The analyst note that morning explains it in one line. “Results in line with expectations.” The company just delivered its best quarter in three years, and the market shrugged, because it had already assumed this would happen. The revenue, the margins, the optimism. All of it was baked into the price months ago.

This happens constantly, and most investors never stop to ask what it means. If the market already prices in what it expects, then delivering on expectations doesn’t generate your return. The return comes from somewhere else entirely.

The Expectations Treadmill

Stock prices don’t just reflect what a company earned last quarter. They reflect what the market believes a company will earn over the next several years, discounted back to today. That’s the core of discounted cash flow logic, and while the math gets complicated, the intuition is simple. A stock price is a bet on the future.

Different companies carry different expectations. Nobody expects a utility to grow earnings 15% a year. The market prices utilities for stability and a dividend. Tech companies carry the opposite characteristics. A stock trading at 40 times earnings isn’t priced for modest growth. It’s priced for something big. When the big thing happens, the stock doesn’t jump because it already moved months ago.

This creates what feels like a treadmill. Companies have to keep running just to justify the price they’re already trading at. When they fall short, the stock drops. When they meet expectations, the stock holds. The only thing that makes the stock meaningfully go up is delivering something the market didn’t see coming, and that something has a name.

The Residual Is Innovation

Innovation, in the broadest sense, is the unexpected advantage. A new product. A new business model. A new way of doing something that the market hasn’t priced because it couldn’t be anticipated.

Consider Spotify. When it launched, digital music distribution already existed. iTunes had proven the model. You could buy any song for 99 cents. The market had priced in digital music. What it hadn’t priced in was a Swedish startup offering unlimited access to the entire catalog for $10 a month. That business model innovation created billions in equity value, not because the technology was new, but because the approach was.

Or think about the iPhone. In 2006, smartphones existed. Blackberry was dominant. The market had priced in mobile communication. What it hadn’t priced in was a device that would restructure the entire relationship between consumers and the internet. The idea was what generated the return, not the fact that phones existed.

Innovation is the most durable source of return that can’t be priced in advance. Everything else (interest rate expectations, regulatory changes, demographic shifts, industry trends) gets absorbed into stock prices as the market becomes aware of it. By the time you read about it, it’s in the price. Innovation, by definition, is the part that hasn’t happened yet.

Sparkline Capital put this well in their research on innovation investing. “In an efficient market, prices accurately incorporate future growth expectations. Even if a disruptive company does ultimately reshape society, its investors will not realize excess returns if this outcome was already priced in.”^[1] The return is the gap between what the market expected and what actually happened.

And the share of equity value that depends on innovation keeps growing. Intangible capital (intellectual property, brands, network effects, software) now comprises roughly 42% of the U.S. capital stock and is growing faster than tangible capital.^[2] The four largest companies in the world by market value don’t need significant net tangible assets to generate their earnings. Warren Buffett pointed this out in 2018. “They are not like AT&T, GM, or Exxon Mobil, requiring lots of capital to produce earnings.”^[2:1] The asset that matters is the one the market can’t easily anticipate.

This doesn’t mean markets are perfectly efficient. They aren’t. Behavioral biases, structural frictions, and information asymmetries create pockets of mispricing all the time. But the directional truth holds. Most of the expected stuff is in the price. Most of the return comes from the unexpected.

The Test Case

If innovation is the source of all true returns, then AI is the stress test.

The hyperscalers are spending hundreds of billions on AI infrastructure this year alone. The assumption embedded in those spending levels is that AI will generate productivity gains large enough to pay for itself many times over. That is a massive bet on innovation. The market is pricing in future AI-driven returns.

Three years after ChatGPT launched, the evidence for those gains is thin. Developer jobs haven’t disappeared. The products people use every day haven’t undergone a visible step-change in quality. AI may be producing gains in harder-to-measure dimensions (faster time-to-market, reduced headcount growth), but if those gains can’t be measured, they can’t be priced in either. And that only reinforces the point.

But part of the reason the innovation hasn’t materialized may have nothing to do with AI’s capabilities. It may have to do with how AI labs make money.

The Token Problem

The dominant revenue model for frontier AI companies is token consumption. You pay based on how much compute your queries consume. This creates a straightforward incentive. The more tokens an agent uses, the more revenue the lab collects.

Now consider the current race to build agents that can run unsupervised for hours at a time. The pitch is autonomy. Give the agent a task, go to bed, wake up to the finished product. That’s compelling. But think about what sits underneath it.

If an agent could deliver your result in one hour but instead ran for eight, you’d never know. You went to bed. The answer was there in the morning. The lab, meanwhile, collected eight hours’ worth of token revenue instead of one.

This isn’t an accusation. It’s an observation about incentive structure. When the revenue model rewards consumption rather than efficiency, the system naturally drifts toward more consumption. Current pricing is also subsidized by venture capital, which means the true cost of inference is obscured. The bet is that models will get cheaper to train and run faster than the subsidies dry up, so the price drop will eventually be organic. We’ll learn much more when these companies go public. Both OpenAI and Anthropic have reportedly discussed IPOs as early as late 2026, though neither has filed. Public filings will force the kind of transparency that private fundraising rounds don’t.

Businesses, historically, have grown in the increments defined by their era’s dominant technology. Ship routes. Factories. Web properties. If the current increment is tokens, then the incentive to consume more of them is the fundamental business model.

What Would Change the Assessment

If innovation is the source of all true return, and AI is the biggest innovation bet in a generation, then the key question is whether the innovation is real but early, or simply hasn’t materialized.

A few signals would shift the picture.

If AI-native startups (companies founded after 2023 that are built entirely around AI workflows) begin consistently outperforming established competitors on product quality and speed, that would be strong evidence that AI is generating genuine productivity gains, even if incumbents can’t capture them. This hasn’t happened yet at scale.

If industry-wide software quality metrics (defect rates, deployment frequency, mean time to recovery) measurably improve over the next 18 months, that would suggest AI is producing innovation surplus in development workflows. The data so far is inconclusive.

The domain where AI’s advantage seems most genuine is medicine, not software. Gene therapy and drug discovery involve pattern-matching at a scale that exceeds human cognitive bandwidth. DNA structures are essentially statistical patterns, and identifying therapeutic targets across millions of sequences is exactly the kind of problem where AI’s architecture fits. That’s entirely different from asking an LLM to write a React component. An Australian AI researcher named Paul Conyngham recently used ChatGPT and AlphaFold to help design a personalized mRNA cancer vaccine for his dog, working alongside university scientists who manufactured and administered it. The tumor reportedly shrank by 75%.^[3] The innovation there wasn’t the LLM replacing the scientist. It was the LLM making it possible for a non-biologist to navigate a domain he couldn’t have entered alone.

Here is the falsifiable version of this argument. If aggregate measures of software quality and worker productivity do not show a measurable step-change by the end of 2027, the “AI productivity revolution” thesis will need serious revision. Not abandonment. Technologies routinely cycle through false starts before finding their moment. Neural networks existed in the 1990s but were ahead of their time. Electric vehicles were the best-selling cars of the early 1900s before being eclipsed by internal combustion, and they didn’t reemerge for a century.^[1:1] The internet took 10 to 15 years to produce the productivity gains economists were looking for. But revision would be warranted, because the spending levels assume the gains are arriving much sooner than those historical timelines suggest.

The Innovation Discount

If innovation is the most durable source of true return, and the market is currently discounting innovation-heavy companies because of near-term pain, then a patient investor is effectively buying innovation at a markdown.

This has happened before. On June 1, 1932, the S&P 500 had fallen 86.2% from its 1929 peak, and a single company (AT&T) made up 12.7% of the entire U.S. stock market.^[4] An investor who bought at that moment of maximum pain would have earned roughly 16.1% annualized over the next 25 years, turning $100,000 into nearly $4.2 million.^[4:1] The point is not that today’s drawdown is comparable to 1932. It’s that the market’s harshest discounts on innovation have historically been followed by the strongest long-term returns.

Tech companies carry long-duration cash flows, meaning their projected earnings are heavily weighted toward the future. When interest rates rise, those distant cash flows get discounted more aggressively, and the stock price drops. This is mechanical, not analytical. It doesn’t mean the innovation is worth less. It means the market is applying a higher discount rate to future earnings.

The last 10 to 15 years created bloat in tech. Zero interest rate policy and pandemic stimulus let companies hire aggressively, spend freely, and defer profitability without consequence. That era is over. The companies that survive the current tightening will be leaner, more capital-efficient, and more focused. The hyperscalers themselves are on a trajectory toward utility status. Just as railroads became the platform on which industrial businesses were built, cloud infrastructure is becoming the platform for the next generation of startups. The returns from being a utility are real but moderate. The outsized innovation returns will come from whoever builds something on top of that infrastructure that nobody expected.

The private market pullback could help accelerate this. If AI genuinely reduces the time and cost of validating a startup idea (faster prototyping, cheaper user testing, simulated customer feedback), then smaller checks could fund leaner companies that reach proof-of-concept faster. The combination of cheaper validation and tighter capital could produce startups that are more capital-efficient than anything we’ve seen.

Or it could simply produce fewer startups. That’s the tension, and nobody knows which way it tips yet.

Where the Return Lives

Go back to the $100 stock. The company announced its best quarter in three years. The stock didn’t move. The investor who bought it expecting a beat earned nothing from the beat.

The return, when it eventually comes, won’t come from the expected growth that every analyst projected. It won’t come from the product launch that was on the roadmap. It will come from the thing that wasn’t on the roadmap. The business model nobody anticipated. The application nobody imagined. The efficiency gain that surprised even the company that achieved it.

That’s innovation. It’s the residual after expectations have been satisfied.

The market will make another all-time high. It always does. The S&P 500 has never failed to recover. The question for every investor isn’t whether to own stocks. It’s where the unpriced innovation is hitting, and whether you’re willing to hold it through the discount.

If this framework is right, the market is currently punishing many of the companies most likely to produce the next wave of unexpected advantage. That’s not a guarantee of returns, but a bet, and it depends on the assumption that innovation will continue to emerge from the same kind of capital-intensive, technology-driven companies that have produced it before. That assumption could be wrong. But the structural pattern is clear enough to take seriously.

This is general education and analysis, not personalized financial advice. The author holds no positions mentioned in this piece.

1. Kai Wu, Sparkline Capital, “Investing in Innovation,” April 2022.

2. Kai Wu, Sparkline Capital, “Investing in the Intangible Economy,” October 2020.

3. Fortune, “This startup founder used ChatGPT and AlphaFold to help treat his dog’s cancer,” March 15, 2026.

4. Jason Zweig, The Wall Street Journal, “The Big Scary Myth Stalking the Stock Market,” February 13, 2026.

The result can be rapid. When enough refinery capacity comes offline or cuts rates, the supply of refined products (gasoline, diesel, jet fuel) tightens before crude oil prices have fully reacted. Pump prices can spike. And if you’ve been watching WTI or Brent futures wondering why your gas station already moved, the refinery step is a big part of the answer. In this situation, the bottleneck isn’t the well. It’s the refinery.

Traders call the gap between crude oil prices and refined product prices the crack spread, the margin a refinery earns by “cracking” crude into usable fuels. When refinery throughput drops, the crack spread tends to widen because refined product becomes scarce even if crude hasn’t fully repriced. Other factors move the spread too (seasonal fuel specs, product inventories, export flows), but refinery disruptions are among the most visible drivers. For consumers, the crack spread captures part of the gap between the headline crude price and the number on the pump, alongside taxes, blending, transportation, and retail margins.

This is the first of five links in a chain that connects an oil supply disruption to your wallet. Most of the damage happens in links that never make the evening news.

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Oil is not oil

The second link trips up almost every policy conversation about energy independence.

Crude oil is not perfectly fungible. Venezuelan crude is thick and heavy, requiring specialized refineries and extensive processing. Crude qualities vary sharply even within a region. UAE Murban is light and sweet, flowing easily and yielding more gasoline per barrel. But most Saudi crude is medium sour, with higher sulfur content that requires different refinery configurations. U.S. shale crude has its own profile. Gulf Coast refineries were built for specific crude slates, and switching from one source to another is constrained by economics and equipment, not just price.

This matters when people say the United States is energy independent. The U.S. became a net total petroleum exporter in 2020, and it exports both crude and refined products. But it remains a net crude oil importer and still brings refined products into some coastal markets. The West Coast is particularly isolated. There’s no direct product pipeline from Gulf Coast refineries to California, so the region depends on waterborne imports and local refining. The East Coast is better connected through the Colonial and Plantation pipeline systems, but regional constraints still create price dislocations during disruptions. Being a net petroleum exporter does not insulate American consumers from fuel price spikes, because the refinery step, crude-quality mismatches, and the geography of fuel distribution all create separate bottlenecks.

Even the domestic energy system has infrastructure gaps that most people don’t realize exist. Pipelines curtailed or restricted the flow of natural gas to manufacturers more than 40 times last year, per Industrial Energy Consumers of America, a trade group representing over 12,000 U.S. manufacturing facilities.^[1] During one winter storm, spot gas prices in parts of the Northeast and Midwest surged to multiples of normal levels.^[1:1] The U.S. has become the world’s largest exporter of liquefied natural gas while its own factories sometimes can’t get fuel.^[1:2] Oil has a version of the same structural problem.

The break-even economics reinforce the mismatch. Saudi Aramco’s upstream lifting cost (the cost of extracting a barrel from an existing well, excluding capital investment) runs in the range of $3 to $4 per barrel of oil equivalent, among the lowest in the world.^[2] U.S. shale is a different story. The Dallas Fed’s 2025 energy survey put the average break-even price for drilling a new well in the low $60s per barrel, though large producers with scale advantages report lower numbers.^[3] As prices rise, previously uneconomic wells become viable and new supply comes online. But “eventually” can mean months, not weeks. And the new supply still has to reach a refinery configured to process it.

The supply chains nobody’s watching

A Gulf energy disruption doesn’t just hit fuel. Oil and natural gas production are closely linked in the region, and natural gas feeds supply chains that most people would never connect to an oil shock. Two of them matter right now.

Fertilizer. Natural gas is the primary feedstock for ammonia-based fertilizers. Qatar accounts for roughly 14% of global urea supply, according to QAFCO, and the broader Gulf and Middle East region supplies closer to 30% of internationally traded fertilizer.^[4][5] A disruption that chokes Gulf gas production during spring planting season creates a problem with a built-in delay. Fertilizer that isn’t available in March and April can show up as reduced crop yields months later. The agricultural calendar doesn’t wait for shipping lanes to reopen.

Helium. This one is less intuitive. Helium is a byproduct of natural gas processing, and Qatar is one of the world’s largest suppliers of the high-purity helium used in semiconductor manufacturing.^[6] Chip fabrication uses helium in lithography cooling and quality testing. The semiconductor industry was already squeezed by AI-driven demand for memory and compute chips. A Gulf disruption that tightens helium supply compounds an existing bottleneck in a supply chain that appears to have nothing to do with energy.

Food is just energy with extra steps

Fuel prices bleed into food prices through two channels. The first is fertilizer (already covered). The second is transportation. Most freight trucking, most rail shipments, and most cargo vessels run on diesel or petroleum-based fuels. When diesel prices spike, distribution costs spike with them. And food is fundamentally a distribution problem as much as a production problem. The world produces enough aggregate calories, but getting them to the right place at the right price (and in the right season, given weather, conflict, and local agronomy) is where the system strains.

This is why analysts and the Fed tend to focus on “core” inflation measures that exclude food and energy. The BLS publishes both headline CPI and a separate all-items-less-food-and-energy index. The rationale is that food and energy are volatile and driven by supply shocks that monetary policy cannot readily offset. But for households, especially lower-income households, food and transportation are among the largest spending categories after housing. When both spike simultaneously from the same upstream cause, the squeeze on discretionary spending is severe. I wrote about how inflation hits different income levels differently last year. The mechanism is identical here.

The Federal Reserve’s January 2026 Beige Book put it plainly. Spending was “stronger among higher-income consumers” on luxury goods and travel, while low-to-moderate income consumers were “increasingly price sensitive and hesitant to spend on nonessential goods and services.”^[7] An oil shock amplifies exactly this divergence. A tech worker who works from home barely notices a 30% diesel increase. A warehouse associate commuting 45 minutes each way absorbs the full impact. Oil-price shocks tend to be regressive because lower-income households spend a larger share of their income on energy-related necessities. The aggregate CPI print masks a lived experience that varies enormously by income and geography.

The sequel that’s already written

The fifth link is the one that investors should be watching most closely.

Demand destruction tends to follow major oil price spikes, though the timing and magnitude vary by cause, macro backdrop, and policy response. The cycle runs like this. Prices spike. Producers rush to bring new supply online as previously uneconomic wells become viable. New drilling starts. Delayed projects get approved. At the same time, consumers cut where they can. Businesses find substitutes, reduce consumption, or pass costs through until demand contracts.

The investment in new supply takes time. New refinery capacity in particular can take many years from permitting through construction, and that lag creates a mismatch. By the time new capacity arrives, consumer demand has often already pulled back. The result, historically, is a glut. After oil spiked above $140 per barrel in mid-2008, prices collapsed to under $40 within six months as demand cratered and new production commitments made during the boom began to flow. After the 2014 spike, U.S. shale producers kept drilling into a falling market, contributing to a global oversupply that took years to clear. Many of the largest oil oversupplies came after large demand spikes. How quickly this cycle plays out in the current disruption depends on variables that are hard to pin down (SPR releases, OPEC+ spare capacity decisions, the speed of alternative sourcing), but the pattern itself is well-established.

Every oil shock contains the seed of the glut that follows it.

Oil shocks have also preceded multiple U.S. recessions. The mechanism is straightforward. Fuel demand is relatively inelastic in the short run, meaning households cut other spending before they cut driving. So higher fuel costs crowd out discretionary purchases. Consumer staples hold up. Cyclicals get hit. Persist long enough, and the spending compression can tip into contraction. After oil prices collapsed in 2014, U.S. oil and gas production employment fell roughly 16% within a year.^[8] The cycle runs in both directions.

What to watch

If you’re following this situation, the crack spread can be a more useful signal than the WTI crude price alone, especially during refinery-driven shocks. When the gap between crude and refined product widens, pump prices are moving faster than the headline number suggests. Watch refinery utilization rates (published weekly by the EIA) for signal on severity. Watch fertilizer prices if you want a leading indicator of food inflation, though the exact lead time depends on the crop, region, and farmer inventories. And track the demand destruction cycle, because the oversupply that historically follows a spike can reprice energy equities that the current panic may be mispricing.

A specific prediction falls out of this analysis, and it’s testable. If the Gulf disruption persists through spring, fall crop yield reports and winter food price data should reflect the fertilizer squeeze. If they don’t (because alternative fertilizer sources filled the gap, or because the disruption resolved quickly), that tells us the supply-chain link is weaker than this framework assumes.

Even in an optimistic scenario, where the conflict ends soon and shipping lanes reopen, full normalization of oil markets would take time. Some Qatari LNG infrastructure damage has been reported as potentially requiring years to repair.^[9] Clearing shipping lanes, rebuilding inventories, and recalibrating investor risk appetite for Gulf-sourced energy don’t happen overnight. The system has inertia in both directions.

The pattern is older than any of us. An oil shock transmits through refinery margins, crude substitution constraints, energy supply-chain dependencies, consumer prices, and demand destruction. The links are well-established. The timing varies. But if the cycle holds, the crack spread will start narrowing before the headlines catch up. In past disruptions, it has tended to move first on the way up. It may well move first on the way down.

This is general education, not financial advice. Your situation is different from everyone else’s, and this analysis does not account for it.

1. The Wall Street Journal, “The U.S. Is Awash in Natural Gas, but American Factories Still Can’t Get Enough”, February 10, 2026. https://www.wsj.com/finance/commodities-futures/lng-us-manufacturers-no-fuel-6c8e48ae

2. Reuters, “Saudi Aramco: The Oil Colossus,” May 30, 2024. Saudi Aramco, H1 2025 results presentation.

3. Federal Reserve Bank of Dallas, “Energy Indicators,” May 1, 2025. Federal Reserve Bank of Dallas, Energy Slideshow, March 3, 2026.

4. QAFCO (Qatar Fertiliser Company), corporate overview, accessed March 2026.

5. Reuters, “How Does the Iran War Affect Fertiliser Supplies, Prices and Food Security?” March 17, 2026. International Fertilizer Association, “Protect Fertilizer Supply Chains to Safeguard Global Food Security,” March 16, 2026.

6. Reuters, “Helium Prices Soar as Qatar LNG Halt Exposes Fragile Supply Chain,” March 12, 2026. U.S. Geological Survey, Mineral Commodity Summaries 2026: Helium and Rare Gases, February 2026.

7. Federal Reserve, “Beige Book Summary, January 2026.” Original: https://www.federalreserve.gov/monetarypolicy/beigebook202601-summary.htm

8. Reuters, “U.S. Oil, Gas Industry Sheds 100,000 Jobs in Slump: Kemp,” February 4, 2016. U.S. Energy Information Administration, “Oil and Natural Gas Production Job Declines Tend to Lag Oil Price Declines,” Today in Energy, June 23, 2015.

9. Reuters, “Cheniere, Venture Global Shares Surge amid Iran Attacks on Qatar LNG Infrastructure,” March 19, 2026.

This is the new reality for a growing share of the workforce. Your paycheck isn’t just what hits your bank account on the 15th and 30th. It’s also what happens to a stock price you can’t control, driven by decisions you didn’t make, in response to forces you may not fully understand.

And the uncomfortable part is that opting out might cost you more than opting in.

Where the Money Actually Goes

When a company gets more productive and more profitable, where does the extra money go?

For most of the 20th century, a reasonable answer was “partly to shareholders, partly to workers.” Wages grew roughly in line with productivity. BLS data shows that productivity and compensation tracked closely for decades, then diverged starting in the 1970s.^[3] The gap has widened since, though its exact size depends on whether you measure wages or total compensation (including benefits) and how you adjust for inflation.^[4]

One major channel for that shift is share buybacks. When a company earns more profit than it wants to reinvest, it faces a choice. Pay dividends. Buy back its own stock. Raise wages or headcount. Or sit on the cash.

Over the past two decades, buybacks have won this contest by a wide margin. S&P 500 buybacks ran close to $1 trillion in trailing twelve-month totals as of mid-2025, according to S&P Dow Jones Indices data summarized by First Trust.^[5] Buybacks eclipsed dividends as the preferred payout method around the turn of the century and have mostly led ever since.^[6][7]

The math is straightforward but underappreciated. When a company buys back 3% of its outstanding shares in a year, earnings per share rise by roughly 3% even if total earnings don’t grow at all. Multiply that effect over a decade and it compounds. Buyback-heavy cohorts of stocks have historically outperformed broad benchmarks, according to S&P Dow Jones Indices research on the S&P 500 Buyback Index.^[8] The contribution to any individual investor’s realized return depends on the company and the period, but the mechanism is real. Buybacks can raise per-share metrics by reducing share count, concentrating ownership among fewer holders and supporting the prices that show up in your brokerage account.

The scale of this shift shows up in the aggregate numbers. After inflation, average hourly wages are up 3% since the end of 2019. Corporate profits have climbed 43% over the same period.^[9] Households’ stock wealth is now almost 300% of their annual disposable income, compared with 200% in 2019.^[9:1]

This isn’t inherently bad. Buybacks are a legitimate capital allocation tool. If a company has no better use for its cash, returning it to shareholders through repurchases can be efficient. But the distributional effect matters. The value flows to people who own shares. Not to people who only earn wages.

How Value Is Captured

Think of corporate revenue as water flowing downhill through a series of pools.

At the top, revenue comes in. The first pool it fills is operating costs, and that includes wages for the workers who generate that revenue. The next pool is reinvestment (R&D, capex, and growth initiatives). After that comes the shareholder return pool (dividends and buybacks). And finally, there’s the equity compensation pool, where companies grant stock to employees as part of their pay packages.

What has changed over time is the relative size of those pools. The reinvestment and shareholder return pools have grown. Labor received 58% of gross domestic income in 1980. By the third quarter of last year, that share had fallen to 51.4%, while profits’ share rose from 7% to 11.7%.^[9:2] Researchers at the Federal Reserve Bank of St. Louis have documented the trend and explored its drivers, from globalization to capital-biased technological change.^[10] The shift is even starker inside individual companies. IBM had a payroll of nearly 400,000 in 1985. Nvidia is nearly 20 times as valuable and five times as profitable (inflation-adjusted), yet it employs roughly a tenth as many people.^[9:3] In the past three years, Alphabet’s revenue grew 43% while its headcount stayed flat.^[9:4]

And the equity compensation pool has become the bridge. It’s a way for companies to let workers participate in shareholder-level returns without raising their fixed cost base.

This creates an interesting dynamic. If you work at a company that grants meaningful equity, you’re drinking from two pools at once. You get a wage, and you get a small ownership stake. If you don’t get equity, you’re only drinking from the pool that’s been shrinking.

And equity comp isn’t evenly distributed across the workforce. It clusters among college-educated workers in knowledge-economy industries, the same group already benefiting from a widening education premium. Between 1981 and 2005, the mean earnings gap between high school graduates and those with college education doubled from 48% to 97%.^[11] Equity comp amplifies that gap further, layering capital-like returns on top of already-higher wages.

The framework helps explain why compensation conversations have shifted so dramatically in tech and finance. Base salary negotiations matter, but they’re not where the growth is. The growth is in the equity grant, because that’s where value capture has been moving.

The Power-Law Problem

This is where equity comp gets uncomfortable.

Wage income, for most people, follows something close to a normal distribution. There’s a range, and most people cluster somewhere in the middle. The spread is real but manageable.

Equity income follows a different pattern entirely. Outcomes are highly skewed. A small number of companies generate outsized returns, and everyone else lands somewhere between fine and terrible. The distribution of stock returns has fat tails, meaning the winners and losers are more extreme than most people intuitively expect. That skewness is what this article’s title refers to.

If you joined Amazon in 2015 and held your RSUs, your equity comp likely grew several times over. If you joined a company that went sideways or down over the same period, your “total comp” looked very different from what the offer letter promised.

The conventional wisdom here is clean and simple. “Sell your RSUs the minute you get them.” Diversify immediately. Don’t let employer concentration risk ruin your financial plan.

That advice is mechanically correct. From a pure risk-management standpoint, concentrated single-stock exposure is hard to justify. You’re essentially making a leveraged bet on one company with a meaningful slice of your net worth.

But the advice also ignores some important realities.

First, many employees feel like selling on vest is leaving money on the table, especially at high-growth companies. The behavioral pull toward holding is strong, and not always irrational. If you genuinely believe your company is undervalued and you have an informational edge (being careful about what that means legally), selling immediately throws away optionality.

Second, for private-company employees, “sell on vest” isn’t even an option. Consider Stripe, founded in 2010, where employees have been holding equity for about sixteen years without a public listing.^[12] The company has run periodic tender offers to provide some liquidity, most recently at a $91.5 billion valuation in early 2025.^[13] But those windows are infrequent and limited. Co-founder Patrick Collison has said there’s “no rush” to go public.^[14] For employees, the equity is real on paper but not easily converted to cash when they need it.

Third, the gap between “what you should do” and “what the system allows you to do” is wide. Vesting schedules, lockup periods, trading windows, and tax consequences all constrain the neat “diversify immediately” playbook.

The Equity Comp Sniff Test

Instead of treating equity comp as binary (hold everything or sell everything), try thinking about it as a portfolio allocation problem with four variables.

Concentration. What percentage of your investable net worth is tied to your employer’s stock? Financial planners commonly flag 10–15% as a threshold where single-stock concentration starts to meaningfully affect portfolio risk.^[15] If you’re above that range, you’re making an active bet whether you intend to or not.

Liquidity timeline. When will you actually need this money? If your liquidity needs are 5+ years out and you can stomach volatility, holding more may make sense. If you’re saving for a house in two years, concentration risk hits differently.

Conviction. How much do you actually know about your company’s prospects versus what you want to believe? Be honest. Most employees overestimate their informational edge. Your perspective from inside the company is valuable but also biased by proximity.

Downside tolerance. What happens to your financial plan if this stock drops 40%? If the answer is “I delay retirement by two years” or “I can’t make my mortgage,” you have too much concentration regardless of your conviction level.

This isn’t a formula. It’s a sniff test. And the uncomfortable conclusion for many people is that the right answer changes over time. Early in your career with few assets, RSU concentration might be an acceptable bet. As your net worth grows and your financial obligations increase, the same concentration becomes harder to justify.

The Bigger Picture

Zoom out and the pattern is clear. We’re living in an economy where the returns to owning things are increasingly outpacing the returns to doing things. Piketty formalized this as r (return on capital) > g (economic growth), the observation that when the rate of return on capital exceeds the rate of economic growth, wealth concentration rises almost automatically.^[16] Buybacks accelerate that mechanism at the corporate level. Equity compensation is the bridge that lets some workers participate. But it’s a bridge with tolls, and those tolls include concentration risk, liquidity constraints, tax complexity, and the psychological challenge of watching your net worth swing with every earnings call.

And if you’re waiting for a correction, history isn’t encouraging. Scheidel’s survey of inequality across civilizations finds that macroeconomic crises produce only short-lived effects on the distribution of income and wealth. Absent a major shock, stability tends to favor the continuation of whatever inequality dynamics are already in motion.^[17] The capital-vs-labor tilt we’re living through looks durable.

Comp negotiation is financial planning. Every offer letter is a portfolio allocation decision, and most people sign them without a framework for thinking about it that way. The Equity Comp Sniff Test is not a solution. It is a prompt to make the allocation explicit before the next earnings call does it for you.

If buyback volumes keep growing relative to wage growth, and if AI reduces labor bargaining power by making some roles less scarce, the gap between median wage growth and median total comp growth should widen over the next three to five years. What would change that picture is evidence that AI-driven productivity gains flow to wages rather than profits, or that labor scarcity in key sectors keeps bargaining power intact. The software engineer who opened that stock alert already found out what kind of bet they’d made.

This post is for educational and informational purposes only. It is not personalized financial advice. Consult a qualified professional for decisions specific to your situation.

Works Cited

1. Dana Mattioli et al., “Amazon Shares Sink as Company Boosts AI Spending by Nearly 60%,” Wall Street Journal, updated February 6, 2026. https://www.wsj.com/business/earnings/amazon-earnings-q4-2025-amzn-stock-996e5cc2

2. Brody Ford, “Amazon to Spend $200 Billion on AI Infrastructure,” Bloomberg, February 5, 2026 (updated February 6, 2026).

3. Michael Brill et al., “Understanding the Labor Productivity and Compensation Gap,” U.S. Bureau of Labor Statistics, June 6, 2017. https://www.bls.gov/opub/btn/volume-6/understanding-the-labor-productivity-and-compensation-gap.htm

4. Economic Policy Institute, “The Productivity-Pay Gap” (regularly updated series). Note that EPI’s methodological choices are debated, but the direction of divergence is well documented across multiple sources. https://www.epi.org/productivity-pay-gap/

5. First Trust Portfolios, “S&P 500 Index Dividends & Stock Buybacks,” October 2, 2025 (summarizing S&P Dow Jones Indices buyback data). Trailing-12-month buybacks near $997.8B ending June 2025.

6. Susan Dziubinski, “Stock Buybacks Are Booming in 2025. That’s Bad News for Dividend Investors,” Morningstar, October 8, 2025.

7. Jeremy Schwartz, “Dividends, Buybacks and the Prospect of Future Returns,” WisdomTree, June 2, 2016.

8. S&P Dow Jones Indices, “Examining Share Repurchases and the S&P Buyback Indices,” research report. Covers buybacks, share count reduction, and index construction tied to repurchase behavior.

9. Greg Ip, “The Big Money in Today’s Economy Is Going to Capital, Not Labor,” Wall Street Journal, February 9, 2026. https://www.wsj.com/economy/jobs/capital-labor-wealth-economy-2fcf6c2f

10. Rosanne Scott, “Why Is the Labor Share Declining?” Federal Reserve Bank of St. Louis Review, October 22, 2020. https://research.stlouisfed.org/publications/economic-synopses/2020/10/22/why-is-the-labor-share-declining

11. Walter Scheidel, The Great Leveler: Violence and the History of Inequality from the Stone Age to the Twenty-First Century (Princeton University Press), page 412.

12. Baillie Gifford (Scottish Mortgage Investment Trust), “Stripe | Holdings” (states Stripe founded in 2010).

13. Anna Irrera, “Fintech Firm Stripe Valued at $91.5 Billion in Latest Tender Offer,” Reuters, February 27, 2025.

14. Emily Mason and Emily Chang, “Stripe’s Collison Says No Rush for Payment Firm to Go Public,” Bloomberg, January 20, 2026.

15. T. Rowe Price, “Helpful Actions You Can Take If Your Portfolio Is Too Concentrated in One Equity,” June 24, 2025. Defines “concentrated” as ~5–10%+ and discusses associated risk.

16. Thomas Piketty, Capital in the Twenty-First Century (Harvard University Press, 2014), Kindle page 1.

17. Walter Scheidel, The Great Leveler, Kindle page 9.

It’s a satisfying story. It’s also almost certainly wrong about the mechanism.

High trade counts like these may reflect a financial advisor or discretionary manager executing an active strategy on the politician’s behalf, though the exact share of advisor-driven versus self-directed trading is unclear. A senator isn’t pulling out a phone during a committee hearing and placing orders on Robinhood. They’re busy, they’re aware of securities law, and direct personal execution would be the most traceable form of fraud imaginable.

But that doesn’t mean there’s nothing to worry about. It means the popular version of this debate is aimed at the wrong target.

What Most People Get Wrong

The standard narrative goes something like this. Politicians trade stocks. Research shows they outperform the market. Therefore they must be using material, nonpublic information. The solution is to ban congressional stock trading.

Each step of that process deserves some scrutiny though.

Start with outperformance. The academic literature is mixed. Earlier studies found evidence of outperformance in certain periods, but more recent work, including a 2020 NBER study by Belmont et al., finds that senators’ portfolios do not meaningfully beat the market on average.^[2] A 2025 VoxEU analysis found that politically powerful members may show abnormal returns around specific legislative events, but that average outperformance across all members is not a settled result.^[3] The point isn’t that no politician has ever traded on an edge. It’s that “they all outperform” is not the slam dunk the headlines imply. There are at least three confounding factors that complicate the picture.

First, reporting lag. Under the STOCK Act, members must report transactions within 45 days.^[4] Any information edge would have been exercised at the moment of the trade, not when it was filed. A stock that gains 8% in the weeks between the trade and the disclosure won’t register as outperformance in studies anchored to disclosure dates. The lag introduces noise in both directions, which is one reason the academic literature produces inconsistent results.

Third, survivorship and selection. Studies that track the best-performing politicians create a narrative that doesn’t represent the full distribution.

Even if stock trading were the primary enrichment channel, it would be the easiest one to catch. Trades are disclosed publicly under the STOCK Act.^[4:1] The SEC enforces federal securities laws (including insider trading), while the House and Senate Ethics Committees oversee disclosure compliance. Case law around securities fraud is deep and well-developed, anchored by standards like the Supreme Court’s materiality test in TSC Industries v. Northway (1976).^[5] U.S. capital markets are among the most heavily regulated in the world.

If you were a rational actor trying to monetize political power, stock trading would be one of the worst ways to do it.

The Perception Standard

The CFA Institute’s Code of Ethics doesn’t ask whether you did something wrong. It asks whether it looks like you could have. When I went through the program, that distinction was treated as foundational.

The CFA Institute’s Standard VI(A) requires members to avoid or disclose conflicts of interest, including situations that create even the appearance of a conflict.^[6] If a portfolio manager takes an action that creates the appearance of a conflict, that’s a violation regardless of intent or outcome.

The reasoning is simple. Trust is the foundation of capital markets. Once the perception of fairness erodes, it doesn’t matter whether the system is technically clean. People stop participating, or they participate with suspicion, and the system degrades.

This standard also illuminates the gray zone between “legal edge” and “insider trading.” Consider an analyst who reads every 10-K filing, focuses on three companies, and develops genuinely superior judgment about those firms. That analyst can legally trade against less-informed participants. This is what the industry sometimes calls mosaic theory. The line between this legal information edge and material, nonpublic information is drawn by case law, and the boundaries are fuzzy. “Material,” under the Supreme Court’s standard in TSC Industries, means there is a substantial likelihood a reasonable investor would consider it important in making a decision.^[5:1] “Nonpublic” generally means not yet broadly disseminated to the investing public.^[7] But information can be technically public (buried in a filing somewhere) while being functionally unknown to most market participants.

If the MNPI boundaries are that ambiguous for corporate insiders, imagine how much fuzzier they get for legislators who influence entire sectors through committee votes, regulatory hearings, and policy negotiations.

The perception standard cuts through the ambiguity. It says the question isn’t “did this cross the legal line.” The question is “does this look like it could.” For public officials with asymmetric information and policy-making power, the answer is almost always yes.

The Transparency-Accountability Matrix

If stock trading is the most visible enrichment channel, what’s happening in the channels that are harder to see?

Map political enrichment channels by how visible they are and how well the rules can be enforced, and four distinct zones appear.

High Visibility, High Accountability. Stock trading sits here. Trades are publicly disclosed. The SEC can investigate. Insider trading law is mature. This is the channel everyone talks about, and it has the strongest guardrails.

High Visibility, Low Accountability. Speaking fees and book deals live in this quadrant. We know about them. They’re reported in financial disclosures. But the rules governing them are loose, and the connection between a six-figure speaking engagement and future policy decisions is nearly impossible to prosecute, even when the incentive alignment is obvious.

Low Visibility, Mixed Accountability. The revolving door between Congress and lobbying firms. Consulting arrangements that emerge after leaving office. Family members hired by firms with legislative interests. These patterns are known in aggregate but hard to track at the individual level, and enforcement is weak.

Low Visibility, Low Accountability. This is the quadrant that should concern us most. Prediction markets like Kalshi are emerging as platforms where information edges could theoretically be monetized without the disclosure requirements that apply to securities. [VERIFY] Informal networks that share “directional” information without crossing MNPI thresholds. Relationships that create value in ways that don’t show up in any filing.

Media and public attention are concentrated on the quadrant with the strongest guardrails. The quadrants where enrichment is least constrained receive far less scrutiny.

Banning stock trading addresses the visible, traceable channel. But if the real money flows through speaking fees, lobbying relationships, and platforms that lack oversight, the ban is necessary but not sufficient. Without addressing the other channels, it’s closer to security theater than structural reform.

Where the Money Actually Flows

So how do politicians actually get rich on a base salary of $174,000 per year?^[8]

Some members arrive wealthy. But many leave office significantly wealthier than when they entered, and the pathway isn’t always obvious.

If stock trading were the primary mechanism, we’d expect to see it in the data. The trades are public. Researchers have been studying them for years. And while some studies find outperformance in specific periods or subgroups, the magnitudes don’t easily explain the wealth accumulation that some members display.

Speaking fees. Former officials can command six-figure sums per appearance. Reporting by The Guardian has documented how ex-politicians across parties have built lucrative speaking careers after leaving office.^[9] The question isn’t whether speaking fees are legal. They are. The question is whether they function as deferred compensation for policy decisions made while in office. If a company executive knows that a senator who voted favorably on industry-friendly legislation will be available for a large speaking engagement after their term, the incentive is established before the vote happens.

Book deals. Advance payments can reach seven figures. Reporting by NOTUS documented sitting members receiving advances exceeding $1 million.^[10] These are legitimate publishing transactions, but the advance sometimes appears disconnected from expected book sales, raising questions about what the publisher is actually paying for.

Lobbying and the revolving door. The path from Congress to K Street is well-documented. Former members who move into lobbying or advisory roles monetize their relationships, knowledge, and access. Current cooling-off periods are one year for House members and two years for senators, and the statutory definition of restricted “lobbying contacts” under 18 U.S.C. § 207 is narrow enough that many advisory and consulting arrangements fall outside it.^[11]

Prediction markets. This is the newest and most underexplored channel. Platforms like Kalshi allow users to bet on economic and political outcomes. If a government insider has directional knowledge about policy announcements, trade decisions, or regulatory actions, prediction markets could theoretically allow them to monetize that edge without triggering securities disclosure requirements. [VERIFY] These platforms are small today. But the regulatory gap is being established while the markets are young.

What Would Actually Work

Reducing conflicts of interest and the perception of them requires addressing multiple channels.

For stock trading. Require broad-market index funds (like VTI or a total market ETF) as the only permissible equity holding for members of Congress and the President while in office. For officials with illiquid assets like real estate or business interests, synthetic overlays or derivative structures could theoretically neutralize idiosyncratic exposure without forcing a fire sale. These aren’t exotic tools. They’re used in corporate M&A and executive compensation contexts regularly.

For speaking fees. Implement meaningful cooling-off periods and disclosure requirements that make the timing and source of fees transparent and connected to the official’s legislative record.

For lobbying. Strengthen the revolving-door rules. Extend the cooling-off periods, broaden the definition of “lobbying activity,” and reduce the incentive to treat a congressional seat as a stepping stone to a lucrative advisory career.

For prediction markets. Establish reporting and prohibition rules for government officials that mirror securities trading restrictions. If officials can’t trade stocks on MNPI, they shouldn’t be able to bet on political outcomes with directional policy knowledge either.

For compensation. There’s a counterintuitive argument that paying politicians significantly more would attract better talent and reduce the incentive to seek enrichment through side channels. Singapore is often cited as a model that benchmarks senior government salaries to private-sector levels, and it scores near the top of Transparency International’s Corruption Perceptions Index year after year.^[12] But the design has a catch. If politicians control their own compensation (which U.S. lawmakers effectively do, subject to political pressure), higher pay could become self-serving rather than performance-linked.

The deeper issue is structural. In the corporate world, we have sophisticated mechanisms to align incentives between managers and shareholders. Boards of directors. Independent auditors. Say-on-pay votes. Proxy contests. Short sellers checking management. Quarterly earnings scrutiny. For politicians, the analogous mechanisms are thin. Elections happen every two to six years. Financial disclosures are filed but rarely scrutinized in real time. Enforcement is fragmented. And the rule-makers are also the rule-subjects, which means they can shape rules that affect their own activities. When that happens, the system needs external checks. This pattern shows up elsewhere in government. The GAO identified 37 states where recent audits of the TANF program found 162 findings related to financial oversight, including 56 classified as material weaknesses, an outcome that’s almost predictable when you combine broad discretion with weak reporting requirements.^[13][14]

The governance gap between how we oversee corporate executives and how we oversee elected officials is one of the most underexplored problems in public policy.

What Would Change My Mind

The testable hypothesis goes like this. If Congress bans stock trading but leaves speaking fees, lobbying, and prediction markets unrestricted, the rate at which members accumulate wealth during and after their terms will not meaningfully change.

What evidence would challenge this? If a comprehensive study showed that stock trading accounts for a majority of politician net worth growth during their time in office (after adjusting for pre-existing wealth, spouse income, real estate appreciation, and normal portfolio returns), the popular narrative would be vindicated and the ban alone would be closer to sufficient.

Conversely, if a breakdown showed trading profits as a small fraction of total enrichment, the thesis here would be confirmed. The ban is still worth doing for trust. But it would be incomplete.

Go back to that database of congressional trades. The high trade counts are real, and they’re worth scrutinizing. But the trades are the part of the system we can already see. The more important question is what’s happening in the channels we can’t.

This is general education and analysis, not financial or legal advice.

1. Quiver Quantitative, “Congress Trading Dashboard.” QuiverQuant.com, accessed Feb 2026. https://www.quiverquant.com/congresstrading/

2. Belmont, William, et al. “Senators As Feckless As the Rest of Us at Stock Picking.” NBER Working Paper 26975, 2020. https://www.nber.org/papers/w26975

3. CEPR/VoxEU, “Political Power and Profitable Trades in the US Congress.” VoxEU/CEPR, Dec 13, 2025. https://cepr.org/voxeu/columns/political-power-and-profitable-trades-us-congress

4. Congressional Research Service, “Stock Trading in Congress.” Congress.gov (CRS), accessed 2026. https://crsreports.congress.gov

5. Supreme Court of the United States, TSC Industries, Inc. v. Northway, Inc., 426 U.S. 438 (1976).

6. CFA Institute, “Standard VI(A) Avoid or Disclose Conflicts.” Standards of Practice Handbook, 12th ed., 2024. https://www.cfainstitute.org

7. U.S. Securities and Exchange Commission, “Material Non-Public Information” (policy exhibit). SEC.gov, Oct 22, 2019.

8. Congressional Research Service, “Congressional Salaries and Allowances: In Brief.” Congress.gov (CRS), Aug 28, 2025.

9. Elliott, Larry, and Jill Treanor. “After-Dinner Mint: How Ex-Politicians Hit Paydirt with Public Speaking.” The Guardian, Apr 28, 2017. https://www.theguardian.com/politics/2017/apr/28/after-dinner-mint-how-ex-politicians-hit-paydirt-with-public-speaking

10. Bender, Bryan. “Congressional Book Club: Lawmakers Earn Big Money…” NOTUS, Aug 18, 2025.

11. Congressional Research Service, “Restrictions on Lobbying the Government: Current Policy…” EveryCRSReport.com, Dec 15, 2016. See also 18 U.S.C. § 207.

12. Transparency International, “Singapore.” Corruption Perceptions Index, Transparency.org, accessed Feb 2026. https://www.transparency.org/en/countries/singapore

13. The Wall Street Journal, “How a $30 Billion Welfare Program Became a ‘Slush Fund’ for States”, Feb 7, 2026. https://www.wsj.com/politics/policy/how-a-30-billion-welfare-program-became-a-slush-fund-for-states-c39b8311?mod=hp_lead_pos2

14. U.S. Government Accountability Office, “HHS Needs to Strengthen Oversight of Single Audit Findings” (GAO-25-107291). GAO.gov, Apr 4, 2025. https://www.gao.gov/products/gao-25-107291

The note arrived through a soft-dollar arrangement (the fund routed commission dollars to the bank in exchange for research access). The cost was real and ongoing. The manager wasn’t paying for the data underlying the note. They could access the same public filings the analyst read. They were paying for the work of turning data into something they could act on.

That work (synthesizing, interpreting, ranking what matters, arriving at a conclusion) is what made the note worth paying for. It required a trained person, significant time, and judgment built up over years of doing it wrong and then correcting themselves.

A good LLM query today can produce something that looks a lot like that morning note. Not identical. Not as good on every dimension. But comparable enough that “worth $40,000 a year” is no longer the obvious conclusion.

This is what AI is compressing. Not the data layer. Not the decision layer. The analyst step in between.

Four Links Between Data and a Outcomes

Think of the path from raw signal to financial outcome as a chain with four links.

1. Data - raw measurements, transactions, prices, signals. Data in isolation is economically inert. A million car-speed readings on a highway are not traffic information. The average speed, broken down by time of day, is.

2. Information - data processed into something that can change a belief or support a judgment. The analyst’s note. The actuarial table. The legal brief. The credit risk score. This is where a large share of knowledge work actually lives, and where AI is producing its most direct cost reduction.

3. Decision - the judgment call that puts information into action. A portfolio manager adding to a position. A CFO approving capital expenditure. A doctor ordering a test. Decisions require context, accountability, and tolerance for uncertainty that information alone does not resolve. They are distinct from information.

4. Outcome - revenue gained, cost avoided, loss prevented. How decisions register on a balance sheet or income statement.

Much of the fear about “AI taking jobs” is really a concern about the second link. AI is not yet replacing the data-generating infrastructure, and in most high-stakes organizational contexts it is not the accountable decision-maker. It is compressing the cost of turning data into information (the analyst step), which was historically the most labor-intensive link in the chain.

The Deflationary Logic

If information is an input to almost every business decision, and the cost of that input falls substantially, then the cost of operating businesses should decline. This is broadly deflationary, at least in industries where information production represents a meaningful share of operating costs.

The historical parallel that fits best is the cost collapse in agricultural production. Farming employed roughly 40% of the American workforce in the early 20th century and about 2% by the early 2000s, with similarly low figures since.^[1] That transition was not a catastrophe. It was a reallocation. Labor freed from food production moved into jobs that didn’t exist when the transition began.

A version of this played out in the private-market research industry within the past two decades. When I was at PitchBook, the product sold was aggregation and structure. Thousands of individual deal disclosures collected, standardized, and made comparable in a single database. Their VC Dealmaking Indicator, for example, was built on top of that. It surfaced deal-flow trends that required the full database to exist and would have been impractical to construct without it. The synthesis was what investors paid for. AI is now moving down the same curve, toward layers of interpretation and analysis that weren’t reached before.

The mechanism is similar for information. When routine synthesis gets cheaper, more decisions become economically viable, because the cost of gathering enough information to support each decision falls. That expansion of decision-making at the margin is likely to create demand for different work, not eliminate work in aggregate.

The evidence so far is consistent with that. Business software spending rose 11% in the fourth quarter of 2025, the fastest pace in nearly three years, as AI was simultaneously lowering what each unit of software output costs to produce.^[2] Cheaper per-unit cost drove more demand, not less total spending. James Bessen at Boston University finds the same pattern across prior technology waves, from textile manufacturing in the 19th century to ATMs in the 1980s.

But the deflationary pressure does not distribute evenly. In competitive markets, cost savings eventually reach consumers through lower prices. In concentrated markets, they accumulate as margin first. The question of who captures the surplus from cheaper information is entirely separate from the question of whether cheaper information is deflationary.

Labor’s share of gross domestic income (the total income earned across the economy) fell from about 57.7% in 1980 to roughly 51.8–51.9% by 2023–2024, while corporate profits’ share rose from about 6.7% to 11.5% over the same period.^[3][4] AI adds another potential mechanism running in the same direction. The surplus goes to capital first, then maybe to consumers, and finally to workers. That last step depends on bargaining power more than on technology.

What Happens to Skill Premiums

The intuitive concern is that cheaper information means fewer analysts, which means lower wages for knowledge workers broadly. This isn’t obviously wrong, but it probably puts the focus on the wrong thing.

The more useful question is what happens to the complexity of the decisions that information supports. Historically, technology adoption has tended to raise skill premiums rather than compress them, because complexity compounds as tools improve.

The college-to-high-school earnings gap in the United States doubled between 1981 and 2005, from 48% to 97%.^[5] This period coincided with rapid technology adoption across the economy. Technology made some tasks cheaper but raised the cognitive bar for the tasks that remained valuable.

Spreadsheet software offers the clearest precedent at the knowledge-work level. When Lotus 1-2-3 and Excel arrived in the early 1980s, bookkeeping jobs shrank. Accountant and financial analyst jobs (the roles that used the same tools to ask harder questions) grew even more.^[2:1] The tool commoditized the mechanical work. Demand for judgment about what the numbers meant rose to fill the space. In 2024, young computer-science graduates earned 63% more than the typical young graduate, up from 47% in 2009.^[2:2] The premium for knowledge work hasn’t compressed during the AI era. It has continued to widen.

The Black Death offers a useful example showing how this works in reverse. When roughly a third of Europe’s population died in the mid-14th century, raw labor became scarce and wages for common workers rose sharply. Unskilled wages rose faster than skilled wages in many documented settings, compressing skill premiums. Not because technology changed, but because the quantity of labor fell suddenly.^[6] That’s a different mechanism. AI is not reducing the quantity of people available to do knowledge work. It is making a specific kind of knowledge work cheaper to replicate. If AI functions as a substitute for routine synthesis and a complement to complex judgment, the premium on judgment rises.

The caveat is that “complex judgment” is not a stable category. Tasks requiring genuine judgment in 2022 may look like routine synthesis by 2028. The floor keeps rising as the tools improve. That is uncomfortable for workers planning careers, but it is distinct from the claim that skill premiums collapse. Historical evidence from prior technology cycles runs the other way.

Which Link in the Chain Describes Your Work

For individuals, the practical question is which link in the chain describes your actual work.

If your primary function is at the data layer (collecting, cleaning, and organizing raw information), the pressure is real and has been building for years before AI accelerated it.

If you are primarily at the information layer, the picture depends on what kind of information you produce. Consider two analysts at the same firm. The first spends most of their time building formatted data tables and writing standard-structure reports. The second spends most of their time deciding which questions are worth asking and pressure-testing where the model assumptions are fragile. AI compresses the first analyst’s workload significantly. It gives the second analyst better raw material to work with. The work that was always most valuable was always the judgment embedded in the analysis, not the formatting. AI is making that distinction visible.

If you are at the decision layer (where novel situations, irreversibility, and accountability are involved), AI is more likely to be a tool than a substitute, at least for now.

The Question Investors Should Be Asking

For investors, the surplus capture question matters more than the technology question.

When a key input gets cheaper, the value that used to flow to its producers has to go somewhere. In competitive industries, some of that value eventually reaches clients through lower prices. In concentrated industries, it stays as margin. The early pattern consistent with this is that firms are capturing AI efficiency gains in profit guidance faster than those gains are showing up in the prices they charge, though a full accounting will take years of data.

The investor question is not whether AI will lower the cost of information production. It probably will. The investor question is which companies sit above that cost compression and capture the margin, and which sit below it and face competitive pressure from buyers who now have better analytical tools of their own.

Incumbents with pricing power and deep customer relationships are better positioned to capture the surplus. Smaller operators in more competitive markets (where buyers can now do more of their own analysis) face more price pressure from the same technology.

The Thing Most Fear Narratives Skip

The anxiety about AI and work usually presents as a quantity problem. How many jobs will remain? The harder and more important version is a price problem. What will knowledge work pay, and who captures the surplus when information gets cheaper?

Being informed enough to make a sound business decision has always been expensive. Getting informed is getting cheaper. Those are not the same thing with the same implications.

The layoffs in knowledge-work industries over the past few years also deserve scrutiny before being attributed to AI. In Challenger, Gray & Christmas tracking, AI-attributed cuts have been a small fraction of total announced layoffs.^[7] A 2025 working paper testing the “ChatGPT shock” finds no statistically significant increase in tech layoffs attributable to it.^[8] Since the Fed began rapid rate hikes in 2022, tighter financial conditions and a post-pandemic hiring correction have coincided with layoffs across tech and other knowledge-work-heavy firms. Rates, constrained venture capital, and pandemic-era overhiring are all well-evidenced contributors. AI is frequently cited in layoff announcements, but cited reasons and causal attribution are different things.

Cheaper information is real. The reallocation that follows is real. But the shape of that reallocation (who bears the adjustment cost, who captures the surplus) is not determined by the technology. It is determined by market structure, bargaining power, and how quickly complexity rises to meet new tools.

That’s the part most of the fear narrative skips.

This post is general education only and not financial advice. Past economic patterns do not guarantee future outcomes.

Footnotes

1. Carolyn Dimitri, Anne Effland, and Neilson Conklin, “The 20th Century Transformation of U.S. Agriculture and Farm Policy (EIB-3),” USDA Economic Research Service, June 2005. Available at https://www.ers.usda.gov/sites/default/files/laserfiche/publications/44197/13566_eib3_1.pdf. The report summarizes agricultural employment share at roughly 41% in 1900 and approximately 1.9% by 2000.

2. Greg Ip, “Tech Has Never Caused a Job Apocalypse. Don’t Bet on It Now.,” Wall Street Journal, February 27, 2026. Available at https://www.wsj.com/economy/jobs/tech-has-never-caused-a-job-apocalypse-dont-bet-on-it-now-d192b579?mod=hp_lead_pos11. Sources cited within include: bookkeeper/accountant employment analysis and software demand data from James Bessen (Technology and Policy Research Initiative at Boston University); computer-science graduate earnings premium from Connor O’Brien (Institute for Progress).

3. Wall Street Journal, “The Big Money in Today’s Economy Is Going to Capital, Not Labor,” February 9, 2026. https://wsj.com/economy/jobs/capital-labor-wealth-economy-2fcf6c2f

4. U.S. Bureau of Economic Analysis. “Shares of Gross Domestic Income: Compensation of Employees (A4002E1A156NBEA)” and “Shares of Gross Domestic Income: Corporate Profits with Inventory Valuation and Capital Consumption Adjustments (A445RE1A156NBEA).” FRED, Federal Reserve Bank of St. Louis, updated December 30, 2025. https://fred.stlouisfed.org/series/A4002E1A156NBEA and https://fred.stlouisfed.org/series/A445RE1A156NBEA

5. David H. Autor, “Skills, Education, and the Rise of Earnings Inequality among the ‘Other 99 Percent,’” Science 344, no. 6186 (May 23, 2014). Available at https://economics.mit.edu/sites/default/files/publications/skills education earnings 2014.pdf. The 48% to 97% figure refers to the weekly earnings premium for college graduates relative to high school graduates. Also cited in Walter Scheidel, The Great Leveler, Princeton University Press, 2017, Kindle page 412.

6. Remi Jedwab, Noel D. Johnson, and Mark Koyama, “The Economic Impact of the Black Death,” George Washington University / IIEP Working Paper (2020). Available at https://www2.gwu.edu/~iiep/assets/docs/papers/2020WP/JedwabIIEP2020-14.pdf. The paper documents examples (including Florence 1348–1350) where unskilled real wages rose faster than skilled wages following the plague, implying skill-premium compression in those settings. Evidence varies by region and skill category.

7. Challenger, Gray & Christmas, “Challenger Report: December 2025,” January 8, 2026. Available at https://www.challengergray.com/wp-content/uploads/2026/01/Challenger-Report-December-2025.pdf. Challenger tracks self-reported reasons for announced layoffs; AI-attributed cuts have represented a small fraction of total announced cuts in recent reporting periods.

8. Manhal Ali, “Narratives vs. Numbers: A Textual and Quasi-Experimental Analysis of AI’s Early Impact on Tech Layoffs,” SSRN, 2025. Available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5987016. The paper finds no statistically significant increase in tech layoffs attributable to the ChatGPT shock, while documenting that AI has become a prominent rhetorical justification in layoff communications.

He had spent months reading the actual mortgage loan prospectuses that almost no one else read, the original documents rather than the ratings-agency summaries. He concluded that the subprime mortgage market was priced as though catastrophic defaults were nearly impossible, and the securities built on top of it were fundamentally mispriced. He had built a position in credit default swaps, instruments that paid out when mortgage pools failed. The trade was costing him money in premiums every month. His investors, watching returns lag while markets hummed along, filed to redeem.

He refused to return their capital and imposed restrictions on withdrawals.

When the subprime market collapsed and credit markets seized, Scion Capital’s housing bets paid out substantially while the broader market fell sharply.^[1:1] The investors who had tried to leave, had they been permitted to exit, would have missed the payoff entirely.

The Burry case gets cited as a story about conviction. It is also a story about what fund management actually requires. Not just being analytically right. Maintaining a correct thesis, at real professional and personal cost, through the extended period when the market hasn’t agreed yet. That is a different skill. Understanding why they’re different tells you most of what you need to know about how to evaluate a fund manager, or pressure-test your own portfolio decisions.

The market requires uncertainty

Capital markets function, in part, because no single participant can reliably predict outcomes. This is more than an empirical observation about how hard it is to beat the market. As a structural matter, the logic runs like this.

If a manager could systematically generate persistent alpha (excess returns above what factor exposures and market risk would predict), the rational move would be to keep concentrating capital into the strategy until their position size moved prices rather than discovered them. At that scale, they’re no longer finding value. They’re setting it. The market’s price discovery function (the mechanism that connects stock prices to real economic information) collapses.

Consider the alternative version. If the edge depended on information others didn’t have, at scale it starts resembling insider trading. The legitimacy of capital markets depends on genuine uncertainty being preserved.

This isn’t an argument that active management is useless. It is a constraint on what active management can honestly claim. A manager who tells you they’ve found a systematic edge generating consistent excess returns, year after year, without a compelling explanation for why markets have failed to compete it away, is most likely describing luck or factor exposure in disguise. A manager who says “we have a process for being wrong productively, for surviving the period when the market disagrees, and for knowing when a thesis has actually broken” is describing something that can exist within the system’s actual constraints.

The distinction matters because the first kind is common and often markets well. The second is rarer and harder to evaluate from a factsheet.

Three edges that survive

Most of what gets called alpha is factor exposure in disguise. Tilts toward value, momentum, and quality are documented, well-studied risk premia that investors can now access cheaply through factor ETFs.^[2] Strip out those exposures from a manager’s reported returns, and excess performance often shrinks considerably. S&P Global publishes annual persistence scorecards showing how rarely this year’s top-quartile managers appear in the top quartile five years later.^[3]

Three edges survive closer scrutiny.

Persistence means consistency across full market cycles, not hot streaks within a single phase. Short-run persistence (beating a benchmark for two or three consecutive years) is mostly noise. The test that actually discriminates is whether a manager has navigated a recession, because that’s when liquidity dries up, correlations between assets spike, and credit conditions stop following the patterns visible in calm markets.

There’s an age asymmetry here too. Younger managers who’ve never worked through a real downturn may be analytically sharp but missing the pattern recognition that only forms by living through one. Older managers can have the opposite problem, deep pattern libraries built from prior crises that they fail to update as the world changes. The ideal profile is a manager who has seen genuine stress and hasn’t stopped updating their priors. It’s a narrower window than most manager searches treat it as.

Mean reversion compounds the persistence problem. Managers who lead a decade rarely lead the next, partly because the factor tilts that rewarded them in one market regime carry costs in the next. Persistent outperformance, adjusted for factor exposure and for the base rate of luck across a 10-year sample, is a genuinely rare finding.^[3:1]

Catalyst discipline is the piece almost every fund evaluation skips, and it’s where the Burry story does its real work. The question isn’t only whether the thesis is correct. It’s what has to happen for the market to agree with the thesis, on roughly what timeline, and what the loss scenario looks like if the catalyst takes longer or never arrives.

Burry’s thesis (that subprime mortgage securities were mispriced) was correct starting in 2005. The catalyst that forced the market to agree, the actual default wave hitting securitized pools at a scale that shattered ratings assumptions, didn’t materialize until 2007. In the gap, his positions were bleeding premium, his investors were filing redemption requests, and he was right with no one agreeing yet. If he’d been forced to sell in 2006, a correct thesis would have produced a loss.

The harder version of this problem is the Kodak corollary. Kodak’s decline was analytically visible for years before the company collapsed.^[4] A short thesis held too early didn’t produce a clean exit. The industry shifted in stages, the stock moved in ways that didn’t map neatly to the narrative, and the timing was unforgiving. Correct analysis. No reward. The market moved on.

Process quality is the hardest to assess from outside a fund, and the research signal here is worth knowing about with appropriate caution. Academic work on investment networks has found that fund managers who share educational or board-level connections with company insiders tend to earn higher returns on their connected holdings.^[5] The causation is genuinely unclear. Great managers may attract proximity to strong networks rather than gain their edge from that proximity, and there’s a plausible selection story where skill and network centrality are jointly determined. That ambiguity limits how much any individual investor can take from the finding.

The underlying question is more useful than the research. How does a manager gather information? How do they challenge their initial thesis? What specific signals cause them to exit a position they still believe in analytically? A manager who can answer those questions concretely is demonstrating the process discipline that survives losing periods. A manager who points to their conviction as the process has told you something honest, just not something reassuring.

The Alpha Audit

These three edges combine into a three-question framework for evaluating any active manager, or any investment thesis you’re building yourself.

Has this manager been right and been paid for it across a full cycle, including a real downturn? Can they articulate what has to happen for their current thesis to be correct in the market’s eyes, and what would invalidate it? How does their process work specifically when they’re wrong?

Concrete answers to all three suggest the edges that can actually exist within capital market constraints. Confident answers about past returns, a compelling narrative, and some version of “we take a long-term view” suggest something different.

The Buffett reference deserves a direct response, since he comes up in almost every conversation about active management. The 10-year public wager Buffett made against Protégé Partners (wagering that a low-cost S&P 500 index fund would outperform a portfolio of hedge funds over the decade starting January 2008) is correctly cited as evidence that passive beats active. It does support that. But the less-cited detail is that in the early years of the bet, the hedge funds partially cushioned 2008’s losses, and Buffett’s position appeared to be trailing.^[6] He maintained the thesis. The catalyst (sustained equity market appreciation over a decade) eventually materialized decisively.

Being correct is a necessary but not sufficient condition for being paid.

What this means for building conviction

The same framework applies when you’re the one making decisions.

Most individual investors who hold individual stocks have a thesis. Far fewer have articulated the specific catalyst that would cause the market to agree with them, or what they would do if that catalyst doesn’t arrive within a reasonable window. The behavioral research on investor trading documents a consistent pattern. People tend to hold losing positions too long, waiting for vindication, and sell winning positions too early, before the thesis has fully played out.^[7] The disposition effect, as it’s called in the behavioral finance literature, is partly a catalyst-discipline failure. The investor believes the thesis is correct but has no clear view on timing or the exit condition if the thesis breaks.

The Alpha Audit applied to your own portfolio is just two questions. What has to happen for this thesis to be correct in the market’s eyes? And what’s the exit if that doesn’t materialize in three to five years?

A manager or investor who can answer both concretely is working with the market’s constraints rather than against them. A fund manager who claims to have solved the guessing problem isn’t offering you an edge. They’re offering you a story. Those are different products, and over most measurement periods, the story has been the more expensive one.

The investors who tried to leave Burry’s fund in 2006 almost missed the vindication. The market eventually agreed with the thesis. By then, they’d been locked in for two years, holding a position that had bled premium and patience in equal measure.

The timing was the variable no one could control. Not Burry, and not them.

That’s the job.

This post is general education only and not financial advice. Past fund performance does not predict future results.

Footnotes

1. Michael Lewis, “Betting on the Blind Side,” Vanity Fair, April 2010. Available at vanityfair.com/news/2010/04/wall-street-excerpt-201004. For Scion Capital’s performance record, see also “Scion Asset Management,” Wikipedia, accessed February 2026.

2. S&P Dow Jones Indices, “The Story of Factor-Based Investing,” research report. Available at spglobal.com/spdji/en/documents/research/research-the-story-of-factor-based-investing.pdf.

3. S&P Dow Jones Indices, “U.S. Persistence Scorecard Year-End 2024,” published 2025. Available at spglobal.com/spdji/en/spiva/article/us-persistence-scorecard/.

4. Chunka Mui, “How Kodak Failed,” Forbes, January 18, 2012. Available at forbes.com/sites/chunkamui/2012/01/18/how-kodak-failed/. The broader point about timing risk in declining-industry shorts is analytic inference from the publicly available Kodak timeline; the specific trading outcome described is illustrative, not drawn from a documented case.

5. Lauren Cohen, Andrea Frazzini, and Christopher Malloy, “The Small World of Investing: Board Connections and Mutual Fund Returns,” NBER Working Paper 13121, May 2007. Available at nber.org. The paper finds that fund managers earn higher returns on stocks to which they have educational or board-level connections. Whether this reflects information advantage, or the joint determination of skill and network centrality, is contested in the literature.

6. Warren Buffett, Berkshire Hathaway 2017 Annual Shareholder Letter, released February 24, 2018. Available at berkshirehathaway.com/letters/2017ltr.pdf. Includes a year-by-year table of S&P 500 index fund returns vs. Protégé Partners’ fund-of-funds portfolio, 2008–2017. For the opposing view from Protégé’s side, see Ted Seides, “A new twist on an old bet with Buffett,” Financial Times, July 9, 2025.

7. Hersh Shefrin and Meir Statman, “The Disposition to Sell Winners Too Early and Ride Losers Too Long,” Journal of Finance 40, no. 3 (July 1985): 777-790. For a large-sample retail investor replication, see Terrance Odean, “Are Investors Reluctant to Realize Their Losses?” Journal of Finance 53, no. 5 (October 1998): 1775-1798.

The part that gets less attention is what inflation does to the speed of economic life. Inflation doesn’t just make things more expensive. It makes the race faster. Every participant in the economy has to move quicker, decide sooner, and adapt more aggressively just to stay in place. And the people who were already moving fast pull further ahead.

I want to work through this idea, because I think it changes how you should think about your career, your savings, and your investments during periods of elevated inflation, which I’ll define here as CPI-U running above 3% year-over-year for at least six months.

The Generic Take (And What It Misses)

Most inflation commentary follows a familiar script. Prices are rising. The Fed will raise rates to cool things down. In the meantime, buy TIPS, own some real estate, and don’t hold too much cash.

That advice isn’t wrong. But it treats inflation as a price problem with an investment-allocation solution. It skips the mechanism underneath.

Inflation does more than raise the cost of goods. I’d argue it also compresses decision timelines, increases the penalty for inaction, and amplifies the gap between those who can adapt and those who can’t. That’s a framework, not an empirical law, but I think it’s a useful lens.

Think of it this way. In a low-inflation world, you can afford to wait. You can sit in a job that underpays you by 5% without feeling it much. You can keep your emergency fund in a savings account earning close to nothing. You can delay a move, skip a negotiation, or put off optimizing your portfolio. The cost of inaction is small.

In a high-inflation world, all of those costs compound faster. If your employer adjusts pay once a year and inflation is running at 4%, that 5% gap to market rates widens in real terms with every month you wait, because your purchasing power is falling while market wages keep moving. If your savings yield is below the inflation rate, your emergency fund isn’t just sitting still. It’s shrinking in real terms every month. The longer you wait to act, the more ground you lose. Inflation turns inaction from a minor drag into an active penalty.

The Race-Speed Framework

To make this concrete, I think about inflation vulnerability along two dimensions.

Dimension one is asset composition. Are your savings mostly in cash and fixed-income instruments, or in equities and real assets that tend to grow with (or ahead of) inflation?

Dimension two is income flexibility. Is your income rigid and slow to adjust (salaried with infrequent raises, fixed-income dependent, or in a field with limited mobility), or is it flexible and responsive (you can switch jobs, negotiate, freelance, or capture new opportunities)?

These two dimensions create four positions.

Cash-heavy, rigid income. This is the most exposed position in an inflationary environment. Your savings are losing real value (assuming yields lag inflation), and your income isn’t keeping up. Retirees on fixed income with money in savings accounts sit here. So do workers in declining industries who haven’t renegotiated in years. Inflation erodes from both sides.

Cash-heavy, flexible income. You’re exposed on the savings side, but you have a lever to pull. You can job-hop for a raise, negotiate harder, or take on side work. If you’re young and early-career with most of your savings in cash, this is probably you. The fix is straightforward. Redeploy savings into assets that keep pace. But the window to act narrows as inflation persists.

Equity-heavy, rigid income. Your investments are working for you, but your paycheck isn’t. This often describes mid-career professionals with solid 401(k) balances but limited job mobility, whether because of specialization, geography, or life constraints. The portfolio hedges inflation over time, but if your income falls behind, you might be forced to draw down savings earlier than planned.

Equity-heavy, flexible income. This is the strongest position. Your assets appreciate with inflation, and you can adjust your income to keep pace or get ahead. Mobile professionals with diversified portfolios, business owners with pricing power, and high-demand workers in growing fields tend to land here.

The framework isn’t about judging anyone’s position. It’s about making the tradeoffs visible. When inflation is low, the difference between these quadrants barely matters. When inflation runs hot, the gaps widen fast.

The Job-Switching Accelerator

One place this dynamic shows up clearly is the labor market. During the 2021-2022 inflation spike, the wage growth premium for job switchers over job stayers widened significantly. The Atlanta Fed Wage Growth Tracker shows that median wage growth for job switchers peaked above 8% in mid-2022, while job stayers tracked closer to 5-6%, a gap much wider than the roughly 1 percentage point spread typical of the pre-pandemic years (2015-2019).^[1]

The logic is straightforward. When prices are rising quickly, companies compete harder for talent. New offers reflect current market rates. But existing salaries often lag, because a large share of employers adjust pay on an annual cycle (a 2024 WorldatWork survey found roughly half of organizations do so),^[2] and raises rarely match inflation in real time. The result is a growing gap between what you earn by staying and what you could earn by moving.

This creates a kind of musical chairs effect. Inflation makes staying in your seat progressively more costly. Switching gives you a reset to market rates, but it comes with risk. New environment, probation period, loss of tenure and institutional knowledge. The faster inflation runs, the more the math favors moving, and the more concentrated wage gains become among mobile, high-demand workers, while a larger group of stayers falls behind in real terms.

The Cash Paradox

The cash side is the hardest part to internalize. Cash feels safe during uncertain times. When markets are volatile and headlines are scary, the instinct is to hold more of it. But when savings yields fall below the inflation rate, cash turns from a safety net into a slow leak. (This isn't always the case. As of early 2026, top high-yield savings accounts offer around 4-5% APY^[3] while CPI-U is running at roughly 2.7% year-over-year,^[4] meaning well-placed cash can be roughly flat or positive in real terms. But during periods when inflation outpaces available yields, the erosion is real and persistent.)

To see how this works, assume $100,000 earning no interest, eroded by compound inflation (real value = nominal / (1 + inflation rate)^years)^[2]. At 3% annual inflation, that $100,000 loses about $2,900 in purchasing power the first year. At 5%, about $4,800. Over five years of 4% average inflation, you’ve lost nearly 18% of your real purchasing power (1 / 1.04^5 = 0.822, or a 17.8% loss).

In practice, your savings earn some yield. The table below shows two scenarios: zero interest (worst case) and a 1% savings rate (a rough proxy for a basic savings account, though high-yield accounts currently offer much more).

If your savings yield exceeds the inflation rate, you’re gaining real purchasing power. That’s the case for some savers today. But in prior inflationary episodes (think 2021-2022, when CPI-U peaked above 9% while most savings accounts paid under 1%), the erosion was severe.

Meanwhile, equities have historically outpaced inflation over long periods. Using Ibbotson/SBBI data, US large-cap stocks delivered approximately 7.3% in real annualized returns from January 1926 through December 2024.^[5] The problem is that “long periods” is doing a lot of work in that sentence.

The Timing Trap

Inflation gets genuinely tricky here, and simple advice falls short.

Equities are a strong long-term inflation hedge. Over decades, corporate earnings and stock prices tend to grow faster than the price level. But in the short term, rising inflation can hurt stocks, and has done so in several notable episodes.

When inflation picks up, interest rates typically follow, either because the Fed acts or because the market demands higher yields. Higher rates increase the discount rate applied to future cash flows, which means future earnings are worth less in today’s terms. Growth stocks with earnings far in the future get hit hardest. This is “duration sensitivity,” the same concept that makes long-duration bonds fall when rates rise.

So the asset class that protects you from inflation over a decade can punish you over a quarter. That creates a real dilemma. If you move from cash to equities during a period of rising inflation, you might suffer short-term losses even though you’re making the right long-term move. And if that shakes your conviction and you sell back to cash, you lock in both the drawdown and the ongoing inflation erosion.

The answer isn't to avoid equities. It's to understand that the timing of inflation hedging matters, and that the emotional experience of doing the right thing can feel terrible for a while.

What This Means in Practice

If I had to distill this into actionable takeaways, the Race-Speed Framework points to a few moves.

Audit your quadrant. Look at your savings composition and your income flexibility honestly. If you’re cash-heavy and income-rigid, inflation is working against you on two fronts. That’s worth addressing, even incrementally.

Don’t confuse safety with inaction. Cash feels safe, but when yields fall below inflation, its real value declines. At minimum, consider higher-yield alternatives for money you won’t need in the next 6 to 12 months. As of early 2026, top high-yield savings accounts offer roughly 4-5% APY^[3:1] and 3-month Treasury bills yield in the high-3% range,^[6] both above current CPI-U of approximately 2.7% (December 2025).^[4:1] That math can shift quickly if inflation re-accelerates.

Watch the job-switching premium. If you’re in a field where market rates are rising faster than your employer adjusts, the cost of loyalty is measurable. That doesn’t mean you should switch reflexively, but you should know what you’re giving up.

Prepare for the timing trap. If you’re shifting from cash into equities, understand that you may experience short-term losses even if the long-term math is favorable. The conviction to hold through that is the actual investment skill, and it’s worth developing before you need it.

Your personal inflation rate is not CPI. National inflation is an average. Your experience depends on what you buy, where you live, and how your income adjusts. The framework works best when you use your own numbers, not the headline figure.

The Bigger Picture

Right now, there are reasons to think about inflationary pressure. The AI capex boom is massive: Alphabet guided $75B in 2025 capex and signaled a sharp increase to $75-$85B or more in 2026^[7]. Amazon projected approximately $200B in 2026 capital spending^[8]. Meta guided $60-$65B for 2025 and $115-$135B for 2026^[9] and Microsoft’s quarterly capex has been running above $20B^[10]. That spending is flowing into the real economy in the form of construction, land, equipment, and labor demand, though the exact magnitude of those downstream effects is difficult to measure in real time. Whether that spending eventually produces enough productivity gains to be disinflationary is the trillion-dollar question, and the honest answer is that nobody knows.

What we do know is that large-scale capex tends to create winners and losers before the productivity benefits (if they come) arrive. The spending hits first. The efficiency gains come later. Maybe.

If you’re sitting in cash waiting for clarity, inflation is the price of that patience. And the more inflation picks up, the faster the race gets.

This is general education and analysis, not personalized investment advice. Do your own research and consult a qualified professional before making investment decisions.

1. Federal Reserve Bank of Atlanta. “Wage Growth Tracker.” https://www.atlantafed.org/chcs/wage-growth-tracker.

2. WorldatWork. “Report: 2024 Salary Increases Lower Than Projected.” Workspan Daily, May 7, 2024. https://worldatwork.org/publications/workspan-daily/report-2024-salary-increases-lower-than-projected.

3. Bankrate. “Best High-Yield Savings Accounts of February 2026.” https://www.bankrate.com/banking/savings/best-high-yield-interests-savings-accounts/.

4. U.S. Bureau of Labor Statistics. “Consumer Price Index Summary (December 2025).” BLS, January 13, 2026. https://www.bls.gov/news.release/cpi.nr0.htm.

5. Lamont, Duncan. “Scared of Investing As Stocks Hit All-Time Highs? Don’t Be.” Hartford Funds (whitepaper), August 29, 2025. https://www.hartfordfunds.com/dam/en/docs/pub/whitepapers/WP849.pdf.

6. Federal Reserve Bank of St. Louis. “Market Yield on U.S. Treasury Securities at 3-Month Constant Maturity (DGS3MO).” FRED. https://fred.stlouisfed.org/series/DGS3MO.

7. Saba, Ismail. “Alphabet forecasts sharp surge in 2026 capital spending.” Reuters, February 4, 2026.

8. Prasad, Ananya Mariam Rajesh. “Amazon projects $200 billion capital spending this year.” Reuters, February 5, 2026.

9. Hu, Krystal. “Meta boosts annual capex sharply on superintelligence push.” Reuters, January 28, 2026.

10. Hu, Krystal. “Microsoft capital spending jumps…” Reuters, January 28, 2026.

• Four hyperscalers plan to spend up to $670 billion on AI infrastructure in 2026, a scale comparable to the largest infrastructure booms in American history as a share of GDP.

• A meaningful share of AI “demand” may be inflated by venture-capital subsidies that keep consumer prices below actual inference costs. When those subsidies end, usage may not hold.

• The Demand Signal Audit is a four-question framework for testing whether an infrastructure buildout is responding to durable demand or a distorted signal.

• Efficiency breakthroughs (like DeepSeek) create a wildcard. They could shrink the need for infrastructure or, through Jevons paradox, amplify it.

• If demand proves inflated, the losses won’t hit evenly. Picks-and-shovels names already got paid. Hyperscalers hold stranded-asset risk. And ratepayers may absorb the grid overbuild.

Remember the first time you took an Uber?

For a lot of people, it was sometime around 2014. You tapped a button, a car showed up, and the ride cost less than a taxi. It felt like magic. What most riders didn’t realize was that many trips were being subsidized by venture capital. Uber sustained large operating losses for years, using promotions and below-cost pricing to build the habit, capture the market, and lock in the network effects. When the subsidies eventually faded, prices increased materially. The demand didn’t vanish, but it didn’t stay where it was either. Some riders went back to their cars. Some switched to competitors. The “growth” that investors had been pricing in turned out to be partly a mirage, fueled by artificial pricing.

Something similar may be happening right now with artificial intelligence. And the infrastructure being built to serve that “demand” could be the most expensive subsidy hangover in history.

The Numbers Behind the Buildout

In the last few weeks, four companies announced capital spending plans that, taken together, make every prior infrastructure project in American history look modest by comparison.

Microsoft, Meta, Amazon, and Alphabet are planning to spend up to $670 billion on AI infrastructure in 2026.^[1] A WSJ comparison chart puts that figure at roughly 2.1% of U.S. GDP (based on their estimate of nominal GDP). For context, U.S. railroad investment in the 1850s averaged roughly 1.7% of GDP, with a peak around 2.6% in 1854.^[2] The scale is comparable to the largest infrastructure booms in American history.^[1:1]

Amazon alone expects $200 billion in 2026 capital spending, nearly a 60% increase from last year.^[2] Meta’s 2026 spending could exceed 50% of its sales.^[1] These are staggering numbers, and markets have noticed. Amazon lost $124 billion in market value in a single session after its capex announcement.^[1:2]

The scale of spending raises a natural question. Is this the right amount?

But that’s actually the wrong question. The better question is this. Is the demand signal these decisions are based on trustworthy?

Follow the Signal Upstream

Here’s how the standard story goes. Cloud revenue is surging. AWS grew 24% to $35.6 billion in the most recent quarter.^[3] Azure grew 39% and Google Cloud grew 48%.^[3:1] AI workloads are driving the growth. Therefore, build more data centers.

That logic is clean on the surface. Demand is up, so supply should follow. But it skips a step.

Who is driving that cloud demand?

A meaningful share traces back to AI startups. Companies funded by billions in venture capital that are burning cash to acquire users. Think about it from the user’s perspective. When you use ChatGPT, Claude, or Gemini on consumer pricing plans, you are likely not paying the fully-loaded cost of what it takes to run your query. Multiple chips fire for every word generated. Large-scale AI inference requires enormous compute.^[4] The per-query cost is real, and many providers appear to be subsidizing consumer usage to grow their user base, though the exact unit economics remain opaque without detailed cost disclosures.

This creates a feedback loop that should make investors uncomfortable.

VC-funded startups subsidize AI usage, keeping prices artificially low. That subsidized usage shows up as surging demand on cloud platforms. Cloud providers report strong AI-driven revenue growth. Hyperscalers use that growth to justify massive capex increases. The impressive capex numbers and cloud growth then validate more VC funding into AI startups. And the cycle repeats.

The loop runs until someone stops funding it.

Anthropic, for instance, aims to break even by 2028 and implements aggressive rate limits on usage.^[5] Rate limits function as a rationing mechanism. They can reflect capacity constraints, abuse prevention, or pricing below fully-loaded costs. The fact that major providers use them suggests current pricing may not reflect the full economics of inference at scale.

OpenAI’s own numbers underscore the point. The company generated around $4.3 billion in revenue in the first half of 2025, according to The Information, and expects an annualized run rate of about $20 billion by year-end.^[9] But it still burned through $2.5 billion in net losses over the same period, largely on R&D and running ChatGPT.^[9:1] The conversion numbers are even more telling. As of July 2025, only about 5% of ChatGPT's weekly active users, roughly 35 million, paid for a subscription.^[9:2] Even in OpenAI's own projections, that figure reaches just 8.5% by 2030.^[9:3] The most optimistic internal case still has more than nine out of ten users on free or subsidized tiers five years from now.

The Demand Signal Audit

I want to propose a simple framework for thinking about this. Call it the Demand Signal Audit. It works for any infrastructure buildout where the demand justifying the spend might be distorted.

Question 1. Who is paying?

Is the end user paying a market-clearing price, or is the price subsidized by investors hoping to monetize later? In the current AI buildout, a large portion of consumer AI usage is priced below cost. Enterprise contracts are a different story, and if enterprise adoption (multi-year, committed spend) drives the majority of growth, the signal is stronger. The key metric to watch is the split between subsidized consumer/SMB API usage and profitable enterprise contracts in cloud AI revenue.

Question 2. What happens at breakeven pricing?

When prices rise to sustainable levels, does demand hold, drop, or migrate? Uber’s experience suggests demand drops but doesn’t disappear. AI may follow a similar pattern. Some users will pay full price because the productivity gains are genuine. Others will reduce usage. The crucial variable is how much of current demand falls into each bucket, and right now, nobody knows.

Question 3. Where does stranded-asset risk land?

If the buildout overshoots, who absorbs the losses? In the current cycle, the distribution is uneven. Nvidia and data center equipment suppliers get paid during the buildout regardless of what happens later. Hyperscalers bear stranded-asset risk on the data centers themselves. And depending on how power contracts and utility tariffs are structured, ratepayers and taxpayers may absorb grid overbuild costs if data center demand falls short of projections.^[6]

Question 4. Is efficiency additive or substitutive?

When the cost of something drops, do people use more of it (additive, per Jevons paradox) or do they consume the same amount for less money (substitutive)? This is the DeepSeek question.

The Efficiency Wildcard

In early 2025, a relatively unknown Chinese company called DeepSeek released a paper showing it had built an AI model at significantly lower training costs than Western competitors were reporting. DeepSeek later stated in a peer-reviewed Nature article that its R1 model cost just $294,000 to train.^[6] The market panicked. Nvidia lost $589 billion in market cap in one session, the largest one-day loss in market history.^[7]

But the panic missed the more interesting dynamic. If you can do the same thing with a tenth of the chips, what happens when you throw all the chips at the more efficient approach? Historically, the answer is Jevons paradox. When something gets cheaper, people use more of it, not less.^[4:1] Whale oil to kerosene to electricity. One lantern per household to dozens of light bulbs to ambient lighting in every room. Computing has followed the same pattern for decades.

But Jevons paradox depends on expanding use cases. Coal got cheaper and usage exploded because industrialization created thousands of new applications for cheap energy. If AI remains concentrated in chatbots, code generation, and content creation, efficiency improvements could genuinely reduce total compute demand rather than increase it. The buildout thesis assumes AI will expand into vision, video, robotics, scientific simulation, and domains we haven’t imagined yet. That may happen. It also may not happen as fast as the capex implies.

The honest answer is that nobody knows which side of Jevons dominates here. And that uncertainty alone should give investors pause when looking at $670 billion in committed spending.

Who Holds the Bag?

If the demand signal turns out to be partially inflated, the losses won’t distribute evenly.

Picks-and-shovels companies (GPU makers, data center builders, networking equipment suppliers) get paid in cash during the buildout. They benefit regardless of whether the long-term demand materializes. Their risk is cyclical. If capex slows, their revenue drops. But they don’t hold stranded assets.

Hyperscalers bear the heaviest concentration of risk. A $15 billion data center that runs at 40% utilization instead of 90% is still a $15 billion asset on the books. Depreciation doesn’t care about demand. The hyperscalers are making a massive, long-duration bet that usage will grow into the capacity they’re building. If it doesn’t, they eat the difference.

Ratepayers and communities face a quieter risk. Data centers require enormous, always-on power.^[4:2] Grid upgrades, new generation capacity, and transmission buildouts are being planned around projected data center loads. If those loads don’t materialize, the cost of overbuilt power infrastructure could fall on local ratepayers or taxpayers rather than on the tech companies that requested it, depending on how utility tariffs and line extension policies are structured.^[6:1]

This distributional question matters for investors. The “picks and shovels” framing sounds safe, but it depends on where you are in the cycle and what’s already priced in. The hyperscaler bet is more concentrated and more exposed to demand-signal accuracy. And the grid/utility play carries its own form of tail risk that most investors aren’t thinking about.

So What

This is not a prediction that the AI buildout will fail. AI is real. The productivity gains are real for many use cases. And unlike the dot-com bubble, frontier AI compute remains tight at leading labs and hyperscalers, even as utilization varies across organizations and workloads.^[4:3]

But “demand is real today” and “demand justifies $670 billion in new infrastructure” are two different claims. The gap between them is where the risk lives.

Here is what the Demand Signal Audit suggests investors should watch.

What to measure. Track the split between enterprise AI contract revenue and consumer/SMB API usage in hyperscaler earnings. If enterprise contracts are growing independently of subsidized consumer usage, the demand signal is more durable. If cloud AI growth is heavily weighted toward VC-funded startup spend, the signal is more fragile.

What risks to watch. LLM provider pricing changes are the canary. When Anthropic, OpenAI, or Google raises prices or tightens rate limits, monitor whether usage holds or drops. A sharp usage decline after a price increase is evidence that demand was subsidized, not organic. Watch also for monetization pivots. OpenAI expects to generate about 20% of its revenue from new products such as shopping and advertising features, a signal that subscription demand alone may not close the gap.^[9:3]

What tradeoff to accept. Picks-and-shovels positions are lower risk in the near term but carry cyclical downside if the buildout plateaus. Hyperscaler positions offer more upside if demand is real but more downside if it’s not. Grid and power plays depend on contract structures that most retail investors haven’t examined.

A falsifiable claim. If AI demand is significantly subsidized, we should see three things over the next 12 to 18 months. LLM provider losses growing faster than revenue. Usage dipping when rate limits or price increases hit. And a gap between enterprise AI contract growth and consumer/SMB API usage growth. If all three appear, the demand signal is inflated. If enterprise demand proves strong and independent of consumer subsidies, the capex is better grounded, and this thesis is wrong.

That’s the point. Not to predict the outcome, but to know what to look for.

This is general education and does not constitute financial advice. You should consult a qualified professional before making investment decisions.

Works Cited

1. The Wall Street Journal, “Big Tech’s AI Push Is Costing a Lot More Than the Moon Landing”, Feb 7, 2026. https://www.wsj.com/tech/ai/ai-spending-tech-companies-compared-02b90046?mod=economy_lead_pos2

2. The Wall Street Journal, “Amazon Shares Sink as Company Boosts AI Spending by Nearly 60%”, Updated Feb 6, 2026. https://www.wsj.com/business/earnings/amazon-earnings-q4-2025-amzn-stock-996e5cc2?mod=business_feat3_earnings_pos3

3. YouTube, “Why Everyone Is Wrong About the AI Bubble”

4. Rui M. Pereira, William J. Hausman, and Alfredo Marvão Pereira, “Railroads and Economic Growth in the Antebellum United States”, Working Paper No. 153, College of William & Mary, December 2014. https://economics.wm.edu/wp/cwm_wp153.pdf

5. The Wall Street Journal, “Anthropic Is on Track to Turn a Profit Much Faster Than OpenAI”, November 2025. https://www.wsj.com/tech/ai/openai-anthropic-profitability-e9f5bcd6

6. Reuters, “China’s DeepSeek Says Its Hit AI Model Cost Just $294,000 to Train”, September 18, 2025. https://www.reuters.com/world/china/chinas-deepseek-says-its-hit-ai-model-cost-just-294000-train-2025-09-18/

7. Bloomberg News, “Nvidia’s $589 Billion DeepSeek Rout Is Largest in Market History”, January 27, 2025. https://www.bloomberg.com/news/articles/2025-01-27/asml-sinks-as-china-ai-startup-triggers-panic-in-tech-stocks

8. Natural Resources Defense Council, “A Better Path to Managing Data Center Load Growth”, September 2025. https://www.nrdc.org/sites/default/files/2025-09/Data_Centers_R_25-09-A_04_locked.pdf

9. Reuters, “OpenAI projects 220 million paying ChatGPT users by 2030, The Information Reports”, Nov 25, 2025. https://www.reuters.com/technology/openai-projected-least-220-million-people-will-pay-chatgpt-by-2030-information-2025-11-26/

It’s a compelling story. It’s also mostly wrong. or at least, it’s wrong about where the costs actually are.

I’m skeptical of this approach. Not because AI hasn’t made building software faster (it has), but because “building the first version” was never the hard part. The hard part, the expensive, grinding, unglamorous part, is everything that comes after.

The Hidden Costs of “Free”

Here’s the pattern I keep seeing. A company pays somewhere between $15 and $1,000 per month for a SaaS tool. Someone on the team says, “We could build this ourselves, especially now, with AI.” And sometimes they’re right. An LLM can generate a working prototype surprisingly fast.

But then reality sets in. That prototype needs to talk to your CRM, your SSO provider, your data warehouse, and three other tools your team relies on. It needs to handle edge cases, the ones you don’t think about until they break at 2 AM. It needs security patches, uptime monitoring, compliance documentation, and someone to maintain it when the developer who built it moves on.

This is classic comparative advantage. A SaaS vendor spreads these costs across thousands of customers. Your internal tool bears them alone. When you do the math honestly, a company paying ~$50/seat/month for 100 users, about $60,000 a year, would need to compare that against the fully-loaded cost of at least one developer dedicated to building and maintaining the replacement. That’s often $150,000+ per year once you include salary, benefits, infrastructure, and the opportunity cost of not deploying that engineer on something that actually differentiates your business.

AI has dramatically reduced the cost of writing code. What it hasn’t reduced, at least not yet, is the cost of hosting, integrating, securing, supporting, and continuously improving software in a production environment. The first version is the visible tip of the iceberg. Everything beneath the waterline is where the real spend lives.

Where AI Actually Shifts the Line

None of this means AI doesn’t change the build-vs-buy calculus at all. It does, but in more targeted ways than the headline narrative suggests.

AI genuinely lowers the bar for certain categories of software: simple internal dashboards, lightweight workflow automation, one-off data tools, and internal apps that don’t need to be production-grade or customer-facing. If the tool is low-stakes, single-user or small-team, and doesn’t require deep integrations, building it yourself might now make sense where it didn’t before.

But for software that’s compliance-heavy, integration-dense, or mission-critical, think payroll, CRM, ERP, security tools, or anything that touches regulated data, the moat isn’t the code. It’s the years of edge-case handling, the audit trails, the API ecosystem, and the institutional knowledge baked into the product. AI doesn’t shortcut that. Not yet.

If I had to map it out, the spectrum looks something like this:

• More buildable with AI: Internal dashboards, simple reporting tools, lightweight task trackers, data pipelines, internal chatbots.

• Still firmly “buy”: Payroll/HR systems, CRM platforms, compliance/regulatory tools, ERP, anything requiring deep third-party integrations or contractual uptime guarantees.

The dividing line isn’t “can AI write this code?”, it’s “can my team own this system forever, and is that the best use of their time?”

The Smarter Threat: Unbundling, Not Rebuilding

Here’s what I think the market is actually (if clumsily) trying to price in: AI doesn’t replace SaaS by letting customers rebuild it. AI threatens SaaS by reducing seat counts.

If an AI agent handles 40% of the customer support tickets that used to require a human, the company doesn’t need as many Zendesk seats. If AI-generated analysis replaces some of the work a junior analyst did in a BI tool, the team might drop from 20 licenses to 12. The software itself isn’t going away, the number of humans who need to interact with it is shrinking.

That’s a pricing pressure story, not a replacement story. It’s real, it matters, and it’s a legitimate headwind for SaaS companies with seat-based pricing models. But it’s a fundamentally different (and slower) dynamic than “enterprises will just rebuild everything.”

For investors, the distinction matters. The “rebuild” narrative implies sudden, binary disruption, and it drove a sharp selloff. The “unbundling” thesis implies gradual margin and revenue pressure, which requires a different valuation framework and a different timeline.

What to Watch

If you’re evaluating software companies through this lens, here’s what I’d focus on:

• Net revenue retention (NRR): Are existing customers spending more or less over time? If NRR holds above 110%+, customers aren’t leaving, they’re expanding. If it drops below 100%, the seat-compression story is real.

• Integration depth: Companies whose products sit at the center of complex workflows (touching many other systems) have higher switching costs. That’s the moat.

• Pricing model evolution: Watch for companies shifting from per-seat to usage-based or outcome-based pricing, that’s a signal they’re adapting to the AI-driven seat compression dynamic.

• AI-native competitor traction: The bigger long-term risk isn’t internal rebuilds, it’s new entrants who build AI-first products that offer 80% of the functionality at 20% of the price. Watch startup funding and product launches in each software category.

The Bottom Line

AI changes the cost of the first version. It doesn’t change the cost of the hundredth update.

Build-vs-buy has always been a question of comparative advantage, not capability. Most companies are better off focusing their engineering talent on what differentiates them like their product, their data, their customer experience, and buying the commodity infrastructure around it. AI makes it easier to build, but it doesn’t make it smarter to build most of the time.

The selloff narrative that “everyone will just rebuild their SaaS for free”, is simpler than reality. And in markets, the gap between a simple narrative and a complex reality is often where the opportunity is.

This is general education and analysis, not personalized investment advice. Do your own research and consult a qualified professional before making investment decisions.

Take dividends, the bedrock of traditional valuation. These have faded as a guidepost. In the U.S., dividend yields have trended downward for nearly a century, falling from 5–6% in the early 20th century to under 2% today (Robert Shiller, Irrational Exuberance).

That drop isn’t about taxes; since 2003, qualified dividends and long-term capital gains have been taxed at roughly the same favorable rates. The deeper reason is corporate behavior: companies increasingly favor share repurchases over cash dividends. Buybacks are more flexible, often more tax-efficient, and allow management to support share prices without committing to a permanent payout. Also, cutting a dividend signals to investors weakness in the company, while a buyback is perceived as a one time event (executed over the course of months or years). In fact, S&P data shows that in many years since 2010, buybacks have outstripped dividends as the dominant form of cash return to shareholders.

Book value fares even worse in today’s economy, where intangible assets dominate the balance sheet of the largest companies. Intangibles now account for ~90% of S&P 500 market value. Google’s search algorithm or Coca-Cola’s brand equity don’t show up in “tangible book value,” yet they’re the true drivers of value for those companies. And P/E ratios? They reveal more about liquidity and sentiment than business fundamentals, expanding when capital is abundant and contracting when it dries up. These anchors may offer comfort, but they rarely explain the real, mechanical, force moving prices.

At its core, every asset’s price is set at the margin, by what the next buyer is willing and able to pay. Each bid and ask reflects not a company’s fundamentals, but the momentary balance between buyers with cash to deploy and sellers needing liquidity. That’s why marginal flows matter so much. Demographics channel retirement savings into equities, central banks add or withdraw liquidity through QE and QT, ETFs pull in capital automatically, and global investors seek safety in Treasurys. Even corporate buybacks act as powerful marginal buyers, supporting prices when other flows weaken. To see where markets are headed, don’t ask what an asset is “worth”, ask who holds the next dollar, and where it’s going next.

This lens matters because it explains some of the market’s most puzzling moves in recent years. The 2020–2021 tech rally, for instance, wasn’t driven by a sudden surge in corporate profits, it was fueled by a wall of liquidity, stimulus checks, and zero rates pushing marginal dollars into growth stocks. Crypto’s booms and busts follow the same script: prices surge when new buyers flood in and could collapse when those flows dry up. Even U.S. Treasurys, once the ultimate safe haven, have shown cracks in this framework. In several recent crises, they failed to rally because global marginal buyers, especially foreign central banks, are no longer stepping in with the same conviction. Foreign official investors’ share of total foreign Treasury holdings fell from 59% to 47% in recent years, with private investors now the larger foreign holder base. What looks irrational through the lens of fundamentals often makes perfect sense when you follow the money and ask where the marginal dollar is going next.

Investors should also ask where the past marginal dollars have come from too, and whether those buyers will quickly turn into sellers when conditions turn. Some capital is fickle and quick to exit, while other flows are effectively permanent. In Bitcoin, for instance, millions of coins are locked away forever due to lost private keys, creating a built-in floor of unsellable supply. Analysts estimate that between 2.3 million and 3.7 million bitcoins, or ≈ 11–18% of the total 21 million supply, have been permanently lost (e.g. due to lost private keys), tightening effective liquidity in the market. In equities, the same logic applies: only the most recent trades determine the current price. Tesla’s valuation, for example, reflects the sentiment of the latest buyers and sellers, not every shareholder. Average daily volume (ADV) measures how much of a stock changes hands each day; when ADV is small relative to total shares outstanding, prices are being set by a narrow slice of investors, and that concentration can make markets more fragile.

Stock prices don’t rise because companies “deserve” it, they rise when marginal buyers keep paying more. Valuation ratios assume capital is unlimited, but prices only move when new investors step in. Mispricings don’t correct themselves without fresh demand. That’s one reason why U.S. valuations stay elevated: the country has deep, steady domestic capital. U.S. household exposure to equities has reached record levels in 2025, reflecting a deep domestic buyer base. The true driver is capital at the margin. Over the next decade, the key question isn’t which companies look best on paper, it’s where global capital will feel safest and most productive. Nations with strong legal systems and enforceable property rights will keep attracting those flows. The edge goes to investors who position early, ahead of where the next marginal dollar must go.

The goal is to avoid becoming house poor, stretching for the biggest home you can afford and then struggling to furnish it, handle maintenance, or save. Because a home is often the largest purchase of your life, mistakes here carry far more weight than everyday financial slip-ups.

Okay, so let’s start with some baseline net worth percentages by wealth tier to understand where rich people have most of their money.

Here’s another chart showing more up to date information from the same survey, but with fewer details on the categories.

The key takeaway from these charts is that wealth breakdowns vary by the amount of wealth, and that’s normal. Your allocations should evolve over time, but the chart offers a sense of traditional benchmarks. The first important observation is that life changing wealth almost exclusively comes from ownership in a small business. If that is something you’re after, a small business seems to be the path to get there. Stocks tend to make up a smaller portion of net worth across wealth tiers, which is odd considering just how great they are at building net worth. To prove this point, here’s a ranked list of assets by appreciation, depreciation, and volatility:

At first glance, Bitcoin looks like a clear winner for asset appreciation. But with only about 15 years of reliable data, we don’t yet have enough history to judge its long-term returns. Its volatility is extremely high, making it a bad place to commit a large share of net worth until it’s tested across more market cycles. My rule of thumb here is that 30 years of data is needed to evaluate how an asset performs under different economic conditions.

So bringing this back to large purchases, the real goal isn’t just figuring out what monthly mortgage you can afford, it’s deciding how much of your net worth you want locked into a house versus left working in higher-return assets. A simple rule of thumb is to cap housing at around 30% of your net worth. So, if you have $1M, that means about $300K could be in home equity. With closing costs factored in, that $300K roughly covers a 20% down payment, which points you toward homes worth about $1.5M. The exact percentages depend on your financial goals, but the principle is the same: set your target allocation first, then let the math tell you the price range you should be shopping in. Think of the numbers not as rules, but as guardrails that keep you from overextending.

Remember, you also need to consider your income and what mortgage plus expenses you can sustain, but the same net worth principle applies to cars: if your net worth is $250K and you cap depreciating assets at 10%, your vehicles shouldn’t exceed $25K in value. Some advisors, especially in the FIRE movement, recommend keeping it closer to 5%. If you aren’t sure what the value of your cars are, I use Kelly Blue Book and Edmunds to figure it out every couple of months. Also, it’s important to note that these net worth targets may be unrealistic early on, but they’re useful goals to work toward.

So what’s a healthy way to allocate your net worth? It depends on your goals, but setting clear percentages early gives you a framework for these big purchase decisions. It helps you see the trade-offs, know what you can truly afford, and avoid overextending yourself. While advisors differ on specifics, a practical rule of thumb is to keep no more than 5-10% of your net worth in cars and no more than 25-30% in home equity. These guardrails keep your balance sheet flexible and your focus where it belongs, on building long-term wealth.

Key takeaways

1. The U.S. dollar’s reserve status is built on more than economic size, it rests on global trust. That trust is being eroded by surging debt, unsustainable interest costs, and political dysfunction. Reserve currencies don’t collapse when they run out of money, they collapse when the world loses confidence in how they use it.

2. Historical transitions show that monetary supremacy fades slowly and unevenly. From Britain to the Dutch Republic, the pattern is clear: persistent deficits, military strain, and diminished credibility degrade reserve currencies over time. The U.S. is showing signs of that same structural decay.

3. Military power is an underappreciated foundation of financial dominance. America’s global reach secures trade, deters conflict, and stabilizes markets, giving the dollar a unique “security premium.” But that premium depends on sustained deterrence, strategic clarity, and institutional strength, all of which are under pressure.

4. Monetary fragmentation is already underway, reflected in both policy and technology. Nations are experimenting with yuan-settled trade, digital currencies, and stablecoins, not as ideological rebellions, but as rational responses to a more uncertain U.S. fiscal trajectory and the weaponization of finance.

5. For investors, the shift doesn’t end global markets, it redistributes opportunity. The real winners won’t be those who guess the next reserve currency, but those who own the productive infrastructure of the future: energy, semiconductors, water systems, defense, and supply chain logistics.

6. The path forward isn’t about fear, it’s about positioning. In a world where trust disperses, value concentrates in what the world needs most. Investors should prioritize usefulness over allegiance, and durability over convention. Currency regimes may rotate, but productive assets endure.

The world reserve structure today

The dollar’s supremacy has long been treated as an inevitability. But inevitabilities have a way of becoming assumptions, and assumptions are dangerous in markets. With U.S. debt surpassing $36 trillion, and annual interest payments now exceeding $1 trillion, the foundation that supports the dollar is beginning to creak. This isn’t just about numbers, it’s about the global perception of U.S. credibility. Reserve currency status isn’t earned once and kept forever; it’s sustained through trust, discipline, and global confidence in your ability to manage power responsibly. And history shows what happens when that confidence slips.

The most immediate crack in that foundation of trust is fiscal: a debt burden growing too fast for even the world’s deepest markets to ignore. When a nation must borrow more just to service the interest on its existing debt, it enters what economists call a fiscal death spiral, a feedback loop where rising debt begets rising interest costs, which then require even more borrowing. This dynamic is exceptionally difficult to reverse without either significant austerity or sustained inflation. America’s national debt has now surpassed $36 trillion, with another $3–5 trillion likely on the way if proposed tax cuts are extended. Interest payments alone have topped $1 trillion per year, now exceeding even the U.S. defense budget. Investors are beginning to take notice. Moody’s has stripped the U.S. of its final AAA rating, Treasury auctions are showing signs of strain, and JPMorgan’s Jamie Dimon recently warned: “You are going to see a crack in the bond market. It is going to happen”.

To understand what might come next, it helps to look backward. The U.S. isn’t the first global power to face doubts about the sustainability of its economic model or the dominance of its currency. From Spain’s silver-fueled empire to France’s post-Napoleonic decline, history is full of examples where financial overreach, political turmoil, or shifting power dynamics led once-mighty currencies to lose their global standing. These stories don’t just belong to the past, they offer a mirror for the present.

Historical Parallels: Lessons from the Pound

Reserve currencies don’t collapse overnight, they erode slowly, often in tandem with rising debt, institutional strain, and shifting global power. Britain’s experience is instructive. In the 19th century, the pound sterling dominated global trade, anchored by the City of London’s financial reach and the gold standard’s credibility. But two world wars, mounting debt, and the rise of the U.S. industrial base slowly unraveled that dominance.

By the mid-20th century, confidence had shifted. The dollar, backed by America’s gold reserves and expanding military presence, overtook the pound, not through revolution, but through realignment. Britain’s decline offers a warning: reserve status isn’t a birthright. It’s maintained through global trust, institutional coherence, and economic scale, all of which can fray long before the consequences become visible.

Britain’s monetary unraveling also revealed a subtler truth: reserve currency decline isn’t always triggered by a singular event, it’s a slow leak of credibility across many fronts. Investors didn’t abandon the pound in one moment; they gradually reweighted exposure as Britain’s relative advantages faded. Inflation crept in. Sterling devaluations multiplied. Fiscal and trade imbalances worsened. Each erosion chipped away at confidence until the pound could no longer serve as the unquestioned store of value it once was. The U.S. today risks a similar trajectory, not because a superior alternative has emerged, but because persistent deficits, polarized politics, and unsustainable interest burdens are degrading the trust that once made the dollar feel rock solid. The lesson isn’t that dominance disappears overnight, it’s that trust, once diluted, is difficult to restore.

But America’s rise wasn’t built on economics alone. What truly set the U.S. apart, and what continues to anchor the dollar today, is its unmatched capacity to project power.

The connection between the military and the strength of the US Dollar

The U.S. dollar’s global dominance is often attributed to America’s vast economy, deep capital markets, and political stability. But those factors, while necessary, are not sufficient. The dollar’s strength is also underwritten by something more forceful: American military power. The U.S. doesn’t merely participate in global trade, it safeguards it. From the Strait of Hormuz to the South China Sea, American naval and aerial presence ensures the security of shipping routes, deters regional aggression, and upholds a global order in which commerce can flow. This gives the dollar a kind of “security premium”, a structural edge grounded in the assurance that the U.S. will enforce the rules if needed.

That military-backed order translates into financial privilege. It allows the U.S. to borrow in its own currency at low rates, to export inflation without triggering capital flight, and to sustain deficits that would be unsustainable for almost any other country. When financial stress hits, global investors still instinctively rush into Treasurys. That reflex isn’t just psychological, it’s rational. It reflects deep confidence that the U.S. will remain the stabilizing anchor in a volatile world, backed not only by institutions but by overwhelming force.

But cracks are beginning to show. One signal comes from the bond market itself. Normally, market selloffs trigger a flight to safety, sending Treasury yields lower. Lately, though, we’ve seen instances where Treasurys sold off alongside equities, a break from historical patterns. This was especially evident during recent tariff escalations, when markets panicked and bonds failed to provide their usual ballast. For some investors, this raised an unsettling question: What happens when the dollar is no longer seen as the unshakable safe haven? When both the financial and military guarantees behind it begin to feel less absolute?

This is why calls to drastically reduce military spending can carry unintended economic consequences. America’s defense commitments don’t just serve strategic goals; they underpin the credibility of the dollar itself. If the U.S. were to retreat significantly from its global security role, it would weaken the perception that the dollar is backed by the enforcement power necessary to maintain global order. In a world where confidence is currency, any signal that the U.S. might no longer underwrite stability could accelerate the search for alternatives.

And that’s where the real risk lies. The slow, internal erosion of trust, not sudden collapse, but steady decay, is what ultimately undermines monetary dominance.

Plausible ways the strength of the US dollar could fade away

Dollar dominance is unlikely to end with a bang, it frays quietly, through institutional decay and rising doubt. The dollar’s strength rests not just on economic output or military might, but on global faith in America’s governance. Repeated debt-ceiling standoffs, runaway deficits, and political gridlock don’t cause collapse, but they do plant doubt. And doubt is how reserve currencies unravel.

The yuan stands as the only politically viable alternative to the dollar, but even then, it’s a deeply flawed contender. China is actively promoting yuan-based trade, forging bilateral settlement agreements, and investing in digital currency infrastructure designed to bypass SWIFT. These moves are not just symbolic; they’re strategic, aimed at insulating China and its partners from U.S. financial leverage. In theory, if Beijing eased capital controls, improved legal transparency, and allowed for more independent institutions, the yuan could attract broader international use, especially among emerging markets already within China’s trade sphere. But that “if” remains enormous. The yuan is still tightly managed, the rule of law is subordinate to political aims, and foreign asset rights remain uncertain. For many global investors, these structural deficiencies make the yuan less a solution and more a hedge of last resort. China may be the only plausible alternative from a geopolitical standpoint—but it’s a reluctant fallback, not a clear successor.

Geopolitical shocks could nonetheless catalyze a shift. A major conflict in Taiwan or the Middle East, for example, could weaken faith in the dollar, not by default, but by breaking the illusion of unshakable U.S. stewardship. In times of stress, nations may fast-track alternative settlement systems, treat dollar-based sanctions as financial coercion, and reconfigure trade to limit exposure to U.S. oversight. In that world, trust doesn’t just erode, it fragments. And once fragmented, reserve currency status is rarely reclaimed on the same terms.

This fracture isn’t just theoretical, it’s already materializing, not only in policy but in code.

Cracks in the Monetary Foundation

The erosion of trust in the dollar isn’t just showing up in bond markets, it’s showing up in the design of new systems. Around the world, central banks are developing digital currencies, and institutions are turning to stablecoins: programmable, instantly settled tokens that bypass traditional channels. These tools aren’t driving fragmentation, they’re responding to it. When faith in governments falters, people look for systems they can verify, not just trust.

Stablecoins bypass SWIFT, settle in minutes, and run 24/7, offering a preview of a future built on distributed rails instead of legacy infrastructure. Their rise reflects growing unease with fiscal recklessness, sanctions overreach, and the fragility of existing systems. They’re not replacing the dollar, but they are hedging against its vulnerabilities.

For investors, this is a signal, not a crisis. Trust is dispersing, not disappearing. In a fractured system, resilience won’t come from picking the next dominant currency, it will come from owning the productive assets that endure no matter what: real cash flows, real solutions, and real economic value.

Investor Positioning in a Fractured World

The erosion of dollar dominance isn’t the end of markets, it’s the start of a realignment. And realignments reward those who position early.

The strategy isn’t to predict the next reserve currency. It’s to invest in what outlasts currency regimes altogether: productive, irreplaceable assets tied to the physical world. Own companies at the heart of global necessities, semiconductors, energy logistics, defense systems, clean water, rare minerals, and digital infrastructure. Favor geographies with fiscal restraint, legal transparency, and demographic resilience. Underweight assets dependent on artificially low rates, government credibility, or U.S. investor flows to sustain valuations.

As the dollar's centrality in global finance recedes, capital allocation will increasingly favor assets with intrinsic productivity and pricing power, particularly those insulated from U.S.-centric capital flows and interest rate policy. Companies tied to physical infrastructure, energy, semiconductors, water systems, and logistics, tend to benefit from inelastic demand, localization of supply chains, and fiscal stimulus tied to national security and reshoring. These sectors often possess high operating leverage, meaning earnings can scale faster than revenues in inflationary or realignment environments. Moreover, real assets tend to reprice upward as fiat trust wanes, serving both as inflation hedges and as new collateral for emerging financial systems. As capital flows fragment across multiple currency blocs, firms with multi-market exposure, pricing power, and tangible output will be better positioned to capture a disproportionate share of global earnings. In other words, the more fractured the system becomes, the more premium accrues to utility, necessity, and productive capacity, not to abstract growth narratives or leverage-fueled valuations.

This isn’t a call for panic, it’s a call for clarity. When the trust underpinning monetary systems disperses, usefulness prevails. In a fractured world, your portfolio shouldn’t bet on which flag flies over the reserve, it should bet on what the world cannot live without.

Key Takeaways

• Chart of the Week - The labor market is cooling, not collapsing, with job openings and unemployment both below average, signaling softer hiring demand that could ease wage-driven inflation and shift the Fed’s stance.

• Beyond Bias - Thinking everyone agrees with you isn’t just a bias, it’s a blind spot that can leave you overconfident, underprepared, and dangerously exposed when markets move the other way.

• Building Wealth - Your relationships aren’t just support, they’re strategy, compounding like capital to open doors, build resilience, and shape your financial future in ways money alone can’t.

• Historical Perspective - From 1945 to 1980, the U.S. quietly shrank its massive debt by letting inflation silently erode savings, offering a warning to today’s investors about the hidden cost of low bond yields and negative real rates.

• Literature Review - A small set of stocks drive most of the gains in factor investing, meaning DIY investors may do better focusing on overlap than chasing dozens of strategies.

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Chart of the Week:

The two data series in this chart—the U.S. unemployment rate and total nonfarm job openings—are foundational indicators of labor market health. Because they are measured in different units and on vastly different scales (unemployment is a percentage, typically between 3–10%, while job openings are raw counts in the millions), both series have been standardized to enable meaningful comparison. Standardization adjusts each series to have a mean of zero and a standard deviation of one, rescaling them without altering their underlying trends or relationships. This allows us to visually assess how the two variables move together over time and detect shifts in labor market tightness.

As of March 2025, both scaled unemployment (–0.744) and job openings (–0.777) sit below their historical averages, suggesting a labor market that is softening but not yet stressed. This position on the Beveridge Curve—low unemployment coupled with declining openings—signals that while layoffs remain limited, employers are slowing down their hiring, reversing the overheated dynamics of 2021–2022 when job openings surged amid labor shortages. That earlier period pushed the curve outward, reflecting market mismatches and hiring frictions. Today’s inward drift reflects normalization, but also growing employer caution. For investors, this is a subtle yet important shift: weakening demand for labor often precedes slower wage growth and household consumption, key drivers of economic momentum. The Fed watches this closely as a proxy for slack, and so should markets—it may be the clearest signal yet that labor-driven inflation pressure is fading.

Beyond Bias: False Consensus - When You Mistake Agreement for Accuracy

The false consensus effect is a cognitive bias where individuals overestimate how widely their beliefs, preferences, and behaviors are shared by others. First identified by Ross, Greene, and House (1977), this phenomenon distorts how we interpret consensus, leading us to believe our views are more common and objectively correct than they actually are. In financial contexts, this bias fuels misplaced confidence: bullish investors assume “everyone sees the upside,” while bears feel validated by their echo chambers. Research by Marks and Miller (1987) found that individuals with high confidence in their beliefs showed an even stronger false consensus effect, reinforcing the idea that conviction often substitutes for evidence. In markets, where diversity of opinion is what creates price movement, assuming that others think like you can be especially dangerous. It leads investors to ignore contrarian signals, dismiss dissenting views, or stay overweight in assets long after the crowd has moved on.

To counter the false consensus effect, investors need to actively seek out dissent, and treat disagreement not as a threat, but as data. One powerful approach is considering the opposite, a technique highlighted in research by Lord, Lepper, and Preston (1984), which found that deliberately generating counterarguments reduces biased assimilation and strengthens judgment quality. Portfolio reviews should include a formal “disagreement checkpoint”: ask, What would I need to see to change my view? or Why might a smart person take the opposite side? Participating in diverse forums, reading across ideological lines, and exposing yourself to performance data that contradicts your assumptions can all help deflate the illusion of consensus. In investing, as in life, the scariest position to be in isn’t being wrong, it’s thinking you’re right and assuming everyone else agrees.

Building Wealth: Social Capital as Leverage - The Hidden Asset Behind Opportunity

Social capital, your network of relationships, trust, and shared norms, is often dismissed as a soft skill. But research shows it functions more like high-leverage capital, capable of unlocking career opportunities, financial resilience, and psychological well-being. Unlike financial capital, which compounds through market returns, social capital compounds through reciprocity and reputation. Sociologist Mark Granovetter’s seminal work, The Strength of Weak Ties (1973), found that people are more likely to find job opportunities through acquaintances than close friends, because weak ties expose us to novel information and non-redundant connections. More recently, Raj Chetty’s Social Capital Atlas (2022) found that upward income mobility correlates most strongly with cross-class friendships, what he calls “economic connectedness”, even more than school quality or neighborhood income. In other words, who you know, and who they know, can directly impact your financial trajectory.

Building social capital is less about transactional networking and more about consistent, authentic participation in meaningful communities. That could mean joining a professional Slack group, hosting a quarterly dinner, or volunteering in a way that lets others see your reliability and values. Research from Putnam’s Bowling Alone (2000) emphasizes that high-trust societies and organizations tend to have stronger economic outcomes because trust reduces friction in cooperation and decision-making. Practically, this means being generous with introductions, following through on small commitments, and offering expertise without immediate return. As with any form of capital, compounding takes time, but over years, a reputation for reliability and generosity can yield asymmetric upside in the form of job offers, referrals, collaborations, and resilience during downturns. In a world where algorithms and institutions increasingly gatekeep opportunity, social capital remains one of the few assets that you can grow organically, and deploy exponentially.

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Historical Perspective: The Financial Repression Era - When Debt Got Inflated Away

In the wake of World War II, the United States found itself with government debt exceeding 119% of GDP, an unprecedented burden at the time. But rather than pursue austerity or default, policymakers quietly engineered a long-term debt workout by manipulating the real returns savers received. This strategy, known as financial repression, relied on keeping interest rates artificially low, maintaining capital controls to trap domestic savings, and allowing inflation to erode the real value of government liabilities. From 1945 through the early 1980s, this approach enabled the U.S. to shrink its debt-to-GDP ratio by nearly two-thirds, even as it ran primary budget deficits through much of the period (Reinhart & Sbrancia, 2011).

At the heart of this strategy was the capping of nominal interest rates. The Fed, via the 1942 Treasury-Fed Accord, had promised to peg short-term rates at 0.375% and long-term bond yields at 2.5%, a policy that remained in place until 1951. During the 1940s and 1950s, inflation averaged nearly 4%, meaning bondholders consistently earned negative real returns. In effect, savers were taxed in a way that never showed up in legislation or headlines. Between 1945 and 1980, the average annual real return on Treasury bills was –0.3%, and for long-term government bonds it was just 1.2%, well below equity or commodity returns (Siegel, 2014).

This repression wasn’t limited to the U.S. Capital controls ensured domestic investors couldn’t easily seek higher yields abroad. Bank regulation (e.g., Regulation Q) limited the interest paid on deposits, pushing savers into low-yielding government bonds. Pension funds and insurance companies were nudged or required to hold large allocations of government debt. The result was a captured investor base funding public obligations at below-market rates. Inflation, meanwhile, was tolerated, and sometimes encouraged. Between 1945 and 1980, the U.S. had 13 years where inflation exceeded 5%, further accelerating the erosion of debt burdens in real terms.

The parallels to today’s macro environment are striking. Public debt has once again surpassed 100% of GDP. Central banks have used QE to absorb government bond issuance, suppress yields, and stabilize markets. Real rates remain deeply negative, and fiscal authorities appear willing to tolerate elevated inflation so long as it supports nominal growth. For long-term investors, this historical precedent raises uncomfortable questions: Are your savings once again being used to solve someone else’s debt problem?

Understanding financial repression matters because it reshapes the risk-reward profile of asset classes. In such an environment, nominal returns on bonds may look stable, but their real purchasing power erodes silently. Investors seeking protection must think more like the postwar generation that turned to real estate, equities, and scarce assets like gold, not because they chased yield, but because they recognized that government debt wasn’t a store of value. The past, once again, may be prologue.

Works Cited

• Reinhart, C. M., & Sbrancia, M. B. (2011). The Liquidation of Government Debt. NBER Working Paper No. 16893. https://www.nber.org/papers/w16893

• Siegel, J. J. (2014). Stocks for the Long Run (5th ed.). McGraw-Hill.

• U.S. Bureau of Labor Statistics. Historical CPI data. https://www.bls.gov/cpi

• U.S. Federal Reserve. (1951). Accord between the Treasury and Federal Reserve.

• Eichengreen, B. (2008). Globalizing Capital: A History of the International Monetary System. Princeton University Press.

Literature Review: The Overlap Illusion – Why Most Factor Investing Gains Come from a Few Stocks

A new working paper by Alexander Y. Chen (2025) quietly upends decades of assumptions in factor investing by asking a simple but overlooked question: what if the same handful of stocks are driving all the outperformance across seemingly different anomalies? Using data from 1926 to 2023 and 164 published anomalies, Chen finds that the vast majority of alpha, a measure of risk-adjusted return, can be traced back to a small overlapping subset of stocks that appear repeatedly in long or short legs of anomaly portfolios. These “overlap stocks” may explain why factor investing often works in backtests, why it fails in practice, and how investors could simplify their portfolios without sacrificing performance.

Chen’s key insight is that a small group of stocks show up across many different anomaly strategies (e.g., value, momentum, profitability), and these overlap stocks deliver disproportionately strong returns. For example, just 10% of stocks each month are responsible for nearly 80% of the cumulative returns across all anomaly portfolios. The study constructs a simple portfolio consisting only of these “top overlap” stocks and finds that it outperforms most individual anomalies, by a wide margin. A value-weighted version of the 90th percentile overlap portfolio generates a Sharpe ratio of 0.65, more than 50% higher than the average unfiltered anomaly. Sharpe ratio, for context, measures return per unit of risk, so a higher value indicates a better risk-reward tradeoff.

Crucially, Chen shows that excluding these overlap stocks from traditional anomaly portfolios causes performance to collapse. Filtering out just the top 10% of overlap stocks cuts average alpha in half. Even for categories like momentum and profitability, widely seen as robust, removing the overlap stocks slashes abnormal returns by 40–80%. This finding holds across decades and across different weighting schemes. For DIY investors or fund managers chasing factors, the implications are sobering: most factor premiums appear to rely on a small, shared set of stocks, not a broad or diversified cross-section. This helps explain why so many factor ETFs underperform, if they fail to capture the key overlap names, they miss the alpha.

What causes these overlap stocks to outperform? Chen finds compelling evidence that investor mispricing plays a central role. Analysts tend to be overly optimistic about short-leg stocks (those expected to underperform) and overly pessimistic about long-leg stocks. This results in large forecast errors, stocks most heavily shorted by anomaly strategies are predicted to return 47% but actually earn only 12%, while top long-leg stocks are forecast to return 28% but deliver 36%. These biases mirror earlier work by Engelberg, McLean, and Pontiff (2020), who found that anomalies often reflect analyst misperceptions. In this paper, Chen links mispricing directly to anomaly overlap and shows that these biases persist through earnings announcements, further reinforcing the case for behavioral inefficiency.

For individual investors, the takeaway is surprisingly hopeful. Rather than trying to replicate dozens of academic strategies or guess which factor ETF will outperform, it may be more effective to identify the small subset of stocks that are repeatedly flagged by different signals. The paper doesn’t offer a ready-made list, but the principle is clear: overlap matters more than category. Investors should also beware of filtering out stocks for liquidity or cost reasons if doing so removes these high-impact names. And while the idea of mispricing may concern purists, the persistence of these errors, spanning decades, suggests that behavioral patterns may offer real, repeatable opportunities. In a world overflowing with smart-beta products, Chen’s paper offers a refreshing reminder: sometimes, simpler and more concentrated portfolios deliver better results, not because they’re complex, but because they’re focused on where the real action is.

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TL;DR: Alphabet’s fundamentals remain strong, but its long-term value proposition is increasingly under pressure. From platform displacement and AI margin shifts to real antitrust rulings, Google is no longer the unassailable tech anchor it once was. Investors must weigh its valuation discount against threats to its dominance in both distribution and monetization.

Alphabet is cheaper than its big tech peers, richer than most countries, and still controls how billions access the internet. But here’s the twist: none of that guarantees it will lead the next era. This isn’t another bullish tech stock write-up, it’s a closer look at whether Google is defending a fortress, or quietly losing the war.

1. A Giant at an Inflection Point

For the better part of two decades, Alphabet, Google’s parent company, was synonymous with digital dominance. It owned search. It built the world’s most powerful advertising machine. It acquired YouTube, scaled Android, mapped the world, and pioneered breakthroughs in artificial intelligence. If you were building a future-focused portfolio, Alphabet was the cornerstone.

But in 2024 and into 2025, something began to shift.

The AI revolution that Google helped spark, publishing the original Transformer paper in 2017, was no longer centered around Mountain View. Instead, Microsoft and OpenAI surged ahead in mindshare, product innovation, and enterprise integration. Meanwhile, Google’s core business started showing signs of strain. Ad revenue growth slowed. User behavior changed. The company’s once-legendary ability to ship intuitive, market-defining products seemed dulled by internal inertia.

And yet, on the surface, Alphabet remains financially formidable:

• In Q1 2025, revenue rose 12% year-over-year to $90.2 billion.

• Net income surged 46% to $34.5 billion, an astounding profit figure in any macro environment.

• The company trades at a relatively modest valuation, with a forward price-to-earnings ratio near 17, well below peers like Microsoft and Amazon.

This divergence, between financial strength and strategic uncertainty, is the tension that defines Alphabet today. Investors are left asking: is this a misunderstood value opportunity or the slow decline of a company whose future is no longer its own?

2. The Cracks Beneath the Surface

Alphabet’s top-line numbers are still impressive. But just beneath the surface, investors are beginning to notice fundamental cracks in the foundation, particularly around user behavior, platform dependency, and advertising resilience.

Despite all its moonshots and diversification efforts, Google’s business remains strikingly concentrated:

• Over 75% of Alphabet’s revenue still comes from advertising, primarily from Search and YouTube.

• The rest, Cloud, hardware, and experimental bets like Waymo—remain too small to offset meaningful pressure in the ad business.

That pressure is building. LLMs like ChatGPT and Perplexity are beginning to displace traditional web search for many types of queries, especially factual or exploratory ones. And perhaps more significantly, social platforms are becoming the default discovery layer for a generation of users:

• Nearly 40% of Gen Z now starts product searches on TikTok or Instagram, not Google.

• These platforms don’t just respond to intent, they shape it. And that upstream control over demand is precisely what Google failed to capture.

The risk here isn’t just user defection. It’s the collapse of the economic logic behind Google’s core model. Fewer searches mean fewer opportunities to show ads. And fewer clicks on those ads, especially if LLMs provide full answers, mean less revenue.

Investors must ask: can a company so reliant on a single, aging paradigm pivot fast enough before monetization erodes further?

3. The Innovator’s Dilemma: Why Google Missed the Moment

Google didn’t just participate in the AI revolution, it helped ignite it. The company’s 2017 paper introducing the Transformer architecture became the bedrock of every major language model in use today. But when AI finally went mainstream, Google was nowhere near the center of the conversation.

OpenAI’s launch of ChatGPT in late 2022 captured global attention. Microsoft quickly followed by embedding the technology across its product suite, Word, Excel, Outlook, rebranding itself as the AI productivity leader. Google, despite having the technical chops and infrastructure, watched from the sidelines. Why?

A few answers point back to culture and incentives:

• Google’s internal culture prizes research and engineering excellence over shipping polished products fast.

• Efforts like Gemini (formerly Bard) have been rushed, error-prone, or confusing to users, undermining trust just as OpenAI was building it.

• Most importantly, Google is incentivized not to disrupt itself. AI assistants that answer questions directly threaten to cannibalize its search ad revenue.

This is the classic innovator’s dilemma: a company so dominant in the current paradigm that it hesitates to embrace the next one. Rather than leading the charge into AI-native experiences, Google has spent much of the past two years in defensive posture, responding, refining, and repositioning while its competitors captured the moment.

For a company with Google’s pedigree, that hesitancy has already proven more costly than any technical shortcoming.

4. Where the Margin Goes: A Value Chain Under Siege

To understand Google’s AI dilemma, we need to follow the money. Building and deploying large language models isn’t just a technical challenge, it’s a capital-intensive, energy-hungry process. And increasingly, the real profits in AI are flowing elsewhere.

The value chain for generative AI spans multiple layers: energy providers, chipmakers, cloud infrastructure, model builders, and interface owners. Alphabet plays a role in several, but it’s losing ground in the ones that matter most.

• NVIDIA is capturing the lion’s share of early AI margin, thanks to dominance in GPUs used for training and inference. The company’s data center revenue doubled year-over-year in 2024, with gross margins north of 70%.

• Microsoft has secured monetization through distribution. By embedding OpenAI models into Office and charging $30/user/month for Copilot, it’s proving that AI doesn’t have to cannibalize, it can compound existing revenue.

• Meanwhile, Google’s CapEx is ballooning. The company expects to spend $75 billion in 2025, largely on AI infrastructure. Yet it has offered no credible explanation for how this spend translates to revenue or margin expansion.

Google still owns some valuable real estate, Search, Android, YouTube, but if users shift to AI interfaces that aren’t monetized the same way, the company risks becoming just another API, not the gateway.

And APIs rarely get the best margins.

5. The Bull Case: Can Google Still Play Offense?

The bear case is clear: Google’s dominance in search is under siege from both AI-native disruptors and legal rulings that strike at the heart of its monetization model. Its cultural DNA, once its superpower, has made it slow to ship, cautious to cannibalize, and reluctant to bet boldly. But that same pressure is now forcing a change. The bull case hinges on whether Alphabet can break its own mold, and in 2025, there are growing signs it has started to do just that.

AI Mode: A Full-Stack Counteroffensive

The most important bullish development is already underway. In May 2025, Google began rolling out AI Mode, a major overhaul of its flagship product, Search. This feature replaces the traditional “ten blue links” with a chatbot-style interface powered by Gemini, offering direct answers, interactive summaries, and even features like virtual try-ons for shopping.

This isn’t just a UI tweak. It’s a full-stack integration of Gemini across Google’s largest distribution channels, Search, Android, and Chrome. AI Mode represents a reimagining of the interface layer, and for the first time, Google appears willing to risk cannibalization of its own search ad model in order to defend its primacy.

Unlike OpenAI, which had to build distribution through partnerships, or Perplexity, which started from scratch, Google owns the gateway. And now it’s using that control to test, iterate, and deploy generative AI at unprecedented scale. AI Overviews already reach 1.5 billion monthly users. If AI Mode sticks, Google will control not just where people search, but how they interact with information in an LLM-native world.

From Engineering-First to User-Obsessed?

The next hurdle is cultural. Google has long prized research excellence over product polish. But the Gemini rollout suggests a subtle but critical shift: borrowing a page from Apple’s playbook, Google is now showing signs of building not just technology, but experiences.

• Gemini’s integration across Gmail, Docs, Android, and Search reflects a broader move toward user-centric, AI-native design.

• Executives are publicly discussing monetization through experience-led ad formats rather than engineering-led infrastructure monetization.

• The company is considering bundling Gemini into Siri through a distribution agreement with Apple, an aggressive pivot in narrative from laggard to partner.

If this cultural pivot deepens, toward product velocity, design thinking, and customer empathy, Alphabet could begin to close the perception gap that opened after OpenAI’s rise.

The Optionality of Scale

Underneath these moves sits an empire of strategic assets:

• Search: Still commands 8.5 billion queries a day.

• YouTube: The second-most visited site globally, expanding monetization through Shorts, Premium, and creator tools.

• Android: The largest mobile OS, ripe for embedded AI integration.

• Cloud: Google Cloud Platform is growing 28% YoY and is now profitable, boosted by AI-related workloads.

These give Alphabet an optionality engine that few can match. With $100 billion in cash and a $70 billion buyback plan, it also has the financial strength to invest aggressively, even inefficiently, and still generate massive shareholder returns.

Valuation as Strategic Mispricing?

Alphabet trades at ~17x forward earnings, more in line with cash-flow utilities than with peers like Microsoft (~29x) or Amazon (~41x). That multiple reflects real concerns: regulatory risk, cultural stagnation, and uncertain AI monetization. But it also embeds remarkably little upside from potential pivots that are already underway.

With $100 billion in cash, a $70 billion buyback in motion, and 8.5 billion daily queries still running through its ecosystem, Alphabet has time, tools, and traffic. What it lacks, so far, is a narrative of execution.

The market is pricing for stagnation. If Gemini gains traction, if AI Mode re-anchors monetization, or if Cloud grows into a true second engine, that perception breaks. And at 17x earnings, that break could rerate the stock dramatically.

In that sense, Alphabet’s valuation doesn’t just offer a cushion, it offers a call option on reinvention.

6. What Kind of Bet Is Google Today?

Alphabet isn’t a pure-play AI moonshot, and it isn’t a legacy dividend stock either. Buying Google today is a complex, high-stakes allocation decision. What you’re really betting on is this:

• You believe Google’s distribution advantage is still underpriced. Despite losing narrative control, it still owns the pipes, Search, Chrome, Android, and AI Mode is a serious play to modernize them.

• You think execution will follow intention. Google has shown it can pivot with AI Mode, Gemini integration, and early monetization tests. The bet is that this culture shift sticks.

• You’re willing to wait for monetization to catch up. You accept that Gemini won’t print money tomorrow, but that the infrastructure, user base, and ad DNA are there to eventually do so.

• You see GCP as an underappreciated call option. It’s profitable, growing, and tied to the same AI workloads that make NVIDIA a market darling, just without the same hype.

• You’re not scared off by antitrust risk yet. You believe regulatory threats may be disruptive, but not fatal, and that the market is already partially pricing in the worst.

But this is not a bet for everyone!

• If you need clear leadership in AI monetization today, look to Microsoft.

• If you want clean financial narratives without regulatory fog, this isn’t it.

• If you favor proven disruptors over late adapters, this may frustrate.

Alphabet’s story is shifting from inevitability to execution. If you think it can still shape the next chapter of the internet, not just defend its corner of the last one, then Google offers asymmetry at a discounted multiple. But it requires conviction in change, not just comfort in cash flow.

7. Antitrust: A Now-Realized Risk to Alphabet’s Valuation

While headlines focus on Google’s AI race, the most valuation-relevant development may be regulatory: the U.S. Department of Justice (DOJ) has now won a landmark antitrust case against Google’s ad tech business. This isn’t a theoretical risk anymore, it’s a judicial ruling that affirms Google’s monopoly power and opens the door to structural remedies. A second case, focused on default search deals, is still pending, but the precedent has been set.

What just happened?

• In April 2025, the U.S. District Court for the Eastern District of Virginia ruled that Google violated antitrust laws by monopolizing open-web digital advertising markets.

• The Court found that Google harmed publishers and consumers by manipulating auctions, acquiring rivals, and engaging in exclusionary practices.

• Remedies have not yet been finalized, but forced divestitures (e.g. of DoubleClick, AdX) are back on the table.

Why this matters for investors

Regulatory risk is no longer just a headline, it’s an evolving impairment to Alphabet’s economic moat. Here’s how that translates to valuation mechanics:

• Ad Tech Business Model Disruption

  If courts force a breakup of Google’s ad stack, vertical integration benefits could unwind:

  • Reduced take rate from fragmented services

  • Loss of end-to-end data feedback loops

  • Operational complexity and margin compression

  • Reputational and legal distractions for years

• Search Business Next in Line

  A separate DOJ case targeting Google’s default search contracts (e.g. with Apple) is still pending. If that case results in bans on distribution payments:

  • Google could lose default status on iOS and Android

  • Jefferies estimates a 25–40% hit to Search EBIT if iOS defaults are lost

  • The $20B+ annual TAC spend (Bernstein, 2023) is a defensive moat, removal could expose user churn

• Valuation Multiple Compression

  With regulatory headwinds now material, equity risk premiums expand:

  • Higher discount rates applied to uncertain cash flows

  • Lower confidence in the duration of excess returns, key to Alphabet’s DCF value

  • Investor base rotation away from mega-cap growth toward more defensible, less exposed names

Has the market already priced this in?

• Alphabet trades at ~17x forward earnings, a steep discount to Microsoft (~29x) and Amazon (~41x)

• Some of this reflects:

  • Lower cloud margins vs. Azure/AWS

  • Uncertainty around AI product monetization

  • Antitrust overhang

• A structural remedy, like an ad tech breakup or TAC ban, would force a wholesale reassessment of Alphabet’s intrinsic value. This isn’t just a priced-in risk; it’s a latent bomb still being processed.

Precedent and Investor Takeaways

• Structural breakups are rare, but so was a clear-cut judicial win of this magnitude.

• A good historical analog may be Microsoft in the early 2000s: legal pressure didn’t kill the business but did stall its stock for nearly a decade.

• Long-term investors must ask:

Are Alphabet’s profits protected by innovation and network effects, or by contracts now deemed illegal?

8. Verdict: Google Is Down, Not Out

Alphabet is no longer the automatic buy it once was. The easy narrative, dominant platform, unassailable moat, AI pioneer, has fractured. But investors shouldn’t confuse fractured with broken.

The new investment case is more nuanced: Alphabet today is a bet on reinvention. It’s a bet that Google can convert its massive distribution surface and technical depth into a compelling AI-native product experience before its traditional monetization layers erode too far. And there are signs it’s finally doing just that.

With the rollout of AI Mode, Google has shown it’s willing to risk disruption of its own flagship product in order to stay relevant. It’s integrating Gemini not only into Search, but across Android, Chrome, and Workspace. These aren’t isolated experiments, they’re full-stack deployments aimed at redefining how users engage with information. If they succeed, Google won’t just preserve attention, it will re-anchor monetization in the AI age.

But execution remains the wildcard. Google must overcome its historical tendency to over-research and under-ship. It must evolve from an engineering-first culture into one that obsesses over product design, user experience, and bold bets on market fit. And it must do all this under the cloud of real regulatory action, with antitrust remedies that could reshape its ad tech and search distribution economics.

For investors, Alphabet may still appeal if:

• They believe AI Mode and Gemini integration represent a real strategic pivot, not just a PR response to competitive pressure.

• They view the valuation (~17x forward earnings) as a margin of safety, offering upside if any one of AI, Cloud, or YouTube reaccelerates meaningfully.

• They trust in Alphabet’s scale, cash flow, and optionality to buy it time to adapt, even in a hostile regulatory environment.

But the stock may be less compelling for those prioritizing:

• Clear AI product leadership and monetization wins today, not promised for tomorrow,

• Predictable margin durability in the face of antitrust-driven structural remedies,

• Or companies already shaping the next internet experience, rather than catching up to it.

The bottom line: Alphabet’s dominance is no longer guaranteed, but its reinvention is underway. For investors who see a latent execution engine hidden beneath regulatory fog and cultural drag, today’s price offers more than safety, it offers asymmetry.

Key Takeaways

• In the News - Tariff turbulence is the economy’s stealth headwind: April CPI was a calm +0.2% m/m and 2.3% y/y, but ADP hiring plunged to 62k in April, down from 147k in March, and NFIB optimism slid to 95.8. Trump’s whipsaw levies, 145% on some Chinese goods one day, a 90-day partial truce the next, are denting cap-ex, hiring, and housing before they hit the CPI.

• Chart of the Week - Time-frame roulette can flip any narrative: YTD-2025 the Magnificent 7 are -6 percentage points behind the S&P 500, but zoom out to Jan-2024 and they’re +60% vs +25%, a reminder that higher-beta winners retrace hard yet still dominate long runs.

• Beyond Bias - Projection bias makes today’s tastes look permanent: Personality research shows more change between ages 20-40 than after 50, yet investors still panic-shift to cash or over-consume on the assumption their current risk tolerance or hobbies won’t evolve. Saving is the hedge against your own shifting future self.

• Building Wealth - Your highest-yield assets don’t trade on an exchange: Time, health, autonomy, and relationships explain more life-satisfaction variance than income (Diener & Seligman, 2004); a weekly walk with a friend or a protected “no-meeting Friday” can deliver compounding psychological dividends that crush the ROI of most portfolio tweaks.

• Historical Perspective - The 1974 Trust Crisis shows what happens when confidence snaps: Watergate, 11% inflation and the oil embargo sent consumer sentiment to record lows and gold from $65 to $180/oz; today’s rush into crypto and bullion echoes that same flight from institutions, consensus is a fragile asset class.

• Literature Review - AI ‘attention signals’ beat ‘action signals’ for durable skill-building: In a 2,484-game chess randomized controlled trial (RCT), action cues lifted accuracy most in the moment, but attention cues delivered 40% of the gain with positive spillovers to later moves and higher perceived agency, designers (and investors) should favor tools that force users to think, not just click “Buy.”

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In the news

The data dribbling in this May all point in the same direction: tariff turbulence is rippling from boardrooms to kitchen tables, cooling a recovery that had finally begun to feel durable. The April consumer-price index looked benign at first glance, headline inflation rose just 0.2% on the month and the year-over-year rate slipped to 2.3%, its lowest since early 2021. But economists were quick to note what wasn’t yet inside the report: the full cost of President Trump’s rolling trade war. With 10% “universal” duties announced on April 2, a temporary 145% levy on Chinese goods days later, and then a surprise 90-day tariff truce struck this week, import prices are still working their way through inventories and supply chains. Fed officials, wary of a summer pass-through, are effectively frozen, hoping the sunshine in the latest CPI isn’t just the eye of the storm.

Corporate behavior suggests they share the Fed’s caution. Big employers from JetBlue to T. Rowe Price are now talking about “selective pauses” rather than layoffs, a strategy that keeps payrolls steady but leaves job seekers with fewer openings. Private-sector hiring in ADP’s April survey rose only 62,000, roughly half of Wall Street’s forecast and barely one-third of March’s pace. The same uncertainty is seeping into small-business sentiment: the NFIB optimism index fell to 95.8 in April, well below its long-run average of 98, as owners shelved expansion plans and reported the weakest hiring intentions since the pandemic. As NFIB’s Bill Dunkelberg put it, “uncertainty is the tax they can’t deduct.”

Housing, which normally benefits from rate-cut hopes when inflation cools, isn’t escaping either. Inventory is finally rising, up 16% versus prepandemic levels, but mortgage rates near 6.75% and shaky consumer confidence have turned the spring selling season into a “dud.” Builders in the Southwest are dangling rate buydowns, and sellers from Florida condos to Texas colonials are throwing in concessions, yet buyers remain on the sidelines. The fear: a tariff-driven cost spike could hit just as a purchase closes, saddling new owners with higher appliance or renovation bills.

Against that backdrop, Monday’s 90-day tariff cease-fire with Beijing looked like welcome relief, U.S. effective duties on Chinese goods will drop to roughly 39% from what Treasury called “embargo-like” levels. Yet, as the WSJ’s Greg Ip noted, the path to this “sensible” pause has been anything but: allies such as Britain are still staring at the same 10% levy China now enjoys, and smaller trading partners that already cooperated on fentanyl or trans-shipping concerns remain under tougher penalties. For companies, the message is that trade policy can swing overnight, so capital-spending and hiring plans need a higher margin of safety.

Investors, then, face a tale of two economies. The household side still benefits from disinflation and a sturdy labor market; the business side is already acting as if a slowdown is at hand. In practice that means headline data will stay noisy, good CPI prints, weak housing turnover, solid headline payrolls but thinning help-wanted ads, and markets will whip around each new tariff headline. The prudent takeaway is less about predicting the next policy twist and more about stress-testing portfolios and budgets for a world where costs and confidence can shift faster than the official statistics. Uncertainty, after all, has become the most reliable leading indicator of 2025.

Chart of the Week: Time, Risk, and Perspective, Understanding Performance in Context

Few snapshots are as misleading as a year-to-date leaderboard taken in isolation. Since the first trading day of 2025 the S&P 500 has inched lower while the “Magnificent Seven”, Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia and Tesla, have fallen twice as far. Look only at that slice and the lesson seems obvious: mega-cap tech is finally cracking and broad diversification is the safer bet. The first chart below, “Same Stocks, Different Story: YTD 2025 the Mag 7 Lag the S&P 500,” drives the point home, plotting cumulative returns from 2 Jan 2025 to 12 May 2025 and showing the tech cohort under water by roughly six percentage points.

Scroll one screen and the narrative flips. Extend the horizon to include the prior year and the Mag 7’s stumble looks more like a breather after a sprint: from 1 Jan 2024 through the most recent close the basket is still up more than 60 percent, trouncing the S&P 500’s mid-20s gain. The second graphic, “Context Is Everything: 2024-2025 Cumulative Returns”, makes that contrast explicit. What appeared to be evidence of structural weakness is, with a wider lens, merely a give-back of last year’s euphoric AI-driven rally. Time-frame roulette distorts more investment decisions than any earnings miss or policy headline; the cure is to check multiple durations before drawing conclusions.

Perspective, however, should not be confused with complacency. The same turbo-charged upside that lifted the Mag 7 in 2024 amplifies drawdowns when sentiment sours. Statistically the group’s beta, its sensitivity to market swings, has hovered around 1.4 versus the S&P 500, meaning a 1 percent move in the index tends to translate into a 1.4 percent move for these stocks in the same direction. That higher “gear ratio” can accelerate wealth creation, but it also demands disciplined position sizing and periodic rebalancing. The lesson from the two charts is therefore two-fold: zoom out before judging performance, and remember that outsized returns ride shotgun with outsized risk.

Beyond Bias: Projection Bias - Why You Think You’ll Always Want the Same Things

Projection bias is the tendency to assume that our current preferences, emotions, and behaviors will remain constant into the future. First identified in behavioral economics by Loewenstein, O’Donoghue, and Rabin (2003), this bias explains why people overestimate how much they’ll enjoy future purchases, persist with short-term emotional decisions, or misjudge how their tastes will evolve. In investing, it can lead to extreme misallocations. Investors panicking during downturns might shift entirely to cash or bonds, convinced they’ll always be this risk-averse. Others might overconsume in the present, buying the “perfect” house or gear for a hobby they later abandon, because they wrongly believe their interests won’t change. Yet over decades, our values, passions, and even personalities shift. Research from Roberts and Mroczek (2008) shows that major personality traits change more between ages 20 and 40 than they do after 50, even though most people believe they are “done changing” by early adulthood.

The antidote to projection bias is humility about the future, and flexibility in how we treat our money. Because money is fungible, dollars you save today don’t need to match your present tastes. They can fund your future self’s needs, even ones you can’t yet imagine. Instead of buying everything that feels urgent now, view saving as a hedge against your own uncertainty. One useful mental model comes from Hal Hershfield’s research at UCLA, which shows that people who vividly picture their future selves tend to save more. Hershfield and colleagues (2011) found that viewing aged images of oneself increased retirement contributions, because it closed the psychological gap between present-you and future-you. If you want to counter projection bias, don’t just forecast the future. Visualize it. Recognize that your future self may not want what you want now, and give them the optionality to decide. That’s the real power of saving.

Building Wealth: The Portfolio of Meaning - Investing in Non-Financial Assets That Pay Psychological Dividends

When most people think about building wealth, they picture a growing investment account. But some of the highest-yield assets in life don’t show up on a balance sheet. Research consistently shows that time, health, autonomy, and close relationships contribute more to long-term life satisfaction than income alone (Diener & Seligman, 2004; Kahneman & Deaton, 2010). Structuring your life like a portfolio means recognizing these areas as capital you can grow. Time spent cultivating physical health through regular exercise, for example, improves cognitive function and emotional resilience well into old age (Ratey, 2008). Investing in autonomy, through skill-building, flexible work arrangements, or reducing dependency on volatile income sources, buys you psychological freedom, even in times of uncertainty. These aren’t luxuries; they’re assets that compound.

The best part is that these investments don’t require significant capital. A one-hour weekly walk with a close friend may do more for your emotional well-being than any financial windfall, especially as loneliness becomes a growing public health issue (Holt-Lunstad et al., 2015). Hobbies that foster flow, deep engagement in a task, can act as both a mental release valve and a source of personal pride, providing ongoing returns in self-esteem and identity (Csikszentmihalyi, 1990). Even something as simple as keeping one day of your week unscheduled can restore a sense of agency that improves decision-making across all domains. In short, your real portfolio isn’t just financial, it’s functional. And like any good investor, the key is diversification and intentional rebalancing toward what actually matters.

Add to your Toolbelt

Take control of your financial future with Rainier FM, your AI-powered financial planning companion. FM stands for Financial Model, and that’s exactly what this app delivers: optimized, data-driven financial plans tailored to your goals. Using advanced optimization techniques and AI-driven insights, Rainier FM helps users navigate everything from retirement planning to wealth building with confidence. Pricing reflects the cost of running the service, but I’m actively gathering feedback to refine and improve it. Here’s an example of an insight this app can help you uncover.

If this sounds like something you’d find valuable, feel free to reach out or sign up, I’d love to hear what you think as I continue developing the platform!

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Historical Perspective: The 1974 Trust Crisis - When Americans Lost Faith in Institutions

By the spring of 1974, America found itself in a moment of profound disillusionment. Watergate hearings played on living room televisions, inflation surged into double digits, and gas lines snaked through neighborhoods as the oil embargo sent shockwaves through the economy. That year, consumer confidence hit its lowest point since records began. According to the University of Michigan’s Survey of Consumers, sentiment plunged to levels that wouldn’t be revisited until the depths of the COVID-19 pandemic nearly 50 years later (Curtin, 2020). Trust in government, corporations, and media collapsed in tandem, not just because of policy failures, but because Americans felt the social contract itself had broken.

The loss of institutional trust didn’t just show up in polls, it reshaped economic behavior. Gold, long dormant in the public imagination, surged in price as ordinary citizens sought something tangible amid the chaos. In 1971, gold had just been uncoupled from the U.S. dollar by President Nixon, effectively ending the Bretton Woods system. By 1974, it had become legal once again for Americans to own bullion, and they rushed to buy it. The metal’s price soared from $65/oz in 1972 to over $180/oz by 1975 (World Gold Council, 2021). Meanwhile, anecdotal reports of hoarding, from canned goods to ammunition, began surfacing as fear over price controls, shortages, and economic collapse circulated through households and headlines.

The crisis also fractured politics. Gerald Ford’s attempt to restore confidence with the “Whip Inflation Now” campaign, a branding effort that included buttons, slogans, and public appeals to thrift, was broadly mocked. No clear policy solution emerged. Monetary tightening from the Federal Reserve was met with pushback from a public already weary of recession. With inflation still surging and the stock market down nearly 45% from its 1973 high, many Americans withdrew not just from markets, but from civic engagement. The 1974 midterms delivered sweeping losses to Nixon-aligned Republicans, but few felt optimistic about the future. In retrospect, this wasn’t merely a moment of economic stress, it was a psychological regime shift. Americans began to internalize that stability wasn’t guaranteed and that leadership, whether political or economic, could fail them.

This history matters now because similar forces are again eroding public trust. Surveys from Pew and Gallup show declining confidence in banks, the media, and even the Federal Reserve. The appeal of crypto, gold, and decentralized systems is rooted in the same mindset that drove Americans to bullion shops and bunkers in the 1970s: a belief that centralized institutions no longer serve the average person. For investors, this has two implications. First, narrative and trust cycles can drive markets as powerfully as earnings or rates, sometimes more so. Second, preparing for volatility is not just about inflation hedges or portfolio allocations, but understanding when the public mood shifts from belief to skepticism. The 1974 trust crisis was not just a financial downturn; it was a collapse in consensus. And consensus, once lost, rarely returns quickly.

Works Cited

• Curtin, Richard. (2020). Consumer Sentiment and the Coronavirus. University of Michigan Surveys of Consumers.

• World Gold Council. (2021). Historical Gold Prices 1971–2021.

• Pew Research Center. (2023). Public Trust in Government: 1958–2023. https://www.pewresearch.org

• Gallup. (2022). Confidence in Institutions. https://news.gallup.com/poll/1597/confidence-institutions.aspx

• Blinder, Alan. (1987). Hard Heads, Soft Hearts: Tough-minded Economics for a Just Society. Addison-Wesley.

Literature Review: Action vs. Attention - How AI Signals Shape Human Decisions

As AI systems increasingly assist humans in decision-making across finance, medicine, and law, a quiet design choice looms large: should these systems tell people what to do (action signals) or merely highlight where to focus (attention signals)? A recent study by Poulidis, Ge, Bastani, and Bastani (2024) tackles this head-on using an ingenious testbed: chess. In this high-stakes, time-pressured, and cognitively rich environment, they conducted a large-scale randomized controlled trial involving 276 players,i ncluding 36 titled experts, who played 2,484 games under three conditions: no AI help, attention signals, and action signals. The design allowed them to measure not only how performance changed, but why it changed, offering critical insights for designers of decision support tools in any domain where human-AI collaboration is the norm.

Their findings were both intuitive and surprising. Action signals, explicitly telling the player the optimal move, were more powerful in the moment, producing the largest gains in move accuracy and game results. But these signals came with a cost. Players who received action signals often made worse decisions on subsequent moves, having followed the AI into unfamiliar territory without understanding the reasoning behind the suggestion. In contrast, attention signals, alerts that a critical moment had arrived, without saying what to do, required more effort but delivered more sustainable results. These nudges caused players to think harder in the moment and, intriguingly, improved the quality of moves even after the signal was gone. This “spillover effect” suggests that attention signals encourage deeper engagement and learning, a finding that parallels research in psychology showing that effortful retrieval strengthens future memory (Roediger & Karpicke, 2006).

The study’s method is especially strong. The use of chess as a domain offers rare analytical precision: modern engines like Stockfish 15.1 can quantify the exact value of every move in centipawns (1/100 of a pawn), allowing researchers to rigorously define a “critical state” (where the best move is at least 60 centipawns better than the next best). By restricting interventions to these states, the researchers ensured that AI help was both needed and consequential. The randomized within-subjects design helped control for individual differences, while post-game surveys offered insights into how players felt about each kind of signal. Strikingly, players perceived attention signals to be more helpful than they actually were, and described action signals as reducing their sense of agency, mirroring research by Dietvorst et al. (2015) on algorithm aversion, where people resist even high-performing algorithms when they feel disempowered.

Despite its rigor, the study isn’t without limitations. Chess is a constrained environment with clear rules and defined outcomes, unlike messy real-world domains like healthcare or finance. One might argue that in investing, where uncertainty, ambiguity, and incomplete information dominate, the clean distinction between action and attention signals is blurrier. Still, the metaphor holds. An AI that tells an investor “buy this stock now” may produce faster results, but it risks disengagement or blind trust. An AI that says “pay attention to this valuation signal” invites effort and potentially deeper understanding. As in the study, effort is the cost, but also the key to long-term improvement. These dynamics echo dual-process theories of cognition (Kahneman, 2011), which suggest that while shortcuts (System 1) are fast, effortful thinking (System 2) is more durable when the stakes are high.

For investors, the takeaway is profound. As AI-driven tools become embedded in platforms, from robo-advisors to earnings prediction models, the choice between action and attention signals affects not just outcomes but behavior. Blindly following a “buy” signal may yield quick gains, but leaves users vulnerable in new conditions. Systems that preserve agency, foster curiosity, and help people learn may yield better results over time, even if they seem less efficient in the short run. In a world awash with AI-generated advice, the investor who stays engaged, like the chess player who pauses to think in response to an attention signal, may ultimately outperform.

Curious about money, investing, or the economy?

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Programming Note: As summer picks up, I’ll be shifting away from a fixed publishing schedule and instead posting when time allows. My goal is to still publish at least every two weeks, though the exact day may vary. These are a lot of fun to put together, but they do take time!

Key Takeaways

• In the news: Shoppers keep spending—up 3.1% YoY on BofA cards—even as sentiment slumps, hinting at tariff-driven “buy-ahead” demand that could fade fast.

• Chart of the Week: China’s equity ETF (MCHI) swung from worst to first in three months, underscoring how quickly policy headlines can flip geographic leadership.

• Beyond Bias: Outcome bias tricks investors into judging decisions by results, not process—keep a decision journal to separate luck from skill.

• Building Wealth: Practicing “mental subtraction” (imagining life without a benefit) heightens gratitude and curbs impulse spending.

• Historical Perspective: Nixon’s 1971 gold-window closure shows that trust, not metal, underpins fiat money—and that monetary regimes can shift overnight.

• Literature Review: A large-scale replication found only 3 of 14 classic experimental-market findings hold up, warning researchers and investors alike to be skeptical of single-study claims.

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In the news

• Consumers keep swiping even as confidence dives: Bank of America card data show spending up 3.1% YoY in mid-April, with pull-forward buying of cars and phones before 25% import tariffs hit.

• Corporate earnings refuse to roll over: after one-third of the S&P 500 has reported, blended Q1 EPS growth ran 10.1% (vs. 7.2% on March 31) despite only 73% of companies beating estimates.

• Tech leadership cracks: the “Magnificent 7” have shed $2.5 trillion in value YTD; ex-Mag 7, the S&P is off just 1.2%.

• Labor market stays in the middle lane: February JOLTS showed openings flat at 7.6m, quits down to 2%, and layoffs still a rock-bottom 1.1%.

Why it matters

• Sentiment–spending gap: historically, confidence that sinks while spending stays lofty is a late-cycle warning. Think 2000 and again in 2007, both episodes flipped from “mood divergence” to demand retrenchment within six–nine months.

• Earnings breadth vs. depth: profits are rising, but half the sectors still post negative YoY growth. Investors who only look at the headline growth rate may miss the rotation underway from energy/industrials toward health care and utilities.

• Market concentration risk: at last December’s peak the Mag 7 made up 36% of S&P market cap; when a handful of names drive both upside and downside, portfolio volatility can lurch more than the index headline suggests.

• Tariff pull-forward distortions: a 1.4% March retail-sales pop looks healthy, but auto dealers report a “beat the duty” rush, sales that may vanish later this summer when sticker prices jump.

Consumers

• Top-income households (+3% spending) are cushioning the aggregate data; lower-income spending is flat. If equity markets stay wobbly, the affluent cushion thins fast.

• Airline spend –13% YoY hints at substitution: higher fuel surcharges and a 10% tariff on imported aircraft parts already leaking into ticket prices.

Corporate earnings

• Average beat size (10%) is the biggest since early 2021, but revenue beats (1%) are the weakest in three years, translation: cost-cutting, not demand, is doing the lifting.

• Forward 12-month P/E finally slipped below the 5-year mean (19.8 vs 19.9). Averages mask a huge gap: utilities trade at 16×, Nvidia at 23× after the sell-off.

Tech shake-out

• DeepSeek’s January AI model pushed investors to re-price the U.S. AI “moat.” Nvidia’s $5.5 bn China-curb charge adds real earnings drag to the narrative.

• Apple’s delayed Siri upgrade is a reminder that AI deployment, not just model creation, drives future revenue. Short-cycle hardware replacement matters when tariffs raise device prices.

Labor tightness lightens, but hasn’t snapped

• Quits rate back to 2019 levels means wage growth should cool later this year, giving the Fed room to cut, one reason gold blasted through $3,400/oz on expectations of three 2025 rate cuts.

What we’re watching next week

• Big Tech earnings blitz (Meta, Microsoft, Apple, Amazon) for any guidance on tariff pass-through and AI capital-expenditure plans.

• ISM manufacturing and Friday payrolls: confirmation that job gains are slowing without turning negative would reinforce the “slow-bleed, not cliff-dive” thesis.

• China’s April PMIs (Tuesday). A downside surprise could propel Beijing toward fresh stimulus and extend mainland equity outperformance since January 2024.

One thing DIY investors may overlook

• Tariffs hit indexes you don’t expect. Roughly 40% of S&P 500 revenue is foreign-sourced; a global trade war can dent “domestic” ETFs just as much as exporters. Check revenue-by-region for any large holding, most fund fact-sheets bury it in the back pages.

The bottom line

Confidence is sagging, tech darlings look mortal and tariffs are warping near-term data, yet earnings and jobs keep the soft-landing narrative alive. The market’s cross-currents reward investors who track underlying drivers (revenue mix, labor churn, policy dates) rather than headline moves alone.

Chart of the Week: When Leadership Flips Overnight: China’s Sudden Surge vs. U.S. & Europe

The lines on this chart track the total-return scorecard for broad-based equity ETFs in China (MCHI), Europe (VGK) and the United States (VTI) starting 1 Jan 2024. Two things jump out. First, performance leadership has rotated sharply: U.S. stocks dominated for most of 2024, but a wave of Beijing stimulus headlines and index-rebalancing flows catapulted Chinese shares to the top of the table in early 2025, at one point showing a YTD gain of almost 47 percent (19 Mar) before sliding back toward the pack. Europe, by contrast, has delivered a steadier, but far more muted, return profile, reflecting a less policy-driven market and the region’s slower earnings cycle. These diverging paths illustrate how fast capital can pivot when macro narratives evolve: tariff escalations, rate-cut expectations, and National People’s Congress support measures sent money sluicing from one geography to another in a matter of weeks.

For investors the message is two-fold. First, a rising home-market index is not a proxy for economic strength, China’s equity surge materialised while its property sector was still deleveraging, and U.S. shares sagged even as domestic GDP prints remained solid. Understanding which catalysts are truly moving prices (policy pledges, liquidity shifts, index-reweightings) is essential for sizing positions and setting stop-losses. Second, the chart underscores the value of geographic diversification and active rebalancing: had a portfolio remained U.S.-centric after 2024’s rally, it would have missed the first-quarter 2025 pop in Chinese equities; by the same token, a China-heavy stance after March would have handed back a chunk of gains. In short, following cross-regional return dispersion, and the policy or flow events that create it, helps investors avoid anchoring on yesterday’s winners and forces a more dynamic, risk-aware asset-allocation process.

Beyond Bias: Outcome Bias — When Luck Masquerades as Skill

Outcome bias is the tendency to evaluate the quality of a decision based solely on its result, rather than on the decision-making process itself. In investing, this means that a risky trade that happens to pay off is often praised as brilliant, while a well-reasoned strategy that underperforms is dismissed as flawed. Baron and Hershey (1988) were among the first to demonstrate this bias empirically, finding that people rated the same medical decision as more or less ethical depending on whether the patient lived or died, even when the decision logic was identical. In finance, this plays out when investors idolize star managers after a lucky streak or beat themselves up for sticking to a sound thesis that happened to underperform in the short term. Over time, this distorts how investors learn, often reinforcing randomness instead of reason.

To counteract outcome bias, investors must separate process from performance, a core principle in disciplines like poker, aviation, and even military strategy. One effective method is decision journaling: before making an investment, write down the thesis, expected risks, and time horizon. After the outcome is known, revisit your notes not to ask “Did this make money?” but “Was this decision sound given what I knew at the time?” Research by Kahneman and Klein (2009) suggests that structured feedback loops are essential for improving judgment under uncertainty. Another tool is “premortem analysis,” where you assume a decision failed and work backward to ask why, shifting your focus from results to reasoning. The best investors don’t just chase returns, they seek repeatable, rational processes that can survive the randomness of markets.

Building Wealth: The “What If It Never Happened?” Tool — How Mental Subtraction Sharpens Gratitude and Spending

Mental subtraction, also known as counterfactual reflection, is a powerful and underutilized psychological tool that can reshape how we value what we already have. In a seminal study, Koo et al. (2008) demonstrated that participants who imagined the absence of a positive event (e.g., not meeting a spouse, not having a stable job) reported greater happiness than those who simply reflected on the presence of the same event. This flips our intuitive logic: instead of maximizing joy by counting blessings, we actually feel more grateful when we momentarily simulate a world in which those blessings never occurred. The reason lies in contrast: imagining life without something makes its value more salient, restoring emotional vividness dulled by habituation. This insight builds on decades of research in affective forecasting and adaptation, which shows that we quickly normalize gains (Frederick & Loewenstein, 1999) and therefore underestimate how good we already have it.

For DIY investors, the application is clear. Mental subtraction can help counteract lifestyle inflation, FOMO-driven spending, and the constant treadmill of material comparison. Instead of asking, “What should I add?” try asking, “What would life be like without this?” Whether it’s your job, your safety net, your health, or your time, this small mental shift brings clarity to what truly matters. It also refocuses financial decisions on value rather than novelty. By mentally subtracting your current assets or experiences, you’re more likely to hold onto what works and cut what doesn’t. In doing so, you foster a mindset of appreciation, not accumulation, which, as research from Dunn et al. (2011) suggests, leads to better financial well-being and more intentional, fulfilling use of money.

Add to your Toolbelt

Take control of your financial future with Rainier FM, your AI-powered financial planning companion. FM stands for Financial Model, and that’s exactly what this app delivers: optimized, data-driven financial plans tailored to your goals. Using advanced optimization techniques and AI-driven insights, Rainier FM helps users navigate everything from retirement planning to wealth building with confidence. Pricing reflects the cost of running the service, but I’m actively gathering feedback to refine and improve it. Here’s an example of an insight this app can help you uncover.

If this sounds like something you’d find valuable, feel free to reach out or sign up—I’d love to hear what you think as I continue developing the platform!

Check it out!

Historical Perspective: The Day Money Lost Its Anchor — The 1971 Nixon Shock

On August 15, 1971, President Richard Nixon addressed the nation with a decision that would change the global financial system forever. In what became known as the “Nixon Shock,” he announced the suspension of the dollar’s convertibility into gold, effectively ending the Bretton Woods system that had governed international currency exchange since World War II. Until that point, the U.S. dollar was backed by gold at a fixed rate of $35 per ounce, giving it a unique position of global trust. But rising inflation, ballooning war costs, and persistent trade imbalances made the peg unsustainable. Nixon’s unilateral move transformed the dollar from a gold-backed currency to a fiat one, backed only by the full faith and credit of the U.S. government.

The implications were immediate and far-reaching. Freed from the discipline of gold reserves, central banks gained unprecedented flexibility to manage monetary policy, ushering in the modern era of inflation targeting and interest rate manipulation. In the short term, however, the dollar plunged, inflation soared, and trust in the monetary system wavered. Annual inflation in the U.S. reached 11% by 1974 and would peak above 13% by 1980, eroding real wages and upending investor expectations. Meanwhile, the dollar’s dominance persisted, not because of gold, but because of U.S. economic might and the lack of credible alternatives. The fiat system that emerged in the 1970s remains in place today, but the lesson is clear: trust in money is not fixed, it is political, historical, and deeply psychological.

Fast forward to 2025, and echoes of the Nixon Shock are everywhere. The U.S. once again faces questions about debt sustainability, inflation control, and global trust in its currency. While modern institutions like the Federal Reserve are far more sophisticated than their 1970s counterparts, the underlying tension remains: can fiat currency systems maintain credibility when fiscal and monetary authorities are under immense political pressure? Recent interest in Bitcoin, gold, and central bank digital currencies (CBDCs) speaks to a broader unease about fiat systems, and the fear that governments might again shift the rules overnight. The core lesson of 1971 isn’t that gold was lost, it’s that public confidence is the true reserve asset.

For investors, the Nixon Shock is a reminder that financial paradigms can shift suddenly, and without consensus. Those who believed the gold standard was unbreakable were blindsided. The same risk applies today to any belief that interest rates, inflation, or currency regimes will remain stable. Just as the 1970s rewarded those who hedged inflation and penalized those who held cash, today’s uncertain monetary backdrop calls for flexibility, skepticism, and a clear-eyed view of risk. Structural shifts in trust, whether in money, government, or policy, rarely announce themselves.

Supporting Research

• Bordo, Michael D. (1993). The Bretton Woods International Monetary System: A Historical Overview. University of Chicago Press.

• Eichengreen, Barry (2008). Globalizing Capital: A History of the International Monetary System. Princeton University Press.

• Volcker, Paul & Gyohten, Toyoo (1992). Changing Fortunes: The World’s Money and the Threat to American Leadership. Times Books.

• Federal Reserve History (2023). “Nixon Ends Convertibility of U.S. Dollars to Gold and Announces Wage/Price Controls.”

• Krugman, Paul (1999). The Return of Depression Economics. W.W. Norton & Company.

Literature Review: Do Experimental Markets Replicate? A New Study Tests the Evidence

In financial economics, few tools are as alluring as the experimental asset market, a controlled laboratory setting where researchers simulate a simplified version of a stock market to study how real people behave when trading assets. These experiments offer rare clarity: unlike real-world markets, researchers know the “true” fundamental value of the asset being traded, allowing them to observe mispricing, bubbles, and crashes without the usual noise. First introduced by Smith, Suchanek, and Williams in 1988, this method has been widely used to explore questions about investor psychology, market efficiency, and behavioral biases. But a major question has hovered over the field for years: do the flashy results from these lab markets actually hold up under scrutiny?

A new study by Huber, Holzmeister, Johannesson, and colleagues (2024) delivers a sobering answer. In a massive replication project, the authors tested 17 prominent findings from four well-cited studies in the experimental asset markets literature. Using a sample more than seven times larger than the original studies, 1,544 participants across 166 markets, the team was able to apply high-powered statistical tests to reexamine previous claims. The result? Only 3 of the 14 original findings reported as statistically significant replicated successfully. The average replication effect size was just 2.9% of the original. In other words, the majority of findings that once drew headlines in the field simply didn’t hold up.

What were these findings? The original studies tested hypotheses like: Do emotions, such as excitement, cause investors to drive prices into bubble territory? Does low self-control make markets more prone to mispricing? Do female-dominated markets behave differently than male-dominated ones? And do cognitive traits like fluid intelligence and “Theory of Mind”, the ability to understand others’ mental states, predict trading success? The new replications cast doubt on most of these effects. For example, the researchers found no evidence that excitement or low self-control increased overpricing, despite dramatic claims in earlier papers. Even widely accepted gender-related findings, such as the notion that female traders reduce bubble risk, failed to replicate.

The study doesn’t dismiss the entire literature. One finding did hold up strongly: market experience matters. Participants who had already traded in one experimental session were significantly less prone to overpricing in subsequent rounds. This aligns with a longstanding “stylized fact” in the literature, also supported by earlier work by Hussam, Porter, and Smith (2008), showing that repeated exposure to trading helps individuals internalize the asset’s declining fundamental value. The study also confirmed that cognitive traits like fluid intelligence and Theory of Mind were modestly predictive of trading performance, though the effects were weaker than originally reported.

The implications are significant. Replication crises are nothing new in psychology and economics, but this study suggests that experimental finance, a field often praised for its rigor and control, is not immune. One possible reason for the original overstatements is low statistical power: many of the initial studies had very small sample sizes, often with just six to ten independent markets per treatment. As statisticians like Ioannidis and Gelman have long warned, this leads to inflated effect sizes and a high risk of false positives. The authors also point to poor randomization, limited incentives, and publication bias as contributing factors. In all, the findings underscore the importance of skepticism, especially when bold claims are based on a single lab experiment. For researchers, it’s a call to embrace larger samples, preregistration, and direct replication. For investors and policymakers tempted to over-interpret behavioral experiments, it’s a reminder: even well-designed labs can produce mirages.

Curious about money, investing, or the economy?

I occasionally answer reader questions in the newsletter—no jargon, just thoughtful, practical insight. If something’s on your mind, tap the button below to send it in anonymously.

Ask a question here!

Key Takeaways

• Chart of the Week: The current market downturn since February 2025 is already the worst post-peak performance of the S&P 500 in over 50 years, largely driven by uncertainty around the Trump administration’s tariff policies.

• Beyond Bias: Choice-supportive bias causes investors to irrationally justify poor financial decisions, but research shows structured self-reviews and decision journaling significantly improve objectivity.

• Building Wealth: Spending money to reclaim time from daily tasks improves life satisfaction and reduces stress more effectively than income increases.

• Historical Perspective: The first recorded sovereign bond default occurred in ancient Athens around 377 BC when military overspending led to unrepayable temple-backed loans.

• Literature Review: Mutual fund managers reduce risk-taking by 6.82% during their zodiac years due to superstition, highlighting that even professional investors are vulnerable to irrational behaviors.

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Chart of the Week: Navigating the Aftermath of Market Peaks—Why This Downturn Stands Out

This chart underscores the severity of the current market downturn, showing that, as of April 2025, the S&P 500 has experienced the worst performance following a major market peak in over 50 years. Such extended downturns often lead to increased market volatility and investor uncertainty, factors well-documented to deter capital expenditures and long-term business investments (Bloom, 2009; Baker et al., 2016). Empirical research indicates that heightened trade tensions and tariff uncertainty significantly depress corporate investment, as companies pause major decisions due to unclear policy environments, effectively putting economic growth into a holding pattern (Handley & Limão, 2017; Amiti et al., 2019). Given the current administration’s aggressive tariff stance, markets are unlikely to stabilize until there’s greater policy clarity, signaling investors may continue facing elevated volatility in the near term.

Beyond immediate market impacts, prolonged downturns also damage investor confidence, reshaping how businesses perceive risk and returns in the U.S. market. Evidence from past tariff-related episodes shows that even temporary protectionist policies can produce lasting negative consequences for investor perceptions, undermining the credibility of policy commitments and fueling long-term uncertainty (Fajgelbaum et al., 2020). The severity of this current decline relative to previous downturns suggests we may be witnessing a shift in sentiment about the reliability of U.S. economic policy, which could prompt firms—both domestic and international—to reconsider their long-term capital allocation strategies away from the U.S., at least until stable policy expectations are restored (Caldara et al., 2020). Consequently, the path forward for U.S. markets heavily depends on the administration’s next moves regarding tariffs and its broader approach to international economic relations.

Beyond Bias: Choice-Supportive Bias — Why We Defend Our Dumbest Financial Decisions

Choice-supportive bias is the tendency to justify past decisions by selectively recalling their positive aspects—even when the outcome was objectively poor. Once we commit to a choice, we subconsciously rewrite history to make it seem like the right call. This bias has been well-documented in cognitive psychology: Mather et al. (2000) found that people remember positive features of chosen options more clearly than those of rejected alternatives, regardless of actual results. In investing, this can show up as emotional attachment to a stock that underperformed, or a lingering belief that “I was early, not wrong.” It fuels overconfidence in flawed theses, discourages introspection, and encourages portfolio inertia—even when the rational move is to pivot.

To protect against choice-supportive bias, investors need structured self-review and decision journaling. Research by Fischhoff and Beyth-Marom (1997) shows that ex-ante reflection—writing down your rationale before making a decision—creates a clearer benchmark for post-hoc evaluation. When you record why you made a trade, what outcome you expected, and what risks you considered, you gain a more objective lens through which to assess results. A quarterly review of your past trades—especially the losers—can reveal patterns of self-deception. Did you exit too late? Hold out of pride? Rationalize bad performance as “temporary”? Tools like post-mortems, trading diaries, or even a “bad trade autopsy” session help create friction between memory and myth. In short: the more structure you add around reflection, the harder it becomes to lie to yourself.

Building Wealth: Buy Time, Not Stuff — Why the Highest-ROI Asset Is Attention

One of the most overlooked levers in personal finance isn’t a financial product—it’s time. A 2017 study by Whillans et al., published in PNAS, found that individuals who spent money to “buy time”—by outsourcing daily tasks like cleaning, grocery runs, or meal prep—reported significantly higher levels of life satisfaction and lower stress, even when controlling for income. Crucially, this held true across a range of socioeconomic groups. The researchers argue that time scarcity—the feeling of constantly being rushed—is a stronger predictor of lower well-being than low income. This aligns with the findings of Kahneman and Deaton (2010), who showed that while income increases life evaluation, it has diminishing returns for emotional well-being. In other words: earning more only helps up to a point—reclaiming attention is what makes it sustainable.

To implement this as a DIY investor, treat time as a capital allocation decision. Ask yourself: “Where do I spend an hour that produces zero future value—and can I delegate it?” Consider outsourcing tasks that cost less than your effective hourly rate or consume your highest-energy hours. Studies on cognitive bandwidth (Mullainathan & Shafir, 2013) suggest that mental load from small but recurring tasks reduces decision quality over time. Freeing up even one or two hours per week can compound—not in the market, but in your clarity, creativity, and capacity to make better financial decisions. Time is finite. Treat it like the asset it is.

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Historical Perspective: The First Bond Default Wasn’t Argentina—It Was Athens

When investors think of sovereign debt crises, they tend to picture Argentina in 2001, Greece in 2010, or Russia in 1998. But the first known government bond default occurred over two thousand years earlier—in ancient Greece, around 377 BC. At the time, Athens led the Delian League, a powerful alliance of city-states that had pooled naval resources and capital to defend against Persian expansion. To fund its increasingly ambitious (and expensive) military campaigns, Athens began issuing debt backed by temple treasuries—specifically, loans from the Temple of Athena and other sacred sites, considered the most secure financial entities of the day.

These weren’t just symbolic notes. Records carved into stone show that Athenian temples lent substantial silver reserves to the state with the expectation of repayment and interest. But as war costs ballooned and Athenian control over the league began to wane, the city failed to make good on its obligations. By 354 BC, inscriptional evidence suggests these “holy loans” were effectively written off. The first sovereign default in recorded history didn’t involve a central bank, credit ratings, or bond auctions—but it had the same root causes we see today: unsustainable debt loads, geopolitical overreach, and misaligned incentives between fiscal and religious institutions.

The Athenian default is more than a historical curiosity—it’s a blueprint for how empires overshoot. As classicist Edward Cohen has noted, these early loans were likely issued under duress, framed as “religious obligations” to pressure temples into compliance. Once Athens lost military dominance, creditors had little recourse. This mirrors a common dynamic in sovereign finance: nations borrow from the institutions they control, but the illusion of creditworthiness fades quickly once power shifts. The same dynamic played out with French assignats, Soviet war bonds, and arguably, China’s shadow banking today.

For investors, the lesson is structural. Sovereign debt crises aren’t black swan events—they’re the downstream result of long-term fiscal and geopolitical choices. Just as Athens borrowed from its temples, modern states borrow from retirement accounts, central banks, and foreign governments. When wars or entitlements outpace revenues, something breaks. The question isn’t if the debt becomes a problem—it’s when, and who gets hurt first.

Supporting Research

• Cohen, Edward E. Athenian Economy and Society: A Banking Perspective. Princeton University Press, 1992.

• Millett, Paul. Lending and Borrowing in Ancient Athens. Cambridge University Press, 1991.

• Harris, William V. War and Imperialism in Republican Rome, 327–70 B.C. Oxford University Press, 1979.

• Davies, Glyn. A History of Money from Ancient Times to the Present Day. University of Wales Press, 2002.

• Finley, M. I. Politics in the Ancient World. Cambridge University Press, 1983.

Literature Review: Superstition and Risk in Professional Investing — What Zodiac Years Reveal About Fund Managers

In a sweeping new study, Chen, Li, Rau, and Yan (2025) uncover a fascinating psychological vulnerability in mutual fund managers: superstition. Analyzing a dataset of over 12,000 fund-year observations in China from 2005 to 2023, the authors find that fund managers reduce risk-taking by an average of 6.82% during their zodiac years — years that align with their birth year in the Chinese lunar cycle and are traditionally seen as unlucky. This effect is strongest among less experienced managers, those without finance degrees, and those with weaker prior performance. Not only do these managers shift away from risky stocks and toward fixed income during these periods, but they also display lower trading aggressiveness and more diversified portfolios. The authors argue this behavior is not random: superstition measurably alters investment decisions — even among professionals assumed to be rational by design.

To test their hypothesis, the authors use rigorous methods, including multiple risk proxies (portfolio volatility, tracking error, standard deviation of returns), placebo tests with “fake” zodiac years, and propensity score matching. They further employ a Difference-in-Differences with Multiple Groups (DIDM) framework to correct for staggered treatment effects and show that only the true zodiac year drives the decline in risk-taking. Perhaps most striking is that this drop in risk is not harmless: fund performance declines in zodiac years as well, suggesting that managers become too conservative in response to superstition. And when managers exit or enter during a zodiac year, fund risk adjusts accordingly — further strengthening the causal claim.

This paper builds on a broader behavioral finance literature that challenges the retail/professional investor divide. Prior work has shown that individual investors are more likely to trade on lucky numbers (Bhattacharya et al., 2018), buy on auspicious days (He et al., 2020), or pay premiums for “lucky” packaging (Block & Kramer, 2018). But this study shows that even seasoned institutional players aren’t immune. The key moderating variables — experience, education, skill, and investment autonomy — mirror patterns in other research on bias suppression (Berk & van Binsbergen, 2015). Importantly, even skilled managers can revert to irrational behaviors under stress: superstition’s effect intensifies during periods of high policy uncertainty, a finding that aligns with behavioral models of stress-based heuristics (Kumar, 2009).

For DIY investors, the lesson is not to avoid mutual funds — but to recognize that even professional managers are subject to the same emotional circuitry we all have. Fund performance is shaped not just by market conditions, but by the mental state and belief systems of the people behind the trades. This study suggests that newer managers or those in more passive or flexible funds may be more vulnerable to bias, especially during times of volatility. Investors evaluating fund managers should go beyond past returns and expense ratios to consider the behavioral stability of the investment process — factors like team structure, management turnover, and the manager’s career stage can subtly shape outcomes. More broadly, the paper is a reminder that behavioral finance is not just a quirk of individual investors — it’s a foundational part of how markets work, even at the institutional level.

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Key Takeaways

1. In the news: AI is no longer just a productivity tool—it’s becoming a hiring gatekeeper, with companies like Shopify requiring teams to prove a human is necessary before adding headcount.

2. Chart of the week: The S&P 500’s strongest five-year returns followed modest 3% monthly gains—not major surges or crashes—challenging the idea that market timing is essential.

3. Beyond bias – Action bias: Investors often act impulsively during volatility, even though frequent trading consistently harms long-term returns.

4. Building wealth – Time audit: Tracking your time in 30-minute blocks can uncover “hidden hours” that, once reclaimed, become powerful levers for building wealth.

5. Historical perspective: This week’s tariff pause doesn’t erase the signal—it reinforces it: protectionism is back in the policymaker’s toolkit. History shows that even the threat of tariffs—when coupled with real investment—can catalyze innovation and industrial renewal.

6. Literature review – Narcissism + gender: A new study finds narcissistic female CEOs significantly outperform their peers, boosting both profitability and valuation more than narcissistic male CEOs.

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In the news

Corporate leaders are increasingly embracing AI as both a productivity tool and a headcount filter. At the same time, U.S. financials are seeing selective strength going into earnings season. But beneath these headlines, Ray Dalio warns that the economic system underpinning it all may be undergoing a rare, generational reset.

Zoom in: Shopify’s AI ultimatum

Shopify announced it will no longer approve new hires unless teams prove AI can’t do the job. CEO Tobi Lütke described a new expectation that employees must integrate AI into their workflows, with AI utilization even becoming part of performance reviews.

“I don’t think it’s feasible to opt out of learning the skill of applying AI in your craft,” Lütke said. “You are welcome to try, but… I cannot see this working out today, and definitely not tomorrow.”

Shopify joins a growing list of firms prioritizing AI fluency in hiring and performance evaluation, while broader white-collar hiring slows. AI-related job postings remain robust, particularly for candidates who can deploy models or fine-tune workflows using generative tools.

The big picture: AI is becoming a headcount gatekeeper.

Rather than augmenting staff, companies are increasingly using AI to replace them—especially in engineering, marketing, and administrative roles. This shift helps explain why tech hiring remains muted despite overall productivity gains.

By the numbers (source):

• IT hiring is down 27%; product management down 23%.

• AI-generated code now accounts for 25% of Google’s output.

• Time-to-hire has climbed to 66 days as hiring slows and candidate pools swell.

Meanwhile: Financials earnings show resilience

FactSet expects the Financials sector to post 2.3% earnings growth in Q1, outperforming six other S&P 500 sectors. However, the growth is highly uneven across industries.

The standout: Consumer Finance is expected to grow earnings by 23%, led by Discover Financial’s projected EPS jump from $1.10 to $3.37.

• Capital Markets earnings are projected to rise 10%, with all three sub-industries (asset management, investment banking, exchanges) contributing.

• Banks are expected to grow 5%, with regional banks outperforming large diversified ones.

• Insurance, however, is a drag: earnings in this industry are forecast to fall -15%, especially in reinsurance (-50%) and P&C insurance (-30%).

Why it matters:

If you exclude Insurance, the Financials sector would be on pace for a 6.7% earnings gain—suggesting underlying health masked by high-variance subsectors.

Context: Macro risks loom large

Ray Dalio warns that today’s headlines—AI-driven job losses, financial earnings volatility, and trade policy drama—are symptoms of deeper systemic shifts. In a widely shared LinkedIn post, Dalio writes that the global order is entering a rare phase of breakdown that mirrors past eras of debt overhang, trade realignment, and civil unrest.

“Deglobalization, debt addiction, and trust breakdowns are forcing a reset of the monetary, political, and geopolitical orders,” Dalio argues.

He cautions that the U.S. cannot indefinitely rely on borrowing from geopolitical rivals, nor assume it will remain the center of global capital and trade flows in an era of tariffs and populism.

What to watch:

• The S&P 500 Financials sector is projected to grow earnings steadily over the next 12 months, from +3.6% in Q2 to +18.9% by Q1 2026.

• Meanwhile, AI adoption is likely to accelerate hiring divergence between AI-native firms and traditional employers.

• Even paused tariffs—if seen as credible threats—could influence corporate behavior, spurring precautionary shifts in capital and supply chains.

The bottom line:

AI is reshaping hiring before it reshapes jobs. Financials are holding up, but macro risks are mounting. As Shopify redefines productivity and policymakers toy with tariffs, the bigger story might be this: we’re entering a new economic regime—one where debt, power, and productivity are being renegotiated all at once.

Chart of the Week: The Market Doesn’t Mind a Bad Month

Conventional wisdom says to ‘buy the dip’ and fear down months—but long-term data tells a different story. Across 140+ years of inflation-adjusted S&P 500 returns, the difference in five-year forward performance between extreme monthly upswings and downturns is surprisingly modest. The best future returns came after modest monthly gains—3% months had the highest median five-year return at 4.3%. Meanwhile, sharp monthly losses of more than 4% still led to a respectable 2.7% median return, not far off from the 2.2% that followed monthly gains above 4%. Even the worst-performing bucket (–1% monthly returns) still delivered a 1.7% median annual return—hardly catastrophic, especially when compounded over time.

What this tells us: not only is it okay to have bad months, it’s also okay to miss them. If you didn’t buy the dip this time, you didn’t miss some once-in-a-lifetime opportunity. The long-term path to returns doesn’t depend on catching every bottom—it depends on staying consistent. The market doesn’t punish you nearly as much for being late as it does for being erratic.

Academic research backs this up. Studies by Barberis and Thaler (2003), as well as Dalbar’s annual investor behavior reports, consistently show that investors underperform the markets they invest in—not because the markets aren’t generous, but because behavior erodes results. Emotional responses to short-term volatility, especially fear-driven selling or FOMO-driven buying, are major culprits. In contrast, staying invested through good months and bad—what Vanguard calls the “stay-the-course” strategy—historically outperforms tactical moves based on market timing (Bogle, 2007). Bottom line: if your horizon is 5+ years, the return of the market over the last few days matters far less than your ability to keep showing up.

Beyond Bias: Action Bias — Why We Do Something Even When Doing Nothing Is Better

Action bias is the tendency to prefer doing something over doing nothing—even when inaction would lead to better outcomes. It’s well documented in fields like medicine, sports, and management, where professionals often take unnecessary actions to regain a sense of control or deflect accountability (Bar-Eli et al., 2007). In investing, action bias shows up as portfolio tinkering, panic selling, or jumping into trades to “make up” for past losses—especially during volatile markets. After sharp downturns like March 2020 or spikes like those in late 2023, investors feel compelled to act, even if the best course might be to simply rebalance and wait. This behavior is reinforced by our discomfort with uncertainty and a cultural preference for decisiveness—what psychologist Dan Ariely calls the “illusion of control.” In short: we often trade activity for effectiveness, confusing movement with progress.

Last week’s tariff scare offers a clear example. Markets started to slide on fears of new trade restrictions—but just days later, the administration paused the policy altogether, triggering a relief rally. Investors who acted on the initial fear may have locked in losses and missed the rebound. That kind of whiplash punishes knee-jerk decisions and rewards those who stayed disciplined. To protect yourself from action bias, you need to build friction between impulse and execution. One proven method is using implementation intentions—clear, rule-based plans for how you’ll act during specific market scenarios (Gollwitzer, 1999). For example: “If my portfolio drops 10%, I’ll review my asset allocation, not place a trade.” Research by Barber and Odean (2000) found that frequent traders consistently underperformed less active investors—largely because timing mistakes and overconfidence compound. In volatile markets, the ability to pause, reflect, and not react can be a superpower. Don’t ask “What should I do?”—ask “What will future me thank me for not doing?”

Building Wealth: The ‘Time Audit’ Rule — Reclaiming Hidden Hours for Financial Gain

One of the least discussed but most powerful tools for building wealth is the time audit — a personal inventory of where your hours go each week. Unlike budgeting money, which is more common and intuitive, budgeting time feels abstract. But for most DIY investors, time is the true constraint. It’s the limiting reagent in better research, more thoughtful planning, and higher-earning opportunities. A 2018 study from the Journal of Consumer Research found that people consistently underestimate how much time they spend on low-value activities and overestimate how productive their days are (Tonietto & Malkoc, 2018). That means many aspiring investors think they “don’t have time” to improve their finances — when in fact, they just haven’t looked closely enough. A time audit is how you look.

Start by tracking your time in 30-minute blocks for three days — either with a notebook, a spreadsheet, or a free app like Toggl or RescueTime. Then color-code or categorize the blocks: productive, neutral, wasteful. You’ll likely find “hidden hours” in surprising places — time spent reflexively scrolling, rewatching old shows, or mentally bouncing between tasks. Those recovered blocks can be reallocated: 30 minutes per week reviewing expenses, another 30 setting up automated investments, and maybe an hour for reading or learning about a new asset class. Time audits also help you spot fatigue patterns. If you always crash at 9 PM, don’t schedule financial tasks then — move them to a morning when your energy is higher. This is about kindness, not discipline. Wealth-building is a long game, and reclaiming just 1–2 hours per week for focused effort compounds faster than most people expect. Think of time like capital: audit it, reallocate it, and put it to work.

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Take control of your financial future with Rainier FM—your AI-powered financial planning companion. FM stands for Financial Model, and that’s exactly what this app delivers: optimized, data-driven financial plans tailored to your goals. Using advanced optimization techniques and AI-driven insights, Rainier FM helps users navigate everything from retirement planning to wealth building with confidence. Pricing reflects the cost of running the service, but I’m actively gathering feedback to refine and improve it. Here’s an example of an insight this app can help you uncover.

If this sounds like something you’d find valuable, feel free to reach out or sign up—I’d love to hear what you think as I continue developing the platform!

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Historical Perspective: Protectionism, Innovation, and the Role of American Power

President Trump’s abrupt pause on new tariffs—just hours after they went into effect—was a striking reversal. But the bigger message wasn’t the pause itself. It’s that tariffs are no longer political third rails—they’re back on the table, and markets now need to take them seriously. Even if this round was delayed, the episode reinforced that protectionism is part of the modern policymaking toolkit, not just a relic of the past.

Historically, tariffs often spark panic—and for good reason. They raise prices, disrupt supply chains, and can provoke retaliation. But history offers a more nuanced view. In the 19th and early 20th centuries, the U.S. was one of the most protectionist economies in the world. From the Tariff of 1828 (the infamous “Tariff of Abominations”) to Smoot-Hawley in the 1930s, America repeatedly used trade barriers to shape its economic development. And during much of this period, it became the most dynamic industrial power on the planet. From steel to textiles to telegraphs, American innovation thrived not in spite of tariffs—but arguably because of them.

Why? Constraints accelerate problem-solving. When cheap British goods were shut out, American firms had to build domestic capacity. Economist Ha-Joon Chang describes this as “kicking away the ladder”—a strategy where wealthy nations endorse free trade only after climbing to the top via protectionism. This isn’t just a U.S. story. Postwar Japan and South Korea deployed protectionist industrial policies to nurture globally competitive manufacturing sectors. If today’s tariff threats on semiconductors, EVs, or steel push U.S. firms to automate faster or reshore production, they could spark a similar investment cycle—even if the policies are walked back.

There’s also a national security angle. In previous eras of geopolitical tension, the U.S. didn’t just levy tariffs—it mobilized industrial policy. During WWII, Roosevelt’s War Production Board and the Defense Plant Corporation seeded capacity critical to the war effort. Cold War anxieties drove DARPA funding and semiconductor subsidies that helped birth Silicon Valley. In today’s context—amid fears over China, AI, and rare earth supply chains—tariffs may be less about trade and more about leverage.

Still, tariffs without a productivity push can backfire. The U.S. steel industry in the 1970s offers a cautionary tale: trade barriers without reform only entrenched inefficiencies. But if today’s protectionist signals are paired with smart investments—in automation, upskilling, and advanced manufacturing—they could unlock overdue innovation. Paul Romer’s idea of “constraint-driven creativity” applies here: limiting the easy path (e.g., offshore labor) often pushes firms to build better, more scalable alternatives.

For DIY investors, the key is not whether tariffs are “on” or “off,” but how seriously companies take the threat. Are manufacturers accelerating capital expenditures? Are robotics, supply chain, and energy efficiency firms seeing new demand? Even a paused tariff regime can change corporate behavior. If firms believe tariffs could return—or are simply part of a broader strategic chessboard—they may act now. And that could filter into real, investable tailwinds in select sectors.

Supporting Research

• Chang, Ha-Joon. Kicking Away the Ladder: Development Strategy in Historical Perspective. Anthem Press, 2002.

• Romer, Paul. “Endogenous Technological Change.” Journal of Political Economy, vol. 98, no. 5, 1990.

• Irwin, Douglas A. Clashing Over Commerce: A History of U.S. Trade Policy. University of Chicago Press, 2017.

• Atkinson, Robert D. and Stephen J. Ezell. Innovation Economics: The Race for Global Advantage. Yale University Press, 2012.

• O’Sullivan, Mary. Contests for Corporate Control: Corporate Governance and Economic Performance in the United States and Germany. Oxford University Press, 2000.

• U.S. Congressional Research Service. “Industrial Mobilization During World War II.” RL33688, 2006.

Literature Review: Narcissism, Gender, and Corporate Outperformance — What a Surprising Study Reveals

In a unique and carefully structured study, Aabo and Rønnow (2024) investigate how CEO narcissism interacts with gender to affect corporate performance. Prior literature has shown mixed results on whether narcissistic traits in CEOs are beneficial or harmful for firm outcomes — often leaning toward the negative due to tendencies like entitlement and impulsivity (Grijalva et al., 2015; Ham et al., 2017). But this paper finds a noteworthy exception: when the CEO is a woman, narcissism becomes a performance enhancer. Using a sample of 849 non-financial S&P 1500 firms from 2007 to 2020, the authors find that narcissistic female CEOs outperform their male peers significantly in terms of profitability (ROA) and valuation (Tobin’s Q). A one standard deviation increase in narcissism among female CEOs correlates with a 19% improvement in ROA and a 21.9% boost in firm valuation — an economically meaningful effect.

The paper offers several explanations for this result. First, women tend to score higher in trait agreeableness, which helps moderate the toxic effects of narcissism — such as entitlement or manipulativeness — while still allowing them to benefit from assertiveness, confidence, and charisma (Ackerman et al., 2011; Wille et al., 2018). Second, the “double bind” of female leadership expectations may actually discipline narcissistic tendencies. Because women are socially penalized for dominant or egotistical behavior, female CEOs must carefully calibrate their assertiveness to remain effective — a pressure not always felt by men (Eagly & Karau, 2002). Finally, the authors note evolutionary psychology research suggesting women score higher in empathy, which may biologically buffer the downsides of narcissistic leadership (Christov-Moore et al., 2014).

That said, the study does have limitations. Only 3.4% of CEOs in the sample were women, which raises the possibility that these findings may not generalize as gender representation increases. The authors do attempt to control for this with firm fixed effects and robustness checks, including alternative narcissism proxies and exclusions of crisis periods like 2008–2009 and 2020. Still, the sample size — 76 female CEOs over 13 years — makes the data susceptible to statistical noise. Also worth noting: the study doesn’t claim causality, only association. Though the authors make a plausible argument that narcissistic female CEOs drive performance (rather than being chosen because of it), they acknowledge that further research is needed to confirm this with larger samples or causal instruments.

For investors, the implications are twofold. First, leadership personality traits do matter — but only in context. This study supports the idea that traits like narcissism should not be evaluated in isolation; gender, social norms, and organizational environment all shape how a leader’s traits affect outcomes. Second, in a market increasingly focused on ESG and diversity, this research strengthens the argument that gender diversity isn’t just an optics play — it can have real performance implications. Investors may want to look more closely at leadership dynamics and not just firm fundamentals. More broadly, this paper is a reminder that so-called “negative” traits like narcissism can sometimes be assets when paired with the right moderating forces — a lesson that applies to how we evaluate managers, founders, and even our own decision-making frameworks.

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Key Takeaways

1. In the news - Hiring for white-collar roles like HR, IT, and engineering has dropped over 25% since 2018, signaling a structural slowdown in professional job markets.

2. Chart of the week - The current 3-year inflation-adjusted return of the S&P 500 is at the 53rd percentile—right around its long-term average—suggesting markets are not historically overextended.

3. Beyond Bias: Ambiguity Aversion - Ambiguity aversion leads investors to avoid unfamiliar opportunities, but using strategies like barbell portfolios can help mitigate this bias and improve returns.

4. Building Wealth: Choice Architecture for Yourself - Simple environmental tweaks—like reminders, visual cues, or added friction—can make wealth-building behaviors easier and more consistent.

5. Historical Perspective: The Nifty Fifty - Even dominant companies can be bad investments if overvalued—reminding investors to consider price, not just quality, when buying into megatrends like AI.

6. Literature review: The Hot Hand in Jeopardy - New research shows that people overreact to winning streaks in unrelated areas—just like investors do—highlighting the danger of chasing recent performance.

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I’m adding something new this week—reader Q&A. If you have a question about the economy, investing, financial decision-making, or historical finance, submit it anonymously using the form below. Each week, I’ll pick one question and provide a detailed response in the next newsletter.

This isn’t financial advice, but I’ll break down ideas, provide deeper research, and share insights on topics you’re curious about. If you accidentally include personal details, I’ll remove them and keep the discussion hypothetical.

No question is too big or too small—if you’re wondering about something, chances are others are too. Submit your question here, and I’ll tackle it in the next edition!

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In the news

Despite the headline-grabbing low unemployment rate, white-collar hiring — particularly in tech — has slowed dramatically. New data from LinkedIn points to a clear white-collar recession, with sharp declines in recruitment for roles in IT, HR, and engineering. While some of the discourse around a ‘white-collar recession’ may be overstated, the numbers are worth paying attention to as we try to understand what this shift really means for professional workers.

By the numbers:

• Hiring in human resources is down 28% since 2018.

• Marketing roles have declined 23%.

• IT hiring is down 27%, quality assurance by 32%, and product management by 23%.

• Even engineering — once considered recession-proof — is down 26%.

• Project and program management hiring has dropped 25%.

• In contrast, healthcare jobs are up 10%, and roles in community and social services are down only 3%.

• Job postings for physicians and physical therapists have surged 80%+, while postings for software developers and data roles are down 20%+.

What’s driving the downturn:

• Over-hiring during the pandemic: After massive recruitment spikes post-2020, many tech companies now face bloated headcounts and are relying on hiring freezes and attrition to rebalance.

• Reduced voluntary turnover: Fewer employees are leaving their roles, with voluntary attrition falling from 27% in 2022 to under 20% in 2025, reducing the need for backfills.

• AI-led productivity gains: Companies are leaning on tools like ChatGPT and AI coding assistants to complete tasks faster, lowering the demand for additional hires. Google reports 25% of new code is now AI-generated.

• Hiring paralysis: With more qualified candidates flooding the market, decision-making has slowed. Time-to-hire has increased from 52 days in 2021 to 66 days in early 2025.

Why it matters:

This is a structural shift, not just a temporary slowdown. Job seekers face intense competition, slower feedback loops, and a market saturated with applicants. While tech roles are beginning to rebound — new postings rose from 144,000 to 223,000 since last year — hiring remains far below pre-pandemic levels.

The bottom line:

White-collar professionals, particularly in tech, face a drastically more competitive landscape. As hiring managers get overwhelmed by applicant volume and lean on AI to boost productivity, fewer roles are opening — and they’re harder to land.

Chart of the week

This chart shows the distribution of 3-year, inflation-adjusted annualized returns for the S&P 500, calculated monthly from March 1995 through March 2025. By using rolling 3-year windows and adjusting for inflation, the analysis focuses on real purchasing power — not just nominal gains. That’s a critical distinction when assessing whether the market is “overextended” or “undervalued” in a historical sense. Investors don’t spend nominal returns; they spend real ones. So to understand whether today’s market outcomes are strong, weak, or typical, it makes sense to anchor that judgment in after-inflation results. The data shows current real returns at the 53rd percentile — slightly above the long-term median — suggesting markets are neither frothy nor cheap by historical standards.

This kind of distribution analysis matters because it adds historical grounding to conversations that are often driven by emotion or recency bias. When markets have recently surged or slumped, it’s tempting to assume we’re at an extreme — but long-term data often tells a more measured story. Seeing where current inflation-adjusted returns fall within a 30-year range helps cut through short-term hype and pessimism. It also reminds investors that markets go through natural cycles, and that mid-range real returns — like the 5.5% annualized figure we’re seeing now — are historically common. That’s useful for setting expectations, tuning portfolio risk, or simply keeping perspective.

Beyond Bias: Ambiguity Aversion — Why We Shy Away from the Unknown (Even When It Might Pay Off)

Imagine you’re offered two investment choices: one is a well-known U.S. company with decades of financial data, analyst coverage, and predictable earnings. The other is a promising biotech firm operating in a new therapeutic space, with limited history and more uncertainty about future outcomes. Even if the second option has a higher expected return, most investors will still pick the first. That’s ambiguity aversion at work. It’s not just a fear of risk—it’s a preference for known risks over unknown ones. In practice, this leads many investors to underweight emerging markets, early-stage companies, or innovative sectors like AI, clean energy, or biotech—not because they’ve done the math, but because those areas “feel” uncertain. And that discomfort nudges portfolios toward familiar names, even when those names may be fully priced or offer limited upside.

To protect yourself from ambiguity aversion, it helps to reframe how you think about uncertainty. One useful tool is the barbell approach—allocate a large portion of your portfolio to stable, well-understood assets, but reserve a small slice (say, 5–10%) for more speculative, high-upside opportunities. This lets you participate in innovation without betting the farm. Another technique: ask yourself, “If this company or market were more widely understood, would the price already reflect that?” Often, unfamiliarity is exactly why the opportunity exists. You can also lean on structured processes, like setting an investment checklist that includes room for conviction-building research—not just headlines. When you recognize that ambiguity aversion isn’t a rational assessment of risk but an emotional discomfort with the unknown, you can start using it to your advantage—by looking where others aren’t.

Building Wealth: Choice Architecture for Yourself

One of the most effective but underused tools for building wealth is designing your day-to-day setup to make smart financial decisions the easiest ones to make. This concept—borrowed from behavioral economics—is called choice architecture, and it’s about shaping your surroundings so that good decisions require less willpower. For example, if you’re trying to invest more consistently, consider placing a post-it on your laptop that says “Have I invested this month?” or setting a recurring calendar event every payday titled “Review portfolio.” These nudges might sound simple, but they reduce the cognitive burden of remembering to act, making it more likely you’ll follow through. Studies like Thaler and Sunstein’s Nudge (2008) show that even small shifts in how choices are presented can meaningfully influence behavior—without removing freedom or requiring discipline.

To implement this, think about where friction is stopping you. If you forget to track expenses, keep a notebook or budgeting app pinned to your phone’s home screen. If you check your brokerage account too often and get tempted to trade, bury the app in a folder or log out after each use—subtly increasing the effort required to act impulsively. You can also use visual cues to reinforce long-term goals: keep a simple progress tracker where you’ll see it often, or store a one-page investing checklist in your wallet or desk drawer. These physical and digital tweaks serve as constant, low-effort reminders that anchor your attention to the behaviors that actually move the needle. Research from Keller, Harlé, and Vlaev (2021) found that well-designed nudges can significantly improve financial behaviors—even in experienced populations. The key is to reduce reliance on memory and motivation, and instead make better choices the default, visible, and easy.

Add to your toolbelt

Take control of your financial future with Rainier FM—your AI-powered financial planning companion. FM stands for Financial Model, and that’s exactly what this app delivers: optimized, data-driven financial plans tailored to your goals. Using advanced optimization techniques and AI-driven insights, Rainier FM helps users navigate everything from retirement planning to wealth building with confidence. Pricing reflects the cost of running the service, but I’m actively gathering feedback to refine and improve it. Here’s an example of an insight this app can help you uncover.

If this sounds like something you’d find valuable, feel free to reach out or sign up—I’d love to hear what you think as I continue developing the platform!

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Historical Perspective: The Nifty Fifty—When “Too Good to Fail” Wasn’t Good Enough

In the late 1960s and early 1970s, investors fell in love with a group of high-growth, blue-chip companies known as the “Nifty Fifty.” These stocks—names like Xerox, Polaroid, IBM, Coca-Cola, McDonald’s, and Johnson & Johnson—were seen as unbeatable, industry-dominating firms that could be bought and held forever. The belief was that their growth was so reliable and their market positions so secure that price didn’t matter. As a result, valuations soared. Some traded at price-to-earnings (P/E) ratios above 50—astronomical by historical standards. This era birthed the concept of “one-decision” stocks: the only decision investors needed to make was to buy.

But like all narratives that ignore valuation, this one didn’t end well. The 1973–74 bear market—driven by inflation, a recession, and rising interest rates—crushed even the highest-quality companies. Xerox lost over 70% from its peak. Polaroid fell nearly 90%. Many of these once-invincible stocks took more than a decade to recover, and some never returned to their prior glory. Investors who had dismissed valuation as irrelevant were reminded that even the best companies can be bad investments if bought at the wrong price (Siegel, 1998).

Fast forward to today, and echoes of the Nifty Fifty can be heard in the rise of today’s AI giants. Nvidia’s valuation has expanded dramatically, and other megacap names like Microsoft, Amazon, and Google are central to both AI infrastructure and investor enthusiasm. While these companies may be more profitable and better capitalized than their 1970s counterparts, the psychology is familiar: investors crowding into dominant firms with world-changing narratives, often without regard to how much of that future is already priced in. Just as Polaroid was once thought to own the future of imaging, many now believe Nvidia owns the future of computation.

The Nifty Fifty story isn’t a warning against innovation or large-cap growth—it’s a reminder that great companies and great investments aren’t always the same thing. When investor excitement runs ahead of fundamentals, it can take years for valuations to come back to earth. This doesn’t mean today’s AI leaders are doomed to crash—but it does mean that disciplined investors should remain sensitive to valuation, concentration risk, and the tendency to overpay for certainty. As history shows, even the most beloved stocks can lose their luster.

Supporting Research

• Siegel, Jeremy J. Stocks for the Long Run. McGraw-Hill, 1998.

• Malkiel, Burton G. A Random Walk Down Wall Street. W.W. Norton, 2003.

• Fama, Eugene F. “Efficient Capital Markets: A Review of Theory and Empirical Work.” Journal of Finance, vol. 25, no. 2, 1970.

• De Long, J. Bradford, and Andrei Shleifer. “The Stock Market Bubble of 1929: Evidence from Closed-End Funds.” Journal of Economic History, vol. 51, no. 3, 1991.

Literature review: The Hot Hand in Jeopardy — and What It Reveals About How We Judge Performance

Rational and Irrational Belief in the Hot Hand: Evidence from Jeopardy! (Kukavica & Narayanan, 2024)

A new study by Kukavica and Narayanan (2024) uses Jeopardy! — the classic trivia game show — to investigate a surprisingly persistent bias in human judgment: the hot hand fallacy. In theory, players should answer questions based on knowledge, not streaks. But the researchers show that both contestants and viewers consistently behave as if winning answers come in clusters — as if someone “on a roll” is more likely to keep answering correctly, even when each question is independent. Using data from over 9,000 Jeopardy! episodes, the study analyzes real-time decisions contestants make when choosing which player to challenge with a buzzer and which dollar amounts to wager. The results reveal a mix of rational updating and irrational bias — and offer important lessons for how investors interpret streaks in performance.

In one striking finding, Jeopardy! players consistently avoided buzzing against opponents who had just answered a string of questions correctly. This behavior could be rational if it simply reflected updating beliefs — for example, believing a contestant who answered three history questions correctly might be stronger at that category. But the study finds that players treated streaks as signals of generalized ability, even when the questions spanned unrelated topics. Contestants who were “hot” saw opponents back off across the board, even when knowledge areas shifted. This is classic hot hand bias — the mistaken belief that past success increases the likelihood of future success, even in random or unrelated domains.

Interestingly, not all hot hand beliefs in the study were irrational. The researchers find that some contestant behaviors did reflect Bayesian updating — where it actually makes sense to infer skill from past performance. When streaks occurred within narrow knowledge domains (e.g., three science questions in a row), other players’ deference was more justified. But even in these cases, players overreacted, giving too much weight to short streaks. In technical terms, contestants showed both rational learning and over-extrapolation — a pattern that mirrors how investors often chase performance. Funds with recent outperformance draw inflows (Frazzini & Lamont, 2008), even when future returns don’t follow. Just as Jeopardy! players overweight recent wins, investors tend to believe that streaks signal enduring skill — even in volatile or mean-reverting markets.

So what does this mean for investors? The takeaway is clear: don’t confuse luck for skill. When evaluating a stock picker, fund, or strategy, a hot hand might not indicate true talent — especially if the wins come in diverse or uncorrelated areas. Look for repeatability within a domain, not across domains. And be wary of performance-chasing behavior, whether in your own decision-making or among your clients. As the authors note, the best decision-makers learn to distinguish when a streak signals genuine expertise and when it’s just statistical noise. Like Jeopardy! players choosing who to challenge, investors are constantly updating their models of competence. But doing so accurately requires more than just counting wins — it requires understanding context, correlation, and the limits of intuition.

1. Chart of the Week - When comparing equal-weighted portfolios of companies exemplifying generative AI and cloud computing, generative AI has outperformed cloud by over 90% cumulatively since each trend’s inception—underscoring just how aggressively the market is pricing in AI’s potential.

2. Beyond Bias: The IKEA Effect - Investors often stick with underperforming strategies simply because they built them—a phenomenon known as the IKEA Effect—which can lead to costly decision-making unless checked with objective reviews or external benchmarks.

3. Building Wealth - Temptation bundling—like only watching your favorite show while updating your budget—can significantly improve financial habit consistency, with research showing it boosts follow-through on tedious but important tasks.

4. Historical Perspective: The Salad Oil Scandal - In 1963, a fraud involving fake soybean oil nearly sank American Express, causing its stock to fall by nearly 50%—a vivid reminder that even iconic firms can be vulnerable to weak oversight and financial deception.

5. Literature Review: How Tweets Distort Our Investing Minds - Investors exposed to positive tweets about a fictional company bought 30 more shares on average—even when the financials were poor—proving that social media sentiment quietly reshapes how we interpret objective data.

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I’m adding something new this week—reader Q&A. If you have a question about the economy, investing, financial decision-making, or historical finance, submit it anonymously using the form below. Each week, I’ll pick one question and provide a detailed response in the next newsletter.

This isn’t financial advice, but I’ll break down ideas, provide deeper research, and share insights on topics you’re curious about. If you accidentally include personal details, I’ll remove them and keep the discussion hypothetical.

No question is too big or too small—if you’re wondering about something, chances are others are too. Submit your question here, and I’ll tackle it in the next edition!

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Chart of the week

The rise of generative AI has captured investor attention like few technological shifts before it, prompting comparisons to past paradigm-defining eras such as the cloud computing boom. This chart explores those parallels by tracking equal-weighted portfolios1 of companies central to each trend, indexed from the start of their respective growth phases, with the return of the broad tech index (XLK) subtracted out2 to isolate the relative outperformance or underperformance of each shift. While some overlap exists between these trend portfolios and the broader tech sector, the intent is not to construct a pure alpha signal—but to show how much investor enthusiasm for each theme has exceeded the tech sector’s baseline performance. The goal isn’t just to measure returns, but to understand the broader market enthusiasm surrounding these technologies. So far, the results are striking: generative AI has massively outpaced the cloud in stock price performance over the same relative time frame. While this could reflect the transformative potential of AI tools in sectors ranging from software to semiconductors, it may also signal signs of exuberance. Much like the early cloud years, current valuations are being driven as much by expectations as by realized earnings.

One critical point is that Nvidia (NVDA), the poster child of generative AI, has contributed a large share of the outperformance in this space. However, by using an equal-weighted approach rather than relying on market cap weighting, this analysis minimizes the influence of any single company and better captures the broader market response to the technology itself. Even with equal weighting, the generative AI portfolio maintains a significant lead—suggesting widespread investor conviction that AI will fundamentally reshape the tech landscape. This divergence raises an important question: is the market correctly anticipating a technology that will prove to be many times more transformative than the cloud, or is it mispricing risk in a moment of collective exuberance? The cloud era matured gradually through steady enterprise adoption and infrastructure buildout; in contrast, generative AI has triggered a sharp, concentrated surge in valuations. That speed may reflect true potential—or it may signal a dislocation that will eventually correct.

Beyond bias: The IKEA Effect—Why We Overvalue Our Own Effort in Investing

Have you ever noticed feeling disproportionately attached to an investment strategy simply because you built it yourself? This common cognitive bias is known as the IKEA Effect—the tendency to overvalue things we have personally invested effort into creating or assembling. First identified by Norton, Mochon, and Ariely (2012), this effect was named after the well-known Swedish furniture brand, highlighting how people place higher value on items they assemble themselves compared to identical pre-assembled versions. In investing, the IKEA Effect manifests when investors become irrationally committed to portfolios or financial plans they personally designed, often sticking with them even when evidence suggests they are underperforming or excessively risky. For example, an investor who personally selects individual stocks might stubbornly hold onto losing positions, reluctant to sell due to the personal effort and pride invested in choosing those assets.

This bias arises because self-created strategies give investors a heightened sense of ownership and accomplishment, triggering emotional attachment and clouding objective evaluation (Marsh, Kanngiesser, & Hood, 2018). To overcome the IKEA Effect, investors should regularly perform objective reviews of their financial strategies, ideally with input from neutral third parties or by using external benchmarks. Asking oneself, “Would I choose this investment today if I were starting from scratch?” can help detach emotions from decision-making. Additionally, implementing rules-based approaches or pre-defined exit criteria reduces reliance on subjective judgment, minimizing the irrational attachment caused by personal effort. By consciously recognizing and mitigating the IKEA Effect, investors can make more rational, profitable decisions, avoiding costly biases tied to their emotional investments.

Building wealth

Many financial habits that lead to wealth accumulation—like regularly reviewing your budget, managing expenses, or contributing to savings—can feel tedious, making it difficult to stay consistent. One powerful, research-backed strategy to overcome this barrier is temptation bundling, which involves pairing a beneficial but less enjoyable financial task with an activity you genuinely look forward to. For instance, you might choose to only listen to your favorite podcast or audiobook while reviewing your weekly spending or updating your budget. This approach leverages your natural motivation for enjoyable activities to reinforce productive habits, transforming financial chores into experiences you actively anticipate. Research by Milkman, Minson, and Volpp (2014) demonstrates that temptation bundling significantly improves consistency in goal-oriented tasks, making it easier to sustain positive financial behaviors over time.

To effectively apply temptation bundling, start by clearly identifying a financial habit you regularly avoid—such as reviewing your bank statements, analyzing investment performance, or updating your monthly budget. Then, link this task directly to something pleasurable, like enjoying a special coffee or watching your favorite show. The key is to ensure you only indulge in the enjoyable activity while performing the financial task you’re trying to reinforce. This deliberate pairing conditions your brain to associate the financial habit with immediate rewards, dramatically boosting motivation and consistency. As shown by behavioral studies, incorporating temptation bundling can lead to long-term behavioral changes, ensuring critical financial routines become second nature, ultimately helping you achieve sustained wealth accumulation.

Add to your toolbelt

Take control of your financial future with Rainier FM—your AI-powered financial planning companion. FM stands for Financial Model, and that’s exactly what this app delivers: optimized, data-driven financial plans tailored to your goals. Using advanced optimization techniques and AI-driven insights, Rainier FM helps users navigate everything from retirement planning to wealth building with confidence. Pricing reflects the cost of running the service, but I’m actively gathering feedback to refine and improve it. Here’s an example of an insight this app can help you uncover.

If this sounds like something you’d find valuable, feel free to reach out or sign up—I’d love to hear what you think as I continue developing the platform!

Check it out!

Historical perspective: The Salad Oil Scandal of 1963—How Fraud Nearly Sank American Express

In 1963, one of the most bizarre financial scandals in U.S. history unfolded, shaking investor confidence and nearly collapsing a financial giant: American Express. Known as the “Salad Oil Scandal,” this incident centered around Anthony “Tino” De Angelis, a commodities trader who devised an audacious scheme involving enormous storage tanks in Bayonne, New Jersey, purportedly filled with valuable soybean oil. In reality, these tanks were mostly filled with water, topped with a thin layer of oil to deceive inspectors. De Angelis used falsified inventories to secure massive loans, leveraging the supposed “oil” as collateral from respected financial institutions, including American Express. When the fraud was exposed in late 1963, the market discovered that hundreds of millions of dollars in collateral effectively did not exist, causing immediate panic and substantial financial losses (Mihm, 2013).

American Express was deeply implicated, having issued warehouse receipts guaranteeing the soybean oil’s existence. As investors learned these guarantees were worthless, American Express’s stock price plummeted by nearly 50%, wiping out around $58 million in market value virtually overnight—equivalent to over half a billion dollars today. Investor confidence eroded sharply, leading to urgent discussions about corporate oversight, due diligence, and fraud detection procedures (Brooks, 1969). The scandal also exposed broader weaknesses in the commodities market, particularly the ease with which warehouse inventories could be manipulated and falsified. The scandal led American Express to restructure dramatically, including the appointment of new leadership and a complete overhaul of risk management practices.

The Salad Oil Scandal is instructive for modern investors and regulators, serving as a powerful example of how fraud can ripple through financial systems, rapidly transforming isolated deception into systemic risk. Similar issues emerged decades later in cases such as Enron and Bernie Madoff, where inflated asset values, falsified statements, and weak oversight again allowed massive deception to go unchecked. Contemporary parallels also exist in today’s investment landscape, where opaque asset classes such as cryptocurrencies and commodities-backed securities can similarly obscure genuine value, inviting fraud and manipulation if robust checks and audits are not enforced (Zingales, 2015).

Today, rigorous due diligence, independent auditing, and technological innovations such as blockchain-based inventory verification have strengthened commodity markets and financial institutions against similar forms of fraud. Yet, despite improvements, financial scandals rooted in deception and misrepresentation remain a real threat. Investors must continue to demand transparency, accountability, and vigilance from financial intermediaries, recognizing that effective fraud prevention is essential to market integrity. The Salad Oil Scandal of 1963 serves as a vivid reminder that even sophisticated institutions can fall victim to basic fraud without robust and persistent oversight.

Supporting Research:

• Brooks, John. Business Adventures: Twelve Classic Tales from the World of Wall Street. Open Road Integrated Media, 1969.

• Mihm, Stephen. “The Great Salad Oil Swindle.” Bloomberg, 2013.

• Zingales, Luigi. “Presidential Address: Does Finance Benefit Society?” Journal of Finance, vol. 70, no. 4, 2015, pp. 1327–1363.

Literature review: How Tweets Distort Our Investing Minds — Even When We Think They Don’t

The relevance and influence of social media posts on investment decisions

A new experimental study by Lars Kuerzinger and Philipp Stangor (2024) shows just how easily social media sentiment can shape our investment decisions — even when we think we’re being rational. The researchers designed a simulated investing platform where 259 participants made real financial decisions based on a fictional company, “Glubon AG.” Each person received a combination of financial data, stock charts, and AI-generated tweets — some positive, some negative, some neutral.

The results are sobering. Investors who received positive tweets bought significantly more stock — even when the financial data was objectively bad. And it wasn’t the tweets themselves that convinced people to invest — it was how those tweets altered their perception of the company’s financials. In statistical terms, financial sentiment fully mediated the relationship between tweet tone and investment behavior. This means the tweets changed how participants interpreted the numbers, not just how they felt about the company.

The average participant in the “positive tweets + negative financials” group bought 30 more shares than their peers in the “negative tweets + negative financials” group — a major swing, especially considering the financial data was identical. The influence of tweets was particularly strong when the financials were weak, suggesting that positive social content helps soften or reframe bad news — a classic example of framing bias in action.

Interestingly, tweets had no direct effect on behavior when measured in isolation. Instead, their power came through their ability to subtly reshape how people processed the underlying fundamentals. This aligns with prior behavioral finance research: Barber and Odean (2008) found that attention-grabbing stories fuel buying, and Tversky and Kahneman (1974) introduced the idea that we rely on mental shortcuts — or heuristics — to make decisions under uncertainty.

What’s especially relevant today is that all the tweets in this experiment were generated by ChatGPT. Participants were reacting to machine-written hype — not influencers, not analysts, not even real people. And yet, the perception of these tweets still shifted their financial judgment. That’s a red flag in today’s world, where AI-generated finance content is rapidly proliferating across X, Reddit, and YouTube.

For everyday investors, the takeaway is this: social media isn’t just noise — it rewires how you interpret the data. Even if you think you’re making a rational, fundamentals-based decision, your brain may be absorbing emotional cues from the content you consume. If a bullish tweet makes you “see” the earnings report in a more favorable light, that’s not insight — that’s influence.

This study doesn’t say that social media is bad — but it does suggest you should be aware of when it’s bending your perception. And if you’re wondering whether a company’s earnings really look as strong as the thread made them seem, it might be worth rereading the numbers without the spin.