What If the AI Boom Really Is Different?
Why generative AI stocks are crushing the cloud—and what it tells us about bubbles, belief, and investor bias.
Key Takeaways
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.
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.
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.
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.
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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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.
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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.
The Cloud portfolio includes: Amazon (AMZN), Microsoft (MSFT), Google (GOOGL), Salesforce (CRM), Adobe (ADBE), Intuit (INTU), Cisco (CSCO), Oracle (ORCL), Autodesk (ADSK), and Akamai (AKAM). The Generative AI portfolio includes: Nvidia (NVDA), Microsoft (MSFT), Google (GOOGL), Amazon (AMZN), Meta (META), Palantir (PLTR), AMD (AMD), Snowflake (SNOW), C3.ai (AI), and Broadcom (AVGO). Each portfolio is equally weighted and rebalanced regularly.
While XLK is market-cap weighted and the GenAI and Cloud indexes are equal-weighted and rebalanced, the comparison is still valid. XLK serves as a consistent baseline for the broader tech sector, reflecting how the market values major tech companies over time. By contrast, equal-weighted portfolios measure the average performance of companies driving each technological trend, reducing the impact of a few dominant firms. This helps isolate whether the trend itself created meaningful stock market value, not just whether one or two outliers performed well.