The Moat Erosion Engine: AI’s Collision with Value Investing and the Mag 7

Over the past six months, many leading experts in AI—current and former leaders of top AI companies—have released manifestos detailing their visions for the future of artificial intelligence and its transformative potential. Many of them are too long to read, but I recommend uploading them to ChatGPT to summarize them. These include Situational Awareness by Leopold Aschenbrenner, published in June 2024. A former researcher at OpenAI and Columbia University valedictorian at age 19, Aschenbrenner provides deep insights into AI’s rapid development. Similarly, Dario Amodei, CEO of Anthropic—the company behind the large language model Claude and a former vice president of research at OpenAI—published Machines of Loving Grace just last month. For those who prefer podcasts and audio books, I would search in your podcast app and listen to it there. Other leaders in the space, like Sam Altman, Miles Brundage, Mo Gawdat, and Marc Andreessen, have also shared their perspectives through essays and public commentary. Add to that the many podcasts I frequently reference, featuring figures like Elon Musk, Eric Schmidt, and Wired magazine's Kevin Kelly, and it becomes clear how much thought is being devoted to understanding AI’s exponential trajectory in the very near term.

When I usually discuss AI, my focus is on helping people grasp how quickly things are changing and what that means for the macroeconomic world. Unless you are using it daily, it is challenging to keep up due to the speed. However, listening to a recent podcast with my friend Adam Parker at Trivariate Research inspired me to look at AI through a different lens. Adam’s commentary on the challenges facing value investors in today’s markets sparked the idea for this paper on AI’s collision with traditional investing. In one of his recent research reports, Adam wrote about avoiding cheap stocks heading into a recession, noting:

“Digging further into our database, nearly all the stocks that are optically cheap are cheap for a reason we can articulate. There is major uncertainty about their potential to turn their businesses around. Hence, a value investor now has to have more arrogance. They have to predict an uncertain recovery, as opposed to what growth investors do—which is to just observe which industries are growing fast. It is which stocks trade at low price-to-forward earnings multiples. Predicting whether or by how much they are under-earning is harder than it used to be. Stocks with the most multiple contraction prior to recessions are no longer ‘discounting enough of a recession to buy them,’ but rather, are indicative of a pending earnings correction.”

This insight, combined with Berkshire Hathaway’s recent announcement that it now holds $325 billion in cash and what that may mean, drove me to reflect on the philosophy of Warren Buffett and Charlie Munger in a time of exponential change. Their value investing approach, rooted in identifying companies with durable competitive advantages—economic "moats"—has long been a cornerstone of investing. These moats, built on elements like brand recognition, network effects, scale, and intellectual property, have historically protected companies’ profitability and market positions. However, with AI advancing at an exponential pace, it is emerging as the ultimate Moat Erosion Engine, capable of dismantling even the most fortified business models.

The Accelerating Pace of Creative Destruction

The lifespan of companies in the S&P 500 has already been shrinking for decades, and the rise of artificial intelligence is poised to accelerate this trend even further. In the mid-20th century, the average lifespan of a U.S. S&P 500 company was 67 years. Today, that figure has plummeted to just 15 years, reflecting the increasing complexity and unpredictability of the modern business environment. This decline is compounded by what analysts describe as "polycrises"—converging and interconnected crises that simultaneously threaten the stability of traditional business models.

AI introduces a new, unparalleled layer of disruption. It compresses innovation cycles and erodes long-standing competitive advantages at an unprecedented pace. My own journey, from taking Python courses five months ago to being able to speak into my phone to build websites without any coding highlights the pace of change.  Technologies and strategies that once provided resilience, such as economies of scale and brand loyalty, are increasingly vulnerable to obsolescence. AI-enabled competitors are leveraging superior analytics, automation, and adaptability to outperform traditional players, rendering even previously "untouchable" companies susceptible to disruption. While some resilient businesses may survive by innovating, diversifying, or acquiring new capabilities, these strategies are no longer guarantees of longevity in an era defined by exponential change.

This shifting reality underscores the critical importance of resilience—not just as the ability to recover from disruptions, but as the capacity to anticipate and continuously adapt strategies, business models, and operations to an ever-changing environment. In this AI-driven era, survival requires organizations to move beyond continuity-focused approaches toward truly dynamic and predictive resilience strategies. The ability to pivot quickly and anticipate change will separate the winners from the losers.

Dario Amodei’s essay "Machines of Loving Grace" illustrates just how transformative AI’s acceleration can be. He argues that AI has the potential to compress centuries of progress into mere decades—or even years. This exponential pace disrupts traditional timelines for innovation and societal advancement, fundamentally reshaping industries. Similarly, Adam Parker highlights that low valuations in today’s market often reflect "structural challenges, not underappreciated opportunities." This insight suggests that moats—once heralded as symbols of enduring strength—may now signal vulnerabilities ripe for disruption by AI. I have always believed that the stock market was the best predictor of what was to come, and Adam’s point answers why Nvidia has a larger market cap than the entire Russell 2000 index.

Moats in an AI World: An Obsolete Fortress?

In the traditional value investing model, moats represent sustainable competitive advantages. These advantages—whether through economies of scale, intellectual property, or customer loyalty—create barriers that protect a business from competitors. But AI doesn’t just challenge these barriers; it renders many of them obsolete.

Marc Andreessen has argued that AI will fundamentally reshape business models and productivity. AI-powered competitors can bypass traditional moats by leveraging data and advanced algorithms to deliver faster, cheaper, and more efficient solutions. For example:

- Brand Loyalty Moats: AI-driven personalized marketing can erode brand loyalty by targeting consumers with hyper-relevant alternatives.

- Scale Advantages: Automation reduces the need for large-scale operations, allowing smaller, AI-enabled companies to compete with established giants.

- Network Effects: AI can accelerate user adoption in new platforms, breaking the stranglehold of incumbents.

For now, the megacap tech giants—the so-called "Magnificent 7"—appear impervious, bolstered by unparalleled scale, vast data resources, and deeply entrenched network effects. However, their dominance comes at an extraordinary cost, as they engage in a capital expenditure arms race unlike anything the corporate world has ever seen. In their bid to outpace competitors in AI development, these companies are investing tens of billions annually into data centers, specialized chips, and vast AI infrastructure. While this spending fortifies their position in the short term, it creates vulnerabilities that could accelerate their eventual decline. Such monumental capex commitments could strain profitability, limit flexibility, and invite scrutiny from both regulators and competitors. Meanwhile, smaller, leaner players leveraging decentralized AI models may avoid these heavy burdens, adapting faster and more efficiently. Over time, the very arms race that is solidifying their moats today could become the fissure through which their dominance cracks, as history has shown that no empire, no matter how fortified, is immune to the forces of disruption.  I won’t go through it now but this is part of my thesis for why Bitcoin is the purest AI trade in my vision.  That’s for a future paper.

Temporal Arbitrage: The Moat Erosion Opportunity

AI’s ability to accelerate disruption creates a unique temporal arbitrage opportunity for investors. While traditional markets may price companies based on historical or linear projections, AI accelerates obsolescence at a nonlinear rate. This discrepancy allows forward-looking investors to profit by anticipating which businesses will face faster-than-expected declines in their competitive advantages allowing for short alpha for investors.  Think Amazon vs the mall retailers.

Sam Altman, CEO of OpenAI, predicts that superintelligent AI could arrive within a few thousand days, reshaping industries at an unprecedented pace. Investors who understand this timeline and can identify early signs of moat erosion will have a significant advantage in positioning themselves ahead of the market.

Navigating the Moatless Future

AI’s role as the ultimate Moat Erosion Engine marks a turning point in investing and business strategy. The philosophy of seeking enduring competitive advantages, which has defined value investing for decades, must now evolve to account for the rapid and unpredictable changes driven by AI.

The future will belong to those who can recognize the signs of disruption, leverage AI to stay ahead of the curve, and adapt to a world where the only constant is acceleration. As Adam Parker’s insights remind us, forecasting and adaptability are more critical than ever in navigating this brave new world. The decisions investors and businesses make today will shape their ability to thrive in a future defined by exponential change—and the disappearance of moats as we know them.