By the late 2010s, value investing faced its longest test.
The strategy had one of the longest track records in finance. Buy cheap stocks, sell expensive ones, wait. Benjamin Graham had codified it in the 1930s. Decades of academic research documented a historical value premium—cheap stocks, as measured by metrics like price-to-book, systematically outperformed expensive ones over long periods. Generations of investors built careers on it.
Then it entered a decade-plus drawdown. From mid-2007 to late-2020, systematic value—rules-based strategies that buy cheap stocks and sell expensive ones—underperformed growth by a significant margin, whether measured by long-short factors or value-vs-growth indices. Disciplined managers who had built decades of credibility watched their core strategy lag year after year. Some clients lost patience. Some funds closed. Many questioned whether value investing was dead.
What kind of problem is this?
When something works and then stops working—without any internal flaw—that is a specific shape of problem. And the shape tells you where to look for insight.
If the machinery had broken, you would call an engineer. If the logic had a flaw, you would call a mathematician. But when something succeeds in one context and fails in another, without itself changing, that is not an engineering problem or a logic problem. It is an adaptation problem. The thing is fine; the fit between the thing and its environment has changed.
And the discipline that has spent 150 years thinking rigorously about the fit between organisms and environments is evolutionary biology.
So let’s borrow.
This is the multi-model move: recognize the shape of a problem, find the discipline that has thought rigorously about that shape, and import its frameworks deliberately rather than reinventing them from scratch.
Both sides of the value debate were asking the wrong question. “Does value work?” is like asking “Is a polar bear fit?” The answer is: fit for what? A polar bear is exquisitely adapted to the Arctic—the apex predator of a frozen world. Drop it in the Sahara and every adaptation becomes a liability.
If we view investment strategies as organisms, their “fitness”—their ability to generate returns—is not an intrinsic property. It is a relationship between the strategy and the environment in which it operates. Change the environment, and yesterday’s perfect adaptation becomes tomorrow’s mismatch.
What the Genome Knows
Evolutionary biology learned this lesson the hard way. Fitness only makes sense relative to an environment—there is no universal fitness ranking.
Christoph Adami, the physicist we met in our previous post, formalizes this precisely. An organism’s genome is essentially a compressed encoding of information about its environment. The polar bear’s white fur, fat reserves, and hunting behaviors are a physical record of what worked in Arctic conditions over thousands of generations. The genome “knows” about the environment, in the sense that it carries information useful for surviving there.
This framing transfers directly to investing. A strategy’s design—its signals, its rules, its parameters—is a compressed encoding of information about the market environment in which it was developed. A value strategy “knows” that cheap assets tend to revert to fair value. But what happens when what the strategy “knows” stops being true?
This is the backtest limitation. Almost every investment strategy that gets deployed passed a backtest—it found patterns in historical data that would have made money. But a backtest is a fitness measurement in a historical environment. It tells you this organism would have thrived in the Pleistocene. It says nothing about whether the Pleistocene still exists.
Value investing’s track record was built in an environment where capital was scarce, information traveled slowly, and investors often overreacted to short-term bad news. Patient investors who bought beaten-down stocks were compensated for providing liquidity and bearing uncertainty. The strategy’s “genome” encoded this environmental structure.
Then the environment shifted. In the US and other developed markets, the cost of capital fell for decades—interest rates dropped from double digits to near zero, making growth more valuable as distant cash flows were discounted less heavily. Information became instantaneous. Systematic strategies proliferated, changing the dynamics of how patterns got traded. The strategy’s genome still encoded the old environment—but that environment had changed.
This isn’t a story about value investing being “wrong.” It is a story about environmental mismatch. The polar bear’s genome is a masterpiece of evolutionary engineering. It just doesn’t help when the ice melts.
Adaptive Lag
Biologists have a concept called “adaptive lag”—the delay between an environmental change and a species’ adaptation to it. If the environment shifts faster than the generation time allows, the species falls out of sync with its world.
Strategies face the same problem with an additional twist: a deployed ruleset doesn’t evolve on its own. A species, given enough time, will adapt through selection. But a strategy sitting in production encodes a snapshot of an environment that may no longer exist.
This is why strategies decay. Not because they were badly designed, but because they were designed for a world that has moved on. The signals that once carried information about future returns now carry noise. The relationships that once held have broken or been arbitraged away.
But there is a deeper problem: in markets, the environment fights back. In biology, organisms adapt to their environment, but the environment doesn’t usually adapt back. Markets are different. When a strategy succeeds, capital flows in. More capital chasing the same patterns can change the patterns themselves. This is reflexivity with an evolutionary flavor: the organism’s fitness changes the fitness landscape.
For some strategies, this dynamic is the dominant story—statistical arbitrage that gets crowded until the spread disappears, or momentum trades that reverse sharply when positioning becomes concentrated. But value’s decade-plus drawdown doesn’t fit neatly into the “arbitraged away” narrative. During this period, value-growth spreads actually widened—cheap stocks got cheaper relative to expensive ones. Something else was happening.
Cheap can get cheaper for rational reasons: if discount rates fall and stay low, growth stocks should command higher multiples. The environment shifted in ways that may have rationally repriced the value-growth gap. The environmental thesis got a partial test in 2022: when rates rose sharply, value outperformed growth—consistent with the framework’s emphasis on environmental fit.
Price-to-book—the canonical value signal—likely became noisier as the economy shifted from tangible to intangible assets. Book value captures factories and inventory well; it captures software and network effects poorly. Many systematic value strategies now use composites (earnings yield, cash-flow yield, sales-based measures) partly to reduce reliance on any single metric. The genome is adapting.
If fitness is relational, then evaluating a strategy requires evaluating the environment—and your beliefs about whether that environment will persist. The strategy’s historical returns tell you it was fit for the past. Your job is to figure out whether the future will look like the past—and to notice when it doesn’t.
Fit for What?
If the evolutionary lens reveals anything, it is the importance of asking what environmental conditions your strategy assumes—and whether those conditions still hold.
Consider the mirror image of value’s long winter: the extraordinary outperformance of the mega-cap technology companies—the hyperscalers. While value languished, a handful of companies delivered returns that dominated global indices. Market-cap weighting mechanically increased exposure to these winners as they rose, amplifying concentration—though whether passive flows drove returns or merely reflected them is debated. For over a decade, “own the mega-cap growers” was the winning strategy.
This wasn’t irrational. These are genuinely exceptional businesses: dominant market positions, network effects, recurring revenues, massive cash generation, and the infrastructure buildout for artificial intelligence. Unlike the dot-com era, these companies sit on cash piles that earn billions in a high-rate world. The fundamentals were real. But the magnitude of outperformance—the degree to which these companies pulled away from everything else—also had environmental roots.
Start with rates. Long-duration assets benefit disproportionately from falling discount rates. When rates fall, the present value of cash flows far in the future rises more than the present value of near-term cash flows. Growth stocks are long-duration assets; their value depends heavily on earnings years or decades away. Four decades of falling rates were a structural tailwind for this kind of company.
Then consider growth scarcity. In a world of sluggish nominal GDP growth, companies that could reliably grow revenues at 15-20% per year became precious. The market paid up for growth because growth was rare. The hyperscalers delivered it consistently, and scarcity justified premium multiples.
Platform economics reinforced the dynamic. Winner-take-all dynamics—network effects, switching costs, data moats—meant that early leaders extended their leads. The market rewarded concentration because concentration reflected real competitive advantages. Leaders extended their leads, and the market recognized that dynamic.
Most recently, the buildout of AI infrastructure created a new growth narrative. The hyperscalers are major financiers and beneficiaries of the AI boom—the cloud platforms, the chip buyers, the foundation model builders. Capital expenditures that would sink most companies became signals of future dominance for these.
The question is whether this environmental fit continues—or whether some of these tailwinds are shifting.
Rates may stay higher—if the four-decade bond bull market is over, the mechanical bid for long-duration assets weakens. AI capex eventually needs returns—if the payoff is slower or more diffuse than priced, the narrative could shift. Growth scarcity could end—if nominal growth picks up, the premium for reliable growers may compress. And regulatory pressure remains an environmental variable that could alter competitive dynamics.
None of this means the hyperscalers are “overvalued” or due for a fall. They might continue to outperform for years. The businesses are real, the moats are deep, and the AI wave may be as transformative as the market believes. But the evolutionary lens asks a specific question: how much of the outperformance was the organism, and how much was the environment?
Any sustained outperformance reflects a set of environmental conditions: falling rates, growth scarcity, platform dominance, and now AI capex. The framework doesn’t tell you to sell. It tells you to ask: which of these conditions are expected to persist, and what happens if that expectation is wrong?
The Adaptation Question
The machine metaphor dominates in many domains. Investment strategies are “built.” Business models are “engineered.” Organizational processes are “optimized.” The language assumes these things are robust and context-independent—that good design transcends circumstance.
The organism metaphor is more honest. Strategies are adapted, not designed from first principles. They carry information about an environment, not universal laws. They can thrive or struggle depending on conditions they don’t control.
When does this lens apply? When something that worked stops working—without any internal flaw. When the machinery is fine but the results have changed. When the debate is “does X work?” rather than “is X fit for this environment?” That’s an adaptation problem, and evolutionary biology has the tools.
The framework doesn’t tell you what to do. It tells you what to ask.
The framework asks three questions. What does your approach “know”—what assumptions are encoded in its design? What context does it assume? And does that context still exist? These questions apply whether you are evaluating a portfolio, a business model, or an institutional process.
The discipline is in asking: fit for what?
References & Further Reading
What is Information?: Christoph Adami
The Origins of Order: Self-Organization and Selection in Evolution: Stuart Kauffman
Adaptive Markets: Financial Evolution at the Speed of Thought: Andrew Lo
Is (Systematic) Value Investing Dead?: Israel, Laursen & Richardson
Darwin’s Dangerous Idea: Daniel Dennett