Most of what I have written about compression has been about valuation.7 The argument was not complicated. Once capital has a price again, debt gets refinanced at rates that no longer flatter it, growth stops being something a company can manufacture, and investors eventually lose the appetite for paying extraordinary prices for earnings that have not arrived yet.
Multiples compress.
There is a second compression behind that one, and I think it matters as much. It is the compression of return itself, and the thing driving it is AI — not because some model is about to find the perfect strategy, but for nearly the opposite reason.
Eventually everyone will have one.
The Market Has Always Paid for Other People's Mistakes
A considerable share of investment return has always existed because markets are imperfect.
- Someone didn't read the filing, or misread the earnings call, or sold because they were frightened rather than because anything had changed.
- Someone let a position run past the size they would ever have chosen on purpose, or never rebalanced, or executed badly on the way out.
- Someone could not hold five thousand securities in their head at once.
- Someone knew an asset was mispriced and had neither the capital, the technology, nor the mandate to do anything about it.
Professional investing has spent decades building increasingly sophisticated machinery to harvest those imperfections, and yet enormous pools of capital are still run by humans working under very human constraints. Attention runs out, analysis costs money, emotion gets in the way, institutions move slowly, and information takes time to travel. Every one of those deficiencies is worth something to whoever is operating better than the person on the other side of the trade, and that is the value AI threatens.
The great investment opportunity created by AI may turn out to be the destruction of investment opportunity itself.
Millions of Portfolio Managers That Never Sleep
The version most people picture is an investor asking a chatbot whether Nvidia looks expensive. That is not the version that changes anything. The one that does is an agent that never stops reading — every 10-Q and 10-K, every transcript and guidance change, every competitor announcement — while it watches rates, spreads, currencies and commodities move underneath the positions it holds. It knows what a given piece of news did the last few thousand times something like it happened. It knows what a position will cost to exit after tax, how much factor risk it is quietly carrying, and which holding has grown large enough to be a problem.
Then it executes. Continuously.
Today that capability belongs to hedge funds, quant shops, and the large asset managers. Tomorrow it is a software subscription. Eventually it is a commodity, and a market in which that capability is a commodity is a different market.
We Have Seen This Movie Before
High-frequency trading is the precedent for what happens when technology finds an inefficiency it can attack. Researchers at the Bank for International Settlements found that more than a fifth of trading volume in their sample occurred in latency-arbitrage races1 — contests over discrepancies often worth a fraction of a tick, fought with enormous technological investment. Once the opportunity was understood, competition collapsed into speed, and microseconds became the product.
AI runs the same process one level up the intellectual stack. The race is no longer about who executes first but about who understands first: who connects a new piece of information to everything else, decides what it matters for, and builds the right response. When millions of agents can do that almost instantaneously, the question stops being who understands first and becomes something more uncomfortable.
What happens when everybody understands at roughly the same time?
Alpha Has a Half-Life
Investment strategies share an inconvenient trait, which is that they stop working once enough capital discovers them. A mispricing exists, someone finds it, capital moves toward it and the mispricing shrinks; more participants pile in and the return falls further; and what once looked like extraordinary skill ends as a crowded trade producing ordinary returns.
That is reflexivity in its simplest form, and AI accelerates every step of it.
A July 2026 NBER working paper by Ralph Koijen and Bradford Levy confronts the problem head-on. As investors adopt AI, prices adjust, wearing away the very patterns those systems were trained to exploit.2 Their real-time benchmark still found that the best-optimized agentic systems, working from earnings-call transcripts, raised the explained variation in announcement-window returns from about 8% to nearly 20%.2
The systems are getting dramatically better at extracting the information embedded in markets, and that success is exactly what destroys the economic value of extracting it.
Intelligence Becomes a Commodity
For most of the history of markets, intelligence was the scarce input. Trained analysts, computing power, financial data, research departments, portfolio optimization and institutional risk management were all expensive, and their scarcity created rents. AI attacks nearly every one of those constraints at once. An individual investor may soon command the analytical capability that took an entire institutional research organization twenty years ago.
Democratizing that capability does not mean everyone earns extraordinary returns. It means extraordinary capability becomes ordinary, and markets do not pay extraordinary returns for ordinary capabilities. The AI-investing conversation keeps asking how much better AI will make an individual investor.
The more important question is what happens when it makes everyone better.
AI does not have to touch the market's return to do its damage. What it compresses first is the spread between investors — the part of the gap between a bad one and a good one that was really a gap in diligence. Picture three of them, earning 3, 7 and 12 percent. Hand each an agent.
- The bad investor stops panicking, stops forgetting to rebalance, stops leaving cash idle, and lands somewhere near 6.5.
- The average one was already close to the market and stays there.
- The excellent one, whose 12 was mostly the reward for reading more carefully than everyone else, finds that everyone else now reads carefully too, and settles at 8.
The market's 7 is untouched. What has been taken out is most of the five points that used to be paid for doing well what machines now do for everyone — and the fight moves up a level, to the one point that is left.
Poor Execution Becomes Less Valuable to Exploit
Many investment mistakes are not analytical at all. They are execution failures:
- Investors panic after declines and chase rallies.
- They let positions grow dangerously large, leave cash idle, rebalance inconsistently, and ignore taxes.
- They sell for emotional rather than portfolio reasons and fail to hedge risks they already recognize.
- They misunderstand correlation, confuse volatility with permanent loss, and take risk without adequate compensation.
Every one of those behaviors is somebody else's opportunity.
Now put an agent between the human and the portfolio. The human can stay emotional; the portfolio no longer has to be. The human forgets; the agent does not. The human ignores four thousand securities; the agent watches all of them and compares them continuously. None of this makes anyone rational. It shrinks the amount of capital that irrational execution directly controls, and that is the distinction that matters.
Important
Markets do not need humans to become rational. They need machines standing between human intention and capital allocation, and that is what is being built.
Then It Collides With Debt
Under different circumstances, greater market efficiency would simply be good news — better price discovery, lower transaction costs, deeper liquidity, less wasted capital. Those benefits are real. But the transformation is arriving in a particular financial regime, and the regime is the point.
The debt calendar tells the same story from the other side. S&P Global now places the speculative-grade nonfinancial maturity peak in 2031, pushed out largely by bond issuance for AI and digital infrastructure, while debt rated B- and below rises to $268.8 billion in 2028, concentrated in the U.S.5
The machines being built to price capital more precisely are being financed, in part, by debt they will eventually price.
That is the collision. Systems capable of becoming extraordinarily efficient at determining the price of capital are arriving inside a financial system that has grown accustomed to capital being mispriced cheaply.
The Market May Become Less Forgiving
This is where return compression joins valuation compression. The old system contained enormous slack, and companies, investors and capital could all afford to be inefficient inside it:
- Low rates made mistakes survivable.
- Rising asset prices concealed the ones that weren't.
- Expanding multiples produced returns even when the underlying business barely improved.
Human inefficiency, meanwhile, gave sophisticated investors a reliable source of outperformance.
Now remove those cushions one at a time. Capital has a price, and debt has to be refinanced at it. AI intensifies competition between companies, erodes information advantages, improves execution, accelerates price discovery and lets capital move faster. None of that has to mean a market that falls forever. It means a market that is increasingly unwilling to reward mediocrity.
Efficient Does Not Mean Stable
A market run largely by AI does not become calmer. It can become more efficient and more violent at the same time. In July the IMF's Tobias Adrian warned that AI is compressing the timelines of financial decisions, and that the flash crashes of the future may come less from coding errors than from many AI systems reacting in parallel to the same information.6
Most of the time such a market would feel remarkably efficient. Earnings are already modeled, macro data already anticipated, the portfolio implications already calculated, so mispricings close quickly and ordinary information moves prices less than it used to. Then something genuinely unexpected happens, and millions of systems independently reach nearly the same conclusion at nearly the same moment. Repricing that once took weeks happens in hours, and what took hours happens in minutes.
The market gets better at reaching equilibrium and more violent in the transition between equilibria.
Three Compressions
AI may ultimately produce three related compressions.8
Margin compression. Intelligence becomes cheap. Smaller organizations gain capabilities once reserved for large ones, competition intensifies, and the rents attached to organizational scale decline.
Valuation compression. Capital is no longer free. Refinancing matters, investors demand more compensation for duration and uncertainty, and extraordinary multiples become harder to sustain.
Return compression. Information, portfolio construction, and execution become commoditized. Easy alpha disappears, and the advantage once supplied by inattentive or poorly executing participants shrinks.
These forces reinforce one another: corporate margins ↓, valuation multiples ↓, excess returns ↓. None of that eliminates investment returns. It changes where they come from.
What Remains Scarce?
Compression becomes recursive. Information advantage compresses, then execution advantage, then portfolio-construction advantage, then model advantage. Eventually even the advantage of having AI compresses, because everyone has it.
When technology commoditizes something, value migrates toward whatever stays scarce. As intelligence, analysis, execution and portfolio optimization become abundant, each becomes less valuable in turn, and return moves toward what cannot easily be manufactured:
- Truly proprietary information
- Control of scarce physical assets
- Unique distribution and regulatory privilege
- Extreme patience and long-duration capital
- The stomach to hold a position when every optimization model says trimming exposure is temporarily rational
Above all, return moves toward liquidity at the moment liquidity itself becomes scarce. The future advantage may belong less to whoever owns the smartest model than to whoever owns something no model can manufacture.
The Odds
Everything above is conditional. Here is what I actually believe, and how strongly.
| Probability | Claim |
|---|---|
| 90% | AI agents materially improve portfolio construction, research and execution |
| 80% | This materially compresses alpha from common signals, slow analysis and poor execution |
| 70% | Retail/institutional behavioral inefficiencies become meaningfully harder to monetize |
| 75% | Alpha increasingly migrates toward scarce information, capital, liquidity and unusual risk-bearing |
| 65% | Agent crowding makes markets faster and occasionally more unstable |
| 60% | This contributes meaningfully to lower excess returns across public markets |
| 50–60% | AI-driven efficiency + expensive capital/debt creates a persistent broader compression regime |
| 25–35% | This becomes the primary cause of a major valuation reset/crisis |
That's why I'd put ~60% on the complete thesis but ~80% on the core mechanism. The uncertainty isn't whether AI makes markets dramatically more competitive. I think that's increasingly hard to argue against.
The uncertainty is where the displaced return goes. That question is the next article.9
The Final Irony
Investors have imagined AI as another tool for generating alpha, and initially it will be. Early adopters will hold an advantage, sophisticated systems will find opportunities humans miss, and agents will manage portfolios better than most individuals manage their own. But technological advantages diffuse, and what begins as alpha becomes infrastructure.
The spreadsheet did not permanently make spreadsheet users rich. Knowing how to search the web stopped being an edge. Algorithmic execution became part of the market itself. AI portfolio management will follow the same path: first an advantage, then widespread adoption, then compression. In the end, it becomes the minimum capability required just to participate.
When that happens, an uncomfortable fact surfaces. For decades, some portion of market returns came not from extraordinarily productive capital but from friction, imperfect information, and poor human execution. AI may remove much of that friction. It will do so at precisely the moment expensive capital and heavy debt loads are removing the cushions that let mispricing persist.
The next phase of compression won't be driven by interest rates alone. It will be driven by intelligence itself: a market brutally efficient at determining what assets are worth, at exactly the moment the financial system can least afford the answer.