Darrell O'Donnell

guilty! 😂

⚡️The professional middle class is entering a structural decline and AI is accelerating the process. For generations, the economic bargain was straightforward. Develop valuable skills, earn credentials, enter a profession, and gradually convert expertise into financial independence. That arrangement worked because sophisticated cognitive labor was scarce and corporations had to compete for the people capable of performing it. That scarcity is disappearing. The collapse in entry-level hiring is the first visible consequence. Companies are discovering that the work once assigned to junior analysts, programmers, designers, and administrators can increasingly be performed by machines. And as those systems improve, the pressure moves upward through the professional hierarchy. The deeper consequence is that corporations are losing their economic incentive to develop human expertise in the first place. Why spend years training someone when the capabilities being developed can be purchased immediately, deployed continuously, and upgraded without waiting for a human career to mature? The entire professional ladder begins losing its purpose. And the next phase is even more consequential. As cognitive capabilities become reproducible at scale, the economic rewards increasingly migrate toward those who control the infrastructure that produces them. Intelligence becomes abundant, but ownership remains concentrated. This will fundamentally alter the relationship between education, employment, and wealth. A generation can spend decades becoming more capable while discovering that the market places progressively less value on those capabilities. The economy will continue producing extraordinary wealth. It will simply require fewer people to produce it. The great repricing of the coming decade is the declining economic scarcity of human intelligence. And the most unsettling implication is that the professional class spent generations building its identity around the very capabilities that technology is now making abundant.

A former colleague of mine just shared one of my old business cycle indicators. I built it. It stayed at Pictet when I left. Funny thing about rebuilding a process from scratch... it forces you to revisit your old work with a fresh pair of eyes. I've done it many times now. Every time, there are improvements to be made. So let me be blunt about this one: it's dead. It’s a composite. Labor market conditions, the yield curve, the output gap, profit margins, consumer sentiment, wages... roughly fifteen inputs, ranked in percentile. Many of those inputs have lost their meaning in a K-shaped economy. They were built to read a broad, synchronized cycle, where labor, credit and profits moved together. That economy doesn't exist anymore. So the signal degrades. You can see it plainly in the chart: the last time this indicator hit the 90th percentile, it should have called a recession. In the old world it would have. This time it didn’t. The changes I've been writing about for the past eighteen months have broken most of these old-school macro models. Or at the very least, made them far less important than they used to be. This is exactly why the loud voices calling for recession these past 2-3 years have all been wrong. I took the other side. I explained why they were wrong... initially in a GMI piece last year, then more completely in one titled The Compute Cycle, one of the last pieces I wrote before I left GMI. Here's the bigger issue. Everyone is building dashboards now. AI handed the world a new set of power tools, and almost overnight, anyone could ship a model. However, AI made it possible for anyone to build tools. What it has not done is make everyone a toolmaker. The difference is judgment. Most people have been handed the capability without the apprenticeship. They can produce the model. They cannot tell you how it works. And if you can't do that, you can't refine it. The oldest rule in this business does not care how good the technology gets: garbage in, garbage out. This old chart of mine is not the only one. The Financial Conditions Index I developed years ago… same story: dying. Why? Because the ground underneath these indicators is moving, and fast. For forty years the business cycle lived inside a cage. That cage held through the dotcom bust, the Global Financial Crisis, COVID, and half a dozen Fed cycles. Then this year global semiconductor sales posted the largest annual increase in over four decades, and the cage broke. Compute stopped being a downstream input to the real-world production cycle and became the binding constraint. Growth now starts with chips and propagates outward. Everything else sits downstream. And that guts the old financial conditions logic. These frameworks were built for a world where cash flow sensitivity to rates, a strong dollar and high oil prices governed the cycle. The dollar still matters, it always will, it's the price of global liquidity. Rates and oil still matter too. But their grip on the cycle has loosened. When capex is funded off corporate balance sheets running a moonshot, and the buildout is a strategic imperative nobody can opt out of (the Red Queen Effect), those old transmission channels matter far less than they used to. When the causality of the entire cycle flips, the tools built to read the old one don't just need tuning. They need burying. For well over a decade I've built asset allocation models and business cycle frameworks for some of the most sophisticated investors in the world. That work, and the discipline of staying relentlessly data-dependent, is what I'm known for. The craft beneath it has always been the same. Statistics and econometrics. The unglamorous stuff you can't shortcut, and can't fake. It comes with me on this next venture. A full suite of new indicators, built from the ground up. I'll be sharing much more in Q4. The macro landscape is shifting. More now than ever. Time to sit up...

Typo alert: Some customers saw quadrillion-dollar AWS billing estimates today. Slight miscalculation on our end (very slight 😅). We're fixing it now. No action needed on your end. Sorry for the confusion. Real question: what will you do with those trillions instead?

⚡️AI is dissolving the social fiction that intelligence and economic value naturally belong together. For most of modern history, higher cognitive ability translated into better credentials, better jobs, higher income, and greater status. Society built an entire moral order around that connection. Study hard. Become skilled. Join the professional class. Convert knowledge into security. AI breaks the conversion mechanism. A machine can now reproduce a growing share of the visible output associated with intelligence. Reports, analyses, forecasts, models, presentations, code, strategy documents, legal drafts, research summaries, and operational plans can be generated at near-zero marginal time. Once the output can be produced cheaply, the market stops paying for the appearance of intelligence. That creates a brutal separation between cognition and consequence. The person who can produce an elegant analysis loses value when thousands of systems can do the same. The person who can commit capital, take responsibility, persuade other humans, absorb risk, build distribution, control infrastructure, or make irreversible decisions gains value. The economy begins rewarding authority over intelligence. That is the real transition. The MBA historically bundled four things: knowledge, signaling, network access, and permission to enter elite institutions. AI destroys the knowledge premium first. Credential inflation weakens the signaling premium next. Network access and institutional permission remain because they are enforced socially rather than intellectually. The degree survives where it functions as a gatekeeping device. That exposes the real truth of elite education. Much of its value never came from learning. Its value came from selection, affiliation, access, and social proof. AI makes that harder to hide. Lower-tier programs collapse first because they sell expensive cognition without scarce access. Elite programs persist because they sell entry into capital, power, and trust networks. The gap between the two widens violently. The labor market then reorganizes around a new hierarchy. At the top sit people who own models, compute, capital, platforms, brands, distribution, regulated permissions, and institutional relationships. Below them sit high-agency operators who can turn machine output into real-world results. Below them sit human roles preserved by physical presence, emotional trust, legal accountability, or social preference. The broad middle gets compressed. That middle was the core of the twentieth-century professional class. Analysts, coordinators, managers, planners, researchers, marketers, consultants, administrators, and junior professionals formed the connective tissue of large institutions. Their work consisted largely of translating information between departments, preparing decisions for superiors, and maintaining organizational coherence. AI can increasingly perform those functions directly. The corporation then becomes thinner. Fewer people control more output. Hierarchies flatten. Middle management shrinks. Junior apprenticeship ladders disappear. The absence of entry-level roles creates a second-order problem that most people are missing. Senior judgment is built through years of lower-level work. If machines absorb the apprenticeship layer, institutions stop producing future experts. They gain short-term efficiency and create long-term capability decay. That becomes a major fragility. Organizations may become more productive while becoming less able to regenerate human competence. The economy then faces a paradox. AI reduces the need for junior human labor while increasing the need for mature human judgment. The system consumes the training ground that produces the judgment it still depends on.