The company is called Mercor. It is valued at $10 billion. Its founders are in their twenties and have never held a conventional job. Its clients include OpenAI and Anthropic. Its premise is straightforward: AI labs need human expertise to train their models, and Mercor connects that expertise to the labs at scale.

Bloomberg called it the startup training AI to replace the white-collar workforce. The coverage has been substantial. The conversation has been almost entirely about disruption which jobs will go, how fast, what comes after.

That is the right question asked of the wrong problem.

What the Coverage Is Missing

Here is the assumption buried inside Mercor’s model and inside the broader AI training economy that almost nobody is stress-testing in public.

AI models do not train once and then know everything. They need to be continuously updated with current, living, practiced human knowledge. The training data that makes a model genuinely useful in investment banking is not a textbook from 2019. It is the accumulated judgement of people who are actively working in investment banking right now making decisions, reading markets, navigating clients, developing the kind of tacit expertise that only comes from doing the work in real time.

Mercor’s contractors are valuable precisely because they are drawing from active, living professional experience. The doctor being paid $250 an hour to help train a healthcare model is not valuable because she read medical journals. She is valuable because she has spent years in consultation rooms making decisions under uncertainty, and that embodied knowledge is what the model is trying to absorb.

If AI displaces these professionals at scale which is explicitly the goal where does the next generation of training knowledge come from?

There are no new bankers developing new banking judgement through live transactions. No new lawyers building new legal reasoning through real cases. No new doctors accumulating the kind of clinical instinct that comes only from years of practice.

Someone will raise an objection here. And it deserves a direct answer.

Once AI models are deployed at scale treating patients, advising clients, executing trades they generate their own outputs. Those outputs become new data. The model trains on what it does, not just on what humans taught it. The loop, the argument goes, is self-sustaining.

In narrow, high-volume, well-defined domains, that is partially true. A model processing ten million radiology scans gets better at identifying the patterns it was trained to find. The loop works within its boundaries.

But a loop that only references itself cannot discover what lies outside it.

The radiologist who gets better at reading scans does not spontaneously develop a new hypothesis about a disease mechanism nobody has previously connected to imaging. That requires a different kind of knowing the kind that comes from being wrong in a real consultation, from a patient describing a symptom in an unexpected way, from the friction of practice in a world that keeps changing the questions.

Knowledge fields do not just deepen. They change direction.

The questions that will matter in medicine in 2035 are not the same questions that matter now. Some of those questions will emerge from the friction of human practice the GP who notices three unrelated patients share an unusual symptom cluster, the lawyer who spots a pattern across cases no database was designed to flag, the banker who reads a geopolitical shift before it appears in any dataset.

A model that trains on its own outputs gets better at the world it was trained on.

It has no mechanism to notice when the world has changed.

That is not a technical limitation waiting to be solved. It is a structural one. The model’s closed loop becomes more efficient and more confident precisely as the gap between what it knows and what is now true quietly widens. It does not degrade noisily. It drifts optimising for a reality that no longer exists, with no signal from the outside world to correct it.

The internet analogy makes this concrete. Large language models were trained on the accumulated output of the web decades of human writing, analysis, and debate. That resource was rich because millions of people had strong economic incentives to create and update it continuously. When those incentives weaken, when ad revenue falls, when publications close, when the economic logic of content creation deteriorates the web stops being a living source. The models start training on an increasingly static archive. The most recent and most relevant knowledge becomes the scarcest.

Mercor’s model, extended to its logical conclusion, creates the same problem in every professional domain it touches but faster, and without the signal that something has gone wrong.

The Second Contradiction

There is a second assumption inside the $10 billion valuation that deserves the same scrutiny.

Mercor’s long-term vision, as its CEO has stated publicly, is that AI will eventually be better than the best consulting firm, better than the best investment bank, better than the best law firm. The technology will, in his framing, transform the economy radically and create abundance for everyone.

Set aside for a moment whether that is technically achievable. Ask the economic question instead.

If AI displaces the professional workforce at the scale the vision requires the bankers, the lawyers, the consultants, the doctors who has the income to pay for the services these AI systems are designed to deliver?

The model assumes a market. The displacement strategy erodes the market that justifies the model. These are not separate considerations to be resolved sequentially. They are simultaneous. The faster the displacement, the faster the market contraction. The more successful Mercor becomes at its stated mission, the more it undermines the economic conditions that make its valuation rational.

This is not an argument against technological progress. Tools have always existed and will continue to exist. Every major technology transition in history has displaced some forms of work and created others. That is not the question.

The question is whether this particular transition is being designed with any serious attention to the economic ecosystem it depends on or whether the design horizon stops at the valuation and the disruption story, with the harder questions deferred to a future that someone else will have to navigate.

The Room This Is Happening In

The Mercor story is being covered as a future-of-work story. It is a more uncomfortable story than that.

It is a story about a room full of very intelligent people, founders, investors, AI labs, enterprise clients who can all see the immediate value being created, and who are collectively not asking the questions that sit one layer beneath the surface.

Not because they are incapable of asking them. Because the incentive structures of the room do not reward the asking. The funding round is closed. The valuation is set. The contractors are working. The revenue is growing. The questions about knowledge source degradation and market erosion are, in that room, somebody else’s problem at a later date.

That is not unusual. It is how most consequential decisions get made in fast-moving industries. The discomfort gets scheduled for later.

What is unusual about this moment is the scale. The room is not a single company or a single sector. It is the entirety of the AI economy, moving at a speed that makes the scheduling of discomfort feel increasingly theoretical.

Both problems arrive at the same time. The model loses its connection to living knowledge precisely when the market it was meant to serve has lost the income to use it. These are not two risks to be managed sequentially. They are the same failure, approaching from opposite directions simultaneously.

The question worth sitting with is not whether the technology will work. It is whether the world the technology is building will still have the conditions required for the technology to keep working and whether anyone in the room has made that their problem yet.

I write about the consequential truths that are visible, evidenced, and systematically underaddressed in the rooms where decisions get made. If you’ve sat in one of those rooms, I’d like to hear what you saw. Join the conversation at Tuskers.

That sounds harsh. It is also true.

When you are young, health converts. Energy, ambition, stamina the world is designed to reward these things. A healthy twenty-five-year-old with no money and a good idea has time, capacity, and a system that is broadly built to back them. Health in youth is a starting gun. It fires. The race begins.

After fifty, the same logic stops working.

Living is expensive. Healthcare is expensive. The compounding that once worked in your favour years of earning, saving, building now runs in reverse if the foundation was never built. Every year of life without financial resilience costs more than the year before. Medical expenses rise. Earning capacity plateaus or falls. Dependence, when it comes, arrives with a price tag.

Reverse compounding. Applied not to a portfolio, but to a life.

Longevity without financial resilience turns time into cost.

The uncomfortable arithmetic is this: if you have wealth and health, health multiplies the wealth. You have energy to deploy capital, judgment sharpened by experience, the freedom to choose your work. Health becomes a genuine advantage.

If you have wealth but not health, it is hard but manageable. Resources can substitute for some of what the body can no longer do.

If you have health but not wealth especially after fifty you are carrying the cost of living without the means to meet it. A long life, in that circumstance, is not a gift. It is a sentence.

We do not say this out loud. The entire healthy-ageing movement is built on the assumption that adding years is self-evidently good. Nobody asks the follow-up question: good for whom, and under what conditions?

The problem is not that people failed to prepare. It is that the system was never designed for lives this long.

The saying health is wealth was written for a world with shorter lives, lower costs, and families large enough to absorb what savings could not. That world is gone.

We need a new saying. Or at the very least, an honest asterisk.

Health is wealth if you have enough wealth for health to matter.

The rest of us don’t just need a more truthful conversation. We need a system that makes that truth livable.

What does financial resilience in later life actually look like? That’s the question the longevity economy has been avoiding. Next piece, I’ll get into it.

Only 25% of Indians are disease-free by age 30. By age 40, that falls to 7%.

These are not projections. These are findings from over three million preventive health assessments conducted across the Apollo ecosystem in 2025, published in the Health of the Nation 2026 report on World Health Day last week.

Read those two numbers again slowly.

By the time most Indians are old enough to have a decade of work experience, three in four already have a condition that needs medical attention. By the time they are old enough to think seriously about career progression, ninety-three percent do.

This is not a story about old age. It is a story about a 28-year-old in an open-plan office in Bengaluru eight in ten of whom are overweight, nearly half of whom are prediabetic or diabetic, two-thirds of whom are not meeting basic physical activity standards. Average age of the working population screened: 38.

The Apollo report is an important dataset produced about India’s population health in a generation. It deserves to be read seriously. I am going to try to read it as seriously as it deserves, which means asking a question the report, for all its rigour, does not ask.

The report’s conclusion is that India needs to move from reactive to proactive healthcare.

• Detect earlier. Screen comprehensively. Follow up consistently.

The Apollo ProHealth platform is designed to do exactly this. The data that 56% of people with high blood pressure improved after following recommended care, and 34% of diabetics did the same, proves the approach works for those who access it.

I do not doubt any of this. Early detection saves lives. The right health check at the right time changes outcomes. Apollo has built something genuinely valuable.

But here is the question the report does not ask.

Why is a generation entering the workforce already metabolically compromised?

Not: how do we find them earlier? How do we detect the prediabetes before it becomes diabetes, the fatty liver before the enzyme levels rise, the atherosclerosis before the cardiac event?

But: what produced this? What is the system that is delivering an overweight, vitamin D deficient, physically inactive 28-year-old to the health check in the first place?

74% of individuals with ultrasound-confirmed fatty liver had normal liver enzyme levels. The standard blood test missed them entirely.

This is cited in the report as evidence that routine blood tests are insufficient and that more sophisticated screening is needed. That is true.

But read it differently for a moment.

Three in four people with a serious liver condition showed no signal on the standard test. Not because the test was poorly designed. Because the disease had been progressing silently, for years, inside a body living inside a system that produced it and neither the body nor the system gave any visible warning until the damage was structural.

Early detection finds what the system has done to you.

It does not change the system doing it.

• The food environment that delivers an overweight 22-year-old to university.

• The city that makes physical inactivity the default for anyone without a car or a gym membership.

• The workplace architecture that produces chronic stress, disrupted sleep, and sedentary hours as the standard conditions of economic participation.

• The social infrastructure or absence of it for a population working longer, living longer, and discovering that the systems built to support them were calibrated for a shorter, simpler version of the same life.

None of these are healthcare problems. They are infrastructure problems. And no health check, however sophisticated, however personalised, however AI-driven, addresses the infrastructure producing the patient.

The Apollo report is a masterclass in measuring the downstream consequence of an upstream problem.

The upstream problem is that India’s economic infrastructure, its cities, its food systems, its workplace design, its social architecture is producing chronic disease as a default outcome. Not in old age. In the workforce. In the university. In the years when the economy needs its people most.

There is one more thing the data reveals that I want to name.

The report notes that breast cancer detection in India occurs at a mean age of 51 nearly a decade earlier than in Western nations. It notes that 1 in 5 Indians under 30 is prediabetic, and that among those who intervene early, 28% reverse the condition. Among those over 50, only 7% do.

The compounding here is not just biological. It is economics.

Every year a structural health risk goes unaddressed in a 28-year-old is a year of reduced productivity, reduced earnings capacity, reduced contribution to the economy that India’s demographic dividend is supposed to deliver. The report notes that closing the women’s health gap alone could add $1 trillion to the global economy annually by 2040.

That number is not a healthcare statistic. It is an infrastructure statistic. It is what the economy loses when the structural conditions that determine health are not built correctly.

Here is what I take from this data.

The NCD crisis is not the problem India needs to solve. It is the room India is standing in. And the room confusion treating the crisis itself as the thing to address rather than the infrastructure producing it is precisely why the structural root stays unaddressed year after year, report after report, commitment after commitment.

The question that no institution in India is yet asking from the right altitude is not how to detect disease earlier. It is what would have to change in the city, in the food system, in the workplace, in the social architecture of a longer life to produce a population that does not arrive at the health check already metabolically compromised.

That question sits in the connection between rooms that are not talking to each other. The health room. The urban planning room. The food systems room. The economic architecture room. No single room can see it. It only becomes visible when someone steps above all of them simultaneously and asks what the connection reveals that no single room can produce alone.

The Apollo report makes the crisis undeniable.

It is a black elephant, large, visible, known, and sitting in plain sight while the rooms below it optimise for what they can measure within their own walls.

The next move is not a better health check.

It is a leader willing to step above the room.

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This piece draws on the Black Elephant framework, developed in a forthcoming book of the same name. If this framing resonates, if you have seen this pattern in your own sector the Tuskers community on this Substack is where that conversation lives. Contact Us.

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