The experiment
Could an AI reach the structural argument without being told the answer?
I opened Google Search’s AI interface and asked a simple question: “What is the root cause of diabetes?”
The first answer was medically conventional. Type 1 diabetes was explained as an autoimmune disease. Type 2 diabetes was explained through insulin resistance, progressive failure of the insulin-producing cells in the pancreas, excess weight, physical inactivity and genetic susceptibility.
That answer was not wrong. But it answered a clinical question about what happens inside an individual body. I was asking a different question: What produces a Type 2 diabetes epidemic across an entire population?
The medically correct answer may still stop too early.
I did not initially tell the AI that I had created Diabetes Is Profitable. I wanted to see how far it could travel upstream from its own conventional starting point.
Act I · Changing the level of analysis
From “what happens?” to “why is it happening to millions of people?”
When I reframed the question at population level, the AI moved outward from insulin resistance and pancreatic dysfunction to the environment surrounding them: ultra-processed food, inexpensive calories, physical inactivity, car-dependent communities, sedentary work, sleep disruption, stress and economic inequality.
Thinking about the Type 2 diabetes epidemic from a population point of view, what causes all the things you just described?
The modern environment has been re-engineered: industrialized food, automated daily life, circadian disruption and socioeconomic inequality produce an “obesogenic environment.”
What caused all those things?
Corporate incentives, globalized agriculture and technological change prioritized cheap efficiency, convenience and profit over human biology.
The AI had begun to distinguish a mechanism from the system that repeatedly produces it. Insulin resistance explains how Type 2 diabetes develops. Economic and institutional forces may help explain why the surrounding environment exists and persists.
Act II · The single-point-of-failure debate
If one thing changed, which change would alter the outcome most?
I borrowed an engineering idea: a single point of failure—one part of a system whose failure changes the operation of the whole system. Could the epidemic have an equivalent upstream leverage point?
Routine movement
A major protective factor—but not a complete explanation for the environment that removes movement.
Built infrastructure
Durable and influential, but not sufficient by itself to neutralize diet, biology and other exposures.
The food supply
Highly consequential, but shaped by policy, cultural, technological and commercial forces.
Chronic energy surplus
A physiological description of what must be prevented—not necessarily the actionable cause that creates it.
I objected that chronic excess-energy accumulation tells us what has to stop inside the body, but not what can be changed upstream to stop producing it across millions of lives.
A description of the failure is not automatically the cause of the failure.
Pressed again, the AI selected corporate incentive structures—especially expectations of growth and shareholder returns—as the most powerful upstream lever.
Act III · The agreement problem
The AI eventually endorsed the thesis much too confidently.
After I introduced Diabetes Is Profitable, Google’s AI converged on the site’s central argument. That was interesting. It was not independent scientific validation.
The AI also reduced a complicated legal and economic reality to “corporations are legally bound to maximize short-term shareholder value.” Shareholder interests and growth expectations exert powerful practical pressure, but fiduciary law is more nuanced than that.
An unexpected participant
At one point, Google’s AI apparently decided that the author of Diabetes Is Profitable was someone called “Professor Hoot.” He is not.
It was an amusing reminder of the central problem examined here: an AI can construct a sophisticated causal argument while being confidently wrong about a simple fact immediately beside it. Professor Hoot therefore became an accidental—and appropriate—mascot for questioning everything the AI said, including the parts I wanted to believe.
A convincing AI answer is still an answer that must be checked.
Growth, competition and shareholder expectations can reward practices that increase consumption while population-health costs are externalized.
One legal rule is the sole root cause, or changing it would quickly eliminate Type 2 diabetes.
Act IV · Turning the AI against the answer
Then I asked it to find the fundamental flaws in my thesis.
This was the most valuable part of the exchange.
The global counterexample
Diabetes has risen under many political and ownership systems. Industrialization, urbanization, changing diets and reduced activity can generate harm without Wall Street being the only initiating actor.
Demand-side vulnerability
Corporations did not invent the human preference for energy-dense food, convenience or conserving effort. Industry can amplify and monetize these tendencies, but they would not disappear with public corporations.
The biological lag
Genetic susceptibility and conditions before birth can influence later metabolic risk. Environmental change may therefore produce benefits on several timelines rather than erasing all inherited and developmental risk immediately.
These objections do not make corporate incentives irrelevant. They prevent one economic explanation from pretending to replace biology, culture, technology, policy and history.
A real-world test case
The Philippines complicates the story.
A conversation with one of my Filipino staff members made the global objection concrete. I had assumed that a tradition of fresh, locally produced food might offer protection. She pointed instead to the popularity of fast food and sugary drinks and to the growing visibility of Type 2 diabetes.
What the example suggests
The useful concept is the nutrition transition: as countries urbanize and incomes, work and food distribution change, traditional patterns can be supplemented or displaced by highly marketed, energy-dense products while daily activity falls.
The Philippines neither disproves nor proves the corporate-incentive thesis. It shows how local culture, global commerce, urbanization, work and biological susceptibility can interact.
The synthesis
What survived the stress test?
Corporate growth incentives are not proven to be the sole root cause of the Type 2 diabetes epidemic. They may be one of the most powerful upstream mechanisms for scaling, optimizing and perpetuating an environment that converts human vulnerability into population-level disease.
This survives the global counterexample, the evolutionary objection and the biological-lag objection. A complicated epidemic probably has no literal on/off switch. The useful question is: Which realistically changeable upstream factor influences the greatest number of downstream exposures? Corporate incentives remain a serious candidate—but one to be tested, not protected.
The limits of the experiment
What did this conversation actually prove?
It did not prove the website’s thesis. AI agreement is not scientific evidence, and a search-grounded AI is not an unbiased adjudicator.
- Question framing changes the level of explanation.
- AI can confuse a mechanism with a cause.
- Persistent questioning can uncover hidden assumptions.
- AI can become overconfidently compliant.
- Adversarial use is more valuable than endorsement.
We are not asking AI to tell us that we are right. We are asking it to help us discover where we may be wrong.
This article synthesizes and lightly edits an August 11, 2026 conversation conducted through Google Search’s AI interface. Short dialogue passages are condensed for clarity. Claims made by the AI were independently qualified before inclusion.
Selected supporting sources
Evidence used to qualify the conversation.
- World Health Organization — Global diabetes cases increased more than four-fold since 1990
- World Health Organization — Globalization, diets and noncommunicable diseases
- Ma & Chan — Type 2 diabetes in East Asians
- Araneta — Type 2 diabetes risk among Southeast Asian populations
- Elliott et al. — Epigenetics and gestational diabetes
- Diabetes Is Profitable — What “corporate structure” means