AI investment priorities reveal more about what we value than any mission statement. Here’s what the numbers are actually saying.
I was sitting in the gym at my son’s school on career day, watching kids rotate through tables staffed by a dog groomer, a police detective, a state park maintenance worker, and me. My topic was AI.
I’d played them a song my son made using AI tools. I’d watched their faces when the music came out of a prompt. I’d thought about what these tools could mean for kids like the ones in that room — kids with different abilities, different challenges, different relationships with the systems that were supposed to serve them. The technology felt genuinely hopeful in that moment.
Then I drove home and opened my inbox.
I work in AI. I’ve spent more than a decade in this space. I believe in what the technology can do — I’ve seen it do things that matter. But I also watch the money, and the money is telling a different story than the hope.
In 2026, four hyperscalers committed to a combined $700 billion in capital expenditure, nearly double what they spent the year before. That same year, federal funding for health and science research took cuts that one analysis described as a screeching, and possibly irreversible, halt for many projects. The Gates Foundation, Novo Nordisk Foundation, and Wellcome jointly committed $60 million to evaluate AI health tools in low- and middle-income countries. Sixty million dollars. Against $700 billion.
That ratio is not an accident. It’s an incentive structure.
Charlie Munger said it plainly: “Show me the incentive, and I’ll show you the outcome.” The incentive right now is return. The outcome is what we’re seeing — infrastructure investment at a historic scale, enterprise deployments producing almost no measurable ROI, and the applications that could matter most to the most people starved of the capital that’s going elsewhere.
The Market Is Working Fine
That’s the uncomfortable part. This isn’t a story about a broken system. The market is functioning exactly as designed. Capital flows to the highest expected return. Consumer AI applications have enormous addressable markets. Infrastructure that powers those applications generates reliable revenue. Health AI for rare neurological conditions has a small addressable market and a long development timeline. The math isn’t hard.
Upton Sinclair put the human version of it this way: “It is difficult to get a man to understand something when his salary depends on his not understanding it.” The people making investment decisions aren’t ignoring the potential of medical AI out of malice. They’re ignoring it because the incentive structure doesn’t reward understanding it. Not yet. Maybe not until it’s too late to matter.
My son has epilepsy. I think about what AI-driven research could mean for conditions like his — better seizure prediction, smarter medication titration, earlier intervention. I think about it the way any parent thinks about something that could help their kid. And I know better than to frame it as epilepsy versus cancer, because that’s a trap. The question isn’t which disease deserves the investment. The question is why we’ve built a system that forces us to choose between them at all, while $700 billion goes elsewhere.
The answer is incentive. And right now, the incentive doesn’t point there.
Progress and Profit Are Not the Same Thing
Companies should make money. Profit funds research, attracts talent, builds the infrastructure that eventually enables the things that matter. The hyperscaler investments aren’t going nowhere — they’re building the compute layer that researchers will use for decades. AlphaFold happened because the underlying models and infrastructure existed. I know this. And the productivity gains from AI deployment are real. The capability improvements are real. Some of what’s being built right now will matter enormously.
But profit and progress are not the same thing, and we keep talking as if they are. We tell the story of AI as the technology that will solve our biggest problems — climate, disease, inequality — while the actual investment pattern optimizes for something else. That gap between the story we tell and the system we’ve built is worth naming.
The tendency to optimize for what’s measurable and near-term at the expense of what’s important and long-term is not something AI introduced. We’ve been here before. But the scale of this wave, its speed, and the environmental cost of the compute it requires compound the stakes in ways that previous cycles didn’t.
The bubble will eventually correct. It always does. Ninety-five percent of enterprise AI pilots are producing no measurable return. The capital is burning, and the patience of the people funding it has a limit. When the correction comes, the investment will contract, the priorities will shift, and whatever window existed to point this technology at the hard problems will have narrowed.
The people most likely to benefit from AI-driven medical research are not the people funding the infrastructure. The people absorbing the cost of the job displacement are not the people who will capture the upside when the next wave arrives. That asymmetry isn’t new. It’s the pattern every technology wave produces.
What I Do Know
The bubble will correct before the incentives do.
That’s the pattern. And when it does, the window on the things that actually mattered will have narrowed — maybe irreversibly for some of them. Climate doesn’t wait for market corrections. Disease outbreaks don’t pause while capital regroups. Federal research budgets, once cut, don’t come back on the same timeline they left.
The market won’t fix this on its own. It doesn’t have to — that’s not what markets are for. Regulation could change the incentives. Public funding could change them. Coordinated pressure from the organizations deploying AI could change them if those organizations decided that the story they’re telling about AI’s potential was one they wanted to be accountable for.
None of that is happening at the speed the problem requires.
Show me the incentive. The outcome is right there.
What I’m Left With
I drove home from career day thinking about those kids. About what the technology could mean for them if it pointed in their direction. About my son and what a different investment pattern might make possible for people like him.
I still believe in the technology. I believe in what it can do when it’s pointed at the right problems. I’ve seen it.
I just think we should be honest about where it’s pointed right now. And curious enough to ask whether that has to be true.
Why are we doing it this way? Does it have to be this way? What would it look like to do it differently?
Those aren’t rhetorical questions. They’re the ones worth sitting with.