Our Innovation Economy Is Solving the Wrong Problems
Capital is flowing into AI, data centers, software, and digital leverage, while aging, climate adaptation, care capacity, and institutional resilience remain systematically underfunded.
In February, the OECD published a short report I have not been able to stop thinking about. The headline number: AI firms captured 61 percent of all global venture capital in 2025. That is $258.7 billion out of a total $427.1 billion, more than double AI’s share from just three years earlier. The report is measured and descriptive. It does not draw the conclusion I am about to draw. But when you read it alongside everything published on demographic stress, climate adaptation, care systems, and institutional trust, the picture that emerges is uncomfortable.
We are not building the future. We are building one part of it, very fast, and leaving the harder parts largely to chance.
This is not an argument against AI
Let me be direct about what this is not. I use AI tools. The productivity gains are real and the downstream applications are significant. Some of what is being funded will genuinely matter, both economically and for human welfare.
But 61 percent is not a portfolio. It is a concentration. And concentrations have a habit of revealing priorities that no one ever explicitly decided on.
An economy that allocates capital also allocates attention, talent, and problem-solving effort. When three-fifths of global venture money flows to one category (even it becomes a general purpose technology), the implicit message is that this is where the important problems are. Everything else competes for the remaining 39 percent. Elder care, flood resilience, antimicrobial resistance, public health infrastructure, institutional competence -- all of it, every other domain, shares what is left. That is worth sitting with for a moment before concluding that the market has this right.
The next 20 years will not be a computation problem
Here is what I think the next decade and a half will actually test.
Populations across the developed world are aging rapidly, and the pace is no longer a projection. It is already visible in labor markets, care systems, and pension finances. The UN’s 2024 population outlook is explicit: decades of low fertility combined with longer life expectancy are driving rapid aging in many countries, with some already seeing population decline. This was not a surprise. It has been in the data for a long time, which makes the absence of a serious innovation response all the more striking.
Climate adaptation is a different story from the one that gets told most often. Public discussion still centers on mitigation: carbon reduction, energy transition, new technology to lower emissions. Those things matter. But for the next 10 to 20 years, much of the lived experience of climate change will be adaptation to conditions already locked in. Heat stress, flooding, water scarcity, the retreat of insurance from entire regions, the redesign of urban infrastructure for a world that is already warmer. The IPCC states with very high confidence that risks and damages escalate with every increment of additional warming. That means the human problem is not only how to stop future warming, but how to make societies physically livable and economically viable under the warming already underway. That is a genuinely different innovation agenda from the one that captures most of the capital.
Then there is institutional trust, which tends to get treated as a soft concern, something governments should fix with better messaging. It is not soft. A society with low institutional trust cannot execute difficult transitions cheaply. Every reform becomes more contested. Every necessary sacrifice requires more coercion or more subsidy. OECD’s Government at a Glance 2025 puts aging, the green and digital transitions, low trust, stagnating productivity, and constrained fiscal space into the same frame, as combined pressures on public governance. That framing matters because it puts institutional competence where it belongs: at the center of the problem, not at the edge of it.
Why capital keeps going where it goes
The market is not irrational. It is solving for its own objective function, and AI fits that function extremely well.
Software scales. Once built, it can reach a billion users at near-zero marginal cost. AI sits at the intersection of software economics, infrastructure scarcity, and what looks like a genuine platform shift. The companies that own the foundational models and the compute beneath them hold leverage that investors can see and value clearly. Elder care does not work that way. Flood resilience does not work that way. Antimicrobial resistance research does not work that way. These domains require patient capital, public coordination, long time horizons, and returns that are distributed across society rather than captured by shareholders.
That is precisely why they are easy to underfund. It is not malice. It is structure. But what makes sense for capital allocation does not automatically make sense for civilization. We are financing the machinery of cognition faster than we are financing the human systems required to absorb its effects. That gap is the actual risk, and it is growing.
Productivity is not the same as progress
This is where the standard innovation narrative becomes evasive, and it is worth naming that directly.
Productivity growth is still treated as though it were automatically social progress. It is not. Productivity growth does not answer the central political economy question: who captures the gains, and what happens to the people whose roles, assets, or bargaining power weaken in the process? A society can become technologically stronger while becoming socially more brittle. It can automate work and still fail to create security. It can lower friction and still deepen distrust. It can raise GDP and still erode the conditions that make growth politically tolerable over time.
The language we reach for -- transformation, disruption, the future of work -- tends to obscure this. It implies that gains eventually spill over, that if the technology is powerful enough, everyone benefits eventually. History is less charitable. Gains spread when institutions, bargaining structures, public investment, and asset access force or enable diffusion. They do not spread simply because the technology is impressive.
The problem health resilience reveals
Take one example that rarely appears in the innovation conversation at all.
One of the next major health threats is not a surprise pandemic but the slower erosion of medicine’s effectiveness through antimicrobial resistance. WHO’s 2025 surveillance report analyzed more than 23 million bacteriologically confirmed infections across 104 countries, covering bloodstream infections, urinary tract infections, gastrointestinal infections, and urogenital gonorrhoea. One in six of those infections involved bacteria no longer responding to standard antibiotics -- rising to one in three for urinary tract infections. WHO frames antimicrobial resistance as a serious, growing threat that is undermining the foundations of modern medicine. This is exactly the kind of long-burn, high-consequence problem that attracts nowhere near the cultural excitement of a frontier model release, and nowhere near the capital.
That contrast is instructive. The problem is real, well-documented, and not going away. The market simply does not find it as monetizable as the next infrastructure layer for AI inference.
The reactive logic and its limits
Some will argue that markets eventually redirect capital when real pain becomes impossible to ignore. When adaptation costs rise enough, they become investable. When care shortages intensify, labor-saving redesign becomes unavoidable. When public systems crack, governments pay attention.
There is some truth in that. But it is a reactive logic. It waits for stress to become expensive enough for capital to care, which means it consistently arrives late, after preventable damage has accumulated. That is a poor operating model for structural transitions that are already underway. Many of the most consequential investments have public-good characteristics that cannot be justified through venture math alone. They create stability rather than hype, they compound differently from software, and they benefit people who are not the ones writing the checks. That is exactly why they are chronically easy to deprioritize.
The benchmark is wrong
The strongest critique here is not a moral one. It is strategic.
We are overfunding the acceleration layer and underfunding the absorption capacity. We are scaling computational power faster than social resilience. The longer that gap persists, the more likely we are to confuse technical progress with civilizational progress. They are not the same thing.
A serious innovation economy should assess investment less by novelty and more by whether it addresses high-consequence bottlenecks in welfare and societal stability. It should be honest that markets systematically underfund domains with public-good characteristics, and design deliberate reweighting -- through public investment, procurement, and institutional reform -- to compensate. It should retire the language that treats all innovation as equivalent, because it is not. Some innovation expands human room to maneuver under pressure. Some mainly intensifies competition inside already overcapitalized domains.
The OECD report is not an alarm. It is a ledger. It tells you where the bets are going. Reading it carefully, the open question is not whether AI will be important. Of course it will. The question is what we will be able to do with it in a society that failed to invest adequately in the human systems that make any technology livable.
We have become extremely good at building what scales. The harder question is whether we can still build what holds.
