Intelligence is not the main bottleneck - by Ruxandra Teslo

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Intelligence is not the main bottleneck<br>Confessions of a naive hamster: why the smartest people in the room keep missing that intelligence is often not the main bottleneck in the real world

Ruxandra Teslo<br>Jul 21, 2026

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At a recent dinner, someone from an AI lab asked me, quite bluntly , why I do what I do — why I write about the regulatory bottlenecks to medical progress, why I spend my time on policies related to clinical trials. He looked at me with something best summarized as pity.<br>I told him what I believe: that in the age of AI, medicine will be bottlenecked more than ever by regulation and the grind of clinical trials, and that billions poured into faster pre-clinical research won’t touch that problem, unsexy as it is. I brought up housing: we’ve had the technology to build better housing for decades, yet it’s more expensive than ever, because housing is a question of political will, not pure capability.<br>Ruxandra's Substack is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

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He looked at me incredulously. Surely, a smart person like me should know that AI, or better said, AGI will be hyperpersuasive soon – already on a bunch of benchmarks it exceeds professional debaters at persuasion. I said, “Hmm.” He said, “Yes, yes”, with the undertone of a man who knows things deeply, things that mere mortals like me, not being AI lab employees, are simply not well placed to grasp. And he looked at me again with that sense of pity, the way one looks at a slightly mentally impaired but cute animal awaiting its imminent slaughter.<br>That night I lay awake, twisting and turning, wondering whether my life had any point at all. Whether every decision I’d ever made had been, in some way, a mistake. What could I have done better?<br>But as the sun came up, I found my way back to the same thought: no matter how “intelligent” AI becomes, if the word still means anything, intelligence is often not the main bottleneck to things changing in the real world. It is hard to hold on to that conviction when people smarter than you, with access to privileged information, insist otherwise. After all, this could all just be “cope” from the naive hamster awaiting its slaughter.

A scared hamster watched over by the AGI<br>But, I shall nonetheless stick to my beliefs.<br>Because the people at these labs made fortunes betting on ideas that once looked insane, the world now takes nearly everything they say on faith. That, I think, is a mistake. It is not a given that AI will solve the problems most people actually care about, including medicine, unless we think about those problems clearly. And at the moment, I don’t believe we are or at least, not to the extent that we could. My case is simple, and it comes from two directions. One is what I observe in the social dynamics of San Francisco, and in how people there talk about all this. The other is what I notice in a field I happen to know something about: medicine.<br>AI and medicine

One of the promises most often invoked to justify AI’s risks is that it will “cure disease.” Every major AI lab CEO says it, and investors seems to agree: any biotech startup with an AI story attached commands an impressive valuation, even as more conventional biotechs struggle for funding and die. But this whole enterprise, as I have long argued, is bottlenecked by many things that have little to do with “intelligence” as such, and the degree to which that often goes unacknowledged is strange to watch.<br>The evidence is everywhere, if you treat scientific advancement as a rough proxy for intelligence and ask whether it alone unblocks progress. Take Eroom’s Law: the number of new drugs approved per dollar of R&D has fallen for decades, even as our scientific tools have grown vastly more powerful, the exact opposite of what the existence of more raw capability would predict. Or take a company like Adaptimmune, which has brought two transformative therapies to market in rare cancers and is nonetheless fighting to stay alive, due to the cost of developing them. Or one can listen to the scientists behind “baby KJ,” the infant saved by a bespoke gene-editing therapy: they have everything they need scientifically and still cannot easily repeat it for the next child, because manufacturing costs, driven in part by regulatory requirements, stand in the way.

Figure 2. Pharmaceutical R&D productivity has steadily decreased since 1960, despite advances in basic science. In the last decade, the deceleration in pharmaceutical productivity seems to have ameliorated , due to a combination of factors including an increase in predictive validity (through e.g., genetics), but also due a larger share of efforts being directed at oncology and rare diseases, where the burden for approval is often lower due to the high unmet need.

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