The Supply of Scientific Attention

Paradigms and economics decide which questions get investigated. AI changes the scarcity that made that system necessary.

My wife asked me to look at something. She had been reading about hydrogen-rich water and had found a study, a real one, published in a journal. She asked ChatGPT whether the research supported what the study claimed, and ChatGPT told her the health benefits of alkaline and ionized water are not supported by scientific evidence.

She brought it to me because something didn’t add up. She was looking at a paper. ChatGPT was telling her there were no papers.

I can read a paper. I am not an expert on molecular hydrogen. But I can recognize a rhetorical move when I see one. “There is no evidence” is one of the most reliable tells in science communication, because it almost always does the same thing: it treats the absence of institutional consensus as though it were a scientific finding. It sounds like a conclusion. It functions as a dismissal. And it works because most people do not know the difference between “this was investigated and the result was negative” and “nobody with resources and incentives has investigated this.”

I’m seeing this all the time now. The language model was not evaluating the research my wife had found. It was reproducing a training consensus. The internet is full of confident dismissals of anything associated with alkaline water machines, most of them reacting to overheated marketing rather than to the actual research on dissolved molecular hydrogen. ChatGPT had learned the dismissal. It had not learned the distinction.

“There is no evidence” can mean two very different things. One means a question was investigated and the predicted result did not appear. The other means nobody investigated the question. Those two situations produce the same sentence. The distinction matters more than it sounds like it does, because one version closes a question and the other reveals that the question was never opened.

Thomas Kuhn explained why.

What the Puzzle Board Leaves Out

In 1962, Kuhn published The Structure of Scientific Revolutions and changed how we talk about how science works. His central concept was the paradigm: the shared set of assumptions, methods, recognized problems, and acceptable solutions that define normal science within a discipline. Paradigms are not obstacles to progress. They are the reason progress happens at all. A paradigm lets scientists stop re-examining foundational assumptions constantly and concentrate enormous effort on tractable puzzles. That concentration is the engine of normal science, and normal science is enormously productive.

But concentration has an inverse.

If a paradigm defines which problems are legitimate and which methods are acceptable, it also defines which problems are not on the board. Some of those excluded problems are genuinely nonsensical. Some are wrong. Some are premature. And some simply lack a paradigm-shaped reason to pursue them.

Kuhn was describing how scientific communities organize intellectual effort. But he was also, whether he intended to or not, describing an attention-allocation system. The paradigm determines where scientists look. Where they look determines what they find. What they find determines what counts as knowledge. And what is never looked at never becomes knowledge at all.

Lynn Margulis spent years trying to get a different puzzle onto the board. Her 1967 paper proposing that mitochondria and chloroplasts originated as independent bacteria that became incorporated into host cells was rejected by about fifteen journals before the Journal of Theoretical Biology published it. The idea spent decades at the margins. Related proposals had surfaced from earlier scientists and been set aside. Eventually, genetic evidence accumulated to the point where endosymbiotic origins became foundational biology, taught in every introductory course. Margulis was not vindicated because she was an outsider who fought the establishment. She was vindicated because the question she was asking turned out to be important, and the paradigm that excluded it turned out to be incomplete. But for years, the question simply did not receive serious scientific attention.

Who Pays to Find Out

Paradigms are not the only system that allocates scientific attention. Economics runs a parallel mechanism that produces the same result through different machinery.

Consider a generic drug that has been off patent for years. Suppose there is plausible reason to think it has a therapeutic application nobody has confirmed. Confirmatory clinical trials cost millions of dollars. If the drug is generic, the organization funding the research may have no exclusivity over the resulting knowledge. Every competitor selling the same molecule benefits from the evidence without bearing any of the research cost.

This is not a speculative objection. In 2019, the NIH, NCATS, and the FDA jointly hosted a workshop on repurposing off-patent drugs that explicitly identified the problem: non-existent financial incentives for drug developers and a lack of clear ownership to address the gaps, even though repurposing generic drugs would be an efficient development strategy. The structural question is straightforward. Who pays to find out?

“There is no evidence that this generic drug treats this condition” can mean the drug was tested and failed. Or it can mean nobody with the resources and the economic incentive has run the trial. Those are not the same epistemic situation, and they produce the same sentence.

The paradigm allocates intellectual attention. The economics of evidence production allocate financial attention. Both mechanisms leave some questions uninvestigated. And historically, there was very little anyone outside the institutions could do about it.

The Barrier That Was Really There

The reason outsiders couldn’t compensate was never simply a lack of curiosity. It was the sheer cognitive cost of serious scientific investigation.

Before the first physical experiment can be designed, someone has to reconstruct the relevant literature. Identify contradictions. Understand the mechanisms proposed by competing hypotheses. Perform or evaluate statistical analyses. Synthesize findings across disciplines. Determine what evidence would actually discriminate between competing explanations. Design a protocol capable of producing that evidence. Assess whether the question has already been answered and nobody noticed, or whether it remains genuinely open.

All of that is cognitive work. It requires years of specialized training, access to literature, analytical tools, statistical competence, and enormous amounts of time. Institutions did not merely control laboratories. They concentrated the cognitive machinery required to know what to do in the laboratory.

Curiosity without that machinery produced anecdotes, not investigations. An outsider could notice a neglected question. Rarely could an outsider mount the cognitive effort required to determine whether the question was genuinely open, what the competing explanations were, what evidence existed, or what experiment would actually resolve it. The institutions held a practical monopoly on serious investigative capacity because serious investigation was cognitively expensive and they were the only organizations that could afford it.

Something Happened to the Price of Attention

An individual directing LLM agents can now perform cognitive work that previously required teams of trained specialists working over months.

Reconstructing a literature. Identifying contradictory findings across hundreds of papers. Evaluating proposed mechanisms. Analyzing public datasets. Performing statistical analyses. Comparing experimental protocols. Generating hypotheses consistent with existing evidence. Identifying what is genuinely unknown versus what is known but scattered. Designing candidate experiments. Criticizing competing explanations. The cognitive work upstream of evidence is where AI changes the economics.

This is not a claim that AI manufactures empirical evidence. LLMs cannot conjure a randomized controlled trial. They cannot substitute for the assay, the mouse study, the clinical observation, or any physical experiment that produces data the world does not yet contain. Thinking is not evidence. Evidence is evidence.

But thinking determines which evidence to seek. And thinking, for the first time in the history of science, is no longer scarce.

A citizen scientist directing a network of specialized agents can now reach a conclusion that used to be available only to institutional investigators: here is everything currently knowable without performing the experiment. Here is what remains genuinely unknown. Here are the competing explanations. Here is the experiment that would actually resolve the question. And here is why nobody has performed it.

When the answer is “nobody has an economic reason to produce this evidence,” the situation is not scientifically closed. It is structurally neglected. And the cost of identifying structural neglect just collapsed.

The Supply of Scientific Attention

Scientific institutions have always solved a scarcity problem. There are more questions worth investigating than there are scientists to investigate them, more promising leads than there is funding to pursue them, more anomalies than there is time to examine them. Paradigms, economics, career incentives, available funding, regulatory priorities, and finite human cognitive capacity have all participated in deciding which questions receive serious attention and which do not.

That allocation system made sense because scientific attention was genuinely scarce. Directing serious investigative capacity toward a question required institutional resources. If the institutions decided a question was not worth pursuing, the question went uninvestigated. Not because it was answered. Because nobody could afford to ask it properly.

AI does not abolish the need for evidence. It does not abolish laboratories, clinical trials, regulatory review, or the physical work of producing observations the world does not yet contain. What it changes is the scarcity of attention that determines which questions get far enough to demand evidence in the first place.

My wife showed me a study that ChatGPT could not see, because the training consensus had already decided the category was not worth seeing. The model reproduced the paradigm’s blind spot as though it were a scientific finding. It did exactly what a well-trained model should do. It summarized what the institutions have produced. The problem is that the existing knowledge base has structural holes in it, and those holes are not random. They are shaped by paradigms and economics and institutional priorities that have nothing to do with whether the questions matter.

Kuhn described a scientific world where paradigms determined where scientists looked. That allocation system was not a flaw. It was a solution to a real problem: scientists were scarce, and their attention had to be directed somewhere. The concentration that made normal science powerful was the same concentration that left some questions permanently outside the frame.

Scientists are still scarce.

Scientific attention may not be.

What happens to the questions that were never on the puzzle board when the puzzle board is no longer the only place serious investigation occurs?