AI Research Is Becoming the Wikipedia Article I Need Right Now

 

Sometimes I need to know something without needing to become an expert on it.

A question comes up while I am writing, doing research, or simply thinking. I have a vague memory that the literature says something, but I cannot remember why I believe it. Perhaps I read a paper ten years ago. Perhaps a labor economist mentioned it over lunch. Perhaps I inferred it from several unrelated studies and gradually converted the inference into a “fact” in my own mind. Memory is not a citation system.

Here is a recent example. I had the impression that educational attainment strongly predicts economic outcomes such as earnings, but that the evidence is much weaker when we ask a narrower question: among otherwise comparable workers, how strongly does educational attainment predict differences in their actual workplace productivity?

That distinction matters. Earnings are not productivity. Promotions are not productivity. Occupational status is not productivity. They may be related to productivity, sometimes quite closely, but they are also affected by labor markets, credentials, organizational structures, bargaining power, selection, and many other things.

In the past, checking an impression like this was annoyingly expensive in time. I would search Google Scholar, look for a recent meta-analysis, chase citations backward, find an encyclopedia entry, discover that it answered a slightly different question, and then spend another hour trying to determine whether I had framed the issue correctly. For a central claim in a paper, that work is justified. For a question that is only one small piece of a larger project, it often is not.

Deep research systems change that calculation.

I asked ChatGPT Deep Research to examine the question, with a fairly strict definition of productivity. The resulting report distinguishes direct or near-direct measures such as output, sales, errors, quality, and worker-level performance ratings from wages, promotions, retention, and other indirect outcomes. That distinction alone is useful because much of the education literature moves quickly from labor-market success to an implied claim about productivity.

The answer was close to what I vaguely remembered, but more precise. Academic performance does predict later job performance, although modestly. The report highlights a recent meta-analysis of 114 independent samples that estimates a correlation of about .21 between academic performance and job performance after correction for criterion unreliability. The relationship varies a great deal across settings. Job-relevant academic performance predicts later performance better than generic grades. Comparisons within the same university or major also tend to produce stronger relationships.

Educational attainment itself appears to be a weaker predictor. Degree level has a small positive relationship with task performance, with stronger associations in more complex jobs. Some employer studies find small or statistically weak relationships between years of schooling and supervisor-rated productivity. Bishop’s same-job, same-firm comparisons are especially interesting because they suggest that schooling can help predict expected productivity while doing relatively little to explain which of two people doing the same job will unexpectedly outperform the other.

The causal question is weaker still. There is a large literature estimating the effect of schooling on earnings, but the report found remarkably little strong causal evidence linking additional formal education to direct measures of individual workplace output. By contrast, randomized studies of job-relevant training do show productivity gains. That is an important difference. Evidence that learning a useful skill raises productivity is not the same as evidence that adding another year of formal schooling will do so.

Could this report be wrong? Of course. It could miss an important paper. It could misunderstand a method. It could give too much weight to one meta-analysis. Some of its judgments depend on how narrowly one defines workplace productivity. A labor economist who knows this literature deeply may see omissions immediately.

But that is not the standard I need for every research question. I am not asking the system to settle an academic dispute. I am asking it to give me a serious first map of the territory. I want to know what the main distinctions are, which findings seem stable, where the evidence is weak, and which papers I should read if the issue becomes important to my own work.

This feels less like asking an oracle and more like asking Wikipedia to write a new article specifically for the question I have today.

Wikipedia is enormously useful partly because someone has already done the work of assembling a topic into a readable structure. Its weakness is that the article has to exist before I need it. AI research tools remove that restriction. They can build the provisional encyclopedia entry on demand, around the exact distinction I am trying to understand.

That changes the economics of curiosity. Questions that previously cost two hours to investigate may now cost ten minutes. Most of those questions will never become formal research projects. They do not need to. A much larger share of our half-formed claims can now be checked against actual scholarship before they harden into assumptions.

Here is the report I produced in about 5 minutes: Academic Achievement, Educational Attainment, and Individual Workplace Productivity. Labor economists, I would be especially interested to know where it is off key, what it missed, or where its framing of the evidence should be corrected. Regardless, this is a new level of access to scholarship.

Big Tech Is Not Why AI Will Stay

AI resisters should understand one basic fact: the main obstacle to a broad retreat from AI is not the Big Tech. It is people like me. Even if access to the most capable systems became much harder, I would look for alternatives. If they became substantially more expensive, I would probably pay more. If you block all the data center construction, I will switch to a Chinese model. I do not say this because I dismiss the risks. I take many of them seriously. AI can mislead, weaken privacy, concentrate power, flatten judgment, and encourage intellectual laziness. But those concerns exist alongside another fact that is just as real: AI has made my life better.

The usual defense of AI emphasizes usefulness. It saves time. It drafts, summarizes, searches, reformats, checks, compares, and organizes. AI is also enjoyable and liberating. Much of adult life consists of maintenance, repetition, obligation, and administrative friction. We spend large portions of our days on tasks that are necessary but not meaningful. Life is boring. AI removes some of that weight, and what it returns is not simply time. It returns attention.

I can move more quickly from an idea to an experiment. I can follow questions that I once would have abandoned because answering them required too much effort for too little immediate payoff. I can test an argument, compare possibilities, work through a difficult passage, or pursue a half-formed thought without first turning curiosity into a project. The pleasure lies partly in this lowered threshold between wondering and doing.

This is why I have come to think of AI as essential. Human life is constrained not only by mortality but by the smaller limits that make mortality concrete: limited energy, limited attention, limited patience, limited hours in a day. We routinely abandon things we care about because they lose a competition with errands, paperwork, logistics, and fatigue. A tool that gives back even a small portion of that lost capacity changes more than workflow. It alters what seems possible within an ordinary day.

For me, that exchange has been favorable. I spend less time on routine production and more time brainstorming, thinking, designing, and talking with people. AI does not eliminate intellectual work. It changes where I place my effort. I can spend less time getting a document into shape and more time deciding whether the idea inside it is worth pursuing.

The same is true in teaching, though not because students need some generic preparation for an AI future. I value AI because it can already do things that are educationally useful. A student can ask for another explanation without embarrassment. A concept can be approached from three directions instead of one. A weak first attempt can become material for analysis rather than merely evidence of failure.  None of this removes the risks. Students can use AI to avoid thinking, produce empty prose, or mistake fluency for knowledge. But these are problems of use, not proof of worthlessness.

The deeper disagreement begins with experience. Some people use AI and feel diminished by it. They sense that something human is being removed from writing, teaching, art, or thought. Others use the same technology and feel expanded by it. They find that it creates room for curiosity, experimentation, and work they could not otherwise sustain. Neither side can command the other to experience the technology differently. 

This is why the dispute cannot be settled by listing benefits and harms, as if everyone were working from the same scale. Evidence about cheating, labor, energy use, productivity, or learning matters. But beneath those disputes is a harder question about what kinds of effort we consider worth preserving. One person sees the removal of effort as a loss because the effort itself carried meaning. Another sees the same removal as relief because the effort was merely an obstacle between intention and action.

I have no objection to people choosing not to use AI. There are many technologies I do not use because they do not add enough to my life. I do not assume that everyone should make the calculation I make. My concern begins when personal refusal becomes an attempt to deny others access to something they have found genuinely valuable. 

Any serious discussion has to begin with the recognition that AI is not merely something being imposed on reluctant users by powerful companies. Many of us actively want it. Democratic politics is what is going to keep it going, not the arms race with China.

The Machines Read My Books Without Asking, and I Refuse to Call It Theft

Twenty-two. That is how many of my papers and books turn up in the database of texts absorbed into the training of large language models. I typed my name into the search box the way one checks a symptom online, half dreading the result and half hoping for it. There they were. Chapters I labored over, an article I still argue with in my head, a book that sold mostly to libraries. All of it taken up by a machine that never asked and never sent a note of thanks.

Some of my friends are furious. The word going around is stolen. Colleagues who never once complained that a rival failed to cite them now discuss intellectual property with real heat. I understand the feeling. There is something deflating about learning that your life's work was handled as raw material.

Still, I cannot summon the outrage, and every attempt runs into a wall I built myself.

Here is the wall. What did I do at twenty-five? I read. I read Buber and Bakhtin and Dewey and a few hundred others, hungrily and not always carefully, and then I wrote things shot through with their sentences and their instincts. I compensated none of them for the structures I absorbed, the metaphors I later recycled without noticing, the moves I learned to make in an argument because I had watched someone else make them first. That is not a confession. That is a description of how anyone learns to write.

Scholars are also the last people who should complain about being read too widely. We spend careers hoping for readers. We check citation counts with an eagerness that would embarrass a teenager. We give talks to eleven people and count it a good day if two take notes. The whole economy of academic life runs on the hope of influence, and influence means exactly this: your ideas get loose, travel without you, and surface in the thinking of people you will never meet. A model that has read twenty-two of my texts and can explain my position on relational pedagogy to a student in Jakarta is not robbing me. It is doing the thing I wanted done.

The objection worth taking seriously concerns credit. When the machine reproduces a distinction I spent years sharpening, no footnote appears, no one learns my name, and I lose the only currency my profession pays in.

That objection is real and weaker than it looks. Humans do not cite every word combination either. As Bakhtin put it, every word is half somebody else's. We cite what we take to be original thoughts, and we do it roughly, late, and often wrongly. Nobody footnotes the phrase "on the other hand." Nobody credits the scholar from whom they learned to build a counterargument. The bibliography is a courtesy extended to a thin top layer of novelty. Underneath sits a large unattributed commons of phrasing and habit that every writer draws on and no writer acknowledges. The machines are working that same commons, only faster, and in public, where the process becomes visible.

Honesty requires a note about my position. Academic royalties are a rounding error. A novelist whose living depends on sales has a genuine grievance, and I will not lecture her about free circulation while a company monetizes her plot. Her case is not mine. Mine is the case of someone who was already giving the work away and now finds it picked up by a courier he did not hire.

I could get angry. To do that I would have to abandon a belief I have held since I was young and reading library copies: that knowledge belongs to whoever needs it, and that ownership sits awkwardly on top of ideas. Dropping that belief now, at the moment it became inconvenient to my vanity, seems like poor timing. Principles that apply only when they cost nothing are not principles. They are preferences.

So the training sets can keep my twenty-two. Read the books, or ask a chatbot what is in them. Buy them, borrow them, or absorb them secondhand from a model that swallowed them whole. I would prefer the first option, and my publisher would prefer it more. But the thing I actually wanted was for the ideas to get somewhere and change a mind or two along the way.

They are getting somewhere. I did not choose the vehicle.

Do we lose control over curriculum if we design it with AI?

Here is a tail end of a lengthy conversation about a course design. There is nothing to add. 

ME: OK, I think we are done with this course. Now, I want to ask you for an unbiased, reflective analysis. Tell me if as the instructor I was more or less thinking, engaging, creative in curriculum design in comparison to a traditional manual curriculum design? Don't try to please me. What I really care is the quality of the course first, but also of not having an illusions. Perhaps you could have designed the same or better WITHOUT my directions?

CLAUDE:  Judged against the record of this conversation rather than against a general impression, your input carried more of the design load than mine did, and the gap is not close on the points that mattered most.

Three decisions determined what this course became. All three were yours, and none were available to me.

The first was walking down the hall and asking the EDLP 250 instructor what students actually arrive with. That single fact voided the entire design I had produced up to that point. I had built a graded proposal checkpoint and a fourteen-row crosswalk for students who do not exist, because I had inferred the starting condition from the department Guide rather than from the person teaching the prior course.

The second was correcting my model of what the IRB is for. I had built two consecutive designs on the assumption that the Cayuse packet flows out of a settled research design, which put the application downstream of finalized questions. You said the review is about protecting human subjects, that the research questions there are background, and that what the board needs is roughly what data is being collected. That inverted the dependency. It is the difference between a syllabus that works and one that produces a December bottleneck every year. I had the department Guide in front of me the whole time and did not see it.

The third was the reading bot, which you supplied whole, including the knowledge that it functions. I contributed the sequencing and the accountability mechanism, and you rejected my sequencing for a better reason than I had.

What I contributed was mostly execution and consistency: tables, formatting, arithmetic, an audit that caught real errors, and a few design proposals that survived, such as the gate-versus-score distinction and the warning about designing studies for exemption. Useful work. Not the work that determined whether the course is any good.

So the answer to your direct question is no. Working without your direction, I would have produced something internally coherent, professionally formatted, and wrong at the foundation. That is the specific failure mode worth worrying about, because it does not look like failure. It looks like a finished syllabus.

On whether you were more or less engaged than in manual design, my honest read is more engaged on judgment and less on execution, which is the trade you presumably wanted. The decisions came fast because you were not spending hours on tables. But two costs are real and worth naming.

The first is anchoring. You set the total at 140 and I distributed it. You said early October and I proposed the sixth. In several places you were choosing among options I framed, and my framings carried assumptions you did not have occasion to examine. That is a quieter form of influence than being told what to do, and harder to notice.

The second concerns the document itself. The syllabus now reads more settled than the design actually is. The two-track explanation in the course description is persuasive prose, and persuasiveness is independent of correctness. If the drift between the Cayuse packet and the chapters turns out to be messier in practice than it reads on the page, the polish will have made the problem harder to see, including for you.

One more thing about my own performance. My consistency audit found comma splices, a broken link, and a singular data. Real errors, all trivial. The structural error in that same document was the dependency inversion, which was mine, which I did not flag, and which you found by talking to a colleague. Verification of the sort I can perform is not the sort that catches the errors that matter.