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AI boosts research careers but narrow the span of ideas explored: study
- dickersnoodle 3mo agoThis isn't a real surprise to anyone who knows how "AI" works.
- xmcp123 3mo ago“Technology that is based on everything humanity has already done, fails to do things that humanity has not yet done”
- runarberg 3mo agoThis may seem so blatantly obvious to us that it need not be mentioned, but to a lot of people I bet it is not obvious at al, and in fact may even be counter-obvious. https://www.youtube.com/watch?v=KtQ9nt2ZeGM https://www.youtube.com/watch?v=KtQ9nt2ZeGM
- esafak 3mo agoAre you following the news? https://news.ycombinator.com/item?id=48863490 https://news.ycombinator.com/item?id=48863490 LLMs don't just 'average' their data.
- Arainach 3mo agoThat doesn't disagree with this article. Proving a theorem that a human already proposed in an existing discipline of math - math, the most formalized and easiest discipline to involve computers in even before LLMs - is very different from expanding the boundaries of science.
- esafak 3mo agoHow is it different? Before there was no proof, and now there is. What counts as expanding the boundary to you?
- Arainach 3mo agoIdentifying what questions to ask is often much harder than answering them. Proposing new theorems - and new areas of investigation - is what expands boundaries. Proving them is confirmation. Once the Pythagorean theorem was proposed, many different proofs have been identified. In art, once a new style is created it's often straightforward for others to replicate. In physics, the idea of Relativity was what enabled the design of experiments to demonstrate its correctness. Proposing the idea is what's essential.
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- pton_xd 3mo agoThey interpolate data in an XYZ dimensional space. The implications of that is beyond our comprehension. I have a hard time believing that all novel concepts yet to be discovered are contained within that space, though.
- esafak 3mo agoYou might as well say AI can only think of things humans can, so even if they invent new maths or science they can't go beyond the space of human thought.
- BurningFrog 3mo agoWasn't Einstein's discoveries based on things humanity had already done? AIs do things no human has done before millions of times a day.
- nathan_compton 3mo agoEinstein's discoveries were based (to a large degree) on negating very specific parts of scientific orthodoxy and then taking the steps forward to carefully derive results with those rejections in place. LLMs are aggressively trained to reproduce facts and consequently struggle to reject orthodoxy. There isn't any reason they can't, in principal, make big new discoveries just by getting lucky, which is sort of also how humans do it, but its ok to acknowledge that current AIs aren't so good at certain things.
- onraglanroad 3mo agoI was under the impression that it was more accepting the othodoxy of Galilean/Newtonian relativety and joining it up with Maxwell's discovery about electromagnetism. So if the speed of propogation of EM waves is the same no matter your frame of reference (along with all the rest of physics) then the speed of light can't be relative (a conclusion that was aided by the Michaelson-Morley experiment) and what are the logical consequences of that. If I'm incorrect in my understanding I'd appreciate any correction.
- nathan_compton 3mo agoWell, sure, but if you want to accept those things you have to revoke the idea that distances and times are observer independent quantities. Even physicists fail to understand this after years of studying relativity. It is very difficult to really understand that relativity says: distances and durations that you measure in your frame of reference are not physically meaningful quantities.
- martinbfine 3mo ago[dead]
- Nevermark 3mo agoAny flattening of discovery due to AI, but will be temporary. We tend to think that obvious potential is the same as realized potential, for new technology. For any specific context, there are generally innumerable smaller adaptations and capability thresholds that have to be crossed. And the price for that journey is often temporary loss off overt productivity.
- Arainach 3mo agoNo, this is significantly more permanent. LLMs are autocomplete generators based off current context, and training generations of people to always ask the planet burners instead of learning to think for themselves - and never having the experience of having to slowly think over the same thing for an extended period - may well mean a permanent cap to human knowledge and a dramatic slowdown or end to new knowledge.
- CuriouslyC 3mo agoYou act like humanity doesn't exist in a competitive environment. If you think AI codegen is a mistake? Just relax, keep writing code by hand and wait for the pendulum to prove you right while showering you in wealth. There are plenty of people making this bet, and I wish the best of luck to you because I'm 99% certain you're on the losing end of it.
- Arainach 3mo agoThe market can remain irrational longer than any of us can remain solvent. The market is not any good at strategic or long-term thinking, particularly if it takes a generation to realize the scope of the damage, as seen by America abandoning its ability to manufacture things in chase of short term profits.
- abalashov 3mo agoThis is exactly the right answer. The supposed "rationality" of capitalism can ruin us before we get a chance to dazzle the world with our contrarian insights.
- Labo333 3mo ago> “It’s not about the architecture per se,” Evans says. “It’s about the incentives.” It would have been useful to check whether less original work was already getting more citations before AI adoption. That could reflect broader trends and network effects: heavily cited research areas attract more authors optimizing for citations, so high-productivity researchers end up clustering on the same topics.
- Diogenesian 3mo agoThey did. The article explains tbat this is a trend which has been getting worse for years, specifically pointing to search engines as a major turning point. Your comment is completely off the mark.
- beepbooptheory 3mo agoThe actual paper is linked there if you are curious. But also just thinking about your point for one second: in your mind, how else would they argue for the conclusion if not by checking the trend over time? Like what is the precise implication here?
- skeledrew 3mo agoAs with other fields touched, AI is merely amplifying what was already there. The aim of many scientists isn't discovery in and of itself. Discovery is a side effect of their primary drive to publish and - hopefully - become well known. And establishments only make things worse, because it's the things that are most likely to produce tangible results (the papers, or economically valuable products) that get the most funding.
- goldenarm 3mo ago100% agree. You could make the same argument for Hollywood : funding & revenue was always the goal, and we've been producing slop before AI was even a thing
- throw94949499 3mo agoYou could also argue the opposite: The aim of many scientists is discovery, publishing is a side chore to survive and to get funding. Automate paperwork and you get more time for discovering.
- skeledrew 3mo agoWell the paperwork is automatable, and things are being automated. But still there're the findings that the article points to: it's leading to far more publishing (and ladder-climbing) than novel discoveries.
- _alternator_ 3mo agoSeems to me that both perspectives are true, and the relative importance of the metric incentive vs the discovery incentive varies. But the metrics and rewards are critical to the perpetuation of the scientific discovery system; its really hard to disentangle.
- analog31 3mo agoDo you know any scientists? Disclosure: Physicist.
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- bwfan123 3mo ago> AI is largely automating the most tractable parts of science rather than expanding its frontiers By definition, creativity cannot be automated, and AI is a fantastic automation machine. It can explore thinking paths at a rate humans cannot match. But creativity is bringing the unthinkable into the thinkable, and that requires sensory experience [1]. Specifically, new definitions and symbols which never existed before. Imagine the concept vector space, and expanding that with new independent dimensions. Is that even possible ? When you look at history the answer is yes !. And each time there was an independent dimension added, it was an act of genius. It is an instructive exercise to name these moments in history where an independent dimension was added to human thought. Some examples in math would be the invention of a number, and in politics could be the idea of democracy. By contrast, LLMs are trapped in the vector space they are trained on, and they lack the feedback loop with sensory experience to be able to create and validate theories. [1] https://philsci-archive.pitt.edu/28024/1/Scientific_Invention_Position_Paper%20(17).pdf https://philsci-archive.pitt.edu/28024/1/Scientific_Inventio...
- psadri 3mo agoI don’t think we have spent enough time on the creativity axis. When we solve problems we usually follow a heuristically guided energy efficient path. We just prune a lot of possibilities based on our existing knowledge and experience. Creativity happens when we consciously (or not) go off the beaten path and explore. Most of those explorations are dead ends. But some will yield unexpected connections, patterns etc that we call “creativity” . An AI system could also go on those kinds of explorations. Today they aren’t it because we are not asking them to.
- mym1990 3mo agoMachine learning systems do have a component of exploring suboptimal path, otherwise they would never get off a singular track. The creativity issue in regards to AI is not about taking unexplored paths, but doing so in a computationally efficient manner when there are infinite combinations of ideas between domains.
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- cynicalsecurity 3mo agoAI has been seriously around for how long? Two years? Isn't it a bit too early to say?
- nathan_compton 3mo agoMaybe its late enough to say maybe we don't need to be devoting half the worlds capital to building data centers.
- seanmcau 3mo agoDid you read the article?
- jdw64 3mo agoI agree with some parts, but not all. I see it as an overfitting problem. Fundamentally, the topic here seems to be that citation indices and similar metrics are actually flawed indicators, and obsessing over them is just Goodhart's law in action. Ultimately, the argument is that the entire design of those metrics is wrong. To be precise, it was a good metric at first, but now that the scale has changed, it's become bad. This is common in programming too—things that are correct in the beginning but become problematic as they grow larger. From an individual researcher's perspective, it's rational. You get more citations, your career accelerates. Everyone knows this. Paper counts aren't everything. Citation counts aren't everything. Journal impact factors aren't everything. You shouldn't only play it safe. But everything is tied to those metrics anyway. Most researchers who give me work are fully aware of these facts. But are they going to change anything? Funding is still distributed based on those metrics. Max Planck said, 'Science advances one funeral at a time.' Science doesn't progress purely through reasoned argument. The authority of the older generation, research funding networks, journals, and school-specific evaluation criteria all move together. And honestly, I think discoveries will keep happening—probably quite rapidly. Because AI doesn't have the factional conflicts or interpersonal issues that humans do. It's very good at connecting papers across schools of thought without bias. In other words, the current human system is flawed at consolidating research, but I think AI is actually strong in this area. I expect AI-driven discoveries will continue for some time. The people who ride this wave will clearly be the winners. Everyone knows things are broken, but no one is trying to fix them. I always think human society is inefficient. I read this post, but I'm more curious about who will actually lead the improvement effort.
- nathan_compton 3mo ago"Science advances one funeral at a time" Well, these AI are never going to die in any real sense, so expect them to make orthodoxy more sticky, not less.
- Marha01 3mo agoAIs get replaced with newer models.
- hiddencost 3mo agoThe entire article seems to rest on their use of an embedding model for clustering garbage science.
- dahart 3mo ago> Scientists who adopt AI gain productivity and visibility: On average, they publish three times as many papers, receive nearly five times as many citations, and become team leaders a year or two earlier than those who do not. To me this effect doesn’t seem to reflect on AI very much, it seems to reflect on humans. Like maybe this is more evidence of the Babble Hypothesis and the incentives in research than AI, no? https://en.wikipedia.org/wiki/Babble_hypothesis https://en.wikipedia.org/wiki/Babble_hypothesis
- koe123 3mo agoYou reckon there could be any selection bias? Some means justify the ends reasoning.
- bloqs 3mo agoThis is superseded/proven by basic psychometrics it seems? Big Five Extraversion is roughly equivalent to "social dominance", how well an individual implements themselves in a social setting. "Extroverts" or people high in the trait are of course more likely to see progression on the basis they are superior at presenting value in a social setting in terms of social ability, which is often (falsely) accepted as a proxy for overall competence. This is why they end up running orgs as well
- cyanydeez 3mo agoid say extroversion likely correlates inversely with bullshit detection and its merely quantity over quality. the last decade of US politics demonstrates just how powerful willingness to produce put strips all other critical skills. AI exacerbates this and exposes fundamental human heuristic frailty.
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- natsucks 3mo ago"Perhaps, says Evans. But he doesn’t think that the problem is baked into the algorithmic design of AI. More than technical integration, he argues, what may matter most is overhauling the reward structures that shape what scientists choose to work on in the first place. 'It’s not about the architecture per se,' Evans says. 'It’s about the incentives.'"
- curious_cat_163 3mo agoWe are headed towards the “trough of disillusion” of this particular cycle.
- DrewADesign 3mo agoSome always refuse to acknowledge that. Like if every hype cycle was a roadrunner bit: some people see the cliff and stop running, others take a few steps off the cliff, look down and pull out a sign that says “uh oh” and plummet, and some people haughtily call the people who pulled out their “uh oh” signs needlessly pessimistic as they careen towards the ground.
- abalashov 3mo agoIt's almost like it's inherent in the definition of LLMs. It's really, _really_ high time we dispensed with the idea that this is "AI". Nobody said they're not useful, but "AI" they are not.
- radarsat1 3mo ago"boost research careers".. seems like a pretty drastic conclusion to draw based on a technology that has existed for like 3 years and only lately is any good..
- fnordpiglet 3mo agoYeah that was my instinct too. What sort of career defining trends are visible with this much historical data? Feels like someone wrote clickbait research to get published.
- m4x 3mo agoThe study looks at forms of AI that have existed for a long time, as well as newer LLMs. From the article: "Their dataset included 41.3 million English-language papers published between 1980 and 2025 in ..." Earlier studies looked at the effect of web search on research. This is all covered near the beginning of the article.
- koe123 3mo agoI enjoy using AI loads. Yet I would be keen to see numbers on actual productivity increases. This reads as yet another datapoint similar to what I’ve experienced: maybe code was the bottleneck at some point, maybe now it isn’t but in my lived experience the bottleneck has simply shifted. Its easy to create “more” but to actually hit the business goals… I don’t see a 2x TRUE productivity boost in anyone in my company. Please feel free to disagree with me! I am keen to hear more anecdotes to get more datapoints.
- et1337 3mo agoThis is the most interesting study I’ve seen: https://unessays.substack.com/p/talk-is-cheap https://unessays.substack.com/p/talk-is-cheap Funny enough, it basically says the exact same thing about software engineering that TFA says about science: First - developer level productivity has improved … Second - overall system flow has slowed down at every step
- koe123 3mo agoInteresting! Thanks for sharing.
- a-dub 3mo agosounds like it is just supercharging the business of science with all of its known failings? it would be funny if by accelerating the enterprise it actually forced an effort to correct the trajectory.
- aborsy 3mo agoA new breed of academics has appeared whose jobs is to put their names in every paper possible. Literally, their job is to work on frameworks to buy co-authorship. They do this in various ways, like establishing paper pipelines, collecting rents on labs and committees, focusing on money layer, using their profiles and citation count to help with acceptance of papers of other people , etc. You talk to them and they can’t explain their papers beyond a superficial introduction. They collect huge citations, travel and give talk on the winner horses, collect credit, which feeds back into this fraudulent scheme. A scientist used to be a scientist not long ago, not a credit collector. I wonder if Google could invent a new metric to expose them (weak ratio of first authorship, etc).
- washadjeffmad 3mo agoIt's not new, and entire disciplines exist because of these dependent structures. It doesn't do to unseat and discredit the connected and well regarded, but you might enjoy some mild comfort in security in numbers through a little citational flattery. It's a game of cultural tribalism. The only thing worse for one than not engaging is to upset the status quo unblessed.
- matthewdgreen 3mo agoA lot of this is downstream of compensation schemes that explicitly reward dumb metrics, like raw paper-count without subjective evaluation of contribution or quality. I don't want to generalize, but this seems to be more common in countries that are not the US or Europe.
- jurschreuder 3mo agoIronically it's also written by AI :) I like LLM's but this writing style is like eating the same dish 4 times a day.
- gammarator 3mo agoAt the present moment I think science is way more threatened by the OMB absconding with the grant budget than it is by AI.
- rbartelme 3mo agoAs a bioinformatics person that's spent time in and out of industry/academia, I agree with some of the article's thesis. While I don't think LLMs or AI are going away, I do think it will allow people in academia to pump out a bunch of inane papers and continue to prop up predatory scientific journal publishing via tenure and promotion. In fact outside of how utterly useless Fable 5 is via their aggressive guard rails for my work, I quite like using statically typed and/or functional languages with other LLMs since there are some baked in guardrails via compiler + type system. I think the flattening of progress is the most interesting dimension to the article. For an example a useful biological product discovery with a nonlinear path to get to there, look at the Taq polymerase (https://en.wikipedia.org/wiki/Taq_polymerase https://en.wikipedia.org/wiki/Taq_polymerase). Without some NSF funded exploratory ecological research by Tom Brock in Yellowstone Hot Springs to test the theoretical limit of life at high temperatures (https://en.wikipedia.org/wiki/Thermus_aquaticus https://en.wikipedia.org/wiki/Thermus_aquaticus) we never get to the Taq polymerase, we never get reliable/robust PCR (https://en.wikipedia.org/wiki/Polymerase_chain_reaction https://en.wikipedia.org/wiki/Polymerase_chain_reaction), which is now a gold standard method in both clinical and environmental testing! It is rather improbable to think that large language models would associate those domain connections across the topic (molecular biotechnology + ecology + microbial physiology). I also did some exploratory work with text embedding models people might use for RAG and challenged them with an open source scientific MCA question dataset, generalist embedders performed worse vs. domain specific embedders trained on scientific corpora (doesn't surprise me at all). However, if everything regresses to the median of the universe of possible knowledge, it seems like scientific leaning frontier models would get locked into this asymptotic flattening before turning cashflow positive for model vendors OR they become so locked down that only big pharma, state actors, or big ag can afford the API rates and vetting process.
- overgard 3mo agoI think we're finding measures of "productivity" in almost all fields are pretty bad and AI is a great way to game them. PR's, papers, etc. We have to stop looking at volume-of-stuff as a useful metric.
- joe_mamba 3mo ago>We have to stop looking at volume-of-stuff as a useful metric. Unfortunately, "volume of stuff" is how governments allocate funding, for stuff like education, healthcare, research, etc Any kind of quality metrics, outside of the free market capitalist "how much profit you made?", will be gamed within a few years to get the same results, for things that aren't designed to turn a profit, since there's no fair and objective metric in that case.
- adevalois 3mo agoI’m watching this play out in financial markets right now. It’s the newest form of the crowded trade. When everyone optimizes against the same signal, individual returns go up right until the aggregate return of the signal decays. And now that fundamental managers and quants alike are wiring the same language models into their workflows, decisions are being influenced in the same direction, and it leaves a fingerprint in market behavior. Crowded trades and crowded research share the same root. The reward is easier to measure than the thing it proxies for. Returns are easier to measure than durable edge, and citations are easier to measure than discovery, so everyone digs the same hole deeper. Which leaves the study’s real question. Are citations measuring discovery at all, or just the crowd?
- itchingsphynx 3mo agoThe original study: https://www.nature.com/articles/s41586-025-09922-y https://www.nature.com/articles/s41586-025-09922-y
- Searching99 3mo ago[dead]