Dan

Why we started

Reinvent Science began as a jam session in the Lewis Science Library at Princeton University. We met up for our first extended conversation, imagining that we’d write one piece together, and ended up with a document full of dozens of essay ideas about how science could be better.

Ben and I both have day jobs at non-profits that help scientists have more impact on the world. That gives us a rare perspective on science as an enterprise. Though we see large-scale structural problems, we also see opportunities for the choices of individual scientists to have an outsized impact. We bonded over a shared and visceral belief that science could be a lot better, not just incrementally improved. We also didn’t know ahead of time how much interest there was from the greater scientific community to read and discuss topics like this and wanted to find out.

Why we’re stopping

There was interest, but not that much interest. Reinvent Science has a bit over 400 subscribers and each post gets about that many views. Many of you are practicing scientists or science funders, and knowing that is what has kept us writing as long as we have.

Our recent post about the scientific literature and LLMs went a little viral, but seemingly because it made some folks angry and I don’t want to get anywhere near the “make people angry to get traffic” spiral. Also it turns out writing the substack is the fun part but you also need to promote the substack and neither of us wanted to do that. Our day jobs include being public faces of our respective organizations and so neither of us could go all-in on the Reinvent Science brand. As a result our day-job audiences remain much larger than the audience for Reinvent Science.

Another aspect is that we’ve said what we wanted to say ourselves. A post per week for over a year turns out to be a lot. We’re not out of ideas, but we are a bit tired of listening to ourselves talk. However, we remain excited to publish content from you and other folks in science to continue to foster discussions about what science could be and how we get there. ’s field report reached a substantially larger audience than our typical posts, which strikes me as a positive sign.

What we learned

Science can be better in a lot of ways. It was never that difficult to come up with a topic to write about. It’s also the case that it’s actually quite hard to get individual scientists to do anything about the situation. Those in privileged positions are happy with the status quo and those who aren’t are under enough pressure that iconoclasm doesn’t seem very attractive.

We also became frustrated with big-picture metascience because of how little credit it gives to the agency of individual scientists. We continue to believe that small tweaks to unblock individuals could do as much for the world as big policy interventions.

We also learned that we share the video game Sid Meier’s Alpha Centauri’s as a formative text for our scientific worldviews.

My favorite posts

Looking back at the archive I’m proud of all of it. I hope you’ll scroll through and read some of our older pieces.

What’s happening now

We’re going to stop publishing weekly, coming back occasionally as individuals when we have something appropriate for this audience and platform (I’ve got something queued up for next week). We also continue to be open for submissions from you and will be glad to edit, publish, and promote them.

The Future

We’re still working on making science better (more than) full time. Come find us here or at our day jobs if you want to help.

Ben

Why we started

At the end of the day we started Reinvent Science to catalogue a whole bunch of ideas about how science could be more awesome written by scientists for scientists.

Unpacking that: it really feels like science is going to change in the 21st century. A thousand problematic threads in science are coming to a head: the massive bureaucracies scientists need to slog through to even do their work, institutional stagnation, becoming a political football. Since we started writing this, AI has added a big piece: creating unprecedented abilities to both comb through information and unleash thoughtless slop, potentially automating labs, and raising broader philosophical questions like “does it count as science if it’s entirely done by a computer?”

It’s a microcosm of the broader “polycrisis.” But as-it-would-be-great-if-Sun-Tsu-said: “Chaos is a ladder.”

But most of the discourse about changing science revolves about some combination of abstract philosophical ideas and policy: “Is Stoke’s quadrant model better than Bush’s Linear Model?” “Should the NSF support more fast grants? The NIH more prize competitions?”

These are important questions, but they largely ignore three critical ideas:

1. The possibility that the day-to-day mechanics of how we do science have been and could be drastically different.

2. Scientists themselves are going to be major drivers of change in how science works; they’re not just agents blindly responding to external incentives.

3. It’s impossible to know a-priori the “right” things that scientists could be doing differently to create more awesome science, abundance, flourishing, rainbows, ponies, etc.

Given those ideas, we thought it might be productive (and fun) to put a whole bunch of provocations, rather than fully-fleshed-out-plans, of how science could be thought about and done differently by actual practicing scientists out into the world.

Why we’re stopping

Honestly, it’s a combination of being very busy and idea-exhausted. Dan got a promotion and now leads Activate’s entire fellowship program; Ben is responsible for an entire organization that is default dead. We came into the project with a massive idea backlog, but it’s actually hard to come up with a genuinely good new idea about science every other week on demand when most of your cognitive juice is being deployed elsewhere.

What we learned

“Short-form provocations about how to bottom-up change science” is niche, but in that niche people care. Only a few hundred people subscribed to the Substack, but the ratio of comments (especially good comments) to readers was way higher than most publications; and just glancing at the list of subscribers, there are some serious and awesome people on the list.

Making up plausible new ways of doing things is shockingly hard. The way things are done now creates such a strong basin of attraction of thinking that the vast majority of ideas are either totally absurd or just tinkering around the edges.

My favorite posts

What’s happening now

Two narratives that were definitely around when we started have exploded into the zeitgeist the past ~year: how science is done as a major political question and AI in science.

(If Google Ngram didn’t just do books and went through 2026, there would be a graph here of the mentions of AI for science exploding.)

In some ways, these trends make what we’ve done here more important than ever. New ways of thinking about and doing science are becoming essential rather than nice-to-have alternatives to muddling along.

On the other hand, the shrill urgency of change means that there are now many other voices and minds seriously tackling the question of how science could be different. A big motivator for us writing was just to get people thinking “oh, things could be different” rather than the belief that we have the best ideas. So to some extent we can now stand on the aircraft carrier and wave a “mission accomplished” banner.

The Future

There is still so much work to be done!
We’ll still occasionally post ideas as they come to us but will not commit to the once-a-week cadence that we’ve kept up for the past year.

If you are a scientist using AI in your daily work, we’re interested in publishing more field reports of this type.

Around January of this year, I realized that AI models, specifically in the form of coding agents like Claude Code and Codex, got quite good. They’re no longer just chatbots: they can write code, use your computer, run software, and increasingly act as an interface to all the other things sitting on your computer. It’s a big deal, but what does that actually mean for experimental scientists in the lab, that do a lot of our work outside of our computers? Is this so-called “revolution” meant only for our in-silico friends?

I don’t think so. They’ve been genuinely helpful for me. The core theme, for me, has been minimizing the cognitive burden of being a scientist at the bench. More specifically, reducing the distance and friction between a scientific question and the operational work required to answer it.

That distinction matters. We tend to think of the scientific process as something like question → experiment → result. But the actual transition between those things is full of small decisions. Specifically decisions around pipetting, reagent arithmetic, inventory management and others. None of these decisions are particularly intellectually interesting. But they are everywhere and they require real cognitive effort before we can even exercise thought on the interesting parts of research. After some time delegating some of this thinking to coding agents, I started to realize how cognitively overloaded I’d been before.

What follows is what that looks like in practice for me and my company. A primer on what it increasingly feels like to go into the lab without that particular kind of dread hanging over my head.

(In this essay I’ll use Claude, Claude Code, Codex, “agent” interchangeably, they are all shorthand for a coding-agent on your computer, not a website-based chatbot.)

Delegating pipetting decisions

One of the most tedious parts of lab work in the life sciences is routinely preparing and running well-plates to answer specific biological or chemical questions.

On the surface, the actual act of pipetting seems like the arduous task. But I’d venture to say that all the cognitive effort needed to go from experimental question to well-plate design can be just as exhausting, if not more so. Every experiment is slightly different, so even a highly optimized Excel template inherited from a senior lab-mate needs significant tailoring and human decision-making for each run. Which reagent goes in which well, which pre-allocation pool or dilution scheme to prepare, where to place conditions and how to keep track of them, how many replicates, which internal control and standard to run, how much reagent it will all consume, whether it fits on one plate or needs two? These are all decisions that drain your cognitive budget every single time an experiment runs.

In theory, if all of this is already decided, the pipetting itself is the easy, if perhaps boring, part. And by the way, having a liquid-handling robot doesn’t excuse you from any of this! Even with a nifty Eppendorf or Hamilton, the burden of all these cognitive steps still lives with you. Experimental science is fraught with these mini-decisions: individually small, but they pile up in your mind, slowing you down, killing motivation, and occasionally causing expensive mistakes.

So where can AI actually help? In practice, it’s difficult to get an AI to understand what you’re doing in the lab. Understandably so. These models don’t have an amazing grasp of the physical world, and you end up writing gigantic prompts trying to explain everything that’s going on, often with questionable results.

An alternative I’ve found much more useful is describing my lab as code.

This serves two purposes. First, it gives the AI an unambiguous definition of the pieces of my lab. Second, it defines an action space for what it can actually do with them, digitally. It sounds strange, but the idea isn’t new. People who build lab robots have needed a programmatic representation of their equipment for years. It turns out that the same representation is very useful for our new digital collaborators like Claude Code, Codex or others.

So I defined a 96-well plate as a Python object, with all the attributes and functions that matter: each well named and enumerated, the 8×12 geometry fixed, volume constrained between 0 and 200 µL, with functions like add_reagent or lookup_well giving programmatic access to the plate.

Next to that, I wrote a paragraph or two of documentation about the assay itself; say, qPCR or a binding assay, the protocol and what the assay needs and consumes. I saved both files (the script and the documentation) and pointed my coding agent, Claude Code, at them. It’s simple, but it gives a coding agent a huge boost in both understanding and, crucially, manipulating a plate in a way that’s much closer to how a human would.

Now all that’s left is to design my first experiment in English:

“I want to understand which extraction condition preserves my protein the best. I’ve already tried these two methods, and I have these three left.”

With the lab pieces and protocol in its context, out comes a proposed plate and a pipetting recipe.

“Actually, I have this fourth condition I’d like to try too.”

The plate gets edited and so does the pipetting recipe. Automatically, a second plate is generated because everything no longer fits on one. The agent can tell me exactly how much reagent and how many consumables I’ll need, and every well and microliter is accounted for. I still check the work occasionally and ultimately I’m not delegating the scientific decision of whether the experiment makes sense. I’m delegating the mechanical translation of that decision into a plate.

It was a strange feeling the first few times executing liquid allocation steps produced by an entity that wasn’t myself or a colleague. I felt like a liquid-handling robot running an experiment for Claude.

Having something like this be reliable is where the code representation framework matters. The code description of the pieces of your lab, along with their constraints, prevents the AI from making up a 97th well or adding a negative volume. This isn’t a prompt saying “NEVER ADD NEGATIVE VOLUMES.” The object itself does not allow that action to happen programmatically. The distinction between telling a model not to do something and making the action impossible is one of the things I’ve found most useful about this whole approach.

The extrapolation is that I don’t have to make pipetting decisions from scratch anymore. It’s a small thing. But I’ve found that removing this small burden has made me more motivated to jump into experiments and answer questions in the lab. I don’t know if I can go back.

Budgeting and assay cost-optimization

Combining deterministic code guardrails with the model’s highly intelligent but stochastic reasoning is powerful, and I’ve found it to be very flexible. Once an assay is specified this way, you can build on top of it.

Each reagent and consumable can be parametrized with a price, a consumption rate for a given assay, and product details, maybe the products’ website URLs as metadata. The important part is that the math behind the scenes isn’t handled in the model’s head. Scripts calculate reagent usage, volumes, and costs. Claude orchestrates them.

Now the agent has enough context and enough tools to actually be useful for lab inventory and budgeting. I can design an experiment as in the previous section and ask what it will cost. Or I can plan a month-long campaign across several setups and get the designs, usage, and budget breakdown back in a couple of minutes.

Then I tried something more ambitious:

“Can you optimize my assay to be cheaper?”

With a clear understanding of my assay, consumables, and lab, it went online, searched through countless webpages for alternate products, combined that with its own knowledge, and ran the calculations using the code I’d already built for the assay. In about fifteen minutes, it handed me a graph showing per-assay cost dropping by more than half, with explanations of the trade-offs and risks of each change. I spent much more time verifying the suggestions than the agent spent generating them. There were zero mistakes in the math or the product recommendations. I bought some of the suggestions, and they indeed worked in the lab. Now I had a >50% cheaper assay per run with what felt like very little effort. Woah. This was earlier this year with Opus 4.5; we’re now at Opus 5! (Again none of this is specific to Anthropic models, I also repeated parts of the exercise with Gemini’s coding models and got similar results.)

Overall, the model finding some cheaper reagent alternatives was a great outcome but this isn’t necessarily the generalizable lesson. It was that once the assay had been represented computationally, the agent could reason across the scientific protocol, the assay inventory, the economics, and identify the relevant product alternatives without me having to manually translate between all of those worlds. This was the kind of work I would normally postpone indefinitely. It’s impactful work, not necessarily difficult, exactly, but it’s tedious. There was always something more urgent to do.

The model did the reasoning and orchestration. The code did the bookkeeping. That clear division of labor feels increasingly important to me.

Chaining things together: lab work and presentations

What’s interesting about these workflows is that they start benefiting from context that I wouldn’t necessarily have called “technical” in the scientific sense.

Quick story here.

Startups have a habit of dropping you into do-or-die situations, often randomly. At our company, we had to scale up to run a pilot with dozens of patient samples, something we’d never done before, with only a few days of turnaround time. Suddenly budget, inventory, reagent preparation, and instrument prep all became urgent at once. Since Claude already had a programmatic and semantic understanding of our lab and our costs, I gave it a try:

“We have to scale up to X samples. I need to know the budget and how much this will consume reagent- and consumable-wise. I’m also fairly sure I have 100 µL of primer for Y, but I’m not sure how many kits of reagent Z we have left in the fridge.”

Off the agent went.

Three minutes later, a wall of text appeared in my terminal. It made a lot of sense, and it came with immediate purchasing and preparation next steps that were forgiving of exactly how little I knew about our own lab fridge.

At this point, though, something else became apparent: the output format matters too. Text isn’t the highest-bandwidth way to present data, and in my experience these models aren’t yet that good at PowerPoint. On the other hand, HTML is a much more expressive language and one that these models speak fluently1. I pointed Claude Code at our company’s branding guidelines (colors, fonts, design philosophy and all) and told it to use that framework to make a slide deck summarizing our budget and plan for tackling the pilot.

The result was a five-page deck with tables, take-home messages, a budget for the work, and warnings about what to order immediately after checking the fridge to confirm the amount of reagent Z. It was shockingly faster for me to digest than the raw text, and much easier to send to my cofounder and others.

I think presentation skill, clarity of thought, and communication are scientific skills. The underlying question of how information should be visualized still deserves real thought, because visualization carries a huge amount of the message and affects how information is interpreted. Clicking around PowerPoint’s toolbar, though, is not a scientific skill. Once I’d decided exactly what information I needed, the model already had the lab context. I could delegate the execution of turning that information into a useful artifact.

It did a better job than I could and in fifteen minutes instead of an entire half day.

Compounding tacit knowledge and machine diligence

An unexpected benefit of using coding agents in this way only became apparent after multiple uses, as data accumulated and the system became increasingly legible to the agent. It started to capture tacit knowledge.

This wasn’t something I’d designed the system to do initially. It happened because the agent was repeatedly involved in the same workflows, and I kept course-correcting, and taking notes, when something went wrong. The tireless diligence of these machines started surfacing nuances in lab-work that surprised me.

I’d given the agent a deterministic script that calculates qPCR melt-curve shape as a typical quality-check check for routinely collected data. It’s something bio-researchers, me included, don’t always do religiously unless something seems off. I mostly trust that the run went fine and move on.

Claude doesn’t cut corners like that. Running through the analysis one day, it flagged inconsistent data in a couple of wells. Melt curves take a characteristically different shape between different DNA/RNA targets, and a curve that doesn’t match historical data can be a useful heuristic that something went wrong; mis-pipetting, contamination, or something else.

It turned out to be a well I’d double-pipetted by mistake, which I had almost forgotten but the agent caught independently. By comparing the experiment I had designed previously with Claude against the historical data, it noticed the discrepancy. Lo and behold I had written that by hand in my notebook but forgot about it. I ended up recording the mis-pipetting incident in the relevant dataset, in case it became relevant in the future for the lab (or for the agent!).

Similarly, one day I noticed the agent had me loading samples into the wells along the outer edge of the plate for a run that was going to sit in the instrument for a long time. On that plate type, edge wells evaporate over long runs. It’s a known quirk of the format, not a law of physics. It’s the kind of thing you learn once, the hard way, after a run fails and you spend an afternoon figuring out why. I said as much, in plain English, the way I’d say it to a lab-mate. It redesigned the layout around the constraint without me specifying a single coordinate. Again, the edge case was captured for future use.

Here, the system starts feeling qualitatively different from a collection of AI productivity tricks. Both times, something real happened in the lab. I corrected the system, and that correction became part of the shared computational description of the experiment. Normally, that kind of knowledge lives in one person’s head, earned, after enough things go wrong. Here, all it took was updating the document the agent always looks at and pushing it to the team’s shared drive. The next time anyone on the team runs a similar assay, if they use Claude Code, it can flag the same anomaly automatically, including for people who’ve never made that particular mistake themselves and wouldn’t necessarily know to look for it. The workflow becomes:

mistake → observation → correction → encoded constraint →sync finding and enable future prevention.

Of course this sort of thing compounds over time, perhaps after enough iterations it converges to some sort of “taste”. But ultimately I think this tacit-knowledge accumulation is what we’ve always called “expertise”, which is different from raw intelligence. That feels like a very different kind of productivity gain. Beyond the fact that the machine works faster; the knowledge generated by doing the work doesn’t disappear when the work is finished, it’s shared to the whole team. There’s something very “hive-mind” about that. Pretty cool.

What exactly am I delegating?

I’ve tried to be opinionated about where I delegate my thinking to AI. As experimentalists, tedious, non-stimulating cognitive tasks get handed over to junior labmates, and perhaps sometimes we hold onto them out of habit and partly, out of a quiet pride in having gotten really good at something over years of practice.

I now ask myself a question when I work in the lab: “Is this actually a scientific skill I need to personally exercise, or is it simply a piece of operational complexity that happens to have historically landed on lab scientists?”

I’ve tried to reclaim ownership of the things I consider important scientific skills: asking clear questions, understanding results, making scientific judgments, and communicating what we’ve learned. I’m increasingly happy to let go of the rest. The lines are obviously blurry. Someone else might draw them differently. Maybe some of the things I think are “just execution” are skills that another scientist quite reasonably wants to own. That’s fine.

As people adopt AI, they’ll develop their own unique preferences and setups, all the better. A lot of this also has to do with building an instinct for what LLMs and coding agents are good and bad at, which you can only really develop by working with them yourself. So ultimately, I don’t think the future is going to be “AI does the science.” It’s going to be a marriage between the style of the scientist, the limitations and capabilities of the machine, and a computational representation of the laboratory that lets the two interact. And I suspect different scientists will draw that boundary in very different places.

Conclusion

So what? I’m aware this can read as small. Somewhere there’s a scientist optimizing a protocol that might matter to a cure for cancer, and here I am writing about well-plates, negative data, inventory, and slide decks.

But I don’t think the size of the problem should be an excuse not to think about improving the day-to-day life of wet-lab scientists. Drudgery is, quietly, one of the reasons talented people leave the bench for a desk. They didn’t stop caring about science. The friction around it just wore them down. Every hour that comes back from liquid allocation, inventory, remembering to avoid mistakes others have made, or making a slide more legible for a finding to land isn’t a minor footnote to the science. It’s freeing up capacity for more science.

And motivation isn’t a soft metric when it’s the thing standing between a scientist and their next good question. Every layer of drudgery I’ve managed to hand off has made me want to stay at the bench longer. If that keeps being true as the abstraction climbs higher, I’m genuinely optimistic about where this new way of doing science is headed.

Old Sturbridge Village, a recreation of an 1830s village in rural Massachusetts, stands out because of its commitment to live demonstrations of period work. On a typical day the village is populated by dozens of professional interpreters (often trained historians) in period costume, performing farmwork and trades such as tinsmithing, blacksmithing, shoemaking and many others using historically-accurate methods and tools.

As a visitor, Interacting with one of these interpreters combines the fascination of watching a skilled practitioner work with the utility of a tutorial, the fun of a live performance, and the value of a history lesson. The overall effect is compelling. Dan’s 6-year-old daughter could have watched the blacksmith work and listened to his descriptions of his tasks and techniques for hours. The experience is categorically different from a “how it’s made” documentary or a youtube how-to1. An interpreter repairing an axe jokingly complained of the many “suggestions” he’d received from decades of visitors about the right way to form a handle. Later, while shaping the end of a fence rail with a different axe that he’d made,, that interpreter described his work as experimental archaeology in which he learned unwritten aspects of 1830s production methods through his experience of using period tools and materials.

As a result Dan now knows more about how to make an axe in 1830 than he does about how to make one in 2026. Moreover, being a blacksmith feels more real to his daughter than being a semiconductor engineer. What if there was an Old Sturbridge Village for cutting-edge industry?

In 1962, a group of business leaders and educators met with President Kennedy to discuss the possibility of a permanent industrial exposition whose benefits were to include:2

1. A dramatic demonstration of United States leadership in science, technology, research, and industrial and commercial capability “for all the world to see and believe.”

2. A display of the application of scientific and technological achievements to the basic industries of the United States and of the interrelationships among these industries in the basic working of the economic system.

3. A stimulus to patriotic pride in American inventiveness, resourcefulness, pioneering development, and technological superiority.

4. The close connection between the competitive industrial system in the United States and the nation’s social, educational, political, and financial structures.

5. A showcase on the various professional fields of science and technology which will inspire and provide incentives for both students and teachers to the end that the future’s requirement for trained manpower is met.

6. An opportunity for study groups and technical missions, both from throughout the United States and from foreign countries, to view a “laboratory demonstration” of the intricate inner workings of the American economic and social systems and the benefits derived from them.

The plan, with an eye-watering proposed budget of 400 million 1962 dollars, never got off the ground despite a few bursts of activity during the 1960s. Nevertheless, it’s the closest we’ve come to a permanent living museum of modern industry and, at its peak, had attracted a number of corporate sponsors interested in demonstrating their production processes.

We find that list of benefits intriguing and meaningfully different from the North American World’s Fairs of the same decade and anything on offer today. Looking past the cold-war jingoism, the point of the exposition would have been to show visitors how American science and industry interacted with each other, how they shaped society, and how individual trades within science and technology worked in practice. Moreover it was all intended to be updated to reflect changing times. This sounds pretty useful!

Imagine a small semiconductor fabrication line constructed with interpretation in mind. Many cleanrooms have glass walls or large windows so all it would take would be the addition of sound transmission equipment and careful arrangement of the tools and stations for visibility. Inside, a skilled technician is manufacturing a wafer of hundreds of experimental lasers. Although some steps happen invisibly inside vacuum chambers, others like lithography, dicing, and packaging remain skilled trades with much to observe and discuss.

Of course for this to work, the technicians operating the fab would need to be trained in talking to the public. They would narrate their process, describe their lives, and talk about what their work means to them and to their industry as a whole. This job, industrial interpreter, doesn’t exist today; it is lost in the interstitial space between industry, academia, and science museums.

Now imagine expanding this facility to include multiple industries. What if that fab were next to other production facilities? Perhaps an automotive assembly line, a set of programmable knitting machines and manual garment assembly stations (don’t show just the automated work!), and a machine shop. Now imagine each with their technicians and engineers trained to interpret the work in addition to doing it. How much more real and understandable would these industries and careers be if we could see them in action and have them explained to us live?

One final thought: a cohort of professionals expert at technical interpretation might be just what we need to comprehend future technical works produced entirely by AI.

We write Reinvent Science to broaden the conversation around science and science funding, and we rely on you to help us reach as many readers as possible. Please subscribe and share to help us continue to reach you and your colleagues.

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The list of things that we call scientific disciplines today was not inevitable. Had historical events gone differently, divisions might be in very different places. For example, top UK universities such as Cambridge have departments of geography while many elite US universities explicitly divided geography into multiple departments, only some of which they label as science.

Although these divisions have real world consequences in what kinds of work are done and by whom, they also hint at a more difficult but potentially more impactful question: what disciplines don’t exist but could? This question is hard to think about because of how immersed we all are in current paradigms, but that’s exactly why trying to think about it, even if doing so feels like flailing, is important. It’s probably the case that we’re somewhere near the middle of scientific progress and so many of our existing paradigms should be wrong in important ways. This means there’s a chance for this kind of thinking to succeed. Moreover, non-incremental thinking is likely to be an increasingly important domain for humans if you believe that AI will handle a growing fraction of incremental work in the future.

We’ll be the first to say we have almost no idea how to do this in practice beyond continuing to flail, so maybe we need a discipline of thinking thoughts that are hard to think. Nancy Nersessian’s Creating Scientific Concepts proposes that making and manipulating conceptual models while treating them as intermediate could be a real approach here, but this is unlikely to be the only way forward.

Maybe what we actually need is a discipline of how to teach our children to do this. Reading is powerful but doesn’t come naturally. Perhaps conceptual leaping will become the next reading.

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Science is a human activity. We spend our efforts researching the questions whose answers we care about, the subjects we find most interesting, and the technologies we wish we had. Machines can make us more efficient at this work (perhaps orders of magnitude more efficient) and help us prioritize what to work on, but letting them choose for us would cede agency that, for our own sakes, we need to keep.

When parenting, it’s often best to let your child make choices that affect their life even when you know better or think you do. Similarly, even if we expect AI scientists to choose what topics to study better than we can, we should approach that expectation with the humility of a parent who has just been calmly and logically corrected by a precocious 6-year-old (Dan is definitely not talking about his own parenting experience here…). We may in fact always know ourselves better than the best machines we can build will. Even if we do not, it may be a fundamental property of humans that we need to participate in discussions of our fate to accept the resulting decisions affecting it.

AI’s success in formal mathematics is unlikely to be the end of its encroachment on human intellectual endeavors. And so we anticipate that Terence Tao’s recent question to his colleagues1 “What are the precise goals, objectives, and values of our mathematical community, and the enterprise of mathematical research? Not just the explicit goals that we communicate to the public (or to funding agencies), but also the implicit goals that we actually seek in practice?” will soon be relevant to many branches of science as well.

In fields where conducting research turns out to be the comparative advantage of machines, we will need to maximize our ability to choose what to research and to articulate the reasoning behind our choices. Otherwise the overall research enterprise will drift toward market signals, what machines happen to be good at and what they decide we need and in 10 years we might find ourselves with cosmetic surgery technology beyond our wildest imaginations and no progress on cancer research. Even worse, we may let this part of our own agency atrophy and begin to treat scientific advancement as a kind of machine-driven weather.

Back when the Turing Test was still a thing, Brian Chirstian wrote The Most Human Human about his efforts to seem as human as possible to judges who would only see his text output. The book takes a deep look at what we humans think makes us human. Although today we mostly conceive of this defensively (how do I distinguish my writing from AI slop?), there is an opportunity to go on the offense. If we get to the essence of why we ourselves do science, we may be able to out-compete machines at choosing what science matters to humans for many years to come.

“Why do humans do science” is an answerable question, at least for any given historical moment. Though many of us have a gut response when asked why we ourselves do science, we don’t spend much of our empirical effort on the collective question of why science is or should be. Nevertheless, we have the tools to start making progress on this topic though we’ll likely need to invent new tools along the way to get to a satisfying answer. Perhaps a deeper understanding of science as a human practice is the route to healthy human-AI partnership.

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Reasons to do pure science all pretty much fall into four categories. Either the activity or the products of pure science are valuable for their consequences or intrinsically. So you might say that doing pure science is, like dancing, an expression of what it means to be human (activity, intrinsic) or that pure science produces knowledge that can lead to unexpected applications (products, consequences). Saying that pure science is valuable because it glorifies God and God expects it of us does not fit into this schema.

Talking about religion as a legitimate motivation for scientists may give us something that’s hard to get in other ways: a deontological argument for doing pure science. What evidence there is (for example this survey of Fellows of the Royal Society) suggests that scientists today are much more likely to be atheists than the general population. What if that’s not because religious folks don’t want to do science, but rather because the culture of science disproportionately turns them away?

We don’t propose to turn all of science into a religious practice, but rather to get out of the way of scientists and potential scientists who want this for themselves. Historically, religion and science have not been at odds (we recommend the works of Lawrence Principe on this point) and by treating religion as taboo we may be doing science a disservice. Who might we have excluded if we had enforced today’s culture earlier1? Who might we let in if we make science more accepting of religious motivations?

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Only 1 in 4 of the inventions developed at U.S. universities are ever licensed1, and fewer still reach a market. We spend billions generating innovation — academic research expenditures topped $109 billion in 20242 — and then fail to move most of that science the last mile into a product.

The usual explanation is a lack of money or market fit. Another deeper problem is what we call dark matter—the tacit know-how, the failed experiments, the undocumented intuition that lives only in the inventor’s head. A patent is the visible tip; the dark matter is the invisible mass that makes the thing work. Strip it away, and you are left with a document nobody outside the original lab can operationalize.

The data backs this up. Across 50 years of spinouts at Stanford University, the top-earning patents were licensed by the inventor’s startup, and self-licenses were 3x more likely to generate >$1M in royalties3. Inventor-led startups have the greatest commercial success because the inventor is the nexus of the invention’s dark matter.

Capturing this dark matter was once thought to be like catching lightning in a bottle, but with accelerating AI, we are beginning to have the right tools. Imagine a digital twin of the inventor: an AI model built by combing through lab notebooks, experimental dead-ends, and structured interviews, then made queryable for the next team. Not a novelty chatbot — a working interface to the reasoning behind the invention. Why did you abandon that pathway? What did you do ‘that one time’ it worked? What would you try next? These answers are available in conversations around the water cooler and lab bench but vanish the moment a team disbands (or a student graduates).

Building a digital twin of an inventor differs considerably from the omnipresent ambitions of building an ‘AI scientist’. Peer-review4 is a far lower threshold to cross than true tech commercialization. Breakthroughs often come directly from mistakes5, unmeasured variables6, and subtle unwritten choices7. A classic example includes the Nobel-winning discovery of quantum dots, which could not be replicated from a new batch of reagents8. Further investigation revealed a single bottle was responsible for all prior successes; it contained trace amounts of oxidized reagents that were essential for producing the successful reaction9. Real innovation lives in the fringes of the wet lab, in the recesses of dark matter in a scientist’s mind – in the undergrad intern’s subconscious preference for a specific reagent bottle.

The opportunity is largest exactly where the loss is worst: in hard tech startup wind-downs, where we estimate 60% of informal IP simply evaporates. A digital twin captured before the lights go out could let the next founder pick up where the last one stopped — instead of repeating a decade of mistakes.

Somewhere right now, a PhD candidate who knows exactly which bottle to reach for is about to graduate — and no one is writing it down. Multiply that by every lab, every wind-down, every “that one time it worked,” and you’re looking at the largest untapped reservoir in deep tech. A digital twin of the inventor is how we finally bottle the lightning before it leaves the room.

We don’t need to wait for an AI that can do science to rescue the science we’ve already done.

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There’s empirical evidence for the negative effect of the academic literature on LLM performance. In 2024 a research team spanning MIT, Cornell, Carnegie Mellon, Google, and OpenAI examined the effects of removing different text corpora from the training data on the performance of an LLM after training (holding the LLM’s structure constant)1. They found that removing ArXiv, PhilPapers, and NIH ExPorter from the training corpus improved the LLM’s performance at answering academic questions and its average performance across all benchmarks while also making the model less likely to generate toxic output. The study is not conclusive on this point because removing Pubmed reduced model performance somewhat, but the mere fact that LLM performance could be improved by wholesale removal of large sets of scientific articles (pre-slop no less!) is pretty suggestive. We wonder whether this result holds up in 2026, but we’d bet that it does.

Scientific metadata is no help either. Once authorship and citations started being used as a metric, all the signal was gamed out of them. Furthermore, high profile institutions and researchers are not immune to misconduct, nor are any particular fields.

This has important practical consequences for using AI to do science. Human scientists use their informal networks to communicate reputational information, verify work through visits and replication, and generally maintain idiosyncratic personal pictures of what work is reliable and what work matters. Because AI agents do not participate in social relationships they cannot access this social metadata except through humans.

This leaves AI for Science efforts with a strange set of choices. They can either create AI agents that human scientists socialize with, frequently record human scientists in private settings, or create an incentive for scientists to provide a steady stream of gossip. Perhaps one way to bootstrap this effort would be to create AI assistants that allow for more scientific work to be done in informal settings. This would amount to surveillance, but perhaps could be worth it for a big enough improvement in our quality of life.

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Small-world networks1 are ones in which most nodes are connected to several neighbors (these groups are known as “cliques”) while a few nodes have long-distance connections. Perhaps surprisingly, small world networks are better at generating new ideas than “fully connected” networks in which almost every node is connected to every other node. In science today it’s easy and expected for everybody in a field to know almost everybody else in their field and adjacent fields. Frequent international conferences, digital journals, email, etc. Your prospects for tenure and funding decrease drastically if you’re relatively obscure. From a network perspective this isn’t great for generating new ideas. Perhaps we need more mechanisms for disconnected science cliques to form and refine wild new ideas before they’re exposed to critique from the entire internet.

These mechanisms could take many forms! Some might be mundane: a department like GMU Economics in the 90s could choose to hire a whole cluster of non-traditional thinkers. Some might be more exotic: the world needs more scientific secret societies!

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Getting out of the lab or office and doing something else is a key creative step in the scientific process. Most scientists we’ve met recognize the power of a long walk and know when to wander alone with their thoughts and when to walk and talk with a colleague. Walks are one of the most common versions of this, but not the only one. Dan and his grad-school roommate once solved a thorny problem in her colloidal crystal research while they were in the kitchen making breakfast.

This effect is well-known enough that corporations are continually willing to invest large sums of money to incorporate natural features and other informal spaces into their R&D campuses. Bell Labs and IBM’s TJ Watson Research Center are the classic US examples, but Huawei’s Oxhorn Campus (completed 2019) LG’s Science Park (completed 2018) and Novartis’s Shanghai Campus (completed 2016) include similar investments. And of course universities also invest heavily in creating and maintaining similar environments, though they serve many purposes at once.

Employers of scientists are willing to spend substantial amounts of money to give them space and time to hang out and take walks. Scientists themselves know that stepping away from their work is a valuable use of their time and an important contributor to their overall productivity. But we don’t have any tools to improve the effectiveness of these interventions. Meanwhile, AI for Science companies are automating literature searches and lab drudgery without regard for the creative processes needed to decide what to read and what to study. No matter how sophisticated machines become at generating and testing hypotheses, science is for humans and so there will always be a role for human creativity in deciding what to work on and why it matters. AI and physical automation may free up some hours for that creativity, but the entire field is currently missing the opportunity to improve our creative hours as well.

So what would it look like to augment thinking while walking? Probably a real-time voice assistant so the walk won’t be disrupted. The assistant could capture and follow up on leads so that detailed analysis is available at your desk when the walk is done. It could provide memory and continuity, reminding you what ideas you’ve considered in the past and offering relevant experimental details and data. It could even kick off relevant experiments back at the robot laboratory and report on the results in real time.

This is a new version of one of the oldest dreams of industrialization: by increasing the value of creative thinking and lowering the cost of non-creative activities, we can carve out more time for those creative activities. In the same way that pencil drafting is no longer part of engineering, what tasks will we let go of from science and never miss? So, AI for science folks, we challenge you to present a utopian vision for the life of a scientist. Can our work be indistinguishable from hanging out in nature with our friends? Can AI assistants connect us to our tools and to each other spontaneously as needed? Can we generate so much value for the world that building us parkland to thrive in becomes an obvious choice?

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We’re all familiar with Planck’s Principle that science advances funeral by funeral1, but what if we did something about it? The healthspan of the typical leading scientist is getting longer, so if we don’t take action and this effect is real, scientific progress will slow down.

The solution isn’t complicated: scientists should execute a dramatic career pivot to a new field somewhere between age 50 and age 60. That’s old enough to have made serious progress in their first field and to have trained several cohorts of junior researchers, while still young enough to leave time for a full second act. They might not start again from the bottom of the ladder, but we expect that they’ll generally start low enough and fresh enough to be out of the way of progress.

Scientists actually do change fields reasonably frequently, but our community rarely discusses it and certainly doesn’t do much to reward it. Perhaps we don’t mention it because it’s usually a response to a researcher’s initial field falling so far out of favor that a major change is the only rational choice. Dan knows a researcher who spent about 25 years in chaos theory and non-linear dynamics before executing a 5-year pivot (when interest in chaos was collapsing) to genomics and bioinformatics where he worked for another two decades.

We think of him as a hero for being willing to start over in a new field, but are protecting his identity because we’re honestly not sure if he’d be judged negatively for this decision by his peers. Our expectations are what really needs to change. Major subject shifts should be the norm.

Imagine celebrating these pivots and even shaming scientists who spend their whole careers in one field. We should have the equivalent of the NSF CAREER award for scientists changing fields mid-career.

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In a recent podcast, Kevin Hawickhorst flagged that the functional bureaucracies of the late 19th century combined everything around a particular subject, bees in the department of agriculture, for example. This organizational principle means having people who deploy money towards the subject (giving grants to farmers to improve bee husbandry) in the same place as people doing research adjacent to the subject (how do bee metabolisms work?), next to people who are trying to directly improve implemented things in the subject (what practices enhance bee health?).

One could imagine reorganizing research in a similar way. (We could even say “innovation” because it would be more than just research.) That is, organizations and departments that were focused on a concrete useful thing in the world all the way up and down the ladder of appliedness. An obvious one would be, say, kidney health. You would have everybody from people funding dialysis to people studying the metabolic pathways for processing urea to doctors who work with patients.

This would in some ways enable more specialization. People could do more incomplete research instead of needing to tell a complete story with every publication, as long as it fed into the bigger picture. A small version of this is a practice Ben heard of once where a lab group that worked on medical device research paid an MD to come and sit in on their lab meetings.

The default approach to curation today revolves around conferences and papers. It is downstream of a world that no longer exists: one in which there were a small number of scientists who all knew each other, the fastest way to send a detailed report was through the mail, and the only way to learn deep technical knowledge was directly from other scientists. Yes, there have been changes around the edges like preprints and now AI literature surveys.

This still works fairly well for small, well-defined, relatively slow moving fields. These are the fields where you can meet all the relevant people at the “Floopion acceleration conference” (to avoid calling out any specific field) and the equipment is so expensive and specialized that you can be reasonably confident that nobody is going to do interesting work in a cave with scraps.

But as science becomes faster, more international, and less institutionalized, scientists will need new curation approaches. New tools like AI agents, the myriad of feeds, and group chats can enable these approaches. We are not actually sure what these approaches look like exactly but we can imagine some possibilities:

A tool that ingests the firehose of twitter, bluesky, mastodon, threads, linkedin, and whatever other social media on which people are talking about science and flags the things that you should actually pay attention to instead of whoever is demanding the most attention. A tool that pays attention to what you’re writing and automatically reaches out to the right person for you to talk to about a certain place you’re stuck. A service that creates a custom journal delivered weekly that is the new preprints that you specifically should read.

If we’re successful we can move towards what Michael Nielsen has called “designed serendipity” – knowing exactly who to talk to or what to read at the exact moment that you need it.

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To lose weight you need to sustainably burn more calories than you take in. But figuring out what macronutrients make you feel full, strategies to avoid overeating, the right amount and kind of exercise, and how to maintain the discipline to do it all day, day in and day out can take a lifetime of work. Work that many people ultimately decide is lower priority than other things.

To run a great research organization you need long enough timelines coupled to useful forcing functions, talented people with just the right amount of independence, and the resources to execute on your goals without becoming too fat and happy.

It’s a simple prescription, but an extremely hard one to nail both because there are many tensions that you need to balance and pressures, both internal and external, can prevent even well-intentioned leadership from ever finding the right setpoint.

To give you a sense of what we’re talking about, we’ve listed a few common pressures that derail research orgs. These are the equivalent of “work stress causes you to overeat” or “time pressures mean you can’t exercise.” Like losing weight, figuring out how to deal with these is hard and organization-specific but we think it’s worth at least calling them out

Intermediate metrics on the wrong timescale. Whoever runs the org or a project has to report upward, and those people (often not researchers themselves) want to see some numbers go up consistently. These intermediate metrics are things like papers, money raised by spinouts, or demos. Each of these can be a real signal of good research, and each is trivially Goodhart-able.

Demanding justification up front. Real research is a hypothesis that might be stupid; some of the best of it sounds stupid right up until it doesn’t. Refusing to fund anything you can’t defend in a memo is a clean, professional, responsible way to strangle the portfolio.

Timescale mismatch. “We expect results in two years” is the research equivalent of a two-week crash diet. It’s not that nothing happens in two years; it’s that the things you actually want take longer.

Side quests. Job creation, regional back-scratching, topics du jour, economic growth — every stakeholder wants the research budget to do double duty. We’ve both worked with funders who care about these metrics and we believe they apply to work downstream from research like technology transfer, but to not research itself.

Over- or under-structuring an org. Leaders often need to put in place rigid procedures and structures to make an organization legible and get the support to start. Research rarely fits into nice structured buckets, so closely adhering to a specific “this is how we do things” can kill output. But at the same time, taking “let researchers do whatever they want” at face value is an equal trap!

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Fundamental principles by which the military forces guide their actions in support of objectives. It is authoritative but requires judgement in application.

Abstracting away from the military context is simple enough:

Fundamental principles that guide an individual or group’s actions in support of objectives. It is authoritative but requires judgement in application.

In recognizing new doctrines as inventions in their own right, Brian Reynolds emphasizes an important corollary to this definition: a doctrine is an idea that confers to its users a tangible advantage in the physical world. For example, The Toyota Production System (sometimes called Lean Manufacturing) is a doctrine whose advantages made Toyota one of the world’s leading automakers and then, as it diffused, drove major efficiency improvements across the global automotive industry and manufacturing in general. Just as the size of its impact marks the Toyota Production System out as a major invention, the history of this doctrine looks like the history of a major invention as well. It was invented by a small group of individuals (Taiichi Ohno is generally considered the main inventor) and then refined over a period of 30-50 years before reaching a relatively stable form. It also took decades for this doctrine’s full economic potential to be realized, with major skepticism at every stage of its development

WIth this in mind, it’s puzzling that the scientific community doesn’t spend more time working on inventing new doctrines both for itself and for society at large. The Toyota Production System shows that inventing a doctrine can lead to large economic impact and international recognition. It’s certainly true that science and technology have advanced enough that new principles to guide how to deploy them would be valuable. One possibility is that new doctrines are in fact so valuable that they’re mainly developed in the private sector. For example, there’s a decent case to be made that customer relationship management software is fundamentally a doctrine for applying relational databases, and it certainly made both Thomas Siebel and Marc Benioff staggeringly rich.

We find that argument unsatisfying because there should be many areas, including science, where new doctrines could create lots of value without making anyone rich. And yet we don’t know of any doctrines by scientists for scientists that are more radical than the preprint. Meanwhile, there is no scientific discipline for developing principles to guide actions in support of objectives1 and the development of such principles is not prioritized inside existing disciplines.

We started Reinvent Science in part to try to develop new doctrines for science in a changing world. Why aren’t more people working on new doctrines? What doctrines are you thinking about?

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The consequences could get weird, fast.

Consider the following corollaries:

1. Speeding up any step that isn’t a bottleneck doesn’t help.

2. Slowing down steps that aren’t bottlenecks is fine as long they don’t become bottlenecks.

3. In fact, slowing down steps that aren’t bottlenecks can be good if doing them too fast ties up resources by letting work-in-process or other inventory accumulate.

Now apply this thinking to any of the outputs we want from science. The counterintuitive point here is that if we neglect supposedly important things in favor of focusing only on true bottlenecks, things we think are “important” in science could turn into raging dumpster fires while the overall enterprise becomes more productive. For example, if the time and attention of our best scientists is a bottleneck, then we could eliminate all of the administrative work that they do. Imagine getting rid of all reporting and auditing without any replacement and then accepting the consequences. Waste and fraud would go up, possibly to an embarrassing degree (cf. raging dumpster fire), but, if scientist time were indeed the bottleneck, the speed of scientific progress would increase as well. The optimal amount of fraud is not zero, but the optimal amount of fraud in science could actually be pretty large. One of ARPA-E’s first 37 funding awards in its inaugural OPEN program was to Craig Grimes, who went to federal prison for grant fraud. Nevertheless, that initial OPEN program is generally hailed as being refreshingly ambitious for government scientific funding1 and delivered a solid return on ARPA-E’s $175M investment.

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We’ve already hinted at the possibilities here with the idea of labs giving each other “payday loans” but that is just one of many possibilities that would enable scientists to trade on their knowledge of things like who does good work or what hyped-up research is actually bullshit. Here are two others to get your imagination going.

Labs could “buy equity” in each other. A PI who is particularly good at predicting which up-and-coming professors are going to do well could give them some extra funds in exchange for some ownership in their lab. A 5% stake would mean that the investing scientist automatically gets to be a coauthor on one in every 20 papers that the lab publishes. Of course there are important questions like “which papers?”, “where did that money come from in the first place?”, and “isn’t it a bad thing if coauthorships remain the main currency of science?”

Another possibility is prediction markets for science. As part of a startup package or just a carve-out in every grant, scientists could have funds to bet on prediction markets about research. Instead of arguing back and forth about the validity of things, scientists literally can just bet on it. These prediction markets would also serve some of the purpose of citations as well – if a market doesn’t have much volume, nobody really cares and you shouldn’t put much weight in the work either as a positive or negative signal. Of course there are many flaws to be figured out: the markets will inevitably be pretty low-volume and there are tricky questions about who can participate.

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As a whimsical example of innovative science financing, one could imagine bootstrapping an entire lab banking system off of “payday loans” for labs. Frugal labs that manage to get research done under budget could loan excess funds to labs who have grants in the pipeline but need to get started now. (Who better to judge how likely it is that a grant will succeed than other scientists!) Once the grant comes in, the lendee lab pays the lender lab back with interest. Once this system is in place, other systems of credit and exchange could follow. Of course, you’re not allowed to just lend money to other labs, but this restriction could be avoided by just happening to need a pile of fungible, divisible experimental supplies like gold or high-grade fiber optic cable. This is an absurd idea, but it hints at the whole space of possibilities for new financial instruments in science.

To some extent, this is already happening – one reason for researchers to work at a university is to smooth out payment timing. Technically, universities are fronting grantors the money for months or years. But extending the system could both enable more research and enable labs across universities or independent organizations to interact.

Slightly more realistically, John Hull and Andrew Lo have proposed massive portfolios with securitized debt for “long shots” that have extremely long timescales but potentially huge payouts like groundbreaking drugs.

There are good reasons why there has been less financial innovation in science. The biggest reason is that it’s not just a matter of pulling captured value from the future to the present. Most financial innovation is based on the idea that some party will have a large amount of money in the future if only they or some other party has a smaller amount in the present. However, the nature of research is that even incredibly impactful work often puts little money in any specific pockets.

It’s likely that any financial instruments for research will need to be backed by something other than pure market value. Paul Christiano and others have proposed “impact certificates” wherein a funder promises a pot of money for solving some problem or achieving some impact like curing a disease or figuring out a way to eliminate mosquitoes cheaply. If that pot is big enough, investors can then finance the projects they think are most likely to win that money by buying “equity” in an eventual payout. The ACX grants actually tried implementing the idea. Impact markets likely only work for a subset of work that has clear outcomes that some deep-pocketed supporter cares about a-priori. But they hint that there may be many other things to try – it just requires a different sort of creativity than most of the people in the scientific community are used to applying. Finance bros, now is your time!

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Historically, it usually takes decades to centuries for math research to become “useful.” There are obvious exceptions like calculus where the math was literally invented to solve a science or engineering problem, but the pattern usually looks more like fast fourier transforms (Gauss wrote the algorithm down in the early 1800s) being repurposed for signal processing in the 1970s, quaternions (invented in the 1840s) being the perfect representation for spacecraft and video game objects in the 1970s or 1980s, or Lie Algebras taking more than 50 years to become a key part of particle physics.

Math becoming useful for a problem requires a special confluence of

1. People who understand the math well enough to apply it to the problem

2. People who understand the problem well enough to see how the math applies to it

3. The computational resources to actually execute the math on real situations

AI may be able to compress the timelines for these conditions to be met and enable a lot more cutting-edge math to apply to real problems.

Consistent with Moravec’s paradox, AI has gotten pretty good at “understanding” pretty complicated math. One could imagine doing breadth-first searches of open science or engineering problems to see whether there is applicable math that has been collecting dust on a shelf could be an unlock. Furthermore, the same abundant computational infrastructure that powers the AI could potentially be part of the unlock to using that math.

The reason to do math research won’t and shouldn’t become purely utilitarian, but it might be cool if more mathematicians saw their work directly applied in their lifetimes.

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Take weight loss as a toy example. Say you and your twin weigh the same amount and have for some months. Now say you go on a potato diet while your twin doesn’t. If you lose 10 lbs and your twin doesn’t, that’s interesting! If your twin then goes on a potato diet and also loses 10 lbs, that’s really interesting and decent evidence that the potato diet works for your genotype.

If you’re a human biologist (twin or not), there are probably twin studies you’d love to run. Some of them are probably very simple. Could one pair of twins generate enough signal to be meaningful to you? If so, just put your theory out there and see if a pair of twins volunteers to give it a shot. You’ll need to take some care not to trip over an IRB requirement, which is why this post is about twins studying themselves rather than anyone else studying twins. However, there’s nothing preventing the rest of us from letting the twins know what the most interesting things to try might be. If you’ve got any ideas, put them in the comments!

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