Some ideas to engineer discovery

Notes on research rotations, curiosity vs. platform science, and rethinking the structures around discovery.

I’ve been thinking a lot about how we organize science: the day-to-day environments where scientists actually do their work. What makes a lab satisfying to be in? What structures help curiosity flourish? And, how can we enable small-scale research teams to pop up like startups?

My own background is in biomedical engineering and neuroscience, so much of my thinking is geared toward biological research. I have worked in academic labs and a non-profit research institute, and now I have transitioned back into the university lab. That mix of settings has made me think more deeply about what I value in research, and about how research environments shape the science that gets done. I outline some of these ideas here at a high level, and hope to explore each more fully through future writings.

These aren’t fully formed proposals. They’re more like ideas that have been bouncing around in my head, shaped by my experience across different research structures, and by reading people in the metascience space like Michael Nielsen, Ben Reinhardt, Eric Gilliam, Adam Marblestone, Jason Crawford, and Matt Clancy, to name a few. I outline three ideas below: the first suggests shifting how we hire scientists, away from narrow skills and toward broader talent; the second explores how different types of research goals require different organizational structure; and the third argues that institutional requirements in science funding are holding back small-scale research startups.

Marie Curie in her laboratory, c. 1900. Source: here.

Integrating PhD and medical rotations into professional research to accelerate breakthroughs

During my PhD at Boston University, the first year was all about lab rotations. You tried three different labs, one each in the fall, spring, and summer, before committing to one lab. While learning new technical skills was part of the process, it was more about seeing how different groups worked, experiencing the culture, and asking yourself if you could spend five years there. You got the chance to make an informed choice, rather than being “assigned” to a professor from day one. That autonomy matters. Looking back, rotations are underappreciated.

The idea actually has roots in early industrial research. At GE’s Research Lab in the early 1900s, new hires were left to wander the floor for a few days to figure out what interested them. Irving Langmuir described how the director of the GE lab, Willis Whitney, would walk around asking, “Are you still having fun?” The ethos of picking a project that excites you and trusting that productivity will follow is powerful. GE still uses a version of rotations today for junior employees, moving them through various departments before settling into a permanent role. GE also has one of the higher employee retention rates, but of course, causal links here are fuzzy.

Another example of rotations comes from medicine. Medical students cycle through various specialties, like pediatrics, internal medicine, surgery, and others, before applying to a residency program. You see different patient populations, learn how teams operate in different clinical settings, and get a sense of what type of work aligns with your temperament and interests. In the end, you make a more informed choice about your specialty.

What if we professionalized this model more broadly, not just for PhD and medical students, but across research organizations? Instead of hiring for narrow technical skills, you hire for scientific talent. Right now, the system for hiring scientists in industry is largely skills-based. But that seems backwards. One of the most important skills in becoming an independent scientist isn’t how fast you can pipette liquids from one tube to another or how many animals you can run through a behavior rig. Those are consequences of good training, not the cause of scientific ability. The real skill is learning how to choose, evaluate, and solve meaningful problems.

By framing job opportunities more broadly, you naturally shift the emphasis from narrow technical skills to individual talent. You bring in great people, give them a structured way to explore, and let them choose what they’re most drawn to. I think that would create healthier environments and, maybe, bigger breakthroughs. Irving Langmuir went on to win the Nobel Prize in Chemistry for work that began as a curiosity-driven side project in the GE Industrial Labs.

Curiosity-driven research and platform science need different organizational structures

Another distinction I’ve been chewing on is curiosity-driven research versus platform science. Curiosity-driven research is what most people picture when they think of academic science: chasing novelty, asking “how does this mechanism work?”, or developing new technology prototypes. It thrives on nimbleness and freedom.

Platform science (I also considered calling it research at scale) is different. It’s still fundamental science, but it comes after that first spark of curiosity. Expansion microscopy is a good example of something that began as a curiosity and evolved into a platform. Originating in Ed Boyden’s lab, the goal was to map neuronal connections in the mouse brain. Their strategy: Instead of developing new microscopes with higher resolution to see the connections of individual neurons, let’s just see if we can find a way to make neurons bigger and use our existing microscopes. Inspired by the remarkable absorbing and swelling properties of a baby diaper, the basic question they asked: can we take brain tissue that has been removed from the animal and make it bigger? This wild idea worked and has been adopted by many labs around the world.

Now, at E11 Bio, a Focused Research Organization (FRO), their goal is to scale up expansion microscopy to map the connections of the entire brain. FROs are (typically) non-profits that tackle large-scale, well-defined, and pre-defined scientific challenges to produce robust platforms, tools, or datasets that the broader research community can build upon. The emphasis is not on novelty for its own sake, but on making methods systematic, reliable, and scalable so they can power further discovery.

I love FROs for this. They’ve carved out a space to scale basic science, and they’re producing incredible resources. But I also worry they won’t fully satisfy the best curiosity-driven scientists. Platform projects require standardization, engineering pipelines, and bigger teams, which can be thrilling if you’re motivated by the scale itself, but perhaps a little unsatisfying if what excites you is the chase for new ideas and exploratory work.

I observed this push-pull between curiosity and platform during my time at the Allen Institute. Founded in 2003, the institute began as essentially an FRO (before the term FRO existed) to build the Allen Brain Atlas, an open-source map of gene expression and cellular anatomy of the brain. Over time, it recruited more curiosity-driven scientists who were either already in academia or would have otherwise gone into academia, and the institute attempted to do both large-scale data generation and exploratory science. But the needs of those two modes are different. Platforms need rigid engineering and stable pipelines. Curiosity needs flexibility, even a little chaos. Putting them together in one place often means neither side is being efficient in scientific progress. But we need both.

So maybe the solution is not to blend them, but to separate them more cleanly. Support organizations built for scale and organizations built for curiosity, rather than trying to make hybrids that stretch themselves thin. Then, we can build clear paths to transition an idea out of curiosity-mode and into scale-mode. What does an academic lab look like if you took it out of the university? Small-scale curiosity-driven research teams of 15-20 people without any specific profit or end goal in mind. Ben Reinhardt’s essay Unbundling the University is an excellent in-depth essay on how separating research from universities could enable small, independent teams optimized purely for discovery. I hope to unpack his essay in a future post.

Institutional affiliation in grant funding is a limiting factor for research “startups” in the life sciences

One of the reasons I suspect that spinning out small-scale research teams is challenging is the institutional requirement for government grant funding. If you’ve ever applied for NIH support, you’ll know the section: you have to detail your institution’s resources (an example I found from Mount Sinai). What equipment is available? What facilities does your university or institute maintain? How cutting-edge are the tools in your environment? The grant application doesn’t just evaluate you or your idea; it evaluates your research environment and institution itself.

For a capital-heavy, curiosity-driven research team, this is stifling. To secure funding, you need an institutional affiliation, but to set up an “institute” (here I only mean it as a small group of scientists), you need funding. This is where most individuals might pivot to a startup and secure VC funding if the idea can be spun to make a profit, or obtain an institute affiliation and start a lab in academia. Is there a third option? Perhaps obtain sufficient philanthropic seed money to establish the basics of an “institute” and then apply for government funding.

I’m not exactly sure when this requirement became so entrenched, but it also weighs heavily on early-career scientists. In biomedical research, landing funding can give you an edge in the academic job market, especially through transition-to-independence awards like the K99/R00. These awards typically cover two years of postdoc work followed by three years as an independent PI. But they also assume you already have an institutional anchor for your postdoc work.

If you don’t have a transition award, there are other early career awards available, but they all also require institutional affiliation. To secure a good position, it helps to come with funding. But to secure funding, you need institutional affiliation. When I looked for grants that would let me propose a project without an institutional home, the options were vanishingly few.

This setup narrows the pipeline. If you’re in a postdoc but want to pivot fields, or your PI isn’t supportive of transition awards, or you simply lose out in an extremely competitive cycle, your chances of securing an independent faculty position at a top research program is lower in the biomedical sciences. At this point, many great scientists pivot to industry positions. Further, the institutional requirement locks you into your current environment and discourages bold shifts in research direction. And what if you don’t want to be in a university setting, with teaching loads and administrative duties, but still want to pursue independent not-for-profit research? Under the current system, there are few viable paths forward.

I wonder if there’s a better way. Perhaps we set up communal science spaces with shared equipment where independent teams could operate. Suppose early-career scientists could apply for funding as individuals, with contingencies built in rather than prior requirements. You’d only receive the money if you secure a position, but you’d have the guarantee in hand while on the job market. That guarantee could enable riskier proposals, career pivots, or independent ideas that don’t map neatly onto your postdoc work.

Bonus: What does a laboratory look like in the age of AI?

This idea is much fuzzier and more of a fantasy right now.

A lot of AI-for-science projects are focused on literature review, hypothesis generation, or computational modeling. Those are very useful. But most scientists working at the bench spend very little of their time reading papers. The bottleneck is the lab itself: building microscopes, running animal experiments, prepping samples, growing and keeping cells alive. Most of my workday is there, not in the library. While LLMs have significantly improved my pace of learning through scoping the literature, it has only had a small effect on speeding up my experimental workflows. And that’s exactly the part of science that is least documented and least automated.

There are glimpses of change. FutureHouse is working on AI hypothesis generation and execution, Cultivarium is experimenting with smart glasses for documenting lab work, companies like Ginkgo and Medra are pushing automation for lab equipment. And Reinhardt points out that “AI scientists” won’t get far if most of science’s real work is invisible in undocumented benchwork. But all of these still assume the human-centered lab as the template.

So what if we designed labs for machines, not humans? Today’s labs, benches, pipettes, and optical tables are built for human bodies. But in a future where experiments could be autonomous, the lab might look completely different. Instead of humans conducting animal experiments, imagine animal housing systems equipped with microscopes that require minimal human intervention (some efforts have been made in this direction). Or, imagine modular wet lab rigs that reconfigure themselves for new protocols. That way, no human would ever have to set foot into a cell culture room. With this, we might finally experiment with biology at its own speed, rather than at our convenience.

Closing

In Michael Nielsen and Kanjun Qui’s essay “A Vision for Metascience,” they outline the process and culture of how we do science as an exploratory space, a playground for trial and error in building new social structures for research. What we want is not a single “right” structure but a thriving ecosystem, one that can rapidly generate and iterate on many ideas about how science could be organized. They say in their introduction:

“We argue that: (1) metascience is an imaginative design practice, exploring an enormous design space for social processes; (2) that exploration aims to find new social processes which unlock latent potential for discovery; (3) decentralized change must be possible, so outsiders with superior ideas can’t be blocked by established power centers; (4) ideally, change would align with what is best for science and for humanity, not merely what is fashionable, politically popular, or media-friendly; (5) the net result would be a far more structurally diverse set of environments for doing science; and (6) this would enable crucial types of work difficult or impossible within existing environments.”

In this sense, it’s about structural diversity. Distinct organizations for curiosity-driven and platform science could reduce institutional friction and provide each type of endeavor what it really needs. Funding models that decouple individuals from institutions might promote decentralization and lower barriers for early-career scientists. Creating an environment that makes rotations feasible might unlock talent in unexpected ways.

As Nielsen and Qui say, “Monoculture is the enemy of creative work.” The way forward is to multiply the environments in which science can happen, create an iterative feedback loop where we learn from our mistakes, and preserve the “free play of free intellects” that has always driven the most important breakthroughs.

If anyone is interested in chatting with me about these topics, please reach out!