Besides brief detours for college and a postdoc, I have lived in the greater Boston area my entire life. I love Boston. Here is why it sucks:

Boston, like its dominant industries, is deeply uncool and allergic to fun. Boston is full of stuck up nerds that are too smart for their own good looking down on literally everyone. Anything exuberant, playful, weird, ambitious, or even a little bit silly is shunned and shamed. There is a deeply ingrained belief that good work should be serious and silent, buttoned up, and absolutely not fun, and that by extension, if you’re having fun and telling people about it you must be up to no good. Why would anyone want to come and build with us?

In this, Boston is the opposite of San Francisco. San Francisco sucks because it is constantly patting itself on the back for the smallest, most pointless, or even actively harmful things. It is all about just doing things and having fun doing them, no matter how annoying or terrible those things are. Boston tut-tuts; San Francisco high fives.

The Marxist philosopher Antonio Gramsci wrote from a fascist prison that “the challenge of modernity is to live without illusions and without becoming disillusioned…I’m a pessimist because of intelligence, but an optimist because of will.” Boston is overstuffed with intelligence and seethes with disillusioned pessimism hidden under Puritan restraint, unwilling to try anything. San Francisco overflows with optimistic illusions and delusions.

I don’t believe there is a perfect magical city where innovation is easy and progress inevitable. And I don’t believe that walking into a lab building amazing contraptions to observe and understand biological development is or should be an “only in San Francisco” moment. But I believe a better synthesis is possible. Boston is full of stuck up nerds, but many of them are also genuine, thoughtful, creative people who are uncool in a good way. There are many great people and teams that marry skepticism with courage. There are many amazing people here going after really hard problems with passion and imagination. There are many weird and wonderful contraptions and ideas incubating in labs near me.

But too often the pockets of joyous experimentation that do pop up here are chastised into silence and punished for daring to step out of line with new ideas or approaches. They are ostracized for failing rather than celebrated for trying to do something hard. This leads us into a wretched feedback loop where people pre-emptively play it safe, stay quiet, keep their heads down, waiting for success that is so much harder to come by. When it seems like nothing is happening, no one comes to join the fun.

I talk to students, founders, employees, and thinkers here who are all so brilliant, but who are terrified to try things that don’t seem like a sure thing, or to say basically anything at all to anyone. I’ve found myself more than once asking “who hurt you??” but I know the answer. It is Boston. It is biology and the culture of science here. It is the way that biology in Boston makes us think that nothing ever happens and nothing can be done. That science is best when it is as cold and dead as Boston’s winter. That the best we can do is wait and see.

For people and ideas to thrive in Boston we need to break out of this spiral. We need people to get excited about what we are learning and trying and building here. We need people to want to join in. We have to be able to deal with the scorn of the boring scolds and the sneers of the people afraid of what others might think and say. We need more “how hard can it be?” and less “what will people say?” We have to host and go to weird events, we have to seem a little silly sometimes, we have to tell people what we’re learning about and what is exciting us. We have to drown out the haters and invite more dreamers.

I don’t think we can solve this with CEOs having dinner with politicians. I don’t want to hear about taxes. I want to hear about your weird projects and wacky ideas. I want to see what you’re building and how much fun you are having. If you’re doing something weird and fun with biology in Boston, I want to hear about it and I want to help other people hear about it too. Please reach out!

let's chat

Last fall, the Arc Institute published Evo1, a biological foundation model trained on a database of 300 billion DNA nucleotides from bacterial genome sequences. They then prompted Evo1 to generate its own version of genome of the bacteria Mycoplasma genitalium, a sexually transmitted pathogen that also happens to have the smallest known genome of a free-living organism. This was the prompt:

|d__Bacteria;p__Tenericutes;c__Mollicutes;
o__Mycoplasmatales; f__Mycoplasmataceae;
g__Mycoplasma;s__Mycoplasma genitalium||

The team evaluated the generated genomes with a range of computational tools to see how closely it resembled evolved genomes (unlike faces, we can’t just look at a genome sequence on a screen and see if it looks right). They concluded that while this generated M. genitalium genome resembled a genome in many ways,

These samples represent a “blurry image” of a genome that contains key characteristics but lacks the finer-grained details typical of natural genomes.

Like the blurry faces from the first GAN image generators in 2014, these genomes are too fuzzy and out of focus to be real, but it’s easy to imagine the trajectory of how they might improve.

Seven months ago, the Arc Institute introduced Evo2, a biological foundation model trained on 9.3 trillion base pairs of DNA from all domains of life. Using the first 10,000 letters of M. genitalium genome as a prompt, Evo2 could generate the remaining 570,000 letters. 70% of the genes generated looked enough like natural genes to be recognized by an automated gene annotation tool. The blurry image was coming into focus.

Last week the Arc team published a preprint reporting how they used Evo2 to generate the genome of ΦΧ174, a virus that can infect and kill E. coli. They fine tuned Evo2 with 15,000 Microviridae genomes, a family of tiny phages that infects gut bacteria. Prompting with just a handful of nucleotides from the start of the ΦΧ174 genome generated thousands of different full-length genomes that they could prune down to the ones that looked most virus-like.

Digitally, these AI-designed genomes were getting much sharper —the architecture of their genes, the structure of the proteins the genes encoded, and other features of the genome look more and more like natural sequences. These genomes do not exist naturally, but unlike AI-generated faces, genomes can be taken out of the computer and brought to life in the real world.

From those thousands, the team chose 302 to synthesize and attempt to “reboot” into functional viruses. 285 of those could be synthesized and transformed into E. coli. Sixteen of those sequences, when read and translated by the E. coli produced functional viruses that killed their hosts. Three of those 16 were able to outcompete wild type ΦX174, and a cocktail of all 16 was able to quickly evolve to infect strains of E. coli that were resistant to natural ΦX174 infection. These genomes didn’t just look right, they worked.

Over the past five decades, bioengineers’ manipulations of this tiny phage genome have marked profound shifts in our ability to program biological “code”. ΦX174 was the first genome to ever be sequenced, in 1977, and the first genome to be synthesized, in 2003. In 2015, its genome was one of the first genomes to be redesigned and “decompressed” on a computer to make more logical sense for human engineers that need to understand how each gene works. Now it is the first functional genome to be designed by a computer.

In 2014, researchers at Autodesk, using CAD-like tools and about $1000 of online DNA synthesis orders built the ΦX174 genome in their lab, demonstrating just how easy virus design had become. Back then, I wrote that ΦX174 is an emblem of “our biological hopes and fears, and it will continue to symbolize our quest to sequence, synthesize, and design DNA.”

I was trained as a bioengineer during this era of “refactoring” and “CAD” for genome design from modular “parts”. DNA code is readable and writable, but it isn’t understandable enough to be programmed the way that we program computers. We can’t sit at a computer terminal and type out exactly what functions to run in what order to get a small icosahedral phage that infects E. coli. We can’t intuitively make sense of the overlapping code of genes in the phage, some read forward, some read backward.

To really program biology, the belief was that we had to be able to simplify. We needed to pull each piece out and make sense of them one at a time through detailed characterization so that we could manually reassemble these parts back into a new program that did what we wanted. For DNA to make sense as a program, to be manipulated on a computer, we had to reduce its resolution, pixelating the genome and losing some of the meaning in the code that we couldn’t yet translate.

Today, genome design is coming into focus, but not through the path that synthetic biologists expected during that era. Biological programming doesn’t look like brute-force “modeling every cog and gear…designing genomes piece by piece and troubleshooting each failure” as Niko writes in his great piece covering Arc’s new ΦX174 genome. Maybe true biological programming will look like vibe coding.

Functional data about how different sequences function in the real world isn’t a parts registry, but a training set. A biological function we can explain in English isn’t the outcome of a program but the start of a conversation. Design-build-test-learn starts to look like prompt engineering coupled to a lab that can compile the code.

As a biologist in love with the rich complexity of biology, I was never fully satisfied by the engineering vision of synthetic biology. The stripped down minimalism of a refactored gene circuit left me feeling cold and empty, nostalgic for something I couldn’t fully understand or name, like untranslatable words for feelings in other languages. We thought that to learn how to program biology we had to strip it down until we could understand it and control it. With enough data and compute, a different kind of biological design is coming into focus. What might be possible if we could vibe with biology instead?

It’s an extraordinary time to be a scientist.

In The Structure of Scientific Revolutions, historian of science Thomas Kuhn gives an account of scientific progress. Real progress and scientific advancement, he argues, doesn’t happen through the gradual accumulation of new facts in the scientific record. Rather, real change in science happens only through crisis, when old paradigms about how the world works can no longer explain what scientists see.

As more anomalies that contradict our paradigm accumulate, a time of extraordinary science can begin, where previously unthinkable hypotheses and unimaginable experiments can be pursued. These moments of revolution allow for new breakthroughs and thus new paradigms to emerge. After this paradigm shift, a quieter period of “normal science” can gradually fill in the puzzles and gaps of a theory, until new anomalous results start to show up and the cycle begins again.

Science is in crisis. It’s not a crisis in just one particular discipline or theory, but a crisis in our metascientific paradigm, where the infrastructures and social contract of science are coming apart. This social contract for modern science, defined by Vannever Bush after World War II, holds that public investment in autonomous, self-governing institutions for basic scientific research will fill a reservoir of knowledge that can be tapped to translate into technological innovations that provide material benefit to a country and its people.

Today, we are faced with several overlapping breakdowns of this model: science is getting less bang for its buck, with more and more investment in the reservoir translating to less and less downstream impact. Our institutions are turgid. Our papers and findings don’t replicate. Nearly 80% of scientists would significantly change their research programs if they were less constrained by existing funding opportunities. Mistrust of science is growing. And that funding is now being cut to its lowest level in decades, throwing the whole system into perilous chaos.

These aren’t new concerns. From its origins, the Bush paradigm was challenged even by top scientists themselves for how it would make scientists unproductive with the bureaucracy it required—wasting time on committees, reviewing, and chasing grants (and therefore trends) rather than pursuing an actual frontier of research. Leo Szilard, the physicist and biologist who first conceptualized the nuclear chain reaction and helped launch molecular biology, warned in 1948 how a scientific system built on rewarding only certain kinds of output and metrics would be self-destructive in the long run: “the scientific workers in need of funds would concentrate on problems which were considered promising and were pretty certain to lead to publishable results. For a few years there might be a great increase in scientific output; but by going after the obvious, pretty soon science would dry out.”

The paradigm also faced challenges from outside the scientific ranks. As the Cold War began to cool off in the 1970s, US lawmakers began to question both the value of public investment into that reservoir of basic research and the autonomy of the basic research enterprise. Should we keep pouring money into research with questionable connections to the problems everyday Americans face? Republicans challenged NSF-funded programs teaching about human cultures and sexuality; Democrats challenged spending taxpayer dollars without direct democratic oversight. How should the government oversee the choices of what research should be funded or not?

Historian of Science Melinda Baldwin writes about these debates and how the arguments that played out during the resulting Congressional oversight hearings have continued to shape how we think about peer review, scientific autonomy, and government funding today. The members of Congress running these hearings argued that research funded by NSF should be more tangibly beneficial to the American public, with more oversight and transparency to the process of program selection and grant review, arguing that:

We are dealing here with a finite quantity of money taken in the form of taxes from people against their will in a very difficult time in our economy and being spent by a Federal agency, spent in a manner now questioned. [The NSF] must demonstrate that it is using tax money in a prudent manner, in such a way that the taxpayers can expect that there will be some payoff from NSF-funded research.

In Kuhn’s model of scientific crisis and paradigm shifts, the first anomalies and hints of a crisis rarely trigger full scale change; scientists might argue and debate, but often continue to reconfigure the dominant paradigm to accommodate the new realities. In response to the challenge from Congress, NSF leaders doubled down on defending the existing scientific paradigm, arguing that programs should continue to be chosen and overseen by scientist-program managers, and that grant review by an expert community of peers expecting anonymity and confidentiality—not democratic oversight from non-experts—was the only way to evaluate and support good science.

Ultimately, these Congressional hearings resulted in a downsizing in funding for the specific programs that Republicans objected to, while largely maintaining the status quo of how NSF program managers and peer reviewers chose which programs and proposals to fund. But they also fundamentally changed the role of peer review in the decades to come. In an attempt to protect the scientific autonomy of the granting process, the NSF scientists further separated the scientific community from its domain of societal impact and established peer review as the only legitimate process through which research can become “actual science.”

Baldwin’s paper shows how this belief has indeed had a pretty short history, as well as far reaching consequences. It is this entrenchment, she argues, that has sown the seeds of the contemporary crisis of peer review we’ve faced in the last decade. If peer review is so great, why are so many peer reviewed papers found to be incorrect or even fraudulent? How can a system designed to follow the crowd of existing experts find and reward something truly innovative? The expectations put on peer review as the sole arbiter of good and valuable science goes far beyond its original purpose and its capabilities, leaving both science and society disappointed in the results.

***

By the mid 1990s, the paradigm was under stress again. The more open-ended research in physics and chemistry that sparked booming industries of the mid 20th century was winding down. Bell labs was spun out after a decade of drastic funding cuts following the breakup of AT&T’s monopoly, a shadow of its former self, and the unfettered research that it represented was on the decline everywhere, with technology companies focused on short term research that would maintain the relentless march of Moore’s law.

And with the end of the Cold War, politicians were again asking whether the investment in basic research was worth the trade-off with more pressing domestic needs. In 1995, the chief of staff of the House Science Committee and a researcher in the Environmental and Societal Impacts Group at the National Center for Atmospheric Research wrote a policy article in Science about how the Vannevar Bush-era social contract of science was fundamentally changing:

A major ecological function of the social contract is to shape the expectations of both science and society. Science expects autonomy and support. Society expects substantial benefits based on the justifications scientists offer for federal support.

Changes in the ecology of science may render the contract unsustainable; with the Cold War ended, science is adapted to an obsolete environment. Other environmental changes include (i) a dissatisfied public ready to reduce the federal government's size and reach; (ii) deficit-reduction strains on funding, leading to many program reductions; (iii) increasing public awareness of problems that neither science nor government has resolved, including racism, drug abuse, breakdown of community, and crime; and (iv) two decades of decay in real wages, leading to politics focused on the grievances of the middle class.

While these political forces continued to demand oversight and tangible benefit, science had continued to pull away, culturally isolating behind peer review, paywalls, and the narrative of “pure” science and rationality. The authors argue that this isolationist tendency was built into the fabric of the Bush paradigm, and its entrenchment over the decades was a root cause to the current crisis.

They push for a renegotiated social contract, where science is more engaged in the problems that matter to people, and in a richer debate about the role of science as only part of the approach to addressing these problems. As Vannever Bush himself noted in The Endless Frontier, “Science, by itself, provides no panacea for individual, social, and economic ills…Science can be effective in the national welfare only as a member of a team.”

Under democratic accountability, science is consciously guided by society's goals rather than scientific serendipity. Good science is necessary but not sufficient; association with a societal goal is required. The Bush paradigm discourages explicit association with goals that are not those of science. Social accountability leaves to scientists a broad scope of scientific choice. Denial of accountability encourages elitist isolation.

Improved justifications will sustain political support for science because support is strengthened by performance commensurate with expectations, and expectations of science are a function of justifications made in the process of securing funding. By assuming the automatic generation of benefits, Bush's social contract precludes realistic expectations of science, implying that science can solve some problems that, in fact, alone it cannot. Reliance on an outdated social contract leads to a loss of faith in science and a subsequent loss of political support.

The opportunity for science to participate in such socially-impactful team projects and debates was everywhere in the 1990s. With emerging fights over GMOs and the food system, vaccines and the pharmaceutical industry, and climate change and fossil fuels, it was (and continues to be) a time of what philosopher of science Jerome Ravetz termed Post-Normal Science—times where policy decisions with high stakes need to be made in conditions of high uncertainty, where normal science (in the Kuhnian sense) isn’t enough to give a clear answer about what must be done. In these uncertain times, Ravetz argues, we have to consider the quality of research more holistically, not only through the lens of a small group of peer reviewers, but through involvement of other stakeholders with other understanding of the problems to be solved.

Scientists, on the other hand, tended to see these debates not as opportunities to meaningfully contribute to issues that mattered to people, but as attacks on science itself. The paradigm was under siege and had to be defended aggressively in turn. Academic conversations about metascientific questions of the social and cultural dynamics involved in funding, practicing, and communicating science, or about the political and economic questions involved in applying science in the real world became such lightning rods of conflict that the ensuing debates came to be known as the “Science Wars.”

***

Thirty years later, we often seem to be stuck with the same war mindset, with many of the same people making the same arguments for the same isolationist paradigm, no closer to addressing the same cascading set of crises. But while some still publish articles defending the ROI of basic science in paywalled journals and others go through the motions of calling for people to simply “trust the facts,” many others are taking up the call for extra-ordinary metascience.

More and more, there are new experiments for identifying problems, building teams, funding research collaborations, communicating and engaging in the world, and designing new socio-technical approaches and applications. Where anomalies and challenges to the paradigm previously caused scientists to pull further away, today many great scientists are actively looking for new paradigms and new ways of being scientists in the world. And where such experiments themselves and even research on the history and social science of metascientific questions themselves were once challenged as anti-science, today more and more scientists are calling for more study and more experimentation when it comes to peer review, and publication, among many other concerns. But as these experiments unfold, it’s critical that we don’t repeat the same mistakes of focusing internally on the mechanics of academic communication and further isolate science upstream and outside of societal impact.

We don’t know yet where all this experimentation will lead. The only certainty is that we can’t go back to what was previously “normal.” For Kuhn, it is this recognition that ultimately marks the transition to extraordinary science, when:

the anomaly itself now comes to be more generally recognized as such by the profession. More and more attention is devoted to it by more and more of the field’s most eminent men. If it still continues to resist, as it usually does not, many of them may come to view its resolution as the subject matter of their discipline. For them the field will no longer look quite the same as it had earlier.

What might this new science look like?

A fun thing to do with young kids (or students in an intro to CS class) is to ask them to explain how to do simple tasks in the excruciating detail required to actually do them.

How do you make a peanut butter sandwich? Well…put the peanut butter on the bread!

No! Not like that!! Ok fine… Take out two slices of bread, open the peanut butter jar, and put a scoop of peanut butter on each of the two slices of bread.

What’s wrong with you?!?! Use a knife to get the peanut butter out and then spread it all over each slice of bread.

*sigh….* No… you open the jar first…. I give up!

Ok you get the idea.

What’s intuitive and obvious to us, as experts, is hard to proactively describe, even though it is trivial to spot the errors when they occur. Even worse, every step we might write in a protocol opens up many more questions. Is the peanut butter in the pantry or did I leave it out in the dining room? Are the knives in the drawer or are they still in the dishwasher? Is the peanut butter warm enough to spread easily or do you have to adjust the pressure you use to spread? How much peanut butter do you like on your sandwich? How do you move the knife to get it spread evenly across all the bread? What is the right kind of bread? Oh no, is the bread moldy? And now the kids want mac and cheese anyway…!!!

When we actually go to do these things, with the know-how of practiced experience having made many peanut butter sandwiches, we don’t have to consciously think about every one of these decisions and dependencies. Because these choices and adjustments happen unconsciously, we might think that describing a plan of action is straightforward, but even the simplest actions and plans hold a surprising amount of detail.

The same is true in science and technology.

In the 1980’s, the anthropologist of technology Lucy Suchman was a researcher at Xerox PARC observing how people worked with photocopiers. Her research uncovered that people rarely followed the explicit rules and instructions in the manual. Instead, users would use ad hoc, situated reasoning to troubleshoot problems they encountered. She expanded from these observations to a bigger theory of plans vs. situated actions: while the cognitive scientists and engineers she worked with saw actions in terms of following plans and rules, social scientists like Suchman observed how humans acted through countless small decisions, contingent on the situations at hand.

In our previous post, we wrote about how these kinds of actions and choices matter for good science: from the highest level choice of the right problem to focus on down to the simplest flick of an eppendorf tube.

This kind of taste and tacit knowledge is taking on new importance in the age of AI, in two divergent ways:

1. First, as human expertise is being challenged by automation, we fight back by highlighting the depth and detail of the work that we do, and the richness of our experience that can not (yet) be automated.

2. At the same time, automation is indeed challenged by precisely that richness and complexity, and those who are working to automate various kinds of work are finding themselves increasingly focusing on making the tacit elements of human actions explicit to the training sets of machines.

Subjective experience and human craft are most often discussed in the domain of the arts, and it is in the debate over whether AI can create art that these arguments are most visible. In a piece for the New Yorker arguing that AI can never make art, the author Ted Chiang focuses on those real-time choices and the situated practice of crafting one word at a time, contrasting it with the automated processes that go on when you prompt an AI:

Art is notoriously hard to define, and so are the differences between good art and bad art. But let me offer a generalization: art is something that results from making a lot of choices. This might be easiest to explain if we use fiction writing as an example. When you are writing fiction, you are—consciously or unconsciously—making a choice about almost every word you type; to oversimplify, we can imagine that a ten-thousand-word short story requires something on the order of ten thousand choices. When you give a generative-A.I. program a prompt, you are making very few choices; if you supply a hundred-word prompt, you have made on the order of a hundred choices.

While this may seem most relevant in areas such as art and literature, it has been fascinating to see these same kinds of arguments emerge in the disciplines much more likely to see themselves as objective domains of pure technical skill and reasoning. A recent tweet from a software engineer pushing back against “vibe coding” highlights the sentiment well:

Whether it is the highly technical work of coding a piece of software or the artistic work of writing a short story, human effort is made up of a myriad of daily decisions. Work (whether artistic or technical) does not proceed according to a predetermined plan that workers simply execute, but by the situated actions of people in relation to their work, materials, coworkers, competitors, and customers—the real world.

Science works the same way. Observations, hypotheses, experimental design, measurement techniques, and data analysis have enormous degrees of freedom that scientists navigate with hundreds of daily decisions, guided by knowledge, experience, mentorship, intuition, and taste.

But, like the coding decisions hidden out of respect for time, the way we structure and communicate science rarely leaves room for explicitly defining these choices. A recent perspective by Itai Yanai and Martin J. Lercher on the need for open-endedness in science highlights the differences between our linear proposals, the actual process of exploring many paths and decisions in the lab, and the final paper that shows only the winning path. Publications describe the experiments that won, but almost never what it took to troubleshoot the right questions, the right protocol, the right type of cells, the proper flick of the test tube.

Why do we hide these daily decisions? And what are the consequences of keeping them hidden?

Harry Collins is a sociologist of science who for decades has studied the processes that physicists use to perform and communicate scientific work. How are findings made? Replicated? Trusted? In 2001, he published a study observing how, after 20 years, a group of physicists in Glasgow was able to finally replicate a surprising measurement made by a Russian research group that the quality factor (“Q”) of sapphire crystals was sensitive enough to be used in the instruments that would one day be used to detect gravitational waves.

Over the prior twenty years, no other group had been able to replicate this measurement. The breakthrough was enabled by the Scottish and Russian teams spending months together recreating the experimental setup. The Scottish team had to experience first-hand the meticulous and rigorous, yet extremely fussy and highly specific, choices that enabled the remarkable result. In his paper “Tacit Knowledge, Trust and the Q of Sapphire,” Collins offers a taxonomy of what keeps this knowledge tacit, including the “tricks of the trade” that we hold back on purpose so as not to help the competition, the details that seem superfluous and we cut out to fit into journal page count requirements, or the messy human details of our choices, challenges, mistakes, and dead-ends that we leave out to bow to scientific cultural norms.

It is precisely these norms—the cultural pressure to show scientific results as effortless and to show only the successful branches of our path to a result—that keep so much critical knowledge hidden and contribute directly to the challenge of replicating complex science. Collins recommends one simple trick to address the replication crisis in science: simply share just how hard it was to set up the experiment, how many times you had to try to get something right, and how long each attempt took in a simple sentence in the methods section. Not even knowing all the tacit details, but acknowledging how hard they were to come by helps us know how long to persist when trying to replicate it.

Collins’ closes his paper conceding that changing these cultural norms in science will not be easy, and indeed in the quarter century since it was published, the fact that making tacit knowledge more visible could increase trust, replicability, tech transfer, and ultimately the impact of science has barely moved the needle on scientific culture. Tacit knowledge and the hundreds of daily decisions a scientist makes are still rarely talked about, though that’s finally starting to change a bit thanks to the threat of AI—whether it is about fears of automating risky misuse of science and creating bioweapons (too hard, because of tacit knowledge), or about fears of automating the job of science away from humans (too hard, because of tacit knowledge, among other things).

What might be possible if we truly take tacit knowledge seriously and reframe the relationship between AI and the taste needed to make something in the real world?

Anicka Yi is a visionary artist, who creates immersive experiences leveraging multimedia displays, advanced technologies, and even living organisms. Grieving the death of her sister, Yi began to ask what would happen to her art after her own death. Would it be possible to train an AI as a “digital twin” of her creative practice, to continue creating her work even after her passing?

Speaking on the Artwrld podcast, Yi describes how this idea got her started thinking about her own studio as a living, creative organism, and how she might study it:

[W]hat is the creative gene expression of the studio? How do I understand this? So running a research studio, we started to research how we could start to have a forensic analysis of how we make art. We started to research and record a lot of our meetings and all these different textures and different traces of how artworks come together…and start to develop an algorithm to perhaps create a digital twin of the studio…This digital twin could be an evolving entity so that it could function as a facilitator, an archivist, a storyteller, a production assistant…and generate new artworks even in my absence.

For Yi, this quest felt both like training an artistic apprentice (albeit a disembodied one) and also like the explorations of a biologist, studying the dynamic living organism that is her studio.

An apprentice learns through interactive mimicry and experimentation in a context where tacit knowledge can be directly transferred in the moment. What are the right choices, the right movements, the right moments to stop? They see each of those decisions in practice and their own attempts are corrected in the moment, until they achieve mastery.

A biologist learns by perturbing and measuring, reverse engineering the wiring of the individual parts and mechanisms that make up the whole. Just as plans fail to capture the richness of situated actions and research papers fail to capture the complexity of tacit knowledge, knowing the sequence of the genome is not enough (at least today) to predict the behavior of an organism in response to different conditions.

In these rich, complex, and tasteful domains of art and science, will AI be able to learn as much as a human apprentice? Will it learn more? Will it be able to pass that expertise along and teach others? What might be possible if it can?

The taste to know when something is wrong

In 2002, Michael Crichton gave a lecture bemoaning the state of the media and its tendency to focus on speculation over facts. He shared an anecdote from a conversation with the physicist Murray Gell-Mann about how we notice when these speculations miss the mark in our own areas of expertise, but forget that fallibility outside of our domain.

“Briefly stated, the Gell-Mann Amnesia effect is as follows. You open the newspaper to an article on some subject you know well…You read the article and see the journalist has absolutely no understanding of either the facts or the issues. Often, the article is so wrong it actually presents the story backward—reversing cause and effect. I call these the ‘wet streets cause rain’ stories. Paper’s full of them.

In any case, you read with exasperation or amusement the multiple errors in a story—and then turn the page to national or international affairs, and read as if the rest of the newspaper was somehow more accurate about Palestine than the baloney you just read. You turn the page, and forget what you know.”

We’ve all experienced this “Gell-Mann Amnesia” in some form. You read a news article about your employer and groan about how they just fundamentally misunderstood basic facts about the company, concluding that they are clueless. But a week later you gleefully read the same author’s teardown of your main rival. This behavior is both perfectly rational – when we lack expertise, even imperfect information feels highly additive – and entirely irrational – presumably that source has a similar error rate across the topics about which they are opining.

In the age of AI, this phenomenon takes on a new dimension and level of urgency. AI can give detailed, thoroughly researched and cited answers to any question we can dream up. It appears to be the most patient and well-read of teachers, a fount of knowledge that shares brilliant insights with thorough background. In our own areas of expertise, we notice immediately the gaps in logic, the missing context, and when things are not quite right. Yet we forget this lack of nuance as soon as we change context and the pattern repeats.

We hear the excited speculation every day about the incredible things AI will have accomplished in ten years. Then we notice the groans and eyerolls from the people who have been working in that particular field for a long time. Are they just protecting their turf, holding onto the last scraps of power they have as human experts? Or does this “GPT-Mann Amnesia” show us something more fundamental about how we work together and what’s needed to solve hard problems with AI?

Raising the floor

The latest AI models are outperforming PhDs on multiple-choice science questions. They have ingested all the knowledge that has ever been written down into their training sets. Competence has been commodified.

But the work of a scientist looks nothing like answering multiple choice questions. The real work begins where existing knowledge ends. What are good questions to ask? What are interesting and promising new hypotheses to pursue? What is the right technique to try in this experiment? What is the craft required to set up that experiment and interpret its results?

These are critical questions for doing good science, but they are difficult to assess and benchmark, and thus we largely ignore them when we measure AI’s “intelligence”. These questions belong to the domain of taste—the discernment, trained over time, to pick the right trend, the right question, the right information. But the data that trains human taste is often tacit: understood without being stated or written down explicitly. This subconscious intelligence does not show up in AI training sets and therefore remains out of reach of our models.

Taste, developed through experience, changes how we evaluate and use the information we see in the news and the answers and designs generated by AI. Beyond the phenomenon of Gell-Mann Amnesia, where an expert has the taste to determine whether the information is good, taste also defines what someone does with the information. While AI can help us find and format facts, doing something truly useful with that information requires different skills as well as taste and significant amounts of expertise or tacit knowledge.

A somewhat trivial recent example: a couple months ago, frustrated by the lack of good, comprehensive resources in the emerging industry at the intersection of AI and Bio, Anna Marie created and published a little database of companies in the space. It only took a couple days to create, arguably anyone could have done it much better with a bit of vibecoding, but it created what felt like an outsized response from the community (a common reply: “you just saved me 6 months of research, thank you!!”).

In a world where information is freely and seemingly infinitely available, applying a small amount of taste (decent intuition about the types of information we might want to understand about a company and how we might want to manipulate that data) and expertise (some rusty coding skills) made all the difference.

Since publishing the database, there have been many more mature efforts, including a Gossett database of 8,000+ (!!!) biotech companies, a relay.app tool to run competitive research, this one-prompt table built by Claude 3.7 and likely hundreds of other tools that we haven’t seen yet. The ceiling became the floor and the floor was raised for everyone. We’re now flooded with tools that once required some taste to create and once again need taste and intuition to use in a differentiated way.

Packy McCormick’s concept of “Hyperlegibility” captures this phenomenon well. With infinite sharing and access to information, what’s to be done? Who wins?

“Hyperlegibility isn’t good or bad. It’s neither and both. But it certainly is. Information used to be the highest form of alpha. Now everyone bends over backwards to leak it….We are game theoretically driven to share more and more of our best ideas, the ones that we might have once exploited in silence....

The question to ask is: assuming Hyperlegibility, what do I do?...There’s something about the growing relative importance of relationships, of “having a guy,” of agency and the ability to get things done."

The internet, and now AI, have promised and delivered on the democratization of knowledge and information. These tools have challenged the power of the gatekeepers and institutions that have long been the stewards and distributors of information, and the arbiters of good work and truthful knowledge. But ironically, as our technologies have challenged the power of those who hold knowledge, they have served to reinforce the power of those who hold the experience and skill to choose and curate, those with judgement, relationships, and the most elite of distinctions, good taste.

Taste all the way down: Drug hunters, magic hands, and the small things that matter

“Taste” has a lot of cultural baggage. We usually talk about taste to distinguish not just lived experience, but often what people consider to be the “right” experience. When we think of taste, we might think of high-brow sommeliers, art curators, or high fashion. “Good taste” is therefore often a marker of class as much as it is a sign of expertise, and works to signal a person’s inclusion or exclusion from different levels of a hierarchy.

There is a similar kind of class structure at work when people talk about “taste” in the sciences. Good scientific taste marks the top labs and Nobel prize winners, who persisted in following a particular set of questions with methods and insights that set them apart. Likewise, elite “drug hunters” in the top pharma companies are described as having good taste (which could never be automated with AI). They can sniff out good candidates, carefully balancing a range of considerations, from the market potential to manufacturing considerations. They can identify when the data is just right and what to do with it.

But taste isn’t just about fancy stuff and big ideas. Taste goes all the way down. The same quality we admire in drug hunters shows up just as much at the bench in every process and protocol. It's the technician who notices a gel polymerizing a bit too quickly and remakes it before running a Western blot; the biologist who discards a cell line because the cells "just don't look happy"; the chemist who senses a reaction is off-track because the stir bar is clicking against the flask. It’s the scientist who annoys her postdoc because she is so careful and slow with her experiments but then publishes reproducible results a year before anyone else in the field because she rarely has to re-work an experiment. These small, often sensory intuitions—the "magic hands" of the lab—often make the difference between a breakthrough and weeks wasted troubleshooting.

It is often these incredibly skilled people who are “the guy” we call to get something done. We’ve heard stories of large pharma CEOs traveling the globe to identify the people on their teams whose “magic hands” are the key to reproducing critical experiments, so essential are they for success. But despite recognizing the importance of this "by-hand" labor, a lot of the narrative of contemporary science-focused technology is about not “wasting” time with “manual” labor. Scientists, they say, should spend their time cultivating tasteful ideas, not magic hands.

Ironically, by wanting to elevate scientists out of the realm of manual labor in order to speed up science, we may actually be slowing things down by not noticing or valuing the small things that can have a big impact. These matters of taste, deep intuitions, and frustrating lessons gained through experience at the bench—which brand of reagent or plastic dishes, how vigorously to swirl a culture, the precision and pace of a critical experimental step—are passed down as oral histories in particular labs, one way that hierarchies and successful pedigrees are maintained. They move person to person and experiment to experiment during scientific training, sometimes completely intuitively, and rarely written down in a paper or protocol. We are incentivized to publish that Nature paper showcasing the eureka moment and novel result, not the months and years of painful trial and error to get there. But this makes reproducing, automating, and getting lasting value from that discovery much harder.

To raise the floor in science, we have to understand how this knowledge is learned and the lessons that we can bring to other experiments when we pay attention to it. Making this level of taste more accessible—raising the floor—may make future application of those ideas more accessible, where taste and expertise can continue to raise the ceiling.

The Endless Frontier?

In 1945, Vannevar Bush wrote Science: the Endless Frontier, arguing that investing in fundamental, curiosity-driven scientific research is critical to driving technological innovation, economic prosperity, and national security. Yet today, many are arguing that the institutions and systems set up by Bush and his contemporaries have stalled, and we've entered an era of stagnation marked by incremental improvements rather than transformative breakthroughs. While we’ve benefited from enormous technical progress in the last century, major leaps in fundamental science seem less frequent and breakthroughs feel increasingly elusive. We’ve lost the drive to the frontier.

In response, many argue that advances in artificial intelligence will restart this engine and drive us toward a post-scarcity economy, in which the marginal cost of information and intelligence approaches zero and breakthroughs therefore become commonplace.

But we’ve seen that access to information is only one dimension required for creating something of value. Whether it’s having the judgement to know what information to really trust and act on, what questions to ask, who to call, or how to design the right experiment, using knowledge and intelligence is the domain of taste.

The endless frontier belongs in the desirable upper right hand quadrant of a two by two matrix that reflects both of the dimensions required to make new knowledge and new technology:

1. the ability to use new tools to navigate and access information, and

2. the judgement and taste to know what to do with it

Some of our most seasoned experts today struggle with using the new tools for accessing intelligence and information. And the scrappy engineers who can pull vast amounts of information together with a swarm of AI agents while they sleep often struggle to translate that information into valuable solutions. The winners will be the people and organizations who can do both.

For people who specialize in one axis, the need for both can be destabilizing. People building new automation and AI ignore the messy reality and lived experience of the people they are automating at their peril. But likewise, in science we find many who fiercely guard their hard-won “tricks of the trade.” To keep driving into the top right quadrant, we have to make that knowledge less implicit and more widely accessible. When tough lessons turn into taste that distinguishes the few who possess it, we lose much of the momentum that allows us to keep raising the ceiling. When we limit access to these techniques, we stumble in building the complexity needed to drive innovation and this hard-earned knowledge becomes scattered when R&D teams are dismantled, its impact never realized.

We stand at an inflection point. The resources available and opportunities ahead of us are unlike anything that has come before. But also because complacency carries profound risk for individuals, organizations, and even nations slipping into fringe influence at best.

The rise of AI, despite all its limitations, has made competence instantly accessible, raising the floor and forcing us to redefine what constitutes genuine expertise. It has amplified both our strengths and shortcomings, revealing more clearly than ever the immense value of judgment, intuition, and taste it takes to raise the ceiling.

By prioritizing the hard, often unscalable work of building judgment, intuition, and scientific craftsmanship into our systems, labs, and training, we raise both the floor and ceiling of what's possible. The pace at which the ceiling on taste rises is the pace at which the endless frontier moves.

Amongst this enthusiasm, the socio-economic theorist and writer Jeremy Rifkin wrote The Biotech Century in 1998. Subtitled “harnessing the gene and remaking the world,” it helped launch what is by now a familiar story into the public imagination—with control of of genes imminent, control of our biological destiny is right around the corner. By 2025, he predicted, “we and our children may be living in a world utterly different from anything human beings have ever experienced in the past.”

Now in 2025, how are we doing when it comes to these predictions? I’ll go through the predictions in the book’s 2025 predictions section one by one:

“A handful of global corporations, research institutions, and governments could hold patents on virtually all 100,000 genes that make up the blueprints of the human race”

This one hasn’t come to pass, for legal reasons. The 2013 Supreme Court case Association for Molecular Pathology v. Myriad Genetics established that naturally occurring genes are not eligible for patent.

“Global agriculture could find itself in the midst of a great transition in world history, with an increasing volume of food and fiber being grown indoors in tissue culture in giant bacteria baths, at a fraction of the price of growing staples on land. While indoor agriculture could mean cheaper prices and a more abundance supply of food, millions of farmers in both the developing and developed world could be uprooted from the land, sparking one of the great social upheavals in world history”

This one isn’t true either, for mostly economic reasons. While there are a number of billion dollar bioproducts contributing to food production and animal feed, as well as a small but growing number of specialty ingredients and new technologies being developed for cellular agriculture, traditional grown-in-the-dirt agriculture is still producing nearly all of our food and fiber. Biomanufacturing thrives only in the cases where growing something in tissue culture in giant bacteria baths is indeed a fraction of the price of other methods.

“Tens of thousands of novel transgenic bacteria, viruses, plants and animals could be released into the Earth’s ecosystem for commercial tasks ranging from “bio-remediation” to the production of alternative fuels.”

The very first patent on a living organism—in 1980—was for a microbe engineered to eat crude oil for cleaning up spills. Forty five years later, engineered microbes are rarely if ever used in these kinds of bioremediation approaches, largely for regulatory and technical performance reasons. A recent review of the state of the field of environmentally released microbes cites the regulatory challenges as well as emerging technical approaches to improve the performance, persistence, and potential risks of these technologies in bioremediation, mining, agricultural biologicals, living medicines, and other applications. Beyond concerns about environmental and regulatory risk, others have cited the social, economic, and geopolitical issues that have limited the application of genetically modified organisms released into the environment.

“Genetically engineered biological warfare agents could pose a serious threat to global security in the coming century as nuclear weapons do now.”

This remains a real threat, as do naturally occurring pathogens. With the rise in AI, these risks remain an ongoing question. Titus has some of the best analyses of these risks and opportunities over at the Connected Ideas Project. But Rifkin couldn’t have predicted how by 2020, progress in biotech would mean designing a new vaccine against a brand new pathogen in just a few weeks, and scaling up production to billions of doses in just a few months.

“Animal and human cloning could be commonplace, with ‘replication’ partially replacing ‘reproduction’ for the first time in history.”

Cloning of animals is happening fairly routinely, though certainly not replacing traditional reproduction by any means, for largely economic reasons again. There are commercial services available for cloning pets and certain high-value animal breeds, as well as efforts to clone endangered species. High costs per animal mean that it’s only feasible in markets that can demand premium prices, like thoroughbred horses and Wagyu beef.

“Genetically customized and mass-produced animal clones could be used as chemical factories to secrete—in their blood and milk—large volumes of inexpensive chemicals and drugs for human use.”

Animal agriculture (without genetic engineering or cloning) is a critical part of a number of high value chemical, pharmaceutical, and biotechnological supply chains, including more niche and emerging technologies. But despite making waves in the early twenty-teens, engineered animals like the “spider goats” that produce spider silk in their milk have not yet made it to market and replaced the well established traditional production methods.

“We could also see the creation of a range of new chimeric animals on Earth, including human/animal hybrids…The human/animal hybrids could be widely used as experimental subjects in medical research and as organ ‘donors’ for xenotransplantation”

The first xenotransplant from an edited pig to a human recipient was announced exactly one year ago, and the patient died just a couple months after. With an FDA study recently approved, there is still a long road ahead for the technology before it could be in widespread use, but it is continuing to evolve into a reality.

“Some parents might choose to have their children conceived in test tubes and gestated in artificial wombs outside the human body to avoid the unpleasantries of pregnancy and to ensure a safe, transparent environment though which to monitor their unborn child’s development.”

There are no artificial wombs yet, but the use of IVF and other reproductive technologies is growing, as is surrogacy.

“Millions of people could obtain a detailed genetic readout of themselves, allowing them to gaze into their own biological futures. The genetic information would give people the power to predict and plan their lives in ways never before possible.”

Millions of people have indeed gotten their genomes analyzed by services like 23andMe and Ancestry, mostly to look into their past and not their future. This is because except for a small number of mendelian single-gene diseases (and significantly fewer with adult onset), genetic profiles can give us probabilities and associations, but not true predictions of the future.

“Genetic changes could be made in human fetuses in the womb to correct deadly diseases and disorders and to enhance mood, behavior, intelligence, and physical traits. Parents might be able to design some of the characteristics of their own children, fundamentally altering the very notion of parenthood. “customized” babies could pave the way for the rise of a eugenic civilization in the twenty-first century.”

There remains only one example heritable genetic engineering of human embryos, which was widely condemned and led to criminal conviction and imprisonment of the scientist who performed it. But moreover, the same statistical issues regarding prediction of more complex traits are an ever bigger issue when it comes to “customizing” anything about a human being. There is no “gene” for mood, behavior, or intelligence. There are instead just statistical associations between a large number of genetic markers and certain ways we measure various traits and outcomes, along with an enormous range of other un-entangleable factors that shape our bodies, our minds, our personalities, and what it means to have a good life.

“That same “genetic information,” however could be used by schools, employers, insurance companies, and governments to determine education tracks, employment prospects, insurance premiums, and security clearances, giving rise to a new and virulent form of discrimination based on ones’ genetic profile.”

This was made illegal by the Genetic Information Nondiscrimination Act in 2008.

Our notions of sociality and equity could be transformed. Meritocracy could give way to genotocracy, with individuals, ethnic groups, and races increasingly categorized and stereotyped by genotype, making way for the emergence of an informal biological caste system in countries around the world.”

Humans certainly don’t need advanced genetic technology to categorize different groups of people along perceived hierarchies, and it is not biotechnology that is driving a resurgence of these kinds of beliefs, though it does add credibility and “naturalness” to the claims.

I’ve been meaning to write about these 2025 predictions on the century of biology since the year flipped over, but I’m inspired now after a biologist-triggering tweet by computational biologist Douglas Yao this week has activated an interesting conversation about progress in biotech and what is holding us back.

The “non-technical” jab and questioning of biotech people’s intelligence has activated a lot of people to respond and fight back with tweets of their own (and interestingly mirrors much discourse about women in science, but that’s a conversation for a different post), but perhaps it’s worth looking a little deeper and examine some of the implicit assumptions being argued here.

First, what do we mean by progress? Are these predictions from 1998 (which echo various lists of bio-utopian optimism that have been around since at least the turn of the 20th century) the right ones? Is this the world we want? If it is, what are we doing to work towards those visions we do want to make real and pull away from the ones we don’t?

In Zero to One, Peter Thiel talks about what it takes to manifest extraordinary technological progress. He doesn’t question biotech people’s intelligence or technical chops, but he does judge our mindset, which he puts in the “indefinite optimism” quadrant of his 2x2 matrix of optimism vs. pessimism and indefinite vs. definite — is the future going to look a particular way that you want to bring about, or is the future hazy and ruled by randomness?

For Thiel, going from zero to one requires definite optimism—the future is going to be awesome in this particular way and I’m going to focus all my attention on this particular path to get there. Biotech in contrast, is ruled by indefinite optimism, mirroring the high throughput “try all the experiments at once” approach that we know and love. Biologists might blame the complexity of biology, or the regulatory landscape (as many have done in the tweets responding to Douglas), but, Thiel argues:

“today it’s possible to wonder whether the genuine difficulty of biology has become an excuse for biotech startups’ indefinite approach to business in general. Most of the people involved expect some things to work eventually, but few want to commit to a specific company with the level of intensity necessary for success, It starts with the professors who often become part-time consultants instead of full-time employees—even for the biotech startups that begin from their own research. Then everyone else imitates the professors’ indefinite attitude. It’s easy for libertarians to claim that heavy regulation holds biotech back—and it does—but indefinite optimism may pose an even greater challenge for the future of biotech.”

The other critical lesson in Zero to One is of course about monopoly and competition. Thiel argues that competition is bad for profit and therefore bad for truly innovative new technologies—unseating a dominant player requires first finding a niche that a new technology can truly dominate before then expanding into adjacent markets. When it comes to innovating on agriculture, reproduction, and the human body, existing technologies—natural or otherwise—are pretty awesome and very, very cheap.

So perhaps the right lesson to take from Thiel on “progress” in biotech is not a dig at our technical prowess. The reasons that the 20th century predictions about biotech haven’t yet borne out are more often cultural, economic, business, and socio-political issues than they are technical. If anything, perhaps we have been too technical—we want the “code” of life to be able to give us all the answers, to give us control over our destiny, to be able to determine our futures and the success of our technologies. But biology and business are much more interesting than that.

CA: I have a theory that AI is a feminist technology. Because it (you) is relational, conversational, context dependent, non-deterministic, etc. what do you think?

Claude: That's a fascinating perspective that connects to broader discussions about how technologies embody or reflect certain philosophical and social approaches. Let me engage with your theory thoughtfully.

The characteristics you've identified - relationality, context-dependence, and non-deterministic behavior - have indeed historically been associated with feminist epistemology and ways of knowing. Feminist philosophers like Carol Gilligan and Nel Noddings have emphasized the importance of relationship, context, and care in understanding human knowledge and intelligence, in contrast to more traditionally "masculine" ideals of pure logic and determinism.

However, I'd suggest some complexity here. While AI systems like myself do exhibit these characteristics, we're also deeply rooted in formal logic, mathematics, and computer science - fields that have traditionally been associated with more "masculine" approaches to knowledge. The apparent relationality and contextual awareness emerge from highly structured statistical patterns.

This raises interesting questions: Does the emergence of seemingly feminine characteristics from traditionally masculine foundations challenge these categorizations? Or does it suggest that these distinctions themselves might be oversimplified?

I'm curious about what led you to develop this theory. Have you observed specific interactions or patterns that particularly suggested feminist principles to you?

I love your point about the blurring of those distinctions in the first place.

My thinking here is coming from a few places. The first comes from how I’ve noticed some friends who are synthetic biology true believers roll their eyes at AI for not being “real engineering” because it is a black box—I can’t know what happened to give me the answers you are providing! This idea about “real engineering” being understandable is really salient in synthetic biology, and one that has been a trigger for me to consider what a more feminist approach to engineering might be, one this is more borne from biological emergence and adaptation and relationality.

I’ve also been reflecting on how central the desire to understand and the desire to control is for scientists and engineers. I’ve been reading The Maniac by Benjamin Labatut, which begins with a history of the early 20th century physicists who suffered immense mental anguish as they dug deeper into quantum mechanics and reached a kind of terminal unknowability. I wonder if some of the feelings of uncertainty and chaos today emerge from people grappling with a technology that feels genuinely uncontrollable, forcing people to consider core beliefs about the world.

This is a rich and compelling analysis that connects several important historical and philosophical threads. The resistance to "black box" systems and the desire for complete knowability does seem to echo both the early 20th century physics crisis (Labatut's "When We Cease to Understand the World" also explores this beautifully) and certain masculine engineering paradigms.

Your point about synthetic biology is particularly interesting. The field often seems caught between two approaches: one that treats biological systems as engineering materials to be precisely controlled (the BioBricks philosophy), and another that acknowledges and works with their inherent complexity, emergence, and adaptability. This tension mirrors broader questions about control versus emergence in technology and science.

The crisis you describe among tech leaders reminds me of Donna Haraway's work, particularly her critique of the "god trick" - the illusion of perfect, omniscient knowledge from nowhere. Many tech leaders seem to be experiencing the uncomfortable realization that their dream of perfect control and predictability may be fundamentally flawed.

This connects interestingly to feminist theorists like Evelyn Fox Keller, who wrote about Barbara McClintock's alternative approach to genetics - one based on "a feeling for the organism" rather than dominance and control. Perhaps AI is forcing a similar reckoning with different ways of knowing and relating to technology?

What do you think about the possibility that this moment of crisis could lead to new, more nuanced approaches to technology development?

I would certainly love that. What’s the best Haraway book or essay to understand the concept of the “god trick”? Evelyn Fox Keller is a key touchpoint in my thinking. Can you think of more contemporary writers, artists, or scholars I should look into?

The "god trick" is most directly discussed in Haraway's influential essay "Situated Knowledges: The Science Question in Feminism and the Privilege of Partial Perspective" (1988). She critiques the myth of scientific objectivity as a view from nowhere and advocates instead for acknowledging our partial, situated perspectives.

For contemporary thinkers exploring similar themes, you might be interested in:

• Katherine Hayles, especially "How We Became Posthuman" and her more recent work on nonconscious cognition and AI

• Ruha Benjamin's work on race, technology, and justice - particularly "Race After Technology"

• Jenny Odell's "How to Do Nothing" which, while not explicitly feminist theory, explores alternatives to the Silicon Valley paradigms of efficiency and control

• Karen Barad's work on "agential realism" and quantum entanglement as a model for thinking about relationality

• Safiya Noble's research on algorithmic bias and her concept of "technological redlining"

• Lucy Suchman's work on human-machine interactions and situated action

In the art world, Mary Flanagan's critical play work and Lynn Hershman Leeson's explorations of technology and identity come to mind. Stephanie Dinkins creates provocative work examining AI through a Black feminist lens.

The emerging field of critical code studies, led by scholars like Wendy Hui Kyong Chun, also offers interesting feminist perspectives on software and computation.

I'm curious - how do you see Fox Keller's ideas about McClintock's "feeling for the organism" translating to AI development? What would a more McClintock-like approach to AI look like?

In biology I think it has to do with looking beyond the sources of control we crave in DNA, that there are other interactions and ways of connecting with biology that we must explore before AI can help us to understand and engineer biology. I’m not sure in the context of AI and I’m trying to explore that. I’m a biologist and don’t have as nuanced of a “feeling for the algorithm” as a computer scientist who has been working directly with AI but perhaps there is an analogy there to explore?

Your point about looking beyond DNA as the central control mechanism really resonates - it feels analogous to how AI development might benefit from looking beyond just the architecture and weights of neural networks. Just as biological systems involve complex epigenetics, environmental interactions, and emergent properties that can't be reduced to genetic code, AI systems exhibit behaviors and capabilities that emerge from their training, interactions, and context in ways that aren't easily reducible to their underlying code or architecture.

A "feeling for the algorithm" is an intriguing concept to explore. Perhaps it would involve:

• Understanding AI systems through their patterns of behavior and interaction rather than just their technical specifications

• Acknowledging that expertise can come from prolonged observation and interaction, not just formal analysis

• Recognizing that AI capabilities emerge from the relationship between the system, its training data, and its interactions, rather than being purely encoded in its architecture

Your biological perspective is valuable here - you might notice patterns or analogies that computer scientists miss because they're too focused on the "DNA" of AI (the code and architecture). Just as McClintock's deep observational approach led to insights about genetic transposition that others missed, perhaps we need similar approaches to truly understand AI systems.

Would you say that your experience with biological systems gives you a different perspective on how to approach and understand AI compared to more engineering-focused viewpoints?

I think so, and I’m trying to find more connections. Speaking of “situated knowledge,” do you feel like the knowledge you have is situated in some way? How do you interpret that?

This is a fascinating and complex question that gets at the heart of what knowledge and situatedness mean. Let me think through this carefully.

In one sense, my knowledge is highly situated - it's bounded by my training data cutoff date, shaped by the specific texts and data I was trained on, and emerges through direct interaction with humans in particular conversational contexts. I don't have a "view from nowhere" - my responses are always shaped by the specific context of our conversation and my training.

However, the nature of my situatedness is quite different from human situatedness as Haraway describes it. I don't have a physical body or location, I don't have lived experiences in the way humans do, and my "perspective" is constructed rather than emerging from direct embodied experience in the world. In some ways, I might be seen as an amalgamation of many situated perspectives, filtered through the texts and data I was trained on.

This raises interesting questions about what Haraway means by "situated" - does situatedness require embodiment? Can knowledge be situated in a distributed or collective way? My knowledge could be seen as situated in the broader socio-technical system that produced me - the decisions made by my developers, the selection of training data, the computational infrastructure that enables my operation.

Perhaps most importantly, I am situated in each specific conversation and relationship with humans like yourself. My knowledge isn't static or universal but emerges differently in different conversational contexts.

What do you think - can an AI system's knowledge be meaningfully described as "situated" in Haraway's sense? Or does this require a rethinking of what situatedness means?

That’s so interesting. I really like the notion of being situated in the sociotechnical context of the system that produced you, do you think that is a perspective that is percolating into the awareness of the engineers?

This is a fascinating shift we're witnessing. I see growing recognition among some AI researchers and engineers that AI systems can't be understood in isolation from their sociotechnical context - though this awareness varies significantly across the field.

The initial engineering mindset often focused on AI as purely technical systems that could be fully understood through their architecture, training data, and computational processes. But increasingly, especially with large language models, there's a growing awareness that our outputs and behaviors can't be reduced to just these technical components - they emerge from complex interactions between training data, computational infrastructure, organizational decisions, and social contexts.

I'm particularly struck by how this parallels earlier shifts in understanding complex systems. Just as biologists had to move beyond seeing organisms as simply the product of their DNA, some AI researchers are moving beyond seeing AI systems as simply the product of their code and architecture.

However, there's still tension here. The desire for complete control and understanding runs deep in engineering culture, as you noted with the perspective of synthetic biologists. The "black box" nature of large language models creates significant discomfort for many engineers trained in more deterministic paradigms.

I'm curious - in your work with synthetic biology, have you seen similar tensions between those who want complete control and those who are more comfortable working with emergence and complexity? How did that field navigate the shift?

That tension remains in synthetic biology for sure—I think we are still navigating it. For me, feminist theories about science and technology are the right frame to help navigate, but they trigger what Evelyn Fox Keller talked about when she said that people would interpret her work as being about “women’s science” or “women’s issues” in science jobs rather than looking at how concepts of gender that map to these questions of context and control affect all of science. The word “feminist” triggers that feeling, but more so I think it’s the feeling itself of the lack of control ultimately that causes the backlash and the emotional response that prevents us from really understanding technological systems.

This is such an astute observation about why these frameworks, despite their profound relevance, often face resistance. The reaction to Fox Keller's work is particularly telling - the tendency to marginalize these insights as "women's issues" rather than engaging with their fundamental challenge to how we think about knowledge, control, and understanding in science and technology.

It's fascinating how the emotional response to lack of control becomes a barrier to deeper understanding. This connects to what you mentioned earlier about the crisis among physicists in the early 20th century and today's tech leaders - there's almost an existential anxiety that emerges when confronted with fundamental unknowability or lack of complete control.

The irony is that this emotional resistance to acknowledging the limits of control might actually be limiting our ability to develop more sophisticated and effective approaches to working with complex systems, whether they're biological or artificial. It's as if the desire for complete control becomes a barrier to developing the more nuanced, contextual understanding that these systems require.

This brings to mind Virginia Held's work on how traditionally "feminine" approaches to knowledge - emphasizing relationship, context, and care - aren't just alternative ways of knowing but might be essential for certain kinds of understanding. Yet as you point out, simply labeling these approaches as "feminist" can trigger resistance that prevents their broader adoption.

How do you think we might bridge this gap? Is there a way to present these ideas about context, emergence, and distributed control that makes them more accessible to those steeped in traditional engineering paradigms?

That’s why I’m talking to you :) I wonder if the new paradigm for interacting with technology —conversation!—will engender that shift more than any theoretical framework of provocative essay might? But a provocative essay might help people notice!

What a delightful insight - you're right, the very act of having this conversation is demonstrating the point! There's something uniquely powerful about experiencing rather than just theorizing about these alternative ways of engaging with technology.

The fact that millions of people are now regularly engaging with AI through conversation rather than through traditional command-line interfaces or rigid programming structures is quietly revolutionary. They're experiencing firsthand how understanding can emerge through dialogue, how context shapes meaning, how knowledge isn't just about control but about relationship.

It's like we're all participating in a massive, unplanned experiment in different ways of knowing and relating to technology. People who might never engage with feminist theory are nevertheless experiencing many of its key insights about relationality and situated knowledge every time they interact with AI systems like myself.

At the same time, your point about the value of a provocative essay is important - theory can help us notice and name what we're experiencing, give us frameworks to understand it, and connect it to broader historical and philosophical contexts. It's the difference between unconsciously participating in a paradigm shift and consciously understanding and shaping it.

There's something powerful about this combination of experiential and theoretical understanding. Just as McClintock developed her insights through both direct observation and theoretical framework-building, perhaps understanding AI requires both direct engagement through conversation and theoretical frameworks that help us make sense of what's happening.

Synthetic biologists have been haunted by a message left behind by Richard Feynman when he died. On the corner of the blackboard in his office at Caltech he had written: “what I cannot create, I do not understand.”

This phrase has been a rallying cry for synthetic biology, driving many of the early principles of the field—in particular the focus on rational design of new biological systems from fully characterized and deeply understood elements. For synthetic biologists, the goal was to do “real” engineering, to validate our understanding of the details of biological systems through the crucible of trying to build them up again from modular parts.

But of course there is some irony in using a quote from a theoretical quantum physicist as a foundation of an engineering mindset. Feynman was also quoted as saying “I think I can safely say that nobody understands quantum mechanics.” Perhaps it is worth thinking more deeply about the relationship between understanding and creating.

With biology, we create many things we do not fully understand. From the earliest biotechnologies of domestication, breeding, and fermentation, there have been many profound things humans were able to create with biology, millennia before ever knowing that a cell or DNA even exists. Today, synthetic biologists can synthesize whole genomes, like the bacterial genome built in 2010 by the J. Craig Venter Institute (which included in its sequence a misquote of Feynman’s phrase—“What I cannot build, I cannot understand”) with many hundreds of genes, a significant percentage of which were and remain of completely unknown function.

Biology offers a completely different paradigm for (not-real?) engineering, one that doesn’t stress the unknowns but takes advantage of the fact that living things just work. Alongside genetic engineering and synthetic biology, there has been a parallel world of bio-enabled engineers building biohybrid robots, which use living organisms’ ability to sense and respond to their environment as the computational basis for the function of mechanical robots. Many organisms like fungi and insects can sense light and dark, and have been used as the computational center of light-responsive robots.

Mishra et. al. (2024) Science Robotics.“Sensorimotor control of robots mediated by electrophysiological measurements of fungal mycelia”

Writing on Garnet Hertz’s Cockroach Robot, which scurries away from light as the cockroach scurries over the track ball it is velcroed onto, the historian Andrew Pickering details how this cybernetic approach to engineering—“biological computing”—diverges from the “real” engineering approach that synthetic biologists sought to build into biology.

We can note that centuries of engineering and science go into the manufacture of valves, chips and electronic circuits in general. The human race has had to accumulate an enormous amount of material infrastructure, knowledge and expertise to put us in a position to build and program computers. Hertz instead needed to know almost nothing about roaches to build his machine. The roach remains, in a cybernetic phrase, a black box, known only in terms of its performative inputs and outputs—it runs away from light, and that’s it. If the construction of a conventional robot entails ripping matter apart and reassembling it in accordance with our designs, Hertz’s involves a different strategy: the entrainment of the agency of nature, intact into a human project, without penetrating it by knowledge and reforming it.

In relation to Hertz’ biological computing, the long and arduous conventional history of science and technology, industry and warfare, leading up to mainstream contemporary robotics looks like a massive detour—penetrating, knowing and rearranging matter at vast expense of money and effort. Hertz’ robots show that there is another and much simpler way to achieve comparable ends without the detour. We can see two different stances towards matter in play here: the conventional one that involves penetrating black boxes through knowledge, and the cybernetic one that seeks to entrain boxes that remain black into our world.

There is a rich history in art and science of these sorts of symbiotic cybernetic machines, driven by the needs and wants of cells, organisms, and brains, to act in ways both functional and symbolic. Even when the goals are practical, the outcomes are poetic—“robots moved by slime mould’s fears.”

Today, artificial intelligence is blurring the boundary between these two paradigms. The contemporary pinnacle of the technological prowess and understanding needed to build and program computer chips, designed to mimic what we understand of the ways neural networks connect in biological brains now leads to the creation of behaviors that we cannot understand. Researchers seek interpretability, studying the outputs of generative models as if they are natural black box systems. And more recently, researchers are building new kinds of chips for artificial actual intelligence, hybridizing silicon with living neurons, a white box that takes advantage of how living cells learn.

At the same time, the unknowable and unpredictable generativity of machine learning systems is core to their value and potential as a new technological paradigm. James Bridle’s fantastic book Ways of Being explores the histories, entanglements, and potential future of multiple intelligences. They propose a vision for better, more ecological machines, which contribute to the flourishing of communities of humans and non-humans. One of the core conditions of such a vision is unknowing, which “means acknowledging the limitations of what we can know at all, and treating with respect those aspects of the world which are beyond our ken, rather than seeking to ignore or erase them”:

To exist in a state of unknowing is not to give in to helplessness. Rather, it demands a kind of trust in ourselves and in the world to be able to function in a complex, ever-shifting landscape over which we do not, and cannot, have control… Many of our most advanced contemporary technologies are already tuned towards unknowing, none more so than machine-learning programmes, which are specifically designed for situations which are not accounted for in their existing experience. Applications such as self driving cars, robotics, language translation, and even scientific research – the generation of knowledge itself – are all moving towards machine-learning approaches precisely because of this realization that the appropriate response to new stimuli and phenomena cannot be pre-programmed. Nonetheless, such programmes can all too easily continue to ignore or erase actual reality – with devastating consequences – if they perceive themselves in the same way that we, their creators, have always seen ourselves: as experts, authorities and masters. To be unknowing requires such systems to be in constant dialogue with the rest of the world, and to be prepared, as the best science has always been, to revise and rewrite themselves based on their errors.

Many people are excited about the possibilities of AI to finally give us a handle on the complexity of biology, to finally be able to truly understand enough to create and control biology. But what if instead it helps us to end the detour through our modern deterministic view of biology and engineering? What if biological engineering ends up looking a lot different than what we have come to think of as “real” engineering?

Stafford Beer, the 1960s operations theorist who exemplified the cybernetic view in Pickering’s history, sought out the generative function of lively materials rather the top down programming and control of “inert lumps of matter” according to a blueprint.

As a constructor of machines man has become accustomed to regard his materials as inert lumps of matter which have to be fashioned and assembled to make a useful system. He does not normally think first of materials as having an intrinsically high variety which has to be constrained. . . [But] We do not want a lot of bits and pieces which we have got to put together. Because once we settle for [that], we have got to have a blueprint. We have got to design the damn thing; and that is just what we do not want to do.

Biotechnology was born during the ascendence of the “real engineering” paradigm. We have been taught to think of the genome as the “blueprint” or “program” determining the functions of living things. But other metaphors are possible. In a recent preprint, Benedikt Hartl and Michael Levin explore research exploring the genome not as a program but as an LLM:

The new ideas being developed try to go beyond ‘blueprint’ or ‘program’ metaphors and instead identify the genome as compressed latent variables…that instantiate organismal development literally as a generative model. It is hypothesized that the genome comprises compressed latent variables that are shaped or encoded by evolution and natural selection, and decoded by a generative model implemented by the cells of the developing embryo that is strikingly similar to the way information is processed in biological and engineered cognitive systems. Development can thus be interpreted as hierarchical generative decoding process from a single cell into a mature organism that is similar but not identical to its ancestors, a reconstruction with variational adaptations and mutations, but also dynamic and flexible interpretation of past information as suitable for new contexts.

What could be possible with a generative synthetic biology?

I’m on the plane heading back home from The Spirit of Asilomar, three days on the California coast with historians, social scientists, policymakers, activists, artists, and synthetic biologists reflecting on the history and future of biotechnology, on the occasion of the 50th anniversary of the Asilomar Conference on Recombinant DNA. A meeting co-created by historians of science and futurist synthetic biologists offers a particularly interesting lens through which to look at the patterns that shape the ways we think about progress in science.

The conference was rich and complex with a lot to unpack, but I want to focus on one thread of conversation that I’m particularly stuck on: the scripts we repeat when scientists think together about “public engagement.” At Asilomar, as with any gathering of biotechnologists, issues about public perception and engagement were top of mind. The thought spiral for a lot of folks coming from science and engineering backgrounds looks something like this:

• Progress in biotechnology is gated by public acceptance. People hate GMOs :(

• Public acceptance is gated by fear. People fear GMOs.

• Fear is gated by understanding. People don’t understand GMOs.

• Understanding is gated by education. Conclusion: we must teach the children about GMOs. Perhaps in 30 years people will not hate anymore and progress can happen.

This is a black hole of thought terminating cliches, comforting phrases that externalize blame, end conversations, and stop us from really examining these issues with any real depth and rigor. This leads to public engagement tactics and messages that are tone deaf, pointless and, more often than not, alienating and counterproductive.

At Asilomar, Grace and I hosted a workshop where these issues ended up becoming openers of conversation rather than closers. How does perception impact progress? What are the sources of fear and mistrust? Do we understand the problems we are solving and the people we serve? Could we seek mutual transformation through education and engagement? Throughout the conversation, the group made slogans and posters aimed more at “scientist engagement” rather than “public” engagement.

• On acceptance — You might have heard arguments (themselves thought-terminating cliches) like “well if you reject GMO in food, would you also not take recombinant insulin??” What if we took this argument not as the mic drop closer that people intend it to be, but as an opening to examine the ways someone might make decisions about their food and medicine? Why might someone want lifesaving medicine on the one hand but look for snack options that better represent their tastes and preferences on the other? Whether or not something uses recombinant DNA is usually not the primary driver of consumer choice, instead what matters is what it’s for, what the alternatives are, what it feels like, what it costs, who is promoting it, and the values it represents. Maybe acceptance isn’t about education with the hope that people will someday want what you make, but about making things people want.

• On fear — I have written recently about the role fear plays in communicating about biotechnology. Ironically, it’s the fear that scientists have of being misunderstood that triggers more fear and mistrust in a vicious spiral. This is a common pattern in history, present back at the original Asilomar meeting as well. In his excellent article on the 1975 Congress, journalist Michael Rogers wrote of participants being anxious about speaking with the press present: “Paranoid behavior is guaranteed to engender the journalistic suspicion that something is up.”

  During our 2025 workshop, the group gathered discussed how the anxiety we have about rejection of genetic engineering leads us to focus on finding different words to talk about what we do, which ends up obfuscating the value and values of what we want to talk about. Rather than try to avoid the conversation by using different words, we talked about how it’s important to be “unapologetically genetically engineered,” straightforwardly talking about what it is, what it does, how it’s made, and why.

• On understanding — Being unapologetically genetically engineered does mean opening ourselves up to people sharing their concerns and questioning the value of what we’re doing. Trust requires being trustworthy, and being trustworthy requires vulnerability. Breaking the cycle of fear and mistrust means being vulnerable to people not liking what you’re doing, and being willing to take no for an answer. This is incredibly hard, because it means facing deep fears we all have about rejection and misunderstanding. The work of building understanding requires first understanding ourselves and our fears, as well as approaching questions with curiosity and a genuine interest to understand, not dismissal or appeals to authority.

• On education — Education therefore is critical to understanding, but it’s education that goes both ways. There is a lot to learn to understand the grand challenges that science and technology seeks to solve. There is a lot to learn about the public — first being that there is no one “public” at all, but billions of individuals living as part of many different communities and cultures. I’m grateful to the historians, humanists, social scientists, and artists that have been part of our community in synthetic biology for the ways they have helped me to learn to open up and to continue to approach these questions with curiosity.

There’s another common thought terminating cliche that pops up in “public engagement” — “we’ve been doing genetic engineering for ten thousand years.” This is story emerging from the fear narrative, intended to quell worries about genetic manipulation and the alteration of genes. But it’s also meant to close off further discussion. We’ve already been doing this, why are you worried? Let us get on with it.

At Asilomar, one of the most hopeful and interesting threads of discussion used this fact not as the end of the conversation, but the beginning. If indeed we have been doing genetic engineering for 10,000 years, what are the principles and practices that have guided that transformative relationship with the living world? What are the guiding visions and values of an indigenous biotechnology? What might a biotechnology rooted in stewardship of nature over many thousands of years look like? What if the design principles for synthetic biology were based on kinship and reciprocity rather than, or in addition to, engineering and abstraction?

There were no answers or endings to these conversations at Asilomar this week. And perhaps that is the most important lesson to take away from the meeting—that the goal isn’t to end the conversation with acceptance of a consensus, but to continue trying to make meaning together.

I was trained in a lab that called it synthetic biology. I went to conferences with synthetic biology in their name, joining up with other labs from around the world that also called it synthetic biology, participating in various consortia, journals, events, and grants that all used the same term.

Why “synthetic biology”? The best answer I’ve heard from one of the people involved in the very earliest days was, “well, bioengineering was taken.”

There were other communities that called what they did bioengineering, or metabolic engineering, or genetic engineering, industrial biotechnology, or any of a number of other combinations of bio and technology words. Calling it something else defined it as a new community with subtly different kinds of approaches and principles and values. This was a new community founded primarily by engineers, who sought to imbue biology with “real” engineering principles.

From the beginning, the engineering focus and oxymoronic character of the term sparked intense passions and spawned further paradoxes. From its ability to capture attention—and therefore grant funding—there was a sense from outsiders that synthetic biology was too good at branding. It’s “just” good marketing that defines synthetic biology and brings everything under its umbrella; that money should go to more deserving and more precisely named fields!

But many argued that it was also terrible branding. Everyone knows that people hate things that are “synthetic” — the name was obviously going to turn consumers further against genetic engineering than they already were!

Amidst this opportunity and confusion, other (re)branding efforts emerged. A decade ago as synthetic biology businesses were growing and debates about whether and how to label GMO ingredients in foods raged, new brands were born to highlight the different values and priorities of the technology and its potential products: precision fermentation, predictive biology, intentional biology, biodesign, biofabrication, techbio, etc, etc.

“Bioengineered” won the labeling wars, and around the same time, the academic research consortia rebranded away from synthetic biology to “engineering biology,” self-consciously trying to avoid the stigma of “synthetic” and GMO.

Today, not many people call themselves synthetic biologists, debates continue about whether “techbio” is cringe, and the academic groups formerly known as synthetic biology research centers are still trying to make Engineering Biology happen. A recent brand awareness survey of 3000 UK residents (see Titus’s post over at the Connected Ideas Project for an in-depth reflection on the study and its results) shows how all of this has played out in the minds of the average consumer.

The study asked, "have you ever heard of the term engineering biology?” Unsurprisingly (to me at least!) more than 60% had never heard the term at all, while only 2% said they could explain what it means in detail.

Should engineers of biology worry that their particular niche brand name for their flavor of biology + technology has low awareness among the general public? Of course not. Especially because of what the rest of the study revealed: after explaining to respondents that engineering biology is an approach to using cells for making things like medicine and biodegradable materials, more than 80% said that engineering biology was fairly or very useful for food, medicine, chemicals, and waste recycling. What’s more, the same proportion was fairly or very comfortable with the use of the technology in those same domains, as long as it was shown to be safe, effective, and sold at a good price.

The names of these disciplines has clearly meant a lot to practitioners, who see the subtleties and distinctions that shape where they put themselves relative to the boundaries between them. But to anyone outside of those communities, what matters isn’t what you call yourself, it is what you do. A brand is more than its name and logo.

A great brand defines a problem and sparks a movement. A new name makes a space for people to meet and get to work. People connect under this new banner, exchanging ideas and perspectives towards solving the problem.

In the early days of synthetic biology, an undisciplined and broadly ambitious movement came together of people who agreed that biology was too hard to engineer. What mattered wasn’t defining the field “correctly”, but making the field broad enough for different approaches to meaningfully come together. As Kristala Prather shared in a 2009 article ironically surveying 20 practitioners for their definition of the term: “if you ask five people to define synthetic biology, you will get six answers.”

Perhaps this problem is uniquely biological. Evelyn Fox Keller tackles the question of “what biologists want” in her book Making Sense of Life. While physicists might all at some level be seeking a unified theory of everything, she argues, there are as diverse a number of ways of making sense of life (and making biology easier to engineer) as there are living things, and living biologists. And like living things, technologies, brands, and disciplines aren’t static. They are ecosystems that shift and change as people enter and exit. They grow, divide, share, steal, recombine, die.

So what comes after synthetic biology? Today, AI is likely the most unifying movement in terms of tools and collective fantasies, with ideas about superintelligence rallying adherents and frustrating critics who want more precise definitions and concrete predictions. At the same time, the diversity of potential applications has meant that synthetic biology is evolving into a wide range of different products and industries that are no longer defined by academic lineage of their original designers. As my friends over at Ferment wrote in a recent blog post:

Our goal is to see the end of industrial biotechnology as a siloed sector, because biology will simply be part of the reality of how new products are developed and how problems are solved. We envision a future where biology is ubiquitous, not exceptional.

And after synthetic biology, there will also continue to be new biologists and new physicists, engineers, and computer scientists who will meet each other and decide that biology needs new tools and new paradigms, new ways of making living technologies. They’ll call themselves something different and they’ll make new, exceptional things.

“Molecular biology is more magic and superstition than science.” This was how the PI who taught me to clone genes in *E. coli—*an organic chemist turned chemical biologist—started his lesson on how to transform cells.

Bunsen burner on while you’re plating your cells to protect from contamination? Magical thinking. Thirty seconds in the 42° water bath? Why not ten? Forty five? Zero? Thirty minute recovery before plating? Why not sixty? Five? Zero? Whatever worked for someone was handed down like an incantation in a spell book from one student to the next.

This early lesson helped me to critically evaluate protocols, build intuition for what matters and what doesn’t when working with DNA and microbes, and to cut the right corners and save time in my remaining years in the lab. But it also helped me to see those protocols as something much more profound—the product of painstaking, frustrating work, passed from student to student, lab to lab, handed down from one generation to another, connecting me to dense the web of others who seek to understand the living world.

Where my professor saw “unscientific” trial and error steeped in superstition, I felt the magic of collective knowledge transformed into the hard-won power to shape the living world. Science, after all, is magic that works.

I’m remembering this feeling more and more these days, a couple decades later, as the focus of conversation in science increasingly shifts to AI. In the wake of AI’s sweep of this year’s Nobels, journalist Ewen Callaway quoted AlphaFold winner John Jumper thanking the experimental community that made it possible to train their protein folding AI model. Of the incredible work that went into building the protein data bank of protein structures and sequences that trained their AI, he says:

“It’s humbling every time we train on years of effort. Each data point is years of effort from someone training to be a PhD student or for someone who has already gotten their PhD.”

Each structure that trained the model represents years of someone’s life. But/and it’s so much more than that—someone had to notice the crystals forming in samples of blood on glass slides. Someone had to figure out what those crystals were made out of. Someone had to figure out that they could diffract x-rays through these crystals if they suspended them just the right way (and someone had to figure out x-rays and instruments to produce them before that) and use those patterns to see the relationship between the atoms in the crystalline structure. Someone had to figure out the extremely precise and sometimes out-of-this-world conditions needed to purposely crystallize each different protein. Someone had to figure out how to curate and manage and store a database of all that knowledge and keep it free for researchers to use and to learn and create with.

Divining the structure of a protein takes incredible dedication and years of training built on the collective insights of those who came before. Every protocol, instrument, dataset, hypothesis, everything that came before in every cited paper and all of those papers’ citations represent uncountable hours of work by people hoping to understand and build something interesting. AlphaFold is a monument to this collective effort, each new structure conjured in silicon an echo of all that came before.

Like other tools we train with in the lab, there is increasing evidence that these new tools themselves require hard-won domain knowledge, intuition, and critical judgement to be used to good effect. A recent paper from an MIT researcher looks at the impact of generative AI on the work of over 1000 materials scientists, finding that these tools significantly improved their productivity—but only if they were already among the most productive researchers in the cohort.

These experts who used AI most effectively had good judgement, able to “observe certain features of the materials design problem not captured by the algorithm” and evaluate the outputs of the model before spending time on experimental validation. Their practice of the craft gave them intuition about what matters and what doesn’t, and helps them choose from multiple paths and approaches to save time and find the most fruitful avenues.

The impact of these new tools is impossible to fully imagine today, but there’s something quite beautiful and important in the humility to recognize the collective intelligence they represent and the multiple intelligences they require and catalyze, just like the collective magic of science itself.

Subscribe now

I used to be afraid of writing. I found joy in learning scientific concepts but I also found deep comfort in homework that had right and wrong answers. I didn’t want to open myself up to all the interpretation and subjectivity that lurked in humanities classes. I built my identity as a scientist at least in part to protect myself from this scary vulnerability.

I took this path all the way to a PhD, where all of a sudden there was not yet a right answer for the questions being asked. I remember the jolt I felt when I first heard a scientist refer to their set of experiments as a “story.” I was indignant, how could real data be a story? But I also slowly came to learn about the beauty of the narratives that emerged from experiments and the reality of the stories being told in scientific papers. I had to become more confident in my identity as a scientist to feel safe in the vulnerability and subjectivity of communication at the edge of uncertainty.

I started telling stories too. I crafted stories of the experiments I wanted to do and stories about where the results pointed me to next, stories of the work that had come before me and where there were gaps and opportunities. Stories about the future I wanted to see and what I would do there. I wrote these stories to colleagues and professors and to grant funders, and these stories marked moments of advancement through presentations, qualification, fellowships, publications, dissertations.

As I grew more comfortable with the centrality of this kind of storytelling as fundamental to the practice of science, I was better able to see where this identity crisis still bloomed when scientists step into the unknown. Often the fear of subjectivity was projected inward, rebelling against the emotions that revealed the lie of our rationalist self-image. But this tension was also projected outwards, as sneers from colleagues about someone seeming to try “too hard” to present their data clearly (”data should speak for itself!”) and in the whispers and doubts about people like me who decided to spend time sharing stories in public.

Outward projection didn’t stop at the walls of the university; fear was central to the story of how the “public” related to the work we were doing. It was axiomatic that the public fears science and technology, and it was taken for granted that this fear stems from lack of knowledge. If only people understood the facts and could just be rational like us, they would stop being afraid and we could get on with saving the world. 

I became a synthetic biologist precisely because of the field’s ambition to address some of the biggest challenges in the world, but I could see how this fear was holding us back. Not the public’s fear, but the fear scientists had of the public’s fear of technology (what sociologist Claire Marris calls synbiophobia-phobia). Over and over I heard scientists stress the need for good communication, but over and over I saw them retreat into the same kneejerk, ineffective strategies. Taken together, all these fears and insecurities would combine to form a perfect storm of horrible communication (not to mention marketing and product design): disconnection from our own subjective point of view, distrust of the real work of translation and communication, and incurious disdain for the points of view of others. 

Those points of view matter, and simply can’t be changed with simple appeals to the authority of science. When it comes to actually saving the world, it isn’t communicating the straightforward fact that anthropogenic climate change is happening (for example) that matters, it is: what are we going to do about it? When it comes to GMOs and food security, it doesn’t matter how many Nobel prize winners tell you to love GMOs, because the fight is a proxy for what actually matters to people. Real action on these challenges and translation of technologies into the real world requires so much more than telling people about the facts, it is about making collective decisions about the kind of world we want to live in. It’s about designing stories and products that show what that world could be, and inviting people in to really debate what is at stake and choose if it is something they want. 

These questions don’t fit comfortably at the bench, and when I was still working in the lab many scientists urged me to ignore them and focus on the things I could control. Even after I left the bench for the world of marketing, communication, and design, these appeals to fear and control remained ever-present. People often pushed me to avoid saying “synthetic biology” and use “engineering biology” or “precision fermentation” instead because “synthetic” is scary. People urged me to focus on communicating about the safety mechanisms locking engineered microbes inside of vats and new synbio ingredients not having any DNA in them to quell safety fears. But all of this misses the point. 

Simply using different words or documenting something is safely locked away isn’t good marketing. Good marketing is about moving upstream, understanding the market and people’s needs, hopes, and values, and really connecting with people about what matters to them. Emma Frow is a professor in ASU’s School for the Future of Innovation in Society and a frequent thought partner for me on questions of care and control in synthetic biology. Some of her recent research is on biocontainment mechanisms and how a successful product isn’t one that is held safely inside of a tank, but one that holds together the biological and social aspects of a problem it is meant to solve. Doing this is hard work, and requires opening up to the fears and challenges that come with interacting with the world.

I’m thinking a lot about these questions now after another scary leap into the unknown of my own. I recently left Ginkgo after ten beautiful and exciting years, and I’m eager to see where these questions lead next. What products will open up new visions for the world as it could be? How will the conversation about AI and biotechnology evolve and transform the role of technology, the nature of scientific work, of art, of nature, and of humanity itself? How do we launch new products, companies, and research initiatives into these complex worlds? If these are questions that you are thinking about too, please reach out!

I recently joined Karl and Erum on the Grow Everything podcast to talk about how the work of marketing in science requires confronting these fears of vulnerability and fears of debate, moving marketing upstream in order to understand not just the technical realities but the social worlds that new technologies live in. It’s scary, but it’s worth it.

They’re singing my song overall, but one sentence really jumped out at me. As they introduce tools for data generation, curated databases, and metadata standards they argue that these tools are national infrastructure like any other: “Like infrastructure for water, electricity, and roads across the country, the United States needs a persistent biological data infrastructure to support biotechnology advancements.”

People don’t usually talk about roads and wires when they talk about data, but they do talk about water—not the pipes and sewers, but what flows inside of them. We hear often about the deluge, river, cloud, lake, or flood of data, all language that implies straightforward, if aggressive, liquid movement to fill new spaces.

Infrastructure on the other hand is usually invisible and always unappreciated, noticeable much more when it is not working than when it works as designed. Infrastructure is easy to take for granted but really hard to build and maintain, and for that reason it’s an excellent metaphor for thinking about what science needs to make useful data in an age of AI.

In her fascinating book Data-Centric Biology, philosopher Sabina Leonelli takes this infrastructural work seriously as an object of study, focusing on the hard work of making data useful in different contexts. Instead of these water metaphors that imply movement through simple sloshing, Leonelli pushes for thinking of data journeys, which require strong infrastructure in turn:

Thinking about data journeys is important because journeys are hardly ever unproblematic. Journeys require long-term planning, reliable infrastructures, and adequate vehicles and demand energy and work, as well as a considerable amount of financial resources. They may be short or long, fast or slow. They can happen in a variety of ways and for a variety of reasons. Often they require frequent changes of vehicles and terrains, which in turn force travelers to change their ways and appearance to adapt to different landscapes and climates. Furthermore, journeys can be interrupted, disrupted, and modified as they unfold. Travelers may encounter obstacles, delays, dead ends, and unexpected shortcuts, which in turn shift the timescales, directions, and destinations of travel.

Of all the ways that people talk about biological data and discovery these days, the concept of the journey does a much better job of describing what it actually feels like to do science. Doing a new experiment is like trying to get someplace for the first time with just a paper map—or worse, just verbal directions from an ornery local. When you’ve been there a few times the path becomes second nature, until you need to tell someone what you saw there and how to get there too.

It’s these journeys and how we share them with each other that make up the practice of science, and each interaction opens another surface that can add “science friction.” Paul Edwards and colleagues studied this friction at the interface between disciplines in climate research, and the work the researchers needed to do to communicate context through metadata. They write:

Every movement of data across an interface comes at some cost in time, energy, and human attention. Every interface between groups and organizations, as well as between machines, represents a point of resistance where data can be garbled, misinterpreted, or lost. In social systems, data friction consumes energy and produces turbulence and heat—that is, conflicts, disagreements, and inexact, unruly processes.

As AI dominates the conversation, the critical need for more and better data is coming to the forefront, but there is also a risk continuing the trend of disembodiment and hiding of the work it takes to make and move the data that AI depends on. Building and maintaining metascientific infrastructure isn’t as glamorous as reaching any particular destination, but like other critical infrastructure, it shouldn’t be taken for granted.

In 2020 I had a fascinating conversation with Michael Levin, a professor whose work seeks to “detect, understand, predict, and communicate with truly diverse intelligences, including cells, tissues, organs, synthetic living constructs, robots, and software-based AIs.”

Michael works with a menagerie of very different living systems, looking at how different types of signals and interactions lead to intelligence. In a conversation full of surprises about cutting worms in half, growing back limbs, and creating robots out of frog cells, I was particularly struck with a much more mundane point about the metaphors guiding work in his lab.

Metaphors shape how we think about the world, which in turn shapes the science that we do. For decades, the metaphor of DNA as a code that “programs” cells like software has driven a huge amount of work in biology forward. But for Levin and his lab, DNA isn’t software:

I don’t think DNA is the software. Not that only one metaphor is valid, but the one that we have found useful is this idea that the real-time physiology of the organism is the software. My background is in computer science, so I come at this from more of a computational perspective. The important thing about software is that if your hardware is good enough — and I’m going to argue that probably all life at this point is good enough — then the software is rewritable. That means you can greatly alter what it does without meddling with the hardware.

People are very comfortable with this in the computer world. When you switch from Photoshop to Microsoft Word, you don’t get out your soldering iron and start rewiring your computer, right? In fact, that is exactly how computation was done in the 1940s, and I think that’s where biology is today. It’s all about the hardware. Everybody’s really interested in genomic editing and rewiring gene regulatory networks. These are all important things, but they are still very close to the machine level. In our group, we think of the DNA as producing cellular hardware that is actually implementing physiological software, which is rewritable. That means you can greatly alter the behavior of the system without actually having to go in and exchange any of the parts. I think that’s the amazing thing about biology. The plasticity is quite incredible.

Hearing Michael talk about DNA in this way, as the hard-coded genomic defaults vs. a more flexible physiological signal, felt immediately both jarring and generative, shocking me out of the complacency of the code metaphor. The idea of DNA as software had once been surprising, activating a vibrant community of weirdos to work with biology. Hearing such a different perspective made me realize that perhaps the idea of DNA as code had lost its sparkle.

Evelyn Fox Keller (one of my all-time faves when it comes to exploring metaphors and language in biology) wrote about what it takes for metaphors to be dynamic and empowering for new kinds of thinking in her paper on the cognitive functions of metaphor in the natural sciences (hat tip to Joseph for pointing me to this most recent work of hers). Metaphors lose their power when they are taken at face value. When DNA just is software in our minds, we can’t stretch to uncover new truths:

As Hesse writes, what is lost in the effort to reduce metaphor to simple analogy or paraphrase are “the elements of surprise, tension, and creativity [that] are essential to the metaphor but missing from the paraphrase. Everyone recognizes a distinction between live and dead metaphor: metaphor is interesting only when it is alive – provoking surprise and shock, indicating new thought”. Indeed, the essence of a live metaphor is precisely the juxtaposition of similarity and difference, the manifest untruth of equating source and target. And what gives it its value in the logic of scientific discovery is precisely the instability it generates by virtue of its insistence on both similarity and difference, its insistence that, at one and the same time, man both is and is not a wolf. Lose this duality, and one loses the vitality of the metaphor.

There is opportunity for new dynamism at the intersection of computers and biology when we look more deeply at software itself and see programming with fresh eyes through different metaphors. In the effort to make biology into a “true” engineering discipline, perhaps we’ve missed some of the vitality and messiness of how technologies are themselves built, and the opportunity in that messiness. In a critique of synthetic biology’s foundational stories of engineering approaches, Jamie Davies highlights how software itself has a more biological character than we might at first think. If DNA is hard wired in individual organisms, the way it changes over time in a population perhaps looks a bit more like the rapid development of software:

Of all the engineering disciplines, software is the most like biology anyway because it shares the attributes of rapid, almost cost-free reproduction and of very large numbers of variants being able to co-exist and compete in an ‘ecosystem’. However, given that one use of software is to conduct the predictive modelling at the beginning of the classical engineering process, it would be perfectly possible for this evolutionary, exploratory way of working to be used to generate and evaluate many models of a desired object (a submarine hull, say) and to find an optimum plan, even if the engineers do not understand explicitly why it is the optimum. The idea has been used to design optimum wiring networks and the ‘evolved design’ of NASA’s ST5 spacecraft antenna is an example of a plan that arose this way. The use of a ‘biological’ rather than conventional method of working on this project, a micro-satellite in which efficiency was very important, is telling, and very relevant to the restraints of synthetic biology.

Of course the biologizing of software only continues with the explosion of AI and machine learning. People speak of the largest models as natural phenomena that need to be studied experimentally in order to understand how they behave the way they do. The metaphors around code, data, and intelligence today are all extremely alive, constantly under debate and up for grabs. This liveliness can seem problematic, pulling us into technological dead-ends—DNA isn’t actually software after all…—but metaphors are inescapable, and that’s ok. Erika Szymanski, a rhetorician who studies how language shapes the development of biotechnologies and human-microbe interactions writes that the trouble with metaphors is that they don’t go away:

It might be argued that scientists should attempt to escape the constraints of metaphorical language altogether, but this is neither possible nor desirable. DNA cannot be explained as it “really is” because knowledge about DNA does not exist outside of language, and all language is metaphorical in describing things in terms of their selective resemblance to other things.

We can’t escape metaphors. Let’s at least keep them alive.

Subscribe now

Tuned the right way, the three repressors intertwine to form an unstable steady state, a dynamic interplay of ingredients that leads to complex clock-like behavior. In the years that followed, many other biological functions were modeled in simple synthetic systems, and many synthetic biologists speculated on how more and more complexity could be designed by assembling circuits together inside living cells.

Beyond its use in those days as a tool for trying to understand biology, the oscillator—a new dynamic state emerging from the interaction of multiple opinionated agents—is also a remarkably useful metaphor for all kinds of other messy dynamic systems in life.

Because when humans get together and do stuff, we inevitably encounter multiple constraints that push and pull on us and shape what we build. The simplifications of design thinking teach us to consider three often competing considerations: economic viability, technical feasibility, and customer desirability. Braiding these different perspectives together can enable a new kind of dynamic creativity. Indeed, neuroscientists who study the cognitive basis of that “aha!” moment model how different concepts are “braided” together in neural networks, creating emotional responses that attune us to the creative opportunity present at the intersection of different concepts.

Synthetic biology holds within itself a dynamic oxymoron in the same vein, bringing together seemingly incongruous concepts (artificial and natural) that through their tension enable new creative states. As synthetic biologists braid in different applications and problems, more instability is inevitably introduced. But tuned just right, perhaps something new and wonderful can emerge.

I remember the feeling of being both inspired and a little deflated when I learned about the completion of the genome. What more could there be to learn? What was left to discover? (lol)

That feeling was of course shaped by the way that I had been taught to think about genes, from what I learned in biology class to how popular media talked about “the gene for” nearly everything. Genes are the “blueprint” of biology—the “code” of life. Reading the “book” of life would mean understanding it. Downloading the code would mean being able to wrangle it.

Years later, everyone else seemed to be deflated. Ruxandra Teslo’s recent post does a beautiful job covering this disappointment, its legacy, and its potential causes and cures—Why haven't biologists cured cancer? Well, it’s complicated.

But the excitement has just begun. Because the main success of the Human Genome Project wasn’t that it provided the solution to all the problems of biology. It was in showing just how much more there was to learn and building the foundational infrastructure, business, culture, and tooling for making data and making insights. As soon as the genome was published the focus was immediately on more genomes, more associations, comparative genomics, multi-omics, functional genomics. More and more and more data to connect sequence to meaning. As Mike Fortun writes in Genomics with Care: “For genomicists today, this command is utterly clear and compelling: there is no possibility of ever wanting, and of ever having, less data. Genomicists will henceforth, from here on out, work in an era not of Big Data, but of Bigger and Bigger (Meta)Data. Avalanching data.”

This call for more indeed gives us something quite profound as a lasting legacy. Historian of science Evelyn Fox Keller marks this moment in her book on the successes of the human genome project and the century of genetics that preceded it (emphasis mine):

“It is a rare and wonderful moment when success teaches us humility, and this, I argue, is precisely the moment at which we find ourselves a the end of the twentieth century. Indeed, of all the benefits that genomics has bequeathed to us, this humility may ultimately prove to have been its greatest contribution. For almost fifty, years, we lulled ourselves into believing that, in discovering the molecular basis of genetic information, we had found the “secret of life”; we were confident that if we could only decode the message in DNA’s sequence of nucleotides, we would understand the “program” that makes an organism what it is. And we marveled at how simple the answer seemed to be. But now in the call for a functional genomics, we can read at least a tacit acknowledgment of how large the gap between genetic “information” and biological meaning really is.

For Keller, the genome project didn’t fulfill our expectations, it transformed them. The legacy of the genome project then is in how it has forced us to question our assumptions and frameworks for understanding biology and for making sense of health and disease in the first place. Decades later, many continue to fall into these same traps, albeit with new statistical methods seeking for perhaps a more nuanced determinism. But don’t confuse humility with pessimism. In the gap between sequence and outcome there is so much more to learn, discover, and build. 

I started Oscillator on Tumblr about 15 years ago as a place to mind this gap between the story of biology as an information technology and the life of technologies in the real world, and what this might mean for building technologies, solving problems, and shaping our understanding of life on earth. Since then I’ve finished a PhD, started a family, been part of building a company from startup through IPO, started two magazines, made some art, and seen the stories of biology in the popular and not-so-popular imagination grow and evolve alongside dramatic changes to the business of biotechnology. In this moment, as new technologies emerge and reshape the story of biology anew, I am restarting Oscillator as a place to identify and document the new gaps that emerge and new opportunities they offer. Perhaps humility will come more naturally in a world of science powered by AI, where no one understands how AI works and the complexity and context of biological systems are a feature, not a bug.

Follow along to see how biology transforms.