Why there are no cracked biotechnologists?
On the total lack of 10x engineers in the lab
There are stories about cracked engineers. The dude who rewrote the whole google maps in a weekend. John Carmack. Elon Musk. The 10x or 10^n x engineer is a well established concept and seems to be pointing at a real thing. High agency people who can completely lock in and achieve incredible feats of programming or engineering by just having an intuitive understanding of the system and its components .
We also keep hearing from tech bigshots like Jensen Huang and Sam Altman that as well as AI, biotech1 is going to be the next big thing and they're super excited about it.
Despite working around the industry in various disciplines for like a decade, and the whole pitch being that we can "program" cells to do what we want, I have heard no tales much less encountered anyone who fits the cracked category in biotech. I'm talking about a go to person who has crazy output and can crack any problem.
(Side note: the closest person at least close to this space is probably Shulgin, who was great, however my read is that he didn't have some massive 10x chemistry/pharmacology skill but a DEA license, a sense of adventure, and a pharmacopia of low hanging psychedelic fruit).
Sure, I have met people who are in the lab until midnight grinding out experiments by putting things in and out of incubators and adding chemicals to Eppendorf tubes (and no disrespect, I admire the grind and sometimes you do just need to Do The Thing). There are people who seem to be relatively successful serial biotech entrepreneurs (although I wouldn't be too confident that part of this isn't some lucky early hits and then grad student selection effects). There are people who *gag* publish a lot of *retch* high impact papers. There's technicians who are the go to for instruments and hardware fixes. There are PCR wizards 2 . There just doesn't seem to be the same category of 10x biotech scientist3 in terms of actually making and releasing Something People Want.
Why is this?
Variable control and system understanding
We cracked the central dogma. Biology is programmed in DNA. The DNA makes the RNA and the RNA makes the proteins, and the proteins do all the stuff. We can read and write DNA. So what's the issue?
The issue is a bunch of bullshit that happens in between each of those steps that each encompass entire disciplines of study in and of themselves. The central dogma is a nice story but pasted on top of it you have oodles of variables and moving parts, all interacting with one another with no overarching design that has been hobbled together by billions of slightly lucky accidents over a few billion years. This may simply be a system that no single human mind can actually grok sufficiently well to engineer it precisely.
The programming problem is "why is the computer doing what I told it to do rather than what I want it to do". The biotech problem is "why is this cell doing things that maximised the comparative fitness of its genes in a wildly different environment, features of which I am not even aware of let alone able to manipulate, and not what I want it to do".
Iteration time
The cells are going to take as long as they take to grow. The OODA loop for actually learning things is much slower than computer programming. Even hardware engineering the cycle time is set by constraints one can somewhat push on and manipulate and there's optionality for swapping and tweaking components. In biology the cells are gonna grow how they grow. Heaven forbid you actually want to study a living organism, or even possibly a human.
To be clear, in reality biotech cycle time is also affected by levers one could certainly speed up (if there exists an analytics department that always has extra capacity and runs samples immediately I am yet to hear of it). I think there's also something of a mindset/culture issue on how long things should take in wet labs that is pretty ingrained and hard to shake. Which brings me to the next issue, which is probably affected by the first two.
Selection
How does someone with that cracked dog in them start computer programming? Maybe they want to solve a problem, and start figuring out how to automate it with some simple scripts. They find it fun, keep tinkering. Start building things. People like the things, and use them. Make whatever they want, they can lock in and grind all night. Eventually they have a portfolio of stuff to show a FAANG recruiter or a prototype to show YC and then someone will pull out a few sacks with dollar signs on them and now they’re a cracked 10x software engineer.
What about physical engineering? Here I'll admit that there is some more limits, but probably you get a circuit board, or a raspberry pi to stick into things. You start tinkering, building stuff at home or maybe at a highschool or university workshop. You're not building Starship on a weekend but you can go build *something*. You can explore the space and for lots of things the only limit is your imagination and how much 3d printer resin you have.
OK so how do you start bioengineering? You take a high school or undergrad course, they hand you a protocol, and you do it. If it doesn't work you did it wrong. Then....that's it until the next lab course. They give you another protocol and you follow it, and so on for a semester. Maybe there's a little bit of creative freedom if you're lucky, but you're basically doing someone else's ideas until post PhD, and even then you need someone with their own lab to sign off and let you come try stuff out. Likely you also need some grant money of your own too (the procurement of which is a whole other kettle of risk averse perverse incentives). There is basically no straightforward way to fuck around and make a thing in biotech (iGem is probably the closest, and that still needs a whole team and structure and supervisor to sign off).
Some personal background:
I figure I am probably top 1% in 'amount of fucking around and trying stuff' during an early lab career in biotech. During my PhD, 95 % of my lab time was working on the nitty gritty molecular pharmacology of the receptor the drug company sponsoring me was interested in. I learned a lot, I independently made some crucial decisions in what turned a pretty good project with some genuinely novel receptor pharmacology stuff. The drug company was happy. I was very much working on a project someone else wanted done though.
As for the other 5%, my supervisor was very chill, so as a side project I ordered some weird Russian drugs I'd read about on Slate Star Codex to throw at a few receptors and see if anything stuck4.
As a post-doc I managed to somehow stumble into an even more chill supervisor. A true old school, Oxford in the 70s, heavy drinking, hates administrators with a burning passion, polyglot professor. Here I started being able to try my own ideas and was told that I should spend 20 % of my time on long shot high payoff ideas. This is pretty abnormal in a wet lab! On top of having the greenlight from the boss I still only really did this because I didn't really care about my publication record having decided long ago I wasn't going to stick in academia. If I was, then I would have been way more on rails grinding our papers.
The point is I was super lucky in which labs I landed in, twice (!), and I still spent a huge chunk of time where I was working on other peoples projects until I was well past my PhD.
I think this selection effect is pretty strong. Intuitively it feels like "crackedness" probably correlates pretty strongly with "likes working on a system I can grok" and "major motivation is making a thing". Combined with a low base rate (1/10000?, 1/100 000?) of cracked engineers in the undergraduate population, even if people come into the universities just as interested in biotech as computer science or programming, we pretty easily get to ~0 cracked biotechnologists on this alone.
Will we get more cracked biotechnologists soon?
Maybe?
There's a few forces pushing in that direction. Automation + cloud labs gives people the power to skip the whole lab skills section and focus on designing smart experiments and looking for insights, but the cycle time and cost is still super high. LLMs lets people skip reading lots of low information density papers and get the basics down more quickly, and is generally a much smoother process of learning a field. This may motivate some cashed up tech types to switch into biotech and make a new and better working environment that attracts the cracked (Arc kind of seems like it's aiming for this, and Astera Institute is doing stuff pushing in this direction too). Price to get setup with the lab basics and some cool toys (an Opentrons, some mini plate readers) is falling, and a few biohackers are trying to build cool stuff in their garage. We're still nowhere near "raspberry pi and a 3d printer" level costs though.
AI could well render this all kind of moot. If it it is just about keeping a complex enough metabolic map in your head and coming up with experiments to poke at it until you can grok it, then this seems a task kind of well suited to AGI (with either robots or cooperative humans). Apparently item 1 on the AGI to do list will be “increase the rate of scientific discovery” so we may well get a whole lot of cracked biotechnologists all at once in, let’s see um, 2030.
I'm using biotech to mean all the stuff under the broad umbrella, pharmacology, cell factories, immunology etc.
The PCR wizard is something of a dying breed as we get universal Tm polymerases and fully automated primer design tools, not to mention just buying whatever you want to clone is pretty cost effective these days.
I’m not counting the people making computer systems pointed at biology like protein LLMs and AlphaFold. These are and will continue to be huge for biotech, and while I’m sure deepmind had some truly cracked programmers working on AlphaFold the nuts and bolts of the work they’re doing is computer science first and foremost.
No hits there, sorry
I think I am your target audience in disposition (you are the first fellow slatestarcodex-reading bio person I've encountered, hello), though probably not aptitude. I was driven to go into experimental biology because I thought my enjoyment of math/cs problem-solving would translate to experimental methods development. The NGS, optogenetics, and cas9 waves were all huge when I was in college, and it seemed like that momentum would continue.
I worked at a metabolic engineering company for 3 years after college to get broader exposure, which was super eye-opening. There were ~50 PhDs there from all kinds of different areas, which was a huge contrast from the more focused nature of many academic labs.
While I enjoyed talking to the people in experimental biology, I didn't enjoy the experimental work itself. I found it pretty stressful and repetitive -- long hours, a single moment of inattention crushing a week of work, hard to translate one's skills to different roles... I wasn't super money-driven, but seeing random FAANG friends working flexibly and making 250k, conspired to get me out of experimental work. I didn't want to be 30 and not have employable computational skills.
I think the repetition of the work deters a lot of people. My swe friends would say that their work too, was repetitive, and complain about "protobuf plumbing." I felt they _fundamentally didn't understand_ how repetitive and time-consuming (and delicate!) doing westerns was just to collect a little bit of information.
I was further convinced not to pursue methods development when I saw a PhD-level job talk from a guy at Big Name Lab at our company whose PhD research had like... not fully worked out. Methods is high-risk high-reward. Interviewers had a positive impression of him and gave him an offer, but were skeptical of how his skills would translate to the company/role, and he also didn't have computational skills.
As you said, I think the lack of agency is another hard part. Since experiments are expensive, it's hard to work on your own ideas. I know lots of experimental grad students and post-docs who have only ever worked on PI's projects... another aspect of this is that since people are spending like ~50-60 hours a week *just doing the same lab protocols*, this leaves a lot less time for actually thinking. I think this probably colors the incentive structure of experimental vs computational fields.
Many people are repeating maybe a dozen protocols hundreds of times, not interacting a lot with people from people from other environments. I remember asking a protein engineering PhD how she thought some reaction worked, and she asked "well what does the active site look like?" It felt like a question out of left-field for me, because Actually Thinking About Structure Details felt like a totally different level of abstraction compared to what I was used to.
Tying into this is that many academic labs are pretty siloed. (I think the biology groups I've worked in have been some of the most intellectually vibrant / fun environments ever though, so not trying to knock everything.) But many people are spending 5-7+ years in a single lab with like <8 people, and conflate that with some kind of Broad View of Science and Society (not claiming to have this, lol). This also means that labs turnover more slowly than companies do, since people might churn in <2-3 years. So I think a company might give one more exposure to ideas, but the constraint of "this has to literally-actually make money in the immediate future" is quite different.
In industry there's a lot of people at the bench who have 20+ yoe. In academia... your PI doesn't pipette, and the most senior person in the lab might be like ~3rd year post-doc. I think this means there's like an entire arc of IC skill development that young academic scientists don't see.
At my old job, I would talk to people with ~20 yoe... every 10-15 minutes throughout the day, and we have a shared context and mission of what's going on. In an academic lab, you talk to your PI once a week-ish, and your labmates sort of know what you're doing, but not really. I felt like I learned _a ton_ from just off-hand technical convos throughout the day with people at the company, and found the academic model sort of isolating.
Another thing from working at a company you pick up is learning to sniff out when somebody with 20+ more years of experience than you... can still be wrong. If there's 50+ people with PhDs, you're going to have a lot of contrasting views, and you have a lot of experiences where Bob, who has a Genetics PhD and 20 years more experience than you, is trying to lecture you about something, but it turns out the other senior people disagree and your hunch of calling bs was right. I think this is relevant in grad school, because your PI is the only senior person you interact with, and while they know more than you, they're also a person with their own idiosyncrasies.
Hey, man. Really liked your post and thought I'd reach out. I found especially interesting that you make the point that 10x engineers are born out of freely tinkering their own ideas around, learning what works n all until they have enough of a system understanding that they become cracked at it. And how you then mention that this 'attempt-result Cycle' doesn't really work for bio stuff, but you're anyway at the "top 1% in 'amount of fucking around and trying stuff'". I myself will be starting university for a major in physics later this year, and hopefully eventually work with some biophysics stuff or neuroscience. In any case, I wanted to ask you if you had any tips, or some important things you learned along the way (and the challenges involved in them!) when it comes to just experimenting with stuff and ideas that you'd want to share for someone that'll be just starting this path to excelence in the field. Would be very much appreciated!