Submitted because I really wanted to comment on this sentence: "For a startup, work needs to be both faster and more rigorous than an academic lab."
In our academic lab, if we knew how to be faster, more rigorous, or both, we'd be doing it.
The notion that a startup can do rigorous research faster than academia is curious. The scientific journals are not full of the output of startups. Industry in general, and startups in particular, can't expend the resources on covering corner cases and tidying loose ends. The profit is generally in getting the gist of an idea, not in writing it down, vetting every detail, and sharing it broadly for free.
I chuckled when I read that. Apart from the uninformed way this was expressed, the author's actual interpretation of this statement comes after that:
"In academia, in order to publish a paper, often you just have to get it to work one time out of ten – so you think, OK, I’ll just keep doing the experiment until it works. We need it to work nine or ten times out of ten."
So the author confuses it with engineering. Then -- allow me to be a bit cynic -- the actual meaning of the article would be something like:
"If you want to be a scientist at a startup, you need to become an engineer."
> "In our academic lab, if we knew how to be faster, more rigorous, or both, we'd be doing it."
There are some cases where it's obvious how to be faster (and occasionally more rigorous): judiciously throw money at the problem. In academia, where grant funding is pretty limited and equipment is expensive, you probably want to only have enough infrastructure for, say, the 50th-90th percentile of load (microscopes, thermocyclers, analysis compute power, what have you), and accept that 10-50% of the time there will be a queue. If the difference between being successful in the market and having your lunch eaten by someone else truly is a matter of weeks, then it makes sense to you and your investors to put some extra money into a widget that will sit idle for most of the time but that helps when things are crunched.
That said, money can't fix everything---9 women can't produce a baby in one month!
You could have 9 women on a constant baby producing staggered cycle so one baby gets produced every month. Then, when you need a baby you will be able to get one in 15 days, +/-15 days.
'Rigour' in the article means 'intensity'. 'Rigour' in science means 'validity'. A study with high rigour means that they've nailed down more loose ends than a study with low rigour. The two concepts are largely orthogonal - and it's disturbing that someone telling scientists what to do has used the wrong version of 'rigour' for that industry.
Ironically, using the 'rigour' of science, startups require much less rigour - startups just need something that works enough, not something that is as correct as can be done.
Academic rigor applied to industry would be a type of overfitting. Just as one example, customers don't care how accurate a fitness monitor is, as long as it is vaguely effective. Yet, few journals or funding agencies would buy your claim of building a fitness monitor unless you can rigorously demonstrate its performance and novelty.
Of course, the story is different in pharma and a few such fields. Theranos is an example of how lack of rigor and openness can damage healthcare startups.
Here is one answer in the form of an analogy -- but I preface this by saying that comparing the set of all academic science with the set of all industry science is fraught at best -- academic science is to cottage industry as industrial science is to factory production.
A related consideration is that training grad students and post-docs is a key component of most academic science. The requirements of training often limit the size of teams working on a single project with the PI-trainee relationship dominating the organizational structure.
As the "PI" of a science startup R&D team, I can start and stop new projects at will with varying team sizes and mandates without the consideration that my folks need to produce a body of published work to further their careers.
The author might have meant aiming for actual results instead of publishability. Academia, in general, is fucked up. Research is being done in order to publish - which means lack of real rigor and lot of fake one. Studies are being performed using bad methodologies, and then massaged and/or repeated until results cross the magic threshold, at which point they get published in 10 papers that say the same thing in slightly different words.
It's a terribly inefficient process that could be optimized by changing the goal from "publishing" to "making something that actually works". Thus you could achieve greater speed and more rigor at the same time.
(Basic research could probably be optimized too, although not through market incentives.)
While I generally agree with your comment, I think the one big advantage of private companies over academia is that they're typically much less resource constrained. Academic labs sometimes take huge shortcuts on cost with strange side effects. Often the way to be more rigorous or faster is "have fewer resource constraints" and even a mediocrely funded startup can have much more money than a well funded academic lab. On top of that, the more experienced team members spend a smaller percentage of their time fundraising.
The same thought occurred to me; I think it's probably more along the lines of "faster and relying on more intuition".
I don't think this is a new concept; Jon Gertner in his excellent book "The Idea Factory" writes how the researchers at Bell Labs switched from basic research to development during WW2 and operated in a similar way. If anything, the urgency of development in the war effort resembled a startup in how it accomplished within a few short years projects that would have taken decades in peacetime.
The title kind of misled me. It's about how scientists with a degree in _biotechnology_ (or a related field) can move from academia to the startup world, not about scientists in general.
For many academic fields there's no equivalent startup tech sector (which is the actual problem for scientists here) since the most important aspect seems to be the ability to become a horse with a horn. This means that before all else, there needs to be a huge potential market.
As a scientist working on a very specific topic, I can assure you that my market would be approximately five companies in the whole world. Although every computer and smartphone user could ultimately benefit from advances in my field of research, my customer would not be the end user, but the manufacturers.
I assume that this applies to the vast majority of scientists so the picture actually looks quite grim if you want to keep working in your profession. Many scientists need to change to an unrelated or remotely related field in order to get out of academia.
I think that scientists in industry are usually desirable for their skill set rather than knowledge in their specific field of study. I bet that you would be able to find industry work in your general field, but, as you say, probably not in the specific subfield that you've studied so far (this is obviously somewhat unsatisfying for people who have dedicated years of their lives to studying something). Out of curiosity, what are you working on?
Simulation of CMOS-Transistors by a semi-classical approach. A self-consistent deterministic solver for the Poisson equation, Schrodinger equation and Boltzmann equation including small signal analysis and noise characterization of the device.
Basically you want to gauge the crude models of circuit designers. But nanoscale transistors are hard to accurately understand. In particular, there's currently no good way to get predictive small signal parameters and noise characteristics of devices at such a scale.
So here I am, doing some of the first decent simulations for transistors at current technology nodes.
Definitely not something a startup is looking for :)
My focus is on biotech :) but what about Thermofisher working on their Ion Torrent platform (although this isn't a startup). They use ISFETs for DNA sequencing. Also there is Oxford Nanopore Technologies which sequence DNA by measuring changes in current as DNA passes through a nanopore. ONT is a startup :)
This article is interesting to me, but I have to take issue with the concept of "thriving" for a scientist. I assume the conditions for thriving will be different for different people, so what follows is my perspective only.
Can a biotechnology startup really provide a better standard of living than academia? Almost certainly yes, from my experience; free lunch and swag are fun, and the pay is much better, but still far out of proportion to the effort put in, which is only slightly less than academia. Can a biotechnology startup really remove the fun and wonder from science? Definitely yes, especially when money questions are prioritized over science questions (always). Is it worth trading the abject discomfort of academia for the semi-comfort of industry and the fun of science for the humdrum of product pipelining? Only individuals can answer that question, and I have not yet answered this question for myself. It is true that industry feels like "selling out" and is seen as such by the academics, however this may be caused by sour grapes style reasoning when jealous.
I hate to say this, but most of the industry people I know are far less intelligent and far less ambitious than the people I knew in academia. They're far less cut-throat, and have far better teamwork, too. The office politics are much friendlier, and non-science people aren't looked down upon in industry in the way they are in academia. More efficient and profitable to have the industry folks around, sure-- but not a single scientific idealist around, and certainly no veritable philosophers of science, meaning that experiments are at worst (either by ignorance or malice) exercises in manufacturing the desired results, just like in academia. At best, I'd say the quality of results are comparable between the two institutions. The emphasis in industry is on production of cash rather than knowledge, which seems fundamentally not science to me.
So, what is a scientist to do? Hard to weigh in on either side with confidence. The trouble is that you can't simply leave academia to "try out" a startup for a while-- it's heresy, and will be punished by refusal of reentry. I guess this may be changing, but up to the present, that's how it's been for people. There is a flow of people to industry from academia, but not in the opposite direction. It's also no secret that industry can be the graveyard of highly intelligent people who were rising stars in academia before switching... sometimes they get lost in a dead-end department (recently, such as many promising folks at Novartis' siRNA team, before it was shut down and hit with layoffs) and other times they get stuck beneath an imperial-style boss-- a similar trap in academia.
This is the style of thinking that leads to MetaMed. "Everything I don't know the tawdry details of is simple because I am smart and radical and more meta than you in my thinking and know the One Weird Trick!" Perhaps you are and do! But it’s overwhelmingly likely you aren’t and don’t.
“Coming from academia, there’s a myth that it’s really risky to join a startup, and in my experience that’s not true at all,”
Tell that to CS PhDs. Granted, this is mostly about bio, but even here there is a lot of hope with the bioengineering applications and the disease modeling. Generally, my anecdotal view is that folks don't want to go to industry as they all see themselves as getting a tenure track job. Then the 2nd postdoc is not a startling success and they change tunes with only 3 months of funding to go.
Also, as other commenters have noted, Matt De Silva of Notable Labs may have had a bad quote attributed to him.
“Think about what a ‘hacker’ was to traditional computer scientists. Startups need the life sciences equivalent of that.”
This, as most people looking at bio from CS, is a laughable statement. I started out as a physicist and got interested in neuro. I thought that it was just because the bio peeps were lazy at learning the math, that a mathy person could sweep in and teach 'em real good-like. Bio is a astoundingly complicated field. You have N overlapping Gaussian curves describing just 2 variables and each interacting in an unknown number of orthogonal (you hope) dimensions and variables. Just take a look at the KNOWN pathways for mammalian mitochondria [0] at some pressure, temperature, salinity, etc. Any variable can throw this off incredibly so, or not, or maybe a little bit. Also, what about muscle cells versus glia? But your study was on mice, not humans, or the humans' mitochondria has a mutation in it's own DNA (yes mitos have their own DNA). Oh, and this is just the people you could get samples from. Bio is soooo very complex. Bio people are nothing but hackers. We have a very small idea of what is actually going on, and we try to exploit that in any small way we can. Look at Rita Levi-Montalcini's discovery of NGF for proof [1]. She is an admirable person and a cherry picked outlier, but a hacker in the truest sense of any CS person. She set up the lab in her bedroom after being fired for being a Jew in WW2 Italy. Her dedication to knowledge for knowledge's sake is amazing. She discovered there is a molecule that pre-destines daughter cells to be neurological by playing with chicken eggs and viper venom in he closet. If that is not a hacker, please define.
“The story of a business is about vision and progress: What are you going to do, how far have you gone so far, and how are you going to make money.”
This is exactly how grants are written as well. They claim a difference, but there really is none. You have to sell your grant just like you sell any pitch. Yes, the grants are mostly on paper and not in front of a person, but the story element is the same.
“In academia, there’s no wheeling and dealing around the grant process."
Yes, there are a lot less places to apply to than there are investors to find, but there is a LOT of behind the scenes stuff happening here as well. If you have a proven track record of getting grants and of getting out research, you get the grants with a lot less questions asked. But, yes, I admit there are a lot less rejections. A grant a month is typical. 2 a month is a little high.
Overall, the article was largely a PR piece. I think to help Ycombinator (the site I am posting on) to drive people to them for investment. Not surprising really. I could feel a bit of the "pick up artist's" technique of 'push-pull.' You insult a little, then you back it off and repeat. I don't think that was intentional though. I like the article, but I feel it had a lot of naivete. Maybe a lot of VCs out there see biotech as a great space, as there is not really a lot of people in it and the ground seems fertile. I would caution against that. Biotech is very very hard to get going. FDA approval is notoriously difficult and expensive. The flip side to that, however, is that once you make it through, there is almost no competition and you essentially have a monoploy (hooray patents).
In our academic lab, if we knew how to be faster, more rigorous, or both, we'd be doing it.
The notion that a startup can do rigorous research faster than academia is curious. The scientific journals are not full of the output of startups. Industry in general, and startups in particular, can't expend the resources on covering corner cases and tidying loose ends. The profit is generally in getting the gist of an idea, not in writing it down, vetting every detail, and sharing it broadly for free.