The reason you don't see more startups in the hard sciences is not due to the lack of hybrid talent as this article surmises. It's because:
1 - VCs are reluctant to fund capital intensive startups that have time horizons for exits that are significantly longer than software based startups.
2 - The product lifecycle is so much longer, which makes it inherently much riskier. In many cases it can be years before you even get to the point where you can get real feedback on the business model.
3 - There are often other considerations e.g. regulations or interactions with existing products, that are entirely outside of the control of the company that can significantly alter the likelihood of success.
This, 100%. The author says how difficult it is for multidisciplinary teams to come together when they don't understand each others skillsets--but the same applies to the investors themselves. When you start talking about these complicated ideas, there comes a point where unless the investor is involved in the industry they're investing in, they simply won't understand the true impact of it.
My company is trying to raise capital now, and that is the exact problem we're running into.
There's a reason for the "janitor as a service" unoriginal ideas--because they're easy to understand, so more likely to be funded. Those kinds of investors are looking for the buzzwords, too, "as a service," "cloud," "social," "AI" that cut off ideas that aren't strictly consumer-facing and infinitely scalable. If you have a modest idea that requires a modest amount of money and targets a modest group of people, you're just not going to hear back from investors. This causes people to have to wrap their idea in buzzwords or lobotomize it into something that allows them to achieve their true goal in a sideways manner.
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Brilliant. The dystopia we find ourselves in is palpable. Your language drives home the insanity of our times because not only is this product absurd but also believable.
I'm pretty sure you're joking but I'm almost certain I could sell this idea if I could keep a straight face through the sales pitch. You have talent :)
There's probably a good German word for the sudden doubt in one's humanity you get when you unintentionally fail a Turing Test. It's like the feeling I get when motion-activated sinks don't see me.
This is describing just one painful route up the mountain.
There are other routes, which usually involve sucking it up, going and working at Wall St or SV for a couple years till you know the "right people" and they know/trust you. After that happens the type of conversations change drastically.
Judging by the lack of coherency / relevance of the profile's other replies, I'm guessing it's a bot whose model someone's attempting to train on HN comments.
A model that has coherency over multiple paragraphs? I don't think that's currently possible - but if I'm wrong about that I'd love a link to a study or a library if you have one. Natural language processing is something I'm very interested in.
> there comes a point where unless the investor is involved in the industry they're investing in, they simply won't understand the true impact of it.
Equity financing is a bad measuring stick for this. But really, when you're competing with, "Buy gold because it's shiny!" - that would have earned investors 26.6 percentage points liquid return YTD - can you really fault people for being skeptical of complex ideas as a class of investments?
Biotech startups are also subject to the fickle nature of living organisms.
When I was in grad school I knew a bunch of grad students who worked on biotech/bioengineering experiments. They would have to take care of their experiments like they were pets, nurturing them and making sure they were well taken care of, because if they died on you, that's months of effort down the drain. Vacations had to be carefully planned, and people had to be delegated to keep those little critters alive.
Whereas folks running experiments with non-living things could actually work 9-6 and take vacations. Computational folks could run their experiments while sitting on a beach in Hawaii (with an LTE signal of course).
Bio is just a different beast.
The worse thing is? Many of these biotech graduates actually struggle to find well-paying jobs after, despite how hot the field seemingly is.
The problem with finding jobs is that the work is far less translatable to other fields unless you take care to make things general which is not really encouraged in academia. As a Physics, Math, or computational PhD you can apply your skills to any domain with a few weeks of learning. You can't really apply PhD level mice work or yeast work to another domain that quickly.
But also, who can blame them. I've seen really stupid companies come out of biotech incubators, including one that was peddling a genetically modified probiotic whose concoction as designed is known to be ineffective pharmacologically (and an equivalent reformulation strategy is not known in their host species), and a company that demoed reconstituted mock vegan meringues that had residual trifluoroacetic acid in their demo day samples. Vcs just don't know how to judge this shit, and it's much harder to pattern match details that require subtle knowledge than "Uber for X"
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if you've ever done an hplc with triflouracetic acid, I promise you there's residual tfa in there, even after a round of lyophilization. Though typically I'm not putting HPLC preps in my mouth. The burney/tingley sensation on my tongue was unmistakable and very reminiscent of the residual feel you get after pipetting 100% TFA in the hood.
I strongly believe that this startup did not know that about TFA, but it's basic common knowledge if you're in a reputable protein biochemistry lab (and you're paying attention). When you're moving fast to show something for demo day, you're going to use whatever you have around to do your protein preps, because TFA is standard practice.
Anyways, if any VCs want a Burton Guster to super sniff questionable biotechs, I'd be happy to do a bit of consulting on the side.
Not chemistry experienced, and a quick internet search isn't doing the trick, but is a "Blueberry" some kind of mass spectrometer? How much would something like that rent for, or is it more of a "if you have to ask" situation?
It was actually a reference to the TV show Psych, whose character Burton Guster (and his many aliases) drove a bright blue Toyota Matrix hatchback affectionately referred to as "The Blueberry".
Considering that only a small number of VCs make any money at all (power law strikes again), it doesn't surprise me that the rest of them are some combination of reluctant and inept when it comes to investing and seeing the long term game of some of these prospective hard science initiatives.
The ones that do succeed end up spending what money they made to keep the deck stacked their way and crush opposition. It's not so much that there's no barriers to entry. They exist, it's all the competitors that have VC money ready to burn to keep you out of the game.
Example: EV wouldn't have really taken off without Tesla battering the living shit out of it. Now the other manufacturers are starting to play catch up after suppressing it for decades. It's not like we miraculously discovered the technology for EV drivetrains a decade ago. It's been there all along, and every single one of those fuckers has been stomping on any and every initiative with a warchest of money to make sure it doesn't happen.
Looking back after all these years, "invest in people not in products" seems like nothing more than glorified lip service.
I want to agree with the article but I have no skin in the game. The only VC tier stuff I was involved it was F&F angel investing and it has worked out quite well, but the scale of money and time needed for "hard sciences" is beyond my level of expertise, and, I imagine, beyond the expertise of most VCs out there.
In short, I suspect most VCs do not know what they are doing when it comes to investing, given the paltry ROIs for most of them. So the article is really restating that in a different sort of way.
This is so true. A mentor of mine has been developing a new cancer therapy with a physicist for over a decade. He's spent millions of dollars and all he has to show for it at this point is a machine for rats that can kill cancer better than the standard of care, for rats. He's building a machine for humans now but by the time he's done he'll have probably invested close to $10M all by himself just to have a single proof of concept that he can take to market. That's an insane amount of investment until as you said, you even get to the point where you can get real feedback on the business model. Ultimately though he understood the fundamental physics and knew that it would work just as well as anyone could.
There's also reproducibility issues in biotech research. Investors are reluctant to believe anything works anymore until they see successful human trials.
SBIR funding is critical for hard science ventures early on. It's also a good indicator to future investors of potential hard science projects that a panel of experts in the area has reviewed and approved government funding for the idea, and the team is at least decently competent to meet the milestones of the SBIR. This helped Ginkgo Bioworks before they received more than half a billion in private investment.
There's decently large amounts of money to be had with SBIRs, but they are a moving target--you end up stitching together many different smaller projects to slowly ping pong your way to the end goal. Of course they're also very slow to apply, decide, reapply, etc
Oh, no one would probably bother to cite it because it's right there in the request for proposals in black and white and they explicitly talk about it repeatedly at the SBIR conferences.
You finish your phase 1 in 9 months to a year depending. Then you apply for phase 2 which takes 3-6 months to review and fund. That's the death valley I mean, the lag between finishing phase 1 and starting phase 2.
If you don't have non-grant funding by then, you're self-funding the company for six months of being strung along waiting for them to make a decision. It sucks, especially when VCs have no interest in funding projects that are as hard to understand as my stuff (catalysts and chemicals and machine learning) when they can just fund Uber for cats or whatever.
I'm too tired to keep trying for it, but at least people are finally starting to recognize that chemical manufacturing infrastructure is pretty critical and that we don't understand much of it at all and that if we want to use bio-sourced chemicals we need to really understand this at a global systems level an awful lot better.
But we won't, we'll just try to bolt on bio stuff to horrible legacy systems and make a marginal improvement instead of a generational breakthrough. But if anyone reading this happens to actually be working on this give me a ring... it's my passion in life to fix this because I see it as reducing energy consumption and also improving agriculture through improved ammonia production processes. I am just unable to work on it because of life. And I really don't want to start another company at this point.
The way I've seen startups make it work is to save part of the indirect for the worst-case scenario of no bridge funding for the 3-6 month period. If you are able to get into an accelerator that takes a small cut and aligns with your area, the amount of indirect saved (.4*250k) available could be sufficient to bridge a low-cost org for 3-6 months. Even then, it's still a gamble as to whether you'll receive the Phase II in that time.
As for timeline, you're able to apply for the Phase II to kick in right as the Phase I is ending. I've seen that work but it requires planning and long hours to perform research and write the next phase proposal. There's also direct to Phase II for ~$2m in one grant.
If you have outside responsibilities and don't want to risk that bridge, you could try and submit the direct to Phase II, which would also help develop your idea to pitch to VCs in clean-tech space.
With hard science ventures that sometimes have a hard time findng the 'killer app' for the technology, I think the experience and learning from ping ponging around is a feature. Yes, startups need to plan for the long and uncertain apply-decide-reapply cycle
I agree with #1 in particular. However, there are always ways around these issues. Some solutions for founders: 1. Use an evergreen fund (such as many family funds) instead of traditional 10-year fund lifetime VC. They are specifically set up to harvest these sorts of opportunities. 2. Demonstrate commitment and operational efficiency. For example spend nontrivial amounts of time and funds on the project to reach relatively advanced stages before asking for outside investment. 3. Regulatory hedging (cross-border operations, and establishing facilities near borders) is one viable strategy.
Full disclosure: I run a nontrivial hardware startup seeking to define and dominate a greenfield segment and have used all three strategies in the last few years.
There are biotech VCs though just fewer of them and the screening process is quite rigorous. You have to get proof of concept in some academic paper first. That being said the AI drug design companies and the transformation of molecular biology with bioinformatics/NGS into more of an engineering paradigm (at least for diagnostics) will definitely change things. I'd expect medical devices to advance first and then new drug development.
Another reason we see duplicate startups within a few domains is that there are many VCs, and each wants to have a horse in the race.
Perhaps they missed on one fintech company, and now it's grown enough to make the market. So they lead an investment in a competitor, or a company in a similar space. This significantly de-risks the investment vs. allocating money towards something entirely new.
There’s a lot of startups purporting to bring hard science to market but most of them are incompetent or snake oil and it’s very difficult for software engineers (or investors) to identify the diamonds in the rough with 0 domain expertise. Meanwhile many of these startups suffer from a lack of exceptional software engineering that is often necessary to solve novel cross-disciplinary problems from first principles.
It would be a boon for society if it were common for great programmers interested in hard problems to take a year off from their lucrative dead-end big tech co careers and study a subject outside of CS that they’re interested in, so at least they’d be able to evaluate the feasibility and importance of technical challenges in that field and apply their skills at a point of high leverage in that domain.
The amount of startups I've encountered in Norway that will "solve" the problem of oil exploration with neural networks is ridiculously high. And they are all run by people with no o&g background but a brand new "data science" masters degree.
Yeah, I was surprised to see the article start from a clearly true starting point and then veer way off into left field talking about not having enough talent or coordination (the founder's problem) instead of the actual issue of VC expectations.
