The dual PhD problem of today’s startups
techcrunch.com
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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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I’ll see myself out. Lol
Menschlichkeitszweifel (Menschlichkeit = humaneness, Zweifel = doubt)
Turingzweifel (Zweifel = doubt)
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.
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?
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.
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"
Yikes! I've had some protein preps go wrong, but never this wrong!
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.
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.
In no other field do I see masters/ PhD from great universities doing such menial work.
And have a known death valley and cash flow issue unless you have other investment already.
But they're a nice to have for sure. Just not enough to keep moving for long enough to get most hard tech startups funded.
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.
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.
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.
like why can't you change a few lines of code, iterate your product multiple times a week and pivot???
because science.
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.
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.
Of course, there are a bunch of new horizons out there [...] Cryptocurrencies and finance.
It seems a lot can change two paragraphs on. Life moves pretty fast these days.
Edited to add: I reject the central thesis of this article, and pretty every one of the supporting arguments. Humans have required teamwork to achieve their goals from the very beginning. Invention has always required the synthesis of ideas from multiple domains. There’s nothing historically unusual about that. What is historically unusual are the diseconomies of scale in activities like software development. That’s provided many market opportunities for small teams in the past four decades, and it will continue to do so unless those economics change.
There are markets with high barriers to entry, and there always have been. Nobody was selling homebuilt aircraft carriers from their bedrooms in the 90s.
From our vantage point, we can’t tell if the seam of potential innovation and market configuration is anywhere close to being mined out in consumer tech, but my sense is that we are nowhere near the point where all startups need to be at the frontiers of all human knowledge of gtfo.
The reason why (most, not all!) VCs are successful is not because they have some secret visionary insights into the future of technology but rather because they have the means of diversifying their investments in things that are more or less guaranteed to happen. Will work be more decentralized in 10 years than it is today? Yes. Will financial institutions move away from the archaic infrastructure it's on today over the next decade or two? Yes. Will education move online and become more personalized in the next 10 years? Yes. So, just invest in 20 remote work SaaS companies, 20 fintech products, and 20 online education startups and you'll have a fair shot at making some money. In other words, most VCs are really just private equity versions of index funds.
Because of this, most VCs lack the experience, understanding, and interest in investing in highly experimental projects (there are exceptions of course!)
As an example, I would be very surprised if any of the major VCs today would have invested in a small set of people who wanted to work on what would eventually become the transistor or TCP/IP. There's a reason why these things tend to start in huge corporate research labs (bell labs) or universities: they're not obvious and they're not obviously profitable.
So, the real reason why these companies are not being built is not that the people aren't there willing to build them, it's because nobody's willing to listen. They're just a bunch of crackpots with crazy sounding ideas... until they're not.
That's a big call during an unprecedented work from home pandemic.
I had a colleague who during her PhD in particle physics wrote from high performance parallel computation frameworks from the ground up in C which was better than Hadoop and Spark in performance. And at my last enterprise AI startup, our CTO had come from a computational neuroscience background. Whether these folks end up in creating startups is a different question, but the talent definitely exists.
The more difficult problem is how to evaluate multi-disciplinary startups and businesses. There usually isn't good empirical evidence unless they follow a more established business model.
Two PhDs that don't speak the same language isn't a great solution, but one PhD who is a jack-of-both-trades isn't the only alternative either. I feel like I've done well with alternating collaborations with biologists who don't have a computational focus, and quantitative methods folks who don't necessarily have a focus in genomics (what we work on).
Is that all that different from a software engineer with little customer facing experience teaming up with a non-technical cofounder who does?
Sure, we had people worked more on the bench and people who never set foot in the lab, but everyone made sure to know exactly how their data came into their hands and it's purpose, so if you were a statistician, you would learn everything about the corn sample you were given to analyze so you could make the correct considerations in your analysis. And if you were that wet lab person and wanted to present a figure that the statistician generated, you would learn everything about the test used, and all the assumptions made when choosing that method of analysis over others. Even in academia, this high level of collaborative interdisciplinary learning can be rare, but makes you a much better scientist who as a much better grasp on the wider project and your role to play.
I think a lot of startups operate with a mercenary mindset. Everyone is hired to play a discrete non-overlapping role, which tends to silo ideas. Central planning from upon high is also the norm, rather than collaborative discussion and solving problems from the bench up.
Depressingly, there are more and more big name academic labs that are adopting this startup oriented top down approach, with a head professor calling the shots and giving marching orders to a few sub research professors with their own postdocs, and grad students, and undergrads. I've known grad students and post docs in these labs who are outright denied to direct the research in their own projects, even if they have good ideas, simply because they didn't come from the top down. Pursuing your own ideas is the whole point of grad school and post doctoral training. On top of that, these labs siphon funding from more innovative and smaller groups by outputting higher numbers of ho hum papers, or affording expensive research with large, multi-institutional grants, both of which are heavily favored metrics in the grant proposal and tenure process.
Today, you can study Machine Learning without having to focus on any particular domain (well, other than stats and applied math, which lays the foundation for the theory).
But, yes, it is tough and demanding to find people that have deep / expert knowledge in both their respective domain, AND machine learning / data science / AI.
I think maybe one way to do it is to just look after domain experts, and learn them enough about ML and DS (if they lack the background) to work as generalists. Enough that they can read and discuss it.
And then, you hire ML scientists and engineers to do the nitty-gritty work, with the input and feedback from the domain experts.
The second key reason why this fails is that people/colleagues/managers do not understand highly diverse skillsets. They do not let you be both. Maybe a culture shift is comming, when more people with multiple skillset will be available, maybe specific roles would be tailored for polymaths, but right now it is not the case.
There are probably workplaces where it is possible, but I have never seen it done properly.
I'm pretty sure that the idea here is that "double PhD types" like you would be the ones to build a workplace where this is possible and breaks the mold.
I actually agree with the author, certain fields are just very complex to develop a solid understand without proper mentoring and support you would get as a grad student.
That said, maybe the real issue is not the fact we need two PhDs each, but the question I want to raise, do PhDs need to take 4/5/6 years? (I'm not even considering the cases where people, like myself, do a 2 year master program before the PhD...). Honestly, in my humble opinion, it is not necessary.
Maybe universities could develop "industry focused dual PhD programs" to target specifically crazy folks like us :)
This might be something worth to fight for.
One of many hiccups is in the submitting and review process. It takes ages. Sure, some areas are less, some are more, but three year long submit/review periods are not unheard of. Reviewers want another experiment, another control, they don't get back to you until February even though you submitted in mid-November, you can forget August as a working month, etc. Unless your PI is well connected, getting published takes forever.
I have a paper that has been in reviewer hell for the last seven years, for example. It's nutters.
Indeed. And the dirty secret of this process is that it has little to do with ensuring research quality (although it does usually succeed in filtering out very bad research as a side effect). Its main purpose is to simply make it difficult to publish, so as to preserve the CV value of a publication in a given journal.
Jack Kilby was able to make the first IC because he worked in a transistor factory.
They are not, really. The field of bioinformatics exists for almost 20 years, as in, you can degree in it - I almost did myself. And the "informatics" part that you get educated about is pretty much data science, that, by now, uses a lot of ML methods and just like ML requires a very serious math foundation.
[0]: https://www.coursera.org/specializations/bioinformatics
For a pure biology undergrad who is probably med school bound, learning ML is superfluous so you don't see it in the curriculum at the undergrad level, unless there are specific concentrations offered for computational biology. A bioinformatics program may even just have you take these ML classes from the statistics or CSE department rather than offer some bioinformatics-specific section within their department.
Much of the groundwork laid for online learning & statistics/bandit algorithms/modern reinforcement learning was produced by biostatisticians working on techniques for efficient experiment design (e.g. Thompson Sampling during the 1930s in Biometrika).
Seems like the author just doesnt get biotech and now that biology is becoming tech is confused where their amateur startups fit. Answer: no where.
Ideally the lucky few who make a ton of cash on easy software apps etc should be using that capital to risk solving hard problems and developing new sciences and technologies.
Going straight for the HN jugular I see.
Successful organizations in the spaces in the article need to prioritize cross-training and collaboration as a first class value. Not doing so will lead to siloing and nobody understanding the whole problem.
In order to find new ground, you may find yourself needing to do something truly out of the ordinary. Try holding board meetings in Swahili. Try “running your business” (which loses context given the next few words) without the concept of ownership or property. Seek sources of knowledge and wisdom outside of the scientific method, religion, business schools, political systems. Of course these would obviously fail (see 3 above) in the current system. We don’t tolerate failure. And we like to see out-of-the-box thinking fail because it further validates our existence systems. We may very well be at a maxima for social and economic development. The cost to try something new would be tremendous. We’re also disadvantaged that we have homogenized culture and thought. Even the simplest changes may require a step back that would be considered another dark age.
Obviously that is not sustainable. If society is to continually innovate, you need to stop building innovative systems and start growing them. One approach to this is true AI; not improving your NLP algorithm by 10% with 3x the math complexity IE the transformer model (quote taken from Michael Stonebrake, although he was referencing database research), but by building math that can grow math.
Hell even math is becoming a road block (try integrating THAT Bayes!). The point is as long as we must learn to build, we will run into a wall as humans have finite lifespans and don't scale horizontally (nor do they want to). If we build something that we can feed or point to un-wrangled, raw information into such that it can learn on our behalf, we might have a shot.
Now BACK to pumping out small improvement papers, innovators!
From what I have seen cross disciplinary innovation usually happens because someone's interest in one domain has created a demand for a solution that can only be fulfilled with a different one. It's rarely about being skilled in both domains, it's about committing yourself to something a single domain expert has no interest in.
An actress basically invented FHSS, and no one understood the applications it would have to future technology until much later on. Just because you cannot think of something new does not mean that no one else can- if you are working in startup funding you need to find the true purple cow. Not the spraypainted one, or the one that only lives for two weeks and has to sustain itself on gold.
Mind you, said actress, Hedy Lamarr, was a fairly brilliant, self taught electrical engineer.
Huh? FHSS was a specific wartime effort with a wartime goal that, aside from that its modern application is mostly civilian, is not terribly distant from its intended use case.
I work in a corporate lab, and it is typical for our teams to have >3 PhDs from different fields solving problems. We acknowledge internally that there aren’t many interesting problems left to be solved by single PhD teams.
One of the upsides of this job is that you get to see everything going on out there in the startup world. One of the downsides of this job is seeing just how many ideas out there aren’t all that original.
Every week in my inbox, there is another no-code startup. Another fintech play for payments and credit cards and personal finance. Another remote work or online events startup. Another cannabis startup, another cryptocurrency, another analytics tool for some other function in the workplace (janitor productivity as a service!)
But I'm glad I kept reading, because there is some good stuff here. I mean, it's not a PhD thesis or anything, but there's some insights worth pondering, tucked away in this article.
The gist is here:
Now, we are approaching a new barrier — ideas that require not just extreme depth in one field, but depth in two or sometimes even more fields simultaneously.
Take synethtic biology and the future of pharmaceuticals. There is a popular and now well-funded thesis on crossing machine learning and biology/medicine together to create the next generation of pharma and clinical treatment. The datasets are there, the patients are ready to buy, and the old ways of discovering new candidates to treat diseases look positively ancient against a more deliberate and automated approach afforded by modern algorithms.
Moving the needle even slightly here though requires enormous knowledge of two very hard and disparate fields. AI and bio are domains that get extremely complex extremely fast, and also where researchers and founders quickly reach the frontiers of knowledge.
I would agree with that sentiment in the general sense. And there's probably some interesting things to be gained by thinking deeply about how to address that problem.
The only part of this I found myself disagreeing with somewhat is here:
We’ve gone through the generation of startups you can do as a dropout from high school or college, hacking a social network out of PHP scripts or assembling a computer out of parts at a local homebrew club. We’ve also gone through the startups that required a PhD in electrical engineering, or biology, or any of the other science and engineering fields that are the wellspring for innovation.
While I agree that it's probably getting harder to come up with something really innovative without that "fusion" approach alluded to above, I'm not convinced that it's not possible. Furthermore, I don't see being "the next no code startup" or "the next cryptocurrency startup" as being a Bad Thing - so long as you do it in a way that's appreciably better than "the other folks" doing the same thing.
Sure, inventing something Brand New is nice, but you can make money making a "nicer version of something that already exists", or by just innovating on business model while the product is unchanged (or mostly so).
None of that was really innovative, yet ended up with massive commercial success. MS-DOS was the not first PC operating system, Facebook was not the first social network on the web, Apple was not the first PC or smartphone maker.
So I don't think anything has changed there at all. You can still create a massively successful venture bringing something out to market in a way that is somewhat incrementally 'better' than what is on offer without having multiple PhDs in different fields on your founding team.
And it's pointless comparing that to the type of startup that is trying to creating something that is completely 'novel' from the intersection of 2 or more technical fields. Managing that type of complexity isn't something new either - it is fairly routine in academia to apply tools from one field to another - which is also how a lot of innovation happened historically as well as how many startups got started. Historically these type of ventures are high risk and the reason we are seeing a growing number of these is more a testament to how saturated the startup ecosystem is and how research increasingly is driven by venture capital rather than by academia and industry.
And a lot of this is very prosaic. It isn't about designing some world-beating technology but finding stuff that works and doing it well. Neither of these tasks are particularly straightforward either, knowing what to do is usually not a big problem in business, knowing how to do it is far more difficult problem (ironically, tech is probably one of the best examples of this).
Just generally: believing in constant progress, believing in the mystical power of technology are common psychological habits of humans (Marxism, to name an example)...but it wasn't any easier to make technological discoveries two centuries ago. Literally, these people had none of the knowledge we had today, there was no way to share information, the barriers were huge. Innovation has never been easier.
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Large companies don't talk about their work in this area much for a while for a variety of reasons. Bell Labs was a thing once....
It's sort of a solvedish problem if you imagine that you are not bound by "silicon valley 2-person startup" rules.
Things close the edge are risky. Things close to two edges are even more risky.
Risk isn't reduced by reward.