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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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"
We're kind of like Uber but instead of calling a car for yourself, you call a deadly pie for your enemies. Our drivers are incentivized by the rating system to operate with discretion that beats similar state-sanctioned programs. Launch flavors include Rhubarb, Almost Almond, and Death by Chocolate.
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.
Every week in my inbox, there is [...] Another fintech play for payments and credit cards and personal finance, [...] another cryptocurrency
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.
This is funny, but to give Danny the benefit of the doubt: he presumably means there are horizons out there in cryptocurrencies and finance that aren't approached by also-ran "fintech plays" or "another cryptocurrency"...
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 because it was forced... I had to fight dozens of companies because "Oh, we need you in the office" and now I reply to each one who hits me up with the original message from their last time.
This assumes that ML and AI research will continue to be silo'd outside of domain specific research. But it's not the case in academia and also increasingly in industry. You have computational neuroscience, bioinformatics, and many other traditional disciplines which have not only incorporated ML/AI methods but also pushed the fundamental methods research forward. We're increasingly seeing interdisciplinary methods and research becoming the norm in academia. In undergrad, nearly all the social science classes and all the hard science classes had some sort programming and quantitative methods requirement. Even in industry we're seeing interesting multi-disciplinary work.Many interesting innovations in time series ML methods have come from the algorithmic trading firms and medical research community has made contributions to computer vision and unsupervised learning approaches.
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.
As a computational biologist I have gained sufficient expertise in both computation and biology to know that most of the magic AI biomed stuff proposed by people who have expertise in only one of these areas is utter nonsense.
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).
Yes, I think that works best. You need an engineering leader who has some understanding of all of the important problem areas the company faces, even though he/she is unlikely to be the company's leading expert in any of them, or more than one of them, so good decisions can be made.
Most of the "interdisciplinary" research I see is just one lab sending a dataset they generated from experiments to some ML collaborator, who just run some fairly low hanging Logistic Regression and RF on the data. I don't see a lot of places where people with deep statistical/computational understanding tackle the problems with gathering data, and working on understanding underlying processes. A lot of this comes down to how short PhD programs actually are, learning both neurology, GPU programming (not just plug and chug torch) and statistics would probably take 8-10 years; and most people/schools won't bare that commitment
That's fair. It's hard to generalize as there is definitely a mixture of good and bad research out there. As a counter point I had to opportunity to observe the Summer Workshop on the Dynamic Brain (https://alleninstitute.org/what-we-do/brain-science/events-t...) which had fascinating interdisciplinary research at the intersection of computer vision, ML, data science, and foundational neuroscience research. There many other programs and research groups I can mention that do quality interdisciplinary research and train interdisciplinary students.
> Today’s startups have a biologist talking about wet labs on one side and an AI specialist waxing on about GPT-3 on the other, or a cryptography expert negotiating their point of view with a securities attorney. There is constant and serious translation required between these domains, translation that (I would argue mostly) prevents the fusion these fields need in order for new startups to be built.
Is that all that different from a software engineer with little customer facing experience teaming up with a non-technical cofounder who does?
As someone from an academic background, it's a bit different than how academic labs are set up. My academic lab had a number of different projects in different research areas, ranging from human health to agriculture, but the unifying theme was big data analysis. The fact we all had this focal point meant we often had shared overlap with common tooling, which meant a lot of collaboration. Whether it was for alzheimers susceptibility or corn yields, you were working with tabular data.
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.
The last two pairs are non-issues, both have plenty of funding. For the former however, one misstep and you have the FDA/DHS or one of state medical unions breathing down your neck.
Interestingly enough, things like Machine Learning grew out of domain fields. A couple of decades ago, there were few - if any - dedicated programs for Machine Learning. The research and grads came mostly from domain-specific fields, like Computer Vision, Signal Processing, Computational Biology / Chemistry, Statistics, Applied Math, etc. Then when things got more cohesive, the most important parts formed into a more or less "pure" field of Machine Learning.
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.
I have an MD and a PhD in machine learning, and in my experience this "double PhD" intuition is completely wrong. The first and main reason is because you do not hold multiple roles in a company, and you are rarely able to apply your expertise in multiple domains - one domain will passively or actively be picked for you. Except maybe for some execs, who might apply their expertise at a high level. If you are in R&D, you will focus on one task. If you are a CEO, it can give you a good bird's eye view but nothing that would match a good board. And as CEO, you will not be researching, and you will need specialists in both disciplines.
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.
> 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'm in the middle of a PhD myself, so take my opinion with a grain of salt.
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 :)
5+ Years PhD programs are completely unnecessary. After quals, you should do some research and write that up. The research may take a year, it may take two, it depends. Write it up and publish it. Do that again, maybe, it depends.
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.
At least 3 years. I knew a guy in Manchester who was on his seventh year, the funding ran out so he'd spent the previous three years living in a cupboard in the department.
This is an argument for the corporate research lab, where you have people on staff who know how to do things. Xerox PARC was able to build the first laser printer because they were in the same building as the people who worked on copier technology. So they had people who knew about photoconductors and paper feeding and getting the toner to stick to the right part of the paper.
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.
That's not a lot of bioinformatics programs that I'm seeing. A lot of bachelors programs seem to focus on teaching almost exclusively the basics of BLAST and all it's boring related algorithms (basically everything in this Coursera course[0]) and their mathematical foundations. Master's programs sometimes are a bit better with a hint of ML, but ultimately most people I've encountered there are still awfully unequipped to tackle ML problems and transfer the advances from mainstream ML to biology/biochemistry problems.
Any bioinformatics program covers machine learning these days. They might not have an explicit class called 'machine learning,' but you can bet it will be covered in the lecture sequence and the cutting edge of the field will be discussed in journal clubs, rather than in lectures which are about established fundamentals.
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.
I agree with this, and this was true historically as well - e.g. Biometrika - a premier journal in statistics - grew out of bioinformatics-like research.
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).
The author has...never heard of biotech VC? Where even the VC have PhDs and the founding team and not just 2 but 10 and also 4 MDs and capital intensive business is accepted and understood?
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.
This is analogous to suggesting Elon should had skipped Zip2 and X.com/Paypal and went direct to building rockets.
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.
Another question that's worth pondering is that perhaps the previous generation of technologies was simply easier to develop than the current generation - I believe the economist Tyler Cowen proposes this theory in his book "The Great Stagnation". For example, it may be that silicon processors are just intrinsically easier to develop than quantum computers, traditional nuclear (fission) reactors are easier to develop than fusion reactors, better fertilizer is easier to develop compared to GMO crops, etc. Perhaps, as Cowen claims, we have already plucked all the low-lying technological fruit, so to speak.
A lot of the impedance mismatch talked about in this article is true for any cross-domain work. Building an application in the medical space, you have to get engineers and doctors to communicate effectively. Building a new semiconductor, you need to get electrical and chemical engineers to get on the same page. Designing a new music venue and you need architects, civil engineers, and sound engineers to get on the same page.
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.
Our hubris is rooted on our systems. We expect accelerating growth without bound. And, by Conway’s Law: “Any organization that designs a system (defined broadly) will produce a design whose structure is a copy of the organization's communication structure.”
We have a social and economic system of Survival of the Fittest, which is loosely based on evolution. Evolution has killed 99.9% of the species that have every existed, and our systems do the same.
My internal dialectic tells me that there’s three responses to this:
1) The metaphor is lost, and that the statement is “all over the place”, exemplifying the point of the article, that, we cannot communicate effectively outside of our limited understanding. That we cannot have new thought, in part because it cannot be clearly stated.
2) Full throated support for the system prevents us from being able to objectively question why were doing the things that we are doing, which is precisely what Conway’s Law is trying to state.
3) Having a understanding, but idealistic perception, of the world means not being able to do anything about it. Survival of the Fittest means that cooperation will be exploited, and definitely not reciprocated.
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.
The author's point is valid - innovation requires more knowledge as the tech that required less knowledge gets built. Today we face the "dual phd" problem, tomorrow the tri.
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!
Yeah, if innovation requires people to receive more and more institutionalized education, then expect innovation to stop eventually.
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.
This is not a new problem when people look at technology all day. You are basically saying that you have seen so many birds that there can never be a black swan. Not without complex xy and z factors. This is a perspective problem that can tie you down into some interesting thoughts such as "There is nothing else to be invented new." -or- "The innovation will happen here, in this little corner, where I and others say it will"
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.
> and no one understood the applications it would have to future technology until much later on
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.
There is actually a whole backstory to people ignoring the idea because she was a woman etc. The navy initially turned down the technology when she presented it. More the the main point I made above- people did not know what they were looking at because they could not think of how important it would be or how it could evolve.
You're correct, and at the frequencies at which permittivity is reasonable, FHSS is much less effective. And Hedy Lamarr was far from the first person to come up with FHSS for communications.
It was always in the cards. At one time a high-school degree was advanced, then mandatory, then college was minimum, now masters, soon phd's then multiple phd's. The issue is physics, the universe is too complex, see the three-body problem [0] (if the entire universe consisted of just 3 particles, that would already be too much for us to calculate). Complexity explodes exponentially. We reach computation barriers immediately. Which explains why stuff like true self-driving doesn't even come close to meeting promises and similar.
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.
Yes, I wonder if eventually all the markets for relatively straightforward startups will be saturated, and capital will need to fund long time horizon projects to get good returns... I wonder how far off we are from that?
And why should startups solve such "open vista" problems? There are universities, corporations, governments better positioned and resourced to solve them.
Don't we already have experience with this? In the workstation era, companies like Sun and SGI did hardware/software co-design. Sun was founded with expertise on both sides and an MBA type CEO in Scott McNealy. So I don't think think this is any insurmountable gap and that any dual PhD companies will have a generalist CEO.
It seems like many areas of human endeavor are complicated enough that you could become an expert of a field as opposed to an expert in a field. For example, could you have university courses that build experts of biological research as a field and not ever pick up a pipet? Does that already exist?
Through the first two paragraphs of this article, I thought it was going to be another silly rant bemoaning the lack of "real innovation" today. That is, another riff on the "They promised us flying cars, we got 140 characters" kind of rant.
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).
>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.
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.
If you are in SV, it can seem like the world is moving very fast (and within SV, the number of things that people are trying and call "technology" is much larger than the number of things that people outside SV either want or need). But we are still at a very early stage of the "computer age". We have definitely seen a small transition towards more modern ways of working but the process is still very far from complete.
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.
The solution involves large companies, not startups. You have an infrastructure and a pool of highly qualified applications that aren't going make or break their personal finances on these problems. These are classic coordination problems, and it requires people skilled at this aspect.
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.
can confirm. went to join a former startup right after the acquisition by a big company. it was a complicated product that would have required years of investment to pull off. instead, the company basically killed all the innovation, micromanaged everything, always wanted immediate results and didn't understand the complexity of what we were doing. we were all let go after 2 years and the project was transitioned to another team that had little knowledge about the product. some months later, they acquired our competitor.
I'm completely lacking in context here, so this is maybe a stupid thing to point out, but the US has a National Lab program and you can get tech from them to develop commercially.
Bell Labs was funded by a government-sanctioned monopoly. Calling it a "corporate lab" is incredibly misleading, but popular among corporate lab types.
Many companies that did not have a sanctioned monopoly used to have significant investments in research labs; those investments have been scaled back drastically.
