The dual PhD problem of today’s startups
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The dual PhD problem of today’s startups
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Re: The dual PhD problem of today’s startups
#2This 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.
Re: The dual PhD problem of today’s startups
#3Through 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).
Re: The dual PhD problem of today’s startups
#4Is this the onion?
Re: The dual PhD problem of today’s startups
#5This 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…
> An actress basically invented FHSS
Mind you, said actress, Hedy Lamarr, was a fairly brilliant, self taught electrical engineer.
Re: The dual PhD problem of today’s startups
#6> AI and bio
> two very [...] disparate fields
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.
Re: The dual PhD problem of today’s startups
#7This 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.
Re: The dual PhD problem of today’s startups
#8This 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…
> An actress basically invented FHSS Mind you, said actress, Hedy Lamarr, was a fairly brilliant, self taught electrical engineer.
I just wanted people to have to google it and learn if they did not know, and here you are ruining that game for me! Anyhow nice film on this for anyone interested: https://en.wikipedia.org/wiki/Bombshell:_The_Hedy_Lamarr_Sto...
Re: The dual PhD problem of today’s startups
#9Every 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.
Re: The dual PhD problem of today’s startups
#10A 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.