
The dual PhD problem of today’s startups - tosh
https://techcrunch.com/2020/07/19/the-dual-phd-problem-of-todays-startups/
======
woeirua
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.

~~~
Andyfilms
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.

~~~
Igelau
> janitor as a service

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staff last cleaned the bathroom?

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Our state of the art MopMine equation incorporates real sweat and toil
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yours!

~~~
ryan_j_naughton
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.

~~~
wsinks
Yeah, that comment belongs on r/TIHI. Too close to home, too soon...

------
hyko
_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.

~~~
zamfi
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"...

------
pontus
The article misses the point, I think.

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.

~~~
haihaibye
>> Will work be more decentralized in 10 years than it is today? Yes.

That's a big call during an unprecedented work from home pandemic.

~~~
WrtCdEvrydy
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.

------
dhairya
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.

~~~
michaelhoffman
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).

~~~
rabidrat
Maybe you need 3 PhDs for 2 fields: one to go deep in each field, and one
'jack-of-both-trades' to mediate and translate between them.

~~~
not2b
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.

------
digitallogic
> 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?

~~~
asdff
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.

------
TrackerFF
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.

------
albertTJames
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.

~~~
hobofan
> 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.

------
klhugo
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 :)

This might be something worth to fight for.

~~~
Balgair
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.

~~~
foldr
> 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.

------
Animats
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.

------
golergka
> 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.

~~~
hobofan
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.

[0]:
[https://www.coursera.org/specializations/bioinformatics](https://www.coursera.org/specializations/bioinformatics)

~~~
asdff
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.

~~~
orange3xchicken
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).

------
dannykwells
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.

------
seebetter
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.

------
altdatathrow
> yes it is a note-taking app, but it runs on Kubernetes

Going straight for the HN jugular I see.

~~~
uncleputin
build it with rust to get more HN karma

------
gautamcgoel
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.

------
wins32767
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.

------
vonwoodson
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.

------
theferret
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!

~~~
imtringued
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.

------
crmrc114
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.

~~~
madhadron
> An actress basically invented FHSS

Mind you, said actress, Hedy Lamarr, was a fairly brilliant, self taught
electrical engineer.

~~~
crmrc114
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...](https://en.wikipedia.org/wiki/Bombshell:_The_Hedy_Lamarr_Story)

~~~
madhadron
Sorry to ruin your fun.

------
Animats
So how is R&D being funded in China? China has government-funded everything.
How's that working out?

------
thinkloop
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.

[0] [https://en.wikipedia.org/wiki/Three-
body_problem](https://en.wikipedia.org/wiki/Three-body_problem)

------
mensetmanusman
Interesting...

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.

------
dustingetz
capital is like water, it floods the low ground first

~~~
epicureanideal
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?

~~~
dustingetz
Another formulation of that is, if progress is happening fast enough, why not
just wait for new short time horizon projects?

------
rabzu
And why should startups solve such "open vista" problems? There are
universities, corporations, governments better positioned and resourced to
solve them.

------
dmch-1
It may be harder to combine two skills such as sales and engineering, than
knowledge in bio and AI, e.g., which are both technical.

------
CalChris
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.

------
PinkMilkshake
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?

------
mindcrime
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).

~~~
starfallg
>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.

------
hogFeast
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.

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k__
Somehow that link redirects me to

[https://guce.advertising.com/collectIdentifiers?sessionId=3_...](https://guce.advertising.com/collectIdentifiers?sessionId=3_cc-
session_059c465a-f5b3-45de-9c18-fa0bc54f85ee)

~~~
stadeschuldt
Same here.

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082349872349872
The solution to the multiple PhD problem might be the "simple matter" of
_implementing_ Engelbart's NLS/Augment.

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pnathan
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.

~~~
not2b
Unfortunately large companies invest much less in longer-term research than
they used to. Bell Labs was a thing ... once.

~~~
octoberfranklin
Bell Labs was funded by a government-sanctioned monopoly. Calling it a
"corporate lab" is incredibly misleading, but popular among corporate lab
types.

~~~
not2b
Many companies that did not have a sanctioned monopoly used to have
significant investments in research labs; those investments have been scaled
back drastically.

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anamax
There's another issue.

Things close the edge are risky. Things close to two edges are even more
risky.

Risk isn't reduced by reward.

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balthasar
Is this the onion?

