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This is what is fundamentally missing from most talk about "tech" startups by VC types these days. They're not actually interested in the nerdy stuff, just in t

This is what is fundamentally missing from most talk about "tech" startups by VC types these days. They're not actually interested in the nerdy stuff, just in the "disruption" stuff. L&S were, and they hired other people who were. They didn't start with a business idea, but with a technical one. They filled in the blanks on the business side after they survived the .com crash. I didn't get to Google until 2011, but it became clear to me after joining that in the past they had gone on a very nerdy mission to hire all the nerdy people and collect them into one place to do nerdy things. (Unfortunately that nerdy thing ended up being selling ads really efficient, but that's another story.) My fundamental point is that the Google story is very unlike the kind of stories that YCombinator or a16z like. It started, like you said, as a set of intellectual/technical interests. The other stuff, that VCs today like, came later. In a way they did the opposite of the usual advice. They started with the hammer (the tech) rather than the nail (the business problem.) They certainly didn't start with a "Like X but for Y" statement like seems to be desired by VC today. And they didn't look like the typical .com story at the time (which was usually: give us lots of VC $$ so we can sell something on the web that is currently not sold on the web, but we'll just use the $$ to buy customers and make no profit...) I would posit that if today's Google came to YCombinator today they'd be shown the "no thanks" door.

YossarianFrPrez 3y ago
Very interesting to hear your thoughts and experience. I wonder if we really shouldn't make a bit more out of the fact that there are different types of startups: those that pick off low-hanging fruit really well, those that combine novel technology with novel problems (for instance, the recent YC LLM meeting note-taking / transcription service), those that are scientific-discovery based (see, for example the NSF Small Business Innovation Research program), etc. I am not experienced enough to create an ontology of startup ideas, but I think it'd be an interesting exercise.
- theGnuMe 3y ago
  The nsf program basically exists to fill the gap between research and product market fit. Except They focus on commercialization plans which we know that in reality is subject to change (aka meaningless).
light_triad 3y ago
Hopefully 2 Stanford CS PhD students hacking on their project would be funded by YC today :) You bring up a good point about starting from the tech rather than the problem. Usually the advice from VCs is to start from the problem and iterate on the tech until you solve it. What was very fortunate for Google is that the tech translated into a great business problem. Open up any CS textbook and 'Search' is always a major section. It was also a great business because the problem is important, frequent, and had not been properly solved by the big players in the mid 90s. "And then we realized that we had a querying tool..." (Page)
- cmrdporcupine 3y ago
  But that's the thing. "Search" was not a great business problem. It still isn't. I used Google for years before their IPO and people were always like "how is this thing going to make money" and believe it or not there was a lot of skepticism even at IPO time that they even had a possible reasonable business model. (Same for Facebook at their IPO, too.) Search is not a good business. Ad sales over top of search turned out to be. AdWords is the thing that catapulted Google towards (insane) profitability. And for the first few years, Google didn't really sell ads, or market themselves as even aiming to sell ads. But AdWords could only be successful because they had already captured the market on search, and so had a captive audience to show ads to (and relevance information based on their searches). AdWords rolled out in 2000. Many of us had been using Google as a search engine since before the company had even been founded (1998).
  - light_triad 3y ago
    Totally agree. What I mean is many research topics can be very interesting from a technical perspective but don't translate into solving problems that are frequent and important enough to build a business around. In the case of Search many business gurus didn't understand at the time that you can make a fortune with a free product. What matters is who has the traffic and who they're selling it to = eyeball meets ad. Page and Brin initially "expected that advertising funded search engines will be inherently biased towards the advertisers and away from the needs of the consumers" (foreshadowing). Also they tried to sell to Yahoo for $1M so it took time to see the full potential, as you describe.
theGnuMe 3y ago
Maybe? YC seems to have an inconsistent view on things that are research vs product. But it’s probably more nuanced than that. Search was a market though at that time. If you pitched an AI LLM before chat-gpt what would the odds have been?
- cmrdporcupine 3y ago
  I dunno, I toyed with the starting phase of YC application a couple years ago, and started working my way through what was involved in their application process and reading their materials, even watching some YouTube content, etc. I didn't really see how a business that was starting from a more R&D angle would make it through. And a lot of it seemed really pitched to the bizdev "hustler" founder personality, not to engineers/nerds.
paulpauper 3y ago
They're not actually interested in the nerdy stuff, just in the "disruption" stuff. I disagree. The AI stuff seems pretty nerdy. Same for GitHub and other levels of abstraction. The disruption talk is just the branding by the media.
- cmrdporcupine 3y ago
  The foundational $$ that has gone into the research to make the LLM stuff happen... was inside the existing big corporations (Google, FB, MS, etc.) and only left there once it had been proven. Ilya Sutskever (OpenAI) for example was employed inside Google doing that kind of work and only left when he ran up against the limits of what he felt he could get done there. But the fundamental R&D had already been done. When I was at Google I saw some of it from a distance before I ever heard of OpenAI. The VCs have come along at the tail end of the R&D cycle on this stuff in hopes of cashing in. Same as they did with crypto and N number of trends before. They're trailing, not innovating. They're primarily interested in successful business models capitalizing on existing technology, not the actual development of new technology. Unless one has distorted the meaning of "technology" significantly.
downWidOutaFite 3y ago
The real trick that made Google possible, and the following web 2.0 era startups, was that linux and commodity hardware was all that was needed at the time so engineers could strike out on their own. Comparing it to the current ML era where you need many millions of dollars of data and many millions of dollars of hardware to compete, the guys that wrote the transformers paper are stuck in bigcorp and aren't going to be the next Larry and Sergey. OpenAI might be one of the few new big companies but it's run by a VC/CEO, not the engineers that figured the stuff out, and he had to sell half the company to Microsoft anyway.
- nsguy 3y ago
  These things always happen at intersections. There are lots of smart people with ideas. The magic happens at the intersection of ideas, time (what technology is available) and luck. Apple Newton was too early. Apple iPhone was just right. Doom 3D was just at the right time when commodity hardware could do what the amazing software needed it to do.
  - downWidOutaFite 3y ago
    The point I was trying to make is what are the situation where an independent small startup can make it big. Your examples are instructive, engineers were able to make id Software big because you didn't need a lot of hardware to invent 3D shooters, but Jobs gets the credit for the iPhone because the engineers needed hundreds of millions of dollars in capital to pull it off. pg is giving advice based on him getting rich from that period of time where the small startup could make it big. But those conditions are probably rare going forward.
    - fragmede 3y ago
      maybe. right now, if a startup has a new ML training technique or whatever, for a specific niche, cloud and VC capital will let them make a model that does way better for their niche than repackaging well funded models from OpenAI.
    - pixl97 3y ago
JohnFen 3y ago
I think there's a connection here to the old adage that "small companies make it possible, big companies make it inexpensive". The purpose of VC can be viewed as trying to take a company from nonexistent to big while skipping the small stage, but small is where the action really is.
- pixl97 3y ago
  Yep, unless some new thing is acquired in a buyout, or if an entire group is spun off with almost no oversight, large companies are horrifically bad at creating new things. It's nearly impossible for them to do it in the case where the new product would affect existing revenue streams.
int_19h 3y ago
> Unfortunately that nerdy thing ended up being selling ads really efficient, but that's another story. The irony is that both the thing and its side effects were anticipated very early on. "Currently, the predominant business model for commercial search engines is advertising. The goals of the advertising business model do not always correspond to providing quality search to users. ... It is clear that a search engine which was taking money for showing cellular phone ads would have difficulty justifying the page that our system returned to its paying advertisers. For this type of reason and historical experience with other media, we expect that advertising funded search engines will be inherently biased towards the advertisers and away from the needs of the consumers." (The Anatomy of a Large-Scale Hypertextual Web Search Engine, Sergey Brin and Lawrence Page, 1998)

YossarianFrPrez 3y ago

Very interesting to hear your thoughts and experience. I wonder if we really shouldn't make a bit more out of the fact that there are different types of startups: those that pick off low-hanging fruit really well, those that combine novel technology with novel problems (for instance, the recent YC LLM meeting note-taking / transcription service), those that are scientific-discovery based (see, for example the NSF Small Business Innovation Research program), etc. I am not experienced enough to create an ontology of startup ideas, but I think it'd be an interesting exercise.

theGnuMe 3y ago
The nsf program basically exists to fill the gap between research and product market fit. Except They focus on commercialization plans which we know that in reality is subject to change (aka meaningless).

light_triad 3y ago

Hopefully 2 Stanford CS PhD students hacking on their project would be funded by YC today :) You bring up a good point about starting from the tech rather than the problem. Usually the advice from VCs is to start from the problem and iterate on the tech until you solve it. What was very fortunate for Google is that the tech translated into a great business problem. Open up any CS textbook and 'Search' is always a major section. It was also a great business because the problem is important, frequent, and had not been properly solved by the big players in the mid 90s. "And then we realized that we had a querying tool..." (Page)

cmrdporcupine 3y ago
But that's the thing. "Search" was not a great business problem. It still isn't. I used Google for years before their IPO and people were always like "how is this thing going to make money" and believe it or not there was a lot of skepticism even at IPO time that they even had a possible reasonable business model. (Same for Facebook at their IPO, too.) Search is not a good business. Ad sales over top of search turned out to be. AdWords is the thing that catapulted Google towards (insane) profitability. And for the first few years, Google didn't really sell ads, or market themselves as even aiming to sell ads. But AdWords could only be successful because they had already captured the market on search, and so had a captive audience to show ads to (and relevance information based on their searches). AdWords rolled out in 2000. Many of us had been using Google as a search engine since before the company had even been founded (1998).
- light_triad 3y ago
  Totally agree. What I mean is many research topics can be very interesting from a technical perspective but don't translate into solving problems that are frequent and important enough to build a business around. In the case of Search many business gurus didn't understand at the time that you can make a fortune with a free product. What matters is who has the traffic and who they're selling it to = eyeball meets ad. Page and Brin initially "expected that advertising funded search engines will be inherently biased towards the advertisers and away from the needs of the consumers" (foreshadowing). Also they tried to sell to Yahoo for $1M so it took time to see the full potential, as you describe.

theGnuMe 3y ago

Maybe? YC seems to have an inconsistent view on things that are research vs product. But it’s probably more nuanced than that. Search was a market though at that time. If you pitched an AI LLM before chat-gpt what would the odds have been?

cmrdporcupine 3y ago
I dunno, I toyed with the starting phase of YC application a couple years ago, and started working my way through what was involved in their application process and reading their materials, even watching some YouTube content, etc. I didn't really see how a business that was starting from a more R&D angle would make it through. And a lot of it seemed really pitched to the bizdev "hustler" founder personality, not to engineers/nerds.

paulpauper 3y ago

They're not actually interested in the nerdy stuff, just in the "disruption" stuff. I disagree. The AI stuff seems pretty nerdy. Same for GitHub and other levels of abstraction. The disruption talk is just the branding by the media.

cmrdporcupine 3y ago
The foundational $$ that has gone into the research to make the LLM stuff happen... was inside the existing big corporations (Google, FB, MS, etc.) and only left there once it had been proven. Ilya Sutskever (OpenAI) for example was employed inside Google doing that kind of work and only left when he ran up against the limits of what he felt he could get done there. But the fundamental R&D had already been done. When I was at Google I saw some of it from a distance before I ever heard of OpenAI. The VCs have come along at the tail end of the R&D cycle on this stuff in hopes of cashing in. Same as they did with crypto and N number of trends before. They're trailing, not innovating. They're primarily interested in successful business models capitalizing on existing technology, not the actual development of new technology. Unless one has distorted the meaning of "technology" significantly.

downWidOutaFite 3y ago

The real trick that made Google possible, and the following web 2.0 era startups, was that linux and commodity hardware was all that was needed at the time so engineers could strike out on their own. Comparing it to the current ML era where you need many millions of dollars of data and many millions of dollars of hardware to compete, the guys that wrote the transformers paper are stuck in bigcorp and aren't going to be the next Larry and Sergey. OpenAI might be one of the few new big companies but it's run by a VC/CEO, not the engineers that figured the stuff out, and he had to sell half the company to Microsoft anyway.

nsguy 3y ago
These things always happen at intersections. There are lots of smart people with ideas. The magic happens at the intersection of ideas, time (what technology is available) and luck. Apple Newton was too early. Apple iPhone was just right. Doom 3D was just at the right time when commodity hardware could do what the amazing software needed it to do.
- downWidOutaFite 3y ago
  The point I was trying to make is what are the situation where an independent small startup can make it big. Your examples are instructive, engineers were able to make id Software big because you didn't need a lot of hardware to invent 3D shooters, but Jobs gets the credit for the iPhone because the engineers needed hundreds of millions of dollars in capital to pull it off. pg is giving advice based on him getting rich from that period of time where the small startup could make it big. But those conditions are probably rare going forward.
  - fragmede 3y ago
    maybe. right now, if a startup has a new ML training technique or whatever, for a specific niche, cloud and VC capital will let them make a model that does way better for their niche than repackaging well funded models from OpenAI.
  - pixl97 3y ago

JohnFen 3y ago

I think there's a connection here to the old adage that "small companies make it possible, big companies make it inexpensive". The purpose of VC can be viewed as trying to take a company from nonexistent to big while skipping the small stage, but small is where the action really is.

pixl97 3y ago
Yep, unless some new thing is acquired in a buyout, or if an entire group is spun off with almost no oversight, large companies are horrifically bad at creating new things. It's nearly impossible for them to do it in the case where the new product would affect existing revenue streams.

int_19h 3y ago

> Unfortunately that nerdy thing ended up being selling ads really efficient, but that's another story. The irony is that both the thing and its side effects were anticipated very early on. "Currently, the predominant business model for commercial search engines is advertising. The goals of the advertising business model do not always correspond to providing quality search to users. ... It is clear that a search engine which was taking money for showing cellular phone ads would have difficulty justifying the page that our system returned to its paying advertisers. For this type of reason and historical experience with other media, we expect that advertising funded search engines will be inherently biased towards the advertisers and away from the needs of the consumers." (The Anatomy of a Large-Scale Hypertextual Web Search Engine, Sergey Brin and Lawrence Page, 1998)