Elad Gil, a successful founder and prolific investor, has already been called the greatest in Silicon Valley. solo venture capitalist given the massive amounts of capital has been investing in recent years, including on behalf of institutions that reportedly include the Harvard endowment.
His track record goes a long way to explain his quiet rise. For example, Gil invested in the Series A round of high-value payment software company Stripe 11 years ago and has invested in many of its subsequent rounds. It also acquired stakes in note-taking app Notion, cloud collaboration platform Airtable, military tech contractor Anduril, and design tool Figma, which it agreed to sell to Adobe for a whopping $20 billion last September, although Adobe is still working on it. to sell Justice Department authorities on the merits of the agreement.
In a conversation late last week, Gil, who occasionally blogging but keeps a basic site — declined to answer specific questions about how much he is managing or some of the amounts he has invested in companies. But the VC quant team TRAC calls him a “super-forecaster” who has funded at least 155 companies, and whose “batting average” is .671, meaning 67% of his initial investments have generated at least follow-on rounds, according to TRAC data. (He says that at least 30 startups in Gil’s portfolio have become “unicorn” companies, though, as Gil himself points out, many valuations are about to change in the next 18 months or so. “Times are coming really difficult,” he says).
When we spoke to Gil, we asked what founders should do if things go from bad to worse. We also talked about his ongoing fascination with AI and some of the early checks he wrote to startups that are now raising major venture dollars, including Character. AIendorsed this year by Andreessen Horowitz, Perplexity.AIendorsed by NEA, and harveybacked by Sequoia Capital.
Last but not least, Gil shared how he is using AI to further his own work. Can hear to our full interview; In the meantime, excerpts from that chat follow, edited for extension.
TC: Years ago, you wrote a book called The high growth handbook, on how to scale startups from 10 to 10,000 people. Do you think now that there was too much focus on growing so fast?
EG: The focus of my book revolved around if you get to that magical product market fit moment, what do you do next? . . I think this mantra of growth for growth’s sake really came about mostly during the COVID period. As capital became really cheap and available, people started to scale up when they had no real product market fit. They started scaling before they had many customers, or before it was clear that they had a moat that would create some sort of defense for their business. I think where things went off the rails was that people started raising money many years before they were. And then they started hiring against that money that they raised instead of hiring against the business that they had.
We’re hearing a lot of stories from employees eager to talk about mismanagement within their companies as things go wrong. Any advice you can offer to companies on how to scale down without fully exploiting in the process?
A lot of the assessment that people are actively doing right now is to ask: where do I think this will be in one, three, or five years? And if it doesn’t work, what should I do? Those are really tough decisions to face. People have to make decisions between downsizing the team and maybe changing direction or trying to sell the company because it’s clearly not going to work. Do they close and return the money? If you look at when people raised a lot of money in the past few years, it happened in a big way in 2021. And if people raise money for three or four years and raise funds when [they] There are nine months left, that means a lot of people will have to start fundraising later this year. So I feel like the really hard times are coming. I think this is still kind of a warm-up period or anticipation.
There’s just this massive backlog of failing companies that should have gone out of business years ago but just persisted.
In terms of your own investments, can you talk about how much you’ve raised in the past few years and how many companies are in your portfolio now? It was raising $620 million for a 2021 SEC filing. .
I haven’t really talked much about it. [and] I don’t really know the exact number [of portfolio companies] right now. Traditionally, I just invested my own money. Then things started to pick up in terms of the allocations I could invest in, so in some cases I did what’s known as SPVs, or single purpose vehicles or investments.
Right now, my model is a bit of a hybrid, where anything that’s a little small, or if people just like me like an angel, I can do it personally. If something gets bigger, I can use a background. If something gets really big, I can use a combination of personal money, fund money, and SPV. I have tried to keep a flexible approach so that as I work with different companies at different stages, I can tailor what I do to what they really want and need. I want to avoid the situation where I have a massive fund and I feel the need to go and invest a bunch of money and push it onto people and they start acting bad.
You were paying attention to generative AI before others. Have you been surprised by what has been released into the world? [on the generative AI front] in the last six to 12 months?
For me, the big moment in a sense was seeing things like very early generative art based on GAN. [which is a class of AI and machine learning algorithms]; it was amazing what non-artists could do. Then a little bit after that, when GPT-2 and then GPT-3 came out, that was clearly a moment where there was a breakthrough between them that was clearly a sea change.
Are you an investor in OpenAI?
I’m not involved with most of the stuff that’s going on at the foundation level, but I don’t want to talk about any specific company or anything.
You sitting on a panel in Los Angeles earlier this month with Ashton Kutcher, whose Sound Ventures just raised a growth fund to expressly support solo six or more foundational model companies — three of which it has already invested in: OpenAI, Anthropic, and Stability.AI. What do you think of that strategy?
There are a handful of companies that are way ahead in terms of developing these basic models. And I do think they are going to have some scale and capital effects, at least for the more cutting-edge models. So, for example, GPT-4 still feels a bit ahead of everyone else, and obviously Google has the capabilities to build something against it. Anthropic has been iterating on its cloud model. There are a few other players. There is Cohere and A121 [Labs] and the like. But for now at least, it seems that the proprietary models are a generation or two ahead of open source, and if you assume that each model will be slightly more expensive than the previous generation of the model, then you can assume that this trend may exist for at least least a couple more years.
That means two things. One is that when there’s GPT-7 or whatever, maybe open source is the equivalent of GPT-6 or GPT-5.5. And GPT-6 will probably be incredibly effective. He’s probably going to do all sorts of amazing things. That leads to the question of what are the really innovative things that you’ll need the most advanced models for, and that’s where I think there’s going to be a lot of the value in the industry, but I think a lot of it will also go to the things that are a or two generations ago. And that’s where I think open source will also play a role.
So I look at it as a world where we’ll have a bunch of very large, closed proprietary models and an oligopoly market that’s kind of like the cloud world where we have Azure, AWS and Google Cloud as the big three players. I think the models can naturally converge there too. But then we’ll have a bunch of open source that people will use for all sorts of things in parallel.
Again, to learn more about Gil, including why he thinks more founders should consider closing and returning the capital while they still can, you can listen to our longer conversation. here.
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