The pricing of these AI models is clearly becoming front of mind for enterprises, and it is very quickly becoming a race to the bottom. Open AI and Anthropic can only play that game for so long because where are they getting their money from? They're not getting it from profits. They're getting it from venture capital.
The biggest thing I've learned is that in venture, your reputation is everything. You can't hide from your track record. Every investment you make, every founder you back, every check you write — it's all public information, and LPs are going to do their homework.
The most counterintuitive thing about private markets is that the biggest source of alpha isn't picking the right companies — it's getting access to them in the first place. Most of the best private companies never need to raise money from you, and if they do, they have more than enough interest from investors they already know and trust.
The number one thing LPs want to know is: can you actually get into deals? Because there are plenty of smart people who can analyze a company, but if you can't get allocation, it doesn't matter. So the access question is really the first filter.
Most LPs will tell you what they want to hear, not necessarily what they think. So the way I tell founders to get signal is to ask them to make an intro to another GP. That is a forcing function — if they liked you, they will make that intro.
The IPO market has in fact become sort of the last stop on the chump train and that is when Google went public I think it was an $80 billion market cap it's up you know 500 fold retail investors have had a chance to garner a tremendous you know 500x return if you to get that from SpaceX after it goes public what would that be two trillion
If a startup launched this product and was able to do the demos that they can do, that startup would be able to raise at I would say easily a billion just based on current market conditions. But they're a startup is evaluated a lot differently of course than a you know public company that has spent somewhere in the range of three half billion dollars building this product.
The biggest mistake most investors make on the S-curve is they get the direction right but they get the magnitude wrong — they underestimate the ultimate size of the market and the speed at which it grows, and so they sell too early.
If a startup launched this product and was able to do the demos that they can do, that startup would be able to raise at I would say easily a billion just based on current market conditions. But a startup is evaluated a lot differently of course than a you know public company that has spent somewhere in the range of three half billion dollars building this product.
In the past five days we've seen the biggest VCbacked IPO ever and the biggest VCbacked strategic sale ever. We've never had a an M&A of a VCbacked company young startup north of 50 billion. Like 60 billion is so so big. We sort of lose sight of it because we're talking about a trillion dollars for this company, three trillion for that company. But 60 billion is beyond a home run.
The best tech investors understand that the S-curve is not just a description of what happened — it's a prediction tool. When you're early on the S-curve, the technology is improving faster than the market appreciates, and when you're late, the technology is maturing faster than the market appreciates.
Bill Gurley doesn't want a crazy pop on day one, doesn't want it to go up 100%. If we're in 10, 20, 30% range for something this big, that just feels like... Everything came together properly.
today's AI companies are scaling faster than any previous generation of startups, and why the eventual outcomes may be significantly larger than most investors currently expect
Unlike most firms chasing power law outcomes, Lead Edge is designed to deliver consistent returns by talking to thousands of companies a year, applying a rigorous eight-point criteria to filter down to a handful of investments.
Unlike most firms chasing power law outcomes, Lead Edge is designed to deliver consistent returns by talking to thousands of companies a year, applying a rigorous eight-point criteria to filter down to a handful of investments, and leveraging a uniquely constructed LP base of world-class executives and entrepreneurs.
These are companies that sit at the intersection of very very advanced software uh as well as hardware. So it's where atoms meets bits um which historically has not been I would say in vogue for the venture capital uh industry certainly over the last 25 years.
You also got Nat Friedman and Daniel Gross. These guys have backed a lot of founders. They've worked with a lot of AI startups. They can understand the team that they're trying to build over there.
