the wildest thing right now is you can start a company that can make tens of millions of dollars literally in 24 months and you can do it for potentially, you know, $2 million, $5 million.

A year ago, I remember many of the startups in the batch would get sort of enterprise proof of concepts or pilots in particular, and there was a lot of cynicism around whether any of those pilots would translate into real revenue.

Fast forward a year, I think we have all firsthand experienced that these pilots have turned into like real revenue. It's still early days, honestly.

Like it, you know, we sort of breathe a sigh of relief right now in 2024, but it's anyone's game honestly.

Like these things are moving so quickly. Welcome back to another episode of The Lightcone. I'm Garry, this is Jared Harge and Diana, and collectively we funded companies worth hundreds of billions of dollars.

Right at the beginning. So 2024, what a year. How are you feeling about this, Harge?

Pretty great. I think this is the year that everything broke in favor of startups. What I've been thinking about a lot recently is when ChatGPT launched two years ago now, the immediate consensus view was all of the value would go to OpenAI and very specifically, do you all remember when they announced the GPT or the ChatGPT store?

Yeah, like the I remember the consensus was everything that was built on top of ChatGPT was a GPT wrapper and the App Store was just going to be released and crush every single person trying to build an AI application and OpenAI would be a ginormous company, but there'd be no opportunity for startups.

Sounds kind of ridiculous to say that Now, because who even remembers the ChatGPT store?

Exactly. The ChatGPT store itself was a nothingburger, but more importantly, what are the big AI applications today?

I'd say outside of ChatGPT itself, the breakout consumer application is Perplexity. The breakout enterprise application is probably Glean, maybe.

In legal tech, you have CaseText, you have Harvey, you have PhotoRoom. The point being, there are many, many applications that have been built not by OpenAI.

It's been a great time to build startups. Yeah, the wildest thing right now is you can start a company that can make tens of millions of dollars literally in 24 months from zero, and you can do it for potentially, you know, $2 million, $5 million.

That's sort of the story of one of these companies, Opus Clip, which never had to raise a real Series A.

And that's something that we sort of see across the YC community as well. Yeah, I think that's a particularly important point that you can do it as a startup without raising tons of capital because post the GPT store launch, I then remember Anthropic and Claude emerged, and the consensus view for a while was all of the value is going to go to one of these foundation model companies, and that the only way you can compete in AI is to raise huge amounts of money, either because you've got venture capital or you're Amazon or Facebook or Google with tons of cash already.

But that if you weren't one of the big foundation models, there would be no value, and the applications built on top of these things would either be built by the foundation model companies themselves or just not be that valuable again.

something that turned out to be completely not true, right? And in particular, what drove that is open source, like the weird series of events where the weights being leaked and like Meta just rolling Tor, yeah, it kind of forced the hand for Meta to launch LLaMA, which was funny, and people thought, oh, it was just this cool open-source model, but it was 18 months behind OpenAI.

And people started doing a lot of derivative work out of it, like Vicuna and all these other animals related to llamas that came out.

And it took Ollama, one of the companies at YC as well, that enabled people to do local kind of Docker development, like models running on device.

It was pretty cool, but people didn't think that they were going to be able to catch up. And the thing that changed from 2023 to 2024 is that during the summer, it was a turning point.

It was the first time that the top foundation model in all the rankings benchmarks was LLaMA.

And that was a shock to the community. Yeah, so it turns out choice matters, and choice means that it's not as much about the model.

I think the model still matters quite a lot. But once you have choice in models, it means you can't have the sort of idea of monopoly pricing.

And you have that model, your competitor also has that model. But all the other things seem to end up mattering a lot more, which is product, your ability to sell, your ability to actually adjust to user feedback, your ability to get to zero churn.

All of those suddenly become far more important than capturing a light cone of all future value through the model, right? A very specific way. I felt this is I remember a year ago working with startups in the batch that are essentially building model routers, just like an API to call like a specific model.

And I remember the um, a lot of the motivation for that at the time was uh, reducing cost. It was like, oh, like you don't want to just like burn up all of your ChatGPT calls, you want to spread them out across like various different models.

And the the the argument against that was just, oh, like the cost of all this stuff is going down to zero anyway, like there's no value to be had in being like a model router and no one wants to build their applications with a model router.

They're all just going to call whatever is the best model. I think fast forward a year, like that's totally not true.

Like from what I can tell, the the model router was actually a really great entry point into just building sort of a new stack for building LLM-powered apps.

And most of the the applications we're seeing, I think they just don't want to be beholden to a specific model.

Does that map with what you've seen? Yeah, actually one of the things we've seen now in the Fall batch that just presented at Demo Day, which was one of the trends that shifted from Summer '24 and Winter '24, was precisely what you're saying.

Companies started to use multiple models for their applications, like the best one for speed at some point because sometimes you need to parse a lot of the input very quickly.

It's fine if it's a bit more lossy, and then you need the bigger model to handle the more complex task.

So a lot of companies in Fall '24 have this actually multiple model architecture to use the best one for the best task, which is similar to the concept of the model router, but it was uh the idea evolved instead of being being more of a routing, it was more of an orchestration.

I think a concrete example we gave uh a couple episodes ago was uh Camper was a company you work with, they use the fastest model for parsing like PDFs, and the more complex ones they use O1.

And that's that's how it's done. And other companies that are doing uh fraud detection, they have this concept of a like a junior risk analyst where they just use like a fast and easy GBD4 mini, and and then they use the bigger one with like O1.

Or the other example is um, I think Cursor talks about it in their episode with uh Lex Fridman, they also have this complex multi-architecture with multiple models.

And this is why it works well, it's like they do one very specific for predicting what you're going to type next, but one for understanding the whole code base.

So very different tasks. So that's definitely happening now. Yeah, the other thing that uh popped up for Fall Batch, there's a company I'm working with um called Variant.

And uh what they're trying to do is take basically state-of-the-art open-source LLM models that can do code gen and then teach them aesthetics.

So uh starting with icon generation. And so they built this huge sort of post-training workflow that should work on, you know, as the open-source models get smarter and better at code gen broadly, they can just, you know, take the next version of that and then uh take their post-training architecture and dataset and then basically teach a given model aesthetics.

So what a certain thing is supposed to look like and uh not in a diffusion sort of way but actually at the SVG level and we think SVG will actually translate into all kinds of Aesthetics so it's an interesting approach and like one of the newer ones and that post training is a whole coherent way to sort of skip the whole idea that um all the value is like acur into the model especially because of Open Source to your point the other thing I I've been having flashbacks to is a year ago I remember many of the startups in the batch would get sort of Enterprise proof of Concepts or pilots in particular and there was a lot of cynicism around whether any of those Pilots would translate into real Revenue um lots of parallels to crypto and how anytime there's some new interesting technology blockchain more specifically than crypto but anytime there's a new technology Enterprises always want to run pilots and P because it's someone's job to like check off yeah we did the like hot new technology thing the chief Innovation officer must have his VI we've spoken about this one of one of our episodes I think and yeah fast forward a year I think we have all firsthand experienced that these Pilots have turned into like real revenue and if anything the startups in the YC badge now are going to sell into real Enterprises faster than they have before and are ramping up revenue and reaching Milestones like a million dollars AR faster than I've suddenly ever seen yeah the fall batch just did this actually again which is actually the first time I think we noticed it was actually the summer batch of this year and uh one of the funnier things that we realized was do you remember when Paul Graham uh would tell us how fast you needed to grow during the YC batch 10% a week 10% a week and uh the wild thing is in aggregate uh across both you know summer and fall batches.

That's what those batches did. Wow. So, which I don't think ever happened, 3x over the course of YC. Yeah, 3x over the course of YC, which I don't think has ever actually happened on average.

On average, it was only the best companies that did that, which is the top quartile or something, right? So, the companies are better.

The general thing that is true is just that the time it's taking to reach $100 million in annual revenue is trending down.

Yeah. And not only that, um, we had dinner with Ben Horowitz recently and remember he was saying when they started Andreessen Horowitz, the common understanding was that in any given year there'd only be 15 companies that year that would even make it to $100 million a year revenue.

And uh, they said they ran the numbers the last 20 years and every decade the number of companies that could actually make it to $100 million went up by 10x.

So, what was 15 per year maybe 20 years ago, I mean, we're talking about 1,500 companies a year that have a real shot at actually making that number.

And when you combine that with what we're seeing in the summer and fall batches, it's not that surprising.

And Jared had a really good argument on our last episode about how vertical AI is going to enable this to have 1,500 plus companies to bloom.

Yeah, that's why it's growing so fast. It's because the value proposition to companies of these products is so incredibly strong that like they're just flying off the shelves because like, companies are smart and they can do an ROI calculation.

And when the ROI is fantastic, all these all these truisms that people believe about enterprise sales cycles and like, quite a test to get big enterprise deals go out the window because companies are smart and they'll make rational decisions.

You know, there's another way that this broken favor of startups that I was thinking about.

Um, it's hard to even remember now, but a year ago, one of the things that people said a lot was that these LLMs are not reliable enough to deploy in the enterprise.

They hallucinate. That was why a lot of people said like, these pilots and POCs like won't translate into real contracts is because, yeah, it's too risky of a technology, um, for people to actually deploy.

Deploy, yeah. And not only is it translating into real revenue, but translating into real deployments that are like being used at large scale, you know, doing thousands of tickets a day.

And I think it's because we've learned how to make the agents reliable via the kinds of techniques that Jake talked about when he was here and just the all this infrastructure has grown up around the models, it's enabled people to make them reliable.

Yeah, that's actually a big trend this year is this concept of thinking of AI more as agentic. That is a term that kind of bubbled up a lot this year.

It was not in the bubble space of conversation last year. Last year was more about a lot of things that were kind of very chat-like.

Chat, I mean, that was kind of the riff on it, but now remixed into a bunch of agents for XYZ.

And we just, I mean, you just put out, Gary just put out a great explainer video about computer use from Claude.

But just the capability of the models keeps pushing in the direction of just being able to do like complex multi-step things and actually take over your computer and call other applications and perform complex tasks that just didn't seem possible a year ago.

What about regulation? Seems like we sort of dodged a bullet there with uh 1047 and uh it looks like some of the Biden EO uh is not that likely to survive the Trump White House, TBD what that means in the longer term.

But certainly one of the things that we were very worried about was that some certain amount of math beyond a certain level would suddenly become illegal or require registration at your local office.

It's certainly been a weird time to be in tech because I've never experienced um software and technology intersecting with politics so much.

And in particular, I'm not used to genuinely caring about national politics affecting startups in a YC batch or just, you know, companies that are less than a year old.

But it did really, it did really for a moment was worrying.

It wasn't clear whether startups would actually be able to build innovative AI applications versus suffering from regulatory capture from OpenAI and a few big players.

We're obviously very glad it broke in the favor of startups. Seems like we're still in the early game, right?

I mean, I mean it's very easy to see that um the platforms themselves really will or could possibly resemble, you know, the Win32 monopoly, right? Windows uh has access to the APIs.

They in fact know all the stats about what's working on their platforms and guess what, they can build it into their platform.

You know, we sort of breathe a sigh of relief right now in 2024, but um, you know, it's anyone's game honestly, like these things are moving so quickly.

I wouldn't totally breathe your last sigh of relief yet, you know, it's we we got to keep working on this.

Okay, so it's clearly been a great year for startups. Um, what else has been happening?

Who else has it been a great year for, do we think? There certainly have been some big funding rounds, right? Like OpenAI, unsurprisingly, has raised huge amounts of capital.

Scale AI. Even within YC, though, we've seen like Scale AI has really broken out this year. $6 billion for OpenAI, $1 billion for uh SSI, the new Ilya Sutskever startup.

Scale, I think, is just worth talking about because it's such a classic startup story.

I mean, you were there in the early days, right? You interviewed them for YC. Um, what, um, tell us what the idea was that they interviewed with and how they ended up landing on what you know is probably one of the best startup ideas of the last 10 years.

The fun thing about the Scale AI story is that it is the sort of epitome of the like classic YC startup story.

There's other kinds of startups that get started, you know, like SSI, for example, that's not a typical YC startup story where like some very well-established people raise a billion dollars with like a PowerPoint pitch.

But like Scale AI is like the classic story of how like young programmers can just gradually build a like $10 billion company over time by being like smarter and harder working than anybody else.

And so yeah, when Alex uh interviewed at YC, he wasn't working on anything related to AI. It was a completely different idea and the idea for Scale AI kind of got pulled out of him by the market.

And it's it's actually still like several pivots because like uh the original idea at YC didn't have anything to do with AI and then for a long time he was basically doing data labeling for the self-driving car companies.

They applied as I remember, they applied like a healthcare-related idea.

Yeah, it was a website for booking doctor's appointments. Okay, cool. And then they pivoted during the batch. Um, do you remember how they came up with the data labeling?

Because this was supposed to be in was this 2016? Yeah, the way they came up with the data labeling idea was that Alex had worked at Quora and Quora had to do some data labeling for like moderation and stuff.

And so at the time the big data labeling service was Amazon Mechanical Turk, and they were deemed unbeatable because they were like run by Amazon and Amazon could throw infinite money at it.

It was always at scale. It was at like quite large scale already. But Alex had a unique insight, which is he'd actually used Mechanical Turk at Quora and he knew that it kind of sucked to actually use it.

And so he he he had this sort of like unique insight on the world and so he just tried to build a better Mechanical Turk, basically the version he would have wanted when he was at Quora.

And as I remember it, like really their early traction came almost entirely from one customer, Cruise, right? Yes, which needed to do tons of data labeling on all the images that the cars were taking as they were driving around San Francisco.

You got to like draw a circle around the traffic light and thing like that.

And the cool thing about Scale is that they've actually caught two waves. So they, you know, accidentally caught the first wave of all these self-driving car companies because ML took off at that time in computer vision.

There was just an unprecedented demand for labeled data for training sets that just hadn't existed before.

And so they were able to ride that wave, and then as that wave was like cresting, LLMs got big, and all of these companies needed to do RHF at a very large scale, and Scale was just like perfectly positioned to move into that business as well.

Yeah, like I think the Scale story is so interesting because it was pre-LLM, it was it was clear a multi-billion dollar business anyway.

And LLM it caught the LLM wave, which has now propelled it into probably it's going to be like a hundred billion dollar plus company.

And I'm seeing that at the ground level too, where many companies I had that maybe finished the batch even pre-the batch didn't have an idea, pivoted into an AI idea that's taking off.

Like I'm just seeing much more success in founders who who waited out and can find an idea that they just couldn't before.

I have a company from a year ago, they they proved the whole batch, they couldn't find a great idea.

It actually took them six months after the batch until they realized one of their parents ran a dentist office, so he just decided to go hang out at the office to see like if there was anything he could automate.

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