How AI is Changing the Way We Build Companies (ft. Guy Wuollet and Noah Citron)

**Robert Hackett** (0:00)
For the first time, it's easier to think about token spend more like headcount. The question no longer becomes how much work can we do, but rather how much money we can allocate to the token budget in order to get work done.

**Guy Wuollet** (0:09)
The absolute numbers have gone from maybe a few thousand a month to a few hundred thousand a month, and in theory could go to a few million a month. And so how we think about that is super interesting.

**Robert Hackett** (0:17)
People have speculated since the AI started getting good that we're going to see all these billion-dollar companies run by one guy or a bunch of agents. Maybe we'll see variations of that where we see much leaner companies. We also might see businesses that are much more horizontal.

**Guy Wuollet** (0:28)
If everyone has access to these models, this will be an incredibly democratizing force where you basically slam shut this dispersion in IQ and it probably becomes much more important not to be particularly smart, but to be particularly gritty or agentic or to have strong desires for things.

**Robert Hackett** (0:49)
Hello and welcome to the a16z crypto show. I'm Robert Hackett, an editor here at a16z crypto. And I'm here with two of my colleagues, Guy Wuollet, GP at a16z crypto, and Noah Citron, head of engineering. And today we're going to talk about AI and the way that it's changing business.
So I'd like to toss it to you, Guy. You know, everybody sort of understands that AI makes things more efficient, maybe it makes people more productive, it makes things cheaper. That's sort of like the basic surface level understanding, the debate that's going on right now. But you're thinking also about how the nature of the company might change in this new world. Tell us a little bit about that.

**Guy Wuollet** (1:27)
Well, Noah and I were having a discussion yesterday, which was a lot of the inspiration for this. And we started by talking about how many tokens each person on his team, on the engineering team, are consuming, which I think was a good jumping off point for basically a discussion on which sort of work is productive and how to think about firm formation. We as a venture capital firm want to invest large amounts of money in companies that need a large amount of money, and will then hopefully be fantastically successful, and occasionally will be abject failures. I think there's a whole new opportunity or space in the market for a sort of company that needs variable headcount, that can be run in a much more asset and headcount-like fashion today because of AI models. It does not need the equivalent of something like venture capital, in fact, not a particularly good fit for it because the terminal outcome, the success case, is not a big enough success for most venture outcomes to make sense. And so we're talking a little bit about the financing models for that. Is it something like convertible debt?
Do these end up looking a bit like a modernized form of private equity, or a bit like a builder form of podshops as opposed to a quant and a trading form of that? I think an interesting place to start is just by asking the question of, for a long time, it was hard to spend a lot of money on AI inference on models. It has become much easier to spend a lot of money on AI inference. And we were talking a bit about that shift for your team and essentially how much everyone is spending, which I thought was like an interesting number.

**Robert Hackett** (3:03)
And that's because a lot of the leading model, there are newer models being released that allow you to use a lot more computation. They're a lot more computation heavy, and so they are a lot more expensive, but they do a lot more. And there's a lot more that complexity they can handle. I think the fundamental change was that, you know, a year ago, or even less than a year ago, six months ago, we really didn't even have to think much about our token spend, mostly because our ability to scale tokens was limited. So we were able to spend, you know, almost within our subscription limits, for the most part, maybe going a bit into the API pricing, but not really a significant spend. But in the past, maybe three months, we've noticed that our ability to get more work done has basically started to get more linear, or at least linear for a little bit longer with the amount of money we're willing to burn on tokens. So now the question no longer becomes, how much work can we do, but rather, how much money can we allocate to the token budget in order to get work done? And this kind of changes the paradigm a lot. So I think for the first time, it's easier to think about token spend more like headcount, right? Where an engineering organization can scale headcount, assuming you have unlimited capital, you can continue to scale headcount, and barring any organizational inefficiencies, you can just keep scaling that and get more worth out. So you have to answer the question of how much work to do is valuable, right? For our given business units, where can we allocate resources to actually increase revenue? This was not the way we used to think about it, but now it is absolutely the way we think about it. So we see, for the first time, I talked to my team about how much they spend on tokens, and every once in a while, I hear on some given week where there was some heavy work being done, and I go, oh, that could cause a problem. I had never been put in that situation before.