Prof G Markets
00:51:32 · Luca Ferrari
[00:51:32] S2: So I think we try to use AI broadly speaking for two things. One is there are sometimes features you can build for customers that are only possible, say today, thanks to AI, that were maybe not possible in the past. You mentioned Eventbrite, think. We haven't done it yet, it's very early, but I think one of the ways AI could could help make Eventbrite better is by greatly enhancing the recommender systems. When you go to Eventbrite, there's roughly 80,000,000 people who go there monthly to find interesting events near them. What you show them as as a recommendation makes a huge difference on whether they are satisfied and whether organizers sell tickets. So it's really like a win win. And that's a difficult problem, especially because you don't have, you know, the data that meta does. I mean, you have very limited data clearly because this... You know, the people only go there for the events. So and AI, we believe could be quite helpful in making that better, and even marginal improvements in that recommender system can be quite valuable for both parties of the marketplace. And the other big ways, and that's completely bespoke to the product obviously. And then there is something much more, say general purpose that applies to almost any business that's using AI and technology more generally, cutting edge technology to be operationally much more effective and efficient. For instance, again, told you we have dozens of proprietary technologies. Most of these are are based in AI, especially these days where if you don't use AI, you're probably giving a lot of value on the table. But some are really AI centric. For instance, we've got something called Alternative Spooner, old ALT Spooner. Each one of us, and Spooners by the way are basically the members of this core team, you know, highly selective recruiting process, and people will move around their businesses fluidly. But we are working now to extend this technology, by the way, to all team members, it takes a little bit more work, but you have this personal assistant that lives in Slack, we use Slack for communication, and it automatically has access as exactly the same level of access you do across all of our toolkit. And you just talk to this agent and you ask it to do pretty much anything.
00:53:39 · Luca Ferrari
For example, you could say, I actually saw this live as I was in a channel on Slack where we post feedback on Evernote, ideas to improve the product, was there to, I was just writing about a bug I found. And I saw our general manager for Evernote, she wrote that she had experienced the bug and asked her alternatives for her to, first of all, investigate it by going to to this tool we built to collect and and categorize customer support tickets and and check whether that bug was prevalent among customers or was this something she she was a corner case, and then go into the code base, identify the root cause, code a fix, and then message the technical lead for that project, Marco, so that it could review the pull request, and if fine, basically approve it so that that fix would go and benefit all customers. And she did that in, I don't know, like five, less than five minutes, and the agent went off and did it. The bug was fixed the day after, because Marco apparently didn't have time to review it the same day, but probably took, I don't know, thirty minutes of a person's time to do something that would have taken optimistically ten hours, even just two years ago. But actually we would probably never have solved that bug because it would never probably have reached the level of priority that we would go after it with the cost. So this is just one example. And interestingly, that technology, you don't even get to choose the model that's used, that's been used. So there is a gateway that the harness is built in house of open source components, and then the gateway is also built in house. So then when a task comes in, we have an algorithm that will determine the best model to, or combination of models to implement the task, to trying to optimize for quality and cost. So we have of course access to the APIs for the frontier models, but also we have self hosted over probably 15 or something different open weight models and combinations thereof. And and for 99% of the tasks, we actually go up and wait, spent almost nothing. We... We're currently, like I said, close to $4,000,000,000 in run rate net revenue.
00:55:37 · Luca Ferrari
We are, as far as I can tell, one of the most aggressive users of AI of any company writing 94% of our code with AI, almost all of our data analysis, almost all of our design, and on a run rate annualized basis, we're spending $15.01, $5,000,000 in all AI related spend, because it's almost sold down with super efficient open weight stuff, and the frontier models are used only for supervision, which is super cheap, it's like if you have a senior engineer just overseeing 10 junior engineers, just taking a look, but not doing the work themselves, that's way cheaper, And then for some super critical tasks, but those are quite rare. So when you bring this to an acquired team, it can make a massive difference in the efficiency with which things get done. And it's not just you cut costs, but also you just launch more features, you can run more experiments in monetization. It's all a virtuous cycle of compounding over time, which is quite powerful, especially if you can deploy it across new acquisitions, because then you can invest more in building these tools because you amortize across a growing base of revenue.
00:56:40 · Ed Elson
[00:56:40] S1: It is a really good endorsement of how AI can make businesses more efficient. Also an endorsement of the value of the open source models, which is that they're cheaper and you're not losing that much so long as you're strategic about when and how to use them, which I think is gonna be a huge shift in the AI landscape over the next few years. But, also, it is kind of an endorsement of the AI will take your job story because you're using AI, it's making the business more efficient, and you're also reducing headcount pretty dramatically in a lot of cases. And it seems like AI is doing a lot of the business work that humans would have been doing in a, yes, less efficient system. But I think if I were worried about AI taking my job, I would point to Bending Spoons as an example. Are you concerned about that? What do you think about this issue?
00:57:37 · Luca Ferrari
[00:57:37] S2: Yeah, very much so. I agree with what you're saying. I also think that I don't believe the answer is telling companies, well, try to be worse than you could be because we like, I ultimately that's not a lasting solution, but I think societally this could be a big problem. Up until a few months ago, I was quite pessimistic about this. I really thought I couldn't see a way for people to be employed even in ten years, at least the bulk of the population, I guess. I've gotten a little bit more positive about it as I've read an article by an economist, I forget his name now, but who made a... So I pointed to people who everybody I've heard say, don't worry, AI will create more jobs. Their claim was ultimately AI cannot do everything humans can, better than humans can. And I think that's a dumb argument. I think it's true today, but our brains are ultimately molecules and electromagnetic signals. Look, we're gonna get there, you know, maybe next year, maybe in twenty years. It's a difficult, but definitely not insurmountable technological problem, and the upside for whoever solves it is so massive that someone is gonna solve it. Maybe it's not LLMs, maybe we need some breakthroughs architecturally that we, you know, but we'll get there. So I think that's a dumb argument, but this economist made an argument that I think is brilliant. And it's like the concept of comparative advantage or opportunity costs or what you will, you are probably, you you or anyone at the company is probably much better at a range of tasks than some colleagues who are doing those very tasks, but your time is limited. And so it's ultimately more efficient for you to focus on certain things, other things you're better at, or they're more value adding and other people will do the other things. AI ultimately, although the capacity it can have, can be orders of magnitude greater than that of a human, ultimately it's limited by something, whether it's energy or chips or, you know, it's never gonna be infinite. And so there will be things that AI cannot do because it would be stupid to use those limited resources for AI to do those things and and humans will very likely be the next best option.
00:59:48 · Luca Ferrari
And in a world where the economy has grown massively because because of productivity gains through AI, maybe maybe we get paid so much more for those things simply because there's so much more money to go around that whoever is enjoying part of that share of the AI pie, to say, is willing to pay a nanny a 100 times more than they're paying her today. And I think nurses are paid a lot more today than they were paid a hundred years ago for very similar reasons. So I've gotten more optimistic, but there's this scenario which could be almost, you know, heaven on in some ways, and maybe heaven on earth is we wouldn't even need to work because we found a way to distribute that wealth and we can all pursue our hobbies and whatever. But this is a semi heaven on earth, I quite like it, but I also got pretty worried that the point AI is so powerful and there's this limiting factor, say energy, it's a slippery slope where if we lose control of AI, then why are we feeding humans? You know, because that's taking away energy that could be fed to giving AI that extra 0.1% to do even more. Like if we're pushing AI to that limit, what prevents those who control it or AI itself turning this into a massive dystopia, a bit like the paper clip typical this stuff. So I've gotten more optimistic, but also this scenario, I think opens up even more concerns on some truly extraordinarily bad scenarios, a lot worse than people are unemployed kind of scenarios. So I'm excited by AI because I'm ultimately I'm a technologist and I think it's fascinating, but I'm also extremely scared about some of the implications it could bring to society.