00:20:48 · John Coogan
[00:20:48] S1: There are a bunch of bold cases. First, I'm gonna tell you about Figma. Agents, meet the canvas. Your AI agents can upgrade and modify your Figma files with design system context. So, yeah, Meta was one of the first companies to sort of reportedly be token maxing and have a leaderboard and all of that. If you have your own model and your own data centers, the incentive to token max is much, much higher because you're just paying the electricity on the cards that you're already depreciating. So you should sort of lean a little bit back into that. Not that you want to be fully token maxing, but you do want your employees using the tools that you've built as efficiently and as effectively as possible. It's just way cheaper to explore when you're not paying margin on another closed source model, and you're you're you're not paying anything else and you're actually improving the model. So it makes a lot of sense for them to roll this out broadly. The the interesting take that Ben Thompson had, which we didn't get to yesterday because we ended up spending the whole interview talking about Xbox, but the the interesting dynamic is that when you are willing to sell API access, you're willing to sell compute directly, and then you're also using your own tool internally, it creates this economic incentive internally that you you you have an incentive to always go with the most profitable, the most the most economically efficient outcome. That can be very good for business, very good for the investments that they made. The the trick is that you can wind up in a little bit of a situation where your business team or your enterprise sales team goes and sells all your compute capacity or all your chips, and then internally, your team is frustrated that they're not making enough progress. So there's a little bit of a dance there, but in general, it's a forcing function on the internal use of their tools to say, hey, wait. Why is someone willing to pay five times as much than what we're willing with the value that we're creating here? Like, we spent a billion dollars on energy consuming our own LLM, and someone showed up and said, wait. We'd pay you 5,000,000,000 for that same compute power to run a different model and do a different task. It's like, why is their model not economically valuable internally? That would be the question. Yep.
00:23:09 · John Coogan, Jordi Hays
The flip side is that they do have low cost, so they should be able to say, oh, yeah. We actually did we we yeah. We we we inferenced MuseSpark 1.1 internally, and we improved the ad model, and boom. We made a bunch [00:23:22] S0: of money. And and these are the same trade offs and decisions that every lab is having to make is how much how much compute do we allocate towards research, towards internal use, towards to the API, to subscriptions [00:23:35] S1: Yeah. [00:23:35] S0: To free plans, etcetera. [00:23:37] S1: Yeah. There was that funny semi analysis deep dive into anthropics forecast. And, in there, I mean, some staggering numbers, really, really optimistic. But, the the flip side was, who was Ed Zitron was was taking shots at the fact that they had EBTIT. EBTIT. Earnings before [00:23:58] S0: Training.
00:23:58 · Jordi Hays, John Coogan
[00:23:58] S0: Training. [00:23:58] S1: Training in for Interest. No. Training in training inference and and everything. No. Earnings before training interest and taxes. And what was odd about it was that Ed Zittrain was was was saying it's like the new community adjusted EBITDA, and it is always odd when a new non GAAP metric pops up. In this case, I think it makes a lot of sense because training runs do fit a depreciation profile. It's a little bit different. I don't know why you wouldn't just put it in in depreciation though, like, just figure out how to account for training runs through a depreciation schedule. And then maybe it's like a non GAAP depreciation metric, but it's still in there instead of trying to get everyone up to speed on a different a different, like, sounding phrase entirely. Yeah. Also, you could just do Ebitra instead of EBITDA, like, earnings before interest, taxes, and training, TRA. Yeah. And that might be a little roll off the tongue a little bit easier. [00:24:58] S0: But I think semi analysis likes to be a little cheeky.
00:24:58 · Jordi Hays, John Coogan
[00:24:58] S0: But I think semi analysis likes to be a little cheeky. [00:25:00] S1: That's true. That's true. But the but the other funny thing is that Ed Zitrin is taking shots at at this, like, oh, like, the the EBIT EBTIT is, like, so ridiculous. But, like, in that forecast, they had, like, net income of $1,000,000,000 in a quarter. So it's like it's like, okay. Well, yeah, you have this, like, funny metric that you could value the business on to get crazy in valuation. But if the business is making money and and actually, like, generating net income, you're not in a disastrous financial position. Yeah. So, you know, you you could debate about the valuation, but you shouldn't. The the whole idea of, this, oh, it's, like, so like, it's it's a very, very different discussion than than WeWork, which was not cash flow positive, which was not net income profitable, and and was using those terms to sort of skirt around the losses that were accruing from the business.
00:25:51 · Jordi Hays
[00:25:51] S0: So Yeah. I was looking back at Ben Thompson's earnings transcript or a script that he wrote for for Mark Zuckerberg. He has a good segment on why AI matters. Ben writes, forgive the long preamble, but this is necessary context for me to properly explain why AI is so important to Meta, and why I'm making the right choice to invest so heavily in both talent and infrastructure. And he goes on and on and on, but he says, what I've come to realize as I've embraced our status as an entertainment provider and ad purveyor is that our nature as a digital business, notwithstanding, we are remarkably well placed to thrive in an AI era. Remember what we learned about humans. They are obsessed with other humans and they wanna connect with them. That obsession and desire only going to increase as we interact more and more with AI. AI is gonna make our properties more essential, not less. Moreover, AI is a productivity tool, but productivity is not the end all be all of the human experience. I've talked over the last year about building super intelligence that helps you get things done, but that's a business story. What we can do uniquely is give people the experiences they want from connection to entertainment to shopping when they are off the clock. The fact that we are investing in AI but not selling solutions to businesses is actually one of our business' biggest advantages. So, of course, this is, just a sort of fan fiction for an earnings transcript. Meta is in fact selling to, businesses now. But who knows over time how big will the API business be
00:27:22 · John Coogan, Jordi Hays
[00:27:22] S1: Yeah. [00:27:22] S0: Relative to how much value they can unlock across their broader Yeah. Business with all of their infrastructure spend. [00:27:30] S1: Let me tell you about console.com. Console builds AI agents that automate 70% of IT, HR, and finance support, giving employees instant resolution for access requests and password resets. We have the perfect guest to talk about all of this with because Eric Souffer from Mobile Dev Memo is with us today in the TVP on Ultradome. Let's bring in Eric. How are you doing? Good to see you again. And congratulations. Is it doctor Seufert now? No. Masters. Right? [00:28:00] S0: Can you hear us?