00:16:49 · John Coogan
[00:16:49] S1: Exactly. Yeah. Let's see what you what sentence you typed and then deleted. Like, what word did you use before minimal impact? Did you say medium impact or whatever? You know? So so he was opted out. And, apparently, I I think all of the Meta employees who were part of that program were able to were were were able to just turn it off indefinitely. Like, you could toggle it on and off. And and the idea was that they wanted to collect information on how work plays out over, like, a twelve to eighteen months eighteen month period, and they couldn't get that from any sort of data labeler because they needed to have very high skilled workers actually chopping wood on projects for a long long time to see how projects go from start to finish. So so, basically, like, how do you compact the longest possible rollout, not just a, like, a single chain of code, but an actual series of meetings and decisions and trade offs and everything that goes into making a decision in a white collar workplace, like how do you actually reason through all of that, it's hard to to distill that from just, oh, well, the code got written this way, so that's the right way to write the code. That that might the code might have gotten written that way because a lawyer said, hey. Oh, we have to do this. And then the marketer said, oh, well, you know, we have an activation with this person, so we need to integrate it this way. And then the business people came in and said, oh, well, like, the margins will be better if we write it this way. And so it's not entirely first principles software engineering all the time when you're actually building real products. So interesting to see him sort of step into the you know, a tough interview and sort of lay out his his side of the story. But Mark Zuckerberg is in Bloomberg today pledging aggressive pricing with Meta's first pay to use AI, which is a funny framing for just an API for a model, but that's the way Bloomberg put it. In a crowded market for AI tools, Mark Zuckerberg wants to win on price. Meta Platforms unveiled a version of its most advanced artificial intelligence model, Muse Spark 1.1, that includes a new paid tier for developers, marking the first time Meta has charged businesses for access to its models and providing a new revenue stream.
00:19:02 · John Coogan, Jordi Hays, Thibault Sottiaux
It'll be among the most affordable options in the market, Zuckerberg said in an interview ahead of the release. Quote, since this is not an open source model, this is, I think, the first time that we're doing a real serious API, referring to the API used to access metas.ai. And the pricing is going to be very aggressive and attractive. Makes sense. I mean, they own the data centers. They're very efficient at building data centers. They should be able to serve a model efficiently. The new model standout improvement is in its agentic capabilities, the Meta chief executive officer said. Agents are a big theme of AI this year with the label applied to systems that can complete multistep tasks on behalf of the user. Zuckerberg described Newspark one point one as having, state of the art or very close to it, agentic reasoning and tool use. The model is also greatly improved when it comes to coding, and Meta employees are using it internally to build products and features for various apps. Yeah. My big question is how how quickly do they move all of their internal workloads Yeah. Onto their own models? [00:20:07] S0: Yeah. So they're buying they're buying they're getting access to models through Google [00:20:12] S1: Yeah. [00:20:12] S0: Anthropic and OpenAI. [00:20:13] S4: Yeah.
00:20:13 · Thibault Sottiaux, Jordi Hays
[00:20:13] S4: Yeah. [00:20:14] S0: I think that a lot of companies will look to Meta's own actions Mhmm. As a way to basically validate whether or not they should be using this model themselves. Right? Because it was just, you know, within the last month that Google had said, like, hey, we don't have Yeah. Capacity. We don't have enough capacity for all of Meta's demand for our models. Yeah. And so, yeah, they can't they they can't get enough AI elsewhere, at least from some providers. And so how much of their workloads will they be able to run themselves is the big question.
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.