AI Labs

“But right now, especially with the labs for the first time, you know, in my career, you can take capital and throw it at a company and it compounds their advantage.”

“And I think one thing that Somewhat distinguishes SpaceX AI from other AI labs is I think our goal is less to build you know, chase superintelligence or some kind of vague aspirational ideal and the goal is actually very practical, which is to build useful AI,…”

“And it certainly feels like we're starting to, or they in the labs really are starting to lose control of these bots. So I don't think this less transparent way of having them run their processing is, is a great idea.”

“Oh, no, to be clear, they're definitely stealing all my data as we go. There's no question about that.”

“The first one is the labs are so desperate that they will engage with startups. And so, like, we actually have quite a bit of signal early on because they're, you know, the labs are, Inking deals with companies before they actually have hardware available. And…”

“I'm hearing about, you know, labs going to Wall Street and trying to buy data from a hedge funds and try to understand how to map and analyze companies.”

“I've seen back of the napkin Back of the napkin ideas of next gen tier one, AI labs that are valued at twelve billion dollars, starting with two billion of cash and are definitely going to raise again in 18 months, you know, to buy now.”

“Josh Gottheimer and Mike Lawler, Democrat from New Jersey, Lawler's a Republican from New York, have jointly worked together to try to raise awareness. They've, I know they've been on TV talking about it. They've reached out to a number of the labs to talk abo…”

“I don't know that the premium for being three to six months ahead is going to last that long. I mean, if you look at, like, enterprise deployments they don't move at three to six months speed.”

“That was the most cynical bullshit I've ever heard from my perspective, even though I do actually like Trump accounts and the idea of the index.”

“I believe within months you're going to realize that these companies are extremely profitable.”

“These are the fastest growing technology companies in history.”

“Ten billion, or 10 gigawatts of powered compute which is, you know, the labs are at, like, three combined across a few of them and we've been on this three X scaling, so this is really just, like, next year's compute.”

“The hyperscalers and the, you know, the biggest the biggest AI labs are buying 95 plus percent of it, right? And then you kind of have the startups, like, fighting over the scraps.”

“Yeah, I mean, I think up until I think three months ago, right, GLP one's like total revenue was more than all of the AI labs put together.”

“LLMs are only going to carry us so far into AI, but all of these labs are not going to find product market fit and business value. Like it's just not going to happen.”

“It's the same. It's the same investors. It's exactly the same cap tables.”

“Yeah, I mean, if a lab tells you that this is an emergent super intelligence behavior, they're just lying to you, and you can read their own safety reports to see exactly how the models are trained, and exactly how they're testing these behaviors.”

“I know some labs have slowed down on their hiring of researchers unless they're above a very, very high bar because the cost isn't the research or it's the compute associated with the person.”

“I do think Neo Labs, without a clear vision for how they're going to impact the world, I do genuinely think there is some risk that they will turn out to be interesting research project, but not a viable company.”

“The shredder approach is obviously More effective, and I believe that this is a way of destroying evidence.”

“This is just, like, VC-subsidized work that labs love. The problem is scaling it beyond a few data points or what one founder or full-time employees can do, and this is the position that we're in, is we're having to compete against Basically founder-led annota…”

“The model release cycle is, look, we're releasing new models, like, every two or three months at this point, and so a model comes out, it takes two or three months to push it to its limits, and then you have another model come out, and so nobody actually knows…”

“I would much rather compete against respectfully all the founders than the labs.”

“Great people are expensive because of the labs, because there's so much money. And so there's a lot of money. There's a big amount of money chasing a finite pool of people. And so salary is a bit up. You need the cash.”

“And I think teams who can raise too much money too fast, too early, who don't have to define what the P zero is, because that's the only thing when you have scarce resources, you gotta invest in. Those cultures end up being the most fragile and brittle, and th…”

“The biggest labs in the world are using multiple different Types of chips to do their inferencing and training on.”

“There are like just labs for the sake of being lab, and I don't think they're going to make it.”

“If we continue this curve within like three years, you know, we're just gonna be so short on all the compute.”

“Most of the AI labs now have their own vending machine running, running a Claudius instance”

“One of the largest things that people underestimate both in the context of AI labs as well as within the enterprise is how significant of a job category it is going to be to train agents.”

“We are finding that the labs tend to prefer partnering with a very horizontally capable vendor that is able to flex across all of the different Verticals and scale extremely quickly rather than working with a hundred different vendors that they have to train f…”

“And now they're focused on how do we get the model to do this end-to-end workflow, coordinating with multiple colleagues for a financial model or a slide deck like we were discussing. How do we get the model to build an entire SaaS application end-to-end? And …”

“Nobody's signing up for more than like one year deals with the labs.”

“These specific products that have taken the world by storm, you can really tell that the AI labs themselves did not know they were going to be as popular as they turned out to be. Obviously, ChatGPT, they had no idea. Cloud code, co-work, like these were small…”

“They've only used the doomer kind of, we've talked about it at length on this pod. Like it's like a Christian theological doomer. Like we are creating a God. You should fear it. They've used this one version of a message. It's actually ancient spiritual techno…”

“I don't think the labs are exempt from this. Like I love their tools. It's great. Like I love, I live in the CLIs, but a regression like every two weeks. Of like a thing that was fixed like three weeks before, and they still can't render a TUI at, I don't know…”

“It's pretty clear even the tier two lab is going to be sold out of tokens, let alone the tier one lab. The tier one lab will have better margins, but the tier two lab will be sold out and probably the tier three lab will also be close to sold out.”

“This technology is by nature very fragmentary, right? That it's sometimes it, you know, like when you have a lot of pressure, you can get a diamond or you can get cracks. Often you'll see diamonds form in pockets, right? Teams of people that really work togeth…”

“Like they have shipped models that were not the snapshots that we wanted, and they have changed the snapshots that they shipped based on the feedback that we give, because our feedback tends to be more enterprise work focused and not coding agent focused.”

“You know, I think it's, to some extent, it was sort of an inevitable outcome because if you think about it, like if you have this AI model that is super intelligence packed into a model, it eventually has to converge on On, you know, all of this, all the same …”

“The labs are not going to, you know, take on the liability for every single use case that you do. They're gonna have very narrow liability that they have around copyright and IP protection and stuff like that, but they're not gonna, you know, they're not gonna…”

“Unless there's some so kind of closed proprietary research event and breakthrough that happens that just simply nobody else knows about, and we have no evidence that we've ever had one of those in AI, like, you know, these things just eventually sort of emerge…”

“Those labs that have Capability to invent new algorithmic ideas are going to start having bigger advantage over the next few years. As the last set of ideas, all the juices being wrung out of them.”

“There are a lot of small attributes, which can probably make a difference, but I don't think at a foundational level, there is a difference in outlook on what the curve is.”

“Definitely one of the top is active areas for almost every lab to invest in looping”

“Part of the potential pathology from the labs is that they have filled that hole with AGI. And so there are things that they assert as empirical that are actually articles of faith. They may be true. They may not be true. I don't know, but they get confused be…”

“What's happened so far is that people leapfrog each other every couple of weeks or every month or two, but because everyone is basically building the same stuff nobody has anything unique in the models.”

“Pretty much anyone who's maybe, I don't know, over 30 and at a large lab has been at Google Brain at some point.”

“Models are extremely good at Rust and Python. They've done a lot of RL on them. They have not done as much RL on Verilog. They've done almost none on, okay, write, write me a markdown file that describes a chip architecture.”

“But they can't go so far as to say like, hey, if you put the pieces together, Then this is how it's going to play out. I think it's too sort of damaging to sort of their reputations and like, you know, their ability to fundraise and things like that.”

“I don't think any lab would ever change the answer that it's giving based on advertising. I think it's going to give you the answer and then it's going to give you related and recommended things from, you know, from the bidding system.”

“ADP Took 20 something years to grow from a billion to ten billion in revenue. And then the next wave of companies like Adobe took about 20 years to go from one to 10. And then you fast forward in time and you have things like Salesforce or SAPs for an even mor…”

“And this is really, really relevant to the AI lab discussion because you can tell that even though all of the leaders of the AI lab say, I don't think about the competition. I don't talk about the competition. I'll use general terms. They're all obsessed with …”

“You know, I think Apple, Google, like the really established brands do have the permission to sort of go higher higher abstraction. And just sort of do, like, these brand films. For the AI labs who are trying to push a particular product, it's, it feels like a…”

“And that's why, honestly, for many years I've been so much an advocate for open science. I just don't believe that you can keep these ideas boxed in unless you're willing to keep people boxed in, which we are not willing to do. And so I don't think we have a w…”

“But in 20, 27, I can't see the checks of these sizes being written again, unless you go to the public markets and you start giving retail Share your retail shareholders and big institutional pension funds, things like that access to try and finance their colos…”

“And I think it's incumbent on the labs as well to, like, start a program pushback into these machines as well, right?”

“We went from, you know, zero to 300 public companies on Brex and, you know, all the major AI labs are on Brex today.”

“The history in the last three years has been everything that looks like it's like the fundamental breakthrough gets, gets basically replicated in lab very quickly. Like, many of the smartest people I know in the field, when I really kind of talk to them, kind …”