Open Source vs. Closed Source, Memory Chips Eat AI Profits, Comcast Restructures | Diet TBPN

Well, on the front of The Wall Street Journal today, this is how you know this is the whole AI 2027 Washington waking up. The AI stories are making it to the front page, the, the, the world news section, not just the business and finance section, more and more. So the picture is about the heat wave, but the lead, the, the story with the largest text is about artificial intelligence. China, resets the AI race with the United States as security models mark gains. We're gonna get into it. This is a fascinating debate because I thought that we'd have a conclusion to the open source AI debate by now. Either they would, the frontier would've collapsed and there would be, you know, perfect commoditization or they would've fallen so far behind.

There will never be a conclusion. It'll just go, it's over, we're so back. It's over.

If you're in open source AI, that's exactly how it feels. The big, the big story is centered around GLM 5.2 from Z.AI. It was officially released June 13th. So it's taken a couple weeks for it to really break through to the front page of the Wall Street Journal. But they're seeing some strong performance on benchmarks, some positive reviews from developers. I have a whole review from Tyler we can go through in a little bit, but we're now entering another round of debates around open source AI. What can the model actually do? Is this a threat to national security? What are the geopolitical ramifications here. And so I'm sure this will be an ongoing conversation throughout this week, probably next week. We have some guests lined up to help contextualize it. But laying down the facts from the journal, security researchers said that a new AI model released this month by China's Zhipu AI, also known as Z.AI, can match the latest US models when it comes to finding security bugs, a development poised to reset the global tech race and pressure the White House in its overhaul of US AI policy. So unlike models from Anthropic or OpenAI, Zhipu's GLM-5.2 is open weight. You can just download it, run it anywhere. You don't need to go to an API. You don't need to go to a private company and pay them. You can run it on your own server provided you have the electricity and GPUs to do so. It is expensive to run, as we'll go into, but it is open weight. That means it can be downloaded, run on hardware operated by anybody and can be modified and used without supervision. Scary stuff. Open weight models are ideal for users who want unfettered access to systems they control, but they're also ideal for hackers who want to run them in the shadow.

Unfettered intelligence.

Unfettered.

Oh, that's a good— We were completely out of names for new NeoLabs.

That's a good NeoLab name, yeah.

Unfettered intelligence.

Unfettered intelligence is good. GLM 5.2 has ranked as one of the top 10 most used AI models according data from OpenRouter, a company that provides access to more than 400 AI models. And what a fantastic business. Alex Atala over there, absolutely cooking at OpenRouter. It's such an exciting way to plug into the AI, the AI race without actually needing to play the benchmark game so much, be the front door. Anyway, in some benchmarking tests, according to cybersecurity company Semgrep, GLM 5.2 bested Anthropic's Claude Opus 4.8 model, which was released in May. When given further instructions, Opus 4.8 and GLM 5.2, can match Mythos in bug binding ability according to researchers. So prior to this launch, and there's a chart that we should pull up here about overall AI capability, we can talk to Tyler about what this chart actually means, but there was this narrative brewing that open source AI was slowing down relative to the closed source frontier. And I saw a lot of American AI fans sort of cheer for this. Hey, the— we have the capital markets, we have the data centers, we have the researchers. And so we are able to push the frontier at a different rate. And if we're actually growing at a faster rate in America within the closed source labs, that will compound and there will be a stronger takeoff in the American closed source AI industry. Now this chart sort of goes back and forth and there's some debate over it. It's in the newsletter. You can go sign up at tbpn.com. While we're pulling that up, let me tell you about Codex. Codex is a powerful workspace for getting work done with AI agents, whether you're writing code, analyzing data, creating creating content, or automating business workflows, Codex helps you move projects forward from start to finish. This chart, which we can pull up, shows progress from GPT-4.0 to 0.1, 0.3 mini, 0.3, Opus 4, GPT-5, 5.2, Opus 4.6, GPT-5.4, GPT-5.5, showing a linear trend in this ELO, which is a blend.

Ryan says GLM 5.2 sounds too much like a gray market peptide I might be taking.

It actually does. It does sound a lot like that. And then you can see the red line are the, are the Chinese models, which are also improving over time, but at a slightly lower rate. And so the question was, are they going to plateau while America's progress continues to advance? This latest model, GLM 5.2, seems it's very hard to apply it to this particular benchmark because this ELO was— can you give us some background, Tyler, on where this chart came from, what this is demonstrating?

Yeah. So this is by CASEI. I think that's how you pronounce it. The Center for AI Standards and Innovation. They have this way to calculate like the ELOV model. It's basically approximation of a bunch of different benchmarks. Some of those like are proprietary, like they're not open. So it's actually hard to run these also because I was basically trying to bench like all the recent models since this was published. Yeah, it was, I want to say May 1st.

Yeah, it'd be great to throw 5.6 SOL, Mytho, and Fable. It would be great to just continue this chart because it's an interesting trend.

So a lot of those benchmarks aren't actually public, so it's very hard to estimate. But I tried, I got, you can look at like some of the benchmarks that are public that you can reference. You can kind of match them up to previous models. 5.2 looks like it is like a big step up from the like Chinese trend line, right? I think the group of benchmarks that were chosen for this ELO definitely accentuate the gap between US and Chinese labs. I think there's a bunch of other like groups like Epoch AI has done chart, they basically see a relatively stable gap between closed source and open source models.

Yeah.

Since like 2023, like a long time.

Yeah. And perhaps at this point, the discussion should be more centered around cost per task more than cost per token.

Yes, definitely. Because even like, you know, new models, a lot of times when they come out, like, okay, maybe the token price is actually the exact same, but the token efficiency is much better than when you do a lot of these tasks, it's like, it's not the price per token, it's price per, you know, something completed. And then you actually see it go down.

And there's a lot of test time scaling laws where you can just throw a million dollars of compute at a particular problem and all the models do really well at it, but it's completely non-viable for any real enterprise use case and probably not even viable if you're trying to be a nefarious hacker or something.

Yes, most people are saying like 5.2 is very token hungry, right? So it uses a lot of tokens. So maybe it, like, it definitely is much cheaper than the frontier models.

On a per token basis.

On the per token basis.

But on the per task basis, it might be more expensive.

Yeah, I mean, on that, that's still, it's generally not. But on, on specific tasks, you can get, you know, if you have low thinking models, low thinking mode on the closed source ones, you can see.

Well, let's revisit John Ludig's post from 2024, May 2024. This is pre-Deepseek talking about his prediction about why the future of foundation models is closed source. He got a lot of pushback from this because a lot of people like open source models, but he laid out a thesis around closed source data flywheels, exponential CapEx intensivity of training. And he said open source will have a home wherever smaller, less capable and configurable models are needed. Enterprise workloads, for example, But the bulk of the value creation and capture in AI will happen using frontier capabilities. The impulse to release open source models makes sense as a free marketing strategy and as a path to commoditize your complements. But open source model providers will lose the capital expenditure war as open source ROI continues to decline. And that was the thesis around the time that the open source AI discussion was primarily driven by Mark Zuckerberg's work at Meta on the Llama family of models. The idea was that Meta would benefit from attracting talent. It was good marketing. It told the story that Meta has an AI story and has AI talent in-house, even if they weren't monetizing it and sharing, you know, a really fast takeoff in ARR around those models. It showed that, hey, they're able to develop these models and, and that might help them cut their costs in the long term. Very interesting that that wound up being very different in 2026. Looking at the news today, which we'll go into about them spending a lot on Gemini, There's been reports about them spending a lot with other closed-source frontier labs that they should have commoditized with their open-source plan. But nonetheless, that was the idea with Meta. But then China sort of woke up and the DeepSeeks and DeepSeeks launch at the start of 2025 and the game theory became way more complicated. So George Hotz sort of summed this up nicely. He has a take in AI Will Be Massively Deflationary, a post from just a few weeks ago as to why China benefits from investing in open source. More than American firms. He says this explains why Chinese— the Chinese are giving the much more moderate resources to train models away for free. They love to see deflationary economics in the US. It is not— it is much less of a service-based economy. And so if they can go and give away free tools that deflate the value of the service sector, that is an advantage to the Chinese economy in his formulation. He says, even if you don't regulatory capture the US government, nobody is getting a monopoly on AI. We don't live in a unipolar world anymore. And so he likens what's happening in DC to sort of rearranging deck chairs on the Titanic. It's a very fun, fun piece. So we're back to this discussion of what are the consequences and the impacts of open-source models, particularly in the United States. And there's been this clip that's resurfacing from Dario Amadei when he was testifying in front of Congress in 2023, and it's now recirculating and it was reposted like he just said it and he did not. So be clear about that. This is from 3 years ago, but some of his predictions were very prescient as of where the frontier is today. So he said, I'm very concerned about where things are going. If we talk about 2 to 3 years for the frontier models, for the bio risks, sort of a bad transcription of what he was saying. He's talking about 2025, 2026. Remember, he was saying this in 2023. We are there now. I think the path that things are going in terms of the scaling of the open-source models, I think it's going down a very dangerous path. And again, if the path continues, I think we could get to a very dangerous place. So he was worried about cybersecurity and bio risks being open-sourced and then not having a counterweight to that. Now, the good news is that we've talked to the CEOs of cybersecurity firms like CrowdStrike and Palo Alto Networks. And they've been working with Mythos and GPT-5.5 Cyber for months now to harden systems from LLM-driven attacks. And so there's still this gap between closed source and open source models. And that gap allows white hat hackers to implement fixes before black hat hackers have a chance to exploit easy bugs. There still will be a bigger discussion here though in DC over the next few months as the frontier models roll out. And the gap doesn't appear to be widening at the moment. So security security stances must adjust. It's not a— closed source is falling behind, so it's never going to be an issue. There will be this gap, and how the American cybersecurity industry and eventually the biosecurity industry implements changes and fixes before open source catches up or commoditizes and makes that particular capability widely available is going to continue to be important. So let's go over to Tyler's quick review of GLM 5.2. Why don't you take me through your bullet points and you can tell us like, what is the shape of this model? How are the reviews?

Yeah. So I think so far one of the main things is like people are saying it's, oh, it's distilled, right? This has been a big thing with a lot of these open source models, especially the Chinese ones. Oh, the only reason that they're good is because they're distilled. It's very hard to actually figure out how true this is. It certainly seems like there's some, you know, I think aspects of Anthropic models.

Didn't Anthropic openly accuse Alibaba of distilling.

Yes, a number of these labs.

Yeah.

And there's also been a big, like, professionalization of the gray market where a whole bunch of different sort of individual groups will connect a whole bunch of different entities and use subscriptions and APIs to then create a front end to like the model that can be served at a very high rate through a VPN, most likely. What's interesting is that you'd think that if you were going to do a training run, you would just find and replace some of the other lab's name before you hit run. Is that not something people can do? I don't understand. Yeah.

I mean, it also depends on what you're actually— like, maybe you're not directly distilling on the API, but you're training on public GitHub repos. And those were all used— those were all made with with, with closed source models, then you're kind of like distilling, but it's not really like, is this really count as distilling? I don't know.

Yeah.

But so, so if you are like, if you're convinced that these are like super distilled, the only reason that they're good is, is because they're just, you know, basically taking the closed sourced, like labs.

There's also this weird thing with distilling where as more and more of the public internet and GitHub broadly and open source repos become LLM outputs you, if you train on that, you are in some ways distilling because an LLM has a quirk like it's not this, it's that in text. And you wind up training on a whole bunch of Amazon Kindle books, you're gonna wind up learning it's not this, it's that. And the same thing applies for different code conventions in open source repos that have effectively been completely rewritten by closed source models.

Yeah. And so I think it's safe to say that like we've generally seen that distilled models generally will generalize worse, right? So you'll see really good benchmark scores. Maybe they're benchmarks, maybe they're not. But even if they're not like directly benchmarks, you still find that they generally—

Yeah, they're kind of accidentally benchmarking. Yeah. Yeah.

So you should always— so I think initially you should just be a little bit suspicious of these super high benchmark scores.

Yeah. But they lack that big model je ne sais quoi.

Yeah. And this is like anecdotally reinforced. A bunch of people have been saying, you know, For coding, these models are really great. GLM, it's a very good model. You know, for creative writing or something like this, where you'd imagine it's a bit harder to kind of benchmark this.

I wonder, have people been testing it with the, like, Tiananmen Square bench? Like, does it reject that stuff? Or because it felt like that was something that was like widely misunderstood by American audiences, that in fact that might not be the biggest deal for the CCP.

Yeah. Also, I think, you know, even if that's true, like the model is open source, you can kind of just fine-tune it to like not do that. And maybe it's a bit harder than that, but I think you can kind of get around like that kind of stuff.

Okay.

Yeah.

So we talked about the token hunger and the API price. And in general, I mean, you said, I'm not convinced that there's a big market for this class of model, especially as frontier models get more efficient. If you look at OpenRouter, the most used models are the smallest open-source models. Presumably being used for specific tasks that need to be repeated over and over again.

Yeah. So I think like what we've seen is, you know, a marginal IQ point of the models is like extremely expensive. Frontier models are getting very expensive. Yeah. People have to cut back, you know, they're token maxing. This is like a massive bill on their balance sheet or whatever. It seems like there's now basically like two classes of models that people really use. There's like the frontier ones and they're using coding agents They need the best thing. If you're doing cyber, like, you just need the best model because, you know, the risk of someone hacking you, it's so great. You just need the best thing. You pay whatever it is.

Yeah.

And then there's the second class, which is like these very small, very fast, very cheap models that you can use for these kind of point solution things. Maybe you have some orchestration where using a really big model to have these little agents using these very cheap models.

Yeah.

I think in the middle, it's hard to actually figure out what is the real use case. Maybe it's like hobbyists using these coding agents and they don't want to pay the super expensive tokens of the closed source labs. You see this on OpenRouter where like, what are the top models by token usage? It's these very small models. It's like, you know, DeepSeq-Flash.

Yeah, because you're spamming them for like, you know, every receipt that goes into ramp gets processed by an LLM at this point. Does it need to be a frontier model? Telling me that I spent $10 on a coffee? No. Yeah, it can just do standard OCR.

That'd be my preference.

Yeah. You want, you want super intelligence overseeing your expenses, most likely. But no, you use the right tool for the job. And that's clearly what's happening.

Yeah.

But also, I think like it is a very good model, right? Like we should not fully dismiss— I think the idea that, oh, the gap is widening, we really don't have to worry about these, these models. I think they are like very good.

Yeah, yeah, yeah.

And maybe if you're super worried about distillation, maybe something changes if the model models are kept to these big partners, right? Like what we've seen recently with government coming in. But I think we can't really fully dismiss these labs.

Yeah, it throws a little bit of a wrench in the monetization potential. Like how long can you monetize a new frontier model? That's more tricky. And then the other one is just like if you're going to keep a model behind KYC, or behind an approval for specific companies like the government has been sort of edging towards and moving towards. It gets a little bit tricky if all of a sudden you just wait 3 months and, oh, I was waiting to get approved for this one, for like GPT-7 or whatever. But by the time I— the government got back to me, my company got access to GLM-6 and It's close enough. And so that, that just throws another wrench that I think the government will have to figure out how it puzzles together with the rest of the strategy, which has been, yeah, back and forth as always.

Google caps Meta's Gemini use as AI demand strains capacity in the Financial Times. Surging appetite for advanced models is turning computing power into the tech industry's scarcest commodity. And they have a picture here of a Google Gemini bicycle, which looks fantastic.

What does that have to do with, with Meta, though?

I think that was just the best Google Gemini picture. Google has put limits on Meta's use of its Gemini AI models after the social media giant sought more computing capacity than the rival tech group could provide, in the latest evidence of the infrastructure constraints facing even the world's largest AI providers. Google told Meta around March that it could not provide all of the Gemini capacity the company wanted to purchase, according to 3 people familiar with the matter, in a move that has disrupted and delayed some of Meta's internal AI projects. So I don't know how much should we— Yeah, yeah.

So, so one, Google spent $200 billion on CapEx.

Okay. So, so, so of course, like around this time, token maxing was becoming a thing. A lot of every company in the world, at least every tech company in the world, kind of going a little bit crazy from a spending standpoint. You know, I could see Meta going and like wanting to basically buy a bunch of capacity and then being told like, hey, we can't fulfill that. Yeah, but I'm wondering, is it worth reading?

I mean, it sounds extremely bullish for Google. Like if they're—

Yeah, no, this tracks with what they talk about on earnings calls. Yeah, yeah, yeah.

Google Cloud acceleration.

You do have to wonder Could distillation be part of this story? Could that be a factor here? I have no idea. I don't know.

Zero Hedge said Meta puts limits on Claude and Codex, fearing distillation, the information.

But so this story is different. This is Meta telling its own employees, don't use Claude and Codex in certain parts and certain parts of our business because we don't want We don't want to accidentally do distillation, is what Meta is saying. So that's, that's different. I was wondering, like, is Google thinking like, whoa, that's a lot of, you know, yeah, cool it, you know. Owing to the restrictions which remain in place as well as a broader push to streamline AI costs, Meta has encouraged staff to be more efficient with AI tokens. Several other Google clients have been affected by the restrictions, although to a lesser extent. Meta has been particularly impacted because of its exceptionally high demand for Google's models. Interesting. Very interesting. On the topic of Meta, Meta shared this morning—

What'd they do?

A new milestone. It is a mind reader.

Mind reader.

Non-invasive brain-to-text decoder research, Brain2Cordy V2, building on V1, which was published today in Nature. Brain2Cordy V2 is the highest-performing end-to-end pipeline capable of real-time sentence decoding from raw brain signals. It advances beyond character-level performance to decoding words and semantics, enabling accuracy for overall communication. So if you thought Instagram was listening to you, it's gonna— if you thought it was listening to your, you know, conversations, now you can have a, you know, new conspiracy at home, which is that they might be just listening to your thoughts.

Do you know, do you know the device? They say this is a non-invasive device. I just shared an image of this device, and I want you to tell Do you consider this non-invasive or invasive? Look at this image of the Magneto and Salaf— and Salaf Graffi device.

No, you got to go high. You need to scroll up a little bit because you can't even see the whole thing here. Scroll up.

It's not invasive.

Because it looks like the device could actually potentially carry on for like a whole half of a block.

It really does seem like it's a— just put yourself in this in this room-sized device. No, of course this will shrink down.

No, I'm giving him credit here.

Non-invasive.

Non-invasive.

Okay.

As long as he—

You're putting this thing on? You're daily driving this thing?

I don't know if I'm ready to daily. I don't know if I'm ready to daily it. This will be a cool demo. Yeah. Like this will actually, when you can just walk in, sit down in a chair and see your thoughts on a screen.

No, we were debating it earlier. My buddy Rob Tave's been on the show twice. Dropped 5 predictions in Forbes recently. We can go through them at some point. He's gonna come on the show. But 4 of the 5 were very, very reasonable. Anthropic's gonna be bigger, and TSMC is gonna face more competition. And then he predicts that in 2030, telepathy will be commonplace, which is a very aggressive prediction in my estimation. How Terry Semmel fumbled Yahoo's Facebook deal deal. How much is Facebook worth? $5 billion, $10 billion, $15 billion? Whatever the number, it's probably a lot more than the $1 billion that Yahoo could have bought it for a year ago. As Yahoo continues its soul-searching, here's an unpleasant rendition of Semmel's catastrophic decision courtesy of Wired. When Yahoo came calling with a bid of $1 billion in cash, the pressure became too much. Zuck relented in July of 2006. He was just like 18 months into building the company, something like that, verbally agreeing to sell Facebook to Yahoo. He said yes. He said he was going to sell Facebook to Yahoo, allegedly. Uh, strategically, it seemed like a good match. Yahoo had hundreds of millions of users, but its foray into social networking was struggling. Facebook had cool tools and was looking for a mass audience. The timing, however, could not have been worse. In the days after Zuckerberg agreed to sell, Yahoo announced it was projecting slower sales and earnings growth. And that the launch of its new advertising platform would be delayed. Its stock price tumbled 22% overnight. Terry Semel, Yahoo's CEO at the time, reacted by cutting his offer from $1 billion to $800 million. He just took 20% off, but Zuckerberg, who had been warned about Semel's reputation for last-minute renegotiations, walked away. And that's probably reasonable. I mean, if they're cutting the price there, you have to imagine that as it gets papered, you get cut down again, then the earnout, you get cut down again, and all of a sudden you're walking away. With barely anything. But 2 months later Semmel reissued the original $1 billion bid, but by then Zuckerberg had convinced his board and executive team that Yahoo wasn't a serious partner and that Facebook would be worth more on its own. He rejected the offer and became famous as the cocky youngster who turned down $1 billion from Wired.

Legendary.

Legendary. It's so interesting to imagine the road not traveled there because the The dynamic, the way Facebook is built as a social network, like, could it have been successful under Yahoo's stewardship? Or would it have been less exciting, attract less talent, ultimately been disrupted? And would they have had the capital and the guts to go and buy WhatsApp and then also buy Instagram, you know, to actually maintain the dominant position in social networking?

What do you think? I think Yahoo should make another offer. I would like to see Yahoo make another bid.

Hey, Meta's trading down, just keeps going.

If it continues at this trend—

it goes down 99.99%, might be able to pick it up.

Anyway, chipmakers are profiting off AI at the expense of just about everyone else.

This is on the COVID of the Business and Finance section today.

We are witnessing an extraordinary transfer of cash from the providers of AI and perhaps one day AI users to memory chip makers. Take us away, John.

Yeah, the explosive growth in Micron Technologies profit in the latest quarter is extraordinarily good news for its shareholders, but it comes at the expense of the artificial intelligence companies to which it sells fast memory chips. Micron, along with the Korea— with Korea's Samsung Electronics and SK Hynix, are to AI what oil producers are to the airlines, makers of an essential input that this year suddenly became much more pricey because there is extremely limited capacity to make the high-bandwidth memory that AI needs. And it takes years to build production facilities. Soaring data center demand simply jacked up prices. Micron's soaring profits are for its customers soaring costs. We are witnessing an enormous transfer of cash, they said. Profit shift of this scale are rare events, and investors should be paying attention to where the money is coming from, where it's being spent, and how long it will keep flowing. In the quarter ended May 28th, Micron increased prices for DRAM chips more than 60% on the previous 3 months while increasing shipments by a low single-digit percentage. It said last week prices for NAND flash memory, also used in data centers, jumped more than 80%. Usually memory doesn't matter that much.. But for Micron, customers paid $18 billion more, and that was just in the quarter. Prices quadrupled in a year, and it's hurting outside AI too. Apple last week raised prices for MacBooks more than 15%. Closer to home for me, the memory I bought on Amazon.com a year ago to build a super quiet computer— I hate fan noise. Good, good color commentary here— has tripled in price and now costs more than the CPU. For an industry in which prices usually drop every year, it's a huge turnaround. In consumer electronics, passing on higher prices helps limit demand for chips, just as higher oil prices reduce consumption. But the AI companies aren't passing on higher prices because they are able to throw money at supply problems. The problem in AI is that the end users aren't covering the cost of the service, with big losses being recorded by AI model producers. Everything is still priced to bring in new customers, yet not yet to make money. So higher input costs create a nasty problem. Either losses will either be bigger or higher prices will be needed, putting off potential customers. And you can see the price of Micron's stock price has been through the roof as the company joins the $1 trillion club.

Tyler, how many trillion-dollar companies are there in Europe?

Out of curiosity, I'm going to go with zero.

That's true.

Uh, NBCUniversal and Sky will separate the company's connectivity business from its film, theme park, and streaming operations. Oh yeah, Universal Studios. Uh, Comcast plans to separate its media and connectivity business.

Building the Anderlecht of theme parks.

It does seem like a—

could there not be an opportunity to create a, a net new theme park business with with modern technology stack?

It's very expensive. Everything needs to be— the modern technology stack in theme parks is expensive.

Oh, it's expensive. You don't believe in the theme park capital markets?

I don't know. I've known people that have worked on theme parks at Disney, and it's tricky because you have to amortize a ride over 20 years. And so, you'll go—

It seems like an absolutely brutal business that is probably harder today, because at the time that a lot of these parks were built, like you didn't have like infinite online entertainment for every single sub-niche. Yes, but instantly available.

I mean, there's a whole bunch of trend pieces right now about how IRL experiences are seeing higher than ever pricing in the face of you could just watch the Knicks game on TikTok highlights, but People still forked over $5,000 to go see the game. And so, you know, you have that like Barbell strategy where Thrive is buying a stake in the San Francisco Giants, a baseball team that should face competition.

They're also exploring the NBA team to Vegas. But at the same time, John, there is maybe an opportunity. There's a stat that came out. There's more sports betting volume than all sales of movie tickets, theaters, theme theme parks, and like a couple other of these IRL categories.

Wait, it's up or down?

Uh, less. It's lower? Less. And the stat was like volume.

Yeah, yeah.

And so it's not exactly like a proxy for like revenue, but still meaningful. Chamath raised $135 million Series A for $80-90. They got Salesforce Ventures, they got Wunder Co., they got Kraft, and they got Launch.

It's the besties.

They got the best.

They got the besties together.

Do you think Friedberg— Friedberg's got to be in.

That's the production board.

Oh, the production board.

Yeah, Friedberg's fine.

Oh, great.

So yeah, you actually have all three of the other besties, and we will see you tomorrow. Goodbye.