Meta Tokenmaxxing, Intel Joins Terafab, Frontier AI vs. China | Diet TBPN

Summary

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TBPN Diet – April 8, 2026

Episode: Meta Tokenmaxxing, Intel Joins Terafab, Frontier AI vs. China
Hosts: John Coogan & Jordi Hays (with guest co-hosts Tyler & Chris)
Duration: ~30 minutes

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Episode Overview

This episode of Diet TBPN dives into a whirlwind of major tech news:

• Meta’s internal obsession with “token maxing” and the resulting questions about Anthropic’s runaway revenue,
• Intel’s new partnership with Musk's Terafab to challenge semiconductor supply chains,
• The realities of corporate retreats (gone wild),
• Frontier AI arms races, including US tech giants banding together against model copying in China.

Energetic, irreverent, and packed with inside info, the hosts bring incisive analysis and first-hand Silicon Valley color to every discussion.

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1. Meta’s “Tokenmaxxing” – Status Games in the Age of AI (00:00–12:10)

Main Story
Meta employees are competing to burn the most AI tokens, chasing “legend” status on a leaderboard called Claudonomics. This has led to rumors that Meta's internal token use represents a vast chunk of Anthropic’s skyrocketing revenue — sparking debate over incentives, waste, and AI-native company culture.

Key Discussion Points

• Tokenmaxxing ≈ Eyeball-Optimizing (00:40):

  • Tyler compares token leaderboard culture to the “eyeball” metrics of early web days: optimizing a metric so hard, its value becomes questionable.
  • Quote (Tyler, 00:55): “When a measure becomes a target, it ceases to be a good measure. Who knows what’s going on internally but Zuck’s pushing everybody to be AI-native. This guy loves spending money.”

• Goodhart’s Law & Meta’s Incentives (01:38):

  • Chris recounts Meta insiders saying the token leaderboard causes unhealthy gaming: “You do not want to be the guy at the bottom … You’re explaining your efficiency while someone else is just burning tokens in a loop.”

• Crunching the Real Numbers (03:00):

  • Headlines suggest Meta uses 60.2 trillion tokens in 30 days—claimed as 1/3 of Anthropic’s ARR.
  • Chris debunks this using cost-per-million-token rates: actual monthly spend likely closer to $55–$136M, not the rumored ~$1B.
  • Quote (Chris, 06:13): “If there are 30,000 engineers, it’s like $4,500 a month per engineer. That’s not absurd if you’re incentivizing usage.”

• Gaming the System (07:19):

  • Reports of Meta engineers writing bots to “burn tokens as fast as they can” because of the leaderboard.
  • Quote (Tyler, 07:19): “It’s similar to how Meta once measured engineering output by lines of code. Useful only if managers dig in, but lazy ones just look at the number.”

• Why Meta’s Bullish on Tokens (08:00):

  • John ties this spend to Meta’s strategic push: a multi-billion-dollar internal AI platform (MSL) is justified simply by saving on these token bills.
  • Quote (John, 09:03): “If they amortize training cost over internal use—they don’t need a viral AI app. Pure vertical integration pencils out.”

• Schizo Theory: Data Distillation (10:19):

  • Tyler suggests the tokenmaxxing is cover for a mass “distillation” run: rewriting all internal data for future model training.
  • John speculates on the legal/moral grey area: “Ship of Theseus world—if Meta pays Anthropic a billion to rewrite all internal data, do they own the tokens for next-gen models? Out-of-bounds per most enterprise contracts, but fascinating fuzziness here.”

Memorable Moments & Quotes:

“Ranking engineers by token spend is like ranking my marketing team by who spent the most money. Maybe we didn’t hit KPIs, but Joe spent $200,000 on a blimp that only flies over his house—he gets promoted to VP.”
— Christina at Linear via Tyler (07:19)

Takeaway:
Big tech is burning tokens—and cash—at unprecedented scale, but optimizing for easily-gamified metrics carries real risks. Meanwhile, Meta’s vertical integration play may be more about internal efficiency than external AI dominance.

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2. Intel, Terafab, and the New AI Chip Wars (12:11–19:09)

Main Story
Intel just joined Elon Musk’s Terafab project alongside SpaceX, XAI, and Tesla—an audacious plan to build a trillion-watt/year fab in the US, breaking the industry’s dependency on TSMC and geopolitical risk.

Key Discussion Points

• Terafab’s Mission (12:33):

  • “Refactoring Silicon Valley fab technology” for ultra-high-performance AI/robotics chips, with on-site manufacturing/packaging in Austin.
  • John: “This is a step change. Chips for robo-taxis, Optimus, and even satellites— Musk wants compute everywhere.”

• Significance for Intel (13:35):

  • Once dominant, Intel has struggled with capacity and innovation. US government now owns 8.4% of Intel.
  • John: “Intel finally has a true demand-side story. TSMC isn’t scaling fast enough. Intel could break the bottleneck.”

• America’s Chip Security (15:26):

  • Tyler: “It’s good to get big buyers to commit to Intel and scale up alternatives to TSMC.”

• Data Centers in Space (17:23):

  • Deploying compute via Starlink satellites: tech’s not impossible, but economics/thermal issues aren’t solved at scale.
  • Chris: “There are GPUs in space today—H100’s even—but widespread space compute is an economic (not technical) frontier.”

Memorable Quote:

“There ARE computers in space! They do heat up because they’re routing packets, but humans have been moving heat around in orbit for decades.”
— John (17:29)

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3. IPO Ticker Battles and Silicon Valley Games (19:09–20:22)

Quick Aside:
Musk plans to use “SPCX” as SpaceX’s ticker, reportedly acquired from ETF entrepreneur Matt Tuttle. Brief inside-baseball banter about ticker squatting and startup priorities.

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4. Corporate Retreat Gone Wrong: Plex in Honduras (20:22–28:04)

Main Story
Plex, a remote tech company, flew 120 employees to Honduras for a “Survivor”-themed retreat—a case study in logistics fails and corporate culture gone wild.

Key Discussion Points

• Disaster Strikes (20:43):

  • Within days, the hotel GM and chef quit. On arrival: guard towers, machine guns, and unfiltered water warnings.
  • CEO became violently ill with E. Coli; arrivals greeted by an IV nailed to a bedpost.

• “Survivor” Hazing and Chaos (27:00):

  • Former Navy SEAL hired to run team-building drills: “Not a super fit group… people passing out, hit by fire ants, alligators, porcupines falling from the ceiling.”
  • Quote (John, 27:00): “It was fire festival for corporate retreats.”
  • Quote (Tyler, 28:04): “A FIRE festival of corporate retreats.”

Takeaway:
A cautionary tale: forced fun plus exotic locations can quickly become a nightmare.

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5. AI Security, Model Copying & US-China Tech Rivalry (28:06–End)

Main Story
Anthropic previews “Mythos,” a model for bug-hunting in critical infrastructure, to selected partners (Amazon, Apple, Google, Microsoft, Linux Foundation), reflecting rising concerns over AI-driven cybersecurity and model copying.

Key Discussion Points

• B2B Go-to-Market Play (28:06):

  • Anthropic’s Mythos is about more than tech: it’s about adoption inside major orgs and preventing “AI apocalypse” scenarios.

• Big Tech Unites on AI Safety (29:00):

  • OpenAI, Anthropic, and Google share info to fight Chinese adversarial model “distillation”—the process of copying/miniaturizing cutting-edge US models.
  • Quote (John, 29:47): “The rare collaboration underscores the severity of the concern … Chinese competitors extracting results from US models to gain an edge and threaten national security.”

• Model Commoditization vs. Revenue Boom (31:48):

  • Even if copying accelerates, the demand for frontier models is insatiable—Anthropic now at $30B run rate (!).
  • John: “Commoditization is more of an AI safety problem… geopolitically, if other countries move faster there’s little incentive for US labs to slow down.”

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Notable Quotes & Memorable Moments

• Meta’s token maxxing culture:

  “You don’t want to be at the bottom of the leaderboard … Someone else just set up an agent that counts one to a million, over and over.” — Tyler (02:25)

• Data distillation and legal grey zones:

  “If Meta pays Anthropic a billion to rewrite all internal data … do they own those tokens for training? It’s a Ship of Theseus world.” — John (10:41)

• Fire festival for retreats:

  “Former Navy SEAL to haze the team … it was 100 degrees, people passing out, fire ants, porcupines. Fire festival of corporate retreats.” — John & Tyler (27:57–28:04)

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Key Timestamps

• [00:00–12:10] — Meta’s tokenmaxxing, incentives, engineering culture, and internal AI strategy
• [12:11–19:09] — Intel joins Terafab: the coming US AI fab boom, chip geopolitics, and space data centers
• [19:09–20:22] — IPO ticker drama and insider Silicon Valley games
• [20:22–28:04] — Plex’s corporate retreat in Honduras: disaster and hilarity
• [28:06–End] — Anthropic’s Mythos, AI cybersecurity arms race, and US-China model copying

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Conclusion

Today’s Diet TBPN episode blends hard AI numbers, high-stakes geopolitics, and the sometimes-absurd human side of big tech. Whether discussing how “tokenmaxxing” might distort incentives at Meta or how global AI rivalry is reshaping alliances and infrastructure, the show delivers punchy analysis and industry gossip with a healthy dose of skepticism and good humor.

For more:
Subscribe to TBPN, follow the live stream, and check out their newsletter.

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Transcript

[John]

Well, there is a whole bunch of news to run through. The first story is that Meta employees are apparently token maxing and competing on an internal leaderboard called Claudonomics for status as a token legend. This is from the information. Over a recent 30 day period, total usage on the dashboard topped 60 trillion tokens. And this sparked a huge debate over how much is Meta actually spending with Anthropic. Of course the other big news is that anthropic just passed 30 billion in run rate revenue with one of the probably the steepest revenue growth chart in human history. Absolutely legendary.

[Tyler]

Yeah, this chasing status as a token legend reminds me of kind of. Maybe it was a year ago at this point you were saying, will tokens ever become eyeballs the way eyeballs were during the.

[John]

Yeah, during the era. Yeah.

[Tyler]

Right. Just optimized for eyeballs. Obviously not every eyeball visit to a website is created equally, but people were optimizing for eyeballs and now, you know, I don't. The reaction to this I think has been generally, at least online, like been, I guess, reassuring. A lot of people are saying Gary Basin says U. Yeah. Marty says Goodhart's Law. When a measure becomes a target, it ceases to be a good measure. And so who knows what's actually going on internally. But we do know Zuck is pushing the entire company to be as AI native as possible. And this guy loves spending money too. Right.

[John]

I have a crazy bull case here that I will run through. Let's get through some of the story first. We gotta pull up this comic from XKCD in the comments here. When a metric becomes a target, it ceases to be a good metric. It's right under the leading post. There we go. And the other counterparty says, sounds bad. Let's offer a bonus to anyone who identifies a metric that has become a target. It is good. I don't think that's going on here.

[Tyler]

Lighter was texting a friend at Meta and sent the post. We just discussed on token maxing and said true. And the person said yes, it's pretty sad, but I mean imagine. So Meta has been. There's been rumors of Meta layoffs for a while now.

[John]

Sure.

[Tyler]

Unclear how many, if any, if any have happened. But if you're sitting there, Zuck is saying like we need to get AI native. Boz is saying we need to get AI native. And then suddenly there's a token leaderboard. You do not want to be at the bottom of the list. I will say that. Right? Yeah. You don't want to be the Guy who's having to explain like, no, well, I, I've actually getting the most out of each incremental token and the other guys just like set up an agent that just counts one one to a million over and over and over or something.

[John]

Yeah, yeah. I mean you have to measure the actual output, the impact on the business. I mean, fortunately, Meta has been a huge beneficiary and a huge winner of AI. The ads are getting better targeting, they're seeing they're delivering more ads and, and the quarterly earnings have been strong. The headline number here that, that sort of took everyone by surprise is that meta staff used 60.2 trillion token 30 days, which would pencil out to about a third of Anthropics. ARR was the number that was thrown out. But both of these claims are pretty questionable. And so Tyler did some back of the envelope math to show that the 1/3 revenue estimate is way, way too high. And I don't know, do you want to take us through some of the reasoning there and then we can talk about the knock on effects of all this?

[Chris]

Yeah. Okay, so 60.2 trillion tokens is the number. Like we can just assume that's true. So basically I'm going to assume all the employees are basically just using Opus 4. 6.

[John]

Yeah.

[Chris]

So then there's basically three numbers you need to look for in like the API cost. So there's like input, there's cached input and then there's output.

[John]

Sure.

[Chris]

So for Opus 4.6, it's $5 per million tokens on input. It's $0.50 per million tokens on cash. Input, cashed and then it's $25 on output. Yeah.

[John]

So if you multiply that 60.2 trillion tokens at the highest possible rate, $25 per million tokens, then you do get like a billion dollars.

[Tyler]

Yes.

[John]

Which is crazy.

[Chris]

That's not what's happen number. But like you have to think about it like, you know, if you're using like Claude code or any of these coding agents, you know, the vast, vast majority of the of the tokens used is input. Yeah. Because like so imagine you're working on some, you know, coding file. Right?

[John]

Yeah.

[Chris]

There's a thousand lines of code in the file. Maybe the model is only changing like 10 at most. Right. So that's a very small percentage. So the output tokens are going to be a very small percentage of the total tokens going in.

[Tyler]

Right.

[Chris]

OpenRouter publishes like a lot of this data, so you can kind of use those ratios to figure out what is actually like, what are the actual numbers of the. You know, input versus cash versus output. Yep.

[John]

So just to get sort of like market standard averages, like baseline benchmarks. Now, Meta could be using these tools differently, but if we are to assume that they're the shape of their agent decoding efforts are similar to the average, this is what the numbers look like.

[Chris]

So maybe there is like some bad incentive where people are just saying to the model, like, count up to a billion and then do it again. So then it's like totally skewed. But if they're doing it relatively normally.

[John]

Yeah.

[Chris]

So on Open router, it's about 98.9% of all tokens are input input. And that's including cached ones. Yeah.

[John]

Because you're stuffing the context window with all your code base or a huge amount of context it's going.

[Chris]

And that's not changing every time. So you can cache it. Yep, yep. So that's like 1.1% as output. Yep. So basically, if you basically get all the numbers, million tokens is going to be $2 and around 26 cents. So that'll get you to something like $136 million a month for the 60 trillion tokens. Right. So that's like way less than the 900. So that would be 1.6 billion a year, like run rate still huge. That's a lot. But that is still in the max.

[John]

Assuming they're in the top.

[Chris]

Yeah, that's assuming that Open Router, the kind of breakdown of how they're using the tokens is the same as Open Router, which I think it's not. If we assume that that's like $4,500 per engineer. If there are, I think 30,000 engineers at Meta.

[John]

Yeah.

[Chris]

Every month, $4,500 on tokens.

[John]

$4,500. That's actually in line with what I've heard a lot of other people spending in terms of their token budgets. $5,000.

[Chris]

That's not like absurd. Absurd if you're trying to incentivize people to use that.

[John]

Yeah, yeah. No, not at all.

[Chris]

But so. So you can actually see the breakdown on OpenRider of how people are using tokens. So 17. The biggest plurality is OpenClaw, which is 17.6%, and then Claude Code is 16.8. So I think if you think about CLAUDE code, you would imagine that in CLAUDE code, the percentage of cache tokens is going to be higher than in Open Claw.

[John]

Yeah.

[Chris]

So I think Meta's usage is actually going to be more heavily based on the cache tokens.

[John]

Sure.

[Chris]

So if you do it just based off, like, Claude code usage, you'd actually see a higher percentage of the input tokens of the total tokens. So it's only like 0.8% is the output. So then if you get all those numbers through again, it's only like 55 million a month, which would be 669 million a year, and each engineer would be like $1800.

[John]

Yeah, that's actually pretty low, which is,

[Chris]

like, I think, very reasonable.

[Tyler]

John Chu over at Coastal says, plenty of my Meta friends told me folks have been building bots that just run in a loop, burning tokens as fast as they can due to this policy. It's an absolutely stupid policy and is similar to how Meta uses lines of code to measure engineering output. Managers are supposed to use it as a proxy and dig in to understand work complexity, but plenty of managers are lazy and just don't. That was in response to Christina over at Linear saying, ranking engineers by token spend is like me ranking my marketing team by who spent the most money. Yeah, we may not have hit our KPIs, but Joe spent 200,000 on a branded blimp that only flies over his own house. So he's getting promoted to vp.

[John]

I'm pro branded blimps, though. I like that idea. So my take on this was that. Yes. Yeah, it sort of ties to what Jensen Huang was talking about at GTC. He was saying that an engineer that's making $500,000 might soon command something on the order of $250,000 a year in token budget. Andhra Karpathy had a similar line. He said it's all about tokens. He said on a podcast last month, what is your token throughput? And what token throughput do you command? And so Meta actually has two different harnesses internally. They have a version of openclaw called My Claw. And then they also, of course, acquired Manus. But it appears that they're running Claude, maybe Opus, under the hood to actually generate the tokens that come through those harnesses. The interesting thing is that at 250k AI budget per engineer, you're at like 20,000amonth. And so, based on Tyler's math, this feels like, okay, there's going to be another maybe 4x to get to Jensen's prediction. I think it makes clearer the strategy with Meta Superintelligence Lab, because if you're looking at, you know, it's clear that they're spending hundreds of millions of dollars on this just for internal code. Gen tooling, like running their business, they are going to spend an inordinate amount of money on frontier inference. And so training a model there, they will be able to amortize the training cost of the next model that they build. Not just over can they get a product out that goes viral and becomes its own standalone chat app that people pay for. Or maybe it's ad supported, like just on the internal usage. They could be running a multibillion dollar token bill that they would have to pay another lab. And so if they develop that internally, it's pure vertical integration. And then you also have everything that's happening on the actual ad targeting and content delivery side. And when you add up all of those, all of a sudden the big question has been like, is Meta going to be able to launch an entirely new AI product like Vibes or something like that? And this is a data point that to me says they don't need to. Just from a pure vertical, vertical integration story the investment in MSL can pencil out. What are you laughing at?

[Tyler]

I just want you to get to your schizo theory.

[John]

What's the schizo theory?

[Tyler]

That this whole like token maxing is like a, is like a barrage while they distill the model.

[John]

Oh, oh, yeah, yeah. I mean there is a, there is a world where if you're running, if you're generating trillions and trillions of a

[Tyler]

frontier model, they're like meta is really like burning through a lot of tokens and you haven't like, oh, we're just token maxing.

[John]

Yeah. I mean there's another story about distilling. We'll get to later in the show. But there is a question about if I have a, if I write an essay and then I have a model rewrite it. Those tokens, they are from that model provider. I buy them, they become mine. Can I train on them? That's probably out of terms of service. So you would think no. But you sort of wind up in this ship of Theseus world where if meta pays anthropic $100 million or a billion dollars to go rewrite every line of code, every email, every slack chat, every internal message, like basically map the entire organization, rebuild it. They wind up with an incredible training corpus that they can use for their next model. But I would imagine that they can't. And I imagine that the, that the enterprise contracts go both ways. They can't, you know, the lab can't train on the corporate information that's standard in all of the enterprise contracts. And I, and I would imagine that the opposite is true as well. Although it is this fuzzy ship of Theseus world where if you're using coding agents to upgrade your infrastructure and then you want to run and train some model on your infrastructure, do you have to pull out the tokens that were revised by the AI lab that you don't have the right to train on? It's all very interesting. Apparently startups that have gone out of business are able to sell their corporate histories for something like a million dollars to data brokerage firms and AI labs.

[Tyler]

Now.

[John]

Have you heard about this?

[Tyler]

Yeah, heard about it. I'm skeptical. I mean, I mean certainly there's a market for it, but basically all the

[John]

code that a company built over a few years, maybe they raised code, but

[Tyler]

also usage within different enterprises.

[John]

Yes, yeah, all sorts of different stuff.

[Tyler]

In other news. Yes, intel is joining terrafab.

[John]

Yes, let's review.

[Tyler]

Intel is proud to join the Terafab project with SpaceX, XAI and Tesla to help refactor Silicon Valley fab technology. Intel says our ability to design, fabricate and package ultra high performance chips at scale will help accelerate terrafab's aim to produce one terawatt a year of compute to power future advances in AI and robotics. And throwing up a post of hanging with Mr. Musk himself.

[John]

Let's go through the Wall Street Journal's coverage of this. Elon Musk is partnering with intel on his ambitious Tarifab project, which aims to build specifically designed chips for SpaceX and Xai as well as for Tesla. In an announcement, intel said it would work with the companies to design, fabricate and package ultra high performance computing chips at scale. The company shared a photo of Chief Executive Lip Bhutan shaking hands with Musk, CEO of SpaceX and Tesla. The partnership is a win for Tesla, which has struggled in recent year. Intel intel, which has struggled in recent years, leading the company to cut production capacity when demand was surging for data center chips and when competitors like Nvidia and AMD have thrived. That was always a just such a tough pill to swallow when you would talk to the ASIC companies like Cerebras and you would say like you're doing something new, you're not doing Nvidia chips. Is there any way you could get off of tsmc and they're like, no, like we still need to be in Taiwan. Obviously there's a huge geopolitical component here. We can get into all that. But last year the Trump administration reached a deal to acquire an equity stake in intel for around $9 billion to help secure the American chip makers business, the US government held 8.4% of intel shares outstanding as of March 20, according to securities filings. The figure doesn't include warrants that could increase the government's equity stake in Intel. Terrafab represents a step change in how Silicon Logic memory and packaging will get built in the Future. Tesla and SpaceX confirmed the partnership in post on X. Musk unveiled the plans for a single facility in Austin, Texas to make chips to be used by SpaceX and Xai, which merged in February, as well as by the publicly traded Tesla. He pitched the project as an opportunity to quickly experiment on chip design by designing and manufacturing the chips in one facility. The FAB will make chips for use in Tesla's Robo taxis, which they're already fabbing I believe at Samsung. Although they do have Nvidia Dojo chips I think that are tsmc. Optimus will also need chips and they are planning to use intel for that as well. So these are two areas of priority for the electrical vehicle maker as it ships it its focus to artificial intelligence enabled products. It will also make chips optimized for use in space where SpaceX is planning to deploy huge numbers of satellites capable of handling AI computing tasks.

[Tyler]

Who else do you think they need to get involved here? Because just the two of these, the two of these got, you know, intel. Yeah And Tesla coming together. It's, it's, it's good to have more involvement. But still I think the entire project.

[John]

No, we've seen, we've seen a few of those like AI leader gatherings in D.C. where you see Tim Cook and Sundar and Sam Altman and Dario and all the leaders are together and I was always hoping that at one of those dinners they would say okay, everyone's going to try and say the biggest number but this time it's going to be how much you're committing to intel and how much you'll buy from them if they come online with a competitive product. Because the demand side is, has, has always been a big problem for intel that they have the capability. They have plans to build the 2 nanometer, 3 nanometer plant like a Frontier plant Leading Edge Fab. But every other company has been so tied to tsmc but we, but I think everyone now acknowledges that TSMC is not, is not investing super heavily in Capex. They're not going, you know, they're not scaling up as much as as the industry would like them to and so lots of folks have sort of signaled towards a chip bottleneck coming in the next few years. And intel has the opportunity to communicate that this seems like the first step in that, in that chain. So companies including Tesla often design their own semiconductors but need a supplier to actually make the so called, make them in a so called chip. Fab. Musk companies have sourced chips from a wide range of suppliers, including Nvidia, Samsung, Taiwan Semiconductor. Oh, I got it. Musk said that Terrafab is needed because his company's demand for chips is slated to far outstrip the supply it gets from partners. I was listening to Chuck Robbins from Cisco talk about data centers in space and the heating issue came up and he was like, I don't really have like a solid answer for that yet, but I do think that if you are bullish on data centers in space, you have to start with the fact that Starlink works in space currently because it is doing compute.

[Tyler]

It's not, you couldn't possibly put, let's be honest John, we couldn't possibly put a computer up there.

[John]

Yeah, like there are computers with like they don't, they can't inference frontier models. They can't, you know, it's not gigawatts in space yet, but there are, I believe across the entire Starlink cluster, megawatts of compute in space with solar panels. And they do heat up because you are running a chip that routes packets across the Internet from one satellite to the next to get you your Internet via Starlink. And so it's not that it's a solved problem is that we are actually, we are on a path to, you know, deploy some level of compute in space. Tyler.

[Chris]

Yeah, I mean we've seen like Philip Johnson, like there are chips in space right now. Like there are GPUs. I think Arthur, he said there were like five or six H1 hundreds, right?

[John]

Yeah, yeah.

[Chris]

So like they do work. It's like yeah. I think most people's problem with space data centers is that it's like economically doesn't make any sense.

[John]

Well so yes, that is the correct angle, but a lot of people are getting hungry. No, no. There is a whole conversation about like, like it is impossible and you need to like move past that into the economic equation which then gets you into timelines and actually thinking about what needs to happen to dissipate that heat. But clearly yes, you can, I mean you can put humans in space on the ISS and cool that they're like, we, we have created ways to, to, to, to move heat around in space for decades. It's a, it's obviously a new challenge. But I think starting with, with the baseline of like there is compute happening in space right now we're going to try and I mean Elon wants to like thousand exit, hundred thousand exit, million exit. I don't even know how to what the, what the scale is, but orders of magnitude and so there's new engineering challenges.

[Tyler]

Speaking of space, looks like Elon is going to use SPCX as a ticker for the SpaceX IPO, which he had to acquire from Matt tuttle, hence the ETF's ticker change shown below. Eric from Bloomberg says we predicted this could happen in a December. Note. Nice catch by Will, who famously gave the meta ticker to Zuck. I did not know that Will Hershey had the meta ticker previously. So you know, we know somebody that

[John]

spots on who, who had the meta ticker.

[Tyler]

A guy named Will Hershey.

[John]

Oh, interesting.

[Tyler]

There's a company called Roundhill, but we know somebody who's not ball. We had somebody here.

[John]

Yes, yes.

[Tyler]

Outside of show hours and say that they were squatting on a bunch of tickers and the, and the idea seems so I think, I think what, what, what might be the reality is that you actually, it needs to be, it needs to be further along than just reserved. I don't know. I think so having it, you can go. If you're a startup today, you can go reserve your ticker today. But I'm not sure that that actually gives you enough leverage to when, when Elon comes knocking, ready for an ipo, you actually have priority. All right, we got to talk about a corporate retreat that went badly wrong.

[John]

Okay.

[Tyler]

Technology company plex took its 120 employees to Honduras for a week long bonding experience. It was a disaster. From the moment they arrived, senior executives at the tech company Plex were eager to treat their 120 fully remote staffers to a week long corporate getaway in a tropical paradise. Pop quiz. Tyler, do you know what Plex is?

[John]

I don't know about Plex.

[Chris]

No.

[John]

Have we seen Plex before?

[Tyler]

I don't know either. So we all failed. But now it's your job to figure it out. I will continue. The plan for the Honduras trip was simple. Company meetings and team building company by powdery soft beaches during the day and island fund at night at a cost of roughly half a million to the company. They'd build the trip around a Survivor theme with teams and challenges. But it'd be fun, not too physically grueling. The CEO of Plex, a free streaming platform, would play a role similar to that of Survivor. Host Jeff Perhaps the executive should have taken it as a sign that just as the first bus of staffers pulled up to the resort, the chief executive was already in his hotel bathroom experiencing the initial waves of violent stomach and infection. What followed was a comedy of errors, including military drills that outpaced anything this group of office workers had in mind. A rogue porcupine, stranded airplanes, and one syringe to the butt of an employee. Corporate retreats are generally assumed to be torture or at least a semi stressful chore, what with their forced fun activities and hybrid workplay environments that leave workers confused about boundaries. Is that, is that like the industry standard? That seems wild.

[John]

I don't know. I think I've ever been on a corporate retreat. I've been on some like Founders Fund events, but those aren't really retreats. Those are more just like conferences. But I don't know, corporate retreat seems, I don't know, unexplored territory for me.

[Tyler]

It's no wonder. The new season of Jury Duty, a comedy series that tricks an unsuspecting non actor into believing his off the wall fictional circumstances are actually happening, is set at a corporate off site. But in real life, Plexcon 2017 beats anything on TV. Here's the story of an all staff company getaway told by six people who were a trip where most everything that could go wrong did go wrong. Nearly a decade later, they're still working together and still talking about it. It's crazy that they.

[John]

It was bonding experience.

[Tyler]

Yeah, well, yeah, it's crazy that this is now, now coming out. So. Sean Ha, 42, founder of Moniker Partners, an independent corporate retreat agency that planned the trip. About three weeks before we arrived in Honduras, we got an email from the hotel's general manager that said, I will be departing. I wish you the best with your retreat. I knew something was off. Three days later, another email. The head chef was no longer going to be at the hotel. 52 Chief Product Officer and Plex Co founder We get there, we've got to take the bus from the airport. Dirt roads, you start getting closer and there are guard towers around the property, people with machine guns and stuff. A lot of people were like, where are we going? Keith, the CEO of Plex54 we usually go a day early and we set up. If there's any little thing, we have to get it right just so the employees have the best experience possible. Keith woke up the day that people were coming in Sunday morning and he is sick as a dog. Everyone there is fried. Basically. People are telling me don't eat the vegetables, don't eat the fruits, don't eat the vegetables.

[John]

That's like the same thing.

[Tyler]

No, no, no, because they clean it, they wash it in water. It's usually not filtered water.

[John]

Right.

[Tyler]

Because it would just be kind of crazy to.

[John]

Yeah, yeah. Here it is.

[Tyler]

I've got to have a salad. Just one salad. So I got E. Coli, which may be the worst thing you could get possibly ever. Just as people were arriving on the buses, I was like, I had lost eight or ten pounds. They had a doctor come to me, which apparently is pretty standard. They nailed an IV bag to the bedpost. Just nailed it. People are arriving for a party that night. The next day is Survivor themed kickoff. There's not one person on the planet more excited about Survivor than Keith and his wife. They have watched every single episode. My wife and I met Jeff, the host of Survivor.

[John]

What.

[Tyler]

What I wanted is when everybody shows up, I do a Jeff, welcome to the island. Here's the theme for the week. But Scott got to do it. The opening Survivor thing was a contest where people on their different teams open up a platter. You have to eat what's on the platter. Sean. Sean, who's the plex. Head of business development. Yeah, somebody is. Somebody is cold texting me, pitching me their. Their startup and they've called me a bunch of times today.

[John]

Is it actually them or is it their agent?

[Tyler]

I wish I could pick up. It's just like a little bit. Yeah, but yeah, cold texting somebody, like getting their number. I don't think that's the new meta.

[John]

No, it's.

[Tyler]

It's bold.

[John]

We heard from an executive in. In tech that they are getting dozens of emails every single day trying to recruit them. And every email comes from a new Gmail account that's like unregistered, brand new, but it's all like LLM written very different, doesn't really do all the research, but has a few keywords in there and it's clear that someone is building sort of like a next gen recruiting agency that's basically just a lot of spam. Feels like the end result will be like a return to relationship building and not like broad top of.

[Tyler]

I should read the cold. The cold text from this morning. I have nothing against cold, cold email and just being bold. But I did read this out loud to you, John, so I'll read it to everyone. So I got a text from an unknown number today at 7:00am all right, Jordy, good news or bad news first? This is blank and I'LL leave the name out and then I just get a PDF of a deck and then a text. All right, Jordy. The bad news is this was an unplanned introduction and on the surface, probably lukewarm outreach. The good news is that there's zero doubt you're now in touch with the founder with the most grit of anyone you've interacted with the past 12 months and likely anyone you'll interact with over the next 12 months. 50,000 seed round passes over the past 10 months here to make 50,000 in one. So. So now you should be coming in being like, I've been passed on 50,000 times. I'm hoping this is the one that gets that.

[John]

Seems like a rough estimate, though.

[Tyler]

Every months of feedback and iterations have made us better. So you're seeing more quality, more quality presentation than rejection. 10,000. Looking forward to your message.

[John]

The chat wants the builder to pitch. They want you to hear this out. Everyone's in favor of this. The chat wants you to get on

[Tyler]

the phone with them, do it live.

[John]

I mean, they wanted to do live. I don't know if you should do live, but you should take the call.

[Tyler]

I will take the call. I will take the call.

[John]

But let's go back to the corporate retreat.

[Tyler]

Okay, so they hire. They hire a former Navy SEAL to basically haze the team on the beach. And you can pull up a picture, an image here.

[John]

The quote is, this is not a super fit group in general. One of our biggest mistakes was hiring a former Navy SEAL to pump the team up. As I'm in my room dying, I could hear them out there doing all the drills and yelling. And so I'm in here thinking, this is terrible, but it sounds terrible out there too. We're doing army crawling on the beach. It was 100 degrees. I bailed out partway through. I went into the ocean just to cool off. I went in probably on all fours because I was tired. There's not a fit group of. Not a super fake group in general. The ex Navy SEAL is like, we can tone it down, no problem. We get up there and it's hot and humid and people are passing out. I don't think he'd ever seen quite such an unfit group. We ended on, I guess, what's probably a golf course. On command, everyone had to hit the grass. Everyone's silent. We're pretending we're Navy SEALs, but I happened to land in the wrong spot. I'm just like, oh, God, what is happening? I was sitting on a fire ant hill. I was Wearing shorts. I jumped and had hives and bumps from the bites. This is ridiculous. Someone saw an alligator on the golf course. Sounds like a ridiculous.

[Tyler]

There was a porcupine that fell through one of the ceilings.

[John]

This is like a fire festival for corporate retreat.

[Tyler]

A fire festival of corporate retreats.

[John]

Anthropic is taking steps to arm some of the world's biggest technology companies with tools to find and patch bugs in their hardware and software. The company is making a preview of its new AI model called Mythos, available to about 50 companies and organizations that maintain critical infrastructure, including Amazon, Microsoft, Apple, Alphabet, owned Google and the Linux Foundation. Cybersecurity researchers and software makers worry that artificial intelligence is becoming so good at exploiting vulnerabilities that it could cause widespread online disruption. Security experts have predicted that AI models will discover an avalanche of software bugs and the effort is set to help companies stay one step ahead of cybercriminals and other threats. This feels like a very good rollout strategy generally, both because we've seen a huge amount of cyber attacks and hacks and accidental releases. Like, even if it's not, you know, there's been. We had a member of the security team from Crowdstrike on the show last week talking about the rise in cyber attacks broadly getting the most frontier models in the hands of big companies early. Great from that perspective. And then also just great as a product demo which will get the entire organization excited about deploying the technology broadly. So very good as a B2B go to market motion. This makes a ton of sense. In some other more positive news, OpenAI anthropic, Google are uniting to combat model copying in China. This is a bigger discussion around AI safety. We've talked about this.

[Tyler]

You look at that.

[John]

Some, some who knew faith and viewers

[Tyler]

who knew that you get along.

[John]

Yes. I mean like, I mean, I'm sure people in the chat have seen the New Yorker article where there's just tons and tons of quotes from various AI leaders all, you know, upset with Sam Altman. And the inter AI drama has been bubbling up since the dawn of OpenAI. Like OpenAI was started as a reaction to Google and then Anthropic leaves and teams up with Google and then Elon doesn't like Anthropic and then Ilya Sutskever and Mira leave but they don't join Anthropic. And so there's been so many personalities and so many disputes. I feel like the takeaway is that this is all extremely high stakes. There's a technological transition happening. A huge amount of money on the table, a huge amount of influence on the table. And so everyone is sort of clamoring for for their share and it's creating a lot of friction. So rivals OpenAI, Anthropic, PBC and Alphabet Inc. S Google have begun working together to try and clamp down on Chinese competitors, extracting results from cutting edge US Artificial intelligence models to gain an edge in the global AI race. The firms are sharing information through the Frontier Model Forum, an industry nonprofit that the three tech companies founded with Microsoft in 2023 to detect so called adversarial distillation attempts that violate their terms of service. According to people familiar with the matter, the rare collaboration underscores the severity of a concern raised by US AI companies that some users, especially in China, are creating imitation versions of their products that could undercut them on price and siphon away customers while posing a national security risk. And so I was trying to square this question of distillation and model commoditization with the news that anthropic has reached 30 billion in run rate and has an agreement with Google and Broadcom for multiple gigawatts of TPU capacity. Like clearly there is insatiable demand for frontier tokens. Frontier models, they're incredibly expensive to train. We saw in the Wall Street Journal

[Tyler]

that these expected training costs from yeah,

[John]

it was training at Andy in France, but it was hundreds of billions of dollars. And so the hope is that you is that you're able to amortize that over at least a couple of years, a long time. Ideally the shelf life of a model after you train it is pretty limited if you're being commoditized and copied. If you're being distilled, it's even faster. At the same time, just staying on the frontier clearly leads to an incredible ramp in revenue. So is commoditization a real problem? It feels like it's almost just more of a problem from an AI safety perspective because you can't have the geopolitical conversation like what Bernie Sanders is proposing around different labs, working together, potentially pausing or slowing down, or just even adding more constraints and reviews before models get released. It's harder to do that if you have a different country that's racing ahead and moving much faster and trying to close that gap. Leave us five stars on Apple Podcasts and Spotify. Sign up for our [e-mail] and we will see you tomorrow. Goodbye.