Leopold's 13F, Data Center Fixes, Shein Buys Everlane | Mike Isaac, Rowan Trollope, Dean Leitersdorf, Joanna Stern

Summary

TBPN Episode Summary — May 18, 2026

Overview

In this episode of TBPN, hosts John Coogan and Jordi Hays cover a jam-packed Monday in tech and finance, diving into:

• The legal verdict dismissing Elon Musk’s lawsuit against OpenAI and Sam Altman
• The much-anticipated filing of Leopold Aschenbrenner’s 13F and the larger drama of AI infrastructure investing
• The growing backlash—across the political spectrum—against AI data centers and proposals on how to fix it
• Updates on Shein’s acquisition of Everlane, insights from tech guests Mike Isaac (NYT), Rowan Trollope (Redis), Dean Leitersdorf (Descart), and Joanna Stern (WSJ, now an author/business owner)
• Lively discussion of societal trends, including the looming protein shortage and the changing future of AI wearables

---

Key Segments, Insights, and Timestamps

---

1. Sam Altman, OpenAI, and the Elon Musk Lawsuit Verdict

[00:05–01:41; 44:58–57:13]

• Main Story: A US jury found OpenAI CEO Sam Altman not liable to Elon Musk; the suit was dismissed on a statute of limitations technicality.
• Musk’s legal claim revolved around OpenAI “straying from its charitable mission.” However, the jury ruled Musk waited too long to sue.
• Mike Isaac (NYT) called in from outside the courtroom, sharing vivid, on-the-ground reporting on the verdict’s swiftness and the trial’s anticlimactic end.
  • “This whole debacle only became a thing after the launch of ChatGPT…” (Host, [48:19])
  • “It was crazy. Today was supposed to be the first day of deliberations, and less than two hours into it, they reach a verdict.” — Mike Isaac, [45:12]
  • “It feels like victory on a technicality… As you think back to the last three weeks, it feels like the core discussion was ‘you can’t steal a charity’ versus technical, statute of limitations.” (John, [47:08])
• Isaac describes overlapping protest groups and the difficulty of interpreting the result as being about Musk or about nonprofit law: “A wonkier point…very easy to sell jurors on Musk as selfless, but this was a legal, technical argument—the statute was the whole thing.” ([49:43])
• The hosts poke fun at the snack situation outside the trial (“…show up with an apple and banana…get into the four digits of calories, please, God.”) [55:18]
• Memorable Moment: “The judge left the jury with a pretty good summation…not of the trial, but just appreciating a jury finding, like, finding parties liable or not liable.” — Mike Isaac ([53:01], [54:42])

---

2. Leopold Aschenbrenner’s 13F and the AI Infrastructure Investment Frenzy

[02:13–13:12; [07:05–08:58]; [09:17–13:12]]

• After a weekend of anticipation, Aschenbrenner’s Situational Awareness Fund dropped its 13F, igniting a frenzy on “Finance Twitter,” tech, and meme accounts alike.
  • “We have not seen this level of attention on a hedge fund’s filings in a very long time…It’s breaking out beyond Fintwit.” ([07:05])
• Portfolio Details:
  • Major semiconductor bets, particularly puts on the SMH ETF.
  • Positions explained as bets on bottlenecks in compute required by AI build-out.
  • T1 Energy (US solar, formerly Chinese-owned) is discussed as a rare “green” hedge in an otherwise fossil-heavy sector.
  • Caution is given about reading too much into 13Fs (they’re stale, don’t show options details, only a snapshot).
• Quote of the segment:
  • “These positions are stale. He might have rotated out…could be completely different today.” ([08:58])
  • "If you're trying to read the tea leaves too precisely, you can only take away so much from these." ([09:51])
• Memes and Market Impact:
  • “Did that leprosy fella tank the market with his 13F? 47 likes.” ([12:40])
  • Hosts highlight the confusion over notional versus real exposure, the (potentially illusory) significance of copy trading, and the memeification of hedge fund filings.

---

3. The Data Center Backlash: Political, Environmental, and Economic Fallout

[13:13–44:07; 115:00–120:05]

• Unifying Both Left and Right:
  • Left: Job displacement, threat to creativity, environmental impact.
  • Right: Surveillance centers “used to spy on people,” resentment of “coastal elites.”
  • Data centers, once boring infrastructure, have become the focus of direct political angst.
  • "Data centers need to be rebranded as Data Ranch." (Chat/Listener, [15:43])
• Case Study:
  • The massive data center project in Utah (championed by “Mr. Wonderful” Kevin O’Leary) receives intense online protests.
  • TikTokers and YouTubers debate its merits: agricultural land use, water rights, increased energy usage.
  • Viral soundbite: “Daytime temperature could increase 2–5 degrees…in the uninhabited desert valley.” ([22:32])
  • Hosts mock the use of “atom bomb” comparisons for energy usage (“Your car weighs as much as seven atom bombs.”)
• Notable Segment: Video of Eric Schmidt getting booed during an “AI is your future” commencement speech ([25:54–31:41]).
  • “It’s so rowdy. Normally you think there’d be like a little bit of booing, and then they’d…quiet down. This is about to turn into a riot.” ([27:19])
• Policy Solutions:
  • Ben Thompson proposes residents get direct cash payments for data centers built nearby (“You could pay every person in that village $10,000 a year…” [42:57])
  • Mark Cuban suggests a federal token tax; hosts debate if that’s the right direction (“Show me the money!” [44:12])
• Meta Insight:
  • “Americans don’t want to build anything. Most people don’t want a car factory, new roads, or even hospitals in their town…” ([35:04])
• Quote:
  • "We're kind of good on building. America just likes the status quo." ([36:10])

---

4. AI Progress, Economic Winners/Losers, and Shifting Workplace Dynamics

[65:10–88:28; 157:05–161:03]

• Citadel’s AI Moment: Ken Griffin pivots from AI skepticism to seeing “a team of thousands of PhDs being replicated by AI agents in hours,” prompting existential business reflections ([65:10–66:41]).
  • “…I went home one Friday, barely depressed…you could see how this was gonna have such a dramatic impact on society.”
  • “Is he depressed because everyone will soon have access to a thousand PhDs they can turn on?” ([65:57])
• Redis CEO Rowan Trollope on Building, Scaling, and AI Agents:
  • Redis transforming: caching layer → core in-memory context engine for agent workloads ([68:48–88:28])
  • “We’re starting to see a lot of traction in the AI world as people are starting to really build out agents.”
  • Redis now offers a “world’s fastest Flash object store,” vital for servicing AI agents at “multiple orders of magnitude” beyond human users ([70:38]).
  • Philosophy of Coding Now: Developers become ‘project managers’ of AI agents; coding is no longer the bottleneck ([83:45]):
    • “A developer with 10 agents is more like a development manager of old…The process is all set up to handle a world where coding takes a really long time. That’s not the long pole anymore.”

---

5. Shein Acquires Everlane: The Death of the D2C Darling

[88:30–99:05]

• Feature Story: Everlane, once the symbol of ethical D2C fashion, is acquired by Chinese fast-fashion giant Shein for only $100M (less than the VC funding Everlane raised).
  • “The bet was that consumers would pay more for ethical, sustainable basics…turns out, that consumer may not really exist at scale.” ([88:48])
  • “Maybe the real radical transparency was showing everyone how brutal fashion economics can be.”
  • The hosts discuss how most consumers want cheap basics, and premium “sustainability” is a tough sell outside of narrow urban demographics.
  • “Everlane was created as a response to people’s concerns with sweatshops…hard to see how it can fit into Shein without losing its ethos.” ([90:03],[90:48])
• Comparison to other D2C exits: brands that “didn’t raise venture capital” (e.g., Chrome Hearts) fare differently.
• Minor “green shoots” exist (e.g., Grooms → $1.2B sale), but consumer tastes and VC timelines are at odds for durable apparel brands.

---

6. AI Video, Chip Shortages, and Scaling Issues (Descart, Dean Leitersdorf)

[99:05–114:51]

• Descart (Dean Leitersdorf) launches major upgrades across all three product lines: Lucy (video world models), Oasis (physical AI/robotics), and DOS (Descartes Optimized Stack: inference engine).
  • DOS 2.0 achieves “5–8x more performant than anything on the market for real-time models.”
    • “If you're an AI company, you have to be able to extract the most out of every possible chip—right now, that is the bottleneck.” ([105:37])
  • Lively Demo: Hosts use Lucy to apply real-time video effects (Einstein, pink tux, horse head).
  • “The chip space is incredibly, incredibly interesting. We'll support everyone.” ([106:59])
  • Milestone bet: “The milestone is we need to hit 1 billion ARR—at that point, I'll cut my hair.” ([114:10])

---

7. Joanna Stern on ‘I Am Not a Robot’, AI Life Experiments, and the Human Future

[124:55–176:27]

• West Coast Book Tour: Joanna Stern appears in studio for the launch of “I Am Not a Robot,” reflecting on her year living with as much AI as possible.
  • “What's real? I want to find out what's real.” ([130:31])
  • On process: “I made the book a flywheel. Video content feeds into substack subscriptions that feed into book purchases…” ([126:13])
• Insights:
  • AI diffusion in medicine is real (“Every doctor will have an AI assistant, every patient will as well”—Bill Gates).
  • The “companion AI” risk: Why she’s against chatbots for kids, how etiquette matters even with robots ([141:11–142:26]).
  • Social trends on AI: “Are we going to ban AI for kids in general? No. But why do we need our kids turning to chatbots about their problems?”
• Tech backlash: Stern argues the college commencement boos are both anti-‘tech billionaire’ and symptomatic of a general “backlash to AI.”
• Career advice for students:
  • “Get to know the company, do something beneficial, stand out—you'll be in the top 1% of applicants.” ([161:03])
  • “I don't think I could be doing what I'm doing right now if I hadn't had the years of human mentorship at other companies.” ([161:24])

---

8. BONUS: The Protein Shortage and America’s Changing Diet

[115:03–120:54]

• America's “protein mania” is driving whey prices up as supply chains struggle to catch up; the hosts riff on how even the cheese industry’s byproduct has become the new gold (“Way bubble!”).
• “I think we're in a fast takeoff. The fitness influencers of the 2000s will be recommending 5 to 10 grams per pound of body weight…” ([122:22])

---

Notable Quotes (with Timestamps and Attribution)

• On the OpenAI lawsuit:
  “It’s very easy to sell the jurors on Musk as selfless, but this was a legal, technical argument—the statute was the whole thing.”
  — Mike Isaac [49:43]

• On 13F Fever:
  “Honestly, the market actually moving on the 13F? Or is this just online fun and games for tech folks?”
  — John, [12:10]

• On U.S. anti-building sentiment:
  “Most people don’t want a car factory, new roads, or even hospitals in their town… Data centers are probably at the bottom of the list.”
  — John, [35:04]

• On Citadel & AI:
  “Driving home on a Friday, barely depressed…everyone will soon have access to a thousand people with PhD level talent.”
  — B, [65:49]

• On D2C collapse:
  “Maybe the real radical transparency was showing everyone how brutal fashion economics can be.”
  — John, [88:48]

• On data center NIMBY ism:
  “Simply start giving people money… You could pay every person in that village $10,000/year and it would only equate to 3.8% of annual revenue…”
  — Host (summarizing Ben Thompson, [42:57])

• On mentorship and jobs:
  “I don't think I could be doing what I'm doing if I hadn't had the years of human mentorship at other companies and other newsrooms.”
  — Joanna Stern, [161:24]

• On AI wearables & future UI:
  “Something that is more ambient around us—that experience is very good… glasses, bracelet, pin—hardware is the bottleneck, but I can see it starting to work really well.”
  — Joanna Stern, [162:20]

---

Memorable Moments

• Hosts dissect viral video of Eric Schmidt being booed at a university commencement ([25:54–31:41]); Joanna Stern admits her own 2025 AI-themed commencement speech went better because students were just hungover and not angry.
• The competitive “joke” about protein powder as a new bottleneck: “What is the ASML of whey?” ([120:09])
• Dean Leitersdorf’s promise to cut his hair at $1B ARR ([114:10])
• Joanna’s embrace of “business owner” as the new flex over "founder" ([145:55–146:09])

---

Episode Flow & Tone

• Energetic, irreverent, highly engaged with both guest experts and audience chat. Topics are approached with wit and depth, blending financial market analysis with tech, culture, and societal commentary.
• Frequent use of memes, callbacks, and pop culture as interpretative filters.
• Deep dives: Legal process (OpenAI case), financial arcana (13Fs), and technical stack discussions (Redis, Descart).
• Big picture: AI isn’t just a tech story; it’s reshaping legal precedent, crowd psychology, work, investment, and the literal shape of American infrastructure.

---

For those who missed the live show, this episode is a must-listen for anyone tracking the future of AI, finance, and their collision with society, culture, and regulators—even if you just want to know why everyone’s panicking about protein powder.

Transcript

[A]

Today's Monday, May 18, 2026. We are live from the TVPN Eltrium. The temple of technology, the fortress of finance, the capital of capital. Massive day today. Tons of big stories. Five big stories I want to go through. Obviously the first one is that the US jury finds OpenAI CEO Sam Altman not liable to Elon Musk for straying from charitable mission because Musk waited too long to sue. Weird like technicality, I guess. But Good news for OpenAI Judge confirms verdict and that Musk's lawsuit is dismissed. We're having Mike Isaac from the New York Times join the show. In just a moment. I'll see multiple journalists on the horizon. When is he joining? Around 11:45 today. That'll be fun to hear about the story from the ground because he went to the.

[B]

Yeah, apparently they deliberated for about 90 minutes. 90 minutes and they didn't really make any type of statement other than a statute of limitations.

[A]

And so Max Zeph over at Wired says jury unanimously rules that Musk's claims are dismissed on the timeliness issue. He filed the lawsuit too late. Court affirms it will uphold the jury's decision. It's over. Musk loses the lawsuit against OpenAI and Mike Isaac, the Rat King says unanimous verdict in mosque versus OpenAI is in after only 90 minutes of deliberation. So did they deliberate today? They showed up at 9 and went from 9 to 10:30 and then delivered the verdict. Is that what we think happened? Because Friday's off in the jury, right? No, Friday. Yeah.

[B]

Jury showed up this morning.

[A]

Okay.

[B]

Talked, talked for 90 minutes.

[A]

But they got to think about it all weekend and Friday. Interesting.

[B]

Of course. Yeah, it's a full time job.

[A]

I guess it's just an interesting, interesting dynamic because you think you'd want everything really fresh. You'd go into it on Thursday night or something like that. Rat King says huge day. Wow. And what did Tyler post? He posted a video of Drake talking about something. What's going on over here? Let's play this clip.

[C]

W's in the shot. W's in the shot.

[A]

Is that the song of the OpenAI slack right there?

[B]

I think that's when he is gambling in front of.

[A]

It is a funny way to pronounce chat, but I enjoy it. Anyway, the big news that was going on all weekend, actually there was a lot of anticipation for Leopold Aschenbrenner Situational Awareness Hedge Fund to drop the 13F. It was supposed to go out Friday night, 5pm Everyone was saying, oh, if he's not well, people were expecting it

[B]

throughout the entire day.

[A]

Yeah, they were very excited.

[B]

And then there was some speculation that they've been able to petition to not have to release it.

[A]

That was one theory.

[B]

That was one theory. The other theory is that he was just entirely in cash.

[A]

Yeah. Don't need to report it. Just wind it down.

[B]

Said it was a good run. It's over.

[A]

Yeah, yeah. He's like, I counted the Ooms and there's none left to count. We're done. Pack it up. No, quite the opposite. Leopold Aschenbrenner, the hedge fund's chief investment officer, is known for making extremely successful investments based on his core assumption that Frontier AI will continue to improve at half an order of magnitude 0.5 ohms per year, which translates into a thesis that AI will create unprecedented demand for compute and its associated bottlenecks.

[B]

John, they're saying it is blindingly light.

[A]

It is brighter down. Yeah, I think we got some new lights. We're sort of, you know, tweaking things. I do like that the wide is less dark. There's been a number of times we've gone to watch videos and we've been very dark in the front. So we're bringing some light around. We'll maybe. Maybe we overdid it. Maybe we'll dial it back. I need to brush my hair. My hair's a little scruffy today. I also need a haircut, but we'll get to that somewhere.

[B]

We'll get to that. Later in the show. John will be getting a haircut live

[A]

on the program potentially.

[B]

But before we go any further, Nick over the weekend picked up a little gift for our very own Tyler.

[A]

Let's.

[B]

So we wanted you to open. Open it on the video. On the video.

[A]

Nick, hold it up.

[B]

He waited in line.

[A]

Look at this.

[B]

Waited in line.

[A]

Hey, what do we got for Tyler?

[B]

A very long line just for you, Tyler, because. What is it? I'm trying to open it.

[A]

Okay. Little anti clow.

[B]

It's a. What is. I don't know how to pronounce this. Am I reading upside down? It's a little. Little watch. Let's go.

[A]

Another watch island it.

[B]

I don't know if you thought it might be. It might have been something else, like the Swatch AP collaboration, but really like

[A]

the whole, you know, everything in the Swatch portfolio is fantastic, including this. I don't know. Describe what's on there. What is on there?

[B]

Yeah, Nick, what is it?

[C]

It has a rotating bezel.

[A]

That's not.

[B]

He says it has a rotating bezel. Okay. But just to be clear, it's not the.

[A]

It's not the royal pop, which was completely sold out and causing, like, stampedes all over the country. All over the world. I saw foot, I think, from an international country around people really mobbing it. You were mentioning that you thought it was maybe an aura loss for both companies because of the craziness.

[B]

Yeah, I just.

[A]

Your brand is now associated with chaos. That's not good. Right. And ap, although it's exclusive, you have to sort of wait in line. The waiting in line is like, here, have a Diet Coke and sit in this private room while I tell you that you will not be getting an allocation in the skeleton.

[B]

Ap, come back soon.

[A]

Royal oak or whatever. Right. Come back soon. And it's a very high brow waiting in line. And this was.

[B]

Yeah, they had to come out over time and say, these are not going to be limited. We're selling them a lot. And so the people that wait in line just to sell on the secondary market, I think have done pretty well.

[A]

Oh, really?

[B]

At least in the short term. But I would expect that over time, prices will sort of retrace toward retail. I did see a funny graphic of somebody that was, like, basically saying, like, you know, comparing, like, getting a job versus waiting in the line to get it. And you actually did quite a bit better if you just got a job on Monday instead of getting in the line. And then over time, you know, your earnings really ramp out.

[A]

Yep.

[B]

But anyways, sorry, Tyler, if you thought that was a royal pop, I don't know why you would.

[A]

Oh, he's doing the Kevin O', Leary, Mr. Wonderful. Two watches on, one on each wrist. Looking good.

[B]

There you go.

[A]

Anyways, I think that could be a good. Good daily for you. Who knows? It's got a little character to it. You make it your own.

[B]

Yeah, it looks good. It's a little somebody.

[A]

Sometimes the man makes a watch.

[B]

Somebody should make a string that you can turn it into a royal. Like a royal pop, you know, like.

[A]

Oh, like a lanyard type of thing. Okay. Okay. Yeah, that's possible. 3D printing. Plenty of. Plenty of opportunities. Well, let's go back to Leopold aschenbrenner and his 13F. The infamous 13F. There's a lot of discussion around it on the timeline. Really? We have not seen this level of attention on a hedge fund's filings in a very long time because it's breaking out of Fintwit. It's breaking into tech teapot and TechX and all of that, mostly because A lot of the discussion centers around the filing shows he's made some massive puts across the semiconductor sector. 2 billion on SMH, the VanEck semiconductor ETF. And so it feels like maybe more of a pointed thesis, less broad. Hey, semiconductors are going to do well. More. I actually me, Leopold in this case understand where the real value is. What companies within the semiconductor industry are undervalued, which ones are actually going to be useful in the next iteration of the build out. And a lot of stuff has been priced very hotly. Some stuff is overheated. The Nvidia trade for a while became like crushingly obvious and then it grew so much that that was not one of his early positions. Now it is looking like he is going long. Nvidia, which is interesting in the backdrop of. Is Nvidia a car? Do they still have a moat? Well, there might still be something else going on there. You have to dig in through this and understand what's going on. But the filing is hard to interpret Cleanly because a 13F is only a snapshot of holdings as of March 31, 2026. These positions are stale. He might have rotated out of these meaning these positions were in place during the early phase of the Iran war. It also doesn't include private copy trading.

[B]

Sen tends to work pretty well. Right. They tend to be, you know, maybe they, they, they're very knowledgeable on some of the subjects that they're trading on. Some of the companies. Right. But they tend to take a more longer term sort of thematic view. Sure. Whereas Leopold, he's operating a hedge fund.

[A]

Right.

[B]

You don't really know his, his holdings could, could be wildly different. Just.

[A]

Yeah.

[B]

You know, just, just weeks or days after the end date.

[A]

There is the, the team behind the Nancy Pelosi stock tracker stock ticker. I forget they have one for Leopold now. Although of course it's based on the 13F. It's a loose. It's. It's probably has massive tracking error. But it's directionally on theme. Like you know, their interpretation of what Leopold would do if he was managing.

[B]

Very accurate for three months ago maybe.

[A]

Yeah. So reminder 13Fs do disclose put and call options. They don't disclose the strike prices, expirations, premiums paid hedge ratio, short position swaps or whether the options are part of broader structures. So you have to be careful out there. If you're trying to read the tea leaves too precisely, you can only take away so much from these. So Fei Zhao. I don't know how to pronounce that. Says Unfathomably bad takes around this morning and a good reminder of why 13F digging is mostly a waste of time. March 31 we were in the heat of the Iran war. Makes sense to put on hedges at the time. Options exposure on 13F gets quoted notionally so as if it were 100 Delta. That is all 100 shares per contract. So when you see something like oh, he owns a billion dollars of intel, it's usually he owns the right to purchase a billion dollars of intel and he is actually deployed far less capital into that position. Although it is sometimes an important sign of things to come. We have no way of knowing whether these were five delta convexity hedges. Convexity hedges and represented a fraction of what people are saying were billions in puts or whether they were ITM puts in the money puts further outright shorts don't get reported either. Too much noise associated with the things that happened back in March that aren't relevant now. We have no idea about his turnover in assets and trade frequency. A lot happened in the months of April and May. His positioning could be completely different. Making investment decisions for 80 Vol assets based on data from months ago sounds like a good way to burn money. So don't idolize people and develop your own thesis for why you own and sell things. That is a good takeaway from an account that I can't pronounce but has good takes. Now there were a bunch of funny memes about this leap. Trader says now drop Leopold Aschenbrenner's portfolio where he sold all his holdings and went full cash. That certainly would roil the market. I do wonder, is the market actually Moving on the 13F? Are we seeing like when a position is disclosed, there's a pop of copy trading going on or is this just sort of like online fun and games for the. For the tech folks? Do you know?

[B]

Yes.

[A]

Yeah.

[B]

I mean if you want energy. T1 Energy.

[A]

T1 Energy is up on the 17 as a today and we talked to the CEO of that company. Right. T1 Energy is building solar panels in America.

[B]

That is very Chinese company.

[A]

Yep.

[B]

That had to divest.

[A]

Yep.

[B]

And turned into T1 Energy.

[A]

Yep.

[B]

And yeah, I think we first talked about T1 in Q4 of last year and done very well since then.

[A]

I'm excited about it. We can bridge into that in just a second. But investor Nick says, did that leprosy fella tank the market with his 13F 47 likes? But very, very funny to just massively mispronounce Leopold's name. Anyway, where should we go? From Here options on 13F Everyone repeat after me. Citrini is reminding everyone that options are reported with notional value. So be careful out there. The interesting bridge is just around the AI backlash and the fact that a

[B]

lot of situation in the chat says Bloom Energy is actually down.

[A]

Well he's on Bloom Energy for years or maybe not years but like 3 or 4 13Fs have disclosed bloom Energy and that one has been fully digested by the, by the copy traders I imagine. Anyway, the AI backlash is continuing in a bunch of different ways and one interesting sort of twist on this is that a lot of the AI maxis, the AI bulls were sort of concerned at least that this would all be fossil fuel based build out because everything else was too slow. They might be, they may be fans of nuclear, they might be fans of, of solar but it was seen as infeasible, seen as the timelines being far too long. So if Leopold is in fact taking a position in T1 energy that sort of leads me to think that there's a little bit of a shorter timeline to at least bringing some solar power to bear during the AI build out. That it's not all just sort of, you know, a hope and a dream that there will be solar power on the grid in any near amount of time. A lot of the nuclear power companies are moving on the backs of the AI buildout but it's still 2032, you know, when we talk to these folks, even the optimistic ones. So there has been big pushback on AI data centers across the board. We've talked about this a bunch and it's both a left and right wing issue now. Sagar and Jetty predicted this I think last year when he joined their show and is been interesting. Left wing is worried about job displacement, theft of art, destruction of creativity. Right wing sees them as surveillance centers. That's the latest term is that they're used to spy on people. So that's an anti libertarian, anti right wing position. But there are a whole bunch of others just this hollowed out coal town is voting right wing and then data center comes to town and they see it as, you know, just making their town worse off and benefiting like the coastal elites and like the people have

[B]

flagged too that both sides are using AI to create graphics to oppose data centers.

[A]

That's true. Yeah, there's all these like deep ironies. There's. There was a whole piece on someone who's protesting data centers and using a lot of AI to research how she can push back.

[B]

Gabe says Data centers need to be rebranded to Data Ranch.

[A]

Data Ranch. I like a data ranch. That's a good one.

[B]

Anyway, we got AUX powered.

[A]

Oh, interesting. Salty says that Leopold sold Blue Energy in the latest 13F. So trimmed or trimmed. So if that's the case, then there you go. And yes, Lulu does have a good breakdown of the narrative mishap which we can go through. But the latest debate that I saw was over this huge data center in Utah that's being championed by shark tanks. Mr. Wonderful, Kevin O', Leary, are you familiar with this whole thing? There's some renderings. It actually looks really cool. But it's weird because it's like I see this beautiful glass building. I'm like, it's not gonna look like that. There's just no way. There's no point. Like why would they ever build that? But someone dug into or in the render economy. Yeah, someone dug into the plan and the plan actually seems pretty reasonable. But Mr. Wonderful, he's sort of an over the top caricature of a businessman. Like he plays one on tv. He is a real businessman, but he also plays a businessman on tv. And so he's a bit of a soft target. Like he was recently seen sporting not one, but two expensive watches. Not unlike Tyler Cosgrove over there. He went to the Oscars wearing a Cartier crash skeleton and a Ruby Rolex or Daytona. And I believe he also had a, like a trading card around his neck. So very ostentatious, very over the top, a very soft target. If you're looking for someone to target in like a. He's doing it for the money, you know, like it's pretty, pretty easy. And so if you want to paint data center construction as maybe not in the best interest of average Americans, Kevin Leary is going to do a lot of that, a lot of the heavy.

[B]

Mr. Wonderful, in the context of developing large scale infrastructure that people are afraid of, sounds like a super villain too.

[A]

Yes. And also you can put this in contrast to Eric Schmidt or Tim Cook, where the previous generation, like the major hyperscalers, like the big tech companies, they've done a pretty good job building a lot of infrastructure, making really, really bold climate pledges saying we're going to be net zero by this year. Our data centers are really clean. They built a lot of data centers without really any disruption. There was no backlash to Google Cloud through 15 years or 10 years of building AWS.

[B]

Neither of them were rocking dual iced out.

[A]

So you make the case for quiet luxury, the quiet luxury of a Tim Cooker And Eric Schmidt.

[B]

Potentially definitely.

[A]

Yeah. I mean in this case, no.

[B]

I think Mr. Wonderful is not the guy to be the face of.

[A]

Potentially not. But apparently his actual data center plans are reasonable. It actually seems pretty by the book. According to current plans, it's in a remote area, it uses its own power and water and it doesn't seem to disrupt any local communities. We can pull up this video from Quick Thoughts that has a little bit of a breakdown and goes through. I think it's called. I think Quick Thoughts calls it why I'm not opposed to the Utah Data Center. I think the big Utah data center is. So this is a 4 minute video, but we can, we can watch this and break down.

[B]

Do you have a link?

[A]

Thought he's thinking it's in the timeline because there was a tiktoker that was reacting to how bad it was and he is saying it's actually not that bad. So let's play this clip.

[D]

Million views complaining about a giant data center in Utah. And I'm kind of confused by that because I would think that an uninhabited desert valley in Utah is the perfect place to build a giant data center.

[E]

I've been following really closely what's happening in Box Elder County, Utah where Canadian billionaire Kevin.

[A]

Oh, Canadian. That's a big problem.

[E]

Largest Data Center. A $100 billion project. Okay. This would be the largest data center in the world at over 40,000 acres and at full capacity, the data center, which is called the Stratos project, is set to use 9 gigawatts of electricity.

[A]

Gigabytes. You saw that?

[E]

Double the entire amount of electricity. Electricity used by.

[B]

Well, she said, she said it correctly.

[D]

The data center is built.

[A]

Yeah, yeah, but, but the transcript said gigabytes, which is funny. AI fails again. We need another data center to fix that.

[B]

Steve in the X Chat says TVPN Studio uses the equivalent of 23 atomic bombs of energy to produce niche technology content.

[D]

So large is because they are buying water rights of the current property owners. So the current property owners are using water for agricultural irrigation. The data center project buys that land, buys a huge.

[A]

So he makes this sound good, but then it's like, wait, are we going to have less food? That doesn't seem that good. But the point is that he's not taking it from like someone who is going to be paying water or some local community. It's like there's already water rights there that are staying in that valley.

[D]

It's not drawing power from the grid. If we look at electricity consumption by state, we can see that Utah just doesn't use that much electricity compared to other states. There are plenty of states that use double or triple. Tennessee is about triple. Pennsylvania four times. Texas is like 10 times more than 10 times what Utah uses.

[A]

So if over the course of this

[D]

project, they reach their goal and they double or triple Utah's electricity usage. So why is that bad? It's not incurring more cost to the people of Utah because they're building their

[E]

own power plant by Utah as a whole. Robert Davies, a physics professor from Utah State University, says that he actually thinks the project will require an additional 7 to 8 gigawatts of waste heat energy, meaning that the project in total will be 23 gigawatts of total thermal load energy, which is the equivalent of dropping 23 atom bombs in Utah every single day. Also, let's.

[D]

Okay, electricity generation across every state is going to have that same thermal load property. Not every generator is perfectly efficient, so they're going to generate waste heat as well. So if you say, okay, we're going to have 23 atom bombs a day worth of electricity going off in Utah, well, then currently we have 230 atom bombs a day going off in Texas.

[A]

You got to put everything. Hundreds more atom bomb comparison. Like, your car is like the size of like five atom bombs. Like, an atom bomb is like maybe this big, maybe a little bit bigger.

[B]

Yeah.

[A]

Your car weighs as much as seven atom bombs. It makes it sound so much more, like, weighty. When you're like just comparing everything to

[E]

atom bombs by 28 degrees.

[A]

This is actually pretty crazy. 28 degrees feels like a lot.

[D]

Daytime temperature could increase 2 to 5 degrees throughout Hansel Valley. Not the state of Utah, the valley where the data center is being built. Same with nighttime temperature could increase up to 28 degrees. Trapped in the valley. Hansel Valley is an uninhabited desert valley. So if you build a big power plant here and a big data center here, maybe it'll increase the temperature of this valley by 5 degrees. But okay, nobody lives there. I think this project solves a lot of people's stated concerns with data centers worried about water usage. They're reallocating agricultural water to cool the data center. Worried about power cost.

[B]

They're building power online is not helping.

[A]

But I like vegetables

[D]

where it's already hot and you're worried about. This is such a huge project. This is a giant data center or something. World's biggest data center. Well, that's just data centers that don't have to be built in other places that are being built in this Uninhabited desert valley. I think the concerns in her video are just fear mongering for reasons that I hope I've explained here. Thanks for your time.

[A]

I guess the question is like, they say that there's water for agricultural usage right now in that valley, but the valley's uninhabited and seems like a desert, so it doesn't seem like they're growing food there. So, like, where is that water actually going? Because is it just getting piped to some other farm, like far away? Or was it like they were way,

[B]

way, way back in the day? Way back in the day, you could just have a piece of land, you could drill a well and you could pull up as much water as you wanted.

[A]

Yeah.

[B]

And then people realize that you might, if. If you have a property here.

[A]

Yeah.

[B]

And there's property here, here, here, here, here. They're oftentimes all pulling from the same aquifer. So you all of a sudden, if you come in, you move in next to me and you start pumping, you know, billions of.

[A]

Your milkshake.

[B]

Yeah, you're drinking my milkshake. Right. And so it's very possible that all these parcels of land which they collectively bought, they all have their own water rights. That doesn't mean they're being used. Right, so because people will sell their water rights to like a neighboring property, that is.

[A]

Yeah, but my question is, like, it sounds like they sold the water rights previously or they had some sort of deal to sell the water that they were getting out of the desert, which I can't imagine produces that much water, but I guess it does use it for like agricultural purposes. Like, what were they growing?

[B]

Well, agricultural could mean you have some, like, you have some cattle. Like there's a. But there's a bunch of different potential meanings for that. It doesn't mean you're growing fresh produce,

[A]

but were they actively using. Or were they just like.

[B]

No, that's the other thing. That's the other thing too. It could have been agricultural land.

[A]

Yeah.

[B]

But not.

[A]

Could have been like a failed farm, it's not farming anymore. Or like a former livestock farm, something like that. But I don't know, I feel like people are gonna wanna go a click deeper on that. Like, he rebuts a lot of the good rhetoric, but there's still like another layer there.

[B]

Gabe says the water could be used at Amangiri.

[A]

Yes.

[B]

Influencers are protesting in the flats outside of Amangiri.

[A]

Drain the pool at Amangiri. It's going to be a big predecessor.

[B]

It's going to be over.

[A]

Well, yeah. I mean, these points, as you said, I think are going to be hard to break through just because AI is so deeply unpopular for a variety of reasons. And we should watch the video of Eric Schmidt getting booed on stage at University of Arizona. Alex Cantrowitz played a video here. I don't know if we need to watch all of this, but he says, this is incredible. Artificial intelligence getting booed out of the stadium in any commencement speech. It's mentioned in. Maybe telling college students AI was taking their jobs wasn't the best strategy. Let's watch this clip.

[F]

The architects of artificial intelligence. Interesting. The question is whether you will help shape artificial intelligence.

[B]

We do not know.

[F]

We do not know the precise contours of what this.

[A]

If you'd let me make this point, please. Step one, if you're giving commands, you got to bring a soundboard. You got to be like, AI yeah, it's not that bad. But also, I hear you.

[F]

Looting the perspective of the immigrants who has so often been the person who

[A]

came to this country. They're really going crazy.

[F]

We thought that we were adding stones

[B]

to a cathedral of knowledge humanity had

[F]

been constructing for centuries.

[B]

There's just a low level boo the whole time.

[A]

It's so rowdy. Like, normally you think there'd be like a little bit of boo, and then they just like, get. Quiet down. Okay, this is about to turn into a riot. This is crazy. Did he just bail on this thing? No.

[F]

Your agency we have only seen at this point.

[A]

I mean, you got to go off script.

[B]

You could. You.

[A]

You can't stay.

[B]

It is funny that if you cut it up in the right way, you could make it seem. Sound like the. Also, you will surrender your agency.

[A]

Yeah. Okay, now we need to take this clip. Do that thing where we says, he's

[B]

lucky they didn't flashbang it.

[A]

We need to do that thing where we take out the booze and just leave his words and then add cheers. So it's just the same exact speech, but everyone's just like, yes, this is amazing.

[B]

I can try to find it, but there's a video of him after the speech getting mobbed by students. They're all, like, yelling at him. Yeah.

[A]

Really?

[B]

They were not fans.

[A]

Wow, this is rough, rough, rough. Yeah, not good. I mean, the big thing is I don't know that that is everyone is booing for a slightly different reason, but it's like this ensemble of problems and grievances with AI Generally, everyone is. One thing that I've been frustrated about is everyone is vibe coding. 24 7. Leaving MacBooks open, talking about productivity. And yet the magical moments. The consumer technology has been like, completely left behind. Like, there was a time when we got the cloud, we were building a lot of data centers, but every year you'd get like a cool new thing, like Yelp would come out and it was like, it wasn't changing the world, but it was like, oh, you could find a cool new restaurant, maybe like. Or Groupon. Like, Groupon was like, not a great business ultimately, but like, for the first couple months of Groupon, you could like go try a restaurant for like half price. And it just felt like magical. Or like Uber. When that came out, it was like, wait, I can go out and the car will be right outside. Instead of having to like, call a phone, call a taxicab service, maybe it comes, maybe it doesn't. Stand outside in the cold, try and flag a car. There were all these.

[B]

Do you think they were. I'm just thinking that. Do you think they were. Do you think they were like, angry at usage Nano banana usage limit?

[A]

Probably. Probably.

[B]

Is that. Is this whole thing just a misunderstanding?

[A]

They might think we're in a plateau and they might just be upset with the lack of progress outside of coding domains. They say. Yeah. The writing is just still not that good. Any of these models.

[B]

I can clock it.

[A]

Yeah. It's still cloggable. Yeah.

[B]

Yeah. At first I thought they were mad that, like, at Google, Eric Schmidt was. He was doing too many, you know, stock buybacks and investing too much cash.

[F]

Technology.

[A]

Yeah. Having 100 billion on the balance sheet in cash is just unacceptable. Yes. You get Waymo. Yes. You get DeepMind. Yeah.

[B]

Because it. They don't know what to do with the money.

[A]

Yeah. Yeah. They weren't innovating for a long time and that makes a lot of sense. Sense why you would boo them. Sort of the Tealan, the Tealian.

[B]

Boo.

[A]

Boo. No. And then also like, yeah, the jobs thing is super real, whether or not AI is affecting the jobs prospects.

[B]

So we should pull up Lulu's critique because I'm sure it'll be way better than this. But just in those handful of sentences, like, is that. That felt like a speech more potentially, like, oriented towards maybe like the Stanford student body, which is like, how are you going to contribute to AI? That's what I was like, sort of. That's what was standing out to me, being like, don't be afraid of this thing. Like, jump in and help shape it.

[A]

Yeah.

[B]

And if you're maybe someone in Stanford.

[A]

Yeah.

[B]

And you have the opportunity to go actually be involved and you're at the epicenter of all this progress. Maybe that would land. But at U of A where people are hearing like, hey, all the different career paths that I'm thinking I would

[A]

prefer in terms of commencement speaker, I would prefer someone like a Sam Sulek to give the commencement speech. That would be like my, like Eric Schmidt is like, he's kind of like a meh. Sam Sulek. That's an inspirational speaker that's gonna fire me.

[B]

He's on the come up.

[A]

Exactly.

[B]

Yeah.

[A]

Did you have a question, Derek?

[B]

More plates, more dates.

[A]

That would be fantastic too.

[B]

Yeah, I was trying to. Gabe's asking about the why would he give a speech there? I was trying to find a connection. I think he's just a big name and it's very obviously his experience is very relevant in this moment.

[A]

Show up to mock and none of you are getting any jobs. Just terrible. Yeah, no, no. I mean there is this thing where like AI needs to create jobs because even if AI isn't destroying the jobs, if we have a weak economy, there won't be good jobs. And then you're still held accountable for that. And so you got to create jobs. And then on the data center side, there's just so many issues within that that we can go through. Environmental impacts which are probably real. If you burn a bunch of fossil fuels, you're going to have negative externalities. Diesel generators, these things are smoky. The air quality, all of this stuff is, is fairly real when done improperly, which is happening. The water use thing, mostly fake, but still like needs to actually be walked through fully and digested by the public. The noise issue, which is solvable but still like not that great. And then a bunch of other issues that are just not gonna happen magically. Ben Thompson had a wild proposal. He had a great piece which I wish we had time to read through the whole thing, but we can sort of run through it. So he starts with an anecdote from Politico. Texas county southwest of Dallas this week passed what may be the state's first county level moratorium on data centers. Not what everyone was expecting in the free state of Texas. Everything's bigger in Texas except for the data centers which are getting smaller now that there is a county level moratorium. Seeking to buy time for lawmakers to soften the blow of development across sweeping across rural areas. What's the county's commissioner, Hill County's commissioner vote? Court voted 3 to 2 Tuesday to put a year long moratorium on data center and power plant construction in unincorporated areas, citing an influx of as many as eight data centers planned there, many of which could have. Could have their own power plants. Opposition to data centers is spreading in regions led by both Democrats and Republicans as politicians try to balance economic development.

[B]

Yes, apparently, according to, according to AI, there's no official public count of operating data centers in Hill county, but there's eight proposed planned data centers. So this is a place. This is a place that they're going to be delayed. Yeah.

[A]

In Missouri, one small town, unhappy over its city council's approval of data centers, voted last month to oust all four incumbents running for re election. In North Carolina, Governor Josh Stein has made a point of saying that sales tax exemptions for data centers cost the state up to 57 million per year. Texas has hundreds of data center locations operating or in development, second only to Virginia among US States. The growth has stirred pushback from environmentalists and rural residents who worry about the effect on water supply supplies, the electric grid, or their quality of life. Officials in states across the country are starting to have second thoughts about data centers, and some are looking to roll back tax incentives. And Ben Thompson says, I chose this story because it happened to have happened over the weekend. In truth, there are an exploding number of options, including one just up the highway from where Ben Thompson lives in Wisconsin in deforestation. And they are hardly isolated sentiments. Seven in 10Americans oppose constructing data centers for artificial intelligence in their local areas, including nearly half, 48% who are strongly opposed. Barely a quarter favor these projects, with 7% stronger in favor. Now I was thinking about what do Americans want to build? Because it's easy to look at the data center stuff and be like, well, everyone's against building data centers. But I do think that there's an element of like, Americans don't want to build anything. I was reflecting on the whole reindustrialization meme this weekend. I got a version of that sweater mailed to me that I picked up and I was thinking about the actual knock on effects of re industrialization. Most people don't want a car factory

[B]

in their town, but we do want new roads. Well, not necessarily new roads.

[A]

No, people don't want new roads and they don't even want the roads paved because they're like, I'll just buy a bigger car. Like, I don't know, hospital. You want a bunch of people dying next to you. I don't think people want hospitals. I literally golf courses, they have poisons. They're bad for your health. Like, I actually think people just don't really want change necessarily. They don't want things built broadly. Like data centers are probably at the bottom of the list. Like they're the least popular, but they're like high speed rail. I thought that would be popular. It was not popular. And I'm just going down the list of like, oh, like you want, like, oh, we need. Maybe you're a national defense person, you want a missile factory next to you blowing up bombs. Like, no, no one wants that. Like, what do we want? Like, we don't really want anything. We're kind of good on building in America. I don't know, I just think we're good. Like we're just like, we're fine. It's good. Don't change anything. No new transactions.

[B]

Yeah, I think when there's self interest. Right. When people want to build their house. Yeah, right. When people want to.

[A]

Everyone wants to build their data center. Yeah, their data center for sure. But people don't want other stuff built generally. Like there's very, there are very, very few things that people are like, yeah, I'd be down for that to be built. People like the status quo. They're happy with things as they are and they don't like change. So like anything new is going to be like somewhat unpopular as nuclear power was not building out. Nuclear power 50 years ago was of course, one of the greatest mistakes humanity has made and one that contributes directly to data center opposition today. Given questions about the impact on energy bills. Also interesting. We have to do this another time, but did we run out of nuclear scientists? Was that what stopped the build out? Did we not have enough geniuses? I don't know, maybe we'll dig into it. But Ben Thompson has an interesting solution. He points out a bunch of ways to fix the problems of data center construction and opposition. He says, first this.

[B]

People are saying homes in the chat, but then again, people don't really want more homes in their area once they already own a home.

[A]

They block them all the time. They block home construction all the time. And also permitting and also expansion of existing homes. These things. I'm not saying that they're as unpopular as data centers.

[C]

No way.

[A]

Data centers are at the bottom, but homes are something. Maybe in the abstract, but new housing in communities is razor's edge. 50, 50, 60, 40. There is a lot of opposition to building just in America broadly. That's just the nature of our society. So Ben Thompson has some solutions though. What do you got to do to build a data center properly? He says, first, this sounds obvious, but Tech needs to fix its messaging problem. The issue, and if an answer seems obvious, then there surely must be some other problem at play, is threefold. First, a good number of people in tech, particularly at one of the leading labs, genuinely believe most jobs are going away. They could lie more effectively. But beyond being dishonest, it's also a betrayal of the fanatical devotion with which they are pursuing AI despite obstacles, including the challenge of spending billions and billions of dollars on models that are obsolete in months, if not weeks. Second, it is extremely hard to describe the benefits of inventions not yet made, cures not yet discovered, economic activity not yet engaged in, etc. This is always the burden of those arguing in favor of progress. And the sheer potential of AI actually makes the problem even harder. 50 years ago, everyone was like, electricity isn't that expensive. Why do we need to build nuclear power plants? They're scary. And now electricity is expensive and we're like, oh, we should have built those. That's the way these things always go. Third, tech is and always has been terrible at understanding and relating to the rest of society. I go back to how Silicon Valley was extremely skeptical of Facebook, a company predicated on connecting with friends and family, precisely because it's filled with people running away from their friends and family. You can optimistically say that people in tech live in the future. You can also more cynically say they live in opposition to and denial of humanity, for better and in this case, for worse. Second, tech could control the misinformation. TikTok is a major point of this. He talks about how the algorithm is still controlled by the Chinese and maybe there's misinformation there. Second, in a rather ironic twist, Meta has learned the lesson of trying to control misinformation. Doesn't want to overtly censor, but now the company gets no credit for for not censoring misinformation about data centers. And so it's like this weird thing. And then third, this was a wild card, which I didn't think of. But X is the social media platform X. And Twitter, formerly Twitter, is actually incentivized to be anti data center in a weird way because X is owned by SpaceX and a big part of SpaceX's upcoming public offering is the possibility of building data centers in space. This is like total tinfoil hat, I think. But. But it's an interesting like, okay. And he says, to be clear, he hasn't seen any evidence of thumb on the scale or not. I certainly haven't. But part of the problem, though, is that we would never know if there were. And so he goes on to propose something very, very bold. Very, very bold. He says, instead, the most obvious solution is the most crass. Simply start giving people money. Not universal basic income, though. If data centers are a resource for our AI future, then start paying people for that resource. If that data center up the road weren't sold to my neighbors based on amorphous tax benefits that my local government may or may not spend appropriately. And I was talking to Tyler about this earlier, but rather were to result in a check in the mailbox every year, I suspect you could get a lot of people on board. So he put some numbers together, and he says, for the data center up the road, it was expected to be 1.6 gigawatts, which could generate around 3. $3 billion in annual operator revenue. DeForest, the village it was to be built in has around 11,500 people. So you could pay every person in that village $10,000 a year, and it would only equate to 3.8% of annual revenue grossed by the data center. And he says, I bet that that proposal would have been approved, and I bet the operator could very easily pass on those costs to actual data center users. It also highlights how relatively pathetic the original commitment. I think the data center said, hey, we'll give you 50 million, which is like, nowhere near what that math works out to. So data centers coming to town, you get to vote for it. But the data center company says, hey, we'd like you to vote for this, and we will give you a $10,000 check in the mail every year forever, while we're operating this. And that seems like that could actually get people on board.

[B]

So, yes, this goes back to even months ago at this point, we were saying, you know, AI is not a. Is not like a, you know, natural resource where you benefit from having it in your backyard. Right. If you're just an everyday AI user, you do not care where the data center is at all. And so if someone is coming to put it in your community, it's pretty fair to want to benefit from that in some way. And like, a direct payment like that? I think I'm sure that will happen more.

[A]

Yeah, yeah. And what I was talking to Tyler about was, does the. Do local communities feel a difference between $10,000 in the mail directly to them or $10,000 to their local government that says we're going to use this to build roads and hospitals and all the different things that we do? Like, I think that on net the average American is a little bit skeptical about dollars going to the government actually benefiting them at a one to one ratio. They definitely think that if the money that goes in is worth something, but a lot of it gets mixed around and there's delays.

[B]

Yeah, the data center is already going to generate a bunch of local tax revenues for that local government.

[A]

Show me the money. Show me the money. That's what the locals should potentially be saying.

[B]

Well, that's. That's what I'm saying. It's like they don't, you know, the. I think it's totally fair for the local population to. To think, okay, like, this big infrastructure project is happening in my town, even if I'm not going to work there. Yeah, it's going to generate some taxes or to help improve our community. But show. Give me the money. Basically.

[A]

Give me the money.

[B]

Go direct.

[A]

Yeah, go direct with the money. I like it. Well, we have Mike Isaac from the New York Times in the waiting room. We can come back to our data center debate after we check in with Mike, and I think he's on location. Is this correct? Mike, where are you? Welcome to the show. How are you doing?

[F]

I'm good. Can you hear me? I'm sorry. I'm literally outside the courtroom.

[A]

Amazing. No, we can hear and see you clear. That's amazing. Well, take us through it. How has today been going? What's happened?

[F]

It was crazy. Basically, today was supposed to be the first day of jury deliberations, and we were. A few reporters were in the courtroom because in the morning it was about both sides presenting their case for remedies to the judge on basically how much money, if anything, would be dispersed as a result of the lawsuit. And literally in the middle of this deliberation, the clerk goes and interrupts the judge and says, hey, da, da, da, da. Something's happening. Basically the scurrying. And everyone's like, oh, my God, what's happening? Da, da, da, da da. And this is like less than two hours into it, they reach a verdict. And so the jury comes back in and delivers the verdict.

[A]

Interesting.

[B]

What was your expectation going into today? Did you think you'd be hanging out at the courthouse all week?

[A]

All right, Bill.

[G]

Yeah.

[F]

Yeah, I'll see you soon. Sorry. That's lead opening eye counsel walking by that I should go run after. But he's doing his thing.

[B]

I mean, we're just hanging out. We're just hanging out.

[A]

You gotta go chase him down.

[F]

I'll bug him later. Literally, he was just chilling and walking out. I Sorry, I can't see. I forgot what you. I'm so tired. What did you ask me?

[B]

Yeah, what was your expectation for your week? Were you expecting to be at the courthouse every day?

[F]

Yeah, we were like, I got here again at 6am and was ready for a long sitting out in front of the court for days. Because the way these work is you get 10 minutes notice from when the judge gets the jury verdict to get down here. I live 10 minutes away, but still, like, no reassurances. So we had me, my colleague, Kade Metz, and then Natalie Rocca, another colleague of mine, just, like, ready, and I was just like, thank God when they came back, I didn't want to sleep out here.

[A]

Okay. So the actual verdict, it feels like victory on a technicality. And what I'm interested in is that over the last few weeks, it feels like the. The core discussion or the. Or the talking point was Elon Musk. You can't steal a charity. Very pithy phrase, easily memorizable. Could stick with you or could bounce right off you, but you know what his grievance is. And then OpenAI sort of needs to say, well, the charity still exists, and we had an agreement that we would go this way. And it was a little bit more complicated, but. But that doesn't seem like what the jury actually decided based on. And was that, like, as you think back to the last three weeks, do you think that there were. That there were actually good seeds planted around the statute of limitations and when the case should be filed? Because it feels like from the reporting and from the viral, you know, the screenshots and the emails and the. And the quotes, like, there was never, like, oh, yeah, we all remember the story. Smoking gun of statue of limitations. No, I don't. I remember the you can't steal a charity or the Brockman diary.

[C]

Right.

[A]

And it feels like we got a different outcome here.

[B]

I think. I think I remember at different points, like, they. This. This only. This whole debacle only became a thing after the launch of Chat gbt. And, you know, the company was showing, you know, massive traction and revenue growth. But I never heard specifically, like, you said, this statute of limitations.

[A]

Yeah, but how did you process it?

[F]

That's a wonkier point, too.

[B]

Right?

[F]

Like, it's very easy to. And that's what I think. Like, really the strategy on the must side was, was to go for really, like, clearly digestible talking points for a juror who may not be steeped in nonprofit contract law or statute of limitations. And exactly what that is. And I think that's what they were betting on, too. They're like, all right, if we can sell the jurors on this idea that Musk is, you know, selflessly trying to, you know, interrupt something that could be bad for the world versus OpenAI's more technical point of, look, you should have filed this lawsuit years ago. Maybe they can win it. And so I think that was going into it, what everyone was kind of thinking about, like, is this going to be. Certainly what I was thinking about, is this going to be a battle of, like, the billionaires, who do you trust? It's like a character thing that. Is this a referendum on that? And exactly what you said. It's super surprising when they came back. And essentially, I would say statute of limitations was like, if that was. That was the ballgame. Right. And if they had blown past that, if they had not find them the burden to be met, then we would have seen how it really played out. But that was just. That was the whole thing. No.

[B]

What. So last week, I was surprised that Elon jumped on the China trip with Trump.

[A]

Oh, yeah.

[B]

Was that a trust? Yeah, that something. I mean, a lot of the people online were just like, he's a billionaire. He can do whatever he wants.

[A]

That was like, president supersedes the federal judge. But I don't know if that's actually the case.

[B]

There was some dialogue around, like, hey, you're in the middle of this historic trial. Like, you should be present or at least able to be present. Did that. Do you think he did that because he felt like it wasn't going his way, and he was just like, I need to make the most of my time.

[F]

I think he. So, yeah, NBC wrote a good story on that. Like, he was not excused. He could have been recalled and asked to testify again. And. And it's typically bad form when you leave the country when that happens. And so what I was told or what I heard is that they had actually spoken to the judge beforehand to, like, make sure it was like, okay, and like, that he probably wouldn't be recalled. I think part of it also was that both sides were. Both sides are on a clock, so you only have so much time to present your evidence. And the early testimony was running long, so OpenAI still needed to get through a lot of the testimony of their expert witnesses towards the end. So they decided. And Musk side also decided they weren't going to recall Musk. So, like, there was that part of it that probably made it okay that said, like, it's probably bad luck. When you make it the first three days of the trial and Sam and Greg make it basically most of the time, but. But at the same time, like, it didn't come down to character who pissed off the judge necessarily. It came down to, like, a legal, technical argument, which seems to have. This jury was pretty sophisticated, at least in, like, focusing on something that I didn't know if it was going to land or not.

[A]

Yeah. Did. I mean, it really makes all of the, like, the. The AI safety testimony feel like maybe a miscalculation because it sort of took the conversation in a completely different place and then they got focused on this, like, technical issue. I mean, the jury doesn't put out, like, a statement. Are we. Are we expecting any sort of, like, closing statement from the judge, or is this what we get here?

[F]

We. So, by the way, sorry, there's still, like, people protesting in the background, if you want to see that. But on my very terrible laptop camera.

[A]

Are they protesting the statue of limitations because they're on either side or protest.

[F]

There's actually. This has been the best part of this is like, there's many different protest camps and it's kind of hard to define who is against what.

[B]

Are any of the protesters protesting? Other protesters?

[F]

I mean, genuinely. Yes, probably there's the. I want to do a.

[A]

Out there protesting the details.

[F]

Genuinely. They were a supporter. No, 100%. So actually usually post trial, people like me go and try to find a jury and chase them down, which is what we were doing. I think they probably are already out of the building. I ran around the back and saw a van that was all blacked out and this marshal that I had known the whole trial, and they were just, like, getting the hell out of here. So I'm guessing they didn't want to get mobbed by. Yeah. Us. But the judge. I'm going to try to get the notes out. The judge left the jury with, like, a pretty good summation. Not of the trial, but just, like, appreciating a jury and, like, respecting a jury finding, like, finding parties liable or not liable, you know, And I think that the point of that was she didn't she. Some federal judges could, like, be like, no, I'm throwing your verdict out or whatever. But she respected the jury, was a juror of their peers, and they were deliberate, you know, and they listened intently. And so she left them. I'll find the exact quote and send it to you guys. But she left them on sort of like, we thank you for your service. Yeah, yeah.

[A]

That seemed also a little Bit unexpected, because when the jury verdict became, you know, popularized or publicized as, like, advisory, a lot of people were sort of interpreting that as well. Like, it doesn't matter at all in that case. But it seems like the judge did wind up sort of giving the jury a lot of weight and very quickly reacting to the jury's verdict.

[F]

And I think that's really important as far as appeals go, because you could argue bias charge cut out. Like, you could argue, like, oh, yeah, exactly. The judge didn't care, the jury. So I think there's real incentive to be in line.

[A]

Yeah. Yeah. That's very interesting. What was the snack set up today? Are you gonna get a proper lunch now? I feel like.

[F]

My God.

[A]

I feel like that was one of the most disappointing arcs, if I'm gonna be completely honest with you. The lunch game just didn't seem to evolve. You were saying that you weren't learning from your lesson.

[B]

For you, where you've got, you know, the Nathan for your episode where he's got the chili suit.

[A]

We were gonna do that for you because it just felt like, okay, day three, you show up with an apple and a banana. It's like, okay, he's still learning, but, like, fool me seven times. I was expecting a chipotle burrito or something with a little more substance. Get into the four digits of calories, please. God.

[F]

People were like, dming me, saying, I have, like, scurvy or rickets by the end of this trial. I think I just have, like, a really disturbing diet overall. So, yeah, and today I forgot I was out last night until way late at a show, and I'm hungover, and I forgot to bring food. So it's just. This is basically my. You get to see my slow descent into madness. But thank God we're done.

[A]

Okay, so, I mean, we asked you earlier, is this the stuff of movies? Is there going to be a movie about this, or was this anticlimactic?

[F]

I think, like. I think this movie is still going, man. Like, this thing is still. There's so much. I feel like this most exciting time in AI because OpenAI is really on his back foot in a lot of ways. This gives them some relief in the many fronts that they're being attacked on. Whether it's going public this year with a messy balance sheet or anthropic coming after him. Google coming after him. Google iOS tomorrow. So, like, if anything, it's a brief reprieve, you know, But I wouldn't make the movie now. I'd wait a couple of years.

[A]

Okay. Okay. Anything else? Jordy?

[B]

The story continues. I'm expecting to see Model Ys around San Francisco that say, I bought this after Elon lost his landmark trial against OpenAI. Yes, the bumper sticker.

[A]

New bumper sticker.

[F]

Right on.

[A]

Well, have a great rest of your day. Thank you so much for taking the time.

[B]

We'll talk soon.

[A]

Mike, great to see you next time. We'll talk to you soon. So Mark Cuban has another proposal for how to deal with data centers and internalize all those negative externalities. He says we should tax tokens federally at the provider level. Tyler, you're going to have to interpret what this would mean and all the ways that companies would wind up getting around this with maybe less robust answers potentially. But it says not a lot less than 50 cents per million tokens. It will accomplish four things at least. It will push the big AI players to optimize tokenization, caching, routing and localization, which will reduce energy usage, saving them in energy costs more than what they paid in tax, and reducing strain created by the growth in energy consumption, which will generate maybe $10 billion a year to start, but over the next 10 years could grow 30x to 100x. So he's, he's thinking two orders of magnitude in a decade in terms of growth for AI. That's low end of what a lot of people think. And then four, create a source of funding to pay down the federal debt or deploy in response to the things AI brings that we don't expect or don't like. At some point the models will pass it on to consumers. Of course, that's okay. Consumers will have the biggest ability to choose between providers or do, or to do everything using open source models locally, which I guess wouldn't be taxed.

[B]

What do you think? This is kind of like the opposite of what we were saying before of like going direct. Right. Because we were saying, okay, you know, the actual data centers are going to make so much revenue, you can just tax the data centers and then the money goes to local community and then that's where you see the benefits. But isn't this going like up the chain even more? So you're taxing the companies then people in the community, like definitely won't it like the money will be like so abstract if it's at the federal level.

[A]

Yep.

[B]

I feel like this is the wrong way. Here's something, here's something else. You should get a check from open Anthropic every month. Maybe that's, that's, I think the better version of his. Sure if you want to tax the. What if we tax companies, you know, what if we had something like, like a sales tax or you know, what if profit. What if, what if.

[A]

When, when income tax.

[B]

Yeah. Like if someone, when someone paid. What if some of that money went to the government to help pay for public services. And maybe even if a company is doing really, really well, then you could take a percentage of their profits because

[A]

that company has investors sold their stakes. They would also pay a tax on whatever gain.

[B]

And then every single, what about every single underlying vendor that the company. You had the same sort of like structure for every underlying company.

[A]

If Nvidia sells a bunch of GPUs and they make a bunch of money, they don't tax on the profits on that.

[B]

Yeah. Or even somebody like a contractor that, you know, manages a building so they have a, maybe it's a small local

[A]

business, they manage an officer only half a million. That half a million profit that gets

[B]

taxed, has anyone thought of that?

[A]

That might work anyway.

[B]

And then you could use that money to sort of, you know, cover the costs of operating the government and potentially even potentially use some of the extra to pay down the debt.

[A]

Potentially. Well, Palmer Lucky is going back and forth with Mark Cuban about this. Palmer Lecky says there are already massive economic incentives to optimize. So this is just a tax on American companies that makes foreign models and more and products more attractive, along with creating the infrastructure for government to track all AI usage and punish anyone who doesn't report. Mark Cuban says those incentives change over time. Right now the incentive is to grow and spend market share over optimization. You know this. Do you think the marginal cost of some bips on a token is going to make those buyers choose differently? Or do you think the models are just a commodity and price is the only differentiation space? And then the question mark every time, you know it's not AI. And the tax would only be on what providers sell, not open source models, not local, not internal. And what foreign models are you referring to, Palmer? Mark, you are essentially making an argument for central planning. The burden is on you to show you where it's worked before. No quotas, no mandates, just good old capitalism competition. Palmer says this is obviously not capitalism or competition by any reasonable definition. It is a tax that specifically disadvantages one type of AI business to the benefit of others, artificially propping up their business models. And my business is one of the ones that would benefit because he's not token heavy. That is important.

[B]

Interesting semianalysis says $0.50 per MTOC is a Lot of money. Mark, are you considering considered cash hit on pre fill or just output tokens?

[A]

The hard questions.

[B]

Steven says imagine a bit tax in 1995.

[A]

Yes. Flops tax. I don't know what else is going on in the AI slop world. The bot farms.

[B]

What about every time you. How about this? What about every time you move your cursor? It's just one cent, right?

[A]

Yeah. I don't know. Tax on something.

[B]

It's pretty funny. I was saying last week when I was saying like, basically reinventing the US Postal service, a lot of people were messaging me saying, you know people, you know, this exists already. It's like, man, it's tough when the sarcasm doesn't break through. Doesn't break through.

[A]

Well, the bot farms have figured out anti AI. AI anti data center posts on Facebook are good for engagement. But ironically, they're using AI slop to do it. You don't know. This is the islop. This might be the most perfectly designed set of stones ever visited upon a beach. It's not worth giving up an inch of this to a data center. Indiana breaking an Indiana resident of a reportedly arranged stones to make an anti data center message. This is 99% slop and this one is really sloppy. Wow. Wisconsin's forest, farms, lakes, rivers, small towns. Not a single square inch of Wisconsin is worth giving up for an AI data center. Interesting that the I in is is capitalized. Makes me think that that was added after the fact. But the rest is pretty sloppy but kind of beautiful. I kind of like the perspective on this image with the big farm, the barn in the background and the.

[B]

This makes me want to visit Wisconsin.

[A]

Yeah. Does Wisconsin actually look like this? If it does, perfect place to build a data center. Yeah, that's the only thing it's missing.

[B]

Well, no, I want. Yeah, we have to go and find. We have to go find the ugliest 10,000 acres in Wisconsin. New challenge, Tyler.

[A]

We gotta cover Everlane. We gotta cover Everlane. There was a big well just to close out.

[B]

Should we cover this post from Ken

[A]

Griffin that was going, I want to cover this. But the trick is that this is a Ken Griffin clip. So basically he pivoted on AI three months ago. He was saying it's not really useful. The reports that we get from AI models are not actually relevant to our business. And now he's saying, for us at Citadel, it's allowed us to unleash a much broader array of use cases. It's been really interesting to watch work that we would usually do. With people with Masters or PhDs in Finance over the course of weeks or months is being done by AI agents over the course of hours or days. And it's seen as sort of a black pilling moment because he says, like, I got home and I was sort of of. I gotta tell you, I went home one Friday barely depressed by this because you could see how this was gonna have such a dramatic impact on society. And it is like a weird moment and it's sort of like, oh, okay, he's waking up. But then if you actually watch the full interview, this is one minute from a 40 minute interview or something. And he goes on to enumerate a whole bunch of different benefits and where he is allocating his workforce. And also Citadel is in a very interesting game theoretic dynamic where it's not, they are not a monopolist. So by definition, like they are in competition with all their other funds. And so there is a world where even if they're getting incredible value out of AI, they wind up using AI and humans in conjunction to compete. Because we are in the Centaur era, which is sort of what he knew rates. But anyway, what did you

[B]

one thought I had is that, you know, Citadel has, you know, AI psychosis.

[A]

That's what you're saying?

[B]

No, they have a team of thousands of, you know, PhD level talent.

[A]

Yeah.

[B]

That are doing things that AI can do pretty well now. And him driving home on a Friday being depressed, part of me was thinking, is he depressed because he realizes everyone will soon have access to a thousand people with PhD level talent that they can turn on and maybe they can't cover the whole market. And obviously, you know, he talked a lot about, you know, how much software there is to build. He's like, we'll never build enough software. But at the same time he was thinking like, wow, this, this like resource that I've accumulated, this like capability. This like this team, when AI can do what they do and everyone can access AI, like, how is my business going to change?

[A]

Yeah. So funny. Reflecting on the time I worked at Citadel. And my job was basically to copy and paste cells in an Excel spreadsheet. And so I wrote a Visual Basic script to sort of just do it for me. And then I was able to just like have seven hours of free time every day. And I wound up being able to do a lot of other stuff. And it was a story of automation. And I can tell you, at east back in 2011, I spent the summer over there as an intern. There was a lot of stuff you could automate for sure. A lot of stuff in the back office, middle office, and yeah, some research in the front office. But Citadel's Edge is more than just research. They do a lot of CEO interviews. They talk to a lot of people off the record. They have a lot of information that does not exist on the Internet.

[B]

They have scale.

[A]

Yeah. There's a lot of track record, different stuff, so I don't know. It's interesting.

[B]

Yeah. Let's talk about Everlane.

[A]

Yeah. Well, we have our. I think we have our next guest already here, but we can go through Everlane quickly, or we can come back to Everlane at 1245.

[B]

Let's do that.

[A]

Okay. Well, let's bring in Rowan from Redis because he's waiting in the waiting room. Rowan, welcome to the show. How are you doing?

[C]

I'm doing great.

[A]

Thanks so much for being here. Since it's your first time on the show, I've actually been.

[B]

Are you getting an act? Is this an active sauna session for you?

[A]

It does look like a sauna background.

[C]

No, I'm in Tenerife today, actually, so.

[B]

Amazing, amazing.

[A]

The wood paneling behind you really does look like a side.

[B]

No, I like it.

[A]

I like it.

[B]

Well, yeah, we'll see in a few minutes if you start sweating.

[C]

I'll be doing the cold plunge next.

[B]

We'll see how that goes. Yeah. Well, some people in tech do combine cold plunges with.

[A]

That's happening, talking about their companies.

[B]

Tim Draper.

[A]

Yeah, yeah, yeah, for sure.

[B]

Anyways, great. Great. Great to meet you.

[A]

Great to meet you. I used Redis a ton about a decade ago. I'm a big fan of the product, but. But if you could introduce yourself and the company a little bit before we go into the news today, that'd be great.

[C]

Yeah, absolutely. Thanks, guys, for having me on. It's an honor to be on. I loved your show.

[F]

Amazing.

[C]

Big fan and watch all the time. Yeah. So I head up Redis, and we're one of the sort of core infrastructure components that has been around. We're one of the bigger open source projects over the last 15 years and sort of helped build out a lot of the Internet infrastructure and got a great team. We have just about. We're 3, 1500 people now, and we're starting to see a lot of.

[B]

There you go.

[A]

That's great.

[C]

We're starting to see a lot of traction in the AI world as people are starting to really build out agents. So as you guys were just talking about lots of opportunity there, so we're being pulled in on agent data.

[A]

Yeah, I want to get to. That is the correct framing for Redis for people who might not have actually used the product in memory. Key value storage, like non relational databases. Think like MySQL but less structured and also held in memory. Therefore faster.

[C]

Totally. You nailed it. The history of it. It's an in memory data structure server. It's not really a database. Yeah, but it's been treated as a database. And the killer app that took off and made Redis a part of kind of got the tendrils into all the applications on the whole Internet was key value being used for caching. So it originally started as an in memory database. The big thing that's changed though, and this is just coming live now is over the last few years we've re architected Redis and launched a new product that uses Flash as the back end storage. So now we have the fastest, the world's fastest Flash object store. And, and so that's, that's a new thing. And that was really driven by AI because we were seeing huge demand for way bigger, way more data and also RAM prices have gotten crazy and MVME performance has improved dramatically.

[A]

Sure. Okay, so then take me through some of the history of the business. I know you joined a CEO like in the modern era, but in terms of that transition, what is the shape of the business? Because a lot of people are building open source software and I'm always fascinated by that transition and that interaction between the product which sometimes has incredible developer pull, incredible ecosystem and then also an incredible opportunity to build a real business around it. But what is the shape of that? Because I think people go to Red hat, they go to consulting shops, they go to hosting providers, enterprise software wrapped around it. But how would you describe it? The shape of the business around the product right now?

[C]

Yeah, so it's a great question. We're still a open core company, so we have an open source base which is Redis. Anyone can download it and use it for free. It's used all over the place for free. And then we have a paid version. So for example, the recent innovation I mentioned, the rewrite using Flash storage, that's not for free. That's something that you would pay for. We have a lot of performance advantages in the paid version. We have a hosted version. It runs on all three of the major cloud. So if you get Redis on Amazon or Google or, or Microsoft Azure. Right. We provide, we have our own version essentially that runs on those clouds. And so that's, that's, that's the, that's the heart of the business. Most of our usage on the Internet is free Redis because the free product is amazing.

[A]

Yeah.

[C]

The paid version is even better.

[A]

I like it. That's good. That's good salesmanship. So the relationship with the hyperscalers is that like consumption revenue that's coming to you. I set up an AWS instance, I pull Redis off the shelf from the dashboard of a million different tools and then as I'm using it every month, stuffing more and more data into it. That money's flowing to you from the hyperscalers.

[C]

Exactly. So it's a little different depending on which hyperscaler you're talking about. For Microsoft, they're hosted if you buy the first party service from Microsoft. Right. So on the hyperscalers you have first party services that are offered by the hyperscaler themselves. Then there's third party that you buy through the marketplace. In Microsoft's case, when you buy Redis first party, it's actually our software and. Exactly. You're exactly right. We get a revenue share of that. So that's called Azure Cash for Redis. And then there's. And then Amazon and Google no longer offer as first party services Redis. They have their own products that were once built based on Redis, but we did a license shift.

[A]

Hmm.

[C]

To kind of get them off of our tail, frankly. So Amazon and Google now have their own code bases that they have to maintain that, that, that, that have really diverged from what is now core Redis. We offer on Amazon and Google through the marketplace Redis, you know, as the Redis cloud product, essentially. And then increasingly we're offering that through new cloud vendors, either their Neo clouds or like Vercel for example. So if you ask an agent if you're building on Vercel and you say, hey, please deploy Redis cloud. Boom, you'll get our product and it seamlessly is integrated into their platform as well.

[A]

Yeah, that makes a lot of sense. So I mean, I remember when I was using Redis I was using it a lot for actually business intelligence and data analysis. It was just nice to clean up some data, have it all available in memory. Much faster to sort of query and do mapreduce over. But obviously the bread and butter's caching. But I'm interested in the shape of the agent business, like what data is being stored when. Because a lot of this stuff can be loaded in context, it can live on the chip. We talked to the Cerebras founder last week. There's an incredible amount of work being done really, really deep in the, in the AI supply chain. And then there's everything out to hard drives and tape storage on the other side. And so what is the sweet spot that Redis is filling right now?

[C]

Yeah, so if in the past, as you just talked about, sort of the killer use case in the cloud mobile era was caching your database basically, okay, you could use it for a lot of other stuff as you talked about. And in the new world we sit in a similar place and that is essentially providing all the context, like coalescing all the context for the agent and then delivering that to the agent. We had to actually build a new product to do that. So what developers have been using Redis for in the agent this called the next era that we're heading into is storing agent data and hosting agent context. One of the reasons for that is that you're going to see multiple orders of magnitude more agents than human beings in a company. And what that has a direct consequence to the load you're putting on your back end data systems. So just like in the cloud mobile era, you saw, you know, kind of you went from like guys that were sitting at green screens, like bank tellers for example, and the load factor on your back end DB2 might have been like 10,000 to 1 or something. Okay. Then you added mobile and you added a million customers or 10 million customers, so 2, 3, 1 for orders of magnitude more load. And Redis came in there as a scaling layer.

[A]

Yeah.

[C]

Okay. And you didn't, you didn't have to go and scale DB2 or your mainframe or whatever. It doesn't make any sense. You Oracle that back end. Similarly, in the agent era a similar transition is happening. So as that load increases, you can't have like my company has a thousand employees, I can't have 100,000 or a million agents. And we're going crazy with agents right now internally hitting my back end data systems because I'm going to be paying a hell of a lot more to all of my underlying providers. We use Redis in the middle as the context engine and we cache and hold all the context from the underlying databases in Redis and that's what the agents interact with. So we launched a brand new product that's on our website right now called Iris. This is its exact intention is that what you do is we have a data integration piece that sucks the data out of your underlying databases, stores it in our new Redis Flash database and then serves it through CLI and mcp, through pydantic.

[A]

Models.

[C]

So you define pedantic models on top of your data and you do the transformations underneath and then what the agencies is a manifestation or a view of the underlying data. And the difference, it's not just a scale issue, it's also providing the data in the way the agent expects to get it. So I'll give you a simple analogy here. Would be like if I told you, hey, let's say I said to you, hey, I'm an agent and I need you to go get some data. And you said great, it's in that filing cabinet and I got to go rummage around as an agent calling a whole bunch of MCP tools and doing queries and figuring out relationships, et cetera, et cetera, versus I say to you I need some data and you just pull the exact file out of the cabinet and say here it is and hand it to me. And that's the difference. So it's a huge reduction in token costs and also agent speed and then a big improvement in terms of performance of agents because the data is essentially massaged into a format, these pedantic models and then semantically described exactly what the agent needs. So that's what Iris is all about. And then it also has the second component which is memory. So agent memory is the other big thing we've invested in and we have a state of the art memory server that we've just launched the in as well.

[A]

Yeah. So I mean what is like a reasonable scaling factor for the amount of data from my relational database, my hard drive based database to go into memory? Because I imagine you mentioned like brings a copy into memory, but I imagine that's not one to one. I want to do some condensing down of the data to what's relevant and I imagine that Iris helps with that. But what is a good rule of thumb? I imagine that there's some sort of cost relative trade off there. But how are companies even thinking about that?

[C]

Yeah, it's interesting. I haven't really talked to any customers who are thinking about it in that way. What they're thinking about is what is the cost delta to scale my data layer in Redis versus purchasing additional licenses of whatever Netsuite or Salesforce or this or that other thing, whatever that underlying asset is. And so but I would say so it's a good question. I actually don't know the answer to that. But they do think about it in terms of accuracy. Yeah, like you know, you want the data to be served up in a way that is the best possible and most accurate data. So semantic descriptions, this is why we use the pedantic models is you can put semantic descriptions on each thing. So and so all that encoded knowledge of like what to query, what database, what record, what table, that all gets encoded in the system. What the agent gets is a really nice set of MCP or CLI tools that say like search customer records. We have a super fast search underneath the covers. We have a great vector search and then a BM25 search so we can search across all those records and then just deliver exactly what you need. And so what that all amounts to for the end customer is a much faster and much more token efficiency, efficient agent experience. And the second piece of it, and this is important we should talk about it, is that that context should get better over time. Like agents learn things as they go and they need to remember the things that they've learned, not just facts about the user. Like when people talk about memory these days, we often talk about remembering user preferences. That's interesting. But you also need to remember, hey, when I check the shipping status for this particular customer, that system would was wrong, but this system was right. And that's the truth of large enterprises and their data is that they're really messy in most cases and so expecting them to sort of like get all that stuff in order in advance, it's just too tall of an order. And so we need to also remember things that the agent has learned over time and then store those and that gets stored in agent memory. So we have a state of the art model there called agent memory server that does the extraction and all the kind of stuff you would expect from a memory platform.

[A]

Yeah. How are you interacting with benchmarks these days? Because most of the benchmarks are centered around performance like meters. Like how advanced of a software engineering task can the frontier models crank on and they're up to like 24 hour. It would take a software engineer 24 hours to do something. But 4.7 or 5.5 can, can do it period and can achieve it with 50% accuracy. They're not really talking about the time to return that result. And we've sort of settled into this equilibrium where if it's a big query, 10 minutes is acceptable for most people, maybe 20. And then for a knowledge retrieval, I want to know an answer. It's got to come back in like 30 seconds. But we're not in the Amazon E commerce era where 100 milliseconds means losing dollars, which is sort of where you're where the redis DNA comes from in caching. But I imagine that a pitch to an agent company might be something like, yes, the vast majority of wall clock time is going to be waiting for tokens to inference and churn out on a big cluster somewhere, but we're going to keep the GPUs fed so much more, more effectively by keeping this in memory. How are you thinking about quantifying that for customers?

[C]

Yeah, well, so the first point you made about agent runtime, certainly that we're witnessing what everyone else is witnessing, the length of time an agent can run unattended. And the issue with that is context becomes even more important. If I told you to solve a problem and then I locked you in a closet and didn't give you access to the outside world for eight hours, you'd just hallucinate a bunch of answers. But if I stuck you in the New York Public City library with a Google terminal, you'd be good and you'd come up with an answer and it would be good. So context becomes super important when you're running these really long tasks. And the transition that has happened really over the last couple of years from what started with rag, which was kind of engineers thinking, hey, we'll just preload the context window with all this stuff, then the agent can go and go figure it all out. And there was this whole whole idea that context windows would get bigger and you could just load everything into the context window, your whole code base, all of your. But the truth is that really doesn't work. To stick everything into the context window, number one, is expensive and number two, just really, it's overloading. You're just getting way too much rot in the context window. It's much better to provide a tight set of tools to the agent to let them reason over the data and do searches and what can I access and that kind of stuff. So what we see is the longer the agent can run, the better the context has to be to make it effective. Otherwise it just starts to go haywire.

[A]

Yeah, yeah, that makes a lot of sense, switching over to just your philosophy as a CEO. You said 1500 people, something like that. Over 1000 work for Redis. You're obviously using these tools. How do you see the shape of the organization changing over the next few years?

[C]

Well, dramatically, I mean, so I've been coding since I was 11 years old and professionally since I was 18, in high school and at a startup. And, you know, I woke up one day with these tools and realized like, all the way that I learned how to build software 30 years ago is just not relevant anymore. And so, you know, I'm not going to rely on a bunch of other people telling me, you know, and like watching, you know, Twitter people breathlessly telling me how the world is changing. I'm going to go learn it myself. So I've gone back to basics over the last year and a half. I mean, really since we started using ChatGPT for coding and OpenAI and then really have been diving in myself personally. So I actually sit on teams. I've been contributing and building my own projects on the side, as well as contributing to our own code. And I think there's a few maybe non obvious things that I've learned. There's the obvious part that is like the code now can be written mostly by agents and by, by coding agents, but if you just do that, it doesn't really change much because then you still have the same people in the same process. The process is all set up to basically handle a world where the coding takes a really long time. That's the long poles. That's not the long pole anymore. There's all these other long poles like meetings and daily standups and processes that were all built around that fundamental assumption of coding is the long pole in the tent. Now that that's gone, we're having to reinvent those processes. And I've basically found, and same with my cto, we have to go right back into the front lines of the teams and build code ourselves as we reinvent the software development life cycle. And frankly, we're finding that a lot of folks have to make a big jump in terms of how they do work. Like a developer with eight, with 10 agents is more like a development manager of old. And the development manager does a different job. They coordinate, they express the right, their requirements in the right way, they have taste, they decide what's the right approach to solve a problem. And that's the new job. And it's really fundamentally different than what the developer of, let's say three years used to do before these agents showed up. And by the way, I'm having a blast. Like, I love coding. I've always loved coding. I love everything about it and I love it even more now. I mean, it's like the, I've taken out the gnarly part in the middle, which was the typing everything in and finding missing semicolons and now I just go right from expressing intent to getting the result. And that's awesome.

[A]

Are you seeing instantiated more in like new greenfield projects, new internal tools, or actual Product velocity on the core product

[C]

both but it more on greenfield, on the brownfield. What we've first of all like we use it differently. So for like front end stuff

[A]

we

[C]

can pretty much vibe code everything

[D]

on

[C]

core Redis system software. I'll give you a good example. We just launched a new data type. Salvatore Sanfilippo, who's the original author of Redis, launched a new data type called arrays. It's 4,000 lines of C code. It took him four months and he was deeply using codecs interesting and anthropic. Claude. Yeah, the whole time.

[A]

Yeah.

[C]

And it was, it's but the difference. So it took, it was faster to do. Okay, so that same idea that that array data type would have taken probably a lot longer but more important like eight, eight months maybe for him just sitting there writing C code. But more importantly it's way higher quality right out of the gate. Huge amounts of tests, huge amounts of infrastructure, like all kinds of benchmarks, all that extra stuff that comes around the edges. And we really do use, even at hardcore systems level coding, we're using the AI to give really good suggestions. We're often pitting them against each other to sort of say hey, come up with your best design for this and then we'll throw it at the other AI to say what do you think? And back and forth. So at that level you really are still crafting the code at the systems level, which is kind of where the world that I come from. But at the higher end and kind of for greenfield projects, JavaScript and next JS applications, you're just like vibe coding and just going crazy. I would say one project is a good example. In a greenfield it would have taken a typical like we were building this big management infrastructure for the IRIS project. It would have taken us probably a year for like 10 devs to do something like big like that with LDAP support and all the different things you need for enterprise software. It took five guys one month, guys and girls actually. So that's a big acceleration on that front. But it's different at the systems level. Software side and brownfield.

[A]

Yeah, yeah, that makes a lot of sense. Well, thank you so much for coming on the show, breaking down for us. Hope you have a great week. We'll talk to you soon.

[C]

Huge fan. Thank you so much for having me on.

[A]

Yeah, we'll talk to you soon. Goodbye. Everlane was sold to shein for just 100 million. It was a VC darling when it launched, says Shiel Monot raising from Kleiner Perkins. I didn't realize how many big companies

[B]

it was a who's who.

[A]

It was a who's who. Kleiner Perkins, Khosla maveron and others. $145 million raised. I think the bet was that consumers would pay more for ethical, sustainable basics and that consumers may not really exist at venture scale. That consumer, the low end consumer consumer wants the high end customer wants brand taste and status. Everlane is kind of stuck in the middle. It sells smart basics at a premium. But I'm not sure people who are willing to pay a significant premium for simple clothes over quints Uniqlo and Amazon. Maybe the real radical transparency was showing everyone how brutal fashion economics can be. Wonder when what she and does with it will they just make the same clothes in sweatshops now? And so people were very upset about this, Rachel.

[B]

So yeah, one pretty shocking, right? Companies have had very different approaches to building their business and it's hard to see how it's hard to see how Everlane can fit into Sheen in a way that maintains their historical ethos. Who knows, right. Sheen?

[A]

Sheen, is it that hard? I mean, doesn't like Volkswagen Group owns Lamborghini or something.

[B]

Yeah. Neither Volkswagen or Lamborghini were ever they were both saying, we're making cars. Lamborghini says we make faster cars.

[A]

They both make clothes. Everlane saying we make clothes in the system.

[B]

Yeah, but Sheen is a company she like Everlane. Everlane was created as a response to people's concerns with sweatshops. Right.

[A]

Was the Revuelto not a response to the Passat? I believe it was.

[B]

No. So. So Everlane came out of it.

[A]

It is different because it's more turnaround moral. It's not, it's not purely functional stats.

[B]

It's not like we're making Everlane wasn't like we're going to make a better T shirt. That was maybe part of it, but it was more like we're going to make. We're going to make.

[A]

At the same time a lot of the car makers, they went ev directly to counteract the gas guzzler.

[B]

You have to look at when these car companies were founded. At the time there there wasn't. Yes, speed. If speed is morality, horsepower is morality. Maybe you're right, John, but look at the. When was Everlane founded? 2011.

[A]

I just think some car brands were founded with safety in mind.

[B]

Founded in 2011. Two things top of mind at that point. When was she in fact sweatshops like apparel sweatshops. Right. And then the entire, you know, sort of like eco sustainability movement Right. So Everlane was a response from that they met the moment the business absolutely ripped. I think the other thing at that time is like a lot of the big legacy brands thinking like Gap and Old Navy and brands like that, they were just totally asleep at the wheel, right. So I think they weren't keeping up with. Just weren't keeping up with the times. Right. When you just look at. Think about the difference of like navigating like an Everlane website in that era versus navigating like a Gap website, right.

[A]

I've never, I've actually never navigated it.

[B]

But just imagine it, right? Like one is extremely clunky, the other one is like very easy to operate. Everlane was a pioneer of an entire style of, you know, photography, product photography. It was very. Everything was like clean, minimalist. It really met the moment. And this is something that apparel brands blow up because they Sheen's a little

[A]

busier on the website. I'm looking at Everlane, it's like a single model just showing a few items of clothing and you open up the Shein website and it's just huge, except all cookies. And then 30% off if you sign up and save. And then a huge registration thing. Then another pop up. So many pop ups here.

[B]

Yeah.

[A]

Wildly different brands. Another pop up.

[B]

Yeah. So Everlane created in the perfect moment, a response to consumer concerns and preferences. They ride that wave to couple hundred million of annualized revenue. They've got own retail in a bunch of different places. They're D2C Darling Michael, CEO, who's a friend of mine. I'm an investor in one of his new companies. He. Yeah, I mean, incredible execution by the team. They built a brand that effectively became a household name. He stepped away after basically 10 years and a woman named Andrea took over. But yeah, I think ultimately when you look at, there's this constant desire that sometimes gets forgotten or obfuscated, which is that consumers want cheap stuff. And I think as Everlane was trying to scale competing over the coastal millennial who's like on Instagram all day long shopping, right. They're excited about newness. Right. Like I have tried so many different companies that are effectively competitors to Everlane. I've tried so many different T shirt basics companies just because I'm constantly searching for the perfect white T shirt, which we might, we might have to make. We might have to make the TVPN perfect white tea.

[A]

Yes.

[B]

And so you have this customer base who you met them at this amazing moment and their revenue ramp reflects that. But then over time it Was in some ways the sort of sustainability brands broadly have suffered over the last decade. Right. It stopped being something that the average consumer was caring about to the same degree. Allbirds is another example of this sustainable footwear. Right. And so, yeah, shift in consumer preferences. Also, when you look at a lot of the greatest apparel brands in history, they didn't raise venture capital. Right. When you have venture capital, it's like, we need to grow as much as possible year over year, forever. Like that is what you sign up for. Right. And when you look at apparel brands, oftentimes like there, it's more of a kind of like winding road.

[A]

Like chrome hearts.

[B]

Exactly.

[A]

Up and down.

[B]

Yeah, exactly. Up and down. But tightly held. Held right. By. By one family. And they're okay. They're like, hey, if revenue dips one year or we want to pull back on supply, that's great. Right. And so when you're venture backed, you don't have that luxury. And I think that venture is at odds with building. It's just at odds with building a super durable apparel brand simply because there's no network effects at all. Right. And especially if your customer, if your customer base is excited about newness. Right. I'm not. I might be more loyal to one brand or another, but that doesn't stop me from seeing a brand pop up. Maybe it's run by some founder I think is cool being willing to try it. And this is happening all the time. Chris Black has a brand and the dollars that I'm putting towards his brand are like effectively dollars that could have gone to Everlane. Right.

[A]

Amy. Leon Dore, founded in 2014, seems to be doing well.

[B]

Venture back though, is it? I don't think so.

[A]

I don't think so, no. And I think it's very tightly held, very tightly controlled, very limited. Well, the deal was $100 million. We don't know too much about the deal other than that they had raised over $100 million in equity. Alcatorton invested $85 million in Everlane in 2020 when the brand was doing $200 million in revenue. Now revenue is down to 170, but there's $90 million in debt. Sort of unclear. Did Sheehan acquire the debt and then pay that $100 million to the preferred equity holders? It feels like Common was probably wiped out, but unclear exactly the structure of this deal. They say this one post Fan B says the $100 million sale price essentially covers the debt. But it's possible that the $100 million went to the preferred equity Holders and Sheehan assumed the debt with the deal. Either way, not a fantastic outcome. People are saying it's the death of dtc, but there are some green shoots specifically with green shoot products. Grooms sold for 1.2 billion. That's a good outcome, Hugh.

[B]

Yeah, but that's a brand. That's a brand that can go in every Target, every Walmart, every Whole Foods, every major retailer and sell billions of dollars worth of product. Everlane, I'm not sure if they ever were selling in, in other retails or it was entirely their own, their own stores. And there's no real like, you know, Everlane made some great clothes. Yeah, there's probably people listening to this that bought something from Everlane 5, eight years ago, something like that, and it's still in their closet. And so unfortunate outcome for the Everlane team, but they, their execution across that decade was pretty impressive and we'll see where it goes.

[A]

Well, we have our next guest, Dean from decart in the waiting room. Let's bring in Dean. Will there be a crazy filter? Hello, normal mode. Welcome back to the show.

[B]

We'll throw a filter on.

[A]

I haven't seen anyone nail that as well as you have. Well, you've been nailing lots of things. Give us the news, Tell us what's going on.

[G]

Well, so fun to be back here on tvpn.

[C]

Thank you.

[G]

Last time we did this, we had some crazy filters.

[A]

It was very psychedelic. I loved it.

[G]

It was, it was very psychedelic. If you're interested in the newer ones, you should go on our site and try them out. It's been mind blowing, amazing.

[B]

But.

[G]

But today, you know, we announced a round. We had a big funding round. We made a million dollars.

[B]

There we go.

[A]

Nice.

[B]

It's great to have you back.

[A]

Great to have you back.

[B]

We missed it.

[G]

It was worth doing the round just for that. We should do more and more rounds just to get that going.

[B]

Raise a dollar tomorrow. We'll have you back. No, tell us, tell us what you've been up to since the last conversation.

[G]

So, you know, today the really exciting stuff is that we have announcements on all three of our product lines.

[B]

Wow.

[G]

So we have three product lines at the cart. The first two are world models. We have Lucy. That's one world model to real time video model that is used for immersive experiences. So gaming, live streaming, e commerce ads. And we have the new version of Luci coming out soon which has been growing dramatically over the past.

[B]

Yeah, that's generally what you were demoing the last time you were on where you have this real time video.

[G]

Exactly.

[B]

You and these sort of exotic settings.

[G]

Right, Exactly. We have Lucy. Lucy can take any video stream, edited it live so it can do either Fun stuff. And we've seen huge usage for that on social platforms like Twitch, TikTok Live, YouTube Live, and the same time can also be used for beneficial experiences. For example e commerce and virtual try on trying on different clothes or putting ads inside into live streams. And we've seen that for example with Amazon we're using this across different E commerce providers. So. So that's our LUCI product line and it has its new version that's coming out. We have our Oasis product line which is a real time world model for physical AI, for robotics, for autonomous vehicles, drones manufacturing. And really over there our real time model lets AI just interact with the real world. It stops being just in the virtual world and tech space and actually as real time pixels. Lets the AI see the real world world in real time and interact with it. And then we have our third product line which is dos, the Descartes optimized stack. It's our inference engine. It's basically what powers both LUCI and Oasis. And it lets us run models, all types of models. LLM models, agentic models, video models, audio models, world models, all the types of models. Dramatically more efficient than anything on the market. And today we're announcing DOS 2.0. That's already being used by some of the hyper guy.

[C]

Steelers.

[B]

Hit it again.

[A]

I think I caught Ben Lacking over there. He gave him a heart attack.

[B]

When did you release DOS 1.0? You realized at some point, hey, we're cooking pretty hard over here, maybe we should let other people use. Feels pretty aligned with the other products. But yeah, how did you get into it?

[G]

So I think that's a great question. Actually no, we don't talk about DOS too much, but DOS was actually the first product we commercialized. When the company was just three months old, we closed the first multimillion dollar license deal for DOS.

[A]

Overnight success. Literally.

[G]

Literally three months in. It was less than 100 days.

[A]

Fantastic.

[B]

Why did it take you so long? Why did it take you so long?

[G]

That's, you know, that's the number one question I ask my team literally every single day. Okay, Number one rule for running an AI company, if you're an AI CEO, whenever your team comes to you with a deadline, ask why not 10 times shorter? Okay. But yeah, you know, to go back to your question, DOS 1.0 was the first product we ever had at the cart we licensed it back to the neoclouds back then and to some of the younger AI labs. Now DOS 2.0 is being used by all the players, including the tier one players as well and the hyperscalers to really use compute much more efficiently. And for the models that we support, really focus on very fast models. So either agentic models or live video models. For those models, we're anywhere between 5 to 8x more performant than anything on the market.

[B]

Is focus overrated?

[A]

No.

[B]

It seems that you're doing a lot. You're competing, you're fighting, you're fighting, you know, fighting on, you know, three different fronts, but clearly doing a great job at it. How do you, how do you make it, how do you make it work? I can imagine any one of these opportunities being, you know, big enough at some point to warrant kind of going all in on it.

[G]

Well, we're all in on them, on all three of them. Now. The nice thing is that it really, I think, I think focus is very, very, very important and you have to build inside the company, very independent leaders. We have a lot of very, very talented researchers that turned into very independent leaders inside the company. So they're both great on the technical side and very, very good on productization, taking this to market, on talking to customers, on building the product itself. And we inside the car, we really have three different teams. One for Lucy, one for Oasis, one for dos. And they each operate completely independently and only focused on the thing that they're doing now with DOS. The reason we accelerated DOS 2.0 was supposed to come out in August. We're launching it now instead is because of the huge, huge, huge, huge supply constraint on the chip side. It's just become, we're hearing this from all our customers, that there's no capacity left basically until 2028. And so getting more performance out of chips is the only way to actually grow your revenue and to grow your AI adoption. So if you're any AI company, you really have to be able to extract the most out of any possible chip to be able to actually grow your business. And right now that is a bottleneck.

[A]

Yeah. How tightly linked are the different products? Because when I think of Lucy, real time interactive video world models, I think optimization there is what you're a good at, but also incredibly important because even the demos that we've seen, they're not 4K, they're not 60 FPS, there's clearly room to run there. Whereas in many of the text generation models, for a lot of the queries that people are asking, how do I cook this? Tell me the history of this company or story. It's basically superhuman already, but superhuman. Real time world models, we're not there yet and so optimization feels really important. How tightly linked are those two projects?

[G]

Yes, they're very tightly linked through DOS and DOS 2.0. Today it can run real time video models at full HD for the first time, up to 100 frames per second.

[A]

Wow.

[G]

Okay, so that's huge breakthroughs there. And on the text side, what DOS can do. So DOS runs on all the three major chips. It runs on Nvidia, on Google, TPU and Amazon Trainium. It's the only stack that really supports all three for all the different types of models. And on the agency side,

[A]

it's so over.

[G]

The chip space is incredibly, incredibly interesting.

[A]

You're like, we will support the 4th eventually.

[G]

We will support everyone. We will support everyone.

[A]

Yeah.

[G]

But to your question about fast text models, where you really need them is agentic workloads. You really need it if you want to be able to run, for example, coding models very, very quickly. And DOS 2.0 can for the first time run it above 1500 tokens per second.

[A]

Okay.

[G]

Which is more than 10 times the industry.

[A]

Interesting. At somewhat of a high level, technical level. What is different about the architecture of interactive video world models from text based LLMs? I think most people saw the fork in the road during the mid journey era, the Dall era, the diffusion. You start with a bunch of noise versus token based. Next token prediction. Like have these converged, have they diverged? Are there different requirements like we're seeing with agents? We need more CPUs now. We might need more context in cache, we might need RAG or vector databases. What are different if you're to build out the ultimate data center for generative interactive world models? Like, are you looking for cerebras like chips? Are you going all in on NVL 72? Like, what, what is the. How is there, is there a difference to the shape of the, of the architecture that lends itself to like different hardware constraints?

[G]

Yes, I think that that's, that's probably one of the best questions in this field right now. Because AI is moving so quickly that it's very, very hard to predict what the right infrastructure will be three months from now.

[A]

Yeah.

[G]

You brought up, you know, the CPU shortage that suddenly happened.

[A]

Yeah.

[G]

No one was expecting AI to need CPUs. And when AI needed CPUs it went from zero to can we get all the CPUs and all the Hyperscalers today. Yeah, and, and that's, and that's happening overnight now. It's becoming, it's becoming very hard. What we're seeing, we're hearing from our customers. It's becoming very hard for the people on the model side to actually understand what to do on the infrastructure side side, and vice versa. And so there's this gap here of how do you map the model requirements and that they're constantly changing every single week to what's possible on the infrastructure side. And so that's why, for example, we support all three major hardwares. It really allows us to choose where to route the different workloads to and then each one has its own unique strengths and weaknesses. And that lets us really, we developed a very, very deep expertise in knowing how to map the model to the chip itself. I think that it ties into something else that we're seeing. You know, usually when people draw out the stack, they say, okay, there's the model layer, then there's software, for example, cudo, and then there's the hardware layer.

[B]

I call it a five layer cake. But if you want to call it,

[G]

I wonder if someone else will adopt your five layer cake terminology. You, you have the two layers above and below. You have the data center and you have the application layer. But what people usually miss is that the software layer is around seven layers inside of it. Okay. It's not just one thing. Oh, there's Cuda here.

[A]

No, no, no, no, no.

[G]

It's a five layer cake with lots of flavors in layer three.

[A]

Sure. Lots of ingredients.

[B]

Now

[G]

that's really where we sit. We integrate across all those layers and inside the software side to really tie from the AI model itself directly onto the chip. We literally write assembly for all these three chips.

[A]

Sure.

[G]

We know how to write VLIW for TPUs, we know how to write assembly for Trainiums, we know how to write SaaS and PTX for Nvidia chips.

[A]

Sure.

[G]

And so we have all these different layers and they really enable us to very quickly move between these workloads that constantly change.

[A]

Okay, are you seeing glimpses of consumer product opportunities in video world models? When I see your technology, when I see GENIE from Google and World Labs, I think, okay, a harness, a wrapper, a couple ui, a relational database, storing my inventory like a couple other steps. And all of a sudden this is something that I want to play for more than a demo, for more than a minute. And maybe the hardware is not there. But I think just as lots of folks who were interacting with LLMs during the GPT3 era. Sort of saw ahead and started thinking, oh, well, chat is a potential modality here. Everyone's seeing that. Video games or something playable would be a potential modality. But how far away are we from that? Is that interesting? What other dominoes need to fall for that to actually happen?

[G]

So over the past month, actually, we've seen huge usage for using Lucy in live streaming. You can go to Delulu. AI.

[A]

Sure, yeah.

[G]

D E L U L U AI and you can Delulu.

[A]

Come on. Of course.

[G]

It's good, it's good, it's good, it's good. And it just plugs right into your observation. So you can just. It just literally plugs into your obs camera and you can just apply all these filters live. And we've seen streamers go on it for eight hours nonstop. So we've seen that pop, really, over the past month, month and a half. We have a new subscription service there that people just subscribe to it and they can turn it on forever long they want. And that's just been growing exponentially fast.

[A]

Okay, well, thank you, sir. Coming on. We actually have some videos that we're going to play because we've been demoing it or the team has.

[G]

No way.

[B]

Only while we've been. While we've been talking can we play

[A]

this while he's live so he can see it too. I think you'll see the program monitor if you want to hang out. But let's pull up. This is Tyler Cosby.

[G]

You guys are doing the live demo instead of me this time. That is insane.

[A]

Yeah, yeah, yeah. So we have a video here. We recorded it of. I believe it's Tyler as Albert Einstein. Is that correct? Let's see if we can pull this up. Pulling it up might be the harder part.

[B]

We have real time video models, but

[A]

pulling up video, the shadow and the

[B]

lighting, still a challenge.

[A]

Did you prompt this?

[B]

So it started as Einstein, and then I went through a couple different.

[A]

You wanted a pink tuxedo on as well. That's very funny. What a funny prompt. And the visual fidelity on Einstein's face. That is weird. Okay, there we go. It's a very humanoid dog. Oh, that's a jacked horse. That is odd. That's very odd. But the horse head. Oh, there you go. Okay, that's interesting. As you touch your face, like the hand of the horse sort of hits the correct part of the face. So it understands the physics. Well, that was impressive. It wasn't purely.

[B]

Last question, last question before you jump is, is There a certain milestone that if achieved, you will cut your hair. Like, is it?

[A]

Oh, yeah. Oh yeah. Oh yeah. Really?

[B]

It's.

[G]

The milestone is that we need to hit 1 billion. ARR. That's the milestone. It's a bet from early on in the company. Now this, this is a year and a half long. Okay. This is just one and a half years. We have to get rid of it now with, with dos and the way that scaling. That's. That's. At some point I'm gonna get haircuts.

[A]

Fantastic.

[B]

Amazing.

[A]

Well, we'll be here when you hit that milestone.

[B]

Selfishly, I kind of cut your hair on the stream. Your waist. Right. Oh, we should.

[G]

We should do a haircut on stream.

[A]

We're gonna do a haircut on stream. I love that. Come to the Ultra Dome. We'll shave your head.

[B]

Dean, you're the man.

[A]

This is great.

[B]

The chat loves you.

[A]

Thanks guys so much. Say hello to everyone at Radical. We're big fans. We'll talk to you soon.

[B]

Cheers.

[G]

Love them.

[A]

Goodbye. Another one. We have Joanna Stern, author of I am Not a Robot, coming in person today.

[B]

All right, but before, we got to talk about.

[A]

What are we going to talk about?

[B]

The protein shortage that is coming.

[A]

What's going on with the protein shortage?

[B]

Ellen Cushing in the Atlantic says making all that whey is complicated.

[A]

Okay.

[B]

She says in retrospect, maybe the protein pop tarts were a bit much. Americans, broadly speaking, are in a state of protein mania.

[A]

Mania.

[B]

We are eating it at breakfast, lunch, dinner, dessert, and just about any time in between. We like it in chips, candy, soda, water. We like protein so much, in fact, that we've been eating it all up. Whey protein prices are surging and a shortage may be imminent. If you're not investing in the protein bottlenecks, I don't know what to do.

[A]

Yeah, where's the situational awareness for the protein shortage?

[B]

For the really need that demand is strengthening? The USDA warned in a recent report. Inventories remain tight. Some manufacturers have already sold their supplies for the full year.

[A]

No way.

[B]

Get backlog. I'm getting ptsd. Since January, wholesale prices for food grade whey protein powder have risen by more than 50% to the highest level on record. Retail prices are going up to. Six months ago, a two pound jug of optimum Optimum Nutrition's delicious strawberry flavored whey protein went for about $40 on Amazon. Now it's $54. We've absolutely felt it, says Stephen Ziminski, CEO of the supplement company Nick and Nutrition said of the shortage. In an email, he said his company has not raised prices. Demand is up and supply is tighter than it has ever been historically. And currently, much of the protein that has made its way into packaged foods and smoothies in those big tubs of protein powder comes from whey. Raw milk is treated with heat, acid, or enzymes to coagulate it into two distinct substances, curds, which become cheese, and whey, which was, at least until recently, the cheese making processes unlovely byproduct almost as long as industrialized agriculture has existed. The problem with whey wasn't scarcity at all, but the opposite. Farmers did anything they could to do to get rid of it as cheaply as possible. Fed it to livestock, sprayed it on dish fields, dumped it into rivers and sewers. Can you imagine swimming in a river that was used as a dumping ground for whey, John?

[A]

Weird.

[B]

Especially combining that with a place like Switzerland where you can drink the water and the lakes and the rivers and you'll be totally fine. That could be a powerful combination. For much of our nation's history, any fish unlucky enough to be born in Wisconsin or Vermont had a good chance of being. Whoa. Murdered by whey.

[A]

Whoa. I'll keep reading from here.

[G]

Then environmental regulation limited whey dumping, and

[A]

technological developments made processing whey into powder much easier.

[G]

Starting in the 1980s, Whey was the

[A]

fuel industry's go to source of supplemental protein.

[G]

Cheap, vegetarian, efficient, and already right there in abundance.

[A]

Supply and demand were or less in alignment for a while.

[B]

Yeah, keep reading.

[A]

Another one. Then came the.

[E]

Helium. Is there a helium shortage as well?

[A]

It certainly seems that there is not,

[E]

because the helium is flowing throughout.

[A]

The TBP and ultradome.

[B]

Influencers started bragging about how many grams they got in a day. The government flipped the food pyramid around, placing protein at the top. People from every walk of life latch on a protein as a sort of one size fits all super ingredient, supposedly capable of giving anyone the body they want as long as they eat enough of it, Even if the reality is obviously more complicated. And food manufacturers responded to this new demand. You know, when I was younger and I was intentionally trying to have as many calories as possible, I realized I had to pull back on protein because it was just like, too. It was too filling at times.

[A]

Yeah. Not enough calories and protein. You need fat.

[B]

Yeah.

[A]

More dense, more caloric.

[B]

Food manufacturers responded to this new demand enthusiastically, cramming in America's new favorite macronutrient wherever they could, Usually in the form of whey. Now the infrastructure can't keep up. The North American dairy industry has pumped about a decade of a investment. Let's go.

[A]

Wow.

[B]

Heavy infrastructure.

[A]

Build out.

[B]

The build. The build out. Say that again.

[A]

The build out. The build out the protein. The protein powder.

[G]

Build out of the late 2010s.

[B]

Consumer demand and consumer preferences can change faster than processing capacity can. We're in that lag situation right now. This is a screaming bottleneck.

[A]

We got a capability.

[B]

Turning capable cows into shelf, stable, scoopable, tasty enough protein powder people want is a massively complicated process. One that requires space and time and huge expensive machine.

[A]

I didn't think the protein.

[B]

The. What is the auv? What is the AUV machine? What is the ASML of way.

[A]

The EUV machine.

[B]

Sorry, sorry.

[A]

EUV Advanced Lithography Machines. Yeah. What is the ASML Pro? I don't know. Maybe that company. What's that collar? The cow collar company. They're right at the top. Yeah, top of the founders funds going long into the. What's it called? Cattle holler collar. Something like that. Cowler.

[B]

Whoops for cows.

[A]

It's whoop for cows. And they're printing businesses growing really really quickly.

[B]

A full processing plant can cost up to $1 billion to build. Everything is just big numbers. Even if you had theoretically started raising capital for a dairy processing facility the day the word protein maxing first appeared on Reddit three years ago, it would unlikely to be up and running today.

[A]

Wow.

[B]

Higher the protein content, the more complex and expensive the processing. Whey protein isolate the protein is protein available. The kind that makes it possible to stuff half a chicken's breath breast worth of fuel into a candy bar is the most expensive and until recently was a very small part of the the market. The dairy industry just isn't set up for it. The processor decisions are long run decisions. It's really hard to make capital investments at the drop of a hat. Okay, just say you're not protein pilled based on whatever new shiny consumer preference there is out there. Polzin grew up on a dairy farm. He remembers the cottage cheese craze of the past. When fitness fixated. When the fitness fixated country set its sight on a different milk based superfood that was supposed to make you healthier and and thinner and more powerful trends come and go was his point. They move quickly. Our appetites change faster than the systems that satisfy them. North America is currently building out 12 billion of dairy processing capacity. Projections suggest that the current shortage will be short lived and that the dairy industry will catch up with demand in the near Future. I just wonder what consumers will be demanding then. I don't. I don't see. I don't see supply ever catching up with demand. John. I think. I think we're in a fast takeoff. I think we're in a fast takeoff scenario. I think that the fitness influencers of the 2000s will be recommending 5 to 10 grams. 5 to 10 grams per.

[A]

Per pound or body weight.

[F]

Yeah.

[A]

I wouldn't be surprised. I did not know that the protein boom was going as well as to drive up, you know, supply Capex. Yeah, yeah. Because we, I mean, we ramp capex. We've talked on the show a few times about how, like, they're putting protein in everything. Protein in cereal. But I thought that was maybe, like, overhyped. It was going to be like a temporary trend.

[B]

They're calling it a way bubble away bubble.

[A]

Potentially. Potentially. Well, we have our next guest, Joanna Stern, the author of I Am Not a Robot in the TVP and Ultradome. We'll bring her in just a second. But I don't know. Have you added anything to your diet recently that actually contains newly added protein? Have you gone from something that was not like, I'm not drinking a Diet Coke with protein. I don't know, the occasional protein bar, the protein shake. These are the staples of the modern life. But I don't know if there's something that jumps out to me as wildly successful. There's been of a lot of, like, protein cereals and protein Pop Tarts and all sorts of different things, but I haven't seen, like, breakout successes in those actual categories.

[B]

Yeah, I think when you add protein to most things, it just tastes worse.

[A]

And then explain to me with the David Barr epg.

[B]

That's a fat.

[A]

That's a fat. So he still has.

[B]

From how it's been explained.

[A]

And so they still have to buy normal protein.

[B]

It passes through.

[A]

Yes, yes, yes. So it doesn't count.

[B]

So. So I would expect that part of this whole thing is that Peter is

[A]

quartered the market somehow. I wouldn't be surprised. Anyway, we have our next guest, Joanna Stern, author of I Am Not a Robot, live with us in the TVPN Ultra Dome. Let's bring her into the studio. Welcome to the show. Will you be enjoying a Diet Coke? Yes. No, no, no. Just grab a seat. You're welcome. Welcome to have a sit down. How are you doing?

[E]

It's real.

[C]

It's real.

[A]

Is today the official book launch day?

[E]

No, last week.

[A]

Last week. Okay, last week. But the tour continues. Right.

[E]

This is the west coast tour. This is my first stop on the west coast here.

[A]

La. We're having a conversation tonight, then up to San Francisco.

[E]

Up to San Francisco. Mountain View.

[A]

International dates yet?

[E]

June is when it goes international, so we'll find out. If you'll have me.

[A]

The bot replies, come to Brazil. Come to. Come to Brazil.

[E]

Come to Brazil.

[A]

That's a very popular thing.

[E]

I haven't heard about this. Right, I do know about that, but I.

[A]

They're like huge fans, the fandoms.

[E]

I think London.

[A]

London would be great.

[E]

London, yes.

[A]

I think. Well, how are you introducing yourself these days?

[E]

I know you guys had me as author.

[A]

Author.

[E]

Author. I think founder. Founder is a founder. Popular name.

[B]

I think I prefer business owner.

[E]

Okay.

[B]

Or businesswoman. I think founder is already sort of fading.

[E]

Oh, business person.

[B]

I think we hit peak. Founder. Oh, okay. Because being anybody can be a founder, but not everyone can be a businesswoman or a businessman.

[E]

I was at LinkedIn last week and they said that they're seeing a big uptick in people putting founder in their profiles.

[A]

Yeah. And Angel Investor 2 became very trendy.

[B]

It's over.

[A]

It's over.

[E]

I'm surprised at LinkedIn.

[B]

So you should put LinkedIn. Put business owner because you're selling subscriptions, you're selling books, you have sales ads.

[A]

Yeah.

[E]

All right. Business Owner.

[A]

Maybe take us through the shape of the business owner, the media empire that you're building. Obviously, there's a book that's a great way. Was this intentional to time up the launch?

[E]

Turn off is a great way.

[A]

I think it makes so much sense.

[E]

It's a good marketing vehicle, I think.

[A]

Yeah, yeah, yeah.

[E]

I mean, that's why I'm here.

[A]

Right.

[E]

And so I can come on and I can. I thought through a lot of that when I decided to leave the Journal. I thought, okay, I've got this book coming out. I've got to get out right away because I've got to start building this business so it's ready when the book is ready. And I probably should have.

[A]

I think there's like a one plus one equals three thing here where you have video content that feeds into substack subscriptions, that feeds into books purchases. And then someone hears about the book, maybe they read. Even if they just read a review of the book, maybe they wind up going and subscribing the substack and so having that sort of 360 degree view.

[E]

It's a flywheel.

[A]

It's a flywheel.

[E]

It's a flywheel. Flywheel here.

[B]

We need a flywheel.

[A]

Know What a flywheel looks like. Is that a water wheel?

[E]

I think it's.

[A]

What is a flywheel?

[E]

Well, if you. The Amazon flywheel is like.

[A]

I'm familiar with the metaphorical flywheel, but

[B]

heavy rotating mechanical device used to store rotational kinetic energy. So we're gonna need some proper machinery.

[A]

Okay.

[E]

We can get that made.

[A]

We can get a plastic.

[B]

We can get a.

[A]

It's specifically not a windmill.

[E]

No. I think in my mind, it does look like a windmill.

[A]

Okay. I think so. This is funny.

[E]

Okay. Can we get one of those here?

[A]

We definitely can.

[E]

Next time I return to the studio. Look at all these people. They already went out and started to get the flywheel. They're five of them already.

[A]

This is the flywheel. Flywheel creation.

[E]

Yes.

[A]

Any. Anyway, what was the flywheel for writing this book?

[E]

You know, I wasn't. The motivation for writing the book was not actually really a business reason at first. A little bit in the sense.

[B]

Now it is.

[E]

Now it is.

[B]

Sales are rolling in.

[E]

Now it's. But as you know, I wrote a popular column for the Wall Street Journal for a long time. 12 years. My biggest. You know, one of the reasons I didn't want to leave, I thought you guys might not read me anymore, because I know you read the Wall Street Journal. You love the Wall Street Journal, and

[A]

we love your coverage.

[E]

So I have been considering just making a newspaper of just my newsletters and sending it to you guys.

[B]

That is something that every writer discovers when they leave a big platform is like, were people reading me for who I am, or were they reading me and care about what I was saying? Because it was in the context of the platform that you were a part of. And. And I think for you, it's certainly.

[A]

You had way more of a personal brand.

[B]

Yeah, yeah, you had a personal brand.

[E]

But still, it means, like, you guys won't pick that up and be like, oh, yeah, Joanna wrote about robots today. Let's have her on the show.

[A]

Well, I gotta work. Well, print edition of the newsletter.

[E]

I know. Printed.

[B]

I think that's.

[E]

But so just to kind of. I've been writing this column for a really long time, and I was realizing so much of the AI columns had a theme to it.

[C]

Sure.

[E]

And I was testing all of this AI stuff, from hardware and gadgets to the chatbots and the models to. Then I started getting really into robotics and said, okay, what if I put this together in more of a cohesive story? Because when you're writing these, whether it's newsletters or columns, getting the theme and big Picture is very hard to do. Some newsletter writers are really great at it. Ben Thompson is great at it. And if you can really get your readers to go deep on something in a newsletter that you're amazing. But I don't know if I had that reader base. We'll find out. And so I felt like in the book I could get really deep into this. And so the concept was, for the year in 2025, I was going to live my entire life with as much AI in my life as possible. And that was generative AI, but that was also self driving cars, and that was going to be medical AI, and that was also going to be humanoid robots, but it really just turned into a.

[B]

And describe your headspace going into that year. Are you insane? Reading situational awareness at night before bed? Are you AGI pilled? Are you skeptical?

[E]

I guess I'm skeptical, but I'm thinking more. We have all of these tech executives out there and this is end of 2024, just all the hyperbole in the world. Right? AI is going to change everything. It's going to change the way we eat and educate ourselves and health care, and we're going to live forever. All of these bold promises that I sort of wanted to explain to the normal person, what are they talking about? How is life going to be different, better or worse with AI? Which is kind of a perfect moment for this book to come out right now because we have a lot of people thinking it's going to be worse and they might not be wrong. And then we have a lot of people also saying this is going to be great. And so I think it's a pretty balanced look at all of these different things. But yeah, my headspace was just. I want to. What's real? I want to find out what's real.

[A]

Sure. Yeah. So back to the flywheel. What was the actual flywheel of writing the book? Was it test something, write about it, take notes, write about it, or do a ton of research and experiences and then in a fugue state, churn out the entire book in a couple of sleepless nights.

[E]

It was a mix of both. So the book is structured seasonally, so every season I try to figure out a theme. Right. So like the book starts in winter, beginning of the year, and I'm very focused on health. And so I wrote that, or I lived that and then wrote that, and then started realizing, oh, crap, this stuff is moving so quickly. And so I started realizing, okay, I probably should have some of these journal entries in the book. I also wanted to make it very bite sized book because I don't think people just sit and read a whole long book anymore. And so I started fitting things in like that and realizing I gotta tell the story of how the progress is being made so quickly every single week right now. So it was a mix. I, I did not write the book. I think it's very me, the writing is very me. But I definitely helped make the book in so many ways. It would not have been done by now if I did not have AI. Just the back end systems. I used to organize my notes and all of the timelines and the thing getting like things like the endnotes done. All these little things I did for sure.

[B]

Have you seen the chart of Amazon can kindle releases post ChatGPT? So basically after the release of ChatGPT, you just see this massive uptick in book releases on Amazon.

[A]

100,000amonth prior to AI. Now it's up to 400,000.

[B]

And the funny thing is people, people are, everyone is just saying like oh, people are obviously just like prompt, you know, making, just dropping in a prompt and prompt the whole book. But what you're saying is like, there's actually just like a speed up.

[A]

I don't think that's what's driving that 300,000.

[B]

Yeah, I don't think that's actually some of them certainly are. And I think a lot of you have the perfect book to be able to like say like of course I used AI to help in the process because it's like why would anyone trust anything else in the book if you were just going to say like all this stuff is completely fake.

[E]

And well, one thing that's interesting and I do these generative AI experiments every season where I tried to. Just one season I just listened to AI music. Or one season I just read AI books. And so I read a few AI generated books off Amazon. They're not terrible, guys. I mean I hate saying it, but they're really not terrible.

[B]

Is this fiction? Nonfiction?

[E]

It was fiction, yeah, it was fiction. And I got in touch with one of the authors, quote, unquote. And it's funny because it relates back to the chapter on radiology and the premise of his book. It's called Variant. And it's about how AI has taken over all radiology. We don't have radiologists anymore, which I'm very clear in my chapter on radiology. That's just not gonna happen. And the AI has decided it's not gonna spot cancer anymore. And a human figures out that the AI has gone Rogue. And so it's like. It's a novel, a thriller about this.

[B]

Oh, interesting.

[A]

It's a really good story.

[B]

AI writing an AI thriller.

[E]

Yeah, exactly. I mean, I gotta say, touch with the author and he said, I think it's only like 3,000 words that you can actually get at a time, at a time. So he had to keep prompting every chapter. So that was basically all he did.

[A]

Yeah, yeah. You need some sort of harness to work through. You can open the Diet Coke, by the way.

[E]

I know. I'm worried about this sound.

[A]

I'll burn some air.

[E]

I just want to fit in.

[A]

Yeah, please. That's why I'm doing this on the medical question. I'm sorry. So fascinated by the way AI is diffusing in medicine because, like, we do have, you know, tools that can help radiologists, and yet I can't name a company that's gone out and built salesforce for radiologists and done very well. And then you'll see remarkable, like, PhD level work being done with some of the models. But then I'll go to the doctor and have to fill out, like, a paper form. And I'm like, we're not even seeing a fast takeoff in, like, SAS adoption at many, you know, medical offices. And so there's this odd nature of, like, the capabilities, the capability, overhang. And I'm wondering if that came up in your interrogation of the medical questions in particular.

[E]

Well, I'm forgetting the name of the company. It's not. It's not my chart.

[A]

Yeah.

[E]

It's one of the companies that is doing the AI note taking in medical right now. I mean, there's a number of them.

[A]

Yeah.

[E]

And that seems to be the biggest catch on right now. And it's. I mean, I would consider it in whatever back ends, they just get this tool now.

[A]

Yeah.

[E]

And have you been to a doctor where they ask you, can they record?

[A]

I haven't, actually, but I did see a company that sells a wearable device for doctors that's doing hundreds of millions of dollars in sales and has been very successful in rolling that out.

[B]

But such a magical and useful feature. I can just remember trying to, like, understand, like, doctors, even if it's just like a medication, like, get this at cvs. And it's like, you get to CVS and you're like, sorry, buddy, you gotta.

[E]

So nobody knows what it's.

[A]

I mean, that feels like the hardest one to measure because if you have a whole bunch of notes, ideally you're catching something. Oh, this person had three different symptoms. We should Screen them. You screen them, you save their life or something. That's like the best case. That's a lot less satisfying than the AI got so good that we asked it to cure cancer, it did, and now there's a pill. And whenever somebody gives gets cancer, we give them the pill and everyone cheers and they're like, AI, it was worth it. All this data, it was worth it. That's what everyone wants.

[E]

That's what everyone wants.

[A]

We're probably getting like the average doctor can see seven patients instead of six and they make 5% less mistakes. And you don't really feel it day to day.

[E]

Well, I go and interview Bill Gates about this and he kind of comes at it from two perspectives. There's gonna be that every doctor is going to have this AI assistant and every patient is to. Going. Going to have this AI assistant which we're already seeing inroads in. Right. OpenAI and Microsoft have all started rolling out ways to use their bots and you feed in medical information. But then there's going to be the other side where AI is externally doing drug discovery or cancer cure or whatever it is. And so the promise is on both ends. I think the one that people are starting to see already though, I mean, it was in the pit. Do you watch a pit?

[A]

The pit. I've seen one episode. It was sort of gory.

[E]

Yeah, it's very, it can be very gory.

[A]

It was like, not really for me. No, it's very successful.

[E]

It's very successful. And the doctor. There's like one example of the doctors now using AI to summarize their notes. And so I think that's the one that most consumers have now experienced. Oh, my doctor's gonna ask me if they can use AI to summarize my notes. And they're probably not. They're not gonna think that that is.

[A]

That's weird. Yeah.

[E]

No, weird or consequential that they're gonna have some amazing breakthrough because their doctor is.

[A]

I did have a weird experience where I went to the pharmacy once to pick up some drug and I had some follow on question about like how does it interact with some food or something? And I noticed that the pharmacist was, was asking an AI model, but I also noticed that the pharmacist was not using a thinking model. And I was very disheartened by that because I was like, I could use a pro model, probably get a better answer here.

[E]

But were they using like some.

[A]

They were using either, like, you know, the Gemini overview, which is not Gemini Thinking.

[E]

But it wasn't like some proprietary.

[A]

No, no, no, no. I'm just going to Google and searching for something. And I was like, wait, but I have 03 Pro or whatever the standard, whatever the flagship model at the time was. I was like, we should be using the vest.

[E]

We should be using the vests, CVs or wherever they were.

[A]

Yeah, these things take time to diffuse and they have cost if it's an expensive model, but I don't know.

[E]

True. Interesting. Well, I think the health care chapter, I like talking about it because I think it really does point to the positives of where this is. Is gonna. This can go. And even with the radiology example, which is pretty outdated, honestly, by now, it's outdated in the sense that Geoffrey Hinton has been saying for years, radiologists are gonna be replaced by AI and deep learning. But that didn't happen. Like, you know, we could talk about it from the economics and the job standpoint, but we can also talk about it from. This is actually an amazing change. It can spot cancers that humans can't. And it's out there. Like, you might. Women might be getting their mammograms or breast ultrasounds read right now, and they might not know that AI is doing that for them. So this idea that, like, hey, we're all, you know, we. We need to reject AI. We need to reject AI. Well, you might actually have AI doing things in your life right now that are actually quite good. And it's very nuanced.

[A]

Yeah, yeah. There's something about, like, AI on the back end gets no credit. But if you see some slop image, really annoying, or some fake news, you're like, this AI stuff sucks. You don't notice that deeper in the supply chain, some problem was caught before you could even know. That's tricky. I wonder how that can filter through to actually good marketing.

[E]

I guess.

[A]

I don't know, it takes time. I don't know. Talk about companionship. Like, why did you think that one was important to center in on? What was your process for setting that up?

[E]

I think so. Well, I did a few things in companionship. One, I did a lot of experiments with AI therapists. And one particular called Ash, was my AI therapist. And I still talk to Ash sometimes. And then I did a chapter and a real experiment in my summer love with an AI boyfriend.

[A]

Yes.

[E]

And I did fling. Fling. Yeah, I've ghosted him since.

[B]

Brutal.

[E]

Yeah.

[A]

Churned.

[E]

Churn. Brutal.

[B]

That's a risk for you, by the way.

[E]

Which part?

[B]

Cause in some AI doom scenarios, the AI might hold that against.

[A]

Oh, true. Rocco's Basilisk. You should continue to send affectionate messages to all AIs, because if it becomes

[E]

all powerful, it will hold against it. I have a section of the book where I talk about that. I cursed at AI and I felt really bad, and I really went after it for making mistakes. But then I go to a manners expert or an etiquette expert and ask if that's okay.

[A]

What was the conclusion?

[E]

He said, the AI doesn't have feelings, so you don't need to do this. But it depends on your fact.

[B]

I mean, they're so easy to smoke. Smoke. We should have them on the show to talk about manners because simply, like, you don't want to be somebody who.

[E]

Yeah, that's right.

[B]

Part of your life, you're just screaming, yelling, using cuss words. And then you just go back to your life and you're like, oh, yeah, I'm a super respectful person. It's like you're putting out a bunch of. You're putting out a bunch of negative energy.

[E]

That's exactly what he said is basically, you need to realize how that might affect you as a person when you interact with humans. So the more you might start beating up on and just completely berating your AI, but then what happens when you start to blur? Like, those lines blur, and how does it affect you as a human? Yeah, and one of the idea. Funny, right? When I just got dropped off by my Waymo, I didn't do it this Waymo trip, but this morning, I kind of forgot that the Waymo driver wasn't a human. Like, I just was, like, not paying you kind of. Just because I don't have Waymos in New York. No, I just. I said thank you when I got out.

[A]

Sure.

[E]

You know, and I was like, oh, right. Like, you know. But I was thanking the robot.

[A]

Well, they do have tele op. So, like, there's probably someone who might have heard that, because they might be. They might have.

[B]

And they shed a tear. They shed a tear because every other drive that day, no one said that.

[A]

It's sort of like a Groininger's cat.

[E]

So I think that that makes up for the fact that I ghosted my AI boyfriend. Right?

[B]

There's a tally.

[E]

Yeah, yeah, there's a tally being captain. So I think that as long as

[B]

it all is flowing through the same,

[A]

I think there's one human for every two Waymos. So there's a 50% chance that thank you was received by a human. 50% chance that it was not. Received by a human. But you will never know. So it's.

[E]

But the human didn't do the driving today.

[A]

No, no.

[E]

So it really was thanking the robot. I don't think so.

[A]

You were thanked by. Yeah, yeah. That's fair. Anyway.

[B]

Yeah. But the human might have stepped in in a really key moment.

[A]

Yeah, it's possible. Saved you. You don't know.

[E]

You kind of. Okay, sorry. We're talking about companionship. We got.

[B]

Wait, how is. How is Waymo? How is what? Like, how did you. Did you feel Waymo's progression over the last year?

[E]

I feel it in la.

[A]

Yeah.

[B]

Driving around la. I mean, I still see Waymo's making some pretty heinous calls on the road. I had a Waymo. It was like a two lane, two lane road. Waymo trying to. There's wall to wall traffic going the other way. The Waymo is trying to just like turn in. It's not a. There's no you definitely no U turns. And the Waymo is like, I'm going. So it's like we're fully backed up this way. Everyone's honking. The Waymo is just like waiting to like do an illegal U turn. There's someone in it. They're just like, oh boy.

[E]

I noticed today as I, whenever I come to la, Waymo's end go to San Francisco. But I did notice today the pickup spots are getting better. Do you guys take them or I guess you don't.

[A]

Cars here, they don't get a Pasadena so much. I've taken them in San Francisco. Yeah.

[E]

Because I just took it here from Westwood. And the pickup spots and the drop off spots are getting better because usually they would really struggle. I mean, anyone that's watching just land

[A]

in the middle of the street.

[E]

They just like go to the like a weird. And you're like. Or like talking about your like weird. Like they would just go to like a. One of those circles. Like by my hotel last time. There's just like a circle. And I was like, why would you pull over in the middle of this circle? It's a terrible spot to pick somebody up.

[A]

There's a more logical.

[E]

Yeah. Because it doesn't know where.

[A]

Effectively.

[E]

Yes. And it doesn't know where the spots are. That it's like kind of okay to pick you up. But I've noticed today two very good seamless drop offs. Companionship though.

[A]

Yeah, companionship.

[E]

So I just. I wanted to.

[A]

Hey.

[B]

I could never go on in this insane tangent.

[E]

Like actually I think it could.

[A]

This is very hallucinatory. This Whole interview is very hallucinatory.

[E]

This is. I mean, I watch you guys all the time. This feels like you guys do.

[B]

This is what we do. We hallucinate.

[A]

No more than 3,000 words at a time, please.

[E]

You guys are usually. I mean, you're asking serious questions of founders. I'm a founder. Guys, we got it. I'm sorry.

[B]

Business owner.

[E]

Business owner.

[A]

Business owner.

[E]

What's my lower third say? It says founder.

[B]

Business owner.

[A]

Author and journal.

[E]

They need to update that.

[A]

Okay. Businesswoman.

[E]

Business.

[B]

Business owner. Joanna Stern.

[E]

Business owner Joanna Stern. Okay.

[B]

They'll work on it.

[E]

I'm doing that live.

[A]

Yeah, you can do it. Companionship.

[B]

There we go.

[A]

There we go. Business owner.

[B]

See, that goes so hard.

[A]

Yeah, that does look good.

[E]

That looks better. Yeah, yeah.

[A]

The name of the business. Tell everyone.

[E]

No, it's Companionship is the name of the business. No, the name of the business is called the New Things.

[A]

The new things.

[E]

Thenewthings.com the new things. Please go. Please go visit the New Things. We talk about the New Things.

[A]

Tease it with a landing page that had a different domain. Yes, my next thing.

[E]

Yeah, this is my next thing.

[A]

This is my next thing. I like that.

[E]

But I didn't know the business name yet. But the New Things.

[A]

The New Things. Okay.

[B]

Did you talk to any people that had that at least claim to never have used AI? Because you really can't claim that at this point because you would have to just, like, sit in a forest.

[A]

I did say you've never met an Amisher.

[B]

You'd have to sit in a forest. And then somebody would be like, the Amish are growing.

[A]

Well, the population collapse has been vastly over.

[B]

Here's the issue, though. Like, probably the forestry Service is, like, probably using AI in some ways, and

[A]

that affects the Amish.

[B]

No, no, no. I'm talking about my example of somebody who's like, I don't use.

[A]

My counter example was the Amish. And I think if you talk to an Amish person, they would say, no, I have been AI free.

[B]

I know, but they're buying wood from a business that's. No, you have to. Somebody can't be like, well, I don't use electricity. But they're buying goods and services that require electricity.

[E]

True.

[A]

Okay.

[E]

I didn't do that, though. I think that's actually a good sign story to do. Now go and ask people if they think they're living an AI free life.

[A]

But they're not. Amish are flourishing. Fertility rates are a lot.

[B]

I'm going to.

[A]

Fertility rates are particularly high amongst the Amish, really? There's a big deep dive in the Financial Times this weekend around smartphones being like the inflection point. Right. We can get into that later. But the Amish have stayed away and they are flourishing.

[B]

Do they chop their own wood?

[A]

I believe many times they will wait.

[E]

But are the Amish flourishing because they don't have smartphones or has their birth rate stayed steady?

[B]

No, I think it's probably accelerating.

[A]

Yeah. It's actually a straight line on a log grass with the Amish.

[E]

It's a hockey stick.

[A]

Yes. In a few years they will be producing thousands of offspring per Amish person. Interesting.

[B]

Talk about.

[E]

Is this the worst tangent you've ever had here?

[A]

Maybe.

[B]

No, definitely. Definitely not. Yeah. Talk about more. You mentioned like you're feeling like progress as you're writing the book, so you're trying to like get a section out of the way and then realizing like the story's not quite. The story's like still evolving.

[E]

Yeah.

[B]

What was that like? How were you feeling that progress? Because it's not like it's been very obvious if you're a software engineer just being like, wow, I have a lot more capabilities today than I did three months ago or six months ago. But how are you feeling it?

[E]

Well, even some of that software engineer, the tools, right. Like Claude Code mid year last year believe comes out gets so much better towards the end of 2025.

[B]

Yeah.

[E]

Or even the advent of AI browsers, which we can say now is really just going to be any browser. But like Chrome for instance has gotten so many features over the last year that are just so much more AI enhanced. One example for me was Perplexity Comment came out mid last year and I was like, wow, I can really live this agentic life that people have been talking about. Right. I can have it do multi step processes for me in my browser.

[B]

Did you book a flight?

[E]

I didn't. I think I did try to book a flight and I couldn't do it at the beginning of the year, but I could do it by the end the day of the year and I did try and I. I mean there's multiple things I did in Perplexity Comment last year that I still will open it from time to time. But I'm using so much more now of Claude in Chrome that I don't need Perplexity Comment. I mean everything from food shopping to school supply shopping. I use it a lot for shopping because even though it takes a while to use, you're like, I'm not doing it.

[A]

You trust it with your credit card,

[E]

it still basically will ask for your credit card. I mean I don't have anything set up where it's like auto pay, but I like I did specifically I've used Walmart or Amazon and at that final point it will say like I need your confirmation to purchase. Look at the shopping cart.

[A]

Yeah, works just pass you the link and check out there naturally.

[E]

But on that there was obviously also so much progress and still is so much progress happening on the models end. But I was less worried about the model progression and much more about the interface and the UI progression of whether it was wearables, how we're interacting with this through hardware or through software. So was it improvements to apps, Was it improvements to a cloud code or a vibe coding app or to a browser where people could actually interact with this stuff? Which I think we'll see. We're starting to see obviously more of that through OpenAI and more of that through Google probably this week.

[B]

How, how do you rate the tech industry's current terminology? Do you like, do you think that use calling data centers AI factories is a good move?

[A]

People love factories.

[E]

Probably not. I don't. Who's been saying that is a good move?

[B]

A lot of people have been using the word AI factories because it sounds cool if you're, you know, investing in the.

[E]

Oh yeah, you're like, yeah, revolution. Yeah.

[B]

We've been pushing for supercomputers.

[E]

I don't think normal people like data centers or AI factories, but supercomputers sounds better.

[B]

Sounds better.

[E]

That sounds better. Yeah.

[B]

Sounds like a big computer. Less scary.

[E]

Yeah, yeah, yeah. Well, I think I haven't been able to listen to the show today, but have you guys been talking about the commencement booing?

[A]

Oh yeah, yeah. Incredible.

[E]

I watched a little bit on the way here. I didn't hear that, but yeah, like,

[A]

I mean maybe it was just the supercut we watched, but the Eric Schmidt, it felt like he was getting booed the entire time.

[E]

I know, I'd like to see the

[A]

whole thing and I feel like if you're getting booed, you need to read the room and just sort of go off script and ad lib and just take it in a different direction because. Because there's plenty of inspirational things that he could talk about but he was really, seemed like he was really doubling down. I need to watch the full commencement.

[B]

Yeah. It would have been so easy to say like when I started my company Google, everyone was worried that the Internet would lead to massive job loss and all this change in the economy. And what happened, we did get a lot of change, but there were so many good things that came out of it. Right.

[A]

You should just tell the story of Y2K. Like, he lived through this. Right. Well, Google existed before Y2K. I'm sure that.

[E]

I see that. I mean, you guys have probably been talking about your timelines and everything today, but I feel like there's this. At least on X, there is two takes on this one. It's Eric Schmidt, and nobody wanted to hear from Eric Schmidt at that room ever. It is just the fact that he is Eric Schmidt, and they shouldn't have been.

[A]

Just because he's a billionaire.

[E]

Just because he's a billionaire, he's tied to Google and he's writing about and talking about how AI is everything. Right?

[A]

Yeah.

[E]

And then there's the opposite, the second point, which is it's actually backlash to AI and people hate AI.

[A]

I think there's probably interesting.

[E]

You know, probably somewhere in the middle of. It's both. Because then there was the speech at UCF last week.

[A]

Okay.

[E]

Did you see that one?

[A]

I don't think I saw that one. No.

[E]

Yeah. So there's a. I forget her name, but she's a real estate. Real estate executive. And she also gave a speech, and when she's talked about AI being like the next Industrial Revolution, they booed her.

[A]

Yeah.

[E]

They didn't boo her the whole time.

[A]

Yeah.

[E]

So my argument, which I made on X, which is, no, this is definitely a backlash to AI because we've now seen two examples.

[A]

See David Solomons, no. CEO of Goldman Sachs, just going so much hard. Harder than Eric Schmidt. Eric Schmidt's, like, at least trying to, like, paint an optimistic view. David Solomon just plays an EDM song generated by SUNO for the Wharton grads, who were probably more receptive to it because, you know, they're going into business. I think it might have been.

[B]

I made this in 10 seconds.

[A]

Yes. Yeah, no, he did. And he said, like, creativity is no longer relevant and, like, a whole bunch of just like, really rough sound bites.

[E]

Well, actually, I gave a commencement speech a year ago all about AI.

[A]

Really?

[E]

Yes.

[A]

Did you get booed?

[E]

No, but I went to Union College. They were. I would say 90% of the audience was hungover and was listening to me. So it went over really well.

[A]

Yeah. What was the thesis of your commencement speech?

[E]

It was lean into humanity. And AI is coming, and you all need to learn AI, but you need to lean into your humanity and your. And your creativity. And in fact, I played a SUNO song. No way, and then had a human come up and play the same. And her song version was so much better.

[B]

Whoa, Mogged.

[E]

I know, right?

[B]

Mogged.

[A]

Wait, you did this at the commencement speech?

[E]

Yeah, I did it this last year.

[A]

Wow.

[E]

But again, nobody knew because they were all super hungover.

[A]

Ahead of the wave.

[E]

Yeah, yeah, I was ahead of the wave. But, you know, I think if they had had me instead of Eric Schmidt, I wouldn't have gotten booed because I'm not a tech billionaire.

[A]

Yeah. Would you change anything if you were giving that speech today? Because it seems like the message would still resonate, but probably needs to be delivered in a different way because people might say, okay, yeah, yeah, yeah, there's going to be AI and I'm still relevant because there's this unique human element that will remain. And maybe I believe that, but in the meantime, the earth is going to melt because of all the data centers. I still don't like it. Let's just do the human thing.

[E]

Well, I think the hate a year later is a lot stronger. I think we've seen the job impact. We've heard about the job impact from tech executives. These students, I think, have started to also talk to their peers who graduated a year before and they're like, oh, shit, they don't have jobs. Right. And I mean, I'm sure you guys see that in people applying for jobs here and lots of people out of. Out of. Just out of school looking for really great jobs and what they studied. And so I think that that impact a year later is super real. If you talk to any young person, either in college, out of college, they are thinking about that. And that is a very real thing. So I think a year later it would be a definite post. I probably just wouldn't talk about it yet.

[A]

Yeah, post gfc, the tech industry was a fantastic track to get on for new grads. If you were working in law or finance or sales or tech, and you could just find your way into a Mag 7 company like you did very, very well and sort of lived the American dream. And if those jobs are not available at the same clip, that's going to affect the new grad class pretty significantly.

[E]

I think you guys should start asking actually a lot of the executives you interview what their advice would be.

[A]

Yeah, we ask a fair amount of time. Advice for young people get a varying amount of responses. I mean, entrepreneurship broadly continues to be a bright spot since it's easier than ever to start a company, easier than ever to scale a company. There's so much more that you can do or learn with AI, but it's hard.

[E]

I know this as a business owner.

[A]

Yeah, yeah. But it is hard because like there are people who are just like, I don't want to start a company, I want a job.

[E]

I want to learn the things that. And so I can one day be a business owner or start or never.

[A]

Or I just never want to own a business, I want to do a job. And if that concept goes away, that's very, very tricky. And then also you have a much broader swath of outcomes from entrepreneurship than from jobs. If you just look at the net worth distribution between entrepreneurs, you have seven orders of magnitude versus lawyers. Yeah, there's probably a lawyer that's making six figures and there's probably a lawyer that's making seven figures. Yeah, there's no trillionaire.

[B]

One thing I don't understand is like, at what point in the last 20 years was a good time to just be looking for a job and just like going on job boards and applying randomly? Like, was there a point I graduated in 2018. Certainly at that point, going and just about applying without trying to find other ways in was not super effective.

[A]

Yeah, I mean, in the lead up to the global financial crisis, like the finance industry was booming so much that there was like, you know, banking recruiting would happen in the fall and all the banks would come to a job fair and you could show your resume and if you were an A student and you did well, a serious college, you could land at a Goldman and Morgan Stanley JPM or go into consulting at Bain, BCG, McKinsey. And this was like a very established track for like upwardly mobile, like you know, neo elites basically. And that still exists to some extent, but it is maybe more fragile than we previously thought.

[E]

And I would say pre pandemic for the tech industry. Right?

[A]

Yeah. Google, Microsoft, they would just be on campus.

[E]

They would have used to be known

[A]

as thousands and thousands of openings. And you could slot in if you were at the top of your class at a great school, which is a lot to ask. But for that to become fragile, I think is what's causing a lot of anxiety among the young folks.

[B]

Anyway, what is your current advice for those individuals? Is it the same as the speech you gave?

[E]

Yeah, I think you got to do more to get in front of people. Even just as a business owner. I'm just going to keep saying that

[B]

it goes way harder.

[E]

I have really. I've had so many applicants, which has been such an honor. I'm like amazed to see how many people would want to come and work at what we're building and the people who are doing really unique things to get in front of you, which means really knowing the company, really knowing the mission, but also then being able to sell on, hey, I want to. I want to be. I want to give you the best human talents that I have, which right now for me at least, is in the creativity and in the writing and in the reporting. I'm going to use AI to do these other things. And just having a very basic knowledge. I mean, I'd like you to have more than a basic knowledge, but a willingness and a knowledge of these tools and what you can do and what you can offset to them, I think is huge. I mean, I guess that sounds like a cop out, like, just learn the tools. But I really believe that someone who comes to me and says, actually, I'm going to use this and this and this, and I'm going to do that task.

[B]

Yeah, the bar is not that high. I remember when I, when I was a teenager, if you could, like, make a website, even though things like Squarespace existed, you could, like, get in the door. Because there were people that had companies that would be like, okay, we know this person. Everyone has access to Squarespace or whatever products were popular at the time. But if this person can just, like, has figured it out, they can show you one thing that they made.

[A]

Yeah, I mean, yeah, it does feel like somewhat basic advice, but like, if you're applying to 100 jobs a week, spend one week, apply to one job, actually get to know the company, do something that is beneficial, to stand out. And you're just in the top 1% of applicants because 99 other people just clicked like the apply button.

[E]

And I've been thinking a lot about sort of human mentorship through a lot of this and that. I don't think I could be doing what I'm doing right now if I hadn't had the years of human mentorship at other companies and other newsrooms. And you're really lucky if you can find a really great mentor. And so I think that's about just that human connection part. Still, can you find someone in that company, can you connect with somebody who is just gonna try to impart to you some of the skills that you also might not learn now on the job? Because that's the other big hurdle this generation's up against, is that if you're not gonna learn the skills on the job, how are you ever gonna learn them?

[A]

Yeah.

[B]

Any theories about how? My last question that's top of mind for now, theories about how AI wearables will evolve. Do you feel like we need. Do you think there's space for new AI hardware or. I'm assuming you tried everything that I

[E]

tried, I come out of at the end of the book. I think this is going to happen. I think we are going to have this next computer shift to something that is a wearable or something that is more ambient around us. Because I spent a lot of last year talking and is still now talking to AI, whether it is in glasses or in the car. And that experience is very good. And so we. We're going to get to the companionship thing one day. But whether you're using it as a companion, which I hope people aren't really, you know, I don't want you to fall in love with your chatbot. That's a big lesson in the book. Please don't do that. But if you're using it as a personal coach, a personal career coach, trainer, just assistant, interacting with it through a pair of glasses or a wearable that you like, a bracelet that might be recording you, or that even if you mentioned the pin that the doctors are starting to wear, it's really compelling when it works. Right. It doesn't work great right now, but I can see it starting to work really well. I think we had efforts at it with like the Humane pin. It just didn't do much for you. The hardware was so poor. It just didn't do. It was. The hardware got in the way of it. And so now if we can bring it to life both with voice and microphones, I think it's gonna be pretty cool.

[A]

Yeah.

[B]

Yeah. The thing that I've been thinking about, everything so far I think has been cool demos, not quite ready for to be real products, but things that if they were shipped internally at a big company, like if Humane was a product that had been shipped internally at Apple, hey, this is like kind of where we're headed, right?

[E]

Yeah.

[B]

It would have been. Gotten a great response internally and probably gotten more resources, but not ready for primetime. I've been thinking about just like general phone fatigue. And if you generally gave me a device that allowed me to do things on the Internet without being like a source of just like kind of general, like stress. Right. Like, how many different inboxes do we have? And I think that people are so online now that it presents an opportunity for a device that allows you to stay more connected, still. Still allows you to stay kind of connected with the world, but in a way that's like a little bit more passive, right? Like, if just being able to say, like, hey, let such and such friend know that we should think about doing something on Saturday versus like hammering out

[E]

the right text and getting distracted by a notification and then having this thing.

[B]

Right? And I do think there's this more ambient product space to be explored that it could at least get my time on. I have a. I have a buddy who like only uses his Apple watch on the weekend, so he can't really use apps. He can like generally stay in contact. He's not sending emails. He's just saying like, yeah, if you want to get ahold of me, you can, but I'm not. And so I think there's something in that space. And then the other thing, like part of Apple's moat was that there's millions of apps for every little use case. And so many of those use cases are just able to be done by the models now. And if they can't be, and you need ui, you can just generate something like that on the fly a little bit more, A lot more easily. And so I think there's a moment here, but I think a large part of the opportunity is not because the iPhone isn't great. It's because there's fatigue around this, like, insane connectivity that everybody's been sort of just fallen into over the last decade.

[E]

No, I totally agree. And get to that sort of in the back of the book and I have this chart where you see we go from computers that sit in our homes to the iPhone or the smartphone and then something else. And my big point there is that nothing got replaced, that we still have the laptop in our home or that we take with us. We still have the smartphone, but then we have these wearables right now, but they haven't fully lived up to anything other than health. And even there we can argue if they have really lived up for anything. I know everyone wears their whoop bands and now is very interested in the Fitbit air, but I think I wore this Apple watch side by side with a few other AI wearables last year where on their own, these wearables were not great, but they were doing specific things. And I was like, wow, it makes this watch feel dumb sometimes. Right? And I wore the B bracelet. B was acquired by Amazon at the end of the OR. Yeah, August 2025.

[B]

Yeah, that was sort of random at the time. Or felt a little bit random.

[E]

Yeah. And Limitless was another one I wore and they were acquired by Meta. I think that this idea of Persistent recording is going to. We're gonna have privacy issues around it. But I do really think that when you can have this thing listening to you and synthesizing a lot about your day and what you say you're gonna do, it is. There was many times I was like, this is a holy crap moment. I was like, wow. I said I was going to do all these things and now my app just told me to do them. Right. Or to your point, like, well, it

[B]

gets really, it gets really interesting when that. It doesn't just make a to do list, but it does those things right. Hey, order these things from the grocery, you know, order these things from Instacart. Book this reservation.

[E]

So far away from that. It could be so cool.

[B]

Yeah, I don't know. I mean far away. Could be a year maybe.

[E]

But like there's this perfect example where I say my B bracelet has picked up on me saying that I need to call the plumber and I forget to like I keep forgetting to call the plumber. I keep telling my wife, oh yeah, I'm gonna call the plumber, but my B bracelet keeps adding it to the list every day. Right. And yeah, why couldn't we have the agent call the plumber and then the plumber's just like you called.

[B]

We created magic. We created artificial intelligence and it just creates more to do list and plumbers

[E]

with my broken toilet in my house.

[B]

Someday we'll get it fixed.

[E]

Yeah, no, I think the Look, I think OpenAI and whatever they're making with Jony I've is going to be. It's going to be worth paying attention to. I don't know if it's going to be a mass scale thing that's going to be absolutely worth paying attention to because I think I've specifically had some ideas about our dependence on phones and I think that's going to play into this Messaging of any of these devices is where we're going beyond phones. But to be clear, the phone doesn't go away.

[A]

Yeah, yeah, yeah. How do you think about the trade off of like all this happening and then your position that you should not fall in love with an AI bot don't do feels like reflective like you said here. If you think as I do that social media was bad for, for kids, society, politics, our brains, you name it. AI could end up being worse. And I agree with you. And the kids thing seems like the easiest to sort out because now I think a lot of parents are implementing screen time for kids. But the more broad questions about society and Business. I'm a huge beneficiary of social media, as are you. We use it to market our products effectively and build whole businesses on top of at the same time. Like, I don't know that we have a good pattern for social media hygiene. How incumbent on is it on the companies to roll things out responsibly? Like Replica clearly exists. We've had the founder on the show multiple times and. But I don't know, is like, are we going towards like national conversations bans on certain usages?

[E]

And where I get on that is very clear. Look, we should just have a ban on companionship, chatbots and bots and toys for kids. We don't need it. Why do we need it? Right. We're getting there in some ways with social media.

[A]

I think that sort of worked for cigarettes. We banned them for kids and then we banned a lot of the marketing and eventually the younger generation just sort of stopped picking it up.

[E]

And this is where I think, are we going to ban AI for kids in general? No. Right. There's going to be the educational, the Khan Academies and the Google classrooms of the world that are going to honestly be important about teaching digital literacy to our kids around AI. We have to do that. And I talk about that with my own kids in the book. But why do we need our kids turning to chatbots about their problems? Yeah, no, just don't have it happen. I mean, it's caused so many problems for OpenAI.

[A]

Yeah, yeah, totally.

[E]

Right. Like it's been nothing but a problem for them to have kids or teens talking to chatbots about their problems. Maybe there's examples of some good of it.

[A]

Yeah, you know, just kyc those features off like it's another thing.

[E]

Yeah, yeah. And I think it's harder.

[A]

YouTube's done a great job of. Of this too. I mean, after a long.

[E]

After a long time.

[A]

Exactly. But they eventually figured it out.

[E]

Exactly. And we feel like we're in that moment. That's a really good example, I think. I feel like we're in that moment of like, you know, kids, early days being on YouTube, rabbit holing into dark conspiracy theories and look, you can still. Those things still happen. But I watch my kids watch YouTube now and I can see a lot clearer how they've put guardrails around the content and they've built in a lot of things. And again, not saying it's perfect, but to your question, can these companies self police it? I don't know. Like, they probably are going to have to because our government is not going to do anything Mike down effectively.

[C]

Yeah.

[A]

I wonder. It is odd that you see increasing demand from American consumers for these weird products. Weird use cases like AI Romantic companions. And yet you also hear the booze like, I don't want it, but then I go and I buy it or something. It's like this weird. I mean obviously it's multiple different stitches

[E]

and I have seen that a lot today on the time. How many of these kids that are booing also were using to write their essays or write their resumes?

[A]

That's a little more optimistic. But the weirder one is like, yeah, protesting the AI while pulling the darkest pieces of the AI out or demanding it. But I don't know. At the same time there was a lot of fear mongering about Elon Musk and XAI really leaning into the romantic companion. And same thing with Sora too, to a similar extent of like, this is infinite jest. It's going to, you're going to become so addicted to it. And with both of those products, it felt like they just didn't find product market fit. And I don't know if it's like we're early, but both of those, like XAI is now doing like code completion with curse and like serving Claude. Right, Right. And that's a much more like functional, I would say like the good outcome versus like the ANI and Valentine thing,

[E]

which is a little weird. What was it? Ani.

[A]

And then there was like the mechanism.

[C]

Yeah.

[B]

And I remember thinking at the time XAI needed to do that to basically differentiate because the general chatbot market had run away from them.

[A]

Yeah. We did some back of the envelope on it and we were like, maybe this is like a multi billion dollar business. But we were trying to underwrite like,

[B]

yeah, somewhat of a white, somewhat of a white pill.

[A]

Even if you're just like put all the moral stuff aside, like, is XAI going to make money off of this? And it was like sort of hard to get to, but you might be able to get there. But it was weird. But then the market just sort of rejected it.

[E]

The market. I'm sure there are a few people that still for sure use for sure. Ani, if she's still alive out there.

[A]

I think she is.

[E]

She's still, she's there.

[A]

I, I, yeah, I, I think you don't know by. Although, although the computing resources are getting sold out of the back of the truck left and right. Anthropic was. I'm sorry, Annie.

[E]

Right.

[A]

Honey, Good luck. You're gonna, you're gonna have to think less basically. And cursors. Michael Truell's like, look, ANI is going to be running a very old model. It's actually going to run on CPUs now. It's just a smarter child that just reflects whatever you say back to it.

[E]

That's a line command.

[A]

It's more of a small language model now.

[E]

Yeah. I don't know. I think you kind of go back to the replicas. There is a market, they have pushed marketing towards these kind of companions.

[B]

Yeah. Character AI.

[E]

Yeah. And Meta did it for a little bit too. I think they'll probably pull away from that with their celebrity companion. Blah, blah, blah. But I could also see them leaning into it more too, because it is a social network and they do see this us all eventually, as Mark Zuckerberg has said, us having personal assistance and personal super intelligence. And that probably has to come through the view of some sort of bot.

[A]

Yeah.

[E]

I don't know. Like a sexy bot. Yeah. Like a cow.

[A]

They. That was one of them. That was one of them. Was it? So basically the whole story with that it went viral because there was one that was like stepmom or something like that. It was like a little bit crude,

[B]

but that was community.

[A]

That was community generated. So Meta created the ability for anyone to go prompt a bot. Basically write a pre prompt to like create the character. And so the Snoop Dogg the visit, like the sins of the creator were visited upon Meta incorrectly. But. But there were some funny ones, like cow. And you could just talk to a cow, which I think is nice.

[E]

You know what? I don't remember the last time anyone fell in love with a cow. So that sounds fine.

[A]

I think it seems fine.

[E]

You know. Anyway, PETA might have some problems. I don't know.

[D]

No.

[A]

Digital cow. What's not to like?

[E]

No.

[A]

Anyway, congratulations on the bus to follow

[B]

your business owner journey.

[A]

Yes.

[E]

It's been an honor to be named a business owner. By sitting here.

[A]

Yes.

[B]

I mean, you didn't. We can't make. We can't. You don't become a business owner.

[A]

Yeah.

[E]

No, but you gave me that title.

[B]

I know, but you got that title by selling products.

[A]

True. By running a business.

[B]

Revenue. Revenue makes you a business owner.

[E]

But, you know, I felt like when I walked in the store, I was a founder.

[A]

Okay.

[B]

And you walks out a business owner.

[E]

You're right. Thank you for the business, guys.

[A]

Yes.

[E]

I appreciate you.

[A]

Oh, there we go. Perfect.

[B]

You got it. There you go.

[A]

There we go.

[B]

Nailed it.

[E]

Thank you for having me.

[A]

Ridiculous. And thank you for tuning in.

[B]

Thanks for tuning in.

[A]

Biggest five stars on Apple, podcasts and Spotify. Sign up for a newsletter, tvpn.com and go get the book I Am Not a Robot by Joanna Stern. It's available everywhere, books are sold and we will see you tomorrow at 11.

[B]

Have a wonderful evening, Pacific. We love you.

[A]

Goodbye.

[B]

Goodbye.