Tim Cook Retires, Mark Gurman Joins, Images 2.0 | Howie Liu, Scott Stevenson, Alex Wiltschko, Spiros Xanthos, Carolina Aguilar, Jake Jurewicz

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Oh no, it's Tuesday, April 21st, 2026. We are live from the TVPN UltraDome. The temple of technology, the fortress of finance, the capital of capital. Massive, massive news. Tim Cook to step down at Apple. This broke yesterday. Gurmanator had the scoop, of course. He's coming on the show later today, but it's on the COVID of the Wall Street Journal today. Heavily predicted, often debated. It's a time to reflect on Tim Cook's legacy and what's up next for John Ternus, the longtime insider.

Incredibly well-executed. Incredibly smooth. They sort of telegraphed it.

Yes.

Wasn't a surprise.

Yes.

It was already fully priced in.

Yes.

I personally was hoping that the market would give Tim Cook a 21% salute.

Yes.

Where when the news went out, it just immediately nukes 21%.

Massive red candle. Let everyone know this is—

we don't like this.

We love him. It's a sign of respect. We will miss him dearly.

Of course.

Rebound. Immediately.

Yes.

But I think that's something that the market collectively should try to do for—

Yes, more symbolism in the candles for sure. Chartology is really the key thing.

It's 21%.

So, but of course, what is Apple at right now?

Is—

are they moving at all? Down 3% today, 2.5%, but up 3% over the last 5 days. Still nearly a $4 trillion company. They're doing fine and they're cooking. So let's go through a little bit of the review of the news and some of the, some of the previous discussions that we've had around Tim Cook and John Ternus, because we're going to be learning a lot more about John Ternus. He's probably going to do a lot more content, a lot more media, a lot more interviews. And we will be hearing from him at keynote events for probably over a decade, maybe two decades. We will see. So in January, Bloomberg reporter Mark Gurman, who's coming on the show later today, predicted that John Ternus would succeed Tim Cook as Apple's next CEO.

Let's, let's just pull up this clip. We have to give some credit to the Germinator.

To the Germinator. He didn't predict it on our show first. I think he scooped it and posted.

Yeah, I know, I know.

But we have this.

This was back in January. I'd love to read the clip. I dropped the clip.

The crux of the argument was twofold. Ternus's relative youth among a pool of potential successors.

Ternus, he's 50.

Quiet.

Everyone else on the Apple executive team, late 50s through their mid-60s, turning 66 this year in the case of Tim Cook.

You're Apple's board.

You like continuity. You like an insider. You like people who know what they're doing, have been there for a while. They know where the bodies are buried.

Mm-hmm.

OK. These guys all have hundreds of millions of dollars, if not more. Pause.

I love Mark Gurman so much.

He's the best.

He's truly the best. And he's coming on— He's coming on in 45 minutes. But continue.

At 50, he's the only one who is— if, let's say, Tim Cook hangs out another 3 to 5 years, You're not gonna appoint another CEO who's 65, 70 years old. He's the only guy. Apple, they get vast majority of the revenue from hardware. He's the hardware guy. Have they screwed up any hardware since he's been in charge? No. He's a steady hand, knows what he's doing. He's really the only choice. You know, there was this New York Times report a few weeks ago basically saying that it could be Greg Joswiak, it could be Eddy Cue, could be Deirdre O'Brien, could be Craig Federighi. It's for sure not going to be Craig. It's not going to be Deirdre. It's not going to be Eddy. It's not going to be Jaws. The only category that makes sense is an operations person because you look at the current CEO, Tim obviously comes out of the ops world. You look at the guy who would have been CEO if Tim Cook didn't stay so long. I'm not saying he shouldn't have stayed so long. He's done obviously a fantastic job for shareholders and employees and what have you. Would have been Jeff Williams. He was the COO. So, Saby Khan, he was named COO a few months ago, but he's really been in that job for the last half a decade, I would say. So anyways, it'll be Ternes or Saby or someone completely out of left field. I don't think this is imminent. So, we'll see what ultimately happens, but all signs are turning towards Ternes. Everyone has an opinion that Ternes is gonna be the next CEO. I'm getting the double thumbs up from you. I've been shouting this from rooftops for the last 2 years. But no one has given evidence like, what is this based on? Right? Has there ever been a baton handoff? Is he getting more responsibility?

Well, they have a, like, a big baton.

You know what you have? You have white smoke coming out of—

sure.

Very environmentally friendly.

So is that the end of the clip?

You know, maybe they have an atomically large baton. Like, like we have our scoop.

Yeah. Oh, Mark Gurman, the scoopinator. He's Scoop Doggy Dogg, Scoop athlete. The scoop, the scoop, the scoop is on fire. We're very excited to have Mark Gurman joining in just 40 minutes. What, what, what a great, what a great run he's been on reporting this story. It's absolutely fascinating. So yesterday evening, Mark was the one with the scoop. Tim Cook will assume the role of Apple's executive chairman and John Ternus will take take the reins of the company. Mark followed up with a scoop with internal memos from both Cook and Ternus announcing the transition.

I don't—

I mean, I understand. I don't know. Maybe I don't understand what it's like to be 65, but I was always optimistic that the Warren Buffett 65 to 95 would be the new trend and that people would just say, you know what? Tim Cook's healthy. He's going for a second.

Everything to date was a warm-up.

Yeah.

It's time to go on the actual run.

That's what I would— I mean, I totally understand. He's like, I got another 30.

I got another 10x. I'm going to— I'm taking us to $40 trillion.

Yeah.

I don't know. I mean, maybe Warren Buffett's in a different world because he's more of an investor, maybe doesn't need to travel as much, doesn't need to be in the arena shaking hands, kissing babies, doing product launches, being in DC, getting wrangled into things. Buffett can be more hands-off and just sort of read the news, review the financials, and delegate.

Liquidity, if needed.

Yeah, it's a more hands-off role. I don't know how— I mean, I imagine that the role of CEO of Apple is incredibly demanding. But I like the idea of just locking in and being like, oh yeah, I'm 65. Everyone expects me to step down, but I got another 30 years in me. But he chose a different path, and he is retiring or stepping into the executive chairman role. Ben Thompson published Tim Cook's Impeccable Timing, which eulogizes Cook's impressive accomplishments as the head of Apple. Ben Thompson kicks it off with an interesting thing. He says, "It's the nature of business that the eulogy for a chief executive doesn't happen when they die, but when they retire, or in the case of Apple CEO Tim Cook, announce that they will step up to the role of executive chairman on September 1st. One morbid exception is when the CEO does die on the job or quits because they're dying, but the truth of the matter is that they were—" is that where any honest recounting of Cook's incredibly successful tenure as Apple CEO, particularly from a financial perspective, has to begin with the numbers. And he— this says that the numbers are extraordinary. Cook became CEO of Apple on August 24th, 2011, and in the intervening 15 years, revenue has increased 303%, profit surged 354%, and the value of Apple has gone from a mere $297 billion to over $4 trillion, a staggering 1,251% increase. And there was some chatter back and forth on the timeline over whether Tim Cook had simply put Apple on cruise control and lucked out, as many big names in tech also saw 10 to 40x increases in their market caps. This is from Brandon Gurel's newsletter at tbpn.com. You can sign up today for free. But this interpretation ignores the fact that many of the biggest public tech companies in 1980, when Apple IPO'd, are no longer even close to the top of the pack anymore. And Brandon cites Xerox, Motorola, Texas Instruments, IBM, and HP, which all fell by the wayside. Iconic name. Over the past 30 years, while Apple built the biggest consumer hardware company on the planet and thrived in the public market. And I was, I was doing some digging on this as well. I pulled up, it's easy to look at like, well, zoom back to the Mag 7. All of the Mag 7 have done fantastically well over the past 15 years during Tim Cook's tenure. Did he do anything special? Is he in a different category in some way? And maybe not when you look at, when you look backwards from the current Mag 7. But if you go back to 2011, when Tim Cook took the reins, and you look at what were the biggest tech companies there then, and how have they performed, it does look like outperformance because Apple was at $377 billion. That was, you know, big, huge, the biggest company at the time. Then Microsoft at $218—

[Speaker:TREY_LOCKERBIE] Yeah, you have to go look at what their peers were at the time where he took the helm. [Speaker:MICHAEL GREEN] Yeah.

And to be clear, Apple, Microsoft, Google, and Amazon were clearly there. FAANG was the term at the time— Facebook, Amazon, Apple, Netflix, Google. For some reason, Microsoft didn't make the cut in that acronym. That has since changed, of course. But there were a lot of companies that didn't go on as significant of runs. You have IBM, Oracle, Intel, Cisco, Qualcomm, and HP, all that were in the the top 10. They were sort of the Mag 10 of 2011 and now they are not in the Mag 7, although many of them have done very well. So, it seems— this was our take from a long time, you know, we like to harp on the failure of Apple intelligence and how Siri is ineffective sometimes and the FaceTime interface is odd and the new iPhotos app is hard to use, but Where it matters.

But we give them credit on Genmoji.

Stop. No, where it matters is did they navigate tariffs? Did they navigate supply chain? Did they navigate the transition to Apple Silicon, delivering a great product consistently that doesn't break? Like we have ordered so many Apple devices throughout building TBPn, and there was a time when you would get a new consumer product and it would just be, oh, it's a bad one. I got a bad one. And I got to take it back. And that's never happened. The quality control is flawless. And navigating a very, very difficult Chip Export Act from Biden in 2022 all the way to the Trump tariffs, to different political swings back and forth and back and forth. And Tim Cook has just done a great job of like keeping the wheels on the train going down the track. And I think that should be celebrated, even though the sexy new features—

Oh, yeah, he's bound to be under, under underappreciated because he wasn't the visionary that Steve was, but he also never— I don't think he ever wanted to be seen in that way. But the consistent operational excellence over almost 2 decades is almost unprecedented at this scale. Totally. Totally. Yeah, just the way you put it, right? The same experience that I had getting a new Apple computer as a teenager, I have today. It is actually almost remarkable how similar the experience is. You open this wonderful box. Just works. You get a great device that works for a long time. And you still get that today. So the consistency and— Yeah, I think he will just get more— when people kind of process the run over time, he will just get more and more respect. Yeah.

And a lot of the other computer manufacturers have had to go into bloatware and pre-installed software, and Apple's been very good at holding the line there. They've, of course, ramped up their services business, integrated advertising in places that were somewhat unexpected, since that was always how they were counter-positioned against Google. But they've done it in a way that hasn't been that annoying. I feel like I don't see that many people complaining about ads in the App Store. People see the ads and they're like, "Wow, this company is bidding on the keyword for their direct competitor." That is, you know, extremely competitive behavior. The same thing happens on Google. Didn't expect to see it in the Apple ecosystem. Of course, it's going to happen. The— uh—

[Speaker] Bogov in the chat says, "Not a single product recall under Tim Cook. Is that possible?" Wow. I think that is incorrect. What did they recall? Says they recalled a 15-inch MacBook Pro in 2019. Did Bengate? Did Apple? AC wall plug adapter in 2016, 2019. Batterygate service program.

They've had some, they've had some back and forth, but, but, but, but, you know, a very, very successful run. The other two things that the chat was mentioning was the Apple Car and the failure of that program. Maybe a little bit. They bit off too much, more than they could chew. I think, you know, looking at what's happened in China with every phone manufacturer launching a car that's extremely impressive, I would have loved to see Apple execute there. And I think that would have been very good for the American technology industry, the electric vehicle industry, a variety of different American industrial efforts. But it was not to be, unfortunately. And there's the Apple Vision Pro, which I think a lot of people look at the churn rates, look at the retention rates, and they just see it as an underwhelming product. I still like it, but I'm in the minority, and I acknowledge that fully. I do think that they made the right decision to turn it into a home cinema, a home theater. They understood that there was not enough of a video game library, a VR game library to plug into in any meaningful away. And so they made the decision. I think one of the lead staff members on the Apple Vision Pro project was from the Dolby Cinema team, which had done Dolby Vision and some of the actual theater buildouts. And so they were able to bring that experience and understand what actually makes for a great movie-watching experience in a premier, you know, cinema, and how can we recreate as much of that as possible in VR. Still, Obviously didn't hit the mark fully because the product has not taken off by any stretch of the imagination. But overall, it was a fun project. And I'm hoping that they continue that. It might wind up pivoting into just camera glasses, and they'll go up against the Meta Ray-Bans, which might be the more prudent business strategy. But I still like these incredible investments in VR. What are you laughing at?

Josh. Over at Semaphore says, "Nobody tell the new Apple CEO that he has a streaming service. We've got a good thing going here, lighting iPhone and AirPods money on fire to make great movies and shows, and we don't need any of that getting any extra attention right now." 5,000 likes.

Yeah, it is interesting. Yeah, Ternus is about as far as you can be from the Apple TV services organization. But I don't know, I talked to a filmmaker in Hollywood, a very successful filmmaker, like, years and years ago before Apple TV was really ramping up. And he was saying that Apple's brand is so prestigious that it's sort of antithetical to the Hollywood mindset, which is much more VC, risk-on, you're going to have flops. Like every movie studio understands that it is impossible to predict the perfect success and have the level of polish that comes from these slight iterations to Apple's.

I think you could do it.

Yeah, yeah, yeah, built different, for sure. And so it's a different culture, because if you have— Apple hasn't— I mean, they've had flops, but even the flops, they still feel very on brand. They don't have a silly movie that is just bad. And running that risk was always a problem. But I feel like Apple navigated it really, really well, especially with the iPhone. One project and has done really well with the content side. And they have held a brand standard that feels almost at the level of HBO pretty quickly. Whereas some of the other streaming services have kind of gone more scattershot, more reality TV, more sort of silly projects that might put— might entertain viewers, but don't fully They don't create a cohesive brand idea of like, what am I getting when I open that particular app on the Apple TV? Anyway, continuing, Mark Gurman said on TVPN in January that Ternus really viewed— really is viewed as the ultimate hardware guy at Apple. Ralph Winkler at the Wall Street Journal published an article yesterday detailing Ternus's pedigree with physical products. Quote, If Jobs was a product visionary and Cook a supply chain guru, Ternus is a hardware savant who exists somewhere in the middle. Ternus, who has a background in mechanical engineering, has been working at Apple for 25 years. Overnight success. And most recently led hardware engineering of all of Apple's products. He played a crucial role in the development of Apple AirPods, obviously a massive success. And he redesigned Apple's computers to use company-designed chips. Instead of Intel's, a massive move that extended battery life and improved performance.

So Ternus is— and set them up very well for AI.

Yeah. Ternus is taking over Apple at a time when the company has largely sat on the sidelines of the AI race, going so far as to outsource the technology powering Siri to Google's Gemini. Ternus will have to— will have to somehow manage this dynamic. Ben Thompson wrote about it in November of 2025. Apple's plans are a bit like The alcoholic who admits they have a drinking problem but promises to limit their intake to social occasions. Namely, how exactly does Apple plan on replacing Gemini with its own models when, one, Google has more talent, two, Google spends far more on infrastructure, and three, Gemini will continue— will be continually increasing from the current level where—

I mean, a number, a number of people have, have talked about, you know, what are the challenges that that Ternus is inheriting. One, supply chain, right? Kind of like, you know, stuck in China in a big way. That presents a pretty meaningful, you know, risk to the business. And then sort of like overall dependency on Google, especially on some of these key products. But without further ado, let's bring in—

Let's bring in Howie Liu from Airtable. Howie, how are you doing? Welcome to the show. Thank you so much for coming on down to the TVPN Ultradome. For those who might be living under a supercomputer, not a data center. Introduce yourself. Tell us who you are.

All right. Howie Liu, co-founder and CEO of Airtable, now also maker of Hyperagent, part of Airtable. Cool. I've been doing this for 12, 13 years now.

13 years. Overnight success. Give us the backstory. Take us from college through early career to the first, the founding moment.

Yeah. So in high school, I kind of got into programming. Like, my dad had this C++ book. Left it in this, you know, corner of the house one summer and I was super bored. C++ is a rough place to start. I mean, this was like 2003, like, yeah, it was like pre— So pre-Python for the most part. Yeah, like, I mean, Python was around, but people didn't really use it.

It wasn't the jumping off point. Java and stuff.

Definitely like early days for even like web apps, right? Like Rails didn't exist, like all that stuff. So learned C++, thought it was kind of cool, and then started thinking about like, how do I turn this into like a real career? Because it was a lot more fun than like classes and like I went to Duke, took some like, mechanical engineering classes, but on the side basically learned how to do web app programming, like first with PHP, then like Rails and stuff. And I stumbled on Y Combinator actually like pretty early on. It was like maybe '06. That's like the first class. Literally the first class. Yes, '05. No, because I remember like I saw Looped, so Walker's first company, and I was doing research like I wanted to do a similar type of company or product, and I was like, nope, you know, I was a nobody in college, didn't know anything. And through that, like, found out about Loop. I was like, damn it, somebody's already got this idea. And then learned about YC and Sequoia. And so that kind of became my first inroad into just learning about that whole world of startups and tech. Eventually, after college, applied to YC with my first company, which was basically— it was called eTax, like contacts with an E. And it was like a personal CRM product. Oh, god. Way back in the— yeah, exactly. Oh, yeah. Yeah, they were like, oh, this is a big problem. Everybody has this problem.

Yeah, we'll fund it right away. Am I correct? —correct. And my take has always been that the people that clamor for a personal CRM really just don't realize that their friends are people that they do business with, and they should either just use a real CRM or just don't and just be friends. I mean, it's—

yeah, I think it's like a very unique target audience for whom it's a very high pain point. So I think there's like a market there, but like it's a very like power user, prosumer audience. And the punchline of it, though, was that like after a year of work, we like worked on this thing, raised some money, hired like a couple people, and then we kind of realized, like I sort of realized, like I think it's a more niche market than we set out to go after, and we had some like different acquirers come knocking, like Salesforce was one of them, but like also big like consumer internet companies who wanted to just buy us for talent, and to me it was like—

And what, this is 2009?

We were winter 2010, that batch, and then the acquisition talks were like 2011, basically, late 2011. And we kind of got to this point where I realized, like, I want to work on a really big problem, like a meta problem, not like, here's one small niche for some people, but instead, what's the underlying problem, which is you could actually build this whole CRM with an app platform, right? Like, you really want something that's a lot more just configurable and customizable. And so we took an acquisition by Salesforce, worked there. And for me, the big light bulb moment was Salesforce is one big data—

What's Benioff like on all hands? Uh, I mean— Because he's electric on the stage.

—there weren't that many all— like, all-hands were quite infrequent at Salesforce at the time. But I went to their big sales kickoff that year. I mean, Marc is a very smart guy and also a very commanding presence. He's a physically— like, if you met him on the street and didn't know who he was, you'd probably think he was like a linebacker in the NFL. He's massive. And he just like— he exudes charisma. Even in a quiet, small room, like, he'd take meetings in his house. I'd go over with— you know, some of the other like— He's the Salesman Final Boss.

I mean, but like, not only is that like—

he's like— imagine going head to head with him. It's over. I give up. It's over.

I forfeit. But he's like, he's just got such a presence even when he's not like, like booming, you know, out loud, like on a stage, like, you know, quiet room.

When he's making the dolphin sound where he goes—

Oh, I don't know about that one, but I didn't get to see that part.

But that's the whole genesis of Salesman. Salesforce. Apparently he came up with the idea while he was swimming with dolphins.

I guess that's what all the, uh, Hawaii motifs are for. Um, it was a fun time. I mean, honestly, it was a really fun company. Like, you know, it was like, for being in enterprise software, it was like one of the more fun experiences. Like, you know, people were kind of super laid back. It was like all aloha and like, or what. But, uh, but learned a lot, you know. And I think like for me, the big aha was like, wow, like all of enterprise software is basically just like a database with some app logic and interfaces on top. And that's basically all that Oracle is used for. That's basically what SAP is. That's what Salesforce is. And if you could create a way simpler version of that that's super intuitive, that might be a big market. And that was basically the genesis of Airtable, was like, I want to go and basically PLG-ify— before that even was a term— this category. Sure, sure.

So yeah, what was the initial— Hunting for a team, raising money, building an MVP? Like, what was the first step?

Yeah, I mean, the second time around— like, so this was my second company then, Airtable. You know, I wanted to do things a little bit differently than the first time. Like, the first time was kind of like, just go and like apply to YC, get in, do whatever it takes to get some traction. Like, it literally felt like this roller coaster, right? Like, every week it was like, launch, get some signups, go and like raise money. And Airtable was a lot more premeditated. We spent 2 and a half years building the product before even launching. Wow. It was actually weirdly a very parallel timeline to Figma. So like both like 2 and a half years around the same time. Yeah. Launched around the same time. Yeah. Very like PLG in both cases. And I think we both kind of exploited like, um, you know, the advent of like rich browser experiences. Yep. Like for the first time. So like you couldn't build— and multiplayer. Yeah. You couldn't build like a rich real-time, like single page app experience before maybe like 2000. Like '11, '12, like, and really became like, you know, kind of really legit in like 2014, '15 with like V8 becoming like really mainstream and dominant. Like, you know, just like the performance of the browser became there. Yeah. Yeah. So we built this product like, you know, the premise was let's make it really, really simple for like anyone, like a small business owner, like, you know, podcasters or even like people within a larger, larger company to build their own app or database. Yeah. And they're like, you know, FileMaker, Microsoft Access, like some of these products existed back in the day but never made the transition to the web. So we kind of built it.

Yeah, my career started shortly after you guys kind of like came onto the scene. And my first ever business, we signed up for Airtable like probably day one and still use it, how many, like 8 years later, something like that. So like just running, like it's been— core infrastructure every single day. That's all I'm saying. Yeah, yeah. And evolved and—

But it turns out databases are pretty sticky, right? Like, think about all the Oracle installs in just random large enterprises that are just still chugging away. You've got your system of record in there and built a lot of customization.

Oracle database as a revenue line within Oracle is growing. Revenue. I'm not surprised. Top line is growing for the Oracle database. Not— their AI stuff is a separate thing. GPUs, yeah. Very different. Valuations, but it is growing, which is, I think, a narrative violation that I think a lot of people would take. Talk about the early go-to-market. I mean, you said PLG, but are you sending this to startup friends? Are you trying to sell this into Salesforce on day one? How are you thinking about enterprise versus mid-market versus startups versus prosumer? There's so many different routes you can go.

Yeah, so this was like 2013-ish, right? And at the time, there weren't that many— I mean, there wasn't really a PLG —like, thing, right? Slack had, I think, just come out when we launched in 2015, so they had launched a little bit before. Dropbox and maybe Evernote were kind of the best, like, PLG pioneers. And they were both very, like, consumer, prosumer first, so, like, solo, like, individual user first.

Drew Houston has the funniest riff on—I don't think he calls it PLG, but he calls it, like, the Web 2.0 growth playbook, which is, like, going viral. But he takes it a lot further, and he's like, so you want to sneeze on as many people as possible. And he refers to that as like, if you send someone a file, that you've sneezed on them, and they might create an account. It's just like a much more visceral way of thinking about viral marketing.

Yeah, I can't even get the sneeze out.

Well, yeah, no, I mean, think about— so my first company is an ad network. So we would— a company would come and say, like, I want to advertise. Here's a $100,000 budget. And then the company would put together a dashboard. —of potential buys, and then the person would go through. And so it was inherently viral. Every customer that would work with us had to log into Airtable and use the product. So that was just happening at massive scale. Yeah, yeah.

And I think that type of— you have some data set, maybe it's for your ad inventory or whatever, maybe it's for your CRM or whatever, you need to collaborate with it. It's a very fundamental construct in data. In just like how knowledge work is done, right? And so I think like the lesson learned for me or like that the principle applied was like, can you go after something that's so foundational that like it's always going to be around, right? Like I think with the personal CRM thing, I kind of felt the like turbulence of like, is this in vogue right now? Is it not? And I really wanted to go after something that felt like it's going to be around for decades. And like, what's more eternal than like people need like databases, a database that you can do stuff with the past 40 years of computing. It's probably going to be around for the next '40. And like, even now with agents, it's like the database layer actually becomes more important, right? You don't want just like a bunch of ephemeral context windows, like for agents. Like they need to like store and collaborate on data along with humans. So, you know, we kind of picked that as the vantage point. And a lot of the early customers were like startup founders, like small business owners. But like, interestingly, we had like, we had written this like fake business plan. It was like basically like a vision deck more than an actual business plan. But like we had said, you know, conjectured like, We're going to have to go after like a long tail of like the kind of prosumer SMB audience, basically like Dropbox. Yeah. And I think what was really surprising is it turned out to be a little bit more like Slack where we got the most virality within larger companies. So like there'd be a big like media company or like even like a scaled startup, like a WeWork or something that would run all of their operations very quickly early on on Airtable and then just grow with the company. Right. And so, like, I mean, WeWork was one of our early customers, had like with like probably 10,000 people, like, you know, when they were at their peak, like it basically was like used by every almost employee there. And like a lot of their operations, building operations, et cetera, were just built on Airtable by default. And I kind of learned the value of like having this like data gravity. Like once you get enough data into a product like Airtable, like it just kind of retains really well within the company and gains more and more usage.

Yeah. How did you think about that?

Until the company—

Well, you index against the industry that you're in.

So I want to get to all the good part, like, right now and all that stuff. But walk us through, since this is your first time on the show, like, you know, you went from being one of the hottest companies in tech during the whole no-code boom, the, like, PLG boom, Xurp. Like, it must have just been, you know, I mean, an insane experience. And then, like, there's kind of this reset in— late 2022, 2023, how has it been kind of like building out of that trough? And then like, I'm assuming it sounds like you've been like very re-energized by this new opportunity.

Yeah. I mean, one of the maybe benefits of like not being an overnight success, because we took like 2 and a half years to build the product. Sure. Even like from 2015 to 2017, '18, I would say like we were getting like a steady compounding of growth, but it wasn't like Slack or like Dropbox where it just overnight became super were easy, right? It felt like we had to really grind. We had to think about, like, how do we need to, like, improve the product and increase the, you know, kind of, like, shareability and the scalability of it? So it was kind of a grind for, like, at least the first 5 years. And 2018, when we got our first unicorn round, is kind of the first year where it felt like it was starting to get easy, right? And so 2018 to 2021, like, very fun, easy years, but also, like, you know—

Everything is so good.

Money just was being printed in the world, unlimited money, and, like— We got to raise a big set of rounds.

How would you like a 100x revenue multiple?

I mean, yeah. And just the absolute scale of funding was huge compared to prior art, right? Now, I mean, you can raise like $100 billion if you're OpenAI. But at the time, we raised— our first unicorn round was like a $100 million round. And we raised another couple hundred, and then a few hundred more. And then our big round was our Series F, which was like— kind of at the peak of like the markets, raised $700+ million in that round at an $11 billion valuation. And, you know, we still have like all of that money on the balance sheet and we're now like cash flow positive. So it's amazing. I think like, you know, it's kind of a fun, fun, fun time to like, you know, kind of get to like ride that wave. And then, you know, but like I always, I think for myself, knew like, you know, you have to build like a durable business, right? And so like valuations are going to rise and fall. It's just going to be macro. But ultimately, either we build a great enduring business or we don't. If we don't, then you could be like a flash in the pan, right? So I think we were always trying to focus, and I tried to focus on what do we actually need to do to compound growth, go after the enterprise. Obviously, at the time, especially, it was clear that was the move, right? You get PLG, but eventually you have to go into the enterprise and win these big multimillion-dollar contracts, like become a really sticky system within these larger companies. And we did that and like, we're still doing that. We have like a bunch of the Fortune 500 like running really critical operations on Airtable, whether it's like content production at a big media company or like, you know, like fund operations at a company like, you know, like financial services company. So these are like the, like almost like modern ERP.

Did you hire a different set of individuals to work on that that were already connected and knew that flow? Or was it something where your best sales reps just sort of got bigger and bigger and leveled up? It was a little of both.

I mean, I think it's a different muscle. I think Rolodex selling is, even at that time, not that effective. I think just knowing somebody at a big company doesn't— even if you're very senior and they're very senior, doesn't actually help that much. We've had some reps come in, they've had a decade-long relationship with, I'm like, you know, the CMO of XYZ company. And I think that gets you like a phone call, it gets you like a meeting. But ultimately, like, buyers are wising up, right? They have been for quite some time where it's not just like, oh, I know this guy I'm going to like, or, you know, gal, like, I'm going to buy this like product from them. Like, you actually have to like show them why this is going to help their, you know, help them in their job, right? And help the company. And so I think it became much more about like transitioning from like, oh, people can just use it on their own and they'll figure it out on their own. To like starting to do more of a consultative sale, like come in and say like, okay, how can we solve like a really big problem for you? Yeah. And maybe like for one company it's like, how do I consolidate like my end-to-end operations for like how we do all of our brand planning, launching new products, all that. And that's kind of like, it's like one part consulting, one part like just thinking about like a big enterprise-scale solution, and then one part like being able to leverage the flexibility of our product almost like in a Palantir-like way to show the customer like, We can actually solve this really deep problem for you quickly.

Is there any sort of like PLG motion or land and expand that happens in the Fortune 100? Like, because there's some small team inside of Coca-Cola or something that's using Airtable, and then you're able to use that as a demo or jump off point? That does happen?

Yeah, I mean, it's like there are some companies where you just can't even get your foot in the door without the top-down. Like a lot of big banks, like we just, we were firewalled out until we got Oh, interesting.

So they can't even—

they literally block you, right? Okay. Your IP is like blocked. Okay. So it might be like hard wall, like request access. Okay. So, you know, there are some companies where you have to come in top down, but like there, you know, I would say 70+% of our current enterprise accounts, including the ones that are like now like $5+ million in revenue, like originated from teams within the company organically adopting Airtable, right? Sometimes it was kind of like, shadow IT, they just figured it out on their own, and they just like, they showed real value from using the tool, right? Like they would build some real operation on it and say like, well, I've been waiting for like IT to deliver me this like old bespoke solution or some like crappy other vendor for like 2 years now. I got impatient and just built the thing myself. And that's like a big, I think, you know, I think of like the enterprise landscape now is like, you know, there's plenty of dollars in enterprise, right? Even now, like, you know, just shifting from like traditional software to now AI. But like, there's plenty of dollars, right? The budget's there. But like, the question is, you're not the only one going after it. And so like, what's your kind of asymmetric wedge to get in there and like take those dollars, right? And if you're a big company like a Salesforce, maybe it's like, we already have the distribution, we have like the customer data in there, we're going to go and attack adjacencies. If you're Airtable, we don't have like the scale of like a, you know, ServiceNow or like an SAP or Salesforce. What we did have is like, the usability of the product. So like the PLG was like kind of the entry point. And then also like even when we pitched to other people in the company that hadn't used Airtable, they had probably heard about it from a friend. Like maybe the CMO's like, you know, partner like uses Airtable in their company, or we can go in and just show them like a really compelling demo quickly. Talk about AI.

What are customers demanding? What have you rolled out? Where does AI fit in well? Where does AI take a back seat?

Yeah, I mean, I think it's crazy because it's like we've seen so many layers of disruption happening almost in parallel, right? Like, you think about desktop to mobile, it was like a single form factor change, kind of easy almost to execute on. That was the one that I experienced at Salesforce. The big thing at the time was like Mark would tell every team, like, show me the mobile UI first before you show me the desktop UI, right? Like, go mobile first, right? And it was like the right move. Move and also kind of a simple move. Now it's like you've got at one level, like, obviously every product should have like AI in it. So, you know, we have the obvious stuff like you can now talk to Airtable's assistant, like Copilot style, and have it do stuff on your behalf in the product. We have what we call field agents, which are kind of like the ability to MapReduce AI calls against like all of your data. So you can have like 20,000 customer records and run like, you know, AI agentic, like, you know, kind of tool calls, like search and like research about the company. Like synthesize, like create a bio— Yeah, exactly, like exactly that kind of stuff. One row at a time. Yeah, and like, you know, we do all that stuff, but to me, like the more interesting, you know, kind of disruptions underneath that are like, one, like, you know, are people, do people even want to come into your interface anymore?

Yeah, that's what I was gonna ask. Like, why you care about like storing the data in a sort of safe, secure way.

Salesforce, they just went like headless recently.

Is there a plan for that, or how are you gonna address this?

Yeah, I think it's like, I think the right move move is like hybrid headless, right?

Like I think the whole, look, like if you wanted just like a backend database, you could use like Postgres, like Supabase, right? Like, you know, it's, and you know, there's like phpMyAdmin equivalents, like modern day ones, right? Like Prisma has its own version of it. Like that are okay or they're good, right? Like, but like I think what most people actually want, especially in a business context is like, you want like the database, but you want to have like proper permissioning. You want to have proper collaboration. And most importantly, like you don't want to exclusively interface with the data through like an agent, right? Like you wanna do that a lot of the time. But like it's really helpful to actually go in and see the actual data. Like I think of it as the equivalent of, you know, even though agentically you can like generate all your code and you should as a frontier developer, like does that mean you never want to inspect any lines of code ever? Like no, like you still wanna see like a diff of like all the actual like code files change, whether that's in your IDE or in GitHub or whatever. And so I think the equivalent here is like you want to be able to drop down into a really nice interface. And we've done some work around like kind of figuring out what's the best blend of the two, right? So like with ChatGPT, for instance, we have a kind of a first-class integration where you can go in through ChatGPT and like interact with your data in Airtable, say like, hey, pull me like all the customers that are like waiting for an outreach for me and pre-draft like outreach messages. But then it can basically compose like a fragment of a view within the ChatGPT interface. So like you can actually see like Airtable, but like, you know, kind of a part of the interface. So it's not completely headless. It's almost like you get to pull out like pieces of its face at the right time on demand. And I think that's a really important kind of like UX form factor.

Yeah. How are you thinking about speed in the context of AI? I feel like the models keep getting smarter, but they also keep getting slower, basically. And while I'm extremely confident that I could point a deep research agent at a massive Airtable with 20,000 rows and get very good results, a lot of times I'm just in my email and I want to find one thing very quickly. And that feels like it has yet to be AI-ified, or at least like LLM-ified. It's very much— it's very much like, okay, well I should probably just fall back to a SQL query or just some Boolean logic or just vanilla search because I want this now.

Yeah, I think both are gonna be really important experiences. And obviously we have like, you know, kind of great like smaller and faster models where like the mini, you know, that are great for like more synchronous interactions and like within Airtable. Like if you go to Airtable or you use ChatGPT with like one of the, the smaller models, you get that faster, almost more real-time experience. But I do think a really important class of work that will come to dominate every frontier company or company trying to reinvent themselves to be frontier is figuring out how to operate in this new modality of— it's like the best developers today don't go and sit there in front of their IDE and synchronously talk to the agent. You have 30 separate branches that are each being worked on by a different agent. And you can have the agents continue to update the branch based on human and other agent feedback, right? So you have like comments back or like, you know, run like tests, et cetera. And I think this whole idea of like, look, it's gonna take like hours for that entire loop to complete, right? Like agent pushes some changes, the changes get feedback from other agents or humans, agent responds to that. Like that whole loop could be hours, not just like minutes. So you're not gonna sit there and like watch it one at a time. But the powerful thing about this is like each one is still actually operating faster than like a human engineer could've like back in the day, right? Like when I think about like the speed with which like our early team at Airtable could build features and we had a very good team, like one agent on one branch can, you know, do the work of like maybe 3 humans back in the day operating probably in like 3 times the time, right? So it's literally like a 10x, you know, kind of leverage factor just for one agent. But the best engineers are now able to multitask and kind of basically say, look, I'm going to oversee my own little team of like 20 to 30 agents working concurrently. And so I think it requires like, it's almost like everybody needs to graduate from being an IC to like an IC manager of agents, meaning like in every function. Like if you're like a VC analyst, your job should no longer be to go and synchronously research one company. It's like you're going to go and research like 30 companies and do them all faster, better, and higher quality, right? Like than, than what you could before. And so it, I think that's the greatest leap that is going to be challenging for a lot of people in a lot of roles to make the leap on, because it's a totally different mentality to how you operate and what your role is than before.

What are you pushing the team to achieve? Mm. So a lot.

I think there's basically 3 different levels of self-disruption we're trying to do at Airtable, right? One is the core product itself. How do we reimagine that for an increasingly agent-led future. So all the like headless hybrid type stuff we talked about and like, you know, like the best testament to that is like, do we see like massively growing, you know, basically tool call volume from, you know, ChatGPT, Claude, any other agent products? Like, are people using Airtable more and more agentically? And is it working smoothly for them? So that's like priority one. The second though is like, I think we have to like really transform how we operate internally. Right? Like, you know, clearly, like, the companies, the best companies in the future are not just going to hire, like, massive armies of people to do everything, right? Like, they're going to hire, like, people who can really effectively leverage agents, right? It's so obvious that's happening in engineering where, like, you know, if you could hire one engineer who could be fully agentically leveraged, you get more output than, like, 30 kind of traditional engineers doing traditional engineering. So that's kind of one internal thing. But then the third is like, I'm a strong believer that like you have to go and skate to where the puck is going, like index against like the big tidal wave coming, right? Like Amazon did this like back in the day against like the growth of the internet, right? Like they, you know, Bezos picked books and like, you know, e-commerce because like he thought that would be the best way to index against the growth of the internet. And so for us, like we get that through Airtable and like kind of hybrid headless Airtable, but we're also placing a big bet on Hyperagent because HyperAgent is basically taking all of the excitement of frontier agents, i.e., OpenClaw and YOLOv0 agents that can just have access to your data and tools and do stuff, really, really long-running stuff, not 10-second tasks, but 10-hour tasks, but for non-coders. And we want to do it in a business-friendly way. So you can go and do this, deploy it into your company, run agents across your entire company. And so that's kind of like a bet on if we believed Airtable, you know, 10 years ago was like the most meta problem, like the largest problem we could work on, which is like software arguably is like was the biggest and fastest growing industry at the time. Right. And like, how do we go after that entire category and index against that? How do we now go against agents and say like, we want to build like the best agents platform that any business and any person can come in and use and just start building agents with, right, and deploying them into their company, right? And so if we do that really well, then like we get to doubly win both as the data layer, but also kind of have a bet on the agent wave.

Yeah. Last question. Should children learn C++? Definitely not.

No, I think—

And is that because of C++ or because of the AI era?

I think, well, I mean, I think both. Like, I think the fundamentals of good technical architecture are going to be the most important thing, but that has like, I think the abstraction that really matters now for creating value is raising up, right? Like, it used to be at one point, like, you know, Bill Gates wrote, like, some of his first programs in, like, literally, like, machine code. Yeah. And, like, would punch it into his, like, PDP-10. Yeah. And, like, clearly, you could be a great startup founder or be a great software engineer and make lots of money, like, without having to go down to that level. And so I think now with agents, like, the bar has raised yet again where, like, what you really need is, like, good product business and like tech architecture sensibilities. Like, how should this system work? Like, where should the different like levels of, you know, kind of responsibility belong? And like, if you can get really good at that, then you have super leverage. If you are just kind of like, like learning the literal kind of like lines of code and how to write them that, you know, a lot of engineers were before, I think that's going to be increasingly below the frontier line of like agents can just do it like equally or better to humans.

Yeah, that makes a lot of sense. Well, thank you so much for coming on the show. Thanks for having me. Have a fantastic rest of your day.

Great hanging. Talk soon.

We have our next guest, Mark Gurman, the Germinator himself, in the waiting room. Let's bring him in to the TVPN UltraDome. Mark Gurman, how are you doing? Tired. How are you doing? Tired. I can imagine. This has been months in the works. You predicted this many times, but also on our show. How did this come together? Did this match your timeline? Were you surprised by this particular Monday that it was announced?

Or walk us through the scoop. Yeah. And get the scoop.

Get the scoop ready. This is the scoop.

We got this one. We got the golden scoop for you. The golden scoop.

Oh, I've never seen that. Yeah. It's awesome.

It's a new prop in the studio.

You'll have to— the new profile.

I like to see you next time in person, though.

The OpenAI deal is already doing work for you guys. Yeah, absolutely. So here's the deal. There's a reason I published my profile of John Ternus just a few weeks ago, right? This was all coming together. Things really ramped up internally at Apple on this at the end of last year. Things have been in motion. The plan was to announce it after the 50 year anniversary celebrations. And it almost felt like the 50-year celebrations were not just, you know, about Apple's 50 years, but sort of a goodbye celebration to Tim Cook and his legacy at the company. So it all came together over, over, over several months. This really started about 2 years ago when Tim Cook identified John Ternus as, as the next one. Ternus had been being, been prepared for this role probably for over 5 years at this point since they put him on the executive team and he became SVP of hardware engineering. But this started in early 2024. And at the time I wrote that, that was the first time I read that he would be the next one.

Yeah. How do you— have you been able to process— I know you've published a few memos. Have you been able to ascertain anything about the internal response? Are Apple employees excited about this? It feels like it's been managed from a communications perspective very carefully. And so it shouldn't have been a surprise to anyone. But are Apple employees generally excited about this? It seems like there's a lot of cause for optimism, but I'm always interested to hear.

Yeah, there was that one article a couple of months ago where clearly they were getting quotes from former employees that were like kind of taking potshots at him. Yeah. He's saying like, he hadn't— he's never made a hard decision.

Yeah, that was the quote that went into the journal. But I don't know, it seems like it didn't matter because he got the job.

Well, got the job 2 years ago, and I think he's going to do a hell of a job in this new role. I am quite optimistic for Apple in the long term with Ternus at the helm. He has product sensibilities that Tim Cook simply doesn't have. He has product decision-making ability that Tim Cook certainly had but wouldn't utilize because he himself knows that product-based decisions is not where he could have the most impact. Just like Tim Cook really oversaw the operations part of Apple as the CEO and left product development to other members of the executive team. My expectation is that John Ternus will be intimately involved with the product side of the organization, as he was in his prior role as CEO, and will leave the operational side to people like Sabih Khan and Priya, the people who run the operations division. At Apple, supply chain, manufacturing, procurement, AppleCare, you name it. So he's going to pick his spots and his spots is hardware and product development. There's a reason that when he chose his successor for the hardware engineering organization, he chose Tom Maryeb, not an innovator, but an incredible execution guy when it comes to hardware engineering, product quality. He did that because his belief is that he will still be intimately involved and sort of be that product visionary for Apple.

In this new chief executive position. Yeah. Does— when I remember Steve Jobs, I think of Jobs as an innovator, as a visionary, as someone who both did Pixar and the iPhone, you know, so many different projects, a lot of them wildly successful. Tim Cook felt like a focusing of that a little bit. But you still had the car, Vision Pro. There's a few different projects going on. Is this more— is this the most focused Apple has ever been and will ever be? Or do you think that there's Do you think Ternus has like some aces up his sleeve where he might want to take a wild swing at something?

I think what Tim or John Ternus is going to have to do is stay the course. Annual iPhone, iPad, Mac, Apple Watch, AirPods upgrades. But at the same time is going to need to do a better job of bringing out new product categories that Tim Cook has done. If you look at Tim Cook's legacy in terms of major new products, it really was on the services side. The AirPods and Apple Watch, you know, those were both really developed by management teams, engineers and people who came from the Steve Jobs era. That's not a slight, but my point being is that we really haven't seen anything wholly new that is also successful since 2016 with the AirPods and the Apple Watch at the end of 2014. The Vision Pro has obviously been a Tim Cook product, a Tim Cook priority, and it's been sort of a flop. At least for now. I know Apple has a very long, decade-long spatial computing roadmap. They eventually want to get to AR glasses. They'll have displayless glasses to compete with Meta several months from now into 2027. But he needs to get cracking. There are 6 major Apple products in development right now, 6 major new product categories: AI AirPods, smart glasses, Pendant. Pendant. Smart Display.

Is that the lamp or the kitchen thing?

No, no, lamp is number 5. Smart Display is different. Okay. The tabletop robot, so that's the lamp, the moving lamp. And then number 6 is a, I'm only gonna say very little about this, but a security camera.

Okay, interesting. Well, at least the Apple Vision Pro has one key fan.

Do you think the lamp is a, a predecessor for humanoid. Do you think Apple would ever do a humanoid?

I do, I do, but I think it's going to be a decade if they do, and they're going to wait.

Yeah, I feel like it says, it says a lot that they're not talking about it, that you don't know about an internal humanoid project yet. Yeah. Oh, I do.

Oh, but, but yeah, they're exploring humans. Yeah, but they're, they're not working on it full throttle, but they have a large robotics initiative. They're working on AI robotics technology and they're also working on robotics hardware. John Ternus actually took control over the robotics hardware team about a year ago. He took it from the AI chief that Apple, you know, got rid of a couple weeks ago, John Gianandrea. Yeah, but they're also looking at— they're actually building— it's really cool— a gigantic manufacturing arm or a gigantic robotic arm that they want to use in manufacturing but also used in Apple retail stores to grab, you know, products off the shelf in backroom and whatnot and bring it into the store. Yeah, that's probably 5 years away. But they're looking at robotics from a manufacturing standpoint, from a retail standpoint, and also most importantly from a consumer standpoint. They've also been exploring a mobile robot, something like an Amazon Astro. But I don't think that's probably going to see the light of day. That's fun.

I feel like Apple has the perfect brand.

Talk about— talk about Ternus's challenge with supply chain broadly, what you think he's he's going to be focused on over the next 5 years. I don't think he will be.

I don't think he will be. I think just like—

but is that not— you're saying like just basically like he's delegating it, broadly ignore that it's a kind of a key risk to the business to have, you know, I mean, his team with the hands on in all hands meeting with Apple employees this morning, he was pretty clear that Tim Cook didn't do everything.

Tim Cook chose his spots and Ternus said that he's going to pick his spots as well. As we know, Tim Spotts was operations, finance, and sales, and he delegated everything else. My sense is that Ternus is going to— Ternus's mandate— Ternus was hired because they believe that he is going to be able to bring Apple back to the forefront of product device innovation. Okay. They already have the best-in-class operations, finance, salespeople. They don't need Ternus to do that. They need Ternus to keep his eye on the prize. Which is products. Yeah.

And what do people point to when they say that there's a risk to Apple staying on the frontier of product development? I saw the Android phone that has the, the, the privacy screen that you toggle on and off. That looked like kind of a cool feature. There's folding phones that they're working on. But are any of these features that exist in other phones? It feels like they haven't actually gotten a groundswell and started pulling iPhone users away from the ecosystem. But are there no features that people are worried about or what?

It's not here yet. Yeah. Okay. It's not here yet. Nothing you've seen is the risk. Okay. The risk is whatever the hell Meta and OpenAI and Haarc and all these companies eventually come out with. The risk is one of those companies doing something really cool. Yeah. And jettisoning Apple from that perspective. But we all know that nobody has done quote unquote cool stuff yet to steal away iPhone users. Nobody is ditching their iPhone for Android. In fact, the switching is going in the other direction, despite the fact that Apple is supposedly the most innovative company in the world and has the least innovative AI technology.

Yeah, but consumers care about value and things like the MacBook Neo really deliver that value. Brand, colors, value.

Privacy. Ternus was only Senior VP of Hardware Engineering at Apple for 5 years. It's a short tenure to be an SVP of a division at Apple. And the reason is because he's— oh, there you go. No, he's been at Apple 25 years. Yeah, I'm kidding.

We use that, we use that ironically.

No, I know. But the point I'm trying to make is that he still has a legacy. And Ternus's legacy is making Apple hardware more performant in terms of speed and battery life and higher quality. He's really focused on the durability and longevity and the reliability of Apple products. And it's meaningful, I think, that the person that they chose to be Ternus' replacement in hardware engineering, Tom Maryeb from Intel, is a product quality and reliability expert rather than a product design person.

Yeah, what was the thinking? I mean, I remember we talked about this, how I got the new iPhone and it has immediately been dinged. What was the thinking on making it disposable? And, uh, but, but it's, but it's better for heat or better for wireless connectivity even though you can't get the color to adhere to the, to the material as much, so it scratches off. Is that the, is that the current trade-off?

Like, yeah, yeah, there's trade-offs with every material. Like, titanium was light, it looked cool, you could bead blast it, it didn't and, you know, interesting. I mean, it looked interesting and it gave them a good marketing point. Like, oh, come buy a titanium phone. Like anyone cares about the material of their phone. But it had really bad properties related to heat. Aluminum, which we've known for 20 years, is an excellent material to build consumer electronics out of. So they went back to the basics. You know, they were really talking about at the end of last year splitting the line between ultra-thin with the iPhone Air and pushing the iPhone Pro to the right as much as possible by making it more performant. My expectation is they're really doubling down on this. Their goal is really to just squeeze as much performance and power in these iPhone Pros as possible. Uh, and for everyone who needs less power, you can get the thinner and lighter iPhone Air. And I think you're going to continue to see Ternus push in that direction, making the MacBook Pro as amazing and most performant as it can be, and pushing everyone else to the MacBook Neo and the MacBook Air. And I think his legacy performance and product quality is, is really important thing to remember. Yeah.

Has Ternus ever talked publicly about AI in any capacity?

He talked about AI in his all-hands meeting with employees this morning. He said that I'm going to check it out.

No, just hold on. I'm kidding.

Hold on. I don't want to give you inaccurate Yeah, fake news. Okay, yeah, just hold on, bear with me.

I think I have internal memos.

No, no, no, no, no. Okay, he said that, uh, he's especially excited to be stepping into this role at this moment because I am telling you, we are about to change the world once again. He said Apple has an incredible roadmap ahead and that I'm not exaggerating when I say this is the most exciting time to be building products and services at Apple in my entire career. AI— there you go— is going to create almost unlimited potential. We're going to be able to keep unlocking possibilities that, that are going to create entirely new opportunities for our products and services, and I'm so excited about what that's going to mean for our users. Yeah. Uh, earlier this month, he reorganized Apple's hardware engineering division, uh, around a new AI platform that they're going to be using to improve product development processes and overall quality.

Interesting. Okay. I saw a post here from Bubble Boy. I want your reaction. Apple is about to become the Mecca of hardware engineers around the world with John Ternus taking over at Apple. Is Apple not already the Mecca? Is there actually somewhere to go but that is up in terms of hardware engineer recruiting? Do you see this as changing the culture in some meaningful way?

I mean, they don't pay like these, you know, OpenAI, Harps, and Metas of the world. Their— Apple has been pillaged by OpenAI and Meta and all these companies as of late. They are stripping apart Apple's hardware engineering division, hiring people from every team they can get their hands on, throwing very big offers at them. And so This has been a really big issue that Ternus has been dealing with over the last year and change. So, but Apple is, you know, the hardware mecca. They're the company that everyone wants to poach from. Yeah. And they're the company that people go to to learn how to build consumer devices. So this is definitely, yeah, I agree to a large extent with you actually.

All right, with Bubble Boy. Yeah, Bubble Boy. What I did, this might be somewhat separate, Just get me up to speed on the folding iPhone. What is the latest there?

Announced in September, Ternus's first big new product. Yeah, super exciting. Super pumped. Yeah, we've talked about this. I'm sick of the candy bar phones. Been the same junk for 15— excuse me, 20 years now. Yeah, I want a foldable. I want a bigger screen.

Yeah, I really hope— exciting.

John wants a newspaper-sized phone. Well, they have those.

I've seen those in China. It's through the trifold, right? But this is a bifold.

Don't get me started on the trifold. Okay, explain the trifold.

Wait, why are they awful? It seems amazing.

John wants pages of screens that he can turn.

Flimsy and they break. Okay, you need a trifold at Apple-like quality in 20 years because, you know, when they do a, uh, when they do— when Apple does a trifold, it'll be good. Okay, okay. You know, I open up a foldable phone right now, you open it up and you you can hear the screen sort of creaking, right? And then you have that big line in the middle, and then it's like impossible to get your thumb in to open the thing. I hope Apple fix that. I don't want to hear a creak. For $2,000, I don't want to hear a creak. I don't want it to stand— sound like I'm stepping on, you know, a wooden floor, right? I want it to just open, and I want it to open quickly and nicely, and it not be like I'm trying to lift a weight.

It's still going to be weird for video consumption though, because I feel like we've done vertical videos, 9x16, and then 16x9 widescreen. But if you open up a foldable phone, you eventually get a square, and that doesn't really make like a movie-watching experience better. No, Apple's is different.

Apple's is like the new Huawei phone where it is iPad screen ratio.

iPad screen ratio when you open it. Okay. When you open it. Yeah, okay.

So still black bars. Any Intel on On, uh, no, no, no, black bars.

Black, uh, yeah, sure, there'll be black bars when you rotate it. Black bars on the top. If you're watching like a cinema film, or even if you're scrolling Instagram, like, you, you won't necessarily get more view because for so long all the content production has been ultra widescreen. If you're making a Tarantino film and it's super cinematic, or if you're on TikTok and you're doing vertical video then you're, then you're gonna have black bars on the side for, for the most part. But for so many other applications, you know, for Word documents and Notes and—

TDPN will look great on it.

Yeah, yeah, yeah.

Uh, something to look forward to.

What do you think Ternus's new comp package looks like? You know, we were— we, we almost marched on Cupertino, uh, multiple times because of Tim Cook.

To get Tim Cook—

you're gonna handcuff yourself to the, uh, spaceship. Yeah, exactly, exactly.

I would— I'm just guessing, I'm just guessing, um, I think a million shares over 10 years. That's pretty big. And can I just tell you why I think that?

Yeah, why?

Because that's what they— because that's what they gave Tim Cook when he was named CEO, a million over 10 years. So sure, I would assume it's the same.

Yeah, but again, I, I don't know. Entry-level researcher salary, but it's a good start if you can. Pretty much. If you can.

Tim Cook is getting, uh, Tim Cook, you know, Tim Cook was getting $100 million a year and then everyone flipped out except you guys, and so he had to cut his pay to like $40 million. And then when things died down, he's like, all right, I'll, I'll take, I'll take my $70, $80 million.

I slept peacefully for for those years. And then you should see my sleep score once.

We were really, we were really the strongest supporters of the, the Tim Cook pay package.

But I, I guess, I guess a million—

I was just, I was just like, you look at the value of the shares, like, baseball player, the same amount as a guy leading, you know, a $4 trillion company. Make it make sense.

Yeah, maybe it'll be 500,000 shares. Maybe it'll be 500,000 shares. I, I don't know. But I know that they gave Tim Cook a million.

You got to get those numbers.

It's time to— really?

You're all in on this already? We should preemptively march.

We're bullish on both. We love both here.

Well, now you get— you get both now.

I know we do. Yeah. Why? Why? Why is 65 a retirement age for the CEO of Apple? Like we were talking about Warren Buffett. He was able to manage a trillion-dollar organization well into his 90s. Is it a more physically demanding job? Is he traveling more? Is it— is his hand ringing from shaking hands in D.C.? Like, why not have another 10 years if you're in that seat?

I don't think the hand situation has much to do with shaking other people's hands. Okay. Why is he not there another 10 years? Well, he needs to give the new guy runway. Okay. I'm sure there are some in— I'll just tell you what Tim Cook— I'm not going to get into it. What I'm going to do is tell you why Tim Cook said he's stepping down. He said he's stepping down because it's the right time and there's an intersection of John Ternus being ready, Apple's finances being in a very strong place, and Apple's future roadmap being in a very strong place. In terms of the real reason why he's stepping down now, you can read some of my prior articles, taking a deeper look at the situation.

Okay. Yeah, makes sense. Cool. I love seeing you.

I love talking to you. Thank you for coming on. Congratulations on all the amazing coverage.

Get some sleep.

Great to see you, Mark.

Keep up the amazing work. We'll see you soon.

We'll see you in 15 years for Termus Junior.

Can't wait. We'll see you. Goodbye.

Let's pull up the OpenAI launch. OpenAI launch. What's going on with ImageGen 2?

I've been playing with this for a while. There's wild, wild examples. They are live streaming.

Sam should have saved the Death Star meme for this, for this launch.

Plant layout into a coherent and organized image. And we think we made a lot of progress in both of this visual understanding and visual generation, both of these aspects.

As a result, being able to handle this kind of tasks very well.

We now have an output for this where you can see 8 different real cool outfits for me. Kymon, what do you like best?

The level of detail in these models is getting so extreme. It's post-SLOP. It is post-SLOP. That's a good point. But I've seen some where people are like, generate the entire periodic table with details about each element and a visualization of each element. And it's just like, like so much information, so dense that you have to wind up zooming in so much just to get all the information. Uh, it's like, like the idea of generating like a single photo of a person in an outfit was, uh, was remarkable just a year ago, I guess, when the Studio Ghibli thing happened. And now you can generate layers and layers of, uh, of detail here. Uh, good teaser. They said this is not a screenshot and post the image.

Something that, uh, prompt— tell everyone about the prompt you've been running with, uh, with Wiki.

Oh yeah, uh, I've been doing this thing where I take a Wikipedia article and I ask, uh, ImageGen 2 to turn it into an infographic. Or you can actually do an Instagram carousel, like 10 images that tell the story of something. I did this with John Ternus, and, uh, it's It's remarkable. I mean, the text is perfectly photo-real. The other thing that's interesting is the brand comes through in an interesting way. It doesn't— it's not like it has like one style for infographic. Like, I had it make an infographic about John Ternus and his career and his path and everything that he's done at Apple. And it put in images of the projects that he's worked on but then also had like an Apple-like brand aesthetic, like a white background, the correct fonts. But then I did the same thing for like the Elden Ring movie that we were talking about yesterday. So I went and got the Wikipedia for the Elden Ring movie that has some details and some leaks of who might be in the— in playing different roles. And it was able to go get images, go get headshots of those people, put those in because it has tool calls now. So you can actually like So if you say like generate an infographic of John Ternus's career, it will do like a pretty good job generating someone who looks sort of like John Ternus. But then you can actually just take his canonical headshot and say, no, use this exact photo of John Ternus and it will just drop it right in. And so it looks perfect 'cause it is just like a copy paste basically on top of the layers. Let's get some audio again. Let's see what else is going on here.

Output. This is particularly useful for very complex prompts. For things that require like web searches, for require you to output multiple images that have to maintain coherence with each other, or even for it to check its work before saying, hey, here's your final output. But let's just like look over some examples of this first. Gabe actually kicked off a few of these examples at the start of the livestream. So let's go to the one on the phone, which is the one of him and Sam, the selfie of them, and they created a manga of it. And if we look at the very first image, we can see, yeah, it does look like Gabe and Sam, right? Yeah. Yeah. But I think what's even cooler about it is that if you look at the follow-up images, they still look like Gabe and Sam, and they still look like the style that was originally maintained.

Yeah, children's stories, coloring books. Coloring books. It's like really interesting. Yeah.

Among pages 1, 2, and 3.

I did that this weekend. It was very successful. Rave reviews in the Coogan household for AI-generated coloring books for mythical creatures that my son came up with, combining different creatures.

We beta tested the instant version of this model on Ella Marina under the codename Duct Tape. A few of you on the internet were really good detectives and deduced that it was us. But we're going to announce that it was us. And so in this prompt, we basically asked that— You got us. —basically GPT Images 2 to go and find social media reactions to this duct tape model and basically quote people. And so we see quotes from Threads, LinkedIn, Reddit, et cetera. And even crazier part is that we've also asked the model to put a QR code to ChatGPT.com so that you can try out this model right now for yourselves. And can we just make sure that it works? Yeah, I try. Oh, nice, nice, nice. So image generation with thinking allows you to do really complex things such as— so in this case, web search, synthesize answers, and put a QR code all in one image. But we have still more, and Alex will talk to you about these new details. It's so interesting.

The tool use is getting really, really advanced. So we have some version of Codex that actually was able to generate a vector diagram to make sure that everything was correct and then regenerate the image on top of that. Of course, we should go to the timeline because there's some people that are having fun with the image generation. Someone made— presidents in Elden Ring. There's Joe Biden, which is a, you know, a Dark Souls-style boss that you can fight, and FDR, Lord of the New Deal. And these images, it just looks remarkable if you ever played any of the Elden Ring or Dark Souls games, because of course, like, the text is flawless. And then you can fight Richard Nixon in front of the Watergate. And this looks like a mod that I think people would play if it actually existed. Blake Robbins said the world is now ready for the rumored OpenAI image model. People are creating Google Street View images that just look perfect and Grand Theft Auto 5 loading screens. And there's a big trend of people creating things that look like screenshots of livestreams, and then they put themselves in the livestreams and have all fun with that. And there's one of, uh, of Satya Nadella presenting, uh, a slide and like even the minor text at the bottom of the slide in the picture. Uh, it's sort of remarkable to think that all of this is generated basically one-shotted. Justine Moore was posting about this, uh, a long time ago, April 6th. This ad was one-shotted by OpenAI's image model. Prompt was literally make an advertisement for the M4 Pro Mac Mini. And I mean, you can see how quickly this will speed things up. The question is just like, what is your source content? What do you want to visualize or deliver via this format? The whole meme of like, turn this essay into bullet points, turn this bullet points into paragraphs. Now you can turn this infographic into text and turn the text back into an infographic. I feel like a lot of slides—

Someone made a calendar for tracking your Uber Eats and FedEx deliveries, and it looks pretty believable. Yeah. We can pull up the screenshot here.

Yeah. People— there's something interesting about this where I've seen a number of slide decks that could be infographics, and I'm wondering if there's going to be a new level of compression where people are they're saying like, just send me a screenshot, like a one-pager screenshot over text instead of like a slide deck that I have to click through. That is a lot of information.

That looks like a dashboard. But if you zoom in, there's like pretty— Oh, it's like pretty accurate. Impressive fidelity.

This is wild. But yeah, I mean, if you're trying to deliver a whole bunch of information that could go into slide deck, condensing it down into infographic feels like potentially a new trend. And I wouldn't be surprised if you see a lot of these flowing out into Instagram carousels and other sorts of content. If you're trying to summarize some sort of content, a history, quotes, you know, any sort of information like that. Anyway, Tyler Cowen, back to the timeline. Tyler Cowen made an incredible call back in July of 2020 in the depths of COVID in Bloomberg. He said, This year is likely to be remembered for the COVID-19 pandemic and for our significant presidential election, but there is a new contender for the most spectacularly newsworthy happening of 2020, the unveiling of GPT-3. As a very rough description, think of GPT-3 giving computers a facility with words, a faculty, oh no, a facility with words that they have had with numbers for a long time and with images. Since about 2012. What a remarkable post. This was 2 months after Goran's scaling hypothesis post and 2.5 years before ChatGPT was released. That is remarkable. There's a lot of folks that are chiming in with, I called it too. Anyway, I believe we have our next guest in the waiting room. Scott Stevenson from Spellbook is the co-founder and CEO. Scott, how are you doing?

Doing great, doing great. How are you guys?

We're good. Welcome to the show. How are you doing?

Good.

Yeah, thanks for having me. Can you— I mean, I want to go into the contracted IRR debate, but let's get the update on Spellbook. How are things going? Where's the company at? How are you feeling? Going very well.

We had a killer Q1, crushed our stretch targets last year. We have over 4,400 customers on board in 80 countries now. 80 countries? Yeah. The most used AI contract review tool in the world.

Why so international so early?

It's been inbound. Yeah, we've had a ton of interest inbound. So yeah, it really, you know, the choice is accept the customers or turn them away. Yeah. You know, we chose to accept them. Yeah.

Is the product sort of multilingual by default?

I would say yes. Like, a lot of it is driven by AI models. AI models have some ability to deal with different— actually pretty good ability to deal with different languages. And then we're able to supplement the models with legislation and norms from many different jurisdictions since we're a legal product.

Yeah. So yeah, where, where else are the key integration points? Like what's the hard work to like bring a country on board or even bring a new flow on board or expand the capabilities of the product as models are just sort of getting better every month by default in the background?

Yeah, I mean, I think for us that the bit, you know, we try to be 2 years ahead of the market and build things that are 2 years ahead of what anyone else is building in legal AI or elsewhere. And we've consistently done that. We've built the very first GenAI product for lawyers back in the summer of 2022. This is like before ChatGPT. Wow. So a lot of like Claude for Word just came out. That was kind of what we launched like 4 years ago. So we're pretty, pretty far ahead from that now. What we really focus on is building unique workflows that are not just chat. I think if you're building something chat-shaped, it's very difficult to make that defensible because there's going to be some really good general AI products for just generic chat-based, based work. What we focus on at Spellbook is really rails for high volumes of contracts and contract workflows. So we sell to like Fortune 10, Fortune 100 companies and really companies of all sizes who are processing, you know, hundreds of thousands of contracts, not just with the legal team, but with their sales team, their procurement team. Sure. You know, we're in manufacturing, shipping, you know, all of these different verticals. And we kind of build these end-to-end rails that allow these contracts to move quickly and safely through organizations. And like, there's a lot that can slip through the cracks when you're dealing with these high volumes of contracts. A lot of mistakes are made. So So, you know, we give like every legal team a second set of eyes on like these massive flows of contracts going through the organization.

Yeah. What drew you to Cargate? Why? Are other, is it legal AI companies that you feel like are getting a little bit dicey on this front or, you know, how do we get here?

Yeah, so I've got some interesting examples to cite. But I think it's an enterprise AI problem. And I'll say first, my goal here with this tweet and what I'm doing is to destroy as much equity value as possible by discrediting this obscene metric of CARR, or at least the way it's being used today, so we can all get back to building real companies. So that's what I'm trying to get out there. Sure. Where this came from is I think I just noticed more and more founders and investors telling me things about ARR reporting, you know, mainly the public reporting, but also some of the internal reporting that was just getting more and more skewed. And yeah, there's all these headlines being published about, you know, ARR records being broken. Sure. And, you know, when the laws of physics are being broken, you have to ask, is it— is AI breaking the laws of physics or or might there be some other kind of illusion going on as well? And I think, I think it's a bit of both. We have really high growth, awesome companies being built, but when you have really high growth, issues can kind of fester and hide underneath. So yeah, I'd heard a lot of more and more stories of people using this metric of CAGR, often using this metric when they're talking to press about their revenue. and then gaming it in some pretty obscene ways. So maybe I just start at the back end.

Yeah, the tweet. So you say the setup. Company signs 3-year enterprise deals. Year 1 is discounted, say $1 million. Year 2 steps up, $2 million. Year 3 is full price. They report $3 million as ARR, even though they're only collecting $1 million this year.

That's a big deal.

The worst part, the customer has an opt-out option at 12 months. It's not actually a 3-year contract. So they're basically like taking the 3-year number, pulling it into the present, even though it's not a— it's a contract that the customer can get out of. Interesting. And they're not actually on the hook. So it's not really— It's rough. So yeah, yeah, yeah.

Just react to that, I guess.

Yeah. So I think that's a specific real example that I heard of in the wild from an insider. Of how these ARR metrics were being gamed to create some amazing revenue charts. But I would say there's a broad category of issues that I can talk about, like a few of them that after the tweet went viral, I got a huge response of other founders and investors saying that they were seeing the same thing and some other examples of the types of gaming that's going on. Yeah, because—

because— folks don't know— you get one person in a category that starts doing this, then the other people —we have to report the same way. —have to start reporting the same way, and it creates this cycle.

Yeah, and it starts pretty innocent, you know. CARR, for folks that don't know, CARR is contracted ARR. So it allows you to count revenue that's not live yet. So maybe you're doing like a 9-month implementation, or you have a 1-year pilot.

Yeah, for short-term stuff, like, hey, this contract is going, you know, this This customer is actually going to be going live next quarter, but we've signed it, and we're just going through the implementation process. Yeah, yeah. But there's nothing that— there's no law that says you can't say, like, we're going to extend the timeline dramatically. It just is not a very grounded way to run your business.

Mm-hmm. Exactly, exactly. Yeah, and I think it's innocent. Like, 3 months extra— credit, arguably useful, but it's a very easy metric to game, especially if you miss those obligations. So I think because we've kind of normalized the forward deployed engineer, which we used to call professional services. And so now you have these really complex implementations where you might be promising a customer like, hey, we'll build this feature. And once we build this feature, then we'll start billing you. What happens if you don't build that feature? So one of the issues you see is companies stacking all these commitments of, they'll switch on billing once they deliver X with their forward deployed engineers. And then what happens if they miss that or what happens if that gets delayed? And then they're reporting it upfront as ARR publicly, but they're not actually at the point where the ARR is live. So yeah, that's another category of issue. And then there's people reporting pilots, just 3-month pilots as ARR and they're free pilots. That, you know, I was talking to an investor yesterday who just sees that all the time from early-stage companies, like, coming out of accelerators saying they have, like, a million ARR, and they look under the hood, and it's just all pilots that haven't converted yet. So there's a host of different issues with the metric. And then the other one is the step-up contract, where, yeah, you're stepping up. Year 1 is 25% of the cost. Year 2 is a little higher. Year 3 is higher. And then people are either amortizing that back over the period to get a higher average or even taking that year 3 amount, like you said at the beginning. So yeah, there's a bunch of patterns that are happening. The other thing is there's early opt-outs. So you can have early opt-outs in these long-term contracts. But there's all— I mean, we're a contract company. So there's a million ways that a contract can be terminated.

[Speaker:ASH BENNINGTON] Seen a few contracts.

Yeah, yeah, yeah. So yeah, yeah, I think, I think it's a really ungrounded metric and people should stop using it to report their enterprise AI companies, should stop using it to report their ARR publicly. I think no one should take it seriously except maybe internally for some projections. You know, it's, it's not a, not a good, good metric.

What is the gold star example of using ARR correctly? Because it's very easy once a company's public to just say, OK, let's just go off of GAAP revenue for the year. And like, what did you actually book this quarter? There's a whole revenue recognition policy. It feels like there's some benefit to tracking ARR month by month if you're a high-growth startup. But what is the best use? Overrated.

Companies should just report their daily annualized run rate. Maybe.

Or hourly. Yeah, and that goes into the debate of annualized versus annual, right? So how have you processed sort of the better cases or like, what is the responsible way to report a revenue metric in 2026?

Yeah, I mean, I think it depends on the company and the shape of the company and whether it's usage-based billing or seat-based billing, which you still have lots of both. Definitely don't do like hourly, you know, annualized run rate based on the hour. That's not good. I think the main thing I would say is it should be live. It's like what revenue is actually live right now for like which customers are you actually billing and are actually paying you? So calculating run rate based on, you know, the month of revenue that you have coming from customers that are actually paying you that you're actually billing. I think that's okay. I think, you know, annual recurring revenue based on, live customers that you're actually billing, that are actually using your service, I think that's pretty good. I think once you start stretching into people who will pay you or, you know, might pay you, that's where things start to— I mean, it can just be so easily gamed, and anything that can be gamed will be gamed.

Yeah. Yeah.

Are you optimistic that anything will change, or do we need to see a massive correction and a dark— and a dark— the dark ages?

Like, uh, I mean, post-2022? Um, I, I mean, I would like to see a steep correction and then back, back to building. You know, we'll see if we can make that happen. Um, you know, my reach is only so far, but, you know, I, I've had— I've spoken to a lot of reporters in the past, like, 48 hours who are like, I'm always going to ask now, like, when the company tells me their ARR, are they talking live ARR? Are they talking, you know, this, like, long-term committed ARR that might come. So I hope, you know, at least the journalists are going to be a little bit more savvy and ask more questions before they report on these numbers.

Yeah, I want to ask about who suffers, but in terms of ARR, like, yeah, there's almost something where you should just report your last month's revenue instead of doing the times 12 thing. And then if people want to multiply by 12, they can. But at least you're just reporting, hey, last month, this is what the Stripe account did.

You can also just say by Q3, we will be at X ARR. Or— Exactly. Because that's a different way of saying— that's better than saying we are at $10 million CARR. Yeah, yeah. So exactly.

Much better. Who suffers here? Is it purely investors? Because I feel like— like a good venture capitalist, their job is to dig into the contracts during due diligence to set prices. And if they want to pay, you know, 1,000 times ARR because they think it's 100 times CARR, like that's their risk profile. Like I would maybe be careful, but you know, that's their job. Or is there a risk that employees see a headline number and think that the business is more stable than it is and they join and then they're rugged. How do you think this affects the— who needs to watch out for this, basically?

Yeah, I mean, I think investors are generally— good investors are generally very aware of the difference between CRR and ARR and aware of the widening gap between these metrics. And in most board decks, you see two metrics. On the press, you usually see CRR, but it's called ARR. In a board deck, you see both metrics. So investors are quite aware. But I don't think it's victimless at all. I think, yeah, employees— are signing up for companies. And as you know, in a high-growth startup, people are committing a ton of blood and sweat to be successful. Part of it is based on the growth of their equity. And they might think their multiple— they might read the headline number. They may not know the number that's actually in the board deck. They might read the headline ARR in the press release. And they might base their decision to join a company based on these headline revenue numbers. Which are really not grounded in reality whatsoever. And by that, I mean, I have literal examples, confirmed examples of the press number being 3 to 5 times higher than the actual live ARR number. So that's a huge difference if you think about a multiple. Yeah. Yeah.

If you're choosing between a public company that's trading at 10x revenue multiple or something, and then you get your offer from from a company and it seems like they're a 10x, but they're actually a 50x, like that is very material for how you should think about valuing that stock that you're working for. Makes a ton of sense.

Then there's the customers, like customers are trying to figure out which company is most mature or least mature. And then there's the, you know, like the whole competitive landscape. It's like if one person, if one company starts doing this, all companies have to start doing it. And it just creates—

Yeah, I mean, we can start doing contracted viewership. So we've signed 3-year deals with people in the audience that requires them to tune into the show every year. They have an opt-out after a month if they don't like it after a month.

You're still advertising based on contracted viewership. I'll sign the contract for the rest of the year.

I'll watch every day, every hour. I mean, I guess that sort of does happen for YouTube channels that— I mean, no one really does this, but there was a time when when YouTube channels were sort of valued on like the subscriber number as opposed to the average view number. And of course there are some channels where every video gets a million views and they only have 100,000 subscribers for whatever reason. And then there's vice versa where someone's been doing, you see this on like old legacy media accounts on X where they'll have like 30 million followers and then the post will get like 3 likes. And it's like, those are two wildly different metrics. Like that happens. It happens all the time. But this is the name of the game in Silicon Valley, the metrics game. Everyone's finding an edge somewhere. Well, thanks for keeping everyone honest, and good reporting, good luck fighting the good fight out there, and spreading the good word.

Don't get too sucked into all this.

I got a business to build, you know? I'm building.

We promise you can come back on in 3 years with your honest ARR, and take a good— Victory lap. No, no.

Become an investigative journalist. Pivot to investigative journalism. Blow the doors wide open on this. Wow.

This goes deeper than I thought. Yeah. Good. Good to see you. Have a good one.

Thanks, guys. We'll talk to you soon. Up next, we have Alex from Osmo building olfactory intelligence. We've talked about this before. Can AI smell? That's our current benchmark for AGI. We say, if, you know, we talk about white-collar work, we see Somaliers as white-collar workers. Unless you can smell, it's not AGI, and artificial intelligence is falling short. But it's your first time on the show. I would love an introduction on yourself and the company because I'm fascinated by this topic.

Let's talk about it. Your Somalier comment and also what you talked about with Max Hodak's been on my mind. Amazing. My name's Alex Gulchko. I'm founder and CEO of Osmo. We're giving computers a sense of smell. I've been working on this problem for 20, exactly 20 years or so. First as an academic, I did my PhD in olfactory neuroscience at Harvard and trained under Bob Datta, who trained with Richard Axel, got the Nobel Prize for discovering the receptors of smell. And my AI mentor trained with Geoff Hinton, who got the Nobel Prize for deep learning. Yeah. And I'm the one weirdo that's like, that's the intersection of those two worlds.

We've been waiting for the entire history of the show.

There's been great prophecies of your arrival for hundreds of years.

I'm so excited. I'm so pumped to be here.

So should we start with maybe like olfactory science 101? Can you set the ground on like how does smell even work? What are the important sort of like building blocks that we should know? And then we can build up to the next generation and how AI is being applied. 100%.

So the chemical slice of reality, all the stuff that's data in the air, we can detect that. Our sense of smell is literally our brain leaving our skull. So when you smell a molecule, whether it's a tree or it's a meal or a drink, like the physical pieces of that thing enter into your nose and touch a piece of tissue about the size of a postage stamp. And that's your brain, right? So like you were in physical communion with that thing. That information gets turned into neural data, which actually skips all of the normal way stations for the other senses and goes right to your centers of memory, the hippocampus, and emotion, the amygdala. So, our sense of smell is very primal in that regard. So, it's the reason why when you smell something, you get dragged into a memory and you cannot stop it. You're just like back in high school or you're back as a kid is because we're physically wired for that. So, that's real.

I've always heard that. I've always heard that phrase, smell is the sense that's most tied to memory, but I didn't know if it was just something you saw on like a t-shirt or something. No, literally neuroanatomically true.

Target wall art.

Yeah, yeah, it feels like Target wall art. I don't know, it's just one of those things that you repeat.

They say a smell is worth a thousand words.

Okay, so that sounds like something that's extremely hard to reverse engineer. Do we have sensors? Is, you know, LLMs, it was so obvious that we had text that was already encoded into data, into ones and zeros. And so transforming that and encoding it, I mean, it was an incredible breakthrough, but it felt like the text was, the data was already in the computer. And I feel like that's not true for olfactory data, for smell data, but how are we, do we need to digitize this before we, do anything with it? How does digitization of smell work?

Yeah, great question. So I was very fortunate to have those guys as my colleagues. I actually spun Osmo out of Google Brain. Oh. And so I was there when all that stuff got invented, and I ran the digital olfaction team at Google Brain for about 6 years before we decided to make it a company through Luxon, through GV. And you have it exactly right. Like, the internet had been accumulating for a while, so we had all this text data, So we could basically slurp that down and start building models. We have chemical sensors. They're called mass spectrometers. There's other kinds of chemical sensors, MOX sensors. There's like a dozen. The history of sensors that can turn chemistry into data is about 100 years old, maybe more. I mean, a lot of it was pushed forward in the Manhattan Project, actually. Wow. But what we've been missing is a map, right? So for sound, low to high frequency is a map. Which lets us build MP3 and speakers and microphones and Spotify, et cetera. And for color, RGB is a 3-dimensional map of color, and that lets us build CMOS, CCD, you know, cameras, et cetera. We haven't had the map for smell, and that's not crazy because there's 3 channels of color information in our eye, but we know there's over 300 channels of information in our nose. Wow. So in a way, we actually did need to wait for artificial intelligence to mature in order to have the ability to extract a 300-dimensional map from data. And that's exactly what we did, starting with our first work at Google Brain. So you got to go get a crap ton of information, right? A bunch of molecules, what they smell like. We've since collected the largest AI dataset for scent in the world. That's what drives olfactory intelligence. We have 5 million sniffs digitized, over a quarter million physical samples created. We've digitized about 6 billion fragrance molecules. So all this is like inside of the company because there's literally nothing on the internet. The fragrance industry has done a phenomenal job keeping everything secret, so we built it all ourselves.

Remarkable.

Jordy, how are you going to make money on that?

It's a good question.

If, if, if you go— actually, let's talk how can we make money on this. So does TVPN have a scent?

Yes, and it's terrible. There's rubber smell in the studio.

There's— in the studio, in the studio there's like thousands of cables and cables, and the cables— we do a good job hiding them, but we have so much gear going everywhere.

It's a lot of rubber.

And so we had to get these, uh, racetracks— racetracks, they're called— to cover all the cables. And it turns out these things smell terrible. Yeah, they off-gas. So we wanted to give our viewers— yes, the, the full experience. I think we actually do not, but It would be like a can that sits on their desk, aerosol, and it would just spray a rubber smell into the room so they could experience what we experienced.

We could capture it, but I think we should fix it. So really concretely, we raised our Series B, we put an additional $70 million in the bank with Two Sigma leading Lux. Love it, got the gong. Congratulations. That was to underwrite building a fragrance factory a fragrance factory. Okay. So we have a robot that's the size of a school bus that makes a new fragrance every 100 seconds. And what we do is we design and manufacture fragrances for brands. Oh yeah, that makes sense. So we, we use olfactory intelligence to design it. So super fast, data-driven, basically, you know, perfect fit for the brand and for the consumer of that brand. And then we actually physically make it. And what leaves our factory is a steel drum of that fragrance oil. We bill them for it. Yeah. We also will do end-to-end. So like if you want to actually make a physical bottle. We'll actually put the fragrance in the bottle for you so the full product comes out. So if you guys want to launch a TVPN cologne or something like that, we could design it for you. I mean, like, if you tell me the prompt right now—

it should smell like burnt rubber. No, no, no, we're not doing burnt rubber.

Burnt rubber.

There's some— it smells like disagreement.

No, it needs to smell like, uh, like, like old $20 bills. Okay. From the 1950s. And mahogany, the official What a business. We need it to smell like mahogany mixed with old $20 bills, the smell of money. That's amazing. Okay, cool.

We've done the smell of money one, which we demoed actually on the New York Stock Exchange floor, which is pretty cool.

That's amazing. That's amazing.

But no, I'll send you, we'll make something, I'll send it to you.

Talk about sensor miniaturization. My phone has 3 cameras and no smelling sensors. Sensor. Can we swap one of these out? Like when you say mass spec, I imagine like a device the size of a living room. I imagine that they are getting smaller. Dishwasher. Dishwasher. Size of a dishwasher. Is there a path to actually shrinking that down to something that's more portable?

So yeah, in the same, there's like many kinds of cameras, right? So the one on the Hubble Telescope, not getting smaller. So if you need resolution, it's gonna be big. But you can make trade-offs and like, when the thing that's reading the data, instead of it being a person, it's an algorithm, you can actually make really intelligent trade-offs, which is what we've done. So we actually have a sensor right now, it's the size of two shoeboxes. Okay. And I kind of use that metric aptly because we've actually used it to smell fake shoes. Oh, so if you're buying a pair of like $500 Air Jordans, the real smell different from the fakes. We can actually pick that up. That's crazy. Yeah, yeah, the, the counterfeiters use cheaper glues, turns out. And the other thing that's interesting is we can actually tell the factory of origin of the shoe 93% of the time. So the smell is a fingerprint. So we're already miniaturizing these devices. Look, the path to get from two shoeboxes to one shoebox is pretty clear. Yeah, we're working on that. To go to something that's like the size of the AirPods case, there's going to be some like hardcore engineering required to have it be a component that fits in your phone. There's some breakthroughs like I can't quite see through the fog yet, but there's nothing like, look, our noses do it. So there's nothing that Mother Nature's saying, I think, impossible, but we just got a lot of work to do.

Yeah, that makes a lot of sense. What about taste? How closely is taste linked? Talk me through the sommelier example.

Yeah, so flavor is everything that happens in your mouth, you know, that's a sensory experience of food. Taste is not, is like 10% of that. It's like what happens on your tongue. Like, you ever eat a jelly bean and like, plug your nose and you just actually can detect very little of what's going on there. It's because 90% of what you experience is actually called retronasal olfaction, where when you're biting, we're biting on something, there's a chimney effect in the kind of almost the steam of what you're eating goes back through your nose and you smell it. Excuse me. And then there's also the texture and everything in your mouth. So we've done tests and our OI models, and this is from a while ago, we haven't revisited it. We're really focused on fragrance right now. But our AI models actually work on flavor surprisingly well. And so the whole world of flavor is there for us when we're ready, but we're really focused on this particular business.

Yeah. I've seen a couple of these sort of, I don't want to call them niche, but like vertical AI projects that are not fully generalizable. There's a DNA model also from Google or DeepMind, and it feels like they're starting to get on scaling curves, on scaling laws. Are you at a point where you feel like, oh, if I 10x the computer, 100x the compute that goes into some of the work?

I believe Alex is ready for a 1-gigawatt data center.

He can be trusted with that. I would trust you. Appreciate it. But how universal do you think scaling laws are? Is there a scaling law here? Is it data-based? Is it compute-based? Both?

How are you thinking about this? The bitter lesson's real. The bitter lesson's super real. I always think about technology as S-curves, right? And like, what's driving you up that S-curve and then how can you hop on the next one? Our current S-curve is data, which is why we're maniacally focused on like generating a ton of data. Like we have a giant fragrance robot that spits out a ton of fragrances. We have mass specs running 24/7. We have sensory panels both domestically, we have a building of people people that just smell all day abroad. I mean, we ship them crates of stuff to smell. And that's how we get the 5 million sniffs, right? Sure. So data, data, data. The size of the models is not the limiting factor right now. Yeah. And it will be at some point. And then switch to the other S-curve.

Yeah. Yeah. Because you don't just have like the open internet to scrape because there's not an existing dataset. Makes sense. Totally.

Double-edged sword, right? So we've had to make it all, right? Which is really hard. But also, Nobody else has it because we had to make it all and had to learn a ton of stuff in order to do that at scale efficiently and all that stuff. Yeah.

Where is the business today? I mean, you've raised money. It seems like there's monetization opportunities for sure. Are you fully in commercialization? Are you still in research? Is it half and half? Like, how do you think about raising more money over time and just growing the business?

Yeah, so we're always going like, We started with like this curiosity-driven drive to figure out how to digitize smell, which is like a pretty wacky thing to do. So we're always going to be trying to push the edge here. But look, we have a factory. We manufacture fragrance for brands. We did this commercial kind of R&D to commercial transition last summer. We're kind of almost at the end of that. And we built a manufacturing organization. We built a sales organization. We have some really amazing partnerships with some big brands. Brands, and we're making fragrances for brands. You can go into Target, buy a product that has our fragrance in it today. Um, and so we're scaling this part of our business. We're still placing bets on the future though, right? So I think we've got really the tiger by the tail in this. It's a whole other conversation. Sometimes you come to the factory, uh, in New Jersey and see how it operates. But like, the fragrance industry is wild. We've got a lot of work to do there, a lot of opportunities.

So we're focused on that. Amazing. Well, congratulations and Thank you for the work that you do. We think it's so important.

One of the most interesting companies we've ever learned about on the show.

Yeah, true science fiction. Awesome. I love it.

We're trying to make science fiction into science fact, but like open invitation to come see how it all gets made. It's pretty crazy in person. So come to the Willy Wonka Chocolate Factory where all this stuff happens. I would love to.

We'd love to. Thanks so much, John.

So great to meet you. Come back on soon.

Yeah, we'll talk to you soon. Have a good rest of your day. You're sorry. We're running a little bit behind, but up next we have Spyros from Resolve AI raising a massive round to build AI that runs production systems. Let's bring in Spyros. How are you doing? Hello guys. Good to be here. Welcome to the show. Sorry, we're running a little bit late. Kick us off with an introduction on yourself and the company.

I'm one of the founders and the CEO of Resolve AI. We're building agents that can help you debug and run production. Think of it as the counterpart to coding agents that produce all this code. And our agents are there to support you. Okay.

Are you always, is your customer always deeply in the throes of vibe coding, has rolled out agentic coding across many organizations? Who is the target customer? Do they have to already be deep in the agentic coding wave? To really get the value here?

They don't have to, but the two are correlated. Like anybody who runs a large software system has this problem. The only solution we've had so far is humans manually solving it, right? Using the tools, being on call. Of course, now AI allows us to automate all of this. But I would say this is true, it was true before. Now with all the AI-generated code, it becomes a necessity, right? So we see strong correlation between the two often.

Yeah. And what are customers coming to you asking? Is it, I want the code that's written, we're writing way more lines of code, we want it to be more readable, or we want it to be more secure, or we want it to be more performant, or all of the above?

The way to think about it is like, for anybody who's delivering their business through software, look at some of our customers, Coinbase, Salesforce, MongoDB, right? To them, reliability is of paramount importance. If anything goes wrong and affects customers, it's a big problem. So Resolve becomes essentially the first level of defense that captures any problem that happens in production that can affect end users, gives you a resolution and a fix, let's say, so you can accelerate that loop, right? And it doesn't take too much human effort, but more importantly, it doesn't cause impact to customers.

What is, like, I mean, the company is now over $1.5 billion in valuation. What has been the key to growth? Is it just product-led growth? Do you have a big sales team? How are you actually scaling the business as you scale evaluation? Yeah.

So this is a very big problem, right? Anybody who has, as I said, delivered their business to software is facing this issue. And whether you're a CTO who pays for, let's say, developers to focus on reliability, or whether you're an individual that has to solve this problem, you'd rather have AI do it for you. So we've seen like huge amount of demand from day one since we launched the company a bit more than a year ago. And we've seen it coming from both big and small companies. We're primarily focused on larger enterprises because we think there is a lot more complexity, given the complexity of the software. And most of the growth, I guess most of the, let's say, the demand comes inbound to us because it's a well-understood problem. And of course, we have both a product-led approach, let's say, but also a self-led approach as we work with large customers.

Yeah. In some ways, the naive approach would be, okay, just point a typical AI agent at the codebase and just tell me where the fault lines are. But I imagine there's some special sauce in the engineering to understand knock-on effects that can happen across a large codebase. Are you actively actively working around context windows or creating like a special harness to understand these problems that can come up before they do? Yes.

So think of it like we have a production ID basically, right? The same thing you have for your code, we have it for all your production systems. Production involves code, involves, let's say, telemetry logs, metrics, like tools like Datadog, Splunk, it involves AWS, right? So you have to deal with all of these, not code. And then we also are training our own models now to improve, let's say, the state of the art, let's say, right? How far you can go? Far enough, let's say, with a good harness and a lot of work, let's say, on the genetic front. But now, and we just announced together, we're funding that we're building a lab to focus on actually training our own models for this domain.

Sure, sure. So what goes into getting relevant data or actually nailing a specific model for this? Because I imagine that you have some great clients, they probably don't want you training on their data. At the same time, if you just grab some open source code, it might not be as complex as like the Coinbase monorepo or whatever they have going on over there. So how do you actually, create enough training data to justify a special model?

What is important here to understand is the training doesn't happen on code per se, right? What happens on is actually the action a human takes to perform a task for the most part. Yeah, yeah. And we're talking about very long kind of, let's say, planning tasks here, right? It might take many, many iterations looking at code, looking at Datadog, looking at infrastructure. And this generally technically speaking, this is not in the training set of models. And software, let's say, in general is both deep and wide domain. So I think if you actually focus on building a model for the types of problems we're trying to automate and how you run and debug production, I think you can have a lot of gains, both in performance, cost, but even quality of outcomes. And that's our goal. And I would say the big labs make it sound, make it look like it's impossible for anyone else to build a model. Model, but I don't think that's the case. And you know, that's what we're seeing ourselves with our investments.

Yeah. How do you put together such a low dilution round? Yeah.

Yeah, tell us about the round. I want to hit the gong. The $40M on $1.5B.

So it is an extension.

We just did essentially the A. We just did the A at $1 billion like 2 months ago, right? And I would say, resolve. There we go.

Sorry, continue.

Resolve is essentially in many ways created this market, right? Like AI for production. Sure. And I think it's well understood by investors. It's also proven given the customers we have. Yeah. So, and we're also a very ambitious company, right? Like we are obviously trying to build the agents and the models for this domain and we have a lot of traction. So I mean, as simple as that, right? Like there's nothing you can do to create a lot of dilution now other than be very Be successful in my opinion these days.

That's a great answer. That's a great answer. Step 1, be successful.

I love it. Step 1, focus on building a business, right? This is my first startup as a founder. I made this mistake many times before, right? Of thinking that raising money is success. It's not. It follows real success on a product. Yep.

No, no, that's 100% right. I love it. Well, thank you so much.

Congratulations on the new round. Yeah, great having you.

Thank you.

And great having you on the show. Excited to watch you guys grow.

Thank you. We'll talk to you soon. Cheers. Have a good day. Goodbye. Up next, we have Carolina Aguilar from InBrain Neuroelectronics building the first in-human study of graphene brain interfaces. What's going on? Welcome to the show.

How are you? Thank you. Very good. Thank you for having me.

Please, since it's the first time on the show, introduce yourself and the company a little bit.

Yes, my name is Carolina Aguilar. A lot of people call me Carola. I am the CEO and the co-founder of InBrain New Electronics, and we are a graphene-based brain-computer interface therapeutics company that actually is developing the most intelligent interface between the neural system and AI to restore health for billions.

Okay, so walk me through brain-computer interfaces and the decision tree that got you to graphene specifically. I'm familiar with like the first decision is probably invasive versus non-invasive. We've talked to a number of founders that have taken either approach. How did you, how did you confront that first question?

Yes, well, I call them implantable and non-implantable. Implantable systems. Yeah. And in our case, we are an implantable company. Yeah. We believe that the real signal processing that is going within the neural system is actually deeper in the brain. And to listen carefully to what it says, what the neural system says, and being able to decode it but also modulate it, we need to be close to those neurons. And interact with those neurons firsthand.

Okay. So when I hear modulate, it sounds like not only processing information that's coming out of the brain, but also potentially writing information back into the brain. Is that the long-term vision?

Yes. And this is the magic of graphene. It's actually about reading and writing very effectively at micrometric precision within the brain. I think that's why we took let's say higher risk to get an advanced material into this funnel because we see that the benefit is incredibly impactful.

Okay. What are the most near-term commercial applications?

Yeah, so the Morgan Stanley report stated the market in $400 billion and we thought that we needed to bring a platform with 3 product verticals to actually penetrate such a big market. So we are creating 3 products. One is, let's say, not implantable actually. So it's a semi-chronic platform, kind of like the modern Utah Array. It's like 100 contacts of graphene that can read and write. We went into tumor and epilepsy resection at the beginning, and that one is pretty pretty close to commercialization. We're almost there. The second product is the implantable platform for the brain. So this is an implant on the brain for Parkinson's disease. So we didn't do, sorry, assistive BCI because we saw a $1.8 billion market that is suboptimal that we could actually displace very easily with this technology. So we decided to go therapeutics into Parkinson's. And the third one is the same platform, but instead of a, let's say, brain sensor, we connect a vagus nerve sensor that is actually able to decode all the fibers that go into the different organs. So we have a therapeutic target for each of the organs just by targeting that nerve in the neck. Mm-hmm.

What about the actual implantation process? We followed from Neuralink, they had to build a whole robot just to drill into the skull. It's incredibly high precision. Are surgeons capable of implanting this at this stage, or will there need to be other robotic devices that are developed to actually deploy this technology safely?

It's an excellent question. I'm coming from Medtronic. I spent 10 years in neuromodulation and another 3 in diabetes. And I think in the future when micro robotics are ready, we will have a very close relationship between our interfaces and micro robots that probably can deliver this implantation in 30 minutes. But today, when there is not micro robots and we are not Elon Musk, we decided to actually have our platform ready for the current surgical workflows that today exist. So we are not changing much from the neuromodulation workflows. And it's an easy procedure, 2 hours, 1 or 2 hours, you know, it's enough. In the case of the neck, it's 45 minutes. Wow. Wow.

That's very impressive. Well, congratulations on all the progress and thank you for the work that you do. And thank you for stopping by the show.

Yeah, great to meet you.

Have a great rest of your day. Cheers. Thanks, we'll talk to you soon. Up next, we have Jake from Blue Energy. He's the co-founder and CEO with a massive raise. It's a gong breaker. Jake, how you doing? Welcome to the show.

Hey guys, I'm doing well.

I got a feeling you have the biggest number for us. I think you have the biggest number.

Kick us off. How much have you raised? And then we'll get into what you're going to do with it. But tell us about the financial situation for the company.

We, we have announced a $380 million raise. Yeah.

And what will you be doing with all that money?

Yeah. So our focus, we're really unique amongst the field of nuclear players right now. Our focus is on building the world's first project-financable nuclear power plant. So we're using this funding to actually put deposits down on long-lead equipment, as well as finishing out the engineering and development licensing on some of our first sites.

Does that mean like less R&D risk or more in like the GE or Westinghouse territory, more like, you know, going with products that have been de-risked, but it's very expensive and maybe the underwriting is different this time around, or are you working on an entirely reactor design somewhere in the supply chain?

No, you had it exactly right. We are not a reactor designer, we're a developer, but our technology is the proprietary approach by which we go about building the plant. Sure. So what bugged me and why I started the company was I had a lot of friends in the nuclear space designing really exciting new reactors, and then you have a lot of incumbents in the space who are working on, you know, kind of the same old technology that we've been operating safely for 70 years. Yeah, but nobody was focused on the core issue of how do we build nuclear on time and on budget. So I grew up in a construction family. I used to be a draftsman for my father's architecture firm. So I just grew up around a lot of construction, did my nuclear engineering and physics degrees in the space and just felt like there isn't— there hasn't been anyone really focused on the root cause issue. So what we're doing is we're borrowing best practices from LNG and offshore oil and gas and offshore wind to prefabricate everything. At existing oil and gas fab yards and shipyards, and then we barge it all as a prefabricated system on the order of 1,000 or 2,000 tons to the operating site. Okay. And then really, you know, basically are just installing it like giant Lego pieces. But what that allows us to do is bring a lot more debt financing to bear. Sure. So we're not taking a lot of reactor technology risk to start in the beginning. We're using mature light water reactor technology to start, But we'll happily work with the Gen 4 reactors as they mature. Cool.

Take me through. I feel like you've been wanting a copy paste.

Yeah, I've been wanting this for a long time.

John's point has been copy paste. Copy paste. Hey, we know how to do this. It works. So this is— We don't need to reinvent. I mean, it's great if we want to reinvent the wheel.

Yeah, we need the next gen tech.

New reactors. But also more of this current gen.

Let's just build some. Yeah. So yeah, my question is, about Vogtle, lessons from the Vogtle project. What do you think they did well that you want to copy, that you want to learn from? What do you— what, if anything, do you want to do differently?

Yeah. So to put some stats on Vogtle, it, like so many other nuclear projects in the West, ended up being about 2 to 3 times over budget and behind schedule. But when you double-click on where that cost was, you realize it wasn't actually in the reactor technology or the equipment. It was on the— over 40% of it was just the construction overhead. So it was the cost of training and relocating 10,000 skilled workers to the site at Vogtle. You know, think about the cost of training, relocating their families, retaining them once they're trained because data center projects are trying to steal that talent. You have to set up the nuclear quality assurance program in the field. So there's all this overhead. It's basically like building a small town. Yeah. And then the hope is that you'd be able to move that traveling circus around from site to site. And then a third of the project cost was just capitalized interest on debt because it took over 10 years to build before it started generating revenue. So this— these are the two big problems we're trying to address is we are moving most of that work offsite. We're keeping the workforce centralized at the Fab Yard and the Shipyard where they already are. So we can start to put nuclear into a learning curve and drive the cost down over time, akin to what we've seen in wind, solar, batteries, and gas turbines. You know, what they did well was it was a mature light water reactor technology. It's a passively safe reactor technology. They really pushed the world forward a little bit in the licensing space and steel composite structures, which we're looking into as well. They actually had— this is not well known— they originally wanted to barge it in up the Savannah River, but they had to then dredge the Savannah. They would have had to dredge the Savannah River for miles and that would have become part of their environmental impact statement. So they ended up— it became such a regulatory and permitting nightmare that they gave up and they said, all right, let's just truck it in. And then they had to truck it in and build a module assembly building on site. So they ended up doing all the welding on site.

Yeah, this is a remodel from Nightmare Remodel. Very interesting. I— yeah, I'm, I'm, I'm I'm fascinated.

Where's the company based? Where are you guys based?

We're in Chevy Chase, Maryland, pretty close to NRC headquarters. And then we've also got a big presence in Edinburgh, Scotland, where there's a lot of offshore engineering talent, particularly from like kind of the history of shipbuilding and offshore oil and gas. Yeah. And then we've also got an office in Houston, also kind of offshore oil and gas capital of the world.

Okay. I read a blog post about one of the potential problems or stumbling blocks that nuclear projects run into. And I want to reality check it with you. The thesis was basically that we have a reactor design, we have a, you know, there's infrastructure around that, cement needs to get laid, pipes need to go here and there, but oftentimes the regulation will change while the project is underway. And so in order to day ahead of the changing regulation, you might have to, you know, jackhammer a bunch of concrete and move a pipe to a different route because the regulation has changed. Is that real? And is there anything that we have done or can do to get to a regime where the regulation is more locked and deterministic so that— I imagine you're not going to build this overnight, but if it takes you a couple of years, you know that the, the contracts that you put in place, the plans that you put in place today will hold and you won't face a massive delay.

Yeah, so that was another one of the big learnings from Vogtle. Vogtle was the first, and still today I think the only project, that did a combined operating and construction license, which means once they locked the blueprint, they really were not allowed to make changes to it during construction. So every time they encountered something and said, oh, we need, you know, the craft labor wound the rebar clockwise instead of counterclockwise. You know, they had to jackhammer it up, or they had to go and re-approve it all. And there's just a— there's a long list of things like that, uh, that they encountered. So one of the things we're doing is we're following a slightly different licensing process, the original licensing process of Part 50, whereby we're going to incrementalize it so that we don't have to encounter that rework, that kind of regulatory trigger a rework situation, but also because we're following this prefab approach and we're moving 80% of the CapEx into a fixed-price contracting environment with these fab yards and shipyards. It forces us to go to something like 60% detailed design upfront and locking those designs because that is what is gonna be coming in prefabbed from the fab yard. Yeah. So it's, it's sort of baked into the strategy, but really this is about taking a lot of those lessons learned and making sure we don't make the mistakes of the past But I'll also say we've never had a more supportive regulatory environment than we have right now. Like, this is a critical juncture in the history of nuclear power that we can take advantage of with where the NRC is presently at.

What is the power output for the first reactor that you're targeting to bring online?

So we are focused right now for the first project using light water, small modular reactors, which the power range the units we're looking at range between 50 and roughly 300 megawatts per unit. Yeah. Yeah. Each site we're targeting doing is going to have multiple units. So it's going to be a gigawatt to a gigawatt and a half per site because multi-unit operations—

that makes a lot of sense— is important.

Yeah. And it helps drive down costs.

And then are you already sharing a timeline? Do you have an optimistic a base case, a bear case? I'm sure you get asked this all the time. It's the worst question, but we talk to a lot of nuclear founders and we hear a lot of 2030s and there's a whole bunch of projects with hyperscalers and big tech companies that are looking at 2032, 2035. It's exciting, better, you know, 2032 than never. But do you have anything to share on like the timeline of rolling out new nuclear capacity? Capacity in America?

Yeah, we'll be announcing things, uh, very soon, uh, but what I can share on the dates— and this is actually another unique thing we're doing, part of our strategy— is we're pursuing this thing we call gas-to-nuclear conversion. So we're actually going to be building half the nuclear plant right away. So the whole nuclear steam turbine system set up for nuclear steam conditions and quality, and we're going to fire it early with two combustion turbines as a two-on-one combined cycle. So it's kind of a Frankenstein combined cycle. We've actually gotten the NRC to buy off on this methodology, uh, through a topical report recently. So that allows us to actually project finance half the CAPEX for a first SMR, and then we will build the reactor and splice in the steam and switch it over from gas steam to nuclear steam. So that actually accelerates our commercial operation date with confidence. Uh, so we're looking at generating first power in 2030, 2031, mostly driven by gas turbine delivery dates today. Yeah. And then our first nuclear commercial operation, we're looking at 2032.

Okay. So is that, is that switch out possible at any legacy natural gas infrastructure site in America currently?

Uh, because that seems like an environmentalist dream. I'm not ready to say yes or no. Yes. I think this opens up a whole new world. Of fossil to nuclear conversions, which we think is an important precedent to set.

Yeah, it seems huge if possible. But I imagine that, you know, it's not exactly USB-C on both sides, but hopefully one day we can build the adapter.

That's right. Yeah. What we're really focused on is, you know, there's a lot of announcements out there, a lot of sometimes, you know, noise of what— there's a lot of exciting things happening in the nuclear sector. We think we've got the first project financable nuclear project and the first one that's going to power— it'll be a new build that powers a new AI data center. Yeah. So we're excited about that and we think our timeline is, is credible, is aggressive but credible and defendable. And we've got the right set of partners around it to make it happen.

That's amazing. Don't use the word data center. We're using the word supercomputer. Supercomputer. They're supercomputers. They're super— no one likes data center. Everyone likes a supercomputer. Yeah. Anyway, thank you so much for taking the time to come chat with us.

Great to meet you, Jake. I'm sure you'll be back on soon. Good luck. I really— we really appreciate your approach. It feels like you're making plays and I like how pragmatic and but innovative the approach is at the same time. It's great stuff. Thank you.

Appreciate the time. Talk to you soon. Cheers.

Goodbye. And we will close out on this. I need your reaction John Coogan's first electric car is priced at the low price of $650,000. An absolute steal.

They're giving them away at that price. Uh, it's electric, right? So you don't have to deal with gasoline. You don't have to do it.

You save a lot on gas.

You don't, you don't have to deal with all the noise that comes out of a V12.

Yeah, no noise. Save on gas. Yeah.

Um, oh well, yeah, I mean, if you're saving on gas and you're driving, I mean If oil keeps spiking and gasoline goes to $1,000 a gallon and you're filling up every week, you could easily be spending millions of dollars a year on gasoline in a normal car. So there's potential cost savings here. So it's sort of a more you buy, the more you save situation, I think, with Ferrari Luce.

There's really going to be a, like, what kind of Ferrari client are you? Moment. Uh, no, I think it'll be a status symbol.

If you see someone with this, you know they can eat $300 grand of depreciation. And you know that they are, that they are high on the list for the F90. Like, they are working their way up, buying in now. And when the F90 comes out in a decade, they're getting a call. Yeah, they're getting a call.

Yeah, very interested to see how this does in the market. And the good thing is, if you are excited about the Luce, but you're not excited about paying $650,000, you will have an opportunity to buy them for far less than that very, very quickly.

Probably.

Probably. But thank you for hanging out with us today. It's been an honor and a privilege to be here with you. We hope you have a wonderful afternoon.

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