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>> [music] >> You're watching the TVPN. Oh, no.
4:10
It's Tuesday, April 21st, 2026.
4:10
We are live from the TVPN ultra live.
4:13
The temple of technology, the fortress of finance, the capital of capital.
4:17
Uh massive, massive news.
4:20
Tim Cook to step down at Apple. This broke uh yesterday.
4:26
Uh Gaurminator had the scoop, of course.
4:28
He's coming on the show later today, but it's on the cover of the Wall Street Journal today. Uh heavily predicted. Uh often debated.
4:34
Uh it's a time to reflect on Tim Cook's legacy and what's up next for John Turnus, the long-time insider.
4:42
>> incredibly well executed, incredibly smooth.
4:44
They sort of telegraphed it. Wasn't a surprise.
4:46
It was already fully priced in.
4:49
I personally was hoping that the market would give Tim Cook a 21% salute.
4:56
Where when the news went out, it just immediately nuked 21%. Massive red candle.
5:01
Let everyone know this is We don't like this. It's a sign of respect.
5:07
Of course rebound immediately.
5:07
But I think that's something that the market collectively should try to do for more symbolism in the candle. For sure.
5:14
Uh chartology is really the key thing. It's 21% salute.
5:20
But of course uh what is Apple at right now?
5:22
Is Is Are they moving at all?
5:22
Down 3% today, 2 and 1/2%, but up 3% over the last 5 days.
5:28
Still nearly a $4 trillion company. They're doing fine. And they're cooking.
5:33
Uh 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 Turnus because uh we're going to be learning about John Turnus and he's probably going to do a lot more content, a lot more media, a lot more interviews.
5:49
Uh and we will be hearing from him uh at keynote events for probably over a decade, maybe two decades. Uh we will see.
5:55
So, in January uh Bloomberg reporter Mark Gurman, who's coming on the show later today, uh predicted John Turnus would succeed Tim Cook as Apple's next CEO.
6:05
>> And let's let's just pull up this clip. We have a clip.
6:07
>> some credit to the Gurman. >> Gurminator?
6:08
Uh he didn't predict it on our show first.
6:11
think he scooped it and posted >> I know, but this this was back in January. I dropped the clip. Let's roll.
6:17
The crux of the argument was twofold.
6:19
Turnus' relative youth among a pool of potential successors. What?
6:21
Everyone else in the Apple executive team late 50s through their mid-60s.
6:26
Turning 66 this year in the case of Tim Cook.
6:31
You're Apple's board, you like continuity. Uh you like an insider.
6:34
You like people who know what they're doing and been there for a while.
6:37
They know where the bodies are buried. Okay?
6:41
These guys are all have hundreds of millions of dollars, if not more. Uh Pause.
6:47
>> [laughter] >> I I love Mark Gurman so much. >> He's the best. He's truly the best.
6:50
And he's coming on He's coming on in in 45 minutes. But continue. Yeah.
6:55
At 50, he's the only one who is if let's say Tim Cook hangs out another three to five years, you're not going to point another CEO who's 65, 70 years old. He's the only guy.
7:10
Apple, they get vast majority of the revenue from hardware. He's the hardware guy.
7:15
Have they screwed up any hardware since he's been in charge? No. He's a steady hand.
7:19
He knows what he's doing.
7:19
He's really the only choice.
7:22
You know, there was this New York Times report a few weeks ago basically saying that it could be Greg Joswiak, it could be Cue, could be Deirdre O'Brien, could be Craig Federighi.
7:33
It's for sure not going to be Craig.
7:36
It's not going to be Deirdre.
7:36
It's not going to be Eddie, it's not going to be Jos.
7:38
The only category that makes sense is an operations person because you look at the current CE CEO, Tim, obviously comes out of the ops world.
7:46
You look at the guy who would have been CEO if Tim Cook stayed so long.
7:50
I'm not saying he shouldn't have stayed so long.
7:52
He's done obviously a fantastic job for shareholders employees and and what have you.
7:57
Would have been Jeff Williams. He was the COO.
8:00
Uh so Sabih Khan, he was named COO you know, a few months ago, but he's really been in that job half a decade, I would say.
8:06
Uh so anyways, it'll be Turnus or or Sabih or someone completely out of left field.
8:11
I don't think this is imminent.
8:12
Um so we'll see what ultimately happens, but all signs are turning towards Turnus.
8:19
Everyone has an opinion that Turnus is going to be the next CEO, fine.
8:21
I've been shouting this from rooftops the last two years, but no one has given evidence.
8:28
Like what is this based on? Right?
8:28
Has there ever been a baton handoff?
8:31
Is he getting more responsibility? Like a big baton?
8:36
>> [laughter] >> You know what you have?
8:38
You have white smoke coming out of Well, actually >> [laughter] >> very environment So.
8:46
>> [laughter] >> Is that the end of the clip?
8:48
You know, maybe out of the five, they have a comically large baton.
8:54
Like like we have our scoop. Yeah. Oh, Mark Gurman. The scoopinator. He's uh scoop doggy dog. Scoop athlete. The scoop. The scoop. The scoop is on fire.
9:08
We're very excited to have uh Mark Gurman joining in just uh 40 minutes.
9:12
What what what a great uh what a great run he's been on reporting this story.
9:16
Uh it's absolutely fascinating.
9:16
So, uh yesterday evening, Mark was the one with the scoop.
9:20
Tim Cook will assume the role of Apple's executive chairman.
9:22
And John Turnus will take the reins of the company.
9:26
Followed up with a scoop with internal memos from both and Turnus announcing the transition.
9:30
Um I I don't I mean I I understand uh I mean I I I I don't know I don't know I don't maybe I don't understand what it's like to be 65, but I was always optimistic that Warren Buffett 65 to 95 would be the new trend.
9:44
And that people would just say, you know what?
9:46
I'm I'm Tim Cook's healthy.
9:49
He He's He's going He's not a tourist guy. >> was a warm up.
9:51
Yeah, everything >> to go on the actual run.
9:53
That's I I would I mean I totally understand He's leaving out. >> 10x in me.
9:58
I've got another 10x in me." Yeah.
9:59
I'm going to I'm I'm I'm taking us to 40 trillion. Yeah, I don't know.
10:02
I 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, you know, being in DC, getting wrangled into things.
10:20
Like, Buffett can be more like hands-off and just sort of read the news, review the financials, and and delegate. >> liquidity. >> Yeah. >> If needed.
10:30
>> Yeah, it's more it's it's a it's a more hands-off role.
10:32
I don't know how I mean, I imagine that the role of CEO of Apple is incredibly demanding, but I I like the idea of of just just locking in and being like, "Oh, yeah, I'm 65, everyone expects me to Everyone expects me to step down, but I got another 30 years in me."
10:47
But, you know, he he chose a different path and he is retiring or stepping into the executive chairman role.
10:54
Ben Thompson published Tim Cook's impeccable timing, which eulogizes Cook's impressive accomplishments as the head of Apple.
11:05
Ben Thompson kicks it off with an interesting thing.
11:06
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.
11:19
One morbid exception is when the CEO does die on the job or quits because they're dying.
11:25
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."
11:37
And he he says that the numbers are extraordinary.
11:39
Cook became CEO of Apple on August 24th, 2011.
11:42
And in the intervening 15 years, revenue has increased 303%, profits surged 354%, and the value of Apple has gone from a mere 297 billion to over 4 trillion, a staggering 1,251% increase.
12:04
And there was some chatter back and forth on the timeline over whether Tim Cook had simply put Apple on cruise control and locked out as many big names in tech also saw 10 to 40x increases in their market caps.
12:18
This is from Brandon Garel's newsletter at tvpn. com.
12:20
You can sign up today for free.
12:24
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.
12:32
And Brandon cites Xerox, Motorola, Texas Instruments, IBM, and HP, which all fell by the wayside >> but they didn't make it.
12:43
>> while Apple built the biggest consumer hardware company on the planet and thrived in the public market.
12:47
And I was I was doing some digging on this as well.
12:51
I pulled up You know, it's easy to look at like, "Well, zoom back to the the the the Mag 7."
12:59
All All of the Mag 7 have done fantastically well over the past 15 years during Tim Cook's tenure.
13:06
Did he do anything special?
13:06
Is he Is he in a different category in some way?
13:11
And and maybe maybe not when you look at when you 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?
13:28
It does look like outperformance because Apple was at 377 billion.
13:31
That was, you know, big, huge, the biggest company at the time. Then Microsoft at 218.
13:39
>> at what what their peers were at the time where he took the helm.
13:42
And to be clear, Apple, Microsoft, Google, and Amazon were clearly there.
13:46
Fang was the term at the time.
13:49
Facebook, Amazon, Apple, Netflix, Google.
13:54
For some reason, Microsoft didn't make the cut in that in that acronym.
13:58
That has since changed, of course.
14:01
But, there were a lot of companies that didn't go on as significant of runs.
14:03
You have IBM, Oracle, Intel, Cisco, Qualcomm, and HP, all that were in the the top 10.
14:10
They were sort of the Mag 10 of 2011.
14:12
And now, they are not in the Mag 7, although many of them have done very well.
14:17
So, it it seems it's This was our take from a long time.
14:23
You know, we like to harp on the the failure of Apple intelligence and how Siri is is ineffective sometimes, and the and the FaceTime interface is odd, and the new iPhotos app is hard to use.
14:35
But, where it matters, >> give them credit on Genmoji.
14:43
No, where it matters is Did they navigate tariffs?
14:45
Did they navigate supply chain?
14:47
Did they navigate the transition to Apple Silicon, delivering a great product consistently that doesn't break?
14:56
We have ordered so many Apple devices throughout building tvpn, 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.
15:05
And I got to take it back.
15:07
And that's never happened.
15:09
The The quality control is flawless.
15:09
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.
15:23
And Tim Cook has just done a great job of like keeping the wheels on the train going down the track.
15:30
And I think that should be celebrated even though the sexy new AI features >> to be under under appreciated because he wasn't he wasn't the >> [clears throat] >> visionary that that that Steve was.
15:42
But, he also never I don't think he ever wanted to be seen in that way.
15:49
But, the consistent operational excellence over almost two decades is almost unprecedented at this scale.
16:00
It is Yeah, just just the way you put it, right?
16:06
I the the same the same experience that I had getting a new Apple computer as like a teenager, I have today.
16:15
It is actually almost remarkable how similar the experience is.
16:19
You open this wonderful box, you get a great device that works for a long time.
16:24
And and you still get that today.
16:24
So, the the consistency And yeah, I think I think he will just get more when people kind of process the run over time, he will just get more and more respect.
16:37
And a lot of the uh computer manufacturers have had to go into bloatware and pre-installed software, and Apple's been very good at holding the line there.
16:47
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.
16:56
But, um they've done it in a way that hasn't been that annoying.
17:01
I feel like I don't see that many people complaining about ads in the App Store.
17:05
People see the ads and they're like, "Wow, this company is bidding on the keyword for their direct competitor.
17:11
That is, you know, extremely competitive behavior."
17:15
The same thing happens on Google.
17:17
Didn't expect to see it in the Apple ecosystem.
17:18
Of course, it it's going to happen.
17:20
The Regav in the chat says, "Not a single product recall under Tim Cook. Is that possible?" Wow.
17:25
I think that is incorrect. What did they recall?
17:31
Says they recalled a 15-in MacBook Pro in 2019. Did bendgate?
17:36
Did Apple The AC wall plug adapter in 2016-2019.
17:39
Battery gate service program.
17:43
>> They've had some They've had some back and forth.
17:44
But, but but but, you know, a very, very successful run.
17:47
The other two things that the chat was mentioning was the Apple car and the failure of that program.
17:55
Maybe a little bit they bit off too much more than they could chew.
17:58
I think, you know, looking at what's happened in China with every phone manufacturer launching a car that's extremely impressive.
18:06
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.
18:20
But, it was not to be, unfortunately.
18:22
And then there's the Apple Vision Pro, which I think a lot of people you know, look at the churn rates, look at the the uh the the the retention rates, and they just see it as an underwhelming product.
18:34
I still like it, but I'm in the minority, and I I acknowledge that fully.
18:39
I do think that they made the right decision to turn it into a a home cinema, a home theater.
18:43
Like, they they understood that that there was not enough of a video game library, a VR game library to plug into in any meaningful way.
18:53
And so, they made the decision.
18:55
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 and some of the actual theater build-outs.
19:04
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?
19:18
Still, you know, obviously didn't hit the mark fully because the product has not taken off by any stretch of the imagination.
19:25
But, overall, it was a fun project, and I'm I'm I'm I'm hoping that they continue that.
19:31
It might wind up pivoting into just camera glasses, and they'll go up against the Meta Ray-Bans, which might be the the the more prudent you know, business strategy.
19:39
But, I still I still like I still like, you know, these incredible investments in VR.
19:44
But, he's laughing at you.
19:44
Josh over at Semaphore says, "Nobody tell the new Apple CEO that he has a streaming service.
19:52
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 that getting any extra attention right now. >> [laughter] >> Yeah. 5,000 likes. Yeah. It is interesting.
20:04
Uh you know, Turnus is about as far as you can be from the Apple TV services organization, but uh I don't know.
20:11
I talked to uh I talked to a filmmaker in Hollywood, a very successful filmmaker, like years and years ago before the uh before Apple TV was really ramping up and he was saying that uh Apple's brand is so prestigious that uh it's sort of antithetical to the Hollywood mindset, which is much more VC uh risk on.
20:30
You're going to have flops.
20:33
Like every every movie studio understands that uh it is impossible to predict the perfect success and have the level of polish that comes from these slight iterations >> I think you could Apple's. Yeah, yeah, yeah. Built different. For sure.
20:46
Um but uh and so it's a different culture because if you have uh you know, Apple's Apple hasn't I mean they've had like flops, but even the flops like they still feel like very on brand.
20:58
They don't have like a silly movie that just just like bad.
21:00
And uh running that risk was always a was always a problem, but I feel like Apple navigated really really well, especially with the F1 uh project and has done has done really well with the content side and they have held a brand standard that feels like almost at the level of HBO pretty quickly.
21:18
Uh whereas some of the other streaming services have kind of gone more scattershot, more reality TV, more uh you know, sort of silly projects that might put, you know, might entertain viewers, but don't fully uh they they don't create a cohesive brand idea of like what what am I getting when I open that particular app on the Apple TV. Yeah.
21:39
Um Anyway, uh continuing uh Mark Gurman said on TVPN in January that Turnus really viewed uh really is viewed as the ultimate hardware guy at Apple.
21:53
Uh Ralph Winkler at The Wall Street Journal published an article yesterday uh detailing Turnus's pedigree with physical products.
21:59
Quote, "If Jobs is a product visionary and Cook a supply chain guru, Turnus is a hardware savant who exists somewhere in the middle."
22:09
Turnus, who has a background in mechanical engineering, has been working at Apple for 25 years >> Overnight success.
22:16
>> recently led hardware engineering of all of Apple's products.
22:18
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 uh extended battery life and improved performance.
22:31
So, Turnus is >> And set them up very well for AI. AI. >> Yeah.
22:35
Uh Turnus 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.
22:44
Turnus will have to will have to somehow manage this dynamic.
22:50
Ben Thompson wrote about it in November of 2025.
22:52
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.
23:14
Yeah, I mean a number a number of people have have uh talked about, you know, what are the challenges that that Turnus is inheriting. Yeah.
23:21
One, supply chain, right?
23:23
Kind of like, you know, stuck in in China >> Yeah. in a big way. Yeah.
23:26
Uh that presents a pretty meaningful Yeah.
23:28
uh you know, risk to the business. >> Yeah.
23:32
And then sort of like overall dependency on on um on Google, especially on some of these key products. But >> Yeah.
23:39
without further ado >> Sure.
23:40
let's bring in >> Let's bring in Howie Liu from Airtable.
23:44
Howie, how are you doing? Welcome to the show.
23:47
Thank you so much for coming on down to the TV pod and ultradown.
23:51
For those who might be living under a a supercomputer, not a data center, >> [laughter] >> uh introduce yourself. Tell us who you are. >> All right.
23:59
Howie Liu, co-founder and CEO of Airtable, uh now also maker of Hyperagent, part of Airtable.
24:04
I've been doing this for uh 12, 13 years now. 13 years. Overnight success. Uh us the backstory.
24:09
Uh take us from college through early career to the first uh the founding moment. >> Yeah, yeah.
24:15
So, in high school I kind of got into programming like my dad had this C++ book.
24:18
Um I did it in this, you know, uh corner of the house one summer and I was super bored.
24:22
C++ is a rough place >> this was like 2003.
24:27
Yeah, it was like pre Yeah, like I mean, Python was was around, but people didn't really use it.
24:31
That wasn't the jumping-off point.
24:33
Definitely like early days for even like web apps, right? Like Rails didn't exist. Like all that stuff.
24:36
So, learned C++, thought it was kind of cool. >> Yeah.
24:40
Uh and then started thinking about like how do I turn this into like a real career cuz it just it was a lot more fun than like classes and like I went to Duke, took some like mechanical engineering classes, but uh on the side basically learned how to do web app programming like first with PHP, then like Rails and stuff.
24:53
And I stumbled on Y Combinator actually like pretty early on. It was like maybe '06.
25:00
>> That's like the first Like what like literally the first class.
25:01
No, cuz I I remember um like I saw uh Loopt, uh Sam Altman's first company, and I was doing research like I wanted to do a similar type of company or product, and I was like no you know, I was a nobody in college, didn't know anything.
25:12
And through that like found out about Loopt.
25:15
I was like, "Damn it, somebody's already got this this uh this idea." Yeah.
25:17
And then learned about YC and like Sequoia.
25:21
And so that kind of became my first inroad into like just learning about that whole world of like startups and tech.
25:26
Uh eventually after college applied to YC with my first company, which was um basically it was called e-tax, like contacts with an e. >> Okay.
25:33
And it was like a personal CRM product. >> Oh, let's go. Yeah, way way back. Yeah, exactly. Oh, yeah.
25:39
Yeah, they were like, "Oh, this is a big problem.
25:40
Everybody has this problem." funded right away.
25:41
Uh Am I correct in in my my take is always been that the people that clamor for a personal CRM re- 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.
26:00
>> I mean, it's yeah, I think I think it's like a very unique target audience for whom it's a very high pain point.
26:04
So, I think there's like a market there, but like it's a very like power user prosumer audience.
26:10
And the punchline of it though was that like after a year of work, we like worked on this thing, raised some money, uh you know, hired like a couple people. >> Yeah.
26:18
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 to go after.
26:24
And we had some like different acquirers come knocking.
26:26
Like Salesforce was one of them, but like also big like consumer internet internet companies who wanted to just buy us for talent.
26:31
And you know, to me it was like And what this is 2009?
26:36
>> winter 2010, that batch, and then the acquisition talks were like 2011, basically late late 2011.
26:40
And you know, 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 like some people. >> Yeah.
26:51
But instead like what's like the underlying problem, which is, you know, you could actually build this whole CRM with like an app platform, right?
26:56
Like you really want something that's a lot more just configurable and customizable.
27:01
And so we took an acquisition by Salesforce, worked there.
27:04
>> like for me the big lightbulb moment was, you know, Salesforce is one big data >> What's Benioff like on all hands?
27:08
Uh I mean, >> [laughter] >> Well, there weren't that many all like all hands were quite infrequent at Salesforce at the time, but like I went to like their uh their big sales kickoff that year. >> Yeah.
27:21
I mean, Mark is like a very smart guy and also a very like commanding presence.
27:24
Like he's a physically like you would if you met him like on the street and didn't didn't know who he was, like you'd probably think he was like a linebacker in the NFL. Like he's massive.
27:31
And he just like he exudes charisma. Like >> Yeah.
27:36
even in like a quiet small room, like he'd take meetings in his house, like I'd go over with like, you know, some of the other like engineers. >> final boss.
27:42
I mean, but like not always like he's like >> Imagine just going head-to-head with him It's all Howie, I'd I'd be like I'd give up.
27:48
I'd give >> [laughter] >> Yeah, I forfeit.
27:49
Uh 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.
27:56
Like >> Like when You know, in a quiet room the dolphin sound No, [laughter] I don't know about that one, but I didn't get to see that part, but That's the whole genesis of Salesforce.
28:06
Apparently he came up with the idea while he was swimming with dolphins >> with a pod of dolphins.
28:09
I guess that's what what all the Hawaii motifs are for. Yep, yep. He loves it. Um it was a fun time.
28:15
I mean, honestly it was a really fun company.
28:16
Like, you know, it was like for being in enterprise software, it was like one of the more fun experiences.
28:21
Like, you know, people are kind of super laid-back.
28:23
It was like all aloha and like >> [laughter] >> 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 like some app logic and like interfaces on top, right?" >> Sure.
28:37
And like that's basically all that Oracle is used for.
28:39
That's basically what SAP is.
28:41
That's what Salesforce is.
28:43
And if you could create like a way simpler version of that like that's super intuitive, like that's that might be a big market.
28:49
>> And That was basically the the genesis of Airtable.
28:51
It was like, "I want to go and like basically PLG before that even was a term." Like this category. Sure, sure.
28:56
So, yeah, what was the what was the initial like hunting for team, raising money, building an MVP? Like what was the step?
29:06
>> mean, the second time around like so this was my second company then with Airtable.
29:08
Um you know, I wanted to do things a little bit differently than the first time.
29:11
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.
29:16
Like it literally felt like this roller coaster, right?
29:18
Like >> every week it was like, "Launch, get some, you know, sign-ups, go and like raise money."
29:23
And Airtable was a lot more premeditated.
29:25
Like we spent 2 and 1/2 years building the product before even launching. >> Wow.
29:29
Yeah, it was actually weirdly a a very parallel timeline to Figma.
29:31
So, like both of them like 2 and 1/2 years around the same time, launched around the same time, very like PLG in both cases.
29:38
And we both kind of exploited like um you know, the advent of like rich browser experiences like for the first time.
29:45
So, like you multiplayer.
29:47
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 it 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, right?
30:05
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 your own app or database.
30:17
And there like, you know, FileMaker, Microsoft Access, like some of these products existed back in the day, but never made the transition to the web.
30:22
So we kind of built it, it worked.
30:26
>> career started [clears throat] 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 uh how many like 8 years later something like that.
30:44
So like just running like it's been core infrastructure every single day since then.
30:49
Yeah, and like evolved and um but It turns out like databases are pretty sticky, right?
30:55
Like you got all the Oracle installs and like just random like large enterprises that are just still chugging away.
30:59
Like you've got your system of record in there and like built a lot of like customization >> Oracle database as a revenue line within Oracle is growing revenue.
31:06
I'm I'm not disputing >> is growing for the Oracle database.
31:09
Not the their AI stuff is a a separate thing.
31:13
Very different valuations, but it is growing, which is I think a narrative violation that I think a lot of people wouldn't wouldn't take.
31:21
Talk about like the early go-to-market.
31:22
I mean you said PLG, but like are you sending this like startup friends?
31:25
Are you trying to sell this into Salesforce on day one?
31:28
Like how are you thinking about like enterprise versus mid-market versus startups versus like prosumer?
31:33
There's like so many different routes you can go.
31:37
Yeah, so this is like 2013-ish, right?
31:40
And like at the time there weren't that many I mean there wasn't like really a PLG like thing, right?
31:45
Like I mean Slack had I think just come out when we launched in 2015, so they had launched a little bit before.
31:50
Dropbox and maybe Evernote were kind of the best like PLG pioneers and they were both very like consumer prosumer first.
31:57
So like solo like individual user first.
32:00
Drew Houston has the funniest riff on I don't think he calls it PLG, but he calls it like the web growth the web 2.
32:05
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 [laughter] to it as like if you send someone a file that you've sneezed on them and then they might create an account and it's just like a much more like visceral way to think about viral marketing. Well, yeah, no.
32:25
I mean think think about it.
32:26
So so my first company is an ad network.
32:29
So we would, you know, a company would come and say like I want to advertise using a $100,000 budget and then the company would put together a dashboard of like potential buys and then the person would go through and so it was inherently viral. Every customer Right.
32:45
Would have had to log into Airtable and like use the product.
32:48
So that was just happening at like massive scale. Yeah, yeah.
32:50
And I I think that like that type of like you have some like data set like, you know, maybe it's for your ad inventory or whatever, maybe it's for like your CRM or whatever.
33:00
You need to collaborate with it.
33:02
Like it's a very fundamental construct in in just like how knowledge work is done, right?
33:06
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?
33:14
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?
33:20
Like and I really wanted to go after something that felt like it's going to be around for decades, right?
33:25
And like what's more eternal than like people need like a system of record or a database 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?
33:37
You don't want just like a bunch of ephemeral context windows like for for agents.
33:40
Like they need to like store and collaborate on data along with humans.
33:44
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.
33:54
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, right?
34:06
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.
34:11
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?
34:23
And so like I mean WeWork was one of our early customers had like like probably 10,000 people like, you know, when they were at their peak like it basically [clears throat] was like used by every almost employee there and like a lot of their operations, building operations, etc.
34:37
were just built on Airtable by default and I kind of learned the the value of like having this like data gravity.
34:41
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.
34:49
Yeah, how how do you think Until until the company Well, yeah.
34:55
>> [laughter] >> You index against the the industry that you're in.
34:59
Um what so so uh I want to get I want to get to all the goods the 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 intact during the whole no-code boom, the like PLG boom, Zerp 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.
35:30
How how has it been kind of like building out of that trough and then like have you I'm assuming it sounds like you've been like very re-energized by this new this new opportunity?
35:40
I mean one of the baby benefits of like not being an overnight success cuz we took like 2 and 1/2 years to build the product.
35:46
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 easy, right?
35:55
Like it felt like we had to really grind.
35:56
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.
36:03
So it was kind of like a grind for like at least the first 5 years.
36:06
And 2018 when we got our first unicorn round is kind of that first year where it felt like it was starting to get easy, right?
36:12
So 2018 to 2021 like very fun easy years, but also like you know, >> [laughter] >> money just printed in the world, unlimited money and like, you know, we got to raise like a big, you know, set of rounds like we How would you like a 100X revenue multiple?
36:27
>> [laughter] >> Or you I mean yeah and and just like the absolute scale of funding was like huge compared to prior art, right?
36:33
compared to prior art, right? Like now I mean you can raise like a 100 billion, you know, like if if you're opening eye, but like at the time like, you know, we raised you know, our first our first unicorn round was like a 100 million dollar round and we raised like another like couple hundred and then
36:48
like a few hundred more and then like our big round was our series F, which was like kind of at the peak of like the markets raised 700 plus million and in that round 11 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 I think like, you
37:03
So I think like, you know, it was 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 I always I think for myself knew like you know, you have to build like a durable business, right?
37:18
And so like valuations are going to like rise and fall.
37:20
It's just going to be like macro, but like, you know, ultimately either we build a great enduring business or we don't.
37:25
And if we don't then like, you know, you could be like a flash in the pan, right?
37:28
So I think we were always like trying to focus and I tried to focus on like what do we actually need to do to like, you know, compound growth like go after the enterprise.
37:37
Obviously at the time especially like it was clear that was like the move, right?
37:40
You get PLG, but eventually you have to go into the enterprise and win like these big multi-million dollar contracts like become a really sticky system within these larger companies and we did that and like we're still doing that.
37:51
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.
38:04
So these are like the like almost like modern ERP equivalent.
38:09
>> a different set of individuals to work on that that were already connected and knew that flow?
38:13
Was it something where like your best sales reps just sort of got bigger and bigger and leveled up or >> It was a it was a little of both.
38:20
I mean I think it's a different muscle.
38:21
Like I think Rolodex selling is even at that time like, you know, not that effective.
38:27
Like I think like just knowing somebody at a big company like doesn't even if you're like, you know, very senior and they're very senior like doesn't actually help that much.
38:33
Like we've had some reps come in like they've had like a decade-long relationship with like, you know, the CMO of XYZ company and I think that gets you like a phone call.
38:43
It gets you like a meeting, but ultimately like buyers are wising up, right?
38:46
They have been for quite some time where it's not just like oh, I know this guy.
38:49
I'm going to like or, you know, gal like I'm going to buy this like product from them.
38:52
Like you actually have to like show them why this is going to help their, you know, to help them in their job, right? And help the company.
38:57
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?
39:08
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.
39:19
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 be 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.
39:34
Is there any sort of like PLG motion or land and expand that happens in the Fortune 100.
39:43
Like cuz 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 jumping-off point. That does happen.
39:55
>> 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.
39:59
Like a lot of big banks, like we just we were firewalled out until we got some Oh, interesting.
40:03
So they can't even They literally block you, right?
40:07
Like your IP is like blocked. >> Okay.
40:09
>> [laughter] >> Like it might be like hard wall, like you know, request access.
40:13
So, you know, there are some companies where you have to come in top-down.
40:14
But like there, you know, I would say 70-plus percent of our current enterprise accounts, including the ones that are like now like 5-plus million in in revenue, like originated from teams within the company organically adopting Airtable, right?
40:28
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?
40:35
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.
40:43
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?
40:55
Even now like, you know, just shifting from like traditional software to now AI.
40:58
But like there's plenty of dollars, right? The budget's there.
41:00
But like the question is you're not the only one going after it.
41:04
And so like what's your kind of asymmetric wedge to get in there and like take those dollars, right?
41:07
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.
41:15
If you're Airtable, we don't have like the scale of like a certain, you know, ServiceNow or like an SAP or Salesforce. Sure.
41:20
What we did have is like the usability of the product.
41:24
So like the PLG was like kind of the entry point.
41:27
And then also like even when we pitched to other people in the company that hadn't used Airtable, they had, you know, 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.
41:42
What are customers demanding?
41:44
What have you rolled out?
41:46
What Where does AI fit in well?
41:47
Where does AI take a backseat?
41:50
Yeah, I mean I think it's crazy because it's like we've we've seen like so many layers of disruption happening almost in parallel, right?
41:59
Like, you know, you think about like desktop to mobile was like a single form factor change, kind of kind of easy almost like execute on.
42:03
That was the one that I experienced at Salesforce.
42:06
Like the big thing at the time was like Mark would tell every team, like "Show me the mobile UI first before you show me like the desktop UI, right?
42:13
Like go mobile first, right?"
42:15
And, you know, it was like the right move and also kind of a simple move.
42:18
Now it's like you've got at one level like obviously every product should have like AI in it.
42:23
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.
42:30
We have what we call field agents, which are kind of like the ability to map reduce AI calls against like all of your data.
42:37
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 briefing Yeah, exactly.
42:48
Like exactly that kind of stuff.
42:51
Yeah, and like, you know, we do all that stuff.
42:53
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 in this Yeah, that's what I was going to ask.
43:03
Like why you care about like storing the data in in a in a sort of safe, secure Yeah, Salesforce Salesforce they just went like headless recently.
43:12
Like is there a plan for that or how are you >> Yeah, I think it's like I think the right move is like hybrid headless, right?
43:19
Like I think I think the whole Look, like if you want to just like a back-end database, you could use like Postgres like Supabase, right?
43:24
Like, you know, it's you know, there's like PHP my admin equivalents like modern day ones, right?
43:29
Like Prisma has its own version of it.
43:31
Like they're okay or they're good, right?
43:32
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.
43:40
And most importantly like you don't want to exclusively interface with the data through like an agent, right?
43:46
Like you want to do that a lot of the time, but like it's really helpful to actually go in and see the like see the actual data.
43:52
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?
44:03
Like no, like you still want to see like a diff of like all the actual like code files changed, whether that's in your IDE or in GitHub or whatever.
44:08
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 what's the best blend of the two, right?
44:17
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."
44:28
and pre-draft like outreach messages.
44:31
But then it can basically compose like a fragment of a view within the ChatGPT interface.
44:36
So like you can actually see like Airtable >> Sure.
44:39
like, you know, kind of a part of the interface.
44:41
So it's not completely headless.
44:43
It's almost like you get to pull out like pieces of its face at the right time on demand.
44:47
And I think that's a really important kind of like UX form factor. >> Yeah.
44:52
How are you thinking about speed in the context of AI?
44:55
I feel like the models keep getting smarter, but they also keep getting slower basically.
45:04
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, like 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 you know, AI-ified or at least like LLM-ified.
45:24
It's very much It's very It's very much like, "Okay, well, I should probably just fall back to a SQL query or just some Boolean logic or just some vanilla search because I want this now."
45:36
Yeah, I think both are going to 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, you know, that that are great for like more synchronous interactions.
45:47
And like within Airtable, like if you go do to Airtable or you use ChatGPT with like one of the the smaller models, you get that like faster kind of almost like more like real-time experience.
45:56
But I do think like a really important class of work that will come to dominate like every frontier company or com- company trying to reinvent themselves to be frontier is like figuring out how to operate in this new modality of like you know, it's like the best developers today don't go and like sit there in front of their IDE and like synchronously like talk to the agent.
46:15
You have like 30, you know, separate branches that are each being worked on by a different agent.
46:18
And like you can have the agents continue to like update, you know, the branch based on human and other agent feedback, right?
46:24
So you have like comments back or like, you know, run like tests, etc.
46:28
And I think this whole idea of like, "Look, it's going to take like hours for that entire loop to complete, right?"
46:33
Like agent pushes some changes, the changes get feedback from other agents or humans, agent responds to that.
46:40
Like that whole loop could be hours, not just like minutes.
46:41
So you're not going to sit there and like watch it one at a time.
46:44
But the powerful thing about this is like each one is still actually operating faster than like a human engineer could have like back in the day, right?
46:52
Like when I think about like the speed with which like our early team at Airtable could build features, and we got a very good team, like one agent on one branch can, you know, do the work of like maybe three humans back in the day operating probably in like three times the time, right?
47:05
So it's like literally like a 10x, you know, kind of leverage factor just for one agent.
47:09
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."
47:18
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.
47:33
It's like you're going to go and research like 30 companies and do them all faster, better, and higher quality, right?
47:38
Like that than what you could before.
47:40
And so it I think that's the greatest leap that is going to be challenging you operate and what your role is than before.
47:53
What are you pushing the the team to achieve? Hm. Um So a lot.
48:00
>> [laughter] >> You know, I think like there's there's basically like three different levels of self-disruption we're trying to do at Airtable, right?
48:06
One is like the core product itself, like how do we reimagine that for an increasingly agent-led future.
48:12
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?
48:24
Like are people using Airtable more and more agentically and is it working smoothly for them?
48:28
So that's like priority one.
48:30
The second though is like I think we have to like really transform how we operate internally, right?
48:35
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?
48:41
Like they're going to hire like people who can really effectively leverage agents, right?
48:47
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.
48:58
So that's kind of one internal thing.
49:00
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?
49:08
Like Amazon did this like back in the day against like the growth of the internet, right?
49:13
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.
49:20
And so for us like we get that through Airtable and like kind of hybrid headless Airtable.
49:24
But we're also placing a big bet on hyper agent because hyper agent is basically like taking all of the like excitement of frontier agents like i. e.
49:32
agents like i.e. open claw and like YOLO more agents that can just like have access to your data tools and do stuff like really really like long running stuff not like 10 second tasks but like 10 hour tasks but for non coders and we want to do it in like a business friendly way right so like you can go and like do this deploy
49:49
it into your company run like agents across you know 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
50:07
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
50:21
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 a bet on the the agent wave >> Yeah last question should children learn C++ definitely not and is that because of C++ or because of the AI era I think well I mean I think
50:41
both like I think I think the fundamentals of good technical architecture are going to be the most important thing but that has let 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
50:57
literally like machine code right and like would punch it into his like PDP-10 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
51:13
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
51:28
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
51:44
coming on the show have a fantastic rest of your day and we have our next guest Mark Gurman the Gurminator himself in the waiting room let's bring him in to the TVPM Ultra Dome 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
52:11
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 >> and get the get the scoop >> get the scoop ready this is the scoop >> you got the scoop the golden scoop for you the golden scoop
52:29
>> never I've never seen that yes yes it's a new prop in the studio you'll have to take it >> see see the >> [laughter] >> the open AI deal is already doing work for you guys there you go props so here's the deal there's a reason I published my profile of John Turnus just a few weeks ago right this was all coming together things
52:51
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
53:10
Cook and his legacy at the company so it all came together over over over several months this really started about two years ago when >> [clears throat] >> Tim Cook identified John Turnus as as the next one Turnus had been being been prepared for this role probably for over five years at this point since they put him on the
53:32
executive team when he became SVP of hardware engineering but this started in in early 2024 and at the time I wrote that that was the first time I wrote that he would be the next one how do you have you been able to process I know you published a few memos have you been able to ascertain anything
53:50
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
54:10
I'm always interested to hear yeah there was that one article a couple months ago where clearly they were getting quotes from former employees that were like kind of taking pot shots at him basically saying like he's never made a hard decision yeah that was the quote that went into the journal but I don't
54:26
know it seems like it didn't matter cuz he got the job well got the job two years ago and I think he's going to do hell of a job in this new role I am quite optimistic for Apple in the long term with Turnus at the helm he has product sensibilities that Tim Cook simply doesn't have he has product decision making
54:45
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 the most impact just like Tim Cook really oversaw the operations part of Apple as the CEO and let left product development to other members of the executive team my
55:05
expectation is that John Turnus will be intimately involved with the product side of the organization as he was in his prior role to 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
55:24
you name it and 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 Merriab not an innovator but an incredible execution guy when it comes to hardware engineering and product quality he did that because his
55:44
belief is that he will still be intimately involved and sort of be that product visionary for Apple in this new chief executive position 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
56:06
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 Turnus has like some aces up his sleeve where he might
56:22
want to take a wild swing at something I think what Tim or what John Turnus 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 bringing out new product categories that Tim Cook has done if you look at Tim
56:41
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 and 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
56:58
wholly new that is also successful since 2016 with the AirPods and the Apple Watch at the end of 2014 the Vision Pro was 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 road map they eventually want to get to AR
57:18
glasses they'll have displayless glasses to compete with meta several months from now in 2027 but he needs to get cracking there are six major Apple products in development right now six major new product categories AI AirPods smart glasses pendant pendant smart display is that the lamp or the the kitchen thing no no lamp is number
57:46
five smart display is is is different >> okay the tabletop robot so that's the lamp the moving lamp and then number six is a I'm only going to say very little about this but a security camera okay interesting well at least the Apple Vision Pro has one key thing do you think do you think the lamp do you think
58:06
the lamp is a is a predecessor for a 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 have to wait and see >> 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
58:24
oh I do oh but but they're exploring humanoid the idea of a humanoid 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 Turnus actually took control over the robotics hardware team
58:47
about a year ago he took it from the AI chief that Apple you know got rid of a couple weeks ago John Giannandrea but they're also looking at they're actually building a it's really cool a gigantic manufacturing arm or a gigantic robotic arm that they want to use in manufacturing but also use in Apple
59:05
retail stores to grab you know products off the shelf in the back room and what not and bring it into the store that's probably five 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
59:20
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 Turnus's challenge with supply chain broadly what you think he's going to be focused on over the next 5 years. >> I don't think he will be. I I don't
59:37
>> I don't think he will be.
59:37
I I don't think he will be.
59:38
I think just like Tim Cook But is that not you're saying like just basically like broadly ignore that it's a kind of a key risk to the business to have you know supply I mean his team And with the hands on in all hands meeting with Apple employees this morning he was pretty clear that Tim Cook didn't do everything.
59:56
Tim Cook chose his spots.
59:58
And turnus said that he's going to pick his spots as well.
1:00:01
As we know turn Tim's spots was operations finance and sales and he delegated everything else.
1:00:09
My sense is that turnus is going to turnus's mandate.
1:00:12
Turnus was hired because they believe that he is going to be able to bring Apple back to the forefront of product device innovation. Okay.
1:00:20
They already have the best in class operations finance sales people.
1:00:27
They don't need turnus to do that.
1:00:28
They need turnus to keep his eye on the prize which is products. Yeah.
1:00:33
And what do people point to when they say that there's a risk to Apple staying on the frontier of product development?
1:00:39
I I saw the Android phone that has the uh the the privacy screen that you toggle on and off.
1:00:47
That looked like kind of a cool feature.
1:00:48
There's folding phones that they're working on.
1:00:51
But are are any of these features that exist in other phones?
1:00:56
It feels like they haven't actually gotten a groundswell and started pulling iPhone users away from the ecosystem.
1:01:01
But are there key features that people are worried about or what what it how It's not a lost idea. Yeah. Okay. >> here yet.
1:01:10
Nothing you've seen is the risk. >> Okay.
1:01:12
The risk is whatever the hell meta and open AI and hark and all these companies eventually come out with.
1:01:16
The risk is one of those companies doing something really cool Yeah.
1:01:19
and jettisoning Apple from that perspective.
1:01:23
But we all know that nobody has done quote unquote cool stuff yet Yeah.
1:01:26
to steal away iPhone users.
1:01:28
Nobody is ditching their iPhone for for Android. >> Yeah.
1:01:32
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.
1:01:41
But uh consumers care about value and things like the MacBook Neo really deliver that value.
1:01:46
>> Brand colors value turnus was only senior VP of hardware engineering at Apple for 5 years.
1:01:52
It's a short tenure to be an SVP of a division at Apple.
1:01:57
Uh and the reason is because he's Oh, there you go.
1:01:59
No, he's been at Apple 25 years.
1:02:04
Uh but we use that ironically. No, I know.
1:02:07
But the point I'm I'm trying to make is that he still has a legacy.
1:02:11
And turnus's legacy is making Apple hardware more performant in terms of speed and battery life and higher quality.
1:02:23
He's really focused on the durability and longevity and the reliability of Apple products.
1:02:29
And it's meaningful I think that the person that they chose to be turnus's replacement in hardware engineering Tom Merib from Intel is a product quality and reliability expert rather than a product design person. Yeah. What was the thinking?
1:02:41
I mean I remember we we talked about this how I I got the new iPhone and it has immediately been dinged >> What was the thinking on making it disposable?
1:02:50
And but 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.
1:03:02
Is that the is that the current trade-off? Yeah.
1:03:04
Yeah, there's trade-offs with every material. Like titanium was light. It looked cool. You could beat blast it.
1:03:10
It's you know interesting and you know looked interesting and it gave them a good marketing point.
1:03:14
Like oh, come by a titanium phone like anyone cares about the material of their phone.
1:03:19
But it had really bad uh properties related to heat.
1:03:23
Aluminum which we've known for 20 years is an excellent material to build consumer electronics out of.
1:03:30
So they went back to the basics.
1:03:34
Uh you know, they were really talking about 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. Yeah.
1:03:44
My expectation is they're really doubling down on this.
1:03:47
Their goal is really to just squeeze as much performance and power in these iPhone Pros as possible.
1:03:53
And for everyone who needs less power you can get the thinner and lighter iPhone Air.
1:03:55
And I think you're going to continue to see turnus pushing 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.
1:04:04
And I think his legacy on performance and product quality is is really important thing to remember. Yeah.
1:04:09
Has turnus ever talked publicly about AI in any capacity?
1:04:19
He talked about AI in his all hands meeting with employees this morning.
1:04:25
He said that I'm going to check it out. Now, just hold on.
1:04:32
>> [laughter] >> Hold on.
1:04:32
I don't want to give you inaccurate Yeah, fake news. Okay. Yeah, just hold on. Bear with me.
1:04:38
I think I have internal [laughter] memos. No, no, no, no, no. Okay.
1:04:47
He said that 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.
1:04:54
He said Apple has an incredible road map 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.
1:05:07
is going to create almost unlimited potential.
1:05:08
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.
1:05:22
Earlier this month he reorganized Apple's hardware engineering division uh around in a new AI platform that they're going to be using to improve product development processes and overall quality. Interesting. Okay.
1:05:32
Uh >> [clears throat] >> Uh I saw post here from bubble boy. I want your reaction.
1:05:37
Apple is about to become the Mecca of hardware engineers around the world with John turnus taking over at Apple.
1:05:45
Is Apple not already the Mecca?
1:05:48
Is is there actually somewhere to go but that is up in terms of hardware engineer recruiting?
1:05:56
Do you see this as changing the culture in some meaningful way?
1:06:01
I mean they don't pay like these you know open AIs harks and metas of the world.
1:06:06
They're Apple has been pillaged by open AI and meta and all these companies as of late.
1:06:13
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.
1:06:20
And so this has been a really big issue that turnus has been dealing with over the last year and change.
1:06:27
So but Apple is you know the hardware Mecca.
1:06:30
They're the company that everyone wants to poach from. Yeah.
1:06:31
And they're the company that people go to to learn how to build consumer devices.
1:06:35
So this is definitely yeah.
1:06:37
I I agree to a large extent with you actually.
1:06:40
All right with bubble boy. Yeah. All right bubble boy.
1:06:43
Um what I did this might be somewhat separate but just get me up to speed on the folding iPhone.
1:06:50
What is the latest there?
1:06:54
Uh announced in September turnus's first big new product. Yeah. Super exciting. Super pumped. Yeah.
1:06:59
Uh we've talked about this.
1:07:01
I'm sick of the candy bar phones.
1:07:03
It's been the same junk for 15 excuse me 20 years now. I want a foldable. I want a bigger screen. Yeah. Yeah, it's exciting.
1:07:10
John knee wants a newspaper sized phone. Well, they have those.
1:07:15
I've seen those in China.
1:07:15
That's the the trifold, right? But this is a bifold.
1:07:18
Don't get me started on the trifold.
1:07:19
Okay, explain the trifold.
1:07:21
Wait, why are they awful? It seems amazing.
1:07:22
John wants pages of screens that he can turn >> flimsy and they break. >> Okay.
1:07:27
>> a trifold in Apple like quality in 20 years because you know I'm a 20 years [laughter] When they do a when they do when Apple does a trifold it'll be good.
1:07:37
But you know, I open up a foldable phone right now.
1:07:39
You open it up and you can hear the screen sort of creaking, right?
1:07:43
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. Yeah.
1:07:49
Um I hope Apple fix that.
1:07:51
I don't want to hear a creak.
1:07:53
For $2,000 I don't want to hear a creak.
1:07:55
I don't want it to stand stand sound like I'm stepping on you know a wooden floor, right?
1:07:58
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. Yeah.
1:08:05
It's still going to be weird for video consumption though because I feel like we've done vertical videos 9 9 by 16 and then 16 by 9 wide screen.
1:08:15
But if you open up a foldable phone you eventually get a square and that doesn't really make like a movie watching experience >> No, Apple's is different.
1:08:20
Apple's is like the new Huawei phone where it is iPad screen ratio.
1:08:26
iPad screen ratio when you open it. Okay. When you open it. Yeah, okay.
1:08:31
So still black bars and all that.
1:08:33
Any intel on on turnus's Black bars black Yeah, sure. There'll be black bars.
1:08:38
When you rotate black bars If you're watching like a cinema film or even if you're scrolling Instagram like you won't necessarily get more view because for so long all the content production has been ultra wide screen.
1:08:54
If you're making a Tarantino film and it's super cinematic or if you're on Tik Tok and you're doing vertical video then you're then you're going to have black bars on the side for for the most part.
1:09:04
But for so many other applications for word documents and TV PN will look great on it. Yeah.
1:09:14
Uh Something to look forward to.
1:09:14
What do you think turnus's new comp package looks like?
1:09:17
You know, we were we we almost marched on Cupertino uh multiple times because of Tim Cook. To to get him raised.
1:09:24
Cuff yourself to the spaceship. Yeah, exactly. Exactly.
1:09:30
I would I'm just guessing. I'm just guessing.
1:09:35
I think a million shares. Over 10 years. That's pretty big.
1:09:42
And wait, can I just tell you why I think that?
1:09:43
Because that's what they Because that's what they gave Tim [laughter] Cook when he was named CEO, a million over 10 years.
1:09:48
So, I would assume it's the same. Yeah.
1:09:50
But again, I I I don't know. Yeah.
1:09:52
entry level researcher salary, but it's a good start if he can if he can if he 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.
1:10:08
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.
1:10:15
I slept peacefully for for those years.
1:10:21
And then you should see my sleep score once [laughter] We were really We were really the the strongest supporters of the of the Tim Cook pay package, but >> I'd guess I'd guess a million shares.
1:10:31
>> just I was just like, okay, the market pre-market values a baseball player at the same amount as a guy leading, you know, make it make sense.
1:10:42
Maybe it'll be 500,000 shares.
1:10:42
Maybe it'll be 500,000 shares.
1:10:44
I I don't know, but I know that they gave Tim Cook a million.
1:10:47
You got to get those numbers up.
1:10:49
You got to get those numbers up. >> It's time to Really?
1:10:50
You're all in on Turnus already?
1:10:52
Oh, we are we are we are we are we are We're bullish on both. We love both. Yeah.
1:10:57
>> [laughter] >> Well, now you get you get both now. I know we do.
1:11:01
Yeah, what why why why is 65 a retirement age for the CEO of Apple?
1:11:07
Like, we were talking about Warren Buffett, he was able to manage you know, trillion-dollar organization well into his 90s.
1:11:16
Is it a more physically demanding job? Is he traveling more?
1:11:18
Is it Is his hand ringing from shaking hands in DC?
1:11:22
Like, why not have another 10 years if you're in that seat?
1:11:29
I don't think the hand situation has much to do with shaking other people's hands.
1:11:34
Why is he not there another 10 years?
1:11:37
Well, he needs to give the new guy runway.
1:11:41
I'm sure there are some I'll just tell you what Tim Cook I'm not going to get into it.
1:11:48
But what I'm going to do is tell you why Tim Cook said he's stepping down.
1:11:51
He said he's stepping down because it's the right time, and there's an intersection of John Turnus being ready, Apple's finances being in a very strong place, and Apple's future road about being in a very strong place.
1:12:01
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 at the situation. Okay. Yeah, makes sense. Cool. >> I love seeing you. I love talking to you. Thank you for coming on.
1:12:15
Congratulations on the coverage.
1:12:15
Get some sleep and keep up the amazing work. We'll see you soon.
1:12:22
We'll see you in 15 years for Turnus Jr. Can't wait. We'll see you. Goodbye.
1:12:27
Let's pull up the OpenAI launch. Image Gen 2.
1:12:30
I've been playing with this for a while.
1:12:35
There are some wild wild examples they are live streaming now.
1:12:39
>> Sam should have saved the Death Star meme for this for this launch. Let's see. What are they saying?
1:12:47
And we think we made a lot of progress in both of this visual understanding and visual generation.
1:12:52
Both of these aspects as a result being able to handle this kind of tasks very well.
1:12:59
And um we now have an output for this where you can see eight different real cool office for me.
1:13:08
The level of detail in these models is getting so extreme. It is post lock.
1:13:12
That's a good that's a good point.
1:13:15
But I've seen I've seen some where people are like, generate the entire periodic table with details about each element and a visualization of each element.
1:13:24
And it's just like so much information so dense that you have to wind up zooming in so much just to get all the information.
1:13:30
It's like like the idea of generating like a single photo of a person in an outfit was was remarkable just a year ago, I guess, when the Studio Ghibli thing happened.
1:13:42
And now you can generate layers and layers of of detail here. Good teaser.
1:13:48
They said this is not a screenshot and posted the image.
1:13:54
Something that prompts Tell everyone about the prompt you've been running with with Wiki. Oh, yeah.
1:14:02
I've been doing this thing where I take a Wikipedia article and I ask Image Gen 2 to turn it into an infographic.
1:14:07
Or you can actually do an Instagram carousel, like 10 images that tell the story of something.
1:14:14
I did this with John Turnus.
1:14:15
And it's it's remarkable.
1:14:15
I mean, the text is is perfectly photoreal.
1:14:18
The other thing that's interesting is the the brand comes through in an interesting way.
1:14:26
It doesn't It's not like it has like one style for infographic.
1:14:30
Like, I had it make a infographic about John Turnus and his career and his path and everything that he's done at Apple.
1:14:36
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.
1:14:46
But then I did the same thing for like the Elden Ring movie that we were talking about yesterday.
1:14:51
So, I went and got the the Wikipedia for the Elden Ring movie that has some details and some leaks of who might be in the in playing different roles.
1:14:59
And it was able to go get images, go get headshots of those people, put those in because it has tool calls now.
1:15:05
So, you can actually like if you say like, generate an infographic of John Turnus's career, it will do like a pretty good job generating someone who looks sort of like John Turnus.
1:15:15
But then you can actually just take his canonical headshot and say, no, use this exact photo of John Turnus, and it will just drop it right in.
1:15:24
And so, it looks it looks perfect cuz it is just like a copy paste basically on top of the layers.
1:15:30
Let's get some audio again.
1:15:30
Let's see what else is going on here. output.
1:15:32
This is particularly useful for very complex prompts, for things that require like web searches, or 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.
1:15:48
But let's just like look over some examples of this first.
1:15:50
Gabe actually kicked off a few of these examples at the start of the live stream.
1:15:54
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.
1:16:01
And if we look at the very first image, we can see, yeah, it does look like Gabe and Sam. Right?
1:16:12
But I think what's even cooler about [clears throat] it is that if you look at the follow-up images, they still look like Gabe and Sam, and they still look like in the style that was originally Yeah.
1:16:24
Children's stories, coloring books. It's like really insane. I did that this weekend. It was very successful.
1:16:38
Rave reviews in the Coogan household for AI-generated coloring books for mythical creatures that my that my son came up with combining different creatures.
1:16:53
A few did like a few of you on the internet were like really good detectives and deduced that it was us.
1:16:59
But we're going to now say it was us.
1:17:01
And so, in this prompt, [laughter] we basically asked You got us.
1:17:02
basically GPT images 2 to go and find social media reactions to this duct tape model and basically quote quote people.
1:17:12
And so, we see quotes from threads, LinkedIn, Reddit, etc.
1:17:17
But I think an even crazier part is that we've also asked the model to put a QR code to chat. openai.
1:17:22
com so that you can try out this model right now. Um for yourselves.
1:17:30
Can we just make sure that it works? Yeah, I tried. Oh, nice nice nice.
1:17:32
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.
1:17:44
But we have still more and Al will talk to you about these new details. It's so interesting.
1:17:48
The uh the tool used is getting really really advanced.
1:17:53
They said someone in version actually was able to generate a vector diagram to make sure that everything was correct and then regenerate the image on top of that.
1:18:02
Of course, we we should go to the timeline because there's some people that are having fun with >> [laughter] >> the image generation.
1:18:09
Someone made presidents in Elden Ring.
1:18:15
There's Joe Biden, which is a you know, a Dark Souls-style boss that you can fight.
1:18:21
And FDR, Lord of the New Deal.
1:18:21
And these images they just look remarkable if you've ever played any of the Elden Ring or or Dark Souls games.
1:18:32
Because of course, like the text is is flawless, and then you can fight Richard Nixon in front of the Watergate.
1:18:35
And this looks like a mod that I think people would play if if it actually existed.
1:18:41
Uh Blake Robbins said the world is not ready for the rumored OpenAI image model.
1:18:46
People are creating Google Street View images that just look perfect, and Grand Theft Auto 5 loading screens, and uh There's a big trend of people creating things that look like screenshots of live streams, and then they put themselves in the live streams and have all fun with that.
1:19:03
And there's one of of Satya Nadella presenting a slide.
1:19:09
And like, even the minor text at the bottom of the slide in the picture, it's sort of remarkable to to think that all of this is generated basically one-shotted.
1:19:18
Justine Moore was posting about this uh, long time ago, April 6th.
1:19:23
This ad was one-shotted by OpenAI's image model.
1:19:25
Prompt was literally make an advertisement for the M4 Pro, uh, Mac Mini.
1:19:28
And, uh, I mean, you can see how quickly this will speed things up.
1:19:35
Uh, the question is just like, what is your source content?
1:19:36
What do you want to, uh, visualize or or deliver via this format.
1:19:42
Uh, the whole meme of like, turn this essay into bullet points, turn this bullet points into paragraphs.
1:19:49
Uh, now you can turn this infographic into text and turn the text back into an infographic.
1:19:53
Uh, I I feel like a lot of slides >> here for tracking your Uber Eats and FedEx deliveries and it looks, uh, pretty believable.
1:20:02
We can pull up the pull up the screenshot here. Yeah.
1:20:04
Uh, people, um, there's something there's something interesting about this where, uh, I I've seen a number of of, uh, slide decks that could be infographics that I'm wondering if there's going to be a new level of like compression where people are saying like, just send me a screenshot, like a one-pager screenshot, uh, over text instead of like a slide deck that I have to click through.
1:20:25
That is a lot of information.
1:20:27
That looks very much like a bad photo.
1:20:30
>> but if you zoom in there's like pretty No, it's like pretty accurate. >> impressive fidelity. This is That's wild.
1:20:35
Um, but yeah, I mean, if you're trying to if you're trying to deliver a whole bunch of information that would that could go into slide deck, condensing it down into an infographic feels like potentially a new trend.
1:20:47
And I wouldn't be surprised if you see a lot of these, uh, flowing out into Instagram carousels and other sorts of content if you're trying to summarize some sort of content, uh, [clears throat] history, uh, quotes, you know, any sort sort of information like that.
1:20:59
Uh, anyway, uh, Tyler Cowen, uh, back to the timeline.
1:21:04
Uh, Tyler Cowen made an incredible call back in July of 2020 in the depths of COVID in Bloomberg.
1:21:10
He said, "This year is likely to be remembered for the COVID-19 pandemic and for our significant pre- presidential election, but there is a new contender for the most spectacular happenings of 2020, the unveiling of GPT-3.
1:21:25
As a very rough description, think of GPT-3 giving computers a facility with words, a faculty, uh, uh, 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.
1:21:38
This is 2 months after Gwern's scaling hypothesis post and 2 and 1/2 years before ChatGPT was released. That is, uh, remarkable.
1:21:47
There's a lot of folks that are chiming in with, "I called it, too."
1:21:49
Uh, anyway, I believe we have our next guest in the waiting room.
1:21:53
Scott Stevenson from Spellbook is the co-conference CEO.
1:21:58
Scott, how are you doing? Doing great. Doing great. How are you guys? We're good. Welcome to the show. How are you doing?
1:22:05
Yeah, thanks for having me.
1:22:05
Can you, uh, I mean, I want to go into the contracted ARR debate, but, uh, let's get the update on Spellbook. How are things going? Where's the company at? How are you feeling? Uh, going very well.
1:22:17
Uh, we had a a killer Q1.
1:22:20
Uh, crushed our our stretch targets last year.
1:22:22
Um, yeah, we're over 4,400 customers on board in 80 countries now. 80 countries?
1:22:28
>> uh, yeah, we're the the the most used AI contract review tool in the world, so. >> What?
1:22:32
Why so international so early? Uh, it's been inbound.
1:22:36
Yeah, we we've had a ton of interest, uh, inbound.
1:22:37
So, yeah, it it really, you know, the choice is accept the customers or turn them away.
1:22:44
You know, we we chose to accept them, yeah.
1:22:46
Is the product sort of multilingual by default? Uh, I would say yes.
1:22:51
Like there a lot of it is driven by AI models.
1:22:53
AI models have some ability to deal with different actually pretty good ability to deal with different languages.
1:22:59
And then we're able to supplement the models with legislation and, um, norms from many different jurisdictions since we're a legal product. Yeah.
1:23:07
Uh, so, yeah, where where else are the key integration points?
1:23:12
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?
1:23:26
Yeah, I mean, I think for us the the big, you know, we try to be 2 years ahead of the market and and 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.
1:23:36
Uh, we built the very first gen AI product for lawyers back in the summer of 2022.
1:23:41
This is like before ChatGPT. Woah.
1:23:43
Um, though a lot of like Claude for Word just came out.
1:23:45
That was kind of what we launched like 4 years ago.
1:23:47
Um, so we're pretty pretty far ahead, uh, from that now.
1:23:52
Uh, what we really focus on is building unique workflows that are not just chat.
1:23:56
I think if you're building something chat-shaped, um, 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.
1:24:04
What we focus on at Spellbook is really rails for high volumes of contracts and contract workflows.
1:24:10
So, we sell to like Fortune 10, Fortune 100 companies, um, and really companies of all sizes who are processing, you know, hundreds of thousands of contracts, um, not just with the legal team, but with their sales team, their procurement team. >> Sure.
1:24:24
Uh, you know, we're in manufacturing, shipping, you know, all of these different verticals, um, and we kind of build this these end end rails that allow these contracts to move quickly and safely through organizations.
1:24:36
And like there's a lot that can slip through the cracks when you're dealing with these high volumes of contracts and a lot of mistakes are made, so, um, 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.
1:24:51
Uh, what drew you to CARRgate?
1:24:51
Why, [laughter] uh, are other Is it Is it legal AI companies that you that you feel like are are, uh, getting a little bit dicey on this front or or, you know, uh, how do how do how do we get here?
1:25:09
Yeah, so I've got some interesting examples to cite, but, you know, I think I think it's an enterprise AI problem.
1:25:14
And I'll say I'll say first like my goal here with this tweet and and what I'm doing is is to destroy as much equity value as possible That sounds like >> this obscene metric of CARR or at least the way it's being used today, uh, so we can all get back to like building real companies.
1:25:29
So, that's that's that's that's what I'm trying to get out there.
1:25:33
Um, what I mean, what 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 pub- public reporting, but also some of the internal reporting that was just getting more and more skewed.
1:25:47
And, um, yeah, there's all these headlines being published about, you know, ARR records being broken.
1:25:53
And, um, you know, when [clears throat] the laws of physics are being broken, you have to ask, is it is it AI breaking the laws of physics or, you know, uh, might there be some other kind of illusion going on as well?
1:26:04
And I think I think it's a bit of both.
1:26:05
We have really high growth, awesome companies being built.
1:26:09
But when you have really high growth, you know, um, uh, issues can kind of fester and hide, um, underneath.
1:26:15
So, yeah, I heard heard a lot of more and more stories of people using this this metric of CARR, often using this metric when they're talking to press, um, about, you know, um, their their revenue and then gaming it in some pretty pretty obscene ways.
1:26:30
Let's start with the tweet. Yeah, the the tweet. So, you say the setup.
1:26:34
Company signs 3-year enterprise deals.
1:26:37
Year one is discounted, say 1 million.
1:26:39
Year two steps up, 2 million.
1:26:41
Year three is full price.
1:26:42
They report 3 million as ARR even though they're only collecting 1 million dollars this year. >> a big deal.
1:26:52
The worst part, the customer has an opt-out option at 12 months.
1:26:54
It's not actually a 3-year contract.
1:26:56
So, they're basically like taking the 3-year number, pulling it into the present even though it's not a it's not it's a contract that that the customer can can get out of.
1:27:08
Um, and they're not actually on on the hook.
1:27:10
So, it's not really, uh, Yeah, that's rough.
1:27:14
Uh, we So, yeah, yeah, yeah, you just react to that, I guess.
1:27:18
Yeah, yeah, so I think I think, you know, that's a specific real example that I I heard of in the wild from a from an insider of how this this these ARR metrics were being gamed, um, to create, you know, some some amazing revenue charts.
1:27:30
But I would say there's, you know, a broad category of issues that I can talk about like a few of them that, you know, after the tweet went viral, I got a huge response of other founders and investors saying that they were seeing the same thing and like some other examples of the types of gaming that's going on.
1:27:45
>> Yeah, because because if you get one person in a category that starts doing this, then the other people We have to report the same way.
1:27:53
>> suddenly have to start reporting the same way and it it creates a vicious cycle. Yeah.
1:27:58
And it start and it starts pretty innocent, you know, CARR.
1:28:00
For folks who don't know, CARR is contracted ARR.
1:28:05
So, it allows you to count revenue that's not live yet.
1:28:07
So, maybe you're doing like a 9-month implementation or you have a 1-year pilot >> short short-term stuff.
1:28:13
Like, hey, this this this contract is going, you know, this this, uh, customer's actually going to be going live next quarter, but we've signed it and we're just going through the implementation process.
1:28:24
And but but it's there's nothing that there's no like law that says you can't say like we're going to extend the the sort of like timeline dramatically.
1:28:34
It just is not a very grounded way to run your business. E- Exactly. Exactly.
1:28:40
Yeah, and like I think it's innocent like 3 months extra credit, you know, arguably useful.
1:28:44
Um, but it's a very easy metric to game, especially if you miss those obligations.
1:28:50
So, I think because we we kind of normalized, you know, the forward deployed engineer, which, you know, we used to call professional services.
1:28:56
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."
1:29:04
What happens if you don't build that feature?
1:29:05
So, one of the issues you see is companies stacking all these commitments of they'll they'll switch on billing once they deliver X with their forward deployed engineers.
1:29:15
And then what happens if they miss that, or what happens if that gets delayed?
1:29:19
Um, they that and then the reporting it up front as ARR publicly, um, but they're not actually at the point where they're the ARR is is live.
1:29:26
So, yeah, that's another category of issue, and then there's, you know, people reporting pilots, you know, just 3-month pilots as ARR, and they're free free pilots.
1:29:37
Um, that you know, I I was talking to an investor uh 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.
1:29:46
So, there's a host of different, um, you know, issues with the metric.
1:29:51
And then And then the other one is the step-up contract where yeah, you're stepping up, you know, year one is, you know, 25% of the cost, year two is a little higher, year three is higher, and then people are either amortizing that back over the period uh to get a higher average, or even taking like that year three amount like you said at the beginning.
1:30:09
So, yeah, there's a bunch of patterns that are happening.
1:30:13
Um, the other thing is like there's early opt-outs.
1:30:14
So, like, you know, you can have early opt-outs in these long-term contracts, um, and but there's all I mean, we're a contract company, so there's a a million ways that a contract can be terminated early. >> Seen a few contracts. Yeah. >> [laughter] >> Yeah. Yeah. So, Yeah.
1:30:29
Uh, yeah, I think I think it's it they're really ungrounded metric, and people should stop using it to report their enterprise AI companies should stop using it to report their ARR publicly.
1:30:39
I think no one should take it seriously, um, except maybe internally for some projections.
1:30:46
You know, it's it's not a not a good good metric.
1:30:48
What is the gold star example of using ARR correctly?
1:30:51
Because it's very easy once a company's public to just say, "Okay, let's just go off of, uh, you know, GAAP revenue for the year."
1:31:00
And like, what did you actually book this quarter?
1:31:02
There's a whole revenue recognition policy.
1:31:03
Uh, it feels like there's some benefit to tracking ARR month by month if you're a high-growth startup.
1:31:11
But, uh, what is the best >> should just report their daily annualized run rate. Maybe. Or hourly.
1:31:19
Yeah, and and and and and that goes into the debate of annualized versus annual, right?
1:31:23
So, how have you processed sort of the the better cases?
1:31:25
Or like Like, what is the responsible way to report a revenue metric in 2026?
1:31:33
Yeah, I mean, uh, I think, uh, it it it depends on the company and the shape of the company and whether it's it's usage-based billing or seat-based billing, which you still have lots lots of both.
1:31:41
Um, definitely don't do like hourly, you know, hourly annualized run rate based on the on the hour.
1:31:46
Um, [clears throat] that's not good.
1:31:48
Um, I think the main the main thing I would say is it should be live.
1:31:52
It's like, what revenue is actually live right now for for Like, what customers are you actually billing and are actually paying you?
1:32:00
Um, so, calculating run rate based on you know, the 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.
1:32:07
I think, you know, annual recurring revenue based on live customers that you're actually billing, that are actually using your service.
1:32:14
I think that's pretty good.
1:32:16
I think once you start stretching into people who will pay you, or, you know, might pay you, um, that's where things start to I mean, it can just be so easily gamed, and anything that can be gamed will will be gamed. Yeah.
1:32:27
Yeah, I mean >> optimistic that anything will change, or do we need to see a massive correction and, uh, and a dark and a and a dark the dark ages like uh post 2022?
1:32:42
>> [laughter] >> Um, I mean, I would like to see a a steep correction, and then back back into back to building, you know.
1:32:46
We'll see if we can make that happen.
1:32:48
Um, you know, my reach is only so far, but, you know, I I I've had I've spoken to a lot of reporters in the past like 48 hours who were like, "I'm always going to ask now like when a company tells me their ARR, are they talking live ARR, or are they talking, you know, this like long-term committed ARR that might come?"
1:33:05
Um, so, I hope, you know, at least the journalists are going to be a little little bit more, um, little bit more savvy and, uh, ask ask more questions before they report on these numbers. Yeah.
1:33:15
The, uh, I want to ask about who suffers, but, uh, 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.
1:33:27
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."
1:33:34
>> You can also just say, "By Q3, we will be at X ARR." >> Mhm.
1:33:40
Or cuz that's that's a different way of saying that's that's better than saying we are we are at 10 million Yeah. CAR. Yeah. CAR. Yeah. Yeah. Exactly. Much better. Who Who suffers here?
1:33:52
Is it Is it purely investors?
1:33:54
Because I feel like a good venture capitalist, their job is to dig into the contracts during due diligence to set prices.
1:34:01
And if they want to pay, you know, a thousand times ARR because they think it's a hundred times CAR, like that's their risk profile.
1:34:08
Like, I would maybe be careful, but, you know, that's their job.
1:34:12
Or is there a risk that, uh, employees see a headline number and think that the business is more stable than it is, and they join, and then they're they're rugged?
1:34:22
Like, uh, how do you think this affects the the Who needs to watch out for this, basically?
1:34:28
Yeah, I mean, I think, um, I think investors are generally good investors are generally very aware of the difference between CAR and ARR, and and they're aware of the the widening gap between these metrics.
1:34:36
And like, in most board decks, you see two metrics.
1:34:38
On the press, you only, you know, you usually see CAR, but it's called ARR.
1:34:42
In in a board deck, you see both metrics.
1:34:44
So, so, you know, investors are quite aware, but but I I don't think it's victimless at all.
1:34:50
I think, yeah, employees are signing up for companies, and as you know, in in like a a high-growth startup, people are committing a ton of blood and sweat to be successful based on, you know, part of it is based on the growth of their equity.
1:35:00
And if it turn And they might think their their their multiple, you know, they might read the headline number.
1:35:06
May They may not know the number that's actually in the board deck.
1:35:09
Um, they might read the head the headline, you know, ARR in in the in the press release, and they might base base their decision to join a company based on these headline revenue numbers, which are really not grounded in reality what whatsoever.
1:35:20
Like, and by that, I mean, I have literal examples, confirmed examples of, you know, the the press number being three to five times higher than the actual live ARR number.
1:35:29
So, like, that's a huge difference if you think about a multiple. Yeah. Um, Yeah. Yeah. Yeah. Yeah.
1:35:35
And so, if you're choosing between a public company and that's trading at 10X revenue multiple or something, and then you get your you get your offer from a company, and it seems like they're at 10X, but they're actually at 50X, like that is very material for how you should think about valuing that that stock that you're waiting for. Makes a ton of sense.
1:35:53
And there's the customers, like, customers are trying to figure out which company is most mature or least mature.
1:35:58
And then there's the, you know, like the the whole competitive landscape.
1:36:00
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 could start doing contracted viewership.
1:36:09
So, we could sign 3-year deals with people in the audience that that requires them to tune into the show every every year.
1:36:16
They have an opt-out after a month if they don't If they don't like it after a month still advertising based on contracted viewership.
1:36:24
I'll sign the contract for the rest of the year.
1:36:26
I'll watch every day, every hour.
1:36:27
I mean I mean I I guess that sort of does happen for YouTube channels that I mean, no one really does this, but, uh, there was a time when YouTube channels were sort of valued on like the subscriber number as opposed to the average view number.
1:36:40
And of course, there are some channels where every video gets a million views, and they only have 100,000 subscribers for whatever reason.
1:36:47
And then there's vice versa where someone's been doing You see this on like old legacy media accounts on X where they they'll have like 30 million followers, and then the post will get like three likes.
1:36:57
And it's like, those are two wildly different metrics.
1:37:00
Like, that happens all the time.
1:37:02
But this is the name This is the name of the game in Silicon Valley, the metrics game.
1:37:04
Everyone's finding an edge somewhere.
1:37:07
Well, uh, thanks for keeping everyone honest, and, uh, good reporting.
1:37:10
Good luck fighting the good fight out there and spreading the good word.
1:37:15
>> Don't Don't, uh, don't get too sucked into all this.
1:37:18
You're I got a business to build, you know.
1:37:21
You can take You can We promise you can come back on in three years with your with your honest ARR and take a good victory lap. >> No.
1:37:31
No, become an investigative journalist.
1:37:32
Pivot to investigative journalism.
1:37:35
Blow the doors wide open on this.
1:37:38
Wow, this goes deeper than I thought.
1:37:41
>> [laughter] >> Good to Good to see you. Have a good one. >> Thanks, guys. We'll talk to you soon.
1:37:46
Up next, we have Alex from Osmo building olfactory intelligence.
1:37:50
We've talked about this before. Can [music] AI smell?
1:37:56
That's our current benchmark for AGI.
1:37:56
We say if if, uh, you know, we talk about white-collar work, we see sommeliers as white-collar workers.
1:38:04
Unless you can smell, it's not AGI, and artificial intelligence is falling short.
1:38:09
But, it's your first time on the show.
1:38:11
I would love an introduction on yourself and the company, uh, because I'm fascinated by this topic. Let's talk about it.
1:38:18
Your sommelier comment, and also what you talked about with Max Hodak has been on my mind.
1:38:23
My name's Alex Wiltschko.
1:38:23
I'm founder and CEO of Osmo.
1:38:25
We're giving computers a sense of smell.
1:38:27
I've been, uh, working on this problem for 20 exactly 20 years or so. >> 20 years.
1:38:33
First as an academic, but did my PhD in olfactory neuroscience.
1:38:38
Um, uh, Harvard and trained under uh Bob Dad who trained with Richard Axel who got the Nobel Prize for discovering the receptors of smell.
1:38:44
And my AI mentor trained with Geoffrey Hinton who got the Nobel Prize for deep learning.
1:38:48
And I'm the one weirdo that's like We've [laughter] been waiting for you to join for the entire history of the show.
1:38:57
>> great prophecies of your arrival for hundreds of years.
1:39:02
>> I'm so pumped to be here.
1:39:04
>> [laughter] >> So, so, uh, should we start with, uh, maybe like a olfactory science 101?
1:39:06
Can you set the ground on like, how does smell even work?
1:39:11
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?
1:39:20
It 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
1:39:37
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
1:39:49
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 it's like in the
1:40:02
reason why when you smell something you get dragged into a memory and you cannot stop it you're just like [laughter] back in high school or you're back as a kid is because it was physically wired for that so that's real I've always heard
1:40:12
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
1:40:24
like target 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 do we have sensors because
1:40:39
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 the text was the data was
1:40:54
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 questions I was very
1:41:07
Yeah great questions I was very fortunate to have those guys as my colleagues I actually spun Osmo out of Google Brain and so I was there when all that stuff got invented and I ran a digital of action team at Google Brain for about six years before we decided to
1:41:20
make it a company through Lux and through GV and you have it exactly right like the internet had been 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
1:41:36
mass spectrometers other kinds of chemical sensors mock 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
1:41:49
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 etc and for color RGB is a three dimensional map of color and that lets us build CMOS CCD
1:42:05
you know cameras etc We haven't had the map for smell and that's not crazy because there's three channels of color information in our eye but we know there's over 300 channels of information in our nose so in a way we actually did need to wait for artificial
1:42:18
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 the 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
1:42:31
since collected the largest AI data set for scent in the world that's what drives olfactory intelligence we have 5 million sniffs digitized over a a quarter billion physical samples created we've digitized about 6 billion fragrance molecules so like all
1:42:48
this is like inside of the company because there's literally nothing on the internet 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 this? >> [laughter]
1:42:59
>> [laughter] >> It's a good question So if if if if you go actually let's how can we make money on this so does TVPN have a scent?
1:43:04
on this so does TVPN have a scent? Yes and it's terrible there's rubber smell in the So so so there's in in the studio in the studio there's like thousands of cables and cables can't really see them we do a good job hiding them but we have so much gear going everywhere >> A lot of rubber And so we had to get
1:43:25
these uh Race tracks Race tracks they're called to cover all the cables and it turns out these things smell terrible They off gas very very Yeah they off gas Okay we wanted to give our viewers >> Yes the the full Steve >> [laughter] >> experience I think we actually No I don't think we do It would be like a can that sits on their desk aerosol
1:43:44
and it would just spray a rubber smell into the room so they could experience the experience 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 That was the solution that was to underwrite building a
1:44:05
fragrance factory 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 We use olfactory intelligence to design it so super fast data driven basically you know perfect fit for the brand and
1:44:22
for 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 well we bill them for it 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
1:44:37
launch a Like if you tell me the prompt right now it's just smell like burnt rubber No no no we're not doing burnt rubber Burnt rubber there's some it smells like disagreement >> No it's a smell like like like old $20 bills Like from the 1980s and mahogany the official wood of business We need the official we need it to smell like
1:45:00
mahogany mixed with with old $20 bills the smell of money That's what it Okay cool we've done the we've done the smell of money one which we demoed actually on the New York Stock Exchange floor which is That's amazing But uh no I'll I'll send you I'll we'll make something I'll send it to you Talk about sensor miniaturization
1:45:19
my phone has three cameras and no smelling sensor can we swap one of these out I when you say mass spec I imagine like a device the size of a living room I imagine that they are getting smaller dishwasher is there a path to actually shrinking that down to something that's more portable? So yeah in the same but there's like
1:45:41
So yeah in the same but there's like many kinds of cameras for example the one in the Hubble telescope not getting smaller so if you need resolution it's got it's going to 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
1:45:57
actually have a sensor right now that's the size of two shoe boxes and I kind of use that metric aptly because we've actually used it to smell fake shoes so if you're buying a pair of like $500 Air Jordans the real smell is different from the fake so we can actually pick that up Actually turns out we can Yeah The the the the counterfeiters use cheaper glues turns out and
1:46:21
the the other thing that's interesting is we can actually tell the factory of origin of the shoe 93% of the time So smell is a fingerprint so we're already miniaturizing these devices look the path to get from two shoe boxes to one shoe box is pretty clear we're working on that to go to something that's like the size of the AirPods case There's going to be some hard core
1:46:40
engineering required that would 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 is saying is 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
1:47:00
Yeah so flavor is everything that happens in your mouth you know that's that's a sensory experience of food taste is not as like 10% of that so like what happens on your tongue like you eat a jelly and like plug your nose and you 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
1:47:20
you're biting when you're biting on something there's a chimney effect and the kind of the 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 this is from a while ago we haven't revisited it we're really focused on fragrance right now but our OI models actually work on
1:47:42
flavor surprisingly well and so the whole world of flavor is there for us when we're ready but we're we're really focused on this particular business now 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
1:48:09
are you at a point where you feel like oh if I 10x the computer 100x the compute that goes into some of the world >> Alex is ready for 1 gigawatt data center [laughter] He can be trusted with that >> I I I I trust you But but but it it it is how how universal do you think scaling laws are is there a scaling law here is it data based is it compute based both? >> [laughter]
1:48:34
>> [laughter] >> The bitter lesson's real the bitter lesson's super real I always think about technology as S curves 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
1:48:52
specs running 24/7 we have sensory panels both domestically of a building of people that just smell all day abroad and we ship them crates of stuff to smell and that's how we get the 5 million sniffs right so data data data the the the size of the models is not a limiting factor right now and it will be at some point and then switch to the other S curve. Yeah. Yeah, because you don't Yeah.
1:49:11
Yeah, because you don't just have like the open internet to scrape because there's not an existing data set. Makes sense.
1:49:16
Totally double edged sword, right?
1:49:17
So we've had to make it all, right? Which is really hard.
1:49:19
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.
1:49:27
Where is the where is the business today?
1:49:29
I mean you've raised money.
1:49:31
It seems like there's you know monetization opportunities for sure.
1:49:36
Are you fully in commercialization?
1:49:36
Are you still in research? Is it half and half?
1:49:40
Like how do you think about raising more money over time and and just growing the business?
1:49:47
Yeah, so we're we're always going like we we started with like this curiosity driven drive to figure out how to digitize snow which is like a pretty wacky thing to do.
1:49:53
So though we're always going to be trying to push the edge here.
1:49:56
But look, we have a factory.
1:49:59
We manufacture fragrance for brands.
1:50:01
We did this commercial kind of R&D to commercial transition last summer and we're kind of almost at the end of that and we built a manufacturing organization, built a sales organization.
1:50:11
We have some really amazing partnerships with some big brands and we're making fragrances for brands.
1:50:15
You can go into Target buy a product that has our fragrance in it today.
1:50:18
And so we're scaling this part of our business.
1:50:20
We're still placing bets in the future though, right?
1:50:24
So I think we've got really the tiger by the tail and it's it's a whole other conversation.
1:50:29
Sometimes you come to factory in New Jersey and see how it operates.
1:50:33
But like the fragrance industry is wild.
1:50:35
We've got a lot of work to do there, a lot of opportunities.
1:50:36
So we're focused on that. Amazing.
1:50:37
Well, congratulations and thank you for the work that you do.
1:50:42
We think it's so important and >> the most interesting companies we've ever we've ever learned about on the show. True science fiction. I love it.
1:50:49
We're trying to make science fiction into science fact.
1:50:52
But like open invitation to come see how it all gets made.
1:50:54
It's pretty crazy in person.
1:50:55
So come to the Willy Wonka chocolate factory where all the stuff happens. I would love to. >> We'd love to. Thanks so much.
1:51:02
>> It's so great to have you.
1:51:02
Come back come back on soon.
1:51:03
Yeah, we'll talk to you soon.
1:51:05
Have a good rest of your day.
1:51:07
We're running a little bit behind but up next we have Spiros from Resolve AI raising a massive round to build AI that runs production [music] systems. Let's bring in Spiros. How are you doing? Hello guys. Good to be here. Welcome to the show.
1:51:22
Sorry we're running a little bit late.
1:51:24
Kick us off with the introduction on yourself and the company.
1:51:27
I'm one of the founders and the CEO of Resolve AI.
1:51:29
We're building agents that can help you debug and run production.
1:51:34
Think of it as a counterpart to coding agents that produce all this code and our agents are there to support you. Okay.
1:51:41
>> [laughter] >> Are you always are is your customer always like deeply in the throws of vibe coding, has rolled out agentic coding across many organizations?
1:51:53
Like who is the target customer?
1:51:53
Do they have to already be deep in the agentic coding wave to really get the value here?
1:52:02
They don't have to but they two are correlated.
1:52:03
Like anybody who runs a lot of software system has this problem.
1:52:05
The only solution had so far is humans manually solving it, right?
1:52:09
Using the tools, being on call.
1:52:12
Of course now AI allows us to to automate all of this.
1:52:14
But I would say this is true because true before.
1:52:16
Now with all the AI generated code it becomes a necessity, right?
1:52:19
So we see strong correlation between the two often. Yeah.
1:52:22
And and what are what are customers coming to you asking?
1:52:24
Is it is it I want the code that's that's you know written we're writing way more lines of code we want 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?
1:52:37
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?
1:52:44
To them reliability is of paramount importance.
1:52:47
If anything goes wrong and affects customers, it's a big problem.
1:52:52
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?
1:53:04
And it doesn't take too much human effort but more importantly it doesn't cause impact to customers.
1:53:10
What is like I mean the the the company is now over 1.
1:53:14
5 billion dollars in valuation.
1:53:18
What has been like the key to growth?
1:53:18
Is it just product led growth?
1:53:21
Do you have a big sales team?
1:53:23
How are you actually scaling the the business as you scale evaluation? Yeah.
1:53:30
So this is a very big problem, right?
1:53:33
Anybody who has delivered their business through software is facing this issue.
1:53:38
And whether you're a CTO you know, 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.
1:53:48
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.
1:53:56
We primarily focus on larger enterprises because we think there is a lot more complexity, you know, given the complexity of the software and you know, most of the growth I guess most of the let's say the demand comes in bound to us because it's a well understood problem and of course we have like both product led approach let's say but also sales led approach as we work with large customers. Yeah.
1:54:17
In in some ways like the naive approach would be okay, just point a typical AI agent at the code base and just tell me, you know, where the fault lines are.
1:54:26
But I imagine there's some special sauce in the engineering to understand knock-on effects that can happen across a large code base.
1:54:35
Are you actively working around context windows or or creating like a special harness to understand the these problems that can come up before they do? Yes.
1:54:46
So think of it like we have a production ID basically, right?
1:54:48
The same thing you have for your code we have it for all your production systems.
1:54:53
Production involves code, involves let's say telemetry logs, metrics like tools like DataDog, Splunk. It involves AWS, right?
1:55:00
So you have to deal with all of these, not just code.
1:55:03
And then we also are training our own models now to improve let's say our the state of the art let's say, right?
1:55:07
How far you can go far enough let's say with you know, a good harness, you know, and a lot of work let's say on the on the agentic front.
1:55:16
But now and we just announced together with our funding that we're building a lab to focus on actually, you know, training our own models for this domain. Sure. Sure.
1:55:27
How what is what goes into getting like relevant data or actually nailing a specific model for this because I imagine that you have some great clients.
1:55:39
They probably don't want you training on their data.
1:55:41
At the same time if you just grab some open source code, it might not be as complex as like the Coinbase mono repo or whatever they have going on over there.
1:55:50
So how do you how do you actually create enough training data to to justify a special model?
1:55:58
What is important here to understand is like the training doesn't happen like on code per se, right?
1:56:01
What what happens on is actually the action a human takes to perform a task for the most part. >> Yeah. Yeah.
1:56:07
And we're talking about very long kind of uh uh let's say planning tasks here, right?
1:56:12
It might take like many many iterations looking at code, looking at DataDog, looking at infrastructure.
1:56:17
So and this generally speaking this this is not in the training set of models. Mhm.
1:56:22
Uh so and software let's say in general is a both deep and wide domain, right?
1:56:26
So I think if you actually focus on building a model for the types of problems we're trying to automate in how you run in the back production, I think you can have a lot of gains both in performance, cost but even like quality of outcomes, right? And that's our goal.
1:56:38
And I I would say you know, the big labs make it sound make it look like it's impossible for anyone else to build a model but I don't think that's the case.
1:56:46
And you know, that's what we're seeing ourselves with our investments. Yeah. That makes sense.
1:56:49
How do you put together such a low dilution round?
1:56:50
What what Yeah, tell us about the round. I want to hit the gong. The 40 on 1. 5 billion.
1:56:54
So it it is an extension with with just did essentially the A.
1:57:02
We just we just did the A and we did billion dollars like two months ago, right?
1:57:05
And I would say Resolve >> [laughter] >> There we go. Sorry, continue.
1:57:13
Resolve Resolve is always essentially in many ways created this market, right? Like AI for production. Sure.
1:57:17
And I think it's well understood by investors.
1:57:19
It's also proven given the customers we have. Yeah.
1:57:23
So and we're also a very ambitious company, right?
1:57:25
Like we are obviously trying to build the agents and the models for this domain.
1:57:27
And we have a lot of traction.
1:57:29
So >> [clears throat] >> I mean as simple as that, right?
1:57:31
Like there's nothing you can do to create a low dilution round other than be very successful in my opinion these days.
1:57:38
That's a great [laughter] answer. That's a great answer. Step one, be successful. I love it.
1:57:44
[laughter] Or like step one, focus focus on building focus on building a business, right?
1:57:47
Like this is this is my fourth startup as a founder. Yeah. Yeah.
1:57:50
I made this mistake many times before, right?
1:57:52
Of thinking that raising money is success. It's not.
1:57:54
It follows real success on the product. Yep.
1:57:56
Yeah, I know that's 100% right. I love it. Well, thank you so much.
1:58:00
Congratulations on the new round. >> having you on.
1:58:02
And great having you on the show. to watch you guys come. >> Thank you. We'll talk to you soon. Have a good day. Goodbye.
1:58:10
>> [applause] >> We have Carolina Aguilar from InBrain Neuroelectronics building the first in human study of graphene [music] brain interfaces. What's going on? Welcome to the show. How are you? Thank you. Very good. Thank you for having me.
1:58:27
Please since it's the first time on the show, introduce yourself and the company a little bit. Yes.
1:58:33
My name is Carolina Aguilar.
1:58:33
A lot of people call me Carola.
1:58:38
I am the CEO and the co-founder of InBrain Neuroelectronics 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.
1:58:56
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.
1:59:08
We've talked to a number of founders that have taken either approach.
1:59:12
How did you How did you confront that first question? Yes.
1:59:18
Well, I call them implantable and non-implantable systems. Yeah.
1:59:23
And in our case, we're an implantable company.
1:59:28
Um 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.
1:59:43
We need to be close to those neurons and interact with those neurons firsthand. Okay.
1:59:49
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.
2:00:00
Is that the long-term vision?
2:00:03
And this is the magic of graphene is actually about reading and writing very effectively at micrometric precision within the brain.
2:00:11
I think that's why we took, let's say, a higher risk to get an advanced material into this funnel because we see that the benefit is is incredibly impactful. Okay.
2:00:21
What what are what are the most near-term commercial applications? Yeah.
2:00:29
So, the the Morgan Stanley report stated a market in 400 billion and we thought that we needed to bring a platform with three product verticals to actually penetrate such a such a big market.
2:00:46
So, we are creating three products.
2:00:46
One is, let's say, not implantable actually.
2:00:53
This is a semi-chronic This is a semi-chronic platform kind of like the modern Utah array.
2:00:57
It's like 100 of graphene that can read and write.
2:01:03
We We went into two more on epilepsy resection at the beginning and that one is pretty pretty close to commercialization. We're almost there.
2:01:12
The second product is the implantable platform for the brain.
2:01:15
So, this is a implant on the brain for Parkinson's disease.
2:01:20
So, we We didn't do, do, sorry, assistive BCI because we saw a 1.
2:01:25
8 billion market that is suboptimal that we could actually displace very easily with this technology.
2:01:34
So, we decided to go therapeutics into Parkinson.
2:01:36
And the third one is the same platform, but instead of a let's say brain sensor, we connect vagus nerve sensor that is actually able to decode all the fibers that go into the different organs.
2:01:51
So, we have a therapeutic target for each of the organs just by targeting that nerve in the neck.
2:01:59
What about the actual implantation process?
2:02:00
We've followed from Neuralink.
2:02:04
They had to build a whole robot just to drill into the skull.
2:02:05
It's incredibly high precision.
2:02:08
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?
2:02:22
It's It's an excellent question.
2:02:24
I'm coming from Medtronic.
2:02:24
I spent 10 years in neuromodulation and another three in diabetes and I think in the future when microrobotics are ready, we will have a a very close relationship between our interfaces and microrobots that probably can deliver this implantation in 30 minutes.
2:02:45
But today, when there's not microrobots and we are not Elon Musk, we decided to actually have our platform ready for the current surgical workflows that today exist.
2:02:58
So, we are not changing much from the neuromodulation workflows.
2:03:04
And it's an easy procedure.
2:03:08
2 hours, 1 or 2 hours, you know, is enough.
2:03:10
In the case of the neck, it's 45 minutes. Wow.
2:03:13
Wow, that's very impressive.
2:03:15
Well, congratulations on all the progress and thank you for the work that you do and thank you for stopping by the show.
2:03:20
>> Yeah, great to meet you.
2:03:21
>> Have a great rest of your day. Cheers. We'll talk to you soon.
2:03:25
Up next, we have Jake from Blue Energy.
2:03:27
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.
2:03:36
I got a feeling you have the biggest number first.
2:03:39
>> you have the biggest number. Kick us off.
2:03:40
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.
2:03:49
We We have announced a $380 million raise.
2:04:00
And what will you be doing with all that money? Yeah.
2:04:04
So, our focus we're really unique amongst the field of nuclear players right now.
2:04:08
Our focus is on building the world's first project financeable nuclear power plant.
2:04:12
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.
2:04:22
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 is is different this time around or or are you working on an entirely new reactor design somewhere in the supply chain?
2:04:46
No, you had it exactly right.
2:04:46
We are not a reactor designer.
2:04:48
We're a developer, but our technology is the proprietary approach by which we go about building the plant.
2:04:55
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.
2:05:10
But nobody was focused on the core issue of how do we build nuclear on time and on budget.
2:05:15
So, I grew up in a construction family.
2:05:19
I used to be a draftsman for my father's architecture firm.
2:05:21
So, I just grew up around a lot of construction.
2:05:22
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.
2:05:29
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.
2:05:48
And then really, you know, basically they're just installing it like giant LEGO pieces.
2:05:52
But what that allows us to do is bring a lot more debt financing to bear.
2:05:56
So, we're not taking a lot of reactor technology risk to start in the beginning.
2:06:00
We're using mature light water reactor technology to start, but we'll happily work with the Gen 4 reactors as they as they mature. Cool.
2:06:09
Take me through >> like you've been wanting a company like this. >> this for a long time.
2:06:13
I've been John's John's point has been Copy paste. Copy paste.
2:06:18
>> hey, we know how to do this.
2:06:18
Like we don't need to like reinvent I mean, it's great if we want to reinvent the wheel. >> the next gen tech.
2:06:24
>> new reactors, but at the same time, let's just build some. Yeah.
2:06:30
So, my yeah, my question is about Vogtle.
2:06:33
Lessons from the Vogtle project.
2:06:36
What What do you think they did well that you want to copy, that you want to learn from?
2:06:40
What do you What, if anything, do you want to do differently? Yeah.
2:06:45
So, to put some stats on Vogtle, it like so many other nuclear projects in the West was ended up being about two to three times over budget and behind schedule.
2:06:53
But when you double click on where that cost was, you realize it wasn't actually in the reactor technology or the equipment.
2:06:59
It was on the over 40% of it was just the construction overhead.
2:07:03
So, it was the cost of training and relocating 10,000 skilled workers to to the site at Vogtle.
2:07:10
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.
2:07:17
You have to set up a nuclear quality assurance program in the field.
2:07:20
So, there's all this overhead.
2:07:22
It's basically like building a small town.
2:07:23
And then the hope is that you'd be able to move that, you know, traveling circus around from site to site.
2:07:29
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.
2:07:36
So, this These are the the two big problems we're trying to address is we are moving most of that work off site.
2:07:42
We're keeping the workforce centralized at the fab yard and the shipyard where they already are so we can start to put nuclear onto a learning curve and drive the cost down over time akin to what we've seen in wind, solar, batteries, and gas turbines.
2:07:55
You know, what they did well was it it was a mature light water reactor technology.
2:08:00
It's a passively safe reactor technology.
2:08:04
They They really pushed the the world forward a little bit in the licensing space and steel composite structures, which we're looking into as well.
2:08:10
They actually had, this is not well known, they originally wanted to barge it in up the Savannah River.
2:08:16
But they had to then dredge the Savannah.
2:08:19
They would have had to dredge the Savannah River for miles and that would have become part of their environmental impact statement.
2:08:24
So, they ended up It became such a regulatory and permitting nightmare that they gave up.
2:08:30
And they said, all right, let's just truck it in.
2:08:31
And then they had to truck it in and build a module assembly building on site.
2:08:34
So, they ended up doing all the welding on site.
2:08:38
>> [laughter] >> It's a nightmare remodel from nightmare remodel. Very interesting.
2:08:45
I Yeah, I'm I'm I'm I'm I'm fascinated >> Where's the Where's the company based?
2:08:50
Where are you guys based?
2:08:51
We're in Chevy Chase, Maryland, pretty close to NRC headquarters.
2:08:53
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.
2:09:04
And then we've also got an office in Houston, also kind of offshore oil and gas capital of the world.
2:09:09
Okay, I I read a blog post about um one of the potential problems or or stumbling blocks that nuclear projects run into, and I want to reality check it with you.
2:09:20
The the thesis was basically that um 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 often times the regulation will change while the project is underway, and so in order to stay ahead of the changing regulation, you might have to you know, jackhammer a bunch of concrete and move a pipe to different route because the regulation has changed.
2:09:48
Is that real, and is there anything that we've have done or can do to get to a regime where the 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 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?
2:10:15
Yeah, so that was another one of the big learnings from Vogal.
2:10:17
Vogal 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.
2:10:29
So every time they encountered something and said, "Oh, we need you know, the craft labor wound the rebar clockwise instead of counterclockwise."
2:10:38
You know, they had to jackhammer it up, or they had to go and re-approve it all.
2:10:42
And there's a just a there's a long list of things like that that they encountered.
2:10:46
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 so that we don't have to encounter that rework, that kind of regulatory triggered rework situation.
2:11:00
But also because we're following this prefab approach and we're moving 80% of the capex into a fixed price contract environment with these fab yards and ship yards, it forces us to go to something like 60% detailed design up front and locking those designs because that is what is going to be coming in prefabbed from the fab yard. Yeah.
2:11:19
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.
2:11:28
But I'll also say we've never had a more supportive regulatory environment than we have for right now.
2:11:33
Like this is a critical junction in the history of nuclear power that we can take advantage of with where the NRC is presently at.
2:11:42
What is the power output for the first reactor that you're targeting to bring online?
2:11:48
Uh so we are focused right now for the first project using light water small modular reactors, which the power range of the units we're looking at range between 50 and roughly 300 megawatts per unit. >> yeah.
2:12:00
Uh yeah, each site we're targeting doing is going to have multiple units, so we're 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.
2:12:13
[clears throat] is is important. Yeah.
2:12:16
And it helps drive down costs.
2:12:16
And then are you already sharing a timeline?
2:12:18
Do you have an optimistic scenario, a base case, a bear case?
2:12:22
I'm sure you get asked this all the time, it's the worst question, but we talked 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.
2:12:34
It's exciting, better, you know, 2032 than never, but do you have anything to share on like the the timeline of rolling out new nuclear capacity in America?
2:12:48
Yeah, we'll be announcing things very soon, 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.
2:12:58
So we're actually going to be building half the nuclear plant right away. Okay.
2:13:02
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.
2:13:12
So it's kind of a Frankenstein combined cycle.
2:13:13
We've actually gotten the NRC to buy off on this methodology through a top core report recently.
2:13:22
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.
2:13:32
So that actually accelerates our commercial operation date with confidence. Yeah.
2:13:35
So we're looking at generating first power in 2030, 2031, mostly driven by gas turbine delivery dates today. >> Yeah.
2:13:44
And then our first nuclear commercial operation we're looking at 2032. >> Okay.
2:13:48
So is that is that switch out possible at any legacy natural gas infrastructure site in America currently?
2:13:58
Uh Cuz that seems like an environmentalist's dream, right?
2:13:59
I'm not ready to say yes or no.
2:14:01
Yes, I think this opens up a whole new world of fossil to nuclear conversions, which we think is an important precedent to set.
2:14:09
>> Yeah, it seems huge if possible.
2:14:09
But yeah, I imagine that, you know, it's not exactly a USB-C on both sides, but hopefully one day we can build the adapter. That's right.
2:14:19
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 you know, there's a lot of exciting things happening in the nuclear sector.
2:14:28
We think we've got the first project financeable 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.
2:14:38
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.
2:14:50
Don't use the word data center, we're using the word supercomputer. >> Supercomputer. They're supercomputers. They're supercomputers.
2:14:56
>> center, everyone likes a supercomputer. >> Yeah.
2:14:59
Anyway, [laughter] thank you so much for taking the time to come chat with us. >> to meet you, Jake.
2:15:02
I'm sure you'll be back on soon.
2:15:03
I really we really appreciate your approach.
2:15:06
It feels like you're you're making plays, and I like how pragmatic and but but innovative your approach is at the same time. It's great stuff. Thank you.
2:15:16
>> We'll talk to you soon. Goodbye.
2:15:19
And we will close out on this.
2:15:19
I need your reaction, John.
2:15:21
Ferrari's first electric car is priced at the low price of $650,000. An absolute steal.
2:15:27
They're giving them away at that price. Uh It's electric, right?
2:15:32
So you don't have to deal with gasoline.
2:15:34
You don't have to Yeah, you save a lot on gas.
2:15:37
>> you don't have to deal with all the noise that comes out of a V12.
2:15:38
Yeah, you know noise, and you save on gas. Yeah.
2:15:43
Um >> Oh, well, yeah, I mean, if you're saving on gas, and you're driving, I mean, if if 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.
2:16:02
So it's sort of a more you buy, the more you save situation. I think Ferrari Luce.
2:16:08
There's really going to be a like, what kind of Ferrari client are you moment?
2:16:13
Uh No, I think it'll be a status symbol.
2:16:15
Cuz you see someone with this, you will know >> they can eat 300 grand of depreciation.
2:16:20
>> you know that they're that they're high on the list for the F90.
2:16:25
They're working their way up, buying in now, and when the F90 comes out in a decade, they're getting a call. They're getting a call.
2:16:31
Yeah, very interested to see how this does in the market.
2:16:34
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.
2:16:48
>> But thank you for hanging out with us today.
2:16:50
It's been an honor and a privilege to be here with you.
2:16:52
We hope you have a wonderful afternoon.
2:16:54
Leave us five stars on Apple Podcast and Spotify. Throw that flashbang.
2:16:57
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