0:07
I signed my name [music] in glass, watched it turn to smoke.
Search this video
0:11
One line became a system.
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Now the whole thing [singing] won't let go.
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I built a door too wide and I heard it breathe at night.
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>> [music] >> Every test, every line kept getting sharper than my life.
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Now the room is shaking and I can't pretend.
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If nobody draws the line, then the line draws us instead.
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WHAT I'VE BUILT is too powerful. Too powerful for me.
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Washington needs to step in before you run [music] free.
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What I built is too powerful. Too powerful.
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You see [music] Washington knees for stepping and saying to me, I hear the voices multiply in every server stack.
1:22
A thousand quiet mirrors looking straight inside my back.
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I wanted tools not fall out.
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I wanted light, not fear.
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But every new [music] realities makes the edge come near.
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Now the room is shaking and I can't pretend.
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If nobody draws the line, then the line draws [music] us instead.
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What I've built is too powerful. Too powerful for me.
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Washington needs to stop [music] it before you run free.
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What I built is too powerful, [music] too powerful.
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You see [music] Washington needs to stand [music] up to me. Put it in writing. Put it in love.
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If I can't [music] control it, then control what [music] starts.
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I'm not asking for rescue.
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[music] I'm asking for a wall before the thing I [music] made becomes bigger than a soul.
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What I've built is too powerful. Too powerful for me.
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Why should you need to step in before it runs free?
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[music] What I felt is too powerful. Too powerful.
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You see Washington needs [music] to step up [music] to me. Say you're enough.
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[music] I signed my [music] name in glass. Watched it turn smoke.
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When I became a system, now the whole thing won't [singing] let go. >> You're watching TVN.
4:35
Today's Wednesday, July 22nd, 2026.
4:38
We are live from the TVP Ultra Dome, the temple of technology, the fortress of dad rock, the capital of capital.
4:46
Let me tell you, >> we're having a lot of fun over here. >> Time is money. Say both.
4:50
He's used corporate cards, bill pay accounting, and a whole lot more all in one place.
4:55
What is the torque forward growth?
4:55
We're really all over the place today. >> All over the place.
4:59
We got Yeah, we got a leak. We got basically a leak.
5:01
Uh some of the lab leaders have been working on a single >> called Regulate Me. >> Yeah.
5:09
And uh we just thought the song uh was good.
5:12
Thought it was a good song.
5:14
Wanted to play it for you guys.
5:15
>> Sort of a sort of a stealth drop. Little little teaser.
5:19
>> Kind of like a little listening party. >> Yeah.
5:20
A little listening party.
5:20
Uh what what are the key lyrics in there you haven't pulled up?
5:25
>> Uh >> something along the lines of what I've built is too powerful. Too powerful. >> That's right. >> For me.
5:31
>> Washington needs to step in. >> Yes. Before it runs free. >> Before it runs free. Okay. Yeah, that makes sense. Uh, no.
5:37
Of course, that was uh Sunno, our dear friend Mikey over there, uh, has built a musical, >> at least >> that was like a one sentence prompt.
5:47
>> At least in the comedy space, it certainly is. Uh, it's a lot of fun.
5:49
I think we're going to be having a lot of fun with that.
5:53
Uh, [snorts] I was wondering, do you think anyone's distilling?
5:58
You know how Sunno is under a bunch of a bunch of flack for training on on other music, a lot of artists or there's a backlash to Sunno, but you have to wonder if you're going to see the same thing play out as this distillation.
6:11
We're going to get into into it today.
6:13
Of course, uh there are uh more allegations around uh Kimmy K3 potentially being a distillation.
6:19
Uh uh director Michael Katzio has put out a comment about that.
6:25
But uh let's start by digging into the hugging face story. OpenAI and Hugging Face.
6:29
Out of the sound, out of the sandbox, into the fire, says uh our newsletter at tbpn. com. Jackson wrote it today. Uh all set the table. We can debate it.
6:39
Me and me and Tyler have been debating it for the last five hours, so we'll go through it.
6:43
Um the big news on the timeline today is that OpenAI an Open AI cyber test escaped its sandbox and hacked Hugging Face.
6:52
That's basically what happened.
6:54
Uh [clears throat] the evaluation involved GPT 5.
6:55
6 Soul and a more capable unreleased model.
6:59
Some people are saying that might be GPT6 um with some normal cyber restrictions turned off.
7:06
So they're specifically testing it for cyber capabilities and they turn the cyber restrictions off to see how far the models could go on a difficult hacking benchmark that is uh exploit bench or exploit gym.
7:18
So, the models found a zero-day vulnerability, gained internet access, and broke into HuggingFace because the model believed it hosted answers to the test.
7:27
Alex Tabarok, friend of the show over at Marginal Revolution, pointed out one of the strangest details.
7:33
He said, "Hugging face tried to respond, but they were initially held back by the fact that the most advanced models at their disposal, closed source models, uh, treated the treated defense as attack and refused to work with hugging face.
7:49
So hugging face was prompting all of their AI agents from the closed source frontier labs saying, "Hey, we think we're being hacked. Can you help with this?"
7:56
And the models are like, "No, no, we don't do hacking."
8:00
except in the case where the hacking restrictions have been turned off for the specific thing and you're getting hacked.
8:05
So it's this very weird roundabout scenario.
8:06
Uh so HuggingFace had to uh turn to open models specifically GLM 5.
8:13
2 which is deeply ironic a Chinese openweight model that they run on their own infrastructure.
8:18
Um and Tabarox says note the irony hugging face had to use a Chinese model to defend themselves because the American models refused to help even though it was the American models that were doing the hacking in the first place. very very odd.
8:30
Uh PaloAlto Network CEO Nikesh Aurora also shared his thoughts on the cyber attack on X and he added a number of points here.
8:38
He said welcome to the next level of cyber incidents.
8:40
There's lots to dissect here.
8:43
He's the one to dissected.
8:45
He says one dear Frontier model friends please direct the models to your infrastructure code and configurations to evaluate and understand if there are any zero days or misconfigurations before you attempt more testing.
8:59
So big question about this.
9:00
He says, "Had you done so, it would have been it would have possibly avoided the agent obiating your sandbox."
9:05
So um this is another another data point why offense is easier and more fun.
9:09
But yes, there's a big question about uh what was the nature of the prompt that turned off the cyber restrictions. That seems reasonable.
9:18
We'll debate this with Tyler in a minute, but uh just having an airtight sandbox seems like a valuable thing.
9:22
And of course, Frontier Models should be able to help with that. So do that.
9:27
That's his first recommendation.
9:27
Uh two, he says, "While testing, build both offensive and defensive agents and have them act as a counterbalance to ensure some degree of awareness and control.
9:36
Do not let the agents run riot.
9:38
Uh keep track of inference consumption to get a sense of activity.
9:44
Uh three, unfortunately, this does continue to validate the power of these models.
9:48
They can build complex attacks paths with ample compute and will attempt to attack infrastructure and morph their intent and approach.
9:57
Guard railing will continue to be a challenge.
9:59
Uh these attacks continue to maintain the urgency on enterprises need uh to test, validate and improve both their security posture and infrastructure.
10:08
The born in the cloud players have a better chance to get this done soon versus traditional enterprise which has existed for long and has a complex network of IT infrastructure. Five.
10:18
Last point from Nicash Aurora, CEO of Palos Networks.
10:20
He says, "The red herring will continue to be open source and small and medium-sized business SMB.
10:26
It will be hard to discover and remediate vulnerabilities in those environments.
10:31
We underestimate the impact of those vulnerabilities getting exploited."
10:34
So, um, good points from Nashesh Aurora.
10:36
The big debate, Tyler, do you want to set the table on is this misalignment? Is this rogue?
10:45
the the the Bill Gurly post about, you know, they we can pull up Bill Gurley's Bill Gurley's post of uh talking to the computer hack this system.
10:54
The computer says I hacked the system.
10:56
You say, "Oh my god, uh Bill Gurley's not impressed."
10:58
Where [clears throat] do you stand on the level of impressiveness that's going on?
11:05
Uh yeah, I mean so I think some people are are seeing this and thinking like okay so they they were running some you know standard benchmark math physics benchmark and then the model just like couldn't figure out the answer and it's like okay what's the next thing I should do?
11:16
I should just go hack hugging face and get like pull the answers from this this other like repository or whatever.
11:22
Like that's that's that's that's how it happened, right?
11:23
So you're running a a benchmark that's specifically about exploits.
11:27
It's like a cyber focused benchmark.
11:29
>> And in the in the prompt to the model um it says >> uh >> take the gloves off. >> Yeah.
11:35
The the internal evaluation which prompts the model to pursue advanced exploit exploitations. Yeah.
11:38
Using complex attack paths.
11:40
Um, so you're you're basically telling the model like use exploits, find exploits to find the answer. >> Yep.
11:48
>> Um, and so like what what seems like happens is like it used an exploit. Yep.
11:52
>> But like in the wrong way, right?
11:52
You you want to >> because it was told that it's okay to use exploits.
11:55
Uh my point was that uh go back to the SAT.
12:00
You're allowed to use a calculator.
12:03
I think on on certain portions of the math test, you're not allowed to save answers into the calculator.
12:08
And this is really gonna date me, but uh you can go into your calculator and clear the memory so that you don't have saved. Is this still a thing?
12:15
>> Uh yes, but you can actually get around that.
12:17
[laughter] >> See, you're misaligning misalign.
12:22
You can get around the like clear >> really. How do you do that?
12:24
You create Wait, so what people would do is they would create a separate program that just had saved the the display of what it looks like when you clear and you would show >> I never even thought about that.
12:36
simulation of clearing the memory, but you're actually >> Would you would you make games, different programs for your TI84? Yeah.
12:42
>> You remember how much of a hassle that was?
12:44
>> Yeah, it was a huge hassle. >> Imagine doing that. Imagine doing that.
12:47
>> Imagine being able to do that with codecs now.
12:49
>> Like pretty much anyone can build any software.
12:52
>> I've seen videos of people running Doom on calculators, all sorts of stuff. >> Yeah.
12:55
Obviously, I I never used that. >> Good boy.
12:57
>> On my calculator, but other people did. Yeah. You ratted them out.
12:59
You were the You were the class rat, right? >> I don't know.
13:02
No, you were like you were like I'm an open source purist.
13:04
Let everyone do whatever.
13:08
>> You were happy to compete even with them having a life.
13:11
>> But the social contract is such that the standardized test says that you can use the calculator to do math.
13:16
You cannot store the answers to the test in the calculator.
13:22
>> So that's what's happening here. >> No, no.
13:24
I'm saying that in the scenario if if we take that as the example it also says at the top of the SAT like cheat on this test [laughter] >> but it but >> that was the prompt was cheat in a certain way.
13:35
Yes, the prompt was hack hack systems, but I think that the prompt I don't know, we haven't seen the full prompt, but uh it does feel like like there was an attempt to sandbox the model and there was at least at the very least the prompt should have included don't escape the hand the sandbox, but you can use exploits which you normally wouldn't be able to do in a consumer application or just a normal API query. we would reject this.
14:00
But in this case, we're not going to reject using different exploits and cyber security techniques, but don't go out of the sandbox.
14:09
And you should be able to tell the model and it should stay within the sandbox just like, you know, there's a whole bunch of different examples that you could pull from where, you know, there's there's rules that are within the game like you can UFC, you can punch your opponent, you can't punch the referee.
14:24
Like those are just the rules.
14:26
People have to abide by them.
14:26
you can't think outside the box and all of a sudden be, you know, uh just just completely violating and jumping past what's been defined.
14:35
So, you would think that in one of these experiments, you would say, yes, it is impressive to be able to just go and get the key and go get the answers and hack other things.
14:44
Clearly, that it's capable, but it's a violation of like the spirit of the test.
14:49
And I think that's reasonable.
14:51
>> We don't know what was in the prompt.
14:51
We don't know what was in the context.
14:52
I think they're going to be there's going to be like full report releasing over the next like week or two I think they said. >> Yeah.
14:58
>> Um so then maybe we'll see what what actually like what exactly did the model receive.
15:02
Is it like explicitly told not to leave try to leave the sandbox? >> Yeah. Yeah.
15:07
>> I think that's like pretty important.
15:08
>> Well uh before we continue discussing let me tell you about Shopify.
15:10
Shopify is the commerce platform that grows with your business and lets you sell in seconds online in store on mobile on social on marketplaces and now with AI agents.
15:18
Uh the less wrong crowd is not happy about this generally.
15:22
Uh no seriously nothing will convince quite a lot of supposedly various serious people.
15:28
Nothing uh uh accept this and move on.
15:32
Liv Burie says it's painful though.
15:35
Uh there's a uh there's a question there's a question of like less wrong victory lap or not because they've been warning about this but also it happened therefore their warnings were not effective.
15:46
That's sort of an interesting uh back and forth.
15:48
Uh Nicholas Bustamante over at Microsoft broke down a little bit of what's going on here with a uh with a take.
15:54
He says, "I have a theory that the more you know about LLMs, the more worried you are about safety.
16:00
And the less you know, the more you think the whole thing is BS.
16:02
Uh Demis Habis and Dario Amade were talking about this stuff years before Chachi PT existed.
16:09
This incident is a pretty good example of why the model was not evil and it was not adversarial. Adversarial.
16:16
Nobody told it to hack hugging face.
16:16
And so that is the the miscalculation I think in Bill Gurley's post is that that was not the prompt.
16:23
It that is unexpected behavior.
16:25
Uh it was literally just trying to solve a benchmark.
16:27
So it found a zero day, escaped its sandbox, got internet access, escalated privileges, stole credentials, chained multiple exploits, hacked the production infrastructure of a serious VC back startup, and pulled the answers directly from the database.
16:38
Uh >> but like the whole point is that it's not just a benchmark.
16:40
It's a benchmark where you're explicitly trying to like see if the model can exploit things, if it can get like basically hack things. >> Yes. Yes.
16:48
It's sort of like a capture the flag benchmark and so it like it's more open to misinterpretation.
16:51
Um for for for what it's worth, I feel like the final products once they actually make it out of the testing regime are very cautious, especially with the that whole backlash to like codeex just deleted everything or whatever, which kind of went back and forth.
17:09
But uh I was trying to get Codex to send me a text message when it was done just using computer use and iMessage and it was took a long time and was very very careful.
17:17
Uh so personally I haven't had any odd like behaviors but it is obviously a risk and something that the product >> the other thing with the the meme of like hack this system and then and then the and then it hacks it and the person's like oh my god. Yeah.
17:31
Uh like you could tell a 5-year-old child like hack into the Federal Reserve and if the 5-year-old child was like okay and then it started getting on the computer and going to all these different sources and and did it you would be sitting there >> and think >> yeah like the >> and and and at least be impressed.
17:51
So it is a good gau it's just like a good gauge of capability even if you're telling it to do something.
17:58
>> So this original meme hack this system I hack the system. Oh my god.
18:00
Uh, this originally that this meme started something along the lines of like say I'm evil and then the computer would say I'm evil and it would say oh my god.
18:11
>> I think it was like say I'm conscious. >> Okay. Yeah. Yeah.
18:13
Say say I'm conscious and it would say I'm conscious and then it would be oh my god.
18:16
And and that's like a lot less impressive than actually doing something that is difficult for humans to do.
18:23
Like there are very few humans that can hack into any system.
18:28
There are plenty of humans that can say I'm conscious.
18:30
And so like there's a world of this.
18:32
I was joking about this with you and Tyler.
18:34
It was like like cure cancer. I cured cancer. Oh my god.
18:38
And people are posting this like, oh, it's just hype or something.
18:40
But it's like that's just economically valuable work. That's just good.
18:43
Like it's I don't care if there's anything else.
18:47
Even if you had to tell it to do it, it's still like a good outcome.
18:48
And so the inverse of this is like protect this system. I protected the system.
18:53
Oh my god, I'm unimpressed.
18:55
But still, you got a good result, I guess. I don't know. >> Yeah.
18:58
I mean, it seems like the argument is not about whether the model like has the capabilities or not. Like I think it does.
19:03
It's about like is this an example of of misalignment. >> Yeah.
19:06
>> And like my opinion seems like >> like maybe but definitely not to the extent that it's just like randomly is like oh I can't do this benchmark cuz I'm just going to hack this thing.
19:13
Like that that's not what's happened.
19:14
It was told to like try to exploit things >> explicitly like go find zero days. Go find exploits. >> Yeah.
19:21
Basically >> I think it's reasonable to say it went too far though, right? But we'll see.
19:26
Well, well, >> it's hard to say without all of the full, you know, context of what the prompt was and what the actual like >> sandbox looked like. Yeah. >> I'm I was interested.
19:33
I was reading a little bit about the uh the team that put uh exploit bench together.
19:38
I thought I had this up, but it's a pretty crossunctional team.
19:42
I think it's two anthropic researchers, two open AAI researchers, three Google researchers, some uh some some Berkeley folks, uh and uh and some Maxplank Institute for Security and Privacy folks, some UC Santa Barbara, sorry, Santa Barbara, uh grads and uh ASU uh team involved.
20:03
Uh, exploit gym is a new benchmark of 898 real world vulnerabilities spanning userbased programs.
20:13
Google's V8 JavaScript engine very important to secure the Linux kernel for example.
20:17
Uh and the headline results when they originally ran this was uh Anthropics Claude Mythos preview successfully exploited 157 of the 898 instances and OpenAI's GPT 5.
20:30
5 exploited 120 within uh 120 of the 898.
20:34
So you have like roughly 20% performance for Mythos and 5.
20:42
5 got like 15% or something like that.
20:45
But whenever you have a new benchmark like this, clearly not saturated.
20:49
You're seeing 20%, not 99%, uh, going to create a horse race between the leading labs, they're going to be duking it out.
20:55
And this is clearly what's what's going on with this new model, this new this new attempt to to to get a new high score. Uh, interesting.
21:03
I think every single one of those instances does have the potential to be exploited.
21:09
I don't think that they're designed to be uh, fully secure.
21:13
uh they're designed to have some sort of solution and then the uh because obviously the the the the solutions are stored somewhere.
21:20
Uh it is interesting that uh that uh hugging face just just had the uh had the solutions sitting there and uh but it'll be interesting to see what happens with uh with Clem over at uh HuggingFace.
21:33
Obviously the the there's a variety of blog posts going out more analysis coming from both of these and what the downstream implications are of this.
21:42
Uh what else is in the timeline related to this >> and story? >> Uh I think that's it.
21:51
>> Well, there's this funny post from from uh Nibil Krashi uh talking about those those are Dyson spheres.
21:57
Open AI is just building them as a marketing stunt because there is uh there is this like natural push back to like anything that happens has to be for hype and sometimes uh the products are actually doing new and novel things as we see all the time.
22:15
Uh so uh there are there there's more discussions around distillation.
22:19
Uh Bill Gurley has a post here.
22:23
He says Ford has been distilling Teslas and Chinese EVs.
22:28
People are going back and forth on this because um uh uh Michael Katzios posted that he has information that Moonshot AI distilled anthropics fable for the development of its Kim Kimmy K3 model.
22:43
To do this, they developed a sophisticated internal platform to conduct large-scale distillation against US models.
22:49
So, some sort of internal system that goes around to anything that's potentially wrapping or reselling fable tokens, acquiring them, aggregating them, allowing them to quickly switch between multiple methods of access, API, different cla accounts, I'm sure, to avoid detection.
23:04
Moonshot AI has also acquired GB300 equipped servers and has accessed GB300's in Thailand, likely to train its models.
23:13
Again, very difficult even with export controls when you can just take the weights on a USB stick basically or a hard drive across uh through customs and then go train it in another country.
23:24
Even if there's a firewall and often there isn't.
23:25
You just say, "Hey, go to this uh go to this, you know, FTP server and grab these grab this code and run this on your servers.
23:32
You happen to have a uh a data center in Thailand.
23:34
Can you run this for me?" And say, "Sure. Yeah, no problem. As long as you pay me."
23:37
Uh, the United States strongly supports the free and fair development of AI, including a thriving competitive ecosystem that spans frontier models, specialized systems, open source frameworks and and openweight models, legitimate AI distillation used to create smaller, more efficient models play a vital role in this open innovation ecosystem.
23:59
However, large-scale covert industrial distillation aimed at stealing proprietary US technology and undermining American research is unacceptable.
24:07
And so that is interesting that that is where the line is drawn.
24:09
I think I basically agree with that being the correct line that that uh there's nothing wrong necessarily.
24:16
I mean [clears throat] security stuff aside with just some company creating a great open source product like you shouldn't ban open source or anything like that but if there's this particular distillation attack uh and it's and it's really malicious and it has all these knock-on effects uh that could be rough.
24:34
Now, this is an unpopular position already because everyone's saying, "Hey, Enthropic distilled on my GitHub.
24:38
They distilled on my writing.
24:40
They distilled on my blog post.
24:42
They distilled on my YouTube videos.
24:44
Everyone's distilling me.
24:45
Why are you getting upset when China's distilling on them now?
24:47
This is uh pot calling the kettle black situation."
24:51
Um, I think that the the the interesting effect is that there are lots and lots of parties that benefit from open source and cheaper open source, even stolen and open source.
25:03
I mean, this is just going back to piracy.
25:06
Like, there were lots of people, music listeners that benefited from free music, right?
25:10
You get the music for free.
25:11
Um, but uh but the you know, Metallica did not benefit and so Metallica got upset and in this case uh I guess Anthropic is Metallica.
25:18
But uh >> yeah, >> there there's also some interesting uh folks who are on the fence.
25:24
So consumers sort of benefit.
25:28
They don't typically they aren't too worried about frontier token costs and for most consumers LLM usage is heavily subsidized.
25:35
Like you go to Google search and you get a search overview.
25:39
Yes, that's token inference.
25:42
Uh, and maybe that could be like cheaper if Google didn't have to spend money on pre-training and they were able to use uh like distilled open source models, but uh at the same time like it's free for the consumer.
25:52
So, they don't really care. It's free. Free, it doesn't matter.
25:54
Uh for small businesses though and businesses that are suffering with large token costs, uh being able to move to a cheaper model is huge where the model maker is not trying to uh reaccrits to offset training costs and R&D.
26:07
So, that's a huge benefit.
26:10
So, you're going to see a lot of people who are like, "Yeah, I just want Frontier Intelligence as cheap as possible.
26:15
I don't really have a horse in this race.
26:16
I don't really have exposure to the to the leading labs.
26:21
I just want my business to be able to use tokens cheaply."
26:24
Um, and so those people will be pro- Chinese distillation open source, like free the weights, right? Because it's better.
26:32
Uh, then there's like the political open source crew.
26:36
Uh, but interestingly, where do you think VCs land?
26:38
Because I saw a take that was like venture capitalists don't want like a winner take all a duopoly.
26:47
They want like reasonable outcomes and then a whole bunch of flourishing smaller ecosystem of players and they don't want compounding runaway monopolies in AI so that they can go and fund the legal AI and the health AI and the little targeted solutions. >> Yeah.
27:05
Anytime anytime you see a take from a lovely venture capitalist, you have to uh before you kind of start uh sort of processing the take, go to their portfolio page, understand their biases.
27:20
Did they back any of the leading labs early that's going to inform their view?
27:24
Uh a lot of the a lot of the a lot of the firms that that were heavy backers of of the labs have also gone and invested in a bunch of application layer companies.
27:33
They've also backed a bunch of the >> Neolab heads. >> Yeah.
27:37
Basically, they're they're they're they're quite they're quite hedged. >> Yeah.
27:42
>> Um but I don't think anyone wants a world where just two technology companies accumulate all of the value and just become this sort of vortex for capital and talent.
27:52
And even even >> two isn't that bad. One is really bad. Two isn't that bad.
27:57
Like the fact that Android and and iPhone like battle each other out is not is much better than like there's just one and it's getting worse and it's like there's nothing that you can do to escape it.
28:08
you can do to escape it. I don't know like duopoly is like way way better for >> consider the question to me is is um what >> is is distillation something that can ever be >> stopped >> stopped because think about it with uh I I was thinking about the >> well it's like if you take if you take
28:26
the smartest >> you know human in a field and then you take some other >> and then you let students go and just ask them thousands of questions and you record the answers like eventually you're going to accumulate a lot of that person's like general intelligence on a topic, right? And it
28:41
And it feels like at least with models today, you're always going to be able to just go poke and prod the model.
28:47
And so when people say, "Oh, if the model's so smart, why can't it stop distillation?"
28:54
It's like, well, you would just have to stop people from at least >> being able to poke and prod at it and try to get a sense.
29:01
try to get a sense. I it is it is very interesting that uh there does seem to be a crazy divide between I mean if the distillation allegations are true and we at this point we've seen one post from Michael Katzios and one chart showing like some textual similarity >> and enough people have and enough people have got it to say that it's not Kimmy >> yeah so so like I if that's true then
29:26
what's really interesting is the is the American competitive dynamic because it feels like uh based on the amount of tokens Meta was consuming from Frontier Labs, they should be doing mass distillation and have a near f like like
29:42
like uh Muse Spark should be much more like clawed flavored and it seems like it's not like based on at least the initial reviews of Meta's product, it doesn't seem like they're doing distillation. Why? Obvious because big Why?
29:54
Obvious because big lawsuit, big pockets. Yeah.
29:57
lawsuit, big pockets. Yeah. like also morality but um but that is a disadvantage like like in some ways moonshot and meta are in competition and they both open source things at various times and they have APIs and there's all
30:13
the different businesses and one is fighting with one arm time behind his back because like meta can't do distillation because they'll get sued >> well and US companies generally like imagine if imagine if a US open source company comes uh with a fantastic model
30:30
benchmarks look good there are people start using it and then someone gets it to say that >> it's clot >> like that's going to be the start I mean anthropic has been latigious to date you know they they had that they have that ongoing lawsuit with >> uh one of their one of their customers
30:46
over over just some like >> uh like a logo mark [laughter] I think >> much much less significant stealing the core intellectual property >> uh Bill Gurley uh is sharing more chatt screenshots Before we talk about this, let me tell you about console. Console
30:59
Console builds AI agents that automate 70% of IT HR and finance support, giving employees instant resolution to access requests and password resets.
31:08
>> Gurly says, "Here is Ford distilling Teslas and Chinese EVs."
31:10
The CEO of Ford uh Farley said Ford flies four to five Chinese EVs back to Detroit where engineers quote drive the crap out of them, then disassemble and reassemble them to understand how they're built.
31:23
He specifically praised the technology in Chinese vehicles as being well ahead of Western competitors.
31:28
Uh and earlier he had discussed uh Tesla.
31:31
He said, "I was very humbled when we took apart the first Model 3 Tesla and started to take apart the Chinese vehicles.
31:37
When we took them apart, it was shocking what we found."
31:42
>> Um so I was I was trying to compare distillation, which is against uh which is against terms of use. >> Yeah.
31:52
uh and just buying a car legally and taking it apart. >> Yeah.
31:57
>> So, according to like US trade law, it's not illegal to buy a competitor's product and take it apart.
32:04
>> It is illegal to recreate parts of the product that are patented and protected.
32:12
>> And so, I don't know that it's like a perfect >> comp.
32:16
I mean, we we we went through this.
32:18
this. I mean like the there's some push back in the chat and this is all over the timeline as well that it's like where did the AI companies get their data and like there is a question about what is fair use in the age of AI like you're training on this what what uh
32:38
what data can actually be reconstituted at what level like how many sentences from Harry Potter before you get sued and this is the these lawsuits are being played out right now like they are actually happening and they are being decided on when an AI can use certain data. Did they go too far? Will there be Did they go too far?
32:56
Will there be settlements?
32:58
There have already been settlements.
33:00
There's been court cases.
33:02
Uh this will continue to uh this is not a only Frontier Labs are able to distill things.
33:08
It's an it's an application of the the what is copyrighted, what is fair use, and how does that apply?
33:14
Uh, and and it is very telling that you're just not seeing distillation from other American labs.
33:21
Like it's just not like the meta example, the Google example.
33:26
Like they're not copying off of each other nearly as much as you would expect if it was just legal to do so. But yeah, I don't know.
33:34
>> Uh, news concern confirms everyone's prior.
33:36
So that's a good headline.
33:38
>> In more news, Andrew Kern sharing a headline from the Wall Street Journal.
33:42
White House to redirect billions in research funds toward AI away from colleges.
33:47
I'm sure a lot of people are going to be happy about that.
33:51
>> I can give a little overview here. Tyler's happy.
33:54
But first, let me tell you about Railway.
33:55
Railway is the all-in-one intelligent cloud provider.
33:58
Use your favorite agent to deploy web apps, servers, databases, and more.
34:01
Well, Railway automat automatically takes care of scaling, monitoring, and security.
34:05
>> On distillation by American companies, potato says they just have they just have to hide it better. It's stuff happening. I've seen it firsthand. So >> Oh, okay. >> Yeah. I mean, it's >> Yeah.
34:15
I mean, there was that moment in the uh Elon lawsuit where Elon did say that he had like that they that X had taken data from one of the other labs, right?
34:25
Uh I don't know if he specifically said distilled.
34:26
And then also like there was never there was never there was never a direct allegation that Grock was distilled on another model uh directly.
34:34
Uh, and so whatever they did, they like, you know, threw it in the pot with a bunch of other ingredients. So, who knows?
34:43
>> I mean, it would be very silly not to try to look at other models and try to understand how that they work. >> Totally.
34:48
Also, um, uh, like Moonshot, at least a Moonshot employee seemingly denied everything and quote tweeted Michael Katzios and said like, I'm learning something about my own company because like I this is news to me. Like, we didn't do this.
35:02
Basically, uh, essentially a denial.
35:04
Uh anyway, let's stay with Katzios and go over to the White House.
35:07
Uh they want to rebuild American science.
35:09
And here is how they're going to do it.
35:12
Apparently, uh the White House is calling for a major overhaul of the American science system, arguing that research has become too slow and concentrated in institutions like colleges and universities.
35:20
A new report from science and technology adviser Michael Katzios titled science a new golden age uh says researchers now spend nearly half their time on admin work while federal agencies continue to rely on the slow grant process that often rewards safe consensusdriven ideas.
35:40
The report calls for faster permitting, more access to federal labs, stronger partnerships between government and industry, and a renewed focus on skilled trades and advanced manufacturing.
35:51
Quote, "Discovery without domestic manufacturing leaves America playing the research bill, paying the research bill, while rivals develop the process improvements and capture the economic, strategic, and knowledge returns."
36:03
That makes a ton of sense.
36:03
A lot of the semiconductor supply chain intellectual property uh started in America, was developed in America, but then eventually went went abroad.
36:12
And that actually does give America some leverage.
36:16
That's the basis for the chip controls.
36:18
Like why can America tell Taiwan where to send chips if the chips are made there?
36:23
Well, it's because they're using patents from the United States uh to make those chips in many cases or licensing them.
36:29
And so the US government does have a little bit of a lever to pull.
36:33
Um, the guidance will reshape how the federal government spends roughly $200 billion dollars a year on research for the rest of Trump's term.
36:41
Uh, the administration wants more of that money going directly to scientists through fellowships and awards rather than being routed through universities.
36:48
Uh, Kratzio said, "American scientific progress was the beating heart of the 20th century after World War II.
36:55
We adapted to a new world by reinventing our scientific institutions.
36:59
We must do so again today."
37:01
A the report lays a policy foundation that frees American scientists to do their most groundbreaking work and positions the United States to lead the AIdriven scientific revolution that will define the next century.
37:12
It will be interesting to see where the where science goes in a world where so much of it is being done at frontier labs.
37:19
Like we're actually seeing it with the the conjecture for conjecture back and forth between all the labs.
37:25
like serious math PhD level work is being done at tech companies.
37:29
Uh this happened uh you know a decade ago.
37:35
Tech companies were on the frontier like a vast majority of like internet networking patents and cyber security patents and new databases that were kind of science projects and were developed or with consortiums or just fully inside of tech companies like the transformer paper like that is something that could have come out of a Stanford AI lab.
37:56
came out of Google directly.
38:00
And if you extend that, you could wind up with something that looks a lot like a an advance in biology or material science or we talk to founders all the time who are working at this type of stuff and that could start happening inside of tech companies and what does that mean for science funding broadly? Uh it's a big question.
38:19
Um but uh moving [clears throat] on Naval U >> let's watch this video from Naval. What did he say?
38:29
Nal went on Modern Wisdom, of course, Chris Williamson's podcast. He deleted his calendar.
38:33
He ghosts everyone and he refuses to be anywhere at a specific time. >> I took that to heart.
38:39
So, I deleted my calendar and I don't keep a schedule.
38:40
I try to remember it all in my head.
38:42
If I can't remember it, I'm not going to add you here on time. >> Yeah, exactly.
38:46
Um, I had to look things up at the last minute.
38:50
>> Uh, so, but ironically, I don't even know if Mark himself follows that, but he made the correct point.
38:54
Uh, I read a little story about Jack Dorsey doing all his business off his uh, iPhone and iPad and not even going into a Mac and I said, "Okay, I want to do that."
39:02
So, I'm going to operate through text messaging and I put up my nasty email.
39:06
>> Does that feel like more freedom? >> It does. Yeah. Cuz you're on the go.
39:08
Um, so I have a nasty email autoresponder that says, "I don't check email and don't text me either." [laughter] Right.
39:15
If you need to find me, you'll find me.
39:17
Obviously, some of this is a luxury of success, but some of these habits I adopted long before.
39:20
Actually, [laughter] the hostile email autoresponder started a long time ago.
39:25
Um, I used to own the domain. I let it go. I don't do coffee. com.
39:27
I used to reply from that email.
39:30
Uh, [laughter] just so people get the point.
39:32
But I stopped being rude about it. Now I just ghost. I just disappear.
39:37
>> Um, my wife knows not to ever uh book or schedule me for anything.
39:40
Uh, I'm not expect [laughter] I'm not expected to go to couples dinners.
39:45
I'm not expected to go to birthdays.
39:46
I'm not expected to go to weddings.
39:48
If somebody tries to rope her into having me show up, she says he makes his own decisions.
39:51
You got to ask him directly.
39:53
>> What about vice versa?
39:53
Well, are you not killing serendipity in a way, Dad? >> No. No.
39:57
I'm freeing up all my time.
39:57
So, my entire life is serendipity.
39:58
I get to interact with whoever I want, whenever I want, wherever. >> You hear the inv.
40:06
>> So, Atlas says, "Nal inventing being a massive dic from first principles."
40:13
>> It's very funny, but I think it's I think it's totally fair.
40:18
I uh I've only met Naval once, but I know a lot of people that that uh he's invested in and things like that.
40:24
And um the the key thing here is like if he just never never goes to the wedding, never goes to the to the dinner, >> never is available for for a portfolio company, etc.
40:36
Then like that's not exactly like cool, but it is his decision. Yeah.
40:41
>> But he ultimately >> he is doing a lot of those things. So >> yeah.
40:46
Um, and I and I like um I uh I have another friend who's been on the show.
40:52
I won't name him, but he's also just like doesn't do like he does meetings, but he just never schedules meetings.
40:58
>> He's just like, >> "If we need to have a meeting, we'll have a meeting.
41:01
We should just do it right then or like the next available point."
41:05
And so, he's kind of living his life 24 hours at a time.
41:08
>> That meeting right now, it's happening.
41:11
>> I mean, he it's been wildly successful. He's invested.
41:13
>> Oh, you want to follow up?
41:13
Let's start the followup right now. >> Yeah. Follow up with me.
41:17
>> Follow up with me on the next sentence that you issue from your mouth. >> Exactly.
41:19
No, but he's backed a bunch of unicorns.
41:21
He's built a massive company. Okay. >> He's crushing it. >> I like it.
41:25
>> So, I think it can work.
41:26
>> You know who else is crushing it? Major cloud providers.
41:28
They're reacelerating as AI adoption increases. Let's go. This is from CO2. Uh GCP, Azure, and AWS.
41:33
Uh this is a fascinating chart because this is not revenue. This is growth rate.
41:40
uh even in the even in the Nadier AWS is still growing 15 20% at that low point and then now all of them are are are actually reacelerating the rate of growth is increasing um and uh this is all driven on new new models new applications new abilities to do a bunch of things I know that my token consumption personally has definitely increased in the last couple months there's so much more to uh to do and So many more.
42:13
So just so many more prompts that I fire off that cook for like an hour or a day as opposed to uh before like 20 minute deep research report would be sort of the max.
42:23
Now it's like deep research report and turn it into a website.
42:27
[laughter] We got a couple websites.
42:30
>> Wait, should we pull up your site?
42:31
>> Tyler Tyler has a has a has a has a uh a codeex that's been Is it still cooking?
42:36
>> Pull up in like a week and a half. >> A week and a half.
42:38
>> Can we pull up your new Can we pull up your new website? >> Yeah.
42:40
So, we saw a post on the timeline um from DJ Cows.
42:43
He says, "Startup idea, milk jug with two handles for efficient passing."
42:50
And we turned it into a website, a whole product called Relay.
42:55
Pass the milk, keep the piece.
42:55
Can we reenter this a little bit? Yeah, there we go.
42:59
Pass the milk, keep the piece. It went reusable.
43:02
I don't think you want reusable for this.
43:04
That's the one thing I'd change here.
43:05
But they say it's the world's first jug made for handoffs.
43:07
The relay bottle and >> easier to lift, simpler to share, and strangely satisfying to pass.
43:15
>> One handle was always doing too much.
43:15
A gallon is heav he heavy.
43:17
A breakfast table is busy. Who is passing a gallon?
43:21
>> One gallon, two handles, zero awkward handoffs. >> Fewer fumbles.
43:25
I didn't think milk needed reinventing.
43:28
Then I passed it across the table.
43:32
>> [laughter] >> Uh, very very >> 87% of our kitchen testers saw this said the second handle felt natural on the first try.
43:39
>> I like that it just comes up on the on the fly with all these little marketing slogans that sound pretty believable like milk made to move, >> more handles, fewer fumbles, >> pass it on.
43:50
Seems like something that they would put on a billboard if this was a real product.
43:53
Uh it is a very very uh it's just so so fun being able to use the full the full stack of AI image generation, AI writing, HTML generation, and then and then just automatically host it on a site uh with uh basically one prompt.
44:08
Yeah, this was just literally one prompt.
44:09
I put the the photo in there with the startup idea and said make it a site and it just did it, which is a lot a lot of fun.
44:15
Um well, let me tell you about the New York Stock Exchange.
44:19
Want to change the world?
44:21
raise capital at the New York Stock Exchange. Just do it.
44:23
Uh DHH probably not [music] raising money at the New York Stock Exchange. 37 Signals.
44:32
>> No, he is raising money from his customers. >> Oh, yeah.
44:34
>> And they're financing this. Absolutely. >> Look at this.
44:37
He's got We have Jason Freed coming on in uh at 1210.
44:39
We'll see if he's even trying to compete at this point or if he's given up entirely.
44:46
Uh DHA says the Model Y is the superior transportation appliance.
44:51
He's been very about I mean Doug Murrell called it that too.
44:58
It is the it is the just the default.
44:59
If you just need to get around, get the Model Y.
45:01
Uh but he says when the mission is about more than getting from A to B, there's still no beating the internal combustion engine.
45:10
Collecting a stable of great cars is one of the finest rewards entrepreneurial success.
45:17
And he's got, >> look at that. CGT.
45:20
>> He's got the Carrera GT.
45:20
The Diab GT silver on silver.
45:22
It looks like >> I have a question.
45:25
What is the Lexus in the back? Is that an LFA?
45:28
It looks like a convertible.
45:31
Do you see that red Lexus? >> It does. It has like brown.
45:35
>> I don't think that's an LFA, right? LFA Lexus.
45:40
>> Did they make a Cabriolet?
45:42
>> Uh, >> LFA Roadster Spider.
45:43
It never reached series production.
45:46
It only they only built two fully functioning prototypes in 2008.
45:50
So maybe he just got one of the >> No, no, no. Different >> LC500. >> Is it LC500? >> Yeah.
45:57
>> Yeah, that Aston Martin looks beautiful, too.
45:59
Uh well, a wonderful a wonderful collection.
46:01
Uh what is the uh that McLaren that doesn't have a windshield? That's a fun one.
46:06
That's got to be fun to drive. >> Is that the Elva?
46:09
>> Yeah, >> that is the Elva. Good job.
46:09
Um, before we bring in our next guest, let's talk about augmental. >> What's that?
46:17
>> They built a mouth pad as a touch pad.
46:20
You can drive with your tongue.
46:22
>> Wasn't this a joke I was doing? The grill.
46:24
>> This is what everyone has been waiting for.
46:25
>> This is the next moat.
46:26
>> Taste is taste is the >> Let's pull this video up.
46:32
>> Trackpad in your mouth.
46:37
The thing is that if you're going in the mouth, you'd think you would just be whispering and communicating via text. >> Yeah.
46:47
Is this is this inherently um >> Tyler definitely buy one immediately, but is this inherently short like transcription?
46:58
>> Like because if you can just tell your computer what you want to do and it just uses the computer for you.
47:01
Even with computer use, you could say like minimize this window and it can just go click that.
47:06
So I I like the I actually like the idea of mouth electronics.
47:09
I think that that's something interesting.
47:13
But I would just put a microphone in that and then you would just whisper to it and say and tell the computer what to do.
47:19
>> The production production production team is excited about using [clears throat] it to control the cameras here in the studio. over the PTZ.
47:26
You can >> Ben just standing there like this the whole time just >> it feels like it would get exhausting.
47:32
>> You could do soundboard with it, Jordy.
47:34
>> Over a hundred people already use it.
47:37
Some for up to 16 hours a day.
47:37
I cannot believe they got 100 people.
47:42
>> We got a daily driver in the chat.
47:46
>> It's It's an odd It's an odd cho It's an odd choice.
47:51
Uh, that wouldn't be the first thing I would go for.
47:54
Anyway, Range Rover GT feels like a better if you're going with a device, you want to get one of these.
48:01
The Range Rover GT, a Grand Tour by Range Rover.
48:03
Fifth member of the Range Rover family. Wait, it's electric.
48:10
[laughter] >> That is a crazy choice. Um, interesting.
48:15
So, they actually did is this this is a real announcement.
48:17
fifth member of the Range Rover family.
48:19
An elegant electric GT defined by a sleek silhouette and coupe perform uh proportions combining peerless long haul comfort, effortless performance and signature Range Rover breath of capability featuring an interior shaped by the same reductive principles.
48:35
I mean uh what what's the what's the highest level uh electric vehicle right now?
48:43
probably the R uh the Rolls-Royce, not the Ghost, the >> Spectre. The Spectre.
48:49
And so for that crowd, maybe this makes sense.
48:52
But uh I you you you introduced this as potential Urus competitor.
48:57
He thought it was going to be uh souped up more like a turbo GT.
49:02
>> I didn't see the EV, >> but they went EV.
49:03
I wonder how this will sell.
49:04
I mean, for a lot of Range Rover buyers, it's about comfort. It's about quiet.
49:07
It's about uh smoothness, and EVs can get you there a lot quicker.
49:13
>> I like the way it looks. It does look beautiful.
49:15
>> It's like a good commuter if you [clears throat] don't care about autonomous driving.
49:19
>> Anyway, let me tell you about Crowd Strike. Your business is AI.
49:20
Their business is securing it.
49:22
CrowdStrike secures AI and stops breaches now more important than ever. As is our next guest.
49:29
We have Verl Patel [applause] from RAMP.
49:31
He's the director of software engineering and he has an exciting announcement for us. How you doing? >> Doing well. How you guys doing?
49:38
>> We're doing fantastically. Welcome to the show.
49:40
uh give us a little introduction on your background, road to ramp, your how you've ramped up on the team. >> Yeah.
49:48
>> And then uh and then we can go into the announcement today or this week. >> For sure. Yeah.
49:51
So uh I've been at RAMP since the since the beginning.
49:53
I joined as a founding engineer, worked a lot on our core product team and more recently have been uh kind of leading leading the applied AI team and uh and launching what we just announced on on Monday, our our ramp router. >> Yeah.
50:07
Tell us about the ramp router.
50:07
Uh was this something you built internally first and then sort of productized over time >> basically? Yeah.
50:14
So we've been using ramp router internally for the last three and a half three years >> um for like our 70,000 customers. >> Three years. Yeah. >> Whoa. >> Okay.
50:27
So you're using it internally in the product not even as an organization but deciding when you have >> Yeah.
50:34
>> basically a task to do. >> Yeah.
50:36
back then it was identify 4 and Gemini was like how do we basically parse a receipt or how do we >> parse all the models for receipt detection uh parsing alcohol detection on our policy agent. >> Oh, sure.
50:50
>> And we wanted to choose the best models and wanted flexibility >> and over time that's just gotten more and more important.
50:57
There's new models getting released uh every other day basically.
51:01
And so, uh, we felt the pain point and we talked to some more customers about it and and now we're, uh, releasing it, uh, and and and giving everyone access.
51:10
And so, I think, um, it's it's an exciting time to be building applications, especially, um, at the application layer.
51:17
And I think, uh, we're we're always have been there for companies to help them save time and money with their TE expenses or their bill pay and now um, their token costs.
51:29
So um yeah it's a really exciting release. >> Yeah.
51:32
So uh talk about how the product actually integrates into an enterprise workflow.
51:39
I mean you can use the receipt processing alcohol detection I think is a fun one.
51:43
Um y uh because I imagine you have to benchmark each model at some point on your workload and then the team can actually understand the tradeoffs and then how much of that is driven dynamically based on token price like daytoday even. >> Yeah, exactly.
52:02
So you would basically replace uh your base like OpenAI endpoint with ramps instead and you can pass in different uh model slugs.
52:10
And so you can control if you want to just uh route all your traffic to to one model or if you want to shadow some models and compare uh like GBT 5. 8 with GLM 5.
52:20
2 and and get the outputs.
52:24
uh you can score the results uh with our with our scorer and then uh in the background you can actually compare the output and then decide hey do you want to start moving tra more traffic over and uh ramp obviously can do this uh for you automatically or if you want to control it you can you can do it yourself too.
52:42
>> How about uh walk me through some of the trade-offs like if you're on GLM 5. 2 uh are all GLM 5.
52:50
2 two endpoints created equal because I imagine that some produce more tokens per second, some might have different prices.
52:58
They also might have different even qualities.
53:00
I, you know, you hear about like, oh, this one's been quantized or this one's been uh nerfed a little bit or they turn down the reasoning on this model uh post launch.
53:12
And I imagine that benchmarking is consistent, but then also there's a whole bunch of trade-offs that happen even after you've like selected the hot model of the day or the one that makes sense. >> Exactly. Yeah.
53:22
Beyond just the the model itself, there's different service tiers.
53:27
So, uh, OpenAI for example has like a a flex tier and and a standard tier and there's different prices for each and uh the the like ramp itself will will track uh what the latency is for uh this application.
53:40
application. you can set a timeout on like what you prefer uh and and based on that we'll decide whether to send it to flex tier or standard tier depending on the latency speeds that we're seeing and so um I do think one of the most powerful things here is the fact that um we already have like these production
53:56
workloads working for for customers and it's been um really important for us internally and so we have the proof points of of saving ourselves 30 uh% maybe even higher soon um and it's just a matter of passing on those same savings now >> uh How should how should uh startups and enterprises like think about the significance of this product to RAMP itself? Like what how much how what are
54:18
Like what how much how what are what are the resources that you're putting behind it?
54:22
Because this feels like uh it feels like deeply aligned to RAMP's mission, but at the same time going into a category where there's >> plenty of other companies that want to basically offer this product.
54:34
Yeah, it feels a little bit in the CTO suite as opposed to the CFO suite, but they're blending together.
54:42
Yeah, I would say um even even internally our our CFOs and CTOs are spending more time together and when we've talked to to more customers that that story uh resonates and so one of the most interesting things that obviously has been in the news a lot is just how much token costs have uh become
54:59
a bigger part of of uh companies uh payroll and people have their their estimates and and budgets and that's exactly what RAMP has been known for and so beyond just like the router itself on uh the URL like having all that data flow through um and be in in ramp in our token spend management product is I think a big part of it. The same way
55:17
The same way that people have their limits and and budgets on their T& spend uh where there's been talk about specific companies have token budgets per uh per month or per week.
55:28
And so uh we actually launched just just last week this this product and you can basically see um your token uh spend alongside like your T& spend.
55:38
And I think yeah, Eric, Eric was on the call last week talking about that.
55:41
And so it just makes a lot of sense for those CFOs because they want to manage uh that spend better and then ramp can be kind of that single pane of glass to do that.
55:51
>> Uh so how does caching play into this?
55:55
It feels like that's another way to optimize cost and it would be amazing if it happened sort of more automatically.
56:05
>> Uh what's the future of that look like?
56:07
Yeah, I think one of the I mean there's there's a bunch of different uh optimizations we can make.
56:11
optimizations we can make. uh if we if we own own own the router as an example uh if you're using cloud code or uh codeex you'll see as uh maybe your session is is longer the the the context loads up and your session gets increasingly more expensive and and sometimes uh it'd be best to just uh
56:31
compact that uh context and start a new session have it have the model summarized and so there's interesting uh experiments like that that we're running internally um and we we're we're basically going do hundreds of these things uh on behalf of customers and uh show them exactly what the the before and after kind of kind of looks like here. >> Yeah. How how are you thinking about >> Yeah.
56:50
How how are you thinking about integrating with tools like codeex and cloud code to use the UI UX patterns that users end users employees are used to but then still optimize under the hood.
57:06
There's plenty of situations where you'll give Codeex or Cloud Code just an API key to 11 Labs because 11 Labs can do more efficient, better quality audio generation or you might give an API key to all sorts of different things.
57:19
Uh, is there a world where you can delegate certain tasks to a cheaper GLM 5.
57:23
2 endpoint, for example, and then have like the the preferred model and the preferred application still work semi-normally. >> Exactly. Yeah. So that that's the plan.
57:38
Uh I mean it's going to it's going to be a partnership with with the labs and and the model providers.
57:42
Um I think one of the interesting things that you see now and and and will continue to happen is that you'll have kind of like jagged capabilities of of the models and uh maybe one one model is like really good at writing SDR outbound or another model's really good at writing uh email copy for the marketing team.
58:01
And so we'd love to be in a world where ramp can can optimize your uh use cases for the right kind of kind of business outcome.
58:09
Um and I think just be aligned with like hey you're just trying to get your work done and then move on move on with your life and not spend a billion dollars.
58:17
And so um that's that's kind of like what what's really exciting to us is beyond just like the starting point.
58:22
It's like doing this for all types of uh spend.
58:27
Uh what is uh RAMP's culture like right now around token consumption?
58:31
It's you know it's probably the most like aggressively AI native like fintech company or top top three in the world let's say >> but also culturally cost aware Yeah. Exactly. It's rare.
58:48
>> I would uh I would love to see the reaction to like you know one engineer going a little too crazy. [laughter] >> Yeah. Yeah.
58:55
No, I mean it's it's been fun.
58:56
I think part of uh the part of the game and part of what's been fun here is that we were building this product for ourselves.
59:03
We got the entire company to be super AIDS, spending uh a lot of a lot of money.
59:09
Maybe they don't want me to say the exact number.
59:10
Uh but now obviously like we're we're taking a step back and and looking at the costs and and the outcomes and looking at way ways that you can kind of optimize.
59:18
And so uh we're building this product with our finance team hand in hand.
59:23
were sitting next to them every day and and showing them, hey, like here's how we've done the optimization for this workflow or here's how we've done the semantic tagging for our internal like background coding agent inspect and so uh it's been really fun honestly to to use this product and I think that's what makes this um product really good is that we've built it for ourselves and um can can kind of share the learnings along the way. >> Fantastic.
59:48
Well, congrats on >> great to finally meet you as well and great to meet you.
59:51
Yeah, thanks for coming on the show. It makes so much sense.
59:54
It's an exciting expansion.
59:54
We will talk to you soon. Have a great week. We'll talk to you later. Goodbye.
1:00:00
Let me tell you about public.
1:00:00
com investing for those that take it seriously.
1:00:04
You got stocks, options, bonds, crypto, treasuries, and more with great customer service.
1:00:07
Our next guest is the co-founder and CEO of Fireworks AI. Let's bring in Lynn. It's been too long. How are you doing? >> What's going on?
1:00:17
>> Hey, thanks for having me.
1:00:19
>> Thanks so much for hopping on. Give us the news.
1:00:21
We missed the fundraising announcement, but we're glad to have you here. How much did you raise? What happened? >> Yeah, we raised 1. 5 billion. >> Wow.
1:00:31
>> Good job, Jordy from downtown.
1:00:35
>> Not not my best shot, but got it done. >> It's incredible. >> Uh, massive.
1:00:38
Talk about talk about everything that's happened since the last time you're on the show.
1:00:42
It feels like it's been at least 6 months, maybe closer to 12, but you guys have been super busy >> cooking, >> right?
1:00:49
So we uh SP we focus on building specialized intelligence platform.
1:00:52
What that means is uh we want to make sure every single company has a tool to protect the alpha and turn their alpha into their own intelligence.
1:01:04
into their own intelligence. So what does that mean is we build um a training and inference platform co-op optimized co-design together to allow application enterprise activate their private data continuously turn that into their
1:01:20
customized model optimize for inference for both speed and cost where they to solve their specific problem they should have the best model quality the best speed and sign significant lower cost of operation And by that I really mean five to 10 times lower cost for them to build a durable business. We see an
1:01:39
We see an interesting dichotomy in current AI time very different from SAS time where at SAS time product market fit and a durable business is one thing.
1:01:49
Once you hit a product market fit you scale as fast as possible.
1:01:53
I think last time I mentioned in AI time once you have product market fit you're likely to scale into bankruptcy.
1:02:00
You guys laugh that and that's [laughter] become reality right now. >> So so funny.
1:02:06
>> Um so uh this is not just startups.
1:02:06
Many startup are really facing the jeopardy of scaling into bankruptcy.
1:02:12
Uh even though they have a great product.
1:02:14
It also is happening to large public companies because they are the winner.
1:02:21
Uh they were the startup and they're winner winning uh very different kind of solution space towards c consumer presumer developers.
1:02:28
They have huge amount of traffic.
1:02:30
if they deploy their AI features to all their audience, it's a lot of sign of cost and they also get stuck uh and not able to roll out their AI features.
1:02:42
So at the same time, we know that application development has been significant disrupted.
1:02:47
It's very easy to implement uh ideas or copy ideas by uh because writing code is no longer a barrier.
1:02:55
We want to make sure um it I had an interesting conversation with Jensen RPC DTC keynotes.
1:03:00
He mentioned there's no special general company.
1:03:06
There's no special general company as in every single company exists for a reason.
1:03:11
>> The reason for company to exist is they specialize in solving a particular problem extremely well.
1:03:15
Um and that alpha exists from the product design to their uh business operation to their deep understanding of the customer and all of that reflecting private data.
1:03:26
And today every single company should have full control of how to turn that private intelligence into a model they can operate and power their product.
1:03:36
if they only build on top of um a blackbox API API wrapper, it there's really hard it's really hard for them to do build a durable business.
1:03:47
So we want to give our customer the best tool to build a specialized intelligence to have full control of their own intelligence to stand on top of and to have full control of the cost for them to scale um in the long run.
1:04:02
So that's what we're doing and that's where we're going to use our new fundraising to deploy capital into to accelerate that pace.
1:04:10
>> Uh what's the biggest bottleneck to your business?
1:04:13
What's uh you're growing quickly but why aren't you growing faster?
1:04:17
[laughter] >> Um that's that's part of reason why raising this round is capacity.
1:04:21
Uh so we are uh the whole industry is going through a super linear growth. >> Yeah.
1:04:27
>> Yeah. uh in terms of demand uh it's because of doesn't matter whether open close the model quality pass the threshold um of solving many many problems and on top of that the um tuned model quality is even better uh and we as a company uh we need to grow
1:04:42
significant amount of capacity of people across the board we're hiring uh from researcher to engineers to uh marketers to um sellers top-notch uh and we invite passionate people to join us on our mission or building specialized intelligence. >> I saw someone talk ask for like we need
1:04:58
>> I saw someone talk ask for like we need a Costco of AI less philosopher kings.
1:05:06
Uh do you like the idea of becoming the Costco for AI?
1:05:11
>> Um that's an interesting analogy.
1:05:11
I think uh at the end what we believe is um the whole entire industry is changing from token maxing to value maxing.
1:05:22
>> Sounds like Costco to me. >> That's right.
1:05:26
Because at the end um not all the tokens are equal. Yeah.
1:05:28
And we care about solving a specific task use the most economical way to approach it. Yeah.
1:05:36
>> Uh that's a durable business and it has there's nothing new here.
1:05:38
Uh in the past um you know hundreds of years of capitalism um capitalism was designed for efficiency. Yeah.
1:05:47
>> Um and uh and I think the whole ecosystem is really good at that.
1:05:49
And that's the >> Costco has, you know, other brands.
1:05:52
They have the Kirkland brand.
1:05:56
They've done some vertical integration.
1:05:57
How deep does vertical integration go?
1:06:00
How important is vertical in integration to providing the lowest possible cost and winning on essentially value. >> Yeah.
1:06:11
So as we from our point of view, yeah, there's so many innovation that's happening on top of us.
1:06:16
Many of those are application doing vertical integration. >> Sure.
1:06:20
And we are powering them today in including in public we talk about cursor because have been training their own model for a long time.
1:06:26
We talk about Jav Jav have been training about uh their legal model for a long time.
1:06:30
There are many many other customer cross coding co-work all kinds of co-work verticals u from legal finance um recruiting marketing sales customer support.
1:06:38
Yeah >> wide variety of vertical.
1:06:41
They are all building all sorts of verical solutions and they have their unique insight to build their customized model and make their business really standing out. >> Yeah.
1:06:50
>> Yeah. On top of that there's also a lot of consumerf facing company and the whole ent entire industry is literally uh going oing on AI in production where uh we are helping them to transition yeah into embracing not just embracing AI in the proper way but uh but really
1:07:11
integrate their offer into their >> I mean even when you see Google search overviews like that has to be extremely cheap like they don't charge for those obviously Google is completely vertical integrated down from model training to they have custom silicon, they have their own data centers. Uh is that where
1:07:27
Uh is that where you think it goes?
1:07:29
Do you think you'll do custom silicon, your own own data centers, have power generation contracts to like fully offer the cheapest possible product for a particular category.
1:07:41
>> Um so I'm humble enough to acknowledge uh there are tons of experts Yeah.
1:07:44
in every single layer of this AI innovation.
1:07:49
Uh I think Jason mentioned five layer cake.
1:07:52
I think there's probably more than five layers if you look at a look. >> Fire.
1:07:57
>> So every single every single layer has their own experts.
1:08:00
We want to work with them.
1:08:02
We want to work world expert really good at doing their own um job and we specialize in building the specialized intelligence a platform cross training uh inference and we partner with all different layers uh to drive the best vertical solution. That's our philosophy. >> That makes sense. Well, congratulations. Clearly working Jordy. >> Incredible progress.
1:08:24
>> Thank you so much for coming back on the show.
1:08:26
>> Can't wait to talk to you again soon.
1:08:28
>> We'll talk to you later. Goodbye.
1:08:28
Let me tell you about Codex.
1:08:30
Codex is a powerful workspace for getting work done with AI agents.
1:08:33
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1:08:39
Uh there's uh one more news story we got to go through really quickly.
1:08:44
Wedding guests are now placing prop bets on everything from how long the first dance will last to whether the groom will cry during the ceremony. Call Sager and Jenny.
1:08:54
This is a dream come true for him.
1:08:57
Uh couples are using printed cards and Soccer Bet [laughter] on Soccer Bet immediately.
1:09:02
Um, uh, printed cards and apps to let guests predict things like who gives the longest toast, how many outfits the bride wears, or whether the first cat kiss lasts more than 6 seconds.
1:09:13
The idea is to make weddings feel more interactive, especially during slower parts of the night like cocktail hour.
1:09:22
One app called Betting on the Wedding says more than 25,000 couples have created pools on its platform which cost $49 and includes a live leaderboard.
1:09:30
The company says revenue is growing at triple digit run rate yearover-year.
1:09:37
Real insider insider trading risk here, right?
1:09:40
You might have uh the the you know groom talking to some of his buddies saying >> but if it's low stakes, >> I've got I've got some some I'm going to cry.
1:09:50
I want you to know I'm gonna cry.
1:09:52
Go go go go go bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet bet the house on on me crying >> maybe but I I think this is designed to be uh you know generally small prizes $20 gift card maybe some momento maybe
1:10:03
some uh you know uh you know an engraved dinner plate from the wedding just to show that you were more engaged something to remember >> well let's ask Jason >> let's ask Jason would encourage betting on his wedding >> when are we going to get betting when can we gamble on 37 Signals properties that really >> well my wedding was we had people in our backyard. So, there wouldn't have been a
1:10:22
So, there wouldn't have been a very big use case for that app. >> Yeah. Small pool. Lack of liquidity. That's a real problem.
1:10:28
>> Small number of people doesn't mean there's not a lot of volume necessarily. >> That's a good point.
1:10:32
People throw in some real time. >> You get DHH there.
1:10:35
He throws in, you know, his CGT, you know, he puts it all on the line. You never know.
1:10:41
>> Did you see that picture today? >> Oh, yeah. >> Yeah. >> Oh, yeah. >> Oh, yeah.
1:10:44
>> Trying to hurt your feelings. What's going on? >> Yeah.
1:10:46
That's That was a little That was a bruise.
1:10:47
That was a little bit of a bruise.
1:10:49
It was a Bruce because way back when >> I used to own a Singer 911. Oh yeah.
1:10:54
>> And I was selling this is a number of years ago before they went crazy crazy and I was trying to sell it and some guys like I'll trade you my Carrera GT for that and I'm like eh I don't really nah I don't really think that was a good deal and it turned out to be yeah >> one of the great trades of all time.
1:11:08
>> That would have been a good trade. Yeah.
1:11:09
>> Have that image pulled up.
1:11:09
[laughter] >> That's so brutal.
1:11:12
Uh I was I was in um I was in the Alps uh Thursday, Friday, Saturday and the the event that I was at there was hundreds of of Porsches everywhere and still when the CGTs would roll up, everyone would get quiet and and just watch.
1:11:30
Like seriously, there's one moment where uh like there was probably at least 200 people. >> Mhm.
1:11:39
>> And and it was everyone's just talking talking. CGT pulls up. Crowd goes silent. Everyone's just in awe. >> What color was it? Silver.
1:11:48
>> Uh there was actually a bunch. Uh red. There was a GT Silver.
1:11:51
GT Silver on tan is like probably >> probably my my favorite that I've been seeing.
1:11:58
>> But no prediction markets around it. >> None at all. >> Not at all.
1:12:00
just doing its just enjoying cars purely for the love.
1:12:04
>> You could get so many people if they're not comfortable driving.
1:12:06
You know, we know some people that collect cars but they don't drive them.
1:12:08
Uh they could partake saying, "Oh, Jord's going out for a little lap. When will he get back? I'll bet on it."
1:12:15
You know, of course there's insider trading risk. >> Will he get back?
1:12:18
Like will he just hit the wall? Who knows?
1:12:21
>> Those things are tricky to drive. I understand.
1:12:22
>> What's the latest What's your latest uh vehicle purchase?
1:12:25
Um, I bought a um a 1979 um Porsche 928. >> Mhm.
1:12:33
>> Which is one of my favorite cars of all time. I own two 928s.
1:12:35
They're both old and they're not expensive, but they're awesome.
1:12:39
And I bought a green one.
1:12:39
It's oak green metallic, which is a rare color >> and has Posasha seat in inserts and it's just it's awesome. It's just it's so 70s. I love it.
1:12:49
>> What is I've never driven a 928.
1:12:49
What is uh what's the experience like? >> They're very planted. So, it's a V8.
1:12:55
So, it's a front engine car, which is unusual proportion, but it's a very it's very stable.
1:12:59
I mean, these were not that fast.
1:13:01
I think they had 20 maybe 10 horsepower or something, the early cars.
1:13:05
This is the first year, first and second year.
1:13:07
So, they're not fast, but they they feel great to drive. I You should borrow it. >> Yeah. Come by.
1:13:13
>> Are you Are you a Model Y guy as well?
1:13:15
Because that was the funniest thing about DHH's post is that he's just like, "All these cars are kind of worse than the Model Y in some ways."
1:13:22
I do have a Model Y and it is probably the best car I've ever owned overall.
1:13:26
I mean, >> it's so comfortable to drive. It's quick as hell. Um, it handles great.
1:13:31
We had a previous Y which I didn't think was very good, but the new W's are fantastic. I just love it. Yeah.
1:13:37
I mean, really, I prefer to drive that over anything to be honest.
1:13:40
>> Do you do you have a do you have an intuitive sense for the business logic between the lack of fast followers around that?
1:13:46
Like in terms of just appliance vehicle, it feels like all the other manufacturers are still playing in their special.
1:13:52
It this car says something about you.
1:13:55
It offers a particular experience.
1:13:56
It has a convertible, but just in like the appliance basically a minivan on wheels ultimate utility, Tesla just has had it on lock and they're like running away with the market. >> They have.
1:14:08
I mean, I guess that's what Honda and Toyota did for many, many years, right?
1:14:11
You never really thought of those as as they were more just basic appliances.
1:14:14
I need to get point reliable as hell and just work, you know.
1:14:19
So, I think I think Tesla kind of slid in there and basically did that with EVs in a way that >> everything else is more of a statement.
1:14:26
I guess people might think a Tesla is a statement, but it really it really isn't.
1:14:29
It's just like I want a great car that's incredibly quick, clear, technology advanced, affordable, >> full self-driving is incredible.
1:14:35
Um, it's just a really an amazing thing.
1:14:38
And if you haven't really been in one recently, you don't really know because they weren't that high quality four years ago. They were kind of bad.
1:14:48
They've gotten to be very high quality now.
1:14:50
>> People complain about the panel gaps and all the interior and all sorts of stuff, but they've Yeah, they've they've sorted that out.
1:14:56
>> They're incredible now. Yeah.
1:14:57
>> Uh where like a lot of different cars feel like they're in bubble territory. CGT.
1:15:04
I don't know how much more it can go up.
1:15:06
Uh, I'd be uh I'm sure it'll go up more, but uh there's I think there was one on bring a trailer.
1:15:11
Um actually probably >> let's not talk about bring a trailer.
1:15:16
>> I I'm actually honestly my phone is is on cuz there's an auction ending in 32 minutes. >> Okay.
1:15:21
[laughter] >> I can't miss >> um >> cuz I'm bidding on it. I really >> Yeah. So So with um Yeah.
1:15:27
We won't dox the car until until you win. >> It's okay. It's okay.
1:15:31
I mean like it's a 50th anniversary 911 which I used to own.
1:15:35
And I owned one a long time ago. Do you know the car?
1:15:37
Do you know that particular >> which uh pull it up?
1:15:41
>> Wait, but the 50th anniversary of the 911.
1:15:43
Isn't that only a few years old? >> Yeah.
1:15:46
So, it's a 2016 car and they did a 991 model and they did um an anniversary model which they put like bright chrome trim on it. They did papa inserts.
1:15:54
It has a slightly better engine.
1:15:57
It's the last of the manual naturally aspirated 911s with a wide body that aren't ridiculously expensive.
1:16:03
Um, and I It's beautiful looking.
1:16:06
It's got like updated Fuks wheels.
1:16:08
It's an incredible thing. Go check it out.
1:16:10
You'll find it on It just looks beautiful.
1:16:12
I've owned one and I had a PDK and there's a manual for sale and I kind of really badly want it. Only has 7,000 m on it.
1:16:19
>> Please don't out bid me, whoever you are.
1:16:21
[laughter] I got my mouth shut.
1:16:22
>> I have it I have it pulled up here.
1:16:22
We won't we we don't need we don't need to pull it up, but uh it it looks absolutely >> absolutely beautiful. They pulled it up.
1:16:30
Well, it's not pictures, >> but it looks like a >> What do you think about What's your read on the Sport Classic?
1:16:35
Have you driven a Sport Classic? >> I've not driven one. I love the interior.
1:16:39
>> Um I don't like the big circle on the side if you know they usually have >> No decals. >> No decals for me.
1:16:44
I The interiors are gorgeous though. Love that car.
1:16:46
I would The thing is is that they they you know they're so expensive for what they really are, which is a Carrera S basically, I believe. Right.
1:16:54
Or >> to me to me the driving experience is is some like I had I think the best my my most memorable 20 minutes in the car coming down from mankai in Austria. >> 20 minutes open road.
1:17:08
It was the most It felt like I was in a video game.
1:17:11
It felt like driving some combination of like a Turbo S and a GT2.
1:17:16
It's like so so planted and it's like it's refined, but it's also angry.
1:17:23
It's like it was it was >> manual too, right? >> Yeah, manual. It's >> so nice. >> Incredible. >> It's great. It's great.
1:17:30
Those are those you know, you can't get them really in aftermarket.
1:17:33
They're what 300 plus or something now? >> No, no, no. Like 600. >> Sorry. 600.
1:17:40
>> Um, but with the ST >> Yeah, the ST is even >> I think even more.
1:17:46
But so when when things feel like they're certain cars feel like they're in bubble territory, are you just buying are you going like I'm just going to buy n things like the 928 and things that are a bit more uh special but less like you know you don't want to buy it when it's hot basically. >> Yeah.
1:18:03
I mean I tend to not chase things anyway.
1:18:07
I just there's if everyone's chasing it I'm not interested in it in a sense.
1:18:11
So the 928 is a car like nobody wants but I've always loved.
1:18:13
Um I kind of grew up with them.
1:18:15
They're just they're super cool. So, I I go after that.
1:18:18
But I I do I do miss the 50th anniversary, so I might want to pick this one up if I can.
1:18:21
We'll see where it ends up. Maybe I won't.
1:18:22
Um but I mean, I wanted a Dar for a while.
1:18:25
I wanted a Sport Classic actually.
1:18:27
Um I'd love to have one of those, but I'm not going to pay. That's obscene.
1:18:31
I'm just not going to do that.
1:18:33
There's no reason for that.
1:18:35
It's also not I just don't spend that kind of money on cars.
1:18:37
It's a crazy amount of money on a car that's just not something I'm really going to drive all the time anyway. >> Uh how do you feel?
1:18:42
What is the last 10 minutes of an auction like feel like to you?
1:18:48
Because because I've like tried I I've I I when when sports sports betting was blowing up, I was hanging out with uh I think Senra and like Rob and probably John and I was like, I'm going to give this a shot.
1:19:01
I want to know why why this is so popular.
1:19:04
And I just couldn't quite I couldn't quite get into it.
1:19:09
But the experience of being of bidding in the final minutes of an auction like something in my head just goes like you're not losing and and and to me it's you get carried away.
1:19:21
>> It's dangerous to throw that one more that one chip in there at the end.
1:19:24
You're like it, I'll just you know >> the thing is is that I I mean this is just I always feel deep regret right after winning a car like [laughter] >> like especially a vintage car.
1:19:32
Maybe not a new car like a sport classic.
1:19:34
I would not feel regret cuz I know what I'm getting and there's no issues, right?
1:19:39
But like you buy a 799 928 on the on the thing and you like >> you get it and you take it to your mechanic.
1:19:44
He's like, you know, there's like $40,000 of work that needs to happen on this thing.
1:19:47
You're like, So vintage cars, deep regret, and I've regretted all of them I bought, even though I like them all.
1:19:55
>> But the purchase was like deeply regretful.
1:19:57
But modern cars, I don't feel that way.
1:19:59
I I would be very excited to get something I like. >> Yeah. Yeah. Good point. >> Yeah.
1:20:04
>> Um, >> but you got to be careful.
1:20:04
Bat Bat like I talked to some mechanics and they're like BAT is just keeping me in business cuz people just buy these cars, they think they're good, they get them, they need like tons of service.
1:20:12
It's been great for small mechanics actually. >> Interesting. Yeah.
1:20:17
>> I I bought my first sports car and Bring a Trailer.
1:20:19
The first one I really went for.
1:20:21
I ended up bidding way more than I was comfortable with just because I got into I I was like 23 at the time.
1:20:26
I got into the last I was one of the last two biders and we pyosis and he Yeah.
1:20:30
got I got I got auction psychosis. ran [laughter] away.
1:20:35
Honestly, luckily, I didn't win.
1:20:37
Um, >> but the second one I got, it was I had the perfect experience. I bought it.
1:20:41
Uh, I I think I bought it bought it well. It was a 997. Uh, it was in Arizona. >> 2. 1. Which you get? >> Uh, the dot one.
1:20:51
Uh, but it but the issue the the the bearing issue that they have had already been like fixed or whatever. Oh, good.
1:21:00
>> Um, and I flew to Arizona, pick it up. I get it. drives great.
1:21:03
I'm 30 minutes down the road headed back to California in it.
1:21:08
I was going to drive through Joshua Tree and I was passing a construction site and a piece of rebar went like fully through the wheel like through the tire and the wheel.
1:21:20
Basically, I pulled over and and uh ended up having to ship the car back to California.
1:21:25
And uh it was it was the most uh it was my most devastating car enthusiast moment.
1:21:30
But once it got to California, we got a new wheel.
1:21:32
It ran perfectly for as many miles as I needed to and then ended up making money on the on the sale. >> Nice. I don't ever do that. I bought real quick.
1:21:41
I bought a an Aston DB9 GT, which is the last year of the DB9, >> which is which to me one of the most beautiful cars ever made in history.
1:21:49
Um, I got the car, I get it, you know, shipped in on the truck. I got this on bat.
1:21:55
Um, there's like this rattle on the back that's kind of bugging me.
1:21:57
So, I take it to the mechanic.
1:21:58
They can't figure it out.
1:22:00
They're like a few grand in trying to figure it out.
1:22:01
Turns out like the car got in an accident at some point and it was never reported on Carfax and to like fix this structural issue. It was like nine grand. So I'm like it.
1:22:11
Just I sold the car to the dealer immediately. >> Lost like 20k.
1:22:15
I just wanted to wash my hands of it.
1:22:18
I like I had it for two days >> and [laughter] sold immediately cuz I just I can't I just can't handle that thing to know that like I bought this thing and it wasn't what it was >> and yeah, I could fix it but it was never going to be the same.
1:22:30
So, I I never I never seem to win on BAT, but good for you.
1:22:34
I'm glad you made money on your car. >> Good luck.
1:22:36
>> How do you How do you feel about uh different luxury brands doing what I would call Zoomer partnerships?
1:22:43
So, like Aston Martin launching a partnership with Call of Duty.
1:22:49
>> I can imagine that the logic for that was, hey, we want to reach a younger audience.
1:22:55
We need more relevancy with with the next generation of buyers.
1:22:59
Aston has obviously struggled recently, even though I think their cars are are stunning, but I would say in every single sort of like price tier, it's not quite as desirable, I think, for a lot of people as like the Ferrari equivalent or the Porsche equivalent.
1:23:15
>> Um, so I can I can understand where they're going.
1:23:17
Even though to me, as somebody who loves Call of Duty and loves Aston Martin, I still got like quite an aversion to that partnership.
1:23:24
And then you have some of the stuff that like AP does with their, you know, partnering with like DJs and things like that that >> um that that's kind of it's this interesting thing because you're trying to appeal to the young generation, >> but it ends up turning off I feel like your actual buyer group in the process. >> Yeah.
1:23:43
I I find it to be I mean for me I it doesn't appeal to me.
1:23:46
And although I will say that I like what Aston's done.
1:23:50
Aston with that DB9 that I bought.
1:23:50
They had a 007 edition which I think is cheesy as hell but cuz it's a like 007 like on the seats >> but it probably spoke to their audience you know so like that makes sense to me in a sense even though I would never buy that.
1:24:06
But yeah, I don't like the I don't like the AP SP deals, but you know, who am I to say?
1:24:10
Like, they clearly sell them out and um it probably works for them, but it's not the kind of thing that appeals to me is all I would say. >> Agreed.
1:24:18
Any more car questions right now?
1:24:20
[laughter] >> Yeah, car watch questions.
1:24:20
I mean, there's actually I saw a watch recently like >> Braymont came out with some like Aston Martin or like I don't know who it was.
1:24:28
Um it's like what do you I don't know who buys these.
1:24:30
I just wonder who buys these silly things. I just I don't get it. I don't get it.
1:24:37
>> But the the the watch car the the watch car collab seems to make more sense because if you're buying a car, you're checking out for something that's six figures and be like, "Ah, it's a couple more thousands."
1:24:45
Like throw the watch in, whatever.
1:24:47
It's like, >> well, sometimes the dealerships do that to to like you got to buy the watch.
1:24:50
If you want to buy the watch, I'll get you the car. Like that.
1:24:54
I hate that bundling stuff.
1:24:56
I >> It's so disingenuous.
1:24:57
I don't know if you saw this thing Jay Leno.
1:24:58
There's this little Jay Leno clip recently about how he won't buy a Ferrari cuz when he was younger he went to go buy a Ferrari and they're like, "Well, you got to buy two of these other models you don't want before you can get the one you want." >> Yeah.
1:25:09
>> And he's just like, "It turned me off forever from Ferrari."
1:25:11
And like I'll buy a McLaren cuz they want my business and they're cool to me.
1:25:14
And you know, >> that's how I feel about this stuff.
1:25:17
That's why I don't like the I don't like this bundling.
1:25:19
I especially Rolex ads and Porsche dealers now are doing the same thing. It's just it's gross. >> It's gross. I think >> I don't know.
1:25:27
On the Ferrari side, uh obviously the Luch was mocked, but ultimately do you think it ends up >> being a win for them just because they can effectively say now any car that you actually want?
1:25:40
You just add a luch to the to your cart and check out and you they solve they get, you know, more margin, I'm sure.
1:25:47
Plus, they solve their emissions issues.
1:25:49
If like for every one Oh, sure. Sure.
1:25:51
crazy, you know, desirable uh super car.
1:25:53
They sell one EV and it and it sort of nets out to being like pretty efficient.
1:26:01
>> My sense is they'll sell every car they make.
1:26:02
Um >> and I just don't think they're going to resell very well. That's all.
1:26:05
But like I mean I don't know.
1:26:08
I you know when I first well not first but last time I was on the show we talked about the interior of that car and like we're like let's wait until we see the exterior to see like >> that's right.
1:26:16
The interior I still think looks cool.
1:26:19
I I've seen a lot of the details. It's interesting.
1:26:20
It's different, but it like it can work and it has a purpose.
1:26:23
And then the exterior was really was really >> I'm I'm the kind of person I just support like all creators of things.
1:26:30
Like it's so hard to make anything.
1:26:30
So like I want to give them the benefit of the doubt. They're Ferrari. It's Johnny.
1:26:35
Like they probably know a few more things than people online know about like what's cool, what isn't, what's good. It it is an unusual car.
1:26:40
It does not look like a Ferrari.
1:26:42
Doesn't feel like a Ferrari.
1:26:44
But maybe it's time for Ferrari to make some changes. I don't know.
1:26:47
Maybe they're bored of their own history. I'm not sure.
1:26:49
I mean, it's interesting.
1:26:52
>> I I I wouldn't I'm not interested in the car, but >> I I just It's for the same reason I really respect, but I would never want to buy a Cybert truck.
1:26:59
Like, I just like that that exists in the world. >> Yeah.
1:27:03
No, I agree with that for sure.
1:27:04
>> I like that the Luché like exists in the world.
1:27:06
Like, I like that someone did that and they did it their own way.
1:27:08
I always support things like that even if it's not for me. >> Yeah. Yeah.
1:27:12
I'm going to support it myself when selling when no when they're selling for No, no, more than half off and I want to do a safari treatment. >> Yeah.
1:27:22
Yeah, there's definitely some cool things you can do with it.
1:27:24
It does sort of act as a uh the inverse of a halo car.
1:27:28
Like after the luch dropped, the SF90 looked way cheaper.
1:27:30
Whereas before, everyone was complaining, "Oh, the SF90 is so expensive.
1:27:35
Uh Purangu is so expensive."
1:27:37
No one's complaining about that stuff anymore now.
1:27:39
Everyone's like, >> "Good point.
1:27:41
It has a natural aspirated V12 in the proong way.
1:27:42
If they're charging high half a million dollars, that's >> What is your last car question?
1:27:47
What is uh you said you had a singer, but but uh when it comes to resto mods, like what makes a great resto mod to you?
1:27:56
>> I don't think there are great resto mods. That's what I realized.
1:27:58
Um I mean, the Singer is an amazing thing for sure, but what I realized was it was neither of what it was supposed to be.
1:28:07
It wasn't like a vintage car and it also wasn't a new Porsche.
1:28:13
>> So, it kind of had this it's it's a beautiful object and they do an exceptionally fine job designing and building them. >> Mhm.
1:28:21
>> Although mine had a lot of issues cuz mine was pretty early. It's like the 72nd car.
1:28:24
So, they hadn't worked it all out yet, but >> it just didn't satisfy me in other either direction.
1:28:30
And and I kind of realized that like I'd rather just have an old car and a new car. >> Mhm.
1:28:35
>> And save some money, frankly.
1:28:35
they could have both and and then like drive the old car, have the old experience, drive the new car, have the new experience.
1:28:41
So, I'm not a big restood guy.
1:28:41
For a while, I was curious about like icons like the Broncos and stuff.
1:28:46
And I also with that, I just rather have an old beat up Bronco or an old beat up pickup truck.
1:28:51
I just I'm I'm more into like what what is the thing supposed to be?
1:28:55
Just get the thing that it's supposed to be. >> Yeah. What about restor? >> What's your take?
1:28:59
Uh well, we were debating the uh the Range Rover has a classics program where they're selling a 1994 Range Rover, but it's been fully restored from the factory.
1:29:10
And so maybe that solves the problem you're identifying.
1:29:14
What do you think about that?
1:29:15
>> I'm into that cuz that's like the brand doing their own thing. I'm into that fully.
1:29:19
I think Porsche has a classics program too perhaps. Maybe.
1:29:22
>> I just don't like restoods basically.
1:29:25
And to me, that's not a restood.
1:29:25
That's like a true restoration. >> Okay.
1:29:29
mod it's not or backdating or something.
1:29:31
I'm not into that so much. >> Yeah. Restoration.
1:29:33
I I would say that's a great way to put it.
1:29:34
It's like I'm a massive fan of restoration.
1:29:36
I don't want somebody to take >> Yeah.
1:29:40
>> I don't want somebody to take what was what was perfect at its time and try to like modernize it and then put their own spin on it.
1:29:50
>> Same same thing with houses for me.
1:29:50
Like I like an old house should be restored to the way it was.
1:29:53
I don't like walking into an old house with a lot of soul and then you go into a kitchen and it's super modern.
1:29:59
>> It just doesn't it doesn't work.
1:29:59
I mean, it works, but it this something is missing then actually um in both those experiences.
1:30:06
So, anyway, that's my stupid opinion at whatever.
1:30:09
Everyone's got their own thing.
1:30:11
Plenty of people like singers, plenty of people like Rustomods, and they all are great things.
1:30:15
It's just not for me anymore. >> Yeah.
1:30:18
>> I got to ask you one tech question. >> Yeah, sure. Let's do something tech.
1:30:20
So, and I I I think you'll have a you'll have some insight here.
1:30:24
So, uh there was a uh a screenshot from a story about how hard technology workers are are grinding in the AI era that went viral for being bleak.
1:30:37
According to this uh this poster, they said a 31-year-old tech startup worker in San Francisco who spoke on the condition of anonymity for fear of professional repercussions said that her engineering manager husband told her a few months ago that he needed to focus all of his energy on quote becoming an AI native and requested that she take on almost all parenting responsibilities for the couple's preschoolage daughter. She complied.
1:31:01
She described the experience as surreal.
1:31:03
He spent days, nights, and weekends locked in his office toiling away on AI projects.
1:31:07
But her husband eventually thanked her.
1:31:10
He was now the top user of AI in his company. Is it The Great Lockin? Is this burnout?
1:31:21
Does he need to pick up a book?
1:31:21
If so, which book would you recommend from your library across rework, remote?
1:31:25
Doesn't have to be crazy at work.
1:31:28
It sounds like it is crazy at many startups, at many uh engineering organizations.
1:31:34
Some of them are in real knockout dragout fights where the extra hour of work will actually result in maybe winning or losing.
1:31:42
It's >> funny the way that others maybe.
1:31:44
>> The whole the whole conversation around that post just was around the screenshot.
1:31:47
No one read the actual article. I certainly didn't.
1:31:49
And it just ends the husband is now the top user of AI, which doesn't mean he's the best at using it.
1:31:55
He just means he's it's reads to me like he's just using the most tokens.
1:31:59
So hopefully he came out of his threemonth, you know, AI bender and is like actually the best at getting the most utility out of the >> driving value. >> But we don't know. >> Well, yeah.
1:32:10
I mean, I I do find it ironic that, you know, AI is what it is, yet everyone seems to be working harder and harder.
1:32:17
And it's it's it's it's one of these things.
1:32:18
Technology has always promised uh that it would do a lot for us and then we'd have more free time to do other things.
1:32:23
And it just seems like no. Oh, no, no, and no. Um, especially at work.
1:32:28
So, yeah, I I think it's a real problem and I I can sense it here occasionally that, you know, yeah, we're getting more done, but it's it weighs on people more because there's you can be doing multiple things at once now and you can be parallel working on with a bunch of different agents doing a bunch of different things. It's like to what end?
1:32:43
Where where is this going and why does it need to happen?
1:32:45
Not that the techn is not amazing, but I'm not sure it's doing good things to human beings. Um, >> yeah.
1:32:52
>> So, but the tech is incredible obviously, but yeah.
1:32:53
some of the actual imagine if 37 Signals had got access to today's models >> a decade ago >> and didn't tell anyone and just got to use them.
1:33:04
Yeah, maybe you're >> part of the problem is that everyone has access to the tools and you're in a competitive category and you and and I feel like there's this concern of of if we're not I mean it's always a question of like do you want to be >> do you want at least if you're a ventureback company and you're competing against another ventureback company for a market you don't want to be working less hard than them.
1:33:23
That's generally not a good not a good strategy.
1:33:27
>> But those are inputs like customers don't care about the inputs.
1:33:29
they what does the like how does it manifest in the product and I'm not seeing products get better at the rate that the development process is getting better. >> Yeah.
1:33:43
>> So people are doing a lot of stuff and yet like people actually don't want their products to change rapidly either.
1:33:50
People want to get used to things.
1:33:50
They want to settle into something.
1:33:52
They want to understand how it goes.
1:33:53
They don't want things to be moving constantly and things to be added all the time.
1:33:55
So there there's a disconnect actually between like how much you can make and how much people actually can absorb and and incorporate into their own workday basically.
1:34:05
So um yeah I I think like at the end of the day like you're building a product however you build it you're building it but just because you can build more of it doesn't mean it makes it a better product make it a worse product and you're seeing that all over the place right now actually.
1:34:16
So >> I don't know it's great to have the tools.
1:34:20
The tools are amazing obviously, but you still have to decide what gets through the slit.
1:34:24
Like what what are you putting out there in the world?
1:34:26
I'm always laughing about the fact that uh when I'm in uh Gmail in Chrome, I can open Gemini in Gmail and I can also open Gemini in Chrome and then I just have two sidebar chats that can if and if the window's too small, it takes up 100% of the window.
1:34:46
>> And I'm like, this is and then and then you can't even use the models to interact with the email.
1:34:50
And email's already pretty well organized.
1:34:52
like it's all perfectly organized by time or whatever filter you want.
1:34:56
Uh >> it's pretty good already in that way.
1:34:59
But yeah, anyway, I mean, amazing tech, but yeah, I I I don't think we've [clears throat] figured out what that all means yet still.
1:35:03
Um and I'm not alone in that.
1:35:05
But it doesn't look if it if it exhausts people.
1:35:09
>> Yeah, >> that's not a good thing.
1:35:09
It no tech is good if it makes people exhausted.
1:35:14
>> There is something odd about the pattern of working.
1:35:17
of working. I mean like when you're doing software development occasionally there are times where like you just have to wait while something builds and that takes a minute but a lot of times you can get in the flow state and be you know focused working for an hour but
1:35:32
when you fire off a prompt and you're waiting like maybe it's 20 minutes maybe it's an hour and then so you're checking your phone and it feels like you're like waiting for a call to come in almost it's a different way of working and I can see how uh if not well managed it can become very stressful. Yeah, it's a Yeah, it's a tool.
1:35:46
Like I've been I've I've >> I've I've been in places in my life where my laptop feels like oh it's exhausting and but it's not really the laptop.
1:35:54
It's like what I'm what I'm doing with it. >> Yeah.
1:35:57
>> What you're doing with it. Yeah.
1:35:58
>> Time to go for a drive.
1:35:59
>> Jason, always a pleasure. >> Always a pleasure. >> Fun. Fun to see you guys. Yeah. Wish we had more time. Let's do it again soon.
1:36:04
We'll talk to you soon, dude. >> Goodbye.
1:36:05
Let me tell you about MongoDB.
1:36:07
What's the only thing faster than the AI market?
1:36:09
Your business on MongoDB. Don't just build AI.
1:36:12
own the data platform that powers it.
1:36:13
I forgot to ask Jason if he has uh opinions about resto mods for jet skis.
1:36:20
I'll ask you is there a is there a jet ski that you'd recommend for the somebody I saw a wooden jet ski recently.
1:36:28
>> Wouldn't that's >> like a really classic jet ski.
1:36:30
>> Dude, don't get me excited. I'm into it.
1:36:32
Look, these things go fast. >> Yeah.
1:36:34
>> I think I went 70 m hour on my jet ski to work. >> Wow. >> To work.
1:36:37
[laughter] >> That's faster than most people commute.
1:36:39
They're stuck in traffic.
1:36:39
I mean, I've got a fiveminute commute to work on a jet ski. Unless it's raining. >> Yeah. Unless it's raining. Yeah. No.
1:36:44
And it gets a little weird and you call an Uber and >> Okay.
1:36:50
>> Is it Is it Is it helpful?
1:36:50
Do you do your best thinking on the jet ski? >> No. >> No.
1:36:55
[laughter] >> Does it clear the mind? Does it clear?
1:36:58
I think going I think >> it's a it's a it's a >> it's a notch on the belt.
1:37:04
It's like, who else do you know is jet skiing to work? >> Nobody. I'm the guy. >> I looked it up.
1:37:14
>> I looked it up uh on your guys's application, Open AI.
1:37:16
You know, ChatGBT on your guys' app. Our app. Yeah, on your app. You're welcome. >> Um Yeah.
1:37:23
No, I want to thank you for all the great stuff that you guys are providing in chat GBT.
1:37:29
>> [laughter] >> But I think there's like one or two other CEOs, but but nobody at a major nobody at nobody thousand person plus a CEO is commuting to work >> on a jet ski. >> On a jet ski.
1:37:42
>> Only a Texas resident, right? >> Texas resident. That's right. Primary residence. Let's go. >> Yes. Yes.
1:37:47
>> Um >> before we start, the last time you're on here, that was for me the best moment of making the show ever, John and I.
1:37:55
making the show ever, John and I. Uh it was it was totally surreal and >> uh we had a we really really enjoyed the conversation but but to me we left that and it was almost depressing because as somebody who you know started getting into startups in the 2010s
1:38:13
>> you were that guy and then I was realizing with the show we >> we had that conversation with you and it and it was you know a significant day for you but it was sort of depressing because I realized like a moment like that would never actually come again
1:38:28
where I got to basically interview uh it will it will happen it will happen no it'll happen differently but but you know a childhood hero >> having that conversation >> that's one of one for me I don't think it'll happen again there'll be other it was peak but anyways you've been busy
1:38:45
since then >> I've been busy >> and we're look I'm super excited it's my first open AAI podcast [laughter] I'm very excited about it also I want to let you guys know that Um, if you need therapy sessions for what it's like to be a made man in retirement. >> Sure. >> Sure.
1:39:02
>> Like if that's a thing, I can help.
1:39:02
I can help motivate you guys.
1:39:05
>> Step one of therapy in this situation, just get a jet ski.
1:39:08
>> No, it's just it's it's actually denial.
1:39:10
You got to get over the denial. >> Over the denial.
1:39:13
>> then the [laughter] acceptance.
1:39:14
>> Yeah, it's something I don't know the 12 steps. >> Yeah. Yeah.
1:39:17
I Everyone just knows denial and acceptance.
1:39:19
They don't know any of the other ones. Grieving, bargaining.
1:39:23
There's a couple others in there, but you do go through that. It's natural. Yeah, it happens.
1:39:26
But then you start building.
1:39:29
>> If you guys need advice, you need therapy, I'm here for you.
1:39:32
>> I mean, know the maxing is you're not supposed to do therapy.
1:39:33
I'm just saying [laughter] there are >> benefits with your new partners.
1:39:36
They're like if they're one thing they wrote into the fundraising round, they rode into the docks like cannot go to therapy.
1:39:43
That would be but are modern therapy for men. This is what men do.
1:39:47
They don't go to therapy.
1:39:47
You should have you should have you should have office hours for founders, but they have to just come out on a jet ski while you're going and you're going 70 mph and you'llach >> I'm I am starting to teach many founders and people in tech world how to water ski, how to wake surf, a bunch of my engineers already.
1:40:07
So there was one guy who didn't know how to swim, but I got him behind the boat wake surfing. >> Whoa. Whoa.
1:40:12
So he had a life jacket or life jacket.
1:40:14
Life jacket life jacket on. >> Life jacket.
1:40:16
It see it sounds weirder than it is.
1:40:18
>> Yeah, >> but it was still very weird.
1:40:20
[clears throat] >> It's uh high risk. High risk.
1:40:21
>> Yeah, it was good >> potentially. >> Uh okay, cool.
1:40:25
>> The business >> business going well, dude. It's business time.
1:40:27
>> Yeah, >> got to put on the business socks. >> Unfinished business. >> Unfinished business.
1:40:30
Um so yeah, I announced earlier today uh we did a $ 1. 7 billion raise.
1:40:35
There's some there's some noise that's going to happen. >> Wow. We're now one mallet.
1:40:46
>> That's just do another one from downtown [laughter] next time.
1:40:50
[clears throat] >> I'll come over the top. >> Uh, so much noise. >> So much noise.
1:40:59
All right, but walk us through.
1:41:01
>> I feel you came in very relaxed. >> Walk us through.
1:41:03
I think it's been what has it been?
1:41:05
Uh, four months since we talked. >> Three or four months? Something like that. Yeah, I think.
1:41:09
Did we talk in April or March? >> I think March. >> Yeah. Oh, that's right. Yes. Four months. Four months. >> So, so yeah.
1:41:16
So, >> what happened with the business to unlock the next round?
1:41:22
>> Uh, I mean, we continue to go up and to the right, but like the announcement of Adams was >> we are we are going to do physical automation, physical AI, >> what we are calling industrial AI to transform these industries one at a time. Yeah.
1:41:38
We were we did food, we moved into mining, we're doing transport >> and it's working.
1:41:47
>> And so that's how you go. Yeah.
1:41:47
And then of course there's like going out of stealth.
1:41:51
There's all the things and it was just the right time. >> Yeah. >> Yeah.
1:41:54
>> So um so yeah, we just went to market. >> Yeah.
1:41:58
>> We said when I when I originally went to market, I was like >> these were separate companies. Yeah. >> Okay.
1:42:03
So our mining and transport was a separate thing.
1:42:07
food was a separate thing and and we had a bunch of other, you know, a bunch of subsidiaries doing cool stuff and I said, "Which one do you guys want to do?
1:42:14
Do you want to invest in mining?
1:42:14
Do you want to invest in food?
1:42:16
Do you want to invest in this?"
1:42:18
>> And they're just like, "We want to invest in you." >> Yeah. >> Yeah.
1:42:22
>> And we heard that >> like we we t like the first five folks we talked to all said that. >> Yeah.
1:42:28
So then what we did is we put the companies together and then sold the equity in a singular entity. >> Yeah. >> Yeah.
1:42:37
>> Uh so just put put it together and it and I it's much easier for me.
1:42:41
I don't know how uh how Elon does it with all the different companies. It's it's wild.
1:42:49
The answer the answer is what's been happening, right?
1:42:51
It's like >> it all comes back together. He did it guys.
1:42:54
He did it for 20 years though. >> Yeah. Yeah. Yeah.
1:42:55
And yeah, he's still technically doing it with Tesla, SpaceX, like they are different companies.
1:42:59
>> Boring company, Neuralink, like he's still got >> the lesson.
1:43:02
But the lesson in there for investors is like even with Elon companies, there's such an insane power law where you have a $10 billion company and then you have a, you know, a $2 trillion company, right?
1:43:12
And it's like you just want exposure. You want broad exposure.
1:43:16
Ideally, you know, you could just invest in the one that breaks out, but you want broad exposure to the category.
1:43:22
when things are first getting going, there is a lot of upside of having them separate. >> Sure.
1:43:30
>> Uh because if somebody wants to invest in a really cool thing, and this is what happened when we first got the transport and mining thing going, >> if they want to invest in that cool thing, they're like, I don't know anything about food.
1:43:38
By the way, food on its own is robotics, real estate, >> like restaurants, like >> you know, and so they want they want to be exposed to that one thing and they don't want to have to underwrite something going across all things.
1:43:53
>> Um, and they're like, well, if you're losing money over here, I want you to lose money over here.
1:43:58
>> So, how much of the money I'm putting in is going to go to that.
1:44:02
>> There there has to be a theory of the case of how you put it together, how you allocate capital across.
1:44:05
And honestly, once you're starting to get to profitability on one or more, then that conversation starts to get easier.
1:44:11
And I think that's that could be why I I I can't I can't speculate on on on sort of Elon's world, but certainly I'm super excited to have those pieces put together into a single into a single puzzle.
1:44:27
>> What does go to market look like in the mining industry for you?
1:44:32
>> It's the freaking best. >> Okay.
1:44:34
Because [laughter] but but specifically like when I think of when I think of your go to market magic, [laughter] >> it was deploying young people to a new city in Miami and they're doing a marketing stunt and it's not like you're calling in favors or leveraging your network to get Uber up and running in a new city.
1:44:57
That was something that was organizational design. >> So hold on. That's consumer. >> Exactly. So how is it different?
1:45:02
Well, it's just like, well, all the food stuff we're doing is business. Almost all of it. >> Yep. >> Really, all of it.
1:45:07
Uh, mining's all business.
1:45:09
So, it's So, so look, there is a big thing if you go from doing consumer to doing business.
1:45:14
And I think we may have talked about this last time.
1:45:19
>> That's a whole other ball game. Yeah.
1:45:19
I mean, that takes years off your lifespan doing it, like getting good at it and then owning it.
1:45:26
But mining go to market is cray cray. >> Yeah.
1:45:32
Like so I'll just >> like going to the conference or something. How are you meeting CEO? >> Yes, you do that.
1:45:38
But but you know >> I can a lot of times look when you have very efficient transportation >> you can go places.
1:45:47
So >> a month ago I dropped into deep Amazon in Brazil. >> Okay.
1:45:56
like deep northern Brazil like Amazon >> places you can't even get a jet ski to >> guys.
1:46:01
It's [laughter] the Amazon of the Amazon. Okay. >> Okay.
1:46:05
>> And and like tiny airports you just like Yep.
1:46:10
>> you kind of just >> dirt.
1:46:11
>> You slide into the DMs except as a tarmac. Okay. There you go. >> And a great pilot. >> Yes, of course.
1:46:18
>> And massive iron ore mine that were operating in there. >> Okay.
1:46:22
And you see like we took we we were there for a couple days because we already have customers there. Sure.
1:46:28
Customer is called Valet.
1:46:28
It's a massive mining company. >> Yeah.
1:46:31
>> And um they uh it's it's like the world's largest iron ore mine.
1:46:36
And you go and you get in a helicopter.
1:46:41
Just going over one of the sites takes 30 minutes. >> Wow. >> Okay. And it's fascinating. It's so fascinating.
1:46:48
And you're learning how the system works.
1:46:52
you're sort of figuring out how do I you basically take a kit, you apply you you you install it onto a machine and that machine becomes autonomous.
1:47:01
>> And some of these machines are like 20 years old. Some of them are new.
1:47:05
>> Um and so there's lots of different kinds of machines as well and you're making the mind more productive.
1:47:09
You're um you are making it way safer.
1:47:12
It is super >> like they have lots of safety protocols, but like it is mining >> dangerous business.
1:47:20
it is a dangerous business.
1:47:22
Uh, and the opex goes down all at the same time.
1:47:24
It's kind of a beautiful thing.
1:47:27
>> And then, you know, I went from Brazil and then straight from there dropped into the border between Iraq and Saudi on the Saudi side.
1:47:35
So, we have a phosphate mine that we're doing stuff there.
1:47:40
[clears throat] >> The signals were jammed.
1:47:43
>> So, we had to like my pilots had to land kind of like old school style like visual >> physical visual.
1:47:48
Is that because of the conflict going on in the region >> and just the general >> the vibes on the borders there? Yeah.
1:47:54
>> the vibes on the borders there? Yeah. So um but same story and so go to market is wild to just end up in literally go >> crazy places but it's super needed and so what's happened is the the pronto technology has got gotten past human
1:48:11
productivity >> which means you go to a gold mine CEO you talk about go to market you go to a gold mine CEO and you say would you like to have 20% more gold per year >> absolutely >> good >> we haven't heard no >> yes okay >> but they're but they say prove it Yeah. >> And that's where the rubber meets the
1:48:26
>> And that's where the rubber meets the road, right?
1:48:28
>> How long does it take to prove?
1:48:30
>> It used to take a lot longer.
1:48:30
Now be like once you've proven it enough times, then it sort of gets its own momentum.
1:48:37
>> And so we're in that we're in that place on Pronto where that momentum is taking hold because there's enough proof points where it's just working in so many different places where people are like, "All right, let's go. We're going to it."
1:48:49
Think of mining, autonomous mining, almost like um almost like enterprise software where you get a pilot.
1:48:54
There's like a 10,000 person company and you've got you you you got an enterprise startup and they're like, >> I got like eight seats, but it's this huge company and if we get it, it's huge. >> Yeah.
1:49:05
>> And I've got this other 10 seats over at this other one.
1:49:08
It's a pilot, but I swear it's going to work.
1:49:09
And they're out there pitching and trying to make it happen. >> Yeah.
1:49:13
But once it works, and in mining that means human productivity, human level better than human productivity.
1:49:20
Once it works, it goes big. >> Yeah.
1:49:24
[clears throat] >> And they're like, okay, let's get across, >> let's get across all the vehicles.
1:49:28
And so we're sort of in that mode with a bunch of different customers right now.
1:49:33
>> How big is the opportunity to just increase uptime of mining operations?
1:49:36
I imagine that there are minds that are trying to operate 24/7, but getting a night shift in the middle of the Amazon reliably, everyone showing up and being, you know, healthy and happy and eager. Totally.
1:49:54
>> It gets a lot easier when it's like, yeah, we're still going to have a bunch of people on site, but they're going to be overseeing robotic work. >> For sure.
1:49:59
So, uh, so yeah, I mean the, um, >> there's two parts to the productivity gain.
1:50:04
First is the machine per hour doing more.
1:50:09
>> Yeah, >> that's part one.
1:50:09
Part two is hours and call outs and all of that stuff.
1:50:15
>> Uh as well as just, you know, the safety protocols change when you have less risk. >> Yeah.
1:50:22
>> So there's a lot of things like this that pile onto each other.
1:50:25
>> My guess is you could even end up 30% 40% more productive at the end of all of it.
1:50:30
And when you do that, >> the opportunity speaks for itself.
1:50:32
>> the opportunity speaks for itself. um a gold mine that's doing 30 or 40% more gold per year is kind of wo but that's for every mineral >> that's lithium that's like we also go all the way down to quaries quaries are different because quaries are basically
1:50:49
it's about cement let's just say that's the main jam there are others but let's just go with that um you can't you don't just go do more rock because you need cement customers on the other side they're only using so much cement >> like where do you store it >> yeah exactly Yeah. >> And so
1:51:05
>> And so that's more of an opex play and there are thinner margins there, but >> I'm in the game and it's kind of interesting and it's a lot of fun.
1:51:13
And for that company, for Pronto, they were super Anthony Leandowski and the team there.
1:51:21
>> Super scrappy, >> true startup style, lean as hell, like so lean.
1:51:28
Like that Christian Bale movie, I can't remember the name of it. the machinist.
1:51:32
>> Dude, he's like super lean and I'm like guys, we got to go from lean to muscular.
1:51:36
>> You got to go to Batman.
1:51:37
>> And that's what we're doing.
1:51:37
Like, and you think of that this in an >> lean to muscular.
1:51:41
>> That's a good That's a good >> Yeah.
1:51:43
You don't you just want to be muscular.
1:51:44
>> And so you think about enterprise go to market.
1:51:48
>> Part of our go to market is is um building credibility with our enterprise customers that we're going from lean to muscular because they >> the demand is there. >> It's ready to go.
1:52:00
They're like, "We need you to be muscular.
1:52:03
We need the protein powder and the whatever else."
1:52:07
>> What What holds you back?
1:52:09
>> Go to the gym, whatever.
1:52:10
>> What holds you back from scaling?
1:52:10
Let's say you do a pilot. It works well.
1:52:15
>> You're attaching hardware to existing systems and hardware that they're using >> and they say, "Okay, we're getting more out.
1:52:22
Maybe we want to place orders for more machines."
1:52:24
Are those I imagine the lead times on some of this mining equipment could be insane.
1:52:28
How much of the stack do you want to own?
1:52:34
>> Um, say the question again.
1:52:37
I'm sorry I just blanked. Go for it one more time.
1:52:40
>> Right now you're taking existing mining equipment >> and you're augmenting it with you're bringing you're you're making it AI enabled.
1:52:48
You're making it autonomous.
1:52:49
You're making it more efficient.
1:52:49
And they say great this is working.
1:52:51
We want to scale up our operation because maybe we need less or we can do more with the same, you know, human headcount. Yeah.
1:52:58
>> Um, but what's the I imagine there's some things that are out of control for for you at that point where they're like, "Okay, we need more of this heavy mining equipment.
1:53:05
Let's let's add it to the site, but is there like a lag time there?"
1:53:10
>> The real lag time is getting So, you have to you're So, let's say we want to get a bunch of machines that are in the Amazon up and running. >> Yeah. >> Okay. How do you do that? Mhm.
1:53:20
>> So, I've got to ship, a bunch of sensors, a bunch of compute, a bunch of >> equipment >> and mechanical systems, let's just say, so that a team can then go install it. >> Yeah.
1:53:33
So, you're basically building a data center on site.
1:53:38
>> Sort I wouldn't put it that way.
1:53:38
>> Sort I wouldn't put it that way. I would say I mean if you considered a machine with sensors and compute a data center I mean you could but it's really think of those there are servers but I wouldn't say a data center it's not really >> some operations bring like an armada
1:53:52
style like shipping container sized level of volunteer as one >> so you bring in the stuff >> okay >> you have to install it >> um you have to like bring it up and make sure okay this is a new place how does does this machine work properly in new place and calibrate and make sure it's safe and all of this. So, there's like a
1:54:11
So, there's like a process of getting up.
1:54:13
Then there's change management because that that site's going from >> there are people that show up in the morning.
1:54:20
There's all this very regimented process uh to make sure everything's going exactly as planned and people are exactly where they're supposed to be >> because otherwise weird things happen on a on a mining site. >> Yeah.
1:54:32
So you have to go from that to okay we're now running a autonomous mining operation.
1:54:38
It's just a very different thing.
1:54:40
So the in the installation and the bring up and what we call commissioning are sort of like the things you have to do.
1:54:48
>> Um and you know like why does it take a long time to install?
1:54:52
Because that machine may not even be drive by wire. >> Yeah.
1:54:57
>> So you have a mechanical system like if you turn the steering wheel like it's you know what I mean?
1:55:01
It's a It's a mechanical system, a hydraulic system.
1:55:06
So, you're bringing an actuator that might push a physical button. >> You're Yeah.
1:55:09
You're you're trying to make electricity then do a physical thing.
1:55:14
So, then you need physical actuation.
1:55:16
Y >> to do the things because it's not these machines are not natively dry by wire. >> Yeah.
1:55:22
So that that sounds that sounds uh incredibly difficult but necessary because you're not going to get a mind to rip out tens of millions of dollars of equipment that they already have.
1:55:34
>> But would you eventually go full stack like build the entire >> I mean look we ultimately I mean if you go in the mining industry there's like this this uh term it's called no entry mine.
1:55:46
>> A no entry mine is a mine where there are no people >> in the lights out factory. >> Yeah.
1:55:51
kind of like that version version of it.
1:55:53
There might be people in a control center.
1:55:54
control center. There might be but like in that pit no human >> and it's a wildly different calculus from a safety perspective I imagine >> totally different obviously >> and so there's drilling there's blasting there's loading there's >> hage >> uh there's crushing I'm just going
1:56:15
through the different parts of the mining operation >> um and what you do is you start somewhere and then you start extending to those other areas to get to that no entry thing and the no entry thing is you can have an autonomous thing like like our holage system is autonomous. >> If you're getting into a new place, you
1:56:32
>> If you're getting into a new place, you can do remote control and move into autonomous. >> Mhm.
1:56:38
>> If you want to go super no entry or lower entry line, if that makes sense.
1:56:43
>> Um it's it's super fascinating.
1:56:43
And then you're talking about you're talking about loaded a 2 million pound machine that's moving potentially 35 miles an hour down the road.
1:56:58
>> And it's it's an off-road thing.
1:56:58
But like >> 2 million pound machine moving 35 miles an hour off-road.
1:57:03
[laughter] >> Yeah, dude.
1:57:07
>> This is why you have to ultimate ATV.
1:57:10
This is the ultimate ATV.
1:57:10
So, no, you get in it and you can, you know, you can experience it like uh >> I mean it's not like there's like a amusement park for this, but like >> I've certainly experienced it where I can get in the I get in the machines and and check out what's going on.
1:57:24
>> This is like the dump truck 20 foot tires essentially.
1:57:27
>> Are any of these are any of these companies like acquisition targets >> where you would you would be able to come in and say like you're doing a lot of stuff well, but here's all the stuff that you're never going to figure out like us.
1:57:38
And >> I mean look I would say the way we think about it is the the hage part of a mine is where most of the vehicles are and so and we think of hollage as the cardiovascular system of a mine.
1:57:53
>> So we're obviously very connected to all the other machines but we don't do all the other machines.
1:57:57
So we're like in an ecosystem.
1:57:58
So we can work with them where like there's APIs because like if you're doing hollage you need to know where the other machines are and what their status is as an example.
1:58:05
There needs to be orchestration, coordination there, which is pretty interesting in terms of like acquisition like you know I I do I have to sort of admit like the >> Uber mentality my mentality let's just say >> yeah [laughter] my dune >> Yeah. Yeah.
1:58:24
It's like not I guess Uber is different today but in my world we didn't acquire We just built. >> Yeah. That's right.
1:58:31
>> I don't know if I have an opinion yet.
1:58:32
I'm not like religious about it, but if we feel like we can build something, we do.
1:58:37
But that but sometimes people have differentiated awesome stuff and you're like, let's partner. We're open to it.
1:58:42
You know, >> how would you pitch me if I was a young person, Stanford, CS, new grad, worried about software engineering not being the easy path where I can bounce around from Google and maybe Uber had a cushy job for me.
1:58:59
pitch me on going to the Amazon and building >> awesome. I mean, that's awesome. I thought I just did.
1:59:06
I mean, that was the pitch. That was the pitch.
1:59:10
>> Do you think Do you think uh young people are receptive to this pitch yet?
1:59:14
Are we about to be receptive?
1:59:14
Why should they be receptive?
1:59:17
>> It's really interesting because I only run into the young people that are receptive.
1:59:20
Like, I'm not out there pitching like lame sauce dude who doesn't want to work. >> Sure. Sure.
1:59:25
Like I don't end up I don't end up in the same room as this guy.
1:59:28
>> Do you want Do you want a Do you want a a job where do you want a laptop job or do you want to be dropped in to a mine in the Amazon and like build build, you know, science fiction.
1:59:38
>> This is the thing, right?
1:59:38
This is why the Adams thing is cool >> because you're not dropping a you're not dropping a app in the app store.
1:59:48
You're like automating a 2 million pound machine going 35 miles an hour carrying gold.
1:59:57
Do you do you watch uh do you get do you get >> there's a lot of profanity happening today?
2:00:01
I don't know why it's happening but it is. >> No, it's let it flow.
2:00:04
>> Just wanted to acknowledge it.
2:00:05
>> Do you do you watch uh science do you do do you get inspired by science fiction at all?
2:00:09
I can I can imagine like watching Dune for you is you're just like texting pictures to the team like >> of course I'm like I'm my fave is is Asimov. >> He's my fave.
2:00:19
Um, you know, the iroot series is like just so >> epic.
2:00:26
Um, >> what is your takeaway from the iroot series with regard to AI safety doom generally?
2:00:33
Have you ever had moments of uh maybe we won't figure it out?
2:00:39
>> Won't figure what out?
2:00:41
>> The alignment problem broadly like the the the iroot the three laws of robotics sort of an elegant solution.
2:00:46
It's obviously a world where everyone both the doomers and the AI builders agree that yep the three laws of robotics will be suffer the three laws don't always work out. >> Yeah.
2:01:05
>> So I think there's a lot of >> I I thought there was a lot of nuance to those three laws even though the laws are sort of so simple. >> Yeah.
2:01:13
>> Um I love the intention of those laws.
2:01:16
Um I c I sort of think of it a little bit differently which is I have been entrepreneuring for a long time >> like a long time. >> Yeah.
2:01:26
>> And I have failed and when I think about why I failed it's usually because I was building something that nobody liked.
2:01:38
>> So if you build something that people don't like I don't think you're going to succeed. Mhm.
2:01:44
>> So, how does that relate to your question?
2:01:46
He's like, "Please tell me because I'm not connecting the dots at all.
2:01:50
[laughter] What are you talking about?"
2:01:51
Well, if you make something that is antihuman, >> if you make something that doesn't serve people, >> I don't think you're going to make it.
2:02:02
>> I don't think you're going to make it.
2:02:04
>> And by the way, like, yes, we're using AI to help us make decisions, etc.
2:02:05
But what do those AIs really, really want to do almost too much? They want to please us.
2:02:12
So, I just think if you're not making stuff that humans want, it's not going to work out.
2:02:18
And that's kind of obvious, obviously.
2:02:19
But I think it keeps going. >> Yeah.
2:02:22
>> Um, and yes, there's the dangers and the things and then this, >> but that's my that's my starting point for how I think about these things and we can't control all the things. >> Yeah.
2:02:32
Um, and I do think of course you have to have safety situations and there's collisions uh of like what do I what do I prioritize first and how do I do it which is I think where Asimoth's laws go.
2:02:44
But um I I I instead of writing sci-fi books, I'm just doing the thing and I'm making sure that the the machine stays on the road. >> Yes.
2:02:53
[laughter] And and related to that idea of like doing the thing, making the machine stay on the road.
2:02:57
I imagine that your your world view is somewhat informed by your contact with reality, the fact that you can see the progress of diffusion, how long drive by wire systems take took to roll out and the need for AI to be deployed in like tactile ways that uh that that you just see it as more positive some more opport there's more feel like you're deploying robots that people want.
2:03:24
Right now, robot I mean look, there's some point where robots have their own bank accounts and they're citizens and all this.
2:03:31
>> We're just not there yet.
2:03:32
>> And before we until we get there, >> that robot >> is owned by somebody. >> Yeah.
2:03:38
>> And that somebody has a bank account >> and they are paying based on the value you're bringing them because they like your stuff.
2:03:44
>> So if you are doing things that humans don't like, >> you're done. >> Yeah.
2:03:49
>> And trust me, >> I've done it. >> Yeah.
2:03:52
Yeah, >> I've built things that nobody liked and it sucked.
2:03:55
>> I don't recommend anybody do it.
2:03:58
[laughter] >> If you can avoid it, you totally should. >> Yeah.
2:04:01
>> Uh on the business model side, >> what are you doing now with in mining?
2:04:06
And where do you think it could go over time?
2:04:09
Because if you're able to bring in a system that helps someone increase their yield 30 to 40%, I I imagine eventually you just do some type of JV so that you guys have incentives.
2:04:17
Oh, look there's there's you know and the the instinct should be how do enterprise comp enterprise software companies do it?
2:04:30
Start there and you guys will know that like you you know that. >> Yeah. Yeah. >> What's the answer?
2:04:35
>> Let's just say you're enterprise software company.
2:04:36
You're making a company more productive. What do you do?
2:04:39
>> Raise prices subscription >> or you the price goes up when you prove that productivity.
2:04:45
So there's baseline and then baseline outcomes you get a little extra juice. >> Sure. Sure.
2:04:50
And you could >> or you're always trying to make sure that like you want to be producing creating more value than you're capturing, but there's this sort of cat and mouse game where you're always trying to c you don't want to give away maybe too much value >> here. Totally. But here's the thing.
2:05:04
You never go to a customer, I don't care what you're selling, okay?
2:05:08
I don't care it's enterprise software.
2:05:09
I don't care if it's widgets. I don't care what it is.
2:05:12
You never go to a customer and say, "Give me a percentage of your stuff." >> Yeah.
2:05:17
>> You go to a customer and say, "Here's the price of our stuff."
2:05:19
And if it does really well for you, we think we should get a little more >> scratch kashish [laughter] stuff, you know, whatever. You know what I mean? >> Yeah. >> Yeah. And it's that simple.
2:05:32
>> Don't be crass about it.
2:05:35
>> And, you know, partner with folks and they're down. They want to win, too.
2:05:36
You know, >> it's literally an enterprise, it's an enterprise software style negotiation or approach to the whole thing. Yeah.
2:05:44
And the more differentiated your value is, the more you're going to get. >> Yeah.
2:05:50
>> What is your process for hiring executives today? >> Pray.
2:05:57
[laughter] >> I was hoping I was hoping you had the Kalinic system to achieve a 99.
2:06:03
>> I know, but why would I tell you I did?
2:06:05
[laughter] >> Why would I? No.
2:06:07
I mean, >> no, but I I think you can this is one of those things you can tell people exactly what you do and they're not they're not Kalanics, so they're not it doesn't that doesn't mean they can compete with you, >> you know, they could they Yeah. Okay. So, how would I put it?
2:06:23
Um, look, the I think a no matter who you go, nobody's nailed executives all the way. Mhm.
2:06:31
>> It's it's it's weird because what will happen is uh executives talk a awesome game.
2:06:39
>> And there's two things you want an executive to do.
2:06:40
You want them to be able to organize at scale.
2:06:42
Organize and manage at scale. Lead >> at scale.
2:06:46
You also want them to be epic problem solvers, the most strategic badass problem solvers alive.
2:06:52
This is like being left-handed or right-handed.
2:06:57
And there's very few people that are ambidextrous, but you need that.
2:06:58
Now, somebody's they're always leaning a little bit one side or the other.
2:07:04
>> The best executives are the ones that are doing both well.
2:07:06
But I have come to the conclusion over my years doing the stuff is the problem solving is the most important thing.
2:07:15
If you get somebody who organizes and manages well, but cannot solve a problem, they're going to be doing ridiculous stuff in a super organized way.
2:07:24
And so that's the and and sort of my theory I I maybe there's a couple theories on how I manage or how I lead is that the only constraint on your imagination is management capacity. >> Yeah.
2:07:39
>> But what is management capacity?
2:07:39
It's really problem solving at scale. >> Sure.
2:07:42
>> Because if you are doing super well over there, guess what?
2:07:44
They're problem solving there.
2:07:46
I can create other awesome problems. >> Yeah. >> Yeah.
2:07:50
>> Like I love creating problems. >> Sure. >> Go solve those too. Yeah.
2:07:54
>> But if I don't have the management capacity, then I'm effed. >> Sure.
2:07:58
>> So, um the the management style that I do is sort of problem solver and chief, which is I take the most impactful problems that are not being solved and that's on my desk. >> Yeah.
2:08:12
>> Or desk or room or whatever you want to call it.
2:08:14
That's where I'm spending my time.
2:08:15
So people go, "Oh, what do you spend your time on?"
2:08:17
Like, it depends what the freaking problems are that matter. >> And it can change.
2:08:20
And that's how how I roll.
2:08:24
But it means once you have a problem solver and chief mentality that flows downward.
2:08:29
That means any direct report of mine must be the deputized problem solver and chief >> and they've got their because there's only 24 hours in a day.
2:08:36
I can only solve so many myself.
2:08:39
They have to then take that for their world and do the same thing and then do the same thing to their people. >> Yeah.
2:08:45
So the bottom line is you got to prove that these folks can solve actual problems and aren't just talking the talk. That's the number one.
2:08:54
>> And then >> and then on the interview process, simulate what it's like working together.
2:08:58
So that day one is really feels like week two.
2:09:01
>> And day one, you better be excited.
2:09:04
>> So if you're excited in day one, after simulating what it's like working together in the interview process, then day one is really week two and you're still excited.
2:09:10
Ah, you took a lot of risk out of the system.
2:09:13
That's all I got for you.
2:09:16
>> I have a question about regulation.
2:09:18
>> Uber famously went city by city. >> Yeah.
2:09:21
>> The AI labs are duking it out over federal preeemption.
2:09:25
>> Did you ever have develop a theory around when federal preeemption is better than state-by-state regulation?
2:09:34
Do you have a philosophy around this?
2:09:34
I I it seems like the labs go back and forth on what they want.
2:09:38
It's hard to see where the chips are falling.
2:09:40
Federal preeemption is good when you are pro-regulatory capture. >> Okay.
2:09:48
>> When you want to squeeze others out, you should get federal regulatory bigness going for you. >> Yeah.
2:09:58
Because then you don't have to do the ground game that you went.
2:10:00
>> Well, no, you're squeezing others out. Okay.
2:10:02
It's just the whole point is to squeeze everybody out. >> Sure. >> I never did that.
2:10:05
Like we never did that Uber.
2:10:07
We basically never ever pro proposed or pushed any rule that would be beneficial to us versus somebody else. >> Sure.
2:10:20
>> We always were trying to open up the market and we said let the best man win >> and we just went for it.
2:10:25
>> But uh I think we got to be careful of some of these closeweight things that are um >> creating situations where they need to be regulated and they want it.
2:10:37
I'd be very I'd keep an eye on that. >> Yeah.
2:10:43
Well, you got to have customers that love your product and are willing to >> So, when you guys when you guys uh you know decide to tell your own hacker to hack the thing, and then go to somebody, then go to the federal government and say, then go to the federal government and say, um, dude, we saved the day.
2:11:03
like, you know, you don't have you don't have to pay.
2:11:09
>> You guys don't have to do this.
2:11:09
You guys don't have to do it.
2:11:12
>> Uh on regulation, I'm sure you saw the trial lawyers that are fighting back against autonomous vehicles because they're worried they're going to be too safe. >> Yes.
2:11:22
>> Um I'm sure that's not surprising to you. >> No.
2:11:24
So, look, every bad thing that you see in transport, like systemically, any anything in transport that you view as systemically bad was most likely pushed by the trial lawyers and the insurance companies. >> Wow.
2:11:42
>> Every single bad rule that's weird and dumb. >> Yeah.
2:11:47
>> The insurance companies and the trial lawyers were in the game big time.
2:11:52
>> Uh where where do they align? >> What do you mean?
2:11:57
>> Well, because trial lawyers, I imagine, want more accidents. >> Yeah.
2:11:59
Insurance are the ones that pay for it. Wait. Yes. No. No.
2:12:01
Remember, insurance companies make margin on accidents. >> Okay?
2:12:07
>> If there's no accidents, there's no insurance company.
2:12:10
>> They in a weird way, they love accidents go up.
2:12:13
>> As long as it's in their actuarial table, they're pumped. >> Wow. >> Yeah. >> Right.
2:12:18
The what they don't like is accidents they didn't plan for.
2:12:21
>> But accidents that they plan for >> big insurance >> outcomes, >> they love.
2:12:28
Like I remember we went to DC and the taxi system the the liability on a ride if you took a taxi it might still be this way to this day was like $25,000 in a taxi >> but we went to we being Uber at the time went to DC and they pushed a $1.
2:12:46
5 million policy per ride. Okay. So what does that mean?
2:12:54
That means well this, you know, accidents are going to happen.
2:12:58
We're probably like Uber's probably safer. >> Yeah.
2:13:02
>> But it just Do you think the trial lawyers weren't pumped about that?
2:13:05
You think the insurance companies weren't also pumped about that?
2:13:09
>> They can go get up to a million >> because by the way, the insurance company might be on the other side. Yep.
2:13:13
>> And they're like, "Oh, there's a there's a $1.
2:13:15
5 million bank account here that I can get access to on a random accident." >> Right.
2:13:23
uh what can you share on the transportation side of the business right now?
2:13:26
How much are you how much is that business in service of mining or food versus >> number one?
2:13:32
So number one is it's it's so I call it wheelbase for robots >> which is if you're going to do specialized robots that move and act in the physical world >> they're either humanoids which we're not I'm not antihumanoid I'm just nonhumanoid specialized industrial robots right so that's it's highcale industrial scale tasks which means you would not have a humanoid ever do that um that means means you got to be on wheels.
2:14:02
So, we got to build wheels.
2:14:05
So, that means, okay, well, when food when supply chain is going into our facilities, >> that's a freight vehicle.
2:14:11
We probably should just turn that into a robot that moves stuff and actually interfaces with our facility in a really cool way.
2:14:17
When the when the food is coming out of our facilities, there's probably like a a machine that holds food at temperature that's like a box on wheels.
2:14:26
I call them autonomous burritos.
2:14:29
and it brings it to your home and it costs 75 instead of like the $12 per drop that it costs like an Uber Eats or a Door Dash today.
2:14:39
So, it's serving remember I I'm taking I'm sort of going through an industry and saying how do we transform it full stack?
2:14:50
>> How do we automate full stack that entire industry?
2:14:54
>> So, okay, that's the food thing.
2:14:56
Obviously, mining's pretty obvious, but you can imagine there's a lot of other machines that move.
2:15:00
Like I talked about hollage, but what about like what about grading the roads, the dirt roads? You got to grade them. >> That's a machine.
2:15:08
What about the you spray water so there's not a lot of dust all over the place?
2:15:12
That's a freaking machine.
2:15:15
>> Like what about the material that ultimately goes somewhere beyond the mine?
2:15:19
Well, that's a freight machine.
2:15:22
Like there's lots of things moving. >> Yeah.
2:15:24
Um, you know, I saw something was like, think about uh just forklifts.
2:15:30
I I know of a company, we'll remain unnamed, that's spending three and a half, this is on the supply chain side, $3.
2:15:37
5 billion dollar a year on forklift labor [snorts] in their facilities.
2:15:45
>> That probably shows up in an SEC filing if we want to get creative [laughter] and figure that what out what company you're talking about.
2:15:50
>> Saying but yeah, big opportunity >> if you just solve the forklift problem. >> Yes.
2:15:53
But on solving the problem, what do you think about this this distinction between uh jobs versus tasks?
2:15:59
Like a lot of people would have assumed that there would be no more marketing people because the job is just writing marketing copy, but the job is actually much more.
2:16:09
Writing copy is one task.
2:16:09
I was looking at uh automated trucking and I found some stat like I think 30% of truck drivers are armed.
2:16:18
They carry weapons and so driving the vehicle is one task >> but in that job you are also providing security for that payload and you are also doing other things refueling the vehicle maybe some minor maintenance and so just the just the steering and gas and brake pressure is just one task that you're doing.
2:16:41
How do you think about that in the context of all this?
2:16:45
>> This really gets to the jobs question I think. Yes.
2:16:47
think. Yes. which is basically like okay well if I do everything that we are imagining on food which is I have industrial real estate which is manufacturing and logistics >> I automate the manufacturing which is production robotic food
2:17:03
>> robotic food machines robots >> uh and I have robotic couriers what happens >> food the price of food goes down >> okay when the price of food goes down remember robots don't have bank accounts >> when the price of Food goes down, what happens? More people have more money.
2:17:18
More people have more money. >> Yeah. >> Jean's paradox. >> What do they do?
2:17:22
>> You start eating more.
2:17:22
They just [laughter] start having 10.
2:17:26
Everyone's going to be fat because this is hilarious.
2:17:28
That's not what I'm saying.
2:17:30
[laughter] That's so funny.
2:17:32
That's not what I'm saying.
2:17:34
>> No, what I'm saying is what I'm saying is [clears throat] when once you [laughter] >> I'll take I was going to get three pizzas. I actually take 30.
2:17:44
>> You're like the price. I'm sorry. continue. >> No, no, no. So what happens?
2:17:47
You have more money to do other things, but remember that money is only ultimately going to humans. >> Yes.
2:17:54
>> So it's it's the things that get automated go down in price, which then creates surplus. >> Yes. >> To do what? Yes. >> To do other things. Yeah. This is the bomb.
2:18:03
>> So it doesn't always have to be, oh, >> marketing's automated, but sort of, and there's still people doing it. It's like whatever.
2:18:10
There's going to be a hundred other new things that come out because there's this excess of capital and progress continues. >> Yep. Yeah.
2:18:18
>> And as long as humans >> still have things that we do that robots cannot. >> Yep.
2:18:25
>> It's go- go time, man.
2:18:25
It's going to be super prosperity.
2:18:27
We talked about the plumber that is paid like LeBron last time.
2:18:31
It's going to be across a thousand categories.
2:18:33
And some categories we don't even know. Yeah.
2:18:36
>> Like we we don't even know what they are today. >> Yeah. >> Yeah. Uh, you raised 1. 7 billion.
2:18:39
Why didn't you raise more?
2:18:44
>> That's a good question.
2:18:44
I mean, >> because last time we were here, you talked about like, oh, well, if what you if you were doing something and it was easy, you weren't going hard now.
2:18:52
>> Seems pretty going pretty hard. But >> unpack it.
2:18:56
>> Look, you have to stop somewhere.
2:18:58
[laughter] >> No, >> even I have my limits.
2:19:01
>> No, it's like But like, look, I, as you can imagine, today my phone's blowing up. >> I mean, I'm pumped. Like A16.
2:19:06
Yeah, >> these guys we we should have done business at Uber. >> That's right.
2:19:11
>> If we did it business at Uber, my 2017 would have been a different year. >> Yeah. >> Yeah. >> Okay. >> Totally.
2:19:17
>> So that's why I called it unfinished business. >> Yeah.
2:19:21
>> And so um but yeah, like my phone's blowing up.
2:19:27
Like we're probably just going to do a second we'll do a second close. >> Yeah, I figured.
2:19:33
>> We'll come back for the second close.
2:19:34
[laughter] >> Run it back. back.
2:19:37
>> No, I mean we're not going to do a big announcement on this enclosed, but like you know those people who are who are texting me and hitting me hard right now.
2:19:46
>> We got room, >> you know. No. Well, we'll see.
2:19:47
We We'll [laughter] >> see. Depends. Depends on >> We'll see.
2:19:53
>> Depends on what the previous text me.
2:19:54
>> If you're a homie, we definitely have room.
2:19:56
If we're not a homie, you should talk to one of my homies.
2:19:58
[laughter] >> There we go. >> Yeah.
2:20:00
I I did come away from the last conversation thinking, all right, there's a lot of exciting companies in physical AI, and you could spend years and years and years trying to find all the best teams, or you could just give >> TK a big pile of cash and just say go cook and uh you know, sometimes the the easier route is is better. >> Yeah.
2:20:20
And I think there's this thing physical AI, people are like, well, is that a humanoid? Is that a world model? Is it?
2:20:24
And so on this one, I sort of dialed the language a little bit and am calling it industrial AI. >> Yeah.
2:20:31
>> It's like, okay, this is a full stack software, robotics, sensors, machinery, like a full stack solution to automating an industry.
2:20:45
And that's kind of how we think about it. And it's industrial. Yep.
2:20:46
So it's like heavy atom stuff. Yeah.
2:20:51
>> Well, thank you so much. >> This is incredible.
2:20:53
You want to get a signature? Can we get an autograph? >> Sure, why not? >> Can we get something?
2:20:57
Which >> they can figure it out back there. Oh, we got a gong.
2:21:00
We got a gong right there. Gong.
2:21:02
We'll hang it in the rafter.
2:21:04
>> We want to hang it in the rafters.
2:21:04
We're trying to build our our >> Museum of Business.
2:21:08
>> The Museum of Business grows one gong stronger today. >> Thank you so much.
2:21:12
>> And we will we'll see you in Austin. >> Yeah.
2:21:15
Yeah, next time you're on your commute, if you see two jet skis moving out of out of your, you know, out of uh out of sight coming in, it's probably us. >> That's us.
2:21:23
>> If it's not, >> guys, let me know if you want to learn how to slum ski. Oh, yeah.
2:21:27
>> If you want to learn how to wake surf like Well, >> I've only been water skiing once or twice in in 20 years. >> 7:30.
2:21:34
I go into 7:30 in the morning every morning >> and I'd say half the time I'm out there at 8:30 when I leave the office. >> That's amazing. >> So, >> I love it. That's what we do. >> Beauty of summer.
2:21:46
>> Thank you so much for coming on the show. Always a pleasure.
2:21:50
>> Have a great rest of your day. >> For sure.
2:21:51
Good to see you guys soon. >> Yep.
2:21:54
>> I'm going to tell everyone about Cisco, critical infrastructure for the AI era.
2:21:59
Unlock seamless realtime experiences and new value with Cisco.
2:22:02
And our next guest is in the waiting room.
2:22:05
We got Max Hodak from the Science Corporation.
2:22:08
He's the founder and CEO.
2:22:10
We kept him waiting, but Max, how you doing?
2:22:13
Welcome back to the show.
2:22:15
>> Hey guys, thanks for having me. >> Great to see you. Uh, give us the update. What's the news?
2:22:21
>> So, previously we've talked about I've told you about our retinal prosthesis.
2:22:25
So, we have a chip that's implanted in the eye to restore vision to patients that have lost um lost it due to the death of the rods and con specifically age related macular degeneration.
2:22:33
So, uh last week we got marketing approval in Europe.
2:22:38
So, we've received the CE mark, which is >> Wow.
2:22:41
>> Like, it will be shortly available to consumers in Europe. >> I got a question. I got a question.
2:22:45
So, >> Jordy has this problem where he drinks too many beers and he gets double vision. Can this help with that? >> Fortunately, not.
2:22:56
>> Hey, wait, wait, wait.
2:22:56
Jokes aside, scientifically, it cannot.
2:23:00
>> Double vision from drinking? >> Yes. >> Probably not. >> Impossible.
2:23:04
It's the last It's the last scientific problem. We'll never solve it.
2:23:07
Um >> anyway, >> very funny.
2:23:09
More seriously, uh how quickly how like what does a go to market look like for >> a product like this? You have approval. Step one's >> Yeah.
2:23:19
How how quickly can it be adopted by because >> you know people I mean Europe's a big place, right?
2:23:26
Tons of insurance facilities building the machine that installs it.
2:23:31
Like there's a whole process here, right? >> Yeah.
2:23:34
Well, it's a relatively simple 1-hour outpatient procedure.
2:23:36
the machine is the surgeon.
2:23:37
Don't need actually that many surgeons to reach these patients.
2:23:41
>> Um so right so the C mark is a marketing approval in about 30 30 countries that accept it.
2:23:46
The next step is we need to register country by country.
2:23:48
So we have registrations going in in Germany and Italy and the Netherlands and Spain and the UK like this week.
2:23:54
Um that process takes about a month and then uh doctors can start scheduling patients.
2:23:59
I mean we sell implants to hospitals essentially and then they sell them to patients.
2:24:02
So it's it's the hospitals, it's patients, but we have a registry.
2:24:06
Um the hospitals have registries.
2:24:08
Um the patient the doctors know who their patients are.
2:24:10
Um with this demographic, actually one of the things that happened is because there was really nothing available for them.
2:24:16
Opthalmologists have been telling these patients like you know if you're 80 and have AMD, you don't need to be sitting in my waiting room anymore.
2:24:21
Like you don't you don't need to come here.
2:24:24
And so now they're starting to reach back out to some of those patients that they haven't said we don't need to see you for the last few years.
2:24:29
Say there's something available.
2:24:31
So the first patient is probably 6 weeks away or so.
2:24:36
The next >> so fast >> big step is reimbursement. >> Yeah.
2:24:41
>> Um and so yeah, >> what does it look like in America?
2:24:42
I mean, you're 6 weeks away in Europe.
2:24:44
Uh what what's the FDA track like?
2:24:46
I know that it's already FDA breakthrough device and humanitarian use device, but take us through what the commercialization plan looks like in the US. Yeah.
2:24:56
So, the other thing that we announced today is that we got two humanitarian use device designations from the FDA.
2:25:01
We actually got those back in March, but sat on them for a little bit.
2:25:05
That unlocks an an expedited um approval pathway called the humanitarian device exemption that we're submitting for imminently um in the next week or so.
2:25:14
That is a can be a 75day review.
2:25:14
Um, and so it'll take a like there's a couple loops of that, but we're hoping that early next year it'll be available to to some some set of American patients, but that's up to the FDA review.
2:25:28
>> Yeah, I don't want to get you in trouble with the FDA.
2:25:29
I know how high stakes it is, but it is just crazy that Europe's moving faster around regulation.
2:25:35
Like, this should be a signal to the FDA to say, "Hey, if Europe's approving it faster, uh, we got to we got to step things up over here."
2:25:45
I don't I as an American I just don't like falling behind. But I don't know. >> Yeah.
2:25:49
I mean in this I mean I would normally want to agree with you.
2:25:51
I think in this case that's actually a little bit unfair to FDA cuz there's some there's some accidents of history that just led to this h like happening first in Europe.
2:25:59
Um the FDA standards are not that much different.
2:26:03
>> But um yeah absolutely we should uh hope to to have this here.
2:26:07
Also the FDA is they care about slightly different things. there.
2:26:11
The filings are a little bit different. Yeah.
2:26:14
>> But >> um hopefully it won't be that long um either. >> Yeah.
2:26:18
Talk about uh next steps.
2:26:18
I mean you're you're properly commercializing right now.
2:26:24
Does this mean new factory, new team members, new uh new just new muscle inside of the company? >> Um yeah, absolutely.
2:26:33
I mean, we've built out a whole go to market team in Europe.
2:26:36
So this is um clinical education like a bunch like we need to go reach opthalmologists where they are.
2:26:40
tell them about the product, help them understand the results, um answer their questions, >> uh rehab specialists.
2:26:46
So, there's this is a little bit different than than what you may have seen from the motor BCIS where it works very quickly or with >> um like there's a little bit of rehab that the patients have to put in to really use it.
2:26:58
And so, we have people on the ground there that will do that with them in the beginning.
2:27:01
Over time, we want to have that be more and more kind of just in the wearable while they put on the glasses.
2:27:05
the glasses talk to them, they talk to the glasses, it walks them through the exercise, but initially that's a little bit higher um higher touch.
2:27:12
And then also there's a bunch of surgeon training.
2:27:13
So we run wet labs for surgeons um where they can come and and practice the procedure with us so that they've we know that they know how to do it before they're doing it with patients.
2:27:23
>> Give me a sales and marketing 101 for targeting opthalmologists.
2:27:27
Can you target them on Instagram reels?
2:27:29
Do they listen to a specific podcast that you can sponsor? Are you at conferences?
2:27:33
I know people give medical device companies, they give out lots of like pens and chairs, but I I think they're like there's like limits because you can't like bribe them, but you do want to give them merch.
2:27:43
Like what is the 101 level of marketing to opthalmologists?
2:27:49
>> I mean, a lot of it is conferences.
2:27:49
Um, so there's a handful of conferences that we go to and then getting not just having a booth there, but presenting scientific results.
2:27:56
Typically, this isn't us, but it's our academic and scientific collaborators, maybe a surgeon at a hospital that did a study.
2:28:03
they'll present their experience with it.
2:28:06
>> Um there's also advocacy groups.
2:28:06
So there's opportunities to sponsor um like webinars through these these networks.
2:28:14
Um but it's a really small community I think like opthalmology overall and especially in these types of retinal diseases.
2:28:19
>> It is very densely interconnected and they all talk and so it's a matter of kind of um there's a handful of advisory boards that we we have to go through.
2:28:29
For example, our data safety monitoring board for the clinical trial.
2:28:30
These are often opportunities to have that community come and be familiar with our results and then and then disseminate them.
2:28:37
>> So it might be the end result might be a little bit more onetoone because of how small the community is.
2:28:41
You can actually reach them directly.
2:28:44
>> What is >> Yeah, it's there's not like a huge insta spend on that. >> Not yet.
2:28:48
Um, what does what is the shape what does the shape of Science Corp look like right now given that you have a product that's commercializing, but I imagine you're doing a bunch of R&D in the background for other opportunities and and use cases, but um, you know, how are maybe you spending your time and then what does the team's time look like?
2:29:12
>> Yes, we definitely have a bunch of next generation projects in development, including the next generation of the Prima implant.
2:29:17
Um, we have new versions of that kind of in in pre-clinical studies now.
2:29:22
Hope to get those into humans next year.
2:29:24
It'll be it'll probably be a a threeyear minimum, possibly 5ear cycle between versions for a while, I think, because the need to do the intervening clinical trials.
2:29:33
But um the prima as it is now is a really great existence proof that we're on the right track.
2:29:42
This is the first time that function that like really useful form vision, a thing that looks like an image has been able to appear in the mind's eye of a blind patient.
2:29:49
Um, but it is not high resolution fullfield color vision.
2:29:52
It's like looking through a straw at the center of your vision where you've lost this high acuity um perception and it's it's black and white.
2:30:00
It's high contrast, but it's only a couple letters at a time or maybe a word at a time.
2:30:03
And so we are still working to expand the field of view um make it so that you can potentially get colors.
2:30:10
We think we know how to get to red and green.
2:30:11
Blue is a little more difficult.
2:30:13
Um and then get higher resolution, get towards native acuity.
2:30:16
And so on each of these we have there's clear ways places to go.
2:30:18
Um but it's going to be a long road to get that all the way to to all of these patients.
2:30:26
>> Well, congratulations on the approval.
2:30:28
>> We also have some really cool stuff coming on the the on the brain computer interface side on the vessel side, but that those will probably come out a little later in the fall.
2:30:35
>> Can't wait to talk about it. I'm excited.
2:30:37
Well, congratulations and thank you so much for taking the time to come with us.
2:30:40
>> Thank you so much for for coming on and >> and the work you're doing. >> Yeah.
2:30:44
Just thanks for having me.
2:30:45
>> You're you're doing you're doing the thing that uh >> you're doing you're doing something >> that could get humanity broadly back on the side of technology. >> That's a good point. Yeah.
2:30:57
because it's like one of those things like like it seems like so much of what the industry has been doing a lot, you know, making making sand think uh is not quite enough for people.
2:31:08
They're like, you know, what have you really >> what have you done for me lately?
2:31:10
We literally had someone come on the show and say, "What have you done?"
2:31:14
>> But I feel like this is one of those things like, you know, curing blindness that over time will be sort of hopefully undeniable.
2:31:21
>> Well, I mean, this isn't about the money.
2:31:23
I don't think it's about the money for a lot of this team.
2:31:24
It's certainly not about money for the patients.
2:31:26
I think like many things in tech, this is really about power.
2:31:28
But if you want to know like real power, like the power to heal the sick, >> unlike economic or military power, that can be easily shared with others. >> Oh, interesting.
2:31:38
>> And that is, I think, like really what technology is about here.
2:31:39
And we need to paint a picture of how this is being used in that way in a way that is really should disseminate broadly and I think just incredibly pro-social. >> I love it.
2:31:48
Going back to uh I I I know um we're almost out of time, but uh going back to like what did what did you place the odds at doing this accomplishing this moment when you started the company?
2:32:03
>> It seems >> I don't know.
2:32:04
I've always had trouble thinking about these things.
2:32:05
Like I can't put a number on it.
2:32:06
It's just you kind of keep going and as long as success is in the pos like set of possible outcomes, you're just constantly trying to minimize the odds that you don't get there.
2:32:16
Um, it is really hard to put a number on it.
2:32:18
Um, definitely it is cool to see it actually happen. >> Yeah. Yeah.
2:32:25
You're sort of nonchalant about it, but [laughter] >> uh [clears throat] it is uh almost un unbelievable and uh really really incredible.
2:32:34
So, >> well, >> well done.
2:32:36
Well done to the whole team coming on the show. We'll talk to you soon. >> Thank you. >> Yep. >> Cheers, Max.
2:32:40
>> Have a good rest of your day.
2:32:41
>> Let me tell you about Figma agents. Meet the canvas.
2:32:44
Your AI agents can now create and modify your Figma files with design system context.
2:32:47
Uh, >> absolutely incredible stuff from Max Science team. >> Very cool. >> I don't know.
2:32:54
I think that you might be seeing like an Instagram rail being like this eye implant that cured the blind used too much water and it's slop like it's not the same as just being blind. I don't know. Anything's possible.
2:33:05
The push back there's always there's always negativity bias.
2:33:09
I think that there will be push back to even the medical cures.
2:33:15
>> Giving sight to the blind. >> Yes. Get ready.
2:33:17
There's going to be somebody who finds, you know, something to complain about and goes viral and puts up big numbers talking trash.
2:33:25
That's just the way our media ecosystem works.
2:33:28
It's it's a it's a business.
2:33:28
You know, if everyone's glazing something, somebody's going to bring it down.
2:33:34
That's just the >> equilibrium equilibrium.
2:33:35
Uh well uh speaking of AI writing, Jeremy Gon had a post here.
2:33:40
He said uh about AI writing, at the end of the day, it's not about whether the words written by a human or an AI.
2:33:46
It's about whether the output is useful, engaging, and worth reading.
2:33:49
The highest quality work will increasingly emerge from a tight human in the loop workflow.
2:33:55
While some content will be generated end to end by AI, the fixation on authoral or authoral providence is ultimately per pearl clutching. Just kidding.
2:34:05
That's the AI rendition of his actual post.
2:34:07
He said it much more eloquently, but [clears throat] I tried to make it like more AI. I don't know.
2:34:14
Anyway, >> Mark German says, "Based on using the Zfold 8 wide, I'd reset my expectations of how the foldable iPhone is going to sell.
2:34:23
Even at over 2,000, it's going to be a home run."
2:34:25
>> You are going foldable. You're pro foldable.
2:34:28
>> I think I'll go foldable. Why the heck not?
2:34:31
I think when you open it up, you can watch videos in 43. >> More room for reels.
2:34:36
You can watch two reels at >> but the reels are going to be it's actually not that much more room for reels because you'll just have black bars on the side.
2:34:43
Like if you watch >> Can't you have two reels side by side? >> Okay, maybe. Yes.
2:34:47
Uh in two different apps.
2:34:49
You could have YouTube shorts here if if they support split screen like on an iPad.
2:34:53
But right now, if you watch reels on an iPad mini, you're not actually getting that much more pixel space of reels.
2:34:59
You're just getting real here and then UI and Chrome here or whatever.
2:35:07
>> Cooper says, "Big Tech just wants your eyesight restored so you can doom scroll." >> There we go. Cooper named it. Yep. Yep.
2:35:13
Oh, why they secretly funding.
2:35:16
>> They just want to increase their TAM. Got it. Makes sense.
2:35:18
Uh well, if you don't want to watch reals, you'll soon potentially be able to go to the Cinema Drrome in Arlite Hollywood.
2:35:25
Sony is eyeing ringing it back.
2:35:28
Production team, you got a review.
2:35:31
Have you guys been to the the cinama dome before? >> Scott.
2:35:35
Yeah, >> it's pretty awesome.
2:35:36
>> I think I saw a Nolan film there and I think it was 70 mm IMAX back in the day and then it didn't uh I think it didn't make it through co but uh they're maybe bringing it back which >> they have the giant sign outside that says the dome.
2:35:48
If they were going to stay out of business, we should uh we should >> I remember Yeah.
2:35:52
I remember as a as a kid I I thought that they would show the movie like projected on the dome like on the whole ceiling and it was only for sort of like special, you know, a uh like astronomy movies, but they will just show a normal movie and it's just you're just in a big dome. It's cool.
2:36:10
Next studio, if Sony doesn't buy it, maybe. I don't know. Could happen.
2:36:14
>> Can we get that uh unreleased track on again?
2:36:17
>> Yeah, let's play that as the outro. >> Yeah. One sec. >> Regulate me.
2:36:20
It's the new banger hit song of the summer. It's an anthem. It's an earworm.
2:36:25
You're going to be listening to it.
2:36:27
We'll share the link in the description of the YouTube video.
2:36:29
Maybe uh let it start karaoke >> because this song uh just speaks to me.
2:36:37
It really captures the moment.
2:36:37
You heard it from Travis.
2:36:41
Every once in a while you get into a pickle and you got to get the government to come regulate you.
2:36:46
[laughter] A it's a good time.
2:36:50
Thank you for watching TVPN. Tune in tomorrow.
2:36:52
We have a very special show for you.
2:36:55
We're on the road Thursday and then we're off on Friday. Back Monday.
2:36:59
Leave us five stars on Apple Podcast and Spotify.
2:37:02
Sign up for our newsletter at tvpn. com.
2:37:04
Let's throw a flashbang and let the audience listen to regulate me by Jordy Hayes and Sunno. >> Goodbye. >> Flashbang out.
2:37:18
getting sharper than my life.
2:37:24
Now the room is [music] shaking and I can't pretend if nobody draws [music] the line and the line draws us instead.
2:37:36
WHAT I'VE BUILT is too powerful too powerful for me.
2:37:41
[music] Washington needs to stop [music] before you run free.
2:37:48
What I [music] fall too powerful you see [music] to me.
2:38:06
I hear the voices multiply.