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TBPN is a live tech talk show hosted by John Coogan and Jordi Hays, streaming weekdays from 11–2 PT on X and YouTube, with full episodes posted to Spotify immediately after airing.
Described by The New York Times as “Silicon Valley’s newest obsession,” TBPN has interviewed Mark Zuckerberg, Sam Altman, Mark Cuban, and Satya Nadella. Diet TBPN delivers the best moments from each episode in under 30 minutes.
You're watching Today is Thursday, 08/13/2026. We are live from the TBPN UltraDome, the Temple Of Technology, the Fortress Of Finance, the capital of capital. Let me tell you about ramp.com. Time is money. Save both.
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Speaker 1:We gotta take a victory lap. You wanna start taking a lap, Jordy? You wanna take a a lap while I tell everyone about our SaaSpocalypse victory lap?
Speaker 2:We got Yeah. So so I was just appreciating some various software as a service companies.
Speaker 3:There's some
Speaker 2:real ways. Charts yesterday. Crazy Crazy Crazy Up like crazy.
Speaker 1:Yeah.
Speaker 2:And I was thinking, I texted John, was like, when did we cancel the SaaSpocalypse?
Speaker 1:Yep.
Speaker 2:And you pulled up our original Substack that we sent back in February. We just decided at that time
Speaker 1:It's not happening.
Speaker 2:It's not happening. We canceled it and
Speaker 1:There was a lot
Speaker 2:Maybe too early to take a victory lap. Yeah.
Speaker 1:Of
Speaker 2:course. In hindsight But
Speaker 1:there were some good arguments. There were some interesting arguments and there was a lot of fear. But there were a lot of companies that were getting thrown in the SaaSpocalypse bucket as just like a pure pile of code. You could vibe code it. And while that thesis might play out over a few years, it's it was a little bit too soon.
Speaker 1:It seemed a little bit too aggressive. And so we wanted to revisit the SaaSpocalypse and the cancellation of the SaaSpocalypse, see where things are now. So just to set the stage, the SaaSpocalypse is a rough, rough go. $2,000,000,000,000 of market cap lost across the the SaaSpocalypse, the major sell off of technology software companies broadly. $22,000,000,000,000 wiped out.
Speaker 1:Gone. Moment silence. Moment of silence. But a lot of it's come back. The iShares ETF attracts tech and software is up 30% just over the last six
Speaker 2:wait. Did we wanna do a moment of Yes. Brought to you by CrowdStrike.
Speaker 1:Absolute terror. Let me tell you about CrowdStrike.
Speaker 2:This moment of silence is brought to you by CrowdStrike.
Speaker 1:Your business is AI. Their business is securing it. CrowdStrike secures AI and stops breaches. No. CrowdStrike's been on a tear.
Speaker 1:So, yeah, it appeared only logical at the time that every company would be vibe coding their own CRM and this would happen imminently and that any company built on a big pile of code would go to zero. Of course, the core thesis still holds over the long term, but it's a lot messier in reality. So, yes, having a huge monolithic piece of software is less of a moat today than it was a decade ago. That's for sure. Competition is increasing, especially for point solutions.
Speaker 1:But many of those SaaS companies that were so beaten up in the SaaS apocalypse were revealed to have sources of strength that didn't fit neatly into the lots of lines of code written bucket. So babies were thrown out.
Speaker 2:Thousand business development representatives. Yeah. Source of strength.
Speaker 1:That's big. Also just what it another thing that's very valuable is what percent of revenue are you claiming from your customers? So if you are going to a customer and you're saying, I'm taking 30% cut, you're probably at more risk than someone who's saying, I'm an IT solution and you're gonna spend one tenth of 1% of
Speaker 2:Shopify is the best example. Right? I talked lot of a lot of ecommerce entrepreneurs ask them, like, what's your biggest expense? Yeah. None of them will say Shopify.
Speaker 2:Yeah. I think even a brand that is like a Dale.
Speaker 1:Shopify pro Shopify Plus for a business that I know very intimately, I think is around $1,000 a month in cost and the business is doing almost 100,000,000 a year.
Speaker 2:And even even One with percent. Even with how models are today, you would need multiple people basically vibe coding around the clock Yeah. To have a product that was comparable. Yeah. And that's not even to mention Yeah.
Speaker 2:A lot of the applications that already tie into to the product. Yeah.
Speaker 1:And you could just put those tokens towards something else that moves the needle and increases revenue by 5% or 10%. Right? There's there's so many other ways to move the needle. So a lot of babies were thrown out with the bathwater. The the most ridiculous one was I think DoorDash, but there were lots of people coming for Spotify and a whole bunch of different platforms that should be very enduring because their source of strength is a network effect or something like that.
Speaker 1:So six months ago, we we identified six companies that we wanted to use as case studies for the SaaSpocalypse. It was Google, Meta, your favorite company, Spotify, Shopify, Roblox, and Salesforce as evidence that large scaled
Speaker 2:software talk about Meta?
Speaker 3:I mean I'm kidding. I'm I'm
Speaker 1:kidding. It always should have been
Speaker 2:Don't get me started. Never should have been Don't get me started.
Speaker 1:It never should have been beat up. Same with same with Google. And then Spotify and Shopify were the interesting ones. Spotify, of course, there is a world where you're listening to AI music, but there's also a world where you're listening to AI music on Spotify. If you looked at Phoenix Flexen's Rubbers, the song of the summer in many ways, probably AI generated.
Speaker 1:I think it's almost confirmed at this point that it's an AI generated song. It has hundreds of thousands of downloads on on Spotify specifically because that's where he chose to distribute it. Because if you had just left it on the Internet somewhere, he's not gonna get any royalties from it and he's not gonna get any distribution. So that's where the audience is and having being an aggregator is extremely valuable. Ben Thompson was writing about this a bunch of the at the time.
Speaker 1:Same thing for Roblox. Yes, you'll be able to vibe code game, but having all the Roblox network infrastructure distribution that that should be valuable in the in the future. So there there were a few different things that might make you resistant to the coming age of agentic coding tools. Marketplace dynamics, network effects, strong go to market organizations. Jordy put it really cleanly after Shopify's last earnings and when Shopify stock popped 20%, he said Shopify isn't a victim of AI.
Speaker 1:AI is a victim of Shopify. And that really it really made me think.
Speaker 2:Yeah. There was certainly some money flowing out of semis.
Speaker 1:Oh, that's actually true. Didn't put
Speaker 4:it that way.
Speaker 2:Into Shopify.
Speaker 1:I guess you're right. I guess you're right.
Speaker 2:It's got to come from somewhere.
Speaker 1:I don't know. Victim is right here. But, yes. Clearly, you know, now you have a long list of unsloppable AI unsloppable SaaS companies. Companies that can't just be immediately spun up and replaced by an AI app.
Speaker 1:There are AI winners now, and it's a lot of who you'd expect. Cybersecurity is more important than ever. You mentioned CrowdStrike, but Palo Alto Networks is also on a tear. Palo Alto Networks, over the past year is up a 121%. It's a $320,000,000,000 company.
Speaker 1:CrowdStrike's a $230,000,000,000 company, up a 107% this year over the past twelve months. Pretty remarkable. Nikesh Arora taking a little victory lap as well. So five months, Palo Alto CEO bought the dip five months later. He must have been reading us because a month after we canceled the SaaSpocalypse, he was like, I I think I like this Palo Alto Networks stock.
Speaker 1:Yeah.
Speaker 5:Like the stock.
Speaker 1:Is the CEO of Palo Alto Networks. So but he put $10,000,000 of his own money into the company. It's now worth $26,000,000. Nikesh Arora invested as investors questioned whether AI could disrupt cybersecurity. Palo Alto has reached an all time
Speaker 2:We gotta give him
Speaker 1:03:15.
Speaker 2:Gotta give him some trouble next time we see him. Why only 10?
Speaker 1:Oh, man.
Speaker 2:10. Should have Does that really move the needle?
Speaker 1:Yeah. He should have been 10 x levered. Yeah. If you really believed it. Come on.
Speaker 1:But, you know, he has 16,000,000 of basically play money now. I mean, it's boy math, you know. You make you make 16,000,000 trading your own stock when you're the CEO. You got to spend that on something fun. And as we know, Nikesh Rohr was recently pictured on the golf course.
Speaker 1:So I was wondering what does 16,000,000 get
Speaker 2:you? Probably buy a golf course.
Speaker 1:You might be able to buy a golf course, but what does $16,000,000 get you if you're a golfer in the Bay Area? You can join, you can pay for the initiation fees of 10 elite clubs. You can become members of SFGC, Cal Club, Olympic, Sharon Heights, Menlo, Burlingame, Pen Meadow Club, Lake Merced, Palo Alto Hills and one or two Southeast Bay clubs for when you get out there. For 6 to 9,000,000, you can pay thirty years of dues at those clubs. And then guest fees, caddies, carts, food, tournaments, that's gonna run you 1 to 2,000,000.
Speaker 1:And so the thirty year lifetime total puts you around nine to 12. Depends on what your tax rate is. I don't know the residency status. But for 16,000,000, you could plausibly fund a lifetime of belonging to essentially every major Bay Area private club that would admit you with plenty left for golf expenses.
Speaker 2:Fantastic.
Speaker 1:I think I I
Speaker 2:Do it in Do it in cash.
Speaker 1:I want to sing about
Speaker 2:Just do it.
Speaker 1:Every single day. Anyway, there were I mean, there are there are SaaSpocalypse victims that have not come back are probably not coming back need to completely reinvent the business because they have been made obsolete by just base level LLM capabilities.
Speaker 2:Classic You are so with 16,000,000 you could buy 640,000 pounds of rib eye.
Speaker 1:That's another good usage of it. I I think that might be up there. Skip the golf course and start bulking season. Chegg is the canonical example of SaaSpocalypse victim that has not made a comeback. The stock is down 99% over the past five years.
Speaker 1:It was hot during COVID. And of course when it comes to looking up answers to homework, the basic free edition of ChatGPT gets you there. Gemini, whatever you want to use is gonna answer those questions and hold your hand alongside your homework while you're doing it.
Speaker 2:Yeah. So they had
Speaker 1:In the heart.
Speaker 2:376,000,000 of revenue in 2025, but that was a 39% decrease year over year from 617 in 2024. Yeah. It's now roughly an 80 or $90,000,000 market cap.
Speaker 1:Yeah. So it's very shrinking business, very difficult. You have to continue cutting every year to make any money, pull any profit out of that business will be very difficult. The the the more the the newer company that is more in the headlines these days is the information is reported that Canva is slipping into a similar situation because a lot of Canva designs can be one shot by image models like images, nana banana pro, grok imagine. I saw the new grok image examples and and a few of them were infographics.
Speaker 1:They clearly figured out over there how to do high fidelity text that doesn't have misspellings or anything like that. So you wind up with a product that if you're designing a, you know, birthday card invite, that's something that you'd probably go to Canva before. Now you can just go directly to the models. And so there are there are there are SaaS companies that have to grapple with their product being more in the direct path of the models. But there are so many other SaaS companies that are buoying the index because they are either in the token path, like if you're a database or a legit or an analytics company or an infrastructure product that the labs are consuming and every AI company is using because they're like, well, we're generating a lot more data.
Speaker 1:We need a lot more Datadog or, you know, any other data company, then those companies are doing really well on the back of that. And then there's also tools companies like Twilio is doing incredibly well. I don't know if you've been tracking this. It's up a 150% over the past year. Another story where huge boom during COVID Wow.
Speaker 1:Sell off.
Speaker 2:$38,000,000,000 company.
Speaker 4:Yep.
Speaker 2:I if you asked me what Twilio was worth before I just checked this, I would have said, I don't know, $55,000,000,000.
Speaker 1:6,000,000,000.
Speaker 2:Yeah. Something like that.
Speaker 1:And and that's where it was a couple years ago. But it's done really really well. And I think a lot of that is that it is difficult to go and vibe code all of the interactions that you need to actually send text messages across the network.
Speaker 2:It's agentic infrastructure, John. If you historically were a SaaS company, pivot to just be calling yourself agentic infrastructure.
Speaker 1:It's it's more like yeah. I mean, it's funny. It's it's more like it's it's infrastructure that will be pulled off the shelf by the agent. And so, I mean, we use Twilio for our app where we want to be able to interface with an application via text message and we use Twilio for that even though we could go and vibe code that all of a sudden you're dealing with the different mobile carriers and the cell networks and are they gonna flag you as spam? And Twilio has all these huge decades of relationships built out in a real network there that that that even though it's just a tool and it's just consumption software at the end of the day, it has this moat and so it's been doing really really well.
Speaker 2:Sebastian in the YouTube chat says, not a fan of these earphone wires. What earphone wires?
Speaker 1:Mine? Mine?
Speaker 2:Mine's yeah. They're going crazy.
Speaker 1:I fight.
Speaker 2:They're going crazy. Thanks for the call out.
Speaker 1:Good call out.
Speaker 2:We have to talk about Yahoo. Oh,
Speaker 1:yeah. Traded.
Speaker 2:He is was took us back in time as well. Let's try to pull up the original traded card that kicked it all off August of last year.
Speaker 1:I signed to
Speaker 2:Yahoo, you went from OpenAI to MSL. It was one of the first high profile exits in that whole saga. And one year later, he just announced this morning, he said, I'm leaving Meta to start a new company, building the TBD lab alongside Mark. And Alex has been deeply inspiring and fulfilling. I'm proud of what our multimodal team accomplished across Muse Spark, Voice Mode, Muse Image, and Muse Video, and even prouder of the team that made it possible.
Speaker 2:Over time, I felt increasingly drawn. He's calling out his laurels, but
Speaker 1:he's not resting on them.
Speaker 2:Over time And
Speaker 6:I felt increasingly drawn to a problem that will matter deeply to humanity's future, yet remains largely underexplored. It now has my full attention.
Speaker 2:More to share as the work takes place takes shape. So anyways, I wanted to take a
Speaker 7:quick
Speaker 2:quick little victory lap because I believe it was Monday or Tuesday.
Speaker 1:The victory lap.
Speaker 3:So Monday
Speaker 2:Yeah. Anyways,
Speaker 1:Monday Are you are you given the situation that you're in right now, are you more of a victory lap guy or a pat on the back guy?
Speaker 2:I like victory lap.
Speaker 1:You don't like patting yourself on the back?
Speaker 2:No. I've always found it awkward.
Speaker 1:Yeah. It is sort of awkward to pat yourself on the back. A victory lap gets the blood flowing.
Speaker 5:Yeah.
Speaker 2:It's healthy. More ergonomic.
Speaker 1:It's more ergonomic. It's
Speaker 2:more ergonomic.
Speaker 1:So take your victory lap.
Speaker 2:I forget what day it was. Maybe it was Monday or Tuesday when I was feeling a a little spicy. But I was just saying that MSL is is basically operating up against the clock, is that they did this massive talent rate across all these companies about a year ago. Yeah. Lot of people 9 figures, 10 figures, many of those people are gonna basically spend a year and hit a point where they're like, okay, I have a $100,000,000 in the bank Yeah.
Speaker 2:And I'm kinda good now.
Speaker 1:25% of a huge pile of gold is still a huge pile of gold
Speaker 2:sometimes. Exactly. Yeah. And a lot of those people are just gonna basically see the number in their bank account and be like, am I happy doing what I'm doing? Is this fulfilling?
Speaker 2:Or do I wanna go build a company? Mhmm. Maybe they've always wanted to build a company. And so, in the case of Yahoo, you whatever package he had, clearly clearly, he he's down to go take a risk. Yeah.
Speaker 2:You could argue how risky is it for him to actually, you know, really start a company. I'm sure they'll raise a massive round out the gates. I'm sure he could always get Aqua hired in somewhere else too even if the company doesn't work. So it's not like he's taking on, I would say, that much risk by going to start a company Mhmm. Right now.
Speaker 2:But that being said, I don't think this will be the last of the exits that we see out of MSL over the next even one or two months.
Speaker 1:What if he starts the social network? That would be risky. What if he's like, I'm coming for it all?
Speaker 2:I mean, says, I've felt increasingly drawn to a problem that will matter deeply to humanity's future. Maybe he's figured out how to make aligned social media. Better reels. Social media safety.
Speaker 1:I like that. Yeah. Well, we should let Tyler pat himself on the back for this
Speaker 2:that make you go like this.
Speaker 1:But first, let me tell you about console.com. Console builds AI agents that automate 70% of IT, HR, and finance support, giving employees instant resolution for access requests and password resets. Tyler, take a get pat yourself on the back because this card was the first TBPN trading card and Tyler whipped it up by himself and it doesn't even have our brand on it. And it also has a literal baseball field in the background. But this video or this this image went so viral.
Speaker 1:The original post got 30,000 likes on axe was
Speaker 3:It did around
Speaker 2:a 100 k on Instagram. Yeah. Random account that again wasn't ours.
Speaker 1:It was crazy. It actually broke
Speaker 2:for not watermarking it.
Speaker 1:And it was the it was the first it was the first moment where we had been talking about a story that really broke through to the mainstream. Because of this trading card, we wound up covering On
Speaker 2:French television.
Speaker 1:French television. That's where I was going. That's where I was going. So but because of this trading card, we talked about this a whole a whole this whole this story over a course of weeks. It was very interesting and dramatic.
Speaker 1:There were a whole bunch of scoops that came out around Mark Zuckerberg making people soup and stuff. It was it was a lot of really entertaining stuff. We were featured in the New York Times daily podcast, I think once or twice for talking about it. They clipped us and included our coverage in their telling of the story for their broader audience. And then French television sent out a bunch of reporters and cameras to come and interview us, which was very funny because they kept asking us like exactly how much money does does did did this guy make?
Speaker 1:And we're like, look, we don't know exactly how much he makes. And even even if we did, we probably wouldn't want to share that but it was a very fair
Speaker 2:tell them there's there's no salary caps.
Speaker 1:That's true. There are no salary caps. Anyway, let me tell you about Figma. Agents meet the canvas. Your AI agents can now create and modify your Figma files with design system context.
Speaker 2:Instagram SaaSpocalypse says, it's over.
Speaker 1:It's over.
Speaker 2:Instagram. The head of Instagram, Adam Masary, just posted a new word mark after ten years
Speaker 7:Okay.
Speaker 2:Leaving it unchanged. Adam says, the word mark at the top of the app hasn't changed in ten years, so it was time for a refresh, cleaner and more modern with references to the original and the simplicity and craft that's always made it Instagram. So, yeah. People people don't like it. I don't I don't have necessarily super strong opinions on it personally immediately.
Speaker 2:I do think I do think the the original Instagram word mark here on the left had started to feel extremely dated.
Speaker 7:Mhmm.
Speaker 2:But it's still iconic and I felt like I feel like we're headed back to this sort of maximalism and branding. Right? Like, however I don't know how many years ago it was, five years ago, all the big fashion houses started going from their historical word marks to updating them and making them much more simple and
Speaker 1:just Yeah. The Balenciaga.
Speaker 2:Yeah. There's like 10 different examples.
Speaker 1:Yeah.
Speaker 2:And so this this move almost feels a little bit like lagging in some ways where I actually like that the Instagram logo was like it did feel dated but it it was so distinct. Yeah. All that being said, people see the Instagram word mark so much that this is going to be normalised probably within days. And people will just forget about it.
Speaker 1:I sent a different treatment that I thought they should go with. It's declined.
Speaker 3:We can
Speaker 1:pull this up. It's right in
Speaker 4:front of I mean, it's not too late.
Speaker 1:They
Speaker 2:could This is their first time updating it but I could see them updating it again tomorrow.
Speaker 8:Yeah.
Speaker 2:If they like your version.
Speaker 1:I think that would speak to me personally a little bit better. But you know, their treatment, it's a choice. It's clean. No. I I think this is
Speaker 2:Trey says, look. I'm not a fan of change. This is a war on history.
Speaker 1:Blanking She's hating change.
Speaker 2:No. I mean, that's actually that's such a funny Yeah. Perfect take because that's people's reactions to almost every logo change. Yeah. It's like, look, I understand that you want a new logo, but personally, I'm just against change.
Speaker 2:I'm just
Speaker 1:against change. That's hilarious. Yeah. This seems like a pretty a pretty minor minor iteration. So congrats to them.
Speaker 1:I'm sure I I I think this will be well received. I wonder if there's going to be more brand unpacking around this. Is this is there a message? Like, will this will this new logo ultimately be tied to, like, Mark Zuckerberg's vision for AI? Is there more is there more discourse that will come from like what the future of Instagram means?
Speaker 1:What the mission and values of the company are in this era? Is there something deeper here?
Speaker 2:Yeah. The interesting thing is like they really just they just tried to combine like a new logo Okay. And the old logo.
Speaker 1:It's very readable. I I think it's fine. It it has a little bit of articulation, a little bit of special
Speaker 2:I treatment. Yeah. I disagree on the on the readability part. Like the the the s which they're taking from the historical word mark is way less readable in the new version. But I again, it's such an iconic
Speaker 4:lot of people are saying the r looks like a z.
Speaker 1:Oh, interesting. When I see the when I see the the s, I see the tour logo for some reason. I see an onion.
Speaker 2:Image of this. Yeah. I do too.
Speaker 1:Little bit like an onion sitting there.
Speaker 2:Yeah. Onion mode. What are they trying to
Speaker 1:message us with? That's subliminal messaging. Right?
Speaker 2:In stag zam says easy e. Yeah. Readable. Joan says that r is so so bad.
Speaker 1:I'm getting I'm getting dragged. Instagzam. Anyway
Speaker 2:Instaguam says Michelle. It's got layers for sure.
Speaker 3:You know company has a
Speaker 1:great logo? Let me tell you about Cisco. Critical infrastructure of the AI era. Unlock seamless real time experiences and new value with Cisco. Cisco has
Speaker 2:says l o l, Jordy just never learned cursive. The original logo is cursive. I'm saying that that is to me more readable than this this Frankenstein. But it'll be fine.
Speaker 1:Did Louis Vuitton ever do the the clean rebrand? It looks like they might have. Is this the actual Louis Vuitton logo? I mean, they still have the classic LV, but I think they
Speaker 2:Yeah. Did But the word mark is
Speaker 1:I think they were a participant in the new word mark. In like the the the clean sans serif font. I think it's time to move away from that. And I and I think, you know, they're they're taking risks here. I'll I'll defend it and I think it will grow on me.
Speaker 1:It's also not the like, Instagram's been through a number of of rebrandings and iterations. So we'll see. But speaking of Louis Vuitton, Louis Vuitton collaborated with Singer and created this insane Singer x Louis Vuitton collaboration for a nine eleven that looks like a handbag.
Speaker 2:They're it the most tacky car.
Speaker 1:I was I didn't know where you were gonna go. I knew I saw this all over the all over the feed. I knew we were gonna be talking about it, but I didn't know where you'd sit.
Speaker 2:Yeah. So Why is
Speaker 1:it tacky?
Speaker 2:It looks pretty Yeah. Let's let's play this video.
Speaker 1:This isn't that tacky. I like the color. You don't like the color? The blue wheels are tacky? That this part looks nice.
Speaker 2:Well, okay.
Speaker 1:The bag Yeah.
Speaker 2:Interior is just brutal.
Speaker 1:The stick
Speaker 2:is pretty cool. The hood with the straps
Speaker 1:Extremely crazy. Yeah. This part's sort
Speaker 2:crazy. Helmets? I I don't hate the helmet.
Speaker 4:There's there's two there's there's multiple cars.
Speaker 1:Yeah. There's another version that doesn't have blue wheels. It's a little more subdued but
Speaker 2:It is so to me, part of the reason why it looks like, you know, a monstrosity Okay. Is that it feels like they just it it feels like AI slop
Speaker 1:IRL. Oh, interesting.
Speaker 2:I was And and and to me it's not actually
Speaker 1:You wouldn't surf with that surfboard, Jordy?
Speaker 4:But the problem is like the problem is
Speaker 2:like I I I think that if Portia had collabed with LV Mhmm. Meet, there would have been like it it would have been toned down like a lot and probably be
Speaker 1:didn't Portia do a collab recently? And we asked We we we talked about this.
Speaker 2:We're talking about the Toy Story collab.
Speaker 1:Oh, that one was really good. I like that one. But I'm I'm going back further.
Speaker 2:But I'm just saying like this this is not this is not sanctioned by
Speaker 7:Mhmm.
Speaker 2:This is like to me like a tacky unauthorized collab. Mhmm. And I know exactly the kind of person that would would buy this. They they I'm happy for them or sorry this happened.
Speaker 1:This when I saw this car, I I was thinking that it it feels less like something that you would drive and more like an art piece that you would put in a in a house that has glass that you look through
Speaker 2:says that the singer looks like BDSM plus Hampton's inheritance minus taste.
Speaker 1:Oh, because of all the leather straps. Wow. That is wild. It
Speaker 2:Maybe maybe they just don't believe that taste is the new moat. And they're just like fading that whole take.
Speaker 1:This is the one I was thinking of.
Speaker 2:The says buckles on the hood. LOL. Perfect for a pilgrim.
Speaker 1:It was the Amy Leon Dor, a l d Porsche nine nine three turbo that we we discussed and I think you were skeptical about as well. Not big on these collabs.
Speaker 2:Yeah. I think I think the main thing is they just went like 300% too hard.
Speaker 1:They did. Yeah. It is very aggressive. How do I add this?
Speaker 2:Avery says the shift boot is pretty bad. Not Let gonna
Speaker 1:me see. Yeah. The ALD was was much more subs subdued now that I'm looking at it. We can pull up these images.
Speaker 8:At
Speaker 2:this one. People are saying next level gluttony.
Speaker 1:Aspirated slop. People do not like it. Well, it's not for everyone. It it feels more like an art piece that you put in a in a luxury apartment that has glass between a seating area like a poker room and a and a garage.
Speaker 2:Like something in Dubai.
Speaker 1:Yes. Probably. It's oh, it's Chubai. That's what you're referring to.
Speaker 2:Yes.
Speaker 1:This is Chubai. Yes. Okay. I understand now. But yeah.
Speaker 1:Pull up the pictures of the the ALD Porsche that was on the official Porsche YouTube channel. So must be real.
Speaker 2:Someone else says that's the ugliest display of good craftsmanship I've seen in years. Yeah. What what they've accomplished clearly was incredibly difficult.
Speaker 7:Mhmm.
Speaker 2:They executed their plan seemingly very well. It's just that the plan was was was way too much.
Speaker 1:Okay. Pull up the ALD Porsche nine nine three turbo collab video because I want you to see I want you I want your review. You gotta pick one. You're either driving driving Louis Vuitton or ALD. Which one you going with?
Speaker 1:Jordy.
Speaker 2:I mean, this was a actual collab between ALD and Porsche. And it was much much much more subtle. It was more subtle. And very well done and the creative is well done. Yeah.
Speaker 2:I thought this was I don't own anything from from ALD to my knowledge. I've never been a part of been a part of that brand. Mhmm. But I I liked this collab a lot.
Speaker 1:Okay. Within this video, so you're going ALD over Louis Vuitton over LV. Correct? You're going ALD over LV if you had to pick between those two?
Speaker 2:100%.
Speaker 1:Within this video, are you going ALD nine eleven or are you going flock of sheep? Which one would you take? If there if you're you can either have the flock of sheep or the Porsche.
Speaker 2:How many sheep?
Speaker 1:Look at how many sheep
Speaker 2:there are.
Speaker 1:There's at least Look at those.
Speaker 2:I think you gotta go. I think you gotta go with the sheep.
Speaker 1:You gotta go with the sheep. Okay.
Speaker 2:I know how much I'm hoping that I'm hoping that the flock is quite a bit bigger. If sheep. Threw If you throw in the sheepdogs and and with the sheep, I think you gotta go with the sheep.
Speaker 1:Okay. How
Speaker 2:Michelle says sheeps are useless. Trey says those are nice sheep. I agree. They look like fantastic sheep.
Speaker 1:Okay. We're gonna find out how much a flock of sheep costs.
Speaker 2:Ben says that was a very wolf answer.
Speaker 1:Okay. About 50 ordinary sheep California, you're gonna spend about $20,000. That's for forty forty eight breeds So
Speaker 2:you're saying I should take the ALB. Nine eleven. Nine eleven. And sell then you can buy
Speaker 1:sheep.
Speaker 2:1,000,000,000 cheap. 5,000,000,000 cheap. No. But I would I would lever up.
Speaker 1:Oh, okay.
Speaker 5:You're using
Speaker 2:I would lever up on the flock.
Speaker 1:You're leveraging up. Okay. Let me tell you about Codex. Codex is a powerful workspace for getting work with agents. Whether you're writing code, analyzing data, creating content, or sorry.
Speaker 1:Or automating business workflows. Codex wants to
Speaker 2:move workforce. Satiris Robotics, which is the company that makes the Oh, yeah. The the what do we call it? The Centaur is in the YouTube chat. Nice.
Speaker 2:They say, pay around $300 a sheep. Oh. Icelandic. We got some. Saint.
Speaker 2:Sheep? Cross. So they don't need shearing. Okay. Good to know.
Speaker 2:Got cheap alpha in the chat.
Speaker 1:We got some other
Speaker 2:they're they're saying that cheap don't depreciate at the same rate. Well But honestly that
Speaker 1:produce. So with your 500 sheep, if you have the right if you have the right mix of ewes and ewes and rams, male and female sheep, you could potentially grow your flock into the billions. Correct? Yes. You could become a a full time sheep farmer.
Speaker 2:Sheep billion.
Speaker 1:Scale it up. Yeah. The new new like shrimp farming hustle is is sheep farming for sure. The last one here is I guess the the supreme nine eleven which that seems
Speaker 2:not authorized. Nick in the team chat says, I'm a sheep the same way Odysseus is a sheep.
Speaker 7:What does that mean?
Speaker 8:Is he a sheep?
Speaker 2:People think he's a sheep, but he's really an absolute Oh,
Speaker 1:okay. Okay. Okay. He's a wolf in sheep's clothing.
Speaker 2:What in the slop is this?
Speaker 1:I can't even tell
Speaker 2:Get that out
Speaker 1:of you. Anymore. Okay. Anyway, let's do let's do some other stories. New bombshell reporting from the Wall Street Journal reveals that North Korea has built a secret workforce inside American companies using stolen identities, AI, and accomplices in The United States to cheat its way into remote jobs and funnel hundreds of millions of dollars back to Kim Jong Un's regime.
Speaker 1:It's a fascinating story. The Wall Street Journal article is posted. It's a thirty minute documentary. They spent over a year investigating this. The FBI says that there are thousands of North Korean IT workers applying for jobs applying for jobs across America.
Speaker 1:After a year long investigation, the journal obtained a trove of leaked browser histories, emails, calendars, and screen recordings from one cell of workers providing a remarkable look at how the operation actually works. The North Koreans apply for jobs at enormous scale. One cell tracked by the journal applied to more than a thousand companies over just three months using AI in nearly every step. And in the documentary, they show a product that is basically, clearly, they will leave a voice agent running while they're being interviewed in a technical interview, give the answer when asked about a particular technology. If they are proficient, they will simply have AI look up the answer, give the answer to the interviewer, get the get the job.
Speaker 1:And in some cases, they're using real Americans as front men. Basically, they will do the interview and then they will ship a remote laptop to say, hey, you're a new you're a new remote worker. You are qualified to work on our software engineering team at this small company. We'll send you a laptop and then you just collect a check, send half of it to the North Koreans and you don't have to do any work. So it's like passive income free money for you, the Americans.
Speaker 2:So you said they're real Americans, but they're not patriots.
Speaker 1:Yeah. That's what I'm hearing. Well, I I the the the documentary is pretty gut wrenching. Like, the the the individual who they talk to who who is participating in this, obviously, is probably going to see some some legal consequences for this. But it did seem like he had a very very tough go and gotten to a very rough situation to be in that situation.
Speaker 1:Was certainly his first choice.
Speaker 2:Ryan in the chat has a hot take. Kim Jong Un is such a lovable rascal. It's hard to stay mad at Boo.
Speaker 1:Boo. The operation also relies on help from inside The United States. North Korean IT workers pay American facilitators, they're called facilitators, to host company laptops in The United States allowing the actual workers overseas to remotely connect to them while appearing to be employees working domestically. Also the North Korean workers, they go to non extradition countries. So they'll go to China and Russia and a few other countries.
Speaker 1:And then from there, they will be remoting into an American laptop. So it just looks like, oh, okay. They're like, the web traffic is coming from it's not coming from North Korea, but it's coming from a country where if the FBI says, hey. Can you send us this person? They're a criminal.
Speaker 1:That country says, no way. We're not we're not sending them to you. So for one job paying $75,000 a year, he said he and the North Koreans split split the salary $50.50. The workers also use stolen American identities to pass employment screenings, often juggling multiple identities simultaneously and holding down several jobs under each name. This all boom during COVID and the remote work boom.
Speaker 1:And the scale of the operation is enormous. Some North Korean IT workers earn as much as $300,000 a year. The Treasury Department says North Korean says the North Korean government can seize as much as 90% of the wages earned by its overseas IT workers and recently estimated that these operations generated nearly $800,000,000 in 2024 alone. Pretty Wow. Pretty big.
Speaker 1:So the journal's thirty minute documentary titled Infiltrated North Korea's Secret US Workforce follows the operation in detail. It's fascinating to watch and I highly recommend that you check it out. So go take a look.
Speaker 4:What do see? I feel like it's important to ask like are they doing a good job? Like, because presumably they're not just like hacking Wow.
Speaker 1:Really? You're gonna steel man this. Okay. I I see where your loyalty is
Speaker 4:there there there's like the skit where it's like, oh, we're gonna rob the bank and we're gonna get a job there. Yeah. Yeah. Yeah. And over 30, we're gonna they're gonna deposit the money straight into our bank account every two weeks.
Speaker 1:And what happens at the end? Yeah. We we walk out of the front door.
Speaker 4:Yeah. It's like yeah. Oh, they're like hacking and they're stealing American dollars by working at the companies.
Speaker 1:Yes. So there are sanctions. And because of those sanctions Yeah. Like, clearly it's America is not allowed to do business with North Korea in any capacity Yeah. Including this one.
Speaker 1:So this is sanctioning
Speaker 4:But, like, it could be much worse. Right? They could just be, like, hacking the company
Speaker 1:That's true. That's true. But they could also be doing both. Because once you let a for a a North Korean IT worker in your systems, they could be planting all sorts of spyware. A very dangerous situation to be in.
Speaker 1:But, yeah, a wild, wild, wild story. So everyone, if you're running a small business with some remote head count, make sure to ask everyone to post something negative about about Kim Jong Un every day to prove their loyalty, I guess, or something. But there is a it opens with a very very funny clip of someone doing a remote Zoom interview and asking someone to say something negative about Kim Jong Un and the guy's like, oh, I can't I can't hear you. I can't I can't hear you. I can't possibly say that.
Speaker 1:Anyway, let me tell you about the New York Stock Exchange. Wanna change the world? Raise capital. At the New York Stock Exchange. Just do it.
Speaker 1:A new luxury hotel canceled a YouTuber's $4,663 stay. Then came the viral feud reports The Wall Street Journal. A dispute between Amman, which we've talked about a lot on the show and content creator Ryan Walker illustrates the growing tension between commercial enterprises and influencers. It's a fascinating story and somebody's got to stand up for the Amman. Somebody has I'm ready.
Speaker 2:No. Honestly, honestly, I don't think the Amman actually needs that much standing up for it.
Speaker 1:No. The Wall Street Journal broke it
Speaker 2:down detail and looks
Speaker 9:bad for this guy. Anyway
Speaker 1:Get it. Let's go through it.
Speaker 2:So Should we should we should we pull up a little of Ryan's video?
Speaker 1:Yeah. Yeah. For sure. For sure. Social video is
Speaker 2:He probably should have deleted it by now because
Speaker 1:It is crazy.
Speaker 2:It's still misleading.
Speaker 1:It is crazy. It's still up. It's in the time.
Speaker 2:And overly dramatic and not representative of what a normal guest experience would be like.
Speaker 1:Yeah. It is it is odd. Let me let me drop it a few different places, see if we can get this in here. Boom. So the Wall Street Journal reports that it was supposed to be the hottest hotel opening of the year.
Speaker 1:Let's play a little bit of his video.
Speaker 4:Don't know me. I am a luxury hotel reviewer here on YouTube. I specialize in very honest reviews of the world's best properties. I'm
Speaker 3:not gonna
Speaker 4:give away too much as to what happened. I want you
Speaker 1:to watch thesis. Like, he pays for the hotels himself. He doesn't get paid the hotel for the review. The hotel doesn't pay for his stay and consequently he can be more independent. This was the Doug DeMiro strategy.
Speaker 1:For years Doug DeMiro said, I'm not taking press cars. I'm not going to your press event. I'm going to borrow the car from just someone who owns it and I'm gonna be able to drive it how I wanna drive it, review it how I wanna review it. And if you're Lamborghini or Ferrari or any other company, you're not gonna be able
Speaker 7:to have
Speaker 1:any any thumb on the scale of my review. Very good in concept, but the execution goes a little bit off the rails in this particular video. So you can see him pulling up to a guard house that looks woefully unfinished. It looks very, very rough.
Speaker 4:You remember Brian Walker? Yes. Yes. I know you. Allow me to verify your information.
Speaker 4:Okay?
Speaker 2:Okay. Thank you. Pleasure.
Speaker 5:What's going on?
Speaker 4:Drama. This is such a strange area. It's kind of like They say cannot enter?
Speaker 2:I cannot enter.
Speaker 4:Apparently, our driver is saying in Spanish that they are saying he cannot enter or pass.
Speaker 7:Good morning, mister Walker.
Speaker 4:Yeah. We don't have your reservation.
Speaker 2:Do you need the number?
Speaker 4:No. We don't have your reservation
Speaker 2:Alright. We can pause. Let's let's get into let's get into the journals piece. Okay. Long story short, that video sparked a bunch of There's thousands of comments on there, people trashing the property.
Speaker 2:Yep. You know, basically fully taking his side. Yeah. But he And his side
Speaker 1:is basically that he showed up. He had a reservation. They didn't let him in. The place didn't seem together. He had a bad experience and he shares that with his YouTube audience, gets a lot of views for it.
Speaker 1:But the journal dug in and provided a whole bunch more interesting perspective. So let's go through it. The travel world was a buzz over the coming August 1 launch of Amandvari, the first Mexican resort from the from Amman, the multi billion dollar ultra luxury hospitality group whose mythos is built on seamless service, total tranquility, and fierce guest confidentiality. The new location is set within Baja California's Baja California's soars, private Costa Palmas community. And the resort promised 18 beachfront casitas.
Speaker 1:So only 18, a very small hotel but very luxurious. So less than a week after its debut, the hotel was the center of a viral controversy in that YouTube video. He said he tried to check-in for a one night stay and he was turned away. So an Amman spokesperson told the Wall Street Journal they went on record and they really clarified a lot. They said the deceptively edited video created by someone unauthorized to be on our property does not reflect the circumstances of the of the incident accurately.
Speaker 1:Their dueling accounts reflect a new reality where cameras are always rolling, virality can outpace truth and public perception is shaped less by what happened than who posts first. So on July 7, Walker booked a stay at Ammanvari. The YouTuber with more than a 150,000 followers, pretty solid giant channel, has built a brand around paying full price for hotel rooms to guarantee honest reviews standing out amid influencers accepting free trips in exchange for positive content. He says, I'm honest and transparent. I'll tell you exactly what to expect and why.
Speaker 1:He had previously paid $26,000 for a Four Seasons yacht trip that he panned in May.
Speaker 2:And sorry for the spoiler. He's not honest or transparent.
Speaker 1:The same month, Walker posted a positive review of Amman Tokyo. Then he traveled to Ammanvari on August 3 to hear Walker tell it in the video he showed the next day, he was a victim of a hospitality nightmare.
Speaker 2:And and one thing that's I think very funny right away is Mhmm. So so you're going to review this hotel by a one night stay, which which means like, you know, maybe you check-in
Speaker 7:Yeah.
Speaker 2:It doesn't look like he he wasn't gonna get an early check-in either way. Let's say you check-in at four, you gotta be out of your room by eleven.
Speaker 1:Yeah. That's not
Speaker 2:You got a really five hour window where you don't even know how could you how could you accurately review the property That's
Speaker 1:a good point.
Speaker 2:When you're missing that incredible window from eleven to four. Yeah. Right?
Speaker 1:Yeah. No. It's a point.
Speaker 2:You just don't have any context. Yeah. I mean, how can I trust your review? So I'm already my guard is up.
Speaker 1:Guard is up. So his video which rapidly surged to more than 700,000 views shows his vehicle approaching an unfinished wooden guard hut with stud walls still visible on the outside. In the interactions that follow which appear to take place between two different gates, staff recognize him, inform him that he has no reservation and according to Walker, eventually call the police to escort him away. Sounds terrible. As Walker drives off, he finds an email he says he missed.
Speaker 1:Sent the day before and apologizes for canceling his stay due to scaled back capacity during opening week. Not looking closely at the timestamp, he tells viewers the email arrived late the previous night when later pressed by the journal Walker revised his timeline saying it arrived the previous morning. Walker did not amend the timeline in his video or in a subsequent livestream. The Internet quickly took Walker's side. Viewers of his YouTube channel, including people who identified as Amman loyalists and travel industry professionals, expressed disbelief.
Speaker 1:We'll never book another Amman property again, read one of the more than 5,000 comments, many of which expressed similar sentiments. Amman's spokesperson said the company does not comment on bookings. Inspect the footage closely though and questions surface notably around an absence of the police invoked in the video's title. Amman said it didn't call police and showed the journal an incident report that made no mention of law enforcement. In the video, at one point in the encounter, a staff member says, I need to call the police now.
Speaker 1:Walker sees her on the phone, says off camera, yep, she's calling the police right now but that might not have been related to him whatsoever. So that's like a very confusing situation. Later, he told the journal he did not see police arrive on-site. When the journal told Walker that Aman said it had not called the police, he said he hoped that was true and that the information would make him reconsider how the result the resort had handled the situation. There is also the arrival gate that does not look like a typical five star welcome.
Speaker 1:And we can pull up a picture of the actual welcome gate because it does look pretty ramshackle. The Amman spokesperson said Walker bypassed the main entrance and
Speaker 2:He was looking for trouble. My theory is he knows exactly what he was doing. And the other thing is, it also came out that he they had reached out, they had called him, they had whatsapped him, they had made numerous efforts to make contact, and I just think he wanted he wanted the drama.
Speaker 1:Probably.
Speaker 2:Wyatt in the chat says, I want to have my hotels call the police on YouTubers.
Speaker 1:So the Amman spokesperson said Walker bypassed the main entrance ending up at staff access points instead. Ambient audio in Walker's footage captures someone saying, quote, this is the employee entrance. Walker told the journal he had never heard the remark and that his driver followed GPS directions so the first entrance showed in the video. You can see it there. So internal documents provided by Walker show the resort emailed him five days before his visit telling him it was unable to allow stays involving content coverage until a month long media blackout and suggested he postpone his travel.
Speaker 1:Walker replied that the staff should consider him a standard guest. The property replied that he was prepared to welcome him, noting that he could post content after the media exclusivity window. So I'm sure they're doing a whole bunch of different Yes.
Speaker 2:So he comes during the first week, books one night, knows that he shouldn't be filming, still decides to film Yeah. And is effectively looking for trouble the entire time. And the crowd over on the Wall Street Journal loves it. The top rated comment, Aman, here I come. Any hotel that bans influencers is doing regular guests a great service.
Speaker 1:Yeah. Yeah. Mean, that's a big thing is that people go to certain places to not have cameras all over the place, and it's getting rarer and rarer. And if if
Speaker 2:It's so it's so it's so actually fascinating to look at the difference. YouTube comments are like, wow. I'm never gonna go to the Amman. And then Wall Street Journal
Speaker 1:I'm looking
Speaker 2:now. Walker is a complete tool, says Ray. Edward says, seems mister Walker struggles with the truth.
Speaker 1:Oh, wow.
Speaker 2:Second most
Speaker 1:highly rated comic So Walker says he didn't
Speaker 2:And then someone else says it takes a special sort of Jack star star star to make me side with the ultra luxury resort resort with holistic wellness temples.
Speaker 1:Yep. But that's the case we're in. A spokesperson for Amman said the resort resort also sent to job. The Oman spokesperson said the resort refunded Walker's booking in full, pledged to cover additional travel and cancellation costs and help him rebook his stay. Walker said he received the refund but didn't take him on up on the other reimbursements.
Speaker 1:He just showed up anyway. The dispute highlights growing tension between commercial enterprises and content creators looking to record on property. Jack Ezzon, CEO of luxury travel advisory Embark Beyond, noted that hotels maintain the right to cancel reservations or deny access. He added that properties immediately after opening should be approached cautiously. You don't need to be the guinea pig.
Speaker 1:Give it six to eight months to grow minimum. In this case, nearly every claim has a competing account. Did Ah Manvari overreact to a content creator's opening week visit to prevent an honest review? Or did Walker turn a misunderstanding at the gate into a viral story? The luxury world may still value discretion.
Speaker 1:The Internet values whoever sees
Speaker 2:He in the X Chat says, imagine staying on a 20 room property and having a YouTuber there. I would be seeing it. It's so I wouldn't be surprised if they update their policies. We gotta talk about McBee before
Speaker 1:Okay.
Speaker 2:Our first guest joins In Wired
Speaker 1:Yeah.
Speaker 2:Reese Rogers says, McDonald's built a 515 page dossier on me. It says I'll never stop eating there. It requested a copy of my data from the McDonald's loyalty program and received an extensive personalized report that algorithmically predicts my next purchase.
Speaker 7:This
Speaker 3:is
Speaker 1:Take me through it.
Speaker 2:Amazing. Pulling up the
Speaker 1:The actual article?
Speaker 2:The actual article.
Speaker 1:McDonald's secret sauce is really commercial surveillance, says Jeff Chester, executive director at the Center for Digital Democracy, a group that advocates for consumer protections. Privacy experts I spoke with said this level of detail may feel invasive, but it's fairly standard for how large companies in The U. S. Run their loyalty programs. The report contains specific pieces of personal information about you that were identified by searching McDonald's systems which contain information about our customers.
Speaker 1:Tyler, you had the the conclusion. What does McDonald's actually know about you? What does McDonald's know about an individual?
Speaker 4:That they like hamburgers.
Speaker 1:That's basically the takeaway. Right? Like the takeaway is like the number one most
Speaker 2:This guy likes hamburger.
Speaker 1:Is a large diet coke, then the spicy snack rack, then the Grinch McShaker fry
Speaker 5:large.
Speaker 2:We should figure out other companies that that have these types of that have this type of data and and request
Speaker 1:Full reports?
Speaker 2:Yeah. We need our own reports. You want I would love to read
Speaker 1:You want your 10,000 page report on what you like from Arrow on? Yeah. You're really gonna go there.
Speaker 2:Because I couldn't possibly know by looking at previous orders.
Speaker 1:Glass bottled water. Mcdonald's
Speaker 2:Anthropic is apparently in talks to Oh, yeah. Cart for 6,000,000,000 Getting mid generation. We've a bunch of awesome conversations with with Dean over at Decart. He's always he's very very early, he was willing to do live demos
Speaker 1:Yeah.
Speaker 2:Of their product, interviews, completely on you know, we didn't even test with him before the show. Yeah. He was just ripping them live. So always had a ton of confidence in the product and they've been just cooking.
Speaker 1:They were valued at 4,000,000,000 a quarter ago. Himanshu has some extra context here. Says, Descartes could be one of the first AI labs focused on world video models to be acquired by a Frontier AI lab. I think this also could mark an initial phase where Frontier Labs start treating world models as a major strategic capability alongside inference optimization as a major offering from Descartes. Yeah.
Speaker 1:Interesting to imagine how this actually links to the core thesis of, you know, b to b and enterprise and and coding. But certainly certainly an amazing technology that feels like on the verge of of a breakout. The demos are amazing, but we haven't had the, like, Ghibli moment for world models yet. Like, it's very much a prototype demo video. Go and see what it looks like.
Speaker 1:But we've talked to a lot of people, Oliver Cameron and Fei Fei Li, about world models. There's a lot of optimism about how this all plugs into the AGI pursuits of the Frontier Labs. Well, we have our next guest already in the waiting room. So let's bring in Ibar Igor Babuschkin. But first, let me tell you about Railway.
Speaker 1:Railway is the all in one intelligent cloud provider. Use your favorite agents to deploy web app servers, databases, and more while Railway automatically takes care takes care of scaling, monitoring, security. And we will bring in our next guest.
Speaker 7:Hi, guys. Thanks having me.
Speaker 1:What's happening? Popping on the show. Welcome. Congratulations on the fundraise. But may maybe let's go back in time.
Speaker 1:Tell us a little bit about your history and journey to starting River AI. And Jordy already has the gong ready, so just tell us the fundraising announcement, I guess.
Speaker 7:Yeah. We we just managed to raise $1,100,000,000 for River AI. Broke the
Speaker 2:gong.
Speaker 1:Broke You broke the the mallet.
Speaker 2:The chosen one.
Speaker 1:The chosen one.
Speaker 2:You are the chosen one.
Speaker 7:Okay. So We'll see if it if it's if it works out. So anyways, I I started my career as a physicist. I was really interested in understanding the universe. Yeah.
Speaker 7:But then realized that there was something really big happening, which was AI
Speaker 1:Yeah.
Speaker 7:Started to happen. And I think AlphaGo was really the moment when I started to feel like, wow, gotta switch gotta switch and learn how to do AI. So I managed to join DeepMind.
Speaker 5:Yeah.
Speaker 7:It was almost ten years ago. Wow. I worked there on WaveNet. We trained a Starcraft agent that was very strong, so got into reinforcement learning there and then switched to OpenAI. One point was really interested in reasoning, coding with alarms, which now turns out to be something that works.
Speaker 7:It was which is pretty crazy to to see. And then end up cofounding XAI together with Elon.
Speaker 1:Yeah.
Speaker 3:So I
Speaker 7:thought it was time for for another Frontier Lab. I'm always in favor of more diversity in AI more. More companies doing different kinds of things, so I was happy to to support him building up building up XAI. Yeah. And now with Brannin, we're kind of taking that to the extreme.
Speaker 7:So I feel like the way we've been building AI is maybe not the way of the future. A few large corporations building these super powerful models. Everybody has to pay them by the token. We wanna figure out how we can distribute AI to everybody in a way in a way where you own it, you're able to shape your own AI systems. Maybe you have the inference running in your home or in your office, that would be the the the best achievement if you can if you can figure out how to do that efficiently.
Speaker 7:And we're running a few different bets on how to help people build up their own AI. We're helping companies build AI with the River API. That product's already out. So if you go on river.ai/api, you can log in and you can start training models based on open weights.
Speaker 3:Mhmm.
Speaker 7:So we support all kinds of very powerful open weight models.
Speaker 1:Mhmm.
Speaker 7:And then we're also using that platform to build out personal AI agents that are increasingly personalized to you. As you're using them, they understand you better and better and should really feel like you're you're building the AI. You're you're creating it.
Speaker 1:Can we go back to your time at DeepMind working on video games? I'm so interested as in video games as a benchmark. I saw someone using codex to play Slay the Spire. And I've played a lot of Slay the Spire. It's pretty difficult.
Speaker 1:Now it's a it's not a fast twitch game but I'm wondering if you like how would you think about the value of video games as a benchmark? There was another story about the FAA hiring traffic flight traffic controllers who had previously played video games. So there's some sort of transfer where if you're good at video games and maybe SimCity, you might be good as a flight traffic controller. And you could imagine a situation where AI gets really good at playing video games and then becomes more useful in a whole bunch of different, you know, related tasks. But it feels like the gaming benchmarks are still toys.
Speaker 1:They're fun. They're people just doing them off on the side. But how do you think about the role of of solving video games or or testing models on video games in the modern era?
Speaker 7:I think it's a great idea, but I I might be biased. You know, I used to play a lot of video games Yeah. Growing up and still do do some gaming from time to time, I I think it's awesome. And I I think the the the idea here is you wanna test your your AI on system on on problems that it hasn't necessarily been trained on directly.
Speaker 1:Yeah.
Speaker 7:So wanna have some level of generalization. And games are are amazing because they have all kinds of complex things you're gonna have to do, all kinds of problem solving. You're gonna have to develop on the fly, and it's kind of measurable how much progress you're making. So if you're getting to the end of the game, you're doing well. If you're making progress from one level to the next, you know, that's, you know, that's measurable.
Speaker 7:So and and have many, many properties that make them pretty ideal Mhmm. For measuring AI capabilities. And the the craziest thing today is we have these powerful agents that have been trained mostly on coding tasks.
Speaker 1:Yeah.
Speaker 7:And I give them some code base and you ask them to to fix a bug or develop a new feature and they go out and they do all this tool calling to figure out how to do it and rewrite your files. But then you can also hook them up to a game. And give them an API like, here's how you control the units in the game or here's how you manage your resources or here's how you move around in Pokemon and other games like that. And I think it's crazy that these models are so capable at playing games and just shows you how much we've how how far we've come in terms of generality and
Speaker 1:Yeah.
Speaker 2:Yeah. Where are the shortcomings though? Because, you know, I can think of one which is like with a game, like, can you basically run an agent, have the agent play a game effectively infinite amount of times. It can fail a lot. It can learn, things like that.
Speaker 2:Yeah. One of the challenges in is in the real world, like let's say someone was making a sales agent, like an agent that wants to help you get customers.
Speaker 3:Mhmm.
Speaker 2:You can't necessarily just let the sales agent run wild in the real world as a business at least because you know, it's gonna mess up a lot. A bunch of customers are gonna have a bad experience and you could make maybe the agent gets slightly better from that experience but you could have lost like a bunch of potential customers or pissed a bunch of people off or things like that. And so how do you think about making the jump from agents that are very effective at at playing these games in a generalized way to agents that can be effective at long running tasks in the real world that involve effectively complex groups that are are third parties?
Speaker 7:That's a good question because most of the training today is done with synthetic environments. So it's you're you're building up these aural environments inside of your AI team and you're training the the models for reinforcement learning. And so you kind of go for a simulation, you could say, and they're not really interacting with the real world when you're training them. I think one of the big frontiers right now, one of the big developments you might see is if you train the training moving into an online setting Mhmm. Where the the models are directly interacting with the users, with the companies that are using them.
Speaker 7:And as they're solving tasks, as as they're figuring out what to do, we update the weights of the model, they get better and better. And it's a big research problem right now. So nobody knows how to pull this off in in general, and the best agents, they're all all been trained in simulation so far. Yeah. But it's one of the things that we're working on at River AI.
Speaker 7:So if any of the viewers are interested in doing some research on this, reach out.
Speaker 1:Yeah. It feels like we're not that far, at least in the gaming sense, to, you know, in the in the training step, just create an environment that's just like, here's a Steam account and a credit card. Go buy every game and try and get the platinum trophy or like complete the game and feed that in. But gaming thing is sort of a pure benchmark at this point because it feels like the labs haven't identified it as something that really they want to focus on. Can you talk about the trade off between like bench hacking for good and bench hacking for bad?
Speaker 1:Because there's the game that the labs are playing but then there's also like if you show up with a product and and it does the task and it classifies all of my taxes, I don't care if you bench hacked on that as long as it gets the job done. Right? So there's Yeah. This push and pull between those. How are you thinking about communicating that with your customers, your the companies you work with who might be fine with a model that's only good at their specific task?
Speaker 7:Yeah. Exactly. I think that's a big opportunity for any company out there today because you own your own data that you've collected from your customers or from the work that you're doing. And if you eval the systems on that data, this is, like, the the perfect eval
Speaker 1:Mhmm.
Speaker 7:For you. If you're able to improve your models based on that, you you might be able and you might end up owning the best model in the world for your particular tasks. I think that's actually huge for companies. They should be building these specialized Evals, they should be trying to build their own models. And the River API makes it easy Yeah.
Speaker 7:To fine tune your own model, given your eval, given your own training environments. But in in general, when it comes to AGI and improving the the intelligence of the of these models, I think we want to hit them with some surprising benchmarks. Want to measure generality then throwing in a game that it hasn't been trained on, that it's never seen before. I think that's a very, interesting measure to see, like, how how far out of distribution can they go can they do interesting things Yeah. And haven't trained them for.
Speaker 1:Huge, huge fundraising round. AMP is in. We've talked to Ajnae a bunch. And he has a very interesting thesis around actually going much deeper in the stack, acquiring compute. How are you thinking about the uses of those funds?
Speaker 1:Because I could see coming to you as a company and knowing that you have the capital to go and really optimize all the way down to the stack and become a Neo Cloud, build a build a data center for me or help me with the more expensive CapEx piece of the puzzle. At the same time, like, I don't really have a rule, like a solid frame of if I come to you and I say I want to fine tune a near frontier open source model, is that actually that expensive and wouldn't you just ask me to pay for that upfront? So that doesn't seem like a huge capital cost to you, but what is the shape of the of the cost that
Speaker 7:you Yeah.
Speaker 1:Are planning on incurring over the next couple years?
Speaker 7:Yes. So we're charging the customers by the token. So if you have a fine tuning run, you wanna do you wanna do it. If have an Aural run, you only pay for the Xudo. So which first app.
Speaker 7:Choose some kind of model
Speaker 1:Okay.
Speaker 7:Training run size that you that's perfect for your task. Some customers, they end up training a really, really small, really fast, efficient model because they've got the best data for the task and end up beating the the largest and most expensive models. Yeah. Other customers want something more general or they wanna utilize these larger open rate models like k m a k free Sure. And others are coming out.
Speaker 7:So that's a that's a more expensive training run. But, yeah, we obviously need a lot of access to GPUs to make that happen. The funding helps with that. But even if you have the funding today, you still need to get access to to GPUs, and GPU prices are increasing Yeah. Steadily.
Speaker 7:So I think what we're going to see is more and more investors collaborating with our portfolio companies around compute Mhmm. Bringing up GPU capacity, distributing it among the portfolio companies. You know, some maybe some of them need a little bit more in one month than others and so on and so forth. And it's a new kind of strategy that we're seeing, I think, to deal with the fact that compute prices are are going up this much scarcely.
Speaker 2:Yeah. Would you say that's like one of the one of the biggest sort of challenges for for River at this point is just compute planning? I mean, we've seen it. We've seen the full spectrum now. We've seen Sam last year, you know, getting really really really aggressive.
Speaker 2:And then we saw, you know, earlier this year, Anthropic just sort of being caught off guard by the growth and it feels like that is as as a CEO, you know, you're,
Speaker 5:you know
Speaker 1:It's a completely new skill set
Speaker 2:for Yeah. It's like a new it's
Speaker 1:a Yeah. There's never a moment where Marc Benioff was like, I don't have enough servers for Salesforce, I imagine. Like, they were way different problem set. But now it's yeah. Demand planning is like a key key key skill set for a CEO.
Speaker 7:Exactly. It's a totally new skill set. That's super important, and it's so difficult because as a start up, by definition, you have this variance for the future. You don't know if you're gonna grow by 10 x or if you're gonna grow by three x Yeah. Over the next twelve months.
Speaker 7:Right? So you have to play this really, really difficult poker game to figure out, you know, what is the right allocation for me or maybe create some deals that are more flexible
Speaker 1:Mhmm.
Speaker 7:So you can actually scale up Sure. Dynamically as the as the demand is is growing. So I think we're gonna see more and more of that's of that happening. And yeah. So so we're bringing up quite a bit of GPU capacity, and it's been important both for research and also to power the API.
Speaker 7:So as more people are starting to tune their own models, it's becoming very, very popular now. A lot of companies are reaching out about wanting to build their own custom models that they own and trying to train on their own data using their own evals. So demand is gonna keep going up, we expect.
Speaker 1:How do you think about custom silicon over the next few years? YouTube, I believe, has a custom silicon chip for encoding video very efficiently because you upload one video, they need it in three sixty p, four eighty, seven twenty, four k HD. And then we saw Thales bake the weights of Lama, I believe, into their chip and prove that that was, exciting enough that AMD acquired the company. And I'm wondering if you imagine a future where a company comes to you, like, you can imagine like the Visa network is like we want to run a transformer based model on every transaction and it's going to be like trillions of prompts effectively or more. And so custom silicon might actually make sense but then the models jump forward and you can do batches and there's so many different tradeoffs.
Speaker 1:How do you think custom silicon will play in the diffusion story of AI?
Speaker 7:Yeah. I think we'll see more and more custom silicon. Obviously, companies like NVIDIA and AMD are also going to do extremely well, especially on the training side. Yeah. It's really unmatched what they're what they're able to do.
Speaker 7:Yeah. On the inference side, there there is some room for optimization because we believe that with personal AI agents coming up, so this is kind of the next evolution of agents after coding agents, which have been super successful. We expect that the demand for tokens will go up even further. Mhmm. So to the point where, you know, we we don't even know how we're gonna serve all these tokens for everyone given limited data center capacity, given all the bottlenecks in the data center supply chain.
Speaker 7:So I think we gotta be smart and start to develop some custom silicon specifically for inference of these personal AI agents. We could imagine being much more power efficient with these kinds of chips. Can we maybe bake some of the transformer architecture into the chip to kind of despite the fact that we know what kinds of models we're gonna run. So that will make it more specialized. You might not be able to run any kind of model anymore.
Speaker 7:So Talos takes us to takes us to the extreme, so you can actually bake bake in the model weights. But today, you're not able to fit weights of very large models on a single chip that way. So that's the big the big bottleneck. Yeah. So you're you're only able to do maybe eight eight billion parameters or something like that, whereas the best models have trillions of grams.
Speaker 7:Yeah. So hopefully we're gonna see some chips that are able to to run those top of the line models very very efficiently.
Speaker 2:Do we need more NeoLabs or, you know, basically like are are are there is there enough idea space that that, you know, more people should be spinning up entirely new labs? I imagine a lot of the people that that would be candidates to spin up labs themselves, you're probably trying to to recruit. But are we past peak neo lab or or is it are we just getting started?
Speaker 7:Hopefully, we're just getting started because, you know, this idea of building powerful coding agents, having APIs and so on, we all had those a few years ago or not OpenAI and other places that that would be the the future. But now that it's actually arrived, I think all of us are feeling like this can be the end of line. Like, there have to has to be a different way Yeah. To own and build AI systems. And so that means there's there are opportunities for research, there are opportunities for new kinds of business models, you know, totally new talents can move in.
Speaker 7:You know, somebody who doesn't have a big name in AI can do something really amazing today because they have to kind of have to think out of the box, come up with something that's, you the the AI experts that's been doing it for ten years, they might not come up with it.
Speaker 1:Mhmm.
Speaker 7:It's such a wild idea. Right? So that's I think we're gonna see a phase of innovation, totally new approaches to how AI systems are built, how you make use of them, and some really cool AI products as well for consumers. So that's what I'm looking forward the most.
Speaker 1:Yeah. What exciting time. Well, congratulations and thank you so much for taking the time to come chat with us.
Speaker 2:Yeah. Excited for the next one.
Speaker 1:Yeah. We'll talk to you soon.
Speaker 7:Thank you, guys. Cheers.
Speaker 1:Have good rest of your day. Goodbye. Let me tell you about MongoDB. What's the only thing faster than the AI market? Your business on MongoDB.
Speaker 1:Don't just build AI, own the data platform that powers it. Our next guest is the founder of CoreWeave. We have Brannin McBee coming back on the show after an insane q two. Congratulations. Give us the headline numbers.
Speaker 1:How'd you do in q two?
Speaker 9:Q two was phenomenal beat for us. Right? That that that couldn't have come in better. We're extremely excited with the performance of the business, and balance of the year is just gonna keep on improving from here.
Speaker 1:What's the biggest bottleneck now for you? Is anything changing based on all the data that you've collected over the first half of the year?
Speaker 9:No. Like, bottleneck remains sludge. Right? Like, that that is the hardest part of this business, and it's also what we're best at.
Speaker 3:Mhmm.
Speaker 9:Right? We have 51 data centers in operation today. We know how to navigate this supply chain. We know how to get this stuff online, deliver to clients, do so on time. It's immensely challenging.
Speaker 9:Right? Like, this is the most important commodity on the planet right now, and CoreWeave is singular in its ability to deliver the best performing infrastructure out there.
Speaker 7:Mhmm.
Speaker 2:You guys are spending a lot of time doing demand planning. Demand is obviously off the charts. What are you we we just had Igor on from from River. We were talking with him about this sort of like new skill set that that technology founders need to have around demand planning, right? You have a, let's say, have a NeoLab, you're developing products, it's very hard to gauge how much demand you're going to have for them.
Speaker 2:The right strategy over the last couple of years was just to assume that demand was near infinite and and sort of plan against that. But for smaller companies that, you know, have smaller balance sheets, it really feels like that is gonna make or break a lot of these companies, especially, you know, NeoLabs that, yeah, that, you know, their margin profiles and and all these other factors are gonna come down to how well they can predict their own, their own demand for their products.
Speaker 9:Yeah. I completely align with that scenario, but I'll probably take it a step further. Like, what happens after that demand is planned for us? How do you finance it? Right?
Speaker 9:And for us, that's been just an absolutely critical skill set that we've assembled, team wise over the last six years. And the the latest financing that we did, DDTL five was a term loan b offering. I thought it a fantastic example of that ability to work with demand planning and get the right financing in place for that demand. That was the first facility that we've done where the contract duration is actually shorter than the amortization period. Mhmm.
Speaker 9:Right? In other words, it asks the investors in that facility to take on renewal risk on the compute versus all of our prior facilities. Investors, were fully covered by the original contract value. Right? If it was a $5,000,000,000 loan, there was $7,000,000,000 worth of revenue sitting behind it.
Speaker 9:Right now, it's opposite. Like, it it flipped. And what that enables us to do is to spend more time in the, shorter duration contract market because we just proved that it's financeable. And for us, that type of client is predominantly enterprise. Right?
Speaker 9:The AI lab cohort, the hyperscale cloud cohort, they like to sit in the five to six year range because they know that they need that compute. They need that scale. They need it for that long duration. Enterprise is, at the shorter end of that curve. And as you guys know, historically, we've been really focused on the longer duration contracts, but having this proof point in the market now that we can go finance these shorter duration contracts is is really important to us.
Speaker 9:So that I I think that goes hand in hand with the demand planning aspect.
Speaker 1:There's a ton of hyperscalers, I mean, that have been building data centers for a long time. There's a ton of neo clouds. How are you positioning differentiation? I imagine that demand is so strong that it's not the biggest thorn in your side by any means explaining the value, but feels like it always has to be in the back of your mind as if demand ever softens, we want to be differentiated. We wanna be differentiated even in a market that's tight on demand.
Speaker 1:So how are you positioning CoreWeave specifically?
Speaker 9:Look. I I think it's widely recognized by our clients, by third party analysts, by our suppliers even that we are the best representation of this technology on the planet. Mhmm. So that demonstration of value will continue to be through our technology product. It'll be through our timeliness and deliveries, our ability to scale across all different types of workloads, whether it's training, fine tuning, inference.
Speaker 9:It'll be in delivering the best performance adjusted flops and token tokens out there.
Speaker 1:Mhmm. Jensen was on CNBC recently talking about a $500,000,000,000 deal with a lot of big banks. What was your interpretation of what that deal means for the industry overall, where that project is going, and how Core Revis fits into that story?
Speaker 9:I think it's wonderful for the space.
Speaker 5:Mhmm.
Speaker 9:Right? It just continues to show the amount of capital that's willing to underwrite the build out of intelligence.
Speaker 1:Yeah.
Speaker 9:As you guys know, we've been at the forefront of financing, this market for years. I I think having more capital in here is just great for the sector.
Speaker 1:Mhmm. How have you been coaching people through there's some folks that are like, I'm worried this is like.com. There's other people that I'm worried about this being like the mortgage, you know, boom. What pieces are different this time? What pieces are, okay, we are actually borrowing from this particular build out.
Speaker 1:A lot of people go to railroads. There was, you know, a huge amount of value created. There were booms and busts in various times. What, how how are you dealing with the various, critics of the build out?
Speaker 9:So one of those points, feel like we talked about this last time I was on, I believe, was depreciation or useful life of compute.
Speaker 7:Yep.
Speaker 9:And, you know, the the variable that we were able to introduce and highlight in our earnings is the eight one hundred SKU. Yeah. Right? This is a twenty twenty SKU.
Speaker 7:Yeah.
Speaker 9:And we still have clients coming in asking specifically for that SKU. Right? It's not like they're asking for Hopper and, like, oh, we only have Ampere, or they're asking for a Blackwell, and we only have Ampere. Right? Like, they are saying, no.
Speaker 9:We want Ampere for workloads because that is the most performing platform for their workload. And we signed a contract in the quarter that goes out through 2029. Right? That that is now an implicit
Speaker 2:So it'll be a nine year old chip View at the by the end.
Speaker 9:Nine year nine year chip. Right? And this is a take or pay fixed term, fixed price contract, and pricing on a 1 hundreds for us have held, solid since early twenty twenty five.
Speaker 1:Yep.
Speaker 9:Right. So it's like I I I think one of those big criticisms was, well, this is two or three year computes, and it's useless after that. We've been very consistent that six year depreciable life is accurate for this infrastructure, and I I think that there is really strong opportunity for it to have material useful life beyond that, and we we see it every day. Right? And it it's it's just in the way that AI workloads are being optimized across different SKUs.
Speaker 9:And it's this concept like there isn't one AI model to roll them all. There isn't one GPU to roll them all. It's just this matrix of different sizes of workloads relative to different sizes of GPUs where it's filling in across.
Speaker 1:Yeah. My thesis has been that there are AI workloads that get that get built out, maybe a recommender system, a basic text transformation system, a translation system. And then those will stay in place for a really long time and those will be used by more intelligent models. But then there is the question of just will the chips burn out? Has any thought changed there or is that pretty well understood?
Speaker 1:Yeah. Right?
Speaker 9:Like this stuff is meant to run the data center. Yeah. It's meant to run for a long duration. We're not seeing any acceleration of burnout or like unexpected rates of errors. The the infrastructure is doing great.
Speaker 2:Yeah. That makes sense. What what is going on on the on the power side? I imagine there's a lot of people over the last year that have realized that how how important power is to this to this whole build out and are trying to front run, you know, players like yourself and get access to that power in hopes that they can resell it. I imagine the utilities are like somewhat sophisticated and at at at this point and and hopefully for a while we'll just call around and try to go directly to the operators, but like what is actually what can you share about like the current dynamics around that that hunt for power?
Speaker 9:I would say the power market is very competitive, but there is power out there. Right? And the bottleneck is less electrons. For us, it's more of powered shell, right, or, like, delivered data center capacity. And that goes all the way down to the components that are in the data center.
Speaker 9:Right? Like, think of the backup battery supplies, transformers, etcetera. But it's also the people. Right? Like, electricians, is a skilled trade that is a significant bottleneck for the industry.
Speaker 9:It has been for some time, will continue to be for some time because that's a trade that takes years to develop the skill sets necessary to be able to work in a data center site. So I believe that that is gonna remain more the bottleneck than power is right now. You're look. Absolutely correct. I I can't cap the number of emails I get a day from people I've never met before trying to offer us powered land and all across The US, but I that is less of the bottleneck, and it's more on delivered, powered shell capacity.
Speaker 1:Are you hiring electricians directly, or does this go through subcontractors for specific projects?
Speaker 9:This predominantly goes through subcontractors.
Speaker 7:Okay.
Speaker 1:Subcontractors. What are
Speaker 9:We have a little bit of self build Yeah. Ourselves where, like, we're going out and sourcing GCs, contractors, etcetera. But for the most part, we lease capacity. Yeah. And that that's been the way that we've scaled business.
Speaker 1:I imagine that there's a lot of folks in the organization that are technologists. They understand the technology and the hardware and the software and how everything pieces together. What are the other areas that you're hiring if somebody wants to join the the the Core Weave organization that draws from a different pool of human capital or talent?
Speaker 9:Going back to the point originally, financing. Right? It's deeply important Yeah. To the business. You know, we've we've done a fantastic job assembling a team up in New York.
Speaker 9:We've raised, I think it's north of $40,000,000,000 in debt and equity over the last twenty four months.
Speaker 1:Yeah. Are you pulling people from Wall Street, investment banking, hedge funds, consulting
Speaker 7:Yes.
Speaker 1:Private equity, like, all of the above?
Speaker 9:Yeah. I I'd say predominantly private equity, private credit
Speaker 7:Okay.
Speaker 9:Investment banking, guys with, like, deep financial backgrounds.
Speaker 1:Yeah. Yeah. That makes sense.
Speaker 9:But look. We're we're hiring across the business. Yeah. Right? It's engineering.
Speaker 9:It's physical deployment teams.
Speaker 5:Mhmm.
Speaker 9:I think we're it it is our HR organization is doing a great job. Let's put it that way.
Speaker 1:Yeah. Busy. How how domestic is the footprint? There's a lot of nervousness about the build out in America, and my default interpretation would be if there's a lot of pushback in America, like, people don't want mind waiting five hundred milliseconds for LLM responses. So, yeah, you can put them in space, but you can also put them in, you know, another country.
Speaker 1:How are you thinking about the international opportunity?
Speaker 9:I think that's right, but I would qualify more in, like, on the county level. Right? Like, we're getting pushed back. The whole industry is getting pushed back Yeah. At the county level in some places.
Speaker 9:Yeah. But that ultimately doesn't change the fact that the demand is there. Mhmm. Right? Getting county level pushed back, state level pushed back.
Speaker 1:Yeah.
Speaker 9:It just means that it's gonna be built elsewhere Mhmm. Because the demand for AI is the strongest it has ever been. The ROI on AI is the strongest that it ever has been as well. I mean, inference, is is absolutely profitable for our client base Yeah. Which means that there's gonna keep coming back to core wheat products.
Speaker 9:We're in Canada. We're in Europe. We recently announced an expansion into APAC as well. I I expect for us to keep keep moving globally, but keep a focus on domestic deployments.
Speaker 1:How strong is the correlation between social media pushback and county level pushback at like a city council meeting? Because I feel like there's sometimes a big disconnect where something can go viral, maybe it has some misinformation, gets a 100,000 likes, but then the actual county and the residents are fine with what's going on and it's sort of this like getting mad on behalf of someone. But do you see a correlation between a story or a news article that happens in this particular area and then there is actual movement on the ground at the local politics level?
Speaker 9:Look. I I'd say it's very specific to the sites.
Speaker 1:Sure.
Speaker 9:Right? Like, we we are engaged in conversations where wherever the local communities would like to understand the the value that we're bringing to that region.
Speaker 1:Yeah. That makes sense. Jordy, anything else? Not for now. Congratulations and thank you so much for taking the time.
Speaker 2:Big one. Thanks guys. It. Great to see you.
Speaker 1:Goodbye. Let me tell you about Shopify. Shopify is the commerce platform that goes with your business and lets you sell in seconds online, in store, mobile, on social, on marketplaces, and now with AI agents.
Speaker 2:It's very interesting. It feels like one of CoreWeave's and and some of the other Neo Cloud's advantages is to just be able to be completely out of the the politics and like the drama of AI. Mhmm. Right? Like all of the the labs Yeah.
Speaker 2:Hyperscalers and all these companies like Mhmm. They have financial relationships. Yeah. They have personal relationships. Some of them Yeah.
Speaker 2:Some of them hate each other Yeah. And want each other dead. Yeah. Can kind of sit there like Switzerland and be like, anyone need tokens?
Speaker 1:Yeah. Yeah. That's true. When a niche hit when a niche hit tweet fails to bang, James Heal shares a quote from Peter Jay. When a sub editor complained that his, that his, the Times columns were too complex, Peter Jay famously replied, I only write this for three people.
Speaker 1:The editor of The Times, the chancellor of the exchequer, and the governor of the Bank of England. True. Not quite audience of one, but audience of three mentality. I think it's a good way to stay sane if you're posting online. Probably don't wanna be too sucked into the algorithm.
Speaker 1:Although this one may go viral. I don't know. Anyway, we have our next guest, Garret Langley from Fox Safety. He's the founder and CEO. He's been on the show before, and we're very excited to talk to him about the latest news.
Speaker 1:Garret, how are you doing? He's back.
Speaker 5:Good. How are you guys?
Speaker 1:We're good. Welcome back to the show. Thank you so much for joining. I
Speaker 2:been saw happening since the last time you came on the show?
Speaker 1:I saw pretty
Speaker 5:chill. Chill.
Speaker 1:I imagine. Well, let's kick it off with the with the report in the journal. FLOC ads, privacy guardrails after surveillance backlash. What happened? What are you actually implementing?
Speaker 1:What is the story?
Speaker 5:Yeah. I mean, there's there's two big buckets. Right? You've kind of privacy and then accountability. And I can start with accountability because I think that's the I think that's the bigger topic, or I think it's the the bigger topic, is low and behold, law enforcement abuses their power at times.
Speaker 5:And that's horrible, right? Mean, it's really bad. And we built a tool about four months ago as a test to kind of scan the audit logs that we've always had to look for abnormal behavior. You know, we solved a million crimes last year. We know what good investigations look like.
Speaker 5:We built this tool, and the headlines over the last few weeks show you that we're pretty good at finding abuse. And I thought this was maybe just an isolated to us. But what I found out is like when law enforcement searches DMV, they search criminal records, there's no audit logs there. There's accountability. So we launched that.
Speaker 5:And I think you're going to see over the coming weeks, you know, more headlines of officers being arrested for abusing their position of power. That's the that's
Speaker 1:that's the big that actual where does the oversight board live? Is this a separate product that's sold to, like, district attorney effectively? Or, like because if if if the person's like, yeah, I checked the audit logs. I was behaving poorly. That doesn't really do anything to stop the process.
Speaker 1:Right?
Speaker 5:No. No. Yeah. So so it it's a free product
Speaker 7:Okay.
Speaker 5:That all of our customers are now required to use.
Speaker 1:Oh, okay.
Speaker 5:Which we're the only company that I'm aware of in this industry that requires their customers to actually hold themselves accountable.
Speaker 1:Okay.
Speaker 5:For most of our cities we work with, there's a a police administrator and then a city manager that is reviewing the audit log. Yeah. So in the hopefully unlikely event that the police chief himself is the one committing a crime, the city manager is still there as the double check.
Speaker 1:Yeah. So the the business has grown a ton and obviously, Flock products are deployed all over The United States. I'm interested in the the the backlash that has grown. You see a lot of videos with a lot of social media attention negative towards flock. Is this actually showing up in your financials at this point?
Speaker 1:Like, because I imagine you have to replace any camera that is destroyed, but I can't tell if there's just one camera that's destroyed and then million people view it and like it.
Speaker 5:Yeah. No. Vandalism real. It's always been real though. Okay.
Speaker 5:Just like weather has been a problem. You know, we deploy these products in Florida and there's hurricanes. I mean, in California, there's earthquakes. And so we're used to that. I mean, if you looked at the data, you'd be hard pressed to say, when did this TikTok trend blow up?
Speaker 1:Sure.
Speaker 5:You know, I'd say the sad part to me is you've got these influencers on TikTok and Instagram pushing this, and they're convincing these 16, 18, 20 year old guys
Speaker 1:Oh, interesting.
Speaker 5:To go do it. Yeah. And they're committing felonies. Yeah. And so I saw this guy that got arrested in New Mexico, and he's gonna face five to nine years in jail.
Speaker 1:Wow.
Speaker 5:Yeah. And I'm like, that's that's horrible. This guy's life is over. He's got a felony on his record.
Speaker 1:Yeah.
Speaker 5:He's going to jail
Speaker 1:Yeah.
Speaker 5:To cut a camera down. Yeah. It just seems really crazy.
Speaker 1:One thing that I've been identifying is it feels like there is a gap particularly between liking a post on TikTok or engaging with anti flock or the deflock campaign and maybe even going out in the world and chopping one down versus at least trying to go to your city council meeting. Because I I just have to imagine, I I've been to social city council meetings. If if there's 50 people there that are all taking the the the podium and saying, look, I I am a citizen and I don't want flock in my city. City council will usually respond to that. Is that is is that disconnect real?
Speaker 1:Like, what is going on there?
Speaker 5:Yeah. I mean, look, I I I it's surprising to me too because city councils are normally pretty boring meetings. Yeah. And there's really only two topics that are being debated right now, which is data centers and flock.
Speaker 7:Interesting. And
Speaker 5:you guys probably have better tools than I do. You can look at social media traffic, and they're fully correlated.
Speaker 7:Okay.
Speaker 5:Which makes me wonder, like, why? Like, why? Because I've I've been asked this question, why now? And I'm like, don't know. We've been in business for almost a decade.
Speaker 5:Yeah. You know, we went to thousands of city council meetings every year, you know, over like 10,000 last year. We're not hiding anything. Yeah. But no
Speaker 2:Yeah. Mean, It's just To to be honest, think, like
Speaker 1:Mhmm.
Speaker 2:When you they like, I I think a large part of it is, well, everyone's been talked to about AI safety one way or another. Right? It might have been a a viral clip of Sam Altman talking about AI in 2015 that gets resurfaced in like an Instagram reel today and people assume that it was said today or and the clip was out of context. And then but but you have to look at like I think I think people are like very scared of of of the technology overall, which I think is fair because they've been told to be scared
Speaker 5:Mhmm.
Speaker 2:In a variety of different ways.
Speaker 5:Mhmm.
Speaker 2:And then they're using AI in their in their life or in their work and and that's cool. But like data centers present something that's like, you know, a data center in your backyard doesn't necessarily give you an immediate benefit. Mhmm. And it could give it could have some real downsides. Right?
Speaker 2:Like noise or potential, you know, the the it it it there's all these sort of like possible negative externalities and then flock, it's like when you when, you know, Dario's talked talked a lot earlier this year about AI getting getting so good at looking over sort of like vast quantities of data and maybe maybe the law is not sort of like staying, know, moving quickly enough to respond to that. And so like Flock is like a very I I can see exactly why people are fixated on that because the product is clearly very very good at fighting crime but then also if you are a bad actor it's like the dream like basically tool set. Right? It's literally like in a video game like God mode. Right?
Speaker 2:And so I think like there's like very real I I I can see why there's like a correlation but I I I don't know if this is where you're going but I don't necessarily think it's like some organized nefarious group that is like, we want to shut down we want to shut down these these two technologies. Mhmm. And I I so anyways. Mhmm.
Speaker 5:No. I think I think there's some truth. I think the other thing that that I think is true is is I I was I asking someone, aren't you like afraid of all the data brokers that buy your location data and sell it? Yeah. And they're like, well, I can't see it.
Speaker 5:I can see your camera. And I think it's like a valid point, is you drive by a flock camera and you say, well, I see it and I say, I I feel a lot safer.
Speaker 1:And some
Speaker 5:people might say, oh, I I feel like, you know, this is this is Big Brother. And I go, it's like, but Big Brother wouldn't have an audit log and wouldn't have transparency and wouldn't do these things. I do think a lot of the feedback is fair, you know, which is like how long should this be stored? And I think the thing we got wrong was expecting local government and state government to follow along fast enough. And I just I don't think they are.
Speaker 5:And this accountability measure, like, no states require this. They all should. And like, we dropped our data retention recommendation from thirty days to seven days, which means the majority of our customers will just simply accept the default. Sure. And state regulators have moved to twenty one days.
Speaker 5:But like we've done the analysis, and we think we'll have a, you know, 10% reduction in efficacy with that reduction. But I think it's a fair trade off. And look, if the government wants to say like in New Jersey, it's a mandate five years of data retention. Mhmm. That's up to the state of New Jersey to decide.
Speaker 5:We'll push for what we think's right. Mhmm. But I I might agree with Dario there in that case, like regulation is a good thing for this type of technology and it needs to move faster, not slower.
Speaker 1:Yeah. It seems like the speed of regulation is a huge issue here because the technology moves much faster and I think folks on the left feel like they could be attacked by people on the right and vice versa. And so if there's a if there's a new technology that rolls out like mid cycle, all of a sudden you're grappling with it and and your worst fears on the on sort of like the extremes of the political spectrum are that the they will be used by the opposing side aggressively against you. Even if Yeah. You are innocent.
Speaker 1:And I think that's where a lot of this is coming from. What do you think?
Speaker 5:Yeah. Know. I I think that's true. Think the other thing, and I don't know who gets credit for saying this. As a technology industry, we still feel like we're this bubble.
Speaker 5:Yeah. This small part of the economy. Yeah. And the reality, technology is the economy now. Mhmm.
Speaker 5:Yeah. And I don't think as founders and CEOs, we've like fully grappled with what that means. Mhmm. And I think about someone like, you know, like Jamie Dimon. I'm like, he he like has so much influence over our fiscal systems.
Speaker 1:Mhmm.
Speaker 5:And I think he probably understands that responsibility because banking has always had this, like, this kind of austere posture of their responsibility in the world. And I think as technologists, we're catching up, and I think I'm in that same boat of, you know, we helped a million crimes last year. Last month, we did 1,025 missing peoples.
Speaker 7:Mhmm.
Speaker 5:That's real human life.
Speaker 7:Yeah.
Speaker 5:There's a real responsibility with that impact, and I'm glad we're catching up. We're not done. Like, we still have a lot of work to do. But I think if I look around the table of my other peers, in public safety, I think we all have a lot of work to do.
Speaker 2:Yeah. I think yeah. The the the the there's been a very real positive impact to the product, and I think, people have every reason to also be concerned because even with these guardrails in place and audits you have even if you assume like a very even a half a percent of police officers in The US are corrupt in some way or or not or shouldn't be in these organizations, that's still thousands of of of bad actors that are in the system. And so I think one of the challenges that that I see is like I feel like this is like too much of an too big of an issue for just you and the flock exec team to be Interesting. To be in charge of.
Speaker 2:Right? Like Mhmm. I think you're you're very smart. I think this company was started for for all the right reasons. I think it is having a positive impact in in many ways like you said but at the same time like this is a national issue right now.
Speaker 2:You know, you're not elected. It's not your job to decide you know Americans like privacy and so I think like my question is like what is happening at the national level? Obviously counties and cities and states are trying to figure this out but I feel like this issue is like beyond I don't feel like this issue should be decided by you, I think it should be decided by our lawmakers because it comes down to our fundamental rights as American citizens. So I actually, I want this the issue is just blown up on social media but I think it's time to like have a much more of a national kind of conversation about this and then ultimately probably new laws put into place to make sure it's something that you can point to and just say like, look, like we are abiding by the law in our country and our our lawmakers who were
Speaker 1:And the correct path to push back on flock is through the democratic process.
Speaker 2:Exactly.
Speaker 1:Yes. Call your representative. That that that does feel like the correct outcome. That that
Speaker 5:that feels very self obvious. It it is interesting, you know, I was getting saw someone push on x, like, you should require a warrant to use your product. And the response I wanted to to make was, yes. When when it when it is the law Yeah. To need a warrant.
Speaker 5:Yeah. We will happily follow the law. Sure. But 40 courts in the last few years have all deemed this isn't, you know, a violation of the constitution. Interesting.
Speaker 5:And this is tricky though because
Speaker 1:Yeah.
Speaker 5:Every state, every city has like a very different point of view on what safety looks like.
Speaker 1:Yeah.
Speaker 5:I mean, look at you guys, you know, hometown of San Francisco, San Francisco's own version of safety has also changed a lot in the last decade.
Speaker 7:Totally.
Speaker 5:And I think it's a better version today than it was five years ago, but it's still changed, and it'll probably change again. And so it is a pretty tricky situation from a setting the right laws because I I struggle to see how the federal government could establish a law that matches everyone's goals. And so my hope is like states we had nine state bills get passed this year. I'd love to see 20 or 30 next year Mhmm. That at least start to move this in the right direction.
Speaker 5:Because I I do worry, like, any federal regulation will just make half the country mad and other
Speaker 7:other half country other half the
Speaker 3:country just
Speaker 1:as bad. In in theory, like, city even city by city, like there is a like I I think people might misunderstand that like flock doesn't put up the cameras. They sell them to Yeah. Police departments. Yeah.
Speaker 1:But the cameras in those police departments have, you know, heads that are elected in many cases. Right? There's like a sheriff that gets elected in
Speaker 5:People who get to pick.
Speaker 1:Vote for. But at the same time I'm sympathetic to someone that basically thinks you put them up because it is an opaque process. Many Americans cannot tell you the names of everyone on their city council and who the mayor is and all these things or when the election is and how how solidified that particular mayor is and whether or not they can actually apply pressure around this issue. It can take years to actually facilitate change in a community. And if you're in the minority, that might never happen for that particular community.
Speaker 1:But
Speaker 2:Yeah. Yeah. I was referencing earlier was like more like friction. Mhmm. Like, we there's historically been security cameras around in a lot of places Yeah.
Speaker 2:Private. Could get access to the footage but there was a lot of friction to doing that.
Speaker 1:Sure.
Speaker 2:Oh yeah. Which is which is good and bad. Right? Yeah. It's good in that a bad actor is gonna have a tougher time like putting together footage to in order to understand someone's movement.
Speaker 2:But again
Speaker 1:Yeah. Yeah. Everyone's everyone's comfortable with like there was an assassination attempt on the president. We should look at all the CCTV cameras.
Speaker 5:We should get that
Speaker 1:first. But then all of a sudden when it's like, okay, like now there's going to be an AI agent that's watching you, Jordy. If you speed at any point during your commute, you're going to get, you know, a point on your license. It just feels like an annoying society that a lot of people don't want to live in and then there's a whole continuum there. And and and and we're grappling with the potential for way, way a way higher level of enforcement and way less of a gray area around our our rule set and our laws, which is something that the laws might need to change or might need to adapt to.
Speaker 1:I don't know.
Speaker 5:No, agree. And I think I think to to your to your point, like Yeah. The I I have a lot of sympathy for our elected officials at the local level because they now need to become experts in AI, public safety, data centers, water. Like it's like and most of these times these are part time unpaid positions. Yeah.
Speaker 5:They're normally lawyers, doctors, real estate agents in their communities. And so, I mean they sign up for the job, but it is it's not straightforward. And, you know, we're we're we like the way the system works today where every one of our contracts goes to city council for vote. Mhmm. That makes the job harder, but makes it transparent.
Speaker 5:Like I saw the other day, you know, was able to find the video from six years ago when we did our first ever city council meeting and how nervous we were because we'd never been to city council. Like, what would that be like? And I've never been to city council, and now it's kind of just a part of our regular day.
Speaker 2:Yeah. What's next? Because Yeah. These updates are good but I don't think they solve I I don't think millions of Americans are are still gonna have are basically probably not gonna read the updates and are still gonna have an issue. So what I mean, what's on the road map?
Speaker 2:What more are you doing to try to find a solution to the to the concerns and and the issues?
Speaker 5:Yeah. I mean, don't think we'll ever be done, because I think this balance of privacy and safety is is a bit of a moving target. Someone asked me the other day, you know, wouldn't it just be better just to turn off all the cameras? And I said, maybe thirty years ago when every single police department was fully staffed, maybe even overstaffed in today's standards. But today, more than 80% of police departments are understaffed.
Speaker 5:So I don't I don't think, you know, of a draconian no more technology is a viable solution. But I do think as a technologist, we have an opportunity to make better products that deliver on that kind of combined goal. And so for us, like, we're excited to get these these tools out there into the wild. I, you know, I anxiously await potential headlines of more arrests in the coming days. I think that'll happen, and that'll be tough because I think a lot of trust in communities will be broken.
Speaker 1:Mhmm.
Speaker 5:I think that trust was broken many years ago, and now the light's being shined on it. But I think for us, we're gonna continue building tools that allow law enforcement to both do their job and build trust with the community at the same time. So we got a lot of stuff coming up. A few more announcements coming up in the in the in the coming months, and, we'd love to come back and share those when they're ready.
Speaker 1:Yeah. That'd be great. Thank you so much for taking the time to come chat with us.
Speaker 2:Yeah. The entire
Speaker 1:Don't that possible take is being enumerated. It's a
Speaker 2:Yeah. It's a very interesting
Speaker 5:want to search jobs?
Speaker 2:Yeah. Yeah. Yeah. I don't think any any anyone in technology envies envies your job right now. But
Speaker 5:The consequence of being consequential is what I tell the team.
Speaker 1:So Yep. Yeah. That's accurate. Well, have a great rest of your day.
Speaker 2:Good to see you, Garret.
Speaker 1:You so much for the update and explaining everything. We'll talk to you soon. Cheers. Have a good one. We've been keeping Sonya Huang from Sequoia Capital waiting too long.
Speaker 1:She's a general partner. She's been on the show before.
Speaker 2:To just talk about Let's bring It'd be great to just talk about venture capital for a little bit. Back
Speaker 1:to AI. Back to 's it going? How's We
Speaker 8:haven't seen each other in a while. Congrats the acquisition.
Speaker 1:Far too long. You. Thank you. We're time. Yeah.
Speaker 1:So temperature check. What's going on with this AI thing? Is there anything there?
Speaker 8:Temperature check. Oh my gosh. There is absolutely something there. The numbers we're seeing from these companies Yeah. Never seen anything like it before.
Speaker 1:Interestingly, less of a power I mean there's power law companies, Anthropic OpenAI. There's lot of companies that are doing really great. But then there's also diffuse community of like NeoLabs and and NeoClouds and so many different application layer stuff where you're seeing just really solid business fundamentals that previously would take a decade to build up. You're seeing it in two to three years.
Speaker 8:Totally. The anthra like, Anthropic and OpenAI and x AI are growing at a pace that nobody has ever seen before. Yeah. But even if you remove them Yeah. This next cohort of companies Yeah.
Speaker 8:Open Evidence, Glean, Factory, they're growing at rates that we've just never seen before. Yeah. One of the most interesting things that's happening right now is we used to have this like separation in our heads of there's the foundation model companies and then there's the application companies. Companies. Mhmm.
Speaker 8:And I would say like one of the most interesting things that's happening now is that all the application companies are starting to build their own research capabilities, their own labs. Yeah. And that's kind of like this concept of democratized intelligence. Yeah. So, would say it's not only revenue that's not just accruing on only the first at the top two players.
Speaker 8:It's it's the production of intelligence itself seems like it's very much democratizing.
Speaker 2:Yeah. We were I don't know when Dylan was on. Maybe it was last week? Mhmm. But Dylan from Figma Yeah.
Speaker 2:We were talking to him. Was like, I want Figma to work on the problem of Basically, Slop is like has been this like, you know, it's been Slop has been getting better and better. Mhmm. But, you know, it's basically like you have a a new breakthrough and then for two months it's like, wow, it's so good now. And then you realize that it can only do like one style over and over and over.
Speaker 2:And I feel like, there's all these application layer companies that are in such a great position to work on some of these like fundamental problems that for better or worse, like the labs are not able to focus enough on. Right? Like maybe it's like only a billion dollar revenue opportunity. Right? And so if you're a lab and you're adding billions of dollars of revenue a month with your core business, why would you work on a problem like that?
Speaker 2:And so I think there's so many examples of that from from Figma to some of the other ones you mentioned.
Speaker 8:Mhmm. Totally. And I think if you look at it from the perspective of the startups, there's just this huge wave towards companies wanting to own their intelligence. And I think a smaller set of companies has been beating this drum for a long time. Like I'm on the board of Fireworks.
Speaker 8:Their tagline is own your intelligence. So they've been advertising this for a long time. Obviously, cursor went on the journey two years ago of starting to train their own models. But what's happening now is like both the giants and the ecosystem are starting to speak up and then the startups are actually getting extremely good at building their own research. So in terms of giants, you have people like Alex Karp talking about sovereign intelligence.
Speaker 8:He he has this phrase, own the means of production, which I freaking love. Saacia talking about your proprietary data. Jensen, championing open weight. So you have all these giants of the ecosystem speaking up of like, hey guys, you should own your intelligence. And then if you look at it from the perspective of the little guys, the startups who we are in the business of backing.
Speaker 8:Yeah. A couple of years ago, they were primarily looking at you know moving some of their intelligence towards these open weight models primarily as a cost rationalization exercise. The thing that's different now is like it is an existential and strategic imperative for them. And so, there's this phrase, you guys probably remember this from the crypto days, not your keys, not
Speaker 7:your crypto.
Speaker 8:Yeah. This idea of like if somebody else is custodying your weights for you, I don't are you like custodying your keys for you? It's not yours. Because something could happen to that brokerage. Something could happen there.
Speaker 8:And I I think like I I think the AI version of this meme is not your weights, your products. Sure. Because fundamentally, if you don't own the weights, if you're just making an API call, you don't have ownership, you don't have steerability, you don't the data file doesn't accrue to you. And so like people are kind of waking up to this. Yeah.
Speaker 8:And so if if your use case is like you want a coding agent, like for that, I'd say
Speaker 7:Yeah.
Speaker 8:Closed model, closed agents, that's fantastic.
Speaker 7:Yep.
Speaker 8:If it is like your core product, I think companies are increasingly waking up to like Yeah. Not my ways, not
Speaker 7:my products.
Speaker 1:Sell to your customers. It should Exactly. That makes a lot sense. Founder office hours coach me through. You're on the board of my hypothetical company.
Speaker 1:Software company, I'm using AI, 500,000,000 ARR, let's say. And I buy this thesis and I come to you Pretty
Speaker 2:confident example there, John.
Speaker 8:500. Not bad.
Speaker 2:There we
Speaker 1:go. We're cooking. We're cooking. No. No.
Speaker 1:No. I mean, like, we have we have fully made it through. We're, you know, an AI winner. We're accelerating, growing. Yeah.
Speaker 1:Growth is great. But I come to you and I say, okay, I'm I'm all in. We're going to own our intelligence stack. Is there a moment where we need to have a conversation about what the talent budget will be? I mean, we saw these crazy MSL deals.
Speaker 1:And for a lot of these companies, they might be unicorns. They might even be decacorns. But they're not going to be able to staff a team of AI researchers and build a full neo lab doing like next generation fundamental research. Yeah. What does the team build out actually look like?
Speaker 1:Is it enough to take your your best software engineers and have them use coding agents to fine tune models for for your team? Or are you hiring entirely new disciplines? What is the what is the correct shape of Totally. An internal AI lab at like a successful scaled unicorn, decacorn software company?
Speaker 8:Yeah. So typically, you're not going to be pre training your own models. There are specific use cases where you actually need pre trained models. That's totally different thing.
Speaker 1:Okay.
Speaker 8:If what you are doing is trying to take off the shelf open weight models and then adapt them and make them really, really excellent for your domain, that's a much larger talent pool. And what we've seen is there's actually two flavors of talent that are really good at this. One is people that have done post training before.
Speaker 1:Okay.
Speaker 8:So, the post training teams at the labs are very, very large at this point.
Speaker 2:Sure.
Speaker 8:There's plenty of these people floating around. And then the second profile is actually interesting is just like engineer or I guess the like just generally smart person.
Speaker 1:Smart, difficult
Speaker 8:person. Pokemon. Because this stuff is actually not that hard and like part of the reason it's even possible for all these companies to have their own labs now is because the actual post training stack has matured.
Speaker 1:Sure.
Speaker 8:So it
Speaker 3:used to
Speaker 8:be that only OpenAI had and Anthropic had the infrastructure in house to be able to do things like post training, reinforcement learning especially. Yeah. But now you have companies like Fireworks that gives you the post training infrastructure. You have LangTrain that gives you the evals. You have Trajectory that helps with the continual learning.
Speaker 8:You have you have Mercur that helps you with the data factory stuff. And so like all these all these components now exist. And so if you as a generally smart person see the see the menu of opportunities, you can actually cobble together your own research stack in a way that wasn't possible a couple years ago. And so like to give you a sense, very, very small teams can get very far. The Harvey team has put out I think pretty extraordinary research.
Speaker 8:Yeah. They're they're they've they just found an entire RL environment last week. Their benchmark is state of the art for legal. Yeah. And their entire research team is seven people.
Speaker 8:So you can get very, very far. We're definitely not, you know, you're not competing with with Meta or OpenAI for size of talent budget here.
Speaker 1:What about one click down? I don't want to invest in building the team owning the stack entirely. There are neo labs that will show up and fine tune a model for me. Is that is that the domain of growth stage startups or is that product going to be more consumed by enterprises that maybe don't have the DNA to just move a bunch of amazing engineers over to do a post training stack and spin up and roll their own? But the NeoLab offers I'm thinking of like a thinking machines, a Tinker, like what happened with Ray Dalio's fund and and and how they were able to fine tune a model, get really good results.
Speaker 1:That feels like its own new market of like post training as a service, fine tuning as a But how does that how does that piece in? Is there a world where you don't necessarily have a relationship with Merkor but your third party does?
Speaker 8:Yep. Look, this is a this is a huge ecosystem and a huge market because I think everyone sees the opportunity of, you know, you obviously the closed model inference market will always be gigantic. Yeah. But I think the open model inference market is becoming very large And for companies to actually be able to make use of open weight models Mhmm. There is a maturation process.
Speaker 8:There is a hand holding process. There is an entire like, you know, come work with us. We will forward deploy people onto your staff Sure. To help you go on that journey. And so there's there's lots of options.
Speaker 8:Fireworks has a fantastic team for this. Mhmm. Merkor has a fantastic team for this. It kind of depends on the specific problem you have. Okay.
Speaker 8:So the specific problem you have is, hey, I I really want to do reinforcement learning on my online data. Fireworks is fantastic for this. If you're like, hey, I can't train on my customer data. I need to get my my model really really good for this specific domain. Can we create a bunch of synthetic data around this opportunity?
Speaker 8:Mercor is fantastic for it. Oh, So, kind depends so on the use case. Mhmm. Generally, I think that it's like really important. It's an and, like companies need to have extremely smart people in charge of this in house.
Speaker 8:This can't be something you outsource like your lunch menu outsourcing, right? This is so core and so you need to have smart people in charge deciding on your strategy, deciding on your technical roadmap, and then making judgment calls of which parts of the stack you want to outsource to others, which parts of the stack you want to lean on others for. Harvey's actually done a really good job of this. They lean on pretty much all of the five or six in your labs I just mentioned as their partners. But you need to intentionally own it and over time, just like we saw Cursor go on this journey, I think a lot of application companies will go on the same journey that Cursor did.
Speaker 8:Over time, you become more and more incompetent more competent in house and you bring more of that expertise in house.
Speaker 2:Sure. Sure. How are you processing the the current price war? Mhmm. You have sort of the lagging labs starting to compete more on price.
Speaker 2:Mhmm. You have leading labs trying to make sure that that they have competitive, you know, models at every Yeah. Part of the curve. But yeah. Anyways, what what's your take and and where does this go?
Speaker 8:Look, think Jevan's Paradox, not to be an annoying VC, but Jevan's Paradox is like freaking wonderful thing. Because what's happening is like, I see the data from all this stuff. Right? From oh, there we go. Bam.
Speaker 8:There we go. Look, Our companies, we see the margins going, their gross margins going up because at the same time as like AI usage is going way up, but their gross margins are also going up because they are the beneficiaries of intelligence getting cheaper and cheaper to meter. On the other hand, I see this from fireworks, see this from the model companies we're in business with, their cohorts are getting better and better and better. So it's not like they're facing price competition and their businesses are going into the gutter. They have phenomenal businesses where because they're able to provide intelligence at increasingly cheap prices, their businesses actually get better and better.
Speaker 8:So I actually think this is an everybody win situation.
Speaker 2:Yeah. Part of the part of the challenge with with I feel like acts trying to process the the price wars and and how open source is fitting into all of this is that when you have Anthropic and OpenAI still as private companies, people just don't have the visibility and they don't realize that even with great open source models and even pricing price cuts across different providers, you're still seeing that just massively accelerating revenue. And it's not like it's not like the biggest customers aren't aware that open source exists and it's good and they are using it in a bunch of different ways. So I think I think Yeah. It'll be very Yeah.
Speaker 2:It's an and. Yeah. And think it'll be very helpful when when more of these players are public just so that everybody has the same access to information.
Speaker 8:Yeah. Totally agree.
Speaker 1:Thank you so much for coming on the show.
Speaker 3:Let's do it
Speaker 8:again soon. Done. Good to see you guys.
Speaker 2:Yeah. Again soon. Great to see you.
Speaker 1:We'll talk to
Speaker 8:you soon.
Speaker 1:Goodbye. Let me tell you about public.com. Investing for those who take it seriously. They got stocks, options, bonds, crypto, treasuries
Speaker 5:friendly IPO.
Speaker 1:With great customer service. You ready for this next one, Jordy? You're gonna love this company. They put a brain in the vat, basically.
Speaker 4:Really?
Speaker 2:Yeah. Here's Sean Cole. Hi.
Speaker 1:It's on Parasma. He's the founder. This is his first time on the show. Sean, how are you doing?
Speaker 3:Hi, guys. Thanks for having me.
Speaker 1:Welcome to the show. Is Brannin in the Vat appropriate or is that derogatory? Should I stay away from characterizing your company that way?
Speaker 7:I think it's pretty good.
Speaker 3:I think it's pretty good.
Speaker 1:Okay. Take us through it. Introduce the company. Introduce yourself.
Speaker 3:So I'm Sean. I'm the founder and CEO of Prisma. Yeah. And we are training brain cells for compute. So previously, I think you guys might have seen, you know, we made brain cells play doom.
Speaker 3:Yeah. And we just launched, like literally just launched, and we got these cells to do token prediction. So kind of the basis for language modeling.
Speaker 2:It's amazing. Okay. So how many cells do you need? Because many people have said that I have seemingly only a few brain cells. And so how many brain cells do you actually need in order to do x token prediction?
Speaker 1:I can predict tokens all day with just a few brain cells. It's no problem. So seems like an easy easy job for you.
Speaker 3:Not that many. So we're we're renting some brain cells out. We're using about 200,000 brain cells to do token prediction, and it's good enough.
Speaker 1:Are these literally, like, donor brain cells from cadavers, or are you growing Your brain cells. Stem cells? Like, where are they coming from? Walk me through the full process. What's your supply chain?
Speaker 5:I think you got
Speaker 3:it exactly. So we're using serotonin stem cells, we're taking these stem cells, differentiating them into different types of human neurons, and putting those in a dish that we can stimulate and get responses from, basically.
Speaker 1:Okay. And then you said you're able to do next token prediction. I imagine that you're not anywhere near the frontier. How are you actually benchmarking? Like, what is capable?
Speaker 1:Because this is probably all predicated on a a very extreme exponential kicking in at some point. But where actually are we in terms of progress?
Speaker 3:Yeah. I I think somebody gave a pretty funny example, which is it's currently at this current stage. The token prediction is basically, you know, is this a hot dog or is this not a hot dog type of token prediction. It's pretty basic.
Speaker 2:Well, can you say say you're alive? And then it says, I'm alive. And you go, oh.
Speaker 3:Yeah. We we could get it to do that.
Speaker 1:You could. Okay. I
Speaker 3:think fundamentally what we've done is we've proved that these types of sequential context based architectures of electrical stimulation Mhmm. Are a viable architecture on these biological substrates. We had a specific example where it was a nonlinear task. So let's say, you know, that to refer to context early in the sentence. Mhmm.
Speaker 3:And if we used a linear decoder, so traditional silicon, it could only maximally get 75% mathematically. Mhmm. But we got above that, so we got 78%. We beat silicon on this very constrained task Sure. Because it was able to model these kind of nonlinear dynamics in in context.
Speaker 2:Mhmm. How long do the do the cells actually last before you need to replenish them?
Speaker 3:So I think currently they last six months. But of course, I think human brain cells last far longer, know, like a hundred years, eighty years. So I think the goal obviously is to kind of extend them for as long as possible.
Speaker 2:Yeah. My brand You were kind of getting at this but but So not not why why why do this? I have some ideas of why you might. But seems like kind of a hassle so you got to have a good reason. Is it is it like idea What do
Speaker 1:you against silicon? Okay?
Speaker 5:Yeah. I'm assuming I'm
Speaker 2:assuming like there's like it like energy efficiency Yeah.
Speaker 1:Yeah. Play it out. If this goes the way you want it to go, is there actually a benefit over just a huge data center or something in space with the solar panel on it? It feels like the current chip stack, the AI stack is is pretty efficient. We've squeezed out a lot of the inefficiencies of being a human potentially.
Speaker 3:Yeah. I think that's where it's really interesting because, you know, regardless of how efficient we make silicon, like Mhmm. Silicon we have right now is is supremely efficient, but we still haven't solved the efficiency aspect of it in terms of power efficiency. I think human brains are extremely power efficient in comparison to silicon. I think there are things that we can harness there on top of other stuff like sample efficiency.
Speaker 3:Human brains learn very quickly compared to silicon, much fewer examples. And continual learning is free on biological substrates because they keep learning over time. Yeah. But I think continual learning is something that has to be expanded on in in the AI space. We're still figuring out what's the optimal approach to use.
Speaker 3:But, yeah, massive massive energy efficiency.
Speaker 1:How do you actually think about that energy efficiency though? Because I've seen I mean, was that that news of, like, $5,000 worth of SOL tokens solved a bunch of math problems. When I think about like not even the salary of a mathematician, but I just think about the food that goes into generating the calories, generates the energy, that generates the theorems from a mathematician, you're way above five k. So Yeah. It feels like the models are actually pretty efficient, but what do I what am I getting wrong?
Speaker 3:I think if you think about, like, the cells as growth, like, humans, you know, we are expensive. We have to feed ourselves and all that. I think with Doom previously, we showed that it's possible for us to inject information, essentially, force these cells to learn much faster than a human would learn. So you don't have to learn the basics of language ADCs. We can tell it's just predict whether, you know, this is a hot dog, this is not a hot dog, something like that, far quicker.
Speaker 3:So we can skip that kind of like prior human learning building phase that would be very expensive normally. Mhmm. So that's kind of the stuff that we are approaching it with.
Speaker 2:Yeah. Is it possible that golden retriever brain cells could be better at long running tasks like chasing a ball?
Speaker 3:Oh, I think that human brain cells for now, empirically, they are the best.
Speaker 7:Oh. However
Speaker 1:Shot's fired.
Speaker 7:Well, you know, we've we've tested it, but Okay.
Speaker 1:We've tested you put a golden retriever in the van. Oh. Uh-oh.
Speaker 3:We tested rat neurons. Right? So they're they're rat neurons and then they're
Speaker 1:human Woah. Brain Golden Golden retriever, rat, these are not comparable animals. Let's let's give it.
Speaker 7:Yeah. Yeah.
Speaker 1:What is the what is like the business going to look like over the next decade? Because I imagine at the end of all of this, there's some sort of business model where like you're selling intelligence. But in the in the short term, there's some venture capital that comes in through Y Combinator. Congratulations, by the way. I do.
Speaker 1:But but the the what's the middle step? Is it partnering with biotech companies? Is it NSF grants or government funding or something like that or partnering with the university? Like, do you keep the lights on and keep the flywheel going? I imagine you can raise more money off of scientific breakthroughs, but I imagine that there'll also be an economic, a commercial flywheel here even before you're, you know, selling the work.
Speaker 3:Yeah. I think something that we're really looking at right now, which is great because, you know, we've just started, is Yeah. With the kind of exponential increase in AI capabilities, we're gonna start building our lab from scratch to be automated. We want to automate the stem cell research to differentiate them into neurons, the optimal compositions, the optimal kind of, like, spacing on the electrodes, let's say. So I think that the automation aspect of our lab can easily be branched out into different things like drug testing.
Speaker 3:Mhmm. And I think that could be significant revenue in the short term to push this all the way to make sure that we get brain cells to be the the fundamental substrate for compute.
Speaker 1:Very cool. Well, congratulations. What a
Speaker 2:fascinating company. Thanks for taking
Speaker 1:the time and have a great rest
Speaker 2:of your We gotta we gotta introduce Sean to the guy that we had on yesterday. What was
Speaker 1:Put him together. Got a whole human. Tissues. He's doing he's doing Yeah.
Speaker 2:Drug testing. VivaDyne. VivaDyne. They are doing some interesting stuff.
Speaker 1:Well, have a great rest of your day.
Speaker 2:Great to
Speaker 3:meet you, Sean.
Speaker 5:Thank you, guys.
Speaker 3:Cool. Thanks having me.
Speaker 1:See you soon. Cheers. Good one. You shaving your head for me? Do you
Speaker 2:mind just just Don't tempt me.
Speaker 1:Me take a saw to the top of your head.
Speaker 2:You think I'm gonna put your brain in a vat?
Speaker 1:You wanna come up to the roof with me?
Speaker 2:I don't wanna go on the roof, John.
Speaker 6:You think I'm gonna throw you off
Speaker 7:the roof?
Speaker 1:I like it's green now. The green's fun.
Speaker 2:We should we buzz should cuts sometime. I think next summer, some buzz for both of us.
Speaker 1:Well, it's gotta be
Speaker 2:a bet. It's gotta
Speaker 1:be, oh, you know, SaaSpocalypse is over or something. What what what some prediction that then we can take a victory lap on or shave our head in in in sadness. Marques Brownlee might have to shave his head. Right? Because he made a bet or he said, if the sire if the cyber cab ships to Elon's schedule, I'll shave my head, something like that.
Speaker 1:And people were going back and forth. And the Tesla fanboys were saying, like, he's gonna have to shave his head. And he's, like, not yet. Because, of course, like, you know, all these all these projects take a long
Speaker 2:I don't even need much of a reason to shave my head. I actually No. Gotten a bunch of summer buzz cards Okay. Over the years.
Speaker 1:She'll be
Speaker 2:like, if the
Speaker 1:stock market moves by more than 1% through the end of the year, I'll shave my head.
Speaker 2:Silver Lake is in talks to buy Workday, sources say.
Speaker 1:That's a big deal. Right? Isn't Workday huge?
Speaker 2:Workday is let's see. I'm sure it's popped. Billion dollar 17%.
Speaker 1:Wow. The take price.
Speaker 2:And so now a $50,000,000,000 company. It was a $43,000,000,000 company at least when this article was written. We'll see. I'm curious what that is a bold, bold bet.
Speaker 1:Okay.
Speaker 2:And but I can imagine there's a bunch of AI transformation that they'd feel like they'd be more easily able to do as a private company. Mhmm.
Speaker 1:So Are you a are you a standard crying face emoji guy? A tilted crying face emoji guy? Or a tear streaming down your face emoji guy?
Speaker 2:I'm normally Going to the Okay. I wanna actually pull this up so I can see more Which one
Speaker 1:are you? Joe Weisenthal has recently transitioned I
Speaker 2:actually
Speaker 1:to a tilted crying face emoji.
Speaker 3:I've actually I don't I
Speaker 2:don't dabble in the tilted. I do I do the tears.
Speaker 1:I do the tears.
Speaker 2:And the straight on.
Speaker 1:Oh, do we do you do the streaming down the face?
Speaker 2:Yeah. Yeah. Yeah.
Speaker 1:Oh, I I need to mix that emoji of 2025.
Speaker 2:Yeah. I went through maybe a decade where I I wouldn't I wouldn't touch crying.
Speaker 1:Oh, any of the crying ones. You do a smiley?
Speaker 2:But I've been I've been laughing a lot Okay.
Speaker 7:For the
Speaker 2:last couple of years. Yeah. Mainly because we've been doing this show. Yeah. That's great.
Speaker 1:You gotta throw I like that in iMessage you can do the but then you can also throw the crying emoji. But I gotta experiment with the tier streaming
Speaker 4:Do you ever go with the cat versions?
Speaker 1:No. I never LARP as the cat.
Speaker 2:Never go never go cat.
Speaker 1:But expect a cat emoji from me soon, Tyler, in our group message, in our group chat because it might be underrated. There might be some alpha there. I don't know. Anyway, there was clearly alpha at this auction. A JPMorgan hand signed mortgage bond from 1886 sold at auction for just $847.
Speaker 1:Someone got a steal, says Dylan Abruscado. This is this needs to go in the Museum of Business. There's a bunch of these good ones. I was looking at the
Speaker 2:the Yeah. How did why did Dylan find this after it had already the auction had already closed?
Speaker 1:I don't know.
Speaker 2:Yeah. Get it together. Get it together, Dylan.
Speaker 1:There's another good one. Can I how do I drop this in the timeline? Microsoft in 1995 1996 published a wine guide. This is from the corporation, the hyperscaler, the mag seven company Microsoft. They released a wine guide.
Speaker 1:The the essential click to the next image and then the next one. There we go. Okay? The wine guide came on CD. The essential reference from vine to glass.
Speaker 1:If you wanted to know about wine, this is where you had to go. You had to get the official wine guide from Microsoft, the software company. Fascinating. Know your audience moment. Lot of lot of Microsoft fans, customers, 1996.
Speaker 1:If you're implementing Microsoft in 1996, probably enjoy a glass of wine every once in a while. So why not
Speaker 2:That's good. Why not be
Speaker 3:a chuck?
Speaker 2:Well, John, I'm gonna read a Vuitton Capital bloke post that I think applies to this exact moment right now. Yes. He says, I think we're very close to the point where caring about AI or talking about it a lot is a bit embarrassing. Move on already. Who cares?
Speaker 1:Move on already. Mean, there is a point where like we don't talk about the Internet anymore. We talk about what's happening on the Internet. We talk about particular Internet companies. This stuff does diffuse.
Speaker 1:If it diffuses fully, you stop talking about the underlying technology. But there's a horse race on, Vuitton. There's a lot of money on the line. The entire global economy potentially.
Speaker 2:That's right. He's just horsing around.
Speaker 1:Thank you for tuning in to TBPN. Sign up for our newsletter at tvpn.com. Leave us five stars on Apple Podcasts and Spotify. Boom.
Speaker 4:Great success.