How the 1% Will Own Compute (and What It Means for You)

The future of AI isn't a smarter chatbot. It's a model that watches your screen, listens to the room, and acts on what it sees. We dug into Thinking Machines' new interaction model, what it means for compute, and the layoff wave that's already here. This week's roundtable: Anastasios Angelopoulos (CEO of Arena, formerly LMArena), Nick Harris (CEO of Lightmatter, photonic computing chips), and Philip Johnston (CEO of StarCloud, building megawatt data centers in space). Thank you to our exclusive sponsor: PayPal Open, One Platform for All Business: http://paypalopen.com/ Timestamps: 0:00 Cold open 1:21 Welcome to Episode 13 2:51 Is China closing the AI gap? Arena's data 5:16 Lightmatter and the photonic interconnect bottleneck 9:42 StarCloud 2, Nvidia Space Ruben 1, and orbital data centers 17:24 Thinking Machines' interaction model: what's actually new 28:22 Whisper Flow and the 3-pedal desk setup 33:48 Real-time desktop and camera awareness as the real unlock 40:25 Why this 100x's compute demand 42:43 The polarization of compute and $10M personal data centers 49:25 The layoff wave: Cloudflare, PayPal, Coinbase, Upwork 54:48 The 10x gap between AI-first and non-AI-first employees 59:52 Unlimited agency and the abundance future 1:00:46 Anthropic's Project Luna runs a retail store 1:03:45 Decoupling labor from value creation 1:05:03 P(doom) round 🔗 Guests: Anastasios Angelopoulos, Arena: https://arena.ai | @ML_Angelopolous Nick Harris, Lightmatter: https://lightmatter.co Philip Johnston, StarCloud: https://starcloud.com | @philipjohnston 🔗 Referenced in this episode: Thinking Machines, Introducing Interaction Models: https://thinkingmachines.ai/blog/interaction-models/ LMArena leaderboard: https://lmarena.ai Lightmatter: https://lightmatter.co Starcloud: https://www.starcloud.com TechCrunch, Cloudflare says AI made 1,100 jobs obsolete: https://techcrunch.com/2026/05/08/cloudflare-says-ai-made-1100-jobs-obsolete-even-as-revenue-hit-a-record-high/ Fast Company, Tech layoffs this week due to AI (Cloudflare, PayPal, Coinbase, Upwork): https://www.fastcompany.com/91538995/tech-layoffs-due-to-ai-this-week-cloudflare-paypal-coinbase-upwork Bloomberg, South Korea floats citizen dividend from AI profits: https://www.bloomberg.com/news/newsletters/2026-05-12/korea-s-massive-ai-boom-triggers-call-for-tech-tax-roiling-market PYMNTS, Inside a retail store run entirely by AI (Andon Labs / Luna): https://www.pymnts.com/artificial-intelligence-2/2026/inside-a-retail-store-run-entirely-by-ai/ Wispr Flow (voice-to-text tool Jason uses): https://wisprflow.ai Fermi Paradox: https://en.wikipedia.org/wiki/Fermi_paradox 🔗 Subscribe and follow: Newsletter and all platforms: https://thisweekinai.ai #ThisWeekInAI #AI #ThinkingMachines #Lightmatter #StarCloud #Arena #Anthropic #ProjectLuna #AIcompute #AIlayoffs #Superintelligence

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Jason Calacanis?

  Hey, it's Oliver from This Week in AI, the brand new podcast from the team at Twist. We're dropping a sneak peek right here in your feed to show you what we've been building. If you enjoy it, join the community at thisweekinai.ai or find us on Spotify, Apple Podcasts, or YouTube. We're discussing the models becoming super intelligent at the same time that the layoffs are happening, at the same time social unrest is happening. Cloudflare cut 20% of its workforce, 1,100 people, while reporting the highest revenue in the history of the company.

• 0:29Speaker 01

  This is the trajectory that we're on. We're trying to bring it with us everywhere we go, which will be enormously valuable and creepy and all sorts of stuff.

• 0:36Speaker 02

  You're going to need 100 times the energy. And that's exactly the bottleneck we're trying to solve for with these space data sensors.

• 0:42Jason Calacanis?

  We are going to have the polarization of compute in our society. The 1% aren't going to be able to just afford a G650. They're going to be able to afford $10 million data center for themselves. It's just going to be exhausting to go to a company, have the company study your work, have AI reinforcement learned and automated, and then get laid off again.

• 1:02Speaker 03

  The capitalist system is such because it incentivizes people to do great labor. We don't create a system where AI just actually makes all the money and we don't know how to make a smooth transition. The core issue, just to highlight it, is that we're decoupling labor from value creation. We are building towards a world that I think in many ways we're unprepared for.

• 1:22Jason Calacanis?

  Thanks to our friends at PayPal, the exclusive sponsor for This Week in AI. Try the payment and growth platform that's trusted by millions of customers worldwide. PayPal Open. Start growing today at PayPalOpen.com. All right, everybody, welcome back to This Week in AI, episode 13. This is our roundtable where we talk to experts in the field of AI about the news of the week. And you can get more about this podcast if you go to thisweekinai.ai. You can sign up for the email and you'll have links to YouTube, Spotify, Apple Podcasts and all those. Anastasios Angelo Pulus is here. He is the co-founder and CEO of Arena.

• 2:04Jason Calacanis?

  I think people referred to it previously as LM Arena, but he was back on episode three of This Week in AI back in March of 2026. Welcome back, Anastasios.

• 2:19Speaker 03

  Thank you, sir.

• 2:20Jason Calacanis?

  How's everything going over there at the arena? What are the trends that you've seen? From the last time you were on the pod, 10 weeks ago.

• 2:30Speaker 03

  Well, as always, AI is moving really fast. You know, Anthropic has been dominant with with the OPUS model for quite a while, but there's a new challenger in GPT 5.5 in the coding arena especially. There's always a great geopolitical race in the US versus China debacle. I would say that open source models continue to be dominated by Chinese providers.

• 2:55Speaker 03

  And we have this new interaction model from thinking machines, which I think is something that, you know, everybody should be discussing and.

• 3:03Jason Calacanis?

  It's our top story today, and we'll get into it. On the, let's double click on the China issue. I know that President Trump is on his way to China. I believe at the time we're recording this with Tim Cook and Elon. What should we take away from DeepSeek 4? And I think it's Kimi 2.6. What should we take away with from their progress? And and are they closing the gap or just maintaining and not falling behind at this moment in time here? May 12th, 2026.

• 3:37Speaker 03

  I would say the gap continues to close, but perhaps not at the rate that others may have expected from previous generations of model improvements. In fact, in certain ways, like just in terms of the first derivative, I would say proprietary American labs have started to sort of pull ahead more and more. So what we've seen is that the gap was closing like six months ago faster than than it is now. Now it's sort of remaining constant.

• 4:07Speaker 03

  And what you'll see in the data is that if you look at the win rate of Chinese versus US, top US proprietary models.

• 4:16Speaker 03

  What's happening is that the Chinese models stay roughly two quarters behind, so half a year behind.

• 4:27Speaker 03

  Which is interesting, you know, it is quite interesting for the industry because there's so much capital expenditure on training the next and next generation of models because that's where the spending goes, right? So why should Anthropic or OpenAI be investing so many billions and billions of dollars into compute? Well, it's because whoever trains the next best model is going to have all the users churn from other platforms onto theirs. And it's going to capture all of that value, all the developers and so on. But if we ever reach a world where that doesn't, where that law doesn't hold anymore and people start saying, hey, you know, whatever I have, it's pretty much good enough and I can't tell the difference.

• 5:08Speaker 03

  If that becomes the world, well, then six months later, once the Chinese models catch up, I think that some of these proprietary providers could have a problem.

• 5:19Jason Calacanis?

  Yeah, that is, I guess, their big existential fear. And I think it's well said, Anastasios. Nick Harris is back with us. Last time he was on was episode seven, so Nick and Anastasios, please meet each other. You guys have been winning the race here on This Week in AI in terms of great feedback, great insights, and of course, being willing to be candid because that's what the audience is always looking for. Nick is the co-founder and CEO of LightMatter. They do photonic computing chips. That means they use light instead of electricity to move data around. Why does that matter? Well, when you're training models, when you're doing inference, you got to get that data between GPUs, TPUs and data centers.

• 6:04Jason Calacanis?

  You have a lot of customers, Nick, and we just saw the greatest report on infrastructure spending by

• 6:17Jason Calacanis?

  Amazon, Microsoft and Google, they are committing to hundreds of billions of dollars this year and also Meta, of course. So of those companies, are you in relationships or situationships with any of those? And what can you tell us from the game on the field? What are you seeing in terms of this build out and the demand for your product and other products?

• 6:41Speaker 01

  Yeah, I would say we're trying to do as many situationships as possible. And in terms of demand, what's interesting is I think these companies are willing to spend everything that they can get access to, even taking on debt, to keep building out the AI infrastructure. I think there's two trends that are happening, which is capacity availability, trying to meet what people actually want to bring online to run inference workloads on models like Claude. And then there's also the challenge of driving the token cost down and getting, you know, more capable models. All of that stuff points to just huge deployments. And the next frontier in AI performance is really about how you connect the chips up. Because I think, as you all know, the computers that these AI models

• 7:24Speaker 01

  run on, they're not a chip. It's thousands of chips, hundred thousand chip, many hundred thousands of chips. And the thing that defines the performance of these systems is really about how you connect them together. And we build really fast connections. You know, we have single chips that are as fast as the cables that connect North America to Europe, hundreds of terabits per second. Your house is a one gigabit per second connection. So I'm powering cities with one chip, 200,000 houses worth of bandwidth. That's what's needed to be able to scale these AI workloads, drive down the token costs, drive up interactivity. I don't like waiting for AI models to respond. I don't know if you like waiting. I never like dial up.

• 8:04Jason Calacanis?

  Yeah. And when will that experience change in your mind? When will we?

• 8:10Jason Calacanis?

  Go from queuing up, turning on our notifications in our browser and coming back to, you know, more instantaneous answers.

• 8:20Speaker 01

  I think 28 is when it'll be like, starting to be broadly available and you'll come into contact with some optics-enabled systems. That's kind of the right timeframe. But what's interesting is, are people going to use that new capability to build even bigger and serve even bigger models with the same wait times? Or is it going to be more optimal to just drive the wait time to zero and host, you know, current models? I don't know the exact answer to that. I think it probably depends on the workload. Maybe some deployments will be on, you know, try to drive the latency down to zero, and some are going to just be offering the biggest, baddest thing they can do. You know, there's OPUS, and then the thing that comes after that Mythos. You know, maybe the Mythos stuff will be all optical.

• 8:59Jason Calacanis?

  The mythological Mythos, which at some point, we're all going to get to play with. Any insight into when that's going to be available to the public?

• 9:10Speaker 01

  I don't have any data on it, but I would love to try it.

• 9:13Jason Calacanis?

  Yeah. Anastasios, anybody reporting in to you in the back channel?

• 9:20Speaker 03

  What so what we're seeing is that actually multiple providers are coming up with their own versions of Mythos. So I shouldn't say, you know, too many details. Anthropic is not the only one that is sort of developing one of these like, security-focused models. And I think what's going to happen is that the sort of Mythos, the like Mythos trend is going to proliferate across the industry. And and we'll see that coming from all sorts of different angles.

• 9:52Jason Calacanis?

  And Philip Johnston is back. He is running StarCloud. As you can see in the background, people are actively building something in the background. I see, I see people moving around. We'll get Philip to describe what they're doing. But the plan is for Philip to build megawatt-scale data centers in space to address all this demand that's happening. Last time you were on was March 10th as well. How's progress and what are people working on back there?

• 10:22Speaker 02

  It's going well. Yeah, that's my co-founder. I see Ezra there. He's our CTO. So we are building StarCloud 2. It's our second satellite launching in eight months or seven months now. It's going to be about 100 times the power generation of the first one. So about a 10 kilowatt spacecraft. We'll have the first Nvidia black roll chips in there and also a whole bunch of other new, new, new and cool stuff.

• 10:45Speaker 02

  Yeah, it's been going well. I mean, yeah, it seems like the data center in space world is, is heating up. Many new entrants and competitors coming, but yeah, it's good for us.

• 10:55Jason Calacanis?

  It's certainly validating the space that you have Elon and the co-founder of Robinhood. And we'll get into that today, joining the fray. The Nvidia chips you're going to put up in space, you're putting up a Gracewell, I think you're.

• 11:10Speaker 02

  Gracebackwell, yep.

• 11:11Jason Calacanis?

  Great. Gracebackwell up in space. Does it need to change in some way in order to survive? The forces that are being sent up? I don't know that they're built for like X number of Gs. And then how do they have to be changed to be in orbit, if at all?

• 11:28Speaker 02

  Yeah, it's a great question. So we actually already launched an H100 in November last year and we had to modify it quite substantially. So we removed, we cut about half the mass of it by doing quite simple things like removing the casing, removing the heat sinks or replacing it. Take up the AC to DC converters, a whole bunch of things. Then we ruggedize it. So stiffen it a little bit for launch for the vibration. And then lastly, we put shielding around it to make sure that the radiation environment of space doesn't stop it from, from running. We're actually now working with Nvidia on designing and building a new space chip called the Space Ruben 1. And if you're, yeah, if you're, if you're at GCC this year, Jensen walked out on stage to the deployment video of StarCloud 1.

• 12:10Speaker 02

  And then he spent about five minutes standing in front of the StarCloud render, describing this new space chip that we're building or designing. And so that's optimized for mass. So slightly, or we actually created a lot less mass. It's optimized for thermal. So you want to run these chips hotter without, you know, the higher failure rate that usually comes with that. And then lastly, it's optimized for radiation shielding. intolerance.

• 12:30Jason Calacanis?

  Ah, and so you're designing it, they're fabricating it. Is that the relationship?

• 12:35Speaker 02

  Uh.

• 12:35Jason Calacanis?

  Or this is they're building it for everybody. Yeah.

• 12:38Speaker 02

  They're they're kind of building it for everybody, to be honest. Um, we we're, there you go. Wow.

• 12:45Jason Calacanis?

  All right.

• 12:46Speaker 02

  Yeah, this was a, this was, this was the opening, like, credits and Jensen walks out just a few seconds after this. Um, yeah, actually, this was the video I take, I think.

• 12:55Jason Calacanis?

  There it is. Wow. Yeah, this is your actual video from your social media. Here's the man in the leather jacket, uh, which now everybody feels obligated to wear a leather jacket. It's getting.

• 13:05Speaker 02

  Yeah, I I did a TED talk. I did a TED talk and I wore a leather jacket on it.

• 13:10Speaker 03

  I'm wearing one right now, baby.

• 13:12Jason Calacanis?

  Everybody's in their cowboy phase. If you're wearing that, you're in founder, true, true founder mode. Talk to us about just the, uh, Philip, uh, before we get to our first story, about the energy that is going to be required. Now, you obviously are going to use solar. There obviously has to be some amount of batteries, I would assume, to keep a steady state uh, and to be charged. But, yeah, ta- talk to us about that balance. How big are the solar arrays? And do they have to flare out like in a science fiction film and build this giant uh, solar array in order to fill the batteries? How big are the batteries?

• 13:48Speaker 02

  Yeah, so actually, one of the very nice things about doing this in space is we can fly in this orbit, which is always in the sun. So this dawn dusk sun synchronous orbit, they call it, which means actually you need very minimal batteries compared to, if you were to build, for example, a solar project on Earth, you would need to charge the batteries during the day to power up night. We need maybe, you know, a thousand times less battery capacity because we're, all we're doing really is buffering between the solar panels and the chips. We're not storing power to use at night, basically, because there is no night in space. So it's a huge advantage and a massive cost saving. Um, so, yeah, the.

• 14:22Jason Calacanis?

  And also no clouds, no inclement weather.

• 14:25Speaker 02

  Exactly. No clouds, no climate weather, no seasonality. So in winter, for example, you have much less iridins in, on Earth than you do in space. Uh, that, so, than, than you do in summer. Um, so it's a, it's a much better place to do it. And also you don't have to pay for permitted land, and that's actually the biggest. Cost in North America of building a new solar project to power data center. So um yeah, it's really the the optimal place to to run. If you wanted to build a solar project for running data centers, space is definitely the place to do that. Um. In terms of how big they are, so it's about two hundred watts per square meter. Um. So like one square meter is about four square, um sixteen square feet. Um. So yeah, you've got about, let's say, four tennis courts is about uh two hundred kilowatts. That's the node that we're going to launch on Starship. You can put about fifty of those per Starship.

• 15:10Speaker 02

  Um. So talking about two hundred tennis courts for ten megawatts of compute, basically.

• 15:14Jason Calacanis?

  And the data between them will be by lasers between them. Yeah, and then getting back the job back down to earth will also also laser, yes.

• 15:26Speaker 02

  Uh. Yeah, oh, yeah, there you go. So, yeah, you can see in this image, uh, we're generating. This would be generating three D video, but it can be any inference workload. It could be coding agents or back office business processing. They come up via either RF or optical to our satellites. That we have a constellation of eighty eight thousand that we've just filed with the FCC. In this always in the sun, you can see it's uh it flies over the day night line, but you know, between day and night on earth. And they're uh connected optically to each other. And then we can fit around, we the eighty eight thousand gives us the ability to deploy about twenty gigawatts. Um. We could fit many terawatts in that orbit, so.

• 16:02Jason Calacanis?

  And they will go to a satellite dish on the ground. Similar to Starlink or a more dedicated one for a data center. How does that work?

• 16:12Speaker 02

  There's a few different architectures, but let's my my expectation is that in the in the end state. Will be direct to from device to uh on orbit. Yeah. So if you were typing in a chat G P T query. It would go directly to a Starlink satellite and then be relayed through that Starlink satellite to one of our orbits for those centers. Um. Yeah, and in fact, if it's going to go through a Starlink satellite anyway, you'd probably rather the data center capacity be close to the Starlink satellites rather than on the ground.

• 16:36Jason Calacanis?

  Amazing. Uh. This is going to be quite a future uh end.

• 16:42Jason Calacanis?

  The idea of protesting a data center is going to go away and the need to take

• 16:49Jason Calacanis?

  energy off the grid is going to go away. And the only thing you really risk is, I guess, based on my deep knowledge of science fiction, that some dust or rocks hit your solar cells. That that is the like legitimate concern is that something hits this massive array that you built?

• 17:06Speaker 02

  Uh, it's a, it is a concern. It's not, we, we have very good data on the frequency that that occurs. Um, so for example, with Starlink satellites, I think there's around 10,000 satellites up now. So they've got around 30,000 satellite years worth of data. And they haven't had a single Starlink failure from orbital dipole or collision. So, um, yeah, we have good data on that.

• 17:28Jason Calacanis?

  So it's, it's great for a, uh, action sequence in a science fiction film, but, uh, not the reality. Uh, Mira Muradi, um, just released a new model through our company, Thinking Machines. She was the, I think, one of the star witnesses this past week, uh, or last week, uh, talking about the OpenAI flip to a for-profit company. We'll leave that on the side, but she's in the news this week for shipping product. Uh, Thinking Machines did a research preview of interaction models. This is an AI model that will process audio, video and text continuously in real time. The goal of the model is to keep humans in

• 18:09Jason Calacanis?

  the loop, of course, uh, and they accomplish this by using two models simultaneously. There's a fast model that listens, watches, uh, and talks live as if they were a person in the meeting with you. And then there's a slower model that's thinking in the background and, uh, maybe feeding to the live agent what's going on. The first model is called TML Interaction Small, 276 billion parameters total.

• 18:37Jason Calacanis?

  12 billion active runs with a mixture of experts, M O E setup, with many specialized sub models inside the main model and a router that activates only the relevant ones for each query. So I think the mixture of experts could be a developer persona or a fact checker. The wider release is planned for later this year. Here's a video of the model correcting the pronunciation and facts in real time.

• 19:04Speaker 04

  So what are you having for breakfast these days?

• 19:06Speaker 05

  I really have been digging these acai bowls.

• 19:09Speaker 06

  It's pronounced acai.

• 19:12Speaker 05

  Oh, sorry, acai bowls.

• 19:13Speaker 04

  Oh, yeah, acai bowls. Yeah, I really wonder where they originated from.

• 19:17Speaker 05

  I think the acai bowls first came from Argentina, if I remember.

• 19:21Speaker 06

  Actually, acai is from Brazil, not Argentina.

• 19:23Speaker 05

  Oh, so sorry, I think it was from Brazil, actually.

• 19:26Jason Calacanis?

  Oh, my God, this is gonna be so annoying. This is like, if you remember Cheers, like Cliff Clavain correcting your facts in real time.

• 19:35Speaker 03

  Totally. It's the, it's the actually meme. It's the, you're pushing glasses up.

• 19:39Jason Calacanis?

  Actually, um, actually, um.

• 19:40Speaker 04

  Actually, it's from Brazil.

• 19:43Jason Calacanis?

  And it's pronounced acai. Like, literally, the most annoying person on the planet is now gonna be replicated. We're gonna have to have, Anastasio, some cultural norm for this, just like we have it for the horrific Ray-Ban recording glasses.

• 20:02Speaker 03

  Exactly, exactly. I think that what we really, what we really needed was models that interrupt us.

• 20:09Jason Calacanis?

  Excuse, um, excuse me.

• 20:10Speaker 03

  Excuse me?

• 20:12Jason Calacanis?

  So what are your thoughts on this? Is it as, uh, cutting edge as it seems in the video? And what's the innovation here that has people, you know? I don't know if people are losing their minds over it, but people are, have engaged it, um, you know, pretty, uh, pretty fervently in the last 48 hours.

• 20:34Speaker 03

  Yeah, so I think it comes as a big release for Thinking Machines, whose last big release was Tinker. And so people are always wondering what is happening with this, you know, mega Neo Lab that has a huge valuation and a lot of the top researchers in the world? What have they been doing? And now they've released this, interaction models. So we should think about what is the main technical development here that separates this from a standard, you know, frontier model?

• 21:04Speaker 03

  And the main technical innovation is the interaction model, which is one of two subsystems in this model they've released and which is trained differently than a standard frontier model is. And it does inference differently, too. The way that the model is structured is it's no longer turn-based in the same sense that a normal AI is. So with a normal AI, it's like ChatGPT. You type your response or you type your query, you press enter, it goes to the model, it thinks, it calls its tools, it does whatever it does, it comes back to you the result. And so it's separated into these turns. But turns are different. Yeah, and here's the diagram. That's exactly right.

• 21:49Speaker 03

  Turns are actually different from time.

• 21:53Speaker 03

  In the sense that, you know, it's turns one, two, three, four, but turn one can take take five seconds and turn two can take two hours. So what they've and what that means is that during the turn, you have trouble interrupting the model and the model has the trouble interrupting you. Instead, what they've done at Thinking Machines is they've said, hey, let's take time and we're going to divide time up into a bunch of micro turns. For every, you know, X milliseconds, we're going to chunk up everything that's happening in those milliseconds, feed it to the model, and the model then going to understand what to do next. And those, you know, X milliseconds, they might include, you know, you talking,

• 22:36Speaker 03

  they might include you moving about, they might include complete silence. And from that, the model is sort of learning how to not just work with you in a turn-based environment, but but interact with you the same way that a human would with all of the, you know, context, implicit and explicit.

• 22:54Jason Calacanis?

  There'll be some nuance if, Nick, I was going to interrupt you in a conversation or if I did a perplexed face, it would know. And we've all had the experience using, you know, Claude coworker, whatever it happens to be, you give it some instructions and then you realize, oh, yeah, and I should have said this. I should have added this to the instruction set and while you're waiting, your mind is just firing off all the things you should have said, the things you should have added to the instruction set. So what's your take on this? And obviously, you've got a horse in the race. The faster this gets, and the more it's able to move data around on the inference side, the more effective thinking machines model would be, obviously.

• 23:35Speaker 01

  I think the demos seem really cool. One of the things that it'd be a little disorienting is there's different things you can ask and the answer to them can take a wildly different amount of time. And when it comes back with that, where you're at in the conversation, all that stuff is going to be wild. So it's going to be all of these thought streams that are remerging at random times. I'm curious to try it. I would say I don't use any voice mode on any of the AI models right now because they're so frustrating. And so any innovation on this is going to be a really big deal. I don't think innovating on snark is the plan because I kind of think the user plot with time with snarky models will kind of drop. But you know, let's see what happens. This is obviously a huge, a huge part of the innovation landscape for AI models. Right now, they're just chatbots. We need to be

• 24:19Speaker 01

  able to talk to them. They need to feel like a person. If Optimus is ever going to get, you know, human rights, it needs to be able to talk to us like we like we talk.

• 24:26Jason Calacanis?

  Yeah. And Philip, when you think about it from a consumer perspective, obviously, whatever you're doing in satellites, I don't think changes this model to a certain extent. But what are your thoughts just from a paradigm shift? Is this going to increase usage dramatically? Is it going to be something that's incredibly annoying when you're trying to do deep work? Is it going to be something that's fantastic for very niche applications? I don't know, I'm driving and I'm trying to get instructions in real time. What is the use case here? And is it paradigm shifting or is it more evolutionary?

• 25:01Speaker 02

  Yeah, I mean, to be perfectly frank, it felt quite incremental to me. Like, it took me a while to understand even what they were, people were excited about. I guess what people were excited about is you could talk over it and it would still be listening. Yeah, to be honest, I wasn't like, it didn't feel like a step change to me, but maybe I'm not deep enough in consumer to know how much of a pain point that is. Certainly for me, that's not a pain point in my daily life. You know, I use AI in a way much more like industrially, either with coding agents or with just general queries.

• 25:39Jason Calacanis?

  So the turn base is just fine for you. You don't see this as like super innovative. You know, I find it incredibly fascinating because I do live podcasts all the time and I'm in live discussions. And I literally on this week in startups, my other podcast, I put a $5,000 bounty out for somebody to build an open source project that listens to a Restream or a Zoom and in real time just does fact checking and puts it along the side. And so they did this with regular models, just chunking it and giving me fact checking. Then I had one do roasting. So I made a persona for roasting and it's possible to do this with the current tools that are out there.

• 26:18Jason Calacanis?

  But it's not as real time as this. I don't know if many people have this use case other than

• 26:28Jason Calacanis?

  the one I just did, like real time fact checking, maybe like in a debate or something. Or I'm moving really fast doing trades or something like maybe I'm trying to think of use case. I'm a stockbroker and I'm trying to build something.

• 26:41Speaker 01

  If you have kids, I mean, Jason, if you've ever had kids in the car and you're talking to like Grok. And my kids are like, I want to ask this question. Every time they do it, the model just stutters and then like goes off. And you can't get anything done. So I think like having a family sort of setup helps, it would help a lot.

• 26:56Speaker 02

  Yeah. The other big one, and my twin brother runs an AI company in London doing voice agents for customer service, basically. For customer service, it's if you're trying to sort of convince somebody that this is a human and not a customer service agent, not an AI agent. And by the way, their like score on how much they like dealing with you is directly proportional to whether they were convinced you were real or not. So if you want to convince somebody of that, as soon as you interrupt any of the current models, they will stop dead. And it's like a dead giveaway. And I think maybe that's the, that's the key is. It will allow you to not be stopped dead.

• 27:34Jason Calacanis?

  That's such a good use case. Yeah, cuz you you frequently are talking to them and you're like, oh, no, I found my membership number and you want to interrupt them. Oh, I got my membership number is here. Or I have my gate number. You want to give it to them and they're, oh, great. Awesome. I don't have to look it up for you and boom, and they give it to you. So that actually feels like a good one. And I think, Nick, you you need to own a Tesla and have kids to have had the experience you're talking about, which is kids love talking to AI. And there's nothing they love more than interrupting you mid instruction to Grok.

• 28:06Speaker 02

  Yes.

• 28:07Jason Calacanis?

  To throw Grok off. This is like the greatest trolling for kids in the backseat ever. Which is trying to get Grok to do inappropriate, stupid things and confuse it after you ask it. Where's the nearest Amy's Ice Cream? A hundred percent.

• 28:22Speaker 01

  Exactly. The the other problem with children is their pronunciation is like very poor. So the AI models just think, what are you talking about?

• 28:29Jason Calacanis?

  Yeah, and they're trying to get it from the backseat so they don't know what's going on.

• 28:33Speaker 01

  Yeah.

• 28:33Jason Calacanis?

  Uh, I I'm in love with Whisper Flow. I don't know if you guys are into Whisper Flow or if I've mentioned it to you before, but I have a three pedal setup under my desk right now. So with no hands, I can switch to my browser. I have a teleprompter here, so I just switch to my comment browser and then I can hit the left pedal and it goes to the zoom window. So I can move the zoom windows with my feet while I'm talking to you guys. And then the middle pedal is to start. If I press it down, it starts Whisper. So any, you know, if I'm in a Slack window or Claude or perplexity, it doesn't matter. And when I release it, then it it hits enter and puts the text in. Unbelievably game changing, but this to me, Anastasia, seems like it would negate me needing to do that.

• 29:18Speaker 03

  I think if you think about what is the difference in the user interaction paradigm here? There's really two things that are being done with this innovation.

• 29:30Speaker 03

  The first is that the model is able to understand implicit signal from the background that isn't explicitly given by the human in any part of the interaction. So when you chat with ChatGPT, you know, you kind of have to just, you just tell it what you want to tell it and you edit your prompt and you're like working for quite a while to like give it the context that you think it needs. And then you press enter and then the model gets that. But here the interaction all I think the purpose of that paradigm. Is that it will be like listening the whole time. And if you want to build, as Nick was saying earlier, a human, that's what humans are doing.

• 30:09Speaker 03

  Humans aren't just waiting for you, sort of, you know, eyes closed and ears plugged for the next signal. They're observing everything and it's possible that that this will result in a better user experience. You know, I think one of the areas, perhaps in a dystopian way, that this will be really useful for is building AI partner. Like AI girlfriend, AI boyfriend.

• 30:33Jason Calacanis?

  Yes.

• 30:34Speaker 03

  Because because what you want is for your AI girlfriend boyfriend to be watching you and like watching your body language and what you're doing. And when you're not saying, maybe you can give AI the silent treatment and then it will start to get, you know, it's

• 30:48Jason Calacanis?

  Anxiety. Yeah.

• 30:50Speaker 03

  It's hard to get anxiety.

• 30:51Jason Calacanis?

  Yeah, I don't know if any of us have ever experienced that before, but yes, you could.

• 30:55Speaker 03

  Never, ever.

• 30:56Jason Calacanis?

  Never. You could get an eye roll. You can get your hands in the.

• 31:00Speaker 03

  God forbid I get an eye roll at me.

• 31:03Jason Calacanis?

  So that's one of the. Pinching of the of the eyes and the nose. That's always a good one. Yeah.

• 31:07Speaker 03

  Exactly. And the AI is going to be like, what? Did I do something to bother you?

• 31:12Jason Calacanis?

  Yeah. Oh, I'm so sorry. It could be even more sick offended. Well, actually, now that I'm thinking about this, because it has multimodal, I could be talking to it, have my camera on, and I could be dragging and dropping, you know, images or sharing my screen with it. And so be like, oh, okay, so there's your Google sheet. Okay, yeah. And then I could go to row two and highlight, oh, I see you highlighted row two. What do you want to do with row two? And you're like, I can average it or or it could just be like, here's the average of row column two. And here's the total and just it does it in real time. So that idea actually is quite compelling.

• 31:49Speaker 03

  Yeah.

• 31:50Jason Calacanis?

  Now that I think about it.

• 31:51Speaker 03

  The second innovation, which I think ties into this, is the fact that it is decoupling the interaction from the model's actual thinking and actions. In the sense that there's a background, it basically spawns a background agent through this like background model that's able to do all the tool calls and blah, blah, blah. And probably what that paradigm will evolve into, if it's not already there, is that the interaction model can just spawn agents whenever it wants to. And then like, listen to those. Basically, it becomes like a pub sub model, like a publisher subscriber.

• 32:25Jason Calacanis?

  Here it is, creating a video, or here's a video of it creating a graph, I guess, alluding to some of the things we've been speculating about here.

• 32:33Speaker 04

  Oh, interesting. Can you visualize them in a bar chart for me?

• 32:37Speaker 06

  Absolutely. Let me generate a quick bar chart for those reaction times.

• 32:40Speaker 04

  In the meantime, could you quickly explain to me why is auditory even faster than visual? That's a little bit unexpected.

• 32:48Speaker 06

  Sure thing. Auditory is faster because sound signals travel a shorter, more direct neural path to the brain than visual input does.

• 32:55Jason Calacanis?

  So if you think of the camera and it understanding what's going on behind you, Philip, that's like one level of interesting. But actually, the desktop is much more interesting, or your phone screen. So imagine, Philip, Siri doing this, or you're working in a CAD software, whatever you do. That, to me, kind of makes it a little bit, or massively more compelling. So maybe you could, maybe we'll give you a second shot. Is this interesting to you if it was real-time monitoring the workshop behind you and your desktop at the same time, and had a camera over your partner's desk there while he's working on that solar panel?

• 33:37Speaker 02

  I think it could be, yeah. I mean, the more I think about the use cases for this, I think the more I can see applications for it. You know, I think, especially if it can understand context about the hardware that we're building. You know, I mean, if I could ask it things about that. No, I can see use cases for that.

• 33:55Jason Calacanis?

  I'm just thinking about like, the security system I have like, at some of my residences, offices, like, I have many cameras, like, many, on the ranch, all different directions, picking up all kinds of things. Like, to be able to have it in real time, and there's like, a camera off-shot here, that is, looks like I'm in a casino with like, every camera, and I can see like, people, you know, or animals on the ranch, deer, whatever, and it highlights it. But to have that talking to me in my ears as I'm walking around, and it's saying like, "Hey, there's a pack of coyotes at the north end of the ranch," or

• 34:37Jason Calacanis?

  "somebody's dropping off a package in real time, talking to me while watching that video screen." Now we're starting to get really compelling. But Nick, what kind of footprint of compute- Is this thing gonna need and what kind of token usage if you were to take a guess of it's got a camera on me, it's got my desktop and it's got four different people talking to it at the same time. Like, am I gonna need a rack of H100s of my own?

• 35:04Speaker 01

  Yeah, I think that's where it's going. Whenever you're sort of interrupting it, it's going off and spinning up another agent, calculating stuff, coming back with another result. People were excited about OpenClaw. This is like OpenClaw on steroids. If this thing is always running and you're just asking it random questions all the time. Right now, we have filters, like what's the, what's the bar at which I'm willing to type something in and give enough context to get an answer? You know, it's some, some level. If it's always listening to you and following you around, this will drive commute, uh, compute demand like crazy, which would be awesome for Philip and Nvidia and all the other companies in the space, including myself. Um, and I think people value time, like we only have so much time. So you want the fastest compute that you can get.

• 35:46Speaker 01

  And that's for you, networking and all that stuff. So, um, I think it's ultimately gonna go there. The thing that we're talking about at the very highest level, what you're saying without saying it, I wish I had an AI with me in the room and it can see what I was doing and what I was seeing. Like, that's actually what you're saying. Like, I wanted to see my screen. I wanted to see what's behind me. I don't want to explain the context because it's exhausting. I don't know if you guys get that feeling. I don't like typing the context a lot of the time and guessing what other things it might need to know to be able to solve a problem. If the model was with you, this is the trajectory that we're on. We're trying to bring it with us everywhere we go, which will be enormously valuable and creepy and all sorts of stuff.

• 36:25Jason Calacanis?

  Yeah. So you have the creep factor. Anastasios, you have the value factor and then you have, what does this do to compute? So we have a billion, over a billion people using these tools already. Like, it's way over a billion now between OpenAI and Gemini. Those two alone are driving, you know, high seven, eight hundred million people in every week. So let's assume there's some overlap there, but at 1.5 billion people are using this probably every week. What magnitude would this be if it was?

• 36:59Jason Calacanis?

  Listening to them persistently and watching their screen persistently.

• 37:07Jason Calacanis?

  In addition to taking text inputs, like what are we talking about here? This is 100x the compute.

• 37:12Speaker 03

  Yeah, it's gonna be huge.

• 37:13Jason Calacanis?

  What do we think?

• 37:13Speaker 03

  It's gonna be enormous. And we haven't even talked about, I think, the most major axis in terms of the difference, which was the fact that it's chunking up time. So the fact is that like, because it's listening to all contexts, not just the context that's specifically curated by a human to go into the model. It's just, it's going to be like completely implicit, watching everything, which is exactly the paradigm that you need when, for example, you're building AI for a robot. Robot can't be turn-based, right? Robot has to be able to take in signals from many modalities all at once into one unified brain. And so I can imagine that this could be a foundational paradigm for that. I mean,

• 37:55Speaker 03

  you know, in the sense that it's taking up time and it's chunking it up into turns in a different way. And I think that the real question there that's, you know, gonna be important is how do we know whether it's working or not? How do we know whether it's doing a good job or a bad job? And it's going to require, because the, the sort of surface of opportunity for those kinds of models is nearly infinite. Right? They're, you know, gonna be measured on both objective and subjective factors. And so we're going to need to be building, you know, it's gonna be like quantitative trading taken to like the 1,000x.

• 38:31Jason Calacanis?

  So what is Arena gonna test then? You're gonna test, can it make a latte art? Can it cut a banana? Can it peel a banana? Can it cut?

• 38:39Speaker 03

  Yeah.

• 38:40Jason Calacanis?

  You know, a carrot? Like, you're gonna have to come up with an actual real-world test. Do you have that already in the works?

• 38:46Speaker 03

  Exactly. Well, we're working on stuff like this. And I think what we would, the way that we would approach it is that we would look at all the actions that's being performed by the model. We would segment them. We would come up with, we would use AI to evaluate the AI to try to understand, you know, what are the success criteria for everything that it's trying to do? And whether it's doing, performing well or poorly against these. For example, if it's Interacting with you. Are you happy with the way that it's interacting? Is it giving you what you need? Is it helping you be more productive? Is it helping you improve as a person? Is it providing positive value in your life? These are the sorts of things that we need to be able to grade at an enormous, enormous scale. And it's going to go past the scale of humans, even though humans are going to have an important role

• 39:28Speaker 03

  as being the sort of consumers and the users of these models for whom we try to create value. Actually annotating that data is going to have to be an automated process.

• 39:39Jason Calacanis?

  Yeah, Nick, when you start to think about the amount of compute, this feels to me like it would be impossible to build even, all due respect to Star Cloud and everybody else working on it. I don't think we can build the compute necessary for every human to have this. This feels like it's going to cost $5,000 a month to have something like this, or $10,000 a month. This is a $100,000 a year product, is it not, Nick? What would this cost to run in my 10-hour working day?

• 40:09Speaker 01

  Yeah, I mean, it's absolutely going to be enormously expensive. There is no way you could deploy this. If you're 100xing, just say where we're at today. You know, tens of gigawatts of compute coming online by the end of the year. You're going to need 100 times that. That is a rate of power coming online that's not going to happen. You're going to need a lot of innovation in the compute, in the interconnect, all the hardware pieces, in power generation. I think you're pulling the future too far forward, trying to deploy something of that magnitude right now. It's probably possible. And what it hints at to me, and I always think about this. I don't know if you guys have the same thought. Imagine what the engineers and the leaders at Google can do with these dedicated compute systems just for themselves, or Anthropic, what they have access to.

• 40:55Speaker 01

  I think there'll be a small group of people who can live in that future, but you can't deploy it to everybody. It's just insanely expensive. Power grid doesn't support it. And we need a lot of hardware advances to bring that economic ease down to the right level.

• 41:09Jason Calacanis?

  So essentially, Nick, what you're saying is this is going to create, we have the polarization of wealth in our society. Has created massive tension, but we are going to have the polarization of compute in our society. The 1% aren't going to be able to just afford a G650, a private island, or mansions, or servants, and, you know, tons of staff. They're going to be able to afford a, I don't know, $10 million data center for themselves. They're going to be able to take their barn or ADU at their home, build it out with batteries and solar and be able to have a level of compute just for themselves

• 41:51Jason Calacanis?

  that will distance them and make them superhuman in their ability to produce,

• 41:58Jason Calacanis?

  which we're already seeing with token usage for developers. This is a whole concept that I don't think anybody's had this discussion to date. Philip, this, I think, makes me very bullish on the fantastical sci-fi vision you have of the world, which is like, most people are like, is that necessary? And when you think about this model, you kind of go, yeah, it's necessary. And there are, if you told me right now I could have this future and all I had to do was put a quarter million dollars worth of compute in my, you know, compute closet at my house, I would do it instantly. I would instantly spend $250,000 on this, which means local compute and buying five Mac Studios and stacking them for,

• 42:43Jason Calacanis?

  I don't know, 15K each, 75K? That's going to give an individual the ability to beat other individuals in any knowledge task on the planet. This is kind of trippy when you think about it.

• 42:56Speaker 02

  Yeah, I think the implication on the amount of energy that we need to power this computer is clear. Like, if you're going to need to run 100 times the number of GPUs all the time, unless there's some dramatic reduction or increase in the efficiency of these things, you're going to need 100 times the energy. And that's exactly the bottleneck we're trying to solve for with these space data centers. Personally, I would be surprised if people end up running these types of models locally. I would have thought if you're going to be spending, you know, 250 grand, that would be, there would be a... In efficiency in doing that on a cloud rather than than running it locally, but could be.

• 43:35Speaker 01

  I I think it can go either way. I I do think that companies like Apple have a huge opportunity to offer personal AI devices with security. Think about like secure technologies, you know, locally at your home. But I also think, like you're saying, servicing a supercomputer, which is what you would literally have to do this is very hard. Think about like the technicians you're going to need for that and, you know, all the work that goes into it, replacing parts like you're going to order cables for high-speed interconnects from light matter, maybe directly. I don't know if there's a consumer future for us, but I think what will happen for sure, we'll have these giant clouds, maybe they're in space and certainly they're on earth. And you'll be able to dial up a huge chunk of it and it'll be proportional to how much money you have, probably true.

• 44:17Speaker 01

  Uh, but there's another thing, which is we don't know the answer to this. How much intelligence do you actually need? If you are an enterprise, it could be a huge amount of intelligence. If you are all the cameras and all the datas you have in every customer interaction for your business, then that could be a huge amount of compute. But if you're just a person trying to solve a DIY project, it's a different amount. So I think it's going to be very dynamic, how much you're spinning up. And it'll be, you know, maybe some people will have custom allocations for themselves. I think there will be people like that. Uh, for sure. And to think about scientific discovery. Uh, if you can have a cluster, a giant cluster, trying to figure out how to cure cancer and you can corner the market on that. I think that will happen. There will be cases where that kind of thing happens. But what's fighting against that is democratization of the models.

• 45:00Speaker 01

  And Anastasia is talking about China's open source models. I think there are a lot of people who work in computer science who are going to fight like heck to make sure that these AI models are open. And they're going to try to enable the world with that. So it's almost impossible. Every time I look at AI, it's almost impossible to figure out what's going to happen because it enables everything at the same time. There's so many things that it's doing at once that I can't quite figure out the trajectory.

• 45:24Jason Calacanis?

  This would be the limitation of the human brain to understand the creation of superintelligence, right? Like if what we're dancing around here is we're trying to conceive of what happens if unlimited intelligence is given to a human. Anastasia, if you give a human unlimited ability to process the world.

• 45:45Jason Calacanis?

  And it understands all the context and you just spitting out your ideas of what should be fixed in the world. Now you add to this, you know, world where you have some unlimited tokens or you're a rich person with a $10 million personal data center. You have your own $10 million data center just for you. Now you add to it 100 optimists or figures. Now this person is sitting there saying, you know, I want to build this and it goes and it builds a satellite and it goes and it builds a rocket ship. Like now we're getting towards, I think, our own limitation as humans to think about what unlimited intelligence and unlimited physical execution and unlimited context means. It's kind of breaking our

• 46:29Jason Calacanis?

  brains. Are we even built to understand what playing every single hand of poker possible at one time is? You know, what does that mean?

• 46:41Speaker 03

  Yeah, I definitely think, I mean, literally, we're not built to understand it, right? These systems ultimately are going to be able to live, you know, 1,000 lives in a day, simply because of how much volume of data they'll be able to process. And I think we should think about, you know, just outlining the steps that it's going to take to get there. I think step one is already there, which is that we have these models that are kind of ubiquitous on the internet that are chatting with, you know, the majority of people, at least in developed countries in the world.

• 47:10Jason Calacanis?

  Yes.

• 47:11Speaker 03

  And they have surpassed the abilities of people on most knowledge, sort of knowledge, knowledge production and knowledge, like recovery, like knowledge access, retrieval, so on and so forth. Logic and so on, models are better at it than we are as humans. You know, I'm not saying there's no human that's better than a model. Of course, all of us have seen failure cases, but in aggregate, they've already surpassed a lot of human intelligence. Step two would be, well, what happens when we, when we make the entire world a reinforcement learning flywheel? And I think this is the part of the problem that Arena is working on, and there's many

• 47:55Speaker 03

  Others working on it as well, which is how can we measure everything, literally everything that's happening across every, you know, sense, you know, sight, sound, touch, you know, smell in the world and turn that into reinforcement learning signal that helps improve models for the benefit of humanity. And that is where we are going next. That's why we have every enterprise in America. Basically, learning how do I access my data to improve my products for my customers? And what that's going to mean is that we're going to have AI learning from all of that data. And then step three at the end is what happens to us?

• 48:35Speaker 03

  You know, what is the role of humans in a world where AI has access to all of this data and where the whole world is a reinforcement learning flywheel? And it's living 10,000 millions of lives in a day. It's able to simulate reality. Well, I think that

• 48:51Speaker 03

  there will still remain, at least, you know, in our lifetimes, room for N of one people that have that it's not enough to know everybody else's life. It's not enough to have simulated the trajectory of everything that the model has seen because it hasn't seen you. And for example, you, you know, have a very, very unique, you were, you know, a workman, then you were, you know, became president of the United States. And then you were a reality show host, you know, we, there's people like this who are N of one. And those people may have such unique experiences and perspectives that they still won't be replaced.

• 49:29Jason Calacanis?

  This is Nick, I guess, getting to the point at which we have to talk about

• 49:37Jason Calacanis?

  employment, what employment has left. This last couple of weeks, we've seen just a flurry of

• 49:45Jason Calacanis?

  what some people call AI washing, which other people call, okay, you, you just don't need as many people. Cloudflare cut 20% of its workforce, 1100 people while reporting the highest revenue in the history of the company. PayPal, Coinbase, Upwork also citing AI just this week.

• 50:03Jason Calacanis?

  And Cloudflare said the cuts span teams. While internal AI usage is up 600% in the last three months, at the same time, OpenAI, to your point, Anastasios, is doing a joint venture with all of these private equity firms in order to build models that get deployed inside of private equity owned companies, which we all know is to train the model to get rid of humans. Okay, it's a sensitive topic for politicians, but hey, for us, I think we see the writing on the wall.

• 50:44Jason Calacanis?

  So much so, that I shared this in the group chat, South Korea floated the idea of a citizen dividend from AI profits. Samsung fell on the news. South Korea's presidential policy chief, Kim Jong-bohm, proposed a citizen dividend funded by taxes on AI earnings before he clarified he meant excess tax revenue, not a new corporate windfall. Samsung posted a 755% Q1 profit jump, crossing the $1 trillion market, crossing a $1 trillion market cap. Okay, this is getting, I think, acute, Nick. I threw a whole bunch at you, but we are, what's your take on this?

• 51:27Jason Calacanis?

  Because this is happening in real time. We're discussing the models becoming super intelligent at the same time that the layoffs are happening, at the same time social unrest is happening. We're soaking in it, I think.

• 51:41Speaker 01

  I think that what's going to happen is it'll be an explosion of companies. A lot of these people who are, you know, being laid off, they're actually really talented engineers and, you know, thinkers and business people. And I think the bar to creating a company is dropping. Everyone's excited about the idea of the first one-person billion-dollar company, which is probably happening right now. I think there's going to be so many different companies you can create. The unique skill that people have is the ability to ask questions. And I think there's a lot of smart people that are dropping out. So I think that'll be part of it. I think there's some crew of people. Who it's going to be harder for if you're working in call centers. I think that problem is probably pretty addressable. Uh, so you have to find something else to do there and maybe it is starting a company.

• 52:23Speaker 01

  You know, you can run a strawberry strength stand a lot more efficiently with AI as an example, like where do I place these? Uh, what's the right price to charge? Track the data for like, how much traffic is coming? How do I optimize this supply demand? You know, uh, there's a lot of smart things you can do. And I think it's going to be super complicated. Uh, which is such an annoying answer, but I actually believe that it's going to be very complicated. Um, UBI, I don't know, seems a little bit demotivating to me like I personally would not be inspired by just getting the paycheck.

• 52:53Jason Calacanis?

  Yeah.

• 52:53Speaker 01

  Um, unless I was a professional mountain biker and, uh, you know, it was.

• 52:59Jason Calacanis?

  Which no, you'd still want to run a race and beat somebody mountain biking, or you would still want to be part of a team that accomplished some trail that nobody had done before. It's just human nature. UBI feels like, uh, you know, like my grandmother, Rustin Peace on my Irish, I would say, you know, uh, idle mind devil's playground. Like go do something like you can't just sit around thinking or you're going to get yourself in trouble, Philip. So i- is this hitting your reality yet where when you run your company? You're just like, ah, you know, I can just figure this out with AI rather than hire somebody. And do you find people in your, on your team asking you for tokens? Or headcount?

• 53:41Speaker 02

  It has definitely made us much more productive. I mean, things that would take like a PhD researcher a day. Like, for example, on orbital mechanics, like a, a question we asked recently is what, what? Uh, percentage of the time in a whole year in a Donda sun synchronous orbit at this altitude? Are you going to be in the sun? And it would normally take like a PhD about a day to figure that out. And you know, AI can do that in 15 or Grok for heavy can figure that out in about 15 minutes. So yeah, definitely makes us way more productive. Where it still acts a little bit is, um, we know, we've been trying all of the different text to CAD models. Um, none of them are.

• 54:23Speaker 02

  They're good for very simple tasks like produce this type of screw or this widget. But they're not good at right, right now, they're not good at, for example, if I say, design me a two hundred kilowatt satellite with deployable radiators uh in CAD, you know, it's not going to come up with something coherent. Um in three years' time, in two years' time, it will come out with something coherent. It will probably be a lot better than anything our engineers can come up with. Um, so yeah, we are like leaning heavily into being, being ahead of that, but yeah, right, right now, those test CAD monitors are not, not right there. And then obviously, at the moment, everything the guys behind me are doing cannot be done by an optimist, but again, in two, three years' time, probably everything they're doing behind me can be done by an optimist too.

• 55:03Jason Calacanis?

  The reality I'm seeing inside my little, you know, twenty one person venture and podcasting operation is the people who are AI first, like really all in on AI have now become at a minimum ten times more valuable than the people not using it. And I literally, like this week, I think I'm gonna have to sit everybody down and just walk each individual on the team through their use of AI day to day and just explain to them how they, just to close that gap. Because it's almost like you're, you're going from people who were tilling the fields with a, with a horse and a plow and like

• 55:47Jason Calacanis?

  then somebody else is on a tractor and it's, you know, just not even comparable, the work product. And I don't know how to get this through to people. I am in your camp, Nick. I do think we're gonna see so many startups. Not because people necessarily want to start a startup, but I think if you've been laid off by Block or Meta, you're just gonna sit there with like three of your friends and be like, do I want to even apply to these companies again? Do I want to go through this charade that I'm gonna get rehired? Then they're gonna replace me again and it's just gonna be exhausting to go to a company, have the company study your work, have AI. Reinforcement learned and automated and then get laid off again.

• 56:30Jason Calacanis?

  I think that's going to be this absurd version of like purgatory for some mid managers or developers or designers. They're just going to be on a flywheel where they're training something for 18 months, getting laid off, get their severance package. Or maybe that's like a nice kind of way to just get a year off paid for every two years of service. That feels like the the future. And then you got to think, what if I could just make a million dollars with two of my friends in profit a year and just chop it three ways and we make our own hours and we can work from anywhere? And then all of a sudden, all these villages outside of Japan where they give you homes for 15K, you're like, yeah, I could live at a beach in Japan. And the end.

• 57:14Jason Calacanis?

  I think this is the ultimate solution. Nick, you wanted to jump in there.

• 57:17Speaker 01

  Yeah, I think that that's the right thought process. I bet some people are working right now at tech companies and they're already spinning up agents in their free time and figuring out what they would do on their own. And they're going to find that there's stuff to do, there's money to be made, and there is like a nice off-ramp. So I think there'll be an entrepreneurship boom, for sure.

• 57:37Jason Calacanis?

  They're looking for their exit ramp. Go ahead, Philip.

• 57:40Speaker 02

  One thing I think will be the last maybe bastion is content creation. Like, I think the job you do might be the last.

• 57:48Jason Calacanis?

  I might be the last man standing. I might be the last guy with the job.

• 57:53Speaker 02

  I literally think you might be.

• 57:54Jason Calacanis?

  I might have figured it out. That's hilarious. Yeah. I mean, I do think if you if you think about, this happened before, by the way. When we had the dot com burst, the bubble burst, and there was a lot of people unemployed, we talked about cognitive surplus. And what could you do with the cognitive surplus of humans? This is before tokens existed. This is 20 years before where we are today. Like, literally in 2006, people were like, what do we do with cognitive surplus? And people started building ideas. Like, I built a blog network because so many people were out of work. I was like, hey, write some blog posts and see if you can entertain people. And we made auto blog and gadget and joystick. And people just were like, oh, my God, I can read 20 stories a day about video games or gadgets?

• 58:39Jason Calacanis?

  That hadn't existed before. So I came up with an idea, like, just make people addicted to coming back to the webpage and hitting refresh. Then Wikipedia happened. Wikipedia was based on cognitive surplus. Like, there's people sitting at home. They're smart. There's nothing for them to do. Give them a wiki to edit. And they would just go edit a wiki. And then people did Mechanical Turk. So what do we do with like the artistic surplus of humans? If it's not necessary for you to make the cup of coffee, well, then what could you do in the cafe? You could play a guitar. You could read poetry. You could help people to their car. You know, there's joyful things you can do. When I went to the Aman Hotel

• 59:24Jason Calacanis?

  in Tokyo, when I was on my book tour, I came in and there was a woman sitting there playing the Japanese. I don't know what they call the Japanese harp, but like it's this giant instrument. And I was just like, oh, my God, wow, I happened to come in here. When she was playing the harp, it's like, no, no, there's somebody playing the harp in the lobby of the Aman Hotel, which is 1,500 bucks a night or $2,000 a night. Like, they're paying for that person to be there for that experience. Experiences with humans. That's it. Which is everywhere you go, there could be somebody playing a flute.

• 59:56Speaker 01

  I think there'll be more art. And there's, there's another experience. I'm curious if you guys are having the same kind of experience here. In some ways, I'm feeling less and less bound. Like, if I have an idea, I can just make it happen. And if you keep playing that forward, at some point, even in the physical world, if you have an idea, you'll just be able to make it happen. That's going to be wild. That's, that's going to be absolutely wild. Where you, you have a thought, you're laying on the couch. And you're like, I just want this thing. And it's like, it just happens. This is kind of where we're headed, like unlimited agency and, and minimis minimizing effort. Uh, that maybe results in more art. Uh, I think it'll result in a, in a lot of crazy things in the world. Uh, it may be exciting, uh, but we're, we're kind of getting to the point where you'll be able to do anything you want.

• 1:00:39Jason Calacanis?

  Complete utter abundance. I want to build a sculpture garden in my backyard. And I also want a rope course and just come back.

• 1:00:49Speaker 01

  Go redo the landscaping, go build this, go build this, you know, uh, playhouse for the kids, like go, you know, do, do anything you want. And it's just like, it just happens. I wonder what, what that'll feel like. That's where it's going.

• 1:01:00Jason Calacanis?

  Well, and on labs, just. Gave an AI a three-year retail lease in San Francisco and asked it to make a profit. So proving you can just go do things, Nick. Luna, a cloud-powered agent, manages a San Francisco retail store with a $100,000 budget. Human staffing, the AI independently interviewed and hired three human employees via phone for $22 an hour. It has security surveillance. Luna actively monitors employees via security camera. After spotting a worker on their phone during a slow hour, she, you know, updated the employee handbook for stricter rules around phone use. It hasn't been perfect. Luna has lost $13,000, botched employee schedules, and accidentally ordered 1,000 toilet seat covers. Okay.

• 1:01:46Jason Calacanis?

  And Luna pays her male employees 24 bucks an hour while paying female employees only 22. Citing X-Files as the reason. So. Yeah, be careful in San Francisco. You're gonna have a protest out there, Luna.

• 1:02:00Jason Calacanis?

  And I'll give you the last word here as we, as we wrap up. Build anything, anytime.

• 1:02:06Speaker 03

  We are building towards a world that I think in many ways we're unprepared for. I think that the, the core issue, just to highlight it, is that we're decoupling labor from value creation. And the capitalist system that we have built is, which has been working very, very well for, you know, for all of us and for many people, for this whole country. The capitalist system is such because it incentivizes people to do great labor. Because people who do great labor are rewarded greatly. And in a world where that is not true anymore and where intelligence and, you know,

• 1:02:49Speaker 03

  the abundant intelligence and it's beyond intelligence, it's also the ability for AI to also take actions because it's going to be agentic as well. In a world where those things are commoditized. I think we need to think hard about how to get to the world that, you know, Nick is wants to target, which I want to target as well. I would love to be in the world of abundance where everyone is taken care of and where their needs are met and where if they have a need, they just think about it and it comes up and it would just be such an amazing society. But I think we will, we need to think very, very careful about how we set up the incentives in that society so that people that so that, you know, we don't create a system where AI just actually makes all the money and we don't

• 1:03:33Speaker 03

  know how to make a smooth transition.

• 1:03:35Jason Calacanis?

  The transition is gonna be, I think, the the challenge, Nick. When you live on a ranch and you have chickens, you start to learn about abundance because these chickens will not stop making eggs and the the eggs are overflowing on the ranch with but five chickens. We lost one, one of my bulldogs killed a chicken.

• 1:03:53Speaker 03

  Sorry.

• 1:03:54Jason Calacanis?

  That's all right. It's part of the food chain. Like, that was the best day of that bulldog's life. He was like, I got to murder a chicken. Fantastic. But I was just talking to, you know, the family and I was like, maybe we should have like 10 chickens because there's no or 20 chickens. There's no difference. Now, it it's just collecting the eggs. But if you have an optimist and you're collecting the eggs, like now you've got unlimited protein. I got two wells. I've got unlimited water. You put in a hydration system or your air capture, you put in solar, like, it does feel like you could get pretty close to abundance with just a wee bit of robotics. And that's pretty exciting.

• 1:04:34Speaker 03

  It is pretty exciting. I think it's gonna create really weird situations. Like, people are gonna build the weirdest things. Like, you'll just be walking around cities and there'll be a tower made of bubble gum or like just absurd things because labor is worth nothing and you can do whatever you want. I think it's gonna be like a video game.

• 1:04:50Jason Calacanis?

  It's gonna be like a video game. Philip, your, your, your, what's your P doom right now? I guess it's, it's fun because this this panel feels like we're really enthusiastic about the future, but you there are some doomsday scenarios here. So do you have a P doom? Like, what percentage doom you think we're living at right now? Where are you in your head? Especially after this hour long conversation, which is just taking us on a real journey.

• 1:05:20Speaker 02

  So actually, like, if I just project forward, I'm relatively optimistic. However,

• 1:05:26Speaker 02

  I have a slightly esoteric take, which is the fact, you know, this like Fermi paradox, how do we don't see life in our galaxy? That's the thing that drives me to having a very high P doom, not necessarily because I'm like worried about any specific AI thing. It's the fact that if there was gonna be, you know, it would only take about a million years to settle the whole galaxy and you could get to the nearest galaxy in about a billion years. So in the last 13 billion years, what we're saying is there hasn't been anything that's as sophisticated as us anywhere in the nearest sort of thousand galaxies. Otherwise, within, you know, a few billion years, they would have been here and the whole galaxy would be flooded with life everywhere, you know? There'd be Dyson spheres and O'Neill rings everywhere, which is the path we're heading.

• 1:06:11Speaker 02

  I mean, if you, if you just extend extrapolate forward where, where we're heading, there'll be Dyson spheres and, you know, we'll be living across the galaxy. So my P doom is actually extremely high, but not for the reason that most people's P doom is extremely high. That is like a hundred percent.

• 1:06:27Jason Calacanis?

  You're, okay. I, I mean, when I hear that scenario, I just think to myself, and perhaps this is just the most egocentric thing I've ever said in a list of very egocentric things. I just think that we just happen to be, somebody has to be first. What if we're just the first?

• 1:06:44Speaker 02

  Somebody does have to be first.

• 1:06:46Jason Calacanis?

  And so maybe statistically, we're just the first to get here and then we're going to make the wormholes and we're going to be the ones who connect the timelines. It's possible.

• 1:06:56Speaker 01

  Or maybe we're already in the simulation and it's already happened over and over and over again. Those are the possibilities.

• 1:07:02Speaker 02

  I think that's probably the most likely scenario, actually.

• 1:07:04Jason Calacanis?

  Yeah. Where's your P doom, Anastasius? Where are you at?

• 1:07:08Speaker 03

  I also am fairly optimistic.

• 1:07:11Jason Calacanis?

  Yeah. So P doom, yeah, my P doom's under 10 right now. I just think it's like a 10% chance somebody does something really stupid with the technology. Where are you at, Nick?

• 1:07:20Speaker 01

  Yeah, I'm not doing it.

• 1:07:21Speaker 02

  What timeframe though? Do you think in the next like thousand years is, is 10% or like?

• 1:07:25Jason Calacanis?

  Oh, well, given like Planet of the Apes kind of scenario where like humans will eventually do something incredibly stupid, yeah, it's a hundred percent over a thousand years. But in the next two or 300, I'm, I'm like 10% chance somebody does something profoundly stupid like. You know, makes a bioweapon by accident or on purpose like I'm 10% less. I feel pretty good about it. All right. Another amazing episode of This Week in AI. Anastasios, Nick, Philip, great job. Give us a URL where people can find more about what you're working on.

• 1:08:02Speaker 03

  I'm just Nick and lightmatter.co, L I G H T M A T T E R dot C O.

• 1:08:07Jason Calacanis?

  Okay. And always hiring, Philip.

• 1:08:09Speaker 02

  Starcloud.com and on X, Philip, at Philip Johnston.

• 1:08:13Jason Calacanis?

  There it is, Philip Johnston on X and Anastasios.

• 1:08:16Speaker 03

  You can go to arena.ai and find me on X at M L underscore Angelopoulos.

• 1:08:21Jason Calacanis?

  There it is, and we'll see you all next time. Bye bye.