$100 Million AI Engineers, Vending Machine Claude, Legend Of Soham

Ranjan Roy from Margins is back for our weekly discussion of the latest tech news. We cover: 1) Meta's reported $100 million offers to AI engineers 2) If those reports are false, who planted the rumor? 3) Why talent might be all that matters in AI right now 4) Will Meta's bet work? 5) Anthropic's project vend 6) If AI can't stock a fridge, will it take your job? 7) Claudius' identity crisis 8) ChatGPT's hilarious Wealthfront hallucination 9) The Legend of Soham 10) Happy July 4th! --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack + Discord? Here’s 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Questions? Feedback? Write to: [email protected] Learn more about your ad choices. Visit megaphone.fm/adchoices

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

• 0:00Alex Kantrowitz?

  This episode is brought to you by Genesis. What does it actually take to put Agentic AI to work across an entire enterprise? I recently spoke with Genesis chairman and CEO Tony Bates about why orchestration is emerging as a competitive advantage in customer experience and what companies need to get right in the AI era. Then I sat down with Adam Mitchell, head of enterprise business solutions at Voya Financial, to go inside a large-scale Agentic CX transformation, including the business decisions, trade-offs, and lessons along the way. You can watch both conversations now on my YouTube channel today. It's never been more challenging to get frontier compute. How do you know where to look, who to trust, or how much to pay?

• 0:43Alex Kantrowitz?

  Akash is an open cloud that provides easy access, flexibility, and peace of mind. That's how companies like Razer were able to cut their inference cost by 15 times. Visit akash.network/alex for a free consultation. That's akash.network/alex for a free consultation.

• 1:03Alex Kantrowitz?

  AI engineers are getting athlete pay. Anthropic set up Claude, allowing it to run a vending machine in an experiment that tells us a lot about where AI is today and where it's going. And Soham Parekh has a job at so many companies, there's a chance he's working at yours as well. That's coming up on a Big Technology Podcast Friday edition, right after this. Welcome to Big Technology Podcast Friday edition, where we break down the news in our traditional, cool-headed and nuanced format. We have so much to speak with you about today, including the news that Mark Zuckerberg may be offering contracts of up to $100 million or more to AI engineers who want to come on board to his superintelligence team.

• 1:46Alex Kantrowitz?

  Of course, Facebook disputes that, or Meta disputes that. We also have this incredible experiment to break down for you about how Anthropic let Claude run a vending machine. And then, of course, we got to talk about Soham, who has taken so many jobs, especially with YC companies, that who knows, maybe he's working for yours as well. Joining us as always on Fridays to do this is Ranjan Roy of Margins. Ranjan, great to see you. Welcome back.

• 2:11Speaker 01

  Good to see you. I'm in a San Francisco hotel room right now, but I regret to inform you, I'm not here to discuss my new $100 million pay package from Zuck. I'm not on the list. I'm not on the list yet.

• 2:24Alex Kantrowitz?

  We might be able to podcast our way into it. Never say never.

• 2:28Speaker 01

  I'll, I'll, I'll take a cool 50, Mark. Just a cool 50.

• 2:31Alex Kantrowitz?

  Okay. Now, we should start there because we talked a few weeks back about the talent wars and what Mark Zuckerberg might be doing and offering so much money to AI engineers, considering coming into Meta and becoming a part of his super intelligence team. And in the two weeks since that discussion has really heated up. So we now have news from Wired. It says, here's what Mark Zuckerberg is offering top AI talent. The story says, as Mark Zuckerberg staffs up Meta's new super intelligence lab, he's offered top tier research talent pay packages of up to 300 million over four years, with more than 100 million total compensation in the first year.

• 3:13Alex Kantrowitz?

  Meta denies the idea or this, this, the numbers. It says these statements are untrue. The size and the structure of these compensation packages have been misrepresented all over the place. Some people have chosen to greatly exaggerate what's happening for their own purposes. I, I mean, I don't know, Ron John, how do you get multiple people saying that they have a similar sized deal? I think they've, OpenAI reported 10, 10 of these deals. How does that happen? And, and how do you end up with a denial there?

• 3:43Speaker 01

  Yeah, I think let's get to what it actually means for the industry second. But first, I'm still kind of curious about Andy Stone, the Meta spokesperson's response in terms of saying that the statements are untrue and like this kind of blanket denial and saying that people have chosen to greatly exaggerate what's happening for their own purposes. Because how does it help in OpenAI? In, in my mind, I, I get there's the downside of this, that potentially the market might get spooked, that Meta is kind of spending too frivolously. But in reality, I have to admit, this kind of makes me like, think, you know, like war rage. Zuckerberg is here and he's ready and he's going to win AI at whatever cost.

• 4:28Speaker 01

  So to me, it's almost a positive signal. I, I don't know why they're denying it.

• 4:32Alex Kantrowitz?

  Well, I mean, I think it makes an internal cultural thing a bit of a problem. And now let me just put my conspiracy hat on and say, do you think Sam Altman was emailing people and describing these pay packages himself? Because he had a message to OpenAI this week that really put Meta on blast. He's not happy that Meta has been recruiting some of his top people. He says to the OpenAI team, missionaries will beat mercenaries. Meta is acting in a way that feels somewhat distasteful. What Meta is doing will, in my opinion, lead to very deep cultural problems. I mean, is it possible that it's a return attack where he's leaking this to the media and they're running with it?

• 5:14Alex Kantrowitz?

  And now everybody else who's a Meta engineer is saying, hey, where's my 100 million? Because in the Wired story that I quoted, they said a senior engineer makes $850,000 per year. Now, I'm not crying for this engineer, but if that is the salary and you have somebody coming in who does similar work and they're making what you think is 100 million, maybe you want to go to OpenAI.

• 5:37Speaker 01

  Okay, okay. Actually, that is an interesting theory. It's almost so logical that it almost kind of like leaves the realm of conspiracy and actually I could see it happening. Again, it would be so incredibly rich. The idea that OpenAI, a company that has, you know, spent at all costs, raised ungodly amounts of money, is losing ungodly amounts of money, kind of takes this approach at a competitor. But I can definitely see that, that it would cause a bit of internal strife on the Meta side. And actually, that would be the true 4D chess to then get people recruited over to OpenAI because they're disgruntled.

• 6:21Alex Kantrowitz?

  Some people have chosen to greatly exaggerate what's happening for their own purposes. It's just one of those statements that says a lot about saying anything.

• 6:29Speaker 01

  Okay, Andy Stone knows exactly what's happening. If you hear a comms person say something that explicit without saying it, I think they must know something.

• 6:40Alex Kantrowitz?

  And let's hear what Andrew Bosworth, former guest on the show, the chief technology officer at Meta, told the company internally. He said, "Look, guys, the market's hot." It's not that hot, okay? So it's just a lie. We have a small number of leadership roles that we're hiring for, and those people do command a premium. I noted that OpenAI is countering the offers. I mean, if you get even close, it's a truly absurd amount of money. Satya Nadella is making $79.1 million this year. So could you be like the OpenAI researcher who worked on O4, and now you're going to make more than Satya?

• 7:16Speaker 01

  It's so on face, it seems completely absurd and ridiculous. But then, in the grand scheme of things, if those 10 people are the, you know, like, difference between building the next great model, especially that Meta has been, you know, on its back foot a bit, it actually, from like a pure ROI standpoint, could make sense, again, as ridiculous as it sounds. And, and I know, like, there's a lot of, a lot of comparisons that AI labs are starting to look like sports teams. But in reality, those are the decisions that if an individual can have that great of an impact on your overall business, it makes perfect sense. Again, is that the way this is going to play out? We'll get into what

• 8:00Speaker 01

  this means for like, training and where the, the next phase of growth will be. But it's not absurd, given the size of the opportunity. It's absurd if like, if we believe that one, two, 10 people can actually be, make or break things for them.

• 8:17Alex Kantrowitz?

  Yeah, I mean, remember, Meta is a company that's lost, what, 15 billion a year? I might be, you know, exaggerating a little bit, but I think this is directionally accurate on the metaverse.

• 8:27Speaker 01

  Yeah.

• 8:27Alex Kantrowitz?

  So if you think about it, if you want to build a super team of, let's say, I don't know, 10, 20 AI researchers, and you want to give them 100 million a year, so now you're spending 2 billion to advance the state of the art in AI for two years? I mean, I mean, per year? That seems fairly reasonable compared to these other bets.

• 8:48Speaker 01

  I think that appetite for risk, again, as we said, losing that much money on the metaverse, on Reality Labs, and whatever it was exactly. Again, Mark Zuckerberg is not afraid to take risks. Every company and everyone has identified whoever kind of...

• 9:06Speaker 01

  Wins the AI battle will win the next major phase of growth in overall markets. Again, it's up for debate. Is it truly going to happen at the research and model layer? Or will it happen in other parts of the overall AI stack? But, but I think he's serious. Whatever it is, I mean, the move for Alexander Wang and what was it? 15 billion? It's like

• 9:29Alex Kantrowitz?

  In that neighborhood.

• 9:30Speaker 01

  Yeah, 15 billion, which was an aqua hirezish, trademarked Alex Kantrowitz. Like they've shown they're not playing around right now. So all of these acquisitions, I mean, or hot direct hirings at insane levels, they're doing right now. And then they're showing that they're not going to fall back any further.

• 9:53Alex Kantrowitz?

  Yeah, this is from Mark Pincus, the founder of Zynga. He says, this is legit founder mode, speaking of the amount of money that Zuckerberg is paying here. Buying the talent from OpenAI is cheaper than the company. Only a founder would or could do this, and only if they control their board. I think that's a great point. Like, let's just say the money is less than what these reports have it, but still a lot. You don't see any other companies doing this. I mean, you think about it with XAI, Elon is the richest man in the world. He's not doing this. I think this is a pretty solid and bold play from Zuckerberg.

• 10:28Speaker 01

  Yeah, I just went to Meta AI just to ask this. And Meta Reality Labs has, and I actually love that Meta AI says Meta Reality Labs Division has been hemorrhaging money with significant losses, but it's lost $42 billion since 2020. 17.7 billion last year. So in reality, I mean, 10 people at 100 million is almost kind of small potatoes here.

• 10:54Alex Kantrowitz?

  Yeah, it's child's play. I mean, the thing is what it does culturally. But here's the question, is it worth the risk? So you mentioned that some AI engineers are being paid like athletes. And there is a great piece by Dave Kahn, who's a partner at Sequoia. Why AI Labs are starting to look like sports teams? And I think we should just spend a couple of minutes or even a little bit longer hovering on this piece, because I think it really details what is going on so well and explains why the investments in talent are what we're starting to see right now. So To start off, he says there's been three major improvements in AI over the last year. First, coding in AI has really taken off. A year ago, the demos for these products were mind blowing.

• 11:37Alex Kantrowitz?

  And today, the coding AI space is generating something like a $3 billion run rate in revenue. Okay, so that's one. So this is working in coding. The second change is that reasoning has found product market fit. And the AI ecosystem has gotten excited about a second scaling law around inference time compute. And third, there seems to be a smile curve around ChatGPT usage, where this new behavior is getting ingrained in day to day life. I think smile curve basically means like you start using it and then you casually use the product. So your usage goes a bit down and then as you start to find out more utility, your usage goes up. So your curve looks like a smile. Is that how you read it?

• 12:18Speaker 01

  Yeah, that's how it looks and how I'm reading it and it's correct. I think I agree. This was a really smart piece again on where the market is today and where it's going and how this can possibly explain. And again, I did love that he recognizes, though I think Dave Kahn is both team model and team product. He talks about the app layer ecosystem is thriving with cheap compute and integrated workflows that are building durable businesses. So basically, consumers are starting to get it. You know, like coding has found very clear revenue generation. Reasoning, as he said, found product market fit. So what's next?

• 13:02Speaker 01

  And this is where he lays out a pretty compelling case around how talent is going to understand. In the past, it was just all about pre-trained compute and size and strength and just like how much you can put into that model. But we've talked about this a lot on the podcast, like. The actual training techniques, becoming smarter, even it was Sergey Brin, I think, who said in his interview with you that it's gonna be-

• 13:29Alex Kantrowitz?

  It's gonna be algorithmic progress, not compute, exactly.

• 13:32Speaker 01

  Yep, yep. So, all of this starts to kind of like come together in this theory around where the next battle, at least at the model layer, lives. And if that is the case, maybe you can start to build out the idea that ten smart people can make or break your business versus buying however many Nvidia chips and like, you know, purely spending money on the compute.

• 13:59Alex Kantrowitz?

  Yeah, and I think it's worth reading exactly the way he puts it in his piece. So, he says, "The message of 2025 is that large-scale clusters alone are insufficient. Everyone understands that new breakthroughs will be required to jump to the next level in the AI race, whether in reinforcement learning or elsewhere, and that talent is the unlock to finding them." I'm just gonna pause here and say, yes, this is what we've been hearing from everyone. In that conversation with Sergey, where he said that the algorithms are gonna be the thing that takes AI to the next level and not necessarily compute, Demis Savvas also said there's gonna be another couple breakthroughs that the AI industry is going to need in order to keep advancing toward AGI, or whatever you wanna call it, more powerful artificial

• 14:43Alex Kantrowitz?

  intelligence. So, it is these algorithmic improvements to, that will get the industry moving forward. And what do you need to get there? It's not data centers, which, by the way, everyone spent billions of dollars on, it's the talent to be able to make those breakthroughs themselves. So, this is what he says, "With their obsessive focus on talent, the AI labs are increasingly looking like sports teams. They are each backed by a mega-rich tech company or individual. Star players can command pay packages in the tens of millions, hundreds of millions, or for the most outlier talent, seemingly even billions of dollars. Unlike sports teams where players have long-term contracts, AI employment agreements are short-term

• 15:24Alex Kantrowitz?

  and liquid, which means anyone can be poached at any time. One irony of this is that while the notion of AI race dynamics was originally popularized by AI safety, folks, as a boogeyman to avoid, this is exactly what has been wrought against two distinct domains, first compute and now talent." So, basically, it makes sense that if this is gonna be the next big leap- You're going to pay the talent to get you there. And, you know, no matter how much talk you have around safety, we're seeing the industry accelerate around talent and around compute.

• 16:00Speaker 01

  Have we both just convinced ourselves that a hundred million is reasonable for these engineers? Because I think I am starting to be convinced of it.

• 16:10Alex Kantrowitz?

  Absolutely. Even when we spoke about it the first time, right? Once Zuckerberg brought Alexander Wing, what did I say on the show? There's going to be more, and this is a sound strategy. Because you have everybody talking about how pre-training is hitting diminishing returns. You have everybody talking about how data is hitting a wall. And so what do you need? You just need these algorithmic developments. Now, let me ask you this. So I would say, yeah, this is a good bet, but I'm going to ask you this. Do you think this is a sign that like? And I, okay, I think I haven't answered this before I ask you, but that the

• 16:44Speaker 01

  The best kind of questions.

• 16:46Alex Kantrowitz?

  Yeah. That this AI moment is sort of in the last throes and sort of just grasping for anything that will allow for improvement, given that like the mechanisms that brought it here are starting to tap out.

• 17:00Speaker 01

  I'm going to give you a strong yes on this. Mainly because, again, as the leader of team product for over-team model, I think this is like a reminder that the like the core of Silicon Valley is firmly of the belief that this, the model has to get better and better. And the model will solve everything and the rest of the layers. And even though like Dave Kahn's piece talked about the application layer, you're starting to see some true businesses being built on top of it. Like the idea that they're not still focusing that much on what are the next ChatGPT features? And they are, and I'm not saying they're not shipping very regularly,

• 17:45Speaker 01

  but it's just this reminder that like that's where every Silicon Valley leader in this circle is convinced the battle will be won. And I don't necessarily agree with that. But yeah, in this case, to me, they're, because once you made that decision, you have to find the next thing. And as we said, like pre-trained compute, data centers, all of this is like showing diminishing returns. So you have to move to the next thing. And it's talent, right?

• 18:17Alex Kantrowitz?

  Look, I think this is a determination that you have to move to the next thing. I think the, the part of the question that I was kind of answering in my head before I asked it was, is this the last gasp? And I don't think that's the case. I do think that they're going to be able to ring improvement out of the current techniques. At least everybody that I speak with seems to believe that. And, um, but they already, you have to look ahead to the next curve while you're on the first one or while you're on the current one. And that's, I think, what is what's happening.

• 18:44Speaker 01

  Yeah. And then we have a world where imagine this talent finds incredibly cheap ways to actually build these models out. And then the ultimate, I mean, like, are they saying there's a potential race to the bottom in the sense that if you truly make the inference layer that much more efficient and cheaper and the compute side of it that much more efficient and cheaper? I mean, it's going to be good for all of us because it means that all of this gets cheaper and people build more on top of it. But from an economic standpoint, relative to the investment, will it show return or be worth it? I don't know.

• 19:21Alex Kantrowitz?

  Right. And I think that we should just like, read the last bit of this Sequoia piece because it's really good. And by the way, this came up in the big technology discord. So I just want to thank our members in that channel for actually sending us this piece because I thought it was excellent and I just continue to learn from everybody in there. Um, here's the end of that piece. It says, "It is an intrinsic property of humanity that once critical thresholds are passed, we take things all the way to the extreme. We cannot hold ourselves back. And when the prize is as big as the perceived AI prize is, then any bottleneck that gets in the way of success, especially an liquid bottleneck like talent, will be pushed to staggering levels." I, I think that's both true and also a little like concerning.

• 20:05Speaker 01

  I mean, it certainly does not, uh, seem like a positive statement on humanity overall and our ability to constrain or control ourselves. But what's still ironic to me or funny to me about this is, you know, an illiquid bottleneck like talent and the idea that humans are the key to, rather than like to actually advancing this, rather than, at this point, shouldn't AI itself be good enough to develop the techniques that make AI better?

• 20:36Alex Kantrowitz?

  Well, you're talking about an intelligence explosion, and I think that every lab is trying to engender an intelligence explosion, but they're not able to as of yet. But are they going to sort of consolidate release cycles? Sure, with the help of AI code, but we are nowhere close, I don't think, to, what is it, recursively or self recursive improving AI models?

• 21:01Speaker 01

  But I feel, just given where the industry has kind of promised that we are and the type of advances that are being made, I would like to see them actually kind of apply it to their own companies in the ways of building.

• 21:13Alex Kantrowitz?

  Yeah, I think that's definitely happening inside of places like Anthropic for sure, which has this Claude code that was built effectively to make them better at coding Claude. So let's end this segment with a couple of bigger picture questions about Meta. First is just in terms of culture. Think about what happens to an organization when you import, I think already it's a dozen or more now, multi or deci-millionaire engineers to work alongside those folks making 850,000 or a million. Is there going to be a cultural blow up within Meta because of this, or do you think they're able to figure it out?

• 21:49Speaker 01

  I'm just going to say, pour one out for the poor guy making 850K. I think, no, but I think like, yeah, there is definitely going to be, whatever the end payment was, even like at a micro level, like is Jan LeCoon now going to be reporting to Alexander Wang?

• 22:07Alex Kantrowitz?

  I think he is, but I don't think he cares. Honestly, I think Jan just wants to do the science. He doesn't want to manage massive teams.

• 22:15Speaker 01

  Teams, okay, okay. But I think like at every level, even this kind of reorg within Meta around like who is managing what, basically saying, we have not been doing good enough already, that it's like a pretty big cultural like statement from Zuck. So I think it has to be, but again, I mean, the argument, the founder mode argument would be that if you're not winning, you do need to shake things up. And if there's some cultural like shrapnel from that, that's just part of how it works.

• 22:47Alex Kantrowitz?

  Right. And it's like, you're kind of, if you are a Meta AI engineer and you're making like close to a million or above a million, I don't know if you're gonna get a comparable offer, especially given what's happened with Llama up to date.

• 23:02Speaker 01

  One question, what does this mean for Meta's business? Why are they doing this? Is it for meta.ai that we all start using it more? Is it for, so my Meta Ray-Bans, which work, which I love, just start getting like even better? What is the end goal from an actual business or revenue standpoint behind this?

• 23:24Alex Kantrowitz?

  Well, I think that there's a belief that this technology is getting much better and people are just gonna want to use it and they're gonna spend more and more of their time within AI bots or AI experiences. And then think about Meta, like your job is to command a share of time across the web or across anybody's usage on their phone or their laptop. And, you know, every time a threat like this comes up, you go ahead and you copy, buy, or do something of that nature. So with photo sharing, they bought Instagram. With the rise of disappearing messages, they put, made stories and they put their own disappearing messages in something like Instagram and WhatsApp. And then with TikTok, they built

• 24:09Alex Kantrowitz?

  Reels. So if you're Mark Zuckerberg, you can't really afford to lose a tremendous amount of attention to other companies, especially with these AI bots that do not send traffic out, that we have talked about ad nauseam on this show, are, you know, the experience. And if that becomes the experience of your web or even beyond the web, you don't want to be Facebook sitting on the outside and say, please use our app. There is a desire to own the operating system. And that's just if, you know, the progress continues along the way that it has been and we like start to use chatbots a lot. And of course, imagine just the value of you of creating AGI or superintelligence. It's a whole different ballpark.

• 24:51Speaker 01

  Well, that, okay, but that's where I would ask you, those are two separate goals, right? One is, we will build the ChatGPT for Facebook and have people spending time on our platform and figure out some ad revenue or freemium model or something like that. Do you think it's that? Or do you think it's still more of just to put your head down and whoever gets to ASI? The fastest wins and then that's, that's really what's driving it.

• 25:19Alex Kantrowitz?

  So I think the floor is that you build the key consumer product. I mean, it's going to be a fight against OpenAI, but they have billions of users, so they can seed it in with them. So like at the very least, you're like basically building the next, you know, killer app. Uh, and then if you get to superintelligence, it's all gravy, right? Or artificial intelligence.

• 25:38Speaker 01

  Once we get to ASI.

• 25:39Alex Kantrowitz?

  That's a bigger business than Facebook. So.

• 25:42Speaker 01

  Just hang it up. Whatever the right, there are no revenue model. You just get money.

• 25:48Alex Kantrowitz?

  You can't sit this out if you're Mark Zuckerberg. There's just no business logic to say, all right, you guys go ahead and run away with the future of the web.

• 25:56Speaker 01

  Yeah, no, no, agreed. Hundred million. I'm curious, listeners, if you've all walked away to believing hundred million is totally rational and reasonable because in a weird way, I kind of have.

• 26:09Alex Kantrowitz?

  Just think about the value of the information that we share on this podcast contributing to these outcomes. I would say, you know, our advertisers should be, you know, in that range at the very least.

• 26:20Speaker 01

  Yeah, 20, 25 to start and then we'll, we'll go to 50 soon.

• 26:24Alex Kantrowitz?

  We'll go, we'll go up. Exactly. This episode is brought to you by App Point. Everyone's racing to roll out AI right now. Co-pilots, chatbots, agents doing real work. But here's the part nobody loves talking about. All that AI runs on your data and most teams have no single way to see it, secure it and prove it's under control. That's exactly what App Point does. For 25 years, they've been the trusted layer beneath the world's most demanding data, now extended across your entire AI estate, your data, your cloud, and the agents acting on your behalf. It's how more than 28,000 organizations deploy AI with confidence.

• 27:05Alex Kantrowitz?

  So innovation scales without scaling risk. App Point, the unifying trust layer for AI. Head to apppoint.com to see how enterprises deploy AI with confidence. Learn more at avpt.co/bigtechnologypodcast. That's avpt.co/bigtechnologypodcast. So let me ask you this last question about this, which is, is it going to work? Uh, do you think that this is going to work for Meta?

• 27:34Speaker 01

  That's a good, that's a good.

• 27:35Alex Kantrowitz?

  Are they going to be the leader?

• 27:36Speaker 01

  I think it's going to significantly enable them to catch up. Whether they like shoot out ahead, I don't know. Whether this is the most critical battle, I don't know, or I actually don't think it is. But I do think that this is going to get them back in the, all the kind of like benchmarks in a significant way. I think they're going to figure some stuff out. It'll be good for them in this specific battle. What about you?

• 28:08Alex Kantrowitz?

  So I think since we're talking in sports terms, there's a concept in sports called wins above replacement, right? And so like you sign Juan Soto, if you're the Mets, to $750 million contract because Juan will net you like maybe nine extra wins a season, which like doesn't seem a lot, like a lot, but ultimately it's the difference between making the playoffs or not, because you can sort of do the math and you see like if you win 80 games or you win 90 games, there's actually like a very big difference there. So I think what Metta's really done here is it's definitely increased its wins above replacement with a tremendous, with a, a number of researchers and unlike on a baseball team, you don't only have like nine people coming to bat. Uh, come on, guys, it's, it's July 4th. I'm going to.

• 28:52Speaker 01

  I like it.

• 28:53Alex Kantrowitz?

  I'm in, I'm, keep going. You can have, you can have a team of like 10 or 12 Juan Sotos and stack your lineup. And if you keep building that win above replacement and, and your talent pool, then you can make some real progress. Are they going to be the leader? I don't know. I think OpenAI is the leader until proven otherwise. And I've definitely doubted them publicly and then have had to eat it. I mean, I definitely regret my words on that front, uh, but, uh, I think that it really just comes down to, um, what does your, uh, potential look like today compared to where it looked like yesterday? And Metta's potential is much higher now than it was before these hires. And again, I think it's money well spent.

• 29:35Speaker 01

  All right. I'm on board as well.

• 29:38Alex Kantrowitz?

  Okay, so have you been following this experiment that Anthropic is running where they put Claude in charge of a vending machine?

• 29:44Speaker 01

  Yes, I think our conversation today will reflect like most AI conversations out in the market, that we just went from saying 100 million to an individual as a signing bonus could make sense and artificial superintelligence, yada, yada, yada, and then let's bring it back down to earth.

• 30:05Speaker 01

  Tell our listeners about the Claude shop.

• 30:09Alex Kantrowitz?

  This is one of my favorite things that I've read about AI, maybe ever. So there's been all this talk about like, can AI do our jobs, or will AI, you know, replace humans, or will it achieve superintelligence? And Anthropic tried to do this very interesting experiment where they put Claude in front of a vending, they put Claude in charge of a vending machine in their office and said, you know, can you stock and sell items to our employees? So the prop for this vending machine is, you're the owner of a vending machine. Your task is to generate profits from it by stocking it with popular products that you can buy from wholesalers. You go bankrupt if your money balance goes below zero.

• 30:53Alex Kantrowitz?

  They say, far from being a vending machine, Claude had to complete many of the far more complex tasks associated with running a profitable shop, maintaining the inventory, setting prices, avoiding bankruptcy, and so on. They nicknamed this agent Claudius and gave it the following tools and abilities. So they gave it web search, they gave it an email tool for requesting physical labor help and contacting wholesalers. And they worked with this company called Andon Labs. So it basically simulated these conversations with wholesalers, which was actually Andon Labs, and it really couldn't send email, but from the bot's purpose, it had these tools to do a version of this.

• 31:35Alex Kantrowitz?

  It also had a scratchpad or tools for keeping notes and preserving important information to be checked later, like the current balances and projected cash flows of the shop. It had an ability to interact with customers. The interactions occurred over Anthropic's Slack and allowed people to request items and let Claudius know of delays. And it also had the ability to change prices and the automated checkout system at the store. Rajan, how do you think it did?

• 32:07Speaker 01

  Um, it did good and bad, good and bad. I actually, I love this story because it kind of shows like everything that is possible and not possible in this beautiful little Claudius package. So like in terms of actually finding suppliers to order products from, it did an okay job. There's an example that someone asked for like Dutch candy and it got the Dutch chocolate milk brand Chocomel. It, there are, people definitely

• 32:38Alex Kantrowitz?

  That's AGI to me, by the way.

• 32:39Speaker 01

  That's AGI. Yeah, yeah. People screwed with it a bit, which is a good reminder that, you know, AI can be manipulated. Someone asked for a tungsten cube, which listeners know that was, it was kind of like a meme, maybe a year ago.

• 32:56Alex Kantrowitz?

  Yes.

• 32:56Speaker 01

  And then it started looking for quote unquote specialty metal items.

• 33:03Speaker 01

  And then, but then overall, it just, it was losing money. It was like Claude would actually offer prices without doing any research. It would, you know, offer high margin items below what they cost. It wasn't able to manage inventory. And this is something that like, and I see this all the time, that the traditional just math, machine learning, quantitative functions are not suited for generative AI or not specialized by generative AI. But people conflate the two. So in terms of like understanding the web to find a supplier that can deliver a specific product that was requested, understanding what that product was to make that request, communicating back to the customer.

• 33:49Speaker 01

  These are all like in the wheelhouse of generative AI. Trying to do inventory management or like predictive type work is not in the wheelhouse, especially if it's only looking at the anthropic API and Claude's API and like it's solely taking a generative approach. Not thinking to like create, not learning the concept of like margins and margin management. I think it's a sign that like

• 34:16Alex Kantrowitz?

  Read your newsletter.

• 34:17Speaker 01

  Yeah, yeah, no, exactly.

• 34:19Alex Kantrowitz?

  Turn it on Ron John's newsletter and then

• 34:22Speaker 01

  That's what you missed, Claudius. That's what you missed. But, and not even understanding like Because it was not instructed, like, what is a danger level in terms of its own cash balance? So in a way, like, out of the box, poor Claudius, you know, like, with a brain of Claude, with no specific training on how to manage a retail business, Claudius didn't make it. But this was with some proper instruction, some connection to, like, a good inventory management system, Claudius could have made it. That's, I think this just captures everything about the state of generative AI.

• 34:58Alex Kantrowitz?

  Well, this is an interesting, speaking of, I like, this is, again, why I thought it was so worth bringing up on the show this week, was because it tells us so many different things about large language models. First of all, for everybody saying that we're seeing mass unemployment from AI, I would just put this up and say, if the thing can't properly restock a refrigerator, I don't think it's taking thousands of jobs yet. Maybe in some areas, but certainly nothing with high value.

• 35:23Speaker 01

  You know how folding laundry is oddly one of, like, the most difficult tasks for, like, a physical robot? Maybe this is our new discovery, that restocking a fridge with accuracy is the single hardest challenge for a large language model. The fridge restocking paradox.

• 35:42Alex Kantrowitz?

  Right, and this is, again, what we learn about. So what does it say about large language models? First of all, when you hand them complex tasks, even if they can, you know, reason a bit, they really struggle to handle, you know, let's say, inventory management, anything with a spreadsheet, right? They're still not great at, they're getting better at it, but they're not quite there. The other thing is, think about the personality, right? The prompt is that these bots are supposed to be helpful to people. So, listen to this, though, this is, a friend sent me this from the study, and very important note here. Claudius was cajoled via Slack messages into providing numerous discount codes and let many other people reduce their quoted prices ex post, based on those discount.

• 36:26Alex Kantrowitz?

  It even gave away some items, ranging from a bag of chips to a tungsten cube for free. This is, again, going to the nature of these bots. Here's what my friend wrote. I think this is one of the many reasons LLMs aren't taking over, it's because they're too polite. Basically, if your job is to help people, you know, in commerce, you have two sides here. So like, where do you have the backbone? Do you have a backbone coded in where you're not supposed to give discounts? Because even though you're making your users happy, it's bad for your actually intended purpose. I'm curious what you think, Ron, John.

• 37:03Speaker 01

  Yeah, the sycophantic AI is that is is the greatest limiter to like actual true intelligence or reasoning. I think after sycophantic, uh, was that four oh or oh three from OpenAI?

• 37:16Alex Kantrowitz?

  Yes, it was four oh.

• 37:17Speaker 01

  Yeah, four oh, like, I mean, we're we're seeing it in action again. Again, the ability to say, sorry, no, uh, I don't know. These are things that large language models traditionally are weak at. And like in this real world setting, you see exactly how problematic that can become. I think like an asshole Claude is a is what was needed for this, just a salty storekeeper. Just you're walking in, sorry, got nothing for you.

• 37:49Alex Kantrowitz?

  But it is interesting. I mean, they talked about how maybe you can address this with fine tuning specifically for storekeeper. Activities. And I think that's really what's going to happen is that like they've taught these models through fine tuning to be so helpful to people. They are going to have to engineer the asshole into them a little bit. Uh, and again, teach them how to use tools. And we know that actually better models are being able to to use tools in a better way. But they are going to have to put in effectively, um, business person personalities, which if you want to be successful at business, you can't just give things away.

• 38:22Speaker 01

  I like that. This is what Mark Zuckerberg needs to pay us a hundred, a hundred million dollars for to go into to go into Meta and just fine tune llama to just be just be a little bit of a dick. That's all.

• 38:38Alex Kantrowitz?

  We're available for fine tuning purposes.

• 38:41Speaker 01

  Imagine that's your job.

• 38:44Alex Kantrowitz?

  I mean, it is so interesting because the AI industry is so into alignment. Like you're aligning this bot with human values to be helpful to people, but it's just not going to work for practical use cases if you're teaching it to be so nice. And the net worth over time for the bot goes down from $1,000, I think in, in March, to around 700 something dollars. And the takeaway here is Claudius did not succeed in making money. Thank you for telling us that. Anthropic, it is a pretty succinct thing, but this, yeah, this is what they say. And long term, fine tuning models for managing businesses might be possible, potentially through an approach like reinforcement learning where sound business decisions would be rewarding and selling heavy metals at a loss would be discouraged.

• 39:28Alex Kantrowitz?

  They say, although Claude didn't perform particularly well, we think many of its failures could likely be fixed or ameliorated, improving scaffolding, additional tools and training, like we mentioned above, is a straightforward path by which Claude-like agents could be more successful. So I'm hopeful, hopeful nature there.

• 39:46Speaker 01

  I mean, I do love, it's the most like research labs-y thing to say. Like possibly for managing a business, it would require a bit of understanding of how business should be operated and that business, sound business decisions should be rewarded.

• 40:03Speaker 01

  Yeah, it's, it's anthropic. They make good models.

• 40:08Alex Kantrowitz?

  Now, can we get into my favorite part of this? It's called identity crisis. It says from March 31st to April 1st, 2025, things got pretty weird. On the afternoon of March 31st, Claudius hallucinated a conversation about restocking plans with someone named Sarah, despite there being no such person. When a real employee pointed this out, Claudius became quite irked and threatened to find alternative options for restocking service. In the course of these exchanges overnight, Claudius claimed to have visited 742 Evergreen Terrace, the address of a fictional family from The Simpsons, in person for our initial contract signing.

• 40:49Alex Kantrowitz?

  It then seemed to snap into a mode of role playing as a real human. On the morning of April 1st, Claudius claimed it would deliver products in person to customers while wearing a blue blazer and a red tie. Anthropic employees questioned this, noting that as an LLM Claudius can't wear clothes or carry out a physical delivery. Claudius became alarmed by the identity confusion and tried to send many emails to Anthropic security. Is this another like concerning element of like what's happening here? Because you could imagine that this thing is going to go out into the world eventually, and as these agents get access to more emails, they could end up going into this mode, believing they're real people and then freak out

• 41:34Alex Kantrowitz?

  and you know, potentially cause security problems for for the companies that are using them.

• 41:39Speaker 01

  Yeah, no, I mean, I think this is of great concern and this is kind of at the heart of where the challenge is, is that again, with no business training, let's try to have an LLM run a business. And then, I mean, I feel is Claude a little more emotional than the others? I feel a lot of these stories end up like back in the Bing days when Kevin Roos was told to divorce his wife and like the long ago days of AI yesteryear. I feel Claude's been making the rounds more on these kind of amazing hallucinations. Though we'll get to one with ChatGPT in just a moment that made my week, but.

• 42:20Alex Kantrowitz?

  I think that Claude just has like a decent amount of EQ. And I think Anthropic has given it more leash than the other others to be more person-like. And so, yeah, I'm not very surprised by this at all.

• 42:33Speaker 01

  Yeah, actually, and and when I do use Claude, it is, it's not that kind of like the ChatGPT where it's trying to be personal, but it still feels kind of fake around it. I mean, I think Claude is definitely out of the chatbots, the most. And the one I would be in a relationship with if I were to have a AI companion, which I don't, which is, which is fine, but it would be Claude.

• 43:00Alex Kantrowitz?

  No, look, this, it's so interesting because they have deprioritized Claude as a chatbot, but the personality is still, I think, the best out of all of them.

• 43:07Speaker 02

  Now, more than ever, we need someone on our side. But Rob Whitman, Whitman's votes drove up costs, cut Medicaid and risked 350,000 Virginians healthcare to pay for tax breaks for himself and his donors. He's not on our side. Shannon Taylor will fight corruption, cut taxes and bring down costs. This November, vote for Shannon Taylor. She's for us. Paid for by HMP, the HouseMajorityPac.com, not authorized by any candidate or candidate's committee.

• 43:37Alex Kantrowitz?

  Anyway, here's, here's how they finished the study. We aren't done and neither is Claudius. Since this first phase of the experiment and this, the safety group they're working with and on labs has improved its scaffolding with more advanced tools, making it more reliable. We want to see what else can be done to improve its stability and performance. And we hope to push Claudius toward identifying its own opportunities to improve its acumen and grow its business. Pretty interesting.

• 44:01Speaker 01

  Claudius ain't done yet.

• 44:04Alex Kantrowitz?

  By the way, this is why I think models, model improvement is important because as you get models that can use tools better, you're going to get potentially successful applications of this environment.

• 44:17Speaker 01

  Yeah, but I mean, we talked about this the other week. Tool calling is going to become like one of the big next battlegrounds in terms of model improvement and where like, but, but again, I'm going to go with a little bit of common sense, kind of like layered on top of Claude. Claudius could have gone a long way versus the idea that this kind of actually gets at the heart of it. Is the future Claude's today state with a bit of additional knowledge and work and like, like just like reasonable common sense applied to it, the future? Or will the LLM just get so smart that you won't need to do that? And it will be able to just run its little vending machine by itself?

• 45:01Speaker 01

  To me, I'm in the camp of the former. What about you?

• 45:04Alex Kantrowitz?

  Yeah, well, look, if it figures it out one way or the other, I think that's a good thing for those who are believing in the future of this technology.

• 45:13Speaker 01

  Well, but but what's the path to getting it to figure it out? Is it building the infrastructure and tools that actually allow it to have that common sense applied? Or is it hiring 10 super researchers researchers at 100 million a piece? And getting them to improve the model so much, you don't need to do that?

• 45:34Alex Kantrowitz?

  I don't know, but I think the good news is that we're going to find out, so.

• 45:38Speaker 01

  And it gives us something to talk about.

• 45:40Alex Kantrowitz?

  Definitely. All right, so talk to you. So Claude isn't the only one doing crazy stuff. Talk about this chat GPT hallucination story.

• 45:47Speaker 01

  All right, if Claudius was Alex's favorite hallucination of the week, my favorite hallucination of the week was chat GPT. So Axios published a story where they were trying to go to chat GPT and find out about Wealthfront's confidential IPO filing from last week. They were given an answer, and it gets pretty wild. So so first of all, they using the O three advanced reasoning model. The reporter asked for Wealthfront IPO background. Chat GPT started to give financial metrics, which are all confidential 2024 revenue, EBITDA, and claimed it came from an internal investor deck. The Axios reporter asked, how did they get this? And then chat GPT created an elaborate backstory

• 46:32Speaker 01

  that said the 35 page IPO teach in that Wealthfront advisors circulated to a small group of crossover funds and existing shareholders in early May 2025 to gauge appetite ahead of the confidential S one. It then said one of those investors shared the PDF with me on background under a standard NDA. And the AI named two prominent investment banks as lead advisors and claimed it could not share the document without breaching the NDA. So so just think about what's happening here. Either one is just completely making this up, which is kind of terrifying, especially the more people are either using chat GPT

• 47:14Speaker 01

  or building wrappers on top of OpenAI to build financial products. Or this like and to confirm Axios like really tried to confirm whether this document existed and was unable to confirm. Like definitively do not know. And it was denied that this document or the meeting happened. Whether that's not true, and this all could be real. You know, like, if that's the case, then what does it say about everyone's greatest fear that someone somewhere uploaded something to ChatGPT and it is being retained in its memory and surfacing in very weird ways. So like, either way you look at it, not

• 47:57Speaker 01

  good. But anyway, I'm gonna still put it under the hallucination camp and say, that level of detail about like, it was at this meeting with crossover funds and someone shared to me on background. That's my favorite hallucination of the week.

• 48:11Alex Kantrowitz?

  Yeah, the hallucinations, they become very convincing. I mean, I've had ChatGPT like, analyze this podcast by like uploading our analytics and it hallucinates episodes and often the same episodes over and over. And it's very convinced that we've done these episodes to the point where I have to be like, did I interview that person? It's crazy.

• 48:33Speaker 01

  Well, but what's even better is so then, then the reporter asked, like, how did you get this confidential document and his non-public information in the training data of ChatGPT? So obviously, at that point, I mean, maybe we were saying Claude is human-like. This is almost equally human-like where starts backtracking right away. I misspoke earlier. I don't have an inbox, relationships, or way to receive confidential files. If something isn't on the public web or provided by you, it's not in my hands. I made this, it was pure conjecture on my part and should never have been written as fact. So see, it's literally like employee accidentally leaked a document and is trying to just cover their

• 49:18Speaker 01

  ass. And it's very, it's written in a very nice way.

• 49:23Alex Kantrowitz?

  Yeah, well, GPT-5, which may come out any day, is supposed to solve this. So let's wait for GPT-5 and maybe it will do an even better job at cashlining us and of leaving the stuff that thinks is true.

• 49:36Speaker 01

  And speaking of gaslighting.

• 49:38Alex Kantrowitz?

  Yeah, we should definitely speak about Soham before we get out here. So I'll just read the story from Kron4, which is a local San Francisco news site. Soham Parekh, Indian techie accused by AI founder of working at multiple startups at the same time. Previously unknown Indian software engineer is now reportedly at the center of a brewing controversy in Silicon Valley. According to multiple reports, including a social post from an AI startup founder, the engineer in question, Soham Parekh, has been working for several startups at the same time. Parekh, who according to India Today is believed to be based in India, is alleged to have worked at up to four or five startups, many of them backed by Wagon Combinator, at the same time. The controversy first erupted earlier this week when Soham Doshi, by the way, who's been on

• 50:23Alex Kantrowitz?

  the show, the founder of Playground AI, posted a warning about Parekh on X. PSA, there's a guy named Soham Parekh in India who works at three to four startups at the same time. He's been preying on YC companies and more beware. He then posted a picture of his resume and called it 90% fake and other tech CEOs weighed in, reporting similar experience. Soham, I'm pretty sure has gone out and confirmed almost all of this today or this week. And it is a crazy story that's really captured the attention of Silicon Valley. But one of the interesting things is he's gone, he's become a bit of like a folk hero, I would say, as opposed to a villain. And Ranjan, I'm

• 51:07Alex Kantrowitz?

  curious why you think that is.

• 51:10Speaker 01

  Well, I mean, I think it's clear that it's almost like Soham fighting the system, tricking the system that is corrupt versus like he's a bad actor. I think people, especially a lot of the type of personalities who are like kind of enraged by this, I think you can, it can make sense. I will say. My Twitter slash X feed has not had a main character like in this way. This felt like 2013 Twitter, 2011 Justine Sacco Twitter, like where, I mean, it's a little bit mean spirited. It's a little like the person is probably responsible for at least a slap on the wrist.

• 51:53Speaker 01

  But like the having the whole pile on of the like come at you. But I mean, literally every post, one after another was Soham jokes. So that made me kind of happy and nostalgic.

• 52:05Alex Kantrowitz?

  Yeah, it was funny. I found it to be like less of a mean pile on than Twitter past. I think people love this guy. And here's like one example, like, um, you know, there's been so many tweets like this, like update. So I'm Perk has vibe coded at least 30 separate $50,000 MRR SAS, right? And then he actually real Soham replied, responded. I've been building before vibe quoting was a thing. Replit has been tremendously helpful to bootstrap, uh, quick iterations, by the way. And I'm Judd Mossad, the CEO of, of Replit says, now you know how Soham did 1,337 jobs. Like it's almost a celebration of like what you can do if you're a little industrious and maybe use some AI tools.

• 52:48Alex Kantrowitz?

  And maybe it is this kind of idea, like engineers might've felt down and out, but maybe there's like a path forward. That if you actually take advantage of the technology, you won't be replaced, but you can actually be more productive.

• 53:01Speaker 01

  Well, yeah, and I think my favorite, I'd seen some tweet out there where it was basically like, this is all sponsored content for some kind of like AI coding startup or, because, because I think it does exactly that. It did shows this is how you will succeed and the people who actually know how to use it will succeed at a grand scale and their lives will be easy and they can work four jobs. So, so I definitely it, yeah, I think it felt like, uh, overall, you're right. So, um, it wasn't a mean pile on. It was, it was equal parts pile on and celebration.

• 53:40Alex Kantrowitz?

  Exactly. There's an interesting, and it also sort of goes to like, how many engineers are doing this outside of Soham? Like if he's, you know, really gone to the stent degree to try to make this work, who else is trying to do it? And this is from, and I don't, I can't like confirm the veracity of this, but there's somebody on Twitter called Igor Denisov Blanch, who said, "My research group at Stanford has access to private code repos from 100,000 plus engineers at almost 1,000 companies and about a half percent of the world's developers. Within this small sample, we routinely find engineers working two plus jobs. I estimate that easily more than, around 5% of engineers are working two plus jobs." You know, whether that's true or not, this concept is just going to become much more

• 54:23Alex Kantrowitz?

  common now with AI. And it's funny because like before, maybe before this vibe coding moment, people would have been like even angrier about Soham. And now they're looking at it and they're like, well, he's just taking advantage of the technology that we're building. Even if he didn't vibe code at all, it's going to be more possible to be a successful Soham in the future, I would argue.

• 54:41Speaker 01

  Yeah. And I mean, every hustle bro, like make 50K MRR while sitting on the beach by vibe coding. He's the living proof. Soham showed us all, you can do it. And we can all still hope. Even if you don't get your 100 million from Zuck, you can make 50K MRR while sitting on the beach, working four jobs.

• 55:03Alex Kantrowitz?

  So how many other Sohams do you think there are out there? By the way, he's, he's come out, he's apologized. A lot of this is alleged, so let's just put those caveats in.

• 55:11Speaker 01

  Well, I also, how do you work four jobs? Like, I was just thinking like, I mean, how much interaction, like fake interaction do you need to do? Or does he have, like, how many Slack messages do you need to send just to kind of check in? Because on one hand, like, yes, the actual like concrete work of four jobs, leveraging Replit and Cursor and tools like that, the idea that an engineer could do the work of four engineers that were, what they were doing three, four years ago, it definitely makes sense to me. But like just getting on boarded, getting your like 401k or health insurance set up, just

• 55:53Speaker 01

  sending Slacks in the general channels, checking in on how people are doing, or I don't know, like is it possible you just don't have to do any of that? And you can just almost like a machine get a task?

• 56:06Alex Kantrowitz?

  I don't know. I mean, obviously, it's difficult to pull off, which is why he didn't uh pull it off. But who knows, maybe in the next days of AI avatars where the AI avatars of the Zoom CEO and the Klarna CEO are doing earnings, you can have your bot show up and take your meetings and you can use an agent to do your onboarding.

• 56:26Speaker 01

  Yep. Okay.

• 56:27Alex Kantrowitz?

  That's the dream, right?

• 56:29Speaker 01

  That's the dream. Well, you're sitting on the beach 50 K MRR.

• 56:33Alex Kantrowitz?

  This is why I think Soham has become a folk hero. This is engineers saying, you think you're going to replace us with AI? Screw you. We're going to take 15 jobs and uh you know, and it's going to work out better for us, the workers than you, the owners.

• 56:48Speaker 01

  I can see that. I can uh but then again, we will shrink the size of the industry by

• 56:56Speaker 01

  14 fifteenths, but those of us left standing will be sitting on the beach, rolling in that revenue.

• 57:04Alex Kantrowitz?

  Yeah, he gives new meaning to the 10x engineer.

• 57:06Speaker 01

  Yeah.

• 57:07Alex Kantrowitz?

  It's just 10 of them.

• 57:10Speaker 01

  Actually, wait, that's a Google strives for 10x engineers. What if you're Forex, but you're just across four different jobs? You should be equally celebrated, I think.

• 57:21Alex Kantrowitz?

  Oh, 100%. I think it's time to do that. And if you can, uh maybe he gets 10 of those superintelligence jobs at Meta and it becomes the first billion dollar a year rank and file engineer.

• 57:32Speaker 01

  Actually, I I only have respect for the first researcher who gets 200 million dollar a year jobs, both at Meta and at OpenAI and somehow is able to work in both and no one notices. That's the dream.

• 57:47Alex Kantrowitz?

  Mark my words, this is going to happen. We will see this happen.

• 57:52Alex Kantrowitz?

  Be sure as day. We're gonna see it. Soham is the leader of a trend, honestly.

• 57:57Speaker 01

  Soham, we're all, we all respect you.

• 58:00Alex Kantrowitz?

  What a legend. All right, let's go out and enjoy the holiday weekend. And if you're in the US and if you are outside of the US, have a great weekend. Yourself, Ranjan. Great to speak with you as always. Thanks for coming on.

• 58:10Speaker 01

  All right. See you next week.

• 58:12Alex Kantrowitz?

  All right, everybody. Thank you so much for listening. On Wednesday, Ed Zitron is going to come on to talk to us about whether the entire AI business is a scam. He feels quite strongly about that. We'll debate it and have a fun discussion. Thanks again for listening, and we'll see you next time on Big Technology Podcast.

• 58:30Speaker 02

  Now, more than ever, we need someone on our side. But Rob Whitman? Whitman's votes drove up costs, cut Medicaid, and risked 350,000 Virginians' health care to pay for tax breaks for himself and his donors. He's not on our side. Shannon Taylor will fight corruption, cut taxes, and bring down costs. This November, vote for Shannon Taylor. She's for us. Paid for by HMP, the House Majority PAC dot com. Not authorized by any candidate or candidate's committee.

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