#250：中間選挙でデータセンター拒否、Anthropicの研究加速、250回の節目、NVIDIAのスパイ疑惑調査

Everything looks different to me right now. Every business, every person, every career path, it just looks fundamentally different based on what's available today and how good it's going to get over the next like 12 months. I just can't look at 2027 the same right now. Welcome to the Artificial Intelligence Show, the podcast that helps your business grow smarter by making AI approachable and actionable. My name is Paul Ratesford. I'm the founder and CEO of SmarterX and Marketing AI Institute, and I'm your host. Each week, I'm joined by my co-host and SmarterX Chief Content Officer, Mike Kaput, as we break down all the AI news that matters and give you insights and perspectives that you can use to advance your company and your career. Join us as we accelerate AI literacy for all. Welcome to Episode 250 of the Artificial Intelligence Show. That is what I said, 250. I'm your host, Paul Reitzer. I'll have my co-host for 230 of those episodes, I believe, Mike Kaput.

Welcome, Mike. All right, Paul. This is a big one. Yeah, it's kind of nuts. We actually thought about it. I think it was last week when I saw the 249. I said, oh, next week's 250. That's a lot of episodes. We should probably do something for that. So actually, we are going to talk about the 250th episode in one of our topics today and give you a little background. But, yeah, thanks for being with us today. Thanks for being with us. Some of you have probably been with us since the first episode, I would imagine. But we appreciate everybody being here for another week of covering AI news. And wow, has it gotten a little bit crazy. You know, we've kind of joked. I guess there is a new like CBS show about Silicon Valley and AI. I think the writers got to go back to work because whatever is happening right now with Jensen Wong and NVIDIA is like off the script. It is. It's beyond Hollywood. And we're going to get into that one, too, because that was one I didn't want to talk about. On Friday, I literally shared it with Mike. I'm like, yeah, I don't think we're going to cover this.

And by Sunday night, it's like, oh, my gosh, we've got to cover this. So if you haven't heard the news on Jensen Long and NVIDIA, if you're wondering why your NVIDIA stock is probably catering, we will explain all that in a few minutes here. Okay, so this episode is brought to you by AI Academy by SmarterX. You hear us talk about AI Academy all the time. This is like the main focus of our business at SmarterX. in addition to our events and this podcast. But there has never been a better time to become a part of AI Academy by SmartRx because we're celebrating our 250th episode with a special offer, which I will get to in a moment. So if you're new and aren't sure what AI Academy is, so AI Academy helps individuals and businesses accelerate their AI literacy and transformation through personalized learning journeys and an AI-powered learning platform. In addition to our collection of on-demand professional certificate course series, there's 21 of them, I believe, Mike, and that's across AI foundations, AI for departments, and AI for industries. So it's a collection of course series within those areas.

New educational content is added every week, so you're always up to date with the latest AI trends and technologies. Plus, as we'll be highlighting during our November 19 AI Academy Day, We have a major focus moving forward on dynamic learning, including more real-time content about AI agents, the inside stories about personal and business transformations, and platform-specific courses, which everyone's been asking for. Those are coming soon for ChatGPT, Claude, Copilot, and Gemini. So if you're learning a development team in a big enterprise that's struggling to keep up with the pace of change, we've got you. We're going to be launching all new course series tied to the platforms themselves. And so now on to the offer. So, again, 250th episode only. This is going to run through November 30th. So you have, what, about 20 days or so to take advantage of this. There's two options here. So if you are an individual and you're buying a license for yourself, you can get $250 off a new AI Academy individual license. So that's the full Academy experience, courses, certificates, live sessions, community,

everything we're building to help you get ahead 25% off your first year. So, again, $250 off your first year. with POD 250. I'll give you that code again in a minute. So that's for individual learners. Now, if you're a business leader who's charged with driving AI literacy and transformation for your team or organization, we have two options here. You can buy 25 licenses or more by November 30th, and I will do a personal, private, one-hour virtual session with your team. We can do AMAs. We can do a state of AI talk. We can just do a consulting session, whatever you want to do. So one hour of my time for 25 or more licenses by November 30th. If you're an existing member and you want to add 25 licenses, we'll extend that offer to you as well. And then the big picker is for 250 or more licenses. So if you want to get a business account with 250 or more licenses, I will actually come to you and do a private session in the United States only. So it's got to be in the U.S. in the first half of 2027.

I hope I regret this. Like, this is one of those offers where I'm kind of thinking about my schedule and saying, how in the world would I do this? But, like, I hope that what I have to solve for is how to make this many trips, because I would love to come and meet with as many companies as we can, hear what's going on in your organization, and try and help, you know, really drive that human-centered transformation. So, again, if you're buying a business account, 25 or more licenses by November 30th, I will do a private one-hour virtual session with your team. If you do 250 or more, I will come to you and we will do a private session in the first half of 2027. So, again, there's never been a better time to join AI Academy as an individual learner or get your organization started with a business account. You can go to academy.smarterex.ai and you can join right there with the pod 250 at checkout for individuals. If you're looking at the team licenses, just reach out to our team and we'll get you hooked up there. And then the last note here, Mike, is we have that AI Academy Day I mentioned on November 19th. We will put a link to it in the show notes.

You can go to smarterx.ai and register for that today. That is a free event on building an AI forward workforce. So while we're going to talk about all of our plans for dynamic learning and what's coming to Academy, it's also going to be sort of an educational session about how we think about learning and development within organizations. so you'll find value in it whether you become an AI Academy member or an AI Academy member or just want to be a part of that day. So, again, academy.smarterex.ai, $250 off individual memberships, and then either an in-person session for $250 or more licenses or a virtual session for $25 or more licenses. So, again, I hope my schedule is packed and I'm regretting that I made this offer, but we were trying to think, like, what is something we could do that we've never done before, and we've never done either of these before. So we hope you take advantage of that. All right. So on to this week's episode, the survey, Mike. Give us the rundown of what's going on with AI Pulse. We always start off with that. Yeah. So we are running our AI Pulse again this week that we've been running the last week or so.

So this question is about how AI has expanded your role and if it's expanded it into work that typically belongs to adjacent roles. So it takes 15 seconds to fill out. If you have not filled it out yet, please go to smarterx.ai forward slash pulse. We'd love to hear from you. All right. This feels like it was already a year ago, Mike, but last Tuesday was the midterms in the United States. And as we've said, we generally try and avoid talking politics on this show. But, wow, there is no way to avoid discussing the implications of the midterms last week on the AI industry, which is where we are going to center our discussion starting today, Mike. All right, Paul. Yes. So this past week's U.S. midterm elections delivered, among other things, a potentially major setback for data center expansion. A few threads to pull on here. So first, voters favored limits on data centers in 40 out of 41. That's 40 out of 41 local ballot measures.

So they either backed restrictions that were on the ballot or rejected proposals supporting development. Now, voters also elected several prominent candidates who campaigned on slowing the data center buildout. So there are incoming governors, including Gina Hinojosa in Texas, Keisha Lance Bottoms in Georgia, and Amy Acton in Ohio, all of whom in one way or another supported pauses on new data center development. And Texas is especially important there. There are varying conditions in each state for allowing projects to proceed, But data centers took a center stage in those campaigns. This push also reached the Senate. Texas Senator-elect James Tallarico called for ending big tech tax breaks and making data centers pay for their own infrastructure. Michigan Senator-elect Abdul El-Sayed supported pausing approvals until federal protections are in place for data centers and for the communities where they're being built. So these positions are reflecting a lot of voter concerns about things like electricity bills, water usage, how much say communities have over these projects.

Now, the election results do not stop a lot of the construction already happening, but they are going to probably influence pretty heavily how planning and considerations go for newer projects in 2027. So, Paul, there's a lot of kind of threads to pull on here. OpenAI is already scrambling to react to the changing political environment. I'm curious, as the electoral wins in the U.S. have shifted so dramatically since we recorded our last episode, what do you see in your mind? What does it mean that the blue waves sweep in Congress and state-level races, what is that going to do to AI policy in the near term? Yeah, I mean, at a high level, obviously, there's going to be a slowdown in the data center buildout. there's going to be lots of people being called before the Senate to explain what's been going on. And I think there's going to be a lot of movement on regulation, probably most likely at the state level. Like, I think things are going to start getting pushed through. So it's just going to make it more challenging, definitely, for the AI labs to move forward at the pace that they were prior to this.

I just what I'll do, Mike, is I'll go through a collection of articles that I was kind of reading, catching up on all of this and just sort of pull some excerpts out, and then we'll kind of close out this one talking about what you were alluding to with the compute campuses as OpenAI is now calling them. Nothing like a rebrand to change perceptions. All right, so Axios. This is from, and again, we'll put links to all of these in the show notes. This is from November 4th, I think. Voters show deep distrust of the AI race Tuesday, approving dozens of data center moratoriums and electing a raft of candidates who favor slowing a $10 trillion buildout, including in Texas, as you mentioned, Mike, the epicenter of the boom, which is also where Elon Musk was building all of his stuff. So why it matters. In an election that hinged on an affordability crisis for many Americans, voters made it clear they view Silicon Valley's AI ambitions not as a technological miracle, but as a resource-training threat to their wallets and communities.

All told, nearly two-thirds of 2028 data center capacity could face delays, although most political analysts don't see Democrats permanently blocking construction because they would then run the risk of impacting the economy negatively, which could then cost them the 2028 election. So it's this, like, fine balance that Democrats not to play. Axios continued AI had its official debut as the biggest issue in American politics. I don't know that I agree with that, But that's what Assemblymember and RAISE Act author Alex Boros told Axios. AI is, this is a quote from Boros, AI is reaching into every part of our society and economy, and it's now crystal clear it's impacting every part of our politics, too. I would not agree that it was the biggest issue in American politics. It was certainly an issue. I would agree that it has the possibility to be the biggest issue in the 2028 elections. All right. So then the Washington Post said in Texas, where industry estimates show a quarter of the tech industry's new data centers are planned to be built.

So a quarter of all data centers plan to be built. Democrat Gina, how did you pronounce her name? I want to. Gina Hossa. Okay. You know, Hossa. Over incumbent Governor Greg Abbott rattled the industry and its investors. She champions a moratorium on new projects along with regulations that could impose aggressive protections for the environment and electricity ratepayers from the effects of data centers immense energy consumption. Now, you alluded to this, Mike. There was one place where data centers won. I thought this was a fascinating side note. It said just one community appeared to give a thumbs up to data centers. Harwood, North Dakota, a town of 800 where three quarters of voters struck down proposed restrictions on industrial development. So North Dakota, we had a winner in the town of 800. All right. Wall Street Journal, the election day revolt that threatens America's AI boom is the title of this one. This one says, this is going to probably, this is the quote, I'll get to that in a second.

So this is going to probably elongate the timelines for the data center build outs, according to Sequoia Capital partner Pat Grady. That's probably the most tangible impact of what happened with the elections yesterday. Now, Brad Gerstner, who we've talked about, the BG2 podcast, very influential in the Trump administration. He said now with this blue wave, I think it'll slow down even more on the all in podcast where he's frequently a guest. adding that roughly 25 to 35 percent of next year's data center build out in the U.S. could be at risk. Quote, that's why I think it will be really challenging for the AI infratrade, he said, referring to shares of data center companies. The article continued, SpaceX CEO Elon Musk poured nearly a half a billion dollars into the midterms, while others in the sector donated tens of millions of dollars to help elect candidates who are more open to data center development. Elon focused a lot of his money on the Texas races because his headquarters are in Texas. And it just didn't go well.

Now, don't feel bad for Elon. Him spending $500 million on the Minterbs is like you and me spending $50 on dinner, but still a lot of money. Data centers in space are looking pretty appealing right now, Musk said on X early Wednesday, referring to his idea of locating the facilities in orbit. He resides in Texas, and SpaceX is based there. The data center buildout has buoyed economic growth at a time when overall hiring has slowed and other economic indicators have stagnated. The boom has flooded many rural communities with investments and property tax revenue. Data centers have been the primary driver of construction spending growth, said a chief economist at the Associated Builders and Contractors Association. The construction trade group said, one can imagine that the data center construction boom is going to really soften next year. Okay, so now on to OpenAI, and they published an article that said, setting a new standard for responsible AI infrastructure. So this is obviously timed, knowing that this was going to happen.

So it said, the promise of AI is that it gives people more power to shape their own futures. It can help someone learn a new skill, build a business, or solve a problem that was previously out of reach. Building more computing infrastructure capacity in the United States can help lower the cost of delivering AI, put these tools in more people's hands, and ensure America stays at the forefront of the technology. The communities that host this infrastructure should have a clear say in shaping their futures, too. Residents should be able to influence how a campus is developed. I'll come back to that word in a minute. Hold developers accountable for covering the cost of powering it and sharing the jobs and business opportunities it creates. How we build these campuses is one of the first tests of whether the benefits of AI will be broadly shared. So they went on to say in this article, today we are publishing our national community compact and a compute campus policy blueprint. So compute campus. The compact sets out what communities can expect from open AI at future campuses that we own and operate.

The blueprint proposes policies to help ensure that compute campus development gives communities a meaningful voice, protects families from new costs, and broadly shares the economic gains. So they're basically taking the hard lessons of the last few years and saying, we understand it. You all don't like the data centers and how we've gone about building them. This is our plan for how to make this work. So the thing that they link to, the Compute Campus Policy Blueprint, then takes you to a PDF that says, AI infrastructure that works for America, our Compute Campus Policy Blueprint. Just grab an intro to there. It says, unlike previous waves of software-driven innovation, AI requires enormous physical investment in American communities, In data centers, energy infrastructure, advanced manufacturing, and the skilled workforce needed to build and operate them, AI training data centers, more accurately described as compute campuses, there's our rebrand,

are unlike most data centers that power Internet searches, email, or phone calls. So they're trying to differentiate between what we used to think of as data centers and what we are actually building. The scale of operations creates massive demand for skilled construction and operations jobs. They give the example of in Pike County, Ohio, where the compute campus is expected to create 2,500 long-term operating jobs with an average annual salary of $80,000 and 26,000 construction jobs. Then they talk about how every future compute campus that they own will have a local compact. They're going to pay their own way on energy and infrastructure. They're going to protect local resources and report on their impact, and they're going to create opportunities for local workers and businesses. So big picture, one final article to cite is tech billionaires face tricky political waters after Democratic sweep. This is from The New York Times, and it said many donated money to President Trump's favored causes and became his allies. Now that political alignment may be a liability.

Tech leaders may find their business dealings under investigation. Quote, they are going to need to plant new forests for all the subpoenas we'll be printing, said Senator Edward Markey, Democrat of Massachusetts, posted on social media on Tuesday. So, yeah, voters don't like data centers, we know. So rename them? Yeah, so compute campuses, super intelligent compute campuses. Yeah, there you go. I was going to ask, is compute campus the new super space intelligence? I don't know. But, yeah, it's – so there's going to be a lot of AI tech leaders before Senate starting in January. You can get used to that. I feel like it's going to be a new weekly session as to, like, segment, what, you know, this weekend, AI leaders visiting Washington. Yeah. And things are going to slow down a little bit. But, like I said before, like, I'm not sure that's a bad thing. But the way it could, I don't know, I'm trying to make, how do you make this tangible to people?

The cost of your licenses may go up real soon for ChatGPT and Claude and Gemini because it's just supply and demand. There's going to be less supply for the inference compute that we all need to use our AI tools. If they can't build their data centers, they're going to raise the cost of your licenses. I hadn't really thought about that until this moment, but I can almost guarantee you price increases will be coming to the favorite AI system near you. Or possibly usage reduction, right? Massive, yes. So they may keep the price anchored where it is maybe. Especially on high inference cost solutions like video, image generation, world models, agents, things that require a lot of compute. you might see like significant reductions in the lower price models. And you could see like, I would not be surprised at all. Mike, actually, as I'm thinking about this, if you don't start seeing the first like thousand dollar a month, $2,000 a month where they're now going to say,

listen, this is replacing the need to hire someone. It's going to cost you $5,000 a month, but it's going to do the work of five analysts and two marketers and whatever. So it could actually accelerate the positioning of this as an alternative to people because they're going to start charging more. because they need to limit the compute usage. It wouldn't surprise me if some of that messaging, too, centers around like, hey, look at this business owner who couldn't hire someone to do this, so therefore they turn to the $1,000 a month plan, you know, as a way to almost say. I'm sure behind the scenes that's not the difference. 100%. That type of thing, like, hey, so-and-so company needed an analyst on demand 24-7. And they could not do that because people couldn't do it or didn't have the skills or whatever. Pick your angle. It wouldn't surprise me, I think. Actually, now you're saying that. I saw an ad for ChetGPT last night, and I was like, oh, this is better than their really bad ones that were running at the beginning of the year where they would, like, scan the text real fast. Like, what is this ad even for? This was one that was more human interest and, like, helping people run a business or start a business.

And so I agree. I think you could see a change in messaging where it might cost you more, but it's powering small business. Right. across the world and from our compute campuses. Right, yeah, we'll see how long that lasts. One final question for you. So obviously it's a super complicated issue in terms of the infrastructure buildup, but it sounds like there's projects already greenlit for 2027. I think a lot of this gets pushed into 2028, but how likely do you think it is we actually see a major blocking of data centers, Or is this going to be a lot of bluster and maybe some local moratoriums? Or do you expect like really existential issues here? I think, well, I think there's like fundamental beliefs about they're not good. Like you have to deal with that. But I do think that more likely because the Democrats understand the importance of the AI build out to the economy and to national security and things like that. it's more of an enormous leverage point now to get money funneling back into these communities

and making a real positive impact. So the Democrats are going to hold a lot of negotiating power here, and I would imagine they're going to use it to an extreme. But the AI labs are going to have to play ball. Like, there's going to be no way around it. So I think they'll probably arrive at it. I just feel like it's going to be really messy. There's going to be a lot of things going on, a lot of articles coming out, a lot of leaks happening. But at the end of the day, everybody knows that we can't just stop AI. Like it would not be good for the economy to do that. So, yeah, I think it'll move slower. But generally, the commitments are usually like two years out. So like 2026, we're building for commitments that were made in 2024. So most likely, you know, the first real impact you would see of this is going to be stuff that would have been coming online in 28, 29 probably. Okay. But anything that was committed in 25, 26 that's already broke ground or started, like I don't think that stuff's stopping. But I haven't analyzed all, you know, 40 bills and stuff like that.

It's like the AI-generated viral video out there where they have all the AI-generated tech leaders saying, we were always blue. Dude, okay. So if you don't know what Mike's talking about, There's one of the best AI slop videos, like, ever created. And we'll put a link to it in the show notes. The guy who did it and just, like, creates AI videos online, it's amazing. It's, like, so funny. It's all the AI tech leaders, like, singing, like, kumbaya, like, we are always, like, Democrats, and this is going to be great. And it's entertaining. All right. So our second big topic this week, this past week, the AI safety nonprofit Meter, M-E-T-R, which we have talked about at length on this show, published an assessment of how much AI is accelerating research at Anthropic. So according to Meter, this is directly from their post, quote, from August 12th to October 29th, 2026, Meter's staff performed a high access evaluation at Anthropic to assess the degree to which AI is accelerating AI R&D, end quote.

So this included examining research data and interviewing anthropic staff. And basically what's happening here is Meter is searching for evidence of what we've talked about in the past, recursive self-improvement or RSI within a major lab. So RSI is that concept of AI getting good enough to be creating and improving itself. So AI creating the next generation of models, which some theorize would lead to basically a runaway explosion of AI development. So Meter did not find proof that RSI is happening at Anthropic at least from what they disclosed We come back to that in a second But they did find some interesting traces of acceleration at Anthropic They say the company has made roughly two years of progress in the past year alone, and that most of the acceleration comes from using AI for AI development. However, they do say this is still primarily AI helping human researchers, not doing

all the research itself. The report does not show AI independently building successive generations of more powerful AI. Now, there's a big, big caveat here. Anthropic redacted much of the evidence from the public report that Meter published. So the conclusions in this whole thing are pretty difficult to check. So Meter actually mentions this frequently in the report that under this arrangement, basically Anthropic was able to redact things and that the redactions make explaining certain conclusions quite difficult. Now, Anthropic said these redactions were, quote, aimed at removing confidential information that could accelerate other AI developers. So, Paul, I'm curious, like, can you walk us through the big picture here, your thoughts on it? I'm just curious, too, why does this matter for your average business leader who's trying to figure out how to better approach this technology. Your company can get disrupted way faster. It's like, so at a very high level, the way I interpret what's going on is things are

moving faster than expected, even within the labs themselves, and even faster than the most aggressive models that we talked about last year, like the models of growth in the AI 2027 report. So if you remember, I don't remember what episode we talked about that on. We've talked about it multiple times. A couple times, yeah. Yeah, it's like basically AI researchers playing out like the scaling laws within AI and how fast things were going to go and if they got to recursive self-improvement, how it could accelerate. And so the most aggressive versions of that, it's actually pacing ahead of that. And at the time, people were laughing at their concepts of how quick things could emerge. Because their prediction got very weird very quick. Yes. You have this runaway improvement. I won't get into the details, but it's like you're dealing with, like, super intelligence. Correct. Like, if you play this out, like, what could happen to businesses, to jobs, and to the economy if the models actually get this good this fast?

So, yeah, like a super high level, and I'll unpack it in a second. It's just like, if you didn't have a sense of urgency to figure this stuff out before, you really, really need to have a sense of urgency to figure this stuff out. either for yourself, for your coworkers, for your organization, for your industry. If you think it's moving fast and hard to keep up with now, it's only going to get harder. And so, like, we just need to be honest with ourselves about where we are right now. So, Meter, you kind of alluded to this, Mike. They said in their ex-post when they shared this research, which, by the way, I think we mentioned this last week, this research, like, blew up in AI circles. But again, our litmus test is always do the people we trust and follow care about something? And every person I follow in this space was talking about this article. So, like, it's a big deal. So they said we submitted a 100 page full report of our assessment to Anthropic. For the public version, Anthropic redacted a majority of the evidence we found most informative.

As a consequence, the public must rely on our judgment more than we consider ideal, meaning they're saying, trust us. We saw things. It is moving really fast, but we're not allowed to tell you exactly how we know that. Now, Sam McCandlish, who I've never heard of before, is a co-founder of a nonprofit. I was wondering, how many co-founders of a nonprofit are there? How many more have we never talked about actually are listed as co-founders? Well, he posted, this is his first post on X. He created his account in November of 2026. I had to go look this up because someone said it was his first post. Yeah, right, right, right. Yeah, he's a co-author on the 2020 Scaling Laws research that Dario Amadei did. I've never even heard of this guy. Yeah, that projected all of this. So, yes. So he was apparently the CTO of Anthropic, and now he's the chief architect. So it just shows you, like, Mike and I are as tapped in as you can be, And like there are influencers lurking within these companies that just like show up every once in a while.

You're like, wow, OK, who's this person? OK, so co-founder, chief architect at Anthropic. He's one of the researchers who helped establish the silence of scaling AI. Again, going back to the 2020 research with Dario Amadei and now helps lead the technical work of training Claude. So this is his post. I'm Sam, one of Anthropic's co-founders, and I lead much of our AI safety work. I'm going to have more to share on the work over the coming months. Many of meter's conclusions match my own. So, again, this is now the guy who basically is probably in charge of this meter research. He's saying, yes, we extracted most of the useful information, but I'm just going to tell you what they're telling you is true, basically. So their conclusions match my own. AI is speeding up our research, but it's hard to say exactly how much. While they conclude AI can't yet make the most important contributions to model training on its own, they think the pace of development could, by default, accelerate further.

We determined that sharing exactly what is driving the speed up isn't the right thing to do, both because it's commercially sensitive and because it risks handing out a roadmap to others to go faster. So he's saying we made the right decision. Like, yes, we extracted a whole bunch of stuff from Meter. But had we put that out into the world, this could go sideways really fast. So he doesn't regret the decision that they've made. He then said, in the coming weeks, we plan to share a report with our own analysis of the speed of AI progress within Anthropic, including where we stand against our responsible scaling policy, which you just alluded to, Mike, that update in the S1, plus new steps we're taking to strengthen our safety and security practices as AI keeps improving quickly. So then I was like, okay, how do we sort of encapsulate, like, what are the implications of this? So we'll go back to Sergey Brin because it's been talked about now for about 6 to 12 months that Sergey is pushing hard on recursive self-improvement at Google, that he sees this as the path forward, which I don't really want to speculate too much.

But my guess is Demis Asabas would not be in the camp of accelerating RSI. I think Demis sees the risks more than others, and I don't know that maybe that's not part of the reason why Demis is no longer, like, leading DeepMind, is maybe the research path took a different direction and he would maybe be comfortable taking it. Okay, so October 15th of this year, we have a Business Insider article that was a prelude to Argonne, which is the newest model. It said, in a roundtable discussion about the creation of Google's new Gemini 4 model, Argonne, company co-founder Sergey Brin said that AI development was accelerating internally and that models are now helping build their successors. So that's RSI, early forms of it. Brin pointed to the release of Google's Flash models before Gemini 4's evidence that this was happening and said the company was, quote, starting to get onto the RSI treadmill. Treadmill continued, and now our researchers and the model work hand in hand to make the next version of the model.

So, again, that is early forms of RSI. Others at Google appear to share Bryn's perspective. In a post on X, Vinyan John, RSI leader for Gemini at Google DeepMind, remarked how embedded the Argonne model had become in its own evolution. He said, when your post-training demand shifts from asking for raw chips to asking for Argon quota, you know the RSI fly reel has reached a new gear. So not even hiding the fact this is happening. Now, the potentials here, so this is a tweet or a post from, I think this was just the other day, Pushmeet Khalil, the chief scientist Google Cloud VP of DeepMind. He said, across our projects at Google DeepMind, we are witnessing a genuine phase transition in the pace of scientific discovery. Yeah, I think I saw this on Sunday. You will see a glimpse of this in our releases in the next few weeks. As this AI-powered acceleration unfolds, our focus will be on empowering the broader scientific community to harness these tools responsibly.

Greg Brockman posted over the weekend, frontier models with lots of test time compute are becoming the engine of scientific discovery. So what they're all saying is like, we are all seeing something you aren't all seeing yet, but we are about to unveil it to you. So imagine what happened in mathematics now, like imagine that being applied to all areas of scientific discovery, which is super exciting, scary, but like exciting at the same time. And then one other one to throw in the mix says like, again, the whole, you have to be aware because things could get disrupted real fast. So it's probably weird to people when we quote Pliny the Elder. This is an ex-user, an anonymous account, who is definitely followed closely by the AI labs. This is someone that is, you know, a verified insider, AI researcher, kind of a hacker, sort of like takes the models, reverse engineers them, releases system instructions, things like that, but has a trusted history, I would say, on X, as, you know, some people follow.

So on November 5th, he, she posts, my spidey senses are telling me the labs are, as we speak, using un-guard-railed versions of their unreleased models to systematically reverse engineer all, in caps, of the Internet software, apps, and games, then feeding the reconstructed source code straight into their training pipelines while simultaneously building guardrails to ladder pull the rest of us. So what he's saying is they're basically taking these, the most powerful versions of their models without the guardrails, letting them loose on the Internet, stealing not just the text and video and images, which is how they train the original models. They're stealing the underlying code of how everything is created and then using that code to build the next generation of everything where they don't need the original sources of that software. It's a wild concept that I hadn't really deeply thought about,

but wouldn't surprise me if that's a new source of training data is all the underlying code for how the Internet was built, how software was built, how businesses were built. Yeah. Wow. So, again, as I started off, and we didn't even get to the Jensen topic yet. Yeah, right. Some things this week are, like, wilder than what we would write in Hollywood. Well, you know, one thing that jumped out of that Meter profile that they did, or a recap they did, rather, is both Meter and Sergey Brin are still talking about humans working with AI to accelerate this. So that's kind of like his first step onto the RSI treadmill. It's not even AI itself improving things autonomously. And so in the meter report, they said that when they had talked about this two years of progress in one year happening at Anthropic, they said this should make you feel old, basically, listening to this.

They said this is roughly the same level of progress from Claude 2.1 to Sonnet 4.5, which has already happened in a crazy small window in half the time. So if you think of even, I don't even think of the last time I said the words Claude 2.0, right, if I even ever have, frankly. So that level of progress in half the time, that's how far they are going, how fast they are going. All right, so let's, I just thought of a way to put this in perspective, Mike, and this actually is a tease up a talk, a segment we have coming up later about education. Chad GPT was released when the current college seniors were freshmen. So November 2022, which would have been four years ago, right? So 23, 24, 25, 26. So if you were a college freshman in November 2022, you are now a college senior,

and Chad GPT just got introduced into the world when you were a freshman. And so imagine the level of change we've seen in the blink of an eye. I mean, think if you have kids in high school or college, you know how fast those four years go. In the matter of time that your child could have gone through high school or college. Same with high school. They were freshman high school. Now they're senior high school. All of this from a simple chat bot that you could text in, text out to talking about recursive self-improvement where the models are writing themselves and taking down fields like mathematics and biology, four years. And what these labs believe is that level of change will start to happen every, like, six to 12 months. Like, that amount of change compressed into, like, months instead of years. And to Pliny's point, it may be happening completely behind closed doors, the things that are possible. We're not even experiencing. We're experiencing barely a fraction of it, and it's still incredible.

Right, and the only way we might hear about it moving forward is if they've called these tech leaders before the Senate. Because they're still going to keep developing the technology. They just won't release it. Yeah, funny to think in that four-year period, too, with those college students, how they voted in 2022 versus voting in 2026 now with some of these issues is wild. Yeah, and I guess to tee up the next topic, Mike, what also has happened in the last four years is... This pulls everything together very well. because this is... Unintentional, by the way. I didn't mention any of that. Yeah, I appreciate this major tea up here. So our third big topic, Paul, is this episode is a big milestone. It is episode 250. And, you know, we can pat ourselves on the back. We have plenty to celebrate. It's no small feat getting to 250 episodes of a podcast. So we wanted to maybe take a few minutes to kind of look back at where the show started, comments on a few things related to that and kind of just overall take a quick little victory lap

and then move on to weightier topics. But I want to kind of just really quickly walk us down memory lane because actually though it is it's 250 but four years ago almost exactly to this date is when this show really kicked off in a big way. So as a reminder for folks the podcast launched in January 2021 as the Marketing Artificial Intelligence show. The first 19 episodes were in a completely different format. They came out periodically. They involved Paul interviewing guests. So they never happened because it was dependent upon me by myself. There were 19 episodes. They were great. But it was not until October 2022, episode 20, that we switched to the weekly news format you all know and love. Episode 20 actually aired on October 14, 2022, it covered, this is just a wild walk down memory lane, these topics. It covered the Biden White House's AI Bill of Rights, which I clicked on the link for

and no longer exists, so not surprising. How to build an AI-driven company and Meta's early text-to-video technology. I'm going to go through a few more episodes because we're about to hit a milestone here. So episode 21 talked about AI's impact on sales, funding rounds for Jasper and Stability AI, whether AI would replace workers or help them do their jobs. Episode 22, Adobe's plans for AI-generated images using DALI 2 for product design and Sequoia Capital's bet on generative AI. 23 was Google's treatment of AI-generated content, responsible AI guidelines, and what AI can mean for local news. Finally, this is the calm before the storm. Episode 24, it was on AI improving workplace relationships, open AI funding entrepreneurs, and Google's latest AI research projects. Then we hit episode 25, which aired. That's funny. It's really interesting. Aired on December 8, 2022. This was the launch of ChatGPT. And looking back at the conversation,

I'm going to share some quotes in a bit, Paul, but we were already asking some really interesting questions. Just wild to see that progression of topics. Also, I thought I was missing something. Those first episodes were like 30 minutes long. I was like, where are all the topics? They were like 30 to 40 minutes long. I was like, no way. There's literally three to four to five topics per episode. So I'll stop there. I'm curious, like, what's top of mind for you as you think about where we started, where we're at for episode 250? Yeah, I mean, I think it's like, you know, it's interesting. A lot of times in business, you know, I was at Ohio University last week talking with students, and we'll get into that a little bit later on the education topic. But, you know, one of the things I stress to them is, like, you've got to play the long game and you've got to have conviction about something. And so, you know, in the early days of this podcast, I've told this story before, but the only reason we did this initially was so that it forced me and Mike to talk about this once a week and figure out what it means for our business. Like, we had no idea if anyone would listen.

So me and Mike would stand around the office and, like, we'd share links with each other each week and then you'd never read them. Like, you wouldn't have time to go through it. We were just going about building the business. And then I went to Mike and I was like, listen, man, like we're just our blog is becoming stale. We're just republishing old stuff. There's all this stuff happening every week. You and I know what's going on. We talk about like, why don't we just start doing a weekly where you and I just cover like three big things and then we'll like hit some rapid fire items. And that was the origin of the weekly podcast. We had no idea how to track performance. We didn't know anything about it. We had the podcast set up because I'd done these, like, you know, 19 or whatever it was at the time. But it's like, let's just start talking about AI each week, and it'll be a forcing function for us. So in 2022, so we started this October 22, we had 4,800 downloads for the year. So we did 18 episodes, 4,800 downloads. Man. 2023, we hit 5,000% growth. So we had 262,000 downloads across 50 episodes.

It's 2024. We hit 400,000 downloads across 51 episodes. 2025, you start to see the compounding value. We grew 137% in 2025 to 953,000 downloads. We did 60 episodes last year. And then this year, we're projected to hit about 1.7 million downloads. So it'll be like 80-ish percent growth. And we've done 56 episodes so far. So in addition to the weekly, we've obviously been doing the AI transformation series that Mike does. We do the Ask Me Anything sessions. So we do AI answers. So we've started mixing some stuff in. But, like, the weekly is definitely the core focus of what we've done. And it's just all organic. And it's all because the interest in the topic has expanded. And, you know, we're, you know, to us, it's, like, an amazing privilege to get to do this every week. Like I've said before, like, Mike and I would do this if no one was listening. It's literally just like it's therapy for me to like wrap my brain around it, get a sense of what's actually going on, figure out the most important things.

So I can start my week each week with like, OK, I know what happened last week. I know what it means to us. You know, we try and share what it means to all of you. And like, that's it. Like there's not like some crazy strategy other than just tell the story of A.I. each week and try and pick the things that are most relevant to people. And somehow that's gotten us into the top point five percent of all podcasts in the world. Like, it's just so, yeah, I don't know. Like, I'm really grateful for people listening. I think it's amazing that when we're in person, you know, we get to hear the stories of our listeners because podcasts, like, you have no idea who's listening. You don't get data on them. We don't track anything other than, like, you know, downloads and streams and subscribers. I think our YouTube channel has, like, 35,000 subscribers now. And then I think between Spotify and Apple Podcasts, we're somewhere around, like, 55,000 to 60,000 subscribers just through there. I don't even look at that data. Like, it's like every once in a while I'll pop and I'm like, oh, cool, we got, you know, 35,000 subscribers over here. Yeah. So, yeah, we're just grateful to everybody for being a part of it each week. And we love hearing the stories.

And the one thing I'll ask, I don't really ever ask for anything in this, is leave us a review. Like, if you're a regular listener, even if you just found it for the first time and you find it valuable, that's what keeps the growth going is the reviews on Apple Podcasts and Spotify. So we would really appreciate that if you do have time. drop a rating and a review in there and help other people discover it. So that was cool, though. The walk-down memory lane, Mike, was pretty fascinating. Unreal. It's unreal to revisit these. You know, one quick thing I did want to share, because I'm sure we'll talk about in the whole segment kind of like the fourth anniversary of ChatGPT, but I want to go back. I pulled some quotes. I was just like, what did we say about ChatGPT on Episode 25? And, like, I'll be honest. I was like I'm always – no BS. Like, I'm genuinely always impressed by, like, the takes and stuff you've got. But, like, this one I was reading, I was like, God damn. Like, we predicted the future here because, like, I'm going to read just a couple quick highlights. So interesting observations from episode 25.

This is where we did an extended discussion of, like, what does ChatGPT mean? What is this thing? These are, like, paraphrased from some of the quotes. So first, Paul compared the moment to the iPhone releasing and the app ecosystem. them basically said, quote, we're talking about trillions of dollars in value and wealth creation over the next decade. The real key to me here is the barriers to entry. Think about the tech. Anyone can try it now. Technical skill, no technical skills required to use it. No code. Very interesting observation. I think that I talked about it as an answer engine and asked what improving answers would mean for people who sell expert knowledge. It's not just about the writing. It's about like how many careers, jobs, or functions are essentially being a human answer engine. You talked about how schools should respond. You talked about your daughter's song writing homework at the time. I didn't notice the exact scenario, but you were like, if AI can, you're like, I realized ChatGPT could probably do this after we completed this assignment. Should I teach her how to use it? She was 10 at the time. Yeah, exactly. Right. We both talked

about what happens to SEO and Google's model when people can get answers directly instead of navigating search results. And then finally, Paul, I want to actually mention the final thing you said here. You explained in this segment a shift in the show's tone. So you said, look, we've been very cautious to not create fear. We've made every effort in our messaging and our content to make AI aspirational, inspirational, drive curiosity, let people explore it. But what I would say now is if you haven't picked up on it, we have an increased sense of urgency. It is very, very important that we solve for this. You have to make understanding AI and figuring out how it's going to impact you and your company a top priority in Q1 of 2023. There's no waiting anymore. I think that's the biggest change with what's happened in the last few months. And here we are again saying the same thing at the start. Yeah, it's funny. When you look at it, like if I look back at the things we've gotten kind of right through the last 15 years or so that I've been in this space,

it always just seems so obvious at the time. Like, I don't feel like I'm predicting the future in any way when we say the things we say on the podcast. Because when you just live in this stuff every day, you can just see around the corner, and you all can do it too. Like, there's nothing uniquely special about the ability to see around the corner if you immerse yourself in a topic. And that's just what we've done, like, for 15 years. Like, I've thought deeply about this stuff. And when you do that, it just seems very obvious. And sometimes it takes, you know, the world or different businesses, time to catch up and see it too. But it happens. You know, I remember going back to, you know, 2018, 2019, talking to leaders of journalism and communication schools. And I'm like, hey, the AI is going to be able to write like humans. You should start preparing for this. You should bring in your alumni and have conversation about what this is going to mean when AI can do this. And, you know, they wanted nothing to do with those conversations back then. And in our book in 2022 that came out right before we started this, the weekly model, there was literally a section of the chapter that says, what happens when I can write like humans?

So it was like, it was obvious then, like, but not to everybody because not everybody was paying attention. And so, yeah, hopefully, you know, that's what this podcast gives you the power to do is see around the corner. It's what I always tell people, like, your best chance to thrive in your career and to, you know, build a successful business is to always see around the corner. Like, you've got to get there first. You've got to be ready for what's coming. And hopefully that's like each week, you know, we help you do that a little bit. I love that. All right Before we dive into rapid fire one more thing in addition to the offers that Paul mentioned at the top of the episode This week episode is also brought to you by our upcoming webinar Introduction to AI Score for Business Leaders So if you are responsible for any facet of AI adoption, the question comes up, how do you know where you actually stand, what your people need, and whether you're making progress? So on Friday, November 13th at noon Eastern, Paul will be walking through AI Score, our new change management platform designed to make AI transformation more measurable and actionable.

Paul will give you a look at business AI Score and personal AI Score, which help establish starting points for your organization and its people. Paul will walk through and explain how assessments in AI score can reveal strengths and gaps, guide personalized learning plans, and inform an AI transformation roadmap. Paul is also going to explore how to organize employees into learning cohorts, prioritize education and change management efforts, and track progress over time. So if you lead AI strategy, learning and development, HR, any type of organizational transformation, do not miss this session. If you have not taken AI score yet, no worries. We'll walk you through it. This will help you understand how to connect this free assessment with your own work. So register for free for live or on-demand access by going to SmarterX.ai. You'll see an option right there on the homepage to register for that webinar. We're hearing you explain that. It's like, man, I got a lot of work to do before Friday. Yeah, I was reading through it, so I was like, am I giving you more to do here?

No, seriously. No, it'll be really good. Is that our public description of the webinar? No, it'll be really good. Yeah, I'm going to basically walk through and do some demos. I may actually show some stuff that's coming, too. I haven't publicly talked about some of the features we built but didn't release. And we may, I have to make some decisions by Friday, but we may show some things that are going to be coming as well to really, you know, increase the value that these tools, you know, give to individuals and organizations. So, yeah, I'm really excited about it. I just got a lot of work to do before Friday. All right. So let's dive into some rapid fire topics, Paul. So, you know, we've had some pretty positive for the most part, even the weighty but interesting topics. It would not be a week in AI without some weird and unfortunately kind of negative stuff going on. So this topic is fast developing. NVIDIA CEO Jensen Wong has admitted to having a romantic relationship with Alice Wong, also same last name, not related. But also maybe not her actual name.

Not her actual name, possibly, because she is an executive at Singapore-based NVIDIA customer Megaspeed, but U.S. intelligence officials also believe that she is a Chinese spy. So NVIDIA's board has opened up an investigation, independent investigation, into this incident. The New York Times reports this was an alleged intelligence operation in which Jensen was targeted as they were seeking information about American AI chips and potentially influence. What you would call in this world a honeypot. A honeypot, like in Cold War espionage. Yes, you attract someone to capture them. So this is linked to Chinese intelligence. They're trying to get information about American AI chips allegedly potentially influence NVIDIA's effort to sell advanced chips to China. The Times also reports that Secretary of State Marco Rubio apparently warned senior Trump officials about this relationship in a classified memo this spring.

NVIDIA says independent directors and outside lawyers will oversee its investigation. The board says in a statement that it currently has no reason to believe national security or NVIDIA's secrets were compromised. and continues, at least for right now, to back Wong's leadership. Wong apologized in a message to the company. He said, I'm just going to read a very short excerpt. He said plenty of things in it. I owe my family and all of you an apologies. I was unfaithful to my wife. My actions hurt her and our children. There's no excuse for what I did. I am deeply sorry. Throughout my life, my family has given me love and support. They deserve my honesty and loyalty. Instead, I let them down and damaged the life we have built together. I take full responsibility for what I did and the hurt I caused, and I accept the criticism that comes with it. So, Paul, I'll be honest. I don't know what my bingo card looks like in AI. This was not on it, really. Like, few companies are more important to AI and the AI economy than NVIDIA, and Jensen is, like, one of the most influential figures in AI. Like, how bad is this and how bad is it going to get?

Worse than people can imagine, I think. So, again, I will try and objectively provide some context here. I will tell you right now, like, if someone doesn't have the rights to a book on this in the next, like, week, I will be shocked. Like, somebody's writing this book and movie rights to follow because it's going to get way more bizarre before it gets cleaned up. So, you mentioned Rubio. How about we go back, Mike, to October 9th, 2025? This is 13 months ago. The New York Times. On a humid June night last year, Jensen Wong, the chief executive of NVIDIA, held court with several of his company's major Asian customers at a bar with sweeping views of Taipei. They stood and toasted the booming artificial intelligence industry. Next to the NVIDIA chief was a woman named Huang Li or Alice Wong, an executive of a Singapore-based data center company called Megaspeed,

which was poised to buy $2 billion of NVIDIA AI technology over the next year. Though Ms. Wong and Megaspeed are little-known players in the AI industry, their association with NVIDIA and its chief executive has recently become a preoccupation in Washington. Commerce Department officials have been investigating whether Megaspeed, which has close ties to Chinese tech firms, is helping companies in China sidestep American export restrictions, according to more than a half a dozen current and former officials and other people familiar with the companies who spoke on the condition of anonymity to discuss an examination that is not public. The inquiry, which is active, calls into question how closely NVIDIA is tracking where its AI chips end up and highlights the possibility of American export laws easily being sidestepped. Megaspeed is also facing scrutiny from Singaporean police, who told the New York Times in a statement that they are investigating the company for breaching local law.

Okay, we'll come back to that. So that was October of 25. If they were already tracking this kind of relationship, whether it was romantic or not at the time, they were enjangled. They knew exactly who Megaspeed was and who this lady was, And that was the New York Times with the story, which means people inside the government knew longer. Here's one I found interesting, and I'll kind of come back to this in a minute. I just end up going a little longer than a normal rapid fire, but I think this is very important. So M.G. Stiegler, who's a journalist, he tweeted something this morning that really piqued my interest. He said the whole Jensen left behind for the big Trump China meeting only to hop on the plane last minute after everyone pointed out how weird it was is now about 10,000 times more wild. Was he left off that trip due to the Rubio memo and did the president override it? Keep in mind, the Democrats take power of Congress starting January 1st or 6th or whatever it is.

there is no way Rubio isn't like first up. Like, what did you know? When did you know it? What is happening? And what we're referring to here is a couple months back, Trump went to visit Xi in China, and it was like, why isn't Jensen on this flight? Like the day before, Jensen's not on the list. And then all of a sudden, morning of, Jensen's like on the plane with everybody else. Okay, so then Semaphore has an article. This is from Sunday. There's little precedent in U.S. corporate history for the crisis-facing NVIDIA after reports that Jensen Wong was targeted by a Chinese honey trap operation. The national security implications of the allegation, the chipmaker's founder, CEO's centrality to his company's growth story, and NVIDIA's pivotal place in the AI ecosystem all mean the fall it is likely to stretch further than other cases of executive infidelity. has been inseparable from commercial success, or Jensen has been,

helming its new product presentations, negotiating the loans to customers that have made NVIDIA the central bank of AI, and dealing directly with President Donald Trump and Chinese leader Xi Jinping at a moment when access to the world's most advanced AI chips has been called the defining strategic question facing the two superpowers. On Sunday, Sequoia's Sean McGuire, a Wong supporter, called him irreplaceable for America's AI leadership and basically said, we have to forgive this. Like, we cannot let Jensen go down for this. It sucks that he had an extramarital affair, maybe with a Chinese spy, but, like, all is forgiven. Don't let Jensen go down is basically what people supporting Jensen are saying right now. The company can also expect litigation from shareholders and possible regulatory action. if Wong or the board held back material information from investors, NVIDIA previously denied the allegations as recently as like last Friday, of an affair to media outlets. And Wong is likely to avoid high-profile investigations from,

oh, unlikely to avoid high-profile investigations from Congress, whose incoming Democratic-led majority is suspicious of big tech CEOs and is already preparing a rash of subpoenas for Trump allies. Wong, unlike many other Silicon Valley founders, is not in control of his own fate. This is a really interesting, important point. He has run NVIDIA as if he fully controls it, but he lacks the dual class stock that shields tech founders like Meta's Mark Zuckerberg and Alphabet co-founders Larry Page and Sergey Brin. While his near 4% stake gave him a net worth of $155 billion at Friday's close, it does not protect him should the board or investors try to oust him. For now, that outcome looks unlikely. There is no obvious successor waiting in the wings. Lawmakers are fully aware of NVIDIA's strategic significance to the U.S., and Wall Street knows Wong is a brilliant founder who has made fortunes for his shareholders. I'll end with the same, Mike, is like someone in Washington wanted this out.

Like if you look at the timing, not only of this, but of some of the other leaks that came out over the weekend, this was a coordinated effort by someone in the U.S. government who had this information to put it out to the Wall Street Journal and New York Times who had the story simultaneously. Jensen has made a lot of enemies. While Jensen is like critical to the U.S., the economy, everything, Jensen has been the biggest supporter of chip exports to China, which is not a commonly held position by most people in power. And he has been the guy pushing for exports to China. He has lost that leverage. So, like, the immediate implication of this outside of all the obvious stuff is, like, the most vocal person pushing for coordination with China cannot be in those meetings anymore. So he is going to have to take a back seat not only for that with China, but as an influential figure in Washington. He's going to have to get out of the room right now.

And the downfall of that, like I, so I just, my guess is over the coming months, we're going to hear a whole lot of like really, really wild stuff because the people who know what's been happening for over the last year may not be his allies. And if Jensen has people who are not his allies in the U.S. government right now, including the CIA State Department, it's going to get real uncomfortable for him very soon. You know, in the reporting you had mentioned that he doesn't control stock. I found this interesting as well. From one of the New York Times articles, it said, Mr. Wong, 63, will continue to lead the company during the investigation, the board said. As a co-founder, he has set up a unique structure at NVIDIA where he dictates all aspects of its business while overseeing some 60 people who directly report to him. So not exactly someone you could totally rip out tomorrow. No, not at all. The company. I mean, unreal, it would be chaotic.

Yeah, just real quick for impact. So their stock started the day down at 8%. So I'm now at 10, 15 a.m. Eastern time on Monday, the 9th. So their stock started down 8% while all their tech stocks were up to start the day. They're now sitting here watching it as we've been talking. It's now 3.7%. So, like, initially cratered. Now it's going to come back to earth. Google's up 3%. Meta's up 5%. Amazon's up 5%. SpaceX up 5%. So everybody else is up 5%. They're down 3%. So just give you a sense of initial reaction. It could change by the time you listen to this episode. All right. So next up, there's a few items in AI and education this week that have us thinking about maybe how complicated or how messy things are getting in this space. And none of these are directly related to each other, but we thought these are kind of worth rubbing together and mentioning. So first, Google is changing its AI policies for schools after months of pressure from parents, educators, and lawmakers about how it has pushed the technology into classrooms.

So this backlash started to really intensify in August of this year when Google made Gemini available in classroom to students of all ages at schools that had already permitted its AI tools. Educators complained they were left scrambling to vet the technology and set rules for it. Last month, a coalition of 13 organizations and experts urged the 150 largest U.S. school districts to disable Gemini on school-issued devices. Google is now saying it will turn Gemini and Search off by default for school-managed K-12 student accounts until administrators enable them. Schools can then turn those services on separately, but Google still is not offering Search without its built-in AI features. So districts are pushing back against kind of having AI being given as a choice, but allowing Google search with AI is mandated or they have to leave search totally unavailable. So there's all these kind of complications here, Paul, with Google and how they're rolling AI out in schools.

Meanwhile, we saw an interesting essay in education in our own community that was published by the president of St. Edward's High School in Lakewood, Ohio, which was titled, What Are We Even Doing Here? And it basically posed this big question about high school education, which is what should a high school education provide as AI becomes more capable? So Casey McKenna, who is the president, said knowledge and deep understanding still matter, but the central question of education becomes what can you do with what you know? He goes through in this essay 11 questions for judging whether students are prepared, things like can they identify worthwhile problems, evaluate evidence, make difficult decisions, create something original, collaborate, teach themselves new skills, and learn from failure. but he also asks whether students can decide what is worth doing and discover a purpose beyond personal achievement. And he ends by saying this means we need to rethink homework, grades, testing, and classroom instruction based on what AI is now changing in education. And last but not

least, Paul, you just spoke at your alma mater, Ohio University. So you're fresh off speaking directly to an educational institution. I'd love to maybe hear your thoughts on these different threads, how you're thinking about some of the, for lack of a better word, messiness of AI in education right now? Yeah, so Casey's actually a friend of mine. We've played a lot of basketball together. Ironically, we haven't really talked AI together, which is unusual. Like, normally at basketball, everybody's asking me AI questions and stuff. But, no, I mean, I've known Casey for a long time. Our kids actually graduated from grade school together. So I say that just as, like, I'm not biased in any way about it. It's a really good essay, but I do know him. And St. Edward's, if you're not familiar, is an all-boys school. So just like as we're saying, like young men and stuff, just know it's an all-boys school in the Cleveland area. So I thought it was really good. I thought it was a nice reflection of where a lot of educational leaders are at. So he said, for generations, education has been organized a relatively straightforward premise. Knowledge is valuable because knowledge is scarce.

Schools existed to provide access to information, help students retain that information, and measure how successfully they could demonstrate what they had learned. But what happens when knowledge is no longer scarce? What happens when artificial intelligence can perform many of the tasks we have spent decades teaching students to perform? And then towards the end, he said, we should be willing to challenge our assumptions about homework, assessment, grades, standardized testing, classroom instruction, and even the traditional boundaries between academic subjects. We should ask whether our students are spending enough time creating, collaborating, experimenting, questioning, and solving meaningful problems. We can help them become young men who are intellectually curious, morally grounded, creatively ambitious, emotionally resilient, and capable of meaningful relationships. We can teach them to use technology as a tool rather than allow themselves to become tools of technology. We can challenge them to think independently, act courageously, and recognize their obligations to the people around them. That was really well said. I think it's, you know, again, it's like the very specific, like, 11 questions we should be asking. I think that's very actionable information.

and I'm just like I'm happy to see the conversation moving forward there. The college campus experience, wow. Okay, so that was an amazing opportunity first. There's a wonderful lady, Betsy Lancaster, who created an endowment in honor of her husband. They had met at Ohio University, and so she created an endowment to bring executive speakers to campus and so I had the privilege to go back and do that as part of like a business school program. And so we had about 150 or so students in the room. I did a talk and then we did a Q&A that was student led and they did a great job with that. A couple of students from the Ohio AI Council, Ohio AI group. And so it was amazing and really inspiring. And I had a bunch of other separate meetings throughout the day with everybody. That being said, you also get exposed to the other side of the angst around AI. So like a dry erase board was shared with me, I guess, like pictures of a dry erase board where they had put somewhere on the college campus. I think it was in the library maybe. It just said, what is your approach to AI?

How is it different for coursework, class research versus personal work? And people would just walk up and write whatever they wanted on these boards. I shared these with Mike, so he's seen this. There are probably some positive statements on those boards, but they are heavily weighted against. And I will just read you a few exact quotes from these boards. Never need it. Never use it under any circumstance. Why would I take a forklift to a gym? Meaning why would I let something else do the work for me? Another, AI is an offense to humanity. Another, I hate AI. Another, F AI. Another, AI magnifies inequality and bias. Another, down with the machine, it's stealing our water and making people stupid. So this is all written. These are students who wrote these. And it's written quite largely often in the image you showed me on a public board where I think they probably didn't know what they were getting into asking opinion. That's a pretty benign question about AI.

And it's overwhelmingly negative on that. 95%. Yeah. So knowing that these feelings exist, because I even asked, like, so the one guy was like the co-founder or founder of the Ohio AI group. And I asked him, you know, a student, like, how many people show up to these meetings each month? And he said, oh, you know, like about 20. And I was like, 20? Every kid from the business school should be at these meetings every week. What are we doing here? But now you understand why. It's like they don't like it. So the thing I said to the students on stage, I did like a state of AI kind of talk and like leading in the AI forward future. And the thing I tried to convey to them is like, listen, I understand it. Like I have tremendous empathy for people who don't like it, who are afraid of it, who fear it for different reasons, who think it does bad things to the environment. I completely understand all of that. And I empathize with all of that. I wouldn't be doing my job here today if I didn't tell you that you have to figure out how to balance those feelings with becoming good at using the technology.

Because if you don't, you won't have a job in three years. Like there is nowhere you're going to go work when you leave this school in one year or five years or four years, whatever, where AI isn't infused into that job. And so you have to find a way to understand the technology more deeply. You have to champion the use of it in a responsible way. But you have to, for your own career potential, you have to find ways to embrace it and get really good at it. Because the best job security you're going to have is being the best at AI in whatever it is you're majoring in. Like, whatever you come out of school with, be the best in AI at it. So one other quick note on the college campus thing, Mike, was this Princeton. So the Daily Princetonian. This is from the editorial board. They said this is November 6th, so just on Friday, I think. Last week, Princeton announced a plan to provide paid versions of Chad QPT to all faculty, staff, and students who request it. However, the university's broad allocation of the more basic plus version lacks pedagogic grounding and takes a fundamental reactive approach to technology with uncertain effects.

We think it is clear that AI is not academically necessary to a large portion of the student body and may have destructive consequences. Since that distinction largely depends on what students are studying, we believe that Princeton should implement a department-specific approval process. The research on how AI affects learning is still an emerging field, but there is some evidence to suggest that in a university setting, the use of chatbots can be detrimental to students. Eric Klopfer, an MIT professor who chaired a committee studying the use of AI at the university, told the Washington Post that AI can lead to, quote, the illusion of learning. Through providing universal access to ChatTPT+, the university grants the same baseline direction to computer science majors who often require AI for coding as English majors whose subjective analysis is largely dependent upon their own critical thinking. Although the university does not describe the pilot as an endorsement of chat GPT or use of generative AI tools, indeed, in parentheses, as one of our colleagues pointed out, it seems to run counter to dean of the college,

Michael Gordon's suggested teaching philosophy. Its choice to provide access to all students incorrectly treats AI as a universal student need. I got a call back to the title of the St. Ed's. What are we even doing here? Yeah. This is crazy. Like you don't have an obligation as a university to prepare them for a future of work. English major, not. I don't care. You don't have to use AI in your English writing. That's fine. But to pretend like we don't have an obligation to teach students what it is and how to use it responsibly is a lack of fulfillment of the duties that are expected by the parents and the teachers, like, or the students. Like, we have to prepare them. I don't care if it's just an intro or the icon. Like, it has to be something. We can't just, like, present it as the evil empire, which is basically how this reads. It's like, I don't know. I get these questions are very challenging, and college is a big thing, but I'd be yanking my kid after reading that. It's bad. Yeah.

It's not a good look at all. It's just like, just to quickly call back, though, to what you were saying in the episode 25 at the end about ChatGPT, like, we don't have any time anymore. This is not like, oh, hey, guys, let's get our act together. It's like there's no time left here. This feels like this could have been written four years ago. Yeah, I mean, go back to the pace of change thing. So in the room I spoke at last week, they were freshmen. So four years from now will be November, what, 2030 or whatever. imagine how much will have changed by the time they are seeing this. You were in college when RSI happened. Yeah, when agents took off. Right, right, right. Unbelievable to think about. All right, so our next topic this week, venture firm Menlo Ventures released its fourth annual State of Enterprise AI report this past week. So this report draws on data they collected via survey in September of this year from nearly 400 U.S. enterprise decision makers that Menlo says are responsible for more than

$4 billion in AI spending. So some highlights from this report include, I'm just going to read off some things that jumped out to me. Menlo found that enterprise AI spending has exploded this year. It reached billion annualized in an annualized pace versus billion last year Since late last year agent inflection point which we talked about on this podcast this year Menlo said that agents quote have now grown to become the biggest consumers of AI models They account for 80% of all tokens on OpenRouter, which is a popular platform to use a bunch of AI models. So it's one proxy indicator. That's roughly 7x the human-driven usage on the platform. Menlo estimates Anthropic, OpenAI, and Google command 69% of all deployment dollars. That's up from them commanding 50% last year. That's 2.3x the rest of the market combined. Anthropic and OpenAI account for 86% of enterprise LLM API spend, again, according to the survey of this cohort.

This is up from 67% last year. I found this super interesting. Anthropic share grew from 40% to 55%. Open AI share grew from 27% to 31%. Google's fell from 21% to 9%. I mean, Google's models have sucked this year. So it'll be interesting to watch the next three months now that they have new models. Yeah, this can change very quickly, I think. Companies are also spending far more than they planned. So average AI spending reached 4.7 times last year's level. They had actually planned on spending reaching 1.9 times the previous level, so a bit more than expected. Coding and enterprise chatbots account for 76% of AI application spending in this group. Finally, 55% of incremental AI spending comes from new budgets versus 22% redirected from existing software budgets. So Menlo, which, you know, has many dogs in the site because they invest in software companies, so take it with a grain of salt. They did find pretty limited evidence companies were replacing core systems like Salesforce or Workday, for instance, with AI.

So, Paul, some pretty interesting data here. Obviously, this is just one snapshot of a specific segment of companies, but among them, my gosh, like enterprise AI spend is on a tear. We talked last week about the self-driving company concept from Omjata Replit. But when you understand the potential of that idea of, like, agents, you know, doing a lot of the work and humans kind of orchestrating those agents, you can start to see this path. Like, I think even in the last seven days for me, based on some things, maybe we'll touch on the use case spotlight. I think we'll talk about controlling AI costs I see as next step. I could see it. Like, if I had a week right now to just automate all the things that I now realize I can automate with agents and some of the capabilities we could put in place that we just don't have right now where there's gaps in human capability within our company. Yeah. I could see 10x-ing our spend in 30 days. And they're being worth it. Totally. I mean, these are crazy numbers, but like 50x, 100x.

There's almost like now that I have a vision for how to apply agents across different roles, like a clear vision, there's almost no limitation to what you could apply them to. And they're very token. They take a lot of tokens to perform their tasks. And you can really start to see how Anthropic and others could be spending more on tokens than they are in human capital. And I'm not saying it's a good thing, honestly. Like in some cases, like there's some things that I'm thinking right now and like seeing what's possible where I'm honestly almost like hesitant to even start talking about it publicly because it's starting to feel to me, and I don't know if I said this last week or not, Mike, but it's kind of starting to feel to me like within the AI labs where you have sort of these un-guard railed models and you have like more powerful versions of something that you're just not releasing publicly. There's a part of me that's starting to like really understand how to build that kind of company,

and I'm not so sure I really want to talk about it because I'm not 100% sure how to do it in the most responsible way yet. So I don't know. I think it's kind of like an iterative deployment of, like, knowledge at the moment. And not because I want to, like, not share it with everybody. I just don't know how to share it responsibly. Like, I just, I think it's very possible right now to build a very different kind of company than I thought 30 days ago. And it kind of like it's exciting, but I actually also find it kind of personally scary to know how to do it. All right. Our next topic is we have a few stories. Again, we're going to kind of pull some threads here together about how companies are navigating AI costs. And, again, while these aren't each related, I think each of these highlights an interesting wrinkle in how we're all trying to kind of navigate AI budgets. So first, the information reports LinkedIn has, quote, flattened the cost of running its AI-powered hiring assistant, which uses large language models, to automate candidate search and outreach for recruiters, even as usage of the tool has steadily increased in the past year, end quote.

So basically, to do this, LinkedIn has replaced models from open AI with smaller models it trained and developed in-house that have been vastly cheaper than these off-the-shelf ones. So basically, subbing in a different model into an existing product has kept costs flat while usage has increased. Second, we had a post from Zapier CEO Wade Foster talking about how companies can save a ton of money by using AI only where it's necessary and automating the rest of a business process with deterministic code or steps. He says, as part of this post, we'll link to it in the show notes, go read the whole thing. He says, quote, with Zapier, your agent builds the automation and Zapier runs it. The agent does the part that needs judgment. It reads your request, figures out the steps and sets it up. Then the automation runs on a trigger like a new email with the same steps every time. If a step breaks, Zapier switches back to an agent to figure out the fix. Now, obviously, he's talking his own book here, but he does claim this approach of understanding where agents probably and their probabilistic superpowers kind of fit in.

and where deterministic code and automation fit in, this approach can cut costs by up to 71%. Now, finally, HubSpot's response to a rough earnings report recently includes that they are pursuing or considering a, quote, headless strategy. That's basically letting customers use ChatGPT or Claude to actually analyze their HubSpot data instead of native HubSpot tooling. So HubSpot basically becomes headless as this data repository. So AI becomes the way you operate the software where HubSpot still holds the customer data and powers the work. They're also testing a pricing model that replaces separate subscriptions to different products with a combo of user seats and credits for usage. Credits cover work like sending marketing emails, resolving tickets, enriching customer records. So the bill can increasingly reflect how much work the software performs. So we're not going to get deep into the HubSpot stuff necessarily, Paul. But the point here is like, okay, LinkedIn is cutting costs by tailoring models better for its core use cases.

Zapier says you can cut costs using deterministic code versus probabilistic agents. And third, HubSpot may be becoming headless as a response to AI and maybe potentially charging by how much work gets done in the platform. So like where is the signal in this noise? because how am I supposed to be thinking about saving money and not breaking the bank using AI tools? Yeah, it's interesting. I'm using HubSpot in this way. Increasingly, I don't go into HubSpot because it's connected for us to our LMs. This morning, as an example, I guess I'm preempting my use case spotlight. We have a couple of webinars coming up, and I was like, I don't even know how many people are registered. So usually, I would either message the team, what is it looking like, how many people are registered, keep me updated, or I would, like, and I get a chance, I would go in and try and find that chart somewhere in HubSpot in the app. Right. And I would get annoyed and, like, it just wouldn't happen. Yeah. So, instead, I go to ChatGPT while I'm making my coffee this morning. Hey, at HubSpot, how many people have registered for the November 13th webinar? Gives me the thing.

Great. Okay. How about the November 19th? Gives me the summary. Great. And it links to the list, so I want to go look at it. I said, you know what? This is perfect. Can you send me a report each morning of registration totals and a breakdown of, like, where they're coming from? So, it's great. Schedules for you. It took about 30 seconds. And out of the data, I just scheduled an ongoing report, and it knew to say, I'll run this through November 19th. Let me know if you want me to extend it beyond that kind of thing. Like what else do you need? In that scenario, what else do you need HubSpot for? Nothing. We don't need dashboards. We don't need reports. We don't need breeze. No offense to HubSpot. This is just an example we use. But, like, what do you need it for? Correct. And then, like, you carry this out. Hey, that's looking a little behind goal. let's send an email to anybody who took AI score in the last three weeks and like draft that for me. So there you're making your coffee some more. Okay, that draft looks really good. Let's get that scheduled and like ping, you know, the marketing team and tell them the draft is ready for review. I am like orchestrating a campaign while I'm making my coffee.

So when I was saying before, like, I just have a different vision right now for what a company looks like. very different from 30 days ago. And when you think about the cost, like now it's this like, okay, now I get it. Now we're going to build analysts for everything. And then we're going to build producers that like work with our teams to like create everything. What the hell is that going to cost us? So that leads me to a new role. So again, like a positive here is like what roles are going to exist? AI value engineer, like cost, whatever you want to call it. We're working on a home project right now. And the project manager for the builder, there's a value engineering component to it. It's like, how do we, we know we want to create, how do we maximize the value creation of this process, reduce the cost to get the same output we want to create? So if I think about the future of SmarterX or other companies like us, and I can see this future where all this, okay, if I use ChatGPT, what would it cost? ChatGPT plus HubSpot. okay, now what if I took Zapier and I did all the deterministic steps outside of the LLM for a hundredth of the cost of the LLM?

That's the value engineer role. Somebody needs to do that. And that's like an immediate thing. That role should exist in every company right now, and I don't know of it. I don't know where that role is, but I might be hunting for that role in the near future. Yeah. So if you're, I'm always curious, like if you're HubSpot, for instance, sorry, HubSpot. It's like catching strays over here. You could say it's of any software company. If your hub's thought, are you going to try to start charging me to use my data more aggressively? Even if you do, can I just use browser usage ultimately and tokens go get cheaper over time and it doesn't matter? Or just pay OpenAI to get enough usage or whatever? Yeah, so that's an interesting one. The browser usage might be a workaround in the near term. So they're absolutely going to charge you for the headless software, that you're going to be accessing HubSpot data in ChetVT or Claude. Like, they're going to charge you for that. They're going to meter that.

But if you turn a computer use agent loose and HubSpot doesn't know that a human isn't the one clicking around using it, and you're not using HubSpot's AI tool, you're just using HubSpot standard data, then how would they ever know that it's not a human doing it? I have this question for every software product, by the way, too. Like every software company. How would HubSpot know? Jeez, oh, man. Okay, so if I have 10 licenses to HubSpot for 10 different people doing 10 different roles, but with computer use, I can have a single agent or a string of agents that function in those roles, but they're using computer use, so they're logging in under a single credential, but then doing all the work of the 10 people. How do you charge for that? How do you even know what's happening? Yeah, there may be technical ways to detect if it's, like, agentic.

Body activity. Maybe, but even then, like, I've found that so far I can tell you from personal experiences, is not reliable because I've done this plenty in certain experiments to see what's doable with software platforms and websites. Like there's no like universal ban that they have figured out to figure out to stop this. Maybe that will change. I don't know. And if it changes, it's like, okay, we're probably going to have to evaluate something different because how do you say we're not going to use computer usage if your company is allowing it or something? and you're just like, wait, we're not going to take advantage of this enormous tool if you're okay with it because of our software that wants to make more money. So then you have to charge by outcome or work product. It sounds like that's where they're going in some of this commentary, but I don't understand, like, are we suddenly then going to be charged based on how many emails we create in HubSpot, it sounds like. Okay. Welcome to variable cost hell.

Right. Right, right, right. And then you actually may just accelerate someone building the alternative themselves. It's like, screw it. Like, this is ridiculous. Like, it's, wow. This is messier than I thought, actually. And at no point is this company, well, it may be today, but at no point has this company been invested in or priced as a headless data repository. It's something pre-AI. Right, and we're just talking about hubs, but this is every software. Yeah, this is not hub software, like me coming at hub software. Salesforce, Workday, like, Project Man, everything. Every SaaS product uses. This goes back to the SaaS Pocalypse, which, ironically, I was looking at, like, one of the most popular episodes ever on the podcast for the 250. If I'm not mistaken, the SaaS Pocalypse episode, I think it was, like, 191 maybe, is the most popular episode we ever did. And I was like, February of this year, I think. Nice. That's funny. May have to do part two on that one now that we're thinking about this. Sounds like we'll have more to say on it at some point. All right. Well, let's move on. This is actually kind of related, at least on my end here. It

sounds like on yours as well. We have our AI use case spotlight segment where every week we give you a quick look under the hood at some real AI use cases we're exploring. So, Paul, I'm going to share one real quick that I want to hear maybe a little bit more about how you are approaching things. So, I have talked about in the past using AI to collect voice memo transcripts. I record a fair amount of voice memos on walks and things. I've talked about building a personal contact system that works across different AI tools. So this week, I have been building on those ideas by building a more complete, more code heavy, to Zapier's point, second brain. So here's basically the overall problem. I do a lot of thinking that happens in different places and mediums. So I record voice memos. I talk through ideas with ChatGPT and Quad, both typing and by voice. I write notes and Google Docs. I save a lot of bookmarks on X of ideas I'm interested in. So I wanted to bring all this together better so AI could help me search and make use of it. So I actually used

Codex to build a searchable database on one of my computers. So it uses SQLite, which is basically just a way to create a structured database in a single file. So each record keeps the original text of a voice memo, a chat, where it came from, all the relevant stuff, basically a big old database. And for AI conversations, it also preserves who said what, like me or the AI, which platform I was on. So I started basically with one source. Voice memos were the kind of biggest immediate needs. I had codex write code to pull Apple's existing transcripts into the database. We set up automatic updates. I recorded a test memo to check that this works. So that's all code. None of that has anything to do outside of using codex to build it with AI. So there's no usage limits, just local code running on your computer. Then I added sources one at a time, X bookmarks, chat, GPT, and quad conversations, selected personal writing in Google Docs, even photographs on my phone of handwritten notes that AI helps transcribe. So there's some AI

getting injected, but still a lot of this is just scheduled code. So I wanted all the repeatable collection of this material handled by code wherever possible. I literally screenshotted that Zapier post and dropped it in. It was like, I want to do this basically. So, you know, AI handles things still like reading handwritten pages and reviewing which parts of a document belong in the database. And then finally, I can have codecs search that archive, pull up relevant source material. I then can use AI on top of this and just point it at this great database that's auto-updated and maintained and say like, hey, like what are some themes that are coming out recently in my writing. What about that idea I had like two weeks ago? Whatever. So this is still a work in progress, but it's pretty cool to be able to have this actual database. There's code in a private GitHub repository now that I finally have, which, you know, years too late. Now, finally here, you could certainly point AI at this stuff and just ask questions, right? Like, there's no problem trying to do that to start, which is what I've kind of done in the past.

I just wanted to challenge myself, again, kind of like what we've been talking about, to incorporate far more deterministic code into the process for reliability, efficiency, and cost reasons. I'm not just ripping through usage trying to point codex on like Astra Max at like my Google Docs, right? So super informative, super cool exercise to go through and felt very empowering. It works really well so far. It's wild. Yeah, mine was pretty straightforward. I feel like I say this every week. Like you give this like insane, like, wow, okay. That was an advanced master class and something. And then I'm like, yeah, I asked Dots what to do with it. No, I actually did. So Dots is in my personal Chachabiti account. And I started playing around with it, the new personal agent. If you haven't, you know, been following along with Dots. I mean, mine bluey. Cute little circle. And so what I found was like there was a specific thing I used it for in my personal life actually. But then it started, like, proactively saying, hey, what can I help you with? Like, you were looking into this last week.

Is this, you still need help with this? And you just immediately start to see, like, oh, my gosh, like, once there's proactive personal agents that are allowed to have access to your business data. Yeah. The amount of, like, you know, this, imagine, like, I program or create an analyst that goes and deterministically does this thing every day for me. Analyze the segment of people who've registered for this webinar. Give me that report. Tell me what they're doing. Just kind of like it does a thing I've asked it to do. But instead, imagine like agents that are just always watching sets of data or business units or production, whatever. And they can be proactively engaging with people throughout the day and say, hey, you want me to look into this or I noticed this fell or like whatever. It's just like the idea of proactive agents is where my brain is like really starting to think very aggressively about and how much that would change the way jobs function today. And that's a little bit of stuff I was saying. Like I'm a little actually afraid of the direction because you can really start to see more and more how a lot of things that people do today won't be needed.

And what does that mean? What does that mean for us? What does it mean for other companies? And I think this gets to that seeing around the corner thing. Like, you can't unsee it. And I kind of feel like I do today with maybe how I did with ChatGVT in 2022. You just, everything looks different to me right now. Every business, every person, every career path, like, it just looks fundamentally different based on what's available today and how good it's going to get over the next, like, 12 months. I just can't look at 2027 the same right now. So, yeah. And it's a monocuous, like, use case. It's like, I'm trying to find this thing, and then you're like, it finds the thing, and you're like, oh, huh, okay. There's a whole lot of things, like this one little thing I just asked you to do that would fundamentally change how I run my company and how I staff my company and what those people do within my company. And, yeah, like I said, you can't unsee the future once you see it. And I just, it's running through my brain a lot right now.

Yeah, I know some of the things you're talking about. I suspect others, but it feels to me, I don't think it's crazy to say this is far bigger than ChatGPT. Oh, God. The ChatGPT moment, actually. Totally. And kind of, not like one technology per se, but the implications of, I think, what you're talking about. Well, and I think, like, for four years, we've let individuals figure out AI systems. Yeah. My concern slash excitement is if you centralize this and you just go do this for people, then the self-driving companies that Amjad proposed could happen way, way sooner. And that's why I'm like, I'm not sure that the economy is ready for, if you have the right people within an organization centralizing this process, things could move really fast. Yeah. All right. Our final segment this week is our AI product and funding updates. I'm going to go through very quickly a handful of updates we've got here,

and we'll wrap up episode 250. So first up, Thinking Machines Lab, the AI startup led by former OpenAI CTO Mira Mirati, is nearing a $5 billion round led by SL at a $40 billion valuation, according to Business Insider. OpenAI has a few updates. First, they launched Images 2.6 across all chat, GPT and codex subscription tiers in its API, adding animated GIFs, more consistent image edits, and generation, the company says, is 50% faster. OpenAI says its experimental AI security pipeline has developed exploits and candidate fixes for hundreds of unique Linux laws that could let a local user gain administrator access. So basically, they're using AI to fix cybersecurity issues. This particular project cost about $1.2 million in API usage. OpenAI also introduced something called LifeVal, LifeVal, a benchmark measuring how well AI completes everyday tasks, such as budgeting, shopping, and finding childcare,

built using time-use studies and a survey of nearly 4,000 people. Anthropic began rolling out personal finance in Claude in beta for US Pro, Max, and Ultra subscribers. This provides read-only access to connect bank and card accounts so users can analyze spending, flag unusual changes, and build financial dashboards. Apple has opened Siri AI to all developers in its latest operating system betas, allowing apps to expose actions such as booking reservations and combine multiple actions into skills. This is ahead of a planned later customer rollout. I have been testing the new Siri AI on my phone. It has gotten smarter. Great. It is not computer use yet, but that is also where they're going. There was an article that just came out about that. We'll talk about it next week, maybe more in detail. They know they're falling behind on personal agents, and it sounds like the CEO has a major focus on that. You can start to see it in Siri now. Cool. That's exciting. And then finally, Replit and Cognition publish security guidance

recommending controlled cloud environments for AI agents, permissions limited to each task, and removing production credentials from developers' laptops. So like everything in here, we will link this. But basically, they have pushed stronger agent security and are offering some guidance around that from some companies on the frontier of all this. So one final reminder, go to smarterx.ai forward slash pulse. Take this week's survey. We would love to hear from you. And with that, Paul, thank you. Happy Episode 250. Yeah, you too, man. Great work. Keep going. We have another 250 in this. I think so. Yeah, and again, academy.smarterex.ai. I use that pod 250. I think a lot of the things we talked about today, Mike, is sort of where I envision Academy going. It's like this agentic frontier, and we've got to figure out what this looks like together, which means we've got to kind of probably build it together and try and figure out how to do it in the most responsible way possible. So, yeah, if you want to be a part of all that, now is a great time to join AI Academy.

All right, thanks, everyone, for being with us. We really appreciate it. It's a privilege to get to do this with you all each week. and yeah, we'll see what next week brings us. It's going to be an interesting one. Thanks, Mike. Thanks, Paul. Thanks for listening to the Artificial Intelligence Show. Visit smarterx.ai to continue on your AI learning journey and join more than 100,000 professionals and business leaders who have subscribed to our weekly newsletters, downloaded AI blueprints, attended virtual and in-person events, taken online AI courses and earned professional certificates from our AI academy and engaged in the SmarterX Slack community. Until next time, stay curious and explore AI.