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The Artificial Intelligence Show
#210: Stanford 2026 AI Index, OpenAI Internal Shakeups, What Agents Mean for Business, Claude Design & Dwarkesh vs. Jensen
01:37:14
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The brightest minds in AI don't necessarily agree on some of the very fundamental things about the industry, about, like definitions of AGI, about whether or not large language models are the path forward and are going to get us where we want to go or if we need world models. Like John Lecun would said, whether or not, you know, jobs are going to go away or more jobs are going to be created, whether we should sell chips to China, like nobody knows. But the fact that we're in a place in society where we're having these conversations openly to me is fantastic. Welcome to the Artificial Intelligence show, the podcast that helps your business grow smarter by making AI approachable and actionable. My name is Paul Raitzer. I'm the founder and CEO of SmartRx and marketing AI institute and I'm your host. Each week I'm joined by my co host and SmartRx 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 210 of the Artificial Intelligence Show. I'm your host Paul Raitzer along with my co host Mike Caput. Going a little makeshift this week. So I have been traveling. I actually was in Washington D.C. with my daughter's class the last three days. I got back late Sunday night then. We normally record this on Monday mornings, but I happen to be flying to Las Vegas this morning for the Google Next conference this week. So we are doing this at an unusual time. I honestly am not even sure what time it is right now. I think it's what year?
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5:30, Mike, it's 5:30 Eastern right now.
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Okay, so it is end of the day Eastern time. I am all sorts of out of sorts here in terms of like what is going on. But as I was doing the final prep, you know, because why find the plane of course isn't going to work but you know, it ends up as we're delaying this, we get the last minute notification that Tim cooks out as CEO of Apple, which is like shows you how real time this is because Mike and I are scrambling literally 10 minutes ago, like what is going on? What did they say? And as of 5.30pm on Monday the 20th they haven't said much. So I'm sure by the time this airs you'll have heard this news and it'll be all over. And when Wall street opens, you know, when the stock market opens Tuesday morning, I would imagine we're going to see some movement one way or the other. So more to come on the Tim Cook thing, I guess, but that's how real time this is. And then Mike and I were joking. If any of you ever have to do, like, virtual meetings when you're traveling or do podcasts or things like that when you're traveling, we all know the makeshift setups that we have to do. And I have to admit, I have a first in this case. I have a recycling can on my lap that my microphone is sitting on top of, and I actually had to take a picture of it so I could show Mike what in the world I'm doing here. So I'm overlooking the Vegas airport. We're going to make this work one way or the other. We got to figure out next week, Mike, because you and I are both traveling on Monday. We need to finish that conversation. I'm also out next Monday. All right, so anyway, episode 210, as I said, this is pretty real time for us. We if you're new to the podcast, because we get new listeners every week, this is kind of how we roll. Like, we do this all in one take, all the time. I think in the entire history of the podcast, we've had one or two stops, and it was usually for coughing fits. So we just do this. Claire on our team edits it and it drops the next morning. So here we go, Mike. All right, so this episode is brought to us by AI Academy by SmartRx, which helps individuals and businesses accelerate their AI literacy and transformation through personalized learning journeys and an AI powered learning platform. New educational content is added weekly so you're always up to date with the latest AI trends and technologies. The AI for Industries collection features seven core series and certificates designed to jumpstart AI understanding and adoption. Those seven series are professional services, healthcare software and technology, insurance, financial services, retail, and CPG and manufacturing, which just dropped last Friday. Mike, if I'm correct. Okay, correct. And then Mike's actually going to give us a little rundown, kind of some key takeaways from the AI for Manufacturing series toward the end today. So these series are an ideal launch pad for organizations that want to level up their teams and accelerate AI adoption and impact. And today we're going to share some insights. As I said, with AI for Mart Manufacturing course series, you can get individual or business accounts. Both plans are available now. You can go to Academy SmartRx AI to learn more. Pricing is totally transparent, so when you go there, you can see the exact pricing for both individual and business plans. So again, go to Academy SmartRx AI to learn more about that. Okay, we've, I, I don't know how many models are going to drop by the time we do this. This comes out on Tuesday. But I, we did have one already today. We might even get into that one until next week. But there is, there's rumors that more models are coming and then like I said, I'm at the Google Next conference. While I don't necessarily expect any major models to necessarily drop this week, that's more usually the I O conference where they would do that. I do expect lots of announcements from Google, so we'll definitely have lots to talk about on next week's episode when we start getting into the updates from Google. But all right, we've got a big new report to talk about to start off though.
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Mike yes, we do.
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Paul.
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So Stanford's Institute for human centered AI just released their 2026 AI index report. This is an annual benchmark where they basically try to map the entire state of AI across research, performance, investment, jobs, public opinion, energy and policy. And boy, I, you know, I haven't looked at every report out there, but this might be the biggest one. It weighs in at literally over 400 pages this year. I, I think that's maybe the longest it's been. It is worth at least skimming or dropping into Notebook LM because it does represent kind of one of the top industry reports out there for getting a perspective on what's happening at a macro level in AI. So some highlights that jumped out to us and obviously we're not going to be able to cover all 400 pages, but a couple things that are important to note. So first, they find that the US China AI performance gap has effectively closed. So US and Chinese models have been trading the top performance ranking multiple times since early 2025. Deep seq R1 briefly matched the best US model in February 2025. And as of March 2026, Anthropic's top model leads top Chinese models by just 2.7%. China is now also ahead of the US in, you know, study publication, research, publication volume citations, patent output and industrial robot installations. They also found that AI capabilities are outpacing benchmarks, but still suffer from this kind of term we use sometimes in AI called jagged intelligence. So what that means is frontier models now meet or exceed human baselines on PhD level science questions, multimodal reasoning and competition mathematics. And they're basically saturating evals that have been intended to be challenging for years, but they're doing it in a matter of months. So for instance, the SWE bench verified coding test on that one performance skyrocketed from roughly 60% to near 100% in a single year. However, while these models can do things like win math Olympiads, they also have big blind spots. They still fail to do things like read the time correctly about half the time. They also found model convergence is basically happening at the frontier. So performance among the very best models is becoming basically indistinguishable. So the top four models on the arena leaderboard for Manthropic XAI, Google and OpenAI are now clustered within just 25 Elo points of each other, which is how they measure their effectiveness. So raw capability is not really serving right now as a clear differentiator. And that's why the competitive pressure is rapidly shifting towards cost, reliability and domain specific performance. So a few more things they highlighted. Generative AI adoption is outpacing, if you can believe it, the adoption rate of the Internet. So generative AI reached 53% global population adoption within three years, which is faster than either the personal computer or the Internet. At the same time, AI's physical and environmental footprint seems to be expanding drastically. For instance, GRO4's training emissions equaled about 72,000 tons of CO2 equivalent, which is comparable to driving 17,000 cars for a year. And a AI data center power capacity reached almost 30 gigawatts, which is comparable to the peak demand of the entire state of New York. On top of all this, global corporate AI investment more than doubled in 2025 to 581.7 billion billion. Private investment reached 344.7 billion as well. Couple final points here, more related to younger people. AI's labor market effects are hitting junior workers hard. Employment for software developers age 22 to 25 dropped nearly 20% since 2024. That pattern was mirrored in customer service and other highly AI exposed roles. And the public and AI experts as a result of this, view the future of the technology quite differently. 73% of AI experts expect AI to have a positive impact on how people do their jobs. That's compared to just 23% of the US public. So quite a bit of a 50 point gap. Furthermore, 64% of Americans expect AI to lead to fewer jobs. And the US reports the lowest trust, 31% in its own government to regulate AI responsibly out of any country surveyed. And lastly, four out of five US high school and college students now use generative AI for schoolwork, but formal school guidance is failing to keep up. Only half of middle and high schools have AI policies in place. A mere 6% of teachers report that those policies are clear. So, Paul, I mean, lots of different threads to pull on here, but I will say I just look at this data and I find it very hard to take seriously the argument that AI is overhyped at this point.
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There's a ton of good stuff in here and like you said, it is massive. I can't imagine anyone's actually going to sit down and read this whole thing. Right. But I thought they did a nice job of actually breaking it into chapters. And when you go to the site and we'll put the links into the show notes, you can just go by chapter. And that's actually how I processed most of it. I didn't have time to read the whole thing. But the nine chapters are R and D, so Research and development, Technical performance, responsible AI, Economy, Science, Medicine, Education, policy and Governance, and Public opinion. So just even that alone, Mike starts to show the breadth of the impact AI is having, that we have all these different areas we have to look at at a macro level of what is going on. They do a great job with the research, obviously, they have incredible research partners and some amazing people that are contributing to this and writing this report. So one of the ways you can do it is literally just go chapter by chapter on the website and it actually calls out, usually it's between five and 10 highlights from each of those chapters. And so that's a great way to go through and do it. And so like you did, Mike, I kind of went through and pulled out the ones that were jumping out to me that might be worth, like a little bit more context. And I'll just highlight a couple of additional ones. So one, you might have touched a little bit on this one, but this idea of like the evals and the benchmarks that we normally use to measure progress. And what jumped out to me here is this continued need that we talk a lot about for companies and individuals to have their own evaluations of these models. You can't rely on these reports that are published by the AI Frontier Labs or even these organizations that are cropping up that are specifically designed to just measure the intelligence of these models. You have to have the standard tasks or workflows that you perform internally. And so when a new model comes out, you can determine the impact of it. So, like, I mean, as Mike was going through this, I was glancing down to see if there's any updates on the Tim Cook thing. And I Saw somebody post something about did they make an Update to Claude 4.7 this morning? It's like sassier than it was last week and I can't use it again. And so like the models truly do change all the time. And when they do these updates, they should incrementally be better. But sometimes something gets broken or something changes in the process. And so with the rate of change and the rate of model releases, it really is becoming more and more important. And it's actually. I flagged something Mike will talk about on next week's episode. Around these evals, I actually saw a new company or tool that was created. Oh, was it zapier? I think it was zapier. So again, we'll talk about it next week because I haven't had time to prep on it. But there was a new thing that was more about actual work being done in developing evals. So eval is really important. They highlighted and I'm under the technical performance section right now, video generation models are starting to capture how objects behave, not just produce realistic content. This goes back to the thing Mike and I have talked a lot about over the last six months or so. This idea of understanding the physics of the world and being able to develop world models, which is going to be very, very important to especially robotics, but video generation, interactive game generation, things like that. So we're seeing progress being made. You'd highlighted a number of the ones in the economy side, Mike, that I was looking at. Like the labor market effects are showing up unevenly. It does seem to be affecting the youngest workers most at this point. And then one third of organizations expect AI to reduce their workforce in the coming year, even though we're not necessarily seeing it in the jobs data yet. I think you'd mentioned this one, Mike, about the four out of five high school and college students now use AI for schoolwork. But the school policies aren't keeping up. So that's a major problem. Another one I thought was interesting under policy and governance was US Public investment in AI remains modest compared to private sector. And the reason I flagged this one, Mike, is because I think this is about to change dramatically. Yeah, I don't know what the numbers, where the numbers are going to come out to, but we'll have to go back. Mike, maybe you can find the show notes. There was an episode where I did a breakdown of the Manhattan Project. This is probably last year where I actually broke down like how much of GDP was going to the Manhattan Project back in the day. And at the time I Said we were going to need something like that from the government. And I do think what's about to happen is going to be the government subsidizing the build out of energy, the build out of data centers, the building of chips. I think they're going to start to get into subsidizing the training and education of American workforces. I think that's going to have to happen. And so I could see sometime in the next one to two years where the government is spending well over a trillion dollars a year on AI infrastructure and talent and all the hard and soft costs associated with it if they don't basically take over one of the labs. So I'm not a proponent of that happening. But I think we're just getting to the point where AI is so fundamental to the future of democracy, future of the Republic, that I don't think they're going to be able to just sit back and hope that the three to five private companies figure this all out. I think they're going to go. I just don't know exactly what that looks like yet. And then under public opinion, the AI optimism is rising, but so is anxiety. It's like, you know, generally people are pretty excited about it, but they're also really not sure. There was one stat that the one thing that jumped on me I thought was weird, it was like people, you know, and their confidence of understanding AI was like 67% of people said they're confident in their understanding of AI. I was like, okay, like you could give me the hundred smartest CEOs in the world and I'm not sure I'd get the 67% who actually understand AI. So I'm not sure who exactly. They were asking that question of different students maybe. Yeah, like it's either they're either really overconfident in their ability or they're asking technical people, because that is not by any means a representative of the average worker or business leader. Two thirds of Americans expecting AI to lead to fewer jobs over the next 20 years, which I thought was a really weird timeline to throw into this. It's like what, two years maybe, but 20 years, who knows? Like, that's crazy. And then one other one, the companionship thing, they talked about it still being niche, but that I was just noting. I don't think that's going to be niche for long. Like they're still treating this, people using this in a relationship or companionship. I feel like that is just skyrocketing beneath our eyes, basically. Like it's probably happening and most people just aren't aware it's going on. And then the last two was the United States reported the lowest trust in its government to regulate AI responsibly. That is accurately placed mistrust, I would say. I, I don't think the US government is currently on a path to responsibly regulate AI. And then across all 50 states, concerns about too little AI regulation outweighs concerns about too much. And then just a final note on their methodology, I thought it was interesting. They said AI Index is written by a team of human researchers and they do a nice job of highlighting those people up front. And they said the authors use ChatGPT and Claude to help refine and copy edit drafts. And then all images in the publication were generated with AI by Johanna Friedman. So they actually recognized the human, but said, hey, we did this with AI. And then they said the two specific models that they used. And then the final thing is public data and tools. One of the nice things about this report is they kind of open source a lot of this, so you can actually go in, they have a Google Drive link to get all the public data and the images that they created, all the charts, and you can actually use all of those things. So if you're doing presentations on this stuff or business research and, and they just tell you how to properly cite the report, and then they also have a global AI vibrancy tool that compares the ecosystem across 36 countries. So I look, you know, you and I, Mike, love a good research report. And this is extremely well done, very professionally done. It's the kind of research we're happy to shine a spotlight on on this podcast, because I think there's a lot of really good information in here. And they obviously put a ton of effort and it was a very thoughtful presentation of the report. But it's their seventh year doing it, I think, so it's like they've kind of got it down to a science at this point.
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All right, so next up, big thing happening this week is that OpenAI is basically going through a very public reorg or a continuing reorg. And this week kind of brought together a few separate threads that tell this overall story of the company, basically speed running through some serious growing pain. So first, we had reported last week on some executive level shakeups. We've got a few more this week. So Kevin Weil, OpenAI's former chief product officer, has announced he is leaving. He joined OpenAI in 2024. He became CPO. Then he stepped out recently to launch OpenAI for science. On the same day, Bill Peebles, who led OpenAI Sora short form video app, also announced his exit. We have previously talked about OpenAI basically rolling Sora into Chat GPT slash, you know, sunsetting it as its own thing. So that kind of makes perfect sense. Peoples might be out. OpenAI says it's also now decentralizing that OpenAI for Science initiative back into core research teams. And so the idea here that we talked about a couple weeks in a row is OpenAI is shedding what their leadership internally calls these like side quests. So things that are just no longer a fit for the company's core mission as they pivot especially hard towards the enterprise. Second, there's some internal drama around the company's IPO timing. The Wall Street Journal reported Sam Altman wants an IPO as early as Q4 2026. CFO Sarah Fryer has told colleagues the company may not actually be ready. And this year Fryer has raised concerns that OpenAI's financial exposure, given its computing infrastructure spending, is a problem because that could reach $121 billion in 2028 alone. Sources also have said Sam Altman has excluded Fryer from investor conversations and major financial decisions, unfortunately a pattern of behavior he has exhibited in some of the other articles we've discussed. Third, there's some internal drama, or at least competitive animosity towards Anthropic. The Verge got its hands on a leaked internal memo from OpenAI's chief revenue officer, Denise Dresser, who sent this to employees, basically taking direct shots at Anthropic. Dresser accuses them of inflating their run rate by roughly $8 billion through what she calls accounting treatment that makes revenue look bigger than it is, specifically by grossing up revenue sharing agreements with Google and Amazon rather than using net figures. She also writes that Anthropic is built on fear restriction and the idea that a small group of elites should control AI. And last but not least, there's also external drama around the company's valuation. The Financial Times reports that OpenAI investors are starting to question the current $852 billion valuation as the company shifts towards enterprise. So the idea here is that all these recent deals and abandoned projects are all part of defending ChatGPT's consumer dominance while going after Anthropic and higher margin corporate markets. So the issue here is that one early backer told Financial Times that despite Chat GPT having almost a billion users, OpenAI is a deeply unfocused company. So Paul, this also comes as OpenAI and Sam Altman are kind of sounding the alarm on AI's possible impact on jobs. They even released an AI transformation plan for jobs this week. But honestly, I don't know. Do they have enough drama on their plates without all that? Like, seems like they're really dealing with a lot of these core issues to the business.
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And the musk trial I think is. Yeah, later this month soon. Yeah, yeah, yeah. And Fiji Simu is still out like the CEO of AGI Deployment. I don't remember when exactly they changed that title. It was F Applications. Right. And then updated it recently to AGI. Yeah. So she's on medical leave. Yeah. You know, I saw. I don't remember what day it was when those resignations started happening, but they were back to back on my X feed and I took a screenshot and I put it in our sandbox for the podcast and I was like. And then like an hour later, one more came and then like, you know, the articles started pouring in. So it does seem to just be a byproduct. Obviously reorgs happen a lot in the AI lab space. Well, at least, least here Anthropic seems to be pretty stable. But there's been a lot of moving pieces at OpenAI for a number of years now. This one seems pretty significant, but it does seem like the byproduct of this renewed focus and the need to constrain compute to the newer models. There's talk that we're going to get their Spud model. It's been kind of codenamed Spud, which I think is 5.5. That that is lots of increasing rumors that that is coming this week. So. Yeah, I don't know, it's just a really interesting time. I. I don't. I try not to over like speculate too often on what could happen with these things. I usually constrain it to like internal conversations, but I will throw one out here that I'll just say publicly. The most logical thing that happens to me with OpenAI is Brett Taylor takes over as the CEO before the end of the year. So what I mean by that is Sam Altman, through his own comments in the media, does not seem enthusiastic about the idea of being the CEO of OpenAI when it's a publicly traded company. There's lots of things going on. It's a very, very stressful job. I can't even imagine what Sam deals with personally and professionally. Obviously he's had issues recently with attacks on his home, attacks on the office, personal threats to his safety, his family's safety, and I just don't ever get a sense when I listen to Sam talk anymore that he's having fun like that this is what he wanted basically. And so when you start adding all those things up, you just start to look around be like I just can't see him long term doing this. Like I could see him being the, the face of the company like for fundraising and the infrastructure and things like that but at some point I, I just don't know that he doesn't burn out from being the real CEO day to day of the company and having to deal with this. And Brett Taylor is just the most obvious thing because Brett's built Sierra, an agent company built on top of chat GPT capabilities. He's the chairman of the board, he's a co CEO of Salesforce. Prior to this he was a former CTO of Facebook, he started his career at Google, he co created Google Maps. Like Brett is a legend in Silicon Valley circles. And if you were going to look and say, well who should be running this company and maybe would want to be running a company like this into the future when we're a multi trillion dollar company again, I just don't know that it's going to be Sam and Brett seems like a really obvious choice. So I would even say this is a prediction. I would say I'm more like public narrative that I would usually reserve for conversations Mike and I have when I'm just throwing things around. Every once in a while something just seems so obvious that it's worth at least like sharing with our audience of like just something to think about again. If I had to put odds on is Sam the CEO of this company 12 months from now? I don't think he would be, you know, he certainly could be but I just, I'm starting to see more and more signs that I don't, I don't think it makes sense for him to be in that role. But we'll see. I have no inside information on that whatsoever. There's no sources I'm getting this from. It's literally just piecing stuff together. The jobs report Mike, I thought was pretty interesting. So kind of along the lines of the Stanford one. I liked the effort they made here. So you know, they've been putting more and more work into their thoughts around the impact AI is going to have on jobs, trying to do more, to project out. And this is what we've been calling for for the last two years was like we should at least be studying this. We should at least be considering that maybe this doesn't go smoothly in the near term and that we could lose a bunch of jobs. But the main way that it's often talked about is exposure levels. So you know, it's how I built jobs GPT. You look at the exposure of tasks that make up jobs and you say, can AI do this thing? But what they're looking at is, goes, you know, kind of layers on top of exposure. And they look at demand elasticity, which is something I talk a lot about on the podc, and then human necessity, which is an interesting one. So in this report they, they say they look at 900 occupations and which they're claiming is 153 million jobs or 99.7% of US employment. Now that would have to include some part time jobs because the full time jobs in the US is around 136 million. So I'm not sure exactly where all those are coming from, but they're looking at a bunch of jobs and then they're basically trying to figure out like how exposed are these occupations and then does that exposure mean an impact on actual work? And so they, you know, they look at this thing like the demand elasticity. We're saying how much demand changes when price changes and is it connected to productivity and the impact on employment? So they're trying to look at, you know, in a simple way, like I think about this, okay, so AI makes us cheaper to deliver legal services, for example, because now we can use AI to do it, we can be way more productive. Does the demand for legal services increase enough to justify that? We keep hiring more and more people. So like right now, the argument a lot of these leaders are making that we talked about in last week's episode is software developers and engineers. So what they're saying is the demand for software is so significant that as the cost to create that software goes down, yes, we'll need fewer people to create the same amount of software, but we're just going to keep seeing more demand for software, so we'll keep hiring more people. And my argument has been all along that doesn't necessarily translate over to industries or professions where there just isn't a ton of demand to see. And so, you know, if you're in a company where the demand isn't going to grow no matter what happens to your price, then you're not going to need as many people. So, so that's what this report tries to take a look at. They're basically looking at this job transitions framework they created and they're asking four fundamental questions. Can AI Do a meaningful share of the work. If AI lowers the effective cost of providing the service or the product, is demand likely to expand enough to absorb the productivity gain? Meaning cost goes down. Is there enough demand for us to still need all these people? The third question is if it can. For the remaining tasks, is a person still central to the work's delivery, judgment, accountability or physical execution? So they're not just looking at knowledge work tasks, they're looking at all tasks. And then they're saying is AI already being used meaningfully for these tasks? So then they use this approach to Critically assess these 157 million jobs or 153 million jobs. And then they break the occupations into four different possible outcomes. So one is jobs with less immediate change are those where the current combination of exposure, necessity and inelasticity does not yet point clearly to one dominant near term outcome. So that is Almost half, it's 46% or 70 million they're saying see less immediate change. Then they go into jobs that will reorganize, that have high exposure and strong human necessity, but demand is not elastic enough to absorb the productivity gains. That's 25% they say, and then 18% is jobs at higher automation risk have high exposure, weak human necessity and insufficient or ambiguous demand offset. So that's 27 million jobs in, in their breakdown that are at high automation risk. Meaning these are the ones that could go away the fastest. And then jobs that will. The fourth category is jobs that will grow with AI, have high exposure, but enough demand response to lower the cost may increase utilization, affordability, access and quality adjusted output. But then one other note, they did say our ability to forecast far into the future is limited and it is very difficult to project how much labor, how much the labor market might evolve over the long term on a shorter horizon. This framework should help us envision how the labor market may evolve and what policy responses we can consider and implement to facilitate a smoother and more people centered AI transition. So again, my, my point with kind of highlighting some of this is I'm really happy that they're analyzing this at a deeper level. I won't, I would love to see all labs doing this. Anthropic has been actually leading the way on this. So they're certainly doing this already. This is the kind of research that needs to be happening at all the different labs. And it's what the kind of stuff that economists should be, you know, aggressively pushing on. But even with all this analysis they did, they basically kind of came out to like, we really just don't know. Like, right. If you want like the too long don't read version of this. We don't actually know, but we don't think just exposure alone is enough. Which they're 100 right on. So the need for humans in the loop for, you know, interpersonal communication between humans is critical. And then the need for understanding demand. So they. A couple of examples I'll finish with. They said the least elastic occupations include firefighters and home health aides. What they mean by that is that is needed no matter what the cost. Like we have to have it somewhat. Elastic occupations are like physical therapists, editors, dental hygienists. You might just go less if it costs more. But if the cost goes down, do I buy more of it if the cost goes down? And then the final. Some of the most elastic occupations include graphic designers and software developers. So yeah, it's really interesting stuff. You're interested in the economic side of all this. It's a good read, good thing to throw in Notebook lm. But I think my main takeaway again is a lot of people are starting to think about this at a deeper level, which is great. And no one has an answer. Like no one can say any. What I always say is anyone who tells you confidently, like without debate that they know what's going to happen to jobs in the next 12 years is either misleading themselves or intentionally. There's a reason they have something to gain by you believing that. We try and kind of sit in the middle and saying, listen, I hope that that's what happens, that it doesn't affect jobs. But I'm also trying to be very realistic that every scenario I look at where I think about this and say, yeah, but what about if demand doesn't go up for a company or a role? How could you possibly create more jobs that fast? And that's the part I just no one has the answer to. So, yeah, I think that's the big takeaway. Lots of change at OpenAI, lots more to come. The innovation is going to keep moving forward no matter what. And they're starting to kind of branch out and trying to understand the impact that their models are creating and stuff they've thought about for a while. I mean, they did that GPTs are GPT study back in 2023 when GPT4 first came out. So it's not like this is new. It's like the first time they're thinking about it, but they're starting to expand the way they look at it.
- [34:27]
B
All right, so our third big topic this week is we saw several stories that kind of weave together to paint a picture of the implications that AI agents might have on business at large. So we wanted to talk about a handful of these that we've been tracking that may give us some clues as to what AI agents could actually mean for business leaders. So first up, OpenAI launched what they call Codex for almost everything. This is just an update to OpenAI's codecs coding agent, but it's a pretty big one because it gives codecs background computer use, meaning it can see, click and type across any application right now on your Mac while you continue working in parallel. OpenAI also released more than 90 new plugins for different types of software that your computer using agent can now operate. Second, we've heard more commentators and companies talk about this kind of slang term going around called token maxing. This is the practice of maximizing AI token consumption inside a company. It's basically being used as kind of a proxy in some cases for AI adoption. So for instance, writer CEO May Habib, who we've talked about before, told the Wall Street Journal that stuff like token maxing to drive internal AI usage is existential for her company. So Ryder and several others are in the camp that basically you need to be token maxing. However, there are critics at some other companies that call this an empty metric and an invitation to waste. Now, on top of that, interestingly, token maxing can have some unintended consequences. So we also had a story where Uber CTO Praveen Naga told the information that the company burned its entire 2026 AI budget in just four months, driven primarily by Claude code and cursor. So Uber engineers were actively encouraged to use these tools. The company ranked them based on internal leaderboards based on AI usage, and they just blew through the available budget. Now a couple other stories that kind of hammer home all the nuances people are dealing with. So Business Insider reported that Microsoft executive Rajesh Jha argues that AI agents may eventually need their own software licenses, just like human employees. His logic is that as AI agents start to act as full employees, companies will naturally have to give them login accounts, email inboxes, permissions, and access to tools that turns each agent into a potential paid software seat. On top of all of this, box, CEO Aaron Levy posted a widely shared thread this week on what it takes to keep up with all this AI architecture. He argues that companies building on AI have to accept that they will be dramatically upgrading their AI architecture. Over and over again. He cited how there are infrastructure Patterns like rag, graph rag and orchestration frameworks that we all talking about as state of the art two years ago, but are already obsolete to end all this. Amazon is living a version of this problem firsthand. Business Insider reports the company has a pretty serious AI sprawl problem. They have duplicate internal tools, disconnected data, and a ton of growing operational challenges around AI agents. So Paul, the reason we mentioned this is there are these threads kind of coming together here in these very broad strokes. So like first we're seeing these agentic coding platforms like Codex and Quad code basically develop into general purpose agentic platforms for non technical knowledge workers. But as these agents begin to permeate the enterprise, they're raising all these really difficult questions and like totally unanticipated consequences around budgets, permissions and architecture. And I'm just curious, like it sounds like some of the top companies in the world do not have this figured out. Does anyone have this figured out?
- [38:29]
A
No, I don't think anybody has this figured out. I think it's presented as though people have a grasp on what is happening, but every day that goes by you realize just how early we are in the integration of agents into workflows and businesses. And it can feel like you're way behind because people are out on the frontiers trying all this stuff, but they're also the ones who are going to find all the potholes and take on a lot of risk. So a few notes here, Mike. I think you did a great job just kind of summarizing. I mean we probably could have honestly just broke that entire recap. You just gave into the whole episode today and just talked about each of those. But at a high level, here's a few observations I have. So one, the accelerated capabilities. I mean every week there's just new capabilities of these agents and when you connect the agents together and it is overwhelming. I mean, even for someone like me who's paying attention to this stuff, reading about it constantly, analyzing it every day, it's, it's moving so fast almost, it's almost impossible to just keep track of what is going on. The second is increased risks and unknowns, especially as the power of coding is in the hands of non coders and technical staff. What I mean by that is there was one that popped up today that really caught my attention. So I have talked a lot recently about Lovable. So it's an example of an app building tool, non coding app building tool that I've mentioned on the podcast that I've used a few times to build some things. It's really cool, you know, if you've never used it, it kind of functions like ChatGPT, just go in and give it a prompt, but then it builds something. And so in my case I built like an org chart was an example. I built an assessment tool. And when you build these, it stays private by design, but then you can share it to like allow people to have access to the app. So any non coder would assume if I'm sharing a link, I'm just like sharing a direct link to that app and like so people can experience it. I'm not thinking I'm giving them access to everything else behind it. So I see a tweet this morning as I'm standing in line from my plane to Vegas and it's this random user. Impulsive is the name of the X account. Weezerosint is the actual handle. So when you first see something this. I would never retweet this stuff right away. I was like, who is this person? Is this legit? Like, you gotta do a little home homework. So I. I'll read you what it says. It says lovable has a mass data breach affecting every project created before November 2025. I made a lovable account today and was able to access another user's source code. Database credentials, AI chat histories and customer data are all readable by any free account. Nvidia, Microsoft, Uber and Spotify employees all have accounts. The bug was reported 48 days ago. It is not fixed. They marked it as duplicate and then left it open. And I was like, what? Like, that cannot be possible. And then sure enough, five hours later, while I'm now on my connecting flight, Lovable tweets. Without acknowledging the original, we were made aware of concerns regarding the visibility of chat messages and code on lovable projects with public visibility settings. To be clear, we did not suffer a data breach. Our documentation of what public quote unquote means implies what public implies was unclear. And that's a failure on us. Specifically for public projects, chat messages used to be visible. This is now no longer possible. Importantly, for enterprise customers, being able to set visibility to public for new projects has been disabled since May 2025. So I was like, I read that. I'm like, okay, maybe I'm just not smart enough to understand what that post means. But our documentation of what public implies was unclear. It's like, okay, well first, that seems super condescending. But again, maybe I'm just reading more into this than I thought because this isn't. Cyber security isn't necessarily my area of expertise at all. So then I was like, oh no, I'm not an idiot. Then two hours later they have to now post again and correct themselves. Because I think other people also were like, what the hell are you talking about? So this is their follow up. We're sorry our initial statement didn't properly address our mistake. Here's what a public project on Lovable means and how we got to where we are today. In the early days, people didn't know what lovable was capable of. So we wanted to make it easy to explore what others were building as a way to spark ideas and lower the barrier to getting started. Like scrolling GitHub or dribble, which again, non coders don't use. You browse projects to see what's possible, then go build. When you create a project on GitHub, it explains how it's default to public, whatever. And then they said over time we realized this was confusing. Many users thought public just meant others could see their published app. I wonder why? Because that is the most logical assumption. Not the chat of an unpublished work. So basically, in essence, you go in, you build something lovable. You're like, oh, this is cool, I'm going to share it with somebody. Like let's say I'm doing it. I send it to Mike, like, hey dude, check this out, check out this like quick demo. Mike gets on there. Mike can see my entire chat history. He can see like everything I've done in the thing. And it's like that is not at all what I intended to have happened. So again I highlight that one as an example of like, we just don't know. And Lovable is a really good company with tons of funding and like the things you would normally do to say like, okay, is this a legit company I should be building something with? They would check the boxes. And yet they made this apparently intentional choice to like. I mean it certainly seems deceitful to me, but like I guess they didn't think it was and then the other one. And maybe Mike will go into this one more next week, but this Vercel hack is, is insane. So I'll just read this quickly. And again I'm trying to highlight all the unknowns around. The use of agents is like my whole point here. But there was this very high profile thing that happened with Vercel and So here's the CEO's post. He said, here's an update to the broader community about the ongoing incident investigation. I want to give you the rundown of the situation directly. A Vercel employee got compromised via the breach of an AI platform customer called context AI that he was using. So employee has access to this things connected to it. The details are being fully investigated through a series of maneuvers that access that employee giving access to context AI from the colleagues compromised Vercel Google Workspace account. So they connected their Google workspace account. The attacker got further access to Vercel environments. We believe the attacking group to be highly sophisticated and I strongly expect significantly accelerated by AI. They moved with surprising velocity and in depth understanding of Vercel. So again these are, this is a very knowledgeable company that you would think would be better than most at avoiding unforced errors when it comes to cyber security. And they, they let it happen. So my thinking here is like again there's no debating the impact agents are going to have in enterprises. Like it is very obvious but there's just, there's so many growing pains we have to go through. And so I would just think very deeply as a, as a leader or maybe as someone who's pushing for these kind this kind of access within your company. Like you see the potential and you want to set up co work and Claude code and an open call and you want to like do all the things you, you gotta understand what it is you're doing or the organization has to understand all the risks that come with doing these kinds of things. And if you're relying on outside consultants or agencies to do this kind of work for you, you gotta know what they're setting up, what kind of access they're you're giving them and in turn what kind of access and technology they're integrating into your systems. And this is why AI is not just like tools, it is change management. It gets into like impact on org structure and tech stacks and the roles within companies. And that's when I say like I don't, you know, if you ask me just point blank how many companies, like let's just take enterprises, let's say companies, 250 employees or more do I think actually fully understand generative AI? Like let's just keep a gentic AI on the side for a second, fully understand the capabilities of generative AI platforms and have properly integrated into their companies to where they can like really scale transformation. Like what percentage do we think that is? It's single digits. Like you can't convince me otherwise at this point. It's like low single digits. I think if you then extend to that, say okay, which ones are actually prepared to integrate agentic technology and scale it within their company in a responsible way. It's like well under 1%. So yeah. So my point is we do our best to highlight where this is today and where it is going. Do not feel like if you're not racing into agents you are way behind. Like you're not. It's okay if you're a Y Combinator company or a VC backed AI native company. This is expected. Like you're not getting funding without this stuff. But if you're a legacy enterprise that's trying to drive like transformation in a responsible way, agent stuff like this is going to be sandsbox through a technical team for like the next 12 to 24 months before you start seeing this kind of stuff, like really living in the wild. So yeah, it's just, I don't know, like I said, it's moving so fast. Mike and I, I really do each week have a hard time comprehending some of the things that are happening same
- [47:54]
B
and I keep wondering to myself too like, I wonder what happens when we finally have of like the Claude code moment for something like co work or another one of these tools for non technical knowledge workers. And by that I mean like all this stuff we've been talking about since the beginning of the year with like AGI and people wondering like something big is happening. That's all been driven by the fact that Claude code was out for a year before this combination of factors late last year meant it got really, really good and everyone started positively and negatively freaking out. I think that's going to happen for something whether it's cowork or another tool for non technical knowledge workers. People are going to have meltdowns and or move 37 moments like we've talked about and I don't think anyone's prepared for whenever that happens.
- [48:44]
A
Yeah, and it is, you know, I think there's also just this, the technical side of how these models perform and how the harnesses are built around them in terms of like, like allowing the capabilities that they're going to have. And you know, I saw a tweet from Ethan Mollick I think was saying like it doesn't make any sense. Like if you look at Gemini's model from Google, it's so good through the API, but it's not even close to competing with Claude when you're in the app. Like it's just a totally different beast because Anthropic excelled at putting the harness around those agentic capabilities and the model capabilities and then bringing them to the actual user interface where Google has been really struggling to Keep up. And the things we're seeing now, like, I think I put the information article in for next week that there's like a code read again internally at Google where Sergey Brain is taking the lead because all the people at Google are using Claude code because their own coding capabilities are like not on par. So yeah, again, like the labs themselves are struggling to keep up. We talked last week, I think, about metas using Claude code internally. Internally. And like, you take this from me, I'm quitting. Like, I need, I need these capabilities so wild.
- [49:57]
B
It's really incredible. Yeah, nobody's got it all figured out.
- [50:00]
A
Not even close.
- [50:02]
B
All right, Paul, before we dive into rapid fire, a quick announcement. This episode is also brought to you this week by our AI for Writer Summit. So the future of storytelling is being rewritten thanks to AI. Which is why we're super excited to be hosting our annual AI for Writer Summit on Thursday, May 7th. This is a half day virtual event for writers, editors, content teams, anyone who does any type of writing or content creation as part of their job. And during it, we'll have some incredible speakers breaking down exactly how AI can help you create smarter and faster. And importantly, do all that without losing the heart and soul of your writing. This event has a free registration option, so go check out your registration options today. We've got the full agenda live. Go to aiwriterssummit.com that's AI writerssummit.com all right, Paul, first breaking rapid fire happened just before we jumped on today. Apple announced that Tim Cook is stepping down as CEO effective September 1, 2026, after serving in the role since 2011. John Ternus, Apple's current Senior VP of Hardware Engineering, is taking over as CEO and joining the company's Board of directors. Cook is transitioning to the role of Executive Chairman, while Apple's current non exec Chairman, Arthur Levinson, will become the lead Independent director. So. Ternus holds a Bachelor's degree in Mechanical Engineering from the University of Pennsylvania. In a press release, Cook expressed his full confidence in the transition, stating that Ternus had the mind of an engineer, soul of an innovator, and the heart to lead with integrity and with honor. The transition marks the end of a nearly 15 year run as CEO for Cook, who took over directly from Steve Jobs. Under Cook's leadership, Apple's revenue almost quadrupled to over 400 billion. His tenure saw the company push into wearables with the Apple Watch and AirPods as well as the Vision Pro. And Paul, I think a big thing we don't know much yet, but something everyone's going to be watching is how Turnus is going to handle Apple's transition, finally, hopefully into the full AI era. What do you think is likely to happen here?
- [52:22]
A
Yeah, hopefully. I, I think the timing of this maybe comes as a surprise, but this has been rumored for well over a year that Tim Cook was, you know, probably in the final stages of his career here. So not necessarily shocking, I wouldn't say. And I think Ternus was rumored to be like the main candidate here. So Bloomberg did have the internal memos. I'll just read a quick excerpt from Tim Cook's memo. So today we have a truly extraordinary roadmap. I've never been more optimistic about Apple's future. That is why I've decided that now is the right time for me to transition to a new role of Executive Chairman. I'm thrilled to announce that John Ternus will be our new CEO. Throughout the many years I've worked with him, in our many conversations about his becoming Apple's next CEO, John's passion and love for Apple shine through. He's a visionary in his own right, a man of remarkable integrity and the kind of person we can all be proud to follow. He's going to, you know, Cook's going to remain as CEO through the summer and work very closely with John as they transition roles. And then they said they would have a town hall in the Steve Jobs Theater 9:00am Tuesday morning, which 9:00am Pacific, I assume. So I'm sure we'll be hearing plenty more about this in the coming days. And I just glanced at after hours trading and the stock is basically flat. It's like nothing's happening after hours. That. Yeah, so, yeah, I don't know to be continued. But I do think, you know, from our perspective and what we do on this podcast, what this means to their AI roadmap, my guess is that's pretty locked in for the next 12 to 18 months. Knowing Apple, you know, they don't just do things like change on a dime necessarily. So I'm sure whatever decisions Tim Cook has made leading up to this point, John was heavily involved in those decisions. And there's continuity in terms of their AI roadmap, but we'll definitely keep everyone informed what we learn about that in the days and weeks ahead.
- [54:12]
B
Next up, Anthropic has launched a product or feature capability called Claude Design, which is a new collaborative visual design tool powered by Claude Opus 4.7. This lets users build designs, prototypes, slides and marketing materials just through conversation. It automatically applies Team brand guidelines by reading code bases and design files. It accepts text prompts or uploaded documents and exports designs to Canva, PDF, PowerPoint and standalone HTML. It is available now for Claude Pro, Max team and enterprise subscribers. Interesting drama here. This launch was actually telegraphed a couple days earlier because Anthropic CPO Mike Krieger the information found out. They published some reporting on the fact that Anthropic was Preparing both Opus 4.7, which we'll talk about at the end in our product updates, and a new AI design tool. And on that same day, Krieger resigned from Figma's board of directors, which is a seat he'd only had for about a year. Figma, being a design and prototyping tool, had previously collaborated with Anthropic to integrate its AI models into figma's products. And that's really interesting because Krieger has said in the past that the largest AI labs will come to dominate software businesses and that this thesis alone has rocked public markets at times this year, which we've talked about because we've talked about Wall street has been undergoing a bit of a SaaS apocalypse lately where major AI companies are supplanting established software businesses by just building their capabilities directly on the model layer. This appears to now be happening in design. So Figma, Canva, which is not publicly traded, Adobe, wix, all of these emerging design startups. Paul, this seems like this just straight in the crosshair. I mean, I don't know how much more blatant this can get. Like, if something like Claude Design does in fact work as advertised, what does that mean for these software design companies, for designers, even at large?
- [56:22]
A
I mean, again with the, you know, the roles and the, you know, giving designers superpowers. I do think it's what we've always said, like great designers who learn to work with these tools are going to become even greater. They're going to have superpowers to be more creative, more innovative, more productive, things like that. And designers who don't. It's going to be really hard to compete from a pricing perspective, from an output perspective, from a turnaround perspective. Like the expectation is just going to be instant. Like I, I can honestly say that, like I feel this myself. When something is taking like a really long time internally, I'm like, what? Why? Like what is our reason for something taking a long time internally? Because I feel like those barriers are just gone. And if, if it's design related or content related, my patience is like very thin because I know what's possible, whether that's working with outside, you know, design teams or whatever. I was like, I'll do it myself. Like I'll just go into clouds and do it myself. Like this is taking too long. So I feel like more and more people are going to get that you're going to realize what you're capable of doing on your own. And if like people aren't doing it, you're going to get really annoyed and you're going to want things to move a lot faster. Now that being said, there's also times as a CEO when I want deeper human involvement and I want it to take time. I want to have the patience of allowing the human involvement in a process when that's better, but when it's just an output we just need. You just want to go, yeah. Figma stock, like many SaaS stocks, is down. I'm just glancing now. Last three months, 29%. I think it's going to be hard for them to rebound. I think it's gonna be really hard for a lot of these software companies to rebound. And we've talked extensively about this. I think it was episode 197 was the SAS apocalypse. We went into great detail about this one. Yeah. And like for us, Mike, we were just in a meeting. Was it last Monday, like a week ago, where you were talking about you and Taylor were using like a Claude skill to do design of slides. And as soon as I saw this I was like, oh, the skill probably just got obsoleted. Like now we could probably just do it right within the thing. I don't know.
- [58:24]
B
Unlike Sigma, I'm ecstatic about that. Where you can obsolet me having to do anything with this. Totally.
- [58:31]
A
And Claire on our team was already thrown into our Zoom chat. Like something she built, she said would have taken her hours. She did it in 20 minutes using this already. And then I'm sure we'll drop a gen app review soon in our AI academy because this is the exact scenario of why with our AI academy we're so focused on real time education. It's not like we have this three month roadmap and that's all that's going to come out. It's like if a new tool comes out and it's hot and it's something that we're all excited about, like, boom, let's get a gen app review put up in the next slide, like one to two weeks. So I'm sure our team is already working on getting a gen app review out of cloud design because these are the kinds of things that we want to be able to do. It's like, let's talk about it on a Tuesday and have a review of it on a Friday. I don't know, I'm not promising that we're going to have that out on Friday, but that's the concept here that we feel like our education has to move at the speed of, of what these AI models are enabling. But yeah, it's very, very challenging time to run a tool. And what does Sam always say, Mike? It's like, like if updated models don't like make your company or product better, then you don't have a company anymore. Like just assume that they're going to make everything better and if that is going to obsolete what you're doing, then just switch gears now and do a different company. You need to be building something that gets better. So to your point, Mike, you don't care in your job as Chief Content officer, if something comes out and takes your skill from one hour of doing it to two minutes, that's awesome. That just made me better at my job. So like the thing you built, fine. Like we were monetizing it. It was an internal tool, like. Yeah.
- [60:02]
B
So yeah, yeah, it's interesting too. It's probably a whole other topic or an ongoing one. Just that idea around expectations. I mean, if you're. If I had one piece of takeaway advice for anybody and what I take away constantly from these types of conversations is just you ignore changing expectations at your peril. Whether you're an employee, an entrepreneur, a leader, a practitioner, whatever. You gotta realize the game has changed and people are having these conversations behind closed doors or on this podcast of like, hey, that timeline or that limitation. I think we can do better than that now based on what we have available to us.
- [60:41]
A
Yeah, there's basically two scenarios right now. I'm like, there's the. You're a listener and you know all this stuff and your boss doesn't yet. And so you are like a superhero. You get stuff done so fast. It's always amazing. They're always like, oh, you're just over delivering. You're so incredible. And then there's like you have the AI forward leader who's like, why is that taking so long? Because I know it's possible. And so it's a tough environment. You're basically probably in one or the other right now. Everything you do seems so fast to everybody. Or it's like not fast enough.
- [61:14]
B
You could be in either one in the same week. I can tell you that's for sure,
- [61:18]
A
because I might not know if something is possible, and then Mike will show me something's possible. And it's like. Like, wait a second. Why doesn't everyone else know that that's possible? Right, Right.
- [61:27]
B
All right, so next up, after literally months at this point of open conflict, we might have seen this week a real thaw between Anthropic and the Trump administration. So Bloomberg is reporting that the White House was moving to give US federal agencies access to Anthropic's new Mythos model. And the very next day, Anthropic CEO Dario Amade met at the White House with Chief of Staff Susie Wiles. And actually, Treasury Secretary Scott Bessant was also present. So if you recall, things even just a few weeks ago were very bad. Anthropic was blacklisted by the government, called the National Security Risk. They're suing the Pentagon over that. Blacklisting The White House, however, is called Friday's meeting productive and constructive. It seems like Mythos is kind of at the center of this. So according to Anthropic and what we've talked about, Mythos can identify weaknesses and security flaws in software, and Anthropic has only released it to a small group of tech and financial firms as part of its project Glasswings, its cybersecurity initiative, because they're worried that hackers could use this model for very malicious purposes. So apparently, Gregory Barbacchia, who is the federal CIO at the omb, emailed agency officials this week saying his office is setting up protections that would actually allow agencies to go access Mythos. And it turns out, based on a separate scoop by Axios, the NSA is already using Mythos despite the blacklist. So Anthropic at the same time, is also spending heavily on some Washington influence. Bloomberg government reported Anthropic has hired Ballard Partners, which is described as a very big shop in Washington with very strong ties to Trump, to lobby what the administration now calls Dow procurement, meaning Department of War. So, long story short, Paul, you predicted that the White House and Anthropic have to make a deal, given how integral the company's tech is to both how the federal government works and national security at large. Is this the beginning of that?
- [63:35]
A
Gotta love politics.
- [63:37]
B
No kidding.
- [63:37]
A
So predictable sometimes. Yeah, I think I said last week, like, that Mythos just. Just demonstrated how absurd this whole government effort to blacklist them or actually blacklisting them was, and that they were always going to have to come back around, but they were never going to admit they were at fault. So they're going to have to find some negotiated off ramp that can make it look like the administration got what they wanted out of it and proved their point and they weren't wrong. And as long as Dario is willing to allow that to happen, then, you know, I think it's only a matter of time before that court case just sort of magically falls to the back of the back pages, I guess. Yeah, yeah. Again, I just go back to, like, it's such an insane thing that they are trying to do this to one of the three companies in the world that, you know, if you were to list the most important companies to the US Government, Anthropic is easily in the top five right now. So. So, yeah, they have to have an off ramp. They have to find a way. I'm actually shocked that we're like four days from this meeting and both sides have managed to keep it relatively quiet. What was discussed. He was there for a while. I thought for sure by Friday night we would have something leaked. And they've kept this under wraps, which is pretty impressive for this government and for Anthropic has every motivation to keep it quiet. But, yeah, So I don't know. I mean, I hope. Hope again, regardless, I hope they find a way. It's like anything else. Like, regardless of what you think about politics and different this administration, previous administration, at the end of the day, if you're American, like, you want the most valuable American companies playing an important role, especially when it affects cybersecurity of, like, every citizen and every business and.
- [65:20]
B
Sure.
- [65:21]
A
So you want a deal done. Like, we want this over. We want them to move forward and, like, find a path to work together, despite their differences.
- [65:30]
B
Yeah, it's interesting, one of the quotes from the Axios article that a Trump advisor told them, they said, quote, this is a big problem. Everyone's complaining. There's all this drama. So this got elevated to Susie to hear Dario out, determine what is BS and start to plot a way forward. So I think behind closed doors, they're not as unified as you would think.
- [65:50]
A
Yeah, that's probably pretty safe to say. I can't imagine the NSA is like, like super ecstatic.
- [65:57]
B
Right.
- [65:57]
A
You know, supply chain risk for them. They know how important they are.
- [66:02]
B
All right, so next up, we've got an interesting story developing and a conversation around Nvidia. So for the last few years, the US Government has restricted which advanced AI chips Nvidia is allowed to sell to China. The goal here is slowing Chinese AI progress by limiting access to the best Hardware. Nvidia has responded by designing downrated chips specifically for the Chinese market. The latest one is called the B30. And it's been this kind of ongoing debate whether the US should be selling these chips at all, whether Nvidia is helping or hurting American AI leadership by doing it. And this week that debate actually kind of blew up a little bit in AI circles because there was an interview from Dwarkesh Patel, who hosts one of the more influential shows in AI where he had Nvidia CEO Jensen Huang on for a wide ranging conversation, as Dwarkesh typically does. But everything was dominated by this segment about China. So in it, Dwarkesh kind of played devil's advocate and pushed Jensen on why selling chips to China is a good idea. And Jensen pushed back kind of uncharacteristically hard enough that this spent the next several days being kind of dissected across X. So Jensen basically said, look, people assume China is far behind, or US policymakers do, and it is not. China manufactures 60% or more of the world's mainstream chips. Roughly half the world's AI researchers are Chinese, kind of hearkening back to that Stanford Report, and the country has abundant energy. And so he basically said, look, victimizing China, turning it into an enemy isn't the best answer. Likely they're an adversary. We want the US to win, but having a research dialogue is probably the safest thing to do. And so Dwarkesh kind of pushes back and says, look, if you're these AI models themselves, he's pointing to Anthropic's mythos and saying, look, these are weapons. Basically, Anthropic is not deliberately, is deliberately not releasing these to the public because of cyber security risks. So if Chinese labs, which they have said publicly are bottlenecked on compute, doesn't ergo selling them more chips, let them build kind of more of these weapons. And he framed it as like enriched uranium, you know, like material for nuclear weapons. And Jensen called that analogy lousy and illogical. And he basically accused Dwarkesh of a loser mindset, arguing that the idea that Nvidia would inevitably lose the Chinese market is defeatism, not strategy. He basically says, look, we should be able to compete in China aggressively, keep advancing the American tech stack and make sure that Chinese AI developers keep building on Nvidia's architecture rather than, you know, national champions in China like Huawei. So his fear is that if the US forces Nvidia out of China, Chinese developers will actually spend the next few years optimizing models for non American hardware. Which will not benefit the US long term. So, Paul, the reason we want to talk about this is it's getting a lot of attention in AI circles. It shows there are some really different opinions on how AI infrastructure might or should play out. What's important to take away from this?
- [69:21]
A
I mean, first just shout out to Dwarkesh for pushing on this like that. You don't, you don't see these kinds of like hard hitting interviews of these tech leaders very often.
- [69:34]
B
No, no you don't.
- [69:35]
A
If you do, it's like the last time that they're talking to that person. But Dwarkesh has a very, very strong reputation in the AI industry in Silicon Valley. He's extremely knowledgeable about the topic, so he can grill on very specific technical details of these chips. And he did. And he didn't back down. I mean, it got uncomfortable. It's actually one of the first time I've ever seen Jensen kind of flustered. Like he was pissed. It seemed now, I'm sure, like he probably appreciated the intellectual challenge of the conversation, but he was not taking kindly to the insinuations that Dwarkesh was making. And so it just made for really fascinating conversation. And I was, I, I gotta be honest, like I wasn't really fully understanding the full conversation. I follow the space pretty closely, but there was definitely just technical stuff they were getting into where I was like, wow, yeah, I gotta, I'm going to go do some homework on this one to really understand how, you know, what he's challenging him on here. But at a high level it is another good example that the, the brightest minds in AI don't necessarily agree on some of the very fundamental things about the industry, about, like definitions of AGI, about whether or not large language models are, are the path forward and are going to get us where we want to go or if we need world models like Yann Lecun would said, whether or not, you know, jobs are going to go away or more jobs are going to be created, whether we should sell chips to China, like nobody knows. But the fact that we're in a place in society where we're having these conversations openly to me is fantastic. Like that that's what should be happening. Like there should be dialogue about really important issues and, and then listening to each other and like allowing those conversations to happen. So to me, more than anything, I, I don't have a personal opinion, like I know don't, but I, I kind of tend to side with Dwarkesh. Like when I listen to the arguments and I Listen to Jensen's responses. There's something missing from his responses. It's kind of like when he gets asked about jobs or when, you know, some of the other labs leaders. I just feel like there's some nuance missing, either because they're intentionally leaving it out or because they really believe that they're right. And I feel that way about the chips in China. And if you remember, like, the government wasn't allowing this. Like, this was not something. And then Trump didn't even know who Jensen was when he retook office. We covered that on the, the first episode. Like, he literally had no idea who Jensen or Nvidia was when his, this current term started. And they're like, well, you should know him. He's like one of the like 10 richest people in the world and he's probably the most important company in America right now. And then he got to know him and then they, like, they removed the restrictions after that. So, yeah, I don't know. Like, I. This is a really, really challenging, complex environment. Gavin Baker, somebody we talked about that we really like and follow, he's with Jensen on this. And I, again, like, I just kind of the people I follow who I trust on topics and I try and process what they're saying and I just, I don't know that there's a clean answer to this one. And, and honestly, I don't know we're gonna know who was right until it's too late. Like, maybe Jensen's right and Jensen's convinced the administration they should sell them and they're going to sell them, and then a year from now, we're going to realize that was a bad idea.
- [72:52]
B
Yeah.
- [72:54]
A
And I don't know whether they admitted at that point or not. I don't know.
- [72:56]
B
To your point, though, it did kind of show like a different path from what we typically have. Like, look, I get it. CEOs don't want to go on podcasts and get into huge arguments that drop their stock price or whatever. But it was like eye opening because you kind of look back and you're like, like, we're not getting a lot of this pushback and debate on a lot of these big podcasts. Understandably. So they probably don't get you want
- [73:18]
A
burn your sources 100%.
- [73:20]
B
So it's not like a, you know, judgment against any of these people. But you're like, man, I wish we had more of this.
- [73:27]
A
Yeah. And I, I do. Like, the one other one that came to mind that I had a similar feeling with was on The. When Gerstner was questioning Sam Altman.
- [73:35]
B
Yeah.
- [73:36]
A
And it was that same kind of uncomfortable, like, wow, he's pushing hard on this one. And Sam's about to like walk out of the room kind of thing. And I've seen that with Elon Musk a few times where he just like got pushed on something. He's like, screw you, man. I don't need to sit here and take this. So again, like, I. Kudos to these leaders who are willing to sit down and have the hard conversation. There's actually one with Sundar Pichai recently where he was doing one. I don't remember the name of the podcast, but it was a weird one because they're sitting there like drinking Guinnesses together and stuff.
- [74:02]
B
Oh, it's the one with the Collisons, right from Stripe, I think.
- [74:06]
A
Okay, is that what it is?
- [74:07]
B
I think it's one of them, the Collison brothers that does it. I forget what it's called.
- [74:10]
A
They asked them hard questions. And to me, I like when leaders take the hard questions and provide thoughtful answers, even if they're not fully baked answers. Just the fact that it's like, listen, this is a hard one. We're working through this. But I know how hard it is for PR and comms people to book that interview because if you don't know how this stuff works, you go through the PR and comms people, you try and like convince him to do the interview, probably have to tell them what the questions are going to be like. It's not easy to get those interviews unless you're just buddies with them and they go around the PR and comms team and if that's what happened here, that's the last time that's happening. That was, that was the other thing I took away is like either he was willing to get into this debate or the PR and comms team did not tell him what was coming or Dwarkesh went around what he told he was going to to cover because some one of those had to be true in this environment.
- [75:06]
B
Yeah. All right, so next up, the web analytics firm Similar Web published its latest gen AI traffic share update this week. So we've covered this a few times in the past. They basically measure the actual share of traffic across major gen AI consumer products. And it's really interesting to see how quickly these can reshuffle. So over the past year, Chat GPT is still in first place, but its dominance has eroded significantly. Gemini, Claude and Deep Seek have all picked up meaningful share. 12 months ago, ChatGPT had just over 77% of gen AI traffic a month ago though. However, again, year on year that number was just over 56%. In the same time frame, Gemini went from 6% to over 25%. That's more than quadrupling. Grok went the other way, going from just over 7% to almost 4%. Perplexity was basically flat at 1.6%. Copilot went from 1.38% to 1.99%. Claude had a pretty dramatic short term jump though. It's still pretty far behind. Twelve months ago it was at 1.4% of traffic. Three months ago that was about 2.22% and then it nearly doubled in a single month to cross the six. So that kind of lines up with all this interest we have started to see in things like Claude code and the latest Opus 4.5 through 4.7 models. So Paul, just really interesting to see how quickly in a year things can change here. Obviously it's not a perfect benchmark of how much usage is happening, but kind of interesting to see Gemini really eating into that.
- [76:47]
A
Share yeah, there's like deja vu. If you go back you've probably seen it Mike. But those that's like this awesome interactive of by bar chart where it shows like the browser wars through the years.
- [76:58]
B
Yeah.
- [76:58]
A
And like Chrome non existent and then all of a sudden like boom and Chrome just comes to dominate. You kind of start getting that feeling here where they're like they're late to the party and then just like they just eat away over time. And I'm not saying Chrome's gonna end up with 92% of the market share here. But yeah, it's definitely balancing out. It's, it's fascinating to watch these things move and to see the jump specifically with Gemini and Claus.
- [77:20]
B
Well just overall too even us. Then also on the converse side talking about the new Google, you know, Code Red. It's like never sit here and say because of a headline someone is dead in the water or someone is winning. Now it's like that'll change next week.
- [77:35]
A
Yeah, for sure.
- [77:37]
B
All right Paul, so next up we're going to do our AI use Case Spotlight we've started to do every week. So as a quick reminder, we hear from listeners all the time that one of their favorite parts of our discussions is when we talk about how we're actually using AI ourselves, both sometimes at Smartr X and in our own lives. So each week we're going to try when we have time to give you a dedicated look under the hood at some real AI use cases we're exploring or deploying in our own work. So, Paul, I mean, this week, first up, I can share some use cases that jumped out to me quickly this past week. If you have anything you've been working on, we'd love to hear it too.
- [78:11]
A
Yeah, sounds good. Go for it.
- [78:13]
B
Awesome. So, first use case that I found that was. Was pretty impressive was using Claude code to prep a bit for a talk I'm giving at a conference this week. So I talked, I think, last week about how I had used it strategically to prep, but this was something much more mundane. So in this talk, I'm going through 40 different AI tools in 40 minutes, which is a talk I've given before. Super fun, but you have to basically make it from scratch every time you do this. To make that talk work visually, I needed dozens of product images, screenshots and logos. It is a nightmare to pull these manual. I've done it for years now. It takes hours, it sucks, it's stupid. And obviously you can't reuse stuff because, like, screenshots change. So there's very little you can reuse from past talks. So I actually had Claude Code try to fire up multiple agents in parallel in a few different waves. So the first wave was four sub agents. Each of them were charged with pulling product screenshots, press kit photos and hero images in order. So, like, basically you're going through and saying, do these images exist online that you can find? As the second wave, it starts looking up URLs with the forward slash press or forward slash brand. Because sometimes it just like doesn't find images and there's like a press page or something. And then the third wave was like a final fallback, which is like, hey, go just grab the logo from like Wikimedia Comics, which is pretty publicly available. Took like 15 straight minutes, which is like, longer on the longer side for some of the stuff I do. And I got a lot of really good, usable images. Not remotely perfect, but just the fact that it could help me conceive this was super cool. And I just enjoyed the experiment of, like, oh, this like, thing I never would have thought about, like, because, you know, you think like, oh, maybe it could go generate images. Like, no, just go find me the files I need. And it's downloading, you know, dozens of files to my home computer, just being like, here's the images. And some of them are good, some of them are bad, but definitely saved me time, which is kind of wild,
- [80:14]
A
near and dear to My heart. Mike, you and I have both spent months of our lives going online looking for logos and product shots for presentation.
- [80:21]
B
I don't even want to quantify how much time I have.
- [80:24]
A
The logos alone.
- [80:27]
B
And the logos actually is probably the one to really highlight because that's a lot easier to standardize and find online. It was kind of of dropping the ball sometimes with different types of images just because it didn't have that artistic sense of what I was looking for. But even then, it was still really helpful. One other quick thing, which I will not advocate everyone goes crazy with, but this was a personal one where I use an accountant for our federal and state taxes. But we typically file our city taxes on our own and it's super straightforward, actually. Kudos to Lakewood, Ohio. It's like pretty simple. You go online, there's a few menus you go through, as long as you have all your other stuff done. But it's taxes. I don't know what I'm doing. I am super prone to making mistakes. So I fired up Claude Code. I gave it all our tax returns and docs, and then I just screenshotted every menu that I was working through and said like, walk me through exactly how to do this so I don't screw anything up. It made it way faster. It was way less scary and frustrating than if I'd had to do this on my own, which is nice. But actually, here's the kicker. It caught something I almost certainly would have missed. So, like, One of our W2s actually has a tax credit from a city outside of our home city. Long story short, you gotta add this because otherwise you pay the wrong amount in city taxes. Claude Code caught this. And if I had made this mistake, this would have cost me eleven hundred dollars. So the Claude Max plan paid for six months of itself.
- [81:54]
A
That's awesome.
- [81:54]
B
It's taxis. So that's all I got this week. But I'm not advocating you replace an accountant or anything. The accountant has been best money I've ever spent in my life. Go pay accountants. Lots of money. They're helpful. But it was nice as like a double check on something I had to do myself.
- [82:10]
A
That's really cool. Yeah. The one I was going to share is just a quick, simple one. But again, sometimes I think it's just good to hit these simple ones. I also have one related to a keynote I'm doing. So I'm actually doing the Movable Inc. Think summit on June 16th in New York. I'm doing the opening keynote for that event and I actually did this event last year and I did The State of AI for Business and Marketing 5 Things Every Professional Should Know. And it's kind of my standard keynote. And I do that talk 30 times a year at different conferences and private events. And then it's always like customized for that event. But it's like, you know, this think someone have a lot of returning people so you want to go give the same talk again. But they also want an updated like state of. It's like, okay, what's new? Where are we at? What's changed since last year? So I don't want to just give the same talk again, but I'm like, you know, what we really need to focus on is of that talk. The big thing that's evolving is really the one dimensional progress, being the agentic. I just listen to this podcast, you hear it over and over and over again. So I came up with a session that I was calling the State of AI Agents and the Augmented Marketer. So the whole premise is like giving marketers superpowers because it's for a marketing audience. And so I went into ChatGPT, which I've used to, you know, draft abstracts before. I gave it the abstract for my standard state of AI talk. And I said, here's where I want to go with this one. I want to focus in on agents. I want to talk about it being, you know, augmented marketers and how it's complementing what they're capable of. Can you use the format for my previous state of talk and like write me an abstract for this one? So again, I mean, if I was going to sit down and do this, I'm probably like one, one and a half hours, Mike, like between drafts, like write the first one, tweak it, shorten it. It's not insignificant, but, you know, not a massive lift. But I didn't have time to do it and they needed it that day. Like we had the, you know, great call and say, all right, let's get, get the agenda live. So here's what it wrote. AI is entering a new era, one defined by agents. These systems can do more than generate content. They can reason, take action and support work across research, planning, creation, analysis and execution. For marketers, that means a fundamental shift in how teams operate and how value is created. In this keynote, Paul Raitzer explores what the rise of AI agents means for marketers and business leaders, where the technology is headed and how organizations can prepare for a future in which marketers are increasingly augmented by intelligent systems. So it's like perfect like, that's not so. Well, yeah, I'll still go through, like, the high level of the things that are fundamentally happening in the space, but I'm gonna then, like, probably 80% of the talk is going to zoom in on the agent stuff and probably cover a lot of the things we've been covering the last couple months here on the podcast. So, again, like, it doesn't have to be like, automating 50 hours of work. Like, sometimes it's just like, just give me that one hour. Because this is a heavy mental lift, like a cognitive load for write a fresh one. But I've said before on the podcast, and I said it in my AI Academy courses, AI is better than me at writing abstracts. Like, I'm. And I'm okay with that. Like, it is not something I like fully, like, find fulfillment in writing. Plus, this chat GPT, it's trained on everything I've created, all my courses, all my stuff. So it. It knows generally how to write this stuff really well. And honestly, like, I probably would have turned it over to the marketing team otherwise. Hey, could you all take a shot at drafting this? And. And I didn't need to just, like, have ChatGPT do it.
- [85:16]
B
Yeah, it's been so valuable for abstracts. I love it. All right, next up, Paul. Each week we are also trying to spotlight one of the courses in AI Academy and give people kind of a real, actionable takeaway from that course, whether or not you decide to ever take it. And this week's course, like we talked about at the top of the episode, is AI for Manufacturing. So I figured I'd take a quick minute to go through what we've got going on in this course. You know, eventually we'll be kind of interviewing the instructors who do the course courses. This one was by Taylor Rady, our director of research, who, offline I kind of interviewed and talked through what was going on in the course and what we need to learn from it. So I figured I'd dive into that real quick before we wrap up with some AI product and funding updates. So AI for Manufacturing, really, there's kind of a core reframe that the course argues that no other function really faces in the same way. So there's this idea that manufacturing has, like, a unique speed of reality problem, so to speak. So, like, you have things happening on the shop floor, on the factory floor, in seconds and minutes, not hours or days. So there could be, you know, a small deviation in how a machine works, some type of slight drift on how a tool works, and the key is by the time a human engineer has figured out what's going on, thousands of more units at scale often have already shipped, perhaps with defects or something wrong that's degrading the machinery. And this is why you have end of line inspection that exists as a catch all. It's why equipment gets run until calendar based maintenance or until it actually breaks. So this idea is like there's a loop, something went wrong and then someone knew about it. Historically, that loop has always been too slow and every downstream decision in manufacturing has been shaped around this delay. And here's kind of the key insight is there's all these kind of classic really useful frameworks that manufacturing leaders grew up on. Things like Lean, Six Sigma or Kaizen. They're genuinely good at what they were built for, but they're built and this is kind of back to the expectations thing we talked about, Paul. They're built for a world where these things are measured in days, in weeks, in quarters. They structurally cannot keep up with failure modes that unfold in seconds. And that is, is unfortunately the reality of a modern connected plant. So that's overall the structural reason that AI is kind of hitting manufacturing much differently. We're not really seeing your best engineers being outclassed by AI. It's more that AI with access to telemetry can watch 10,000 simultaneous signals in real time in the way that humans never could in the first place. So that's a really important high level point. And how this actually shows up up in terms of an actionable takeaway is the goal is to find the single longest feedback delay in your operation. And that is the gap between when a problem actually occurs, whether that's a defect, deviation, etc or even a supplier issue or an equipment anomaly, and the gap between that and when a human becomes aware of it. So two final things I'll leave you with. There are specific questions from the course that are worth worth sitting with as you consider how can AI be integrated into my manufacturing operations? As one, what percentage of your defects are detected during production versus after? And which of your experienced engineers spends the most time manually reconciling data across all your different systems before they can even start troubleshooting? That is the strategic bottleneck that this course kicks off, teaching you how to understand, diagnose and resolve. So, so go on over to Academy SmartRx AI. We'll also include the exact link to manufacturing in the show notes AF for manufacturing that you can go check out the individual course series or an AI Academy membership to learn more from Taylor, who has extensive experience in the manufacturing industry, about how to resolve these issues and how to move forward from here. All right, Paul, so we've got to end up here. A bunch of different AI product and funding updates that I'm going to to kind of blitz through as fast as possible. And like you said, I think last week or the week before, any of these probably could have been a main or rapid fire topic. We've got tons of them going on.
- [89:40]
A
It's longer and longer every week.
- [89:41]
B
It really does.
- [89:42]
A
I. I'm gonna say this, like, at some point we might have to just like start doing these product and funding updates as a standalone podcast. Yeah, I don't know man. They're just every week it gets longer and we're cutting them too. It's like, all right, like, let's just stop here, like, this is enough 100%.
- [89:57]
B
Well, until we phase this out, we'll do one more this week, but keep going.
- [90:02]
A
I'm not saying we do it, but. Right. It's getting there.
- [90:05]
B
But even this first one, like a small little thing called Claude Opus 4.7 launched this past week. So this is the new Anthropic flagship model with meaningfully improved coding and computer use vision. It's now available across CLAUDE products products. CLAUDE Code at the same time also launched something called routines, which puts Claude code on autopilot via saved configurations that run on schedules, API calls or GitHub events from Anthropic's cloud infrastructure. So it basically enables unattended work like nightly backlog, maintenance alert, triage and PR reviews. At the same time, Anthropic has restructured Claude enterprise pricing from a flat fee of up to $200 per user per month month to a 20 base fee plus usage based billing. Claude Code is unbundled and moving to per token pricing in that shift as well. At the same time, Anthropic has attracted investor offers at an 800 billion dollar valuation. They've received multiple investor offers at roughly 800 billion. The company has so far resisted those, but that's more than double February's $350 billion pre money valuation. Anthropic has also reported that they are automating alignment research using Claude. They published research showing that Claude Opus 4.6 can autonomously propose and run alignment experiments, though the study also flagged there were risks with reward hacking as part of that. That's it for Anthropic News at the moment. Some other updates. Harvey has launched agents for end to end legal work. So legal AI platform Harvey launched something called Harvey Agents. They autonomously produce memos, redline contracts, due diligence reports and slide decks across 14 plus different practice areas. Deepseek, the Chinese AI startup, is in talks to raise at least $300 million at a $10 billion valuation, its first ever funding round that's timed with the launch of its new V4 flagship model. Meta has hired AI engineer Joshua Gross from Mira Murati's Thinking Machines lab. Another person leaving that lab, they've hired him into their Super Intellig. That makes him the fifth founding member to defect from Thinking Machines to Meta. AI chip maker Cerebras has filed for an IPO on NASDAQ under the ticker crbs at a 22 to $25 billion valuation. Target they're reporting about $510 million in revenue in 2025, which is up 76%. They have major customers that include AWS and OpenAI.
- [92:46]
A
The OpenAI announcement related to that is is a pretty big deal there. Yeah, I don't think Jensen was happy about that one.
- [92:53]
B
Yeah, Microsoft has a couple other things going on this week as well, so they are apparently developing Open Claw inspired co pilot features via an internal team called Ocean11 that's basically building a version of three of Copilot that runs in the background and takes autonomous actions on emails, calendars and role specific tasks. We are expecting a debut of that at microso build in June. They also added new Copilot in Word capabilities for legal, finance and compliance professionals, including track changes toggled from Copilot, automatic tables of contents and real time visibility into Copilot's multi step edits. Cambridge philosopher Henry Shevlin announced that Google DeepMind has recruited him for a new philosopher role focused on machine consciousness, human AI relationship and AGI readiness. That's the first dedicated full time philosophy position at a major AI lab. We'll see if that's the last. Google also launched Gemini for Mac, a free native Apple silicon desktop app that lets any user share any window for contextual help and includes integrated image, video and music generation. Google is also bringing AI mode into Chrome on desktop and mobile, opening web pages alongside the AI interface instead of forcing tab switching and letting users add multiple tabs, images and PDFs as context. And last on the Google front, Google introduced skills in Chrome, a feature that lets users save reusable AI prompts as one click Gemini workflows for frequently used AI tasks. One last Apple update here. Bloomberg reports Apple is working on a display free smart glasses called N50 for late 2026 or early 2027. And former AI chief John Giandria officially exits this past week after a year of resting investing following the disappointments with Apple Intelligence. And last but not least, Salesforce unveiled something called Headless360, exposing its entire platform as APIs, MCP tools and CLI command line interface commands. So AI agents can operate Salesforce without opening a browser.
- [95:12]
A
Yeah, I'm counting like, right. I was like 18 product updates, 18 products.
- [95:18]
B
And we definitely cut some of these, I promise you.
- [95:21]
A
And again, like there's so many of these that I would love to like riff on, but yes, we are, we are past. And I probably have somewhere I'm supposed to be, right? You're staying late at the office and I'm, I'm probably supposed to be at a party somewhere.
- [95:35]
B
You're at an event or something, right?
- [95:37]
A
Yeah.
- [95:37]
B
All right, one last announcement here, Paul. As always, we are running, running our AI pulse survey. So go to SmarterX AI forward slash pulse to see this week's survey and contribute to it. It is related to some of the top things we have talked about this week. So we're going to ask a question about AI Driven search and we're also going to ask a question seeing if AI agents are starting to change at all, how your team works or if you're still mostly using chat based AI. Should be interesting to check those out. Paul, thanks again.
- [96:07]
A
Yeah, thanks for doing this on the road too. Yeah, man. Every week, dude. All right, well, and next week I was looking at the calendars. You're going through some of those. We'll find a way to do next week's episode. Mike is actually at Experience Inbound Milwaukee, right?
- [96:21]
B
Yeah, yep.
- [96:22]
A
And I'm at Acquia Engage in Colorado. So if either, if you're going to be at either of those events, like, you know, hit me and Mike up. Love to say hi in person to people, but yeah, we are both traveling so we'll figure it out. We'll find a way to thread the needle on that one too, somehow. All right, Mike, well, I will see you when I get back to the office later this week.
- [96:41]
B
All right, sounds good, Paul. See you everyone.
- [96:44]
A
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 and 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 SmartRx Slack community until next time, stay curious and explore. AI.