The Artificial Intelligence Show Podcast - Ep. 205: AI Labs Refocus on Agents and Enterprise, Trump’s New AI Framework, & Meta’s Rogue Agent

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All the labs realized what Claude code unlocked, and it wasn't like it was the first coding agent; it was just the best. They did something different with the harness, how they enabled it to do what it does. All these labs see not the finish line, but the next mile marker of agentic capability and their ability to automate AI research, and their ability to then, as Logan Kilpatrick's deleted tweet said, start disrupting everything.

Welcome to the Artificial Intelligence Show, the podcast that helps your business grow smarter by making AI approachable and actionable. My name is Paul Ritzer. I am the founder and CEO of Smarter X and Marketing AI Institute, and I am your host. Each week, I am joined by my co-host and Smarter X 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 205 of the Artificial Intelligence Show. I am your host Paul Ritzer, along with my co-host Mike Kaput.

We are recording Monday, March 23rd, about 10 a.m. Eastern time. Some big stuff happened last week, Mike. The whole last week was just crazy. We were on a company retreat for two of the days, so I almost feel like I lost track of time for that. Then my entire week was spent getting ready for the company retreat to create you and I both taught workshops, which we will talk a little bit about, to the team. I did five presentations and workshops, I think, on the first day. So, it was a little bit of a crazy week, but in between all that, we had over 50 different sources in the podcast sandbox this week. So, as usual, Mike did an amazing job of curating the topics for today, and we were updating what we were going to say even about 3 minutes ago before we came on. We still may adapt it as we are moving forward.

There is just some big stuff open, and their kind of shift, but it is sort of a larger trend about what is going on with the labs. There is some new polling data about AI. Meta has a rogue agent there.

There is a lot to unpack this week. So, this week's episode is brought to us by AI Academy by Smarter X. If you are a regular listener, you hear us talk about AI Academy a lot. This is the core focus of what I do at the company, and it is a huge part of what Mike does at the company is building the content and the curriculum for AI Academy. It is designed to help 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 are always up to date with the latest AI trends and technologies. Our AI for Industries collection features six course series and certificates that are designed to jumpstart AI understanding and adoption across industries. The six that are available right now, and they are part of the overall AI Mastery Membership program, or you can buy them individually. We have AI for Professional Services, AI for Healthcare, AI for Software and Technology, AI for Insurance, AI for Financial Services, and the newest one that just came out Friday, last Friday, was AI for Retail and CPG. So, these series are an ideal launchpad for organizations that want to level up their teams and accelerate that AI adoption and impact. Mike teaches a number of them, including AI for Professional Services. So, later on in the episode, we are actually going to get some insights from Mike of some of the big takeaways he had from that series. Again, this probably is going to be part of a new element of the podcast. We are going to start trying to drill into some of these core series we are creating. We are spending so much time researching and building these things, we want to bring some of those core insights to everybody as part of this podcast. We are going to start doing some of those. So, Michael will tee that off this week with AI for Professional Services. So, individual and business account plans are available now, or you can buy those single courses and series, as I mentioned, for one-time fees. You can go to academy.smarterx.ai to learn more. All right, Mike, we have our AI pulse this week. So, this is smarterx.ai/pulse.

You can participate in these pulse surveys each week. They are informal polls of our listeners where we ask a couple of questions related to that week's episode. So, last week, Mike, we had Atlassian lay off 1,600 workers and explicitly cited the AI era as the reason. What is your reaction? So, we had 39% said this is the new normal, AI-driven restructuring is real and accelerating. We had 26% say it is AI washing, a fast-growing company using AI as cover for cost cutting. And 25% said it is too early to tell; we need to see if the roles are truly replaced. And then 11% said, "I am more concerned about the total tech layoffs in 2026 than any single company." I do not have anything really surprising there. It is a pretty balanced response overall, but I think 39% is the highest response rate that it is kind of the new normal.

And then in a New York Times quiz, 54% of readers preferred AI-written prose over human originals. What is your reaction? I wonder if it is the exact same people who answered this. It says 39% said not surprised; AI has gotten genuinely good at clean, polished writing. 28% said writing quality was never the real moat; taste, judgment, and point of view are. And then we had 20% said this is a wakeup call for professional writers to differentiate beyond surface quality.

And 14% said the quiz was flawed. It is kind of an irrelevant result.

Okay.

So, we will give you the two pulse questions for this week later on at the end of the episode, but again, smarterx.ai/pulse if you want to participate in those pulse surveys each week. All right, Mike. So, the first one is, it started in our sandbox as a bunch of OpenAI news. There was a whole lot of stuff.

I am going to let you unpack what happened with OpenAI across the 15 articles that we were looking at. And then I am going to do my best to sort of zoom out and say what is actually going on at all of these labs, because this, I think, is a major shift happening. And when you start looking at the collection of all of this information at the same time, you start to kind of see the trend of where this is going.

All right, Paul. So, right now, OpenAI is in the midst of executing what might be one of the more dramatic strategic pivots it has done so far. So, it is simultaneously restructuring how it sells, what it builds, and who builds it, all while preparing for a potential IPO later this year. So, on the enterprise side, Reuters reports that OpenAI is pursuing partnerships with multiple private equity firms in deals potentially worth a combined $10 billion. These firms include places like TPG, Advent International, Bain Capital, Brookfield Asset Management, and others.

And the PE investors would contribute approximately $4 billion and receive equity stakes, board seats, and influence over how OpenAI's technology gets deployed across their portfolio companies. So, the logic here is that private equity firms control massive portfolios of enterprise companies and influence their tech spending. So, this partnership gives OpenAI a distribution channel directly into those businesses.

Now, notably, Anthropic is also reportedly courting private equity, including Blackstone, signaling that this may become a standard go-to-market playbook for some of these frontier AI companies. Now, on the product side, OpenAI is consolidating its web browser, ChatGPT as a whole, and its Codex coding tool into a single unified desktop, what they call a super app. So, Fiji Simo, who leads OpenAI's applications division, confirmed this move, saying the company is cutting back on "side quests" to focus on coding and business users. At an all-hands meeting on March 16th, Simo laid out the commercial goal that they want to convert OpenAI's 900 million users into "high compute users" by turning ChatGPT from a consumer chatbot into a productivity instrument built around agentic AI. Now, interestingly, they are facing quite a bit of competitive pressure on this front. So, according to enterprise software vendor Ramp, the proportion of businesses using Anthropic increased from 1 in 25 to nearly 1 in 4 within a single year. And Anthropic currently wins approximately 70% of direct comparisons against OpenAI in new enterprise contracts. Now, meanwhile, at the same time, OpenAI is also going all-in on fully automated AI research. So, founding OpenAI member Andrej Karpathy went viral this past week. We talked about this last week, describing an experiment where he deployed an autonomous AI coding agent to run continuous research for 2 days. It is called Auto-Researcher. And basically, this agent, as we discussed, executed hundreds of experiments, discovered new optimizations, and sped up how well the model itself worked in terms of its training time. Now, interestingly, Shopify's CEO tested the same approach on his internal company data, running an agent overnight that conducted dozens of experiments and improved performance by almost 20%. Now, the point here is that Karpathy says all frontier AI labs will adopt this approach, calling it "the final boss battle" these labs face.

So, there are reports that OpenAI is following suit, going all-in on this idea of trying to build an AI researcher. Now, lastly, they are reportedly nearly doubling their headcount, according to the Financial Times and Bloomberg, over the next year as they scale across all these initiatives simultaneously. So, Paul, maybe connect the dots for me here. OpenAI is making some pretty big, pretty sudden changes.

Yeah.

So, the trend I was referring to is this goes back to episode 189 of the podcast on January 6th. So, right out of the holidays was when Claude Code sort of blew up and it became very hot over those last two weeks of 2025, and we spent an entire segment of the episode talking about what was happening with Claude Code and how it, something had definitely changed. And so that was the starting point, and all the major labs are in this accelerating race for autonomous agents and enterprise customers. So, that is the thing I reference. When we first started the outline for this podcast yesterday, there was just this focus on OpenAI, but when you look in the totality of all the articles we are looking at, all the tweets we are seeing, you see that this everything has changed to this refocus on agents and enterprises, which was not really OpenAI's core. It is not like they were not going after that audience, and they were not building agents before, but Claude Code changed things. And you and I, Mike, can attest to this. It is incredible within Claude, the ability to build things. I will give an actual example a little later on in this episode, but it changed things, and they are ahead of everyone, very clearly ahead, when you use the product. So, I am just going to break down a little bit the OpenAI thing, but then I want to get into the bigger picture. So, you mentioned the Fiji Simo's talk about private equity firms that they are in these advanced talks, and that both OpenAI and Anthropic are aggressively courting these PE firms, which makes a ton of sense, and we have talked about this a little bit before on previous episodes. But Anthropic, as you mentioned, is winning in this space. So, OpenAI's enterprise business, according to Reuters, is $10 billion out of the total analyzed revenue of about $25 billion right now. And I said that is a run rate; they are not actually at $25 billion yet in a year, but that is the run rate they are on right now. And she tweeted on March 16th, this news came out a little earlier than we planned. "We are excited to be building a deployment arm and will share more details soon." So, that is what we are talking about, this idea of kind of getting out with these frontier alliances where they are actually working with the consulting firms and stuff. So, there is just a lot going on where they are trying to get to where the enterprise customers are. And then when it started getting into this idea of refocusing, which is interesting because I remember last fall we were talking about this; all of a sudden, Sam Altman is everywhere.

They are going to do space stuff. They are going to do robots again. They are going to build the video gen apps and social networks and devices with Jony Ive. They are just everywhere. And it was like, "Whoa, you are getting crushed right now on the model side. Why do not you focus on the model side?" And it appears they have come to realize that. So, Fiji tweeted on March 19th, "Companies go through phases of exploration and phases of refocus. Both are critical, but when new bets start to work, like we are seeing now with Codex, which is their version, like Claude Code, it is very important to double down on them and avoid distractions. Really glad we are seizing the moment." I remember when I first saw that tweet, I was like, "That is weird." It is just a weird tone on a tweet, almost like people were questioning whether she was behind this focus because that is not what she was brought there to do. She was in part brought to diversify based on her background. So, I think some people may have taken this news as almost like a slight against what she was supposed to be doing there. I do not know, but that is how I read that tweet was like, "Wow, that is a really interesting trying to set the tone that you are behind all this rock-and-roll stuff." So, that was in relation to the Wall Street Journal article that said, "OpenAI Plans Launch of Desktop Super App to Refocus, Simplify User Experience." In that, there was a quote that said, "We realize we were spreading our efforts across too many apps and stacks, and that we need to simplify our efforts." That was from Simo. "That fragmentation has really been slowing us down and making it harder to hit the quality bar we want."

That said, top executives, including Altman, Chief Research Officer Mark Chen, and Simo, have spent the last few weeks reviewing OpenAI's product portfolio and looking at areas to de-prioritize. And then at an all-hands meeting, she told employees they could not afford to be distracted by those "side quests" you mentioned, and that they are in this major battle with Anthropic, and it is basically like a code red internally. This is all related to this idea of a fully automated researcher, which is not news, and we have talked about this being something they are working on for at least the last year.

But I think the timeline is maybe starting to become more clear. So, they said their new research goal is the North Star for these next few years, pulling together multiple research strands, including work on reasoning models, agents, and interpretability, meaning knowing what the models are doing and why they are doing it. And then there is even a timeline. OpenAI plans to build "an autonomous AI research intern," a system that can take a small number of specific research problems by itself by September. A lot of what Andrej Karpathy is talking about is sort of a prelude to this stuff. He said the AI intern will be the precursor to a fully automated multi-agent research system that the company plans to debut in 2028.

It is a weird timeline to me. I do not know why it would be that long, but anyway, this AI researcher, OpenAI says, will be able to tackle problems that are too large or complex for humans to cope with. You mentioned the idea of these again, these side projects. What that means when you hear "side projects," it could be things like the Sora video generation app, the standalone app. I have got to think the planned hardware devices fit into this bucket if you did not spend the $6 billion on Jony Ive. But I have got to imagine that there is a chance you get delays in the hardware because that is hard. It is a difficult thing to pursue, and that could definitely be a major distraction. And then e-commerce features in ChatGPT, you could see those kind of get sidelined. So, there are lots of interesting things they have been doing that could get sidelined in all of this. And then you mentioned, in the same time, they are doubling headcount. So, they are aiming to grow to about 8,000 employees; they are at about 4,500 today, according to the Financial Times. And then, overall, it is like it creates this muddied relationship continuing with Microsoft as well. So, again, when I started zooming out, it is like, well, what is going on with all the other labs?

Like, we hear so much lately about the challenges Microsoft and OpenAI are having as they try and reimagine that relationship so that OpenAI could get in a position to go public. And in the process, they allowed them to start developing partnerships with people like Oracle and AWS, which I will talk about in a moment. So, then we get into the Microsoft thing. Now, we will talk a little bit more about this one in rapid fire, and we will drill into this, but the premise is Microsoft made a major shift last week where they are moving Copilot under Satya Nadella. So, they are actually moving it under another executive, Jacob Andrew, but then he reports directly to Satya. And they are taking Mustafa Suleyman, who was in charge of Microsoft AI, and he is going to run the super intelligence lab. It sounds like at the same time, Microsoft, according to the Financial Times, is weighing legal action over $50 billion Amazon-OpenAI cloud deals. So, now we have this weird muddying of relationships between Amazon and OpenAI. You have XAI. So, one of the other major labs, basically five major labs in the US. XAI, Elon Musk tweets on March 12th. This is following lots of turnover at the AI lab; a lot of the co-founders have left in the last 60 days. He tweeted, "XAI was not built right the first time around, so it is being rebuilt from foundations up. Same thing happened with Tesla." So, you have XAI, one of the major labs, is, according to Elon Musk, in basically a complete reset mode. And this is a month after on February 12th, they got acquired by his other company, SpaceX. So, just 40 days ago, SpaceX, who also is an Elon Musk company, said on Monday it had acquired XAI, the AI company controlled by Musk, to consolidate his empires and kind of build this one unified company.

So, that combined company now includes X, like the Twitter platform, and includes XAI, and then they have a deep relationship now with Tesla, his other company. At the same time, Musk is suing OpenAI, and that is supposed to go to trial in like April, right? I think that is moving to an act. So, you have this crazy thing, but Elon Musk is watching what has happened with agents and enterprise. He wants a piece of that, and he realizes, "Wow, we did not build this the right way. Let us just hit the reset button." And nobody hits the reset button faster than Musk. Like, if something is not working, he is going to blow it up.

Then you have Meta. We talked about this. So, March 12th, Meta delays rollout of new AI model after performance concerns. So, they are spending what, over $15 billion last year just on talent acquisition. So, they are investing heavily. They are rumored to be spending $135 billion this year on like CapEx to build out the future of everything, and it does not seem to be working yet. They have not released a major model since they acquired Alexander Wang and Scale AI. So, Meta is sort of in upheaval; they have kind of fallen off.

Let us say them and XAI are just sort of like down at the bottom right now. You had Yann LeCun leave XAI, but then Meta shows up and buys Molt Book, the AI agent social network that went viral because of fake posts back in earlier this year. So, Meta is trying to get in and have a piece of this agent game. They would probably love to play in the enterprise world, but that is not their natural thing. Then you have Jensen Huang talking last week about OpenCLAW being the next ChatGPT. So, there is a CNBC article. It says Jensen Huang, the CEO of Nvidia, on Tuesday pointed to a fast-rising AI project called OpenCLAW as a major step forward in how people interact with artificial intelligence. He said it is now the largest, most popular, the most successful open-source project in the history of humanity. This is definitely the next ChatGPT. OpenCLAW is an open-sourced autonomous agent platform that goes beyond traditional chatbots. Instead of answering questions, these agents can complete tasks, make decisions, and take actions with minimal input from users. Nvidia moved quickly to build around OpenCLAW's momentum. The chip leader on Monday announced NemoCLAW, an enterprise-grade version of OpenCLAW that layers Nvidia's software stack and tools on top of the platform.

And then you have Google DeepMind. So, Google, you know, came in hot with Gemini 3. It was great. It is powerful. They have just last week announced some major improvements to Gemini within Google Workspace, which we experience, Mike, every day. We use Google Workspace and we embed Gemini.

They have had kind of a runaway success with NotebookLM.

Even though, I mean, when you talk to the average business leader, they have no idea what NotebookLM is. So, like in our bubble, NotebookLM is amazing, and we talk all the time, we have courses on it. The average person has no idea what it is or how to use it. So, they have had success building these individual apps like Notebook and Gemini. They announced a major investment last week in AI Studio where they are trying to get into the vibe coding game, so trying to play along with like how Claude Code and stuff is.

But the reality is AI Studio is still for developers. I cannot, I do not know how to use it. I went in there last week. I was like, "Okay, maybe it is ready for me to use it." And it is like, "No, it is not."

So, Gemini, while amazing, Google DeepMind, incredible, they have no answer to Claude Code right now. It is running circles around them. And based on what we like, what a Google engineer said. So, we talked with this on episode 189. Janelle Dogen, a principal engineer at Google, on January 2nd, tweeted. So, this is them saying it, not us. He said, "I am not joking, and this is not funny. We have been trying to build distributed agent orchestrators, which is exactly what we are talking about with like OpenCLAW and Claude Code at Google since last year. There are various options. Not everyone is aligned. I still cannot believe this tweet was allowed to go out. I gave Claude Code a description of the problem. It generated what we built last year in an hour. It was not a very detailed prompt, and it contained no real details. Given I cannot share anything proprietary, I was building a toy version on top of some of the existing ideas to evaluate Claude Code.

It was a three-paragraph description." And then, "When will Gemini get to this point?" I think someone asked. "We are working hard on it right now, the models and the harness." And then I thought this was really interesting, Mike. So, Logan Kilpatrick, who is sort of like, you know, head of AI developer relations, basically. So, he is like a major player within Google DeepMind, came from OpenAI.

He tweeted, "I could not believe I saw this tweet, and I was like, holy, that is going to come down fast." And it did. He deleted it. It said I think this was on Saturday. "All the industries you thought were not going to be disrupted by AI are about to be disrupted." They are not allowed to say that.

Google customers are reading that saying, "I am sorry, what?"

Yeah.

100% true. You cannot say that.

And so someone got that down real fast. So, Google is sort of like in this crazy phase where they are trying to build it into Gemini, they are trying to make it like function within the productivity tools that they have, while DeepMind is telling you that like every industry is going to be changed.

So, then I will wrap here with what I think is, if you want to be ready for the technical stuff when you listen to Andrej Karpathy. Like, he is Mike and I talk about Andre all the time. He ran Tesla computer vision for 5 years. Co-founder of OpenAI, did a bounce back to OpenAI for about a year.

Now he is an independent researcher. He has been on fire on X the last like 3 weeks, just like all these crazy things he is working on, but he did an interview on the No Priors podcast. Again, if you are ready for the technical side of this, listen to this episode. We will put it in the show notes. A few key notes that I was listening to this yesterday, actually. So, a few key things. He was talking about how fast these models have evolved and how it is largely a skill issue, which is funny because that is a term my son tells me. Like, when he beats me in a video game, he is like, "It is a skill issue, Dad." Like, if I lose in Mario Kart, I am like, "Ah, it is the wrong character." He goes, "No, skill issue."

So, apparently, that is like the lingo right now. So, he was saying it is a skill issue if you cannot get value out of these models. There is this idea of token maxing, which is a very technical concept, but it actually makes a ton of sense. So, every time you use one of these models, you are basically burning through tokens. So, tokens is when a large language model does something, where an agent does something, it is basically making predictions using tokens. Tokens are like pieces of words, in essence. And so you get an allotment of these tokens. So, let us say I use a million, two million tokens, whatever.

So, he was saying, like, if you are an engineer, you want to know what your token budget is. Like, how much AI can I use in my job? And so this idea of token maxing is like, for the average user like you and me, Mike, I have a Claude license, I have a ChatGPT license, and I have a Gemini license. And if I am not maxing out my subscription every month, I am leaving like intelligence and outcomes on the table.

And so he was saying there is this pressure right now, especially on coders, to max out your available tokens because if you do not, you are just like, you are not getting the full value.

And I think that concept is starting to carry over eventually into knowledge work, where you are like, "We have these AI tools, we are not fully utilizing them, and we are just leaving value on the table by not maxing out our tokens each month."

And in a similar place, he talked about this idea of running projects in parallel, which I do. Like, I will go into Claude and be like, "Okay, I am going to give it this project. I am going to go over to ChatGPT and have it work on this project." And so there are times where I am running three projects simultaneously with AI agents while I am doing my other work. So, like I am going to do an email or something else, and I have got. And so that is a big thing. And then he talked about the compression of timelines to complete projects, which I am going to talk about in an upcoming topic here about our company retreat.

But I think that is a very important concept, that things that used to take 5 hours, 10 hours, 20 hours now might take 5 minutes. And that is a weird environment to be working within. And then he also talked simultaneously about this idea of compression of software stacks, where we used to have a CRM tool and a social tool and all these tools.

And it is like, I am just going to have a swarm of agents, and they are going to go talk to all this software, and I am just going to have like a single user interface. And then the final one I will say with Karpathy, and again, this is all relevant to what these labs are doing.

These, if you listen to the Karpathy interview, all the labs are realizing what Karpathy is realizing on agentic capabilities, and they are now in a race to do what he explains in this. And that is why this podcast episode is so important. He is telling you point blank what all the labs are trying to do with agents, and you will walk away with a better understanding of the moment. But then he said at one point, "Working with these agents is like simultaneously talking to a PhD student and a 10-year-old." So, like, sometimes you do something with it, and it is like it was like giving it to a top PhD student, and then the next moment it is like some stupid simple thing, and it just cannot do it. So, it is that idea of the jagged frontier and the jaggedness of these models.

So, zoom out recap. All the labs realized what Claude Code unlocked, and it was not like it was the first coding agent; it was just the best. They did something different with the harness, how they enabled it to do what it does. All these labs see not the finish line, but the next mile marker of agentic capability and their ability to automate AI research, and their ability to then, as Logan Kilpatrick's deleted tweet said, start disrupting everything. And so it is an all-out race for agents, and they are seeing a pot of gold with enterprise adoption, which is why Anthropic and OpenAI are doing deals with PE firms. It is why they are doing alliances with major consulting firms. They are trying to get in and get where this is going to be because the labor replacement value of being the model they go to when they reduce workforces and put it all into AI models to token max to get work done. They see that future coming very fast. And it is important, like, I if I just covered a lot in like 20 minutes here. I think it is very, very important that you understand what we just covered, like that is what these labs are doing, and it is going to become very apparent, I think, in the next like 3 to 6 months that this is full go where they are headed.

Probably a pretty good time to be an enterprise buying AI technology. I am assuming these labs would like to court you.

Yeah. Yeah. You get a lot of credits, especially like, "I will give you the first million free."

All right. Next up, we have got three separate developments this week that are painting an increasingly complicated picture of how Americans actually feel about AI and how Washington is responding. So, first, we had some new polling. David Shor, who is head of data science at Blue Rose, appeared on the OddLots podcast with some interesting polling data. So, his organization has found that over the past year, AI rose in issue importance, and its importance as an issue, faster than any issue his firm tracks. It is now more important to voters than climate change, childcare, and abortion.

According to their polling, 79% of voters are concerned the government does not have a plan to protect workers from AI job losses. 77% are concerned about entire industries being eliminated. 56% are worried about personally losing their job to AI. This is hitting at a time when 61% of Americans say life has gotten less affordable in the last year. Only 25% feel confident in their financial future. Only 34% say that they are, in their opinion, have a secure job. So, what Shor's data shows, and he is polling from this perspective of trying to kind of find political messaging for the Democratic party, is that this whole idea of, "Hey, everything is going to work out just fine," that message is dead on arrival. They actually found when leaders in government and tech say AI will not cause widespread job losses, net trust is -41. And when they say AI will create economic productivity that benefits everyone, net trust is -20. Now, you are starting to see this play out across the political spectrum because, second, up this week, we got dueling AI political declarations. So, first, there was a coalition that involves a lot of unlikely bedfellows, including Steve Bannon, Susan Rice, Richard Branson, Ralph Nader, Yuval Noah Harari, and others, who released the Pro-Human AI Declaration.

This basically called for a prohibition on superintelligence development until there is broad scientific consensus it can be done safely, as well as a number of other manifesto points about keeping AI pro-human. So, over 40 organizations signed this, and they also found in their own polling that Americans would rather prefer human control over the speed of AI development by an 8:1 ratio. However, another organization called Build America AI published a direct counter to this manifesto titled "We Cannot Afford to Pause AI." They argued safety and innovation are not opposites, and the US already has regulatory tools through existing authorities to manage AI development. Now, third, the Trump administration unveiled a national AI legislative framework with seven pillars. This is a short document, but basically gives legislative guidance on how they think legislation should evolve related to AI. And this framework takes a pretty clear "try first rather than regulate first" posture. It opposes creating any new federal AI regulatory bodies. It defers copyright questions to the courts rather than legislating. And it recommends Congress preempt state AI regulations that impose undue burdens on developers, establishing what it calls Americans' "right to compute."

There is an interesting part in here in shifting responsibility for protecting children online from tech companies to parents. So, rather than imposing strict industry standards, they are actually shifting more to empowering parents with tools to protect kids online. The framework also calls for Congress to empower Americans to challenge federal agency efforts to "dictate the information provided by an AI platform."

So, basically trying to make sure that there is no undue influence on what information is provided by AI. So, Paul, I am curious. There are a number of threads going on here. If you are in the AI industry or just observing or trying to navigate these changes yourself, how are you thinking about these numbers and the moves on either end of the political spectrum?

Like any research, we always talk about you have got to know who is doing the research and what their goal is, what kind of bias might be in the research.

That being said, it is going to become more political. And as we have said many times in recent months, these are all trial balloons. They are trying to figure out what do Americans think about AI, and is there an opportunity to move votes a few percentage points one way or the other by taking a strong position on AI, which Republicans and Democrats have not really done for the most part with voters.

So, the one thing that is becoming more interesting to me is, you know, I always read this research and think these people do not know what AI is. Like, you are asking them questions about something that they do not understand.

And now I am actually thinking out loud here of like, that is maybe an advantage for politicians that want to manipulate and persuade people to vote one way or the other. So, if you do not know what it is, then you can create whatever you want, right?

100%. So, if AI, if people generally like, "I do not know," like, whatever, then it is like, "Okay, let us hammer the message of it is going to take jobs, and it is going to, data centers are going to ruin communities," and now that is all AI is to people.

So, this is a maybe a dangerous slope we are going down here, where we are seeing the early efforts to try and gauge what is perception so that we can then influence perception of what it is to move votes one way or the other. So, David Shor, I did not know who he was.

I did not know his organization. So, that is always the first thing I do. It is like, "Okay, we see some cool data, and it is getting shared everywhere on X."

"Who are these people?" was always the first thing I ask. "What is their mission?" So, David Shor is head of data science at Blue Rose, based in New York, originally from Miami. "I try to elect Democrats." That is his ex-profile. So, what I just read is his ex-profile. So, there is no hiding what the point of this is. Blue Rose helps campaigns make higher-quality strategic decisions by democratizing access to act measurement. That is on their About Us page. The name Blue Rose symbolizes turning blue what is now red.

So, again, there is no hiding what this is for. David Shor is a prominent American data scientist, political consultant, and expert in public opinion polling. Now, that does not mean it is not valid research. We are just saying, like, there is a perspective here. That is the whole point of understanding this. He actually worked for Barack Obama's 2012 re-election campaign. So, the survey, just to put it in a little bit of context, when it says AI is like the fastest-growing issue, you have to understand it is actually 29th out of 39 issues right now, though. So, yes, it is growing fast, but the top five issues for Americans are cost of living, the economy, political corruption, inflation, and healthcare. Those do not really move; those are pretty common top five. Then if you go down to like 25 to 30, so just to put in context of where AI falls, you have war in the Middle East at 25, international trade, income inequality, voting rights, then artificial intelligence, then race relations. So, while it is growing fast on the surface, Americans do not really care. It is not something that would jump out to you as votes are going to move based on that. But it is changing fast. You talked about some of these key ones, Mike. "The government not having a plan to protect workers from job loss driven." Oh, the question was, "How concerned are you about?" And then it said, "The government not having a plan to protect workers from job losses driven by AI." 79%. So, you do not need to understand what AI is to be like, "Yeah, it kind of worries me. They do not have a plan." And that is 100% true. They do not have a plan. Or if they have a plan, they are certainly not talking about the plan. So, everyone should be concerned that the government does not have a plan. Then it said, "How concerned are you about young people entering the workforce and finding fewer job opportunities because of AI?" 79%.

They should be concerned. That is happening. That is a real thing right now. So, again, but whoever is asking these questions, Republican, Democrat, independent, it does not matter. That is a fact. It is harder to find jobs right now. Entire industries being eliminated by AI faster than new ones are created. That is a ridiculous question. We are not getting rid of industries; companies being disrupted, sure. Career paths. So, that is an absurd question.

You could just throw that one away. AI changing the job market in a way that drives down wages for people like you, 72%. You could replace AI with any variable, anything you ask. It is like, "Are you concerned with, you know, something driving wages down?" Well, of course I am concerned. I do not want my wages going down. So, it is like, whatever. You or someone in your family losing their job in the next year because of AI, 56%. That is a reasonable concern. And then when they said, "When leaders in government and tech industry say AI will not cause widespread job losses, net trust," as you mentioned, is negative 41. Distrust it somewhat, 35%; distrust it completely, 32%.

So, 67% distrust it somewhat or completely. Now, that may align with 67% of people do not believe anything the government tells you. So, I have no idea.

So, again, just framing where the data is coming from. Then there is another one, Data for Progress, which is a progressive think tank and polling firm that provides data, research, and messaging strategies for the progressive movement. They produce polling on policy issues and support campaigns. They came out with new research on February 27th, which is worth mentioning here.

This is 1,200 US likely voters nationally using a web panel. So, they are asking about how frequently they use AI in their daily lives, whether they have favorable or unfavorable views of the tech, and how confident they are in their ability to spot AI-generated content. This is a pretty short survey.

We will put the link in. It is only like five pages. You can read it for yourself if you want. But some of these questions are pretty interesting. "Do you have a favorable or unfavorable opinion of the following people or institutions?" They asked about AI. Democrats, -3 net favorable; Republicans, +11; independents, -5. They asked, "When it comes to AI tools such as ChatGPT," so now again, they are trying to qualify for you what are we talking about when we talk about AI? So, if you understand what ChatGPT is, at least you have some concept. "When it comes to it in your personal life, have you mostly embraced or resisted using them to assist your life? Or have you found areas where you could use AI in your personal life?"

Embraced: Democrats 32, Republicans or Democrats 34, Republicans 32. Resisted: Democrats 35, Republicans 33. And then, "I have not found areas that I could use AI in my life." Democrats 30, Republicans 32, which is totally balanced. Like, there is really nothing there that would indicate any anything they can do with that data to move people one way or the other. Then they had another one. "Sometimes people use AI to make fake or edited photos and videos that they post online. How confident do you feel in your ability to spot that stuff?" Very confident, 15%; somewhat confident, 35%. So, that is 50% think they can figure it out.

Oh, they do. Yeah, right.

That they cannot. Yeah, they are wrong. Yeah. But and then they did an interesting one where they were like comparing data from August 2025 to February 2026, where they asked, "How frequently, if at all, do you use the following? Using AI such as ChatGPT for your job." So, right now, 14% say multiple times a day. 44% rarely or never. 11% a few times a month. So, you have 55% of these people being polled in February 2026 that a few times a month, rarely, or never. So, again, if you think everybody is doing this, they are not. And then the one you mentioned about the Pro-Human AI Declaration, again, it is important to kind of know where the counter is coming from. So, the AI industry super PACs, we talked about this last year. CNBC had this as well as others. There is a super PAC called Leading the Future, and the contributors to this are Andreessen Horowitz, OpenAI co-founder Greg Brockman, Palantir co-founder Joe Lonsdale, and Angel investor SV Angel founder Ron Conway, and AI software company Perplexity. So, these are people like pushing the super PAC, which is all about acceleration, and it is all about like rapidly accelerating what is going on, and they are basically saying that this stuff is ridiculous. So, the Build America AI is in essence led by this group, and they are saying, "We cannot afford to pause AI." So, this is a TechCrunch piece that highlights the re of the Pro-Human AI Declaration. The document you mentioned, the goals behind that effort are understandable.

People want AI to be safe, and they want clear rules. Those are fair concerns, but this is still the wrong direction. So, these are the super PAC people.

Pausing frontier development will not solve the problems that supporters claim it will solve. If anything, it risks making several of them worse. It would slow the research that helps us understand how these systems behave in practice and weaken Americans' position at the exact moment our adversaries are invested heavily in advanced technology. We cannot hand hostile actors on the world stage a strategic edge. That is what would occur if we paused AI.

And then that leads to the AI legislative framework from the government, which is just the starting point. I think that is the most important thing to take away from that. It is just like guidance on where they think legislation should go. It is not doing anything yet, but you covered some of it. It is like protecting children, safeguarding and strengthening American communities, respecting intellectual property.

That is a really funny choice of words.

Respecting intellectual property rights, meaning they do not want you to have property rights as a creator, and supporting creators, preventing censorship, and protecting free speech, enabling innovation, and ensuring American AI dominance. That is probably the most important one. And because all the other ones fall under that one, and educating Americans and developing an AI-ready workforce, which I am definitely intrigued to hear what they have in mind there. So, yeah, again, it is just, I think what we are seeing, we have said this like recently, every week there is now going to be more and more on the political side. We are moving into the midterms. We are moving into the moment where the political parties have to decide whether or not Americans care. And this election cycle is either going to be all about AI or it is just going to fade away.

And you are seeing the push towards data centers being bad, job loss being bad, and then you have got the Leading the Future super PAC people who are like, "All of it's great, and it's all going to create an abundant future for all of us." And if you do not believe that, then believe we have to beat China. Like, that is basically the messaging.

You know, it is like, "Choose your fighter." Like, that is it. And I do not know where the middle ground is here, but right now, neither side really knows. But the super PAC, the Leading the Future people, they are going to push hard on this stuff, and they are going to try and make you believe it is all going to work out and jobs are not going to be lost. And you always have to, what I would just encourage people to do is, like, do not get stuck in whatever your traditional political silos are. You know, if you only listen to one perspective on this, this is an issue where you cannot just be listening to one perspective that you have always followed.

I think it is really important to realize neither political party knows the answer here. They are both trying to figure it out.

And so it is really important that you open your own mind and look behind who is saying things and what the goal they have behind saying that is or where their research is coming from. It is going to be very important to try and keep a level head on this stuff and be listen to arguments of both sides.

To your point about people often being polled who do not know what AI actually is, that is the point of some of these numbers. We would throw out half these questions if we were doing actual research. But if they surface a strong opinion or view on AI, even if that view is wrong, that is really useful polling to certain people because it tells you exactly what you need to say and hit on using that ignorance almost as a weapon in some ways.

Yes, facts and lies mean nothing in election cycles. It is all about what can you say that will get you to remain in power. And that is, again, I do not think that is a controversial perspective.

It is what it is. Like, they are going to tell you whatever you want to hear to stay in power or to get in power, both sides. So, form your own opinion, like, form your own informed understanding of the situation. And then from there, you can take more logical actions to make sure you know, I do not know, it is like situational awareness, I guess, about what is happening with this issue. It is going to become a major issue. I think they are going to find that they are going to find the levers to pull. They are going to find the wedges to create frustration and anxiety around AI, and that could get very dicey. Our third big topic this week is about, Paul, this Smarter X annual meeting and retreat we had over the last couple days of last week with our team. So, this was super inspiring. We spent a couple days together collaborating. Day one, we talked about vision, goals, KPIs, priorities, and growth initiatives. Day two, we ran AI productivity and AI innovation workshops, which are designed to accelerate responsible AI adoption across business units and teams. And the reason we wanted to cover this and dive into it is because it has some signals, maybe some lessons here about overall company transformation with AI, because Paul, I will let you kind of unpack this for us, because what we were able to achieve over just two days, both in how we were approaching AI and by actually using the technology, I think can teach us quite a bit about how AI is changing the way businesses operate.

Yeah.

I mean, a couple of things, and Mike, you and I have not talked about this, like it is so if you have other perspectives or things to add, let me know, but yeah, the reason I wanted to highlight this is a few things came to me. So, it was like two days. There was a part of me that thought it was a great example of what you can do with the time you gain from AI. So, the fact that we use AI so intelligently within our own business gives us a little freedom to say, "Yeah, let us take a full two days. Let us go do this thing. Let us go think, let us go spend time together, build camaraderie, like do all the things we should be doing." And as I was sitting there, I kept thinking, "We have got to do more of this." Like, what does an AI-forward company look like? And how do you take the benefits you gain from AI, the efficiency and productivity gains, and redistribute that in some way? I am not a 4-day work week guy. I do not think that is reality. I do love the idea though of like, "Let us do more of this."

Let us have like, once a month, let us just take like an afternoon and just think and talk and work on big ideas. I find that to be more like enables the work to be more fun and more fulfilling if it is not just, "Let us token max every minute of every day." So, I think there is like, in some ways, I want to build, I want to maximize what we can do, but I also want to make sure we are getting the benefits of it. It is not like a race to some endgame or like some competitive race.

So, yeah, the way we set it up was, day one was, as you mentioned, sort of the company day, vision, goals, KPIs, building scorecards, a rocks workshop, or setting priorities for the coming quarter. And then, I think, just like the thing we teach, which is setting expectations for everyone of what an AI-forward professional looks like.

And in some ways, modeling that by showing in real time how we are using AI and making sure everyone on the team understands the capabilities. So, Mike, you did on day two, you led off with this AI productivity workshop, and you talked about the idea of not only jobs as tasks, but tasks as workflows, which I loved that framing. And then you went through like an AI capabilities overview of like, what are all these things the models can do so that people started to think a little differently about their own daily lives at work. We demoed JobsGPT, CampaignsGPT, and InnovationsGPT. I did that one in mine.

But those are some of the free custom GPTs we have built that we make publicly available. We use them in our own teams. Like, we literally use these tools to train our own teams. And as an example of like this AI-forward idea in real time, so Mike is doing his workshop, which was awesome because I have never sat through one of Mike's workshops. So, Mike and I do these things all the time for other companies.

We do them at our MakeOn event, but like we do not have time to sit in each other's workshops. So, he is doing this workshop, and he is showing AI kind of layering over workflows and reimagining workflows. And he showed this AI capabilities slide, and then he turned it into it was like a spreadsheet with like 90 rows or something, like 90 different capabilities and features across some of the major AI tools. So, you can quickly like pick and choose and filter and map things to all the individual tasks you are doing as part of a workflow, for instance.

So, like reasoning capabilities, video capabilities. So, yeah, it is like, so I was like, "I love this." And I was looking at this thing. I am like, "I wonder if we could turn this into something." So, as he is talking, I take the spreadsheet and I put it into Claude Code or or just Claude. And I am using Sonnet 4.6 at this point. And I said, "Help me visualize this. We wanted to help professionals understand the full capabilities of today's leading AI models so they can apply them to their work." That was the entire prompt.

So, it did it, and I was like, "This is really cool." And I said, "Is there a way to turn this into an app that I can demo internally?" So, like three minutes later, I had this functioning app. So, Mike does not know this is happening.

He is just on stage doing his thing. But the best part, and this, I am still like trying to wrap my head around this.

Mike, we do not have a Claude license for the team. So, when Mike built his capability slide, it was what? Google, uh, so Google Gemini, ChatGPT, NotebookLM, and I spun out kind of deep research for both tools as kind of its own capability set, right? Okay. So, this 90-row worksheet does not have Claude in it, but I am talking to Claude to build this interactive demo. So, Claude says, it first asks me a question, "How would you want to share run it?" And I said, "Standalone and HTML files, fine." It then said, "What should people be able to do beyond browsing? Select all that apply."

And then I just, simplicity, I was like, "Just browse. That is enough." Then this is the question that blew my mind. It said, "Should Claude be included as a fourth tool?" So, it was aware that it was not part of the spreadsheet he created, and it asked me if it should add itself to the spreadsheet. I literally laughed out loud when I saw this. I was like, "What?" And so I said, "Yes, add Claude." Great. And it did. And it followed the exact model he had done for the. And then it built this interactive capability thing. I mean, it honestly blew my mind. And as I said, like, we do this stuff every day. I see this stuff every day, and there are still moments where I am like, "I cannot even believe it was capable of doing this in real time." And so when I say Claude is like running circles around what some of these other apps are capable of doing, AI says it is capable of doing. This is a perfect example of it.

Yeah, you oneshotted a 90-item capability database. More than 90, because it added in probably 25 different things from Claude. Oneshotted it in a way that genuinely was professionally designed, extremely intuitive. It was great.

It was functionality experience. Yeah.

Unbelievable. So, the other one I will share, and again, we will touch on some of this kind of later on, was rocks, and I put this on LinkedIn on Sunday, and I actually featured this in my newsletter, The Executive Insider newsletter. So, I am just going to read what I wrote because it summarizes it really well. So, I basically was saying, like, we went through this retreat, and like one of the things became apparent to me as an example is this idea of rocks. So, we use a modified version of rocks from the EOS system, in which departments and individuals establish three to five priorities per quarter, and then the rocks allow us to align our time, energy, resources on what matters most, and it provides transparency. So, if I want to see what are like the five things Mike is working on in Q2, I can go. Or if I want to go see what the studio that Mike leads is doing, I can go see that. So, the thing that became abundantly clear to me is the time to complete rocks is compressing, and that it requires a complete rethinking of business operating systems. So, for example, during a live session where I was actually demoing, so this is part of the company day, I was demoing a new AI assessment tool we are developing that I will share more about in probably a month or two. And I so I had used Anthropic Claude Code, again, Sonnet 4.6, in real time to build an interactive reporting dashboard that visualized and analyzed responses from 17 people. So, I had built this assessment in Google Forms as like an MVP, and then Mike and I tested it the day before the retreat just to make sure it worked. And so I had my data and Mike's data, and then I had everybody else take it, and then I exported that CSV from Google Sheets.

So, that was it. That was like the entire process. Zero coding, zero design abilities to do this thing, and I give this to Claude and I said, "So, this is while we were taking a lunch break. I ran this." I so here is my prompt. "I had 17 team members take the assessment. Can you come up with an elegant way to visualize the results based on the format model you already created?" So, I had to create one for me and Mike. And the CSV is attached. So, in a previous life, which I said, like, aka 3 months ago, before Claude Code really started working, this would have been my entire Q2 rock, like, "Create an interactive dashboard to visualize assessment results for teams." I would have spent 10 to 20 hours researching dashboards and developing a brief. Then I would have invested time and money hiring a designer and developer to conceptualize, build, iterate on the design and capabilities. Then we would have gone through weeks of internal testing and revisions. And then maybe by the end of Q2, I would have actually had a minimum viable product that I could demonstrate to the team and pilot with users. Instead, in about 5 minutes, while I got a plate of pasta, Claude did the entire thing with one prompt, and the final product was beyond anything we could have possibly created." And I told Mike, I was like, "I am going to try this. I am going to do it." And then he and I are both just like waiting, like, "We should go check the laptop. Did it do it? Did it do it?"

It was insane. It was totally interactive, better than anything I could have possibly designed myself or worked with a developer to build. And I am now going to use that to actually turn it over to a developer and say, "Here, let us build this and like take this, you know, live in like 30 days, hopefully." So, yeah, we share this as a little bit of like behind-the-scenes of how we think about Smarter X as like an AI-native company, like event and media and education company. And two, just to bring to life the fact that you do not need any coding ability to all of a sudden now just build stuff, and it is totally compressing the timelines to do everything in business, and it is changing the way every day that I think about how to run our own company and how to advise other people to build their companies.

Yeah, I would argue we have quite well-done and clear and ambitious rocks, at least, you know, in our department that we were working on during this workshop. But yeah, it is actually kind of laughable that all five of them should take three months.

Yeah. I mean, I really think my guidance to the team was like, "Five. I want you to have like five for your department."

I actually think you need 20. Like, I because Right. Right. And and you need like some categorical thing of like, "Hey, this is, you know, this would take 10 to 20 hours of human labor. We think we can do it in 10 minutes." Like, there are honestly things that are just going to be like that. There are going to be all these quick win rocks where it is like, "Well, it used to be 3 months worth of work, but it is probably 3 days now with mostly AI." It is like level three AI. It is going to do most of the work. So, the companies that figure that out and realize that and restructure how they are building everything stand to do really well. And just two final really quick notes here, but to piggyback on what you did with AI during these workshops. So, the AI capabilities map I built, which was again like 90-ish rows of all these different capabilities and features.

That is a lot to figure out on your own. And what is really cool is I determined the framework I wanted to use and worked back and forth with Claude to say, "Okay, what is the most sensible way to organize these once I am, you know, once we have them? I do not have them yet."

And then it is like, "Okay, we have got a really solid system. How do I get them?"

Typically, you might go do a bunch of research. You might have to sort through all sorts of documentation. I just went into each tool and screenshotted all my menu options and dropped them into Claude and said, "Guess what? We are going to go create the spreadsheet based on the framework that you and I came up with and go have at it." And then it basically oneshots a 90-row spreadsheet. It is incredible. And the same type of thing during your innovation workshop. I fed Claude a lot of different context about my department, the content studio, some of my context around our organization, and then using your framework that you developed, layered that on top of that context, and what Claude is now able to do and got better innovation ideas than I could have come up with. First, on my own at all, and second, in an entire day, I did it in like 20 minutes. So, it is so powerful, not only just using the right tools, of course, but having these proven frameworks and models and ways of thinking layered over them. All that stuff we have spent lots of time developing as like IP or as unique models to approach these things with in our workshops. It is like rocket fuel at this stage.

Yeah.

Yeah. And I think there is just something like a lesson to be taken from how we structured it because obviously our team is probably more informed than most teams about AI capabilities, but honestly, I do not know that they even were aware of a lot of the things these models could do.

Um, and so it was very intentional how we did this, and I would advise other companies to think about a similar model where you have this kind of like state of AI, like what is it capable of? And that is often what I will go in and do with enterprise, like I will do like a state of AI for business, and here are the capabilities, here is what you need to understand. Then you do the productivity workshop, where it is like, "How do we get efficiency and productivity in our tasks and workflows?" Then we will often do a problem-solving one too, but the innovation one is how we closed, and I intentionally wanted to close with that. Because once you understand what it is capable of, and once you have solved like the lower-level efficiency and productivity things, now you open your mind to the possibilities. And I mean, and then we go around the room, and each person gives us like one or two innovations they are super excited about.

So, then you leave after 2 days actually feeling like ready to go, not like drained. It is like, "Okay, that was amazing. I want to go do those things now." And that was what I people come up to me like, "Okay, can we do these things that we just talked about?" So, I think it is a really cool format for people. And so, if you are trying to get your team on board, you know, borrow that format of like, make sure they are understanding of it. And if you need help with it, give us a call. This is like what Mike and I do all the time. We run boot camps and workshops, and so if nothing else, we can advise you on ways to do it. But if you know, if you are in big enterprise and you need help with it, just like, you know, we can come in and do stuff like that, too.

All right, Paul, before we dive into rapid fire, quick message here. This episode is also brought to you this week by our upcoming webinar, which is unveiling our AI for CMOs Blueprint, presented by Google Cloud. Now, this is actually happening the week you are listening to this episode, Thursday, March 26th, at 12:00 p.m. Eastern, 9:00 a.m. Pacific. And in this session, me and our CMO, Kathy McFillips, are going to break down the insights from this AI for CMOs Blueprint we put together in partnership with Google, where we break down real-world state of AI for CMOs, use cases, tools, strategies, and more.

We will also be doing some in-depth discussion and live Q&A. So, registration is free. All registrants will receive ungated access to the full AI for CMOs Blueprint. So, go to smarterx.ai/webinars to go register.

All right, let us dive into rapid fire, Paul. So, first up, Microsoft CEO Satya Nadella is taking some more direct control of the company's Copilot product, personally overseeing a restructuring that consolidates consumer and commercial Copilot into a single organization. Jacob Andrew, a former Snap SVP who joined Microsoft last year, now reports directly to Nadella as the new EVP leading Copilot experience across both segments. The restructuring frees up Mustafa Suleyman, the DeepMind co-founder who became CEO of Microsoft AI in 2024, to focus entirely on what he calls the company's super intelligence efforts. This move apparently comes as Copilot trails quite badly in the AI assistant race. Copilot has 6 million daily active users compared to ChatGPT's 440 million. That is according to a CNBC article. Gemini has 82 million. Claude has 9 million. Nadella wrote to employees that Microsoft is "doubling down on our super intelligence mission with the talent and compute to build models that have real product impact."

So, Paul, what does this tell you about where Microsoft is headed with AI? Like reading this, I was like, I know it sounds like Mustafa is excited, but this feels more like he is getting sidelined, and we need to get real serious about Copilot real quick, which is kind of what we have heard anecdotally from users of Copilot.

Yeah, there are lots of variables going on here. I mean, one is the shift in their relationship with OpenAI. You know, they are they they were obviously a major investor in OpenAI. They are a major equity holder. I think it is somewhere around like 27% they own of OpenAI, but all of their efforts were being built on top of OpenAI's models.

And now, again, if you go look at what we were just talking about with Claude, it is like you are almost at a disadvantage as an organization if you cannot use when a breakthrough happens, when somebody builds just a better thing. You are at a disadvantage if you cannot use that thing. And so if Microsoft was stuck using OpenAI technology, and all of a sudden Claude races ahead in some really important component, that is not great. And then if you are Microsoft, and you are one of the three biggest companies in the world, the fact that you are not building your own models is probably a disadvantage moving forward. And so I think there was this shift where they realized that probably, you know, a year and a half, two years ago, that they were going to have to remove their reliance on OpenAI. It probably happened the day Sam got fired when that became like, "Oh boy, we are all eggs in one basket, and it could go bad real fast."

So, you know, I think there has been this ongoing shift where they knew they needed to invest in their own technology, build their own models. They need to have kind of an off-ramp over time from their reliance on OpenAI. And then in November of last year, they announced this "humanist super intelligence" movement. So, we talk about in episode 179, which was on November 11th, but Mustafa had tweeted, "It should not be controversial to say AI should always remain in human control. That we humans should remain at the top of the food chain. That means we need to start getting serious about guardrails now before super intelligence is too advanced for us to impose them." And then there was, you know, linking to an article from November 6th that was called "Towards Humanist Super Intelligence," where he said, "At Microsoft AI, we are working towards humanist super intelligence, incredibly advanced AI capabilities that always work for in service of people and humanity more generally." So, again, we have kind of known this was happening at that time. I think I pulled what I said. I said, "Maybe Mustafa stays at Microsoft to realize this vision, but I cannot help but feel like this vision will eventually clash with the need to justify their investments in AI." And so I think what they are basically saying is, "You go focus on this stuff, you know, focus on the future and the building of this thing, but Copilot is critical to our business right now, and it is not where we want it to be."

And that now needs to get much closer to Satya. And that is basically, I think, what has happened here. I have no idea if Mustafa stays and keeps doing what he is doing, if they really do believe in this humanist super intelligence thing, but I do not see Wall Street loving the humanist super intelligence. I do not think stock prices are going up because of that blog post or that vision.

They want to know how you are going to compete with Claude and work with Anthropic, and that is all that Wall Street is going to care about. And at the end of the day, Satya and Microsoft have a fiduciary responsibility to return shareholder value. And I do not think that messaging plays. So, I do not know.

We will see. It fits into that whole thing I started off with where, like, these AI labs are shifting focus, and you are going to see a lot of reorgs, a lot of, like, they tried something, it did not work. Like Meta burned $10 billion on the metaverse and changed their name to be Meta, and it is done. So, just there are going to be lots of big efforts, big misses, and you have got to move quick when it does not work. And I think this is an example of that.

Not to mention, anyone who is a Wall Street analyst of any type is almost certainly using Microsoft Excel and thus Copilot and sees it firsthand. So, or they used Claude in Excel and realized it was better than Microsoft.

You are right. That is what I was saying.

Right. They have a very close experience with perhaps some of the inadequacies of this tool. Yeah.

All right. Next up, an AI agent inside Meta took unauthorized action last week that triggered an actual security breach at the company. So, an employee used an in-house agentic AI to analyze a colleague's question on an internal forum. So, pointed the AI at the question, said, "Analyze this for me."

The agent then posted a response to the question on its own without being directed to do so. The second employee followed the agent's advice, sparking a domino effect that gave some engineers access to Meta systems they should not have been able to see. The security breach was active for 2 hours before it was contained. A Meta representative confirmed the incident and said no user data was mishandled, though the company's internal report noted unspecified additional issues that contributed to the breach. A source told The Information there was no evidence anyone exploited the unauthorized access or that data was made public, though the reporting notes that may have been the result of dumb luck more than anything else. The agent had also passed every identity check in Meta's system. That exposes some pretty serious fundamental gaps, might talk about in enterprise identity and access management. So, Paul, I am curious, how close are most companies to having this kind of thing happen to them? I do not know, but it is certainly a very viable thing. This is why I said in recent episodes, you have got to listen to it. I mean, there is a reason why some enterprises are moving really slow, especially when it comes to adoption of agents. And like, you know, we talked about the Jensen thing where he was like, "OpenCLAW is like the ChatGPT." I was like, "Okay, maybe, but you know how hard it is going to be in enterprises to do anything close to what that does?" This is the exact issue.

We just talked on episode 203 about something similar happening with Amazon, where it just like went rogue and started doing everything. And I think I joked at the time, like, "We could just do a rogue AI agent episode segment every week." Like, this is going to be a recurring theme, and it is going to become a major issue. The concerns around oversight and governance of these agents, and then these agent swarms that are just given access to stuff, and the breakdown you might then see in permissions controls. We had this conversation at our own company meeting.

It is like, "Can we connect this to that?"

"Can we connect that to this?" And it is like, "No, because I do not know yet the risks associated with that." So, yeah, that is again, it is one of these situations where the tech can do things, but it does not mean you should let the tech do things because there are so many potential risks. So, yeah, I mean, this is a crazy one. You should go like read the articles about it. It is pretty nuts.

Yeah.

I almost found this was like more notable because it happened. It was not like some super incredible agent just like giving access to it to your whole codebase or whatever. It was just like a total consequence of something that's actually like probably a pretty normal use case on the surface, saying, "Hey, let me use AI to analyze a question one of my colleagues posted on a forum." And then you are like, "Oh no, I realized that now this thing can choose what to do and how to do it." And that is like totally a weird way to start thinking here, right?

Yep.

Yeah. And again, go listen to Andrej Karpathy's No Priors podcast episode, and you will understand this stuff at a deeper level. He talks a lot about these risks and even himself not knowing. He talked about like setting it up to run his house, and he is, "Oh yeah, I gave it access to." I was like, "Go find Sonos," and it like goes into his network and finds the Sonos speakers, and then it goes security, and he just gave it access to everything. And he likes Dobby the Elf, he calls it.

Yeah, it is hilarious. So, again, this is a recurring theme. Really important to understand where agents are going, where these agent swarms are going, how they will eventually be used to run organizations, and how some people are willing to be out on the edges right now setting these things up and connecting them to their own company data. And we are all going to learn plenty of lessons from their early efforts.

All right. In our next rapid fire topic this week, the Anthropic versus Pentagon saga continues. The Department of War has fired back at Anthropic's lawsuits in a 40-page filing in California federal court. The Pentagon calls Anthropic an "unacceptable risk to national security," arguing the company might attempt to disable its technology or preemptively alter the behavior of its model during warfighting operations if its corporate red lines are being crossed. Recently, now nearly 150 retired federal and state judges, appointed by both Republicans and Democrats, have also filed their own amicus brief supporting Anthropic. We talked last week about how tech companies like Microsoft and Apple are all filing their have all filed their own briefs, basically arguing that this designation of Anthropic as a supply chain risk could mean the entire government procurement system becomes contingent on political favor rather than the rule of law. So, Paul, the big piece here is really this idea that a bunch of ex-judges are coming out and saying that they also support Anthropic in this. We have talked about if this is going to get resolved anytime soon.

There is a hearing on whether or not to grant Anthropic some temporary relief that is actually set for March 24th, the date this comes out. Where do we stand with this?

I do not know. The only context I might add is, I think it is just still this like "he said, she said" thing, like where the government is saying one thing and they are doing the other thing behind the scenes, but they are trying to give this perception that they are all on the right here, and Anthropic is this horrible company, and it is this huge risk. So, there was a tweet thread from Roger Parloff, who is a senior editor at Lawfare, and he, I will put the link in. He said some Anthropic updates. On March 4th, just hours before Hexith declared Anthropic a supply chain risk, allegedly due to threats of sabotage and data exfiltration, his undersecretary wrote to Anthropic, and they have the screenshot of the email, that they were very close to a deal, asking to change a prepositional phrase.

So, while Hegith is getting ready to like go on and blast them on X and say they are done, they are actually still negotiating behind the scenes, and they have screenshots of it. Then, since then, the government has claimed that Anthropic sought a veto over Department of Defense actions, but two top Anthropic officials assert it never did. And this is actually, like, legally, they submitted a briefing saying this is not what happened. Similarly, the government's purported fear that Anthropic might disrupt the military was never raised with the company and is a technical impossibility. So, they actually explained, like, "We cannot even do the thing they are claiming we would do." And then, as of for Anthropic's refusal to allow its product to be used for autonomous lethal warfare and mass surveillance, Hegith himself said those concerns were understandable, and the commander of US CENTCOM echoed those sentiments. Anthropic's head of policy writes. So, they submitted these briefs saying, like, "They agreed with us. We weren't even raising something that they didn't themselves think was an issue." And then he had one last update.

In the government's response Tuesday, it backed away from the secondary boycott Hegith called for in his February 27th "final decision" post on X, admitting it was lawless, but also taking no responsibility for its devastating impact. The hearing is coming up on March 24th. So, uh, yeah.

So, these are declarations, legal declarations from Anthropic's head of policy, Sarah Hec, submitted as part of their response to the case, and then they also their head of public sector.

So, uh, yeah, I mean, they are basically saying, like, "Well, here, I'll testify to like this never happened or this is what they said." So, the whole thing, as I've said many, many times, it has become this political thing. It has become a battle of egos on the government side. And, you know, I think that everyone sort of sees through that why they are actually doing this, and we will see what the courts have to say, I guess. It is impossible to tell, but based on that new context, it almost sounds like there is one possibility where did like Hegith jump the gun on tweeting about this when they were nearing the deal, and that... Yeah. Right. So, well, not only jump the gun, but like claim some power that they actually do not have. Like, Right. Right. Where like he's posting so aggressively when the deal's almost done before this all blows up, and now it's just like doubling down on a mistake, maybe. I do not know.

Right.

Or just you are just going to do harm either way. So, you do not really care if it is legal or not. It does not like the repercussions. Nothing is going to happen to me if I do this and say this other than hurt that company and try and use it as leverage to get them to do what I want them to do, which is not an unusual political tactic.

All right, next up, Google DeepMind has published a cognitive framework this week that attempts to answer the question: If AI actually achieved AGI, how would anyone know? So, the team here proposes a cognitive taxonomy with what they claim are 10 measurable traits of general intelligence on which to measure AI and its progress towards AGI, and it is divided into two categories. So, this first category covers eight building blocks of human cognition: perception, generation, attention, learning, memory, metacognition, and executive functions. And these combine to form two composite faculties that DeepMind considers equally important, which are problem-solving and social cognition.

They basically define these as the ability to process and interpret social information and respond appropriately in social situations. So, their proposed test here is pretty straightforward. They want to run AI models and humans through the same cognitive benchmarks.

And then they theorize you would get a measurable estimate of when a single AI can meet or exceed human capabilities across all 10 of these areas. DeepMind actually launched a Kaggle hackathon with a $200,000 prize pool to crowdsource evaluations for the five areas where the gap between testing capabilities right now is the largest, which are learning, metacognition, attention, executive functions, and social cognition. So, they say their goal is to move the conversation around AGI from one of subjective claims and speculation towards a grounded, measurable scientific endeavor. So, Paul, does this change anything about how we talk about AGI? Are we getting any closer to really defining what it is and actually measuring it?

Yeah, I mean, Google DeepMind has done the best job of trying to get to that point. You know, they had a paper last year as Shane Leg led on where he was trying to sort of define the different general capabilities and performance and trying to put some way to measure it. So, I like the effort to try and quantify it, making it more meaningful, try and get some maybe eventually universal agreement on what it is. The first thing I thought when I saw this is like, "Well, how do you not saturate these tests?" Wouldn't the models eventually learn what the tests are and just be able I do not know how they would do that to keep them like sandbox so the model does not end up with the training data, basically, that it eventually learns how to look like it has AGI because it just learned what the test was ahead of time? But I think the most important thing for our audience is that we just keep coming back to this. AGI is a really interesting topic. It is fascinating to sort of follow along progress towards it. It is a meaningless term related to what it does to impact your job, your company, the economy more broadly. So, we do not need to reach AGI, whatever that definition is. We do not need to agree on a definition for AI to transform businesses, the economy, and society. This idea of capabilities overhang, we talk about that Andrej Karpathy episode that I mentioned touched on this quite a bit. But just go back to that example I shared of rocks.

It is like if you have a company like ours that knows this stuff, we understand what AI capabilities are, and we look at an operating system of our company and we are like, "Oh, we are just going to reimagine the whole thing."

Rather than five rocks a quarter, we think we could do 15 or 20, like, easily, and here is how we are going to do it. So, we understand the capabilities and we are applying them to the best of our ability. Then take some other company that does not even have GenAI tools for their team yet. They have not even got them Copilot licenses or ChatGPT licenses. Like, they have done no personalized training, and they have never run a workshop internally. Like, they are not even taking advantage of any of the capabilities other than maybe using it as like an answer engine or a chatbot.

So, there is this overhang of we have all these capabilities, and so few companies are actually doing anything with them. Not just companies, educational institutions, governments, practitioners on an individual level. So, that to me is the most important thing.

So, I am all for this. I think like quantifying it so we can just get to the point we agree on what it is makes total sense. But do not be misled by that or wait around for that definition. Be like, "Oh, okay. I will worry about it when we get closer to AGI." It is already there.

All right. Next up, Anthropic has published results from the largest multilingual qualitative study ever performed on AI attitudes. They did nearly 81,000 interviews with Claude users across 159 countries and 70 languages. So, these conversations were actually conducted by an Anthropic interviewer, a variant of Claude trained specifically to conduct and then analyze interviews, which we have talked about in past episodes. Interestingly, they found out that the top fear of people expressed in these conversations is actually hallucinations/unreliability of AI, which ranks as the number one concern with 26.7% of people mentioning it. It is ahead of jobs and economic impact, which is at 22.3%.

And loss of human autonomy and agency at 21.9%.

Interestingly, Anthropic finds that people value AI often for the same capabilities that they fear most. So, 50% of respondents experienced time savings from AI, yet 19% felt pressured to simply work faster as a result. 33% cited learning benefits, while 17% worried that it would actually facilitate more cognitive decline when you are relying on machines to think for you. And it is interesting that people experiencing one side of attention are typically three times more likely to also worry about the other side, meaning these are kind of inherent contradictions in the same people using AI. Now, what is really cool is they actually asked what people actually want from AI. 18.8% of those who answered said that they seek first professional excellence from AI. 13.7% said they were seeking personal transformation. 13.5% said better life management. And 81% report experiencing some progress towards their vision in those areas.

Paul, I am interested what you took away from this data. Pretty interesting way they went about getting it.

The Yeah, it is the approach to research that I found most intriguing. So, I mean, the data is great. I do think again, as I referenced earlier, you have got to keep in mind, like, who are the people responding to these questions, things like that, when you look at the data so you are not just making some broad assumptions. In December of 2025, before Claude Code really took off, and before the government issues, and before like this movement to where the Claude app became like the number one app on the App Store, they have a heavy technical user base. Lots of like coders, lots of AI researchers using Claude. So, when you are looking at this, even though it is 80,000 people across all these countries, it is still likely skewed toward a more technical user. So, just for reference sake, that is important to keep in the back of your mind. So, I love the approach, this like dynamic approach based on responses. It adapts it. Not great news for people who run focus groups and who are like consumer research people for a living. This is definitely one of those ones where you are either adapting, or the whole new way of doing research is going to kind of run you over. They said their next Anthropic Interviewer study, launching shortly to a small subset of Claude users, focuses on Claude's effects on people's well-being over time, whether Claude is actually making people's lives better in the ways they want, and how it could do so more effectively, which I thought was interesting. And then they said, "This is a new form of social science that is qualitative research at a massive scale, and we are in the early stages of learning how to do it. Surveys and usage analysis tell us what people are doing with AI, but the open-ended interview format helps us get at the 'why.' Conducting this research has moved us and challenged us. We did not expect so many deep, open, and thoughtful responses. By far the most common reflection from our team was that this, it was viscerally moving to see Claude impacting people's lives for the better, and equally motivating to hear their concerns. We were equally gripped by the fears and downsides. People saying that the same availability making Claude useful is what makes it hard to put down, or knowledge workers worrying about outrunning AI's economic impact. When you come into contact with this much raw human experience, it knocks you sideways. They said the usefulness is real. And the question for all of us is how to claim the benefits without incurring undue cost." I thought that was really interesting to note, Mike, because this actually came up during our company retreat. This idea that we are all sort of at the frontiers of figuring all this out and using it, and it is awesome for productivity and innovation and efficiency and growth and all these things, but it also has this like very messy, complicated other side where it has this human impact, and maybe your friends or your family hate it, and they do not even like the fact that you are working on it, and they have these perceptions about what you are doing because you are in AI or because you are one of the people who talks about it. I honestly think about that sometimes from, you know, what we do on the podcast, Mike, where I think about like, "God, I hope at some point like people do not like we are trying to do the humanness in a rush." Like, we are trying to educate people so we can have a positive outcome, right?

But sometimes the truth does not matter, and like I do worry about that. It is part of the reason I do not read comments on social ever. Like, I do not, I do not look at our comments on YouTube and X, and maybe sometimes LinkedIn, but I just prefer to like try and just do our thing and like know we are trying to do a positive thing, but that does not change the fact that there is like darkness to this, and there is uncertainty and fear and anxiety and hatred, and like all those things are very real. So, I am really excited actually that Anthropic is going this research direction. Yeah, that is why I really actually like the findings here.

Obviously, to your point, they are skewed towards a certain type of people, but yeah, when someone asked at our offsite, "How do you stay grounded when you are dealing with such heavy and sometimes horrible, dark AI topics in the news?" That was like my answer was that focusing, not to the detriment of the negative, but focusing on the positive things that I have been able to do with these tools. Like, I have been able to do things, achieve goals, get results that I never dreamed possible. Like, it is genuinely, as technology has made me a better professional, leader, thinker, strategist, even husband and father. So, that is kind of the flip side. So, I love to see in this data people saying, "Hey, I am using this. I am trying to get out of AI, professional excellence, or personal transformation, or better life management. I have done all those things with AI, and it is glorious what you are able to do."

Doesn't Yeah. It doesn't get rid of the negative stuff or like the concerns, but it Yeah. It's trying to focus on the positive. So, yeah, this is something we're going to come back to too. We actually, so you, we brought on a director of research a few months back, and it's one of the focus areas she has is actually on the humanities side of this.

It's like to, you know, so we're actively, Mike and I and Taylor are actively talking about more research in these directions and the kinds of things around the human impact. And so, yeah, it's something we're going to probably be doing a lot more about on the show and then even with our academy is starting to talk about that stuff. So, very important, and maybe even on our event side, we might be looking at doing some stuff we can bring people together to have these conversations because they're critically important. Um, okay, well, as we wind down, Mike, we had mentioned at the start, the AI for Professional Services, which you taught as part of our academy. And so, again, one of the ideas we have is to do little spotlights on these where we, you know, without having even to take it, we give you a little bit of insights into some of the key things we learned in building these courses. So, Mike, with AI for Professional Services, any like key insights or takeaways that you think would be helpful for people to hear?

Yeah.

Sure, Paul. So, as part of this, you know, four-course series, which comes with its own certification, you know, we're breaking down both from a high level, what is happening at the industry level that you need to know about, and then getting into the actual tactical A to Z of here's how you identify your own use cases and match AI tools to them in your own professional services career. So, a couple things that just jumped out as part of both building this course and as someone that was in professional services before we did the whole AI thing is, number one, and we've talked about this on the podcast, one of these trends that really, really, really needs to be appreciated is the idea that the billable hour model is maybe not only on borrowed time, but is dead. Like, if you are on a billable hour model as a professional services organization, AI is a major threat to that because many, many organizations still have not adequately figured out what happens when you can now do things in a fraction of the time that you used to do them in using AI. You cannot simply charge the same amount of hours and hope to get away with it. So, you see a lot of industry professionals and leaders trying to figure out how do we adapt our business model without tanking our entire organization. So, one of the big takeaways there is the firms that are going to win are the ones that figure out sustainable, defensible value-based pricing first. So, pricing on outcomes, not hours. Because again, you can do so much more in the same amount of time.

There's no chance your clients, your customers are not going to demand that you pass along those savings to them.

And then I would also say another big area here is figuring out how your human intelligence within your professional services firm becomes your superpower and your competitive advantage. Because unfortunately for a lot of professional services firms, there are very intelligent AI models out there that now have been, for better or for worse, trained on a lot of your expertise. So, figuring out how your humans, with all their experience and background and domain expertise, can actually leverage be leveraged and scaled with AI is going to be the entire battle moving forward.

So, you really want to look almost at any frameworks, any experience you have internally as almost like your own IP if you're not already, because AI can scale that, and you can have that be a competitive advantage. But if you do not do that, if you are playing at the commodity level of, "Hey, we're experts in marketing," like so is AI now. So, you have to figure out what kind of expert you are and how you are differentiated. And then last but not least, I would say there's always these questions in professional services about like, "We'd love to get started with AI, but we work in really sensitive industries with clients that have privacy and data concerns about using this stuff." We have not figured that out yet. Totally valid.

We talk about that more at length in this course series. But the advice here is actually start with your back office stuff. If you have these kinds of challenges, if you are still trying to navigate data and privacy concerns, your back office stuff, I guarantee you can become dramatically more productive by applying AI, often at a very low-hanging fruit type of level. We go into very specific use cases and tools in the core series to help you do that. But there are these areas where that don't touch client-facing stuff that you can actually start your AI journey almost in the back office and achieve massive immediate profitability gains just from doing that alone. So, tons, tons more in the core series, Paul, but those are kind of three big takeaways there.

Yeah.

The other thing I think about, Mike, is just from the buyer's perspective, understanding the professional services and how it's evolving and how I should be looking for like AI-forward professional services firms. So, even for me as the CEO, we outsource legal, IT, accounting, advertising. Like, we work with an advertising partner. So, I just think about just those four, like understanding how their business models are evolving and the importance of working with AI-forward versions of those companies and our points of contact, things like that. So, yeah, it's great, and I appreciate you obviously building this series and, you know, this ongoing effort we're doing to try and sort of create content across all the departments, all the relevant industries, and then even into like businesses, try and make that stuff super relevant for people. So, hopefully, like these little spotlights will be helpful for people to get a little taste of what's going on in these different industries. We'll touch on departments.

We'll touch on some of the GenAI things we're doing, and just try and bring some of that value from the academy to the podcast each week.

All right, Paul, we've got a number of AI product and funding updates here to wrap up this week. So, I'm going to run through these. If there's anything that jumps out to talk about further, let's do it. But otherwise, I'm going to run through these. So, first up, Jeff Bezos is trying to raise a hundred billion fund focused specifically on AI manufacturing. This fund would represent one of the largest single pools of capital ever assembled around AI infrastructure.

Google has launched something called Stitch, an AI design tool that turns natural language prompts into high-fidelity UI design. So, the tool lets you describe what you want in plain English and generate production-quality design outputs. So, Google is kind of in this emerging "vibe design" category. Google also rebuilt AI Studio from scratch as a full-stack vibe coding platform. They said they spent actually four months on this rebuild, and the new version lets developers go from prompt to working application entirely within AI Studio. OpenAI has released smaller, cheaper tiers of GPT-4. So, GPT-4 Mini and Nano give developers access to the model family at lower cost and latency.

In some other legal news, a court temporarily allowed Perplexity's AI shopping agents to continue operating on Amazon. Perplexity's agents browse Amazon on behalf of users to actually find and purchase products. And this ruling lets the service remain live while their ongoing legal dispute, which we covered on a past episode with Amazon, plays out. On X, the company is rolling out AI-generated article summaries that appear when users share links on the platform.

Researcher Ethan Mollick noted the irony that many of the articles being summarized are themselves obviously AI-generated. So, we're creating an interesting loop where AI summarizes AI, and then it trains the Grok language model, and then it, I think it's part of the reason why they made articles such a prominent feature is to get a lot more training data that was proprietary to them potentially.

Yep.

And finally, and we'll be keeping a close eye on this one. Demis Hassabis, CEO of Google DeepMind, Nobel Prize winner, is teasing his upcoming book called "The Infinity Machine," set for release on March 31st, that covers the story of DeepMind and Hassabis' vision for the future of AI. I'll be looking very closely at that one, Paul. That looks interesting.

This one, I pre-ordered this one. I will, this is a good way to end today's podcast. I am actually going to read the excerpt because I think this is really fascinating. So, this comes from the, was it called "The Infinity Machine"? Okay. So, it says, "The true reason to build artificial intelligence," Hassabis was now saying, "went beyond Kant and Feynman. The goal was to draw closer to what might be called God, to the intelligence that may presumably have designed everything around us." Hassabis quote, "I am first and foremost a scientist. My goal is to understand nature, but doing science is sort of like reading the mind of God."

"Understanding the deep mystery of the universe is my religion, kind of. We humans, we have these faculties. The world is understandable. But why should it be that way? I think there is a reason. Computers are just bits of sand and copper. For Hassabis continued, now sounding more urgent. Why should these combine to do anything? I mean, it's absurd. The electrons move around, and then that creates an AI system that can defeat a Go master. Why should that be possible?" "This table," Sebastian Hassabis rapped his palm on it for emphasis. "Why should it be solid? This is beyond evolutionary coincidence. We can build electron microscopes and interrogate reality down to the most minute detail. We can build systems that detect black holes colliding more than a billion years ago. I mean, what is this?"

"What the hell is going on here?" There was a pause, but Hassabis was not yet finished. "I sit at my desk at 2:00 a.m. and I feel like reality is staring at me, screaming at me, literally screaming at me, trying to tell me something if I could just listen hard enough. That's how I feel every day. So, you can see why I'm trying to build AI. I've felt that since I was very young, that there's a deep, deep mystery about what's going on here. You can frame it how you want. You can call this God's design, or you can say it's just nature."

"I'm open-minded about the description, and I don't know what the answers will turn out to be. But at the moment, we don't really know what time is or gravity is or any of these things. So, there's a mystery waiting to be solved, and it encompasses just about everything. I would like to understand before I croak. I would like to understand." And then I'm perfectly fine to shuffle off my mortal coil.

That was awesome. Incredible.

Yeah.

So, that's um, and again, as we've said on the show many times, like Demis thinks very deeply about. Elon actually commented on that when he's like, "I like I share Demis' like urgency here and thoughts here." So, I think it's important to understand why one of the people, one of the five, why he's building AI, and it is for a much bigger solve: intelligence, and then solve everything else. That's been his mission, you know, for the last 30 plus years of his life, 40 plus years of his life.

Incredible.

All right, Paul, just one quick note here as we wrap up. Go to smarterx.ai/pulse to take this week's survey. We're going to ask a couple questions about the topics this week. One is about OpenAI's enterprise deployment with that private equity backing we discussed. The second one is about Anthropic's study and some of the findings there and how you feel about them. So, we'd love to hear from you. And Paul, really, really appreciate you breaking down everything for us this week.

Yeah, good stuff. Busy week as always. I think we just have one episode this week. I do not know. I did not check my calendar yet this week. Maybe we have a second one. But, uh, we will be back next week. And then I think I will be on spring break then for like 10 days. So, Mike, next week might be, we might be on a break after next week. So, yeah, thanks for being with us. Have a great week, everyone. And, uh, we will be back with you next week.

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