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The Artificial Intelligence Show
Ep.183: AI Job Automation, Is There an AI Bubble?, AI Political Divides, ChatGPT Turns 3, Claude Opus 4.5, Google vs. Nvidia & DeepSeek V3.2
01:23:44
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- [00:00]
A
If we stopped development of AI models today, if we shut off all the AI labs and all we had was today's current models, everything changes anyway. Like people don't comprehend how disruptive the tech we already have is. Welcome to the Artificial Intelligence show, the podcast that helps your business grow smarter by making AI approachable and actionable. My name is Paul Raitzer. I'm the founder and CEO of SmartRx and marketing AI institute and I'm your host. Each week I'm joined by my co host and 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 183 of the Artificial Intelligence Show. I'm your host Paul Raetzer along with my co host Mike Put. We are recording December 1st, Monday, December 1st, 11am a day after the anniversary of ChatGPT Mike, which we will talk about. Three year anniversary of ChatGPT. New models dropping already. We had two drop this morning so we will touch on both of those. Probably not in great depth because neither Mike nor I have had a chance to get into Deepseek and then the new video generation model from Runway. But those already happen. There are lots more releases coming. I think we still like last year, Mike, we had the 12 days of shipments, I believe is what OpenAI called it and I anticipate we are going to get that again. Some variation of that. So I don't know, buckle up. I think the first 20 days of December are going to be very, very busy with model releases. So lots to talk about. We've got some macro level stuff to talk about related to the AI bubble, what's going on in politics, a couple of new reports from MIT and McKinsey. So a ton to get into. I hope everyone had a great holiday week. I know I was out for the week myself. Like I did. You guys, you guys travel too, right?
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B
Yeah, we did as well.
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A
Yeah, it was nice. It was good. I actually, I mean outside of keeping track of everything for the podcast, I, I really unplugged for like four or five days, which was just amazing. So it's good to be back though. Good to be catching up with everything and starting to kind of wind down for the, for the year. I'm trying to do a ton of 2026 planning and I am leaning on AI heavily to assist me in that planning. So I don't know, maybe I'll share some of that stuff as we're going in the next couple weeks too. Some of the cool things we're doing. All right, so this episode is brought to us by AI Academy by SmartRx. AI Academy helps individuals and businesses accelerate their AI literacy and transformation through personalized learning journeys and an AI powered learning platform that we just launched. I guess it was. Was it last month? Yeah. Last month, right? Yes. Yeah. Losing track of months. There are nine professional certificate course series available on demand now with more being added each month. One of those featured certificate course series that I teach is piloting AI. This is actually the original thing we launched. This is the third edition of Piloting AI. So we've been doing this each year. So I did a completely updated version for this year. So the piloting AI series is a hands on guide to moving from AI theory to tangible business impact. In the series you will master a complete methodology for identifying, prioritizing and building high impact AI pilot projects. There are four courses in this series. Course one is piloting AI in business. Course two teaches the use case model for prioritizing use cases. Course three is the problem based model which teaches you how to take a different view of challenges within your organization and use AI to solve them more intelligently. And then course four is how to build your CO X. So it's about creating AI assistants that augment human potential. So I walk through how I created CO CEO and then we show how to apply that to other areas so you can learn more about AI Academy and our AI mastery membership program at Academy SmarterX AI. You can also use pod 100 for $100 off either an individual course series or the annual AI mastery membership. So again, use pod 100 at Academy SmartRx AI. All right, Mike, the AI pulse surveys. This is our third week of doing these. Third or fourth week. That's our fourth.
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B
Yeah.
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A
Okay. Our fourth week. So last week we asked, how frequently do you currently use Google Gemini? Any model of Google Gemini in your professional workflow? This one was surprising. So again, these are informal polls of our audience with 95 responses to this one during a holiday week, which is great. We pretty sure everybody taking the time to do it, 46% say they use Google Gemini daily. Mike, that was. I don't know, I mean, surprising. Yeah, I don't know. Like that's great. We're not comparing this to like vs chatgpt or anything like that. But apparently our audience are big Google Gemini users. So daily is 46%, 22% is weekly, and then only 16% say rarely. Or never. And 16% said occasionally. So yeah, I guess we have a big Google Gemini user base in our audience, which is cool. Yeah. And then the second one from last week was US Senator Mark Warner has warned that AI could spike unemployment for recent grads. We asked, has your company changed its hiring strategy for entry level roles? Now this one, Mike, I think like we, we maybe don't have a lot of HR people like in our audience like a dominant. So there's a chance that this one is just like they just don't know. And we actually had. What did we have? I'm not sure. Yeah, 23%, which is probably like they have no insight into it. 46% said no, our hiring plans have not changed. But 23% said yes, we are hiring fewer entry level staff.
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B
Yeah, that's pretty significant, I feel like.
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A
Yeah, because even if they're not in hr, they might be hiring managers in their departments and so they, they know this. So, yeah, that's interesting. Again, like our intention with these surveys is right now they're informal polls. But once we get to a threshold of, say, I don't know, four or five hundred responses, you know, it starts to become more projectable data based on our audience, our listeners. But right now it just kind of looked more sentiment and indicators of where things are going. So that's pretty interesting to see. Okay, so this week's Pulse survey, you can go to SmartRx AIPulse and you can participate in the survey. We have two really interesting questions this week. The first is with recent reports of AI related layoffs. How secure do you feel about your specific role over the next 12 months? I'm going to be really interested to see the responses to that one. And then the second question is, do you believe we are currently in an AI investment bubble? Now that question is going to become much more relevant once we go through the main topics. Today we're going to get more into this idea of an AI bubble. We touched on it in episode 182, but we're going to kind of expand on that line of thinking in today's episode. So again, how are the recent AI layoffs affecting your view of your role over the next 12 months? And then how do you feel about the, you know, if we are or not in this AI investment bubble? So go to SmartRx AI Pulse. Again, these are informal polls. We are not collecting email addresses. This is not a marketing thing. This is purely research to get a sense of where, where our audience feels about different topics that we're Talking about. All right, so let's kick off the main topics today. If you're new to the show again, I know we have new listeners each week, so it's probably good to do a quick reminder. These weeklies that Mike and I do, we go through three main topics and then we usually go through about seven to ten rapid fire items. So that's the format. And today's first main topic kicks off with a recent study from MIT that got a lot of headlines last week. Mike. As well as some background information as well on a new McKinsey study.
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B
All right, Paul. Yeah, so first up, we had a study from MIT and Oak Ridge National Laboratory that utilizes a simulation tool they created called the Iceberg Index. And this model found, among many other things, that current AI systems can replace today 11.7% of the US workforce, representing approximately $1.2 trillion in wages. And that's what's getting the headline. And the researchers note that while the tech layoffs in tech are visible, there's significant exposure that lies beneath the surface to AI in logistics, finance, and human resources. Hence the name of this Iceberg Index. Now, separately at the same time, a report from the McKinsey Global Institute estimates that demonstrated AI technologies could theoretically automate 57% of current US work hours. And the firm projects that by 2030, this shift could unlock $2.9 trillion in annual economic value. Now, McKinsey frames the transition not as pure displacement, but more as a move towards what they call, quote, skill partnerships between humans and agents. They note that 72% of skills are required for both automatable and non automatable work. Interestingly, as a result, they find that consequently, employer demand for what they call AI fluency has increased sevenfold over the past two years. So, Paul, that's just kind of a very high level look at what these two big studies, two big pieces of research are looking. I'm curious what jumped out to you here, because like you said, the MIT one's getting quite a bit of headlines. The McKinsey one's flying a little bit more under the radar, but has some really interesting stuff in it.
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A
Yeah, both make for great headlines, that's for sure. You know, the ones you kind of really want to dig into. So I, you know, last night when I was getting ready for this, I wanted to go through both the reports, as we always guide people, like, look at the methodology, you know, kind of see where the reports are coming from. We had the other MIT report we talked about a couple months ago that, you know, didn't really hold up in terms of its methodology. This is a different research approach. I mean, this is a very thorough research effort from mit. The project Iceberg is honestly a bit confusing. So I did download the full report and I actually read the whole thing and I was trying to comprehend what exactly this data point was saying and also just what they were trying to do with this project. So the website itself, again we'll put the website address and there's just Iceberg mit. Edu. You can go and look at this yourself. There's not a lot to the site yet. I was hoping when I went there I was going to be able to explore this data. They talked about all this analysis they did and very deep analysis and I couldn't figure out how to actually get to the data. So I couldn't actually assess these skills they were looking at and things like that. But basically the best way I can explain this is it seems to be a sandbox for policy evaluation. So they're actually trying to work with state level officials to guide on what these AI models mean to workforces. And so they're looking at identifying reskilling and training priorities and then evaluating investments across occupations, industries and communities. So it is more around like policy and economic development, workforce development, it seems. So the research report itself actually starts off with really strong validation as to why this research is important. So I'm going to. I'll go through a couple of elements here. So they said evidence and I think this is actually from the abstract of the research. If I remember correctly, evidence suggests workforce change is occurring faster than planning cycles can accommodate. Payroll data covering millions of workers shows a 13% relative decline in early career employment ages 22 to 25, which we've talked about that research on the podcast recently for AI exposed occupations. Analysis of job postings across 285,000 firms for 62 million workers reinforces the pattern. The demand for entry level positions have subdued while the focus has shifted to hiring for experienced roles. These shifts alongside widespread restructuring in the technology sector indicate that the pace of change is accelerating across the economy. States are committing billions to workforce programs while key workforce dynamics remain invisible to traditional planning tools. So there's a lot to unpack there. But this is definitely reinforcing key topics and data points that we have touched on in the last few months on the podcast. A lot. But this, this angle that we haven't really got into, Mike, that I really like the direction of where they're going here is when you look at like economic developments like in, in In Ohio, we have jobs Ohio. And they look at this, you know, training and reskilling of, of, of employees and trying to prepare for, well, where is the economy going so we can prepare people in our states for that type of work. And their point here is if you don't understand what AI models are capable of and where the crossover is of what these models are capable of doing, then your state isn't going to be prepared to upskill your workforce. So it's actually a very, very important point. And which is why I kept like, okay, I want to really dig into what they're doing here. So in the abstract, continued. By the time these changes appear in official statistics, policymakers may already be reacting to yesterday's disruptions, committing billions to programs that target skills already displaced. Without forward looking capability to test strategies before implementation, states cannot distinguish investments that prepare workers from those that arrive too late. Existing workforce planning frameworks were designed for human only economies. They track employment, wages and productivity, but were not designed to measure where AI capabilities overlap with human skills before adoption reshapes occupational structure. So I'll give a little bit more context in a moment. But Mike, as I'm saying this out loud, it actually I see the educational components of this as well. Like this is the kind of stuff at a high school level and higher ed. We should be doing the exact same things like what are the majors that we're offering that we're having kids going through four years, six years of college for? Are the skills they're leaving school with even relevant to the current workforce or where these models take us?
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B
So yeah, it's interesting. I don't know about you, but as I've talked with schools about designing curriculums, especially around AI, it's like they're in such a hard position where it's like you have to plan this curriculum out years in advance, at least a year in advance. It's really, really difficult to do. And they kind of speak to that in this research about how we're all just kind of playing catch up using traditional methods.
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A
Yeah, and even, you know, again, as we think about 2026 planning and you think about staffing for your company, these same things hold true. This is why I create jobs. GPT was to try and like project this stuff out. So like, are our staffing plans for next year even relevant or are the AI models so capable of some of the skills we're hiring for? We just don't need as many people as we did previously. So, okay, a little bit more on the index because again, now that I'm, like, talking out loud about this, I'm actually seeing a lot more relevance to this research. So it says the index measures where AI systems overlap with skills used in each occupation. And this is really important, related to the CNBC headline, Mike, that you led with. A score reflects the share of wage value linked to skills where current AI systems show technical capability. For example, a score of 12%, which 11.4 is what the headline said, means AI overlaps with skills, representing 12% of that occupation's wage value, not 12% of jobs. So it's all about skill overlap, not job displacement. And this is where I started getting confused. It's like, what exactly is the headline saying? And, yeah, you know, what are we trying to convey here? So then they actually have an FAQ on the iceberg page, and it says, does the index predict job loss or displacement? No, the index reports technical skill overlap with AI. It does not estimate job loss, workforce reductions, adoption timelines, or net employment effects. So again, they're stressing, like, this is all about trying to educate policymakers on where these overlaps exist. And then they're doing this through. And this is where I was like, I wish I could learn more about this part of it. So here's how they say they do this. They use what they call large population models to simulate the human AI labor market, representing 151 million workers as autonomous agents executing over 32,000 skills across 3,000 counties. So counties being within the states in the United States and interacting with thousands of AI tools. I don't know if I missed it, Mike, but, like, that's the part I really wanted to say. How in the hell did you do that? Like, what is the technology behind it?
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B
They're bearing the lead here, I feel. Yeah, the 11.7% is really interesting and important, but, like, wait a second. We have something that can model this, right?
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A
Where is this technology? Can I use it in advertising? What is this? So, yeah, that was the part that jumped out to me. And again, I may have just not been looking at the right stuff. I looked at the addendums, like the appendices in the report I looked on the site. So I guess stay tuned. We'll try and figure out more about this. But that's where the headline comes from. This idea that this 11.4, 11.7%, and 1.2 trillion in wages. That's what it means, is there's an overlap of AI capabilities and skills. So then the second one like that you touched on is this McKinsey study. This is a really solid report. Lots of data. It does get pretty technical. They also were taking a look at the exposure of jobs and skills to AI models and they use what they called a skill change index which shows which skills will be most and least exposed to automation in the next five years. Again, I think there's a couple of just highlight data points here I would encourage. If you do want to dig into this one, do throw it in notebook. LM play around with it, look at the data. It just gets kind of dense. And I honestly like I love McKinsey research. I had the hardest time reading their graphs. I don't know if it's just me and like I'm not like a researcher by trade but like I, I have to really really spend time on their charts to figure out what the hell they're showing me. So I often just lean on whatever their call out says. The chart is telling me otherwise like I have no idea what I'm looking at. So the demand for AI fluency as you mentioned Mike, the ability to use and manage AI tools has grown sevenfold faster than any other skill in US job postings. That's a pretty big deal. I like they call this out. They said integrating AI will not be a simple technology rollout but a reimagining of work itself. Redesigning processes, roles, skills, culture and metrics so people, agents and robots create more value together. That is such a critical point. It is something we stress all the time. You cannot treat AI adoption and scaling of AI as a technical problem. It is a people problem. It is a change management issue. And so it does take all of this deeper thinking. So I really like that they, they touched on that there's 2.9 trillion of economic value by 2030. Again I'm not 100% sure where they're getting that data point from. I've seen similar data points from them before, so it may actually be referring to a previous study. But they then do say if organizations prepare their people redesign workflows rather than individual tasks around people, agents and robots. You mentioned that I think this highlight about 57% of US work hours could basically be done today with automation that is currently capable. And they actually break that out then into 44% of US work hours today that are more knowledge based stuff and then 13% that are more manual or robot based. They did do a cool breakdown. They share these seven archetypes that I would recommend people just again this, this was actually a pretty understandable graph. So they show less automatable and Then more automateable on a spectrum basically. And so they break it into seven. They have people centric, which is future work done mostly by people, people agent, which is future work done mostly by people with agents. And that's gonna be 21%. According to them, 34% is the people centric. And then agent centric is future work done mostly by agents. That's 30% of current workforce. And then they get into people robot, robot centric and then people agent robot and agent robot. So again, they're looking at the people, the agents and the robots and trying to look at the future of all work. This isn't just knowledge work. So quick kind of synopsis here. This is, this touches on a lot of what I covered in my move 37 moment for knowledge Workers keynote at Macon. So I just figure I'll pull in a couple of key points from that talk that are sort of like supported by this data. So the first thing is the future of work hypothesis. So what I've been working on since spring of this year is this hypothesis that is within one to two years, AI model advancements and agent capabilities will force a radical transformation of talent, teams and organizational structures. Leaders face conflicting pressures to take a responsible, human centered approach to AI adoption while leveraging AI for near term gains in efficiency, productivity, creativity and profitability. The second point is we do not need to reach AGI however we define it. And we'll touch on this a little bit more in a couple of upcoming topics today. To completely transform business, the economy and society, companies will put a premium on AI literacy, which we just talked about. AI fluency demand a sevenfold increase, interpersonal communications, creativity, critical thinking, curiosity, emotional intelligence, imagination and adaptability. These are the kinds of things we think think it'd become more important in the hiring process and the development of talent. Two critical aspects of everyone's work, especially in knowledge work. Knowing what questions to ask of the AI assistants and knowing what to do with the answers and then knowing how to talk, to collaborate with and learn from AI. One other key point here, Mike, that we've talked a lot about is this idea of an economic Turing test. So while we look at this iceberg index and the skills index and all these things, at the end of the day when things really start to change is when businesses make a decision to hire an AI agent or a collection of agents working together instead of a person, not just for tasks and projects, but for full jobs. And that's the part where we're like anxiously watching for this to become the thing Right now, the job loss we're seeing is because humans can get work done more efficiently, they can produce more, not because AI agents are actually doing all the work that would replace an actual human. It's just efficiency. So that's why it becomes extremely important to consider exposure as the models get smarter and more generally capable, which is sort of what the McKinsey report is starting to look at as agents and robots. So again, really important research. They seem like well done both of these reports. Both, you know, noteworthy for sure, but yeah, like kind of dig into them, but at a high level. I think those are some of the key things for people to think about again, as we enter 2026 planning.
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B
Yeah, and I would just encourage our audience too. If you're a knowledge worker like me, not an entrepreneur owning a business, invert the economic Turing test for yourself and say, why would someone hire you over an AI agent or a swarm of connected agents instead of just hiring you. I mean, that's really helpful and a useful question to ask as you kind of evolve your own career. All right, next up, famed investor Michael Burry, best known for predicting the 2008 housing market collapse, has launched a public campaign betting against the AI industry. After deregistering his hedge fund Scion Asset Management, Burry is using a new newsletter to argue that the sector is in a bubble comparable to the dot com era. His central thesis, developed with researcher Phil Clifton, posits that the massive capital expenditure on AI infrastructure, which is projected to reach trillions over the next five years, far outpaces actual end user demand. Burry specifically challenges accounting practices at companies like Nvidia, alleging they overstate the useful life of chips that quickly become obsolete in order to justify the costs. Now Nvidia has actually aggressively pushed back against this. They sent a seven page memo to analysts and defended their accounting and stock based compensation as consistent with industry peers. Palantir CEO Alex Karp took a more informal approach and dismissed Berry's short positions as batshit crazy. According to him. Burry, however, maintains the industry faces a reckoning similar to telecom Cisco's crash in the late 1990s. So, Paul, I guess the question here is like, is there a bubble? And then also this jumped out at me in some of the reporting on this. This is a point from Burry and Clifton in their research saying the investment world is, quote, expecting far more economic importance out of this technology than is likely to be provided. Just because a technology is good for society or revolutionizes the world doesn't mean that it's A good business proposition. What would you say to all that?
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A
Yeah, so I don't know. We touched a little bit on this. On episode 182 we were talking about Nvidia's earnings and I think I even mentioned Burry at that time. I'll just, in case you didn't listen to episode 182, I'll just do a really quick recap of the points I was making there. So one, we're on the leading edge of an intelligence explosion. AI is going to be everywhere and in everything. That to me just is inevitable. Nvidia's rise and a lot of the related stocks and certainly the valuations of some of these AI startups has really come from these foundational models. Now Nvidia in particular from the training of these models. But the bet that all of these companies are making and this gets to the demand, will the demand be there, as Burry and his partner are questioning, comes down to the amount of intelligence that society will demand and specifically inference. So again, the models are trained and then they go through post training. And so the Nvidia chips are used to do all these things and to build out of data centers. And the requirements for all this energy, all of it has largely been about building the models moving forward. It's all about delivering those models to all of us through devices, through AI assistants. But those models aren't just doing text anymore, they're doing reasoning, they're doing image generation, video generation, audio, 3D worlds, AI agents, robotics, all of these things are going to require dramatically more computing power, dramatically more energy than what is currently available in the world, which is why they're all spending tens of billions, hundreds of billions on the build out. So most people I talk to, and I talk to a lot of very savvy investors and I get asked all the time about different stocks. And the general response I always give is like, people, even very savvy investors, have no concept of where this all goes, how these models advance, what the demand is going to be for this sort of stuff. So as I've said, there'll be losers in this. There will be multibillion dollar companies that just go away. There will be dramatic drops in stock prices. We'll touch a little bit, I think, on Nvidia's drop last week when it came out that, you know, Google was doing deals for their TPU chips. So there's going to be all kinds of stuff happening. But you have to play the long game. You have to look at where this is. Like, I think back to like the doubt investors had in Google after ChatGPT was released. Like Google got destroyed for a while. Like there was just all these questions about can Google survive. It's like, you forget who, who created all this. Like, this is why I was always like, why would you bet against Google? I never understood this. They were better positioned than everybody. But everything has become about the next earnings call. And so that's why these AI bubble. All this talk is like, everyone always anxiously awaits the Nvidia earnings call. Microsoft, Amazon, Meta, Google. They're waiting to see are they going to keep building out, are they going to keep investing in the future. Do they have high conviction about where this goes? So specific to Burry, I have no idea. I mean, obviously the guy bet right in 2008. He has bet wrong numerous times since then. He obviously knows more about this stuff than I could ever know in terms of like the actual accounting side of everything, the finances. He picked a fight with Elon Musk this morning. He's just going after everybody. So who knows if he ends up being right on some of this. But I don't know. I have pretty high conviction about the overall premise of where this all goes. Without getting into the fine details of balance sheets and accounting methods people are using to, to look at the depreciation of chips over time and like things like that that Burry's assessing. I've just always looked at things like I'm not smart enough as an investor to challenge people on that level of thinking. For me it's about the companies I'm exposed to, the people I talk to, my belief in, like where this all goes. So the advice I would take here and sort of like my big takeaway on this AI bubble thing is you have to consider your own position from a personal investing perspective, but also from a career and business perspective. To your point, Mike, about if you're an employee or if you're a business owner, an entrepreneur, or if you're just thinking, where do I invest my retirement money? I think what you have to do is have a long term thesis about AI and then you have to decide what your level of conviction is. So again, I always stress I'm not giving anyone investing advice on any stocks. I don't, I get asked by friends, family, co workers, like, I don't give advice on specific stocks. But for me, I'm 47. I bet everything personally and professionally starting in 2016 that AI would change the world. Every business and every industry would become AI, native AI, emergent or obsolete. So at that time I personally started betting on companies that I thought were best positioned to grow when demand for intelligence scaled. And then I built my own companies to help professionals and businesses accelerate their AI understanding and transformation. So personally, from a personal investing perspective, from a businesses I'm building perspective from where I'm betting my own well being and financial strength on, I believe we are on the leading edge of an intelligence explosion. I think it's just starting. I don't think we're at this like, oh, Nvidia is screwed because people are buying TPUs from Google. Like, okay, it's not like their business model became irrelevant overnight. So that doesn't mean that every AI company survives. As I said earlier, there's going to be winners and losers. There's going to be ups and downs in the stock market. There will be variables that are out of our control that will impact the markets in the months and years ahead. So in my AI timeline course that I teach as part of AI Academy, I have this, this breakdown of like what accelerates progress, like what actually moves this faster and then what slows it down. So on the what slows it down part, there could be a breakdown in the AI compute supply chain due to like earthquakes, hurricanes, physical or cyber sabotage. Like there's things that we just have no idea. There could be catastrophic events that are blamed on AI, which gets into the political side of all this stuff. There could be lack of value, created enterprises, like maybe it just doesn't work. I don't think that's going to happen, but that's a possibility. Landmark IP lawsuits that impact access to training data, legality of existing AI models, restrictive laws and regulations. We talk a lot about state regulations, societal revolt against AI due to job loss, politics, perceptions, fears, unexpected collapse and scaling laws. I don't think, but that's talked about a lot. And then voluntary or involuntary halt on model advancements due to catastrophic risks where the labs say, hey, we got to stop. Like it's, it's getting too good, too powerful. So at a very, very high level, I think there's a far greater risk in sitting back and assuming this is all a bubble than positioning yourself and your company to thrive in the age of AI. That doesn't mean there's no risk. It doesn't mean that there aren't going to be bubble like events where companies just disappear or valuations drop 10% and so you have to again, if you're late in your career, if you're sitting on a retirement fund, you're like, should I keep it all in Nvidia. That is on you and your investment advisor to talk about the risks you're taking. As I said, I'm kind of middle of the road here. I'm 47. I got a long investing career ahead of me. I got a long business career ahead of me. I'm in it for the long game and I'm placing my bets accordingly. But everybody's got to decide their own thing. But again, have a hypothesis about where the future goes and then decide what your level of conviction is in that hypothesis and let that then guide your decisions.
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B
I love that. That's great advice. So our third big topic this week, the ongoing political battles around AI are getting a bit more intense this week based on a number of stories that we're tracking. So first, the New York Times reports that a new network of super PACs is seeking $50 million to back candidates who prioritize AI safety regulation. This effort aims to counter a super PAC we've talked about in the past, Leading the Future, a hundred million dollar group funded by Andreessen Horowitz and OpenAI insiders that targets regulation friendly lawmakers. ABC News notes also that there is some fracture within the Trump Maga movement around AI. So while the Trump administration and AIs are David Sachs advocate for DE regulation to win the AI race against China, former strategist Steve Bannon has been very vocal about urging the base to resist AI acceleration, citing threats to working class jobs. There's also a big policy fight happening around federal preemption. So TechCrunch reports that industry lobbyists are pushing for a federal standard for AI that would nullify state level safety laws. We've referenced that a little bit on past episodes and this is a move currently being blocked by a bipartisan coalition of lawmakers and state attorneys general. Now, on top of all this, the White House just released an executive order titled Launching the Genesis Mission, which is a coordinated national effort to accelerate scientific discovery through AI. They describe this in the text as an undertaking comparable in urgency to the World War II era Manhattan Project. And the mission aims to harness massive federal data sets to train scientific foundation models and create AI agents capable of automating research workflow. So Paul, a number of different threads here happening in the last week or so. We've got certain elements of, say, the Republican or conservative MAGA movement going all in on acceleration through the White House, but also many, many people pushing back in that movement. And then also on the other side of the aisle, Pro regulation Super PACs getting lined up here. What's your kind of. How are you starting to signal from the noise here?
- [35:34]
A
You know, it's funny. Like, I think the 2026 may be the year of like AI and UAPs. It's like aliens might be here and UFOs following that story. Like, watch Age of Disclosure. It's wild. But like, it's so fascinating because all of a sudden there's like these bipartisan sides, like these factions being formed where Democrats, Republicans, like half of them, like want disclosure of all this stuff, half of them want state regulate. Like it seems to just cross the aisle. Like no one can decide what their side stands for when it comes to these major issues. So I'm like half joking about the UAP thing, but I actually do think that that is there's a really good chance you're going to get something relatively soon from the administration on that. So I mentioned to Mike in our sandbox last week, I said we should probably just start doing a political roundup because I feel like the floodgates just open. I don't know, like the last like six weeks where there's this endless tweets from senators, there's these different missions being pursued, executive orders. Like, it is becoming very political for sure. And I think because it's starting like politicians are starting to realize AI touches so many areas. Jobs, the economy, geopolitics. The regulation issue is a huge thing. The environment, you don't hear as much about that, but that's, you know, sort of sitting there still. Impact on defense and autonomous weapons, which is going to become a major issue. Scientific advancement, which is what the genesis mission is focused on. So there's just so much going on. David Sacks, you mentioned we talk a lot about David Sacks. He's sort of the Aizar right now. There was a bit of a hit piece on him in the New York Times that got a ton of at least mentions in my feed over the weekend. Said Silicon Valley's man in the White House is benefiting himself and his friends. So David Sacks is one of the all in hosts and. But it's basically about how he's controlling the future of AI and as a result, like benefiting his own companies and his friends. Companies and things like that. So just for context, again, not, not making a statement one way or the other on David Sacks. I don't, I don't know him. The side of people who are like, hey, this is what we need is actually people who are very successful who are willing to sacrifice their own careers and investments to go do things for the Government, like this is the best of us is kind of that. Then the other side is like, oh, I just do it at the profit and everything. So just for context on what's going on, he's a. He's a polarizing figure right now, I would say in the government and in AI. So it's worth kind of following that story. So I don't know the Genesis mission. I love the idea of this. The areas that they're focused on are advanced manufacturing, biotechnology, critical materials, nuclear fission and fusion energy, quantum information science and semiconductors, and microelectronics. I would imagine the one that's not on that list that I could see being added to that list is space exploration. And there's just so much talk and we haven't touched on it much in this, on this podcast yet, but there is so much about. I think the mining of materials in space is still decades off. But the thing that's all of a sudden got everyone talking is this idea of data centers in low Earth orbit. Basically that within like this decade, you have Xai and Elon Musk with SpaceX, you have Google talking about it, you have Amazon talking about it. Like all the labs are all of a sudden talking about like, let's get the data centers off Earth because then we have the sun as the energy source. And so I think you're going to. That would be, again, it's sort of like a missing component here that sort of touches like probably three or four of these areas. But I think that's the kind of thing we're going to probably be hearing a lot more about next year too.
- [39:20]
B
Yeah, and also what jumped out at me reading a number of these articles was just a few narrative threads that seem to be popping up. We'll see how long lived these are. But what jumped out at me is like, especially within conservative circles, there's a lot of talk about pro family and child safety movements related to AI and as well as a religious component because people start feeling like we're potentially losing our humanity. You might hear more about that. I thought the worker thing was interesting. On the right, it's more about working class families. On the left, it's more about like affordability. And data centers become this kind of weird lightning rod as well around a lot of that stuff, which is fascinating. So I'm curious to see which of those have legs going into the midterms.
- [40:03]
A
And again, I think what we're seeing, Mike, is just this like trial balloons. Like you're just both sides are sort of floating these talking points and trying to see, okay, are there any votes sitting behind this idea? And yeah, and then once you, once someone finds the wedge, it's like, okay, now, now let's go, let's build the campaigns around these things. But right now I think they're, yeah, it's just like testing the waters and trying to see what, what people care about.
- [40:27]
B
All right, let's dive into rapid fire topics for this week. So, first up, Chat GPT officially marked its third anniversary this weekend, three years after its November 30, 2022 release ushered in a breakthrough time for the AI industry. So since then, OpenAI's chatbot has grown million weekly users in its first few months to 800 million active weekly users today. Data from the Pew Research center indicates that about one third of all American adults have now used the tool, which nearly doubles to 60% when you look at adults under the age of 30. And Paul, interestingly, this episode will go live on December 2nd. And on December 2nd, 2022, you wrote a blog post on the Marketing AI Institute website where you said, quote, I just tested ChatGPT from OpenAI. My immediate reaction after five minutes is that the marketing profession, business world, and society are not even close to ready for what is about to happen as a result of rapid advancements in AI. So do you still believe that what's happened since you wrote that?
- [41:29]
A
Yeah, I think that I put that on LinkedIn yesterday. I said, like, these words ring true today. Still kidding. Yeah, interesting context here, I think, for people who, for so many people, ChatGPT was the moment when they woke up to AI that they realized, oh my gosh, there's this thing and it's going to start affecting us. But like, I started the AI Institute in 2016, started researching AI in 2011. Like, we've been thinking about this stuff for a long time. And so I just to put in context that moment three years ago because it did change everything. You have to remember that ChatGPT was a research preview. Like, they had no idea if it could work. I shared on LinkedIn yesterday Sam Altman's tweet and it said, Today we launched ChatGPT. Try talking with it here. And he gave a link and then he said, language interfaces are going to be a big deal. I think talk to the computer, voice or text, and get what you want. For increasingly complex definitions of want, this is an early demo of what's possible. Still a lot of limitations. It's very much a research release. And then he said, soon you will be able to have helpful assistants that talk to you, answer questions and give advice. Later you can have something that goes off and does tasks for you. Eventually you can have something that goes off and discovers new knowledge for you. So imagine here we are, like those are all true. And so part of this is like explaining to people sometimes the future is hiding in plain sight. Like at that moment Google had similar tech. You could actually experiment with this stuff in OpenAI's AI studio. Like what they released was just a user interface. Like the tech was already there. And so Mike, I actually went back and I grab if you're watching on YouTube, like cover of our book I'm showing Mike and I wrote Marketing Artificial Intelligence. The book in what spring of 22 Mike, does that sound about right? Came out like some or really final edits in the spring of 20. Like we started writing in 2021. So you know, at least a year probably before ChatGPT. And there's a section in the book that I wrote that says what happens to marketing when AI can write like humans? So again, keep in mind this is a year before ChatGPT came out. This is the Things We Knew says there is a race to train AI systems to generate human language at scale. When achieved, the implications, both good and bad, are immense. OpenAI, an AI research company originally backed by billionaire technology leaders like Elon Musk, Peter Thiel and Reid Hoffman, builds AI models to do just that. It started with generative pre trained transformers called GPT and GPT2. These are AI language generation models that automatically produce human sounding language at scale. GPT2 wowed the world when it was released in 2019, three years before ChatGPT, with its ability to construct long form content in different styles using huge amounts of content from the Internet. And it says, yet in May of 2022 or 2020, OpenAI introduced a dramatically more powerful model called GPT3. So again, if they, if you're not aware, like GPT3 was in the world for two years and you could use it in their AI studio, it was able to produce human like text. In early experiments, the model was used to produce things such as coherent blog posts, press releases and technical manuals, often with a high degree of accuracy. And then we wrote GPT3 is still in its early days as of 2022 and the validity of the model has not been fully explored. But the speed of improvement in OpenAI's language models should be top of mind for every marketer, writer and business leader. So Mike, my point in saying this is like this is why you listen to podcasts like this. This is why you pay attention to AI. Like, sometimes we can see around the corner. Now, we were seeing years around the corner when we created AI Institute, when we wrote the book. And maybe years around the corner is kind of hard right now. But I mean, go look at the studies we started with, the MIT study, the McKinsey study, where they're looking at, okay, what are human agents and humans and robots? Like, how do they work together? That's the point of all this. Like, when ChatGPT dropped and I tested it a day later, there was nothing it did that I wasn't aware was already going to be possible. I had been advising my journalism school, I graduated from three years earlier, to prepare for a world when AI could write like humans. And people laughed at it. They thought it was ludicrous. So my point here is you have to see around the corner AI bubble, no AI bubble. Like you have to have conviction about what happens and you have to pay close attention to what these labs are doing and saying, because it often provides a preview of where we're going. Six months, 12 months, two years out. And that's the opportunity and the advantage you have by being one of these kind of early adopters who's paying attention to this stuff and trying to figure it out.
- [46:28]
B
Yeah, I love that we've talked a number of times on the podcast about, hey, go read Sam Altman's essays. He tends to predict the future. So I think you. This stuff is not a secret. It's very interesting to see that it is in plain sight if you know what to read and what to look at.
- [46:43]
A
Yeah, and we'll talk about. Ilya Sutskova made an appearance and so we'll touch on that later. But again, the reason we talk about these things, they seem abstract at the moment, but it's all connected and you can actually start to see the future a little bit when. When you zoom out and like see all these pieces.
- [47:00]
B
All right, next up, Anthropic has released Claude Opus 4.5, A New Frontier model that they describe as the. It's their most intelligent system to date for coding agents and computer use. According to the company, the model scores higher than any human candidate on Anthropic's internal performance engineering exam when it was constrained to a two hour time limit. It also tops benchmarks in seven out of eight programming languages on SWEBE Multilingual. And it also introduces now a configurable effort parameter for the API. So that allows developers to prioritize either speed or maximum capability when using the model. So Paul, this is seemingly from what I've seen online based on the feedback, it's another hit from Anthropic. People seem to love this model. I mean as a non coder I would say don't sleep on Claude. It's a pretty unique and incredible model. I did find it interesting they're testing this on real take home exams for engineering candidates. They have leaned really hard into Claude, either augmenting or automating depending on who you talk to. Software engineers.
- [48:03]
A
Yeah, they're all in on the AI researcher and then using the AI researcher to take off. You know the more powerful AI. Well we know from interviews with Dario and others at Anthropic is this is not their most powerful model like they. It may be the baseline model, but this is, this is not state of the art in terms of what this and that being said, I've seen the same things as you Mike, that people are loving. The model compares very favorably to other state of the art models. But again with all of them, whether it's Google or OpenAI or Anthropic or others, what we're getting is not the best they have. I don't know how else to stress that these models are capable of far more than what you and I are going to be able to do with them. It may just be that they're not safe enough to do those things with them. And Anthropic in particular has shown great restraint in releasing their most powerful models due to alignment and safety concerns.
- [49:03]
B
Which also adds another layer of sincerity to when some of these leaders are warning about impacts on the economy or how this is going to impact society, as they are seeing what is actually possible, not just what we all have access to. Correct? All right, next up, OpenAI has introduced shopping research. This is a new ChatGPT experience designed to automate the creation of personalized buyer guides. So unlike a standard search query, this feature engages users in a conversation to determine specific constraints about a product or item they're looking for, like budget or usage requirements. It then scans the Internet for reviews, prices, availability and then the resulting guide it produces outlines top products, key differences and trade offs based on the user's specific needs. So users can interact with these results through a visual interface, marking items as not interested or requesting similar options to refine the research in real time. This is rolling out now to all logged in users across free plus and Pro plans. And OpenAI is offering nearly unlimited usage of it through the upcoming holiday season. So Paul, I tested this out really, really briefly. And you know, I found this really helpful actually. In just a few minutes I came up with a, almost like a mini deep research report specifically tailored to comparing products. That made it pretty useful for me and easy for me to make a decision that each one has kind of a big visual card, it's linked to the vendor. So kind of an interesting feature here that might have some bigger implications for E commerce.
- [50:40]
A
One of the big questions, Mike, we've talked about numerous times moving forward is how does consumer buying behavior change? And this is certainly, you know, heading in that direction where it just starts to change the way you seek out information, you evaluate products, you make purchasing decisions in some cases directly from the chat interface you're in. So I think that's one of the big things to watch moving into next year is how buying behaviors change and how these AI assistants that we spend all our time in just start to more and more become the place where you just do everything. And then the other thing I've been starting to hear a lot more sort of murmurs about online that I, I would think are sort of related to this is I would not be surprised at all if we don't see ChatGPT ads as part of like December release schedule. I think there's elegant ways they could integrate ads into this kind of stuff. And I wouldn't be surprised at all like knowing how much revenue they need to generate. They gotta find a way to do that outside of just the $20 a month subscriptions. Yeah. And ads seems like the, you know, multi billion dollar thing that's just sitting in front of them to be solved. So. And you know, I think from a business perspective we're all trying to figure out, well, how do we show up in language models, how do we get our brands found when people are looking for things. And so I think that there's value in that from a business perspective. To be able to enable a platform to people to get there without hopefully alienating users who don't want ads, you gotta find a way to do it in a very creative and value added way. So yeah, a couple things to watch where buying behavior changes and ads integrated into AI results. Google's certainly playing in that world as well.
- [52:22]
B
In the meantime, just go try it out. You just click the little plus sign in ChatGPT and you can trigger shopping research. Super easy to use. Next up, Google is intensifying its competition with Nvidia by pitching a new way for companies to access its custom AI chips. So traditionally customers could only rent Google's Tensor processing units, or TPUs, by accessing them through Google's own cloud servers. But now the information is reporting that Google has begun negotiating with major clients, including Meta and large financial institutions to let them run TPUs directly inside their own data centers. According to this report, Meta is currently in talks to spend billions to deploy these chips on premise by 2027, alongside renting additional capacity next year. Google executives reportedly aim to capture up to 10% of Nvidia's revenue through this expansion, telling customers that on premise, TPUs can better meet strict security and compliance needs. Now, to support this, Google has developed software called TPU Command center, which is also designed to chip away at Nvidia's dominance and developer tool. So in response, Nvidia CEO Jensen Huang has ramped up investments in customers like OpenAI and Anthropic to secure future hardware commitments. So I'm curious, Paul, how worried should Nvidia be about Google encroaching on its core business here?
- [53:48]
A
This is what I was referring to earlier with like Nvidia's stock drop to the point where they released this tweet. This is so not Nvidia. Like it was a very bizarre tweet. So I get alerts from Nvidia's newsroom is how I saw this. They actually tweeted. I think it was like the next day when their stock dropped like 5%. We're delighted by Google's success. They've made great advances in AI and we continue to supply Google. So people know Google is a massive Nvidia customer. Nvidia is a generation ahead of the industry. It's the only platform that runs every AI model and does it everywhere computing is done. Nvidia offers greater performance, versatility and fungibility than a sics which are designed for specific AI frameworks or functions. That was the whole tweet. It was such a bizarre tweet. They got roasted for it. It was just like instant meme worthy. But I mean again, this is one of those been in plain sight forever. Like TPUs have been used internally since 2015, they're made available in 2018. It's no secret that they have these and that they use them themselves and that there's a massive opportunity for them to take a piece of the market, which I expect based on my, my own personal hypothesis, to get much, much larger. They're both great companies like I. This, this is where that overreaction to short term news is like it. The markets are often just illogical when it comes to this. But again they're illogical if you think long term. They're, they're logical if it's about, you know, day trading and, you know, trying to, you know, beat the market and things like that. So I don't know, they're both great companies. See how it plays out. I, I just, I, I still feel pretty good about Nvidia's business model and I think Google is a great company that is going to do extremely well. It was interesting, I saw an interview with, with Elon Musk that came out, I think yesterday and they asked him about, like, you know, which AI companies he believes in or would invest in and he's like, well, I don't really invest personally, but like, Google's going to be worth a lot of money. And so it's like the fact that he came out and just was like, they got a lot going for him that, you know, I think both companies are going to do really well in the long run.
- [55:59]
B
In our next news item this week, the AI music generation startup Suno has announced a partnership with Warner Music Group that will transition its AI platform towards models that are trained on licensed audio. According to suna, the collaboration will support a new generation of models built using Warner's music catalog. This deal also creates a system for Warner Artists to opt into the platform, allowing users to generate tracks using their specific voices, likenesses and styles in exchange for compensation. Now, Suno frames this deal as a product evolution, but Ed Newton Rex and oftentimes generative AI like Usage Critic in terms of copyright and ip, characterizes this as a huge win for creatives. He notes that the agreement effectively forces SUNO to shut down its old unlicensed models and shift entirely to systems trained on licensed audio. He argued that this outcome validates the efficacy of copyright litigation, emphasizing that was only a lawsuit against SUNO that compelled them to admit that they had originally trained on musicians work, work without permission. He does warn, though, against premature celebration. He points out that Suno still faces a bunch of other active lawsuits from other major record labels and independent musicians whose catalogs are not covered in this agreement with Warner. So I thought this was a pretty interesting development and interesting perspective here, Paul, from Ed Newton Rex. And he said, actually, so when AI boosters tell you it's too late to do anything about the exploitation at the heart of generative AI, when they tell you Pandora's box is open, point them to this settlement. What did you make of this?
- [57:34]
A
Yeah, we've talked a lot about these different lawsuits related to the intellectual property. Specifically in this case to like the training data and things like that. This is kind of how I always assume this plays out, is there'll be these massive lawsuits. There might be some wins in court from both sides, but at the end of the day, the artists, the media companies, they're going to see the opportunity for the revenue from this. And I just think a lot of licensing deals are going to be signed. I think the model companies eventually find ways to train on less data, more highly curated data through these licensing deals, get similar capabilities from the models. I don't know. I mean, I think this is, it's probably a preview of how a lot of these lawsuits end with, with licensing deals. But it doesn't solve everything. As Rex was pointing out. Like, there's all these independents that are left behind and like, you know, is it really worth it to these artists? Like, how much money can you actually make versus, you know, the traditional model? Like, I don't think this really solves anything, but it is definitely a direction. I see more and more of these lawsuits probably going.
- [58:49]
B
Next up. Major insurance providers, including AIG and WR Berkeley are seeking regulatory permission to exclude AI liabilities from standard corporate policies. Now, according to the Financial Times, the insurance industry is moving to limit exposure to what it views as unpredictable and opaque technology. So one proposed exclusion from WR Berkeley would bar claims involving any actual or alleged use of AI, including products sold by a company that merely incorporate the tools. Now, the shift follows several costly incidents, including a deepfake scam that cost engineering group ARUP $25 million and a tribunal ruling that forced Air Canada to honor a discount invented by its customer service chatbot. Now, while some insurers are introducing specific, what they call endorsements to cover AI risk, these add ons often come with strict caps. For example, the insurance company Chubb has agreed to cover some risks, but specifically excludes widespread incidents where a single model failure affects many clients at once. Now, Paul, this can get kind of a little wonky. It's still a pretty early trend, but definitely seems like this could have big ripple effects over time. If insurers won't protect firms from AI risks, you would imagine the demands from companies related to the reliability of things like AI agents would just be so sky high there'd be such room for error that they would be very, very gun shy about adopting.
- [60:22]
A
I find this one really fascinating. So I, you know, Mike, you and I worked together at my agency for a long time. But my agency that I owned for 16 years, we did a lot of work in the insurance industry. With on the commercial side with carriers as well as agents, independent agent networks. And so I spent a lot of time thinking about the insurance industry for well over a decade. I honestly hadn't really stopped and thought deeply about the implications of AI on insurance policies. But now that I saw this topic and kind of read through this, my mind is kind of racing with this one. I want to like talk to some of my friends in the insurance industry and get some insights into what is actually going on here. I think we actually have on Mike, we have a AI for insurance course that we're series we're thinking of doing and some blueprints like that. Yeah, this is, this is something we probably want to like dig into on the research side. So yeah, more to come. I mean if you're in the insurance space or if you deal with contracts for your company, this is something that's probably very near term for you. It is, is an area I had not really contemplated deeply when I was thinking about things that could slowly progress down. Yeah, but there are definitely risks, especially as we start getting more and more into the agentic side of this that I would imagine most businesses have not contemplated yet in relation to their insurance. So yeah, it's a fascinating one. I'd be interested to keep this conversation going next.
- [61:58]
B
Yeah, I'd be interested too if anyone working for vendors of AI technology that feels inclined to reach out. I'd be curious how you answer these questions when you get them from enterprises, if you're getting them yet.
- [62:10]
A
Yeah, well, even on the model side, like the, the insurance for these model companies has to be just absurd right now. Yeah, yeah, I don't know. My mind's going with a lot of different directions on this one.
- [62:21]
B
All right, next up we reference this briefly. Former OpenAI chief scientist Ilya Sutskever is offering a rare look into the strategy behind his new venture, Safe Superintendent Intelligence, or ssi. He just did a new interview on the Dwarkesh podcast where he argued the AI industry is moving from a quote age of scaling back to a quote age of research. He contends that the era of simply adding more compute and data is reaching its limits because pre training data is finite. Instead, SSI is focusing on quote reliable generalization, aiming to replicate the sample efficiency of human learning rather than just increasing model size. So backed by 3 billion in funding, the company is considering what they call a straight shot approach to development. So this is Paul, I know something you were excited about because you know, big Dwarkash podcast fan, big Ilia fan. When they tease Us coming out, like, what did you take away from this episode?
- [63:18]
A
I thought I was going to take away more, honestly. So when I. Because I think I saw the Day Before Dark questions, Dwarkesh tweeted that this was coming. I was like, oh, man. Like, Ilya's gonna finally, like, say, what's going on with Save Super Intelligence. It was not that, like, there. So I would say it's. It's pretty technical. I don't know that I would, like, advise everybody go listen to this episode. I don't think you're gonna get a ton out of it. If you want to understand Ilya more, he's obviously an extremely important figure in everything we're going through right now in AI and probably where it goes from here. So it's good to hear him talk. He does not do this often, which is why I was so anxious to see this. He. He has tweeted three times since July, and two of them was in the last, like, 72 hours. So he. He doesn't talk at all. Like, he hasn't really, to my knowledge, I don't think he's given an interview on Safe Superintelligence, so I don't think so. Yeah. And he, again, if you don't know who he is, he was the central figure. We talked about him recently. When Sam Altman got fired, Ilya was the board member who. Who led the firing, the. The pursuit of that firing. So he's a very influential figure. A few things I'll note that did jump out to me a little bit. Still no clarity on revenue plans. Dwarkesh said, how will Safe Superintelligence make money? He said, my answer. This became a meme right away, too. My answer to this question is something like this. Right now we just focus on the research and then the answer to that question will reveal itself. I think there will be lots of possible answers. So there's some great memes with that quote. He said, is the plan still a straight shot to superintelligence? Now, this answer I found very intriguing. Ilya said, maybe. I think there is merit to it. I think there's a lot of merit because it's very nice to not be affected by the day to day market competition. But I think there are two reasons that may cause us to change the plan, which is interesting. One is pragmatic. If timelines turned out to be long, which they might second, I think there's a lot of value in the best and most powerful AI being out there impacting the world, I think this is a meaningful Meaningfully valuable thing. I think on this point, even in this straight shot scenario, you would still do a gradual release of it, which is what OpenAI does, this iterative deployment. That's how I would imagine it. Gradualism would be an inherent component of any plan. It's just a question of what is the first thing that you get out the door. That's number one. So that's really interesting, Mike, because that is a variation of a straight shot to superintelligence, I would say. We were told from the beginning we're not releasing anything until we're there, basically, and we're sure it's safe. And now he's sort of hedging, saying, well, maybe the safe way to do it is actually iterative deployment like OpenAI is doing, which would change the entire dynamic of what that company was built to do. So then Dwarkesh on this idea of continual learning, says, you're suggesting that the thing you're pointing out with superintelligence is not some finished mind which knows how to do every single job in the economy. Because the way, say, the original OpenAI charter or whatever defines AGI is that it can do every single job, every single thing a human can do. You're proposing instead a mind that can learn to do every single job, and that is superintelligence. To which Ilya said, yes, again, this is very, very different. So this actually maybe is like the one thing worth listening to this podcast for. So dark Kwesh said, but once you have the learning algorithm, it gets deployed into the world the same way a human laborer might join an organization. To which Ilya said, exactly. Then Ilya expanded on this. He said, there has been one idea that everyone has been locked into, which is the self improving AI. Why did this happen? He asks? Because there are fewer ideas than companies. But I maintain that there is something that's better to build, and I think that everyone will want that. It's the AI that robustly aligned to care about sentient life specifically. I think in particular, there's a case to be made that it will be easier to build an AI that cares about sentient life than an AI that cares about human life alone, because the AI itself will be sentient. So this is, I guess, point two, it was worth listening to. So then he said, I think it would be really materially helpful if the power of the most powerful super intelligence was somehow capped, because it would address a lot of these concerns. The question of how to do it, I'm not sure, but I think that would be materially helpful when you're talking about really, really powerful AI systems, Darkesh said. Speaking of forecasts, what are your forecasts for this system you're describing which can learn as well as a human and subsequently as a result become superhuman? So again, the whole premise is develop a model that continually learns, put it out into the world in an iterative way, and then allow it to learn like a human would on the job or a teenager that is capable of lear many things. So Ilya said, I think like five to 20 years. And then Ilya did tweet as a follow up to someone summarizing his things, he said scaling the current thing will keep leading to improvement. So this idea like scaling laws are broken and we're entering the age of research in particular, it won't stall, but something important will continue to be missing, which is echoing what Demis Hassabas, Yann Lecun have been saying is like, like there's missing stuff to get to the superintelligence. But the scaling laws are continuing to build smarter models.
- [68:41]
B
All right, so Next up, the AI 2027 report is a prominent forecasting scenario that we talked about on a previous episode that predicted that AGI could arrive within the next few years. But now the project's authors are revising their estimates, acknowledging that progress appears to be moving slower than their original model predicted it. So co author Daniel Cocotoglo recently stated that his personal timeline for AGI has shifted to around 2030, and he notes there's still lots of uncertainty involved. Fellow author Eli Lifland clarified that while 2027 remains a possible arrival date, their median forecast has moved back roughly to 2030. So this created a bit of a firestorm from AI skeptics, including prominent skeptic Gary Marcus, who argues that that this doomsday scenario has been officially postponed. He noted the original aggressive timelines were influencing high level policy discussions, including comments From Vice President J.D. vance and White House advisors. And he actually contends that significant economic and national strategies are currently being built around what he calls a fantasy that is no longer supported by its own creators. Now Paul, we talked about this project first on episode 143 and this revision of the timeline has drawn some flack online. We got White House senior Policy advisor Sriram Krishnan essentially arguing the whole project as fear mongering. He's advocating the authors should retract or rename the project to better reflect now what they say they were trying to do. So why is this important? Does that what does this mean for kind of the overall timelines or expected arrival, if any, of AGI.
- [70:25]
A
Why I think that one. It just highlights the uncertainty around all of this that no one really knows. It may be 2030, it may be sooner, it may be later. Like, there's just so many variables, some of which are known and many of which are probably unknown. Honestly, I would say if people want to dig into this, we did spend quite a bit of time talking about it on that. Episode 143. I believe episode 141 is also when I did my AI timeline. Like the road to AGI timeline. I would say go listen to both of those if you want the context here. And if you're an AI Academy member, go take the AI timeline course that I just did in September. So that's like a fresher look at that. This is why when I talk about AI timelines, I include ranges. It's like, I don't. AGI is probably like, between 2026, 2027 and 2030. Like, we. We just don't know. And it also depends on how you define it. Yeah, but the whole premise. And again, this goes back to the point I made earlier. Whatever they're defining as this thing that's going to happen in 2030 really doesn't matter to you, to your company. If we stopped development of AI models today, if we shut off all the AI labs and all we had was today's current models, everything changes anyway. People don't comprehend how disruptive the tech we already have is. And so, like, I wouldn't get too caught up in these, like, oh, it's now 2030. Okay. Like, I've got a few years now. It's like, no, like, just. Just move forward with a sense of urgency to figure this stuff out and get ahead of everybody else and then pull them along with you. Because otherwise, when it does show up, you're going to have your chat GPT moment where, like, we knew for years it was coming and, like. And then it just shows up and you're like, what is this? Just be prepared. Like, I don't know. It's interesting conversations. It's good to go back and listen to the, you know, the context around it from the last episode and 143 we talked about it, but don't get too caught up in it. There's just lots of uncertainty in these forecasts.
- [72:22]
B
It's a good mantra for 2026. Be prepared.
- [72:25]
A
Yeah.
- [72:26]
B
All right, next up, Google DeepMind has released a documentary offering an inside look at the lab's pursuit of AGI titled the Thinking Game, this film is now available for free on YouTube to celebrate the fifth anniversary of AlphaFold, the company's breakthrough biology model. This documentary is filmed over the course of five years by the same award winning team that produced the film AlphaGo. It centers on DeepMind co founder Demis Hassabis. It traces how his early life shaped the company's mission to unravel the mysteries of intelligence and life itself. Now, following its world premiere at the Tribeca Festival and a subsequent international tour, the film is now being released publicly. So, Paul, I was curious, why is the Thinking Game and its subject matter worse worth paying attention to here?
- [73:11]
A
It's so good. You know, I was talking a lot about AlphaGo documentary and that was the basis for the Move 37 Moment keynote I mentioned earlier that I did at Macon this year. I would absolutely watch this. It's, it's fascinating on numerous levels. One is a personal story about Demis. Two, just like behind the scenes of the conviction he had for, for over a decade, like you know, know 20 years, probably from the time he was little, that he was going to change the world with AI it's just so fascinating, like solve intelligence and then solve everything else. So I know for me, like for years, I mean, going Back to like 2016 when I was doing public speaking on AI and doing keynotes about AI, I would use the definition Demis gave of AI is the science of making machines smart. And I would poll people like how many people have heard of Demis Sabas and I was like lucky to get one or two hands in the room. Even as late as like last year, I would do this, this and like people just don't know who he is. And I would be like, he's going to win multiple Nobel prizes. He will probably be the most consequential person of our generation. And like no one knows the guy. It was wild to me. So I don't know. A few quick points I'll make about the movie that I think are worth noteworthy again. The whole thing is awesome. But one, they start off testing Project Astro. They talked to it as Alpha. That was fascinating. I had not seen the inside story of how they did that, but they created this whole room to test their, their, their vision agent basically. So that was awesome to see that done. The early iterations of what became Project Astro, that they now is living within the Google Gemini app. You can actually use that technology. His decision to sell DeepMind, I'd never heard him talk about that. That was awesome. So when he decided to Sell Google. So again, there was multiple founders. Shane, Shane Legg, Mustafa Salomon. Peter Thiel was their original investor, and I assume he was referring to Peter Thiel when he said this. But like, their investors didn't want them to sell to Google. They sold for, I think it was reportedly about US$650 million, which if you think about the context of today, it's like, oh, my God. Safe superintelligence from Ilya is worth 32 billion and they have nothing, no product roadmap, no plan for anything. And here we have maybe the most consequential AI company in human history, sold for 650 million. But there's a. Eric Schmidt, the former CEO of Google, is talking in the documentary. He says, after the acquisition, I started mentoring and spending time with Demis and just listening to him. And this is a person who fundamentally is a scientist and a natural scientist. He wants science to solve every problem in the world, and he believes it can do so. That is not a normal person you find in a tech company. So then they go to an excerpt where he's riding in the back of a cab in London and he says, we were able to not only join Google, but run independently in London, build our culture, which was optimized for breakthroughs, and not deal with products, do pure research. That has since changed. But our investors didn't want to sell, but we decided that this was the best thing for the mission. This is, this is the part where I was like, you almost get chills. So in many senses, we were underselling in terms of value before it more matured and you could have sold it for a lot more money. And the reason is because there's no time to waste. There's so many things that got to be cracked while the brain is still in gear, you know, while I'm still alive. There's all these things that have to be done. So you haven't got. I mean, how many more billion would you trade another five years of your life for to do what you set out to do? Okay, all of a sudden we got this massive scale compute available to us. What can we do with that? So the whole point was like, yeah, I could have like made billions more, but like, if I can buy myself five years of having access to all of Google's infrastructure to go solve intelligence, like, what is that really worth to me? So it was just, it was so fascinating. Then you saw the human side where when they finally crack protein folding, which is Alpha Fold, he's sitting in a conference room and they're like, hey, we did it. Like, we finally figured out how to do this grand challenge in biology. What do we do now? Like, this is probably worth trillions of dollars. And somebody said something like, well, we could just open source. He goes, well, yeah, do that. And like walks out of the room. Like, literally gave humanity these predictive models for protein folding, which advances medicine by probably decades. And the dude just like, yeah, do it go. Like, doesn't even contemplate the alternative. It was so cool. So I don't know, like, I always say if, if you have to bet on one lab. And this is not really a commentary on anybody. I don't know any of these people personally. But if you think about the people who are leading the charge to build this now, super intelligence, we're sort of moving past the AGI thing. Have Zuckerberg, you have Elon Musk, Dario Amade, Sam Altman, Satya Ndela, probably throw in there and Demis Hassabis. And then you ask yourself, who do I actually want controlling this? Like, who. Who would be the person that, if you could pick someone to solve this, who do you want that to be? And for me, I want the pure scientist who is doing this because he believes intelligence solves everything else. And when you watch this documentary, you realize from the age of eight, that is what this dude has been doing. And so like, you just have a different, you have a different understanding of him and why Google doing well in this scenario may be a really good thing for humanity, I guess is one way to think about it.
- [78:38]
B
All right, we've got a couple more updates this week. First, DeepSeek, a Chinese research lab focused on open source model development, has launched DeepSeek v3.2 and a high compute variant of that model. So the standard v3.2 model introduces a new architecture known as deep seq sparse attention, which is designed to significantly reduce computational complexity while maintaining performance in long context scenarios. The company highlights that this model harmonizes efficiency with reasoning and notably, it integrates a thinking process directly into tool use tasks. And according to its technical report, it performs comparably to GPT5 on reasoning benchmarks. It is now available via the company's app, web and API. And Paul, this seems like a pretty powerful open source, openly available model from Deep seq. They keep churning them out.
- [79:33]
A
Yeah, they're a player. I mean, that's the thing is I'm talking about different labs and I was talking about the US based labs. Obviously Deepseek is a major player in this and can be a Disruptive force to what the models in the AI labs are doing in the U.S. we obviously, Mike, haven't had time to, to play around with this. It came out like, you know, six hours before we came on today, but definitely noteworthy. And it's, I would imagine this affects, you know, stocks. I was just trying to glance real quick at stocks today, but I don't know what other variables are playing out. But usually when Deep Seek does something it, it has an immediate trickle down effect into the US stock market.
- [80:07]
B
Yeah. And perhaps one of those reasons Meta has really gone cold on open source models.
- [80:11]
A
Yeah, that's, it's honestly. Yeah, you're, you're probably right. Like that's, that's probably the biggest threat would be this. Deep Seek is one upping Zuckerberg at what he intended to do, which was like commoditize the model market with open source models. And they, they've sort of beat them to it multiple times now.
- [80:27]
B
All right, our last topic this week. Runway has introduced Gen 4.5, a new video generation model designed to deliver higher visual fidelity and precise creative control. According to Runway, the model represents significant advancements in pre training data efficiency and post training techniques. Currently holds the top position on the artificial analysis text to video benchmark. And it's developed entirely on Nvidia GPUs. And the model aims to improve physical accuracy, ensuring objects move with realistic weight and liquids flow with proper dynamics. The company states gen 4.5 will support existing control modes like image to video and keyframes and is available at pricing comparable to previous subscription plans. So access is being rolled out gradually to all users over the coming days. Now just another example, Paul, of like some of the amazing video generation tech we are getting in the last several months.
- [81:25]
A
Yeah, this one was getting some buzz because there was a, you know, what's it called? Whisper Thunder or what was that? I don't know, there's some code name for it, but it was like tops in the charts and people were like, which one? Which model is this is the Sora, is it? The next VO ends up it was Runway, which is, is sort of like an OG of, of Videogen. I remember my first keynote, Makon 2019. I featured Runway as like an example of technology that was coming. So they've been around, they, you know, they've, I think when Sora and VEO showed up a lot of people started to kind of forget about Runway. But they're, they're a player, they're doing really cool things. I, I, I again, no inside information here. I just think these guys get acquired at some point. Like, yeah, I don't know how they continue to compete as more and more goes into video generation. So I would think someone would swoop in and grab these. It could be an Adobe Google OpenAI, you know, whatever. It's just good tech here. Probably a lot of talent. I don't know that they can sustain competing as these models keep getting more powerful, but they're doing really cool things. So. Yeah, and I think there might be some more video stuff still to come in December. I think not the last we're hearing of video generation models, but certainly 2026 is going to be a huge year for video.
- [82:36]
B
All right, a couple final announcements here, Paul, as we wrap up. First up, if you have not left us a review on your podcast platform of choice, please do so. It takes only a minute or two and really helps us out, helps us improve the show and reach more listeners. Also, as a reminder, the AI Pulse survey will be live when you listen to this, so go to SmarterX AI forward slash pulse and check that out. We'd really appreciate your participation. And Paul, as always, thanks for breaking down this week in AI.
- [83:05]
A
Yeah, thanks for being with us everyone. We will be back again next week and probably have some more model news for you to digest. Have a great week. Thanks for listening to the Artificial intelligence show. Visit SmarterX AI to continue on your AI learning journey and join more than 100,000 professionals and business leaders who have subscribed to our weekly newsletters, downloaded AI blueprints, attended virtual and in person events, taken online AI courses and earned professional certificates from our AI Academy and engaged in the SmartRx Slack community. Until next time, stay curious and explore AI.