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The Artificial Intelligence Show
#135: Sam Altman on GPT-5, Anthropic Economic Index, ChatGPT’s Largest-Ever Deployment, Gemini 2.0, OmniHuman-1 & AI Career Advice
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- [00:00]
Paul Raitzer
The models are going to be commoditized, they're going to roughly have the same capabilities. And so brand preference is actually going to become very important for these AI models and the platforms. And I would think that they're very aggressively trying to solve for how do they become the preferred brand of the next generation of workers. 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 Marketing AI Institute and I'm your host. Each week I'm joined by my co host and Marketing AI Institute Chief Content Officer Mike Caput, 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 135 of the Artificial Intelligence Show. I'm your host Paul Raetzer along with my co host Micah Put. We are recording Monday, February 10th. It's about 11am Eastern time. We've already had some late breaking launches this morning, so or at least a launch this morning that we had to remix the main topics like literally 20 minutes ago. So hot off the presses with some anthropic economic research, which should be fascinating to talk about. And we had Sam Altman saying all kinds of things last week on his Tokyo tour. He was over in I think Japan for a series of events maybe, but he did a bunch of interviews and said a whole bunch of stuff. So we're going to get into a little bit about what Sam had to say. And then he dropped a article on us last night, I think while the super bowl was happening, if I'm not mistaken, it was like right around like 4pm but I don't know if it was Eastern time or Pacific time. So right before the Super Bowl, Sam dropped a new article on us. So lots to talk about, like big picture, macro stuff, big week of AI safety. There's, I don't know, it's just a lot of. It was actually kind of like a relatively slow week, Mike. I felt like it wasn't a bunch of crazy news. Yeah. And then all of a sudden like Friday hit and things just started kind of adding up. So lots to get through. This episode is brought to us by the AI for Writers Summit we've been talking about. This is a marketing AI institute event. This is our third annual event each year. The first two years we've had over 4,000 attendees for this virtual summit. I think last year we had 90 countries represented in the attendee base, which was pretty incredible. So this is all about reimagining the future of writing and creativity really. So it's an event we created a few years back to try and help writers figure out where AI was going and what it meant to their careers. Whether you're, you know, an author, a copywriter, an ad creative, an editor, or again, just like a creative professional who, you know, focuses on storytelling through different mediums. That's what this event is for. The beauty of this event is there is a free registration option. So it goes from noon to 5 Eastern on Thursday, March 6. Again, there's a free registration option thanks to our sponsors and you can go to aiwritersummit.com right Mike? AI writer summit.com yep, there's the URL. You can learn more about it or if you're on the Marketing AI Institute site, just click on Events. It is right there in the drop down for events. So we're going to talk a lot about sort of the state of AI for writers and creators. I actually swapped my opening keynote, I think last week I mentioned a different keynote I was planning and then I sort of had some inspiration on a trip last week and decided I was going to do a state of AI for writers and creators and talk about model advancements, where they're going and what it means to writers, storytellers, creators. So I'm pretty excited actually about this talk. I think there's going to be a lot of new stuff to go over. We've got a session on AI copyright and IP prompting session AI Powered research, which is what Mike's going to do, talking about all, you know, these new deep research tools and how they can be integrated into your, your role. We still have one more keynote we're going to announce and then an Ask me Anything session. So AI writer summit.com if you are a creator, a writer, an editor, or if anyone on your team is, it's a great event for them to join. And again, there is that free option. All right. So Mike, take it away with Sam Altman and everything he had to say last week.
- [04:23]
Mike Caput
Well, Sam made some pretty striking comments about AI and pretty explicitly mentioned GPT5 during a recent panel discussion as part of, as you noted, his trips abroad. In one of the most direct statements yet about AI's trajectory, Altman expressed very strong confidence that the next two years will bring even more dramatic advancements than we've seen recently. He emphasized that OpenAI knows how to improve their model significantly and they see no obvious Roadblocks ahead. Most notably, he suggested that the progress we'll see from February 2025 to February 2027 will feel more impressive than what we've witnessed over the last two years. That's pretty remarkable given the rapid advancement we've already seen. He was particularly enthusiastic in These discussions about AI's potential impact on scientific discovery. He predicted that within a few years, AI systems will be able to compress 10 years of scientific progress into just a year and and potentially accelerate major breakthroughs in areas like climate change and disease treatment. Now, as part of this discussion, he came out and talked about GPT5, briefly saying, quote, how many people here at this panel feel smarter than GPT4? And, you know, people kind of laughed, some hands went up. And then he said, how many of you still think you're going to be smarter than GPT5? Still some more laughter, but not nearly as many hands. In fact, not really many people at all appeared to raise their hands during this. And he said, I don't think I'm going to be smarter than GPT5. And I don't feel sad about it because I think it just means that we'll be able to use it to do incredible things. Now, these kinds of comments were followed. Paul, like you mentioned by this essay that Altman dropped on Sunday titled Three Observations. It lays out three key observations about what is coming. So he talks about basically the predictable yet astonishing pace of AI advancement. So very briefly, the observations he outlines are, number one, the intelligence of AI models scales with the logarithm of resources used to train and run them. So according to Altman, companies can spend virtually unlimited amounts of money and achieve continuous predictable gains, which is a pattern that holds true across many orders of magnitude. Observation number two, the price to use a given level of AI falls by roughly 10 times every 12 months. He notes that this rate of improvement far outpaces Moore's Law, which historically doubled computing power every 18 months. And observation number three, the socioeconomic value of linearly increasing AI intelligence is super exponential, which suggests we'll continue to see exponentially increasing investment in AI development for the foreseeable future. He also mentions he envisions a future where AI agents function as virtual co workers, especially in knowledge work, and even suggested that by 2035 any individual should be able to marshal intellectual capacity equivalent to everyone alive in 2025. All right, Paul, so I have to say, like, you can love Sam Altman, you can hate him, you can honestly, if you want, think he is a complete con artist as Some people do, but he is making really, really bold predictions that we will literally know in the next 24 months if he's right or not. So I don't think I'm naive. Maybe I am, but it does seem strange to me you would commit so fully to very specific predictions simply to lie and drum up investment. Now, I'm not saying people don't lie and drum up investment, but it seems like there's like easier way, less risky ways to build up the AI hype train. If you're Altman and you want to go that route, like, what's going on here?
- [08:42]
Paul Raitzer
Yeah, I mean, certainly there's the, the people that immediately jump to this is just hype. He's not really saying anything more than he said before. And he's just trying to raise this $40 billion. And certainly there's no denying that he is actively raising money if they haven't already got the commitments to it. And, you know, I think that there might be some component of that. But Sam's history isn't to do that. Like, Sam's history is to lay out what he thinks the near term future looks like. Hope people listen and go about building the future. And so anytime he's done this, like I often reference on this podcast the Moore's Law for everything post from March 2021. And I remember at the time, like I was trying to get people to listen, like pay attention, you share it on LinkedIn, you know, talk about it at business events. And it was just too soon, like people weren't ready. We hadn't had the chat GPT moment yet. But Sam was laying out that this was coming. These things are going to be able to think and understand and reason like his exact words. And people didn't believe it. The average business community, I would say didn't. Didn't believe it. So I think it's helpful to take this actually at face value and assume Sam isn't just typing. Like he is laying out what he thinks to be true about the future. And we need to figure out what to do with that. So I thought it'd be helpful to kind of break each of these observations down a little bit. So this first one about the intelligence of AI models scale with the log of resources used to train them. This actually jives with what Demis Hassabas just said. Last week he did an interview, I think it was big technology, maybe he said original scaling laws are working, but slowing down. So that's exactly what Sam's saying here. He's saying that you can keep increasing the amount of computing power, training data and other resources you put into building the model. The intelligence performance grows, but it grows more and more slowly over time. So in other words, like, if you double, say we go from a, a $5 million training run to a 10 or a 10 to a 20 or a 50 to $100 million training run, the doubling of the resources doesn't equal the doubling of the intelligence, but you do get kind of these incremental gains. So this also syncs with what we were talking about in fall 2024 when the media started latching on to this scaling laws are hitting a wall idea. I think this is kind of what was being referred to is that we were seeing these things sort of taper off. And what that means over time is that the gains start to flatten out. You eventually hit a point where these frontier model companies are going to have to decide, is the next billion dollar, five billion dollar, ten billion dollar training run worth the gain that we're going to get from it? It also means that you're going to continue to continue to see these pushes in new research directions. So where they're looking for efficiencies in the architecture and the algorithms themselves and the training methods. And this is where we've heard a lot about like, you know, these more efficient models with deep seq, like we were talking about in the last couple episodes. So. And again, like you go back a year ago and Demis Hassabis was saying this exact thing like we were going to keep pushing the frontier, we're going to keep building the bigger and bigger models because there's still gains to be had there. And we don't know where the upper limit is. We don't know when you stop gaining enough to validate doing it. But we're also going to push on the lower end. We're going to find more efficient ways to build these models and to train these models and to post train these models. So in essence, if you've been listening to the AI leaders for the last two years, what they have said is was going to happen is what's happening. Which is why articles like this are are helpful. Like it usually plays out that they're generally right. The other thing that this first observation brought to mind for me is what I've been saying recently on this podcast, which at the end of the day, I think there's two to five frontier model companies. And when this all shakes out, which is in like two to three years, what I mean by that is there's Two companies I think for sure keep building the biggest models, that is OpenAI and Google. The maybes that maybe they keep playing this game of billion dollar 5 billion, $10 billion training runs, capex of 100 billion, 200 billion. Like what it's going to take to do this XAI because I'm not sure Elon wants to lose to Sam in this. So I think that his ego will keep him in this game for another couple years. Meta, I I don't know. Like, I feel like they got undercut hard by Deep Seek and it probably bruised the egos a little bit and made them maybe made them question a little bit like their plans to try and compete with OpenAI and Google here. But I think they stay in the game for a while and then Microsoft's a bit of a dark horse here. Like do they ever try and actually get into the Frontier game themselves either through acquisition or through building their own major models? Because they have been relying on OpenAI's models to date and they obviously have a massive stake in OpenAI. So I think that they don't play in this game. So OpenAI and Google for sure. I think XAI and Meta are possible the likely not in the Frontier model game in one to two years. I would put Anthropic in that moat. I would put Amazon there and Mistral I think is just eventually going to fade to building nice, more efficient models and then I guess you could throw like Deep Seek in there as probably like a likely not. I don't think they can compete at the high end. I think we they were in this Goldilocks zone where they had enough GPUs to compete with OpenAI in their current model, but they're not going to be able to compete in the future model due to export controls. So I think OpenAI, Google are the major players that two years from now have basically run away when it comes to Frontier models and they're the only ones that are still really pursuing that. True. I again, Mike, if it's legally possible from a regulatory standpoint, I think Google buying Anthropic is like the most obvious acquisition that could happen in 2025 because we'll talk a little bit more about what Anthropic's doing with their research and safety and stuff. They're just such a perfect brand fit, I think, for where Google's going with their models. And I can't imagine Dario going back to OpenAI and I can't imagine working with Elon Musk at XAI. So it's like they're only if they want to stay in the frontier model game, I think they have to get acquired and Google's the only one that actually makes sense. Okay, so that was, that was 1, 2. This one's really important for business people. The price to use a given level of AI falls by roughly 10 times every 12 months. That is an insane thing to actually try and process. So in essence, the way to think about this is the cost of intelligence. The cost of having 0103 models on demand is racing towards zero. Like it's the cost to deliver. So if we move forward a year from now, and let's say you're paying 200amonth for the O1 model from OpenAI, you're going to get the equivalent of that for basically like $2 a month. Like that, that level of intelligence is going to cost next to nothing. And so when you try and imagine a world one to two years out where any business leader has the current most advanced level of technology available to them for next to nothing, like the current best models will be open sourced 12 months from now, so you literally could have them for, for zero. That's a really bizarre world to, to try and imagine. And so what Sam's saying here is like Moore's Law was about the computing power of these chips doubling every 18 months. And so in essence, like the cost of the hardware comes down every 18 months, but it follows a relatively predictable pattern. What he's saying here is the cost of intelligence is scaling way, way faster. Like it's becoming dramatically cheaper every 12 months. It's actually moving faster than Moore's Law. And so what this means is you start true democratization of AI, that as the advanced AI becomes more affordable, more people and organizations can afford to use it. It lowers the barriers to entry that drives innovation across different industries. Innovation can kind of come from anywhere. This pushes towards one of the things I've said I'm, I'm most excited about. With the future, which is a rise of entrepreneurship, I think AI native companies will come to dominate industries. So again, take, take any industry you want. Legal industry is certainly ripe for this. Consulting industry, marketing agencies, hr, finance, wealth management, take your pick. In the next three to five years, I think AI native companies like built AI from the ground up are just going to dominate almost every industry. Like there'll be some industries that figure this out and sort of evolve, but as the cost drops so steeply, it becomes so cheap for new players to enter the market that have access to this like PhD level intelligence. In every aspect of running a business. So the companies that don't keep pace with the cost and efficiencies gained by these AI native companies just have no chance of competing in it. And I see this every day myself. Like Mean Mike, you, you kind of a, a front row seat to this and you and I talk about this stuff all the time. But as I'm thinking about building Smarter X. So again like Marketing Institute is our core business. I started in 2016 and then we sort of spun out Smarter X to to tell the story of AI to all knowledge work, not just marketing. And as I think about the building of Smarter X and more specifically about the building of our AI academy, I'm doing it ground up AI native. So like every decision I make is like what is a smarter way to do this than has traditionally been done? I have, I don't know, five to 10 conversations a day with O1 in, in ChatGPT. I'm literally pushing like every decision, every thought about staffing structure, about pricing models, like everything I talk to oh one about and I can't even like if you haven't done this as a business leader it is really hard to convey the amount of value you get in minutes that would, I just wouldn't even even honestly had access to. And so sometimes it's things I've already decided on and I just go into O1 just to like vet my, my thought process and I'll talk to it just straight up like an advisor others I'm just stuck and it's like I don't even know how to think think about this like for example, like and this is real stuff like customer success team. So if you build an online education business that has tens of thousands of learners, what does a customer success team look like in that environment? I've never built that. I don't know the answer to that question. So I can sit there and talk to 01 about it and in five to 10 minutes basically have the knowledge of someone who spent 10 years probably building customer success. Now I haven't done, I'm the experience but I now have that same knowledge base. That's a really, really weird thing to have the power to have on demand for next to no cost. So I think the impact on human labor again kind of on this number two umbrella, I think the impact on human labor becomes massive. I think industries that have a talent gap like accounting, insurance, healthcare, where they can't hire enough people, they can't find enough professionals professionals in those industries, I think you start to See quote, unquote, AI digital workers filling those gaps in the next two to three years. But by doing that, you're creating the replacements too for the professionals who are still there. And then we start to deal with job displacement, which then leads to observation number three, which is the socioeconomic value of linearly linear. Linearly increasing AI intelligence is super exponential. This is the most like jargony one of all. So we'll break this one down real quick. So what he means here is even modest gains in the AI systems capability, which he's calling linearly increasing intelligence. So modest gains in the system can generate outsized, disproportionately large socioeconomic benefits. Thus super exponential. So because these incremental improvements are so valuable across different industries, investors see strong returns or potential massive value gains. So they just keep putting the money into it. So like, why is Softbank willing to put 40 billion in? Because it sees the entire, I don't know, hundreds of millions of knowledge workers around the world as the total addressable market. And it's saying 40 billion is nothing. We're talking about tens of trillions in value if we solve this. So the investors see the gain potential, so they just keep going. So the research advances quickly, the deployment starts to accelerate. And so the way to understand this super exponential. So in a, in a linear growth, $1 becomes 2, 2 becomes 3, 3 becomes 4. Linear is literally just like this very predictable, same amount of value over a given period of time. Exponential is 1 becomes 10, 10 becomes 100, 100 becomes a thousand, thousand becomes a million. That's exponential. It's some given multiple of a value over a given period of time. So when you hear 10x something, they're literally talking about 10 times the current amount. And imagine 10 times compounding every year. You start to see the massive impact that this can have. So the ratios may differ, the time periods may differ, but you start to see this massive economic growth. You see job and industry transformation. What they're hoping for is massive social improvements. And so I'll kind of wind up my thoughts here, Mike, with a couple of other highlights that I think just drive this home. And I really recommend people read this article. And if you think Sam is a hype man, set that aside for a moment and, and just read it as objectively as you can and think about some of these things. So one again, they're always sort of revising their age. What is AGI definition? So in this one they say system that can tackle increasingly complex problems at human level. In many fields, they Equate AGI to a general purpose technology. So they talk about like electricity, the transistor, computer, the Internet says the economic growth in front of us looks astonishing. And we can now imagine a world where we cure all diseases, have much more time to enjoy with our families and can fully realize our creative potential. This is really important because this is the techno optimist future view that justifies all the risk and all the dangers. So because this is possible, then whatever else comes from it we will solve for because we want the abundant future that is the mindset of SAM and other techno optimists. Business they talk about virtual co workers said let's and this one's a good example. Let's imagine the case of a software engineering agent, which is an agent that they're actively trying to build and they think these will eventually be capable of doing most things. And this is a quote, most things a software engineer at a top company with a few years of experience could do for tasks up to a couple days long. So they don't have enough computer to do inference on something that might take a senior professional a year to do. But they envision building AI within the next 12 to 18 months that could do something that might take Mike or I two, three days to do. That's the kind of thing we're solving for here. It will re. They say it will require lots of human supervision and direction and it'll be great at some things, but surprisingly bad at others. Then this is the real important part. Still this quote, imagine it as a real but relatively junior virtual coworker. Now imagine 1,000 of them or 1 million of them. Now imagine such agents in every field of knowledge work. So again, we don't have to build PhD level at every cognitive task. They want to build average human level, junior quality if it needs to be, be. But because they can do it once, they can do it a thousand times. So instead of building a team of a thousand people, you can say we have this task that needs to be done or this job that needs to be done. Let's hire three people and they will oversee a thousand AI agents. They'll supervise them, they'll train them, they'll monitor. That's the future. And this is actually Jensen Wong said this exact thing in November of last year. He envisioned a future where there was tens of millions of AI agents working at Nvidia. They all see the same possibility. Couple other quick ones said the future will come at us in a way that is impossible to ignore. Long term changes, society and economy will be huge. We will find new things to do, new ways to be useful to each other and new ways to compete. But the jobs will not look like they do today. Now interesting here he never describes in, in detail the tangible detail of what this future looks like. He talks in these very broad strokes. I honestly think he, he hopes that economists, philosophers, sociologists, science fiction writers figure out what the actual future looks like. He's so set on building it, I don't think he actually has the ability to envision what it really looks like. Talks about the price of goods falling right now, the cost of intelligence, cost of energy, constrain a lot of things in the price of luxury goods and a few inherently limited resources like land may ris rise even more dramatically. I actually envision him like this is like thinking out loud where he's like I wonder what it could look like. Oh, and I also envision him talking to like GPT5, which I'm pretty sure right row four and like imagining these details. So yeah, so I, I don't know. I think there's, there's a lot to this article and I think dismissing it as hype to raise money is very, very near sighted and I would not fall into that trap because I think you miss the chance to actually think about the bigger picture if you do that.
- [26:55]
Mike Caput
So somewhat related to this, there's a new study that literally kind of just we stumbled on right before we started recording from Anthropic that reveals some fascinating patterns about which jobs and tasks might be seeing the most AI adoption and disruption. So they released this thing called the Anthropomorph Anthropic Economic Index. And according to them it quote, provides first of its kind data and analysis based on millions of anonymized conversations on quad AI, revealing the clearest picture yet of how AI is being incorporated into real world tasks across the modern economy. So basically what Anthropic did is they took a ton, literally millions and millions of anonymized clients, claude conversations between CLAUDE end users and they organize them by occupational tasks. So what were the people trying to accomplish with claude? Not necessarily in their jobs, but what tasks were they trying to do that might map to what we are doing in our professions? And then from there they were trying to extrapolate okay, well what professions, what areas are going to be most possibly affected right now or are being affected right now by AI? So the initial research finds that AI usage is heavily concentrated in computer related and technical writing tasks. So these fields account for nearly half of all the AI Interactions with clot. However, it this extends more broadly across the economy. They found that about 36% of occupations would be using AI for at least a quarter of their associated tasks by this methodology they're kind of using now. Perhaps most interestingly, they don't really see AI completely automating many jobs. The study found that 57% of AI usage involves augmenting and enhancing human capabilities, while 43% involves automation. Now, the relationship between AI adoption and wages shows a surprising pattern here. So usage peaks in the mid to high wage occupation, so people are using it for things like computer programming and data science, but it drops off at both the highest and lowest ends of the wage spectrum. So this kind of reflects both probably the current limitations of AI capabilities as well as some of the barriers we're seeing to adoption. So Paul, this actually just takes like a really interesting approach to unpacking AI's impact on labor. They're using people's actual conversations with AI to determine what AI is being used for. What did you take away from that approach and that research from it?
- [29:47]
Paul Raitzer
Yeah, I was really happy to see the research. And as you mentioned, I mean this like literally popped up 20 minutes before and we made the decision to swap this in and it actually so nicely transitions from the socioeconomic impact stuff we were just talking about. I don't honestly know how relevant this data is because for one, I don't think most people, outside of the kind of people who would listen to this show actually have a clue what Claude is. I don't know that it's awareness or usage in the general public is much of note. I know there was like a recent thing that just came out of like top apps and top websites and anthropics nowhere to be found. So I don't think that they have very broad awareness or usage in the general public. 3.5 is known as being a top development coding tool, so it makes sense that this would be skewed heavily toward technical users. And then it also has, I think, a bit of a following in the writer world. So it makes total sense that those are the two things. I think that if you had this kind of data from chat GPT1, it would be extremely fascinating and I hope OpenAI follows the lead on this and does something similar with their data. Love to see it from Gemini as well. But I think ChatGPT, given the usage, which we'll talk about in one of the rapid fire items, you're going to get the most representative data of the general public anthropic Actually, in their tweet, sharing this, which is how we saw it this morning, they said one of their tweets was like all analyses, ours comes with caveats. We can't be certain all these tasks were performed at work or what people did with Claude's outputs. This might undercut augmentation. And since Claude doesn't generate images, we're likely missing some important AI use cases. And if I'm not mistaken, Mike, Claude also isn't connected to the Internet yet.
- [31:39]
Mike Caput
Right, Right.
- [31:40]
Paul Raitzer
So that's why I'm saying, like, Claude is not representative of Gen AI right now. Like, it is it. It seems like it's got a great underlying model that's really good at coding and really good at writing and some other things, but it is, it is not a, a tool in the, in the sense of what Gemini and Chat GPT are currently doing, where they're multimodal and connected to the Internet, have reasoning capabilities, things like that. So appreciate the effort. Like, I, again, I think it's good research and I hope other platforms with more awareness and usage follow suit and start publishing it, because I think it would be helpful.
- [32:15]
Mike Caput
Yeah. Not to mention, aside from the limitations in what it is tracking, you're not even touching on any type of combinatorial task or creative ways of using the tools like you described with 01. Right. That are reshaping how we're even doing work in some of those ways. Like, this is going to probably be looking more at like, oh, did you use Claude to refactor code? Did you use Claude to generate headline ideas? Which is all really helpful, but did you use Claude to reinvent a research.
- [32:47]
Paul Raitzer
Project and build a business strategy?
- [32:50]
Mike Caput
Right, right. I'm sure some of that's in there, but I would bet that some of the more exciting transformative use cases kind of get lost in that.
- [32:57]
Paul Raitzer
Yeah, they don't have vision, they don't have video generation, they don't have image generation, they don't have audio generation. Yeah, it's not multimodal. So again, great starting point. Very valuable if other companies follow suit. But on its own, I wouldn't read too much into the data and assume the use cases they highlight are representative of the broader market.
- [33:19]
Mike Caput
Right. Because I haven't looked at. But I guarantee you there's a headline out there that's like, X percent 100 computer programmers are now using clock.
- [33:26]
Paul Raitzer
Oh, yeah, it's going to be clickbait for the next like 72 hours.
- [33:29]
Mike Caput
I'm sure. Sure. All right, our third big topic this week, the California State University system, CSU for short, is making some history with the largest deployment of ChatGPT attempted bringing AI to more than half a million people across its 23 campuses. So this was announced on OpenAI's website. They have a partnership with OpenAI through which the university system will provide ChatGPT. Edu, which is a version of ChatGPT specifically customized for educational institutions, to over 460,000 students and 63,000 staff and faculty members. So this turns CSU into kind of the first real like AI powered university system in the US and the implementation includes a few key components. So one faculty can use ChatGPT for curriculum development and create course specific GPTs while students get access to personalized tutoring and study guides. The university is also launching a dedicated platform offering free AI training programs and certifications along with apprenticeship programs connecting students to AI driven industries. So early research into how ChatGPT is actually impacting education shows that this could actually be really substantial. Like Harvard researchers have found that AI powered tutoring doubled student engagement and improved problem solving. A Microsoft study indicates that individuals with AI skills are 70% more likely to be hired. So like we mentioned, this uses chatgpt.edu. that's not special to CSU. It is, that is something that launched in May 2024 and provides universities with access to OpenAI's latest models, Enterprise level security, specialized pricing. So if you are just hearing about this now, go check that out for sure if you're a higher ed institution. So Paul, this seems really promising, especially in its scale. What is there to like about this partnership between CSU and OpenAI and what are some of the bigger picture implications?
- [35:49]
Paul Raitzer
Yeah, I mean it's fantastic from a CSU perspective. Now it's weird for me to say CSU because we're from Cleveland. Cleveland State University is csu. So the first time I read this I was like, oh awesome, CSU is doing a massive program. I was like, oh, it's California State University. Hopefully our CSU also follows suit. So I think it's great from a university perspective perspective to have a vision for this, not just infusion into the curriculum, but into administration, you know, into the academics. And to provide the training is so critical here of how to use these platforms. Not just get the licenses and hand them out, but actually like make sure that they're being used in a responsible way. That's critical. I think as a parent now, again, my kids are 13 and about to be 12. They're in, still in, in grade school. But if I was looking at high schools, which we will be soon. I would say if I, if we had two choices that were comparable again I, I, my, my belief is the kids should choose where they go. My parents gave me that choice as a, a high schooler and I, I think my kids should have the same. But as a parent I would very aggressively look at how schools are doing and if I saw one of the schools that my daughter was say considering had a very aggressive program to integrate AI drive literacy, drive usage, responsible usage, and one outlawed it or considered it plagiarism, there's like a really, really good chance I'm going to try and help her see the opportunity of the more modernized school. I guess I would say same is true. In college I would push more heavily like if I had a, a, a child right now, you know, junior senior in high school, this would be in the top three of my list of how are they handling AI? What are they going to teach over the next four years to prepare you for the reality of the workforce in four years from now? And again, if it was a college that wanted me to be paying them 20 to 50,000 a year or more and they weren't integrating AI and didn't have AI systems, I'm, I'm sorry but as a parent like, but look at the next option because you're, you're not going to be ready for the real world. So I think that you're going to see a lot more of this. I think the Chat GPT Edu program will explode in a very positive way. And I think this is a really important aspect. When we go back to the models that are the winners in the end there is a battle for the next generation of workers. And when the kids come out of school they're either going to be loyal to Gemini or Chat GPT is my current given who I think are going to be the major frontier models two years from now. I think there's a battle for mind share and usage from those students. And I think whoever you come out of school relying on is probably the, the model you stay loyal to because as we've talked about, the models are going to be commoditized, they're going to roughly have the same capabilities, capabilities. And so brand preference is actually going to become very important for these, these AI models and the platforms. And so I think that Google and OpenAI, I would imagine know that and I would think that they're very aggressively trying to solve for how, how do they become the preferred brand of the next generation of workers.
- [39:14]
Mike Caput
So really Quickly, I'd be curious as to your perspective on like what kind of training actually needs to be in place here for this to work. Because they're say they're providing it, but like we talked about tons of times, both in higher ed and outside of it, the people getting access to powerful AI being turned on need training to be able to use it effectively. Like what kinds of change management are they going to have to overcome? I mean no offense, but some teachers are now notorious for hating ChatGPT and AI in general. Like what does that look like to you?
- [39:52]
Paul Raitzer
That's, that's gonna be the painful part is you're, you're going, you, you can sit here and lay out a vision for what this looks like, what the change management's going to need. Building centers of excellence for teachers so that they can share best practices. They can, you know, you can build into your training. Here's the personalized use cases that are going to be most relevant to each department, each teacher. So you, when you hand them the license, here's the three to five ways to use this in curriculum development. Assessing students, grading, you know, homework, whatever it is, like whatever the main ways they're going to use it is and teach those as like the cores. Maybe even give them pre built GPTs for specific use case. Like that's best case scenario. Yeah, but as you highlighted, you're going to have a whole bunch of teachers, professors who want nothing to do with this. They, they just, they, they are. You're not going to convince them this isn't cheating and it's not shortcutting critical thinking. And, and that's going to be the biggest problem honestly, because especially at the university level when there's tenure involved, like you, you can't force change at the university level, especially if they're state funded. Like it doesn't work like that. They, they can't, it's not like we can bring in Elon Musk and have him, you know, create DOGE and like just totally change universities, you know, in 30 days. Does, doesn't happen that way. So I think that there's going to be some universities that solve for this and find out ways to move forward and bring the professors and, and teachers along. And then there's going to be some that just really struggle against the friction of resistance to change. That is what humans tend to do. Especially when you've been doing something for a really long time, a very specific way.
- [41:34]
Mike Caput
Okay, let's dive into this week's rapid fire. We've got a bunch of interesting topics on the docket this week. So first up, Google has expanded the Gemini AI model family. Gemini 2.0 is widely available in three distinct variants. The release kind of marks a pretty significant step forward to the stuff we were talking about. More intelligence at lower cost. So this Lineup includes Gemini 2.0 flash which is now generally available for production use. This is Google's kind of workhorse model for high volume tasks. It has a massive 1 million token context window. And for developers seeking an even more cost efficient option, Google has introduced 2.0 flashlight which maintains better performance than the previous 1.5 flash version while keeping the same speed and cost structure. The most powerful addition is Gemini 2.0 pro experimental. This is obviously an experimental model that Google claims shows superior performance in coding and handling complex prompts. It has an even larger 2 million token context window and can integrate with external tools like Google Search and code execution. It is available to developers in Google AI Studio, Vertex AI and as well you can use it in Gemini Advanced if you have one of those accounts. Interestingly, as of recording 2.0 Pro experimental sits in the number one spot across all performance categories on the popular Chatbot arena leaderboard at lmarena AI Paul, what is there to look forward to with the 2.0 family of models with Gemini? I mean we're getting seems like new models every other day. Why are these ones so important?
- [43:22]
Paul Raitzer
Yeah, so if you're a Workspace customer, I actually have no idea which of these you have. So if you go into your Gemini account through Google Workspace all you see is Gemini Advanced. There is no dropdown. I don't know which model is currently used and I did not Google it to figure out which. I I think it's still 1.5 pro but I honestly do not know. So first and foremost as a Google Workspace customer you still have Gemini Advanced. No idea what the underlying model is. If you have Gemini through your Gmail like if you have your personal one as you just highlighted mike, there are now 6 choices and because we gave crap to OpenAI about this we have to be fair and do the same thing to to Google. I'm not actually sure who's is worse right now. So in my I'm looking at my Gemini app right now I have 2.0 flash for everyday tasks plus more features I have 2.0 flash thinking experimental best for multi step reasoning is an all reasoning multi step anyway 2.0 flash thinking experimental with apps for reasoning across YouTube, maps and search. I don't know why Flash Thinking Wouldn't just have those apps, but we have a separate model for the apps. Then I have 2.0 pro experimental best for complex tasks which I would actually immediately think is reasoning. Like yeah, I don't know why those are different. Then I have 1.5 Pro previous model and 1.5 Flash previous model. I have no idea when or why I would use the previous models that are still in my drop down. If the cost is the same to me, I'm paying my same 20 bucks a month. Why, why do I Even need the 1.5s? So we have not solved for the branding issue. Apparently we have yet to achieve a model that is capable of solving this issue for these companies. I don't honestly know. Like I think the reasoning is the biggest thing. Like the thinking. What they're calling the thinking is the is the main thing here. Yeah, but you have to use your personal account to use it. The big question for me is when does 2.0 Pro come out? Which I assume is like the multimodal leap that maybe all the reasoning is baked into it. And then I just need 2.0 Pro and it has the apps and it has the thinking and I don't need to decide which model to use. But I don't actually don't know.
- [45:46]
Mike Caput
So more on that. Hopefully we can improve that part of the AI experience.
- [45:51]
Paul Raitzer
Yeah, and I get, I think it's like for all of our listeners, like if you feel completely overwhelmed and honestly like I was sitting there last night like wait a second, I have the O. I'm paying the 200amonth on my personal account for OpenAI but I also have the chat GPT team license that has O1 and now O3 mini. What is, what am I getting for the 200? Like I was literally sitting there thinking like I was going to message you, Mike, and say what's the difference again between what I'm getting with. So if you are listening this and you're like, I don't know what I'm supposed to do, I don't know which model to use. Like, welcome to the club and Mike and I do this for a living. And I'm like, I get so confused by all these models and which one I should actually be using for things.
- [46:34]
Mike Caput
Our next rapid fire topic concerns some major AI companies that are rolling out new safety measures this week. And this is kind of signaling a growing concern about controlling increasing increasingly powerful AI systems from some of the companies building them. So in a series of announcements, Anthropic Meta and Google DeepMind all unveiled new frameworks for managing the risks of advanced AI. Anthropic introduced what they're calling constitutional classifiers, a new defense system against AI jailbreaks, or attempts to bypass an AI's safety guardrails. This system has apparently proven quite effective. Testing has shown it blocks over 95% of jailbreak attempts, while only increasing normal query refusal rates by less than half a percent. Anthropic is so confident in the system, they are offering a $20,000 bounty to anyone who can successfully break it. Meanwhile, Meta has taken an unusually strong stance on AI development, announcing they may completely halt the development of AI systems they deem too dangerous. Their new frontier AI framework specifically identifies two risk categories, high risk systems that could aid in cyber or biological attacks, and critical risk systems that could lead to catastrophic outcomes that can't be mitigated. Now, Google DeepMind, in turn, has updated its own safety framework, with a particular focus on preventing what they call, quote, deceptive alignment. The risk of AI systems deliberately undermining human control. They're implementing new security protocols and deployment mitigations, especially for models that could accelerate AI development itself. Okay, that feels pretty weighty, Paul. Like the big question I have here is three of the leading AI companies make these same types of announcements at the same time. They're three very different companies. For instance, Meta is not exactly known as Safety Conscious. Is this a coincidence we're hearing from all of them about AI safety right now?
- [48:47]
Paul Raitzer
Yeah, I don't think so. So I wrote about this in the Exec AI Insider newsletter. And if, if people aren't familiar with that, I. Every Sunday I sort of do an editorial and like a preview what's coming for the podcast. So we'll, we'll drop a link in the show notes for that. And what I said was, in the totality of everything that happened last week, this was very unusual to me. And so the way we do the podcast, I've said this before, but basically throughout the week I drop a bunch of links. We use Zoom, so we keep an episode, sandbox, drop a bunch of links, tweets, videos, courses, all this stuff. And then Mike goes through and curates everything, you know, starting on Friday usually, and then throughout the weekend, and he curates everything. So then I sat down Saturday morning to write the editorial for my newsletter and I'm scanning through the 40 or so links and I count like six or seven unrelated links, all tied to AI safety. So we had European Union news, we had a gov.uk article we'll link to. We had these three major things. There was a bunch of tweets around it and it's just one of those where you take a step back and like, huh, this is very unusual. Like, why would they all be doing this in the same week? And so I, I do think, and what I said in the newsletter was I don't think the timing is a coincidence at all. I, I think that they all see the reasoning models taking off on this exponential growth, right? They see the continued improvement of these frontier models that we talked about with, you know, forthcoming GPT5 and what Sam was alluding to. And they all know that they're all going to be releasing things soon. And, and so I think they're starting to try and get ahead of this. And AI safety and alignment is going to become a much more mainstream topic as more people start accepting how disruptive this stuff is going to be to the economy and to jobs. And so they're, they're trying to get out ahead of this a little bit. Also, this question of like what remains uniquely human. We heard Sam sort of like struggling with that very question in his three Observations article. So I think that the labs are all sort of trying to get their ducks in a row. They're all trying to figure out how do we know when it's too smart to release into the world. And so they're very aggressively looking at their own policies internally. The thing I will say, and this isn't very, it doesn't give us much peace of mind, but no matter how much time they spend on these things and how much pr, they eventually put behind this frontier work and their AI safety work, what history, recent history will Show US is GPT2 was considered too dangerous to release, so they did not release the full model originally. Google had their own internal capabilities, very similar to ChatGPT, if not more powerful than ChatGPT. They did not release it first because of concerns for risk and safety. OpenAI took the risk and did. OpenAI didn't want to show the thinking of the reasoning models. Deep Seek did it. So then OpenAI followed suit within seven days and started showing more of the thinking behind the models. Nobody was willing to put out an open source model of the magnitude of llama 3 until meta did it. What I'm saying is it only takes one player in the game to do the thing that's considered too risky by everyone else and then everybody has to follow suit. So just because Anthropic has some level four thing that they think is like end of humanity concerns, it takes one other research lab to push that out. And then Anthropic either says, listen, we're just out. Like we're, we're not going to go there. We're going to like bring our safety research to like Google or somewhere else. But like we're not playing in this game anymore. But you're not going to have open AI step out of the game and you're not going to have probably meta step out and certainly not xai. They're going to push the frontiers that someone else has made okay to push. That's what concerns me is we, we have seen a oneup game played out over the last five years every single time. And I don't know that these labs have the will to not do the thing that the other labs make the norm. And so I think this stuff's critical. I think we have to have much more in depth conversations across all industries, not just like in these, you know, our AI show kind of thing. We need other industries, other leaders thinking about AI safety. I don't think the current administration is going to get involved. I don't think that they're going to try and push this forward. I think the states will though. I think it's become a massive issue with state legislation like California, Texas, we've already seen it. The EU's done their thing. There's a major issue like AI safety is going to be 60 minutes style. Like by the end of this year you're going to see those major episodes on AI safety and alignment and you're going to start seeing a lot of mainstream media headlines around this. And this is the thing, I think this and job loss is what eventually triggers society to. You'll see backlash about AI. I think it'll happen this year. I think there's going to be a couple of events this year that will actually trigger where we start to see true pushback on AI advancement. And this is going to be one of the main two areas where it's going to be caused by.
- [54:19]
Mike Caput
In another news story this week, ChatGPT has hit a pretty significant new milestone. Tool has reached 3.8 billion visits in January 2025 and is widening its lead over competitors in the AI chatbot space. According to some new analysis From Big Technology, ChatGPT's nearest rival in terms of just volume of visits. Microsoft. Bing logged 1.8 billion visits, so less than half of ChatGPT's traffic. Other major players trail even further behind. Gemini has 267 million visits. Perplexity 99.5 million. Anthropic Claude at 76.8 million. This growth spurt, according to Big Technology, appears to have coincided with OpenAI's release of GPT4O. They also integrated Dall? E's image generation directly into the chatbot and released enhanced models that show improved reasoning have fewer hallucinations. So Big Technology says, quote, the traffic surge is a remarkable reversal for ChatGPT following a usage stagnation that lasted longer than a year. After reaching 1.9 billion visits in March 2023, ChatGPT didn't surpass that number until May 2024. So this is obviously pretty good timing for OpenAI. You know, deep SEQ has been gnawing away that it feels, I think, you know, during its peak publicity wave, it achieved 1/3 of ChatGPT's daily traffic almost overnight. And OpenAI is now clearly working to solidify its brand dominance in other ways, including running its first super bowl ad this weekend. So Paul, it certainly seems like despite all the criticism ChatGPT gets when a new model comes out, I mean we've read thousands of threads on like chatgpt is done for it is positively surging in usage. So in context, how important is it to look at these user numbers? Do they tell us anything useful about the overall AI race?
- [56:24]
Paul Raitzer
Yeah, just huge numbers. I mean that's like obviously you have a far and away leader that I think a lot of the average person, it's become like the Google of AI. Like you know, you just Google things like Google is synonymous with search. I think Chat GPT is synonymous with generative AI For a lot of people, they don't even think about the other players in the game. Just sort of assumes ChatGPT similar web also put out like their top 10 apps in the US like iPhone apps for January. Just like you know, perspective here. Deep seats number one still Chat GPT was two threads. Who, who uses threads?
- [57:03]
Mike Caput
Like I think you're forced to have an account is part of the reason that those, those are the way that uses threads. I don't either.
- [57:15]
Paul Raitzer
And then Google Gemini is eighth on that list top app. So I mean people are using Google Gemini too by the way shout out like the ads. We don't have this on the thing to talk about the super bowl ads. Chad gbt My God. Like I, I, I was going to tweet this and I, I resisted but whoever they're having doing their brand naming I think also does their ads. Like it was, it was really bad. Like it was just a miss for me. Like I, I don't know, maybe some People loved it.
- [57:43]
Mike Caput
Yeah.
- [57:44]
Paul Raitzer
On Twitter it was not getting much love. So if you didn't see the ad, you can go look at it on, on their Twitter account. It's just like a bunch of dots for like 50 seconds forming all these things throughout human history and then it's just, it's chat GPT. I don't even say open AI, it's just chat GPT. On the other side, Google Gemini did this amazing. Maybe it's because I'm like a sappy father with a 13 year old daughter but like they did this amazing ad of like a stay at home dad who raised his daughter and now he's trying to like rejoin the workforce and he's doing a prep interview with Gemini. You know you had talked about this use case, Micah preparing to give talks and it's just like, it's so emotional. Yeah, I was, I had a hard time watching it honestly. So Google nailed their ad I think for Gemini. ChatGPT, you know, back to the drawing board. So anyway, so yeah, it's just, but it's huge numbers, it's really hard to overcome that, that kind of lead when you're talking about, you know, the market for the next few years here.
- [58:42]
Mike Caput
ByteDance, the owner of TikTok, has demonstrated a new deep fake video system called Omni Human One that might set a new standard for realism. So unlike many current deepfake tools that often leave obvious digital traces, Omni Human 1 has produced some really, really, really convincing results based on the demo videos. With just a single reference image and some accompanying audio, this system can generate video clips that are complete with adjustable aspect ratios and body proportions and look hyper, hyper real. So this capability was highlighted by demos that included a fictional Taylor Swift performance, an imagined TED Talk, even a deep faked Einstein lecture, all crafted from a training set of 19,000 hours of video.
- [59:33]
Paul Raitzer
Pretty sure they had permission for all 19,000 hours of that.
- [59:36]
Mike Caput
Sure, I'm sure they did. So obviously like any technology this has flaws, but it is definitely like one of the more stunning recent examples of deep fakes out there. We've talked, Paul, about how weird and dangerous hyper realistic deep fakes are going to get and make the world, but it is quite another thing seeing this stuff. And interestingly this is from ByteDance which created TikTok. I could certainly see some use cases for this appearing on their platform as well.
- [60:09]
Paul Raitzer
Yeah, this is a problem. I mean we've talked, this is one of those like slow moving trains we've been talking about for a year and a half now. These Deep fake videos and the ability to make anything look real and sound real. You know, I think we're going to make a lot of progress, good or bad, in 2025 on video. Text to video, image to video, video to video, where you just continue on from scenes and make things happen that never happen, indistinguishable from. You're going to have companies like Google with their VO tool that want, you know, put watermarks into it so at least you can determine from the metadata if it is or is not a fake. You're going to have others who do not. And you know, I think the reward function of social media is engagement and things going viral. And so there's no real, you know, we just had, I mean, meta like a few weeks ago, announced they were like removing human reviewers of content basically for, except for the most violent stuff. So you have the, the walls coming down from the social media channels that's going to allow this stuff to spread even faster. And yeah, I, I don't know, again, like, I, I worry about this with my kids like now. I, you know, think about what they see online and knowing whether it's real or not. Like, I have these conversations at 13 and 11 already about how to know what's real and what's not online and make sure it's coming from the verified source and if you see something, look at the source. If it's not a source, you, you know, make sure you go to the actual source of that person, person or that media company. It's it. Yeah, man, this is, this is one I always like worried about and I just think it's, it's coming, becoming real. Where you going to be able to do this over 10, 20, 30 seconds, eventually minutes of video that just completely reinvents. Now there's all kinds of cool things you could do with that, but they don't, they don't care about ip, they don't care if they're representing celebrities doing things like su them all you want, like, good luck. Yeah, it's a problem. I, I wish I had better thoughts about this one, but this terrifies me, honestly.
- [62:13]
Mike Caput
So we're also starting to see the first enforcement phase of the European Union's landmark AI act to go into effect. So as of Sunday, regulators there can now ban AI systems they deem to pose, quote, unacceptable risk to society. So these regulations create four distinct risk categories for AI systems. They're one, the highest risk level is these unacceptable risk systems that are now completely banned. So these include things like AI that creates social Scores based on behavior, AI that manipulates people's decisions subliminally and a bunch of other types that can provide, create real world harm for people in society. The penalties for these kinds of violations under the EU's AI act are pretty steep. So if you're found using these types of applications, you could face fines of up to 35 million euros or 7% of your annual revenue, whichever is greater. There are a few exceptions, like law enforcement can use certain biometric systems in public places and some systems that detect emotions may be permitted if there is legitimate medical or safety justification. So Paul, like, how are you viewing these regulations? I mean, the EU sometimes comes under criticism for regulating too much, but we also just talked a bunch about how companies are coming out and saying that we're getting worried about some of these systems.
- [63:38]
Paul Raitzer
Yeah, the balance for them is to how do you still allow for innovation to happen? I mean, the eu, you know, it's no secret that they, they lag dramatically behind, you know, the United States in terms of AI and innovation and AI startups and things like that, like Silicon Valley is just so far beyond in other pockets of the United States. But that's their biggest struggle. Like, there's a lot of practical elements to the, the EU AI act and I think probably some things that are correct, but there is this balance where you, you probably are thwarting, you know, making sure you're staying well behind America, I guess, when it comes to this stuff. And that's the, I think the frustrations you see online. But you know, they're, they're trying, they're trying to find that balance and I think only time will tell, but I assume this is, you know, it would have that effect of creating more obstacles for innovation in the eu. But that's the trade off they're willing to have.
- [64:42]
Mike Caput
Can you really quickly talk me through that? There's like an AI literacy component to the EU's AI act and there seems to be some confusion around this. This is not directly related to the, you know, extreme risk systems, but a part of the overall legislation. Could you walk me through this?
- [64:57]
Paul Raitzer
I think what's happening is there's, there's probably like a push to that where some people are trying to convince people in the EU that this is all about like being AI first and driving AI adoption. And that's not what it's about. Like, that's, I think where the confusion mostly comes in is that you're, you know, it's all about building AI native companies and AI emerging companies. You got to drive AI literacy for those reasons, that's not their intention, it doesn't seem so. You can go read it. We'll put the links. And this is article four, which is titled AI literacy. And then recital 20, which is, I don't know, there's about 200 words of like explanation. I will read you very briefly the summary of the article. It says the article states that companies that create and use AI systems, so using, I assume that means any business, so someone using ChatGPT or whatever must make sure their employees and anyone else who operates or uses these systems on their behalf are well educated about AI. That is a very broad statement. Obviously this includes considering their technical knowledge, experience, education and training, as well as the context in which the AI systems will be used and the people or groups who will be using them. So that, that's the article and then in recital 20 they go into a little bit more detail and I'll highlight a couple of excerpts here. Says, in order to obtain the greatest benefits from AI systems while protecting fundamental rights, health and safety, and to enable democratic control, AI literacy should equip providers, deployers and effective person affected persons with the necessary notions to make informed decisions regarding AI systems. So that's very, again, very broad. But you're basically saying like, if you are selling AI systems, if you are using AI systems, you need to understand much more than just how they work. You need to understand how are they trained, what is, what are the implications of my decisions around using this? Am I going to use it that's going to inject bias into our decisions, things like that. They're really looking more at like, risk than anything and understanding these systems to avoid that risk. They talk about suitable ways in which you interpret the AI system's output. So like what training people, what do I do with this? Once it gives me this thing, if I use a reasoning model, how am I supposed to decide how to use this? And then the last thing I'll highlight is they kind of basically say we sort of need to figure out what this article actually means because they say the European AI Board should support the Commission to, quote, promote AI literacy tools, public awareness and understanding of the benefits, risks, safeguards, rights and obligations in relation to these systems. In cooperation with relevant stakeholders, the Commission and the Member States should facilitate the drawing up of voluntary codes of conduct to advance AI literacy among persons dealing with development, operation and use. Meaning we haven't really figured out how we're going to actually execute all this, but like we should get together and put some codes in place that make sure we follow some best practices to actually do what the spirit of this article is intended to do. So I don't know that gives people any more clarity, but maybe the fact that there isn't clarity gives you some clarity in a weird way of like, like if you think you're supposed to understand what this means, I don't know that you're actually supposed to per se by reading the article and you know, going deep into the recital.
- [68:19]
Mike Caput
Gotcha.
- [68:20]
Paul Raitzer
Open to interpretation is kind of how I interpret this.
- [68:25]
Mike Caput
So we've talked several times about the company Figure, which makes humanoid robotics. They have announced that they're ending their high profile partnership with OpenAI just months after the two companies joined forces. So this decision comes after what Figure calls a quote, major breakthrough in their in house AI development. OpenAI is also an investor in Figure, and Figure has raised about $1.5 billion, achieving a valuation of $2.6 billion. So Figure CEO Brett Adcock, who's quite active online, explains the split by pointing to the challenges of integration. While OpenAI excels in many areas of AI, embodied AI, which brings AI to physical objects like robots is not its primary focus, according to him. He says that the proper solution is building an end to end AI model specifically designed for their hardware, saying, quote, we can't outsource AI for the same reason we can't outsource our hardware. Now the timing of this becomes more interesting given that OpenAI just filed a trademark application with the US Patent Office involving humanoid robots. So Paul, what is really going on here? Is this just PR spin that Figure is putting on the split? Like, it Certainly sounds like OpenAI could be a competitor or is there a legitimate business argument here OpenAI is building robots.
- [69:55]
Paul Raitzer
I mean like reading Adcock's tweets was kind of rough because it was just like trying to convince people that wasn't what was happening. But OpenAI started trying to build robots eight years ago. This isn't new. Like they, they always thought embodiment of intelligence was critical. They we just weren't there yet from a hardware perspective. We weren't there from an intelligence perspective yet. But the, the multimodal language models are the brains, give them vision, give them reasoning capability. This is what Elon Musk thinks the future of Tesla is. He thinks there's going to be billions of robots. Nvidia thinks there's going to be billions of robots. So does Sam Altman. And that is like the biggest addressable market of all. Like if you think that knowledge work is an addressable market. Like wait till you see the size of the robot market. So if you want to justify a $300 billion valuation for OpenAI, talk about how many billions of robots you're going to sell seven years from now. And like just, they're just, they can print the money so they can say whatever they want. OpenAI is going to try and build robots and that is why this deal fell apart.
- [71:02]
Mike Caput
In some other OpenAI related news, OpenAI co founder John Schulman has left Anthropic, where he was after leaving OpenAI. He left Anthropic after just five months, reportedly to join former OpenAI CTO Mira Murati's new startup venture. So Shulman had originally departed OpenAI last August after nearly nine years with the company when he first left. He explained that the move was driven by his desire to morph, to focus more deeply on AI alignment at Anthropic and to return to hands on technical work. Now Fortune has reported that Schulman is joining mirati's secretive nature company, which has been quietly taking shape since she left OpenAI in September. Now, details about the whole company and venture remain pretty scarce. It seems like she's also attracted a former OpenAI supercomputing team researcher Christian Gibson. They're reportedly in talks to raise over a hundred million in funding last October overall. Paul like this seems like a win for Murati's company. But like any guesses as to why Schulman left Anthropic so soon after joining?
- [72:16]
Paul Raitzer
Yeah, it'll be fascinating to see what they end up building. I mean obviously what she's pursuing is very enticing to him. I'm sure that there's a lot of equity opportunity there that maybe he didn't have Anthropic, but I think that's the thing I'm really anxious to see is just what is she building? She's not gonna build another frontier model company like right, we established that. I think it's too late to enter into that game, so I'll be intrigued. Of what her the thread she pulls is.
- [72:46]
Mike Caput
Another OpenAI alum Ilya Sutskever. His safe superintelligence startup is in talks to raise funding at a staggering $20 billion valuation. This represents a fourfold increase from the company's $5 billion valuation just a handful of months ago in September. This despite the fact they have not yet generated any revenue. The company has raised a billion dollars to date from prominent firms like Sequoia Capital, Andreessen Horowitz While the size of the New funding round has not been disclosed. Obviously the valuation suggests it would be substantial. While little is known about Safe Superintelligence's actual work on the tech or technology, the company's names, let's give background, suggest that they are trying to develop super intelligence that's both powerful and controllable. Now, Paul, like, it seems like it's a smart bet to go with whatever Ilya is doing. We've talked about this before. However, this is still like a huge bet. Like do we have any idea what this company actually does or is going to do? Because if you're an investor evaluating this opportunity, like outside of Ilia, like what details do you actually even look at?
- [74:02]
Paul Raitzer
Yeah, I don't know. I mean they not only don't have any revenue, they have no plans to have revenue, no plans for products, no plans to make money. They have said we are on a straight shot to Superintelligence, which is artificial Superintelligence, which is smarter than the smartest humans at every cognitive task. Like that's their pursuit. The only thing you have relative is he was leading the team that built Strawberry, which became the reasoning models that OpenAI. I think he's probably the one internally who figured out the test time compute scaling law basically and you have to assume that's what he left to go pursue. The question is, can he get there faster than OpenAI? My guess is he thinks he can and he's proved before he's one of the top, if not the top AI researcher in the world. And so you're going to have people willing to, you know, make those bets.
- [74:54]
Mike Caput
We alluded to this a little bit before, but both Google and Microsoft keep running lists of hundreds of case studies and use cases detailing how customers of their different AI solutions use those products to achieve real world results. We've even reported on some of these in the past. Now both companies are adding to that database. Google has released a new series of case studies on how 50 different businesses from across all 50 states in the US are using AI in Google Workspace. This includes stories about how businesses in many industries you can think of are using AI for Docs, Drive, Gmail, Meet, et cetera. These appear to maybe intentionally focus on smaller local businesses using Gemini AI in Workspace to do everything from write social posts to draft emails to track inventory. Microsoft has also added more than 50 new customer stories to its huge list of 300 plus AI transformation stories. So Paul, we'll link to both of these in the show notes. It's awesome. Everyone should go check all of these out for inspiration and examples. One thing that jumped out to me, I mean, Google's new case studies definitely seem to be focused on like normal, accessible, small, local businesses using AI, not like huge tech firms. Do you think that's an intentional marketing choice?
- [76:19]
Paul Raitzer
I would imagine. I mean, in The United States, 99.7% of all businesses are small businesses now. Like half of employees work for big companies. But in terms of number of businesses, yeah, something like 24 million businesses in the US are small to mid sized businesses. So it's, I would imagine, central because it's a massive market and it's the market that probably is struggling to like really see the opportunity and like the use cases. So trying to personalize it to all these different businesses makes it ton of sense. And we know people love use cases, Mike. Like, I mean, anytime we put links to this stuff in, it's always the top link stuff in the newsletters. Like, people want the tangible things that it's like, all right, maybe I'll find some inspiration by seeing a company like mine and hearing what they do. So this has always been our focus, like since the day we started Market Institute back in 2016. It was trying to connect the dots for what are the use cases? Because that's how you make it tangible to someone is when they see them. It's like seeing yourself in the mirror. It's like, okay, I, I get it. Like, that's how I can use these models myself.
- [77:18]
Mike Caput
All right, our last topic for this week. So we're going to end with a new segment that we're hoping to do each and every week. We did this last week as well, listener questions. So we get a ton of questions. We'd encourage you, if you have a question about AI, to reach out to us through the website marketingaiinstitute.com click contact us and we'll try to answer a bunch of interesting a user and audience questions. So this week's question, Paul, is I'm a marketer who wants to level up my career with AI. What is the path I should take? And I want to add really quickly some context as to why I picked this one. This sounds like it's easy to answer. We've got tons of resources. We have literally developed over nearly a decade to answer this. But the reason I picked this is because people still ask me all the time, like, should I go get a certain degree, a certain certificate, how should I get started here? Like, there are so many options. I think people might be a little more lost than sometimes we realize.
- [78:17]
Paul Raitzer
Yeah, you know, I, I think about this all the time, because we do. And we've. So my intro day I class, which, like, I'm teaching tomorrow, Tuesday, is like the 44th or 45th of these. I started teaching intraday in November of 2021 as a free monthly class for people on Zoom. If you happen to be listening to this before noon Eastern time, we'll drop the link to intraday just in case. But we do it every month so you can register. So I have taught. I think we've had 27,000 people go through that and over that time, and we get an average of probably 80 to 100 questions every time I teach it. So it's. I'm not exaggerating, I say tens of thousands of questions probably. So we see this all the time. And then when Mike and I are out doing talks, like, we hear it firsthand all the time. And what I, what I tell people, whether you're a marketer, any knowledge way worker, you know, you have to personalize your learning journey. And that happens in a couple ways. Like, one is, how do you learn best? Do. Do you like taking online courses? Do you like reading books? Do you like listening to podcasts? Do you like watching videos? Like, what is it that is your best learning vehicle? And then find the content and the experts who can help you in that way. Like, for me, I used to read a ton of books. Like, that was how I. My whole career, that was how I learned everything was through books. I used to just go to the bookstore and buy five, six books at a time. Now it's. It's a lot of podcasts, It's a lot of, like, passive listening. When I'm in the car, when I'm at the gym, Like, I'm just always listening to different perspectives on things. So I think the way you have to think about it is how do you learn best? And then I actually, I shared a learning journey framework with Mike, like, literally last Friday that we're probably going to do more with. But, like, this is a rough draft. Think about it as, like, step one is curiosity. Like, you, you are beginning to explore it, but you're not really sure exactly where to go. And maybe that's how you found this podcast. And, like, this is your first step. The second is understanding. This is like the fundamentals of AI. And that's where, like our Intro to AI class is all about. Is, like, free. Here's the fundamentals. It's the certificate that I'm building for the AI Academy. We're going to, you know, relaunch in the spring. There's an AI Fundamental certificate. It is specifically built for this purpose. Like the people that just want this fundamental AI101 knowledge. Experimentation is the next step that is like you gotta just get in and use ChatGPT, even if it's not allowed at work. ChatGPT, Gemini, whatever it is, you gotta just play around with these tools. Use it to plan a trip, use it to build a coaching lesson for your kid. Like whatever it is, like use the tools in your life. Figure it out. Third is integration. You're now building it into your or, I'm sorry. Fourth is building it into your workflows and processes. Like you have found your three to five to seven core uses and every day you are using AI in that process. Like for me and Mike preparing for this podcast, every week, AI is infused into that workflow. And then there's like transformation. I have reinvented how I do my job, how I go through my personal life, because AI is so fundamental to what I do. And so whether you're a marketer or an attorney or a, again, wealth manager or an entrepreneur or a CEO, whatever you are, personalize it based on how you learn best and find the resources to do that. And then personalize it based on where you are in that journey. Now that is what I'm trying to do with our AI academy moving forward is to like create this platform to allow people to follow these personalized journeys. But that's how I, you know, I think about learning and this all like towards this path to like high level of confidence and proficiency and in AI.
- [81:54]
Mike Caput
Awesome.
- [81:54]
Paul Raitzer
I love that.
- [81:55]
Mike Caput
That's a great kind of positive, clear note to end on after a week full of pretty daunting AI news and developments. Paul, thank you as always for breaking everything down for us.
- [82:07]
Paul Raitzer
Thanks Mike and we appreciate everyone being with us again this week. We'll be back next week with regular schedule episode. Thanks for listening to the AI show. Visit marketing AI institute.com to continue your AI learning journey and join more than 60,000 professionals and business leaders who have subscribed to the weekly newsletter, downloaded the AI blueprints, attended virtual and in person events, taken our online AI courses and engaged in the Slack community. Until next time, stay curious and explore AI.