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The Artificial Intelligence Show
#145: OpenAI Releases o3 and o4-mini, AI Is Causing “Quiet Layoffs,” Executive Order on Youth AI Education & GPT-4o’s Controversial Update
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- [00:00]
Paul Raitzer
If you're a service provider, every time you put a proposal together, you need to be asking yourself, Can O3 do this? Like, I'm about to send a proposal to somebody for ten thousand, twenty thousand, a hundred thousand, a million dollars, whatever it is, if this is a AI emergent business like ours would be, could they just use O3 to do this? 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 SM and 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 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 145 of the Artificial Intelligence Show. I'm your host, Paul Raitzer, along with my co host, Mike Putt. We are back after an extended break. We were on spring break. I was actually in Aruba, which was incredible. I had never been there. It was, if you have a chance to go, I would highly recommend it. My family and I went and enjoyed seven Days Mike. It was crazy and it was cool because my kids are at the age where, you know, downtime Siberia just kind of chills. They do Minecraft or whatever they're working on. And so I actually get like a couple hours on vacation each day to myself to just kind of think and work on bigger picture things. Plus you got seven hours in the planes. So I actually had like a, an incredibly productive and relaxing trip, which is like my favorite combination.
- [01:45]
Mike Kaput
The best kind.
- [01:46]
Paul Raitzer
Yeah. So I was like keeping up on the AI news and, you know, filling the, our sandbox for the podcast with stuff. Still did my newsletter, you know, a couple, a couple of Sundays, but there was no lack of things happening while we were away. So Mike and I are gonna do the all rapid fire approach again. So anybody who's new to the show, normally we do three main topics where we, you know, kind of linger for 7 to 10 minutes per topic and then the rapid fire is usually like one to three minutes. So to try and get through everything from the last two weeks, we're going to try and go all rapid fire. Now there are a host of topics that each could definitely be a main topic. So I would say follow along here, but go to the show notes if you want to dive into anything that we cover. If you, if you're unaware. The show notes always have the links to everything we cover. So if there's any topic that we don't get, you know, really far into today that you want to go explore more, you know, check out those show notes and, and go explore those topics as well. So ton to get to first I want to touch on. This episode is brought to US by the AI for B2B Marketer Summit, presented by Intercept. So if people again, are kind of new to the show, don't know how this all works. Marketing AI Institute is Mike's Chief Content Officer of Marketing Institute. I'm the founder and CEO of Marketing AI Institute and Smartr. So Marketing Institute, I created in 2016 that that business is very much focused on education, but also events. And so we have four core events through Marketing Institute. One of them is our AI for B2B marketers summit, which is a virtual event. We just announced the agenda so you can go check that full agenda out. It's happening virtually on June 5th. So this is. We have three virtual summits that we do. AI for writers, AI for agencies, and AI for B2B marketers. And so this one's happening June 6th at noon Eastern. You'll learn real world strategies to use AI for growth, better content, stronger customer relationships. And thanks to Intercept and our sponsors, there's actually a free ticket option so you can register at B2B Summit AI. You can go learn more about it, check out the full lineup and get registered. So as I said, there is a free registration option. There's a paid option as well, but you can register for free. And again, thanks to Intercept and our partners for making that possible. And then the other, our flagship event at Marketing Institute is Macon. This is our marketing AI conference. We started this in 2019 was the first one. So Macon 2025. We will be back in Cleveland, Ohio, which is our home base, October 14 to 16. We've already announced 23 speakers, dozens of breakouts, mainstay sessions, and the four hands on immersive workshop shops that happen on the 14th. So this is our sixth year where we're bringing this, bringing thousands of marketers together. We're expecting probably 1500 plus this year. And you can check that out. Ticket prices go up May 31st. They kind of go up every 30 days roughly. So, you know, try and get in there in these early bird pricing specials. Save yourself a few hundred dollars. So you can go to Macon AI, that's M A I C O N A I to learn more about about that event. We still Have a lot more speaker announcements coming, working on some really exciting things. But again, you can check out the first couple dozen speakers and learn more about the breakouts and mainstay sessions that are going to be coming up there. So, yeah, check out AI for B2B Marketer Summit for a virtual event on June 5th and then join us in Cleveland October 14th to the 16th for Macon 2025. Okay, Mike, we got new models, we got research reports, we got calls for interpretability from Dario Amade. We, we got a whole bunch going on education initiatives from the government. Like let's, let's kick it off.
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Mike Kaput
All right, Paul, so, and as a reminder for what Paul said previously, we are covering the last couple weeks of news. So some of this stuff may not have happened exactly just last week, but it is stuff that we have not yet covered. And first up is a big one, which is OpenAI just launched two major new models. There's now O3 and O4 mini. So these are their smartest, most capable models yet. And what sets them apart isn't that they're just better at math and coding and writing, it's that they can also now reason about when and how to use tools inside ChatGPT. So these things come out of the box, ready to search the web, run code, analyze images, generate visuals. All of this starting to be chained together without you needing to necessarily, you know, select a bunch of different tools or prompt every single feature or functionality. So O3 is setting new records across academic benchmarks and real world tasks. O4 mini offers lightning fast, affordable reasoning, which is ideal for high volume work. And both models can think with images, not just about them. So there's a whole new level of multimodal problem solving. And right now you can access up to 100 messages a week with O3 and 300 a day with O4 mini, if you have a chat, GPT plus team or enterprise account. According to OpenAI Pro, users have, quote, near unlimited access to these models at the moment. Now what's really interesting here, Paul, is that O3 in particular is making some serious waves due to just how intelligent this thing seems. There are some prominent voices out there, there, including the popular economist Tyler Cowan, who have said straight up, they believe this model is essentially artificial general intelligence or AGI. So, Paul, I know you and I have both been really impressed with O3. Maybe walk me through your initial impressions, maybe give me a sense of what you think about all this commentary about it being actual AGI.
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Paul Raitzer
There's definitely been lots of the AGI context I think a lot of people starting to wonder if we're not, you know, really on this accelerated path to it and if this isn't kind of an early preview, because I think O3 Pro is going to come out soon as well. Like, I think. Yeah. So there. There's a more powerful version coming. I've also seen quite a few reports that hallucin. Hallucination rates are higher with O3. So just sort of, you know, a user warning. It seems super impressive and it really is. But again, if you're depending on this thing for work that you're going to turn in for things that you're going to put out into the public, you have to be very vigilant on the accuracy and reliability of the outputs. So just kind of a note there, a couple of people that surfaced for me when I was looking at reactions here, Alexander Wang, who we've talked about numerous times on the podcast, the CEO at Scale AI, which is a company that works with all these big model companies to do the trick training, you know, provide the data, things like that for the training. So he said OpenAI03 is a genuine, meaningful step forward for the industry. Emergent agent, agentic tool use, working seamlessly via scaling, reinforcement learning is a big breakthrough. It is genuinely incredible how consistently OpenAI delivers new miracles. And then Bob McGrew, who's the former chief research officer at OpenAI, tweeted that the defining question for AGI isn't, quote, how smart is it? But quote, what fraction of economically valuable work can it do? The spotlight for O3 is on tool use because intelligence is no longer the primary constraint. The new frontier is reliable interaction with the external world. So just a reminder, like, you know, as we talk about AGI and, you know, again, people who follow the show know we have an entire new series dedicated to, like, kind of following this road to AGI and beyond. Um, I think it's really important that people continue to remember we don't need to reach it or agree on it, that we have reached it for it to transform everything. So just using O3 myself over the last week, you really start to increasingly see it doing the things that I would otherwise be paying advisors and consultants to do or the things that we would traditionally be hiring someone to do. So as an example, while I was in Aruba, we had to make a kind of a relatively quick decision on Internet for the office. So we have Internet in the office. We need to upgrade it. It is not my area of expertise. It's not something, as the CEO of The company I've even had to think about for, like, five years because we did this before and it's been working fine. But as we're scaling up our company, we have to rethink how we're handling the Internet. Make it more reliable, more stable, things like that. So we get a quote from a vendor. Tracy, our coo, sends it to me. She and I go back and forth. I've got questions, she's got questions. Neither of us are experts in this field. So I was like, screw it. Like, I'm just going to go into O3 and like, let's just have this conversation. Hey, you're a senior IT advisor. We're trying to solve for this problem. And it analyzed things in ways like, I've been paying IT people for 25 years running my companies. It helped me understand more deeply how to solve this than any IT person I've ever talked to to. And I was able to just, like, continue. I don't understand this. Can you explain this for me better? Can you give me examples of why I would care that this is the difference? And so rather than, like, me reaching out to my IT person and then waiting five hours for a response that I might not understand in the moment when I had 20 minutes, I just did it myself. I just solved the thing. And so you start to realize, like, I don't necessarily have to have deep expertise here. I know enough. Having managed my Internet as a CEO for 20 years, what I need and don't need, I just needed some guidance and, like, some. Some frameworks to help me make a decision. So in a matter of about 20 minutes, talking to oh3, I made a decision, replied to Trace. I was like, okay, let's go and here's what we're going to do. And then I shared that chat with Tracy so she could also see the context of why we were making that decision. And then she could continue on and see if she had any other questions as well. So that is a prime example of something I would have absolutely paid an advisor for. Same time, I'm working on this massive organizational design strategy for our company because again, as we're kind of scaling up and new complexities around size of the staff, compensation models, all these challenges that we haven't had to really face. And even when I was running my agency, we peaked at like 20 employees or something. So I never had to design an organization that could scale to 100 plus employees. Which is what I'm now having to kind of, like, envision is like, okay, we have to make decisions now that can get us to stable growth from like 50 to 100 employees if we choose to go that route. But now I'm, I'm out of my league. Like, this isn't what I've done. I haven't run a company with 100 plus people. So again, I could pay and probably 50 to $100,000 for the specific thing that I was looking to do, or I could do it myself with O3, which is what I did. And what I realized in the process of doing this over a few days on vacation was rather than paying someone to give me a report and say, here's what you should do, that I would then have to sit there for hours reviewing, analyzing, trying to make sure I understood the recommendations so that I could then make an educated decision. I just did all the work myself with O3. Now I, you know, I kind of knew the prompts to give it, like, the questions to ask. But the main value of the project became my ability to critically question the outputs of the model. Be like, well, why are you saying that? Where are you getting this data from? And it would show me the citations. So it became this immersive experience to where I am going to have a much greater confidence level in the final output because I was bought into the process and I was able to ask all my questions along the way in real time. And so it really just starts to change the way I think about how we do knowledge work. And we talk about this Mike a lot on the show. But like, these are very practical examples where I just saved myself probably 100 plus hours of time and work and probably a hundred thousand dollars in expenses. And I actually feel better about the end product, which, by the way, the other thing I'm going to do is take that end product before I operationalize it. And I'm going to use other models as critics to evaluate what I ended up at working with O3. So I'll take the final output, I'll put it into Gemini 2.5 and say, just basically start from scratch. Hey, here is the organizational structure I'm looking at. Here's the decisions I've made. Please assess this for me. You know, please criticize or look critically at different areas and challenge these decisions. And now I'm not just dependent upon a single model that might be hallucinating. I can actually vet it against one or two additional models that maybe take a different perspective. And again, I end up at, you know, a place where I'm just more confident in the final product. So I don't know, Mike. Like, it. It just changes things. And I. I know you and I talk about this all the time, but, like, when you know this stuff, like, when you can do what I just explained, when you start to look at problems in your business differently because, you know AI can help you do it. You can run a business or a department or a team or a campaign in entirely different ways. When you know how to work with these tools, it really is, like, hard to comprehend if you're not actively doing it, but it's so transformative.
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Mike Kaput
Yeah. And I would also say, too, I'm certainly biased here, but this alone justifies the cost of 200 bucks a month having unlimited access to it. I mean, you just described I just.
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Paul Raitzer
Saved $5,000 in my IT bill.
- [15:57]
Mike Kaput
Yeah.
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Paul Raitzer
One project. I could write it up.
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Mike Kaput
Yeah, exactly. And I also would just to kind of wrap up here, be really blunt, honestly, and say, if you are a professional services provider, like a lawyer, an accountant and IT consultant, what have you, you need to run and not walk to go spend $200. Or you can get in a Plus account in limited usage and put this thing through the paces of the hard questions clients ask you. Because I would be really starting to think about how do I become the guy that they then go hire after they've done this initial thing themselves.
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Paul Raitzer
Every. Every time, if you're a service provider, every time you put a proposal together, you need to be asking yourself, Can O3 do this? Like, I'm about to send a proposal to somebody for 10,000, 20,000, 100,000, a million dollars, whatever it is, if this is a AI emergent business like ours would be, could they just use O3 to do this? Or 80% of this? Because the answer is going to increasingly be yes as we get to a higher level of awareness and AI literacy for leaders at these companies. Right now, it's still early, and we're still very much in kind of the early adopter, innovators phase, where very small percentages of companies and leaders are aware they can do this in place of hiring you. But it's going to change pretty quickly.
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Mike Kaput
And, you know, the second topic we're covering is kind of related to this because we're seeing some reports, especially according to the information, that CEOs are quietly making a big bet. And the bet is the more AI we're using, the fewer jobs they're basically going to need to hire for. So the information came out with a report where they wrote, quote, executives at more than half a dozen companies said AI has affected their hiring plans, though most were careful to avoid saying AI was effectively replacing existing employees. So, for instance, they cite all these case studies where PayPal says AI now handles 80% of customer service tickets, cutting support staff dramatically. Cloud giants like Microsoft and Google are literally pitching AI as a full replacement for junior sales reps. IT staff, in some cases even software engineers and executives are starting to admit that if a role can be automated in these ways by AI, especially as they're fearful of kind of possible economic issues coming up through some of the policies we're seeing, through some of the headwinds we're experiencing. If a role can be automated by AI, they're starting to say it's either going to be frozen or outright eliminated. So one leader at Ernst and Young at EY put it bluntly and said most of their clients now expect slower hiring or headcount cuts across their entire business. And we've also seen a couple related reports where others are starting to kind of, let's call it, say the quiet part out loud. In an exclusive report with Axios, Anthropic's chief information security officer said that the first fully AI AI employees are a year away. There's a new startup that's on our radar called Mechanize, which is backed by heavy hitters like Nat Friedman and Daniel Gross, Patrick Collison of Stripe, Dwarkesh Patel, Jeff Dean and others. And their explicit mission is to develop, quote, virtual work environments, benchmarks and training data, data that will enable the full automation of the economy. So, Paul, there's kind of all these threads coming together, and I think you summed up the central point here really well in a recent LinkedIn post where you said, quote, my belief is that quiet AI layoffs have been happening for the last six to 12 months, for instance, masked under a return to work policy. And they are accelerating. Companies have been replacing staff with AI, or at minimum not hiring new staff due to AI, but they don't just don't want to admit it because it's bad pr. Can you maybe walk me through what you see going on right now? Like, what do you expect to be happening in the near future with AI's.
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Paul Raitzer
Impact on jobs, disruption and displacement of jobs, which is what we've been saying for the last 18 plus months on this show, is that this is coming. And I just don't think people were ready to hear it. Like, I don't think people wanted to admit it, or maybe they just didn't understand fully what these things were going to be capable of doing. And they were just in denial that it was going to be possible. But it's absolutely what's happening. It's what I've been hearing sort of behind the scenes now for six to 12 months. It's what we're now seeing people saying publicly. It's not going to be evenly distributed across industries. So I think that some people may hear this and be like, yeah, I'm just not seeing it. And it's like, that's fine. Like maybe in the legal industry they're just slow moving, or banking or financial services, whatever it may be. There's different reasons why different industries might not move as quickly, but it's absolutely what's going to happen. And, you know, I think we had this return to work policy was the natural cover initially. So it started in the tech space. Like, it's like, okay, you got to be back in the office four days a week, three days a week. They know 20% of people are going to refuse to do that and they're just going to leave the job. Great. We just cut 20% of our staff without having to say we were replacing them with AI basically over the next two years. And now the latest cover is going to be tariffs in the economy. So, you know, things aren't great in the economy right now. There's increasing chatter that we're heading towards a recession. And that's going to give the impetus to say, well, we got to cut costs anywhere we can. And if that means people, it's, it's people. Now again, they're not going to say we're doing this because we don't think we're actually going to need as many people because we're going to use AI to do a lot of this work. That won't be the lead talking point, but it's probably going to be the underlying thing that's actually causing this is there's increasing confidence by C suites and boards that they don't need as many people to do what they do. And I think they're probably right. This is the thing I've been saying all along is like, we just don't need as many humans doing the current work. So, like, if you take, you know, of all things being equal, we say like, we do these 100 things or we create these 100 widgets every month. Moving forward, we have 50 people creating those a hundred widgets. Maybe we can do it with 20 instead. So if you're, if the creation of the output, the product or service remains flat, you just don't need as many people to do it. Now, if there's tremendous demand for what you do, and you have a ton of growth and new markets and new products, then great. You, you may keep hiring people, but you don't need as many. And your revenue per employee number should, in theory, be skyrocketing over the next two years.
- [22:34]
Mike Kaput
Years.
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Paul Raitzer
Because you should be able to create more output, generate more revenue per employee. If you don't, you're doing something wrong. If you don't start to generate a higher revenue per employee number, you are mismanaging your company and it's because you just don't need as many people. So this why I've said, like, it's the best time ever to be a startup, because you can just build more intelligently, you can build with fewer people, build with smarter processes. You don't even have to deal with the quiet layoffs. You just grow smarter. But if you're an existing company that has dozens, hundreds, thousands of employees, you got some really challenging times ahead to manage that headcount. Now, the mechanized thing, Mike, that you mentioned, man, like, that's wild. Talk about just coming out and saying it like, so this is the kind of startup we have absolutely expected. I remember we talked a few episodes back. I don't know, maybe like 10 episodes back, could be longer. We talked about Y Combinator and how they were like, investing in vertical agent companies. This is exactly what we were talking about, that people are going to build companies that automate workforces by industry. Now, mechanized just comes out, says, we're just going to automate the entire economy, Literally their mission. So even though we've known this was what people were going to build, it was what venture capitalists were going to invest in, it's jarring to actually see someone come out with the mission statement. So spend a moment on this. And I know we're not doing main topics, but man, we got to talk about this. So Mechanized was launched on April 17 via a Twitter post by its founder, who's a researcher, Tame Besser Besorgalu, who is Epic AI was, I think he created it, or he's lead researcher there, which is a research institute that investigates key trends and questions that shape the trajectory and governance of AI. So the startup goal, according to Bessorgalu, is the full automation of all work and the full automation of the economy. Now, in the tradition of recent AI major startups like Safe Superintelligence and Thinking Labs, their website is a single page of text. No images, nothing. It's just like text and some links and in there, they kind of go over some of the information that they tweeted, which is today we are announcing Mechanized, and this is just direct quotes, a startup focused on developing virtual work environments, benchmarks and training data that will enable the full automation of the economy. We will achieve this by creating simulated environments and evaluations that capture the full scope of what people do at their jobs. This includes using a computer, completing long horizon tasks that lack clear criteria for success, meaning there's no goal you can set. It's just like you got to figure out what the milestones are along the way, coordinating with others and reprioritizing in the face of obstacles and interruptions. Now, I'll pause for a second here before I continue. One of the things we've been talking about recently is the need for evaluations and benchmarks and AI to not be tied to IQ tests but to actual jobs. This is exactly what they're doing. So they're using that to then inform the building of smarter AI models. Okay, continue. Mechanized will produce the data and evals necessary for comprehensively automating work. Our digital environments will act as practical simulations of real world work scenarios, enabling agents to learn useful abilities through reinforcement learning. Now here's where it gets kind of crazy. The market potential here is absurdly large. Workers in the US are paid around 18 trillion per year in aggregate. For the entire world, the number is over three times greater, around 60 trillion per year. So this is their total addressable market is 60 trillion per year. The explosive economic growth likely result from completely automating labor could generate vast abundance, much higher standards of living and new goods and services that we can't even imagine today. Our vision is to realize this potential as soon as possible. So they're directly saying the thing no one has been willing to directly say, which is they plan to take all knowledge work and use AI to do it as quick as possible. Now the part that was somewhat shocking to me was the investors. Now you highlighted them, Mike, but like Nat Freeman is the GitHub CEO, so he's. GitHub is Microsoft, right? Microsoft bought GitHub, I think. Yeah, yeah. Tech investor Daniel Gross, Stripe co founder and CEO Collison, you mentioned Dwarkesh, who actually just did a podcast with these guys. So I'll come back to that in a second. Jeff Dean, if you're not aware of that name, is Google's chief scientist. Jeff Dean is like one of the godfathers here of like modern AI and then a couple of key investors. So the fact that these people, these guys are all behind a company that is directly saying we plan to intelligently automate all knowledge work in the economy. And I haven't seen a comment from any of these guys. Like, I'm really curious to hear their positioning on this. But. So if you want to go deeper on this, I know, like, I'm flying to Boston in two hours, so as soon as we get off this, I'm jumping on a plane to Boston. I know what I will be listening to, which is Darkesh's podcast with TAME and EGE Ertalu, who is, I think his partner in this, but he just dropped the podcast the day this got announced. And then in a TechCrunch article, Tame referred to a research report that he and Ege published in 2023 called Explosive Growth from AI Automation. A review of the Arguments. So now I think this paper is actually the prelude to Mechanize. And so in that paper he said, we examine whether substantial AI automation could accelerate global economic growth by about an order of magnitude 10x akin to the economic growth effects of the Industrial Revolution. We identify three primary drivers for such growth. The scalability of AI of an AI labor force, restoring a regime of increasing returns to scale, Two, the rapid expansion of an AI labor force, and three, a massive increase in output from rapid automation occurring over a brief period, period of time. We conclude that exclusive growth seems plausible with AI capable of broadly substituting for human labor. But high confidence in this claim seems currently unwarranted. Key questions remain about the intensity of regulatory response, physical bottlenecks in the production of economic value of superhuman abilities, and the rate at which AI automation could occur. So the key takeaway here is Mechanize is not alone. They're just the first ones to come out publicly and say this is what they're doing. Andreessen Horowitz is probably funding 10 companies that are trying to do this exact same thing. Like this is going to be pursued. I'm not saying it's achievable. I'm not saying like the total address of market is reliable. I'm just telling you venture capital is going to pour hundreds of billions of dollars. If you look at something with a tens of trillions of dollars of market potential, that means they're willing to put in hundreds of billions in the next three years to pursue this idea. Yeah, so I know it wasn't supposed to be main topic, but how do you talk about this without doing this? There's so much uncertainty about what this all means, and I get all the anxiety. The thing I keep coming Back to is like, if this is blowing your mind, file it away, know it's happening, and go back to your work and just do the next best thing to increase your own literacy and capabilities here. Like, it's not going to happen overnight. It is, you know, I think I've said it's a, you know, it's kind of like climbing up a hill, not falling off of a cliff at the moment. And so, like, you have a chance to sort of be out on the frontier here and like, figure this stuff out as it's going. I get that it can cause anxiety, but, like, I wouldn't let that overwhelm you. I would just like, go, go do the next things.
- [30:15]
Mike Kaput
I would also say it. The silver lining to me here as well is if they think this is possible, you know your own job better than very likely better. These people do. So you can go start figuring this out for yourself. Not necessarily automating everything away, but whatever productivity and performance gains they believe are possible, you probably are the best suited person to go figure that out in your own shop.
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Paul Raitzer
Yeah, and I do think that there's quite a window here. Like, I don't see what they're trying to do as like a 2027 outcome. You know, I think it's going to be by industry. But again, I think if you're in like AI research, financial analyst, lawyer, like, there's just going to be some really obvious industries that this stuff's going to hit sooner than others. And I would not be ignorant to it. Like, I think that's the key is, like, you have to educate yourself on not only what these, these models are capable of, but what they're capable of in your industry. Because things like we started this with, people are going to stop accepting your proposals because they know they can do the work cheaper. People are going to stop hiring you as a professional because they just don't need as many of you in their, in their company anymore. So you're going to start to see earlier signs here, sort of like canary in the coal mine kind of stuff, like, it's coming. But what they're envisioning isn't probably like a near term, like three to five year reality now. Anything beyond that, as I've said on the road to AGI podcast, like, I can't help you. Like, I don't know beyond like three years. It's really hard to project the reality here.
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Mike Kaput
In our next topic, President Trump has signed an executive order to make AI education a national priority starting from kindergarten. This new order creates A new White House task force that will coordinate AI programs across government, aiming to get foundational AI training into every K to 12 school and expand opportunities for lifelong learning. It also calls for a national AI challenge to spotlight student innovation and set some aggressive deadlines. Within 90 to 120 days, federal agencies must launch partnerships with tech companies and universities, create online AI resources and start funneling grant money towards AI focused teacher training. This plan also aims to go beyond schools. It pushes for more AI apprenticeships, industry certifications, and even encourages high school students to learn to earn college level AI credits. Now, Paul, I found it really interesting the federal government is actually starting to at least talk about this in a serious way. How much substance do you think there is to this initiative?
- [32:55]
Paul Raitzer
I don't know. I mean, it's the first time I've heard this administration say anything on this topic. So it kind of came out of nowhere in my opinion. I don't know who's actually the driver of this. I think it's, it's a very smart initiative. Like I say, I don't know who is the sponsor of this. Like, I'm not sure where that's coming from, but it's the kind of initiative that we've been calling for on the show for a couple years that the government had to get involved in AI literacy. This is like absolutely essential. And I like the idea at least like again, that much is known about this. It's like, hey, in 90 to 120 days, come back with a plan is basically what this executive order says. But we have to teach the teachers, we have to make the technology accessible to students, which we've seen. I think OpenAI, Anthropic and Google have all kind of made their models free for college students. I think in the last month we've seen announcements around that we have to teach the responsible use of it. This can't be handled the way enterprise adoption has been, which is, hey, here's a thousand licenses to copilot. Go figure it out. If we're going to give the technology, starting at kindergarten all the way up, we have to actually teach the students and the teachers how to use the technology. So as I said, like, I'm a bit skeptical because I. It's the first I'm hearing of this and like, I don't. AI was not mentioned on the campaign trail once by either, you know, potential administration. So this idea of like this massive investment in AI literacy, while I love it, I don't necessarily know that they're really committed to it or that they even really understand the importance of it. Whoever wrote this seems to, but I don't know that the administration at large actually believes this is like, critical. I really would love to be wrong on that though. Like, I'm totally open minded and I'm very optimistic about the approach. In the fact sheet, they said the executive orders to create new educational and workforce development opportunities for America's youth, fostering interest and expertise in AI from an early age. Love that. That's great. Early training in AI will demystify this technology and prepare America's students to be confident participants in the AI assisted workforce, propelling our nation to new heights. Absolutely. Again, whoever's writing this, it might be.03, I don't know. But like, whoever's writing this, like, it's really good. Preparing our students to be leaders in AI technology requires investing in our educators 100%, providing them with tools and knowledge to both train students about AI and utilize the tech in the classroom. And then they said lifelong learners also need new resources to develop technical skills for rapidly evolving work environment that increasingly incorporates digital technology. So again, on the surface, this sounds like a really, really positive direction. Anything that involves more government interest, action and funding around AI literacy I am absolutely for. So I would love to see this come to light and be real and to actually have like full government support.
- [35:46]
Mike Kaput
And just to reiterate our previous topics, again, if you haven't used O3 it, 100% could have written that with the right input.
- [35:53]
Paul Raitzer
So no doubt AI is really important to America's youth. Go create a fact sheet and draft an executive order.
- [36:00]
Mike Kaput
I say that because of how good it is. Like, it is that good.
- [36:02]
Paul Raitzer
It's really good.
- [36:04]
Mike Kaput
All right, so for our next topic, I'm going to run through a bunch of OpenAI related updates because they had a ton going on since our last episode. And then Paul kind of just let you weigh in on whichever of these you find the most noteworthy team.
- [36:18]
Paul Raitzer
Sounds good.
- [36:18]
Mike Kaput
So first up, on Friday, April 25, Sam Altman posted that GPT4O got an update that quote, improved both intelligence and personality. So according to the company's model release notes, this included making what they call subtle changes to the way it responds. But this may not be that subtle because a bunch of people online claim that right after the update for those personality became kind of annoying and very focused on being basically like a yes man. Like, like telling you only what you want to hear in this, like really annoying, enthusiastic way. And Altman actually responded to these claims and said the company is working on a fix there. OpenAI also launched GPT 4.1 in the API, which is only available in the API won't won't be available in chat GPT the tool and it's focused specifically on real world developer needs. So it's got made huge leaps, they claim in coding, following instructions and it has long context understanding with a context window of up to a million tokens. OpenAI is also apparently building a social feed inside chat GPT. According to some either rumors or facts being reported on by the Information, this new feature would let users post and share how they're using the chatbot. Basically kind of like a mini social network internally. What they are calling making a post in the feed. Posting to the feed is called a yeet. Yes, that is really the word you are thinking of. And this idea is to help ChatGPT's massive user base, which is now over 500 million people a week, better understand what this chatbot can actually do now. Also, according to internal projections seen by the information, OpenAI expects to hit 125 billion in annual revenue by 2029 and 174 billion by 2030. That is roughly the size of something like Nvidia or Meta today. And they're betting a lot of that growth will come from agents and new products like shopping assistance and free user monetization. They even project that by 2029 agents alone could bring in 29 billion a year. They could be selling high end AI workers ranging from $2,000 a month knowledge agents to $20,000 a month research agents. Now also, OpenAI is getting taken to court again by another major publisher, Ziff Davis. The company behind sites like PC Mag, Mashable, Lifehacker has filed a lawsuit accusing OpenAI of copyright infringement and trademark dilution. They are seeking hundreds of millions of dollars in damages, according to insiders, and OpenAI says it is using the material in a way that is grounded in fair use now last but not least, a number of AI leaders have added their name to an Open letter calling on U.S. state attorneys general to investigate OpenAI's plans to transition from a nonprofit to a for profit company. This letter is titled not for Private Game and it's signed by literally dozens of top AI researchers, legal experts, and even Nobel laureates. Some of the notable signatories include Jeff Hinton, one of the godfathers of AI, AI ethicist and researcher Margaret Mitchell, and at least 10 former employees as far as I could count from OpenAI. And the letter argues that the company's plan to restructure in hand control to a for profit entity violates its original nonprofit mission, which we've talked about before, which was to ensure that AGI benefits all of humanity. They think allowing this structure to go forward could essentially allow private investors to capture and monopolize the value of AGI. So Paul, it's been a busy couple of weeks as always for OpenAI. Did you, did these like jump out to you as particularly noteworthy?
- [40:27]
Paul Raitzer
Yeah, I mean we could obviously talk about any one of these at length. So I'll just, I'll stick with the 4O thing. Yeah, I think one, it's interesting to note just they have this iterative deployment plan. That's their strategy at opening eyes. Just like just keep putting things out into the market, see what happens, see how people respond to it. So Sam basically just tweets like hey, we've made Some updates to 4.0. No context at all as to like what those updates are, like how it's different. But if you go to they have a model release notes page which they honestly don't really keep that updated as regularly as well. But when I saw his tweet I'm like there's got to be something more to this. So I, I went there and they had in fact to put an update a little bit of what it was. So what the Update was on April 25th is when this, they published this, it said we're making additional improvements to GPT4O, optimizing when it saves memories and enhancing problem solving capabilities for stem. We've also made subtle changes to the way it responds, making it more proactive and better at guiding conversations toward productive outcomes. We think These updates help GPT4O feel more intuitive and effective across a variety of tasks. We hope you agree. And then as you alluded to on April 27, after lots of feedback of this thing is really annoying, Sam actually tweeted this would have been Sunday night. The Last couple of GPT4O updates have made the personality sycophanty and annoying, even though there are some good parts to it and we are working on fixes asap. And then the part I thought was most interesting, he said at some point we'll share our learnings from this. It's been interesting. This just alludes back to the thing we talk about a lot on the show is like they don't know how these things work, like they don't know why it became annoying and like there's something that happened, some changes that they made where this thing all of a sudden just started becoming a yes, man. Like you said, like it's just like, oh, you're great. Like I love you. Like, oh that's so brilliant. And rather than being like a critic and helping you and so but like how that happened and what they have to do to try and fix it from being annoying, they don't know. And like they gotta kind of go in and try and like figure this out and, and that's just weird. And you know, we'll talk a little bit more about this in an anthropic topic coming up. But like these models, they aren't programmed the way traditional software was programmed to just follow instructions, right? Like they, they have this sounds weird but like they have a mind of their own and sometimes it's on the research to try and figure out why they do what they do and sometimes it's just not very obvious why and like what needs to be done to fix it back.
- [43:04]
Mike Kaput
So in some Microsoft related news in response to a post from Microsoft CEO Satya Nadella, prominent AI expert Ethan Malik has criticized the company's Copilot AI tool or product. Now, Nadella recently posted about a bunch of new features within Copilot that he was excited about. This included its researcher and analyst agents, which we covered on a past episode. And in reply, Malik said, quote, Microsoft keeps launching Copilot tools that seem interesting but which I can't ever seem to locate. Can't find them in my institution's enterprise account, nor my personal account, nor the many Copilot apps or copilots to apps or agents for copilots. Each has their own user interfaces. So we wanted to just quickly highlight this because it does come from a prominent, credible voice in AI. I mean, Ethan Malik is one of the top people out there to follow. I mean, Paul, this just certainly doesn't seem like a good look for Microsoft. It starts to kind of explain a disconnect. We've heard from people about the value that's actually being created by Copilot. What do you think?
- [44:12]
Paul Raitzer
Yeah, so again, we don't use Copilot Internet, so I can never speak directly to, you know, copilot experience. What I can tell you is if you have kids and you've ever tried to manage their Minecraft account through Microsoft, you know exactly how this goes. Like it is the craziest thing ever, how complex it is to manage Microsoft accounts, especially if it's across multiple products. So if the Copilot experience is anything like being a parent of a child who uses Minecraft, like, good luck, what I will say is Contextually, we have Google Workspace, so I can speak to Gemini's experience. It sounds like Gemini is probably a little bit better, but I suffer from the exact same thing with Google. So they announced what Veo, I think, like, the video model supposedly was available in Gemini. I don't have it. And like, and then I saw a tweet, like, five days later, it's like, oh, like, we're starting to roll it out. It's going to take a little while. And it's like, okay, well, that would have been nice to lead with. Like, when you announce that Veo is now available. And then I always laugh because I have my personal Google account with Gemini that generally gets this stuff before our workspace account gets it. And so I'll go in, like, I just have both tabs open. It's like, each day it's like, oh, nope, not there yet. You never know when you're going to get the thing or like, which version you have. OpenAI seems to probably do this best in terms of, like, they roll the models out fastest to their, their, their customers. Like, if they say something's coming, it usually happens pretty quickly, but it's still confusing as hell. Like, I still don't. And same thing. I have a personal chat GBT account I pay the 200amonth for, and then I have my business account. I never know which things in which account and which models underlying, you know, custom GPTs. And so again, like, Mike and I live this stuff 24 hours a day and I'm lost half the time. So, like, if you're a listener and you're like, oh, I have no idea, like, which model is Gemini using? Or what am I supposed to do? And where's this view? Like, welcome to the club. Like, it is it. It is rough. And it sounds like if you're a Microsoft user, it may be worse than all of them. I don't know.
- [46:23]
Mike Kaput
And sorry to poke more fun at Microsoft, but I saw a great post the other day that someone said you can go find if a startup has actual customers by seeing if they have a Microsoft login option. Because no one would build this on their own. It would only be a customer request. So I think that maybe there are some issues there. All right, next up, a new paper from two AI researchers, one of whom works at Google DeepMind, paints a very interesting vision of AI's future. So according to this paper, AI is about to enter what the researchers are calling the era of experience. So here's the idea. They say that until now, most AI models have been trained on human generated data, you know, writing code, papers, whatever, but that data is running out. And crucially, it only gets AI to human level of performance, not beyond it. The next leap, according to David Silver and Richard Sutton, the researchers behind the paper, will come from AI learning the way we do through its own experiences. They say that in this new era, AI agents won't just answer questions, they'll interact with environments, set long term goals, adapt strategies, and even form memories across months or years. Instead of being judged by human preferences, they'll optimize based on real world outcomes, like how much they've improved, a health metric, for instance, the scientific discoveries they've made, or energy efficiency they've achieved. So the researchers say this is a huge shift we need to prepare for because there are major risks. Agents could act autonomously for long periods, making it harder for humans to intervene. But it also offers a safety benefit. Experiential agents can adjust if their goals or environments change, rather than getting stuck in perhaps destructive loops of behavior. So the bottom line, according to them is, quote, ultimately experiential data will eclipse the scale and quality of human generated data. This paradig shift, accompanied by algorithmic advancements in reinforcement learning, will unlock in many domains new capabilities that surpass those possessed by any human. Now, Paul, this can, you know, maybe get a little dense or forward thinking, but there's, or seems like there's a really important point here that despite the breathtaking rate of AI progress so far, these researchers seem to be saying me have barely scratched the surface of what's possible.
- [48:54]
Paul Raitzer
Yeah, I mean, generally language models have gotten us to this point, but all the AI research labs seem to agree that they are not the end game like they, they are. You know, some, like John Lecun, think they're a distraction. Like he literally has been on record saying like, don't, if you're coming out of college now, don't work on language models. They're not the future. But there's different beliefs as to, like, what the unlock is. Silver is like a legendary AI researcher, so he led the Deep Re DeepMind AlphaGo effort. So if you again go watch the AlphaGo movie if you haven't seen it and you'll understand, you know, the context here. But he was the lead researcher on AlphaGo and I believe on AlphaZero, which came after. So the difference was AlphaGo was trained to play the game of Go through examples from like top players. AlphaZero was not given examples like it learned to play a number of different games and solve problems without human data. What they realized was the human data may actually bias the system, that these systems might be able to learn better without the prior human data. So we wrote about AlphaGo in our book Marketing Artificial Intelligence in 2022. We actually quoted Silver in the book. So I'll read this excerpt real quick because I think it gives a glimpse into what Silver is referring to, about what they're working on and what DeepMind is focused on moving forward. So again, I'm just reading an excerpt here from Marketing Artificial Intelligence. Cade Metz, an author and technology correspondent with the New York Times, was in Seoul, South Korea, covering the match for Wired magazine. This is referring to the AlphaGo match in 2016. He spoke with DeepMind's David Silver, the lead researcher on the AlphaGo project, about move 37 happen. Metz summarized what happened in this way. So AlphaGo learns from human moves, and then it learns from moves made when it plays itself. It understands how humans play, but it can also look beyond how humans play to entirely different levels of the game. This is what happened with move 37. AlphaGo had calculated that there was a 1 in 10,000 chance that a human would make that move. But when it drew on all the knowledge it had accumulated by playing itself so many times and looked ahead to the future of the game, it decided to make the move anyway. And the move was genius. In AlphaGo, the movie, Silver said of Move 37, that AlphaGo, quote, went beyond its human guide and it came up with something new and creative and different. But in the documentary, Silver also made the point that this is not human versus machine, but rather human plus machine quote. AlphaGo is human created. And I think that's the ultimate sign of human ingenuity and cleverness. Everything that AlphaGo does, it does because a human has either created the data that it learns from, created the learning algorithm that learns from that data, or created the search algorithm. All of these things have come from humans. So really this is a human endeavor. So the reason I share that is because this kind of goes back to, like, the topics we've been building on throughout this episode. This is the future. Like, they think that they can build systems that can go into any industry, any job, and learn potentially to do it better by running simulations of it, by basically learning from itself, by identifying reward mechanisms. Because if you give something where there's like a finite outcome, like, you know, the end game, you know, the goal is this, and we want you to achieve that. So if you give the AI a goal, it can work towards that goal and then it can know if it achieved it. But if you're playing into a game like business, which has this like infinite ending, like we were just, I was just listening to a BG2 podcast where they talk about like finite versus infinite outcomes and like business is infinite. There's no like end goal. Like there might be a near term revenue goal or something like that, but like winning isn't like an end point. And so the idea of being able to put these AI systems into these environments where there's just, they have to figure out what the reward mechanism is. It's not always just we, we achieved this outcome. There's like these in like difficult things to define along the way. And what they're saying is we can build systems that can figure that stuff out. They can find the reward mechanisms for themselves, they can create their own data, they can run simulations and they can learn better than if humans were to provide the data for them or just learn from the best humans. And so the challenge today of AI systems is they can't invent something new. There is, there's nothing like they can, they can connect dots just the same way a human would of like all these things and create a new product idea but they can't invent new physics, they can't like invent, you know, a new proof in, in math. Like they don't come up with something that isn't somewhere in the training data. But the belief is they can. Like there's no reason that they wouldn't be able to do that. And so the approach DeepMind is taking as well as other labs are probably going to try and pursue this. But Google has a distinct advantage in this and that they invented this like they have been doing this for 15 years. So if you want to understand what DeepMind is working on, where they're going, go study Alphazero like And there's a podcast that just Came out, the DeepMind podcast that actually has David Silver. I listened to this on the flight home from Aruba where it's called Is Human Data Enough? And he actually like tells the story of what they're working on. David does a great job of like talking and non dense scientific ways. Like it's, it's really good. Listen. So we'll drop the link to the YouTube video in the podcast in the show notes.
- [54:23]
Mike Kaput
Our next topic is a new report from Digiday which shows that major brands and agencies are racing to appoint chief AI officers or C A I os which is a role that's quickly moving from a novelty into actual table stakes at these companies. So in this report they show in the past year, companies like General Motors, Mastercard, PwC, Zocdoc, Accenture, they've all hired AI chiefs and so have some of the ad giants like WPP and indie agencies. And the reason is these companies want to move beyond experimentation and actually realize efficiencies. And to do that they need dedicated leadership. So go their argument to get AI initiatives off the ground. So in some cases, some people are working on this internally. Like at wpp, for example, the AI chief Daniel Holm says his role is about placing the right AI bets internally. Others are focused on customers real world impact, like a PwC where their AI chief is working directly with people in their ecosystem to scale AI. And some are just focusing only on helping their companies rebuild processes with an AI first mindset. So Paul, we first actually talked about the trend of the rise of the chief AI officer. I looked like way back in episode 82 in February 2024, because the new York Times did a story on this. So back then we kind of talked about how these titles were probably going to become more common, but also that it would be interesting to see how long they last because AI is or should be the responsibility of every single executive, not just one leader. Have your thoughts evolved here in the last year or so?
- [56:15]
Paul Raitzer
Yeah, I mean we're hiring for one right now. So I'll, I'll, I'll put the job description in like, because I thought deeply about this role as I was building out like our, our organizational structure, as I was thinking about the things that are needed. So the way I think about Smarter X, which is, you know, we talk about as like a sister company and marketing institute, it's, it's really more of like the parent company. Marketing Institute is like a marketing focused area within Smartr X. But like we think of SmartR X as an AI transformation company. So we want to educate and empower leaders to reimagine business models, reinvent industries and rethink what's possible. We want to do it with ourselves as well. So like everything we do, we look at and say, okay, is there a smarter way to do that? Is there a smarter way to build that department? Is there a smarter way to run that campaign? Is there a smarter way to build that process? What Smarter X means, it's like smarter version of everything. And so to do that you need to have. And again, I think every company should be doing this. You should be saying what is the smarter way to build our business? To build this team, build this department. So I think that a chief officer is the logical role that should lead that. Now does that person need to be highly technical? I don't know. Like I could see arguments for it actually being like a marketing person or it being, you know, I don't know, not finance. Like it could, it could be technical, but it doesn't necessarily have to be. So in our world I actually saw it as a combination of like a CIO role and elements of a CTO role. So I was seeing it being more technical. I'll read real quick the description. So role overview it says the Chief AI Officer, again this is straight from the SmartRx website, is responsible for spearheading AI driven innovation and automation throughout the organization. The role will focus on developing and deploying AI agents, intelligent automation of processes and workflows, and optimizing our technology stack to enhance operational efficiency, productivity, revenue growth and innovation. The Chief AI Officer will work closely with leadership and cross functional teams to ensure AI is leveraged effectively to scale our offerings and create sustainable competitive advantages. And then it goes into responsibilities of strategy and innovation, agent and intelligent automation, tech stack optimization infrastructure and IT cybersecurity and compliance, which becomes increasingly important with the AI agent side, AI education and adoption data strategy and then AI tool, app and product development which is more of an exploratory phase. So yeah, like if you want to go look at, we'll put the link in there SmartRx AI and you can go look at them. But yeah, so I, I do, I, I think this is going to become like a standard C suite role. I, I think it's going to become a prominent part of it.
- [58:54]
Mike Kaput
All right, so we alluded to this next topic a little earlier. As AI systems grow smarter, some researchers are asking a surprising question. If AIs become conscious, should they have rights? At Anthropic Co. Behind Claude, this is not science fiction. Last year, according to the New York Times, they hired their first AI welfare researcher, Kyle Fisher, to explore whether their models might one day deserve moral consideration. Today's AI, according to Fish, probably isn't conscious. Though he does say that there's a 15% chance in his estimation that Claude or another current AI system is conscious. But he thinks that in the next few years, as models develop more human like abilities, companies may need to take this idea much more seriously. So to do that, to actually figure out how do you tell if an AI is conscious, Fish suggests combining mechanistic interpretability, which is studying how essentially how the A high's Thought process and brain works with behavioral probing and watching what models prefer or avoid over time. Now it has never been more important. Regardless if you think this is necessary or, or science fiction or crazy, it's actually still never been more important to understand how the models actually work, especially as they become more powerful. And this comes from Anthropic CEO Dario Amade in a new essay he wrote called the Urgency of Interpretability. In it, he argues that being able to increasingly understand what's actually going on in AI systems, regardless of they become conscious or can become conscious or not, will help us more easily manage the dangers of AI, like it possibly acting deceptively or exhibiting other dangerous capabilities. Now Paul, this topic, this conversation can really quickly get into like technical and philosophical weeds. But I think Amade put the bigger point here really well in that essay. He said, quote, people outside the field are often surprised and alarmed to learn that we do not understand how our own AI creations work. They are right to be concerned. This lack of understanding is essentially unprecedented in the history of technology.
- [61:17]
Paul Raitzer
Yeah, so this isn't fringe research. This can sound really crazy. Yeah, I think it's just still a bit of a taboo topic. Kind of like the quiet AI layoff thing like it's happening, but like no one wants to talk about it because people kind of think you're nuts if you talk about this. And it just kind of veers into the sci fi stuff. It plays into the fears people have of like what they've seen in Hollywood the last 30 years about AI is like, what if it is conscious and aware and then it needs its, you know, needs rights and it needs all. It's just weird. Like it gets very bizarre very fast. Now, 15% being like maybe Claude is. My guess is that was a number he was comfortable saying publicly. I'm going to guess that he probably feels it's higher than that. That, that was like, I can't say 50%. So let's say 15 because it just seems like a sort of arbitrary number. So all I would say here is like, again, Mike and I aren't living in the labs, like pushing these systems to try and determine things like this. So this is like, you know, us observing the space and listening to lots of interviews and things like that. But based on everything I know, this is a legitimate concern or at least a legitimate possibility in the minds of leading AI researchers to define consciousness. Just so we're all on the same page here, a few different definitions. State of being awake and aware of one's surroundings. The awareness or Perception of something by a person, the fact of awareness by the mind of itself in the world. So the way I think about it is like knowing it exists. Like the simplest way I think about is like the model knowing that it's an AI model in a world talking to humans like it's aware of that. So when I say this is legitimate. Just two weeks ago, Demis Hassabis was on 60 Minutes and this question was asked of Demis Hassabis, who I consider the most reputable, authentic AI researcher in the world. Like, Demis is like, you know, my Mount Rushmore Demis is like the first one. So like, if, if he's talking about it, then I generally like find him to be, to be the most believable person around these topics. So he said, I don't think any. And we'll link to this that this is. You can watch the, or read the transcript in this quote. I don't think any of today's systems to me feel self aware or you know, conscious in any way. Obviously everyone needs to make their own decisions by interacting with these chatbots. I think theoretically it's possible. So then Scott Pelley said, but is self awareness a goal of yours? Demis replied, not explicitly, but it may happen implicitly. These systems might acquire some feeling of self awareness that is possible. I think it's important for these systems to understand you self and other. And that's probably the beginning of something like self awareness. So Peli says, but if a machine becomes self aware, we may not recognize it. Demis replies, I think there's two reasons we regard each other as conscious. One is that you're exhibiting the behavior of a conscious being very similar to my behavior. But the second thing is you're running on the same substrate rate. We're made of the same carbon matter with our squishy brains. Now obviously this with machines, they're running on silicon. So even if they exhibit the same behaviors and even if they say the same things, it doesn't necessarily mean that this sensation of consciousness that we have is the same thing they will have. This is wild. Like you just. So then real quick on the amade thing. And again, like I, you know, I feel like his writing is, is like bordering on a little bit too much. Like I, I feel like they're just like maybe crossing the line slightly into like the hype side or like over exaggerating. But then there's parts of me that think, but maybe he's just right and it looks like hype right now, but he's Actually, like, right. So I try and be very objective with Amade's right. Writings that, like, I try and take him out of his word, but I think he's, he's made some very exaggerated statements in some of his recent writings. So I would just take that in context of what we're telling you. So I wrote about this in my Exec AI Insider newsletter yesterday, and it basically like, here's the couple of key points he said in the context of AI. Interpretability refers to the degree to which a human can understand the reasoning behind AI models decision. It involves understanding how the model arrives at its output. This is kind of like what interpretation interpretability needs. Essentially, it's making AI systems more transparent and understandable that allows a human to grasp what it is. So what I was saying is like, the, the labs know that the models are getting smarter and more generally capable. They know if you give them more data, you do reinforcement learning, you give them better data, that they get smarter. And they know that if you give them time to think like the reasoning models do, that they tend to reduce hallucinations and again, get smarter on the IQ test. But they can't see the inner workings. It's analogous to, like, how scientists and doctors don't really know how to explain how the human brain does what it does. Or if the human's doing something weird, like they can't like, look in the brain and figure out exactly what's going on. That's kind of how this works with these AI models. And so what Amade was saying is he thinks that some of the recent research breakthroughs have put them on a path to be able to understand this, to do the interpretability, like you were saying, where they can actually look into the model and figure out why it's doing what it's doing, and then that can lead to the building of more interpretable models in the future. And then his main thing was calling on other labs like OpenAI and DeepMind to put more resources into interpretability, which they may be doing. They're just not talking about it as much as what anthropic is.
- [67:03]
Mike Kaput
In our next topic. According to a report from Bloomberg, Elon Musk's XAI holdings, such as the newly merged entity combining X, formerly Twitter, and his AI startup Xai, is in talks to raise around $20 billion. So if that closes, it would be the second largest startup funding round ever, behind only OpenAI's $40 billion haul earlier this year. And this new cash would value this entity at more than 120 billion some of that would be used to pay down debt that Musk took on to privatize Twitter. And obviously plenty of it would be spent on developing Xai's AI chatbot, Grok. @ the same time, Grok now has a feature called Grok Studio, which allows Grok to generate documents, code reports and browser games. And according to the announcement, Grok Studio will open your content in a separate window, allowing both you and Grok to collaborate on the content together. Grok users can now attach files from their Google Drive, can now work with documents, spreadsheets and slides. Yet Paul, despite all this, this is, you know, noteworthy fundraising, cool features, they keep shipping. You, however, did run a poll on LinkedIn recently that suggests perhaps Grok isn't catching on, at least with some users, the way that some of the other AI tools have. Can you maybe tell us a little bit about that?
- [68:31]
Paul Raitzer
Yeah, I was honestly just curious. So like I, I've said on this podcast many times, I'm very active on X in terms of monitoring the AI space. It's where a lot of the links and resources and things that we find actually comes in from a highly curated list of a couple hundred AI researchers and entrepreneurs and leaders. So I see Grok all the time. It's embedded in Twitter. It's like they talk about it nonstop. And so if you're living on X, you could get this impression that Grok is relevant. And, and so I was just curious now you could jump over to LinkedIn and obviously people like the kind of the ex elitist, like think LinkedIn is ridiculous and would never go there. So it's like almost two different bubbles in a way. But I just went over there, I was like, okay, has anybody tried this? Like, I was just curious. So I said, have you tried Grok by Xai, Elon Musk's ChatGPT competitor? It was a poll I put up. And so the, the survey results was 30% said yes, 62% said no, and 8% said never heard of it. So 70% have never tried Grok on LinkedIn. Now this was 18,000 impressions and 1400 votes votes. So it wasn't an insignificant poll. Like it was reasonable. Now There was also 98 comments and I did not export all these comments and like analyze them. But Mike, if you looked at the comments of the people that said no, I think it's reasonable to assume like 50 of those is because they hate Elon Musk.
- [70:11]
Mike Kaput
Yes, it was.
- [70:12]
Paul Raitzer
There was definitely commentary about like, like not liking him and interestingly, like some European comments Like, I would never buy anything from that guy or using it. So I don't know how much his Persona plays into it. It's certainly impacting like Tesla sales and stuff right now. But I do think more than anything, it's, it's just, it's not a mainstream thing right now. It's very much living in that X bubble. Not, I'm not saying anything against the product itself. Like, they're making a lot of advancements, they're copying other capabilities very quickly. They're a fast follower right now. They're not innovating, it would look like, yet. They're just like anything OpenAI comes out with, like three weeks later, it seems like they come out with something like it. But yeah, that's, that's kind of where we're at is 70%, at least on LinkedIn in my network of, you know, 50,000 people, whatever it is, 70% have not tried it or heard of it.
- [71:07]
Mike Kaput
Interesting. All right, Paul, so to kind of wrap us up here, I'm going to go through a few other quick AI product updates and then get us into our last segment, which is listener questions. So chime in here at any point if any of these product updates jump out of you. Otherwise we'll just ease right into that final question here. So in terms of some AI product updates, we alluded to this one before. Google has released v2 or is in the process of releasing v2 in the Gemini app. So Gemini advanced users can create short, high quality, quality videos with this newest video generation model. You write a detailed text prompt describing a scene and VO2 transforms it into an 8 second 720p video clip. At the moment, these are not blurry AI clips. They are built to capture real world physics, human motion and fine visual detail. So definitely worth checking out if you're interested in video generation whenever it becomes available, available to you. Next up, descript has announced an Agentic AI video editor feature in its video and audio editing platform. CEO Andrew Nason calls it Cursor for Video in reference to the popular Agentic AI coding assistant. According to the company, you can give this AI co editor instructions and it will go do the thing right in descript. So an example might be, which they provided, you can give it a screenshot from Wikipedia and say, can you write a script about this or tell it to break this video into scenes and add layouts and stock media? Last but not least, a new startup is getting a ton of buzz because it's aiming to replace your CMO or your entire marketing team. With AI, it's called Icon and it bills itself as the world's first true AI Chief Marketing Officer. Now this is backed by Founders fund and execs from labs like OpenAI and Cognition, so definitely some heavy hitters behind it. Icon claims to be able to plan, create and launch thousands of ads end to end and then learn and improve from real performance data, not just guessing. So definitely one to watch if you're.
- [73:21]
Paul Raitzer
My one note there is, I didn't dig into this company deeply, but if they think that all a chief marketing officer does is run ads.
- [73:28]
Mike Kaput
Exactly.
- [73:29]
Paul Raitzer
They might be looking at quite a limited total addressable market. So I don't know if they have the right name for that.
- [73:37]
Mike Kaput
Yeah, yeah. All right, so, Paul, let's wrap up with our new recurring weekly segment Listener Questions. Here is the question from our listeners and from our audience. This week I've been hearing about AI assistants or AI agents. Are these real things or just built out versions of a custom GPT?
- [74:01]
Paul Raitzer
I feel like we could answer a different version of this question every week. It is absolutely the hot topic when I go do talks like it's, everybody asks me about AI agents. It comes up on our intro to AI class like it's all everybody's talking about. And part of the confusion is like everyone's just labeling everything AI agents now. So things that were previously templates or apps or, I don't know, workflows, like whatever, they're just, they're just being called agents. So it's a super confusing space. I, I empathize with anybody trying to understand whether agents are real or not. Basic premise is an AI agent's like a system that can take actions to achieve a goal. And sometimes those actions are defined by the human. You know, there's a rules given to this agent to do something, but oftentimes there's some element of it that it chooses its own path.
- [74:54]
Mike Kaput
Path.
- [74:54]
Paul Raitzer
There's an ability for it to write its own rules and figure out a direction. So the example I like to give is deep research. OpenAI or Google, both have their Deep Research product. You give a prompt and it figures out what tools to use, it figures out what sites to visit, it summarizes those sites, it comes back, it creates a report for you, it builds its research plan that is agentic. That is a good example of, of a real AI agent and I think a prelude to much more technology like that that can actually build and perform its own plan. And the human is there to kind of give it the goal and then like review and Vet the quality of the product and things like that. So that's what an agent is. I think what's happening right now is a lot of tech companies are just piggybacking off of the AI agent terminology and calling everything agents. But in reality it just means like if, if there's a project you need to do, if there's a, you know, something, an activity you need to perform that might require 5, 10, 20 steps, these things are increasingly going to be able to do those and even plan for what to do versus you having to go in and say, I want you to do this, then this, then this, then this, then this. That's just automation. Like if you can, if you just go in and define the 10 steps and then you, you know, set up through make or zapier, whatever, like, like a way to do it, that's just automating something. With an AI agent, there's some level of intelligence happening. There's the thing, there's doing some planning or decisioning on its own and then creating the output. So it's confusing, but it basically just means there's a series of actions that are taken by the AI system to achieve an outcome or a goal.
- [76:35]
Mike Kaput
Cool. Paul, that is a ton going on in the past couple weeks in AI. I really appreciate you as always breaking everything down for us.
- [76:43]
Paul Raitzer
I thought today was for sure going to be an hour and a half for him. Looks like we knocked this out about an hour 15. So nice, nice work curating and organizing it all because there was no less than like 75 links in the sandbox this week. So Mike does an incredible job every Sunday night of pulling this all together and getting it organized for us to talk about. And it was a, it was a lot of work this week. So thanks everyone for listening and giving us the grace of a week off to enjoy spring break. And we're, we're back now for the foreseeable future. I don't, I don't think we have any known days off coming off. So we will be back every week as originally planned and we'll talk to you. I guess it'll be May next time we talk to you, boy. All right, thanks, Mike. I'm off to Boston. All right, all right, later. 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 Marketing AI Institute Slack community. Until next time, stay curious and explore AI.