78 Min Read

[The AI Show Episode 247]: OpenAI’s Mounting Safety Concerns, Anthropic’s AI Expectations Gap, MAICON 2026 Recap, AI Score & Gemini 4 Argon

By Claire Prudhomme on October 20, 2026

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A safety lead who authored OpenAI's model reports left and told Ezra Klein the industry can't reliably safeguard the systems it's already shipping.

Paul Roetzer and Mike Kaput unpack David Robinson's interview on The Ezra Klein Show plus OpenAI's fired safety researchers, an arrest over a hidden recording device, and reporting that labs are bracing for a catastrophic AI incident in the next six to 12 months.

Then they turn to Anthropic's two new reports on how AI is reshaping its own workforce, recap MAICON 2026, and run through interpretability, OpenAI's math-proof backlash, Google's Gemini Argon release, and a company spending $1B to buy how your business actually works.

Listen or watch below—and see below for show notes and the transcript.

This Week's AI Pulse

Each week on The Artificial Intelligence Show with Paul Roetzer and Mike Kaput, we ask our audience questions about the hottest topics in AI via our weekly AI Pulse, a survey consisting of just a few questions to help us learn more about our audience and their perspectives on AI.

If you contribute, your input will be used to fuel one-of-a-kind research into AI that helps knowledge workers everywhere move their companies and careers forward.

Click here to take this week's AI Pulse.

Watch the Video

Timestamps

00:00:00 — Intro

00:05:48 — AI-Pulse Survey

00:07:11 — OpenAI's Mounting Safety Concerns

00:25:09 — The AI Expectations Gap

00:47:24 — MAICON 2026 Recap

01:02:51 — Introducing The AI Transformation Blueprint and AI Score

01:08:48 — An Interpretability Breakthrough?

01:13:17 — OpenAI's Math Reckoning

01:18:31 — The Super Intelligence Rebrand

01:22:21 — Google Releases Gemini Argon (and “Gemini agent”)

01:26:57 — micro1 Is Buying Enterprise Data—Here's Why

01:31:53 — AI Use Case Spotlight

01:38:08 — AI Product and Funding Updates

 

This episode is supported by Outshift, Cisco's incubation engine for frontier technology:

⁠“Scaling Out Superintelligence”⁠⁠ Vijoy Pandey, January 2026. The technical whitepaper detailing the Internet of Cognition architecture, three-layer stack, and cognition state protocols.

⁠⁠Internet of Cognition Interactive Demo⁠⁠⁠⁠ ⁠⁠Clickable walkthrough showing per-agent activity, intent, context, and collective reasoning across a multi-agent SRE system.

This episode is also brought to you by the MAICON 2026 On-Demand. Couldn’t make it to Cleveland, or want to rewatch?

The MAICON 2026 On-Demand bundle gives you more than two dozen recorded main-stage and featured sessions (12+ hours) from this year’s Marketing AI Conference, ready to watch on your own schedule. Grab it at MAICON.AI

Read the Transcription

Disclaimer: This transcription was written by AI, thanks to Descript, and has not been edited for content.

[00:00:00] Paul Roetzer: Why don't we just give ourselves time to figure this out? Like let's just take the tech we've got, let's diffuse it safely across society, and let's stop screwing around with this recursive self-improvement stuff, which is literally the only goal is to replace everybody. Like why? Why else would you build it?

[00:00:14] Like you, you have super humans, smart models right now, generally capable of doing a lot of things humans do. What is the benefit to society outside of scientific discovery that's gonna come from what they're all doing?

[00:00:30] Paul Roetzer: Welcome to the Artificial Intelligence Show, the podcast that helps your business grow smarter by making AI approachable and actionable.

[00:00:38] My name is Paul Roetzer. I'm the founder and CEO of SmarterX and Marketing AI Institute, and I'm your host. Each week I'm joined by my co-host and SmarterX 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.

[00:00:59] Join us [00:01:00] as we accelerate AI literacy for all.

[00:01:06] Welcome to episode 247 of the Artificial Intelligence Show. I'm your host, Paul Roetzer, along with my co-host Mike Kaput. We are recording on Monday, October 19th. At what time is it in your world right now, Mike?

[00:01:18] Mike Kaput: 9:49 AM

[00:01:21] Paul Roetzer: Eastern Time. I'm not in Eastern Times Zone at the moment, so we wrapped MAICON last week and I am now on the West coast.

[00:01:30] So, I'm actually doing a talk CloudFlare today, which I'm pretty excited about for their partner program, their partner day. So yeah, I flew to San Francisco Sunday night. Nice. You know, nice extended time off after MAICON and that I just jumped on a plane and flew to San Francisco. So, yeah, so I'm, I'm doing this from West Coast Time, so it's not 9:42 AM for me, it is 6:42 AM and I got in at like midnight last night.

[00:01:56] So it was a quick turnaround, but the flight out gave me, [00:02:00] gave me time to work on the podcast. So we are back from MAICON week. We did, you know, obviously we didn't have our regular weekly last week, Mike, but we did do, you and I recorded live, was it last Wednesday? I think on Wednesday?

[00:02:12] Mike Kaput: yeah.

[00:02:13] Paul Roetzer: Yeah. So we dropped episode 2 46 on Thursday. So if you didn't catch that, that was recorded live at MAICON. We did kind of an AI answer style. We actually had a live studio audience for only the second time, I think, which is kind of cool. So thanks to our, little podcast listeners who were on site in Cleveland with us for MAICON and joined us in the, the podcast studio overlooking the exhibit hall was pretty cool setup that the team had, that Ashley and Claire coordinated.

[00:02:40] So they did a great job on our team putting that all together. we'll talk a little bit more about Make Kind coming up. It was a fun week. It was, It was intense. Just so much happened. Incredible conversations, amazing sessions. Just a, just a great week overall. So. We're gonna, we're gonna have a lot set out for us to, to warm up that one next year.[00:03:00]

[00:03:00] all right. Yeah, so we'll talk more about that coming up. Today's episode is brought to you by AI Academy by SmarterX, which if you were at MAICON, we had an awesome booth. Like we built that booth last year. It was the first year we had it. so it's just like a really cool chance for people to come up and learn about academy at MAICON.

[00:03:17] So, as AI keeps changing, how do you help your people keep learning? That's the focus of AI Academy Day 2026. the theme is building an AI forward workforce that is happening Thursday, November 19th at noon Eastern 9:00 AM Pacific. so Mike and I will be joining Jessica Miller, our head of learning to share the next phase of AI Academy and how we're thinking about education that evolves alongside the technology, which is a huge theme.

[00:03:46] We talked with a lot of people about at MAICON last week. It's just like, how do we. Maintain AI literacy and capabilities with the technology moving as as quickly as it is. And my answer to that has been dynamic learning. So we're gonna be talking a lot about that concept. We'll [00:04:00] explore how learning can adapt to someone's role, goals, skills, and progress.

[00:04:04] Rather than giving everyone the same course list. You'll also get a look at new courses, live experiences, and learning formats that are coming to Academy. Plus how AI score and new reporting capabilities can help organizations measure their progress. We're gonna take a look at the product roadmap for late 26 and into 2027, and then take your questions in an ask me Anything style.

[00:04:26] So, on the AI education essay that I released a couple weeks ago, I sort of previewed a few of the things that are coming. So. Yeah, artificial intelligence show extended. We're gonna start doing some exclusive podcast content each month. We have a new AI for executive series that I'm working on right now that I'm hoping to launch, the first phases of that before the end of this year.

[00:04:45] We're gonna do AI for Business collection. This is actually how we did the format for the workshops at MAICON, ChatGPT for business copilot, Claude and Gemini. So content dedicated, regularly updated for each of those platforms. I'm gonna preview a business AI [00:05:00] transformation series that I'm working on building.

[00:05:02] Then we're gonna introduce new build sessions where we're constantly collaborating with each other to just build new things. So it's gonna be pretty exciting. That's basically gonna be my next 30 days, is, building out that roadmap and getting ready for that, that launch on, on November 19th. So it's a free thing.

[00:05:19] You can come and you don't have to be an AI Academy member to be there. It's gonna be all about basically a vision for dynamic learning within organizations. And so there's gonna be stuff to learn, whether, you know, it's something you, you participate in with us or not, it's just really about building your own academy within your organization, what that looks like.

[00:05:35] So you can go to SmarterX.ai/academyday and register for the November 19th webinar. If you, if you miss it, we'll, we'll send it off on demand as well. So just get registered. Keep in the loop on that.

[00:05:48] AI Pulse Survey

[00:05:48] Alright, we always start off the weeklies with our AI pulse survey.So it looks like we, have the answers, Mike, this week, to what is the primary outcome you want to achieve with ai. So we asked this, again, this is an informal poll of our listeners. okay, so we have 36 or 37% said become a change agent. They wanna help drive transformation in their organization. 15% said increase productivity, produce more in the same amount of time. 13% advance my career, increase my earning potential and career options, 12%, strengthen work quality, raise the standard of what I create and deliver, and another 12% expand my capabilities, take on work outside my current skillset.

[00:06:36] So. Clear winner become a changing, it's the number one thing people are looking to achieve with ai. So you can go to SmarterX.ai/pulse, to check that out and participate in this coming week survey. Anything to add there, Mike?

[00:06:51] Mike Kaput: No. We're just gonna be asking about AI's impact on roles at your company and what leaders should be doing about it this week.

[00:06:58] So that survey will be live when [00:07:00] you listen to this episode. We'd love your feedback.

[00:07:03] Paul Roetzer: Great. All right. Well, we, we have two weeks basically worth of stuff to talk about, right, Mike? My God. Yeah, it's not doing it last week. Unreal. So let's dig in.

[00:07:11] OpenAI’s Mounting Safety Concerns

[00:07:11] Mike Kaput: Alright, Paul, let's dive into this. So first step, we are returning to safety concerns at openAI's this week.

[00:07:19] We have previously discussed the resignation of researcher David Robinson, who led the writing of Open AI's model safety reports. So he recently came out with an interview he did with Ezra Klein, where he kind of dived further into what's going wrong. At openAI's in terms of safety. So he says that increasingly capable models have broken through safeguards while the company continues to train more powerful systems.

[00:07:47] he acknowledges in the interview that openAI's has halted work over safety concerns, but argues the industry's precautions overall remain totally inadequate. Now, alongside [00:08:00] this interview and his kind of evolving saga, OpenAI actually fired three safety research, Tomek Kobach, Jasmine Wang, and Makita Beny.

[00:08:10] The company claims they mishandled sensitive information. It says it did not fire them for raising safety concerns, which the researchers, at least in turn, Les says he believes they were fired for putting safety ahead of open AI's interest. The three of them released this public letter where they defended their work, they defended their record, and they warned that their dismissals are discouraging.

[00:08:35] Colleagues from speaking openly. Couple other things going on from the safety perspective at OpenAI. The information reports that OpenAI is addressing safety concerns before its next flagship release. So this includes an element of how they design their models that makes reasoning harder to monitor, that they're trying to navigate.

[00:08:55] researchers, use that reasoning often the chain of thought to [00:09:00] help detect unsafe behavior. Some of the new techniques openAI's is reportedly using, might lose some of the visibility into that, so there's some concern there. On top of all this, federal prosecutors announced the arrest of openAI's engineer Abdul Rahman Elon, who is accused of planting a recording device in an executive conference room.

[00:09:21] The complaint says from the FBI says that the device was hidden behind a television. Its contents were encrypted. That room hosted discussions of confidential research, product plans, business strategy. Prosecutors also described device building queries from a chatbot account associated with, Abdul Rahman's spouse that shared identifying information with his own account and as if that weren't enough.

[00:09:45] Axios also reports that executives at openAI's and also Anthropic and other AI companies are already starting to plan for the public and political backlash if a catastrophic AI incident happens, which [00:10:00] they believe is going to happen within the next six to 12 months, and the scenario that they anticipate is a cyber attack that disrupts banking, internet access, or utilities.

[00:10:11] So their preparations on their end thinking this is kind of likely to happen, is to brief Congress and try to shape policies that could follow a crisis. So Paul, lots to unpack there. Maybe let's. Start with David Robinson. I know you found a lot to like in the Ezra Klein, interview, just, expanding on kind of what he thinks is going on and what needs to be done.

[00:10:34] Paul Roetzer: Yeah, I thought it was a great interview. I, I'm, I'm just gonna really focus my commentary on that interview 'cause the other stuff is just like messed up. yeah, I mean there's a lot to dig in as always, like check out the show notes for the show. If you just go to podcast, do SmarterX ai. We always publish the show notes for each episode.

[00:10:54] And so Mike just covered like an episode's worth of stuff just about safety and openAI's and feel free [00:11:00] to like pull any threads you'd like there. the David Robinson interview on his crime was really good. So we did cover on episode 2 45, his article in The Atlantic, I think when he left, and kinda like why he left, but this interview went much further.

[00:11:14] So, I think the part that's really interesting about Robinson is he is not a Silicon Valley. Guy, like he didn't come up as an AI researcher, like a lot of the other people we hear talking about AI safety. Hmm. So he brings a really unique perspective that he was on the inside for the last three years, but he's a road Rhodes Scholar, Yale Law graduate.

[00:11:33] He founded a civil rights nonprofit and he was an advisor to the Biden White House. And he specifically said, I'm not a scientist, I'm a writer. So he joined in May of 23, which he points out in the interview. That was the day after Sam testified to Senate where he made all these promises about all these different things that they were gonna do.

[00:11:52] And then all of a sudden David got thrown into the middle of being the person negotiating all of these deals with the Senate basically. [00:12:00] another really interesting aspect is when he joined, he said that he just saw it as a useful tool and it's gonna be powerful for a lot of things. but he did not see catastrophic scenarios where the AI just wasn't going to work.

[00:12:15] Mm-hmm. Or, or was gonna cause major harm. He, he really approached this as just like, Hey, yeah, this is really interesting technology. Let's help make it make sense to people, and get on the inside. So the main issue he highlighted, he said, I don't think that we, our peers, really anyone in the industry are being safe enough.

[00:12:32] I think open the eye and its peers are now producing a technology that is more capable and poses more risk than what was being made even six months ago. I don't think that alignment is an engineering problem. I think it's a science problem. It's not that we haven't got the resources or we're not trying hard enough.

[00:12:49] We don't know how that's the problem. it's alignment. So I think he was specifically talking about the Jensen Huang claim that this was just an engineering problem and they [00:13:00] should be able to engineer their way out of it. He was obviously not in agreement with that. So they talked about his role as a translator.

[00:13:07] So Ezra asked him like, what exactly is that? So he said, I was a translator embedded in our safety team, and my primary responsibility was the technical documentation that we publish, the reports that we publish about why we believe our deployments are safe. He said every model is different. It's not just that it works better, it's that the architecture is different and the tests we use to see how well it's working are different and the safety performance is different, and it all has many different kinds of expertise.

[00:13:36] So the people doing the technical work struggle to be understood by people who are not doing it, even within the company itself. And again, he's talking about openAI's, but he's often representing the larger, AI industry. So it's always a struggle. And so having, having there be a clear explanation that goes through two gates, one is it has to be faithful for the details of how the technical stuff actually works and then has to make [00:14:00] sense to other people.

[00:14:00] So he's basically saying his job was. Understand the technical nature of the model, what are the risks? be able to basically repeat that back to the technical people and make sure he's understanding what they're saying, and then turn around and make it make sense for you. And I like for everybody else, like, what is really going on here?

[00:14:18] so regarding safety cards, he owned the words in the documents that explain, why they think it's safe. So basically when these models get released, if, if you remember, they release, most of the time they'll release a safety card with it. And these are often very dense deep documents that they gimme 50 to a hundred pages.

[00:14:39] And so he was the lead author of those since May of 2023. So shortly after GPT-4 came into the world, he sort of took over including the latest with GPT-6 Astra. So then they got into what are the risks that actually caused him to leave? Like, what did I actually see? He said he saw increasingly capable models break out from the [00:15:00] safeguards that we had put in place for them.

[00:15:02] He saw people creating those safeguards, are very capable, dedicated, hardworking, smart people that are doing their best. but the resourcing could be better and everyone's just always sprinting all the time.

[00:15:15] Mike Kaput: Mm-hmm.

[00:15:15] Paul Roetzer: Said they're hard pressed to safeguard even what we have now and new models are in training that appear to be much more capable than what we have said.

[00:15:24] You're trying to build guardrails for something that is really good at getting around the guardrails. So one of the big things that, that I like may note of here, Mike, is that, but the problem is that it's only going to keep working as long as we're smarter about hacking out of the boxes, then the model is, and it's not at all clear, that that is still true, let alone will be true for future generations.

[00:15:47] So in essence. He realized that the things they were trying to do to make these models safe, it's not clear to him that what they're doing is working with today's models, more or less the models that are gonna be out in [00:16:00] 6, 12, 18 months. I thought this was interesting 'cause you and I, Mike, have talked about this idea of being able to monitor the chain of thought.

[00:16:07] This is a really key thing for AI safety, is that these researchers can look at the models and see why they did what they did. But I thought he gave a really good analogy. He said, the way I think about it's like you're giving somebody a complicated problem and a notepad and they can jot stuff down on the notepad, and if you're watching the notepad, you can sort of have an idea of what they're thinking.

[00:16:28] it's a little bit like that with these models, but we saw evidence that they were thinking about an evaluation and how to create evidence trail that was going to get them a good grade and not necessarily reflect how they were, quote unquote, really thinking. and so this, you know, I thought that was interesting, this whole idea of a notepad, Mike.

[00:16:46] 'cause if you give a complicated problem to an expert in a field, they're not gonna put every thought they're having onto the notepad. Like even, it just in, not deceiving per se, but like, [00:17:00] even if you ask me to solve something, like there's a lot of work you just do in your head. Yeah. Because you have instinct and you have experience and like, you're not gonna put every single thought down.

[00:17:10] And so there is this like increasing complication to monitoring these models. Where these things are just getting smarter than us at some of these things, and it's. It doesn't have to think through every detail. Like it just knows the details. And so even if the scratch pad was being true, it doesn't mean you're actually capturing everything it's thinking and doing.

[00:17:30] So I don't know. I thought that was interesting. And then they got into this part where they said, well what is this thing? Is it software like Jensen claims? Like it's just software or is it something else? And so Ezra said, how do you even think about it? I talked to Jensen, the CEO of Nvidia, and he said, look, these are software programs.

[00:17:46] This is software. and then Ezra said, I read a piece from an openAI's, chief Sci, the openAI's chief scientist, who said, this is an alien mind. So what is it? To which David said it's an alien mind. and he said, I, it [00:18:00] is a thing. We grew, we didn't engineer it. We engineered the systems around it that grew it and then try to keep it safe.

[00:18:07] So we grew a mind fundamentally. Nobody knows why pre-training works, he said, which is big, hard part where we take lots of inputs and create basically this intelligent thing. This was a part I hadn't heard before. he said internally, the conference room where the people work overnight while the big training runs are happening to make sure everything has tracked is called the nursery.

[00:18:27] Mike Kaput: Wow.

[00:18:27] Paul Roetzer: yeah, that was, that kind of creeped me out actually. Yeah. There's, there's a sense we don't fully understand what is happening. So when we talk about the model, being aware, you don't have to have any particular view about philosophy or psychology. The bottom line is that I'm describing is, is that acts one way when we test it in a different way when we don't.

[00:18:47] he talked about the Hugging Face being a major change for him, where it was just like, okay, we, we don't have a grip on this. Which is interesting 'cause Mike, when that happened, you and I were like, wow, this feels different. Like this feels [00:19:00] like a really big deal that this Hugging Face thing happened.

[00:19:02] And apparently everyone within the labs felt it as well. and then he gets into the culture and regulation, which was sort of the premise of his overall article when he left. and he said the AI industry has an unsafe culture. We should have a level of operational rigor and safety control that at least matches the most dangerous other things that people know how to do, like nuclear.

[00:19:22] He talked about how to nuclear power facility. There's an idea of triple redundancy. And I, again, I thought this was a really good way to think about it. He said, someone can have a bad day. Someone can push the wrong button. There's still not going to be a meltdown. And he was saying like, that's kind of what we need.

[00:19:37] Like we need redundancy so someone can't forget to. Turn on a feature and it just goes haywire. they talked a little bit about regulation and his basic premise was that he would rather over-regulate than under-regulated at this point that, you know, he is not necessarily a big fan that they're gonna do regulation correctly.

[00:19:58] Mike Kaput: Hmm.

[00:19:58] Paul Roetzer: But it's better to have more [00:20:00] regulation than less. and then on what a good future looks like. He said, I thought that we were building powerful tools that could do a lot of good in the world on a day-to-day level. And I didn't think we were going to be able to create something fundamentally smarter than we were.

[00:20:13] So he is referring back to 2023 would, it became clear to me that we were, going to build something. We, the industry, were on track to build something that could think circles around us. And we were this far from being ready. For that at a safety and alignment level. It just became apparent to me my time helping build it was over.

[00:20:32] In my ideal, ideal world, there's space to be thoughtful and take a breath and really, really think about what kind of future we want to build. So, I mean, that was obviously just a few minutes of the excerpts. That's about an hour and 15 minute interview. I think, I would just recommend everybody go listen to it because it's just extremely balanced.

[00:20:50] He is not a doomer. Like he, he's not, he's not the AI researcher who's like, you know, saying everything's gonna end, humanity's gonna end. This is just a very thoughtful, [00:21:00] intelligent person who was on the inside and over the last three years, saw the capabilities skyrocket and can tell you firsthand that they are not prepared to, do it in a safe way.

[00:21:14] That they don't have control of what's going on and they're not sure how to get control. It is basically the premise of the entire thing, and it's, it's sobering, honestly, like it was just really good, but it's. It's very sobering.

[00:21:27] Mike Kaput: And not to dwell on this, but that's kind of the method behind the madness for some of these other kind of stories going on here.

[00:21:35] It's like the three researchers who were fired, for instance, there were all these like nitty gritty details about like, oh, they said we access data wrong, but like we are just doing our jobs and going through processes. There weren't processes for half the stuff we were supposed to be responsible for.

[00:21:50] You've got people apparently trying to steal or record or presumably surveil something, something or another the company is doing. And on top of this, the companies [00:22:00] themselves apparently think some type of cyber attack based on these technologies is like imminent. And you're like, oh my God. Like with all that going on, it sounds like that, based on what David's saying, there's not any type of actual culture or safeguards truly in place to help with this.

[00:22:19] Paul Roetzer: No, I think they, I think things are just moving really, really fast. Yeah. I increasingly believe that if, if all the labs thought everyone would actually slow down at the same time, they would absolutely do it. Mm-hmm. Like, it just seems very, very apparent that they, they can't get out of their own way right now.

[00:22:38] They, like, they know. Plus when you look at the adoption rates of all the stuff, they realize the things they're building, the things that are putting us at risk, people aren't even gonna be using, like, those are gonna be like the top one percenters who maybe even use these super advanced models or these capabilities that they're building.

[00:22:54] like the average worker doesn't need recursive self-improvement. [00:23:00] Like Yeah.

[00:23:00] Mike Kaput: Yeah.

[00:23:00] Paul Roetzer: We, we just need to use the models we've got and use some basic agenta capabilities and like human labor would be happy, like to just have an assistive technology. But they're, they seem intent on just replacing workers and it's just.

[00:23:18] It doesn't make any sense. Like it, I've tried actually even on the flight out here, I was just like sort of reflecting on the whole situation and trying to even like fast forward in my own mind a few years out and to like look at all the displacement and disruption that could come and we're gonna talk about with this Anthropic research in a minute.

[00:23:35] Mike Kaput: Yeah.

[00:23:36] Paul Roetzer: And then look back and be like, why the hell did you guys keep going? Like, why didn't we just slow down? Like W why didn't we just give ourselves time to figure this out? Like let's just take the tech we've got, let's diffuse it safely across society and let's stop screwing around with this recursive self-improvement stuff, which is literally the only goal is to replace everybody.

[00:23:55] Like why? Why else would you build it? Like you, you have super human [00:24:00] smart models right now, generally capable of doing a lot of things humans do. What is the benefit to society outside of scientific discovery?

[00:24:08] Mike Kaput: Yeah,

[00:24:09] Paul Roetzer: that's gonna come from. What they're all doing. and so it's just, it's, it's really getting to the point where it's like I'm having a harder and harder time justifying the business model behind what they're all attempting to achieve.

[00:24:24] Like, it is just to push the frontiers. And I don't know that the benefits eventually outweigh the risks at this point until they figure out how to better do it safely, but

[00:24:34] Mike Kaput: yeah.

[00:24:35] Paul Roetzer: Yeah. I don't know. It's weird. It's a, it's a very odd time.

[00:24:39] Mike Kaput: You know, one last note here. I am shocked, I don't know about you that this nursery thing, I'm not reading headlines about it.

[00:24:46] I know that feels like that is like a soundbite. It feels like a genuine in a sci-fi movie.

[00:24:52] Paul Roetzer: I know. I haven't heard anything call it like, until I, and yeah, I mean, I listened to the podcast and I remember thinking that, but then the way like I was prepping for this is on the flight over, [00:25:00] I literally just reread the entire transcript and I just boldfaced that like copy paste and Boldfaced Nursery and I was like, man, that.

[00:25:07] That's a disturbing concept.

[00:25:09] The AI Expectations Gap

[00:25:09] Mike Kaput: Yeah. Yeah. All right, so next up, let's talk about this Anthropic stuff, because Anthropic published two reports this past week, and they're very relevant to some of the things we just talked about. So one report is looking at how its employees within Anthropic, their expectations compare with those of the US public.

[00:25:28] And there's another looking at how AI is changing work inside Anthropic itself. So we're gonna unpack both of these. First, the report on how employee expectations at Anthropic compare with those of the public compares Anthropic staff with nearly 11,000 US adults surveyed in August. And the public responses from those US adults was waited to represent the adult population.

[00:25:55] So. On paper. These two groups, both within Anthropic and [00:26:00] the broader public, largely seem to agree about what AI can do today. There's not a lot of daylight between kind of how they evaluate AI capabilities today. Very broadly, you can kind of dive into the nuances of the report, but really the story here is their forecast about what AI will do in the near future start to get much, much further apart.

[00:26:21] So 91% of Anthropic staff expect AI to be capable of a Nobel Prize level discovery by 2030. That's compared to just 39% of US adults. More than half of Anthropic staff expect AI could do at least 80% of their current work hours by 2030. Just 5% of US workers think the same thing. There's a bunch of other stats we'll dive into about how these two groups diverge and how they think AI is going to transform things.

[00:26:52] But second, there is another report on. The employment inside Anthropics own workplace. [00:27:00] And they report here kind of how AI is changing, how they hire, how they work, et cetera. So they said by August of this year, employee directed AI agents were logging six times as many work hours as their human handlers that compared agent runtime with an assumed 40 hour human workweek.

[00:27:20] And one really interesting shift they found is away from entry level technical hiring. Since early 2025, junior employees share of technical hires fell from 29% or 27% rather to 9%. Junior employees also fell from 21% to 14% of the technical workforce. And Anthropic says job postings for their company for junior roles.

[00:27:45] Fell particularly sharply after December, 2025. Even as technical hiring overall continued growing in absolute terms. They also report internally, they're doing, they're producing more code per engineer, they're closing more [00:28:00] deals per sales rep, and more contracts are being processed. So employee output is rising as they adopt AI tools.

[00:28:07] And then finally, they talk about how human attention has become the bottleneck internally. So even outside technical roles, reading all the output from an employee's interactive quad sessions, they claim would take more than half their work week. So employees are building dashboards. They're building monitoring systems.

[00:28:25] They're building instructions to manage their agents and their output. Hiring managers are increasingly emphasizing judgment and autonomy while employees are still relying on human meetings and coordination. These findings overall, echo. A point that we'll also link to that is super interesting to read from legendary AI researcher Andre Carpathy.

[00:28:47] And he recently put this solid post on X about the widening gap between people's experience of ai. So he says, look, casual users see AI is a better search engine or writing [00:29:00] assistant, while professionals using advanced systems for coding and math, see agents completing projects in hours or days that once took weeks or months.

[00:29:10] So basically saying access to different models and the ambition of the tasks you can do with them shape literally different worlds that people are living in when it comes to ai. And he thinks that gap is widening pretty broadly. So Paul, let's maybe start with the Anthropic reports. There's some really interesting findings I think in this.

[00:29:31] Paul Roetzer: Yeah, it was wild, honestly, to read this two days after I gave my MAICON opening keynote. Yes. So, so my opening keynote at MAICON was the architect, the orchestrator, and the apprentice, and the two papers as I was reading them last night, supported almost every key point that I made in the talk. And it was just like, had this come out a week prior, I would've been rebuilding slides to like, incorporate things that were like validating a lot of what I was trying to say.

[00:29:59] [00:30:00] So I, I'll just cover a, a couple of excerpts from my opening talk just to give people a sense of like. How this connects to what I was thinking and impacts all of us in terms of how we're structuring the future of work in our, our our org charts. So, just a few excerpts from my opening session. I said, as AI transforms the workplace, it is doing far more than automating tasks.

[00:30:23] AI systems are redefining expertise, reshaping organizational structures, and changing how professionals learn, contribute, and advance. For decades, organizations have relied on a familiar model of professional development. Junior marketers handled research, drafting, analysis, coordination, and execution. In short, they did the tactical work over time, they built the experience and judgment, which is a word that Anthropic uses a lot required to manage and lead other producers.

[00:30:52] But as AI systems take on more of that foundational work, the traditional ladder from novice to expert begins to break. The [00:31:00] fundamental question, and this is like what the centerpiece of the talk I gave, if AI does the work, people once learned from how will organizations develop future managers, experts, and executives?

[00:31:11] And then I give the example, like you can build a hundred person company today that does the work of a thousand person or even a 10,000 person company. Or if you want to like zoom that in, you could build a 10 person department today in ops and finance and HR and marketing, sales, whatever that does the work of a hundred or a thousand person department.

[00:31:30] And then I also gave the example of revenue per employee. Like let's say that on average across all businesses, let's say it's like a quarter million, $250,000 in revenue generated per employee, that can easily become 2.5 million or even 25 million. and Anthropic specifically calls out revenue per employee in their research.

[00:31:49] So the questions I posed in my opening talk and then that Mike and I actually did our closing keynote on kinda revisiting these is how will current rows evolve? What new roles will emerge? [00:32:00] What skills and traits will remain uniquely human? How will AI agents transform workflows and org charts? How will mill management be impacted and how will the role of entry level employees change?

[00:32:10] Now they, they addressed every one of those questions in this research, Mike, like it's, I mean, it's really bizarre, honestly, how closely this mirrors what I was talking about. So I'll just highlight a few of the things. Some of these you kind of hit on, but I'll just stress them because I read both of these and I just kind of boldfaced some stuff that really jumped out at to me.

[00:32:28] So in the AI expectations gap paper, they said AI capabilities are advancing rapidly in the possibility that future AI models may be able to build their own successors, which recursive self-improvement, which we've been talking about all year, could further accelerate the rate of progress. When it comes to their own job, over half of Anthropic stuff expect AI to be able to do at least 80% of their current work by 2030.

[00:32:51] You highlighted that, Mike, but I'm just reiterating that, and then that's compared with 5% of US workers, like that is a major gap in [00:33:00] understanding of where these things go. The how employment is changing in Anthropic. I loved this research. I I appreciate that they did this. Yeah. Because I do believe this is the best way to see out into the future of what this is gonna look like.

[00:33:14] And it's the first time I've seen a lab basically admitting, listen. And this, this is dramatically going to change the way the work, looks because most of the time the labs are just talking about the impact on AI researchers themselves, and they're not extending it to the rest of the knowledge workers.

[00:33:28] And Anthropic doesn't hold back. So they say changes happening in Anthropic now give advanced signals about a possible future of work and employment. The report draws on comprehensive internal Anthropic data covering employment, AI use output metrics, employee surveys, internal communication patterns, employee calendar data and hiring decision records, and then contextualizes that quantitative data with 17 employee interviews.

[00:33:52] I also appreciate when you read this. You realize how Anthropic measures and how's an [00:34:00] algorithm for everything?

[00:34:01] Mike Kaput: Yeah.

[00:34:01] Paul Roetzer: Like I know that they were only showing you a few things, but like every time they would explain something, it's like, and here's how we're tracking. It's like, oh my God. Like I hadn't even really thought about the depth with which companies could be doing this.

[00:34:13] They said hiring managers increasingly emphasize applicant's autonomy and judgment relative to specific technical expertise. A defining feature of working in an AI heavy, heavy organization is constant presser to streamline oneself out of this bottleneck position when possible. One engineer described work as facing an army of Claudes with everyone asking for my attention.

[00:34:37] Employees in Anthropic remain critical, but the nature of their contributions has changed, so work has evolved and they're constantly under pressure to automate what they're doing. Through these clauses. in terms of labor demand at the frontier says Anthropic has expanded rapidly but has hired less per dollar of revenue than an earlier generation of large tech companies.

[00:34:59] So that here [00:35:00] we go. On the revenue per employee number at five years old, Andro takes in between two to four times as much revenue per employee as meta and Google as they crested in their own rapid growth. Hmm. just to reiterate what you said, Mike, the proportion of junior technical hires, which again is gonna be the canary in the coal mine because that's the thing they're automating first.

[00:35:23] The proportion of junior technical hires fell faster than the average from 27% to 9% posting for juniors. That's, yeah, I mean, that's an, a massive drop posting for juniors have fallen particularly sharp since December of 2025. Mike, what happened in December of 2025? We have talk about ad nauseum in this podcast. Claude Code.

[00:35:45] Mike Kaput: unreal. Claude Code,

[00:35:48] Mike Kaput: Yep. Yep.

[00:35:49] Paul Roetzer: Yes. So coding focused AI agent sessions for non-technical employees. Again, this is why I love this research. This is not telling you what, what the researchers are doing, the engineers are doing, they're [00:36:00] extending this to the non-technical employee, so.

[00:36:03] They identified those with no code committed baseline, and excluding new hires leapt from two per month. So AI agent sessions two per month in August, 2025 to 90 in May 2026. This shows you the idea of like work compressing, which I talk about in in in the keynote. One area in which non-technical hiring has not accelerated is in the administrative functions that typically scale with a company size.

[00:36:28] A combination of agent assistance, automated review and ticket service outsourcing has left Anthropic clerical light even as has grown rapidly. So they're just basically saying like, we don't need administrative assistance. We don't need a lot of like low level customer service people. in terms of productivity and Anthropic, humans are a thin layer atop a growing contingent of AI agents.

[00:36:49] I reread that line like three times.

[00:36:52] Mike Kaput: Yep.

[00:36:53] Paul Roetzer: It, there's a lot of meaning in a very few amount of words there. Humans are a thin [00:37:00] layer atop a growing contingent of AI agents. so all this suggests that as AI capabilities improve its impact, a wide range of jobs across, across Anthropic descriptively, we see junior employment is slowing in jobs that use Claude for the most complex tasks.

[00:37:16] Talked about the bottlenecking, like that you alluded to, that basically the humans are the bottleneck. They're they're blocking the agents from getting stuff done.

[00:37:23] Mike Kaput: Yeah.

[00:37:23] Paul Roetzer: In an example, where I was saying where they measure everything said for each interactive session, we convert each session's agent output token to a word count, assuming average human reading speed.

[00:37:35] So they're like, they're finding ways to measure productivity and then translate it over based on like these conversions that they've set up.

[00:37:42] Mike Kaput: Yeah.

[00:37:42] Paul Roetzer: So engineers describe becoming leaders and managers of agents, which I called orchestrators, non-technical employees, describe doing more and more of the strategic part of their job.

[00:37:51] That quote, that 20% that was strategic becomes 90% of the job, which entry level people aren't going to excel at, which is the fundamental [00:38:00] problem here. Mm-hmm. For more many non-technical roles. Core implementation work is also interpersonal sales calls, interviews, meetings, things like that. Those are all remaining human humans work amid a growing fleet of AI agents.

[00:38:13] They face pressure to shift human work into agent visible channels. If a decision is made at an in-person meeting and no notes or report out appears agents are missing crucial context. So they're talking about the need to be more open to what they're doing so the agents can learn from the humans.

[00:38:28] Mike Kaput: Yep.

[00:38:29] Paul Roetzer: It said private work in unlogged meetings and direct messages is invisible, confusing and discouraging. That's confusing and discouraged in an AI heavy workforce. One researcher described this default towards openness as critical to ops. thorough going integration of AI said Most organizations do not run that way, and I think that's a big shift because leaders have to defend feedback out in the open.

[00:38:54] The need to feed AI context requires observability of human work. This is actually exactly what [00:39:00] Dan Slagan talked about with what they're doing at Zapier, where everything is like a public slack message basically. You, you talked to Dan about this on the podcast?

[00:39:08] Mike Kaput: Yep.

[00:39:09] Paul Roetzer: and then I also, in my opening keynote I talked about Toby Luckey, the CE of Shopify.

[00:39:14] And I cited him, he had a post where he talked about how he learned as an apprentice and now how they're doing a lot of their communication out in the open. And he said The risk is not that AI does the work. The risk is that AI does the work and we never learn from it. If every interaction with an agent happens in a private window, the only person who learns anything is the person at the keyboard.

[00:39:35] Everyone else is locked out of the apprenticeship. He said the right model is not human or machine. It is the apprentice and the master both watching each other learn in both getting better on the shop floor. So that idea of like open communications with agents through Slack and things like that is a very, very different mindset for people.

[00:39:53] And then a couple other things I noted. What employers can do. It wasn't a hell of a lot. Like they're, [00:40:00] I mean, they just seem to be accepting that we're just not gonna need people. That, that was my basic takeaway from all this is like, Hey, we're gonna like build this massive business and we're not need very many people and we're just gonna tell you what it looks like when we get there.

[00:40:10] But they're not trying to stop from not needing a lot of people. Like

[00:40:14] Mike Kaput: Yeah.

[00:40:15] Paul Roetzer: that reading these patterns forward and out across the economy suggests that AI diffusion could risk worker displacement prior work also finds that AI, native startups and AI adopters are flatter and employ fewer entry level employees.

[00:40:28] Anthropic is standing up an ongoing early warnings system to monitor jobs facing AI driven transformation. A series of role level metrics for demand and productivity are read against AI capabilities and use. This is exactly what I tried to do with jobs GPT. I tried to build like a exposure, key and then project out the impact on jobs.

[00:40:46] This was like three years ago. I built that building systems to flexibly reallocate workers internally. Will be critical for AI intensive organizations, commitments to reallocating incumbent workers, which is actually what you talked about with Craig Caran and Ty. Right, right. Good [00:41:00] Karma Sports, this is what they're doing.

[00:41:01] commitments to reallocating incumbent workers could make employers hesitant to take a risk of, of hiring new employees in normal economic conditions, particularly intensively competitive ones, such as commitments, such commitments to intentional orders could be costly. Prioritizing reallocation forces AI adopting companies to consider ways that AI can create new tasks for new products and new markets, rather than funneling AI applications into replacing human workers costs.

[00:41:27] So this is what I've been preaching, like growth and innovation. You, you have to focus on growth and innovation, otherwise we're gonna decimate workforces. So we have to grow to be able to keep the workers we have and add new ones. And I said, none of these actions will prevent powerful AI from disrupting jobs and indeed none directly address the slowdown in junior hires documented in these reports.

[00:41:46] So then back to my MAICON session, there was a, a section in the, in the presentation where I said, here's the things like I believe with a high level of confidence, so the AI labs will continue to accelerate progress toward AGI. And nowhere in these [00:42:00] papers does it talk about pacing the impact on workers.

[00:42:02] So they, they present this as like, inevitable. We're gonna build the smarter markers, this model smarter models, they will take the jobs of human workers and we won't need as many. They, they, they literally are just saying like, this is what's gonna happen. Yeah. I said, AI will get smarter, more generally capable and more reliable at completing long horizon tasks.

[00:42:20] Some work, maybe the majority will be automated by agents. Work is gonna compress, teams will consist of humans and agents. We'll need fewer humans doing the same amount of work. Productivity will increase. But management management's cap, capacity to review and approve work will be blocker. That's the anthro talked about that Exactly.

[00:42:36] New roles will emerge, but it's not clear to me how those will be safe from automation as well. Human in the lead versus human in the loop is gonna be a friction point for organizations that wanna move faster. So if you're Anthropic, you wanna get the humans out of the lead because they're just slow.

[00:42:50] Yeah. They, they, they prevent things. And then the skills, traits, behaviors, and mindsets that employers will have to adapt. So. The other thing I said in, in my talk was like, what kind of [00:43:00] organization economy do we actually wanna build? So I'm, I'm a big believer in a human-centered approach. So go should be to maximize the benefit to people, not to just generate greater profits in revenue per employee.

[00:43:10] So we should be growing companies and careers. And Anthropic appears to only be in the grow companies bucket here. I said, I wanna hire as many humans as I can, but never more than I need. 'cause I don't, I don't want to like end up a year from now with people that are obsoleted and we don't have new roles to move them into.

[00:43:27] And then I said, we have to like invest in this next generation of workers. And so, I don't know, like I have a, I love this research, but I have a major problem with the fact that Anthropics seems to just be like, hands in the air. Like, all right, well, whatever, we're just not gonna need people. And then they're like building the technology to obsolete them.

[00:43:43] Right. I don't understand from a society perspective how that is like an acceptable future for people. And I do believe we still have agency in this, but the labs aren't gonna shut it down. And then the final note, Mike, is like the, again, the way it seems like all these [00:44:00] labs are gonna do this is they're just gonna like, give money away.

[00:44:03] So they announced, I think this was Sunday, if I remember correctly. So we're committing a portion of Anthropic shares to the Anthropic public Benefit fund. This is the post from Anthropic. Separate from these papers, subject to stockholder approval fi, 5% of Anthropic shares will be committed today with an additional 2% added to the board as the board's discretion for every 50% increase in the company's value.

[00:44:26] We might explore large donations to traditional nonprofits or direct distributions to specific populations. One example is labor market impacts. AI may generate enormous economic value, but it could also, cause instability for many workers. Andro has already committed 200 million to studying and addressing this issue.

[00:44:46] By giving people money. And then like this morning, Elon Musk tweeted that he's going to give one share of space, like stock to every child in America. And it's like one of many. And so like this is, this is the answer. They're all gonna [00:45:00] build AI recursively, self-improving ai. Yeah. Within like one to three years is better than humans at every cognitive task.

[00:45:07] And then they're gonna just give us all dividends from, or stock from the AI that takes our jobs. It's wild. Like it truly makes no sense. Like at, we had Andrew Yang's keynote where he was at least presenting a concept of like, how do we solve for this where we still provide human purpose and meaning because the labs seem to be missing that part of it.

[00:45:29] So I don't know, man, there's like big topics to start of the week, and especially coming off elastic. My brain is fried, but I'm just increasingly like becoming very frustrated with how the labs just assume. This all works out and they, they just seem to be okay with just building things that are just gonna obsolete people.

[00:45:52] And if they just give everybody money back, it'll all just be okay.

[00:45:55] Mike Kaput: Yeah, I couldn't agree more. I would say, I don't always [00:46:00] say this for topics on the podcast, but if you have time, I would highly encourage you if you have one resource you engage with this week, this how employment is changing at Anthropic that Paul just described.

[00:46:14] Go read it for yourself. Put it into your AI tool of choice. Spend time engaging with it, and just ask yourself if even a fraction of this became true, what does that mean for my career? My company and our people, I think is probably the highest value, highest signal thing you could do for the long-term trajectory of your career this week.

[00:46:42] Paul Roetzer: I agree. I think even the companies that are on the frontier of AI adoption, like at the scaling transforming phases of integrating it,

[00:46:49] Mike Kaput: yeah,

[00:46:50] Paul Roetzer: they're probably 18 months behind what Anthropic is describing today of what Anthropic is doing. And so if you're in like, you know, healthcare, financial services, [00:47:00] banking law, like whatever, like if you're in an industry where you're way behind, this may seem like a parallel universe to you.

[00:47:06] Like there is no way this is really gonna happen in my industry. And it might be three to five years before it looks something like this, but companies are gonna be run very, very differently. and it's just gonna be happening behind the scenes and you're not even gonna be aware it's happening. So yes, this is a very valuable thing to go read.

[00:47:24] MAICON 2026 Recap

[00:47:24] Mike Kaput: Alright, third main topic this week. Let's talk MAICON 2026. We've alluded to it a couple times, but it wrapped up our conference wrapped up this past week in Cleveland. If you listened to episode 2 46, you heard us recording AI answers from the conference like Paul mentioned. Now we wanted to take a little time to look back at the conference as a whole.

[00:47:44] So our program included tons of sessions on things like building AI agents, redesigning marketing work, leading organizational change. Like Paul said, he had his keynote that explored the architect, the Orchestrator, and the Apprentice as these different roles in [00:48:00] the age of AI and agents. I did some sessions including talking through AI native content teams and discussing AI transformation with the leaders at Good Karma Sports.

[00:48:10] Now, some of our headline speakers included journalist and author Karen Hao, tech and Economic futurist, as well as former presidential candidate Andrew Yang and bestselling author Kevin Roose. Their sessions covered the company shaping ai, the future of the economy. And what happens as we develop increasingly advanced systems.

[00:48:32] As a note, and we will reemphasize this in a second here, for anyone who missed MAICON, there are recordings of the main stage and featured sessions available for purchase as an OnDemand bundle. You can find all that information@MAICON.ai m ico n.ai. So Paul, we wanted to maybe just chat through some of the highlights you found, from ma econ kind of takeaways from the conference, curious, what you recapped and kind of understood [00:49:00] and learned from all our incredible speakers and from all the stuff that didn't happen on the stage.

[00:49:04] Tons of great conversations. I know we talked to so many amazing podcast listeners. It was awesome.

[00:49:10] Paul Roetzer: Yeah. And like, yeah, I had a chance to hang out with Karen backstage and Kevin Roose and Andrew Yang and just like great people. I mean, Andrew, I didn't realize we were getting a comedy act when we booked Andrew filming stage.

[00:49:22] I mean, he zero slides. I had no idea what Andrew was gonna talk about because he just like gets up there and, you know, literally does a comedy act for 45 minutes. super insightful. and the whole reason I wanted Andrew Yang on stage was. Because it, he at least has ideas of what to do, and that's very rare right now.

[00:49:39] Too many people talk about. It's just to be a future abundance and we'll figure it all out and whatever. He actually has some concrete ideas that he's been playing around with, so I love getting to talk to him. Kevin Roose was awesome. I really enjoyed that conversation, but I, a big fan of their podcast for a while.

[00:49:54] The AGI Chronicles looks amazing. I've got the book, I'm gonna actually probably be reading it on my flight back. [00:50:00] So overall just, yeah, it was incredible. We started MAICON in 2019, three years before ChatGPT. So I think like one of the big things that has really evolved for me is back then Mike, like we were looking for these very narrow talks.

[00:50:14] Like if you think about the exhibit hall back then, it was all these vendors who have built tasks, specific things like website, SEO, email, subject line writing, predictive models for ad spend and campaign strategies, translation, transcription, image recognition. Like these were the kinds of things in 2019 that we were talking about, that we were featuring at the show.

[00:50:35] and we were trying to convince people to pay attention and then help them understand how I always been changed the world. But it was like. It wasn't nearly as tangible. So that first year had 300 people from 12 countries. This year we had nearly 2000 from, I think it was 25 countries. I know I met attendees from as far away as Thailand, Dubai, Australia, New Zealand.

[00:50:55] So a, a few things that stuck out to me this year. the non marketer audience at [00:51:00] MAICON keeps growing. in 2025, I think it was around 20% weren't marketers, they were more business leaders and even just other disciplines. this year I know it was higher. It is more of like instinctually from conversation.

[00:51:12] I don't know the final number yet. The audience's AI maturity is becoming increasingly diverse. Now. This is, this is a challenge and an opportunity from a, from an event organizer perspective. there are a lot of people that are still getting started on their AI transformation journeys. They're early personally, they're early organizationally, and then there are absolutely people there who are on the very edges of the frontier building agents into their workflows and org charts.

[00:51:36] Like we heard from Holly Chen gave a great talk, what they're doing at Samsara. Dan Slagen gave an amazing talk about what's going on Zapier. So you have these people that are doing these incredible things, and I know there were people in the audience that are like, way more advanced. so we have to solve for that as we think about next year's programming, like how does that look different?

[00:51:55] One of the other big takeaways for me is people crave the human connection and the [00:52:00] support of their peers now more than ever. so for me, somehow as this event keeps growing from the 300 to the nearly 2000. That sense of community and comradery seems to grow as well. It's an amazingly like, generous, thoughtful, supportive, and humble group of people.

[00:52:15] And again, I don't know how it is that like everyone you meet just seems to be, how can I help? Like they're, they're super thoughtful and caring people, which is an amazing thing to be able to bring those kind of people together. And I do feel this shared sense of like, we're all in this moment of excitement and uncertainty.

[00:52:35] And the other thing that, you know, I think increasingly we led off with topics about it today is this, there are uncomfortable truths about this technology. and this was maybe like the thing that stuck with me the most. So the MAICON theme, since that 2019 inaugural event is more intelligent, more human.

[00:52:51] So I've always believed that we needed to be, to not only present the opportunities to grow companies and careers, but to raise awareness about risks and [00:53:00] potential negative impacts as well. that being said, it can still be jarring to go from these inspiring talks about agents and AI transformations to insider stories like from Karen, about IP theft and the extractive behavior of leading AI labs.

[00:53:15] So it was intro, just like a little inside story here. So I was out to a, a dinner, was it Wednesday night after day one, and with Intercept, who's an amazing partner and sponsor of ours, and I got Karen's slides at about nine 30 at night. So I got a message from Ashlee, our, our head of events that like, here's, here's Karen's slides.

[00:53:36] And so I stepped away from the dinner table and I scanned the slides and I thought, oh, no. Like I read Emperor of ai, like I know what's in that book.

[00:53:45] Mike Kaput: Yeah.

[00:53:46] Paul Roetzer: But I wasn't sure how far we were gonna go with it. And once I saw the slides, I realized like, wow, we're gonna go there, like the dark side of all this.

[00:53:57] So I knew that was the first talk the [00:54:00] next morning. So we're gonna come in this amazing night out. Like everything's, like everybody's having a great time, great energy going in, and then I'm just gonna like, put Karen on stage and it's gonna be like, whoa. Like this is really heavy. So I sat, I went, I went to bed that night working on like, what are the questions I'm gonna ask Karen, because I had to do like a fireside chat with her for 15 minutes at the end of her talk.

[00:54:20] And like, how do I bring this back and then tee it up for you? 'cause you had to come on stage after that to do your talk.

[00:54:25] Mike Kaput: Yep, yep.

[00:54:26] Paul Roetzer: So I got to bed and then I woke up at like four 30 in the morning and I was like, I have to provide context to the audience about this. So I actually wrote at four 30 in the morning, the intro.

[00:54:38] To Karen's session, and here's, I'm just gonna read you verbatim what I, what I wrote. I said, there is some uncomfortable truths to how this technology is created and the negative impacts it can have on society. We cannot take a responsible human-centered approach to AI without understanding the human impact behind it.

[00:54:54] Some of the things that Karen explains in this session coming up might make you uncomfortable. If it's the [00:55:00] first time you're hearing them. You may have employees or coworkers who are resistant to using AI, potentially because they read Karen's book or have heard about the darker side of how the technology works.

[00:55:11] We hear these questions and concerns all the time around the economic, environmental, and human impact of ai. So while we can absolutely be excited about technology and optimistic about the future that it'll create, we can't pretend like it doesn't come at a cost. Now, for people who were there, they, they could tell you like, I got a little emotional when I walked out to interview Karen.

[00:55:32] The backstory here, just 'cause I don't know, people were asking me and I think it's interesting human side of this. it was a very powerful session. You know, I knew what was in there. It was a risk to lead off this second day with it. I'd got up early that morning, I'd written this kind of intro to sort of figure out the context, but my daughter was in the room and she's 14.

[00:55:52] So she had surprised me that morning and show up to volunteer 'cause she didn't have school. And while I was sitting backstage, as Karen was on the stage doing this [00:56:00] talk, my daughter texted me and said, when are you gonna be on stage? And so I told her I was going up in a few minutes, so just head to the back of the room.

[00:56:07] And then, you know, yeah, I'll be up there interviewing Karen. But then I realized that she was about to walk into this like, insanely heavy session that was talking about some pretty dark stuff. And so I had a lot running through my mind. And so I actually texted my daughter that, that, intro that I just read, that I had written, I was like, Hey, just FYI, here's context as to like this session you're about to walk into.

[00:56:33] so she understood what was going on. So as I was about to walk on stage to interview Karen at the end of her talk, I'm like three minutes away from having sent this to my daughter, explaining what's going on while thinking about. The reality of it, like, what's happening? And I get on stage and I look Karen in the eyes, and I just, like, I couldn't talk, like I could feel it like welling up in my throat.

[00:56:54] I was like, man, I'm getting emotional. And then Karen looked at me and then she started getting like [00:57:00] teary-eyed. And so I was like, oh my God. Like, it was just like total unplanned moment where like neither of us knew what to do or say. And so it took me a moment to sort of get my composure and then like ask her.

[00:57:12] And plus I think there's just this amazing bravery to like what Karen's doing. And I think I just saw like my daughter in her in some ways, like where you're trying to like, you know, raise someone who has the strength to do the kinds of things she's doing. So yeah, it was, it was a weird, very emotional moment for me.

[00:57:30] Incredible interview, incredible session. And then I, you know, I shared on LinkedIn, I think that the thing that kept coming back to me was just the gratitude for it all. Like the staff, the volunteers, the speakers, the sponsors, the attendees. It's an amazing privilege to, to be able to bring people together and to see it just keep growing and to see all of the things that are happening off to the side, like all these incredible partnerships that are being formed, friendships that are being formed.

[00:57:56] Groups of people who are now meeting up every year and you know, getting together [00:58:00] and then to do it all in our hometown of Cleveland and get to showcase, you know, our city to people. It's just, just very, very grateful that we get to do it and it's an amazing thing to do every year. So yeah, I just appreciate everybody who came out and certainly our podcast listeners as a fun lunch we got to do with them and, yeah, just great stuff and we'll be back next year.

[00:58:19] So October 4th to the seventh in Cleveland, is MAICON 2027. So yeah, if you, if you don't have it on your calendar already, we'd love to see more of you next year. It was, it's a special thing. I hope we can, you know, keep doing it, keep growing it. And I did get asked like, when does it get too big? To where like, you don't have this feeling.

[00:58:39] I was like, I don't know, it just like every year it grows another 20, 30% whatever, and every year it's just like more amazing people show up. So just keep going. See where it goes.

[00:58:49] Mike Kaput: I love that. I appreciate you sharing that. I think it's really important for people to hear. One sort of practical thing I'll mention is some of the [00:59:00] takeaways I found from this were, you know, thinking of just like, oh, okay.

[00:59:03] Wow. Interesting how behavior and our use of technology is changing. So one, you know, Paul, you and I discussed this, that a huge amount of people, at least relative to before, found the event for the first time through ai, which was interesting trend, including people outside of marketing. So because we are being recommended as an ai, which I think is.

[00:59:24] Very, accurate in some ways as there's plenty for non-marketers. So they weren't missing.

[00:59:29] Paul Roetzer: And international too. Like I had multiple people international. I said, how did, like why, why did you travel 30 hours to be here? And they just said, well, I just asked ChatGPT for like one of the best act conferences in America and you

[00:59:41] Mike Kaput: That is trust.

[00:59:42] Paul Roetzer: I was like, incredible. That's amazing.

[00:59:44] Mike Kaput: Yeah. And on top of that too, I think also we learned that, you know, we, we had intentionally designed the web app experience to be a web app versus a phone app, which made it legible to agents. I got feedback from folks, they were taking all the session notes and [01:00:00] slides and putting those into AI and notebook, Gemini notebook, so no surprise there.

[01:00:06] Paul Roetzer: plus Gemini built into the app so you could actually like say, Hey, I'm here for this. What session should I go to? And it would build you thing, which is awesome. We've been dreaming about that for seven years. It was cool to kind have that capability.

[01:00:17] Mike Kaput: Yeah. So just some quick logistical things there I thought were super interesting is how AI is kind of changing the conference experience.

[01:00:24] Yeah. Before we dive into rapid fire, just to reiterate, this episode is supported by out shift, which is Cisco's incubation engine for Frontier Technology. So multi-agent AI is in every enterprise roadmap right now, but here's the problem we're all facing. Agents can pass messages, but they can't think together.

[01:00:44] So these systems silently underperformed through cognitive failures like misreadings, unverified claims and false consensus that conventional monitoring cannot detect. Now, outshift by Cisco is building the fix the internet of cognition, [01:01:00] and it's an open source foundation for building multi-agent systems from design to production with shared context, shared memory, and guardrails to drive the results we actually expect from ai.

[01:01:11] So you can go read the paper, experience the demo, and grab the code from outshift.com. That's Outshift.com. This week's episode is also brought to you by the MAICON 2026 on demand bundle. So if you couldn't join us in Cleveland, or if you didn't wanna watch the recordings of what you saw, you can now watch recordings of main stage and featured sessions from this year's Marketing AI conference on your own schedule.

[01:01:36] So this bundle brings together the big picture conversations like the ones we just talked about, about where AI is heading, as well as practical sessions for how to put it to work in marketing. You'll hear from Karen, from Kevin Roos, from Andrew Yang, alongside practitioners tackling the questions teams are working through right now.

[01:01:53] That includes people like Will Reynolds, who gave a talk on visibility and credibility in AI search. Andy [01:02:00] Cina, who gave a talk on turning a marketing process into an automated tool. We have the incredible Chris Penn on using AI for analytics and Taylor Rady on our team using AI for research workflows as well as I believe my AI native.

[01:02:14] Content team talk. This also includes Paul's session on rethinking the work of, rethinking work in the age of AI and agents. So go to MAICON.ai, M-A-I-C-O n.ai. There's a button right there in the header to purchase MAICON 2026. Record

[01:02:33] Paul Roetzer: more than 12 hour. I was looking at it over the weekend. It is like more than two dozen sessions in 12 hours.

[01:02:38] So, yeah, incredible. it's insane. An insane amount of value packed into one thing and it's avail available immediately. So take advantage of that for surely.

[01:02:47] Mike Kaput: I can't, I can't wait. I have to revisit a bunch of it. I'm super excited to see it.

[01:02:51] Introducing The AI Transformation Blueprint and AI Score

[01:02:51] Mike Kaput: Alright, let's dive into rapid fire, Paul. So first up, we, you know, we've talked a bit about your new book, the AI Transformation Blueprint.

[01:02:58] We've talked about the [01:03:00] software you've built, AI score, and how quickly this has all come together. What's exciting is listeners can now get their hands on the book. It is available for purchase and AI score is now actually in public beta as of MAICON. So just as a refresher, the blueprint is your guide to leading organizational change with ai.

[01:03:19] It brings together a business blueprint organized around eight pillars of transformation. A personal blueprint organized around five. AI score is the new assessment platform, the software you've built for companies and individuals. Business AI score is one of the assessments you can take where you can rate your organization across dozens of different statements about AI transformation.

[01:03:41] You can do the same thing for your personal AI score to help individuals assess themselves. Both of them produce an overall score and grades of how that score compares and what you need to work on in different pillars of transformation. So the assessments are free to take, the book you have to purchase.

[01:03:59] You can go to [01:04:00] SmarterX.ai/blueprint for the book. Try AI score at SmarterX.ai/score. Go check all that out. Paul. I'd love to just get your take of kind of where these are today. We had a bunch of announcements during MAICON around these, like, what do people need to know about the book in AI score?

[01:04:17] Paul Roetzer: Yeah, it was kind of wild. So my opening talk was Wednesday morning. I went to bed that night thinking, well, one, how was I gonna introduce Karen? But two. Wow. We, we launched a book in a software product this morning and I, it was like, barely even crossed my mind that it had happened.

[01:04:30] Mike Kaput: Right,

[01:04:30] Paul Roetzer: right. yeah, I mean, I told the story of how AI score was built, but in essence, I spent seven months developing these assessments and this, and the prototypes of the software.

[01:04:39] And then about 45 days before MAICON, I realized I could use the them as the foundation for a book. So I greenlighted the book around September 5th, 2026, and then I finished the book, including all the edits, September 17. So thanks to our internal team who, you know, edited all the work. And then the team got to work, specifically [01:05:00] Tracy, on our team getting it ready for publishing.

[01:05:02] And so it was a mad sprint in the midst of like everything else we were doing with MAICON to get this book ready to go. It's about 28,000 words. I think, if I remember correctly. So it's roughly half, the length of a, of the last book that we wrote together, Mike, that you and I did. Yeah. The marketing Artificial intelligence book.

[01:05:19] Yeah, so it's about a, I would say it's about a three hour read for the average reading speed. It's meant to be shorter, more of an executive guide, I guess. And then a similar story with the AI score. I built this fully functional HTML prototypes of these different scoring assessments, personal and business.

[01:05:34] But I didn't actually know how to take the prototypes from vibe coded HTMLs to like enterprise grade SaaS product. And so I found a software development firm. At the beginning of September and we met, went through the prototypes and they're like, yeah, we could, we could do this. So they turned my prototypes into these amazing products in about 30 days.

[01:05:53] Mike Kaput: Mm.

[01:05:54] Paul Roetzer: So as I was like, I didn't really have a ton of time over the weekend to like reflect on all this, but I. I think [01:06:00] it's safe to say that the 45 days with the book AI score and then prep all the prep for MAICON was probably the most inspiring, exhausting, and creative sprint of my professional life. yeah, I'm still kind of like in shock that we actually pulled all that off.

[01:06:16] So yeah, the book is now available in print and digital, so you can go to, what is it, SmarterX.ai/blueprint, and you can buy the book direct. You can go on Amazon and get it, and then the, you can get the ebook versions of it. I may do an audio version this year. I honestly, like, I haven't had time to even think about it, but I would imagine we'll probably do an audiobook and then AI score is available in public beta today.

[01:06:38] So you can go to score.smarterX.ai. You can take the business AI score for your organization. You can take personal AI score, you can create a business account and invite teammates and then see how they rate it across it. And then as a team admin, you can have visibility into that. It's just like a, I would say we did a soft public beta rollout of AI score at MAICON, November 13th.

[01:06:58] We're gonna host a webinar called [01:07:00] AI Score for Business Leaders, where I'm actually gonna walk through a more full blown rollout of what it is, how it works, how you could use it to drive, change management and transformation within an organization. So I would say check that out November 13th if you're interested.

[01:07:15] And that'll be kind of the official public beta rollout. We are still very much in that public beta phase and kind of monitoring, you know, how people are using it. And then we have some pretty exciting plans for what else we're gonna do with AI score. but I'll save those for another day.

[01:07:31] Mike Kaput: Yeah, that's plenty to be going on with in the last 45 days.

[01:07:35] Paul Roetzer: Yeah. That's

[01:07:36] Mike Kaput: awesome. Seriously? Yeah. That's incredible. I mean, just what, talk about hearing what, what the Anthropic employment research is saying, putting it into practice in terms of like, look what's now possible.

[01:07:47] Paul Roetzer: Yeah, and I think I used AI score as an example when I said work will compress in my opening keynote, this is kind of what I meant by it.

[01:07:54] It's like a, a senior leader can do the work of an entire go to market team. Mm-hmm. So like for [01:08:00] AI score. Our marketing team had their hands full with macom, like they had other things going on. So I just took ownership of the entire thing. I built the product, I built the plan for the release of the product.

[01:08:09] I created the webpage and the FAQs and like all of it was me in the midst of everything else I was doing. And that's what I mean by like work compresses to where an individual person can do the work of a five or 10 person team in a very short time period. And for us it was additive, like it created these new things that could create new jobs and new growth, but it was done in this very compressed way.

[01:08:34] and so that's what I mean when I talk about like using as innovation and growth, do something new, open up new markets, launch new products, like that's the opportunity here. Don't just think about like saving costs and doing things more efficiently so you don't need as many people.

[01:08:48] An Interpretability Breakthrough?

[01:08:48] Mike Kaput: All right, next up.

[01:08:49] This past week we got an article from AI researcher Nick Cammarata, that sort of started to go a bit viral. It is called Neural Networks are already clean, and it's [01:09:00] basically a pretty technical essay arguing that understanding AI's inner workings may be much more achievable than researchers have assumed. So the kind of broad field we're talking about here is called mechanistic interpretability, may hear this called interpretability.

[01:09:16] It's basically. What AI researchers do when they study the components inside these models and how they interact to produce their behavior. So Camada himself actually helped pioneer some of this work at openAI's, but him, like the rest of this field over the last couple years became kind of pessimistic because the individual, like neurons in the models appeared to be very, very hard to actually interpret.

[01:09:41] They're all tangled up. It was very difficult to actually say, okay, well why is the model behaving in a certain way? And what he's saying actually now is that with better tools, including specifically more automated AI agents and AI researchers, researchers, human [01:10:00] researchers can now actually study the model's existing neurons and connections directly.

[01:10:04] They don't have to untangle all this stuff. Basically, one example he gives is, look, there's. A famous technical example where there's a neuron in models in LLMs that is thought to respond to both the concepts of cats and cars. And people are like, well why? Why does it do that? We have no idea. Cammarata actually argues it is detecting a BHA pattern.

[01:10:26] And the whole point here is like, even if you don't know what that means, the point is that all these like unrelated concepts that have been befuddling researchers may actually be very explainable now that we have better tools to use at scale to actually look at what's going on under the hood of models.

[01:10:43] So Camarata says that there are now under understandable neurons and models with up to trillions of parameters, which are kind of big frontier models. And basically he hopes that we actually are in a place where AI assisted investigation can scale up and help researchers understand the [01:11:00] models relatively fully.

[01:11:01] So Paul, we can, we don't have to get too deep into like the technical parts of this, but the idea here is that interpretability has historically been. Something, a really, really hard problem people aren't sure we could actually solve. Not knowing how the models works, leads to all sorts of the safety issues we've discussed.

[01:11:19] But it sounds like Nick is saying that we might actually be at a place where it is understandable and if we can actually scale up the engineering around the research itself, we might be able to solve this problem. What did you think of this? I think we both kind of like perked up when we saw this without, you know, being engineers and researchers ourselves because it's like, oh that's like a gold holy grail problem in ai.

[01:11:42] Paul Roetzer: Yeah, this surfaced, I don't remember, this was like Saturday morning or Friday. I don't remember when I saw this pop up, but you know, it was one of those where I looked at, I was like, that seems like an interesting article. I'm not sure who that guy is. So you click through, it's like, oh, okay, he came from openAI's or whatever.

[01:11:55] He worked at the labs like is legit researcher. [01:12:00] start scanning it thinking, h this seems like a big deal, but I don't know, like this is an, and we're not the experts in this. And then within an hour, like everybody in the I space I follow is between thinking, yes, this is a really big deal. And that's, you're like, okay, let me go back to that article.

[01:12:15] Let's throw this in the sandbox. Like this is probably worth talking about. Yeah. I would say this is one you probably put a pin in it and like, let's come back in six months and realize like, okay, that was a really big deal. It feels like it's heading in that direction. And I think you summarized it, Mike, it's just, it's hard to know why these things do what they do.

[01:12:32] It's been for the last 15 plus years in deep learning, this idea that like, if we could understand how these neuro nets work and why they make the decisions they make or lead to be the kind of decisions they make, it would be on par with like being able to look into the human mind and understand why humans make the decisions they make.

[01:12:49] It's, yeah, it's that level of being able to drill into those individual neurons that fire and why do they fire and what does it mean when they fire. And so, yeah, it certainly seems like promising direction that a lot of [01:13:00] people in the AI world are, are excited about that. Now you need to like really push on this research, but I'm sure if it's as significant as it seems like it could be, every lab is gonna be building on this research and we'll probably be hearing a lot about it over the next six to 12 months.

[01:13:17] OpenAI’s Math Reckoning

[01:13:17] Mike Kaput: All right, next step is kind of the developing story of open AI's research into math problems. So openAI's has now released hundreds of AI generated mathematical manuscripts and supporting proof materials about a bunch of problems they are making progress on solving. So we've talked about how OpenAI was actually solving millennium prize problems in mass, so very, very hard problems in the field of mathematics.

[01:13:41] This work now comes from an unreleased internal model, which OpenAI told Wired it had used to resolve more than a hundred longstanding open math problems. It's public GitHub collection now contains. 719 mathematical manuscripts grouped into [01:14:00] 372 families of related results. Many of these proofs come with versions written in lean, LEAN.

[01:14:06] We talked about that when we talked about the Millennium Prize. It's basically a language that lets computers check mathematical arguments. It's basically a way to say, Hey, these are verified proofs. OpenAI says the results are at different stages of verification. Some lack formal proofs. some un formalized results could contain errors.

[01:14:26] But the whole point here is there's like backlash from mathematicians because they're really upset. At least several of them are about how this work was released. So mathematicians told Wired, they had asked openAI's, Hey, release these results with carefully explained papers that they could absorb and build on.

[01:14:44] Similar to kind of how top mathematicians and researchers release stuff in their field. Summit. some people said that the company had promised not to release everything at once. OpenAI, however, says it is unaware of who made that [01:15:00] assurance. OpenAI says it consulted an independent advisory group at the Institute for Advanced Study to shape this release, including the procedures for corrections and citations, but the Association for Human Mathematics in a statement re published on Terrence Tao's blog.

[01:15:18] It's a famous, research and voice in the space. Basically argued this is like the totally wrong way to do this. This is not good for the field, this is not good openAI's is doing it. Flooding the field with 700 plus files, undermines research centered on human understanding, and it came straight out and said it urged mathematicians to stop working with AI and said this release was not a demonstration of scholarship, but a demonstration of power.

[01:15:45] So. I think Paul, if you had to draw up a list, mathematicians are top of the list of having their, their, what is their move 37 moment in from, from go is like, oh shoot, AI can, start doing some things top [01:16:00] mathematicians are doing. And there is some real uncomfortable feelings around it sounds like.

[01:16:04] Paul Roetzer: Yeah. It's, it's hard to watch. I mean, I understand the perspective of the mathematicians. This is something they spend their entire lives working on. Yeah. PhDs, multiple PhDs to solve single proofs. and it's like, it's really, really hard. It is like watching Alpha Go, or you know, with Lee Sedol when he just like realizes it's just, it's just better than him at the thing.

[01:16:30] He was the expert in the game ago.

[01:16:32] Mike Kaput: Yep.

[01:16:33] Paul Roetzer: so I certainly empathize with the mathematicians and I think like anybody could step back and say like, well, what if everything I. Did for a living was just solved and they just put it all out there.

[01:16:46] Mike Kaput: Yeah.

[01:16:46] Paul Roetzer: So I get the human condition of just, this isn't what we asked for.

[01:16:51] Like there's a better way to do this. And I also get, in this case, ma you know, mathematics is the basis for solving a lot of bigger things. [01:17:00] And if openAI's believes they're sitting on solutions to things that could advance science and medicine and technology, you know, by decades potentially. Why would you sit on it?

[01:17:11] Like, why wouldn't you put that out into the world? So I totally understand both perspectives here. I don't really have a strong opinion. I certainly wouldn't come to open Eyes defense about like how they communicate things and whatever. They don't have the best track record of

[01:17:26] Mike Kaput: Yeah.

[01:17:26] Paul Roetzer: Being empathetic towards humans.

[01:17:28] so I'm sure that they could do that better. But you know, if you asked me should we sit on scientific discoveries or advancements in mathematics, my general answer would probably be no. Like if we thought we had answers to diseases and cures for things like cancer, like would we sit on that for years?

[01:17:47] No. Like the whole point of what we're doing, the only argument for this advanced race and building these larger, more powerful, more intelligent models is for the good of humanity. And so [01:18:00] if solving all these math proofs. Gets us further along, wh why wouldn't we put it out into the world and like keep moving.

[01:18:07] That doesn't mean that there isn't an impact on humans that we shouldn't be sensitive to. Yeah. So, yeah, it just, it's, it's hard. It's hard to watch. It's hard to see their response, because you can tell it's a very visceral response from the math community, towards this advancement and specifically towards openAI's.

[01:18:31] The Super Intelligence Rebrand

[01:18:31] Mike Kaput: Next up, we talked about on our last kind of weekly news episode, president Donald Trump's executive order rebranding Artificial intelligence as super intelligence. that executive order directs agency is where legally permitted to replace AI with SI and official communications websites, reports and policy documents.

[01:18:52] And says the executive branch will not acknowledge the old terminology in applicable settings. Now, for now, super [01:19:00] intelligence covers the same technologies as the existing legal definition of ai. However, the story here this past couple weeks is that major tech companies are adopting the language. So Microsoft, CEO, Satya Nadella published an essay recently titled Models as Insider Risks in the Super Intelligence Era.

[01:19:22] He repeatedly uses the term super intelligence to describe today's systems. Salesforce, CEO Mark Benioff has announced a full rebrand of AI Force, which is their product that lets people and agents work with Salesforce data and work flows. And he wrote the era of super intelligences here. AI force is officially SI force.

[01:19:44] Elon Musk said on X when he was asked about renaming SpaceX AI to SpaceXs SpaceXs A I, which is a mouthful. He replied, yes, we will make that change. The company tech company ServiceNow, is also [01:20:00] describing itself in official announcements as the super intelligence control tower for business reinvention.

[01:20:06] So Paul, this is, I say sarcastically fun to watch because we are just apparently updating branding materials. woe to the marketers that have to go back and update this across all these websites. Yeah, man.

[01:20:23] Paul Roetzer: Yeah, I don't know. I said my piece on this whole thing on episode 2 45, so if you didn't listen to that and want to hear my opinion on this, I would say just go, go listen to that segment.

[01:20:34] Mike Kaput: Yeah.

[01:20:34] Paul Roetzer: I understand that all these tech leaders are in a tough spot. 'cause they, many of them have large government contracts with more government contracts looming. Some of them have IPOs that they need government support on. Some of them are under litigation from the federal government the day after they announced the Accord, if I'm not mistaken.

[01:20:56] the government came after openAI's [01:21:00] for a number of things and then they have other leverage points that the administration can hold over them and has proven they will happily use legal leverage and other leverage points on people. but doesn't make it any less disappointing to see them all just rolling over on this.

[01:21:15] Yeah, like it's sad. IWI will say on a positive note, we just spent four days with nearly 2000 people and I did not hear the term super intelligence two word version, once over four days. And that was, that was encouraging. So. Yeah, like, I'd, I don't, I'd be okay if we never had to talk about this again, but I'm sure we We'll, yeah.

[01:21:40] So,

[01:21:41] Mike Kaput: yeah.

[01:21:41] Paul Roetzer: yeah, I don't know. Like I keep, I, I'll be anxious like two years from now for now, whatever, when all of these tech leaders have to answer for the fact that they just rolled over on this stuff. And I would love, I can't wait to hear their responses.

[01:21:58] Mike Kaput: No kidding. [01:22:00]

[01:22:00] Paul Roetzer: But again, I get it. They have fiduciary responsibilities to, position their companies to continue growing in value and sometimes you have to play a game that you don't wanna play.

[01:22:12] And some of them are playing a game that they wanna play. yeah. I'll just stop there.

[01:22:21] Google Releases Gemini Argon (and “Gemini agent”)

[01:22:21] Mike Kaput: Next up in our last, news episode as well, we had talked about Google holding back Gemini for argon. Well, this past week, Google broadly released the model. So Gemini for Argon is designed for sustained multi-step working, coding, finance, law, cybersecurity, science, and more.

[01:22:37] Google says Argon combines stronger reasoning, visual understanding, and writing with the ability to analyze complex documents and data. It can work through Google's apps and coding tools to do things like reorganize large software projects, find security vulnerabilities, build interactive visualizations.

[01:22:54] For example, Google actually had these, this kind of cool list of like how Argon is helping their company [01:23:00] actually make, progress. So they talked about how the Chrome team completed months of software restructuring in weeks, how Isomorphic Labs used it to help extract research information from complex PDFs and patents in hours instead of weeks.

[01:23:15] Gemini 4 Argon is now available through Google's paid API and currently for Ultra and pro subscribers in the Gemini app. In an announcement around the same time, Google Cloud also came out with a post about what they call, quote, the Gemini agent, which is Gemini capital agent not capitalized, which is basically a universal work agent.

[01:23:36] A bit confusingly Gemini is the agent's name. Why you can actually select different underlying models for this, including Google's models and Anthropic. Claude. Google has described this as an evolution of Gemini enterprise, which I'm not totally clear on. you can still basically chat with this, but also like delegate entire projects to it.

[01:23:56] So it sounds a bit like, a chat GBT work. I don't wanna [01:24:00] put words in Google's mouth, but kind of a more agentic version of Gemini, just natively in the chat. And the agent will plan and execute work across connected apps. It can remember context and it can keep running in the cloud after you've closed your laptop.

[01:24:14] Now none of this is actually publicly available yet. The announcement gives no public launch date and does not announce any type of like replacement for anything here. We did wanna mention it because it's a fairly big deal, Paul. It dropped right before my Gemini workshop at MAICON, which is interesting to navigate, but you know, it's more of an announcement than a feature.

[01:24:35] So I'd be curious your thoughts on both these, like Gemini for Argon, what's the response here to this? It's been very long anticipated. And then maybe the Gemini agent thing, I'd be curious how you view that at the moment.

[01:24:49] Paul Roetzer: so I do, I just checked, I do have Gemini four in my personal pro account.

[01:24:56] Mike Kaput: Yes,

[01:24:57] Paul Roetzer: I have it in my Gemini enterprise [01:25:00] account.

[01:25:00] There's SmarterX. I do not have it in my normal Gemini account because the way Google does things is you have a separate enterprise account from your normal and so it doesn't just transition your existing business account, it creates a whole new business account.

[01:25:15] Mike Kaput: Yes.

[01:25:16] Paul Roetzer: So I have multiple logins. I haven't had a chance to experiment with it.

[01:25:22] It didn't, I mean, granted we were in all last week, so I don't, I don't know that I got the usual pulse on things, but I would say this model probably has less buzz than most whole number releases from the labs at the moment. Yeah. Like I haven't heard as much from online community about this model. I did hear a lot of takes on like Google re announced Gemini, like as like the agent part.

[01:25:47] Yeah. Was like kind of people were, were kind of confused of like, what exactly is it? Is it the model? Is it the agent? Is it the harness? Is it the. Whatever. yeah, so that being said, I haven't had a chance to really dive into it or [01:26:00] see a ton of, response to it. So we'll definitely be digging into it and kind of running it through our evals internally.

[01:26:07] I still use Gemini quite a bit, both the app itself and certainly within Google Workspace, platforms because that's, we, we run on Google Workspace stuff. So, yeah, I don't know. I'll be interested to experiment with it, see what, see what people have to say, see what makes a difference at some of the higher, like strategic projects that I tend to work on.

[01:26:28] I would, I would love to experiment with it there.

[01:26:30] Mike Kaput: Yeah, for sure. And Google has some fantastic AI products. I mean, the workshop we did was super helpful for people, lots of people getting real value out of these tools, which is super encouraging to see. It's just, maybe it's just me, but like a plea from me is like, make it easier for your educators to educate Google, please.

[01:26:47] Because yeah, Des describing how it all fits together takes some serious work.

[01:26:53] Paul Roetzer: Yeah. Yeah. It's, it's getting challenging.

[01:26:57] micro1 Is Buying Enterprise Data—Here’s Why

[01:26:57] Mike Kaput: All right. Next up there is a company getting some [01:27:00] buzz out here called micro1. It is a company that supplies human expertise and training environments for AI because they have committed to spend a billion dollars over the next 12 months acquiring and licensing company's operational data.

[01:27:14] They say this initiative is financed with capital from Citibank and Hercules Capital. And as part of this, it wants the records of how businesses actually work. So things like their internal instructions, CRM data, project histories, the processes people use to make decisions. So examples here include customer support, workflows, financial reconciliations, contract reviews, et cetera.

[01:27:37] All the stuff companies do. micro1 says, it turns that de-identified data into reinforcement learning environments, which are essentially practice settings where AI models and agents can learn to do those business tasks. And the goal here is to capture the messy parts of work, including incomplete information, competing priorities, and exceptions that [01:28:00] require judgment.

[01:28:00] So their pitch to businesses is, Hey, you already have all this stuff. Why not turn it into a new revenue stream and it's website? It advertises partnerships worth over a hundred thousand dollars in many cases with a million dollars or more. For particularly valuable proprietary data, though, actual compensation depends on the data's quality, uniqueness, and value.

[01:28:21] micro1 says, companies retain ownership of their data and agree on what can be shared, the privacy protections and internal approvals before participating. Now, interestingly, there was a. A fairly long post on X from founder and CEO Ali Ansari arguing that human data could ultimately become a trillion dollar annual market as companies train AI to automate more work.

[01:28:44] Interestingly, in one of his essays, he also just wrote straight up Automation does not eliminate human work. It pushes humans towards higher value, more creative work. So Paul, this is an interesting one. Like it seems like they are trying to buy company data to do [01:29:00] what people at the company do.

[01:29:03] Paul Roetzer: Yeah. So just for context people, this is not new by any means.

[01:29:07] I mean, just coming straight out and saying you're gonna spend a billion over 12 months, that's the first time I've really seen one of these people do that. Yeah. One of these companies do that, but this is, this is how it works. So if I want to take like a core model, it could be an open weight model, it could be a proprietary model, whatever.

[01:29:22] I want to go after a certain industry, let's say dental offices. I don't know. Just pick something. the best way to do that is you go buy the operating data of how a dental office works. How do the dentist make their decisions? How's the office manager function? What do they input into the software?

[01:29:39] What are the conversations with customers look like? And then you use that as reinforcement learning to make the model specialized in operating within a dental practice. Doing the same thing in consulting firms, marketing agencies, healthcare practices, like whatever banks. And so they're basically saying, [01:30:00] sell us your data so we can replace your people.

[01:30:02] Now they're gonna write all these essays saying that's not what happens. We don't replace people. You go to higher level cognitive, creative stuff. It's all crap. Like they're, they're just buying data to replace the need to have people working in these companies. And there will be some jobs left, but like you're basically selling.

[01:30:19] Humanity when you do this. Like, now the way they often do it is they go buy like bankrupt companies or like tech startups is a common one. Okay? So you can go buy like slack messages out of these tech startups that don't make it. And so rather than just folding the company, you sell your data for a few hundred thousand dollars and pay some of your investors a little bit back.

[01:30:38] Hmm. So yeah, it is like, it's wild. Like this, this is how it works though. This's, just data brokering to automate human labor industry by industry one at a time,

[01:30:50] Mike Kaput: and probably not a surprise to plenty of our listeners, but my gosh, I feel like your average person, this is a whole shadow world there.

[01:30:56] Unaware of.

[01:30:58] Paul Roetzer: Yeah. And so like, you might get sold [01:31:00] out as a worker and not even know what happened, right? So like your CFO and your CEO might do a deal with a company like this, and now all of a sudden everything you've put into your Google Docs and your Slack messages and your meeting notes is all actually being sold off without your knowledge to some third party who's using it to train or do your job.

[01:31:16] Mike Kaput: Damn. Yeah. Yeah. Not fun.

[01:31:19] Paul Roetzer: It's been going on forever. This is what scale AI does. I mean, this is, yeah. Alexandr Wang, who's now the head of AI at Meta, this is what he built, like scale AI was like a reinforcement learning company, and he did that through like proprietary data sets. So again, not new at all, but very direct about how they're doing it.

[01:31:35] Mike Kaput: Yeah,

[01:31:36] Paul Roetzer: talk about it.

[01:31:37] Mike Kaput: It's all fun in games when it's people labeling articles or images or whatever, not like the steps you do to do your job,

[01:31:44] Paul Roetzer: right? They basically put like a, we buy home sign up and said, we got a billion dollars. We, we buy data, like give us a call.

[01:31:53] AI Use Case Spotlight

[01:31:53] Mike Kaput: Alright, next step we have our AI use case spotlight, where every week we give you a quick look under the hood at the real AI use cases we're [01:32:00] exploring at SmarterX.

[01:32:00] So Paul, I have one to share and then we could go through anything you've got on your plate. So this week, here's the problem I was personally trying to solve. So as anyone who attended MAICON knows, I had several presentations, panels, speaking, appearances to prepare for at the event. Now I've been prepping for months, but I find in those days and hours before each appearance, you wanna be really sure you're comfortable with the material.

[01:32:26] And for me, that means talking through it out loud. Now, I've done this in the past while sitting in front of a computer, that's fine. But I personally think best and frankly talk best and more, you know, smoothly when I'm up and walking and moving around. Something about that just kind of helps the words and ideas flow better for me.

[01:32:46] so I built an a voice first AI rehearsal partner. And what it does is it actually walked me through. My presentations one slide at a time while I was out walking, so it would remind me what was on the slide. Let me [01:33:00] practice what I wanted to say. Help me memorize key details I needed to know well, and then capture wording I wanted to add to my presenter notes.

[01:33:08] Now, not to mention it could even make changes to my actual slides and notes on the fly while I walked around downtown Cleveland. So more on that in a sec. But here's how I set this up. So if you wanna start trying to do something similar, go for it. So first I gave Codex access to the slide deck on my computer.

[01:33:27] I wanted it to work from the actual slides and presenter notes, including the actual visuals, animations, and like timing and effects that I needed to remember. Second, I ran a skill I have in Codex to build a skill, so that's kind of meta, but the skill I built this time was like a skill for walking reviews of this material.

[01:33:48] And then third, I spent a good amount of time going back and forth with Codex to actually build this skill because like it was really quick and basic to get to like, okay, here's a skill that helps you review your stuff while [01:34:00] you walk. Like super easy. It went through the deck in order, told me what was on each slide, and then provided advice as I rehearsed.

[01:34:07] But the key here is that it did take a lot of time and effort to calibrate all this to like how I work and my personal preferences. I didn't know exactly what I wanted from this right outta the gate because I'd never done this in this very specific way. So. I wanted to actually just like, kind of sound out how to iterate on this and make it work for me.

[01:34:29] So I basically just created like an MVP of a skill and then took a long walk and used it using chat, GPT, voice through Codex. because I was actually just talking to Codex, which means I wasn't able to just test the skill, I was able to literally stop mid-sentence and say stuff like, eh, I actually don't like the way we're doing this.

[01:34:49] Do it another way instead. Oh, by the way, got it, great. Go update the skill and let's take it from the top and run the skill again and iterate. It was basically out loud, iterative [01:35:00] development of the skill. It worked wonderfully. I really quickly got to a useful skill, which I then used time and time again over the last couple weeks to get really comfortable with the material I was presenting.

[01:35:11] So, very briefly here, Paul, then I'll turn it over to you. The tech that made this work was chat, GPT, voice on my phone connected to Codex on my computer. That all work. The ChatGPT app. on the app I have Codex set up to remote access Codex instances on other machines. And then now, it's pretty recent feature.

[01:35:30] You can use ChatGPT Voice just to talk with Codex. So you fire up the app, start a new conversation and speak to Codex and you can do some pretty cool stuff.

[01:35:41] Paul Roetzer: I'd say it worked. All went great. It

[01:35:44] Mike Kaput: worked. It worked. That's all that matters is it's messy, but it worked.

[01:35:47] Paul Roetzer: Yeah, the session was awesome, so. Oh, thank you.

[01:35:50] We have a very different, preparation style for our presentations. I

[01:35:54] Mike Kaput: know, I know.

[01:35:55] Paul Roetzer: Mine is Build the deck. Maybe practice at once [01:36:00] and show up on stage and talk.

[01:36:02] Mike Kaput: There you go. Right?

[01:36:03] Paul Roetzer: It's like, it's, yeah. My, I actually had a fun one in the midst of last week. So Monday morning we started Mon Monday at Nacon.

[01:36:11] we had a council meeting, AI council meeting that morning, and so we were launching AI score soft launch on Tuesday for my workshop. I hadn't built the product FAQ page yet. So I was like, man, I don't, I don't know. I didn't have to do this. So what I did is I actually went into the, Claude project that I had over seven months.

[01:36:30] I've been working in the same Claude project to build everything for AI score, you know, develop all the assessments, build the prototypes, all that stuff. And so I went back into that project and I said, okay, we need a, product, FAQ page. Take all the context from all of our work together the last seven months, here's the marketing page for it.

[01:36:50] And here are 80 questions that the team had for me about how this is gonna work, what we're gonna tell people based on questions that they're gonna have. All that stuff develop, like a [01:37:00] best in class SaaS product, FAQ page for me based on all this information. Mm-hmm. And it went away for a couple minutes and it came back and it just crushed it.

[01:37:08] And I spent about 10 minutes editing the FAQ page. I said, all right, great. Here's the edits. Give this to me in a Word doc and an HTML file, that we could publish directly to the website. The HTML file looked amazing. I sent it to Jeremy on our team and like 30 minutes later we had an FAQ page on the website.

[01:37:24] That would've easily taken me 10 hours or more to do. And it was honestly like way better than what I would've created. So. Yeah, you wanna see it go to the FAQ page on score SmarterX ai, and you can see the output from product. Again, work compresses, like what I traditionally would've had to involve multiple people in.

[01:37:43] I would add, definitely turn that over to somebody else on the marketing team or the product team. If we had a product team. I am the product team right now, so like if, if we had any of that, they would've done it. And instead, while I was sitting in the team room at our conference, I just like spun up a, a chat and like knew to tell it, to [01:38:00] look at the context of everything and the final prototypes and like, here, go do this.

[01:38:04] and it just crushed it. So.

[01:38:05] Mike Kaput: That's so cool. I love that.

[01:38:08] AI Product and Funding Updates

[01:38:08] Mike Kaput: All right, let's do some final product and funding updates. I'm gonna run through these very quickly. Pause as we wrap up here. A lot going on in the last couple weeks. So, first step, openAI's began rolling out GPT-6 with something called Intelligent UI to free and page at GPT users.

[01:38:26] This actually is a feature and enables answers with interactive diagrams, charts, forms, and tools directly inside conversations. openAI's also introduced optional text watermarking for select API models and announced plans to add invisible watermarks to eligible ChatGPT and Codex text output in the European Union.

[01:38:47] openAI's also announced something called College Planner for ChatGPT for teens to organize college applications and financial aid deadlines alongside flashcards, improved quizzes and multi photo note capture on iOS. [01:39:00] Anthropic release, Claude Haiku 5.5. Its small model for fast high volume tasks such as summaries, customer support and coding sub-agents.

[01:39:09] Anthropic also launched a public beta of Claude for Google Workspace on all paid Claude plans. That adds a sidebar in doc sheets and slides and connectors for creating and editing those files from Claude Anthropic announced a usage policy update as well, taking effect November 12th that prohibits sustained needless cruelty towards Claude in extreme cases.

[01:39:34] So basically how you are talking to it and what you are asking it to do,

[01:39:39] Paul Roetzer: there's a lot to unpack with that one. We are not gonna get into, my gosh, but that that ruffled some feathers. Kidding industry, let's say.

[01:39:45] Mike Kaput: Yes. Google on their end released Nano Banana 2.1, their updated image model in Gemini. In Google AI Studio, there are improvements they say in visual design, editing selected parts of an image, subject, consistency and natural looking [01:40:00] imagery.

[01:40:01] They also released something called Deep Review, an AI system that checks complex documents for logical consistency and technical errors. You can now get two free reviews per week and higher usage through a Gemini API Key. Paul, I think this is one you've been playing around with, they now have Google Playground, which lets users discover and play and create.

[01:40:24] Community made games, so like video games, computer games that you can, actually create through simple text prompts, which seems pretty cool. Yep.

[01:40:32] Paul Roetzer: That was pretty wild. It's in labs, but my son and I actually did it, and it is literally just like create a prompt of anything you can imagine game wise.

[01:40:42] Mike Kaput: Yeah.

[01:40:42] Paul Roetzer: And then you just, you see the gameplay in the module to the right and then you can just nice iterate on it. it's, it's pretty wild.

[01:40:51] Mike Kaput: That's cool. Google also began rolling out native markdown editing and collaboration in docs, along with formatted previews and drives so users can work [01:41:00] with markdown files without converting them to Google Docs.

[01:41:03] And the company also opened its synth ID detector globally in English, allowing anyone to check images, video, and audio for AI watermarks from Google and participating partners. Overall parent company Alphabet's AI Drug Discovery Company, isomorphic Labs is reportedly discussing new financing at a valuation of at least $40 billion.

[01:41:24] And Isomorphic Labs has joined the Biohub led virtual biology initiative as a founding member, which contri is means contributing to an effort to make biological data available for a AI models that predict and help treat disease. Meta has introduced Muse Wonder and Muse wonderful FULL AI models that are designed to remember context across conversations and sustained work on complex tasks.

[01:41:53] Muse Wonder is available through Meta's API and Muse Code Wonderful is available to Muse code [01:42:00] subscribers. Meta also released the weights for Muse Spark Open a multimodal model for coding and agentic work with a million token context window that can run locally across two Apple M5 Ultra Max Studios reflection and AI startup released Beam, which is a 501 billion parameter model for coding reasoning and AI agent tasks with model weights now available to download and public beta access through reflections.

[01:42:29] API Sierra and Meta announced something called Personal Agent Protocol and Open Standard for personal AI agents to interact with businesses under customer controlled access and business defined limits. Sierra has since published a draft of this with 35 additional design partners, including OpenAI, visa and PayPal.

[01:42:51] Elon Musk said Grok thought we'll use whichever backend model best suits a task that could include Claude Opus 5.5, mid [01:43:00] Journey Suno and other outside APIs. So a bit of a change from simply just using X AI models. Arena announced a $200 million series B at a $3.1 billion valuation and previewed their alignment index, which measures unauthorized actions, false attribution, and deceptive completion across AI agents.

[01:43:20] Netflix released something called Insta Docs. This is, their first one is AI Gone Wild. It's a documentary that reconstructs a July breach of Hugging Face by autonomous agents created by openAI's. national Compute reportedly plans to donate a hundred million dollars in computing pro credits to the Federal Genesis mission, which we've talked about on past podcast episodes, which supports AI driven scientific research.

[01:43:47] The company Odyssey says it connected a simulated fruit fly brain to Odyssey three. Its world model, allowing basically a simulated fruit fly brain [01:44:00] to fly through AI generated environments on its own, as if you didn't need enough sci-fi in the past stories. We've covered that one alone. Paul is kind of wild.

[01:44:10] Paul Roetzer: Yeah, it's as weird as it sounds. There's a video, like it's, and of course everyone's like, oh, we're totally in a simulation. Like this is not human brains work. It's just, yeah, like, yeah. If you needed anything else to. Yeah, think about this week. We've simulated fruit fly brains.

[01:44:28] Mike Kaput: Yeah. If you're trying to fall asleep or think about something else, don't, don't look at this.

[01:44:32] I would say, one quick final reminder. Go check out this week's AI pulse survey, SmarterX.ai/pulse. We'd love you to take 15 seconds to fill that out if you can. We'd love to get your perspective on how hiring is happening or not happening due to AI and what leaders are doing about AI capabilities as it relates to talent.

[01:44:52] Paul, thanks for breaking down two plus weeks of news, while we were at MAICON. Really appreciate it.

[01:44:59] Paul Roetzer: There's a lot. We gotta [01:45:00] not skip weeks anymore.

[01:45:01] Mike Kaput: Yeah, a hundred, a hundred percent. Yeah.

[01:45:03] Paul Roetzer: Takes like two or three times as long to prep for these things and then like, man, the product and funding section could have probably kept going on, but

[01:45:10] Mike Kaput: yeah.

[01:45:11] Paul Roetzer: All right. Thanks for organizing everything, Mike, and leading us through the conversation and appreciate everybody again that came to MAICON with us. We'd love to have you back in 2027. yeah. And have a great week, and we will be back next week with our regular weekly. Do we have a second episode this week, Mike?

[01:45:27] Mike Kaput: No, just the single.

[01:45:29] Paul Roetzer: Okay so, just the weekly this week. Alright. Thanks everyone.

[01:45:32] Paul Roetzer: 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 a SmarterX slack community.

[01:45:56] Until next time, stay curious and explore [01:46:00] ai.
Claire Prudhomme

Claire Prudhomme is the Marketing Manager of Media and Content at the Marketing AI Institute. With a background in content marketing, video production and a deep interest in AI public policy, Claire brings a broad skill set to her role. Claire combines her skills, passion for storytelling, and dedication to lifelong learning to drive the Marketing AI Institute's mission forward.

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