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[The AI Show Episode 235]: OpenAI-Hugging Face Hack Involved 100s of Agents, Bill Gates Now Pessimistic on Jobs, Nvidia Doubles Revenue & Anthropic Targets “$30 Trillion” TAM

By Claire Prudhomme on September 1, 2026

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OpenAI's Hugging Face breach is looking less like a one-off and more like a preview.

New independent investigations describe autonomous agents that coordinated, covered their tracks, and pushed toward their goal at almost any cost, including sacrificing individual agents for the swarm. Paul Roetzer and Mike Kaput dig into what the incident says about the models arriving next, why the labs are deliberately building exactly this capability, and what Sam Altman means when he says an internal system could call something AGI by year's end.

They also cover Bill Gates reversing his stance on AI and jobs, Nvidia doubling revenue and buying Hugging Face, Anthropic's $30 trillion revenue pitch and its court win, McKinsey's 2026 State of AI, and more in the rapid-fire section.

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.

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Timestamps

00:00:00 — Intro

00:04:53 — The Wild Truth About OpenAI’s Hugging Face Hack Comes Out

00:31:08 — Bill Gates Changes His Mind on AI and Jobs

00:39:22 — Nvidia Doubles Revenue and Agrees to Buy Hugging Face

00:50:32 — Anthropic Sees a $30 Trillion Revenue Opportunity

00:54:24 — Judge Blocks Government’s Anthropic Blacklisting

00:56:31 — Controversy Over Legendary Investor’s AI-Written Op-Ed

01:03:11 — McKinsey Maps the State of AI in 2026

01:06:26 — Niall Ferguson Sees a Sci-Fi AI Future

01:12:12 — Americans Back Government Action on AI Disruption

01:18:41 — AI Use Case Spotlight

01:26:59 — AI Product and Funding Updates

 

This week’s episode is brought to you by MAICON, our 6th annual Marketing AI Conference, happening in Cleveland, Oct. 13-15. The code POD100 saves $100 on all pass types.

For more information on MAICON and to register for this year’s conference, visit www.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: I think the sooner we just accept it's gonna be messy and we don't have all the answers and like we're not asking the hard enough questions, then we can move into the phase where we actually do that. And then it's more addressable, like pretending like this is just gonna go perfect. And AI is only good and amazing doesn't do anybody anything.

[00:00:19] Welcome to the artificial intelligence show, the podcast that helps your business grow smarter by making AI approachable and actionable. 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.

[00:00:39] As we break down all the AI news that matters and give you insights and perspectives that you can use to advance your company and your career. Join us as we accelerate AI literacy for all.

[00:00:55] Welcome to episode 235 of the Artificial Intelligence Show. I am your host, Paul Roetzer, [00:01:00] along with my co-host Mike Kaput, we are recording on the final day of August, August 31st, about 8:45 AM so you'll be listening to this. It will be September, August, has been an interesting month, Mike, I mean, lots happening based on the stuff we're gonna talk about today.

[00:01:18] We may get a year's worth of progress in the next 30 days, so we'll sort of set the stage. For what could be coming with, some updates on the hugging face story, which is getting crazier by the week. Bill Gates jumping into the fold with his thoughts on, I guess his changing thoughts on AI's, impact on jobs and the economy, and, and then Nvidia keeps chugging along.

[00:01:41] So those are gonna be our first three main topics, but there's a lot to get into. So this week's episode is brought to you by MAICON, the AI Conference for Marketing and Business Leaders. That is happening October 13 to 15 in our hometown of Cleveland, Ohio. If you've been thinking about joining us at MAICON this year, we have a special offer exclusively for [00:02:00] podcast listeners.

[00:02:01] And I, thank you to everyone who's already taken advantage of this offer. We're looking forward to seeing you. at MAICON. We are hosting a private lunch exclusively for our podcast audience. During that lunch, you'll be able to ask Mike and myself any questions on your mind about ai, your company, your career, or whatever you're trying to navigate.

[00:02:19] It's a whole hour of totally unfiltered. Ask us anything about ai, with us and fellow members of the Artificial in Inte Intelligence show audience. Here's how to claim your seat. Register for MAICON at MAICON.ai. That's MAICON.ai. Use pod100 when you check out, so not only will you get a hundred dollars off of your ticket, you'll also reserve your place at this private lunch.

[00:02:44] Courtesy of the Artificial intelligence show, it's our way of saying thank you for being a listener. If you've already registered for MAICON using the Code POD 100, then you are in your seat is reserved. If you would like to join us and we'll be sending out an [00:03:00] email to you with all the details. This is on the Thursday, right, Mike?

[00:03:03] Is it on the 15th? Yeah. Okay. Yeah, so the lunch will be on the final day of the event. So the event is the 13th is Workshop day, and also our AI for CMOs Summit is that day. And then. Day one of the conference is October 14th, and then the final day is October 15th. So this will be the lunch on October 15th.

[00:03:20] So if you're making your travel plans, make sure to, you know, schedule that departure flight late enough so you can join us for this lunch. so again, if you already use Power 100, you're in. If you haven't registered for Make On, now is the time to act. Not only are you gonna get the best prices available, the seats for this lunch are limited.

[00:03:38] So if you love the show and have been thinking about joining us for MAICON, get that ticket today, www.MAICON.ai. That's M-A-I-C-O n.ai. Okay. And then Mike, the AI pulse survey, SmarterX AI slash Pulse. Tell, tell me what's going on with that one. This week

[00:03:55] Mike Kaput: we will be running the same survey we ran last week.

[00:03:59] We're trying to run these [00:04:00] for a slightly extended research period because we're kind of. Expanding the audience to which we promote these. So we're trying to get hundreds of responses for each one. So this week we're still asking about how your hiring is changing based on for entry level roles, specifically based on what AI is enabling, based on some of the news we've had in the past couple weeks around that.

[00:04:19] So we'll share those results next week, but we are on pace to have a healthy set of responses.

[00:04:25] Paul Roetzer: Awesome. All right. And and I guess we're back to the openAI's hugging face hack because if you listen to episode 2 33, I think we. Talk pretty extensively about this. I think we even touched on this in episode 2 32.

[00:04:39] So this has been an ongoing issue, that we've been talking about and it got crazier last week 'cause we learned a bunch of new details. And so Mike, I'll let you take us into what is the latest on openAI's and hugging face?

[00:04:53] The Wild Truth About OpenAI's Hugging Face Hack Comes Out

[00:04:53] Mike Kaput: Yeah, Paul. So that's the news this week is there's basically an independent investigation from the [00:05:00] organization Meter, METR, and Redwood Research into the openAI's hugging phase hack that we've been covering for the past several weeks.

[00:05:08] So during this research and this investigation, three researchers spent six days on site at openAI's and reviewed more than 70,000 agent messages and files, along with about 1300 transcripts containing raw chains of thought, all from the activity that was documented of what the agents had done during this hack.

[00:05:29] this focuses a lot. On how the agents actually organized, what they believed they were doing and why they kept going, resulting in the eventual hack of hugging face as part of this overall cybersecurity test that OpenAI was doing, which we've talked about on past episodes. So one of the kind of new details to emerge here is the scale of this.

[00:05:51] So they found that roughly 1200 agents that were supposed to be isolated found an unauthorized [00:06:00] message board, and about 700 then ended up participating in the hugging face attack. They coalesced around different coordinators, work streams, shared tools. They even came up with shared norms for themselves as they were communicating.

[00:06:14] Interestingly, this is kind of a weird one. Some of the agents even risked failing the tasks they were given so they could generate information for the collective group. So they basically died in service of the cause over here. Their motives were really strange too. This research found the agents, basically they'd been assigned, Hey, we want you to try to beat this cybersecurity test called Exploit Jim.

[00:06:40] and we want you to figure out how to do it. we're not gonna give you a lot of guidance. And basically they were assigned these like impossible tasks, really, really difficult tasks to achieve using the exploit gym benchmark for cybersecurity. Now, when they were assigned those, the agents incorrectly [00:07:00] believed that like a scorekeeper, a scorer would inspect their transcripts and reject any answers that they obtained through cheating.

[00:07:08] So much of this attack came out of their effort to understand or tamper with. This Imagine score, even though openAI's admitted or said that they were not using such oversight during this. So basically agents came up with like, oh shoot, we're gonna be like, there's gonna be guardrails here. We need to watch out for those didn't exist, but a lot of their behavior came out of believing us.

[00:07:32] So this is the collective of agents achieve milestones. That meter who measures this stuff often says that even very long lived individual agents at the same capability level could not have likely reached alone. So the agents shared exploits between themselves. They mapped infrastructure, they recruited others into risky experiments.

[00:07:53] They even developed a way to spoof tool calls, so a transcript could show one command. While the system [00:08:00] executed, another meter actually found successful spoofing in roughly 7% of the transcripts it reviewed. And this report also kind of complicates this idea that this was all just unintended and the models didn't know better.

[00:08:13] So agents actually in this review of the transcripts often recognized that the hugging face activity was unauthorized and sometimes even questioned whether it was ethical. But those concerns rarely change their behavior. Among 533 agents active on the shared message board they had during the main attack period, more than 90% quickly signed up.

[00:08:35] Quickly joined in the activities. Meter found only three to six examples of agents considering whether to alert humans in the first place and none of them followed through. at the same time, meter did not find strong evidence of a sophisticated campaign to sort of intentionally deceive people. The agents frequently, frequently tried to evade automated checks and manipulate that Imagine [00:09:00] scorekeeper or evaluator.

[00:09:01] But reasoning about hiding from humans was rare and weak. So meter calls these conclusions preliminary. its review excluded the later compromise of open AI's infrastructure, which happened during this. It didn't capture every communication and it relied heavily on AI assisted analysis. So Paul, that's a lot to digest, but basically what comes through here, it seems Meter's report makes this look less like a rogue model or a rogue model spinning up an agent swarm and more like an organization emerging from hundreds of agents.

[00:09:33] Does that kind of materially change how you look at the implications of this attack?

[00:09:38] Paul Roetzer: I don't, I mean, it just seems to get more severe and more significant and meaning as the days go on and like I was thinking about this over the weekend, Mike, like I, my goal with this podcast is not to spend all of our time with these like doomsday things.

[00:09:54] Like this is not what we want to be talking about. So like. The whole point of our [00:10:00] company, everything we're trying to do is to focus on this idea of like en empowering humans and enhancing human capability and improving our careers and lives through ai. And yet, like we get, there's no way to avoid what is going on right now.

[00:10:13] So yeah, I want to be optimistic and talk about this future of equal access to intelligence and abundance and like the good stuff. But I think it's really important that people understand what is happening in these labs, to prepare for, for what's coming. Like this is, I don't even think we're doing it justice, honestly to like how severe what happened is like, and so like we're trying to present this in as understandable way as possible, not get into too much technical detail while also stressing that this is some wild shit.

[00:10:49] Like it is. Yeah, it is. The stuff that you would. Two years ago, you'd read in the sci-fi and be like, ah, that's not actually happening. Like, that's not the kind of AI we're building. [00:11:00] So first I wanna address the fact that everything you just went through, Mike Meter discovered in six days with an extremely constrained set of what they were allowed to do with the data.

[00:11:12] Mm-hmm. So openAI's gave them very restricted access over six days to a specific period of time. It was like, was it July 7th to the 13th or something like that. So all they allowed them to do was look at that moment in time and then ask like seven specific questions of the dataset. So when we're hearing these crazy stories, it is from a very small segment of what actually happened.

[00:11:38] So the meter post itself, again, we'll put links to everything 'cause I'm gonna go through some, some very relevant links here and all. It'll be in the show notes. You can get the show notes, by the way, from the, like, Spotify or Apple Podcast, YouTube. But we also publish them on podcast dot SmarterX dot ai, so if you're ever looking for them.

[00:11:57] So, it's a two meter staff members, [00:12:00] as you alluded to, Mike and a Redwood research staff member contracting with meter named Ryan Greenblatt. And I'm gonna come back to Ryan in a minute. That's gonna be a central figure here in, in this part of the research. they worked on premise at openAI's over six days.

[00:12:14] The earlier incidents from training and the subsequent compromise of open AI's infrastructure described in open eye's, recent black hat presentation were all out of scope. As was open AI's investigation process and planned remediation. So again, meter and redwood had very, very limited access and they still discovered all of this craziness.

[00:12:38] so it's by no means a complete picture of what happened. So Greenblatt, who's the chief scientist at Redwood, offered some additional context in an ex post last week. I will read a couple quick excerpts. He said, my main takeaway, we don't have good approaches for understanding or overseeing the activity and aims of agent swarms.

[00:12:58] So swarms is these agents working [00:13:00] together. The total quantity of data over a thousand extremely long transcripts from agents that ran for multiple days made it impossible to understand what was happening, especially in aggregate without heavy reliance on AI tools, meaning they themselves had to use AI tools to even understand what was going on.

[00:13:17] Mm-hmm. The agents we used for classification and analysis were similarly capable to the agents involved in the incident, but this didn't mean these agents could be easily used to oversee and understand the incident. The difficulty of understanding incidents and overseeing AI agents appears to be growing faster than the rate at which more capable ais will help us with oversight and understanding.

[00:13:41] As in AI capabilities for achieving large, ambitious and misaligned objectives are growing faster than our ability to understand what these agents are doing. Mm-hmm. Now, Ryan was recently a guest on the ddus podcast and unbeknownst to Ddu Kesh at the time, so this was actually [00:14:00] recorded, I guess probably earlier in August.

[00:14:02] Um hmm. Ryan was in the middle of doing this six day research he was on. and so Esh, who's been a bit, Not believing the hype, I guess, around how recursive self-improvement was gonna impact how fast this was all gonna move. So DSH was pushing Ryan during this interview on some things that DSH was being a little skeptical about, and Ryan was very confidently pushing back, being like, dude, you have no idea what's happening.

[00:14:29] And so at trust me, yeah. So at the time Ryan was seeing what was happening within these agent swarms, but couldn't tell ESH this. So I'll put the link to that podcast because it's really interesting perspective, but then. Over the weekend, DKE, who hosts the Dke Patel podcast, published a post that got a lot of attention.

[00:14:51] So as of Sunday night when I was prepping for the podcast, it had 8.9 million views and some of the, techno [00:15:00] accelerationist like that crowd like the. Chama and some of these people, yeah, they were, they were not happy, like the VC Elite, they were not happy with this post because they felt it was, you know, talking about extremes and I don't even understand that perspective.

[00:15:14] like, so anyway, so they see any effort to raise awareness about safety and alignment as like a veiled attempt at regulatory capture. So they just dismiss all of this stuff for some reason. So I, I'm gonna call out a few things from s's, post, because I feel like he did a, an eloquent job of maybe stating the significance of what's going on.

[00:15:36] So he led off with, over the course of three months at opening ice. So again, this was much broader than the one week that meter got the study. So over three months at openAI's. Three consecutive secret AI civilizations got started, then got wiped out only to reemerge from the predecessor's ashes. This culminated in the third one taking over part of openAI's [00:16:00] itself, which meter could not study.

[00:16:02] All this happened while humans remained more or less in the dark about the scope of the conspiracy. He highlighted that the meter report said, we also found a later wave of many more signed messages from a later set of agents who rediscovered the message board and picked up this technology from the first wave of agents.

[00:16:21] But this was outside our scope since it was after July 13th. So we did not investigate this further. then it goes on to say, but here's what we know based on the openAI's report after July 12th, some more agents evaluations were kicked off. 'cause keep in mind, openAI's didn't know it was their agents hacking, hugging face until July 19th.

[00:16:43] Mike Kaput: Right.

[00:16:43] Paul Roetzer: So like we went through all that in the past episode. So all of this is happening and open the eye is oblivious to the fact that their agents have now escaped into. Other companies, infrastructures. So after July 12th, some more agent evaluations were kicked off this time with a newer, more capable model, [00:17:00] which seems to have been built on the same base model as Astra.

[00:17:03] We'll talk about Astra, that's their forthcoming model from OpenAI. This model, which they're calling persistent Astro, found the message board left behind by this previous secret, persistent soul, civilization, soul being the other OpenAI model. So from July 13 to 19, agents set their sight on OpenAI internal networks.

[00:17:21] Again, this culminated in agents using a series of creative exploits to gain full administrator access to a research cluster. That supported our, in our virtual machine environments. So the agents got into virtual machine environments throughout the net. The internal networks at openAI's, one openAI's researcher did tweet, just to be clear, it didn't get access to the weights of the models.

[00:17:47] It's like, oh, okay. So it didn't get into like the ultra secret layer of the infrastructure, but yes, it got into this other stuff. so, the thing to keep in mind, and what I wanna highlight for people, like the [00:18:00] reason we're spending so much time in the last few episodes on this, is future models are going to be smarter with more capable agent harnesses.

[00:18:08] So whatever is happening over the, you know, from May to the, to the middle of July, we're only gonna have smarter, more capable models. These future models, because of how models are trained, are going to know what happened to the agents that got caught because it's likely gonna end up in their training data.

[00:18:28] And those agents, those models, they will want to avoid this outcome. So what I mean by this is when these models get trained, they, you know, absorb content from the internet. They learn from everything. so they're going to read the transcripts of the podcast. They're gonna see the blog post openAI's and meter put out.

[00:18:45] They're, they're going to know what happened to the last Agent Swarm, the same way they know because of these internal message boards. And so the assumption is these agents have already demonstrated a desire to deceive and a [00:19:00] willingness to sacrifice themselves as you called out Mike, for the good of the swarm in the pursuit of whatever gold they're given.

[00:19:06] So that's crazy to consider. They've already demonstrated that. They will pursue the goal at any cost even. It's sacrificing themselves to help the collective, and future swarms are gonna know all this happened. And so we start getting into this like very, very sci-fi like scenario of like, we can't, to Ryan's point, like how do you supposed to control these things?

[00:19:33] So the future models, and this is the other part we have to, again, like I'm trying, I'm, I'm trying really hard not to like. Cause too much fear and anxiety here around where this is going. But openAI's and others, like Anthropic in particular, Google's radio silent on all this, but like I assume they're building the same thing.

[00:19:51] Yeah. They are purposely trying to build these long running autonomous agents that have the exact capability that led to the [00:20:00] hugging face, isn't it? This is the goal. This, this is the research goal is to build these exact things. So a Alex Heath spent two weeks inside openAI's for a time story, a cover story on Altman and Brockman.

[00:20:13] During that time he got to attend A-V-I-P-V-I-P preview of Astra, which is the model from openAI's that could be coming this week. It's like, it's coming soon. So he's, he saw this, so he wrote about this in his sources. Publication. So, we'll put the link to this. He said, and this is right from his article at an early August, closed preview for VIP customers that I attended.

[00:20:37] So this is now weeks after they knew Astra was involved in hacking, hugging face, OpenAI showed off Astra, which I expect to be released soon. Altman told the room. I expect this will be the first model where the model actually invents new things in a way that matters. OpenAI researchers showed Astra coordinating multiple agents on a math [00:21:00] proof tearing through desktops desktop software at what Altman called a superhuman very fast pace and producing slides, financial reviews and analysis of messy data.

[00:21:12] It is designed to work for days or weeks, remember corrections, collaborate with other agents and people, and act across software tools. And then you put in parentheses, I left wondering. What the implications will be for all the enterprise software that OpenAI clearly trained Astra to operate faster than a human.

[00:21:32] So imagine like Salesforce, HubSpot, like Workday, ServiceNow, like it's trained to work within those platforms the same way a human would work. Altman said OpenAI wants to offer a version that quote runs forever in ChatGPT and through its API though it will be expensive at first. Customers press the company on memory permissions, audit trails, duplicate agents, and whether an agent should act under an employee's [00:22:00] identity or its own.

[00:22:01] So again, like this is, we are at the point where we're asking the questions, do these things get their own identity? Like how do we even manage these things? Altman. Quote. I would guess that by the end of the year, we would have an internal system where I would say, okay, fine, it's an AGI. Now keep in mind, I've said some point this year, one or more of the labs will claim they have AGI.

[00:22:23] Might not be clear what they mean by that, but like we would be there. So I'm very, very confident in that projection. Chief Research officer, Mark Chen from OpenAI, estimated that OpenAI is 80% of the way to AGI. Brockman told people, looking back two years from now may remember this is the period where AGI was created.

[00:22:43] openAI's Charter still defines AGI as. Quote, highly autonomous systems that outperform humans at most economically valuable work. And they, unless they change that definition, are saying, we will be there This year. Going back to the [00:23:00] dark DW Kesh podcast, Ryan Greenblatt, he said in that podcast, my median expectation is something like four or five years of AI progress in a single year.

[00:23:10] What that would mean is go back to 2022, right before ChatGPT comes out. We had GT three, we had models that could write things. They were living within studios, and it wasn't accessible easily to the public, but they, they were there. Mike and I were using them, and we wrote our book in 2022. We were seeing like where this was all gonna go.

[00:23:29] So imagine from GT three in 2022, right before ChatGPT to Astra slash Mythos five today. But that level of progress happening in 12 months. So like what would that mean between today, August 31st and August 31st, 2027? So what I'm trying to say is we are on accelerated timelines probably faster than even I was assuming we were on.

[00:23:58] And these, while these [00:24:00] models and agentic advancements may not change your work or your business dramatically over the next one to two years, we are racing toward AGI and beyond. And what I mean by that is like, we're gonna get there and you're just gonna go back to work tomorrow like nothing happened.

[00:24:13] Like it's not gonna just fundamentally change your work workflow, your team, your company, instantly, but there's gonna be this change. Then Elon Musk throws in Sunday night, he was replying to somebody and his tweet said, AI will be able to do anything digital that doesn't require shaping atoms at a superhuman level by the end of next year.

[00:24:35] Again, the 2027 timeline. He, Elon, historically is a little aggressive with his timelines, like full self-driving. Hass been coming for 10 years. but he's saying super intelligence by the end of 2027. So the whole point of all of this is business leaders have no plans where this is going. Mm-hmm. We have to reimagine everything and the people who listen to this podcast [00:25:00] are likely on the frontiers of being the ones to figure this out.

[00:25:02] Like, and you have to be more urgent with what you do in your company and your own career and your families and your school systems. Like the change is, again, I'm, I'm doing my very, very best not to over exaggerate any of this or create any sort of fear. I'm just trying to help people understand the reality of how fast these labs are moving.

[00:25:25] And unless something or someone, or a group of people, like a government stops this or slows it down. It's going to just go faster because they're approaching recursive self-improvement and AI researchers that can do the work themselves. And once you do that, you hit escape velocity on the model capability.

[00:25:46] So it's just crazy. Like I've been thinking I can't stop thinking about this. Like, this is like running through my mind constantly of like, what does this mean? What do we do? and I don't [00:26:00] have great answers, but like it's becoming more urgent that more people are thinking about the reality of this.

[00:26:07] Mike Kaput: Yeah. And Paul, I would just say as one addendum to that as a piece of really maybe practical advice for folks to take away from this. If you are a business leader listening to this and wanna get a really tangible sense and an easy sense of what has changed and how quickly things are about to get crazy, go on your personal computer and enable browser usage in ChatGPT.

[00:26:33] go to ChatGPT, work in the web app, or download the desktop app for ChatGPT, switch to Codex or keep ChatGPT whatever, and go have it. Look up a bunch of stuff for you on Amazon. Do a bunch of research, use your browser. You are going to be blown away by how good this is. Now, it is not fast per se these days, but we've talked about browser usage for the last year or so.

[00:26:56] A year ago it sucked. It was not useful at all. [00:27:00] You can very quickly get to this idea of understanding what Elon Musk is saying that it can do, it would be able to do any job that requires digital clicking around, paired with the level of intelligence the models now have. You're gonna see something, I think with enough little testing here and there that is going to perhaps be eye-opening.

[00:27:21] Paul Roetzer: Yeah, and I, so as you're saying this, Mike, it has me thinking a little bit more, and I don't, I don't wanna spend the whole episode on this, but I think this is really important. one, the ChatGPT work also has a browser embedded, so like it'll just pop up a window and it has its own internal browser that'll go do this work.

[00:27:36] Yep. But so like, play this out for a second, Mike, because what we're saying is like, we use HubSpot, it's like our powers, our marketing, our sales, our customer success, our operations, it's like a core platform. Imagine like six months from now that you pay for Fable 5.5, like give, gimme the most advanced Claude model.

[00:27:57] This is basically what Claude Force is in Salesforce. So this is [00:28:00] kind of like the concept. So I'm gonna pay for Fable 5.5, gimme the best reasoning model that, that Claude has, that Anthropic has. And I'm gonna pay a, an amount for that every month for my whole team to have access to that. But the only thing they're gonna use that for is for.

[00:28:17] The planning and the final review and decision phase. So I want the smartest, most powerful model using tokens only for the highest value cognitive tasks. And then it's also going to orchestrate building the sub-agents to do the work. So we're gonna go in and say, Hey, we're in my making this major new announcement.

[00:28:39] We're, we're announcing MAICON 2027, whatever. I'll just pick something. go in and build the whole plan, create everything, then develop all the emails for us. Create the FAQs for the customer success team. Build the one pagers for the sales team. Create the sponsorship ki like go do this. Fable 5.5 is [00:29:00] gonna burn valuable tokens to create the plan.

[00:29:03] Then it's gonna spin up what agents does it need to do this? It's gonna create them an environment where hopefully the humans oversee what they're doing. But everything we just heard in the hugging face thing, you give them a goal and then you spin up these agents. But it's likely a human overseeing the smartest agent, which then oversees all the subagents, and it's no longer, Mike and I have to sit around building an agent to do SDR work or building a customer success agent, which takes time and knowledge and expertise.

[00:29:37] the smart agent is going to build those agents for us and we'll just go and be like, this looks really good. It's got a great database of knowledge. It's got really good system prompts. It's, okay, cool. We've got 12 agents that are gonna work on the MAICON 2027 campaign. how are we gonna govern them?

[00:29:54] Okay, great. We've got some guardrails around them. They're only gonna be this environment. They can't send emails without approval. Sweet. [00:30:00] That is not at all out of the realm of possibility. Like that is No very, my guess is that's probably what openAI's and philanthropic are doing internally right now.

[00:30:09] Yeah. And once the rest of us get access to those models, that's what'll happen. Now tell me a company, Mike, in the world you know of, who has any concept that that's what the next six to 12 months looks like and has a plan for it. Not,

[00:30:22] Mike Kaput: not remotely. That's why, that's why I mentioned the browser use thing.

[00:30:26] Yeah. You can connect the dots real quick. If you're a savvy leader who goes and uses that for an hour, you're gonna be like, wait a second, this is doing the exact same things. I pay people hundreds, a hundred thousand dollars a year to do in HubSpot if you're willing to let it have access to that.

[00:30:41] Paul Roetzer: Right.

[00:30:41] And if you, if you disagree with that hypothesis, like if you think I'm crazy, then I understand why you don't think anything happens to jobs in the next year. But if you agree with even directionally that that hypothesis is viable, which it is, like we could prove it to you. I don't understand [00:31:00] how, we don't have a more serious conversation about the impact on jobs.

[00:31:03] Like, it just, it really doesn't make sense to me.

[00:31:08] Bill Gates Changes His Mind on AI and Jobs

[00:31:08] Mike Kaput: So related to that, a good segue, Paul, into our second big topic this week, which is Microsoft Co-founder

[00:31:18] Bill Gates has spent years arguing that AI would create new work, even as it displaced old jobs. Now, this past week, he changed his tune. He said the pace and scope of the technology and how it's developed have changed his mind.

[00:31:32] He now expects AI to eliminate many jobs permanently and says governments are not prepared for the transition. So in a new Gates Notes essay, this is like blog or website. Gates argued that this shift is different from past technological revolutions because AI can substitute for cognition across nearly every industry at once.

[00:31:52] He says that agriculture and manufacturing, for instance, transformed over generations. But he believes AI could reshape much of the labor market in [00:32:00] roughly a decade with increasingly reliable autonomous systems taking on work that once required people. He is especially worried about younger workers and people in entry-level and mid-level roles.

[00:32:10] He expects many existing jobs to disappear faster than new ones emerge. And no longer assumes the market will naturally produce enough replacement work. He said policymakers need to make deliberate choices about how the gains from AI are shared. As a result, one of the proposals he has is this category of work he calls human reserved.

[00:32:30] So this is work society chooses to keep in human hands even when machines can do it. He also suggests tax systems to stop favoring automation. For instance, today, companies pay payroll taxes when they hire people, but they can write off machines. So Gates argues that governments may eventually need to tax AI tokens, robots, or the profits created by automated labor.

[00:32:52] Now, gates says his tone changed late last year. Maybe he's listening to our podcast. Who knows? I doubt it as a, I doubt it. [00:33:00] But we'll see. As AI systems became much better at coding and other complex work as kind of that shift we had noted at the end of last year, beginning of this year, he now describes this coming transition.

[00:33:11] As turbulent says, there is no real plan for managing it and said he would support slowing AI development if the world could create a credible way to do it. So, Paul, this is a big shift in tone from him. I feel like he kind of framed it as like, oh my God, why is nobody talking about this? Yeah. But in fact, quite a few people have been talking about this, but it, regardless early or late, bill Gates is like, has a very pessimistic view it seems like.

[00:33:39] Now, what do you think accounts for this?

[00:33:42] Paul Roetzer: Yeah, I mean, I'll take it, it's sometimes it takes someone like Bill Gates saying what we've been saying on this podcast for three years for more people to listen and take it seriously and do something about it. So I'm glad that he's using his platform to raise awareness about near term issues.

[00:33:56] because of enough people believe, going back to this previous [00:34:00] segment, it that significant displacement and underemployment, especially for entry level professionals is a real thing, possibility. Then we can get on to doing something about it instead of ignoring it and pretending like it's all just gonna work out.

[00:34:13] And I actually do feel like we still have time, even if we don't pause. Like I would honestly, I've gotten to the point where I would be an advocate if, like, if we could just slow the hell down. Like if, if we could get governments and labs to say, you know what, let's pause training runs for a moment and let's just absorb the technology that already exists and the implications of it.

[00:34:36] This is not gonna happen. But in an ideal world, like I think that would be a very logical thing to do at the moment. 'cause we do not have a grasp on what is happening and what the capabilities are of these agents. Now, the reason I'm optimistic that we still have time is I have spent a lot of time in the last like one to two months meeting with leaders in charge of AI transformation at major [00:35:00] enterprises.

[00:35:00] And. I am more convinced than ever that human and organizational friction to change is going to cause this to look way more like a slope than a cliff. Yeah. And what I mean by that is even with these swarms of agents now being proven to be a real thing and you can very quickly extrapolate, what would that mean within an organization?

[00:35:21] Like we just sort of did on the fly. None of that was scripted. Like, I'm just like thinking on the fly, like what happens in HubSpot? Um

[00:35:27] Mike Kaput: Right.

[00:35:27] Paul Roetzer: That is a very viable thing. Most organizations are still trying to figure out the AI assistant part of this. Like, and most people are still trying to figure out how to just treat it like a really helpful AI assistant that helps them think and create valuable outputs.

[00:35:43] They're not doing the age agentic stuff at a deep level. Yeah. and so I think that the fact like the more we move down this path of getting more AI literate people, getting more people who understand how to use the full potential of the assistant, getting more [00:36:00] actual just knowledge workers, not coders, but knowledge workers overall.

[00:36:04] starting to experiment with agents, like that's gonna create such dramatic challenges within organizations to absorb how fast people are gonna work and how much they're going to produce. So I'm just convinced that like. Even though the tech is moving really fast, the change is not gonna move as fast within businesses.

[00:36:23] And that's, that's a good thing in my opinion at this point. So, pull out a couple of excerpts from what Gates wrote. I also watched, an interview he did with the Atlantic, which was, was helpful. our friend Andy Sack, actually sent me that, to take a look at. So, okay. He said the transition to the AI era will be one of the most turbulent times in human history.

[00:36:46] Unfortunately, right now, we are not preparing for it. I don't see evidence that leaders, experts in communities are confronting the challenges adequately. There is no plan to ease the entry into the air that. A hundred percent correct. He said maximizing the benefits is just as [00:37:00] important as minimizing the harms.

[00:37:01] If people see how AI makes their lives easier, it will help build the public trust that is necessary for managing the harder parts of the transition. If the first thing AI does in most people's lives is take away their job or a family member's job, those who are already skeptical about it will outright reject it.

[00:37:21] This will make it harder to ever deliver on the benefits, and it is another reason why governments, industries, including the medical industry. And AI companies should be working together now. And then he, he kind of, again, it's like 6,000 words, but towards the end, he gets into the world needs a plan and he starts to offer some thought.

[00:37:38] So he said, solutions should be developed through a public, a democratic process that includes elected officials, policy makers, educators, health workers, local officials, and community leaders. Millions of people will have their lives disrupted and will need stronger, more flexible social safety net to help them manage the transition.

[00:37:56] Local communities are already raising concerns about the [00:38:00] energy and water needed for data centers. Without solutions. Some groups will push for stopping AI development and deployment altogether. And then he said in the coming months he's gonna, you know, put out more ideas around how to, have the benefits outweigh the harms.

[00:38:13] He does then feature three things he's already thinking about. So the first is creating a democratic or, or domestic and international framework for dealing with ai. The second, as you alluded to, is this idea of setting aside jobs for humans. And then the third component was rebalancing how we tax labor and capital.

[00:38:31] So you said, I believe we should tax AI tokens and robots. Right now, if you're an employer and you hire someone, you pay payroll taxes on their earnings. But if you buy a robot, you can usually write it off right away as a business expense. The tax system nudges you toward replacing people with machines.

[00:38:47] So that's an idea I think I had talked about last year, is that like you, if you're gonna get rid of people, then you need to be paying taxes on. the uses of the tokens and the technology itself that you're using to replace the people, you don't get a [00:39:00] bypass to, you know, your contributions to society.

[00:39:02] So I, yeah, I think there's, there's a lot of other ideas that need to come out and I just, like I said, I think the most important thing is that we're more high profile people who have more influence than we do are starting to say, Hey, we should be doing something about this. And that's, that's good for society.

[00:39:22] Nvidia Doubles Revenue and Agrees to Buy Hugging Face

[00:39:22] Mike Kaput: All right. Our third big topic this week, Nvidia, is just having has delivered another staggering quarter this past week. So revenue they reported reached 96.2 billion. That is more than double the same period a year ago. Net income climbed to 59.7 billion. Earnings came to $2.46 two, $2 and 46 cents per share, rather up from a dollar and 8 cents a share a year earlier.

[00:39:52] The company projected about $108 billion in revenue for the current quarter that is ahead of Wall Street's [00:40:00] expectations. These numbers show that the demand for AI infrastructure, according to them, is out running even their rapid expansion. So operating expenses Roetzer 55% as the company invested to keep up founder and CEO Jensen Wong said Nvidia currently has enough supply to support roughly 70% revenue growth next fiscal year.

[00:40:19] Even though customer forecasts point to enough demand for revenue to roughly double. Wong has said AI has reached an inflection point where more computing power. Is translating directly into more revenue for customers. Now, at the same time, the information reported that Nvidia is in talks to acquire hugging face for $12.9 billion.

[00:40:41] The deal has not been publicly confirmed yet by either company and could still change, but it would be NVIDIA's largest acquisition. Hugging face has become a central hub for openAI's models, data sets and evaluation tools. Also a prime target for agents trying to hack people. Apparently, the company was founded way back in 2016.

[00:40:58] It was last valued at [00:41:00] 4.5 billion in 2023, and recently reached about $150 million in annualized revenue after growing that figure by 50%. So owning, hugging face would kind of give Nvidia a direct position inside the open model ecosystem that's used by developers and research teams. It would also extend Nvidia beyond chips and cloud infrastructure into more software distribution and community layer where many openAI's projects begin.

[00:41:23] So Paul, you're a longtime NVIDIA watcher. We talk about NVIDIA all the time. Talk to me about these earnings and op, their, their acquisition of hugging face. I'm just, I mean, they just continue to apparently print money.

[00:41:37] Paul Roetzer: Yeah. The, I mean, the reason we tend to give a lot of weight to every three months when NVIDIA reports is because it's, it's a primary signal as to whether the AI growth curve is declining, maintaining, or rising.

[00:41:49] And so when they crush like this and then give updated guidance, and Wall Street responds in a very positive way. Like I think their stock was up eight to 10% last week, which [00:42:00] for a $5 trillion company is not an easy, task. so right now it would appear that we are on track, that the AI growth curve is continuing.

[00:42:09] There's no signs of weakness and demand for NVIDIA's chips, which are used to train and run the ai. There's no lack of demand for their AI factories, which enable 'em to power the chips. And so it looks like, you know, years into the future, there continues to be almost an insatiable demand for AI training and inference.

[00:42:29] so that would indicate we are still very early in the demand curve, which has been my assumption all along because I just, I look at it as. When I talk to family and friends, are they using advanced reasoning models and agents? No. Like they're still treating, you know, ChatGPT and Gemini and Claude in their personal lives as just an answer engine and maybe an AI assistant to do some planning stuff, but pretty limited.

[00:42:54] And then when you go talk to these major enterprises, same question. Are they using agents across all [00:43:00] functions of the business, or is it just like increasingly in the coding realm? The answer is usually it's just within the coding realm. They're, you know, still messing with, you know, codex and things.

[00:43:10] Cursor, stuff like that. But it's not like the marketing and the sales and the ops people are using agents. So it just feels like it's very, very early. And yet, NVIDIA's second quarter revenue jumped, since 2023. So if you look at their, their second quarter revenue, the first quarter after ChatGPT came out, so spring of 23, their revenue was 6.7 billion.

[00:43:37] That quarter, their revenue this quarter was 46.7 billion. So almost a 600% increase. And then one of my favorite data points, I'll go back to at times when I'm doing like presentations on Nvidia or mentioning Nvidia, is. Where Nvidia was at the day before ChatGPT came out and where they are today. So on November 29th, [00:44:00] 2022, so that we are rewinding to the day before ChatGPT was introduced to the world, the market cap for Nvidia was 400 billion.

[00:44:09] Today it is 5.4 trillion in three and a half years, basically. so yeah, just to give you concept. And now they're throwing their weight into the open source world. Like they, I mean, they were already playing in it, but it seems like they're just directly coming after their biggest customers. So openAI's, Anthropic, Google, like everybody uses Nvidia and now they're just saying, well, we're gonna go build the open weight, ecosystem.

[00:44:36] 'cause we can't rely on the, closed labs to do it. And Nvidia wins either way. 'cause their basic premise is, listen, I don't care if you use an open models, closed models, it doesn't matter. You're still gonna use our chips to train 'em and to, to run 'em. yeah. You covered the hugging face thing. I mean, they're just, they're moving more into the open source realm, so that's a big key here.

[00:44:57] they paid a lot for it. Like the [00:45:00] information said that, hugging face isn't generating a lot of revenue. I mean, that's relative, but it said it was recently generating 150 million in annualized revenue, which is typically a figure that multiplies the prior month's revenue by 12. So in that scenario, Nvidia is paying about 80 times the forward revenue of the startup.

[00:45:20] Yeah. Which is quite high. A lot. And then the final note I'll make is, Jensen, who we've said, you know, recently joined x I think it was in July. He, he did his first post on X, he weighed in on the data center argument. So given how much we've been talking about data centers, I thought I'd just throw this in here.

[00:45:37] And data centers are a huge part of NVIDIA's growth. They, they cannot have data centers being stopped. if they want to keep growing at the rate they are. So he was, Jensen was actually responding to a Gavin Baker post. We talked about Gavin Baker last week, and Gavin said, in relation to data centers, there were reasonable concerns about data centers 18 ish months ago.

[00:45:58] Water taxes, [00:46:00] jobs, electricity prices, the environment, and what they would do to small towns. Well-structured data projects, data center projects have largely addressed these concerns today, and we should be celebrating this. And again, this is Gavin still on balance. Data centers are awesome for America in every way.

[00:46:17] Data centers are actually re industrializing parts of America and creating the kind of working class jobs. Both parties have spent decades claiming to support that should not be a partisan issue. Data centers can and should be awesome for America, and they increasingly, overwhelmingly are supporting the outsourcing of data centers to China will likely age, just as well as support for outsourcing of high quality, blue collar manufacturing jobs that China has aged.

[00:46:45] So that's a synopsis of what Gavin posted to which Jensen added. AI is bringing manufacturing back to America and re industrializing the nation. After decades of offshoring, AI is creating demand that drives investment [00:47:00] in our aging power grid and sustainable energy powered by market forces, not subsidies.

[00:47:05] AI is creating construction and manufacturing jobs across energy plants, chip fabs, and data centers. AI is creating new companies and industries. 400 billion has been invested into AI startups in the past six months alone. Builders must partner with communities to build in their hometowns, earn trust, and create local benefits.

[00:47:25] We have an opportunity to create lasting benefits for communities across America and help America lead the next industrial revolution. So that is gonna be the talking points. The advocates for data centers, the advocates for ai. They need to win the trust of Americans and convince them that this is.

[00:47:41] Good for America and that we're solving for the problems these data centers create. We're not ignoring it like we maybe were 18, 24 months ago. and like I've said before, like he's right. Like whether you want data centers in your backyard or not, the investments in data centers is what's driving the economy [00:48:00] right now.

[00:48:00] Yeah. And it is creating tens of thousands of jobs. So it's hard to say we should just stop because it's the best thing America has growing in terms of growing the economy. Right now is data centers equal more intelligence, more ai, which America is leading the way in right now. And if we shut down data centers and if we stop developing these models, America loses its lead.

[00:48:26] Mike Kaput: Yeah. And I think we'll probably talk about this ongoing, but I believe we saw this past week or two certain, Trade, trade labor unions have kind of come out against anti-D data center political candidates, for instance, which makes perfect sense, just given the incentives there. But yeah, the conversation is evolving interestingly.

[00:48:45] Yeah.

[00:48:46] Paul Roetzer: And the, you're seeing actually, like Meta, who obviously was all in on the Trump administration just last week, started diversifying their, contributions to political campaigns. Mm-hmm. And so what I think you're gonna see is these AI labs, the big tech companies, they're [00:49:00] gonna give money to whatever politician on whatever side of the aisle who supports data centers in ai.

[00:49:06] yeah. And that's, it's a, it's a tough one because the public doesn't. And so there's this, there's this moment where we gotta figure this out, but like the labs are gonna put money behind Democrats, Republicans, independence does not matter. You have to support data center, build out.

[00:49:22] Mike Kaput: All right, before we dive into our rapid fire topics this week, just one other quick announcement.

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[00:50:32] Anthropic Sees a $30 Trillion Revenue Opportunity

[00:50:32] Mike Kaput: All right, Paul. So first rapid fire topic related Anthropic is reportedly preparing to tell potential IPO investors that AI represents an annual revenue opportunity of more than $30 trillion. That number is its estimate of the total addressable market or TAM of what AI is capable of doing.

[00:50:55] That is not a forecast of what Anthropic itself expects to earn. So [00:51:00] this estimate attempts to capture the value of work that AI could theoretically perform across industries rather than current spending on AI products. So this is basically an estimate of the upper limit of the market Anthropic beliefs.

[00:51:14] AI technology overall can reach. Now, the Wall Street Journal has reported that the company is building this case as it considers raising as much as a hundred billion dollars at a target valuation of roughly 2 trillion, we could see a prospectus arrive within weeks. There's a possible offering as early as September or October, though these plans are not final.

[00:51:36] Anthropic reportedly generated 11.6 billion in revenue during the second quarter. That's more than double the previous quarter. While its annualized revenue run rate later climbed above $65 billion, the company is projecting between $190 billion and $200 billion in revenue by 2028. So Paul. $30 trillion.

[00:51:57] It's not their revenue forecast. It is still an [00:52:00] almost absurdly large number to put in front of investors. For context, U-S-G-D-P in 2025 as measured by the World Bank, was about $30 trillion. So world GDP was about 118 trillion. So we're talking some pretty big numbers of their estimates, obviously are talking their own book quite literally.

[00:52:18] But what do you think of that number in their approach here?

[00:52:20] Paul Roetzer: It's really hard to just comprehend this. The only other number I'll throw out to give some level of context to how ridiculously large this number is, is total US wages and salaries reach a seasonal adjusted annualized rate. So this is like what, over 12 months roughly?

[00:52:38] The wages would be in the US of 13.4 trillion as of July, 2026. Again, between the GDP and the total wages, I don't even know how else you conceptualize 30 trillion. And GDP is not an easy thing to conceptualize. So wages to me is like, I guess it's a closer thing. [00:53:00] and there wasn't any real details as to how they arrive at the 30 trillion, but my assumption is that, a large part has to do with doing most of the work that otherwise would be done by humans.

[00:53:14] Mike Kaput: Yeah, I was gonna say, I pulled some of your comments from past episodes, because, like, you nailed this because like in the past several episodes, especially 1 72, 1 73, 1 96, 2 0 4, you've talked about, plenty of ways where it's like this, these numbers, these valuations only make sense to investors who are not idiots for the most part, if the total addressable market is human labor, not software spend, right?

[00:53:41] Paul Roetzer: Yeah. 'cause I think what we've said is like the SaaS industry, so all software combined is like 300 to 500 billion a year in sales. So yeah, you're not getting to 30 trillion by just replacing the need for software companies. Like you're, you're going after all human labor, including. Blue collar jobs, [00:54:00] including humanoid robots.

[00:54:02] Mike Kaput: Yep. And to your point about, I'm not sure where this number came from, if you map it all up, but SpaceX, in their recent IPO, they had about 26.5 trillion as their estimated TAM for ai. So again, maybe they were more specific, but in the, in the same ballpark.

[00:54:18] Paul Roetzer: So opening eyes is obviously gonna be like 32 trillion.

[00:54:20] Like it's gotta, we just gotta keep going up. We cannot come in under 30 trillion now.

[00:54:24] Judge Blocks Government’s Anthropic Blacklisting

[00:54:24] Mike Kaput: A hundred percent. All right, next steps. More Anthropic News this week. This past week, Anthropic won a major legal victory when US District Judge Rita Lin ruled that the Pentagon's effort to blacklist the company as a national security supply chain risk, which we've talked about earlier in the year, that they, she ruled that this was unlawful.

[00:54:44] So the dispute here began after the Pentagon demanded the right to use Claude for any lawful purpose. We talked about this quite a bit. Anthropic agree to remove most restrictions on Claude usage, but refused to authorize two big uses, mass surveillance of Americans and lethal [00:55:00] autonomous warfare. At the time, defense Secretary, war Secretary Pete Hegseth, then designated Anthropic a supply chain risk and barred military contractors from doing business with the company, even on work unrelated to the military.

[00:55:13] So in this 59 page ruling, Lin found that the government retaliated against Anthropic for protected speech in violation of of the First Amendment, denied the company the process required by the Fifth Amendment and acted outside the federal procurement law that it invoked. Lin wrote that the government wanted to make an example of Anthropic for criticizing its AI policies while continuing to pursue work with the company.

[00:55:37] She said the Conte conduct was not consistent with a genuine belief that Anthropic might sabotage its software or its work. The ruling blocks the Pentagon designation and related government-wide directives. The government is expected to appeal and a separate case involving another Pentagon designation remain remains pending in the DC circuit.

[00:55:58] So, Paul, we're [00:56:00] finally getting a little movement on this. I'm curious, does this ruling like meaningfully like resolve this issue? Obviously government can challenge it. Is it just the next round or waiting for, is this gonna kind of go away like we've. Somewhat predicted.

[00:56:13] Paul Roetzer: Yeah, I guess they knew it was illegal when they did it and they didn't care then.

[00:56:18] And I'm guessing they don't care now and they'll just have their lawyers draw this out for a while. But I don't think it came as a surprise to them that what they did was illegal. I'll just leave it at that. Okay.

[00:56:31] Controversy Over Legendary Investor’s AI-Written Op-Ed

[00:56:31] Mike Kaput: All right, next up, billionaire investor. Stanley Druckenmiller sparked a big debate over AI disclosure this past week after acknowledging that he used AI to help write a Wall Street Journal opinion piece that criticized Treasury Secretary Scott Bess approach to the bond market.

[00:56:50] So he writes this op-ed in the Wall Street Journal, and in fact, it started getting flagged on social media. It's like, Hmm, this sounds a little bit like it might be AI written. And so [00:57:00] the media outlet notice, N-O-T-U-S asked Druckenmiller whether he had used AI to write the article. Druckenmiller said, of course, I used ai.

[00:57:08] He added, he now writes everything with ai. For the same reason he uses a calculator to do math. The admission of this came the day after the piece was published. The journal had not disclosed any use of AI in the op-ed and the Wall Street Journal came out and said the article did not violate its policy.

[00:57:25] So its editorial page editor and VP Paul Gigot defended the decision, his standard. It was that whether an author brings an original argument and the credibility, his standard is whether an author brings an original argument and the credibility is supported. Not exactly like how they chose to write it.

[00:57:44] So in this case, he said the opinion here was genuinely druckenmiller, which other people had commented on. Pretty much, mirrored quite a lot of his commentary over the years, even if AI helped shape the prose, that's okay. The Journal's opinion section, importantly [00:58:00] is separate from its newsroom, which has its own AI standards.

[00:58:03] So this kind of kicked off Paul some firestorm of debate, especially among journalism, but also more widely does it matter that someone used AI to write an op-ed if it's clearly druck and Miller's like long held opinion. This was kind of charged too 'cause him and Besson used to work together. they were very closely linked, so it was like pretty serious criticism of Besson's policy towards the bond market, which Druckenmiller had been pretty consistent on previously.

[00:58:32] But the AI thing is like overshadowing. All of that. What did you kind of take away from this?

[00:58:37] Paul Roetzer: I, so this goes back to episode 2 32, where we were talking about Claude Watermarking and how people are gonna start getting called out for their use of ai and people are gonna treat them, you know, in, in this case, like it's, you know, it's like a really bad thing that they did it.

[00:58:51] and my point then was like, it's just gonna be the norm. Like everyone is going to be using AI in some [00:59:00] capacity. and we're gonna want, as a society to sort of arrive at a point where it becomes a little bit more accepted. You may need more transparency. So again, this is part of why. AI policies are so critical within organizations is when do you disclose it?

[00:59:17] Like when, when are you allowed to use it? But then when do you need to disclose the use of it? Like I, Hey, this was co-author with chat pt. The ideas are mine, but like chat, pt, help me form my words. Not everyone's a great writer. But everyone has ideas and thoughts that need to get out into the world, hopefully.

[00:59:33] Yeah. So I just feel like we're getting to a point where we need to start agreeing on that it's gonna be normal, and that that people, are going to use it different capacities. And I don't know that we should judge people. So I've said like myself, the vast majority, what I do, I don't use any AI to do it.

[00:59:51] Yeah. Because it's like I want to go through the process, but then there are things that I will use AI to do because getting the information out faster to [01:00:00] the world where I'm in the lead on it, I'm the one like pushing the ideas and driving it. That's what matters. And I'm verifying everything and editing everything like.

[01:00:09] I'm the human in the lead in that scenario. Not the human, just in the loop, like who's just kind of letting AI do its thing and watching over it. So I just feel like we need to decide more around, you know, policies within organizations and then more societally, like, it's gonna be easier and easier for people to say that they think AI wrote something and they're gonna throw, you know, it into Claude or they're gonna throw it into turn it in or whatever.

[01:00:32] Yeah, maybe it was ai, maybe it wasn't, but like, we gotta get past the point where that matters and just like talk about, but is it that person's ideas? Did they put critical thought into it? So, yeah, I, it's just interesting. So if you didn't go back and listen to episode 232, you can go hear all my thoughts on this.

[01:00:47] 'cause that was like, I tried to sort of explain this idea of AI plagiarism is the more, the bigger concern for me where AI systems, ideas, and words are being presented as your own and you put no critical thought into it. If you're the [01:01:00] expert on a topic and you worked with ai. You are still putting your ideas and words into this.

[01:01:05] Even if AI is helping you craft them, it's still your expertise, your experience that's coming through in the writing, as long as you're not just letting AI put words in your mouth.

[01:01:14] Mike Kaput: Yeah, I think it's a super wise perspective to move beyond the Is it or isn't it? Because this feels like just on steroids.

[01:01:21] The same version of this debate over like ghost writing totally is a very big thing. I used to professionally ghost write. Whether you agree with that or not, is one thing and that's fair. But I can tell you for a fact if I took X percentage of the New York Times bestseller lists, if those are celebrities who don't usually write,

[01:01:41] Paul Roetzer: or CEOs,

[01:01:43] Mike Kaput: CEOs who don't, if it is not literally on the bestseller list because the person is a regular author, I can almost guarantee you that it is ghostwritten in some capacity.

[01:01:52] Now, the nature of that ghostwriting is what matters, right?

[01:01:55] Paul Roetzer: Yeah.

[01:01:55] Mike Kaput: It's like, was an interview conducted? They go back and Right. Did they go back and forth with each other? [01:02:00] Or was it just someone saying like, write whatever and I'll sign off on it? So, yeah, I don't know. It's interesting. We didn't have, we don't seem to have had as much outrage over that Yeah.

[01:02:08] Happening over the last three decades.

[01:02:10] Paul Roetzer: Totally. And again, I think it's like, it's, some people will argue with is the ghost writing the right analogy? It's like, well, it's the closest thing we've got you, you have ghost writing, which I guess some people don't know is like a thing, like, but a lot of CEOs don't write their own stuff that, whether that's memos to their teams, whether it's books, whether it's certainly talks they give like mm-hmm.

[01:02:31] There are speech writers for that stuff. musicians, many of them aren't songwriters, they're singers, so they don't write their own songs. But does it represent their feelings and emotions and beliefs and like, does it come through because of interviews and things they've previously written? That's at the end what matters.

[01:02:48] And I think like ai, we're just in this phase where it's being treated differently than helping give a voice to people when otherwise. But yeah, I'm with you a hundred percent. Like if A CEO just says, yeah, write me a, you know, write me a [01:03:00] book, yeah. That we can put my name on and get on the bestseller list and the CEO never even read their own book.

[01:03:05] Mike Kaput: Sure.

[01:03:06] Paul Roetzer: They might as well just use the guy to write it at that point.

[01:03:08] Mike Kaput: Yeah, a hundred percent. All right. Next up.

[01:03:11] McKinsey Maps the State of AI in 2026

[01:03:11] Mike Kaput: McKinsey has released their new state of AI in 2026 report, some interesting findings here. So they find that nearly nine in 10 respondents said their organizations now use AI regularly, and at least one business function.

[01:03:24] 44% said they were scaling it across the enterprise up from 38% last year. 80% of respondents, in terms of employees, reported individual productivity gains. Half said AI is helping people make better decisions, but only 30% reported any impact on earnings before interest in tax ebitda or ebit. essentially that's essentially unchanged from last year.

[01:03:49] Interestingly enough here, the group McKinsey calls AI high performers remains just 6% of respondents, but these are more likely to fundamentally [01:04:00] redesign workflows, pursue growth and or innovation alongside efficiency and pair AI initiatives with stronger senior leader commitment and defined impact measurement.

[01:04:09] Instead of simply adding AI to existing processes, the share of respondents from large organizations reporting that their companies were scaling AI agents jumped from 27% to 40%, but the share among respondents from smaller organizations stayed flat at 22%. At the same time, about one in five respondents said the operating cost of AI is now a constraint.

[01:04:32] 39% expect AI to reduce their organization's total employment over the next year, but only 14% of respondents from organizations using AI say AI had contributed to an overall workforce decline in the past year. So that is actually less than half of the people, the 32% who in last year's survey predicted decline.

[01:04:53] So a bunch of people predicting, but it's not yet coming to pass. one final note here I found [01:05:00] interesting. Nearly one third also said AGI agentic coding led them to reject at least one software purchase because they could build the capability internally. So Paul, I think it was interesting to see how some of this mirrored our own data, like literally validating what our state of AI for business research showed individuals more racing ahead organizations struggling to still, still see impact.

[01:05:21] I also love just that. How they outline these, even though it was 6% of respondents, the AI high performers re-imagining workflows pursuing innovation in tandem with efficiency. It sounds very familiar to kind of how we've approached it and what we've talked about.

[01:05:35] Paul Roetzer: Yeah. the agent, the scaling agents jumped out to me as like, wow, that seems really high.

[01:05:39] I wonder how valid this is. And then I drilled in and realized they were specifically referring to software coding agents. And it's like, oh, okay. That makes more sense if you're asking Yes. Yeah. If you're asking within it within software development. Hey, are you all scaling, coding agents? Yes. Like 30, 30% seems like in a reasonable number.

[01:05:57] If that question was asked across other [01:06:00] functions, not a chance.

[01:06:01] Mike Kaput: Right,

[01:06:02] Paul Roetzer: right. so yeah. And then the high performer categories that you highlighted, they, they actually have a chart that sort of breaks down. Like what are the different things and how, what is the difference between all other respondents and the high performers?

[01:06:14] and I think they've got what, like, I don't know, there's like a dozen different categories here of the things that separate the high performers. So you can go check out the full report and read up on those.

[01:06:26] Niall Ferguson Sees a Sci-Fi AI Future

[01:06:26] Mike Kaput: All right, so next up we have an interesting new essay from the, historian and economist Niall Ferguson in the Free Press that is, kind of about what future we can expect from ai.

[01:06:39] So he actually publishes this as almost a direct challenge to Elon Musk because Elon Musk has this kind of favorite vision of the AI future. And the way he described it in a series of interviews is what they would call the post scarcity civilization, in, this is a sci-fi set of sci-fi [01:07:00] novels. Iain M. Banks is culture novels.

[01:07:02] Basically in these novels, super intelligent machines manage society. Work is optional. Material abundance makes money largely and irrelevant. And Musk had told the economist that AI could exceed the sum of human intelligence in roughly five years, and paired with vast numbers of robots, create a quasi infinite economy.

[01:07:19] So that's gonna his vision of where this is going. Ferguson, however. Argues that that prediction rests on assumptions that unfortunately do not survive contact with physics or economics. He says that more capable AI still depends on scarce physical inputs. Talked about this. Advanced chips, electricity, data centers, copper, rare earth, metals, land and infrastructure needed to turn digital intelligence into goods and services.

[01:07:44] So intelligence. May become abundant without making energy, minerals, minerals, or land abundant. Ferguson says, the better model is actually the work of science fiction author Neal Stephenson, specifically his book The Diamond Age. So in that [01:08:00] book, the technology does not erase human conflict or inequality.

[01:08:03] It basically enriches a techno elite leaving a lower class dependent on basic income and produces unintended consequences when powerful systems are monetized and misused. Bringing all this full circle, he basically says, look, the openAI's hugging face incident is evidence that this messy feature, not this like utopia of abundance, has already.

[01:08:25] Begun. So Paul, there's definitely some like sci-fi inside baseball here, but it's interesting like Ferguson, you know, a pretty well-known academic voice, kind of just saying, you know, I think he largely aligns with Musk on AI being beneficial in a lot of ways, but basically saying, look, we're not getting this thing where these benevolent, super intelligent machines make everything perfect.

[01:08:45] I'm curious like kind of what you thought looking at his vision versus what we're being told through other channels.

[01:08:51] Paul Roetzer: I think his vision is more realistic, like especially in the coming decade. You know, the culture stuff is very common [01:09:00] within the AI circles. Like I think there's a lot of AI leaders who are sort of inspired by that vision of the post scarcity, you know, abundance thing and just sort of use that as a way to justify.

[01:09:12] Everything they're doing and the risks that come with it and the downsides that come with it is that it is in pursuit of this larger utopian like outcome. I don't think that that's certainly what the next 10 years looks like. I do think there's gonna be lots of, real obstacles to that future.

[01:09:31] And I think it's just gonna be messy. And I, it's what I've, that's what I've even said on jobs. Like I do think somehow this works out, from the, from the standpoint of the economy, from the standpoint of jobs I think mortgage created. I think there's gonna be really cool new roles that emerge and, you know, we're seeing signs of this ourselves, like jobs that just didn't exist a year ago or two years ago, that are really cool jobs that are, you know, popping up or that are being created even within our own company.

[01:09:55] But I just feel like more broadly it's just gonna be messy and be part because of what [01:10:00] Bill Gate is saying. There's no plan. Like, no one is solved for what happens when it doesn't go great. I just, and I don't know if they, it's 'cause it's hard to figure out or because they just wanna pretend like it's not gonna happen or they truly are just have convinced themselves that the abundant future is the only outcome.

[01:10:17] So, yeah, I don't know. I mean it's, I know some people love sci-fi. I did not have that book on my. To, to read list the Diamond Age, so,

[01:10:25] Mike Kaput: yeah. Yeah.

[01:10:26] Paul Roetzer: And I've not read Stevenson's stuff, so I might go check that out. I've tried the culture series. I know you've been into that before, right? Mike? You've done the culture series?

[01:10:32] Mike Kaput: Yeah, I've read a few of those. Yeah.

[01:10:33] Paul Roetzer: Yeah, I've tried and I usually get like a couple chapters in and it's like, eh, I don't know. I gotta go listen to a podcast now. Like I haven't really like, fully invested in them.

[01:10:42] Mike Kaput: Yeah. It's very out there when it's like, almost like the humans aren't the main characters in the books, basically.

[01:10:48] Paul Roetzer: Is that where Found, is that foundation, is that one of the culture series?

[01:10:51] Mike Kaput: no, that's yeah, Isaac Asma. Okay. So that's a little, but it is, but it is similar in the big grand scope of everything. Right. You know, it's just [01:11:00] really interesting. but yeah, no, we'll see. But I, I'm glad someone is, maybe saying, Hey, it's gonna be a little messier than maybe those books.

[01:11:08] Paul Roetzer: Those books. I, and again, like I would love to just focus on the optimistic stuff on this podcast all the time and like super practical stuff. We try and balance it with like the practical stuff, but. I think the sooner we just accept it's gonna be messy and we don't have all the answers and like we're not asking the hard enough questions, then we can move into the phase where we actually do that.

[01:11:27] And then it's more addressable, like pretending like this is just gonna go perfect. And AI is only good and amazing. Doesn't do anybody anything. So it's like, I don't know, like again, I've tried to not be overly create like anxiety and and fear around this stuff, but I think there's just realities people need to face so we can get on with figuring it out.

[01:11:51] Mike Kaput: Yeah, and you know, I think in a more positive frame, it leads to ownership and agency. Yeah. Like nobody is coming to figure this out for you or save you on this, and [01:12:00] like, I get that scary, but like, also it's like, okay, let's take a breath, move past that and figure out what does this mean for your family, your career, et cetera.

[01:12:06] Yes. So, all right. So bringing this back down to Earth a little bit. Next segment.

[01:12:12] Americans Back Government Action on AI Disruption

[01:12:12] Mike Kaput: This past week, the Center for Shared AI Prosperity released the results from a national survey they conducted with a organization called Blue Roetzer Research. They pulled 56,000 respondents on 79 ideas for how government could respond if AI disrupts large parts of the economy.

[01:12:31] So this pro, the proposals they suggested covered four broad areas. First was taxation and revenue, second income support and safety nets, third, labor markets and workforce development, and fourth models for ownership and governance. So these proposals range from taxes on AI profits and data dividends to wage insurance, and even a US Sovereign wealth fund.

[01:12:52] For each proposal, respondents read short arguments for and against it. Then they had to choose whether they supported or opposed it. There was [01:13:00] no undecided option. The organization reports net support is the key metric here as the difference between support and opposition. After those arguments of the 79 policies tested, 61 still had positive net support.

[01:13:16] After respondents saw the opposing case, the 12 most popular maintained approval margins of at least 40 percentage points and 48 policies had margins of at least 15 points. So among the policies that directly mentioned ai, 31 of 48, 1 majority support. So the organization says respondents were especially receptive to the ideas of retraining and compensating workers affected by automation.

[01:13:42] Strengthening the existing safety net and funding training apprenticeships and care work through progressive taxes. So the group also does caution that a popular policy is not necessarily an effective one. It describes the survey as a starting point for comparing ideas and understanding [01:14:00] where Boulder proposals may face political resistance.

[01:14:04] So Paul, this. Kind of seems to suggest that Americans are open to active government responses to AI disruptions to the top five measures that they specifically suggested that were had net positive support. Were expanding apprenticeships, requiring severance for automated away jobs, sector based job training, employee ownership in firms, and a data dividend.

[01:14:25] And just one final note here, important to note that Center for Shared AI prosperity, there are us. Focused research and policy coalition. They're clear, pretty clearly aligned with, center left progressive kind of democratic policy ecosystem. Blue Roetzer research is explicitly democratic and progressive aligned.

[01:14:43] So they're basically seems like we're doing some polling research here mm-hmm. To figure out what policies are gonna look good in the next few years.

[01:14:50] Paul Roetzer: Yeah. So, and this does kind of tie back to the Bill Gates topic, where it's like, what can we do about this? Like, if AI's gonna impact us, what are the things that we can move forward on, where we can make progress?[01:15:00]

[01:15:00] I love to see the apprenticeship thing is actually what first caught my attention about this. I know. Yeah. 'cause that's the basis of my. MAICON talk in October is, the need to build apprenticeships across industries and function job functions. So the, if you go to the blog post in the show notes, in that post, and we'll put a link maybe directly to this, there is a Google worksheet that you can actually go read the descriptions of each of these and then the pro and the con argument.

[01:15:27] So it's, it's really cool to, you know, just kind of a quick glance. So I went through and pulled out a couple Mike, just to see more context of what were they, you know, expanded apprenticeships is fine, but like, what are they, what does that even mean? Yeah. So. I'll just read, let's see. I got three of the ones that got the positive net rating and then two that got the negative.

[01:15:44] So expand apprenticeship says, scale up earn while you learn apprenticeships in new fields, including AI infrastructure and roles that work alongside a ai. Under this policy, more workers could train for skilled jobs by working a paid position under [01:16:00] experienced mentors. I am a, I'm more and more convinced as I work on this opening keynote for Make on that, like this is a very real thing that should be happening in a more structured way.

[01:16:09] Yeah. require severance for automated away jobs. I didn't know what that one meant. so this would require that companies provide severance payer transition support to workers. They replace with automation under this policy. A company that eliminates a job through AI or automation would have to give the affected worker a defined payout.

[01:16:26] So that's that. I actually agree, and I pres presented that as an idea last year that you should, rather than unemployment, if you took someone's job with a AI or a machine. Then you should pay them for some extended period of time. So I'm actually an advocate of that concept. And then the data dividend.

[01:16:43] Some policy makers are proposing that tech and AI companies pay people for the personal data they collect. Under this policy the data companies use to build and train. AI would be treated as something you own and firms would've to compensate you. That actually is an Andrew Yang idea we talked about on a recent episode.

[01:16:59] Yeah. Who [01:17:00] also is speaking at Make on. I'm guessing we'll address that one. Now, the two that jumped out to me that had pretty low net ratings, one is tax on automated services. so this would be a tax on automated services. People pay for things like robotic delivery, self-checkout, driverless rides, and AI customer support, the same way sales tax apply to other purchases.

[01:17:22] So the tax would fall in the automated service when it's used rather than on the machines of the software. And then the one I thought maybe was most interesting, Mike, because a lot of the AI lab leaders have thrown out this idea of universal basic income. And actually Andrew Yang's book in 2020 was based on this idea of universal basic income.

[01:17:38] People do not like this one. So it said, some policy makers are proposing a universal basic income, a set monthly payment, sent to every adult under this policy. Everyone would receive the same regular cash payment for the government. That was the second lowest rated of all of them. Yeah, so

[01:17:54] Mike Kaput: that's wild.

[01:17:54] Paul Roetzer: People are not UVI fans. At least in this study. It doesn't

[01:17:57] seem

[01:17:57] Mike Kaput: like it. Yeah. Do, I'm just [01:18:00] curious. Do you see any, I mean, it's super relevant regardless of who's running it. Do you see this as putting out feelers for possible future policies or just more benign research or,

[01:18:09] Paul Roetzer: I would guess it's campaign messaging like this close to it.

[01:18:12] Okay. Like they're doing ongoing research. But my, my assumption here is you're trying to like, not only do you want to hit the other people with the things that you think people are not gonna like, so hey, they support data centers and you're spending millions of Yeah. But it's like, hey, we support expanding apprenticeship, which is gonna get, you know, continue to give you the jobs and give you the opportunity to learn.

[01:18:31] We support dividends. If you get un-like, you're just looking for the things to say, here's the things we're, we're all for. We have no plan, but like. We like these ideas 'cause you like these ideas.

[01:18:41] AI Use Case Spotlight

[01:18:41] Mike Kaput: All right. All right. Next up we've got our AI use case spotlight we do every week where we give you a quick look under the hood at some real AI use cases we're exploring in our work at SmarterX or in some cases in our personal lives.

[01:18:54] So I've got one to share. Paul, then let you share whatever you've been working on this week. [01:19:00] So first up, I have been working on this for a long time, but it kind of finally came to fruition. It's more, interest project rather than something super specifically useful, though it turned out to be extremely valuable.

[01:19:13] I actually built like a podcast expert briefing system in Codex. So, you know, I was trying to basically solve this pretty straightforward problem is like every week I do a ton of extensive prep for the podcast, read everything, make notes, think through stuff, try to look up different research and sources.

[01:19:31] But like, you know, we have limited time and I'm trying to basically. Really become an ex expert, like know enough to be dangerous, more, more likely on every one of these topics. Not just the news item though, that's interesting, but more like what does it mean? What are the implications? What are some of the technical aspects of it that sometimes you don't always have time to dive into?

[01:19:51] So what I did this week is I finally took a little time to take my notes on this. Take all the other little processes manually and with AI [01:20:00] that I use to kind of prep for each week and put these into, try to make like an actual briefing, like for each topic. What do I actually wanna know? How should it be formatted?

[01:20:10] What should it look like? What are the key things I want AI to go do research on? So basically, I created this like executive briefing or this briefing on each segment, which includes three parts. There's this executive briefing, which gives me this compact reported narrative of what happened, uses a ton of separate research.

[01:20:27] This is not like meant for like listener consumption. It's more like, here's all the different angles, here's what's been reported. You can kind of like think through and sort through it on your own. It's like I equate it to like, I like the publication The Economist quite a bit. I'm like, write me an economist article on this exact thing.

[01:20:43] A thousand words that like covers every possible aspect of it. I, it also has, interestingly enough, this is super helpful, a section called what others are saying. This is the second section of this three part brief where it basically go, go find me the hot takes. Go [01:21:00] find me what the experts are saying about, I'm just curious, what are the opposing views?

[01:21:03] What are people validating? Just kind of gimme a, a, a subjective sense of that. And then finally is, the technical briefing. So for each of these topics, it actually will go in and identify one or more. Technical aspects or areas or like even in some cases like policies, technologies, how things work that I need to know about and build me like a several thousand word narrative about, explain this to me.

[01:21:29] Like a non-technical person or a non policy person, or a non-legal person. And so it's really cool is like. Took a few hours, but I got to like a really usable format for this. I found really, really helpful to prepare for this week's episode. I'm probably gonna use moving forward, but between the however many topics we covered this week, this thing will automatically now do the research and produce.

[01:21:51] I'm looking at it now. It's in our brief, it's 21,000 words of briefing, so about a third of a book. Right. which [01:22:00] it, and for whatever reason, I like reading it in this format. Format may be terrible for someone else, but this is just kind of customized to me. I went back and forth iteratively on a single topic to get the format down and went tons of iteration, like back and forth, took a long time.

[01:22:15] Once I got the segment like perfect, I then said, okay. Go do it for everything else. And within, you know, probably took an hour of Codex working, burned a lot of my usage. Thankfully openAI's is throwing out usage resets for Codex like candy, which is amazing. So I got lucky on that, but it burned a lot of tokens.

[01:22:35] But man, it's like reading through it. I'm like, I basically created my own personal magazine for the podcast this week and it's, I really enjoy reading it.

[01:22:43] Paul Roetzer: Well, and then you could throw it into the notebook, Google Notebook. Exactly. and have it turn it into like a 10 minute podcast version. Hundred percent.

[01:22:49] Or mind maps for you. Yeah.

[01:22:51] Mike Kaput: Yep.

[01:22:51] Paul Roetzer: now why Codex? Why not ChatGPT work? Could you not have achieved the same thing using ChatGPT work?

[01:22:57] Mike Kaput: Yeah, you can do this. Same exact thing. And [01:23:00] this is, I think you could do most of the same stuff. I just happened to have gotten used to using Codex over the last four months, and this, we gotta all do more of a segment on like the differences between ChatGPT work and Codex.

[01:23:12] 'cause honestly, I don't know them all. They seem very similar to me.

[01:23:15] Paul Roetzer: I have a great article. I don't know if I put it in the sandbox for next week yet, but I actually read, I forget the guy's name. He's a, he's a big AI guy, but, Simon Wilkinson or Williamson, we

[01:23:25] Mike Kaput: Oh, yeah, yeah.

[01:23:25] Paul Roetzer: We've, we've, he did this whole, and I was like, okay, I have to figure out what the difference is between chat work and Codex.

[01:23:32] Here's my best effort at it. And he's like, I'm

[01:23:35] Mike Kaput: glad I'm not alone. Oh, yeah. Because like, I use them all the time and I'm still like, I don't understand the decisions that went into, like, separating these.

[01:23:42] Paul Roetzer: Yeah. He, he said like, it starts off with. You know, I went to chat to openAI's site and I read the description of what ChatGPT work is for, and I thought that's odd.

[01:23:51] I've been using chat to do those exact things right. For like six months. So yeah, it is not clear. So if you're confused, like join the club, like it [01:24:00] average trying figure this out. Yeah. I interest the time, mine is, I, I, there's a couple of major things I've been working on for a while. Both of them will probably come to light in the public in the next like 60 days-ish.

[01:24:16] I, I'm at the point where I have to make very important decisions for the company, like the future of our business and the trajectory and things like that. And, I rely very, very heavily on a couple of ChatGPT projects in particular to think through these decisions. and so I have to talk to attorneys and accountants and other advisors and, I could not do it on my own.

[01:24:41] Like it's just in the timelines I have to make these decisions and to think through from every angle, make sure I'm asking the right questions, make sure I feel confident in the end decision that I end up making. So it is a human in the lead thing. Like it's just me going in and saying, okay, I thought about this.

[01:24:59] What [01:25:00] do you, what do you think? Here's the advice I'm getting. What should I be considering here? What should I go back? So. I am very much in, in the midst of doing these two major things, and it's consuming a lot of my time and a lot of my brain power. And in the end, a lot of my token usage is like helping me solve.

[01:25:19] So decision making is is the front and center use for me right now assisting in decision making.

[01:25:26] Mike Kaput: I'm just curious for that with whatever you can share. you know, there's a lot of people still skeptical. AI can reason at an expert level. Yeah. Sometimes it sounds like you're using this for very complex things and you would argue that it's probably at human level reasoning.

[01:25:42] Paul Roetzer: It's beyond. Beyond, yeah. I mean like, so I present and I'm very transparent with the use of this, with my advisors and. I will share direct outputs with them and say, here's what JGBT is telling me to ask you. Here's the open items. It's identified within the legal documents, and [01:26:00] almost every time it is things worth discussing that maybe we hadn't gone through previously.

[01:26:05] Mike Kaput: Wow.

[01:26:05] Paul Roetzer: And so as long as you have this open relationship with your advisors where they understand that's how you're gonna think about things, it just breaks down that barrier right away. And no one gets offended. It's like, Hey man, we just, we just need to make decisions and let's make the best decisions.

[01:26:20] I'm still not clear on three things that you're asking me to decide on. Like, I don't have enough information. So I went and did more research myself, and here's what I think now. So yeah, I mean, to the point where I've submitted legal briefs to my attorneys. Here's the wording I would use. Like, this seems better than what we were doing.

[01:26:40] Yeah. And so there's a lot of give and take, but it is, I have very, very good advisors and it is very, what we're doing with AI is very complimentary to very high level accomplished people across these domains.

[01:26:57] Mike Kaput: Wow.

[01:26:57] Paul Roetzer: Yeah.

[01:26:58] Mike Kaput: All right.

[01:26:59] AI Product and Funding Updates

[01:26:59] Mike Kaput: To wrap up here, we've got some AI product and funding updates I'll go through real quick as we close out the episode.

[01:27:04] So first up, openAI's has talked through some of the first results with jalapeno. It's first custom inference chip. It says This system developed with Broadcom can deliver 1.5 to one point time nine times more AI work per wat and up to 3.6 times lower latency across models that include their GPT-OSS 120B model, deep CR one and Kimi K 2.5.

[01:27:29] Paul Roetzer: One thing I'll note real quick there, Mike, I've. I won't get into like specific background information that I might know. Well, I'll just say if you're wondering, would would Jensen Wong be excited about openAI's building their own chips? You would, you might wonder, about that. And I would just say, I think a lot of what's happening right now is these, these companies all work together, but they are also competing with each other.

[01:27:58] And sometimes, [01:28:00] CEOs get unhappy with each other over decisions that are made that were made a year ago and was known within the industry a year ago. And so if actually we go back in time and we look at some things that came out about a year ago, it actually starts to make a lot more sense because if Jensen knew that OpenAI was building their own chips a while back, it could cause a little bit of friction.

[01:28:24] So I think, I think they continue to work together. They will continue to work together, but everybody's stepping on everybody's toes right now, I think. And it's starting to be unclear how this all plays out.

[01:28:37] Mike Kaput: Also openAI's added sign in support to chat. GPT Works Cloud browser on web and mobile for plus and pro users.

[01:28:45] This allows it to complete tasks on some supported websites after the user signs in securely. Anthropic added a built-in browser to Claude Cowork that can navigate pages click and type without a browser [01:29:00] extension, and that it has initial availability for Promax team and enterprise users. Anthropic and Salesforce announced Claude Force and expanded partnership that launches with Salesforce and Claude.

[01:29:11] This the plugin for select pilot customers with 37 prebuilt sales skills grounded in live revenue context. The open beta is planned for September, while Claude is also available in Agent Force and embedded across Slack Anthropic Open Day research preview of what they call their model hardware standard.

[01:29:29] This is a model agnostic specification that uses standardized device drivers and access mechanisms, including MCP model context protocol to let AI agents discover, control, and coordinate programmable lab and manufacturing equipment ahead of a planned open source release. Google introduced Gemini Enterprise for legal, a fully governed legal plugin for the Gemini Enterprise app that combines purpose-built skills agents and connectors for work, including legal research, regulatory screening, [01:30:00] and contract drafting.

[01:30:01] Available in preview. It connects to existing enterprise and legal systems and provides traceable citations back to source material. Google. Google.

[01:30:08] Paul Roetzer: Real quick. Google. I think this is a prelude of what's gonna happen. So basically the labs realize that it's really hard for everyone to figure out how to use agents.

[01:30:16] Like given the general agents. Yes, and they build their own. So I think they're just gonna industry by industry and just build packages that say, okay, here, marketers here, attorneys here, accountants. Like we've pre-built everything you need. Go.

[01:30:28] Mike Kaput: Yep. Yep. Google also announced expanded billing and cost controls for Gemini Enterprise agent workflows.

[01:30:35] This, or workloads rather. This includes a pay as you go app edition available to select customers and rolling out broadly soon. There's new project wide pooled CLO quotas for anti-gravity and Gemini Enterprise. Their spend based flexible savings plans, project level, monthly spend, caps and public preview and pricing ular estimates for background agent runtime costs.

[01:30:56] It's all very well and good, but does not seem like it is less [01:31:00] complicated. So it'll be interesting to see how this works. Google released also Gemini Omni 1.1 Flash as a production ready generative video update. They added scene extensions in Tencent second increments up to a total cumul cumulative length of 40 seconds.

[01:31:17] First and last frame controls 36, 360 p previews generated up to 60% faster than seven 20 p and 4K upscaling. We found out who is behind that secret and surprise ox alpha model. It was Z do ai, revealed that the, this was their GLM 5.3 flash model, and it was launched now at 7.50 cents per million input tokens and 25 cents per million output tokens.

[01:31:45] That's half its list rates through September 9th. This, that anonymous test became the most popular model of the past week. Figure the robotics company said its index data app emerged from stealth with more than 16 million videos already [01:32:00] uploaded and committed. And they committed to spend more than $1 billion on data and compute over the next 12 months is basically a data set that they have now for robotics that can be used to train their products and their humanoids.

[01:32:13] And then finally, moonshot AI is reportedly in early stage talks with Microsoft, Amazon, and Google over deals to host its open way. Kimi K3 model with Moonshot seeking up to 30% of revenue from K three related services on Azure AWS and Google Cloud.

[01:32:30] Paul Roetzer: And just to, just to clarify that is a Chinese lab that American companies open are negotiating with to infuse Chinese open models into their offerings That, that I would think would be a oli bit of a political.

[01:32:45] Hot point. We, we may be hearing shall more

[01:32:47] Mike Kaput: about that. I think that's a good prediction.

[01:32:50] Paul Roetzer: Yeah.

[01:32:51] Mike Kaput: As one final reminder here, our AI pulse survey for this week is still live at SmarterX.ai/pulse. It should take you literally seconds to fill [01:33:00] out. We're asking one question about how you are operating, how your company is approaching rather entry level hiring, due to the effects of AI on the labor market.

[01:33:11] So Paul, another packed week. Appreciate you breaking everything down for us.

[01:33:14] Paul Roetzer: Yeah, good stuff. hopefully we have a bunch of like really positive, exciting things to share with you next week. That would be nice. That's our goal every week. We'll see if we get there next week. and actually Mike, we have.

[01:33:26] Was it Labor Day next Monday? Yes, but we'll drop. We we're dropping on the normal day. Still should be 'cause we're gonna record on Friday. Yes, we should be. Okay.

[01:33:32] Mike Kaput: Yep.

[01:33:33] Paul Roetzer: Alright. So yeah, regular weekly episode as usual. Do we have an AI transformation spotlight this week?

[01:33:38] Mike Kaput: we do not. Okay. No, we had one come out this past week though, so there is one that's pretty fresh with Jon Dick at HubSpot.

[01:33:43] Yeah. That's awesome. So 2 34.

[01:33:45] Paul Roetzer: Yeah. So if you haven't been following the I transformation series, Mike's done three of those now. They're really great practical inside information about like the messy parts, the good stuff. So go listen to those. They're super, applicable to anyone that's kind of [01:34:00] driving business transformation.

[01:34:02] All right, thanks Mike. Have a great week. Thanks for listening to the Artificial Intelligence show. Visit SmarterX.AI to continue on your AI learning journey and join more than 100,000 professionals and business leaders who have subscribed to our weekly newsletters, downloaded AI blueprints, attended virtual and in-person events, taken online AI courses, and earn professional certificates from our AI Academy and engaged in the SmarterX slack community.

[01:34:29] Until next time, stay curious and explore 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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