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[The AI Show Episode 215]: Musk v. OpenAI Round 3, AI's Hot New Job, The AI Jobs Apocalypse Debate & The 2026 State of AI for Business Report

By Claire Prudhomme on May 19, 2026

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The Musk v. OpenAI trial is finally in a judge's hands, but what it really put on trial was the industry's own credibility. Three years of private texts, personal journals, and boardroom maneuvering were revealed to the public, and no one came out looking great.

Meanwhile, a coordinated push from prominent voices tried to kill the AI jobs narrative this week. Scott Galloway, a16z, Andrew Ng, and Derek Thompson all argued, within the same seven-day window, that fears of a job apocalypse are manufactured. Paul and Mike don’t believe it, and SmarterX's own State of AI for Business data says why: 71% of professionals now believe AI will eliminate more jobs than it creates, up 34% from last year alone. They also dig into Forward Deployed Engineers, the hottest new role in enterprise AI, and why OpenAI's $4B "Deployment Company" launch is really a grab for the $6 trillion knowledge work economy.

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

00:03:46 — AI-Pulse Survey

00:06:27 — Musk v. OpenAI Round 3

00:12:13 — "Forward Deployed Engineers" Are AI's Hot New Job

00:30:17 — The AI Jobs Apocalypse Debate

00:48:49 — An AI Hate Wave Is Here

01:00:34 — Two Scenarios Could Unfold in the US-China AI Race

01:05:19 — AI Threats Have the US Government (and Labs) Worried

01:09:04 — The Rise of "Headless" Software

01:14:53 — Publicis Acquires LiveRamp

01:16:36 — How AI Is Changing the Way We Work (Second Brains, Apprenticeships, and More)

01:24:39 — AI Use Case Spotlight

01:29:30 — AI Product and Funding Updates

 

This episode is brought to you by AI Academy by SmarterX.

AI Academy is your gateway to personalized AI learning for professionals and teams. Discover our new on-demand courses, live classes, certifications, and a smarter way to master AI. Learn more here.

Read the Transcription

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

[00:00:00] Mike Kaput: What happens when you have a nation of people that used to be doing okay with white collar jobs and now have to drive DoorDash or Uber or something like you're gonna have a lot of angry people?

[00:00:10] Paul Roetzer: Welcome to the Artificial intelligence show, the podcast that helps your business grow smarter by making AI approachable and actionable.

[00:00:18] My name is Paul Roetzer. I'm the founder and CEO of SmarterX and Marketing AI Institute, and I'm your host. Each week I'm joined by my co-host and SmarterX chief content Officer, Mike Kaput, as we break down all the AI news that matters and give you insights and perspectives that you can use to advance your company and your career.

[00:00:39] Join us as we accelerate AI literacy for all.

[00:00:46] Welcome to episode two 15 of the Artificial Intelligence Show. I'm your host, Paul Roetzer, along with my co-host Mike Kaput. We are recording on Monday, May 18th, 9:30 AM. Which could be relevant to our first main topic, which is gonna be the [00:01:00] musk versus openAI's, because jury is in deliberations, and I don't know how long it's gonna take.

[00:01:05] It might be one day, it might be five days. So by the time you listen to this, who knows? We, we may already have the verdict in the case. we've got some big, big picture things to talk about today. I don't, Mike, like, I guess there's a bunch of updates in the like product and funding, like no major models last week, but just some massive topics that I think are going to.

[00:01:27] As the year progresses, take on far greater importance, I would say, within business and society. So, buckle up for today. We've got some big topics. today's episode is brought to us by AI Academy by SmarterX, which helps individuals and businesses accelerate their AI literacy and transformation.

[00:01:46] Through personalized learning journeys and an AI powered learning platform. as I will note today, when we're going through a couple of our topics, one of the really important things about our AI Academy approach is it is a human-centered approach. So we are [00:02:00] trying to teach responsible use of the technology, and that is gonna be increasingly important, especially to this next generation of professionals who are coming into the workforce as we speak, graduating in May and who aren't really loving AI as we will discuss.

[00:02:15] So, if you're hiring that next generation, you need to be thinking about how do we teach AI in a responsible, human-centered way because they are not going to have it any other way. it's gonna be a really important balance people are gonna have to figure out. So. With AI Academy, new educational content is added weekly, so you are always up to date with the latest AI trends and technologies.

[00:02:37] We feature a number of collections of courses, and so one of those is AI for Industries, which has seven course series and certificates available on demand right now. These are designed to jumpstart AI understanding and adoption, those, collection features, certificates and professional services, healthcare software and tech insurance, financial services, retail, and [00:03:00] CPG and manufacturing.

[00:03:01] And we are adding new industries every month. So stay, informed if you're not, hearing your industry there, there's other collections for you, including AF for departments. And the Foundations collection. and like I said, new stuff getting added every month. So these series are an ideal launchpad for organizations that wanna level up their teams and accelerate responsible AI adoption and impact in their organizations.

[00:03:25] Individual plans are available as well as business accounts for five or more licenses. visit Academy SmarterX AI to learn more and for individual plans, you can use POD100 for $100 off your annual subscription. So again, that's POD100 at Academy SmarterX ai.

[00:03:46] AI-Pulse Survey

[00:03:46] Paul Roetzer: Alright, so every week we do an AI pulse.

[00:03:49] This is just a quick, informal poll of our audience to see how they're feeling about topics we've covered on that podcast. Last week we had a poll. The first question was, should new powerful [00:04:00] AI models be vetted by the US government before they're released to the public? This one is. Almost exactly split.

[00:04:08] we have, yes, but only for the most powerful or highest risk models. Got 40%. We have no voluntary safety testing by labs is enough at 30%. And then we have no, the government should not be involved at all at 27%. So that's 57% saying no.

[00:04:27] Mike Kaput: Mm.

[00:04:28] Paul Roetzer: And then we had a very small sliver that says, yes, mandatory pre-lease vetting for all frontier models.

[00:04:34] So, yeah, again, informal poll, of our listeners. The second question is, is your organization actively rela replacing roles? With AI today. Okay. This is dominantly, no, not yet. 69, 70% at, no, we are not doing that yet. Next closest is 15%. Yes. But unofficially through attrition or free hiring freezes, and then 9% at [00:05:00] we are considering it, but have not acted yet.

[00:05:02] And then a sliver at, yes, we have cut head count specifically because of ai. That's around what? Seven or 8% like it

[00:05:09] Mike Kaput: looks like. Yeah, looks like it. Yeah.

[00:05:10] Paul Roetzer: Yeah. So interesting. So keep that one in mind. Today we are gonna be talking about, jobs a little bit. There's been a lot of news in the last week related to jobs.

[00:05:18] So, right now a very informal poll. no, not yet is 70% in terms of organizations. Alright, Mike, onto the main topics. If you're new to the podcast, again, we have new listeners every week, so it's, you know, helpful reset here. What Mike and I do is we go through probably between, I don't know, 70 and a hundred sources each week between podcasts and ex posts and research reports and articles and videos and keynotes and all these things we consume throughout the week.

[00:05:47] We curate those down. We pick three main topics. Mike does an amazing job of this on Sundays. He puts in the work on a Sunday. He goes through, curates the three main topics, and then we try and do about seven rapid fire [00:06:00] items and those. so we try and get to 10 total, sometimes a little more, a little less, and then we consolidate all the product and funding news into one update at the end because that alone could be 15 to 20 topics every week.

[00:06:12] It's becoming burdensome to try and manage just selecting like the 10 or 15 we feature. So, that's the, that's the gist of how we run this three main topics, and then we get into the rapid fires from there. So Mike, kick us off with main topic number one.

[00:06:27] Musk v. OpenAI Round 3

[00:06:27] Mike Kaput: Alright. Yes Paul. So a lot going on this week. We talked first about this ongoing Elon Musk verse openAI's trial.

[00:06:35] We have talked about this on the last couple of episodes and in the days since, of course the trial wrapped closing arguments and a nine person jury is now in deliberation. So, openAI's, co-founder and former chief scientist Ilya Sutskever took the stand Monday. He testified that he spent roughly a year gathering evidence of what he called Sam Altman's consistent pattern of lying, and he had prepared a 52 page [00:07:00] document for OpenAI's board detailing those concerns, which we've talked about before.

[00:07:04] Interestingly, for those keeping score at home, ver disclosed that his openAI's stake is now worth approximately $7 billion up from about 5 billion in November, 2025. Microsoft, CEO, Satya Nadella also testified Monday he said he never believed Microsoft's 13 billion in investments violated OpenAI's nonprofit mission, and that Musk never once contacted him to object despite the fact that they, he says, have each other's phone numbers.

[00:07:33] Sam Altman himself testified Tuesday. He said Musk tried multiple times in 2017 and 2018 to merge openAI's into Tesla or convert it into a for-profit. He would majority own Altman called One Moment. During that time, particularly hair raising Musk told co-founders reportedly that CO control over openAI's could pass to his children when he died in Thursday's.

[00:07:58] Closing arguments. Musk's [00:08:00] lawyer Steven molo accused Altman and OpenAI President Greg Blackman of stealing a charity. The. OpenAI's lawyer countered that Musk never cared about the nonprofit structure. What he cared about was winning and showed evidence that Musk himself proposed turning openAI's into a for-profit in 2017, in which he would've held over 50% ownership.

[00:08:20] Now, as a reminder, Musk is asking for about $150 billion in damages, altman's removal from the openAI's board, and an unwinding of OpenAI's for profit conversion. So in this case, actually the jury's role is technically advisory. judge Yvonne Gonzalez Rogers will decide the actual penalties. Obviously, everything's kind of on the table.

[00:08:42] The options range from granting Musk everything he wants to throwing the verdict out entirely. So Paul, we've been covering this trial now for weeks. What is your read on what actually matters here? Where do you think this is gonna go? The verdict itself. I'd be curious on your thoughts on, on [00:09:00] where that's headed.

[00:09:01] Paul Roetzer: Yeah, I have no idea where the verdict goes. I think, I mean, what largely has been accomplished is a whole bunch of private text messages, personal journals, emails from these tech companies that the public would never see or generally even know how these people think were just laid bare for everyone. I don't know that either side wins in the court of public opinion.

[00:09:24] I don't, I don't know that either of them came out looking great here. Microsoft spent most of the trial trying to distance themself from everything. but other than that, it, it's like billionaires fighting over trillions of dollars. And so I think that that's one of the things that, you know, will play into.

[00:09:42] One of our topics a little bit later is just how society's feeling about all of this. And I don't know that for the most part, people really care, like we care because it's in the AI industry. And openAI's is obviously a major player as a Elon Musk. So it is like intriguing, certainly from yeah, just the [00:10:00] show of it all, and the inside information and, you know, a lot of validation of these, you know, journalists who were uncovering these stories along the way and being told that that wasn't true and it was misinformation, and then all of a sudden they have to admit to all this stuff in court.

[00:10:15] So I think that's all fascinating. I don't know where it goes. I don't know. I can't imagine that it ends up leading to an actual changing of the structure of openAI's or the Ouster of Altman and Brockman, or, you know,the, dissolving of the for-profit, like Right. I just can't see that.

[00:10:39] But I don't, I don't know. Like, it's, it's gonna be really interesting to see how the judge, 'cause like you said, the jury doesn't decide this. They're basically the nine person, jury is making recommendations, in essence, to the judge. And then the judge is gonna actually make these decisions. I thought it was notable that Altman and Brockman were there last week for the final stages of the trial while Musk was, [00:11:00] in China with Trump.

[00:11:01] So, and apparently he wasn't allowed to leave 'cause he was on a callback, whatever, I don't know legal term for it, but he needed to be available for callbacks.

[00:11:09] Mike Kaput: Hmm.

[00:11:09] Paul Roetzer: I'm sure he doesn't care about that. Like, I almost feel like Musk feels like he accomplished what he wanted to, which was probably to embarrass them and like make it really, their lives really difficult and now he just doesn't even care.

[00:11:21] Like Yeah. Whatever they decide, they decide. I'm, I'm off to China now. that was at least the, you know, perception I had of it. So, I don't know. We could be shocked. We could be sitting here next week talking about. An unexpected outcome that throws, their plans for an IPO into chaos and resets the competitive structure of the AI landscape.

[00:11:43] Maybe, but I just don't expect that I could see some sort of penalties, but who knows? Yeah. I haven't heard any good real analysis of like what the likely outcomes are. Right? It doesn't seem like anybody has a clue and I've been following the journalists who've been in, [00:12:00] the room, you know, following along and I, everybody's just kind of documenting what's going on.

[00:12:04] No one seems to have a clue how it actually plays out.

[00:12:07] Mike Kaput: I'm glad we're not the only ones who are not able to see around the corner on this one.

[00:12:13] "Forward Deployed Engineers" Are AI's Hot New Job

[00:12:13] Mike Kaput: All right. So Paul, our second big topic this week. This past week, a title called Forward Deployed Engineers, or FDEs became basically the most talked about role in enterprise ai.

[00:12:25] So these are essentially engineers who embed inside customer organizations to design and deploy AI systems alongside frontline teams. So the reason this is becoming such a hot topic now is that openAI's made a huge move on Monday that we had kind of teased last week, with the launch of this thing they call the deployment company.

[00:12:46] So this new business unit is dedicated to embedding FDEs inside customer organizations to identify high value AI workflows, redesign critical processes around them, and turn the gains into durable [00:13:00] production systems. So this deployment company actually launched with over $4 billion of initial investment.

[00:13:06] It is. The in on the investing side, led by TPG with Advent Bain Capital, Brookfield, and Goldman Sachs, among the capital backers, alongside also partners who are systems integrators, including Bain and Company Capgemini and McKinsey. Additionally, openAI's is acquiring a company called Tomoro, an applied AI consulting firm whose clients include Tesco, Virgin, Atlantic, and Supercell.

[00:13:31] They're bringing literally 150 experienced fds into this from day one. Through that acquisition. On the same day as that was announced, Google Cloud, CEO Thomas Kurian announced a new AI focused organization inside Google Cloud's go to market team. They have plans to hire a ton of additional FDS to scale customer AI transformation.

[00:13:53] This sits alongside a previously announced $750 million ecosystem commitment to help Google's [00:14:00] 120,000 member partner network deploy a agentic. And finally we had a ton of commentary online about this role. So Box CEO, Aaron Levie posted that FDEs are about to become one of the most in demand jobs in tech.

[00:14:15] He's arguing that deploying agents is far more technical than deploying traditional software because vendors need to deeply understand the customer's business process and deliver work output, not just software. He actually also urged college career counselors to start steering students towards FDE roles.

[00:14:32] Ali k Miller, a prominent AI voice, however, did push back on this. Warning enterprises that treating FDEs as their entire transformation plan can be a very expensive mistake due to all of the other change management, communication and education that is required to achieve these changes, not just technological deployment.

[00:14:52] So. Paul, there's a ton of talk about fds now. It seems like everyone is chatting about this or investing in this. [00:15:00] What is your read on this? Because we've kind of, I think, talked about the need for some type of role that might look and feel like this in the past, but is it purely an engineering role? What does that look like in your mind?

[00:15:12] Paul Roetzer: We did touch on this topic last week, so I think I mentioned I was kind of getting tired of this term already, but I don't, I don't think that's gonna change anything. I think we're only gonna hear more and more about this term. Yeah. As we mentioned then, Palantir sort of made this job title popular. You know, 10, 15 years ago.

[00:15:29] it is in essence, if you're wondering, isn't it just a consultant? Yes. That the answer is yes. It's a consultant who has technical ability to actually go in and like customize software in this k ai, AI models and agents to solve problems for businesses. So they have more technical ability, to go in and do that kind of stuff.

[00:15:47] I've, I've talked with friends who work with Palantir and organizations like that and worked with these fds and what they're often doing is going in and just trying to find business problems to solve, to, to charge money for. So now the beauty with the [00:16:00] FDE role is they can actually charge on an outcome pricing basis.

[00:16:03] You know, we talk a lot about how, what are the pricing models of these, AI companies and how they're kind of metering it and charging by tokens. The beauty of the FDE role is they can go in and say. What is a business problem? They identify, one they can solve with their AI technology, and then they say, okay, this is worth a hundred million dollars to you, so we're gonna charge you 25 million, or whatever that is.

[00:16:23] Yeah. So you can do true outcome-based pricing, which makes a massive difference. Now, at a high level, while they're not gonna say this outright, they're gonna tell you they're working with consulting firms, you know, the Deloitte and the McKinseys and things like that, but they're a hundred percent going after them.

[00:16:37] Like did They're, they're coming for your work, like they can be partners as much as they, you know, want, but the reality is there's 6 trillion in, in labor wages in the United States for knowledge work roughly. And they're coming for it. So the FDE serves a couple of roles. One is. It's hard to do this adoption.

[00:16:58] Like it's hard to [00:17:00] apply these AI models and the agent capabilities within organizations with the existing staff of those firms. They don't, you know, a lot of, enterprises aren't structured to know how to do this deeply. And so there is absolutely like a functional need for this, but there is also.

[00:17:16] a capitalistic need there. There's a need to generate massive amounts of revenue to justify their valuations and their IPOs. and so FDES is, is a bit of a Trojan horse, in my opinion, for what they're trying to do. yeah, I was doing a little prep work on this when I found an article from Salesforce in March of 26, and we, I don't think we covered it at the time.

[00:17:38] We weren't really talking too much about this, but, we'll put the link in the show notes. It was a blog post called Today's Hottest Role Forward Deployed Engineer, and I thought they had some good context in here, so I'll just read a few excerpts. They said, consider the role of natural reaction to the times AI has burst onto the scene faster than many companies can adapt.

[00:17:56] Fde are like a personal tech guru, business [00:18:00] consultant, and hand holder all in one. They work closely with companies to remove blockers and accelerate AI adoption, as well as share customer feedback with product teams to make AI agents better. The software company Palantir pioneered the FDE in early 2000 tens when it embedded engineers directly with customers, mostly government agencies at that time to help implement products.

[00:18:22] Palantir called these engineers Deltas at the time until 2016, they had more deltas than software engineers. The role evolved and gained huge traction this year when tech giants like OpenAI announced they were hiring FDE teams. Analysis by Indeed and the Financial Times found that jobs postings for this role soared by more than 800% between January and September, 2025.

[00:18:45] Salesforce alone has committed to building a team of 1000 F Ds. The role varies from company to company, but it's Salesforce, which launched its team in April 25. FDEs work in several ways. Many work individually with a customer, but some [00:19:00] are starting to work in pods that consist of one deployment strategist and two FTEs.

[00:19:04] I found that interesting. Hmm. The deployment strategist identifies the best use cases for a company and creates the overall AI strategy. The fds then design, build, and deploy the agent. They're the team's technical architects and primary coders. Pods focus full-time on one client for about three months.

[00:19:24] thus the Ford deployed part. They go in and just literally work with this client for three months or as long as it takes to successfully deploy an agent for one or two use cases. So they're very focused in their efforts. Sometimes they even travel to the customer and embed themselves in the customer's day-to-day work.

[00:19:39] It may sound like FDES do the same work as Salesforce partners. This is an interesting commentary, but they play different roles and their collaboration can help customers launch agents more successfully. Partners still do the nitty gritty work of helping customers implement technology. But FDEs can provide behind the scenes knowledge from Salesforce that a partner may [00:20:00] not have.

[00:20:02] quick context there. What they're doing is they're talking to their solutions partners who are value added resellers of Salesforce. So these are like agencies and technical partners who make their living providing services on top of Salesforce. And Salesforce is trying to thread a line here saying, we're not competing with you.

[00:20:19] We're, we're enhancing what you're doing, not taking work from you. That's a tough line to draw. I'll come back to that one in a moment. and then they finish. One reason competition for FDEs is so fierce is that the role requires an unusual combination of skills. Not only do you need to be a tech whiz, you also have to communicate well and be comfortable in a customer facing role.

[00:20:41] FDEs and deployment strategists have to listen to customers understand how business works and offer solutions in language. Non-techies can understand. That is a very. Rare mix of capabilities. It is not often the person who can build the solution is also a strong customer facing, professional, so you [00:21:00] can get at it.

[00:21:00] So why all of a sudden, like, what is going on here? A couple of of notes. So my basic take is the technology is advancing faster than enterprises are able to understand and adopt it. And AGI agentic ai, which as we've talked about many times in this show starting in December of 2025 in particular, AGI agentic AI capabilities have accelerated the urgency and the complexity of scaling ai.

[00:21:25] Hmm. in the Levie post you mentioned he wrote, deploying agents is far more technical of a task than most people realize. Often, far more involved than deploying software. Software generally works the same way every time and generally for the past few decades has been updated, versions of an existing technology or concept, which basically means easier to the enterprise to adopt their workflows on a newer system.

[00:21:48] With agents, you're actually deploying the equivalent of work output within the enterprise. The customer's effectively using you as a professional services provider for a task which they expect to [00:22:00] get solved, nearly end to end. Now, this means you need to actually deeply understand the business process as a vendor and get the customer from a current to the end states seamlessly.

[00:22:09] So FDs are technical, but the same opportunity and need exists for non-technical consultants. So this is, you know, my commentary here. So we think about agencies. So again, if, if you're not familiar with, our history, I owned a marketing agency for 16 years. We were HubSpot's first var, first value added reseller solutions partner back in 2007.

[00:22:28] So I grew up in this ecosystem, this idea of partner ecosystem and a solutions partner ecosystem. And so when I look at the needs right now, these fds that openAI's and Google Cloud and, everybody is building Salesforce, HubSpot, I would imagine is probably like getting ready to announce they've got one.

[00:22:46] What they're building is solving the technical side of this and the agentic stuff is making the technical side higher in demand. But as Allie Miller alluded to, it's the non-technical stuff. The support around adoption and change [00:23:00] management and communications and education and training like that need is gonna be as bigger or bigger than the technical lead, in my opinion.

[00:23:07] And all of this is based on the premise. Like when I was in the HubSpot ecosystem, they had a report, and I think I might've mentioned this last week, $1 in software sales equals $6 in services. So the premise is if you're spending a a million dollars with openAI's. You're gonna spend six to 10 million on the services to make that million dollars work.

[00:23:29] And so that's the money everyone is now going after. So openAI's and Google Cloud and others can sit back and wait for a solution partner ecosystem to a a, a vert. Emerge that can provide this level of service, but they don't have time to wait. They, they need their AI models and agents working right now, so they're just gonna go hire all these people.

[00:23:50] And so even if they say they're not directly competing with the consulting firms, they're a hundred percent competing for the talent that those consulting firms would otherwise hire. So this is [00:24:00] what happens, this is how it works. Like HubSpot, in the early days, we were part of, in essence, a pilot test to prove to them that solutions partners could drive retention, and growth of accounts.

[00:24:11] And so what then ended up happening is over time, as the ecosystem sort of leveled out and you had different levels of capabilities across the different partners, oh, HubSpot realized that they could not depend on the same level of service and quality from all the partners. So they started offering the onboarding services themselves and started charging, I don't know what they charge today, but it used to be like $5,000.

[00:24:32] To do an onboarding installation because that's how they ensured that the client would actually get value. So this is a constant conflict going back decades where technology companies sell a solution, but then they need services on top of it. And your options are build a solution, partner ecosystem, or do it yourself.

[00:24:51] And sometimes they try and balance between those two. And then the final note, Mike, here I will make is, you know, we released our state of AI for business [00:25:00] report last week. We'll put a, a link in the show notes to it. I think it's state of business.ai. I think you can go and get it. So the two, yeah, the two key things.

[00:25:08] We asked the question, what are the barriers to adoption? Number one, lack of education and training. Number two, lack of awareness and understanding. And so this is the stuff that has to get solved. They, they don't know how to do it. They don't know what the models are fully capable of and they can't train their people fast enough to do it.

[00:25:23] So a a, a complimentary approach is you bring in the experts to do it and like I said, there's just massive amounts of revenue and they're gonna look at that $1 equals $6 to $10 in services, but the bigger number they're all looking at is the 6 trillion in labor. That every year. So the US is like 11 trillion, I think, overall.

[00:25:41] And about, you know, 4 to 6 trillion is it's knowledge work. And so they're totally going for that. I don't blame 'em like, it's a logical thing. You wanna call it an FDE or a con technical consultant, or whatever you wanna call it. It's needed.

[00:25:56] Mike Kaput: You know, when you were talking I just couldn't help but like nodding [00:26:00] along, especially at the non-technical point because I read about F Dees and I think it makes sense as a concept and I can certainly see the need for it.

[00:26:08] But if we think that this is the only role that needs to be in this kind of forward deployment of aI think we're wrong on that. Like this doesn't work just with fde or to your point, it has to be an FDE who's uncommonly good at all those other things, which is not a lot of engineering minded people, I would say.

[00:26:27] Paul Roetzer: Totally. Yeah. I mean we're doing something similar. I don't think I've talked about this publicly, but we're doing something similar with AI Academy. So yeah. You know, AI Academy SmarterX, the way I think about this is in essence, I told the team months ago, we basically have to build an internal agency.

[00:26:41] To help organizations do the education and training. So there's a lot of enterprises that have l and d teams in place that, you know, can take, AI Academy and run with it. But then there's a lot of organizations that don't or that, like, we're working with a marketing team that maybe doesn't have the full support of the l and d team to do what they're trying to [00:27:00] do.

[00:27:00] And so we think about what we do through Academy as, as AI transformation, not just selling courses. so again, I've mentioned before like Coursera, LinkedIn learning, like there, there's great places to get courses. That's not what we're trying to do. We're trying to actually get in there, identify what are your goals, what does success look like?

[00:27:16] What are the barriers to, you know, preventing that, you know, in terms of communications with employees, dealing with cohorts of employees who don't want to do this stuff, don't wanna learn ai. So I see it as like we have to build a similar concept. I would not call 'em FTEs, right? But we have to function as advisors to help our AI Academy customers drive, transformation and change within their organizations.

[00:27:42] And you have to be sensitive to all these complex issues. And so I think about a similar approach where we have to build advisory services on top of AI Academy to go do this. And then we may also empower an ecosystem of partners to support in that way. But similar concept, like we're offering a solution that on its own [00:28:00] needs, additional guidance within many organizations to make it happen.

[00:28:03] And so I'm building a system to do that, you know, and supported by different technologies. But we're also looking at the service side and realizing we're gonna have to build that capability pretty quickly.

[00:28:13] Mike Kaput: Yeah. And I'm just curious as we round this out, what your thoughts are. I kind of found myself agreeing with Levie when he was like, Hey, this is a huge opportunity for possibly recent college grads or more entry-level people.

[00:28:25] He focused on the cus computer science aspect of it. Hey, like if you have a computer science background, FDE is an amazing path that we should be counseling people to go down. I could almost see what, whether you call it an AI accelerator, just an advisor, consultant, whatever, I think this is a potentially large opportunity for, not just entry level careers, but if you are an AI forward version of anything, a marketer, a salesperson, operations person, there's a real interesting path here in my mind of how you could add value to an organization doing this [00:29:00] kind of thing

[00:29:00] Paul Roetzer: at an individual level.

[00:29:01] Definitely. Yeah. You know, I think that premise holds true. I also look at it as, You know, from an organizational perspective, if you're an outside solutions partner

[00:29:11] Mike Kaput: And

[00:29:11] Paul Roetzer: you wanna specialize in openAI's or Anthropic, like again, like we did with HubSpot back in the day, I just bet everything, the future of our company, I just betted on HubSpot, we went all in.

[00:29:19] We didn't try and learn Marketo and Salesforce and Pardot and all the players at that time. We just said, let's just get great at HubSpot. And so every employee was trained deeply in HubSpot's capabilities. We would often get on calls with their customer support team and we would solve things that they didn't even know were problems.

[00:29:36] And that's what made us a great solutions partner, um Is you, you get to know the product oftentimes better than the own marketing, sales, and customer success teams of the technology company. And so I think there's a tremendous opportunity right now for service firms to go in that direction.

[00:29:53] Whether you, you know, specialize in capabilities across a collection of AI platforms and companies or. [00:30:00] You just make your bet. We're gonna go in Google Cloud, we're gonna go in, you know, with openAI's or Anthropic or whatever it is, and you just get great at it. These companies aren't gonna hire the FDs fast enough.

[00:30:09] the issue you're gonna have is them then coming and hiring your people, like, yeah, yeah, that's a real. A real concern. Yeah.

[00:30:17] Mike Kaput: All right.

[00:30:17] The AI Jobs Apocalypse Debate

[00:30:17] Mike Kaput: Our third big topic this week. So in the past week, we've seen a range of very prominent voices pushing back very hard against the narrative that AI could fuel a job's apocalypse.

[00:30:29] So this included people at Andreesen Horowitz, the notable, commentator, Scott Galloway, Andrew Ng, Derek Thompson, the journalist. All of them made this case from kind of different angles that the fear of job loss from AI is largely manufactured. So Scott Galloway wrote an essay that framed the AI jobs apocalypse as narrative driven, engineered by people who profit when you're scared.

[00:30:53] He pointed out that US Tech employment has stayed flat at 9.6 million for three years. He argued [00:31:00] that recent layoffs from Meta Microsoft and Oracle are mostly returns to pre pandemic headcount, not AI displacement. And he basically claimed that the AI job. Apocalypse Narrative is just a marketing strategy for the labs to make their products seem more valuable and to be able to charge more for them.

[00:31:17] Over at Andreessen Horowitz, David George argued that the AI doomer position is the old lump of labor fallacy with new branding. So this fallacy assumes a fixed amount of work. So if AI does, more humans must do less. George pointed out that history contradicts this. For instance, agriculture went from a third of US employment in the early 20th century to about 2% today.

[00:31:41] But output tripled and workers flowed into entirely new industries. Andrew Ng, called the AI Employment Narrative, irresponsible and damaging, pointing out that software engineering is the field most directly affected by AI coding tools. Yet hiring remains strong, and overall US unemployment is still a healthy [00:32:00] 4.3%.

[00:32:01] he also argued that AI labs have an incentive to make AI sound powerful enough to replace workers. So that they can justify much higher pricing by anchoring the value of their solution to salaries rather than your typical SaaS company pricing. he actually expects we're going to have an AI job-a-palooza, where we're gonna see a ton more jobs created from AI rather than destroyed.

[00:32:25] Derek Thompson released a podcast titled The Smartest Case Against the AI Jobs Apocalypse. He argued that human desire and status seeking are insatiable, and in his view, even if AI automates many tasks, new categories of work will emerge wherever people are willing to pay for scarcity or status. And finally, kind of on the other side, Brookings researcher, Molly Kinder, who we've talked about before.

[00:32:47] She published an essay called The Messy Middle, where she argued the binary between today's intact labor market and a future post AGI world of abundance. Should we get to something like that? Ignores the long [00:33:00] stretch of disruption in between. She warned the AI may reverse the decades long skill premium for knowledge workers with losses concentrated in the highest paid cognitive roles.

[00:33:10] So, Paul, curious what you think is going on here. We've got some really prominent people arguing, it seems like all in the same week, that we're not going to lose as many jobs due to AI as people, I guess the doomers we might call them, or just maybe realistic people might think, what, what's going on here?

[00:33:30] Paul Roetzer: by last Wednesday, I was trying to figure out like, did an email go out to everyone saying counter the jobs narrative this week? Yeah, it was just like everywhere. So I don't, I don't know. I mean, they can say whatever they want, but it's not changing public sentiment. It's going in the opposite direction.

[00:33:45] So the state of AI for business research that I just mentioned, we ask a question in there, and we've been doing this for six years now. We've been asking this question now for six years. What do you believe the net effective AI will be on jobs over the next three years? This was actually the starkest finding in this [00:34:00] year's data.

[00:34:00] 71% of respondents believe AI will eliminate more jobs than it creates in the next three years. Only 13% expect net job creation. And then 12% say they simply don't know. and then we noted in the report that what makes the finding so notable is the consistency. So the belief that AI will be a net job eliminator does not vary meaningfully by roles.

[00:34:24] CEOs and VPs, 73% think, you know, eliminate more than it creates directors and managers, 71% and specialists, 64%. So, it also doesn't vary by function, marketing, engineering, sales, operations. They all converge with a few, within a few points of the 71% average. And then when we put it in context, this is where it gets really interesting.

[00:34:45] Mike, in 2023, when we asked this question, so this would've been right around the time GPT-4 came out, we would've had the survey in the field around that spring of 23. So right after ChatGPT in 2022. [00:35:00] 40% of respondents said AI would eliminate more jobs than it creates the following year. And we asked the surveys in the field again, roughly late spring every year.

[00:35:09] So this is a, a, cyclically around the same time. the number was 47%, so nothing crazy is seven percentage point jump. That's about, 20% or so. Last year it was 53%, so 2025, 53%. So it's inching up six, seven points each year. And then this year, it jumps 18 percentage points or 34% it jumps up. so that is a significant change in the sentiment and how the public is viewing this.

[00:35:40] And we ask 20 more than 2100 professionals across industries geographies. So, and most of the people who answering this, again, there's some bias in every data set, but ours is that they're more likely to be AI for professionals and leaders because. They're taking our survey, which means they're seeing it through our podcast.

[00:35:57] They're hearing about it in our newsletter, things like that. [00:36:00] So these generally are more informed people who are using AI themselves and seeing what's possible and realizing this is, this is significantly going to impact jobs. And then I think there's just the reality within companies, and I'm not sure.

[00:36:12] Like the people that are saying this are, are incredibly educated and well-informed people. Yeah. Whose opinions we listen to on a lot of issues. So this is not like, you know, people who generally just like to say the opposite of, of what's actually happening. For the most part. There's definitely some in there who I think just say the opposite regardless.

[00:36:32] But everybody we talk to and every company I talk to and across every industry, flat headcount is a common goal. Like, that's their best case scenario is they're, they're trying to slow down hiring and they're trying to keep it flat while increasing revenue. these, the people who are trying to take this alternate position that it's gonna be amazing, we're just gonna create a ton of jobs.

[00:36:54] It always works out. 'cause historically it always has. the job [00:37:00] displacement we're seeing is from over hiring is the thing they keep saying. And I just don't get it. Like, I don't see how that is like a standard, belief system that makes you just ignore. The possibility that that isn't what's gonna happen.

[00:37:15] So on a positive note, Mike, the one thing we are seeing is more companies that are, displacing workers, but saying, listen, we're going to hire, we're just gonna hire AI forward employees moving forward. And the chick trick there is they just not gonna need as many of them. So we had this happen with General Motors.

[00:37:34] TechCrunch reported this, general Motors has laid off more than 10% of its IT department, or about 600 salaried employees in what they're calling a deliberate skills swap, clearing out workers who expertise no longer fits and making room for some with AI focused backgrounds. So I think this is something we're gonna start hearing more about.

[00:37:53] They said the most sought after capabilities are AI, native deployment, data engineering and analytics. This is sounding a lot like the FDE [00:38:00] thing, cloud-based engineering and agent and model development. Prompt engineering and new AI workflow. So they're looking for the people who are AI literate.

[00:38:09] Mike Kaput:

[00:38:10] Paul Roetzer: Now, the thing that I think is gonna be interesting, Mike, is all these people who are very, very confidently saying jobs aren't going anywhere, that's just gonna all work out. My general feeling is one by one, they're gonna come to the realization that they might have been giving false hope to people. this happened with Ken Griffin just this week.

[00:38:28] So Ken Griffin, the CEO of Citadel was a prominent AI skeptic and he now says AI is real. So this was Business Insider. we'll put a link in. So many CEOs have been saying for years that AI can do the work of many white collar professionals and remaking their companies accordingly. Griffin has been a notable holdout at the beginning of the year during a panel discussion at the World Economic Forum in Davos, which we reported out at the time.

[00:38:54] The hedge fund billionaire said AI was impressive on the surface, but as soon as you dug deeper, it's all [00:39:00] garbage. This is like three months ago.

[00:39:02] Mike Kaput:

[00:39:03] Paul Roetzer: so their article at the time, AI is not going to be a game changer for Investment. So this was, the investment business. This is for May or March of 2000, 2025.

[00:39:13] He said it saves some time. It's a productivity enhancement tool. It's nice. I don't think it's going to revolutionize most of what we do in finance. So machine learning is going to come with a cost to society. So even though he is saying that he's not gonna pick their business, he was acknowledging that there was like a bigger picture here.

[00:39:31] So, so he quote, machine learning is going to come with a cost to society, a cost that we need to understand. How do we help these people land on their feet so we don't end up with a backlash against AI machine learning. So then coming back to his comments at the Stanford Business School this past week, or earlier this month, he took this starkly different view.

[00:39:50] He said, I gotta tell you, I went home one Friday, actually fairly depressed. You could just see how this was going to have such a dramatic impact on society. Again, this is three [00:40:00] months later, Griffin said AI had become profoundly more powerful than it was nine months ago, and had allowed the head hedge fund to unleash a wider range of use cases for the technology.

[00:40:12] He said for the first time, AI is real. And then he said, quote, to be blunt. Work that we would usually do with people with masters and PhDs in finance over the course of weeks or months is being done by AI agents over the course of hours or days.

[00:40:28] Mike Kaput: Hmm.

[00:40:28] Paul Roetzer: He emphasized that this goes beyond what he called mid-tier white collar jobs that are now being automated with AGI Agentic ai.

[00:40:36] So my argument, Mike summarized, and I've made this argument now for multiple years, but again, I know we have new listeners to the podcast all the time, so I'm gonna kind of highlight our view on this once you have a deeper understanding of the full capabilities of today's AI models, including the agenda capabilities, and a general concept of how these models are going to improve over the next one to two years.

[00:40:59] [00:41:00] Your view of the future of work and your business changes? Specifically, as I said, this is applying to the increasing autonomy and reliability of agents. That's what's happening to Ken Griffin. He's coming to this realization of, this isn't the same technology that we had nine months ago. He's now seeing what you should have been seeing nine months ago, which is where these agents were going.

[00:41:20] And what if you stop now and look ahead and say, oh my gosh, like where are we gonna be nine months from now? I don't understand this argument that we won't have fewer jobs. Like I really just. Struggled tremendously with this. So if you aren't growing, so I think what happens oftentimes is they look at Anthropic and openAI's and Salesforce.

[00:41:38] They look at these companies like, well, they're hiring, they're hiring developers nonstop. Right? They're hiring fds. Yes. And they're also growing 40 to 60% a year. So like, of course they're hiring. If you aren't growing or if you are in a business that's growing, let's say less than 10% a year, if you don't need fewer people, then you are not properly applying [00:42:00] ai.

[00:42:00] Like you, you just don't need as many people to do the same level of output or the same amount of revenue. So employees who are AI forward, and able to drive efficiency and productivity gains are going to reduce the total number of people needed to do the same amount of work. That's what General Motors is doing.

[00:42:16] We're gonna get rid of these IT people, the 600 of them who don't like AI or aren't good at it, and we're gonna hire 200 back who are like AI forward and they're gonna do the work of 2000. Like that's the premise here. so if you are in big tech and you're growing 20%, or if you're outside of big tech and you're growing at 20, 30, 40%, then yes, you are probably still hiring and you can get this feeling.

[00:42:40] Like everybody should be hiring. 'cause you live in a world where growth happens. But if you work for a private equity owned company, a VC-backed company, or a public company, the pressure to increase revenue for per employee is going to be massive. So if you just look at a simple metric of how much revenue does each employee in our company generate, [00:43:00] everybody is going to pressure that KPI and there's two ways to improve it.

[00:43:05] You reduce your costs largely by reducing payroll, or you increase revenue with the same headcount. That's it. So I don't, I, again, I want to be wrong on this. I really wanna look out three years from now and saying, Nope, they were right. Like, jobs just kept growing. It was awesome. Everything worked out great.

[00:43:24] But I also don't understand the premise that a, a responsible leader of any organization wouldn't plan for the alternative. That maybe there is this chance that we do go through a one to three to five year period. But we're just gonna need fewer people and it's gonna cause displacement. It's gonna cause us underemployment across the economy.

[00:43:43] And yet they seem to all have this unshakeable confidence that they're right. and so I guess that's my big thing is like we need AI to drive innovation and growth. We need to be realistic about this. And human centered in the fact that we should be preparing for the possibility that [00:44:00] there's at least displacement, that college students are gonna have a hard time getting jobs that highly paid people like PhDs and people with masters aren't gonna be needed as much in companies like Citadel.

[00:44:12] and I don't understand the premise of pretending like that's not a possibility. It just seems like a disservice to humanity to just not at least say maybe, maybe one to three to five years is gonna be kind of messy. Maybe ten's gonna be amazing 10 years out. It's like it's all gonna work out, but.

[00:44:27] That's easy to say when you're making a half a million a year. Right. More you 5 million, whatever. And it doesn't affect you. but I just feel like, and we're gonna talk about it in the next topic, there's a hell of a lot more people who don't feel that way. Our own research says 71% a jump, 34% increase in people who think it's gonna eliminate more jobs in the next one to three years.

[00:44:52] that's a major problem that these people are basically just glossing over with a future of abundance. And historically it's just always worked out. I don't understand that [00:45:00] premise.

[00:45:00] Mike Kaput: Yeah. I was gonna mention to that last point, this just strikes me. Such a tone deaf argument because like people are already upset right now, and I'm no economist and I agree with you.

[00:45:11] This is the number one issue that keeps me up at night. I genuinely hope I'm proven so wrong. Like it would be my most joyful outcome would be like five years now I'm being like, I worried so much about that and it was not a thing to worry about. Great. I, you can poke fun at me for the rest of my life for being wrong on this.

[00:45:27] But like, it's really hard to hear these arguments of like, well, you know, people get displaced and move into other jobs. Like, well let's talk about the nature of that displacement. Are you gonna move into another white collar job that pays you the same amount or more? In some cases, perhaps, but probably not.

[00:45:45] What happens when you have a nation of people that used to be doing okay with white collar jobs and now after Drive DoorDash or Uber or something, like, you're gonna have a lot of angry people. And as we're gonna talk about, they're already really upset

[00:45:58] Paul Roetzer: about this issue. And [00:46:00] again, they keep coming back to, but look at software developers, look at the need for fds.

[00:46:03] It's like, okay, well the out of work marketing manager isn't becoming an FDE. Like, I'm sorry. Right. And like I I, and again, like we're doing our best to hire as many people as we can at SmarterX. I'm not hiring people who aren't AI forward ever again. Like it's, and that's just the reality. And if I talk to other companies, it's what I advise them.

[00:46:26] You cannot hire people who aren't AI forward. I, yeah, it's. It, it is a, it's a disservice to your organization and you have a responsibility as leaders to do what's right for the organization. So you have to offer the ability to reskill and upskill people. But if they choose not to learn the technology, there's not a hell of a lot more you're gonna be able to do as a leader.

[00:46:47] But I don't see job opportunities for people moving forward that don't buy into the technology, which right as we're about to talk about, is gonna become a [00:47:00] major problem for the next generation entering the workforce this year.

[00:47:03] Mike Kaput: Alright, Paul, so before we dive into that and all of our other rapid fires, just one other announcement.

[00:47:08] This episode is also brought to you by MAICON, our marketing AI conference that we run every year. This year's is happening October 13th to the 15th. Here in our home base of Cleveland, Ohio, we are super excited to announce that Dan Slagan, SVP of Marketing at Zapier has joined the 2026 speaker lineup. He leads growth for one of the world's most connected AI orchestration platforms.

[00:47:32] Previously, Dan was@cmoattomorrow.io. He's been recognized by Forbes as a top 50 entrepreneurial CMO. His work has been featured all over the place with what he was doing@tomorrow.io. He has previously spoken at MAICON. We can't wait to have him back on stage. And what's really cool is Dan joins a 2026 lineup that Paul is just shaping up to be incredible.

[00:47:54] We've got Karen Howe, the award-winning AI journalist and author of Empire of ai, Andrew [00:48:00] Yang, former presidential. Candidate and tech and eco economic futurist. Of course, Paul, you yourself are speaking at MAICON Plus, we've got a ton more speakers ready to be announced. So this is three days of workshops, keynotes, and sessions and conversations built specifically for marketing and business leaders who are actively figuring out how to adopt, operationalize, and scale AI across their organizations.

[00:48:22] So one important note here, if you are thinking about joining us at MAICON this week and next week are a really good time to do that. Ticket prices go up on May 30th. If you register before then, you'll get the best pricing available. Also, you can use the code POD100 at checkout and save an additional a hundred bucks on top of the current rate.

[00:48:42] So go visit MAICON.ai, that's M-A-I-C-O n.ai to register.

[00:48:49] The AI Hate Wave Is Here

[00:48:49] Mike Kaput: Okay, Paul, so let's dive into this first rapid fire we've been alluding to throughout our conversation so far. So an Axios piece this past week was published called An AI Hate Wave is Here, and it captures what is now starting to look like sustained backlash against AI in the us.

[00:49:08] And they actually write in their opening here. If AI were a candidate for political office, it would be losing in a landslide because they cite a bunch of recent data that does not look great for sentiment about ai. So a recent Gallup survey found only 18% of young people ages 14 to 29 feel hopeful about ai.

[00:49:28] An economist slash u gov poll released this past week found that over 70% of Americans think AI is advancing too quickly. With the figure consistent across parties. 68% of Republicans, 77%, Democrats U Gov's tracking shows negative views of AI have risen from 34% three years ago. To just over 50% today. Axios opened their piece as well with a Florida commencement address earlier this month that went a bit viral because real estate executive Gloria Caulfield sparked a chorus [00:50:00] of boos when she told graduates that AI is the next industrial revolution.

[00:50:06] the backlash is starting to have real effects too. There's a record number of data centers that were canceled in the first quarter of 2026. Amid community resistance. Morgan Stanley analyst wrote that public pushback is emerging as a binding constraint when we're thinking about investments, particularly around data center buildout and Jeffrey is told clients these setbacks are sapping investor confidence.

[00:50:27] So, Paul, let's dive into this. This really does seem like sentiment is going not only the wrong direction, but is going to stay there for a bit. This doesn't seem like a passing fad anymore.

[00:50:38] Paul Roetzer: Yeah. As you're, as you're going through this, I had this thought like. So the students graduate right now would've started in 2022, right?

[00:50:48] That, yeah. 23, 24, 25. Yeah. so they went through college with that minimum ChatGPT. Mm. Like their, you know, came into to being in their freshman year of [00:51:00] college basically. high school students today won't know education without it. I mean, they've gone through high school, they'll go through college with it.

[00:51:09] And one of the problems we faced in the early days of generative ai, and even today many schools, is these students have been told it's cheating. They've, they've been raised, raised, I'm gonna use loosely over the last three or four years that AI is cheating. They're being taught by professors who want nothing to do with it.

[00:51:29] There was a lack of effort at a higher education level to move with urgency to embrace it in a responsible way. And we just like. Outlawed it, or called it plagiarism, whatever. So part of this issue, Mike, and again, I'm kind of thinking out loud here, is if you're told something is bad long enough, like you come to believe it's bad, right?

[00:51:53] I, I don't, I'm actually trying to think like, where else would the negative sentiment have come from? Because up until [00:52:00] the last six months, nobody gave a shit about this politically. Like it wasn't right. It wasn't like this was like some big effort by one side or the other to vilify it. I mean, certainly we're heading in that direction now, but I don't know, like, I'd be really fascinated actually to like, dig into why the hate exists, I guess, or why the distrust exists.

[00:52:21] It is the first thing that came to my mind is it's just right. Just that's what they've been ingrained in them.

[00:52:26] Mike Kaput: I would imagine the school piece is significant. I could imagine. I have no way to prove this, that as certain things like data centers have become more prominent, that there's probably a solid ecosystem of commentary online that perhaps people are seeing videos on TikTok and things that are blowing up around these kind of polarizing issues.

[00:52:46] But to your point, that wasn't the case a year ago.

[00:52:49] Paul Roetzer: Yeah. Someone's funding. Campaigns to make. Yeah. Whether it's just virus, that

[00:52:53] Mike Kaput: wouldn't surprise me.

[00:52:54] Paul Roetzer: Yeah. Yeah. So, to, to bring this point home, we'll put a link in, in, in the show notes for this one. But [00:53:00] you, you gotta watch the six and a half minutes or so of Google's former CEO Eric Schmidt giving, uh.

[00:53:06] the commencement speech at the University of Arizona last week. I will read a couple quick excerpts here from A-C-N-B-C article, but it says, Schmidt, who led Google for a decade opened his remarks by reflecting on his own student years and the rise of the computer of device named Time Magazine's person of the year in 1982.

[00:53:25] Back in his day, he traced its evolution into the laptop and smartphone and its per proliferation through the internet and social media. While the computer connected people democratized knowledge and lifted many outta poverty, it also carried a darker side. Schmidt said the same platforms that gave everyone a voice like you're using now as they were booing him, also degraded the public square.

[00:53:47] He said they rewarded outrage. They amplified our worst instincts. They co in the way we speak to each other. And that way, and in the way that we treat each other in is in the essence of a society. Schmidt [00:54:00] then drew a parallel between AI and the transformative impact of the computer, and was immediately met with boos.

[00:54:05] And they are, if you listen to the clip, it is awkwardly painful. Like the boos got louder and louder as he kept talking. So he said, I know what many of you are feeling about that. I can hear you. There is a fear, there is a fear in your generation that the future has already been written, that the machines are coming, for the jobs.

[00:54:24] Schmidt's reception was not an isolated incident. Earlier this month. Real estate executive, Gloria Caulfield was similarly booed at a commencement speech at the University of Central Florida. After mentioning the controversial technology, the rise of artificial intelligence is the next industrial revolution, she said is the crowd erupted in boos.

[00:54:46] So again, when you, when you watch the Schmidt talk, it is like honest to God, like it was like a dystopian feel. Like I found myself. Having lot like a flood of thoughts [00:55:00] about it. I've mentioned on previous episodes that the industry has a major PR problem. Like they have, they have for too long ignored their own impact on jobs and impact on society with this whole future of abundance thing and just assumed it was gonna just work out.

[00:55:16] And I think they're now realizing that society doesn't work that way. Even when Schmidt through the boos transitioned, I thought he was just gonna walk off stage at one point. Yeah. But he transitioned to the good it's doing, how it's gonna solve cancer, it's gonna do all these things. They booed louder when he tried to highlight the good stuff and I was like, oh my God.

[00:55:36] Like this is maybe a larger problem than I was expecting it to become. Mm. and then I had this moment where, well, well, I guess I'll play, play it out for a second. If you're a politician and you want to win. We are about to see a tripling down of those people on the fear mongering. Like we are going to hear so much negativity [00:56:00] around data centers and job loss and all this stuff because these boos opened the floodgates for that messaging to work.

[00:56:07] Yeah. and that is a very dangerous thing to do to, to, to, to shift power. I guess the societal backlash is very real though. And the thing that really worries me, I mean, there's a lot of this that bothers me. but the thing that I really was worrying about Mike as I was listening to those boos, was you aren't gonna find a job.

[00:56:29] Yeah. Like if, if you are sitting there booing him, you can boo Eric Schmid. You can hate Google, you can hear Eric sch, I don't whatever. Like, I'm not standing up for him or like Yeah. You know, whatever. He is done. I don't know the guy personally. I don't think they're booing him though. I think they're booing.

[00:56:44] What he stands for as a leader of an industry that builds this technology that they feel threatened by.

[00:56:52] Mike Kaput: Yeah.

[00:56:53] Paul Roetzer: But if you're in that crowd or any commencement crowd, or you're having these feelings as a 21-year-old, a [00:57:00] 22-year-old, I am sorry, but you are not getting a job. Like you, you can have those feeling towards ai.

[00:57:08] And I empathize with them a hundred percent. Like I understand the fear, the anxiety, even the hatred towards it. The tech isn't gonna stop. It's not gonna go away. And unless you find ways to embrace it and go work for companies that are doing it in a responsible way, but if you show up to an interview and you say you hate ai, they're not gonna show you the door fast enough.

[00:57:33] Mike Kaput: No.

[00:57:35] Paul Roetzer: and so that is a really, I don't know how we solve this. Like it's gonna be hard enough to find jobs for these. The students graduating right now. If you layer on top of that, a hatred for ai Yeah. And a refusal to learn how to use it in a responsible way. Yep. You have no job prospects. None like it.

[00:57:55] It, this just doesn't work. And this is like, now I'm like, I'm [00:58:00] concerned about jobs on a totally different level after the last 24 hours, the more I've kind of sat on this and watched that video.

[00:58:06] Mike Kaput: Yeah.

[00:58:06] Paul Roetzer: Of what happens to these students who just resist the change that is inevitable. You're, your booing isn't going to stop AI from changing work.

[00:58:17] And I don't, I don't know what to do with that, Mike. I really don't. I'm just like, I'm, I'm really conflicted right now and like Yeah. Struggling with this one.

[00:58:25] Mike Kaput: You know what's also really disturbing to me to consider is, look, I again, couldn't echo more what you just said, but there's the kids booing and then there's the kids who are friends with the kids booing who might on their own be.

[00:58:39] Curious about ai, but I would imagine if the boos are this bad, there's a lot of peer pressure where I would imagine among your friend group, your cohorts, it's probably not a good idea to be talking about this stuff. And I think that's such a shame where like there might be people who are put off on experimenting or exploring AI because of the sentiment, even if they [00:59:00] don't agree with the boos, which I would just encourage you if you feel like you're in that spot or have a kid who is like, try to work against that.

[00:59:07] Paul Roetzer: Yeah, we've, we've had these conversations internally, so again, you have to keep in mind like at SmarterX we are not an AI model company. We're not building this technology. We're trying to teach how to use it in a human set of responsible way. And we have humans within our company who. Are scared about the future too, like

[00:59:23] Mike Kaput:

[00:59:23] Paul Roetzer: These are real conversations that we have all the time where, you know, either we have an employee who's like, I am, I'm actually terrified about what it's gonna do to jobs. And we like, okay, that's why we're doing what we're doing. You have employees we talk to, like coworkers of ours, Mike, who's friends, don't like the fact that they work in ai.

[00:59:41] Right. Who judge them based on that. Yep. and like hate it. And so we are, we are living in this world. We are not living in the Silicon Valley tech bubble of like, all progress is amazing and abundance is, you know, guaranteed and it's just gonna work out kumbaya. We're living in the [01:00:00] world where we don't necessarily all feel super positive about the future.

[01:00:06] And where we have friends and family members who don't know why we're in AI 'cause they don't understand what we're trying to do in ai. it's, it's a very weird. Time and I don't, I, this is definitely one where I just don't have the answers, but It weighs on me. Yeah. Like every day.

[01:00:26] Mike Kaput: Yeah. I couldn't agree more.

[01:00:27] I think we're, this is not the last time, unfortunately, we're gonna be exploring the implications of this. Yeah.

[01:00:34] Two Scenarios Could Unfold in US-China AI Race

[01:00:34] Mike Kaput: Okay, next up. This past week, Anthropic released a paper called 2028 Two Scenarios for Global AI Leadership. And in it they laid out the view, their view of the US China AI race and what policymakers need to be doing right now.

[01:00:48] Now, this was interesting timing because this dropped right in the middle of President Trump's visit to Beijing. So in the paper, in the first scenario, they outline a pathway where America [01:01:00] defends its compute advantage. Policymakers tighten chip export controls, they disrupt China's distillation attacks on US models and accelerate democratic AI adoption.

[01:01:11] Anthropic projects that this locks into a 12 to 24 month US lead in AI by 2028. In the second scenario, the US fails to act. The Chinese Communist Party catches up to near frontier intelligence deploys subsidized AI globally and authoritarian regime shape the rules and norms of the technology. Now Anthropic argues the window here to pick a path or get on a path is closing fast.

[01:01:36] The paper says there is a high likelihood that we will look back on 2026 as the breakaway opportunity for American ai, and they pointed to their own mythos model as a wake up call for the acceleration ahead. Now meanwhile, in Beijing, the summit between President Trump and Xi, the leader of China produced no signed AI agreement, treasury Secretary [01:02:00] Scott Bessant told CNBC.

[01:02:01] The two AI superpowers will start talking and set up a protocol in terms of how do we go forward with best practices for AI to make sure non-state actors don't get ahold of these models. Trump told reporters aboard Air Force one, the two sides discussed possibly working together for guardrails.

[01:02:17] Interestingly, Nvidia, CEO Jensen Huang was initially left off the CEO delegation that accompanied Trump in China to avoid some awkward optics on chip controls. After the snub, leaked to the press, Trump personally called him and Huang boarded Air Force one at an Alaska refueling stop. So Paul, it's interesting Anthropic releasing this while Trump is in China.

[01:02:39] There is, it sounds like from Anthropics view, a window that is closing for the US to establish American AI dominance. What did you make of all this?

[01:02:49] Paul Roetzer: I am nowhere near enough of an expert on geopolitics to like, comment deeply on this one. I always suggest AI superpowers by Kai Foley. If you wanna [01:03:00] understand US and China relationships, chip war is another great one.

[01:03:04] If you wanna understand, significance of Taiwan and, you know, why the conversations are centering around China's ambitions with Taiwan. The all I guess all I'll say from an, just from observing and studying this stuff through the years is whatever they would agree on to work on together with AI safety, I think is probably, probably not worth the paper it's printed on.

[01:03:34] Yeah, right, right. They're so deeply, Embedded in like espionage and stuff with each other. And I think Trump even boasted at one point, toxi, whatever you you're doing to us, just know we're doing it way worse to you. Or, I dunno the exact words were, but Yeah. Yeah. Like, yeah, you think you've got us. Wait, wait, you should know what we've done to you, kind of thing.

[01:03:58] And so that stuff isn't [01:04:00] stopping. Like they're not gonna just all of a sudden be like, all let's just work together this be amazing and figure it all out. It's like, no, they need each other at a high level for lots of different things. but you know, if you think that they're gonna stop trying to steal the weights of the mythos model and things like that, like that, come on.

[01:04:18] So yeah, whatever It's, it's a trade trip. It's trying to open up, you know, money and trying to demonstrate power and things like that. I don't, I, I just don't have high hopes for it personally. I, and I think Taiwan, I, again, I don't want to get into like super. Hot button political issues, but, if you don't know why Taiwan is such a sticking point, you should learn it.

[01:04:45] Mm. And they said Chip war is a, a great book to understand it, at least at a technological perspective, but I think it's safe to assume that it's gonna become a much [01:05:00] more mainstream story in the not too distant future based on things the US is doing. in other countries and, what China's ambitions are with Taiwan.

[01:05:12] I just, I feel like it's probably an important topic for Americans to be educated on. I'll

[01:05:16] Mike Kaput: just kinda leave it at that for right now.

[01:05:19] AI Threats Have the US Government (and Labs) Worried

[01:05:19] Mike Kaput: So, next up, some more politically related news. Axios reported this past week that 32 house lawmakers in a bipartisan letter led by Republican Bob Latta. Urge the White House to act on AI cybersecurity threats.

[01:05:32] So one of the triggers here was Anthropics Mythos model has already identified thousands of high severity zero day vulnerabilities across major operating systems and web browsers, which we've talked about. These are include flaws that survived years of human reviews. Palo Alto Networks, one of the few companies with early access to something like Mythos told Axios it found 75 vulnerabilities in its own products in the past month.

[01:05:58] That is seven [01:06:00] times its normal rate. And Palo Alto estimates organizations have just three to five months before attackers gain broad access to these capabilities separately. Google's threat intelligence group reported the first known case of cyber criminals using ai. To develop a zero day exploit in the wild.

[01:06:18] Google's chief analyst said the AI vulnerability race has already begun, and that for every zero day we can trace back to ai. There are probably many more out there. Their broader report also detailed Russia, North Korea, and Beijing backed hackers using AI to scale up cyber attacks. Alongside all of this, openAI's announced Daybreak its broader cybersecurity initiative.

[01:06:42] Which has cyber capable versions of GPT 5.5 being provided to launch partners that include CloudFlare, Cisco, CrowdStrike, and Palo Alto Networks. on top of all this, the Trump administration has been weighing executive action on Frontier Model [01:07:00] Cybersecurity. Though Axios reports that process has been delayed by internal disagreements and the runup to Trump's China trip.

[01:07:06] So Paul, we've talked about this before, but just especially with the Google research coming out, are companies anywhere close to ready for like what is about to hit them from a cybersecurity perspective?

[01:07:19] Paul Roetzer: It doesn't seem like it. It really doesn't. No. I mean, if you're not gonna be an, forward deployed engineer, then be a cybersecurity expert.

[01:07:28] Yeah. Right. Those jobs are gonna be in really, really high demand.

[01:07:31] Mike Kaput: Might be stressful, but probably in demand. Yeah. So

[01:07:34] Paul Roetzer: you're that outta work marketing manager, you know, maybe level up on the cybersecurity side of things. I don't know. it, I've said it before in the fact this, this part terrifies me.

[01:07:44] Like what, whatever the most advanced models today are doing, six, nine months from now, the open source models will be doing and based on the reports we're getting, which is just the stuff that's leaking out Yes. Of this. Like, this isn't even the worst of it. it [01:08:00] is, these models are gonna be prolific at causing major problems for companies.

[01:08:07] And, I don't, I don't know what the solutions are. I, Google has their IO conference this week. Yeah. And when I was at Google next, they talked a lot about security and governance. so I think you're gonna hear a ton about it from these big companies. I just don't know. They're gonna be able to move fast enough.

[01:08:26] But Cybersecurity's always been that way where it's just like you're trying to racing to sort of stay ahead of the bad guys. Yeah. So man, we're, this is not like the most uplifting episode, to be honest with

[01:08:37] Mike Kaput: you, Mike. So far it is not like we've got a couple of things that aren't like directly gloomy in here, I promise.

[01:08:44] But yeah, I'll throw

[01:08:45] Paul Roetzer: up in a ball after we're done with this episode for a little while. Yeah,

[01:08:48] Mike Kaput: this one, this one's hitting all the usual suspects of things that scare the hell outta me. Yeah, really.

[01:08:55] Paul Roetzer: And then the next one I'm looking at headless. It's like, oh, well yeah, this one, I know what it means, but this just look at me [01:09:00] like this is just like continuing the

[01:09:01] Mike Kaput: concept.

[01:09:01] Yeah. Don't worry. This one's not as morbid as it sounds.

[01:09:04] The Rise of the "Headless" Software

[01:09:04] Mike Kaput: So the, what we're talking about here is last month Salesforce launched what people are calling a headless product. So basically opening its APIs and betting that in an AGI agentic world, its value as a business lies in the data layer rather than the ui.

[01:09:19] So this past week we actually got a piece from Andreessen Horowitz that used that announcement to kind of ask a broader question here, which is, when agents replace humans as the primary users of business software, what is actually defensible? A 16 Z's argument is that the user interface is actually doing far more work than it got credit for.

[01:09:41] So the interface enforced data hygiene, created shared vocabulary like leads, opportunities, and accounts, and built muscle memory across thousands of users. That stickiness they claim is why something like Salesforce gets brought from job to job. So agents, however, do not need a ui. They [01:10:00] read and write directly.

[01:10:01] To the underlying data. So Andreessen Horowitz argues defensibility in this environment shifts in two directions. So some of the old moats stay the operational logic, compliance, critical data like payroll connectivity across siloed systems all stay very defensible and important. But there are several factors that become newly important for AI native startups.

[01:10:24] So for instance, proprietary data. The product uniquely generates owning the full action loop from decision to outcome, the network effects across multiple parties, and how all this is executed in the real world. So. Their bottom line is the next generation of systems of record will likely not just be databases of human entered data.

[01:10:44] They will be AGI agentic systems that capture context, initiate actions, and produce their own data exhaust. The most interesting ones will actually extend into real world execution or the mediation of multi-party workflows. So Paul, it's a little bit kind [01:11:00] of in the weeds and technical here on like the insider baseball of like software companies and moats, but in it is an interesting overall trend that when agents start using software, what changes and what do we need to be thinking about as business leaders?

[01:11:15] Paul Roetzer: I'll just try and like I'm, you know, processing this as you're reading this one, Mike. Like, I will use HubSpot as an example. So HubSpot is our CRM. It powers a lot, our marketing, our sales, our customer success operations to a degree. And increasingly you're starting to say, okay, well if Gemini's connected to HubSpot, or Claude is connected to HubSpot or ChatGPT, or like, whatever the interface is.

[01:11:37] 'cause those companies don't want you to leave their interface. Yeah. Like their, the AI companies want you to live within. Whatever the U UI is gonna be that they're gonna create. It might be the chat like we see today, or it might be some new way to interact with information, but their whole premise is like, whatever you want is just living there.

[01:11:57] And whether you want to run a workflow, say, Hey, launch [01:12:00] a campaign for me, send an email to this. Like you're just talking to your AI assistant all day long. It just goes and does stuff. And so the premise here is I don't have to log into HubSpot anymore, right? Maybe every day, like Claude just pulls my HubSpot dashboard and shows me the 10 KPIs that matter and tells me what to do about 'em, and then writes my customer emails and does my sale.

[01:12:19] Like it just all happens right within my Claude interface, as an example. And I never go to HubSpot anymore. And then like, let's say we do that for 20 some employees, like, Hey, everybody, like all your workflows, everything you need, it's just gonna live right within c Claude. Just log in every morning and.

[01:12:35] Boom. Like it just does it all for you. And all of a sudden, like they don't have to go to HubSpot anymore and do things like, they just do it all right through there so that if you're a software company, it's like, well, what, what is our purpose then? And so the premise here, if I'm understanding correctly, is like, well, we're where, where the data lives.

[01:12:51] Like yes, we are. So then the question becomes like, okay, but if you're where the data lives, because historically that's where the data lived, but [01:13:00] if moving forward there's new interfaces that exist where the data starts to live.

[01:13:07] Mike Kaput: Yeah.

[01:13:08] Paul Roetzer: Then do I need. That system that was built for humans to work within? I don't have an answer.

[01:13:13] I'm just like, I'm sort of like regurgitating here how I'm synthesizing the current situation. Right. it's a very tricky place for these software companies to be, and it is why there's so much uncertainty around the future of software and they're all trying to kind of figure this out. But I do think that it's reasonable to have this assumption that at some point it's gonna be agents that are going into these software programs and extracting the data more than it's gonna be humans.

[01:13:39] That it's kind of like, I assume we're gonna have more agents coming to our website than humans at some point. Yeah. Right. Based on what industry you're in. Exactly. Just the agents are gonna do the work for the people and so this is a really intriguing thing to follow along.

[01:13:52] Mike Kaput: Which is why for a Salesforce and or a HubSpot, if they can somehow figure out how to get the ownership of the [01:14:00] agents on their platform and your ability to build them beyond their platform.

[01:14:03] That's interesting. I'm not sure if that will be the case, if that will happen, but it seems like where they're headed, because otherwise Yeah, you're just a database or a data repository that Right. Someone else's agents are accessing.

[01:14:16] Paul Roetzer: Yeah. And I don't, like, some of this I feel is like, it's just above my pay grade to like comprehend, or I don't, I don't live in this world every day and like, think about this challenge.

[01:14:25] I observe what all these software companies are doing. All I know is Wall Street's not convinced yet. Like the software stocks aren't, you know, throttle, you know, throttling up like the way the AI companies are. And so I think that there's naturally just a lot of unknown about, okay, well maybe what they're doing is gonna work.

[01:14:43] Maybe this, you know, switch plays out and it, it ends up being good. but yeah, I think most of it is just uncertainty right now.

[01:14:51] Mike Kaput: Yeah. Next step.

[01:14:53] Publicis Acquires LiveRamp

[01:14:53] Mike Kaput: This past week, Publicis Group agreed to acquire LiveRamp for $2.2 billion in an all cash de deal. The French advertising holding company announced the agreement in, on Sunday, and Publicis is betting the acquisition positions.

[01:15:08] It as a leader in what the company is calling Agentic Transformation, meaning the use of AI agents to automate and collaborate on corporate workflows. Publicis estimates the agentic transformation opportunity at roughly $8 trillion. LiveRamp specializes in what Adweek describes as data collaboration.

[01:15:25] It basically lets different companies share and build new data sets and data models together. Publicis is framing those capabilities as a foundation that can power AGI Agentic AI frameworks, Publicis Group Chairman and CEO Arthur Sadoun told Adweek the deal is core to that strategy. So Paul, it really seems like.

[01:15:45] Publicis just dropped a couple billion dollars to position itself for, the AGI Agentic transformation market. Is, did this make sense from your perspective as an ad holding company kind of positioning itself to win in AGI agentic ai?

[01:15:59] Paul Roetzer: [01:16:00] Yeah, it's gonna be an interesting partnership, acquisition. I actually just, I did a keynote for LiveRamp, I think it was like two months ago.

[01:16:07] A chance to spend time with their CEO, you know, I was really impressed with the organization. but yeah, they do like data collaboration where, you know, brands can kind of input their data anonymously and then you can kind of share and build off of that data to do better targeting, things like that.

[01:16:21] So it's a, I mean, I guess it's a pretty logical play for Publicis because a lot of, you know, they probably have a lot of mutual customers.

[01:16:28] Paul Roetzer: and so being able to kind of go deeper into the data side of that and the agentic side makes a ton of sense.

[01:16:35] Mike Kaput: All right.

[01:16:36] How AI Is Changing the Way We Work (Second Brains, Apprenticeships, and More)

[01:16:36] Mike Kaput: Next up, we've got three different stories that surface this past week that when you kind of put them together, point to some interesting ways that AI.

[01:16:44] Is beginning to change how we're all starting to do our work. So first, go to market leader, Kieran Flanagan published a long post on the, what he calls the AI second brain he built before taking over a 400 person team. So. This [01:17:00] system runs on a piece of software called Obsidian and Claude Code. It basically has hooks that load all of his notes, his strategic context at the start of every session, and write structured summaries back to his file so he could basically query this kind of ongoing second brain of all his information notes, what's been happening in his day and in his work.

[01:17:20] Second, we got a post from Shopify, CEO, tobi lutke that shares how his company built an AI agent called River that lives in public Slack channels. So in the past 30 days, just about 6,000 Shopify employees worked with River across, almost 4,500 channels, and about one in eight pull requests merged into Shopify's code base last week were authored.

[01:17:42] By river. So river only works in public, never in dm. So the whole company can essentially use this from through Slack and watch and learn, what everyone else is doing. Also, interestingly, the AI researcher Andrej Karpathy, posted this interesting viral tip at the end [01:18:00] of any LLM query. Go ask the model to structure its responses, HTML, and view the file in your browser.

[01:18:06] Karpathy argued, markdown is today's default for AI output, but HT ML is the better next step because vision is how the human brain prefers to receive information. So Paul, the reason we're kind of talking about all these, we've got one go-to market leader building, essentially an AI second brain for his job, his function, where he can have this instantly queryable and conversational database about basically everything going on.

[01:18:31] Shopify's doing an interesting version of something in public where employees can essentially query and use a centralized agent. And then I just thought the HTML thing was interesting 'cause I know you and I both have often used Claude to output HTML as a better way to kind of visualize and review output.

[01:18:49] So all of this is really just pointing to like, there's these weird, interesting nuances that are changing how we work because of what AI seems to enable.

[01:18:57] Paul Roetzer: Yeah, the HTML trick is like a key [01:19:00] unlock. Yeah. I love, I love it. 'cause you can make it interactive and everything. Yeah. That's huge. yeah, I, so I've alluded this a couple times, but I think Mike, I'll just like explain what I've been working on real quick because I think it's super relevant here.

[01:19:14] So. about two months ago, I had a, an interesting day, I had a breakfast with a higher education leader, a a top leader at a major university. And then that same day I went and visited nasa. So we're lucky in Cleveland to have NASA Glenn in our backyard. about 10 minutes from my house right next to the airport, and as I was touring different labs within nasa, there was one I went in where they work on the wheels for the rovers that you send to the moon and Mars.

[01:19:46] And, as, as I was getting the tour, a scientist walked by and somebody was following him and, I got introduced and the guy following him was an apprentice. And so I'd spent the morning trying to talk to a higher [01:20:00] education leader about what the future of jobs look like and what their. You know, graduating students might be doing when they got out into school, outta school.

[01:20:09] And then I, you know, go to NASA and I'm, I'm seeing this, you know, idea of this apprentice. And so I left NASA and I stopped on my way home and I was getting something to eat and, was waiting for the food take over for my family. And I started thinking about this apprentice idea. And so I actually went into chat BT and I was like, let's talk about the history of the Apprentice and like, what is the origin of that role?

[01:20:28] Where does it come from? What are the possible applications? And I'm just like, just having a conversation. And so I started landing on this idea and I messaged I think Mike and some others at the company like in the moment. And I was like, I think I. Have the idea for my Make on Talk this year. And so this idea of like the organizational structure is something I've been thinking deeply about for a while.

[01:20:49] We did that AI talent report earlier this year, Mike, where we kind of shared some of the findings from our council, or maybe it was the year last year. It's all right. I was beginning of this year

[01:20:56] Mike Kaput: actually, it seems like a million years ago. It really does.

[01:20:59] Paul Roetzer: So I'm [01:21:00] just gonna read something because again, I alluded to this on an, event last week, and so I've kind of like publicly said I was doing this, so I'm just gonna explain what I'm doing.

[01:21:11] And I'll leave it at that. We'll come back to this later on. I don't think this is publicly on the MAICON site yet, but I It's gonna get announced soon enough, so I'll just explain it. So here is the premise behind my keynote for MAICON. The title is The Architect, the Orchestrator, and the Apprentice, rethinking Work in the Age of AI and Agents.

[01:21:29] As AI and autonomous agents transform the workplace, they're doing far more than automating tasks. They're redefining expertise, reshaping organizational structures, and changing how professionals learn, contribute, and advance. In the process they're giving rise to three defining roles that may shape the future of knowledge work.

[01:21:47] The architect, the orchestrator, and the apprentice. The architect designs the system. This role defines the workflows, governance, team structures, and human machine operating models that make AI useful, scalable, and aligned with [01:22:00] business goals. The Orchestrator runs the system. This role frames problems directs AI agents and human collaborators.

[01:22:07] Evaluate evaluates outputs and turns distributed intelligence into decisions and actions. The apprentice learns within the system. This role develops judgment, context and discernment. In a world where much of the entry of the work people once learned from is being absorbed by ai. For decades, organizations relied on a familiar model of professional development.

[01:22:28] Junior employees handled research, drafting, analysis, coordination, and execution Over time, they built the experience and judgment required to lead. But as AI systems take on more of that foundational work, the traditional ladder from novice to expert begins to break. If machines do the work people wants to learn from.

[01:22:46] How will organizations develop future experts, managers, and executives in the architect, the orchestrator and the apprentice? I'm gonna try and present a new framework for understanding this next era of work. The future of competitive advantage will not [01:23:00] belong simply to the companies that adopt AI tools fastest, but to those that redesign work management and talent development around these roles, we're gonna look at historical, traditional hierarchies, why judgment becomes more valuable as execution becomes more a.

[01:23:16] How middle management must evolve from supervising production to scaling expertise and why apprentice may become more important, not less in the age of intelligent machines. So the way I'm basically thinking about the future of teams and pods and orgs, like however you wanna think about, it's like.

[01:23:31] You have the leaders, obviously at the top you have these architects or planners that are strategists. You have builders who are like, develop the apps and agents, which is why we created SmarterX Labs. So that, that's actually like where this labs concept came from. Then you have the orchestrators who are the human and agent project managers, basically the apprentice.

[01:23:48] And then you have agents. So while I'm focusing on architects, orchestrators, and apprentices, when you layer in the builders and the agents, you, you actually have the forming of an organizational structure. So it's kind of like a working [01:24:00] hypothesis, I would say. I'm like observing all of these different models that we're hearing about from Dorsey and others and Yep.

[01:24:06] And trying to like, for how that aligns with the direction I'm thinking, I'm having conversations with leaders who are like working on this within major enterprises and bouncing ideas around. So yeah, like if you're working on something along these lines, if you're thinking about this at an organization, reach out to me on LinkedIn.

[01:24:22] Like I'd love to, like compare notes and, you know, just chat a little bit because I want to do a discovery process behind this. I don't wanna just like write this. Say, here you go. so yeah, reach out if you've got something interesting to talk about on this.

[01:24:36] Mike Kaput: I love that. Can't wait to hear more. Yeah.

[01:24:39] AI Use Case Spotlight

[01:24:39] Mike Kaput: All right, so next up we have our AI use case spotlight, where every week we give you a quick look under the hood at the real AI use cases we are exploring, building, or deploying in our own work at SmarterX. So Paul, I'm gonna share a quick use case. Then if you have anything to share, we can talk through that too.

[01:24:55] Paul Roetzer: Sounds good.

[01:24:56] Mike Kaput: So for me, I'm gonna actually just read a couple excerpts out of [01:25:00] a message I sent to our team because as we've talked about several times, we launched this past week our state of AI for Business report. So kudos to our director of research, Taylor Radey, for taking the lead on that. As part of that, me and Taylor actually sat down to, build out how we activate this report internally.

[01:25:19] So I'm gonna share a little bit of what I shared with the team about how we're using AI to do that. So I started the message saying a testament to the velocity at which we can now move. Thanks to AI Taylor Radey and I met today to finalize our plans for activating the state of state of AI for business report both internally and externally.

[01:25:36] These plans also serve as the template for how we'll activate all our research moving forward. So knowing what AI was capable of, we expressly set out not just to plan all this out, but solve as much of it as possible during a single meeting. So we had about two and a half hours. Blocked off for this, and we used the following playbook first.

[01:25:55] The first half of the meeting was spent on a deep strategy session, architecting [01:26:00] what, you know, what we might call an activation machine, could look like. Every bit of that was recorded. The recording number two was then fed into Claude, along with the context into what we were trying to build. Number three.

[01:26:12] While that processed, we listed out and gathered all the context documents we'd need to build the activation machine we envision. And number four, we spent the second half of the meeting actually building and iterating directly in Claude. The result was that in that time we had a working prototype of a Claude project that takes any piece of research and in minutes generates an internal activation brief that presents customized data takeaways and next actions for each individual team at SmarterX so they can go make optimal use of the research for things like marketing, sales, et cetera.

[01:26:45] So we still have a lot of iteration to do to fully get this right, but it's pretty incredible. We are just blown away by when you really set out with that mindset and that approach, you can make so much progress in building things and just getting them solved and done in [01:27:00] a single meeting.

[01:27:00] Paul Roetzer: Yeah, this was super impressive.

[01:27:03] this is one of those where I just wanted to like. Call Mike and be like, okay, explain exactly what's happening here, how this is working, what are the implications, the other workflows we have. so yeah, it was really cool and you and Taylor are just doing awesome stuff, in the, from a research perspective and content creation perspective.

[01:27:19] Mine was actually a pretty cool one. super simple though. I'll put the prompt in in the show notes. a few weeks back, Anthropic shared a, prompt you can use to extract memories and personal context from another platform. So if you wanted to shift from openAI's over to Anthropic, I think they did this when openAI's was getting in a mess for something.

[01:27:43] I forget what they had

[01:27:43] Mike Kaput: done. Yeah, I forget what prompted it, but they did this pretty recently.

[01:27:46] Paul Roetzer: Yeah. There was like some backlash to openAI's. And so Anthropic was like, Hey, you wanna switch over? Like, here's a prompt. And so I was like, wait a second. So I have a, a Claude account that I've been using for about a year [01:28:00] and I wanted to switch, 'cause we got, we got Claude for all SmarterX employees now.

[01:28:04] So it's SmarterX. We have ChatGPT, Gemini, and Claude. Everybody gets all, all of them. And so I was not using the SmarterX Claude account, but I didn't want to have to maintain two work accounts. So I was like, oh, lemme try this. So I go grab Anthropics prompt. I put it into the historical Claude account I had, and then I took the output and put it into the new Claude to train it on all the history.

[01:28:29] And it was insane. It was so good. That's awesome. And the prompt is no more than, I don't know, 250 words. It just, it literally says, export all of my stored memories in any context you've learned about me. From past conversations, preserve my words verbatim where possible, especially for instruction and preferences.

[01:28:45] And then it gives categories, output in this order, instructions, identity, career projects, preferences, and then like how to organize it and things like that, and what to output it in. So I just, I dropped that in, into my old claude account [01:29:00] and about a minute later I get like the output and it's, it's way more than 250 words, but it's, it was really good.

[01:29:09] and so I just copied that and I put it into like the system instructions or the memory within the new Claude and boom. And then I just picked up where I left off and started working on projects in the new one. So, pretty cool little trick. again, sometimes knowing how to prompt is, is still a super valuable skill.

[01:29:25] Mike Kaput: Yeah, that's super valuable. It's definitely gonna help a few people out. I bet. Yeah.

[01:29:30] AI Product and Funding Updates

[01:29:30] Mike Kaput: Alright, final, item for this week, Paul is our roundup of AI product and funding updates. So I'm gonna rapid fire through a bunch of these as we wrap up today's episode. So first step Anthropic is raising new funding at a reported valuation of 900 to 950 billion according to some reports from the Financial Times and the New York Times.

[01:29:52] At the same time, Anthropic announced a $200 million partnership with the Gates Foundation, focused on applying Claude to global health [01:30:00] and development priorities, as if that weren't enough. Anthropic also launched Claude for Small Business, a new offering, bringing Claude's Enterprise capabilities to smaller companies with a range of different features.

[01:30:13] openAI's launched a preview of a new personal finance experience in ChatGPT for us pro users that lets them securely connect financial accounts through a service called Plaid across more than 12,000 institutions. They can then see a dashboard of spending investments, subscriptions, and upcoming payments, and ask chat ChatGPT questions grounded in their real financial.

[01:30:35] Context. There is, support from the FinTech company Intuit, and they're planning a rollout to the plus tier. Next openAI's also reorganized its executive ranks, officially making president Greg Brockman head of product strategy in addition to his work on AI infrastructure merging ChatGPT Codex and the developer facing API into one core product team that is now [01:31:00] led by Codex Head, Tebow Sotio, and moving longtime ChatGPT head, Nick Turley to lead enterprise products and naming former Instagram VP Ashley Alexander.

[01:31:10] Head of Consumer Products. SpaceX is aiming to go public on June 12th and was expected to be the biggest IPO of all time with Elon Musk's Rocket Company planning to raise as much as $80 billion or more and list on the Nasdaq. The information reports a couple items related to openAI's and Microsoft.

[01:31:29] openAI's apparently expects to save approximately $97 billion by 2030 under its latest revised deal with Microsoft, which we talked about in past weeks, and they reported that Microsoft has now spent over a hundred billion dollars on its OpenAI partnership to date. At the same time, OpenAI announced work with Codex from anywhere expanding access to its Codex coding agent across more environments and surfaces beyond the original command line interface.

[01:31:57] Google was busy as well. They launched Gemini [01:32:00] Intelligence on Android, an upgraded set of Gemini AI features directly integrated into the Android operating system. And Google DeepMind unveiled an early research concept that reimagines the computer mouse pointer for the AI era. They designed it in a way to give users a more intuitive way to interact with AI across applications.

[01:32:20] Amazon has launched Alexa for shopping a personalized agentic AI assistant designed to help users discover products and complete purchases on Amazon. This past week also saw the launch of Recursive Super Intelligence, a new AI startup focused on building AI that safely conducts experiments to improve itself.

[01:32:40] And last but not least, isomorphic Labs. The DeepMind Drug Discovery spin out led by Demis Hassabis announced a series B funding round to accelerate its AI driven drug design pipeline. So Paul, that is all. We've got. One more quick announcement. We talked at the top of the episode [01:33:00] about our AI pulse survey. Go take this week's survey at Smarterx.ai/pulse.

[01:33:04] This week we are gonna ask about how concerned you are about AI's impact on your own job over the next year, and getting, asking a question to kind of gauge where you personally are at with AI versus where your organization is at. So go to Smarterx.ai/pulse to take the survey.

[01:33:24] Paul Roetzer: Yeah, one quick note.

[01:33:25] Just don't sleep on. As I said before, those AI product and funding notes that Mike does at the end here, there's so much significant information in just the ones from this week. Like the ISR Isomorphic Labs is major. If you're not following what they're doing, follow them. The recursive super intelligence is a huge deal, like, yep.

[01:33:46] Some of these things are just sign very significant and we'll, we will mean more, in the near future for people, but yeah, don't, don't breeze past those. They're, they're good. It's good information to know.

[01:33:58] Mike Kaput: Well, Paul, thanks [01:34:00] again for breaking everything down. Sorry it was a little gloomy this week, but I think it was an important conversation nonetheless.

[01:34:06] Paul Roetzer: Yeah, they all are. I mean, it's, it's important we're having these conversations, but I am, if you're feeling the weight of 'em listening and trust us, we're feeling the weight of them talking about 'em sometimes. Yeah. Next week we're gonna try and try and get the bright, some bright spots. I

[01:34:20] Mike Kaput: will, I will do my best to find some, some bright spots.

[01:34:24] Alright, thanks Mike. Thanks Paul. Appreciate it.

[01:34:27] Paul Roetzer: Thanks for listening to the Artificial Intelligence Show. Visit SmarterX AI to continue on your AI learning journey and join more than 100,000 professionals and business leaders who have subscribed to our weekly newsletters, downloaded AI blueprints, attended virtual and in-person events.

[01:34:44] Take in online AI courses and earn professional certificates from our AI Academy and engaged in a SmarterX slack community. 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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