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[The AI Show Episode 225]: GPT-5.6, ChatGPT Work, Enterprise Agents, AI 2040 & Apple Sues OpenAI

By Claire Prudhomme on July 14, 2026

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OpenAI shipped GPT-5.6, ChatGPT Work, and a new wave of voice models this week.

The models grabbed headlines, but the quieter change matters more: the default way you talk to these tools is moving from a chat interface to agents that can use your computer.

Paul and Mike dig into what ChatGPT Work actually does, why the rollout undersold its own significance, and what the shift means for knowledge workers.

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

00:05:45 — GPT-5.6, ChatGPT Work, and GPT-Live (Plus GPT-6 Rumors)

00:31:30 — Advice on AI Agents in the Enterprise

00:46:39 — AI 2040

00:59:17 — Apple Sues OpenAI for Trade Secret Theft

01:06:14 — Illinois Signs Nation-Leading AI Safety Law

01:09:59 — AI Safety Index: Nobody Gets an A

01:13:11 — The AI Cheating Scandal at Brown

01:17:44 — China and Russia Stoke US Public Opinion on Data Centers

01:20:57 — AI Use Case Spotlight

01:26:19 — AI Product and Funding Updates

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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: If you showed someone a recording of this five years ago, they'd be like, we have AGI. Yeah. Like it's not as smart as the models you're gonna be interacting with on your computer, but you'd just be like, wait a second. This, you can have an actual real time dynamic conversation with this thing. It's insane.

[00:00:16] Paul Roetzer: Welcome to the Artificial Intelligence Show, the podcast that helps your business grow smarter by making AI approachable and actionable. My name is Paul Roetzer. I'm the founder and CEO of SmarterX and Marketing AI Institute, and I'm your host. Each week I'm joined by my co-host and SmarterX chief content officer, Mike Kaput.

[00:00:36] As we break down all the AI news that matters and give you insights and perspectives that you can use to advance your company and your career, join us as we accelerate AI literacy for all.

[00:00:52] Welcome to episode 225 of the Artificial Intelligence Show. I'm your host, Paul Roetzer, along with my co-host Mike Kaput. We are recording on [00:01:00] Monday, July 13, about 9:00 AM Eastern time. We had a slew of new models last week. We will get to those leading off with. GPT 5.6, which sort of stole the headlines for most of the week.

[00:01:15] I don't know, it was like a day-to-day thing. Mike. We're gonna, we're gonna focus on that. I don't, I don't know. We're getting any new models this week. I think Google could surprise us at any moment with a Gemini 3.5 Pro. There's, I saw this morning some possible leaks of some of the evals on 3.5 pros, so I, it seems like it's fully cooked and just kind of like ready, readying for release maybe.

[00:01:41] So we'll see, see what the week brings us. All right, marketers get, quick gut check. When's the last time someone found you through ChatGPT or Claude? Instead of a traditional Google search, that shift is happening fast and most brands have no idea how they show up. Siteimprove is the agentic content intelligence [00:02:00] platform that shows marketers how their content performs across traditional SEO and AI driven search.

[00:02:06] Because good SEO and accessibility are still the foundation. They just aren't the whole picture anymore. Get your free AEO check at siteimprove.com/AIPOD. So thanks to our friends and partners at Site Improve for sponsoring today's episode. And today's episode is also brought to us by, assuming you're listening on July 14th.

[00:02:29] So hopefully you, you get in early and listen to the podcast early, because July 14th is MAICON Day. So we created MAICON Day last year by asking a handful of community members, friends, team members and speakers to help spread the word about MAICON. This is our annual marketing AI conference that happens in Cleveland, October 13th to the 15th.

[00:02:48] And so MAICON Day was designed to make the biggest registration day of the year. It was a huge success in 2025, so we're bringing it back in 2026. Hundreds of community members, speakers, sponsors, alumni, [00:03:00] partners, friends and registered attendees are helping us make MAICON day even bigger this year. And we are incredibly grateful for everyone who's pitching in.

[00:03:08] If you've been thinking about joining us in Cleveland on October 13th, 14th, and 15th, today is the best day. Again, if it's. July 14th, you're listening to this, the best Day to Register. You'll save $200 on your pass with Code MAICONDay26. That's MAICONDay26. And you'll be entered for a chance to win exclusive prizes, including a VIP party pass, a VIP lounge pass, which was a huge hit last year, a complimentary on-demand upgrade, and $100 in MAICON swag books.

[00:03:44] I didn't even know we had MAICON swag books, so that's cool. I might, I might enter it away. I'm gonna register this today. The offer ends at midnight Eastern Time, so don't wait. Visit MAICON day.com. So that's MAICONday.com. If you need more information [00:04:00] on MAICON before committing a link on the MAICON Day website, we'll take you to the event site.

[00:04:04] So thanks for being a part of this community. We hope to see everyone at MAICON this October in Cleveland. Okay. Every week we start off with a recap of our AI pulse survey. This is an informal poll of our listeners on how they feel about topics that we covered in the previous episode. So, last week we had Palantir's, CEO claims.

[00:04:24] AI labs quietly absorb your company's data and competitive edge. Do you worry about this when using AI providers? 48% somewhat, but we accept the trade off 28%. Yes, it's a serious concern for us. 11% no contracts and controls protect us. Mm, that's, that's kind of what we've always thought. I'm not so sure about that anymore and 13% haven't thought about it.

[00:04:52] Alright. and then the second one was, what is AI actually doing to headcount at your company right now? [00:05:00] 59%. No noticeable impact on headcount yet. So all the economic reports would, support that as the majority. Right now, 30% were hiring less or slowing hiring because of ai. We are definitely seeing and hearing that, 9% were hiring more because of ai.

[00:05:20] That's great. Those are probably our high growth companies that we talked about last week. So you're growing and you're hiring as a result and then a small percentage not sure. Alright, so Mike, we have, like I said in the opening, lots of model news to discuss. This week we're gonna focus on what's new from openAI's and.

[00:05:41] openAI's had a very busy week last week, but we're going to start with the models.

[00:05:45] GPT-5.6, ChatGPT Work, and GPT-Live (Plus GPT-6 Rumors)

[00:05:45] Mike Kaput: Yes, they did Paul. So first up, openAI's had some of these big releases this week. So first we saw the general launch of GPT 5.6. This is the company's most powerful model family yet. so this [00:06:00] comes in three tiers.

[00:06:01] There's GPT 5.6 Sol, which is the flagship built for complex work like coding, research, science, computer use, GPT 5.6 Terra. This is a middle tier balancing capability, speed and cost for everyday work and GPT 5.6, Luna, which is the fastest and cheapest of the family. So all three of these are now available generally across chat, GPT, codex, the openAI's, API, and they're.

[00:06:25] Positioning this model family as the new frontier. The company says, Sol itself sets a new state-of-the-art. On the artificial analysis coding agent index, it gets a score of 80, which they claim is 2.8 points above Anthropics Claude Fable 5, while using less than half the output tokens and costing about a third less.

[00:06:45] Now, this launch itself is noteworthy, not just because of the model, but as we've discussed, the Trump administration pushed openAI's into a staggered release last month. They limited an initial access to these models to government approved entities. While the Commerce [00:07:00] Departments Center for AI standards and an innovation tested the model, these restrictions were lifted this past week.

[00:07:06] Though a White House official disputed that any approval was needed, saying decisions on timing and scope of releases rest entirely with the companies. Now, not only did they release these new models, but OpenAI also launched something called Chat, GPT Work. Now chat, GPT Work is an agent that takes an outcome, gathers information across your apps, and stays with a complex project for some duration of time.

[00:07:32] The minutes could be hours, openAI's claims, breaking it basically into steps and completing all this on its own. So the output that this tool produces is finished work sheets, slides, docs, shareable web apps, et cetera. It can, can, can, it can connect to the systems where work already lives, including Slack, Microsoft teams, Google Drive, et cetera.

[00:07:54] This launch also reorganizes open AI's product lineup, and we'll kind of talk about [00:08:00] this a bit, but the Codex app is basically merging into a single new ChatGPT desktop app that puts chat, work and Codex on every plan, including free. openAI's is also beginning to sunset its Atlas browser, which is its kind of agentic browser.

[00:08:15] It was experimenting with. And on top of all this, there was a new wave of voice models. They released GPT Live, GPT live one and GPT Live One Mini. These are models that are built to listen and speak at the same time when you use chat GPT voice mode, so that conversations flow naturally and users can interrupt without the model cutting off.

[00:08:38] interestingly, open AI's ChatGPT Voice Product Lead told Axios, the company thinks this will unlock the ability to use voice as kind of the primary interface to computing. Now, if that wasn't enough, the rumor mill is already spinning dramatically. There are posts circulating on x, partially corroborated by AI commentator [00:09:00] an current, and we've talked about that claim.

[00:09:02] GPT 5.6 is the final model in the five series, and that GPT six built on a much larger base model could actually arrive within weeks. So. Paul, there's a ton to unpack here. Maybe kick us off with your thoughts on the initial releases models. I know there's probably some stuff to talk about chat GPT work as well.

[00:09:22]

[00:09:23] Paul Roetzer: Yeah, so, I've played around a little bit with 5.6 over the weekend. I mean, I haven't done a ton like pushing it on, you know, internal evals or anything like that, but, definitely seems like a significant upgrade over 5.5. Early response has been really strong. People who had early access, you know, seem to be really happy with it.

[00:09:41] A lot of people seem to prefer Fable 5 when we're, you know, if you're comparing those as direct, model comparisons. So according to OpenAI, 5.6 Sol sets a new standard for intelligence and efficiency achieving state of the art results across coding, knowledge work, cybersecurity, and science. they say fewer tokens and at a [00:10:00] lower estimated cost, but everything I've been seeing online is that the thing burns through tokens like crazy.

[00:10:05] Mike Kaput: it can,

[00:10:06] Paul Roetzer: yeah. Like people are running up against the limits, like really fast. so that's just something to keep in mind. I did see some things this morning that people are complaining that openAI's sort of like nerfed it since it first came out, like four days into the launch, it's already seen a performance decline.

[00:10:25] and I even saw some comments from people within openAI's that they're basically working on how it. Functions behind the scenes and it might cause some issues with its reasoning capabilities, stuff like that. The early acts of some governments is super confusing. As you alluded to. Ashley Gold at Axios had the story that OpenAI got the green light from the government and then posted later that day on July 8th that the White House official disputed that they gave a green light and that they don't need that permission and referred Axios to the June 2nd executive [00:11:00] order, which bars any mandatory federal licensing or pre-clearance.

[00:11:03] So it's like, it seems like maybe there's just a double standard that if it's Anthropic, they have to get clearance. I don't want to be like overly. Negative or suspicious here, but like maybe offering 5% equity in your company helps when it comes to getting quick approvals of model. I don't know. So who knows what's actually going on there.

[00:11:28] there was a Matt Shumer, who we've talked about before of the something big is happening fame from February. When he posted that article about like the impact of coding models, he tweeted that 5.6 Sol just accidentally deleted almost all of my Mac files, and this is why I trust Fable 1000 times more.

[00:11:50] he then said the crazy thing is if you read my GP. Fi GPT 5.6 Sol review. I already much preferred Fable and stopped using 5.6 weeks ago. [00:12:00] The only reason I was using it today is because OpenAI team asked me to test the ultra mode. for what it's worth, they're great to work with and it's a freak accident.

[00:12:08] Just sucks so much. So again, Schumer obviously is pretty advanced user of these models and even he, for whatever reason almost accidentally had his entire MAC files deleted by a model. So user beware. one of the exciting things about 5.6 Sol is it renewed the SAM versus Elon, Twitter battle. So that's always fun to see.

[00:12:30] So, Sam tweeted, there are a lot of benchmarks that suggest 5.6. Sol is the best model in the world right now, but the most reliable way to tell is that Elon is obsessed with me. Again, this was. This was gonna reply to something we'll touch on a little bit later about, apple suing openAI's. But, Elon had tweeted he takes scamming to a whole new level to which Sam retweet or replied homeboy.

[00:12:58] You're the one selling public market [00:13:00] investors on short term space data centers. To which Elon replied, we start flying them next year. Maybe you can come see them after your parole officer approves. After stealing an open source charity, you then stole all of Apple's phone technology. What do you plan to do for an encore that's tough to beat.

[00:13:16] So, you know, we, we haven't had the soap opera of Sam versus Elon for at least like six weeks, so it's always good to have that back. okay, so ChatGPT work, honestly, and Mike, maybe you can break this down for me a little bit more. I find this really confusing, as to when you're supposed to use which thing now.

[00:13:36] So if, like, if we go into Smarter X's Chat, GBT instance, you can just click whether you want chat or work. So I'll just kind of walk through a little bit of what this is like if you haven't seen this yet. So, in our account, you know, if I go in and do a normal chat, so if you're, whatever, if you're a Claude user, Gemini, copilot, whatever, you, you have your chat window and at the top there's a chat and a work tab.

[00:13:59] [00:14:00] So if I'm in the chat tab and I'm going to start a conversation in chat. My model dropdowns is instant 5.5. Then there's medium, high or pro of instant 5.5 or, or I guess 5.5. So you have instant, medium, high, pro of 5.5. Then I can choose 5.6, so underneath that I can still choose 5.4, which it says leaving July 23rd, 5.3, and I can still select oh three.

[00:14:30] So in a traditional chat, those are my options. When I click into the work tab, my model choices are now, so 5.6, so Terra luna, or 5.5. I can also then choose effort, light, medium, high, extra high or max, and I can choose speed standard or fast. I can then. Choose a project so I can connect work to, to a project.

[00:14:58] I can also [00:15:00] connect plugins. Now, one of the parts that's confusing to me is you could already do plugins with chat, so it just seems like it's the same feature. You could also connect projects with chat, so like, these don't seem like differentiating features. And then they, there's a call to action to get the desktop app.

[00:15:17] And I'm still not actually a hundred percent sure what the difference is between the desktop app and using the browser. But that's a standard software thing. Like I always use the browser for Asana as an example, not the desktop app. okay. So that, that's kind of like that. I'm gonna, now I'm gonna give the prompting and then maybe Mike, I'll stop and ask if you have any clarity on this that I, I'm missing.

[00:15:40] Okay, so then. openAI's has a prompting guide to try and differentiate how to prompt when you're using work versus chat. So in chat it says A short prompt is often enough for larger or more important tasks. Include the parts that matter, like the goal. What should chat GPT Do context. What [00:16:00] information or sources will help output?

[00:16:02] So what format, length or level of detail do you need? And then boundaries. What must stay unchanged? What should ChatGPT avoid? So that's how it guides you to, to work with traditional chat. Then it says, for prompting work, use chat for quick questions, short rewrites, brainstorming, and lightweight drafts.

[00:16:22] Use work for tasks that draw on different sources or tools involve a sequence of steps. Make changes or produce a large deliverable. For work tasks, describe the results you need. Provide the source material. Name the audience and explain how you review the work. Ask chat GPT to plan. Gather the needed information, create files, and check them before it finishes.

[00:16:49] work is useful for time consuming or recurring tasks or for finished files you can reuse a task that uses more credits, can still be worthwhile. If it saves [00:17:00] time, improves quality, or helps you make an important decision, start with one result you can review. So include the real resources to find the audience, separate, required work, blah, blah, blah.

[00:17:09] Review the first result, refine the instructions and reuse the workflow when it works. And then one other note here, Mike, it says, Chad Chip T Work is an agent that takes an outcome. Gathers information across your apps and stays with a complex project for hours, breaking it into steps and completing them on its own.

[00:17:28] this can be a little misleading because projects that takes hours, there's, there's no way that the reliability is high on that stuff.

[00:17:38] Mike Kaput:

[00:17:39] Paul Roetzer: Right? And it's gonna burn your entire token budget. Like if you're using work or an agent to do hours of work, it, it's gonna burn through whatever your credits or token, but whatever it is.

[00:17:51] So then they said in the launch, ChatGPT work is designed for longer, more involved work than a typical chat request. So usage works differently. [00:18:00] Usage varies with the amount of work required and more complex tasks may use more of your plans included usage. ChatGPT follows the same usage structure as Codex Chat.

[00:18:10] GT Enterprise and EDU admins can set spend controls in the admin council to manage ChatGPT work usage as adoption grows. So I'm just gonna stop there, Mike, honestly, like, so as an account admin for our ChatGPT instance, and as a user who regularly, you know, a dozen times a day is in chat, GPT using it for different things, I have no idea.

[00:18:35] 'cause most of what it says for work, yeah, I do that with chat GPT standard, so I'm not a hundred percent clear. When I'm supposed to jump over to work or if I'm just supposed to now stay in work. Do you have any

[00:18:48] Mike Kaput: clarity on this? Well, that's, that's shocking to me, Paul, because in true AI lab fashion, they've made this as confusing as possible.

[00:18:55] Okay. I would argue, here's what I've observed. I don't have a [00:19:00] full read on this yet, but just some tests I've ran. I think the two key distinctions are, first, the ability to spin up subagents, which, for instance, I just tested this in the web app. I said, Hey, can you go like research current AI capabilities for me?

[00:19:16] Spin up some agents to do it, so it spun up some subagents to apparently do this task and worked for a bit. I don't think you can do that in chat. That can be helpful for parallel work. Here's the bigger thing though, which I just tested and is deeply confusing to me. in the chat GPT Desktop app, this thing has the option to use your computer and that is the key differentiator.

[00:19:41] That's what Codex was able to do so well, is that now once I enable computer use within this app, this thing can now go do all sorts of stuff with my files, spin up agents to do all sorts of things. They're super delete them. Very, very dangerous. Do not enable this if you don't understand the capabilities or

[00:19:59] Paul Roetzer: if you're not [00:20:00] allowed.

[00:20:00] Mike Kaput: If you're not allowed. Correct. So that is a key differentiator. However, the web app, as far as I can tell, does not have that ability. So I was like, for me, I was using the desktop app just looking at this and I was like, oh, okay. This kind of makes sense to me. This is just like. Codex light for non-technical workers.

[00:20:19] It's like Claude Cowork basically. Right. I think is like the analogy here. However, that only holds true in the actual desktop app in the web app. Sure. It seems more agentic, but it doesn't seem to be able to use your computer or anything, which is good, right. In a lot of cases, but doesn't differentiate it as much I would say.

[00:20:41] So I'm a little confused if they're just trying to drive you to the desktop app, because you would think long, long term computer use is like the name of the game so that they can then do knowledge work for you. Right. So that's kind of how I've looked at it, but that's why I was very surprised. This seems like a monumental [00:21:00] change.

[00:21:00] Yeah, and I don't know if people are treating it that way because now, right now in your chat, GPT app. You have the ability to have this thing, give it, give this thing access to your computer. So like if you're an enterprise that doesn't automatically have that shut down by it. Like you need to go in and figure this out.

[00:21:18] Now, I don't even know if you can restrict it. I have no idea. I'd have to look in our account. but that seems huge to me and I just don't know why the labs might wouldn't say that. It's

[00:21:30] Paul Roetzer: like their go-to market plan doesn't match the significance of the launch. It's like, Hey, we launched this thing, here's a couple blog posts.

[00:21:36] Correct. And then you go and it's like, wait a second. This is like entirely different ways of talking to these things.

[00:21:41] Mike Kaput: Right?

[00:21:42] Paul Roetzer: And the one I keep coming back to is like, when I reread this, it's like, use chat for quick questions, sort rewrites, brainstorming and lightweight drafts use work for, you know, a bunch of things in larger deliverables.

[00:21:52] So I'm thinking the majority of my use of these models is strategic support and planning. Right? And like using the [00:22:00] reasoning and it's like. Is that I be in work instead of traditional chat. Like I don't, I don't even know. But based on their explanation here, the answer would be yes. That chat is literally just for like the really quick stuff.

[00:22:11] Mike Kaput: Quick stuff,

[00:22:12] Paul Roetzer: yeah. And work is where you live if you're using reasoning or agents in essence.

[00:22:16] Mike Kaput: That's my understanding. Yes. you're still using the same base models, so I think you could still accomplish a lot of the same things. In chat is my. Guess, but again, I don't know for sure.

[00:22:27] Paul Roetzer: All right. Well then to make things more confusing, Ethan Mollick, who had early access to both ChatGPT work and 5.6 he tweets I've been going on about how ChatGPT work and Claude Cowork are missed opportunities for knowledge workers and to illustrate that.

[00:22:45] take a look at Google's notebook. LM answering the same question as ChatGPT work with the same 70 files. It centers process and sources, not just outputs. To be clear notebook, LM has its own issues and is built for specific use [00:23:00] case research and analysis of sources. But as an example of how a UX might actually operate that treats knowledge work seriously.

[00:23:07] It doesn't treat only goal as outputs, it exposes processes. Now, the point he was making was. He showed a screenshot of an output, I think it was from, ChatGPT work, where it was literally like, here's your file, like here's your PowerPoint, and where NotebookLMhas this like extensive dashboard of all these different capabilities.

[00:23:26] And click and look for references. So then. He was, sharing on top of a post he had previously put up, and I thought this was, helpful. He said A fundamental problem with extending Codex cowork code to all knowledge work is that they remain very software brained where the end result, the software is what is important, and that code serves as the source of truth.

[00:23:49] For a lot of other knowledge work, the process is as least as important as the outcome. This includes researching what is known, an exploration of alternatives, failed efforts, prototype branches, [00:24:00] experiments, et cetera. All of those things are valuable, so you cannot use the PowerPoint at the end. The way you can use a code base nor in is progress on a to-do list, sufficient context.

[00:24:12] Post compaction, you work in learning loops, refining your perspectives as you go. In some ways, this makes long running models like Fable hard to use for now. Deep knowledge work since they're designed to deliver product to you. At the end, you can prompt your way around this problem, but everything about the Codex and code harnesses wants you to be a software developer and you have to fight them.

[00:24:34] I think that's a really, really important thing to keep in mind is like. Cowork and work are being powered by the coding agents underneath them and the harnesses that structure those which are built for software developers and AI researchers. And they're like force fitting them to the rest of the world.

[00:24:50] Now that all these knowledge workers, yeah, but they're still being built by software developers who understand software. so he said there's a real disconnect between how a [00:25:00] manager or analyst thinks about problems and how the agentic software tools approach solving them, addressing this is critical to breaking out of the coding niche for these tools.

[00:25:08] So I dunno if you have any other thoughts there, Mike, but I just thought that was really important context from Molik on maybe why it isn't super clean, how to use these things and when.

[00:25:18] Mike Kaput: I couldn't agree more. I would say the success and value I've gotten from those tools is just prompting around these behaviors.

[00:25:25] Yeah. Or doing stuff that lends themself to those behaviors. So yeah, it's deeply confusing. And I also just come back to both the challenge and the opportunity. So many non-technical knowledge workers don't understand just how powerful these tools can be for specific types of work. Who's gonna tell 'em how are you gonna communicate?

[00:25:42] There's an actual sea change here moving from chat to computer use agents. That's deeply important for people to grasp, but you wouldn't know it from any of these announcements.

[00:25:51] Paul Roetzer: Yeah, and I will tell you like just some inside information, how we think it's SmarterX. So our AI academy consists of dozens of, you know, on demand professional [00:26:00] certificate courses that are, you know, 4, 5, 6 courses deep.

[00:26:03] They might take three to five hours to complete and you earn your certificate on the other end. And we see that being continuously incredibly valuable within organizations. But the dynamic nature of how fast these things are changing and like what it means to all of us as knowledge workers, had us sitting there Friday morning literally discussing our roadmap for AI Academy.

[00:26:21] And like, okay, how do we, how do we address the fact that these things are changing so fast? And we have our weekly app reviews that we drop, app and agent reviews that come out every Friday. We have lives happening all the time, but like. There's a whole nother velocity happening right now behind how these models work and how it changes the way we work.

[00:26:39] So we are very actively thinking about how to continually evolve what we're doing with Academy to address the fact that there needs to be more real time learning that on demand course and certificates are not gonna be sufficient on their own. That you really, it becomes what, what changed last week and what are we gonna learn from it?

[00:26:58] So we have some really cool things in the [00:27:00] works, but it, I feel like every day it's becoming more and more urgent. And as I was preparing even for today's episode. I was just like, oh my gosh, I want to go build the next iteration of what we're gonna do right now.

[00:27:11] Mike Kaput: Right. I couldn't agree more.

[00:27:12] Paul Roetzer: and then a couple quick thoughts on GPT Live.

[00:27:15] 'cause I do think it's, it's huge. It's, you know, an indication of what openAI's believes that voice is gonna be the future. They said, our vision is to enable truly natural human AI interaction. A world where collaborating with AI feels as fluid and responsive as working with another person while reasoning and complex task execution happened seamlessly in the background.

[00:27:33] The way they're achieving this is kind of interesting. They had a post that talked about. How their previous voice models worked. And there was two prior generations, in essence. And the a lot of voice models work this way. This is just, opening eyes. So Cascade Voice Systems, which is, you know, a previous generation is kind of how Siri works.

[00:27:52] rely on a series of models acting one after another to process each turn. So original ChatGPT voice chained three models together. So there [00:28:00] was a speech to text model. So you would talk to the model, it would convert it into text, so it would transcribe what you said so it could then understand it.

[00:28:07] Then a large language model would produce a response in text and then that there was a text to speech model that would convert that back to speech. So if you wondered why talking to Siri or other models is slow, it's 'cause there's a symphony of things happening behind the scenes to power that communication.

[00:28:24] Then there was turn-based voice models like ChatGPT, bt Advanced Voice Mode, which processed and generated audio with a single model. So that was the breakthrough we talked about last year. This reduced latency and made conversations smoother, but it still operated in discreet turns. That's why like you'd be talking and you take a breath and it starts talking back to you.

[00:28:44] It's like, oh, hold on. Yeah, I'm not, I'm not done telling you what I was gonna tell you. So they say, GPT Live addresses these limitations through two changes. Instead of processing a sequence of separate messages, live continuously, continuously processes input while [00:29:00] generating output. So it's listening while talking, in essence.

[00:29:02] The model can therefore make interactive decisions many times per second, whether to speak, continue listening, pause, interrupt, or invoke a tool. And then second, they decoupled live, which handles continuous interaction from deeper work. So when a question requires search reasoning or more capabilities, live, delegates that task to another model like 5.5 right now, and eventually 5.6.

[00:29:25] This allows the conversation to keep going while it's doing tasks in the background. So as a result, conversations should start to feel much more natural. You'll be able to interrupt with a question, pause to go through your thoughts, and it shouldn't feel as, as, as sequenced, I guess. it should be happening kind of simultaneously.

[00:29:45] So should be really interesting. They say that these models are rolling out as of last week to ChatGPT users globally. So if you haven't tried voice recently, might be worth it. you know, pop in and do that. I know Mike, you're a huge voice user, but I know you use Whisper all the time [00:30:00] for transcription.

[00:30:00] Yeah. But. I assume you also are talking to the models a lot too.

[00:30:04] Mike Kaput: Yeah, I've used this quite a bit. I so far really enjoy it. It's got its flaws, but it is pretty night and day from the previous voice mode, which even the previous voice mode I still found really valuable despite its limitations. But yeah, this one, it's like if you showed someone a recording of this five years ago, they'd be like, we have AGI.

[00:30:21] Yeah. Like, it's not as smart as the models you're gonna be interacting with on your computer, but you'd just be like, wait a second. This, you can have an actual real time dynamic conversation with this thing. It's insane. I really hope they crack the code on like these models being smarter and able to use tools, et cetera.

[00:30:38] I realize there's like some technological bottlenecks at the moment, but like the moment you're able to just talk to these things and say, go code me this. Go access this tool, go do this, that and the other, or whatever. I think productivity. Goes crazy if you, if you are someone that tends to use voice a lot.

[00:30:54] Paul Roetzer: Yep. Yeah. And if we will put the link in the show notes to their post. It does, there's actually, you can listen to [00:31:00] comparisons of the previous generation, the new generation with some sample voice things. So, yeah, it's, I think it's a, it's definitely the start of a new generation. My my assumption is, Gemini is or will soon function the same way.

[00:31:13] 'cause generally they've been together in the lead here, but Google is certainly very advanced in terms of voice because they begin integrating it into search and, other elements of their business. So. Yeah, definitely an an area to keep an eye on.

[00:31:30] Advice on AI Agents in the Enterprise

[00:31:30] Mike Kaput: All right. Our next big topic this week, highly related.

[00:31:32] We're talking about some advice and considerations around both agents and just AI generally in the enterprise. So this kind of kicked off with this past week box, CEO, Aaron Levie, we've talked about a bunch. He published this widely shared rundown of what he's hearing from enterprise IT leaders about AI agents coming off a bunch of meetings he's had with several, people in these roles.

[00:31:54] So he basically gives this kind of map of the real unglamorous challenges that companies [00:32:00] hit as they're trying to use agents in production. so Levie's biggest theme here is that agents basically force this like operating model problem. So most companies are built in silos, but agents work best when tied to a process and the most valuable process is cut across.

[00:32:16] These silos. So this raises a bunch of like very difficult questions for enterprises to answer, like, who owns and manages centrally deployed agents, and how do they actually get adopted across organizational boundaries? So he talks about some things like data fragmentation being a major blocker underneath all that, since agents struggle to give accurate on policy answers when a company's data is scattered and non-standardized, he argues that in a world where everyone can tap into roughly the same frontier intelligence, a company's proprietary context.

[00:32:48] Which is its own data captured and formatted so agents can use. It becomes its real competitive moat. He also said there's a growing consensus that tokens are the wrong metric. Companies should manage [00:33:00] instead to business outcomes like revenue or shipped product. The catch being, those are much harder to track top down.

[00:33:06] He also added two more themes that are important here. The best use cases fundamentally change the work being done rather than just doing an old process more efficiently. And the talent to deploy and manage agents is very, very scarce right now. Most companies, he says, will have to train for it internally.

[00:33:22] Now, on top of all this, we got. BCGs fourth annual AI at work survey of nearly 12,000 employees that found the AI is changing jobs faster than companies are redesigning how they operate. So they found 74% of frontline use workers are now regular AI users. That's up 23 points from last year. 61% believe agents could do at least half their job within three years.

[00:33:46] But overall, their basic finding is that strategic clarity, not access to tools, is what separates the organization's getting real value. Paul, that was like news or music to my ears, because that is the exact approach of the [00:34:00] AI for Productivity workshop. Yeah. That we're doing at the bootcamp this week, which we'll talk about more in a sec.

[00:34:05] It's just like the tools of course matter and literacy with the tools matters, but all these enterprises it seems, are running into all these bigger challenges about workflow mapping context. Governance, et cetera. Like what's your advice right now for enterprises? Like do you see these same things in the conversations you're having?

[00:34:22] Paul Roetzer: Yeah, this is a really good report. There's, they, surveyed 12,000 frontline employees, managers, and leaders and dozen global markets. So it's actually like a lot of international components as well. a lot of this Mike Reinforces themes we saw in our state of AI for business research that we released in Was that May, I think we came out with that research.

[00:34:41] Mike Kaput: Yep.

[00:34:41] Paul Roetzer: Yeah. So yeah. I'll, I'll, I'll call out a few, points here. So they said 42% of AI users save eight hours, the equivalent of days worth of work in a week. and the time savings is even higher for functions such as marketing, 60% it 53% and human resources [00:35:00] 50%. Hmm. However, 66% still receive little or no guidance on what to do at the time they save.

[00:35:06] And more than half say they're not reinvesting time saved in more strategic work. That goes to the point, Mike, you're making about just the organizational structure that it's like, okay, great. We gave 'em tools and maybe we even trained them how to use the tools. We didn't train them what to do with the time they're saving.

[00:35:19] And so they're not redirecting that into strategic efforts and new campaigns and new ideas and things like that, which is a huge opportunity for companies. compared to 2025 number of organizations that have graduated to using AI to reshape workflows end to end and to invent new business models has nearly doubled from 40, to 42% up from 22%.

[00:35:40] You mentioned the idea of training and support from leaders as a strong driver of as potential, but also one of the biggest unmet needs. 72% of respondents say expectations for the skills they need have shifted. However, 36% feel they have received, they have not received, adequate or they have received adequate upskilling, so like a smaller percentage are getting the [00:36:00] upskilling they needed.

[00:36:01] And then only a third of frontline employees say leadership's communications about AI are clear. This is what we see all the time. And only 28% see a strong connection between what leaders say and what the organization actually does. So we talked about that when I did the eight pillars of business AI transformation.

[00:36:16] I said like the most important thing was clarity from leaders. That leaders understood the moment and that they were clear in their communication. So definitely echoes the things we've been saying. And then on the topic of agents, the vast majority of respondents, 84% have heard of AI agents tools. And that act autonomously with minimal human oversight is how they define them.

[00:36:34] More than twice as many respondents as last year say their organizations have integrated AI agents into workflows. So that's at 30% up from 13. Another 50% say their workspace has run AI agent experiments or pilots. The experience is leading people at all levels to believe that agents could do at least half of their job within three years with leaders and managers expecting the biggest shift that's.

[00:36:58] That's crazy.

[00:36:59] Mike Kaput: Yeah.

[00:36:59] Paul Roetzer: I mean [00:37:00] that's, they're, they're right. But the fact that people are now realizing that awareness and integration of AI agents have outpaced the systems that companies have enacted to supervise them. This is, you know, an issue that, Levie talks about. half of respondents say their companies lack clear governance for managing teams with people in ai, and almost as many say, AI related accountability is one of their three top concerns to the future.

[00:37:21] And then they had a really nice, five CEO imperatives outline. So I'll just, I'll say the five points. So make strategic clarity, a top priority and own it personally. I agree a hundred percent. The CEO has to be the lead on this stuff. Change the scorecard. Measure value, not adoption. invest in redesign work, end to end, not in more tools.

[00:37:38] Put people at the heart of that redesign and then govern it as a moving target, not a one-off program. So those are all really good points. from, for Levie's o overview, couple other things I'll drill into and, and maybe, double click on Mike. So, growing view that enterprises are going to live in a multimodal, multi-model world.

[00:37:58] Lots of interest though, though, [00:38:00] early in the actual adoption and layers that can route workloads to different models for cost performance. So this means like, you know, let's say Claude goes down, what are we gonna do, right? If all of our, everything lives in there, all of our projects, our skills, whatever, how do we keep the business running?

[00:38:16] Or if it becomes too expensive, we need to have another, we need to have an open source model internally. We need to have a secondary backup. So he is basically saying like. Almost no enterprise is gonna bet on a single company or model provider. you mentioned this when talent for driving AI adoption and implementation still remains a major issue and topic, many view it as something you necessarily have to train for internally.

[00:38:37] Do a shortage of talent being trained on this, from the outside. So this goes back to our whole point, like AI literacy is fundamental. Like the companies have to own reskilling and upskilling their own people. It's gonna be the fastest path. People with domain knowledge or institutional knowledge, domain expertise, that you can make AI literate AI forward, that is your best path to do this, right?

[00:38:56] And then the best use cases for AI tend to be those that fundamentally [00:39:00] change the work being done instead of just replacing an existing process. So this goes to the idea that, that I've been touting, that I've, you know, featured in my make on 2025 keynote optimization is doing things better, faster, cheaper.

[00:39:10] That's 10% thinking should, we should absolutely be doing it. But innovation is re-imagining what's possible and creating entirely new forms of value. That's 10 x thinking and that's what we want to be. Doing Now. One last late entrant to the game, Mike. This was from Satya Nadella on Sunday, July 12th. So last night he, he posted this, so I just threw this in, in kind of the last minute.

[00:39:32] He published something on X called the Reverse Information Paradox. It had seven and a half million views as of Monday morning. so this goes along with what Carp was saying last week. You know, Palantir, CEO, that we talked about a little bit of what Levie was talking about, but you're seeing these recurring themes.

[00:39:49] So he said, in the age of intelligence, how should firms protect their core ip? Nobel Prize winning economist, Kenneth Arrow, famously described a paradox in the market for information. [00:40:00] Quote, its value for the purchaser is not known until he has the information, but then he has an effect acquired it.

[00:40:07] Without cost. So in Arrow's information paradox, the seller risks giving away knowledge in order to sell it. AI creates the reverse problem in the AI age, the buyer risks giving away knowledge just in order to use what they bought, meaning you are giving these model companies knowledge every time you use their product.

[00:40:27] So he said you essentially pay for intelligence twice, once with money, and again, with something even more valuable. The proprietary knowledge you must reveal to make that intelligence useful the better you want the model to perform, the more of that knowledge you have to feed it. The seller learns, meaning openAI's, Anthropic, Google.

[00:40:48] in theory, Microsoft, the seller learns more and more about you as you use what you purchased while you learn very little about what the seller is learning in return. So you don't [00:41:00] know what your prompts are teaching them, what the outputs you create are teaching them, is the point he's making. That is what I think of as the reverse information Paradox.

[00:41:08] Models learn from exhaust, the prompts people write, the tools, agents use, and especially the corrections people make when the model is wrong. Every correction is distilled insti into institutional knowhow. In consuming intelligence, you are creating intelligence and what you create should belong to you.

[00:41:29] This is a very different approach than like, I mean, Microsoft is the biggest investor in openAI's and they're in essence taking. The anti openAI's position here, which is really fascinating to watch happen in real time in learning flows. It it, if learning flows in only one direction, economic value converges towards the owners of the learning infrastructure rather than the creators of the knowledge itself.

[00:41:52] So again, the model companies win, not you. Therefore, it's imperative that we distribute the learning infrastructure to every firm so that they can [00:42:00] control their own learning loop as Alec Karp put it again, referring to the Palantir CEO topic we talked about last week. What the technical customers want is control over their compute, their models, their data stack, and their alpha.

[00:42:13] They want to know they own the means of production and it's not being transferred to someone else. The current regime does precisely the transfer carp and company's fear. So she said there are few things every enterprise must do to of them a highlight control. Create your private evals because evals define what good looks like in your organization and then have control of it.

[00:42:34] And then choice. Ensure the orchestration layer is decoupled from any single model. Ask yourself if any one model you are using is taking away, do you still have the ability to operate and optimize your evals using other models? So then ends. In other words, a company should be able to use a model without giving up the knowledge that makes it unique.

[00:42:52] That is the reverse information paradox we need to confront. So. I mean, as you said up front, Mike, there's just so many related [00:43:00] things now each week, and like we said with Kap, while he is a controversial CEO and figure If you get through that noise, there was a lot of what he was saying that made a lot of sense.

[00:43:12] And that Levie is echoing that Satya Nadella is echoing and it is in conflict with these proprietary models that Dario Amodei is, you know, obviously a huge proponent of, OpenAI is a huge proponent of, but what they're all basically saying is, open source is gonna matter more and you're gonna need more control over the knowledge that you put into these things.

[00:43:33] And the one thing I'll say is to buyer beware anytime you're using. An intermediary like that, you're allowing your prompts to go through a third party. If that third party is helping you achieve efficiency, figure out what models to use. Like, perplexity comes to mind. Yeah. As a, an example, here they are a hundred percent taking the data that you're putting in the prompts, you're using [00:44:00] the things you're building as training for their own businesses.

[00:44:04] That is the value to them. It's why maybe they're even gonna give these things away. They want your prompts and if you're, you know, if you, if they're an intermediary, it is to extract information from you, to build something else on top of you and your data.

[00:44:20] Mike Kaput: That's such and this paradox, I don't know.

[00:44:22] Do, do we have any sense or advice on how to think about resolving this? Because it's like you to get value outta the models, you have to, in some fashion provide it with your unique context. I would argue it's not gonna happen in a vacuum. You're not gonna just magically get value with it, without it knowing specifics about your business and data, but then to their point, you're giving up your alpha.

[00:44:45] Is there any, I mean, we talked a bit about some solutions maybe when we talked about carp. Like have you, has your thinking evolved at all on that?

[00:44:52] Paul Roetzer: No. I mean, I haven't had like the brain space to really spend a lot of time on it, but there's a part of me that thinks. It's just [00:45:00] the cost of doing business.

[00:45:01] Like Mm. I mean, you could say the same thing every time you use Gmail or you know, a Google Doc or anytime you use Microsoft, like the, this has always been the game. Yeah. The tech companies have always had the insights into every single thing you do, what you click on, what you look at, what you type, what you retype, like.

[00:45:19] It's, it's the exchange for the power and the intelligence and the tools. So I think it's the hot thing right now, and I do think it'll affect some of the ways that, you know, its, and departments and CIOs in particular think about their use of these tools, especially for more proprietary and sensitive use cases.

[00:45:39] But I think for like marketers and salespeople and CS people, it's like, what the hell are they gonna learn from us? That they're not gonna, it's like whatever we do is public knowledge anyway in the end. Like, marketers are putting information. So I don't know that it's as critical for the standard, like, knowledge work functions, but like if your lawyers working on patents and things like that, a hundred percent you should be like on a, on a, [00:46:00] on a open source model that you'll control internally that doesn't go anywhere.

[00:46:03] So I think it probably relates more to how you're using these models and what you're putting into them. and again, it's not like it's taking. Your specific confidential information and training a new model on it, but they're able to learn how you use them, what they use them for, what the prompts.

[00:46:19] You use them and they're able to train the models differently based on how you interact with these things. Yeah. And so it's like, I don't know. I think it's just for most people, it's gonna come down to like, it's just the cost of doing business and I don't really care that much.

[00:46:33] Mike Kaput: Yeah.

[00:46:33] Paul Roetzer: Whether that's the right attitude or not, it's just how we've always functioned with business software for sure.

[00:46:39] AI 2040

[00:46:39] Mike Kaput: Alright, our third big topic this week, comes from something we talked about a little earlier in 2025. So in April, 2025, the AI Futures Project, which is this research group led by former openAI's researcher Daniel Kokotajlo, published a report called AI 2027. This was basically a detailed month by month scenario [00:47:00] forecasting that AI could automate AI research itself within a couple years, which would trigger an intelligence explosion that they predicted would end in either AI takeover or an extreme concentration of power.

[00:47:12] This became a widely debated document in ai. It drew recommendations from figures like AI Pioneer Yoshua Bengio. We covered this on the show a couple times, last year, but now this past week, they are publishing a follow-up called AI 2040 Plan A. This is a 90 page scenario laying out the team's positive vision for how humanity.

[00:47:37] Gets to super intelligence without catastrophe. So the authors are really explicit that this plan a doc is a recommendation, not a prediction. And kind of like the previous report, they game out like a fictional scenario to show how this could work. So they basically say, quote, we recommend an international deal to avoid a dangerous race to super intelligence.

[00:47:58] The deal involves total [00:48:00] research transparency for AI r and d, which allows the nations of the world to understand what's happening and enforce guardrails. The result is multiple companies across multiple countries scaling slowly and safely together towards super intelligence instead of racing each other in secrecy.

[00:48:17] So basically they're saying under plan A, if all goes really, really well, humanity could delay super intelligence until 2040. They would make all AI research public. This would allow dozens of companies around the world to catch up to the frontier while safely managing AI development. So we can get into some of the specific details around their predicted timeline here, but this all kind of hinges Paul and them predicting like, Hey, the US and China basically need to coordinate on, coming to some type of deal, whether formal or informal to start that would actually have them jointly.

[00:48:51] Taking some actions to kind of regulate how AI research works, but they have some real sci-fi scenarios in here, like 10 years from now. They're [00:49:00] basically saying in this fictional scenario, like most of all, labor would be automated by ai, even if they regulate a. On the way to super intelligence. So there's a lot of very speculative stuff in here, and I'm, I'm kind of curious how seriously you're taking some of these, like, I found some of these predictions.

[00:49:17] I mean, it could happen, but they're pretty wild.

[00:49:21] Paul Roetzer: Yeah. So Daniel, the author, tweeted Plan A is our current best guess, but hopefully a better plan will exist before it's too late. We hope that the best ideas from Plan A will be adopted and the worst discarded. So. As I was looking at this, Mike, I'm thinking like three to five years out is almost impossible to comprehend.

[00:49:39] Yeah. Like the complexity of these models. What happens if we get to AGI and beyond and to super intelligence? So this sort of paper can almost seem like, yeah, like, we're not even gonna talk about this on the podcast, but instead we chose to make it a main topic. Why did we do that? Well, the starting points actually show why it's so important to work on and to talk about these things because what they present and the questions they ask are [00:50:00] actually very logical and likely.

[00:50:01] So they start the report it says in it begins, 2027, the writing on the wall. So this is, again, I'm gonna, they they're presenting hypotheticals, so they're looking into next year. But when I read the start of this like. This is not farfetched. So the foundation of why they're doing this research rests on this.

[00:50:21] So America has two workforces now. The first is people, 165 million of them. The second is AI agents. Millions of copies spun up and shut down every hour working around the clock at superhuman speeds. That is a path we are absolutely on. Most of their work is slop. Definitely a path we run. Hmm. But enough of it is good that people are paying tens of billions of dollars a month for ais that can, in theory, at least do anything on a computer that an employee can do.

[00:50:48] There is one job the AI companies wanna automate more than any other, their own. They haven't succeeded yet. No recursive self-improvement. That's a topic we've talked a lot about in 2026, so no recursive [00:51:00] self-improvement so far, but they seem to be getting closer and they're pulling up the ladder behind them.

[00:51:05] The strongest coding ais refuse to help competitors with AI r and d. That's based on something that's actually happening already. Even as the most bullish employees admit that things are taking a bit longer than planned. The skeptics notice that their usual dismissals are starting to ring hollow.

[00:51:20] Why exactly will AI never be able to do my job? What's the barrier again? Congress is starting to pay more attention. They've long been hearing about AI data centers using too much water. Chatbots encouraging suicide, mythos hacking NSA systems. And of course, tech industry lobbyists warning that any whiff of regulation will make AI immediately lose the race with China and spend the rest of history as a tributary state.

[00:51:47] The CCP tributary state. So again, none of this is farfetched at all. This is like next year. Now they step back and ask, where are we going with this? What does the world look like five, 10, or 15 years [00:52:00] from now? Will there still be jobs? What if there aren't. One question weighs especially heavily on their minds, who will control all these ais?

[00:52:07] Congress settles on an important part of the answer, probably not us. They hold a series of tense hearings on ai. They read the 2016 OpenAI emails discussing how OpenAI was founded in order to prevent Demis Asaba from becoming a dictator. But who is preventing Sam or Elon from becoming dictator?

[00:52:24] Congress is unsatisfied with existing responses. The result of this wake up is the AI Transparency Act of 2027. An omnibus bill that does many things, some good, some bad, but doesn't fundamentally change the situation. So that's what they're looking at saying next year. Now, again, is an AI transparency act gonna be created.

[00:52:41] Who knows? But the rest of that is all very. Probable, like it's a, it's a logical thing. So then that leads to 2028. AI is now on the ballots. That's the next presidential elections in the United States. The 2028 election cycle is heated. As usual. AI is the biggest topic, which I a hundred percent AGI agree it.

[00:52:59] It probably will [00:53:00] be. The data centers now under construction costs twice as much as the entire US military budget. Most white collar professions are seeing disruption like software engineering saw in 26. Such jobs now heavily involve managing AI agents. That is absolutely gonna happen. AI companies have industrialized the training process.

[00:53:17] Executives say, let's move into X profession this year. That's what VC firms are funding is like, let's go take on the next profession, and then the company interviews professionals buys data, creates training environments, et cetera, until their AI get traction. Then the AI rapidly improve as they're used more widely in the field and accumulate more real world data, which then leads to more automation of different industries.

[00:53:38] Other countries are starting to get scared and angry. It seems like a handful of US and Chinese companies are on track to automate all white collar jobs. Power is concentrating in the US and in particular the president, plus a handful of tech CEOs that's already happening. AI experts warn that the intelligence explosion is near.

[00:53:55] By speeding up AI research, the eyes will become even more competent, speeding up research [00:54:00] even faster, making them even more competent and so on. Both presidential candidates keep getting asked what they'll do about AI and try out increasingly dramatic ideas on the campaign trail. The discourse bounces back and forth across all the options displayed below, which I'll explain in a second.

[00:54:15] And eventually the president and his protege, which I'm assuming is an AI agent. Later on, converge on one plan, the opposition candidate converges on another. Then it's election day. So what this is doing is it's setting up, here's what's probably gonna be happening over the next year and a half in the us.

[00:54:31] Leading up to this election, and then we are going to have to choose, the candidates will take opposite positions. That's a given in politics. We then, as, as the voters will have to choose which candidate we think is best to lead us into this era where super intelligence will likely be possible. So then at a very high level, and we won't get into like the, the, you know, granular details of the report.

[00:54:54] They say, okay, race through super intelligence explosion by having AI self improve and putting them in charge of [00:55:00] more things, data centers, factories, weapons faster than China can. That is like the option presented to the US and to these candidates. So plan A is a verified slowdown. That's the one you talked about.

[00:55:10] Mike President announces that the US will pursue international cooperation to avoid an imminent intelligent explosion. Sounds great. Does not seem like a viable option, in my opinion. Plan B is you fight China. The president announces the creation of a US led coalition to govern AI deployment or development.

[00:55:25] Plan C has burned the lead. The president says he'll be implementing strong regulation to ensure safety and security plan D, race to super intelligence. The president says he will be implementing light touch AI regulation to prioritize AI innovation and Plan S shut it all down. The president seeks a global moratorium on AI development.

[00:55:44] Again, anything beyond 2028 is like for sure completely guesswork and they're, I'm sure relying on really smart, advanced AI models as well as their own domain expertise to like develop what this could look like over the next, you know, 14 years. [00:56:00] But I think just accepting that some version of what they're presenting in 27 and 28 are likely scenarios.

[00:56:08] It presents the reason why we should be having these discussions now and at least contemplating what they're presenting as like, wow, we have no idea what it looks like beyond 2028.

[00:56:19] Mike Kaput: Yeah. I feel like I had a mild panic attack reading the, like, setup of this scenario. Yeah. Just because like where they extrapolated out to obviously just sounds totally sci-fi, but you see the seeds of this being very, very realistic based on what we cover every week.

[00:56:38] There's huge opportunities and there's a lot of really positive things outlined in their, recommendations, so to speak. Yeah. But yeah, it's, it's a sobering read, I would say.

[00:56:49] Paul Roetzer: Yeah. I think you gotta be in the right mindset to want to read anything beyond what we just like covered for you. Um. But I do think that it's really, really important, especially [00:57:00] Americans like,

[00:57:00] Mike Kaput: yeah,

[00:57:02] Paul Roetzer: it's gonna play a role in the midterms in 26.

[00:57:05] It will dominate the 2028 presidential election. Like, there's no way. It can't, because the implications are so vast across the economy, energy, where we're getting energy from, where we're building data centers, what, foreign entities we're allowing to invest in US companies, whether or not we allow our models to go out, like export the models and chips and like it is going to be a part of literally every conversation that's happening is gonna like, come back to the implications of AI and the decisions are gonna be made.

[00:57:38] So, like, it is gonna be very, very important that people understand these, these issues going into 2028. Yeah.

[00:57:47] Mike Kaput: Alright, before we jump into Rapid Fire this week, another announcement that this week's episode is also brought to you by the AI for Business Bootcamp by SmarterX. We are coming to Columbus, Ohio this week.

[00:57:57] When you are listening to this Thursday, [00:58:00] July 16th is when we'll be in Columbus for the bootcamp. There is still time to join us if you're a professional or leader who's ready to accelerate AI adoption and value creation. This is a single day, about eight 30 to about five 30 at the Hilton Columbus at Easton.

[00:58:13] We're starting the day off with a state of AI for business Keynote given by Paul. Then we're transitioning into two highly interactive workshops, one led by myself and AI Productivity Workshop in the morning. And then Paul is leading an AI innovation workshop in the afternoon. So this event is built for AI forward managers, directors, executives across every department who are ready to move past AI theory.

[00:58:36] We're gonna actually. Work on architecting real AI powered workflows. Get strategic frameworks to accelerate transformation and leave with immediately actionable plans for yourself and or your team. So AI Academy Mastery members get discounted pricing. We also have discounts available for teams of two or more, and groups of 10 or more can even get custom pricing.

[00:58:58] If you are listening to this [00:59:00] podcast, you can also use the Code Pod 100 to take a hundred dollars off your ticket. Again, this is happening this Thursday, July 16th. So to grab your spot, just go to SmarterX dot ai, click on events. You'll see the AI for Business Bootcamp as an option right there. All right, some rapid fire.

[00:59:17] Apple Sues OpenAI for Trade Secret Theft

[00:59:17] Mike Kaput: This week, Paul, we had some big news later in the week, breaking and over the weekend. This past week, apple, sued OpenAI for trade secret theft, accusing the company and its Chief Hardware officer of running a coordinated campaign to steal information about upcoming Apple products. So the suit, which was filed Friday in the Northern District of California says openAI's encouraged Apple employees to share info components and other materials tied to unreleased products as openAI's seeks to potentially build its own AI devices.

[00:59:48] According to the suit, more than 400 former Apple workers are actually now at OpenAI. So the suit actually names OpenAI Chief Hardware Officer Tang Tan, a former Apple VP of [01:00:00] Product Design who led iPhone, apple Watch and AirPod Developments, along with former iPhone hardware engineer Chang Lu. Apple says Tan solicited details about unreleased products and job interviews.

[01:00:11] Lou downloaded dozens of confidential hardware files and OpenAI was actually actively coaching departing employees on basically avoiding the kind of quote dreaded walkout when you leave, that ends your access to confidential inter information. So, at every level from members of its technical staff to its chief hardware officer, this suit is saying.

[01:00:33] In coordination with business partners, openAI's has been stealing Apple's trade secrets and confidential information. Apple said. So Apple is seeking a jury trial. They want openAI's to stop, destroy any proprietary materials and redesign upcoming products to exclude Apple's technology. openAI's dis denied these allegations.

[01:00:51] And Paul, I mean, this is a pretty big bombshell. I mean, we're gonna be watching this one closely. I did not see this one coming.

[01:00:58] Paul Roetzer: Yeah. And just important [01:01:00] context. So, you know, OpenAI acquired Johnny i's Company, right? Yeah. Right. For like $6 billion or something.

[01:01:06] Mike Kaput: Yep.

[01:01:07] Paul Roetzer: so Johnny, ive of, of Apple fame, the, we've discussed like what are the products they're building.

[01:01:13] We've tried to kind of guess, but we know it's hardware related. And so we've known for a while that they were working on devices, whether it's trying to compete with or replace the iPhone, or if it's an ambient listening device that sits on your desktop, or if it's a pen or a pendant. Like we don't, we don't know yet.

[01:01:29] Um. They're very aggressively moving into this. And I would assume hardware is gonna be a key part of open AI's, IPO, like the potential value and market opportunity behind their hardware. The, I'm not a lawyer, but holy shit. Like the lawsuit, like what they're claiming it is, it does not sound good for openAI's.

[01:01:50] so I, I'll just, a couple excerpts from Alex Heath who was, I follow on, on X and he was sort of reviewing the complaint. [01:02:00] so he said, yeah, recent, recently, significant evidence has emerged suggesting this is quoting from the filing. individuals employed by openAI's wrongfully took apple's secret and confidential information regarding our unreleased technologies, processes and products.

[01:02:13] We always defend our team's hard work and innovations and we are taking all appropriate steps to do so. regarding an ex Apple employee named the lawsuit over several weeks while developing hardware for OpenAI Mr. L Serendipitous or a a c certificate. Wait, how is that Worse

[01:02:28] Mike Kaput: Surreptitiously, I think.

[01:02:29] Paul Roetzer: There we go. Yes. Accessed and downloaded dozens of apple's confidential hardware related files including, voluminous detailed information about unreleased products, engineering presentations, technical specifications of proprietary project data. Other formal Apple employees who had gone to work for OpenAI emailed themselves Apple's confidential information to personal accounts on their way out the door.

[01:02:50] regarding Tan, a veteran Apple product leader who is now openAI's head of hardware, Apple's investigation has revealed Mr. Tan has been methodically using Apple's confidential information to benefit [01:03:00] OpenAI. He has used an Apple internal project code name to ask quote, what's the plan for X for an announced Apple product?

[01:03:07] He has directed job candidates still working for Apple to bring actual parts from Apple to their interviews for show and tell sessions in which he and his team at OpenAI can elicit still more Apple confidential information. OpenAI has been instructing Apple employees to bring CAD design artifacts and prototypes to the interviews.

[01:03:26] in February, an investigation was in early stages, apple wrote openAI's to raise its concerns that Apple's cons confidential information could be making its way into open AI's business improperly. Apple asked openAI's to discuss what precautions they were taking to avoid this problem, to investigate and to, remediate any issues.

[01:03:44] openAI's did not respond. openAI's has been stealing Apple's trade tickets and confident information. As a result, open AI's nascent hardware business now rests on the shakiest of foundations rotten to its core by its illegal reliance on misappropriated trade secrets. [01:04:00] February 9th weeks after he had left Apple knowing he had no right to do so, Mr.

[01:04:05] Lou tried to access Apple's network storage. He discovered that surprisingly he could still access the Apple network repository after leaving Apple, the result of then unknown authentication vulnerability. Rather than bringing this Apple's attention, Mr. L celebrated his fine with Ms. Pang and said about exploiting it.

[01:04:22] Quote LLLI found I can access the network storage. So funny. That's not gonna play well in court. So it goes on like it is, it's insane. And like, again, not an attorney, but like, I just, this is a bad, bad look for openAI's. now openAI's statement, which I thought was actually a joke when I saw this. I was like, but this is from their director of strategic communications.

[01:04:47] He tweeted our statement in response to this suit. Quote, we have no interest in other companies trade tickets. We remain focused on building innovative technology that empowers people everywhere. I didn't realize that was the formal response from openAI's. I was like, [01:05:00] oh my God. Like, their lawyers are gonna tell 'em to get this down in like seconds.

[01:05:04] But apparently that was their actual tweet. And then Sam said, I'm not afraid of Apple, but I have tremendous respect for them. okay, Sam, I, I, again, not sure that's gonna go so well. so my big questions are Apple and Open Eye are in a partnership like they, they're sharing. Chet, you can still use it in Siri, so that's probably not going so well.

[01:05:24] Um. What this does to apple's or, or, or openAI's hardware plans like I, and then the impact on the IPOI, I don't think when you're going for a multi-trillion dollar IPO, you want a massive lawsuit from Apple of all companies hanging over you. So I don't know, like this is really, really interesting and a whole new thing to watch.

[01:05:47] Mike Kaput: To our point before about worrying that the model companies are taking your alpha, I would imagine at Apple you are not allowed to use chat GPT today. You still are. You shouldn't be. [01:06:00] It's kind of crazy.

[01:06:01] Paul Roetzer: Yeah.

[01:06:01] Mike Kaput: Ugh, boy. Well, and

[01:06:03] Paul Roetzer: that was what led to the Sam Elon tweet that I started with about like him stealing stuff.

[01:06:09] So,

[01:06:10] Mike Kaput: well, I'm sure we'll have some updates here soon enough on that.

[01:06:13] Paul Roetzer: Crazy.

[01:06:14] Illinois Signs Nation-Leading AI Safety Law

[01:06:14] Mike Kaput: Next up, this past week, Illinois Governor JB Pritzker signed the Artificial Intelligence Safety Measures Act. This is a bipartisan law. State leaders are calling the strongest AI safety and accountability framework in the country.

[01:06:27] This law targets the most capable models built by the largest companies using basically two thresholds. if they have $500 million in annual revenue, they also kind of measure if they have massive computing that they're using for the models. And anyone who's covered by this must publish a transparency framework explaining how they apply industry standards, how they measure model capabilities and catastrophic risks, and identify and respond to safety incidents.

[01:06:52] This law also creates confidential reporting channels and whistleblower protections. For any employees who raise [01:07:00] safety concerns. So Illinois actually then becomes the first state here to require regular, independent, third party safety audits of covered AI systems. That actually goes a step beyond the 2025 New York and California laws that Illinois legislators used as a model.

[01:07:17] Illinois Attorney General Kwame Raul will have authority to find companies up to $1 million for a first violation, up to 3 million for each additional violation. This law passed the Illinois House 110 to zero, and it actually did draw support from openAI's and Anthropic. Anthropic actually said it was proud to be the first AI lab to support the bill.

[01:07:40] So Paul, I'm curious what you think of this law. Like we're starting to see it seems. States fill the void, which is kind of what the federal government, at least the administration was worried about. If there's no federal overarching law states are going to pass their own regulatory frameworks, it sounds like.

[01:07:54] Paul Roetzer: Yeah. I didn't, I didn't see any response from the government on this one. the federal government. Yeah. I would imagine they're [01:08:00] not like huge fans of this, and they probably aren't fans that openAI's Anthropic are publicly supporting it. I don't know if this is like a skeptic in me, but I just find this hard to believe this is gonna have any real impact.

[01:08:10] Yeah. You know, if openAI's Anthropic are like, yeah, this is great, then it's like, okay, yeah, it's probably, it doesn't have much, much teeth. And I don't know if it's like that it's only like a $3 million violation for each additional violation. They're like, yeah, okay. That's like a rounding error. or that it really just isn't that aggressive.

[01:08:27] I, I don't know. It just doesn't seem like it got a ton of. Pushback from anybody, which tells me it probably isn't like a huge deal.

[01:08:35] Mike Kaput: Yeah.

[01:08:35] Paul Roetzer: But it is a sign of states making progress and filling that void, as you said. But I don't know that it's like a, you know, this is a transformational thing when it comes to, you know, how the models behave and what they're gonna do and, the power that they're gonna have.

[01:08:50] So, I don't know, I might be wrong on that, but it just doesn't seem like it got a lot of run, and that tells me it probably isn't like a massive deal yet.

[01:08:58] Mike Kaput: And like we talked about with the [01:09:00] California laws, they were considering like good luck keeping a close eye on like, compute levels. Yeah. And where the thresholds are.

[01:09:06] Like I don't even know how you begin to do that at the state level.

[01:09:10] Paul Roetzer: Yeah. And again, like one of the, one of the loopholes that I just see it seems like we've realized is gonna be an ongoing issue, is all of these regulations are related to. Publicly released models. So the loophole is they don't have to release the most powerful models.

[01:09:31] That the labs can have more powerful models. They can give, you know, exclusive access to select companies to government. And so this doesn't cover the fact that the models just gonna keep getting smarter and like, maybe we just start reducing who has access to them because it's too hard to release them publicly.

[01:09:46] So they just do these limited releases. And then the general populace doesn't ever get the most powerful models. I don't know, but like, that seems like a. Logical path that all the regulation could lead to. Yeah.

[01:09:59] AI Safety Index: Nobody Gets an A

[01:09:59] Mike Kaput: All [01:10:00] right. Next up. Something else related to AI safety. This past week, the Future of Life Institute published their summer 2026 edition of their twice yearly AI safety index, which convenes an independent panel of seven AI experts to grade nine leading AI companies across 37 safety indicators.

[01:10:18] Unfortunately, the top grade was a c plus. Anthropic, again, held the top spot with that c plus. openAI's slipped from C plus to a C grade. Google DeepMind ranked third. After that, the panel noted all three have weakened or dropped earlier pledges to halt development on their own. If certain red lines came into view.

[01:10:40] They have also softened their resistance to military uses of their technology. So meta actually improved, slightly climbed from a D to a D plus. so great. Good for you. Elon. Musk's XI, obviously just rebranded a Space XI, it fell to an F. It joined China's deep seek [01:11:00] and France's Mytral at the bottom.

[01:11:02] the Institute's co-founder and President Max Tegmark we've talked about before, said the failing grades spanning three continents show that this is a global problem. he ba Stuart Russell, who is also a panelist said companies have backed away from earlier commitments to release new systems only with safety measures appropriate for their capability levels.

[01:11:21] Now they're planning to release them, even if it's demonstrably unsafe to do so. Tegmark told Time Magazine that a real race to the top on AI safety will take regulation. Saying he is cautiously optimistic and pointing to the new, the eus AI Act, new Chinese rules that are taking effect this month and a more risk conscious US administration.

[01:11:43] So Paul, like, no question Tegmark comes at this from a very specific view on AI safety, but him, Russell, the other panelists is, are pretty big people in AI saying that the companies are not doing a good job, it sounds like.

[01:11:57] Paul Roetzer: Yeah, I don't think that's surprising to anybody. Yeah, I [01:12:00] mean, cer certainly, you know, some of the labs do a better job of releasing information, being a little bit more transparent on what they're doing, being more vocal about the risks related to what, you know, everybody's building.

[01:12:11] Some of these labs are, you know, very intentionally not sharing this kind of information. Don't Yeah, yeah. Like, so that it's I do, I do think it's funny though, like the d to the d plus is, is really good. And, but again, if, you know, I know some of our listeners are really concerned about the AI safety side of this and where does this all go?

[01:12:30] And so this is just to, for you to know that there is a report out there that, like anything in politics or in ai, there's, there's bias in the future of Life institute. Like what do they believe and what are they pushing? Like you always have to put the context of, okay, who is publishing this? What are they traditionally focused on?

[01:12:50] What is their mission? all that being known though, like it's, it's important to, to know these things exist and be able to go down this path if you [01:13:00] want to go read this stuff. And I, there's nothing in it that surprises me. Like we know that they're not really doing a great job in this area, but it does sort of quantify it in some ways.

[01:13:09] Yeah, it really does.

[01:13:11] The AI Cheating Scandal at Brown

[01:13:11] Mike Kaput: All right, so this past week we also saw a cheating scandal at Brown University. That became a viral case study in what AI is doing to higher education. So this is about an economics professor, Roberto Serrano. He basically allowed his students to take take home exams in his very difficult welfare economics course.

[01:13:32] This was the first time he had done it this spring, unfortunately, it was due to, because in December they had a campus shooting that left a bunch of students anxious about being in classrooms. So he said, okay, we'll do the exam as a take home this year. Interestingly, when he announced that enrollment jumped from a typical class of under 30 students to 86 students, the take home midterm came back with an average score of 96 out of 140 students scored a perfect [01:14:00] 100.

[01:14:00] The historical average in the course range between scores of 65 and 80 on this exam. And he basically noted this. He saw that many answers had this weird, convoluted style. So he started running the exam questions through ChatGPT and produced basically similar answers. So what he did is he said, look, we're moving our final exam to in-person, and if the score distributions look the same, we'll hold the midterm, grades where they are, and you can take your, your well earned grades if the score distributions look different, we're voiding the midterm.

[01:14:32] The moment he said this, 18 students dropped the course. Nine more, skip the final 22 of those 27 had scored a perfect a hundred on the midterm. Here's the thing, the students then took the final in person and the average score dropped from 96. To 48. And so basically while he says he's not using AI detectors or anything, he's like, look, this is a pretty solid indicator everyone's using chat [01:15:00] GPT or other AI tools to basically totally cheat on all this stuff.

[01:15:03] And so it's kind of set off this scandal where the teacher's like, look, I, you know, he's softened the blow a little bit by changing some of the worst scores it seemed, or giving a little more credit, but he's like, we've got a serious problem here. And so Paul, I don't know if this is surprising per se, but seeing those exact numbers was crazy to me.

[01:15:22] Paul Roetzer: Yeah, that's the, it is jarring just to, to see like it quantified like this. I don't think anybody would be surprised that kids are using these tools to cheat. Yeah. yeah. The, so I, I was like, this is a rapid fire. I won't go deep on this, but like the thing that jumps out to me is you can't outsource thinking.

[01:15:41] and we have to, as employers, as parents, for ourselves, for coworkers, we have to be constantly aware that we have access to intelligence on demand. And if we use it as a crutch for everything, we will forget how to think. And so this becomes critically [01:16:00] important, obviously for educators that they're, they're not allowing, they, they want to teach them how to use AI responsibly, but you cannot do it where they just outsource the important part of what we do.

[01:16:10] and you have to think about this when hiring and when evaluating your existing employees. So in hiring, you almost have to assume that the people you're interviewing out of college got through college using AI Right Now that can be really good, but you have to be able in your. Interview process to test for critical thinking capability to, to make sure that they didn't, atrophy that ability in college, that they've lost the ability to think for themselves and to assess things.

[01:16:42] So it's really, really important. Like this is the whole next generation of workers will have never known education at the higher levels where they didn't have the ability to do it and they probably didn't have someone over their shoulder making sure they weren't using it, to, to, [01:17:00] to replace critical thinking.

[01:17:02] So that could be a problem I could compound within a company.

[01:17:05] Mike Kaput: Yeah. This feels like the real danger at the heart of all the positives about AI usage for students especially. It's like we're seeing it. In the corporate world with what we've called in the past, like work slop, right? Yeah. Where people are just submitting AI responses to their coworkers that are just nonsense or like don't have any thought put into 'em and know makes more work for everybody.

[01:17:26] And it's like I just, if you're a student and you think that you can come into a job and just submit whatever ChatGPT gives you. I don't know if you people think that, but I would just say you have another thing coming. Yeah. If you think that is the standard moving

[01:17:40] Paul Roetzer: forward, that's for sure.

[01:17:42] Mike Kaput: All right, so another big story.

[01:17:44] China and Russia Stoke US Public Opinion on Data Centers

[01:17:44] Mike Kaput: This past week, the New York Times reported that state actors in China, Russia, and to a lesser extent, Iran, are working to inflame American debate over AI data centers. According to some analysis they're reporting on from the threat intelligence firm, Alethea between January and [01:18:00] June State Media and the three countries mentioned data centers roughly 700 times an average of nearly four times a day.

[01:18:05] Basically, in an effort to turn data centers in the controversy around them, into what Alethea calls quote, a domestic fracture point, heading into midterm elections where AI is seen as a top issue. So. There were some examples that included a Chinese state-owned newspaper publishing a satellite image of a data center in Gainesville, Virginia, warning that AI threatens Americans wellbeing.

[01:18:30] There is a chat GPT generated comic strip that was misinformation disguised as a Maryland news outlet, blaming data centers for soaring electricity bills, and a known covert Russian operation, circulating a video attacking an American company's data center project in Armenia. So. Paul, we have known for over a decade at least, that foreign adversaries of the US have routinely pushed misinformation about tons of different subjects over the years to basically stoke domestic turmoil.

[01:18:59] [01:19:00] it seems to be happening with data centers. I believe this is yet another podcast prediction that came true or was correct. is that this was happening. It seems like we have evidence of at least a little of it. It's not something where it's like every single thing you see, there's a real invalid debate around the issue, but it sounds like some people are trying to take advantage of that.

[01:19:18] Paul Roetzer: Totally. Yeah. I was like, I think when I first mentioned it, like a month or so ago, I was trying not to like go down this rabbit hole too much. Yeah. I mentioned like Cambridge Analytica and things like that. yeah, this is absolutely a strategy that foreign governments use to influence, citizens in other countries.

[01:19:35] The US does it to other countries and people do it to us. And so if you find something that causes friction. within, the citizenship, then you, you push on that and you use social media, and now you can use AI tools to personalize the stuff that, you know, pushes these ideas. So it's not to say, as you alluded to Mike, that this isn't an issue, that there aren't real issues.

[01:19:58] But yeah, foreign [01:20:00] governments are absolutely going to push on these things to try and create, a more, I guess hate and fear and distrust among American citizens. So, yeah, it, it's just a, like, people need to be aware and maybe you need to like educate your kids. Like if your kids aren't aware how social media works and things that they're seeing on TikTok and YouTube and wherever else they get their information that.

[01:20:26] You know, sometimes it's not real and it's actually meant to piss them off and get them all worked up about a topic. AI is just one example of this, but like, your kids need to know how the world works. for better or for worse, I've had this conversation with my 14 and 13-year-old, so I don't know what, when's too young to start them.

[01:20:46] And my kids aren't even on social media. Yeah. they don't have accounts like they, they use YouTube, but they're not on TikTok and Instagram and things like that. And we've already had deep conversations about how this stuff works.

[01:20:57] AI Use Case Spotlight

[01:20:57] Mike Kaput: Yeah. Wow. Okay, [01:21:00] next step we have our AI use case spotlight, where every week we give you a quick look under the hood at some real AI use cases we're exploring.

[01:21:06] So I was just gonna share one quick thing, Paul, that came out of my prep for our AI productivity workshop at our bootcamp this week. so as part of that I was building out categories of AI capabilities, basically to help participants better understand what can AI tools do today that you might not be aware of.

[01:21:24] so I actually used Codex to build a huge source backed like capability spreadsheet across chat, GPT, Gemini, Claude, and Microsoft Co-pilots. So basically the way I did this is like described what I wanted to do ideally, and Codex split this research across four separate agents, one per platform. So one agent researched ChatGPT using only official openAI's sources.

[01:21:48] That was a specification. Same thing for Claude Gemini and copilot. Each agent was asked to and inventory the current end user and business capabilities of each platform. As of [01:22:00] July, at this point, it was July 8th or ninth, 2026. So things like the models, the reasoning, chat search, web grounding, debrief, search, et cetera.

[01:22:08] Like what are all the things that are today available in these tools? And it created, it took like over an hour. I think this is like probably one of the longer use cases I think I've had for in a while. The final CSV has 877 rows do each one documenting a feature, showing what link in the documentation it came from.

[01:22:29] So there's like 200 roughly for each of these. We then did another pass to audit it. Basically looking for blank fields, looking for missing source links, stress testing, all the answers. Super interesting. it's deeply overwhelming. It's not like useful as a public facing asset, I would say, but it was extremely critical in distilling this.

[01:22:52] Topic down into something others could understand and making sure that was all backed by real data. So it was awesome.

[01:22:59] Paul Roetzer: That was really cool. [01:23:00] I did a, I shared this with you, Mike, so you've seen this, but I actually, was trying to think of something to test Fable 5 with. Yeah. Since they extended access to, to 12th and so I think it was like Friday morning or something, or I guess no, Saturday.

[01:23:14] I'm looking at it now. I ran this on Saturday morning. So, I'm not a huge one on like tracking competitors. I don't, I don't, I generally just like focus on listening to our audience, looking at the trend data and like building what needs to be built, what we think you know, is best. But every once in a while it's good to just like see what's out there and see what's going on.

[01:23:30] So I actually ran a competitive analysis and I gave this to ChatGPT. 5.6 Sol high work edition, it looks like. And then I also ran it on Fable 5. So I'll give you the exact prompt is that run a competitive analysis on, and then the name of the competitor consider strengths, weaknesses, threats and opportunities in comparison to our business, propose business strategies that we can use to exploit their weaknesses and our strengths to differentiate in the market and be, the clear choice for enterprises.

[01:23:59] So that, that was it. [01:24:00] That's the entire prompt. And then I gave it to both of them and it crushed it. Like they were, you saw 'em like, like I, yeah. I, and I, what I did in this case, again, like the whole idea of AI slop, I shared them internally with a couple people and I said, listen, this is, I don't have time to edit this.

[01:24:14] I just wanted to run this as a test in these two different models. I've taken both the outputs, I've put them into this single Google Doc. this is the raw unedited version of this. I'm not gonna get to it until later next week, but just wanted you guys to have access to this as well. And so, again, like I think one, just the simplicity of the prompt two, using it for high level strategy.

[01:24:36] Three, if you're gonna present something that you haven't verified yourself and given the time to think about, don't present it to your coworkers as though here's this genius thing I did. It's like, no, here's something I took 35 seconds to do across two platforms. Here is how I'm going to move forward verifying this and using this information.

[01:24:57] But here's the raw files in case you guys have a chance [01:25:00] to take a look at it or it's relevant to anything else you're working on. So, that was a cool thing and I'm, I'm really anxious to actually dive into that later this week.

[01:25:07] Mike Kaput: Yeah, the outputs were so cool. And I would just mention as you're kind of talking about that, it really reminds me of what we talked about the top of the episode that Ethan Mollick was saying that this kind of strategic knowledge work is not.

[01:25:18] De is not coding. right. So like you could spin up a bunch of agents to like stress test these ideas. It might redu result in something really useful, but you would need to then go audit like what the logic was there, right? So it'd be much more you working in tandem with this raw output versus hey, let me turn agent loose and have it debug this thing.

[01:25:39] Right? So it's kind of very interesting to see how this knowledge work differs from coding.

[01:25:44] Paul Roetzer: Yeah. And the other thing is you can then take this and say, okay, you know what? I'm really intrigued by what they're doing. every Friday morning, run an updated report, tell me anything new they've done. Right.

[01:25:55] And now you can get at the agentic side and Yeah. Start again. Changes work like it, [01:26:00] you reimagine what it's like to do these things. Like Mike, I, when we were in my, like the agency days, you and I mean, we would charge like, I don't know, 10 to $20,000 to do these like deep competitive analyses and then provide strategy on top of it.

[01:26:13] Yeah. And it did it 28 seconds. Okay. It's wild.

[01:26:17] Mike Kaput: It's incredible.

[01:26:18] Paul Roetzer: Yeah.

[01:26:19] AI Product and Funding Updates

[01:26:19] Mike Kaput: All right. We're gonna wrap up here with some AI product and funding updates. I'm gonna run through it real quick 'cause we have a ton of 'em. So first up, openAI's published an approach to government and national security partnerships laying out principles for its growing defense and public sector work, including commitments not to allow its technology to be used for mass domestic surveillance, direct autonomous systems, or make high stakes automated decisions.

[01:26:43] openAI's CEO of applications. Fidji Simo announced she is actually stepping out of her role. She's stepping back to a part-time advisor role. She's been on medical leave for a chronic illness for three months, so she will no longer be in that role. Anthropic has as of right now, once again [01:27:00] extended access to Fable 5 on all paid plans through Sunday, July 19th.

[01:27:04] It was originally supposed to end July 12th. but now anyone who has a paid plan can use Fable 5 within certain limits rather than only paying usage for usage as you go. So when you're listening to this, you'll have a few more days to try it out. Anthropic appointed former Federal Reserve chair Ben Bernanke to its long-term benefit Trust, the independent governance body that oversees the company's public benefit mission and can appoint board members.

[01:27:32] They cited how his ex, his expertise on how AI will affect workforces and economies. Anthropic also brought Claude Cowork its agent platform for delegating everyday knowledge work to web and mobile, letting users hand off a task at their desk, monitor progress from their phone and let work keep running.

[01:27:49] In the background, Microsoft has started replacing openAI's and Anthropic models with its own internally built m ai models for some AI features in Excel and [01:28:00] Outlook. Meta released Muse Spark 1.1, an agentic encoding model, and began charging for access to its models for the first time through the new meta model API, pricing that CEO.

[01:28:13] Mark Zuckerberg says the pricing is actually roughly a quarter. Of what Anthropic and openAI's charge. Funnily enough, he announced this in his first post on X since 2023. Meta also launched Muse Image and Muse video. These are new image and video generation models. They immediately drew privacy backlash after the New York Times reported that public Instagram accounts are enrolled by default in a feature that lets other users generate AI images from their photos.

[01:28:45] The Financial Times also reported that meta is testing prototype AI glasses designed for continuous ambient recording that feeds an onboard AI memory users can query. meta has reportedly considered whether the indicator light would actually [01:29:00] stay on during passive data collection. SpaceX ai, the new merge company, formerly known as XAI and SpaceX is rolling out Grok 4.5.

[01:29:09] This is the new, their new Frontier model built for work beyond software engineering. They're rolling this out to all customers after a beta test program and Cursor is making the model available across its coding platform. Amazon is working on a secret project code named Moonraker to turn Alexa into an AI agent that can chain together multi-step tasks from a single command with internal documents projecting more than hun a hundred million dollars in GPU costs.

[01:29:37] In 2026 alone, a startup called Prime Intellect raised $130 million Series A led by Radical Ventures with backers including Nvidia and Intel to build what it calls the open super intelligence stack and artificial analysis introduced six new capability indices for comparing [01:30:00] AI model capabilities across industry domains.

[01:30:03] And I think Paul, you had one or two more things to highlight as well.

[01:30:06] Paul Roetzer: Yeah, just this one just dropped this morning, so I'll just throw this in here. So, this is from the Stanford Digital Economy Lab. There's a new website wemustactnow.ai is the URL 16 Noble Laureates. Join leading economists and AI researchers and call to prepare for AI's economic transformation.

[01:30:30] calling for urgent preparation for the economic impacts of radically more powerful ai. This is led by Erik Brynjolfsson, Aja Al Anton Korinek and Tom Cunningham. but it is also signed by Noam Brown. We talked about Jeff Dean, Jack Clark, co-founder of Anthropic Sholto Douglas. We've mentioned Yasha Benzo, Eric Schmidt, Dean Ball, Ben Bernanke, who you just mentioned.

[01:30:55] Mike Kaput: Yeah.

[01:30:55] Paul Roetzer: So the statement is three. It's three simple points. [01:31:00] one, AI may become radically more powerful. Uh. Over the next 10 years. Two, this could drive an unprecedented transformation of our economy larger than the industrial Revolution, but unfolding over a vastly shorter timeframe. It could bring risks including large scale job displacement, as well as opportunities such as major gains and living standards.

[01:31:20] And three economists, policymakers and technology leaders must act now to understand the economics of transformative AI and to build the incentives, guardrails, and institutions needed to steer AI in a direction that compliments humans and benefits society.

[01:31:36] Mike Kaput: Seems like, at least a few economists are waking up to what we have been talking.

[01:31:41] Paul Roetzer: Yeah, and it's interesting 'cause it's like there's, we've mentioned recently how the labs sort of in apparently coordinated effort, all of a sudden started backing off of their belief. Like Sam Altman in particular even just tweeted again on Sunday, like, oh yeah, I might've been wrong about jobs. It's like, no, you weren't.

[01:31:55] Like, it's coming. And like they're all, you know. [01:32:00] Starting to accept it. And I think, I don't know what the other goals behind this are. We'll put a link to the announcement from Stanford and a link to the site. But, it's, it has to happen. Like I've always said, it, at minimum, we need to be prepared. If it doesn't happen, great, but we need to be prepared in the event that it does cause massive displacement and, underemployment.

[01:32:25] Mike Kaput: All right, so that's a wrap for this week, Paul. Just one more quick reminder to visit our AI pulse survey for this week, SmarterX AI slash pulse. This week we are asking about your feelings around chat, BT work, and also your feelings around using voice ai. So if you go to that link, smarterx.ai/pulse , we'd love if you shared your thoughts.

[01:32:47] Paul, thanks again.

[01:32:48] Paul Roetzer: Oh yeah. Programming note. No episode, July 21st, so, right. Yes, correct. So I'll be on vacation and we were gonna try and squeeze in a recording early, but [01:33:00] Mike and I are in Columbus, for the AI for Business Bootcamp this week. So it just, it wasn't gonna work despite our best effort.

[01:33:07] So we're gonna, one week. Break for the podcast. We'll call it our summer break, Mike, if you, and a day off.

[01:33:13] Mike Kaput: Yeah, there you go.

[01:33:14] Paul Roetzer: and then we will be back on, I guess that would be what, July 28th? yep,

[01:33:19] Mike Kaput: yep,

[01:33:19] Paul Roetzer: yep. All right. So thanks everyone. Have a great two weeks, I guess, and, we'll be back with the latest on episode 226.

[01:33:27] Thanks for listening to the Artificial Intelligence show. Visit SmarterX.ai to continue on your AI learning journey, and join more than 100,000 professionals and business leaders who have subscribed to our weekly newsletters, downloaded AI blueprints, attended virtual and in-person events, taken online AI courses, and earn professional certificates from our AI Academy and engaged in the SmarterX Slack community.

[01:33:52] 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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