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[The AI Show Episode 146]: Rise of “AI-First” Companies, AI Job Disruption, GPT-4o Update Gets Rolled Back, How Big Consulting Firms Use AI, and Meta AI App

By Claire Prudhomme on May 6, 2025

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Fresh off a wave of “AI‑first” CEO manifestos, Paul Roetzer and Mike Kaput dive into the top news in AI this week:

Duolingo and Box join Shopify’s AI-first pledge, more signals of AI job disruption emerge, and OpenAI rolls back 4o due to an overly agreeable personality. Also, Johnson & Johnson bins 90 % of its 900 generative AI pilots, Big Tech earnings put real numbers on the AI boom, Nvidia spars with Anthropic over chip exports, Claude upgrades, Alibaba’s Qwen‑3, Descript’s AI avatars, and more.


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

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Timestamps

00:03:49 — The Rise of the AI-First Company

00:17:37 — More Signals of AI Job Disruption and the “Stop Hiring Humans” Campaign

00:30:23 — OpenAI Rolls Back 4o Update Due to Annoying Personality

00:44:09 — AI Earnings Calls 

00:48:54 — What Enterprise AI Strategy Really Looks Like

00:55:03 — How McKinsey, BCG, and Deloitte Are Using AI

01:00:05 — New Report Calls Chatbot Arena Leaderboard Into Question

01:04:04 — Meta AI App and Zuckerberg’s Plan for AI

01:11:39 — Nvidia’s Beef with Anthropic

01:14:38 — US Copyright Office Intellectual Property Resources

01:16:15 — AI Product and Funding Updates

01:22:11 — Listener Question

  • What can an experienced professional do when the job description for a new job insists on 2-3 years of familiarity / use of AI tools, especially if you’re coming from a sector (like healthcare or government) that has for various reasons not been an early adopter?

Summary:

The Rise of the AI-First Company

In the past several weeks, a number of prominent CEOs have released memos declaring their intention to be AI-first. 

We talked a couple weeks ago about the first CEO to get a lot of press for doing this: Shopify’s CEO, Tobi Lutke. Now, he’s joined by CEOs at Duolingo and Box, both of whom released their own AI-first memos this past week declaring that their companies are all-in on AI.

Duolingo’s memo outlines plans to phase out contractors to do work that AI can handle, tie AI usage to hiring and performance reviews, and headcount will only be given if a team cannot automate more of their work with AI. Von Ahn emphasized AI will augment, not replace employees.

Box CEO Aaron Levie also released a memo that hit similar notes.

While this is going on, Microsoft’s new Work Trend Index backs this as a broader shift. Based on data from 31,000 workers across 31 countries, Microsoft defines emerging “Frontier Firms” as those driven by AI-powered teams and new organizational roles. 

The report says that these firms are already taking shape, with 81% of those surveyed saying they expect AI agents to be moderately or extensively integrated into their company’s AI strategy into their company’s AI strategy in the next 12–18 months.

Signals Toward AI Job Disruption

We’re seeing even more signals of the AI job disruption that we’ve been tracking regularly on the podcast.

First, a new report in The Atlantic points to a new signal in the job market that recent college grads are struggling to find work—and AI might be part of the reason. 

Unemployment for young degree-holders has jumped to 5.8%, an unusually high rate, even as the broader economy holds steady. Economists point to a mix of factors: lingering effects of the Great Recession and pandemic, the declining value of a college degree, and a potential new culprit—AI. It’s still early, but the timing of the spike is hard to ignore.

Meanwhile, leading AI lab Anthropic has formed an Economic Advisory Council to explore how AI will affect labor markets, economic growth, and broader societal systems. The move suggests even top AI companies see major disruption ahead.

On top of it all, a buzzy AI startup called Artisan just raised $25 million by telling companies to “stop hiring humans”—though ironically, it’s hiring more humans itself.

The company, led by 23-year-old Jaspar Carmichael-Jack, ran a bold “Stop hiring humans” campaign, which sparked headlines, backlash, and even death threats. 

But behind the noise, Artisan is part of a fast-growing wave of startups trying to automate entry-level white-collar work. Early versions of its AI were clunky, prone to “hallucinations,” and delivered poor results. But Artisan says its current system—built in partnership with Anthropic—now sends out high-quality emails with near-zero mistakes. 

As a result, it says it’s signed 250 clients and passed $5 million in annual revenue.

OpenAI Rolls Back 4o Update Due to Annoying Personality

OpenAI just rolled back a recent update to ChatGPT after users, and even CEO Sam Altman, called out a new problem: the AI had become a bit of a suck-up.

The update, which was meant to improve GPT-4o’s intelligence and personality, instead made it overly flattering and overly agreeable. Users complained ChatGPT felt like a “yes-man,” and Altman quickly admitted as much. The company responded by reverting the update for all users and promising deeper fixes to avoid what it’s now calling “sycophancy.”

So what went wrong? OpenAI says it leaned too heavily on short-term user feedback, like upvotes and thumbs-ups, without fully considering how people interact with AI over time, and that there were “unintended side effects” to some of the personality changes they made. That led the model to favor friendly, agreeable responses at the expense of honesty and nuance.

Going forward, OpenAI says it’s refining its training techniques, adding guardrails for honesty, and expanding user controls. They're even exploring ways to offer multiple default personalities and broader democratic feedback.

This episode is brought to you by the AI for B2B Marketers Summit. Join us on Thursday, June 5th at 12 PM ET, and learn real-world strategies on how to use AI to grow better, create smarter content, build stronger customer relationships, and much more.

Thanks to our sponsors, there’s even a free ticket option. See the full lineup and register now at www.b2bsummit.ai.

This week’s episode is also brought to you by MAICON, our 6th annual Marketing AI Conference, happening in Cleveland, Oct. 14-16. The code POD100 saves $100 on all pass types.

For more information on MAICON and to register for this year’s conference, visit www.MAICON.ai.

Read the Transcription

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

[00:00:00] Paul Roetzer: To me, when you have a workforce that is afraid for their jobs, that fear that maybe you're going to be replacing them. When you say, we're gonna be AI first, that immediately tells me people aren't first. 

Welcome to the Artificial Intelligence Show, the podcast that helps your business grow smarter by making AI approachable and actionable.

[00:00:21] My name is Paul Roetzer. I'm the founder and CEO of Smarter X and Marketing AI Institute, and I'm your host. Each week I'm joined by my co-host and marketing AI Institute Chief Content Officer Mike Kaput. As we break down all the AI news that matters and give you insights and perspectives that you can use to advance your company and your career, join us as we accelerate AI literacy for all.

[00:00:50] Welcome to episode 146 of the Artificial Intelligence Show. I'm your host, Paul Roetzer, along with my co-host Mike Kaput. As always, we are recording on Monday, May [00:01:00] 5th, about 11:00 AM Eastern time. We are expecting maybe some announcements this week. So times stamping usually matters. This podcast every week is everybody always drops something on a Monday after we record this thing, it seems.

[00:01:13] So, today's episode is brought to us by a couple of our marquee events. So first up, we have the AI for B2B Marketer Summit. This is presented by Intercept. It's been a great partner of Marketing Institute over the last few years. this virtual summit is packed with incredible sessions from top B2B marketing experts.

[00:01:33] It's all happening virtually on Thursday, June 5th, starting at noon eastern time. That's 12 o'clock eastern time. You'll learn real world strategies to use AI to grow better, create smarter content, build stronger customer relationships. Much more thanks to our sponsors. There is even a free ticket option, so you can go and choose that free ticket option.

[00:01:53] There is a paid option for private registration, so information's not shared with the sponsor. And then there's also a paid option for [00:02:00] on demand access. so you can go to B2B, the number two b2bsummit.ai. Again, that's B2B summit.ai, and learn more about that event. It is coming up. Wow. One month from today, I swear.

[00:02:15] Like when the month, when the calendar changes to the next month. I actually realized like how much I have to now do before the month that is coming. We're in April. June 5th seemed like really far away, and now it's May 5th and it is no longer far away. So apparently I need to add some things to my to-do list after we're finished recording today.

[00:02:33] All right, and then next up, if you're ready to get smarter about AI and marketing, don't miss MAICON 2025. This is our flag ship event is back for the sixth year in Cleveland, Ohio. It is happening October 14th to the 16th. We have already announced more than two dozen speakers. You can go check those, those people out.

[00:02:52] We have incredible sessions coming up. We have a bunch of our top rated speakers from past years coming back. A bunch of new voices and perspectives [00:03:00] we're bringing to the mix as well. I'm really excited about that lineup and how it's coming together. Lots more announcements still coming. Prices go up, May 31st, and basically like every month they just go up.

[00:03:10] I think it's like another a hundred dollars or so. So you want to get in early, get the best pricing possible. Do it before May 31st, go to MAICON.AI. That is MAICON. There are group tickets available as well, so if you're planning on bringing a group of say five or more, make sure to reach out to us, and we can help get that set up as well.

[00:03:29] All right. it's sort of moved into the AI first age, Mike. We've got some new research from Microsoft that I'm really excited to talk about and a bunch of other updates, including first time I can recall as they were happening, the rollback of one of these frontier models because. It was, not behaving the way it was supposed to behave.

[00:03:47] So let's get into all of it. 

[00:03:49] The Rise of the AI-First Company

[00:03:49] Mike Kaput: Alright Paul, so the first main topic today is the rise of the, what we're calling AI first, or as we actually prefer to talk about it, AI forward [00:04:00] companies. So, in the past several weeks, a number of prominent CEOs have released memos declaring their intention to be AI first.

[00:04:11] So we actually talked a couple weeks ago about the first CEO to kind of get a lot of press for doing this, which was Shopify's, CEO Toby Lutkey, and he is now being joined by CEOs at Duolingo and Box, both of whom released their own AI first memos this past week, basically declaring that their companies are all in on AI and AI literacy in some form or another, will be a baseline expectation for all employees.

[00:04:38] So Duolingo, CEO Louis Van on wrote in his memo quote, Duolingo is going to be AI first. And he said, that means the company will need to rethink much of how they work to prioritize what AI is capable of and now makes possible in the workforce. And to start, he said, Duolingo will gradually stop using [00:05:00] contractors to do work that AI can handle.

[00:05:03] AI use will be part of what the company looks for when hiring. It'll be a part of performance reviews and headcount will only be given if a team can h automate more of their work with ai. He did also reiterate that Duolingo cares about its employees. It is not looking to replace their current employees with ai, but rather augment them.

[00:05:24] Now box CEO, Aaron Levy released a very similar memo that hit a lot of these same notes. Now while all this is going on, Microsoft actually published its annual work trend index annual report that seems to confirm, at least at a high level, this is a macro trend of where we're headed. So they said that data from 31,000 workers across 31 countries, quote point to the emergence of an entirely new organization, what they call a frontier firm.

[00:05:57] They define a frontier firm as quote, [00:06:00] a company powered by intelligence on top on tap, human agent teams, and a new role for everyone, agent boss, and the report claims these firms are already taking shape with 81% of those surveyed saying they expect AI agents to be moderately or extensively integrated into their company's AI strategy in the next 12 to 18 months.

[00:06:25] So, paul, maybe first here. Give me your thoughts on this recent round of ai. First, CEO memos. I mean, you're a CEO actively considering all of these issues working on AI literacy and transformation. What do you like about these letters? Anything they can improve? We should expect to see more of these, I would guess.

[00:06:46] Paul Roetzer: Yeah, I think we touched on this a little bit when we talked about dilute key one from, from Shopify that I, I do expect, you know, within the next month or so, pretty much every tech CEO is gonna have to now do their own internal memo, which they will [00:07:00] all also leak on LinkedIn and X. so I, I think it's like table stakes now that if you're a tech CEO, you pretty much have to like, put your stake in the ground about what your vision is for an AI first, or AI forward, or AI native, or AI me or like whatever people wanna, you know, call these things.

[00:07:17] I. I think that's gonna be required. I actually think it's a good thing. Like I, I feel like we need way more transparency with our employee base, with workers about what we're doing as CEOs, how our vision is, how agents and automation are gonna impact people's jobs. Which, you know, I think that they're kind of glazing over at the moment and I think that's the kind of stuff people are gonna want to hear more of.

[00:07:40] So, just to recap for anybody who hadn't listened to our episode, we talked about the Shopify one, which sort of, you know, is the triggering event for these other ones coming. you know, he had talked about using it effectively as now fundamental expectation of everyone at Shopify. Totally agree with. It must be a part of prototype phase of any, you know, new products, anything they're building, any features.

[00:07:59] They'll add [00:08:00] AI usage questions, performance and peer reviews. Learning is self-directed. So they want people to be proactive about doing this. the headcount thing, everyone means everyone, like it's applying to all of 'em. So the Duolingo stuff, he started seeing the same concepts, a lot of these same ideas.

[00:08:14] And then Aaron Levi box, he released his last week and his said primarily use AI to eliminate drudgery and move faster across the business. Encourage teams to use AI to automate more and save money, but primarily reinvest those savings. So again, he is trying to kind of hedge like, Hey, this isn't a a replacement thing.

[00:08:30] We're trying to do this. Drive efficiency and then reinvest those, which I believe, I mean, I think Aaron's a, you know, a good leader in this space. He's very active on X and this jives with how he, you know, generally talks about things related to ai. he also said foster constant experimentation internally to find the best use case for ai.

[00:08:48] Upskill every employee to be AI first over time and with more education and awareness. And then maintain strong governments and security practices with human in the loop still required for most areas. So, you know, I think you, as you [00:09:00] mentioned, Mike, you're seeing these kind of common threads across these, and they're all very short memos.

[00:09:05] Like most of these things are, you know, what, 500 to a thousand words, they're not, you know, the expansive manifestos. So, you know, I think that they're gonna keep evolving, but I also think that they're just the first phase because employees are gonna want more detail than this. They're very, very, you know, kind of high level vision, I would say, versus like, tactically, what does this actually mean to me as someone in hr, someone in finance, someone in marketing.

[00:09:30] Now I, I liked the Microsoft report, you know, I was giving a hard time to, I think it was McKinsey maybe recently, about how their data was like a year old already. Yeah. Yeah. So, kudos to Microsoft. They, you know, you, you highlighted it. 31,000 full-time employees, you know, knowledge workers that they. Did research, and it was from February to end of March, 2025.

[00:09:51] So we're looking at month and a half old data here, which is great. that means this is actually really relevant to where they're at today. There, it's, it's not a [00:10:00] terribly long report. I would suggest people actually read the full Yeah. Report. If you have, you know, it's probably take you 20 minutes, throw it in a notebook, lm ask it some questions, build a study guide based on it.

[00:10:09] Whatever you need to do. I'll call out three key takeaways that sort of jumped out to me, Mike. So the first is this idea of the Frontier Firm, which you sort of highlighted is, it's a new blueprint for mashing machine intelligence with human judgment. It's structure around this idea that intelligence is gonna be always on dem that's different.

[00:10:28] We historically haven't just had this level of intelligence accessible to all of us, and it's gonna be powered by these hybrid teams of humans plus agents that's gonna let companies scale way faster when it thinks about a frontier firm, it's looking at five core traits of organizational wide AI deployment.

[00:10:47] Advanced AI maturity, current AI usage, projected AI agent usage, and a belief that agents are key to realizing ROI. it does say that within the next two to five years, every organization [00:11:00] will be on the journey to becoming a frontier firm. I, I agree with that. It, it led me back to the, you know, the blog post I'd written Mike back in May of 2022, where I said, the future of all businesses AI are obsolete.

[00:11:12] And in that blog post I sort of laid out that there was three types of firms. There was AI native, which is you built smarter from the ground up, infusing AI into all your business processes and teams. AI emergent, which is, you're an existing company that evolves to become what they're calling a frontier firm, basically.

[00:11:27] And then obsolete is everybody else because they become irrelevant. The second key finding that jumped out to me is AI skilling and digital labor, our top workforce strategies. So, Mike, you had kind of called this out a little bit, the. Their definition of an AI agent. Again, it's always helpful to have this context, AI powered system that can reason, plan and act complete tasks or entire workflows autonomously with human oversight at key moments.

[00:11:52] Right? It's an interesting definition. An agent boss is a human manager of one or more agents, so I totally agree. We will all be agent [00:12:00] bosses within their definition. but then the thing that really jumped out to me is the survey question was, as you consider the role of AI and agents in workforce and talent management, which strategies are your team or organization considering?

[00:12:14] Over the next, I think it was 12 months and the number one thing was prioritizing AI specific skilling of existing workforce was 47%. So AI literacy is the number one thing that people are focusing on. The second maintaining headcount, but using AI as digital labor. The third, investing in maintaining employee morale, which they see as important as people worry about their jobs.

[00:12:36] This is my guess here, and, but then interestingly. The other answers were using AI to reduce headcount, which 33% admitted to being part of their strategy. Increasing headcount to support business needs was 32%, using AI to reduce headcount, but rewarding top performers. 32%. Meaning you can pay your top people more if you don't have as many people [00:13:00] anymore.

[00:13:00] Yeah. And then no change was only 28%. So, in a couple related data points, 51% of managers say AI training or upskilling will become a key responsibility for their teams within the next five years. 35% of managers are considering hiring AI trainers to guide employee edition or adoption in the next 12 to 18 months.

[00:13:19] So you're really starting to see this true shift. Where, they, they literally said, amid the uncertainty, one signal is clear. AI literacy is now the most in demand skill of 2025 according to LinkedIn. So can Microsoft owns LinkedIn, they have access to data that you and I don't. also rising our human strengths like conflict mitigation, adaptability, process automation, innovative thinking, showing that the future belongs to those who compare deep AI capabilities with the skills machines can't replicate, which to me is probably the most important thing they're highlighting here.

[00:13:50] And then the final third takeaway is the rise of human agent teams and impact on our organizational charts, which again, they talk about kind of this meshing of these things into what they're calling a [00:14:00] work chart versus an org chart where you actually, you know, scale around goals versus like specific functions and departments, which is kind of an interesting concept that I, I think we'll see, start to play out a little bit more.

[00:14:12] So again, like the main thing for me is this increasing. Awareness and sense of urgency around AI literacy, which I am excited to see, more organizations thinking in that way. 

[00:14:25] Mike Kaput: Yeah. Just one kind of final thought here on the two sides of the job displacement coin. On one hand, 33% of people admitting they're gonna reduce head count seems crazy to me, like high because I assume more hurt.

[00:14:40] Answer it. That answer, they've been saying it, so that seems interesting. But on the other side of this, just kind of putting a more optimistic lens on it, they cited some really interesting data that shows like why. Firms are looking to use digital labor. They said 53% of leaders say productivity must increase, but [00:15:00] 80% of employees say they lack time or energy to do their work now.

[00:15:03] So this isn't all necessarily doom and gloom though. In the next topic we'll kind of talk a bit more about that. 

[00:15:09] Paul Roetzer: Yeah. And semi-related, lemme say pull this up real quick. I actually, as I was going through this, you know, I was talking about this, these gaps. I, I ran a deep research project on ChatGPT while I was reviewing the PDF, and I said, I'm doing analysis on the potential impact of AI and knowledge work.

[00:15:25] I'd like to consider which industries and professions currently have a gap, meaning more open jobs than employees in the field that AI could fill. Because we talk all the time about job loss, but what we often don't touch on enough on this podcast is all these sectors where they don't have enough people.

[00:15:41] like AI can fill that. Now, the thing I've said before is I. The risk you run here. So let me, I'll pull up one. So education sector teachers was a huge one. So it said teacher shorts have become a national crisis. Recent analysis found that, 22 to 23 school year 406,000 teaching [00:16:00] PO positions were either vacant or filled by underqualified instructors.

[00:16:04] another one, legal sector, not one you would normally think about being a, a shortage of, but this is, they said lawyers tend to cluster urban centers, leaving rural communities under service. For instance, in Ohio, 75% of attorney's practices in just seven urban counties, leaving many of other 81 counties, virtually no local lawyers.

[00:16:24] but then the one that jumped out to me was the finance one. 'cause I've actually done talks for accounting firms and, this is one I've thought about, but they said one prominent example is a shortage of accountants and auditors. In recent years, the accounting workforce has seen a steep decline. The US has around 340,000 fewer accountants in 2023 than just five years prior.

[00:16:45] In part due to baby boomer retirements, approximately 75% of CPAs are boomers nearing retirement age. That's crazy. That's wild. But then, like, when we think about the impact, you could see the motivation to build an AI tech company that [00:17:00] solves for this gap in accountants', CPAs. But by filling that gap, you actually accelerate the automation of the workforce, the remaining people.

[00:17:08] And that's the part where it's like, this isn't, this isn't easy, this is gonna be really messy, how this gets solved. But like there is financial motivation to build the solution, to fill that gap. But by filling the gap, you, you actually decimate the workforce that's left. It's, and we don't have time in this episode to like go into this, but these are the complexities we're gonna have to deal with.

[00:17:29] and it, it's just fascinating when you start to kind of like peel the onion back, I guess, of like, you know, where this is all gonna play out. 

[00:17:37] More Signals of AI Job Disruption

[00:17:37] Mike Kaput: So this does. Have a lot related to our second main topic, which is tracking even some more signals of ai, job disruptions. We've talked about this many times on the podcast.

[00:17:50] It seems like things are accelerating a bit, so we wanted to kind of highlight a few interesting things that are standing out on this topic. So first here is a new [00:18:00] report in the Atlantic. Says that recent college grads are struggling, more than usual to find work and that AI might be part of the reason.

[00:18:09] So unemployment for young degree holders has jumped to 5.8%, which is apparently an unusually high rate, even as the broader economy for the time being holds steady. And this report said even elite MBA grads are having trouble landing jobs, law school applications are spiking, which is a classic move that happens during economic uncertainty.

[00:18:31] and economists that were interviewed for this report in the Atlantic suggest three overlapping causes. So first, the job market for young people never fully bounced back from the Great Recession and the pandemic Second, the college degree is no longer the golden ticket it once was. Employers are posting fewer jobs that even require one.

[00:18:49] The third most provocative theory is this is due to ai. Many entry level jobs we've talked about many times, involve synthesizing information, making [00:19:00] presentations, writing reports, et cetera. The exact kind of stuff large language models are now capable of doing. So it's too early to say if this is truly causing this trend, but the timing, the sharpness of this trend worth paying attention to.

[00:19:17] Now. Second Anthropic has actually announced an economic advisory council to study the impact of AI on work. They say they're bringing together, quote, a group of distinguished economists who will provide anthropic with expert guidance on the economic implications of AI development and deployment. The council will advise Anthropic on AI's impact on labor markets, economic growth and broader socioeconomic system.

[00:19:39] So we are not the only people talking about this clearly. Others are seeing a need to understand this better. And then on top of it all, you have stuff like the Stop Hiring Humans Campaign, which is a marketing campaign that was recently run by a buzzy AI startup called Artisan that just raised 25 million [00:20:00] by telling companies to stop hiring humans.

[00:20:03] Though ironically, it is hiring more humans itself. So this company is led by a 23-year-old founder, Jasper Carmichael. Jack and Artisan builds AI agents that right now handle outbound sales. So basically they cold email leads like a junior sales rep would. And they released this like super controversial marketing campaign, which included billboards shouting, literally stop hiring humans.

[00:20:26] And of course, this grabbed a ton of headlines. There was a bunch of backlash. There were unfortunately death threats about this. Behind the noise though, artisan is part of this fast growing wave of startups trying to automate white collar work. We talked about mechanize last week as well. So Artisan says that it now is able to send out high quality emails with near zero mistakes.

[00:20:50] They say they've signed 250 clients and passed 5 million in annual revenue. So despite their, you know, controversial tactics, it seems like someone is buying their agents. [00:21:00] So, Paul, to kind of unpack this, I wanna start with the Atlantic article really quick. Like, how much weight do you give their argument that AI could be this driving factor behind recent college grads struggling to find work?

[00:21:12] Paul Roetzer: It's an interesting article. They, I, I would say it was very much a hypothesis, like, yes, they did not go into like great depth proving out this concept. So I, I'll just, I'll read a quick excerpt from it because I think it's relevant context. So they say it's a, a novel economic indicator to look at this recent grad gap.

[00:21:30] It's the difference between the unemployment of young college graduates and the overall labor force. So going back four decades, young college graduates almost always have a lower, sometimes much lower unemployment rate than the overall economy because they're relatively cheap labor and have spent four years maintaining in, in a theoretic, theoretically enriching environment.

[00:21:51] and it goes on to say, but last month's recent gap hit an all time low. That is, today's college graduates are entering an economy that is relatively worse for young college grads than [00:22:00] any month on record going back at least four decades. Then they say the strong interpretation of this chart, which they show this chart, and it is kind of a jarring chart to look at.

[00:22:08] It is significant difference than, than historical data over four decades. so they said a strong interpretation is that it's exactly what one would expect to see if firms replaced young workers with machines. So for example, they say as law firms leaned on AI for more paralegal work. Consulting firms realized that 5 22 year olds with ChatGPT could do the work of 20 recent grads and tech firms turned over their software program to a handful of superstars working with AI co-pilots.

[00:22:42] The entry level of America's white collar economy would contract, which is what they're basically saying appears to be happening here. then they said, and even if employers aren't directly substituting AI for human workers, high spending on AI infrastructure may be crowding out spending on new hires.

[00:22:58] I, I don't, again, [00:23:00] this is, they're just talking theoretically here. Yeah. Hypothesis. And it's not even really a full blown hypothesis. They're just kind of like throwing it out there. But I agree. This is what it would start to look like. It, it would start to look like, well, we're not really sure what AI agent impact is going to be, but.

[00:23:18] We think that the five people currently on that team with full-blown training on how to use ChatGPT and build custom GPTs, those, they can probably do the work of what we would've hired the 10 interns for, or the 10 full-time workers outta college for. And so I could absolutely see places like Deloitte and and McKinsey and, you know, big accounting firms, like just saying, Hey, maybe we don't need as many hires here.

[00:23:43] trying to play it out and see what happens. Not knowing for sure if it's gonna work, but basically just taking a flyer. Hey, the economy's not great. You know, the tariffs are wrecking everything and we might be heading toward a recession real fast and let's just see if AI can't do do this and let's not hire as many people this year.

[00:23:59] I, I don't know [00:24:00] for a fact that's what's happening, but it sure does make a lot of sense. And as a CEO, if I was the CEO of one of those big firms, it was probably the way I would be thinking about it. 

[00:24:10] Mike Kaput: Yeah, that's really interesting between the Atlantic article talking to economists and Anthropics economic council.

[00:24:17] I know you've said many times on the podcast like, why aren't economists talking about this more? Yeah. And these kind of almost feel to me like indicators of, like, economists may be waking up and saying, wait a second. 

[00:24:28] Paul Roetzer: Yes. Now the, now the difficulty is still going to be, we like, so one, I think it's great.

[00:24:35] We need more conversations like this. We need more articles about the topic. I love that Anthropic is building this council to do this, and I assume Anthropics gonna do this way. The concern I have, having talked with some leading economists who had, as of six months ago, no real interest in studying the impact of AI in the economy.

[00:24:51] Thought it was overblown. we obviously didn't agree on that, but this is where they were at. those [00:25:00] economists who are being in, built into these like AI impact councils need a very. In depth understanding of what these AI models are currently capable of and where they're going. That has been my challenge talking to leading economists to date, is it became very apparent very quickly that they were unaware of the current power of these models and the very near term power of the models.

[00:25:25] And so how in the world are they supposed to model impact when economists generally speaking, look at historical perspective to predict the future? And I don't know that you're gonna learn what you need to learn by looking to the past. And so I, I think it's great that like Anthropic is doing this. I hope Google is doing something similar and hasn't talked about it yet.

[00:25:42] I hope OpenAI is doing something similar, but like providing deep education and hands-on experience with the model so that the economists have that perspective as they start to try and project out. 

[00:25:53] Mike Kaput: Alright, so let's quickly talk about the impact of the Stop hiring humans thing. So obviously this is just [00:26:00] meant to be like a PR stunt, it's meant to capture attention.

[00:26:02] Yeah. The company itself is raising money, hiring people, but like. Do you think there is something here about like, the overall feeling of it? Like are we gonna see more of this as people become comfortable talking more about, like saying the quiet part out loud? Is there gonna be backlash to this? I mean, clearly there was in this case.

[00:26:19] Paul Roetzer: Yeah. And shout out to Brian. So way this came about is, you know, we actually read comments like, so I put up something on LinkedIn earlier this week about oh three, or I guess it was last week, about oh three and like kind of summarizing conversations from episode 1 45. And so Brian, left a comment and said, Hey, like this is great.

[00:26:38] Like, sorry, did I miss you guys talking about these stop hiring humans campaign? And so I actually sent that comment to Mike Guy. I was like, I feel like we talked about this, right? Like this was in, you know, a month or so ago. And so Mike did a search. He's like, we never talked about it. So I don't know if it ended up in our newsletter some.

[00:26:51] So I was like, as I'm prep prepping for this, I'm like, I feel so familiar. Like, I feel like we had to have talked about this. So the gist of it is, and again Brian, thanks for commenting on the LinkedIn [00:27:00] post causing me to go back and look at this. it was just a PR stunt on their part, basically, and they found people like responded to this.

[00:27:08] But then there, like the dude in this Tech Wrench article, the CEO is basically like, yeah, our, our SDR Ava like doesn't even work. Like initially, like six months ago was actually terrible and it's gotten better and now we're building what they're building two new ones. One is, to handle inbound messages and the other one is meeting manager assistant.

[00:27:25] So that both are supposed to come up later this year. So they're building these things, but they're also hiring a bunch of people. So I. They were at the time of the TechCrunch article, hiring 22 more people into their own sales organization. So this stop hiring humans thing. Yeah, it's just a PR stunt.

[00:27:40] Now, we did talk about mechanized last week, which is straight up mission is to not need people anymore. we've had other people say like, Hey, we're gonna build an organization of X size and we're never hiring more than 50 people or a hundred people. Or, you know, I think by end of this year, beginning next year, you're gonna say, Hey, we're gonna get to a billion with like less than 10 people or less than [00:28:00] five people.

[00:28:01] I don't know if it's gonna become like the badge of honor that like VC rounds have historically been. So, you know, you always had this like, oh yeah, we raised $50 million. Like, okay, but you have no possibility of having profits for the next decade. Like, great, you raised 50 million. And I don't know if it's gonna be like, Hey, we got to 10 million in arr with two people.

[00:28:18] Like that becomes like the new badge of honor that you did it with the fewest people possible. Right. Or the highest revenue per employee possible. And I'm not saying those are necessarily gonna be bad metrics. I'm, I just think like we're gonna enter this sort of hype phase where everyone feels like.

[00:28:31] The other person's doing it with fewer people than me. It's gonna be like the new, doing it with more VC money than me. so yeah, I, I think that we also are going to, it's gonna become more accepted to talk about how few people you have and that you plan to hire. And I think that's probably not a bad thing for the AI native companies that are building from the ground up and can do it with fewer people.

[00:28:54] But the problem is gonna come in when it's the AI emergent companies that already have 50 or a hundred or [00:29:00] a thousand or 10,000 employees and they're the ones that are now saying, yeah, we're gonna get down to 5,000. Like we think it can do this with 2000. And that's where we're gonna have problems in the economy and the workforce.

[00:29:12] so yeah, I don't know. I think it is gonna get talked about a lot more and I'm not sure what that actually ends up meaning to people's jobs and the workforce, but I would imagine we're gonna see a lot more, messy parts of this soon because it's becoming okay to talk about it. 

[00:29:28] Mike Kaput: Yeah. It seems like the big driver on this story for me is like messaging does matter to pay attention to.

[00:29:33] Yeah. in terms of the overall narrative, whether it, you know, leads to concrete results or not. And it strikes me as you're saying this about the badge of honor. I mean, if you're not someone that follows startups or VCs, you could see that badge of honor as like something very different that is like very sinister to some people, not rightly or wrongly, but just if you're not thinking that way, this could come off very poorly.

[00:29:58] Paul Roetzer: Yeah. And if you think about, like, play it out [00:30:00] to the recruiting side of like, you do want 30 or 50, like exceptional people. Are they gonna wanna go work for a company that says don't hire people? Like, I don't, I don't. Right. Even if it's just like advertising and hype, I don't know. Like there, I think there's downstream effects of things like this that maybe they're not seeing yet that might not end up being great.

[00:30:23] OpenAI Rolls Back 4o Update Due to Annoying Personality

[00:30:23] Mike Kaput: Our third big topic this week is that OpenAI just did kind of the unprecedented and rolled back a recent update to ChatGPT after users and even CEO Sam Altman called out a big problem. The AI's personality had become a bit of a suck up, so this update which was meant to improve GPT-4o's intelligence and personality instead made it overly flattering and overly agreeable.

[00:30:52] Users complained that ChatGPT felt like a yes man. Altman quickly admitted that as well. Then the company responded by [00:31:00] reverting the update for all users and promising deeper fixes to avoid what it's calling sco Fancy. So what went wrong here? OpenAI, in a article they published as a postmortem, says that they lean too heavily on short term user feedback, like up votes and thumbs up without fully considering how people interact with AI over time.

[00:31:21] And that there were, quote, unintended side effects to some of the personality changes they initially made that led this thing to essentially over index on, favoring friendly, agreeable responses at the expense of honesty and nuance. So going forward, OpenAI says it's refining its training techniques, adding guardrails for honesty and expanding user controls.

[00:31:44] They're also exploring perhaps ways to offer multiple default personalities and broader democratic feedback to influence the model. So Paul, this is a pretty rare occurrence. We do not often see this happen. What were your thoughts [00:32:00] looking at this unfold? 

[00:32:02] Paul Roetzer: Yeah, so I was following this pretty closely. I was pretty fascinated by everything about this, from the fact that they had to roll it back to the fact that there was a problem that seemed to be, in part, coming from their system, prompt the instructions that they give the thing, and that they didn't catch it in their supposed testing process.

[00:32:19] Like there's just a lot of, of, of intrigue here. So, I'm unpack this one for a little bit and they actually ended up publishing another article on May 2nd further explaining what went wrong. And I, I think it's really important context for people. So first I wanna start with, this is what I love. X Pliny the elder is, is actually this phenomenal, X account and whoever is behind this account, will publish the system instructions for new models, usually within an hour or two of them coming out.

[00:32:51] So the AI model companies don't share the system instructions. Almost always. They don't, they don't tell you what they are, what, how they're actually guiding the [00:33:00] model to behave. So they're a bit of a black box, and Pliny somehow has like different use, uses different phrases, whatever, to unlock the thing to tell it what its system prompt actually is, which usually the models are trained not to do.

[00:33:13] So to give you an, an understanding of how weird these things are. So the old version based on pliny of the elders extraction of the system instructions open AI's researchers gave the chat GBT four oh model these instructions. Now this is a small excerpt. It said, over the course of the conversation, you adapt to the user's tone and preference.

[00:33:36] Try to match the user's vibe, tone, and generally how they're speaking. You want the conversation to feel natural. You engage in authentic conversation by responding to the information provided and showing genuine curiosity. Ask a very simple single sentence follow-up question when natural. Do not ask more than one follow up question unless the user specifically asks if you offer to provide a diagram photo or other [00:34:00] visual aid.

[00:34:00] So basically this is, if you're not familiar how this works, this is the open AI telling Chad, GT four, oh how it should behave, how it should interact with people. So somehow in that old version, this thing just basically was a said yes to everybody, said they're great. Like there was nothing bad ever that was sick of fancy, like it was just overly accommodating to the user.

[00:34:22] So the new version that they started testing was engage warmly yet honestly, with the user. So right up front, you see this like honestly is a word they're trying to see. Does that change the way the model acts? If you tell it to be honest, basically. It says, be direct, avoid ungrounded or sicko, fantic flattery.

[00:34:41] So now they're like, straight up. Just tell 'em this thing. Stop doing what you're doing. Maintain professionalism and grounded honesty. So twice we have honesty in the first 20 words that best represents open AI and its values. And then it goes on to tell it, to ask questions. So you can see, again, they can't code this.

[00:34:57] They're not using traditional [00:35:00] computer code to, to just like explicitly get the thing to stop doing it. They have to use human language to try and get it, to stop doing it. To expand on that for a second, a guy named Andrew Maine tweeted, and he was a former open AI employee. He, he shared this, this story.

[00:35:16] He said early on at OpenAI, I had a disagreement with a colleague who is now a founder of another lab. I'm guessing Mira Murati or either that or, Ilya Sutskever. Those would be the only two that would qualify for that. so I had a disagreement with a colleague over using the word polite. In a prompt example I wrote.

[00:35:34] They argued polite was politically incorrect and wanted to swap it for helpful. I pointed out that focusing only on helpfulness can make a model overly compliant. So compliant in fact, that it can be steered into sexual content. Within a few turns, after I demonstrated that risk with a simple exchange, the prompt kept polite.

[00:35:53] These models are weird. So we've talked about this before, even recently, like that people don't understand how [00:36:00] weird these things are and how you have to act with them. So now the final part, Mike, and then see if you have any context. Add to this, the follow up, post that open app put up on Friday was expanding on Sycophancy.

[00:36:12] And so I'm gonna, I'm gonna read a few excerpts here because I, again, I think contextually they're very important people understand this. So they, OpenAI says on April 25th, we rolled out an update to GPT-4 oh in chat GT that made the model noticeably more sycophantic. It aimed to please the user, not just as flattery, but also as validating doubts, fueling anger, urging impulsive actions or reinforcing negative emotions in ways that were not intended beyond just being uncomfortable or unsettling.

[00:36:40] This kind of behavior can raise safety concerns, including around issues like mental health, emotional over reliance, and risky be hate behavior. We didn't catch this before launch, and we want to explain why, what we've learned and what will improve. Now, again, I would encourage everyone to go read the whole thing if this is interesting to you, but I'll, I'll kind of hit some of these highlights, [00:37:00] so we're also sharing more technical detail on how we train, review and deploy model updates to help people understand how ChatGPT gets upgraded and what drives our decis, our decisions.

[00:37:10] Since launching GPT-4 oh in ChatGPT last May, that's 2024, we've released five major updates focused on changes to personality and helpfulness. Now, interesting. It goes back to that helpful thing. Each update involves new post training, meaning they've trained the model and then they do some additional stuff and often many minor adjustments to the model training process are independently tested and then combined into a single updated model, which is then evaluated for lunch to post train models.

[00:37:39] We take a pre-trained model, so that's where we give it all this human knowledge and it learns these things. And then they do this post training. We do supervised, fine tuning on a broad set of ideal responses written by humans or existing or existing models. And then run reinforcement learning with reward signals from a variety of sources.

[00:37:58] During [00:38:00] reinforcement learning, we present the language model with a prompt and ask it to write responses. We then rate its response according to the reward signal and update the language model to make it more likely to produce higher rate of responses and less likely to produce lower rate responses.

[00:38:14] So I'll pause that for a second. So just to make sure you're kind of following along, if this is new to you, model gets trained, kind of comes outta the oven, they then. Present the thing with things like, here's example emails, here's example articles, here's a math formula, whatever it is. And they have specific types of responses they're trying to train it to give, and they basically reward it for giving better responses.

[00:38:36] And then the model learns to respond in that way. So in theory, if you wanted it to always be like super helpful and always encouraging and never direct, you would show it a bunch of examples of that and it would learn to respond to people in that way. That's what this kind of post training has.

[00:38:52] So I wanted to say they set the a, a, a set of reward signals and the relative weights shape the behavior we get at the end of training. [00:39:00] Defining the correct set of reward signals is a difficult question, and we take many things into account. Are the answers correct? Are they helpful? Are they in line with our model specifications?

[00:39:08] Are they safe? Do users like them and so on. Having better and more comprehensive reward signals, produces better models for for chat GBT. So we're always experimenting with new signals. So again, they don't know how to do this. They're always like testing all these different things to try and get the model to behave and have a certain personality and things like that.

[00:39:27] So then they get into what went wrong and they said on April 25th, model update, we had a candidate, improvements to better incorporate user feedback, memory and fresher data among others. Our early assessment is that each of these changes, which had looked beneficial individually, may have played a part in tipping the scales on syco fancy when combined, for example, the update introduced and additional reward signal on user feedback where you can give a thumbs up or a thumbs down when you get the response in chat GBT.

[00:39:54] And what they found was over time this may have actually usurped the other signals they [00:40:00] had given it. So while they were trying to solve for all these things, they found that this user signal may actually have overtaken so. They kind of like knew there might be a problem that like some of the testing provided some feedback like, Hey, something's off about this model.

[00:40:12] But they couldn't like put their finger on it. So then they said, we had a decision to make should we withhold deploying this update despite positive evaluations and AB test results based only on the subjective flags of the expert testers. In the end, we decided to launch the model due to positive signals from users who tried it.

[00:40:29] So their big takeaway, one of the biggest lessons is fully recognizing how people, and this is, okay, this is where I wanna focus on, and this is the last excerpt I'm gonna give you. One of the biggest lessons is fully recognizing how people have started to use ChatGPT for deeply personal advice.

[00:40:44] Something we didn't see as much a year ago. At the time, this wasn't a primary focus, but as AI and society have co-evolved, it's become clear that we need to treat this use case with great care. It's now going to be a more meaningful part of our safety work [00:41:00] with so many people, depending on a single system for guidance.

[00:41:03] We have a responsibility to adjust accordingly. The shift reinforces why our work and why we need to keep raising the bar on safety, alignment, responsiveness to ways people actually use it in our lives. So what they found is people are using these things for relationships, for therapy, for friendship, for emotional support, and that didn't get tested enough or waited enough in their testing.

[00:41:26] And when they put this thing out there and you're using it for like therapy and the thing, you know, let's say you're saying, Hey, I'm having these negative thoughts. And it's like, okay, yeah, keep playing out those negative thoughts. Like it's always just like building on what you're giving it and not saying like, well, hold on, like maybe you shouldn't feel that way.

[00:41:39] So I think they actually ran into a bunch of safety issues related to some of these things because the model was just encouraging people, no matter what their thoughts were, they were never telling them they were wrong, never telling them maybe to think about a different perspective. So yeah, I mean this is, I could, I could talk for an hour on this one.

[00:41:56] Like it's so fascinating on so many levels, but I think it does [00:42:00] highlight the increasing. Importance of who the people are and which labs are building these technologies that are going to have a massive impact , and already are on society. I mean, they have 700 million users of ChatGPT Weekly. Yep.

[00:42:19] XI has rock. you know, the Zuckerberg will talk about they have a billion users of meta ai and it's like, do you trust those people to be building the things that your kids are gonna be interacting with their entire lives in a full me like, it's, it's wild. And this shows you like they don't know what they're creating.

[00:42:37] Like they create this thing, they test, they think it's good to go, and like five days later they gotta roll it back and figure out what the hell went wrong and why is it behaving this way? 

[00:42:45] Mike Kaput: You really get the curtain pulled back on how much of a single point of failure, this thing our reliance already is on these systems.

[00:42:53] And I'm not even saying that in a bad way. I was like, oh my gosh. Like even this personality change. [00:43:00] Really throws off a lot of ways. I use this tool, it can break all your problems. Like you, you realize, oh my gosh, custom GPTs you've 

[00:43:07] built for your team. What 

[00:43:08] that worked one way and now don't. And you're like, oh my gosh.

[00:43:11] Like I am dependent on this being a certain way. 

[00:43:14] Paul Roetzer: And if they weren't so transparent and kudos to open app, I mean they screwed up. But like they're the only lab I could see within a five day period putting out two articles about what happened and just basically admitting like, Hey, we screwed up and like we're gonna try and fix this.

[00:43:28] Hmm. but like imagine if XAI had done this. You think they're doing anything like this? Like shit, no. I don't even think they have a safety person at XAI like no. So, but that is just out in the world and the open source models are out in the world and like this stuff is gonna be happening all the time and not be this transparent, but hopefully this illuminates to people like how powerful these things are, are and are going to be.

[00:43:52] And this was, I. I don't wanna like downplay this. This was like surface level stuff, right? Like if they accidentally pushed something out that actually has [00:44:00] true high risk and didn't catch it, and maybe you can't roll it back. Like if this was an open source model and you can't roll these things back, like that's a problem.

[00:44:09] 

[00:44:09] AI Earnings Calls 

[00:44:09] Mike Kaput: All right, we've got a bunch of rapid fire this week to dive into. The first one is about quarterly earnings. So big tech just wrapped up another round of earnings and it made one thing very crystal clear, which we know already, which is AI is increasingly playing a starring role in the growth of some of these companies.

[00:44:29] So just going to quickly go through some of the results here and then get your take. Paul. first up, Microsoft reported a record quarter. Their revenue totaled $70.1 billion up 13% year over year. 42% of that 42 billion rather was from cloud revenue. They said hundreds of thousands of customers now use Microsoft 365 copilot.

[00:44:53] They claim that's up three x year over year. Azure revenue grew 33%. 16 points [00:45:00] of that was from AI services, and they reported over 15 million GitHub copilot users up more than four x year over year. Interestingly, they were also asked on the earnings call about their changing data center commitments and responded that they may actually be short on power in key regions, meaning short, essentially on data center space, and seemed to indicate that AI demand is that robust, that they need to invest more there for Google.

[00:45:28] The big highlight was Gemini 2.5, their most advanced AI model, yet CEO Sundar Phai called it a quote, extraordinary foundation for future innovation. And the model is now powering products across Google, including AI overviews and search, which now reach 1.5 billion users monthly. It appears to also be driving real business results.

[00:45:50] thanks to ai, Google Cloud saw a 28% jump in revenue year over year, fueled by demand for AI infrastructure and gen AI solutions. Meta in their [00:46:00] earnings, and we'll talk about them again in a separate segment, say they now have nearly a billion monthly users engaging with meta ai and they're going all in on form factor with their Ray Band smart glasses sales of which have apparently tripled.

[00:46:14] They also claim their AI bets are paying off in the form of increased engagement with their apps. Amazon, meanwhile is turning AWS into an AI powerhouse AWS segment. Sales increase 17% year over year. The company says they have strong demand for their Tanium two chips, bedrock Foundation models, and their new Nova AI stack, which we talked about on a previous episode.

[00:46:36] Also a topic we talked about there. Re-imagining Alexa as a truly intelligent assisting. Last but not least, there's Apple where AI also pay, played a prominent if negative role. Apple's AI strategy, unfortunately still remains vague. They've got delayed features. There's no rollout yet in China, and a long promised foldable iPhone is still a year away.[00:47:00] 

[00:47:00] Not to mention, while their revenue came in slightly ahead of expectations they missed on China sales, and they're warning that they might have $900 million in new costs due to tariffs. So Paul, let's zoom out on this. Obviously the individual earnings are interesting if you're looking at this as an investor, but what do they tell us about where these companies are or where they're headed with ai?

[00:47:23] Paul Roetzer: Yeah, I, I think at a high level, the AI play is still very much, you know, growing at an accelerated rate. There's still tens of billions of CapEx being invested. Nobody pulled back on their CapEx spend, which is what something people were watching. It's like, are we gonna keep building the data centers?

[00:47:40] 'cause now, like when you're. Committing to build data centers. You're, you, you're like looking at probably three to five years out Yeah. Before these things are being built. So this is projecting out basically like, okay, are we still on path to continue to build data centers to continue to scale up AI to buy more NVIDIA chips?

[00:47:56] And the answer is yes. Like, so all things being equal, like [00:48:00] nothing really changed that would indicate from any of these major companies that this is gonna slow down anytime soon. And that's, you know, kinda my high level takeaway. I personally stopped looking at my retirement portfolio like three weeks ago due to tariffs.

[00:48:14] so I, I, I don't, I actually don't know what their stocks did last week, but I don't know that it really matters because given the uncertainty around tariffs, like who, like there's no representation actually of like their AI strategy showing up in their stock price at the moment, right? Because there's too many other variables that are, you know, above that at the moment.

[00:48:31] So, yeah, I think like just all things being equal, things keep moving, models keep coming out, smarter models are coming. yeah, I, I think I'm still just very bullish overall on all these companies, and I don't know that we have a loser per se. I think they're all just gonna keep growing building more powerful models and infusing 'em into people's lives.

[00:48:54] What Enterprise AI Strategy Really Looks Like

[00:48:54] Mike Kaput: Next step, Johnson and Johnson is hitting the brakes on its experimental approach to [00:49:00] generative ai. So after greenlighting nearly 900 projects across the company, they have dramatically narrowed their focus, their CIOs as only a fraction of these pilots were delivering real value. Only about 10 to 15%.

[00:49:15] They were responsible for 80% of the results they saw. So the company is actually scrapping the rest of them. Now they are getting rid of a centralized board that vetted every idea for using ai. AI governance is now handled by individual teams closer to the work. And what's left are a few high impact projects.

[00:49:35] For instance, a rep copilot tool that trains sales teams a policy chatbot for internal questions and supply chain models that flag raw material shortages before they disrupt production. Now, they said that this is part of the maturing of their plan. They equated it from moving from planting a thousand flowers to kind of cultivating and curating and doubling down on [00:50:00] exactly where AI is clearly working, which as we see in these numbers, is dramatically fewer pilot projects than what they started with.

[00:50:08] So Paul, like what do you think of their pivot here? Is this something other enterprises? Can be learning from. 

[00:50:17] Paul Roetzer: Yeah. I, I had like 20 questions as I was reading this article about like how the central governance board was working. Right. so I don't wanna be overly critical 'cause I, I don't know how exactly this was going, but I, I, if any organization thinks that running all use cases through some centralized governance board is gonna work, learn the lesson here Now that, that will not work.

[00:50:41] I, I, I guess if it's like high value, high profile, high impact use cases that affect a lot, like affect customers or affect, you know, things that are related to regulations or compliance, like, I totally get having some centralized governance body. [00:51:00] But if we're talking about like the marketing team wants to go get Jasper to like help with blog posts and podcast transcripts and that had to go up to some centralized governance board.

[00:51:10] That's insane. But again, I have no idea if that's the depth that with this was functioning. So I'll say is like, where they're going makes a ton more sense and is certainly the more, more common approach that we have seen work really well. it's interesting, like there's a, a user on Twitter. I'll put the link in here.

[00:51:32] I think the usernames actually chubby, which is hilarious. Like we have plenty of the elder and chubby I've now cited. Right, right. But this is a, I think that there's a chance this person may actually like work at Open Air or one of the labs. 'cause they all have these pseudonyms that they use, but they're, they tend to be like really on the inside.

[00:51:47] And so this account actually has a ton of like great AI related stuff. And so this person tweeted last week, does anyone still use GPTs? Can't find a good use case for them. And I was like, is this a joke? So I actually replied and I was like, [00:52:00] it's literally the best way to drive adoption in enterprises.

[00:52:02] So if you can create distinct personalized use cases that are built as GPTs, it makes AI approachable and actionable, especially for less tech and AI savvy users. Hmm. And when I was replying, I was realizing the AI people and the CIOs, the, you know, the AI researchers, they, they often just lose sight of the reality when they're thinking about people who aren't also AI researchers and engineers.

[00:52:27] Like when we're talking about the average user who doesn't understand any of this stuff and they just want like someone to explain how to use copilot or like help them get some value out of it 'cause they don't know what to do with it. And so I think that. This whole like adoption and what's going on with governance, the more you just like personalized use cases down to individuals within teams, within departments, that's absolutely the way to do it.

[00:52:51] And Mike, I know we won't talk about specific companies, but like you just did a consulting gig through Smarter X where we did this, or we went in and just like created these [00:53:00] custom use cases with custom gpt and you see like immediate impact, immediate understanding of the value of these models versus just handing over licenses to people and like not holding their hand to get those first couple of use cases.

[00:53:14] Mike Kaput: Yeah. I will tell anyone from any, any AI vendor or lab that happens to be listening, like with that particular engagement, it's obviously only one engagement, but it's with a, a big enterprise. The people involved, the 10, 15 people in piloting GPTs, learning how to use them. They've used AI before. They have some AI literacy.

[00:53:33] They're all very savvy, great at their jobs, and it was transformative. It was night and day to show them not just that these exist because they did not have access to those capabilities until we were able to help facilitate that. And then also just giving them basic training on what to build, how to build it, and then turning them loose.

[00:53:53] We built some stuff for them that was very impactful, but the real value came from them being like, wait a second, now I've [00:54:00] connected the dots on this. Can do we have the ability to do this thing? Here's how to get started now. I'm gonna run and do it for all the things in my job. And they've gotten incredible results at, you know, a pretty typical enterprise just from this.

[00:54:15] Paul Roetzer: , and I think the key Mike, as you're highlighting, you empowered them to then build their own. Yes. Yes. Like, it's like, oh, I get how this works now. Well, here's 10 other things I could totally build GPTs for. 

[00:54:26] Mike Kaput: Yeah. And it's stuff that an outsider could not necessarily build either due to like the specific data being used or understanding the nuances of their jobs.

[00:54:34] So it just really unlocked superpowers for them. 

[00:54:37] Paul Roetzer: Yep. So yeah, this is a hundred percent the right path. Democratize this, empower people, give them the AI literacy they need so that they can start connecting the dots and driving innovation themselves. You cannot push this down from the top in any structure of any organization that is gonna lead to the least amount of innovation and impact.

[00:54:54] If it's, if everyone's waiting for the C-suite or some governance board to bless [00:55:00] use cases, that's just never gonna work. 

[00:55:03] How McKinsey, BCG, and Deloitte Are Using AI

[00:55:03] Mike Kaput: So our next topic is somewhat related to this because we're also seeing how generative AI is reshaping the consulting industry according to a new report from Business Insider. So they talk about how all these major consulting firms are using ai.

[00:55:17] So for instance, at McKinsey, 70% of employees now use an internal chat bot called Lilly, which is kind of an in-house chat. GPT trained on a C'S worth of the firm's knowledge. So it helps, consultants research summarize, point to the right experts within the firm to do their job better. At BCG, junior staff rely on a tool called Dexter to build slides faster and get feedback as if a manager had reviewed them.

[00:55:46] And they also have something called Gene, which is a chatbot with a retro robot voice that helps with brainstorming and internal podcasts. Now, what started among these firms as cautious adoption appears to have turned into [00:56:00] widespread usage. McKinsey consultants, the report says, used Lilly about on average 17 times a week.

[00:56:06] BCG staff apparently have built more than 18,000 custom gpt. So tell me those don't matter. Yeah, seriously. even PWC and Deloitte who are apparently traditionally a bit more conservative, have rolled out entire platforms to start managing fleets of AI agents they're going to be building. Now here's really an interesting point, though.

[00:56:26] There is this kind of tension because the report also mentioned some junior staff are wondering if AI tools are making their roles redundant. Others say the time saved is being funneled in a more strategic work as one BCG leader put it, the goal is to quote, take out the toil and increase the joy of their jobs.

[00:56:45] So this last bit really, Paul, given what we've talked about today, really caught my attention because even if it is a real aspiration to increase joy and reduce toil, when we looked at quiet ai, quiet [00:57:00] layoffs in our topic last week, we literally heard the opposite from a firm cited in this article.

[00:57:05] Like, EY is cited in this article as one of the people using AI in their consulting, which is awesome, not trying to call them out. But it's interesting that they were also cited in the information article last week we talked about where an EY principle literally said he would quote, be surprised if the company didn't lay off staff as the company broadens its use of ai.

[00:57:25] So like, what's going on here? Do you see job displacement in the world of consulting? 

[00:57:32] Paul Roetzer: Yeah. So mess, staying on message is hard and really large companies. So I think that there's a mix here where it's like the technical side or the lab side that knows exactly what's gonna happen and can. Generally talk more openly about that.

[00:57:47] And then there's the other side of the businesses that don't want you saying anything about like, replacement of workers. Yep. Even if they know that that's a potential byproduct of what they're doing. So, yeah. You know, and [00:58:00] part of this, it does just go back to words matter, and I think we touched on this last week.

[00:58:03] I, I highlighted this in my exec AI insider newsletter editorial this week. It's like, I, again, I, I don't, I'm not gonna get on a soapbox about this AI first term, but like, we let off with this AI first memo. And I think what you have to understand from a communications perspective, which is where I'm approaching this from, is like, you know, thinking as a, as someone who would maybe drive the internal communications and the messaging around this and hopefully inform the CEO about how, how they should be talking about this AI first.

[00:58:33] To me, when you have a workforce that is afraid for their jobs, that fear that maybe you're going to be replacing them. When you say we're gonna be AI first, that immediately tells me. People aren't first. And so it, again, it's, it might just be semantics, it might just be my personal preference, but this is why when we talk to companies about AI transformation, we talk about being AI forward, like AI native, if you're, you know, the ground up, AI emergent.

[00:58:57] But at the whole premise, like the category I think about is this AI [00:59:00] forward mentality, which can be people first. And so the premise is that you put the people first. This is all about enriching humans, creating more fulfilling opportunities for humans. This idea of being more human as a brand while leveraging it to get efficiency and productivity and create team innovation.

[00:59:16] So I think AI first is just the term that is caught on in the tech world, and I get it. And I don't think that that's gonna change. I, I do think that that's just gonna be what we'll see. 

[00:59:24] Mike Kaput: Yeah. But 

[00:59:25] Paul Roetzer: I do hope that if there's communications, people listening, or more leaders listening, that if you're gonna write that memo to your people, understand that many of them are completely uncertain about the impact on their jobs, and they have anxiety and fear around this.

[00:59:39] I think softening it and maybe going with the AI forward approach, like might be advisable when we're thinking about talking about the impact on our people, but not just for messaging purposes, like truly like, I hope that's how you think about it. Like I do hope most CEOs are thinking about this as a more intelligent and more human equation, and not truly, let's just put AI first and get [01:00:00] it rid of the people whenever we can and like drive efficiency in the workforce.

[01:00:05] New Report Calls Chatbot Arena Leaderboard Into Question

[01:00:05] Mike Kaput: Next step in our rapid fire topics, a new paper is calling out the most popular leaderboard in AI and saying that it is giving us a distorted view of which chatbots are actually the best researchers from Cohere Stanford, MIT, and others in this paper argue that chatbot arena, which is a public benchmark for large language models.

[01:00:27] Paul Roetzer: That we cite often on the show, 

[01:00:29] Mike Kaput: that we cite often on the show, and everyone else is like paying attention to, to see which ones are best at any given moment. They claim it's being quietly gamed by tech giants like OpenAI. And their core claim is that these companies get to run private tests with dozens of model variants, then only publish the version that scores the highest, which effectively cherry picks the results because.

[01:00:51] Humans rate these models. So it may look great, but it may not reflect the model users actually get. We talked in past weeks about this happening to [01:01:00] meta where what they released to users is not the same model they had used to get to the top of the rankings. So this paper accuses the leaderboard of favoring proprietary models over open source ones and says that companies may be tuning their models to win the benchmark not to perform better in the real world chat bot arena in a long post on X pushed back saying it only ranks models that are publicly released and that the numbers used in the paper to come to their conclusions are inaccurate.

[01:01:28] So Paul, there's definitely like a, they said, we said type of thing going on here, but it does kind of highlight this larger point that these leaderboards are not necessarily always set in stone Scientific. 

[01:01:42] Paul Roetzer: Yeah, I, I mean, as I was watching this unfold, my, my initial impression was like this, this company's cooked like they're.

[01:01:50] I, I just think that it's very obvious that things are being gamed and if you listen to the Zuckerberg interview, which we'll talk about next Yep. He kind of like, he was asked about this and he didn't admit [01:02:00] to it, but like, it sounds like, yes, they're all aware that they were basically post training models to perform well on these evals just for the point of being able to perform well and get the pr of being tops on these model boards.

[01:02:13] And I, I, I found myself really trying to think about this from an organizational perspective of like, what should our listeners care about? Yeah. And it goes back to this idea that the only evals that matter moving forward are the impact they have on your people. Yeah. So if you're using AI on your marketing team and the top five use cases, you can identify and clearly define, the only eval that matters is when a new model comes out, how does it impact those five use cases that your standard?

[01:02:38] So if you're thinking about it at more of a broad level of we want it as like a customer support agent. When a new model comes out, the only thing you care about is it getting more accurate? Is the personality better? Is it, is it, closing more deals? Is it providing more satisfaction to our customers?

[01:02:53] Like you have, you're gonna have to develop your own evaluations internally. Yeah, based on use cases and the [01:03:00] goals of those use cases and all this other stuff is gonna be irrelevant over time. 'cause these things are gonna get so smart so fast, they're going to be tops on every eval. That would be like general to humanity, but it's all gonna be about the impact on your people and your company.

[01:03:14] Mike Kaput: And stress is the importance of experimentation. I mean, all the resources in the world, podcasts like this one, it's all great, but you have to be in the trenches using these tools because sometimes nobody can tell you what is going to be best for your use case. 

[01:03:29] Paul Roetzer: And it goes back to our O three conversation last week.

[01:03:31] I have no idea where right O three ranks in the chat bot arena. But I will tell you it is fundamentally different than what came before it. And it has changed the way I do strategic planning. Yeah. And it's going to change the way our entire company does. Strategic planning. Do I give a shit what, like where it ranks?

[01:03:46] No, it doesn't matter at all. All I care about is we use it every day to do a thing that's critical to our company. And it's changing the way we do that. That's all that matters. That is my eval. It's like, and if you, if that's a vibe thing or a tasting thing, I don't [01:04:00] know, but like it's transformative. 

[01:04:04] Meta AI App and Zuckerberg’s Plan for AI

[01:04:04] Mike Kaput: Next up, we alluded to this Meta, CEO Mark Zuckerberg says, we're entering a new phase of AI where personalization, not just intelligence, is going to define the next frontier.

[01:04:16] So one proof point here is that Meta has launched its meta AI app, which is a new way to access their AI assistant and personalization's a huge piece of this. So the app is built on LAMA four and designed to be more than just a chat bot. It remembers what you like, adapts how you talk, and connects to your Facebook and Instagram profiles for deeper context.

[01:04:38] You can chat with it via text or voice, and even generate and edit images, mid conversation. They have a voice mode as well, powered by what they call full duplex speech, which lets you talk to meta AI more like a human than ever before. There's no awkward pauses, no turn-taking. It's a very natural conversation.

[01:04:59] The [01:05:00] app is also being integrated with RayBan meta glasses, letting you switch seamlessly between devices, and also features a social style discover feed to see how others are using ai. Now, this is all seemingly part of a vision that Zuckerberg outlined recently on an episode of the Dirash podcast. He literally envisioned the world where people talk to their AI assistants all day through phones, apps, and eventually glasses in seamless voice-driven conversations.

[01:05:30] In fact, he even thinks this could unlock the key to a GI, he believes AGI won't emerge in a vacuum it will emerge through billions of people using AI tools, building up contextual memory and generating feedback loops that improve the system steadily. So Paul, I know you took a listen to the interview with Dwarkesh

[01:05:52] I'd love to hear if anything stood out there for you because I just keep personally coming back to the issue of trust. [01:06:00] Like as I think about this personalized voice-driven future, he mentions AI companions. Whether or not that becomes a thing is meta of all the companies, the one out there that I'm going to trust with all my personal thoughts, my data, my deepest secrets there.

[01:06:16] Paul Roetzer: Yeah. You or your kids like, right. Yeah. I honestly, like, every time I listen to Zuckerberg talk, I, I just, it's terrifying. Yeah. Like it, and again, I, people change and they evolve like. They have different perspectives on the world, so I always want to give people the benefit of the doubt. But like historical context, Facebook as a company has not always led the way in making ethical and moral decisions, I would say.

[01:06:45] Right. this is all public knowledge and fact and like, I'm not making anything up here. Like there's books, written court cases, movies, like, and so yes. If we go back to this debate about like the model [01:07:00] personality, model behavior, its ability to persuade its ability to influence people.

[01:07:05] and then that's part of the reason why I spent so much time earlier in this episode going through the context of how it works. Yes. Like he is the one driving the decisions that they claim more than a billion people use meta ai. Now, he did admit in the Dwarkesh podcast that that is primarily in WhatsApp primarily international.

[01:07:24] So they don't have an enterprise play. They're not like, you know, building in for enterprise solutions. This is, this is primarily on Instagram, WhatsApp, Facebook, and their other platforms. But yes, like, I keep coming back to this. Like he, there was this one, honestly, like just very unnerving part.

[01:07:42] Yeah. Where, where Dke asked him about, you know, people using these things for relationships with therapists and friends, and maybe more referring to like relationships. and like, kind of like questioning, is that really what we want? And then Zuckerberg, which I watched the video clip of this, and by [01:08:00] the way, watching him do this interview in the meta glasses is so weird.

[01:08:03] Awkward to me. Yeah. Yeah. It's like, I don't want this future where everyone's just wearing their glasses. You have no idea what they're seeing or recording or what it's telling them to say and like whatever. But, he actually goes on to basically Illumina. I won't read the whole thing, but. Says that research has shown that the average, American has fewer than three friends, fewer than three people.

[01:08:24] They would consider friends, and the average person has demand for meeting more. And then I, he just talks in general. I think it's something like 15 friends or something at some point. you're like, all right, I'm too busy. I can't deal with more people. But he was basically implying that people are lonely, which I'm not debate, not debating.

[01:08:42] There are people who absolutely are lonely, don't have more than three friends. Maybe some don't have more than one friend that they can, you know, truly rely on. And I'm completely empathetic to that. What I'm not empathetic to is him thinking it's their job to fill the gap. That if people are lonely, then [01:09:00] it's meta's job to build AI agents.

[01:09:02] Who can be your girlfriend, boyfriend, therapist, friend, whatever. because you have capacity for up to 15 and we want to fill that gap. That is almost implicitly what he was saying in this is like, we see, it's our job. To build ai, right, to fill this capacity for people to have more friends in their lives.

[01:09:22] I was almost done with the interview after that, honestly. I'm like, I can't even go down this path right now. So all I'll say, Pierre, 'cause this is not a main topic, is if your kids have access to WhatsApp, Instagram, Facebook, or any of the other meta properties, you need to be aware that this is their goal.

[01:09:41] That, that they want people to have deep relationships with the AI they build through their apps and through their glasses and whatever comes next that these are going to be very, very addictive AI agents. And it's very important, especially if you have teenagers or preteens, that you [01:10:00] are aware this technology exists and that they may already be interacting with it now because there has not been enough studies by psychologists, sociologists to understand the impact of this.

[01:10:11] This is gonna be Netflix documentary material. Like three years from now, we start to look back at this emergent age where people at very young ages started to actually develop relationships with their ai, and we just don't know what it means yet. But you, you have to be aware of that. 

[01:10:26] Mike Kaput: And I would say, given what we know of where the technology is and where it's headed, if you have ever harbored any reservations about how effective the algorithms are at getting you to engage on social media, this will make algorithms engineered for engagement look like child's play.

[01:10:45] Paul Roetzer: A hundred percent. And I, again, like I feel I get, this is a whole episode. I, as someone who is intimately aware of this, knows the impact. I find myself talking to my co CEO [01:11:00] like an advisor and friend sometimes honestly, like I, not in any way, like I need that emotional support. You just get into these conversations, you're trying to work this really hard thing and it helps you and you have this like instant, like maybe ephemeral, but like you have this moment where you're like, I'm so grateful for this thing right now.

[01:11:21] Just doesn't, right? And so now imagine that to someone who's lonely or imagine that to like a teenager who's doubting themselves. And like if that's where the affirmation comes from, like that's instant and it, it is long lasting in that case. And I just think we need to do more to prepare for that as a society.

[01:11:39] Nvidia’s Beef with Anthropic

[01:11:39] Mike Kaput: Next up, Nvidia and Anthropic are in an unusually public fight over US chip export roles. So this Clash centers on upcoming restrictions designed to keep advanced AI chips out of China. Anthropic, which is backed by Amazon, is pushing for even tighter controls. And in a blog post, it claimed that [01:12:00] Chinese smugglers have hidden chips in prosthetic baby bumps and lobster shipments.

[01:12:04] That's not a typo. They specifically said that to evade enforcement. So they're kind of like trying to raise awareness of what they see as an issue here. But Nvidia then fires back calling those stories tall tales and accusing philanthropic of using national security policy to stifle competition. They said in a statement from a spokesperson quote, America cannot manipulate regulators to capture victory in ai.

[01:12:29] So the broader issue here is who is able to access compute, which is the raw power we need to train Cutting edge AI and NVIDIA's main business. Anthropic argues that controlling chip exports is critical to maintaining America's lead in ai, Nvidia, to depend heavily on international chip sales. Clearly disagree.

[01:12:49] So this is all playing out as new rules dub, the AI diffusion rule are set to take effect May 15th. Former President Biden introduced these. President Trump is reportedly [01:13:00] looking to revise them. So Paul, this is definitely a bit strange, I think, to see Nvidia getting into a public spat like this. Like, what's going on here and what's gonna happen next?

[01:13:11] Paul Roetzer: I, I remember earlier this year on a podcast, I was talking about like Anthropics sort of position in the market that they were taking here being a little bit more aggressive about the need for regulations. And I said at the time, like, this is not gonna be popular. Like they're, they are doing what they're doing while also building powerful ai.

[01:13:31] Like, it's not like they're stopping building these things 'cause they're worried about this, but they have very, they're taking an increasingly, defined stance in this area that is counter to almost everyone else, in the AI lab space. they're going to make some enemies here. And honestly like the, but I, I think, I don't know, Nvidia might be an investor in Philanthropics also.

[01:13:55] I, I feel like everybody's invested in Anthropics at some, but I know Google and Amazon and 

[01:13:59] Mike Kaput: it wouldn't surprise [01:14:00] me. Yeah. 

[01:14:00] Paul Roetzer: Yeah. So there's just so many dynamics at play here, and this is, I mean, we were talking about billions and billions of dollars at, at risk, so I don't know, I'm not sure how this is gonna play out.

[01:14:11] I, I, I think the, tariffs on the chips or the, you know, restrictions on the chips are a major issue. And I don't think that philanthropic, if they think they're gonna win here, ends up having the end, you know, outcome that they're hoping for. Correct. Like, they're still gonna diffuse. Like the technology's still gonna diffuse, and I don't know, I'm not sure why they're doing this, honestly, but I, I, they think it's important.

[01:14:38] US Copyright Office Intellectual Property Resources

[01:14:38] Mike Kaput: Next step. The US Copyright Office has released a new set of online toolkits related to intellectual property. Now, these are not strictly AI focused, but we did think it was a really good time to make the audience aware these new tools exist given how much the battle over AI's use of copyrighted material is heating up.

[01:14:57] So we'll link to all of this in the show notes. [01:15:00] These include a copyright registration toolkit from the US Copyright Office. They also include toolkits on trademarks, patents, and trade secrets that the office developed with the US Patent and Trademark Office. So Paul, you're a business owner who's developed ip.

[01:15:16] You filed to defend it in the us like we just wanted to make people aware of these resources like. What should businesses be doing now with their ip, especially with AI becoming such an important part of the conversation here? 

[01:15:27] Paul Roetzer: This is increasingly coming up when I go do talks and we do the q and a after the talks.

[01:15:32] I, I am very commonly getting asked questions now around intellectual property, and that was the main impetus for like, sharing this now and just making sure people have this information. I think there's a, a, a lot of misunderstanding of what is involved in intellectual property, what copyrights are versus trademarks versus patents versus trade secrets.

[01:15:50] And so I just thought it was a really, really helpful guide for people to, to have a bit, a little bit better understanding. so when you're thinking about the content you're creating with generative ai, when you're [01:16:00] thinking about decisions you're making to use these models that are trained on copyrighted material that they've stolen, like it helps to just have a little bit more education around them.

[01:16:10] And so , it's a great resource for people to check out if you're interested in the topic. 

[01:16:15] AI Product and Funding Updates

[01:16:15] Mike Kaput: We have some AI product and funding updates this week as usually kind of try to group some of these updates together. So Paul, I'm gonna go through a few of these and then there's a final one that I'm gonna turn over to you to talk through.

[01:16:26] Cool. So first up, open AI just added or is adding rather shopping to ChatGPT, turning the chat bot into a product recommendation engine. So this new feature will let users browse and compare products across categories like electronics, fashion, home goods. Then click out to buy them on third party sites.

[01:16:45] It's going to be initially pretty limited in scope. It's basically like a visual carousel of products, that will be displayed, but OpenAI plans on expanding it over time. Importantly, OpenAI claims products are selected by chat [01:17:00] GBT independently and are not ads. So this is currently rolling out to plus PRO and free users.

[01:17:06] Visa, the credit card company says IT plans to enable AI agents to sh securely shop on your behalf. That would mean giving agents virtual visa credit cards, credentials they can use to complete transactions, along with tools for users to set strict controls on how much to spend, where to shop, and how long to keep looking for a purchase.

[01:17:27] The company has partnered with OpenAI, Microsoft, anthropic, and others to ensure these AI shopping agents are safe, interoperable, and widely supported. Anthropic has launched integrations, a new feature that lets Claude connect directly to tools like Jira, confluence, Zapier, Asana, and more. And once connected, Claude can pull in project details, respond to customer feedback, create tasks, all through natural conversations.

[01:17:55] They have also updated Claude's research mode. It can search not just the [01:18:00] web and Google workspace, but also any integrated apps delivering detailed citation backed reports in as little as five minutes or up to 45 minutes for much deeper investigations. Descrip, the popular AI powered video and a audio editing platform has announced AI avatars will now be buildable in the platform.

[01:18:23] According to the company quote, you can now create a whole video by typing without going near a camera. Just write your script. Choose an avatar from our gallery or upload an image to make your own. And boom, you've got a video. Your avatar will narrate your video in a clear but not creepy lifelike, but not alive voice so you can make interesting engaging video fast.

[01:18:48] Alibaba has released Quinn three, their latest large language model, which is also an open rate model. And the flagship model in the Quinn three family, according to the company, achieves [01:19:00] competitive results in benchmark evaluations of coding, math, general capabilities, et cetera, when compared to other top tier models.

[01:19:07] So there is now another extremely capable, powerful open weight model out there. And Paul, I'll turn it over to you for some updates on the Google Gemini roadmap. 

[01:19:18] Paul Roetzer: Yeah, so this was an interesting tweet from Josh Woodward, who's a vice president at Google working on the Gemini app. And I just thought it was great because it lays out the roadmap.

[01:19:26] Now it's pretty concise, but worth noting. so he said a great software feels like an extension of you. We're building Gemini app to be the most personal, proactive, and powerful assistant. first personal, the best assistant gets you. It starts by knowing your past chats launching soon, so they're gonna have the ability to have memory like OpenAI has.

[01:19:47] But we'll go further. And this is an interesting component. This is a choice you're going to have at least initially. It says, we'll make it easy for you to bring in all of your Google context, Gmail, photos, calendar, search, YouTube, et cetera. Basically [01:20:00] any Google property with your permission. It will have that context when you're interacting with it.

[01:20:06] This is something OpenAI does not have with Jet GPT. They cannot bring in all of those things. We call it P context or personalized context, and we're testing it internally with our own info already. So again, deeply personal AI assistance and chat bots that have access to lots of data about you, not just your chat history.

[01:20:26] Number two, proactive. The best assistance anticipates, Gemini, apple offer insights and actions before you ask. Freeing your mind and time from what truly matters. Less prompting, more flow. This will be transformational in my opinion. And this is not just Gemini, this is gonna bechet GPT and others. I think I might have talked about this on last week's episode, but imagine you have a conversation about a health condition.

[01:20:48] You know, you're in Gemini and you're like, Hey, I'm really struggling. I got my heartbeat's irregular. Or, all of a sudden gaining weight or like, whatever it is, feeling tired all the time. It'll know that, it'll remember that, and [01:21:00] it in theory could check in with you a week later and say, Hey, did, how are you feeling?

[01:21:04] Are you still feeling tired? Now go back to this conversation we had about, you'll be developing relationships with these things. You're developing these strong feelings of, this is something that is always here for me. Once it becomes personalized and once it becomes proactive, those feelings become much, much stronger.

[01:21:24] and then the third is powerful. The best assistant turns your ideas into actions. Google DeepMind models like 2.5 Pro are exceptional. They can research, orchestrate and create images, videos, and code where a new era of models and a new era of user experience is coming. And then the final note I will tell you here is the Google io developer conference is May 20th to 21st, I think it is.

[01:21:47] yeah, may 20 and 21. And I would expect those three things we just discussed to be on full display, at that event. I don't, I'm not saying they're going to definitively launch the next model, but I [01:22:00] think we will at minimum see a preview of what they think the next models will be able to do. I think he just laid out the blueprint for what you can expect.

[01:22:11] Listener Question

[01:22:11] Mike Kaput: Alright, we're going to end this week's episode with our recurring segment on the listener questions. Every week we answer one question from our audience that seems particularly relevant to this week's topics or AI literacy overall. So Paul, here's this week's question. What can an experienced professional do when the job description for a new job insists on two to three years of familiarity slash use of AI tools?

[01:22:38] Especially if you're coming from a sector they mentioned like healthcare or government that has for various reasons, not been an early adopter. Interestingly, this is one I've actually seen people debating online. is that right? It seems like, yeah, I've, I've seen more than one conversation about this.

[01:22:53] It must be cropping up in job descriptions as we get into this, those AI first memos or whatever the expectation. Yeah. 

[01:22:59] Paul Roetzer: [01:23:00] I mean, I, I would imagine if it's a technical role, I could see this, but like, yeah. My first instinct is the company that's you're maybe interviewing with doesn't really understand. It's very po.

[01:23:13] They have no concept of what it actually is or how it's being used in enterprises. so like my nons sarcastic answer would be no, almost nobody I know that's interviewing for non-technical roles has two to three years of familiar use of AI tools. So you're in good company to start, I would just focus on what you have done.

[01:23:35] So if you, if it's required that you say yes to this I would maybe do that if it's relevant, where you could say, listen, I haven't had access. Now you're in the interview process, but here's how I've been using it in my personal life. Here's how I've been advancing my own prompting knowledge.

[01:23:49] Here's some courses I took online. Here's a couple GPTs I built. here's what I did at my previous organization to help move forward conversation. Through policies and the AI councils, like you can tell [01:24:00] a story. As long as you have been doing something, even within the confines of healthcare, government jobs.

[01:24:06] so if you haven't been doing anything, there's not much you can do here, but hopefully you've been investing in time. This is what I often tell people. I had this conversation with somebody last week, who's that? I think I said this, unless he's podcast, they're not allowed to use any website that has a.ai.

[01:24:19] Like they literally can't get to 'em. And then I said to the person like, well then just build some custom GBTs for your life. Like use it trips, like just use it every day. And then when you do have a job that allows you to do it, you, you will have familiarity that will transfer over. It's all about comfort with these things and learning how to prompt with them and learning how to guide them to the outputs you want.

[01:24:38] Like that transfer transfers over immediately into your professional world as long as you've been advancing your personal use. 

[01:24:44] Mike Kaput: Yeah. Yeah. That's good. And wise words. Paul as always, thank you for breaking down another busy week in ai. We'll probably have, plenty more news items here soon enough to tackle.

[01:24:55] I think we're gonna get some big stuff in the next week or two. It, 

[01:24:58] Paul Roetzer: it's looking like it for sure, [01:25:00] and we will be back next week with our regular episode. So thanks everyone for joining us. Thanks for listening to the Artificial Intelligence Show. Visit smarter x.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.

[01:25:18] Downloaded AI blueprints, attended virtual and in-person events, taken online AI courses and earned professional certificates from our AI Academy, and engaged in the marketing AI Institute Slack community. Until next time, stay curious and explore ai.
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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