This episode may just be the calm before the GPT-5 storm…
We’re back with another rapid-fire episode: there was just too much AI news to cover any other way. In this episode of The Artificial Intelligence Show, Paul Roetzer and Mike Kaput dig into the possible release of GPT-5, unveil what’s coming in our reimagined AI Academy 3.0, and examine how AI is transforming job markets, consulting, and enterprise strategy. They also break down key updates from OpenAI, Microsoft, Meta, Apple, and Google, and what listeners need to know as AI’s impact accelerates across business and education.
Listen or watch below. (And see below for show notes and the transcript.)
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Timestamps
00:00:00 — Intro
00:10:27 — OpenAI’s Explosive Growth
00:16:52 — Microsoft and OpenAI Near Contract Agreement
00:23:23 — ChatGPT Study Mode
00:28:42 — How We Talk About AI’s Impact on Jobs
00:36:16 — Microsoft Paper on AI Jobs Impact
00:41:24 — AI’s Impact on the Consulting Industry
00:47:01 — Apple AI Acquisition Speculation
00:51:26 — Earnings Reports
00:58:16 — Gemini 2.5 Deep Think
01:04:29 — Meta’s Vision for Superintelligence
01:12:46 — ChatGPT Shared Links Indexed by Google
01:15:05 — AI Product and Funding Updates
Summary:
OpenAI’s Explosive Growth
OpenAI just crossed a massive milestone: $12 billion in annualized revenue, according to The Information ($13 billion according to The New York Times), nearly tripling its pace from the start of the year. That breaks down to about $1 billion a month.
That explosive growth comes with equally aggressive funding. OpenAI has already raised $8.3 billion as part of a $40 billion round, five times oversubscribed, with heavyweights like Blackstone, TPG, and T. Rowe Price jumping in.
The single largest check? $2.8 billion from Dragoneer Investment Group, one of the biggest VC bets ever.
Behind the numbers is a user base that’s ballooned to 700 million weekly active users, plus five million paying business customers. To keep up, OpenAI has upped its projected cash burn to $8 billion this year, including massive outlays on chips and new data centers, some in partnership with SoftBank.
There’s also a strategic shift: ChatGPT isn’t just a chatbot anymore. It’s evolving into a productivity suite with tools for presentations and spreadsheets, directly targeting Google and Microsoft.
Microsoft and OpenAI Near Contract Agreement
Microsoft and OpenAI are deep in talks to rewrite the terms of their relationship, and it all comes down to AGI.
Right now, Microsoft’s $13.75 billion deal gives it access to OpenAI’s models until 2030 or until OpenAI declares it’s reached artificial general intelligence, a milestone vaguely defined as AI that outperforms humans at most economically valuable work. If that happens, Microsoft could lose access to OpenAI’s tech overnight.
That’s a problem, since Microsoft has built Copilot, Azure, GitHub, and much of its AI strategy around OpenAI’s models. So the two are working on a new deal: one that would let Microsoft keep using the tech even after AGI is declared, while also negotiating a potential equity stake in the low- to mid-30% range.
But there’s friction. OpenAI wants more revenue, looser constraints on who it can sell to, and stricter guardrails on how Microsoft deploys its models. Microsoft, meanwhile, has blocked some of OpenAI’s acquisitions and isn’t afraid to walk away if the terms don’t work.
ChatGPT Study Mode
OpenAI has rolled out a new “study mode” in ChatGPT.
Instead of just solving a problem, study mode walks users through concepts step by step, using prompts, hints, and checks for understanding to guide the process, which helps students actually learn instead of just getting quick answers.
What sets it apart is how it’s built. It leans on insights from teachers and learning scientists, using techniques like Socratic questioning, cognitive scaffolding, and metacognitive prompts—all tailored to the student’s level and memory from past chats. It even includes quizzes and open-ended questions to help ideas stick.
In early feedback, students described it as “a live, 24/7, all-knowing office hours” and praised it for breaking down tough topics into something finally graspable.
Study mode is free for all logged-in users and can be toggled on or off during chats. For now, it’s powered by custom instructions layered on top of ChatGPT, but OpenAI says the long-term plan is to bake these behaviors directly into its models.
This episode is brought to you by our Academy 3.0 Launch Event.
Join Paul Roetzer and the SmarterX team on August 19 at 12pm ET for the launch of AI Academy 3.0 by SmarterX —your gateway to personalized AI learning for professionals and teams. Discover our new on-demand courses, live classes, certifications, and a smarter way to master AI. Register here.
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] Mike Kaput: It’s a really, really good time to be an expert who has a lot of real world background and context, and I don’t know how long that’ll last. If you’re in knowledge work and you are an expert that has all this domain expertise and background, don’t waste this moment.
[00:00:15] Paul Roetzer: Welcome to the Artificial Intelligence Show, the podcast that helps your business grow smarter by making AI approachable and actionable.
[00:00:23] My name is Paul Roetzer. I’m the founder and CEO of SmarterX and Marketing AI Institute, and I’m your host. Each week I’m joined by my co-host and 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.
[00:00:44] Join us as we accelerate AI literacy for all.
[00:00:51] Welcome to episode one 60 of the Artificial Intelligence Show. I’m your host, Paul Roetzer, along with my co-host Mike Kaput. We are recording Monday, August [00:01:00] 4th at 9:20 AM and we may be in the week of GPT-5. So Timestamping might be highly relevant this week. I’ll keep an eye on Twitter while, while we’re on your mic and see if anything drops while we’re doing this.
[00:01:14] All right. This episode is brought to us by AI Academy by SmarterX. The, 3.0 version of Academy is launching on August 19th. So we have been working on this for, well, I’ve been kind of conceiving of this for a couple years, but intensely working on this since November, 2024. So we have completely reimagined our AI Academy and I, our AI Mastery membership program.
[00:01:39] They were first launched in 2020. and it’s been a, a part of what we offer, you know, online courses, cer professional certifications, but this is, on a whole nother level. So on August 19th at 12:00 PM Eastern Time, we will hold a launch event. That’s going to, you know, share the vision and roadmap [00:02:00] for where we are and where we’re going.
[00:02:01] preview all the new on-demand courses and professional certificates. Introduce AI Academy Live, which is a new component. we will give a preview of the new learning management system. It’s an AI powered LMS that’s gonna be coming out later this year talk about personalized AI learning journeys and how you can build yours within, the, you know, the online education space.
[00:02:23] take a look at new business accounts. This is something that we’re introducing that’s new. Five or more, licenses can be part of our business accounts. So we will preview all that and then we’re gonna have an ask me anything session with me and Mike and Kathy on our team. And so Mike and I thought we would be talking about this for the last couple episodes.
[00:02:39] So that’d give you a little bit of a preview of what we’ve been doing because this has been the better part of my professional life for the last three months in particular, building all this new content. And so the three main things that I’ve been creating, for this August nine 19th launch are.
[00:02:57] our AI foundations category, so [00:03:00] AI Fundamentals, which is a brand new series. This is an eight course on demand program. So there’s intro to ai, AI concepts 1 0 1, which is a brand new course I’m, I’m really excited about. It’s actually one of my favorite ones. We built state of AI that goes to the five things Everybody needs to know the AI timeline, which takes a look at sort of AGI and beyond generative AI 1 0 1, prompting 1 0 1, which is actually a fun one to build.
[00:03:23] that was, I found it super helpful myself to go through that one. AI Agents 1 0 1 and then AI and You, which is sort of like a personal look. then I just yesterday finished the new piloting ai. This is actually, this was our flagship course series a few years back, so this has been completely reimagined.
[00:03:41] So piloting AI, third edition. and so that one’s four courses. It’s piloting AI and business, the use case model, the problem based model, and how to build your coax, which is all about building AI assistance. And that one was. Again, like kind of one of my favorite ones to build. I think it’s super actionable for people.[00:04:00]
[00:04:00] And then I’m finalizing this week, the second edition of our Scaling AI series. So this was first launched in June, July, 2024. So I’m doing a, a refresh of that, series. That’s probably the most evergreen of the courses we’ve created. but I’m gonna do a refresh of those this week. So that one’s eight courses.
[00:04:20] You have AI Forward organization, the AI Gaps, which is a new one. the AI Academy, the AI council, generative AI policies, responsible principles, AI impact assessments, and the AI roadmap. So in total, I’ve recreated or updated 20 courses for this August 19th launch, but that is just the beginning. Mike, what have you been working on the last.
[00:04:44] Couple months.
[00:04:45] Mike Kaput: Yeah. So Paul, I’ve been working on the first couple installments of our AI for industries and AI Depart for departments course series that we’re putting out. So these are just the tip of the iceberg. But unlaunch date we will have AI in professional services, which is, [00:05:00] which is a four course series actually that is going to go through kind of your opportunity in AI as a professional services professional, or a leader or an owner of a firm.
[00:05:12] We’re gonna talk about, course two is AI and the future of the professional services firm. the third course is all about finding kind of your AI advantage in pro services. So really finding your own specific use cases in that industry. And then finally, we are going to go through a ton of sample use cases and tools in the fourth course of that series.
[00:05:32] And then kind of a similar cadence. The ai, in marketing series is going to go through kind of the high level opportunity marketers have to increase productivity and performance. With ai in course one, course two, we do this great deep dive into the state of AI for marketing. So even if you’re kind of newer to this topic, you’ll come away with like a really good grounding and the actual state of things in our industry.
[00:05:56] course three is about the AI forward marketer and really [00:06:00] how you can invent your, reinvent your career and your work very practically and tangibly using ai. Then we also, in course four go through a ton of AI tools and use cases specifically for marketers. And last but not least, in course five, we do a whole applied AI for Marketers section where we just kind of put you in the deep end.
[00:06:18] Not really, we give you plenty of instruction, but you get started with some of the top tools out there. We just go through sample prompts and projects where you can just go kind of from zero to 60 very quickly with ai.
[00:06:30] Paul Roetzer: And then you’ve got the Gen AI app series, which is brand new to AI Academy as well, which is gonna be weekly product reviews, like 15, 20 minutes each.
[00:06:37] We’re gonna drop those every Friday. And I think you’ve got a few queued up to,
[00:06:40] Mike Kaput: yeah, so we’ve got a couple, a couple queued up. you know, as of right now the plan because we, you know, these are meant to be almost in real time being created and published. So we are going to be creating one on custom gpt, you know, with the caveat for our valued listeners.
[00:06:56] If GPT-5 comes out and blows up custom [00:07:00] gpt got it. To the point, which I really hope they don’t ’cause that would really ruin my week. But we might swap in one of the other tools, but we’re going to be doing things, don’t, you know, quote me on this order. But like notebook LM from Google, Google Deep research, OpenAI’s deep research, some of these really core capabilities that.
[00:07:18] Business professionals in any function can get a ton of value out of. That’s gonna be our focus for the first few of these.
[00:07:24] Paul Roetzer: Yeah. So we just wanted to give you a little bit more perspective since we’ve been talking a lot about this and as Mike said, this is sort of the tip of the iceberg. There’s a ton more planned.
[00:07:31] There’s a whole AI for department series, there’s a whole AI for industry series. Yeah. AI for businesses, AI for careers. So the whole idea is to, we’ve reimagined AI Academy to allow people to build these personalized learning journeys that really move them to, to a point of like mastery over this topic.
[00:07:46] So, yeah, so, so, appreciate, you know, everybody giving us a few minutes here upfront to talk a little bit more about this. It has been a massive lift. but, you know, I think at the end of the day it’s gonna be in incredibly valuable to people and we’re really [00:08:00] excited to get these in everyone’s hands come August 19th.
[00:08:03] okay. So you can learn more about that at SmarterX dot ai. We will also put the link to the webinar, the August 19th launch webinar in the show notes. So. That’s what’s coming up with AI Academy. And then the second, is brought to us by our Make on Marketing AI conference coming up October 14th to the 16th.
[00:08:21] As I said last week, we’re, we’re trending in a really strong direction from a ticket sales perspective. we’re expecting very strong attendance. We had 1100 last year. It’s far outpacing that 1100 number this year. So we’d love to see you in Cleveland, August, or I’m sorry, not August, October 14th to the 16th.
[00:08:39] That’s at the convention center in Cleveland, right across from the Rock and Roll Hall of Fame in Lake Erie and the Cleveland Brown Stadi at least for a couple more years where they move out. but come join us. You can check out the agenda. It’s MAICON.AI, . You can use pod100 for a hundred dollars off of your ticket.
[00:08:57] most of the agenda is live. we’re [00:09:00] gonna have some announcements coming up soon on the general session, the kind of featured talks and keynotes. we. Maybe next week we will see. We may announce a few of them next week. But, that’s, that’s taken, shape and the main stage is really all about like the macro level.
[00:09:15] So I like to do talks on like education and the economy and the future of jobs, like bigger picture things. And they all obviously have relevance to marketing, but I really like to use that main stage to sort of expand people’s minds and introduce new topics and speakers that maybe they wouldn’t otherwise see at, at events like this.
[00:09:33] So, love to have you in Cleveland, macon.ai. Again, it’s M-A-I-C-O n.ai. Alright, Mike, we’re gonna, it was a busy week, like lots of big topics and so we, we decided on Friday we’re gonna go with the rapid fire style again, there’s a couple of these, I may expand out a little bit past rapid fire, but idea is to try and go rapid fire on these because there’s a bunch going on last week.
[00:09:56] Mike Kaput: Well, you know, as I. Want to start saying, you know, [00:10:00] any rapid fire could be a main topic if you try hard enough, Paul, so seriously. So shoot for the stars over here. but yes, we’re gonna go all rapid fire. I kind of see this as almost like the qualm before the GT five storm, since I imagine that’ll be a leading topic.
[00:10:14] And I think at some point I think
[00:10:15] Paul Roetzer: Google’s holding their next model. Yes. Like I’m starting to get the sense that it’s like, who’s gonna release first thing right now with OpenAI’s and Google? There’s like a little game of chicken going on.
[00:10:26] Mike Kaput: No kidding.
[00:10:27] OpenAI’s Explosive Growth
[00:10:27] Mike Kaput: So first up, OpenAI’s just crossed a huge growth milestone.
[00:10:31] They are now tracking somewhere around 12 billion, according to the information in annualized revenue, or 13 billion according to the New York Times. Slight difference in those numbers, but this is nearly triple. Their pace of growth from the start of the year and breaks down to about a billion dollars in annualized revenue happening per month at the moment.
[00:10:52] And this comes with some equally aggressive funding. They have already raised $8.3 billion as part of a $40 [00:11:00] billion round, five times over subscribed, and they’ve got now some institutional heavyweights like Blackstone and t Rowe Price jumping into this round. And the single largest check as part of this funding is $2.8 billion from Dragone Ear Investment Group.
[00:11:16] And this is one of the biggest VC bets basically in history. behind this are some pretty serious user base numbers. the user base has grown to about 700 million weekly active users, plus 5 million paying business customers. That was 3 million just a couple weeks ago when we had this Also as a topic.
[00:11:38] the, to keep up with all this OpenAI’s has upped its projected cash burn. To $8 billion this year. That includes massive spending on chips and new data centers, some in partnership with SoftBank. there’s also a strategic shift happening, which we’ve talked about a little bit. It’s not just a consumer chatbot anymore.
[00:11:59] You can see [00:12:00] chat g PT kind of evolving into a productivity suite that’s starting to directly target some of the wheelhouses of Google and Microsoft. So Paul, I’m never surprised that OpenAI’s is thriving. I guess I am surprised that they keep hitting these crazier and crazier speed and scale of growth numbers.
[00:12:17] Like what is driving. Just this massive jump in revenue, which is tripling its pace, and the cash burn is up to $8 billion from, I think they projected like 1 billion. So what is going on here?
[00:12:31] Paul Roetzer: Yeah, the cash burn I would expect is just gonna keep going up as long as the demand, you know, long term is there.
[00:12:37] and that’s largely gonna be coming from, you know, what they’re building out the fut the future infrastructure. plus it’s just the demand on, you know, the cost of delivering this intelligence. So as more individual users, you know, want it, that costs money to serve up that intelligence in every chat That happens, especially as you get into video and image and reasoning, which draws on more compute [00:13:00] than a standard text chat.
[00:13:01] And then the revenue is coming obviously from the business user side. So to go from 3 million in June to 5 million, here we are in August, like. Those are crazy numbers, and that’s just probably the surface of where they’re going, in terms of enterprise adoption. And so that’s where the revenue’s gonna come from.
[00:13:20] But, you know, I don’t think that they expect to be profitable anytime, yeah. Soon or this decade. Like they’re not, that’s not the goal right now. It’s to stay ahead of the cash burn and sort of hit escape velocity when it comes to, especially the business user side of things. So, you know, the other thing is GPT-5 does appear to be imminent.
[00:13:39] We, we don’t know, like there’s been rumors that actually might come out today, August 4th. There’s been other things I’ve seen online that say it, you know, could be later this week. But it does seem as though like we’re entering this phase. Sam Altman tweeted a couple days ago, we have a ton of stuff to launch over the next couple of months, new models, products, features, and more.
[00:13:58] Please bear with us through some [00:14:00] probable hiccups and capacity crunches, although it may be slightly choppy. We think you’ll really love what we’ve created for you. So lots more to come and then. Sam is just like flaunting the fact that he has GT five, like he’s not hiding it at all. Mm-hmm. He talked about on the podcast with the Theo Vaughn podcast, I think last week we mentioned, then he did a tweet.
[00:14:19] I think this was on Saturday or Sunday. He said, Pantheon is such a good show. A, a user replied, did GPT-5 recommend this? And Sam says, turns out, yes, with a screenshot of GT five as the model chosen. Now this was a really interesting tweet ’cause I didn’t know what Pantheon was, and I thought he was just doing like a cutesy thing, like they’d named their next model Pantheon and he was just like, you know, doing what Sam does and having some fun with it.
[00:14:43] So then I went and did like, well what is Pantheon? And, I don’t know if you’re familiar with this, Mike, but it’s a new Netflix show. So Pantheon is an American, this is straight from Wikipedia, by the way. because the description on Netflix was like 10 words. so Pantheon is an American adult animated science fiction [00:15:00] drama television series, based on a series of short stories by Ken Li.
[00:15:05] Set. In a world where mind uploading technology is on the verge of mass adoption, it follows a disparate trio of protagonists. Maddie Kim, a grieving teenager whose father was uploaded without her knowledge. Caspian Keys, a gifted teen unknowingly raised in a constructed environment and invented Shada. A brilliant computer engineer uploaded against his will as they placed themselves at the center of a global conspiracy.
[00:15:30] They also deal with societal consequences and existential crises brought forth by rapidly evolving technology. The series has received praise from critics, particularly for its animation, voice acting, emotional and philosophical, death and portrayal of the singularity. Amen. I watched, dude, I watched the first, it’s like a minute and a half, two minute trailer on Netflix.
[00:15:55] Oh my God. Like. I dystopian probably, but [00:16:00] like just chills. Like, I was like, oh no. Like I don’t, I don’t know if I’m ready to watch this. So Sam doesn’t tweet stuff like this by accident. This is like, you know, preludes too. He is obviously become obsessed with the singularity and super intelligence, but yeah, so apparently it was a 2022 like a MC show that got dropped and then it was picked up by Amazon Prime video and then dropped from there.
[00:16:23] And now it’s like got new life on Netflix. So I will, I will be watching, I will tune in, but it’s you know, I don’t think it’s a coincidence that he’s sharing tweets about things related to the uploading of intelligence and singularity and stuff.
[00:16:38] Mike Kaput: Yeah, it sounds like I’m going to watch this. Love it.
[00:16:40] And then immediately lose sleep over it.
[00:16:43] Paul Roetzer: No, I might lose sleep over the trailer. It’s, it’s you. You’ll go watch it. It’s you like the trailer itself is like, whoa.
[00:16:52] Microsoft and OpenAI Near Contract Agreement
[00:16:52] Mike Kaput: All right. Next up some more OpenAI’s related news. Microsoft and OpenAI’s we’ve been talking about are deep in talks to [00:17:00] rewrite the terms of their relationship, and a lot of it comes down to AGI because right now Microsoft’s $13.75 billion deal with OpenAI’s gives it access to OpenAI’s models until 2030 or until OpenAI’s declares it has reached AGI.
[00:17:17] Artificial general intelligence, this milestone is very vaguely defined as maybe AI that outperforms humans at most economically valuable work. Now, if that happens, if there is some agreement that AGI has been reached, Microsoft could lose access to OpenAI’s technology. Now, this becomes a problem for Microsoft because they’ve built copilot Azure.
[00:17:39] GitHub and much of their AI strategy around OpenAI’s models. So currently the two are working on a new deal, one that would let Microsoft keep using the tech even after AGI is declared, while also negotiating a potential equity stake in the reportedly low to mid 30% range. [00:18:00] However, there is plenty of friction and details to work out here.
[00:18:03] OpenAI’s wants more revenue, looser constraints on who it can sell to, and stricter guardrails on how Microsoft deploys its models. Microsoft meanwhile has blocked some of OpenAI’s acquisitions and may not be afraid to walk away if the terms don’t work. So Paul, I mean, we’ve been following this back and forth for some while now, like, seems like there’s plenty to work out, but it does actually seem, based on this newer information, there’s some movement happening.
[00:18:31] So do you think they’re going to work this out in any timely fashion?
[00:18:36] Paul Roetzer: I don’t know. I mean, I th. Without being in the room, obviously, and only being able to read whatever, you know, the information or BBO Bloomberg gets access to, you, you have to assume they eventually just find a way to, to get this deal done.
[00:18:51] Like both sides need this deal. Yeah. but there’s so many complexities here. There’s the definitions of AGI and who gets to decide when it’s been [00:19:00] reached. There’s Microsoft’s access to OpenAI’s models. There’s the fact that OpenAI’s compute needs are what’s driving largely Azure’s rocket growth. Like the growth of the cloud computing business for Microsoft.
[00:19:12] Billions of dollars per quarter probably being spent with Microsoft. There’s Microsoft’s own AI ambitions under Satya and Mustafa Solomon, who’s the CEO of Microsoft ai, former Google DeepMind co-founder and inflection AI founder competition for business customers. They’re increasingly coming up against each other and selling against each other.
[00:19:33] Opening eyes, desire for more compute beyond what Microsoft can or is willing to provide, which means they’re having to go to people like Google and Oracle, opening Eyes, IPO desires the their need to change their business structure, opening eyes funding, which is dependent upon them changing their business structure.
[00:19:49] So like there’s all these things that we’ve talked about over the last year and a half on the podcast. It’s not an easy deal to get done there. There’s lots of variables here. And then the other thing to throw into the [00:20:00] mix here, Mike, is I was listening to this podcast last week. I never actually listened to this podcast before.
[00:20:05] It’s, Lenny’s podcast. It’s called Lenny Ky. I dunno if you’ve ever listened to this one before. Yeah. But he had Benjamin Mannon, who’s a co-founder in ai safety researcher in Anthropic. He started Google DeepMind. but he was talking about the economic Turing test. And I think he might be the guy who kind of coined this, like the way I’ve seen it now.
[00:20:25] Talked about on a couple, he was a no priors podcast. He talked about this as well. And I don’t know that he gave attribution to someone else. So if someone else came up with this concept, you know, we will, we will mention them in a future episode, but he’s the guy talking about it right now. So his idea of the economic Turing test is sort of like everyone’s trying to figure out how do we define AGI?
[00:20:43] And they’re basically trying to move the goalpost and say, well, let’s just like not even try and do that. Let’s just set an economic Turing test. Mm-hmm. So the Turing test being, does the human know if they’re interacting with a machine or not going back like 70 years? And that’s sort of been achieved, like we’ve passed that.[00:21:00]
[00:21:00] So what they’re saying, the economic Turing test is, this is how you described it, said it’s this idea that if you contract an AI agent for a month or three months on a particular job, if you decide to hire that agent and it turns out to be a machine rather than a person, then it’s passed the economic Turing test for that role.
[00:21:18] So what he’s saying is, like a human hires someone virtually, they don’t, they don’t know if it’s a human or a machine that. Human or machine does work for a month or three months on a particular job. And then the human hi, the person doing the hiring has to decide, am I gonna hire, you know, professional A, professional B, and then unbeknownst to them, they choose to hire the agent over the human.
[00:21:41] And so that’s what they’re saying. The economic turning test is, is where human doesn’t know that it’s an agent doing the work. And he said, and if the agent can pass the economic Turing test for like 50% of money weighted jobs, then we have transformative ai. So he’s saying as an economy, once we get to the point where a [00:22:00] human in a blind taste test basically chooses the agent over the human worker, 50% or more of the time for like 50% of jobs than we have entered this age where it is inescapable, like we are in true transformation of the economy, 10% plus GBP growth every year.
[00:22:16] And he talks about this as 2027, 2028. Hmm. And Anthropic tends to usually be more conservative than others. You know how all that kind of stuff factors in I’m, whether this is part of the negotiations with Microsoft and OpenAI’s, I don’t know, but it seems like this sort of more concrete quantitative test to say, Hey, we’re past AGI.
[00:22:39] This is what’s missing from the AGI conversation, basically. So it’s, it is just interesting to note. And it’s a really, really good episode. Like I will put the link in the show notes. It’s, it’s one of those I’ll probably listen to multiple times and take notes on next time.
[00:22:52] Mike Kaput: Well, as a funny corollary to that, that’s probably happening in some way with humans getting jobs they’re not remotely qualified [00:23:00] for, because they’re using ai Yeah.
[00:23:02] To ga to game hiring like we’ve talked
[00:23:04] Paul Roetzer: for Sure. And probably like five or in sites like that where you have humans that are applying for jobs, getting jobs. Mm-hmm. And then they’re letting the agent do the work. Exactly. Right. And like the person hiring them has no idea that that’s what’s happening. I can almost guarantee you that’s happening a ton.
[00:23:18] And people are making a lot of money, having agents doing most of the.
[00:23:23] ChatGPT Study Mode
[00:23:23] Mike Kaput: All right. Next up, OpenAI’s has just rolled out a new study mode in ChatGPT. So instead of just solving a problem or answering your question, study mode will walk users through concepts step by step, using prompts, hints, and checks for understanding to guide the process.
[00:23:40] So this is meant to help students specifically actually learn instead of just getting a quick answer to an assignment or a question. And this actually leans on insights from teachers and learning scientists. It uses techniques like Socratic questioning, cognitive scaffolding, and metacognitive prompts.
[00:23:59] And [00:24:00] these are all tailored to the student’s level and memory from past chats. It even includes quizzes and open-ended questions to help ideas stick in some early feedback reported by OpenAI’s students described it as, quote, a live 24 7 all-knowing office hours, and some praised it for breaking down tough topics into something they could finally grasp.
[00:24:22] Study mode is free for all logged in users. It can be toggled on and off during chats. And for now it’s basically just powered by custom instructions layered on top of ChatGPT. But OpenAI says the long-term plan is to bake these types of behaviors directly into its models. So Paul, I thought this was really great to see.
[00:24:41] I mean, just based on my own usage and how I get value out of AI models, I’ve been so bullish on AI for real personalized one-to-one learning. Like the amount of things I’ve just been able to learn, even hacking together my own prompts has been incredible.
[00:24:54] Paul Roetzer: Yeah, this is a, a cool advancement. the post they put up did say that it was [00:25:00] created with college students in mind.
[00:25:01] Yeah. I’d be interested to see if college students actually use it in this way. I know some college students who, you know, don’t really want it to function as an advisor. They just want it to do the work. So they did say in their post also was a first step in a longer journey to improving learning and chat.
[00:25:15] GPT it is currently powered by custom system instructions, meaning they didn’t change the model at all. Like the underlying model is still the same. They’re just giving it specific instructions. but they said that, you know, if this works, and once they make improvements, they plan on training this behavior directly into their main models.
[00:25:33] They’re also exploring functionality to make study mode more engaging and helpful, such as clear visualizations for complex or text heavy concepts, goal setting for progress tracking across conversations and deeper personalization tailored to each student’s skill level. So this is great and I I do think that there’s, you know, two, three years out, this is just how you learn.
[00:25:55] Like, I think it’ll be adopted pretty quickly. Interestingly, Mike, I was [00:26:00] actually working on, and you were talking, you and I were talking about this right before we got on today. so my daughter’s 13 and she’s, a very gifted artist, like, you know, illustration and painting and things like that.
[00:26:11] She gets that from my wife, but she’s taken a keen interest in creative writing. And so I’ve been working with her on kind of teaching her how to use ChatGPT, but for the, this exact way it’s like, Hey, don’t have it right for you. Like talk to it. Like, Hey, I want you to help me. I want to give you my writing.
[00:26:29] I want you to like, tell me how to improve it. Don’t just rewrite it. Like, tell me what you’re doing. And so I’ve been trying to figure out a way to like do this without her unintentionally using it as a crutch to learn the process herself. And so, ironically, I was, yesterday actually going through, it’s like, how do I build like AGI PT for her that functions in this way that doesn’t do the work for her, but actually helps her be a creative writer?
[00:26:52] Because I’ve, I mean, I’ve written three books. I write for a living, but I’m not a, I don’t write fiction. Like, I don’t really know the process of writing great fiction. [00:27:00] So I went into, study mode this morning and I said, I’d like to create a AGI PT using study mode to help my teenage daughter develop her writing skills.
[00:27:08] How do I get started? And it said, Hey, this is a great idea. Study mode can make learning fun, personalize, and effective. And it said, you go through and define the role and tone of the GPT, you know, guide, not give answers, use questions and prompts and feedback to build skills, match your daughter’s age and personality.
[00:27:23] And then it gave me a sample. It’s like you’re a creative writing coach for a teenage girl. Your tone is warm, encouraging, and curious. You follow study mode rules, meaning you guide, ask questions, and help her build her writing, voice and storytelling skills through prompts, feedback, and gentle challenges.
[00:27:37] You never write for her. You help her express her ideas more clearly. So this is. This is interesting, like this is the direction I’m really excited. But then it actually said like in when you’re building your GPT in the capability section, choose study mode and then this will force the GPT to do it. And I was like, oh my God, did they build this into gpt?
[00:27:57] And so I went and checked. That is not actually [00:28:00] possible, but that is like if they go there, which I assume they have to like, this is a no brainer to make this work. So right now, if you go into ChatGPT, you pick study mode in a normal conversation, but you can’t set that as the default. But if you can build GPTs where you can choose study mode as a capability, now I can build that creative writing assistant for my daughter.
[00:28:21] Yeah. And know it’s gonna follow study mode, or teachers can build GPTs for their class and set study mode as the default capability. So I assume opening eye is going to do that. and it’s on the roadmap because that, it seems like an obvious thing. And I think this is great. Like I would. I would build GPTs for my kids all day long if I could set ’em in study mode.
[00:28:42] How We Talk About AI’s Impact on Jobs
[00:28:42] Mike Kaput: Yeah, for sure. It seems definitely something to keep an eye on. Yeah. Next up. According to some new reporting in the Wall Street Journal and Gizmodo, we are seeing the trend further cemented that we’ve been tracking for a while now. They are talking about how CEOs are not only saying the quiet part out [00:29:00] loud when it comes to AI’s impact on jobs, but in some cases they increasingly seem to be referencing cutting headcount thanks to AI efficiency gains as basically just a badge of honor.
[00:29:12] Executives are openly celebrating smaller workforces as signs of efficiency, technological progress, and investor discipline. For instance, they mentioned the example of Wells Fargo, whose CEO told investors. The bank has shrunk for 20 straight quarters, down 23% since 2019. Verizon says its workforce is going down all the time.
[00:29:32] And Bank of America, which once had 300,000 employees, is now sitting closer to 212,000. It sounds like from some of these examples and people they talk to, these aren’t mass layoffs in the traditional sense, but a lot of times companies are letting attrition do the work. They’re leaving roles unfilled, combining responsibilities or automating tasks, and AI is often the rationale here.
[00:29:56] So at Bank of America, ai now reconciles trades [00:30:00] summarizes client info and writes code, which reduces the need for new hires. And one AI consultant who spoke to Gizmoto just kind of had a really money quote here, whether you love it or hate it, and it says, he said quote, CEOs are extremely excited about the opportunities that AI brings.
[00:30:17] As a CEO myself, I can tell you I’m extremely excited about it. I’ve laid off employees myself because of ai. AI doesn’t go on strike. It doesn’t ask for a pay raise. These things that you don’t have to deal with as a CEO. Now, Paul, this kind of reminded me a little bit of maybe the darker side of AI adoption that you’ve mentioned in the past.
[00:30:35] You know, companies may get only focused on shortsighted, near term efficiency gains and just like go use AI to chop headcount as much as possible and not really consider the full consequences of something like that. Is that what’s happening here?
[00:30:53] Paul Roetzer: Yeah, I mean, I think there’ll be times where there’s some pullback and they realize they probably went too far.
[00:30:58] But it’s, it’s just [00:31:00] amazing to me how quickly this has gone from no one talking about this to everyone, right. Accepting this as the norm. I mean, I don’t know what episode it was, but I remember vividly like saying, I don’t get why this isn’t a conversation. Like this is the inevitable outcome. And at the, it was probably like early this year, like 2025 or end of 2024.
[00:31:20] Yeah. where, where we’re just like pleading with people to like accept that this is gonna happen. Why isn’t anyone saying anything? And because we were hearing it in conversations with executives, so it was, it was always inevitable that as c-suites, boards and investors increased their awareness and understanding of ai, that, that we would have layoffs as a result of it, or reductions in hiring.
[00:31:42] The thing I I would caution though is we’re still very early the, but the financial pressures for publicly traded companies, VC backed companies, private equity owned companies to reduce human labor costs is going to grow. Like AI agents aren’t even reliable yet. and we’re already seeing CEOs straight up [00:32:00] saying, yeah, we’re gonna cut workforce 10% because aIt’s like, it’s not even that good yet.
[00:32:05] So as it becomes more reliable in the, you know, the next 6, 12, 18 months, you know, go to that economic turning test idea as we, we enter these phases where it’s actually reliable. Like this isn’t gonna slow down. Like we’re, we’re going to see continued disruption of this, and I think we’re gonna go through a very challenging period.
[00:32:25] For jobs in an extended transition period for the workforce. I would expect this to pick up steam in 2026. I, as I said before, I think this is gonna be a major, topic of discussion and sticking point in the midterm elections in the United States starting in spring of 2026. we saw it just last week in America.
[00:32:44] The jobs reports wasn’t, what the current administration wanted to see, so they fired the person in charge of publishing the data. So I think it’s just kind of inevitable that this is gonna happen and I, again, I don’t want [00:33:00] to be like the, the bearer of bad news, but I think a full blown economic and societal crisis around this, I don’t wanna say like, is totally inevitable, but I It’s greater than a 50% probability by 2027, 2028.
[00:33:15] And it’s because when you go listen to the heads of all these AI labs who all believe this, they all think that this is gonna happen. None of them have a solution. Like they’re all starting to look at it and research it. we will talk about the Microsoft paper next. So they’re looking at it, but nobody has a plan.
[00:33:32] And that’s the thing that worries me, is like, I think they all now realize what’s gonna happen and they don’t have a plan, so they can’t really talk transparently about it. but yeah, I, this is why I always said like, I just didn’t comprehend why people were ignoring this. And I think it just came, came down to they, they didn’t know it was gonna happen yet, and now they do.
[00:33:57] And so now we get earnings reports where they talk about how many [00:34:00] people they get rid of and how efficient they can become because of ai.
[00:34:03] Mike Kaput: And I don’t wanna be a dor here as well, but I had to just check this again because you know, I think people sometimes lack perspective on some past. Periods in history that we are starting to, you know, make analogies to, right?
[00:34:18] Like, I think we all think like, okay, the Great Depression, which none of us live through, we’re thinking like, oh my God, nobody has a job. It’s the worst thing ever. It’s such a big anomaly. The unemployment rate during the Great Depression in the United States peaked at 25%. Is that right? That’s not that high.
[00:34:33] I mean, that’s huge and has huge effects. But that’s one in four people, right?
[00:34:36] Paul Roetzer: So what, like three or 4%
[00:34:38] Mike Kaput: now? Something like that? Yeah, I think it’s like three or 4%. So just to show, I mean, that’s an enormous number relative. You’d have to, you know, what, multiply eight x the amount of unemployment. So I get that that’s a huge and anomalous event, but it doesn’t have to be like nine outta 10 jobs go away due to AI for there to be a massive crisis.
[00:34:57] Like the one you’re talking,
[00:34:58] Paul Roetzer: I, any, anything [00:35:00] touching 10% is, is like crazy. Yeah. and so I don’t, again, I’m, I am not an economist. I don’t know where the break points are. I like, I don’t know. When we get, all I know is like family, friends, things you hear just in conversations. It’s different right now.
[00:35:19] Like I do with the job numbers wrong, right? All I know is it starting to feel pretty different. And when I talk to people who are unemployed or underemployed, the prospects of jobs just don’t seem to be what they were before. And I don’t know, like It’s just a really, really important conversation and, it’s something we need to be following and closely on this podcast, but like, you all need to be thinking about this in your own companies and starting to kind of try and look out six to 12 months and see what the impact in your company and your industry is gonna be.
[00:35:56] ’cause it’s gonna be uneven. Like this isn’t gonna happen everywhere, all [00:36:00] at once. But I can, I can just start to like sense that it. Once the C-suite and board and investors get it, which they seem to be getting it now, mm, then the dominoes start falling way faster than they have the last two years.
[00:36:16] Microsoft Paper on AI Jobs Impact
[00:36:16] Mike Kaput: So related to this, we actually also got pretty big new study from Microsoft that was analyzing how exposed jobs are to ai.
[00:36:26] And what they did is they actually analyzed 200,000 real world interactions with Microsoft copilot. And the researchers dug into what people actually do with generative AI at work, what the AI does in return, and which jobs that touches the most. And the results they found are People in this study are using AI most often for gathering information, writing, explaining things.
[00:36:49] In other words, classic knowledge work. And AI for its part tends to act more like a coach or assistant rather than, at this time, a full replacement. in [00:37:00] fact, they found 40% of the conversations they studied showed no overlap between the user’s task and what the AI actually did. And what they did is they looked at all these different occupations and how the highest and lowest ones exposed to what AI was being used for.
[00:37:15] And some of the top affected jobs, according to their methodology, include interpreters and translators, sales reps, writers and authors, customer service reps, news analysts slash reporters slash journalists and editors. And Paul, you know, I mean, it’s always good to see research like this. I like they’re taking data, they take data right from actual usage of co-pilot and they like that.
[00:37:37] They share in the methodology. They also align that data with occupation and work activity info from O net, which is this public database of occupational data. And that actually kind of mirrors. Some of the ways we actually think about this stuff when we’re building courses and when we’re doing kind of some of our work.
[00:37:55] Trying to look at that, looking at Bureau of Labor Statistics and kind of total addressable [00:38:00] markets there as well. The one thing is this data was gathered January 1st, 2024 to September 30th, 2024. So it kind of falls into that same thing we always kind of complain about, which is the data is not exactly new.
[00:38:12] What
[00:38:13] Paul Roetzer: good is this? Yeah,
[00:38:13] Mike Kaput: right, right. Though I do like it’s based on how people are actually using one model at least, though again, it’s not gonna be the most powerful model that we have today. So,
[00:38:21] Paul Roetzer: yeah, so those are great points. You know, we’ve talked about, Anthropic has done some work in 2025 with similar approach where they’re looking at actual usage.
[00:38:30] Problem with Anthropics data is, it’s predominantly used for coding, right? So you don’t really get this great subset of data. Microsoft Bing is obviously a, a more broad data set, 200,000 anonymized users. That’s that’s great. The fact that it was June to September, 2024, we didn’t have reasoning models yet.
[00:38:47] So oh one wasn’t introduced until September, 2024. Mm-hmm. And we know that the vast majority of people still don’t even know what reasoning models are or how to use them. And that’s the most disruptive technology potentially [00:39:00] to, to high level knowledge work. So this is great. We, we need more study, we need more real time data like this.
[00:39:07] Like ideally, we would see more data like this from Google and OpenAI’s. it cannot be year old data. Like we, we have to get this stuff in more real time. the, this is why I created the I Exposure key. So when I created Jobs, GPT, which we will put a link to, and if you, if you haven’t seen it or used it before, so Jobs, GPT, is a custom ChatGPT, that was meant to enable people to do impact assessments on jobs.
[00:39:35] It actually trained it on the O Net database. So in essence, what happens is. You can take any jobs if you, if you’ve never used the on net database, it’s great. Like you can go up there, there’s like seven or seven to 900 jobs occupations in there, and it’ll give you the standard tasks of every job. And so the whole concept behind Jobs GPT is to break a job into a bundle of tasks and then do an assessment of how that job will change [00:40:00] as the models get smarter and more generally capable.
[00:40:02] So what jobs GPT does is, is it doesn’t just look at what current models can do, which is what the Microsoft Study is looking at. It’s like how are people who probably aren’t even trained to use Gen AI using gen AI with today’s models? That’s, that’s good. What we actually need though, to figure out the impact this is gonna have on the workforce in the next 18 months is we need to project out where are these models going and how could different knowledge workers use these models once we get there.
[00:40:32] So the AI exposure key, I created. Looks at as image, as video, as audio, as voice, as advanced reasoning, as persuasion as ai agentic capabilities, as ai vision, as all of these abilities are built into these models and become more reliable than what happens to writers and attorneys and consultants. So yeah, it’s important data, but again, you always have to look through the lens of when [00:41:00] was it taken, who were the people that did it, and are they considering future known improvements to these models?
[00:41:07] Like we know what’s gonna happen in the next 18 months, roughly. So I would say like play around with jobs. GPTI actually built a capability in a couple months ago that lets you future cast any job or college major and so you can put it in there and it considers that exposure key against that job.
[00:41:23] Mike Kaput: And somewhat related to this.
[00:41:24] AI’s Impact on the Consulting Industry
[00:41:24] Mike Kaput: We also got a report in the Wall Street Journal talking about how McKinsey, the Consulting Giant, is kind of facing a bit of an existential crisis because AI can do much of its work faster and cheaper. And that reality and says the journal quote is pushing the firm to rewire its business. So they have quietly deployed over 12,000 AI agents that write in McKinsey’s signature tone, draft presentation, summarize interviews, and even check the logic of arguments that their consultants are making.
[00:41:56] And the firm’s global managing partner told the journal [00:42:00] the goal is one agent per employee in the not so distant future. Now meanwhile, since 2023, its head count has dropped by about 5,000 people. Now what’s interesting here is kind of how the math has changed, because traditionally, McKinsey built teams of about 15 consultants per project, and they were aiming to like any service business charge based on the scope and duration of that project.
[00:42:25] But AI is both speeding up the consulting work and means that fewer people are needed to work on each project. So today McKinsey puts three consultants on the same project. He used to use 15 for plus bots. One consulting industry. Insider even told the journal that junior employees will likely be most affected immediately in consulting by these kinds of factors and that you can expect slimmer staffing to ripple through the entire consulting industry.
[00:42:51] Food chain. The insider said quote, you have to change the business model. You have to make a dramatic change. Now, Paul, this definitely [00:43:00] seems to align with, I mean, what we’ve seen, we have a huge professional services audience. You know, I mean, I was reading through this, just nodding and like, and saying out loud.
[00:43:08] Yes. Because it’s literally, it just proved I was on the right track with the AI for Professional Services course I’m doing because it literally touches on these factors. These are the structural issues that are sending the consulting industry and professional services at large towards a cliff. The fan needs to navigate, you know?
[00:43:26] So can you kind of unpack for us, like what’s going on here? What do consulting firms or other service firms need to be thinking about
[00:43:34] Paul Roetzer: it? It is tough. Like, so I’ve said this before and we’ve talked about, you know, consulting firms and agencies. It’s a great time to be an AI native consulting slash professional services firm.
[00:43:45] So if you’re building one from the ground up and you can start fresh with a, you know, a, a pricing model and a talent structure and a service mix that’s adapted to, you know, where the market’s at today and you can build from there. It’s great. You can build a more dynamic, more [00:44:00] efficient one, you know, fewer people, more revenue, and you can, you can be adaptive to the market much easier.
[00:44:07] It’s a very difficult time to transform an existing one into an AI emergent firm. I owned a marketing agency and consulting firm for 16 years. Sold it in 2021. Mike worked with me there as a senior consultant and leader for nine of those years. So this is our world. Like we, we lived this for a long time.
[00:44:26] the economics of that model are being reinvented. There’s no obvious answer to what that’s gonna look like. The impacts on staffing and compensation models for your staff. Like there was pretty standard ways you determined how much you could pay somebody based on what their billing rate was and how many hours per year they would do and things like that.
[00:44:45] That’s being kind of, tossed upside on its head. service demand is shifting faster than ever before, and the AI models are advancing like we just talked about. If you built your service mix, not knowing that AI models could do [00:45:00] reasoning tasks, and then all of a sudden they can do reasoning tasks pretty reliably, what does that do to your service mix?
[00:45:07] And so as your clients become more educated on these capabilities, their expectations of what you’re gonna deliver to them and at what price you’re gonna deliver to them as is changing every day. And that’s a really hard environment to manage a firm of that size with all that legacy stuff. And all the people who built their careers making a million dollars a year or something close to that who may be that expertise isn’t as valued anymore.
[00:45:37] And like, they don’t want AI to come in and do their job, they don’t think it’s capable of that. There’s just gonna be a, a tremendous amount of friction. And I’m, I’m sympathetic to it. Like It’s hard. There’s gonna be a lot of change within the professional service world in the, in the next, like one to three years.
[00:45:56] A lot of turnover of top firms that, you know, kind [00:46:00] of get disrupted by AI native upstarts. I think it’s a massive opportunity. And if you’re at one of those big firms, like, I mean, I don’t wanna provide career advice to people per se, but like, there’s never been a better time to do your own thing.
[00:46:13] Like, I believe that for a lot of people, that’s gonna be the path is to like, start fresh, you know, be dynamic, be nimble, and building a, a, a, a more dynamic model. That being said, listen, there’s great people working at these firms and there’s great leaders at these firms. I’m just saying it’s gonna be hard for them to solve this, but, you know, this is why you get paid the money as a leader to, to, to kind of write that ship.
[00:46:38] We’re gonna talk about Apple next, like right, Tim Cook’s going through this right now. it’s hard to be the leader of a big established company, not just in consulting, in, in any firm, in, in any professional service firm, in any business. It’s a very, very disruptive phase, and there’s very little historical precedent that leaders can look back to that’s gonna get them through the next few years.
[00:46:59] It’s, it’s [00:47:00] unprecedented.
[00:47:01] Apple AI Acquisition Speculation
[00:47:01] Mike Kaput: All right, so let’s talk about Apple because some speculation is heating up that Apple may be getting serious about ai. So on the heels of a $94 billion quarter, CEO, Tim Cook said Apple is open to AI acquisitions and is reallocating a fair number of people internally to focus on AI features.
[00:47:21] Now this obviously makes a bit of sense because we’ve talked about Apple’s. Siri revamps behind schedule Meta is poaching talent like nobody’s business. apple has reportedly considered deals with OpenAI’s and Anthropic. They’ve floated acquiring perplexity behind the scenes. They reshuffled their leadership this spring, which we’ve talked about.
[00:47:43] They moved Vision Pro Head Mike Rockwell to lead Syrian AI efforts. And Siri is still struggling with some AI issues. However, Apple’s fundamentals remain strong. we will talk a bit more about their earnings in a sec, but their iPhone sales are up. Services revenue hit 27.4 [00:48:00] billion. There’s a new iPhone on its way this fall.
[00:48:02] So like Paul, we’ve been talking about Apple’s need to catch up here for some time. Is an acquisition or acquihire of a leading AI company the answer? Is it partnership with one of them? How does this play out?
[00:48:15] Paul Roetzer: I don’t know that it solves it. I think I mentioned this on episode 1 59. Maybe like the more I was thinking about it, If they go, I think they will do acquisitions, but I I just wonder like, will those people stay there?
[00:48:26] Like, right. so I don’t know, like it is such a fascinating case study here because they, they haven’t really been penalized from a market cap perspective for largely sitting on the sidelines since November, 2022 when ChatGPT hit like Apple Intelligence is useless largely. They have incredibly failed at making siriany better.
[00:48:49] Like, and yet they, they’re crushing it. And so it just goes to show the strength of their brand and distribution and the quality of their products. I feel like they have like one more [00:49:00] chance. It’s like the market has given them one more chance to figure this out. in, in the all hands meeting that Tim Cook called, which is an unusual thing for him to do, he, Bloomberg says the executive gathered staff at Apple’s on Campus Auditorium Friday in Cupertinos.
[00:49:14] This was last Friday, telling them that the AI revolution is as big or bigger, quote unquote as the internet smartphones, cloud computing and apps. Quote, apple must do this. Apple will do this. This is sort of ours to grab. Cook told employees we will make the investments to do it. He then went on to say, we’ve rarely been first there was a PC before the Mac, there was a smartphone before the iPhone.
[00:49:38] There were many tablets. Before the iPad, there was an MP play, MP3 player before iPods. I’m, I’m laughing. Like, I’m wondering how many, like, of our younger listeners don’t, never had an MP3 player. Didn’t know there were tablets for that, but I think Apple invented all these categories. They didn’t.
[00:49:55] but he said Apple invented the modern versions of those product [00:50:00] categories. This is how I feel about AI That went on to say echoing comments he made during the earnings call, cook told employees the company is investing in AI in a big way. He said 12,000 workers were hired in the last year with 40% of the new hires joining in research and development roles.
[00:50:16] So my feeling on Apple is they have the money. Do they have the culture and vision? Because many of the top AI researchers, they wanna work on AGI and super intelligent. They don’t wanna build consumer products or like make sirismarter. So can Apple do enough to attract and keep those people even if they hire or acquihire or straight up acquire.
[00:50:39] Can they keep those people there and compete with other AI labs? Yeah. but then the question falls back to like, well, do they need to do, do they just need a few hundred people who aren’t the top, you know, billion dollar researchers that everybody else is fighting over? Do they just need great AI researchers to execute because they have massive distribution through all their, you know, Mac and [00:51:00] iPhones and iPads and Vision Pro and all these other, you know, devices.
[00:51:04] So, I don’t know, like I, yeah. Again, I’m not gonna give investing advice here, but I’m like, so I don’t know, A Apple’s a really fascinating play for the next few years in what they do with ai.
[00:51:15] Mike Kaput: Right. And there needs to be some movement here, though, I think in a positive direction. Yeah. They, they’ve gotta
[00:51:19] Paul Roetzer: get this, like, I feel like they’re gonna really, by 2026, they’re gonna have some problems if they haven’t nailed this.
[00:51:26] Earnings Reports
[00:51:26] Mike Kaput: All right, next up we got a bunch of quarterly earnings coming out from some of the big leaders in ai. So Paul, I’m gonna go through just a handful of these and then we can kind of talk about these in aggregate. Or if there’s anything that jumps out while I go, feel free to stop me. But first up, Google, alphabet.
[00:51:43] Their parent company, posted some strong earnings, but there are some complications here. So their parent company saw revenue jump 14% last quarter to over 96 billion and profits were up even more. Sundar Pcha, CEO, credited AI for driving strong momentum [00:52:00] across the business, but there’s still like a much bigger question.
[00:52:03] Can Google stay on top? As AI reshape search, the company is apparently all in on AI mode, but they now expect to spend 85 billion this year on CapEx, especially around ai. 10 billion more than planned for now. Investors seem cautiously optimistic. Their shares have recovered from some earlier dips.
[00:52:25] Microsoft also just posted some jaw dropping numbers that show its AI BET is paying off big time. They reported 27.2 billion in profit last quarter, up 24% year over year revenue hit se 76.4 billion. Also beating expectations and basically AI and the cloud are driving this growth. They poured 88 billion into new data centers this year to keep up with surging demand from AI services services, especially through the OpenAI’s partnership.
[00:52:55] Azure, the cloud platform brought in $75 billion over the [00:53:00] last year and despite already being massive Azure’s growth rate jumped from 26% to 39%. Meta also posted a blowout quarter revenue of 47.5 billion. They beat expectations. 3.848 billion users across meta apps. And Zuckerberg, which we will talk about in a little bit here, is also saying that meta is all in on building what he calls personal super intelligence.
[00:53:25] At the same time though, they are seeing a lot of CapEx. They’re reality labs, they’re kind of vr, ar, augmented reality as a multi-billion dollar loss. but Wall Street loves the direction they sent the shares up by more than 10%. And then Apple, like we just talked about, had its strongest quartering years.
[00:53:43] There’s a surge of iPhone sales revenue, Roetzer 10% year over year. That’s the biggest jump since 2021. That’s thanks largely to the iPhone 16. Their Mac line of pedestal quarter, their services is now $27 billion a quarter business, which [00:54:00] Roetzer 13%. cook obviously said the company is significantly growing its AI investments and wants to acquire to accelerate that roadmap.
[00:54:08] And sounds like they’ve got the money to do it like we just talked about. So Paul, I am by no means an expert investment analyst, but if I had to boil down these trends we’re seeing this quarter, it seems like if anyone was worried these AI bets wouldn’t pay off, they shouldn’t be worried because they seems like investors are rewarding the AI bets, even though these companies are doubling down on these really large CapEx expenditures.
[00:54:35] Paul Roetzer: Yeah, and just so, CapEx, we, we use that term a lot and every once in a while, like, good to stop and explain what, what it means. So capital expenditures, it’s a big thing that Wall Street looks at in these earnings reports because what it’s doing is it’s referring to funds that a company uses to acquire, upgrade, or maintain long term assets that are expected to provide benefits for more than a year.
[00:54:56] So it’s like forward looking stuff. So if they continue our [00:55:00] to invest in cloud infrastructure, data centers, research and development acquisitions, these are things that they’re like longer horizon. And so if they, if the companies like Google and Amazon and Microsoft and Meta are increasing their CapEx number, that means they’re continuing to see value in these long-term AI plays.
[00:55:20] That’s like a synopsis of what it is. So every quarter investors are kind of holding their breath to see, is this a short-term AI bubble? Like is this just a frothy period where it’s gonna eventually like collapse and like all these investments aren’t really gonna deliver the kind of value that are expected?
[00:55:36] Or is it a long-term transformation of the economy with AI as the underlying, underlying operating system. So they watch for things like cloud computing, numbers, revenue growth, usage, data of ai, and these CapEx commitments this year and beyond. and they’re looking for guidance from these companies. So when they’re saying, Hey, we were already at 70 billion, we’re going to 80 billion this year, that’s a really good [00:56:00] sign for like the bulls, the long term AI bulls who think this is gonna keep going for the next five to 10 years.
[00:56:05] So. For me, like I bet everything personally and professionally. Back in 2016, that Wall Street was missing the big picture with AI that investors didn’t realize what would happen to the economy as AI progressed and was infused into every profession and industry. And I’m not, again, giving investing advice, but more commentary on the state of ai.
[00:56:26] So I think generally speaking, most investors and business leaders still don’t fully comprehend how early we are in this intelligent expo explosion and what the implications will be as we move into the age, age of AGI and beyond. Like we’ve been talking about all throughout this episode. I think there’s gonna be downturns.
[00:56:44] I think there’ll be doubts in the months and years ahead, and there will certainly be some friction and resistance as AI starts to have a greater impact on jobs. But the end game for these labs is omnipresent intelligence, like it’s infused into every piece of software we use, every device that we use.[00:57:00]
[00:57:00] And I think that we are still at the base of an exponential growth in consumption of energy compute from data centers and the underlying models that these companies are building and serving up. So I’m not saying there aren’t gonna be like quarters where earnings don’t meet expectations or where CapEx doesn’t like hit that number that Wall Street wants to see.
[00:57:21] But I think when you zoom out, we are still at the very base of this exponential that this is gonna, it’s, it’s hard to comprehend because the human mind thinks in linear paths. Like when, when we think about why didn’t people get it in 2016 when I was thinking this was obvious, why didn’t people get it 2022 when it seemed obvious?
[00:57:38] Like there’s been these moments where you look at it and it’s, it’s because it’s really hard for the human mind to think about something that looks totally different than what we see today and tomorrow. And when you look at the exponential though of the scaling laws that are driving all of this, and the seemingly insatiable desire for intelligence that consumers have.
[00:57:58] Yeah, those two [00:58:00] things kind of indicate we are, we are just at the base of this continuing to grow. So I don’t know. I like, like I talked about in the Road to AGI episode, there are obstacles, there’s things that could slow this down, but overall, I think we’re just at the start.
[00:58:16] Gemini 2.5 Deep Think
[00:58:16] Mike Kaput: So our next topic actually kinda looks at a bit like how quickly we are progressing here, because Google just launched what they call deep think inside the Gemini app.
[00:58:26] And this is a souped up version of its AI that is designed to reason more like a mathematician. It is actually based on a variant of the Gemini 2.5 model that recently hit gold medal performance at the International Math Olympiad. That version took hours to solve problems. This one’s much faster and aimed at real world use.
[00:58:45] It still hits bronze level performance on the same math benchmark. The trick here is something Google calls parallel thinking rather than sprinting to an answer. Deep think explores multiple ideas at once. Revises them and even combines them before [00:59:00] landing on the best solution. That makes it useful for way more than just math.
[00:59:05] Google says it shines at web design, scientific reasoning and algorithm development. Basically anything that requires building up ideas step-by-step. So this also tops a bunch of leading benchmarks for code generation and reasoning. The only catch here is right now you need to be a Google AI Ultra subscriber to use it in the app that is the ultra subscription that costs almost 250 bucks per.
[00:59:29] Now, Paul, I don’t know why I’m surprised, but it is pretty incredible to me to see we can, we literally went from talking about this on like experimental cutting edge model, winning the international math oad gold one week, like a week or two ago. Then we get consumer access to a version of it, literally a week or two later, even if it is hundreds of dollars per month.
[00:59:53] I find that incredible.
[00:59:55] Paul Roetzer: Yeah. And this is the sort of advancement that’s really gonna impact these high level knowledge work, [01:00:00] jobs and consulting firms like we just discussed. You know, if you think about it, one senior strategist or researcher with these advanced capabilities for two 50 a month, which is nothing, nothing.
[01:00:10] If you’re talking about like, if you know how to use them, we will be able to do the work of 10 or more people. Like, so if you’re a McKinsey firm and you have access to this kind of technology and you can highly train people how to use this stuff, or a law firm, or you know, a marketing agency or a business with a c you know, your C-suite to your director level, VP level, and you train them how to do this stuff, you’re talking about transformation of work.
[01:00:37] Like there’s no, there, there is no like one, two x thing, like this is 10 x stuff. Mm-hmm. And again, I just don’t think that most business leaders are even aware stuff like this is possible. Like they don’t really know how reasoning models work and how they can. Augment, or in some cases replace human labor.
[01:00:58] Now, it doesn’t solve for the verification [01:01:00] gaps, like the AI gaps we talk about like verification thinking and confidence. If we go back to episode 1 55 where we kind of, preview this idea of AI verification gaps, and I mentioned I built a whole course now on this in AI Academy, but you have the verification where someone still needs to validate the work that comes out of it.
[01:01:16] You have the thinking gap where someone’s gotta apply the critical thinking to it, and then the confidence gap of like, you actually have to understand the material to be able to present it and talk about it. But the AI labs are trying to solve for verification and thinking with other agents that are trained to do verification and thinking, like, and that’s kind of what Deep Think does.
[01:01:32] It’s this self-improving mechanism that checks its own work and verifies it and then creates a, you know, a, a, a more polished, finished product, I guess that then just needs the human to do some level of oversight. So, I don’t know. I mean, I, these are those little product announcements that, you know, OpenAI’s got something like this, you know, philanthropics got something like this.
[01:01:53] At six months from now, it’ll just be commonplace that you can use models like this. I don’t [01:02:00] know, like it, these are the kinds of things that are, I think, gonna end up being way more disruptive than most people realize in the moment.
[01:02:07] Mike Kaput: Yeah. And just one more note about the verification gap. Like it is a, we’ve talked about this, it’s a really, really good time to be an expert who has a lot of real world background and context.
[01:02:17] And I don’t know how long that’ll last, but I echo the advice you gave in the consulting industry that now is a good time to start something, whether you start something or not. If you’re in knowledge work and you are an expert that has all this domain expertise and background, don’t waste this moment because there is at least a gap here where you are very, very, very valuable.
[01:02:39] More so than before.
[01:02:40] Paul Roetzer: I agree. And I’ll, I mean, I’ll, I’ll think out out loud here, and maybe this is a little more proprietary information than I should probably be saying out loud, but So like Mike heads up our AI content studio within SmarterX, and it’s like an emerging area within the company that oversees the creation of all of the content, all the research, all the courses.
[01:02:57] And like I’m wondering Mike, like, [01:03:00] do, do we need like a AI verification team? Like is one of the things we build actually just a team of experts who verify the outputs within, you know, the research and things like that. Because you’re gonna have the high level experts, the lead researchers, the course instructors who need to have expertise in this need to do the deep thinking, need to have the confidence and the presentation of the material.
[01:03:21] But it’s possible you actually have a team of people whose job is largely to verify the outputs and work with the models and do some additional prompting. And so those are the kinds of things that I think about for like future org charts. And again, Mike and I’m literally, I’m thinking of this in real time, like we’ve never had this conversation.
[01:03:36] But yeah, that’s the kind of stuff I think people are gonna solve for now Is that needed five years from now? I don’t know, but Right. Like it’s certainly needed right now and for the foreseeable future.
[01:03:45] Mike Kaput: Well, we’ve even talked about just how valuable it can be in certain contexts and we, you know, eat our own cooking in this respect of people having really hardcore journalism skills.
[01:03:54] Yeah. Because while journalism as an industry is very economically struggling, I can [01:04:00] translate those skills really well with some AI literacy to becoming a very good AI verifier or someone with those skills can, so that’s interesting to also just think about instead of even will this job exist? Like how do we, I guess, retrain or reframe some of the existing skills out there too.
[01:04:16] Paul Roetzer: Yeah. And if we have any, anybody in, at the university level who is involved in journalism schools, something to be thinking about. Mm-hmm. Like that, that may be a, a future role, very near future role for your graduates.
[01:04:29] Meta’s Vision for Superintelligence
[01:04:29] Mike Kaput: All right. Next up, mark Zuckerberg has recently shared his vision for the company’s AI future, and this focuses on building what he calls personal super intelligence.
[01:04:38] So he released a statement, a video, and then kind of an, extended statement titled Personal Super Intelligence. That’s kind of like a letter to employees and to the world, I guess. So he starts this letter by saying, quote. Over the last few months, we have begun to see glimpses of our AI systems improving themselves.
[01:04:56] The improvement is slow for now, but undeniable, developing [01:05:00] super intelligence is now in sight. He then says that while many in the industry are focused on using AI to automate work at scale, meta has a different vision. They don’t want centralized control, but personal empowerment. So instead of building this single AI brain to run the world, meta wants to give everyone a deeply personalized assistant, one that knows your goals, grows with you, helps you become the person you want to.
[01:05:25] And Zuckerberg basically explicitly calls this out as part of what they’re building. He says, this is distinct from others in the industry who believe super intelligence should be directed centrally towards automating all valuable work. And then humanity will live on a dual of its output. He rejects that vision and says Meta is going a different direction.
[01:05:45] Now, Paul, obviously, I mean, medic comes with plenty of its own baggage here. It is not always the most altruistic company on the planet, but I did personally at least appreciate the tone of his vision. He is saying super intelligence quote, has the potential to [01:06:00] begin a new era of personal empowerment where people will have greater agency to improve the world in the directions they choose.
[01:06:06] I thought it’s at least a nice idea.
[01:06:08] Paul Roetzer: Yeah. Okay. I’m gonna, I’m gonna come back to this Mike for a second. So I was actually, while you were doing that, narrative, I was scanning to see if, GPT-5 had been announced yet. it’s not, but ironically. The thing that pops up is, the information has a headline why universal verifiers are OpenAI’s Secret Weapon.
[01:06:29] Mm. So literally, we just talked about the verification thing. It talks about how they’re using reinforcement learning to train models to verify the outputs of AI AI models.
[01:06:39] Mike Kaput: Wow. So our idea, welcome to Project the future of work is already outdated. Right? Yeah. So, like I said,
[01:06:45] Paul Roetzer: they may just have AI agents that do all this checks.
[01:06:47] Yeah, exactly. So, so 1 61, we will come back to that article, but, okay. So back to the meta super intelligence thing. so self-improvement. So if you’re, if you’re again, like kind of newer to this [01:07:00] stuff, and I get into this in my AI concepts 1 0 1 course, kind of explain these concepts of how these models learn and how they’re trained and things like that, and what the dimensions of improvements are.
[01:07:08] Self-improvement is right at the top of the list for everybody, and that’s the idea that it’s like a key unlock once the models can improve themselves, improve their own outputs, improve their own training data, things like that. Then we have potential escape velocity for the intelligence that we can truly get to super intelligence.
[01:07:27] I don’t even know that there’s anything beyond super intelligence, but like once we get there, we, we can unlock everything that’s possible. It’s also very slippery slope because once they can improve themselves, it becomes harder to interpret what they’re doing, why they’re doing it, things like that.
[01:07:42] So, just, just know that self-improvement is a known thing that has been pursued for years in ai. and by all these AI researchers and him alluding to the fact that they’re seeing that. I heard something similar from Sam Altman recently. I saw, I think Ben Benjamin [01:08:00] Mann on the episode I, podcast I referenced earlier.
[01:08:02] He talked about it. This is something you’re gonna be hearing a lot about the ability for these things to sort of improve themselves. And the verification thing I just mentioned from OpenAI’s is one of those ways, the ability to check its own work and then improve based on that. So there was a couple of excerpts.
[01:08:16] Mike, I’ll, I’ll add to the mix that, in addition to what you were talking about. that I think are just interesting. So he said, in some ways this will be a new era for humanity, but in others it’s just a continuation of historical trends. As recently as 200 years ago, 90% of people were farmers growing food to survive.
[01:08:31] Advanced Syntech have steadily freed much of humanity to focus less on sub sub subsistence and more on the pursuits we choose. at each step, people have used our newfound productivity to achieve more than previously possible pushing the frontiers of science and health, as well as spending time on creativity, culture, relationships, and enjoying life.
[01:08:53] He’s very optimistic about super intelligence, which will help humanity accelerate our pace of progress. Intersection of technology and how people [01:09:00] live is meta’s focus and this will only become more important in the future. Again, to your point, Mike is meta the company. We really want determining this to be determined.
[01:09:09] if trends continue, then you’d expect people to spend less time in productivity software and more time creating and connecting. personal intelligence that know, super intelligence that knows us deeply, understands our goals and can help us achieve them, will be far the most u by far the most useful personal devices like glasses, which obviously they’re making huge bets on that understand our context because they can see what we see, hear what we hear, and interact with us throughout the way The day will become our primary computing devices.
[01:09:36] So that’s kind of putting a stake in the ground, which we knew, but I don’t know. He is been saying it quite as directly. They think that the interface of the future is gonna be through things like glasses. we will change from sitting in front of our computers and using our phones to things we wear that just, you know, know everything that’s going on around us.
[01:09:53] And the rest of this decade seems like, likely to be decisive period for determining the path of the technology will take and whether super [01:10:00] intelligence will be a tool for personal empowerment or force focused on replacing large swaths of society. So yeah, he’s definitely taking the opposition to the other labs and it’s gonna be interesting to see how that plays out and.
[01:10:12] Honestly the implications of if he’s right and they win. I, you know, I don’t, I don’t know, like I don’t, I I will say like, well, I don’t even know what I should say. I, like, if I think about my kids like 12 and 13, I would rather at this moment in time that they use AI built by Apple than built by meta.
[01:10:38] Like, and I don’t, you know, I don’t know, I don’t say anything too truly controversial here. Like, I think I would rather at, at this time, the thought that goes into Apple’s devices and intelligence and the principles with which they build that for, versus, you know, what a social media [01:11:00] company has been built around, which is all about engagement, keeping people on their apps like.
[01:11:04] I don’t know. Like it, I work again, like I don’t, and nothing against meta. Like I meta’s done some great things too. I just, I think these are the kinds of things we’re gonna have to grapple with as parents, as business leaders. Like which companies do you bet on? Which companies do you believe in?
[01:11:19] Which companies do you think is most closely aligned to the values of, you know, your company and your family? And those are the decisions we’re all gonna have to make. And we’re gonna have choices. Like they, they’re all gonna be building this stuff and everybody’s gonna make, you know, their choice around it.
[01:11:35] But, yeah, I mean, I, and companies change, people change, you know, maybe they hadn’t a, a good direction and this ends up going well for society. I don’t know.
[01:11:44] Mike Kaput: And, you know, incentives matter as well. Yeah. I think looking at how the company makes its money is a helpful way to Yeah. Start gauging that
[01:11:52] too.
[01:11:52] Paul Roetzer: And I will say like, I have friends within Meta and, and I will say there, there’s really good [01:12:00] conscientious people working on these products. Who do care deeply about the human side of this. Like you can’t, meta isn’t just Zuckerberg. Yeah. It isn’t just like that one person and that you maybe, you know, 50% of people love him, 50% of people maybe don’t.
[01:12:13] but there’s tens of thousands of other people working at Meta and many of them are really good people with great intentions and, great hopes for humanity. And so I don’t want to like, so I don’t really like saying I do or do not like meta I do or do not trust meta. It’s not just one person. And sometimes meta more than many companies gets bucketed into that one person and how people feel about him in particular.
[01:12:37] Mike Kaput: Yeah. It’s a good reminder, especially with how personality driven some of these places can, can seem in the media. Right. Yeah. All right. Next up.
[01:12:46] ChatGPT Shared Links Indexed by Google
[01:12:48] Mike Kaput: Though the issue is now resolved for at least a short time, some ChatGPT chats started being indexed in Google search results. And this wasn’t really an accident.
[01:12:56] This was happening when users clicked share on a [01:13:00] conversation and opted to make it visible on the web, which is an option, but many apparently didn’t realize that option meant the whole world could find it with a quick Google search. So there was kind of this freak out for a while where thousands of intimate exchanges, some of them discussing trauma, mental health, specific family details were now publicly indexed.
[01:13:19] One user talked about their PTSD, another described their personal history in vivid detail. Some named people in their lives in different ways. They were conversing with ChatGPT. Now the good news is as of August 1st, OpenAI’s has now patched the issue. Shared chats are no longer visible in Google search.
[01:13:37] Our good friend Chris penn@trustinsights.ai posted about this. He recommended that people regularly and routinely check their chat settings by going to settings, data controls, shared links, then manage and then get rid of anything you don’t need to share or that you didn’t mean to share in the first place.
[01:13:55] So Paul, I have to imagine this is like quite a wake up call for some people. ’cause I [01:14:00] know, I know for a fact lots of users are not paying as much attention as they should to their ChatGPT privacy and security. And we’ve also talked more and more people are relying on ChatGPT for really personal stuff.
[01:14:13] Yeah,
[01:14:14] Paul Roetzer: yeah. I think it’s just a user beware kind of thing, and. I mean, I just generally take the position and I think I’ve said this before, like anything you share online, just assume,
[01:14:25] Mike Kaput: yeah,
[01:14:25] Paul Roetzer: you know, your parents can read, your boss will read like whatever. Like you think you’re doing it in a private for don’t assume it’s private.
[01:14:32] If you’re sharing a link that only people with the link can access, don’t assume only the person you sent it to is gonna be the one accessing it. So I think this is just more of a general awareness about overall user behavior on online and certainly a bit of a not great look for OpenAI’s that they enabled this feature.
[01:14:53] Right. Without being clear about it. They did fix it quickly, but yeah, it’s like it shouldn’t happen, [01:15:00] but it’s going to happen more and more and I think people just have to be aware of that
[01:15:04] Paul Roetzer: For sure.
[01:15:05] AI Product and Funding Updates
[01:15:05] Mike Kaput: Alright, Paul, we’re gonna end up here with, some AI product and funding updates that I’m just gonna run through real quick here.
[01:15:11] So first up, Anthropic says it is rolling out new weekly rate limits for Claude Pro and Claude Max in late August. it sounds like Claude code is to blame here. Anthropic said some of its biggest fans are running it continuously in the background 24 7, which is very costly. They said one user consumed tens of thousands of dollars in model usage on a $200 a month plan.
[01:15:34] Anthropic cover says these rate limits are only going to apply, they estimate to less than 5% of subscribers based on current usage. Now at the same time, Anthropic is also closing in on a massive due funding round that could raise up to 5 billion. That would push its valuation up to a staggering 170 billion, which is nearly triple where it was earlier this year.
[01:15:54] And finally, in some other Anthropic news, HubSpot has launched their first ever CRM connector for Anthropics [01:16:00] Claude. So basically this makes the AI assistant far more useful for teens already running on HubSpot. So Claude can now tap into realtime CRM. And respond with tailored summaries, charts, and next steps.
[01:16:13] Also, Microsoft is now testing something called copilot mode in its edge browser. This turns the browser into a full blown AI assistant. So with copilot mode, edge can scan all your open tabs, summarize comparisons, book restaurants to all sorts of stuff through natural language. And with your permission, copilot can access your browsing history, passwords, and saved credentials to complete tasks on your behalf.
[01:16:40] Ramp. The corporate finance startup, which is known for its AI powered expense platform, just raised half a billion dollars, bringing its valuation to 22.5 billion, which is up from 16 billion barely a month ago. This cash is fueling their push into AI agents. Their first agent launched in July, and it’s used by [01:17:00] thousands of customers to flag expense reports and check compliance, basically like a digital accounting a.
[01:17:06] One finance manager said it’s doing the job of an entry level clerk, and RAMP says its system will reason through policy, docs and predict expenses, and future agents will handle tasks like procurement and budgeting. Last but not least, Google just gave Notebook LM a big upgrade and it’s all about turning complex material into something you can understand better.
[01:17:29] This new standout feature is called Video Overviews and basically it uses AI to generate narrated slides and mix visuals, diagrams, quotes, and key data from your documents. You can then go ahead and customize these videos based on what you know, what you wanna learn and who the content is for. All right, Paul, that’s a wrap on a busy, busy week in ai.
[01:17:50] I have to believe that this next week is gonna be a big one as well.
[01:17:54] Paul Roetzer: Yeah. One quick note. The the continuing soap opera between the AI labs. [01:18:00] when Anthropic tweeted and then posted that they were like rate limiting people the next day they also shut off OpenAI’s access to the model. Mm-hmm. So it kind of like appeared as though maybe it was OpenAI’s that was abusing it, that someone within OpenAI’s was logging in, like using their agent nonstop to test it and stuff.
[01:18:18] And so, OpenAI’s, somebody at OpenAI’s tweeted like, Hey, we, we still give Anthropic access to ours, but Anthropic said that OpenAI’s was using it against the terms of use and whatever. So just the constant, like little digs back and forth at each other, it’s always entertaining. All right, man. Good stuff.
[01:18:36] thanks again for curating. Mike and I are both still in the lab all week creating courses for the Academy launch, so definitely join us on August 19th. Lots to share with you all. And, yeah, I expect another busy. I think we’re heading into crazy season. I think August is gonna be interesting. I think September may.
[01:18:57] Yeah, on a whole nother [01:19:00] level when it comes to AI news and product releases. So stay tuned everyone, and always, something interesting to talk about. Thanks for being with us. Thanks for listening to the Artificial Intelligence Show. Visit SmarterX.ai to continue on your AI learning journey and join more than 100,000 professionals and business leaders who have subscribed to our weekly newsletters, downloaded AI blueprints, attended virtual and in-person events, taken online AI courses and earned professional certificates from our AI Academy, and engaged in the Marketing AI Institute Slack community.
[01:19:33] Until next time, stay curious and explore ai.
