OpenAI and Microsoft's 'bromance' on the rocks? Plus, AI's looming impact on your career. Join Mike and Paul as they unpack the growing tension between OpenAI and Microsoft, Brookings Institution's eye-opening report on generative AI's potential impact on the U.S. workforce, and Sequoia Capital's latest market analysis, which predicts a new era of "thinking slow" AI. All this, plus Google NotebookLM’s updates, Adobe Max 2024, a new Midjourney update, Agents in Microsoft Copilo, and more in our rapid-fire section.Listen or watch below—and see below for show notes and the transcript.
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Today’s episode is also brought to you by our AI for Agencies Summit, a virtual event taking place from 12pm - 5pm ET on Wednesday, November 20.
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00:05:12 — OpenAI + Microsoft’s Strained Relationship
00:18:18 — GenAI Job Exposure
00:31:15 — Sequoia Market Analysis
00:42:23 — Google NotebookLM Is Becoming a Very Big Deal
00:48:23 —Adobe Max 2024
00:53:05 — Major Midjourney Update
00:55:10 — Playground Releases Playground v3
00:58:08 — AI for Customer Success from Ex-HubSpot Exec
01:03:08 — Bain + OpenAI Extend Partnership
01:06:54 — AI Content Scraping Opt-Out Model
01:09:22 — Agents in Microsoft Copilot
01:13:22 — Demis Hassabis Speaks at Times Tech Summit
OpenAI and Microsoft’s Strained Relationship
The partnership between OpenAI and Microsoft, once dubbed "the best bromance in tech" by OpenAI CEO Sam Altman, has started to sour, according to The New York Times.
The partnership began with Microsoft investing $13 billion in OpenAI, providing the startup with essential funding and computing power. However, after OpenAI's board briefly ousted Altman last November, Microsoft reconsidered its approach to further investments, says The Times.
OpenAI, facing significant financial challenges with expected losses of $5 billion this year, has been seeking additional funds and computing resources from Microsoft. However, the tech giant has been hesitant to increase its commitment, leading OpenAI to explore other options.
In response, OpenAI has been attempting to renegotiate its deal with Microsoft, aiming to secure more computing power and reduce expenses. The AI company has also broadened its investor base, recently closing a $6.6 billion funding round.
Meanwhile, Microsoft has started to hedge its bets. In March, the company invested at least $650 million to hire most of the staff from Inflection, an OpenAI competitor. Mustafa Suleyman, Inflection's former CEO, now oversees a new Microsoft group working on AI technologies that could potentially replace OpenAI's offerings.
This move has caused some friction, with OpenAI executives and employees expressing frustration over Suleyman's presence at Microsoft and concerns about the sharing of technology between the companies.
Generative AI’s Impact on the American Workforce
A new report from the Brookings Institution takes a data-driven approach to analyzing the potential impacts of generative AI on the American workforce.
The study highlights that generative AI could significantly disrupt a wide range of jobs, with over 30% of workers potentially seeing at least half of their tasks affected by AI. It also finds that 85% of workers could see at least 10% of their work tasks impacted.
Unlike previous waves of automation that primarily impacted routine, blue-collar work, generative AI is poised to affect "cognitive" and "nonroutine" tasks, says Brookings, especially in middle- to higher-paid professions, according to the report.
(Fields such as STEM, business and finance, law, and office administration are among the most exposed to potential AI disruption.)
The report emphasizes that while generative AI presents both opportunities and risks for workers, society is currently underprepared to address these challenges.
Sequoia Market Analysis
Famed VC firm Sequoia Capital has just released an updated market analysis of the generative AI landscape titled Generative AI’s Act o1 (in reference, of course, to OpenAI’s new o1 model).
In it, Sequoia partners Sonya Huang and Pat Grady break down how the generative AI ecosystem is evolving, writing, “Two years into the Generative AI revolution, research is progressing the field from “thinking fast”—rapid-fire pre-trained responses—to “thinking slow”— reasoning at inference time. This evolution is unlocking a new cohort of agentic applications.”
They find that the foundation layer of generative AI is stabilizing around major players like Microsoft/OpenAI and Google/DeepMind.
They also find that the focus is now shifting to the development of a reasoning layer, the latest, most significant advancement being OpenAI’s new o1 model, which enables more deliberate, "System 2" thinking, as opposed to the quick pattern matching of earlier models.
This has also led to a new scaling law emerging, they claim: the more inference-time compute given to a model, the better it can reason. This is shifting the focus from massive pre-training to scalable inference clouds.
This is going to have some transformative effects on business as usual—and the authors even anticipate a future "AlphaGo moment" in generative AI, where these systems demonstrate truly novel and superhuman capabilities.
Disclaimer: This transcription was written by AI, thanks to Descript, and has not been edited for content.
[00:00:00] Paul Roetzer: There is massive disruption coming. No one seems to be talking about it. Like economists don't seem to be talking about it. Industry leaders don't seem to be talking about it. Government leaders don't seem to be talking about it because they don't seem to really realize what's about to happen. And we don't have answers to what happens to jobs in all these different industries.
[00:00:20] Paul Roetzer: Welcome to the Artificial Intelligence Show, the podcast that helps your business grow smarter, grow better. by making AI approachable and actionable. My name is Paul Roetzer. I'm the founder and CEO of 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.
[00:00:49] Paul Roetzer: Join us as we accelerate
[00:00:57] Paul Roetzer: Welcome to episode 120 of the Artificial [00:01:00] Intelligence Show. I'm your host, Paul Roetzer, along with my co host, Mike Kaput. We are coming to you on, well, it's Monday, October 21st, 10 a. m. in Cleveland, a beautiful fall day in Cleveland, I must say. I've got my pumpkin coffee with me. I am like, I'm, I am all falled out this morning.
[00:01:17] Paul Roetzer: I love it. I was thinking about we're, we're, just under a year away from MAICON 2025, Mike. I was like, Because next year's event is October, I'm going to get this wrong because I'm saying it off the top of my head, like 14th to the 16th. Oh my gosh. So it's literally like one year away from MAICON. but it's fun because you see like the beautiful fall colors and it's just such an amazing time of year in Cleveland.
[00:01:39] Paul Roetzer: So I'm excited to have everybody back here in Cleveland next year at this time. in the meantime, I'm just going to enjoy my pumpkin coffee. I have like one month out of the year where I like, I have to have a pumpkin coffee every day. all right, we've got some big items to talk about. Last week wasn't like another crazy week of launches and new models and stuff, but there's a few [00:02:00] articles and reports that came out that Mike and I are going to really, drill into today that, I think provide some really great like macro level perspective about what's going on.
[00:02:11] Paul Roetzer: So I'm, I'm excited to talk about those. first I'm going to say this episode is brought to us by Rasa. io. Talk about a common challenge we all face making our email newsletters truly engaging. Well, Rasa is changing the email newsletter landscape. Imagine each of your subscribers receiving a newsletter tailored just for them.
[00:02:30] Paul Roetzer: Sounds impossible, but Rasa.io makes it possible with their AI powered platform that makes personalization easy. We've known the team at Rasa for about six years now. They've been a longtime supporter of our Marketing AI Institute. And if you are running or planning to launch a newsletter for your business, you should definitely check out Rasa.
[00:02:51] Paul Roetzer: io. Head over to Rasa.io/MAII and use the code 5, number 5, M A I I, and you'll get a 5 [00:03:00] percent discount. Give it a try. Your subscribers and your engagement rates will thank you. And the episode is also brought to us by our second annual AI for Agencies Summit. This is a virtual conference that's taking place noon to 5 p.
[00:03:15] Paul Roetzer: m. Eastern time on Wednesday, November 20th. So that's the live version and then you can also purchase on demand. so if you can't make it on November 20th from 12 to 5 p. m. Eastern, you can always watch the replay. So the AI for Agency Summit is designed for marketing agency practitioners and leaders who are ready to reinvent what's possible in their business and embrace smarter technologies to accelerate transformation and value creation.
[00:03:40] Paul Roetzer: During the event, you'll join hundreds of other forward thinking agency professionals to consider ways to recruit AI savvy talent and upscale your team. Explore AI tools can boost creativity, productivity, and operations. Hear insider stories. I think we have six different, agency leaders presenting on their case study within their agency.
[00:03:58] Paul Roetzer: So it's going to be all [00:04:00] about, the agencies that are actually doing this and what they're learning and being able to share that with you. Understand how it impacts, impacts your pricing models and service offerings. That's a topic we're going to talk a little bit about in today's episode. A lot of things happening in that space.
[00:04:13] Paul Roetzer: And then connect with like minded agency professionals and leaders. Who are in the midst of their own AI transformation journeys. It's all presented by a group of world class agency leaders and experts. You can get your tickets at AIforAgencies.com. Click on register now. So when you do, use the code POD100 and that'll get 100 off of your ticket.
[00:04:34] Paul Roetzer: So that's AIforAgencies.com and code POD100 for 100 off. Alright Mike, there's some brewing drama. We've thought that there was some stuff going on with OpenAI and Microsoft, there was some, some rumors, but the New York Times sort of blew some, stuff out with, a bunch of inside sources.
[00:04:58] Paul Roetzer: And, [00:05:00] yeah, there's a lot going on in that Microsoft OpenAI relationship. The former best, what was it, best relate, bro, bromance in tech was what it was called? maybe not so much anymore. So let's, let's get into that.
[00:05:12] Mike Kaput: Yeah, so first up, as you mentioned, OpenAI CEO Sam Altman used to call the partnership between OpenAI and Microsoft, quote, the best bromance in tech.
[00:05:26] Mike Kaput: But, according to some new reporting by the New York Times, that partnership has started to sour a bit. So, if you recall, this began with Microsoft investing 13 billion into OpenAI, basically giving them a bunch of essential funding and, importantly, computing power. However, after OpenAI's board briefly ousted Altman last November, Apparently, Microsoft started reconsidering its approach to further investments, according to the Times.
[00:05:57] Mike Kaput: Now, OpenAI [00:06:00] has a lot of financial challenges. They have expected losses of 5 billion this year, and they've been seeking additional funds and computing resources from Microsoft specifically. However, apparently they have been hesitant to increase that commitment, which has led OpenAI to explore other options to fund and support their growth.
[00:06:20] Mike Kaput: Shh. In response, OpenAI has also been attempting to renegotiate its deal with Microsoft, aiming to secure more computing power and reduce expenses. They also recently, which we reported on, broadened their investor base, closing a 6. 6 billion funding round recently. Now, Microsoft has also started to hedge its bets, it sounds like, in March.
[00:06:42] Mike Kaput: The company invested at least 650 million in what you might call an acquihire to get most of the staff from Inflection, which was an OpenAI competitor. Mustafa Suleyman, Inflection's former CEO, now oversees a new Microsoft group working [00:07:00] on AI that could potentially replace what OpenAI offers. And that's also been causing some friction, it sounds like, based on this reporting.
[00:07:09] Mike Kaput: OpenAI executives and employees expressed frustration over Suleyman being at Microsoft and concerns about sharing of technology between the two companies. So, Paul, there's never a dull moment at OpenAI. Like, can you maybe break down some of the complexities for us here? Like, Microsoft has this huge Vested interest in OpenAI's success, at least it seems like, but they also appear to be creating obstacles to the company's success.
[00:07:38] Mike Kaput: Kind of helping them grow, but not too crazy. OpenAI increasingly seems to be in a competitive, not a collaborative position here. Like, what's going on?
[00:07:48] Paul Roetzer: Yeah, it's wild. We've talked about this relationship a number of times on episodes over the last year, year and a half. And, you know, I recall in the last couple months, just the first time that [00:08:00] Microsoft identified OpenAI as a competitor, like, officially, I think, within, like, their earnings reports, and, we've, you know, obviously, when inflection was brought, kind of, through that acqui hire, that seemed odd, you certainly could understand how that could ruffle some feathers on OpenAI's side by putting Mustafa in the position he was in, especially as, sounds like, the main point of contact for her.
[00:08:22] Paul Roetzer: Microsoft and OpenAI. So, I think this article, again, I would highly recommend people read it. It's by Cade Matz, who's the author of Genius Makers and heavily sourced within all these companies. Mike Isaac and Aaron Griffith at New York Times. There's quite a few details in here I had not heard yet. That usually every time Cade is involved in doing an article on AI, there's something comes out that you previously weren't aware of.
[00:08:48] Paul Roetzer: So, I'll zip through a few of the things that I, you know, did a double take on or kind of made specific notes on as I was going through it. So, I had not previously heard [00:09:00] that OpenAI was trying to raise billions from Microsoft prior to November 2023 when Sam was temporarily ousted. If that had been reported, I had not seen it anywhere.
[00:09:11] Paul Roetzer: So, the article said Nadella was initially willing to keep the cash spigot flowing. But after OpenAI's board of directors briefly ousted Mr. Altman last November, Nadella and Microsoft reconsidered, according to four people familiar with the talks, and then, you know, as you mentioned, that he was shocked and concerned by the firing.
[00:09:31] Paul Roetzer: The OpenAI has been trying to renegotiate the deal. This makes sense in the context of we know they're trying to shift to this for profit entity, but not only do they have the non profit current structure that is restrictive, their agreement with Microsoft is highly restrictive. And for, for the for profit change to occur and for them to eventually IPO, I would imagine there needs to be a re imagination of the OpenAI Microsoft relationship.[00:10:00]
[00:10:00] Paul Roetzer: And Microsoft may be in no hurry to do that, because if I'm not mistaken, they have 49 percent ownership of the for profit entity. I think we reported on a recent episode, there was like a certain percentage of their revenue, like I think it was 20 percent of all revenue or something goes back to Microsoft.
[00:10:16] Paul Roetzer: And I believe there was a profit cap too, where like the first hundred million or a hundred billion or some crazy number was Microsoft's money too. Like it's, it's crazy. I mean, Microsoft's got a sweet deal here, plus they get inside access to the technology. and it's just like, it's not ideal for where OpenAI wants to go.
[00:10:36] Paul Roetzer: It was like fundamental to get them where they are, but it makes sense that they would be trying to negotiate this. So the article said over the past year, the AI company repeatedly tried to renegotiate a lower cost on compute and allow it to buy compute from other companies because they didn't feel like Microsoft was, giving them enough.
[00:10:54] Paul Roetzer: It did say Microsoft agreed to an exception in the contract recently that allowed OpenAI to sign a [00:11:00] roughly 10 billion computing deal with Oracle. I had not seen that reporting. That one I would assume has been out because that's a pretty big deal for Oracle. So I would guess that information is out there somewhere.
[00:11:10] Paul Roetzer: and it said in recent weeks OpenAI and Microsoft renegotiated a change to a future contract that reduces how much Microsoft will charge. the company for the computing power, you mentioned that there's these concerns internally, about, you know, Mustafa's management style and, maybe what Microsoft is doing.
[00:11:30] Paul Roetzer: Said in, some have complained that if another company beats, this is an interesting one that leads to an issue later on. Some have complained that if another company beat it to the creation of AI that matches human, the human brain, so basically AGI. Microsoft will be to blame because it hasn't given OpenAI the computing power it needs.
[00:11:48] Paul Roetzer: So that's kind of interesting. the, you know, some of the issues around Microsoft, like it almost seems like what Microsoft is doing, and this may not be correct, but it seems like [00:12:00] OpenAI thinks they're slow playing them on the compute access they're giving them so they can build their own internal competing tools and not be reliant on OpenAI anymore.
[00:12:12] Paul Roetzer: So you bring in Mustafa, Mustafa now owns the relationship with OpenAI, but he's also in charge of building OpenAI or Microsoft's own capabilities with Kevin Scott and the team there. And so it's like, let's just slow play this over the next 12 months. We'll build up an equivalent model to what OpenAI has.
[00:12:30] Paul Roetzer: We have all the inside information on what they're building next. So like, we can get there where they get there and then we're not relying on them anymore, which I asked, like, there was two episodes ago where I said, sure seems like Microsoft just relies on OpenAI for all this innovation, if I'm wrong, someone from Microsoft, like, call us.
[00:12:46] Paul Roetzer: Like, but it does appear that this is basically validating what I was saying, that Microsoft's aware that they're too reliant on OpenAI. So they're trying to build around them. So imagine Microsoft sitting there a year from now, maybe they have a competing [00:13:00] model, they're not relying on open eyes tech anymore, and they still own 49 percent of the company.
[00:13:04] Paul Roetzer: So Microsoft seems to have all this leverage, but then the most fascinating part of the article brings back a unique nuance of the relationship that some people maybe either forgot or didn't know, which is the contract is void. If OpenAI achieves AGI, so in the original deal with Microsoft, with all the, you know, the access to the technology, everything else, if OpenAI achieved AGI, Microsoft doesn't get access to that technology.
[00:13:34] Paul Roetzer: And that was a clause they put in because they wanted to make sure, supposedly, that it would be used responsibly and they didn't want a third party having access to like this AGI technology. So, that's the article said the clause was meant to ensure that a company like Microsoft did not misuse this machine of the future.
[00:13:52] Paul Roetzer: But today, OpenAI executives see it as a path to a better contract. According to a personal familiar with negotiations under the terms of the contract, the [00:14:00] OpenAI board would decide when a GI has arrived.
[00:14:03] Mike Kaput: Hmm,
[00:14:03] Paul Roetzer: that is the most fascinating part of all of this. So imagine Microsoft has all this leverage, like, we're not going to give you more compute, we're going to charge you exuberant, exorbitant fees for the compute.
[00:14:13] Paul Roetzer: we're going to build our own tech and OpenAI seems kinda helpless in this scenario, relying on Microsoft and their contract, but they have the trump card which is, Oh yeah, by the way, GPT 5 is AGI, our board has decided, so you don't get access to it. So, both sides are going to try and play leverage, and if the contract truly is that, You know, cut and dry that if AGI is achieved, which is decided by the board of OpenAI, which is what we've heard over and over again, then all the board of OpenAI has to do is say AGI.
[00:14:45] Paul Roetzer: And, and then we're done, like Microsoft doesn't get it. And so that's the game we're playing, which makes me like go back to last year, Sam Altman did Lex Friedman podcast, or maybe it was early this year. And I remember Lex asking him about like GPT [00:15:00] 4 and did he think it was AGI? And Sam's like, I don't know, like, do you think it is?
[00:15:06] Paul Roetzer: And knowing how Sam approaches things, there's this part of me that now looks back and thinks that was a totally intentional thing to like a shot over at Microsoft saying, maybe this, maybe we're maybe we're already there in this, this contract situation we're having is, you know, we're willing to play that leverage if we need to.
[00:15:27] Paul Roetzer: So that led me to like, Oh, maybe we're going to get to AGI in society just because OpenAI wants out of their contract with Microsoft.
[00:15:34] Mike Kaput: Yeah. That struck me too, is like, we know, and we talk about all the time that the leaders at these companies do appear to genuinely believe they are on the path to building AGI, but now there's a direct incentive for them to also contractually say what they have is It's fascinating.
[00:15:53] Mike Kaput: Yeah.
[00:15:54] Paul Roetzer: So I definitely read the article. There's a lot more that I, you know, I'm not touching on here, but there was a lot of really fascinating [00:16:00] elements to this and it got into like Apple's relationship with them a little bit. And like the one thing I thought was fascinating is, so back in 2022, as OpenAI was developing the technologies that would drive ChatGPT, Altman and Kevin Scott, the CTO of Microsoft Met with executives at Apple to explore those three companies working together.
[00:16:20] Paul Roetzer: This is before ChatGPT. I had no idea they were talking with Apple back then. and that Microsoft would be involved in doing a deal with them. I mean, just wild. Like, I would love to Like, click into that story angle, because they just kind of left that paragraph hanging there. It's like, whoa, hold on a second.
[00:16:36] Paul Roetzer: This feels like a whole other chapter of the book.
[00:16:39] Mike Kaput: I'm selfishly hoping that this is a prelude to Cade's next book. Me too. I've joked online with them a
[00:16:47] Paul Roetzer: couple times, like, when is Genius Makers 2 coming
[00:16:49] Mike Kaput: out? Yeah, right. Hee! So to kind of wrap this up, like their OpenAI is obviously facing this issue with Microsoft, but also like in the past weeks, like we've talked about Ilya starting his [00:17:00] own venture, OpenAI tries to restrict its own investors from funding that company along with a few others.
[00:17:05] Mike Kaput: This week, we got a report that former CTO Mira Murati is now raising purportedly a ton of money for her new startup as a hundred million dollars quoted. she's also The startup aims to build AI products based on proprietary models, according to Reuters, like we've talked about there's only a handful of companies out here that can actually like build these frontier models, but like how worried does open AI also need to be about, like, it's ex employees forming companies to compete with them.
[00:17:35] Paul Roetzer: It seems like probably very, I don't know, I mean, like, their secrets are just going out the door every week with all these leaders. I mean, there was a visual I saw in one article where it was like, I don't know, like, 14 founding members and actual, like, co founders of OpenAI, and the only people left are, Greg and Sam, and I think there was one other person on that list.
[00:17:57] Paul Roetzer: But all, all of them are gone [00:18:00] and, seemingly doing their own thing. So, yeah, it's, it's, it's a fascinating story. I mean, I, I don't know. That's why, like, every week we end up talking about OpenAI because it's just always new angles to the story. And it is so instrumental to where this all goes.
[00:18:18] Mike Kaput: Alright, so our next big topic this week, we just got a new report from the Brookings Institution, which is a well known non profit.
[00:18:26] Mike Kaput: And in this report, they take a data driven approach to analyzing the potential impact of generative AI on the American workforce. And some of these findings are worth paying a little bit of attention to because the study highlights that generative AI could significantly disrupt a wide range of jobs, with over 30 percent of workers potentially seeing at least half of their tasks affected by 85 percent of workers could see at least 10 percent of their work tasks impacted.
[00:18:59] Mike Kaput: And to [00:19:00] assess this, Brookings quote, utilized estimates shared by OpenAI relating the predicted ChatGPT 4 exposure level of thousands of the tasks that make up the hundreds of occupations defined by the Department of Labor's O Net database. Now, unlike previous waves of automation that. Impacted routine blue collar work, generative AI, they say, is poised to affect cognitive and non routine tasks, which means it is getting into middle and higher paid professions.
[00:19:31] Mike Kaput: They highlight fields such as STEM, business, finance, law, office administration. All of these are the most exposed. to potential generative AI disruption. And they also make the point that while there are opportunities and risks for workers with generative AI, society is currently under prepared to address these challenges.
[00:19:52] Mike Kaput: Now, Paul, we don't, I don't think we directly know anyone at Brookings Institution, but this is sounding very familiar. [00:20:00] So, A note on the methodology here, Brookings used estimates shared by OpenAI relating the predicted exposure level of thousands of the tasks that make up occupations using the O Net database.
[00:20:13] Mike Kaput: That sounds a lot like the methodology that you use to create and part jobs GPT and campaigns GPT. Like, how seriously are you taking this study? Can you talk a little bit about this methodology? See why it might make sense.
[00:20:27] Paul Roetzer: Yeah, I think it's wonderful. I mean, this is what is needed. So this is what I've been, you know, kind of pleading for is more high level involvement from institutions that can move the needle, have the ears of key government leaders and industry leaders.
[00:20:41] Paul Roetzer: So I am very happy to see this effort. I like the questions they're asking. So, you know, the report starts off with how do we ensure workers can proactively shape generativized design and deployment? What will it take to make sure worker benefits meaningfully from the gains and what guardrails are needed for workers to avoid harms as much [00:21:00] as possible?
[00:21:00] Paul Roetzer: I like that they're taking a multi year approach. They said, this is just a start of the initiative. Brookings Metro has embarked on a new multi year effort focused on raising awareness and shaping societal responses. Wonderful. they said they're drawing on insights from a workshop date where they convened 30 experts from policy, business, innovation, investment, labor, academic, think tank, research, civil society, philanthropy.
[00:21:24] Paul Roetzer: Brilliant. More of this. Like, this is all phenomenal. what they did from best I can tell is they actually took the GPT's, our GPT's paper from August 2023, which is where the exposure methodology originated from. So this is an open AI paper where Mike and I have talked about this on the show before. We talked about it in the jobs GPT episode, I think it's 110, which is when GPT 4 came out.
[00:21:53] Paul Roetzer: So GPT 4 comes out in March, 2023. OpenAI, I think in partnership [00:22:00] with Microsoft, or it might have been mostly OpenAI, created a paper, a research paper called GPT's are GPT's, generative pre trained transformers, the foundation of language models, are general purpose technologies was the point that these AIs are capable of doing many cognitive tasks is kind of the premise of a general purpose technology.
[00:22:20] Paul Roetzer: And so it seems like what Brookings has done is zoomed into that research. So this doesn't seem like new research on their part that's looking at like, you know, 30 percent of all workers could see at least 50 percent of their occupation tasks. I think they just further analyzed OpenAI's data, which means the data is a year and a half old that they're referencing.
[00:22:42] Paul Roetzer: So it's probably not even relevant per se. But if that's what they did, which is what it seems to be, then I have the same I don't know, concerns isn't even the right word. Like the same reason I built JobsGPT [00:23:00] and the exposure key that I developed, which goes beyond current and it looks at future capabilities of advanced reasoning and persuasion and digital action and computer vision, you know, having agents in the world.
[00:23:12] Paul Roetzer: So when we build JobsGPT. The idea was to project future impact on jobs, to look at the exposure of the models we know that are coming. And so I think that there's a lot to build on with what Brookings is doing. If we can assist them in any way, like please, someone reach out, we're happy to, you know, provide any data or help wherever we can.
[00:23:31] Paul Roetzer: Because this is the kind of initiative I think we need more of, is just, you know, these higher profile institutions really aggressively pursuing answers to very difficult questions. And, you know, I think at the end of the day, like if you go back to episode 105, Mike, you and I were talking about this, the Carl Schulman interview, one of the co founders of OpenAI, the early people there, he did that 80, 000 hours interview.
[00:23:56] Paul Roetzer: And in that one, he talked about the economic [00:24:00] impact and, and. so we, we kind of went pretty deep into there, and I, I went back and I pulled the excerpt, where I said, like, what Karl was talking about in that episode, and what I think Brookings is hopefully trying to get at here, is there is massive disruption coming.
[00:24:16] Paul Roetzer: No one seems to be talking about it. Like economists don't seem to be talking about it. Industry leaders don't seem to be talking about it. Government leaders don't seem to be talking about it because they don't seem to really realize what's about to happen. And that even if we don't get to AGI and we just get smarter models.
[00:24:34] Paul Roetzer: That follow these scaling laws, that's sufficiently disruptive, and we don't have answers to what happens to jobs in all these different industries. So, yeah, I mean, at the end of the day, I'm just really happy to see this, and I hope they really aggressively pursue this plan that they have. They talk about, you know, defining and supporting good employer practices, enhancing worker voice and power, [00:25:00] and developing public, public policy solutions as sort of like their three main priority areas going forward, but they address that there's just so many unknowns and so many questions that need to be explored.
[00:25:12] Paul Roetzer: And that's kind of where I'm at. It's like, I don't have the answers, but it sure looks, when you look out a year or two from now, like, We're going to see some pretty significant disruption across a lot of different industries that no one is preparing for.
[00:25:24] Mike Kaput: And kind of like you alluded to in the Shulman episode, you expressed, I would say, correct me if I'm wrong, like bafflement that more people aren't talking about this and it made you kind of feel like, When you were kind of very early to AI and you were like, I can see where the impact is going to be.
[00:25:41] Mike Kaput: I'm surprised more people aren't talking about it because a thing that they mentioned in here is that they said this data does not even attempt to project future capability enhancements from next generation AI models likely to be released. So they're saying like, these are the numbers we're citing to you of exposure of just today.
[00:25:58] Mike Kaput: Right. And we've talked about [00:26:00] we're on the cusp of GPT 5 already. Right.
[00:26:03] Paul Roetzer: And that's, I, I, like. I get why they wouldn't do that yet, but I also don't see why you couldn't. Like, I mean, it's, I built the Jobs GPT exposure key by just studying the space and looking at what all these research labs are telling us.
[00:26:18] Paul Roetzer: It's pretty obvious what they're all working on. Like you don't have to search really that hard to, to reasonably project what the next versions of these models are going to be able to do. And so why can't we model that to specific industries? And that's what needs to happen is. Take the exposure key I've created, like go to smarterx.
[00:26:37] Paul Roetzer: ai and go to the jobs GPT page and like it's, the exposure key is right there. Think about your industry, if you're an accountant, customer service, lawyer, doctor, I don't care what you are, entrepreneur. Take that exposure key and think about your own career paths and industries. Like, this isn't that hard to do.
[00:26:57] Paul Roetzer: For whatever reason, we haven't had [00:27:00] economists and institutions like Brookings do it yet at scale. And that's, that's what has to happen is like, we can only think kind of horizontally. And we think obviously about marketers through our Marketing Institute brand. But, like, I spent Saturday doing a talk on AI and entrepreneurship in K 12 education.
[00:27:17] Paul Roetzer: And so for that, that afternoon, my brain is locked into, like, what does this mean to educators? How do we prepare students at, you know, my daughter's in 7th grade, so I was actually talking at, like, this morning I was advising the 7th grade entrepreneurship initiatives at the school. And so, like, sometimes I'll zero into a specific vertical.
[00:27:34] Paul Roetzer: But most of the time we're just zoomed out and you and I, Mike, are talking like macro and then hoping people will take that inspiration and go run into their domain. And that's where I think this needs to go is, and Brookings isn't going to answer all that either. So if you're a listener to this podcast, like take this knowledge and you go figure out what it means to your industry, your business over the next one to two years because nobody else is doing it right now.
[00:27:58] Mike Kaput: So, just very briefly, it definitely touches [00:28:00] on, you know, in our last episode when we talked about one of the main topics was Dario Amodei's AI manifesto, how this would, AI would impact work and society, and you kind of ended with this really interesting call to action, I thought. We were like, look. We don't have the funding to do this massive, whatever, research project, deep study, but someone like the government or Google or Microsoft, somebody needs to make this their thing and do deep studies about the future of the economy, you had said.
[00:28:28] Mike Kaput: And we noted that we kind of have listeners of some of these companies. I just wanted to push like a little further as we wrap this up. Like, Bye. If you did have the funding or partners with that kind, the kind of funding it might take to actually take this seriously, like, would you both go broad and deep like you just described?
[00:28:45] Mike Kaput: Like, what would be kind of that next big step that needs to happen?
[00:28:49] Paul Roetzer: Yeah, I, I think so. I mean, you and I talk so much about, you know, even when I'm thinking about our own business model and how much, you know, we try and tackle, we always come back to empowering [00:29:00] other people. Like, I think that more than anything, what I have focused our energy on is.
[00:29:05] Paul Roetzer: Distribution of knowledge and information and tools that empower other people to go figure it out in their own domain because this stuff's going to move too fast for one council or, or one, you know, government initiative to achieve everything. And so I think by, you know, distributing the knowledge, into people who can then take it and apply it to their domain, that's critical.
[00:29:27] Paul Roetzer: But I do believe that there needs to be like an Apollo level mission on literacy and reskilling and upskilling workforce, not just on the building of the technology. And it seems to me like most of the focus at the big frontier model companies and at the government level is all about like, how do we build the smarter technology and maintain a competitive advantage over other countries?
[00:29:50] Paul Roetzer: Not what does this actually mean to our society and our workforce and our educational systems? And how do we? In a very quick time period, prepare for that. And I think, I don't know if I ever [00:30:00] used that analogy on the last episode, but for better or for worse, the pandemic is the closest thing we have to how quickly we can mobilize change in business, in society, in educational systems.
[00:30:12] Paul Roetzer: And I'm not saying we need like a 30 day plan. But it sure wouldn't hurt to have like a next 12 month plan from somewhere on high that's pushing for dramatic, action. You know, again, I don't, I don't know exactly what the change looks like. This isn't a 5 10 year play, like, if Dario and Demis and Sam and all these other people are right, We don't have five to 10 years, like we got two, five, maybe, before like just completely disruption and transformation.
[00:30:47] Paul Roetzer: So yeah, I just, if you're in a position of authority or if you're in a position of power at one of these bigger entities, you know, nonprofit, government, frontier model company, we got to do something way [00:31:00] faster on the societal, educational level.
[00:31:05] Mike Kaput: So in our third big topic this week, the famous VC firm Sequoia Capital just released an updated market analysis of the generative AI landscape. Now they titled this Generative AI's Act 01 in reference to OpenAI's new 01 model. And in it, Sequoia partners Sonia Huang and Pat Grady break down how the generative AI ecosystem is evolving.
[00:31:30] Mike Kaput: They write, quote, two years into the generative AI revolution, research is progressing the field from, quote, thinking fast, rapid fire pre trained responses to, quote, thinking slow, reasoning at inference time. This evolution is unlocking a new cohort of agentic applications. So they find things like the foundation layer of generative AI is actually stabilizing around some of these major players like Microsoft slash OpenAI and Google [00:32:00] slash DeepMind.
[00:32:01] Mike Kaput: Focus, they believe, is now shifting to the development of a reasoning layer. The latest, most significant advancement being, of course, OpenAI's O1 model, which enables this more deliberate system two thinking we've referenced on multiple episodes. As opposed to, say, a quick pattern matching that earlier models have done.
[00:32:22] Mike Kaput: And they also think we've gotten to a new scaling law that's emerging. And they claim that the more inference time compute that's given to a model, the better it can reason. So we're shifting a focus from massive pre training to scalable inference clouds. So basically, the authors argue this is going to have some major, major effects on business as usual.
[00:32:43] Mike Kaput: They even anticipate what they call a future AlphaGo moment in generative AI, where these systems demonstrate truly novel and superhuman capabilities. So Paul, I know you found a lot to like in this analysis, and obviously Sequoia [00:33:00] is like investing on the bleeding edge of AI, so they're absolutely worth paying attention to.
[00:33:05] Mike Kaput: Like, what was really worth a look within
[00:33:09] Paul Roetzer: this
[00:33:09] Mike Kaput: report?
[00:33:10] Paul Roetzer: I think for our regular listeners, it's a really nice, concise, summarization of what we've been talking about for the last, like, you know, 8 to 12 episodes. So as we knew that Strawberry was coming, so we were talking about Strawberry, you know, 10 episodes ago and, and then we eventually got the O1 model.
[00:33:30] Paul Roetzer: We've talked since January about the system one, system two thinking that actually came Andrej Karpathy had that intro to LLM's video in January of this year on YouTube that we featured and talked about. That was one of his big things, is giving the machine time to think. We talked about with the Noam Brown episode and his efforts in poker play and diplomacy that the more time you give the computer to think, the smarter it seems to get or the better it seems to be at solving things.
[00:33:58] Paul Roetzer: So, yeah. There wasn't [00:34:00] anything, like, groundbreaking and new that came out of this if you're a regular listener. If you're not, it's a really good five to seven minute read where you can kind of catch up on some of the key things that are going on that you highlighted in your overview there, Mike. there's a few things that I thought were interesting just from Sequoia's perspective that I wanted to kind of click into for a minute.
[00:34:24] Paul Roetzer: One is the foundation models, you know, you mentioned this idea that they thought a couple years ago that, that there would end up being like a single model company, which I assume they assumed was open AI at the time, or maybe Google, that basically just made everybody else irrelevant and everyone else would kind of play for the, you know, the scraps.
[00:34:42] Paul Roetzer: That doesn't appear to be true. When we talked about this, I don't know, maybe five, six, six episodes ago where I said, the key to me is. Is GPT 5 a breakthrough, or is it a continuation and just like more compute, more training data went into it, more reinforcement learning? [00:35:00] because it seems like everyone has sort of caught up to OpenAI for the most part, like with Meta and Google.
[00:35:08] Paul Roetzer: XAIs, you know, on, on their heels to a degree, where it's hard to know how they're going to truly like leap ahead and spend another year and a half with a better model than everybody else. So, it, Sequoia was sort of saying this, that it just seems like we're now like every three to six months, the other model companies catch up to whoever had the most recent, most powerful model.
[00:35:32] Paul Roetzer: The inference time compute thing came out of the O1 paper from OpenAI, and again, Noam Brown, who was at Meta and is now at OpenAI, this is like his main thing, it's why he went to OpenAI, it's to work on this reasoning thing, which is, the basic premise is, The system one, system two thinking. So system one is what's the capital of Ohio.
[00:35:53] Paul Roetzer: And it's like, okay, it's Columbus. Like real quick, it is Columbus, right? Like I'm thinking of the top of my head. So [00:36:00] that's system one. Like it's just a fact based thing. There's no real thought process other than memorization that goes into this retrieval and memorization. That's it. That's system one thinking.
[00:36:09] Paul Roetzer: System two thinking is, you know, why did they decide to make Columbus the capital of Ohio? Well, now I got to stop and actually kind of like, think about this. And I got to do some research and I got to go through some steps. Like that's a more system two style thinking. And so that's the basic premise is what they're saying is when we give the machine time to think, it seems to be able to do much more complex things in like math and biology and chemistry and all these things.
[00:36:36] Paul Roetzer: business strategy, you know, when you start to bring this back to our world. And so that's what they're looking at. the one thing that they didn't really say, like explicitly within this, that I sort of like was thinking about is, The more and more I think about this, the more I believe that what ends up happening is Google, Meta, OpenAI, Microsoft [00:37:00] probably gets back in the game with their own frontier models, NVIDIA.
[00:37:04] Paul Roetzer: They're all going to spend, they're anthropic, they're going to spend their billions of dollars building these massively powerful, approaching AGI, eventually getting to AGI models that are generally capable of almost all cognitive tasks. But those models are always going to cost the most money to run and to use.
[00:37:24] Paul Roetzer: And the reality is, many of the use cases in business, like helping us write our emails or brainstorming ideas, building a marketing strategy, those don't require a 10 billion dollar frontier model to do. We could do that with like GPT 4 level stuff, like that might be good enough. Right, right. So I'm envisioning this world where there's four or five dominant frontier models that are all approaching or at AGI a year or two or three from now.
[00:37:53] Paul Roetzer: But we don't use those models daily in our lives. What's happening is that model almost functions as like the project [00:38:00] manager, like the overseer of all the other models and agents that live within it. And so when we go into ChatGPT, instead of having to pick from one of the four models, which makes no sense from a user experience, how am I supposed to know which model to pick?
[00:38:12] Paul Roetzer: I just put in my prompt. And then the most powerful model figures out which model is actually best to solve that, calls on that model to do its thing, and maybe there's like a symphony of dozens of these things, and unknown to us, we're just putting our prompt in. But behind the scenes, you start to get this symphony of agents and models working together to do the thing for us.
[00:38:35] Paul Roetzer: And I'm like, I'm almost like 99 percent sure that's what ends up happening, because us picking models for ourselves makes no sense from user experience. Only for developers does that make sense. To the average user, a marketer, an entrepreneur, a lawyer, like, how do they know which model to pick? O1 Preview, O1 Mini?
[00:38:53] Paul Roetzer: 4040 Advanced, Gemini 1. 5, like who knows? Right. So I think that [00:39:00] that happens. And then the thing they called out is like, and you and I have talked about this, this idea of like these AI software companies that are just wrappers for the model, meaning. If I work directly with OpenAI, I use ChatGPT. I'm using the model directly through OpenAI.
[00:39:17] Paul Roetzer: If I go use, I don't know, like Conmigo, I'll say, because I was using that example on there. If I'm using Conmigo, that's a wrapper for OpenAI's models specifically tuned for education. They don't build their own models, at Khan Academy. They're building on top of it. So they're a rapper, a software rapper.
[00:39:34] Paul Roetzer: Those rappers kind of got, no pun intended, a bad rap like a year ago. We all kind of assumed that they would just be eaten up. What did, what did Sam say? They would steamroll them or something, I think, like if they were useless. So we assumed these rappers that in the first, you know, six months after ChatGPT were raising 10 million, 20 million, whatever, that they were just going to become worthless.
[00:39:56] Paul Roetzer: What Sequoia is saying is, no, that these rappers are [00:40:00] actually critical. Because you, it requires domain expertise to build like a legal assistant or a customer services assistant or a marketing agency assistant. And that that's actually where the knowledge or the value will accrue in the venture capital world is at the wrapper layer for people that build these domain specific things.
[00:40:21] Paul Roetzer: So, I don't know, like, again, nothing in this article, to me, was like, oh my god, I didn't know that, because we're living it every day. But it definitely summarized it in a really nice way, and it allowed me to sort of step back and have some more macro level thoughts that I maybe haven't, like, given my brain time to think about for a few weeks or a few months.
[00:40:41] Paul Roetzer: So, again, a really good read. It's a pretty approachable read. It's not highly technical. If you're struggling with it, drop it into NotebookLM and like, you know, ask it to explain it to you at a seventh grade level. Like, and I'm not joking about that. Like, it's a truly great function of NotebookLM, to do it that way.
[00:40:58] Paul Roetzer: So, yeah, [00:41:00] again, all three of the things we've covered, these main topics, all really good reads and really good kind of macro level stuff for people to understand.
[00:41:08] Mike Kaput: Yeah, that's why I like the big picture, like zooming out a bit, because even if you are a listener, it can be a regular listener and you're following this stuff, it can be really hard sometimes to frame like, you know, this is a very different landscape than it was six months ago or a year ago.
[00:41:23] Mike Kaput: Like that doesn't seem like that long of a time. But really we're starting to see how this is evolving and just you can't use the old mental model of like, Oh, well it's like just a prediction machine or it's just like making predictions or it's like, ah, the prompts are okay. Like, no, go read this article.
[00:41:39] Mike Kaput: You'll see quickly how things are moving.
[00:41:41] Paul Roetzer: Yeah. And it's helpful just to like, I mean, it's a fire hose, like what we're all trying to like process right now is just a fire hose of information and names and companies and model numbers. It's a lot. And so whenever you can get these articles that just do a nice job of giving us like, Hey, here's the three to five themes to be thinking about right now.
[00:41:59] Paul Roetzer: Like, [00:42:00] that's what we try and do with our podcast, but we love it as just, you know, people seeking knowledge too. Like, I always love when I can just step back and say, okay, what are the three to five things they're saying? And does it jive with what we're thinking? Or is this actually Like, a different perspective that we should be considering when we're talking about on our show.
[00:42:15] Paul Roetzer: So, yeah. Good. You know, appreciate Sequoia and Brookings and New York Times for putting out some good stuff.
[00:42:23] Mike Kaput: All right. Let's dive into this week's rapid fires. First up, we're going to talk some more about Google's Notebook LM. This is the company's AI powered research assistant. This thing is just quickly becoming a darling of the AI community.
[00:42:39] Mike Kaput: Google actually says over 80, 000 organizations are already using it, and it's getting even more powerful updates. We've had a slew of recent updates. And first off, Notebook LM is actually getting rid of the experimental label. It is now ready for primetime. Millions of users appear to already be using the AI powered [00:43:00] notebook to engage with intricate topics.
[00:43:03] Mike Kaput: The star of this that kind of put Notebook LM really got a bunch of its current buzz and put it on the map. With this audio overview feature, where you had your own personal AI podcast hosts that discussed the content you had uploaded to the notebook. They just made an update to enhance this feature, where you can now actually guide the virtual hosts.
[00:43:24] Mike Kaput: So you can customize their focus, And their expertise level, it's basically they equate it to like slipping the hosts a note just before they go live, like shaping how they present the material that you want to consume. So if you want them to focus more on specific topics, you can do that. You can have them adjust their expertise level to suit their audience.
[00:43:47] Mike Kaput: And, you can also listen to them now while you're actually working within NotebookLM, which is a new feature, so you can be querying your sources, getting citations, all while listening to this audio overview. [00:44:00] Interestingly, Google is now also rolling out NotebookLM Business, which is going to be available soon through Google Workspace.
[00:44:08] Mike Kaput: This is tailored for organizations, universities, and businesses. It is also focusing on data privacy and security, so you can rest assured that you're keeping your information safe and secure as you're using Notebook LM. So, Paul, like, Notebook LM just feels like it's kind of caught lightning in a bottle at the moment.
[00:44:29] Mike Kaput: I mean, it's been around for longer than we've been talking about it, but right now it's just having this incredible moment. Now, for instance, we've been using it, like, inspired by some experiments from our podcast listeners that they posted about stuff they were doing with it. I actually just put all of our 2024 podcast episodes into a single notebook, which I can then query and converse with.
[00:44:52] Mike Kaput: We were literally using it right before recording to really quickly find some, like, obscure quotes that we'd been talking about. and Mike [00:45:00] was like, Oh my
[00:45:00] Paul Roetzer: God, it actually works. Like you found exactly what we were looking for. Yeah. I typed in
[00:45:04] Mike Kaput: some very vague search of something we were trying to remember if Paul had said, and it narrowed it down quite quickly.
[00:45:10] Mike Kaput: It was really impressive. Have you, how have you been using Notebook Ellen?
[00:45:13] Paul Roetzer: Interesting enough, last night I was prepping for the podcast today. Like the way I, like I've said before, but if you're a new listener, like this is kind of how we do it. Every article we talk about, podcast we talk about, video we talk about, I listen to, watch, or read every single one of them.
[00:45:29] Paul Roetzer: I don't use AI to summarize these things for me and then just regurgitate bullet points for people. Because my feeling is like, I don't grasp the topic then. Like, I don't deeply understand it if I don't personally consume it. So, the way I do things is like, let's say I'm listening to a podcast, or like, go back to the Sequoia example.
[00:45:46] Paul Roetzer: I will read it and I will be copying and pasting excerpts from it into a kind of a sandbox and then I'll boldface the things that I want to specifically call out. So what I tested last night for each of the main topics is I created a new notebook and notebook [00:46:00] LM for each of them, gave them the source note, New York times doesn't work because it's a paid subscription and you can't get it into there even though I have a paid subscription.
[00:46:08] Paul Roetzer: So maybe a future integrations with paid subscriptions would be cool. but then I created a briefing doc and a table of contents as the starting point for them. And what I then did is I went through and looked at the themes I had pulled out personally and compared them to the themes and summary that NotebookLM created to see if I missed anything or if it called out something that maybe was more interesting than what I was selecting as a theme.
[00:46:34] Paul Roetzer: And so I actually, last night was my first time to sort of pilot using it as a theme like a research assistant almost in prepping for the podcast. And like, it was cool. Like I haven't, I haven't perfected the workflow at all, but it definitely helped, help me again, not replacing doing the work, but definitely assisting me in doing it.
[00:46:54] Mike Kaput: Yeah, for lack of a better phrase, I feel like it's extremely helpful in this like connecting [00:47:00] the dots research where it's like not just dropping in one complicated source to understand it. It is very valuable for that. But like, for instance, I dropped in three of the top Papers or manifestos on AGI last week.
[00:47:13] Mike Kaput: Dario Amodei's, Sam Altman's, and then Leopold Aschenbrenner's situational awareness paper. And then you can start saying, like, what commonalities did they hit on? Did they disagree on? Things like that. It's really insightful if you want to kind of dial in on a topic. Oh, yeah. I
[00:47:28] Paul Roetzer: mean, you and I could probably sit here and brainstorm, like, 50 ways to use it.
[00:47:31] Paul Roetzer: Because as you're saying that, I immediately was like, okay, so if we're profiling Demis Hassabis, let's go grab the transcripts from his last five interviews, and let's, like, Summarize, you know, those things, yeah, it could, yeah, or we could like, if we were talking about like AI in the workforce, which is the thing Mike was trying to look up before we started is like the last time I talked about jobs in the workforce, we could go back and say, let's just grab every episode where we talk specifically about jobs.
[00:47:55] Paul Roetzer: Let's create a notebook LM dedicated to the transcripts of those. And now we have everything [00:48:00] that we've talked about previously about jobs. So like, yeah, it's again, like pick a single tool with ChatGPT, Notebook. LM, and like, go deep on, let's find three to five use cases that are just going to be really valuable to us.
[00:48:12] Paul Roetzer: And let's, let's lock those in and like adjust our workflow. And then you can always experiment, keep finding more, but like, just drill in and nail those and you create a ton of value for yourself.
[00:48:23] Mike Kaput: Alright, next up, Adobe just wrapped up its annual MAX event, and during this they announced a bunch of new and interesting AI updates.
[00:48:31] Mike Kaput: So the star of the show was Adobe's first generative AI video model. This has been teased for a while. It's called their Firefly video model. It is now launching across a handful of new Adobe tools, including right inside Premiere Pro. So, there's a tool now in Premiere Pro in beta called Generative Extend, which can be used to extend the end or beginning of footage that's slightly too short, or make adjustments mid shot, like [00:49:00] correct shifting eyelines or unexpected movement.
[00:49:04] Mike Kaput: Adobe also announced a bunch of AI powered features across its Creative Cloud apps. One, for instance, is in Photoshop. It is called Distraction Removal, and it can automatically identify and remove common distractions in images like people or wires with a single click. Adobe is also pushing the boundaries with experimental tools.
[00:49:28] Mike Kaput: They have one called Project Turntable, which allows designers to rotate 2D vector images as if they were 3D objects. This would typically require you to completely redraw the image. Another interesting development is something called Project Know How which can help combat misinformation by tracking image ownership across various platforms.
[00:49:50] Mike Kaput: Adobe is also teasing some future developments including something called Project Concept, a planning app that allows real time collaboration on mood boards with AI powered [00:50:00] image remixing capabilities. And interestingly, Adobe during this event signaled a shift in its approach to AI. So Scott Belsky, the chief product officer, announced that the company is moving away from the prompt era of AI, which he suggests cheapened and undermined the craft of creative professionals, and instead Adobe is entering what they call the control era, focusing on integrating AI in more specific ways to enhance creative workflows without replacing the human touch.
[00:50:31] Mike Kaput: Now Paul, I confess that. Twelve to eighteen months ago, with everything we were seeing, all this stunning stuff coming out in image and video generation, I was like, I worry Adobe's in a lot of danger and they are not going to move fast enough to deal with this, given their established business. But I don't think I had to be worried because they have like been on a tear with embracing generative AI.
[00:50:55] Mike Kaput: They've got their own video model ahead of Sora, and they beat out Sora to actually get to [00:51:00] market. Like, how bullish are you on what Adobe is up to?
[00:51:03] Paul Roetzer: So interestingly enough, if you go back to like 2019, 2020, 2021, when I was doing keynotes about artificial intelligence, I actually often featured Adobe as one of the forward thinking companies.
[00:51:14] Paul Roetzer: They were doing a ton in, you know, what we'll call like the machine learning era before generative AI really took off in 2022. like I specifically remember this, like, I think it was their CEO and they had a slide. This is back in like 2019 and they go, we're a hundred and some AI features within the Adobe platform.
[00:51:31] Paul Roetzer: So it's not like Adobe. Wasn't thinking about AI and doing AI, but there was definitely that window when generative AI emerged in 2022, where it's like, what is Adobe doing? Like they just seem to get caught flat footed by the innovation in image and video generation and editing. And then when they came out with like their first version of Firefly, it was kind of unimpressive.
[00:51:54] Paul Roetzer: And so, yeah, they do seem to be. Catching their stride now, and I was following along online with some people who [00:52:00] were at their event, and it seemed like people were responding very positively. I know they focused on, I think it was their Firefly video model, that they were positioning as like a responsible model that's actually trained only on licensed data and their own internal stuff.
[00:52:14] Paul Roetzer: So, yeah, I mean, it definitely accompanied your watch. I think that While we thought there was a lot of probability of disruption in the early days, almost going back to kind of how Sequoia was talking about this, with these, you know, kind of wrapper companies that were showing up and they're going to threaten Adobe, it does sure seem like we've shifted more toward the incumbents who figure out how to apply AI seem to still have an advantage.
[00:52:41] Paul Roetzer: They have the data, they have the money, they have access to the compute to build models yeah, so it's, you know, I don't know where Adobe's stock has been, like how Wall Street's been responding to their moves. but it does seem like they're heading in a good direction. [00:53:00]
[00:53:00] Mike Kaput: All right, we've got a couple other design and imagery focused updates here.
[00:53:05] Mike Kaput: So MidJourney, which is the company behind one of the most popular AI image generation tools out there, has announced plans to release an upgraded web tool that allows users to edit any uploaded images from the web using MidJourney. So this new feature is apparently set to launch sometime this week, according to their CEO.
[00:53:26] Mike Kaput: This will enable you to edit existing images and also re texture objects within them. So users will be able to essentially repaint colors and details of objects based on text captions, which opens up all sorts of creative possibilities. However, there are, understandably, some concerns here. The ability to easily edit and manipulate existing images is raising questions about copyright infringement and potentially spreading deepfakes.
[00:53:55] Mike Kaput: Thanks for watching! MidJourney, to address this, is planning initially to [00:54:00] restrict the release to a subset of its current communities and implement increased human moderation along what they call new, more advanced AI moderators. So Paul, given just like how popular and powerful MidJourney already is, this seems like kind of a big deal and certainly something I imagine Adobe is looking at pretty seriously.
[00:54:21] Paul Roetzer: Yeah, and it's that, you know, it sounds like Adobe is really steering toward their traditional customer base of the design community where mid journey is likely going to open up to the non design community and more and more cater to people like me who have zero design capabilities but still wants to You know, messing around with logo concepts and, you know, tweak designs and improve images that otherwise I would have no, you know, right doing.
[00:54:45] Paul Roetzer: So, yeah, it's, it's kind of interesting. The responsible rollout, good luck. Like anything mid journey can do, someone can do with open source, like it's just not a solution. So, I, I just, when I see messaging like that, it's like, yeah, okay. Like it's just like playing the PR [00:55:00] game of trying to, you know, sound good, but let's all be realistic that this, Tech is going to be readily accessible to anybody and they're going to be able to do whatever they want with it.
[00:55:10] Mike Kaput: So Playground, which is another popular AI graphic design tool, just released Playground V3, which is their latest text to image model that achieves state of the art performance across a bunch of testing benchmarks. And they basically said that our new model's focus was to be the best at prompt understanding and control.
[00:55:29] Mike Kaput: There's that control term again. Going beyond aesthetics, which has saturated as a benchmark, it outperforms all the most popular image foundation models in its class. Now, the company says that they actually evaluated this model across popular graphic design categories. So users consistently chose Playground B3's designs over human made ones in categories that may sound familiar.
[00:55:53] Mike Kaput: Things like logos, social media post designs, cards and invites, and even memes. [00:56:00] It can also handle prompts with more detail and longer token lengths than any other image model according to the company. It excels at generating accurate text within context in the image, which is something that historically these models have struggled with, and they say that Playground V3 shines in all these areas thanks to its LLM integrated structure.
[00:56:22] Mike Kaput: It understands and follows detailed composition, layout, and style directions while also grasping cultural references like holidays, memes, celebrities, sports teams, and more. Paul, we've talked a bit about Playground in the past, certainly not as much as the usual suspects like Adobe and Midjourney, but it's really interesting.
[00:56:44] Mike Kaput: They're pretty clearly pivoting or positioning themselves as AI for graphic design. Should we expect more of this in the graphic design space?
[00:56:54] Paul Roetzer: Yeah, I haven't tested Playground in a while. It's probably been four or five months since I've been in [00:57:00] there, but I used to enjoy it because you could choose the different models and you could kind of play around with the The creativity and the temperature, I guess, for lack of a better way of saying it.
[00:57:09] Paul Roetzer: so yeah, I'll have to, I'll dive back in and play around with it a little bit. I don't, I mean, it sounds like they're saying they're building their own models. I find that hard to believe, but maybe they are, versus tuning on top of someone else's models. Yeah, I mean, it's, they've been around for a while.
[00:57:24] Paul Roetzer: They've been kind of, I forget, they rebranded at some point. It wasn't called Playground. Yeah, they did. I forget what the initial name was. Yeah. But yeah, it's, again, like it's worth using as a, like, as a non designer. I was able to kind of get in there and do some stuff. But like, imagine from, Google Gemini is getting really good.
[00:57:42] Paul Roetzer: DALL E's, you know, increasingly getting better. I think both of those will probably have these kinds of capabilities native within it. So I think it's a hard play. Like if I was Getting pitched to invest in a company like this, I would probably struggle to understand how they're going to differentiate 12 months from now when [00:58:00] DALL E and IMAGINE have all these capabilities baked in and Adobe's got them all, but maybe there's a market there for it.
[00:58:06] Paul Roetzer: I don't, I don't know.
[00:58:08] Mike Kaput: So next up, Elias Taurus, who is the former VP of Engineering at HubSpot and the co founder of Drift, which sold for 1. 2 billion, has launched a new AI startup called Agency. Now Agency just emerged from stealth mode. It secured 12 million in seed funding led by Sequoia and HubSpot Ventures.
[00:58:30] Mike Kaput: And the company's mission, they say, is to automate many of the tasks traditionally handled by customer service managers, CSMs. Things like onboarding, training, and upselling new features to users of things like complex B2B software. So, Taurus actually conceived the idea for Agency while consulting for OpenAI in early 2023.
[00:58:51] Mike Kaput: He was basically helping work on AI solutions for OpenAI's enterprise customers. and realized that everyone could benefit from AI powered [00:59:00] CSM work. It's basically designed to understand each customer deeply by analyzing data from different sources, like emails, CRM, chat, and phone conversations. This allows agencies AI to anticipate customer needs effectively, automate routine tasks like scheduling, follow ups, customer onboarding, and meeting prep.
[00:59:21] Mike Kaput: The product is currently in an invite only beta phase. But it is being tested by companies like Haygen. In a post that describes the company Taurus wrote about the name, quote, the company is called agency because that's the vision. Just like hiring an agency, our product will handle the work for you and without the meetings, contracts and back and forths.
[00:59:43] Mike Kaput: Paul, we're obviously very familiar with both HubSpot and Drift. Elias's background alone makes this worth paying attention to. Like, what do you make of agency, given your experience with. His background in these companies and this kind of problem set. [01:00:00] I'd also kind of love to get your thoughts on this name, because this is total speculation on my part, but this is customer success focused right now.
[01:00:07] Mike Kaput: But that last quote I read sure sounds to me like this is meant to expand to other areas reserved for agencies. Am I wrong in that?
[01:00:14] Paul Roetzer: Well, I mean, it interests us. Sequoia, the investment, Brian Halligan is at Sequoia now. Brian Halligan, the chairman of HubSpot, co founder, former CEO. I think Brian is involved, obviously, in this deal.
[01:00:27] Paul Roetzer: Brian is the guy back in 2000, so my agency, again, long time listeners know this, so I created PR2020 back in 2005, we were HubSpot's first partner back in 2007, we were the origin of their partner program that at one point accounted for 45 percent of their revenue, so yes, I have intimate, you know, knowledge and experience with HubSpot, I built an agency on the backbone of their agency ecosystem.
[01:00:51] Paul Roetzer: man, I probably have a lot of thoughts about this one, but this is just a rapid fire, so I'll be concise here. As soon as [01:01:00] I saw the name, and as soon as I saw the description, I thought, well, that seems like a pretty direct service as a software play, which Halligan has previously tweeted about and Sequoia touched on in their paper that we didn't really get into.
[01:01:11] Paul Roetzer: With this idea that the AI provides the services, and it sure sounds like Torres is directly saying that, like, we're just going to , you don't need the agency, like, we'll build the agency for you, and you'll automate it, and it might be a collection of models and agents, like I was explaining earlier, like a symphony of agents, and, and it does the work for you.
[01:01:32] Paul Roetzer: So, Yeah, interestingly enough, I mean, this is, so my keynote for AI for Agency Summit on November 20th is like AI agents and the future of the agency. And I don't, I don't even know what I'm going to say yet, honestly, and it's like a month from now. But this is the exact thing I was trying to prepare agency leaders for.
[01:01:51] Paul Roetzer: So I don't obviously own an agency anymore. I don't really have a stake in the game. but when I look from the outside in, knowing what goes into running an agency for 16 [01:02:00] years, I, I would be very seriously, exploring what the future of the agency world is when people are literally creating AI companies called agency and saying they're going to do your job for you, but the client doesn't have to deal with all the BS that goes with managing an agency relationship.
[01:02:17] Paul Roetzer: Microsoft Mechanics So, I don't know, maybe, maybe they're going to sell to agencies as like a future, maybe that's a distribution channel for them, I don't know, but, yeah, from the outside looking in and without doing a bunch of additional research or talking directly to them, this sure seems like a direct shot at saying, let's just go take on the agency world and, you know, the multi billion dollar industry that it is, let's, let's go get a piece of that.
[01:02:41] Paul Roetzer: And I, honestly, It's there to be had, like I, you know, I think it's a threat to agencies. I think it's a very smart market for Sequoia and, and it's interesting that HubSpot Ventures is involved because, I mean, HubSpot was built on the back of agencies and they have over the recent years, [01:03:00] let's just say that partner program has evolved in its focus.
[01:03:04] Paul Roetzer: So fascinating.
[01:03:08] Mike Kaput: So, next up, another kind of related, Bain Company, which is one of the giants in the world of consulting, has announced a significant expansion of its partnership with OpenAI. So, the two companies have been collaborating since 2022. They had a global services alliance announced in 2023, but now they're expanding in a couple different ways.
[01:03:30] Mike Kaput: So, Bain is establishing a dedicated, what they call Center of Excellence, COE, staffed by a team with extensive experience in open AI technologies. Bain and OpenAI will co design and deliver initial solutions for, they specifically call it Retail and Healthcare slash Life Sciences, with plans to expand to other sectors.
[01:03:52] Mike Kaput: This center of excellence will be equipped with the technical resources to use OpenAI Frontier technology to deliver [01:04:00] client solutions. And so far, Bain has already deployed OpenAI platforms, including ChatGPT Enterprise, to its employees worldwide. They say that the partnership has already delivered concrete results for clients like Coca Cola.
[01:04:13] Mike Kaput: And, in addition to this partnership, Bain is going to continue to offer AI transformation consulting services that include stuff like strategy development, process change, and organizational development. So Paul, we have covered plenty of partnerships between these consulting firms, like Accenture, McKinsey, with OpenAI and other AI companies.
[01:04:35] Mike Kaput: This is kind of, you know, the latest expansion of this type of team up. I found the focus here on retail and healthcare interesting. is that kind of a signal that those two industries are what people have their eyes on when it comes to AI transformation? I
[01:04:50] Paul Roetzer: mean, they're just big market value industries, obvious, you know, use cases.
[01:04:55] Paul Roetzer: But yeah, I mean, what's going on here? Like, again, go back to HubSpot in 2007. What [01:05:00] HubSpot eventually decided was that the way to push and distribute their software into the market was to go through trusted relationships with agencies. So, you know, Build an agency partner program with people like, you know, my agency and you introduce HubSpot software into that client base.
[01:05:16] Paul Roetzer: And so that's what's happening. And this has been going on for a couple of years. I've been involved in some of these conversations with some of these bigger firms where, you know, if you're OpenAI or you're Google Gemini or, you know, Anthropic or whomever it is, whatever the deals we've been hearing about with Accenture, McKinsey, and Deloitte, instead of building your own sales force and scaling it up immediately and trying to go in and sell these enterprises that you don't have relationships with, it's way faster to train up an existing, client base or client relationship, team.
[01:05:50] Paul Roetzer: at Bain or Accenture or McKinsey, and, and then have them introduce your technology through their solutions. So you're just leveraging the trusted [01:06:00] relationships of Bain and Accenture, McKinsey, and, and let them go do the work and the onboarding and sell services on top of it. And I think back in the day with HubSpot, it was like for every dollar of software people spent, I'm going to get the number wrong, but I want to say they spent four or five dollars on services.
[01:06:17] Paul Roetzer: And so it's the same premise here. If you go spend. 2 million a year on OpenAI enterprise licenses for your entire, you know, organization. You're probably going to spend 10 million on the services to onboard and implement and function the change management, build the solutions around it. That, it's, this is a playbook that's been going on in tech for, you know, 50 years.
[01:06:36] Paul Roetzer: So, it's, it's a very natural thing to see happen. Alright, a couple final Until agency AI puts them out of business. I'm just kidding. That's not going to happen. Oh boy.
[01:06:47] Mike Kaput: That, we might have to roll that into another main topic next week. I feel like there's more to unpack there.
[01:06:54] Mike Kaput: All right. So a couple of final topics here this week.
[01:06:57] Mike Kaput: The UK government appears to be planning [01:07:00] to consult on a controversial, what they're calling opt out model for AI content scraping. So the Financial Times is reporting. That under this proposed opt out model, basically AI companies would be allowed to scrape online content from publishers and artists unless those parties specifically opt out.
[01:07:19] Mike Kaput: The UK government plans to unveil their consultation on this opt out model in the coming weeks. And this is described as the government's quote, preferred outcome by sources that are close to the matter. Publishers and creatives are not exactly happy about this. They say this kind of model of Regulation or legislation is unfair and impractical.
[01:07:42] Mike Kaput: They claim it would create a huge administrative burden for smaller companies. The creative industry, you know, prefers this opt in model, which allows for licensing agreements and fair compensation. Interestingly, the European Union has a similar opt out model in the AI Act. [01:08:00] Paul, obviously this is exclusive to the UK at the moment.
[01:08:03] Mike Kaput: Kind of interesting though to see that this approach, which was heavily lobbied for by AI companies, is the preferred outcome. Like, given that the major cash and influence that AI companies wield, not just in the UK, should we expect to see this approach kind of become
[01:08:22] Paul Roetzer: I have no idea. I mean, I feel like, I think Japan has, their laws, basically there is no, like, copyright doesn't matter.
[01:08:30] Paul Roetzer: Yeah, they're very liberal. So, I mean, I'm kind of surprised to see in the UK, but maybe I, I misunderstand how they generally have approached AI. I mean, the EU certainly has taken what seems to be quite a conservative approach. Yeah. And this does seem to almost be the opposite, but I, I don't know. Like, it's, it's, it's I'd be interested to see, like, we should dig back into the, like the U.
[01:08:55] Paul Roetzer: S. Copyright Office and see if there's been any updates. To my knowledge, there hasn't been from the [01:09:00] listening sessions they were doing like 2023. I don't know if there's been any movement or anything. But yeah, we'll have to circle back around and see if there's any updates on what's going on in the U. S.
[01:09:09] Paul Roetzer: on this topic. I mean, even back in MAICON in September, there wasn't anything new that I'm aware of. And we had that whole panel on copyright. Yeah, I know. I don't think
[01:09:18] Mike Kaput: I've seen anything come out that's definitive.
[01:09:22] Mike Kaput: Alright, so next up, Microsoft has announced that there are new Autonomous Agent capabilities coming for Copilot.
[01:09:29] Mike Kaput: So first, you'll be able to access a public preview of the ability to create Autonomous Agents within Copilot Studio starting next month. And second, Microsoft is introducing 10 new Autonomous Agents in Dynamics 365 for things like Sales, Service, Finance, and Supply Chain. Here's a quote from Microsoft in this announcement on kind of what they're doing here.
[01:09:52] Mike Kaput: They say, quote, Think of agents as the new apps for an AI powered world. Every organization will have a constellation of agents ranging from [01:10:00] simple prompt and response to fully autonomous. They will work on behalf of an individual, team, or function to execute and orchestrate business processes. Copilot is how you'll interact with these agents, and they'll do everything from accelerating lead gen and processing sales orders to automating your supply chain.
[01:10:19] Mike Kaput: So this feature within Copilot has been private. Previously it's been used by a few select customers, people like McKinsey and Thomson Reuters. Now it will be in a public preview, which means more people have access. Microsoft also provided some examples of like what these Dynamics 365 agents look like.
[01:10:38] Mike Kaput: One is a sales qualification agent that basically prioritizes sales opportunities. Another is a customer intent and knowledge management agent that improves customer service by learning to resolve issues. Now, obviously Microsoft is heavily interested in promoting this service, but says that some of their early results from using agents [01:11:00] include a sales team, achieving 9.
[01:11:02] Mike Kaput: 4 percent higher revenue per seller, and 20 percent more closed deals. And an HR team having 42 percent greater accuracy answering employee questions with an agent. You can apparently start building agents in Copilot Studio today, but this autonomous agent capability is rolling out next month. Paul, can I have a couple things just jump out here?
[01:11:27] Mike Kaput: Like one, we're obviously like all hit on agents. Microsoft is not the first one to be doing this. We've talked about Google, Salesforce, and others. Two, this kind of struck me as like a really broad definition of agent. Like, they seem to be considering them as anything from simple AI assistants to fully autonomous.
[01:11:48] Mike Kaput: Like, Is this going to get really confusing for Pyres and users if you're not following this closely?
[01:11:54] Paul Roetzer: Yeah, I'll tell you the thing that's confusing me is why is Satya Nadella tweeting this at 6. 30am Eastern time on [01:12:00] a Monday morning? Like, I'm, seriously, what, what else is happening this week that they felt the need to get this out?
[01:12:06] Paul Roetzer: They don't like to drop events that I'm aware of. I just did perplexity search and said, what is Microsoft doing for AI this week? And I can't see anything. The only time you see news like this is when, like, OpenAI is about to announce something and they're getting out ahead of it. Or maybe anthropic or something, but like, very weird.
[01:12:24] Paul Roetzer: Announce, like it specifically says, today we're announcing new, it's like, okay, why today? Like, is this stuff you normally announce at an event or not at 6 30 in the morning? So, which again, Eastern time, like then I'm in West Coast. So, and it's the CEO tweeting. Like, usually when Sundar or Satya tweets something, there's something more to the story.
[01:12:47] Paul Roetzer: Yeah, I don't, I don't know. It just seems like a continuation of like, we know they're building agents, we know it's under co pilot, like I'm not sure what exactly is actually new here that wasn't already out per se, but I think I'm more [01:13:00] intrigued by now what happens the rest of the week because if they did this just randomly on a Monday morning, then it just isn't following the patterns that, AI leaders have been following over the last two years, which is to preempt each other on other news.
[01:13:14] Paul Roetzer: Interesting. We're going to bookmark this. Yeah. I went and looked at Twitter. I wonder if anybody's announced anything while we're sitting on this.
[01:13:22] Mike Kaput: Alright, so our last topic this week is that Demis Hassabis, the head of Google DeepMind, recently sat down with The Times, which is a British publication, at their recent tech summit.
[01:13:35] Mike Kaput: to discuss building AGI with safety in mind. And Paul, I know you, were paying close attention to some of the things Demis was saying here. Do you want to kind of talk us through, like, what he was talking about that, that is worth paying attention to here?
[01:13:51] Paul Roetzer: It kind of goes back to that Sequoia article where, you know, if you're a regular listener, paying close attention, nothing groundbreaking per se, but when I listened to the podcast, there were [01:14:00] definitely a few things that, jumped out at me.
[01:14:02] Paul Roetzer: So I'll just call it a few pieces. So. When asked specifically about timeline for AGI, he said, probably within 10 years, he then did give his definition of AGI, which, you know, we've quoted numerous times and it changes a little bit, but I thought it was worth noting how he defined it here. So he said, the goal of DeepMind is to get to AGI, which means a general system that's capable out of the box of doing any cognitive tasks that humans can do.
[01:14:29] Paul Roetzer: So fully general, capable of computing anything that's computable. Now, interestingly, he does not distinguish there between, the performance level, which is in the levels of AGI that Shane Legg and the DeepMind team published earlier this year. It's, so it says doing any cognitive task at what level? At a 50 percentile level of like 50 percent of all humans?
[01:14:53] Paul Roetzer: At a virtuoso level, like 99th percentile of humans, like PhD level? So, again, like a [01:15:00] definition, but with some vagueness to it. He does say that multimodality is a key to AGI, which we know, that the models from the ground up, like Gemini, are being built with image and video and audio capabilities and coding capabilities and reasoning right within the model, it's not just a text in, text out model, which is what GPT 3, GPT 4 were, he said he thinks there's two to three big innovations needed from here until we get to AGI, and then he kind of hints at what areas those might occur within.
[01:15:29] Paul Roetzer: So he said Project Astra, which we've talked about, which is kind of like the vision capabilities of your phone or of glasses that can see and understand the world. He specifically says memory, personalization, are coming in next gen universal assistants. So memory and personalization are two key things to think about.
[01:15:47] Paul Roetzer: He talked about current chatbots or passive question and answer systems. They want agent based systems, so again, agents coming in. He said they need to be able to do planning, like chain of thought, reasoning, take [01:16:00] actions, have basically nearly infinite memory, remember everything about your interactions with them, and be personalized, so it remembers your preferences.
[01:16:09] Paul Roetzer: And so those are the kind of the keys, like somewhere within those are the two to three breakthroughs he's talking about. That if we can get some breakthroughs that allow us to achieve infinite memory, true personalization, advanced reasoning and planning that we can then get to that point. the one that like kind of stuck with me was he was asked about the doomers, versus the people like the techno optimists who think that every all acceleration of technology is great.
[01:16:35] Paul Roetzer: And they, they said like, why are you sort of more of a cautious optimist? Like, why, why are you concerned? And so he said specifically, so if you go back to like AlphaZero, which is a system that they designed that could learn like gameplay basically from scratch. He said, I've seen this in the microcosm of games, something I understand well like playing chess, where you start with a system, alpha zero, that's random in the morning.[01:17:00]
[01:17:00] Paul Roetzer: By morning coffee break, it can beat me, and he is a, like, world class chess player. By lunchtime, it's better than the world champion. And then by afternoon, within eight hours, it's the best chess playing entity the world has ever seen. I've watched that process over an eight hour period. So he's basically saying all these techno optimists were like, we'll figure it out.
[01:17:21] Paul Roetzer: We'll give it goals. It'll only do what humans want it to do. He's saying, no, I've seen them from zero go to like virtuoso, world class, superhuman at a thing. In eight hours, so anyone who thinks we can't have a fast takeoff doesn't understand how quickly these things can learn when developed this way.
[01:17:42] Paul Roetzer: And so I think it was just kind of like a call of caution, but a very practical way of like, I've been there, I've built the systems that do it, like it can take off. So I always, I mean, it's only like a 25 minute interview. We'll put the link in there. I always just love listening to Demis talk. I mean, I learn something every time.
[01:17:59] Mike Kaput: [01:18:00] Yeah. And I certainly wouldn't characterize him as someone who is over hyping things often. So if he says something like that, I would pay pretty close attention. All right, Paul, that's all we got this week. Just a couple of quick housekeeping notes. If you have not checked out the Marketing AI Institute newsletter, it is marketingainstitute.
[01:18:19] Mike Kaput: com forward slash newsletter. It is called This Week in AI, and we'll give you an in depth breakdown of everything we just talked about, plus all the other news we didn't get to in this episode. Last but not least, if you have not left us a review and you have the ability to do so on your podcasting platform of choice, we would very, very much appreciate it.
[01:18:38] Mike Kaput: It helps us get better and reach more people with the shows. Paul, thanks for demystifying AI for us this week.
[01:18:47] Paul Roetzer: Good stuff as always. Thanks, Mike. And, we'll talk with everyone again next week. Appreciate you listening. Thanks for listening to The AI Show. Visit MarketingAIInstitute. com to continue your AI [01:19:00] learning journey and join more than 60, 000 professionals and business leaders who have subscribed to the weekly newsletter, downloaded the AI blueprints, attended virtual and in person events, taken our online AI courses, and engaged in Slack community.
[01:19:16] Paul Roetzer: Until next time, stay curious and explore AI.