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The Artificial Intelligence Show
#122: ChatGPT Search Is Here, McKinsey: AI Worth “Trillions” in Coming Decades & Microsoft AI CEO Calls AI “New Digital Species”
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- [00:00]
Paul Raitzer
We are in the very, very early innings of this, probably the top of the first inning of like adoption and transformation from AI. But it is going to be massive. And so studies like this from McKinsey help put in context. How massive? But it's all a guessing game. It's big though.
- [00:19]
Mike Kaput
Welcome to the Artificial Intelligence show, the podcast that helps your business grow smarter by making AI approachable and actionable. My name is Paul Raitzer. 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. Join us as we accelerate AI literacy for all.
- [00:55]
Paul Raitzer
Welcome to episode 122 of the Artificial Intelligence Show. I am your host Paul Raitzer along with my co host Mike Kaput. We are recording this November 4th, 10:20am Eastern Time. I have a feeling that might be relevant this week. I don't know, like there's just, there's so much happening. We talked so much about like all the model updates and releases on the last episode I think it was. And it seems like we're probably going to get some more stuff before the holidays here. So yeah, these dates are becoming important and we are now what about 26 days away from the 2 year anniversary of Chat GPT Mike. So that'll be interesting to see what OpenAI does with that. I'm expecting search wasn't the final thing we're going to get leading into the two year anniversary. So lots to talk about today. We've got ChatGPT search. As I alluded to, we've got a new report from McKinsey we're going to dive into. We've got some interesting interviews with Mustafa Solomon and Sam Altman and then a whole bunch of other updates. So we're going to get into that. But first, this episode is brought to us by our AI for Agency Summit. This is our second annual virtual event. It's taking place from 12pm to 5pm Eastern Time on Wednesday, November 20th. So that is coming up fast. I should probably start building my opening keynote for that one. I have notes on it like I generally have a direction but I'm unsure because I'm talking about AI agents and like the future of agencies. And I'm not settled yet on what exactly I think is going to happen with agencies, but I gotta find some mind space in the next week or two here to really sit back and think about that one. So 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. Important note here for the brand side, if you are a business leader, marketing leader, whatever your role is, if you touch the marketing agency relationship, you're going to want agencies moving into 2025 that have are actively trying to solve for this stuff. So if you have some agencies that are core to your partnerships, maybe pass this along to them to take a look at, could be very valuable for them and valuable for you moving into next year. So during the event you'll join hundreds of other forward thinking agency professionals to consider ways to recruit AI savvy talent and upskill your team. Explore how AI tools can boost creativity, productivity and operations, hear insider stories from agencies that are piloting and scaling AI successfully, and understand how AI impacts your pricing models and service offerings. We're going to have, I think there's six presentations from agencies that are actively doing this. We're going to have kind of case studies that they're going to share inside information about what they're doing and seeing across different areas of AI integration in their companies. You can go to ai4agencies. That's f o r a i4agencies.com Click the register now button. Use promo code pod100. That's pod100 for $100 off your ticket. So again, check out ai4agencies.com There is going to be an on Demand option. So if you can't make it on November 20th or you're in a different time zone and can't be there live, no worries, you can get the on demand option. So yeah, AI foragencies.com check that out. And Mike, let's get into our main topics.
- [04:17]
Mustafa Suleiman
All right Paul, like you alluded to, first up this week is ChatGPT can now search the web far better than before thanks to this week's major update with us getting ChatGPT search. So OpenAI wrote in an announcement this past week, ChatGPT can now search the web in a much better way than before. You can get fast timely answers with links to relevant web sources which you would have previously needed to go to a search engine for. This blends the benefits of a natural language interface with the value of up to date sports scores, news, stock quotes and more. Some key features of this new search feature Natural language queries that automatically trigger web searches when needed, direct links to source material and citations specialized Displays for categories like weather, stocks, sports, news and maps. Real time information access for current events scores and market data. And integration with major news publishers including AAP, Reuters, Financial Times and others. The rollout of ChatGPT search is going to be tiered based on your subscription type. ChatGPT plus and team users have immediate access right now. In the coming weeks, enterprise and education users will get access to this. But if you're a free user, you're going to have to wait for months. In the coming months, eventually you will get access to this feature. So, Paul, I want to talk a lot about what this means for Google, but first, can you just share your initial impressions of search within ChatGPT now? Like, how useful do you find these new capabilities?
- [06:05]
Paul Raitzer
Yeah, my impression is that they're, they're going to change the way you use ChatGPT, honestly. So I think the user experience is really solid. When you choose, you can choose a search. So if you haven't tried it yet, you can actually Click search. Or ChatGPT just intuits that you're conducting something where search would be valuable and it automatically will use search. So I personally tested on like planning a trip into Florida, you know, this fall. Um, and so I just said like planning a trip to Florida, specifically, it's the city. And it immediately gave a very logical structure. It pulled in pictures, it had a getting there header or where to stay, must see attractions, activities, dining, weather, tips. So all this, like, all the structure that we're used to with ChatGPT where it automatically kind of curates these outlines, but now it had inline citations. I tried one for, I think it was like Friday morning or something, or Saturday. Give me a summary of the Cavs Cleveland Cavaliers game from the, from last night. And it pulled in Reuters and Talk Sport articles and summarized it, but then linked right to those articles. I tried one just out of curiosity, like, when was the last time the Cleveland Cavaliers started 6 and O? And it pulled in Wikipedia, NBA and then it actually pulled in real time stories from Reuters in the New York Post from the previous night's games. I tried one like, why are tech stock down, tech stocks down? So I was just like experimenting with it. And my overall take is I really enjoy the no ad experience. I went over to Perplexity and I tried some of those same searches again. And honestly, like Perplexity started to feel a bit obsolete to me. More from the user interface. And it feels very cluttered all of a sudden, which, I mean, if you're used to Google, Google's Cluttered as is. So perplexity didn't seem that big of a difference, I guess. But when you have this totally clean interface with no ads or anything, perplexity just felt different to me all of a sudden and not as modern and clean. And that's before perplexity infuses ads, which we know they're working on. And so that was kind of like an initial reaction. But to go back to, you know, you had mentioned kind of like the bigger picture here about like Google and search. I actually was like, I think you and I talked about this in March 2024. I didn't go back and look for the episode would have touched on it. But Sam Altman did an interview with Lex Friedman. It was episode 419 of Lex Friedman's podcast. We'll put the link in the show notes. And I, I, I remembered like as soon as I saw this last week I was like, wait, what was that Lex interview where he talked about this? I remember noting it at the time. So anyway, I went back to the transcript and again we'll put the link in. And Lex asked Sam specifically about search in March 2024. So I think it's important for people to realize like, this isn't a new thing. OpenAI has been thinking about search and how they would handle it for, for years and Sam has openly talked about it for a while. So in that interview, Lex said, like, how are people going to access information? You know, people show up to GPT as a starting point. So is OpenAI really going to take on this thing that Google started 20 years ago? And Sam said, I find that boring. I mean, if the question is will we build a better search engine than Google, then sure, we should go. People should use the better product. But I think that would be to understate what this can be. So again, kind of quoting him, Google shows you 10 blue links, well, 13 ads and then 10 blue links, kind of taking a shot at Google. And that's one way to find information. But the thing that's exciting to me is not that we could go build a better copy of Google Search, but that maybe there's some better way to help people find and act and synthesize information. Actually, I think ChatGPT is that for some use cases and hopefully we'll make it be like that for a lot more use cases. He went on to say, but I don't think it's that interesting to say how do we do a better job of giving you 10 ranked web pages to look at than Google does? Maybe it's really interesting to go say, how do we help you get the answer, the information you need? How can we help create that in some cases, synthesize that in others, or point you to it and yet others. But a lot of people have tried to just make a better search engine than Google and it is a hard technical problem, it is a hard branding problem, it is a hard ecosystem problem. I don't think the world needs another copy of Google. But then he does talk about integrating the ChatGPT and he says that's cooler. Like if we could build search right into it, as you might guess, he said, we are interested in how to do that. Well, the intersection of large language models plus search, I don't think anyone has cracked the code yet. I would love to go do that. And then Lex said, well, what about ads? Have you thought about how to monetize it? And he said, and this is important. This kind of gives a preview of how they may not go, the direction perplexity is going to go. And the way Google is built on says, I kind of hate ads as an aesthetic choice. I think ads needed to happen on the Internet for a bunch of reasons to get it going. But it's a momentary industry. The world is richer now. I like that people pay for ChatGPT and know the answers they're getting are not influenced by advertisers. I'm sure there's an ad unit that makes sense for language models and I'm sure there's a way to participate in transaction stream in an unbiased way. But basically he's just not a fan of ads at all and he really wants to build their business model based on subscriptions instead. And then the other, you know, a couple points of context here in. So it was it in June, I think of this year, June or July, they introduced Search GPT prototype. So again, we've known this was coming. How exactly they executed it wasn't clear. But even back then they showed how they were thinking about it. Said, we're testing Search GPT a prototype of new search features designed to combine the strength of AI models with information from the web to give you fast and timely answers. So, and then like one of the key reasons, so getting into like, why does search matter? Like, why is this important? You have to remember that like large language models have a cutoff data on their knowledge base. So GPT4O's knowledge cutoff, like when it was trained, is October 2023. So the data in 4.0, when it's not connected to the Internet, it doesn't know anything about the world from the last 13 months basically. And so it was pre trained on the data up to October 2023. So what the benefits of search in the LLM is access to real time information. In theory and so far research has proved this out. A reduction of hallucinations so you can have greater accuracy and reliability. If you can vet the outputs with real time data and search information, it improves the potential for personalization and localization of results. So you know, a lot of times that's not being infused into these LLMs where, you know, Mike and I may have the same experience even though, or different people and maybe in different locales. And it also enables the verticalization of information of these LLMs to specific industries or domains. So imagine in like law if you don't have the latest legal cases, or in healthcare if you don't have the latest research or reports, or in sports, finance, stocks, all of these verticals require up to date information that these models don't have. Inherently they're not, they're not traditionally a search engine like Google are used to getting the most updated information. And then my final thought here is just like the concerns or weaknesses, this isn't all like sunshine and rainbows. Like there's, this does open up a greater potential for misinformation. You know, once people figure out how to kind of trick these systems, the information they get to be indexed and found can become an issue. So we don't know how reliable they are. There was a tweet I saw from Emily Bender, who's a University of Washington professor of linguistics and somebody I follow pretty closely on Twitter. And she was saying like one of the biggest challenges to this is people come to believe these things are accurate. So one of the beauties of what Google has done for 20 years is generally, you know, get really good at presenting you accurate information and you can kind of trust it and then verify sources and things like that. But she was saying we don't know how reliable these things are, how much they hallucinate. And the problem is that, and this is to quote her, a system that is right 95% of the time is arguably more dangerous than one that is right. 50% of the time people will be more likely to trust the output and likely less able to fact check the 5%. So if people start assuming that search and chatgpt is right all the time and they don't even click on the links and maybe its error rate is only 5 or 10%, that's a significant error rate and that can affect how people use the system and how they trust it. Bias and manipulation become a thing again. Think about search and how there's the shadow industry of basically just figuring out the algorithms and trying to stay ahead of it. So you already have people trying to figure out how to game these algorithms. It sounds like they're largely using Bing's index, not their own index of the web. Yep. So now you have SEOs, you know, that are probably racing to figure out how to game the Bing algorithms to get injected into these search results. Yep. Creates an echo chamber because they're going to license like a lot of the stuff that's going to get surfaced is going to be licensed from specific sources like a Reuters who are willing to do a deal and let's say politically or religiously or whatever you know, you want to throw in there. They do deals that heavily lean toward one side or the other. Now all of a sudden, or geographically government wise now all of a sudden they lean, the results lean in those directions because that's who they did licensing deals with. So again it's, there's all kinds of challenges around this as well. So on the surface, awesome tech, great user experience, it's clean, I like it. There's a bunch of other macro level stuff going on here though.
- [16:06]
Mustafa Suleiman
And just as a very small example of what this could mean for marketers, like I just typed in what are the top AI tools today? And it says as of November 2024, several AI tools gained prominence. Blah, blah, blah, lists out 10 different tools from very clear sources, none of which I will probably ever click through to because like, seriously, like I have the name of the tools I'm then going to go search. So like this is the exact type of top of the funnel query typically you might be targeting to really get someone's eyes on your website. Obviously we talked about for over a year now that kind of strategy is going the way of the dodo. But like this is very clearly giving me the answer I have and enough information to ask follow ups with links that I'm probably not going to be clicking through to nearly as often.
- [16:54]
Paul Raitzer
Yeah, yeah. And it, it, you know, it's kind of like the step toward the one like with the one answer that you get from voice. So again like if let's say advanced voice. So let's say advanced voice, which I, I don't know if it has search built in or not. I, I would guess maybe it does. I haven't tried that yet. But let's Say that you become reliant on voice interface and like, you know, 6, 12, 18 months from now, you're basically just either talking to Apple Intelligence or Advanced Voice or Google Assistant, whatever when you need something. Well, now your world is living within whatever bubble that company's algorithm is deciding the right answer is to things. Right? Whether it's what you're gonna buy or what you're gonna believe or, you know, just real time information you've now like. The beauty of Google's link strategy is a diversity of places to click through to form your own opinions and points of views and beliefs. If we become too reliant on a single interface and assume that whatever it presents to us is right, and in the case of voice you're only getting one, then you start to run the risk that we're like really forming people's beliefs and opinions in, in the light of whoever's controlling the algorithms or the models that present that information, which again, you could get into all kinds of ramifications around that of like, who's building the models, what government is supporting those models, things like that. So it's infinitely fascinating. Like there's so many threads you could pull from a simple. And this is again, part of our goal with this show is, yeah, chat should be searched. Awesome. But like, let's step back for a minute and think about like the bigger implications here and the questions that it opens up that we don't necessarily have the answers to, but it's, it's good to not just take the technology on its surface and assume it's, you know, all great. They gotta like think about the other angles here.
- [18:47]
Mustafa Suleiman
So to kind of wrap this topic up, like, how bad is this for Google? Like, not only does this come out, but in the past week we got reports that Meta is trying to put its own AI search engine into Meta AI to explicitly reduce reliance on Google. And much Microsoft perplexity, despite some legal challenges, obviously is kind of full steam ahead as an alternate search option. Though they may get hurt even more by this. They're going to start charging for ads. Like, how worried am I if I'm the Google search team right now?
- [19:20]
Paul Raitzer
Yeah, I don't know. It's a difficult question. We have, you know, Bing has certainly seen improvements. So I'm just like the current. From what I can see on Statcounter, Google owns 90% of the search market, Bing's around 4%. But Bing has gained ground. I think we talk a lot about perplexity because we all live and a lot of Our listeners live in this AI bubble and its perplexity seems like it's much larger than it probably really is, just because we talk about it a lot, but it doesn't even show up in the top six search engines, seven search engines, things like that. So their, their market share is I think well below a half a percentage point. If you know, now it's growing. Yeah. And I don't know, like, I think part of this is an interface question, like what is the interface of the future? Will 2, 3, 4 years from now, are we largely going to rely on voice? Are we going to just live within our language models and they become the primary interface to everything and it's connected to everything and in which case then, you know, you could see this being a major threat. So I would imagine that the teams at Google are kind of playing this out as like, what are the possibilities here? What, you know, what's the impact on our ad revenue? You know, how does this change if adoption of ChatGPT continues to skyrocket? And I don't know what they can, what their market share is in language models. But I mean you're the study we did where we asked our users like 1800 people in our state of marketing AI report this year, I think it was like 55% said they had chat GPT licenses at work and that was by far the number one when compared to Co Pilot and Gemini. Now that may flip in the enterprise market like the big enterprises, maybe Copilot's higher adoption. But yeah, I think there's just too many unknowns right now about where this goes, where search goes, where interfaces go, where language model adoption goes. And that'll kind of dictate this. But I'm sure people at Google are paying attention and you know, playing this out of possibility, possible paths that this could go.
- [21:28]
Mustafa Suleiman
All right, so our next big topic this week, the McKinsey Global Institute recently released a report called, quote, the Next Big Arenas of Competition. This is a whopping 213 page report that explores 18 what they call arenas. These are future areas that could reshape the global economy and generate a staggering amount of revenues by 2040, anywhere from 29 trillion to 48 trillion more in revenue combined by 2040 from these 18 future arenas. They basically define arenas as industries that transform the business landscape and have two core characteristics, high growth and dynamism. One of these arenas is particularly important to us and our listeners. It is AI software and services and the implications of some of McKinsey's data on the growth in this area are pretty interesting. So for the purposes of their report, McKinsey defines AI as, quote, a machine's ability to perform cognitive functions that we usually associate with humans. And they kind of include in that both traditional machine learning capabilities where we're using AI to predict outcomes and behaviors, as well as generative AI applications. Notably, though they exclude from this kind of arena this categorization, they exclude hardware like Nvidia chips. So with all that in mind, here's kind of what McKinsey reports about this arena. First, investors are flocking to companies developing advanced AI, particularly Gen AI. Equity investments in that technology jumped from 5 billion in 2022 to 36 billion in 2023. Developments in analytical AI and Gen AI are poised to drive the industry's growth by improving business and worker productivity. In certain modeled scenarios, the Arena's revenues grow from 85 billion in 2022 to 1. 5 trillion in a lower range of scenarios in 2040. And that can go up to as high as 4.6 trillion. In some of the more optimistic scenarios, that's a compound annual growth rate of anywhere from 17 to 25%. Now past McKinsey research also kind of analyzed more than 500 uses for analytical and gen AI and estimated their potential economic impact. One of them, analytical AI such as machine learning and deep learning could amount to an estimated 9.4 to 15 trillion in revenues by 2040. Gen AI could produce 2.6 to 4.4 trillion of economic impact through enterprise use cases. McKinsey highlights that three quarters of that value would be in only four areas. Customer operations is number one, number two, marketing and sales. Number three is software engineering and lastly, R and D. Now all these enterprise use cases do not even account for all the productivity gains of individual knowledge workers who are automating aspects of their occupations. Incorporating all these cases of individual worker productivity enabled by Gen AI, in addition to the enterprise use cases could unlock a total of 6.1 trillion to 7.9 trillion of value annually. So if you add all this together, these components together yield an estimated range of total economic potential from AI software and services of one of 15.5 to 22.9 trillion annually by 2040. Now that's a lot of numbers. Some of those are like eye watering. I don't even believe them half the time looking at them. Obviously Paul, we cannot fully predict this economic impact of AI like 15 years from now, but as you've noted in some of your posts and writing from the past week, it is, it seems safe to assume we are looking at trillions of dollars being invested in and produced by AI in the coming decade. So that certainly seems to me like reading all this, that despite all the hype in gen AI and even some of the disillusionment that's already creeping into the market, it sounds like we're just still so, so early.
- [25:49]
Paul Raitzer
Yeah. And it is like the numbers are confusing and I mean listening to you say them now, it's like hard to wrap your brain around. I, I read like the key findings like five times just to try and comprehend what they were saying because they, it's weird. They, they mix AI revenue and economic impact. Yeah. And then they blend like some new data with a report that they did, I think earlier this year that had to do with where they analyzed 500 AI use cases. And I was struggling to like interpret what was the new data versus what was the previous thing they were referencing. But I think like to unpack this for people as you explain, Mike, the key takeaway here is it's huge. It's trillions. And we're at, we're just at the start to try and put it in context. So I went and pulled the latest gross domestic product numbers, GDP numbers for the United States. So the GDP October 30, 2024 was 29.4 trillion. So again, to put trillions into context, the GDP at the moment is 29.4 trillion, and it's up roughly 5% from the previous year. Now the GDP is the total value of all goods and services that are produced in this case in the United States within a specific period, which in this case is, you know, an annual basis. It is a key indicator of economic health. And it's also when you look to the future and you try and say, well, what's the impact of AI going to be? It is one of the key things you would look at and say, well, what's the impact going to be on gdp? Now I've had this conversation with a few people who are skeptical of AI having like an outsized impact because on average, you know, the GDP is going to grow maybe two and a half to 3% in a given year. Now we've had a jump in the previous 12 months, but it, it's pretty common for it to stay in that range. And so if you have someone show up and say, well, it could be 10%, like a year, or in the case of Leop Leopold Aschenbrenner and his situational awareness, he's talking about like 10, 20, 30% per year and an annual rate and like compounding over time. And a lot of people just say there's just no way, there's no historical context to that, that it's impossible. And so the way that AI could impact gdp, so the economic impact I will come back to the revenue is increase productivity. So we just produce more, we can output more services, we can output more tangible products because we have more time to make more things, basically because AI is assisting us so we become more efficient. The efficiency enables us to do more productivity. The other area is innovation and new product development. AI is going to help us identify new markets, new product ideas. It's going to help us drive innovation that creates growth. As the AI does more and more of people's knowledge work jobs, you have the potential to reallocate the labor force. So we have this kind of finite amount of people that can do work in the United States. I don't know how many people we have in the US is like 300 million or something like that over.
- [28:58]
Mustafa Suleiman
Yeah, a little over 300.
- [28:59]
Paul Raitzer
And there's about 136 million full time jobs. So imagine if AI 10 years from now can do 20 to 30% of the cognitive labor of those a hundred million people that do knowledge work jobs and you can redistribute those people to do other things. So now you have the existing GDP from what we are already doing today. You layer AI's ability to do another 20, 30, 40% of that work. And then you redistribute that 20, 30% of work to other things. You're increasing the output. So a reallocation of labor into new roles, new markets, new businesses. You have industry and sector growth. AI enables growth of different industries, they can produce more. And then you potentially have a boost in consumer demand, which drives the growth of outputs because you can personalize experiences and products to them. And so these are all like fundamental ways, very tangible ways that you can start to make an argument that AI will have a massive economic impact on gdp. Now I mentioned Leopold, like, let's go back real quick to his notes and we'll put this, this was From June of 2024, his situational awareness papers. We'll put the link in the show notes again, but a few key excerpts here. He says, as we. And this is one of the things like I don't think McKinsey's taken into account AGI and superintelligence. And again, this is one of the big flaws I keep saying we see from researchers and economists is like they're not considering the future models. They're considering what we know to be true about basically current models. But in Leopold's papers he's saying we're going to get to superintelligence and like when we do, we're going to see massive economic growth. So he says we could see economic growth rates of 30% per year beyond quite possibly doubling a each year this way. And then he does give a caution this may well be delayed by societal frictions, which I 100% believe to be true. I think it's a large part of why we're not seeing the massive economic impact right now. And he calls out specifically, arcane regulation might ensure lawyers and doctors still need to be human even if an AI system were better at those jobs. Surely Sam will be thrown into the gears of rapidly expanding robo factories as society resists the pace of change. And perhaps we'll want to retain human nannies all which could slow GW statistics. Now a really practical recent example here is we had the east coast shut down and a strike because of the shipping and it was all over, well, at least in large part over the future possibility of automating those ports, which is already being done in other countries. And so this is, could we ship more, could we receive more if we were automating the ports? Absolutely. Will we be allowed to automate the ports due to whatever regulations or union or whatever it is that slows that down? That will be a barrier to GDP growth even though the AI could do it. And so that's an example where, and I'm not saying right or wrong either case. I'm just using it as an example of there's going to be friction, there will be resistance and then when you get into the AI revenue. Leopold made the point companies will make large AI investments if they expect economic returns to justify it. He gets into trying to project out like OpenAI Microsoft's revenue. He said one estimate puts Microsoft at 5 billion in incremental AI revenue already. He said every 10x scale up in AI investment seems to yield the necessary returns. So we're already seeing this like the reason they're putting in 500 million a billion 5 billion 10. But whatever it is is because their expectation is a return. They're not doing this just out of like hope that it's going to work. They're seeing the results. Leopold then says a key milestone for AI revenue that I like to think about is when will a big tech company, Google, Microsoft, meta hit a hundred billion revenue run rate from AI? Specifically these companies have an on order on the order of 100 billion to 300 billion of revenue today. A 100 billion from AI alone would be a huge, represent a huge opportunity and a big fraction of their business. And then you could start to extrapolate out massive growth. And he's thinking we might get there by 2026 based on current models. And so he's just pretty much saying I like the example Leopold gives about Microsoft. He said it may seem like a stretch to be talking about these crazy numbers, which leads us Back to the McKinsey study of trillions. But he said like how unrealistic really is it If Microsoft has 350 million paid subscribers to Microsoft Office? And as we think about these reasoning engines and these more advanced models one to two years out that are basically doing the work of people who are making hundreds of thousand dollars a year, would you as a company be willing to pay a hundred bucks a month for that model? Hell yeah. Like yeah. I mean I, so I haven't, I haven't talked in depth about this yet. Maybe we'll come back to this, but I built a co CEO GPT. Like I built a strategic partner for myself to run my business. I'm, I'm telling you right now, I'd pay a hundred bucks a month to have access to this one custom GPT I've built. Like it is wild how much value every day I get from this thing as a strategic partner for me to run my companies and like kind of build plans and envision the future. So I think once these tech companies get better at explaining the value of their products in a true value oriented form. Not this assumption that I should only pay 20 bucks a month. But the, but what is the value I'm getting out of this as either a replacement or an augmentation to a strategic consultant or advisor replacement or augmentation to a full time employee that's making 150, $200,000 a year, a hundred bucks a month is nothing like to be able to get that value exchange. And so once these companies get better at demonstrating that and then get way, way better at onboarding AI literacy like training and education internally so that the people buying the software are from day one getting value from it, that's where they're, they're all missing the boat right now. But as soon as you do that, we are, we are talking about hundreds of billions and trillions of revenue coming from the companies selling these. But then that trickles down to all the other verticals in industry. So I mean at the end of the day the Thing as you alluded to, like, it's impossible to project this accurately 15 years out. It's possible that production is actually 5 years out. Like I don't even try and do it five years out. Like two, two to three maybe. But it's going to have a massive impact. And the thing we keep coming back to, and if you're a listener to this show, a defaulting to taking action every day, saying, how do I get better at this? How do I learn more about AI? How do I infuse it more into what I'm doing? How do I help my company move forward? The people who take action and accelerate their literacy and capabilities have the greatest chance of thriving as we move forward. And the same holds true for businesses. We are, we are in the very, very early innings of this, probably the top of the first inning, to use a baseball analogy of like adoption and transformation from AI. But it is, it is going to be massive. And so McKinsey studies, like, studies like this from McKinsey help put in context how massive. But it's all a guessing game. It's big, though. Yeah, yeah.
- [36:31]
Mustafa Suleiman
And we talk about that a lot. That it's probably more important to just be directionally correct. Like whether it's 10 trillion or 100 trillion, honestly doesn't really matter to your average business leader. But do we know today that double digit productivity gains are possible across a wide range of knowledge work? Yes, we do. What would that be like two years from now? It could be triple digit gains. I mean, so that's the directional trend.
- [36:54]
Paul Raitzer
Correct?
- [36:54]
Mustafa Suleiman
Act accordingly.
- [36:55]
Paul Raitzer
Yeah, yeah. And yes, take, take action. Do not wait around.
- [37:01]
Mustafa Suleiman
Yeah. All right, Our third big topic this week, the popular podcast Masters of Scale, just dropped a new interview with AI leader Mustafa Suleiman that we think is well worth paying attention to. So Masters of Scale is a podcast primarily hosted by Reid Hoffman, the co founder of LinkedIn, a former OpenAI board member, current Microsoft board member. He is also with Mustafa, the co founder of the AI company Inflection. So Mustafa is a former co founder and CEO of that company. That company basically got Aqua hired by Microsoft. Now Mustafa is the CEO of Microsoft AI, which means he's not only on the bleeding edge of AI development, he's also a key player in both Microsoft's AI strategy and something we'll talk about a little bit more this episode, the company's relationship with OpenAI. Mustafa is a key player in governing how that works. So, Paul, you kind of found some things that jumped out here in this episode that were worth Noting can you walk us through them?
- [38:03]
Paul Raitzer
Yeah. So I do think it's worth listening to and as you mentioned in the context of Solomon's role with Microsoft, it is very instructive of where they're going. And so as one of the key players it's, it's definitely worth paying attention to. So I'm going to highlight a few of the key points but one he talked about upfront is recursive self improvement. Because the key thing with this interview is he was kind of timestamping these key elements and when he thought they might be occurring. So recursive self improvement is basically the ability for the models to, to identify their own weaknesses and flaws and hallucinations and fix themselves. This sounds awesome on the surface it also sounds terrifying because this is one of the things that the doomer side worries about is these things develop the ability to recursively self improve which could enable like a fast takeoff where they just become really really smart, really really fast when they can fix themselves basically and find these flaws. So he said he sees that coming into view in 2025 that teams will start experimenting with that. We have heard about this from I think Claude, they've talked a little bit about it. I think we heard a little bit about this with 01 preview from OpenAI as you develop the ability to do reasoning and chain of thought, part of that process is to identify when the chain of thought breaks, when it's no longer true, when like a falsehood has been found or misinformation is found within it to be able to go back and fix that. And so that is something to watch. It is, you know, we talk a lot about that. Andres Karpathy Intro to LLMs YouTube video from January 2024 Recursive self improvement is one of the things he got into. One of the other things I was not surprised to hear them talk about was EQ versus iq. So intelligence versus you know, emotional quotient versus intelligence quotient and or emotional intelligence. And the key here is this is what he was trying to build an inflection. That's why I wasn't surprised at all. And they're that they're bringing that to Microsoft. So IQ is what these models are really good at. It's often how they measure them is their cognitive abilities, intellectual potential, Logic, problem solving, math skills, analytical skills, reasoning, language, comprehension, those are all the natural things. But emotional intelligence is more the ability to recognize, understand, manage and influence one's own emotions and those of others. Self aware awareness, self regulation, motivation, empathy, social Skills, not what you would traditionally expect from an AI. And so in his case, he said, it turns out that actually tone, style, emotional intelligence of these models, the extent to which they will ask you questions, the extent to which it reflects back on the type of language that you might use and so on, that delivery vehicle for the substance is perhaps more important to the majority of consumers than just an objective regurgitation of Wikipedia, he said. He went on to say, I think that's going to be one of the key capabilities. I think everyone's starting to wrestle with that now as we look at this agentic future is what is the personality of these models and the good and bad. And we talked about, you know, the challenges of when these things become too human, like when they have a high emotional intelligence that if that is not properly protected or contained, then you have humans developing unhealthy deep relationships with AIs that seem very human. Like, so this is a very slippery slope. I mean right now we've just talked about emotional intelligence and recursive self improvement. Now Mustafa talks about these things as inevitabilities and like that they're actively looking to build this in. Now keep in mind Microsoft may do both of those things more ethically than others will or more ethically than maybe an open source model would allow third parties to build. He got into AI agents. He said the first step for the agentic future is that your co pilot has to have the ability to see. This becomes extremely important. We've talked multiple times about Microsoft's efforts with this. We have Project astra from Gemini OpenAI is working on this. They want these things to be able to see the screens Claude computer use we talked about. So he says the AI companion has to be able to see. And having an aide or an assistant or a companion that is really seeing the pixels that you see on the screen in your browser, your desktop, your phone, means there's a new level of sort of awareness about your sensory input that enables the companion to observe what you're seeing and be able to do things. Now ironically, last week Microsoft's Copilot Twitter account tweeted, if only your browser could see what I see. Oh wait, Copilot Vision will be able to very soon. So this is aligned with what Microsoft is saying they're going to do. So that was important. One memory was another one. We've talked a lot about memory being one of the next key unlocks. So memory is the ability to go from conversation to conversation and Inflection or ChatGPT or Microsoft Copilot and have the AI remember everything and be able to personalize everything it's done based on your history. Now, OpenAI has been working on this. They've talked a lot about memory, but he said we're going to nail memory. I mean, I'm really confident 20, 25 memory is done, permanent memory. If you think about it, we already have memory on the web. Copilot has really good citations. It's updated about 15 minutes ago, knows what happened in the news, on the web, so on, and we're just compressing that to do it for your personal knowledge graph. When you add in your own documents, emails, calendars, stuff like that, memory is going to completely transform experiences because it's frustrating to have a meaningful conversation with like a Gemini ChatGPT inflection and then go on an interesting exploration around some creative idea and then come back three or four or five sessions later and you have to start again like it doesn't remember anything. So memory is a really key thing. He also talked about models, said the good news is the models are getting bigger and smaller, which we've talked many times on this show about, that they're going in both directions. But he does think that the biggest models have a lot of room to go, that there's plenty more data that they can infuse into these things. They're not going to see a slowdown in the frontier models for at least the next few years. And so the frontier models are going to kind of have an outsized impact, but the smaller models are going to be critical to the future. I like the example he used here, which we've shared before, like why small models make sense. He said, small is definitely going to be the future. Because if you think about it, the very large model, when you ask a query of these frontier models, it's lighting up the neural representations of billions of pathways which are not relevant to the query at that hand. It's like if someone asks me something and my entire brain fires to answer it. That's not what happens in our brains. There's very small pieces of our brain tied to specific cognitive tasks. And so our brains are highly efficient because we don't use every neuron for each cognitive thing. Right now, the large language models are basically firing every neuron. When you say, what was the score of the Cavs game last night? Like, every neuron's kind of. And so they're trying to build these small models that allow for these things, and then I'll kind of end with his final thoughts. And this Sort of aligns with what we just talked about, with the potential of, you know, growth and economic impact. So I think that this is a moment to found companies, scale companies. It's a moment to really pivot careers. Even if you're not an entrepreneur, even if you're an activist or an organizer or an academic, this is the moment to really pay attention, because by 2050, the train will have left the station. It'll be quite different. And this is a moment where we really do have a chance collectively to shape and influence things. And nothing is predetermined. It's really, it really is within our reach to shape it for the best of humanity. And I think that's quite. We're very lucky to be alive at this moment. It feels incredibly empowering, and it's a great responsibility. Oh, I don't agree with everything Mustafa says, and there's elements of what they're working on that I don't, I'm not that excited about as a, as a human. I, I, I like his thoughts at the end, and I, I kind of echo those. And I've, I've said it many times, like, the only way we do what we do, and I think as much as I do about this is because I believe we have the possibility of an incredibly abundant future. And I choose to be optimistic about it. Despite the concerns and fears and the uncertainty. I don't find worrying constantly about that stuff does me any good. And so I choose to try and take actions to ensure the greatest possible outcome for myself than my family and everybody else. And that's kind of how I keep going each day with this stuff.
- [46:57]
Mustafa Suleiman
Yeah, I feel like we need a regular segment on how to stay optimistic.
- [47:01]
Paul Raitzer
And keep our sanity.
- [47:03]
Mustafa Suleiman
Keep your sanity while covering this space. All right, let's dive into some rapid fire for this week. So first up is something that actually hit the docket, like, right before we started recording because a new interview with Sam Altman just dropped. It was an interview on the 20 VC podcast, which we've talked about before. The host is an investor named Harry Stebbings. And basically over 35, 40 minutes, they covered a ton of ground. They covered things like the trajectory of model improvements. They talked about whether or not scaling laws will continue, and also Altman had some really, first draft predictions about where we're headed, among many other topics. So, Paul, I guess I was going to kind of maybe pull out a few things that jumped out at me. And, you know, if you have any thoughts on some of these topics, we'd love to Kind of hear you putting them in context.
- [47:52]
Paul Raitzer
Yeah, sounds good. I'd see. So I had seen some clips of this last week because it was done live and then they published it on Monday morning as a full podcast. So I have not had a chance to listen to a full podcast. I just saw some clips of it last week. So yeah, I'm kind of like following along with the audience on this one and seeing what you pulled out of it.
- [48:11]
Mustafa Suleiman
So. And again, I would highly encourage you listen to the whole thing. Like we've said before, listening to what these key players are saying is really important. This is not a very long podcast relative to some of the longer form stuff out there. So I'm just going to cover a few things that I found to be important. So first up is, it's very clear OpenAI is betting really heavily on O1 reasoning. Sam said we want to make things better across the board, but this direction of reasoning models is of particular importance to us. He says it's going to unlock all sorts of possible things that can drive things forward. He said, quote, so you should expect rapid improvement in the O series of models and it's of great strategic importance to us. Interestingly, this is a great question I loved Harry asking. He said, how do you think about the definition of AI agents today? What is an AI agent to you? And he's basically getting at a point that we've talked about at length, which is the semantics around the agents. The how we talk about them is getting very muddy and unclear. So Altman said, this is like my off the cuff answer. It's not well considered, but something that I can give an agent, he's saying, is something that I can give a long duration task to and provide minimal supervision during execution. 4 Now Stebbings then asked as a follow up, what do you think people think about agents that actually they get wrong? Altman goes on at length to kind of describe, look, normally you hear people talking about agents as like this thing that'll go do stuff for me on the Internet. You know, go like order from a restaurant or something or book me a flight. And he's like the category I think though is more interesting is not the one that people normally talk about where you have this thing calling restaurants for you, but something that's more like a smart senior coworker where you can collaborate on a project and the agent can go like do a two day task or a two week task really well and ping you when it has questions but come back to you with like a great work product. Now, Paul, that's kind of his first draft, thinking about, I found that to be much more helpful in kind of formulating how I think about a definition of agents.
- [50:19]
Paul Raitzer
Yeah. And I mean it. Part of it plays into this further confusion of what an agent is and how we're going to define these things. He's definitely aligning his view of it more with their reasoning models, their own models. You know, it's interesting, I hadn't really thought about this until now, but when we go back to the previous conversation around what would you be willing to pay per month, especially if you're talking about like senior level support, I almost wonder if there isn't a value based model kind of more similar to like a fiver, one of those task oriented sites where there's a marketplace where you say I'm willing to Pay, you know, 500 for a logo. I wonder if there isn't a way for ChatGPT and Gemini and others to have this value based model where you say, here's the problem I'm trying to solve. I'm willing to pay $15,000 to solve this. And then you have your agent in their world, your, your reasoning model that maybe goes off for a day or a week or a month to work on that problem because the cost of inference is going to be higher as it has to go through dozens or hundreds of steps. And so I just wonder if there isn't a marketplace for agents that are value based where you just let the market decide what they're willing to spend to solve something. And I don't know, it's kind of a fascinating idea. I don't know if they've considered that approach or if people are thinking about that. But I think as these tech companies, like I said earlier, go back to a better approach to educating people on the value these models create and get away from this expectation that's 20 or 30 bucks a month for all the value. I wonder if there isn't a way to do that where you just present a problem and is what I'm willing to pay to solve because otherwise I got to hire an advisor or a consultant or a staff member to do this thing.
- [51:59]
Mustafa Suleiman
Yeah, that's really interesting. And it's interesting too, how much of this conversation somewhat parallels what Mustafa was saying. Because as part of this as well, Sam Altman was asked about, like, hey, where do you think we're going in the next year or two? And he said, among other things, quote, without spoiling anything, I would expect rapid progress in image based models, which was Kind of what Mustafa was also getting at. He was asked, when we think about scaling models, like scaling these AI models, how many more model iterations do you think scaling laws will hold true for? He said, without going into detail about how it's going to happen, the trajectory of model capability improvement is going to keep going like it has been going. And I believe that it will be doing that for a very long time. So that's also very interesting that he's committing to that.
- [52:47]
Paul Raitzer
Yeah, it was funny. He tweeted, I think this was on Sunday or something. November 2nd. I heard O2 gets 105% on GPQA. So I wasn't really even sure what GPQA was. A graduate level, Google proof, Q and A benchmark. So there was a research paper in November 2023 about a challenging data set with 4 and 48 multiple choice questions written by domain experts in biology, physics and chemistry. And then Sam tweeted, damn, wrong account. And he was kind of. So I think he's being like, trying to be funny. Like he has burner accounts and he usually tweets this stuff from burner accounts. But, you know, I think again, there's always some element of truth to what Sam does with this stuff. He's joking around where, you know, these models are going to get smarter and they're pretty confident in their ability to make them significantly smarter.
- [53:37]
Mustafa Suleiman
And then I'll end with this final kind of comment he had. And they, you know, Harry had asked him, like, hey, what is the 5, 10 year horizon look like for OpenAI for AI in general? I won't read his whole answer, but he kind of made this really important point that he's like, you know, I think in five years it looks like we have an unbelievably rapid rate of improvement in technology itself. He then goes on to say, the pace of progress is totally crazy. And we're discovering all this new stuff both about AI research and also about the rest of science. If we hit kind of this AGI moment, and that feels like if we could sit here now and look at it, it would seem like it should be very crazy. But then the second part of the prediction is that society itself actually changes surprisingly little. An example of this would be that if, I think if you ask people five years ago if computers were going to pass the Turing Test, they would say no. And in fact, we kind of, roughly speaking, have already passed the Turing Test. And society didn't change that much. He said it just sort of went whooshing by. That's the kind of example of what I expect to keep happening, which is progress, scientific progress, keeps going outperforming all expectations and society in a way that I think is good and healthy. So he is sticking to his plot points of things are going to move very, very quickly.
- [54:56]
Paul Raitzer
Yep, yep. As I expected the interview would say.
- [54:59]
Mustafa Suleiman
All right, so next up here in our rapid fire session is we've got a bunch more news this week about AI agents, which are always a hot topic. So first up, Salesforce has announced the general availability of AgentForce. This is its new AI layer that allows companies to build and deploy autonomous agents that can take action across business functions. And while Salesforce is gathering steam, Google has revealed a bit more of a measured timeline for its AI agent ambitions. At the same time, CEO Sundar Pichai has announced that Project Astra, their AI agent initiative, won't launch until 2025 at the earliest. So this is aiming to kind of create AI assistance that can both understand the world through smartphone cameras and then perform complex tasks. That was first demoed by Google at their I O Developer conference. In May of this year, LinkedIn is getting into the AI agent game. They are releasing their first autonomous tool, Hiring Assistant. This is an AI recruiting agent that can handle everything from writing job descriptions to sourcing candidates and engaging with them. And finally, in the startup world, a company called Sierra, which is led by the former Salesforce Co CEO Brett Taylor, is making waves with a possible funding round that could value the company at over 4.5 billion. They have AI agent technology that focuses on automating customer service tasks. So kind of bringing it back full circle to that McKinsey report, which showed that customer service support was one of these big, big areas of opportunity. So, Paul, it certainly seems like we can soon expect AI agent capabilities, however people are defining them to be in a bunch of these platforms. Did any of these like jump out at you as particularly noteworthy?
- [56:45]
Paul Raitzer
Yeah, it's interesting to see LinkedIn starting to kind of get in the game and infuse. I mean, obviously they're owned by Microsoft, so they're, you know, they have access to a lot of the technology and I think you're going to start seeing more and more of that stuff infused into LinkedIn. The Sierra one. Back on episode 116, we talked about like an interview with Brett Taylor that might be worth revisiting for people, but I actually laughed. Someone had one of those AI agent landscapes already. I was like, oh man, oh boy, we are. We already hit the hundreds or thousands of agents in a landscape moment. So there's no turning back now. So I don't know how in the world you'd keep that landscape accurate and updated. But yeah, agents, whatever they actually are and however we end up defining them, are going to become a key part of your life. And sometime between now and November 20th, I'm going to figure out what I have to say about them, because that's what my opening keynote is for. A four agency summit.
- [57:43]
Mustafa Suleiman
So in some other news, Microsoft and Andreessen Horowitz have joined forces to come out against burdensome AI regulations. So Microsoft CEO Satya Nadella and Microsoft President slash Chief Legal Officer Brad Smith joined a 16Z, as Andreessen Horowitz is colloquial, colloquially known. They joined Mark Andreessen and Ben Horowitz, the two head people at this firm, in publishing a joint policy statement that advocates for less AI regulation. So key aspects of this position, which was published online, include pushing for market based approaches over government regulation. Advocates for unrestricted access to data for AI training, argues that machines should have the quote, right to learn, similar to humans. Supporting open source AI development is another piece of this, while opposing regulatory frameworks that might restrict it. And they also call for regulation only when benefits outweigh costs, with the industry determining those calculations. This statement also notably reframes copyright concerns around AI training, suggesting that copyright law shouldn't prevent AI systems from using data to learn. So the companies are framing their position as protecting innovation and startups, but critics point out that their stance primarily serves to prevent meaningful oversight of AI development. So Paul, this stance that they have on these regulations doesn't particularly surprise me. But why are we getting a joint formal statement from these two companies now?
- [59:16]
Paul Raitzer
I, the only thing I come up with is related to the election. I don't know, like, I think they're timing wise. Obviously in the United States the election is November 5, so it seems like they're just getting out ahead of this from whichever administration is going to be in office standpoint, sort of stay in their claims. I don't know, not sure what else is happening that would time this. Um, I, I, there was nothing new in it per se. I mean we kind of knew their points of view. The, the big one, just to reiterate, is they want regulations to focus on the application layer, not the model layer. Meaning, I think, you know, let, let's, I don't, I don't like using the guns example, but electricity, nuclear, whatever that like it's, it can be good or bad, it's how you use it and they want AI models to be treated that way, that the models can do good or bad, and it is the application of the models that should be regulated and penalized where they're used. And then the copyright one I thought was interesting to just blatantly come out and say like we don't think copyright should be a thing. Like we don't think that should prevent at all that the AI models have the right to learn, just like humans. I don't necessarily agree with that standpoint, but there's a lot of things A16Z would say that I don't necessarily agree with, but there's also things they say that make a lot of sense. And then the one that I most aligned with, I would say at the end is they said help people thrive in an AI enabled world. This very much aligns with how we talk about it. Said policy should fund digital literacy programs that help people understand how to use AI tools to create and access information. It should also support workforce skill development and workforce retraining programs to help people secure jobs in an AI driven world. I mean, 100% on board with that final point.
- [61:04]
Mustafa Suleiman
Nice. All right, so next up, we also got some more comments from Sam Altman in a Reddit ama, and he revealed that the company's AI projects are facing significant delays because they simply don't have enough computing power. So apparently a computing bottleneck is affecting Several high profile OpenAI projects. That includes Sora. Reports suggest the current version of sora takes over 10 minutes to generate just one minute of video. And the computing constraints are also impacting things like the vision capabilities for advanced voice mode in ChatGPT, which have been postponed indefinitely it sounds like. And it turns out that April demo of Voice mode was rushed to compete with Google's IO conference. Despite internal concerns, the technology was not necessarily ready, which leads to this postponement and also the fact that Voice mode did not come out untimed. So OpenAI is apparently working to address these limitations. Reuters has reported the company has been collaborating with Broadcom to develop its own AI chip. Altman has said the company in the meantime is focused on improving its O1 series of reasoning models. And he also said, quote, we have some good, very good releases coming later this year. Nothing that we are going to call GPT5 though. So Paul, let's connect some dots here. Like why is this a problem for OpenAI right now and how much of this is related to what we've talked about in past episodes about the relationship with Microsoft, who's supposed to be helping them Keep access to capital and compute.
- [62:40]
Paul Raitzer
Yeah, I would say, I mean one part you could read into this, that Microsoft is purposely throttling access for some reason. I don't know that that's true. I think the more likely scenario is that Microsoft has their own vision and AI ambitions now with Mustafa at the head, and they have far greater need for access to compute themselves to be doing what they're doing. And so there's only so many Nvidia chips to go around and so many data centers to build on. So I think it's just that the demand for this stuff is massive. And so Microsoft, I believe we talked about like allowing OpenAI to do deals with like Oracle and others to get more compute and then obviously Sam's trying to wrangle their own compute moving forward. So I think that'll kind of continue on as a story. I think, you know, they've said before they probably weren't going to announce GPT5, but I, I don't know if like 01, when they get out of just preview mode, I'm not so convinced that O1 isn't sort of the next model. They just aren't going to call it GPT5. So I, I think we're probably seeing the elements of GPT5 coming to light that just not under that name.
- [63:57]
Mustafa Suleiman
So some other Microsoft related news. GitHub, which is owned by Microsoft, has announced that it is expanding beyond its exclusive relationship with OpenAI by bringing multiple AI models to their popular CoPilot coding assistant. So GitHub CoPilot will now integrate three major AI providers, Claude 3.5, Sonnet, Gemini 1.5 Pro, and OpenAI's latest O1 preview and O1 mini models. So this timing is kind of notable because it comes amidst all these reports that OpenAI is worried about Anthropic taking the lead over them in code writing capabilities. Now, GitHub CEO has framed this as a response to developer demand for choice. He said, we're not saying one model is better than the other. We believe it's for developers to decide. So Paul, we know that like the major AI companies are pretty seriously interested in creating models and products to aid developers. So totally natural there's going to be a bunch of competition in this space. But is this another sign still that there's trouble in this relationship between OpenAI and Microsoft?
- [65:11]
Paul Raitzer
It seems like this was unexpected. I would imagine OpenAI was aware this was happening, but it was pretty big news in the AI developer world when this occurred. And so again, I'm not sure that that world was expecting it to happen. But I don't think that this is an insignificant move by Microsoft and GitHub to enable this for the developer community. So something to keep an eye on in the ongoing OpenAI Microsoft relationship.
- [65:41]
Mustafa Suleiman
All right, so next up, Apple has an AI rollout that is facing some early growing pains. The company has kind of taken this staggered approach to releasing Apple Intelligence features. The first wave, which is arriving in iOS 18.1, brings kind of modest improvements that many users might find underwhelming compared to all the promises we heard at wwdc. The initial release of Apple Intelligence includes basic features like writing tools for text editing, improved Siri interactions, smart replies and messages. Nothing. That's really kind of a wow moment here. Now more transformative features are being held for iOS 18.2 in early December. This includes things like ChatGPT integration, an image playground for AI image generation, and visual intelligence for real world object recognition. Now the company is kind of making this bet that even if they're late to market, their implementation will be more secure, more reliable than competitors. So, Paul, you are kind of an Apple power user. Like, what do you make of the Apple Intelligence rollout so far?
- [66:48]
Paul Raitzer
It was wild. So October 28th is when it came out. So I did, I think I said in the last episode, I finally went and bought the new iPhone. And knowing August 20th or October 28th is when they were going to release 18.1, if I get the new phone, the phone's the same phone as the previous phone, basically. I had a 14. There was like no noticeable difference initially. So I download the features and I'm like, or I download 18.1 and the next day I'm like, where is it? Like, I thought this thing was supposed to glow. When you talk to Siri, it's not glowing. And so I even went to Siri. I was like, is this the new Siri? Like, is this the new features? Like, where is it? And I was so confused. Like, I had no idea why I didn't have these features. So I finally go into, like, my settings and I find this Apple Intelligence feature in there and I click on it and then it says, join the wait list for Apple Intelligence. I was like, join the wait list? What are you talking about? Like, why do I have to join a waitlist? It is so bizarre, like, the whole experience. And I just kept thinking about it, but I talk about this stuff and follow it for a living. And I didn't know I had to go in and join a wait list to then get like about 36 hours later, I think it showed up. And then you get in there, you start playing with. It's like, oh my gosh, this is it. Like, this is what running all these TV ads for and having Tim Cook personally tweeting about the Apple intelligence age. Like, oh, it's, it's just bad. Like, it's so, it's so disappointing. So I don't know, I guess we'll wait till December 2 and see what they come out with then. And then They've announced like 18.4 will be in April of next year. And that's when maybe Surrey actually gets better, I don't know. But it looks like this is going to be a long game for sure. And it is highly disappointing, I would say, thus far.
- [68:33]
Mustafa Suleiman
All right, our final piece of news this week we got a pretty thought provoking post from the CEO of Runway, the AI video generation company. His name is Christobal Valenzuela. And in this post he argues that we have reached the end of what he calls the, quote, AI company era. Not because AI has failed, but because it has become fundamental infrastructure, much like electricity or the Internet. He argues this transformation marks a crucial shift in how we should think about AI and its role in business. So as a result, he's kind of announced that Runway, which is originally known as an AI company, has basically reframed itself as a media and entertainment company with a singular vision to use AI as a fundamental tool for storytelling. So Valenzuela here kind of draws a historical parallel, comparing their work to the invention of the camera. It's not just a device for capturing images, but it's a catalyst that spawned entire industries, cinema, television, social media, et cetera. So basically they have this new vision where they see AI as infrastructure rather than an end goal, similar to how companies kind of stop being Internet companies once the web became universal. They want to focus on creating new forms of expression and storytelling rather than just simply advancing AI technology. He talked a bit about the concept of what he calls, quote, universal simulation and world building, where content can dynamically generate itself in response to viewers. And they're looking to break down the traditional one way media consumption models in favor of interactive and generative experiences. So basically they're saying the next wave of innovation here won't just come from improving AI models, but it'll come instead from companies that kind of understand how to use AI to create new forms of media and experiences. So Paul, for a pretty short post, there's like a lot to unpack here. Like, I Want to kind of take this in two parts. Like first, what do you make of his claim that we've reached the end of AI companies? And then what does this actually mean for like runways? Direction and focus.
- [70:44]
Paul Raitzer
I'll actually go in reverse here. I don't think it means anything to their direction and focus. It's just how he wants to describe it. The reality is he's living in a bubble. Like so I honestly. So when I started Marketing Institute in 2016, I thought by 2020 I wasn't sure we were going to need the like AI in the name. Like I thought Marketing Institute might just be redundant and like everything's just going to be an AI company and we won't need to differentiate. I was very wrong on that. It was like Elon Musk, you know, projecting full self driving by 2020, like we were just off, like, and so I'm off by five years at this point. Like, like we're still nowhere near companies not needing to differentiate whether they're AI forward, AI first, whatever you want to call it. So I don't agree with him that we don't. I mean in his world, fine like that. I get that he wants to just be known as a media and entertainment company, but the reality is the fact that they're an AI first, AI native, whatever you want to call it media entertainment company is what makes them different. So he doesn't need to position it that way if he chooses not to. But that there's the vast universe of media entertainment companies aren't AI literate yet. Like they're not really doing this and infusing it in. So the fact that somebody is or is not using AI is actually different. And you could do that for healthcare, law firms, marketing agencies, SaaS, companies. Like there's plenty of software companies I talk to who I wouldn't even consider AI forward yet. And so it's a differentiator. Like when we're considering our own tech stack at the institute, I want to know whether I don't want to know it's a software company. I want to know it's an AI first or AI native or AI for whatever you want to call it that they're infusing AI and intentional about and they have a vision for building smarter software. Like that makes them different and it will for the foreseeable future. So I don't agree with him that we've arrived at a point where companies are just companies again. That is, we're nowhere near that yet in most industries. But again for their positioning Fine. Like, he'd say whatever he wants. To me, Runway is an AI company. Like, it's going to continue to be. It's one of the first ones we started following back in 2018, 19. And it's still what makes them unique is their integration of these models to do amazing things and move their industry forward. But getting rid of AI and that just, it's just a personal choice. So, yeah, I mean, it is worth following. I think they do amazing stuff. It's a company we've been paying attention to for a really long time. But I don't, I don't agree that that is a relevant positioning for most people that would listen to our show.
- [73:16]
Mustafa Suleiman
All right, Paul, that is a wrap on this week. We've got a big week ahead of us. Probably some AI news, some non AI news and stuff tomorrow as well. Making dominating the airwaves.
- [73:28]
Paul Raitzer
But they saying everybody, whatever, whatever, whatever direction you're going with the election, whether it goes your way or not, remember, we're all in this together. And like we all got to pick up the pieces the next day and do our thing, whether you're candidate one or not. So just be kind to each other. And, you know, we don't want this show to ever be political, but I'm just going to be from a human perspective saying, like, let's, let's move forward together in a positive direction, whether you're in the United States or globally, whatever. But yeah, it's, it's going to be an interesting week one way or the other.
- [73:59]
Mustafa Suleiman
No kidding. And if you want a distraction from anything going on this week, go take a second to leave us a review if you have not. We really appreciate all feedback we get. It helps us make the show better. So if you haven't done that and you have the ability to do it, please do that. And last but not least, go check out the Marketing AI Institute newsletter. It has all of this week's news and AI, including a bunch of stuff we did not get to on today's podcast. So go to marketingai institute.com newsletter. Paul, thanks again.
- [74:32]
Paul Raitzer
Thank you Mike and thanks everyone for listening. We will be back back next week with our regular weekly episode.
- [74:38]
Mike Kaput
Thanks for listening to the AI show. Visit MarketingAI institute.com to continue your AI 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 the Slack community. Until next time, stay curious and explore AI.