OpenAI Finally Ships Its Superapp, Meta’s AI Price War, ChatGPT Cheating At Brown

Ranjan Roy from Margins is back for our weekly discussion of the latest tech news. We cover: 1) OpenAI debuts its new superapp 2) What happens when all AI products converge 3) Are consultants the key to winning in AI? 4) Are all AI products commoditizing? 5) Meta's new Muse Spark 1.1 model is very cheap 6) Zuck confirms Meta is thinking about a cloud business 7) Is it bad economics to rent your compute to competitors? 8) Instagram's loose Ai reuse settings 9) Oh man, Meta is relevant in the AI discussion again 10) Professor accuses students of cheating with ChatGPT 11) Was professor wrong? Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack + Discord? Here’s 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Learn more about your ad choices. Visit megaphone.fm/adchoices

• 0:00Speaker 00

  Opening AI's super app finally arrives. Are all AI apps starting to look the same? Meta undercuts the Frontier Labs on pricing and Ivy League students use AI to cheat. That's coming up on a Big Technology Podcast Friday edition, right after this. This episode is brought to you by Genesis. What does it actually take to put Agentic AI to work across an entire enterprise? I recently spoke with Genesis Chairman and CEO Tony Bates about why orchestration is emerging as a competitive advantage in customer experience and what companies need to get right in the AI era. Then I sat down with Adam Mitchell, Head of Enterprise Business Solutions at Voya Financial, to go inside a large-scale Agentic CX transformation, including the business decisions,

• 0:44Speaker 00

  trade-offs, and lessons along the way. You can watch both conversations now on my YouTube channel today. It's never been more challenging to get Frontier compute. How do you know where to look, who to trust, or how much to pay? Akash is an open cloud that provides easy access, flexibility, and peace of mind. That's how companies like Razer were able to cut their inference cost by 15 times. Visit akash.network/alex for a free consultation. That's akash.network/alex for a free consultation. Welcome to Big Technology Podcast Friday Edition, where we break down the news in our traditional cool-headed and nuanced format. We have a great show for you today. OpenAI's long-awaited super app is finally here,

• 1:29Speaker 00

  and it's starting to look like all AI apps are going to look the same. So what does that mean? Meta comes out with a new model, MewSpark 1.1. It's almost as performant in some areas as the Frontier Labs, but in some areas, it's 25% of the cost. So has the price war arrived? And finally, we will discuss the Brown students cheating with ChatGPT. Take home test for the midterm. Everybody gets 100, it seems like, in class at Brown University for the final. And it is, it's failure city out there. So what does this mean for our youth? Joining us as always on Fridays to do it is Ranjan Roy of Margins. Ranjan, great to see you.

• 2:13Speaker 01

  Good to see you, Alex. I'm glad you, as an Indian person, I'm glad you clarified it was Brown University and not the Brown students.

• 2:19Speaker 00

  There were so many jokes about that on Twitter and, and like people being like, you know, who tweeted this study like, why do you gotta bring race into it? And I was like, be careful.

• 2:31Speaker 01

  Alex, remember to say Brown University.

• 2:36Speaker 00

  And of course, I messed it up. But yes, Brown University. Let's move on until we return to that. All right. So I'm not even, there's no segue. All right. Let's talk about what happened this week in tech. OpenAI finally unveiled its Claude cowork competitor and its desktop super app. This is according to the information, OpenAI is part of its effort to attract more business customers, announced a new agent called ChatGPT Work, which taps into corporate data to automate the creation of spreadsheets and presentation and can also handle more complex tasks like, like financial forecasts and conducting research. ChatGPT Work is OpenAI's answer to Anthropic's popular Claude cowork product,

• 3:18Speaker 00

  which the startup has used to expand the market for AI coding to non-technical users. OpenAI also unveiled a desktop super app that marries ChatGPT with Codex and the new ChatGPT Work offering. This reflects OpenAI's recent realization that Codex is better than ChatGPT in handling long-running tasks that involve multiple steps and require the use of external tools. Let's just start here. Rajan, it seems like all, I mean, we've talked about this on the show a bunch, but like now you can finally see it with your eyes. All AI products seem to be converging on this one use case, which is that like, you know, you might have some chat, but really AI is there to

• 3:58Speaker 00

  get things done for you. How are AI companies, and we talked about this a bit with MG on Monday, but how are AI companies going to differentiate themselves if they're all offering effectively very similar version of the same product?

• 4:12Speaker 01

  Well, this is front and center in my life, working at Writer and, you know, like delivering enterprise AI and what we've been selling for a year now, natural language-driven agentic workflow building, which sounds very buzzwordy, but is basically this. It's that kind of marrying of what Codex has done in the commands line, but in a more, in a more chat-based interface. So have definitely been thinking about this a lot, played a bit with ChatGPT work, was definitely underwhelmed. It felt a bit rushed in terms of like how Claude Co-Work was kind of slowly built out. And now has been integrated into the main Claude interface as like a separate button, which is another topic, whether it's getting too convoluted and messy.

• 4:56Speaker 01

  But I, it's funny, like I go to a lot of conferences and now every booth is starting to look the same. I've been in meetings where someone's like, what do you do? Don't say enterprise agentic AI. And you're like, well, it is such enterprise agentic AI, but like it's making it easier for business users to actually build agents. Like it's actually crazy how Cursor just released something that's actually more in this direction. Notion has built an entire ecosystem around this very same thing. I think like the exciting part of it is that I feel validated. I started talking about this last October. It's, this is every product going forward.

• 5:40Speaker 01

  And I think, where I think the differentiation is going to be, what we focus on is sales and marketing across enterprises. But like the actual like context layer, intelligence layer, how you actually build out the systems that help build that within an organization. That's where differentiates differentiation is going to be. And I actually think it's good for the more verticalized companies and it's bad for Anthropic and OpenAI.

• 6:08Speaker 00

  Wait, sorry, explain that and, and do it without making the Writer commercial.

• 6:12Speaker 01

  No, no, no, but, but dude, this is my life right here. This is like all I think about and read every day and we have to deal with it.

• 6:19Speaker 00

  Take Writer out, take Writer out of it.

• 6:21Speaker 01

  No, no, but, no, but.

• 6:22Speaker 00

  Explain what the differentiation is going to be.

• 6:25Speaker 01

  Yeah, yeah, so, I mean, it's difficult when, uh, this is.

• 6:29Speaker 00

  I'm challenging you here.

• 6:30Speaker 01

  All right, all right, let's go, let's go. So if you are a marketing organization within an enterprise.

• 6:38Speaker 01

  There are different levels of like the foundation beyond brand. When you say brand, everyone thinks like, oh, just put a tone of voice document or something. Like building out skills that actually and or a foundation of knowledge. So when people go to build agents, they're actually doing good work or correct work. Using this stuff out of the box, everyone, no enterprise actually sees any kind of value. Realize like they don't, things don't work well. So like actually building the system. So if you, okay, if you take, you're creating an event and like the big next big technology summit.

• 7:19Speaker 01

  Like making sure that the Eventbrite or what system do you use for it?

• 7:24Speaker 00

  Luma.

• 7:24Speaker 01

  The ticket, Luma, like making sure the connectors all work really well and out of the box for customers. Connecting to various CRM systems so you can actually remarket, having your messaging and and like whatever kind of guidelines around how you like to communicate already built in. So like those are the layers where there's going to be a lot more competition. And we can get into price wars and the the cost side of the equation, but actually making it so. Again, you're a large consumer goods company, like how your product knowledge is ingested into the overall system. Makes every agent either work or not work. And does that make sense? I'm curious.

• 8:06Speaker 00

  Okay, yeah, but let me push back a little bit because, all right, first of all, like I was able to use a lot of Claude Co-Work, Claude Code and ChatGPT to set up the event. So for instance, Claude Code out of the box uh made the website. Now I gave it some references of what I wanted it to look like, but it built a great website. It embedded Luma for me, right? So it did that.

• 8:30Speaker 01

  Yeah.

• 8:30Speaker 00

  And then hold on, I'm not, I want to finish. So then Claude Co-Work helped make like the prospectus for the uh for the event that we sent to potential speakers and sponsors. And of course, I I refined what was in there and helped give it reference material, but it did a good job there. ChatGPT designed, you know, with me, of course, you know, all the graphics for the back of the stage, right? This was like a generative AI production. We went as hard as possible into Gen AI as possible. And then the scheduling, for instance, you know, was done in a single threaded chat with within ChatGPT. Where like I would say, okay, we have this change and it would move the scheduling. So we didn't need any bespoke software there.

• 9:11Speaker 00

  We just basically needed like some references for the AI to go off of. And then it was able to take it from there.

• 9:19Speaker 01

  So now imagine you have 221,000 different websites that you manage. You have 17 different regulatory agencies that you actually are responsible to. You have six different large brands that each have their own like entire architecture. Now try to do that in the system that you just did. That's to me where the battleground is going to be. And that's like every action you took right there is it like, that's like the beginning of how this all works, but that does not work at large organizations in that way.

• 9:54Speaker 00

  So let's go back to what David Karp from, uh, no, Alex Karp from Palantir was saying. David Karp is the Tumblr guy. Um, Alex Karp from Palantir was saying. Nobody knows where he went. He just sold the blog to Yahoo for a billion and then he disappeared. All right, let's talk about the relevant Karp.

• 10:13Speaker 01

  Okay.

• 10:13Speaker 00

  Alex Karp, relevant Karp. So basically what he was saying is, you know, he was, he was at CNBC talking about how he was going to build this open model. Uh, based, you know, uh, based off of Nvidia that you would effectively use with his Palantir consultants to put the AI into action. Right? So is that kind of what you're saying, Ranjan, is you need a version of what Karp is saying? And, um, and is this maybe, you know, we've kind of joked about how OpenAI and Anthropic are bringing the consultants in with their, you know, so-called forward deployed engineers, which by the way, is also a Palantir thing. Um, and so is, you know, is where we're going.

• 10:58Speaker 00

  Um, the customization of these tools for the purposes that you spoke, spoke about. Um, with the assistance of, uh, some of these decidedly non-military, but military sounding forward deployed engineers. You tell me.

• 11:13Speaker 01

  Yeah, it's a, apparently I read once Alex Karp said he got the term for how French restaurants have the waiters work in the kitchen. To like truly understand the the dishes before they go out and sell them.

• 11:25Speaker 00

  Okay.

• 11:26Speaker 01

  Even though.

• 11:26Speaker 00

  Let's pause.

• 11:27Speaker 01

  I know, that's what he said.

• 11:28Speaker 00

  This guy is so good at marketing.

• 11:30Speaker 01

  I know.

• 11:31Speaker 00

  Legitimately renamed what a consultant is. And he's told you that his consultants are like fine French dining.

• 11:38Speaker 01

  I know. No, no, and it was like a military term. He's so good.

• 11:41Speaker 00

  Nothing against consultants.

• 11:43Speaker 01

  Yeah.

• 11:43Speaker 00

  But let's, why do we have to like, we're, we're, um, we're looking for an apartment right now. And it's like some industries like, uh, the wine industry, the real estate industry, and I guess Alex Karp have the most audacious, um, verbal flourishes to describe very simple things. Like you can't just have a standard two bedroom. It has to be a gloriously situated, you know, mini palace with, you know, two layers to rest your head. This is what, what Karp is doing.

• 12:15Speaker 01

  Well, I mean, I do consider myself a bit of an AI sommelier, so, you know.

• 12:22Speaker 01

  Okay, so.

• 12:23Speaker 00

  Ending the show now, ending the show.

• 12:27Speaker 01

  That's it. That's the next wave of job in this, in this economy. Fifty percent of white collar jobs wiped out, but AI sommeliers are going to be, uh, the future. So for deployed engineer, so that actually is exactly it. But that is the high lift in person layer of this, and that's how things have worked to date. What I'm saying is, and I'm seeing this, and I'll avoid the writer, commercial part of it. But like, this is across competitors as well. The Sierras of the world are like, anyone who has like a very clear Harvey's of the world. Like they're building their platform, but is being differentiated. I mean, we say verticalized, but to me it's less about verticalization and it's more about expertise

• 13:11Speaker 01

  within a domain and being able to actually integrate that into the overall platform. And that's what's going to happen. And it's going to be part. F D E A I sommelier, whatever you want to call them, people going in and working with you.

• 13:24Speaker 00

  Don't call it A I sommelier.

• 13:25Speaker 01

  No, I'm sticking with that one. I'm sticking with that one right now. It's going to be part that, but it's also going to be reflected in products more and more. That's where I see this going and that's where I see the differentiation. And I think like Claude for science already is kind of that. So like even the labs have their, their versions of it. But like, that's where the next battleground is going to be. And I call this battleground. So I'm telling you where the next one's going to be.

• 13:53Speaker 00

  Okay. So more verticalized style, uh, applications of these catchall applications is where the differentiation is going to happen.

• 14:03Speaker 01

  And, and I, for some reason, I don't like the word verticalized or verticalization because like in traditional SaaS, it was such a specific thing. And now it's going to be more about the type of work being done rather than the type of company it is.

• 14:20Speaker 00

  Explain that. So like, if you're like a sales and marketing person, you'll use one type of tool. And if you're like a researcher, you'll use another.

• 14:27Speaker 01

  Yeah, yeah. Which I don't know. This is, I'm still thinking through this one, but like the way people's minds go to when it's like Salesforce, it, it wasn't a vertical, i- it wasn't like for all sales and marketing, it was for CRM. And then they had to buy more companies and try to like, put together this larger offering versus now the A I is built for that person and that function rather than the like type of company it is. Does that make sense? I'm still, I'm still workshopping this one, but I don't like.

• 14:59Speaker 00

  Okay. So, so I want to go back to our, our sort of meta debate that we've had on the show for like four years at this point, which is,

• 15:08Speaker 00

  is it the product or is it the model? I almost decided to make the whole day about product versus model, but I decided to put the gas on that. But I'll put the brake on that. But now I'm coming back to it. Okay. Why? So, so I think what you're, you're, you're sitting in one of these, whether it's not verticalized or it's specialized type of companies, right? You're sitting there and you have that point of view. If I'm the lab, what I'm going to tell you is, The consultants are a bridge. These specialized products are a bridge.

• 15:38Speaker 01

  I know.

• 15:39Speaker 00

  What's going to happen is, you know, I'm speaking again, like if I'm in the seat at the big AI model companies, my model is going to turn into AGI. And at that point, the model will have enough intelligence that these bridges and we've called it scaffolding in the past. All this stuff is going to matter much less because the pure intelligence will be able to take on this specialized work with much less handholding and prompting. Let's go back to your example about the 117,000 websites or whatever it was. All right. You're, right now, it takes a lot of effort to feed that, but maybe in the future, when these models take their next leap,

• 16:22Speaker 00

  it will just be, let's say, a day of saying to your model, like, let's say I'm going back to, you know, my summit, right? Go ahead and crawl everything we do, get our voice, get our branding, and then, you know, you come up with a plan and go ahead and execute it. And you could just do that at scale because the model has that much more intelligence. So why doesn't it go that way?

• 16:44Speaker 01

  I know. Do you know why I love this? The debate has not been settled because that is the, it still remains the entire debate. It's like, it is, it's product versus model because like, I'm sure within the labs, the assumption is burn money to invest in more capable models and they will subsume all scaffolding I like is like, architecture might sound too buzzwordy scaffolding. It's like the things that are required to make agents work today. I'm sure they all think one year from now, two years from now, none of that will matter. And it's a, it's an interesting one because in reality, like, I mean, the progress that's been made in the last two years,

• 17:27Speaker 01

  the things you had to do two years ago all went away. So like, you know, like I remember parsing a PDF, the simplest thing. Two years ago, you had to like, define the tool and like potentially upload a tool like a Python script or something like that versus now like, all that stuff is just intelligent. Like, so the debate lives on and it's going to be the central, I think, to the next one to two years of who wins and who loses.

• 17:55Speaker 00

  Okay, so now let me take us, let's say, three years into the future, assuming one version, not necessarily the version that will happen, but one version of this future, where that vision does play out, right? So you have your AGI, and it's in Codex, and it's the new ChatGPT app, and it's in Anthropic's app, and Meta's figured it out some way, and Apple has it in the iPhone, for whatever reason. No, I should be nicer to them, they're making progress.

• 18:24Speaker 01

  I appreciate, I still haven't downloaded the developer iOS 27, but I'm meaning to, yeah.

• 18:30Speaker 00

  So, okay, so we get to that point, where, like, what's the value? Because it's going to be four companies that are going to be doing the same thing, and we're already starting to see signs that the premium is going to be on lowering costs. And does everything just eventually go to zero? For example, Sam Altman, talking about GPT-5.6 this week, said it was 54% more token efficient than others, right, than its previous model. So, like, if you're a client and you're thinking about your ROI, well, the I is going to matter a lot, the investment of the return on investment calculation, and you can get a higher ROI

• 19:12Speaker 00

  if the investment is lower. And if you have all these products doing the same thing, or similar versions of the same thing, it seems like there's a chance that even though you're providing an extremely valuable service, it's a race to the bottom.

• 19:25Speaker 01

  See, I'm still about the R. I think, okay, this is our next one, you're the I guy, I'll be the R.

• 19:33Speaker 00

  Bring on a third person for the O. Like, what's your role in this podcast? I just do ON, actually. Ron John does return, Alex does investment, I'm here for ON. Why are you here? Well, someone had to do ON.

• 19:47Speaker 01

  Someone had to. I mean, it is required.

• 19:51Speaker 01

  So, so I think, I think cost becomes important. I don't know, like, on this cost, and we're gonna get into Zuck's comments, like, it is almost comical to me at points, how dramatically the conversation now shifts. It was one Uber quote, like, even, I mean, telling you, like, talking to C-suite people six months ago, no one brought up tokenomics and cost, and now it's on everyone's mind. Meta's coming in hard as like, make, and I listened to the Boz episode around like, you know, like, they don't, like, they have more capital, they have more cash, they have more profit, so like, actually investing in these things.

• 20:33Speaker 01

  They have more strength versus some of the other frontier labs in this case. Um, but still, like, we're not there yet, because people don't have this stuff working at scale outside of software. Like, it, it hasn't. It's, there's bits and pieces and promise, but I can tell you definitively, like, this, we are so early on this, that like, to even think or worry about cost optimization, before you've actually figured out how to make it work well, I think is like, this is more, like, everyone in the small groups within these companies is, realize that cost will be a factor, and it wasn't before, but

• 21:17Speaker 01

  I feel the pendulum, again, just keeps swinging too far each way, and just, just let us work, people, just let us work.

• 21:24Speaker 00

  Okay, there's two separate things here, right? First is, is this cost discussion overblown? Maybe to some degree, right? But you also have, because you were right, that 2026 is the year of agents, and I give that to you once again.

• 21:40Speaker 01

  Thank you.

• 21:41Speaker 00

  Um, the agent workflow is that much more token-intensive that people who previously were using generative AI and didn't really care, you know, what it costs, because it wasn't costing a lot. You know, now we're seeing 10x this year, and are starting to worry. Like, end of the year last year Anthropic was at a $9 billion ARR, despite the fact that it is a flawed measurement. Now I just saw, maybe they're at a $69 billion ARR. In five months, you know, they've 7xed. Or more, ADEXed, right? So that's why these costs are starting to become real to people. And that's sort of the driver of this, of this discussion is there are people within companies who are like spending this and telling leadership it's justified.

• 22:25Speaker 00

  And that was an easier sell when it was one seventh or one eighth of the cost than when it is now, like leadership is actually going back to them and being like, we need to see the productivity increase and the return. But you know, that's one side of it. The other side of it is, so that's a discussion we will continue to have. But to me, the more pressing thing here is that whether that discussion is merited or not, we are, we are literally going to be in the middle of a price war here between these companies. And again, it comes from the centralization of the AI product experience into this like cowork, Claude code type experience, right? The super app experience and the fact that there are some companies that are quite motivated

• 23:09Speaker 00

  to, to drive the price down. And you mentioned Boz, so let's go to Facebook. So Facebook this week, they introduced their Muse Spark 1.1 model. This is according to Bloomberg and they are going to do something to strategically bring this market down. Here's what Mark Zuckerberg told Bloomberg. "Since this is not an open source model, I think this is the first time we're doing a real, a serious API and the pricing is going to be very attractive and aggressive," is the Bloomberg story. "The API will be used to collect fees from developers.

• 23:47Speaker 00

  API pricing is roughly 25% the cost advertised by other top models from OpenAI and Anthropic." I mean Zuckerberg has said there's some, some good margins, that the labs make some good margins here. And so he's like, well, we have the compute, we have a model, it's almost as performant as everything else. We are going to go not, not a half, not a third, quarter of the price of, you know, the models it's competing with. Now, obviously it's not at the same level of intelligence. But I just want to hear your perspective on what is the consequence here if all AI pricing starts to just crumble, you know, as this commoditization era kicks off?

• 24:28Speaker 01

  Oh, I think it's a massive, I'll use the term headwind. Liberally there where it's like, I mean, this changes the entire battle. It does, and it already has. Like, again, becoming model interoperable, uh, whether like having the right model for the right task. Everyone, and I don't think that's unwarranted. I think everyone is rightfully thinking about that. In any normal, like, development of a new economic cycle or whatever, like new industry, you would think, okay, this is all pretty normal. It's, uh, you know, where new technology, kind of the economics of it are being understood,

• 25:09Speaker 01

  the technology itself, the application, it's going to cost a lot at first and then price is going to come down. Like, that's all pretty standard. Um, I think what that, I mean, the two companies that affects the most are OpenAI and Anthropic. And like, and again, that wouldn't be a problem if it wasn't their like actual cap tables and just the way they've raised money. But it is, like, if everything is about the near term and rush to IPO for them, I think that's a big issue.

• 25:42Speaker 00

  Yeah. I mean, I think Zuck would personally be thrilled to play the spoiler here. And by the way, didn't I say last week that Zuckerberg was going to come out and complain about the concentration of power among the frontier labs?

• 25:55Speaker 01

  Oh, Zach, love him.

• 25:57Speaker 00

  And what he could do about it. And we, you said, you heard it here first, within the next week or two, you're going to hear Zuckerberg make his attack. And he did. And this is the quote I was looking for. "The price from some of the other labs is very extreme and has very high margins. We think that there's a real ability to offer frontier or very high level intelligence at a much more affordable cost." So knives out, right? And again, this is because power has consolidated between, within OpenAI and Anthropic. Um, you know, the, there was inevitably going to be a player who's going to come out and say, well, screw that, let's even the playing field. Right? And that, by the way, goes to my, again, this idea that this is, this could commoditize. And, you know, lo and behold, Zuckerberg raised his hand, you know, the week after we said he would.

• 26:41Speaker 00

  And he, Leroy Jenkins, his way right into the, into the competition.

• 26:46Speaker 01

  I mean, when I think about people that must hate concentrated industries and power and high margin monopolized areas like platform advertising.

• 26:57Speaker 01

  I think of Mark Zuckerberg. I mean, you know, he.

• 27:00Speaker 00

  You know, it's it's funny, it's funny to make that joke. The reason why Zuckerberg has as much power as he does is because when he's seen a threat to his business, he has often masterfully forwarded it, whether that is, you know, copying stories from Snapchat, copying reels from TikTok. AI has yet to, he's yet to be able to do that. Now, obviously, they have this quest to build personal superintelligence, but I think, you know, with that taking longer than expected, as he said last week, you know, the other option is,

• 27:35Speaker 00

  you know, just just run in there. Now, he had, he had open source models, right? And they were free. So he's charging for them now. So is it really that different? But I think what what he's doing is very interesting. He's saying, all right, I've spent all these billions and I'm going to at first use it to commoditize the model layer. Like Boz said, and Boz agreed with you, it's the product layer that matters.

• 28:00Speaker 00

  And so for for Meta, the idea that AI would be costly, does not serve their purposes. They want it to be free, so the product can be built on top of it, whether it's theirs or others.

• 28:14Speaker 00

  And in particular, you know, they they also want to be able to use this, as Boz said, as negotiation leverage when they rent models from OpenAI, Anthropic and Google.

• 28:23Speaker 01

  Well, I think for them, it's almost, it's like a two layers. It's one. Like, I think you had made the point that just like an existential threat that ChatGPT competes with social media, that could compete, like, people could be spending more time talking with their ChatGPT rather than scrolling Instagram. That is a threat and like kneecapping the companies that are coming after you on that would be kind of like, you know, classic zuck and kind of amazing. But then I think also, it's like you said, if personal superintelligence. Whatever that may mean, but like something around being that like for every day for the

• 29:06Speaker 01

  consumer, for like helping you manage your life and everything, not enterprise, whatever. I think that is another area that the cheaper everything gets, the better for them. And again, they, how many, is it four billion people use their products a month, three billion, whatever, I mean.

• 29:25Speaker 00

  In that neighborhood.

• 29:26Speaker 01

  Everyone stopped counting, I feel like. It's basically the world.

• 29:30Speaker 00

  Well, they saturated anybody on the planet with a phone.

• 29:33Speaker 01

  Yeah, you, you own distribution. So like being the one to actually be the front door to all AI, um, which is interesting. Like, as I'm saying this, like, they really could be competing more head to head with Apple, uh, going forward, I think. Like, if Siri iOS 27 becomes a, a bit of personal superintelligence, ambitious statement there, but like they could be competing a lot more directly soon.

• 30:05Speaker 00

  Oh yeah, they, they certainly will. Right. And this, by the way, you know, and in the, uh, you know, in a, in a roundabout way, you know, there's, you know, we've now given all the explanations for like, why they're happy just to cut off the economic benefits from the other labs for other purposes. In a roundabout way, this might actually help their AI efforts, uh, in general. This is from the information. Now, what Meta's doing is no way to make money in AI, but that clearly isn't Meta's strategy. It knows that the cost of AI has become a paramount issue for many businesses, so much so that they're trying various methods to reduce it, including the use of open source models or routing some AI work to older and more cost effective models. In that environment, Meta presumably

• 30:47Speaker 00

  thinks that by undercutting everyone else's own price, it can persuade users to at least try out its new model and potentially hook them. If that approach works, Meta could jack up the price later. So it's like, it's like the, the, um, you know, maybe it's using the old, uh, I don't know, the drug dealer model of economics where I give you a taste, you become hooked. And then I say, all right, you know, if you want to keep using it, it's going to be a little bit more, more pricey.

• 31:14Speaker 01

  I mean, Anthropic and OpenAI both, I think, have definitely been, uh, Anthropic more than anybody. Went that route. So, so yeah, I think everyone gets that like, and again, overall consumption of AI and compute will exponentially increase and it's going to make its way into more and more parts of our lives and the ways enterprises work. So I think like, it is interesting. Everyone is trying to find where they fit best in that equation, under the assumption, which I do agree with, that it will happen. So I guess it's good. Before, everyone was

• 31:54Speaker 01

  just kind of riding on, if the explosion happens, it's just great and like you will, you will be valued at a trillion dollars or whatever. But now everyone's starting to actually try to like map out where do they fit in that future?

• 32:10Speaker 00

  Yep. Okay. So before we go to break, you know, one of the underwriting themes or underlying themes on this show is always, has always been, you know, we believe that there's real technology here, but we just wonder whether there's a business here. So given what we've discussed for the last 31 minutes, what do you think all this means for the business prospects of OpenAI and Anthropic?

• 32:35Speaker 01

  I mean, it, I don't see how it's good. I really, and I know I like compete against them in some cases. So like, but, but I'm just being like, like, can you map out in a world where models are interchangeable and interoperable and cheaper and cheaper? How that could be good? The where, the only way I see it being good is if they still maintain that model that subsumes all the verticalized slash functional offerings or are so good that somehow you just stop using social media and then like, that becomes

• 33:18Speaker 01

  your personalized super intel. Like, I mean, they're still making the bet. That's why it's kind of almost like weird to me that when Sam Altman starts talking about 50%, 4% more token efficient, that's not their game. Their game has always been, we are going to build AGI and then that's why we win. And it feels pretty binary. So.

• 33:39Speaker 00

  Yeah. Well, they've also said they want to build intelligence too cheap to meter.

• 33:45Speaker 01

  Wait, I thought he said it will be like electricity and metered.

• 33:50Speaker 00

  I guess they've had mixed messaging.

• 33:52Speaker 01

  Okay, no, no, but but explain to me, what would be your take on in a in a world where AI gets cheaper and models interoperable and application layer, etc. How do they win?

• 34:08Speaker 00

  Ah, I don't know. I mean, I think I think this this has always been the problem here has always been you're building on something that is very difficult to hoard. And I don't know if there's an answer to that yet. So they've they've started to build products, right? And, you know, maybe get to go back to the laptop example from last week. You know, you're building something that's like a laptop can be used by a personal folks folk can be used by people for personal reasons, can be used by people for business reasons. Um, and so like your um, your your you can there can be multiple laptop makers. But ultimately, we see what happens. They compete based off of cost and um, based on cost.

• 34:51Speaker 00

  And, you know, Apple's, you know, doing well in its MacBook business and there are some others. Uh, but, you know, it's it's not like world beating businesses. So that to me is is like the real question here. Um, in terms of where they go. But um, I mean, the other side of it is you could say, let's say we take your line right here that the product matters most. Uh, they both have built compelling products, you know, OpenAI with ChatGPT, Anthropic with Claude Code and Claude Co work and those will continue to grow and they've been the engine behind their um, behind their growth and maybe that won't uh, slow down, even if the pricing power goes down a bit. So that's kind of the way I think about it. Totally unsettled.

• 35:33Speaker 01

  Yeah, I mean, it's I don't know, the the next few months, I'm just waiting to see an S one. I want to see the numbers, but it's definitely uh, it's it again, all of this stuff in a one, two, three year time horizon. It's just such a different conversation over the way this is going to play out the next six months.

• 35:53Speaker 00

  Yeah, well, the S one isn't gonna tell us. It's not gonna tell us anything because the battle that like what we're talking about right now. Battle's just starting. It's not even, we're just approaching the start line. Between because like it was what, four years? It was four, I'll just say it. There were, it was four years before these companies figured out the trajectory of what they could build and the form factor. And now they finally centered around it. So now it's game on. Everything else was a wind up.

• 36:23Speaker 01

  But do you think if numbers come out and they're atrocious and then that affects like stock price and then that affects like employee and researcher attrition and that point like, do you think they are two separate things or do you think one could affect the other?

• 36:46Speaker 00

  I don't know. I mean, of course it can affect, it can affect the future without a doubt. Like we've talked about this, but ultimately, you know, it's all prologue. They would argue and I would argue too. The businesses they've run, they've run up until now are not the businesses they're going to run from now on. Except for maybe Anthropic. We have a preview of that with Anthropic.

• 37:10Speaker 01

  That's not, that's not how S1s work. They're backwards looking.

• 37:14Speaker 00

  That's what I'm saying. I'm saying that like it's there, there, well, it's not good. That's why I'm saying this, the S1 in this case isn't going to tell us anything and this is entirely narrative based.

• 37:24Speaker 01

  All right, all right, fine. SpaceX showed us the alternative, so remains to be seen.

• 37:32Speaker 00

  Dude, SpaceX trading below its first day, opening price 148 right now. Opened at 160.

• 37:41Speaker 01

  Still valued 714 billion dollar market cap.

• 37:46Speaker 00

  No, 1.96 trillion.

• 37:50Speaker 01

  Yeah, sorry, sorry. That's Elon's share. I vibe coded an app is ElonMuskaTrillionaire.com if you want to go over there. And he is currently a trillionaire, but it's at 1.01 trillion. So he's right now is like right on the border.

• 38:06Speaker 00

  Well, prayers out to Elon. Hopefully he'll get through this this difficult time. Hopefully we'll get through this break and on the other side, we'll talk a little bit more about the cloud business that Meta is considering and of course, the students at Brown University using ChatGPT to tweet. We'll be back right after this. This episode is brought to you by AppPoint. Everyone's racing to roll out AI right now. Co-pilots, chatbots, agents doing real work. But here's the part nobody loves talking about. All that AI runs on your data and most teams have no single way to see it, secure it, and prove it's under control. That's exactly what AppPoint does. For 25 years, they've been the trusted layer beneath the world's most demanding data, now extended

• 38:51Speaker 00

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• 40:24Speaker 00

  And we're back here on Big Technology Podcast Friday Edition. All right, so the rumors that Facebook may start its own AI cloud business, there seems to be something to it. I shouldn't say rumors, I should say reporting. This is from Bloomberg. Meta Platforms Needs All The Computing Power It Can Get, Mark Zuckerberg Said. But in a market star for resources nearest necessary to run and develop artificial intelligence products, he's also considering whether some of Meta's AI infrastructure could be more valuable if rented to outsiders. This is what Zuckerberg told Bloomberg. The offers that you get for using the compute are so high that it may make sense in some cases to rent out or consider those kind of deals instead of your own internal uses. Zuckerberg said the potential for a cloud business

• 41:09Speaker 00

  is certainly there anytime we want to build it. I just want to say this. So we got this comment on the show. And by the way, I love the comments, keep them going. But sometimes we'll want to respond to them. This is on Spotify. You're not thinking like economists, opportunity costs. If demand for compute is outstripping supply, why not sell off your excess and make profit rather than struggle with figuring out what to do with it? Rent it out for a profit and let others figure out what to do with it. Let me, I just want to address this. Why is, let's say Anthropic, willing to pay so much money for your compute? The answer is because they have built a product

• 41:53Speaker 00

  that even if they're paying you such high rates, they can market up and be profitable, or at least build a business for the future. The way tech companies work is they invest early in a product and they end up, you know, building something that people want and then profiting on it later. It's like this idea of like, you know, the opportunity cost. There's an opportunity cost on your VC money, right? You don't see startups taking their VC money and loaning it to other companies and saying, "Well, we're going to make a profit here." To me, it just shows a lack of either imagination or what's probably happening is success with

• 42:38Speaker 00

  the product. And there's the gap between the winner and the loser, where you have one company, let's say Anthropic, willing to pay you that much that you can't say no. And then finding an even more valuable use for it via their product while you sit at home, you know, or in your office and try to figure out what to do with your compute. Does that make any sense? That's how I see it.

• 42:58Speaker 01

  Yeah, but where that actually all, I think, like, is a logical line makes perfect sense. Where I still think that actually kind of brings back the whole like race to the bottom commoditization conversation even more is exactly what you described. Anthropic has a very expensive product they can charge a lot for, and so they're willing to pay a high amount for compute. And then Facebook is willing to realize that they can sell them that product at a high price because they're willing to pay. But if that cost structure, like a cost battle happens, then that goes away. Then suddenly Anthropic has to default to cheaper models even within Claude or whatever

• 43:43Speaker 01

  Claude cowork slash code. Like even within Claude, the moment they have to start defaulting more to cheaper models, then they won't be able to pay as much for compute, which means that it becomes less of an attractive business. So like, actually, what I love here is like, kind of Zuckerberg is basically taking both sides, right? Like, the cloud business is if it's prices high and people are willing to pay, then we'll just sell compute via the cloud business to Anthropic. If prices are low, which we're also helping make happen on the other side, then we'll kind of kneecap them, put them out of business and make our personal super intelligence even better. So suddenly Meta is looking pretty good in this.

• 44:25Speaker 00

  Yeah, I just love that Zuckerberg said it doesn't, you know, he told Bloomberg, "It doesn't mean Meta's overbuilt or has excess computing power available." I mean, that, you know, it's either one or the other. You either have, yeah, exactly. You either have excess computing power. That you can sell or you don't.

• 44:46Speaker 01

  No, no, no. It's like, what's the wire line? There's always a buyer at the right price or something like that. I'm sure there's some Stringer Bell line that's in there, but it's like, they, they don't have, you could argue, you don't have excess computing. You have that internal demand at a price. But when the Facebook is mass market, massive, like, you know, I mean, they've high margins, high profit on advertising, but like the way they would deploy to customers, you know, who don't pay them, like, like that is a, they would need that internal demand, but at a low price for compute. Whereas if it's coming in higher, then maybe you, you just do it. Again, you could, he can forever argue.

• 45:31Speaker 01

  You have four billion phones with like meta AI kind of jammed into Instagram and Facebook Messenger and stuff. You could just couple of growth hacks, get everyone adding prompts and using some kind of compute and doing AI stuff very easily. Half of Facebook feeds are probably AI generated anyways now. So like, you could do it.

• 45:51Speaker 00

  Shrimp Jesus.

• 45:52Speaker 01

  So you're, yeah, shrimp, shrimp, shrimp, Jesus. I gotta say, like, we can get into the World Cup stuff. Like, actually, just AI content overall is getting pretty good. Did you see peptide Seinfeld?

• 46:06Speaker 00

  No, I have to see that, though. I've, I've watched all the Harland memes. Have you seen them? The Erling Harland memes of like the Norse versus the British, like a battle of old and modern. The AI slop, this, did you say this? This is the week that AI slop has really transformed to like AI majesty. It's amazing, right?

• 46:25Speaker 01

  Yeah, it's not slut. Like, I think the word slop, it's kind of fascinating. Again, like, watch peptide Seinfeld. It's like, well done. It's George goes on peptides and suddenly good looking and like, kind of like jacked and like, it's so well, and it's not just like that it looks like them and it is Jerry talking. It's just actually good, a good story and funny and in the Seinfeld tradition. Those world, I think the World Cup is going to be when AI video found its moment and everyone realized like, you can actually make good stuff. Like, it's no longer gone are the, do you remember Will Smith eating pasta from two or three years ago? Yeah, we're, we've come a long way, come a long way. That's where the compute demand is.

• 47:10Speaker 00

  That's where it is. This week, my perspective on AI videos did change, right? I went from, oh, man, I've been fooled to like seeing so many of the England versus Norway videos that I started seeking them out and sharing them. Like this week, I'm telling you, my wife, who is a very big Harlan fan, as I think are many women here who are watching the World Cup.

• 47:35Speaker 00

  Her WhatsApp inbox is filled with AI videos from me, where I'm just like, watch, laugh, send. I'm like a robot. Watch, laugh, send. These AI slops, not even slops.

• 47:46Speaker 01

  It's not, no, it's not.

• 47:47Speaker 00

  Beautiful AI art pieces.

• 47:49Speaker 01

  No, no, I mean, I think the word slop is, needs to be like reminded. It's kind of like everyone uses vibe coding for, in a certain way. I feel slop is overused as well, because like slop is when it's bad. And man, videos are getting good. People are being creative. Like they're being genuinely, they're making good content, just in a different, a different tool.

• 48:16Speaker 00

  Yeah, no, it's pretty cool. And you know, as OpenAI has gotten out of that business, there's an opening for Meta, but as Meta tends to do, the company just can't help itself. This is from The Guardian. Instagram's AI image generator alarms privacy experts. Meta has sparked blowback from privacy advocates for allowing its new AI image maker to generate photos of users with public profiles by default. Users, users of Meta's Muse Image AI Tool released Tuesday can tag public Instagram profiles and generate pictures that pull from faces of people featured in these social posts. Instagram users are not notified when their posts are integrated into what the company describes as its most advanced image

• 48:56Speaker 00

  generation model yet. So basically, if you want your Instagram photos and videos to not be able to be used for this AI engine, you actually have to go and opt out. You tap the hamburger menu on the top right of Instagram. You go to sharing and reuse and you toggle the button that allows other people to reuse your content off. That's our PSA. But you know, it's sort of like, gosh, Meta, Meta almost had like a really good week in AI and had to sort of spoil it with this typical, you know, privacy shirking behavior.

• 49:28Speaker 01

  I can go to anyone else's. I've not used this yet. I'm definitely going to go look at this now. So I can go use anyone's public profile and use their likeness to generate new images.

• 49:45Speaker 00

  Okay, I don't want to say definitively yes, but it seems like that is, that is, you know, either that or they can incorporate it. Bottom line is your stuff is ingestible by these AI tools and then apparently remixable. So I turned it off from my personal profile, but I kept it on for the big technology podcast Instagram feed because I guess if people remix and reuse that stuff, we'll be happy about it.

• 50:10Speaker 01

  Meme us, just meme us. We're ready for it.

• 50:15Speaker 00

  By the way, Meta, you know, it's so interesting that last week, we talked about how there are basically two points of failure in this AI industry and Meta, as it seems to always do, has just inserted itself right into the conversation again. And how much have we talked about Meta this week? You know?

• 50:34Speaker 01

  Yeah, they're back, they're back.

• 50:36Speaker 00

  They're back.

• 50:37Speaker 01

  We just had July 4th. We're waiting for the next one, Zuck, on the hydrofoil American flag.

• 50:43Speaker 00

  Hydrofoil. Yeah, Boni would be in, what would you call it if it's not AI slop?

• 50:50Speaker 01

  I mean, it's just, no, no, it's actually something, it's something I've thought about a lot. It's just, it's just a video. Like it is.

• 50:58Speaker 00

  Interesting.

• 50:59Speaker 01

  I think like where the whole debate around is it AI or not? Okay, if it is, if it's pretending to be something real, that's, if it's like a true deep fake in that sense and like it's trying to convey that it's real, I think then that's a deep fake, that's a problem. But otherwise, it's when it's peptide Seinfeld, it's, it's just content that's pretty funny and good and um, yeah.

• 51:29Speaker 01

  It's just content.

• 51:30Speaker 00

  New era, new era. All right, so let's close out this week talking about the cheating at Brown University. So, so this is the story from, I think, Institute of Higher Ed or publication like that. I should really cite them. I'm gonna cite them. This is from Inside Higher Ed. Okay, there is this professor at Brown. For the first time since he started teaching welfare economics and social choice theory nearly two decades ago, Brown University economics professor Roberto Serrano gave his students a take-home midterm this spring. Quite a few students had expressed anxiety about being in a classroom after a gunman killed two students and injured nine in a December mass shooting at Brown, so it was appropriate, he said, to allow the students to take their exams home.

• 52:15Speaker 00

  By the end of the semester, Serrano regretted the decision. Dozens of students in the class likely used artificial intelligence to cheat and earned perfect or near-perfect scores on their midterm, he said. Serrano, in turn, made the final exam in person, which led to more than a dozen students to drop the course and even more to fail it. His welfare economics class typically attracted up to 30 students by the spring. He had taught 86, an increase he attributes to the promised take-home exams. When the midterms came along, the average score was 96%. Historically, that was 65 to 80%. So the professor knew something fishy was going on, and he and his graders ran the test through ChatGPT. The AI gave answers that mirrored what his students had written, which were kind of correct, but very off,

• 53:01Speaker 00

  with a very convoluted style. So he said, okay, the final exam is gonna be in person. Here's what happened. 18 students dropped the class. Nine stayed enrolled but didn't show up to the final exam. So you already have 27, very acting, very fishy. Three got a zero. The average score in the final was 48.6%. Again, this was for the kids who had an average of 96% on the midterm. By far the historic low. Previously, the average on the final exam never dropped below 68%. He has a great quote, the professor, at the end of this. "We cannot afford to have a society in which a significant fraction of our best young minds think that cheating is okay. This leads to a declining society, to a failed society. We cannot choose to become idiots."

• 53:47Speaker 00

  Your thoughts?

• 53:49Speaker 01

  I don't know if we can, uh, we might have crossed that Rubicon, uh, a while back as a society. But, uh, before AI, AI didn't do that one. Um, I, I, I've thought about this, I mean, again, education like should fundamentally change based on the tools that are available. And like, I would, exams should somehow be like, all right, you all have access. Who's going to put out the best work? And I think like the way tests and exams have always been structured, it was less around like, the information will, and the calculations will get done, but your understanding of it,

• 54:32Speaker 01

  like your ability to kind of extract new insights from it, that's where the value is going to be. And like, to me, actually, I gotta say, like, Serrano, come up with new exam, like to do stuff in the exact same way you've always done, and then say it's a problem, given how much things have changed. I think that reflects more on the institution and the teacher than the students. Of course, they're going to do it. I don't think it's cheating in that sense. I also love like, could you imagine the three students who earned a zero, how much they were sweating, sitting there in the exam hall, just like, even the average score for it. Imagine you just don't know any of it. This is like the stuff people have nightmares for. And like, you're just sitting

• 55:17Speaker 01

  in there and you have no clue what's going on. You're just staring blankly at that paper. And I say

• 55:25Speaker 00

  Reminds me of my, uh, yeah, go ahead.

• 55:28Speaker 01

  No, I mean, I, I feel, it's never been that bad, but I feel like there's probably been like, maybe an exam, I never did, no zeros in my time, but like, where you're just like, oh shit, I did not prepare for this. Now imagine that 10, 20, 30x, that's gotta

• 55:46Speaker 00

  Oh, yeah.

• 55:47Speaker 01

  gotta be some sweating.

• 55:49Speaker 00

  No, it reminds me of my childhood where I would bring a zero home from school and my parents would say, nice work, but you couldn't get a three?

• 55:57Speaker 00

  That's a joke, high standards joke. You know?

• 56:00Speaker 01

  Wait, what?

• 56:01Speaker 00

  Anyway, you know, it's a joke. It's like the typical parent, but no matter what number the kid comes home from school with, they say, all right, nice work, but couldn't you have done a few points higher? Anyway, didn't land.

• 56:15Speaker 00

  I guess we're at that point on a Friday, aren't we? But uh, but

• 56:19Speaker 01

  July, it's Friday.

• 56:21Speaker 00

  That's all right. So quick, no, a personal story and then, uh, and then some thoughts about this. Seriously. Yes, I have gotten a zero on a test, um, or a one of five on the AP physics, uh, which

• 56:35Speaker 01

  Oh, no, no, one out of five, not one out of a hundred.

• 56:39Speaker 00

  Right. No, but I shouldn't have even gotten that one because I think that was the lowest you could possibly get. If there was a zero, I would have qualified for a zero. I took AP physics in my senior year of high school and realized I was completely out of my depth on it. I mean, I remember a few things like specific heat, but other than that I was toast. Like trying to calculate the trajectory of some object, uh, based off of like the mass and the velocity. I'm just like, you know what? This is not for me. You guys figure this out. I'll take the other stuff. Anyway, so I show up for my AP physics exam and, uh, you have to sit there for at least two hours, two of three. I'm done in like 30 minutes because I'm just like, I'm failing this one pretty bad. Uh, and you don't get college credit unless it's a four or five.

• 57:19Speaker 00

  So I sat there for the next hour and a half, like drawing pictures of the spaceship, the solar system and spaceships and stuff like that. And I handed it in and my, uh, my physics professor came up to me or teacher came up to me later and he goes, hey, Alex, I heard you were the first to finish the exam. You must've done really well. He, he knew I failed. He knew I failed and I failed. Okay. So that's a, that aside, I've been there. I've been there. You know, students at Brown University who got that zero, I empathize with you. I feel, I feel your pain. Okay. But I, I agree with you before we go, I agree with you, Ron John. You know, there's been this dialogue. I wish we had more time for this. There's been this discussion since we're offloading so much of our thinking to AI, you know, our brain's going to rot. I think it's actually the opposite.

• 58:05Speaker 00

  I think now that I've handed so like all the like lesser activities to AI. I'm thinking through much harder problems and I feel like my brain's getting stronger because of it, not weaker. Like I'm actually able to like really think about the tough stuff. And so I think you're right that the professor needs to realize that that's going to be the universe that we're we have going forward. And this binary just doesn't fully capture what you're supposed to be testing for at a university.

• 58:35Speaker 01

  Yeah, I always think back, I know I can try, I grew up outside of Boston Lexington, Mass. And then basically like junior and senior high school drove a lot. And I can navigate more where I grew up without a map than I can in the New York City area where I've been for ever, or especially like I can't get to JFK without a Google map because I'm just so dependent on on maps. And that's one part of my brain I outsourced. And nowadays, I'm not making slides anymore, which is the greatest thing ever. You're just having the slides made for you. So you actually think about what's in there. So I think there's, there's I'm sure pitfalls and people who are not going to actually take advantage

• 59:20Speaker 01

  of this, but do better, Brown administration, not students. I'm team students.

• 59:28Speaker 00

  Team students. I mean, the disparity was amazing. Like, one kid got a 95 on the midterm and a 95 on the final. So low average on the midterm, obviously the best on the final. And then one kid got like 100 on the midterm and a zero on the final. So go figure. All right. Great speaking with you as always, Ron John, and good to have you all with us again here on the Big Technology Podcast Friday Edition. We'll see you next time on Big Technology Podcast.

• 1:00:08Speaker 02

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