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

Episode Date: July 10, 2026

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

Opening Eye 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. In the face of ongoing disruption and opportunity, TMT leaders need to deliver tangible results, not just ideas. When pace and performance matter most,

PWC combines market insights and deep sector experience with AI, cloud, and emerging tech to accelerate your transformation and drive measurable ROI from strategy to execution. PWC can help you anticipate what's next, outpace disruption, and compete. For more information, visit pwc.com. Welcome to Big Technology Podcast Friday edition when we break down the news in our traditional cool-headed and nuanced format. We have a great show for you today.

Open AI's long-awaited Super app is finally. here 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 it has the price war arrived. And finally, we will discuss the Brown students cheating with chat chitp-t, 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 Ron John Roy of margins. Ron John, great to see you.

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. But 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 got to bring race into it? And I was like, be careful. Alex, remember to say Brown University. 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 going. There's no segue. All right, let's talk about what happened this week in tech. Open AI finally unveiled its Claude co-work competitor and its desktop super app. This is according to the information. Open AI is part of its effort to attract more. business customers announced a new agent called Chad GPT Work, which taps into corporate data to

automate the creation of spreadsheets and presentation and can also handle more complex tasks like financial forecasts and conducting research. ChatGPT work is OpenAI's answer to Anthropics popular Claude Co-work product, 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 chat GPT with Codex and the new chat chipt work offering. This reflects OpenAI's recent realization that Codex is better than chat GPT in handling long-running tasks that involve multiple steps and require the use of external tools. Let's just start here.

Ranjan, 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, 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? 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 command line, but in a more chat-based interface. So I've definitely been thinking about this a lot. Played a bit with chat GPT work. It 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 cloud interface as like a separate button,

which is another topic, whether it's getting too convoluted and messy. but 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 an enterprise agentic eye, 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. This is every product going forward. 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 open AI. Right. Sorry, explain that and do it without

making a writer commercial. No, no, no, 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. Take rider out of it. Explain what the differentiation is going to be. Yeah, yeah. So, I mean, it's difficult when this is... I'm challenging you here. All right, all right. Let's go.

Let's go. So if you are a marketing organization within an enterprise, 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, 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, no enterprise actually sees any kind of value realization. Things don't work well. So like actually building the system. So if you take, you're creating an event and like the big next big technology summit, like making sure,

sure that the event bright, or what system do you use for it? Luma. 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 remark it, having your messaging 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 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. Does that make sense? I'm curious. Okay, yeah, but let me push back a little bit because, all right, first of all, 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 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. Yeah, but... And then, hold on, I want to finish. So then Claude Co-Work helped make, like, the prospectus for the event that we sent to potential speakers and sponsors. And, of course, I refined what was in there and helped give it reference material, but

it did a good job there. Chat Chitp.T 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 within chatypT, 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. We just basically needed, like, some references for the AI to go off of, and then it was able to take it from there. 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 entire architecture. Now try to do that in the system that you just did. That's, to me, where the battleground's 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. So let's go back to what David Karp from, no, Alex Karp from Palantir was saying, David Karp is the Tumblr guy.

Alex Karp from Palantir was saying, where is David Karp now. Nobody knows where he went. He just sold the blog to Yahoo for a Billy and then he disappeared. All right. Let's talk about the relevant carp. Go. Okay. Relevant carp.

Relevant carp. So basically what he was saying is, you know, he was at CNBC talking about how he was going to build this open model based, you know, 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 Carp is saying? and is this maybe, you know, we've kind of joked about how Open AI and Anthropic are bringing the consultants in with their, you know, so-called forward deployed engineers, which by the way is also a volunteer thing. And so, you know, it is where we're going the customization of these tools for the purposes that you spoke about with the assistance of some of these decidedly non-military, but military sounding forward deployed engineers. You tell me. 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. Okay. Let's pause. No, no, that's what he said.

This guy is so good at marketing. I know. He's like legitimately renamed what a consultant is. And he's told you that his consultants are like fine French dining. I know. No, no. And it was like a military. He's so good.

Nothing against consultants. Yeah. But let's, why do we have to like, we're, we're looking for an apartment right now. And it's like some industries, like the wine industry, the real estate industry, and I guess Alex Karp, have the most audacious 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-pourish. with, you know, two layers to rest your head. This is what Karp is doing.

Well, I mean, I do consider myself a bit of an AI somelier, so, you know. Okay, so. Ending the show now. Ending the show. That's it. That's the next wave of job in this, in this economy. 50% of white collar jobs wiped out, but AI someliads are going to be 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. Or like anyone who has like a very,

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. expertise 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 FDE, A.I. Somelier, whatever you want to call them, people going in and working with you. 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 versions of it, but like that's where the next battleground is going to be. And I called this battleground, so I'm telling you where the next one's going to be. Okay. So more verticalized style applications of these catch-all applications is where the differentiation is going to happen. 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. 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 research or you'll use another. 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 wasn't a vertical, 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 AI is built for that person and that function rather than the like type of company it is. Does that make? I'm still workshop in this one.

Yeah. Okay. So I want to go back to our sort of meta debate that we've had on the show for like four years. this point, which is, 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 break on that. But now I'm coming back to it. Okay. Why? So I think what 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. I know. 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 of 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 now, right now it takes a lot of effort to feed that. But maybe in the future when

these models take their next leap, it will just be, let's say, a day of saying to your model, like let's say I'm going back to my summit, right? Go ahead and crawl everything we do, get our voice, get our branding, and then, you know, you come up with the 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 does it go that way? 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 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 as 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 an interesting one because in reality, like, I mean, the progress that's been made in the last two years, 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. Okay. So now let me take us, let's say, three years into the future, assuming one version, not the 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 chat chit-a-tap and it's in Anthropics app and meta has figured it out some way and Apple has it in the iPhone for whatever reason. No, I should be nice to them. They're making progress.

I appreciate. I still haven't downloaded the developer iOS 27 but I've been meaning to, yeah. So, okay, so we get to that point where, we're 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 GBT 5.6, this week said it was 54% more token efficient than others, right, than its previous model. So, like, the, 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 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. 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. Bring on a third person for the O. 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. Someone had to. I mean, it is required. So, I think, I think cost

becomes important, I don't know, like on this cost, and we're going to 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, making, and I listen to the Baws 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. They have more strength versus some of the other frontier labs in this case. But still, like, we're not there yet. Because people don't

have this stuff working at scale outside of software. Like 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 realized that cost will be a factor, and it wasn't before. But I feel the pendulum, again, just keeps swinging too far each way and just let us work. people. Just let us walk. 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. Thank you. 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, at 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 out a $69 billion ARR in five months. You know, they've seven X'd or more, eight X'd. 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. 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'll continue to have. But to me, the more pressing thing here is that whether that discussion is merited or not, 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 co-work, Claude-Code type experience, right, these super app experience. And the fact that there are some companies that are quite motivated to drive the price down. And you mentioned Bos, so let's go to Facebook. So Facebook this week, they introduced their Mew Spark 1.1 model. This is according to Bloomberg. And they are going to do something to strategically bring this market down. There'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 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.

its API pricing is roughly 25% the cost advertised by other top models from OpenAI and Anthropic. I mean, Zuckerberg has said there's 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 performing as everything else. We are going to go 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? Oh, I think it's a massive, 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, whether like having the right model for the right task. Everyone is, and I don't think that's unwarranted. I think everyone is rightfully thinking about that. So 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, you know, where new technology, kind of the economics of it are being understood, 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. I think what that, I mean, the two companies that affects the most are open AI 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 raise 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.

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? Oh, Zach. Love him. And what he could do about it? And you said, you heard of 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 is consolidated within opening eye and anthropic, you know, 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.

And that, by the way, goes to, again, this idea that this could commoditize. And, you know, lo and behold, Zuckerberg raised his hand, you know, the week after he said he would. And he, Leroy Jenkins, his way right into the competition. I mean, when I think about people that must hate concentrated industries and power and high margin monopolized areas like platform advertising. I think of Mark Zuckerberg. I mean, you know, he... You know, it's funny to be ironic sarcastic.

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 thwarted it, whether that is, you know, copying stories from Snapchat, copying reels from TikTok. Doc, 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, you know, just run in there. Now, 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 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.

And so 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. And in particular, you know, they also want to be able to use this as Boss said, negotiation leverage when they rent models from opening I Anthropic and Google. Well, I think for them, it's almost, it's like at two layers. It's one. Like, I think you had made the point that just like an existential threat that chat GPT competes with social media. They could compete like people could be spending more time talking with their chat GPT 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 super intelligence, whatever that may mean, but like something around being that like for every day, for the 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, in that neighborhood. Everyone stopped counting. I feel like, it's basically the world. Well, they saturated anybody on the planet with a phone. Yeah,

you own distribution. So like being the one to actually be the front door to all AI, which is interesting. Like, as I'm saying this, like, they really could be competing more to head with Apple going forward, I think, like, if Siri iOS 27 becomes a bit of personal superintelligence, ambitious statement there, but like they could be competing a lot more directly soon. Oh, yeah, they certainly will, right? And this, by the way, you know, in the, you know, 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 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 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 thinks that by undercutting everyone else on 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, you know, maybe it's

using the old, 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 pricey. I mean, Anthropic and Open AI both, I think, have definitely been anthropic more than anybody went that route. 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 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. Yep. Okay. So before we go to break, You know, one of the underwriting themes or underlying themes on the show 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 Open AI and Anthropic? I mean, I don't see how it's good. I really, and I know I like, computer. compete against them in some cases. So like, 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 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 your personalized super intelligent. 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're going to build AGI, and then that's why we win. And it feels pretty binary. So tell them. Well, they've also said they want to build intelligence too cheap. to meter. Wait, I thought he said it will be like electricity and metered. I guess they've had mixed messaging. Okay. No, no, but explain to me what would be your take on? In a world where AI gets cheaper and models interoperable and application layer, et cetera, how do they win? I don't know. I mean, I think this has always been, the problem here has always been your building,

on something that it's very difficult to hoard. And I don't know if there's an answer to that yet. So 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. It can be used by personal folks. Can be used by people for personal reasons. Can be used by people for business reasons.

And so, like, your, you're, you're, there can be multiple laptop makers. But ultimately we see what happens. They compete based off of cost and based on cost. And, you know, Apple's, you know, doing well in its MacBook business. And there are some others. But, you know, it's not like world beating businesses. So that to me is like the real question here in terms of where they go. But, 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. They both have built compelling products, you know, open air. with Chat GPT, Anthropic with Claude and Claude Co-work, and those will continue to grow. And they've been the engine behind their growth, and maybe that won't slow down, even if the pricing power goes down a bit. So that's kind of the way I think about it, totally unsettled. Yeah, I mean, I don't know, the next few months, I'm just waiting to see an S-1. I want to see the numbers, but it's definitely, it's, 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. Yeah. The moment those S-1s hit. It's not going to tell us anything because the battle, 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. 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 windup. 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? I don't know. I mean, of course, 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 up until now or not the businesses they're going to run from now on. Except for maybe Anthropic. We have a preview of that with Anthropics. That's not how S-1s work. They're backwards looking.

That's what I'm saying. I'm saying that, like, It's, they're, 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. All right, all right. Fine. SpaceX showed us the alternative.

So remains to be seen. Dude, SpaceX trading below its first day. Opening price, 148 right now. Open that one, 60. Yikes. Still valued $714 billion market cap. No, sorry. 1.96 trillion.

Yeah, sorry, sorry. That's Elon's share. I vibe-coded an app is Elon Muskatrillionaire.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. Well, prayers out to Elon. Hopefully you'll get through 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 those students at Brown University using chat GPT to tweet. We'll be back right after this. This episode is brought to you by Accenture. When your advertising operations fall out of sync, everything else follows. Spotify and Accenture are working together to reinvent the rhythm of ad sales, using automation, analytics, and smarter workflows to simplify campaign delivery and access better data across the business.

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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, narrows. 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

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 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 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. Yeah, but where I, 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 describe.

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 Claude co-works slash code, like even within Cloud 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 Zuckold. Zuckerberg is basically taking both sides, right? Like, the cloud business is, if it's prices high and people 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 superintelligence even better.

Suddenly, meta is looking pretty good in this. Yeah, I just love that, though, 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. 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 Stringerbell line that's in there. But it's like 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, they've high margins, high profit on advertising, but like the way they would deploy to customers 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 just do it.

Again, he can forever argue. You have 4 billion phones with like meta AI kind of jammed into Instagram and Facebook Messenger and stuff. You could just a 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. Shrimp Jesus. Yeah, shrimp. No, shrimp Jesus.

I got to 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? No, I have to see that though. I've watched all the Harland memes. Have you seen them? The Erling Harlan memes of like the Norse versus the British. Yeah,

like a battle of old and modern. The AI slop, did you say this? This is the week that AI Slop has really transformed to like AI Majesty. It's amazing, right? Yeah, it's not Slot. Like I think the word Slop,

It's kind of fascinating. Again, like, watch peptide Seinfeld. It's like, well done as 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 a good story and funny

and in the Seinfeld tradition. 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. It's no longer gone or the, do you remember Will Smith eating pasta from two or three years ago? Yeah. We've come a long way. Come a long way.

That's where the compute demand is. 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. Her WhatsApp inbox is filled with AI videos from me, where I'm just like, watch, laugh,

scent. I'm like a robot, watch laugh, send. These AI slops, not even slops. It's not, no, no, it's not, you can't use the word slops. 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 creatives. Like they're being genuinely, they're making

good content just in a different, a different tool. Yeah, no, it's pretty cool. And, you know, as Open AI 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 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 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 top 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. 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. Okay, I don't want to say definitively yes, but it seems like that is 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. Meme us. Just meme us. Yes. We're ready for it. 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, for like a half hours. They're back. They're back. They're back. They're back. They're back. They're back. They're back.

They just had July 4th. We're waiting for the next one, Zuck, on the hydrofoil, American flag. Hydrofoil. Yeah. Yeah. Bonia would be an, what would you call it if it's not AI slop? AI. I mean, it's just, no, no, it's actually something.

It's something I thought. about a lot. It's just, it's just a video. Like, it is. Interesting. I think like where the whole debate around is it AI or not. Okay, if it's, 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 just content that's pretty funny and good and yeah it's just content

or new era new era all right so let's close out this week talking about the cheating at Brown University so this is the story from I think Institute of Higher Ed publication like that I should really cite them

I'm going to cite them this is from inside higher ed okay so what happened was 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. By the end of the semester, Serrano regretted the decision. Dozens of students in the class likely used artificial intelligence to cheat and earn perfect or near-perfect scores on their midterm, he said. Sorano, 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 and increased the attributes to the promise 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

ChachyPT. The AI gave answers that mirrored what his students had written, which were kind of correct, but very off with a very convoluted style. So he said, okay, the final exam is going to be in person. Here's what happened. Three students, oh, 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 on 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. Your thoughts. I don't know if we can

we might have crossed that Rubicon a while back as a society, but before AI. Hey, I didn't do that one. I've thought about this. I mean, again, education

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, like, your ability to kind

of extract new insights from it, that's where the value's going to be. And, like, to me, actually, I got to 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 all 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 in there and you have no clue what's going on. You're just staring blankly at that paper. And I say this. Reminds me in my, yeah, go ahead. No, I mean, 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 got to, Oh, yeah. Got to be some sweating. 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.

That's a joke, high standards joke. You know. Wait, what? 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, it didn't land. I guess we're at that point on a Friday, aren't we? July, it's Friday. That's what we're on. So quick, no, personal story and then some thoughts about this. Seriously, yes, I have gotten a zero on a test or a one of five on AP physics, which I took AP physics. Not one out of a hundred.

Yeah, right. No, but I shouldn't have even gotten that one. 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 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 you have to sit there. 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. And you don't get college credit unless it's a four or five. So I sat there for the next hour and a half like drawing pictures of the spaceships, the solar

system and spaceships and stuff like that and I handed it in. And my physics professor came up to me, our teacher came up to me later and he goes, hey, Alex, I heard you were the first to finish the exam. You must have done really well. He knew I failed. He knew I felt. And I felt. 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 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, are our brain's going to rot? I think it's actually the opposite. 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 have going forward and this binary just doesn't fully capture what you're supposed to be testing for at a university. Yeah, I always think back, I know I can, 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 forever. Or especially like I can't get to JFK without a Google map because I'm just so dependent on maps.

And that's one part of my brain I outsourced. And nowadays I'm not making slides anymore, which just. 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 some sure pitfalls and people who are not gonna actually take advantage of this, but do better Brown administration, not students.

I'm team students. 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, best on the final. And then one kid got like a hundred 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 in the Big Technology Podcast Friday edition. We'll see you next time on Big Technology Podcast. If you want a $3,000 a month payday for life, what would you feel free to do?

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