Anthropic’s Cost Advantage, ShopMy's $1.5B Valuation & Wildfire Fighting Drones | Nov 10, 2025

0:13Welcome, everyone, to the Informations TI TV. My name is Akash Pasricha. It is Monday, November 10th. We have got a great show lined up for you today. First up, we are comparing Anthropic and OpenAI's projections for the coming years. We're then talking to the founder and CEO of ShopMy, a creator e-commerce company that just raised new funds at a$1.5 billion valuation. We're also taking a closer look at how AI could help fight wildfires. And we are also bringing on AWS to talk about how to accelerate the adoption of AI agents. And finally, we are ending with a discussion I am really excited for around the James Bond style security systems that are being built around AI data centers.

0:56It is a jam-packed show. And so let's get right on into things. Anthropic and OpenAI have both made some stunning revenue projections for their business. But a new story we published today takes a deeper look at how their margin structures could compare over the coming three years and why Anthropic could in some ways be more efficient. Joining me now is Sri Mupiti, who wrote the story. Sri, welcome to the show. It's great to have you. Excited to be here. So let's talk about Anthropic and OpenAI. We have done a lot of great reporting. You have done a lot of great reporting, I should say, on the company's revenue projections.

1:30And you had a new story today out that talks a little bit about their margin structure, about their compute processing. Tell us a little bit of what you found. Of course. So last week we wrote a story about Anthropic raising its revenue projections to be higher than before. So some of the findings that we found was that Anthropic projected to have$70 billion in revenue and$17 billion in cash in 2028 in its most optimistic projections. But that same year, OpenAI expects to generate about$100 billion. Anthropic also expects to be cash flow positive as soon as 2027 and generate about$3 billion in cash.

2:06Comparing it to OpenAI, OpenAI actually has 35 billion in cash burn in 2027 and won't be cash flow positive till 2030. As for efficiency, which was about today's story, we find that Anthropik's compute costs are actually much lower than OpenAI. For instance, this year, Anthropik expects to spend about six billion across compute for training and running its AI models, versus OpenAI expects to spend about$15 billion. And then by 2028, Anthropic expects to spend$27 billion versus OpenAI's$111 billion, which also includes about$40 billion in backup servers that OpenAI has reserved for any unexpected breakthroughs or viral product hits.

2:47And do we have a sense as to why their strategies are differing so much? Yeah, I think part of the reason could be the different types of customers that both companies serve. So for example, Anthropic serves roughly 80 % of its, makes about 80 % of its revenue from selling its API or AI models through an API to its business customers versus OpenAI generates a vast majority of its revenue from ChatGPT, many of which users are actually free users. The second piece could also be just the fact that Anthropic, for example, has a different type of chips that they use. For example, they use NVIDIA, Amazon, and Google chips versus OpenAI primarily uses NVIDIA chips.

3:32And because Anthropic leaders also say that they match those chips to specific tasks, that could also be helping towards driving down compute costs as compared to OpenAI. And the last piece I'll just add is that, as I mentioned to the types of customers that OpenAI and Anthropik respectively serve. OpenAI's R &D costs are across many different types of bets. For example, we had recently reported about an AI music model that OpenAI might be working on. And so I think that really points to the diversity in types of products and research that OpenAI is working on as compared to Anthropik. And at a more fundamental level, one of the things that we've talked about on this show is OpenAI's broad strategy here with a number of different products, whether it's the browsers, the agents, the social media side of things with the generative video features.

4:22I mean, OpenAI really is going for what seems like world domination in some senses. Anthropics' mission seems to be a little bit more focused. And so I wonder, this strategy that you've sort of helped us unearth with respect to financials, is this reflective of Anthropics just saying, hey, we want to be a little bit more narrow with our focus? Or is it OpenAI saying we want to do everything? Help us understand sort of which side of that is more dominant here. I think we see that with the types of products that both companies have released and also just where a lot of the revenue seems to be coming.

4:56As I mentioned, Anthropic generates 80 % of its revenue from business customers through its API versus OpenAI has a vast majority of it coming from ChatGPT. Other areas that OpenAI has also said that it generate revenue from is like new products or free user monetization which we've learned to potentially be advertising or affiliate fees um so there there is a diversity in the way that open ai expects to generate money but also um the types of research that we're seeing uh open ai work on as i mentioned around like music and browser technology and things like that how often do projections like these end up changing i would say that companies probably are doing quarterly projections.

5:40These projections that we had reported on are from over the summer. And so even just in the last couple of months, it's likely that OpenAI and Anthropik's compute spending has gone up. I know, for example, Anthropik has a number of unsigned deals that haven't been announced yet. And OpenAI, as we know, has signed 1.4 trillion in deals just to boost its compute access. And so these projections have likely gone up and compute spending is probably higher than what we even reported on. But even still, this seems to bode pretty well for Anthropik's fundraising efforts, should they sort of be marching towards that.

6:15Yeah, last week, we had reported that if Anthropik were to raise, just given that they had increased their revenue projections from the last fundraise process earlier this year, Anthropik could raise at a valuation of between$300 billion and$400 billion, and that's a step up from its last valuation. So I think that these projections are really helpful for investors to know what's up with both companies and decide if they want to invest at the time. Great. Well, Shree, I want to thank you for coming on. It's a fast-moving story and we'll have you on again when you've got more to share with us. Thanks.

6:49Okay. The creator economy and e-commerce sectors are becoming increasingly intertwined and businesses building at the intersection of these two industries are getting a lot more attention from investors. ShopMy is one company in that category. The curated creator e-commerce company raised$70 million last month at a$1.5 billion valuation. Joining me now is Harry Rain, founder and CEO of the company. Harry, welcome to TITV. It's great to have you. Thanks for having me. I'm a big fan of what you guys do with the information. So it's an honor. Well, thanks a lot. I've been looking on your platform for some new outfits that I can wear.

7:26I know that there's a lot of selection. And I know that the creators have a lot of options for me. So I also am watching what you do. Let's talk about ShopMy. Tell us, for those of us who aren't familiar, who does the company sell to? What is the actual product? How do you make money? Talk to us about that. Yeah, I love it. So we so we're building the infrastructure for the future of curated commerce. We serve three different stakeholders. First is obviously content creators. We help them get paid promoting the products they love. The way they do that is through affiliate links, these digital storefront products that we built, as well as brand partnerships.

8:01Second piece is we serve brands directly. So we give them everything they need to build a world-class career marketing program. So that's everything from discovering talent based on data, that's gifting, that's affiliate tracking, social listening, performance tooling, everything you might imagine. And that's really our bread and butter is a SaaS product for brands. The third piece is now we've involved shoppers. So we're giving them a shopping experience, incredibly well-structured, easy way to shop the recommendations of these top curators. And how much revenue are you guys generating right now?

8:38We've been on kind of a tear the last couple of years. So we ended two years ago about$4 million in revenue. Last year, 27. This year, we're in about$80. We were driving about a billion dollars in GMV to our brand partners. we have about 200 ,000 content creators on the platform, 10 million shoppers shop each month. Things have been kind of explosive. And I think it's just indicative of the way that shopping's changing and the commerce landscape's obviously overwhelming. And this idea of this curator layer taking the massive amount of options and filtering it down for the end consumer has been a really powerful force in commerce.

9:15And so$80 million in 2025, that's where you're expecting to end the year and you're profitable? Yeah, we've been profitable since 2024. So obvious question is, why do we raise? I think the answer is the opportunity here is so big. This commerce is shaking up so massively with LLMs, with AI, and we think humans and taste play a big role in that future. And we kind of want to own that lane. And so own that lane. So what do you plan to spend the money on? Yeah, there's a few things. So it might be helpful actually to take a step back and talk about kind of how we got to the$1.5 billion valuation and what we changed in the market.

9:52So if you think of the last 10 years, put yourself in the shoes of a brand, there's been two real ways to grow from a marketing standpoint. One is creator marketing. And the value of creative marketing is it's very authentic. You're selling through content creators that have a relationship with their audience, but it's hard to scale because you're working with people and inherently working with people is hard to kind of allocate meaningful budget. The second option is performance marketing, right? So you build a couple of ads, you can experiment incredibly well, and it scales very effectively.

10:23So you can put in millions of dollars a month and see direct ROI. Downside, not authentic. It's someone making an ad about themselves, pushing out like, hey, look at me, look at my new product. So what we've done is taking kind of the best of both worlds, and we're effectively calling creative performance marketing, which allows you to scale at the scale of performance marketing while maintaining it authentic. And the way we do that is through a ton of, we track everything automatically. We have a price and budget allocation engine to figure out what creators you should work with, how much you should pay them.

10:53So it scales incredibly effectively. And I think any brand that we work with that has really leaned in has seen it become their real dominant new channel. When you talk about scaling though, one of the things I noticed is that one of the ways in which Shopify differentiates itself from something like a TikTok shop is you guys are a lot more upmarket in terms of the products that you seem to be advertising with your brands and creators. How do you sort of think about scaling then if it's sort of a narrow set of products, but then also it's sort of limited by the number of creators that are available to tout these products, right?

11:31Yeah, yeah. I would say we work in the product space where it requires taste and a level of human trust. So that's anything that you put on your body, anything you, like, what you wear, how you decorate your house. The TikTok shop is kind of more like lower-end products, quick, impulsive buys. This is a much more intentional, thoughtful world. And it's enormous. I mean, the amount people spend on things that require taste is massive. You're right that we're limited by the number of curators. And I think, like, one of the main things we're spending money on here is to try to expand that creator pool and serve more of the brand's budgets that they're trying to, like, Are you concerned at all with this shift towards people turning to chatbots to find recommendations for products and the idea that we used to use creators and we still do?

12:20I'm not saying it's gone away entirely, but there is a bit of a discussion around, hey, could people turn more to ChatGPT to find recommendations rather than turning to creators? Are you worried about that at all? Yeah, it's a great point. And I think we, so there's kind of two forms of shopping in that sense. One is what we call hunt-based shopping, which is I know exactly what I want. I need to go find it. High-rise black jeans. LLMs are phenomenal at that. They're going to find you exactly what you want. The other side is what we focus on is more the gatherers, where it's I'm going out in the world.

12:52I'm finding things that you might be interested in buying and bringing them to you. You can't really start that with the chat because you don't know what it is. So it's exploratory. gathering form that we're celebrating and helping bring into a platform. Now, you mostly sell, you said, to the brands and to the creators. You also talked about sort of targeting the consumers. I tried to register for the app actually over the weekend. And so I see that there needs to be sort of an invite process or I got to apply. And so the way I understand, there's no actual consumer facing app right now, right?

13:28Are you thinking about going into that space? We are two weeks away. So unfortunately, two weeks from now, but stay tuned. What's coming in two weeks? Tell us about that. Yeah. So we have, we're launching our app. We're really excited about it. It's for, um, it's for shoppers to consume the recommendations of these tastemakers. It's very, it's an incredible platform. You can wishlist things, favorite things, get a feed, shop, search the feed through all the obviously AI LM tactics. Um, yeah, it's amazing. I can't wait. So then how do you think about then competing with all these social media platforms which are the discovery engines this is um the intention is never to bring the creators off of those platforms they still post through those channels we just aggregate behind the scenes our like our intention with the app is not to be a new entertainment destination it's to be a shopping site built just for you so you're choosing which curators make up your recommendations and then you get what is feels like a pretty general commerce experience with all the like kind of amazing parts that you can build into a custom app.

14:31And it's clean. It's awesome. So it's when you're actually looking to buy something, you can come and say, all right, I have 15 people I trust. Let me search for a summer dress or whatever you're looking for and get that recommendation that you know you're going to like ahead of time. Great. Well, Harry, I'm looking forward to seeing that app and congrats on the fundraise and we'll have you back on the show again soon. Thank you so much. Okay. A new startup is tackling a big challenge to reduce the frequency and severity of wildfires. Seneca recently launched with a$60 million financing ground.

15:05It is building an autonomous fire suppression system that uses AI to stop fires and cut response times to under 10 minutes. Joining me now to discuss this is the company's founder and CEO, Stuart Landisberg. Stuart, welcome to the show. It's great to have you. Thanks so much for having me. So talk to me about how you got so attached to the mission here and what your company actually does. So Seneca makes autonomous early response systems. And fundamentally, what we try to do is focus on use cases that today are inefficient, unsafe, or impossible for firefighters with existing technology. Our first product in practice is a fleet of five autonomous suppression copters.

15:47You can call them drones, but they're very large, weigh 250 pounds. Each carries over 100 pounds of Class A foam. And they can respond earlier than is possible today, hopefully getting to fires within five or 10 minutes. And that can keep them from spreading. They can access places that are hard to reach, are really dangerous for firefighters. And they can operate in situations that are really inefficient for firefighters to get it, giving sort of a force multiplication effect to folks on the front lines. And so these are large drones. I've seen them. They kind of put them on the back of a pickup truck, and they're bigger than sort of the remote control ones that we've seen sometimes on Instagram.

16:26How many of these have you sold? So we're not talking specifically about numbers, but we do have a number of fire agencies who've agreed to deploy them next year. And we think about them as going in a group of five. And the magic of a group of five is that across five aircraft, and you're right, they're sort of like the average of your little hobby drone and a large helicopter. Across a group of five, you can get about as much suppression power as a wildland fire engine. And there's something, too, if you can get that there in the first five to 10 minutes post-detection, our modeling shows that you can stop even a 95 % risk fire or at least give the ground troops enough time to get there to prevent real devastation.

17:06So if we're not talking about how many have you sold, then how many agencies are using it? But, you know, give me a sense of scale. Who are the customers? So our customers today fall into three major groups. They're fire agencies, some of the most forward-thinking folks in the country. We've already talked publicly about partnerships with San Bernardino, the largest county in the country with over 20 ,000 square miles in their jurisdiction in Southern California, obviously very close to our friends in the Palisades last year. We've talked about flying with agencies across the Mountain West, including Aspen, Colorado.

17:38But we've flown this for dozens of agencies, thousands of missions across California, Wyoming, Montana, Colorado. And so expect to see it widely deployed in 2026 and even on even a broader scale in 2027. And then I'll speak quickly just beyond fire agencies. There's also utilities, developers, and various landowners who obviously have a really think hard about making sure they have the right protection for their property. And a bit of firefighting 101 here because I don't know as much as I should. But the drones are big, but where does the water come from? I mean, they can't house that much water, I think, in them, right?

18:15So the magic of five is that each of them comes preloaded with a little over 100 pounds. The capacity today is about 14 gallons. And when you use Class A foam and you spray it at really high pressure, the airflow actually sucks air in. And with the foam, you get what's called an expansion ratio of somewhere between 5 and 15 to 1. So 500 pounds will end up playing more like 2 ,500 to 5 ,000 pounds, which gets you to approximately the 500 gallons of suppression power you'd see in a wildland engine. So it may not be intuitive at first, but actually you get quite a bit of suppression power just from the payloads that's on board the aircraft.

18:53And I missed that part. So it's not actually water. It's a special foam that is getting. Okay. Okay. And now talk to me about the long-term vision here. I mean, firefighting, I don't know how big a market this is. I know the problem is getting worse, but in many ways, you're selling to the public sector in terms of them wanting to buy the technology. Are you also looking to sell more to the private sector? What is the long-term vision for the company? So there are lots of stakeholders who care about this, right? Obviously, we've seen utilities. For many of them, this is an existential risk, stopping fire danger.

19:28But you also see everything from timber farmers to major developers who have got properties, high value properties in the wildland urban interface. But if you look at the long term, fundamentally, if we can't stop homes from burning down as a result of wildfire across the American West, we are going to lose home insurance across the American West. We're not going to socialize with the fair plan and other things, the losses from wildfire. So we have to stop this problem. And so when I look out over the long term, I think of this as something that every single community in a high-risk place, which unfortunately much of the Western United States needs to be taking a proactive look at because it is only a matter of time before we all start losing our insurance, which means we're going to start losing our mortgages, which is going to threaten the American way of life across half our country.

20:16So we look at this as an existential crisis for the American West and one that technology can play a real role in solving. So it's exciting and passionate and we're grateful to have terrific partners across public and private sectors. Great. Well, Stuart, I really appreciate the work you're doing and really appreciate you coming on the show. Thanks for coming. A pleasure. Back anytime. Okay. Our next segment is with our presenting partner, Amazon Web Services. We have written a lot at the information about how challenging it is for businesses to adopt AI agents. And there's no one thing that makes it difficult, really.

20:50There are a multitude of issues that companies face every day. And so I want to bring on Sean Nandy, a director at AWS, to help bring us a view from the ground on what these obstacles look like and how they can overcome them. Sean, welcome back to the show. It's great to have you. Bosh, it's good to see you on such a fun topic. Let's talk about agents. You know, I saw the report this morning, McKinsey actually put out this weekend, talking about AI adoption. And the headline of the report really was that a lot of companies have adopted AI. Scaling AI is a much different story. And it was a much smaller proportion there.

21:26And so I want to talk to you about what the challenges are scaling agents. What are the challenges that customers are telling you they're facing with respect to actually rolling this out across their entire companies? Yeah, absolutely. Look, this is such a topically relevant concept because everyone's talking about it. We're seeing customers deploy these first agentic wins and some customers are in a massive scale, but I think we're seeing a common thread of challenges. And maybe I'll first tell you how big it can be. I was with WPP on stage, well-known advertising, et cetera, technology now company, and they've deployed over 70 ,000 agents to production.

22:05So people can do this at massive scale. I think the challenge has been doing it at scale safely, securely, quickly, cost effectively. And I'll just give you a really simple analogy, Akash, that I think will resonate with people, a human analogy almost. If you had the luxury of having a team of humans help you in life, which is what agentics is going to look like, you'd get a legal advisor, a financial advisor, maybe even a travel advisor, right, a planning advisor, you have all these advisors. And having one advisor is pretty easy. They keep their notes in their notebook, they call you, they know you pretty well, they have access to your stuff.

22:38But when you have a team, you got to think about like, what access does everyone have? Does my travel advisor have access to my investment strategy? No, but they probably need some basic financial info. How do they share notes? And how do you constrain all these advisors? How do you make sure they're spending the right amount of time on things? And that's some of the challenge with scaling agentic is looking at how to deal with all that I can break down those pieces for you in not technical but in terms. Yeah, I mean, look, what we've been hearing on the ground from our end here is one category of the problem is that the product itself needs a little bit of polishing.

23:16Another category is the predictability of costs. I mean, how do you even tell how much you need to spend on this stuff? Are I'm curious in those two categories, which of those seems like a bigger problem right now? Yeah, look, I'll put it in a couple of ways. Most organizations built their data and their infrastructure and application foundations targeting maybe business intelligence and machine learning, even generative AI, but not agentic. And so the problems you laid out, the problems don't tend to be super technical in nature. We talked on a prior show about the people side of it and chief AI officers.

23:48But on this side, it's not cost on day one. Cost matters when you scale. Cost is what holds you back from getting really big, but not getting started. It probably is things like access and security and privacy that are pretty important, as well as how agentic is so different on distributed systems. So when you think about building an agent, you really need to think about how it might scale and access things. Like an agent might run for five hours or it might run for five seconds, depending on the task. And when you ask agents to reason and think on your behalf, how many things do you want them to go in touch?

24:22Do you want them to adopt the identity that you have as the user or like a super user? Do you want them to be able to create things on your behalf or draft them? Having all those guardrails, all of this is technically solvable today, Akash. I think where we've been focused helping customers is making sure customers don't reinvent how they do that. Because if you're sitting there building this foundational stuff, these guardrails, you're spending all your time on that and not in innovating. I do want to go back to what you were saying about the cost side here, though, because one of the things that I've been thinking about is, hey, you unleash this agent, you put it into your system, and you say, you're going to do the work for me.

25:01I mean, you don't know how much work it's going to take. You don't know how much compute it's going to use. I mean, conceivably, if I hire 10 people here today, they could stay until the wee hours of the morning. Then you get into issues of overtime and stuff like that. How do you know how much an agent is going to cost you? How do you sort of constrain that issue? I love that. I love that analogy, Akash. You nailed it. And look, this is not a new concept, even though it feels new. When cloud started, cloud has been around 19 plus years at this point. And when cloud got to the enterprise, all these CFOs were like, what if all my developers spin up things in the cloud?