# How Cheap AI Could Derail OpenAI And Anthropic's IPOs

## Метаданные

- **Канал:** CNBC
- **YouTube:** https://www.youtube.com/watch?v=aKNaXGpJ7WM
- **Дата:** 20.05.2026
- **Длительность:** 27:17
- **Просмотры:** 15,272
- **Источник:** https://ekstraktznaniy.ru/video/51522

## Описание

Chinese AI labs like DeepSeek are matching American frontier capability at a fraction of the cost, and a wave of American and European challengers are building toward the same price point. Adoption is already shifting, with Chinese models taking a growing share of enterprise AI traffic. That's a problem for OpenAI and Anthropic, which are pitching IPO investors on a premium moat that's eroding fastest in the enterprise segments they need to dominate. CNBC's Deirdre Bosa explains how cheap AI from competitors could derail OpenAI and Anthropic's upcoming IPOs.

Chapters:
0:00 - 1:07: Introduction
1:08 - 4:06: China undercuts the frontier
4:07 - 6:26: American challengers move in
6:27 - 7:43: The SpaceXAI proof case
7:44 - 27:16 Full interview with Cohere CEO Aidan Gomez

Anchor and Columnist: Deirdre Bosa
Produced by: Jasmine Wu
Edited by: Matt Soto
Animations: Emily Park, Jason Reginato, Christina Locopo
Senior Director of Video: Jeniece Pettitt
Additional Editing: Erin Black

» Subscri

## Транскрипт

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Are OpenAI and Anthropic actually worth a trillion dollar each? Well, soon Wall Street is going to find out. Both companies are courting investors for IPOs at valuations north of $800 billion. — People really believed in this AI revolution and they wanted to put their money to work behind it. — The pitch, they're the next Microsoft, the next Google, tech giants with pricing power that will last for decades. In the first quarter of this year, we saw, if you were to annualize it, 80x growth per year. — But here's the problem. That pricing power, it's already cracking. Chinese open source models, they're eating the low end. American competitors are coming for the high end. And the moat that these companies are selling to public investors, well, that's shrinking in real time. — The gap between the two countries, it's closing rapidly. — I'm George Bosa. the biggest IPOs of the year. They're priced for a market that is already splitting underneath them.

### - [1:08]

— So, as enterprises go from a few experimental AI projects to now rolling out across entire workforces, they're beginning to ask, is it worth the cost? — Where their spend is going, what's the nature of the task, and if other models may be more performant, and how much could be saved by shifting volume based on the right level of task. I think this is going to be a mega theme for the next year. Well, let's do the math. Say your company has a $10 million AI budget. Run it on Claude Opus, that's Anthropic's top model. You could burn through it in weeks. Run the same budget on DeepSeek, that's China's open- source model. That may stretch across most of a year. The benchmarking firm artificial analysis crunched the numbers. Claude costs nine times more than the cheapest Chinese alternative to do the same work. Even models that are outofdate 6 months old are perfectly performant for most of the nature of tasks being done. — And not just cheaper, they're competitive. Chinese Labs, Moonshot, Xiaomi, Deep Seek, GU, they have shipped open source models in the last 4 months that match or nearly match American Frontier models on the benchmarks that matter. — Our models are better overall. Open AAIs is better. Anthropics is better. Gemini's, you know, better. However, their open-source models are well ahead of us. — Adoption is following the price on Open Router, the largest AI traffic aggregator. Three of the top five models this month, they're Chinese, and they went from 1% of usage in 2024 to more than 40% this year. — The reality is that so far the ones dominating open source AI are Chinese company. For the first time in 2025, the volume of download of open models uh from Chinese providers was higher than from American providers. Right? Even in the US — for Chinese labs, constraint became the strategy. Cut off from Nvidia's best chips amid export restrictions, Chinese labs, they had no choice but to get creative. So, smaller models, cheaper training, more efficient inference. the best AI researchers in the world because they are limited in compute they also come up with extremely smart algorithms. If most advances came from algorithms and computer science and programming tell me that their army of AI researchers is not their fundamental advantage and we see it deepseek is not inconsequential advance. Meanwhile, American Frontier Labs, they are spending hundreds of billions of dollars in AI infrastructure, training ever larger models on the most expensive chips that Nvidia sells on a power grid that cannot keep up. That gets passed to the consumer. So, the edge that justified premium pricing, that's eroding. Chinese open source is closing the gap on capability. While American competitors, they're coming for the sensitive, high trust workloads where customers will pay up.

### - [4:07]

American Frontier Labs. They do have one stronghold left. Trust. Banks, grid operators, defense, healthcare, regulated industries that won't touch Chinese models no matter how cheap or good they get. — For governments, for regulated industries, critical industry, they're not going to be using and leveraging that technology. Chinese models just simply aren't an option. And so organizations that need to trust the models, the decisions that they're making, the code that they're writing are going to be willing to pay a premium to access Western democratically aligned technology. — That is where premium pricing holds. But it's also where OpenAI Anthropic are about to get squeezed by American competitors building exactly for this gap. Take Cohir, founded by Aiden Gomez, one of the authors of the paper that kicked off the modern AI era. Coher builds for that niche, smaller, more efficient models specifically for regulated industries. — I see things moving heavily uh in Coher's direction. We've seen that over the past year with our revenue 6xing uh last year and continuing to grow very rapidly this year. — And then there's Nvidia, a company that US enterprise already trusts. It's now shipping its own open source models called Neotron, positioning them as the alternative to both Chinese options and the closed Frontier Labs. — I think Nvidia recognize that they need everyone to be able to build AI, not just a few players. And so that's why they supporting open-source AI so much. That's why they became, in my opinion, the American king. — Palunteer, Salesforce, Service Now, Crowdstrike, they're all already adopting NVIDIA open source. And then there's Reflection AI, a startup that just raised at a multi-billion dollar valuation, building open source frontier models as an American alternative to DeepS. All three are going after the same gap. Capable models at a fraction of frontier prices on infrastructure US enterprises already trust. — If we can see more and more American startups contributing to open source, uh we can definitely catch up to Chinese open source. Open AAI and Ananthropic, they don't just have a China problem. They have an America problem, too.

### - [6:27]

Even Elon Musk isn't betting on a standalone AI lab anymore. In February, he merged his AI company, XAI, into SpaceX. — Elon Musk's rocket company SpaceX, acquiring Musk's artificial intelligence company, XAI, in what would be the largest M& A deal in history. and ahead of a possible blockbuster IPO for SpaceX. — The stated reason was more capital for XAI. Before the deal, XAI was reportedly burning roughly a billion dollars a month. But after the deal, that burn would be backstopped by SpaceX's billions in annual revenue. So, the man who built the largest AI supercomputer in history, who has bet more of his own money on AI than maybe anyone else decided his AI lab needed to diversify, other businesses to support it. Open AI and Anthropic, they do not have that option. So, Wall Street will have to judge them on AI economics alone. So, here's where we end up. Open AI and anthropic, they're being priced as if they own enterprise AI, but the evidence says they own one slice of it. And even Elon Musk hedged his bet. The pitch was pricing power for decades, but the evidence is stacking up against it. Public investors, they're about to decide who's right.

### 27:16 Full interview with Cohere CEO Aidan Gomez [7:44]

You heard from Aiden Gomez in the piece. He co-authored the paper that started the modern AI era and he now runs Coher, building for regulated industries. We think the full interview is worth your time. He gets into why he thinks the market is moving coher's direction, how he sees that China threat, and where premium pricing actually holds. — Is premium AI, Frontier AI, is it still worth it? — I think you can see in the demand that people are willing to pay and you can see in the level of capex spend and the reduction in free cash flow that companies can see the future demand ramping. So yes, people will continue to spend to get access to extremely high quality models. In terms of the enterprise and how their adoption of AI is being shaped, two of the core bottlenecks to that are cost and security. And so trust is really deciding who they're going to choose. And to your point about DeepSeek V4, I think that's an example where for governments, for regulated industries, critical industry, they're not going to be using and leveraging that technology. Chinese models just simply aren't an option. And so organizations that need to trust the models, the decisions that they're making, the code that they're writing are going to be willing to pay a premium to access Western democratically aligned technology. talk a little bit about Coher's business model then because my understanding you guys serve the enterprise and you're a model builder. How are you thinking about it and the cost payoff and benefits? — Yeah, absolutely. So we build our models from scratch. Uh our business is exclusively on the enterprise side. So we focus in particular on uh the high security settings. So think uh grid operators uh financial services, telco, uh government. Um and in those settings, the data and the systems that these models are accessing are really national security concerns. And so there's just no way that there's going to be a reliance on a non-democratically aligned technology stack. And what Coher does uniquely well among all the labs is very secure deployments. So whether it's onrem or completely airgapped, we can deploy in a way that there is no cyber risk. We can deploy inside a customer's data center, we can deploy into a submarine, a kilometer under the surface of the ocean with no ability to talk to the internet. And so that level of security just gives us a completely unique value proposition. It also presents constraints. We have to deploy on extremely limited compute footprints. And so we talk about how these large models are driving massive buildout and spend uh for the hyperscalers uh but for these high security settings they can't get their hands on enough chips either. And so we need to be able to deploy on two to four GPUs. Uh and so that presents a completely new technological constraint that basically rules out massive models. Uh and so we focus on something that is right sized for the market that we serve. — Right. It you know two of the biggest frontier labs open AI and Anthropic certainly turning their attention to security with mythos and you know Sam Alman Open AI were out with you know big plan last night. How do you compete with those ones? Um and those bigger models you know mythos the whole narrative surrounding it is it's so powerful they can't release it. when they do, how does cohhere fit into that picture? — Yeah, so the massive models, you know, uh rumor is that it's something like 10 trillion parameters in scale that does not fit on two to four GPUs and so it's not going to be served inside of these extremely compute constrained environments. So we don't really run into those models when we're serving the market. What I will say is that these massive models that are capable of uh very sophisticated cyber offense and defense use cases are a very interesting new capability uh that exists in the world. uh we can now at scale find exploits inside very sensitive software. And so it reinforces the significance of private deployments, ensuring that our grid operators aren't putting the code that operates the power that flows into all of our homes, all of our businesses in a place that it could be accessed by a third party. uh where that third party could use AI models to find exploits that they could use to shut off the grid or the same thing in financial services or in healthcare etc. If we expose the infrastructure the software that powers our economy whether it's in finance whether it's in healthcare whether it's in energy um the risk now that someone will be able to exploit vulnerabilities and use them against us is higher than it's ever been. So I think the notion of private deployment is essential. Uh we've known that these cyber capabilities were going to emerge in models for a while. Uh and as we've started to see software increasingly be written by these large language models instead of by humans, you're going to care a lot about which models you're using, whether they're like who they're coming from essentially. So, if you're going to be replacing your entire software engineering team with models that are writing your code, you probably don't want that coming from China because it might be subtly introducing vulnerabilities that you're not going to catch. And so, the trust barrier that I was describing, the trust barrier to enterprise adoption continuously gets higher and higher, — right? And it sounds like you know coheres models have a very specific use case when you can have a model on prem and you need the utmost security. Um when you say though that um sort of the whole so I think also what you're talking about is sort of the back door right that's been a concern with these Chinese models. You can self-host them but you don't know um if there's a back door that can get into your data. Why then does AWS, the hyperscalers, Michael Dell, why are they hosting and promoting these open source Chinese models? — Well, for certain applications, I think they're great, right? Like in low sensitivity settings, it's pretty reasonable to use a Chinese model. I think uh for startups that are looking for the lowest cost option uh and aren't operating at scale yet, it seems like a reasonable and very helpful tool. Of course, I think everyone would prefer to be using a democratically supported and aligned piece of technology. So, I hope there is and coher is contributing to much more uh democratic open source. Uh we want to continue to put that out there. But you know there is a market for these and they are cheap as you say and for the less secure settings maybe there's a place for them — right and you know you see sort of usage go up and we hear all the time about how work compute constraints so is that calculation for enterprises changing especially if you're not in an industry that requires so much security um is that calculation changing especally because some of these open source models are getting very close to the frontier and that sort of lag is closing. — Yeah. Cost control and then the other thing is like the compute bottleneck, right? So there's simply not enough compute to support using these massive models. So we need efficient small models that are good enough uh for the use cases that people are pursuing. Uh otherwise we're just not going to have sufficient compute to meet demand. Um, so there is a shift in the market. I think everyone over the past year has been racing to, you know, at all costs adopt AI. There's going to be a another phase that we enter into where CFOs are looking at the expenses being spent on some of these models. They're going to try to optimize. They certainly aren't going to pull back and it's going to continue to grow, but we're going to need to find ways to use smaller models, more efficient models. Um I hope that doesn't mean shifting over to a Chinese tech stack to satisfy that. I think there's companies like Coher and others who are building uh you know more aligned technology that satisfies that efficiency need — right but it does sort of beg the question are the American bottle are the American models better than Chinese ones? Like what is the gap? Is Deepseek V4 a real threat, especially when we see sort of it matching or surpassing some of the frontier ones on the benchmarks? — I think it's very close. Uh the gap between the two countries, uh in addition to Canada, in addition to France, Germany, um it's closing rapidly, — right? And you're saying that there's options. I mean, Coher's working on it, Nvidia's working on it, the startup Reflection is working on open source. Is there enough companies working on it in America? And what happens to open AI and Anthropic if they aren't working on this sort of open source model race? — I think there's a lot of folks contributing to open source. Um, Coher, we've been investing in it for years now. I'm not so concerned about the number of players. I think there will continue to be good open source options coming from democratic nations like the US, like Canada, like Germany. But I do think that um the thing that China is doing that's setting it ahead is effectively distilling the large models uh which is against the terms of service of a lot of these large models. Um and so that gives them a shortcut. uh they can very quickly catch up just from distillation of someone else's work. Uh whereas for the rest of us that are trying to build a completely independent stack from scratch, it requires more effort and more work to build competitive great models. Um but I do see things converging. I do see capab capabilities converging uh between all the major providers. So what does that tell us about sort of the huge infrastructure spend that we see from the hyperscalers and meta just talked about this coming off a massive earnings days they're all planning to spend more than $700 billion in infrastructure this year um from where you sit especially as Kier is building these very specialized models on just a few GPUs does that money come back or do you think that the trend is going towards these smaller more specific models the kind that coher is I definitely think it's pointed towards more efficient models. There's going to be a big wave uh in the market seeking to reduce costs and make things more efficient. Um it's easy to do PC's on some of these large models, but then as soon as you push that into production and you're dealing with production level scale, suddenly the price tag just changes the buying equation and cost becomes a huge constraint. Uh so I see things moving heavily uh in coher's direction. We've seen that over the past year with our revenue 6xing uh last year and continuing to grow very rapidly this year. So I do think there's going to need to be a lot more infrastructure because the demand is virtually insatiable. Um but as that infrastructure comes online, we're also going to make a big push towards efficiency. So there will be much more AI in the world but those AIs will be using less GPUs uh to do the same work as they were doing six months ago. — Are the hyperscalers plus OpenAI and anthropic are they building for that kind of world? — Uh the hyperscalers are definitely building the infrastructure necessary to serve um a significant amount of this demand. Although it's important to say that beyond cloud on-prem deployments, private deployments, they're growing massively as well. And this is becoming an increasingly well understood security threat that moving too much to the cloud is a mistake and exposes you to vulnerabilities, especially in this new frontier of potential cyber uh attacks thanks to these models. And so on-prem deployments, folks building their own data centers, neo clouds, you're going to see a whole new array of compute providers to satisfy all of this demand. Um, so certainly the hyperscalers are building out the infrastructure uh that is needed. Um, but they won't capture the entirety of the market. They'll probably capture half of it and the rest will be uh these on-prem secure deployments. So where does that leave an open AI or anthropic that's not you know the cloud business is not the main business it's frontier models. — I think the demand for frontier models all models is going to be extraordinary. I think there's going to be — so it's basically a rising tide lifts all boats. Everything is going to benefit from this. — I I'm not too worried about the demand for their products or our own. uh there is a huge market out there. The applications for AI in the enterprise side, it feels like we're just scratching the surface. Like we're still doing very simple things. We know the models are capable. from 18 months ago are capable of way more than we're doing actually out in the industry. So even if you don't update the models for a year and a half, the industry is still catching up. So right the demand piece I feel extremely confident there's a lot to go do in the global economy. Um, good question from Carson Allen, who's a regular viewer of our live streams, asking, um, are is your pricing increasing as you see these compute constraints? — No. So, we don't actually charge our customers for the compute. We deploy inside of their uh, private deployment. So, it's their own compute. Uh, what we see with our customers purchasing their own compute, — yes, the GPUs are becoming more expensive. There is a scarcity. um you know it's something that we try to help our customers with but our model of sales is not to provide the compute with the model instead we just provide the software. — So are you in the server building business as well? Is that sort of what this looks like in the future? — Yeah. No uh no infrastructure from us. We just focus purely on the models, the agentic platform that the models power. So that integrate with all the different tools and data that enterprises use to automate things, right? Like to automate workflows uh for big banks, you know, inside capital markets divisions, wealth management, investment banks. Um we automate work and augment employees inside those organizations. And we don't build the actual data centers themselves — or help source it. They do that and you come in and implement the tools, the AI tools. Got it. — Um, now Aiden, um, part of the reason I want to talk to you so much as well is, of course, you co-authored that famous paper that started essentially the modern AI age. This is a really general question. Watching where it's gone in, you know, a small number of years. You've got infighting at the top. Musk versus Altman's a big focus in San Francisco this week. Um, you see growing backlash in the public. What do you think? Did you expect to be here? Do you think that the industry has mishandled the public image side of AI? — I certainly uh I mean it's a completely general technology. It's like computing, right? Like it can be deployed into any sector. Uh against any use case. It's the most general piece of technology humanity has created. And so it's going to have very sweeping impacts and touch everyone's lives both professionally and uh as consumers. So of course it was very important that technology be developed in a way um and communicated to the public in a way that was accessible uh that they could try for themselves that um they could test and find the faults and give feedback. in terms of how the sentiment has evolved throughout that process over the past three years. I am concerned. I do think it's um you know there's certain folks who feel uh skeptical of the technology whether it's AI slop and in the creative industry there's a lot of resistance to this uh technology. I think we need to better compensate artists and ensure that they can contribute to the development of the next generation of models. um or whether it's the energy crisis, right? Like to power all of this adoption, we need a ton of energy and if that is making things more expensive for people, I think that's a hugely regrettable outcome. So investing in energy infrastructure, ensuring that prices don't go up uh for consumers as a result is a massive priority. Um I think people are rightly skeptical. I think criticism is a good thing. Being aware of the weaknesses, shortcomings about how the techn is being rolled out, the potential consequences is net positive in the long run. It's a tricky thing that both sides are figuring out, both the public and the companies building the technology. We both want it to go well. — That's a really thoughtful answer. Um, do you think we have the right spokespeople? Thinking of Daario at Anthropic and Sam Alman at OpenAI, Elon Musk. um what's needed here? It is kind of like a narrative and image problem where you did mention really real things, but also, you know, there is does feel like this narrative problem at the top. — Yeah. You know, uh since you're a Canadian, I can say it. Uh I try to bring a Canadian touch to to things. I think we need to be empathetic, kind, uh you know, thoughtful in this and not just uh steamroll people. Um, I think it's — Yeah, it's really great that the conversation is happening. I think it's essential. Um, there should be criticism. Uh, we should respond to that criticism with action to try to mitigate the potential downsides for people. — Well, we hope to hear a lot more from you, especially that sort of nuance, thoughtful answer, acknowledging both sides of this and the real criticisms. Uh, Aiden, thank you so much for taking the time. We really appreciate it. And from fellow Canadian to another, thanks again. Thank you for having me.