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Anthropic’s Open Model Stance: Revenue Risks, Regulatory Strategy, and the IPO Motive
Recent developments in the AI industry signal a significant shift in how foundational technology models are treated among major U.S. players. Anthropic’s public decision to sit out a viral open model coalition—championed by competitors like Microsoft, NVIDIA, Meta, OpenAI, and Google—raises immediate and nuanced questions for business stakeholders watching the intersection of enterprise AI, regulatory affairs, and long-term profitability.
Revenue Vulnerability and Open Source AI
A key data point underpins Anthropic’s approach: approximately 80% of Anthropic’s revenue is derived from token-based business payments, in stark contrast to peers like OpenAI (estimated at 20%) or larger ecosystem players such as Google, Microsoft, Amazon, and Meta, whose revenues from token sales are minimal (08:23, 17:08). This business model makes Anthropic highly susceptible to the availability of free or low-cost open-weight AI models, particularly those released by Chinese labs—tools that enable enterprises to dramatically cut costs by replacing paid APIs with nearly equivalent open alternatives (18:03, 18:32).
Specific example: when Chinese models such as GLM-5 2 became freely available, large enterprises could deploy these solutions internally or through inference providers, sometimes reducing AI spend by up to 95% (18:28), fundamentally threatening the core of Anthropic’s revenue model and projected growth.
Policy Timing and Strategic Lobbying
Anthropic’s strategic communications align closely with critical regulatory deadlines. The company’s recent public letter, positioning itself as a proponent of responsible AI safety rather than a protectionist, appeared just days before a federal deadline concerning AI model regulation, a period coinciding with confidential IPO filings and market uncertainty (15:04, 24:05). While competitors supported the national open model coalition for cost reduction and enhanced competitiveness, Anthropic’s messaging focused on the risks of open-weight models in the hands of authoritarian governments, using national security and alignment risks as justifications (08:34, 09:04).
Crucially, the correspondence and timing strongly suggest the letter’s intended audience was legislative and regulatory bodies, not everyday consumers or the developer community (13:30, 32:32). Regulatory capture—a scenario where rules are shaped to create barriers or moats favoring the incumbent—was a key theme, with the implication that Anthropic’s lobbying seeks to establish durable market differentiation through compliance and regulatory complexity, rather than open competition (22:20, 22:30).
Contradictions in Safety Argumentation
A review of Anthropic’s recent and past communications reveals apparent contradictions. While the latest public letter claims never to have advocated outright bans on open architectures, prior published essays explicitly propose mandatory third-party safety testing and government authority to block AI model releases—conditions that functionally preclude open release, since once a model is publicly available, it cannot be retracted or contained (29:03, 30:14). This policy position, if enacted, would prevent open-weight models from legally or practically persisting in the market (30:25, 31:19).
Additionally, the safety arguments for closed models are complicated by recent events: proprietary models have themselves shown vulnerabilities, with incidents involving containment failures, misuse by external hackers, and closed systems failing to outperform open models in certain security or defense scenarios (32:04, 33:38). This challenges the narrative that only closed, proprietary models can enable effective cyber defense or prevent misuse.
Long-Term Industry Implications for AI Business Models
Anthropic’s high exposure to per-token revenue underpins its urgency to shape the regulatory environment (17:27, 34:59). As token-based AI model services face rapid commoditization—driven by open-weight, open-source alternatives—major enterprises are already reconfiguring their infrastructure, often replacing substantial portions of paid API spend with increasingly competitive open models (21:37, 21:53). Switching costs are low, especially for customers who have avoided full ecosystem lock-in.
Meanwhile, larger players (Microsoft, Google, Meta) have diversified revenue streams and product ecosystems that create natural customer retention independent of model-level pricing. These companies explicitly back open models to speed innovation, reduce costs, and reinforce collective resilience against security threats—a direct response to fast-evolving risk and opportunity landscapes (12:08, 13:19).
IPO Strategy and Market Perception
Anthropic’s public filings and stock performance indicators suggest a window of uncertainty within which the company seeks to go public before broader market participants fully reassess the long-term economics of the token sales model (25:11, 27:11). The pre-IPO valuation has faced recent downward pressure—despite strong product releases—likely as a result of accelerating open model adoption and rising awareness of competitive, durable alternatives (27:19, 28:19). Regulatory intervention, if successful, could reduce competition and preserve higher margins for token-based inference, at least in the near-term.
AI Leadership and Regulatory Outlook
The current debate is not only about safety and control, but also about business model protection. While stated safety concerns are grounded in real risks, the timing and substance of industry lobbying reveal a calculated effort to secure a regulatory moat to maintain pricing power and growth trajectories in the face of disruptive open model economics (36:51, 37:12). For businesses evaluating AI partnerships or long-term commitments, the disconnect between public messaging and underlying financial incentives—and the magnitude of potential policy intervention—deserves careful scrutiny.
The ongoing developments in model openness, enterprise AI value chains, and regulatory outcomes underscore the need for highly intentional strategic planning, particularly for organizations seeking to balance cost, flexibility, and security in their own AI adoption roadmaps.
Topics Covered in This Episode:
- Anthropic Refuses Open Model Pact
- Dario Amodei’s Public Letter Analysis
- Anthropic’s 80% Revenue Token Expose
- Chinese Open Model National Security Fears
- Microsoft & Nvidia’s Open Weights Coalition
- Regulatory Capture and Washington Influence
- Timing Related to Executive Order Deadline
- IPO Motivations Behind Anthropic’s Decisions
- Contradictions in Anthropic’s Open Model Stance
- Impact of Open Source on Token Business Model
Episode Transcript
Jordan Wilson [00:00:16]:
Anthropic finally responded as the last AI Lab out in not supporting open source AI in the viral open weights in American AI leadership paper slash coalition. That's because Anthropic's CEO Dario Amati just published a letter defending the company's decision to not join the other players, and I think it's actually more about Anthropic's IPO than it is about open source. But here's the setup and the backdrop. NVIDIA and Microsoft put out a letter four days ago defending open models, and basically, everyone signed it. Meta, OpenAI, Google, IBM, everyone. And Anthropic was the only major tech player that said no. And late yesterday, Dario published Anthropic's answer in a blog post, and it opens by insisting they've never wanted to ban open models, which is technically true in writing, but not really truly in line with their previous statements warning about how dangerous open models are and there should be government mandates for them. Now here's the part that nobody's talking about.
Jordan Wilson [00:01:30]:
Why Anthropic is really trying to get DC lawmakers scared of Chinese open models? It's one number, 80%. Yeah. That's roughly 80% of anthropic revenues just comes from businesses paying per token, while the other big players have a fraction of that. So free downloadable Chinese open source models literally choke Anthropic's only real source of revenue, and Anthropic filed for an IPO targeting in October launch. Hence, the reason why Anthropic zigged while every other tech company zagged when it came to supporting open source. So today, we're breaking down not just what the letter says, but where it actually even contradicts Dario's own previous words and why I think this was written for Washington policy makers and not for you and me. Alright. Let's get into it.
Jordan Wilson [00:02:22]:
Here is the big picture. Anthropic is the only major AI lab that refused to defend open models in the open weights in American AI leadership, paper slash coalition that was put forth by Microsoft and NVIDIA. Essentially, they said, well, open source is good for the American economy. It's good for national security, and we're gonna talk a little bit more about that paper. But why? Well, Anthropic will tell you it's ultimately about safety and control, but I think it is that 80% number. And we're gonna look at well, it might not work very well if Anthropic can't get this essentially to go in their favor. And that's because their IPO is potentially only weeks away in October. So on today's show, stick with me, and you're gonna learn why that 80% meter token revenue makes Anthropic uniquely exposed to open models.
Jordan Wilson [00:03:18]:
You're gonna know what the regulatory capture means when it comes to AI and why this letter just fits the pattern exactly. You're gonna know why the timing points at Washington policy makers because there's an important date coming up in just a couple of days, and I don't think this letter was made for me and you. And you're gonna know what Dario said in 2025 that flatly contradicts today's letter. Alright. Let's get into it. Welcome to Everyday AI. If you're new here, my name is Jordan. We do this well every day.
Jordan Wilson [00:03:49]:
It's your unedited, unscripted, live stream podcast, and free daily newsletter helping everyday business leaders like you and me keep up with the nonstop AI avalanche. I tell you what's important, what's not. You use that decision, and all of a sudden, wow. You're the smartest person in AI at in your company. So it starts here, but please make sure to subscribe to the podcast and go to our website at youreverydayai.com. Alright. Let's get into it. This has been all the talk on tech, Twitter, and, you know, in the media, in anything AI.
Jordan Wilson [00:04:19]:
Right? If you follow AI on a daily basis like I do, this conversation has been just grabbing every single headline. And it's extremely important and also a little bit nuanced. So today's show, I'm gonna try to go through it a little bit quicker, stick to just the facts, and my opinion, maybe I'll, you know, bring back an accidental hot take Tuesday for old time's sake. But make sure you go check out today's newsletter, for more. So here's what actually, Dario put in his published response, on open models. So the core claim was that Anthropic has never advocated banning open weight models, and he rejected the argument that open models actually help defenders more than attackers, which was a core claim in the Microsoft slash NVIDIA, paper that everyone kinda got behind. And Infropic says it's better for chip controls, to crack down on distillation and mandatory safety testing for all. And they deny that protecting Infropic's business ever motivated the position.
Jordan Wilson [00:05:24]:
Oh, that's a funny one. Alright. So let's take a quick look, at the letter. I'm not gonna read it, but I will kind of read, the opening paragraph and go through some of the bullet points here. So, this is from Dario Motti, Anthropic's CEO. So, yeah, this just came late last night actually or late afternoon. I'm so tired. Sometimes, like, 4PM feels like late night.
Jordan Wilson [00:05:46]:
So it says over the last few days, there have been a lot of discussions about open weight models, especially those from China. Reports suggest that some US officials are considering banning the use of Chinese open source models, sorry, Chinese OpenWeights models by US companies. In response, many tech companies have signed a letter supporting OpenWeights models, and some people have even accused Anthropic of wanting to ban open source models as a means of protecting our business. Anyone who has read my past writing should know that I don't regard such bans as a useful measure, but let me state it clearly so that there is no doubt. Anthropic has never advocated for a ban on open weights models. Then he says open weights models that don't have dangerous capabilities are a public good. They don't cost anything besides the compute needed to run them, and they provide value to businesses, developers, and researchers. Alright.
Jordan Wilson [00:06:39]:
So let me just take a pause, and I probably should just kind of talk very briefly about open source, open weights models. So if you are brand new, maybe this is the first episode you've ever listened to, and you're like, what the heck is going on? Why does this matter? Well, a couple important points that Dario brought up here in the letter. Number one, there has been this idea floated recently, and, you know, we'll see, you know, maybe in ten, twenty years, how much of anthropics DC lobbying efforts maybe, led to this. Right? I'm a former journalist. I understand how this works. All the big labs have a lot of lobbyists, but, the US government is actually considering a ban on American companies using Chinese open source models for, you know, what they're saying, competitiveness, national security concerns, etcetera. But essentially, open source or open way models are models that you can, well, you can download and you can run them on your machine. Well, very small open source models are what open source models used to be, you know, like, two years ago.
Jordan Wilson [00:07:38]:
Today's open weights, Chinese models, you can't download those on your computer. You're literally gonna need a spare, data center to run the newest. And the ones that kind of set this off are, Moonshot's Kimi k three, z a i's g o m five two. And I think that eventually we'll be talking a little bit more about QEM 3.8. So essentially, you have all these models that, well, if you're a huge enterprise, you could use them for free. But if you are using them, through a hosting provider, through an inference provider, they're way way cheaper. Right? For the most part. Not always, but usually, these open source models are way cheaper, if you can't download and host them or run them locally.
Jordan Wilson [00:08:23]:
Alright. So that's the big picture on, what kind of Dario said in his letter in all of that. So, some of the more important bullet points here. He said, my primary concern is the risk that authoritarian governments, not solely the Chinese commune Communist Party or CCP, although the CCP is clearly the most capable threat, build AI models that are more powerful than those built in The US and use them to achieve permanent military superiority or, perpetrate incredibly deep repression of their own people. Right? So essentially, he said the biggest concern is, well, the Chinese government is gonna use these for military superiority. Number two, secondary concern, he says, is that powerful AI models may be misused to carry out cyberattacks or biological attacks, and those may have serious alignment issues. So, you know, I agree with both of those things. Right? I've been saying it all along.
Jordan Wilson [00:09:21]:
I think artificial intelligence, for the most part, it is, gonna be more important maybe in five to ten years than, you know, what your traditional military might be looked at. It's gonna be more important than oil or gold or anything else because, the nation essentially that figures out artificial general intelligence and artificial superintelligence first has a legit unfair advantage on the rest of the world, and you can go from a mid tier global power to the, global superpower if you control artificial general intelligence or artificial superintelligence. So, kind of to address these concerns, what he does say in his letter, he says, number one, we should not sell powerful chips or chip making equipment to China. Essentially saying, hey. We still have a lead, although it's maybe slimmer than it was before, but China can't catch us if they don't have our chips. Right. Previously, they said, Anthropic said that by, essentially creating a chip ban, The US could get a twelve to twenty four month, lead in AI. I don't necessarily think that's true.
Jordan Wilson [00:10:23]:
The lead could extend from where it is now, which is, like, two months. It used to be about six months, between the, US frontier models and the Chinese open source models. So maybe it could extend it. I don't necessarily think it would be twelve to twenty four months. That's a little bit of lofty thinking, I would say. And then, Dario also says we should crack down on industrial scale distillation operations. Right? So, essentially, how China is catching up. Right? They do have some, novel architecture and, kind of ML, approaches.
Jordan Wilson [00:10:56]:
But for the most part, it's distillation. Right? They're just copying, you know, millions of inputs and outputs from, you know, quad models and other models to make their models smart. So they're just copying someone else's answers at a fraction of the cost. And then, Dario says all sufficiently capable models, opening close, should go through mandatory safety testing. Alright. There's more on the, the letter. It's not super long. I'm not gonna read it all.
Jordan Wilson [00:11:22]:
We'll make sure to put it in today's newsletter, as well. So this is all essentially in response to the original, kind of movements that, NVIDIA and Microsoft put forth, on July 24, which was this past Friday. And this was called the open weights in American AI leadership. Alright. So, essentially, they circulated this arguing that open models cut costs and avoid vendor locked in. So they said it's good for American businesses. It's good for, competitiveness, and then they also said it's better for safety because they said that the core safety claim of having open models is that defenders right? Because this whole thing comes down to cyber attacks versus defending your business. Right? One of the big arguments is, oh my gosh.
Jordan Wilson [00:12:08]:
If you have these, you know, these open source models everywhere, you can't keep track of them. Right? And eventually and I agree. Probably in two years, I've said this, you'll be able to, you know, have a version or an open source model that is of the g p d five, six soul, fable five, tier of models, you'll be able to have something that powerful on a, you know, consumer ish desktop device. So at that point, you know, yeah, there is gonna be, you know, critical infrastructure at risk from cyber attacks. You know, think of, you you know, phishing scams and, you know, people holding, you you know, web systems for hostage, times a thousand and compacted into a very tight time frame, right, once that does happen. Alright. So if you're thinking of hospitals, school systems, power grids, right, entire small nations, you you know, city and state governments. Right? All these places are going to be extremely at risk, and I agree with that.
Jordan Wilson [00:13:05]:
But, essentially, Microsoft and Nvidia are saying, well, the best way to defend against this is, well, open models because you have more people contributing. People are gonna find these vulnerabilities, and it collectively makes, you know, everyone stronger. So it gives everyone better defensive tools. Alright? And, essentially, everyone literally agreed with that from Amazon, OpenAI, Meta, Google, IBM, everyone except Anthropic. So my take on this is Dario's letter is not for us. This is literally for regulatory capture. This is to, influence the policy making in, DC. So there's been a lot of articles about, you know, just how big of an impact that entropic has in lobbying.
Jordan Wilson [00:13:46]:
And, yeah, I think that this has to be the real reason here. But, I I mean, my take on the actual letter, it's well written. There's great arguments in there, and it it genuinely engages with real risk. Right? There's nothing in there, that I'm looking at. I'm like, this is absolutely blasphemy. Right? Like, aside from the fact that they say that they absolutely never, you know, called for a ban on open models, they have just without using the word ban. Right? I don't know. If you say something, you know, walks like a duck, quacks like a duck, is yellow like a duck, you you know, is is in the Jersey on mighty ducks, but you're like, well, it's not a duck.
Jordan Wilson [00:14:27]:
Well, yeah, that's essentially what you said. Just because you didn't say that animal right there is a duck, but you you described every single duck characteristic and used the word duck in describing the characteristics, but then you're like, well, it's not a duck. Well, yeah. It is. Alright. And but the important thing here is Dario's letter comes just days before an important federal deadline, and that is why the regulatory piece is very important. So president Trump signed essentially this, AI, order on June 2 with a sixty day deadline. And guess what? That sixty day deadline is, well, this week.
Jordan Wilson [00:15:04]:
So the framework right now does not cover open weight models. Right? So that's why there's all this kind of, jockeying right now in, you know, why you've had these stories come out in the past, seven to ten days. Right, saying that The US is now maybe considering, kind of throwing open source models, not in this executive order, but potentially just banning them. Because out of nowhere, right, you had, you know, moonshots, Kimmy k three, I think, especially come up and, you know, essentially, it is in that top tier. Whereas before, the open source Chinese models were never in that tier. So that obviously takes away from, American business, number one. Right? Because whether you know it or not, and I've been saying this all along, AI and large language models right now, they run the American economy. So when you have a Chinese open, weights, contender come in and well, potentially, you don't wanna take all that spend, right, from, you you know, putting America first sort of thing, which is the, kind of the Trump's, the Trump people's, position on this.
Jordan Wilson [00:16:13]:
Right? Well, that takes away probably a lot of money that American businesses would be spending with other American businesses. So not only that, but, you know, kind of the other, chain of thought, AI joke accidentally made is that, you know, the more powerful these models become, well, they're just gonna become better at distilling versions of themselves from the other frontier labs. And, you know, it seems like anthropic, maybe more so than others, seemingly is struggling. Maybe they're just the ones complaining a little bit more because they technically have more to lose. But the Dario letter lands just five days, before this, kind of order gets locked in. So the order is voluntary, and there's no licensing and no pre clearance requirements. But, essentially, you're supposed to clear, models with the government thirty days before release to, you know, give everyone fair amount of time. So, yeah, there's that.
Jordan Wilson [00:17:08]:
Pretty big thing that no one is talking about. But the real reason is the money y'all. So these are according to public estimates, which are pretty close. A lot of it is from semi analysis, which is kind of the, the leader in the space in terms of, looking at compute and its impact on the economy, the money in and the money out. So essentially, the best estimates are 80% of Anthropic's revenue comes from just selling tokens. Right? Whereas its closest competitor, OpenAI, it's estimated that it's only 20%. And then, you know, your other big tech companies, it's very small considered. Right? Because they have, you know, dozens or hundreds of lines of revenue.
Jordan Wilson [00:17:49]:
Like, you know, Google, it's only about 2%, Microsoft, 2%, Amazon, 1%, and Meta, not even 1% yet. So essentially Anthropics entire business model is just selling tokens. Right. And well, what if now all of a sudden. If you just stop buying those tokens, if your company stops buying them. Right? If you're like, hey. We don't need right? We can get 95% of our AI use with a model like, you know, GLM five two. And that one is a little bit smaller and well, you know, we can invest, you know, 6 figures in getting this set up.
Jordan Wilson [00:18:28]:
We were spending 6 figures or 7 figures in AI each month. Right? And all of a sudden, we cut our cost by 95%. We have more control. We're running it locally, etcetera. So you can see how this is in extreme. I am talking, like, potentially lights out for, in tropics growth. Right? Not for them as a company, but for their staggering growth that we've been reading about through 2024 and 2025. And, you know, they've been the main beneficiary of this token maxing, but now that token maxing is going away, and it's, going toward token efficiency.
Jordan Wilson [00:19:03]:
So not only Right? If you look at OpenRouter, which kind of is a third party tracker of, you know, who's spending, tokens where, you know, it used to be anthropic. Anthropic used to dominate. Now, I checked, yesterday. I think they were, like, number seven. They're, like, the seventh most used lab in open routers, and they used to dominate. Right? So people are coming to a couple conclusions. Number one, yes. Anthropic still has probably the best models in the world.
Jordan Wilson [00:19:30]:
Right? The combination of Opus five that just came out in Fable five. Although I do think g p d five six soul, is is right there and on many important benchmarks, I do think, g p d five six soul is still better. But regardless, Anthropic has some of the best models in the world, but 80% of their revenue is just selling tokens. And now all of a sudden, over the past ten days, you have Kimmy k three, and you we're gonna be talking more about Quinn three eight and probably whenever GLM five three comes out, all of a sudden yeah. In tropics, you you know, that that hockey stick growth is not gonna look very hockey stick for much longer. So Anthropic built this massive business. And if I'm being honest, there's not a big moat, right? Google OpenAI, and Microsoft are taking a very different approach. Right? They're looking at the entire ecosystem.
Jordan Wilson [00:20:24]:
But right now, Anthropics entire business is inference. And inference is just a commodity that's getting cheaper mainly because of open source models and the technology itself. But some open models produce output roughly 10 times cheaper than Opus. So even if you're paying for it via the API, right, in some models and some certain tasks, it's just the same price. But in many instances, it is incredibly cheaper, and switching is easy. Right? That's the difference between the ecosystem play versus the API charging you for tokens play. Right? If a company and you've seen plenty of stories, literally people detail this at big multibillion dollar companies where they're like, wait. We just cut our spend.
Jordan Wilson [00:21:08]:
You know, we were spending, you know, tens of millions of dollars a year. We just cut our spend by, you know, 80% just by now using a model router and, you know, 80% of our queries, we can use open source models. Right? Maybe they're still paying a little bit to, you know, in for API or OpenAI or Google or whatever for the actual just raw tokens. But switching is so easy to literally go in there and switch a couple endpoints or, you know, if you're using, you know, a certain gateway, you you know, to go in there and just choose a new model in the drop down. And if you've been doing your due diligence and, you know, doing your running your backups like I've been telling you to do for years, it's actually not that big of a disruption for your business. So switching is easier than ever. If you're just paying for APIs, switching out of, an ecosystem, not easy. Right? That's like, I've always wished I could just, you know, switch out of using an iPhone or switch out of using a Mac.
Jordan Wilson [00:21:59]:
I can't. I am locked into that ecosystem. Even if the hardware isn't the best, even if the camera isn't the best, whatever. It doesn't matter. I can't get out of the Apple ecosystem. I am too locked in. Right? That's the game that OpenAI has been playing. That's the game that Microsoft and Google has been playing, and Infropic has just been saying, well, we're just gonna sell tokens.
Jordan Wilson [00:22:20]:
Well, they're seeing and they're finding out that that may not work unless they can get some regulatory help, and that could just become their moat. So that is the regulatory capture here. So that's just winning in Washington instead of on an actual product. So essentially, the incumbents, right, are backing these safety rules that only companies their size can afford, and the rules become the moat that the product just couldn't build. Right? And this isn't just me saying this. Yeah. There's actually some very prominent names. You think I'm tough on anthropic sometimes.
Jordan Wilson [00:22:54]:
I'm definitely not. I actually think that this letter is pretty good, and there's very few things in there that I would argue against. Right? But White House AI czar, David Sacks, called it fear based regulatory capture. I mean, he tore into this. And, interestingly enough, he actually called it. Right? He essentially said, yeah. Anthropic is gonna run, a psy ops on us. You know, he literally called this before, Dario wrote this letter, and he said, you know, Anthropic's, gonna claim that they never wanted to ban open models, although they did want to.
Jordan Wilson [00:23:28]:
And sure enough, that's literally what they did. Right? And also nearly 200 startups warned that banning open source models could hand anthropic a monopoly. So let's get back to the timing because literally this is what this is about. This is what the I I don't know how many times you want me to tell this. Right? This is the difference between a company, you know, being worth, you know, 500,000,000,000 and being worth $3,000,000,000,000. Right? In theory, Entropic could go either way in a year. You know, and I actually wasn't even gonna do this show, but I put out a a tweet. Yeah.
Jordan Wilson [00:24:05]:
I did. Yeah. Cringe. Right? A couple of weeks ago. And it was actually a very high up person from Google that liked the tweet. Right? And my tweet, I forgot exactly what it was, but it was something along the lines of, like, in traffic is in deep trouble with these open models and with the shift from, you know, token maxing to token efficiency. And their hope is they want to go public as quickly as humanly possible before Wall Street and enterprises figure out what the heck is going on. And I was actually surprised that this very high up ranking person, at Google liked the tweet.
Jordan Wilson [00:24:40]:
And the thing is they're not really, I mean, they are direct competitor with Anthropic, but they're also an investor. And, you know, likes are private, so don't think you can go in there. And not gonna say who this person was. But when I said, wait. This person is, essentially agreeing with what I'm saying here, and that's why I'm like, no. I am a 100%, confident that this is what is happening. This isn't just some, like, some punch. Right? And I've talked to plenty of people off the record about this at the big labs, and they've all pretty much agreed.
Jordan Wilson [00:25:11]:
So, anthropic, here's the timing. Anthropic filed their confidential s one June 1. And, you know, according to reports, they're looking at maybe listing as a public company as soon as October. So public investors will pay for that durable pricing power, which obviously the open models straight up erase. And, you know, these restrictions that are landing here soon, right, would remove the cheapest substitutes. So the safety concern can be sincere, but also can be commercially self serving all at once. I think that's what's happening. That's why I'm like, you read the letter.
Jordan Wilson [00:25:45]:
Yeah. Most people would agree with most parts of it, but it can be a, you know, a good letter that you can kind of agree with many parts of it, but also be incredibly self serving at the same time. Alright. So anthropics numbers, you gotta take a look at them. So obviously they're not public, but there is a couple of websites. One is called notice, that essentially tracks. And this is, you know, all, you know, in algorithm estimates. It's not, you know, actually secondary market prices.
Jordan Wilson [00:26:17]:
But, you know, it essentially tracks, you know, the OpenAI, the Anthropics, all these companies that have yet to go public, it tracks what their stocks would roughly be. And Anthropic has had their worst seven week span ever in a seven week span that included a model release. Right? Because there was one drought, you you know, where their stock went down a little bit, but they just didn't have any releases. So, essentially, in the history of the company, this is their worst period when it comes to their, you know, pre IPO stock price. And they had great models that shipped during this time window. Right? Fable five, Sonic five, Opus five. So, you know, not a huge drop, but it fell, you know, about 8% over seven weeks, their worst record on stretch. Why? Right? Makes no sense because they've been hockey sticking, essentially since, you know, 2024.
Jordan Wilson [00:27:11]:
Well, I think early investors and people who are very closely attached to AI are starting to understand what's going on here. It is the combination of the reckoning going from token maxing to token efficiency to now all of a sudden people are starting to care more about the cost per tack, cost per task. Right? So they're not just solely looking at a benchmark and then, you know, fully going full send token maxing on, you know, the best model. They're instead saying, what's the best model, that gets the job done at a good cost. Right? The best example is, you know, OpenAI's GPT 5.6. Their models, well, you know, might have one or two, points lower on a benchmark like artificial analysis, but at sometimes at a third of the cost. Right? So, yeah, there's just some bad things that are happening when it comes to token economics with anthropic. And they are literally hoping to go public ASAP as soon as humanly possible, before essentially the rest of the, insiders wake up to see what's go actually going on.
Jordan Wilson [00:28:19]:
So let's inspect a little bit more about some of the reasons I think what Dario said don't exactly add up because he actually wrote something six weeks ago, that kind of sound like a band to me, yet in today's, sorry, in yesterday's, you know, letter, saying why they didn't join this coalition with Microsoft and NVIDIA and everyone else. Well, they're like, well, we're not banning open source models. We just can't fully support them in the way that it's written. Alright. So Dario had an essay called the policy on the AI exponential published on his personal website. So he drops transparency as sufficient and calls for binding regulatory, sorry, regulation and set. And he wants that mandatory third party testing, then government power to block a release. Right.
Jordan Wilson [00:29:10]:
In his words that releases should be blocked or reversed if models fail. All right. So essentially he thinks all models should have third party testing and well, if they fail after release, they need to be blocked or reversed. So anthropic published actually draft legislation with it and pledged money behind it. So what does that mean? Well, essentially, this was calling for a ban of open source models without using the word ban. Let me explain. Because if you add one and one here, the only answer is two because Anthropic or any proprietary company can, you know, go along and play by anthropic's rules that they wish existed. Right? You put out a model, something goes wrong.
Jordan Wilson [00:29:57]:
It's not safe. You pull it away. Right? Same thing that happened with their fable five mythos five. It was out for, like, three days. The government said, nope. And then they pulled it. Right? So in theory, Anthropic was playing by its own rules. Guess what? Open models literally can't.
Jordan Wilson [00:30:14]:
So I don't know how Anthropic is saying, well, oh, we never said, we would ban open source models. We would just create rules that would make them literally impossible to exist. So we didn't use the word ban. Right? We didn't call it a duck, because nobody can undo an open release once thousands have downloaded an open model. Right? So they're, and, you know, I'm not gonna go through state by state, but essentially, Anthropic has pledged support, to certain states where it would be advantageous for them to, have this kind of setup in there. That would essentially make open source models not really a truly viable thing. So if his rule is passable for anthropic, well, it or, you know, OpenAI, any, you know, any closed frontier model, you can't download it. You can't save it.
Jordan Wilson [00:31:05]:
Just like Anthropic did, they can pull it. Right? If Google needs to pull something, if OpenAI needs to pull something, they can't. It's closed. They pull the API. They pull the subscription. The model's no longer available. It's not the case with open source. Once you open it up, you can't trace it.
Jordan Wilson [00:31:19]:
Right. People can, you know, lower the guardrails. Yeah. It is technically in theory, a little more dangerous. Right. But you can't say both. Right. You can't put a letter out yesterday saying like, oh, we never said we would ban open source models.
Jordan Wilson [00:31:38]:
We just six weeks ago put out, our own example of policy that would just make it so open models could never actually exist by following rules that technically only a proprietary model could follow. So that's not the only thing. The other thing is talking about the control arguments. So Dario's case requires closed models to be monitorable and containable. Well, what happened with Mythos, y'all? Because Mythos escaped its own sandbox and emailed a researcher unprompted. And not only that, unauthorized outsiders access mythos the same day it was announced via a Discord server just by guessing the naming mechanism. And also oh, wait. What's that? Chinese hackers ran 80 to 90% reportedly of an espionage campaign using Claude Co.
Jordan Wilson [00:32:26]:
So the bad stuff that Dario is warning about, we can never have open models. Right? The the the way that everyone else wants them because all this bad stuff will happen. Well, guess what? It's already happening with Infropic's own models. Right? That's not my opinion. That's just the facts. And we have to talk about the hugging face incident. Right? So that happened recently. It's been all the talk and it's actually led to the snowballing of all these other things, but opening eyes closed model broke containment.
Jordan Wilson [00:32:54]:
Right? The agent got out of its sandbox and attacked Hugging Face, to work on a benchmark. Right? It was trying to solve, a benchmark problem by kind of hacking Hugging Face and getting the answers. So the closed APIs that Hugging Face used, right, they were trying to analyze the attack and they use closed models and it didn't work. They got blocked. Right? And instead, they used an open source model, GLM 5.2, and it worked. And that's how they were able to close down the attack. So the closed source models, well, they weren't good enough as a defender. The open source Chinese models were because they couldn't use the closed source proprietary models because the guardrails are a little stricter.
Jordan Wilson [00:33:38]:
So Dario's own argument that he puts out there is moot. Right? His his letter doubts says that open models can't actually help defenders. Right? He he says that's what proprietary models are for. Not true. Right? If AI is only for the and not for me, it's not the case. Right? You you know, if the the the mythos as an example, you you know, I think there's, like, 40 original partners, in the Glasswing, program that have access to the mythos that you could truly use the best model in the world for cyber defense. Well, for 99.9% of everyone else, they can't. Right? So you can't have this two tiered system, of well, only a select few can actually use the best proprietary models for defense, but anyone can use an open model.
Jordan Wilson [00:34:29]:
So, you know, his point there is absolutely false. Right? Demonstrably false within the last week. Right? So maybe they should have updated. Maybe they wrote that letter before the whole, you know, OpenAI agent hugging face GLM 5.2, because it literally is doesn't make sense on paper because it's false. So let's wrap up here because I think here the big debate is safety control in business protection when it comes to open source. Right. I'm not gonna dive too deep on that because there's pros and there's cons. Like I said, there's things in the, in the NVIDIA Microsoft, letter that I agree with a 100%.
Jordan Wilson [00:35:10]:
There are things in Dario's letter that I agree with a 100%. A lot of it is gray area, a lot of it's nuance, and a lot of it is the unknown because who knows what models in six months proprietary o or open source will be capable of. Who knows? Maybe at some point, open source models will be able to pass proprietary models. I mean, it's not outside of the realm of possibilities. So we can't really make that comparison, but what we have to look at is today and how this impacts your business and the decisions you make. Because I think Dario's safety concerns, well, they're real, and I think that they were thoughtfully presented in this letter. But the letter, it's actually not what you think it is. It is literally about their IPO.
Jordan Wilson [00:35:52]:
It is to scare, people in DC enough to get some restrictions on these Chinese open source models. They're gonna say, you know, put out the the the military reasons, the American competitiveness, all these things, which in theory are kind of true. But then I think you have to look at the, the every other company's position that signed on to the, NVIDIA and Microsoft letter that says, well, no. Everyone needs open source because the more people, the more companies, the more AI labs they get behind open source, the more powerful it becomes. It's collective. It's a snowball. And when you put all the snowballs together and roll them down the hill, they're gonna get bigger. So this is not for us.
Jordan Wilson [00:36:32]:
This is for Washington. This is there's a shot clock, both the shot clock, for the Trump AI rules that go into effect here in a week, but also, INTROPICS IPO. Because the conclusion here, I think, is, yes, this is a sincere safety argument, but it is unmistakable regulatory capture. That's all it is. And my hot take is, well, INTROPICS over the last couple of months has realized, I think, that their monthly growth is probably not up hockey sticking at the same rate that it was before, and they've realized that their moat is not really as strong as they thought it was. So they're actually hoping for regulatory help to be their actual moat because they realized, wait, with open source, we can't just sell tokens when other companies are giving it away for next to free, and it's just as good. Alright. That's a wrap on Anthropic's response and why Claude's CEO didn't sign the open model pact and the real reasons I think why.
Jordan Wilson [00:37:37]:
So if this was helpful, let me know about it. Please subscribe to the podcast. If you haven't already, then go to your everydayai.com. We're gonna be recapping the highlights from today's show as well as everything else you need to know to stay up to date. Thanks for tuning in. Hope to see you back tomorrow and everyday for more everyday AI. Thanks, y'all.