OpenAI vs. Anthropic IPO Showdown: Trillion-Dollar Valuations, Price Wars, and China's Open-Source Pivot

Compiled & Edited: Deep Tide TechFlow

Guests: Chamath Palihapitiya (Founder of Social Capital), Brad Gerstner (Founder & CEO of Altimeter Capital), David Sacks (Partner at Craft Ventures)

Host: Jason Calacanis, All-In Podcast

Podcast Source: All-In Podcast

Original Title: OpenAI vs Anthropic IPOs, Anthropic $3T, Zuck's Price War, China Ends Open Source?, Trump Accounts

Air Date: July 11, 2026

Key Takeaways

In this 280th episode of All-In, with Friedberg on vacation and Brad Gerstner filling in, the show begins with a trillion-dollar IPO race: SpaceX has already gone public successfully with a $1.75 trillion valuation. Anthropic confidentially filed on June 1st, with OpenAI close behind. Gavin Baker predicts Anthropic could surpass $100 billion in revenue this year, potentially reaching a $3 trillion valuation at IPO. Brad doesn't hesitate to say Altimeter would buy heavily into both IPOs.

But Chamath pours cold water on the enthusiasm. He finds his own company's token costs doubling every 45 days, with downstream productivity gains of at most 5%. He asked Claude 5 a question: how much EPS growth has AI contributed to the S&P 500? The answer was 50%. However, stripping out the portion where Nvidia sells chips to Amazon, the real EPS growth for the S&P 493 is only 9%, mostly driven by pricing power above inflation and buybacks. The actual AI ROI sits between 0% and 2%. Chamath's verdict: if you can IPO now, do it now, before these numbers seep into the market's waterline.

The latter half of the show shifts focus to China. Reuters reports that the CCP is considering restricting overseas access to China's top AI models, classifying AI research leaks as national security crimes. Sacks has discussed this in Washington, D.C. with the White House and Treasury. His assessment: China's strategy mirrors Sam Altman's old playbook—open-source before you catch up, close-source after you do. He also reveals that GLM-5.2 contains distillation watermarks from US frontier models, and the US government will likely take action against distillation. The show concludes with Brad spending nearly an hour on Trump Accounts, a plan giving $1,000 to every newborn American, invested in the S&P 500. The app opened 1.5 million accounts and absorbed over $1 billion in deposits within 24 hours.

Highlights of Key Views

On IPO Timing

• Chamath: "If you can go public now, do it now, before these numbers seep into the waterline. Because I think that's when you can sell at a high price and raise a lot of money."
• Brad: "Today, Altimeter would buy both IPOs, at scale and size."
• Brad: "Anthropic's annualized revenue could exceed $100 billion, while SpaceX's forward revenue is $35 billion. Based on SpaceX's success, this will be a phenomenal IPO."

On AI ROI

• Chamath: "My token cost doubles every 45 days, and downstream productivity may be at most 5%. My costs are doubling, but returns are essentially flat."
• Chamath: "The S&P 493's EPS growth is 9%, the vast majority coming from pricing power above inflation, with another 3% from buybacks. The actual AI ROI is between 0% and 2%."
• Brad: "We've never seen revenue growth like this because we've never seen a TAM this large. Intelligence is the largest addressable market in human history."

On Open Source vs. Closed Source

• Sacks: "The spirit of enterprises is willing, but the flesh is weak. They want to move away from closed-source models, but they can't."
• Sacks: "Open source's share of enterprise spending is actually declining, from 19% last year to 11% this year."
• Brad: "Whether you use a $3 cheap model or a $15 frontier model to replace a $200-an-hour consultant, the price difference is irrelevant."

On China's Shift in Open Source

• Sacks: "China's strategy is clear: open-source while you're catching up, and close-source once you catch up. Sam Altman did the exact same thing three years ago."
• Sacks: "GLM-5.2 contains distillation watermarks from Mythos. The US government will act to crack down on distillation, and it should."
• Chamath: "The best thing for the US would be for China to also have a doomer community."

On Trump Accounts

• Brad: "If you get $1,000 at birth, with someone matching some of it, and you save $10 a week, by age 18 that's $50,000. All invested in the S&P 500."
• Sacks: "If a Trump Account is maxed out from the start, based on the market's average returns over the past 30 years, that child would be a millionaire by age 28."
• Jason: "This could replace Social Security. It replaces the Giving Pledge."

Main Content

Chapter 1: The Trillion-Dollar IPO Race: SpaceX Sets the Pace, OpenAI and Anthropic Prepare to Enter

Jason: Let's start with IPO updates. A trillion-dollar IPO sprint is underway. SpaceX has gone public, and its stock is trading around the issue price. Perfect pricing. Next up, theoretically, are two more: OpenAI and Anthropic. SpaceX's stock hit $200 at one point, now back to $150, right around the issue price. Current market cap is $2 trillion, making it the seventh-largest company globally. Anthropic confidentially filed on June 1st, and Polymarket gives it a 65% chance of going public this year. Gavin Baker said two weeks ago he believes Anthropic's revenue will exceed $100 billion by year-end and turn profitable. If it IPOs now, it could be valued at $3 trillion. Chamath, you said earlier that Elon going public first was a good move. What's the probability these two come out this year or early next year?

Chamath believes both are excellent businesses, but the core question is where the market clearing price is. It depends more on the market's appetite for new issues and at what price level it can digest them. OpenAI and Anthropic are at different stages. OpenAI's last disclosed information showed its cash burn is still high due to business diversification and greater reliance on consumer spending. Brad mentioned earlier that Anthropic might have unexpectedly turned profitable. Chamath shared a detail: he asked his CTO about token spending, and the CTO said, "It's currently doubling every 45 days." He pressed for downstream productivity gains, and the CTO said, "At most 5%." Costs are doubling while returns are essentially flat. The CTO explained that the next iteration improvement requires consuming significantly more tokens due to diminishing returns.

Chamath's verdict: If you can IPO now, do it now, before these numbers seep into market perception. That's likely the window to sell high and raise substantial capital.

Brad, as an investor in both companies, offers a more optimistic assessment. SpaceX's IPO was textbook: raising $75 billion at a $1.75 trillion valuation, with forward revenue of about $35 billion, and the stock has already risen 25%. Anthropic's revenue is rumored to potentially exceed $100 billion this year, and if true, its GAAP revenue next year could far surpass that. Based on SpaceX's successful precedent, Brad believes this will be a phenomenal IPO. SpaceX set a precedent in total IPO size, pricing, liquidity, index inclusion, and lock-up arrangements, and both Anthropic and OpenAI are learning from it.

Regarding the controversy over index inclusion, Brad explained that previous rules existed for a reason, as most newly listed companies are younger, with less revenue and weaker profitability. But SpaceX is too large and important; excluding it from the index would be illogical. Exchanges and index providers made adjustments, avoiding stuffing it in at the peak, which prevented the typical 30% post-IPO drawdown from compounding onto passive investors.

Brad also shared the latest on OpenAI: revenue has recovered to around $70 billion annually, and GPT-6 might launch within 30 days. While this is only twice SpaceX's revenue and less than Anthropic's rumored $100 billion, as one of the two leading frontier labs, growing at this pace makes a trillion-dollar-plus IPO reasonable. He doesn't see a race between the two; both will act when the time is right. OpenAI's corporate structure restructuring is more complex, so it might come after Anthropic.

Chapter 2: Token Cost Doubles Every 45 Days, AI Investment Returns Near Zero?

Jason: We've been discussing the ROI of token spending for the past few weeks. CTOs and CEOs in the industry have started responding publicly on X. Uber's CTO Pinen shared their approach: 99% of engineers use AI tools, over 70% of pull requests come from local or cloud agents, and engineers have built 200 agentic skills. They deploy engineers as "frontline deployment engineers" across departments to work with department heads on process optimization. Brad, what do you think of Uber's approach?

Brad believes Chamath is right; it's just a matter of time horizon. A lot of money is currently being spent in experimental buckets that might not have direct ROI. But enterprise AI adoption is still in its early stages. The addressable market is every company on Earth, larger than ever before. Revenue distribution is not concentrated either, with millions of customers making independent, rational daily decisions.

Brad makes a bold prediction: If Anthropic's year-end revenue exceeds $100 billion, their revenue next year could multiply by 3 to 5 times. From $100 billion to $300 billion, that incremental $200 billion in revenue is unimaginable in Silicon Valley's history.

Chamath's skepticism focuses on the sustainability of ROI. He asked Claude 5 two questions. First: How much EPS growth has AI contributed to the S&P 500? The answer was 50%. But he realized this figure includes Nvidia's revenue from selling chips to Amazon. So he asked a second question: What is the EPS growth for the S&P 493 (excluding the Mag 7)? The answer was 9%. Breaking it down, the vast majority came from pricing power above inflation, with another 3% from buybacks. The ROI actually attributable to AI is between 0% and 2%.

Chamath argues the enterprise side looks shiny, but the problem is that smart investors like Brad and Gavin will eventually ask companies: What's your ROI? Where is the actual EPS uplift? If the answer is "I'm not sure," and you don't have sustained pricing power, the enterprise side becomes fragile. The consumer side, in contrast, becomes a safe haven because you have tens of millions of buyers with much smaller price points. The two orders of magnitude difference in buyer count shields you from ROI scrutiny.

Jason adds a perspective: This technology's uniqueness lies in its ability to touch everyone in an organization. When Excel came out, the accounting department was excited, but HR and Marketing didn't feel much. AI is different. In a 1,000-person organization, everyone is using it, spending $200 per person per month, doubling to $400. Compared to a $150,000 annual salary, that's only a 3-4% increase. The key question is: does it make that person 3 to 5 times more efficient? If so, that explains why token spending is skyrocketing.

Chapter 3: Open Source vs. Closed Source: Revenue Concentrates on Frontier, but Enterprises Want to Flee

Jason: Sacks, CTOs are starting to discuss intelligent routing on X, sending tasks to open-source models first and falling back to Claude if unsuccessful. What's your take on this trend? As an investor, when frontier model CFOs start asking, "Can we get it cheaper?" how do you view the growth of frontier models?

Sacks believes enterprise CTOs do want to shift token consumption to cheaper models. They're watching their token costs skyrocket and are looking for ways to slow or control the spending. Add to that the discussion from last week about AI sovereignty—companies worry about entrusting their core alpha to a frontier lab that might eventually become a competitor.

Sacks' core assessment: Enterprises want to move away from closed-source models, but most lack the technical capability to do so. The spirit is willing, but the flesh is weak.

Coinbase and DoorDash have achieved this by building token routing middleware, sending frontier tasks to frontier models and non-frontier tasks to regular models. But the average enterprise doesn't have this capability. That's why the wallet share of closed-source models is actually increasing. Open source's share of enterprise spending dropped from 19% last year to 11% this year. This doesn't necessarily mean usage is declining; it might just be difficult to track because using open-source models involves only paying hosting fees, not the lab itself.

Sacks also cites the viewpoint of Decagon's founder: when you know exactly what to do, small, cheap open-source models are fine, but you need data and post-training. If you don't yet know what to do, you want the most powerful general intelligence. Use open source for mature use cases, frontier models for immature ones.

Jason mentions a discovery by Databricks founder Ali: by just changing the harness (task orchestration framework) for the same model, costs can be cut in half. GLM-5.2 paired with a specific harness performs exceptionally well, directly halving the workload. Jason's personal experience echoes this: he built an agent for hourly trend discovery. After optimization, token consumption dropped by 80%. With cheaper tokens, he changed the agent from daily to hourly runs and split the single agent into three parallel tasks. Waking up to find 14 tasks completed felt entirely different.

Brad's perspective on this: The core debate is whether intelligence will converge. Eighteen months ago, during the "DeepSeek moment," the market dropped 40%. Many thought frontier models were finished and open source would kill them. But 18 months later, the opposite has happened. A tweet from Jesse Zang points out that the wallet share of frontier labs is actually rising, even though token usage is growing on both sides.

Brad presents a counter-intuitive hypothesis: Maybe intelligence won't converge at all. If superintelligence becomes self-recursive, smarter models make more money, more money buys more compute, and more compute builds better models. The gap might not be closing over the next 2-3 years; it might be widening.

Jason also mentions interviewing Anton, the CEO of Lovable. Their product launched about 30 months ago, and revenue grew from zero to $600 million. He also asked Matti, the CEO of 11Labs: "You're a major customer of frontier models, spending tens of millions annually. Are you worried about data leaks and competition?" Both said they are developing their own models. These are eight- and nine-figure customers.