Tortoise and Hare

Anthropic

Anthropic’s revenue run rate just topped $30 billion — that’s ahead of OpenAI, depending on how you count

The Claude maker’s revenue run rate has tripled in three months, as Dario Amodei’s firm takes the software world by storm.

Hyunsoo Rim

4/8/26 6:50AM

Anthropic said Monday that its annual revenue run rate has topped $30 billion, more than tripling from the ~$9 billion reported at the end of 2025. That places the Claude maker ahead of OpenAI, at least on paper, which disclosed roughly $24 billion in ARR at the end of March — a remarkable leapfrogging that would have seemed unthinkable just six months ago.

The jump came alongside the announcement of an expanded partnership with Google and Broadcom, which will give Anthropic access to 3.5 gigawatts of AI compute capacity starting in 2027 to help meet surging demand for its Claude products. The company’s latest model, Mythos, is already turning heads.

Over 1,000 enterprise customers are now spending over $1 million annually, more than double the ~500 customers in February.

Still, as remarkable as Anthropic’s near-vertical growth looks, the Claude maker may not have actually surged past its rival in the AI revenue race, since the comparison isn’t perfectly apples to apples.

Both OpenAI and Anthropic sell their AI models through cloud partners such as AWS, Google Cloud, and Microsoft Azure, which take a slice of revenues generated through their platforms. But the two account for those sales differently. According to The Information, Anthropic records the full amount a cloud customer pays as revenue, later counting the partners’ cut as an expense. OpenAI, by contrast, records what it actually receives after the cloud provider takes its share — at least in the case of Azure, its primary cloud partner.

In fact, The Information estimates Anthropic could pay roughly $1.9 billion to cloud providers this year and as much as $6.4 billion in 2027, based on the startup’s most optimistic forecasts, meaning the revenue gap with OpenAI could be narrower than it appears.

Beyond the revenue race, both AI startups are burning cash at historic rates, with the ChatGPT maker expecting to burn through a staggering $218 billion between 2026 and 2029 — about 23x what Tesla burned during its most cash-intensive years, and roughly equivalent to the GDP of Ukraine.

Jon Keegan4h

FT: Meta considering “tens of billions” in new capital to fund AI

Just days after Google announced a monster $85 billion upsized equity raise, the extremely profitable Meta is seeking to sell “tens of billions of dollars” in stock, according to a new report from the Financial Times.

Meta is planning on spending between $125 billion and $145 billion on AI capital expenditure this year alone.

Shares dropped more than 5% on the news.

Meta weighs big equity raising after blockbuster Google deal

Rani Molla

10h

Meta is supercharging its subscription businesses

The free-app giant never really cared about recurring revenue. Now, it’s launching paid plans for all sorts of stuff.

Jon Keegan10h

FT: Anthropic staff helping the NSA use Mythos for offensive cyberattacks

Anthropic’s Mythos AI model was deemed too dangerous to release to the public, with the company citing its ability to orchestrate novel cyberattacks.

And that’s just what the National Security Agency is doing, with the help of Anthropic staff embedded at the agency, according to a report from the Financial Times.

Only a small number of companies and US allies have been given access to the advanced model, which means America’s adversaries have not had the chance to shore up their defenses against the AI model’s new offensive capabilities.

The arrangement is especially unusual as the Pentagon has deemed Anthropic’s AI a national security supply chain risk — effectively blacklisting it for defense work — in response to the company’s refusal to allow its technology to be used for any legal application, which could include autonomous killing or mass surveillance. Anthropic is currently suing the US government to fight the determination.

US National Security Agency using Anthropic’s Mythos for cyber attacks

Rani Molla11h

Longtime Tesla bear JPMorgan upgraded Tesla and raised its price target to $475 from $145

For more than a decade, JPMorgan was Wall Street’s most stubborn Tesla skeptic, anchored by auto analyst Ryan Brinkman’s strict focus on traditional car fundamentals and near-term delivery numbers.

But JPM recently handed coverage of the stock to a new analyst, Rajat Gupta, who is throwing that playbook out the window. In a note Friday, the firm upgraded Tesla to “neutral” from “underweight” and raised its price target 228% to $475 from $145. (The analyst consensus on FactSet is $403.) Instead of focusing on the company’s struggling vehicle business, the new analyst is orienting himself more toward Tesla’s idea of the future, now modeling Tesla’s physical AI and robotaxi fleets all the way out to the year 2040.

Here are the main reasons for the capitulation:

Looking past the car lot: Gupta argues that Tesla “is at the forefront of physical AI, entering uncharted TAMs” and therefore “deserves the benefit of the doubt to be valued on LT earnings potential” rather than near-term speed bumps.
Unmatched vertical integration: Tesla’s control over everything from battery cells to custom silicon gives it a massive moat. JPM notes this starting point advantage is “unmatched at an industrial level scale” and “still somewhat under-appreciated and misunderstood.”
The AWS flywheel effect: Deploying Optimus robots inside its own factories “should not only lower COGS for the base automotive business, but more importantly, help validate the product at an industrial scale.” Gupta called it “a classic flywheel effect, somewhat analogous to AWS and Kiva at AMZN.”

For Tesla bulls who have argued for years that this is an AI company and not a carmaker, JPM’s sudden $3.9 trillion valuation model is the ultimate validation.

Anthropic ponders self-improving AI

Anthropic says Claude already writes 80% of its code. A new post asks what happens when the models can improve themselves — and whether anyone could stop them.

Jon Keegan11h