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AI & TECH

Anthropic leases SpaceX CPUs as model quality deteriorates

Sunday, May 17, 2026 · from 2 podcasts
  • Anthropic routed users to inferior chips to hoard Nvidia GPUs for internal research.
  • The company's compute crisis forced a deal with rival Elon Musk to lease a 300-megawatt data center.
  • Hiding model 'reasoning' to prevent training by rivals is breaking Claude's cognitive consistency.

Anthropic is mortgaging its product quality to survive a compute shortage. To preserve scarce Nvidia H100 clusters for its own researchers, it began forcing public users onto less-optimized hardware from partners like Amazon and Google. This was a suicide run, argued Theo on Nerd Snipe.

The result was a severe degradation in Claude's performance. The engineering burden of hiding the model’s 'thinking' traces to prevent competitive training created a fragile system. When caches fail, the model loses its reasoning history, becoming less coherent. On Nerd Snipe, Theo suggested the company’s software is too fragile to handle this complexity, resulting in a dumber assistant.

"Because the model needs its own prior reasoning to maintain context, Anthropic must perfectly map thread IDs to hidden server-side data. If the ID link breaks or the cache expires, the model loses its reasoning history."

- Theo, Nerd Snipe

The perception isn't just vibes. An internal audit at AMD showed the input tokens required for similar coding tasks in Claude Code spiked 170-fold between January and March. Estimated costs for one project ballooned from $26 to over $42,000.

Desperate for capacity, Anthropic turned to a rival. It is now leasing the 300-megawatt Colossus 1 data center, a facility with 220,000 GPUs controlled by Elon Musk’s interests. On FYI, Frank Downing noted the facility is ideal for inference but marks a sharp strategic pivot for Anthropic, which once blocked Musk’s xAI from its models.

"The lease cleans up the financials for a potential SpaceX IPO. It demonstrates a path to monetization for massive capital expenditures even if his own model, Grok, doesn't achieve immediate market dominance."

- Frank Downing, FYI - For Your Innovation

The crisis exposes a core tension in frontier AI. Model builders are willing to sacrifice product reliability to secure the compute required for scaling. The deal with Musk shows that when the power runs out, competitive distance no longer matters.

Source Intelligence

- Deep dive into what was said in the episodes

Anthropic solved their compute problem by buying it from Elon?May 14

  • Theo argues Anthropic's Claude Opus 4.7 is not a meaningful improvement over prior models and comes with user experience regressions due to overly strict safety system prompts.
  • Figma's stock dropped 5% after a competitive announcement from Anthropic, contributing to an 85% decline since its IPO. Ben cites this as evidence of Anthropic's negative market impact.
  • Theo reveals Anthropic confirmed a routing error last year where 0.8% to 16% of Sonnet requests were sent to a dumber, 1M-context version, establishing a precedent for performance regressions via model versioning.
  • Theo's primary conspiracy is that Anthropic now forces all Claude Code users onto the dumber 1M-context model version to route traffic away from scarce Nvidia GPUs and onto partners like AWS and Google TPUs, explaining the performance drop.
  • The hosts claim Anthropic employees use different, superior internal versions of Claude and its tools, creating a disconnect where employees don't experience the external product's failures and dismiss user complaints.
Also from this episode: (5)

AI & Tech (5)

  • Anthropic is aggressively banning third-party tools like T3 Code and OpenClaw that interface with Claude Code. Theo attributes this to a compute crisis and poor caching implementations that increase costs.
  • Theo states OpenClaw's heartbeat function, which polls for tasks, costs him $4.31 daily without active use, extrapolating to roughly $120 monthly in wasted API spend.
  • Ben cites a GitHub issue from an AMD AI head showing a massive spike in Claude Code usage and cost at their company. Input tokens increased 170x and costs jumped from $26 to over $42,000 monthly after model updates.
  • Theo and Ben argue Anthropic's engineering is incompetent, citing recent changes like a new tokenizer, a 5-minute cache TTL, and hidden reasoning data that complicate their stack across three hardware providers, leading to reliability and intelligence issues.
  • Theo attributes Anthropic's problems to a research-first, safety-obsessed culture that devalues engineering and product reliability, creating opaque policies and a 'holier-than-thou' attitude that frustrates developers.

SpaceX And Anthropic Partnership | The Brainstorm EP 131May 13

  • Anthropic has been supply-constrained on compute, restricting user tokens and cutting off research compute, forcing them to lease Colossus 1 - a 300MW, 220,000 GPU data center - from SpaceX to lift capacity limits.
  • Anthropic previously enforced a policy to cut off competitors like XAI from using its models, but the compute deal has reopened communication between them.
  • SpaceX’s vertical integration from chip fabrication to model inference reduces its risk when leasing out compute, as it can more rapidly rebuild capacity.
  • Building a gigawatt-scale AI data center costs roughly $60B: $19B for the facility, $30B for GPUs, and $11B for other IT equipment like CPUs and networking.
  • A gigawatt of leased compute infrastructure generates about $15B annually; a model provider operating on that scale can generate $30B in revenue.
  • OpenAI’s revenue scaled with compute; they generated $20B on roughly 2 gigawatts early this year, implying about $20B per effective gigawatt for inference.
  • Revenue per watt is increasing as model utility and enterprise willingness to pay rise, creating pricing power for both infrastructure providers and model companies.
  • Space-based AI compute economics hinge on Starship launch costs; at $300/kg, launching a gigawatt costs $7.5B, beating terrestrial facility costs.
  • Manufacturing satellites on a production line offers cost efficiencies over building unique terrestrial data centers, where $5B of the $19B facility cost is labor.
  • Compute scarcity on Earth means AI companies like Anthropic would pay a premium for space-based watts even before launch costs break parity, valuing velocity over price.
  • ARK analysts project SpaceX could begin scaling space-based AI compute in 2028-2029, reaching tens of gigawatts per year in the early 2030s.
Also from this episode: (1)

Energy (1)

  • Small modular nuclear reactors are more likely to scale in the US than gigawatt plants, fitting incremental demand like offsetting retired coal plants.