Why Google's Cloud Strategy Is Starving DeepMind
Dylan Patel explains why Google Cloud sells compute away from DeepMind, eroding its AI lead against rivals OpenAI and Anthropic.
Which GPU Clouds Are Actually Good? | ClusterMAX 3.0
With swyx, Jordan · Sunday, October 4, 2026
SemiAnalysis researchers Jordan and Dylan join swyx to break down the findings of ClusterMAX 3.0, evaluating the managed GPU cloud landscape. They analyze severe cybersecurity vulnerabilities in NeoClouds, extreme compute shortages driven by profitable inference providers, Nvidia's defensive acquisition strategy with Poolside and Hugging Face, and the future of multi-silicon and Chinese AI hardware.
Key takeaways
- Google possesses massive TPU capacity, yet internal teams like DeepMind have less dedicated R&D compute than OpenAI and Anthropic. Read more →
- Frontier labs like Anthropic and OpenAI used to rent GPU clusters in blocks of 8,000 chips; now they aggressively secure slices as small as 1,000 GPUs. Read more →
- Reinforcement learning workloads now match or exceed pre-training compute volume, forcing data centers to shuttle jobs between different specialized chips instead of relying on uniform GPU clusters. Read more →
- SemiAnalysis built ClusterMAX 3.0 to test managed software layers rather than raw data center construction or bare-metal server specs. Read more →
- Nvidia pulls in nearly $50 billion of free cash flow each quarter, giving its corporate strategy team roughly $200 billion annually to deploy defensively. Read more →
- SemiAnalysis audits of GPU NeoClouds revealed that researchers could view other tenants' private training runs, storage volumes, and active workloads. Read more →
6 articles from this episode
Why Profitable Inference Squeezed GPU Supply for AI Labs
Inference providers hitting 60% gross margins and frontier labs renting 1,000-GPU slices have wiped out AI compute supply.
Why RL and Huawei Are Ending the Single-GPU Era
RL workloads and Chinese chip production are forcing AI labs off pure Nvidia clusters and into heterogeneous silicon.
Why Great Data Centers Still Fail at Managed Slurm
SemiAnalysis explains why data center power pipelines do not equal reliable AI training clusters, and how ClusterMAX scores compute providers.
Why Nvidia Buys AI Startups to Protect Its Cash Engine
SemiAnalysis reveals how Nvidia uses $50B quarterly cash flow for defensive acquisitions like Poolside to create captive GPU demand.
Why NeoClouds Leak Your Private AI Models and Data
SemiAnalysis audits reveal how unpatched GPU NeoClouds exposed national security workloads and private tenant data.
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