OpenAI closes $122B at $852B — most valuable private company in history
·Crunchbase News
OpenAI closed its $122B primary+secondary on March 31 at an $852B post-money, passing SpaceX to become the most valuable private company in history. D.E. Shaw and MGX co-led, with Thrive, Coatue and Temasek participating. Revenue run-rate hit $28B on the April 1 board update, up from $12B a year earlier. The round funds OpenAI's $500B Stargate commitment with Oracle and SoftBank plus a reported $70B custom-chip program with Broadcom and TSMC aimed at halving training-compute cost per token by 2027.
OpenAIValuationStargateBroadcomTSMC
Why it matters
Symbolic passing of SpaceX is less important than the $500B Stargate commitment and the $70B custom-chip program with Broadcom/TSMC. Those lock in multi-year compute supply and a credible path to halving token costs — which is what actually moves the competitive frontier, not the headline valuation. The capital stack now matters more than the models.
Impact scorecard
7.5/10
Stakes
7.5
Novelty
5.5
Authority
8.5
Coverage
9.5
Concreteness
7.5
Social
8.0
FUD risk
3.5
Coverage70 outlets · 14 tier-1
Wall Street Journal, Financial Times, Bloomberg, Reuters, CNBC, New York Times, …
Valuation figures from private rounds are inherently mushy — primary sources are leaks to WSJ/FT. Revenue run-rate comes from a board-deck leak. Treat $852B as the number people agreed to pay, not the number the business is worth. Stargate and chip numbers are better corroborated.
@hardmaru (David Ha) flagged a paper adapting Sora-style video-diffusion architectures to build a learned world model of an actual Linux desktop. The model ingests 9,000 hours of screen-recording + keyboard/mouse traces and learns to predict next-frame UI state conditioned on user input — effectively a probabilistic operating-system simulator. On a held-out eval of 50 common tasks (opening files, running commands, navigating web UIs), the model achieves 73% next-event accuracy at 2-second horizons and 41% at 30-second horizons, beating the prior SOTA (Meta AI Habitat-UI) by 18pp. Direct application: train agents in fully simulated computer environments without real-system rollouts — cuts RL data costs ~40x and eliminates the safety risk of letting agents touch production systems during training.
EE Times deep-dive on AMD's ROCm 7.0 and whether it can finally dent NVIDIA's CUDA moat. AMD's MI400 (96GB HBM4, 5.2 PFLOPS FP8) now runs PyTorch, vLLM and SGLang out-of-the-box — but reviewers testing MLPerf Inference v5.1 still see 1.6–2.2x gaps vs H200 on representative LLM workloads, driven by kernel-library maturity rather than raw silicon. Breakthrough of the cycle: AMD hiring 600 CUDA-kernel engineers in 12 months, plus open-sourcing HIPify tooling that auto-translates 83% of typical CUDA kernels. AMD claims Meta, Microsoft and OpenAI are all now shipping production MI400 pods. NVIDIA's response: CUDA 13 with tensor-core autotuning targeting the same eval suite, launching Q2.
Anthropic announced the advisor strategy on the Claude Platform: pair Opus 4.6 as a planning/critique advisor with Sonnet 4.6 or Haiku 4.5 as the executing model. The advisor inspects partial outputs, suggests corrections and redirects the executor mid-generation. On SWE-bench Multilingual, Sonnet+Opus-advisor scores 2.7 percentage points higher than Sonnet alone, at roughly 1.3x the cost vs 7x the cost of running Opus end-to-end. General availability today via the Claude Console and CLI; pricing is existing Claude API rates for both models (no advisor premium). Anthropic positions this as the first first-class multi-model inference primitive in any frontier-lab API — not just routing or cascading but explicit advisor/executor roles with shared context.