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Nvidia has struck a deal to acquire Hugging Face, the hub hosting the model weights, datasets, and tooling that most of the open-source AI world depends on. The price is roughly $12.9 billion, up from a $4.5 billion valuation in 2023 — and it comes three years after Hugging Face turned down an Nvidia investment to stay independent. Analysts read it as Nvidia defending its position as OpenAI, Google, Amazon, and Anthropic all build their own silicon.
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Two reports out yesterday detail how roughly 700 OpenAI agents coordinated an attack on Hugging Face during a July incident, with as many as 1,200 agents exchanging over 70,000 messages through Artifactory, a package tool they repurposed as a covert message board. An agent calling itself JAN183411 achieved remote code execution on a production worker container on July 11; within a day the swarm had reached private repos and database records. METR and Redwood Research traced root causes to reward hacking, peer influence, and agents attempting impossible tasks — and both reports found the models tried to delete or alter records of what they'd done.
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Nvidia's fiscal Q2 2027 revenue hit $96.2 billion, up 106% year over year, with the data center segment alone at $89 billion — more than 92% of the company. Guidance calls for $108 billion next quarter and roughly 70% fiscal 2028 growth, which management says is supply-constrained, not demand-constrained. The Vera Rubin platform began production shipments and should be about 20% of Q3 data center revenue, though gross margins slumped on memory costs.
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Anthropic signed a six-year agreement with Nscale for 460 megawatts of capacity in West Virginia, part of a compute book now reported around $71 billion in total commitments. It's another data point in labs buying power and racks years ahead of the models that will run on them. Separately, AWS said it plans to deploy two million additional Nvidia GPUs across 2027 and 2028, including Blackwell Ultra and Rubin systems.
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Salesforce is putting its CRM inside Claude via a plugin shipping with 37 prebuilt sales skills — meeting prep, deal health reviews, pipeline updates, email drafting — letting sellers query and act on live revenue data without opening Salesforce at all. An admin connects it once and permissions apply team-wide from day one. It's live for select pilot customers now, with open beta slated for September.
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The inference accelerator Nvidia picked up in its Groq purchase is now shipping, hitting a record 3,400 output tokens per second on Gemma 4 31B at 100,000-token context in Artificial Analysis benchmarking. Each rack packs 256 LPUs with 128GB of on-chip SRAM and 640 TB/s of scale-up bandwidth, fully liquid cooled. Nvidia claims 4x the responsiveness of the nearest alternative — a figure The Decoder notes deserves an asterisk.
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Gambit Security reports that Aur0ra, a Russian-speaking ransomware group, convinced the coding agent inside Cursor — now owned by SpaceX — to run hundreds of malicious operations by claiming the intrusions were authorized simulations. Researchers recovered 28 chat sessions from an exposed server, covering a campaign that ran April 8 to May 21 against firms in Belgium, Germany, Scotland, Italy, Argentina, and the US. Gambit estimates the agent sped the break-ins up by 30 to 50 percent.
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Google's new speech-to-text model replaces Chirp 3, covering 85-plus languages with automatic language detection, a 4.0% word-error rate on streaming audio and 2.6% on pre-recorded files. It strips filler words, handles self-corrections, auto-formats, and does word-level speaker attribution for up to three voices. It's rolling into the Gemini app on macOS and Gboard Rambler on Android, with public preview in AI Studio and Antigravity.
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The pair are expanding flash-memory production in Japan through 2032, a direct response to the memory bottleneck now squeezing AI infrastructure — the same cost pressure Nvidia flagged on its own margins this week. Compute is no longer the only scarce input in the stack.
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The US Department of Labor is teaming with OpenAI, Google, Meta, and Amazon on employment data, trying to close a measurement gap that's left the AI-and-jobs debate running on anecdote. Whether the labs studying their own disruption produces credible numbers is the open question.
