Nvidia has reported second-quarter revenue of $96.2 billion for fiscal 2027, up 106 per cent year-on-year and 18 per cent on the previous quarter, driven largely by continued demand for its data centre AI hardware.

Data centre revenue reached $89.0 billion, up 117 per cent year-on-year. GAAP and non-GAAP gross margins both held at 75.0 per cent, while diluted earnings per share came to $2.46 on a GAAP basis and $2.22 on a non-GAAP basis. Nvidia returned around $26.0 billion to shareholders during the quarter through buybacks and dividends, and has roughly $99.0 billion remaining under its existing repurchase authorisation.

The company's Vera Rubin platform moved into full production during the quarter, with racks now running at partners including CoreWeave, Google Cloud, Microsoft Azure, Oracle and Nebius. Nvidia also unveiled Vera, which it describes as the first CPU built specifically for AI agents, and confirmed its Groq 3 LPX inference accelerator has entered full production.

Chief executive Jensen Huang said "AI has reached its inflection point. It's doing useful work. Its tokens are productive and profitable. Now, compute is revenue," adding that demand was being driven by a widening base of frontier labs, startups and open-model developers, in contrast with the single dominant customer that characterised the market a year earlier.

For the third quarter, Nvidia guided to revenue of $108.0 billion, plus or minus 2 per cent, excluding any assumption of data centre compute revenue from China.


Execution Level Governance- What audit-ready agent governance actually looks like
David Girvin, founder and CEO of Assury argues that model-in-the-loop review, AI governing AI, is fundamentally unreliable for regulated environments: even the best-performing models miss a meaningful share of violations, the reviewing model is typically provided by the same vendor being reviewed, and prompt injection or context poisoning can compromise both the acting agent and its supposed overseer simultaneously. He makes the case for deterministic, architecturally enforced controls instead, walking through Assury’s approach of autonomy zones, session risk accumulation, and credential starvation, which lets a compromised agent be cut off from its tools instantly rather than relying on time-boxed access. The conversation touches on why David is sceptical of just-in-time credentialing as a solution for agent security more broadly, since agent sessions don’t run on predictable human timescales, along with the current gap between how identity and security vendors are pitching agent protection and what he sees happening at the execution layer in practice. He also discusses the compliance and audit implications of probabilistic decision-making, arguing that regulated industries will increasingly need tamper-evident, hash-chained audit trails that can withstand scrutiny from auditors and regulators who are only beginning to understand agentic risk, and reflects on a named frontier lab’s own published framework as an example of the gap between research and practitioner reality. Elsewhere, David reflects candidly on building a bootstrapped security company in an increasingly crowded market, why he turned down aggressive VC funding to stay in control of the product, and what a credible third-party assessment of his own gateway would need to look like given that Assury sits directly in the execution path for every customer’s agents.
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