🌏 From OpenAI to Google, India hosts global AI summit ↗
A major global AI summit landed in New Delhi with a decidedly stacked guest list - top execs from OpenAI, Google, Microsoft, Amazon, and Anthropic, plus political heavyweights. The prevailing tone is “developing countries should get a say in AI governance,” which feels overdue.
On stage, it’s framed as “AI is opportunity,” while the quieter subtext reads “AI could chew through a lot of jobs,” especially in services-heavy ecosystems. The juxtaposition is a little awkward, and very of-the-moment.
💸 Anthropic's revenue run-rate doubled in India in 4 months, says CEO Amodei ↗
Anthropic says its revenue run-rate in India doubled in a short span - which points either to genuine enterprise pull, or to a market scrambling not to miss the boat - or both.
What stands out is the “run-rate” framing. It’s a momentum story, not a neat quarterly box. Still, it fits with India as a massive proving ground for AI products at scale (complex, multilingual, high-velocity - basically AI’s treadmill).
📊 India Country Brief: The Anthropic Economic Index ↗
Anthropic published an India-focused slice of its “Economic Index” work - an attempt to quantify how AI use shows up across tasks and industries. This kind of measurement is surprisingly hard, because AI usage is everywhere and nowhere at once, like glitter.
The punchline is less “AI will do X jobs” and more “here’s how the work-mix is shifting,” which is the more candid lens, even if it’s less headline-friendly.
🧠 New Data Shows NVIDIA Blackwell Ultra Delivers up to 50x Better Performance and 35x Lower Costs for Agentic AI ↗
NVIDIA is leaning hard into “agentic AI” as the workload to optimize for - the kind of systems that plan, call tools, retry, and generally behave like a caffeinated intern who never sleeps.
The headline numbers are huge (bordering on cartoon-scale), but the deeper story is the direction of travel: performance-per-dollar for multi-step agent workloads is becoming the bragging-rights battlefield. Compute isn’t just compute anymore... it’s compute that can think in loops without bankrupting you.
🪖 Anthropic’s Pentagon Talks Snag on AI for Surveillance, Weapons ↗
Talks between Anthropic and the Pentagon reportedly hit turbulence over boundaries - surveillance and weapons use-cases, the kind that turns “AI policy” from a comfy panel topic into a cold-sweat problem.
If there’s a theme, it’s that “we won’t do X” gets stress-tested the moment a major buyer shows up with a checkbox list. And governments tend to arrive with a lot of checkboxes.
🔬 Gemini 3 Deep Think: Advancing science, research and engineering ↗
DeepMind is positioning Gemini 3 “Deep Think” as a research-and-engineering focused mode, which amounts to saying: fewer party tricks, more lab bench. It’s a clear strategic signal - they want to be the model you trust with hard problems, or so it seems.
Also, “Deep Think” as a label lands a little funny - like naming a workout plan “Very Strong Legs.” Still, if it meaningfully supports scientific workflows, the branding poetry earns its keep.
FAQ
What was the global AI summit in New Delhi, and why did it matter?
The New Delhi global AI summit brought together a high-profile mix of leaders from OpenAI, Google, Microsoft, Amazon, and Anthropic, alongside major political figures. The public framing leaned on “AI is opportunity,” but the subtext pointed to real economic disruption. One of the clearest signals was that developing countries want a substantive seat at the table in AI governance, not a passive role receiving rules set elsewhere.
Why are developing countries pushing for more say in AI governance?
The summit’s prevailing view was that AI governance should not be defined solely by a small cluster of wealthy nations or companies. Developing economies often carry different exposures: labor-market vulnerability, uneven digital infrastructure, and multilingual realities. A common push is for shared standards and enforceable accountability that reflect where AI is deployed at scale, not only where it is invented.
How could AI affect jobs in services-heavy economies like India?
The tension on stage - “opportunity” versus “job churn” - is especially sharp in services-heavy ecosystems. Many roles are task-based, and AI tends to reshape tasks before it replaces whole jobs. In many pipelines, the near-term impact looks like a shift in work mix: more oversight, exception handling, and client-facing judgment, with some routine components compressed or automated.
What does it mean when Anthropic says its India revenue “run-rate” doubled?
“Run-rate” is a momentum snapshot, not a neat quarterly result. It signals what the current sales pace would look like if it continued, which can spotlight acceleration even before it appears cleanly in formal reporting. In India, that could reflect genuine enterprise pull, a market moving fast to adopt AI at scale, or both. It remains a directional metric, not a settled accounting figure.
What is the Anthropic Economic Index India brief trying to measure?
The India Country Brief is positioned as a way to quantify how AI use shows up across tasks and industries. That is difficult because AI can be embedded almost anywhere - sometimes visible in tools, sometimes obscured inside workflows. The more practical takeaway is less “AI will do X jobs” and more “here’s how task composition is changing.” It offers a lens for tracking shifting work patterns over time.
Why is “agentic AI” suddenly a big focus for NVIDIA and model makers like DeepMind?
Agentic AI refers to systems that plan, call tools, retry, and iterate through multi-step workflows rather than answering once. NVIDIA’s Blackwell Ultra messaging leans into this, with headline claims like up to 50x better performance and 35x lower costs for agentic workloads. In parallel, Gemini 3 “Deep Think” is framed as more research-and-engineering oriented - suggesting the next competition is reliable, loop-capable systems for serious workflows.