Ok, so here is my take on the Fable ban, sovereign AI, Sarvam, etc. The event is interesting as it has implications from many perspectives. For AI users, it is clear that you should not confuse access with ownership, or adoption itself as advantage. And if the most significant tech differentiator you are leveraging has external control loops, then you have to accept you are vulnerable. For AI talent, it is now a precedent that you would be *seen* aligning to national interests more than company interests. And even if its just a whim for now, this trend will be hard to reverse as the world gets more automated… For AI labs, their offerings will be stratified - general purpose AI would be available as utility, but frontier AI would be gated. This is a fantastic business model for labs - *democratized* AI sucks in all the data liquidity of the world which is locked in higher margin frontier offerings. I think for the world to be a better place, all three of the above are bad vectors. We need to have more countries and companies owning their own destinies. And in the post AI world, that means being able to use and improve AI systems within their own perimeters - what one may call Sovereign AI. At Sarvam, Sovereign AI in India was the founding thesis a couple of years back, and continues to remain the core operating principle. From our vantage point, it is super clear that India will build, leverage, and create massive business value and societal impact with sovereign AI. The following is precisely how we at Sarvam are contributing to make that happen.

Compute: We were the first team in India to train at scale. We trained the sovereign models at the scale of ~3400 H100s. We are now putting serious capital behind the next step. India's first Blackwell cluster is now online and used by us, and we are building momentum towards operating 10s of megawatts in compute on Indian soil by 2027. Models: With Sarvam 105B, India's first sovereign model built from scratch, we showed that highly capable models can be trained here, independently. More importantly, the capability is now compounding across data, training, evaluation, systems, alignment, and deployment intuition. And we are scaling up to trillion-parameter class models, with larger runs built for coding, agents, and security. A coding model is coming soon... Inference: We already host our own models, with third party usage tripling in the last three months. We are soon taking live a production-grade token factory with the price, throughput, latency, reliability, and governance that banks, governments, enterprises, startups, and developers need for real systems. Products: Our products are now reaching India scale. Voice was our first wedge. It powers millions of interactions per day, doubling in the last three months, while we continue to optimise costs. Like voice, another modality at scale in India is documents, and we are hitting exponential growth of our new document intelligence product. Our fully managed agents product is live with enterprises and is being launched for all next month. Deployment: Most of the value in AI is unlocked in the last mile. We learned that by doing it across engagements in enterprises, government, and strategic sectors. Now we are turning that learning into a platform that allows every organisation to hill climb on its own use cases - whether it is building an agent, customising the harness, creating the data/tool backbone, or finetuning the model on custom data. Talent: Serious researchers are joining us across pretraining and RL, including people who have done meaningful work at the frontier. We are also starting our San Francisco office as the conduit for frontier AI ambition for India first, then the world.