Interviewer: When is Jio expected to launch its first batch of satellites to serve communications in India? Akash Ambani: “I don’t want to give a precise answer about the launch date.” Starlink is available in over 170 countries today that could deliver real connectivity solutions for Indians now.

Interview with Hu Sheng: Whether we are talking about the Middle Way or independence, neither matters more than exiled Tibetans being able to return to their homeland. I have a very naïve dream: if President Trump could help arrange for His Holiness the Dalai Lama to take some people back to China—even for just seven or ten days—to return to Tibet itself, with each person taking a handful of soil or filling a bottle with water, I think that would answer the deepest longing in the heart of every Tibetan in exile.

interview question I like - multi-modal RAG w/ an agent can go high level & also rabbit hole into context engineering, tokenization, harness design, search, how models are trained, etc Setup: there’s a folder with multi-modal data - markdown files, PDFs, image files, video files Let’s build a Q&A system over this data. Rabbit Holes & Follow-Ups: - How would a ReadFile() tool work per modality? Let’s talk about preprocessing, tokenization & ingestion into the context window and if they differ per modality? Anything to be careful of? - How would you design the search harness? say we care a lot of about reducing costs from unnecessary reads? or we care a lot about recall - What if we had A LOT of video data, how would your approach change? Would you consider preprocessing (ex: transcripts)? how would that match to how the model builder recommendation based on how it was trained? - what model(s) would you use and why? if multiple, how would they work together in harness? - here are some query patterns. the data is mostly about sports events (transcripts, gameplay footage, stills, reports). most people ask questions about when an event happened. some ask “how” it happened in the real world. let’s talk about how to research and design around these patterns by looking through our data you can go infinitely deep here. i’d like talking through mental models around model context & the harness for preparing context. also details around how to do efficient search and when to pull context into the context window vs operate over it via code very useful to talk through different levels of abstraction with someone, can tell pretty quickly how much they’ve considered agents and the data they operate over, also would just be a pretty fun convo!

INTERVIEW INCOMING 🎙️ TOMORROW AT 1PM ET, NVIDIA'S SENIOR DIRECTOR OF AI SOFTWARE, ALI GOLSHAN, WILL BE JOINING OUR OWN EVAN @StockMKTNewz LIVE They'll be talking about the AI safety announcements NVIDIA $NVDA made today. Don't miss it. Tuesday, Sept. 29 at 1:00 p.m. ET.

Interview with an industry expert on why hyperscalers remain the right choice for sensitive workloads ( $AMZN, $MSFT, $GOOGL, $CRWV ): - The expert highlights a new approach to data platform modernization that replaces traditional data engineering with agentic packages built with AI labs, to avoid spending months and millions of dollars building out hundreds of ETL and analytic data products the conventional way. The engagement typically runs one to two years, covering platform setup, installation of agentic packages, and training the client's team, after which the client decides whether to take operations in-house, bring in another partner, or continue the relationship. - The expert notes that the expected shift toward open source models never materialized. Until now, frontier models with strong reasoning capabilities have consistently outperformed open source alternatives when layered with agents, and the market moved toward agentic adoption rather than fine-tuning. - On cost, the expert explains that open source models require procuring GPUs, training, deploying, and managing clusters that run 24/7, making the economics only favorable at very high API call volumes that most enterprise use cases have not yet reached, leaving pay-as-you-go frontier models as the more practical choice at current scale. - Security and compliance present another barrier, with most large organizations having legal and security teams that have not approved open source or Chinese models for use on actual customer data. Frontier models from Anthropic and OpenAI have a clear advantage here as they are natively integrated within major cloud providers like AWS and Azure, making them far easier to clear through enterprise security policies. - The expert emphasizes that enterprise AI adoption is still in very early stages, with most Fortune 500 companies having done a handful of POCs but very few having actually deployed full-scale agentic solutions in production. Confidence is low, adoption is low, and the expert sees an enormous amount of runway still ahead, both for large enterprises and the broader mid-market. - The expert believes hyperscalers remain the right choice for sensitive workloads given their established security and data privacy frameworks, while newer neoclouds are being engaged for different, less sensitive types of workflows. The distinction matters because the kind of work being put through neoclouds is significantly different from what runs through hyperscalers, reflecting a practical split based on security requirements rather than performance preferences.