Ali Ghodsi never wanted to be CEO. In 2015, he was interviewing for a professor job at Berkeley when the @Databricks board handed him the interim title. Revenue that year was $1.5M. This episode with @alighodsi will go down as one of my favorites. 10 things I took away: 1. Focus the entire company, orders of magnitude of attention, on its single biggest bottleneck. Like a laser, almost to an extreme. The cycle is 1-3 years, not weeks. If your focus changes weekly, then you’re just in firefighting mode. 2. There is nothing worse than a conflict-averse CEO. They are wonderful people, but they are in the wrong job. Conflict is the gym for a CEO: nobody likes it, but everyone has to go. 3. Study your enemy carefully, understand their weaknesses and apply your strengths to those weaknesses. Snowflake had 2x his revenue. He didn’t copy them. He found three weaknesses (proprietary, no AI, expensive) and hammered them account by account for four years. Watch the competition, never follow it. 4. The concept of a Lakehouse was ridiculed internally and online. No one wanted to market with this new term. So he made the whole company religious about it anyway, killed the ads that converted better without the word, and put it in the sales comp plan. It worked. All hands on deck, no exceptions. 5. Be willing to take a step back for a much bigger vision, even when the company is already succeeding. At multiple hundreds of millions in ARR, he was unhappy, because the vision he pitched investors wasn’t the company he was running. So he took one step back to go ten forward. 6. On the flat org, player-coach model that a lot of people have talked about this year: “it’s BS.” Separate how the company thinks (AI, ontology) from how the humans get managed (they are after all, still humans). His staff meets 3x a week. I asked if it could just be coordinated in a Google doc. His answer: do you meet your wife and kids, or coordinate that in a Google doc? 7. His test for a sales leader: can they build the car, or just drive it? Ron Gabrisko, the Databricks CRO, had seen $0→50M and $50→100M+, and hadn’t changed jobs in 10 years prior to joining. Now he has been the CRO for over a decade. He built and drove the car the whole way. That almost never happens. 8. Hire execs ahead of the curve because by the time you need them, it’s too late. A real search takes 6-12 months. The extra time helps you increase false negatives and decrease false positives. Do an insane number of backdoor references because 80% of ‘front door’ references are bs. 9. The best salespeople are not super technical, so stop trying to force them to be. Square peg, round hole. The best win with professional aggression, high EQ, and mapping the real power base (how decisions get made high up in an organization), not technical depth. 10. Yes, your best AEs will annoy people. One of the first at Databricks got a meeting nobody could get, but got banned from the customer’s building for it. He told Ali, “what are you complaining about? I got the meeting.” Professionally aggressive is the bar. One bottleneck, zero wussing out. He reminds me of @elonmusk that way. Chapters 0:00 – Introduction 1:22 – The secret CEO search and why the board bet on a founder 4:11 – Professor or CEO? Always taking the harder option 8:18 – Pour everything into one bottleneck 12:16 – Killing PLG and learning what great enterprise sellers actually have 19:09 – Hiring ahead of the curve: sales leaders, execs, and back-door references 27:02 – The Snowflake rivalry: study your enemy, never copy them 33:22 – Lakehouse: conviction, ridicule, and the case for second acts 41:02 – The killer instinct and why conflict-averse CEOs fail 44:10 – Dunbar's number and rethinking the org chart around AI 48:00 – AGI is already here — enterprises just use it as a chatbot 52:55 – Does he still code? Two days for a connector vs. three quarters 57:18 – A day in the life, and why the Monday meeting isn't theater 1:04:53 – Why Databricks will go public, just not yet 1:08:09 – Get over conflict aversion, or don't be CEO 1:11:07 – Brian's takeaways Link to more in the comments.

Exciting day for NVIDIA and @huggingface. Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. They allow every developer, startup, university, industry and country to build with, customize and benefit from AI. Thank you @ClementDelangue for coming to me. NVIDIA is going to be a great home for Hugging Face, its community and the future of open models. 🤗

Introducing Smart Routing in Unity AI Gateway. Stop overpaying for AI. Smart Routing matches each coding task to the right model and harness based on what the task needs, so higher-cost models can differentiate on intelligence while lower-cost models differentiate on cost and performance. Match frontier quality and cut task costs by 30%+.

Today @databricks we're publishing a detailed analysis of techniques we used to drastically reduce our internal AI spend while aggressively growing adoption. Savings come from layering in several techniques, which combine to drive unit costs down as much as 90% in some scenarios. Tl;dr, the wins come from: 1. Shifting defaults to more efficient models, including OSS models such as GLM. Maximum intelligence models simply aren't needed for many coding tasks, and "good enough" models are quickly becoming very cheap. We shift traffic between models using Unity AI Gateway. Approximate savings: 50% or more. 2. Using smart routing to automate model selection. Routing can further squeeze efficiency by dynamically selecting the model or harness that can most efficiently execute a particular task. Our task-level routing leverages @omnigent_ai. Approximate savings: 30%. 3. Providing user visibility and adaptive budgeting. Every user can see how much they spend, and users receive hints on how to contain spend. Heavy spenders encounter progressive friction as they ratchet spend above certain levels. Approximate savings: 10%. 4. Managing context bloat by pruning tool call results and tuning harness settings. Extraneous context costs $$ and delivers no value. Tuning cache settings also help lower average token costs. Approximate savings: 10%.

We found that improving data agent quality also improves efficiency. Sometimes more is less: agents that take long, exploratory random walks are often less likely to arrive at the right answer. We put Genie Code head-to-head against three leading general-purpose coding agents on 400+ real user data tasks. Result below 👇

Next up on @AltimeterCap First Pass: A conversation with @matei_zaharia from @databricks on Omnigent Agents are in their infancy, and so are the ways we develop and build them. Omnigent is a meta-harness aimed at solving many of the gaps in agent development today

Excited to be releasing FrontierFinance, the largest and most challenging open benchmark for evaluating AI agents across the full investment workflow! FrontierFinance is substantially harder than current finance benchmarks: Existing benchmarks like FinanceBench and Finance Agent focus almost entirely on data extraction. FrontierFinance spans diverse use cases across the full investment process: Screening & Discovery, Company Research, Sector/Industry/Macro, Earnings & Events, and Coverage & Catalyst Monitoring. Created for ambiguous, long-horizon agents: 220 examples paired with 11,543 expert-crafted rubrics, following Samaya's Criteria Eval methodology. The rubrics are what let us evaluate the reasoning and steps behind a true expert-level output, not just a plausible-looking one. Evaluations: We evaluated Claude Fable 5, Claude Opus 4.8, GPT 5.5, Gemini, open-source models including GLM and DeepSeek, and others. We used the same public rubric and a standard harness for financial tasks. Samaya's AI system reached state-of-the-art accuracy at 50.8%, at 4x lower inference cost than Fable 5. Next best was Fable 5 (49.2%), then Opus 4.8 (45%) and GPT 5.5 (43.5%). We're releasing the benchmark, methodology, and full evaluation results - see link in comments. Future releases: FrontierFinance was curated from Samaya's larger internal set of ~5,000 examples, and we plan to release subsequent, harder benchmarks as well as a more detailed technical report!