We trained a robot to play football. How? Self-play for 140 years in a virtual World Cup. Meet the #Messinator:

We trained a robot to play football. How? Self-play for 140 years in a virtual World Cup. Meet the #Messinator:

Find the bottleneck. Swarm the bottleneck. Shatter the bottleneck. If every CEO that I coach focused like @alighodsi, I wouldn't have a job. His strategy: - Identify the main bottleneck - Focus the whole company on it (to an extreme) - If you overdo it, thats great. You did it right. @databricks put all their attention on one thing at a time, and in this story it was figuring out how to get the commercial engine humming. Complete obsession. Now at a $7B run rate. Link to full episode in the comments

We trained a robot to play football. How? Self-play for 140 years in a virtual World Cup. Meet the #Messinator:

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.

Well.. @levie and I filmed this episode of Training Data a week or two ago, when the “current thing” was Doug Leone’s novacaine root canals instead of pacing the frontier… Simpler times! But Aaron’s advice on reinventing yourself and your company for AI is timeless. Aaron founded @Box 20 years ago. It sits on hundreds of billions of enterprise files, and he's bet the company on agents that can read every one of them. He's also one of the most wired-in people in AI, on every cap table and, by his own admission, 95% Twitter-educated. He’s the rare CEO who can straddle both the internet AND has the ear of CIOs. His core argument: (1) the gap between what a model can do and what an enterprise workflow actually needs is vast, and closing it is a lot of software; (2) diffusion of AI outside of coding will take far longer than Silicon Valley thinks, and that slowness is exactly where the applied layer's value comes from. The conversation covers: — why application companies are the hottest neolabs, and why the LLM-wrapper thesis is finally working — the fox-guarding-the-henhouse problem with letting model providers route your tokens — work slop, and why we accept AI-written code but flinch at AI-written decks — how Box built its agentic harness and why it beats raw API access on accuracy and latency — the open-weights paradox: closed labs and open models both growing exponentially at once — what continual learning has to solve before it works for a lawyer with five matters and a Chinese wall — why 90% of enterprise tokens in five years will come from tasks no human kicked off — the mandate for founders right now: whoever gets it to the customer wins 0:00 – Introduction 1:55 – Are application companies the hottest neolabs? 6:56 – Will the labs move up the stack? 12:34 – Box and betting the company on AI 16:50 – Hero use cases: reading a million contracts and long-running agents 18:42 – Work slop: why AI code is embraced but AI content isn't 24:08 – Building Box's agentic harness and the evals that matter 27:23 – The state of the model race 29:25 – Open-weight model adoption in the enterprise 32:34 – Memory, continual learning, and what belongs in the weights 37:29 – Box Labs and systems of record in a world of agents 44:55 – Will chat be the dominant UI for enterprise AI? 48:00 – Why coding diffused fast and the rest of knowledge work hasn't 54:31 – Staying wired in, making a company AI-first, and what it takes to win

We did it again. Temporal just raised a $550M Series E round at a $12.55B valuation. Learn more about how we got here: temporal.io/blog/temporal-ra…

We just hit 100M ARR within 10 months of starting deployments. We are in factory lines. On construction sites. In kitchens. In data centers. Cleaning. Welding. Building. Cooking. Deploying.

Introducing Phoenix-4.5, the fastest and most expressive real-time human rendering model on the market. More natural than ever before: richer facial animation, more expressive emotion and micro-movements, and movement that now extends through the upper body. It is the closest AI has ever come to passing the Turing test face to face.