Recent posts
What they write about when they write about running… I’ve recently finished Haruki Murakami’s What I Talk About When I Talk About Running, and had previous read (and written about) Nick Thompson’s The Running Ground: A Father, a Son, and the Simplest of Sports. There are many parallels between the two, but also differences. Here’s my take. - Both writers are seriously extreme runners. They’ve both run multiple marathons and completed 100k races. Thompson is probably somewhat more competitive, and holds the 50k American record for runners over 50, but I wouldn’t want to race Murakami. - Fun fact: Murakami runs while listening to music; Thompson prefers meditative silence. - Both have a separate day job, and are at the top of their game there (Murakami is a world-famous writer, and Thompson CEO of The Atlantic). - If it weren’t already obvious from their accomplishments in both running and their day jobs, the books make it clear how ambitious, disciplined, and hard working they are. - The reporting of their running exploits is similar in that it is factual. They report impressive accomplishments, but don’t brag (humbly or otherwise). Murakami does show a certain amount of self effacing, more than Thompson, perhaps reflecting the different cultural backgrounds. - They both draw lessons from, or parallels between, running and other facets of life. For Thompson this means mostly relating running ethics to work ethics. For Murakami it’s more relating running to existential questions of the human condition. Maybe this reflects the difference between a journalist and a fiction writer. - Murakami’s tone is philosophical. Thompson’s psychological. Indeed, a central theme in Thompson’s book (as reflected in the subtitle) is his relationship with his obviously talented, but equally obviously troubled, dad. Murakami mentions family (mostly his wife) only in passing. Thompson often mentions not only his dad but also his wife and kids. - So it’s surprising that the tone of Murakami’s writing is more extroverted than Thompson’s. Murakami often speaks directly about how he feels, whereas Thompson relays the most difficult personal situations (and some of them must have been excruciating) in a measured, matter-of-fact, almost third-person tone. He doesn’t hold back on facts, but the reader is invited to project their emotions on the situation. This again may reflect the different cultural backgrounds and upbringings. The upshot is that both books make for compelling reading, certainly for someone with an interest in running. I made a stronger connection with Thompson’s book, perhaps because of the personal story, and perhaps also because our orbits overlap; I know him personally and like him. But I enjoyed - and recommend - both.
No hype, just well-researched data, and responsible commentary.
Introducing the #AIIndex2026: Our most comprehensive, independently sourced data analysis of AI’s trajectory, with a clear-eyed assessment of the critical gaps that remain. As AI advances rapidly, can the systems built around it keep up? Explore the data: https://hai.stanford.edu/ai-index/2026-ai-index-report
It’s just getting better and better
🚀 Today we’re launching a brand new @relay! If you want an AI team that works for you, now’s the time to start. Here’s what makes our AI agents different: Anyone can create agents. You work with AI agents just like you work with people. You ask your agent to do things for you and give it feedback to get better. No code, JSON, terminal, or MCP needed. Agents are predictable and reliable. You teach your agent skills with simple prompts, and it turns those into easily understandable, consistent workflows. Plus, your agent can keep a human-in-the-loop for anything high stakes. No random actions you can’t explain. To try it out, head over to @relay and get started for free. I can’t wait to hear your feedback. p.s. like and RT to get a bonus code for 500 extra AI credits per month for a year. 🙏
Also renaming the company The Boring Company. Is that ok with you @elonmusk?
Boring AI Agents? They aren't players, they are workflow slayers: - Oliver: Humor in beta. Grounded in data. - Nancy: Dull in chats. Doesn’t invent facts. - Karen: Cold and distant. Highly consistent. In enterprise AI, boring is a feature not a flaw. 👉 See how we build predictable, accurate and consistent boring AI agents: https://t.co/Uu0dqTrX9g
Welcome aboard Russ, Carla, Virginia, Vipin, Elham and Dan. What a wonderful addition to the AI Index @indexingai Steering Committee! https://hai.stanford.edu/news/stanford-hais-ai-index-welcomes-six-new-steering-committee-members
Everyone talks about agentic flows, but few actually tackle the hard stuff to make them work. There’s no free lunch; prompt-and-pray is not a strategy. AI21 Maestro takes a different approach. See it in action on SWE-bench. https://www.ai21.com/blog/test-time-compute-swe-bench/
There’s a lot written about, and sw offered for, agentic workflows. Most of it is not deep. This is.
1/6 Long-horizon agentic tasks are breaking our mental models. More tokens, bigger models, and best-of-N only go so far. Orchestrated approach to test-time-compute scaling is what long-horizon tasks need. Here’s what we learned using SWE-bench as a test case. Read the blog for a deep dive: https://t.co/IcN9kWbMiO
Sometimes the important stuff is not the glamorous. Although this is actually quite sexy, beside being critical for the enterprise. Especially if you’re turned on by things like steerability and long context.
1/4 🚀Introducing Jamba2, a memory-efficient open source model family built for total enterprise reliability and steerability.
This does for MCP what two-phase commit did for DBs
1/4 MCP works great… until you run multiple subagents on the same task and they need to write files (not just read). Parallel reasoning is easy. Parallel writes are where things explode. 🧵
On-device LLM with 256k context window, low latency, and SOTA on benchmarks (with all the necessary caveats about the latter). You’re welcome. https://www.ai21.com/blog/introducing-jamba-reasoning-3b/
Just read this. Not sure why. If it were written in 2025 it would be silly. But it was 1943.
This one was particularly fun. Erik’s and Nick’s smarts are matched only by their clear communication style.
“AI and the Future of Work: From Hype to Real Impact” - we tackled this big question with some of the sharpest voices in #EnterpriseAI: ✨ @yshoham – Co-Founder & Co-CEO, @AI21Labs ✨ @erikbryn – Director, @DigEconLab ✨ @nxthompson – CEO, @TheAtlantic Key insights: 🔹 The reality of AI today vs. the hype 🔹 Why enterprises struggle to scale pilots 🔹 The future of human + AI collaboration 🎥 Watch the full replay 👇 https://t.co/MKohBVEgZJ
Really enjoyed the chat with Jason
Orchestrating Smarter AI Systems with AI21 Labs’ Yoav Shoham In this episode of AI Basics, @Jason sits down with Yoav Shoham (@yshoham) — Stanford professor emeritus and co-founder of @AI21Labs, creators of Jurassic-2, Wordtune, and the new orchestration system Maestro. • Why enterprise AI struggles with reliability • What orchestration really means (and why LLMs alone aren't enough) • The pitfalls of “agent-washing” • Small vs large models, agent-to-agent protocols, and where real opportunities lie This one is for founders building with AI — if you're navigating hallucinations, chasing automation, or exploring multi-agent workflows, this episode is a must.
Dinner at the Elysée Palace hosted by President @EmmanuelMacron to discuss AI strategy. His mobilizing of the ecosystem to support French AI should be studied by every national leader.
Always fun speaking with @Scobleizer - thanks for having me!
It's not every day I get to interview a former principal scientist who worked at Google, and is a Professor Emeritus at Stanford University, about the state of AI. But here we go. Introducing an hour with Yoav Shoham, @yshoham, AI pioneer and cofounder of @AI21Labs . This will make you smarter, not that all my videos aren't that way. :-) ++++++++++++++++++ Here's what we discussed (this part was written by Chat GPT after I gave it the transcript of the video): 🚀 The State of AI Today •The pace of AI development is unprecedented, likened to a “universal firehose” of innovation. •Everyone—from your plumber to enterprise CTOs—is using AI. But not all use cases are equal or enterprise-ready. 🏢 Enterprise vs Consumer AI •Enterprise adoption is still slow compared to consumer. Shoham cites AWS data showing only 6% of AI pilots go into production. •Enterprises demand reliability, cost control, and explainability, which raw LLMs like ChatGPT don’t fully offer out of the box. 🧱 Beyond the LLM Hype •Shoham explains that pure LLMs aren’t enough. Enterprises need “compound AI systems” or “AI agents” that: •Use tools like calculators for arithmetic instead of relying on the model •Integrate with company databases via RAG (retrieval-augmented generation) •Plan, reason, and execute tasks through orchestrated workflows •AI21 Labs built Maestro, their orchestration system, to do exactly this. 🔐 Enterprise Concerns •Enterprises worry about IP leakage, data privacy, and hallucinations. •AI21 addresses this by running models on-prem or in VPCs, ensuring data doesn’t leave customer control. 📉 Why Models Still Fail •LLMs generate “authoritative bullshit” — convincing but wrong answers. •Shoham says “prompt-and-pray” doesn’t work for serious business tasks. •Real-world enterprise deployments need robust evaluation frameworks, not just leaderboards. 📊 Case Study: French Retailer Auchan •Auchan deployed AI21’s system to automatically generate product descriptions—a clear ROI, but required careful iteration to build trust. 🧰 What’s Next in AI21’s R&D •Working on planning systems, action models, and ways to estimate cost/accuracy trade-offs before running tasks. •Focused on enterprise AI orchestration, not flashy multimodal generation. ⚠️ Agent Washing Warning •Shoham warns against the buzzword “agent” being overused. His advice: “Translate ‘AI agent’ to ‘software system that does X.’ If it still makes sense, keep going.” 🤖 The Human-AI Hybrid Future •Shoham sees a world of hybrid teams: humans and AI agents working together. •This transformation will affect everything from org charts to HR policies. •The AI-powered worker is scalable, reliable, and multilingual — changing customer service, operations, and more. 🗣️ Closing Thoughts •Enterprise leaders need to move beyond the fear and hype to start small, test carefully, and scale based on value. •“AI won’t replace humans,” Shoham says, “but humans using AI will replace those who don’t.”