The LangSmith Signal: Azure's share of OpenAI traffic grew nearly 4x in under 3 months. We're sharing how devs are building agents, by the numbers. While most orgs started by connecting directly to OpenAI, over the past 10 weeks we've watched Azure's share of that traffic grow from 8% to 29%. We've analyzed this trend via LangSmith Observability data across more than 6.7 billion agent runs. Our hypothesis: 💡 Early adopters moved fast and went direct, but the enterprise wave is now arriving in force 💡 Azure gives teams the compliance, security, and procurement infrastructure they already have in place 💡 Azure traffic 4x-ing in 10 weeks likely indicates AI development is maturing quickly
Yeah, I agree. Plus two years from now Robotaxis from a number of companies, including Tesla, will be in many neighborhoods. I seriously doubt I'll buy another car to put in my garage again. Waymo customers tell me that they never will buy one again.
Totally. Been interviewing Waymo customers and they love them. Getting three rides in one changes them deeply. Many say that before they got that experience they thought they "loved to drive" or "could never trust a computer to drive." After? They say: 1. I'm never going
Here we go: Gamma 4 released: ""Outperforms models 20x its size" Google dropped Gemma 4 under Apache 2.0, full open-source, big licensing shift. Built on Gemini 3 tech, four sizes: E2B, E4B, 26B MoE, 31B Dense. Price-performance: 31B is #3 open model on Arena AI, 26B MoE is #6
Gemma4 is amazing. You'll read that everywhere. Let's focus on what is HUGE here: the revenge of dense models…. Throw away your b200, not needed anymore, throw away the millions of lines of code we had to write to make MOEs faster, training stable etc… throw away your router-aware kernel, your EP DEEP GEMM, throw away the auxiliary loss function. Welcome to simplicity, dense is the new king. FINALLY hating MoEs is back to being chad. For those who know me: I was always a moe doomer
Talk about moving goalposts. This one from MSFT’s Suleyman may well take the cake. “Superintelligence” just went from intelligence beyond all humans to merely “delivering product value”.
A first-of-its-kind study from MIT measures how successful AI is in completing thousands of tasks done by workers in the US economy. Across these real-world tasks, they found that AI capabilities are improving quickly, but performance is rising smoothly: bit.ly/3Q1JQZD [Translated from EN to English]
Totally. And my autonomous car is feeling normal. I noticed that by interviewing Waymo customers. They see it as normal life now. One guy has taken one to work every day for two years.
What do successful deployments of AI have in common? It was awesome working with Elisa Pereira and @AGraylin on this research. We studied 51 companies and summarized the results. Alvin has a nice summary below. Check out digitaleconomy.stanford.edu/… for the full report. Alvin Wang Graylin (@AGraylin) New Research💡: “The #Enterprise #AI Playbook — Lessons from 51 Successful Deployments” Excited to share new research from Stanford @DigEconLab, I co-authored with @erikbryn and Elisa Pereira . We spent 5 months interviewing executives across 41 organizations, 9 industries, and 7 countries — focusing exclusively on AI deployments that actually delivered measurable value. Not hype. Not predictions. What’s working right now, and why. A few findings that challenged even our assumptions: The hard part isn’t the AI. 77% of the toughest challenges were invisible costs — change management, data quality, process redesign. Technology was consistently described as the easiest part. Same use case, wildly different timelines. One company deployed AI customer support in weeks. Another took years. Same models. The difference was always the #organization — its #leadership, processes, and willingness to fail. #Agentic AI works — but most firms haven’t tried it yet. Only 20% of our cases were agentic, but they delivered 71% median gains vs. 40% for high-automation. This gap will widen fast. The model is increasingly a #commodity. For 42% of implementations, model choice was fully interchangeable. The durable advantage is in orchestration, data, and process — not the foundation model. With productivity increase, headcount #reduction is common (45%), but not the majority outcome. Redeployment, hiring avoidance, and acceleration strategies accounted for 55% of cases.🚨 The window for experimentation is closing. This is no longer a question of whether AI delivers value. It’s whether organizations can evolve fast enough to capture it — and whether leaders will take responsibility for smoothing the transition for workers and communities along the way. Full report (free): digitaleconomy.stanford.edu/… @StanfordHAI — https://nitter.net/AGraylin/status/2039729157676921185#m
