We believe openness to be an advantage for AI safety. Openness provides more visibility into the behavior of models and, most importantly, greater means of turning safety research into actionable and transparent controls than closed-source. This is why Baseten and Base Labs are building a stronger safety and security standard for open models, with the launch of our safety infrastructure. Base Labs will develop and publish methods for training and monitoring open models, and Baseten will integrate that work into its deployment infrastructure, live at runtime, and offer this work as a managed service. This will be a standard that is transparent and built into how our models are trained and deployed. We invite the open-source community to contribute, and are proud to partner with @huggingface and @GoodfireAI to bring this vision to fruition. Together, we are building an ecosystem of open models that are safe and accessible to all.

🚀 Introducing DeepSeek-V4.1-Flash: smarter, faster, more efficient. 🔹 Introducing the smallest model in our new architecture family, with native visual understanding. 🔹 Designed for greater capability, faster inference, higher throughput, and scaling to larger models. 1/6

Today we're announcing Base Labs, a dedicated research organization focused on advancing open-source AI. We believe in a healthy, open frontier model ecosystem. To enable this, we are working on: - Blue-sky research on continual learning, the science of RL, and how models learn across their full lifecycle, with every experiment and recipe shared openly. - The BaseHub Data Foundry: the highest-quality open RL environments, training data, and real-world benchmarks, built for anyone to train and benchmark on. - Post-post training: taking open-source models and making them better, safer, and more aligned through continual post-training, built on our research, and deployed with our frontier safety stack so organizations can use open-source models with confidence. - Making models cheaper and more performant through our model performance research. This is a mission-driven research effort, not a commercial product. We believe the health of the open-source AI ecosystem matters and that the best way to advance it is to do science in the open. We’re hiring engineers, researchers, and research fellows to advance this mission.

Introducing Atlas: The world's first multimodal world model that generates image and video frames with pixel-perfect camera control and reconstructs them in 3D. Model the world, move the camera, and simulate space & time.

Most companies today are renting their intelligence a token at a time. That's @baseten @philipkiely's read, and it's the take from our conversation I wanted to highlight. AI equals @OpenAI, @AnthropicAI, @GeminiApp tokens flowing into whatever you're building. Which means the thing you're built on isn't yours. A price hike, a deprecated model, a rate limit you didn't see coming and your product breaks on someone else's schedule. You don't own your roadmap. You're leasing it. The companies Philip watches are done with that. Fine-tuning their own models, running open weights, owning their tokens end to end. The GPT wrapper era is over. He's blunt about it: cool in 2023, uncool by 2024, dead now. What replaces it is companies that own the thing they're built on. His other take:"I am not worried about an AI bubble." I didn't have to take his word for it. Since we filmed, the receipts came in. @Baseten raised a $1.5B Series F at a $13B valuation — their fourth raise in 18 months, inference volume up 40x in a year. The line in their announcement: Their customers are "more motivated than ever to own their intelligence." Same phrase Philip used with me on the pod. The companies that own their intelligence just got $1.5B of validation. The ones renting it got a deadline. 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Intro (02:30) What Inference Actually Is (05:00) Why You're Renting Your Intelligence (08:00) OpenEvidence & When Latency Means Lives (11:00) Writer Who Engineers, or Engineer Who Writes? (15:00) Joining Baseten Before ChatGPT Shipped (19:00) What He Got Wrong About Inference in 2022 (23:00) GPT Wrappers Are Dead. Good. (27:00) Writing 256 Pages in a Field That Moves Weekly (32:00) The Chapter to Read With the Most Skepticism (36:00) The 24 Hours After DeepSeek Drops (42:00) Why 8 GPUs Is the Magic Number (47:00) Rent vs. Own: Where the Break-Even Sits (51:00) "I Am Not Worried About an AI Bubble" (56:00) Every Wafer Gets a Chip Pressed Into It (01:00:00) Will NVIDIA's Moat Ever Crack? (01:05:00) Is There a Black Market for GPUs? (01:09:00) Inference Engineering as a Career (01:13:00) There Are Only Two Kinds of Models This is a @Composio "Agents at Work" podcast, where I chat with founders building the next leap of AI. Follow for more :)

Here's my conversation with Philip Kiely. A deep dive into inference engineering, his book his available online for free, and it's worth reading. Inference is becoming more important to people and organisations by the day, learning about the topic will prepare you well.