@01Advisors
Today we’re announcing that OpenAI will acquire @pierrecomputer to accelerate our vision for making Codex a place where teams can build software together with AI, from first idea to the version they ship.View quoted post on X
Pierre’s open-source developer tools already power parts of Codex today. Together, we’ll build on that foundation for the agentic era.
openai.com/index/openai-t…
Baseten’s day 0 bet was that inference was the technology that would enable the best user experiences AI could deliver–fast, smart, reliable, secure. And that those experiences would rely not only on a handful of giant general intelligence models, but millions of specialized models built by companies for their specific customers and use cases.View quoted post on X
Whether you’re a doctor, developer, lawyer, mechanic, researcher, construction worker, marketer, etc, you’re accelerated by specialized tools worthy of your craft. To me, this is one of the most meaningful promises AI can deliver on.
We’re starting to see it now. Many of the main-character AI companies on the application layer are built on highly-specialized models for highly-specialized workflows–Abridge, Clay, Cursor, OpenEvidence, Hebbia, Mercor, Notion–these businesses are booming because customers love specialized tools.
There are probably hundreds of custom models in production today. Soon, there will be thousands and then millions. All enabled by a high-performing inference layer.
Inference has emerged as one of the hardest problems in modern AI systems. Delivering reliable, low-latency experiences requires deep coordination across distributed infrastructure, kernel-level performance, and software ergonomics—even world-class teams struggle to do this well. As a result, as consumers and developers, we’ve grown to accept sluggish performance, frequent downtime, and inconsistent quality across both application companies and model providers.
Meanwhile, the demands on inference are accelerating: AI adoption is trending towards ubiquity with reasoning models that are orders of magnitude more compute-intensive. This will only increase as more companies catch on to the virtues of owning their end-to-end IP rather than relying on black-box model APIs on shared infrastructure. Whether we can realize the impact of this generational shift will depend on our ability to serve these models reliably at scale.
We knew we could make the technology work, but the biggest delight of it all has been seeing what our customers do with it. The (many-model) future is bright.
fortune.com Term Sheet Next: How Facebook’s former chief revenue officer is coaching the next generation of... Why 01 Advisors partner David Fischer went from Google and Facebook to venture investor. the shortcut for the LLM age: cmd + option + cView quoted post on X
copies the full details of the @linear issue & docs, including comments and customer requests, which you can use as a context for your LLM workflows
