YC is literally telling where are they going to deploy money 1. Company Brain: turn company rules into skills AI agents can run 2. Software for Agents: build for agents, not humans 3. AI-native services: sell the work, not the tool 4. SaaS Challengers: rebuild legacy software AI-native 5. Inference chips: silicon built for agent workloads 6. Counter-swarm defense: stop drone swarms cheaply 7. Chip supply chain 2.0: modern tooling for semiconductor logistics 8. AI for farming: fewer pesticides, more food Which one are you building? ycombinator.com/rfs

Every company can rent the same frontier models, so the edge comes from what a company's agents learn about its own business. Databricks' answer is a system of record built for agents. Before an agent can be trusted with real work, that record has to tell it: 1. What's true: the company's own data 2. What it means: how the business defines things (grounding agents in it raised accuracy from about 50% to 84.5% in Databricks' testing) 3. What it's allowed to do: an identity, a budget, and guardrails 4. Whether it worked: grading against the company's own definition of good Feed every correction back into the record, and the company keeps what it learns even when the model changes. "If you replace the AI model at your company and the new one inherits your definitions, permissions, evaluated cases, customer history, and hard-won exceptions, that is a foundation for a sustainable competitive advantage." From the latest Profiles of the Future by @bhorowitz and @mikemcg0 on Databricks & the Future of the Firm: a16z.news/p/databricks-and-t…

This is correct. The vast majority of enterprise workflows have not actually had agents applied to them in any meaningful way. The economy probably has this deployed < 1% right now. At best you have a few automated systems in a company that do a single pass of an LLM to make a decision or provide feedback, but very little real “automation”. Some of this is a statement about the adoption rates of technology in enterprises, but the vast majority of it is really just due to the capability of AI itself. That’s all changing. Now with computer use, long running agents, harness improvements and surrounding infrastructure, we’re finally at a point where we can start to apply agents at more complex workflows. We will see self-improving systems that can take any particular workflow goal and improve the execution rates over a volume of instances. And with the emerging form factor of agent swarms, you can just choose to deploy any amount of compute at a problem and agents will off and do the work. This won’t be about and users prompting agents to complete a specific task for them, but instead the organization reengineering the underlying workflows to take advantage of agents in their processes. This will have a massive impact in finance, life sciences, healthcare, and other information-heavy domains. Lots of opportunity to build and deploy these systems right now.

Si hoy empezara a desarrollar con agentes, haría esto. Lo he aprendido a base de equivocarme durante meses con proyectos reales en producción. Guárdalo, que es largo 🧵

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We believe by the end of 2030, every Fortune 500 will be a Self-Driving Company. That doesn’t mean AI replaces people. It means people remain in the driver's seat leading judgment, creativity and taste, while agents fuel execution. This is the Self-Driving Company. replit.com/blog/rise-of-self…

We need to bring back that unexpected edits trend 😭😭😭