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Once a year, Pat Grady and I sit down with our trusty AI collaborators 🤖 and zoom out to the big picture on what’s happening in Generative AI. Here’s our 3rd annual take… 1: The foundation model layer of Generative AI (large, pre-trained language models) is stabilizing around key players like OpenAI, Anthropic, Meta, Google DeepMind, and xAI. What felt like a dynamic and volatile market a year ago is now stabilizing. 2: The next frontier is the development of the reasoning layer. o1 🍓 represents a significant advancement in general reasoning capabilities achieved through inference-time compute. This was Generative AI’s AlphaGo moment, achieved by deep RL for the first time in a general setting. 3: There’s a new scaling law in town: the more inference-time compute given to a model, the better it reasons. With whispers of diminishing marginal returns in the pre-training world, it’s deeply exciting to be staring down the starting line of a promising new scaling law. 4: State of the art models, and the AI applications built on top, will shift from "thinking fast" (rapid responses from pre-training) to "thinking slow" (reasoning at inference time). 5: Better reasoning is finally unlocking the promise of agents. A new cohort of agentic applications is emerging across various sectors, expanding markets by reducing the marginal cost of delivering services. 6: These AI-native agent companies look different than their SaaS counterparts. Increasingly, AI companies are selling work outcomes rather than software licenses, targeting the multi-trillion dollar services market. Services-as-a-Software is the new SaaS (h/t Brian Halligan). 7: Two years ago, application level AI companies were derided as just a thin skin on top of a model. Now, it’s becoming clear that there is a ton for application builders to get right to bring value to end users, including engineering cognitive architectures (h/t Harrison Chase), systems design, and novel UX paradigms. 8: As investors, we are increasingly shifting our attention towards the application layer. Many exciting Sequoia Capital investments in applications from law (Harvey) to customer support (Sierra) to coding (Factory) to security (XBOW) to general knowledge work (Glean). What did we miss? What did we get right and wrong? What’s next that we should be keeping our eyes out for? DMs open! Full essay linked below. And thanks o1 for the assist 😏