Daily Brief

Full episode: https://www.youtube.com/watch?v=QbdbAhaJoCQ Me on twitter: https://x.com/dwarkesh_sp

Full episode: https://www.youtube.com/watch?v=OS1NZLgKM2c Me on twitter: https://x.com/dwarkesh_sp

Decagon builds the customer support agent that answers when you email a brand, and making that agent good for a specific enterprise is the forward deployed job. It splits in two: one side configures the agent brain, the instructions and the handoff rules for when a human should take over; the other works like product engineering, figuring out what a new enterprise needs and anticipating the requests that have not been made yet. Sunny Rekhi's point is that the line between forward deployed and product is thin, because a customer ask is often just a product feature waiting to be built. What cha

At Palantir, forward deployed started as a literal description: you were deployed, physically, at the customer's site, and the onboarding project was keeping the platform from falling over. Natalie Meurer's dirty secret is that the title never settled after that. It stretched across DevOps, data integration, ontology work in Slate and then Foundry, and solution architecture, until forward deployed engineering meant so many different jobs that the label stopped meaning much. Every company hiring for it, whether they call it forward deployed, customer, or deployed engineering, is describing a sl

It's Friday night, an enterprise sales rep needs an SAP S4 HANA integration to hit quota, and the reflexive Forward Deployed answer is yes. Leo Mehr's first principle is to pause instead: always be scoping. Saying yes to everything buries the team and often does not even serve the customer, so the job is to weigh what actually matters against the rest of the queue and decide with that context. Ramp's FDE function looks different from its Palantir origins, pointed at enterprise customers, but the discipline is the same: scope hard before you roll up your sleeves and ship. The second half is wh

Most of the coding agent market quietly optimizes for token usage; Cognition's deployed engineering team measures the opposite, the outcomes a customer can actually see, and reports something like an 82% reduction on the work they targeted. Jia Wu's argument is that you measure before Devin ever lands, then again once it is fully activated inside the customer, so the value is a real delta and not a vanity number. The way that value shows up is not linear: one team using the agent is a step function, a whole enterprise using it is parabolic, because the products Cognition builds and the custome

From building no-code products at Airtable to leading Core Product Engineering at OpenAI, Akshay Nathan has spent much of his career trying to make the power of software accessible to people who do not write code. In this episode, Akshay joins swyx and Vibhu to unpack the launch of ChatGPT Work, why Codex unexpectedly took off among nondevelopers inside OpenAI, and the company’s broader plan to bring useful agents from software engineers to knowledge workers and eventually everyone. We go deep on the shared agent harness behind Codex and ChatGPT Work, why OpenAI brought the experiences togeth