Sources
Show notes: my writeups of the talks, interviews, and videos behind the essays. Not essays themselves, just the source material, distilled and weighted.
Show notes: The hottest AI job is an old job, renamed
Jean Lee, twenty years in tech, on whether forward deployed engineer is really the highest-paid job in AI or a familiar pattern: a hot new title that is mostly an old customer-facing role repackaged, with the word engineer chosen to pull builders into work they would otherwise refuse, and a durable skill hiding under a temporary name.
Show notes: Do things that don't scale, at scale
Bob McGrew, who helped invent the forward deployed engineer model at Palantir and later ran research at OpenAI, gives the operator's playbook: why the strategy is a last resort not a default, the gravel road the engineer lays and the superhighway the product team paves, the two team roles, the margin curve that proves it isn't consulting, why AI agents have no incumbent to copy, how to price an outcome, and why the real opportunity is the gap between what AI can do and what anyone has adopted.
Show notes: One title, four companies, four different jobs
A South Park Commons panel with forward deployed leaders from Ramp, Nominal, Dataland, and OpenAI shows the same title meaning four different jobs: Ramp's defensive sword and shield, Nominal and Dataland's bespoke agent work, OpenAI's frontier push. Where four very different companies independently agree is the real signal, and where they disagree, on whether AI collapses the role into one person or splits it into specialists, is the live question.
Show notes: The title means nothing, and that is the point
Natalie Meurer of Sierra, a Palantir forward deployed engineer from the origin years, gives the dirty secret: the label has been stretched to cover so many jobs that it means nothing, the real work began as 2 a.m. DevOps babysitting not helicopter heroics, and the jobs to be done only ever accumulated. What survives when the title dissolves is a single invariant, accountability to the customer, and as code gets cheap that invariant is spreading until nearly everyone becomes a forward deployed engineer.
Show notes: A consultant who can actually build
Soleyman, an independent tech consultant, gives the plainest working definition of the forward deployed engineer: a consultant who can actually engineer and build, sitting in the gap between a slick AI demo and a working system inside a messy real business. The value here is the how-to: the six-step job from understanding the real problem to feeding the learning back into the product, the skill stack it demands, and the career advice to build the capability, not chase the title.
Show notes: The hard part is not the build, it is the trust
Colin Jarvis, who runs forward deployed engineering at OpenAI, gives the operator's field report from a frontier lab: trust, not capability, is the binding constraint, the discipline is determinism-first with evals as the starting artifact, one customer's fix becomes an open-source primitive and then a shipped product, and the whole function is run as a research feeder that can refuse services money because OpenAI monetizes the model elsewhere.
Show notes: Forward Deployed Engineering 101
Kevin Bai (Anthropic, ex-Palantir, founding Rippling FDE) on the forward-deployed model: sell the outcome not the software, the one quadrant that actually needs it, a design partnership scaled to the enterprise, the platform that keeps it from becoming a dev shop, and why every agentic product in 2026 quietly puts its customers in Palantir's old shoes.
Show notes: Who's afraid of Chinese models?
Ben Thompson's case that the Kimi and Qwen panic is mostly an economics misread. Marginal costs are back, intelligence is the commodity not tokens, and today's prices are a compute-shortage umbrella. The one real fear is cybersecurity.
Show notes: Agents are the new SaaS
Greg Isenberg's playbook for building an agent business: sell the job done, not the tool. Find a workflow with a paycheck, shadow the human, ship the smallest useful agent, prove it with evals, then productize the pilot.
Show notes: Why AI won't make you rich in 2026
Alex Hormozi on why AI-maxing businesses are not making more money: AI raised capacity, not judgment, and it did not cancel any other form of leverage.
Show notes: What SaaS buyers actually want in 2026
Rob Walling and Einar Vollset on the five moats private equity now requires before they will even look at a SaaS company.
Hormozi's AI test: are you making more money?
Alex Hormozi spent two hours on The Diary of a CEO arguing that founders are misallocating the AI boom: wrapper companies the models will eat, years of an $11,000 bill spent automating a non-constraint, and 'token maxing' that never touches the P&L. His one-question test, where he says value survives when intelligence is cheap, and the deliberately unglamorous program he prescribes instead, with his frameworks drawn out.
Reinforcing clip: Hormozi, AI does dumb things faster
A 3-minute MoreMozi clip that restates the two-post Hormozi thesis in sharper form. Not its own post: woven into the existing pair as independent corroboration. Where each line landed.
Show notes: Hormozi on DOAC, the AI misallocation episode
Business and AI themes only from Alex Hormozi on The Diary of a CEO (July 2026): the stop-chasing-AI thesis, reality as the moat, the value equation, pricing, talent-as-funnel, and the numbers he cites, all labeled as his claims. Personal segments excluded by design.
Doing the wrong things, faster
Alex Hormozi asked his wealthiest friends how much AI they actually use. Almost none, it turned out, and their businesses keep compounding anyway. The gap between token bills and profits has an old explanation: AI raised everyone's capacity to work, and capacity without judgment flows straight into the backlog of things that never deserved doing.