Pedro Franceschi (co-founder and CEO of Brex) on Peter Yang’s channel — 48 minutes, two live demos: Brex’s AI recruiting employee, and the OpenClaw harness Pedro runs his own life on. All data shown is Brex demo data.

Build employees, not agents

  • The framing: don’t build an open-ended agent that could do a million things — build a virtual employee with a specific job. The test is whether it behaves, looks and feels like a real person.
  • Jim, Brex’s AI recruiter: sources candidates, filters all inbound, and serves as the recruiting analytics layer. Running since February.
  • Three-stage pipeline: sync Greenhouse (the ATS) → ingest candidates (resume, LinkedIn profile, GitHub data) → evaluate every candidate against role-specific criteria.
  • Recruiters talk to Jim in Slack — “which role has the highest average candidate score?”, “top 30 applicants for this role” — and it posts standout candidates with links. Pedro’s point: it behaves like a coworker.
  • A self-improvement loop: Jim proposes things to build that would automate more of the process, then you implement them as cron jobs and skills.
  • “The harness doesn’t really matter as much as just the skills.”
  • The thing most teams never build: agreement-rate measurement between humans and the agent — candidates a recruiter passed that Jim rejected, standouts Jim flagged that no recruiter reached out to.

Why the PM playbook changed

  • Pedro’s tweet: the PM playbook of writing PRDs and aligning stakeholders is dead. His argument: the quality of a product org is how fast it compresses signal → ship, and AI eats most of the ingest-and-digest half.
  • “PM as mini CEO” is out at Brex; engineering and design craft sit on equal footing with PM.
  • The roles are conflating: PMs write code, designers build, engineers make calls that used to be PM territory.
  • What PMs do now: ingest six months of Gong calls, unearth insights from unstructured data, validate with cheap prototypes — so much conviction accumulates that you ship the right thing first.
  • Because building is cheap, discernment matters more than ever: pick the one true lever, minimize surface area, go deep on one thing rather than shipping five or ten.

Half the job is reviewing work

  • The case-law model: leadership review is the Supreme Court that sets the standard, then reviews cascade down to the IC building the thing.
  • Two review types at Brex: problem alignment (which problem is worth solving) and solution alignment (is this the right fix).
  • Brex keeps a repo of PM skills so a review arrives already digested — humans then spend time only on what is idiosyncratic to that problem.
  • Standing questions: what were the alternatives, what is the actual bottleneck, how does this create more throughput on it.
  • “We manage the work, not the people, and we manage people around the work.”
  • Hiring: the interview is a builder loop — you have to build something with AI proficiency. Open source presence is a positive signal; its absence is not a negative.

CrabTrap: securing agents at the network boundary

  • Brex open-sourced CrabTrap, an LLM-as-a-judge HTTP/HTTPS proxy that sits around the agent and controls all its network traffic (brexhq/CrabTrap).
  • Reasoning: if the agent can run code — and it should — it can always cross a network boundary. Constraining tools alone doesn’t hold.
  • Every request hits static rules first (URL prefix/exact/glob → immediate decision, no LLM call); anything not matched goes through a policy prompt evaluated by an LLM acting as judge.
  • You don’t have to write the policy: CrabTrap replays observed traffic, summarizes it with a model, and proposes the ruleset (~30 minutes to run).
  • This is semantic, not allow-list: it can research candidates on GitHub but not push to repos; it can’t delete repos or change Okta configs. Granola was blocked — the agent could reason about why.
  • Cost of a block: about 1,000 tokens in, 104 out, ~2 seconds on Sonnet. Audit-trail mode shows every request live.

Magpie: the token bill is coming

  • Brex built Magpie for AI cost visibility, split into three pillars: corporate AI (internal productivity), operational AI (serving customers, automating processes), product AI (shipped features).
  • The point is granularity by caller: transaction tagging costs $0.10 a call ($28k/month), disputes ~$2 a call — a 240x spread. Plotting cost-per-call against call volume shows what actually changed.
  • You can see which harnesses dominate (Claude Code first, then Codex, Cursor), cost per customer, per employee, and who is tokenmaxxing.
  • Pedro’s sequencing advice: let people go deep first, optimize later. Brex gives unlimited token budgets with a few caps, then surgically attacks low-ROI usage — caching, or moving work to cheaper models.
  • The reconciliation angle: Brex is the card and the rail, so usage data can be matched against the dollars that actually clear the bank account. This becomes a Brex product.
  • Pricing is shifting from per-seat to per-token, including products that merely wrap tokens (Cursor) — so even an all-Anthropic shop has token spend scattered everywhere.

We are still early — the 2,500-box chart

  • Pedro’s favorite slide: the world in 2,500 boxes, each dot 3.2 million people. Red = never used AI; green = free chat; orange = paying $20/month; and a tiny box = people using agents effectively.
  • His thesis on jobs: role conflation, everybody a builder, a market for 10x employees — raise the floor and the ceiling at once, with the ceiling rising far more.
  • Dispersion in productivity is normal (10x engineers); AI widens it while lifting the baseline.
  • Why shouldn’t a company founded today be you, a big token budget, and agents?

Autopilot: the harness that runs his life

  • Pedro’s personal system, Autopilot, is an OpenClaw harness with two building blocks — people and programs — plus signals: Slack messages, email, meeting notes.
  • A signal collector runs on a schedule; an aggregate job then goes through every person and program and updates the markdown files (status, blockers, risks, action items).
  • Drafts appear automatically from signal injection: a Slack DM asking about a conference became a task to decide on it, with the DM attached as context.
  • Everything is markdown files committed to a repo, with scripts generating searchable and editable UIs; an interaction log traces each task back to the signal that created it.
  • On OpenClaw reliability: “an acquired taste… definitely takes a bunch of time to do it well,” but worth it.

Selling the work, not the tool

  • The SaaS subscription era is blurring: the currency changed to tokens, but you’re still paid for solving a problem — you now charge a markup on tokens instead of a seat.
  • The bar for a good AI product is higher, because you can sell the work itself rather than the software.
  • “The reality of all good AI products is they’re all the same thing: an agentic loop and a measure of tools.”

“You expose the tools to the model, you run it on an agentic loop, and just let it do its thing.”