Karpathy's autoresearch broke the internet
A 24-minute solo breakdown of the AI experiment-loop tool that went viral — and 10 businesses you can build on top of it.
March 11thA step-by-step playbook for building and selling AI agents as done-for-you labor instead of software seats.
Selling AI agents as completed work instead of software seats taps a market bigger than SaaS ever was, because the buyer is paying for labor, not licenses.
The video argues that agent-based businesses are a bigger opportunity than the SaaS era because they sell completed work, not software licenses, tapping the multi-trillion-dollar labor market instead of just software budgets. It lays out a seven-step playbook: find a workflow that already has a paycheck attached to it, shadow the human who currently does the job before building anything, ship the smallest useful agent (draft-and-approve, triage, coordinator, or bounded-action), wrap it in a trust layer of logs and approvals that makes it feel like software, sell it manually as a pilot before productizing it, price it like labor with a setup fee plus a simple recurring fee, and grow through workflow-teardown content that contrasts the old broken process with the new agent-run one. It closes with a concrete 30-day, zero-to-pilot execution plan.
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The host states the thesis that agents are the new SaaS, argues the labor market makes the opportunity bigger than software alone, and previews the full playbook: niche, workflow, first agent, proof, packaging, and selling agents as labor.

Frames the core mental model: SaaS sells software, agent SaaS sells work. Uses a restaurant phone example (Slang AI) and a home-services dispatch example (Sameday) to show agents replacing a specific job rather than providing a tool.

Lays out five traits of a good agent workflow (frequent, clear finish line, touches existing software, edge cases are learnable, buyer feels the loss) and a scoring method for picking a workflow inside a chosen niche.

Argues founders should shadow 10-20 runs of a human doing the job before building anything, extracting trigger/context/tools/rules/escalation/success criteria from the real (not assumed) workflow.

Introduces the minimum useful agent (MUA) concept and its four starting shapes — draft-and-approve, triage, coordinator, bounded action — citing Anthropic's guidance that agent problems should start as predictable workflows.

Explains that the trust wrapper — logs, approvals, controls, and a way to test the agent before going live — is what separates a real agent SaaS product from a plain automation script, plus the role of a 50-example eval set.

Describes selling a manual, AI-assisted pilot to three customers in one niche before productizing, with sample pricing structures (setup fee plus flat monthly, or setup plus per-outcome pricing).

Covers distribution via workflow-teardown content contrasting the old broken process against the agent-run process, and recaps the zero-to-100 sequence in one slide.

Gives a concrete day-by-day plan: pick niche, interview operators, pick workflow, write spec, run manually, build MUA, build eval set in week one; sell two pilots in week two; add the product wrapper in week three; publish teardown content in week four.

Closes by restating that the opportunity is finding the smallest painful, repeating workflow in a niche you understand and making it disappear, and invites comments on what to cover next.
Buyers pay more readily for a job that disappears than for a new tool to learn, so the highest-leverage AI businesses package agents as completed labor and only build the software wrapper once a manual pilot proves the work.
“Building agents is the new SaaS.”
“SaaS sells software. Agent SaaS sells work.”
“The agent does the work, but the wrapper creates the trust.”
“You want to be in the business of selling painkillers, not vitamins.”
“Start with the job. Then build the agent people pay for.”
See every word as it's spoken — crank it to 2× and still catch all of it. The same dual-channel trick behind Amazon's Kindle + Audible.
The host opens by declaring building AI agents the successor to the SaaS boom, then spends the episode turning that claim into a concrete seven-step playbook for finding, building, pricing, and selling an agent business.
A scoring checklist for picking which workflow inside a niche is worth building an agent for.
A specification checklist to fill out before building any agent, used to avoid building 'agent slop.'
Four increasingly autonomous starting points for a first agent build, in order of how much trust/autonomy each requires.
The full end-to-end sequence the episode is structured around.
“let me know what you want me to cover next... and subscribe if you want more of this stuff in your feed”
Soft, low-pressure close asking for comments and a subscribe, no hard product pitch — consistent with the video being free educational content for the podcast's audience.
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25:53A 24-minute solo breakdown of the AI experiment-loop tool that went viral — and 10 businesses you can build on top of it.
March 11thA 30-minute screenshare where boring arbitrage ideas become cash-flowing businesses with a few prompts and a Slack webhook.
May 11thHowie Liu, co-founder of Airtable, walks through the macro case for the agent economy and then live-demos HyperAgent — a cloud-native, UX-first agent platform built for running a fleet of digital employees.
April 29thA 54-minute live demo where Cody Schneider runs seven AI agents simultaneously to build a full GTM machine — ads, outreach, cold email, data analysis — with Greg Isenberg watching.
March 2ndA 64-minute masterclass where a Codex true believer converts a Claude Code skeptic, live, on camera.
April 27thAlex Finn walks through every surface of the new Hermes Desktop app and shares the session management insight that turns a $1,000/month bill into almost nothing.
June 6th