Making $$ with AI Agents
Limited BONUS: First 1,000 builders get $1,000. Claim yours while supplies lasts.: https://startup-ideas-pod.link/hyperagent I sit down with Howie Liu, co-founder and CEO of Airtable, to talk about the agent economy and the launch of HyperAgent. We walk through Sequoia's charts on AI agent deployment, the economics of token-based work versus human labor, and why frontier agents have crossed a threshold that changes how companies get built. Howie then does a live show-and-tell of HyperAgent, including a custom "Greg Isenberg contrarian AI" skill he spins up in real time. This one is for anyone building a solopreneur business, operating a fleet of agents, or trying to figure out where to place their bet in the agent ecosystem If you want more workflows and tactics to build a business with AI, check out this free workshop: https://www.ideabrowser.com/workshop Timestamps 00:00 – Intro 02:22 – Sequoia's AI agent deployment chart reaction 04:41 – Copilot vs Autopilot territory and the $1T+ opportunity 08:13 – Agent economics vs human labor costs 11:12 – Fastest enterprise adoption curve in history 14:48 – The agent command center and fleet of 20 agents 18:03 – What is HyperAgent? 19:43 – Live demo: hyperlocal real estate market reports 22:38 – HyperAgent as the founder, not just the developer 23:21 – Street View, Zillow redesigns, and visual tool power 24:15 – Command center view across a fleet of agents 25:48 – Skills as the key primitive for frontier agents 26:30 – Building the Greg Isenberg contrarian AI skill live 32:31 – HyperAgent vs Perplexity Computer, Manus, OpenClaw, Codex 34:52 – Reviewing writing skill 36:55 – The arbitrage of persistence 41:31 – Confidence milestones: first dollar, $10K/month 35:27 – Reviewing contrarian tweet drafts live 45:05 – Giving the agent feedback and building rubrics 50:15 – Connectors, OAuth, and building custom API skills 53:03 – How to get started with HyperAgent 01:01:54 – Credit giveaway for listeners 01:03:31 – Closing Thoughts Key Points * Frontier agents have crossed a threshold in the last 4–5 months where they function as true autonomous coworkers, not just chat assistants. * Reframe agent cost by value delivered: a $150 token spend for a board memo beats hours of human time, so anchor on opportunity cost. * The real arbitrage is persistence: 99% of people quit after one shot, while daily practice for 30/60/90 days produces top 1% operators. * Skills are the most important primitive in frontier agents, turning generally intelligent models into domain experts through playbooks. * HyperAgent's differentiation is a low floor plus a high ceiling, with rubrics, LLM-as-judge evals, and fleet-wide observability for scaling. * Aim for $100B companies with under 5 employees, built on fleets of always-on agents mapped to human job roles Section Summaries 1. The Under-Penetration of AI I open with Sequoia's chart showing software engineering at nearly 50% agent deployment and most other categories in single digits. Howie argues even 50% understates the shift, because frontier teams now run dozens of Claude Code instances in parallel with full autonomy, while most industries are still catching up to three-year-old state of the art 2. Copilot vs Autopilot and the Agent Economy We dig into the trillion-dollar agent opportunity Sequoia flagged. Howie frames the real TAM as the entire white-collar GDP across the Western hemisphere, arguing the unlock happened with Opus 4.5 roughly four to five months ago when agents started shipping clean PRs on multi-hour tasks autonomously 3. HyperAgent Show and Tell Howie walks through HyperAgent live, starting with a hyperlocal real estate market report generated from one of my open-source startup ideas. The agent did market research, Reddit validation, competitive analysis, a V1 app build, a marketing site, and ad creative in a single workflow. 4. Skills, Rubrics, and Fleet Management Howie builds a "Greg Isenberg contrarian AI" skill in real time, letting HyperAgent research my voice and distill it into a reusable primitive. He then explains rubrics, essentially eval rubrics pinned to agents, with a separate LLM as judge scoring outputs on dimensions that matter. This is the observability layer that lets you actually run a business on a fleet of agents The #1 tool to find startup ideas/trends - https://www.ideabrowser.com/ LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND HOWIE ON SOCIAL X/Twitter: https://x.com/howietl Hyperagent: https://www.hyperagent.com/ Airtable: https://www.airtable.com/-
