Serial Founder: Stop Using AI Like Google! Get It to Do Real Work

Serial founder and Crazy Egg CEO Hiten Shah thinks most people are still using AI like Google: they type a short request, accept the first answer, and stop. His alternative is to give AI real context, show it what good work looks like, and let specialized bots carry a job further—while a person keeps the final judgment. In this episode, Michael and Hiten compare the friction of earlier agent setups with Grok Bot, which gives each bot a computer it can use. Hiten explains how he connects research, product-marketing, and librarian bots; uses private GitHub repositories as shared context; and limits risky actions with read-only access, explicit constraints, and approval steps. The examples are concrete. Hiten’s bots made 1,800 GitHub commits in a day before he corrected the behavior and set a limit. In another workflow, a local AI system reproduced a desktop-app bug, gathered screenshots and video, debugged the problem, and prepared a pull request for human review. In this episode: - Why Hiten calls today’s shallow AI use “the Google problem” - What changes when a bot has its own computer instead of only a chat box - How a research bot, product marketer, and librarian can work as a team - Why Hiten stores bot-created context and artifacts in private GitHub repositories - How he handles access, permissions, read-only connections, and approval gates - Why he does not use one chief-of-staff bot as a bottleneck - How to get better output by showing AI examples of what good looks like - Why human judgment remains the final mile before anything is published or shipped - How local models can support QA and move work from a bug report to a reviewed pull request - The product question Hiten now asks repeatedly: “If we were to build it today, what would we build?” - Why distribution and go-to-market may need to shape the product from the beginning Try one low-risk experiment: choose a task you already understand, give AI the relevant context and one strong example, constrain what it can change, and review the result before it acts publicly. About Already Here: Michael meets people already living with personal AI in everyday life. We look at the tools, but the real story is what changes in how work is assigned, how decisions are made, and where people spend their attention. Subscribe for more conversations with people already living with personal AI. Then comment with one task you are still treating like a search—and what it would look like to delegate the whole job. Official links mentioned in this episode: Hiten Shah: https://www.hiten.com/ Crazy Egg: https://www.crazyegg.com/ Typeahead: https://www.typeahead.ai/ Grok Bot: https://x.ai/bot Cursor: https://www.cursor.com/ OpenClaw: https://openclaw.ai/ Hermes Agent: https://github.com/NousResearch/hermes-agent Last 30 Days: https://github.com/mvanhorn/last30days-skill GitHub: https://github.com/ ChatGPT: https://chatgpt.com/ Claude: https://claude.ai/ Codex: https://openai.com/codex/ Granola: https://www.granola.ai/