Y CombinatorHow To Get AI Startup Ideas
Garry Tan on from Hackathon Hacks To Billion-Dollar AI: Finding Real Ideas.
In this episode of Y Combinator, featuring Garry Tan and Harj Taggar, How To Get AI Startup Ideas explores from Hackathon Hacks To Billion-Dollar AI: Finding Real Ideas The hosts of the Lightcone Podcast explain how aspiring founders can systematically generate strong AI startup ideas by going beyond shallow, hackathon-style projects and instead pursuing hard, ambitious problems.
From Hackathon Hacks To Billion-Dollar AI: Finding Real Ideas
The hosts of the Lightcone Podcast explain how aspiring founders can systematically generate strong AI startup ideas by going beyond shallow, hackathon-style projects and instead pursuing hard, ambitious problems.
They emphasize two main paths: looking deeply within your own past work, internships, and skills for unique founder–market fit, and aggressively going outside your comfort zone to embed yourself in real-world industries and workflows.
Through many concrete YC portfolio examples, they show how founders uncovered valuable ideas by leveraging specialized experience, following cutting-edge technology, shadowing or working in tedious jobs, and serving overlooked niches now made viable by AI.
They argue that despite apparent competition and investor skepticism, this is an unusually good time to persist in AI startups, because capabilities and opportunities are expanding so quickly.
Key Takeaways
Start from genuine expertise or experience, not from what’s trendy.
Many of the strongest AI ideas came from founders who had spent years at the edge of a domain (e. ...
If you lack expertise, deliberately go get it in the real world.
Founders created great companies by shadowing relatives, working a day in a dentist’s office, getting a medical billing job, or closely observing friends’ boring jobs, then using AI to automate the painful workflows they saw.
Treat early exploration like research, not short-term revenue chasing.
When you don’t yet have a strong idea, the hosts suggest acting like a researcher or PhD: go to the edge of some field, build understanding and prototypes, and let insights about real problems emerge over time.
Aim for harder, more ambitious ideas that truly capture imagination.
Founders tend to gravitate to what can be built in a weekend; the podcast urges them to push toward ideas that are technically hard, have big upside (e. ...
Look for jobs and workflows that are outsourced, repetitive, or consultant-heavy.
Tasks already offshored or requiring armies of consultants (drive-thru order-taking, RPA deployments, government-bid monitoring, back-office insurance processing) are strong signals that AI can create high-value automation startups.
Use personal networks and proximity to ‘edge’ companies as idea generators.
Internships, prior roles, and friends at bleeding-edge firms (Cohere, Scale, Apple, Pinecone ecosystem) repeatedly produced billion-dollar ideas because they exposed founders early to emerging problems and capabilities.
Ignore superficial signals of competition and investor disbelief if users love you.
Even in crowded spaces like AI customer support, technically superior teams won big customers by actually making the tech work; founders are advised to trust paying users and on-the-ground learning over investors’ social-media-level views.
Notable Quotes
“There is a very important question that all founders have to ask when they commit to an idea, and that is: if not us, then who?”
— Garry
“Often when people are thinking about startup ideas, they tend to gravitate towards things that seem very easy to build a first version of… most of the best startup ideas are actually at least somewhat hard to ship the first version of.”
— Jared
“Work on something that captures the human imagination.”
— Chris, as quoted by Jared
“If you’re building a startup working on cutting-edge AI, even if you haven’t found the right idea yet, why give up and go back to Google or college or something?”
— Harj
“You can’t stay in your house or sit on your toilet doomscrolling X. You have to either look very deeply within you… or you need to radically get out of the house.”
— Garry
Questions Answered in This Episode
How can I systematically uncover the unique intersections of skills and experiences that might give me founder–market fit for an AI startup?
The hosts of the Lightcone Podcast explain how aspiring founders can systematically generate strong AI startup ideas by going beyond shallow, hackathon-style projects and instead pursuing hard, ambitious problems.
If I don’t have a strong domain background yet, which industries or job types would be the most leverageable to ‘go undercover’ in for finding AI automation opportunities?
They emphasize two main paths: looking deeply within your own past work, internships, and skills for unique founder–market fit, and aggressively going outside your comfort zone to embed yourself in real-world industries and workflows.
Where is the line between a problem that’s merely tedious and one that’s valuable enough to sustain a large AI company once automated?
Through many concrete YC portfolio examples, they show how founders uncovered valuable ideas by leveraging specialized experience, following cutting-edge technology, shadowing or working in tedious jobs, and serving overlooked niches now made viable by AI.
How should I decide whether to persist in a crowded AI space (like customer support) versus looking for a more obscure niche?
They argue that despite apparent competition and investor skepticism, this is an unusually good time to persist in AI startups, because capabilities and opportunities are expanding so quickly.
What concrete steps can I take over the next 3–6 months to ‘live at the edge’ of AI and notice what’s newly possible before everyone else?
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