How do you find your next startup idea in AI?
AI is moving so fast that it’s becoming harder to know which ideas are worth exploring.
I’m curious how other founders approach this:
Where do you usually discover new startup ideas?
What sources do you trust most for AI trends?
How much time do you spend researching opportunities each week?
Have you ever found an idea too late because someone already built it?
Would a daily list of promising AI opportunities be useful to you?
Would love to hear how you think about this.
YC's Hacker News, arXiv papers, or trending GitHub repos are what I use. I don't spend an enormous amount of time each week, but even just a small bit of scrolling and interacting with the trending front-page discoveries + ideas can lead to some interesting patterns.
On the note about a daily list of promising opportunities, I think it is a great idea, but are just wondering how you may go about it. If, in terms that sense, there is a constant refresh every day, how do you plan on gathering the brisk information to process and deploy? And how do you plan on long-term sustainability if said promising ideas are being proposed each day?
Just some thoughts that came to mind based on my interpretation. Overall, I'm digging the idea.
Thanks for the thoughtful questions! I think the key challenge is not generating more ideas, but building a system to identify meaningful signals. My current thinking is that the workflow looks something like: Market signals → opportunity discovery → demand validation → finding potential users → feedback → product iteration. The signal layer is built from real-world data: developer discussions, GitHub activity, research, product launches, complaints/pain points, and emerging conversations. The goal is to identify repeated patterns instead of isolated ideas. For sustainability, I don't see it as a knowledge model that simply generates startup ideas. The long-term value comes from accumulating structured market data over time: - which problems appear repeatedly - which technologies are gaining adoption - which opportunities were validated or rejected - how builders respond to different signals Eventually the feedback loop makes the system better: signals → opportunities → users/builders → feedback → improved recommendations. Still early, but I'm experimenting with turning this into a continuously improving AI opportunity radar rather than just a daily idea list.