AI has made it easier than ever to build software, ship fast, and look productive. The hard part is still the same: finding startup ideas people actually want to pay for.
Dr Alex Young (a surgeon and founder of multiple AI companies) argues that most businesses fail for one main reason, they never land on that small but important “1 percent idea” that connects a real problem to a clear solution.
His approach is simple to explain and hard to fake: start with publicly available signals, turn them into a long list of possible ideas, narrow to a short list with validation, then earn the right to build by speaking to real people. The goal is to move from “blank canvas” to “I know what to build next” without guessing.
If you want updates like this, Alex shares more through his newsletter for founders and builders and posts regularly on his personal blog.
The 5-method framework (from zero ideas to customer interviews)
Alex’s framework is built around five methods that stack together. Each one answers a different question:
- What’s already trending and “pre-validated” by serious investors?
- What are real users complaining about in public?
- What are paying customers saying in structured reviews?
- Which businesses have obvious pain you can point to today?
- What do customers say when you talk to them directly?
Here’s a quick overview of how the sources differ:
| Method | Source | Signal type | What it’s good for |
|---|---|---|---|
| 1 | VC “requests” and thesis pages | Pre-validated themes | Starting from a blank page |
| 2 | Reddit and forums | Unfiltered pain points | Finding problems and language users use |
| 3 | G2 reviews | Verified buyer feedback | B2B feature gaps and positioning |
| 4 | Google reviews, Glassdoor | Visible business weaknesses | Outreach and service-style offers |
| 5 | Communities and interviews | Direct, high-trust feedback | Confirming what to build and why |
Method 1: Start with pre-validated startup ideas (VC signal)
When there’s no clear niche yet, Alex starts with ideas that have already been researched by people whose job is to scan markets all day.
Where the “pre-validated” ideas come from
Two examples he calls out:
- Y Combinator’s Requests for Startups list (also called RFS), a curated set of problems and themes YC wants founders to tackle
- VC thesis pages (he mentions an AI agent thesis from a16z as an example)
A useful starting point is YC’s live list: Y Combinator Requests for Startups. For extra context on how YC thinks about these ideas, there’s also YC’s latest Request for Startups article.
The point is not “build exactly what a VC wrote down.” It’s that these lists often highlight themes that are showing up across many companies at once, so you’re less likely to chase a dead end.
Using ChatGPT to generate a large idea list
Alex then takes that list and uses ChatGPT as a fast ideation partner.
He shares a prompt pattern that includes:
- who you are (your background)
- where you want to play (the niche)
- what source to use (the YC list)
- a specific outcome (generate 100 ideas)
- constraints that create an advantage (what you know that others don’t)
In his case, he’s a surgeon and wants to build in medicine. He gives examples like:
- “something that revolutionizes surgical training”
- “a voice agent for automating medical note-taking”
That background matters because it changes the quality of the output. The more specific the constraints are, the more practical the ideas become.
The limitation of ChatGPT at this stage
Alex also flags a real problem: this kind of ideation is still not customer contact.
ChatGPT can generate lots of ideas quickly, but early results can be vague because the model isn’t reading your buyers’ minds. It’s a starting gun, not the finish line.
One clean way to think about Method 1 is: it gets you moving, then the next methods add proof.
Method 2: Social media listening on Reddit (pain points in plain English)
Method 2 shifts from “investor themes” to “user pain,” and Alex’s favorite place for that is Reddit.
Reddit works because people:
- complain in detail
- name the tools they use
- explain why something broke their workflow
- sometimes share what they paid
That last part is especially useful because pricing is part of validation.
How Alex used to do it manually
Before automations, Alex says he would “live” inside subreddits as a lurker.
He gives a specific example from early on: medical exam prep. He would read threads in communities like USMLE and medical doctor subreddits, looking for:
- frustrations with existing exam prep products
- requests for help preparing for exams
- clear signs people were already spending money on solutions
From that, he could see what to build, how to position it, and what price range the market was already accepting.
Automating Reddit listening with Gumloop
Now he automates the same kind of research using a workflow tool called Gumloop (he calls it one of his favorites).
The flow he describes looks like this:
- Choose a subreddit tied to the niche (example he gives: a marketing subreddit for blog post and SEO tools).
- Scrape conversations about a topic or tool.
- Run the scraped text through OpenAI’s API to summarize it.
- Output a list of problem statements based on a prompt.
He also shares a tactical twist: don’t just scrape broad industry subreddits. Scrape competitor subreddits too.
His example is microlearning. If a product competes with Kahoot or Quizlet, their subreddits can show exactly what frustrates users, which makes it easier to decide what features to prioritize and how to position against incumbents.
A simple “no-workflow” shortcut
If AI workflows feel like too much at first, Alex describes a lightweight option:
- take a screenshot of a Reddit thread
- upload it to ChatGPT
- ask for a summary of problems, pros, and cons
- ask for possible differentiators (he mentions “killer features” and USPs)
That keeps the same logic, even without automations.
Method 3: Validate B2B startup ideas with G2 reviews (paying customer signal)
Reddit is great, but Alex points out a key issue: many Reddit users are individuals, and not always paying customers.
If the goal is B2B software, he switches to a source where reviews are tied to real buyers.
Why G2 changes the quality of validation
Alex calls out G2 because:
- reviews are validated
- reviewers are often enterprise buyers
- feedback is about real-world buying criteria, not just opinions
He references doing this kind of competitive review mining for Virti (his business) and looking at what customers liked and disliked in competing products.
(If you want context on Virti as a company, he links it as Virti’s website.)
Scraping and summarizing G2 at scale
He describes the same automation pattern here:
- scrape G2 reviews
- feed them into an automation tool like Gumloop (he also mentions alternatives like n8n)
- summarize with an LLM
- extract repeated complaints, feature requests, and language customers use
And again, for a simple shortcut, he suggests screenshots: capture the G2 review page and have ChatGPT summarize problems and opportunities.
For anyone who wants extra depth on what YC is seeing in the market right now, Alex’s Method 1 pairs well with an overview like Specter’s roundup of YC requested startups, because it can help you spot clusters of similar ideas and potential white space.
Method 4: Find “obvious pain” via Google reviews and Glassdoor
Method 4 stays in the social listening lane, but it tilts toward outbound and service-style business opportunities.
The sources change from “software users talking to each other” to “customers rating a business in public” and “employees reviewing a workplace.”
Using Google reviews for local business targeting
Alex describes scraping Google reviews for local businesses and focusing on negative reviews.
The reason is straightforward: negative reviews usually contain clear pain, written in normal language, with examples of what went wrong.
He mentions setting up this kind of scraping with Gumloop and also calls out Clay as a tool people use for local business targeting.
Using Glassdoor for employee experience pain
He also mentions Glassdoor as another source of signals, because low employee scores can point to operational problems.
In this method, the outcome isn’t just “collect ideas.” It’s also “reach out with a problem you can prove.”
Turning reviews into outreach
Alex gives a sample outreach style message that references the review data directly, for example:
- noticing customer service complaints
- proposing an AI tool that could automate part of that support load
He also describes automating the outreach itself: reviews go in, an AI generates a tailored email using a custom prompt, and the output becomes a ready-to-send message.
Method 5: Customer interviews inside real communities (where ideas get real)
After mining public data, Alex moves to the part most people avoid: talking to customers.
The goal here is clarity. Social listening gives patterns, interviews give reasons.
Where to find people to interview
He names a few places:
- Reddit communities
- Facebook groups (he calls out marketing and sales groups as examples)
He also notes that ChatGPT can help find the largest Facebook communities for a topic, which can save time when you’re trying to locate where your target users already gather.
Just like earlier methods, he mentions scraping tools (like Clay and Gumloop) to pull out the comments and sentiment in those groups, but the bigger idea is participation.
Building trust before asking for anything
Alex’s approach here is not “drop a link and pitch.” It’s closer to:
- join the community
- share helpful advice
- become a familiar, useful presence
- introduce what you’re building only after you’ve earned attention
- invite people to user interviews organically
He shares a story to show why this works: Adam Robertson (he references a webinar with Clay’s go-to-market lead) joined and spent a year talking to people in marketing and sales communities. Instead of pushing automations or hard sales, the focus was learning pain points and feeding them back into the product so growth became product-led.
Alex uses that story to underline a simple point: people love products that solve a real need, and communities are often the fastest way to understand what “real” means.
He also mentions that AI will likely automate more of the interview and survey process in the future, but for now, this method is still about direct conversation.
What I learned from Alex’s approach (and why it works)
A few themes stand out in this framework, especially when you look at how the methods build on each other.
First, the process starts broad on purpose. Alex goes from pre-validated themes (like YC’s RFS list) to messy, human feedback (Reddit), then to structured buyer feedback (G2), then to business pain you can point to (Google reviews), and finally to direct interviews.
Second, he treats speed as a feature, but not as a substitute for truth. Automations help him compress time, especially for scraping and summarizing, but the end of the funnel still requires understanding people in context.
Third, the methods keep pulling you toward clearer positioning. Competitor subreddits and competitor reviews are basically a shortcut to “what should I not copy” and “what’s missing that buyers keep asking for.”
Finally, his credibility is tied to outcomes he states directly: he says he’s scaled multiple AI companies to over $1M in revenue in their first year, and he’s used versions of this process while building companies valued at $50M+.
Conclusion
Good startup ideas don’t come from staring at a blank doc, they come from patterns you can prove. Alex’s five methods move from investor signal to user pain, then into buyer feedback and real conversations, which makes it much harder to build the wrong thing. If you want to see more of what he’s building, he shares updates on his X profile and posts behind-the-scenes content on his Instagram account. If AI content is part of your go-to-market plan, he also mentions a tool he’s excited about, Clipyard for AI UGC videos.
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