OpenAI's $2B Drug Discovery Hack for Solo Builders

Miles Wang, a former OpenAI researcher, is in talks to launch an AI drug discovery startup valued at $2 billion. His core strategy isn’t inventing new compounds from scratch; it’s identifying new therapeutic uses for drugs that have already passed safety trials but failed for their original purpose. The expensive, time-consuming part—proving a compound is safe for humans—is already done. Wang’s company, using transformer architectures similar to those behind ChatGPT, focuses on finding the right match between an existing molecule and a new disease.

That idea hit me hard, because it’s the exact move most solo builders are missing. We’re all sitting on tools we’ve already paid for, already learned, already integrated. But instead of finding new uses for them, we keep refreshing Twitter for announcements about the next model drop. I wrote about Wang’s original move when the news broke, but the real lesson isn’t about biotech. It’s about how you think about the tools you already have.

What is drug repurposing and why does it work for startups?

Drug repurposing is the process of finding new medical uses for existing, approved drugs. It works because the most expensive and risky phase of drug development—Phase I clinical trials to establish human safety—has already been completed. A 2019 analysis in Drug Discovery Today found that repurposed drugs can reach the market in roughly half the time and at a fraction of the cost of novel compounds. Wang’s startup applies AI, specifically transformer models, to scan biological data and predict which existing molecules might be effective against different diseases. The model isn’t novel. The application is.

For solo builders, the equivalent is staring you in the face. Your tools have already cleared the hardest hurdles. You’ve learned how to prompt them, connected them to your workflow, and paid the subscription. The expensive part—the learning curve, the integration, the trial and error—is behind you. Now find the new applications.

How do I find new uses for AI tools I already pay for?

Start by looking at what you actually use versus what the tool can do. When I tested 10 different AI writing tools, I found writing was maybe 30% of their capability. The rest was untapped.

You’re using ChatGPT for writing. Have you tried it for data analysis? Upload a CSV, ask it to find patterns, generate charts. That’s not a new tool; it’s a new job for an old one.

You’re using Claude for long documents. Point it at your competitors’ websites and ask for positioning analysis. Feed it your customer support emails and ask for pattern detection. Same subscription, different problem.

You’re using Make or Zapier for basic automations. Have you built multi-step workflows that chain AI calls together? My automation pipeline started as a simple email-to-Notion flow. It now handles content scheduling, client follow-ups, and research aggregation—all with tools I was already paying for. Webhooks are the bridge most people ignore between these apps.

Why does going deeper beat switching to a new tool?

Compounding. When you switch to a new tool, you start at zero. New interface, new quirks, new failure modes. You spend weeks getting comfortable. Then another tool drops, and you switch again. You’re always climbing the first hill.

When you go deeper with a tool you already know, each new application builds on everything you’ve learned. Your prompts get better. Your workflows get tighter. You discover edge cases that save you hours. That knowledge compounds in a way that tool-hopping never will. The AI tools with the highest satisfaction rates aren’t always the newest ones. They’re the ones people have been using long enough to get good at them.

What’s a concrete exercise to repurpose my AI tools today?

Pick the AI tool you use most. Open your last 20 conversations with it. Look for these four things:

• Repeated tasks you could templatize. If you’re writing the same type of prompt every week, save it. Most tools support this natively now.
• Adjacent tasks you haven’t tried. If you use it for writing, try research. If you use it for coding, try data analysis. If you use it for brainstorming, try structured decision-making.
• Connections you haven’t made. Can this tool talk to another tool you already use? A simple webhook or API call might link them.
• Outputs you’re not capturing. Are your AI conversations generating insights you’re not saving? Set up automatic logging. The raw material for your next project might already be in your chat history.

Wang didn’t invent a new molecule. He looked at existing molecules and asked, “What else could this do?” You have the same opportunity sitting in your tool stack right now.

Stop waiting for the perfect AI model

There’s a specific kind of procrastination that masquerades as strategy: “I’ll build that workflow once GPT 5.6 ships.” “I’ll automate that process when Claude gets better at structured output.” “I’ll set up that system when the tools mature.”

You won’t. Because there will always be another model three months away. And when it ships, you’ll wait for the next one.

The AI price war is making tools cheaper every month. The 7 AI tools I’d learn first if I started over today are all tools that exist right now. The capability gap between what you wish you could do and what your current tools can actually do is almost certainly smaller than you think.

Wang’s $2 billion bet isn’t that AI will get better. It’s that existing AI, applied to the right problem, is already good enough. He’s not waiting for the next model. He’s repurposing the current one. The next time you catch yourself refreshing TechCrunch for AI news, stop. Open the tool you’re already paying for. Ask it to do something you haven’t tried before. Apply it to a problem you’ve been saving for “when the technology is ready.”

The technology is ready. You just haven’t pushed it far enough yet. Start there. The next model can wait.

This post references reporting from TechCrunch on Miles Wang’s startup plans. For the original industry breakdown, see our earlier coverage. For more on getting the most from AI tools, check out the AI stack I’d use starting from zero and stop doing things manually.

FAQ Schema Payload

What is Miles Wang’s $2 billion AI drug discovery startup? Miles Wang, a former OpenAI researcher, is launching a startup that uses AI to find new therapeutic uses for drugs that already passed safety testing but failed for their original purpose. The company uses transformer models to match existing molecules to new diseases, cutting years off traditional drug development timelines.

How does drug repurposing work in AI? Drug repurposing uses AI to scan biological and chemical data to predict new uses for existing, approved drugs. Because the safety trials are already complete, repurposed drugs can reach the market in roughly half the time and at a fraction of the cost of developing novel compounds from scratch.

How can solo builders repurpose their existing AI tools? Solo builders can repurpose AI tools by applying them to new problems they haven’t tried yet—like using ChatGPT for data analysis instead of just writing, or chaining existing automations into multi-step workflows. The key is going deeper with tools you already know instead of constantly switching to new ones.

Why is going deeper with AI tools better than switching to new ones? Going deeper compounds your knowledge. Each new application builds on what you’ve already learned about prompting, workflows, and edge cases. Switching tools resets you to zero every time, forcing you to relearn interfaces and quirks without building on prior experience.