I looked at data from 100,000 RunwayML images to figure out what kinds of prompts get the best results. The findings made me rethink some common advice about generating images.
The Framework That Actually Works
Here is a simple framework for evaluating RunwayML. It's not complicated, but it requires discipline.
Step 1: you need to move fast and break things
This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: the best solutions are often the simplest ones
Once you have the foundation right, this becomes much easier. I've watched founders struggle with this for months when the answer was staring them in the face.
Step 3: Iterate relentlessly
Nothing works perfectly the first time. The companies that get RunwayML right treat it as an ongoing process, not a one-time project.
The Counterintuitive Truth
In RunwayML, the best practitioners often do less, not more.
The lesson: the best solutions are often the simplest ones. It sounds simple. It's incredibly hard to execute.
Lessons From the Trenches
I want to share a few specific lessons I've picked up over the years. These aren't theoretical. They come from real companies, real failures, and real successes.
Lesson 1: The best time to start thinking about RunwayML was yesterday. The second best time is now. Don't wait until you have the perfect plan.
Lesson 2: Hire for attitude, train for skill. The best people at RunwayML are rarely the most technically gifted. They are the most curious and persistent.
Lesson 3: Your competitors are probably getting this wrong too. That's your opportunity. While everyone else is following the same playbook, you can zig when they zag.
This connects to broader themes around AI design tools, Stable Diffusion, Midjourney, AI video that I've been thinking about a lot lately.
What's Next
Best practice in RunwayML is changing fast. What worked last year might not work next year. That's both the challenge and the opportunity.
My advice: stay curious, stay humble, and stay close to the people who are actually doing the work. Read less thought leadership and do more experiments. Talk to fewer consultants and more practitioners.
If you're building in this space, the best time to get RunwayML right is before you need to. Don't wait for a crisis to force your hand.
I'll keep sharing what I learn. This stuff matters too much to keep to myself.
Frequently Asked Questions
Q:What was the biggest challenge in this case?
Almost always, the biggest challenge is people and alignment, not technology or strategy. Getting the right team focused on the right problem is harder than any technical challenge I've encountered.
Q:What would you do differently looking back?
I'd move faster on the things that were working and cut the things that weren't sooner. Most founders, myself included, hold onto failing strategies too long because of sunk cost. Speed of learning is everything.
Q:Can these results be replicated?
The specific numbers will vary, but the underlying patterns and principles are transferable. The key is understanding the context behind the results, not just copying the tactics. Every company has unique constraints that shape what works.
Q:How long did it take to see results?
Most meaningful business results take 3-6 months to materialize. Anyone promising overnight success is selling something. The companies in my portfolio that grew fastest were the ones that stayed patient and consistent.