The Bullseye method was built for human bandwidth constraints — but agents change the math on testing, not on judgment. Knowing which channel *deserves* your concentration still falls to you.
Gabriel Weinberg and Justin Mares wrote Traction as a corrective to founders who fall in love with their product and neglect the machinery that gets it in front of people. Their core argument is that distribution failure kills more companies than product failure, and their prescription is structured: map all nineteen traction channels, run cheap parallel experiments across your outer ring, identify which two or three show real signal, then pour everything into the single best performer. The discipline is in the narrowing, not the brainstorming.
The Bullseye's original constraint was human time — you could only run so many cheap tests before the team collapsed. Agents dissolve that bottleneck. An AI-native studio can instrument cold outreach sequences, content experiments, SEO probes, and community plays simultaneously without burning a person per channel. What does not change is the judgment call at the center of the bullseye: reading which signals are real, which numbers are flattering noise, and making the concentrating bet. Founders who treat agent-driven testing as a reason to postpone that decision will scatter forever and mistake activity for traction.
- Test more channels in parallel than your human headcount ever allowed, but set your evaluation criteria before the agents run
- the signal-reading step is yours and cannot be delegated
- concentration still wins — agents accelerate the path to the one channel, they do not replace the choice.
