Chris Cooper
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I’ve been spending a lot of time inside early-stage AI projects lately, some greenfield, some half-built, some already bleeding time and money.
What keeps showing up isn’t a tooling problem. It’s a decision problem.
Most founders don’t actually struggle with:
What I see most often:
The hard part is answering, clearly and early:
Curious how others here are handling this:
What keeps showing up isn’t a tooling problem. It’s a decision problem.
Most founders don’t actually struggle with:
- Choosing models
- Automating workflows
- Shipping MVPs
What I see most often:
- AI ideas that sound promising but don’t survive contact with reality
- Projects stuck in “iteration mode” because no one wants to kill them
- Automation efforts that optimize the wrong thing
- Founders building because they can, not because they should
The hard part is answering, clearly and early:
- Is this worth executing?
- What’s the fastest path to real signal?
- What must be proven before more build?
- At what point do we stabilize, pivot, or shut it down?
- Diagnose quickly
- Decide ruthlessly
- Execute only what earns execution
- Stabilize or stop — no zombie projects
Curious how others here are handling this:
- How do you decide when an AI idea is worth pushing forward?
- Where do you draw the line between “iterate” and “kill”?
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