Seth Godin went on The Diary of a CEO this week and said something that should be printed on every founder's wall: "The dip is not the dead end."

He was talking about quitting. Specifically, why smart people quit too late. They confuse the predictable hard patch with a structural dead end. They keep feeding a cul-de-sac because they already spent the money, or they bail on a dip because it feels like failure.

But here is the part that matters for anyone deploying AI right now: the same confusion is killing good projects and keeping bad ones alive.

AI deployment has a dip

The received wisdom says AI adoption is a straight line. Buy the tool, automate the task, save the hours. When that does not happen in week two, the conclusion is immediate: AI does not work for our business. Or worse, AI does not work at all.

The truth is messier. In our experience, serious AI deployment has a dip. The first month can be slower than the old way. The output needs more review than you budgeted. The team resents the change. The ROI spreadsheet looks like a lie.

This is not necessarily evidence you chose wrong. It can be the cost of crossing.

Name the dip before you buy

Godin's framework is blunt: before you start, name the dip. How long is the hard stretch? What resources do you need to survive it? Who else has crossed it, and what proof do you have that more effort reaches the other side?

A common AI deployment failure begins when nobody asks these questions. The business buys the demo, not the deployment. It expects the tool to work like a hire who shows up productive on day one. When the dip arrives, the team calls it a dead end and goes back to manual work.

This is why we argue that an AI project needs a running operating record, not just a promising prototype. The evidence should show whether each revision, integration and workflow change is moving the system closer to useful work.

Do not feed a cul-de-sac

The other failure mode is worse: staying in a cul-de-sac because the invoice is already paid. The AI tool that generates content nobody reads. The automation that creates more review work than it saves. The chatbot that answers questions nobody asked. Past spend is not a reason to keep feeding a dead route.

The test is present-tense recommitment. Knowing what you know now, would you start this project today? If the answer is no, the sunk cost is not a strategy. Kill it.

If the answer is yes, and the other side is visible, the question becomes practical. Do you need more time, better source material, a different model, tighter acceptance criteria or a human reviewer who knows what good looks like?

The goal is not maximum automation. It is one useful business loop that produces a measurable result and gets better with evidence.

Use AI to make the humans more valuable

This is where Godin's AI point lands. He argues AI should widen the human dividend, not strip it. Use AI to remove preparation, administration and the repetitive first draft. Then reinvest the saved hours in judgement, connection and better service.

The companies winning with AI will not simply be the ones automating the most. They will be the ones that can show where the saved capacity went and why the customer received something better.

The dip is not the dead end. But you have to know which one you are in.

Further reading

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