MMarketing Against The Grain
← All frameworks
Self-MasteryNathan Labenz

AI Serenity Prayer

Adopt durable AI tools, ignore distractions, and revisit uncertain bets later.

Difficulty
Easy
Time to result
~ongoing to results
Steps
5
Confidence
93%

The AI Serenity Prayer is a compact decision rule for navigating an overwhelming stream of AI products. It divides choices into three responses: move quickly on tools whose capabilities and usefulness are likely to endure, deliberately ignore tools that are mostly transient experiments, and exercise judgment when the distinction is unclear. Durability includes more than whether a startup survives; it asks whether the capability will remain useful, whether an incumbent product will soon absorb it, and whether integration costs justify immediate adoption. For uncertain candidates, the framework recommends a scheduled reassessment rather than continuous monitoring. This preserves focus while allowing practitioners to benefit from meaningful advances without rebuilding their workflows around every short-lived application.

Origin

Nathan Labenz shared the AI serenity prayer, originally written as a popular joke tweet, during Marketing Against The Grain.

Core principles

  • 01Not every impressive tool deserves adoption.
  • 02Durability matters when workflows carry switching costs.
  • 03Waiting is an active choice when the market is moving quickly.
  • 04Users need judgment as much as technical agility.

How to run it

  1. 1

    Define the Use Case

    State the practical job the tool would perform and how frequently it would be used.

    Pro tip Ignore features that do not connect to an actual workflow.

  2. 2

    Test Six-Month Relevance

    Ask whether the capability, workflow, and user need are likely to remain meaningful in six months.

    Pro tip Evaluate the capability separately from the current vendor.

  3. 3

    Check Incumbent Absorption

    Assess whether software already embedded in the workflow is likely to add the same feature soon.

    Pro tip Consider waiting when migration costs would exceed the short-term benefit.

    Watch out Do not assume every startup feature will remain a standalone category.

  4. 4

    Choose Adopt, Pass, or Revisit

    Adopt durable high-value tools, reject low-value distractions, and assign a review date to uncertain candidates.

    Pro tip Record the reason for the decision so novelty does not reopen it immediately.

  5. 5

    Review the Outcome

    Reassess adopted and deferred tools against actual usage, quality, and market developments.

    Pro tip Remove tools that never became part of regular work.

    Watch out Sunk integration effort is not evidence that a tool remains useful.

In the wild

Standalone AI Writing Tool Decision

A marketing team considers integrating a new AI writing application. The capability is useful, but the team expects its existing office suite to add comparable generation and editing features soon. It uses the current tool only for a bounded trial and schedules a review rather than migrating the full workflow.

The team learns from the capability without creating unnecessary switching and training costs.

Common mistakes

Chasing Every Launch

Constant experimentation consumes attention and leaves teams with fragmented, abandoned workflows.

Evaluating Only the Vendor

A vendor may disappear while the underlying capability remains highly relevant, or survive while its feature becomes commoditized.

Waiting Without a Review Date

Unstructured patience can become permanent inaction even after a tool proves durable.

Is it for you?

Best for

Professionals deciding which rapidly emerging AI tools deserve sustained attention and workflow investment.

Not ideal for

Research teams whose explicit role is to test the full frontier regardless of near-term durability.

From the transcript

God grant me the agility to use the AI tools that will still be relevant in six months, the patience to pass on the rest…

Nathan Labenz · 31:30

There's so much stuff coming at us, and it's like, well, there's a lot of cool stuff.

Nathan Labenz · 31:30

I honestly think that the application layer, not super bullish on it long term.

Nathan Labenz · 31:30

From the episode

GPT-4 Beta User Reveals What Jobs It Will Destroy In 2023 with Nathan Labenz (#103)

Nathan Labenz