Outcome-Based AI Activation Ladder
Measure AI activation by completed outcomes, not prompt volume.
- Difficulty
- Moderate
- Time to result
- ~weeks to results
- Steps
- 6
- Confidence
- 99%
The Outcome-Based AI Activation Ladder replaces interface-centric onboarding metrics with escalating evidence that a user achieved the product's purpose. In an AI application builder, prompting is only an input and can become an anti-metric when repeated prompts indicate failure. A stronger first threshold is completing and publishing an application. Stronger evidence follows when the creator uses the application, colleagues adopt it internally, or external customers begin using it as a product. The ladder helps growth teams classify power, medium, and non-core users according to achieved outcomes rather than raw interaction volume. The final activation definition should correlate with later retention or monetization, ensuring that the metric represents experienced value instead of convenient instrumentation.
Origin
Elena Verna described Lovable's activation definition and its effort to push beyond prompting and button clicks toward published applications with real users.
Core principles
- 01Define activation through realized value rather than interface activity.
- 02Treat excessive prompt volume as a possible friction signal.
- 03Use a sequence from generation to publication to real usage.
- 04Distinguish power, medium, and non-core users by meaningful behavior.
- 05Prefer behavioral evidence over button clicks.
How to run it
- 1
Define the intended outcome
State what the customer ultimately wants to create, complete, decide, or operate with the AI product.
Pro tip Phrase the outcome in the customer's language.
Watch out Do not begin with whichever event is easiest to instrument.
- 2
Separate inputs from value
Classify prompts, clicks, and generated drafts as inputs unless they independently deliver the intended benefit.
Pro tip Investigate whether higher interaction counts indicate struggle.
Watch out Optimizing prompts can reward inefficient product behavior.
- 3
Set the first completion threshold
Choose the earliest observable event showing a finished, usable result, such as publishing an application.
Pro tip Require completion rather than mere initiation.
Watch out A button click may not prove that the resulting artifact works.
- 4
Add real-use thresholds
Measure whether the creator, an internal teammate, or an external customer actually uses the result.
Pro tip Build a ladder from private use to broader adoption.
Watch out Publication without use may still represent abandoned output.
- 5
Segment by outcome depth
Classify power, medium, and non-core users using the frequency, depth, and audience of meaningful outcomes.
Pro tip Use these segments to compare retention and monetization.
Watch out Avoid persona labels that are unsupported by behavior.
- 6
Validate the metric
Test whether reaching each threshold predicts continued use, payment, or another durable success signal.
Pro tip Promote the threshold with the strongest practical predictive value.
Watch out Correlation should be reviewed as the product and market evolve.
In the wild
The company ignores raw prompt totals as its primary activation metric. It records whether a user publishes an application, whether the creator uses it, whether colleagues use it internally, and whether external customers begin using it.
→ Activation reflects escalating customer value and supports meaningful user segmentation.
Common mistakes
Counting prompts as success
More prompts may mean that the model failed repeatedly or that the customer cannot reach a satisfactory result.
Stopping at publication
Publishing is meaningful, but stronger activation evidence comes from actual use by the creator or another person.
Choosing a convenient event
An easily measured click is not useful if it does not predict experienced value or retention.
Is it for you?
Best for
It is best for generative and agentic products where users interact through a prompt box to create a concrete result.
Not ideal for
It is not ideal for conversational products whose primary value genuinely is the conversation itself and has no downstream artifact or action.
From the transcript
“We do define activation at lovable is uh you publishing an app.”
“we're not just gonna optimize a number of prompts that you do, because that's almost an anti-metric once you start going past a certain point.”
“And then we're also pushing our activation definition. So that's like an ultimate that you not just didn't click the button, but you actually started…”
From the episode
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