AI products break traditional go-to-market, so it shouldn’t be surprising that they also break traditional product metrics. DAU/MAU, time-on-platform, and feature adoption were built for the attention economy – not for systems where value compounds through interaction depth, accumulated memory, and reasoning loops.
This is the central shift most teams miss: AI products do not scale through usage volume — they scale through contextual intelligence. Traditional metrics optimize for dopamine loops; AI-native metrics optimize for defensibility, switching costs, and compounding value creation.
To build an AI product that survives, you need to track the things that actually matter: depth over breadth, intelligence over activity, and memory over engagement.
Legacy productthinking still anchors on the same stack: drive signups, increase engagement, push activation, and optimize conversion. These metrics assume the platform’s value comes from what the user does. But AI products invert that relationship. Their value comes from what the system learns.
This is why legacy metrics fail:
1. DAU/MAU Ratios
DAU/MAU was never a measure of value. It was a measure of habit strength. For AI products, habit strength without accumulated memory is meaningless. A user who returns daily but resets context each time contributes no defensibility, no compounding intelligence, and no moat.
High DAU/MAU with shallow memory is a red flag, not a win.
2. Time on Platform
In AI-native systems, the most valuable path is often the shortest one: Get the right answer with minimal friction. Time-on-platform metrics punish efficient value delivery. You end up optimizing for longer sessions, which pushes productdesign toward distraction — the opposite of what AI products should do.
Feature usage is not a proxy for value. Users might try features out of curiosity, not need. And in AI products, features matter far less than the memory that powers them.
You can have high feature adoption and low retention if the product never becomes irreplaceable.
The Shift to Memory Metrics
AI-native products behave like compounding systems. Every interaction doesn’t just serve the user — it improves the system. This requires an entirely new measurement stack.
Below are the four memory metrics that predict defensibility, growth, and market dominance.
1. Memory Depth Score
The master metric. Memory depth measures the contextual intelligence accumulated for each user over time:
their preferences
their workflows
their problem-solving style
their domain shortcuts
their constraints
their long-term objectives
Memory depth is what makes a product feel like it “knows you.” It also creates the switching costs that make users stay.
A deep memory profile is worth more than years of marketing spend.
High memory depth = high retention. High retention = high defensibility.
2. Reasoning Improvement Rate
The second critical metric is how fast your platform gets better at solving problems across users.
This measures how effectively pattern extraction works:
tool-use sequences
decision pathways
domain heuristics
problem decomposition patterns
It’s the intelligence equivalent of a network effect. The more users solve problems, the faster the system learns. The faster the system learns, the more valuable it becomes for all users.
Low activation means memory is being stored but not used — usually a sign of poor orchestration or retrieval.
This is the metric that proves: “Memory creates value, not just context.”
4. Depth-to-Breadth Ratio
This ensures that as you scale, you don’t dilute intelligence.
Breadth = number of users Depth = memory per user
If users scale faster than memory depth, your product becomes shallower. If memory grows faster than users, your product becomes more defensible with every cohort.
This is the sustainability metric: Depth at scale = durable moat.
AI-native companies that dominate will be the ones that:
accumulate deep contextual understanding
extract collective reasoning patterns
activate memory to improve outcomes
scale without diluting intelligence
When you measure memory, you measure the actual engine of compounding advantage.
This is the strategic inversion at the core of AI companies: Value is created not when users act, but when the system learns.
The Business Engineer Synthesis
To build and scale AI-native products, you need a metric stack aligned to the economic engine of these systems — not the relics of ad-driven platforms.
What matters isn’t how often users show up. It’s how much value accumulates when they do. This is the foundation for:
The companies that win the AI era will master this inversion. They will optimize for memory, not attention — depth, not breadth — intelligence, not engagement. They will build systems that get smarter with every interaction and irreplaceable with every month.
If you measure the wrong things, you build the wrong product. If you measure memory, you build the future.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
