AI Strategy
The Activity Graph: Why AI Context Can't Be Bought, Only Accumulated
Models get matched. Compute gets cheaper. Clever prompts circulate within a week. The one asset in AI that doesn't decay is a multi-year record of how work actually happens — and you can't buy it from anyone.
Every claimed AI moat eventually evaporates. The model that's untouchable today is matched in eight months. The compute price drops in half on a calendar nobody controls. The clever system prompt circulates in someone's Substack within a week of being clever. Almost everything in AI has a half-life. Data doesn't.
The activity graph is the asset that doesn't decay. It's a longitudinal record of how work happens — every application opened, every decision made, every friction point hit, every recovery path taken — captured continuously at the desktop where the work is happening. It accumulates. It doesn't transfer. And it can't be bought from a vendor because no vendor has the record of your specific company.
The math of catching up is brutal. A competitor starting today gets to year five in five years, not faster. By the time their record has any specificity, the incumbent's record has another five years of depth and per-tenant fit that a generic dataset structurally can't match. The gap doesn't close — it widens with time.
This matters because the quality of any AI suggestion is bounded by the quality of the substrate underneath it. Generic context produces generic agents. Specific context produces an agent that recognizes an exception when it appears, because it has seen the precedent enough times to know what counts as an exception.
Dataken's activity graph holds more than ten billion records across five years of continuous production, growing at roughly a million records per day per deployment. The number isn't a brag — it's the only honest explanation for why the suggestions are worth surfacing instead of dismissing.
Key takeaways
- Model and compute moats have a half-life; data moats compound
- The activity graph is a longitudinal, per-company record of how work actually happens
- A new entrant doesn't catch up by writing better code — they start at year zero of the record
- Suggestion quality is bounded by substrate quality; specific context produces specific agents
- The numbers (10B records, 5 years, 1M/day) are the explanation for why the output is useful, not marketing
Numbers cited
- — 10B+ activity-graph records across 5+ years of continuous production
- — ~1M activity records per day, per typical deployment