AI systems see everything.
They understand almost nothing.
Netflix processes 500 billion events per day. Most of that data answers one question: what did users do? This research builds a framework for the harder question — why.
YouTube detects that you watched three political videos and floods your feed for weeks. It read your behaviour correctly. It misread your state entirely — situational curiosity became a permanent profile update. That distinction is what this framework addresses.
73% of companies cannot use their behavioural data for anything beyond basic segmentation.
That number comes from Forrester. McKinsey found that companies excelling at personalisation generate 40% more revenue than the average. The distance between those two numbers is a gap most teams have never formally named.
The problem is not a lack of data. Netflix has too much data. The problem is a lack of interpretation. When a student pauses for 30 seconds before answering a question, the system records the pause and moves on. It cannot tell confusion from careful thinking. When a customer abandons a product flow at step three, the system logs the drop-off. It cannot tell frustration from distraction from simply running out of time.
This matters because what and why require different system architectures. Predicting the next click requires historical data and a good model. Understanding motivation requires a different kind of observation - one that most deployed AI systems have never attempted.
Four layers. Each one sits beneath the last.
The ABSD framework maps the inferential path from what users do to why they do it. Each layer is more useful than the one below it - and harder to reach.
The key insight is directional. From a generative perspective, Drive shapes State, State produces Behaviour, Behaviour manifests as Action. The user's motivation generates their behaviour. AI systems must work in reverse - inferring upward from the observable layer to the motivational one.
Seven patterns that bridge the layers. All of them are probably already in your data.
These signals appear across JITAI research, affective computing, and learning analytics. They are not invented for this framework. The contribution is naming them clearly and mapping them to the states they indicate.
Netflix and Khan Academy are best in class. Both stop at Layer 1.
Two case studies examined through the ABSD lens using publicly available documentation. The finding is consistent: class-leading AI systems are excellent at capturing actions and partially capable at detecting patterns. Almost none attempt state inference. None model drive.
Netflix processes 500 billion events per day and uses deep neural networks to rank content recommendations. The system logs when you pause, when you rewind, when you abandon a film at the 40-minute mark. It cannot tell whether the pause was anticipation or confusion. It cannot tell whether the abandonment was dissatisfaction or distraction. Both signals get filed as consumption data.
Khan Academy tracks exercise attempts, hint usage, and mastery thresholds per skill. Khan Academy is a genuine Layer 2 system - behavioural patterns feed the mastery model. But the 45-second hesitation before a question a student has answered correctly before carries no different weight than a 4-second answer. The cognitive process is invisible. VanLehn's 2011 research showed that AI tutors underperform human tutors precisely because they lack this psychological understanding. That gap remains accurate today.
What this is not.
Open. Practitioner-led. Live now.
This research is being conducted in the open. The survey is live. Expert interviews are in progress. Findings update daily.
53 references across six source clusters - Self-Determination Theory, affective computing, JITAI research, learning analytics, CX and industry reports, AI and recommendation systems. The literature synthesis found minimal cross-citation between the four primary traditions. ABSD is the first framework to connect all four.
The research question is empirical, not theoretical: do practitioners recognise the signal gap described by ABSD in their own systems? If they do, does the framework provide vocabulary and structure useful enough to act on?
The survey takes five minutes. The voice interview takes twenty. Both are open to any practitioner who builds AI products in CX, EdTech, or adjacent domains.