Behavioural Economics Series - Habit Theory: Building Adaptive Loops in an AI-Driven World

For decades, therapy has framed habitual behaviours as triggers, actions, and rewards. Approaching my third decade as a clinician comes with an increasing focus on functional analysis; the key to solutions so often hides not in the behaviour itself - these are actually quite fungible - but what those behaviours are for.

Modern AI can amplify that framework, turning personal data streams—sleep patterns, stress logs, even sentiment analysis from typed messages—into precise cues for micro-interventions. Think of generative AI that crafts real-time nudges to disrupt negative habits; behavioural therapies like ACT and CBT are often most effective when the sticking point is dealt with in the moment. Or perhaps a retrieval-augmented system that references past successes (and failures), reminding a user how they overcame a tough moment last month.

So often, it is our network that reinforces our behaviour. This network is multi-layered - intrapersonal networks of cognitive, emotional and sensation patterns, interpersonal networks of repeating social dynamics, the interface we have with our environment and of course the digital networks that now connect all prior layers in both supportive and deranging ways.

For example, often when I contact my friends I am deliberately reaching for some kind of validation or support, whether I consciously realise that at the time or not. As we march together relentlessly into an increasingly AI-integrated world, we have the opportunity to deliberately construct positive networks at the individual and collective level.

On the developer side, it’s a serious engineering challenge: designing pipelines that glean subtle changes in user context without drowning them in notifications. We want “adaptive loops,” not nagging devices. Notification is fatigue is real - I often find myself going through my phone app list and just smashing the toggle on all but the most crucial. But by meshing habit theory with machine learning, we can scale individualised mental health support in ways that were inconceivable a few years ago.

Credit to Quantum for doing much of the heavy that inspired the thinking behind this post. I've been going through my feature roadmap with a different lens and it's paid dividends. They have a fascinating playbook on behavioural economics free to download. It's refreshing to see such innovation in a world where easy and quick wins seem to take most of the pie.