Industry Dynamics: Engines of Extraction

How Personalisation Drives Market Concentration

Are those who master our attention the true winners of the digital economy?

In this essay, adapted from my recent simulation study “Behavioural Surplus Extraction and Market Concentration”, I explore how data extraction and personalisation transform competition itself — and why our current policy tools are still blind to the tipping points that define digital markets. Beyond the paywall, I will address the following:

From Personalisation to Dependence and Control — power now lies with those who train algorithms, as personalisation evolves into behavioural manipulation through self-reinforcing feedback loops.

The Feedback Economy — engagement and prediction form a continuous cycle where data fuels dominance, replacing traditional competition based on price or quality.

Why Markets Tip — and Why It Matters — once platforms like Google or Meta reach critical behavioural data mass, learning effects cause markets to tip toward concentration and lock-in.

Policy and Strategy Implications — regulators must focus on data mobility, interoperability, and limits on manipulative personalisation to maintain adaptability in digital ecosystems.

The Broader Lesson — behavioural extraction has become the dominant business model, turning user data into self-compounding informational capital — the ultimate contest is not over products or profits, but over control of the architecture of human choice.

Under the Hood — an agent-based simulation reveals how personalisation strength and switching friction interact to produce tipping points in digital markets, showing that as predictive accuracy rises and user mobility falls, ecosystems rapidly shift from open competition to entrenched dominance.