How understanding is built
A three-essay sequence on knowledge, composition, and the history behind a working system.
Professor & Head of Translational Research, Deakin Applied AI Initiative
On frontier AI, and what it changes.
I write about how intelligent systems are built, how understanding forms, and the human judgment that makes both useful.
These essays are a place to work through ideas.
Questions these essays take up
When AI can do more of the work, what still depends on human judgement?
Read The Hollow Middle, Human Purpose vs AI Capability and The Integration Problem.
What happens to expertise when AI does the work people used to learn from?
Read The Ladder Is Gone, The Residue and the Trace and Can AI Agents Replace Experts?
How do we build AI systems that people can rely on?
Read Creating Reliable Systems from Unreliable Parts, Trust as Cognitive Multiplier and Bounds, Not Goals.
If AI can produce the answer, how do people still come to understand?
Read The Encoding Gap and Two Compressions.
Now that AI makes so much easier to build, what is worth building?
Read AI Changed What's Possible and Human Purpose vs AI Capability.
Recent essays
All 32 essaysThe Symmetry You Choose
The choice of what should remain invariant can matter more than the model that exploits it.
Two Compressions
Finding a pattern in observations is one achievement. Discovering the mechanism that generates it is another.
The Integration Problem
Moving AI into professional practice creates new demands for expertise, evaluation, and accountable integration.
The Regulator's Mirror
Metacognition as a governing layer: how an agent might monitor and regulate its own operation.
Reading paths
Short sequences of essays, to read in order.
Designing the agent
A suggested route through persistent agents, inherited knowledge, latent intelligence, and self-regulation.
From capability to practice
Begin with reliability, then consider the human and organisational work of making AI useful.