Product strategy for AI that feels like yours
Working at the personalisation layer.
/ Philosophy
Concentrated on personalisation and adaptation. With a design background, my focus is on how a system represents human preference, how it stays modular as underlying models shift, and how craft and probability hold together across the experiences a brand puts into the world.
/ Principles
I work with these principles
1.
What users click, save, and skip reveals more than what they can articulate.
Behavioural signals over stated preferences
2.
Designing for variance means building a 'solution buffer', a curated backlog of adjacent, solvable problems.
Design for probabilistic systems
3.
How something looks, sounds, and moves shapes whether users trust it, return to it, and recommend it.
Aesthetic decisions are product decisions
4.
The default state of AI is to amplify consensus; without human intent, it simply scales shallow thinking faster. The human mind remains the only genuine filter. AI is for multiplying original thought, never a substitute for thinking itself.
The human is the source of intent, AI is the multiplier
5.
Modularity organises the system for a scalable trajectory, ensuring that testing is rapid, targeted and structurally sound.
Modularity
/ FAQ
Ask away, start with a thread below.
/ Blog
A look into the subjects I’m currently researching and writing about
Product strategy for AI that feels like yours
Working at the personalisation layer.
/ Philosophy
Concentrated on personalisation and adaptation. With a design background, my focus is on how a system represents human preference, how it stays modular as underlying models shift, and how craft and probability hold together across the experiences a brand puts into the world.
/ Principles
I work with these principles
1.
What users click, save, and skip reveals more than what they can articulate.
Behavioural signals over stated preferences
2.
Designing for variance means building a 'solution buffer', a curated backlog of adjacent, solvable problems.
Design for probabilistic systems
3.
How something looks, sounds, and moves shapes whether users trust it, return to it, and recommend it.
Aesthetic decisions are product decisions
4.
The default state of AI is to amplify consensus; without human intent, it simply scales shallow thinking faster. The human mind remains the only genuine filter. AI is for multiplying original thought, never a substitute for thinking itself.
The human is the source of intent, AI is the multiplier
5.
Modularity organises the system for a scalable trajectory, ensuring that testing is rapid, targeted and structurally sound.
Modularity
/ FAQ
Ask away, start with a thread below.
/ Blog
A look into the subjects I’m currently researching and writing about
Product strategy for AI that feels like yours
Working at the personalisation layer.
/ Philosophy
Concentrated on personalisation and adaptation. With a design background, my focus is on how a system represents human preference, how it stays modular as underlying models shift, and how craft and probability hold together across the experiences a brand puts into the world.
/ Principles
I work with these principles
1.
What users click, save, and skip reveals more than what they can articulate.
Behavioural signals over stated preferences
2.
Designing for variance means building a 'solution buffer', a curated backlog of adjacent, solvable problems.
Design for probabilistic systems
3.
How something looks, sounds, and moves shapes whether users trust it, return to it, and recommend it.
Aesthetic decisions are product decisions
4.
The default state of AI is to amplify consensus; without human intent, it simply scales shallow thinking faster. The human mind remains the only genuine filter. AI is for multiplying original thought, never a substitute for thinking itself.
The human is the source of intent, AI is the multiplier
5.
Modularity organises the system for a scalable trajectory, ensuring that testing is rapid, targeted and structurally sound.
Modularity