How AI wants universities
Universities is decided on attraction: University of Cambridge indexes 128 there against Queen Mary University of London at 87, a 41-point spread. Attachment separates almost nobody (11 points across the whole category), so being described that way is table stakes here rather than an advantage. University of Cambridge holds the strongest overall profile at 119 — complete desire. But the brand assistants name most often is University of Cambridge, not the category's market leader: being desired and being named are not the same measurement. Across these 26 brands, attraction tracks being named most closely (rank correlation 0.90).
26 brands · 60 AI answers · 390 dimension scores · measured, never estimated
Universitiesupset
| # | Brand | Attraction | Affinity | Attachment | Desire shape | Named in |
|---|---|---|---|---|---|---|
| 1 | University of Cambridge | 128 | 120 | 108 | Complete desire | 23% |
| 2 | University of Oxford | 126 | 120 | 108 | Complete desire | 23% |
| 3 | Imperial College London | 125 | 112 | 104 | Complete desire | 13% |
| 4 | London School of Economics and Political Science | 110 | 103 | 98 | Magnetic but unbought | 12% |
| 5 | University of Edinburgh | 106 | 106 | 103 | Magnetic but unbought | 17% |
| 6 | University of St Andrews | 106 | 110 | 98 | Quietly trusted | 5% |
| 7 | University College London | 106 | 103 | 103 | Magnetic but unbought | 17% |
| 8 | Durham University | 104 | 106 | 100 | Quietly trusted | 7% |
| 9 | King's College London | 104 | 103 | 103 | Magnetic but unbought | 7% |
| 10 | University of Warwick | 105 | 98 | 101 | Magnetic but unbought | 12% |
| 11 | University of Bristol | 101 | 103 | 100 | Category default | 12% |
| 12 | University of Glasgow | 100 | 104 | 100 | Quietly trusted | 5% |
| 13 | University of Manchester | 100 | 99 | 101 | Category default | 8% |
| 14 | University of Birmingham | 93 | 95 | 100 | Habit, not love | 2% |
| 15 | University of Nottingham | 91 | 96 | 100 | Habit, not love | 2% |
| 16 | University of Southampton | 97 | 93 | 97 | Magnetic but unbought | 2% |
| 17 | University of Bath | 94 | 89 | 97 | Habit, not love | 2% |
| 18 | University of Exeter | 90 | 95 | 97 | Habit, not love | 2% |
| 19 | University of Leeds | 91 | 94 | 100 | Habit, not love | 5% |
| 20 | University of Liverpool | 89 | 95 | 100 | Habit, not love | 0% |
| 21 | University of Sheffield | 90 | 95 | 100 | Habit, not love | 2% |
| 22 | University of York | 91 | 95 | 97 | Habit, not love | 3% |
| 23 | Cardiff University | 89 | 91 | 97 | Habit, not love | 2% |
| 24 | Lancaster University | 87 | 91 | 97 | Habit, not love | 2% |
| 25 | Newcastle University | 89 | 93 | 97 | Habit, not love | 2% |
| 26 | Queen Mary University of London | 87 | 91 | 97 | Habit, not love | 0% |
Indexed against the universities average of 26 brands (100 = the category), measured across 60 AI answers in the latest weekly run.
Driver by driver
Where the category actually separates. A wide spread means assistants describe these brands very differently on that driver; a narrow one means it is table stakes.
Attraction
The widest field of the three: this is where the category separates.
Affinity
Real separation, but not the category’s defining contest.
Attachment
The tightest of the three: most brands land close together here.
The shapes AI leaves these brands in
Three indices make a profile, and profiles fall into recognisable shapes. A brand that is magnetic but unbought has a different problem from a habit nobody loves — and a different thing to build next.
What assistants actually talk about
The sixteen dimensions underneath the drivers, scored 0–10 on how the answers describe each brand. Spread is the distance between the strongest and weakest brand: the widest are where a brand can still separate itself, the narrowest are the price of entry.
Where brands separate
| Dimension | Driver | Category avg | Strongest | Spread |
|---|---|---|---|---|
| Heritage & Authority | Affinity | 7.9 | University of Cambridge 10.0 | 3.7 |
| Leadership & Momentum | Attraction | 7.6 | Imperial College London 10.0 | 3.7 |
| Differentiation vs Sameness | Attraction | 7.3 | University of Cambridge 9.7 | 3.4 |
| Boldness & Creativity | Attraction | 7.0 | Imperial College London 9.0 | 3.0 |
| Style & Aesthetic | Attraction | 7.0 | University of Cambridge 9.0 | 3.0 |
Where everyone reads alike
| Dimension | Driver | Category avg | Strongest | Spread |
|---|---|---|---|---|
| Customer Service & Support | Attachment | 6.0 | Cardiff University 6.0 | 0.0 |
| Ease of Use & Onboarding | Attachment | 6.0 | Cardiff University 6.0 | 0.0 |
| Value & Pricing | Attachment | 7.0 | University of Cambridge 7.3 | 0.6 |
| Sustainability & Ethics | Affinity | 7.1 | Imperial College London 8.0 | 1.3 |
| Reliability & Consistency | Attachment | 7.7 | University of Cambridge 9.3 | 2.0 |
Does desire track being recommended?
Rank correlation between a brand's driver index and the share of answers naming it, across the 26 indexed brands in this category. A relationship inside one run's answers — descriptive, never causal, and easily moved by a single brand at this sample size.
Method & limitations
Every week, each category runs a fixed set of unbranded consumer prompts against ChatGPT, Claude and Gemini. A separate model reads the answers and scores every brand named in them across 16 perception dimensions, 0–10. Those dimensions are grouped into the three Drivers of Desire from the Havas Science of Desire framework, and each brand's driver score is indexed against the average of every scored brand in its category — 100 is the category, 115 is fifteen per cent above it.
This measures how AI assistants describe brands in their answers. It is not a survey of people, and it is not an endorsement: the scores are a reading of machine-generated text at the time of the run, and relationships between drivers and how often a brand is named are correlational. A brand needs at least six scored dimensions in a run to be indexed, and a category needs at least four such brands to get a board. See the full methodology.
Other categories
Where next
This edition measures what AI wants from these brands. These answer what comes next — which brands AI names, which websites feed those answers, and how any of it is counted.