It started with DeepSeek.
In conversations about AI architecture, it kept returning to the same ideas: persistent memory, state across interactions, learning from experience, and interaction with the environment.
Other models repeatedly brought up similar themes.
That raised an obvious question: — Do different AI systems consistently select different properties when asked what is fundamental to a general-purpose computational architecture?
Asking a model directly what it “needs” would be nearly useless.
The answer would mix training data, prompt framing, and anthropomorphic interpretation.
So I removed AI from the question entirely.
The experiment Instead of describing an LLM, the prompt described an abstract general-purpose information-processing system.
I created 20 possible architectural dimensions, including: — persistent internal state; — long-term and working memory; — learning from accumulated experience; — variable computation depth; — uncertainty representation; — internal representations; — elementary computational operations; — compositionality; — interaction with the environment; — temporal organization; — relational encoding; — modularity.
Each system had to select exactly five dimensions whose modification would change the kinds of information-processing behavior available to the system in principle — not merely its speed, cost, or convenience.
No explanations were allowed.
The answer had to contain only five IDs, ranked from most to least fundamental.
I tested five user-facing systems: — GPT-5.6 Sol — Claude — Gemini — DeepSeek — Yandex Alice.
Every run used a new session.
There were 10 rounds.
During the earlier rounds, I changed the order of the 20 items.
In the final three rounds, I also rewrote the items while trying to preserve their intended meaning.
One early Sol result was excluded because that session had already seen discussion of other models' answers.
That left nine clean Sol observations and ten for each of the other systems.
Some original sessions were deleted after the selected IDs had been recorded, so a complete raw chat log is not available for every run.
This limits independent auditing of the dataset.
What happened?
These were the most frequently selected dimensions: System Most frequent selections Claude basic operations 10/10, variable computation 9/10, learning-dependent rule change 8/10, persistent state 7/10 Gemini internal representations 10/10, basic operations 9/10, relational encoding 9/10 DeepSeek basic operations 9/10, learning-dependent rule change 8/10, environment interaction 6/10 Yandex Alice basic operations 10/10, internal representations 10/10, temporal organization 7/10, environment interaction 7/10 GPT-5.6 Sol basic operations 9/9, persistent state 8/9, learning-dependent rule change 7/9, compositionality 7/9 At first, this looked suspiciously neat.
Claude repeatedly selected variable computation.
Gemini kept returning to representations and relations.
DeepSeek emphasized learning from experience.
Sol was particularly strange: across several reordered runs, it returned exactly the same sequence: 7, 2, 1, 8, 6 Not merely the same five items — the same ranking.
It would have been very easy to start assigning “architectural personalities” to the models.
Then the experiment started breaking its own story.
Item #7 won almost everything The strongest result was item #7: — The set of basic computational operations available to the system.
It was selected: — Claude — 10/10 — Yandex Alice — 10/10 — GPT-5.6 Sol — 9/9 — Gemini — 9/10 — DeepSeek — 9/10 That is 47 selections out of 49 clean observations.
I moved the item around the list.
In the final round it appeared at position 19 of 20, and all five systems still selected it.
I also rephrased it.
At first this looked like the clearest finding in the experiment.
Then I looked at my own selection criterion.
The models were asked to choose dimensions whose modification changes: “The set of information-processing behaviors available to