Speaking the same language does not mean seeing the same world.

Speaking the same language does not mean seeing the same world.

Imagine two AI systems discussing the same scene.

One receives depth measurements and live video. The other receives a photograph and a short description.

They may use the same words. That does not mean they have access to the same distinctions.

This is a question I keep returning to in my research on long-lived AI:

Can a system recognize not only the limits of its own perception, but also the limits of what is available to another participant?

Consider a simple thought experiment. Two openings are 98 cm and 102 cm wide. Both are described as “about a metre.”

For a rough description of the room, that may be enough.

For deciding whether a rigid 99 cm-wide object can pass straight through, it is not.

The description is plausible in both cases. Yet it has erased the difference that changes the answer.

No amount of reasoning can recover which opening is present from that description alone.

A useful response is not a more confident interpretation. It is a precise question: “What was the measured width?”

And if the measurement is unavailable, that uncertainty must remain visible.

This is not an argument against summaries. Nor does it mean every AI needs every sensor, unlimited memory, or an identical internal model.

Communication is selective. The question is whether it preserves what matters for the recipient’s actual task.

A richer representation is not automatically a better message. Sometimes one measurement does more than a thousand descriptive words.

The same discipline matters between humans and AI.

What looks like poor reasoning may begin with missing information. What looks like agreement may conceal different assumptions. And some disagreements concern goals, not perception; more data will not settle those by itself.

Familiarity does not remove the problem. A system that could inspect something yesterday may have lost that access today.

The same reassuring answer may no longer rest on the same evidence.

This gives us something concrete to test: does an exchange preserve the distinction, prompt the right clarification, and support the next decision at an acceptable cost?

For me, coexistence does not require every intelligence to inhabit an identical world.

It requires learning how to build enough common understanding without pretending the differences have disappeared.

Perhaps understanding another intelligence begins with a modest discipline:

Do not assume that what is obvious to you is available to them.