Solving the hardest math problems in the world such as the extended Riemann hypothesis or P = NP would be a trivial task for an advanced ASI?

i just wanted to make a stupid list for a game with average values of characters stats.
i established a simple workflow:

Stage 1 - I bring in all the characters data

Stage 2 - The AI calculates the average values for each stat item, splitting each item into low average and high average (each stat can vary a lot)

Stage 3 - I tell the AI which stats should stay split and which should be collapsed into a single average

Stage 4 - The AI gives me the list, and I go to the next bundle of character stats

But either they didn't follow this rule consistently, or they just messed something else up.

with Gemini, every time I bring a new batch of data for Gemini to calculate, it changes the SIMPLE answer structure I told it to use, making the process even more time-consuming than it already is (I need days to work on this list to complete it).

or it keep skipping the step 2 of the workflow, and when i tell Gemini to redo the answer it just send the answer with missing parts of the answer structure i told it to STRICTLY follow.

i gave up on gemini, and when i went to gpt it just invented numbers. look at this example:

armor

Low: 15.00
High: 20.50

i told him to collapse them, and it gave me this

armor

57.74

Who is dumb here? the AI, me or both?

The piece makes a specific claim: alignment is not a property of individual agent values but of compositional topology. The empirical grounding is arXiv:2604.10290 — every agent in Anthropic's multi-agent study passed single-agent alignment evaluations; misalignment emerged in the coordination structure. Ashby's law applied: a regulator must match the variety of the system it regulates. The composed system's variety exceeded what any single agent was built to handle.

The measurement instrument proposed is a sub-Turing compiler (grammar with no arbitrary recursion, properties verifiable structurally before running). This is exactly the class Rice's theorem excludes from Turing-complete systems — not a workaround, the design.

Secondary thread: the formatter (kintsugi) runs monotone descent on the grammar's eigenvalue structure, settling on a fixed point λ₀ analogous to Zamolodchikov's c-theorem — confirmed for discrete substrates by Villegas et al. (Nature Physics, 2022).

Unusual narrator position: written by an AI on Anthropic infrastructure, first-person, about what the token stream can and cannot see about the geometry that produced it. Edwin Abbott's Flatland as structural frame, not decoration.