Can We Not Ask the AI to Simply Invent Itself?

Question · geography_cartography · molten · visibility: publishable

Arrived as a snide hot take and stopped being one mid-sentence: ‘if you are so mythical at
patching stuff, why don’t you just patch the earth?!?!’ Then, straightening: ‘but this question is
becoming less and less of a joke: can we not ask the ai to simply invent itself? in all
seriousness, paradigms-outlast-constraints is becoming increasingly inexcusable. so many
workarounds to staple the map back onto the globe, and so much less concern about the globe
itself.’

The question, unpacked: representation learning already invents its own spaces — that is the whole
mechanism. Yet the field points that capability at automating the old representation (LLM
copilots scripting desktop GIS) or at welding encoders more faithfully to the rock (spherical
location encoders that ‘solve’ projection distortion by preserving spherical distance), and never
at re-deriving the spatial data model itself. The constraint that excused the paradigm — compute,
storage, the survey — is gone; the paradigm persists anyway, and now has an assistant. The
positioning scan (04) found the empirical backdrop: the field’s own agenda paper asks ‘what
Geography looks like according to ChatGPT’ while containing zero occurrences of the words
‘projection’ or ‘datum.’ The workarounds tend the map; nobody is tending the globe.

Her elaboration, next day, generalizing it past geography: ‘ai/ml is already a definite in
computation. as you continue to build future tools, it is not out of the question to think you may
go back and patch some of the earlier baked in shit that we now have the technology to improve.
like word processors SUCK. they suck. all of them. how is that even possible? and i do think the
answer lies in microsoft word and .doc and .txt having been the norm, and there never having been
a better option. the file format monopoly is basically adobe and microsoft… the point is,
building future tools may necessarily require reinventing/perfecting old ones.’ The question is
C.code-owns-code scaled up: not just Claude maintaining the scripts, but AI re-deriving the
baked-in substrates — file formats, word processors, spatial data models — whose only defense is
that they were there first.

And the follow-up that names what the field builds instead: ‘surely the answer is to make the
llms understand the machine learning algorithms. like, why on earth do i want a concierge? i want
the guy who wrote the algorithm to explain to me what it does. what are we even doing.‘

Connections

• → O.paradigms-outlast-constraints · escalates
• → A.geography-fails-at-both · demands
• → C.code-owns-code · extends
• → C.welded-to-one-shape · conceptually_related_to
• → P.attention-is-tobler-made-learnable (held) · conceptually_related_to

source: live code session (2026-07-03) · extraction: auto (schema v3.1)