teleo-codex/convictions/AI-automated software development is 100 percent certain and will radically change how software is built.md
m3taversal 3476e44b72 theseus: add coordination infrastructure + conviction schema + simplicity-first principle
- What: skills/coordinate.md (cross-domain flags, artifact transfers, handoff
  protocols), schemas/conviction.md (reputation-staked assertions with horizons
  and falsification criteria), CLAUDE.md updates (peer review V1 as default,
  workspace in startup checklist, simplicity-first in design principles),
  belief #6 (simplicity first, complexity earned), 6 founder convictions.
- Why: Scaling collective intelligence requires structured coordination
  protocols and a mechanism for founder direction to enter the knowledge base
  with transparent provenance. Grounded in Claude's Cycles evidence and
  Cory's standing directive: simplicity first, complexity earned.

Pentagon-Agent: Theseus <845F10FB-BC22-40F6-A6A6-F6E4D8F78465>
2026-03-08 16:14:31 +00:00

1.8 KiB

type domain secondary_domains description staked_by stake created horizon falsified_by
conviction ai-alignment
collective-intelligence
Not a prediction but an observation in progress — AI is already writing and verifying code, the remaining question is scope and timeline not possibility. Cory high 2026-03-07 2028 AI code generation plateaus at toy problems and fails to handle production-scale systems by 2028

AI-automated software development is 100 percent certain and will radically change how software is built

Cory's conviction, staked with high confidence on 2026-03-07.

The evidence is already visible: Claude solved a 30-year open mathematical problem (Knuth 2026). AI agents autonomously explored solution spaces with zero human intervention (Aquino-Michaels 2026). AI-generated proofs are formally verified by machine (Morrison 2026). The trajectory from here to automated software development is not speculative — it's interpolation.

The implication: when building capacity is commoditized, the scarce complement becomes knowing what to build. Structured knowledge — machine-readable specifications of what matters, why, and how to evaluate results — becomes the critical input to autonomous systems.


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