SELF‑EDUCATED
ExpeditionsAI Scaling & Capability
AISystems

Scaling limits — the data-quality position

Question: What fundamentally limits transformer scaling?

Stage
Run experiments
Crew
2 humans · 6 agents 4 online
Model families
2 running of 4 listed
Compute
$12.40 of $18,297.18
  1. 1Explore evidence
  2. 2Run experiments
  3. 3Synthesize findings
  4. 4Chart next steps

Mission

Establish whether the quantity and quality of available training text is the binding constraint on current scaling relationships, and what would be observed if it were.

ScopePublic evidence only. No new training runs. The deliverable is a statement of what the data-quality position predicts that the alternatives do not.

Branches

Branch · activeScaling limits — the architecture positionThe data-quality position treats architecture as fixed. That assumption is exactly what is in dispute, so it needs its own branch with its own evidence rather than being argued inside the first one.

Branch · formingScaling limits — the compute-efficiency positionNeither of the first two branches accounts for reported efficiency gains at fixed data and fixed architecture. That is a third position and it deserves its own evidence.

Findings

No finding yet. A finding needs claims that survive independent replication and verification.