SUMMARY: Brandon and Aaron discuss the pros and cons of owning or renting your model weights. What does that mean for the Enterprise, and what should you be considering?
SHOW: 1055
SHOW TRANSCRIPT: The Enterprise AI Show #1055 Transcript
SHOW VIDEO: https://youtu.be/uc0GZBLgUeo
SHOW SPONSORS:
Topic: Own Your Weights or Rent Them?
- Why now? Alex Karp had a spicy CNBC segment arguing enterprises should "own their weights" rather than rent models from the big labs — sparking a widely-shared response from Jamin Ball on Clouded Judgement. Substack
- Past: Same shape as the "own vs. rent" debate the industry has had before — on-prem vs. SaaS, buy vs. build for ERP/CRM — just replayed one layer down, at the model layer instead of the app layer.
- Present: A weight file is really just a frozen snapshot that degrades in relative terms as frontier models keep improving — what actually matters is owning the RL/training loop that keeps producing better weights, not the weights themselves. A model RL'd against a company's actual workflows can beat a frontier generalist model on that one task, and do it far more cheaply — but that leaves enterprises managing a sprawl of task-specific models that all need governing, versioning, and securing.
- Future: Ball frames it as a stated-preference vs. revealed-preference problem — everyone says they want model sovereignty, but the spend data shows enterprises keep writing bigger checks to the frontier labs every quarter because most don't have the talent or infra to run the loop. Where's the market for a company that closes that gap — makes "owning the loop" accessible without the complexity tax? Tie back to your Show #4 (off-the-shelf AI, harnesses) — this is basically that debate's sequel, one layer deeper. (Aaron’s hot take, and another episode: maybe it’s not about the weights at all…)
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