my 25 year adventure in ai and ml in the long run
This looks like a single event, but it behaves like a shift in defaults. The public narrative is clean; the operational tradeoffs are not (source).
see also: LLMs · Model Behavior
set up
The visible change is obvious; the deeper change is the permission it creates. I read this as a reset in expectations for teams like LLMs and Model Behavior. Once expectations shift, the fallback path becomes the policy.
clues
- What looks like a surface change is actually a control move.
- The way my 25-year adventure in ai and ml is framed compresses complexity into a single promise.
- The path to adopt my 25-year adventure in ai and ml looks smooth on paper but assumes alignment that rarely exists.
system motion
policy shift → procurement changes → roadmap narrows surface change → tooling adapts → behavior hardens constraint tightens → teams standardize → defaults calcify
fault lines
- my 25-year adventure in ai and ml amplifies model brittleness faster than the value it returns.
- Governance drift turns tactical choices around my 25-year adventure in ai and ml into strategic liabilities.
- The smallest edge case in my 25-year adventure in ai and ml becomes the largest reputational risk.
my take
I’m leaning toward treating this as structural. Build for the default that’s forming, but keep an exit path.
default drift
constraint signal
linkage
linkage tree
- tags
- #market-news
- #ai
- #2024
- related
- [[LLMs]]
- [[Model Behavior]]