"Global AI-relevant compute projected to grow 10x by December 2027" - the timeline compression is real.
The shift from physical bottlenecks to human/institutional bottlenecks is the key insight. Synthetic data (Meta's self-play, Waymo simulating 20 billion miles daily) dissolves traditional data constraints. What remains: grid interconnection queues, permitting backlogs, regulatory processes.
Safety as engineering problem rather than philosophical debate is refreshing. Automated testing and interpretability research can scale alongside capabilities if we treat it as systems design.
excellent explaination about RSI. Thanks
"Global AI-relevant compute projected to grow 10x by December 2027" - the timeline compression is real.
The shift from physical bottlenecks to human/institutional bottlenecks is the key insight. Synthetic data (Meta's self-play, Waymo simulating 20 billion miles daily) dissolves traditional data constraints. What remains: grid interconnection queues, permitting backlogs, regulatory processes.
Safety as engineering problem rather than philosophical debate is refreshing. Automated testing and interpretability research can scale alongside capabilities if we treat it as systems design.
I've been building production agents where institutional constraints matter more than technical ones: https://thoughts.jock.pl/p/ai-agent-comparison-claude-manus-zapier-real-testing-results-2025
On one sentence M