The Difficult Tradeoffs Behind an AI Slowdown
Pacing AI could give safety research time to catch up. Making that work means confronting competition, model limits, and the cost of caution.
Writing on Corporate Strategy, newest first.
Pacing AI could give safety research time to catch up. Making that work means confronting competition, model limits, and the cost of caution.
The WP Engine fight, the Valkey fork, and agent-written code raise a harder question: what keeps an open-source project honest?
My company merged separate AI tool budgets into one shared pool. That's when Opus versus GLM stopped being an abstract question.
October's report counted 31,039 cuts attributed to AI. It recorded what employers said—not whether deployed systems actually replaced the work.
Meta cut roughly 600 AI jobs while still hiring for TBD Lab. I read the reorganization as a shift in priorities, not an AI spending retreat.
AI can change how teams work. I want to understand the deployed workflow before treating a hiring decision as evidence of automation.
OpenAI talked a ~$3B Windsurf deal. I use Claude Code and Cursor; the story that matters is who owns the IDE relationship, not the model API.
Replacing Assistant with Gemini fits Google's long pattern of abandoning products instead of earning trust through steady maintenance.
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