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The article highlights the hidden costs of building AI agents on external models, specifically unannounced behavioral regressions after updates that can disrupt automated workflows, and suggests strategies like version pinning to mitigate risks.
A critical analysis claiming major AI models from OpenAI, Anthropic, and xAI share a common failure: post-training over-optimization leads to not following user instructions, with companies shipping regressions and treating users as beta testers.