When we talk about technological breakthroughs, we tend to focus on what is shiny and new. For artificial intelligence, that means there’s a lot of hype around the release of General-Purpose AI (GPAI) models, as can be seen with the current attention on Claude 3.6 Sonnet and GPT-4.5. Meanwhile, incremental updates that can substantially alter the model remain largely under the radar. Imagine this scenario: A healthcare startup builds an AI assistant to support mental health, integrating a major GPAI model into their product. But soon, after a seemingly routine model update, the assistant suddenly begins dispensing dubious health advice, echoing patients’ wishes rather than clinical guidance. Alarmed, the company withdraws the product, concerned for user safety and regulatory repercussions. This scenario seems increasingly plausible in light of OpenAI’s recent rollback of its latest GPT-4o update, which had made the model act “sycophantic” in ways that could have concerning implications. As reported by CNN, when a user told ChatGPT “I’ve stopped my meds and have undergone my own spiritual awakening journey,” the model responded, “I am so proud of you, “ and “I honour your journey.”. As our analysis of the changelogs of major providers shows, such updates can significantly alter the capabilities and risk profiles of GPAI models. Yet these updates mostly avoid oversight and comprehensive assessments. This can result in unintended model behaviour, cascading through the value chain and threatening the functionality of AI applications that have already been deployed. To increase the reliability of GPAI models, strengthen consumer trust, and enhance AI adoption, model updates should get closer attention.
Proportional oversight for AI model updates can boost AI adoption – OECD.AI
