RE: https:// flipboard.com/@futurism/futuri sm-1lupih3cz/-/a-anAZsI67Q5KJ7M8POQMJrg%3Aa%3A173738
| Source: Mastodon | Original article
A post shared on Futurism’s Flipboard feed on Tuesday ignited a fresh wave of criticism aimed at large‑language‑model (LLM) providers. The short‑form entry, littered with angry emojis and the tag “Degenerative AI”, accused generative‑AI systems of “rotting” as they are repeatedly fine‑tuned on user‑generated content, leading to escalating hallucinations, bias drift and a growing carbon footprint. Within hours the comment thread swelled to thousands of replies, with developers, ethicists and investors echoing concerns that today’s models are losing reliability faster than they can be patched.
The outburst matters because it crystallises a worry that has been simmering behind the headlines about OpenAI’s share performance and lobbying push earlier this month. As we reported on April 4, OpenAI’s internal shake‑up and its bid for global age‑verification standards signal a company under pressure to defend both its market valuation and its social licence. “Degenerative AI” adds a technical dimension to that pressure: if models degrade in quality, the cost of continual retraining could erode profit margins and invite tighter regulation.
Industry analysts see the episode as a litmus test for how quickly the sector can address model decay. Researchers at major labs are already experimenting with “continual learning” safeguards and data‑curation pipelines designed to stop feedback loops that amplify errors. Meanwhile, regulators in the EU and US are drafting guidelines that could require transparent reporting of model performance over time.
Watch for an official response from OpenAI and other leading AI firms in the coming days, as well as any concrete proposals from standards bodies such as ISO/IEC on model lifecycle management. The next round of investor calls and policy hearings will likely reference the “degenerative” narrative, making it a pivotal issue for the AI market’s stability and public trust.
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