The AI bubble has reached its 'Fried chicken' phase (www.ft.com)

Journalists and commentators are arguing that AI’s hype cycle has hit what they call the “fried chicken” phase — a moment when an industry saturated with genuinely useful innovation starts producing a flood of derivative, low-value products riding the technology’s momentum. In practice this looks like a surge of startups and vendors slapping “AI” onto trivial workflows, one-off verticalized tools, and marketing-first applications built on the same large pretrained models rather than novel algorithms. The signal: excitement, easy access to powerful models, and abundant funding are driving quantity over quality. For the AI/ML community this matters because it highlights systemic shifts and risks. Technically, commoditization of foundation models and widespread API access enable rapid productization via fine-tuning, prompting, and lightweight adapters, but they also amplify problems around benchmarking, reproducibility, dataset bias, and model hallucination. The business side sees inflated valuations and talent diverted from core research to short-term productization, while infrastructure—compute, data pipelines, inference costs—becomes a bottleneck. The takeaway: expect a crowded market where rigorous evaluation, defensible data assets, domain-specific engineering, and meaningful user outcomes will distinguish long-term winners from the “fried chicken” noise.