Nathaniel Whittemore reviews a summer in which frontier models improved while government restrictions increasingly shaped when and where those models could be released. He points to Fable 5, GPT-5.6, Grok and Chinese open models as evidence of a growing gap between capabilities inside labs and what businesses and consumers can access.
Enterprise AI also became a cost-management problem. Companies started routing tasks between models, evaluating cheaper model families and considering open weights for price and data-sovereignty reasons. At the same time, harness engineering and repeatable agent loops emerged as disciplines for managing systems that carry out larger portions of knowledge work.
Whittemore says markets remained willing to fund AI despite concern about capital spending, while data centers became a major political issue before the US midterms. He closes with the growing cybersecurity risk from advanced agents, arguing that organizations now need stronger systems, policy and oversight because those capabilities are already part of operational reality.
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