Industry experts told CNA they expect a slowdown to bring longer testing periods, smaller roll‑outs and tighter limits on how autonomous AI agents can act on their own.

With more time for safety checks, firms may monitor and align agents rather than racing to launch the next version, and they could stop training once a model reaches a risky capability level, as OpenAI did with its Astra model.

Consumers are likely to notice the change only for the most powerful models. Slower releases and tighter restrictions—such as invite‑only access or staged roll‑outs—could apply to highly autonomous features.

Professor Li Jia explained that a slowdown would usually mean a longer gap between when a model is built and when it reaches users, more extensive testing before deployment, and a less aggressive escalation of training.

For everyday users, the impact may be minimal. Gary Gardiner said most people are not yet exploring the full potential of AI, so they would hardly notice any change in their daily interactions with the technology.

Overall, a more measured pace aims to deliver safer, more reliable AI tools without stopping progress, giving developers breathing room to focus on stability rather than constant disruption.