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Zhou Hongyi 360 集团 · 创始人、董事长
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Zhou Hongyi says agents are becoming the main vehicle for AI deployment and that inference compute demand is exploding, surpassing training.

We made two forward-looking predictions: first, agents become the main vehicle for AI deployment, with competition shifting from parameter comparison to real-world deployment capability. Second, inference compute demand explodes, with inference usage and growth surpassing the compute needed for model training.
Original (zh) 一是智能体成为AI落地主角,智能体应用走进千行百业,深入生产和业务系统,行业竞争焦点从参数比拼转向落地能力比拼。二是推理算力需求爆发,人们使用智能体完成复杂行业任务或需求时,需要多次调用大模型进行推理,算力消耗巨大,推理算力使用规模、增速全面超越大模型训练所需算力。

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Zhou Hongyi says cybersecurity underpins national security, and as everything becomes software-based and intelligent, vulnerabilities will inevitably be exploited and attacked.

There is no national security without cybersecurity. Cybersecurity is closely tied to every individual and to the development of every country. Especially once everything becomes software-based and intelligent, there will certainly be vulnerabilities, and vulnerabilities will be exploited and attacked.
Original (zh) “没有网络安全,就没有国家安全。”全国政协委员、360集团创始人周鸿祎在接受“国家安全大家谈”采访时表示,网络安全跟我们每个人、跟每个国家的发展休戚相关。特别是当所有东西都软件化、智能化之后,一定会有漏洞,有漏洞就会被人利用,就一定会被人攻击。
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Zhou Hongyi says future project teams may consist of one leader plus a group of AI employees with different capabilities, with the leader responsible for goals, trade-offs and results.

未来项目组可能是一个负责人加一群不同能力的AI员工,负责人负责目标、取舍和结果。
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Zhou Hongyi warns that long-half-life foundational principles and judgment must not be outsourced to AI, or people will be unable to spot AI's mistakes.

长半衰期的基础原理和判断力不能外包给AI,否则无法识别AI的错误。
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Zhou Hongyi suggests a company's core competitiveness may no longer depend on headcount but on how many AIs take part in real work internally.

公司核心竞争力可能不再只看员工数量,而看内部有多少AI在参与真实工作。
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Zhou Hongyi says enterprises are shifting from training to inference applications, which will drive hundred-fold short-term growth in inference compute that surpasses training compute.

Enterprises no longer frequently train large models from scratch, but instead call AI directly through inference applications to solve real problems; this shift will drive a short-term hundred-fold growth in inference compute demand, exceeding training compute in both scale and growth rate.
Original (zh) 企业不再频繁从头训练大模型,而是更多通过推理应用直接调用AI解决实际问题,这一转变会推动推理算力需求呈现短期百倍级增长,规模和增速都将超越训练算力。