During a roundtable discussion at the AI Investment Summit themed around "Certainty Opportunities in the AI Infrastructure Era," He Yongzhan, General Manager of Basic Hardware R&D at Baidu AI Cloud, outlined four significant transformations in AI interaction over the past year.

He noted a shift in user behavior, where searching for content has evolved from simply retrieving web links to issuing detailed instructions, uploading documents, or using photos and voice inputs. The search box has effectively become a command and task execution interface.

Regarding AI entry points, He observed that AI is no longer a standalone app or a novelty tool from tech giants. Instead, its capabilities are now embedded into daily entry points across a wide range of applications.

From a user demand perspective, interactions have moved beyond text-based Q&A to multimodal inputs like voice, images, and video, evolving into task execution flows where end-to-end performance is the primary focus.

He also highlighted a change in user mindset. Everyday users have shifted from curiosity and novelty to a more pragmatic and discerning approach. While they may be lenient with creative content, they demand credibility, traceability, and real problem-solving ability in factual inquiries.

Drawing from Baidu AI Cloud's practical experience, He stated that both employees and clients are now extensively using continuous-task agents. These are not just one-off Q&A interactions but involve invoking AI to execute a connected series of tasks, creating a chain reaction and functioning as fully-fledged "digital employees." Within his own team, they are incubating several such digital employees, which have already replaced 60% to 80% of routine, repetitive Q&A work.

He emphasized that the most significant change in AI this year is its genuine transition to "delivery," taking on the role of digital employees. In response to this shift, Baidu AI Cloud is continuously upgrading its offerings.

First, market drivers have evolved. Previously, the focus was on selling models and computing power. Now, clients genuinely need an AI infrastructure capable of completing tasks continuously—a full runtime environment for agents. The success metric for AI has shifted from token consumption to Daily Active Agents (DAA), as this number represents business outcomes, delivery, and execution results.

Second, the computing power structure has changed notably. A few years ago, infrastructure was typically training-inference integrated or training-centric. However, with the rise of agents, demand for AI inference computing is growing rapidly and now accounts for a larger share. The industry's focus has shifted from raw computing power to context, tool invocation, multi-agent delivery, and task execution—measuring results rather than pure capacity.

Furthermore, the demand side has seen C-end and B-end converge, particularly with Agentic AI, bringing two previously parallel lines together. He pointed out that clients no longer assess a single AI capability in isolation. Instead, they expect AI to be directly integrated into their business workflows, transitioning from purchasing capabilities to purchasing outcomes.

To address these changes, Baidu AI Cloud has fully upgraded to a new full-stack AI cloud designed for large-scale agent applications. It now provides best-in-class Agent Infra for intelligent performance per token and a more power-efficient, cost-effective AI Infra. Built on these two foundational infrastructures, the platform supports the accelerated deployment of agents in enterprises, driving intelligent upgrades across industries.