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"there are quite a lot of misunderstandings about big models in the outside world." recently, an internal speech by robin li was exposed. robin li believes that the gap between big models may become larger and larger in the future. he further explained that the "ceiling" of big models is very high, and it is still far from the ideal situation. therefore, the model needs to be continuously and rapidly iterated, updated and upgraded; it needs to be invested for several years or even more than ten years to continuously meet user needs and reduce costs and increase efficiency.

regarding the industry's statement that "there are no barriers between the capabilities of large models", robin li gave a different view: "every time a new model is released, it is compared with gpt-4o, saying that my score is almost the same as it, or even exceeds it in some individual items, but this does not mean that there is no gap with the most advanced model."

he said that in order to prove themselves, many models will compete in the rankings after being released, and will try to guess test questions and answering skills. judging from the rankings, the capabilities of the models may be very close, "but in actual applications, there is still a clear gap in strength."

li yanhong pointed out that the gap between models is multi-dimensional. the industry often pays more attention to the gap in understanding, generation, logic, memory and other capabilities, but ignores the dimensions of cost and reasoning speed. although some models can achieve the same effect, they are costly and slow in reasoning speed, and are still not as good as advanced models.

li yanhong also said, "before the era of big models, people were used to the idea that open source meant free and low cost." he explained that, for example, open source linux was free to use because people already had computers. but this is not true in the era of big models. big model reasoning is very expensive, and open source models do not provide computing power. people have to buy equipment themselves, which makes it impossible to achieve efficient use of computing power.

"open source models are not efficient," he said. "to be more precise, closed source models should be called commercial models, where countless users share the r&d costs, machine resources and gpus used for reasoning. gpus are the most efficient. the gpu utilization rates of baidu wenxin big model 3.5 and 4.0 have reached more than 90%."

li yanhong analyzed that the open source model is valuable in fields such as teaching and scientific research; but in the business field, when the pursuit is efficiency, effectiveness and lowest cost, the open source model has no advantage.

at the level of large-scale model applications, robin li believes that the first to appear will be copilot, which will assist people; the next will be agent intelligent bodies, which have a certain degree of autonomy and can use tools, reflect, and evolve themselves; if this level of automation is further developed, it will become ai worker, which can independently complete various tasks.

he also said that although "many people are optimistic about the development direction of intelligent agents, to date, intelligent agents are still not a consensus, and there are not many companies like baidu that regard intelligent agents as the most important strategy and the most important development direction of large models."

li yanhong believes that the threshold for intelligent agents is indeed very low. many people do not know how to turn large models into applications, and intelligent agents are a very direct, efficient and simple way. it is quite convenient to build intelligent agents on top of the models.