What Does the AI Industry Compete for When Large Language Models Are No Longer Scarce?

Homegrown AI in China is shifting from the "parameter race" to the "delivery race".

If you follow AI news for the past six months, you will find that when large models are no longer a scarce commodity, the number of people arguing is decreasing, while more and more people are taking practical actions.

In May 2026, domestic large models began to launch paid packages in a concentrated manner. The industry-wide debate over "whether AI can make money" has largely faded, and the question has shifted accordingly — from "whether the model has sufficient capabilities" to "whether it can generate profits stably and in compliance with regulations". The benchmark performance scores and parameter scales that everyone once chased are no longer that critical in this round of industrial competition.

This signal means that the industry is shifting from "user growth" to "value verification" — the logic of simply acquiring customers for free to drive traffic is being re-examined by industrial clients.

Relying on the long-term monitoring capability of CTR Market Research (CTR) for serious news from mainstream central media, CTR Media Convergence Research Institute has systematically sorted out all AI-related news and information on the large-screen end of China Media Group from May to June 2026, and finally formed 1,840 structured records. These records not only fully reflect the recent development characteristics of the AI industry, but also show what the national authorities are paying attention to at the policy level.

I. AI competition has moved out of laboratories and entered the scene of engineering implementation

According to the distribution of reports, there are 332 reports on robotics and embodied intelligence (18.0%), 295 reports on governance, ethics and standards (16.0%), and 263 reports on computing power chips and infrastructure (14.3%). The three categories add up to 890 reports, accounting for 48.4%. Nearly half of the reports are talking about the same thing: AI is stepping out of the screen and entering construction sites, workshops, procurement contracts and regulatory documents.

This means that the competitive boundary of AI has expanded from model capabilities to "whether large-scale deployment can be realized" and "whether institutional governance can be achieved". Just as a power grid is not only a generator, and logistics is not only a truck, the AI industry will next compete in scheduling systems, compliance systems and delivery capabilities.

What truly determines the market position is the capability to enter real scenarios at controllable costs and form a trusted delivery closed loop.

II. From "being able to move" to "being able to work": Robots are getting through the last mile of the physical world

There are 332 reports on robotics, accounting for 18.0%; these reports collectively point to a change — the industry has moved from the past demonstration stage of competing for "being able to move" to the operation stage of competing for "being able to work".

Orders are concentrated in scenarios with high repeatability and controllable environments — industrial handling, warehousing and logistics, high-risk operations, and professional cleaning are the first tracks to achieve large-scale development. Clients no longer pay for one-off demonstrations, but focus on the success rate of a single task, total operating hours, and the ability to continue operating at the established rhythm.

At the same time, agents are also closing the "dialogue window" and entering the "controlled execution" stage. Agents that can operate software, call tools, and collaborate across multiple applications are replacing "chatty assistants" and becoming a new generation of product form. As a result, permission grading, operation logs, and exception rollback will change from bonus items to hard indicators in procurement contracts.

III. Computing power bids farewell to the "era of piling up GPUs", and operational efficiency has become the new measurement standard

There are 263 reports on computing power, chips and infrastructure, accounting for 14.3%. There are fewer reports focusing on "how many GPUs each party has"; instead, the focus has shifted to scheduling, utilization rate, inference optimization, heterogeneous collaboration and energy cost.

The policy orientation is also obvious — the priority is no longer to "build more computing power centers", but to weave scattered computing resources into a national network that supports monitoring, grid connection, scheduling and billing.

What is happening simultaneously is the hierarchical collaboration between industry-specific models and general-purpose models: general large models serve as the base, while industry-specific models and rule systems undertake professional constraints.

The moat of enterprises will increasingly lie in data assets and process assets, rather than parameter scale.

IV. Compliance is not a toll fee, but an admission ticket for procurement

There are 295 reports on governance, ethics and standards, accounting for 16.0%. The superposition of themes such as standardized application of agents, reporting of AI irregularities, terminal intelligence grading, and ethical review indicates one thing — the governance objects have expanded from content platforms to models, agents, terminals and physical equipment.

For To B and To G businesses, this change is first reflected in the procurement process. Large institutions will pay more attention to whether the product has been filed, whether it has passed third-party evaluation, whether the content identification is clear, how the data is processed, how the audit is conducted, and whether there is an emergency response mechanism in case of accidents. Compliance maturity directly affects the transaction success rate.

Conversely, this means that enterprises that have made early efforts in compliance are queuing up to get the admission tickets for the second half of the game.

V. The "monthly subscription seat" model is fading out, and result-based pricing is rewriting the commercial rules of AI

There are 145 reports on large models, agents and commercialization, accounting for 7.9%. The launch of paid services by domestic large models is only one of the superficial phenomena, and the deeper change is that the billing logic is being rewritten.

The enterprise-level market will not pay for "how many months the service has been used", but focus on professional capabilities, task completion rate and stability. In scenarios such as customer service, marketing, R&D, and industrial operation and maintenance, hybrid billing modes based on call volume, task volume and even business results will become more and more common.

The old "monthly subscription seat" model will be eliminated by more industrial clients. Whoever can shift the focus from "what has been done" to "what has been successfully accomplished" is likely to gain the pricing power.

Closing Remarks

The second half of the AI industry is no longer a single-point competition for parameter scale, but a comprehensive competition that requires capabilities, cost control, scenario adaptation and governance all to be satisfied. However, under the bustling narrative, the problems that enterprises face every day are very specific — what exactly are the real demands in a certain scenario? What exactly are the compliance risks of a certain type? Which part of the cost structure is causing unnecessary losses? These problems need to be solved with solid data and research.

This article is from the WeChat official account "Dewai No.5" (ID: dewaiwuhao), the author is Sun Jialin, and it is released with authorization from 36Kr.