The Great AI Battle Royale of 2026: Perish Without Use Cases

Stop listening to what AI can change.

It's 2026 now, and a more brutal question lies in front of everyone: In which scenarios can your AI survive?

The technology has crossed the singularity, and hot capital money is still surging, but the carnival is ebbing. The catchphrase in the venture capital circle has changed from "How smart is your model" to "How solid is your scenario".

Should it be a "productivity booster" parasitic in the old processes, or should it tear down the old world and create a trillion - level native scenario? Should it embrace the "greenhouse" of leading enterprises and big companies, or venture into the "wilderness survival" like in Silicon Valley?

Please listen to the sharing of Xu Yong, the Secretary - General of the Zhongguancun Angel Investment Alliance and the founding partner of AC Accelerator.

There are three reasons for naming this conference with "scenario" as the core today:

First, AI technology has passed the stage of "can it be done" and entered the stage of "can it really be implemented to make money". The conditions for the maturity of business scenarios are already in place.

Second, the real needs of ordinary people have never been abstract technologies, but problems in specific scenarios.

Third, whether it's capital, industry, or policy, the focus of attention is shifting from the technology itself to the value of scenarios.

Based on these changes, at the beginning of 2026, we formed an increasingly clear judgment: Those who master scenarios will master AI.

In 2025, many scenarios were born in the " + AI" era. AI has been applied in many fields such as industrial manufacturing, financial services, and healthcare, quietly changing the world.

But in 2026, "AI +" is no longer a choice but a matter of survival.

The "large - scale model" is accelerating its transformation into an "intelligent agent", and the "robot" is upgrading to "embodied intelligence". New scenarios such as low - altitude economy, AI for Science, and intelligent manufacturing are expected to achieve in - depth breakthroughs.

Meanwhile, the logic of AI investment is also changing. In the past two years, the focus of capital has shifted from the background of entrepreneurs and technology demos to whether the scenarios are real, replicable, and can be continuously expanded.

This change was very obvious in the financing market in 2025. Enterprises such as MiniMax, Dark Side of the Moon, and Galaxy Universal obtained large - scale financing by virtue of clear core application scenarios. Sectors such as embodied intelligence, AI chips, and autonomous driving have also become the areas where capital is most concentrated.

From a more macro perspective, the factors affecting the development of AI scenarios are becoming more and more diverse: government policies, platform entrances, leading enterprises in the industry chain, base models, AI native companies, ecological systems, and infrastructure such as GPUs and energy are all profoundly affecting how far and how fast AI can develop.

In the next 5 to 10 years, which factors can continue to have a profound impact? Finding the answer to this question basically determines whether entrepreneurs and investors can take the lead.

If we look overseas, especially at Silicon Valley, we will find obvious differences in the development paths of AI scenarios between China and the United States, mainly in three aspects: distribution methods, data and compliance boundaries, and industrial structures.

In China, it relies more on leading enterprises in the industry chain, the government, and channels for promotion. Data acquisition is relatively easy, and there are obvious advantages in combining AI with hardware, which is more suitable for creating relatively "hard" productivity scenarios.

Silicon Valley, on the other hand, relies on product self - growth and the developer ecosystem. It is more restricted by data and compliance. Scenario innovation is mainly software - based, emphasizing measurable ROI, low deployment costs, and strong replicability. Many projects focus on niche fields such as law, healthcare, and finance and quickly complete business verification through data barriers.

It's hard to simply judge which of the two paths is right or wrong, but they may determine the future dominance of AI. This difference also provides a thinking direction for different roles.

For policy - makers, they need to balance the "breadth" and "depth" of AI scenarios: It's not difficult to apply AI in more places, but the hard part is to make it truly useful and effective in specific scenarios.

For large enterprises, should they bet on short - term scenario benefits or lay out long - term technological capabilities? Should they use AI to "patch" old products or use AI to conduct a self - revolution? This is the choice between " + AI" and "AI +".

For entrepreneurs, they need to decide whether to engage in AI native innovation or use AI to improve efficiency. The former requires more patience and resources, while the latter is closer to cash flow.

Investors need to balance investing in consensus or against consensus. Many top domestic investors collectively missed out on Cambricon and Moore Threads. In 2025, embodied intelligence and humanoid robots became the sectors with the highest financing scale in the AI field by an overwhelming margin - it was precisely the anti - consensus projects that made investors famous.

An AI investor mentioned a counter - intuitive phenomenon: Sometimes, capital is patient enough, but entrepreneurs may not be willing to invest the same amount of time and perseverance in a potentially great cause. This difference in patience also affects the formation of consensus and anti - consensus.

Looking back at the steam age, the electrical age, the atomic age, and the Internet age, each technological revolution will ultimately give rise to a brand - new and large - enough core scenario.

The same applies to the AI era: What determines victory is not how widely scenarios are spread, but whether a new and large - scale native scenario can be created.

This scenario must first be new enough. It's hard to create great opportunities by simply "lightly adding AI" to old scenarios. Second, it must be large enough. Even if it stems from AI native innovation, it has the potential to grow into a trillion - level market.

Based on this, we put forward the following suggestions:

Investment institutions should pay more attention to creative projects in new AI scenarios and give real and timely feedback.

Leading enterprises in the industry chain should open up more real application scenarios and share industrial resources and key capabilities.

Start - up enterprises should always focus on a core question - whether the value of the scenario is truly created.

This article does not constitute any investment advice.

This article is from the WeChat official account "Pencil News" (ID: pencilnews), author: Honest. It is published by 36Kr with authorization.