New AI trends worth paying attention to in 2026, from multi-modal agents to local large models, see this article

New AI trends worth paying attention to in 2026, from multi-modal agents to local large models, see this article

Entering 2026, the word AI no longer carries the aura it had two or three years ago. It is being pulled back into a more practical context and being repeatedly evaluated. The most intuitive feeling in the past year is that the absolute parameter scale of the model is no longer the center of the topic. What everyone is more concerned about is how well the model can achieve specific tasks, whether the inference cost is controllable, whether it can be run on one's own computer, and whether enterprises have improved efficiency after using it. This process from concept to effect verification is a stage that any new technology must go through, and AI is no exception. This article does not intend to list nouns, nor will it draw a big picture. Instead, from the perspectives of multi-modal Agent, local large model, AI video generation, AI programming, enterprise-level implementation, privacy and compliance, we will talk about the directions worthy of attention in 2026, and what unresolved practical problems there are behind these directions.

Multimodal Agent moves from demonstration to daily life

Agent has been a concept that has been discussed repeatedly in the past two years. Various manufacturers have made demonstration videos showing that they can surf the Internet, adjust tools, and write code, but not many can actually run it stably in daily work. The most obvious change in this direction in 2026 is the shift from single text input and output to multi-modal closed loop, that is, the Agent not only reads text, but can also view screenshots, video frames, listen to voice commands, and reversely output the results into the same multi-modal form. The actual ability brought by this closed loop is to take over work scenarios that are closer to real people, such as writing a summary based on the chart in a PDF report, such as filling out a form while watching a web page and then submitting it, or listening to a meeting recording to generate structured minutes and automatically arrange follow-up tasks. While capabilities are improving, maturity bottlenecks are also obvious. The stability of long tasks, error recovery mechanism, and cross-tool context transfer. These links that were edited out in the demonstration video are the key to whether the Agent can enter daily life. What deserves attention in 2026 is not what extra work Agent can do, but which vendors can really make long tasks repeatable, monitorable, and rollable.

The maturity and differentiation of local large model ecology

After the cloud large model has gone through the explosive period, the local model will show strong momentum in 2026. There are several driving forces behind this trend. First, the open source community continues to invest. Open source models such as Meta’s Llama series, Alibaba’s Qwen series, and DeepSeek’s DeepSeek are constantly narrowing the gap between them and closed source models in terms of effectiveness. Some tasks even have their own merits. Secondly, there is the evolution of consumer-grade hardware. The unified memory architecture of Apple chips shows unique advantages when running large models. NVIDIA consumer-grade graphics card memory is also continuously upgraded. It is no longer difficult to run models with billions of parameters on personal computers. The third is the dual demand for privacy and cost. Enterprises are unwilling to send internal data to external APIs, and individual users have begun to have the awareness of data retention. The differentiation of the local large model ecosystem is reflected in user stratification. Ordinary users can use fool-proof tools such as Ollama and LM Studio to run it on their own computers. Developers use vLLM and llama.cpp for more refined deployment optimization. Enterprise users deploy open source models on their own GPU clusters for internal applications. This differentiation means that the local large model is no longer a toy for geeks, but a truly layered ecology.

AI video generation enters controllable stage

The impression of AI video generation in the past two years has mainly been a few stunning demonstration clips. If it is really used for serious video production, there are still many shortcomings, such as characters' faces drifting between different frames, unnatural movements, and lack of consistency in lens switching. The obvious progress in this field in 2026 is in controllability, which has evolved from just trying your luck with a prompt word to being able to specify the duration, camera movement method, character consistency, stability of background elements, etc. Controllability means that this technology has begun to have the possibility to enter the actual production process. From a self-media blogger to make an opening animation to an advertising company to make a complete commercial, AI video is beginning to be used as a tool in the draft generation stage. There are still many unresolved issues in this direction, such as the consistency of long shots, the rationality of complex physical movements, and the precise synchronization of sound and images. These details determine whether AI videos can move from short videos to film and television-level production. At the same time, compliance issues with copyright and real-person images are more complex than image generation. The risks brought by deep forgery have led regulatory authorities in various countries to treat AI videos as separate compliance objects.

AI programming from code completion to pairing partners

The application of AI in the field of programming is one of the fastest-growing directions in the past few years. From the initial code completion tool to the current project-level understanding and multi-file collaboration, the evolution path is quite clear. The obvious change in 2026 is that AI programming tools begin to play the role of real pairing partners. They are no longer just completers for writing one-line if statements, but can understand the entire project structure, track function calls across files, and complete multi-step modifications based on natural language descriptions. Anthropic's Claude Code, various large-model-based IDE extensions, and command-line programming agents are all iterating in this direction. For developers, the actual working model has begun to change. In the past, it was developer-led and AI-assisted; now it is closer to developers who describe the tasks clearly, AI completes the first draft, and developers do review and refinement. This change in working mode also brings new problems, such as how to avoid AI from producing overconfident erroneous code, how to keep the code understanding ability from declining during the review stage, and how to get team members to reach a consistent quality standard for the code written by AI. These are engineering cultural challenges that cannot be solved by the tools themselves.

The real implementation scenario of enterprise-level AI applications

When talking about enterprise AI applications, we have to go back to a simple question: how much money did the company spend, how much manpower was saved, and how much new revenue was brought in? Enterprise AI applications in 2026 present a more realistic picture, and the scenarios that are truly common are concentrated in several categories. The first category is customer service and work order automation, handing over standardized inquiries that were handled manually in the past to AI; the second category is document processing, such as contract review, report summaries, and internal knowledge base Q&A, which AI is good at; the third category is the assistance of data analysis, business personnel can use natural language questions instead of writing SQL, lowering the threshold for analysis; the fourth category is the batch generation of marketing copywriting and creative materials to increase the speed of content production. The common features of these scenarios are clear task structure, high fault tolerance, and quantifiable cost savings. What is relatively less optimistic is the attempt to use AI as a core decision-making system. In scenarios such as risk control, medical diagnosis, and legal judgment, AI is more of an auxiliary than a substitute, because the issues of responsibility attribution, explainability, and the cost of serious errors have not yet been completely resolved. The real picture of the implementation of the enterprise is that it starts with the scenarios that can save money and speed up the process. It will take time to reach the deep water area.

The game between inference cost and hardware innovation

The other side of improving model capabilities is the cost of inference, which is often ignored in the past, but is actually the key to determining whether AI applications can be rolled out on a large scale. There are two lines of concern in this direction in 2026. One is optimization on the model side. Technologies such as sparsification, quantization, inference distillation, and expert hybrid architecture continue to reduce the computing power consumption of a single inference, allowing models with the same effect to run on cheaper hardware. The other is innovation on the hardware side. NVIDIA continues to dominate the high-end training market. AMD and Intel are accelerating to catch up in inference chips. Various types of dedicated inference chips are emerging in data centers and edges. Apple chips and Qualcomm Snapdragon have their own layouts in end-side AI. Together, these two lines push the cost curve per thousand inferences down, which is a good thing for application developers. It means that more scenarios that were not cost-effective in the past have become feasible, such as embedding AI into ordinary consumer apps, into smart home devices, and into local office software. The balance between cost and experience is still something that developers must continue to weigh. Free seems to be the best, but service sustainability is more important. This is a reality that any company making AI applications must face.

New challenges for privacy and compliance

As AI penetrates into more scenarios, the boundaries of privacy and compliance become more complex. In the past, when users used search engines or social software, they focused on whether data was collected; now when users use AI assistants, they have to consider whether the content uploaded to the model has been used for training, whether it has been manually reviewed, and whether it is flowing across borders. Mainland China has issued a series of generative AI service management measures, requiring service providers to complete corresponding filings, and clearly stipulates the sources of training data, content review mechanisms, and user identity verification. This framework will continue to be refined in 2026. The EU's AI Act takes effect in phases, imposing stricter compliance requirements for high-risk AI applications. Although there is no unified legislation at the federal level in the United States, states have successively introduced specific regulations on deepfakes and automated decision-making. For individual users, when choosing AI tools, pay more attention to the terms of data usage in the terms of service. If this switch is turned off for training authorization, turn it off. For enterprises, when adopting AI tools, using data compliance as a hard indicator for procurement evaluation, rather than remedial measures afterwards, will reduce a lot of unnecessary trouble.

Several observations on the AI ​​ecosystem in mainland China

Returning to the local perspective, several characteristics of mainland China’s AI ecosystem are worth mentioning separately. In terms of open source models, Alibaba Qwen, DeepSeek, Kimi, ChatGLM, etc. all continue to release open source weights, and their performance in Chinese scenarios is generally better than that of overseas open source models, which brings real convenience to domestic developers. At the application level, the product matrix of major manufacturers has been formed. Byte, Alibaba, Tencent, and Baidu all have their own AI assistant products, which are deeply integrated with their original businesses in search, content generation, office, social networking and other scenarios. In terms of vertical industries, companies specializing in large-scale industry models have emerged in the fields of law, medical care, education, customer service and other fields. These companies do not pursue general intelligence, but develop in-depth industry knowledge. The supervisory side continues to improve the filing system and makes clear requirements for training data sources, content review, and application scenarios. At the developer level, the AI ​​middle-end services provided by cloud vendors lower the threshold for model deployment, and small and medium-sized enterprises can also use capabilities that only large companies can afford. Overall, the maturity of mainland China's AI ecosystem in 2026 will be significantly higher than one or two years ago, and the actual input-output ratio will be easier to measure.

Some practical suggestions for individuals and businesses

Finally, I will give some pragmatic suggestions, in no particular order, based on the scenario. At the individual user level, it is more important to use it first than to worry about which one to choose. Start with daily scenarios such as writing documents, checking information, translating, and writing code, and gradually form your own workflow. For learners, understanding what AI can and cannot do is more important than chasing new models every week. The key is to develop critical judgment about AI output, and the facts that should be checked still need to be checked. For a small team, it is more cost-effective to choose one or two main tools to thoroughly understand them than to spread out ten tools for simple use. The cost of tool switching is underestimated by many people. For enterprises, they should first find one or two scenarios that can generate quantifiable benefits as pilot projects, run through the process, and then consider spreading them horizontally. Most of the projects that pursue company-wide AI from the beginning have unsatisfactory results. For industries with strict compliance requirements, priority is given to evaluating local deployment solutions. Although the initial investment is large, long-term data security and controllability are more guaranteed. AI is still evolving rapidly, and the judgment in 2026 is only a snapshot of a point in time. What is really important is to establish a habit of continuous learning, not to be biased by the popularity of concepts, and to always focus on places that can bring practical improvements to yourself and your business.

FAQ

Is it necessary for ordinary people to deploy large models locally?

The daily use of cloud AI assistants by ordinary users can meet most needs. The core value of local deployment lies in privacy-sensitive scenarios and offline scenarios. If your work involves data that cannot be uploaded to an external API, such as internal documents, personal diaries, and unpublished project code, on-premises deployment can resolve compliance concerns. If the computer performance is good, running an open source model with a parameter level of one billion to several billions will not put much pressure on the hardware, and it will be smoother to use after one trial and error. It is not required for general users, but it is worth spending a night or two to try it as a hobby.

Can AI video generation be used for commercial advertising?

Technically, we already have the ability to produce drafts and short materials, but there are still several issues to consider when using them for commercial advertising. The first is whether there is clear authorization for commercial use in the tool’s terms of use. The free version usually does not allow commercial use. The second is that if real-life images, brand IP, or famous scenes appear in the generated content, the corresponding portrait rights and copyright issues must be confirmed before commercial use. The third is that advertising regulations in some regions have labeling requirements for AI-generated content. For example, it is necessary to clearly state in advertisements that the content is generated by AI. It will be less troublesome to sort out these pre-existing issues before putting it into production than to find out that it cannot be used after finishing it.

Will AI programming put junior developers out of work?

There will not be widespread unemployment in the short term, but the content of work will change significantly. AI programming tools are responsible for a large amount of boilerplate code, repeated implementation, and unit test writing. This part was originally a link for junior developers to accumulate experience, but now it is compressed by the tools. This means that junior developers need to quickly build up their understanding of business, architecture, and debugging, rather than just writing code. Looking at the industry as a whole, AI has improved development efficiency and may make more new projects possible. The total number of developers required may not be reduced, but the ability structure is accelerating adjustment, which is real pressure.

What is the actual effect of the domestic open source model?

In public evaluation and actual use, domestic open source models such as Alibaba Qwen series and DeepSeek series generally perform better in Chinese scenarios than overseas general open source models. In English and code scenarios, they have their own strengths as overseas open source flagships. For domestic developers, the greatest value of these models is that they have open weights, can be commercially used, have complete Chinese documentation and community support, and have lower deployment costs and adaptation difficulties than overseas models. When it comes to model selection specifically, it is recommended to do a small-scale test run based on your own task type. The performance differences of different models on different tasks are greater than everyone thinks. It is easy to get into trouble just by looking at the list scores.

Which AI tool is the most cost-effective?

There is no one-size-fits-all answer, you need to choose based on the situation. For daily general conversation and writing, mainstream large-model assistants in the cloud can handle it. The difference is more about usage habits. For code-related tasks, IDE integrated tools with project understanding capabilities provide a better experience. For privacy-sensitive tasks, the locally deployed open source model is the most stable. For multi-modal creation, select specialized image and video generation tools and use them with universal assistants. For small teams with sensitive budgets, first use the free version to run through the process before considering a paid upgrade. Don't buy the package right away. Treat tools as extensions of productivity rather than status labels, and the choice will be much simpler.