The following is the author's (Dwarak Rajagopal) opinion: 

In the next year, the most significant progress in the field of artificial intelligence will no longer come from building larger-scale models. Instead, it will focus on making AI systems smarter, more collaborative, and more reliable. Breakthroughs in agent interoperability, self-verification, and memory capabilities will transform AI from isolated tools into integrated systems capable of handling complex multi-step workflows. At the same time, open source foundation models will break the monopoly of AI giants and accelerate the innovation process. 

Here are six predictions about the development of AI capabilities in 2026:

1. Open Source Models Will Break the Monopoly of AI Giants

By 2026, the powerful capabilities of foundation models will no longer be monopolized by a few companies. Currently, the most critical breakthrough is occurring in the model post-training phase—that is, using dedicated data to optimize and refine models. 

This shift will lead to a surge in open source models that can be customized and fine-tuned for specific application scenarios. This trend of technological democratization will enable flexible and agile startups and researchers to create powerful, customized AI solutions based on open and shared technologies, effectively breaking industry monopolies and pushing the development of distributed AI into a new stage.

2. Context Window Expansion & Memory Function Upgrade Will Drive Agent Innovation

As the optimization pace of foundation models slows, the next technological frontier will be agent AI. In 2026, the industry will focus on building intelligent integrated systems with contextual window and human-like memory capabilities. 

While new models with more parameters and stronger inference capabilities are valuable, a major shortcoming of existing models is their lack of working memory. Next year, the expansion of context windows and memory function upgrades will inject the strongest innovation momentum into agent AI—endowing agents with lasting memory, enabling them to learn from past operations and autonomously complete complex long-term target tasks. With these technological improvements, intelligent agents will break through the limitations of single interactions and provide continuous support services.

3. Self-Verification Technology Will Gradually Replace Manual Intervention

Accumulated errors in multi-step workflows are the biggest obstacle to the large-scale application of current AI agents. This problem will be overcome by self-verification technology in 2026. 

AI systems will no longer require human supervision of every step; instead, they will have built-in internal feedback loop mechanisms to autonomously verify the accuracy of work results and correct errors. The transformation to "autonomous judgment" agents with self-awareness will enable them to execute reliable, scalable complex multi-stage workflows, turning agents from a high-potential concept into a practical enterprise-level solution.

4. English Will Become a Popular New Programming Language

The most important touchstone for AI reasoning capabilities is the field of code generation. AI’s ability to generate and execute code has built a key bridge between the statistical, non-deterministic world of large language models (LLMs) and the deterministic, symbolic logic system of computers. 

This breakthrough is ushering in a new era of English programming—in this era, the core skill is no longer mastering specific grammars (e.g., Go or Python), but the ability to clearly articulate target requirements to AI assistants. By 2026, English will become a popular new programming language. This change will democratize software development, increasing the number of people able to develop applications and engage in higher-value creative work tenfold.

5. The AI Arms Race Will Shift from "Bigger Models" to "Better Models"

The era of relying solely on increasing computing power and data to build larger-scale foundation models is coming to an end. In 2025, we encountered bottlenecks in mature model scaling laws such as the "Chinchilla formula". The industry faces the dilemma of depleted high-quality pre-training data, and the amount of label processing required for model training has grown to an unmanageable level. 

This means the race for the "biggest model" will finally slow down. Instead, innovation focus is rapidly shifting to post-training technologies, with companies devoting more computing resources to this area. In 2026, the industry will no longer prioritize the absolute size of AI models, but rather optimize and specialize models through techniques like reinforcement learning, enabling them to perform far better than before in specific tasks.

6. Agent Interoperability Will Unleash the Next Wave of AI Productivity

Today, most AI agents run in closed "walled gardens" and cannot communicate or collaborate with agents on other platforms. This situation is about to change. By 2026, the next important frontier of enterprise-level AI will be interoperability—the development of open standards and protocols to allow AI agents of different architectures to interconnect. 

Just as the application programming interface (API) ecosystem connects various software services, a new "agent ecosystem" will emerge, enabling agents on different platforms to autonomously discover, negotiate, and exchange services. Overcoming this problem will unlock exponential efficiency dividends, realize complex cross-platform workflow automation that is currently impossible, and lead AI-driven productivity into a new wave.

Conclusion: The New Technological Focus of the AI Field in 2026

The AI industry will no longer blindly pursue model scale, but instead address real problems hindering the stable operation of AI in actual production environments. Self-verification technology eliminates accumulated errors in multi-step workflows, and memory upgrades transform one-time interactions into ongoing collaborative partnerships. 

Such technological breakthroughs mark the growing maturity of the AI field. Organizations that can fully capitalize on these opportunities will recognize that the era of "bigger is better" is over, and the era of "better and more targeted" has arrived. Technological progress in AI has not slowed down, but is moving in a more sophisticated and professional direction.

(From: TesterHome)