The Year Ahead: AI Trends for 2026
By Vicktor Moberg
2025 saw incredible advancements in artificial intelligence (AI) and the capabilities of large language models (LLMs). These capabilities gave rise to AI Agents- pieces of software that could execute functions and tool calls and were controlled by an LLM as it’s “brain”. By the end of 2025, we began seeing AI Agents in all sorts of places, accomplishing tasks and in some cases taking over the jobs of various positions. 2026 will continue to see many new advancements.
The Evolution of Agents
While we already saw the beginning of agents coming to life, in 2026, they will be more complex and the norm. Agents will be able to better reason and plan, and teams of them, a mix of experts, will begin doing tasks. They will be able to self-correct and become smarter through learning from their mistakes.
Embedded AI Systems
In 2026, we will see more AI embedded systems- that is, AI built into more applications such as web browsers, operating systems, smart cameras and other devices, and other systems– all operating with a local AI model and no need for API calls to an AI service, like Gemini or ChatGPT. This actually means more privacy for users– your data stays local to your device– and cuts down on response time. Google has already released Chrome with a small version of Gemini, Gemini Nano (Google) built into it with various tools, and OpenAI released ChatGPT Atlas (OpenAI) as a direct competitor, but these require small models to fit nicely inside without disrupting the user’s experience.
Outside of apps, user devices like laptops, and browsers, we will also see AI embedded in robotics in more sophisticated ways. Rather than relying solely on pre-programmed routines and ML models for object recognition, robots will begin learning through real-world trial and error. For example, a warehouse robot might autonomously experiment with different navigation patterns to optimize efficiency, or a robotic assistant could learn household preferences through repeated interactions—adjusting its behavior based on feedback rather than explicit programming. As these machines become more capable at human-scale tasks, natural communication becomes essential. Embedded language models will enable workers and users to interact with robots through conversation rather than control panels, making the technology more accessible and intuitive.
Security and Threats
As AI becomes exposed to more sources and is embedded in more systems, the risk of using them and their attack surface increases. AI models are still susceptible to prompt-injection attacks, hidden prompts that instruct Agents to act maliciously and pass user information to bad actors and these risks will increase as agents are used more and more (Forbes). These prompts might be hidden on legitimate websites, or fakes, as text made to look like the background of a page or in hidden files that the agent may access and read. These instructions can also be stored in the agent’s long term memory and turn the agent against the user down the road (Manila Times).
A persistent risk in corporate environments is that of the “super user”, a user with high level permissions, able to read and write critical and confidential information, and with access to the internal systems of a corporation. As more work is offloaded to agents, companies and developers need to make sure they still use zero-trust and follow the Principle of Least Privilege to ensure their agents can only access the data it absolutely needs, and not be able to execute commands that can damage internal systems, like a Replit agent did (Fortune).
Governance
AI moved too quickly for most governments to keep up and many spent months passing laws to protect users, often long after the threat emerged and people became victims, knowingly or not. Three such pieces of legislature from the United States are the, “NO FAKES Act,” the “ENFORCE Act”, and the “DEFIANCE Act”, which prohibit the publication and distribution of AI generated images of people without their consent, close loopholes making AI generated images of children, not based on real children, just as illegal as real child pornography, and provide federal civil remedy for victims of non-consensual AI-generated pornography. In Denmark, it was ruled that people own the copyrights of their likeness and voice, making it illegal to use them in AI generated content without permission (360Busiess). However, these came rather late, and many people have only recently become aware of the abuse of these image generating models. In recent weeks, it was discovered that users on X, were using Grok to alter pictures of women and place them in sexually explicit poses and clothing (Reuters).
This year, having seen what AI is capable of, they will be moving faster to be more proactive. However, there might be challenges over these laws as in the US, federal bills are being drafted to limit what sort of regulations the states can impose. We may see similar conflicts as what one country allows AI to do, another bans.
Chip Development
Currently, there is a shortage of memory chips for graphical processing units (GPUs). This has driven the price of DDR5 chips into the hundreds and thousands of dollars. Several companies have been forced to pivot their strategies in response to this scarcity 1 . While rumors briefly circulated that Asus would start manufacturing its own RAM to bypass the bottleneck, they recently denied those plans, choosing instead to double down on partnerships with giants like Samsung and SK Hynix (Tom’s Hardware). In the meantime, the industry is bracing for a long haul, with experts predicting that this high-bandwidth memory (HBM) crunch will continue to inflate prices and delay hardware rollouts until at least mid-2027.
In Conclusion
2025 saw rapid advances in AI development and 2026 will be no different. The evolution of AI continues and it is up to us to ensure it does so with a human first-approach.
As we stand at this technological crossroads, the questions before us are not merely technical but deeply ethical. The integration of AI agents into our daily systems, the embedding of intelligence into our devices, and the unprecedented capabilities these technologies bring demand more than innovation—they demand wisdom. The security vulnerabilities, governance challenges, and hardware constraints we face are symptoms of a larger truth: technology advances faster than our collective ability to guide it responsibly.
Yet there is reason for hope. The very concerns that surfaced in 2025—from prompt injection vulnerabilities to the abuse of generative models—have catalyzed a growing awareness that artificial intelligence must serve human dignity, not simply human convenience. The legislative efforts, however imperfect and delayed, signal a societal awakening to the need for guardrails. The shortage of GPU memory chips, while frustrating, reminds us that limitations can slow our rush toward implementation without understanding.
The year ahead will test whether we can build AI systems that amplify the best of human capability while respecting the boundaries of human autonomy and privacy. As agents become more sophisticated, as AI embeds deeper into our infrastructure, and as the tools become more powerful, we must ask ourselves repeatedly: are we advancing technology in service of human dignity, or simply for the sake of advancement itself?
The answer to that question will determine not just what AI can do in 2026, but what kind of world we will inhabit because of it.