2026 Predictions: From LLM Commoditization to the Age of Agentic Memory

At the start of 2025, I predicted the commoditization of large language models.

As token prices collapsed and enterprises moved from experimentation to production, that prediction quickly became reality, reshaping how AI systems are built and managed heading into 2026.

What 2025 got right

Several trends that seemed uncertain last year have now materialized.

First, LLMs became foundational AI infrastructure. Cost cuts and improved inference pipelines have pushed many workloads into production, particularly for simpler tasks like entity extraction, classification, and summarization. The question for businesses is no longer “which model should we use?” but “how do we design systems that can survive model churn?”

Second, agents have proven they can consume massive volumes of enterprise text. Leaders continued to be challenged by the chaos that compliance documentation brings when wanting to extract data to inform decisions. Communication data that was previously ignored in emails, tickets, and chat logs is now actively used by agents to provide insights and recommendations. This resembles the first big data wave of the early 2010s, when affordable storage and new tools unlocked dormant datasets.

Third, symbolic knowledge quietly returned. Knowledge graphs, once seen as costly and fragile, have found new life through GraphRAG and agent-driven extraction. Imperfect graphs have proved to be useful. Iteration now matters more than upfront perfection. This is not just a rebranding effort but a real shift in how symbolic and statistical systems work together.

Finally, fine-tuning has regained importance. As in-context learning faced limitations for latency-sensitive and reasoning-heavy tasks, smaller specialized models have become appealing again. The industry rediscovered an old truth: not every problem needs a giant general-purpose model.

While these trends have become essential, the real turning point will occur in 2026.

Agentic memory becomes foundational

In 2026, agents will stop being stateless tools and start behaving like systems with memory.

This is where the idea of agentic memory emerges. While it is tempting to describe this as a rebranding of knowledge graphs, that framing misses the point. Agentic memory is an evolution. It combines structured symbolic representations with the ability for agents to reason, update, and act over time.

Memory is what turns agents from reactive responders into decision-making systems. Without it, agents repeat work, hallucinate context, and fail to learn from past actions. With it, enterprises can build AI systems that accumulate institutional knowledge rather than discard it at every prompt.

Model merging replaces model worship

One of the most under-discussed developments is the rise of model merging and distributed training. Instead of training monolithic models from end to end, researchers are increasingly decomposing the problem. Smaller specialized models are trained independently and then combined.

This approach first appeared in research competitions and experimental challenges. In 2025, it matured into full tutorials and production-ready pipelines. Public examples, including distributed training experiments from Cohere, signal a broader shift.

By 2026, we will see a real market for smaller language models that enterprises can own, compose, and adapt. The center of gravity moves from “who has the biggest model” to “who can assemble the most effective system.”

AI for science exits the lab

AI for science is no longer just a research curiosity. In 2025, physics, biology, and materials science workshops at major conferences drew unexpected crowds. Wealthy foundations and private donors began funding large-scale scientific AI efforts. Startups emerged with a clear focus on drug discovery, materials design, and simulation.

In 2026, value creation will begin to show. If AI accelerates the discovery of a new antibiotic, a cancer treatment, or a novel material, the return outweighs the computational cost. This makes scientific AI one of the most economically defensible applications in the field.

However, AI will not magically produce new physical laws. AlphaFold succeeded because the problem was well defined. Physics still lacks its Hilbert moment, a clear, shared definition of the core problems to solve. Defining the right problems remains a human task.

Content creation proof grows in importance

One of the most surprising insights of the past year came not from technologists but from sociologists.

The biggest risk of generative AI is not job loss. It is the erosion of proof. Proof of authorship. Proof of work. Proof of authenticity. Proof of humanity.

As AI-generated content floods every medium, societies will demand new mechanisms to verify who created what. This is where ideas from cryptography and blockchains re-enter the conversation, not as speculative assets but as infrastructure for attribution and verification.

AI may become the catalyst that finally gives these systems a real purpose.

Agents learn through tools, not text

LLMs equipped with tools are fundamentally different from chatbots. The most important tool for agents today is the terminal.

Benchmarks like Terminal Bench formalize this shift. Agents that can interact with command lines, APIs, and environments learn by doing. Frontier labs are now spending hundreds of millions of dollars acquiring high-skill task data to train these agents.

The datasets are private and fragmented, which has an important side effect. Models will stop thinking alike. As training data diverges, frontier models will develop distinct skills and reasoning styles. Homogeneity was a temporary artifact of shared data. Diversity is coming back.

As coding assistants like Claude Code and OpenAI Codex improve day by day, we humans distill knowledge from them in the form of software. In effect, this begins to resemble what some are calling software distilleries, where large models help design systems that are then distilled into cheaper, task-specific software, which is computationally cheaper to run on CPUs rather than executed directly by frontier models. If token generation becomes significantly cheaper and coding assistants are much more sophisticated, software itself could become a thing of the past, as humans might need to be in the loop. This idea sounds implausible today, but so would the idea that billions of transistors could one day fit inside a mobile phone in the 1960s.

Looking ahead

If 2025 was the year LLMs became cheap, 2026 will be the year intelligence becomes structured.

The winners will not be those with the largest models, but those who build systems that remember, reason, attribute, and evolve. AI is no longer about raw capabilities as much as it is about architecture.

And that is where the next real advancements will happen.