2026 Predictions for Legal AI

The frontier of AI is happening in software engineering, and it’s several months ahead of any other field. Foundation labs, such as Anthropic and OpenAI, are incentivized to focus their efforts on software engineering, since any improvement they make helps them build the next generation of models and tooling. 

Despite this, model intelligence doesn’t get full credit. With tools like Codex, I’ve managed to measurably triple my engineering output from November to now – even though between GPT 5.1 (November) and 5.2 (December) there was only a 7.5% improvement in general intelligence per the Artificial Analysis Intelligence Index.

The difference came from three changes in how software tools work. I predict these same changes will affect legal tools in 2026.

1. Background agents become the default way work starts.

With Claude Code, I start most engineering work by writing a detailed plan, then handing it off to run in the background while I move on (or even craft a detailed plan that executes while I sleep). My role shifts to judgment and orchestration: specifying requirements clearly, reviewing output for accuracy, and deciding what runs next.

This unlocks parallelization. I’m no longer constrained to one workstream at a time. I move from plan to plan, queuing up several times as much work while agents execute simultaneously.

2. Search becomes effortless and immediate.

Improvements in search have given AI agents the ability to instantly find anything across thousands of files and millions of lines of code. The most effective software tools, like Cursor, combine approaches: fast keyword lookup, regular expression matching, structure-aware search that understands document types, and semantic retrieval. The system routes each request intelligently based on what you’re asking. 

As an engineer, this means that I don’t have to memorize the entire codebase anymore. If I have a question, I can ask it; if I’m looking for something, I can find it.

Flawless context retrieval will be ubiquitous in legal tools in a few months as teams catch up, and search will no longer be a sufficient product in itself. 

3. Context and specificity become the differentiators.

Models are getting better, for everyone. The differentiator is building environments where models can be precise. Agent harnesses like Claude Code support specific “skills” that allow a generalist model to pick up new contexts dynamically. You can use skills in clever ways, such as creating multiple distinct subagents that work together to complete a complex task.

Reusable workflows matter here – guided processes that move through real tasks with strong opinions baked in. Output quality comes down to how well you constrain the task and how effectively you feed the right context

In 2026, everyone will have access to strong models, but not everyone will have strong context. And in litigation, correctness is the product.

At &AI, we’re building for the future of patent litigation with these shifts in mind. Book a demo to learn more.