Ten Legal Tech Predictions for 2026

From Tool Shake Out to Market Reshuffling

Picture credits: AVDR

Elgar Weijtmans and I regularly spar about legal tech, because we look at the same developments from different angles and that tends to sharpen both our views. This time we decided to turn those conversations into ten concrete predictions for 2026, precisely to place those two perspectives side by side and show what happens when you look at the landscape both from the supply side and from day to day practice.

Elgar’s predictions are about product logic, infrastructure, which providers will remain standing, how data and models will organize themselves, and where technology will truly add value. I look more from practice and market dynamics, what AI does to client behavior, to competition between firms, who will win market share, and who will fall behind. Combined this resulted in ten predictions for 2026, five from him and five from me, as a sober snapshot of where we are and where things might be heading. Let’s dive right in!

1. A defensive layer of bots will emerge between clients and lawyers

Over the past two years, many clients have developed a new pattern. They first run their case by ChatGPT, stitch together their own advice, and then go to a lawyer asking for a stamp of approval. “Can you confirm this?” is the new intake.

The problem is not that clients use AI. The problem is the quality and the starting point the AI gives them. The AI advice is often too simplistic, based on American jurisdiction (although the user is based in Europe), or legally just wrong. The lawyer first has to untangle what is actually written by the AI, then explain why it is incorrect, and only then reconstruct what the real question was. That takes time, and above all energy. You are not communicating with a human who explains their situation clearly, but with a robot that blurs your client’s story.

What makes this extra painful is that the client often thinks the AI is saving them money, while the lawyer has to go back to square one. The conversation starts with dismantling instead of building. This behavior will only increase as generative AI becomes more embedded in society.

That is why I expect a new layer to arise on the side of law firms in 2026. Not to keep clients out, but to filter out noise. A bot that separates sense from nonsense in client input, recognizes American assumptions, flags legal errors, and keeps probing until the real question surfaces. Once that has been taken care of the human steps in.

Firms that set this up well win twice. They save time on pointless repair work, and they start the real conversation with a client who feels better heard because the core issue becomes visible faster. Intake shifts from human to machine, so the human advice improves.

2. The AI bubble will not burst

If you read the headlines, you might think the AI bubble could burst any moment. OpenAI is losing billions. The investments are not sustainable. But that analysis misses nuance.

Yes, OpenAI invests massively in compute, research, and talent. But if you link those investments to the models they produce, you see a different story. Those models are profitable. They generate more revenue than they cost to run. The point is not that the business model does not work, the point is that profits are immediately reinvested into the next generation.

These are not the signs of a bubble, they are the signs of scaling. This is what Amazon did in its early years, deliberately accepting losses to invest in the future. The difference from earlier tech hypes is that real fundamental value is now being created. Companies pay for AI because it works, not because it is fashionable.

In 2026 the bubble will not burst. On the contrary, we will see consolidation and further growth. Valuations may become more realistic, but the underlying trend will hold.

3. Frontrunners will cash in on their AI adoption

The past two years were mainly experimental for many law firms. Pilots here, proof projects there. Interesting, but the business case was still unclear. In 2026 that changes for the frontrunners.

They have now built enough experience to deploy AI structurally. They know which processes lend themselves to automation, which tools really work, and how to bring their people along. That will translate into operations, faster turnaround times, lower costs, better margins.

The upside will also become visible to clients. They will notice that some firms are simply better, faster, sharper. That creates a flywheel of more cases, more data, better AI, even more cases and so on. Early adopters will monetize their lead.

While frontrunners cash in on their AI advantage, laggards will run into trouble. In 2025 they could still get away with waiting. “We are watching how this develops.” “We will move when AI really works.” In 2026 that stance becomes risky.

Clients will compare. Why does it take three weeks here and three days at firm X? Why does this cost ten thousand euros here and three thousand there? Competitive pressure from firms that do use AI will be brutal.

The problem is that AI adoption takes time. You cannot catch up in three months with what others built over two years. Laggards need to move now if they do not want to fall hopelessly behind in 2026. The gap between leaders and laggards will become painfully visible this year.

4. Google will overtake OpenAI

OpenAI has set the agenda in recent years, but that will change. With Gemini 3 Pro, Google showed it is back at the top level, and it has a few strategic advantages that are hard to ignore.

First, scale. Google’s infrastructure was built from the start for AI services at massive scale. Second, data. Google has spent decades indexing and understanding information. It has access to an unparalleled trove through Search, YouTube, Scholar, and all its other services.

Third, DeepMind. The combination of commercial pressure and scientific excellence remains a powerful engine for innovation. While OpenAI is mainly focused on the next version of its own model, Google can draw on different research teams and approaches.

In 2026 I expect Google not only to deliver equivalent models, but to be better on certain fronts. Especially in multimodality, combining text, image, video, and audio, and in real time processing it has an edge. For legal use cases this means better document analysis, better speech recognition for transcripts, and smarter search. OpenAI and Anthropic remain formidable players, but Google takes the lead.

5. Alternative Legal Service Providers will put pressure on Big Law

ALSPs, Alternative Legal Service Providers, have existed for years but until now they operated mostly on the margins. They handle standardized volume work, the kind of work Big Law does not get excited about. But AI changes the playing field.

ALSPs can pair AI driven efficiency with flexible scale and lower overhead. They do not have to fight centuries old partnership structures or billable hour models. They can move fast and launch new services quickly.

In 2026 we will see ALSPs no longer just picking up crumbs, but seriously competing for substantial work. They offer better prices, faster turnaround, and more transparent processes. For certain services, think contract management, due diligence, and compliance, they become the first port of call instead of the cheap alternative.

Big Law will have to respond or lose market share.

One story with two perspectives

Those are my five predictions. Elgar’s five relate to these more from the supply side. He looks at what happens to products, infrastructure, data, and providers. Which legal AI players will not make it, how the big legal AI players will move toward legal service delivery, why publishers will need to embrace platform thinking, and how technical choices such as small language models and legal embeddings will make the difference. Combined those ten points tell one story: the clean up and reordering of the ecosystem and the consequences for clients, firms, and competition.

At the end of 2026 we will compare our list to reality to see where we were right and where we need to adjust our predictions. Did we miss any predictions? Let us know in the comments!