Articles | jerlash.labs

What if AI were first and foremost a way to read better

Sat, 13 Jun 2026 00:00:00 +0200

There is a lot of talk about generative AI as a drafting assistant. Less as an outstanding reader. It is not (only) a matter of marginal productivity, of a few minutes saved here and there. It is a deeper shift: the ability to digest, cross-reference and prioritise masses of information that no human could absorb alone. Entire functions of analysis, monitoring and preparation are being redefined — not automated as they were, but transformed by tools that bear no resemblance to their predecessors.

In a law firm, this shift is not immediately visible. You do not replace a lawyer with a prompt. But something else is starting to show: legal professionals who read faster, who are better at extracting the useful signals, who cross-reference heterogeneous sources to form a sharper opinion. Where others still open their five tabs of legal commentary, a model has already read, compared and synthesised them. It is another way of working, more fluid, more precise and faster. And it is because the tool does not “do everything on its own” that it becomes useful: it prepares the ground, it sharpens the analysis, it allows the legal professional to bring into play what gives them their value.

Let us take a very concrete example. You receive a 150-page report to analyse urgently — a due diligence, an expert report, an opinion from a public authority. Traditionally, you would have to spend hours on it, annotating, summarising, structuring. Today, some tools make it possible to extract the main points, identify the points requiring attention, and even cross-reference this content with other sources. In a few minutes, you have a structured overview you can query. It is impressive. But it is also the moment to stay alert: have we properly understood what the tool did not read? What did it rely on to reach its conclusion? What biases or errors crept into this quick synthesis? AI gives the impression of having read everything. In reality, it has selected, filtered, interpreted — often without explanation. This is where the legal professional’s role comes back into its own: checking, nuancing, cross-checking, rephrasing.

The point is not to be distrustful on principle, but to work differently. To limit the risks, you need to learn to “steer” AI methodically. First, by structuring your requests: do not ask directly for an answer, but for a process. For example: “summarise the parties’ arguments for me”, then “what are they based on?”, or “can you quote the passages of the report that ground this conclusion?” Next, by testing any blind spots: “which points are missing or under-treated in this text?”, “what alternative interpretations could be considered?” Finally, by cross-checking: never a single answer, always verification by other means. It is not a complex art, it is a (new) discipline. And like any discipline, it is cultivated.

For example, what makes a conversational agent strong is not the first answer. It is the conversation. It is in the back-and-forth, in the rephrasing, in the objection that the tool becomes truly useful. Asking it a question is good. Pressing it again, pushing it to justify, to refine, even to doubt, that is where the real value emerges. This dynamic resembles strategic dialogue more than automatic delegation. You do not use AI like a search engine; you use it as a partial interlocutor, sometimes vague, but incredibly fast and available. In that sense, conversational AI does not distance the lawyer from their reasoning. On the contrary, it can help sharpen it — provided one keeps an active, critical and somewhat playful relationship with it.

To go further, some choose to create assistants dedicated to these complex tasks. A preconfigured conversational agent, trained to analyse a report, answer precise questions, cite its sources, rephrase or flag the limits of a line of reasoning. It is a bit like having a specialised associate, always available, always structured. By feeding it your methodological instructions — set out the context first, isolate the arguments, ask for justifications, flag the grey areas — you turn a generic tool into an operational ally. It does not replace your judgement, but it frames the reading, saves you time and, above all, pushes you to think more clearly. A well-designed “analysis assistant” can be called upon at any moment, in a familiar interface, and carry out its task without drifting. The gain is real. The rigour, intact.

AI is not going to replace legal professionals. It is going to redefine the way they read, reason and decide. To those who see it as a drafting gadget, one can reply that it is far more formidable — and more useful — when it helps us understand better. It is not simply a question of efficiency. It is a question of stance: choosing to remain in control of one’s analysis, to steer the tools demandingly, to cultivate exchange rather than dependence. Reading differently does not mean reading less. It means reading with more discernment, more method, and perhaps — in time — with a little more freedom.

What AI will never replace. Rethinking the lawyer’s profession from what endures

Sat, 13 Jun 2026 00:00:00 +0200

Everyone is worried about whether AI will replace lawyers. What if we asked the opposite question instead: what, in this profession, has stood the test of time — and why? A shift in perspective to refocus practice on what matters.

Everyone is stirred up about the same question: is AI going to replace lawyers? It is obviously the wrong question.

The real question is: what has withstood the centuries, and why? If we want to understand how artificial intelligence will transform the practice of law, we need to change focus. Rather than looking at the latest developments in GPT or a legaltech company’s product announcements, we need to look in the rear-view mirror — over the long term. AI, like any technology, will merely reveal what holds firm, and what was incidental.

The right tool for thinking about this is the Lindy effect: the idea that the longer something has lasted, the more likely it is to keep lasting. And in law, certain functions have been around for millennia. Why? Because they answer fundamental human needs: organising trust, settling disputes, taking a position in the face of uncertainty.

Before courts, there were village elders. Before articles of law, there were promises, debts, exchanges. The lawyer’s profession — this stance of a third party able to defend, to negotiate, to advise with responsibility — is far older than our codes. It was not born with legal scholarship. It was born with trade, with conflict, with life in society.

What AI is going to sweep away is not the lawyer’s profession. It is everything that has built up around it. All the tasks that exist only because we had nothing better: sorting, proofreading, copying, reformatting, producing on an assembly line. Those are not “Lindy”. They have only lasted a few decades, and their sole justification was technological: they took time. Today, they take too much.

Let us take a concrete example: contract review. A well-trained AI can now identify risky clauses, detect deviations from a reference baseline, and generate suggested rewordings. No more need to spend hours on it. That is not where the value lies. The question that remains is: should this clause be accepted, given the context? And there, we come back to what endures. Judgement. Experience. The balance of power. The client’s intention. What goes unsaid.

Another example: mass litigation. Semantic analysis tools can already draw out the main trends from hundreds of decisions, extract patterns in case law, and summarise the most frequent arguments. But they will not tell you whether this particular case, with this particular client, is worth pleading. Nor how to plead it, nor what tone to adopt, nor whom to talk to face to face.

The same goes for advisory work: you can ask an LLM to simulate a contract or to summarise a reform. But no model takes a risk in your place. None will say: “Here is what I recommend. And I take responsibility for it.” That sentence lies at the heart of the profession. And it cannot be replaced.

Some firms have already begun this refocusing. Far from grand announcements, they have used AI not to “do AI”, but to get back to the essentials.

One employment law firm, for instance, began by automating its most routine tasks: clause extraction, generation of standard arguments, assisted case-law research. The result: its associates gained several hours a week, not to “do something else”, but to do better — take the time for a conversation with a client, prepare a negotiation, adjust a strategy.

The firm did not try to “transform itself digitally”. It simply reinvested its time where it is most useful, where law is not a stock of knowledge but a lever for decision-making.

What matters today, in the age of the hegemony of AI agents, are the fundamentals. Listening, judgement, clarity, the ability to stand behind a recommendation with commitment. None of this is new. And none of this can be replaced.

As AI reshuffles the cards, it is not technologies that should fascinate us, but invariants. What remains when everything else changes.

The lawyer’s profession does not fit into a task description. It is a role and a function: owning a point of view, defending a position, making a decision amid uncertainty. That role is played by no tool. And it is the one clients come looking for — more and more.

The issue is to find and decide what we are going to deepen. Where does our value lie? Where are we irreplaceable?

The rest can — and must — be automated, and not to follow a fashion. Automate to make room. Do not let technological agitation distract you from what is essential. AI does not kill law. It forces you to ask yourself what you want to make of it. And what, truly, you want to be of service to.