If AI Does the Junior Work, Who Trains the Lawyers of 2035?

The problem in one sentence

The tasks which AI can automate most easily are often the same tasks through which the profession historically expected junior lawyers to acquire experience.

Five things to understand first

1. AI is not simply making junior work faster.
In some workflows it may remove substantial parts of the task altogether.

2. Clients are unlikely to keep paying for avoidable manual processing.
If a task can reliably be completed in minutes, billing many hours for the same mechanical work becomes increasingly difficult to justify.

3. Repetitive work and legal judgment are not the same thing.
Reading thousands of documents may expose somebody to a case. It does not automatically teach them how to advise a frightened client or decide which point should be abandoned.

4. Junior lawyers still need experience.
The disappearance of low-value tasks does not remove the need for supervised practice. It means the supervision model must change.

5. AI competence is not enough.
The valuable lawyer will need both technological literacy and the distinctly human skills needed to test, challenge and improve machine output.

The traditional bargain: clients paid while juniors learned

Legal training has historically contained an informal economic bargain.

Junior lawyers performed large quantities of labour which had to be done:

• document review;
• legal research;
• due diligence;
• chronology preparation;
• first drafts;
• bundles;
• disclosure exercises;
• precedent adaptation;
• and checking large amounts of factual material.

Clients paid for those hours.

The firm generated revenue.

And somewhere along the way, the junior was expected to become a lawyer.

The theory was rarely stated explicitly.

It was simply how professional formation worked.

You did the work.

You saw more files.

You absorbed the craft.

You became more senior.

Eventually, somebody else did the repetitive work and you exercised the judgment.

AI disrupts that sequence.

The machines are moving into the bottom of the pyramid

The work currently most susceptible to automation is disproportionately work historically allocated to junior lawyers.

Legal AI systems can already assist with:

• large-scale document review;
• extracting clauses across contracts;
• identifying dates and events;
• constructing draft chronologies;
• summarising judgments;
• producing first drafts;
• comparing pleadings;
• organising disclosure;
• identifying potential authorities;
• and standardising documents against templates or playbooks.

None of that means the work no longer requires supervision.

It means the human labour required to produce the first version can collapse dramatically.

At the summit, practical examples included work which previously took hours being reduced to minutes, subject to human review.

That is commercially attractive.

It is also professionally disruptive.

Why would a client continue to pay fifteen hours for a ten-minute task?

This is where the training issue becomes an economic issue.

For generations, clients effectively helped finance junior development because junior labour was necessary to complete the work.

That bargain becomes unstable where technology can undertake the mechanical component substantially faster.

A client may reasonably ask:

“If the firm has technology capable of doing this in minutes, why am I paying for somebody to perform it manually for a day?”

That does not mean every AI-assisted task should suddenly become cheap.

The value may reside in:

• supervision;
• verification;
• strategy;
• risk;
• professional responsibility;
• and the consequence of getting the answer wrong.

But it does mean time spent on avoidable mechanical processing becomes harder to defend as the core source of legal value.

That puts pressure on both the billable-hour model and the training structure built around it.

Perhaps we have confused endurance with education

There is an uncomfortable possibility here.

What if spending eight years carrying out repetitive legal processing was never the reason somebody eventually developed good judgment?

What if judgment developed because, around that work, they also:

• watched experienced lawyers make decisions;
• sat in client meetings;
• saw negotiations succeed and fail;
• received detailed corrections;
• observed hearings;
• made mistakes under supervision;
• watched evidence collapse under scrutiny;
• learned which theoretically available argument was tactically foolish;
• and gradually acquired professional pattern recognition?

If that is true, removing repetitive work does not necessarily destroy legal education.

It may expose which parts of legal education were actually valuable all along.

Perhaps the problem is not that AI will stop juniors learning through drudgery. Perhaps the problem is that we never designed the learning deliberately enough in the first place.

The regulatory definition of competence is already much wider than technical processing

The Solicitors Regulation Authority’s Statement of Solicitor Competence is instructive.

It does not define competence simply as the ability to retrieve law or produce documents.

It includes:

• ethics;
• professionalism;
• judgment;
• communication;
• client relationships;
• working with others;
• managing work;
• legal knowledge;
• and recognising one’s own limitations.

The SRA’s 2026 assessment of continuing competence is particularly interesting in this context.

It identified a continuing tendency for some learning and development to focus too narrowly on technical legal knowledge rather than the wider capabilities required for competent practice.

It also placed greater emphasis on ethical reflection and the ability to deal with unfamiliar professional dilemmas.

That matters because AI is making technical retrieval and first-draft production progressively easier.

The harder skills are becoming more visible.

AI changes the starting line, not the finishing line

One of the most useful propositions from the summit was that AI output should be treated as a first draft, however polished it appears.

That is exactly the right mindset.

The dangerous feature of modern generative AI is not merely that it can be wrong.

It is that its first draft may look far more finished than a human first draft.

That creates temptation.

The junior may think:

“This looks complete.”

The supervisor may think:

“This looks good enough.”

The client may never see the gap.

But the real professional work begins with questions such as:

• What did the system miss?
• What has it assumed?
• Which authority actually controls?
• What evidence contradicts this?
• What would the other side say?
• What is technically correct but commercially foolish?
• What is legally available but ethically wrong?
• Which point should we deliberately not run?
• What does this particular client actually need?

Those are not merely drafting questions.

They are judgment questions.

The “centaur lawyer”: machine capability plus human judgment

Walters used the history of computer chess to illustrate the point.

When computers became better than humans at brute-force calculation, the lesson was not immediately that human chess insight had become worthless.

For a period, combinations of human strategy and machine calculation proved extraordinarily powerful.

The legal equivalent is useful.

The AI can:

• read fast;
• compare fast;
• retrieve fast;
• draft fast;
• and calculate across large amounts of material.

The lawyer needs to:

• frame the problem correctly;
• understand the person affected;
• test the output;
• exercise ethical judgment;
• balance risk;
• recognise uncertainty;
• understand institutional context;
• and accept responsibility for the advice.

The future lawyer is therefore not valuable because they can compete with a machine on reading speed.

They are valuable because they know what the machine’s answer means and whether anyone should act upon it.

But there is a genuine deskilling risk

It would be naïve to say that removing routine work carries no downside.

Some repetitive tasks teach useful things.

Reading a hundred judgments can develop a feel for judicial reasoning.

Drafting repeatedly can improve structure.

Building chronologies can reveal how factual patterns emerge.

Reviewing disclosure can teach what evidence looks like in the real world rather than in a textbook.

If the junior presses a button and sees only the finished answer, some of that learning disappears.

The answer is not to force people to perform obsolete labour for educational theatre.

It is to preserve the learning outcome while changing the exercise.

For example:

That is harder training.

It is also closer to the work senior lawyers are actually paid to do.

What should the new apprenticeship teach?

If we were designing junior legal development now rather than inheriting it from the last century, I would make at least eight capabilities explicit.

The SQE era makes this especially timely

The profession is already reconsidering how competence is demonstrated.

The Solicitors Qualifying Examination assesses knowledge and practical legal skills against the competencies expected of solicitors.

But qualification is only a starting point.

A lawyer develops professional judgment through repeated exposure to uncertain, human and ethically difficult situations.

That is precisely the material which cannot simply be automated away.

AI therefore strengthens the case for:

• high-quality supervision;
• observing experienced practitioners;
• structured reflection;
• feedback on reasoning, not merely drafting;
• simulation of difficult professional choices;
• and deliberate exposure to client-facing work.

Qualification frameworks will increasingly need to ask not only:

“Can this person produce the legal work?”

but:

“Can this person supervise technology producing legal work?”

A junior lawyer should not become the human rubber stamp

There is one training model we should avoid completely.

AI produces the work.

A junior is told to “check it”.

The junior has never performed the underlying task.

They are under time pressure.

The output looks polished.

They do not know where the model is most likely to fail.

They approve it.

That is not supervision.

It is procedural decoration.

A person can only meaningfully supervise a task if they have enough competence to recognise failure.

This creates a transition problem.

The generation entering practice now may need to learn both:

• how the underlying legal task works;
• and how to supervise technology performing it.

That is likely to require more intentional training, not less.

Done properly, this could also improve access to justice

There is an optimistic version of this transition.

If technology removes genuinely mechanical labour, legal professionals may be able to deliver more assistance at lower cost.

The Government’s 2026 Legal Services Advisory AI Growth Lab explicitly identifies improved productivity, affordability and access to justice as potential benefits of responsible legal AI.

That matters enormously.

There is no public interest in preserving expensive inefficiency merely because it once formed part of professional training.

The challenge is to redesign training while allowing consumers to receive the benefit of lower-friction legal work.

We should not make clients pay for obsolete processes in order to preserve a professional apprenticeship model.

We should build a better apprenticeship model.

What this means for Family Court work

The distinction is particularly important in family justice.

AI can help organise:

• messages;
• chronologies;
• court orders;
• allegations;
• disclosure;
• Cafcass material;
• school records;
• medical records;
• and draft documents.

But the difficult work remains profoundly human.

Is this evidence relevant to welfare?

Does a pattern actually exist?

Is an allegation established?

What alternative explanation has been considered?

What will this proposed arrangement mean for the child?

What should be said to a traumatised client who has a technically arguable point which may nevertheless damage their case?

AI can accelerate the processing.

It cannot relieve the human professional of responsibility for those judgments.

The JSH Law test: what should remain above the line?

Before automating a legal task, ask two questions.

What part of this task is processing?

and:

What part requires professional judgment?

The lawyer of 2035 may need fewer mechanical skills — and more difficult human ones

None of this means legal knowledge becomes optional.

You cannot evaluate a machine’s legal answer if you do not understand law.

You cannot detect evidential distortion if you do not understand evidence.

You cannot supervise drafting if you cannot recognise bad drafting.

But knowledge alone will no longer differentiate lawyers in the same way when basic retrieval becomes cheap.

The lawyer’s value moves towards:

• judgment;
• discernment;
• strategy;
• ethical reasoning;
• human understanding;
• advocacy;
• responsibility;
• and trust.

That should be good news for the profession.

Those are the reasons many people wanted to become lawyers in the first place.

We should not train tomorrow’s lawyers to compete with machines at the work machines do best. We should train them to become exceptional at the work for which human judgment still matters.

AI may remove parts of the old apprenticeship.

Our responsibility is to replace them with something better.

JSH Law: AI should augment judgment, not replace it

JSH Law’s work on responsible legal AI focuses on the point where technology meets evidence, human judgment and access to justice.

For litigants in person, defined-scope support can include reviewing AI-assisted documents, organising evidence, creating chronologies, preparing statements and position documents, analysing Cafcass material and preparing for hearings.

The objective is not more AI-generated legal material. It is clearer evidence and better human decision-making.

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Related JSH Law analysis

• AI Can Write 300 Pages. That Does Not Make It Advocacy.
• AI Slop, Costs and Litigants in Person
• Can AI Tell the Truth?
• Should AI Become a Legal Person?

Sources and further reading

• Clio EMEA AI Summit 2026 — event presentation
• Solicitors Regulation Authority — Statement of Solicitor Competence.
• Solicitors Regulation Authority — Annual Assessment of Continuing Competence 2026.
• Solicitors Regulation Authority — Misuse of AI Warning Notice, August 2026.
• UK Government — Legal Services Advisory AI Growth Lab, 2026.