Justic-IA Has Finally Come to Mexico

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The story of a judge who leaves early and has no backlog

1.

Jorge has been a judge at a civil court in Mexico City for seven months and has not issued a single ruling.

Zero. Not one. Seven months, and his judgment counter is still blank.

That should set off alarm bells. In the Mexican judicial system, backlog is the norm, not the exception. A judge who issues no rulings sounds like a judge who isn't working — or who is buried under a mountain of unresolved case files. But Jorge is, by any metric you care to use, the most productive judge on his bench. He moves through eleven or twelve hearings a day — more than any of his colleagues. His minutes are ready before noon. He leaves early. He has no backlog.

So how is it possible that the judge who issues no rulings is the one with the least backlog?

To understand that, you first have to understand the courtyard.

2.

In Jorge's courthouse there are eight judges. Every morning, before hearings begin, they are all there: seated in a courtyard, reading case files. Four hours. Sometimes more. They read through pages, underline, try to retain the names of the parties, the dates, the arguments. It is a test of memory and endurance. If you ask them why they work this way, they look at you strangely — as though you had asked a surgeon why he washes his hands. It has always been done this way.

Jorge does not sit in the courtyard. Jorge arrives five minutes before the first hearing. Sometimes in sneakers. He puts on his robe and begins.

What he does is feed each case file into an artificial intelligence model. Before the hearing he has a chronological summary, a table with the key points of each party, and the applicable statutes organized by issue. He walks in knowing more about the case than the attorneys themselves. If something comes up that he did not anticipate, he calls a five-minute recess, consults the system, and returns.

When he explained this to his colleagues, one of them looked at him as if he had said he read tarot cards. "That's illegal," she told him. "Why would you do that?"

3.

In 1847, a Hungarian physician named Ignaz Semmelweis discovered that women were dying of puerperal fever in Vienna's hospitals because doctors did not wash their hands between the autopsy ward and the maternity ward. The solution was so simple it was offensive: wash your hands with a chlorine solution. Semmelweis implemented it on his ward and mortality dropped from 18% to 1%.

His colleagues rejected him. Not because the evidence was weak — it was overwhelming — but because accepting that handwashing saved lives meant accepting that they, for years, had been killing patients. The solution was not difficult to implement. It was difficult to admit.

Semmelweis died in a psychiatric institution at the age of 47. It took medicine another twenty years to adopt what he had already proven.

Sociologists of innovation have a name for this. They call it the "Semmelweis reflex": the instinctive resistance to a new idea, not because it is wrong, but because its correctness threatens the professional identity of those who hear it. It is not ignorance. It is something deeper. It is the psychological cost of admitting that there was a better way — and you were not using it.

"That's illegal. Why would you do that?"

4.

In September 2023, a team of researchers from Harvard, Wharton, and MIT published what remains to this day the most rigorous study on the effect of artificial intelligence on the work of knowledge professionals. They took 758 consultants at Boston Consulting Group — not interns, not junior staff, but experienced consultants who bill hundreds of dollars per hour — and divided them into two groups. One was given access to GPT-4. The other was not.

The results were not subtle. Consultants with AI completed 12% more tasks, 25% faster, and with 40% higher quality. But the most telling finding was not that. It was that the consultants who scored lowest at the outset — the least experienced, the ones who struggled most — were the ones who improved the most: a 43% jump in performance. AI did not merely make the good ones more productive. It turned the mediocre ones into good ones.

Behavioral economists have a concept for what AI did to those consultants. They call it "discretionary time": the portion of your workday that is not consumed by mechanical tasks — the space where you can actually think, create, and decide. For most knowledge professionals, this time is surprisingly small. AI did not make them smarter. It gave them back the time to use the intelligence they already had.

Now think about the eight judges in the courtyard. Four hours every morning reading through pages. That is not discretionary time. That is mechanical work dressed up as preparation. And when those four hours are over, you arrive at the hearing exhausted, with a general sense of the file but without the precision you would need to do something truly useful with that knowledge.

Jorge recovered those four hours. And what he did with them is what turns this story from a productivity anecdote into something far more interesting.

5.

Because when you truly know a case file — not from having read it in a hurry, but from having broken it down, from being able to see each party's position with a clarity that normally takes weeks to achieve — you can do something that most judges simply do not have the time to attempt.

You can sit with the parties at the hearing, in oral proceedings, and help them see where they actually stand. What they stand to gain if they keep fighting. What they stand to lose. How much time, how much money, how much attrition it will cost them. And you can do it with such precision — because you know every angle of the file — that the parties, time and again, choose to settle.

Jorge refers them to alternative dispute resolution with a concrete proposal. And it works. Every time.

Seven months. Zero rulings. Not because he avoids making decisions, but because the parties no longer need a third party to decide for them.

The most widespread fear about artificial intelligence in the courts is that it will replace the judge. That an algorithm will issue rulings. That justice will become automated, dehumanized, cold. But what happened to Jorge is precisely the opposite. The tool did not adjudicate for him. It freed up the time and attention for him to do the most difficult and most human thing a judge can do: persuade two people to reach an agreement.

AI did not replace the ruling. It made the ruling unnecessary.

6.

If you look at the timeline, what is unfolding in Mexican justice follows a sequence that says more than any official speech.

In August 2025, Magistrate Juan Jaime González Varas did something no federal judge had done before: he issued a ruling — Civil Complaint 212/2025 — in which he disclosed that he had used artificial intelligence as an auxiliary tool to calculate bond amounts. He did not hide it. He documented it.

He published the methodology, the input data, the models he used, and even the discrepancies he found when running the same calculation across three different systems. And he set out four minimum principles for the ethical use of AI in the justice system: proportionality, data protection, transparency, and human oversight. His judicial criteria were published in the Semanario Judicial de la Federación that same day. It was the first regulation of artificial intelligence in Mexico to arrive through a court ruling.

Months later, Jorge — who had probably never read those criteria — began using AI to study case files and discovered something González Varas could not have anticipated: that the deepest benefit lay not in the precision of the calculations, but in the time that remained once they were done.

And on March 3, 2026, the Federal Judiciary issued Circular 1/2026: the first institutional document with formal rules for the use of AI in judicial activity. Principles of proportionality, transparency, human oversight. The same four principles González Varas had already established seven months earlier in a ruling.

First, a magistrate did it and documented it. Then a judge did it and took it further than anyone had expected. And in the end, the institution wrote the rules for what individuals were already living.

Innovation never flows from the top down. It comes from those who dare to move first — and then, if we are lucky, the institution runs after them to give it form.

The eight judges are still in the courtyard every morning. Not because they are bad judges — they are dedicated, responsible, and hardworking. But they are trapped in a system that consumes all their time on the task of knowing the file, and leaves them no space to do anything with that knowledge.

Jorge still arrives five minutes early, in sneakers, with his little bag. He still issues no rulings. And the question no one has asked him — the one that truly matters — is not how he manages to leave early.

It is why the other eight have not tried to do the same.

Jorge is a real judge. He uses AI exactly as the criteria and the circulars say it should be used. Without fear and without ignorance.

Jorge is mindful of privacy, uses dedicated accounts for this purpose, and takes all the necessary precautions for the proper handling of the information he works with.

If you are a judge, I implore you: be like Jorge.