Most people don't think about location when they ask a legal question.
They think about the event.
“I was hit by a car.”
“My landlord kept my deposit.”
“I slipped on ice.”
“I got hurt at work.”
But there is another question hiding inside all of these:
Where did it happen?
That question can completely change the answer.
Law isn't just about what happened
If someone asks a general AI:
“Can I sue after slipping on ice?”
The model might produce a reasonable-sounding explanation about premises liability.
But reasonable isn't necessarily useful.
The rules governing that situation depend on the jurisdiction.
Even something as specific as Massachusetts snow and ice law has its own history and rules. Massachusetts moved away from the old “natural accumulation” distinction after Papadopoulos v. Target, which changed how these cases are analyzed.
So “I slipped on ice” isn't really a complete legal question.
It is closer to:
“I slipped on ice, in this place, under these circumstances, on this date. What law applies?”
That is a very different problem.
Location should be part of the input
This is one of the things I find interesting about building legal AI.
In a normal chatbot, location might be treated as optional context.
In a legal system, it can be one of the most important pieces of information.
A useful intake process might look something like:
What happened?
↓
Where did it happen?
↓
Which jurisdiction governs?
↓
What authority applies?
↓
What facts are still missing?
↓
Explain the relevant rule
The location isn't simply added at the end.
It helps determine which legal sources should even be considered in the first place.
Massachusetts is a good example
Jimmy Knows is intentionally focused on Massachusetts rather than trying to answer legal questions for every state.
That constraint is actually a feature.
The platform's answers are grounded in Massachusetts statutes and case law, and its content is localized across Massachusetts cities and practice areas.
That means a question about a workplace injury in Brockton isn't treated as an abstract “workers' compensation” question.
The system can start from the fact that the matter is in Massachusetts and work from the relevant Massachusetts authority.
That sounds obvious.
It isn't.
A general-purpose AI has to deal with an enormous amount of potentially conflicting law. A user can ask a perfectly clear question and still get an answer that is based on the wrong jurisdiction.
The answer may even sound convincing.
That's the dangerous part.
The problem isn't always hallucination
When people talk about AI mistakes in legal applications, the conversation usually goes straight to hallucinated cases or invented citations.
Those are serious problems.
But there's another failure mode that is easier to miss:
The AI gives you a real rule from the wrong place.
The statute exists.
The case exists.
The explanation sounds professional.
It is still the wrong answer.
That's why jurisdiction isn't just a filtering feature for legal AI.
It should be part of the reasoning process.
Maybe the best legal AI is narrower
There is a natural temptation to make AI systems broader.
More states.
More practice areas.
More questions.
More sources.
But legal information may benefit from the opposite approach.
Less can sometimes mean better.
A system that knows exactly which jurisdiction it is supposed to operate in can spend more effort understanding the facts and finding the relevant authority within that jurisdiction.
For a person looking for a first answer after something has happened, that can be more useful than a chatbot that knows a little about everything.
The interesting question isn't:
“How much law does this AI know?”
It might be:
“Does it know which law it is supposed to be using?”
That distinction could become increasingly important as AI moves from general information tools into areas where the answer depends heavily on jurisdiction.
