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Speaker 1: This is Bloomberg Law with June Grossel from Bloomberg Radio.

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Speaker 2: A lot of work goes into legal research and writing,

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Speaker 2: so using AI seems like a way to replicate some

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Speaker 2: of the very time consuming tasks, such as researching court

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Speaker 2: cases and citing them in legal briefs. And the legal

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Speaker 2: profession has seen a rise in the use of AI

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Speaker 2: in recent years. With lawyers facing increasing scrutiny and sometimes

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Speaker 2: finds from judges for the alleged misuse of AI as

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Speaker 2: fake case citations and other errors show up in legal filings,

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Speaker 2: you may ask how can lawyers fail to vet the

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Speaker 2: output of AI generated legal documents? And now another question,

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Speaker 2: how can judges fail to vet AI generated orders before

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Speaker 2: they go out from their chambers. In a response to

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Speaker 2: an inquiry from Senate Judiciary Chair Chuck Grassley, two federal judges,

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Speaker 2: one in New Jersey and the other in Mississippi, have

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Speaker 2: admitted that members of their staff used artificial intelligence to

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Speaker 2: help prepare orders that Grassy said were ever ridden. Joining

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Speaker 2: me is former federal judge Paul Grimm. He's the director

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Speaker 2: of the Bolt Judicial Institute at Duke Law School. Are

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Speaker 2: clerks and interns are they using AI? And doing research

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Speaker 2: and drafting opinions. Is that sort of the norm.

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Speaker 1: Yeah, I think right now the genies out of the

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Speaker 1: bottle on that Two or three years ago, the law

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Speaker 1: schools were really struggling with whether or not students should use,

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Speaker 1: be able to use artificial intelligence in their class work,

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Speaker 1: particularly during exams. And I think that probably the needles

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Speaker 1: moved to the point where the law schools recognize that

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Speaker 1: this demographic, these young people, they're using this in all

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Speaker 1: their aspects of their lives, and to try to expect

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Speaker 1: them not to use it in the practice of law,

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Speaker 1: especially when law firms have invested heavily in the use

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Speaker 1: of generative AI in their own business and practices, is

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Speaker 1: a futile thing. And so I think what the law

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Speaker 1: schools are trying to do now are teach the students

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Speaker 1: the proper use of it and are trying to teach them,

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Speaker 1: you know, the dues and don'ts of how it should

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Speaker 1: be used. And I think that as for the intern,

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Speaker 1: I think it was in the District of New Jersey

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Speaker 1: that use of AI was inconsistent with the judge's own

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Speaker 1: internal policy and chambers and also inconsistent with what the

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Speaker 1: law school policy was for use of this, So I

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Speaker 1: think that while there's no way of knowing whether it's

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Speaker 1: most students coming out of law school now, I think

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Speaker 1: probably it is the overwhelming majority are using this and

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Speaker 1: probably are at schools where they're being taught the proper

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Speaker 1: way to use it. And it's clear that, you know,

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Speaker 1: it's been over a year that the American Bar Association

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Speaker 1: it's Ethics Committee released formal ethics opinion five one two,

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Speaker 1: five twelve that deals with the ethical issues associated with

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Speaker 1: the use of artificial intelligence in the practice of law.

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Speaker 1: So that's over a year old, and the fact that

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Speaker 1: the ethics people are already starting to tell people how

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Speaker 1: you use it ethically is pretty clear indication that it's

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Speaker 1: being used broadly in law schools. And I think that

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Speaker 1: the students realize that they're going to be expected to

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Speaker 1: have this skill because the law firms are expecting it,

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Speaker 1: and that's how they're going to survive and thrive in

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Speaker 1: the law school environment. So I think that probably the

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Speaker 1: answer is, yes, they're using it, and that will only increase.

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Speaker 2: In the New Jersey case, the use of AI resulted

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Speaker 2: in a temporary restraining order that referred to parties allegations

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Speaker 2: and quotes unconnected to the case. So even if you

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Speaker 2: proofread the restraining order, wouldn't you see that there were

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Speaker 2: all these problems?

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Speaker 1: Yeah, in both of those cases, both the Southern District

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Speaker 1: of Mississippi and also the Digitut of New Jersey. I

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Speaker 1: think that in retrospect, it looks like if there had

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Speaker 1: been rigorous proofreading by the law clerks led by the

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Speaker 1: court the judge themselves, that this should have been caught.

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Speaker 1: And indeed, the Administrative Office of the US Court has

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Speaker 1: a task force which is dealing with the use of

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Speaker 1: AI by judges and chambers. That should be a national

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Speaker 1: policy across the United States in all of this SCT courts,

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Speaker 1: and that was issued on July thirty first of twenty

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Speaker 1: twenty five. And in his response to Senator Grassley's letter

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Speaker 1: asking for comments from the Federal Administraty Office of the

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Speaker 1: US Course about any policies dealing with the use of

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Speaker 1: the generals of AI and chambers, Judge Conrad, who is

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Speaker 1: the director of the AO, made reference to that. So

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Speaker 1: there's even interim guidance, and I think that one of

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Speaker 1: the things that is pretty clear from that guidance is

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Speaker 1: that the judges are responsible with in their chambers to

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Speaker 1: make sure that AI is not used for any core

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Speaker 1: judicial functions such as decision making or adjudication, and strongly

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Speaker 1: recommends independent verification before any generative AI is used for

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Speaker 1: any purpose. There are a number of purposes for which

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Speaker 1: it probably safely can be used, and there's guidance on that.

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Speaker 1: The National Center for State Courts, which is a terrific

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Speaker 1: organization that focuses on the state courts, and of course

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Speaker 1: they are far more state court judges than there are

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Speaker 1: federal judges, issued a seventeen page document on August seventh

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Speaker 1: of twenty twenty four titled Artificial Intelligence Guidance for the

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Speaker 1: Use of AI and Generative AI in the Courts, And

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Speaker 1: again it goes through extensive definitions of what AI is,

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Speaker 1: what kind of software applications, There are the ones that

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Speaker 1: are problematic, the ones that are more useful, how chambers

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Speaker 1: should operate. And similarly, this year, the Sedona Conference, which

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Speaker 1: is a think tank that has been around for many

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Speaker 1: years and is very influential in dealing with cutting edge

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Speaker 1: technology issues as they apply in the law, issued a

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Speaker 1: monograph in their journal called Navigating AI in the Judiciary

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Speaker 1: that was co authored by three statehourt judges and two

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Speaker 1: federal judges and a professor from Waterloo University that gives

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Speaker 1: an eight pages some really good guidance to judges as

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Speaker 1: to how they should and shouldn't use it. So I

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Speaker 1: think the key takeaway here is, you know, the genies

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Speaker 1: have a bottle. The stuff is being used. There's a

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Speaker 1: growing guidance in terms of how it can properly be

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Speaker 1: used and how it should probably not be used. And

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Speaker 1: just like courts initially were sanctioning and cautioning lawyers who

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Speaker 1: didn't use it correctly when they were citing JENAI created

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Speaker 1: content without checking it, I think the judges themselves are

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Speaker 1: learning that lesson that they're responsible crediting comes out of

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Speaker 1: their chambers and they have to manage interns in law

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Speaker 1: works and make sure that their procedures are complied with.

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Speaker 2: Coming up next, I'll continue this conversation with Judge Grimm.

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Speaker 2: So how much do judges actually vet the work of

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Speaker 2: their clerks? You're listening to Bloomberg. Two federal judges have

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Speaker 2: blamed faulty rulings on the use of artificial intelligence tools

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Speaker 2: by staff members at raises questions about how much they

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Speaker 2: scrutinize documents issued under their names. Federal judge Julian Neils

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Speaker 2: in New Jersey and Henry Wingate in Mississippi admitted to

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Speaker 2: the ai foibles in letters to the Administrative Office of

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Speaker 2: the US Courts. The answers were sent in response to

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Speaker 2: questions by Senate Judiciary Committee Chair Chuck Grassley. I've been

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Speaker 2: talking to former federal judge Paul Grimm, director of the

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Speaker 2: Bolt Judicial Institute at Duke Law School. So you have interns,

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Speaker 2: you have clerks. Does it depend on the judge how

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Speaker 2: much a judge puts into let's say, an opinion. Do

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Speaker 2: most judges at least read them over.

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Speaker 1: Yeah, it's a great question, and the answer is it

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Speaker 1: varies greatly with the chambers. I think as a bottom line,

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Speaker 1: I don't think it's a federal judge on the court. Now,

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Speaker 1: where it's take court judge who's hearing cases, they wouldn't

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Speaker 1: say that they read everything that goes out of their

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Speaker 1: chambers with their name on it. I mean, unless it's

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Speaker 1: a scheduling order, it's automatic. You know that says you know,

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Speaker 1: counselor will by such and such a date provide me

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Speaker 1: with the following information and we'll have a scheduling call,

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Speaker 1: but certainly no order coming out of a judges chamber

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Speaker 1: that deals with a substantive ruling on a case or

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Speaker 1: an evidentrary motion, you know, something that's going to decide

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Speaker 1: the merits. I don't think any judge would say that

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Speaker 1: it's appropriate or a judge to issue in order that

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Speaker 1: deals with the resolution of a substantive issue in a

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Speaker 1: case without having read it and verifying to the satisfaction

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Speaker 1: of that judge that it's legitimate. Now, some judges have

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Speaker 1: different ways of doing it. What I would do is,

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Speaker 1: once I had the initial conversation with them and they

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Speaker 1: sent me a draft, I would ask them to attach

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Speaker 1: to the draft a PDF copy of the cases that

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Speaker 1: they cited as this positive. Now, as you're aware, lots

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Speaker 1: of times there are cases that are just sort of

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Speaker 1: like a string site in five cases, and there's one

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Speaker 1: at a recent Court of Appeals, the Supreme Court case

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Speaker 1: that had the ruling and you're going to rely on it,

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Speaker 1: I would have them attached a PDF copy of that

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Speaker 1: and highlight for me in yellow highlighting the language that

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Speaker 1: they were relying on for what they said, and so

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Speaker 1: I can independently check it. That way. By giving it

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Speaker 1: to me that way, it's sped it up for me.

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Speaker 1: I didn't have to go on West Law or Nexus

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Speaker 1: and input it all and read it a lot of times.

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Speaker 1: You know, there's a lot of part of the case,

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Speaker 1: the initial part talking about the jurisdiction or the standard review.

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Speaker 1: That's really not important to what the ruling is. But

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Speaker 1: you want to look at the discussion on one particular

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Speaker 1: point of law. And by having the actual case and

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Speaker 1: having the highlighted section, I then had at my fingertips

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Speaker 1: what I needed to go through. Look at the opinion,

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Speaker 1: look at the authority, decide whether I thought, you know,

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Speaker 1: it was adequate for what was necessary, look at the

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Speaker 1: parties briefing again, and verify that before it went out.

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Speaker 1: But to your question, to you know, the caseloads of

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Speaker 1: an average federal judge probably runs between a low of

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Speaker 1: two hundred to three hundred cases to a high of

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Speaker 1: a thousand cases. And it depends upon what kind of

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Speaker 1: an order it is and how busy the court is.

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Speaker 1: And you know, no matter how hard the judges working

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Speaker 1: or what kind of safe cards they have in their chambers,

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Speaker 1: it's possible for things to slip through the cracks and

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Speaker 1: when it deals with new technology that people are perhaps

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Speaker 1: not as familiar with. There's going to be a learning

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Speaker 1: curve at the start, but I think the I think

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Speaker 1: the cast out of the bag now that the learning

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Speaker 1: curve time is over and you need to start using it,

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Speaker 1: you've got guidance that tells you how to do it.

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Speaker 1: I am quite sure. I'm quite sure that Judge Robin Rosenberg,

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Speaker 1: who is the director of the Federal Judicial Center, which

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Speaker 1: is the legal education arm for the Federal judiciary, she's

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Speaker 1: a fantastic judge. And I am quite sure that the

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Speaker 1: Baby Judge schools, the judge training programs that they have

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Speaker 1: every year for new judges and for judges, magestate judges,

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Speaker 1: bankruptcy judges, district judges, and circuit judges. I am sure

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Speaker 1: that there will be a segment on the proper use

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Speaker 1: of AI there because Judge Rosenberg will want to make

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Speaker 1: sure that the judges have the most recent guidance and

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Speaker 1: are aware of the pros and the pitch balls.

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Speaker 2: Let's say, have both sides using AI. Do you think

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Speaker 2: that this will lead to less innovative legal theories or

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Speaker 2: legal thinking because they're both using you know, whatever is

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Speaker 2: on AI.

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Speaker 1: That is a worry. There's a worry among law professors

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Speaker 1: that if students are taught to use AI, that their

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Speaker 1: own sort of creative approach to a subject will actucy

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Speaker 1: the critical reasoning skills. And I suppose if the lawyers

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Speaker 1: are doing that as well, there's always a risk that

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Speaker 1: it applies to them as well. What they submit, even

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Speaker 1: if they verify it and they get rid of the

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Speaker 1: whucinations and they submit the stuff which is real that

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Speaker 1: they got there through a curated analysis that was done

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Speaker 1: for them by AI. There's some law and technology people

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Speaker 1: computer scientists who have raised the specter of a problem

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Speaker 1: that can occur when you've got these large language model

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Speaker 1: programs like generative AI, is that you know, one of

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Speaker 1: the things that you worry about with AI is biased,

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Speaker 1: and it could be intentional bias, or it can be

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Speaker 1: inadvertent bias. But when you use a generative AI tool

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Speaker 1: and you put sort of a narrative inquiry, you know,

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Speaker 1: what are the key defenses to an any trust case

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Speaker 1: to the legs the following things in the second circuit

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Speaker 1: of the United States courts. And you ask a question

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Speaker 1: like that and it comes out and gives you a list,

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Speaker 1: and you follow up with a series of follow on questions,

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Speaker 1: which is sort of a natural progression that the algorithm

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Speaker 1: senses what you're interested in, senses what you're looking to do,

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Speaker 1: and starts to provide you with answers, even if they're

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Speaker 1: based upon legitimate legal sources, consistent with what it into

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Speaker 1: its your desired outcome is. And that's a real problem

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Speaker 1: because you know, I'm a dinosaur. When I started doing

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Speaker 1: legal research, you know, we had digests. There were no computers.

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Speaker 1: You would go to like contracts and you'd look through

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Speaker 1: this digest. It was like an old fashionist cyclopedia and

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Speaker 1: you'd be looking, you know, and you say, it's this

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Speaker 1: close to the problem I have. And then you would

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Speaker 1: go to a real book and you'd open it up,

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Speaker 1: you read the case, and then when you got a case,

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Speaker 1: you had a whole series of processes to make sure

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Speaker 1: the case was had not been overruled or modified, called shepherdization.

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Speaker 1: And what you did was in reading and kind of

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Speaker 1: going triangulating to all these different things. You might say, well,

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Speaker 1: you know, I thought it was contract, but it's really warranty,

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Speaker 1: which is sort of a sub issue of contract, but

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Speaker 1: it has its own set of rules, and you kind

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Speaker 1: of would learn through this trial and error that this

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Speaker 1: intuitive approach to what the answer was, and sometimes it

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Speaker 1: would be from a different field that you had initially

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Speaker 1: thought it might be from you miss all that when

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Speaker 1: you don't have that kind of give and take thinking

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Speaker 1: about it, I can remember sitting in the law library

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Speaker 1: with like fifty books open, you know, all surrounding me,

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Speaker 1: you know, looking at something to try and figure out

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Speaker 1: what my particular case really needed to have to be

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Speaker 1: the solution for the position that I was taking. And

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Speaker 1: I think there's worry that you lose that now. Some

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Speaker 1: people would tell you, though, June, that if the parties,

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Speaker 1: one thing you can do one accepted use of AI,

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Speaker 1: if it's done properly, is you can feed into the

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Speaker 1: artificial intelligence generator of AI search program the briefs of

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Speaker 1: the party and ask the particular tool if any of

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Speaker 1: the cases cited by the parties our host nations are

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Speaker 1: not proper And you can also say did they miss

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Speaker 1: any controlling cases in this jurisdiction that deal with this

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Speaker 1: subject matter? And so it may be possible that AI

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Speaker 1: will allow the judge to sort of check to see

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Speaker 1: whether or not the lawyer's joint use of it has

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Speaker 1: been siloed to where they missed some authority that the

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Speaker 1: judge really thinks needs to be reflected upon. But I

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Speaker 1: think that right now, people who talk about the appropriate

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Speaker 1: use of AI worry that that sort of initial creative

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Speaker 1: and analytical process, the thought process that law schools have

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Speaker 1: championed for decades as to critical thinking skills that lawyers

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Speaker 1: have to have, that they may become a sort of

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Speaker 1: atrophy and a little bit less well used if people

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Speaker 1: rely upon AI too much to the exclusion of the

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Speaker 1: old skills that are that are learned. Now. We heard

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Speaker 1: some of those same kind of problems about the use

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Speaker 1: of computers back in the day. I remember lawyers saying that, well,

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Speaker 1: you could never use computers in litigation. You could use

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Speaker 1: it in wills and the states because you're using basically

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Speaker 1: forms the same forms over again, but you'll never use

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Speaker 1: it in litigation. I think that people would split their

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Speaker 1: size open laughing at that notion today, because computers are

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Speaker 1: used in every aspect of law. So you know, we're

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Speaker 1: at the beginning of this. And keep in mind, you know,

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Speaker 1: AI has been around since the nineteen fifties, right in

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Speaker 1: one form or another, but generative AI because of technological advances.

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Speaker 1: No one was talking about generative AI. I know this

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Speaker 1: because I used to write opinions as a judge and

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Speaker 1: scholarship as an adjunct professor and a professor of Duke

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Speaker 1: about evidentiary issues associated with technical evidence, and no one

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Speaker 1: was talking about generative AI until about twenty twenty two

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Speaker 1: when the whole chat GPTV came out. So this is

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Speaker 1: still relatively recent. And what's confounding is how explosive the

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Speaker 1: use is become in such a short period of time.

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Speaker 2: Explosive is the right word. Thanks so much as always,

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Speaker 2: Judge grim that's Judge Paul Grimm, director of the Bolt

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Speaker 2: Judicial Institute at Duke Law School. NBA Commissioner Adam Silver

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Speaker 2: spoke out on Friday about the bombshell gambling scandal that's

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Speaker 2: rocked the league.

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Speaker 3: I was deeply disturbed. There's nothing more important to the

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Speaker 3: league and expands and the integrity of the competition, and

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Speaker 3: so I had a pit in my stomach. It was

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Speaker 3: very upsetting.

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Speaker 2: Shock Waves ripple through the sports world after Courtland, Trailblazer's

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Speaker 2: head coach Chauncey Billups and Miami Heat player Terry Roser

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Speaker 2: along with thirty others, were indicted as part of two

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Speaker 2: separate criminal schemes related to illegal sports betting. Joseph Nocella,

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Speaker 2: the top federal prosecutor for the Eastern District of New York,

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Speaker 2: described the two schemes. The first involved sports betting.

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Speaker 4: Between December twenty twenty two and March twenty twenty four,

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Speaker 4: these defendants perpetrated a scheme to defraud by betting on

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Speaker 4: inside non public information about NBA athletes and teams. The

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Speaker 4: non public information included when specific players would be sitting

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Speaker 4: out future games or when they would pull themselves out

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Speaker 4: early for purported injuries or illnesses.

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Speaker 2: The second involved rigged poker games.

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Speaker 4: These defendants, which include former professional athletes, used high tech

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Speaker 4: cheating technology to steal millions of dollars from victims in

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Speaker 4: underground poker games that were secretly fixed. The games in

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Speaker 4: the New York area were backed by the Banano, Ambino

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Speaker 4: and genal Vezy crime families.

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Speaker 2: Billings and Rosier face money laundering and wirefront conspiracy charges.

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Speaker 2: Joining me is Derek Hogan, a partner at Tully Rinky,

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Speaker 2: tell us about the charges here.

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Speaker 5: Broadly, you know the first one involved providing let's say

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Speaker 5: inside or confidential information to people placing wagers on games. So,

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Speaker 5: for example, the NBA player involved, Terry Rogier. There are

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Speaker 5: certain bets that an individual can places, you know, whether

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Speaker 5: a team wins or loses, the score goes over a

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Speaker 5: certain amount of points, or there's something called individual prop

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Speaker 5: bets where betters can make bets on whether a certain

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Speaker 5: individual is going to score more or less than a

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Speaker 5: certain number of points, get a certain amount of rebounds,

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Speaker 5: certain amount of assists, whatever statistical categories they put out there.

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Speaker 5: And in this instance, it's been alleged that mister Rogier

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Speaker 5: purportedly told individuals that he was going to leave a

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Speaker 5: game early, you know, before he reached a certain amount

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Speaker 5: of points, rebounds, sist, and that if the individuals bet

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Speaker 5: the under on that, then you know, they would obviously

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Speaker 5: win their bets. So it's alleged that he passed along

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Speaker 5: that information and then that other people took that information,

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Speaker 5: distribute that information to others, and distributed information to others

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Speaker 5: to make bets on them. And I think it's important

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Speaker 5: to note in those types of situations that each league,

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Speaker 5: whether it's the NBA, NFL, Major League Baseball, national hockey, league.

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Speaker 5: At some point they issue like an injury report, and

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Speaker 5: that injury report will let people know who's playing, who's

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Speaker 5: not playing, who may be playing, probable, questionable things of

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Speaker 5: that nature, and that information is released, so ultimately the

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Speaker 5: book makers can put odds out there that are, you know,

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Speaker 5: the equivalent to who's playing, who's not And so what

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Speaker 5: was happening mister Rosier's situation. You know that information was

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Speaker 5: allegedly being disclosed to others prior to any type of

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Speaker 5: injury report or not even making it on your injury report.

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Speaker 5: Inside information think about like in a stock tip, right,

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Speaker 5: Like you might know about a company going public before

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Speaker 5: it's supposedly out there, and then then you use that

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Speaker 5: inside information to benefit to yourself. The other involved an

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Speaker 5: illegal poker game in a sense that it was a

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Speaker 5: cheated card game. It was through the use of technology

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Speaker 5: from what I read, whether it's like technology within the

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Speaker 5: table that the players were using, technology within the cars

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Speaker 5: that they were using. Essentially that it was a rigged

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Speaker 5: poker game. And where the high profile bendit, mister Chauncey Billips,

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Speaker 5: who was the head coach of the Portland Trailblazers. It's

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Speaker 5: being alleged that he was kind of used to lure

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Speaker 5: individuals to that game, to lure like an unsuspecting victim

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Speaker 5: to that game, because think about it, that person might say, oh, Wow,

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Speaker 5: I'm gonna play poker with Chauncey Billups. Well, I don't

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Speaker 5: believe mister Billips has been accused of, you know, cheating

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Speaker 5: the individual a lot of money. What it's been alleged

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Speaker 5: is at those poker games, it was a rigged poker

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Speaker 5: game and then ultimately some person loses a lot of

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Speaker 5: money and then that money is distributed among the co

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Speaker 5: conspirators and the individuals that ran that game.

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Speaker 2: Is there a connection between these two indictments or they

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Speaker 2: just decided to announce them on the same day. And

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Speaker 2: you know, out of more than thirty people, just three

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Speaker 2: are associated with the NBA.

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Speaker 5: Right, So I mean, I think it's yes and no.

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Speaker 5: Obviously it's a whole high profile matter. And when you're

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Speaker 5: talking about Terry Rogier, he's a current NBA player, Chauncey

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Speaker 5: Billups Hall of Fame player, current NBA coach, they are

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Speaker 5: kind of interrelated. But also it's been alleged and you

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Speaker 5: can't really you can't really tell from the indictment involving

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Speaker 5: mister Rosier, but that indictment mentions a co conspirator who

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Speaker 5: lived in Oregon at the time and had previously played

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Speaker 5: in the NBA. The co conspirator, it can surmised to

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Speaker 5: be it's Chauncey Billups and the reason that is related

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Speaker 5: to the indictment with mister Rogier. It's alleged that the

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Speaker 5: individuals and I don't even think mister Rogier, but the

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Speaker 5: other co defendants in that indictment receive specific information about

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Speaker 5: certain individuals on the Portland Trailblazers that weren't going to

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Speaker 5: play that night prior to any release to the public.

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Speaker 5: That's who Chuncey Billibs was coaching at the time. So

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Speaker 5: while mister Bilbs has not been charged in connection with

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Speaker 5: the call it the betting scandal, the NBA betting scandal,

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Speaker 5: it is potentially he's listed as a co conspirator. But

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Speaker 5: to your question, no, I think these were more released

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Speaker 5: on the same day due to the high profile nature

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Speaker 5: of the cases. But I don't really see a link

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Speaker 5: between the two because one is about an illegal poker game,

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Speaker 5: the others about other individuals using inside information to make bets.

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Speaker 2: In the inside information. How much money do they say

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Speaker 2: was involved here?

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Speaker 5: Well, I do know from the indictment that the individual

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Speaker 5: that provided the information allegedly from mister Rogier. So mister

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Speaker 5: Rogier allegedly provides this information to an individual. That individual

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Speaker 5: gives it to other people who then use it to

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Speaker 5: make bets. And it's alleged that that individual received one

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Speaker 5: hundred thousand dollars just for providing that information. And so

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Speaker 5: then it you know, you can surmise that if that

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Speaker 5: individual is getting paid one hundred thousand, I'm sure that

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Speaker 5: there were multiple wagers made by multiple people because it's

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Speaker 5: all information. Like if you're one of the people at

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Speaker 5: the top of the pyramid, if you had this information,

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Speaker 5: you're going to tell your web of betters to make

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Speaker 5: these bets. So I would imagine that the money gain

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Speaker 5: from that was in the hundreds of thousands. If they're

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Speaker 5: paying this one person allegedly one hundred grand just for

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Speaker 5: that info.

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Speaker 2: But it seems like it's a drop in the bucket

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Speaker 2: compared to the fact that sports betting accounts for about

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Speaker 2: eighty four billion dollars a year.

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Speaker 5: I certainly see the juxtaposition, and I don't necessarily agree

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Speaker 5: with you. You know, like a lot of these leagues, NBA, NFL,

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Speaker 5: they are now in partnership with these gambling companies. You know,

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Speaker 5: before it was taboo to bet on sports, right, and

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Speaker 5: all the leagues pressed against it. They didn't want anything

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Speaker 5: to do with Now people could bet illegally for you know,

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Speaker 5: going back to however long. But once it became legalized

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Speaker 5: in most of the fifty states. I know, it's legalized

434
00:25:00,760 --> 00:25:03,120
Speaker 5: throughout the country, but in most of the states it's

435
00:25:03,119 --> 00:25:06,679
Speaker 5: now legalized. So these companies essentially got in bed with

436
00:25:06,760 --> 00:25:10,840
Speaker 5: these gambling companies to form sponsorships, you know, collaborations whatnot.

437
00:25:10,880 --> 00:25:12,920
Speaker 5: I agree with you that it might be a drop

438
00:25:12,920 --> 00:25:14,560
Speaker 5: in the bucket, but if you think about it, it's

439
00:25:14,600 --> 00:25:17,480
Speaker 5: about the integrity of the game, right, Like you can't

440
00:25:17,520 --> 00:25:20,840
Speaker 5: have players taking themselves out voluntarily for the purpose of

441
00:25:20,840 --> 00:25:23,800
Speaker 5: getting a bet. You can't have individuals, you know, using

442
00:25:23,840 --> 00:25:27,720
Speaker 5: inside information to divulge that prior to the betting public

443
00:25:27,920 --> 00:25:30,359
Speaker 5: gets it because it does it does call into the

444
00:25:30,359 --> 00:25:33,159
Speaker 5: integrity of the game, and it does call into in

445
00:25:33,240 --> 00:25:35,960
Speaker 5: question the actual results of those games.

446
00:25:36,160 --> 00:25:38,159
Speaker 2: So now tell me where it fits in that the

447
00:25:38,280 --> 00:25:42,840
Speaker 2: league investigated ros You're in twenty twenty three, but cleared

448
00:25:42,920 --> 00:25:46,720
Speaker 2: him when the physician showed he had a genuine foot inflammation.

449
00:25:47,160 --> 00:25:49,520
Speaker 2: Does that fit into his defense anywhere?

450
00:25:49,840 --> 00:25:51,960
Speaker 5: I mean, I think so. These a von Brosier's attorney.

451
00:25:52,080 --> 00:25:54,040
Speaker 5: You know, first of all, I don't know if he's

452
00:25:54,040 --> 00:25:57,400
Speaker 5: going to use the NBA's investigation, but I think he's

453
00:25:57,440 --> 00:25:59,719
Speaker 5: going to say, listen, we cooperated with it. We were

454
00:25:59,760 --> 00:26:02,919
Speaker 5: clear by the NBA he had a legitimate foot injury.

455
00:26:02,960 --> 00:26:06,240
Speaker 5: You know, unless you can establish a direct connection between

456
00:26:06,600 --> 00:26:09,600
Speaker 5: you know, this individual and mister Rogier, who he allegedly

457
00:26:09,680 --> 00:26:12,840
Speaker 5: got the information from, Yeah, I think that will because

458
00:26:12,840 --> 00:26:14,440
Speaker 5: that's going to play in all their defense, Like, how

459
00:26:14,440 --> 00:26:17,720
Speaker 5: can you prove that I provided this information? In Rogier's case,

460
00:26:17,720 --> 00:26:19,800
Speaker 5: how can you prove that I didn't have a legitimate

461
00:26:19,800 --> 00:26:22,320
Speaker 5: foot injury and I voluntarily took myself out of that

462
00:26:22,400 --> 00:26:25,480
Speaker 5: game because in fact, I did not want to damage

463
00:26:25,480 --> 00:26:28,240
Speaker 5: my career further. You know, these athletes, their bodies are

464
00:26:28,240 --> 00:26:31,239
Speaker 5: their temple, right, they're their money makers, and you know,

465
00:26:31,400 --> 00:26:34,560
Speaker 5: you can't necessarily fault an individual for taking himself out

466
00:26:34,560 --> 00:26:37,159
Speaker 5: of a game with a legitimate injury that could ultimately

467
00:26:37,200 --> 00:26:39,800
Speaker 5: affect his future earnings. You know, when it comes to

468
00:26:39,800 --> 00:26:40,560
Speaker 5: play on the court.

469
00:26:40,920 --> 00:26:45,600
Speaker 2: Jim Trustee, a lawyer for Rogier, said prosecutors had relied

470
00:26:45,720 --> 00:26:51,920
Speaker 2: on spectacularly incredible sources rather than actual evidence of wrongdoing,

471
00:26:52,040 --> 00:26:54,800
Speaker 2: and that a long time ago we reached out to

472
00:26:54,840 --> 00:26:57,520
Speaker 2: these prosecutors to tell them we should have an open

473
00:26:57,560 --> 00:27:03,880
Speaker 2: line of communications. Chized Terry as a subject, not a target.

474
00:27:04,359 --> 00:27:07,720
Speaker 5: Right And so normally, you know, as an attorney, you

475
00:27:07,800 --> 00:27:10,960
Speaker 5: might get correspondents from the FBI or the federal government,

476
00:27:11,000 --> 00:27:12,720
Speaker 5: you know, in a sense that you're a target or

477
00:27:12,720 --> 00:27:15,240
Speaker 5: subject of the investigation, or you're just kind of like

478
00:27:15,320 --> 00:27:18,119
Speaker 5: an external person you're looking for information from. So it

479
00:27:18,160 --> 00:27:21,080
Speaker 5: seems like for mister Rosier's attorney statement that, you know,

480
00:27:21,119 --> 00:27:23,920
Speaker 5: because I remember reading articles myself that you know, Rogier

481
00:27:24,040 --> 00:27:26,800
Speaker 5: was being investigated by the NBA. They were upfront and

482
00:27:26,840 --> 00:27:29,520
Speaker 5: they were forthcoming. And it's my guess is he's probably

483
00:27:29,520 --> 00:27:32,199
Speaker 5: a little upset that he said to the FBI, hey, listen,

484
00:27:32,600 --> 00:27:35,440
Speaker 5: we're here if anything comes up, you know, I represent

485
00:27:35,520 --> 00:27:38,280
Speaker 5: mister Rosier, please come to me before you do anything.

486
00:27:38,640 --> 00:27:40,560
Speaker 5: And then next thing you know, he's getting a call

487
00:27:40,680 --> 00:27:42,760
Speaker 5: that his client has to do a purp walk. You know,

488
00:27:42,840 --> 00:27:44,960
Speaker 5: I think Rogier was at a hotel the morning he

489
00:27:45,040 --> 00:27:47,480
Speaker 5: got arrested. So I think that's where the lawyer's ire

490
00:27:47,560 --> 00:27:50,000
Speaker 5: comes from, because I've done this myself, where you reach

491
00:27:50,040 --> 00:27:52,640
Speaker 5: out to law enforcement. You tell them, hey, you are

492
00:27:52,680 --> 00:27:55,800
Speaker 5: represented by counsel, or this individual is represented by counsel,

493
00:27:56,080 --> 00:27:58,200
Speaker 5: and if something comes up, let me know. I'll bring

494
00:27:58,240 --> 00:27:59,679
Speaker 5: them in. We don't got to do this. Song and

495
00:27:59,760 --> 00:28:02,399
Speaker 5: dance will make it a lot easier. I think that's

496
00:28:02,400 --> 00:28:03,879
Speaker 5: that's where his eyre is coming from.

497
00:28:03,960 --> 00:28:07,119
Speaker 2: When you're told that your client's a subject, not a target.

498
00:28:07,520 --> 00:28:10,840
Speaker 2: I mean, does that often turn into a target not

499
00:28:10,920 --> 00:28:11,639
Speaker 2: a subject?

500
00:28:12,200 --> 00:28:14,600
Speaker 5: I guess it all depends on ultimately what information they're

501
00:28:14,640 --> 00:28:17,480
Speaker 5: gaining from him. You know, if he's a subject, I'm

502
00:28:17,520 --> 00:28:20,720
Speaker 5: guessing that the lawyer said, hey, let's sit down, let's talk.

503
00:28:21,000 --> 00:28:23,479
Speaker 5: Here's what the investigation with the NBA did, so on

504
00:28:23,520 --> 00:28:26,080
Speaker 5: and so forth, and you know, if something comes up,

505
00:28:26,160 --> 00:28:28,200
Speaker 5: let us know. So I would think in that time,

506
00:28:28,440 --> 00:28:31,040
Speaker 5: because the federal government, the FBI there, they're powerful. It's

507
00:28:31,040 --> 00:28:33,639
Speaker 5: a powerful agency, right and you know they're probably not

508
00:28:33,680 --> 00:28:35,719
Speaker 5: going to come after you unless they think they have something.

509
00:28:36,040 --> 00:28:38,120
Speaker 5: But I would think if my client's just a subject,

510
00:28:38,120 --> 00:28:43,000
Speaker 5: he's not necessarily a target. I'm not necessarily overly concerned

511
00:28:43,120 --> 00:28:46,880
Speaker 5: about him getting arrested, specifically, once I've already notified the

512
00:28:46,960 --> 00:28:49,719
Speaker 5: FBI that hey, call me, let me know if something's

513
00:28:49,760 --> 00:28:51,320
Speaker 5: going to come up, because that you know, we'll take

514
00:28:51,320 --> 00:28:52,120
Speaker 5: care of it from there.

515
00:28:52,520 --> 00:28:55,240
Speaker 2: The poker games, I take it that there are illegal

516
00:28:55,280 --> 00:28:58,600
Speaker 2: poker games happening across the country all the time. What

517
00:28:58,760 --> 00:29:03,600
Speaker 2: made these show worthy of an FBI investigation and end indictments?

518
00:29:04,040 --> 00:29:06,760
Speaker 5: So you're right, there's illegal poker games going around, you know,

519
00:29:06,880 --> 00:29:10,000
Speaker 5: even if you're playing for you know, minimum money technically, right,

520
00:29:10,040 --> 00:29:12,320
Speaker 5: and that's an illegal poker game. But I think this

521
00:29:12,440 --> 00:29:15,520
Speaker 5: drew the fbis I for a couple of reasons. There's

522
00:29:15,640 --> 00:29:18,760
Speaker 5: allegations of the mafia being involved in it, right, and

523
00:29:18,880 --> 00:29:21,640
Speaker 5: what they were doing is allegedly is okay, you get

524
00:29:21,640 --> 00:29:24,920
Speaker 5: this one individual that might lose hundreds of thousands of

525
00:29:24,920 --> 00:29:28,080
Speaker 5: dollars at the poker table, and now they're using that money.

526
00:29:28,120 --> 00:29:32,000
Speaker 5: The people involved are allegedly you know, distributing it amongst themselves.

527
00:29:32,280 --> 00:29:35,680
Speaker 5: They're laundering that money. Further, it's my understanding that when

528
00:29:35,680 --> 00:29:38,400
Speaker 5: the individuals didn't pay up, then there was you know,

529
00:29:38,520 --> 00:29:41,600
Speaker 5: threats of force, maybe even actual physical force, used. So

530
00:29:41,680 --> 00:29:43,920
Speaker 5: that's why I think the FBI is getting involved in this.

531
00:29:44,280 --> 00:29:46,080
Speaker 5: Like you said, you're runn of the mill poker game.

532
00:29:46,080 --> 00:29:48,360
Speaker 5: But when you have an MBA star, you have you know,

533
00:29:48,440 --> 00:29:51,160
Speaker 5: hundreds of thousands, maybe if not millions of dollars at stake,

534
00:29:51,520 --> 00:29:54,160
Speaker 5: and then you have alleged mob tized and then also

535
00:29:54,480 --> 00:29:57,920
Speaker 5: allegations of people using force to collect on those debts.

536
00:29:58,160 --> 00:29:59,400
Speaker 5: That's where they come into play.

537
00:30:00,000 --> 00:30:04,240
Speaker 2: Tell how strong the cases are against the defendants from

538
00:30:04,280 --> 00:30:05,120
Speaker 2: the indictments.

539
00:30:05,800 --> 00:30:08,560
Speaker 5: I mean, you can't necessarily tell from the indictments, because

540
00:30:08,560 --> 00:30:11,280
Speaker 5: the indictments are just gonna list out some basic facts

541
00:30:11,640 --> 00:30:14,200
Speaker 5: as they see them, and then the counts they're charged with.

542
00:30:14,560 --> 00:30:17,040
Speaker 5: I mean, yes, if everything was all true that they say,

543
00:30:17,080 --> 00:30:19,720
Speaker 5: then that's you know, that's a pretty tough case. However,

544
00:30:19,840 --> 00:30:23,000
Speaker 5: you know, it's not like discovery has been provided the public.

545
00:30:23,040 --> 00:30:26,160
Speaker 5: It's not like certain information has been provided to the public.

546
00:30:26,400 --> 00:30:28,840
Speaker 5: So ultimately you can't say whether or not there is

547
00:30:28,880 --> 00:30:31,080
Speaker 5: a strong or is not a strong case. I will

548
00:30:31,080 --> 00:30:34,040
Speaker 5: say two things about BEF though. Number One, the Feds

549
00:30:34,080 --> 00:30:36,640
Speaker 5: have a high conviction rate. I'm not saying every federal

550
00:30:36,680 --> 00:30:39,400
Speaker 5: defendant gets convicted, but I would imagine that the Feds

551
00:30:39,400 --> 00:30:41,480
Speaker 5: aren't going to come after someone unless they have a

552
00:30:41,520 --> 00:30:44,320
Speaker 5: lot of evidence, you know, in their back pockets. Certainly

553
00:30:44,360 --> 00:30:47,400
Speaker 5: one of this kind of you know, public nature, right,

554
00:30:47,440 --> 00:30:50,200
Speaker 5: a lot of media, you know, a lot of press

555
00:30:50,240 --> 00:30:52,800
Speaker 5: around it. And the second part though, to whether it's

556
00:30:52,840 --> 00:30:55,040
Speaker 5: a strong case or not, it's always going to hinge

557
00:30:55,040 --> 00:30:57,840
Speaker 5: on the credibility of the witnesses. I would imagine that

558
00:30:57,920 --> 00:31:00,520
Speaker 5: some of the individuals that have been providing animation from

559
00:31:00,520 --> 00:31:03,200
Speaker 5: the government might be cedy. Individuals might not have the

560
00:31:03,200 --> 00:31:06,040
Speaker 5: best track record. So if I'm a defense attorney, I'm

561
00:31:06,040 --> 00:31:09,560
Speaker 5: going to go full bore attacking that witness's credibility. Say,

562
00:31:09,600 --> 00:31:12,240
Speaker 5: you know, how can we believe person A when they've

563
00:31:12,280 --> 00:31:14,160
Speaker 5: done this, that, and the third throughout the course of

564
00:31:14,160 --> 00:31:17,640
Speaker 5: their life. So I think defense probably have a strong case,

565
00:31:17,640 --> 00:31:19,800
Speaker 5: but you can't really tell that from the indictment, and

566
00:31:19,800 --> 00:31:21,280
Speaker 5: that still has to play out in court.

567
00:31:22,200 --> 00:31:26,800
Speaker 2: How do you think Billups and Rosier's celebrity would play

568
00:31:26,800 --> 00:31:27,640
Speaker 2: into a trial?

569
00:31:28,160 --> 00:31:30,480
Speaker 5: Think get at place huge, right, you know, because you're

570
00:31:30,520 --> 00:31:33,600
Speaker 5: gonna get supporters, you know, on both sides of the coin.

571
00:31:33,680 --> 00:31:35,920
Speaker 5: You know the fact that you know, Chauncy Bills Hall

572
00:31:35,920 --> 00:31:39,520
Speaker 5: of Fame player, current head coach. You know, Terry Rogier,

573
00:31:39,640 --> 00:31:42,240
Speaker 5: he's known like that celebrity, You're going to have people

574
00:31:42,240 --> 00:31:45,200
Speaker 5: that support them regardless. You know. You look at Sean

575
00:31:45,240 --> 00:31:48,200
Speaker 5: Puffy Combs right like, he was alleged to done awful

576
00:31:48,240 --> 00:31:51,040
Speaker 5: things and yet he still had his public support or anybody.

577
00:31:51,040 --> 00:31:53,000
Speaker 5: I don't want to just use you know, mister Combs

578
00:31:53,000 --> 00:31:56,000
Speaker 5: as an example, any high profile defendant. You know, OJ

579
00:31:56,160 --> 00:31:59,200
Speaker 5: Simpson still got plenty of public support, right, you know,

580
00:31:59,360 --> 00:32:01,600
Speaker 5: and so that is going to play into it. And

581
00:32:01,640 --> 00:32:03,800
Speaker 5: I think the defense is going to you know, this

582
00:32:03,920 --> 00:32:06,680
Speaker 5: is mister Billbs, hall of fame, career head coach. Why

583
00:32:06,680 --> 00:32:09,120
Speaker 5: would he be risking all that on just you know,

584
00:32:09,160 --> 00:32:11,920
Speaker 5: a poker game, maybe a poker game he participated in,

585
00:32:12,240 --> 00:32:15,040
Speaker 5: but unwittingly as far as you know, it being a

586
00:32:15,120 --> 00:32:17,840
Speaker 5: rigged game, or others being you know, losing on purpose.

587
00:32:18,240 --> 00:32:19,360
Speaker 5: You know, same with mister Rogier.

588
00:32:19,400 --> 00:32:23,320
Speaker 2: I would think they've both made spectacular amounts of money

589
00:32:23,320 --> 00:32:26,880
Speaker 2: in their careers. Billips made about one hundred and six

590
00:32:26,920 --> 00:32:31,960
Speaker 2: million dollars in earnings over his career. Rosier made about

591
00:32:31,960 --> 00:32:34,800
Speaker 2: one hundred and sixty million. I mean, why would they

592
00:32:34,920 --> 00:32:38,200
Speaker 2: get involved in this kind of a scheme? It seems

593
00:32:38,240 --> 00:32:39,840
Speaker 2: like small potatoes, you know.

594
00:32:40,120 --> 00:32:42,800
Speaker 5: I you know, I've asked myself that same question. But

595
00:32:42,960 --> 00:32:45,880
Speaker 5: I think it goes to two reasons. And I don't know,

596
00:32:45,960 --> 00:32:47,440
Speaker 5: you know, I'm not saying I have any rhyme or

597
00:32:47,480 --> 00:32:50,200
Speaker 5: reason to believe this, all right, But one, you know,

598
00:32:50,360 --> 00:32:52,880
Speaker 5: a lot of the times, at least in mister Rogier's case,

599
00:32:53,280 --> 00:32:55,840
Speaker 5: it is a led that he provided this information to

600
00:32:55,880 --> 00:32:57,880
Speaker 5: an individual he grew up with, that he had known

601
00:32:57,920 --> 00:33:00,960
Speaker 5: since childhood, right, And I think that goes to sometimes

602
00:33:01,360 --> 00:33:04,360
Speaker 5: these individuals, no matter how much money, power and respect

603
00:33:04,360 --> 00:33:07,560
Speaker 5: that they have, sometimes there's individuals that you grew up

604
00:33:07,600 --> 00:33:09,360
Speaker 5: with that you can't you know, I'm not saying you

605
00:33:09,400 --> 00:33:11,600
Speaker 5: should cut them out of their circle, but if they're

606
00:33:11,640 --> 00:33:14,640
Speaker 5: there for nefarious purposes, you know, sometimes you can get

607
00:33:14,800 --> 00:33:17,080
Speaker 5: entangled in that, right And I'm not saying that's the

608
00:33:17,120 --> 00:33:19,360
Speaker 5: case with mister Rosier. It could be one of his

609
00:33:19,400 --> 00:33:21,760
Speaker 5: best buddies, that's a great person, you just never know,

610
00:33:21,800 --> 00:33:24,520
Speaker 5: But I think that's one you get entangled with these

611
00:33:24,520 --> 00:33:27,400
Speaker 5: individuals that are up to no good. The other part is,

612
00:33:27,440 --> 00:33:30,280
Speaker 5: and I can't say, people might have an addiction, right,

613
00:33:30,360 --> 00:33:33,320
Speaker 5: and this gambling addiction could get out of hand. Regardless

614
00:33:33,320 --> 00:33:35,280
Speaker 5: of how much money they make. And I'm not saying

615
00:33:35,280 --> 00:33:37,760
Speaker 5: that's the case, because I don't think it's alleged that

616
00:33:37,840 --> 00:33:40,680
Speaker 5: Rosier even you know, shared in any of these profits.

617
00:33:40,880 --> 00:33:43,600
Speaker 5: It's just that he provided this information. So I think

618
00:33:43,640 --> 00:33:45,560
Speaker 5: it goes it could be a gambling addiction, or it

619
00:33:45,600 --> 00:33:48,360
Speaker 5: could just be you know, individuals, whether they grew up

620
00:33:48,360 --> 00:33:50,520
Speaker 5: with them or not, they're just individuals that aren't looking

621
00:33:50,600 --> 00:33:53,480
Speaker 5: out for their best interests. That's ultimately how you could

622
00:33:53,480 --> 00:33:55,000
Speaker 5: lose that much more. You know, you could risk so

623
00:33:55,120 --> 00:33:57,000
Speaker 5: much when you make multi millions of dollars.

624
00:33:57,040 --> 00:34:00,160
Speaker 2: But the FBI said, oh, this is not over or

625
00:34:00,200 --> 00:34:03,920
Speaker 2: we're still investigating. Does it look like this is an

626
00:34:03,960 --> 00:34:08,080
Speaker 2: instance where other NBA players could be implicated.

627
00:34:08,480 --> 00:34:10,200
Speaker 5: I don't know if it would be anytime soon. I

628
00:34:10,239 --> 00:34:12,600
Speaker 5: feel like if they you know, because this is talking

629
00:34:12,640 --> 00:34:14,640
Speaker 5: about games from twenty twenty three, I think here we

630
00:34:14,680 --> 00:34:17,000
Speaker 5: are in twenty twenty five, we're almost going into twenty six.

631
00:34:17,320 --> 00:34:20,080
Speaker 5: So for what they have now, I feel like if

632
00:34:20,120 --> 00:34:22,600
Speaker 5: they had more, they would come out with it. That

633
00:34:22,760 --> 00:34:25,640
Speaker 5: being said, you're going to start getting now that all

634
00:34:25,640 --> 00:34:27,719
Speaker 5: these people are arrested. People are going to flip. People

635
00:34:27,760 --> 00:34:30,960
Speaker 5: are going to start talking, and then when that happens,

636
00:34:31,040 --> 00:34:34,359
Speaker 5: that may lead to other information coming out. Not saying

637
00:34:34,360 --> 00:34:36,839
Speaker 5: it will, but I think it's more likely than not

638
00:34:37,000 --> 00:34:40,000
Speaker 5: that if there is another quote quote scandal, this is

639
00:34:40,040 --> 00:34:41,920
Speaker 5: just the beginning of it, right, and this is just

640
00:34:41,960 --> 00:34:43,839
Speaker 5: going to start to come out, and you might see

641
00:34:43,840 --> 00:34:46,840
Speaker 5: it come out in other league NFL, NHL, Major League Baseball,

642
00:34:46,840 --> 00:34:47,680
Speaker 5: things of that nature.

643
00:34:47,960 --> 00:34:51,399
Speaker 2: I'm wondering about the timing of this announcement by the

644
00:34:51,800 --> 00:34:57,280
Speaker 2: FBI and Federal prosecutors at the beginning of the NBA season.

645
00:34:57,680 --> 00:35:02,239
Speaker 2: When the sports betting scheme allegedly curred between December of

646
00:35:02,480 --> 00:35:06,799
Speaker 2: twenty twenty two and March of twenty twenty four, and

647
00:35:06,880 --> 00:35:10,800
Speaker 2: the rig poker game that Billips is accused of participating

648
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Speaker 2: in took place in April of twenty nineteen. So why

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Speaker 2: announce this on the eve of the NBA season.

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Speaker 5: It might just be a great coincidence, or you might

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Speaker 5: be right in that, you know, here's the start of

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Speaker 5: the season. You know, here we are, the Federal Bureau

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Speaker 5: of Investigation, your big seasons about the start, were about

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Speaker 5: to drop this hammer, you know, to make sure that

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Speaker 5: all eyes are in connection with this. It could be

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Speaker 5: a coincidence doesn't seem.

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Speaker 2: Like it was, though, especially with the fanfare with which

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Speaker 2: the indictments were announced. Thanks for joining me, Derek. That's

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Speaker 2: Derek Hogan of Tully Rinky Turn It, and that's it

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Speaker 2: for this edition of The Bloomberg Law Show. Remember you

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Speaker 2: can always get the latest legal news on our Bloomberg

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Speaker 2: Law Podcast. You can find them on Apple Podcasts, Spotify,

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00:36:00,880 --> 00:36:05,920
Speaker 2: and at www dot bloomberg dot com, slash podcast Slash Law,

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Speaker 2: and remember to tune into The Bloomberg Law Show every

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Speaker 2: weeknight at ten pm Wall Street Time. I'm June Grosso

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Speaker 2: and you're listening to Bloomberg