When Algorithms Find Fraud: The New False Claims Act with Tom Suddath and David Bender

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Hello and welcome to the Emerging Litigation Podcast.
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I'm your host, Tom Hagy.
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Today we're going to talk about Lincoln's law.
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This is the law that goes back to the Civil War days when military contractors were ripping off the government for whatever lame meals, lousy food, uniforms that fell apart.
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Nobody needs that.
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It's called the False Claims Act, as anybody who's familiar with it knows.
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That has to do with identifying fraud against the government.
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And a lot of the tips about fraud, lower list fraud, have come from insiders, people with knowledge of the conduct and practices of companies, how they bill and how they overbill, overcharge government.
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But now litigants, False Claims Act enforcers, relators, investigators, and others, they're relying a lot now on finding trends in data.
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Specifically, we're today we're talking about healthcare data.
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So if you go through the troves of data that's available publicly from the government and you spot trends, you can see that there may be some irregularities that may warrant some additional attention and scrutiny from the government.
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That is, they may be getting ripped off.
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I think that's what it comes down to.
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That's not specifically in the law itself, but you know, in my summary.
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So what we're going to talk about today is recognizing the use of data mining and data safaris or fraud safaris or fraud hunting.
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Uh, the government is encouraging uh folks to use these large data troves to help them find cases.
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Their objective is enforcement.
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They want to find these people, they want to persecute, persecute them, they want to find these people, and they want to prosecute them.
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In addition to identifying fraud, uh they believe that use of this data will also help the government build stronger cases.
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Joining me to discuss this are two attorneys from Reed Smith.
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You've heard of them.
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One is uh lives near me, outside of Philadelphia.
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He is Tom Suddath.
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He is a former assistant U.S.
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attorney.
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He now focuses on False Claims Act cases, white-collar crime, and government investigations, particularly in the healthcare sector.
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So I appreciate Tom coming back uh on the podcast.
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We talked about this before, so I'm glad to have him back.
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You know, he may be questioning his life choices.
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Joining Tom is David Bender.
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He's counsel in ReadSmith's Washington, D.C.
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office.
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And there, his practice includes healthcare litigation, regulatory matters, Medicare and Medicaid disputes, compliance counseling, and of course, the topic today, false claim back.
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So with that, here is my interview with Tom Suddath.
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And David Bender of Reed Smith.
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Hope you enjoy it.
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Tom Suddath.
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David Bender, thanks for joining me on the Emerging Litigation Podcast today.
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I appreciate you taking the time.
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Um Tom, we're, you know, I've explained the FDA a bit in my intro, but today we're talking about something called Focus.
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Uh can you give us a little bit of background on what that is first?
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Sure, Tom.
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Uh and thank you.
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Thank you for having us here.
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It's good to be here.
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Um, so the Focus Initiative was uh an initiative um announced by the Department of Justice at the end of April of 2026.
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Uh it's an acronym that stands for the Broad Oversight Through Careful Use of Statistics.
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And the bottom line is it's an effort by DOJ to try to establish some rules of the road or guardrails for the use of data mining in connection with false transact cases.
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Um, data mining uh in false transact cases has been around for a while.
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DOJ has used it primarily through the use of non-public information.
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But what we're seeing now is an upsurge in cases brought by uh whistleblowers, data miners, who are not typically insiders with insider information, which is what we're typically seeing in these cases.
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Instead, they're uh folks who are collecting publicly available data, analyzing it, and then going to the government uh with the PTAM case, uh saying that they they think they found indicia of fraud and asking DOJ to investigate and hopefully uh intermediate case.
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So they're kind of like data data fraud hunters, is that is that the right way to look at it?
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Yeah, it it is.
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What they're doing is uh they're using the uh what we're seeing is increasingly powerful and sophisticated AI analytic tools um to analyze uh publicly available data uh and then going to the government saying that we we we've we found uh evidence of fraud.
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Uh primarily it's what we're seeing is a big uptick in the healthcare fraud space because there's a lot of money there and there's a lot of statistics and a lot of publicly available information.
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But yeah, basically they're yeah, they're data miners who are you know hunting for fraud.
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Gotcha.
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Okay.
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And um so what what is what is driving this?
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Is it the availability of data, the as you said, the AI analytics or the economics of litigation?
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What would you say is driving this?
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I think it's all three, uh, Tom.
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You know, that there's uh you know, healthcare remains a top priority for Department of Justice, healthcare fraud, that they continue to be committed to investigating, uh prosecuting those cases.
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Uh it's yeah, it I don't see that changing anytime soon.
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Um and it's the availability of uh a lot of data out there, uh information that uh had previously not been publicly available, it's now publicly available.
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And then also it's the increasingly powerful um and sophisticated AI tools where they're running algorithms, they're they're testing thesis, and then uh coming to the government and saying that we believe we found some uh evidence of uh fraud and asking government to take a look at it and intervene if possible.
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Okay.
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David, do you have anything to add to that?
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I think Tom, the other thing I would add to that is I know in the introduction you outlined some of the unique aspects of the False Claims Act.
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And one of them is that when you have a whistleblower or a relater under the False Claims Act who initiates a False Claims Act lawsuit, they stand to receive a portion of the ultimate recovery.
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And so in this particular instance, there's a financial incentive for data miners to bring these cases to DOJ's attention, um, with the idea being that it's mutually beneficial.
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It's mutually beneficial for DOJ to prosecute alleged fraud and abuse and waste.
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And it's also mutually it's also beneficial to the relator, or in this case the data miner, to bring those cases to DOJ's attention and stand to benefit financially as well.
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Okay.
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I don't know why I'm laughing, other than um as kind of a freelance uh free agent out here.
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I'm always looking for new uh revenue opportunities, so maybe this would be a side gig for me.
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That's not what I'm gonna do at all, but moving on.
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So uh let's move on.
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You mentioned obviously healthcare, we're here to talk about that too.
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Why is healthcare uh ground zero?
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It pr I mean it produces enormous amounts of billing and reimbursement data, obviously.
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So it has has it become the proving ground for these data-driven SCA enforcement actions?
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I I I think it's certainly uh one of the proving grounds.
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You know, we've also seen a lot of these cases uh brought under the uh uh Payment Protection Uh Act in those cases.
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Uh but you know, as those cases wind through the system, yeah, health care fraud is yeah has staying power.
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You know, it it's going to um I think what we're gonna see is increasing use of this uh in health care fraud cases.
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Health yeah, government subsidies of health care uh is not going away.
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Um there's just in there's just so much money uh involved here.
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And so, you know, it's a you know it's a potentially loop as David Fender said, you know, it's a potentially lucrative ground for people who are not insiders.
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Um these are people who have uh are just mining the data and running algorithms and you know coming back with information they believe uh shows some type of reliable correlation uh between the statistics and some indicty of fraud.
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Okay.
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The other thing, Tom, is that you know, under the under the False Claims Act, there's also a uh there are statutory per claim penalties.
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And in the healthcare space, um that arena is conducive to very, very high numbers of claims that are submitted to the government.
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Think of Medicare and Medicaid, for example, when you go to a doctor and they and they submit a bill for your for your um care, we're talking about over the course of a year or two years or three years for a health system, you could be talking about thousands or even millions of claims.
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And so under the FCA, given there are uh there's availability for per claim penalties, what we see is that there are quite a few FCA cases in the healthcare space, and that's one reason.
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There's a lot of money at stake, not just the damages, but there are a lot of claims, and a lot of claims means a lot of penalties.
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Well, let's hope we continue to have uh health care.
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Okay, little side jab for a little side jab from a senior citizen.
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Um how how do you distinguish between evidence of fraud and like innocent statistical anomalies?
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I mean, typically what what how you uh distinguish between the two is you have an insider who who um identifies information.
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They, you know, their insider information is buttressed with statistical information.
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Um but that's not what's what we're seeing here.
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You know, what we're seeing here in these this new generation of cases are cases brought uh not by insiders, not with people who have inside information, um, but in but cases that are brought based upon data analytics.
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Of course, there are some cases where that that is supplemented with some insider information.
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Um but I mean what what courts generally uh require is that there be both uh indicia of fraud, but also and falsity, uh, but also that the the fraudulent, the allegedly fraudulent claims were claims that were submitted with Ciencia, with with knowledge uh that it was improper.
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Um what what we're seeing with these data mining cases is that they're they're trying to bring the cases based upon this driven by the statistics and the the uh the through their analysis, they've come up with what they believe to be uh predictive and reasonably reliable correlation between the the the claims and then the uh yeah, the falsity and the sienter.
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Okay.
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David, anything to add to that?
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I I echo what Tom uh mentioned there.
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I think to me, the biggest difference between um an innocent error and the makings of a of a of a provable false claims at case is sienter.
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The the idea that the defendant um knowingly submitted claims that were in excess of the amount to which they were entitled under the law.
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That sienter or knowing aspect of the case is really what separates um an overpayment from fraud.
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I guess the next one is is kind of obvious.
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You know, DOJ said it wants analytical rigor and legal legally sufficient allegations.
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So what what problem is DOJ trying to solve with focus?
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It's the you know the onslaught of these cases, Tom, in a nutshell.
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Um yeah data analytics has been around for a while.
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Governments used it um in a lot of their cases going back a number of years.
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Uh what you know, DOJ is no, you know, this is not breaking news to anybody, but you know, they're you know their their staff is down.
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Um the people who are on the uh on the front line uh addressing these issues have a lot on their plate.
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Um and so I think what DOJ is trying to do here is establish um is to try to uh tell the data miners, look, if you're gonna come to us with these cases, here's what we expect.
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Um and we will prioritize those cases that we believe have been um properly analyzed and uh analyzed and presented to us uh with a understanding of what the legal requirements are.
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Um and so I I really think that this is uh an effort by DOJ to try to establish some um uh an ability to try to winnow through um the the you know the number of false claims at cases that they're getting.
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That they're you you've seen the statistics that they're just yeah, the number of false claims that cases in 2026, you know, they're being filed at a record pace.
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And that coupled with the fact that DOJ ha has a smaller staff to deal with these, I I think it just yeah, this is an effort by DOJ to try to uh provide some guidance um on the cases they're gonna prioritize.
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Yeah, it must be crazy times at DOJ right now, among other agencies.
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Uh uh David, did you have anything to add to that?
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One thing I would add is that, you know, DioJ is also acutely aware of one of the main hurdles in an FCA case, which is what's known as um Rule 9B under the Federal Rules of Civil Procedure.
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So under Rule 9B, a party who alleges fraud has to plead fraud with what's known as heightened particularity.
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Uh it's an elevated standard where a plaintiff alleging fraud really has to plead specifics on the who, what, where, when, why, how of the fraud.
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It's a higher bar than in an ordinary non-fraud civil case.
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And so DOJ understands that typically one of the first lines of defense for an FCA defendant is to move to dismiss the case on the posture that the relator has failed to satisfy that Rule 9B bar.
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If you have data miners who are standing in the shoes of the role of relator who maybe have a theory but don't really understand the case and aren't able to articulate the specifics of it in the complaint, DOJ understands that that's the type of case that defendants are going to have a higher success rate when they move to dismiss.
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So DOJ really wants to kind of vet these cases and know that data miners that bring the cases to their attention really understand the cases and are able to clear that Rule 90 bar by drafting the complaint that would be likely to pass.
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Okay.
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Well, when you're talking about the complaint improving case, can you talk a bit about um the kind of evidence that is required for a strong case?
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Sure.
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I mean, I I I think the type of evidence that typically a DOJ looks for, as I've mentioned before, it's um insider information.
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It's um you know, the the cases that don't pass muster are cases where there's just the relator tries to establish if there's correlation, there must be causation.
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Correlation does not equate to causation.
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Um what DOJ is saying here though is if there's a reasonable or reliable uh basis for the correlation, that may be, you know, that that may be enough uh for them to uh pick up on this, pick up on these cases, so long as that analysis is also tethered or tied to a legal analysis to to uh identify issues such as Center's, issues such as falsity.
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But typically what they look for is, as we mentioned before, the insider information, the information that uh that suggests that what is being presented to DOJ, these are not just mistakes, these are not just sort of innocent one-off filling irregularities, but this is a pattern and a practice um by defendants to overbill um Medicare.
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I think that's a really important point, Tom, because we if we step away for a moment, you step away from the data mining case.
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You know, in your garden variety false claims that case that's predicated on a whistleblower who's a a c a company insider, if you will.
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They're able to tell DOJ the story.
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They're able to tell DOJ not just what was allegedly causing an overpayment, but also the why.
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The idea that, hey, you know, this was an initiative.
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This was a corporate-wide scheme.
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In data mining cases, it's tougher to do that.
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It's tougher to understand why folks within the company did what they did.
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It's harder to make out that story.
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So it becomes all the more important for VOJ to be able to find patterns within the data because they have to sort of overcome what um is ultimately a bit of a hurdle, which is um not having a company insider who can explain how things were really happening boots on the ground.
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Gotcha.
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So you can't do a podcast on an emerging issue these days without talking about artificial intelligence.
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And and so uh it sounds like that technology is is becoming more and more of a powerful tool in in a lot of ways.
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But so we're looking at its use in detecting fraud, fraud.
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So can you talk about that?
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How what's the influence of is uh is AI making these kinds of claims and this kind of analysis uh stronger and more possible?
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Yeah, Tom, I don't think there's any question, but that this is um fueled in significant ways by AI.
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Um and I I think that's you know what what you're seeing is not only use of AI in doing data analytics, but also using AI to help craft complaints.
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Um I think that's yeah, that that is something that is contributing significantly to the uptick of false clients that cases.
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Um you know that the AI is being used to not only do the data analytics, but then once you have those data analytics, it's being used to help craft complaints.
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Um and so it's you know whereas before um putting together a false client set case may require a lot of uh hours in in going through information, sifting through information before you present it to DOJ, um AI just cuts down on on that workload significantly, not only just collection of the information, but then using that information, plugging it into a complaint.
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And I think that's yeah, uh in my mind, I don't think there's you know much debate that what's what's happening here is being driven significantly through AI, which is you know, it it can be a powerful tool, but it can also be, you know, a double-edged sword.
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That's for sure.
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Mr.
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David, any comment on that?
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Uh picking up on the double-edged sword theme, you know, typically when a when a data mining relator brings the case, they're going to try to put forth their best examples of what they would deem to be thought.
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They're going to try to put those in their complaint.
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But if the case moves on to discovery, you know, AI can also be a really powerful tool for defendants.
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Um, one of the things that defendants can use AI for when it comes to the data is really sifting apart the examples that were showcased in the complaint from maybe counterexamples that are reflected in the data.
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That's often one of the one of the best ways to take some of the air out of a plaintiff's balloon and explaining that you know there might be a difference between uh a one-off overpayment and a whole swath of thing on the other side that demonstrates a pattern of compliance.
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Okay.
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Yeah.
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And I was I always on my soapbox about AI, because I use it quite a bit in my business, but um and lawyers certainly are learning this too.
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And lawyers have a lot more, um, there are a lot more traps, I think, for lawyers in you in the use of AI that I don't have to have.
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Like if you're using chat and you're entering confidential information into the chat, is that breaking attorney client privilege or whatever?
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Are you sharing work product?
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But I don't practice law, so I thank God for a lot of people, but I don't have to worry about that.
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The um the other thing though is is oh my gosh, the human involvement.
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You talk about uh David, you know, writing complaints and stuff and uh law firms.
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Are learning the hard way, aren't they?
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That you must have a human overlooking things.
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And my my whole thing is treat AI like it's uh like it's a really smart, really fast junior associate uh with you with you.
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Um that's my uh my my soapbox for now.
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I I do ask AI how why is it so good?
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And I it's it's be it's it's getting weird that it even mentions my wife to me now when I'm talking about things.
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And it's it mentioned it makes fun of me when she talks to it, which is just it's kind of amusing, but uh also terrifying.
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But I've asked AI, like, how do you do this?
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How are how is it you're able to give me back answers?
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And I asked complicated questions.
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It said, well, you know, Tom, you have to think, you know, you've got one brain.
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I've got a multitude of brains all working at the same time uh on massive amounts of data, and I'm learning all the time.
00:22:56.000 --> 00:23:01.200
So it's like, yeah, I just told it never to uh never to you know come after me.
00:23:01.359 --> 00:23:15.039
Um, are requiring um you know changes court to court, but some courts are now requiring certifications from counsel as to whether they did or did not actually use AI in the course of blocking a um submission to the court.
00:23:15.440 --> 00:23:19.359
Yeah, I feel like I feel like that's that will end up being the wrong question.
00:23:19.359 --> 00:23:28.079
I don't know if you agree or not, but the question will be uh because in my mind the lawyer owns it no matter how it's done.
00:23:28.079 --> 00:23:33.920
So, you know, are you certifying that you are responsible for this and you have checked it?
00:23:33.920 --> 00:23:35.119
You know, that kind of a thing.
00:23:35.119 --> 00:23:39.920
But um because it's uh yeah, but I I see what you're what you're saying.
00:23:39.920 --> 00:23:51.920
You because there are too many instances where uh firms, I don't know, I would just be horribly embarrassed if I were a firm and I went to uh went to an appeal or a motion or something with with uh cases that didn't exist.
00:23:51.920 --> 00:24:05.839
And um I've seen it do it, uh, but when you push back on it, because I was looking once for an antitrust case, and um it was giving me cases that I just didn't that just didn't exist.
00:24:05.839 --> 00:24:10.559
And I I gave them to a really good paralegal and he said, Well, and he was being kind to the AI.
00:24:10.559 --> 00:24:15.039
He said, Well, maybe it's maybe it's in some small county in western Texas.
00:24:15.039 --> 00:24:18.160
I'm like, a big antitrust case wouldn't wouldn't be there.
00:24:18.160 --> 00:24:20.480
But so man, you gotta be careful.
00:24:20.480 --> 00:24:25.119
One time it was it said, uh, I said, I don't believe this this part that you just gave me is real.
00:24:25.119 --> 00:24:32.640
It said, Oh, I thought you wanted me to just make up placeholders for that you could replace with real with facts later.
00:24:32.640 --> 00:24:36.240
I'm like, okay, AI, never, ever do that.
00:24:36.240 --> 00:24:38.400
And it's learning.
00:24:38.400 --> 00:24:41.279
Uh so it's it's it's a crazy time.
00:24:41.279 --> 00:24:45.519
But um, well, anyway, I wanted to just just kind of wrap up.
00:24:45.519 --> 00:24:50.960
Um so uh no, I think we've you know, we've so we've talked about AI.
00:24:50.960 --> 00:24:54.720
Do you would you say, are you optimistic about the focus program?
00:24:54.720 --> 00:24:58.160
Do you think uh you know a few years from now we're gonna say, yeah, that was a good thing?
00:24:58.480 --> 00:25:04.559
So I I I think that the uh I think it's too early to tell time I'll give you a clear answer.
00:25:04.559 --> 00:25:06.640
We're only three months into it.
00:25:06.640 --> 00:25:16.960
Um I I think that um yeah, DOJ has put down, you know, a pretty clear marker here of what their expectations are.
00:25:16.960 --> 00:25:33.279
Um and I you know I I think that part of what we may be seeing here is laying the groundwork for DOJ to exercise more of its power to dismiss meritless TJM cases.
00:25:33.279 --> 00:25:44.000
Um because you know, e even if a um TJM case that's driven by AI, driven by data analytics, can can be filed.
00:25:44.000 --> 00:25:49.279
Uh and perhaps it will survive a motion to dismiss, maybe not.
00:25:49.279 --> 00:25:55.279
But these are tremendously expensive um cases to litigate.
00:25:55.279 --> 00:26:04.160
Now, and it's expensive and time consuming for the defense, but it's also tremendously time consuming also for the uh for DOJ.
00:26:04.160 --> 00:26:10.640
Uh who, yeah, as we talked about before, you know, if they're doing trying to do more with less.
00:26:10.640 --> 00:26:20.079
You know, they're getting more of these cases filed, they have less personnel to handle them, to sort of separate the weak from the cash.
00:26:20.079 --> 00:26:41.119
So I what I'm gonna be interested in is um you know seeing whether this may ultimately see uh you know, be an effort by DOJ to kind of flex its dismissal power, which it has, and it's signaled a an increased willingness to use that in the right cases.
00:26:41.119 --> 00:27:04.559
But if you know if the flood of these cases continues to be filed at DOJ unabated and if the uh data miners don't take the focus initiative seriously, I I think what we're gonna see is you know DOJ increasingly considering the exercise of its uh power to just dismiss these cases.
00:27:04.880 --> 00:27:05.119
Okay.
00:27:05.119 --> 00:27:05.920
David?
00:27:05.920 --> 00:27:27.759
I completely agree, and I would say um even short of dismissal, I think we'll see DOJ declining to intervene in cases that they don't feel um uh as comfortable have been fully vetted by data miners who understand um some of the unusual aspects of prosecuting the false.
00:27:27.759 --> 00:27:28.559
Okay.
00:27:29.279 --> 00:27:48.160
Yeah, and you know, in a larger view, you you you both are probably aware that there are companies now that are using AI generally to find uh actionable cases or potentially actionable uh cases in all sorts of areas from personal injury to antitrust and all kinds of things.
00:27:48.160 --> 00:27:52.640
So um it's a it's a it's a wild time for sure.
00:27:52.640 --> 00:27:55.039
Look, is there anything else you wanted to say about it?
00:27:55.039 --> 00:27:57.920
Because you've answered my questions beautifully.
00:27:57.920 --> 00:27:59.359
So uh anything else?
00:27:59.680 --> 00:28:08.079
I guess Tom, you know, the the one uh yeah, data mining is yeah, it can be a powerful tool for uh the relators.
00:28:08.079 --> 00:28:14.880
Um but also I I think you know it can be a powerful tool for companies from a compliance perspective.
00:28:14.880 --> 00:28:36.400
And yeah, we we haven't touched much on sort of the use of this in compliance, but I think what you're gonna see is companies increasingly using AI and data, um data mining to identify, try to get out ahead of the curve and and identify potentially problematic crisis.
00:28:36.400 --> 00:28:45.279
Um, so that they you know, they they know about it and they can take corrective action before you know it hits the phase.
00:28:45.680 --> 00:28:46.240
That's really good.
00:28:46.240 --> 00:28:47.279
That's a really good point.
00:28:47.279 --> 00:28:55.440
Rather than having somebody use AI and and whistleblow on on your conduct, apply it to yourself first.
00:28:55.440 --> 00:28:59.039
That's a that's a that's an interesting uh that's great advice, I think.
00:28:59.039 --> 00:29:00.480
Not that you're giving legal advice.
00:29:00.480 --> 00:29:01.519
Nothing here is legal advice.
00:29:01.519 --> 00:29:02.880
That I'm giving legal advice.
00:29:02.880 --> 00:29:06.400
No, no, and nothing here establishes an attorney client relationship.
00:29:06.400 --> 00:29:08.160
David, anything that you wanted to add on?
00:29:09.200 --> 00:29:27.519
Yeah, I I I agree with Tom, and I was just going to add that, you know, even in in that particular instance, if someone does blow the whistle, the corrective action that the uh defendant engaged in is is a fantastic fact if and when the case goes um to discovery.
00:29:27.519 --> 00:29:36.559
Because defense counsel can then argue that that corrective action really inhibits the plaintiff's ability to demonstrate fraud.
00:29:36.559 --> 00:29:45.599
In other words, demonstrate the Center or the knowing um the knowing intent to um submit a false claim.
00:29:46.000 --> 00:29:46.319
Okay.
00:29:46.319 --> 00:29:54.720
And just I I always like to ask attorneys because um I'm surprised sometimes at the answers I get, but how did you guys end up here um doing this?
00:29:54.720 --> 00:30:01.279
Is this when you were uh in elementary school, were you thinking, you know, that Lincoln Law.
00:30:01.279 --> 00:30:04.880
Um uh Tom, what uh what about you?
00:30:04.880 --> 00:30:06.480
How did you end up in this?
00:30:06.799 --> 00:30:14.319
So I was uh uh I was an American studies major, uh, and I knew there wasn't much of a you know lucrative future doing that.
00:30:14.319 --> 00:30:25.200
So I guess when I was, you know, took a year off and and was a ski bum, I decided I'd really that was not also not a uh not a road to riches.
00:30:25.200 --> 00:30:26.000
Really?
00:30:26.000 --> 00:30:26.880
Yes.
00:30:26.880 --> 00:30:30.160
So it was fun, but it wasn't really a road to riches.
00:30:30.160 --> 00:30:42.079
So um I went to law school and yeah, all kidney side, I I did uh I thought that I wanted to become a class and become a classic then let me to do the defense work.
00:30:42.079 --> 00:30:42.640
Yeah.
00:30:42.640 --> 00:30:43.119
Okay.
00:30:43.119 --> 00:30:47.200
It's sort of been an evolution into healthcare quad space.
00:30:47.680 --> 00:30:53.759
Well, if you ever seen me you've if you ever saw me ski, you would definitely know that wouldn't have been a future for me.
00:30:53.759 --> 00:30:58.960
I I hit a certain level and and I never got past it.
00:30:58.960 --> 00:31:05.519
I was always like once in a while going down the mountain, and all of a sudden I am f I am parallel to the earth.
00:31:05.519 --> 00:31:12.400
I am flying with one ski on and the other one is I uh it doesn't end well usually.
00:31:12.400 --> 00:31:14.400
No, no, they well, yeah, yeah.
00:31:14.400 --> 00:31:15.119
What do they call that?
00:31:15.119 --> 00:31:15.920
A yard sale?
00:31:15.920 --> 00:31:18.640
I was in many, uh was in many of those.
00:31:18.640 --> 00:31:20.079
Uh David, what about you?
00:31:20.480 --> 00:31:21.359
Similar to Todd.
00:31:21.359 --> 00:31:31.039
I guess for me it wasn't being a ski bum, but once I realized that the the dream of being on the PGA tour as a golfer was was just that a a dream and not a reality.
00:31:31.039 --> 00:31:35.519
Uh I came back to Earth and um, you know, I I went to law school.
00:31:35.519 --> 00:31:39.519
But no, from the time I was a kid, I I've always had an advocate's heart.
00:31:39.519 --> 00:31:42.880
Um I went through many years where I wanted to be a doctor.
00:31:42.880 --> 00:31:56.400
And in the end, I I decided that instead of being a scientist, I would like to sort of get up to speed with the science, but then use the advocacy to um uh be someone who who defends healthcare clients.
00:31:56.400 --> 00:31:58.160
And so that's how I got here today.
00:31:58.160 --> 00:31:58.799
Okay.
00:31:59.359 --> 00:31:59.680
Yeah.
00:31:59.680 --> 00:32:13.839
Golf is something I've never really got into uh although as I've got once once I had teenage daughters, and now that I've been now that I've been working delightfully with my wife, um, we are together 24-7 or even longer.
00:32:13.839 --> 00:32:15.279
Um no, it's wonderful.
00:32:15.279 --> 00:32:15.920
She's great.
00:32:15.920 --> 00:32:21.599
And I wouldn't uh she's very easygoing, but there are times when I think, you know what, maybe I should golf.
00:32:21.599 --> 00:32:26.799
Maybe I should go or just get one club and stand on the course.
00:32:26.799 --> 00:32:28.960
Are you watching that show, the uh The Hawk?
00:32:29.279 --> 00:32:30.240
I am, I am.
00:32:30.240 --> 00:32:31.759
But you know what they say about golf?
00:32:31.759 --> 00:32:34.400
It is a four-letter word, so it's great.
00:32:35.440 --> 00:32:36.480
Okay, good.
00:32:36.480 --> 00:32:39.200
Well, I don't know, maybe I'll try fishing.
00:32:39.200 --> 00:32:41.839
All right, well, guys, very thank you very much.
00:32:41.839 --> 00:32:43.279
This is really informative.
00:32:43.279 --> 00:32:46.480
It's obviously an it uh an interesting and important area.
00:32:46.480 --> 00:32:48.799
So uh let's check back.
00:32:48.799 --> 00:32:51.759
Uh maybe we'll check back next year and see how things are going.
00:32:51.759 --> 00:32:53.039
So thank you guys very much.
00:32:53.039 --> 00:32:53.759
Thanks, Tom.
00:32:53.759 --> 00:32:55.200
Tom, it was a pleasure.
00:32:55.200 --> 00:32:55.839
Okay.
00:32:55.839 --> 00:32:57.200
All right, guys.
00:32:57.200 --> 00:32:59.680
If you could uh nice job.
00:32:59.680 --> 00:33:02.000
Um, I I just love doing this podcast.
00:33:02.000 --> 00:33:04.240
I just love uh I love law.
00:33:04.240 --> 00:33:06.640
Uh I and I don't know why, but I do.
00:33:06.640 --> 00:33:17.119
It's uh maybe I said this to you, Tom, before that to me it's like a philosophical argument a lot of times that ends up but has real world consequences at the end, you know.
00:33:17.440 --> 00:33:18.960
Tom, let me ask you the question.
00:33:18.960 --> 00:33:20.480
How'd you get into this?
00:33:20.799 --> 00:33:21.759
Oh, yes.
00:33:21.759 --> 00:33:29.759
Well, um I uh loved writing, and I didn't even know how much I loved it until I got to college.
00:33:29.759 --> 00:33:34.880
And there's one one professor that said, Oh, why don't you take advanced expository writing?
00:33:34.880 --> 00:33:36.799
I'm like, what is that?
00:33:36.799 --> 00:33:39.759
And um what time is the class?
00:33:39.759 --> 00:33:41.680
And it was eight o'clock, eight a.m.
00:33:41.680 --> 00:33:42.720
on Monday.
00:33:42.720 --> 00:33:49.039
And I'm not gonna kid you guys, I was not a very serious young man, and I was not a very good student.
00:33:49.039 --> 00:33:51.920
So I'm like, so I think, yeah.
00:33:51.920 --> 00:33:55.200
Um, but so I love writing and I loved law.
00:33:55.200 --> 00:34:10.559
My grandfather was a lawyer, my uncle was a lawyer, my sister was a lawyer, my dad wasn't a lawyer, but he dealt a lot with uh labor relations law in his is he was head of industrial relations at a at a metals plant, and um so I was exposed to it and found it interesting.
00:34:10.559 --> 00:34:15.760
And and then I went to my high school uh counselor and I said, Oh, I think I want to be a lawyer.
00:34:15.760 --> 00:34:18.239
He said, Have you thought of the trades?
00:34:18.239 --> 00:34:27.679
Um I was so mad at him, and then later on I'm like, Yeah, yeah, he was right.
00:34:27.679 --> 00:34:30.559
Uh but just to be clear, there is nothing wrong with the trades.
00:34:30.559 --> 00:34:33.440
It's honorable work and very smart people working them.
00:34:33.440 --> 00:34:35.760
That said, I didn't have the math chops.
00:34:35.760 --> 00:34:52.239
But anyway, I got into the writing, started doing a lot of writing uh and loved law, moved to Philadelphia, and I interviewed for a job as a junior uh editor at a small startup legal publisher who was paid $8,500 a year with no benefits.
00:34:52.239 --> 00:34:54.719
And um, I thought that sounds perfect.
00:34:54.719 --> 00:34:59.599
And so, but but it was great because I was sitting next to two grizzled journalists.
00:34:59.599 --> 00:35:04.400
They were old, they were 41 and 45, and uh I was 23.
00:35:04.400 --> 00:35:06.880
And uh man, I learned so much from them.
00:35:06.880 --> 00:35:15.440
And then, but every day, guys, I was reading opinions, talking to lawyers like I'm doing right now, um, and then having to write about cases.
00:35:15.440 --> 00:35:22.159
And so I don't know, I was in some form of law school, I think, for 10, 15 years, and that's how I I got into it.
00:35:22.159 --> 00:35:27.440
So then I started my own business, and well, we we I was out of business, we sold it, and then I started my own.
00:35:27.440 --> 00:35:32.559
And so now with podcasting, it's more like this is like the best of all the stuff I used to do.
00:35:32.559 --> 00:35:35.440
Yeah, to talk to guys and learn about law.
00:35:35.760 --> 00:35:53.840
Well, you're uh you know, that background comes through, Tom, uh, because your questions are yeah, that they're solid questions, that they're you know you you tee up the issue well for the you know for your audience and the people who are on the receiving end.
00:35:53.840 --> 00:35:55.440
So um thanks.
00:35:55.440 --> 00:35:59.360
No, it's it's it's yeah, it's it's like I looked at your questions.
00:35:59.360 --> 00:36:01.840
I was like, this guy's done some, you know, he's done his homework.
00:36:02.079 --> 00:36:02.480
Oh yeah.
00:36:02.480 --> 00:36:03.760
Well, thank you, thank you.
00:36:03.760 --> 00:36:05.199
Well, I do, I do.
00:36:05.199 --> 00:36:08.880
And part of it is because I am curious about law, I just really am.
00:36:08.880 --> 00:36:21.519
And uh and so looking at the political and electoral landscape and everything, I'm just um I'm just like I'm one of those guys that ends up defending lawyers.
00:36:21.519 --> 00:36:23.519
That uh you can say what you want about lawyers.
00:36:23.519 --> 00:36:31.440
There's there's like dirt bags in every profession, but the ones I encounter are like serious guys, and if you uh have an issue, you want them.
00:36:31.440 --> 00:36:37.360
And um, and we're seeing that now as law firms are being challenged and judges are being challenged.
00:36:37.360 --> 00:36:39.599
Um it's more important than ever.
00:36:39.599 --> 00:36:40.639
So all right.
00:36:40.639 --> 00:36:42.639
Well, good luck uh golfing or skiing.
00:36:42.639 --> 00:36:44.239
I don't have to talk to ski anymore.
00:36:44.239 --> 00:36:45.679
Yeah, I do.
00:36:45.679 --> 00:36:46.639
Good for you, man.
00:36:46.639 --> 00:36:47.519
We'll keep it up.
00:36:47.519 --> 00:36:48.320
Well, thanks, guys.
00:36:48.320 --> 00:36:55.840
The Emerging Litigation Podcast is a production of Critical Legal Content, which is part of the awesome brand HB Litigation.
00:36:55.840 --> 00:37:05.599
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