Anish Koka: Okay, we are live on X with Doctors' Lounge and today we have a special guest. Our guest is Kevin Bass ⁓ and course co-host Dr. DiGiorgio from the People's Republic of California.

Anthony DiGiorgio: No, am ⁓ broadcasting live from the Big Easy. I'm in New Orleans. Yes, we are. Live, yeah, live from Louisiana.

Kevin Bass: you

Anish Koka: ⁓ that's right. I remember you saying that New Orleans. That is the big easy. All right. Wonderful. About to have some ⁓ you like, what are you doing in New Orleans?

Anthony DiGiorgio: Well, Gumbo's fantastic. Yeah. I'm giving a course on spine surgery. brought the family. You know, I did my training here. So wife hasn't really been back since we left for California. So now it's a good chance to revisit the good city.

Anish Koka: Yeah. Wow. ⁓ Outstanding. Well, Dr. Bass, thank you for coming. Dr. a slip there. Your Kevin Bass PhD. Dr. Bass, you for on. Your arc has been one that many have been watching, partly because you are ⁓ prolific and much followed on the platform known as X.

Kevin Bass: Technically I am Dr. Bass, so there we go.

Anish Koka: You know, first, I first heard of you like a while ago because you were a ⁓ teller about, you know, nutrition science and a bunch of different stuff that was kind of, ⁓ you a weak fatty type stuff. And you were, know, you were kind of had, you know, you're, you're a very effective communicator. so you would, you know, get to the heart of the matter in terms of what was BS and what was not. and so that's when I, that's how I first heard of you. And then, ⁓ and then, and then kind of COVID happened. and you really blew up with COVID because you went from kind of being what the establishment was saying in terms of vaccines and ⁓ lockdowns all that. then as went on, mean, partly because you had a vaccine and then you actually had paracorditis from the vaccine, as I recall. And that was in some ways, red pill moment for you and then you started to kind of, you know, closely evaluate, you know, what the institutions were saying. ⁓ that led to you writing a pretty viral piece in Newsweek talking about how you were essentially apologizing for kind of being on the other side and kind of being condescending ⁓ et cetera, when you on that side. And that piece went mega viral. You eventually were went on. Tucker Carlson, is now this is your, you're an MD, PhD student at Tech Medical School. ⁓ And, then, and then kind of, you know, ⁓ crap hits fan at that point. And I'm very interested your, I you to tell, you know, the story since then, because it's incredibly instructive story for ⁓ the lay public as well for medical trainees, especially to kind of, you know, know about your story. So Kevin, welcome, thanks for coming on, thanks for giving us the time to do that.

Kevin Bass: Yeah, no, thanks for having me on. Appreciate it a lot. I'm looking forward to talking to both of you.

Anish Koka: Yeah, so tell me Kevin, go from, ⁓ know, if that's okay, can you describe of what happened? Can you take it from where I left off in terms of your story?

Anthony DiGiorgio: Likewise.

Kevin Bass: you yeah. So, ⁓ you know, as soon as I had the music piece, I thought, you know, you know what my perspective was when I was writing about nutrition and it was ⁓ a similar perspective when I started writing about COVID. I thought, you know, the institutions work, they get some things wrong. They get some things wrong. Usually they're minor. It's not going to be that big a deal and they self correct eventually. Maybe it takes 10 or 20 years longer than it should. And you know, maybe they change the cholesterol guidelines, the cutoffs, ⁓ ⁓ they become more aggressive. think anyway, in general, get things right and they're self correcting and that's sort of ⁓ the belief. And truly believe that I thought everybody who thought that the institutions are not self-correcting, I thought they're crazy. I thought they're completely crazy. Those were quacks to me. But on COVID, you I realized ⁓ ⁓ there's a lot of stuff. And the reason I believe ⁓ the narrative... It's because I didn't look into it. So I was really good at nutrition, but I just assumed, you know, of course, if they're saying these really making these very strong statements about masks and lockdowns in particular, ⁓ and a lesser extent, I think vaccines, but it happened with vaccines too. Although maybe many listeners will completely disagree and think it's the opposite, but I think, you know, they made these strong statements. It had to be right. And so you should support The public health, they're using science, they're trying to save lives, right? And anybody who goes against that is a quack and probably a really bad person. So when Jay Bhattacharya started like sending replies to me, ⁓ like reading the material he was sending me felt like transgressive. I was reading this material, was like, ⁓ my God, this is bad stuff ⁓ Jay Bhattacharya, he's a bad guy. You know and that's sort of the establishment perspective right like if you're on that side of things you still think that way about Jay and about Vinay and you're like cheering that Vinay's out right now and you hate You know Marty Makary so I thought all that but then I studied like the more I got into it the more so I was like holy crap like this actually At minimum at the time I was like at minimum. This was a really big ⁓ debate that should have happened. And it should have been in public because ⁓ prominent, ⁓ really important experts, the best experts in some ways before the pandemic happened. For example, ⁓ the guy, Johns Hopkins, his name is escaping me. He wrote the paper with, anyway, he a 2006 paper about how to deal with pandemics. The best, some of the best experts had a legitimate... counterpoint, but there was sort of a mob mentality, especially around a pandemic. It's an emergency, obviously, and this happens all the time, actually, with pandemics. You have this sort of hysteria, and you have a lot of group think it's happened for hundreds of years. There's been there's been controversies like the one I was involved in for hundreds of years. It was a hard won battle where we stopped actually doing mass quarantines in Great Britain in the 18th and 19th centuries. And so what I realized is that we had had a hysteria, we'd had group think. ⁓ then once people started saying, like, Kevin's a quack, he's grifting for Twitter money, which, you know, like, you're not making any money on Twitter. You're not making any money on social media at all. So, but you know, he's grifting and he's doing this for whatever reason. Or, you know, at my school when got the internal records ⁓ from my requests, I got these records I think at the beginning of last month. Internally they were writing that like I had psychological issues The reason that I'm saying the things I'm saying is because I have a psychological problem Probably related to childhood trauma. I want attention. I have there. Here's a here's a really great phrase these I have intellectual narcissism. Okay intellectual narcissism I enjoy the notoriety, but so when people started criticizing me back Internally they were writing that like I had psychological issues The reason that I'm saying the things I'm saying is because I have a psychological problem Probably related to childhood trauma. I want attention. I have there. Here's a here's a really great phrase these I have intellectual narcissism. Okay intellectual narcissism I enjoy the notoriety, but so when people started criticizing me back And saying all these things I doubled down because I'm like, have the, is like an important thing that I'm saying. and then you're trying to get me kicked out of med school for saying the truth. Right? Like I'm saying, something that's at least really important ⁓ for us to have debated. And ⁓ many the things that were said by and Wallensky were categorically wrong, like categorically, scientifically wrong, like indisputably wrong. And yet you're not allowed to say. And saying all these things I doubled down because I'm like, have the, is like an important thing that I'm saying. and then you're trying to get me kicked out of med school for saying the truth. Right? Like I'm saying, something that's at least really important ⁓ for us to have debated. And ⁓ many the things that were said by and Wallensky were categorically wrong, like categorically, scientifically wrong, like indisputably wrong. And yet you're not allowed to say. that and you're going to get kicked out of med school for adhering to science, right? Like adhering to the highest standards sets and things that are very straightforward and simple in my opinion, that were categorical, you're going to get in trouble for that. So I doubled down as I should not have done as talked to Anish on the phone, you're like, they're going to kick you out. ⁓ that and you're going to get kicked out of med school for adhering to science, right? Like adhering to the highest standards sets and things that are very straightforward and simple in my opinion, that were categorical, you're going to get in trouble for that. So I doubled down as I should not have done as talked to Anish on the phone, you're like, they're going to kick you out. ⁓ You're on the phone Anish you're like they're gonna kick you out. But like definitely gonna and you were like one of the only ones who saw it clearly because like my understanding is that you understand how things work like really on this side. And I was yeah, I'll figure out how to it. We'll figure out how to get through this thing. It was savage. And yeah, they did. You're on the phone Anish you're like they're gonna kick you out. But like definitely gonna and you were like one of the only ones who saw it clearly because like my understanding is that you understand how things work like really on this side. And I was yeah, I'll figure out how to it. We'll figure out how to get through this thing. It was savage. And yeah, they did. guys about sham peer review. It was sham peer. ⁓ am I?

Anish Koka: conversation that we had because your Newsweek piece went viral, right? I just just to go over, I tweeted the snapshot of it. You know, as a medical student researcher, sorry, the title was it's time for the scientific community to admit we were wrong about COVID and it cost lives. As a. Yeah. Right, right. Right, right.

Kevin Bass: That wasn't my headline by the way. And a lot of people like read just that piece and see the headline and they say always a you know, okay so.

Anish Koka: And then, but you you said as a medical student and researcher, I staunchly support the efforts of the public health authorities when it came to COVID. I believe the authorities responded to the largest public health crisis of our lives with compassion, diligence, and scientific expertise. was with them when they called for lockdowns, vaccines, and boosters. I was wrong. We the scientific community were wrong and it cost lives. ⁓ And think ⁓ that, know, coming from an establishment, MD, PhD guy, you know, I mean that went, mean that Newsweek's piece for that reason went super viral and you're writing that actually, that's you're writing that January 30th, 2023. That's like years after this stuff has gone on. It should be relatively obvious by then that everything you're saying is true. yet ⁓ then I think you you you you you you got a call from the ⁓ producer Tucker new Tucker Carlson and that's when I think we had the conversation about Should you know ⁓ what about ⁓ should go on Tucker or not? And I and I specifically said I think we still have the messages saying you know don't do it You will you will not graduate from medical school if you go on Tucker

Kevin Bass: You're right.

Anish Koka: But when I told you that, I know you internally, I mean you heard it and stuff, but did you not believe it or did you believe that if they tried to do that, that the system wouldn't allow that to happen?

Kevin Bass: I believe that if they tried to do that, the system wouldn't allow it. And I still think that's the case. mean, I may not be a doctor, but I still think the system will eventually work.

Anish Koka: Right, Did the, did the...

Kevin Bass: But I didn't know that would be removed for sure. didn't know I would end up, it would go this far. Yeah.

Anish Koka: Yeah, right. Because it's insane thing. it's not, I mean, I think most folks believe that we live in country where you can ⁓ say you want. And as long you're not trying to harm or hurt people, like there shouldn't ⁓ some crazy response that. so how, mean, now been through this terrible thing. I I felt watching you kind of go through this. Can you, like, how did it? ⁓ whatever you're allowed to say because I know, you I don't want you to say anything that'll get you in more trouble or anything like that, but can you describe some of the things, I mean, you've been tweeting about it and you you've had a Freedom of Information stuff that, Information Act request for documents and stuff that you've been putting out, but. Can you give me a sense and can you give the audience a sense of what happened and how they went after you? Because they didn't go after you because you published something or that you want. They didn't say he went on Tucker. I'm going to cancel you. Right. And I said that is not going to happen. That is not how they're to do it.

Kevin Bass: So the only problem with this part is I a lot of facts ⁓ that used to establish sort of the plausibility of what they did and it's all in the docket. I have I think 130 paragraph third amendment complaint that still needs to get approved by the judge and it's solid. And they just file, they just try to dismiss it. But it's all in the docket, so that's not a problem. The only thing is I can't, well I probably can to some extent connect the dots. So here's ⁓ they did. ⁓ And I'll try to.

Anish Koka: You say it, yeah. You can speak generally. don't have to... You're just generally speaking,

Kevin Bass: Yeah, basically, you know, there was an escalating series of professionalism complaints, something on the order of, you know, 50 separate allegations that were made. if you look at the internal record, which, as I said, had gotten January, ⁓ there was an escalating series of emails internally. I never knew about this. Any of this was happening. I never knew that there was ⁓ nobody you know, emailed me and said, let's, we need to talk about this. Nothing, ⁓ but was an escalating series of emails. starting the day that my ⁓ piece was published there was a big flurry of emails among the senior administration at the school about incident they called it an incident my the publication of this article was an incident that they to they had meetings about and they had to figure out a response to, which is incredible. And to me, it's this sort of bureaucratic language where truth and whether it's truth, true or false is not an issue. It's about managing sort of a bureaucratic problem, specifically the PR problem of my publication of this piece and the reaction it was getting. And that's all that the bureaucrats were concerned about. And then, you know, by a couple weeks three weeks before I started getting the first hits or as two or three weeks you know the the lead the Dean the Dean of the medical school Stephen Burke asked for my you know the honor code that I had signed as a medical student again this is all internal I didn't know any of this is going on He asked for it. They gave it to him. They're all CC'd on me. And he's like, okay, what ⁓ rotation is Kevin on? What rotation is Kevin on right now? then in three weeks, I started getting hammered. And then maybe about one or two weeks after he sent that, internal emails started hammering me that I didn't know about. Suddenly, have nowhere, you got these really long narratives about everything I was doing wrong sent by the clerkship directors. ⁓ to the administrators. this is That's that is the factual set of connections you know Yeah, and the original, the initial complaints were nonspecific. Nobody would tell me what I had done wrong. would tell me what I had done wrong for months. But kept getting more of them. ⁓ And would go to these hearings and they wouldn't tell me what I did. I would be defending things that I would be guessing what they might be thinking. And I'm like, this, you know, and I'd be explaining things that like they wouldn't tell me. They wouldn't put it in the documents. They would have meetings with me making specific allegations. I would be like, you guys need to put this in writing. They would refuse to put any of it in writing. Some of the allegations were incredible. They're outrageous. And I was like, you guys got to put this in writing. And they would, they would like, They would put it in, they'd write a page where they would actually not say what happened. So, you know, this was a part of a plan that they've probably done this to other people many times. Then, then I won the appeals. had two hearings. It ⁓ already, it was the stressful thing ever. I knew something was wrong, but I was also being advised by people to, you know, to ⁓ basically not fight it be like, Not basically be like, this a conspiracy guys? Like is this, I was playing by the system. I was assuming that I just respond to these things and ⁓ by the system, then it'll work itself out. So won after the appeal hearing, which I won. I won the appeal hearing probably because the lawyers are like, you can't have him lose this thing because this looks really bad. You have to let him win. And I won on the basis that They were there. The allegations are nonspecific. Nobody knows what I did. There's nothing. There's no, know, and performance is good. ⁓ But I ⁓ have professionalism, even though I was like considered cleared, they also said in ⁓ the to the second hearing, the appeal hearing, if I have another professionalism complaint on immediately triaged back without talking to the administrators, they'll be able to work it out. immediately charge back to the professionalism community again. I knew that whenever I got that letter, I was cleared, quote unquote, I knew I was screwed. I knew that they were gonna hit me again immediately, which is exactly what they did. Within like a week, had another one. But this time, ⁓ the new set of ⁓ professionalism complaints were extremely specific. There were crazy distortions of things that happened falsehoods, like straight up falsehoods. And then I started getting hammered with those. And then at that point I started filing grievances because I was like, you're guys are gonna put me through more hearings again. I'm not really gonna be able to defend myself. You might actually find me guilty this time. We'll have these, I'll actually tell the truth of what happened in the grievances since I'm not gonna be able to do it during the hearings, the first hearing I never got a chance to. And we'll work it out through the grievance ⁓ system, I thought was, and according to the student handbook, I had this right, and I had this right to have the grievances heard before the hearings. So I dumb enough to make a tweet, although, ⁓ know. I don't know if I regret it. mean tactically it was dumb. I made a tweet and you're going to love this extra tidbit about it. You probably know about the story, but the tweet was basically, know, we're all going to die. We're all going to die very soon. And, know, given this, why do we worry so much about what other people say or think about us? Obviously it's a reflection on like vanity, but then they took the, all going to die very, very soon. And they said, You know Kevin basically there's already this hysteria about me at the school Kevin's a crazy person He's actually ⁓ sort of making a veil threat to kill everybody basically And it's not like I laugh because I have to right. It's such a critic. It's so crazy I have documentation showing that at the time contemporaneously, eight administrators knew this was false, including the person who signed the threat assessment team thing that said I needed to be removed from campus, the criminal trespass warning. ⁓ This knew it was false at the time he signed it. The day after he told me he knew it was false. He told me he knew it was false that this was a threat. ⁓ I him on recording. I have that on a recording I took of that conversation. But people felt threatened, ⁓ quote by the grievances I had been filing. They were afraid. Let's hear interpretation. ⁓ They were the truth of them lying about me in clinical evaluations was going to come out through these grievance hearings. Right? So, got removed off campus, actually banned from campus for a year. had to go with police officers in order to be on campus. They consolidated everything into a consolidated hearing where they me beforehand, you know, you're going to have the grievances heard and we're to go issue by issue. None of that happened. They hired a person from the outside to do this. This guy's name is Darren Gibson. Darren Gibson is a Harvard trained lawyer who works for Littler Mendelson. He's one of the defendants on my lawsuit. Darren Gibson is responsible for, throughout a significant number of institutions in higher education, he's responsible for the grievances and conduct hearing type things in many universities, creating the protocols that handle those, as well as Title IX. he's often taken on as an independent adjudicator during these hearings. ⁓ Now gets even deeper than this. I wish I could really go deep about this particular issue because it's very interesting. This is guy in charge of about half the systems, the public systems in Texas. And he's also the guy who adjudicates these things. I'll go ahead and say it. He has the way that his system is set up for these hearings. He has set up certain sort of discretionary clauses ⁓ ⁓ given ⁓ decision point where he has ⁓ officer discretion. What that means is ⁓ this paperwork ⁓ you alleges you have due process at all of the major decision points, you don't. But it's beautiful system. You have many, many pages laying out this beautiful system where it's very professional by this Harvard trained lawyer at one of the best employment one of the best employment ex employment law experts one of the best law firms ⁓ course he's doing this this has to be legitimate ⁓ so he's the guy who they took on ⁓ adjudicate my hearing he was there for 12 hours adjudicating it ⁓ He's paid, he's been paid, you know, over $400,000 by the University of course accelerators. He continues to have a contract with them I know through public records requests. ⁓ what he did is everything I was told about how it would be fair was not the case. They basically piled ⁓ 10 adverse testimony of them. saying these horrible things about me and then they're like do you have any response? I'm like what do you want like what am I gonna do like I can't you can't rebut 10 hours of that and also I look crazy if I'm trying to do that it would take me literally 10 hours to talk about it and then I would look like a crazy person I don't have 10 hours they give me 30 minutes So there was no real chance to win that thing. you know, of course it was unanimous. They kicked me out. I then wrote a twenty-thousand. ⁓ in retrospect, I did everything right here and I'm like shocked. And I think it's because ⁓ like medicine has biology as its foundation. So if you know biology really well, you can say a couple of things about medicine. If you know the arts and ⁓ the and humanities really well, you'll understand what's fair and what's what's ethical and right. And you'll do the right thing. So 20,000 word appeal on my... Dismissal and it was so long because there's so much to say everybody's like, you know If you read like the doctors talking about they're like, oh he was manic Why why did he write it to it? It was 20,000 words because there was so much craziness that happened and I addressed all of it and that That that appeal document was is gold. It's gonna be gold So because I actually did hit a lot of legal things. They didn't even know existed but like I got them Yeah, but of course they sent me an email saying, you know the dismissal is finalized as like two words or two sentences about how, you know, we don't accept this appeal and it's like fine.

Anish Koka: And, and the, there like a top line of what the dismissal was ⁓ on? ⁓ okay. But fundamentally you're a threat to other students or what did they, and there was no like.

Kevin Bass: Yeah, there were like 10 different things. There were 10 different things. ⁓ I've addressed... No, it was a whole range of different things. All these things that had been piled up over time. I haven't talked about them publicly. I want to get that stuff in court done. It feels weak to be like, like they accused me of this, but I'm like, gonna like argue with them. They're not arguing with me with me yet, but if we can get to the court, then that's I want to do it. Yeah. ⁓

Anish Koka: Okay. Yeah. Yeah. the system is...

Kevin Bass: Which is not to say, by the way, just for legal purposes, I'm not actually trying to relitigate the dismissal. I don't want to go back into medicine. I'm not interested in that because I don't want go back into this system. And also, I'm so smeared, it'll just be such a tough grind to the entire time. I'll always be under the microscope, it's horrible. So I don't want to go back. So I'm not relitigating anything. What I'm doing is I'm basically... But what will happen is this stuff will have to come up because lawsuit is over First Amendment retaliation and American Disabilities Act stuff and so they're going to try to go... They're going to, it's going to have to, they're going to try to, they're going to relitigate this, not me. I'm alleging that this is retaliation and then it's going to have to be established. Well, what were these professionals and things real? So I'm not relitigating these things, but they will come up in the, court because they have to in order for, ⁓ us to establish whether it was really retaliation just for legal, you know,

Anish Koka: Yeah.

Kevin Bass: I probably already messed this up. They're probably going to take this out of context. I'm going to have to respond, you know, whatever. It's fine. Go ahead.

Anish Koka: Yeah. Dr. DiGiorgio, what is your thoughts in ⁓ of the process you have been a part of it ever with regards to the system that's in place for evaluating medical students this fashion? Do you feel like it's, can it be fair system or is it like ripe for abuse and how does it normally

Anthony DiGiorgio: I mean, whole process reminds me of the LaVentri-Barrera quote, right? Show me the man and I'll show you the crime. It's ironic because on one you are clearly here, but on the other hand, ⁓ is incredibly, ⁓ it's more in residency, but it's incredibly difficult to dismiss subpar residents, at least from medical training. And, what we largely find is that they, it's almost easier to just shuffle them along to the next rotation and the next rotation until they graduate residency. And ⁓ that makes it's very difficult. Like, you you have stories of residents getting through because the school doesn't want to handle the paperwork of dismissing residents because you do such a paper trail. which is disheartening to hear that they can essentially fabricate one for you, Kevin. when you, you know, if you actually have a resident or a med student who you think is going to harm people, they are not able to get rid of them because you need such a paper trail. So this is, you know, this is incredibly disheartening in that you can take competence is not an issue, but political beliefs are. And that seems to be at least why you were targeted.

Kevin Bass: Yeah, ⁓ Yeah.

Anish Koka: Yeah. why I am ⁓ back, Kevin, why do you think the system, you're talking about the meta system, you're not talking about the local medical school system. That does not appear to work, but ⁓ still have belief in the larger system in terms of justice and legal system will eventually slowly grind way to the right place.

Kevin Bass: I mean, in my case, yes. ⁓ think that it's what Dr. DeGiorgio said. Maybe medical students have protections, or maybe they just me so much that they were willing to, I don't know what the answer is. I mean, so they can't just say you things and then just like. kick you out based on them like they can't just do that right I mean that's basically what they did I never had a real opportunity to respond to anything that they said like ⁓

Anish Koka: And prior to the Newsweek article and stuff, you'd never had issues.

Kevin Bass: PhD advisors didn't like me sometimes ⁓ I was too honest. a of the stuff... This is gonna... ⁓ A lot the science there was... There was research and I got retaliated against about that also. Not to say I try to rock the boat. I was never trying to rock the boat in the sense of filing whistleblower stuff or anything. I never did. In fact, the opposite. ⁓ But I would hold ground. ⁓ was, you know, the basis of smearing me all the time. Some people's whole, you know, three to five paper research programs were negated by data that I would publish during that I would present during seminar. ⁓ And And so ⁓ there was narrative about me already being a back then. But again, that was the same kind of retaliation. And that's not a part of the lawsuit, but that's going to come up, too, because they're already talking about that in the dock on the docket. ⁓ But yeah, apart from that, ⁓ no formal anything against me ever because if they had done anything like that, people knew that it was gonna be a big problem because I had the facts on my side. I the same thing would hold true here.

Anish Koka: But that

Kevin Bass: As far as the broader legal system is concerned, it's hard to say. Honestly, until sort of this AI era, I would be in big trouble because they have the lawyers. They know how to file the stuff. I don't. But with good AI pipeline, you can actually file superior quality legal ⁓ filings to assistant attorney general, which is what I'm doing. ⁓ My legal are far better than the assistant attorney generals are. But that wouldn't been possible ⁓ if three years ago. ⁓ in general, actually, I don't think the system does work. They can actually do this, but maybe at least now, there's a weapon it, if you understand the engineering well enough, ⁓ which I've done lot on. ⁓

Anish Koka: Yeah, I think I may have... Sorry, go ahead, Anthony.

Anthony DiGiorgio: Well, I was just going to make a quip that in New York you won't be able to do that soon enough because they're trying to ban the use of AI for law or medicine in New York. So they're going to take that away from you again there, Kevin.

Kevin Bass: Yeah. Yeah, they've got to reestablish their firm hands.

Anish Koka: control. I was going say, Kevin, I think I was the one that erred in telling you that, you know, I was like, yeah, Kevin, just keep your head down. You know, just disappear a little bit, become a doctor. And then, you know, once you're a doctor, you can start, you know, opening your mouth. Even then though, you got to be careful, you know, because they can always get you in one way the other. but I may have been given your arc afterwards. So this gets to the, I think the very interesting part in terms of what you've done since being dismissed, you've of course been, you know, fighting legal battle in terms of what happened to you, ⁓ which you've detailed. ⁓ ⁓ know, you have really been very, very, mean, you're, I don't want ⁓ make your head too big, but from a data ⁓ standpoint, my goodness, have clearly really been an early adopter. ⁓ of AI and its ability to kind of AI to analyze data and up with some very interesting stuff. So ⁓ that's what been kind of doing. ⁓ Can you me a little bit about what you've been doing since all this happened on that front?

Kevin Bass: Yeah, it's the same. Basically, it's the same theme. If you're really going on a meta level, everything I've been doing, even in nutrition, it's the same thing over and over again. It's the same concern I have, which is, you know, what is what is true, right? And AI allows you to to dramatically enhance your Analytical capabilities actually it goes far beyond this. I wish I could share with you sort of the Breakthrough that I've had I don't think anybody's doing what I'm doing right now like literally nobody and Even okay, so let's talk about that. I Figured really cool out and I don't think

Anish Koka: What? Why is that?

Kevin Bass: Like people know about it within the tech community, especially like the niche, like really hardcore tech people, they know about this kind of stuff that I'm doing, but they don't understand the application to a lot of the contemporaneous issues and the specific sort of ways you could target these techniques. It hasn't been put together, so I've put that... For whatever and I don't think anybody like I actually have talked to recently people who are deeply involved in this particular field me and you have been ⁓ me ⁓ you've been talking about ⁓ a lot of Doge stuff, We've been talking about I've been talking to people who are in that world And as far as I can tell nobody knows about the insane like the sort of like 100x thing I'm I cooking right now But basically in general, AI allows you to code. if you basically have any questions like scientific questions, right? You're like, OK. How do you establish causality of this and that issue? We did this whole illegal immigration and the different policies that are the policy constellation that sort of incentivizes legal immigration with respect to voting. And I got, I went viral for a little bit on that. You can just ask these questions as a scientist. And if you know how to use the platforms, the analysis will be done for you. And if you have a scientific mind and you're able to wait a second, need to establish causality. You can interrogate sort of the data and the code, the code part is sort of taken out of your hands. So it basically has this knowledge ⁓ facilitates and lubricates. capabilities because you don't have to hire that engineer to do that analysis. if you're smart enough and you are truly trying to read team or you're trying to criticize your own sort of conclusions, ⁓ you have the AI do that for you as well. you can where it's going wrong and going right. ⁓ And so being scientific enough to do that, you take the technical burden off. And so, you know, I've worked a little bit with, you know, Jay's nonprofit for a while doing really fundamental stuff on natural language processing. I still need to get that paper out for him. I've been telling him I'm going to do that for a while. I need to establish myself financially, but that is and once I get maybe the next jump in AI, it'll be so breezy to get out. And I could talk about that paper forever. It's a really cool paper. When it comes out, it'll be, it'll be, it's not like super, super cool, but it's pretty cool. And then we recently did the whole Epstein stuff. So you can take all of the Epstein files, you can put it in a database and with the right kinds of tools to query that database, you can basically say, okay, everything substantial that's said about Trump or everything substantial that's said about Reid Hoffman in those files. Give it to me. And of course it takes like a day because there needs to be a pipeline. It's actually not easy, even easy for the AI to do. The AI has to go document by document, parsing everything. But the AI can then tell you what's important. And once it tells you everything, it can construct a story, right? And you can ask, okay, what are the story threads here? What is this actually saying? Give it to me objectively and from both ends construct a story. I can rewrite it or I could just post it raw. I'm trying to do more. rewriting because some people don't like it being posted raw. but you can essentially take massive amounts of data and make sense of it at a speed at which you could never do it before. In the past, all this stuff could be done. Journalists did this all the time, and newsrooms, teams did this over the course of months. As a single person, you could do this in a couple days, what they would take months to do for, say, the Epstein files. So that's really cool. Now, imagine, and this is where I'm probably gonna say too much, but imagine if you can get all of the data, not just analyze it and put into a database but you suddenly have the ability to get all the data almost instantly from any issue and imagine that there's public data about everything. okay it's really incredible like I've discovered this about a week ago the amount of public data about like who owns what companies what their financial transactions are what their lawsuits are what properties they own related to Medicaid or Medicare, you know, linked to them. There's like 10 layers of different data sets. There's so much data that's available that is currently collected. It's been collected for a long time by law ⁓ that people can't access. Now, what if you could access that instantly and then could put that into that kind of database and then do the same sort of Epstein files that with regard to fraud. And that's what I'm kind of doing. And I've said too much. It's fine. I couldn't help myself, but I haven't told you what the technique is. ⁓ There's some crazy ways to do this that I didn't. It changes the way that I understand the Internet. but like, yeah.

Anish Koka: Well, Anthony, Anthony, you've talked about, know, Anthony's a, you know, pretty, you know, researcher, ⁓ know, ⁓ of the, one of the very, one of the extremely good health policy researchers and one the guys who spent time on this stuff. Anthony, isn't part of the problem that the data sets themselves are very messy. is, is, there, is it, I mean, so what, ⁓ how is that a problem that Kevin's going to run into? Because

Anthony DiGiorgio: Yeah. I mean, I was just...

Anish Koka: Maybe he doesn't know how messy the data sets are or like how do you validate all that?

Anthony DiGiorgio: Yeah, I was just thinking how much we've lost by not having you in the medical field, Kevin, because the, know, so I, for example, doing research with these large data sets, we had one where we're trying to look at traumatic brain injuries and it just so happens that the coding, when you see a doctor, the coding is so sloppy, we call it coding hygiene or a lack of coding hygiene, that in this data set, about 40 % of people had codes that were mutually exclusive. So they were coded for both traumatic brain injury and non-traumatic intracranial hemorrhage, just because nobody knew what the correct code was. So both codes ended up getting entered into system. So it makes it really hard to work with these data sets because they're so, so messy. Now I imagine if you, again, in healthcare, if you had AI that not only had access to the codes, but to the clinical notes, to the radiographic images, it could probably get to a closer ground truth. than just going through a large administrative data set. But that of course requires a different level of access that you don't really have with any sort of these public data sets you really need to get. And even working in the institution, I don't have access to the clinical notes on all the patients in these administrative data sets. So I think it's really just going to be a level of access. What you're talking about, I mean, you're talking about people that pay for engineers to do all this data cleaning and data collection in healthcare. You know, we pay for nurse practitioners, you know, these are six figure jobs, high six figure jobs. We pay for them to do pre-charting before clinic and just get all the patient's story and history together before the physician walks in the room. So you're paying someone a ton of money for something that AI could do. This whole idea of having somebody do your pre-charting when an AI, I mean, this is like the perfect application for everything you're talking about is taking this messy patient chart. I mean, I don't know how it is for your patients, Anish, but a spine surgery patient that's had three operations and seen five different pain management specialists had seven different MRIs and CT scans. mean, the data is all over the place. It's incredibly messy. And Kevin, you, like if you were just ⁓ our office being able to make an AI bot that could give me a one page summary before I walk into the patient room would like triple my efficiency instead having to pay a nurse practitioner to do it.

Kevin Bass: So, you guys are probably super, you know more about this than I do. My understanding about medical AI is like all the big players are doing it. Like that's like one of the big things that the big players are, that's one of the reasons why. But I also have friends in Silicon Valley who working on, that stuff is gonna be hopefully available before the end of this year on a. widespread basis. I hope so. It takes time to roll out though right because of the regulators. That's probably the biggest problem. Yeah. Yeah.

Anthony DiGiorgio: Well, it does. yep. Yeah. So nobody wants to give access to an AI bot into their institutional epic because then that, that cloud bot, starts posting it online or it starts, you know, giving you Kevin Bass, all of, all of the data of the, institution I think is going to just lead to disasters.

Anish Koka: Now, tell me about the issue of the messy? mean, Kevin, ⁓ just heard that, you know, this is an issue, right? Like when a neurosurgeon looks at a chart and looks at a code, looks at medical we're talking about, ⁓ hopefully neurosurgeons aren't looking at Python code. But when looking at billing codes. ⁓ But, you saw, you heard that, you know, you have charts which have two billing codes are

Anthony DiGiorgio: A lot of them are, but I can't interpret that.

Anish Koka: mutually exclusive and you're trying to use the billing codes to figure out who has a traumatic brain injury but chart has ⁓ know things can't happen you can't have a traumatic brain injury and a non-traumatic brain injury and like how the heck do you figure out who had what ⁓ how are you know is there concerns about ⁓ how the data may be because the folks that ultimately put in this stuff ⁓ put in very messily so like ground truth becomes very hard ⁓ to and ⁓ if just have AI kind of searching stuff without access as Anthony is saying to the underlying clinical chart to validate the patient had a traumatic injury or non-traumatic brain injury. ⁓ does one get beyond that? Meaning you get a lot of garbage in garbage out there. Are you worried about that? How would you validate some of the stuff that ⁓ trying to get? ⁓

Kevin Bass: So, you know, the kinds of questions you can answer depends on the data that you have, right? So if you're interested, if you're, the answer to your question depends upon that distinction between traumatic and non-traumatic brain injury and you can't access the underlying data, then you just can't answer the question, right? So some of the stuff that I'm doing right now doesn't rely on that. It relies on a whole bunch of other records that actually are like really good.

Anish Koka: Yeah.

Kevin Bass: That are non-medical it there's an interaction between medical and non-medical for some of this fraud stuff now Yeah, it'll probably be an issue on some things like data access And then fighting for data acts. I don't know how that's gonna play. don't even I'm not there yet But yeah, of course, that's definitely an issue So for sure Yeah, you're limited. like one of the things I was trying to establish with the immigration stuff was causality between of the democratic policies, the Democrat. Yeah.

Anish Koka: Describe for us, just to give the audience a sense, what analysis did do and what conclusions did you come to?

Kevin Bass: It's a little bit stale right now, my memory. And I didn't like publish on this, I did it very fast, so I have to ⁓ back. Because you have to create content fast. So the first set of analyses I did was ⁓ on the between welfare benefits to illegal immigrants ⁓ and what was wasn't immigration rates, was, there was actually a relationship between different policies, welfare benefits to illegal immigrants and, yeah, voting, voter ID laws. And I just showed in ⁓ a right? ⁓ In ⁓ with amounts of ⁓ welfare there's just like three or four different categories that are very important. You also saw ⁓ really lenient or nonexistent voter ID, ⁓ laws. And so that's interesting. Is it that trying to incentivize immigrants, illegal immigrants to come and then to vote? I don't think that's the mechanism. I think it's part of ⁓ the incentive do think that they're sort of, I don't think it's engineered. It's just part of like how Democrats know that supposed to think about things and it just so happens to benefit them. And so they push constellations of policies that do benefit them. I don't think that's the main mechanism through which policies are, know, so that I showed that connection. went like, you know, Elon loved it, blah, blah. And then it was, you know, people thought it was really cool. And then I think after that, I looked at maybe like immigration rates and some of these policy constellations. And so I did that. have a couple and I probably need to put it on sub stack. And it's all transparent on GitHub. Nobody's challenge the analysis. It's all like everybody can literally look at the data and look at the code ⁓ see. ⁓ I did try to establish causality. Like, okay, ⁓ is there causal relationship? Do they put these policies in place? And what happens is you get. vote more votes for the Democrats. Like do you get ⁓ there a causal relationship? I couldn't establish that ⁓ because There's a selection effect when Democrats get power. They put these policies in place ⁓ It's necessarily that the policies and so but the data are limited to the extent you actually can't answer that question Because the just you don't have the granularity of data to answer the causal question for a lot of the reasons that dr Giorgio is saying ⁓ on another of kind of data set but a similar sort of issue ⁓ Yeah, so that's what did there. I don't remember the next thing I did. ⁓

Anish Koka: Yeah. Go ahead, Anthony.

Anthony DiGiorgio: Yeah, I was going to ask, can you figure out Medicaid fraud for us? I this is a, think we're just trying to scratch the surface, but one of the arguments that I keep having with people is some people say Medicaid fraud is 1 % of all Medicaid spending. Some people say ⁓ 80%. I mean, just fact that there's this huge range in the public discourse about how much of Medicaid spending is actually going to a fraud. And I consider things like, a hospital charging a 30X markup to the Medicaid program also as fraud. If you're charging $18,000 for an MRI when it can be $500, I would consider that sort of soft fraud. But are we ever going to be able to get a handle on the amount of actual fraud in Medicaid?

Kevin Bass: Well, do you mean by including soft fraud? Because that's like, you know?

Anthony DiGiorgio: I mean, it out. Like the true hard fraud, the Somali daycare, luring centers and all that. And then the sort of softer fraud, like you could actually get a better deal on some of these services.

Kevin Bass: Or another kind of fraud like you don't need a treatment. The doctor knows and is knowledgeable enough to know that you could do things a different way without that treatment and maybe even better. But then they still encourage the patient to get that intervention when they don't need that intervention. That happens all the time, right? You're rewarded in the hospital.

Anthony DiGiorgio: Yeah, the so-called supplier induced demand. Yeah.

Kevin Bass: So, they call that incentives, right? Like that, way they, have these incentives, but like, it's not just incentives because the doctors, some of the doctors, they know better than to do it, but they're doing it anyway. Is that fraud? ⁓ I think that's also another kind of soft rub. ⁓ I, I, so ⁓ least and I'm new to this field, and I just literally had a conversation with the guy who's an expert about this. earlier today, the legal bar for showing fraud is quite high, right? So even though ⁓ something like really looks like fraud, or even though it's almost like, it's almost certainly fraud, you may not be able to take that case to court. Is that considered fraud? Or is it only the cases that you can take to court that are fraud? And so it's kind of a spectrum, right? So ⁓ we like we've already in this conversation, like looked like four different things. are all those four things fraud and then how do you show them all? So I don't know if we'll ever actually have a clear answer to that, but as far as solving the problem, that is like what I'm working on, like literally, because that's where, you know, there's a lot of money there for me, for me potentially, but it's also an important issue. And, and there are clear cases though. And I think that, so, you know, and I think that using data in this, in this new way can, can help to at least

Anish Koka: What's the-

Kevin Bass: the other issue is that ⁓ of these people who are doing this fraud, especially the big operations, and I believe that there are, let's say this, I believe that there are massive operations on a scale that you couldn't comprehend, centralized, I believe that. But the people who running these are incredibly sophisticated and smart. So these are the worst cases. Some of the people are incredibly, they know how to hide it. But I also think that with access to a lot of this data, it would take teams, ⁓ like a 50 team, like six months to, I believe that you can show, like for example, you my, ⁓ this is not fraud, just to be clear it's not fraud, but you take my medical school case for example and you look at the patterns of obstruction with respect to records requests, ⁓ patterns of apparent retaliation ⁓ I file a lawsuit, know reopening debt collection as soon as I file a lawsuit, ⁓ you at just many different patterns that are in the documentary record they don't ⁓ they don't dispositively the thing that I'm claiming, they're so pervasive and there's so many patterns and so many parts of the record, they start to put together a picture that can help create a case. That kind of stuff has become possible. ⁓ But the is, as I'm saying, some of these people understand how not to be detected, ⁓ but the paper that they produce in trying to avoid not being protected is itself a piece of evidence. so that's something that I'm not a piece of evidence, but that's actually is case. So that's the problem is they're smart, they're smart operators. My hope is that we can, is that new ways of analyzing the data at a much larger scale can reveal the ways that they've been hiding their operations, but we will not be able to expose everything because it's a cat and mouse game.

Anthony DiGiorgio: Well, this is, and this is one area I think Anish and I sort of differ on opinion is, least in Medicaid, ⁓ don't think that you necessarily need to show a legal case for fraud, but if you have enough evidence, because so much of Medicaid goes through managed care organizations, if you can show evidence to managed care organizations, they can then use their ability to tamp down future fraud by introducing like utilization management. For example, if there was a large MCO, that was dealing with behavioral health services instead of just the behavioral health services billing fee for service directly to the state. If it instead went through an MCO and an MCO caught wind of it because someone like Kevin Bass gives them a lot of data showing that a lot of these claims are fraudulent, then they can simply institute utilization management strategies to prohibit that fraud, to prohibit those claims from going through the same way they do it, know, oftentimes to our chagrin, to doctors. by doing excess utilization management. But that's one way, at least I think, that having a private intermediary can help tamp down some of that fraud. so I guess the point is you don't need a legal case, you just need to show this is outstanding, need to be looking at this ⁓ party payer.

Kevin Bass: There are, yeah. Yeah, so the legal side is the part that I'm most interested in because that will allow me to build a base of capital I'm successful there. However, if fails, ⁓ there many lanes and if ⁓ you up a legal case, then obviously you can go down those other lanes even easier.

Anthony DiGiorgio: Of course.

Kevin Bass: that's ⁓ going to be part of it as well. ⁓ it's interesting because some these fraud networks, there's a lot of complaints that have been filed against these people. ⁓ Some them have indictments, some of them have convictions. Some of operations are so large that investigators have only found one piece, but they're doing lot of other things. Well, so the legal side is also important because you want to shut them down completely because they can keep going. So, but yeah.

Anish Koka: Yeah, so I think I was wrong in terms of where you would have the most impact because it may very well be that Kevin Bass unleashed from being on clinical rotations, doing rectal exams. I don't think they do rectal exams anymore, but doing whatever Dr. DiGiorgio is making his poor medical students do, what you're doing now may be more impactful.

Anthony DiGiorgio: We do, especially in spine surgery.

Anish Koka: sphincter tone, very important.

Kevin Bass: I mean if I had to do a rectal exam as a medical student I certainly that would have been a great I would have gotten a professionalism complaint 100 % they would have they would have made they would have had something ready for that and that would have been horrible so just saying

Anish Koka: I'm glad they got to you before that. No comment. Yeah. So Kevin, mean, I'm looking forward. mean, you your analyses have been and you're all I mean, it's like the of non medical stuff that you're doing is very interesting. And it is certainly you seem have a nose for what a lot of people will be interested in. I did not foresee that you would.

Kevin Bass: Ha ha ha ha.

Anthony DiGiorgio: No comment.

Anish Koka: take the entirety of the Epstein file dump and, you know, analyze it and then set up, you know, these brilliant, these brilliant analyses of like, okay, because everyone keeps talking about Hoffman, Trump, this, that, and you're just like, okay, go through it here. Let me show you how many times and what the timeline is for Reid Hoffman and what he did. Let me show you Elon. And of course, Elon shows up 40 times, Reid Hoffman shows up, you know, a bajillion times and, and all the, you know, all, and you contextualize. the Elon stuff. I mean, it's the it is the best journalistic review in analytical way of the Epstein files that I've seen and it's done by Kevin Bass. Like what heck is going on here? Like ⁓ what is Yeah, PhD, Dr. Yes. So I so I'm yeah, I'm looking I'm looking forward to you know, ⁓ what you're going

Anthony DiGiorgio: PhD.

Anish Koka: bring to us as citizens to kind of illuminate and shine light because yeah, think you have a combination of an analytical brain, ⁓ ability to ask the right questions, ask the right prompts and time you ⁓ are doing rectal exams.

Kevin Bass: funny because those skills that I built for the Epstein files those were built Creating my lawsuits so if I so So I like after I had these lawsuits filed I was like, my god I have all these tools for the Epstein file just use them like and ⁓ I didn't I honestly I didn't know anything about the Epstein files at all, but I knew that like they would work in this context, so I just literally moved them over to Epstein files. Now I've learned so much from the Epstein files and I've learned actually a lot from do this Medicare like technically from an engineering perspective. And by the way, that journalism was all it was really just engineering, right? So I've learned so much now I'm going to move all those techniques back to the lawsuit again. So soon Texas Tech has to face the nightmare machine. So it's it's it's it's it's yeah.

Anish Koka: Okay, what advice, do you have any advice to ⁓ terms of how we be using AI or is there anything that we should be doing to interrogate things or ⁓ it's ⁓ far our grasp and we should just ⁓ find some person to tell us what to do.

Kevin Bass: So this is where I think one of the other qualities about me that I think you didn't hit whenever you were explaining why I'm able to sort of pivot and do this kind of stuff is like a really high level of openness and I just love like new things and learning about new things and like whenever I learn something like a lot of people like a lot of doctors will be like oh I've learned my trade I've learned my craft and like I'm set now and like that's alien to me because to me it's like wow like always new things to learn and I and like whenever I learn something new I feel like it's like the it's like the best feeling in the world literally it's like I'm like discovering something right so um so so to get so to get to your your answer your question. I know that me and you, have, me and for like the last like, I don't know how long we've been talking about this, but at least over a year, we have maybe a different view about AI. I'm like a total AI tech bro, like super enthusiast, accelerate. I think we are literally this year and this is where so I ⁓ is why I did all this preface because it's a totally new world. ⁓ I don't know if necessarily ⁓ You know, we're gonna get like source recursively improving AI and it's gonna be sky net or whatever. don't know know if I believe exactly that, but I believe that the capabilities of AI continue to improve. It appears at a linear rate. ⁓ haven't hit the wall. ⁓ thought maybe a year ago we'd hit the wall and we've kept going and we haven't hit the wall at all. Now the capabilities are insane. I think it's much crazier than I think most people realize, especially if you know how to leverage them in really careful pipelines. It's not just a chat bot. When you leverage them that way, you're able to create leverage that one person's never had. So then the question becomes, can we keep going at this linear rate? And maybe I should not get off this topic, but like if we keep going this way It's kind of hard to know what it's gonna look like it so if it stops like right now My advice would be what like I don't know what the medical AI people are doing but like Certainly everything dr. DiGiorgio said is true How to get ready for the future your practical advice let's get down to like practical stuff ⁓ I don't know what the work the future is gonna look like in the issue. Sorry. I can't answer that question. It's like, yeah

Anish Koka: That's a good answer. Yeah, no, I think it does speak to, know, I folks realize that there's a huge potential here and it's ⁓ worthwhile, that have an open mind to kind of... take a look and look under the hood in terms of what the capabilities are to see it can be useful to you. And I think it's going to be very different for ⁓ a private practice cardiologist versus an academic neurosurgeon ⁓ a health service researcher, right? So very, very, very interesting. I'm actually super interested to see how the research enterprise ⁓ may both for the good ⁓ and better. Like, you know, ⁓ I've a long, just as an example, I've been a long critic about all sorts of different health policy things, right? And you know, it's like, how do you go and I mean, you can, can write blogs and I can write op-eds fundamentally. Maybe, maybe I have a, I have a piece that's slightly insightful and it gets read, read, read a little bit, at the end of the day, the real way, you know, in.

Kevin Bass: Don't put yourself down like that. You're actually getting trolls from stat news on your Twitter. You're making an impact. It's good.

Anish Koka: But the real way, as Anthony is that the ⁓ way to rebut researchers in a certain point they have ⁓ actually generate other research. And for the longest time, ⁓ thought the problem with much public health research is, I'm just giving one example, ⁓ public health policy research, ⁓ has that ⁓ it from one monolithic ideologue and it comes from the left. ⁓ And all the research you produce, if you're a leftist ideologue, ⁓ has a certain kind of flavor, even unconsciously, you the decisions you're making leads you to a certain outcome, right? so, and folks that are libertarian or free market folks are ⁓ kind frozen out because well, ⁓ they let you complete medical school if you, that's a haha, not really. But ⁓ now, really to if ⁓ there are folks that outside the establishment that would be able to produce ⁓ high quality research from databases. you know, the Medicaid database that was just data dump released, right? It has been accessible only to a few people, a few academic groups around the country. Now that same database is fundamentally accessible to like random person sitting on sitting on the internet, right? Who has, who has the benefit now of having, you know, a bunch of AI agents that can parse that data. So, you know, it does for somebody that can ask the right questions, similar to what you're doing. you know it'll be interesting to see if ⁓ if if if ⁓ be easy to see what the establishment does ⁓ response to that like you know would ⁓ you know some journal you know publish something from some random guy who's done analysis that looks exactly the same like some analysis from say ⁓ the yale group right ⁓ it's coming from somewhere so And it's all public and it'll be open source and it'll be on GitHub, right? And you can be like, can look at it and you can test it. I'm just not making it up. So I think we're on a very interesting in an interesting place. I don't know if like the average 45, 50 year old physician clinician is necessarily going to be able to pivot to do that. But certainly, you know, there's there may be some elite 45 year olds that can. But certainly the younger folks who are growing up in this era that are like, I'm not doing a PowerPoint. I'm just telling Claude what to do so that Claude does a PowerPoint for me. That group of folks. Yeah, so I think we may be on a, it'll be interesting to see what happens in the next 10, 15, 20 years. So sorry, very long comment, but Dr. Giorgio, we'll get out of here because we've used up enough of everyone's time here.

Anthony DiGiorgio: Yeah, no, it's, I agree with that, a niche completely. Kevin, it's been a pleasure having you on.

Anish Koka: Thanks so much, Kevin, all the best, man. Good luck to you. are rooting for you in your fight against ⁓ the establishment. And I'm super curious to see, like I said, what ⁓ going to produce on multiple different fronts and ⁓ try to get some more sleep. ⁓

Anthony DiGiorgio: Hahaha

Kevin Bass: Thank you.