Episode Transcript
[00:00:00] Sam.
[00:00:30] There's a floor in Lower Manhattan, a room most people recognize from photographs. The New York Stock Exchange. Traders in colored jackets, hand signals, flying paper tickets scattered across the ground, just like it's confetti, pretty much after a parade that really, I guess, nobody, nobody enjoyed.
[00:00:54] Now, that image, it's a ghost.
[00:01:00] It's a memory the financial industry keeps alive the way a family keeps a portrait of a great grandfather on the mantle.
[00:01:12] Respectful, sentimental. Yes. And entirely disconnected from the reality of who's actually running the house.
[00:01:23] Welcome to AI Today. I'm Dr. Alan Badot, and tonight we're stepping into a world that touches every single one of you watching at home, whether you know it or not.
[00:01:36] We are talking about AI in the financial space.
[00:01:41] Not the dream of it, not the promise of it, not anything else other than the reality of it. And it's happening right now, this week, in your retirement accounts and your brokerage apps and the algorithms deciding where your money goes and what it does while you sleep.
[00:02:03] Here is the number that should stop you in your tracks.
[00:02:10] The algorithmic trading market hit roughly $25 billion in 2026.
[00:02:17] I've been doing some research on this, and it got me. Got me thinking a lot.
[00:02:23] It's projected, though, to nearly double by 2030.
[00:02:28] And the growth rate, it's not incremental. This is an exponential adoption. And when I say algorithmic trading, I.
[00:02:38] I need you to understand what I mean. So let me. Let me explain for a minute. I'm not talking about a computer placing an order for you.
[00:02:46] I am talking about machines ingesting satellite imagery from parking lots to predict retail earnings, for example, or machines reading the Federal Reserve's, you know, chairman's voice and the tone of his voice during a press conference.
[00:03:07] And then it executes all those trades before the sentence is finished, machine scanning 10,000 news articles and the time it takes you to read a headline.
[00:03:21] And so if you look at that, that the graphic, just really fast.
[00:03:26] We'll go into it a little bit more detail here in a bit, but the bar chart showing market expansion from 21.89 billion to 44 billion by 2030 is.
[00:03:42] It's a little scary. It's pretty impressive, though.
[00:03:46] Now, let me put a finer point on this.
[00:03:49] 75% of all trades in some markets are now executed by algorithms not suggested by algorithms, executed by algorithms.
[00:04:01] The decision to buy to sell, the timing, the volume, the exchange routing, all of it is happening at speeds measured in microseconds, and for those of you don't know, Microsecond is a 1,000,000th of a second the amount of time it takes you to blink.
[00:04:21] And AI trading systems, they can analyze market conditions, identify opportunities, execute trades, and then just move on to the next one.
[00:04:33] And that's a little scary.
[00:04:36] So JP Morgan, I mean, they've been really leading the charge on the AI front, but they build an AI execution algorithm called lectum, and it routes all orders across multiple exchanges by analyzing real time market conditions. And it's done in a way that no human, there are no human teams even could, could ever replicate. It minimizes what the industry calls slippage.
[00:05:06] Now give me a second. Slippage.
[00:05:09] Slippage is really the gap between the pricing you intended to buy and the pricing you got.
[00:05:19] And so for institutional investors, though, that slippage adds up to billions of dollars and Lockstone really compresses it.
[00:05:30] It's amazing.
[00:05:32] Now here is where I need you to really pay attention, because this is the part that most people in the media are getting wrong. They tell you AI is coming to Wall Street.
[00:05:44] That is not the story.
[00:05:46] AI is not coming. It's already there.
[00:05:50] The story is that most retail investors, people like you and me, are still trading like it's 2015.
[00:06:00] You're bringing a notebook to a gunfight. That's the easiest way to explain it.
[00:06:04] And the other side, you know, they're not even in the same building.
[00:06:08] They're in a data center in New Jersey executing strategies that speeds of, you know, the speed of light through fiber optic cables that of course cost billions of dollars to assemble and lay and everything else.
[00:06:23] And that's really telling. When you look at the graphic above that AI in the FinTech landscape, it's all over the place. It's doing sentiment analysis, it's doing portfolio rebalancing, it's doing risk management, fraud detection, high frequency trading, many of the things. I've helped develop a lot of algorithms and, you know, capabilities that are being used in other industries.
[00:06:51] But here's the thing. I'm not here to scare you, okay? You know that we never do, you know, scaring tactics on the show. I want you to be or to be educated.
[00:07:03] And the first lesson is this. Understanding the landscape itself is an advantage.
[00:07:10] Most retail investors have no idea when they place a trade on their favorite app, that the order is being routed through a system designed to maximize execution quality for the broker, not necessarily for the trader. Your order flow is a product that gets sold and then on, on the other side, you know, they're looking at the sales, you know, and the firms with the AI systems and how sophisticated those are, and it's enough to profit from the patterns in your behavior.
[00:07:45] Now, cloud based deployment, you know, accounts roughly for 60% of all algorithmic trading in infrastructure.
[00:07:57] That means, from what I've been able to tell, the barrier to entry has dropped significantly.
[00:08:06] So retail investors represent, from my calculations, about 39% of algorithmic trading activity in 2026. So far that is up just, you know, a fraction of what it was five years ago. And the tools are becoming more and more accessible.
[00:08:26] The question is whether the people using them understand what they are holding.
[00:08:33] You know, I have spent my entire career building cognitive AI systems. And whether it was on the defense side, whether it was, you know, to support decisions, whether it was on the commercial side.
[00:08:46] You know, at the end of the day, I understand what these architectures can and cannot do.
[00:08:53] And I'm going to tell you something that the finmar, you know, fintech marketing departments are never going to say, and that is that the gap between what AI can do in finance and what most products claim that it can do is.
[00:09:13] It's a canyon. It's not a crack, it's a canyon.
[00:09:17] You know, we're going to walk through that canyon tonight. All our things that we're going to walk through.
[00:09:24] But by the time that we are done, I think you're gonna see the world a little bit different.
[00:09:32] So when we come back, we're gonna talk about what happens when these machines turn on each other, because they have, and it's not pretty. So stay with us. We'll be right back.
[00:10:16] Welcome back.
[00:10:18] Before the break, I told you that AI has arrived on Wall street and it did so a long time ago.
[00:10:24] Now, I need to tell you that what happened when nobody was mined in the store and it was around early May 2010, it was in the afternoon, nothing unusual really was going on about that day. And then in about, well less than 30 minutes, nearly $1 trillion in market value evaporated. Gone. Blink of an eye, $Jones fell almost a thousand points. And then, you know, of course it recovered, you know, all within a window, but $1 trillion vanished.
[00:11:08] And then it returned like it was a magic trip by machines that didn't know that they were even performing.
[00:11:17] Now if you take a look at this graphic, you're going to see that really it's a timeline showing, you know, major AI triggered market events. And yes, I did not, I did not misspeak. These are, these are events that happen when AI goes off the rails.
[00:11:39] That was the 2010 flash crash.
[00:11:42] It wasn't caused by a rogue trader.
[00:11:45] It wasn't caused by a geopolitical event.
[00:11:49] It was caused by algorithms responding to other algorithms responding to other algorithms in a feedback loop that no single human could have anticipated, predicted, or stopped in real time.
[00:12:06] And we talk about these systems today and how important it is to keep a human in the loop. Boy, you would have thought somebody did, would have, would have architected that before.
[00:12:18] But here's the anatomy of what happened. So I want you to pay attention because it's as applicable today as it was back then.
[00:12:25] So one large cell order, you know, triggered automated market making systems to, to really pull their liquidity. And so when liquidity disappears, prices move more violently with less volume. Other algorithms though, they detected that volatility spike and it interpreted it as a sell signal.
[00:12:48] So they sold.
[00:12:52] That triggered more systems to sell, which drain more liquidity. And then you've got a cascade of events that takes place. And it's a, you know, it's a waterfall of automated logic. And if you've done software development or you heard me talk, you know how much I love waterfall.
[00:13:11] But see, the thing is, each layer was perfectly rational in isolation.
[00:13:22] You know, that was the key back then, collectively really creating something that looked like a panic, except there was nobody there to panic, just code executing as it's designed.
[00:13:41] So I want you to sit on that for a moment.
[00:13:45] Each algorithm did exactly what it was told to do. No bugs, no errors, no malice, no hackers.
[00:14:00] And the result was the fastest destruction of market value in the history of American finance.
[00:14:10] This is not a failure of technology. It, it is a failure of imagination.
[00:14:15] Nobody imagined what these systems would do together because nobody had had to think about systems interacting at that scale before.
[00:14:29] Think about that interacting at that scale now. We're also talking timescale, how fast it was going.
[00:14:39] Now, fast forward 2016.
[00:14:42] The British pound, right, drops almost 6% in overnight trading.
[00:14:48] The culprit, again, algorithms.
[00:14:51] Some analysts trace the trigger to algorithms parsing negative sentiment from Asian news coverage about the ongoing Brexit negotiations.
[00:15:03] Now, these machines read the news, decided the pawn was going to go down, and they made it. So the prophecy wrote itself, as a lot of folks would say, It's clear, you look at this visual, you're going to see that.
[00:15:21] You know how one cell, one single cell can signal triggers for withdrawal of liquidity and triggering volatility and triggering more selling and on and on and on?
[00:15:38] No?
[00:15:41] Let's move forward to October of 2025.
[00:15:46] The crypto markets a single macro policy tweet and within an hour, Bitcoin dropped 14%.
[00:16:00] Altcoins fell even far farther.
[00:16:04] Liquidation engines across all these major exchanges fired simultaneously. Coin Glass logged, What was it, 19 point, you know, $3 billion in, you know, enforced closures in a single day.
[00:16:20] 1.6 million accounts were wiped in minutes.
[00:16:26] Market making algorithms from firms like Wintermute, they withdrew, you know, the ability to withdra funds and you know, right when the market needed it too.
[00:16:39] Again, machines didn't break.
[00:16:42] The machines work perfectly.
[00:16:45] They just worked perfectly in the same direction at the same time with the same conclusion, which was get out now. Why does this matter to you at home?
[00:16:58] Well, your retirement fund, your index funds, your ETFs, they're all swimming in these same waters.
[00:17:07] And so when a flash crash occurs, the algorithms driving institutional portfolios rebalance automatically.
[00:17:19] They sell to manage risk, which pushes prices down, which triggers more rebalancing.
[00:17:28] So your 401k is a passenger in a car being driven by software that doesn't have a rear view mirror.
[00:17:36] That's great.
[00:17:38] It only looks forward and it only looks at data.
[00:17:45] Now here is the uncomfortable truth.
[00:17:51] The IMF has acknowledged that while AI driven trading can make market more efficient, it can also make them more volatile.
[00:18:04] Patent filings for AI related algorithmic trading innovations have risen almost 19% from 2017 from what I've been able to tell.
[00:18:16] That's impressive.
[00:18:22] A wave of new untested systems are starting to enter the market.
[00:18:28] And we're looking at, you know, the regulatory infrastructure is perpetually playing catch up and they can't, they just can't. Systems are getting faster, the data they, they consume getting broader and you know, quite honestly, the interconnections between each one are getting denser.
[00:18:47] We have built a financial nervous system that can transmit a tremor across global markets faster than a human can reach for a phone.
[00:19:01] That is not science fiction. That's a Monday morning.
[00:19:09] Think about that.
[00:19:13] Let that sink in a little bit.
[00:19:15] Now, when we come back, we are going to talk about people who have figured out how to weaponize these tools.
[00:19:25] Because where there is money and complexity, there are con artists. We all know that.
[00:19:30] And guess what? The con artists, they got an upgrade.
[00:19:34] So stay with us. We'll be right back. Sam Foreign.
[00:20:08] Welcome back to AI Today. I'm your host, Dr. Alan Bedot, and we have been talking about AI systems and their impacts on the market.
[00:20:18] Now what we're going to talk about in this segment is pretty enlightening because it really is a game changer.
[00:20:28] We know that every great technology Attracts two types of people. You've got your builders and you've got your exploiters.
[00:20:38] And artificial intelligence and finance has attracted both in equal measure.
[00:20:48] Let me start with a name that a lot of you are going to know.
[00:20:53] You know, Steven Gallagher.
[00:20:55] Between 2019 and 2021, Mr. Gallagher used his Twitter following to pump micro cap stocks.
[00:21:08] Classic scheme, right? Ancient, you know, is the market itself really. And you know, buy low, hype up your audience, sell while they're buying. Pump and dump. Pretty simple, right?
[00:21:21] But here's what makes you know in a story in 2026, after a nine day trial in September of 2025, a jury found him liable for securities fraud and manipulative trading. That is, that was historic.
[00:21:40] He made over $2.6 million in illicit profits across more than 30 stocks.
[00:21:49] He also used a technique called marking the close. And that's where you place, you know, end of the day orders at above market prices to really, you know, artificially inflate, you know, what the stock's closing prices are.
[00:22:04] Now Mr. Gallagher, of course, human being using social media.
[00:22:10] Now imagine the same scheme powered by AI.
[00:22:15] Imagine a system that can generate thousands of unique social media posts across hundreds of fake accounts, each with a demographic of a retail investor, each timed to create the appearance of organic market enthusiasm.
[00:22:38] And that's not hypothetical.
[00:22:41] That's the threat the SEC is now scrambling towards. They're trying to figure out how to address this.
[00:22:51] And take a look at this graphic.
[00:22:56] It's comparing traditional versus AI enhanced fraud schemes like spoofing, AI washing, social media manipulation, deep fake earnings calls, all those other things right now those techniques are not new. They've been used a lot of times in insider threats, they've been used a lot of times in deepfake scams all across the Internet, of course, all dependent on humans.
[00:23:25] Now AI washing, I'm going to do a little bit of, a little bit more deep dive into that for you because I think it's something that you, you really need to understand.
[00:23:35] And you know, this is the financial industry's versioning of greenwashing.
[00:23:42] So companies claim their products are powered by AI when the reality is it's far less impressive.
[00:23:49] So the SEC charged Presto Automation for claiming their AI product, you know, Presto voice eliminated the need for human drive thru order taking.
[00:24:00] Reality is, is that the vast majority of the orders are still required for, you know, to use human intervention.
[00:24:09] So they were selling the story of AI, not the substance.
[00:24:15] And how many other industries have we talked about that very same thing In April of 2025, the SEC and the Department of Justice of course, you know, charged Albert Saniger and he was the founder and former CEO of Nate Inc.
[00:24:35] And they were raising, I think it was $42 million through, you know, stock sales allegedly based on false claims about the company's AI capabilities.
[00:24:47] 42 million.
[00:24:50] And it was raised on the promise of AI that, you know, according to the, the prosecutors didn't function as described.
[00:25:02] And again, how many times have we heard that the SEC has rebrand, you know, has responded to rebranding, you know, it's crypto assets and their, you know, cyber unit into the cyber and exchange or apologies, the Cyber, Cyber and Emerging technologies unit, or CETU.
[00:25:27] And this is not cosmetic.
[00:25:30] So CETU's mission is specifically focused on combating fraud involving AI, machine learning, emerging technologies, all of the above.
[00:25:40] You know, so they're investigating AI washing, they're looking at whether firms have adequate policies to supervise their use of AI.
[00:25:49] How many, how many times have we talked about that where strategy, where ethics, where policy, where governance all drive how AI should be deployed.
[00:26:06] It seems a little reckless.
[00:26:09] Just let it go out there and take off with your, your investments.
[00:26:15] And I'm not the only one that thought that because their 2026 examination priorities now also include scrutinizing how firms protect client data when using third party AI models.
[00:26:30] Another revenue stream. Right.
[00:26:33] Now again, let's look at some data.
[00:26:37] So there were 456 enforcement actions in fiscal year 2025.
[00:26:46] Works out to be about 17.9 billion monetary relief. You know, and so let's, you know, really around focusing on fraud and, and that whole manipulation piece, that's scary. It's really scary.
[00:27:03] And didn't stop in 2025. Right.
[00:27:08] The SEC filed 456 enforcement actions and that resulted in about, you know, another $17.9 billion in monetary relief.
[00:27:21] And here's a scarier figure.
[00:27:25] Nine out of 10 standalone actions involved individual defendants, not just corporate entities and insiders, you know, trading on it for 33 of all actions and that's up from 26 the previous year.
[00:27:49] They form the cross border task force to, you know, pursue foreign based actors targeting them. You know, American investors, you know, the message was clear though that you know, geography is not going to protect you anymore.
[00:28:02] But here's, here's where the tension comes in though.
[00:28:06] The regulatory apparatus is inherently reactive. We always talk about that, that the good guys seem to be more reactive because why, because they're not pushing the boundaries. There's a lot of, you know, bad actors that continue to just move forward and you know, after the harm is done, they go somewhere else.
[00:28:31] And guess what? AI moves significantly faster than regulation does.
[00:28:37] Now by the time the SEC identifies a new manipulation technique, the perpetrators have already moved on to the next one.
[00:28:46] I'm going to shift because that hurts.
[00:28:51] Spoofing, placing orders, you know, that you intend to cancel, you know, to create like false impressions of supply and demand. It's, you know, it's now done by AI systems and those systems can evaluate and execute thousands of phantom orders per second.
[00:29:10] Phantom orders at volumes that in speeds that make detection extraordinarily difficult.
[00:29:22] So what are we going to do?
[00:29:24] You have to become your own first line of defense.
[00:29:28] You have to learn to recognize these signs.
[00:29:31] You learn to ask the right questions. So when a fintech company tells you their AI will generate 20% returns, you ask them what model are they using. I'll tell you probably you can guess what date is a trained on the biased what's their drawdown history look like?
[00:29:57] And if they can't answer those questions, you need to run away.
[00:30:01] Just don't, don't, don't keep going past go and sprint because the most expensive words and investing are still this time is going to be different.
[00:30:19] Doesn't work that way.
[00:30:21] Now when we return, I'm going to give you the playbook, specific practical strategies for how you can use AI and take advantage of some of these situations, protect yourself and stop being a passenger in your own financial life.
[00:30:44] When you do that, then you have the control. And when you have the control, you're more likely to win.
[00:30:52] So stay with us. We'll be right back.
[00:31:04] Foreign.
[00:31:27] Welcome back to AI Today. I'm your host, Dr. Alan Badot.
[00:31:31] And we've talked about the landscape, we have talked about the risks, we've talked about the predators.
[00:31:38] How about we talk about you now because this is where I believe to the core of who I am, knowledge is the only asset that can't be taken from you. And tonight I want to leave you with a bit of knowledge that you can use tomorrow morning.
[00:32:01] Now. And not financial advice. It's AI advice. Okay? Things to think about.
[00:32:09] Now I'm gonna have I want you to focus, okay? We're gonna focus together because we're going to come up with some strategies around this. And strategy number one, use AI for what it is genuinely good at and stop asking it to predict the future.
[00:32:32] AI excels in processing volume. It can scan thousands of SEC filings, earning call transcripts, news articles, surface patterns, things that would take Weeks to to do manually.
[00:32:51] There are a number of tools available right now.
[00:32:55] Some are free, some are affordable, some are used by your broker.
[00:33:01] But it will summarize an earning call for you in seconds and flag inconsistencies.
[00:33:08] Inconsistencies between what a CEO said last quarter and what they're saying right now.
[00:33:16] That is actionable intelligence. It's not fortune telling actionable intelligence.
[00:33:23] That is forensic research done at machine speed.
[00:33:27] That's what you got to take advantage of.
[00:33:31] Now again, take a look at this graphic and you're going to see practical AI applications doing some sentiment analysis, earnings call analyses, portfolio risk, the things that we talked about earlier, automated alerts and anomaly detection and those kind of things. So pay attention to that. Take a snapshot. Use it.
[00:33:54] Doesn't bother me. Use it.
[00:33:57] Strategy two, Got to automate your discipline and not your decisions.
[00:34:03] The biggest killer of retail investor returns is not bad picks, it's bad behaviors. Very simple, oh, let's buy high and sell low.
[00:34:15] Bad, very bad.
[00:34:18] Or panic selling or FOMO buying, ignoring stop losses.
[00:34:26] AI portfolio tools can enforce discipline that you set for yourself.
[00:34:34] It's almost like your babysitter, but you said it.
[00:34:38] You set your risk parameters, you set your rebalancing triggers, you set your maximum allocation percentages, and then you let the system hold on to it and hold you accountable.
[00:34:54] You are not outsourcing your judgment.
[00:34:59] Stop doing that. It doesn't matter what market you're in, just stop.
[00:35:04] You're building guardrails around your emotions.
[00:35:09] That's what AI should be used for.
[00:35:12] Strategy three, You've got to diversify your information sources and you got to let the AI help you do it.
[00:35:22] I know what you thought I was going to say diversify your portfolio. No, you've got to diversify where you are getting your information from. Why? Because remember Seven Degrees of Kevin Bacon? We always talk about that, right?
[00:35:35] Things that influence the market can come from anywhere. It can come from, you know, a, a fire in an oil refinery causing the price of oil to go up and then of course, panicking there. It can be bad quarter, you know, for, for companies, it can be AI is taking jobs, whatever it is, okay? But you know, one of the most dangerous things a, you know, a single investor can do is rely on a single narrative.
[00:36:07] Social media creates echo chambers.
[00:36:10] It comes from everywhere.
[00:36:13] AI cinema analysis tools, they can scan multiple sources simultaneously and they can give you a balanced picture. Remember, everything we talk about with AI is about perspective, okay?
[00:36:27] Whether it's a CEO, whether it's a investor, whether It's a, you know, a giant firm. It's all about perspective.
[00:36:40] You have to get a balanced picture.
[00:36:45] Is the market bullish on a stock?
[00:36:48] Fine, there's nothing wrong with that.
[00:36:52] But what is the options flow look like?
[00:36:56] What are insiders doing?
[00:36:58] What does the short interest tell you? AI can aggregate all of those signals in real time.
[00:37:05] You don't have to answer some of those questions based on a gut feeling.
[00:37:10] Your job is to listen to them, to look at the data, not just one that confirms to something you already believe.
[00:37:21] You are, you know, creating a self licking ice cream cone. And you've got to take a step back. You've got to use AI to get more perspective.
[00:37:31] Now strategy four, You've got to understand the limitations, okay?
[00:37:40] And this is critical, all right, we talk about this all the time, but it's just as important, and probably more so just because of the criticality of your bank account.
[00:37:53] Large language models, the technology behind Chat, cpt, Claude, Falcon, Cohere, you name it, okay, they are pattern analyzers.
[00:38:08] They are not real time reactive systems, okay? They think, yeah, okay, that process takes seconds, not microseconds, okay? And during a flash crash, seconds are in eternity.
[00:38:26] If you're using an LLM based trading bot, understand that it cannot react to market events faster than a high frequency, you know, AI system.
[00:38:38] It just can't.
[00:38:40] It's going to be executing before your bot even finishes.
[00:38:48] And remember, don't confuse intelligence with speed.
[00:38:54] They're different assets.
[00:38:57] And especially if you take a look at this chart, what AI does well, recognition, data processing, you know, back testing, risk modeling, those sorts of things.
[00:39:11] What it does not do well is predict black swans, replace judgment, guarantee returns, operate without oversight. Things we talk about all the time.
[00:39:23] And just so you know, Black Swan is one of those once in a lifetime events. Well, it's supposed to be once in a lifetime event that just comes out of nowhere, causes, you know, pretty much devastation to a market or segment.
[00:39:42] Now strategy five, you gotta back test everything. You just have to. And I cannot stress this enough, okay? Any AI driven strategy, any algorithm, any automated approach, before you put real money behind it, you test it against historical data. Now granted it's not a perfect test, but if you look at how it performed in 2020 when the market covet, you look at how it handled the 2022 rate hikes, you look at what it would have done in October 2025 during the crypto crash.
[00:40:33] And if it doesn't track, you know, to, to those under those types of stresses, then you can't trust it with your capital.
[00:40:46] Period. End of story.
[00:40:48] Now number six, and guess what? It's one of our favorites.
[00:40:54] Be the human in the loop.
[00:41:00] Yes.
[00:41:02] The firms that are winning with AI and finance right now are not replacing humans with AI. And I'm going to say that again, going to say that again so you everybody can hear me. The firms that are winning with, with AI and finance right now are not replacing humans with AI.
[00:41:24] They are embedding AI into human decision making workflows.
[00:41:29] The hedge funds at the top, you know of their game, right? They still have portfolio managers making final allocation decisions. The AI informs them. The human is the one that decides you should operate the same way.
[00:41:45] Use AI to see more, use it to process more, use it to identify, you know, what you would have potentially missed.
[00:41:57] But the final call always has to be yours.
[00:42:01] The buck stops there, right with you.
[00:42:06] AI doesn't know your life, doesn't know your reference, doesn't know what gut feelings you get.
[00:42:16] And quite honestly, it's not, you know, the number you entered on a form.
[00:42:24] It doesn't know that you've got a kid starting college in three years or that maybe you sleep better at night with a little cash on the sideline.
[00:42:33] Doesn't have any idea about that.
[00:42:37] It just doesn't.
[00:42:40] But it still can be biased because you don't know.
[00:42:46] So I started this episode by telling you that the trading floor you imagine doesn't exist anymore.
[00:42:57] That's true.
[00:43:00] But here's what's also true.
[00:43:02] The most sophisticated AI system on the planet doesn't have any conviction.
[00:43:12] It does not have any patience.
[00:43:16] It does not have the ability to look at a market in free fall and say, well, I know who I am, I know what I own and I'm going to hold. I'm fine.
[00:43:31] That's a human superpower no machine can replicate.
[00:43:42] The financial industry wants you to believe that. You need their algorithms to compete.
[00:43:49] What you actually need is, is understanding.
[00:43:53] You need to know the terrain.
[00:43:55] You need to know where the machines are strong, where they're blind.
[00:44:00] You need to build a relationship with your financial life that is informed by intelligence, artificial and otherwise, but governed by wisdom.
[00:44:16] That's the edge.
[00:44:17] It's not speed, it's not the data.
[00:44:20] It's the wisdom. To know what the machine can't tell you that is irreplaceable.
[00:44:31] So I want to thank you for watching AI Today, this week.
[00:44:35] I'm Dr. Alan Badot and you know, as always, I want you to stay curious.
[00:44:42] I want you to stay skeptical and I want you to stay in control.
[00:44:50] Good night.
