The Ryan Hanley Show - The AI Trap: Why Buying More Tech Won’t Fix Your Broken Business

Episode Date: August 7, 2026

I help founders & executives generating more than $10M in revenue find their Easy Mode. Start here: https://ryanhanley.com/subscribe Watch this episode on YouTube: https://youtube.com/ryanmhanley If you buy Salesforce to avoid fixing a broken sales process, you fail. If you buy AI to avoid fixing a broken business, you fail faster. David Bach, MD, is the founder and CEO of Optios. Optios is a neurotechnology company building the human-state layer for AI. He is a Harvard-trained neuroscientist, a physician, and an entrepreneur who understands what happens when hard science hits the reality of the market. We break down why treating AI like a magic bullet is destroying capital, and why the leaders who win will be the ones who pair aggressive adoption with disciplined skepticism. We cover the danger of outsourcing your judgment, why most enterprise AI pilots stall, and the story of an investor who made a capital decision based on a competitor deck that ChatGPT hallucinated out of thin air. We also explore the future of physiological AI. Sensors that measure your attention and brain state could train you to perform like an expert in a fraction of the time. The question is not whether your company uses AI. The question is whether the tool improves a clear process or hides a weak one. AI does not lower the standard for leadership. It raises it. You cannot outsource taste, positioning, or strategy to a machine. Connect with David Bach Website: https://optios.ai/ LinkedIn: https://www.linkedin.com/in/david-bach-md-0b81b29/ Follow Ryan Hanley Website: https://ryanhanley.com Instagram: https://instagram.com/ryan_hanley Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

It turns out chat GPT hallucinated the entire thing. If you don't actively news AI, you're going to fall behind. It's not going to replace humans. You're dealing with a very young but very powerful capability. If you can't articulate what you're doing in a way that makes sense, nothing is going to save you. The theory that AI is going to replace us all, I don't see that happening. David, I really appreciate you taking the time, man. And this is a topic that is near and dear to my heart.

I'm an armchair kind of mindset, peak performance psychology. And I love talking to guys that actually know what they're talking about and are trained and think about this stuff every day. So I appreciate your time and looking forward to getting into it, man. Thank you. Thank you. I'm excited. Yeah.

So one of the things when your people reached out and I was digging in and I got really excited to chat with you was, you know, you kind of live right now and in your career in general, but right now you live in this space between, you know, AI and P performance and kind of the mindset and neuroscience that sits in the middle. And as we talked before him, before we went live, like you have, you have the platform that you're building and the technology you're building, as well as this is what you've done for a very long time for a living as a career. You've been a consultant, a coach, and you've done this. So what I love is kind of, this isn't just you did a 201 regression analysis in an MBA class

and said, oh, there's an opportunity in neuroscience and AI. Like, this is your life. And what I kind of want to start the equation or start the equation, start the conversation with is a conversation around, like, what's real in this space? Being that you have your hands in AI, but you've also done the work offline, like, if someone's, if someone is sitting here going, you know what, I'm a little nervous to hire a coach and go all the way to hire someone, but I'm not really sure how much AI can actually help me or how real it is or what kind of real insights can I get.

like maybe we just level set the playing field for like how much like what is real today and what isn't in terms of what someone can extract from say an AI tool in general like can they trust these things can they dig in or is there still human in the loop necessary just I'll leave that kind of there and let you roll as you ask that question I'm hearing two very different questions and let me just start and I tell you what I think I heard because those questions lead to different paths. Yes. What we do at Optives

is we are building the capability to make AI aware of your physiology. And so our technology allows the AI to know, are you paying attention? What's your cognitive

workload? Are you in the zone or are you in kind of a tilt state with the theory that allowing the AI to become physiologically aware will make the AI more effective of working with you. And so one way of interpreting your question is how real is the stuff we're doing, you know, in terms of take a bunch of brain data, analyze it, and can you actually read what's in the brain in a meaningful way? Can you use it to make the AI better? but I heard a completely separate question,

which is kind of how real is AI generally, which is like forget the brain data, but like if I go on to chat GPT or Claude, can I use it as it is it useful? And that's like independent of us. And I'm happy to talk to either of those things, kind of my expertise is in the first one, but you know, I live right in the middle of the AI world,

so, you know, I'm happy to take either one or both. Why don't we start at the higher level and work our way down to your specific product and what you're doing? You know, my company, we're always in fundraising mode, right? And so one of my existing investors had a friend, called him up and said, hey, you should look at this company Optios

and think about doing an investment, right? And the guy went into ChatGPT, and he was like, you know, look, here's what Optios is doing. Tell me if Hybracillers are doing the same thing. Like, you know, give me a sense and, you know, tell me what Google is doing, right? And it gave him a PowerPoint presentation and said, okay, this is what Google is doing, you know, which Optios is doing. And the PowerPoint presentation said Google is building kind of the same thing. They're further behind, but they're kind of doing the same thing as Optios.

So he's like, you know, and I'm not interested because, you know, Google's going to eat these guys. right? And so, you know, he sent us the PowerPoint presentation. And, you know, we freaked out a little bit because we were like, we didn't know about this. And so I spent like last night doing a bunch of research. It turns out chat GPT hallucinated the entire thing. It created a powerpoint deck for this guy saying this is what Google is doing. And on very deep research, he prompted it in such a way. So it was just like, trying to please him, and it created a picture, which is not at all what Google is doing. And in fact, you know, for a variety of reasons, our belief is they're going to be very interested in what we're doing in a year or two. But it was a real, you know, it was an amazing thing, and it sort of speaks to, you know, I mean, this guy made an investment decision based on chat GPT, making shit up completely. And so it is, you know,

the problem that we're all having with AI is like when it works, it's insane, and it sounds really confident, but every once in a wall, it's either completely idiotic or it just makes stuff up completely. And so, I mean, at a high level,

I and everybody I know spends a lot of their time using Cod, using Open AI, and everybody I know gets value out. If you're a coder at this point, either Plod is doing all of your coding or it's doing 80% of your coding. And with that, like, you go to bed at night, you tell them what to do,

you wake up in the next morning and then, like, it just did three months of coding. at the same time, you've got to know it makes a lot of mistakes, and it's not, you know, so it's like a, it's like when you hire an intern and they don't know what they're doing, like they'll do a lot of work and you've got to check everything, you've got to just sort of, you have some judgment about it. So what I would say is, yeah, AI is insanely real.

if you don't actively use AI, you're going to fall behind. And the problem is every month that's getting better. And, you know, it's essentially a force multiplier. But at the same time, at least today, and I think, you know, for the foreseeable future, it's not going to replace humans. And you need to give it a really short leash.

because it'll make stuff up, it'll lie to you, it'll, you know, it'll think it's doing the right thing and it goes wrong thing. So, I mean, that's kind of my answer, but I don't think anyone could survive without it because where it is good, it is so staggeringly powerful. I completely agree.

And that story is scary. And unfortunately, I deal with a decent number of early-stage companies, both helping them get investments and I do my own angel investing. And I have seen multiple times that scenario that you just described. A company is looking for investment. They send over their thesis or their SIM or whatever they're using over to someone. They throw it into chat, GPT, run a quote unquote competitive analysis. And now all of a sudden these investment decisions are being made.

And not just investment decisions. This is all kinds of things, whether to use the tool or not. I see this all the time. We were talking before we went live. I tend to work in the property casualty insurance industry a lot. That's kind of what I would say like my home industry is. And a lot of, they call them insuretechs, a lot of insured tech startups, they get, they're getting killed right now by users who think they can either quickly code something

or just hack a solution that they have created in an enterprise tool inside their clawed or their chat, GBT. And one, what I think is really interesting. is the lack of awareness of cost, right? Like they're using the $20 basic min version, you know, the first step up from free. And there's no guardrails. They're not using skills.

They don't have plug-in. You know, so there's no sophistication to the tool. They're not even using custom projects, which may have a little more context in a little more guardrail. And they're using this and making decisions. and I find it to be, I understand why they're doing it, especially if there maybe tend to be more of a Luddite or just not as technologically advanced or haven't spent any time with it. But the scary part is they're taking it as truth. And that is crazy to me.

And even simple things like not understanding how a context window works can take your entire conversation with a chat prompt way off the rails if you're not, you know, opening up new sessions or compacting the context and that kind of stuff. And I don't mean to get too technical guys if you don't know what all these terms mean, but what David just described is a very real scenario. And I'm interested in your take on maybe how you've thought about fixing that issue. I built myself just a little application where it's actually a skill where let's say Claude gives me an answer to something. If I want, I can go to my committee

and the committee skill that I built will then reach out to Grock, chat GBT, and I think I added deep seek just for like an open source model. And it essentially creates a committee. And like, and I'll, it'll say, you know, act as a critic of this opinion and poke holes in the argument, you know, and it'll hit this committee of models. And then that tends to, to get me closer to truth. And certainly gets rid of some of the sycophanism. But I'm wondering, like, have you thought about a solution or this?

How do you make sure you're not just riffing off of data that that's been hallucinated? The industry writ large, you know, the Googles and Open AIs and the like, are very focused on this. And there are, I mean, really tens of billions of dollars going into trying to figure out how to solve this set of issues. And it's, you know, it's, you know, It's a combination problem where hallucinations happen still today, like the story I told you. But the other thing is, you know, there's just this huge issue that AIs make assumptions that are just wrong.

You know, they're, you know, and that's not to talk about the psychophantic kind of nature of it, although, you know, I think you can actually work with that with the prompts, the sycifantic issue. I mean, I guess what I would say, Ryan, is as a general principle, there's two things. First of all, you can't be lazy. Like, you know, the bottom line is you're dealing with a very young, but very powerful capability. And if you take it at face value, you shouldn't be in business,

is the bottom line. But if you also become a Luddite and you're like, you know what, it's too complicated, I don't trust it, I'm going to just do it the old way. There are some industries where that is going to work, but more and more and like month by month, you're going to get rolled over by anyone is using AI

because it is so intensely powerful. So the bottom line is it's just kind of you've got to you've got to deal with it where you're aware of its limitations and you manage these things and there's not

there's not like a silver bullet there's not like oh open this window or do this prompt you have to be a power user of of AI I mean it's the bottom line it depends on your

whatever, but you know, where the competitive advantage is, if you know what you're doing with AI, you know, it used, like in coding, they used to say whatever, you know, a good, a really good coder's worth 10 or 20 times, what a mediocre

coder is, that's now like a hundred times or 300 times or something because a really good person can train an army of agents. Now all of a sudden they've got this whole army and, you know, a mediocre one screws it. So, you know, it's just kind of kind of the good news and the bad news is working hard, being smart, is, has always been a competitive advantage. And it's equally, if not more true, today. And that's my answer.

No, no, and I agree with you. Would you say that, so I was talking to somebody the other day and so this isn't my, original idea, but I just wanted to put this by you. So essentially what this guy said was he felt like his business had missed the digital era, that he's in his late 50s. And, you know, he just was skeptical of for whatever reason. He had a very successful business. You know, his father had done it a certain way for a long time. And when he took over, he was fully invested in that model and he kept it rolling and he didn't want to be disruptive and they were making money. And he's like, he's like, look, like we made money through the kind of 2010 to 2020 digital era.

He's like, we were making money. But we didn't really invest in it. We were always kind of behind. We were always the last one to take on a tool, you know, that kind of stuff. And he's like, I'm looking at AI as a chance to say, okay, I was wrong. You know, as much as we still had a business, we still did a fine. He's like, I could have doubled or tripled what I did if I had kind of gone all in. and AI, his take was he saw AI as an opportunity to not make that mistake again.

And by going kind of quote unquote all in on AI, learning it both himself and figuring out how to, how to integrate deeply into his company, he felt like I can now say, yes, I may have missed the digital era, but I can leapfrog a lot of that by going all in and AI. Do you see that as an opportunity to almost, if someone's sitting here and feels like they may have missed the digital wave for whatever reason, that this is an opportunity for them to get back in and start pushing again, that this can leapfrog them back into a great position? There was a study done, this is now more than a year old, so, you know, in the AI world, that's ancient news. But MIT took a look at a bunch of companies who made an investment in

AI, and basically what they concluded is 95% of them lost money. on the investment. But basically, they put a bunch of money in, they built AI, and it didn't actually add value. All it did is added overhead to the companies. But 5% of them,

it was incredibly valuable for. I don't know what that number would be today if you did the study, but I will tell you, you know, there's a really, and look, I deal with much larger companies

than kind of the people who you've, my client base is people with whatever, 20 employees. Yeah, you're doing it with big enterprises. Yep, I got you. Big enterprises, you know, but the theme is the same. Right now, if you are not Microsoft or Google or, you know, Anthropic, but you know, you're, I won't name a company, but just like a big company that's not a kind of an AI native company. Right now, your board

has been screaming at you for six months about what are you going to do in a because everybody's freaked out about it. And there is this sense like if we don't get on this bandwagon, we're going to lose out. And it's kind of a weird phenomenon.

And this was true in the days when the Internet was starting as well, where it's incredibly easy if you want to pacify your board to go hire whatever, a big consulting firm or go hire a bunch of AI people and do a big project in AI. And as a general rule, kind of the way the way stuff works is you're going to wind up just wasting all your money and it's not going to do anything. So you're going to sort of discover we've got a way of working. It works.

This is just going to mess us up. and, you know, but at the same time, the boards are right, and it's true, and if you don't sort of get on the bandwagon, you're screwed. And, you know, I guess what I would say is, you really have to be thoughtful about what you're doing. So you got a company, your dad had your company, you have the company, it's working, it's making money, you got your processes, you've really got to go in very rigorously and try to figure out what allows us to kind of move to the next level. And there's an interesting phenomenon in Silicon Valley

where they talk about whether a company is AI native or not. My company, by virtue, when we were born, is AI native, right? So we live and breathe AI. We have some of the better AI scientists in the world. We have this insane competitive advantage of our company that's like five years older where they did it one way

and now they've got to kind of retool themselves. And it's just really hard. I mean, even doing a technological redo 10 years ago was hard. but AI is like super hard because the field is evolving really fast and it's technically difficult. So what I would say is I agree with your friend

it is my prediction that kind of when the dust settles three years from now and we look back a lot of companies are going to go out of business because they're not doing AI but there's going to be even more companies who have spent the next three years is investing in AI and get nothing of value out of it.

So it's like really, it's kind of like the world has shifted so profoundly because of AI. You don't want to be hasty and you really want to go in with a theory of the case about am I an AI company? Am I not an AI company? If I'm going to be an AI company, what does that actually mean? And you rethink the business from the ground up as an AI native. company. But I think if you just like plop AI on top of it and like, okay, I'm going to replace customer service with an AI agent, right? Or, you know, I mean, remember, the other thing is, you know,

for people listening to your podcast, they don't have 20,000 employees, right? And they don't have, you know, okay, I can throw a couple billion dollars of this. You know, and it is an unfortunate, it is a game where, you know, the stakes are pretty high. And so I think, you know, you just got to be talked about. But at the same time, there's these insane stories of like two guys in a garage build a billion dollar business in a year.

So, you know, doable. If you're smart and if you're thoughtful and if you do it, right. So again, it's just like, you don't want to be impulsive. You really want to have a theory of the case and say, this is how I'm going to use AI. This is why I believe it's going to work.

This is why it's doable. Here's a plan and you don't spend a dollar until that plan is super clear. Yeah. So, okay, two things I want to say there. One, and I'm going to fix the way that I ask them because it leads into the next question. So those cases that you just broke down where large enterprises are implementing AI and putting millions, if not tens of millions of dollars behind these projects and then, you know, six months later popping their head up and going, hey, we're not really seeing any improvement. That to me is like the old adage of, like the middle manager who says let's buy Salesforce, like that person's never been fired, right? Because if you just recommend, you know, if you just take the broad thing and say,

hey, let's do, you know what's going to fix our sales process? It's not our scripts or our leads or our flow or even my leadership because we don't have sales force. And all we have to do is get Salesforce and everything will be fine. It seems like like that mentality is being applied to AI and that just to your point doesn't work, right? Because you have to be thoughtful. we need to have a plan.

So what I have learned through my own dabbling, playing, and I've built a few things as well. Most of them are just personal tools that I use myself, is the planning process. And in many of the, whether it's the CLI function or whatever you're using, there's a planning mode to these tools now and almost no one that I come in contact with uses it.

Yet when you use this, And you don't have to use the planning mode. I want to talk about planning in general. But like when you use that planning mode, what you get as an output is exponentially better. It's not even close. You can't even compare the results to a general prompt and planning first. So with that said,

how do you, you know, kind of maybe taking it into your own work, your own business, like in this idea of how important planning is. Like, how are you thinking about planning? What does planning actually mean before you deliver it to an AI to execute on a task?

Like, do you have thoughts, ideas, or a system around creating a plan that then you can give or just some high-level ideas? Because, David, I don't think anybody is doing this, or at least most of the users are not planning before they use these tools. I'm kind of a bad person to ask for this because, again, we're an AI-native company. Yeah, yeah. And so, like, I don't think we would know how to do. do it without planning. It's just like, it's so in our blood,

you know, because we're an AI company, right? So, and everybody is there, and so, and, you know, look, as a result, we've, you know, we've got a competitive edge.

Because it's just like, right, we live, breathe, and speak the language. I think you know, so again, like, I'm not a great person because I haven't even dealt with, I've dealt with big companies

who are idiots in terms of what they're doing around AI and I've seen what they've done wrong. You know, but I haven't kind of seen the specific use case. But what I would say is this, there's nothing that replaces common sense and kind of strategic thinking. And so I've had the great privilege in my life of knowing a bunch of people who've made a bunch of money building businesses,

right? So I'm an entrepreneur, you know, I've got a ton of friends, and I've got lots of friends who've built, you know, multi-billion dollar businesses, like from the ground. You know, and I think one thing which characterizes successful entrepreneurs is they think long and hard, and, you know, business ends up being a pretty common sense kind of thing. So, you know, independent of the tools or how do you write the prompt or what agents do you use, if you can't articulate what you're doing in a way that makes sense, kind of using a pencil and a piece of paper, nothing is going to save you. Right?

And, you know, so if you've got a business and you're like, okay, I've got this business. It's 100 years old. I'm going to retool it. You got to have a theory. And, you know, it's like, okay, so we have these business processes. Am I going to use AI to fix, you know, let's say I've got 18 steps in my business process. Am I going to use AI to say these three steps are going to get more efficient? Or am I going to say, I'm throwing out all 18 steps and we're starting from scratch, right?

And either way, it's like you've got to be able to tell a story. So I, you know, it's very interesting. you know, again, I'm just sort of random, but it's a kind of a probably useful metaphor. And so we've done a lot of stuff in my company working with financial traders, and I've worked with a bunch of big trading firms, right? And, you know, nowadays with AI and machine learning,

you know, a lot of trading comes from, you build a computer system, whether it's an AI or machine learning model, you build a really complicated black box model, and then you throw it into the market and have a trade, right? And as a result of that, I also know a bunch of people who invest

in these trading terms, right? And, you know, there's some very famous firms. Everyone I know who's made money investing in trading firms has told me the same thing, which is like, if they can't explain their strategy in a way I understand it in like less

than 90 seconds, a long invest. Like, they're just like, you know, I mean, Warren Buffett actually had this great line where he's said, beware of geeks bearing formulas. Right? I mean, it's just kind of,

and the reason I say that is, it all comes down to just like fucking common sense. It's like, if you want to use AI, you better explain what you're doing and it's got to make sense. And if you can't explain it and if it doesn't make sense, you're going to fail. You have to have a theory of the case, you know, and the minute you go into AI and you have it planning,

you're effectively doing that deeds with formulas. Right now you're trusting the AI to understand your business and how to make your business more effective. And it's like zero chance that'll work. Zero, right? But if you're like, look, I'm going to replace customer service with AI and this is why. And here's how I'm going to do it. And these are the tools I'm going to use.

And it currently costs me this and I'm going to drop my cost of this. I'm going to do sales this way. I'm going to enable it, and now, you know, my return on investment is going to go from here to here, and this is why. That's the kind of like, okay, makes sense. And so, I mean, I think, you know, again, kind of so much of business to be successful is just kind of stupid simple. And the more AI comes along, the more important that becomes.

Because it's just so easy to get caught in, you know, oh, my God, out this is incredible. And it just, it is, but it's so easy to get, like, misuse. I'm going to tell you just one more thing. I was just at an AI conference a month ago,

and there was a slide that was put up by a guy from Anthropic. And it was really interesting. It showed the number of new apps that have been released year by year, and then it showed, like, app sales near by year,

and kind of the number of successful apps. And what the slide showed, and it was really staggering, is the number of new apps released, like basically per month, has been growing exponentially. Because now you can go to Cloud and you say, okay, I want to build an app, you go to bed, the next morning it's worth a for that for you. And so everybody in their brothers are releasing apps.

Dollar sales for apps, the number of apps that have become successful hasn't changed. So what you're seeing is you're seeing a world where it's just harder to build a successful app than it was a year ago. But kind of, you know, the message is if you just go to Cloud and say, build me an app, you're not going to succeed.

You've got to still have something which is a good years of user experience, which is also a real problem. And if you do that, yeah, sure, your costs come down and you can succeed. But it's just kind of, I mean, that's the theme is AI doesn't replace,

business judgment or common sense. So what I heard you say basically, you know, is the core tenants, the core ideas, the core structural drivers of what makes a business successful or not have not changed. And essentially what you're saying, and correct me if I'm wrong here, is that we can't outsource the ideation and decision making to the AI, that judgment, taste, these things are still incredibly important and may become the most important concepts that a human actually brings to the AI is the judgment and taste behind it, what the customer experience is, what our hook is going to be, who we're serving, how we're serving them, et cetera. And if we're just vaguely throwing these

things into an AI and hoping somehow it's going to like make these decisions for us, that is just an absolute recipe for failure. I would concur with that. And it does kind of speak to a much larger question about whether and when you're going to see the rise of autonomous AI and I mean, of course you're going to see it. But there's a huge debate in the industry.

You know, there's a lot of debates. Like people fight about everything there. It's like he's AI conscious and they're going to reach AGI if we've already reached it, right? But one of the things, you know, people are really trying to figure out is, are we building an AI that's autonomous, or, you know, is the model for the future more AI teaming with humans where, you know, there's always going to be a person in the loop and you need that? And it's actually, I mean, that particular question is worth,

hundreds of billions of dollars to kind of figure out the answer to. And, you know, there's a, you know, there are people with very strong views on both sides of that equation. And, of course, obviously, you're already seeing Autonomous AI, right? So, you know, you're seeing it with missiles and you're seeing it with cars and there's, there's plenty of situations where, you know, the AI is effectively just operating on its own. you know, I think I don't have any special

wisdom or knowledge on this. So I'm just speaking as like a guy, but based on my experience, based on what I know about neuroscience, based on what I've seen, I think that the theory that AI is going to replace us all is

I'm very skeptical about that. You know, the whole five years from now, we're going to wipe out 70% of all the jobs and everyone's going to be unemployed and working for the AI overlord. I mean, I just, I don't see that happening. Yeah.

That doesn't mean I'm right. But, you know, as of today, AI has added jobs to the economy. And sure, it's replaced a few things here and there. But as of today, and, you know, the truth of, there have been many, many technological advances over the last 200 years

where every time it happens, people are like it's going to eliminate people. And so far, it would be very hard to point to a technological advance, which led to lower employment rates. I'm with you. I'm huge techno and AI optimist in general, while optimist for the future of humans and their relationship to AI, huge optimist. I guess I have to say that.

You could be, I guess, an anti-human optimist and just think, yeah, I was going to take over and that's how you're optimistic. But I guess very optimistic for human in the loop and humans in general. And I think a lot of the conversation around, you know, the dumer conversation around AI, all of those arguments when you boil them down,

they tend to misrepresent one primary piece of information, in my opinion. and the kind of, I guess, analogy that I would make is, it would be like saying the Industrial Revolution wiped out all the farmers without telling you that all of those farmers became factory workers. They didn't lose jobs.

They still had jobs. The job just wasn't the same. So yes, we didn't need as many farmers because we had tools and machinery to help them do things that used to take humans. But whether it was running the, those tools or moving into the factories that came along with that technological innovation, those people were not dying in the farm fields because they didn't have jobs and couldn't

pay for things. They just transitioned to where they work and how they work. And I, in every one of these technological advancements in which someone, you know, the, in this, most of the time, it's, it's the incumbents with territory to lose who make these arguments. They never reference where the jobs went. They just reference the jobs that were lost as if that happened on an island. And that's the part that I find maybe not purposely disingenuous, but certainly something that we always have to consider when we read those arguments. So I'm with you. I want to transition to Optios and you said something in the green room that I have been

dying to get to, which is you kind of sit in this world of hard science, AI, an actual practical performance. And the example you said was you can use your tool to analyze someone shooting file shots, but at the end of the day, if they don't make more file shots, then the AI doesn't do its job. So taking now, let's specifically talking about. about Optios and the work you're doing there, how do you marry and how do you think about and build towards all of this, you know,

like you said, every day there's a new model coming, a new idea, a new way to get data out, a new way to structure speed, okay, with, like, what your clients want at the end is a practical outcome and improvement. Like, how do you marry those two things and how do you make sure you're getting them? Because I think to something you said

in the very beginning, there are a lot of people today that are implementing AI, using AI, building AI, without any real idea of what the practical outcome should be or even tracking what the practical outcome is? I think a good starting point for that question is to tell you about a research experiment that was done 15 years ago out of DARPA. For those of you listening,

DARPA is an agency in the government, starting in the 50s, to basically fund frontier research which is kind of too early for corporations and it was built out of the defense industry and a lot of the most important inventions that have ever been made in the U.S. came out of DARPA funding.

So 15 years ago, DARPA had a theory that you could use neuroscience to improve a war fighter. And to test the theory, they asked just a really simple question, which is, can I use neuroscience, is to measure when someone's in an optimal brain state.

And the task they started on was marksmanship, because it's the military easy to get a bunch of data. And everybody wanted to shoot a gun back then. Incidentally, that was much less relevant to the military today. But back then, that was hugely interesting. So they took several hundred marksmen, and they scanned their brains when they were shooting. And, you know, sure enough, they disres.

discovered that there is an optimal brain state associated with shooting a rifle. The extric marksmen were pretty good at getting into the state. Novices did not get there. And now you had the ability to measure what people call, you know, the flow state or the zone state with regard to marksmanship. So that was actually a big deal with study. But then they went on and did something really interesting. They said, and now that we can measure the zone,

can we use technology to train it to accelerate learning? So they invented, you know, arguably the first nor feedback device ever. Now these things are everywhere, but it was basically a sweatband. It had sensors and it's just measuring if your brain's in the zone or not, attach you to a haptic motor that clips out of the collar. So the way it worked was if you're not in the zone, it's vibrating on your neck, and then as you get into the zone, the vibration goes away. That's the term neurofeedback.

They had novices train with this for like total two hours over the course of a month, and the results were just stunning. What happened is because the brain is plastic, they rewired their brain over the month. They learned to access that expert state very quickly, and with that, these novices moved 80% of the way up to learning curve to one of the big experts. So it seemed like six months or a year of training time. And then they went out and they showed that work with our intermediates and they showed to work with experts.

And so, you know, kind of the core thing that came out of that was this idea, if you can measure things in the brain that are relevant to performance, you can exploit that information to help accelerate learning or to help improve the performance. So you talk about the fall shot, and you can imagine the same thing. if you're trying to learn to shoot a free throw, and if you can get metrics about, is my body moving in the right way, is my brain in the right state,

you can teach yourself how to go through a pre-shot routine to get into that optimal state, and that will improve your free-throw accuracy more quickly than just normal shooting in basket. And there had, you know, since that time, the government spent like $7.5 billion during research all around this notion of, can you measure the brain? Can you use it to improve performance? And there's hundreds of

studies that have come out where you can process information faster and you can improve your memory and you can learn of like 250 times of speed and the law. And so that's kind of the underlying science that's informing what my company does. Okay, you're with me so far? So, you know, what we are doing is we're focusing on two problems. Problem one is to kind of get really good metrics physiologically that you can use in the real world. Because all this DARPA stuff was done in a laboratory, like graduate students or like snipers or whatever, but, you know, it hasn't been deployed. And so we've got this massive database of brain data and eye data and stuff in association with tasks.

So you can basically say, you know, was it trade profitable or not? Did the sniper make the shot? You know, we've gotten stuff with basketball players and football players and traders and pilots and the like. And it's all about kind of building an AI kind of model to say, now I can do real-time measurement in the brain. Right. And to do it in a way where like millions of people can. could use it. And then the second thing is to actually build a closed loop system where you actually use the data to have an impact on making someone learn fast or, you know, or improve performance,

or even to just tell an AI, here's the state of the human, so here's how to interact with the better. But some kind of human in the loop system where we're kind of really providing that human state later or that physiology data to make the AI better. Okay? Makes sense. So you're with me so far. Right. And, you know, so I guess what I would say is, I think the evidence is beyond dispositive that this can make people better. I mean, it's just kind of like, if you give someone information about their brain state or you give an AI information about the brain state, it will improve performance, 20%, 30%, 300%, you know, depending on the use case.

But like every time we've tried this, every time somebody else has tried, it kind of works. Right. So, and it, you know, if you think about it, it makes sense, right? You manage what you measure. So if, you know, if you're trying to learn to speak Spanish,

and you've got an AI agent, and now that agent knows, are you paying attention? What's your car? cognitive workload, you know, is the information getting in there and it modifies what it's doing, it'll double your learning speed. Like, it's just going to happen. And, you know, we've done stuff in golf where we showed you can improve patting accuracy by like 30% and stuff with, you know, pilots where you can improve performance at a simulated flight test by, you know,

somewhere between 30 and 70% and stuff with the National Geospatial Intelligence Agency where you actually got a tripling of productivity. So, kind of the science, I think, is very real. To say something is real and works in a lab is super different than you got 10 million users and they're using it and now you'll never learn Spanish and last year kind of wearing a headset or monitor in your eyes because it gets better. But I think kind of the science is compelling enough and the problem set is compelling enough. It's very hard for me to imagine the three years from now or five.

years from now, you're not going to see this kind of toolkit built into all these AI agents, or at least, you know, certainly the ones for education and gaming and, you know, sports and stuff. And then we're already seeing kind of a big movement in that, and there's a lot of Silicon Valley money kind of supporting the thesis I just gave it. Yeah, it makes sense that I think intrinsically we all understand that if you train your body, you do it in a deliberate way, you watch and listen for feedback and iterate off of that feedback towards positive performance that your body physically starts to respond. And what I hear you saying is now with AI and the ability to scan the brain, we can do the same exact thing with our brain.

We can understand the mechanisms, the states, the processes that need to happen in order to improve our functionality from not just a physical perspective, but how our our mental state, our mindset, our focus, etc., also improves our performance on a task. And not just, so in the case of like trading, right, not just hitting a baseball or taking a foul shot or a put, right? It's actually, are you saying like even the decision making that we're making on, say, like a trading floor if we're, you know, trading stocks or something? We've done three studies where we showed if you take a day trader and you put a headset on them, you can predict in advance of the trade's going to make money based on whether he's in a

good state of mind. We did a project with a professional baseball team. You look at someone's brain before they step into the batters box like 75% accuracy in predicting the outcome of a swing just based on their brain state in advance. Like it's real and oh yeah, no, this is real published like not the trading thing and the baseball thing aren't published, but no, they're solid. You know, and if you If you think about it, kind of the world of AI is transformational for this problem.

Because what's AI about? AI is about training really large models where you give it a massive amount of data that's too big for a human to comprehend. And you say, build a model, right? So, you know, now you've got self-driving cars. those were trained with just gobs of information where now it's like, okay, that's a stop sign, that's a puddle, that's another car,

that's someone crossing the street. You can do the same thing with the brain data, right? So we've got this, you know, we get like 100 million data points per hour out of the brain when we put sensors on and we've got data from the heart and from the eyes. You know, you take that and you build a large enough data set and you're like, this is what it looks like when you made a free thrower, you didn't,

or you sank a golf ball, or you did a training decision. Well, over time, the model is like, oh, okay, now I know what the brain looks like when you do well or poorly. And what's interesting is, because it's AI, when you give it information about basketball, that informs how it looks at a trader, and when you give it information from a trader, that informs how it works with golf.

It's all about large data sets, right, and diverse data sets. So, yeah, I mean, it's real. I mean, it is, the results are staggering, and, you know, I think it feels utterly inevitable that it's just going to be part of the AI ecosystem, that, you know, if you're a football player and you're watching film, the AI is going to track your eyes and track your brain movement, and it's going to be like,

hey, are you paying attention? Hey, you know, you were supposed to look at this coverage. Did your eyes look at it? Did your brain register that? And that'll be part of the film watching experience. It's like, you know, it's just like one out of eight million examples.

You know, if you're playing a video game, the developers are going to want to know what your brain is so that they can create the game to make it meet what you're, I mean, it's just going to be part of, to be AI tech stack, I think, inevitably. Do you think there will be a wearable that maybe isn't like a whole brain scan hat and all the devices on you? Will there become a day where, say, I'm just, I want to say just, I'm a salesman,

and I have 10 sales calls today, and I pop on my necklace, contact lens, whatever, right, earpiece and it's able to help me make sure I have my mind in the right state that it needs to be in order to be successful on that sales call or at least position myself. You know, like that kind of practical everyday use. Do you see that as like a wearable that we have and getting real feedback from? Sort of. I mean, here's what I think is going to happen. If you're a, you know, let's say you're a tele-salesperson, right?

So you're sitting there in front of a monitor and you're making whatever, 100 calls a day. 500 calls a day, whatever. There's going to be a system that tracks your physiology in association with those calls. And it's going to be built on top of data from tens of thousands of salespeople. So it's like, this is good, this is bad. Now that is going to need to be multimodal. So it's going to need to look at your eyes.

What's the size of your cupels? Where are you looking? It's going to want your brain. It's definitely going to want sound from the audio. You can get a lot of information. So you're going to use AI to decode the audio, not only what were the words, but what's the intonation. And you've got to realize once you get a large enough data set,

the AI will be insane. The AI will know in advance of a call, are you likely to close this or not. And that'll be useful information that you can use to manage people and screen people and train them, right? but if you think about the nature of it being multimodal, you can't really do it through a wearable. You're not going to have,

it's not going to be like a whirling or a Fitbit where now you wear it. I think it's going to be integrated into the overall system through a number of sensors which are interchangeable. So I don't think you're looking at a hardware solution. you're looking at a software solution coupled with, you know, a commoditized set of hardware devices, right? And so, you know, you already have data from microphones, so you're going to be able to pull that.

I think there's going to be much better cameras because right now you can't pick that much up. So you're going to, I mean, there'll be much better cameras where you can track the eye movement and look at the eye. I do think there's probably going to be sensors in the headphones. Remember, these guys are already wearing headphones. So what you'll do is we'll throw a couple sensors into it. Now you can read the brain data.

And it's going to be part of the headset. My guess is everyone who makes these headphones, five years from now is going to be putting sensors in because it'll just be going to. And then there'll be some sort of software platform, which I hope comes from Opios, which integrates it and then feeds that information as a model contact protocol into the sexes.

I think that's almost certainly where the future is, headed. You know, what's going to be interesting, which is the bigger issue, is how many of those salespeople are going to have been replaced by AI. Right?

That's a bigger question. But for the people who are still on the phone, I don't see any way that's not happening. And it's going to be in the next whatever, two to five years. You know, and this is one of the places where I think being a Luddite is going to hurt you.

Embracing that type of technology, embracing understanding, like, hey, let's say Johnny's your number one salesperson, and he shows up in the morning, and every sensor attached to him is signaling that he's stressed out, overwork, something's on his brain, he's not in a great place.

You know, you can cut him off from making maybe his first 25 sales calls and maybe sit him down or just tell him to take a break or have a meeting, you know, something to help him recalibrate before he wastes the first three hours of his day banging on calls that are never going to be successful

because your censored dad is telling you he's going to be short on the phone because he's just, you know, didn't get enough sleep or whatever's going on. You know, you're pulling that. And the ability to manage in that way, you know, one of the things I think is really interesting, and this is where I'd like to close out our conversation today,

is just we've talked a lot about planning, talked about feedback, you know, the massive of, amount of data that's going to be both at our fingertips as a whole as well as leveraged by AI tools that can kind of synthesize it and produce outcomes. My position is, and this is what I'd love your take on, is I think AI moves more of the burden of success off of the mainline production, producer of the value, say a salesperson, a customer service person and puts more of the burden on leadership today.

And the reason I say that, and I'll finish up this idea and that I'm very interested in your take, is that because I now have insights into, let's say it's two years from now, Johnny's mindset when he first shows up at work, and I can kind of jump in and sit him down or maybe just grab a cup of coffee with him for 10 minutes and try to help him reset his brain and get him into that right state or, you know, grab a customer service person who's maybe had two or three really tough calls in a row and, you know, whatever, whatever. I believe this, you can't check out as a leader anymore, right? There's no excuse in either terms of delay of data, lack of data, lack of insights,

et cetera, lack of ability to train. Like, it feels like more and more and more of the burden of success is moving to the leadership layer. And it's now paramount that we have high quality, thought. full leaders versus the main line, right? Where a great mainline person could make up for a poor leader. I think today you have to have that high quality leader or your boots on the ground people are going to really struggle to be successful.

Does that make sense? Does that argument make sense? I agree completely. I mean, the problem my having is I don't know what to say other than I agree. I think your analysis is correct. So if I say anything beyond this, all I'm going to do is just repeat what you just said.

Well, that's okay. That's okay. I mean, it goes back to what I said before about the difference between a really good coder and a good coder has been magnified by AI. that's true everywhere, right? I mean, you can now do so much more with so much less because of AI. And what that means is leadership and strategy become more of a differentiator of businesses

than they were five years ago. And it's always been the key differentiator. But I just, I agree, it's more so, it's more so today. And, you know, look, I just think you're going to see more and more division between winners and losers than ever before. Because if you use AI correctly, you're going to be able to eat the lunch of others. And the speed of development, like, you know, things that used to take 10 years in business now take six months. And so you have to be smart and all the rules just keep changing. I mean, it's just kind of if you're good and you're hardworking and you're smart,

you can make much more money much more quickly. And if you're not, you're more likely to get wiped out than ever before. I kind of agree with you. I think that's a great place to finish our conversation. I couldn't agree with you anymore. I do want to say. Yeah, please. Please keep gone.

I want to go back to the thing I said at the beginning. So let's talk about this guy who runs the sales center. And I just want to kind of temper what I just said. I think if you're putting your head in the sand, you're not investing in technology, you're going to die. But I think if you approach it, you know, like with the energy of a five-year-old kid

who just walked into a candy store, you're going to probably kill your business as well. And so today, I, you know, there's a lot of limitations to AI, right? They just are, and you, in many ways, the old ways are still better than the new ways.

And it's like you, what we were saying about sales force, right? And so it's not a Band-Aid, it's not a silver bullet. And so I think it's important to be very aggressive with technology, but also very conservative and skeptical. And so, you know, it's interesting. I do a lot of stuff in sports, right? And sports is, you know, it's really,

famous for having kind of older coaches who believe they know what they're doing and resist change, right? And if you remember like the movie Moneyball, it's all about this, like we have this new technology, it's going to change everything. And you remember, it's all about like this whole guard,

you know. But the people who are successful, like the great coaches today, embody what I'm talking about. They look really hard at technology. I mean, I know a university basketball team that did incredibly well recently, and their coaches very tech forward

and does a lot of things that are just not standard in basketball. You know, using force plates and how high someone jumps as a screening tool, to decide who you're going to put into the game kind of thing. And it's got 30 things like that. You know, cameras everywhere using AI and so on. But like what characterizes this guy is, he's very tech forward, but he's also very skeptical, very common-sensical,

and is really willing to call bullshit if he's not convinced and it's giving him an edge. So the reason I want, you know, the reason I wanted to close with that is I think you have, have to keep both things in your mind. You do have to be very aggressive. You know, it's idiotic to pretend AI is not going to change the world,

but you also don't want to be impulsive and you never want to think the technology is going to solve the problem that you have to deal with in just like you got to think hard and you got to be commonsensical. And I think if you can manage that balance, you know, it's the best time to be an or ever in the history of the universe.

I love that. And I'm actually glad you came back around. I think that really, I think that is one of the more important ideas that we have discussed. And I'm very glad that you did that. I know my audience is going to want to go deeper in your world. Where can they learn more? And is there any socials that they can follow and kind of hear your voice and what you're trying to do?

I'm now doing podcasts. I'm like, everybody asks me for a plug in. Like, we just suck at social media. So I've got, like, every once in a while,

we post things on LinkedIn, but I like, you know, basically, it's not easy to follow us. We don't have, like, a newsletter or an Instagram

or anything like that. So, um, you know, maybe, maybe, well, we'll send them to the website and the LinkedIn is a good place to start.

There's not even any social links on the website. So awesome. But thank you so much. It was a great pleasure talking with you. Yeah, no, and I love your perspective. I really do. I think there is a nuance to how you are talking about this stuff that that is the vein. And I think it's the exact right way to be thinking about it.

I'm so glad that you shared it. Appreciate you. You know, anytime you want to come back on, I would love to have you on because I love this topic. And I feel like we just started scratching the surface. So I appreciate you, David. Thank you so much. Thank you so much.