The AI Trap: Why Buying More Tech Won’t Fix Your Broken Business

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 say to 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 in. 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 look forward to getting into it, man. Thank you. Thank you. I'm excited. Yes.

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 AI and peak performance and kind of the mindset and neuroscience that sits in the middle.

And as we talked before before in my life, 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, 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 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, 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 is it 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 asked that question, I'm hearing two very different questions and let me just start and 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, in terms of take a bunch of brain data and analyze it and he actually read what's in the brain

in the meaningful way. Can you use it to make the AI better? And 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 you, pt or clawed, kind of you know, can I use it as it is 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 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 were always in fundraising about it, right?

“And so one of my existing investors had a friend called up and said hey, you should look”

at this company, opios and think about doing an investment, right? And the guy went into chatch pt and he was like, you know, look, here's what Octios is doing. Tell me if I per scalars 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

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.”

It's Octios. I mean, so it's like, you know, 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've read down 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 chatch pt hallucinated the entire thing. It created a power quump back 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 there can be very interested in what we're doing in the year, too.

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 chatch pt making shit up completely, right? 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 fun to them. But everyone's in a wall, it's either completely idiotic, or it just makes the problem.

And so, I mean, a high level, I and everybody I know spends a lot of their time using the cloud using up an AI, and everybody I know gets value out of it, like, you know, if you're a hoder at this point, either cloud is doing all of your coding or it's doing 80% of your coding. And with that, like you go to bed at night, do you tell them what to do?

You wake up in the next morning and then like it just didn't dream them to coding. But 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, it's like when you're hiring internally, don't know what they're doing. They'll do a lot of work, but you've got to check everything, and you've got to just

sort of, you have some judgment about it. So what I would say is, yeah, AI is insane, you know, if you don't actively news AI, you're going to fall behind.

Again, in the problem is every moment 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”

speech, because it'll make stuff up, it'll widen you, you know, you know, you know, it'll think it's going to work, and I think that's the 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, but 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 code on quote, competitive analysis, and now the sudden these investment decisions are being made, and not just investment

decisions, this is, you know, 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 private cashier insurance industry a lot. That's kind of what I would say, like my home industry is, and a lot of, they call them insure texts, a lot of insure text startups.

They get, they're getting killed right now on, 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 GPT, and one, what I think is really interesting is the lack”

of awareness of cost, right?

They're using the $20 basic min version, you know, the first step up from free, and there's

no guard rails, they're not using skills, they don't have plug in, you know, 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 guard rail, and they're using this and making decisions,

be more of a lot, or just not as technologically advanced or haven't spent as a 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 the chat prompt way off the rails, if you're not 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, you know, what David just described is a very real

scenario, and I'm interested in your take on maybe how you 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, will then reach out to Grock, chat GBT, and I think I had”

a deep seek, just for like an open source model, and it essentially creates a committee, and 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 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 or 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 Google's and OpenAI's and the like are very focused on this, and there are, you know, there's, 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 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 AI's make assumptions that are just raw, you know, 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”

psychophantic issue. I mean, I guess what I would say Ryan is, as a, is a general principle, there's two things. First of all, you can't be lazy, like, you know, the bottom line is, dealing with a very young, but very powerful capability and if you take it at face value and you shouldn't be in business, it's the bottom line, but if you also become a lot of it, and you're like, you know, it's too complicated, I don't trust that I'm just doing

the old way. There are some industries where that is, got to work, but more and more and, like, month by month, you're going to get rolled over by anyone is using AIR because

it is so intense, like, powerful. So like, the bottom line is, it's just kind of, you've

got it, 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, you have to be a power user of AIR, I mean, it's”

the bottom line, it depends on your, whatever, but you, you know, where are the competitive advantages is, if you know what you're dealing with AIR, you know, it used, like, encoding they used to say whatever, you know, a good, a really good code of worth 10 or 20 times what a mediocre code is, that's now like 100 times, if 300 times it's 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 off. So, it's just kind of, kind of the

good news in the bad news is working hard being smart has always been a competitive advantage

and it's equally, if not, we'll travel to that, and we'll let our answer. No, I agree with you, would 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

been 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 trend, 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 started 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 an 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 or 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. The 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 my charger companies and then kind of the people who you've, my kind of basis, people with whatever, 20 employees. Yeah, you're doing 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 in, you know, anthropic, but you know,

I will name a company, but it's 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 AI? Because everybody's freaked out about that. And there is this sense like, if we don't get on this bandway again, we're going to lose out. And it's kind of a weird phenomenon because, you know, this was truly the days when the internet was starting as well, where

“it's incredibly easy if you want to pacify your board to go higher,”

whatever, be consulting firm or go higher, 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 start to discover, we got away, we're working, it works, this is just going to mess up stuff. 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 bandway again,

you're screwed. And, you know, I guess what I would say is, you really, you really have to be thoughtful about what you're doing. So you, 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 is, there's an interesting phenomenon

in Silicon Valley. But they talk about whether a company is AI native or not. You know, my company, by virtue when we were born, is AI native, right? So we, we live and breathe AI,

company, the bandage of a 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, I mean, it's just a really hard. I mean, even during a technological redo 10 years ago was hard, but AI is like super hard. Like, because the field is evolving really fast and it's technically difficult. So what I would say is, I agree with your friends, it is my prediction that kind of when the dust settles three years

from now and we look back, you know, a lot of companies are going to go out of business because they're not doing AI, but there's going to even, you know, there's going to be even more companies who spend the next three years 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 profound, but because of AI, you don't want to be hasty and you really want to go in with a theory of the caves and

about MIA and AI company and my god, and AI company, if I'm going to be at AI company, what does that actually mean? And you rethink the business, turn 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,

okay, I can throw a couple of 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 just got to be talking, but at the same point, like this is in the same stories of like two guys, any garage, build a billion dollar business, any year. So, you know, you will, 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 caves 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, 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 pop and they're 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 sales force, like that person's never been

fired, right? Because if you just recommend, you know, if you just take the broad things, hey, let's do, you know, it'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 sales force and everything will be fine. It seems like that mentality is being applied to AI and that just to your point doesn't work, right? Because you, because you have a thoughtful need to have a plan. So, what I have learned

through through my own dabbling playing and, 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, um, like the whether it's the CLI function or whatever you're using, like 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 I'm, you don't have to use the planning mode. I want to talk about planning, generally, 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, 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 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 it without planning. It's just like, it's so in our

look, as a result, we've, you know, we've got a competitive edge, because it's just like,

“right, we live brief 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 have seen what they've done wrong. You know, but I haven't kind of seen this 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 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-building

business, like from the ground. You know, and I think one thing which characterizes success entrepreneurs is they think one in hard and, you know, business ends up being a pretty common that's kind of thing. So, you know, independent of the tools or how do you write the problem or what agents do you use, kind of, 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, um, nothing is going to save you, right? And,

you know, so if you've got a business, you know, like, okay, I've got this business that's

held in the years old, I'm going to retool it, you got to have a theory. Like, 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 to 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, um, you know, again, I'm does a sort of random,

but it's a kind of a probably useful metaphor. And so we've done a lot of the stuff from my company

“working with financial traders, and so I've worked with a bunch of big trading firms, right?”

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

Everyone I know is 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 the less than 90 seconds from our invest. Like, they're just like, you know, I mean, Warren Buffett actually had this great line where he 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. So 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. Right? 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, I don't 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 work high

customer service for the 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 dock my customers. I'm going to do sales this way and 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,

it is, but it's so easy to get, like, messages, I'm going to tell you just one more thing. I was just

“setting the AI kind of for some months 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 year by year and kind of the number of successful apps. And what, 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've got a bad next morning. It's built with them for you. And so, everybody in the brothers are missing apps. Dollars 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, you just go to cotton, say, build me an app, you're not going to succeed. You've got to

still have something which is a good year. So to use our experience, we're also a real problem. And if you do that, yeah, sure, your costs come down. You can succeed. But it's, it's just kind of, I mean, that's the theme is, hey, I've done some more plays 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 are not have not changed. And essentially, what you're

saying in correct me from wrong here is that we can't outsource the ideation and decision-making

“to the AI that that judgment tastes, these things are still incredibly important and may become”

the most important concepts that a human actually brings to the AI is, is the judgment and

taste behind it. What, what the customer experiences, what our hook is going to be who we're serving, how we're serving them, etc. 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, a much larger question about weather 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 an ZAA conscious. And we're going to reach AGI. We 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 kind of you need them. 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 if there's plenty of situations

“where, you know, the AI is effectively just operating in its own, you know, I think I don't have any”

special wisdom or knowledge on this. So, I'm just speaking as it's 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, the theory that AI is going to replace us all is for, yeah, 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, um, um, I don't see that happening.

Yeah. That doesn't mean I'm right. But, um, 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, in the end of the truth, there have been many, many technological advances over the last 200

you know, it'd be very hard to point to a technological advance, which led to lower employment rates.

I'm with you. I'm huge, uh, Techno and AI optimist in general, while, uh, optimist for the human, uh, uh, for the future of humans and their relationship to AI, huge, huge optimists. I guess I have to say that. You could be, I guess, uh, an anti-human optimist

“and just think the AI was going to take over and that's how you're optimistic. But I guess very optimistic”

for human and the loop in humans in general. And I think a lot of the conversation around, you know, the, the, the doomer conversation around AI, uh, all of those arguments, when you boil them down, uh, they tend to misrepresent one primary piece of information in my opinion and the, uh, 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 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, just transition to where they work and how they work.

And I, in every one of these technological advancements in which someone, you know, the, uh, 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, 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've been dying to get to, which is, you kind of sit in this world of hard science, AI, and actual practical performance. And the example you said was, you can use, you know, your tool to analyze someone shooting file shots,

but at the end of the day, if they don't make more file shots than the AI doesn't do its job. So taking now let's, let's specifically talking about Optios and the work you're doing there, how do you marry and how do you think about and build towards, you know, 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 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 at a DARPA, you know, for those of you listening DARPA is an agency in the government started the 50s to basically

fund frontier research which is kind of too early for corporations and, you know, 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 warfighter. And to test the theory, they asked just a really simple question,

“which is can I use neuroscience to measure when someone's in an optimal brain state?”

And the task they started on was marksmanship because, you know, it's a military easy to get a bunch of data and everybody wanted to shoot a gun back done. All right, incidentally that was much less relevant to the military today, but back then there was an hugely interesting. So they took several hundred marksmen and they scanned their brains when they were shooting. And, you know, sure enough, they discovered that there is an optimal brain state associated with

shooting a rifle. The extra marksman were pretty good at getting into the state. Now, obviously, it's did not get there. And it was, and now you had the ability to measure

So that was actually a big deal of study. But then they went on and did something really

“interesting, they said, "In 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 Norfolk back device

have, or now these things are everywhere. But it was basically a sweat fan, and it's sensors, and it's just measuring if your brain's in the zone or not, actually, to a haptic motor that comes 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. And that's the term neurofeedback. They had novices trained with this for like total two hours over the course of a month.

And the results were just stunning. What happened is, because the rain 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 the learning have to want to be an expert.”

So it seemed like six months or a year of training time. And then they went up and they showed that work with like the mediates, and they showed it worked 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, help it's all rewarding or to help improve the performance. So you talk about the fall shock, 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 grain in the right state. You can teach yourself how to go for a free shot routine to get into that optimal state. And that will improve your free throw accuracy more quickly than just normal shooting basket. And they're had, you know,

since that time, the government spent like seven and a half billion dollars in research all around

this notion of, can you measure the brain, can you use it to improve performance? And so there's hundreds of studies that have come out where, you know, you can process information faster, and you can prove your memory and you can learn of like 250,000 feet and the law, right? And so, that's kind of the underlying science that's informing one of my company does. Okay, you with me so far. So, you know, well, 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. And because all of this stuff was done in a laboratory with 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 I data and stuff in association with tasks. So, you can basically say,

you know, was a trained profitable or not did the cyber make the shot, you know, we've got some stuff for 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 right, right, and to do it in a way where like millions of people 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 some more investor, you know, or performance or even to just tell an AI, here's the state of the human. So, here's how to interact with that. 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 bad. Okay, 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 disposative 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, and it, you know, if you think about it, it makes sense, right, your manager would you measure. So if, you know, if you're trying to learn to speak Spanish,

workload, you know, is the information getting in there, and it modifies what it's doing, and 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 could improve patting accuracy by like 30% done stuff with you know, pilots, where you can improve performance at a similar, if I'd task by, you know, somewhere between 30 and 70% and stuff with a national geospatial intelligence agency where

“you actually got a tripling of productivity. So kind of the, 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 unless you're kind of wearing a

headset or monitoring your eyes because it makes it better. But I think, and in the science is compelling enough in the problem, it's compelling enough. It's very hard for me to imagine that three years from now or five years from now. You're not going to see this kind of tool that built into all these AI agents, or at least, you know, the circumference of one's education and gaming and, you know, sports and stuff. And then we're already seeing kind of

about big movement in that and there's a lot of Silicon Valley and money, kind of supporting the thesis I just gave you. Yeah, it makes sense that I think intrinsically we all

“understand that if you train your body, you do in a deliberate way, you watch and listen for feedback”

and iterate off of that feedback towards positive performance that your body, you know, physically

starts to respond. And what I hear you saying is now with 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, et cetera, also improves our performance on a task and not just, and so in the case of like trading, right, not just

hitting a baseball or taking a file shot or a put, right? It's actually, are you saying, like, even the decision making that we're making, I'm saying, 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 if the trade's going to make money based on whether it's in a good state of mind, we did a project with a professional baseball team,

they look at someone's brain before they step into the batter's box, like 75% accuracy in predicting the outcome of a swing, just based on the brain's dead in advance. It's real and, oh yeah, no, this is, this is real published, like, not the thing. The trading thing and the baseball thing aren't published, but another, they're solid, you know, and if you, if you think about it, and the world of AI is transformational for this problem, because what's AI about AI is

about training, really large models where you've given 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, you know, that's almost crossing the street. You can do the same thing when the brain data, right? So, we've got this, you know,

there's like, we get like a hundred million data points per hour out of the brain when

the consensus, I don't know, 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 throw where you didn't, you're saying you golf ball or you did a training decision, well, over time the model is like, 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 to my 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, well, yeah, I mean, there's the, 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

did your eyes look at it, did your brain register that, and that'll be part of the film watching

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 it's like, you can create the game to make it meet, but you're, I mean, it's just going to be part of the

“AI tech stack, I think, inevitably. Do you think there will be a wearable, that you maybe isn't”

like a whole brain scan hat, and, you know, all the devices on you, well, they're coming day where say, I'm just a, I honestly just, I'm a salesman, and I have 10 sales calls today, and I pop on my necklace, contact lens, whatever, write earpiece, and it's able to help me make sure I have my mind in the right state that it needs to be in in order to be successful on that sales call or at least position myself, you know, like, that kind of practical everyday used.

Do you see that it's like a wearable that we have in getting real feedback from? Sort of,

but, I mean, here's what I think is going to happen. If you're a, you know, let's say you're a

tell a salesperson, right? So you're sitting there in front of my, I mean, you're making whatever hundred calls a day, five hundred 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 multi-mobile. So it's going to need to look at your eyes,

what's the size of your pupils, where you're looking, it's going to want your brain, it's definitely going to want sounds from the audience. You need 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 in like the AI will know in advance of a call, or you'd like to close this or not. And that'll be

“useful information that you can use to manage people and screen people entering, right?”

But if you think about the nature of it being multi-modal, you can't really do it through a wearable. You're not going to have, it's not going to be like a normal ring or a fit that we're 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. I think 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 it's going to be much better cameras. It's like 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 AI movement and work at the AI. I do think there's probably going to be sensors in the headphones. Remember, these guys are already wearing headphones, right? So what you'll do is we'll throw up

the sensors into it. Now you can read the green data and it's going to be part of the headset. My guess is everyone who makes these headphones, like this one, I was going to be putting sensors at, because it will just be, kind of, and then there'll be some sort of software platform which I hope comes from our videos, which integrated into the feeds that information as a model

“how to describe it all into the sensors. 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 be 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 that's going to be in the next whatever, two to five years. You know, and this is, this is one of the places where I think

being a lotite is going to hurt you. Embracing that type of technology, embracing understanding, like, hey, let's say Johnny's your number one salesperson. 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 meet, 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 sensor data 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

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 mass of amount of data that's going to be both at our fingertips as a whole, as well as leveraged by eye tools that can kind of synthesize it and produce outcomes. My position is, and this is what

“I'd love your, 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 sales person, 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 then 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 couple 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 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 responsibility and more and more of the

burden of success is moving to the, to the leadership layer, and it's now paramount that we have high quality thoughtful leaders versus the main line, right? Where a great main line person could

“make up for a poor leader. I think today, you, you have to have that high quality leader, or your,”

your boots on the ground, people are going to really struggle to be successful. Does that, does that make sense? Is it, is that argument, makes sense? I agree completely. The problem with 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, or people, do you just said, um, 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 to differentiator. But I just agree, it's more so, it's more so to”

that. 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. Why, and if I agree with you. I think that's a great place to finish our conversation. I couldn't agree with. I do, I do want to say, please, please keep going. 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 tell your business as well. And so today, there's a lot of limitations

And it's like what we were saying on that 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 Money Ball, I get it right and 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. You know, I mean, I know a university basketball team that did incredibly recently. And their coaches very tech forward and does a lot of things that are just not standard in basketball to, 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. He's also very skeptical, very common sense of call 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 want to pose with that is

“I think you 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 common sense of call. And I think if you can manage that balance, you know, it's the best time to be. 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 not doing my jazz. I'm like everybody asks me for a plugin like we just suck at social media. So I've got like everyone to know all 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, you know, maybe we'll send them to the website and the link is a good place. That's all right. There's not any 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 I think there is a nuance to how you are talking about this”

stuff that is, 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.