In this episode of The AmpIntel Show, Zach Hammer and Charlie Madison dive into the question: Is AI overhyped? They explore where the technology delivers on its promises and where the reality falls short of the marketing hype. Join us as we discuss how to leverage AI effectively, enhance productivity, and avoid common pitfalls. Whether you’re skeptical of AI or eager to adopt it, this episode is packed with insights to help you navigate the evolving landscape of artificial intelligence.
Listen now to uncover the truth about AI!
What do you think about this topic today?
Charlie Madison: There better be steak, not just sizzle. That's what I'm saying.
Zach Hammer: I could go for a good steak. I can always go for a good steak. Shoot, I'll take steak. I'll take ribs. Ribs would be good. There's lots of
Charlie Madison: I had a good Tomahawk bison steak this weekend at a Jiu Jitsu tournament. Like, How manly is that?
Zach Hammer: Man, I don't know. Like smoking a cigar and maybe, like, I don't know. I don't know what else you throw into the mix, but, Yeah, because I mean you had punching people. Well, Jitsu, you may not have been doing a bunch of punching, I guess, but grappling people.
Charlie Madison: Did have someone get knocked out.
Zach Hammer: there you go.
a video recently that talked [:And so maybe let's lay that out. let's lay out some of what we're seeing, cause you and I both work a lot with AI in our day to day work. What we're seeing in terms of like the arguments that, when credence to this, that maybe it is overhyped, maybe that we've been sold a bill of goods? right. And really for me, I know probably one of the biggest pieces of evidence is what the companies announce versus what actually even comes out. And then also what is announced and demonstrated versus when you actually go into the tool, what it feels like to try and get close to that result.
A great example of this is Sora from OpenAI. That one was launched or what was talked about at this point over a year ago, I believe. I think they might have just launched. I just saw a video of somebody talking about it so maybe maybe they're in the process of launching right now. But
arlie Madison: Which is four [:Zach Hammer: Yeah, yeah, like, they talked about it. It was really cool. It's a, text to video generator. That was one of the first things that we saw examples of having you know, object permanence where like, if my hand goes behind my hand where you can't see it and it comes back out the other side that like the AI understood that. And it wasn't like my hand comes out the other side and becomes a banana. That sort of idea. It was one of the a system that was cool for that. It was announced and it's been like over a year and some people got to play with it, but most of us in the real world didn't. And so you see a lot of that, you see a lot of you know, people talking about these cool, impressive features.
ked like this. They put in a [:What about for you? What are some of the things where you've seen before we got on this call, you mentioned that there's times where I feel like AI is actually getting dumber. What are some things that you're seeing on that front?
o update the code. And it is [:Zach Hammer: Right. Yeah And you know, really I think, like what we're left with here is we're in an interesting zone where there are some aspects of this that are maybe a bit unprecedented. Yeah. Where we might be on the cusp of some of what's being hyped actually being able to happen. right?
an end user wants. right? It [:And so there is this aspect of what gets somebody to invest and the hype that gets somebody to invest doesn't always align with what you're actually seeing delivered. And really, I think there's a few ways that this could be going. One is that in some cases, I think we might even be seeing that because that's where the focus and incentive is, it's causing problems to surface in the product itself that may not be like we've seen. Some of the things feel like they're getting worse because they're not building with that in mind. That's not where the incentive lies.
In some cases, it might be that it was just never there. Some of the things that are being touted and promised, it's more they're selling on future vision rather than what they've actually been able to achieve. And so we're seeing some of that too.
his? Is it all hype with, no [:It's not all smoke and mirrors. There actually is, right now, present, still, a great deal of powerful technology, powerful stuff that can really change your business. But you have to have a realistic view of what that looks like and how that comes together. does that sound right?
Does that sound like what you've seen as well?
stic. And so like I've found [:Zach Hammer: Right, yeah. And so that's one of the skill sets that's definitely like right now in this present world, one of the key ideas is be skeptical.
Be skeptical not only of what you hear the company's promising, but also of what you hear the AI saying. And so there's a couple of ways to navigate that. If you aren't familiar with something, know that you really can't expect it to be reliable, and so you will either have to also research it, or you will have to use a tool like perplexity or ChatGPT with search in order to be able to look at the sources and see how it's arriving at its answers or conclusions, to see if you agree, to see if you align with it.
hen you talk about something [:But it's still useful and worthwhile to be directed toward those things, to gain a better understanding. Use it as an opportunity in these areas where you are interested, where you are curious to gain a better understanding, to learn more about what's real and what's not real, to, have it reveal your blind spots, while also having you get a better understanding over over, the subject matter at hand. Note that, right? There's value in that. This thing that actually feels like a problem may actually be part of what helps you, as long as you're expecting it. Cause I, I've found that. I've been, like, I ask a question and I'm like, is that real? That doesn't feel real. Is that actually the case? And then I go in and I read the article and I'm like, Oh, I get what you're saying. I get why you said this, but it's wrong. Or here's what's off about it. And now I know better how to ask the question or how to think about it, or what really is working or not.
And [:On the other end, just like you said, you know, if you want to use it as a tool to help you step outside of your own blind spots, that could be useful as like a research partner, to research. But don't trust what it says, it sparks the trailhead. It gets you started on the journey that you go and you actually read the source material. You actually research the thing to gain a better understanding and know that you're going to have to, if you're not an expert in that thing.
hat I know that somebody who [:They'd be able to get done what I did more effectively, just because they understand how these different pieces mix together. Whereas, I'm almost having to like back into an understanding, where it's like, I don't know what I'm doing. Make this functionality do this thing for me. And then it has to try two to three times to use a function that doesn't exist and be like, I keep getting this error. What's this issue? For it to finally be like, Oh yeah, this tool doesn't use that thing. And I'm like, Oh, okay. So now when it comes up again, I'm like, that doesn't exist in here. Do it this way.
But so like, I actually gained that expertise through that process. But it takes time. I have to know that I'm going into that to learn something. I have to know that I'm going into that in that way. Whereas somebody like you, you could potentially leverage it and get to that end result quicker, but you'll still see what it's done.
Does that make sense?
Charlie Madison: Yeah. a hundred percent. 100%. And you know what, what I've found, I think, it's a skill on how to use AI.
Zach Hammer: Right.[:Charlie Madison: And the cool thing about programming is the machine tells you if it works or not, like you get an error code. The problem with other domains, there's no instant feedback. You know, It's like the lawyer that was arguing against using AI in uh, I think in, court, and his paper used AI and had fake court cases in it.
Zach Hammer: Right.
Charlie Madison: They printed out so much material. He didn't, you know, like You can't have AI fact check itself like well, as much as, you gotta have some outside validation.
And so with programming, it's the machine doesn't do what it's supposed to do with the other stuff, like when I'm using it to negotiate or something like that. It's my gut feel, in my experience.
h my real world experiences, [:But the other step is, there is that real world feedback. If I ask ChatGPT for a well structured Facebook ad that's gonna drive, clicks, or signups or whatever, the ad either works or it doesn't. I either get the clicks at a cost that makes sense or I don't.
And so long term, you might ask the thing to give it to you in one way, but really, you validate what works or not through your real world experience, seeing what actually happens. And you either do that through doing the work and getting there, or you can leverage other people who have already validated that to say, I've seen what works and so I'm going to guide this AI system into what's already working.
That general principle of being skeptical, You have to, if it's information, you got to validate that it actually makes sense. If it's, this is the right way to do it, you gotta see if it actually works in the real world. And do trust me. your experience on that.
nteresting. When you partner [:Like it could do that really well, and that's really where the opportunity is that I think a lot of people are missing. What they're hoping for is they're hoping for, you know, Rosie the robot that's gonna go and do your dishes and take care of Astro, and do all of that, and we don't have that right now. And to be completely honest, I don't know if it's even on the horizon, based on what we've seen with how AI is developing, and what the real world results are, looking like.
d unprecedented time that we [:But if you already know what a process is and you build out smart tools, smart systems to do it on autopilot, we have seen massive time savings and things like that. Turning, I record a 10, 15 minute video transcript into an SOP that now can be used over and over and over and over again. Taking videos like this and being able to chop them down into, smaller bits, to be able to leverage in a marketing system that we already know that works, and surface the right, the right clips at the right time. We've seen that work. We've seen that be effective.
What are some other ways that you've seen, what's working right now, still be really effective?
htened emotions, right? Like [:Again, it took me a while, it probably took me about an hour, maybe it wasn't that long, it felt that way, but and it actually helped me get to the core. I was actually wanting to say and do. And I'll tell you what, the conversation could not have gone any better.
Like, This was one of those things where a year ago, I talked to my wife about having this conversation. And she said, don't do that. I still want to be friends with these people.
Zach Hammer: Right,
oints, you know, his trigger [:But When I saw it going back and forth, it was able to really encapsulate so much more information than I can. I think that's, what it is. Can I encapsulate so much more information, you know, I said, use Chris Voss and Richard Bandler. Completely different people, and so it's you know, a lot of times I'll say, take this idea and teach it to me from the Farnham Street mental maps, or from first principles. Or I'll say, you know, so it's able to take completely different things and mash them together. So Those are the things that really stick out to me.
mazing how powerful it could [:It's not impossible, but it takes work, it takes effort, it takes energy. And we don't always have like that peak energy to do that. But with AI, like it never runs out of that energy. You could say, I want you to pretend you're a robot pirate that always answers every other word with beep.
f seeing how this might play [:What would this look like if somebody was like this. And like these are things that we could do ourselves, but they take a lot of mental energy. you could ask yourself, how would I write this if I was this person that I know really well? You can put yourself in that flow and you can try, and you'll learn things that way. But when you can literally just ask, I want you to rewrite this as if you were George Lucas, writing this story. Or I want you to rewrite this as if you're Adam Sandler, describing the same story.
What would change? What would be different? Why would it be different? And you learn things in that process that you wouldn't learn otherwise. Because it's it's hard for you to think that way, right? cause you're not that person. That can be really useful and really helpful.
There's this aspect of discovery and having the secondary brain that really what it's doing is it's allowing you to look at your own thoughts from different angles, and your own expertise from different angles, and that you wouldn't typically be able to. So it gives you some ability to do that.
en is, it certain aspects of [:But yeah, when you have that, it allows you to do a lot of these things at scale that you couldn't before. But it's still, it's own skillset, and it's own way of developing out processes. And ultimately what we're seeing right now is, people are hoping that AI is going to completely eliminate the need for people, or it's going to completely eliminate a lot of the people that you need.
opportunity is, I know that [:And then further in the same way, teaching your people to do the same thing. Right, Where they can bring consistency to their work that they might not normally be able to, by being able to say, this is how I always want to answer these emails. This is how I always want to do this. Now let me take that process and turn it into a system so that it could just happen on autopilot so I could free my mind up to focus on the things that actually matter. Not stepping out of thinking at all, but leveraging the things that I've already thought about and figured out and make them consistent. Rather than expecting the machine to think for me, it's I did the creation part of the thinking, and I'm just getting the consistency and repetition to the machine.
Does that make sense?
Charlie Madison: Yeah.
Zach Hammer: But if you don't do the thinking on the front end, whether it's you, or somebody on your team, or anybody else, it'll give you hot garbage.
Charlie Madison: Hot garbage. Yeah.
Zach Hammer: Or as [:And it's more like, you got the paycheck, and you got somebody that's working for you powerfully. How do you 10x what they're capable of doing? That's where the real opportunity is.
e, is so much nicer when you [:And the skill is how to know whether it's right on track or not and how to guide it. But imagine, if every one of your best team members was able to 10 X their productivity and you know, maybe even better the way it saves money. Like, What if you could let go of the people that aren't performing?
people that maybe aren't all [:So it's like, this interesting thing where it's, yes, there is opportunity to reduce costs, but it's maybe not in the ways that people are thinking. And really a big part of this is, it is its own skill set. It is its own expertise. And you almost need people who are trying some of this, stuff out so that you could ask the questions, see what's actually working and see how that looks.
Cause it's not typically the same way that it's working otherwise. Your processes for writing something with AI look different than maybe how you set up a human to write successfully. right? There's similarities, but they're also different. And so that's really where the opportunity right now still lies.
out of what's available, but [:And that's what we seek to do at the AMP Intel elite group, where we come together and we talk about what's actually working for us. You get the opportunity to learn what we've already proven, what we've already vetted, but you also get the opportunity to get unblocked and unstuck in whatever you're working on, see what the opportunities are, see what, processes could potentially be leveraged this way so that you're able to you know, really bring together your team, you know, empower a team to leverage AI, both by learning it yourself, but also having your team learn it as well.
so yeah, if you're interested in learning that information and learning how you can put that into practice and having the space to develop that skillset, we'd love to have you. And uh, you can reach out at ZachHammer.me/contact, and find out more about what that looks like. But otherwise, that's what we have for you today.
Charlie, what's your biggest takeaway or what's your biggest thought on this one?
Charlie Madison: The name of your program is AMP Intel. Amplify your intelligence that you already have.
ntion to the first releases. [:It's like Iron Man with his armor, like it really can make you 10x productivity in the skills you have. So now, my suggestion, find someone that's got the skill, and already knows some AI, or find someone that's got the skill, and get them connected with Zach, so your whole team, then it comes that 10x amplified intelligence where, now you don't have to have an army of people, but you can have a small, maybe so task force that all 10x times 10x plus 10x. That's, That's a big difference.
g able to do more with less, [:In fact, it could cause people to deploy a lot of time and a lot of effort on things that will never work, because they aren't the understand what's good or what's not. But having that those realistic processes and having that ability to leverage it to 10x what's working, that's where the real power is.
So absolutely, Charlie, thanks again for coming on. And everyone else, thank you for joining us for another episode of AMP Intel. And we'll catch you on the next one.