How Leaders Think with Kenny Lange

Kenny Lange [00:00:00]:
If the platform or whatever, the service is free, you are the product.

Alex Bratton [00:00:05]:
Yep. And that's something.

Kenny Lange [00:00:07]:
And I don't think enough people understand that.

Alex Bratton [00:00:09]:
Exactly. Exactly. Well, and I think the. So we're getting into some of those crossovers here. We talked about the phone. The other thing that lots of folks have in their homes is the Alexa or other voice activated types of things. And all we are doing is feeding our personal information directly into the cloud engines.

Kenny Lange [00:00:34]:
Welcome to the how Leaders think podcast, a show that transforms you by renewing your mind and giving you new ways to think. I am your host, Kenny Lang, and with me today is the Alex Bratton. He is the CEO and chief geek of Lextech. He's also an applied technologist who helps world leading brands create employee experiences that empower people to thrive at work. Couldn't we all use that? He's also the author of Billion Dollar Apps, an adjunct professor at, at the computer science department of Northern Illinois University, and holds two patents related to mobile video delivery, which is super cool. He has championed new international aviation standards, improving the employee experience and mobile tools for pilots. He is also the first guest to be on using the Apple Vision Pro. Welcome to the show, Alex.

Alex Bratton [00:01:27]:
Thanks, Kenny. It is awesome to be here. Coming to you virtually this time.

Kenny Lange [00:01:32]:
Yes, virtually. It's like virtually in real life. I don't know. It's just your augmented reality. Your Persona is making an appearance. You may actually be doing something totally different. This is. It's just all robots and AI now.

Kenny Lange [00:01:47]:
So I'm trying to figure out how to get a generative AI version of me to conduct these so I can start to crank out more podcasts quickly. But next time, I definitely, I want to have a Vision pro to Vision Pro podcast episode. So we'll make that happen. But tell me today, Alex, what is on your mind?

Alex Bratton [00:02:09]:
I am really seeing this year 2024 rolling back. We had the industrial revolution. We had the information revolution. I am seeing the pace of change hitting everybody. This year in 2024 is faster than it's ever been. I think we have entered the innovation age, and I think that is really hard for leaders to understand what that means and how to absorb it and how to help their teams get there. We've got to do more faster, and we've got to learn really quick.

Kenny Lange [00:02:44]:
Right. And I know there's a lot of people talking about how taxing that is. I think it's interesting, you know, denoting that, you know, we're entering, or have entered the innovation age. Obviously, we've been in sort of like the information or Internet age, depending on who you talk to. But what are you seeing in your circles or in your reading and conversations about the current thinking or the prevailing wisdom about the space and time that we're at? Because you see a lot of people are struggling to either acknowledge or keep up and embrace the point in time that we're in.

Alex Bratton [00:03:24]:
And I think for me, the one really encouraging thing is that I think that there's a lot more folks that are being forward enough to say, I'm not where I want to be, I'm not absorbing fast enough. There's so many new things, I'm not sure where to go with it. So that's been encouraging, is that folks don't have, have their head in the sand, which I would say has maybe been the past to just set the stage for me. And I've been trying to share this as much as I can with folks. If we go back to the iPhone, I would call that a singularity event. It brought mobility, it put technology in our hands. It changed everything. We have three different things of that scale hitting us at the same time this year.

Alex Bratton [00:04:05]:
Not just one, three. We have spatial computing. What I've got here, not virtual reality, we'll talk about that later. But spatial computing, of bringing us together virtually artificial intelligence. It's here, it's real, we can all use it. And commercial spaceflight, any company that wants to, can put a satellite in orbit relatively inexpensively or access sensors from orbit. What happens when I can start rolling out an app where you know what, it's the parking app of the future, and I have a real time view of Los Angeles, and I can actually see where all of the open parking spots are. We're there.

Alex Bratton [00:04:40]:
So thinking about all of those different things, not that we should go run out and implement a bunch of stuff, but we need to think, where does it mean? How does that affect me and my industry?

Kenny Lange [00:04:51]:
Right. And how does that end up trickling down to us as the end user?

Alex Bratton [00:04:58]:
Exactly right.

Kenny Lange [00:04:58]:
It's one thing for a large corporation like a Microsoft, which is obviously a large shareholder of, of OpenAI, and those two are always linked. What does it mean for them internally and their products? But then for you and I and the common everyday user, how does that start to impact us? Or how should we engage with it? Because I think a lot of people that aren't in the technology space or frequently using technology or barely using it are fairly apprehensive about some of the things they see and they hear and they read and trying to you know, in some cases I try to help people realize you've had some. Some artificial intelligence or some automation things that you use in everyday life. You just may not have ever called it that. Right. Like even cruise control is a form of automation.

Alex Bratton [00:05:55]:
That's a good thing to say. Yeah, exactly.

Kenny Lange [00:06:00]:
So how do you see it trickling down, like in terms of impact? Because it usually impacts the larger organizations that are the early adopters or the innovators first, and then it just rolls on down. So how do you see that?

Alex Bratton [00:06:15]:
And it's been interesting. Some of these I've seen actually flip an impact on the smaller side first, actually. Let's start there. So commercial space flight again? No, we're not all visiting the space station this year. We're not there, but it completely changed Internet access for millions of people with the Starlink satellite system. I've got that as my backup down here in Florida in case there's hurricanes or otherwise if the cable company goes out. I had to use that during, after a hurricane previously and powered up the neighborhood, part of the neighborhood here for ten days and it worked beautifully. That type of technology now or this year we're going to start seeing cell phones talking directly to satellites.

Alex Bratton [00:06:54]:
Every one of us can have that. So if we're in connectivity challenged areas, that's here. That's awesome. So the statement of always on Internet, wherever I am, we're kind of there. So I think that's huge. I think some of the other ones, as you mentioned, AI especially, I'll use the iPhone as the poster child for this. There is so much AI baked into that phone. Every picture we take, it takes three, four, five pictures and intelligently merges them together and gives us the result of an amazing picture.

Alex Bratton [00:07:28]:
I think that's actually the killer app for AI, is where it's helping us do something where we didn't have to think about it as AI. It just does a job, make my picture better, help me do a thing. And I think that's where it's going to get really exciting.

Kenny Lange [00:07:43]:
Gotcha. Sort of the going. Don't want to go down a rabbit hole. I've been known to do that. But like Clayton Christensen's jobs to be done framework example where we can think about it not as this ethereal, like all knowing being like we're going into the matrix sort of thing of AI. But just I hired AI to do a job and I don't even think about it. I just think about the job got done or got done faster, more effectively, or I didn't have to do sort of the minutiae of this. Like in the photo example, I didn't have to load it up into Photoshop, or was it lightroom or any of these other apps to clean it up and find these pieces and make it all happen.

Kenny Lange [00:08:30]:
And now it can happen pretty much near instantly.

Alex Bratton [00:08:35]:
And I think that's a then jobs to be done is a perfect framework for how we should be thinking about AI, both personally and on the business side. And I think that's actually where I've seen this is where I think the big companies really had problems with it over the past five or ten years, especially in the machine learning, analyzing data fashion, where a lot of companies wanted to throw AI at problems and they said, hey, we've got all this data, throw AI at it. But they never asked the jobs to be done question of what problem are we trying to solve?

Kenny Lange [00:09:07]:
Just because you can doesn't mean you have to start.

Alex Bratton [00:09:10]:
Exactly. Exactly. Or hey, what could AI do here? Well, what's worth doing? And I'm a big champion of user experience and employee experience. We've got to get down to what actually matters to the human being we're trying to help.

Kenny Lange [00:09:25]:
I think that's important. And one thing I did want to pause on before we go further, I do want to mention that if you are interested in the jobs to be done framework, go check out competing against luck. Phenomenal book, easy to read for anybody. You don't have to be a Harvard graduate to read it. But could you break down the difference between AI, which gets used quite a bit? And I know that there are levels of AI as well, if you want to speak to that, and machine learning, because those two get used together quite a bit. And I think it can be confusing to understand are they related or we saying the same thing? When you see AI and ML for machine learning learning, can you help break that down?

Alex Bratton [00:10:15]:
Happy to. And it is a really messy quagmire in particular, because a lot of people use these terms to mean different things. So AI, artificial intelligence, let's just say that that is the overall bubble that all of this exists in, and let's just leave it at that, which is somehow a computer is helping deduce something from data. If we go to ML machine learning, that was really based on, let's feed it some data with a very specific problem. So as you were talking about, as the example, cruise control, or if we want to talk about self driving cars in the future, where there's a very defined problem, keep the car on the road, don't run into anyone and feed it a whole ton of data. That's a great example of machine learning. What's really hit us, and that's been around for a long, long time. I mean, thermostats have that to know, hey, when should I set back based on energy trends and all of that kind of stuff? So it could be little tiny things or as insanely complicated as a self driving car.

Alex Bratton [00:11:21]:
Moving on from the machine learning side of things. Actually, sorry. The other part that we can get into there is life sciences organizations that are doing DNA analysis of, hey, folks that have this kind of data might be more susceptible to this kind of disease. Another great example of machine learning.

Kenny Lange [00:11:37]:
Okay, gotcha.

Alex Bratton [00:11:39]:
So call it advanced data processing to get to some kind of answers. And there's lots of stuff related to that, been around for a long while. The stuff that really hit us this year is the LLM large language models, which the best way to describe it is it's a conversational model where you can talk to a computer. And that has been what has been a huge enabler for folks so that I can now ask human language questions. As a quick tangent, I fully expect the most common programming language today. There's lots of different programming languages that people build technology. I think the most common programming language of the future is going to be English. And we're going to do that by just mapping out, hey, here's the problem I'd like to have solved.

Alex Bratton [00:12:29]:
So the large language model means we can now talk to a back end. Another element inside this AI bubble is something called generative AI, and that's where you see all of the AI generated pictures and videos and music and deep fakes and all that kind of scary stuff. And that's something where maybe I'm using a conversational interface to talk to it, saying, hey, create a video showing someone walking down the street, then there's lots of other pockets in there. So when we say AI back to what we talked about earlier, it really should come down to what problem are we trying to solve? And then what's the interface to that back end? In that earlier Apple example with the photos, there's no user interface. It just does what it's supposed to do.

Kenny Lange [00:13:17]:
Right? Yeah, just clicking the photo button triggers the AI to go into thing. I'm glad you brought up the LLMs now, the most notable one being what OpenAI and chat GPT are doing. That was it. December 22, January 23, I think is when the big rush, just over a year into this, as of this recording, I think the furthest one is GPT four. In terms of versioning, I've heard that they are training GPT five. I think they're always going to be training a new model or something like that. But for those of us, again, for long, large language models, we may have heard of GPT four or just chat GPT in general, it's useful for all sorts of things, but I know that people may also have concerns about, well, what is it taking in? I mean, there are data privacy, data security, data integrity, a lot of those things have been in the news for the last several years. Obviously, I know you and I are Apple fanboys, and there's a lot about on device processing and making sure your data is secure so that it's not going up in the cloud.

Kenny Lange [00:14:40]:
Nobody's looking at your stuff. Can you talk a little bit about what does this mean for large language models where we are speaking in conversational ways with artificial intelligence programs? What is it learning? How does that get incorporated? What are the maybe security concerns or non concerns that we should have?

Alex Bratton [00:15:07]:
And they're definitely concerns. One of the examples there is so chat GPT in their licensing, if you're just using the free or inexpensive version, anything you're feeding into it, they can use to train future models. In addition to chat GPT, there's another 15 to 20 leading competitors that do very similar things. So that's one of the huge confusions right now, is OpenAI is doing amazing things. They are in continuing war of escalating technology with this other ten to 20 companies of who's better today, who's better the next day, who's better the next day, and it's always getting better. So our one takeaway from that isn't company x is the best. It's, this is going to keep evolving, it's going to evolve rapidly, and it's only going to get faster.

Kenny Lange [00:15:59]:
Right, so as more users come on board. Right, because that's feeding more data.

Alex Bratton [00:16:04]:
Absolutely. Yep. Feeding more data and more opportunities of what do we do with it? That's actually the biggest challenge right now. Is that, okay, I can go to. So again, chat GPT is essentially the typing based interface to talk to the OpenAI system. But there's lots of other organizations, including OpenAI, that have essentially an API where if I'm writing software, I can power it and add an AI brain to my software by tapping into OpenAI or all of these other companies. So one of the things that we're seeing is pretty much every software provider out there is trying to figure out how to bring artificial intelligence into their product. We're seeing it in the Windows operating system, we're seeing it in Excel.

Alex Bratton [00:16:50]:
So it's going to be extremely common that we'll see it everywhere. But again, back to your key question. Who owns the data? Is it my data? Where is it actually going? And that is super hard to find in all of these licensing agreements. Something that I, I'm a big believer in personal privacy and securing all of that information, and especially as we build systems for the enterprise, same thing. Our data is our data. The one company that I'm really seeing championing that you mentioned Apple earlier, is the fanboys. They are huge privacy and security champions. And the way Siri works, I think, is a great example of that, of the Siri voice processing happens on the iPhone itself, not bouncing off the cloud.

Alex Bratton [00:17:35]:
Apple has released in the last four months some really interesting white papers showing the ability to run those large language models on my phone.

Kenny Lange [00:17:46]:
Wow.

Alex Bratton [00:17:46]:
So I don't have to use the cloud, I don't have to give up my data. I think we're in for privacy tech wars happening here in the next year or two. We've already had it. Between Apple, Facebook, and Google, who owns my data for advertising, it's going to get ten times harder when it comes to an AI perspective to really understand where's my data flowing. I think Apple's going to champion it. I'm not sure about the others, right?

Kenny Lange [00:18:10]:
Because I could see with as much data as is already present in these large language models, add in, you know, thousands, millions of users that will contribute to it. It becomes a bit like we as humans when we get some sort of source amnesia, because we heard something a few years ago, we've integrated it into our way of thinking. We espouse it as though it were our own thought, but in fact, we learned it some time ago. Tracing, and it may not be accurate, right? And it may not be accurate. So tracing the source of that is difficult here. I imagine it'll be similar as we continue to accelerate into feeding these large language models more and more data that we're thinking about and questions we're asking.

Alex Bratton [00:18:59]:
Absolutely. And I think where it really gets interesting, and this is where the Apple developer conference is in June, and I'm fully expecting them to start announcing some of these things. Where this all gets super interesting, is where that language model or the AI engine knows who I am, they know my day, and apple is uniquely positioned that if it's running on my iPhone and if I trust it. And if it's got access to my calendar and my contacts and where I am, that gives it amazing abilities to make suggestions for me about what's around me, who else is around me, what I'm going to do next. So, I mean, we've already seen that where when you get a calendar recommendation of, hey, you should leave for that next appointment because traffic's heavy. But imagine that for everything potentially in your life of recommendations. So that personal assistant might finally be here.

Kenny Lange [00:19:53]:
Yeah, I mean, I know it'll sense when I connect via Bluetooth to my car and it'll set and it'll pop up a map notification that says it should take about this long. And if you want to use the map to navigate through traffic, then you can do that because I found multiple routes in saying that there's what I feel are lightweight versions that exist today. And I want to keep going back to examples so people can kind of spot these things in the wild, so to speak. But, like, on my watch, one of the watch, because I have Apple Watch Ultra, just the first one. So wait, you know, we'll see if I get the second one. But on any of the more recent Apple watches, one of the watch faces is a Siri based one. And it, and it has tiles and you can see going down of different things, but it, it auto generates what those things are. You don't dictate what those recommendations are.

Kenny Lange [00:20:52]:
It'll say, oh, the next most important thing is that you have a meeting in 15 minutes. But then the following thing may say it's someone's birthday, here's their phone number if you want to call them or text them, and then it may recommend something else from the data that it has access to, which is obviously, if you have an iPhone, too, it's able to access a lot more than if you only have to watch. Conversely, on my iPhone, on my home screen, because I live and die by my calendar. So I have a calendar widget on the home screen, but below that there is a widget. I forgot the exact name of it. I think it's called like, Siri recommendation, but it's like a set of two rows of four apps. And every time you open your phone, it will change what those likely, it'll change those recommendations. So, like, when I'm getting into bed at night, it pops open my sleep app, calendar app, maybe ESPN because maybe I'm reading an article, but, like, right now, it's popped open weather, Gmail, text messaging, slack group me.

Kenny Lange [00:22:06]:
And that's a perfect example, because it's.

Alex Bratton [00:22:07]:
Got to know you. Yeah. And that. Well, here's the question.

Kenny Lange [00:22:11]:
Right? It knows when I'm saying, does that freak you out? It doesn't freak me out, but I've, for as long as I can remember, I've been a bit more open to embracing tech before it's widely accepted. But I know for some other people, they may not want that or they may question that of, like, what is that doing? Or why is it doing that? People freaking out about seeing being targeted with a Google Ad or a Facebook or Instagram ad because they were watching tv. There was a commercial on, if you happen to have commercials still, there was a commercial on about something you had no interest in, and now all of a sudden, you're getting targeted ads. Right. Like, I know that part freaks people out, but, and it should.

Alex Bratton [00:23:04]:
It should. That's a great example of our information is up in the cloud, and there are companies that, again, were their product. They make money because of that data.

Kenny Lange [00:23:13]:
Right.

Alex Bratton [00:23:14]:
Which is different than, hey, it's on my phone and it's locked in my phone. That I prefer.

Kenny Lange [00:23:19]:
Yes, yes. And amen. One of the phrases that I share with people because people know, like, at least in my circles, I'm the resident nerd and tech support and all these sorts of things, but they, you know, they'll talk about how that sort of stuff freaks them out or, you know, they're unsure of this, that and the other thing, but they want to keep using these social platforms. I use it because I have a, I have a business. I need to promote it. That's where people are. I got to go where the people are. But I tell people one of the, my favorite phrases that I heard a while back, and you may know who's credited with it, but if the platform or whatever, the service is free, you are the product.

Alex Bratton [00:24:02]:
Yep. That, and that's something.

Kenny Lange [00:24:04]:
And I don't think enough people understand that.

Alex Bratton [00:24:06]:
Exactly. Exactly. Well, and I think the. So we're getting into some of those crossovers here. We talked about the phone. The other thing that lots of folks have in their homes is the Alexa or other voice activated types of things, and all we are doing is feeding our personal information directly into the cloud engines. While I will use Siri, I know where it's going. None of those others are allowed in our house because it's just an open gateway for my data to be fed to those companies.

Kenny Lange [00:24:36]:
Trey, I'm glad you bring that up. And I also feel a little less crazy knowing that someone else has those same rules for their home. I do know, and this is, again, a bit of a rabbit trail. My editor is probably going to have to cut this to make it sound like I'm more focused than I'm feeling right now because I love this topic and I don't get to talk this way with everybody, but there are people who will knock Siri for being. And there's articles, too. Not just people in my personal life, but articles that say, well, Siri is lagging behind in terms of effectiveness. I know accuracy for AI models gets measured and things like that, compared to, say, a Google Assistant, Amazon Alexa, another big home one is Josh AI. And I know there are several other large entities that will work in very particular spaces, but Siri has gotten a bad rap.

Kenny Lange [00:25:40]:
But I'm wondering if that's because they're not, that they've chosen to sacrifice some efficacy for the sake of security. And what would you tell someone, or what questions would you recommend someone think about as they evaluate what AI platforms, tools, entities might I be comfortable with? Where are the trade? There's trade offs everywhere, but how would you recommend someone think about trade offs when considering their phone watches, any smart device that they bring into their home, including thermostats, but especially these assistants that are typically in a small speaker of some sort.

Alex Bratton [00:26:24]:
And it's interesting you mentioned thermostat. So I have a Nest thermostat that I have intentionally kept separated from the Google back end. So it's still a nest only login, not the Google smart home, because of exactly what you're talking about. Feeding the data back into the Google machine, not something. Again, I'm not paranoid, but I don't want to be giving that data away. So, folks thinking about that? Again, Apple is a big champion of it when it comes to other platforms. Now, I want to be really clear. AI is here to stay, and it's something we all have to be leaning into.

Alex Bratton [00:27:02]:
There's a lot we can do with it, and it's really powerful, but it goes back to exactly where we started. What are the jobs that I'm trying to get done? That tech can help me do that? AI can power. It's not about the AI for AI sake. And I think that's where a lot of folks got hung up on. On Siri. And Siri is obviously, yes, it's been around for a long time. My company is an Apple enterprise partner, so we get to do lots of interesting things with companies, with Apple. And the one thing that constantly comes through is Apple caring about privacy and Apple caring about the experience and that as soon as you open up some of the gateways that you see in some of these other platforms, Apple or the company providing it, has no idea what's going to happen.

Alex Bratton [00:27:48]:
They have no idea what the answers are that might be surfaced from the raw Internet to a question. So they've been very focused on, you know, what we want to have an experience that we know is going to be positive for folks. Now. I think they're going to add more brain power to the phone going forward, and we'll be able to do more with it in a secure, private setting. So I'm excited by that. That said, I think every single person should be experimenting with chat, GPT, everybody, and learning, because the expertise we all need. Back to that innovation side of things. We need to know how to talk to a large language model and get what we want out of it.

Alex Bratton [00:28:26]:
It's not as simple as talking to a human, and that's a skill we're going to have to learn.

Kenny Lange [00:28:32]:
Yeah, my kids actually have brought some of this up. Of course, last year, they were like, oh, my gosh, I don't have to do homework anymore.

Alex Bratton [00:28:43]:
And I'm sure their school loved that. Yes.

Kenny Lange [00:28:45]:
Oh, yeah, the IT departments are freaking out all over the country. But one of the things that I've had a conversation about with them regarding AI, because they know, like, we're tech forward family usually, you know, playing around with some new way to make their lives easier, but difficult at first. Cause, you know, I experiment with it, is, I think the skill, more and more, it's gonna be how to write a prompt. Like my son was saying. It's like, oh, well, this makes everything easy. You know, like, I'm. He's about to be in high school, and then, you know, soon after college and, like, I won't have to do all this. I was like, it's not as easy as just, like, throwing, like, hey, write a paper on the war of 1812 and cite it MLA style.

Kenny Lange [00:29:36]:
Like, I mean, you could throw something basic out like that, but the results are not going to be great.

Alex Bratton [00:29:41]:
Exactly.

Kenny Lange [00:29:42]:
They're going to be overly simplistic. And I really see there's a skill set of prompt writing that people need to start learning. I'm trying to learn that myself. You know, I bumped up, it's a $20 subscription to get GPT four and dolly and stuff like that. And so I'm starting to experiment, and I'm figuring out the way I might throw something into Google where I to go out and search for an image for a LinkedIn post or source material doesn't translate one to one in how I would communicate with chat, GPT, or another platform that's using its API. So apart from just spend some time doing it, whether free or paid, how do you recommend people in particular, let's draw it back to, say, leaders and business, whether small business or medium or enterprise, what do they need to do to upskill in their ability to write and get the most out write prompts and get the most out of the AI that's available to them?

Alex Bratton [00:30:53]:
Great question, and it's something that we've been wrestling with. So I've got a team of about 35 folks, and one of the things I think that we as leaders need to understand is that while we need to be leaning into embracing and exploring, our entire team needs to be doing this, and we have to be encouraging folks to do that. We actually added a quarterly initiative as a part of our quarterly objectives. Every single person on the team, everyone, accounting, design engineers, everybody, had to go pick an AI tool and experiment and spend a few hours trying it out, try experiments. And all they had to do is capture, hey, what worked, what didn't, what would you recommend other members of the team take away from that and potentially add to their workflows? And we have folks who are now, they're presenting a couple a week in our alliance sessions, and they're, they're sharing their learning. So having an environment of this, always learning, is critical. And in that way, we can expand our personal brain space. Let's let our teams help us with this.

Alex Bratton [00:31:58]:
This isn't that only the geeks should lean into this. No, some of the best ones I've seen. One of our sales folks created a set of prompts where it would go out and research a company and an individual and pull out information about the organization, their industry, likely pain points of issues they were facing based on any public statements and some other background material. And he was able to craft that in probably five or 10 hours worth of work over a month or two. And it's now become our standard that we can just run something through and we can get a three page report out. That's awesome.

Kenny Lange [00:32:33]:
Yeah. Yeah. I mean, how much? As somebody who's done a ton of sales still has to, right? I'm a company of one, but I've trained sales teams before in my agency days. Is like the administrative side of sales is like it's the bane of their existence.

Alex Bratton [00:32:55]:
Exactly.

Kenny Lange [00:32:55]:
And to quote, there's a book that I read a year or two ago called the prosperous coach Steve Chandler. It's fantastic, but I think it's great for anybody in coaching, consulting, probably even just regular salespeople. But he says no sale ever happened outside of a conversation. And so what your salesperson has done is allowed your salespeople to stay in more conversations and out of the admin, but also be able to intelligently approach sales opportunities. Because now I have some research, I have some intelligence, and that doesn't, that doesn't take away what I hope no one. If you're a salesperson, listening to this, thinking, you don't have to stay curious in a conversation. You can just blurt out all the research material. It spit up.

Kenny Lange [00:33:47]:
You can't, but at least would help you craft more intelligent questions and gives you content. Ask somebody who.

Alex Bratton [00:33:53]:
Exactly.

Kenny Lange [00:33:54]:
Yeah. As somebody who gets pitch slapped regularly, usually through LinkedIn, um, the ones that stand out to me that I don't anger me or I will consider responding to are where people asked a thoughtful question that showed that maybe they did a little research, or at least they had a. They were. They wanted to know something instead of telling me how much they needed.

Alex Bratton [00:34:14]:
Exactly.

Kenny Lange [00:34:15]:
So I think that's a really cool.

Alex Bratton [00:34:16]:
And it was, again, it was. Someone on the team brought it to the table, and we were able to share it with the team. Uh, but the. The key for everybody at the same time was the, um, being able to also tap online courses in prompt writing. And as you said it before, that is a critical skill. So we, as leaders, whatever world you're in, we've got to be able to get better at these prompts. We have to envision we're talking to. It's an Android, you know, a fake person across the table from us.

Alex Bratton [00:34:44]:
It understands our words, but it doesn't know how to answer our question. It doesn't have any of the context of going through a school system or having conversations with people to really understand what we're asking for and how do we be explicit. And just to be clear, the average really good prompt is probably ten to 100 lines long. It's not two sentences.

Kenny Lange [00:35:09]:
Wow. Yeah. I think, again, going back to, you can't just port over what you were doing in Google or Duckduckgo or any of these other search engines to where it usually was, you know, at most a pretty lengthy single sentence.

Alex Bratton [00:35:25]:
Yep.

Kenny Lange [00:35:26]:
Um, and. And typically only just, like, a few words. But, um, that's interesting. Um, I I've started finding that I'm like, man, I needed to write more context clues because um, it's exposed how many assumptions I make in. Isn't that asking a question to another human? And I was like, yeah, I was just. I was just messing around, making some custom backgrounds for my iPad and my iPhone this week. So I've been playing a lot of halo infinite, and then I started watching the shogun show on FX or Hulu or whatever. And I love it because I was born in Japan, so I've always been drawn to the culture and history, but I was like, I want to merge these two.

Kenny Lange [00:36:13]:
And so I started writing some prompts to create a background that merged, like cherry blossoms and halo and all this stuff. And the first couple of things I wrote, it came back with, like, a ridiculous and absurd version of a photo. I was like, that is nothing like what I had in mind. And I looked at it, I was like, I made so many assumptions that it would know, take this piece, this piece, this piece. And I became far more explicit and said, it needs to be oriented this way and this resolution and move this back and move this forward and space this out. It was a lot more involved than I think, anecdotally, a lot of people are talking about. So that's just. No.

Alex Bratton [00:36:51]:
Agreed. And the fact that there's different ways to think about the prompts. Some of them are, for example, there's a prompting style called few shot examples where you're giving examples of things. So there's actually a lot of definition around how you craft these prompts. It's not just write an english paragraph. So one of them might be you're giving examples of the kind of output that you want back, and then you give it the question, and it has figured things out based on the examples or others where you're actually mapping out. Here's the logic I want you to apply. Now apply it to this.

Alex Bratton [00:37:25]:
So there's all kinds of things. There's actually a couple of great free courses on Coursera. Yeah.

Kenny Lange [00:37:31]:
Okay.

Alex Bratton [00:37:32]:
Highly recommend tapping into those. There's a couple of them. They take you three to 5 hours. There's one specifically for business leaders to help you start thinking through those prompts. But some of the basic ones of just how do I do this? And what are the different examples? Again, not as simple as just writing what you would put into Google, but hey, this might be one. I want to give an example. One last example that we've seen work beautifully is because the large language model is now conversational, it becomes the ultimate sales coach, if you will, where I can have a simulated conversation and tell the LLM hey, I'd like you to be a play the role of an industry executive in the manufacturing space, in heavy industry, working with these kind of products with these kind of problems. And I'm gonna be pitching you on our product set and you can actually role play with it.

Alex Bratton [00:38:29]:
That's awesome.

Kenny Lange [00:38:29]:
Wow, that's huge. Especially, I mean, if you're somebody in my position where you don't have a sales team or a sales manager or something like that, and you're coming up, the opportunity to do that is it's, it can't be understated because you can get some reps and sets in a way in a safe, and I would say a safe environment to where you're, if you fail or screw up or something like that, it's, it's non fatal failure. It's not going to put you out of business. And I know that there are some sales tools that are starting to add a little bit of that too, in terms of summarize this email thread and pull out some relevant pieces or give me some feedback.

Alex Bratton [00:39:15]:
Those are interesting little tactical examples. But again, what's the job to be done of, ooh, I'm pitching in this industry and I don't really know anything about it. Okay, that might be an interesting thing to explore then, right?

Kenny Lange [00:39:27]:
Yeah. Or if I say this, am I going to lose them? Or is it too wordy and complicated or something like that? So that's a really intriguing idea. I feel like we could probably go on another 45 minutes or more talking about this because I love this. And again, don't get to talk like this. Typically with, all I see are my family and my wife and kids will just kind of give me a squint and be like, yeah, whatever, what's for dinner?

Alex Bratton [00:39:53]:
Well, this is the applied tech of why does tech matter? It's supposed to be helping us and that's what this all comes down to.

Kenny Lange [00:40:02]:
Right? So I really appreciate you making, bringing this from the abstract, ethereal sort of things down into the practical every day, because I think when that happens, more of us can say, can one make it less scary? Right? It doesn't have to be this unapproachable or terrifying monster of a thing, as we can give it a name, we can see its application go, oh, well, that's approachable. I can do that. You've mentioned Coursera, which we'll grab some links and put that in the show notes in case anybody wants to go check that out. But if somebody wanted in particular, business leaders, founders, executives, things like that, if they wanted to start to incorporate AI, machine learning. Some of those thinking these essential pieces of this innovation age we've stepped into, as you put it. What would you recommend they do to get started in the next 24 hours? That would cost little to no money.

Alex Bratton [00:41:03]:
So the two things I would recommend there, number one, if you're not actively using, let's use chat GPT, just because it's easy and free, get it, install it on your iPhone, because you can then flip to a mode there where you can talk back and forth to it. You need to experience that. So it's a full audio back and forth verbally, so that that's a great one. And then yes, you can build your bigger prompts, but you need to experience that. Number two is I would really look around at your team and see how you can bring folks in. Not challenging people to, hey, everybody, go do this in a wink, but how can you give people a little bit of flex space but say, you know what, AI is impacting everything in the world right now. How does it impact us? And how can you engage your team in some of that? Experimenting with an expectation that they're going to give a three minute readout of some kind to the group. That's where you're going to get those learnings is from the people who see friction in your organization that AI might be applicable to.

Kenny Lange [00:42:05]:
I love that. If I could add a tack on to that, which I don't normally do, but I know a lot of people are going to listen to this, hear it and think, okay, great for businesses, for profit enterprise, you know, and I think we've illustrated that it can work from one person companies up to the multi thousand, tens of thousands, there's application there. Could you briefly state like, how this also applies in, say, the nonprofit or faith based houses of worship, like places that may say, yeah, no, we're really in the people business, or, that's not for us. That's great for sales teams and large for profit companies, but not for us. Is there anything you would say or encourage to show them that this could really help them in their mission to make a positive impact on the world?

Alex Bratton [00:42:57]:
Absolutely. And actually, I'm glad you brought up those groups because given the common thread that those are the groups that are the most resource constrained and trying to have an impact on the world around them, that's actually where something like this, and being able to empower members of a team to do more, that's, that's huge. Those same organizations, you're going to have things that are repetitive tasks. Or again, they're, they're friction, they're grunt work. They're filling out paper or research or otherwise examples, being able to load a document in and summarize it. Or hey, does this ten page contract have in it a clause that we would have a problem with any of those kind of things? So I think that's super helpful. But even just as individuals, we should be experimenting with this. So, for example, hey, here are the ten ingredients I've got in my kitchen.

Alex Bratton [00:43:51]:
Give me three different meals I can make with it and we'll get an answer.

Kenny Lange [00:43:55]:
I can use that for lunch.

Alex Bratton [00:43:57]:
Absolutely. But it's anything like that. We've got to get curious and experiment and explore. This is applicable for 100% of humanity. And the only folks that are going to struggle as we go forward are the folks that say, yeah, that's not for me. Well, no, we've got to switch into that learning and experimenting mindset. That's the key.

Kenny Lange [00:44:18]:
I love that. Well, Alex, thank you so much for your time and sharing your wisdom and knowledge. And yeah, it'll be more fun once I get my apple vision pro and we can make that episode happen. But if people wanted to know more about you or maybe ask some follow up questions or something like that, where would you send them?

Alex Bratton [00:44:39]:
So, best place. I'm alexbratten on LinkedIn. We'll post that link. I'm also alexbratten on Twitter X and our company website is Lextech L E x t dash E C H.com, and you'll see some examples of this vision pro thing and how we're seeing it affecting the world there as well.

Kenny Lange [00:45:00]:
Excellent. Well again, thank you so much. Look forward to having you back in the future and celebrating the worldwide Developer conference this summer. So I'll probably be texting you about it, but for all you listeners, if you got value out of this conversation or any of the other episodes, please like subscribe, leave a rating or a review. It helps more people find this. So if it helped you, pay it forward and help somebody else find it so that they can make progress in their leadership and in their life. But until next time, remember, change the way you think you'll change the way you lead. We'll see you.