Tech Solutions for Business | Alex Natskovich, MEV

This transcript was generated automatically. Its accuracy may vary.

[00:01:01] Mike: Alex, for those who haven't come across you online, introduce yourself and tell our listeners what we're talking about today.

[00:01:09] Alex: I'm Alex Natskovich. I'm the CEO and founder of mev. We're a technology services provider. We're building Complex software systems for life sciences and pharmacy space. We have been for almost 20 years. And I think today what might be interesting for listeners I'd love to share some of the experience that I had in really helping businesses win with technology and really finding the right solutions, right technology solutions for business problems.

[00:01:38] Mike: ALex, a lot of our listeners are pharmacy owners like I am, and we can typically get stuff out of the box now. The pharmacy software, maybe some delivery, capture of signatures Obviously credit card processing, all that kind of stuff. But once in a while things come along and there's just nothing out there for this.

And so years ago we would do medical equipment rental and there was not a good program for this like 20 years ago. And so I hired a company in my town and. They got going on something called, this might be before your time, but I think it was FileMaker Pro.

[00:02:20] Alex: Oh,

yeah, 

[00:02:20] Mike: Now probably you can get this stuff just from a Google document. But back then you couldn't. So I went out and looked for him and he charged me X thousand dollars. And he said, the cool thing about this, Mike is once we build it, then you'll be able to make adjustments.

So I spent this money on a computer scientist, but I'm not incompetent around computers. The guy makes all this and the big change I was able to make the one that I felt comfortable with on this program was I was able to turn the total button 

I could change it from red to gray. So it wasn't going anywhere and I just dropped the thing, but it sounds to me, Alex, like your kind of stuff your company does is maybe getting to that level where it's like, all right, you maybe own a few pharmacies or you own a business, you're a CEO or this or that.

And maybe out of the box stuff can do some of this. But sometimes there is a link where it would be great to have the computers talking, but it's made it past where you can go.

[00:03:30] Alex: Yeah. Yeah. No, definitely encountering this type of situation that I would probably split it into sort of two different categories. Things that we typically deal with, you what I encounter quite often . It's, yes, like there is some kind of a business problem, it's, you it would be nice that this system talks to this system, or something would happen automatically.

And so often, you know, we come in and kinda look at like, how can we solve it? your business problem with technology. And so that may entail really building something custom, making those two systems work, but it also may entail actually figuring out, Oh, maybe there's a third system that has both of those use cases.

that, you cover it. so you would maybe migrate to another system, Right. That's a very frequent scenario. And The other kind of interesting use cases, very often. to founders and uh, business owners who. You know, they run the business and they see a particular behavior, right? And overall customers or clients, 

 The staff. And they thought well, it would be nice to have a, like an app maybe, or some kind of a software that would do this, so maybe it is possible. Maybe it is impossible, but this is what I love to really talk to them about: are the business problems they have, the opportunities they see?

And is there a possibility to really help them kind of realize that business value by. Either building something or buying something off the shelf. Like quite often, I engage in conversations about my job really, and what I see myself role is, as really helping them find the right solution.

Not necessarily kind of, uh, one that we build, for example, because it's really, you as an engineering company building something that already exists, you're kind of reinventing the wheel, so why do it like we want to do something that is really impactful and innovative, to be honest with you.

often if I'm seeing a use case where, Oh, you know what? Like it actually could be solved by using this system and this system. And yes, maybe we would like to build that bridge for you. But you don't need to kind of rebuild the whole package so to speak like the whole system so that not necessary So this is really where you know, I love I'll have to go deeper on understanding like what business Like what's driving your business, like what impacts a particular problem or an opportunity And what effect you wanted to have on your business and on The 

market if you build in like a product that you want to watch for example, for your, for your, clients. 

[00:05:58] Mike: You and I were talking, Alex, there's already some cool programs. One is Zapier. I think the other one is, if this, then that, or something, 

[00:06:05] Alex: yep You're exactly right. Yeah. 

[00:06:08] Mike: It's like another Zapier, but basically what these do, and I use them for a couple of things, basically what these do is they'll help somewhat popular products, talk to other products.

Where you wouldn't think for example, maybe LinkedIn would do something with your email, or maybe Todoist does something with QuickBooks or something like that. But even those programs, there are some pretty cool ways that people can do stuff. So there's a lot there.

But your company When you're building stuff, you can actually take it sometimes one level higher, at least one level higher than what we would do as average business people that can tinker around a little bit with things.

[00:06:59] Alex: You're exactly right. And quite often, I guess if you look at the trajectory of technology over the last 20 years, back in the day of FileMaker or Microsoft Access, you have a lot of the use cases covered by those, great tools, frankly speaking.

 Even today, like every now and then, I miss Microsoft Access functionality because it was great for many use cases. Today you have a lot more specialized tools. often they cover a lot of those use cases, but, frequently you need to integrate them together. And I think this is where there's a lot of opportunities for you, something like Zapier or if this and that. But as you go kind of deeper into a use case you start kind of pushing limits, like pushing against the limits of those systems.

And they, that may be limits of functionality, where, I wish it done something like this, and there's just not, there's just no kind of action or there's no integration that they support, because it's, it may be very specific or it may be, limitations of maybe it's compliance or regulatory, in the pharmacy business, like you under a lot of regulatory oversight.

So those tools, they do not really get, geared towards regulated environments. They're more consumer and business friendly. But if you start to like exchange, patient data that requires HIPAA. So there's a lot of those things where if yes, early on, it's kind of cool to use it.

But then when you really get into a business use case, there's more things you need to be thinking about. So that's what I often try to do with folks I talk to, to kind of really help them truly understand what they are doing, like what they are trying to achieve. Because many times people don't understand what technology can do for them.

So I'm trying to kind of broaden that horizon, but also what are some of the elements that are influenced, like the ultimate decision about the solution, so I generally kind of try to split things into two areas. problem space, let's define what the problem you're trying to solve,

 And what influences that problem? And then the solution space, what are the possible solutions? And maybe, it's buying something that gets you to 80%. Or it's building something that gets you to 90 percent or building something that gets you to 100%.

There's variability, of course. So my role is really to help navigate both the problem space and the solution space and hopefully find something that works. 

[00:09:14] Mike: Alex, now the other day I was telling one of the guys in my pharmacy, I said, with chat GPT now, which can

Give you HTML or CSS programming and so on, if you tell it kind of what you want it to do, I thought with that and API, which I want to ask you about, I thought with the two of those that I could do. And that more, I told my staff, I said, I should learn this. I don't know what the hell, why just for fun to see what 

I can do it. Explain what API is and explain how maybe AI could. Merge those. And is that something that an old fart like me could have fun with? Or is that beyond, you've talked to me now for five minutes.

You know what I got upstairs, but am I missing something? Is that the thing? the API and the puzzle piece, maybe with chat GPT, what else am I missing in that equation and what could I do with that stuff?

[00:10:19] Alex: It's a great question. So first of all, I think ChatGPT is a very powerful tool and in general, technology over the last 20, 30 years has been built on two kinds of competing philosophies. One is really about democratizing access to it, and the other is kind of more closed source, like more kind of business.

And both have a place in business. I'm not like a proponent of open source versus closed source. But my belief is that everybody Not only could, but actually should try to experiment with technology, at the depths. as Deeply as they can go, really, so let's talk a little bit about what APIs are and maybe how working with something like ChatGPT can help you just get a better sense of it of APIs and capabilities of different systems.

So API stands for Application Programming Interface. This is really kind of a way that computers and systems and applications can talk to each other, so it essentially defines four external worlds. What you can talk to me about, so think about Maybe. Let's use a very simple example.

Let's say a door, so what can you do with the door? You can open it. You can close it. You can lock it, for example, so those three different actions, that's your basic API. Basically with the door you have three different kind of endpoints and you can, do this action, to the door, 

so that's a very kind of basic way to explain what an API is. For any software application, an API can essentially exposes. to the world what can be done with that application through the API, you can ask the system to do something, You can get some data out of the system or an application. and that's really, without going too much into details, what it is every API

 consists typically of endpoints, So those are, those actions that you can kind of run against, And then with endpoints, you can pass some data to it. Give it some parameters, like going back to the example of a door.

Maybe you can close a door halfway, so you would call closeDoor and pass a parameter halfway to it And then the door would close halfway like a silly example, but I hope it can help you. So, A lot of the systems have public APIs. What it means, there's a public API and a private API. Private APIs you typically can only call, you have specific access, maybe it's for internal use or maybe it's for some sort of registered users or paying users or whatnot. But, public APIs are the ones that you also as a user can typically call and execute some kind of action against.

If you look at the system that's kind of maybe familiar to your listeners, let's say MailChimp, which is an email system that delivers email. I don't know exactly the API, off the top of my head, but I'm pretty sure they have an endpoint that allows you to manage the 

And the meaning of the going to use. It's the return value of the last word, which is a, the ending of the sentence or the first limit of the sentence. And so, you know, you can use that method, where you will return the value And then you will use that method to create something called a response.

So once you have the ability to call something, you can say, Oh, what is this response? the question? This response. do that? Oh, it does. Does it? And then that will give you the So when you're working with something like chat gpt, what really helps is have some basic understanding of how, put a simple program together, let's say in a language like Python.

 There's a lot of great resources online today. I mean, back when I was kind of learning software engineering, we had no documentation of any kind. You would learn by basically just experimenting with looking at the code. Now when I look at younger engineers, it's like our kids these days type, moment for me It's like man, you guys have it so good 

anyway going back to what I was trying to say learning just very basic Aspect of how you can program in something like Python or pick any language really but I recommend Python because it's very easy Would help you with two things 

It will really make your sessions with chat GOT a lot less frustrating because you would kind of Know what to do with what it tells you, right? Yes. It can provide instructions and everything, but you would at least get a better sense of like, how do I actually do this? But it'll also, you know, start helping you understand how machines work, Like what they do? And then, you know. Maybe it doesn't go anywhere beyond a basic experiment. but I think it can also help you kind of in the long run just with understanding of what is possible. So my recommendation is to take a very quick course on something like Coursera or any other site. I don't really have any affiliations with any of them.

Um, To learn the basics of a language like Python and then with ChetPET, When you're experimenting especially with APIs, Let's say you want to. experiment with some kind of a, let's say benefit verification API, we talked about this before the show. And if it's a public API, it's a known API.

They publish the documentation. You can go to the site and go to the developer section and look at the API. There is a high probability that ChatGPT would actually know what that API looks like. So you can ask it and say basically, Hey, help me write the program that does this against this API, 

Maybe the MailChimp API is a good example. and be very explicit in terms of step by step instructions. What do I need to do to write it, to run it? And how to really work with it so you want to tell it to sort of treat you in a more step by step way, okay, like it gave me a bunch of code, but what I do with this code, like how to actually run it, 

so help if you're more explicit with those strengths, I think it gives you good enough instructions to be able to do it. But if it's a private API, if it's an API, like with this benefit verification system, most likely you would need to actually pass documentation of that API. And here you need to be careful with two things.

One, if you're working with a third party company and they gave you access to their API, you want to make sure you're actually legally allowed to kind of, take the API documentation, effectively upload gpt, so that's first thing, number one. But secondly, it's almost always protected by some mechanism.

So chat gpt wouldn't really know that, and you would need to tell it that, and in API documentation, if you read it, it'll tell you like, here's how to log in into it. So effectively you take that, upload it to ChatGPT and instruct something like using this API documentation, help me write, a program that does benefit verification for patients, given their, insurance card information, 

so, those are very broad kinds of two approaches. one of

[00:16:41] Mike: The things that the APIs could do that something like Zapier could not do is Zapier seems to be more having your information.

In a program, talk to your information in a program, like I said, email to, LinkedIn or something like that. It seems like API more, if it's allowed, you can get into certain databases of information and pull that out where something like Zapier may not do that.

[00:17:18] Alex: Yeah. I think you're right. When you work with Zapier, what you see a lot is, want to support the most common use cases, so they, if you need something a little bit more complex that API supports, because Zapier also uses API under the hood, 

 they don't have magical access to the system. They also use

the same 

[00:17:35] Mike: ask that. 

[00:17:36] Alex: So they use the same APIs, but they only may use 20 percent of it or 30 percent of it. And they also may not give you the full list of maybe they limited to certain, range of the parameters, 

so having direct access to API gives you way more flexibility It also. allows you to construct use cases that are more elaborate, for more elaborate ones, you probably want to talk to someone like me or an engineer that you know, because they would require a more complex setup, but they allow you to do a little bit more interesting things.

Like uh, maybe something called a callback, where basically you're not really going to, the door. The door opens automatically based on a particular event. And then, it calls your system and then that's something Or it may be a more real time system, where you have the data constantly flowing through So it's like a bank. send a message to your system and something is happening based on that. Or maybe it's based on some other kind of a less typical use case where it's not this transaction, it's not . If this happens, then this, It's a lot more, if this starts happening like more than five times a day, then do this, That's another example where the logic could be a lot more elaborate with those APIs. there. 

[00:18:46] Mike: So you have an idea for a handheld program for your business or something.

Is happening is that you think computer technology can help and you have these APIs and you maybe have them talk to each other through a little bit of programming. What is the landing point for all this? So for example, for a business like that, our listeners have, it might be Wix or Weebly or 

Squarespace or something like that.

What is the landing spot though for. Having an API program. Are there similar things I've just talked about, like those website programs? Is there similar things to build APIs connecting on

[00:19:35] Alex: There are more tools coming on like these that allow you to kind of integrate different APIs. ones that kind of come to mind and one that we use. For a much more complex type of interaction. Something like WebFlow, for example, is a pretty good tool. Uh, We use it ourselves, pretty extensively.

 Now we're talking more about kind of low code or no code solutions, what they call, so this is really a relatively new thing, but the talk of it has been around for the longest time. I remember back in like 90s, there was this concept called visual programming, and it was predicted that software engineering is going to be obsolete by year 2000, because you would be able to just kind of move blocks together 

 Obviously it didn't work out this way, but no code and low code tools actually have a lot more promise than, earlier day visual programming tools. They allow you to really kind of, interact with systems and build systems in a more intuitive way, and the barrier for entry there is a lot lower, 

If you, for example, it might take you like a month to learn Python. to do some basic things with a low code tool or no code tool. Maybe it will take you a few days to get to something more productive. I certainly recommend experimenting with them. there's a lot of them like Webflow is actually one of them.

Another one that's interesting called Bubble. They also have a fairly robust tool. Google has their own tool. There's, there's a lot of them kind of on the market and it really depends on.

and, this is like a good start. If you want to essentially kind of combine the two API's, maybe, and you want to land data in some kind of a database and then expose it somewhere, to your internal system, or if you want to expose it to some kind of a web page that you build. Yeah, So that's kind of a good set of tools to experiment with those things.

[00:21:23] Mike: Talk to me about how AI has made your job or your day easier. And we know AI as being large language models from eight months ago or a year 

ago. And I know that depending on how AI is defined, AI has been going on for 20 years, but 

in the last year, has any of this made your.

Job of finding solutions for companies that have problems to solve. Has it made that easier and more engaging?

[00:22:01] Alex: Interesting question. I think you're right, aI actually goes back way, way back. I think if I'm not mistaken, probably the sixties, maybe late fifties when the whole concept of really foundational elements of AI started to get built. And so you go through these waves like false promises.

 you can even see it in in the literature, because every time there's new development of technology, you start seeing the impact in literature and kind of, pop culture here's AI is coming, but so this one, this kind of iteration of AI revolutions, let's call them, I think has a lot more practicality to it.

 I think earlier iterations of AI have a lot of practicality, but it was more specialized, because it gave you tools that are a lot more specialized and would only be used in a particular use case.

Now we have, what looks like in LLMs, a tool that is a very general purpose tool, that you can kind of apply to a lot of different problem sets and see what kind of happens. I don't really know that we, as a business society or even as a society in general, kind of figured out what LLMs are really Um, what is the, like, the best use case?

 you see a lot of people experiment with and a lot of new tools

 coming, but I don't think we fully realized its potential yet. And it's very early on to kind of really say, Oh yeah, like this is great for this. So for us, we've experimented with LLMs a lot as well. I mean, a lot of our clients come to us and basically say, Hey, like I want to integrate LLM into my product, or I want to use it for this particular business challenge, we work a lot with data. And when that kind of comes together, we have a kind of track record of using AI and more specifically machine learning and NLP, natural language processing to solve different kinds of business problems. So I have some expertise in that space. So that's one thing that kind of over the last, year that interest certainly exploded, 

there's really a lot more kind of incoming interest there. But for us from an operational perspective. We still, basically at our company, everybody has access to it, so you can freely use it. We don't really limit it. Obviously, there's like limitations of what data you can put in it.

Going back to my earlier point, like you can't really put customer data in there or anything that's sort of confidential. So you need to be careful about this. And if you, as a business owner looking to really help your staff experiment with my recommendation would be. Like just having a conversation, explain the concept of different levels of confidentiality with the data and like how you classify it, like what is considered confidential, what's not.

It's a good training to have actually on an annual basis to sort of refresh it in everybody's memory. So we're going to let it open for everybody to experiment with, tools like ChatGPT but also BARD and other language models from Facebook, for example. There's a lot. Milenkovic is a great model too.

And we use it mostly right now for generating copy in different ways and helping with communication. The challenge we face is this. I don't think for us specifically it's there yet because the copy that it produces is too machine like. In my liking, it is too grotesque in a way, 

there's a lot of unnecessary words. You would never actually hear a person speak like that. So what we have to do is prime it, to follow our particular tone of voice. And it gets us about, I would say, 60 to 80% there, depending on the type of content we're creating. And so the remaining, 40 to 20%, we have our kind of copywriters and uh, marketing team to sort of review and use.

So that's one big use case that really helped us. And same for internal communication. But another interesting thing that we use, for engineering we try to use those tools. Frankly speaking, tools like Co Pilot from Microsoft or GitHub more specifically they're interesting tools, but I personally haven't found them to be Particularly useful.

They're great as just kind of, get some basic things done but they are not there yet. I don't think they're there yet to really build something complex. Again, focus on more complex use cases. So for us, here and there we use them, but not across the board. for engineering, for development.

Another, sort of a third area where I find them pretty useful and we as a company also do. That is really more exploratory so contact what is out there tell me about you know This particular domain that maybe and it really gives you a good jumping point And this is I think kind of a what a lot of folks are really using it for as well Is really for brainstorming and bringing up things that maybe you didn't know about 

and it gets you on this path of deeper discovery. So those are, three most common use cases. thAt I think here to stay. The other things that are sort of maybe less common that we do is, we use it for code transcribing and then just insights and summary.

So we use a tool called Firefly's AI, which is pretty popular these days. It's a relatively big organization. We have a hundred people and they are all in different parts of the world. Even in the U S we have like multiple times or so, we can't all be on the same calls all the time.

So having a tool like that allows you to very quickly to kind of catch up with what happened, overnight in Europe, or what happened, in, in the Eastern time zone or whatnot. And there's some, in some of those meetings. And unlike just transcribing tools, it gives you. Um, uh, Milenkovic, uh, Milenkovic, uh, 

Milenkovic, um.

Let's go back and watch part one of this. Um, we have, um, uh, the action items of the action item. have, uh, something called the call transcripts, well, not actually the call transcripts, if you've seen the previous part. But this is, um, the, the, the, the, the, the, the call transcripts and the action Internal project management system. So like we talked about it, it captured the action items and then it put into project

management system that exactly So we're not there yet. We're kind of experimenting with this thing, but it's a pretty easy step, so for us, I think we're going to be there in the next quarter or two and that already kind of cuts down the amount of time we need to spend for purely administrative thing that doesn't add any value to the customers, 

it helps us to communicate better as a team and just helps us to create kind of a more cohesive 

business, I think.

[00:28:08] Mike: Alex I want to tell a couple scenarios here. Then I have a question on that path. You hear a lot about the dangers of AI, two come to mind. One would be all of a sudden, you have an 11 year old in your basement and they're, making up, our president or a president across the pond, saying something and, we're 10 minutes away from bombing this or that. It's like in the old days, you could say this on the news at 6. 30 with NBC's logo and stuff, but nobody watches news that way. Now, anyways, it's just all floating around. So that's one thing I know that people could be concerned about. The other one I heard is there's a bunch of examples of this, but it's you tell AI, you say, AI our company wants to not use as many paperclips, something like that.

And AI comes back and it says. Kill everyone, that's one way to not use any more paper clips. So those are just some anecdotal things on the dangers. What truly are some of the dangers with AI as, as you see it, if you do see them?

[00:29:19] Alex: Oh, sure. I think with any technology you have dangers and unforeseen consequences. And frankly, I think you gave two interesting examples . But I don't think we, as technologists fully understand yet the impact that AI will have on the world. And I think the same with every technology, frankly speaking, like you're going back to the famous quotes of CEOs of IBM and Microsoft and they didn't see market for more than 30 or whatever it is computers in the globe so I think we're in that times today where I don't really know and I don't think anybody truly knows What the real dangers of AI are and what the real positive impact of AI, is so I think the question of like disinformation and using AI as a tool to really create that source of disinformation, I think it's certainly possible for sure.

And I think what it forces us to do as a society is be a lot more cognizant about how we consume the data and where it's coming from. And really go an extra mile to verify its validity. 

And so really, I think as a society, we have to kind of go through this transformation and get to the point where, you know, everything we see or hear, and I think frankly, it's already happening, it's already like people kind of healthily mistrusting everything they see online or trusting particular sources, which, there's a problem.

and It's all right. But I think we're still in the early stages of both. It's kind of a malicious usage of AI, and a positive usage of AI, where maybe we have more of those tools to be able to validate, oh, is that really the voice of some world leader? Is that really from a trusted source? And I think there are many tools.

starts adding some of those kinds of validation verifications. Cryptography is naturally one of them, but there's other things. So that's one thing. In terms of a paperclip, doomsday scenario, I think AI may be not as smart yet to be able to exterminate the human race.

It might get there one day, hopefully it doesn't. And hopefully we're more aligned with it or it aligned with us. But I think we are far from it. And, for something like that to happen, I think it needs to be a lot more integrated into the real world, so to speak.

Today, for all intents and purposes, AI is more, it's a program that runs on a huge computer that, you can turn off if you want 

to, 

essentially. So that's a valid concern. I think we're not too far away from a scenario where, you know, not exactly with paper clips, but with some other means of delivering some kind of damage to society.

It could be possible. And as technologists, as business leaders. I think we need to be cognizant of that and kind of keep an eye on it. But I also think we shouldn't be too scared of it just yet, because theory is really one of those things that kills a lot of possibility for innovation and a lot of progress and, going kind of too quickly too.

The realm of like regulation or stopping any development. I think we're not there yet. 

[00:32:24] Mike: Was that the stopping of this? Was that face value? Were they trying to? Be cautious of this, or were there deeper levels of why some companies wanted to stop? I mean,

[00:32:42] Alex: Mm 

[00:32:43] Mike: in a nefarious way that some companies wanted to catch up with others and things like that.

Do you think it was true, and I don't even know if you'd ever stopped something, but was it a true attempt to look at it? Can we take that at face value?

[00:32:58] Alex: It's my personal opinion, but I don't think we can, frankly. Because, I've been in the AI space for pretty long, before the LLMs and everything. And,

[00:33:06] Alex: I know a lot of it, but not everything, and then you look at somebody who's really You know, can't really tell the difference between different models, for example, 

or different approaches. And you're asking them to create the regulatory framework to really enable innovation and reduce the risks. Like it just doesn't make sense to me, so I don't think it's really like a very good attempt. It's too early in my opinion to really regulate anything AI related.

And, I'm sure there's a lot of different factors that need to be considered. And I think it's a good time to start the dialogue, but I don't think it's a good time to really kind of go into action and start kind of limiting anything. In terms of whether that was, I think you alluded to like regulatory capture, 

So maybe I don't really know for sure, but it sure seems like this because why else would you be really having leaders of some organizations basically coming in and saying, yes, please regulate us at this junction because they are so far ahead of everybody else. And you limit your competition there. But for better or worse, I think the cat is out of the bag at this junction.

I don't think it's really physically possible to stop it because, you have other countries that are already experimenting with this, 

[00:34:21] Alex: you have open source models. And really. I think it would be just wasted effort if we, as a country or as a society come off to the AI and really, because other countries are going to ignore that, 

they're just going to blow past us. Yeah, exactly. And yeah, so we shouldn't, I think we should embrace it. I think that's, again, for better or for worse, I don't really know if at the end of the day, AI is going to be net positive or net negative for society. I don't think anybody knows, but you know, we as humans.

I kind of bet a lot on technology to this day, and it's so far produced, pretty good results, I must

[00:34:55] Mike: Sure, exactly. Alex, did I get that right that there's so many different kinds of AI that for them to stop it and try to understand it and regulate it all, it's like you'd basically have to pull the plug on it because nobody would be able to have a real good answer for all of it because there's just so many different areas to know, you'd have to basically stomp down the whole thing and really never open it up again. Cause as soon as you open it up again, you're going to have all those different ways of doing it. And how would you control that? 

[00:35:32] Alex: Yeah, I think you're right. I was thinking about that too. I don't think there's really kind of a realistic way, even if you have regulation, so regulation without enforcement is 

really useless, right? so enforcing that, that disjunction I don't really know if it's I don't know what you, anything short of going on the way to some sort of a, dystopian, super big brother like state, I don't think that's a possibility, 

[00:35:58] Mike: I was thinking about AI now, and it seems there's a lot of people worried about, okay. My head. My face is going to be on the body of this person, whether they're robbing a bank or they're unclothed or they're whatever. Alright, so everybody's worried about this, but in my opinion, you get past that and within another few months here, people can say what are the odds of that being Mike?

 So now you might look at something and say, Oh, look at what so and so's doing. I see their faces and they're torturing this cat or something. A year from now, almost everything you see, you're going to take with a grain of salt.

You're going to say, no, that's probably not Mike torturing a cat. It's probably something else until you 

can prove to me it is Mike.

 And now maybe that's going to be a total untrusting of society and so on, but I don't think people are just going to be sucked into believing everything they see forever is true.

I think it's actually the opposite. People are going to be having a real skeptical eye on things, which might help people

 like me. If I was actually a cat torturer, I'm not saying I am, but if I was. I'd probably get a free ride because you say Mike's not torturing cats. When in fact I might be,

[00:37:24] Alex: Yeah, that's an interesting point.

I, 

Yeah, I think we have to find a way, almost like social etiquette. And I think in some way, like this, I think it kind of goes in waves where, if let's say 10 years ago, you were kind of a lot more trusting to this broader world and the internet, 

so now maybe like your Let's call it cohort of trusted sources is going to be reduced to more people that you actually know in real life, so in some ways, I think we're going to, maybe, and again, I don't want to make any prediction, but in some way, I think we're going to go to value in those social connections in real life more than the value kind of the connections online.

And, the pendulum kind of swings back towards that until we probably figure out kind of like a way to really. And then maybe it starts going the other way too. So who knows? But, I think it's an evolution we have to go through. It's not dissimilar to something like social networks, that came online in the mid 2000s.

And so I think we all kind of went through this phase where Like, how do we really use this? What is it? What role does it play in our life? What is an acceptable way of using it or what is not an acceptable way of using it? And, it'll differ between different social groups, I think.

But, in my opinion, we have to go through that process of really experimentally kind of figuring 

out,

[00:38:52] Mike: was just thinking about this. Let's say there are some. Religious stories, whether it's the resurrection of Jesus or whether it's, Mormon, Joseph Smith, I think, getting this revelation or whether it's Muhammad or whatever.

and you think to yourself those are just stories from humans. why didn't this stuff happen in the year 2000 when you have pictures and videos and you can capture and all that kind of stuff. But now it's funny, cause we're almost going full circle where you say I don't believe in videos.

I don't believe these accounts. I have to talk to somebody about this. 

[00:39:27] Alex: Yeah. No, you're right. You're right. I think that I feel like we're going towards that, frankly speaking.

[00:39:32] Mike: Alex, you mentioned something about, I think you said cryptography or something. 

Are there ideas of ways to, almost like a blockchain, like to prove where the information's coming from in the videos and things like that?

[00:39:46] Alex: Oh yeah, I think there's a lot of ideas in that realm right now. I think what we have to be careful of is really avoiding creating something like the ministry of truth, where there's a record of okay, this is the truth. And then well, 

[00:39:58] Mike: already seen that fall apart 

with, Twitter and Facebook and all 

that kind of thing. Whose truth is it, 

[00:40:02] Alex: yeah, exactly. So I think the model that I probably see a bit more democratic is where I can verify what I actually said or did. So I can verify with my cryptographic signature that yes, this is a true video of me and everything else that lacks that cryptographic signature. Basically, that's not me. 

[00:40:27] Mike: you almost go on to something if you saw something of you or you produced it You'd go on and almost put your Fingerprint on it 

[00:40:35] Alex: exactly. 

[00:40:36] Mike: I acknowledge this

[00:40:37] Alex: Yeah, exactly. I think that's probably more, that doesn't really require any sort of centralized authority of any kind, political, social or technological, like a blockchain. Yes. It's a kind of a publicly accessible record, but then it's still the record that, that sort of.

 exists, it's not like I do not control it necessarily. But I think to get to the model that I suggested, I think we need to kind of go through a few iterations where cryptography is also a lot more democratic and easily acceptable. And I think we're gradually getting there.

And I think, frankly, like crypto is helping us to get there a little bit faster. For all the bad stuff that's going on there. And I think there's very good pieces of technology that can come out of it and good tools as well. So yeah, I mean there is a pathway to it, but I think it all comes back to what is going to be socially acceptable.

I think technologically there's a lot of ways to do this, but what we as humans are gonna kind of accept as, okay, like this is convenient enough, this is reliable enough, and. We all agree that, yeah, that's a way to kind of validate the truth, 

 There's this Biologist guy I was listening to on Rogan today

[00:41:48] Mike: and I thought this guy had a bit of Quackery to him. I wasn't sure where I was going? I went under reddit,

where you've got you know 300 people talking about this and 

it's almost like Those comments of this guy being a quack kind of raise up to the top and then they showed a picture of a mugshot from him, seven years ago and I guess you could almost say the Amazon rating scale is like that, although you don't know if those are real or not, but it's almost like crowdsourcing of reality or crowdsourcing of truthfulness.

Yes. 

Until you hear what everybody else is saying about it, only 

[00:42:31] Alex: Yeah. 

[00:42:31] Mike: don't want to just say that it's real, but when you see people talking about it in. Opening on it, you get maybe a better feeling and maybe it's gonna be kind of a crowdsourcing truth in a way. I know Wikipedia is sort of there, but I know that's got some biases and things too.

But it's probably closer than just reading one article online that you don't know where the hell it came from.

[00:43:00] Alex: Yeah, it's an interesting problem because with crowdsourcing too, you have If the crowd is small enough, you're still gonna have different narratives that's going to drive that end result, How do you get large enough crowds? I don't know what you guys specifically refer to on Rogan's podcast, maybe he just has a lot of people who hate him.

So they showed up for that Reddit so you never really know. and it's very challenging I think for us as humans to kind of really, Get to the quote unquote truth and it's and truth itself is fairly kind of objective thing, so who knows what it is, but you know, I I think over time it maybe we get to the point where Kind of getting more and more people involved in this process of kind of contributing to things like on, acts slash Twitter, you have this concept of community notes, for example, that I don't know if you've seen them or use them.

I think it's something like that. It really helps the community and has enough of the kind of people to contribute to something that is maybe objectionable or maybe kind of misrepresenting something. So you start kind of at least seeing Oh, maybe I shouldn't just automatically trust what this person is saying.

And there's more. So it's not necessarily replacing the truth, but hopefully it gives you enough of a desire to go and do your own research.

[00:44:21] Mike: Is there a separate section that talks about posts or news or something? 

[00:44:26] Alex: they have a fairly interesting algorithm. I don't remember exactly how they did it. I was listening to, I think, an interview with Elon Musk the other day and he explained it. But. Yeah, so on some of the tweets, I don't know what they call them now, it was this rename. You can kind of see, especially the ones that have maybe a lot of exposure, but also something that is questionable, 

I think people can contribute to it and it automatically shows what they call community notes. And basically provides more context. Like, because a lot of times in this medium, you see things that are taken out of context, and it kind of creates this impression that like you said something horrible and in reality.

It was like, And I think the way they do it. On the back end,they basically try to collect people who typically have opposing views. But have the same view on that particular tweet. So is a pretty robust approach to it. and the ones that I saw they were pretty good, you know in my 

opinion, so 

[00:45:22] Mike: so 

It's almost like a side wiki sort of thing.

[00:45:25] Alex: Yeah kind of like that 

But it shows kind of in place like so you see it right there You don't have to go like extra step to research it, 

and I think it helps to combat this information there and still probably a lot of issues with it. But that's definitely a step in the right direction.

So I'd love to see more of things like that, just kind of appearing. And I think that would help us navigate this information landscape better 

as a society. 

[00:45:50] Mike: I was talking to Somebody a while ago on the show, and we were talking about blockchain as far as some of the values of it, and one of the things they were mentioning is that they say all the technology kind of has its day in the sun, as it comes out. And then if it's not really needed, don't spend the time and money doing it.

So blockchain, from what I've heard, it might kind of just. Gradually kind of be there without maybe making it into the news and so on. But they were saying that for things like pharmaceutical studies and things like that, 

just. Knowing that data is good. And so if you're taking, weights of somebody, that it was 190 pounds and not 109 pounds or something like that.

Just having that secure data. 

[00:46:41] Mike: What's your take on that, Alex? Do you dabble in any of that with your stuff or is there not a need to dabble in it? Where are you on the blockchain?

[00:46:53] Alex: I think it's an interesting technology. You kind of came across it, I want to say about 10-9 years ago. I've been dabbling in it for a pretty long time. And I'm of the opinion that it certainly fueled a huge hype cycle that I think, frankly speaking, hurt its practicality.

 In my opinion, it just sort of shifted focus to Kind of gold rush, quick money making without any foundation. 

[00:47:21] Mike: NFTs of cartoon characters and all that stuff. 

[00:47:25] Alex: Yeah. Oh, yeah. I mean, the NFTs are probably less toxic in a way. Or we're less toxic of an event. I think, frankly, NFT is going to come back and be here to stay.

 I think NFT is It's interesting concept. its adoption is really what remains a big question mark but I see more and more activity in that space. 

[00:47:47] Mike: That might have to be for things like concert tickets and those kinds of things that maybe have some more substance than just bored apes.

[00:47:55] Alex: Yeah, exactly. Or, other types of collectibles like, baseball cards, for example, things of that nature. 

 I think the whole, what you've now seen, or I guess already saw was things like FTX and just the fraud that was happening in space. that really detracted from valuable applications. But that said, I think blockchain is here to stay. We as a company have some clients that operate in that space and we do some work that's a lot more kind of on a practical side, in my opinion.

I do see its application gradually being added to more areas where it has a positive impact. I think maybe a few years ago, you would have folks who would come in and they basically just want to use blockchain for the sake of blockchain, 

But I think it gradually, like, was one of the things that sort of came on pretty quickly. There's a huge hype cycle, we didn't fully, meaning the technologies didn't really have a very good grasp on the positive impact it could have on real life businesses. A lot of new types of businesses were invented and a lot of them proved to be not really businesses but just kind of useless use cases.

But now I think from the ashes of that, we start seeing new things that emerge that I think have potential. And, I think things like blockchain and AI and, metaverse and, AR, VR, I think they gradually sort of coalesce towards a different way of engaging with, computers, if you will, and really kind of different reality almost that is powered by all of those things that hopefully will enhance our human experience but that's kind of my opinion on blockchain.

I think there's still quite a bit there And I think over the next years we're gonna start seeing it being integrated more into our lives maybe not in a very apparent way, but in a more sort of Obscure way you wouldn't even know that you're 

[00:49:42] Mike: You don't know it 

Slow creep coming in. Yeah, and you had the whole idea that the blockchain was a kind of decentralization, but then like you said with FTX, it's like You need something rather than a guy and a girlfriend's garage kind of thing.

[00:49:56] Alex: Yeah. And frankly speaking, even today, I think crypto has, it's a pretty difficult thing to do securely, like you, you have even for me, and I'm an engineer. Like I know how all this stuff works, but every now and then it gets a little bit too confusing. Like, how do you do there's all different networks and there's all different tokens and like this and that.

And it's not human friendly. I think the huge hurdle that crypto hasn't overcome is really making it. Human friendly and easier to use than other systems in our lives.

[00:50:26] Mike: And they tried with Coinbase and stuff like that, but then there's always talk about how secure and this and that. But. I think you're right. And maybe it'll never get there, 

and I think that's why the LLMs have done well, because, of having chat GPT in your hand or dally or something like that, 

it's that user interface. That's there. 

[00:50:49] Mike: Alex, we talked about a lot and all the different technologies, the pros and cons of those, and there's more that are out there, the voice technologies and drone delivery and all this kind of stuff, someone like you, who's at the forefront of business needs

What kind of things are out there that look cool for you five or 10 years down the road that maybe your customers haven't asked you for it yet, but you can kind of see a little hints or glimmers of what might be cool five or 10 years out

[00:51:23] Alex: Yeah. It's a great question and I mean, I wish I had a crystal ball and it worked, but I really, so to me, technology is really, it's really great when you almost don't notice it. And I think this is where I feel a lot of things like, AI, I think is really getting there where, 

 you have some kind of an assistant, if you will, and there's obviously products that already tried to do it, but they're not quite there yet, but some kind of a, Semi intelligent entity, hopefully more intelligent, that can really help you to navigate the world better and in a more seamless way, wish we can kind of pull our eyes away from the phones and start looking at the world around us and seeing all this beauty, and we have.

I still have access to the internet and all the information there, but through a different type of interface, so that's kind of my personal hope. That's one thing. The other thing I really hope technology will do in the near future is you know start bringing people closer together and The people that are very far more like you and I kind of looking through zoom on each other we have the video conferencing and it's really exploding popularity, obviously But I think the next frontier is really things like, the Vision Pro, for example, from Apple.

I'm really curious about how it works and all the reviews that I listened and read, they seem to be pretty positive. So I'm really curious how that kind of transforms the world of business interaction, we can not just kind of see each other, but almost feel the presence of each other.

Because I think that dynamic changes when you're in the room with somebody, it's a different type of conversation. So I'm really excited about trying things like Vision Pro or, Matt is working on theirs as well and seeing how that could be adapted in business settings and really bringing people together, 

Again, technology, not for technology's sake, but really. To the end of it, helping us communicate better, helping us do more, together and really bridging physical barriers, if you will need to travel somewhere. And, I still think that's an important thing to do, but, that's an exciting area that I'll be definitely monitoring and hoping for a positive progress 

there. I don't really know where it's going to take us. Maybe we as a society just rejected as like a 

silly idea of wearing a computer on your head, like we did with Google glass. 

[00:53:54] Mike: You had the Apple Newton that was out, I don't know, 10 or 15 

years before the iPod. And like you say, Google Glasses are kind of coming and going and they'll resurface again and so on.

[00:54:03] Mike: I Remember there was some debate with Zuckerberg at Meta and they were talking about the two things.

One's virtual reality, one is like augmented reality and one you're like totally taken away and the other one you're sort of more like the Google Glasses you're. Interacting in your life and you might be able to see something there, those are kind of 

the two different routes that this could take.

[00:54:27] Alex: Yeah. I think you're right. I mean, augmented reality is about. helping you navigate the real world, that may be interacting, just get more relevant information to you, when it's needed. And I think virtual reality is really replacing that real world, a virtual one.

And I think both are great pieces of technology. I think it's a great area. And I'm pretty sure over the next 5-10 years, there's going to be a lot of experimentation there. Because I don't really, I mean, we've already had Google glass and we in Oculus, and there's it's a new area.

The market there is not too big. I think only guys like, meta and Apple and Google can really afford to experiment in it. Cause they spend billions and billions of dollars building that. So that's the challenge for startups really that don't have such deep pockets, experimenting with those things.

And that's why I also think about the pace of innovation. May not necessarily be very fast, but hopefully it will be pretty steady and we'll see better and better products coming on the market and that help us to live better lives.

[00:55:30] Mike: It's nice to see the meta stock is back up now. I know Zuckerberg sold some stock cause about where it was before he did all 

this virtual reality. Frankly, since they went over to the meta name and so on. So it's cool to see that risk and then the market not being afraid to kind of support that company still, even though they had some risk in taking some different directions to the future and so on.

Yeah.

So Alex, boy, we had so much fun covering stuff. Now we started with Zapier and such. And 

knowing that a lot of things that people come to you with, there might already be solutions for. Give me an example of something that maybe is just out of reach with the Zapier stuff and those kinds of things.

What's an example of a. Project that maybe you take on that's just out of reach of some of this off the shelf stuff.

[00:56:24] Alex: That's an interesting question. If we're talking about a lot more complex systems, you may look at things like, having a proprietary data feed that you want to analyze in a particular way and maybe create some kind of a visualization that your customers can access.

So that would be more complex. Stuff like that is, certainly we do quite a bit. Things that are a bit beyond Zapier's capabilities are when you have. you have multiple systems and you try to connect them together, or maybe like they have relevant information that would be nice to see in, in, in one place, but not just that, because that, that maybe you can get with Zapier or even no code tools like Webflow together but also enabling some kind of a behavior around it.

So maybe that's for your internal staff, maybe you want to expedite processing of some kind of data, or maybe you want to enable it. A new way of really engaging with your clients, whether through some kind of online tools or maybe, through messaging of some kind, 

So this is where you start moving from just integrating systems to now enabling certain behavior, and that's where we can really help make that a reality.

[00:57:30] Mike: Boy, cool stuff. Alex, tell the listeners where they can reach your company.

[00:57:38] Alex: Sure. So thanks, Mike. It was good, the conversation too. I really enjoyed it. Your listeners can find us at mev. com. It's a pretty short domain name. Yeah, always happy to talk to anybody and see how we can be of help.

[00:57:52] Mike: Alright Alex, take care. We'll talk again soon.

[00:57:55] Alex: Thanks. Bye.