Leveraging AI

24 | AI is the New ROI: Practical Ways AI Drives Profits and Growth for Your Company with David Hirschfeld, CEO of Tekyz

August 08, 2023 Isar Meitis and David Hirschfeld Season 1 Episode 24
Leveraging AI
24 | AI is the New ROI: Practical Ways AI Drives Profits and Growth for Your Company with David Hirschfeld, CEO of Tekyz
Show Notes Transcript

Are you ready to transform your idea into an AI-powered product that adds value and generates revenue?

In this  episode, I sit down with the brilliant David Hirschfeld to talk into the fascinating world of AI and its implications for product development. This is a deep-dive discussion on how AI can simplify the journey from a mere idea to a valuable product.

Topics We Discussed:
🧠 Leveraging AI for business efficiency and career advancement
💡 The magic of transforming an idea into a tech product
🎯 The role of AI in making product development smoother
🛠️ Breaking down the process of AI-powered product development

David Hirschfeld is a thought leader and expert in the field of AI and product development. With a wealth of experience and a passion for innovation, David brings a unique perspective on the practical and ethical ways to leverage AI for business growth and product value creation. Connect with David on LinkedIn and join the conversation on the future of AI and product development. 

About Leveraging AI

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Isar Meitis:

Hello and welcome to Leveraging ai. This is Isar Metis, your host, and in today's episode, we're talking to David Hirschfeld. He's the CEO of Tekyz, and he's going to share how his company is applying AI to more or less everything they do in the business. How he is creating an environment where his employees are encouraged to use AI and how they're creating a safe environment for them to test different AI's capabilities so they can then deploy them across everything that they do. It's a fascinating episode with a C E O that really gets it and that understand the potential benefits and that he's willing to invest the time in order to rip the benefits later on. And the later on is actually in the immediate future. If you are in any leadership position in thinking how to get started or how you can apply AI in your business, this is definitely gonna be an amazing episode for you. If you're anybody else in business is still gonna be really. Interesting. At the end of the episode, I'm gonna share some news as always on what happened this past week that is relevant in the AI world, now let's dive into how you can apply AI to everything in your business. Hello and welcome to Leveraging ai, the podcast such as practical, ethical ways to leverage ai, improve efficiency, grow your business, and advance your career. This is Isar Metis, your host, and as some of you may know, I have been in multiple tech startups in leadership positions, either as a C E O or as head of sales and marketing in different stages of my life. But developing new products in the tech world is one of my biggest passions, and I still find it fascinating and an amazing thing to do. I'm fascinated by the fact you can take an idea from somebody's head and make it to a product that actually helps people, provides value and generates revenue to several different companies, including the company that developed it. AI makes a lot of steps in that process easier, but that's a very big statement. How do you take that statement and break it into actual. Building blocks that you can use when you're developing products. Our guest today David Hirschfield, is doing exactly that. David is, first of all, a serial entrepreneur himself. He has founded and sold more than one business. He's currently running several different businesses as a CEO, and in the past several different years, he's running a company called Tekyz Corporation spelled T E K Y Z, but sounds Tekyz, where he helps startups launch a. They're products, so going from idea to revenue in the shortest amount of time, and in this past year, he's been focused a lot on adding and infusing AI into all of these processes in order to make that process for his client even faster and more efficient. Now, because I think that every single company that develops anything that, especially that develops new products, will have to include AI in the processes or their competition will, and then they will not be competitive. I find this as a critical aspect that every single company has to learn how to do, and hence, I am really, excited about the conversation that we're gonna have with David. The conversation is gonna be around what aspects, what steps in the journey from an idea to a working revenue generating product. You can have AI help you do faster and better, and exactly how to do that. And since, like I said, I believe this is critical for the future of every company. And since I'm personally passionate about it, I'm really excited to have David as the guest of the show. David, welcome to leveraging ai.

David Hirschfeld:

Thank you for that great introduction, Isar. David,

Isar Meitis:

let's really start with, if you want high level, what are the steps, like bullet points that you help new startups go through before we talk about ai? So people have the framework, I come to you as a potential client, say, David, I have this idea, I wanna build this into a product. What are the five sec, 5, 6, 10 different steps that we have to go through in order to go from an idea to an actual running product?

David Hirschfeld:

Okay. That's a great question and there's a couple of different ways to go about that if with us, with Tekyz one's the classic way, which is where you have an idea. Sometimes it's five bullets on a napkin and it's gonna be the next unicorn. And and so always is. And it always is, right? And we spend and we spend a lot of time then basically distilling the requirements that the application will have that we need to build that will deliver these five bullets, okay. Of, wonder and to the world. And so we start there and we come up with an estimate for the project because we gotta have some idea of what it's gonna take to deliver that. And then we try to scope it back to what has to be there in the mvp. And this is very difficult for a lot of founders to. Not only scope it, but then to maintain the discipline as we're building it to live within that scope of the minimal viable product. So then we start to develop, then we do the design and we build a prototype, a high fidelity prototype that looks like the finished product with all the user experience so that we can see exactly whether this idea holds together and that the design is appealing, but more that the user experience all holds together. Then we build it. And we try to build it as quickly as we possibly can and limit with some limitations in the first version to keep the cost down so that we can get something out quickly and see whether or not there's product market fit. And nine outta 10 times there isn't. And now it's a matter of pivoting and hopefully finding that product market fit before you run outta mind. That's one way. The other way as to test the market first. So we still do the estimation, we still do that high fidelity prototype, but we also go through a rigorous niche analysis, identifying that early adopter niche, their value proposition and their top two or three issues that they need to solve. Because they're the, and we know they're the early adopter, cuz those two or three issues that are their big problems have a higher perceived impact to them and a higher cost combined, those two independent numbers than every other niche and all of the problems that you might solve for them. And so when you've nailed that down you have the early adopter niche and then we, after we build the prototype, we build a marketing stack and we go out and try to do pre-launch sales. And that way you find out very quickly you're forced to face the truth. Do they really want my product or not? Because if you can't sell it in a pre-launch sale, you're probably not gonna sell it once the product comes out. But now you haven't spent all that money, haven't spent all that time, and you can pivot quickly and cheaply cuz you're not having to pivot a finish develop product. So those are the two paths that people can choose to go to. And surprisingly, a lot of people still pick the classic path. They, even if they say they're gonna do the other path first, and we go through the whole process, we get ready to market, they'd say once it comes out it would be so much easier to sell. I was like, no, don't do that. Go out and face the music now.

Isar Meitis:

Yeah, I love that. And, I think especially most people when they develop a new product, they have a client in mind. Meaning they have their previous employer, their previous client, a yeah. An area of expertise that they have with relationships that they have. That they have the opportunity to do exactly what you said is somebody that will be willing to be their beta test for something quickly because they really have a real need and they really want a solution and they'll be willing to accept a not perfect thing. So it's, I think in many cases from a founder perspective, it's doable. And I'm actually surprised that most people don't take that quicker path. But let's go back now that we understand the framework and figure out where are you using AI right now, and where are you planning in your process to use AI further in a way that can help other people who are in the same boat or planning to develop a product or developing a product to learn from that and implement it myself. So implement it themselves. So you started in the beginning with you. You know what, I'll let you follow your path of which steps in the process you're using AI and how.

David Hirschfeld:

Okay. And and it's probably best if we just, if we talk about it, almost like our journey, our AI journey. Okay. I'll jump ahead. I won't go into all of the open AI stuff we did last year, but earlier this year we started using it to write or, to refactor queries just because we had some big applications in law enforcement in particular with one client, and we had huge stored procedures that were complex and some of them had some performance issues. So we started to literally take that code just for the stored procedure. Nothing is exposed to the world, right? Put it into AI because it's, and then ha say, we'd like this to run faster and more efficiently. And the first time we did that, it was 150 lines stored procedure and it's 40 lines about. 10 seconds later and it ran faster and it ran the first time. We were somewhat floored. This was in January, I think with, chat G p T. So we had done other stuff in with OpenAI prior to that, but that was our first experience with chat G P T. And that was in, I think that was in January. So

Isar Meitis:

we, yeah, we jumped far into the process, into actually writing code as far as the journey that you described. But I do have a follow up question that I'm very fascinated by since we started talking about this. ChatGPT is limited with how long the prompt can be. So you can't drop, you cannot drop a whole chunk of code in there. You have to break this down into specific segments. Is this what you did? Like you took just shorter segments and run it through ChatGPT?

David Hirschfeld:

No, we put the whole thing in and it was that much code 150 lines. There's quite Oh, okay. Quite a You can, but now you can put quite a bit into there now. Yeah. Like 10 pages of, content. Actually more if it's code because, but 10 pages of written content can, you can put, it in a prompt. If you're using open ai, you have a bigger range Yeah. That you can feed it and then you can do things to chain it together. But that's all getting like way too technical, right? Yeah,

Isar Meitis:

Okay. Interesting. So yeah. So from your experience, the first thing you try that was like, oh my God. Was literally just letting you, letting it help you write a more effective

David Hirschfeld:

code. It it rewrote code that we had written. Yeah. And I'm, and I think about that evolution of that piece of code and and the time to debug it and everything else that we had spent on it. And then just gave it to ChatGPT and 10 seconds later we had 40 lines that worked perfectly the first time. And that's not always the case, but it did there, but it was so eye-opening. And now all of a sudden we're thinking, okay, maybe we need to go back and, refactor a lot of our code. Especially areas. And then we said, okay, we want our code documented. So we'd paste the code in there and say, document this, and it would write beautiful documentation of the code, no change to the code, things like that. So these are all like little baby step things that we did really early on. But we started realizing very quickly that this is the, this has to be the direction the world's going. Tech stacks keep changing. You're, stuck in your tech stack based on your skillset typically, right? Unless you're willing to just have lots of different tech stacks and lots of different skill sets on your team, or get very, senior people that have broad experience, which is great to have and our people are experienced, but still, you're ability to, unless you're one of those brilliant developers that picks up a new language overnight there's still gonna be limited to a fixed number of tech stacks that sometimes is not the most efficient way to go. There might be other things that are better suited. And one thing we noticed about ChatGPT is we can start writing code in tech stacks that we're not so familiar with, and we learn that tech stack almost overnight because it's writing the code for us and we can see how it's structured. An API call how it's called a function, how it creates an object or references on whatever those things are, how it's building a class structure, how it's creating microservices in a particular textile and, then all of a sudden we are starting to get competent like very quickly. Even though we don't really need to be competent, we just need to be good enough programmers so that if it runs into a problem we can then have it debug itself. We can know what have it anyway. The whole process is shifting and we have l and we keep learning. That's gets us to where we were a couple months ago when we started to say, we need to formalize this whole thing. In addition to that so, the process for developing anything is write requirements. Then you take those requirements and turn'em into detailed requirements. A design user stories, test plan, test cases, test script, if you're automating your testing deployment and then all the various pieces in the in your deployment. And your CICD and your automation and orchestration and all that, right? And all of that requires different specialties and different skill sets and different experience levels, and it has to be really well planned and really well orchestrated. And in, in a competent team, this works really pretty efficiently, as long as you don't throw any big changes into the mix. So just thinking about, okay, how does a competent team function? So where can we where do we want to use AI to start to automate this process? And so, we do a lot of point solutions like with writing user stories. So our QA group is, using it to write all kinds of user stories and test plans and test cases, or our business analysts for the user stories.

Isar Meitis:

So I gotta, so I gotta ask you a question before we go. Yeah. So the user stories to test a new product. Are based on a deep understanding of the problem. Meaning it's not just understanding the software side of it, right? It's understanding the specific niche for which you're developing the solution, right? It could be right on one day you might be developing a travel app, the next day you might be developing a law enforcement app. The third day you might be developing an accounting app. How do you use AI to help you with writing those user stories?

David Hirschfeld:

First of all, ai the OpenAI any of the large language models have an amazing deep level of understanding of all these different domains, okay? And of all these different users and their behaviors, and it's amazing, right? I, we still don't assume it's gonna know how to. Exactly how we need to create this piece, this particular piece of software and test it. But we're not counting on ai, at least not yet to do all the user experience design. So all of that is done first and we do do deep analysis in that process with our clients and with that domain and with all of our past project experience and all these other domains, cuz it all comes in and applies. But when we ask it to write a user story, we're writing a, prompt that gives it enough context about what we're trying to accomplish with this particular function or screen or whichever we're writing it for. Or an API or user story for an API, for example. And it writes that user story with a lot of competence and and some of the mistakes that we might make because we just miss things. It doesn't miss things. It may go in the wrong direction sometimes, but we're reading that and evaluating and saying, no, this isn't what I meant. And then we re-engineer the prompt and then we're getting the result we want. Question.

Isar Meitis:

Yeah. First of all, I love this. I think this is a, really great way to use, just like you said this, these large language models have read most of the stuff on the internet, at least the interesting stuff. And definitely, yeah. Software development stuff. It has read and has those expertise on how to do these documents in a very efficient way. Yet, like you said, you have to give it the right context. Yeah. And you have to give it the right formatting for it to work for your particular use case. Can you give me the structure of the prompt that you're using to do that? So obviously not the whole prompt, because I assume it's a pretty long prompt, and I assume some is proprietary to your client, but if you had to break it into components, What components do you put in a prompt like this to write a user story? And if you can read some of it of what you actually wrote in those components, that would be awesome. I think that will give people a better understanding of what you mean by prompting for writing user stories.

David Hirschfeld:

Okay. I'm actually gonna go at different direction than user stories only because I'm not the one, it's my people on my team doing the user stories. They're the ones developing the prompting. I will give you an interesting example, which I used to train my team how to think about this, and it's nothing to do with software. Okay. But it requires you to rethink your relationship with technology, in terms of your relationship with ai because it is a relationship. Because what happens is that pe we've been doing things a certain way for so long, we don't realize that we have an expert available for everything at any moment. And it really, and what does that even mean? So my, we just moved recently to Vista, California from Scottsdale. I know we went the wrong direction. Everybody goes the other direct, but still direction. I was Arizona. Long story, how, why that happened. But anyway, and my wife loves to garden and here you can garden all year round and it's a very different gardening here than it is in Scottsdale. So she likes to do square foot gardening, so she wanted to come up with a square foot gardening plan and for our backyard. And so I said, okay, how about we ask ChatGPT, and she's rolling her eyes anyway. So while she was thinking through all this and she was on the phone with a friend talking about it, I started a conversation with ChatGPT. I said and I even put in, my wife wants to create a square foot garden in our backyard. There's two of us. How many square feet do we need? We're not vegetarians. But we like to have vegetables with most meals. And it told us, okay, for two people, and it told us how many square feet we would need and how we should probably arrange the beds. And I said, okay, great. Based on that what are the, different types of vegetables that we can grow in our garden and with, along with their companion plants, because when you're doing gardening, you have companion plants. Certain plants like to grow with others and they don't like to grow with others, so they'll hurt them versus help them. And so then it built a companion plant plan together, which beds this should all go into, right? And a nice list. And then I said, okay, what about companion flowers? Because those are flowers you wanna plant near certain vegetables because they draw the bugs that like those vegetables off. So then it gave us all the companion flowers. I said, okay, come up with a yearly plan that includes succession planting. So after one planting is done, something else will go on that same ground, but you wanna put the right thing in there because the soil is gonna like to grow something else. So then it did a whole plan for the whole year for three different seasons with the successions. I said, okay. Consider a square foot gardening number, the beds, and give me a table with all. And it built us all out. And then five minutes later, I said, is this what you were looking for? I just showed my wife and her mouth just fell open. There were a few more prompts along the way, but this is this is what I mean by a relationship with the ai. So while we're writing user stories or whether we're asking it to write code or requirements, there is this, sort of evolutionary process that we go through in terms of making sure that our relationship and the way we're communicating with it is getting through in the right way, in the right context. And, the, they we're giving it the proper feedback to continue to improve on the result.

Isar Meitis:

So I, first of all I, really love your example, and I want to take it really to the direction that you're saying of generalizing it, of how you want to use it. And if I generalize what you said, I. The first thing is you need to have some level of expertise in the thing you're asking about because you need to know what questions to ask. The second thing hold on. I know what you're gonna say, but you need a good starting point. The second thing is you have to be curious, quote unquote, in order to continue asking questions to get solid information. And the cool thing is, like you said in some of the stuff you're saying I don't know what other plans I need, but you can ask it. Okay, what else do I need to know in order to make this successful? And then you will tell you like, I don't know about supporting plans. I didn't plans, I didn't even know that's a thing. But if you would say, okay, what else do I need in order to make this successful? It will tell you, and they'll say, okay, that thing that you told me, What do I need to know about it more and so on. So you have to Yes. A lot of people that I work with, and I u I do this as a consultant and as a training in courses that I teach they, get the first answer like, oh, that's not really good, so I'm wasting my time. And the whole process here is a, you called it a relationship. I'm just saying it's a process. Like you gotta go the back and forth and back and forth and be curious and committed to asking continuous questions. Because in question eight, you're gonna hit

David Hirschfeld:

gold, right? That's right. And, what I, so let's say you wanted to do gardening, right? But you didn't know about it. You've heard about square foot gardening. And you could start with the prompt. I'd like to do gardening and by the way, when I gave this prompt, I said, in Vista, California. So it was giving me all of the planting guidelines for the weather in our area. Yeah. But I could have asked it, what questions should I ask if I'm want to start doing square foot gardening in my backyard? And then it will list all the questions and if those are questions that I can answer, then I'll say, okay, given these answers for these questions, and then I could just continue to go through this process. So I don't even have to know anything about square foot gardening. I could have easily ended up in the same place maybe a minute or two later. I love that. I think

Isar Meitis:

that's a great addition. So let's go back to your company and let's go back to the process you described in the beginning. You said that the first thing you do with clients is to try to estimate the scope

David Hirschfeld:

of the project. Yes.

Isar Meitis:

Do you use AI for

David Hirschfeld:

that as well? Yes, we are now doing that. We're in right now. I know this isn't the most important thing, but we are building an estimation tools, using ai to estimate our project, which is a many step process in AI to do that. And but it, but in the process of doing that, there's a lot of other benefits that come out of this. Number one is it expands the requirements to fill in gaps. And one of the steps are questions that come out of this that we might need to clarify with our clients because there's not enough known in a certain area to have enough detailed requirement to build something. And we ask AI for, to identify those patterns so that we, know that the estimate we're giving is more complete and more and richer and comprehensive. So then we want those requirements to then be expanded out in a full functional spec and AI's doing that for us. Then we want to build a traceability matrix out of that, a requirements traceability matrix, and it does that. And then it asks us what tech stack we plan on using. Right? And it has, it pulls all these tech stacks out and we don't have to use those. We could actually write in one that's not in the list. And then based on that tech stack, it starts to come up with risk factors and effectiveness scores and all of these other things. So we can see whether this is really direction we want to go or not before we commit to that. Anyway, so it's this multi long step process and at the end we end up with a full breakdown of the project and effort estimate for each of the modules in the, or each of the elements in that project, not even on the module level, but at a functional level. And then we have two different ais that we use so that we can crosscheck the results, it also tells us team size. It'll also tell us and, if we give it the duration of the project, it'll tell us team size or we say, here's the team we have on this, and that'll tell us what the duration of the project. Yeah. Incredible.

Isar Meitis:

Okay, let's break this down because what you said is mind blowing by itself, even if you don't touch any of the other points. First of all, how does it know? So I'll start with the first thing you said. Yeah. The first thing you said is understanding where there are gaps in the requirements, how do you do that? Do you feed it the existing requirements and ask it? Do you see any gaps? If, this needs to do 1, 2, 3, and these are the requirements, where do you see gaps? That's the idea.

David Hirschfeld:

We yeah. So we feeded a set of requirements and if it can, it will just fill in those gaps and build out the rest of the requirements. If let's say we're starting with five bullets, then what it will, then we can just ask it to, what questions do we need to ask to get a full set of requirements from our client? But if we feed in, let's say, five bullets, we say, just go ahead and create, a functional spec at it for this, for a full product, and it'll do a decent job. But then we'll take the result of that and put it back in and say, okay, now take this and continue. And it'll build it out at even a more detailed level until we feel like two times through is pretty much it. You don't really need to go beyond that until we know we've got everything that needs to be there. Now, the

Isar Meitis:

estimation part of it, it has to know how to estimate the development. Which means it needs to know either your team. Or an average team, I don't know if that's worth anything because you need it for your team because you're, trying to make money in the process. So yeah. Did you take previous project information and fed it somehow into ChatGPT, wherever two, you're developing as a training process so you can get better

David Hirschfeld:

results? There's a, there was actually an easier way to do it. We just look at our team in terms of what we've done and how long it took us to do it. And then we ask ChatGPT to say, how long should it take to build this thing? And and where our numbers were coming out, some percentage of that number, right? In some projects, a little bit more, some projects a little bit less based on what ChatGPT knew. So that gave us. A good understanding of how it was estimating projects based on what it understands.

Isar Meitis:

Oh, I understand what you're saying. So you it's

David Hirschfeld:

a lot easier. You

Isar Meitis:

now have a benchmark knowing that for the projects you have done and you know exactly how long it took, you ask ChatGPT how long it thinks it would take, and now you know percentage wise, up or down, depending on the type of project. This is where your team will score compared to what ChatGPT thinks it will take.

David Hirschfeld:

We were starting out with all estimation where we just take our requirements and say, give us an estimation for this. And it would say, here's the duration, here's what it should cost, and we compare that about to what we came up with and to see if we were wildly off. Compared to what it thinks, maybe we're missing some piece of it. Anyway, so that's how, where we started months ago, right? And then we started to refine this process, but now it's more like a coefficient or whatever it comes up with. We know that means for our team in the classic development mode. Now, of course, it's all changing, right? Very quickly. So we think that projects that were, might have taken us six months to do. Right now, starting on it today without all the tools that we're gonna be building, but just doing point solution stuff we should do in two months or maybe even six weeks the same six month project with the same size team. As we snapped together more of this capability, we think that could even be cut in half, where we can be producing pretty sophisticated projects in two or three or four weeks. That would've taken us six or seven or eight months with better quality. That's brilliant. And, a higher confidence factor of the functionality.

Isar Meitis:

You know what I'm gonna ask you a tough question that Yeah. That you don't have to answer if you don't want to, but the situation you're talking about right now is very relevant to a lot of service companies Yeah. And not necessarily in the tech industry. So I assume you guys charge by the hour. Yes. Meaning you give an estimate and then whatever is actually happening is what you charge for, right? So law firms are the same way. Consulting companies are the same way et cetera, et cetera, which means now you're faced with an interesting scenario from your company's revenue perspective, right? Because there are several ways you can take this. You can say I can, it was supposed to take six months. Now I can do it in six weeks. I can charge for three months, and everybody's happy, right? The question is, what happens two years from now? When everybody can do it in six weeks and now you quote unquote lost 70% of your revenue. Unless you can hire clients fast enough to do a project every

David Hirschfeld:

six weeks. Yeah, first of all, I think we're on the leading edge of this. I think everybody's playing with it to some degree. Anybody that doesn't have their head in the sand is playing with this to some degree with their team. But I think we're a little ahead of that. And so that my whole I, basically made it really clear to my team, this has to be our number one focus is wrapping our entire process around adopting AI driven everything, which means when everybody, we always have been this way everybody's a critical thinker. Everybody if Malcolm Gladwell, it's everybody's job not to fly the plane into the side of the mountain, if you know any of his writings. Anyway and, everybody has an equal voice in a project. There's no there's no hierarchy in our teams the way we run projects. So we already have pretty good critical thinking skills, but they really have to step it up, step up the curiosity, what you were talking about step what's possible, don't think beyond what you are, how you do things today, and start to, and literally have, think that AI will do it all for you. And so you're the one in control of the process now, not delivering the result directly. And because, yeah, go ahead.

Isar Meitis:

No I, wanna ask a very interesting question because I, this raises. Another topic that is, again, if most of the people listening to this are senior business leaders and you're now transforming your business into an AI centric business, which is incredible to me because most people are just like how do we use this to do a very simple thing task here and there, and you're looking at this, okay, my business is a different business tomorrow if I do this. How did you, or how do you not, did you, I'm sure it's an ongoing process. How do you address this from a time investment of your employees? Who in the teams are in charge of the transformation versus who is in charge of delivering projects? What percentage of the resource that you have internally from a time, it's less money? I think with ai, because I think most of the tools right now are relatively cheap, but more of the time of your people. Is invested in figuring this out. And what you told me is amazing, right? You said we're now developing a tool to do scoping. I assume you're developing a tool to do design. I assume you're developing a tool to do prototyping. I assume. Like it's just gonna be a process where exactly, where slowly you're gonna roll everything. Who's in charge of that? Who is doing the day-to-day work? How much time a week did you assign to people to work on that versus work on projects?

David Hirschfeld:

So obviously the project work comes first because our clients expect us to deliver things. So that always comes first. But the reason we started with the estimation part was because my most, the most. So costly people, and when I say costly, I mean they have the biggest impact on the business. And when they're busy doing estimation work, they're not busy doing things that make the business better. And so that's why including myself, that's why we started there. And not on automating QA or not that our QA isn't critical to the success of our business, but but their time isn't as critical in terms of the impact of the forward movement of our business. So we start, that's why we started with estimation. And it immediately after that, we're even going back a step behind that where we are, remember I talked about the niche analysis part where you have all these problem statements and these niches and you try to figure out which one has the highest impact, which where they cluster and cost we're that will be our next AI project because that is very hard work for a founder to do. And they often get fatigued in that process and don't get it all the way done. And so

Isar Meitis:

when you say your founders is your clients, the people who are clients,

David Hirschfeld:

founders clients, my clients, yeah. That are founders, that are have, that are at the idea stage. Not all my clients are that, but a lot of them are. So if I can automate that process with ai, I've just given them the ability to get to revenue many weeks sooner in the whole launch first with a lot more confidence. Because they won't be fatigued a ChatGPT or OpenAI doesn't get fatigued in doing these things. Yeah So that'll be the very next thing we do is as far as a formal project. But everybody on the team is looking for ways of, improving the quality of what they do, not just reducing the time, but improving the quality is really the biggest focus using ai. And in the process of doing that, they reduce the time often, dramatically, sometimes from several days to a few minutes on some point tasks. It's stunning occasionally what we're able to accomplish.

Isar Meitis:

And it's, is it the. Mandate of every single employee to do that. Meaning it's something you've defined for the, there is no one person that is in charge of the transition, but every person is encouraged to go and experiment and test and look for ways to do things with AI that will be better, more efficient and higher. Yes.

David Hirschfeld:

Quality. Yes. Everybody is tasked to do that. We have one formal project right now at a time that is actually automating a whole a context for us that will probably continue that way. We might accelerate that and have two projects running at the same time, but we're able to do one project without one project at a time without having any negative impact on client delivery stuff. And that's our critical success factor. So we have an internal project of product that we're going to be building for business networking. And it's not important what that is, but we're going to kick off the development of that in probably two weeks. And that will be the first project, because it's our own internal project that we are going to be completely developed with AI cuz it's okay for us to use that project as learning cuz it's our own I don't have a client delivery issue. Yeah But that'll be a hundred percent AI built.

Isar Meitis:

David. Yeah, this was a fascinating conversation. You and I can probably go on for another two days. I, really wanna summarize quickly some of the things we talked about and then I'll let you add if you have anything to add. And, I wanna actually not go back to the process we started with, but go back to what you ended up on. I think the direction that you're taking is the direction that every business owner has to take right now. Meaning what processes do I have in my business? How can AI improve those processes? Improve can mean a lot of things, but higher quality, less time, less money, better results whatever, the case may be. Improve the processes. What are the ones that are the most impactful in combination with risk? And like I said, you are willing to go on in, on something that is an internal project. But you're, taking the necessary cautions when it's a customer deliverable. That's another thing. The other thing that you mentioned, which I absolutely love is, encouraging and moreover, mandating that every employee in the business, Is continuously aware that there might be better ways using AI to do what they're doing. And encourage them to do so and experiment again within the level of risk that is acceptable. And the last thing is, once you have identified the lower hanging fruits or the highest targets, is actually go and start implementing them in a gradual way. So don't say, okay, now I'm stopping everything that I'm doing in order to do this, which is not, doesn't make any sense in any business, but find the steps in which you have bandwidth to actually go and implement things that will be force multipliers. Like you're saying, I'm now developing a tool which, meaning I'm investing actual re development resources, in developing a tool that will then allow me to eliminate the need to take the most critical path people in the company from doing estimations, which means the company could grow faster and do more things. I absolutely love the process that you define, and like I said, I think this has to be the thinking of every business owner or business leader out there today. Do you have anything to add? Because again I, literally I'm, speechless with what you're describing.

David Hirschfeld:

You got it really pretty good. There are a couple things. One is it also reduces the cost and cycle time for our customers, which is critical, right? And it is, and. We also have lots of compliance requirements with some customers that are healthcare, our law enforcement customers. We got SOC two level two certification compliance, right? We have all this overhead. And so in that SOC two level two, we have to document any time we're gonna take a piece of code and, then paste it in basically paste that into AI or use AI in any way. We have to document how we're doing it and how it's secure. And, so those types of things have to be thought through really carefully so that you're not creating some kind of liability exposure because you're using it in an irresponsible way. But all those things are possible. They're very doable and nobody, has a choice, right? If, other companies like me think that they really don't. They, really don't have to do this. They can just go along the way they are and maybe make, they won't have a choice about that a third of the time. So half the not as many billable hours and all that, it's, you don't have any choice. It's, that is just the reality. So figure out how to build your business and be effective around that reality. Six months a year, 18. And it's fast. It's coming so damn fast. Excuse me. No,

Isar Meitis:

you're, I you're, spot on. I, I, you I would've used harsher words. David, I can't thank you enough. This was an incredible conversation. I think what you're doing is amazing, and like you said, I don't think anybody has a choice. So if you can, if this conversation that you laid out if, the, what you shared can help people and underst understand. In concept where they need to go, then. Then, we've done a good job and I think we have, thank you so much for

David Hirschfeld:

sharing. Yeah. And thank you so much for having me, and this was a really fun conversation. Hope we get to talk some more. Amen.

Isar Meitis:

Thank you. What an amazing conversation with David. I love the fact that they are building internal tools with ChatGPT as a way to learn how to use the platform. Because doing so a enables them to do stuff that is low risk because those tools are not exposed to their clients or the external world. But at the same time, it teaches them how to use ai and it also gives them an immediate benefit once these tools are working'cause it enhances their internal efficiencies. I also love the fact that they mandate their employees to find ways to improve quality and reduce time while using ai. That's another really important thing that I think every leader in every business should do today. And now let's jump to some news from this week. First interesting piece of news is that Open AI has launched a GPTbot, which is a web crawler that will help them gather more information in a way that is aligned with future requirements and regulations. So it's basically. A bot that crawls the web, just like Google has its own bots, and that will allow GPT-4 updates or maybe GPT-5 learn more from more websites. They have reported that it should strictly filter out any firewall, paid restricted data sources that violates their policies Also, they will not gather any personally identifiable information, also known as P I I. This is definitely good news. The other interesting piece of good news is that in your website, in your robots.TXT file, you can block the GPTbot from browsing through your information and collecting it. So any company wants to do that can just go to its robot txt file and define the G P T bot as disallowed. It won't be able to crawl and collect data from your website. So interesting and actionable piece of news. If you wanna block chat g p t from training on your data, Google introduced another interesting research paper this past week that they call embedding for language image aligned x-rays or elixir for shortcut. And what it basically does, it's a multimodal tool that allows to connect their large language model Palm-2 to X-ray imagery processing with ai, which allows it, and I'm quoting Achieve state-of-the-art performance on zero shot chest x-rays, meaning it becomes very, very good at identifying and also explaining and describing what it sees in chest x-rays. My take on this is Twofolds. One is that it's a fascinating move in the medical field where we are gonna start seeing more and more of those capabilities that are gonna get embedded into our medical processes, whether with doctors or maybe even without doctors in the future where we can get. Better, faster results to different tests that we're doing on a broader scale. This is a highly targeted multimodal solution, and a multimodal basically means that it has more than one input, meaning in this case text. Large language model combined with image processing, and I think, and I believe we're gonna see more and more of those meaning we're gonna see highly targeted, relatively small light and fast multimodal. Models that will be able to help on very specific tasks. And then we'll be able to do those extremely well and provide a lot of value in that very narrow field. This is just another great example of such an implementation. This is only a research paper right now, but the more of these research papers that we have, the more practical solutions will come out of them and hand stuff that we'll be able to use on our day-to-day. Another ai interesting piece of news comes from Apple. Apple reported their earnings this week and they obviously related also to AI in their call with investors one of the things that Apple is focusing on right now is they open a lot of new job postings that are looking to integrate AI capabilities into specifically the iPhone. And the quote from Tim Cook, he said, we view AI and machine learning as core fundamental technologies that are integral to virtually every product that we build. So while he says every product, the focus, and obviously the biggest thing that makes money for Apple is the Apple iPhone and all of its ecosystem. And so, Their goal in this by people are analyzing this is how to run smaller, lighter models and different AI capabilities on the iPhone itself to get many benefits. I don't think that's unique to them. Google has done more and more of this in their latest 2 Google phones, and they've built hardware that will allow them to do these kind of things and run AI locally on the device. I think it's just just a trend that we're gonna see growing, so just like we're seeing right now, more and more companies embedding AI into their software offerings. There's no doubt in my mind that any company that generates hardware for anything today will look for ways to integrate AI into their core offering, whether it's computer vision analysis in cameras, or sound analysis in anything that has to do with voice or sound enhancements, if it's headphones or speakers and stuff like that. So I think we're gonna see more and more. Of these lightweight, very specific models that are developed for specific tasks run on dedicated hardware in order to achieve more benefits and more good things for us, the users at the hardware and software level, which I think is good news for everyone because we'll get more capabilities from the stuff that we hold in our pockets, put in our house, or use in our businesses. The biggest news from this week comes from OpenAI, the company that gives us ChatGPT shared by one of their executives. these are going to roll out, they're saying next week, which maybe that some of you already have access to that. and there's a bunch of them, and I'll just follow what he shared. So first of all, example prompts. You're not gonna be starting with a blank page, Prompt engineering will become less and less important as you're gonna get examples of prompts for stuff that you're trying to do That's in step one. In step two of the conversation, you're gonna get suggested replies, so just G P T will generate potential follow-up questions, which means you'll be able to have better. Conversation that will reduce your fatigue and get you better results when you are having those chats with ChatGPT GPT-4 is gonna become the default engine when you run ChatGPT. So right now the default is G P T 3.5 and most of us sometimes remember, and me sometimes don't remember to change it to g PT four. So that's gonna be the default for the paid users. Another one that's huge for anybody like me who became a code interpreter junkie is the ability to upload multiple files at the same time to code interpreter. I think that's a amazing benefit that, will allow people who understand the benefit of that tool that do stuff that's nothing short of magical. It's literally like having a data scientist in your pocket and the ability to load multiple files, just amplifies that. They're introducing keyboard shortcuts that will allow you to do some of the things faster and you can see those shortcuts in ChatGPT itself. A lot of great new small improvements from ChatGPT. And that's it for this week. If you're enjoying this podcast, please share it with anybody who can benefit from it. Also, download rate and review the podcast on the platform you're on. If you're on Spotify or Apple Podcast, I would really appreciate a five star review and write me what you actually think and if there's stuff we should change. As always, if you find anything interesting or if you wanna gimme any feedback, please connect with me on LinkedIn, ISAR Metis, I S A R M E I T I. Ss. Go explore ai. Test it out, share it with other people, share it with me, and until next time, have an amazing week.