PTW3 052: Innovating with AI: Transforming Industries and Building the Future with West Stringfellow

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Donna Mitchell

00:00:08 - 00:00:24

Thank you. Good morning, good afternoon. Good evening. Welcome, welcome, welcome. This is Donna Mitchell. And I have west string fellow today. And I'm excited and I'm giggling because I'm really excited with the companies that he has worked with. He's high end.

Donna Mitchell

00:00:24 - 00:00:59

He's in the C suite vice president. He's working on the big companies. He's been there, Amazon, Target, PayPal, a lot of these places that we kind of look at, try to invest in. But he's going to give us a lot of outstanding information, excitement in AI, innovation and what's happening today and how we can move forward in the future and be successful because he has an awesome past. So, Wes, say hello to your audience and ours and tell them how you got in this space and who you are right now today, because you're a big, I'm glad to have you here.

Donna Mitchell

00:00:59 - 00:01:36

Well, thank you for saying that, Donna. Thanks for having me on the show. It's great to be here. And hello, everyone and Donna's audience. I'm west and I've spent the last over 25 years in technology helping companies try to figure out how to use emerging technologies. I started doing database work in 1997 and just kind of plowed through as a database administrator and website designer and developer. And I ended up developing a pretty unique skill. I was one of the world's best data managers for book data and very pretty, pretty useless skill.

West Stringfellow

00:01:36 - 00:02:10

But Jeff Bezos, the CEO of Amazon, happens to love books. And so when he wanted to build a book marketplace, there were only a couple people he could contact. And Amazon contacted me and I went and helped them build their book marketplace. And that's when I really learned how to use technology. Before that, I was just playing around. And when I got to Amazon, it was the real deal. And since Amazon, I got to, if you've ever watched the video on Amazon, I was on the team that launched the first version of Amazon Prime Video, if you've ever used an AWS web service. I wrote the product spec for Amazon's first billable web service.

West Stringfellow

00:02:10 - 00:03:00

So I got to be there at some of the beginning of their largest products. From there, I went and I was the vp of innovation at Visa. Then I was the chief product officer at Rosetta Stone and then the vp of innovation at Target. Target bought my company and I joined as an entrepreneur in residence. And then I helped them innovate and they asked me to be the vp of innovation. And then now I have howdo.com, where I've open sourced all the processes that big companies use. Because what I've learned by building my own companies and helping the world's largest companies innovate is everyone basically does the same thing. And I noticed this big gap between, I talked to executives and entrepreneurs at big companies and then I, everyone else who has a business, the tens of millions of Americans who have businesses, that I noticed that most people just don't know the processes.

West Stringfellow

00:03:00 - 00:03:12

And so I open sourced those, made them free and available on howdo.com, and now I'm walking through those processes and showing people how they can use AI to accelerate their innovation. And I'm doing that on YouTube.

Donna Mitchell

00:03:13 - 00:03:56

Well, that's really exciting. And that was really a mouthful with some high end positions with a lot of impact experience in scale, especially for the gap, that gap of information that's definitely out there. And I could speak to that as well. There was a gap of the sequence, the process coming out of corporate and then coming into the entrepreneurial game. There is a significant difference. And with the new technology that's available today, there's really something I had to take some time to wrap my arms around and then get my head around. So tell me, with the AI that's taking place today, how does that really benefit a solopreneur entrepreneur brand? It benefits everyone. It's in every sector, it's everywhere.

Donna Mitchell

00:03:56 - 00:04:06

It's everywhere in the industries. Tell us, how is that making such a significant difference and how you help people really utilize that and implement.

West Stringfellow

00:04:07 - 00:04:49

So with AI, you're right, it is everywhere. And it's definitely the most powerful technology trend I've ever seen, and I've been in it since 1997. And so it says something. The way it's useful to different companies is unique. Unfortunately, right now AI is not one size fits all, but there are some common problems that we all have or common opportunities that we all have that AI can help us with. All of us have customers. AI is fantastic at helping us understand our customer. And so one of the easiest things that we can do is if we get email communications from our customers or if we have any sort of documentation of our communication with our customers.

West Stringfellow

00:04:49 - 00:05:45

I always find AI as a very powerful tool to ensure that what I say to the customer is what they hear. And so if I could get a profile of my customer, who they are, what their job title is, where they work, what potential age they are, maybe even some more specific demographic information about their education. I feed that into AI. Then I feed what I'm saying to them in my email, on my website, in my ads into AI, and I ask AI to align those two things. I find a lot of times there's some big differences. And the more we have precise communication with our customers, the smaller amount of communication we have to have with our customers, the more precise we can get, the higher engagement, the higher conversion, the more they buy from us. So that's the most common usage of AI that I see, is simply using it to accelerate our communication, our sales pipeline, and make our marketing more efficient, make our communication more personalized.

Donna Mitchell

00:05:46 - 00:05:59

So with the different AI's out there, is there one AI that you use over the others? They have degenerative machine learning, they have the different. Could you explain a little bit about that for the audience and myself?

West Stringfellow

00:06:00 - 00:06:43

Absolutely. So right now, everyone calls a big category of technology AI, and there's a lot of different AI's in there. And the one that everyone's talking about right now is large language models. That's OpenAI or anthropic, has a version called Claude, but the big one is OpenAI chat GPT. That's what everyone talks about. And so, jet GPT is a large language model, and what it does is it goes out and literally reads the Internet. It reads everything that's on the Internet that can be publicly accessed. And then it builds a big model that is just the statistical average of what it reads.

West Stringfellow

00:06:43 - 00:07:37

A lot of people think that the chat GPT is thinking, or that it has logic, or that it is smart or intelligent. And really all it is is a pattern predictor. It's read several trillion sentences, literally, and think about that. It's just an incredible amount of reading that it's done. And because it's done so much reading, it can predict the next word, or even the next letter in a word for most sentences. So if we ask OpenAI's chat GPT a question, it says, what are the words in this sentence? What do those, where on the Internet have these words been near other words? And then I'm just going to give you the other words that your input sentence was. So it's not intelligence. It seems intelligent because it's had so much training, because it's read trillions of sentences.

West Stringfellow

00:07:37 - 00:08:20

But what it really is is just regurgitating the average thought on the Internet. And so when we think about it, you know, a lot of us have the me included. You know, I only know what I know, and I'll tell you. I know I don't know most things. So when I use chat GPT, I learn a lot from it. But what I, but I contextualize what I learned from it in the form of this is the average of what is known. This is not the best of what is known because chat GPT can't read Harvard or McKinsey or Deloitte, PowerPoint, Dex, it doesn't have access to that. It does read a lot of things that are wrong.

West Stringfellow

00:08:21 - 00:08:40

It's a good tool to get a direction. It's a good tool to enhance our thinking, but it's not a good tool to replace our thinking or to really replace doing some good strategy work or communication work with our customer. I find it creates options, not solutions.

Donna Mitchell

00:08:41 - 00:09:14

So as one is going forward with their strategic plans and trying to keep up with technology, what process do you think they should use when they want to start implementing AI for some of their systems and, and the different things that they're doing, that's taking forever. That's annoying. That could probably be with AI. How do they know when they need AI or AI could do it? Is there some kind of assessment or process assessment or is that something on your website? How does someone know what they need?

West Stringfellow

00:09:15 - 00:10:00

That's literally what we're trying to build right now. Unfortunately, it doesn't exist. And the reason that tool that you're suggesting, which is very needed, right. Doesn't exist, is every business is different, every culture. And the more I get to work with small businesses, the more I find out how important culture is, how they have their own way of doing things, and how most small businesses don't really want to completely change how they do business entirely. And when we put something like AI into a business, it implies pretty significant change. You know, you're going from. Whereas let's just say I was just working with a small architecture firm and they had a software process that they'd use since the 1980s.

West Stringfellow

00:10:00 - 00:10:43

I mean, their server was literally not even supported by Microsoft anymore. But funny story, because it wasn't supported by Microsoft anymore, they were not affected by Crowdstrike. When Crowdstrike took out all those computers, they were fine. But they're trying to integrate AIH, and that means a couple of things. That means they have to upgrade their Microsoft server, which means they have to change their software and they have to change the processes that they've used for decades. And even having those conversations with the team members is very emotional for the team members because they're coming from a place of, I know what to do. I come in, I don't even think about it. I can get it done 100 times a day without even blinking.

West Stringfellow

00:10:43 - 00:11:18

And then, and then we're suggesting maybe you're going to do everything that you've always done different. And they're like, that doesn't sound fun. That sounds scarier than fun. And so, you know, because every business is different, there are these unique software requirements, there are these unique cultural requirements that company, some people will change with AI, some people will keep the old system. That's where we had to land. Some people were like, if you make me do, if you make me do the thing that I know how to do, that I know that I've known how to do for two decades, different. I don't want to do it. And it's more important to keep the people than it is to have the AI.

West Stringfellow

00:11:19 - 00:12:12

And so, you know, it's different for every business. There is no one size fits all, right? Now it's important to find the people in the business who are excited about AI. I'll say that if you have someone on your team who's excited about playing with AI and learning what it can do for the business, that's the best place to start. And if you, if they don't know where to start in terms of what to do with AI, just get on YouTube, you know, I mean, or if you, if you want to hire consultants, there's a lot of people out there I would just be weary of. Uh, anyone who says that AI provides solutions and speaks in general terms, try to get real specific. Uh, you know, we provide services if people need help. But the, the reality of this situation is most people in small businesses, solopreneurs, uh, they, they can figure this stuff out by watching YouTube and, and what they'll see by playing with it. And I always kind of characterize it by playing with it.

West Stringfellow

00:12:12 - 00:12:32

You know, we have thought of technology as a one way street for a long time. I tell the computer what to do. It does it. I put my email in the computer. It sends it. Maybe it spell checks it before, but it sends it. I put my numbers in the computer, Excel tells me what they are. I put my PowerPoint, etcetera.

West Stringfellow

00:12:32 - 00:13:04

And so where AI is different is AI is a coworker, almost. It's a thought partner. And that's why I say it gives us options, not solutions. Like email is a solution. I want to send the email, I type the email, I press send solution complete with AI, I may want to send an email. Maybe this is a really important email. Maybe I really need to close this client. And so maybe before sending that email, I take all the client communication and that I've ever had, let's say it's with the CEO.

West Stringfellow

00:13:04 - 00:13:46

Every time I've ever emailed with that CEO, any notes I've taken from a call with the CEO, I put it into GPT, I just dump it in there. And then I say, please tell me the tone of this person's language and I can learn how they like to speak. I say, please tell me the requirements you hear in this person's language. What do they need? And I'll get some very precise, very precise language. And then I say, please put their requirements in their words, and it will. So, three questions. What's their tone? What do they need? Write what they need in their words. Then.

West Stringfellow

00:13:47 - 00:14:04

Then I send the email, because what I'm doing then is I'm using their words to explain to them what they need. And that is so much easier than me having to sit down and think about how am I going to make them understand this? Well, I'll make them understand it by using the words they use to explain it to themselves.

Donna Mitchell

00:14:04 - 00:14:32

Wow, that's outstanding, because I've done the tone. I want a professional tone, but something that's very receptive with this, that, and the other. And you write your little prompt, but I never thought about reversing it. That's really a great idea. Let me ask you this in the nonprofit space, and I'm curious, because I work with a lot of nonprofits. Technology in a nonprofit space is tough. Yeah, it's real tough. Okay.

Donna Mitchell

00:14:32 - 00:15:46

Especially those old nonprofit organizations that are there with the charities and the disasters and everything else. What do you suggest is the best way? I think you really said it. One size doesn't fit all, and it's going to be some excitement. Being able to look at some of the agencies and their different functionalities and capabilities, outcomes, and their mission to see how to bring in the new technology. Have you seen any outcomes or experiences? I know UNICEF is involved with some of the blockchain and doing different things with technology. What have you seen in that space at all? Is there anything that you can share in regards to how they're shifting or changing their minds or getting away from the old best practices? And give us an example on how it's working and what's going on. Nonprofit. It's probably brands to profit, but nonprofits came to mind because so much really impacts nonprofits with blockchain and different new decentralization apps.

Donna Mitchell

00:15:46 - 00:15:59

But they're scared to leave the technology, the fear and the pushback. I was in a meeting and I just mentioned a couple of things, and it's like, oh, my God. I thought, you know, it was really interesting. So what do you suggest?

West Stringfellow

00:16:00 - 00:16:05

Well, I'd love to know what their feedback was. What was their feedback? What were their opinions?

Donna Mitchell

00:16:05 - 00:17:18

I would say, as we do bring in some of the new technology, we have to remember that we're moving into the AI space, but more importantly, the blockchain technology space, and some of the home office or national office, I probably should say, is already looking at blockchain and SAS and different things that they, that they didn't use before in self help. They didn't use these things in regards to, you know, providing security or some of the needs that humanity, people need human dignity and just meeting the needs and human services, I really should say. So. There was just a lot, but you can't do that there. And I'm like, well, why not? You know, they didn't think they could do that at the airport. You know, it may not be for everything and everywhere or every space and place, but I'm just wondering, do you have any examples that people could hear about or any suggestions on how to make that ball kick that can down the road a little further? Maybe that's my question, because you've got a lot of experience there. So I think I need to pull out everything we can for myself and the audience.

West Stringfellow

00:17:19 - 00:18:29

Fair enough. And so my first job out of college was with a nonprofit, was with the Global Philanthropy Forum, trying to get for direct investment overseas. And I built a big database for them because I was a database guy. The thing that we did with data that really impressed the donors was, in the first instance, start to collect data around the needs, rather than just saying, here's a PDF or here's a handout with a pamphlet. We used actual on the ground metrics to define the specific use cases where their money would go, what the impact would be, the efficiency of that all the profile of investment. And then we did a survey of the donors and we simply mapped them together. Now, I use that example because that was easy to do in 2001 if we had the right data that required. Getting that data required a ton of work, both in terms of asking folks on the ground in Africa and Central America and Latin America, like, what are you doing? How are you doing? And standardizing that blah, blah, blah, and then getting doner data and actually mapping it.

West Stringfellow

00:18:29 - 00:20:04

It was a lot of work, but it wasn't hard. It's just work now, with GPtheme, you can intuit, because GPT has read most of the Internet. You can ask it questions like, what are the needs of the people? What are the needs of the constituents who are receiving this benefit? What are the needs of the customers of the nonprofit? And you can start to profile donors. You can say, what do these donors, what motivates them? How are they thinking about what it is that they want to have the impact on? So if you can align the impact that needs to happen with the intent of the donor, who wants to create the impact? And again, I find that with most nonprofit work, it's about communication, it's about messaging, it's about helping people see. And this is true with even technology changes, technology transformations like you're trying to create with blockchain. If we help the customers see what the technology gives them, what their investment gives them, it motivates the customer to want to do it. And so I always try, like, I always try to reverse my language and think about, how can I create an investment opportunity? And that could be for a company, that could be for a donation, that could be for an investment in technology. How do I create a compelling pitch that the investor just can't resist? And I used to spend months and hundreds of thousands of dollars, literally trying to build strategies to convince investors to invest, invest in things.

West Stringfellow

00:20:04 - 00:21:22

So, like, you know, in my career, in total, I've raised close to half a billion dollars in capital for investment through all the big, different companies and my own companies, and trying to get people to invest hundreds of millions, tens of millions over multiple years. You know, you spend a lot of money and time trying to figure out how to get them to do that. I can get those strategies that, again, used to cost me. You know, the most expensive strategy I think I built was close to $800,000 just for the strategy, just to make the ask. Now, I can get that done in a week by myself with GPT for free. So if you can convince the people who are trying to make the investment, either on the donor side for the nonprofit or on the technology side of the nonprofit, into blockchain or into whatever technology it is that you see as the future that lowers their cognitive resistance, it starts to make them more inclined to want to do it, and then we get better outcomes. I always try to think about, from a first principles perspective, what is a nonprofit trying to do? It's trying to get services to people in very simple terms. Now, to convince the nonprofit to get those services to people, we have to understand what is their motivation in doing that.

West Stringfellow

00:21:22 - 00:22:15

What are the outcomes they want to see? And those, I think, are the inputs and outputs. Their input is their motivation and resource. The output is the result from the customer. If we can put those inputs and outputs in their language, and we can tie it to the needs of the customer and present it in the context of a technology solution, like blockchain or AIH, or an investment into the nonprofit from a donor, it's all the same structural problem. But AI is a radical accelerant for that. Because we can use the language of the nonprofit, we can use the culture of the nonprofit. Everything that's written on the website, everything that's written in the donor material to be uploaded into GPT. And then at a minimum, we can ask GPT to help us characterize the needs of the recipients of their services.

West Stringfellow

00:22:15 - 00:23:09

And at a maximum, we could go out and actually speak, go speak to three or five of their recipients, the people who receive whatever it is the nonprofit does. Record those conversations, put it into GPT. Literally, just record them like on your iPhone, transcribe it, which you can do with AI in minutes, put it into GPT and then say, align these. Align these intents with these quotes, and then say, how does blockchain connect this intent with this customer's quote? And that starts to present options for us to pitch better. And those options we can test with the customer, and that increases our likely of either a sale or funding or purchasing services or whatever it is. You know, business is all, at its core about communication. The better we can communicate, the faster business goes.

Donna Mitchell

00:23:11 - 00:23:29

Well, that was. Thank you for answering that. I. Yeah, that would be very helpful. I never really thought about it. See, that's why you're here. So I have one other question for you. So, with all of that said, you've had global impact.

Donna Mitchell

00:23:29 - 00:24:06

You've been involved, and you've seen a lot globally. What's happening on the governance front? I mean, I'm hearing things about people leaving the country or starting their businesses overseas or home offices over in Europe. You know, America's taking too long. You know, what's happening on the governance stage, what can you share there with the differences in the cultures, the governance, and, of course, what's happening here, what you might be able to share and know, why can't we just get this going so we could do the smart contracts? Everybody's, like, looking around, wait. Well, no, we don't know.

West Stringfellow

00:24:07 - 00:25:04

I find I've gotten to be at the beginning of a couple things that we would call industries today, but at the time, they weren't industries like when we built Amazon's first web service, we didn't know we were doing that right. We saw a customer problem and we built it. Now there's a lot of, a lot of regulatory gray area, let's call it, with a lot of the technology that I built where we're going to share data in a way, we're going to charge data in a way. No one's done that before. Who's going to regulate us? Who's going to slow us down? Let's say, yeah, that should happen. Yeah. And especially, especially in payments, like at PayPal and at Visa, as we were trying to build new solutions for businesses, we're actually trying to help businesses grow multiple times. We had to be reactive to the government coming in and saying, hang on, what you're doing looks scary.

West Stringfellow

00:25:05 - 00:25:48

Stop that. We had to very much prove, for example, when I was the head of product for PayPal in Australia, we helped over 5 million businesses in Australia accept payments. Before us, it was a seven to eight week application process to get payments accepted online. After us, it was 2 hours. And so we unlocked e commerce for Australia. The government saw that as a threat, not an opportunity. And so we had to help the government understand, no, actually we're creating opportunity in your economy, not risk. And, you know, for us it was about doing a couple of different activities.

West Stringfellow

00:25:48 - 00:26:46

Number one, hiring great lawyers, because the government likes to work with lawyers. And then on our end, we made sure that everything that we did was above board, was in compliance. You know, we instituted our own AML. So the government was particularly concerned with money laundering or terrorism financing and any sort of identity verification around that. And so we created our own processes. And I mean that like, we had to, my teams and I had to invent ways of preventing money laundering and terrorism financing. I had to do that at Amazon and at Visa and at PayPal to make sure that we could be in business. And, you know, technically it's difficult, but once we did it, it was a competitive advantage because all the other businesses that wanted to do what we did couldn't, they couldn't make sure that they did not have the ability to prevent money laundering.

West Stringfellow

00:26:46 - 00:27:44

They did not have the ability to prevent terrorism financing. They did not have the ability to identify their customers. And if they created those capabilities, they had to use old technologies, which meant that we could move faster. So at Amazon, for example, we were able to authenticate, verify the identity and the legitimacy of a marketplace seller in minutes. And some of the machine learning, actually, the first machine learning that I built ever, it was in 2005. And again, machine learning is a subset of AI. We reduced the verification time so the amount of time it took for the system to determine if something was fraud or not, from around 3 seconds to 300 milliseconds, so much, much faster. Because of that, we had ten X more transactions than eBay because for every additional second that a customer waits, they're eight times more likely to leave.

West Stringfellow

00:27:44 - 00:28:32

They'll just close the window. And so because we made our technology faster, we grew faster. And that's some of the stuff that we would take to the government. We would say, look, if we want our economy to grow, we actually have to have speed in business, and here's how the speed that we put into our company has allowed our company to grow. Now, think about if we put this speed into our economy, it'll help the economy grow. And I helped the national high tech crime unit in Scotland Yard understand that. I helped some of Amazons acquiring banks like World bank of Scotland and put big, big banks for visa understand that, and then ultimately had to help the australian government understand that, too. And it's natural for the antibodies to progress.

West Stringfellow

00:28:32 - 00:29:01

I think in any organization to come out, they're paid to be those antibodies, so they're just doing their job. I don't blame them for it, but it's about finding that common ground of interest where it's like, look, we're all aligned on one end, which is what we want the economy to grow. We want our citizens to do well. We want a healthy middle class. Well, then we got to get government out of the way in some of these things. And let me show you how we're doing our job as a company so that you, as a government, feel safe.

Donna Mitchell

00:29:01 - 00:29:35

Wow, thank you for that. I can talk to you. I could talk to you all day, but I can't do that. So let me be respectful of your time. With everything that you've been involved with, seen and observed and had hands on, or was able to peek behind the curtain, where did you have to raise your eyebrow? Where did you see? Where did you have a jaundice and saying, I didn't know, or, I'm a little concerned about this. Is there anything that you really have some concerns about or wonder about?

West Stringfellow

00:29:36 - 00:30:14

Yes is the short answer. But, you know, I was always on the fraud side of things, meaning I was preventing fraud, I was preventing risk. And because I'm a data person, just by training and by my kind of professional upbringing, data doesn't lie. If something is right, you can measure it. If something is wrong, you can measure it and demonstrate it. So I've certainly discovered some things that were wrong in the data and presented them, and I've had different emotional responses to that discovery.

Donna Mitchell

00:30:14 - 00:30:15

I'm sure you did.

West Stringfellow

00:30:15 - 00:31:01

Yeah, exactly. Some people like the best companies that we can think about, Amazon, visa, target. When you discover those things, and I'm not suggesting that I discovered those things there, but let's just. But if you discover those things, let's say they are very, very grateful. Thank you for figuring this out. Let's fix it right now. What do we need to do? How do we take care of the customer? How do we make sure that we're in compliance with the law? Let's go other companies, companies that are smaller, more is at stake. I find that they're more willing to live in gray areas, maybe even areas that are illegitimate.

West Stringfellow

00:31:01 - 00:31:46

And I just don't stay at those companies or support them very long. I kind of just kind of, if I discover that emotional reaction, I'm just like, oh, well, that's where I leave. So, yeah, absolutely. There's a lot of people, especially with the kind of money that I have to deal with. Whenever you get tens to hundreds of millions of dollars in one place, um, people tend to try to take advantage of that structure. And so, uh, absolutely, there's some things that have been untoward in there. And then I also, you know, I think sometimes the way that companies take care of their people, uh, take care of their customers, and I think it's very important. I really love working for companies that provide a good service, good value, don't take advantage of the customer, and then also pay their employees well, treat them well.

West Stringfellow

00:31:46 - 00:32:14

Um, and I I've seen the other side of that as well. And again, those are places I don't stay. But business is business. And I'd say if you look at, everything's a bell curve. I'd say probably 95% of businesses are kind of in the middle, and they're just good businesses, good folks. You got a couple, maybe two to 3% on the end that are pretty sketchy, and then two to 3% on the other end that are really good. They really take care of the people, really take care of the customer.

Donna Mitchell

00:32:15 - 00:32:41

So I said I didn't have many more questions, but I do. Blockchain technology, let's just circle back to that a little bit, just for the audience. Can you explain what blockchain technology is for the audience? Because you've got so much experience, I'm sure it is going to land well and increase awareness and understanding and what blockchain technology is and why it's so important today and why it's being discussed so much.

West Stringfellow

00:32:41 - 00:33:09

Yeah. So I love blockchain. I think it's fascinating. And it's technically, like, marvelous. Like in the truest meaning of marvelous. Yeah. And so I see blockchain as being the potentially the future foundation for most data storage and most kind of read write. So without getting technical, what I'll say is, you know, in your computer, and I'm speaking to the audience now.

West Stringfellow

00:33:09 - 00:33:46

Right. So in the computer, there's a part of the computer that thinks, and there's a part of the computer that stores what it's thinking about. The hard drive blockchain is potentially, when it's implemented correctly, it's the world's hard drive. So we no longer have data locked inside server closets that no one can access, where bad things happen, where nefarious things happen. Now we can have data that's open and available and everyone can see. So we have trust around the data rather than thinking what's going on in that company. I don't know. I don't know.

West Stringfellow

00:33:46 - 00:34:17

And then the CEO comes out and says 20 words once a quarter. You can actually see into what's going on because it's publicly available now in traditional databases. One of the biggest reasons that you don't want people to see what's happening is because the ability to see it means that you can change it. So if I can see inside a database today, I can write over what I've seen. Read, write. I can read it. I can write it. Blockchain is different.

West Stringfellow

00:34:17 - 00:34:57

Blockchain is immutable. And so what that means is when I write it, it can't be rewritten. So I can write it once and it's true forever and people can see it. And so if you can see it and not change it, that creates trust because I know what's in that database. I can read it with my eyeballs. And because it's blockchain, because it's a secure ledger, which all that means is a very small number of people have permission to write and you can't unwrite it. You can't erase it or change it, that creates trust in the data in the system. And so let's just, I'm going to make a use case up here.

West Stringfellow

00:34:57 - 00:35:35

But, you know, let's say the government budget, because we're talking about governments. If the government put on blockchain how they were spending their money or a nonprofit if a nonprofit put on blockchain how they were spending their money, everyone who they wanted to have access to it, and potentially everyone on earth could see how they're spending their money. And that is, that cannot be changed. They can't go make that up. They can't. It's publicly auditable. And because of that, it builds trust in the institution, it builds trust in the government, builds trust in the nonprofit. We feel better about giving them our money, and they have more public accountability.

West Stringfellow

00:35:36 - 00:36:01

Blockchain is potentially extremely transformational because by shifting how the data is stored so that everyone can read it and making sure that it can't be changed so we know it's true, then what we're doing is increasing our ability to have faith in the institution, not by relying on what the institution says, but by looking at what the institution does.

Donna Mitchell

00:36:02 - 00:36:06

That's exciting. So it sounds like it might help us get back to the world of facts.

West Stringfellow

00:36:06 - 00:36:10

100% and some trust. 100%.

Donna Mitchell

00:36:10 - 00:36:17

I like that. This is a good note to end on. So how can people reach out to you, talk about your website. What is the URL again?

West Stringfellow

00:36:17 - 00:36:25

How do.com? howdo.com? And then I'm best reached either there or on LinkedIn.

Donna Mitchell

00:36:25 - 00:36:41

Okay. Well, Wes, I am so glad that we ended up connecting. I definitely would like to have you back and everyone. Thank you. Thank you. Thank you. Good morning, good afternoon, good evening. And we are definitely pivoting to web three and shaping tomorrow together.

Donna Mitchell

00:36:42 - 00:36:51

Thanks for checking in the pivoting to web three podcast. Go to pivotingtoweb three podcast.com to download and listen, or web three game plan to check out the videos.

Donna Mitchell

00:36:51 - 00:36:51

Thank you.

Donna Mitchell

00:36:52 - 00:36:53Exclude

0:00 - 36:53

We're shaping tomorrow together.