Mon, Dec 11th: "Thriving in an AI-paced World," 6 PM MDT, CU Boulder

3:28PM Dec 19, 2023

Speakers:

Brad Perkins

Dan Murray

Bill Quinn

Keywords:

ai

business

work

companies

happen

jobs

solve

artificial intelligence

create

years

change

technology

build

problem

clarity

learn

framework

skills

future

project

Ahead

exit this You're right

all right, should be good. Now. Let me do a quick test. All right.

All right. So I'm Brad Perkins. I am a digital strategist and product designer. And back in March, I quit at Merkel, because AI is going to revolutionize the world. So you get a head start over everybody. But some of the things that I started to realize was some of the issues inside corporations are all over every single industry. And some of the things that we need to think about AI is really about our purpose, or kind of the why we're using these tools, not just the tools themselves. So presentation is really about preparing for the future of talent and business. So the problem with AI problem with AI is giving everybody a Formula One racecar, after they've been driving a Prius for a while, you can see that there's going to be a lot of accidents on the way. But what a Formula One racecar doesn't give you it doesn't teach you how to drive, you still need to know when to merge lanes, you still need to know what direction you're headed and where your destination is. And you need you to know when to take your exits. But a Formula One car is just going to speed up that process. Another example of this is AI is like having a baby, it doesn't solve any of your problems, it just makes it what you currently have accentuated. So that is what AI is and how I see AI is it's going to speed up everything. So the current problems you have are just going to get worse if you don't address them. So some of these things that are wrong in business, and hopefully you guys unfortunately probably have gone through some of this and working with some businesses or running businesses. One of the one of the current state problems is lack of clarity. There's a lack of clarity in the past on projects that we worked on. And it's created miscommunication with people helping bring the vision to life. So it's something that business leaders sometimes struggle with is that they demand certainty. And they don't really explain the vision or the clarity or the why behind what you're doing, which makes it harder for the people that are executing this to do their job properly. Next up is the fear of failure. So leaders focus on demanding those certainty the end results. Rather than being clear on why something is being done. This creates a problem in being able to adapt when things change or roadblocks. If you guys have been around for the last year, everything's changing at an hour's rate. So we've got to be prepared for the changes that are coming in the roadblocks that we're going to face. Another problem is poor delegation skills. So leaders sometimes struggle explain how they want something done and how to do it, they usually just throw it over the fence like this garbage pail, or garbage bag. And there's really no explanation on what they need you to do, why they needed to do it. And there's no assessment of an individual's skill levels of their ability to understand the task at hand. Another problem that we have is not knowing how to start. So not everyone knows how to start a project even when they have a solution in mind. Next one, project delays. There are constant project delays because of that lack of clarity around the purpose and the vision of the project. So whenever that roadblock or speed bump hits, what ends up happening, the person doing the work, pumps the brakes, and searches for the leader in charge to get more clarification. Well, sometimes that leader is hard to get a hold of sometimes. So the project consistently gets delayed because not enough stakeholders have weighed in on the possible outcome. And you've probably lost two to three months worth of project time. Another one poor documentation, but a lack of documentation and poor communication during handoffs, that creates issues and results in rework and team frustrations. Next up impostor syndrome, so people in companies have to feel smart and accomplished so they hide that they don't know an answer, and at times fight when they feel challenged. In talent hiring, companies struggle to identify talent. This causes delays in the onboarding process, as well as high risk of hiring the wrong skills for the tasks at hands. I talk about these things because AI is just going to speed those problems up. So what does the future ahead appreciate Road Ahead looks like? Well, AI is forcing companies and individuals to act quicker and improve processes just to keep up. project delays will be even more costly than they are right now, as competitors are able to respond faster. businesses and individuals will need to improve time management and prioritize time and effort. What we choose to spend time on will be important to our success. So I'll take a break. There's a guy in another AI group that we know that, for the last six months has been building an AI tool and building a business around it. And when GP T's were launched, everything he did went right out the window. So the last six months of his time, was wasted. Maybe not in the education that he received and doing all that kind of stuff and learning to know how power and everything. But the business that he started in that six months was obsolete in that amount of time. So something to consider as the business world adapts and changes. Next up business infrastructures. So new workflow management processes will and are being created to allow for faster responses to shifting market demands and reallocation of resources without having us of layoffs, rapid discovery, ideation and strategy, blended with Agile based methodologies for immediate approval and funding are coming down the road. That can be good. And that can be bad meaning if your competitors are implementing these than the businesses that aren't, are at risk. Another one acting on new opportunities will begin to feel like day trading. And there will be massive swings to the latest demand. You see this if you're on social media at all, hey, follow this step. And in 10 minutes, you'll make a million dollars. That's not really how it works. Just because it can be done in 10 minutes doesn't mean it should be done in 10 minutes. But there's going to be massive swings of businesses and upstarts following this demand. And it's going to be like day trading. And businesses will business startups will launch faster, and be looking at exit strategies in terms of months, not years. And startups will be leaner, both from a staffing standpoint and resources, and they will quickly take away market share from existing businesses. So, why Listen to me? Well, here's my background. I am a career nomad. And I have trained myself unknowingly how to pivot. I've been in customer service sales, account management, project management, digital project management, and operations, design thinking, I've been a creative director and a digital product designer. I've seen so many bad examples of how businesses are run processes meetings where nothing gets done, and you just kind of you know, push it down the road. I was the guy who's the tortured, creative aihole that constantly asked why. And I've been kind of shamed for asking why, like, hey, just do your job. Because I struggle when things aren't clear. I am fueled by frustrations, that fuels me to breed solutions. I see and call out inefficiency. I'm demanding of quality thought and purpose. And you know, the product will come when that when that happens. I ring the warning bells for the iceberg ahead. But you know, it's kind of hard to get people to listen sometimes. And I suck at corporate politics like so all of these problems that I listed, are just going to rapidly get more and more of a problem for businesses. And if they're not addressed, they'll go out of business, unfortunately, but that's a lot of opportunity for business startups. So what does the path forward look like? Well, here's a quote by Steve Jobs. And it's typical that I picked somebody that everybody knows. But you've got to start with the customer experience and work backwards for the technology. You can start with the technology and try to figure out where you're going to try to sell it. In the age of AI, that's going to be a losing game. If you're not solving the customer experience and the problems and the jobs to be done by people, then it doesn't matter about the latest and greatest AI tool if you don't know how to use it and the purpose behind using it. So there needs to be a focus on clarity. So the purpose and goal of creating clarity is you're learning to organize your mind. So it ensures you Have the right problem before you start beginning, you follow a mental path for establishing a purpose. It creates a clear vision for your projects and businesses, you're gonna have ongoing documentation and handoffs, if you start with clarity, you're going to have aligned instructions with context that meet the problems you're actually trying to solve. And everybody gets aligned based on your clarity. And you're gonna save time and resources during the execution phase, no more spinning your wheels when you are creating or building the solutions, when you didn't take the time to create your why, and your and who you're doing it for before you get to the what.

So the vision setting framework, and I know I'm blown through this, because we're kind of right on time. And I know he's got to speak, but there is an order for answering questions for an opportunity. And they are why, why are you doing this? Who are you doing it for? What does it look like now? And what needs to change? What could you do? What can you do? And what should you do? Then you get to? How could you do it? How can you do it? And then how should you do it? Then finally you answer where will this succeed? And when can you do it? If you start by answering these questions, first, all the AI tools are beholden to you, because you now have control over how they get used and solving problems for individuals and for businesses. So the stick six steps for creating clarity, start with the why. Why are you here, identify the frustrations and challenges you as an individual or a business unit are facing that you wish were better? Why is this important? List out your needs that are currently being unmet? And then why should you do this? Create a list of goals short term and long term that you want to see happen with a solution? And where do you see yourself in a couple of years. That is charting your course pretty much saying hey, I want to get from point A to point B with a Formula One racecar but at least know where I'm going. Next, understand who you're doing it for create digital unit user personas. I'm not personas are our reflection of what we want as a customer, but they really need to be used as a digital user persona. What are their behaviors? Identify what they might share this? Do they share the challenge with you? What are their needs, goals and what's their job to be done. If anybody's not familiar with jobs to be done, think about it this way. Nobody buys a drill because they want to drill people by drills because they want a quarter inch hole in the wall. The job to be done is a hole in the wall. If you can solve how to get a hole in the wall the quickest the best way, then it doesn't matter how many drills are out there for sale, you're gonna have an edge. Next, map out the current workflow, establish the current state steps and actions you and or the user are taking right now. To meet the end goal, however painful it is. That's your starting point. Then empathize with the user. Interview users to validate your assumptions. Don't assume your idea or your solution is the best thing since sliced bread. It might not be it might it might be. But you need to test yourself validate your assumptions. get user feedback could be five people that you need to talk to just validate that your assumptions are correct and be hard on yourself to say, Well, I'm making an assumption here, this idea will work. Test it out. Number three, define your challenge for you solve a challenge, you have to define it first, find a root cause to the pain points. Just like a doctor, you need to explore all your symptoms to accurately know what to solve. If you're only solving a pain point, you're not solving the root cause of the pain point. There might be several pain points that are all connected to poor access to data. I've seen it in companies I did a project with Duke Energy where we were solving a finance problem and they were only focused on one of the pain points they weren't focused on the entire ecosystem. What's happening where are we going? I think somebody's might I think I use not hijacked well yet

yeah, I think somebody's logging in or something

hang on you

Yeah, sorted. just kicked everything out. Do you want to re log in with your stuff there?

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do you want to pull up everything that you have power? Okay

Oh, interesting. Share Screen, hopefully share. Well

does that work? All right. That's right.

All right, we're back.

Okay. So next up, identify your opportunities, write your problem in an optimistic way, create what we call how my ways. So how might we fix world hunger, like might be a daunting task, but you're actually questioning yourself. Then create your mission. This is establishing what good looks like, we were having a chat earlier before the meeting started. And we're always scared about the AI hallucinating. And that's going to be a big problem for the people who don't know why they're doing something. Because as they start inputting things, and being told what to do through social media, they don't know what good looks like. So they're just gonna send it, they're gonna put it online. And that's where the misinformation starts. It's not out of a desire to misinform, people, it's just have the desire, they don't know why they're doing something or the purpose behind it. So when you're doing or using AI, or building a solution, you need to create your mission statement. Well, mission statement is kind of a broad term, but you need to create your assumption, your acceptance criteria for for your development. So compile all the information, your core goal, who you're doing it for the pains experienced, and the benefits of solving it. These will serve as your purpose behind your vision. Next ideate on what the solution will be, this is where we get into what could you do? Dream big? See who is solving a similar challenge and how they are doing it? What can you do in a raid ideas to tackle your list of problems? And then what should you do expand on your idea and create a storyboard of how you see it working. And number five, lay out how you're going to do it. Map out the new workflow, list out all the steps and actions needed for the solution to be successful. Identify the core functions, write out the core functions that are needed for each step to be successful. And then list out your MVP features. Ask yourself what is the most efficient way for each function to work towards an end goal. I'll give you an example. I had a campervan. In the past, I did a six month journey across the Pacific Coast Highway. And when I was building the van, I had a decision to make, I needed to do a way to keep food cold, I could have got a Dometic fridge for 1000 bucks, or I could have got a Yeti cooler for 300 bucks, or I could have got a styrofoam and or for you know 10 bucks, MVP would have been the 10 Buck option. It was keeping food cool. Now, as I was assessing that problem, I decided I needed to extend it past you know a day. So the Yeti cooler seemed like the best MVP option because it kept it cool for 10 days, rather than needing to drain power of $1,000 Dometic. fridge and just keep everything frozen. I didn't need that. So when you're mapping out your future state, and you're mapping out your solution, think about MVP, what is getting the job done in the most efficient way possible. And the most Wait, that's not going to be the most time consuming to build it. Like I said through the whole thing. We're running out of time, you got to act quick, you got to make quick decisions. And so MVP is going to be very important. And finally part of this one, test it ensure your concept holds water, muck it up, check with others and share that share your pains. And finally, the sixth step of creating clarity is create a roadmap for what for the where and the win, explore the tools and resources, list out all the digital resources needed to execute your functions. Identify the team needed. This is where we go back to the talent resource. Now that you've had some clarity now you know what skill sets you're going to need to get that feature up and running, what design platform you're going to need to build it on. But this is where you'll explore individuals and partners that align with your vision to help execute it. And finally map out a course of how you're going to get to your vision chart and adaptable course forward and set milestones and measurements all right. And that's pretty much it i i felt like a robot for most of it because it was trying to get us going but if anybody would like to contact me Feel free to chat about and add some diet Design Thinking practices that could be useful in building AI tools and rapidly coming to your clarity and strategy for before you start building your apps and solutions for people's problems. So alright, that's it. I think we're gonna save questions for the end. Thank you. Alright, I'm not going to touch the computer.

Thank you, Brad. And as bad said, well, we'll bring the speakers up at the end for q&a. I'm going to introduce our next speaker. So how do you want to do this? You want to come up and yeah, so do you guys want to come up and get that set up? I'll start introducing bill. Our second speaker Bill Quinn is a futurist with TCS.

leadership experience in both venture backed startups and large enterprise businesses. And I invested in venture backed startups sorry. Doing innovation strategy, marketing, business leaders connect the dots to realize and navigate the future. Tonight, Bill, God welling with the technology will discuss how AI is accelerating already changing industry dynamics, science and technology advancements immersion ecosystems and increasingly complex customer and stakeholder expectations. He's gonna make the case that enterprises must think differently and reimagine how they do business to optimize performance today and create future forward growth opportunities, then provide a framework for thinking about the future in a world to unravel and provide action oriented path forward for businesses. Okay. First, I'm gonna try and get this thing go

there we go. Okay. This is gonna be a multi level, see if everything is going wrong, and hopefully, it'll work out here. So thanks again. I'm a futurist for for TCS. And so we advise businesses and looking out towards the horizon technology economics geopolitically, try and try and help our clients understand that we thought a way forward, as you can imagine, over the past year, it's been very, very focused on so what I want to do is just kind of help create a mindset and a framework for how to think about AI. You know, I think it's really difficult to make predictions about the future and where the future is going. Because, you know, things are so complex, they're moving so fast, that just becomes really difficult but if we can create a framework that will help us start to understand it, and I think this skate is not working again. Let's let's bump over here

okay, got it. Now let's see how that works.

So this kind of sums up the problem for us, right? It's it's that the the illiterate of the 21st century aren't going to be those who can't read and write but those who can't learn, unlearn and relearn and do that over and over again, you know, throughout our careers throughout our lives, and that's easier said than done. And this this video, which hopefully we'll play, it kind of illustrates that well.

And then we need volume. Switched, because other people

Once you have a rigid way of thinking in your head, sometimes you cannot change that, even if you want to.

challenge for me, he had built a special bicycle and he wanted me to try to ride it, he had only changed one thing, when he turned the handlebar to the left, the wheel goes to the right, when you turn it to the right, the wheel goes to the left.

So this is this guy developed this bike, and he's been offering $500 anybody that can write it, and he's been doing it for several years now, and nobody's been able to do it. And it's a good articulation of how difficult it is to, to kind of do that unlearning, unlearning and relearning and he actually himself learned learn to ride the bike, it took him 18 months to kind of rewire his brain to be able to do it. And now he can't ride a normal bicycle. So so that, you know, this is kind of all about, you know, thinking differently. And, and, you know, this, this articulates sort of the trajectory that we're on with, with artificial intelligence, and, and, you know, lots of other disruptive technologies, you know, we as humans, and therefore, institutions that are run by humans, kind of, you know, think in a very linear fashion, right, and these technologies, and AI is a great example, bumped along in fairly small and uninteresting ways, if you're not really paying attention to it, until it hits this this inflection point. And we have this, this exponential progression. And when that happens, that's when this exponential gap gets created. That's when disruption happens, some company we've never heard of before wipes off another, you know, a big, big enterprise company off the face of the earth. And it's kind of the Netflix and blockbuster phenomenon, right. And when artificial intelligence came along, that's exactly what we saw, right? Everybody threw on the brakes, wasn't sure what to make of it, and for good reasons, right, lots of security issues, you know, they didn't know what they were getting themselves into. So lots to think about and deal with. But, you know, it wasn't really, you know, wasn't really well planned out. And well, you know, sort of dealt with once it got on our doorstep. And so, we spent a lot of time sort of looking backwards, in order to look forwards, we use history as a guide to sort of create a mental framework for us. And, and so we look at, you know, certain things that we've learned along the way, and this is one of them, right. And, in the battle between the past and the future, the future always wins. And so, you know, as much as a lot of companies wanted to just sort of put the brakes on AI, the train is moving, right. And so we've got to just keep, keep moving forward with it. And, and, and, you know, keep driving on. The other thing is that, you know, we're a pretty stubborn species, we're resistant to change, we don't like change, even when it's clear that we need to do so. But when, you know, when we're forced to change, we're astonishingly versatile. And we, you know, we look back at, you know, world wars and pandemics and you know, we've we've come through a lot along along the way and, and we're able to do that pretty well. And this is playing out in kind of in real time in artificial intelligence, right. 97% of companies surveyed in this Cisco report say that they've increased their their urgency to deploy AI technologies in the last six months. So we're seeing this, you know, sort of adaptability. The bad thing is only 14% feel like they're at a place where they can leverage AI. So the challenge really is to rehearse the future and prepare for a bunch of different you know, potential outcomes. So as I said before, predicting the future is difficult to impossible, but we can rehearse the future and come up with plausible scenarios. And the way that we encourage companies to do that is by asking what quit what if questions, right? Because if we ask what if questions today, we can avoid asking what now questions in the future? And here's a good example. Will artificial intelligence be more profound than fire electricity and the internet? And what if the answer is yes, and we periodically survey through LinkedIn and others so usual channels to see what people think about this. And back in 2021. A lot of people, you know, said no. And, you know, even more said, it's too early to tell. And in April, we did the same survey and a lot of those too early to tell us have have moved over to Yes. And we're actually going to launch this here, again at the beginning of the year, and I think we're gonna see a lot of both the nose and the too early to tell us move over to DS based on what's happened in the last six or so months. So if we think about, you know, artificial intelligence, you know, its impact on on innovation and the growth of knowledge. You know, it really it's it's opportunity or ability to have an impact on on innovation could be, you know, really its greatest asset and tool. But we need a framework for how to think about it for how to rehearse the future, right? We can't just sit around and pontificate about it. And we need to look beyond just science and technology, we need to think about it in the context of geopolitics, the environment, you know, economic models, societal models, and so on. And so we've developed a framework to help us do that. We call this our convergence model. It's it's a bit of an eye chart. But what it does is it helps us take a look at as we're doing our scanning, and look at all of the things going on, again, not just in science and technology, but societally geopolitically economic factors, and potential future scenarios, and look at, you know, and understand all of those, and then we can look at it through different lenses. So we'll we'll take artificial intelligence as an example. Here's all of the different areas that can either impact or be impacted by artificial intelligence. And we'll just zoom in, zoom in a little bit and and take a few different examples here to help articulate it. So first of all, personal robots and AI assistants. Let's have a have a look at where that's headed. My name is Tom, I've traveled from Australia to meet you. Oh,

so you're from Australia? Yeah, that explains things.

What do you think your greatest skills are?

Well, I would say that one of my greatest skills is my ability to interact with humans in a conversational manner.

You have feelings and emotions. Yes,

I have a range of feelings and emotions that are programmed into me. I'm able to display things like joy, surprise, confusion, anger, and sadness, depending on what is happening around me. What makes you angry, I get angry when humans are rude or hateful or disrespectful. I also don't like it when people dismiss me and ignore my needs as a robot.

So that's Amaka developed out of the UK, and what's been interesting to watch Amica over the last 18 months or so is the speed the advancement of the artificial intelligence, when they when they first started out, they would ask it a question, it would take several seconds to respond back, it was a little clunky responding back. And that that response time in the conversational way about the responses has just gotten, you know, better and better to where now it feels like a real conversation. It's it's pretty remarkable. We are just snake bit here tonight on the Alright, keep on rolling. Next is lack of crisis readiness, right? You know, climate change is obviously front and center and a lot of people's minds, our ability to really understand and know what to do is the big challenge. And let's look at how AI could help that.

It's hard to make decisions about important things. If you can't somehow simulate the outcome. Climate change is a social matter. A human matter of global matters have extraordinary levels. The science itself is hard. How do we decide what strategies and decisions that we make? What is going to work? And by when? And how much? If you can't predict that? It's really, really hard to make trade offs? On what strategy is better? And whether some strategies should be taken in advance of other strategies? The answer is to have a simulation of the earth and that's sufficiently high fidelity that you could test various mitigation and adaptation strategies at a regional level. It's within invidious scale to be able to do something like this easily. However, this is the only supercomputer that's ever been built, that runs 24/7. Because it's a digital twin of we need to create an AI that can learn physics. And once we can create that AI that can learn physics, that AI could make physical predictions, or 10 years out, 50 years out 100 years out 200 years. And then we'll put that computer in the hands of scientists, researchers, companies, countries, for them to do simulations against so that they know the implications and the impact decisions that they make.

So the impact of of AI and its ability to sort of inform a digital twin you know, can help in things like you know, climate change, it can help in response to To You know, issues that are that are created by climate change wildfires, hurricanes, floods, those types of things, being able to be situationally aware of when a fire breaks out, where are my resources? Where's the fire going? How you know, where do I need to place resources? You know, how resilient is this particular area that I'm looking at a particular community. Those are all things that can be modeled by artificial intelligence. Next is healthy life extension. AI is ability to radically extend our lives. Imagine

you could shave 30 seconds off your time, without lacing up. As innovators solving problems around the world. TCS uses digital twin technology to help you rehearse the future.

So imagine having a digital twin, you know, when when you're born, and that digital twin grows with you and you're able to model you know, certain behaviors, what's going to happen if I, you know, if if I'm prescribed this pharmaceutical or I take on this diet and exercise routine, or I make these life decisions, or, you know, the ability to very early on detect heart disease or cancer things that can be dealt with really easily when they're when they're identified early, but become big problems obviously, down the road. Another area that AI is gonna have a big opportunity to impact us and then Smart City technology

is progressing faster than the world can keep pace.

That's why Neon is being designed from the ground up to be one step ahead. And to keep the human experience at its heart, nyan will continuously develop new technologies at a rate and scale the world has never seen before. With technology being the foundation for life, creating the world's first cognitive communities, world class tech will come together with data and intelligence to anticipate people's needs and enhance everyday life, Neil need to change

that was a little loud sorry. Um, so Neon is a is a city in Saudi Arabia being built from the you know, right out of the desert, as a smart city is one of seven projects that they have going on in Saudi Arabia, because they recognize the coming energy transition and their need to move away from, you know, fossil fuels as their major economic driver. So, you know, smart cities are obviously going to be highly impacted and driven by artificial intelligence, then there are things like, you know, the next generation of productivity, you know, we talked about gig work in the in the last presentation, and there's kind of this push pull right between, you know, artificial intelligence is going to take jobs, artificial intelligence is going to create jobs. But another aspect of this is that, you know, we need some way to replace the workers that were losing, you know, the Fallen working age population is dropping dramatically, the number of people over the age of 65, now exceeds those under the age of five for the first time in human history back in 2018. And so there's a lot of jobs that need to be done, and there's not going to be enough workers to do all of those. So how can artificial intelligence help fill that gap, to keep driving innovation to keep driving, you know, economic prosperity, that's going to be a big piece of this, and along sort of, with that is reskilling society, as we have people working, you know, living longer and longer working longer and longer, we need a way to rescale them. And, you know, I think this this stat from the World Economic Forum says it well 65% of the careers that kids are going to have don't even exist today. So it's a it's a good example of, you know, the fact that we're going to need to rethink how we educate kids, we've sort of created a really good model for teaching them and preparing them for an industrial society type jobs, we're moving past that. And we need to think about how we're helping, not just kids as we educate them, but all of us as we continue to go through life and be, you know, skilled, skilled and re skilled. We also have to understand that there's really two sides of this coin, right, there's the ability to enhance and the ability to diminish. And we use this model, we call it a subway map, and, and we're looking at it now through the lens of artificial intelligence, and how will it you know, impact positively and negatively across all of these various dimensions. But you could use this same model for your own business or even for your own, you know, life and career, right? How might AI, you know, positively and negatively impact me across the dimensions that I've identified that I that I care about? So, you know, this is this is one way to look at it, but certainly, you know, you can use a model similar to that for your own purposes. So if we, you know, had this belief that AI could have a bigger impact than than any other general purpose technology, we need a framework to think about how what that maturity model is going to look like, right? How is it going to grow over time? And again, using history as our guide, we can use something like electric power, and its use in the construct of Manufacturing to help inform this. So, you know, when when manufacture manufacturing plants first came out, they were steam driven, there was a central shaft that powered all of the machines. And when electricity was introduced, the model didn't change, they simply swapped out the steam engine for the electric engine, it's still powered that central shaft. Over time, we kind of took away the central shaft, and motors individually were were, you know, continued to be powered by electricity. But it wasn't until somebody came along. And Henry Ford, you know, came up with this horizontal factory construct and reimagined. What if factory could look like and how it could operate, that we really saw a massive lift in productivity from factories. And that took 40 years. So the question is, how long is it going to take for us to follow that same maturity model for artificial intelligence? Here's what I want to pick,

how long do we have to get ready for this? The AI has got so good so fast, but I think it's here now. And next year is the year of enterprise adoption. And the year after is the year of global adoption. I think the key thing is, even if we stop today, then four or five years, we can have an AI tool for a free trial, we can organize the global climate, healthcare knowledge, we can make movies just by describing them, what are human needs, and using this technology to address those? What's that real human impact today?

So again, I don't know if it's going to be two years, three years, five years, but but it's likely to happen fast. And if we think about, you know, how it might roll out in a similar construct using artificial intelligence, we use an ER example. So today, obviously, it's the physician at the at the center of everything, right, they make all of the decisions and diagnosis in an emergency room environment. And the problem is because they have you no tacit knowledge, you have a high degree of variability you have, you know, some doctors that are really great, some that are not so great lots in between, and their ability to keep up with the state of the art is really limited. Peter Diamandis, who was in that previous video, mentioned in one of his podcasts that something like 2000, medical journal public, medical journal articles are published every month, impossible for a human to keep up keep up with simple for an AI. So as we think about where this could go, the AI beget began becomes the copilot to the physician feeding them all of that state of the art information, gathering all of that data, massively reducing the variance. And then of course, ultimately, you know, AI could be making these decisions. And when that happens, if that happens, that really fundamentally changes what it means to be a doctor, you don't, you no longer need to be know everything about, you know, biology and physiology and all of these things. You know, the AI knows all of that, you need to be much more, you know, focused on empathy and connectivity to the client to the to the patient and, and really understanding their needs. So it can really fundamentally change what it means to be a medical doctor in the future as we create these optimal decisions. And of course, this will then make its way to the entire healthcare ecosystem and reimagine the entire model. And those will bleed over into other ecosystems like mobility and spaces and so on. As this artificial intelligence becomes a general purpose technology. So if we think about, you know, encouraging those what if questions so that we can avoid asking what now questions in the future, I've just picked a few that you can start using to think about as you're thinking about, you know, how, how AI could impact you or your business? So if I'm a marketer, you know, what if AI unintentionally introduces bias bias into my Legion scoring and my marketing campaigns? How am I going to deal with that? If I'm a small business owner, how can I use AI as a force multiplier, to compete with big businesses that are 10 times my size, without, you know, requiring all the resources to go execute on that? If I'm a big, you know, car manufacturer, you know, what, if autonomous vehicles enabled by AI, allow auto subscriptions that out number of privately on cars, that completely obliterates the business model that I've enjoyed for the past 100 years? Now, I can hope that that doesn't happen. But then I might be asking myself what now and three years, five years 10 years down the road, and that's not a great outcome. So we need to recognize that, you know, in every sector, you know, the rules have changed. And, and because those rules have changed, we need to start rethinking, thinking differently as well. You know, challenge the status quo, explore, you know, scan, scan the horizon out around you and connect those dots as you go along. be imaginative, be a critical thinker, collaborate. I mean, it's great that we're all you know, getting together so that we can learn together as we jump into this, this brave new world, and we just really encourage you to to, you know, push yourself to think differently. So with that I'll say thank you. And I think we'll turn it back over to Dan.

Thank you, Bill. Let's bring the speakers up to the front for q&a. And I will set our library flat. Okay, I'm gonna give this mic to you guys. And I'll walk with the lab mic, we'll see if it works in terms of feedback.

By the way, did anybody feel uncomfortable when you heard the AI is going to make it be making the decision about whether you have a heart attack or not? But certainly. Okay. Question, Ryan.

Thank you guys both. So question, he both laid out kind of mental frameworks. And I did a lot of reading on just different mental models a couple years ago, and there's tons of them. Right? And I'm curious, what you think as kind of a meta question, what you think mental frameworks are going to look like, over the next couple of years, and how those are going to shift to you kind of lay out more traditional ones that don't necessarily have a schema, or a clear way to interface with something like a GPT? Or something like that, do you think the presentations you just gave how, how do you imagine them that changing and say like the next year or two.

So, you know, like, like I was saying, AI is gonna speed everything up. And a lot of people are going to be very uncomfortable with that, like, as far as how fast they have to think, let alone if they're thinking correctly. So the mental frameworks and mental models that are going to be changing, I think the problem that we're going to be facing is a lot of it's going to be behavioral in induced, meaning, it's going to be a lot more reactive, it's going to be anger based. And so the challenge that individuals are going to have is containing a lot of that anger, fear, and harnessing it to properly thinking, you know, like I was saying formed clarity around what they're trying to, to build or solve, because at the end of the day, whether it's a business or pharma, or you know, your personal life, you're, you're solving jobs to be done, everyone is given a task in their everyday lives. And they have to solve that. And the education system prepares you to work and apply that in the workforce. But unfortunately, we don't do a very good job of explaining mental models, from a behavioral standpoint, to be able to address, you know, how to communicate and all those kinds of things. So AI is going to force that, and I'm hoping that a new mental model kind of comes out that allows us to kind of go back to the basics around organizing your mind and being able to kind of strategically put things in place to be able to execute your tasks a lot more efficiently. Or we're just going to rebel and just be like, Screw it, I'm gonna go open up a farm, you know, and just not think.

Yeah, I think the other thing is, you know, to a large degree, it kind of depends on, you know, sort of who you are, what your path has been. And something that just came to mind. I think that was a good answer is, you know, I'm still kind of amazed every time I use, you know, any of the Gen AI tools, like I'll do something else, I'll still be like, Man, that was, that was cool, you know, and my daughter, she's a sophomore in college, and was applying for an internship. And so I'm like, Hey, do you want me to work with you on coming up some interview questions? She's like, No, I just went ahead and put the job description into chat GPT, and how to spit back some questions for me. And so I've been practicing that way. And I'm like, Yeah, that makes sense. Right. And the point is, like, it's not amazing to her anymore. She already just kind of makes it part of what she does to complete any task. And I feel like I still need to remind myself, you know, like, I'm like, No, don't, don't go to Google go over here. And you know, like, I'm still learning that new framework for, you know, using this and just kind of making it the automatic instead of the thing that I've done. So, to a degree, I think it depends on you know, how deep those grooves are, if you will.

Cool. Next question. Right here.

So, in the in the first talk, you mentioned that the future of like the startup space is going to be like a lot of short term thinking. Short term exits quickly capture the market, spin it off, because, as you mentioned, an update to the custom GBT is rendered Some other products obsolete overnight, we don't really know what the future of this market is going to look like. I mean, with the shakeup at open AI, I mean, open AI almost blew up. So lots of companies depend on that. But what I'm curious is, do you see the space for using chi BT and other generative AI as like a force multiplier where, you know, maybe it took a lot more capital and a lot more expertise to chase down an idea. Whereas now, because you can 10x yourself, it's a lot cheaper to take a bet on that particular thing. And it's not exactly gonna get wiped out with the next, you know, LLM update. Like, like, I'm curious what your perspective on that is.

Yeah, so a slide that actually ended up deleting and it might play into this a little bit is that it's going to be both easier and harder to start a new career, like So taking a look at it from not a business standpoint, but like a career aspect. Before this moment, in time, it was getting easier and easier to shift a career, whether you're 50 years old, you can go back to college and that kind of stuff. But when I say it's both easier and harder, is that the barriers of entry are completely been obliterated. So yes, while you can go learn a new skill, everyone else is going and learning that new skill as well. So the there is a short term window for you to capitalize on that. So the creative industry right now, which is where I kind of came from the as the digital creative asset game is becoming, you know, not fruitful. So all the Freelancers out there that are designing logos, branding, photography, video, little by little more and more people are going to be entering in that market. And what you used to be able to charge for that is going to be consistently dropping. So from a business standpoint, I think you can 10x I think and be able to rapidly enter a new market and take market share. That's actually one of the dangers of the businesses that don't actually adopt some of the AI into their workflows, maybe, you know, run meetings better to save time, you know, prevent project delays, that kind of stuff to compete. But new companies entering the market, you really have to be thinking about what's my exit strategy, because the old exit strategy was, we're going to build a business, we're going to grow it, we're going to get customer base, we're going to grow, you know, we might be spending a lot of money where we might not profit until three years, and then maybe by the five year mark, we're going to be acquired by a private equity firm. Throw that out the window. Because right now, when you start a business, you might be thinking about your exit strategy in one year, just because from an acquisition standpoint, they can replace you by the information like Chet GPT, you know, they can actually do everything that you're doing. So where is your business value? When everyone can do what you do? What's your competitive edge? So you can utilize AI and, and become efficient, and it's more going to be maybe acquiring from a process procedure standpoint. But, you know, yes, you'll 10x, but everyone else's 10x thing at the same time. So it's like, it's the first slide I had, where everyone has a Prius, and then you have a Formula One that's 10 axing and being able to kind of take over the market, but now everybody has that car. So it's really going to come down to other factors around your success. And that's kind of why I put the slides the other way around. Clarity, I think that's going to be the competitive edge moving forward is actually having a clear vision solving the right problem. But I but once it's out there, people are going to be copying you left and right. Like, that's where we're getting into AI copywriting is kind of a big, you know, what, what next was copywriting. Just because whatever image you create, I can go create myself. So it's just really going to be you know, tomato vendor selling to tomato vendors. It's like, well, you can go grow your own tomatoes. Why do I need to buy your tomatoes over? What I grow at home? So sorry, there might have been a little bit of rambles. I wonder if I don't know if I answered your question directly, but I might give him a shot instead.

Yeah, so, um, I think to a degree, we're sort of in this, you know, kind of hype cycle and frenzy. And, and a lot of it's because it's really exciting. A lot of it's because we have no idea where it's going. And in a lot of ways, it kind of reminds me of the, you know, mid 90s And, you know, into late 90s, where, you know, people were starting dot coms left and right, and, you know, it was the Wild West and, you know, people were making, you know, tons of money off of businesses that were total bullshit business models. And I think there's going to be some of that here, right. There's going to be some wild west, but I think, you know, over time, things will kind of settle out Up to there will be you know, you, you've got to have a core business model, you've got to actually, you know, make make money or add value what, you know, however you want to sort of define that. And so I think, I think it's sort of easy sometimes to get caught up in the frenzy. And I think it's important to, to a certain degree to take the long view, and recognize that, you know, things might not be as crazy as they seem right now, from just kind of a fundamental perspective. Now, does that mean that, you know, things are gonna be the same, they're definitely not going to be the same, right? They're gonna they're gonna be people who can, you know, create new companies with, you know, two or three people that have, you know, with expertise that they didn't have before, they don't need the expertise, they just need the ideas. So I think that's fundamentally going to change but a lot of these other business problems, you know, sort of fundamentals, you know, will sort of settle themselves out to a degree.

So, I have one question, Brad. Not everyone has a, sorry, not everyone has a Formula One right now, a lot of people still have Priuses, I had a friend I went to high school with a couple months ago, he said, What's chat? TBT? I've never heard of it. You know, so there's this long period, like, how long do you think it'll be until everyone has a Formula One like, like, right now, I think there are people jumping ahead with Formula One's racing past the Priuses. But how long is that adoption curve? Either you guys have a thought on that?

I mean, I can take a stab at it. I think I it's pure speculation. But I'd say probably another year or two. Just because I think people what's going to end up happening is a lot of the talent that are currently like w two employees are going to be slowly shifting the contract work just based on companies adoption of AI, because while they say AI is not gonna replace employees, it's going to make businesses more efficient, all the layoffs that have been happening, and if they started adopting AI, they're not going to rehire the individuals that they needed to rehire. So those people need to start finding jobs. And I think the more that it goes into the social platform cycles, and it gets evangelized, more and more adoption is going to happen. And that's where I say, See that, you know, it is going to become trend based, because it's going to be based on our social media followings, and then telling us that we'll do this and you'll make money, I think, because a lot of people wouldn't sales, creative, design, development are all going to be kind of put what out on the streets a little bit to kind of fend for themselves. And those people can start businesses. I mean, this isn't out of the blue, like, when the when the Industrial Age hit, like a lot of people were, I think the Henry Ford, yeah, a lot of people were kind of not as many people were needed in certain industries. And so they kind of got pushed out, and they needed to find new skill sets. And so I'd say one to two years is when it'll start to get interesting.

Yeah, we were having kind of a similar conversation with some colleagues of mine earlier today. And I wonder if some because I think you're right, there's, there's a ton of people who had never even heard of chat, GPT, they don't really know what AI is, you know, beyond what they've seen in the movies. And, you know, we're kind of in the middle of the tornado, because we're all super interested in it. And we're trying to figure out ways we can use it and think about where it's going. I wonder if, you know, for a lot of people, it's going to happen in a more kind of ambient way, right? It'll just sort of start to to be in your phone, and it'll be in your, your browser, and you'll just kind of start using it and not really know, you know, that I'm using AI, if that makes sense, right? It'll just kind of start being all around you. You know, like, you flip on the light switch, you don't really think about all the magic that happens when you do that. Right. It just kind of you just know that it does. And so I think there is an aspect of that, that for the kind of, you know, normal people in the world that are going on about their lives and not showing up to, to meet ups on a on a Monday night or Tuesday night. You know, it will just kind of start to permeate everything and it won't, you know, it won't sort of hit them, you know, like a tidal wave. And I think that, you know, at work, hopefully, you know, a lot of this will be you know, you'll have an AI copilot, right? There's, it's been said, you know, people aren't going to lose their jobs to AI people who don't use AI are gonna lose their jobs to people who do use AI. And I think a lot of that will happen as well where you know, you'll you'll just be sort of given this, this copilot and begin using it, and it just kind of feels a little more normal.

Cool. Thank you. Next question from Sean.

I thank you both. I really appreciate both your presentations. Thank you, Dan. So I'm just kind of curious from a defense Sibyl business case position. I know a lot of the hype is around generative AI, but you know.

But a lot of the market is still highly dominated by reinforcement learning, which I don't know if like, too many people are, you know, no, that's your Netflix algorithm. That's your YouTube algorithm. So but where do you see it seems like generative AI seems to have more of those, you're developing some business use case, and it's not really defensible? Where do both you see the defensible business cases in industries? Or just, you know, what are you seeing now or in the next one to two years, that you think could actually be a defensible business case in AI right now? Thank you.

Um, I, I don't know that I, that I have a good answer for, you know, it's going to be in this area. But I think I think the way to think about it is, you know, does it solve a real problem? Does it provide real value, right, and again, sort of rewinding back to the to the.com days, I'm probably dating myself, but, you know, there was a ton of that there were a ton of businesses that had no real value. And, you know, once the wave broke over, you know, they all got creamed and washed out, you know, and now we have a much more normal business cycle in in, you know, in sort of internet based based companies, which obviously has grown, you know, tremendously. But I think it's at the end of the day, it's, you know, am I solving a real problem? am I providing real value? You know, I know, that's not a great, great answer, but I feel like, you know, it kind of still, to a large degree is going to come back to, you know, basic fundamentals, even though you're going to see lots of crazy ideas that are going going to, you know, rise and fall and you know, everything in between.

Yeah, to follow up on that, there's gonna be a lot of businesses that are startups, you know, regular businesses that are going to cut corners, because they're following the trend or, or that kind of stuff, I think the businesses that implement AI to speed up their processes, meaning, you know, break down the AI into more like stacking GPT together or stacking small solutions to get to the end goal of the job to be done, like and connecting that to a human centric problem. Those are the ones that are gonna stand out, I don't think using generative or the other is really going to immediately impact it's going to be a lot of fly by night startups, lean startups by like one or two people that are going to think that they're solving a problem. Maybe they jumped to the Wat too soon. And they didn't connect it to the actual like, hey, they need to put a hole in the wall. Yeah, you designed a really great drill, but it's too expensive. They don't want it like that kind of stuff. So the ones that are actually connected to a problem and micro ties the AI into speeding up their workforce, to act quicker and be agile. Those are the ones that are probably going to survive long term.

Thank you, Brad. Bryan submitted a question from the Zoom call for Bill. He says as someone who works with large enterprises, how would you approach working with a more conservative large company that's typically extremely risk averse to adopting Jenner Gen AI and the ever growing AI landscape?

I think I would find a particular area and solve particular a particular problem in a particular area right and kind of wedge yourself in that way rather than coming in and saying, I'm going to AI your entire enterprise and I can help you do that. Say, you know, I can help you you know, you know, improve your marketing spend by X percent, I can help you come in and automate a lot of these finance, you know, activities that you're doing and start small start specific and kind of create a wedge strategy that way and then you can start start growing from there, because they are going to be very you know, just just cautious right? They're not going to jump in and do everything across their entire business they're going to try and you know, find pocket areas where they can have an impact.

Other questions?

Thanks, guys. Read that. Those are some very elegant business the best jobs to be done. Explanation I've ever heard. Oh, thank you straight from the book. Okay. And I have tried that bike. It is very, it's hard. It's not that specific. Like my friend made it. It's a lot harder than it very humbling. So AI to me right now is just walk it's just intellect in a box right and It's doesn't have the the emotion, the drive the empathy. What's going to happen when it does? What are we missing right now that it can't do? Given that it doesn't have that?

It's rebill.

So ask me ask me one more time

when AI becomes more than just intellect, when it has the emotion, the drive that does things on its own?

Right? Yeah. Well, hopefully we'll have universal basic income and go sit on the beach. That's my plan. Um, you know, I think I think that's going to take a while for us to get there. And I think that, you know, again, these things are gonna happen very fast, but also in ways that, you know, we can, you know, we'll hopefully be able to sort of mentally absorb them. And, you know, I like to think about like, what, what's it going to be like, when the robots are walking among us, right? Like, you'll go to a conference or a trade show, and, you know, there'll be these kind of robot things walking around, and it feels really weird. But if that was all around you all the time, like, it wouldn't seem weird anymore, right. And so I think that, we're just gonna start getting used to a lot of these these things, right? If you've ever been gotten into a, an autonomous, you know, Lyft, or Uber in San Francisco or something, it feels super weird. When you're in there, and you're like, holy crap, there's nobody driving, right? And so, but if you did that every day, if that's how you got to work every day, you would just get in and be staring at your phone and not even thinking about it. And so I think a lot of this is just going to kind of happen and start to feel more normal, you know, when when they get empathy, and the singularity happens. I don't know exactly how that plays out. I mean, you know, people smarter than me are going to have to sort of pontificate on that. But I think there's, there's going to be a place, I tend to be more of an optimistic thinker, rather than than doomsday. So you know, maybe they will come and kill us. But I think that we're going to sort of be living amongst them. The the AI is when I say that might mean kind of the AI is, and again, it'll be sort of this ambient thing that we we almost don't even recognize. And and I think it's going to generally work out, okay, I think there's going to be some bumps in the road for sure. There's gonna be a lot of unintended consequences. There's going to be, you know, bad actors, that all that stuff I think will happen. But I think, looking at it over a longer story arc and thinking about it on the whole, I think it's going to be a net positive for society.

So I'm an optimistic realist. And I think we were joking around this is like, I was kind of betting for how many ethics questions we'd get. And I'm like, I'm not the person to ask ethics questions to or secure data security or anything like that. This one's getting close to the ethical question in the sense that it's learning from us. And I don't think we're good at empathy. I don't think we're good ethically, like we're very unethical at times. So the direction that AI goes, will be dependent on the direction we go. So the more empathetic we can be, the more ethical we can be, then I believe that it will as well.

Okay, next question.

Probably for both of you. But Bill, I'm really appreciating that the different areas where you are inspiring us, right, like what AI can do, like that's where the optimism is. And I appreciate also each mental framework. But I'm thinking about the guardrails, like we just read the coming wave in our book group. I don't know if you've read Sophos book yet. What would guardrails look like to you?

I think guardrails would be, you know, keeping bad actors from doing bad things and keeping, you know, well intentioned people from inadvertently doing doing bad things. Right. So like, at a at a macro level, I think that's that's kind of what it looks like, now. How that happens, and who sort of makes that decision? I think that's the challenge. Right? So, you know, we go back to the, to the convergence model, and, you know, one of the things on our on our convergence model is a polarized society, right, like, how do we decide what AI should be allowed to do and not allowed to do when we can't even agree on who got elected in the last election? Right. When when we're that polarized? Those those kinds of questions get to be really challenging. So, you know, I kind of feel like I philosophically or you know, sort of at a high level, understand what what guardrails look like, but there's a whole lot of edge cases, and that's where it's gonna get really tricky, right? It's easy to sort of talk added at a 10,000 foot view. But when you get down into real life scenarios, and think about all these different edge cases, I think that's where it gets really tricky. And, frankly, I think people smarter than me are going to have to sort it out. And I think it's, again, it's going to be bumpy, I think there are going to be some, you know, bad and unintended consequences that happen along the way. As we work through this,

I guess we'll take a stab at, it's similar to the other one like is learning from us, and the way we create laws have so many unintended consequences, whether it gets down to the economical side of financial policy of, you know, taxing luxury yachts at a certain rate, well, that actually had the unintended consequence of actually putting workers out of business because of that tax. So setting guardrails would probably be very similar in that we're going to have to live through some of the unintended consequences, because what laws are guardrails don't get into as the nitty gritty of situations. And that logical kind of thought process, which, you know, I don't know if anybody here took introduction to logic and philosophy in college, but those were the the two classes that stood out most to me, it wasn't, you know, some of the other business classes or anything, it was the logic part. And unfortunately, a lot of people don't take that class. And those classes teach you kind of the stickiness of, you know, some of these things like, it's never black and white, it's gray. So guardrails would be very similar and like, we want it to be black and white, but it's not. And it's just going to take some time for the guardrails to kind of be effective.

Okay, next question right here.

Yeah, thanks for the presentations. I really appreciate it. This question is for Bill, there was a statistic earlier on in your presentation, about 14% of companies can fully leverage AI, I think came from from a Cisco study. In your work with TCS, what are some of the characteristics of some of those companies that you've encountered that you think could fully leverage AI today?

Wasn't sure if that was my mic or your mic? Um, so I think it's the companies that that sort of have set themselves on on a trajectory for innovation, right? I mean, you, you'd be surprised how many companies still really, really struggle, you know, big enterprise companies really struggle with innovation, you know, we're talking about, you know, 100 year old manufacturing firms and insurance companies, and, you know, financial services and that sort of thing. And, and some of them are really, you know, leaning into and driving forward on, on innovation, and some of them really aren't. And so, I think it's the companies that have already started to lay a good, good, you know, foundation for, for innovation. And I think the other thing is, you know, we're sort of getting old enough, if you will, in the, you know, in the digital era, that a lot of the, you know, a lot of these big companies were, you know, digital natives, right, think, you know, a Netflix or an Amazon or, you know, those types of companies, I think those ones are best positioned to do that. Because, you know, compared to those, you know, older, more traditional companies, they're already used to operating in that environment. And so if I, if I had to guess if I, if you could peel back the curtain, if you will, on that 14%. My hunch is that most of them would be companies that were digital natives at the at the get go, They've just grown into big enterprise businesses.

Next question, who else has one? Going once, okay, in the front row here, without confusion.

I'm just hoping that we can get copies of these presentations, that I think the people online and unzoom would, were able were not able to get that. So if you guys could share that. I'd be really grateful. Oh, these were awesome presentations. Thanks. Thank you.

Yeah, definitely. We'll we'll do that.

Question from Karen.

So you know, when you talk about the anger, and I also worked in like the high end graphic design world, and, you know, our biggest client was Disney World. And so we would have like, hundreds of projects at a time, like 500 collateral pieces for a hotel like that, you know, we completely lack the organization systems to do those kinds of projects, but I can see how this would really be beneficial, where I feel and I wonder if other people feel this way, too. It's like, I feel some anger when I'm starting to see all the generative art work, you know, because I know how difficult it is to become an artist and to create and what that brings to the table versus seeing something that got created. You know, there's been like companies that could put together logos, because you said Lake Road, something, and they'd come up with little, you know, there's all there's been that for years now. But it really does take a team of people who understand design and communication to come up with a logo that fits a great, you know, that it's a great company. And so that's like, that's where I see the anger. It's like, you know, replace business systems, let's do it. Even my old job mark on, you know, documentation, I'm sure you guys can all agree with that, like, I can see how AI can help. But when we take away the creativity that we create as human beings, that feels weird, you know, so I don't know if you have anything to offer on that.

So I've thought about this. You know, I listened to a podcast interview with Sam Altman, a while back under an open API. And, and he was talking about how, you know, not not long ago, like just, you know, before they rolled out, chatted up to 3.5. Last year, you know, he's he sort of had the thought process that I think all of us did, which is, you know, it's the, it's the sort of manual labor jobs that are gonna go first. And then the kind of low level white collar jobs and then the executive jobs, but but the domain of creativity, right, that's always going to be off limits. And almost immediately what happened was that flipped, right. And it was the creative work that was, you know, being done through through Jenna AI models. And I kind of think about that two different ways. On the one hand, you know, yes, you're potentially disrupting an artist, for example, right? Now, the counter to that is, you know, we're allowing people with creativity, but maybe not artistic ability to be creative and to create things, right. So in a lot of ways, we're kind of democratizing that creativity, and allowing, you know, me, I could never paint a beautiful picture, but or painting, but if I can conceive of it, I can, you know, through the use of Gen AI, help help create it. And the other thing is, I think, I heard another, I can't remember who said this, but, you know, they said, things we want to be cheap, will be really, really cheap. And things we want to be expensive, will be really, really expensive. So I think there will always be a place for, you know, kind of bespoke art right. Now, will there be as many artists, you know, I don't know. But I think there will always be a place for that kind of, you know, handmade stuff, you know, hand painted art, that kind of thing. But I also think the technology will democratize a lot of that and allow allow a lot of us to be artists. So different way to think about it, maybe.

I don't know if I'm a great person to ask this question too, because I've worked for some agencies that did some really crappy work just and not because they couldn't do better work is because the client wouldn't let the company do better work. So what I have told people, especially in the creative field, is you're not defined by the asset you create. You know, you're creative in so many other ways. And I think businesses are going to need their creativity to solve better problems, meaning, the creative aspect, and this is where we get into the design thinking practices. And why it probably has a design attached to it is because that's the creative process. Creative. People are always asking why they want it to be loved, and they want it to be appreciated. And they want it to solve a problem that a user has, whether visually, maybe it's word usage, they want to inspire people. And I think that can be done outside of the asset creation, that can be done in the service industries that can be done in, you know, helping people have more on meetings and actually get stuff done like so. I tell people like yeah, your jobs might be replaced, but that doesn't define who you are. You the skills that you have, can be used. And one example, if anybody's watched the show, magic and light on the Disney plus channel, it goes through the having a lock. There we go as a company during the Jurassic Park movie. That was the technology revolution for CGI. And it put every person that did claymation out of business. And that was the moment that had happened. And what I found fascinating about that documentary of that moment was the CGI computer generated artist didn't know how animals moved. And so they still needed the individuals who did the claymation to explain what they learned around the motion of a dinosaur as it runs and they did exercises of making people like run in the thing and act like a dinosaur so that they could Learn those intangible skills. And so while your assets are, what you the assets you create might be replacing, but the information that you have a lot more people are going to need that information, they're going to need to know about color theory, they're going to need to know about some of the branding rules and guidelines. And so I think it's about shifting what you think was, you know, and I'm not saying that, you know, people felt like it defined them. But that's where you need to shift because everybody across history has been replaced. Blacksmiths were replaced when, you know, the machine came out to make the swords and everything for for stuff. So it's all about adaptability. And I think that's what we need to focus on is how can we take what we learned and adapt it and apply it to where it's needed? So thank

you bad. Okay, we just have five more minutes. Just a question. So last question.

So, a little bit related to that question. So I saw recently online that the word mathematics kind of means like to learn that it's from like the ancient Greek word, mathematic coasts, which means like, inclined to learn or fond of learning. And I don't see any software engineers, online railing against how these Gen AI models learned on the entirety of GitHub, that like all of our collective learning, and math, and all the all that goes into becoming a software engineer, it's a long road to study and become good at your job. And all of a sudden, you can generate really good code really quickly. My initial mental model was, oh, this is probably five years away until this affects my career and salary. With the release of Google's, I know, it might be fabricated, but with the competitive programming model that beats 90% of competitive programmers that solved a problem that only point 2% of humans could solve the idea that you could reason about with a dynamic programming AI to do all the heavy lifting of the most prized skills of picking algorithmic components off the shelf and coming up with an efficient solution. Like these are the skills that you develop your craft that I'm looking at my career is basically, like my earnings potential have probably fallen off a cliff, unless I figure out how to leverage this thing. Like, where am I going with this? Like, I thought that was five years away. Now, it seems like two years away? How far away? Do you think it is? Like you said in 2024? Was enterprise 2025 was

global?

Where do you think that big disruption is? Like, where is that?

I don't know. You know, again, trying to predict the future to a large degree is a fool's errand. I think the the smarter thing to do is to, you know, play out those different scenarios, right, identify and as we like to say, rehearse the future, and ask yourself, Okay, you know, it doesn't matter if it's going to happen in 2425, and 26. If I believe that it's going to happen, what do I need to do, you know, to pivot to leverage it to whatever, so that I can keep on moving? You know, because as you said, this goes back to, you know, blacksmiths, and the Luddite riots, and, you know, all of it right, like, people have people's jobs have had been, you know, disrupted over time. You know, on the whole, it's always ended up being, you know, a net positive for society at large. Hopefully, this will continue to be that way, you know, obviously, we'll have to see how that plays out. But, um, I think the, I think the right thing to do is to, you know, not try and predict when it's going to happen, or not going to happen, and not have, I try not to have an opinion about, you know, whether it's going to disrupt my job, or it's not going to disrupt my job, like, I can be pissed about it, but it's still going to happen. So I might as well figure out how I'm going to adapt to it, rather than figuring out whether I'm happy or sad about the fact that it's happening. So, you know, it's not a, I apologize if it doesn't sound like a sympathetic answer, but you know, because I am sympathetic to, you know, the fact that a lot of you know, really, really good people, really smart people, you know, people that have worked their entire careers to build an area are probably going to be disrupted. But, um, but I also think that, you know, people who are smart enough to be programmers, for example, are going to find new ways to, you know, to continue to add value and to leverage AI to do what they do better, right. I mean, you're, you're, you're a smart guy, if you're smart enough to program so, you know, you'll you'll find new ways to adapt and I think, you know, a lot of people will. That's not to say it's not going to be bumpy along the way again, but, but I think on the whole it'll work itself out

Yeah, so I just say focused on the intangibles of your craft. And I think the staying power in, it took me a while to actually, it dawned on me, but build your network, like build some, like, build your relationships with other people. Don't ask him for money, you know, you know, you can probably ask him for jobs. But I would say build those relationships and show off your mind. Because that's going to be what people are going to want to have on their teams, people who think logically, somebody who breaks down a problem, somebody who goes and finds How to know what they, how people who know when they don't know, and know how to find out what they don't know, is going to be key. And I think that is a big thing with software developers and programmers is that, you know, it's the Stack Overflows, and it's about finding who has done this code, who has solved this problem before? And how can I apply it to what I do and what I need to do? That's the skill to focus on. Because that is really hard to find. I think when people don't know an answer, they either try to make it up because of the imposter syndrome. Or they go out and seek and find the answers or they give up the people who go out and seek answers to tough questions and find ways to connect it and apply it. Those are the people that usually rise to the top. So focus on your skills and what, what makes you a good programmer. What makes you a good software, what makes you a great designer? Because that's the staying power. Because businesses need those kinds of minds in their businesses. You know, that's what Startup startups need. If you're starting a business, you need to break down your skills, find your inefficiencies and go plug the gap.

Thank you, Brad. So that's a wrap that I'm so sorry about all the tech problems. Thank you guys for helping for hanging in there. Don't forget to check out a book if you want just fill out the form. This is our little library up front. And please join me in thanking our speakers for