Generative AI Playbook: How to Identify and Vet Winning Use Cases with Jason Schlachter

28:17Because when that new capability emerges, and we saw it when, you know, chat GPT4 hit the market, there were some companies that like overnight had applications. Some of them are bogus and kind of borderline fraud, but those have kind of fallen away. And now we see like Adobe deploying image creation models inside of their Adobe platform so that you can completely generate a new background for your foreground, or you can erase an object and then ask it to generate a new object and it will do that in the application. So those are starting to become more mainstream for sure. Yeah, that's one we're playing around with at Hatchworks.

28:57right now. I think it's Firefly is the name of the Adobe product, similar to like a mid journey or something like that, but it's within the Adobe ecosystem. I think this is where we'll start to transition into some of, you know, you have ideas, you have, you know, a list of ideas you've generated, but how do you begin to test that the viability of we should do these over these, you know, that's the, one of the most important things is how do you start to prioritize some of these use cases. And there's a bit of a, call it a rubric or analysis, you take it through. So Jason, start to take us down this path of how you begin to wait and prioritize some of these ideas.

29:40Absolutely. And Matt, let's throw up our use case that we're going to use to illustrate. Yeah, let's do it. Okay. So yeah, you set it up. You got the real story. And I'd say too, we got a couple. There's one related to the stock market. There's one related to chat with a customer, customer interactions. We may play around with a couple of those later. But yeah, let's hit the main one. Jason's taking a big trip in about a week or so. So Jason, set up the use case for us. Okay, awesome. Yeah, and Matt, let's make sure we get those questions in too. So the use case I'm most focused on right now is travel.

30:19So I'm heading out to Japan in a bit with the family and trying to book our travels. And, you know, I kind of want to be on the edge of, you know, the sort of like touristy kind of stuff. I don't want to be like deep in it, you know. And so that means I'm looking for like experiences that are just like a little bit off the beaten track. And so booking hotels, looking for national parks, trains, buses. Do they, you know, can it says that the hotel room sleeps four, but I only see two beds. Like, are they charging us here for kids? Like all this kind of stuff. And it's a huge amount of time to kind of really dig into it if you want to make it right.

30:57And I don't really want to hand it off to a travel agent because I like the idea of being in the details. I like the idea of having the controls. But with Expedia or Priceline or TripAdvisor, what I'm having to do is I'm having to like break down the larger itinerary in my own mind, and research all these different places of which most of them, which I'm not going to go. Some of them I don't really understand. and then look at for individual things. So can I find a train from point A to point B? And what does that mean? And how much does it cost? And how we work our luggage? And can I find a hotel in this city?

31:29And like, I don't really know which district to stay in and all this kind of stuff. So if I had the ability to give myself a team of staff that were going to work on my behalf as a generative AI might, I would want to say to the generative AI, I'd like to take a travel. I'd like to take a trip to Japan with the family. we want to be outdoors hiking we want to we want to get our hands dirty doing archaeological digs we want to take lots of photos we want to be at local cultural events um we want to be at the yon festival in kyoto on these dates and super mara world super important super important to my kids and to me um so we want to go to that as well um give me some itineraries and and sort of like figure out all the connection points and show me like cost structures and explain to me sort of which ones are better than the others and why.

32:20And from that, it's like as if I had my own executive team working on this for me. And then I could look at it and I could say, well, this looks cool, but I don't want to go there. Or like I could even query it like, hey, why are you putting me in this city? Like I didn't ask for that. And it could even sort of respond with like, well, we found that like people like you who have gone to Japan and visited the city, you know, really enjoyed it for these reasons and it fits comfortably, you know, with your schedule here and there, like, it would just be like a very easy conversation. And from like a, like an, let's say like a trip advisor perspective, like it's all AI driven.

32:53There's no customer service agents, it's scales, there's no time. So that's to me like a, a use case that very clearly is going to become dominated by generative AI. Yeah. There's one catch and we'll kind of get into this moment with the things to way it needs to be right do not want to be stranded with with two kids at a bus station with a hotel that only sleeps two people even though it booked it as four um and that's where generative ai is not so great um and so we'll talk about that yeah what are the stakes and first of all i'm jealous of the trip that's awesome you're getting to do this but we can put ourselves in like the seat of xpedia or a company looking to disrupt xpedia uh how should they be thinking about this.

33:39And frankly, I mean, Expedia should be very wary because this is the type of emerging technology that literally could upend an entire business model. And just as an aside, we've got an episode coming out later with Andy Silvestri, he leads up our design practice. There's potential for this shift from kind of a imperative to a declarative approach, a point and click kind of approach to declarative where I'm talking and interacting. with the interface. So it changes how user interfaces are designed. So be able to look out for that. It'll be coming out in a few weeks. But Jason, start to take us through, you know, you just kind of set up the context.

34:21What are the different dimensions that you can start to weigh a use case to determine how viable it is? That's right. Okay. So we'll start with business value, but we'll keep it really short because business value, you know, is something that is well studied. And so you want to be able to assess the business value. To assess the business value with generative AI, you may want to rapidly prototype. You may want to do sort of Wizard of Oz kind of things where maybe you give a customer a chat bot and you label it so that it's very ethical and transparent as you're talking to a generative AI bot. And it's very expressive.

35:01It can look through all the documentation, all the manuals. It's not just dumping technical information to you, but it can reformat it and answer your questions. But at the same time, you have this whole thing that it's AI bot driven. What you could really be doing on the back end is you could be having some of your expert customer service people quickly typing stuff out. And so you haven't really implemented anything technologically, but you've started to assess the viability of a customer accepting that they're going to engage with an AI and understanding how they engage with an AI. if they structure their queries differently, if they scope their requests differently.

35:40So that's an example of a business value where you could start to get to it. Next, and this is a really big one, is fluency versus accuracy. So for these generative models, fluency means generating content. And that's what they do. They generate content really, really well. Accuracy means that the information is factual. And so if you asked the generative AI model, text-based generative AI model, to help me write a short story about, and you explain what you wanted to write. It could dump a story to you. And it's probably going to read really well. It's probably going to be great for creators that need help structuring their content or want to sort of like add some details to their content.

36:19It just really speeds up that kind of workflow. In that case, things like hallucinations, which is a term for when generated AI models say things that aren't true. There's a lot of technical reasons why that happens, but they do that then in that case it's okay because like fantasy creativity you know abstract thoughts like those are all interesting aspects of like a short story um but if you have an agent that's meant to give you medical advice and you're asking it you know do go to the hospital like what's going on with me you really want it to be accurate and it's not as important that it generates creative content or that.

37:00And this is a new kind of dimension. I feel like with, you know, AI and generative AI, the importance of this one moves very high of the list of considerations where it wasn't as nascent as a concept. I think in the past, you mentioned business value. That's still critical. Always going to be there. Yeah. This one's interesting because, you know, it literally thinks it can go rogue. It can hallucinate. like you mentioned, and what is the risk or the outcome of if something goes wrong, right? Yeah. Yeah. So you have to think about your use case. Is it a use case that demands fluency? In which case it's something that you can address more easily with the models.

37:46And if it's accuracy, there's ways to mitigate this. So if you do demand accuracy, you're able to train models, you're on your own, you're able to tune some of the existing models. So there's like foundational models emerging for generative AI. These are like OpenAI's ChatGPT4, but also Google has BARD, ETA has I think LAMA. So a lot of these companies are building their own models. These are foundational models. They have very large representations of language and semantics, and then they layer on top of that this ability to be prompted and respond appropriately. So these are models that you could use off the shelf for some of your business use cases.

38:31And if fluency is your goal, those are probably great sets. But if you have like a need for accuracy, you may need to tune them on your own data. And so this is where, you know, you start to ask yourself, do I have enough data to do that? So it wouldn't be impossible to generate a model that answers medical questions. It's a great use case for generative AI if it is highly accurate and probably highly regulated. It may be reviewed by a clinician in certain or many use cases. or if it reaches a state of, you know, getting into the unknown, unknown territory, can the model be geared in a sense to where it's not spitting out a random response, but it is saying, I don't know.

39:22You know, there's that element of it as well, which, you know, how do you start to actually monitor that? That may be a bigger, totally different problem, right? Yeah, there's not a lot of self-reflection is a challenge right now for these models. They know everything, even when they don't, because what's in their mind. Yeah, exactly. I mean, they've been trained on a certain set of world data, and they have sort of a partial understanding of that data. And they look pretty convincing when they talk about what they know. But when they're asked to talk about something they don't know, they don't necessarily say, I can't talk about that.

40:00They try to answer it in the context of what they do now. And because they have like partial understandings of what they do now, there's not like an explicit, like expressive representation of these concepts in some kind of, you know, logical reasoning, causal kind of way. It's very probabilistic. You get very weird emergent phenomenon because you can find weird edge case paths through the probabilities of these models. So fluency and accuracy is a cornerstone of how you should think about your use cases. The other really big one is low risk and high risk. So, you know, we talked about this just a moment ago, but like, what's a low and high risk?

40:37Like Expedia sending me to a foreign country with my family and telling me to go stand somewhere in a corner because there's going to be a bus and there isn't, it's kind of high risk, right? But me, you know, jumping onto like T-Mobile's website and asking a question in natural language and getting back like a personalized explanation is pretty low risk. Especially, and this is interesting, so you can do retrieval augmented training on these models where in order to suppress errors and to build confidence for the user, you can force it to only say things that it can back up with a document that's retrieved.

41:16So it could like pull up some kind of like knowledge base article that exists in T-Mobile's, you know, you know, data set. And it could say like, this is the thing I found, but I'm not going to make you read it. Here's like the two sentences that directly answer your question. But if you need to dig deeper, like this is the document that I use to kind of generate this answer. And this is taking it a step further than just like, let's just get the OpenAI, ChatGPT API and just integrate. Now you're starting to weave in some of your own company's data information to enhance the experience, the model, all of that.

41:58So that's kind of up leveling it a bit versus just slapping AI on your product service or process. us yeah yeah exactly and then that's a fundamental question too like you know there's a lot of use cases you kind of lock with off-the-shelf stuff but there's a lot you can do to tune these models and so when you tune these models it's a question do you have the data because if you're tuning let's talk about if you're tuning them so if you're tuning a model why would you do it you might do it because you need more accuracy in the kind of use case we explained and in that case you need to ask yourself, do I have the data to tune it?

42:31And so what do you need to tune it? Well, you need your own documents that represent the knowledge sets and the way of speaking about the things you care about. So in T-Mobile's case, it could be like their knowledge bases, their technical documentation. You also need, you may need prompts and answers. So one of the ways these models get built is a very labor intensive step where, where people literally write out a prompt and then write out an answer. And then they show the model both. And they use those to train the model as to like, this is what a good answer to this prompt should be. And, and some of these bigger companies like Google and Microsoft, they have like thousands, if not tens of thousands of people employed full-time, like writing prompts and answers.

43:17It's a very labor intensive part of the process. So that might be something you do to tune a model. The other reason you would tune a model, if not for accuracy, might be performance. So maybe you don't need a huge model. Like maybe you can run with a really small model that takes less compute. You can run it on a locally on a device or just cost less. Um, but you need to tune it because you're, you're building an auto mechanic helper generative AI system that, uh, that helps your auto mechanic, you know, rather than reading car manuals, um, for cars that he hasn't worked on for a while. He just asks the question and gets the immediate answer with reference back to the model, the manual pages or something like that.

43:56Like in those cases, it could be small. It could run on device. So those are some considerations there. And then the other piece here is what's defensible and non-defensible. So, you know, is it important for you that the model that you're using and the use case you're building is defensible from a business perspective? So let's get back to the travel example. Like, would it be defensible if TripAdvisor built that capability? I'm going to pause. I'll throw you the question. Yeah. And folks in the audience, too, if y 'all want to answer. You know, it's interesting if it's simply, you know, if you could do the same thing, referencing ChatGPT or some large language model that's open to the public, I'd say no, it changes the whole business model and defensibility of their business.

44:52Now, if it's leveraging, to your point, data that an Expedia or a TripAdvisor has that they can supplement into the model, then I think it does begin to have an element of defensibility. But what's your take? I'm curious. Yeah, it would have to leverage custom data from TripAdvisor. They're not going to get anything that's capable of doing that kind of use case off the bat. They're going to have to spend a lot of time and a lot of money leveraging their own data to tune those kind of models. And even then, I think it's really going to struggle with being accurate because there's so many connection points, right?

45:34Transportation hubs, hotels, sites. But if you think about what they have, they sort of can trace member trajectories through like cities and tourist areas and restaurants. So I do think there's a lot to it that they probably could do. I think it's partly defensible on the model basis. It's partly defensible because Expedia might be able to do the same. Priceline might be able to do the same. Booking.com might be able to do the same. I would argue that there's nuances that TripAdvisor has that they capture, like extensive photos from users and sort of very multimodal, like hotels, cars, you know, hiking, restaurants.

46:17Everything is so across the board. But I think, like, even if it's not fully defensible, they still need to do it to be competitive in their industry space. So it's somewhere between like differentiated and highly defensible to like the competitors might go to the same, but maybe not quite in the same way. But I think ultimately what's interesting is like non-defensible doesn't make it bad either. Like things can be very high value, but non-defensible. So in this case of TripAdvisor, like it might be that the model is non-defensive. Like it might be that they can build this model, but like, so can every other travel service.

46:59So then there's sort of like other levels of defensibility, right? Like, like use cases and business models were defensible before AI came along. So what other ways is it defensible? Like it could be that, that their brand alone is, is helping to make it defensible. Like I don't necessarily want a startup, an AI startup, even if they're well-funded, sending me and my family out to Japan. for a while, I might not trust it. I'd much rather go with the trip advisor. It might be defensible in that they have partnerships and integrations in a way that this actually works, right? Because the rubber has to meet the roads still, like if they're going to book these itineraries.

47:40So there may be other ways to make it defensible that isn't the model. So I think when you think about these use cases from business perspective, a defensible model is great if you can do it, But you're not going to get a defensible model without spending a lot of money and having a lot of data. So it may not be critical. I think it deals with, is it connected to your inherent value prop, the customer-facing side of the business, the business model itself? Then this defensibility question becomes really important. But you mentioned brand. Brand actually is a differentiating thing. now, I'd say most folks, it's the level of apples and the big ones where that's where you see the brand defensibility truly shining through.

48:23But that's a critical piece of this. I've seen there's websites that track how many AI startups are being created every day. And there's some where they're literally just putting a skin on top of a foundational model. and there's no inherent defensibility to it. Somebody could have spun it up over the weekend. And it's like, how do you kind of weave through that? In essence, is there something, is there substance behind it that makes you unique, right? I mean, that's an interesting example because those companies were sort of serving a market need in some ways, like in the very early days, the average non-technical person probably didn't know what OpenAI was, didn't know they had a website, I didn't know they could go to the website and subscribe to their model, just saw it in the news.

49:15And then they get a friendly cartoonish bot popping up on their, you know, their, their iPhone ads, you know, for access to the model. And it's like, you know, that, that was, that was a marketing niche that OpenAI was neglecting. I think they're picking up on that now, but. Well, a great example is that the ChatGPT app, they didn't have an app for a little while. And there were competitors that created an app just leveraging ChatGPT. and they were able to get some amount of probably actually crazy scale. But then ChatGPT, OpenAI came out with their app and you probably just completely killed their whole business model.

49:50So that's like the whole defensibility piece. How easily can a competitor are in and just take it over? Exactly. Okay, so there's two more things I want to touch on here. One of them is like, whether it's internal or external facing, like this kind of relates to risk, but it's not directly related to risk. So if you think about internal versus external, Like, you know, if you're using it to create, and this is really where, where these generative models have the most value to create content, um, where fluency is, is the highest need and risk is low. So this internal facing use case of like, you know, help me compose emails to my colleagues faster or help me create marketing content that I can post online faster or like generate blog posts for me that I can just tweak and send out.

50:37Or like, you know, summarize to me this document that I received from one of my partners. Or like, explain to me, you know, this chain of emails. Like, those kind of things can really boost productivity. They're fairly low risk. There's a human in the loop. Human in the loop is maybe the magic word here. If there's a human in the loop and it's just proposing information or helping to accelerate something, low risk. And those are often internal facing. But when you're customer facing, there's higher risk. So that's another thing you want to consider too. And then part of that is doing the AI ethics component.

51:17So in all of this, there's a need to consider the implications, the ethical implications of using AI models, even in your own business, but especially if they're affecting customers. um you know at elevance you know we're we were building ai models for health care and we were impacting people's ability to get care with those models yeah our intent was to improve their health outcomes and to make things better but you know things can go wrong and even when they go right you know there's always risk that you have to assess and so we would hold these these ethics workshops and the idea here is to to dive deep into what it means to build this and so So I'll spend a moment on that, Matt.

52:00Yeah. But I think this happens really early on. It's not like something you do kind of at the end of your use case pitch when you've got your funding and you just need to move forward. It's really early on in the process of the viability of the idea and the business value. And so there's ethic workshops you can do where you can work with a team of stakeholders and you kind of start off really small. you know low low overhead an hour or two get the basics and as you grow your your business case and your your your plans and your your funding then that's when you start to lay in kind of more and more layers of this and this is actually something that we do for our customers we help them to to work through these kind of ethics workshops where you you want a third party that has experience running these and understands how things go wrong to to run this internally and so you look at your users, you look at your stakeholders, identify all the stakeholders.

52:57You try to understand, you know, the values and the interests that the users and stakeholders will have. What kind of tensions might arise? Like, how are you going to test your assumptions? You think about the impact you could have, changes in behavior that might emerge. Like, great example for me is like cars. Like Atlanta, where we live, was built after the invention of the car primarily because you know the original atlanta city was um burned down and they rebuilt it really after cars came to be and at the time the um the mayor of atlanta said i i dream of building a city um that is a car first city and it's like that seems like anathema today for us but that was the ai of the time they wanted to build an ai first city a car first city right And now Atlanta is like really difficult to walk in and traffic is bad, congestion is bad.

53:51And we're slowly peeling back the layers of that a hundred years later. So that's an example of like changes in behavior. If there was an ethical review committee for, you know, the car for a city, like maybe some of those things would have come up. So there's also things like the group interactions that emerge. So how it is about groups. There's questions around data and privacy, explainability. um so if a model is is impacting your life like you kind of should be able to understand why it's making those decisions we don't want to take um sort of the the distributed bias and distributed failures of our our current sort of like business ventures and centralize them in a way that nobody can question and understand them um there's questions around um sort of like do you have a human in the loop how do you monitor performance how do you mitigate things um you know how do you get feedback.

54:44And so all these kinds of things are discussion points, like what is fairness? What does it mean to be fair in this use case? So this is part of the validation cycle, but you just start late an hour or two on the first pass. And by the time you're funding a big use case in a big program, it should be very rigorous. There should be processes in place, accountable stakeholders, and all that stuff. No, that's awesome. And a great example of something that can be facilitated with AI Empowerment Group and Hatchworks there. So we get about five minutes. I'm wondering, Jason, we could jump into some of these questions and topics in the chat if you're up for it, unless there's something else you want to cover.

55:22That's great. Yeah. Jacob had one, does anyone use AI for scheduling appointments? And I don't specifically know of a tool. I'm sure there's several folks trying to achieve this. But this is like a perfect example of a use case that you could disrupt a Calendly or, you know, products that exist out there. You know, how could that impact that workflow of I need to schedule appointments, plan out my day. I don't want to be the person having to, you know, reach out to somebody and say, hey, does this time work? Does that time work? Jason, that was kind of an interesting one. Any thoughts on that?

55:59I mean, yeah, I think there are use cases like Calendly that do that today. And I think there's other AI startups out there that do something similar. But I guess I would challenge the notion of what is the real task that you want done or that I want done. It's not strictly that I want to schedule a meeting with Matt. And so I want Calendly to go figure that out for me. That's sort of still that process level where I have to get it done. I would love to just have like a more robust agent or where I said like, Hey, I want to talk to these 10 people this week, go figure it out. And then Matt gets an email from Calendly saying, Hey, Matt, Jason has identified you as somebody you'd like to speak with this week.

56:44Like, what is your availability? in which you just did there is you took the question from earlier like if i had a team or a staff that could go and do this how would they solve the problem versus me having to be like the main point of failure bottleneck in the process that's a great example of how to kind of reframe how you think about a use case uh i like uh chris is creating movie scripts about batman's early days which is kind of yeah it's funny but like it does change how that whole industry works potentially from a creator perspective and you know all of that right we are so for people who are not like deep into uh you know stable diffusion or dolly there are models out there right now generative ai models creating uh movies um and writing the scripts for those movies and And so it's emergent.

57:38Like I believe like in the next year, we're going to see like TV shows where the script has been written. The actual animations have been completely created by the AI. They may not be successful. I don't know, but it's happening. But this is like, this is one of those big transformational disruptive type of things. You think back to the day from like music going digital, same kind of thing. And there's going to be the movies, the studios trying to fight this, change of AI, generative AI playing a role. But it has the feeling of something similar that's happened not too far in the past. What if it's like, make me a commercial that's going to cause people to hire an AI empowerment group to help them with AI strategy, create music for it, some kind of amazing techie kind of humanistic background and write the script and then use my voice to create it.

58:33Because it can speak like me, because it's trained on my voice, like it will just speak for me. Yeah, it's awesome. Yeah. And Klaus brings up an interesting one. How can businesses leverage the potential of utilizing ChatGPT to enhance customer interactions, streamline various business processes while ensuring data privacy and compliance, particularly when it involves sending data via the API back to OpenAI Cloud? I think this is kind of an inherent like risk type of aspect, right? This is a good one. Yeah. Yeah, so Klaus, you mentioned you're with a German company and the EU is passing measures to require that any use of generative AI be approved by committee and be licensed, I believe.

59:20And I think we're going to continue to see pushes for that. I don't necessarily think that we should be regulating generative AI or AI at that level in the broad sense. I think there's specific use cases that should be regulated, just like, you know, we regulate food with the FDA or drugs. I mean, certainly in certain domains and where there's a certain need for precision, it should be regulated. But I think for a lot of these startups with low risk, they should be able to get out there and do it. But in Europe, you're probably going to be faced with that challenge. One way to mitigate what you're asking about is not to send it to OpenAI.

59:57Run your own models, run them in your own cloud, host it in your building. push it to the end user, run it on their client machine. And so in doing so, you're not necessarily sending their data to open AI. There are open source models that are emergent in generative AI. And some of them are pretty mature. Stable Diffusion is a great example. It's a first class generative AI model that's open source. there's a lot of large language models and chat gbt type capabilities on the open source side i'm a firm believer that the open source models will overtake the closed source models given time um so yeah it you may not there's even like a well there's a leaked document i think from google i believe it was real but they were kind of cautioning at this exact thing internally that hey open it the it's funny they call themselves open ai it's not really per se but you look at like metas to kind of taking that strategy and there's other kind of foundational open source models out there but they have the potential to overtake things that are being developed internally right yeah meta is a great example so like you know open ai originally founded with you know elon musk and and others to to open source these AI models so they wouldn't be closed source.

1:01:23Then sort of strong-armed, overtaken by Microsoft. Now Microsoft owns it. They make them closed sourced. Meta and Zuckerberg has surprisingly shown up to be like the big open source creator of these models. And I think from a business strategy, it makes sense. Like Google's playing to win. Microsoft's playing to win. They want to be the winners in this generative AI race. I don't think Meta wants to do that or necessarily needs to do that. They're playing to not lose. If they raise the water for everybody, then everybody is okay and nobody loses. And I think that's meta's play. And that's a good strategy against these two giants that are dumping all their money into it.

1:02:06There's a network effects element there too, right? If they're at the foundation of it, it kind of raises their business. I mean, it happened with stability, stable diffusion. Like there's thousands and thousands of versions of stable diffusion being spun up because it's open source. And, you know, DALI has its trajectory. Yeah, we are at time. You know, we could go a little bit longer. I just to kind of close it out there. I'd love to last comment there. You know, he's heard that these tools are an expansion to your imagination. They totally agree. One of my favorite uses of ChatGPT is telling it to graphically describe any concept.

1:02:46great foundation for any type of media creation, but it's an interesting concept. It's like that co-pilot and it's like a whole nother topic. Yeah. Yeah. Matt, there's one. Yeah, we can keep you on it. Let me do just kind of the call out and then we can stick on for another couple of minutes. But yeah, so like we mentioned earlier, Hatchworks and AI Empowerment Group, we are partnering together. So like these type of custom workshops is the exact type of thing we can take your organization through, you know, Jason, you mentioned kind of the ethics based workshop, but you know, this is the part where having an expert is critically important and, you know, hit up Jason or I, and we can kind of help you help get that facilitated, but any other closing thoughts and then maybe we can jump to a few other chat items.

1:03:33Yeah, Matt, totally agree. I mean, I love, I love the ideation process, the creative problem solving piece. And I love hearing about the kind of problems that are real and concrete. And those kind of opportunities would be a lot of fun and productive for both of our organizations. So hopefully we'll hear from some of you. I would love to pick up this one question from Monica Lopera, which is the biggest fear for some people is that AI can replace some jobs or even professionals. How do you balance the pros and cons that AI brings to the world? So great question. We're not going to answer it in the last moment here, but I think it's a great question just to surface because there is immense responsibility.

1:04:13This is really the dawning of an age in which how we work and how we live and how wealth gets distributed and who has what is going to dramatically change. And there's a lot of hype out there. Generative AI is not everything that it's hyped up to be. And it's going to take a long time for a lot of these things to happen. But the reality is that we overpredict the short term change, but we underpredict the long term change. And so this is a great question of service. And I think we just have to really be deliberate in the ethics of all this and try to build the world that we want to make, you know, and not the world that we can, right?

1:04:54They're just tools. I'd say too, do you have kind of the opportunistic mindset or the negative or positive, I'm forgetting the correct terminology here. But think about like 20 years ago, majority, a large portion of jobs that exist today did not exist previously. So a lot of times transformational disruptive things like this create new opportunities we don't even know exist yet. So I think this is like one of those things that has the potential as well, even though it may be replacing some jobs, I think it's going to create a whole host of new ones in the process. Absolutely. And, you know, a lot of what it's going to do is not replace jobs or replace tasks.

1:05:36So like a medical claims reviewer, like I'm just taking a wild stab in the dark here. You might not love reviewing medical claims. well and you have some training that makes it appropriate for it or it's easier than being out on the er floor all night but you may not love all aspects of medical claims processing and so this is where i think ai can remove some of the burdensome tasks that you don't enjoy so that you can focus on the stuff you do enjoy so what if you could focus on like the really interesting like clinical challenges or like the really like puzzling situations and not sort of the mundane minutiae of like comparing numbers or checking dates or, you know, understanding, you know, the timelines and stuff like that.

1:06:21So I think, I think for a lot of people, for most people, it's gonna, it's gonna remove the mundane, more automatable tasks, but, but not their jobs. There certainly will be people whose jobs are lost. But like you said, it's, it's always changing. Yeah. It's like back to jobs to be done. And, you know, communication is the job that's existed for a very long time from talking to physical mail to email to, you know, Slack and keep going. The job remain the same. It's just how you did it changed. Right. And just the last one, just because Chris is hitting on it, you know, how is it going to impact the stock market?

1:07:01Anything being done to regulate that? I'd say, I don't know. I think there's a lot of stuff already being done today, leveraging AI in terms of stock trading. And that's kind of already prevalent in a lot of ways today. But I don't know, any thoughts there to wrap us up with the last kind of Q &A question? I don't know. I mean, yeah, I would imagine that most stock trading right now is already done by AIs. So maybe the question is sort of like, if the AIs get better, like, what does it mean for us? Yeah. I don't know. I mean, the only stage advice I can give on that is put your money into a index fund and forget about it.

1:07:38Anything else is a gamble, whether it's AI driven or not. That's right. That's right. Well, cool. That was really appreciate you being on, Jason. Thank you, everybody that came and participated. We will be putting this out there on the podcast and sending out the recording to everybody that joined. We really appreciate the time, Jason. And everybody have a good rest of your day. Thank you, Matt. Thank you guys for the questions. Thanks, everybody.

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