EP 102: Future of Work - What's Next For Creative Orgs?

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What's next in the future of work, especially for creative organizations? You know, when we talk about generative AI and how it can really augment and supplement creativity, what does that mean for the rest of us? That's one of the things that we're going to be talking about today on Everyday AI. Welcome. My name is Jordan Wilson. I'm your host. And we do this every single weekday, Monday through Friday, bringing you experts and everyday people on the best ways that you can learn AI, but how you can also leverage it to help grow your company and to grow your career.

So.

So if it's your first time listening, welcome and thank you. If you're regular, it's great to have you back. As a reminder, this is a live stream and a podcast, so if you are joining us live, please get your questions in. What do you want to know about the future of work and creative organizations? We have a great guest who's going to be joining us here in just a minute, but before we do, we always recap what's going on in the world of AI news. So let's do that real quick. All right, so is ChatGPT and the GPT4 tech going to be dethroned soon? All right, so a recent report says Google is nearing the release of its highly anticipated new AI offering, Gemini. So Gemini is Google's next large language model and it is reportedly five times stronger than GPT4. So keep your eyes on that. It was supposed to be released fall, and apparently it's the fall already. You know, my, my brain is still in summer mode, so keep an eye out on Gemini and what that means and also make sure you go to your everydayai.com and sign up for the newsletter. We always recap all of these news pieces in the newsletter. All right, second piece of news. Salesforce is putting its money where its mouth is for generative AI. Wow. So they are planning to hire at least 3,300 employees with a heavy focus on generative AI solutions. Specifically, it's its own AI model called Einstein Studio. So Salesforce is also planning to rehire a lot of the former employees that they laid off in January. I think it was around 8,000 employees. All right, and last but not least, there's even more AI coming for your Amazon shopping carts. So we reported this a couple of weeks ago when it was announced, but Amazon has started to now officially release some new AI powered features where essentially you can use AI to write your product description. All right, so they've announced having AI to help you sort through reviews. A lot of different, you know, even their own kind of chatbots that you can better make purchases with. So now helping on the seller side as well. So we have that and a whole lot more in today's newsletter. So as a reminder, make sure to go to your everydayai.com and sign up for that. All right. But we're, we're here to talk the future of work and creative organizations and I'm excited for it. So please help me. Welcome to the Everyday AI Show Camilo L. Cruz. He is the Chief Innovation officer for Sparks and Honey. Thank you for joining us.

Thank you very much. It's a pleasure to be here and happy. 100. Oh my God. This week, right? Or, or was very recently. Right?

Yeah, thank you. Yeah, we hit, we hit 100 episodes, I think, think on Wednesday. So yeah, it's been wild. And we've talked to people from all, all walks of life. But, but, but Camille, tell us, tell us a little bit about Sparks and Honey and what you all do there.

Yeah, absolutely, absolutely. We are a strategic foresight and innovation consultancy. We started, we're still a very young organization, I would say started about 10, 11 years ago, really. The mission is to help organizations in both the public and private sector to understand change and really see how the change that we experience in our everyday lives has an impact on the people they serve, whether it's their employees, the consumers or customers, citizens. So really help them quantify what's changing, name it, identify it, and then formulate strategies, whether it's developing new products or a new marketing campaign, or adjusting strategy and messaging or perhaps even imagining the role of their company in the next decade or so. Right. So that's sort of the mission of the company. When we started, we realized we needed a few ingredients to make this happen. Right. One is we need like, you know, to be connected to a network of super smart people. We built an advisory board. Today, 70 strong is our people who are like really interdisciplinary thinkers in many, many different areas. We also built a cultural intelligence platform that essentially is aiming to categorize change in culture and quantify it. We call it Q. And it's been up and running for a while. At some point we decided to take that platform and turn it into a SaaS business. The company. If you think about how our business model is, you know, half is consultancy and the other half is hands on Keyboard access to our platform.

You know, it's, it's, there's a lot to digest there. But you know, one thing Camilla, I really want to talk about. So even when we think of the future of work and you said, you know, one thing that you all are trying to do at Sparks and Honey is to quantify what's changing and to help companies, you know, adjust to the strategy and messaging. How does the introduction, and I use that term loosely because generative AI has definitely been around for a very long time, but it seems like it's really just starting to infiltrate our daily lives. In your experience, how has generative AI already started to kind of shape and shift consumers mindsets and then what can companies do to, to kind of prepare for that?

Yeah, absolutely. So look, I mean I would say that we are from a consumer standpoint we're at the very, very beginnings of what's going to be massive change. So it's a great moment to ask a question. We have advice clients who are releasing or launching new features powered by generative AI and in the go to market strategy there's always the question of what is in consumers minds. If you are in the industry, if you're a professional working in the space and following generative AI, you're going to see there's a lot of philosophical dialogue even now in Congress this week about the existential risks of AI and all that. But if you're a consumer, your views on AI are going to be contextual. They're context dependent, meaning if you present it in the context of a use case. So I'm going to use, I don't know, instacarts plugin to be able to more easily sync my shopping list with my lifestyle, then it's like oh my God, yes, give me more. Right. So I think, you know, if you're a CEO, you're looking at, you're looking at different data points. You're looking on one hand at this very high level sort of philosophical perspective and on the other hand you're looking at more, you know, promising and demand type customer data or consumer data. That I would say is perhaps sort of a good starting point if you want to understand where things are going, which in my view is accelerating pretty fast.

Yeah, I'd say that is a fair statement. Fairly fast. Right. And hey, as a reminder everyone joining us, thank you Jackie. Wishing everyone good morning. Mike Harvey talking about Gemini, Brian saying good morning from the Gulf Coast. As a reminder, if you have a question for Camilo about the future of work in the Creative industry or just anything. Make sure to get your questions in now. So, you know, speaking of that, speaking of how fast things are changing, because it seems like, you know, almost every week, you know, there's something big going on in the world of generative AI. How, how do you think, you know, companies can even keep up, you know, trying to, you know, use these different AIs, AI tools and systems? You know, we talked at the top of the show. You know, Salesforce is, is really investing heavily in generative AI. Yesterday we talked about, you know, ey, major consulting firm, you know, building their own large language models. How can companies even prepare for this if they aren't some of the biggest companies in the world? How can they still take advantage of what generative AI has to offer, but still stay in good contact and in good communication with their consumers or with their customers?

Yeah, yeah, that's an excellent question and very, very common these days. Right. And I would say step one is you have to understand the why, right? I mean, why would you bring generative AI technology? Why would you create general generative AI driven features or products and release them out into the wild? Right? And I think in that sense, when you're thinking about specifically the future of work and what you would do inside your organization, there are a lot of really good answers to why you would do it. I mean, there's productivity. There's simply the fact that this is a train that's already leaving the station. If you don't jump on that train, it's going to be really hard for you to catch up to what everybody else is doing. But there's also, I think the question that's a bit more human, that relates to how can you enrich the experience of the people you serve, including the people who work in your organization? Right. And that is a question that I think has a lot of different answers when it comes to using generative AI. And there's a lot of really good examples out there in the world where people are starting to think about, okay, how do I think about this concept of an AI companion or an AI agent and put it in the service of students? Like when you look at, for example, what Khan Academy is doing with Kanmigo, for example, if you're watching and you're not familiar, there's a ton of different podcasts that are covering Canmigo and you can go check it out. I mean, they're essentially flipping a little bit the equation when it comes to learning. They're saying rather than seeing generative AI as a Threat as I need to catch students who are using ChatGPT, I'm going to use large language models to actually engage in a Socratic, you know, discussion with the student. Right. And I think that there's a lot to learn about what they're doing that we can apply to the world of work.

Yeah. And it's, it's, it's very interesting too. And I'm actually going to, instead of following up on that, I actually want to talk about here. Jackie has a great question or a thought, but I'd like to get your, your take on this. So she's saying, I wonder, as generative AI tools monetize, will large, so will the largest corporations have the edge because they're able to afford all of the tools and the ability to create their own? So what's your take on that? And, and then what can all the other companies that. Not like Salesforce, not like ey, you know, if you can't kind of create your own generative AI tools, maybe how can you still keep up and even thrive?

Look, I mean, if you look at. That's a great question, by the way. Right? I mean, if you look at history of media and technology, you see that there's always this dance between decentralization and centralization, right. I mean, radio, you look at some of the first accounts of radio when it was introduced as a technology, they read like the Internet in the 90s, right. It's like, oh, everybody's going to have the power to communicate and, and create their own communities, et cetera. And then it was centralized and a few players sort of reap all the benefits. The Internet was the same thing. It was decentralized and then it became sort of centralized in walled gardens. I think we had a lot of discussion about Web3 last year and the decentralization and the power that's still sort of more in the background when it comes to generative AI right now. This is the moment in, if you are a small guy, if you are a small business, to really take advantage of this decentralization stage. Right. I can't predict how fast it's going to go back and centralize. There's a lot of different unicorns that are being minted, like seemingly every month in the space. So there are really large companies behind all of it and they're connecting the dots really fast. But if you're playing with ChatGPT and now you can, through the premium version, get a plugin that connects it to the Internet, now you have a really powerful capability at your fingertips. I would say this is the moment where you're going to try to do everything in your power to create a competitive advantage for yourself using these very powerful tools.

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Yeah, and there are so many competitive advantages to be had. And I'm actually glad you even talked about connecting generative AI to the Internet. To the Internet, because we did a whole episode on that yesterday. But I do want to talk a little bit and you know, sometimes we, we look into the future, but what, what do you think the future of work looks like? You know, you kind of mentioned, you know, this, this thought of, you know, AI agents or AI companions. Is that, is that kind of where you see the future of work going? That we're all going to have a suite of generative AI tools built around kind of, kind of our daily role or where do you see this going?

I think that's a, that's a, that's a fair extrapolation in terms of if you look at where the money spent right now, you look at the last year Q2, 2022, Q2 2023, the vast majority of capital going into generative AI is going into AI assistance and AI companions. You combine those two and that's way more than half of all the dollars. Right. So there's definitely a lot of appetite from an industry to be able to move fast in that space. We've also seen a lot of good, good indications that these large language models are not just mastering language, which is so already impressive, but they're even Communicating with a level of empathy that becomes really, really interesting. There's a paper that came out a few months ago that was comparing the responses of ChatGPT for it was a blind study versus responses from a Reddit medical community. And the responses of ChatGPT4 were ranked by the participants in that study as higher in empathy and many other directions, which is very counterintuitive. But I think that when you look at the power of a personalized experience that stays with you, whether it's education or work, I feel like that is definitely a space where I think we all need to pay a lot of attention right now. There's also, I don't know if you've seen Kai Fu's AI 2047 book. I'm sure if you have, your geeking out on it is Kai Fu and Stan Lee, who's a very well known writer in Science fiction writer in China. And they're looking at different scenarios of AI in 2047 so out in the future. And you know, one of them is based on education and it's in this idea of agents. Right? I said AI 2040 says 2041. This was published in 2021, the old one there.

Okay.

They were looking at 20 years in the future. So the first. So there's like a. Each chapter is a short story written by the science fiction writer. And then on the back there's Kai Fu, who's one of the biggest thinkers in AI out in the world, explaining the technology that would make it happen. Right. And this idea of AI companions has been widely explored in that book. And I think it makes sense. I mean, when you start a new job, what's the first thing that happens? You get a bunch of different people putting a bunch of different meetings in your calendar for like two or three weeks. You're not really very productive and you're learning. Right. That experience could happen the minute you join the company with a tool that's also understanding you as a person. Right. Your learning style, your role, who you interact with, what makes other people in your circumstances more successful. So, you know, it definitely has a ton of potential.

As we look into the future. Yeah, 2021. 202047 or 2041. I think, I think there's so many different ways that this can go. But I think one thing that people always look back to or you know, a common roadblock or obstacle is they say, okay, generative AI can't be empathetic. And then Camilo, you know, you just referenced some, some different studies that have looked at generative AI's ability to be empathetic. But there's also been, you know, similar studies on the creative side. So, you know, two of the biggest things is people say generative AI can't be empathetic or creative. So working in the creative space. What's your thoughts on that? Do you have any hot takes? Can. Can generative AI be as creative as humans?

Well, I think. I think I would. I would look at an example in chess, right. And I think. I think if you like, you know, there's a. There's, I think a crop of writers and thinkers that are investigating this idea of intelligence, Right. So the I in AI, Right. Which I think it's very important for us to sort of think deeply in this world where now we're creating a type of intelligence. James Brittle is one of them. He wrote a book called Ways of Being Recommended to Everyone. And then he wrote a piece and an article on the Wetransfer magazine that was looking at the example of chess, Centaur chess. Are you familiar with centaur chess?

Oh, no, I haven't seen.

So, so, so, you know, AI today can be like go players, which it was thought to be impossible, right? So it. Because it requires intuition. So you can do that. It can easily beat any human on chess, right? Pretty easily. Right. But a human and an AI partnering can beat any AI playing chess, Right. And so there's this sort of very geeky, sort of Centaur league of chess, if you will. Right. And it's people who play with. Alongside AIs against each other. Right, Right. So I think that in the. That that chess is strategic and creative, and I think that it's a good analogy for, you know. Yes, absolutely. I mean, AI can be created. There's some experiments. Adobe with Dream Catcher, like, five, six years ago, started experimenting with generative design. There's a couple of TED talks that are. If you watch them today, you get goosebumps because, you know, this is something that's been going on for a while, Right. And designers are sort of confronting this AI and saying the only way for the AI to actually do its job is if I put my ego in check and if I sort of take a step back, right. And think about my role as differently than what it is. Right. So, you know, definitely can be creative, but can it be more creative than a system that seamlessly gives a role to the human in that context? Right. And I think that the answer to that remains to be seen, but I would dare to say no.

That's a great example that you Brought up on, you know, because, yeah, we've had the, the AI systems beating the best humans in chess for decades. But when you combine the human and the AI, that is the best. Right, right. That is the, there's no other combination that, that, that can win in a game of chess. So how, because this, this conversation, we've gone all over the place, but I want to, I want to kind of end on next steps. So how can people, you know, so they, they're hearing you talking, they're hearing, you know, even what you all are working, honey. So how can they create that, that, that combination of humans working with AI? How can organizations start to do that? Because it seems like, it seems like there's a divide. There's the companies that got it and they got it right away. Then there's companies that are really just struggling. So how can they start to create that, that unbeatable pairing that, that augmented relationship of the brightest humans working with these very powerful generative AI tools?

Yeah, absolutely. So, number one, I think about it as a system, right? And you have to design a little bit sort of the system. So the human has a role and the AI has a role. You have to sort of write a job description for the AI and then rewrite the job description for the human. Look at your frameworks, the things that you put out, whether it's a product or service or whatever it is, and make sure that you understand what inputs are coming from which side and when you create opportunities to seamlessly sort of collaborate, if you will, like. So, for example, if you're creating a type of art direction document and you're going to give a generative design an opportunity to create, you need to figure out what is that brief the prompt, beyond the prompt, where is the data? Are you going to use what's available out there? Are you going to create your own data rooms? We're using vector databases to create dedicated data rooms. It tends to outperform just the normal standard large language model. I can talk a little bit about that if that's important. But I would say know the data, know the inputs, know what each is going to contribute and create an environment, a sandbox. Right. It could be as easy as a, as, as a, as a document that's editable, which, that has sort of the, the output of the AI, Right. Where the human can go and interact with it. Right. So I think design with intention, think about it as a system and then audit all the different components of the system. Easier said than done. I know, but, you know, I know we don't have a lot of time.

No, it's. That's great because you actually laid it out step by step right there. And I love. Hey, yeah, maybe it's because I'm a dork, but I love when people say, start with data. You've got to get your data right.

Yeah, oh, absolutely. Look, I mean, these large language models are very impressive in, in terms of their abilities, but they have limitation when you go into specific domains. Right. And that's why if you're a large corporation, if you're a small business, you want to Definitely connect your ChatGPT to the Internet and because the data becomes richer, et cetera. But if you're a large corporation or a bigger business, you have the opportunity, you have the opportunity to use vector databases, for example, to dedicate expert data spaces. Then you have the opportunity to train the responses via the work that you have done over the years. For example, we at Sparks and Honey, we've been doing consulting work for 10 years. We can show our own models how a great insight looks like, what a prediction, what a forecast looks like. So when we pre engineer a prompt that's about a forecast, we get an answer that's trained on our own thing, on our thinking. Right. And that gives you at least a starting point that feels more competitive than if you go with the standard outputs.

Yeah. Wow. We took in so much everything from vector databases to creating the best kind of AI augmented relationship, talking about the future of creative work. Camilo, thank you so much for walking us through this journey. Really appreciate it.

My pleasure. Thank you for the invitation. And here's to the next 100.

Hey. Absolutely. And hey, if you couldn't, we. We talked about a lot, but if you couldn't keep up, don't worry. We're going to be sharing a lot more, you know, a little bit about Sparks and Honey and just going over everything in depth in the newsletter. So even if you're listening on the podcast and you're like, this sounded good, I want to know more. Okay, well, make sure to go to your everydayai.com we every single day we recap the, the live stream, the podcast in the newsletter. I think it's fantastic. A lot more in there, keeping up with everything that's going on in the AI world. So make sure you do that. And I have to say this, we have a huge, huge giveaway going today. If you're struggling to integrate generative AI into, into your workplace, you're going to want to sign up for the newsletter. We're doing a huge giveaway and it lasts for three days only. Helping companies actually put generative AI to you, so you're not going to want to miss that. But thank you again, Camilo and I hope to see everyone back for another episode of Everyday AI. Thanks y'.

All. Thank you very much. Er.

And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going for a little more AI magic. Visit youreverydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.