EP 367: How AI Can Make Generalists as Valuable as Specialists

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We've been taught our whole lives and trained and rewarded to be good at one thing, right? To be a specialist, to own your domain, to find that niche and really dig down. But is that how we should be approaching it now with, with generative AI, should we be tried to like, should we be trying to be the most specialized specialist ever? Or with generative AI, should we be looking at maybe being a generalist? And is maybe being a generalist just as valuable as being a specialist now? Well, things are changing and we're going to be talking about that today and more on Everyday AI. What's going on, y'?

All?

My name is Jordan Wilson. I'm the host of Everyday AI and this thing is for you. It's a daily livestream, podcast and free daily news that are helping us all learn and leverage generative AI to grow our companies and to grow our careers. So speaking of grow our companies and growing our careers, another way you can do that is with Work Lab from Microsoft. So why should you listen to the Work Lab podcast from Microsoft? Because it's made for leaders who know they must, must adapt to stay ahead. Work Lab is the place to find real world lessons and actionable insights to prepare for your for the next phase of AI at work. So that's W O R K L A B no spaces available wherever you get your podcasts. Yeah. So make sure. Thank you to our sponsors at Microsoft. New episode just dropped, so make sure to go check that out and make sure to go check out our website, your everydayai.com we're going to be recapping today's conversation and a lot more. So make sure you go check out our website. All right. Before we get into today's conversation. Right. Because I think this whole, you know, AI making generalists as valuable as specialists, it's a conversation that I really am excited for. But before we do, the world of AI news has been crazy, y'. All, so let's just quickly recap. So first in AI news, Meta has launched Llama 3.2, its first multimodal large language model. So Meta has unveiled Llama 3.2, a significant advancement in large language models. So it does feature different varieties. So they have a 11 billion parameter and 90 billion parameter and those are the multi model variations as well. As they announced two new edge or on device kind of models, the 1 billion and 3 billion parameter kind of versions as well. So the model supports a 128k token context length, allowing users to input more extensive text. Also, Meta's CEO Mark Zuckerberg emphasized the importance of open source technology in AI, stating it is becoming the industry standard AKA the Linux of AI. And yeah, I know there's a lot of, you know, heated discussion on a is llama actually open source? Is it open weight? I'll let you guys debate that. But Zuckerberg claimed that Meta AI is on track to become the most used AI assisted globally, indicating a shift in how users might prefer to engage with technology. Meta also released an updated headset with its Meta Quest 3s as well as augmented reality glasses called orion. All right, OpenAI, yeah this y' all the last 48 hours have been crazy so OpenAI is facing a leadership shakeup as CTO Mira Morati and other key figures departed yesterday. Yeah, all at the same time. So OpenAI is undergoing some significant leadership changes with three prominent executives stepping down, raising questions about the company's future direction. This news comes just ahead of the company's annual Dev Day conference in San Francisco, making it particularly noteworthy. So mir Marathi is OpenAI's chief technology officer, announced her departure after six and a half years, emphasizing a desire to create a space for personal exploration. So alongside Murati, Bob McGrew, the chief research officer, and Baritz Zoff, the VP of post training, also are leaving the company. So CEO Sam Altman acknowledged the abrupt nature of these departures, but emphasized that leadership changes are common in rapidly growing companies. So speaking of rapidly growing, OpenAI is changing in a big way. So according to an exclusive report from Reuters, OpenAI is planning to restructure from a nonprofit company to a for profit Benefits Corp. So OpenAI is making even more headlines, gosh, in the last 48 hours as it gears up to transform its core business into a for profit benefit corporation, a move intended to attract more investors after its governance structure. So this shift is significant as it marks a huge departure from the nonprofit control that has defined the company since its inception. So the restructuring will allow OpenAI to operate without the oversight of its nonprofit board, which is which has obviously raised concerns about governance and AI and safety among critics. So CEO Sam Altman is set to receive equity for the in the for profit entity for the first time, a decision that could potentially increase his wealth significantly as the company's valuation is projected to reach $150 billion. So Sam Altman will reportedly have a 7% equity stake, about $11 billion right now. And the existing nonprofit will still hold a minority stake in the new for profit company, ensuring some level of continuity in its mission to create beneficial AI. It's, it's wild. And you know, there's SORA news. There's so much we can't even get to it all. So make sure you go check out the newsletter for that. But I'm excited for today's conversation and this kind of, this change in the workplace about how now AI can make generalists just as valuable as specialists. So enough of me blabbing on. I'm excited for today's guest, so please help me welcome to the show Ian Bcraft, the CEO of Signal and Cipher. Ian, thank you so much for joining the Everyday AI show.

George, it's great to be with you. Thanks for having me on the show.

This is an exciting one, y'. All. So, hey, good morning also to our live stream audience. Kurt, Chris, Cecilia, Harvey. Too many to name. Michael, thanks for joining us. If you have any questions for Ian, make sure to get them in now. But let's start. Ian, tell us a little bit about Signal and Cypher and what it is y' all do.

Absolutely. So we're a change management firm specializing in working with creative services industries, so advertising, marketing and innovation. We kind of started actually as an agency ourselves and we realized that the way that we were going about our work by really leaning into AI and re engineering the process around how we typically did our work when we worked at agencies and worked in marketing departments was actually more valuable than the product we were putting out as an agency. So we made the pivot. We were already doing work that was considered impossible by other agencies and said this is something of value that we can teach to these different groups, the marketing departments, agencies, innovation teams, about where this is going, what's happening with this technology, how does it actually tangibly change both the output and the process, but also the work that the people in your teams are doing?

And I do want to, later in today's show, I want to double down on this change management piece because I think it's huge. But I want to get to kind of how I started off the top of the show. So, you know, I think that for many, many decades, right, we've all kind of been. Been trained or we've learned to be very, very good at one specific thing. Is that not how the world is going to work in the future? Is the business world going to maybe devalue specialists a little bit and maybe value generalists a little bit more?

Well, it's certainly changing. And one of the things that AI does, essentially it abstracts the years of discipline, expertise and exposure needed to perform proficiently at something. Now, notice I didn't say at an elite level. So I'm not walking in and replacing the best in the game or the people at the top of a department, but I am starting to even the playing field. And oftentimes, especially in smaller teams and startups, and even sometimes in enterprise, good enough is good enough. And being able to expand skill sets into spaces people didn't have the ability to operate in before helps create progress within a team. And that's one of the things that's shifting this dynamic between experts and specialists. And the typical debate now is, well, I know more about this thing and you can't replace that skill and that's necessary and there's truth to that. But at the same time, one of the challenges people aren't looking at is when we're waiting on expertise, and expertise is the bottleneck, then we get into spaces of alignment and approval and waiting on those types of things before work in progress. So it's not about the speed or skill or the output of an individual person or team. It's about all those other artifacts of corporate life that come from having to wait on scarce resources and on talent to make progress. And we talk about specialist versus generalists, we can get into it, but there's this symbiotic, you know, circular ecosystem that's built off the two of them that also makes specialists capable of being more generalist in nature as well. So it doesn't actually set up this dichotomy of one versus other. They actually need each other. But what it does is it does elevate the generalists so they are on the same playing field.

Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on Genai. Hey, this is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft and Nvidia have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use Gen AI. So whether you're looking for ChatGPT training for thousands or just need help building your front end AI strategy, you can partner with us too. Just like some of the biggest companies in the world. Do go to your everyday AI.com partner to get in contact with our team, or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on genius. So many great points there, Ian. You know, one thing, though, that I keep thinking about is in my head when I'm, you know, saying, oh, this is going to be a great podcast for young people to listen to. But it's not even necessarily that because, you know, the, the buzzword over the last 18 months has been upskilling and reskilling and, you know, skill sharing. Right? And all these crazy skill words. So should people even mid career, right? Maybe they've, they've made themselves, they, they've made their career off being a specialist, off being, you know, a leader in their domain and having that very niche expertise. Should they be looking at kind of generalizing their skill set, maybe?

Well, the first thing I want to go back to is actually a statement of mid career, because I think that term doesn't exist anymore. We are in a paradigm shift where you're going to have to constantly regenerate your own perspective on work and your, your skill set. And for many, that sounds exhausting. For some that sounds exciting. But either way, it's kind of a statement of what is at the moment. So we're in this space where I do actually believe that everyone's going to have to learn how to learn a little differently. We're in a space where we're unlearning 25 years of digital behaviors that took us a lot of time and millions of repetitions. So a switch doesn't happen just like that. It's not that easy. But I do believe that becoming more generalist is something that's going to be important for everybody. And I'm not saying that expertise goes away or that you should say, ah, your expertise is worthless now. It's actually not. It can be incredibly important. But by developing a more generalist skill set, you have the ability to understand how your expertise affects more roles, functions and outputs. And that actually gives you a higher landscape or survey of the landscape of which you're going to impact. And that makes you even more valuable.

So I'm going to put the devil's advocate hat on here, Ian. So, you know, for maybe those that are in the camp of, hey, it is still worthwhile, right, to, to, to invest in being a specialist and invest in, you know, having that domain knowledge. Well, what about Those that say, well, hey, guess what? A large language model knows every single thing that a specialist knows and probably a whole lot more. Right. The human brain, even those who are specialist leaders in their field, can only know so much and retain so much information. How do you kind of square that one away when, yes, large language models, they're getting more powerful. They know essentially everything. Right. Versus, hey, that the smartest human in the room. How do you square that one away?

Yeah, it's. If you take a look at it pound for pound, the amount of actual information that is accessible to large language model, there's no way any human could compete with that. That's not really what's right now, but we have examples of how this works in the past. Let's take a look at chess. You know Gary Kasparov, who's world famous for having been beaten by Deep Blue back in the early 90s. He actually runs a chess league of sensuar chess players of both person and machine. These are the quintessential definitions of the greatest experts in the world on a subject saying that the game's going to be more interesting and more nuanced. And if we're playing together, not us versus them, it's not. We're all replaced. We're all going, you know, I'm out. The game is done. It's, you know what this is? This is a new way, a new paradigm of thinking about person plus machine. And I don't think it has to be an either or. I think a lot of people are opting into this false dichotomy of it has to be one or the other, and it really doesn't, because we're going to get to a point where right now we're at a blip in time that is kind of the beginning of the rest of the history of the Earth. And it's going to continue to move exponentially. This will look so quaint in even five years.

So I think that there's this still struggle, and I even find myself going through this a lot when we think about manual knowledge work. Right. And that's something as a specialist, Right. Specialists have hung their hat on that for many decades. Right. Being able to sit down, down and to, to analyze information, to, to distill, you know, and synthesize important pieces out of that information and then to create something new out of it. What should those specialists be doing now that large language models can do that maybe way better and way faster? Should they still be specialists be handing over some of these important tasks to AI and trying to, you know, find this augmented relationship, or is there still value in kind of being that old school, you know, oh, hey, I'm do this without a large language model?

Yeah, I think there's value to both. I think everyone needs to be engaging with the tools because it's kind of like saying, I'm going to stick with my leather notebook. You kids could go play with your computers. And then 40 years later, what happens? But I don't think that we're devaluing the expertise of specialists. We actually need specialists to help create and monitor what's happening with these models as we're doing more specialized work. Because the models aren't perfect. You see garbage in, garbage out. If you don't know how to prompt all, you're not going to get good outputs. It's also a very jagged frontier as far as what models are good at. So it might do something very, very well. And then you take this thing that is analogous, almost exactly the same as that previous task, and the model just completely falls apart and can't do that. An expert can find those nuances far faster and more effectively than somebody who doesn't have expertise in a specific space or, or output. So I think that there's a number of places that expertise still has extraordinary value. The other is especially internally at organizations, as we're trying to build internalized and encoded expertise that's built on the knowledge inside your organization, that expertise is even more important. So that when you get an output, you're not having a generalist without a lot of depth of knowledge there saying, yeah, that looks good to me. Check it. That's we're training on. Whereas a specialist might say, hey, hey, there's something you're not seeing here in this data. Let me explain why this is not the right output, why this doesn't work. So all of a sudden you're not training on bad data, you're not infusing the culture in your organization on faulty data. And that's where that symbiotic relationship comes in between specialists and generalists, because the specialists can help refine what's actually happening with the models, the outputs, the inputs, how we use them in a way that really gets to the core of the value we're putting out as a team or an organization. And then the generalists, as that gets better and better, their skill set starts to expand the amount of friction between them and a final output starts to drop. And the whole group as a team starts to become more productive where they're not waiting on Those resources and the barriers to getting it done start to fall.

So I've had this thought called first party company data where I think in the future specialists are going to be working internally and grabbing all of this specific domain expertise in their company, rounding it up and then making sure the models are correct in kind of absorbing that knowledge. Is that an actual future we might see where specialists are maybe just spending the majority of their time making sure that the data that the rest of the quote unquote generalists are working with is highly domain specific and accurate? Is that a future that we might actually see?

I could easily see that especially as like a transition type of role too, because eventually that is something that models will be able to effectively impact as well. Especially as we're starting to see this relationship between multi model outputs, not just multimodal, but like using models as antagonists to each other, models that are checkers and governors of other models and supervisors. A lot of the conversation right now is about single model outputs, inputs and outputs from things like ChatGPT and Copilot. But where we're going and everyone's starting to talk about all the multi agentic agentic is great, but what about multi model agentic and orchestration across all these different types of things? We're leaning into a space where we're building on top of models that already evolve at a fast pace. And if you build your foundation on top of that, you have to be ready to shift at a moment's notice.

All right, let's shift right now to moments notice real quick, just to shout out our partners here on this show, Microsoft Work Lab. So real quick, the Worklab podcast from Microsoft is made for leaders who want to understand how work is changing. Effective leaders adapt. They stay ahead of trends, they embrace any edge they can get. They also know that AI powered organizations will be better at spotting opportunities, creating new products and business models, and maximizing value. So for real world lessons and actionable insights to help you stay ahead, check out the Worklab podcast. That's W O R K L A B, no spaces available, wherever you get your podcasts. All right, so thank you again to Microsoft for sponsoring our show. So Ian, I do want to now get back to one of these points that we talked about Initially was change management, right? How important is that right now? My hot take has always been, you know, proper AI implementation isn't a technical project, it's a change management project. Can you speak a little bit about how important change management is for successful work workplaces?

A thousand percent. I mean you just spoke into the, the value we saw in change management for ourselves. And unfortunately the, the way people look at AI right now is we just sprinkle a little magic AI pixie dust on something, all of a sudden you've got an AI strategy. We tend to chase shiny objects that way. But change is a matter of people challenges, process challenges and strategy. And it's not just about technology. Technology usually comes last. Technology enables that change to happen. You have to observe what technology is changing and then go change the other structures that are impacted by that. And that starts with your people, that starts with the processes within your organization. Starts with how you actually reshape both the outputs and the processes to get that value you create as an organization. And then say, go to your people and say, hey, we need to change the way we're operating. This impacts your roles, this impacts your outputs and the tools you're going to be using. So we need to bring you along for the ride and say, we're going to do this together. Otherwise you get a top down perspective of, hey, we're changing everything. You just need to adapt. And that's what people are used to in terms of digital transformation initiatives. And we're going to implement an ERP or we're going to implement some sort of new transformation initiative. And when we're done, you all just have to adapt, figure it out, we'll train you on it. But you're going to be working for the system, not the other way around. And that's one of the things I think is really unique about this new paradigm, is we can actually flip that on its head and say, yes, technology is revolutionary. It's amazing. You do so many things, but it should be in service of what we do as human beings, not the other way around. And I don't think it has to be that dystopian narrative of AI taking over all of our work and we no longer have an identity.

Yeah. And Ian, you know, I'm curious, how does this kind of shifting paradigm of the specialist generalist relationship, how does this change the makeup of teams?

Yeah, it's definitely going to change the makeup of teams. Because if you have one person who can do a lot more than they typically could, because if you take a look at the structure of an organization, everyone has a title and a level which helps you matrix the organization and navigate it effectively. That's essentially a structure that's 150 years old. The org chart came out, I think in 1858 for a train line and not much has changed. Since the theory and management style that we use still echoes the work of Frederick Winslow Taylor from the 1800s. And what we've seen is everything else about it has changed, but the structures that we exist within have not. So the relationship between work has changed, the expectations of work have changed, the outputs of work have changed, but the structures of work have not really kept pace. And I think that the new relationship between work, between specialists and generalists is going to be one of those things that pushes this forward and says, now there's actually more power with the teams and than there used to be before. So you'll have these smaller teams that can achieve a lot more with a broader set of skills. So one person might be able to do the work of, you know, 3, 4, 5, or even a team based on those skill sets that were previously necessary or those scarce resources. A group of people, two or three, could do the work of an entire department. And then as you start to kind of build out, you're seeing these groups of people have even more and more massive impact. Now, some people might be hearing that say, well, don't I just downsize and have, like, maybe 20% of the staff do the same thing? Well, the argument I would make to that is we actually have reference to this. We go back to the first industrial revolution. We talk about how we looked at the construction industry. We actually replaced physical labor with mechanical labor. And a lot of the people who were shoveling dirt to make way for foundations looked at these steam shovels and said, okay, you have the strength of 10,000 men in that machine. My career is over. And if that's all you looked at, then that's a straight line between those conclusions. We're looking at AI the exact same way. And I think we can all tell that the construction industry didn't just create all the stuff that was already on contract and say, we're done. We built everything. There's no more work to be had. We continued to innovate. We looked at how things were changing. We built new infrastructure and economies around that as a result. And that's how things have become what they are. So I think we're kind of at that inflection point right now where a lot's going to change very quickly. But the specialist and generalist relationship, I think, is going to be one of the first paradigms people encounter themselves that gives them a taste of where that's going.

I think, Ian, you said something that's very pivotal to focus on there, you know, about specialists and maybe everyone saying, well, where can my skills be valued? How should we even be looking at that? That's something that I even, you know, think about myself all the time is where can my current skills be valued in the future? And how can we all be building, you know, maybe, maybe more future proof skills when skills and knowledge are seemingly starting to become kind of commoditized. So how can we be looking toward the near future and where we should be spending our time to be skill building?

Absolutely. So it's actually kind of counterintuitive and it's about giving your skills away to those who don't have them. And what I say by this is there's a way that you can, we'll call it encoding knowledge. So taking the knowledge of a specialist and using that knowledge to advance the ability to automate some of the work around that knowledge. So many people as specialists are spending probably 70% or 60% of their time doing things that are lower level work, that are administrative, that aren't the highest use of their time. I'm sure they'd love to be using their intellectual horsepower on something that is more fulfilling, satisfying and a higher level of transformation and focus on taking that bottom 70% of work and encoding it in a way, using it to build models around it, to build automations around it and then give that to your team. Whether that could be in the form of GPTs or even agents or multi agent workflows, there's a whole bunch of ways to do that. But what that does is then that says, hey, you know that thing you come to me for and I spend like 70% of my time doing it over and over and over again. I can actually help or with the help of another specialist in the area of automation, start to automate some of these things and distribute that knowledge across my team. And we see that happening all over the place. And what that does is it gives rise to more effective generalists who are able to do those things that would usually have to go to somebody else to get done. And it's a lot of people like, but then I'm losing my value. Like the thing that keeps me employed is this and yes and no. I liken it to one of the changes in the content world where we have all these people who became thought leaders by giving away things for free. You know, you take a look at Gary Vee, he's one of the biggest people who talk about this like, just give, give, give, give, give, give, give, give, give. With no element of return. And of course corporate, you have to get a return but by doing that, you become the go to resource for this kind of stuff and you're helping elevate other people. He's worth hundreds of millions, if not a billion at this point in time. By giving things away free. Everyone else told, don't do that, that's your value. We're in a similar place when it comes to AI, where if you can create value for other people in your organization, you actually become indispensable because you're helping raise the floor for the other generalists in your organization. And that's that symbiotic flywheel I was talking about. By taking your expertise, encoding it, or putting it into the models, into the workflows so it can be distributed across your organization. And then all of a sudden everybody else elevates their game and you elevate your importance.

That's, that's an interesting one. But I, I, I do want to dive in a little bit deeper on one of those points that you just made there. This, this concept of, you know, hey, a specialist, you know, maybe spends 70% of their time, right, doing this lower level work, doing this, what we call, you know, manual knowledge work, or work that in theory, a large language model could either do better or faster, or a combination of the two. Right, so then what does that mean for the future of that 70% of the time? Let's even say it's 50% of the time, right? Give, give benefit of the doubt or say we got to spend, you know, 20% of the time overseeing, you know, agentic workflows that are now doing that more mundane work. What does that mean then for, for specialists who are, you know, able to, you know, offload, let's just say some of their more mundane work. Do we just have 50% more time to sit around and think creatively and strategically and you know, do we go to a three to four day work week? Do we just, you know, force. All companies now have to find 50% more business to, to compensate for this kind of time saved. How does that piece get worked out?

Yeah, I think it, honestly, it could be all the above. I think the beautiful thing about where we're headed is the answers don't have to be so monolithic. It doesn't have to be one or the other. And I think what we're going to see is this massive fragmentation of different paths that companies take. We'll see new business models, we'll see new operating models, we'll see new team structures, we'll see new forms of value, we'll see new ways of working. And what's beautiful about this is the volume and the tool sets allow us to experiment with these things rapidly and get the data back. Say this is working, this is not. Let's explore the frontier using these tools and get that information back on what's going to work and what won't. But I don't think that there's any one answer. I think that there will be some companies say, hey, it's a four day work week from here on out. I would love to get to that point. Others are going to say, you know what, we're going for the gold. It's now we're leaning even harder. We're going to work, we're going to produce more. I think more will probably go that route, especially in the beginning. But there are going to be others that say, hey, let's explore entirely new business models. Let's work into adjacent spaces that we didn't have any business being in before. But as an organization, we can also be a generalist. We can start to expand to the edges where there's, you know, uncaptured opportunity. Right for the table where someone else's process is your opportunity. And that's one other thing that comes from this dynamic is, you know, Jeff Bezos is famous for saying, your margin is my opportunity. Well, when it comes to AI, it's about your process is my opportunity. And if your process is cumbersome, it's hard to do it long and complex. That's an opportunity for someone else to come and disrupt that. Oftentimes it's the specialists that are going to identify those opportunities for that disruption.

So many important takeaways from today's conversation, Ian. But you know, as we wrap up today's show and, and we've covered a lot, maybe what is the one most important piece of advice that you can give to business leaders out there that are thinking through this transition of AI being able to make generalists maybe just as valuable as specialists. What's that? One biggest takeaway?

Yeah, I would say find something that you're really passionate about that's going to force you to use the tools in a different way. And if you find something that's meaningful, you're going to continue working through all of the challenges and all the things that break and don't work and don't work the way you're supposed to. And the nuance you'll understand about how these tools are developing and growing and the direction they're going in is going to be invaluable. That's One of the biggest challenges for adoption of these tool sets is people don't have something that's meaningful to work towards. So the second they get to any friction or any stopping point, they say, ah, it doesn't work. They want it to. I'm done. And adoption starts to plummet within your organizations. So do that for yourself, but also try and find and help people in your organization find those things that they're passionate about, whether it's personal or professional. And you'll see adoption start to rise. You'll see people start to push through those challenges of where the models, the interfaces aren't really great yet to get to some sort of solution. And then all of a sudden that internal knowledge of how this stuff is evolving becomes so, so much more robust.

Ian, so many great points that you bring up that I think are really going to help us think of our time at work, what it means to be a great employee, what it means to work on meaningful work. I think today's discussion was an extremely important one. So thank you so much for your time and for joining the Everyday AI Show. We very much appreciate it.

Jordan was awesome to be here. Thanks again.

All right. Hey, there was a lot in today's episode, y'. All. I appreciate you guys tuning in and I'd also appreciate it. If you haven't already, please go to your everydayai.com Sign up for the free daily newsletter. Yeah, there was more news than we can even get to and Ian just dropped so many valuable insights as we kind of mull over this transition from, you know, specialist to generalist. It's an ongoing process, so make sure you go check that out and make sure you check us out tomorrow and every day for more Everyday AI. Thanks, y'.

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