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The Role of AI in the Future of Work
Is your business ready to embrace the promising horizon of AI-dominated workplaces? Our world is witnessing an era where over 70% of information workers employ AI to amplify productivity. The landscape of work is evolving at an unprecedented pace, with 'shadow AI' emerging from the integration of tools outside of organizational provision. Yet, as we delve deeper, there is an intriguing contrast between the employees eager to adopt AI and leaders prudent regarding its integration and risks.
Shaping AI-Centric Skills for Future Management Roles
AI provides unlimited possibilities and is no less than earlier breakthroughs such as typewriters, laptops, and the internet. Still, AI systems also require uncharted management skills, significantly different than those needed to manage humans. The emphasis leans more towards soft skills, resilience, communication, and empathy, emphasizing personal experiences over purely technical skills like swift engineering. This scenario could revolutionize who is considered adept for leadership roles, offering opportunities for those often overlooked.
AI Economy: Momentum Shift Towards Ideas
Within this rapidly evolving AI economy, effective delegators are seen thriving. With an opportunity to turn their ideas into reality supported by AI, there is a clear shift towards an idea economy. This uncoupling of application from ideas and the increased value of ideals harbors enormous potential for businesses ready to adapt.
Adapting to AI: Chasing Growth and Efficiency
As the world grapples with AI advancements, numerous workers are accustomed to the simplicity, convenience, and enhanced efficiency that AI provides. As a result, these individuals are often reluctant to return to the traditional methods, signaling a shift towards further AI adoption in the professional landscape.
Human Creativity: The Irreplaceable Asset in an AI-ruled World
Although the advancements in AI are monumental, it's essential to understand that AI can't imitate the intricate charms of human experiences, storytelling, or songwriting. While it can undoubtedly supplement a myriad of tasks like data analysis, human ingenuity involved in storytelling, for instance, remains unparalleled and irreplaceable.
Preparing for the AI Era: Hone Your Unique Skills
The key to surviving and thriving in the AI era is to concentrate on developing AI-related skills while simultaneously enhancing personal strengths, interests, and unique human ideas. Remember, your greatest contribution to the AI era might be your ingenious idea waiting for its due spotlight.
Conclusion: Balancing AI and Human Skills for Entrepreneurial Success
Undoubtedly, AI brings newfound conveniences and opportunities for business enhancement. But remember to balance these advancements with the imperative value of human connection, creativity, and unique skills. As we move towards a future dominated by AI, the potent combo of AI aptitude and unique human skills will recreate the rules of the game. In such turbulent times, your key to survival may lie in the balance you strike between the two. Stay agile, stay adaptive, and most importantly, stay human.
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Topics Covered in This Episode
1. Microsoft's Research
2. Skills in the AI Era
3. Short-term Skills and Long-term Skills
4. AI Adoption and Use
5. Impact on Workflows
6. Generative AI Shifts
Podcast Transcript
Jordan Wilson [00:00:16]:
You know one thing that large language models are great at? Knowing a lot of things. That same skill that humans have, We've hung our hat on that. Right? Our ability to be intelligent, our ability to know things. You know, I think for so many business leaders out there, it is that intelligence. It is those things you know that have paved the way for you to accomplish what you have in your career. So what happens next? When we talk about the future of work and artificial intelligence, large language models, as they get more capable, more robust, and they know more things, as large language models make intelligence more democratized. What are those skills us human needs? What skills do we need for the future? Well, we're gonna be talking about that and a lot more today on Everyday AI. What's going on y'all? My name is Jordan Wilson, and I'm the host of Everyday AI, and this thing is for you.
Jordan Wilson [00:01:20]:
It's your daily livestream podcast and free daily newsletter, helping us all not just learn about AI, but how we can leverage it to grow our companies and our careers. If that sounds like you, you are definitely in the right place, and I'm excited for today's conversation. But before we get started, have to give a quick shout out, to our partners at Microsoft WorkLab. So why should you listen to the WorkLab podcast from Microsoft? Well, because it's the place to find actionable insights to guide your organization's AI transformation. Tune in to learn how to think more creatively about unlocking the full potential of the technology. That's worklab, no spaces, available wherever you get your podcast. And speaking of Microsoft, we have to thank them for, you know, sponsoring everyday AI, but, also, we have a great guest today, from Microsoft. But if you are tuning in and listening for the AI news, yeah, generally, we bring that, to you right before we get started every single day.
Jordan Wilson [00:02:21]:
Technically, prerecorded show, debuting live. So if you do want that, AI news, it's gonna be in the newsletter. So make sure to go to your everydayai.com and sign up for that free daily newsletter. We'll be recapping today's conversation and a whole lot more. So let's talk about that big thing. Well, when intelligence is democratized, what skills do us human needs? What what skills do us humans need? Apparently, we need communication skills. Right? Alright. So, I'm excited, for today's guest.
Jordan Wilson [00:02:50]:
So please help me welcome on the Everyday AI Show. There we go. We have our, Alexia Khambhan, the senior director of research at Microsoft. Alexia, thank you so much for joining the Everyday AI Show.
Alexia Cambon [00:03:02]:
Thank you so much for having me, Jordan.
Jordan Wilson [00:03:04]:
Kat's got my tongue this morning. So, Alexa, tell tell everyone a little bit about what you do in your role of senior director of research at Microsoft.
Alexia Cambon [00:03:14]:
Yeah. Sure thing. So I head up research for a team at Microsoft that is called the Copilot and Future of Work team. So pretty much what it says on the tin, we essentially take all of the billions of signals that we collect through telemetry, through surveys, through qualitative and quantitative research. And then we try to understand what that means in terms of the future of work. So we can really try to start to predict and project, yeah, what the employee experience is gonna look like. Some of our, biggest research deliverables are the work trend index reports. So a little shout out to those.
Alexia Cambon [00:03:50]:
That's where most of our innovative research lives is in those annual reports.
Jordan Wilson [00:03:55]:
Yeah. And, yeah. You have to shout out that report. We've shared it in our newsletter, multiple times over the years. I I I think it's one of the most all encompassing and best looks, at artificial intelligence and its impact on work. So yeah, shout out to everyone that worked on that. But, you know, speaking of, you know, work, billions of data points. Alexa, like help us better understand, like, where the heck do all of these data points come from? And how do your you and your team use all of these data points to help us hopefully make smarter decisions about the future of work?
Alexia Cambon [00:04:31]:
Yeah. I mean, we're very privileged at Microsoft, obviously, to have access to a type of data that's called telemetry data. And telemetry data, you know, essentially is emitted from all of the products that we create. So that really helps us understand things like time spent in meetings, time spent on email, number of emails read, number of documents edited, all of those signals that essentially make up work. And obviously, important to note that we only look at them at the aggregate. But they really allow us to understand, how work gets done. And that's obviously important as an observation in the wild, right, seeing how behavioral patterns look, out there, in the wild. But we also do a really big global survey that goes out every year to 31,000 people in 31 different countries.
Alexia Cambon [00:05:23]:
And that allows us to get more of the sentiment based data, so really getting a pulse on how people are feeling at work. And combining those two data sources is really what makes up the work trend index reports.
Jordan Wilson [00:05:36]:
So, I I do wanna get back to that, Alexia, but I I also wanna fast forward to the very end. Right? Let's just give away the good stuff. So, you know, what skills do we all need? Right? Like, that's that's what I wanna know, about the most. Right? Because these large language models, they're getting smarter and smarter. And if you know how to use them, it seems like you literally have the world's intelligence at your fingertips. Right? Both in in multimodality capabilities, voice, all these things. So what skills should us humans be focusing on?
Alexia Cambon [00:06:08]:
I mean, essentially, the key question you're asking there, Jordan, I think the most important question is what skills will AI make rare?
Jordan Wilson [00:06:14]:
Mhmm.
Alexia Cambon [00:06:14]:
What skills will AI verify? Because AI is gonna bring to the table whole new skill sets that we can use and adopt from AI. But that also means that it will probably rarefy other skill sets that perhaps aren't considered with as much esteem today. So that's a really interesting question to sort of dig into. I think we should also look at what skills will we need in the short term and what skills will we need in the long term. In the short term, very much we need AI aptitude. Like, far beyond, you know, this this future era in 5, 10, 15 years where all of us are AI experts and it's just interwoven into how we work. Right now, all of us are building the aptitude to actually use AI effectively. And that is a hugely important skill set for everyone, to be acquiring.
Alexia Cambon [00:07:04]:
And we see that, you know, within the LinkedIn research, for example, that people are adding AI skills to their profiles. In the long term, the question then becomes, okay, so if AI enables you to write better, if AI enables you, you know, to to create better, if AI enables you to code, if AI is giving you all of these technical skills that are part of your learned experience, then what are the skills that we rely on humans to give us to contribute to the team? And so my hypothesis here is that essentially with with AI giving us a lot of those skills that we would acquire through learned experience, the skills that humans will, essentially possess that are rarer are skills from our lived experience. And so those are all of the types of skills that we acquire in our personal lives as well as our professionals. So our ability to, for example, you know, coach a soccer team, a kid's soccer team. Right? That really builds your resilience, your patience, your empathy. You know, maybe some of you have a neurodivergent family member. Right? So you have acquired skills that allow you to create really important routine structures, that have allowed you to develop really important communication skills. And those are all the skills that you wouldn't necessarily get through AI or through a degree.
Alexia Cambon [00:08:26]:
Right? They are skills that you have acquired through your lived experience. And essentially, that is the more human skills that we possess. So my hypothesis would be that those are the ones that become rarer and therefore more valuable.
Jordan Wilson [00:08:37]:
Mhmm. And that's it's interesting. Right? Because sometimes when I think about the future of work, it seems very robotic to me. Right? It's it's this, you know, a single person orchestrating, in a gentric ecosphere, right? But it seems like maybe that's not the most important or the most, you know, skill that could be rarefied by AI. So you're saying it's maybe more of these not interpersonal skills, but some interpersonal skills and but soft skills, empathy and patience. Why do you think those skills in the long run are maybe more important than, you know, being able to, you know, prompt engineer the heck out of Copilot?
Alexia Cambon [00:09:19]:
Yeah. I mean, the prompt engineering skills in the short term for sure will be important. The skills you mentioned around, you know, a single person managing, an agentic work force, that is a management skill. Right? A lot of our managing skills do come from our personal experiences. They do come from our lived experiences. And that is probably a future job somewhere, right, the the job of AI manager. And if you think about what that job would entail, like, right now, managers of humans, you know, they hopefully have the types of skills that enable humans to progress in their career, right? So empathy, to your point, patience, kindness, investing in your career. AI doesn't need you to invest in its career.
Alexia Cambon [00:10:01]:
Like AI doesn't care about climbing the corporate ladder. Right? So the the manager of an AI agent or a group of AI agents or even an AI agent workforce probably needs fundamentally different skills from the manager of a human. And so that then becomes very interesting is we we will now be blowing up the definition of a manager and maybe opening up the role of a manager to people who normally wouldn't gravitate towards being a human manager. So, you know, I think those a lot of those soft skills, they often get a bad rap. Right? Soft skills, it's like it sounds like it's unimportant and it's not. Those are the things AI can't give you. Like, AI, you know and maybe one day they will. I don't know.
Alexia Cambon [00:10:42]:
But right now, if you wanna learn how to be empathetic and kind and patient, it probably comes through coaching your kids' soccer team or, you know, those more lived experiences that make up most of our lives.
Jordan Wilson [00:10:55]:
You know, Alexia, you you brought up a super interesting point, about how and and, you know, part of this is, you know, I'm putting, you know, 2 and 2 together that hopefully equal 4. But, you know, I'm thinking, you know, maybe some people that rise up to management, you know, maybe they're not the best, right? Maybe they're, you know, they have a certain charisma or persuasion skills, but then once they're managing people, maybe not the best. Might we see a future of work in the medium or long term where, you know, you have these smaller companies that can maybe accomplish much more because you have a different human, you have a different human pilot that maybe would not have been considered, for a leadership role, to historically how we kind of judge management? Is that is that offer? Is that like a thing?
Alexia Cambon [00:11:44]:
No. I think you're absolutely right to be questioning, what are the types of people that will be in which types of roles. We're already seeing, some, you know, some insights that suggest that people who are very good at delegating and very good at managing are actually really good with AI. Because that's essentially what AI right now in its form as a personal assistant is. Right? You are delegating tasks to it. So those skills serve those people really, really well. I think in the future, what will be interesting is, how will that dynamic play out, in terms of giving opportunities to people who might not otherwise have had easy access to them? And beyond the management and the leadership question, one of the things I've been thinking a lot about is, the idea economy, essentially, the ideas economy. If every great contribution in art, in literature, in music, you know, in history, every great contribution from humans towards society has required 2 things.
Alexia Cambon [00:12:50]:
It's required the idea, And then it's required, you know, the the knowledge, the resources, and the skills to bring that idea to life. So if you think about, for example, Mozart, right, and his symphonies, he had to have the idea for the symphony. But then he also had to have, you know, the the 10 years experience and the genius and the resourcing to to bring the symphony to life, to actually play it. Right? And in a world in which AI is giving you a lot of those skills, it's essentially decoupling the idea from the application of the idea. And so now you could have a great idea for a song. And you could hear it in your head. And you could know what the lyrics are, but you wouldn't know how to actually write it. You wouldn't know how to play the instrument to do it.
Alexia Cambon [00:13:31]:
And you can now go to an AI music generator and it will allow you to bring that to life. So that will probably really create huge openings for people who've always had great ideas but who haven't had the resources or the skills or, you know, the experience to bring those ideas to life. And that is when we start to see the the idea become one of your most valuable commodities as a human.
Jordan Wilson [00:13:57]:
That got me that got me, like, weirdly, like, pumped up, right? Kind of this decoupling of required skill sets, you know, it really is a new era of work. But, you know, could you take us back to where are we at right now, right? Like where's the state of AI? I think, like, I know things have changed very much and they change quickly, right? Anyone listening to this podcast knows that I was just at Microsoft Ignite, and I think, you know, the things that you all, you know, at Microsoft have been working on for many months, The world just sees them now, and I you know, the future of work, when I think about it, my thoughts are so much different than they were maybe 2 or 3 weeks ago. So where are we at right now, Alexia? What is the state of AI at work?
Alexia Cambon [00:14:41]:
Yeah. Great question. So we see the large majority of information workers, over 70% say they're using AI at work. So we know that AI has arrived at work for sure. But within that population, we also see that over 70% of people, are using tools that weren't provided to them by their organization. Not exclusively. They probably are also using tools provided by their organization. But they're also using tools that weren't.
Alexia Cambon [00:15:09]:
And so that's creating this phenomenon that, you know, is dubbed BYO AI, bring your own AI, which is essentially also known as shadow AI, which is when your workforce is using AI on the fly. And that creates a whole degree of risk for the company. Right? So that also shows that employees are moving very, very quickly. And that contrasts a little bit with what we're seeing on the leadership side where leaders are perhaps lagging a little bit and are very worried about how to bring this into the organization and about the risks that it, you know, presents and really wanting to take things much more slowly. And so that is creating, you know, a bit of a dichotomy between leaders and employees, employees who want to go fast and leaders who are, you know, taking a little bit more slowly. So I think that number will only increase over time. I think, you know, when we run the survey again next year, we'll probably see that number has jumped massively. And we're also seeing that employees who have tried out AI and who have been using it for a consistent amount of time don't wanna go back to a time in which they didn't use it.
Alexia Cambon [00:16:10]:
And it's kind of, I think, the comparison of like once you've learned how to, you know, write on a typewriter or
Jordan Wilson [00:16:17]:
on
Alexia Cambon [00:16:17]:
a laptop, you don't wanna go back to writing things manually by hand. Right? You just can't you can't grasp the annoyance of having to spend time and energy on something that could be done a lot easier and a lot quicker, through technology.
Jordan Wilson [00:16:31]:
I I feel that. Right? Because, sometimes I accidentally find myself like reading 10 different tabs and 10 different web pages. And I'm like, why didn't I just use, you know, Copilot or ChatGPT for this? So yeah, that one hits home. So, you know, what you told us, and we'll make sure to include this in our newsletter because great things from the work churn index annual report from WorkLab. But so 75% or over 75% of of global knowledge workers are using AI. Is is it working? Are are are people more productive? Are companies making more money? Is AI working so far?
Alexia Cambon [00:17:08]:
Yeah. So I think one what's interesting is the speed at which AI has been adopted is pretty unparalleled in terms of the history of technology. So within 2 years to see this amount of people using it it's pretty incredible. What we also know about AI and the way people are using it is that they are using it primarily as a personal assistant. And as a personal assistant, it's giving them, you know, an uplift in productivity across certain universal tasks. So we know there are, you know, tasks that create a lot of stress and really pose a barrier to productivity for all information workers. Things like time spent searching for information, right? You you would not believe how much time we all spend searching for information on our day to day. That does not yield a lot of ROI in terms of your finite energy and time, how you are spending that budget.
Alexia Cambon [00:17:58]:
I wouldn't want you spending it on search. Right? And AI's ability to retrieve information quickly and easily is very important. But, and I will say this very emphatically, AI is not a search engine. And I think this is one of the issues that we're finding is that we look at that, you know, search box, that Copilot presents or ChatGPT presents, and our brain automatically thinks, oh, search engine. And that's how we've been treating it. And we know that AI is not a search engine. AI is not a command based tool. It is a conversation based tool.
Alexia Cambon [00:18:33]:
And when you treat it as a partner, a collaborator, you know, when you use it to, think through ideas and brainstorm, you actually get a lot more of the value than when you're just using it as a tool, or a search engine. So I think that's the big shift we have to move towards now is in this early era of it being a personal assistant and helping you find documents and helping you catch up on meetings and, you know, helping you write first drafts. All of that's amazing. Provides enormous productivity uplift for employees who desperately need it, by the way. But the real exciting transformation, the place that it's really gonna disrupt, you know, workflows, ways of working is when we treat it as a thought partner, a collaborator, a game changing, addition to your team, if you will.
Jordan Wilson [00:19:20]:
Yeah. That's such an overlooked, aspect. I I think, 6 or so months ago, I dedicated an entire episode to that very concept. So, yeah, we'll make sure to put that in the show notes as well. But I wanna get back to something that we were talking about a little bit earlier, right? So, you know, having access to these, you know, billions of data points, you know, not not, you know, one individual, it's just a collective. And in seeing these trends, what are, you know, maybe a few things, you know, that you've been able to identify that have changed with generative AI. Right? Because, I'm sure, you know, Microsoft has had, you know, historically access to a ton of data, but, you know, what are some of these big shifts that we're seeing across the board? Not saying that we're in a quote, unquote post, you know, generative AI world, but, you know, since November 2022, what are those big things that, you know, the data is already telling us that it's shifting?
Alexia Cambon [00:20:16]:
Yeah. Well, first, if we think if we look back to the early days of AI, or right before AI joined the scene and we look at what the problems were there, you know, the way I categorize it in my brain is we had big problems with email. We had big problems with meetings. And we had big problems with, like, document and collaboration, type activities. And so I'll give you an example for emails. You know, we saw in some data a couple of years ago that 85% of emails are read in under 15 seconds. And the typical person has to read about 4 emails to everyone they send. So like we, we saw this idea of digital debt, you know, that you are just submerged by information constantly.
Alexia Cambon [00:20:58]:
And know, if you can imagine, you know, just the emails like piling on top of you and you're trying to, like, gradually, like, move your way on top of it, that feeling is what people experienced at work. And it bed out in the data. So we were very interested to see, well, what happens with emails once you give people access to AI? And so we did this very important study. It was done as a randomized controlled trial. For those of you who don't know what an RCT is, it's the way pharmaceutical companies test out new drugs. So it's the most scientific robust way of doing any sort of comparative AB test. And we essentially partnered with 58 Copilot customers, to, essentially look at their data over a period of 12 months and to compare data from their Copilot users with data from their non Copilot users. And the Copilot licenses were randomly allocated.
Alexia Cambon [00:21:52]:
So there was no risk of bias. There was we could be completely confident that the differences we were seeing were because of Copilot as a result. And the greatest effect we saw was definitely on email. Like, we saw a significant reduction in time spent on email for these companies. And that just had me really intrigued about if we're seeing that already in, like, year 1. Are these are these employees having access to Copilot? What does that mean in 5 years? Like, do we are we just all spending no time in our inboxes in 5 years? Do we have an agent, like, tapping us on the shoulder and saying, look, here are the things from your inbox that you need to be aware of? Because Copilot's ability essentially to help you retrieve the most important information you need from an email, to summarize a super long chain, you know, to to write a first draft of an email. All of those, all of those innovations essentially are changing our relationship to email altogether, which is hugely exciting.
Jordan Wilson [00:22:50]:
Alright. I have 1 or 2 more questions for you that I can't wait to get to. But first have to give a, very quick another thank you to our partners at Microsoft. So why should you listen to the WorkLab podcast for Microsoft? It explores the questions business leaders are asking. How can they guide their organization's AI adoption journey? How can AI help them maximize value and create new products and business models? How should they help their teams reskill for the new era of work? And why is it important to be completely transparent about when and how you use AI? So find the answers on WorkLab. That's worklab. No spaces available wherever you get your podcast. So thanks again to our friends at WorkLab for sponsoring, you know, everyday AI.
Jordan Wilson [00:23:36]:
So one thing that I wanna get to. Right? So we we already talked about this a little bit, Alexia, but, you know, kind of the the skills that become important in the short term and the long term. Right? To oversimplify it, we said, hey, you know, working with AI and AI agents is gonna be very important in in the short term. And, you know, maybe some of these more, you know, interpersonal skills in the long term. But I'm curious for you, someone that, you know, knows the the the future of work probably better than, you know, 99.9% of anyone else out there. What skills are you focused on building right now?
Alexia Cambon [00:24:10]:
I think for me, if I think about what is the unique value that I provide Microsoft, right, in my job and what is the unique value that when, you know, AI is able to do a lot of the tasks that I do either autonomously as an agent or, you know, with heavy assistance from myself in 5 years, what is the thing that AI will not be able to do? And one should never compare oneself to Taylor Swift ever. And so I don't want listeners to think that is what I'm doing here. But I was thinking recently about what is the difference what would the difference be in my own personal experience between listening to a song that I knew AI had written 100% and listening to a song that Taylor Swift had written? And regardless of quality, right, let's assume both are really high quality songs. The difference is when I listen to Taylor Swift, the thing that goes through my brain as, you know, a 34 year old female who, you know, is who can see herself in Taylor Swift in many ways is, oh, wow. She went through that. And I went through that. And I she knows exactly how I feel. And there is someone else who knows exactly how I feel, and I don't feel alone.
Alexia Cambon [00:25:20]:
And that I will never get from AI because AI is is not a human, is not a sentient being. And I'm not gonna get that sense from an AI written song. And so as I think about, you know, my own career and what it is I do, there's the data and there's the crunching of the numbers. And then there's the looking for the patterns. There's the trying to dissect what is happening. All of that, I bring enormous value to. All of that, I've been using AI to help me do better. Right? But the thing that, hopefully, Microsoft will never outsource fully to an AI agent is my ability to story tell and to be on a podcast with you right now and to tell you about my experiences and my hypotheses.
Alexia Cambon [00:25:57]:
And all of those have come from my unique experiences in life. Right? And, you know, I've been having this conversation with a bunch of people I work with who also feel the existential threat. I think it's quite natural when you see, you know, this incredibly powerful, intelligence generator and curator suddenly doing all of these tasks. You start to wonder, well, what is my unique value as a human? And it is all of the personal, creative, human based contributions that we provide to the organization that cannot be automated. So my data scientist, who I work with very closely, who does all of the data analysis, who does all the coding, his ability to look at all the patterns and relate to the story that he's seeing between the lines and to figure out, well, this might be the cause, that all comes from his experience. And that is really important and valuable to me. You know, I don't I don't want to get rid of that. That is something I really need on my team.
Alexia Cambon [00:27:00]:
And so that's, again, going back to this hypothesis of it's humans' lived experiences that will really count in future. It's those skills, the storytelling, the creativity that will become more important or become rarer. That's where I'm hopefully investing in my future career is becoming a better storyteller, becoming better at using, using my experience to spot the patterns and project the future.
Jordan Wilson [00:27:24]:
That's a deep that's a deep answer. I I like, I love that. Right? I'm just I'm just sitting here and and thinking about this this concept of, you know, rarefied skills and human connection, and, you know, kind of this maybe brighter and more energy giving side of AI that I don't think a lot of us spend, you know, time looking at. But, you know, as we, you know, kind of transition here toward the end, I'm wondering, so in the same way I asked you, like, hey, what skills are you working on? What skills should everyone else be working on? Right? And I know that's, you know, you can't give a blanket answer, and we kind of already did talk about this, you know, short term versus long term. But, you know, if someone's listening out there and they're like, wow, you know, the future of work, I have a completely different viewpoint on it. What should they be focusing on?
Alexia Cambon [00:28:14]:
Yeah. Well, I would say build your AI aptitude. Do not neglect that. Do not wait to see if it's a passing hype. I don't think it is. Invest the time and the patience and the energy in becoming really good at AI. That I don't think you can wait on. And then I think narrow down the things that you personally are really good at and the things that you really enjoy and really sharpen those.
Alexia Cambon [00:28:40]:
Because if we're moving into an era where your idea is one of your most important contributions, Because remember, like, prompting AI, you are putting in the seed. Like, the seed comes from you. You are the person who is creating the the flow of work that the AI will do. And so your ideas as a human will become your most valuable commodity. And so essentially, sharpen the things that you are already good at and that you already enjoy because that's probably where your best ideas are coming from. And if you can hone and develop that over time, then you will have a lot to offer and a lot to contribute in an era where your ideas are the most unique aspects to to your work and the rarest and most in demand aspect, of you as an employee. And again, saying this with 90% of what futurists say turns out to be false. So I'm I'm not pretending that this is the actual reality that will for sure a 100% happen.
Alexia Cambon [00:29:41]:
Right? This is all my own personal hypothesis based on the research that we've done so far. But that would be that would be what I would tell my my imaginary kids.
Jordan Wilson [00:29:51]:
Well, I think a lot of us feel uncertain about the future of work. But, Alexia, I think you helped a lot of us, you know, myself included, feel a lot better. So thank you so much for taking time out of your day to join the Everyday AI Show. We really appreciate your time and your insights.
Alexia Cambon [00:30:09]:
My pleasure. Thank you so much for having me on for the great chat.
Jordan Wilson [00:30:12]:
All right, y'all. And that was a lot. So many, I'd like, maybe you can hear me typing over here, but I'm typing over things. I'm like, that is amazing. I've done this thing, you know, 400 plus times and still, getting great insights that you haven't even heard here on the show before. So if you haven't already, please make sure to go to your everydayai.com. Sign up for the free daily newsletter. We will be recapping all of the great advice that Alexia just gave us, links to the, work, work trend index annual report, and a whole lot more.
Jordan Wilson [00:30:43]:
Thanks for tuning in. Please join us tomorrow and every day for more everyday AI. Thanks, y'all.