Preparing All Learners (and Ourselves) for Jobs that Don’t Exist Yet
Dr. Michelle Weise—podcast host, author, futurist, and Chief Impact Officer of The Kern Family Foundation—spoke to a riveted audience at InstructureCon 2025 about preparing learners for a future that won't look anything like the present.
Please welcome Doctor. Weiss to the stage. Thank you so much for having me here. I'm honored to be here with all of you. Over the last few centuries, we have gotten really good at delaying death. There are some estimates, and according to the authors of The One Hundred Year Life, since eighteen forty, we've been adding on an extra three months of life expectancy every single year.
There are now some futurists and experts on aging and longevity who are predicting that the first people to live to be a hundred and fifty years old have already been born. A hundred and fifty years old. I've had people tell me, kill me now. Kill me now if that means my work life is gonna be sixty, eighty, or a hundred years long. But it's actually not as far fetched as we might think.
Today, have workers who are fifty five and older who are staying in the workforce at historically high rates, well into their sixties and seventies. And many of them are actually retiring with an average of twelve job changes under their belts. Twelve. So for our younger generations whose average tenure in a role is less than three years, they may have to contend with twenty or thirty jobs as the new normal. This is the concept of long life learning, where if we have to somehow sustain a longer work life, we are going to have to make ongoing skill development a way of life.
But this is complicated because work is changing. The very nature of work is transforming. We are seeing the ways in which technology is transforming how and where and when we do work. Right? And so the other piece of this is that technology is not only transforming how we think about work, it's also transforming how we think about our own lives and our own work and our own anxieties about automation and unemployment. It used to be that the people who worried most about automation and unemployment were those without college degrees.
But today, we find ourselves in a new world where the people who are actually most worried about job obsolescence are those with college degrees. This is impacting both blue collar and white collar workers alike. We see now some predictions that maybe ninety or more percent of the jobs out there are going to be greatly impacted in some way by this new proliferation of generative artificial intelligence. Right? And so this future of work just feels deeply unsteady. It requires constant adaptation.
It's not going to slow down just because we opt out of it. In fact, it's accelerating at this exponential rate, which means that the longer we stay in the workforce, the more tectonic shifts we might witness in quick succession. The skills we retire with will not be the ones that we start out with, and the way we thrive in our work will have everything to do with how we learn. We're gonna have to harness the power of education over and over again to ride out these potential disruptions of AI and automation. And so to have one conversation about the future of education over here and another conversation about the future of work here in parallel makes no sense.
When we use this concept of long life learning, we realize that these futures are one and the same, that the future of education is this future of work and vice versa. So rather than thinking about our lives as somehow a three part approach where we learn, earn, retire, and rest, we're gonna have to face this new future where it's learn, earn, learn, earn, repeat, often doing both at the same time. Excuse me. But then when we think about our infrastructure today, right, in this longer future of work, in an ideal world, we could all sort of enter the workforce highway after we do this kind of front loaded education in the first twenty to twenty five years of our lives. We would enter the workforce highway, and as we move through our careers, we could seamlessly take one of these cloverleaf exits, get what we need to skill up and retool ourselves, and then seamlessly reenter the workforce highway.
That would be the ideal. But this does not exist. Right? We think about what we have to do today with how rigid and brittle our systems are. Adults in particular have never had easy access to these fluid upskilling on ramps in and out of work. Instead, we've been somehow forced to think about how we force fit our very nonlinear realities into a rigidly linear system that was geared for eighteen to twenty four year old learners, those full time students.
And this is where I really began to focus my research on the who. Who was being left behind as we thought about this present and future of work. And so we began to do about a hundred one hour interviews looking at a particular population of learners. We just talked about universal design. We were looking at the people who were most excluded by our systems, who could not access a thriving job in order to to earn a living wage.
And at the time, this is prior to the pandemic, we had over forty million Americans who were falling through the cracks of our education and workforce ecosystem. And when you think about designing for the future, we have to look at the folks who are falling through the cracks. Because when we actually seek out the perspectives of those who are being most excluded, who are going and facing the greatest burdens, we're actually going to gain the greatest insights into how we begin to move forward. And this was a way to move beyond all of these scary statistics about the millions of people who might lose their jobs to automation and computerization and roboticization and instead begin to design for the future. And in these in these interviews, we kept hearing over and over again five recurring principles that were needed for a better functioning learning ecosystem of the future.
We needed to design a new learning ecosystem that is fundamentally more navigable, supported, targeted, integrated, and transparent. If we do this well, we can actually cut into the curb. When sloped curbs actually became the new normal in the United States, it went far beyond enabling those who maybe were in wheelchairs. It helped people pushing strollers and dollies, people wheeling their luggage, runners, cyclists, skateboarders. Curb cuts benefited everyone through accessible, usable design or universal design.
Cutting into the curb in our particular context means focusing on those who are faltering in those cracks today. And so in order to do a better job, we need to focus first on career navigation. So many of us have a real hard time trying to understand where we are today relative to where we wanna go. We need a clearer road map to make this more easily navigable. I need to know what skill sets do I bring to the table that I could transfer and launch from as I maybe move into a different industry domain.
And in interview after interview, we kept hearing displaced workers use terminology like, there's no road map. Where do I turn? I have no GPS. I have no one to guide me. We asked what they needed, and in interview after interview, they said, help me, oops, build my confidence. Help me begin with the end in mind.
Help me not waste money and just study what is necessary. Help me sharpen and reaffirm my skills. Help me play into my strengths. Help me share my hidden talents, and help me prove to you that I can do this work. People really need a bird's eye view of the job market and all the career pathways that are open to them based on their interests, skills, and abilities, and past training and experiences.
We need to know that we might be thirty percent of the way there towards being a system network analyst or eighty percent there towards being a human resources manager. And here are the skill gaps that I need to fill, and here are the learning experiences, by the way, that might help me fill those skills gaps. This is all happening as we speak with different kinds of platforms. But the really exciting piece that we need to think through is that we are moving from this moment of AI into generative artificial intelligence. And as scary as that can feel sometimes, this also affords us new opportunities to rethink where we are stuck today.
Because in this future where we have our own personalized artificial intelligence, our own agents, it can recommend personalized learning pathways, projects, skills that we can work on, a new course that we might want to think about. And it'll be real time adjusted to labor market needs. We're gonna figure out which of those in demand skills we need to focus on and which certifications might actually help me launch to that new opportunity. It's this idea of illuminating a better way forward that we really need help with when we talk about this idea of a system that is more easily navigable. How amazing would it be if I could have my own personalized career adviser, my skill coach, my storytelling agent that will help me share my story to a prospective hiring manager.
The second principle is this idea of wraparound support services. And this is where we actually spend a lot of time in higher ed because we have a much older adult learner population that is coming back realizing that they need this ongoing skill development but still have to juggle a lot of other responsibilities that lie outside of our classrooms. And it's really hard if we don't have supportive systems that are tailored to meeting the needs of the kinds of things that dog us as we try to pursue and advance our education. Our barriers to success rarely actually lie in the classroom. It can often be outside of the classroom, and we need to manage those aspects of our lives.
Sometimes it can be a small thing. I just need a subway card. I need access to a clothing closet. I need food, just a food pantry access. I need eyeglasses or steel toed boots or culinary knives.
But then other times, those support services can be much more substantial. They can be around mental health services, financial services, access to food stamps, transportation, child care. These kinds of wraparound support services are critical, and it also includes that human touch that is aligned with those career support services. If I want to move forward, I need accountability coaching. I need someone who is on me asking me if I'm moving forward in my educational journey.
The third piece of this is targeted education, and this is just the idea of the most direct route to a destination. Right? We want the right skills, the right path at the right time. It's precision learning, which is inordinately difficult to do these days because we face over one million different kinds of educational and labor market credentials out there flooding our markets. So which one am I supposed to choose? How am I gonna know that this one learning experience that I select will have the signal power I need to share to an employer that I can do the work ahead? Right? How do we know which one to pick, especially when we have all these various narratives around, well, you need to get some STEM skills. You need AI and tech literacy.
Nope. You need human skills. Which one is it? So through research, we've actually looked at some of the data out there and the millions of credentials and the pathways and resumes that are moving through our entire labor market. And what's fascinating to see is that from a demand perspective, we can begin to actually design better the kinds of specific credentialed pathways that will help people move in that more precise and targeted way. So one quick example of this that I love to share is just take the idea of going into journalism.
We would assume that in order to get a good job in journalism, we need to be able to communicate really well and have that verbal and oral communication down. But when you actually take a look at journalism fields today, the field of journalism today, it looks a whole lot more like an information technology market. You have to be able to display these kinds of hybrid skills, both human and technical skills. You have to be able to engage in data visualization. So it's this perfect idea of a T shaped individual, right, where you're moving across these human and broad based conceptual skills plus some vertical technical expertise.
It's the instantiation of what author David Epstein calls range. It's this ability to dance across disciplines and connect ideas from various domains while knowing when to intervene at the right time. It's this kind of intellectual dexterity alongside that technical vertical expertise. Right? So imagine it as being emotional intelligence, artificial intelligence, ethics and logic, communication and programming. Right? It's the simple approach to both and.
Problem, though, generative AI is complicating our approach to thinking about human plus technical skills. Today, we are seeing results today that are showing that generative AI can pass our US medical licensing exam no problem. The bar exam. It can perform better than ninety eight percent of the population on general IQ examinations. It can solve thirty five puzzles in twenty five minutes, things that we would find quite difficult to accomplish.
And so then we keep hearing these narratives, okay, well, let's focus on our human skills, our uniquely human skills. That's what's gonna keep us future proofed as we face all of this uncertainty. Another problem. These generative models are actually mimicking empathy. They're mimicking our social and emotional reasoning skills to the point where we've had now this is from McKinsey data showing that experts used to imagine that maybe AI can take over some of these skills, these human skills, in the twenty seventies or twenty eighties.
But because of how rapidly evolving these technologies are, they are now pulling back and ratcheting back on their expectations thinking, oh, this might happen in the early twenty thirties. Right? So then what are we supposed to do? This is where all of us get to do the fun work of beginning to name the work of the future. It's really important that we begin to do this. Because just remember, in two thousand fourteen, those top five jobs that existed on LinkedIn had never existed five years prior. We have to begin to imagine some of those words.
And I just put in some crazy ideas in here. Right? What would it look like to have a mindfulness technologist or an empathy engineer? The reason why this is important is it begins to help us understand, are we building the right skills for the future? And what do we need to begin to help our learners understand that is core as they think about those human and technical skills that will make them robot proof or future proofed. The other piece of this is as we think about that t shaped learner, that simple idea is no longer going to be sufficient. We are going to have to think about it more in this jagged model where over time we're gonna not only have to broaden our human skills and deepen and get more sophisticated in our human skills, we're gonna also have to figure out when we need to go deep into that vertical tech technical expertise. Cybersecurity, you can't just have a shallow understanding of.
Cloud computing, I could maybe just have enough to get my foot in the door for an for an interview. Over time, we're gonna need to think about this model as that learn, earn, repeat model for the future. But it's not just linear. It's three d, three-dimensional. So imagine it as a spiral staircase instead, where just because we achieve a specific competency doesn't mean that we actually are one and done, we nailed it, we don't ever have to come back to it.
Instead, especially as we think about these human skills, we're going to have to get better and better at them over time because we all know what it looks like and feels like to work with folks who lack those human skills. Right? Finally, sorry, second to last, we have this idea of integrated earning and learning. We are all going to have to figure out how we learn in the flow of work, and the onus is gonna be on our employers because they have seriously disinvested from this space. Over the last four decades, we have seen two and a half weeks worth of training a new employee go down to less than eleven hours per year. Today, forty four percent of our employers offer zero upskilling opportunities for our future, right, to build us toward those emerging jobs of the future.
We have to figure out how we get in the flow of work and carve out time in the workday to begin to build those skills for the future. And finally, transparent hiring practices. We're gonna have to figure out how we make our hiring processes less opaque and give people an opportunity to prove that they can do the work ahead. This is kind of a big, part of the book, but I'm gonna sort of spend a little bit less time on here because I think what the main goal here is just to say that in order to begin to move towards those seamless on and off ramps in and out of the future, in and out of work, we have to actually think about how we knit together these five principles. I think all of us can think of a or a couple of solutions that do one or a few of these things.
But if we were to ask any of our friends or a person off the side of the street and say, how are you gonna navigate your next job change? They would all have different answers because we have not figured out how to make this system more easily navigable. So together, a system built out of these five principles will help us figure out how we begin to harness the power of education over and over again throughout that longer work life. Because it's important for us to remember this. When we think about the future workers, it's not someone else, it's about us. We are all going to have to contend with those ten, twenty, or thirty jobs to come.
And if we are thinking about, oh, I have a really great job today. I have a degree. I have an advanced degree. I have multiple degrees. I love my job.
I don't need to think about this yet. We are all, no matter how fulfilling we find our lives today, we will find ourselves having to flex and move as these working learners. We're gonna have to juggle both working and learning at the same time. And when we return to learning, we are going to have to face these same constraints on our current learning ecosystem, these same limited options, the same confusion as the people who are struggling the most today. And knowing the future that we don't want, we can articulate what we do want and begin to build toward that.
So the question becomes, when we are seventy or eighty and looking at another twenty or thirty years in our work lives, what do we want our learning ecosystem to look like? It's time to build. Thank you. Thank you so much. Thank you.
There are now some futurists and experts on aging and longevity who are predicting that the first people to live to be a hundred and fifty years old have already been born. A hundred and fifty years old. I've had people tell me, kill me now. Kill me now if that means my work life is gonna be sixty, eighty, or a hundred years long. But it's actually not as far fetched as we might think.
Today, have workers who are fifty five and older who are staying in the workforce at historically high rates, well into their sixties and seventies. And many of them are actually retiring with an average of twelve job changes under their belts. Twelve. So for our younger generations whose average tenure in a role is less than three years, they may have to contend with twenty or thirty jobs as the new normal. This is the concept of long life learning, where if we have to somehow sustain a longer work life, we are going to have to make ongoing skill development a way of life.
But this is complicated because work is changing. The very nature of work is transforming. We are seeing the ways in which technology is transforming how and where and when we do work. Right? And so the other piece of this is that technology is not only transforming how we think about work, it's also transforming how we think about our own lives and our own work and our own anxieties about automation and unemployment. It used to be that the people who worried most about automation and unemployment were those without college degrees.
But today, we find ourselves in a new world where the people who are actually most worried about job obsolescence are those with college degrees. This is impacting both blue collar and white collar workers alike. We see now some predictions that maybe ninety or more percent of the jobs out there are going to be greatly impacted in some way by this new proliferation of generative artificial intelligence. Right? And so this future of work just feels deeply unsteady. It requires constant adaptation.
It's not going to slow down just because we opt out of it. In fact, it's accelerating at this exponential rate, which means that the longer we stay in the workforce, the more tectonic shifts we might witness in quick succession. The skills we retire with will not be the ones that we start out with, and the way we thrive in our work will have everything to do with how we learn. We're gonna have to harness the power of education over and over again to ride out these potential disruptions of AI and automation. And so to have one conversation about the future of education over here and another conversation about the future of work here in parallel makes no sense.
When we use this concept of long life learning, we realize that these futures are one and the same, that the future of education is this future of work and vice versa. So rather than thinking about our lives as somehow a three part approach where we learn, earn, retire, and rest, we're gonna have to face this new future where it's learn, earn, learn, earn, repeat, often doing both at the same time. Excuse me. But then when we think about our infrastructure today, right, in this longer future of work, in an ideal world, we could all sort of enter the workforce highway after we do this kind of front loaded education in the first twenty to twenty five years of our lives. We would enter the workforce highway, and as we move through our careers, we could seamlessly take one of these cloverleaf exits, get what we need to skill up and retool ourselves, and then seamlessly reenter the workforce highway.
That would be the ideal. But this does not exist. Right? We think about what we have to do today with how rigid and brittle our systems are. Adults in particular have never had easy access to these fluid upskilling on ramps in and out of work. Instead, we've been somehow forced to think about how we force fit our very nonlinear realities into a rigidly linear system that was geared for eighteen to twenty four year old learners, those full time students.
And this is where I really began to focus my research on the who. Who was being left behind as we thought about this present and future of work. And so we began to do about a hundred one hour interviews looking at a particular population of learners. We just talked about universal design. We were looking at the people who were most excluded by our systems, who could not access a thriving job in order to to earn a living wage.
And at the time, this is prior to the pandemic, we had over forty million Americans who were falling through the cracks of our education and workforce ecosystem. And when you think about designing for the future, we have to look at the folks who are falling through the cracks. Because when we actually seek out the perspectives of those who are being most excluded, who are going and facing the greatest burdens, we're actually going to gain the greatest insights into how we begin to move forward. And this was a way to move beyond all of these scary statistics about the millions of people who might lose their jobs to automation and computerization and roboticization and instead begin to design for the future. And in these in these interviews, we kept hearing over and over again five recurring principles that were needed for a better functioning learning ecosystem of the future.
We needed to design a new learning ecosystem that is fundamentally more navigable, supported, targeted, integrated, and transparent. If we do this well, we can actually cut into the curb. When sloped curbs actually became the new normal in the United States, it went far beyond enabling those who maybe were in wheelchairs. It helped people pushing strollers and dollies, people wheeling their luggage, runners, cyclists, skateboarders. Curb cuts benefited everyone through accessible, usable design or universal design.
Cutting into the curb in our particular context means focusing on those who are faltering in those cracks today. And so in order to do a better job, we need to focus first on career navigation. So many of us have a real hard time trying to understand where we are today relative to where we wanna go. We need a clearer road map to make this more easily navigable. I need to know what skill sets do I bring to the table that I could transfer and launch from as I maybe move into a different industry domain.
And in interview after interview, we kept hearing displaced workers use terminology like, there's no road map. Where do I turn? I have no GPS. I have no one to guide me. We asked what they needed, and in interview after interview, they said, help me, oops, build my confidence. Help me begin with the end in mind.
Help me not waste money and just study what is necessary. Help me sharpen and reaffirm my skills. Help me play into my strengths. Help me share my hidden talents, and help me prove to you that I can do this work. People really need a bird's eye view of the job market and all the career pathways that are open to them based on their interests, skills, and abilities, and past training and experiences.
We need to know that we might be thirty percent of the way there towards being a system network analyst or eighty percent there towards being a human resources manager. And here are the skill gaps that I need to fill, and here are the learning experiences, by the way, that might help me fill those skills gaps. This is all happening as we speak with different kinds of platforms. But the really exciting piece that we need to think through is that we are moving from this moment of AI into generative artificial intelligence. And as scary as that can feel sometimes, this also affords us new opportunities to rethink where we are stuck today.
Because in this future where we have our own personalized artificial intelligence, our own agents, it can recommend personalized learning pathways, projects, skills that we can work on, a new course that we might want to think about. And it'll be real time adjusted to labor market needs. We're gonna figure out which of those in demand skills we need to focus on and which certifications might actually help me launch to that new opportunity. It's this idea of illuminating a better way forward that we really need help with when we talk about this idea of a system that is more easily navigable. How amazing would it be if I could have my own personalized career adviser, my skill coach, my storytelling agent that will help me share my story to a prospective hiring manager.
The second principle is this idea of wraparound support services. And this is where we actually spend a lot of time in higher ed because we have a much older adult learner population that is coming back realizing that they need this ongoing skill development but still have to juggle a lot of other responsibilities that lie outside of our classrooms. And it's really hard if we don't have supportive systems that are tailored to meeting the needs of the kinds of things that dog us as we try to pursue and advance our education. Our barriers to success rarely actually lie in the classroom. It can often be outside of the classroom, and we need to manage those aspects of our lives.
Sometimes it can be a small thing. I just need a subway card. I need access to a clothing closet. I need food, just a food pantry access. I need eyeglasses or steel toed boots or culinary knives.
But then other times, those support services can be much more substantial. They can be around mental health services, financial services, access to food stamps, transportation, child care. These kinds of wraparound support services are critical, and it also includes that human touch that is aligned with those career support services. If I want to move forward, I need accountability coaching. I need someone who is on me asking me if I'm moving forward in my educational journey.
The third piece of this is targeted education, and this is just the idea of the most direct route to a destination. Right? We want the right skills, the right path at the right time. It's precision learning, which is inordinately difficult to do these days because we face over one million different kinds of educational and labor market credentials out there flooding our markets. So which one am I supposed to choose? How am I gonna know that this one learning experience that I select will have the signal power I need to share to an employer that I can do the work ahead? Right? How do we know which one to pick, especially when we have all these various narratives around, well, you need to get some STEM skills. You need AI and tech literacy.
Nope. You need human skills. Which one is it? So through research, we've actually looked at some of the data out there and the millions of credentials and the pathways and resumes that are moving through our entire labor market. And what's fascinating to see is that from a demand perspective, we can begin to actually design better the kinds of specific credentialed pathways that will help people move in that more precise and targeted way. So one quick example of this that I love to share is just take the idea of going into journalism.
We would assume that in order to get a good job in journalism, we need to be able to communicate really well and have that verbal and oral communication down. But when you actually take a look at journalism fields today, the field of journalism today, it looks a whole lot more like an information technology market. You have to be able to display these kinds of hybrid skills, both human and technical skills. You have to be able to engage in data visualization. So it's this perfect idea of a T shaped individual, right, where you're moving across these human and broad based conceptual skills plus some vertical technical expertise.
It's the instantiation of what author David Epstein calls range. It's this ability to dance across disciplines and connect ideas from various domains while knowing when to intervene at the right time. It's this kind of intellectual dexterity alongside that technical vertical expertise. Right? So imagine it as being emotional intelligence, artificial intelligence, ethics and logic, communication and programming. Right? It's the simple approach to both and.
Problem, though, generative AI is complicating our approach to thinking about human plus technical skills. Today, we are seeing results today that are showing that generative AI can pass our US medical licensing exam no problem. The bar exam. It can perform better than ninety eight percent of the population on general IQ examinations. It can solve thirty five puzzles in twenty five minutes, things that we would find quite difficult to accomplish.
And so then we keep hearing these narratives, okay, well, let's focus on our human skills, our uniquely human skills. That's what's gonna keep us future proofed as we face all of this uncertainty. Another problem. These generative models are actually mimicking empathy. They're mimicking our social and emotional reasoning skills to the point where we've had now this is from McKinsey data showing that experts used to imagine that maybe AI can take over some of these skills, these human skills, in the twenty seventies or twenty eighties.
But because of how rapidly evolving these technologies are, they are now pulling back and ratcheting back on their expectations thinking, oh, this might happen in the early twenty thirties. Right? So then what are we supposed to do? This is where all of us get to do the fun work of beginning to name the work of the future. It's really important that we begin to do this. Because just remember, in two thousand fourteen, those top five jobs that existed on LinkedIn had never existed five years prior. We have to begin to imagine some of those words.
And I just put in some crazy ideas in here. Right? What would it look like to have a mindfulness technologist or an empathy engineer? The reason why this is important is it begins to help us understand, are we building the right skills for the future? And what do we need to begin to help our learners understand that is core as they think about those human and technical skills that will make them robot proof or future proofed. The other piece of this is as we think about that t shaped learner, that simple idea is no longer going to be sufficient. We are going to have to think about it more in this jagged model where over time we're gonna not only have to broaden our human skills and deepen and get more sophisticated in our human skills, we're gonna also have to figure out when we need to go deep into that vertical tech technical expertise. Cybersecurity, you can't just have a shallow understanding of.
Cloud computing, I could maybe just have enough to get my foot in the door for an for an interview. Over time, we're gonna need to think about this model as that learn, earn, repeat model for the future. But it's not just linear. It's three d, three-dimensional. So imagine it as a spiral staircase instead, where just because we achieve a specific competency doesn't mean that we actually are one and done, we nailed it, we don't ever have to come back to it.
Instead, especially as we think about these human skills, we're going to have to get better and better at them over time because we all know what it looks like and feels like to work with folks who lack those human skills. Right? Finally, sorry, second to last, we have this idea of integrated earning and learning. We are all going to have to figure out how we learn in the flow of work, and the onus is gonna be on our employers because they have seriously disinvested from this space. Over the last four decades, we have seen two and a half weeks worth of training a new employee go down to less than eleven hours per year. Today, forty four percent of our employers offer zero upskilling opportunities for our future, right, to build us toward those emerging jobs of the future.
We have to figure out how we get in the flow of work and carve out time in the workday to begin to build those skills for the future. And finally, transparent hiring practices. We're gonna have to figure out how we make our hiring processes less opaque and give people an opportunity to prove that they can do the work ahead. This is kind of a big, part of the book, but I'm gonna sort of spend a little bit less time on here because I think what the main goal here is just to say that in order to begin to move towards those seamless on and off ramps in and out of the future, in and out of work, we have to actually think about how we knit together these five principles. I think all of us can think of a or a couple of solutions that do one or a few of these things.
But if we were to ask any of our friends or a person off the side of the street and say, how are you gonna navigate your next job change? They would all have different answers because we have not figured out how to make this system more easily navigable. So together, a system built out of these five principles will help us figure out how we begin to harness the power of education over and over again throughout that longer work life. Because it's important for us to remember this. When we think about the future workers, it's not someone else, it's about us. We are all going to have to contend with those ten, twenty, or thirty jobs to come.
And if we are thinking about, oh, I have a really great job today. I have a degree. I have an advanced degree. I have multiple degrees. I love my job.
I don't need to think about this yet. We are all, no matter how fulfilling we find our lives today, we will find ourselves having to flex and move as these working learners. We're gonna have to juggle both working and learning at the same time. And when we return to learning, we are going to have to face these same constraints on our current learning ecosystem, these same limited options, the same confusion as the people who are struggling the most today. And knowing the future that we don't want, we can articulate what we do want and begin to build toward that.
So the question becomes, when we are seventy or eighty and looking at another twenty or thirty years in our work lives, what do we want our learning ecosystem to look like? It's time to build. Thank you. Thank you so much. Thank you.