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Good afternoon and welcome to the City Club of Cleveland, where we are devoted to conversations of consequence that help democracy thrive.

It's Friday, October 24th, and I'm Andrea Lyons, board chair of the Deaconess Foundation.

I'm pleased to introduce today's forum, which is in partnership with the Foundation and Honor of the fifth Annual Deborah FEC Systems Change Champion Award.

Deborah v c was the longtime president and CEO of the Foundation.

She retired in 2020, and in honor of her leadership, this award is given annually to a highly impactful systems change or innovation initiative and workforce.

Earlier today, Deaconess announced this year's award winner Towards Employment.

Please join me in congratulating them.

There's no doubt that all workforce systems and frankly, all workers will be impacted by one of the innovations which is on top of everyone's mind these days.

Artificial intelligence.

But where is it going and how do we prepare?

It's with this in mind that we welcome Tiffany Hsieh.

Tiffany is the director of Innovation programs in the Innovation.

Practice of Jfr labs.

Tiffany is responsible for strategy and operations for the center for Artificial Intelligence and the Future of Work.

In this role, she works with innovative organizations to test and scale new models for learning and workforce development.

Prior to joining Jfr, Tiffany worked with corporations as both a strategy and a benefit, and upskilling consultant.

Tiffany graduated from UC Berkeley and has an MBA from the Fuqua School of Business at Duke University.

Today's conversation will be moderated by Janine Kaiser of Compass Policy.

Janine consults for workforce practitioners, funders and policymakers across the country, but it's located right here in Cleveland.

If you have a question for our speaker, you can text it to (330)541-5794, and the City club staff will try to work it into the Q&A portion of the program.

Members and friends of the City Club of Cleveland, please join me in welcoming Tiffany Hsieh and Janine Kaiser.

Good afternoon and thanks so much to the City Club and Deaconess Foundation for having us here today and raising such an important topic for this community.

And I want to thank so many friends and colleagues, familiar faces and friends that we don't know yet for joining today's conversation.

I think Michelle's comment about the energy in the room is dead on.

I'm feeling tremendous excitement, trepidation and probably also from many here, in part because this topic is so transformational at this time.

I think as someone that spends time working with different communities, talking with education providers, workforce development leaders, economic development leaders across the country, I know that we are constantly thinking through and studying both what the opportunity is relative to AI, but also what the risks are, who will win and who will lose, and what can we do to get ahead of, of and mitigate any risks that emerge?

Today, I'm hopeful that our conversation can help bring some insights, not necessarily to the answers to those questions.

For Cleveland, where this is very real fast paced change that's underway, but also to help us think more about how to think about this.

How can we think together about solutions that will help our economy thrive during this transformation?

How are businesses and organizations on the education workforce development spectrum thrive and help our workers at the ultimate at the end of the day?

All right, so I'll just say I am I have the best job in this room because I get to ask my do my most favorite thing, which is ask be able to ask smart people good questions.

So I get to I get to start us off.

And then as we move into Q&A today, I imagine I'm going to hear some really great questions coming up from the room today as well.

And I'll say, in preparation for this, I have, been so impressed by Tiffany's both knowledge in this space, but also her groundedness.

And so as someone again, that works really closely on the ground with partners, I think this is such she's such a great resource.

And I'm very grateful that you're here with us to be able to speak a little bit more about this, help us think better about this transition.

So I'm going to start us off with the first question just to get us rolling and get oriented.

So my first question for you is, tell us more about jobs.

Jobs for the futures.

The center for Artificial Intelligence in the Future of Work.

Yeah.

So hello, everybody.

I am so honored to be here.

So I work at jobs for the future, which is a national nonprofit.

We work at the intersection of education and workforce development and really to drive economic mobility and quality jobs for all people.

And so, you know, two and a half years ago, when, actually almost three years now, when ChatGPT came on the scene, we sort of recognized the major impact that this was going to have on our work and for the people that we really serve and care about.

And so we started the center for AI and the Future of Work to understand what is the impact that AI is going to have on jobs and skills.

What are the use cases for AI that can both drive efficiency gains, but also transformation and economic empowerment?

And then what are the implications for our ecosystem to be able to respond.

Right.

And so that's what we're really focused on getting a little bit deeper into that.

And I can share some insights from that today.

So I want to start us off with a really basic question.

And I've heard people complain about the fact that AI's used so widely that it doesn't mean anything anymore.

So tell us what it means.

And why is this been such a transfer?

Or why is it taking the world by storm in recent months or years?

It's a it's a fair complaint because artificial intelligence is really can mean different things at different people depending on what context you're talking about.

People refer to AI as in many different technologies, but generally what we mean by AI is that it's technology that emulates human intelligence in some way.

Right?

In the past, it is referred to things like machine learning, where you are taking large data sets, creating algorithms and to be able to make predictions off of that data.

So your Netflix algorithm, for example, is based off of machine learning.

And then, of course, you know, three years ago we heard about generative AI.

And that's really what has brought AI more into the discourse of the public narrative, because it is allowing the more people to be able to use it more easily.

And what I mean by that is you can use natural language just the way that you speak and talk, to be able to ask questions of AI to get the answers that you want.

And it generates a new, novel content or, you know, images or video or whatever you choose to use it for.

And so because of that, it just made it more accessible to more people.

And then also opened the aperture of the use cases that it has on a variety of industries, occupations.

And that is obviously then having, transformative effects on our economy.

Goodness.

Yes.

So many, so many ways.

I'm curious.

So what do we need to know about how businesses are using this now relative to AI and how the technology is advancing?

Yes.

So the technology is advancing very quickly.

Is, if you've tried at all to keep up with all of the advancements around AI, I myself am overwhelmed on a day to day basis of all the information in the news coming out about, you know, new models, new tools, and so I think, you know, every six months we probably get some sort of leap at this point in AI advancements.

What I think is important to know about where the technology is going is two things.

So one, the technology companies are working towards something called artificial general intelligence, which is essentially the point at which AI is equal to or surpasses humans on any task.

Right?

So we're sort of there in some cases.

But I also still struggles to count the number of hours in strawberry.

Right.

So we're sort of at this kind of like Jagged frontier, if you will, of that technology.

But knowing the end goal is important for us, because that kind of puts into context where the technology is going, because right now you are actually inputting a prompt into the system and getting an answer back.

But we're moving towards technology that is becoming more autonomous.

So a genetic AI is sort of this next frontier where the systems will be operating more autonomously to complete, you know, full workflows, for example.

And so knowing where the technology is going can help us predict and, prepare for the impacts that AI is going to have on occupations and understand how we train people now in that new context and where jobs are going.

And so I mentioned we're in this sort of jagged frontier, period.

And so businesses are also still figuring out exactly how to use AI.

There was a recent study, out of MIT that said 95% of AI pilots have not reached ROI or gotten kind of clear impacts on their bottom line.

And that's because, again, we're still we're still testing.

We're still figuring it out.

So that means that businesses are still figuring it out.

Therefore, education and training and decisions are still figuring out how to respond and what skills are needed.

But I can tell you that, what we're sort of seeing right now in terms of what businesses are using AI for, we're seeing a lot of work done in kind of marketing and sales.

So, you know, I is very good at content generation.

You can imagine like being able to produce marketing material, social media marketing, much more easily, much more quickly.

You're also seeing it in software development, obviously, because these big tech companies really know that space well.

So there are a lot of use cases for generating code and making that process a lot easier.

And then we see a lot in customer service too.

So being able to, you know, answer questions from customers using the body of knowledge at a company has more easily.

So you mentioned a lot of different kinds of ways that our use cases for companies.

I think one of the things many of us are grappling with is, what does this mean for jobs?

Are we seeing, modest shifts?

I know there's discussion of augmenting roles.

What do you perceive and what are you observing so far about how those roles are shifting?

Yeah.

So that is, million dollar question of the day.

And I'm here to tell you that I don't know and nobody knows.

And I'm going to ask you all to sit in that uncertainty and that discomfort, but also that opportunity for where we want, you know, an AI powered world to go.

But what we do know now, in terms of at least the data that we have on some of the signals in the labor market in terms of AI impact is two things.

So one is that we're seeing across like as a total economy, we're not seeing huge shifts yet that's attributable to AI.

Right.

And you know it's hard to parse out is this a yeah.

Is this the economic environment or is it another factor.

But I think we've had some good research come out recently.

In the past couple of months.

There's one out of the Yale budget lab, one out of Stanford called Canaries in the Coal Mine, if you're interested.

I would definitely read those.

But, you know, I think overall, on a macro level, we're seeing various minimal impacts so far.

But there is one segment of the labor market that we are seeing impact, and that is with entry level workers who are in roles that are AI exposed and, really lend themselves to automation.

And so those are things like software development, the customer service roles, marketing and sales that I just mentioned.

So you kind of see that correlation between where companies are using AI and then where we're seeing those impacts.

And I know many of us here today, work closely either in workforce development or in education.

And, one of the things folks are trying to think through is specifically which kinds of roles, which kinds of careers are most at risk.

So we wanted to go through just sort of a couple of rapid fire, examples or just have you share a lot quickly.

And I want to ask about sort of three categories.

The first one and again, just quickly kind of right off the cuff, what comes to mind, careers where there's going to be a lot of disruption, known disruption.

What are they and what is the disruption that you're seeing?

So that's the first one.

Yeah.

So, you know, entry level, what I just mentioned.

Right.

We are seeing diminished hiring in the data.

And I think we'll continue to see that.

And that kind of makes sense right.

You have entry level workers who don't know as much or don't have as much expertise.

And so right now, you know, companies are making the choice to stay with their more senior, more experienced roles.

The other thing to think about in terms of occupations is occupations that really lend themselves to the strengths of generative AI.

So those are roles like clerks, administrative assistants, and have those roles that have a lot of routine, repetitive tasks where those do lend themselves to automation.

But then I think we're seeing some, you know, I, resilience in roles that are kind of more require that more in person element, require more expertise.

So health care, skilled trades, etc.. Great.

I'm going to ask one more.

So you kind of blended we had a question a little bit about, folks that are safer or sort of roles that are experiencing less this disruption, but because you sort of alluded to some of those, I'll give you I'll give you a pass on that one.

But how about for folks that don't have a lot of digital skills today?

I know digital divide is a big challenge that Cleveland has been grappling with for a long time.

Say more about how, folks that are have lower digital skills now may be impacted.

Yeah.

So I mean, lower digital skills, obviously we do need to close the digital divide.

And there is a risk that we are exacerbating or even creating a second digital divide.

Right?

So you know, these AI tools are actually widely available.

And they all basically have free versions for folks to use.

So that's actually really positive.

That expands the potential for access to these tools.

But the more advanced and more capable models are hidden behind payables and you have to pay for them.

And that then creates a, you know, that second digital divide.

So I think there's a combination of both providing access from a monetary standpoint to more advanced capabilities, so that those folks are not being left behind just because they can't pay.

Right.

But then, we also need to just think about broadly the AI literacy that is necessary to be able to use these tools in the first place.

Right?

And that's not just how do I use ChatGPT, how do I use Claude?

But is how do I think about, evaluating the outputs?

How do I understand kind of underneath the hood, how this technology works, so that when all of these developments and changes happen, I'm prepared to be able to adopt and use it in my own contexts.

Maybe not great, but yes, I hear both the opportunity and, challenge in that space.

So I have one more question that's about sort of what's what's happening.

And then we're going to start shifting gears, get your thoughts on what should be happening.

What's next?

What what should we do?

What what is preparation look like?

What does planning look like in this space.

But my last question is thinking a little bit more about skills for success in this moment.

We've we've heard a lot about durable skills, about sort of problem solving skills.

I'm curious for you what's jumping out for your in your work and in your research for, skills that do help workers succeed in this moment, be they they be those the entry level folks that are getting a toehold in the labor market and beyond.

Yeah.

So I think at this point, we know I think there are three big areas.

So one is AI literacy.

What I just talked about second is durable skills.

What you were just referring to.

And the third is domain expertise.

Right.

So on the AI literacy front we do need to understand these technologies, understand how to use them and understand how to use them in context, respond swiftly and safely.

Secondly, those durable skills are really going to help to be able to break down problems but also retain what the human element of humans are being able to collaborate, communicate.

And then lastly, we are really seeing domain expertise kind of rise to the forefront to what I mentioned in terms of that difference between entry level workers and more experienced workers being able to have that deep domain expertise in that experience, building to be able to respond to different situations that are edge cases, that is really going to make a difference for, for people.

And we're really seeing that reflected too, in some of the labor market data.

So like, Cass had a great report recently, that showed that there were, you know, wage gains associated with AI literacy skills as well as just the proliferation of durable skills being put into job postings.

So, we are seeing validation of some of those hypotheses around what's important in the future.

Just for anybody that is not used to that language.

Can you say just a quick note on what durable skills are?

Oh, sure.

Durable skills are, skills like communicate and problem solving, critical thinking.

You know, those some people have called them soft skills in the past, but we're calling them durable skills because they, you know, stand the test of time and actually are going to really come to the forefront in an AI powered world.

Right.

Thank you.

All right.

So we're going to start shifting gears.

And we want to we want to think a little bit about who's in the room here who might be listening and sort of take ask for your insights on what different actors can do to make a difference in the space, both for their own businesses, for their own entities, and also for the community.

Our first question is about, education workforce providers, which are I know many of us are here today, too.

So considering skills changes, role changes and the new threshold for entry level, what can education institutions, training providers and beyond do to be helpful or for it to support success for workers?

Yeah, absolutely.

So we did a survey last year at JF of workers and learners on their perceptions and experiences with AI.

And we found that, about I think 77% of respondents said that they believed I was going to have an impact on their careers in the next 3 to 5 years.

We also asked how many of you, actually are making changes to your careers in response to that?

And that number was more like 19%.

And so I think there's a little bit of a disconnect and maybe even just a navigation problem to really support people in this time of transition.

They may not know kind of where to go, what they actually need to be pivoting to, or what kind of skills they should be getting.

And so I think that is kind of the biggest call to action for me, for education and training providers of you have a great role and opportunity to play here, to be able to really support workers and learners in this time.

And so I would I would encourage you all one to I think we heard from was at El Barrio earlier.

You can upskill your own staff.

Right.

Start there.

That's step zero.

We make sure that you understand AI how it works.

Start using and leveraging these tools in your own practice, so that you can better serve the workers and learners and clients that you all support.

Secondly, think about those skills that we just discussed, right?

How can you integrate AI literacy, durable skills, building and domain expertise into the programing that you have for, you know, the individuals that you serve so that they have, are set up for success to be able to have those skills as things continue to change around AI.

And then what that can maybe look like too, is how are you bringing kind of contextual, work based learning, partnering with employers to know what that looks like?

Back to the way that you're preparing workers and learners, so that they have that experience building, especially for those early career workers, to have that body of knowledge and expertise to build upon.

As you know, expertise becomes more and more important.

I think, there's so much change happening on that front and so much more to go.

And I it feels like businesses have such a big role to play as well.

So I'm curious in your work and what you're hearing, nationally, what should and could businesses do to make a difference?

Again, to support success of workers to support their own success, their growth in this moment?

Absolutely.

Businesses have a really great role to play here as well.

In that same study that I referenced, we we heard from workers that only 30% of them felt like they were prepared to use AI in their jobs.

So to me, that says businesses also need to lean into AI training for their workers to make sure that they, you know, take foundational AI literacy and then apply it in context and have the tools to be able to do that.

I think we also found that only 16% of workers had access to paid AI tools at their workplaces.

So, you know, we know that businesses are still figuring this out, and that's totally fine.

But, you know, combining those two, making sure that you're investing in tools and and training for your employees is going to be really critical for both their success as well as your success.

I think the other thing is to consider what are worker centric ways that you can bring workers to the table as you roll out AI adoption and implementation.

Right.

What are ways that you can bring worker voice to the table, to even democratize how you identify what are the right use cases?

Right.

I think, we see, you know, two kind of path in terms of AI adoption.

You can go kind of the top down route.

You can say that, you know, these are the tools you use.

This is exactly how we want you to use them.

Or you can just provide the tools to folks to try to start figuring it out.

Where does it fit in your workflow?

Where are you finding value?

Where are you finding efficiency?

And then those can then ladder up into the broader strategy.

And a lot of companies have found that really valuable because that is much more, you know, contextual lies.

And and you can run more experimentation, if you will.

So those would be the two that I would recommend.

So our last sort of what we should do question has to do with, civic actors sort of at the, at the system level, at the community level.

And we're wondering especially, given the knowledge and information infrastructure needed for AI, and the state of our digital divide, we're especially thinking about here.

But you could like sort of contextualize everywhere.

I think many places have the same challenge.

So how do we make sure that AI doesn't make inequality worse?

Yes, that that is one thing that keeps me up at night.

Right.

And I think, you know, going back to we were talking about the digital divide earlier.

Right?

So thinking about how are we making sure that we are, you know, providing access to these tools, providing access to the skills and the knowledge to be able to use these tools to everybody?

That's going to be really, really key.

Secondly, I think that we need to listen to workers and learners to on their perceptions and experiences and build our systems around that, instead of, you know, not a problem in search of a solution, or a solution in search of a problem, but a problem in search of a solution.

Right.

So, for example, you know, we I think a lot of the narrative right now is around, oh, you know, this group might be being left behind in this AI race in the digital divide.

But actually, when we looked at the data, people of color in particular were using AI at much higher rates than other demographic groups.

And so that forces us to kind of interrogate our own assumptions maybe then at a systems level, what that looks like is leveraging their expertise.

Right?

Do you want to, you know, bring young people into the forefront to then teach older folks to be able to use AI?

Are you also thinking about, hey, if we have a lot of folks who are using AI for entrepreneurship to start their own businesses, right.

We hear that a lot about a potential for this technology.

But are the systems in place to help them succeed?

Are we making sure that we're investing in a wider variety of businesses?

Do we have those mechanisms and supports in place?

Those are ways that we can start to to tackle the challenges that we know are coming out of AI, and that's going to take collaboration and across the system.

Right.

We need that can't be done in a silo.

We need education institutions.

We need employers.

We need training institutions all at the table to be able to work together at a systems level.

So okay, I still do have Mike.

Okay.

So I have one more question or two more questions for you.

And I know we will be shortly turning to Q&A, but, I'm curious if there are any organizations or places that are inspiring you about how they are approaching and integrating AI or thoughtfully planning for AI?

Sort of implementation in there at the community level or the organizational level that you would refer us to for future learning.

Yeah.

So, I mean, I think there are a lot of great organizations doing great work.

I will call out specifically an organization called the Skill Up Coalition.

So Skill Up is a nonprofit organization.

They work nationally in a number of different cities to provide a platform for job seekers, particularly those without college degrees, to be able to go and find opportunities, for living wage, high potential careers.

And so recently, they created, tool, an AI tool, basically a coach to provide personalized recommendations for folks using the data on their platform.

And what I really admire about their approach is one they took a partnership approach.

Right?

So they're not necessarily building this in-house.

They work with other partners to be able to work together.

So adapted bright high for some of their data side.

They worked with a technical partner, to really put this all together because we know, again, we can't all do this alone.

And then secondly, they're really taking an evidence based approach and looking at the data.

So they partnered with Medici to do an evaluation of the work that they were doing to see if they were getting the outcomes that they wanted to from this tool.

You know, really the ultimate goal is if you get more personalized recommendations, then hopefully then you get a better job.

Right?

And they were very transparent about their initial results from their pilot, which, you know, actually didn't meet their expectations.

And I actually, you know, to take the courage and vulnerability to admit that is really great.

And they were using feedback from their users to be able to inform how they might improve in the future.

Right.

Some of the things I found, I think were, you know, it it's kind of hard for people to give all of the information to kind of an anonymous, chat bot that will then actually inform really personalized recommendations.

Right?

So how can they think about think differently about the way that they can support getting that information and actually tailoring that, that recommendation.

Right.

So things like that.

But those are the types of ways that we want to be approaching AI, right?

We want to make sure that we're being strategic about the use cases that we're identifying, making sure that we're not just using AI for AI sake, but we're actually doing it in service of a goal and continuously iterating and making sure we're looking at the evidence and getting feedback from directly from, you know, the populations we serve also speaks to that example.

It seems to also speak to the importance of sort of the human piece that intersects with the technology.

So that's that's great.

So I know I'm going to get the hook in a second, and we're gonna have to go to a, we will go to, group questions.

But my last question for you is just a really open field one.

What advice do you have for Greater Cleveland as we grapple with this transition and transformation?

I mean, the fact that you are all here and engaged in this discussion is probably step one.

Right?

And I think I would just echo some of the themes that I've talked about.

In the last half hour, which is one, make sure that you're all collaborating.

Right.

We know that this is a team effort.

We know that we're living in a time of uncertainty.

We don't have perfect data, but we can work together to get the information we need.

Secondly, what are the no regrets moves we can make now, right?

I think that I, you know, is actually just exacerbating issues and foundations like cracks in the foundation that we had, for a really long time.

Right.

We know that our education systems have we need to change to revolutionize and provide more like practical experiences for our students.

We know that durable skill development is really important.

Right?

And so what are the things that we can do now in the absence of more information, to be able to set students, workers, learners up for success in the future?

So I would imagine integrating work based learning or project based learning experiences would be number one.

Also take a look at the systems and, you know, how are you actually supporting workers and learners in transition?

What can we what can you all do in Cleveland now to set yourselves up for success?

As we get more data and we get more information about who exactly is going to be impacted, I appreciate that so much.

And I know that I've learned a ton even in this conversation building on past ones.

So we are going to switch gears in just a second to, the sort of group questions.

And so I've been given a script that I will read at this time.

So we are about to begin the audience Q&A for those of you joining via our live stream and radio audience, I'm Janine Kaiser, I'm the principal of Campus Policy Strategies, and I'm a moderator for today's conversation.

We are joined here by Tiffany Hsieh, director at the center for Artificial Intelligence and Future of Work Jobs for the future.

We are discussing the evolution of AI and its use and workforce development.

We are welcoming questions from everyone city Club members, guests, students and those joining via our live stream at City club.org or live broadcast at 89.7 KSU Ideas Stream Public media.

If you'd like to text a question, please text it to (330)541-5794.

That's 33045415794, and City Club staff will try to work it into the program.

May we have the first question, please?

Hi.

My name is Deanna McIlrath.

I'm a senior at Georgia Early College.

And my question to you is, do you think as AI continues to grow as I know it is growing fast, do you think human nature will cease to exist?

I don't think that human nature will cease to exist, but I imagine deep buried in that question, kind of the implicit question here is, you know, what does that do to, what does using AI more frequently do to our human relationships connection, but also maybe our, our thinking and our, critical thinking?

I know there's a lot of discussion about how, you know, using AI more is going to make us dumber.

And, and how, you know, folks turning to say AI companions for a relationship or companionship is eroding.

You know, maybe the in-person nature of communication, collaboration and relationship building in person.

And so what I would say to that is that we can make choices now, right?

We're still early in this game.

And I think that we can find use cases for AI that actually bring us together rather than pull us apart.

And that's an optimistic take, I recognize.

But, we if we know that there is a world that we don't want to go down, then why don't we take the steps now to be able to prevent that?

You tax question.

Okay.

There are currently hundreds of billions of dollars invested in AI companies.

Those companies are producing only 5 to 10 billion in revenue.

Is this a bubble?

And what opportunity costs as this over investment in AI bring?

In other words, what meaningful initiatives are struggling to find investment while everyone pause and say AI companies?

Great question.

Is it a bubble?

I'm not an economist.

I think there are some worrisome signs about the circular nature of some of the investments that we're seeing right now.

But I do think that to what I was saying earlier, we're still in this kind of messy middle period where we haven't exactly figured out how to use this technology.

Right?

There are some use cases, for example, in like drug discovery and in, in medicine that that have a little bit more traction.

But on the whole, I think the economy and companies and people are still figuring it out.

So I think that the value is is going to come.

It also, I think the costs of all of this technology of compute, of training is going to come down because the technology companies are incented to bring that down so that they improve their own bottom line.

So I do think that is likely to happen.

Now, on the question of what is the, kind of trade off between investments in AI and investments and other things?

I think that that is a really fair question.

And I don't have a great answer, which is that there is a lot of emphasis on AI right now.

And I think what we as kind of in the education and workforce ecosystem can do is think about what are our strategic ways to actually leverage this technology, how does that work with funding for the funders and philanthropists in the room?

To be able to actually, you know, put money into where, the challenges actually are.

So, again, kind of listening to workers, listening to learners, making sure that we really, truly understand the problem.

And elevate those.

I think that's what we can do now.

Hi, my name is Linda from GI Early College.

I'm a senior, so a lot of people in here to my question is, what if people who have these jobs that have been working in these fields for a long time can't coexist or not willing to, work with these advancements dealing with AI?

Can you repeat that last part?

Okay.

But, okay.

I say, Tom, just repeat the question.

Sure.

Okay.

Okay, okay.

What if people who have these jobs have been working in these fields for a long time can't coexist?

We're not willing to work with these advancements of AI.

I think that there are jobs like we were just discussing, that are a bit more AI resilient, and that will have less impact from AI.

Right?

So I think, you know, one, we need systems in place talking about systems and what the the community can do to be able to help folks navigate.

What are the queries that are match their interests, their skill sets, and how do they actually get the training and upskilling and reskilling to be able to get into the roles that they want?

And how do they navigate what roles are existing now?

What roles will exist in the future?

What's going to happen to their current roles?

So if they are in an AI impacted role and they don't want to work with AI systems, then would they consider, you know, moving into a skilled trades, to be able to, you know, work more with your hands?

If that is, you know, your choice and your preference.

Fascinating.

You said that because a CEO of Nvidia said that the next generation of millionaires are going to be people working in the trades.

So it's interesting you just said that my question is about that.

The algorithms are coming after me.

They're telling me to learn about AI.

There's a system called cursive and it has like, has like 30 different icons.

And, you know, it's a 30 day program.

What do you think?

Should I do it?

I assume you're not asking about this course specifically, but just generally the barrage of information you're getting about learning about AI.

So on the.

Should you learn about AI?

Yes, I think you should.

I think someone said it earlier that AI is here to stay.

Even if we're afraid of it, that doesn't mean it's going to go away.

So better understanding the technology is and how to use it is going to, you know, set you up for success.

I also think, in my personal opinion, using these tools, you will be able to see what the tools are good at and what they're not good at.

Right?

I think there are things where I, you know, look at an AI output and I say, it's not that good.

I can do better.

Right.

And so you'll start to learn those and then you can be able to use them more strategically.

In terms of maybe the, the other like kind of implicit, question in your original question is what do we do?

How do we sift through the noise right around everything that's coming at us around AI and like where I should go for upskilling?

I think that's a problem.

Another community problem that can be solved, right?

AI that you know how do we vet, whether it's tools, whether it's training of what is quality, what has value in the market.

So all of the large tech companies, for example, have their own training materials like those likely have more market value.

If you have a credential from like a Google AI certificate, then, you know, a smaller or lesser known, credential.

And that's just, you know, acros I'm Jonesborough, I'm director of Dolly Parton's Imagination Library at the Literacy Cooperative.

And my question is, given the recent reports of a 40% decline in the number of Americans reading for pleasure in the last 20 years, and the lowest reading scores of high school seniors in the history of the National Assessment of Educational Progress.

What are the possible implications of possible overreliance on AI for reading proficiency in literacy and what are safeguards we can put in place as a community and society?

Thank you.

First of all, thank you for sending my daughter books.

Every week for every month I. So I think that, you know, to my earlier point about the importance of domain knowledge and expertise, the foundations still matter.

And I think we we all need to, maybe as an ecosystem, make sure that that narrative is not being lost in, you know, how we prepare workers and learners and students.

So, you know, people liken this AI a lot to, oh, you know, we had calculators, or we didn't have calculators before.

Once we had calculators.

Now, like, we're really bad at math.

But we still learn math in school, right?

We still learn the fundamentals of, of, general math principles.

And so I think the same thing can be applied in a world of AI where you still do need the foundations, because how are you ever going to know if an AI output is good or correct?

If you don't have those foundations in place?

And so I think they're still really critical.

And I strongly believe we need to continue to invest, especially given, you know, the data that you're saying.

Good afternoon, Ronnie Cannon towards employment.

And it may be a segue to the previous question, but from a policy standpoint, is there any initiatives is taking place on in Washington, DC that kind of, prevents certain privacy?

How would you say, scuse me?

Pause for a second.

Basically, like if I'm Senator, from a standpoint of I want my privacy protected, is there any policy that is currently being negotiated or spoken about in Washington, DC that kind of excludes certain things from happening?

And I from personal standpoint, as a citizen of the United States.

Great question.

So not that I know of, I think the actions at the federal level so far have been executive actions and plans.

So there was an executive action on for K-12 education that really touted AI literacy as a really important investment.

There was an, an EO on, America's kind of like talent strategy as it relates to AI.

So it was talking about the need to invest in workforce programs.

But I think that right now they're leaving it up to the states to kind of experiment and put into place some policies, that, you know, and that's generally how a lot of our policies have worked in the past, too.

We see, for example, California just recently passed, bill that related to privacy and data around AI.

I'm not going to quote the specifics because I'm not a policy expert, but I think states who are kind of leading the way on that will likely set some of the standards that we'll have kind of across different states.

The National I, a Xavion Walker John area call it my question was what is your take on like the I would say like battle is going on between like, generative AI and I would say it's kind of hard to explain, like create like creative people who have, like, creative jobs, like, say, artist animators, illustrators even put, like, sets in their musicians, like people like that.

What would you what would be your take on like, like AI and do you think that there's like a reason to worry about people who have creative jobs like that, getting their jobs taken from AI?

Great question.

I'm getting some really tough questions today.

Especially from young folks here.

Yeah, yeah, the young people, so on the creative side, yes, I think that, you know, there is definitely a risk.

There are, you know, there was just, big update to OpenAI saw model, which is their video generation model that is, you know, highly advanced.

But I think I see hope in a couple of different places.

So one is, power of, you know, collective action and bargaining.

Right.

We saw the Actors Guild, you know, really band together to really put down their own guardrails around AI.

And I think we can really see that from from creatives overall.

And secondly, I do think that there are still people who are actually turning more towards kind of like live music or live entertainment and creative because they want to be able to see kind of who is creating the art that they're they're experiencing and really want to have a depth of, you know, experiences that are actually informing the art.

And that is important.

And to me, that, that that would be important for me.

And there's a subsection of folks who will accept kind of AI music, hey, it's it's a bop.

It's that's probably not the right term from a young person perspective, but, you know, like some people who like, well, just listen to music for music sake.

And then there are others who actually want the deeper like understanding and like history of like, why something was created.

And I think we're actually seeing trends of people kind of turning towards really knowing the people who are creating now.

And so I see hope there.

Hi, my name is Alan Caskey.

I work in higher ed.

I think most organizations would say that their most valuable thing or the people that work for them.

But related to that, one of the most valuable things is the data that they have and the data being the practices, the information and whatnot.

I noticed that there's kind of a myth of inevitability, that all these companies like, whether anthropic or, OpenAI, are entitled to everybody's data that's available.

I'm curious, what do you see in terms of companies walling off their data?

And also like creating like rag models and things like that, where they create AI for their own departments, for their own staff, and to be able to work from their.

Yeah, I think that that's, a big choice for organizations to make.

Right.

So one on the kind of creating your own tools and creating your own models, that is kind of the safest way to protect data in your organization or in your company, but those that also comes with very large investments.

Right?

And so sometimes in resource constrained environments, people will turn to existing tools to be able to use, and therefore then their data is kind of at risk.

The other thing we're seeing too is, I don't know if you all saw OpenAI released, a, AI powered browser.

And so that's another way to kind of capture more information about you and your habits.

And, and frankly, the tools do work better when they have more information about you.

So it's sort of a catch 22 in some ways.

But I think we can all, you know, protect our own data by making sure we're following strict data privacy guidelines, not putting PII or sensitive or confidential information into these tools, and still retaining the human element of making some of these decisions on our own.

But it is certainly a systems wide problem.

my question was, do you think that African-Americans should be concerned about AI growth within the community?

And that's something that they need to work on and something that they should take more serious?

So, should African-Americans be using AI more?

Is that concerned about concern that the trend of using AI.

Okay, sorry.

Sorry I think I got is a Janine.

Janine caught me up.

So should they be concerned about the trend of using AI more?

Yeah, right.

I think that if we learn about AI and we use it in responsible ways, we can mitigate and be aware of some of the biases and inaccuracies that come out of these systems.

Right.

So, you know, in the career navigation front, for example, if you are inputting into a system that you know, hey, I'm a career coach and I'm working with a, you know, 16 year old, African-American woman who is, you know, interested in these types of careers.

And they're recommending you know, jobs like, oh, do you want to be an administrative assistant or, you know, very like kind of like biased information.

That is one way to mitigate some of the risks that are coming out of these systems.

I think the the worry that people have around AI is completely fair.

And AI, there are limitations of the system.

And at the same time, I think that, you know, if we don't recognize and understand these systems, then we can't necessarily take action on the concerns that we see around it.

I, it really interesting discussion about inequality and the potential for AI to exacerbate existing gaps in society, based on the digital divide.

And I think it's interesting to think about when we have large scale disruption like this that creates winners and losers.

If we flip that on its head and think about, is this an opportunity then to try to address those equity gaps, through use of AI and technology and access?

And I'm curious if you have advice for, for us as a community on ways to kind of think about that, or if there are others across the country that are thinking about about, you know, this change period is an opportunity to really remedy and address, an income inequality.

Absolutely.

And I think that's really the the potential and opportunity in this moment.

Right.

If we can find those strategic uses of AI that are really addressing the challenges that we're seeing with the individuals that we serve, that is going to be the unlock, right?

So if you think about, you know, a lot of these AI career coach tools are coming onto the market.

They are filling a real need and a real gap, because not everybody had access to quality information about careers, programs that might lead them to those careers.

And democratizing access to that information is really, really valuable from an economic mobility or income inequality perspective.

Right.

The other thing that, that I think hasn't necessarily been addressed very much is the social capital gap.

Right?

So how can we make sure that I can help folks?

One, you know, practice how to how do you actually do outreach and do informational interviews to build relationships with people that you don't necessarily know, but maybe can be helpful to you in your career?

How do you practice some of those interviews?

There are there are some tools that are coming on the market that are trying to address that, but that's another place where we haven't seen a lot of progress or movement on a challenge that we know has been around for a long time, and there's a lot of potential for AI to be able to solve it.

My name is Jane.

I have a question.

When it comes to, like, prompt engineering, what kind of advice or tips do you have for us?

Who are using AI every day and making sure that we are leveraging it to its fullest capabilities and getting the best outputs out of our inputs?

Yeah, absolutely.

I don't know that we're turning this into a prompt engineering training session, but, there are lots of good to the other point about training.

Like, you know, you can there's lots of frameworks to think about prompting, but I think what's most important is to, break down what you're asking of an AI system into component parts, right?

So what is the task you're asking?

What is the context you can provide about, you know why you're asking this?

What are, you know, any reference documentation that you might want to provide in terms of like models for what you want or just more information?

So there are lots of different components and just making sure that you're including more of that.

But then the most important step is to evaluate and iterate upon whatever comes out of those systems.

Right.

To make sure that you're using your own expertise, your own context knowledge to be able to see, okay, this is what I'm getting.

It's not quite right.

This is how I want you to, you know, adapt and tweak and iterate upon the the ultimate outcome that I'm trying to reach.

how should we be thinking about how to discern what we can trust in an era where we can just use AI to do anything?

Yeah, that is one of the use cases that terrifies me.

But, I think again, I, I do think it goes back to really understanding the technology and what it can do, right?

And not just what it can do now, but continuing to stay updated on how it's progressing so that use cases like that.

Yeah.

So scammers for example, you know, emulating voices and you know saying I've kidnaped your daughter and you know, give me $100,000 is is happening and it's real.

And so I think to the point of digital literacy generally making sure that AI is incorporated into that, to be able to really interrogate what we see, it is, you know, I think some of the, the technological companies are thinking about, you know, what our watermarks or like other methods that we can kind of signal to folks when things are real or not.

But it is right now incumbent on us to be able to, to make that discernment.

Tiffany Hsieh and Janine Kaiser, thank you so much.

I'm Dan Moulthrop, chief executive here at the City Club, reminding you that forums like this one are made possible thanks to generous support from individuals like all of you.

You can learn more about becoming a Guardian a free speech at City club.org.

Our forum today is presented in partnership with the Deaconess Foundation as part of its fifth annual Deborah Veasey Systems Change Champion Award, which was earlier this morning presented to Towards Employment.

Yes, give it up for our friends at towards employment.

We would also like to welcome students joining us from John Hay Early College High School, who provided half of the Q&A today.

As well as Davis Aerospace and Maritime High School.

Additionally, we'd like to welcome guests.

The table is hosted by the center's El Barrio Workforce program, citizens Bank, Deaconess Foundation, Greater Cleveland Career Consortium, Greater Cleveland Works, Ohio, Guidestones, and once again towards employment.

Yeah!

Woo!

Next Friday, October 31st will host our 2025 annual meeting.

Thompson.

Hines.

Robin.

Mentors smyers from our board will lead a company.

With biographer David Greenberg about his recent biography of John Lewis.

The book is titled A Life and His.

And we'll be discussing how his story, John Lewis's story, may serve as a model of optimism and leadership for us today.

Also just announced this giving Tuesday, December 2nd.

We're partnering with Glen Mead to give back to you.

Don't miss our free breakfast forum on the state of charitable giving, and how we can all rediscover the joy of fundraising.

Meghan Bush grants in Within bloom.

Hey, it is, it is.

Believe me.

And if you'd like to learn more, come talk to me about it and bring your checkbook.

Meghan Bush grants in within bloom and Dan Mansour with good works will be, will be leading that session.

You can learn more about everything coming up at City club.org.

Thanks once again to Tiffany and Janine.

Thank you to all of you for joining us today.

I'm Dan Walter.

Have a great weekend.

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