Just Economy Conference – May 6, 2021
Are the alarms being sounded by community advocates about the profound impact of artificial intelligence (AI) on work, and is the potential for social unrest exaggerated or on point? Four million drivers may lose work when driverless vehicles are perfected and approved. Blockchain and AI innovations are threatening real estate and other industries with massive job loss potential. Yet, today, over seven million jobs remain unfilled because America’s workforce training systems fail to link interested workers with the appropriate training and education needed to fill those jobs. This workshop presents both a view of the anticipated job dislocation and promising answers to the technical challenge of linking workers to new skills in ways that meet industries’ real time needs. Hear how the Godzilla of “stackable credentials” can help defeat the Kong of job dislocation in a post-pandemic world.
Speakers:
- Frank Cicio, CEO, iQ4
- Ed Gorman, Chief, Community Development, NCRC
- Michael Martin, Director, Network Development, SignalFire
- Shelly Steward, Director, Future of Work Initiatives, Aspen Institute
Transcript
NCRC video transcripts are produced by a third-party transcription service and may contain errors. They are lightly edited for style and clarity.
Gorman 01:13
Afternoon everybody, welcome to the NCRC conference, I hope you’ve been able to enjoy earlier workshops in the great plenaries that we’ve had on days one and two, welcome to day three. If this is your first workshop, you can see some housekeeping issues in front of you please take a note of those. And speaking of housekeeping, I want to mention those of you who are interested in workforce issues, we should also give, at least you notice about the program that we’ll be having on Tuesday, May 11th, starting at 11:30. That’s entitled beyond failure, organizing grassroots job training programs that work for black and Latin x communities each year. So let me start out by saying, you know, this is a subject the subject of the future of work in AI that we wanted to do last year. And of course, unfortunately, our conference was canceled. So we’re happy that it’s that we’re able to talk about it this year. And it seems even more relevant to me at least. And I hope to you that the future of artificial intelligence and machine learning, may well be a job disrupter going forward. Although it’s pretty hard to understand that when you’re looking as we are now at 7 million jobs that are unfilled in part because we just don’t have people with the credentials to fill them. And so that’s the system, as it exists already needs a lot of work in order to take willing workers and get them the credentials, they need to be able to take on these positions. But we also kind of want to take a long view here and think about what is artificial intelligence mean to the future of work? So with us today are three really, really terrific panelists that we’re lucky to have. And I’ll do quick introductions, and I’ll ask Michael Martin, delete us off. So Michael, is a fellow that I met four years ago, at a wedding of all places, and we had the most fascinating nerdy conversation about the future work in AI and the ethics of whether AI, there’s Michael, the wet the ethics of, of what job disruption in the AI industry means Michael was running the XPrize. And I’ll let him talk about that. After Michael, we have Shelly Steward, who is a very accomplished economic sociologist, and applied scholar at the end is director of the Aspen Institute on the future of work initiative. And Shelly is I think, uniquely positioned to talk to us about sort of the state of play now and going forward on the future of work in AI. So welcome, Shelly. Glad to have you. And then finally, my friend, Frank Cicio, who runs a company called IQ for that, I think, is on the cutting edge of how workers will be prepared for the future using something called stackable credentialing. So, Frank, welcome, and thank you for joining us. Um, let me say, we our intention here is to keep this to about 35 to 40 minutes total. And then to open it up to questions. And so we’ll go back to the sort of Meet the Press format, once everybody’s had a chance to present. But in the meantime, Michael, if you would take it away, please talk to us about the state of play of AI, if you would.
04:50
Sure. Thanks, Ed. And thank you, everyone, for joining us today. You know, I’m gonna speak briefly I’ll give a quick introduction. I’ll speak briefly on you know, the technical side of it. I’m not a technical, another technical engineer, but I am a technologist. And then I can dive in a little bit to how I see that, you know, that state of play as a regard in regards to the technical side kind of leads into more of the economic or workforce development rather than like the economy at large. And then from there, I can kind of dive into where I see some potential for, for solutions and is more from like a political economy side. So my background, and perhaps why I hopefully I’m qualified to speak to those issues is I am a city planner by trade, the community organizer, worked on that in New Orleans post-Katrina, for about four years, then transitioned into more technologies, and the tech sector working at a digital strategy agency. And then as Ed mentioned, I also was the manager of the artificial intelligence XPrize. The XPrize is a foundation that’s focused on using incentivized prize competitions to, quote-unquote, solve global grand challenges. In this case, they had a prize that was focused on a $5 million prize for using AI to solve global grand challenges. So global grand challenges are, you know, a very broad topic. But they are, let’s say, roughly aligned to the Sustainable Development Goals, the UN Sustainable Development Goals. In time, we were able to actually partner with the United Nations, and we’d launched the first excuse me, the AI for social good conference, or AI for social good summit, it happens annually. Now, the first one, I believe, is in 2017. And the next one is upcoming. And you can just Google that, and you should be able to find actually a bunch of content as it relates to be broad subjects that we’ll be discussing today. After the X Prize, I went and did two things simultaneously. This was sort of in in reaction, if you will, to the 2016 presidential election, co-founded an organization called free machine, with some friends free machine is focused on using gamification, to add public participation to drive really like, you know, folks getting engaged in the development of tech policy. Part of you know, I think that tech policy is extremely important as we’re kind of discussing now, it’s has massive impacts, yet, it’s kind of often hidden behind sort of the opacity of whomever is the elected that is then appointing people to run these, you know, all s t, for instance, or let’s say, like the CTO of California, you know, our idea was to bring that stuff forward, specifically in regards to AI. Because these types of technologies are imprinting us on a very tangible basis, every day, everything from you know, people’s potentially having tested their jobs automated all the way to jobs being fully automated down to everything from facial recognition software being deployed, let’s say through like CCTV, if you are in Britain, nor even through police cameras here in the US. At the same time, I joined a venture capital firm called signal fire, we’re based in the Bay Area. And my role there is the director of network development. So I work with our external network of advisers to leverage their expertise and bring them, bring them into content, conduct sessions, and really be supportive for our portfolio companies. We have a number of portfolio companies that are focused on leveraging AI and machine learning. And, as a result, just kind of get to be the front row seat from that perspective, and you could have really interesting conversation with people who are extensively building the future. So this is all to say, what kind of want to share now a little bit where I see where we are in relation to an automation in the job market. I think it’s important to think of this all as a spectrum. I don’t think automation in and of itself is a single thing that’s going to kind of come like the meteorite and just destroy an entire sector at a given time. I think the spectrum ranges from task, augmentation all the way to full replacement. So for instance, you can imagine like one example that you may even use today is autocomplete in Gmail, where your sentence is finished based on using natural language processing from what you would previously read in emails. That’s augmentation, that’s not going to replace anyone that role. It will just help you individually do your job better. I’d say the next stage. And this is all still very considered narrow, artificial intelligence. There’s narrow and then there’s general so we are still very much in the narrow AI. And narrow AI in and of itself can be very disruptive, but this isn’t what we’re talking about. General AI is more of the You know, to us, it really, really is a tired example is more of the Terminator-style, if you will, or fully driving cars, I guess as a more mundane example. Anyways, this is all to say. So you have task augmentation, then you have task replacement. So services like Zapier, which is kind of you put together a formula. So when x, when x does, when you do X, Y happens, which then pushes to Z. This stuff is again, similar to it augments your job, it may replace portions of your job, but I don’t think it’s going to be massively replacing anyone’s role, then you start to get into things like natural language processing, and machine learning for the creation and editing of contracts, leases, etc. These are easily ingested data. It’s structured data. And this is the kind of thing that I wouldn’t say is easy to do from a machine learning and natural language processing perspective, but it is something that is currently being worked on. In fact, by a number of our portfolio companies are working on stuff that’s in this broad universe of working on those types of tasks. I see those things potentially replacing things like replacing role rules like paralegals, for instance. But again, this might be something that a paralegal then operates as opposed to, you know, maybe the the attorney who’s ever running the firm, maybe they just don’t want to do that anyway. So at the end of the day, I don’t see that as being that could be more disruptive, but I don’t think it’s full replacement, per se. Then you move into things, you were jumping a little bit moving into things like automated vehicles. I’m very bearish on these happening anytime soon. I know that there is, you know, a lot of talk about a V’s replacing the entire trucking industry. And for instance, you know, a V’s replacing, like, you know, Robo taxis. I think even if you look at the news in the past six months, you can see that that hasn’t been probably will not pan out anytime soon, Uber, sold their automated vehicle division at a loss of $40 million. And Lyft just dissolved theirs and sold their to Toyota two weeks ago. It’s just from the business perspective, it’s not making sense. And I can get to that in a moment. But then also from the technical perspective, you know, people that the technologists, the methods that they would that would be necessary to create fully automated vehicles are just simply not here yet. The most advanced method of artificial intelligence that we currently have under our control, if you will, is called Deep Learning. This was kind of developed by a man named Geoffrey Hinton, in 2012, or 2013. And right now, we’re just kind of moving the pieces on the chessboard with deep learning. We’re not creating new chessboard, we’re not creating new games yet. And we wouldn’t say we’ve plateaued in terms of technology. But in the next few years, based on the research that’s out there doesn’t seem that we’re going to, like, figure out what’s after deep learning. So one thing I do want to touch on, though, and this is kind of gets into, like the economy pieces, you know, for instance, Uber and Lyft. You one could argue perhaps, that the reason that they have sold their AV departments is not just because the technology is hard, but because it’s also for them now cheaper to continue to employ people in precarious situations. I mean, this is the you know, the precariat, if you will, is the the new proletariat, for instance. And, you know, with the passage of Proposition 22, in California, and the kind of codification of the gig worker as a 1099 employee, you it’s much cheaper for them to continue to effectively underpaid people than it is to have them be to continue to invest in AV, which they may still be 10 or 15 years out, I think you can see that across the economy at large. If you’re talking about manufacturing, it’s still, you know, it’s analogous to reshoring. The reason that reshoring happened wasn’t because, you know, America just got so good at quote, unquote, making things but rather that the Chinese labor markets got too expensive. So it was now cheaper to make things and employ non unionized labor in the south than it is to, you know, make things plus import them from China. And this is not happening fully at scale. Obviously, reshoring is something that is not taking over the entire manufacturing economy, but it’s something that’s happening. And you could say the same thing. Similarly to automation. The reason that you perhaps aren’t seeing automation at scale in the manufacturing sector is because it’s just not costly. Effective, this isn’t to say couldn’t become cost-effective in the future. But today, and I would argue in the next few years, combined with the lack of technical breakthrough, plus those costs, I don’t think you’re going to see massive, massive disruption that some folks might might otherwise see down the line. And I want to wrap up in just a couple of minutes. I think maybe Brenda could do this. ping me how much longer I have. But I will say, a couple of pieces think in terms of like, what are our long term solutions? You know, like I said, I think 10, let’s say 10 years, well, we need to plan for 10 years. We can’t just say, well, let’s wait until 2031. To figure this out. I’ve my personal perspective, and perhaps belies my role in the venture capital world is, I would think that one of the best ways to combat worker displacement is going to be figuring out ways for workers to be stakeholders and shareholders in the actual companies that are doing the automation, so how can the people who are employees actually own their labor in a way that own their labor and own the means of production in a way that allows them to not only, you know, leverage their voice for worker protections in the face of automation, but also really get the payout, as automation happens, and productivity increases, and their, you know, their amount of time spent laboring will inevitably decrease? So I think that this is a really important component of all of this. And I don’t think that we can stop the train, I think that the genies out of the bottle in terms of what we as a society consider growth and efficiency. So I think we really need to reconsider the governance and ownership of firms and figure out ways to not only engage workers who are being automated, but also engage the public in a meaningful way, so that they’re aware of the trade-offs and so that they are they and we are aware of how this technology will impact society and what you get out of it, you know, you may get cheaper goods, but that the cost of your neighbor’s job? And if we think that that’s inevitable, do we want to give NLRB more power? Do we want to give the DLL more power to really ease this transition in a way that is equitable? That’s inclusive, that’s, you know, if you will kind of adjust transition using the same language as the climate movement. So with that, I know that it’s kind of a quick run through all of this, I hope it was educational as regards to the technical limitations. And with that, I want to turn it over to Shelly from the Aspen Institute.
Steward 18:03
Thank you so much, Michael. And thank you to everyone who’s turned in tuned in to hear this conversation. I’ll provide, as Michael did some sort of overall remarks about my take on what’s happening with automation and artificial intelligence, and focus on you know, what are the real problems that we’re seeing, and I think in a lot of ways, my perspective is, is very aligned with my goals, but also some some some key differences and kind of how I see the problems shaping up and hopefully the solutions to come. So as Ed had mentioned, I’m a sociologist by training. And I think that comes through in a lot of how I see some of these questions. Now, we’re talking about economic trends, labor market trends, but we can’t think of these out of their social context. There are influences of sexism and racism and power dynamics that come through in how any automation plays out and how any economic process carries out. And that that sort of complicates the challenges that that we are facing. And I’m currently the Director of the Aspen Institute’s future of work initiative, which is a program of the larger economic opportunities program. And, you know, we start from a perspective that the future is not some point off in the distance that we need to predict. It’s happening, become especially apparent through the pandemic with remote work, and all of the various trends that you know, we see playing out in front of us. So so to get to the future of work, we want we really need to hone in on what are the issues going on today, both for workers and for employers, there’s real challenges for everyone involved, that we need to work on addressing and we tried to address them through state, local And federal policy, as well as through practice, equipping, advocates, equipping leaders, equipping business leaders to take a stance and start fixing things through their everyday practices. So with that as a kind of background as to where I’m coming from, as, as Michael sort of pointed to, automation is probably not leading to kind of mass unemployment, mass displacement on some huge scale. At any time in the near future in the next few decades. There will be jobs for people. The question is, if those are good jobs are those jobs that provide people the type of living that we want to have for everyone in our society. Technology has long been changing the labor market, you know, this is not a brand new set of trends coming from nowhere. What is new is the pace, technology is developing faster and faster. And the ways that technology then impacts the work is also happening faster and faster. And as we see this displacement is happening. It’s just not at the apocalyptic rate that some headlines seem to suggest. So the disruption that’s happening is largely by task and often within sectors or industries. So one of the more often quoted stats on this is from McKinsey that as of the post-pandemic analysis, up to one in every 16, workers may need to transition to some degree to new sectors to new occupations as a result of unfolding automation. So the risk is there, we do face these challenges. But we also see, you know, the US is actually behind Europe and parts of Asia in terms of how far automation has progressed. And those other places are not facing these, these mass employment crises. So that provides us a little bit of reassurance that this is not a robot apocalypse just around the corner. But as I said at the start, the biggest problem is not that automation is impacting the labor needs, it’s that the jobs that are then available, tend not to be good jobs. So the risk is millions of people trapped in low-paying on predictable jobs that lack career ladders. So if you look at the Bureau of Labor Statistics, occupational growth projections for the next decade, the top three growth occupations in terms of number of jobs available, all have incomes below $30,000 a year, you know, the highest growth occupations are concentrated in low wage care work, which is essential, we’re sort of in this moment where there’s increasing attention to just how essential care work is, but as a society, we do not treat it like it is it? You know, not that’s inseparable from the fact that it’s held disproportionately by women of color, and often immigrant women of color. This is work that has not been valued for a very long time that was explicitly carved out of a lot of the labor protections and labor laws that we do have. And, and remains pretty, pretty bad jobs for the vast majority of people who hold it. And that’s where the growth is, we see the decline in manufacturing jobs in some of these jobs that do experience higher rates of automation, and tasks that are at risk of automation. And then we have this growth of this work that we typically have not valued as a society. And that’s where kind of the uncertainty surrounding the future really sits. And and though it can feel pretty, pretty gloomy at the moment, I think there is a lot to do, there’s kind of several paths forward. One thing to do is to focus on those transitions, people will have to transition whether it’s learning new tasks, to perform the same job, whether it’s transitioning to a different job within their their industry, or transitioning to a different industry altogether. You know, we need a robust unemployment insurance system that’s connected to workforce development. And we need mean meaningful training and lifelong learning opportunities that are targeted towards actual growth occupations. coding is important for some jobs, but probably not a majority of the jobs that have openings in the next 10 to 20 years. Now we need to think about what are we training people to do and are those opportunities out there and are they good opportunities, and that’s where linking training and also credentialing to actual jobs. comes in, we can offer training, offer a set of skills and then expect that to just land people into a better a better place a better job, we need to ensure structurally that those jobs exist, and that we remove any additional barriers people face to getting those jobs. But in addition to the training, which itself is is absolutely necessary. And then the sort of last component of solutions that I’ll mention and i think in many ways, it’s the most important is addressing the question of, of job quality. And why do we have so many jobs that are not good jobs, a lot of it has to do with the power dynamics between workers and employers, we’ve been on a long road of declining worker power that needs to change to change this overall situation, you know, a college degree reduces the chances of low wage employment by 33%. For someone joining a union reduces the chances of low-wage employment by 39%. So as much attention as we have on building education systems and providing skills, that is important, we need to keep doing that we need to improve that system, we also need to think about organizing workers and empowering them to to be successful and to have have good jobs. And there’s a component of that as well. And Michael spoke to this also of involving workers and worker organizations, including unions in the introduction of some of these technologies, to into workplaces. So that can take place through worker ownership. As Michael mentioned, unions play a part in the training programs that happen with on-the-job and job adjacent training to ensure that people are learning the skills that they need in order to succeed on the job in a way that that is empowering. And that is retained by workers and their organizations. So there’s kind of a range of strategies to employ to address this. And we also need to think about the exclusions of existing worker organizations and labor law. And this goes back to, to the questions of race and gender and care work. When we think about how can we boost the quality of those jobs that are not at high risk of automation, that are incredibly dependent on people’s social skills, interpersonal skills, you know, some people refer to these as low skilled jobs that they are far from it, they are very high skill jobs, but they are undervalued jobs. And that’s where we see the growth and where we need to focus a lot of of our attention moving forward to ensure that that the economy is successful is prosperous, as automation doesn’t hold and as technology as it has for centuries continues to impact the market. So I think I’ll leave it there for some some of my introduction, but very much looking forward to our conversation and questions from from our viewers as well. And so I would turn it over to Frank, though.
Cicio 28:22
Thank you, Shelly. Thank you, Michael. Michael, that was a great context. And Shelly, love your perspective. On the state of affairs, I come from the technology perspective. So I am a tech entrepreneur and emerging technologies and been working on solving business problems for the last 40 years, believe it or not take the state my age, in technology, and how do we use it to automate for the better? So I’d like to share two key perspectives with you. One is where I believe any technology advancement has impacted jobs and career. And then the second is, what do I think we really should need to do to solve the problem. And again, you’re gonna obviously hear the word technology, at least a core part of it. You know, if I look at today, artificial intelligence, you could, you could, you can wrap it under the umbrella of data science, machine learning, etc. But it’s all about machines getting smarter, and the ability to write code to understand patterns and progressions and all kinds of data, to reuse that data in ways that could try to make certain decisions as things go along. But also, if you could recall, and maybe not many of us, but quite frankly, myself. I mean, I remember the day when there were hardly any computers and when they calculator actually made a huge impact. It also took 10 years to adopt. And everybody afraid that their jobs in the financial side now that the computer that the calculator has been invented, won’t have a job. Over the last 3040 years, we’ve gotten very smart with software and technologies. And in today’s day and age, you know, data science analytics, these are all real types of technologies that have replaced jobs that used to do this manually in an automated way. However, it’s also increased substantially the amount of new jobs, different types of jobs, but new jobs. And I quite frankly, do not believe that. And I agree with Shelly that AI is going to create this massive sudden job loss sector and on the planet, I believe it’s actually going to create more jobs than we have today. And I’ll share with you an example in cybersecurity what we’ve seen going on there. But if you just took an example of listening to the speakers, if you took technology and how Amazon has applied it to logistics, and supply chain, just think about it for a minute. Before the internet, we were going to a store, we’re buying things the store would call the warehouse, I need this, the warehouse would call the manufacturer, I need that. And these things, merchandise would be fulfilled today, it’s all automated. And a net result of that, for example, last year, when Amazon was looking at coming to New York, for example, my hometown, they were going to create 45,000 new jobs alone in Queens just for the warehouse. They’re creating millions of jobs for logistic workers, warehouse workers re-skilling folks that have been in and it’s using technology, it takes technology that is driving that transformation. So that’s just an example a tiny one of where we believe that that can happen. And so I think what I’d like to share is, you know, from that perspective, that AI will create new jobs, AI is not going to eliminate it will definitely eliminate certain things. But it’s going to create, I believe, more opportunities, then it’s going to eliminate, however, that said, we do need to pivot and transform and learn the new skills. Learning, as Sherry said, shall, he said is a lifelong experience. And we must be able to apply lifelong learning to our everyday way of life, I don’t believe if we if we’re sticking the mud and sit there in terms of what we know today. And don’t take advantage of where the market is going in the industry and the world is going, then we will be at a disadvantage. So I can for and it’s not a pitch, I’m going to share with you 10 years worth of work that we’ve been working on, we thought when we took on the skills gap, which I admit in terms of many other technologies, will draw drive skills gaps. Whether it’s in healthcare, or tech, or banking, or finance or whatever the industry, manufacturing, distribution, retail doesn’t matter. We felt that the key issues in the market was that there were no technologies that underpinned lifelong learning, there was no technology that massively scaled the ability to create a trend, a trance translucent between what industry needed in terms of their skills, and what education could do, to try to help the learners understand the skills they need to enter into a great career. Not a job, a great career and a great marketplace. And so fast forward a lot of our work in this skills gap. What we also realized is beyond the ability to capture lifelong learning, we actually needed to transform into a skills marketplace, as a as a planet. Just like Amazon has provided an eBay, a marketplace for merchandise for supply and demand. We need to do the same for skills. And if we don’t, we will always be challenged with these massive issues of under-skilled DNI disadvantage. There’s no reason for it. mean everybody has talent. Everybody has the ability. If we hired based on skills and we taught based on skills, we would transform the world. So in cyber, I’ll just give a few minutes on this. We We use technology to solve this problem to create this human supply chain, if you will, this copper supply chain. And what we realized was that industry really needed to become more productive and forward-thinking in terms of how they work with the workforce, to be able to develop, find and retain great talent. And so we selected cybersecurity back six years ago, because it was at the time there 300,000 open jobs. And this was a big issue in Wall Street. The challenge that actually machine learning and data science and others had brought on the cybersecurity potential threats and, and then all of the cyber technologies that were used to, to breach the many different stories you hear about the loss of data. It brought on a monster was job market today, the markets, almost 4 million jobs are open right now in cybersecurity, and it’s not technical. A lot of it is governance and compliance and analytics and human behavior. It’s across the board. And it’s also not for a specific set of talent. It’s for anybody, anybody with almost any skill can take on the cybersecurity market. So what we felt was, it wasn’t just technology, what would what the what we needed to do as an ecosystem was, first and foremost, identify the skills we needed through this notion of what we call macro Prudential, kind of like the Department of Labor’s apprenticeship program. It’s based on occupations that have a set of skills. And it’s based on the apprenticeship model, which teaches and applies those skills, through mentorship, to be able to develop the workforce. The second is to be able to use the technology to find develop and retain talent. The third do it at massive scale, and the fourth to capture that data based on standards and the result. From what we’ve experimented with. So far, we’ve gotten over 7000 students from you know, we decided to go into the school systems like the City University of New York, where the household incomes and average of 20,000 a year 500,000 students, and take students that knew nothing about cyber get, provide awareness in an area that they knew nothing about. And then provide them roadmaps and career pathways based on the skill sets that we are able to extract and using MLA AI to extract those skill sets from a resume or transcript and help them understand career pathways or where they could go to get phenomenal jobs. And so there’s only a couple of slides I just wanted to share with you. I won’t go through the details. But the cybersecurity market is massive in terms of its growth in open jobs, it’s going to be 6 million open jobs in the next couple of years. And the cost of the cyber attacks are in the trillions. These are this is not a small feat to resolve. So the last two slides that I’ll share is it takes a village. This is not something that could be done by a technology company, it takes educational institutions to develop the mobility programs and platforms that we believe that are needed in the market to capture and understand what are the skills that industry is looking for, and do it in a way that’s not it’s disruptive, but it’s not disruptive to the model in education. And the same with industries to drive the standards that are needed to create the common taxonomies that they can interrelate with the schools and with individual learners. So that we could have a common ontology terms of the skill sets that we speak, the business case is huge. You apply these types of things, you can have people learn skills, and have the extended workforce experience in 1/5 of time and 1/10 the cost. So the numbers on the ROI are huge. And then of course the partners to scale. And there’s a lot of organizations in the in in the country and around the world that are really want to address the problem of skilling in general, not just AI. And here’s an example of the types of organizations standards bodies, technology partners. blockchain mean, I believe, at the end of the day, and I’ll close with this that technology is a key catalyst, but also the models the consortium has. The ecosystem that’s fragmented, can be brought together using technology to solve the problem. There’ll be a ton of jobs using AI and a ton of opportunities to develop AI. Thank you And I think you’re on mute.
Gorman 40:07
I sure am sorry about that. I was trying to make sure that I didn’t. I want to get the questions. And if the group could be brought up, so we could go into our Hollywood Squares mode, I’d appreciate it. So thank you, folks, for a really terrific set of thought-provoking presentations. It’s almost hard to begin, because each one of you has raised separate and really important issues. The one point of agreement seems to be that, that the dislocation we may experience from AI will be a little bit like the lobster in the warm water that doesn’t realize it’s being cooked until it’s too late. But I think, you know, perhaps it won’t be in a massive, immediate dislocator. But it’s still worth our consideration. And I’m kind of glad we ended the way we do with you, Frank, because you provide some hope, I think on on some solutions that that, you know, because our questions that our members are going to have here and already have sent in, I’m going to get to those right now. We’re going to be, you know, what we experienced from the 1980s forward is, is a dislocation from different other from other things, then AI certainly, as finance came in, as we leverage debt, and did leveraged buyouts in the 80s, folks got dislocated as trade adjustments occurred as work went overseas, we lost work that way. And we lost it disproportionately in rural areas, and in some cities, clearly, but the textile mills of North Carolina, the tool and dye shops in the Midwest, the effect of NAFTA in Ohio and other states, that really has been a game-changer. You know, I think, what I think what we’re looking at now, it seems to me, is this little slower moving process. And what you just said, Frank indicates that you think that this could be the great leveler, that in fact, ai could be helpful and technology could be helpful in eliminating place as the obstacle to employment. Would you talk a little bit more about that? Because you believe that the cybersecurity stuff will be addressed? And it can be done from almost any place? Yes.
Cicio 42:33
Yes, yeah. We’re living in a virtual world, everything that’s connected is vulnerable. And we’ve connected the world with the internet, right? So I think, technologies, there’s going to be a lot of advancements, like we’ve all discussed in AI, etc. But I also believe that that creates opportunities on both sides of the fence, like I had mentioned the example of, of the trucking and logistics industry with Amazon. But let me take it to cyber. So if you look at cyber, you had X amount of jobs all working typical security jobs, cybersecurity was never wasn’t around seven, eight years ago, or what I would call it risk. All right, we call it cyber, it’s the fancy word, but it’s really Information Technology risk. And what that means is what’s the risk of assets based on potential threat, and very advanced technologies that are using AI? So I could actually spin this into a well AI could be a bad guy, right, from the perspective of trying to attack good guys. And so and they’re doing it, I mean, it’s very advanced. So I think that I’m, I’m not sure if I’m answering your question, right. But I believe that AI has two to two personalities. One is the development and I see some questions here. Yes, there’s going to be development of advanced technologies and AI and data science and machine learning, no question about it. That’s a very specialized field. And that, as Michael said, that’s going to take a very long time to, to come about, but I think on the other side, the opportunities that it creates, will be more than the opportunities that it replaces. And, again, the Amazon was just one example. They really automated the logistics supply chain and has created millions of job opportunities. companies that don’t pivot will suffer, Walmart pivoted, but the internet and online trading replaced a lot of brick and mortar. So you know, you have to be able to also not just individually pivot but from an organization that that may answer your question it or
Gorman 44:37
it does. And let me kick it over to Shelly and Michael, for a discussion. You know, Shelly, you talked about the importance of labor unions in organizing better work, better-paid work, that I know our members really care about the income disparity that has been created and long sort of held sway in the labor market. For the jobs you’ve talked about, that are considered low wage, and we probably need to mute, I need to mute in a minute. Could you talk a little bit about what you see in terms? And Michael, you mentioned worker co-ops. I know, we had a question about this. How can we introduce worker ownership more substantially into this and if not ownership, how can labor unions play a bigger role in sort of increasing wages, and also the learning opportunities for people who are working in Union working with you and
Steward 45:36
I can jump in and then and then invite Michael to speak, especially to the employer ownership piece, I think we have quite a few examples of unions and other worker organizations playing a role in training workers, that the problem is the rates of unionization and the opportunities to organize in and out of traditional labor unions. And so there’s a policy component there. And there’s a business practice component. And there’s a worker empowerment and training about organizing that needs to happen to really boost unionization and organizing so that it can fill that role that it has, in quite a few instances, to train workers, empower workers, have workers be directly engaged in the introduction of new technologies. Michael, do you want to speak to the employee ownership aspect?
Martin 46:30
I do? Yeah. So I think, you know, and I don’t think anyone is saying this. But I do think that it’s an all of the above, sort of, set of solutions. Like, if we’re talking about worker ownership, that the pyramid really needs to come, you know, that needs to start with worker education, training, organizing, etc. Because you can’t start to even think about ownership until you’re organized as a group of in identify as workers and not a group of workers, not just employees, worker ownership, though. No, I think the model is really and thankfully, we wouldn’t have to do too much work with this, from a policy perspective, because Germany has been doing something called worker co-determination, since 1974 73, I believe, which is basically allows for think it’s everything from firms of 500, or more people to have elected, rather, doesn’t allow it requires those firms to have elected workers on the board of directors. Elizabeth Warren, in their presidential campaign actually introduced what’s called the accountable, accountable capitalism Act, which stipulates similar numbers. I think it’s around 40% of people who are elected to board of directors in so it’s basically a corporate governance structure that you need to reform. And Elizabeth Warren and her policy team has put this together already. It’s kind of it was part of her presidential planning. So who knows where it is today, but it’s something that has been outlined and certainly thought out and has been tested in other Western countries. So I don’t think that it’s impossible. And I think that it’s in fact, something that could really be, you know, certainly representation is one thing and control over capital, and its distribution is another. But I think getting organizing, first, participation second, and then distribution of capital. Third, I think that we could see that happened. And we have that we know how to do the first two
Gorman 48:32
You know, this question, and I was going to bring up Aesop’s and thank you, Shelly, for sharing the employee ownership resources from Aspen on this, you know, the number of us I’m a labor lawyer by trade, I worked on Aesop’s back in the day and more often than not they failed, in part because they were sort of a the opportunity of last resort to save a company that was otherwise going to go under. And so they simply use the employee stock as a debt leveraging capability and that didn’t work hasn’t worked, at least in large scale. So we presumably, will you do you guys think we need to rethink the employee stock ownership model? And, you know, otherwise, are you really going to get there? Or should we simply focus on workers owning their skills, and return to the days of the old guilds, when workers own their skills, and we’re portable with those and rely on things like stackable credentialing that Frank CiCio is working on? That’s open for everybody. Got it? Oh, I’m sorry.
Steward 49:47
I was just going to say that I was I would repeat Michael’s call that you know, this is not a one-solution problem. It’s a both and Aesop’s are not going to solve all of our problems, but they do have potential, as do a lot of other approaches, including more more portable, more effective training that workers can carry with them through their careers.
Cicio 50:12
Yeah, and I just like to share with you folks for maybe two minutes, just a current situation that we’re finding. So the whole thing about DNI, right? diversity and inclusion. I work a lot with the banks, they have got literally billions of dollars that they’re putting up to try to tap in and font and, and help to transform that marketplace. Here’s the question. They don’t know how to do it. They really don’t know how to do it. I mean, I’m in for I don’t want to name the banks. And it’s, it’s okay. It’s kind of like trying to find a really great new, you know, mousepad. And not even having, knowing where to start, right? Or are for those who have kids? are kids coming out of college after we’ve all spent the fortune trying to understand where do I go with my career? We’ve got to get down to that. That empowerment. Right, that we talked about that mobility, empowerment, the ability, we just finished a project last year with the American workforce policy advisory board, on identifying helping the learner whoever that may be, in the enterprise, outside the enterprise DNI, what veterans whatever that may be, we have to get back to the gills where people do understand the skills they have, we must do that. And we must do it at a granular level, not just a competency. For example. I know how to program Python, but the ability to program Python in a double encrypted technology or the ability to ship Python technology to people who want it, right. I mean, whatever,
Gorman 51:59
Python and Python technology, because I don’t think most people know about it.
Cicio 52:04
Oh, it’s coding. It’s just a type of coding a type of technology. But it could be anything, it could be a warehouse worker, the ability to stack shelves in half the time because of my ability to understand you know, supply and demand of the warehouse logistics, whatever everybody has uniquenesses is where I’m going. And today, it’s not captured in a way that can be machine-read, machine-learned, and empower the individual to understand, most importantly, where can I go? What roles are out there? What’s the gap between what I know and what I need to know? And where are the jobs right now that I could get. And if I do that, one more second, we can do that, then employers can find them. And employers can then find and develop the talent by leveling the playing field for their need.
Gorman 52:55
Alright, so we’re going to do a lightning round, because we’re running out of time, Phyllis, I raised the question of project labor agreements and their benefits. But I’m going to just throw this out, give me the one reform in the workforce development system, that if you were the president for a day and could do it by executive order, you would put in place to connect more people to better jobs and higher pay, and maybe overcome some of the things we’ve some of the hurdles we’ve talked about. I’ll start with you, Shelly or not?
Steward 53:34
Yeah, I think my answer to your question is contained in the question itself, which is connecting workforce development systems to those quality jobs, and ensuring that the training that we are able to provide people with is connected to good jobs, and that we ensure that that is the case and take action when it’s not.
Gorman 53:58
All right. Michael, what’s your thought?
Martin 54:03
Perhaps a little bit less, you know, ai II, if you will, but I’d band Right to Work laws. I think that it’s like, you know, Matt, when we’re talking about we’re talking about like really wonderful things here. let’s organize let’s get workers involved in corporate governance. But in what 30 states in the nation we can’t have people aren’t allowed to organize like, we are so far in the Stone Age in this country as a relates to the ability to workers to organize that. I think we need to really think like, Okay, what are these most basic steps to get us to what we need to do? So yeah, ban, right to work ASAP.
Gorman 54:39
Revolution begins here, Frank,
Cicio 54:42
fund emerging tech folks like us that are trying to automate it. I think the government spends so many billions in education and Department of Labor on great initiatives. But at the end of the day, there’s some great technologies out there that can help solve this problem, we’re not going to solve it just by elbow grease, we need to automate it. And I think that, you know, if you look at Israel, the government in Israel funds technology companies big time, they put an awful lot of money and incentive to help young, innovative entrepreneurs address those problems. It’s called authentic problem-based learning. Right. And so that’s what I think we should have an initiative that helps fund authentic problem-based learning, which will help solve problems and develop skills and mobilize that ecosystem.
Gorman 55:34
I would be remiss if I didn’t try to shoehorn Phyllis, his question in here. project labor agreements, and I would add community benefit agreements, because NCRC, as many of you know, has been at the forefront of getting banks to understand community needs, through these community benefit agreements they’ve been developing, including an $88 billion agreement reached last week with PNC on their BVA merger. So when you combine pls and community benefit agreements, what you have to me, it seems is, is this amalgam of impact, both from the workforce side and from the investment side. Have you all spent much time working in that area? Do you see any role to play in this space for community benefit agreements and project labor agreements?
Maritn 56:25
You know, I’ll I’ll speak, I can’t certainly speak to the specificity of the pls and CBA is at the scale that you’re discussing. But from a city planning perspective, CPAs are often part of a community planning and development. And as relates to that, and that’s my experience with them. As it relates to the automation of roles. I do think that there’s a place for CPAs, right? This kind of gets down to like, in this gets down to having a worker organization that can negotiate that CBA within, say, an organization itself, it’s like, okay, we want to automate 30% of the jobs. Well, okay, sure, then that’s, that’s fine. What do we get out of it? Right. And this is sort of like you need to have, but in order to have that you need to be able to have a negotiating partner. So then workers need to be able to negotiate. So again, Dan, right to work.
Gorman 57:13
Well, alright, so I want to, I know we have to wrap up. We didn’t get to talk about things like guaranteed income, sort of the stranglehold that academia has on certifications, and how we break that. There’s so much more to all of this. I hope that the folks on Tuesday can pick up the baton a little bit on some of those issues. But in the meantime, let me just thank you on behalf of our members, and all the folks who are part of this conference for the presenters, thoughts on what is I think a fascinating topic. So Shelly, Frank, Michael, thank you. And thank you all for participating. I look forward to your comments. now and in the future. Thanks, guys.
