Kaveh Vessali: Sure. Thank you, Adel. So I actually started my career not in technology, I started my career as a policy economist urban planner. And I'm old enough that back in my day in grad school, as a poor grad student, I had to basically figure out both how to put my sort of personal computer together, and start doing some, what would now be considered techie work, to make the data accessible. So I ended up learning just enough to be dangerous from a technology standpoint, because as a consumer of data, trying to build economic models and so on, it was critical for me to make sure that the data sang, that it worked, that it was trustworthy and that it answered the questions I was trying to ask.

I then accidentally more or less stumbled into software and technology because I happened to be in the Bay Area in the late nineties when the internet was exploding for the first time. And as I was finishing my PhD program, friends invited me to start a startup with them basically as software consultancy. And it seemed like a very exciting thing to do. So I jumped in and the next thing I knew I had spent more than a decade in software and startups, mostly focused on data and analytics, and then enterprise software companies. And so I ended up basically working initially as a consumer of data, then as a provider of data within sort of business processes to customers, and always with a focus on government, because I was interested in public policy. I was always interested in helping policy makers make better decisions and make them based on data and evidence rather than not. So that's basically where I spent all that time in the Valley.

I then sort of had a midlife crisis and decided to quit tech and go back to policy, and the route I chose to do that was joining the foreign service. So I became a diplomat for the United States and I was fortunate enough to be assigned to Dubai for my first and what turned out to be only assignment. Because once I got to Dubai, I realized that I loved what was going on here in the region, the speed at which things were changing, the ability for people like me to actually get stuff done with governments that were extremely active and motivated and forward looking, which was not exactly what I had found in the US. It was more frustrating trying to get things done with the government in the US. Of course, the way things are structured here, more centralized decision making.

And while I was here, Dubai announced their interest in being, their intention in becoming the smartest city in the world, they announced their smart city strategy, and it just seemed the perfect combination of everything I had done, urban planning, policy making, data driven decision making to make a city more livable and better. So I ended up working on that project and spent the first several years after I left the embassy working with Smart Dubai. That was absolutely foundational, really fantastic work. We were able to talk about things like open data and data sharing across government and how to really valorize data, how to draw the value out of this. Information's only useful, it only has value when it's shared.

And for too many, whether it's because they don't trust their data and the quality of their data, or whether it's because they've sort of grown up in a culture of weaponizing data, of overly protecting it, of ensuring that they have data that others don't, it's hard to actually get entities to share data, especially government entities. But here in the region, many of the cities and the countries have really realized the importance of that and have pushed hard for it. So I ended up spending the last eight years or so working here in Gulf and the broader Middle East, helping governments with these major digital transformations that involve database policy making.

How would you define a smart city?

Adel Nehme:  So fast forward from your early days in public policy to leading data and digital transformation programs and the MENA region today, last year we had Dr. Amen Ra Mashariki on the podcast, who is the former chief data analytics officer of New York City. He worked a lot of smart city programs for the City of New York. He defined the smart city as a city that is enabled to be both reactive and proactive with technology and data. How would you define a smart city?

Kaveh Vessali: Not too differently, I think than Dr. Amen did. I usually, when we talk about smarter cognitive cities, try to focus on two things and really package it up as this virtuous cycle of gathering more and deeper insights about what constituents, residents and various stakeholders in the city need to make their lives better and their experiences better, and then leveraging those insights, in fact, to design better experiences. The smartness of the technology and the data is as it should be, an enabler only for the actual mission of the place. In a city the mission of the place is to provide a fantastic experience for residents, for visitors, for investors, for everyone.

And to me, it's this virtuous cycle of insights and experience that define a smart city, which means that cities have to start with trying to identify where the experience needs work. They've got to be problem focused, as opposed to technology focused. They've got to be demand focused, as opposed to supply focused. And they need to build that supply of technology and data very specifically to address those problems that they see, the highest priority demand to fix the places where they can bring the most improvement to the experience and make the cities as livable as possible and sustainable, of course.

Adel Nehme: That's awesome. And do you mind maybe expanding into some of those examples of problems tackled by smart cities and the type of solutions being deployed?

Kaveh Vessali: Sure. I mean the problems, so I was trained as an urban planner, these problems aren't new problems, they're the problems of being in a city. They're the problems of lots of people trying to move around. They're the problems that come from high density living, and in more modern times, the strange dual problems of high density and sprawl, sort of all in the same mix. So these are problems associated with mobility, problems associated with sort of pollution and quality. They're problems associated with the actual programming of life in a city. So in addition into the physical space, what does it take to get things done? What does it take to be able to feel safe for yourself and your family? What does it take to be able to grease the wheels of sort of economic transactions, right? Of starting your business, of running your business and so on.

So they're really, the problems are the same problems we've had in cities for centuries. I think what we have now, what makes today interesting is, again, when I was in school, the bigger problem we had was a lack of data. My biggest problem was how do I get the data to answer the questions that I have. That is rarely the problem now. The problem now is that we have data coming at us in all directions, in higher velocity than we can manage, a higher variety of data. We don't know what to do with all the information we have now. So I think what's happening is we've just sort of turned our problem on its head, can we solve mobility problems by tracking everybody's movements all the time and figuring out what the best way to distribute it is? Versus trying to figure out where on earth people are actually going when, and then planning a system around it.

But look, I think when you look at cities, you're going to see the same five, six dimensions of life. It's basically people either interacting with the physical space, or interacting with one another. So it boils down to making sure people are well informed, they're well educated, they're healthy, they're safe. And they have the freedom and the opportunity to move around, to take advantage of opportunities. Cities are historically, they are by definition aggregations, their aggregation economies. So what they do is they give you a more condensed set of opportunities, and that brings with it some of the challenges of now having to sort those out and deal with some of the negative side effects of all of that condensation, if you will, right? The pollution and the crime and the traffic and so on.

Transforming Public Sector Institutions

Adel Nehme: That's great. And you've definitely architected a lot of these programs. And given that, what do you think are the main levers or drivers for change that can transform public sector institutions?

Kaveh Vessali: So I think there are a couple ways to think about this. As I mentioned, I think the two most important things, the three most important dimensions by far have to do with understanding the needs, attitudes, behaviors of your stakeholders, of your constituents. So really understanding what's going on and what people need is the beginning of anything. We can pull levers, but if we're doing it a bit randomly and we don't really know it's going to have the biggest bang for our buck, we are going to end up wasting a lot of time and money. So I really want to highlight the importance of deeply understanding the needs of the constituencies, the priority is the paint points, and things that maybe they don't even realize they need yet. That's one.

The second piece is with respect to gathering this information and these insights, it's just as important to get them to the right place at the right time for decision makers. And by decision makers I don't only mean the leadership in a city trying to plan and manage the city properly. I mean every individual household trying to decide what mode of transport to take, where to make their residential location decision. Where should I live given what I need to do? The more we can get the information in real time where it's needed in a way that's digestible and actionable, the more we allow people themselves to make better decisions, and we can incense those decisions.

So for me, the most important lever is finding a way to scale the city manager's role, and basically crowdsource it through partnerships, through the private sector, and into individual households where they can feed me the insights I need, they can have the information they need to make better decisions. And as a policy maker, I can focus on incenting the longterm behaviors on those. Or I can focus on building systems that incent sustainable behavior, as opposed to non-sustainable behavior. And those will fall into some of the obvious places. We need to do a better job of longterm planning around our transportation networks and our land use. We need to do a better job of putting an infrastructure in place that lends itself to the kind of behavior we want.

It's very difficult to get people to use mass transit if your city doesn't have concentrated areas of economic activity and of residential activity, if it doesn't have good mixed use neighborhoods where you can get most of what you need done in a few minute walk or a short public transit ride. If you're going to ask people to take public transit in Los Angeles, you're going to get the problem Los Angeles had of getting people on those trains, where are they going to go, right? So I think that you've got to run sort of parallel longterm planning to change the fabric of the city as you try to create better experiences in the short term, given the fabric that exists.

But if all you focus on is trying to make incremental changes, given the existing fabric, I think you're going to end up frustrated 20, 30 years later. You've got to also work on changing that fabric. Which makes doing work in the region particularly interesting because there are a lot of greenfield opportunities here. There are a lot of cities being built from the ground up where we get to sit and try to lay the place out properly, to begin with. And also put in place processes that are agile and flexible, so that as the world changes under our feet, we are not stuck with what we thought was going to be the case 30 years ago.

Adel Nehme: This is fantastic. And it touches upon that virtual cycle thinking that you mentioned. Now you mentioned here specifically the City of Dubai and the MENA region. This is something I'm especially excited to talk to you about because I'm Lebanese and I come from the MENA region myself. But you're someone that worked in the technology space some way or another across the world throughout your career. So where would you say the MENA region sits today in terms of data maturity? And what are some of the exciting things being done in MENA that are not being done anywhere else?

Kaveh Vessali: Sure. So let me first tackle the data maturity question. In general, data maturity is much lower globally than we sort of think it is or than we want it to be, right? Generally speaking, any individual will ask a question that will help them make a decision and it'll be much harder to actually put the right information together to feel like you got a good answer, in organizations of all sizes and even worse when now you look across organizations for something like a city. In some ways the MENA region, again because of more centralized government decision making, in some ways they're ahead, and in some ways they're behind what I've seen in the West and in Asia. And I'll explain what I mean by that.

There does tend to be a bit more central data gathering that I see here in the region, but a little less data sharing. Now the data itself is often not being managed to sort of best practices of data governance and quality and management standards. So you will find private sector organizations generally better off than public sector organizations. And here in the MENA region, generally speaking, it's our public sector organizations that are the thought leaders. And very often the private sector is being pulled forward by very forward thinking public sectors. Whereas that is generally the opposite in other places in the world. Generally, what we find is slower governments and innovation is happening in the private sector. So I'd say as far as public sector, government cities are concerned, the data maturity here, we tend to have more centralized data, but less broad gathering of the data.

Let's take the US, for example. The US has been gathering census data, economic census data, labor statistics, et cetera, for decades and decades, if not centuries. The data is sitting somewhere, but it's very siloed. As the US national government, there's no one place to go to get access to the data. You're going to get energy data out of the energy department. You're going to get labor data out of the labor department. Now, if I've got a question that involves combining that and trying to figure out for people whose jobs fit these kinds of descriptions, what do their transportation decisions look like? That's a bigger challenge potentially in a place like the US than it would be in Saudi or the UAE where they may not have there as much data for as long, but it's a little bit easier to pull that data together and ask a cross sectoral question and get a good answer.

So I think it's a little bit of both, more mature and less mature. However, again, because the leadership in general in a lot of the countries here in the region are very forward looking and what I would describe as somewhat risk loving in an economic sense, right? They're willing to make big bets and try things that are very difficult to get done in this lower environs of western or sort of east Asian governments. So I think we end up with Dubai style smart city initiatives. We end up with neones and the Red Sea projects and national data programs where we actually are creating national information centers in the countries here in the Gulf. We are leveraging blockchain. We're trying to identify use cases where right now we can get value out of using AI, out of building distributed blockchain network. So I think we have this appetite for leapfrogging. And in some cases you can do that. With respect to data, I think there's a very, very critical set of early foundational steps that are required before you can then leapfrog.

And this is the biggest challenge I see here in the region is that we do often see our clients, and sort of governments, really excited about jumping forward, believing or hoping that they can get that AI driven process automation next year. And in theory they could if they had enough historical high quality data in one place where we could throw the machine learning algorithms at it, figure out some patterns and then continue to train those going forward, but very often we're not starting with that. So we've actually got to make sure we get the data in the state it needs to be. We've got to prepare the information first before we can throw some of these tools at it. So it's a little bit of making sure that we understand the steps, we understand the road map. The target, the true north is a fantastic one, but it may not be achievable for everyone in one or two or three years. Because year one, two or three may need to be taken up with this homework, the data management homework.

Adel Nehme: I'd love to deep dive into this. You know, there's oftentimes a misconception when organizations once start doing data science or AI, where they want to start working immediately on highly imaginative AI use cases that you often find in the news or in sci-fi movies. But these use cases are not necessarily the most value driven. I'd love to know how you manage these conversations with government stakeholders, where you need to get buy-in on the need to build foundations rather than use cases. And how you're able to explain the need to bridge the gap between what's value driven and what is an imaginative AI use case.

Kaveh Vessali: Sure. I think there are two parts to this. So the way we usually have this conversation is A) to make sure that we are always describing something as a journey of maturity that has a series of steps. We want to agree very much that the objective is aligned. We want to get to this place where the future state looks like this. And if in, which is sort of level five on this maturity framework. And if we find ourselves in level four right now, fantastic. Let's go. If we don't, let's figure out where we are from level one to five and make sure that we do two things. And this is the other critical part of this conversation. If I end the conversation with you're at level one and it's going to take eight years to get there, we need to focus on level two, that's not exciting. That's not going to get the top executive thinking these are the visionaries I want to work with and they know what I need.

So it's really critical while we have that sort of longer term foundational work stream if you will, that we also absolutely focus on identifying the no regret, quick win moves, that will bring benefits now, that will help you not only see value from your existing systems and data, because there's still value in them. There's definitely something that's going to work for you that you're going to get value out of in the next few months. We just need to identify in your particular case, where is it? Which of your units, which chunk of data and insights is the most mature? Who is the most motivated? And can we make a hero out of them? Because you've also got the hardest part of technology which is always the people. We're always ahead of the game in terms of the tools we have available to us compared to what people can absorb and the change that people can manage. The change management is always the hardest part of any technology initiative.

What I've found in now a pretty long career is that if we can identify the hero, if we can identify that really motivated stakeholder who is a bit more risk loving and has a bit more maturity, and we can bring them value, then they, as your customer, as our customer, will become by far your greatest evangelist. I don't need to go talking about what my firm or some other partner can do for you government agency. If I can help one of your units, one of your folks who is willing to jump in and has already done some of the homework and is fairly mature, if we can show value there, they will become a hero in the organization and everybody will want a piece of what they've got.

So we try to have this conversation in parallel streams. Let's look at the overall maturity, let's target that ideal future state and let's work along the steps to get there. But in the meantime, let's bring value where we can immediately and make sure that you've got something that is a winner, and you're not going to regret. It's going to be reusable. It's a good learning experience for all of us. We start to build that muscle, people jump on board. We start to actually make change, right? What we don't want to do is spend two, three years planning something the world will have changed underneath our feet, right? We've got to be, we've got to take an agile approach to all of us.

Framework for Identifying Quick Wins

Adel Nehme: I completely agree with you on the need to generate excitement with a quick win that shows the value of AI, and this is regardless of industry, whether you're in public sector or private sector. You mentioned here the importance of showing a quick win. What is a step by step framework you can propose that helps with the quick win identification process? And how do you take a look at what's possible and available to be able to generate this excitement early on?

Kaveh Vessali: It's really a matter of creating a Venn diagram, really with two things in mind and looking for the intersection. The first is talking to the business about what their priorities are. They know their priorities, they know their pains, they know what keeps them awake at night. You want to be able to have a very clear sense of what those things are and what the benefits of solving those problems will be to the people actually do the work of the organization. That's one circle. The other circle is just feasibility. The other circle is just looking at whether you've got the data to solve that problem. Whether there are dependencies. As much as you would love to solve that very high impact problem, there are three critical steps to getting there and you don't have any of those in place. Now, it doesn't mean you don't prioritize step one in that process for a really high impact item, but don't expect that to be a quick win. Expect that to be part of what happens alongside the other quick win.

So we really try to do both a business first and a top down view of what the biggest impact wins will be for you and your organization. And at the same time, bring in the unfortunate reality check of feasibility and cost and so on, and figure out what can actually be done. We need to look at your people. We need to look at your systems. We need to look at your data. And we need to figure out what is the right pocket of maturity in all three of those dimensions in order to make that high impact high priority win work.

So depending on the organization, how well they know themselves, that's something that could take a few weeks. A lot of times organizations come with their own sense. They've already been thinking really hard about this. They know what they think their biggest problem is. And it's just a matter of us coming in and confirming, okay, can that actually be tackled in a short period of time? Are you mature enough in terms of your systems and your data and your people? And if so, then, great, let's jump on it. If you don't know, if nobody's done that sort of cataloging of the business priorities, then we can't create a digital roadmap that aligns with the business strategy because you haven't sorted out your business strategy and priorities yet. If you have, makes that easier.

Adel Nehme: So you mentioned here data maturity and how it's divided into key components like data, people, culture, and so on and so forth. I'd love to unpack this, but I'd like to focus on the people component. You mentioned here how change management is the most difficult aspect of any technology initiative, especially in a public agency setting. I wonder if you can unpack this challenge into different sub-components and what you think are the most challenging aspects of it. This is especially relevant, because I'd love your take on workforce transformation and how it relates to upscaling.

Kaveh Vessali: Sure. Yeah. So I think there are two parts to this. There's the part where we have to make sure we have dedicated and already knowledgeable and skilled people in place to drive this change. And that has two parts. A) it entails ensuring that the leadership, that those who are capable of driving change, believe in this. Because if we're trying to drive change only from the middle or the bottom, and at the top they're not bought in, then somebody somewhere is going to end up saying this doesn't look worth it to me. And there won't be the support you need from the top to make it happen. So I think executive sponsorship understanding the importance of this transformation, again to the organization as a business, it's core mission, having nothing to do with technology or digital or data, but understanding that we think we've got an approach to making the organization better at its mission. That's probably step number one.

Step number two is then making sure that there's at least a small dedicated group, people who wake up and go to sleep every day with their mandate being this transformation. Not somebody's night job. Not something that will get, because this is strategic work that requires kind of being protected from the operation. Like any innovation initiative in any organization, it benefits from being protected from the day-to-day rush and crush of the operation. So you need to sort of protect a strategic knowledgeable group charged specifically with making these changes. And you've got to make sure that you've got a couple of folks there that know what they're doing. With some top down executive sponsorship to lay out the roadmap, to create this program, this marathon of cultural change that you're about to try to motivate. That's the one side of it.

The other side of it then has everything to do with finding the points of value and benefit to the masses who you're trying to bring on board. If it's strictly a mandate from the top, it might happen, but you're not necessarily going to have motivated people. You may get a lot of resistance and it will go slower than you want. But if you can identify something that makes my life better right now, I will be much more likely to invite that transformation for me myself, and see that upscaling as something that is helping me right now do my work better, rather than something I need to do to satisfy someone else's reporting needs.

We used to, I think I worked at one of the best. I've worked at couple of the best companies in the world. I'm at one of them now, and I was at another at salesforce.com. And one of the really amazing things we did at Salesforce in the early days of the cloud, was go to the business directly, go to VPs of sales, rather than trying to transform all of IT with this cloud notion and say, look, we've got something that will make you and your team more effective. You'll make more money by the end of this year because you've been able to track your own sales process better. And you're not going to waste your sales meetings finding out what everybody's been doing, because they're going to put it in this amazing tool, you're going to spend your sales meeting strategizing and advising on how to do things better. And they were that sounds great. Let's jump on board. They had their benefit immediately. That made it much easier to make those salespeople adopt the tool because it made them better salespeople.

And that's the kind of approach we need to bring in government also. We need to look at, in my experience, people working for government entities, they're typically not the best paid in the world. They have an interest in making the world a better place, making their country a better place, making their city a better place, and providing better services to their friends and neighbors. They're motivated by doing better work. The more we can help them do better work, the more they'll take it on themselves. A lot of this comes down to having a benefit driven mindset rather than sort of a mandate driven mindset.

Key Skills Governments Need To Scale To Become Data Driven

Adel Nehme: I wholeheartedly agree with the importance of getting buy-in for these programs specifically around upscaling for creating excitement as well as for creating a community practice around data skills. Now, given the importance of data skills and data literacy in government settings, what do you think are the key skills governments need to scale to become data driven and to drive their workforce transformation and their data literacy transformation?

Kaveh Vessali: Look, I think in line with what I was just saying about being demand driven, I think the best way, the most important first skill is to make people better evidence based decision makers. Because once they realize that they can use data to make better decisions, then it's much easier to get them to manage that data better. Generally speaking, we don't need an armies of people with the technical skills to manage data. What we do need armies of are people who have basically this notion of a citizen sort of developer or citizen analyst. We need people who can use data in their work. And so I actually think it's more of an analytics skillset that we need to focus on than a sort of technical or engineering skillset. There will always be the specialists that we need, but we don't need to create an army of those. In fact, fewer and fewer of them.

What we do need are armies of people that know how to look at information and make sense of it. That can tell whether the information in front of them is answering the question they had or not. That can recognize whether the process that that data went through, leaves it whole and trusted or sort of poked away at its value and they're now left with something that isn't so great. And one of the best things about the schooling I got was a lot of my sort of data analytics schooling was designed around helping us be smarter consumers of studies and research, right? The idea was if someone comes to us and says, here's my chart, and it says the following, understanding what the right questions are to ask to make sure that is indeed the case. We need better consumers of data, more than we need better producers of data. A lot of that can be done with smaller numbers of people with better tools. But if we're not all really good consumers of the information, we can't tell if what we're looking at makes sense or not.

Adel Nehme: And to add to your point, having that baseline data literacy for data consumers creates better work for data producers. Since data consumers can better spot automation opportunities, use cases for data and it leads to this virtuous cycle of work. Now, given the importance of this analytics mindset, how important do you view in government the importance of data ethics, given the data products governments are producing can change and affect people's lives directly?

Kaveh Vessali: Look, I think it's absolutely critical as we have access to more data, to do a better job of generating the frameworks within which we agree the use is ethical. I have a little bit of a radical view in terms of data collection in that I believe that we should be very transparent about the data we're collecting and very transparent about the way it's being used and very transparent about being able to check up on that use. I would much rather collect all the data in the world and be super transparent about where it's going, where it gets used, and allow a degree of sovereignty, a huge degree of sovereignty over how my personal data gets used, but ensure that society is benefiting from my ability to analyze data. I would much rather collect everything and focus on managing it properly and transparently than I would be interested in ensuring we don't collect.

So I'm an eternal optimist that way, in terms of sort of human nature. I think the more we shine lights, shine a light on things and are transparent with one another, the more we'll be sort of forced to use information ethically. Now that's in terms of sort of data privacy, data sharing. There's another side though to ethical AI usage, of course. Which is sort of capture typically in the trolley problem, right? So as we are automating vehicles, what kind of ethical rule set are we going to build into a vehicle's decision making if it's got to choose between protecting the life inside the vehicle, its owner, versus the life in front of it, if there's no option. And I think that opens up a whole interesting AI can of worms that requires a lot of additional thinking.

Adel Nehme: Now to harp on the AI kind of worms concept here, what do you think of the view that some ethical concerns of AI or AGI can be circumvented or alleviated with better public policy? Taking the trolley problem, as an example, do you think we can prevent the trolley problem by having roads optimized for autonomous vehicles? Roads and laws, of course?

Kaveh Vessali: I think in some areas like, that's exactly the approach, right? There are other examples where I think it gets much trickier. For example, using AI to predict illness. I think it's one thing to throw AI at predicting diabetes, it's another thing to throw AI at predicting mental illness. And 100% the answer to dealing with this is in public policy. But I think some of these lines, our thinking is just so, it's so new. We are so immature in terms of our ability to think about it yet, that I see a lot more questions than answers. It's not that we can't resolve this. We definitely can. And it's going to come down to policy. But in a lot of these cases around I think ethical use of AI, we've just started thinking about this. And we are definitely yet ready to feel like we are incorporating all the different variables, envisioning all the different scenarios.

So again, it's another one of those cases I think where the process is the product. We need to make sure we have a good process or addressing these, right? We need in the same way that agile software development was a process solution to a product problem, we need some of that with respect to public policy. It's what planning has historically always been about. But with a lot of these new technologies, we're always going to be a step behind in terms of our policy. The tech is going to move much faster than our understanding of how to manage it, so having a framework that keeps some of it in check, as we figure it out, is really helpful. And in my humble opinion, the best baseline approach to that kind of policy making, is openness and transparency. We have to be able to have these debates in public at scale.

Final Thoughts

Adel Nehme: I agree with you, especially since the major challenge here is that institutions are going to have to be reactive instead of proactive. And even the challenge about being proactive here with AI is that it's easy to fall into analysis paralysis. And here transparency and openness can set the stage at least for much faster and more effective reactivity. Now, Kaveh, as we end on a slightly optimistic note, any final words you have before we wrap up for today?

Kaveh Vessali: Sure. For me the most interesting problem right now, the most interesting question at hand, is how are we going to manage all of the, how are we going to manage the distribution of all of the benefits that a lot of these new technologies are bringing us? From where I sit, without a doubt, we are now at a place where we are producing enough material wealth to meet everyone's needs globally. But we're not meeting everyone's needs globally because of a distribution issue. And as we get more and more productive through the use of technology and AI and process automation, we will need less and less labor, fewer and fewer people to produce the same amount of value. And what we've watched happen over the last century from a sustainability standpoint is that when we let growth be the only real driver, we're obviously destroying ourselves.

So for me, the most interesting question is how do we address the fact that with all this additional productivity, when do we start and how do we start to look at our needs and meeting them, meeting them fairly sustainably and not sort of worshiping at the altar of never ending growth, and make sure that we distribute this wealth a little bit better. But a lot of smarter people than I are looking really hard at the numbers here, but I have to envision that for my kids' generation, they'll be able to have everything they need with 20, 25% of the effort.

Does that mean we're going to have this horrible sort of violent painful transition where the 20, 25% of us who are more on the cusp of generating that value are going to continue to disproportionately sort of consume that value, and the crush of the rest of humanity is going to be as painful as it seems to be right now? Or we going to get much smarter about how we can spread this in a better way and free all of us to be developing a different relationship with work and with consumption and with production and really just sort of transforming our relationships with one another and with work and production to have a little bit more Star Trek like future.

Adel Nehme: I couldn't agree more, especially around that last note, as I do think income inequality is one of the biggest challenges of our time since it's really at the crux of most of the world's problems. And the possibility for AI to alleviate this is massive. Kaveh, thanks again for coming on DataFramed. Really enjoyed your insights.

Kaveh Vessali: Same here, Adel. My pleasure. Thank you for having me.