Dan’l Lewin: Reinventing education in the AI era

I don’t I don’t have a ton of hope at the moment. The educational system has been set up to like most regulatory systems to resist change for the most part and the generational you know evolution of how and what how instruction is delivered and done and what information is presented. What AI presents is a personal GPS for every learner.

So is it it’s Dan? Danel is my given name. Yeah.

Apostrophe L. That’s right. It’s my given name.

You’re the only D I know. It’s my mother’s gift to me. It’s a talking stick.

I like to say that I’m foul challenged. I threw away the I and the E. It’s really just a contraction for Daniel and a family nickname that my father earned at one point.

I’m not going to do a very good job giving your your bio. You’ve accomplished and done a lot. What really sparked my curiosity to have a longer conversation with you was hearing hearing some of your war stories from your time at Apple where you as I understand it were leading their strategy to bring computers into the education sector.

Right. The time where there were no computers in schools and I remember my school that’s right first time we had computers it was iMacs. Right.

That was the initial impetus for this conversation, but before we dive into even that story and and anything else that you’re, you know, willing to share with us. Yeah, maybe you can give whatever your version of the the Daniel elevator pitches. Oh, just my my background or my interest and story.

Sure. So yeah, long story short for me is I grew up in in very western upstate New York and with hindsight look back very fondly on the educational system that I got to work through public school system. And in particular a second grade teacher when I was seven taught us binary and it was easy for me.

It just happened. When I came to Silicon Valley, there’s a lot that occurred. I had a a very atypical family life.

I I attended Princeton and found out very quickly that I was not a mathematician like within a matter of minutes of arriving on the campus and ended up studying and spending my time in the politics department based upon some professors that I met who believed that politics as they called it as opposed to political science was best defined as relationships between or among people and that you could scale it structurally to organiz organizations and that of course organizations have power as individuals give up certain things to participate but the collective comparative advantage if you will of of large clusters of people can accomplish great things. So I was moved by the social issues of the day thought in the end that I would be a civil liberties lawyer. I focused on organizing principles.

The 18-year-old vote was clicking in when I was college. That dates me if you want to look that one up. It was women’s rights, civil rights, and rock and roll that organized people towards social and positive change.

And as far as I was concerned, positive change. Anyway, so through that educational system, in the end, I became a very good student and decided to take time before I went to law school, lost a family bet and came to California, moved to PaloAlto in 1976. The people that I met as I arrived were all clustered in this large house on the top of a hill in Los Altos Hills overlooking the Stanford Research Park.

And they were doing various and different things. Eight or nine unrelated adults living in this large place. One was designing integrated circuits for Watkins Johnson for guided missiles.

One was in med school. The owners of the house, one was running the Kestrel Institute, which does a lot of crypto work for the NSA among others, and a lot of math work. And the other one was a woman named Peggy Karp who was working on what became DNS for the working with Larry Roberts.

So, like, aha, where where am I? And then the long story short is through a roommate in college, I ended up going to work for Sony in Certino. And that happened to be a 600 square f foot office where the the next door neighbors the very week that I started became the people who left Apple’s garage.

So Steve and Steve and a small mattering of others, Chris Espinosa and rest of them that were just hanging around in the garage, showed up in that office space. I spent a couple years I got to know them but I spent a couple years working for Sony in the storage area. They were bringing out three and a half inch floppy and storage and kinds of things and that eventually found its way into the Macintosh as most people know as I brought that stuff to Apple.

But I became keenly interested in the broader implications of what was going on with microprocessors and computing and in particular graphics. And at one point I interviewed to go to work for a company that had these 3/4 of a million half million dollar CAD workstations because I was really interested in graphics and simulation and understood a little bit about microprocessors and all the rest. And anyway, so the long story short there is I turned down that opportunity for just behavioral reasons on my part.

Told me that I was the most qualified person that they interviewed, but I only had four years of experience and they needed someone with five years experience. And I smiled and said, "Well, if if that’s the case, then I’m not interested in working for you or your company because that makes no sense to me whatsoever." And and they said, "Well, but but" and I said, "No, no, sorry. The good news is I went to work for Apple soon there." And it was because you could kind of get your arms around what was happening.

These personal devices or these worlds within which individuals were programming and doing interesting things at this time like the vast majority of Americans do not have a computer do not even have access and maybe haven’t even seen one. No, this is exactly the case. I mean, you got to realize there were 50 at the same time.

I I know that was a sort of long-winded thing. There were 50 or so companies in Santa Clara County more or less that were doing microprocessorbased personal computers. Promeco and and then Commodore and Atari, all the rest of them started to emerge.

Apple was the one that was being run by real adults, if you will, in the sense that the the board, the investors in Apple brought in highly talented people from HP and other companies who knew how to build the infrastructure to scale that business. And Steve was the, you know, the goose that laid the golden egg. He was this entrepreneur with all this energy that needed to be harnessed and so they had adults harnessing him and doing all those things and and Apple became the golden child with the IPO in December of 1980.

I went to work soon thereafter. I missed the IPO. So anyway, when I worked to work at Apple, it was the Lisa system which was the precursor to the Mac.

Mac wouldn’t exist without Lisa and all the work that was done by that team. That product schedule ran out. I don’t know another 12 or 15 months because they made some fundamental changes after I started and so I took my own time with permission and brought in people from the university community.

I was posing the question to the executive staff of the company. Why try and sell these systems to the Fortune 500 when it was all IBM mainframes and terminal emulation and color because that was a new new thing these color terminals and all that other kind of stuff and all fourrand programming and Apple’s all about Pascal and the rest and I said you know the university and research community will buy these products you won’t have to sell them because you’re inventing what they or you’re building what they invented into real products. The, you know, the company was not interested in that.

The sneak preview program that I ran, we brought 90 corporations through. I wore a suit and tie and did a full day briefing with corporate CEOs. Had to be the CEO and other officers.

So, they all had to be corporate officers. Ran about 90 of those programs over the course of a year. So, I was in front of those people interacting with them and then absorbing all that.

In that window of time, Mary Kay Cosmetics came in. They were an early adopter of Xerox Star, which is some of the first stuff that was kind of like a Macintosh and like modern-day computing. And Richard Rogers, who was Mary Kay’s son, was the CEO.

Steve Jobs came to that sneak preview, and watched what I was doing and called me afterwards. He he hired me as well at Apple, so that was pretty straightforward. But he, he said, "You should come to work for us." He had just taken over the Macintosh project.

And he said, "We have a passion about what you’re interested in." because I’d been writing these reports about, you know, Fortune 500 is not interested. We should be thinking deeply about these other markets. And for all these obvious reasons.

So, the long story short there was, I went and looked at what they were doing. There were about 10 people in the group in this little Texco towers office space that had been set up. And I looked at what they were doing and they had a little blue blueprint to consider the higher education market.

Joanna Hoffman had done that work. She she deserves all the credit for thinking it through. But the work needed to be retoled and redone because I was really smart about channels and distribution and how to figure those things out because back in those days you could not mail order a computer to an individual.

You had to go to a retail. It was against the law to mail order. I mean you when you’re saying were there computers?

No, there were no computers. There were very few in homes. They were hobbyists.

They were kits and that was basically it. I think right now about the the exuberance around artificial intelligence, you know, because I was sort of working in AI like 10 years ago and it was there was a similar level of exuberance, but it was like very narrow. It was like people here, people in Boston, people Cambridge actually, not Boston.

And you know, chat GBT kind of like massively expanded the almost everybody now is sort of thinking and talking about AI and there’s almost like a preconceived notion the future is here. Like if you could put your finger on the moment where people started to realize like oh computers are like this is the future. This is what we need to be focusing on.

It wasn’t in 1980 it sounds like. No, it definitely wasn’t. My interpretation of the evolution of personal computing to the mainstream.

My view of that comes from really desktop publishing and the notion of a graphics interface that human beings could reach into and the idea that what you see on the screen you could print. So laser printing if you will. So that’s where Macintosh broke through and broke out.

We had already left Steve and I and others was Steve first obviously but then we left to start next but that was the moment I think. So you went to next that’s so cool. Yeah.

And so so that to me was really the beginning of it. The the fundamental difference between what happened what I would say in the first 50 years of microprocessorbased computing and where we are today in this window this 1975 and not 50 years total. So the first 25 years and the second 25 years is is kind of the following.

Computers are really good at at optimizing rational tasks, calculating numbers, putting characters on a screen, you know, manipulating an image over time, those kinds of things. And so it was all you put things into the system and then they were stored and you could manipulate them. And there was no networking, there was no wireless, there was no way to connect.

So communications were really nent in all all things considered. It wasn’t until you know Macintosh kind of broke things out with desktop publishing. Then you had Microsoft and all the channel play through 1995 when the launch of Windows 95 and Office 95.

That was the breakthrough. There was a cultural moment. You know, you had the Tonight Show.

You, you know, got Start Me Up with the Rolling Stones. You got all this stuff going on cultural. Again, going back to my earlier comments about, you know, women’s rights with rock and roll, there were there were cultural movements that really occurred.

1997 Is when XML started to become a topic of conversation about with browsers. Going back to the next machine which Tim Burnersley wrote the browser right the web if you will on on on top of next it was 97 till that early 2000s and that was where Yahoo and others started to take off and then Google you started to see the breakdown of the the image of a page into these little components XML komus and then you had these web standards to move those payloads around over the network that was basically everpresent started to turn the radio network the cell networks into ways for people to connect and then you started to get social media and those kinds of things to connect people. So for 25 years from more or less 1975 to about the year 2000 because you had Y2K run up with all the enterprise infrastructure and all the money flowing in because everybody was you know last time you could sell people things on a promise that you know the world would fail if not.

So that 25 years was was one thing 97 with XML or yeah rolling up into the you know 2015 2020 and today that whole period has been about markets reducing to a unit of one where I could market to you but the way I marketed to you there was the trade was your identity and your data in exchange for free access other than your connectivity charge basically. And it’s it’s the organization and the mining of all that data and turning it around into a system using the techniques that you and others been pioneering for 20 25 years. But you now had cloud-based scale infrastructure.

Storage was effectively free. Communications was ever present and everyone was walking around with a device and everyone had access to a device. He even had people like Warren Buffett buying into Apple, right?

Why was that seize candy? Apple, you know, it’s like what do people buy when times are good and when times are bad? They buy chocolate.

Do they have a cell phone? You betcha. You know, so the cultural and social connections came about with the inverse of the business model.

The challenge right now in my estimation is that as has often been the case, the hobbyists and the early access point right now, that’s the free-for-all of people running around on the web with AI and all the goofy stuff that’s happening. The next phase, I think, will be behind the corporate firewall where there’ll be a lot of very focused and highly tuned and highly valuable uses of these AI techniques associated with enterprise value. And efficiencies.

I don’t know what that’s going to do to the job market and andor people and all of that, but there’s going to be a ton of that activity in the next 5 years or so. How that all turns into a use case for education and learning is is is an interesting challenge because back in the day the benefit of a Macintosh in the university setting which was the initial market entry and then it went everywhere after that. It was the vet professor.

I mean vet schools harder to get into than med school. There was a vet school professor who bought into Macintosh and and and he was an evangelist for the company because his point was when he was in vet school, there was a small chapter in a large textbook about some feline distemper thing and cats. And here he was 10 years later he’s practicing.

There’s three textbooks on the topic, two journals and this that and the other thing. So the question is how do you gain access to the information that you really need? So part of it was organizing information, right?

And then obviously then you had the web and then you have access and all the rest. So so breaking that down into you know what you trust and how individuals will be will find their way is going to be the big challenge when you start to think about the use of of AI and education. Yeah.

Because it’s interesting that you talk about the sort of like digital transformation within these sort of big bureaucracies and one of the questions that I sometimes get is well if AI is going to display so many jobs and we have all this AI yeah I mean that the unemployment rates at like 4%. It it seems very clear to me that there is a disconnect between the capabilities of the technology and institutions ability to deploy those technologies because the bottleneck is no longer what can be done, what a what can AI do. It’s more like do we have the organizational structures, the people knowledge and capacity and also just the political capital.

Yeah. And the regulatory framing and all the rest. Regulatory framing and and my my instinct is that the the big push will be when there whenever there’s a next recession I’m not going to predict when that is but whenever there’s a next recession that’s you know suddenly it becomes a top priority to figure out how do we do more with less because we just rift you know 10% of our staff and I’m curious if that is that how like what do you see as sort of the critical inflection points for for computing and maybe the internet where it sort of pushed past all of that institutional like morass in the past.

Yeah. Well, Y2K was, you know, one interesting moment for that because people had sold systems into the enterprise, but there were competing stacks for the solutions. And so the interoperability of data became the big challenge.

And so that was the recession that occurred after the Y2K sort of glot where everybody sold everything in to you know save the day. Then the question was the enterprises turned around and looked to the vendors and said now you need to make this stuff work together. So the systems integrators did particularly well of organizing and bringing the data so that it was interoperable.

You didn’t have the the ISO stack. People were still flushing that out, you know, in terms of the layers and, you know, there was what Sun was doing and what Microsoft was doing and, you know, even in the PC industry in that period. There was what the issa bus crew was doing with compact and the lead and everyone else, all the clones and the BIOS that would allow sort of Windows to run on a PC stack and then IBM split and went with a micro channel, a different architecture.

Of that was crazy. And then so they got left out in the cold for a little while and that’s when we started partnering with them when I was at Next and things and because they were they were in left field. So these stacks they just didn’t work together and so that was a that was a big challenge.

The tools are much more sophisticated and the problems I think are more they’re more humanoriented today. Yeah. Today.

Exactly. They’re they’re they’re more associated with individuals learning new and different ways to do work and in most cases using these tools, they’re going to be highly optimized in what they can actually do. I just even back in the beginning of the PC industry, there were the number of product managers required to bring a product to market and the time it took and the lead time for tech publications that were going to print the paper.

I mean, you had magazines embargoed for six months because they had a 90-day print cycle to even get the magazines to Mar. So, I mean, just this the pace and scale of things is just it’s it’s it’s lightning speed right now. And I think the bigger the bigger problem right now at the broad level for society is how will people learn how to live in those systems and use those systems and deploy those systems and and it’s always generational.

So the question of if the technology takes a decade and now you can see that same decade back in the day it would take a decade now you can see the same thing occurring in 24 months or less sometimes. How will people adapt? And yeah, employment is 4% but the number of people who are unemployed who are midlevel managers in the tech sector right now it’s huge.

Sort of like the qualitative data from being in the valley in San Francisco, you know, like two years ago someone would get laid off from from Meta and they would have two jobs lined up and it’s like great, I’m getting severance and then now I’m getting a second, right? You know, right? And the question was like, "Oh, how much fun employment do I get?" And now like I have I have friends who are, you know, eight months, great resume, like blue chip, big tech.

And that seems to actually be I don’t I would I wouldn’t say it’s the rule, but it is it’s sort of this thing that’s percolating and it isn’t necessarily reflected in the data. And I don’t know that it’s AI. I mean, I think there was just like, you know, interest rates are really hot and low for a while.

There was a lot of like maybe overhiring but there is this odd confluence of you know companies are saying oh well like you know we’re now AI first and a quarter of the code that’s written at our company is written by AI and I think a lot of that is actually just trying to signal to investors who are chasing anything AI but there’s I mean my instinct is there’s something to it like AI is really good at coding and then there’s like that much harder to get a job as a software engineer like there’s something there. I totally agree. I have personal experience within my family knowing that with highly talented kids and how long it’s taken them in between jobs.

And it isn’t like I don’t have access to people who will say, "Sure, I’ll I’ll take their email and I’ll hand it off to the most senior recruiters that we have and they’re and highly capable, but they’re not hiring." and and some of it is just the retooling and some of it is like you said the the runup for a period of time was people they overhired right one of the common refrains is that AI is going to going to create all these new jobs and look back at past industri you know technology revolutions industrial revolutions they all coincided with increases in employment I mean surely computers displaced a lot of the tasks that people were doing if I think about like before Microsoft Excel. I mean, the fact that people were handwriting spreadsheets is is wild to me, right? I I think there’s also a challenge now where companies are trying to figure out at what point do we start to raise the bar of expectation given that we know that employees can now do more with less.

Mhm. But it’s uneven in terms of which employees actually have access or have the training or even the like capacity to learn. And and I’m curious with computers how that was navigated where, you know, as these tools were starting to go and become more common, but they weren’t necessarily in front of every single employee, like at what point did companies say, well, you know, if people are bookkeeping, they should, you know, stuff that used to take a week now takes a day and that’s just the expectation.

Was there like was there like a moment or was it did it just kind of happen gradually? I think it just happened gradually. I, you know, I remember the cover of a Business Week magazine saying, you know, the office of the the paperless office of the future.

You know, that was 1975. It was on the verge of not using paper anymore because you do things electronically. I I think a lot of this was just again generational.

I think it was a slow and gradual and systemic process change and then before you know it, you’ve got next generation coming in. Spreadsheets and Wall Street, you had that whole run up with Mike Milin. It’s like without spreadsheets, they wouldn’t have been able to do all that work.

It wouldn’t have been possible. So structural change occurs and the economy just radically evolved. I mean the financial services industry go back to the 70s was certainly sub 10% it’s 20% of the economy now so there’s just the world’s a bigger place now the marketplace Thomas Freriedman the world is flat all of that allowed for structural change to occur efficiency to occur corporations to rise up to scale and what we’ve witnessed obviously is the tech sector turning into this, you know, as I said, you know, in my time at the museum, life doesn’t exist without computing, period.

When did that happen? Slowly and suddenly. And what were the motivating factors for that?

Communications, beginning of cell phones. I mean, I was on the board with a guy who was at Motorola who was in the executive suite who when they brought in the first phone that had a camera in it, he laughed and said, "Who would ever want a camera in their phone?" Right? He said, "I was that guy." So, so everything that built up as a result of these communicators, right?

This goes back to Star Trek in the very beginning. Many of the people who built the industry as we know it today were Star Trek fans, right? And and then there was then there was Alan K in the very beginning of it all, right?

Saying, "Okay, here’s the way will likely evolve." And it did. We forgot about the education component. And and the notion of that when the Apple reaction to us leaving Apple was the creation of the knowledge navigator video to to show that the company had foresight for the way the world was going to evolve.

And that’s pretty much what we have now. I think we’re in for a very different place. I think the just the credentiing associated with higher education just even the structural stuff that’s going on in the world today and some of the pressures whether it’s US politics aside but just higher education and credentiing and learning those kinds of things we’re seeing more around you know badging and credentiing and I can go learn these things and I can prove that I have these skills and that’s that’s very very doable.

Some of the very early people in the in the in the computer industry that I knew didn’t have college degrees, but they were really good at what they did and because they dove in. I I think this this is just going to happen a lot faster. That’s my two cents.

A lot faster. Yeah. Because the the like past industrial revolutions, past technology revolutions had these like very slow physical bottlenecks.

Like you know you had to literally make sure that you were buying computers and then you had to like hire these like system administrators and you talked about the interoperability. That’s exactly right. And you know, I think it’s, you know, sometimes I think it’s a bit of a van vanity metric when people talk about like Chad JBT reaching I think it was like 100 million users in a month or right some crazy.

But I think it’s also instructive that like we have basically been getting to this moment for you know let’s go back to like the Turing computer right and you know the fact that we now have you know widespread access to supercomputing over you know 5G broadband speeds you know I think the key question is like are is education able to retool at pace or ahead of industry based on your what you saw in terms of the education systems ability to adapt because surely there was a lot of change that came with computing. Yeah. Does that does that give you hope or like like what are going to be the big challenges?

I I don’t I don’t have a ton of hope at the moment. Because the the educational system has been set up to like most regulatory systems to resist change for the most part. And the generational you know evolution of how and what how instruction is delivered and done and what information is presented.

What AI presents is a personal GPS for every learner, right? I mean you make a wrong turn in your car, what happens? Reroute, right?

Because you want to get to a destination. If you get 90 on your algebra test, that’s an A. But that last 10%.

Why weren’t you rerouted? And why didn’t you get 100%. Because the system could easily reroute you to find out what those little things were that you should know which maybe would make a difference 5 years from now or some period of time in your life, you know, or your credentiing for some opportunity of work of some sort.

So that’s where the tools can come in. How the system will deploy them and whether the teachers and the and the and the structure of the teachers unions would allow for those things. That’s where I’m concerned.

I just think that’s I think that’s that’s that’s my big work. So, you do see alternative approaches in some of these different types of schools and I’m hopeful for some of those. But at a wholesale level, when you break it down to the state level and how our systems are administered, it’s hard to be really optimistic because I mean the last major structural change that strikes me goes back to 100 years ago which took us to the microprocessor era.

And what was that? It’s well human beings traveled at one horsepower. 25% Of the agriculture production in the United States went into feeding horses.

People cleaned up after them, cleaned up the horses, right? And then we had structural steel, right, because of Ford and then eventually highrises and then aerospace and all the things associated with that. So that took a long time, but that was transportation and moving people around and that fed industry and war and all the rest and all the military stuff.

And obviously Silicon Valley exists because of the military stuff because the Russians had better rockets and we needed to reduce the weight on our payloads and so semiconductors got fed and Shockley lived here because his mother was in PaloAlto. So there you go. So we we’re seeing now a very different change because it’s it’s everpresent.

People’s lives don’t exist. If the computers go off, we all die and the indigenous people somewhere survive and there’s a new world. So, so it’s a very different change now than what we’ve ever experienced before because we have this ubiquitous access in requirement that these systems function and it’s on top of that and we’re being fed the data that we’ve given away in exchange for free access.

We’re being fed that back. So who’s juring, filtering, organizing, and presenting that in a way through the structural systems that society has created for itself in a world of agriculture, which is goes back to that 100-year-old thing. It was farming and horses and all that.

And that’s where the educational system was set up to support that educational mean and and endgame. So that’s where I I seriously do worry as a society especially in an affluent society as opposed to one where people are hungry, right? And and that’s so the opportunity for for the Africans perhaps, right?

And people we’ll see more and more of that over time and and we’re seeing it in other countries as well. Yeah, that’s an interesting almost almost contrarian take. I’m not sure I’m not sure if we disagree on the potential for like the the intersection of AI and education.

I I think what you described as sort of the I don’t even want to say utopian because I think it’s actually totally plausible if you had the right expertise and systems to be able to implement it. No doubt AI could significantly enhance teachers ability to personalize learning. M you know there’s questions of like you know in Harry Potter they have the sorting hat and sort of and then there’s like you know there’s other sort of dystopian novels where people your job is sort of dictated for you when you’re born.

I think the world that you described where you’re you’re you know let’s say the education system has enough of your data to be able to personalize learning in that way. It seems at some point we’re going to say, "Well, it doesn’t really make sense for us to have students apply to college because first of all, they’re using AI to write the the applications. We have all this data.

We in fact, the AI probably can predict to a far higher degree of accuracy than some human reviewer which not just which college you should go to, but frankly, which career you should pursue." You know, perhaps there sort of like we put our foot down and say, "Well, that’s a bridge too far." Well, that’s, you know, the way slippery slopes work is, you know, we you eventually do get to this place where there’s so and and maybe that’s just sort of the inevitability of like this like and and I think this is hard because I don’t know what your take on AGI is, but sometimes I feel like there’s a a rhetorical challenge. If if somebody’s assumption about AGI is that we’re going to achieve AGI in the next, let’s say, 5 to 10 years, then a lot of these conversations are a bit moot. I try to assume that like if it if it comes it comes but you can’t plan around sort of this what might actually be a really difficult gate you know for us to achieve a breakthrough technology so let’s just write off like let’s assume we don’t achieve AGI but am I missing something there in terms of you know the once you start relying on these tools just feels very hard it’s like imagine going back and doing using a like a paper spreadsheet yeah I I think The human component of answering that question is complicated because of the cultural differences and societal norms.

Who’s it tuned for? In that in that sense, that’s the one thing that I’m I kind of struggle with. It’s the self-driving car routine, you know.

And can you explain that because I think it’s well just you know in certain in certain societies if if the choice is go off go off the road and kill yourself or the choices to kill the baby and the mom walking across the street which where do you go and and I don’t know was the car programmed in some country in Korea where it’s different than if it was programmed here. So what are the choices that get made? So I do think I do think that and it gets into the kinds of things that that I’ve been that I studied and that I’ve been reaching back and reading more again.

So cybernetic stuff from Norbert Weiner, right? So like about human interaction with machines. Starting to look at, you know, the a sense of western society and human if you go back to one of my first experiences had a professor and a adviser in college who studied he was politics professor but he was in the state department and he was working on Iranian affairs at the same time that the US government was working with the sha of Iran and aiming to implement a social security system because EDS had done that in the United States in a rant and you’re giving a number to every individual in a society that has no sense of I or self because of the nature of their culture and emanational nature of their belief systems.

So as these systems get grown up around the world, my curiosity is around how they will be tuned and housed and guided in a world where internet protocol takes information everywhere in real time pretty much. And you know you can So that’s the hard part. So even in the United States down to the 50 states and the different schools and the districts and and all that kind of stuff, how will they be how will they be deployed, right?

Who will be the who will be the judge? And that’s the that’s the stuff that you know, you pose a question, you know, the next 5 to 10 years, what is that going to look like? I don’t I I see smart people trying to wrestle with these questions.

I get that. But but I don’t I I I don’t have a crystal ball. This is the This is the trouble of when you talk to people who are really informed, they don’t actually make predictions.

I I’ve actually learned like you can you can basically discount somebody’s expertise if they’re too sure of what the next because it used to be you might say like the next 10 years. I think five years is actually unknowable. Yeah, exactly.

I think you’re totally right. I think you might be able to extrapolate. I mean, AI has changed the equation a little bit, a lot.

I I would say not a little bit, but it used to be that you could you could kind of inside of a large corporation having spent 17 years as an officer of Microsoft in the end and having access essentially to all information and a thousand PhDs doing research and looking and the way the modeling gets done and everything, you sort of look at it and go, you might be able to look out 18 to 30 months or something like that, you know, just in terms of but then something comes in and then and it adjusts. But the big machines take a while to to to realign. But the pace of change it’s it’s stunning right now because because again we’ve reached the stage where every individual functions as a result of computing.

I mean, everything they do all day long requires it. Yeah. To me, it’s it’s a real question of what does it look like once you have capable agents and I know agentic AI and the challenge of this is like there actually is I think simultaneously I think people are underestimating the the scale and scope of the change.

They’re sort of like so distracted by like the individual widgets. Yeah. And and and yet I think there is also a lot of overhype in terms of like so balancing that balancing act like yes we’re I mean and I guess like going back to personal computers and the internet I mean that there were there was a hype cycle and it would still have been correct to predict that this is going to be the future.

The goal might not necessarily have been let’s go build a website right now. Maybe that’s what you need to do. But yeah, but I mean in in those days we were still I mean that’s the some of the earlier comments I made.

We were we were still struggling with the stack and the communications infrastructure and the cost of storage and the cost of this and there was a researcher in in sorry in addition to all of the institutional bureaucracies that had to be changed. So like you still had all the same challenges you had with AI but also the physical logistical challenges. Yeah.

The logistical challenges of the industry and the and the competing stacks and the competing approaches and the way those things were going to work and the lack of interoperability and all of those things made is much more bulcanized kind of a kind of a situation. And that’s you know that’s the that’s the thing that right now the business models that have created this abundance of data are the things that the data is out of the bag. You know the genie’s how it gets restructured and placed into society is is the big challenge.

And there weren’t any real regulatory issues. I mean IBM was held accountable as a monopoly and Microsoft as well in those periods of time. But that was that was very small scale compared to what the world is facing right now and the structures that need to be considered for the for the social implications of these devices and what they can do and will do whether we like it or not.

Can you give an example because I think there’s, you know, going back to these conferences, there’s a lot of like, well, we have to harness the upsides of AI and minimize the downsides. And I think sometimes there’s almost a generalization of what and even like AI ethics. Yeah.

And so I, you know, you can go as far as to say, okay, algorithmic bias. Sure. But if you can help paint a slightly more sort of u you know higher fidelity picture of like the type of things where if it’s not regulation at least sort of like having standards and systems in place are going to be important.

Yeah, there’s a you you asked me a question in in one of our exchanges about some of the readings and things things that I find that are informing some of my my gut reaction to these is a book that Verity Harding wrote called AI needs you which is a really good assessment of three different technologies. The internet being one IVF in vitro being another that took 20 year arcs for society to absorb them and for there to be regulatory oversight in some way shape or form. One could argue whether net neutrality etc. But she looks at these these three things that took societal change and structural change and courage for them to materialize and become part of the fabric of of modern culture at least in the West.

There’s another book Jamie Suskin wrote a book called the digital republic. Is very well organized into bite-sized chunks about structural and political change and how the the technologies can fit into that and what kind of courage we need to deploy new structures and new regulatory frameworks on that front. There are scholars who are working on some of these things.

What we need is again generational change in the governance and the people’s skills in government to be able to appreciate that and put those things to work. And that’s because back in the day this the the motivator was was life-threatening. It was world war, right?

And so those were the motivators where we actually had science adviserss and things like that that people really organized to save society as we know it. Right. Yeah.

I love that your your emphasis on history like as someone who studied history in college. And you know it’s funny my background is not technical at all like I’ve never written a program. Yeah.

I my background was in political science and politics. By accident fell into into AI. I was working for this AI company doing ML and climate tech space and doing like policy and work for them.

Sure. And I just it’s a long story but basically landed at this AI company like the first company to to basically build language models and linguistics and predictive coding for the legal sector. Interesting.

And and the CEO was this Nicholas Econo. He was sort of this visionary almost like a philosopher king. He was a student of history and he was sort of intuiting this this risk that he’s like I see the technology now being experimented with and deployed including by our company and there’s no sense of like what competence or accuracy or quality is.

There’s no guidelines as to like who should even be equipped to ask the right questions and certainly judges weren’t. And so I started actually building AI literacy for judges. Interesting.

And and then discovered that our schools weren’t teaching AI about AI and I was like well okay well the future work is probably also no I didn’t know that about you that I really appreciate where your questions are coming from now as well when we rolled out the Macintosh I had a consortia of 24 institutions participating in receiving the systems under this specialized pricing and sort of give and take relationship and one of the requirements was that They encourage faculty to do interesting things with the computers and then to share those little mini programs and things among themselves and it was a guy from Boston College who organized the a book of stuff and we printed it all but the distribution of that software we cut a deal with Kinko because Kinko’s distribution strategy was to set up shop next to the major research institutions of which there are 200 00 and they would build these books. It was called Professor’s Press of chapters from various different publications and snap together the book for this professor’s syllabus for his course or his or her course. And they were actually manufacturing the floppy discs with with these little quantbased things for the humanities and all these little programs and things that and programming languages that the physics professor at Reed College Bill called Rascal.

Rascal because of Pascal all these things and and it all went back to using the existing infrastructure and distribution infrastructure. The challenge now is it’s the distribution in the in the in the infrastructure for sharing the information is ubiquitous and everyone is is a unit of market to attack and that’s the difference now. There were filters by which these things were delivered before and there aren’t any.

So how we how we harness those filters and what kind of leadership and structures are being proposed that’s going to be the trick. And it isn’t that there won’t be these capabilities where you can look at the bright side. There will always be the down the downside and anything that can be used at scale can be weaponized.

Right? So, and and and this can be weaponized by a person or a small group of people in a very different way than obviously other types of weapons that require, you know, nation state action and and and things like that. So, that’s the worry that I have and and the good news is there’s a lot of smart people who who are putting energy into trying to solve at least point out the areas that need the work.

And some of the structures that that could be put in place and that’s I sort of try and hunt down that type of reading where I can. Yeah, I think that’s the that it is a glass half full argument as to you know sort of putting language models into the public zeitgeist you know some I’ve heard it sort of talked about as reckless but I think prior to ChatGPT because AIDU we I founded AID in 2019 okay I was doing the work in 2018 before even it was a very small it was a very small world and you know a lot of people did not take meetings with me who they’re not banging on our door but it’s you know we have no like my I have no issues getting meetings it’s more about like how much where do I spend my time right in what do you what do you how do you exchange what do you what questions are you asking what are you looking for right and and so there’s there’s a tremendous power to I mean if you think about like like presidential campaigns and you know yes it’s hard to mobilize a really desperate you know decentralized system like the US education system, but we do it every four years, twice every four years. With presidential campaigns, you’re mobilizing, you know, what about half of the electorate is actually turning out.

And you have a very short timeline to do it. And it is it is not just about hiring a bunch of field organizers. That’s part of it.

And you know, the Obama campaign did that really well. The Trump campaign really didn’t. In fact, they outsourced it.

What I think what I think there where there are common threads is there is this sort of centrality to and like simplicity to the to the the message or even just and sometimes I think people confuse message and policy not policy but it’s like what is the reason that is like getting you to take pay attention to this take an action and obviously Obama had that obviously Trump has that and I think AI actually poses this the fact that now it’s you know teachers are banging like filling conference centers you know hundreds of teachers hundreds of superintendents are taking time out of their day and we did we did one big AI summit in Cincinnati and the Ohio Department of Education was there and we’re like we’ve never seen a this level of excitement. Yeah. And so there’s like the question is how do we channel that attention to what’s actually important as opposed to just try to sell.

I you’re on it and that’s that’s good to hear what you just said. It’s important. I my last role at Microsoft I did campaign technology for both sides of the aisle for the 2016 election cycle.

So I started in 2012 and I had a red team and a blue team and understood and studied everything that the Obama folks did and then because it was all Microsoft underlying technology but no Microsoft data. So we watched all of that happen including what happened with Cambridge Analytica and Ted Cruz and all that stuff. So we watched all that stuff and I think The harder part is it’s a great analogy that you know registered voters is a no is a known list and they’re all technically adults.

So it’s the the children the kids who will have access whether we like it or not and and that that behavior pattern. So, I I love the idea that you’re spending time on that sort of regulatory statewide education conversation to get people to be thinking about this and there will be good models that emerge and then people will share them and ideally they’ll share them easily and quickly because of the infrastructure that exists. The challenge will always be, which is what I experienced in my personal life because my kids grew up with their, my oldest was 18 months old when I had a Macintosh prototype at home in 1982.

And so he grew up like a duck with that menu system imprinted in his brain. And when he got into, you know, junior, middle school, whatever, where they had a little lab and he was controlling the systems and was getting in trouble, but he was wasn’t really getting in trouble. He was just doing the things that he knew how to do and the the schools weren’t ready for that.

So that’s the one question I will have is what will the schools do when the kids come in yes, more empowered? What will they do? This is I’m obsessed with this.

I’ve been the by far the number one question that we get request for help is like, "Help us deal with cheating. All the kids are using chat GBT." and you know, I’ve heard some people in the space actually, well, their response goes something like, well, it’s not cheating. Get with the program.

This is the future. We need to empower students to use these tools. And if they don’t know how to use the tools, they’re going to be left behind.

And so, it’s not cheating. Just you need to just change what you’re doing. I I actually don’t agree with that because I think what teachers are actually they they don’t they can’t quite put their finger on it.

But what they intuitively understand is that kids today are already running laps around us. Like the idea that we need to that teachers are going to teach students about how to use AI is ridiculous. There.

Let’s be very clear and we I just actually had someone from Stanford who has like an AI maker space and the the students at Stanford are the mentors for the faculty like that that that will be the the model to the extent that we’re going to be trying to figure out how to use the tools. There’s there’s something sort of fundamental about like part of school like part of and like I didn’t do I really didn’t do very much in alignment to what I studied. I mean I studied politics but I it was I really just chased the interesting professors.

So, I studied the history of Brazilian politics part two, right? And I did the same thing. The one thing I learned in school was just the the persistence.

Like I wrote a lot and so I spent a lot of time, you know, and often the night before a big essay was due, I’d have to sit down and sort of like push through the writer’s block and get something written. Mhm. And I I I and I really I just I I just wonder what it’s going to be like in a world where you know replace AI with like a really helpful parent and let’s say the parent is like really good about not giving you the answer, not writing the essay.

It still be like something would be off if somebody went through college, went through high school, and they always had their their parents sitting next to them like, "Oh, like are you having trouble with that? Can I help you? Like let’s talk about it." Like you’d be like, "No, that’s I totally agree.

You need to be by yourself sometimes. I totally agree the the notion of deep immersing and reading and deep thinking sort of deep structure as opposed to surface structure because I think what you get back you ask you learn how to ask good questions and you get back interesting information but it’s surface it’s a surface level it’s not the deep structure and the underlying it’s sort of the chsky language and mind stuff right it’s just not it’s not the same and that’s the one thing you know that I that I worry about like you’re pointing out the same thing and I had the same thing the professors that I was same exact example I was I got a degree from the politics department I took five courses out of the department that was everything else were cognates that were tied into things that I was focused on sociology of the family the politics the relationship between men women all this other kind of stuff because I was trying to figure out how do you organize what are the organizing principles for driving change in society So I got to apply them to the computer industry in the early early phases. And so that’s what I look back on now and say okay what are those structural things that we need to be thinking about and the reality is that like you I took one test in college and other than that I wrote papers right and it was it it caused me to think and the last piece of work that I did for this professor which was the last thing I did in my quote unquote college career I can’t call it an academic career but you know I got my degree was ask yourself three questions and answer them no more than five pages each.

We won’t judge what you ask or how you answer with anything that you read or discussed in the precept which was a small, you know, gathering or from the lectures. Took me a month to think that through because I knew that was the end game. It’s like what do what do I want to and why?

And how am I going to express that in a concise way rather than 50 pages of ary really hard problems and so that’s the thing that I the time the contemplative time that’s the one thing and I don’t know what that will turn into for it it just it will change the nature of the human being and this is the jar linear stuff I mean it’s just going to change us but I so I I worry that it’s like the digital divide. I was just talking to Tony Juan about this from Reach Capital. Who also interestingly like he’s in a he’s an in venture capital now.

He spent 10 years building Ed Surge. So he has a sort of background as a journalist. And so he has a very unique unique take as as a venture capitalist that you don’t always hear.

But but basically the the the wondering I have is whether the digital divide will actually look something like the the poor kids get all the AI you know the you’re in a private school you’re reading uklidities you’re writing pen to paper you have a teacher in your classrooms and sure I think there’s still AI there I think the teachers are still using AI to maybe personalize I think personalized learning as you alluded to is such an obvious especially if you think about like students are special needs, certain types of learning and certain types of content, right? Subject matter. But but I I I and the reason I’m focused on that is because like to the to the point about what does it look like to have a very clear and crisp message and what I have been really pushing and fighting for is AI readiness, not AI literacy.

AI literacy is how do you use a technology? Readiness is how are you ready for the world? And that might mean math and reading and writing.

It might not actually include that much illiteracy. And I go back to 2007, which is not even nearly as far back as you’ve taken us, but it it’s it would feel quite silly in 2007 to be like the thing that schools need to focus on is mobile phone literacy. Right.

It’s also correct to say that mobile phones changed the world and you couldn’t do a job without a phone, right? But it’s it’s like necessary but insufficient. Right now.

I I’m I’m with you. It’s it’s a it’s a question of the book writing to learn. I mean, we we we’ve as as humans have learned from taking a stick and putting it in the dirt, you know?

I mean, and it’s like it’s over there, you know? So I I that’s the question of like you just said whether it’s a digital divide whether you have or economic divide whether you have the e access to someone who can help you with the nature of humanity as it’s currently embodied in most people. And yet, just you get the the, the email from the school these days.

My my partner, my fiance’s got a son in high school. It’s there’s going to be a policy about cell phones that they can’t be on, you all right, we know this. They shouldn’t be on in school, you know.

So, but they are So I don’t know I don’t know we are in an inflection point that is unique and you know on the global scale the again I I look at it at the at a macro level just the level of cultural differences and societal norms the rituals the symbols and the way in which the world will evolve and we’ll we’ll know when we know and it but It’s it’s it’s happening happening faster than ever. Do you feel like to go back to this your conversation about access to information and I feel like we’re actually sort of like past the peak like if I if I had to guess it would be around the 2012 time frame where there was a really rich universe of like highquality content. Like the publishing and news industry hadn’t collapsed yet.

Social media hadn’t quite pivoted to information. It was still like friendships and social networks and connections, right? And now today it’s like you you know my own I mean I read some mainstream publications but I really my the time I spend is like it’s on YouTube really and maybe a little bit of Twitter or acts but everybody I talk to I mean like their information silos are like it’s it’s staggering.

They’re real. Yeah. And so I almost wonder like do we it you said we’ll know but I but I I Yeah.

Like I feel like some people will know. Yeah. And other people it will just sort of just happen and they’ll be oblivious to it having happened.

Yeah. I I I don’t disagree with what you’re saying. And I think that time frame that you point out is is very rational because it’s in that period that I started asking questions of people in the group that I was looking after inside of Microsoft and they were of another generation than mine and and I was asking them about Facebook versus LinkedIn.

I mean, I went to the first public developer event that Facebook had because I was at Microsoft and I was here and we had a little programming tool that was going to help and obviously back in that day it was the enemy your enemy’s your friend and so we made the investment in Facebook all that kind of stuff relative to Google and all this things and Facebook was where you basically would be you’d communicate with people only those people that you would this is what people said to me then you’re at the beach and you’re with your family and you’re in your swimsuit you would send those photos to the people that you would friend on Facebook and that was the use case was that level of sharing and community and some level of intimacy and filter and then LinkedIn was a professional framework and that was it. Can I ask you like as as someone who is informed and thinking deeply about this? I I don’t want to make it sound like other people are sleepwalking and I’m sort of smarter than them because like I’ll be the first to admit I’m curious if this is the case for you.

I have started to become more and more reliant on the like the Google search synthesis where I mean it’s it gets it wrong a fair amount of the time, but most of the time it’s right. Most of the time it gets me and even when there’s like potential for a hallucination, I’m sometimes just like, well, let me just try it out. Like if I’m troubleshooting something.

And so even someone like me who is really attuned to this, I my behavior is like pretty has changed and even and I’m curious if you’ve I mean have you taken any are there any sort of like meaningful or intentional things that you’ve done to try to ensure that you don’t you know sort of half-hazardly slip into this rabbit hole into well just inadvertently placing too much trust or reliance on AI. I may be more of a a lite than than most in that sense. I mean, whether that’s the right phrase or not, I don’t know.

I tend to read more than than most people that I know. And and I just find it sort of I had a list of list of books that I’ve been, you know, like I picked one out the other day, The Revolution. Very self-s social change in the emergence of the modern individual from 1770 to 1800.

It’s like it’s just like like what was going on back then and how did the modern human of that era emerge and what was what was propaganda back then and what’s propaganda now because the systems we have right now it’s they’re basically just it’s propaganda. So, I’m I try to like you I think what you just described is if I’m searching for something, you know, I’ll try Google, I’ll try Bing. I I look at both.

I don’t know. So, you’re not using like deep research. No, I’m not.

And and it’s kind of like maybe it’s that maybe it’s just me and maybe I’m stuck because I don’t know. I’ve got about 500 maybe 700 old albums that I have digitally that I like that music too. You know, there’s a certain certain and I’ll it isn’t that I won’t explore other things and things like that, but the majority of my time I find that and then when I when I’m with my kids and my grandkids and stuff like that, then that’s when I sort of I listen and learn and I follow their leads.

We used to spend the last couple minutes talking about music. Do you So, you don’t use streaming services. You actually have albums.

Maybe they’re on iTunes. Yeah. I mean, I have a digital I have I have Yeah, I have I was an early Sonos customer.

I’ve got a whole bunch of zones and all that kind of stuff. And so, I’ve got a little server and I put all of it up there. Yeah.

And and there are streaming services that Laura will put them on as well, but I’ll I’ll I’ll just I like jazz and I got a bunch of old stuff that I really like and things like that. So, and I you know and I studied Bob Dylan and classical charismatic leadership theory and so the new move it’s an interesting one as well about that. So, anyway, yeah, what I’m getting at is cuz I also collected CDs.

I mean, I my the best gift I ever got was my cousin basically gave me the like this giant binder of CDs and I just let me rip them. So, I just like ripped all the CDs into iTunes and I just sort of like immediately teleported into, you know, this was like sort of like indie rock, let’s say, sort of like the general theme. And so I was like by far had the coolest music taste of all of my friends because I was listening to like Radio Head and the Flaming Lips.

And you know, John Cra, a lot of jazz, a lot of like just like a guard all over the place. And looking back, that was by far the the golden age of my and still sort of like informs my music taste today. Yeah.

Today my m my my my my ability to conjure up a a a song or an artist that I like has diminished so much because the unit of measure is no longer an album. It’s the radio playlist that’s the algorithm creates for you and different and it’s and it was sold to us under the premise of like this is going to help you discover new music and the actual and I’ve talked to enough people that have experienced the same thing where it’s you just you’re tapping into a sound stream. Yeah.

You’re not it’s not the same. You don’t have any you’re not you’re not invested in it because you haven’t it’s not about even buying the album. It’s like listening to an album all the way through.

It’s the surface structure and the deep structure. It’s the same thing that me that was again a takeaway. This is a couple reads from my you know from school was language in mind you know Chsky’s stuff was really fundamental structure of scientific revolution sort of structural change which is Coon’s book if you’re familiar with that I’m sure most people and because that they were I think he wrote that in 72 or something like that and I was in school in 73 to 76 you know so it’s like so they were fresh and Weiner stuff was older But information theory, some of those things became, you know, were really fascinating.

And so, you know, we were, what you’re what you’re getting at is just the notion that you actually invested enough time to go deep into and and so you as a human something locked something locked in. You learn something and that’s a structure from which and a filter by which you see the world. And that’s what’s changing in in a really unpredictable way.

And so the question and that’s why I I air on the side. And so I want to learn more a lot more about what you’re doing as well over time just that this how how are the societal structures going to filter and what will we be trusting you know and so your point about well is it an economic divide where there will be some where there’s a human engagement where there’s a level of humanity or will will it be more dystopian people will just be flitting around at the surface like a water bug? Yeah.

I don’t know. Well, I like I I spent enough time in, you know, World Economic Forum, the UN, the World Bank, all these all these sort of like organizations and there was a lot of thought leadership. There’s a lot of thought leadership today.

There was a lot of thought leadership about AI. I mean, the World Economic Forum coined the fourth industrial revolution back in 2016. Sure.

And at a certain point I realized that there’s there’s probably not a pathway to policy leaders and the thought leadership class actually solving this because what I kind of and I and I will give myself credit for this and by the way, I don’t think this has actually come to terms yet, but I really think that there’s like AI is right now the protagonist in the story. Maybe like a an anti-hero if you’re you know depending on who you are but it’s sort of the the most conversation about AI is about like the opportunity it’s going to bring and yes there’s going to be changes and downsides I think in the next and it will be the next recession but I think it once once job displacement hit gets to a certain point where it becomes unequivocal I think I think public perception shifts and my worry is that there’s If we don’t build some foundational knowledge about what this is, it’s very unpredictable what what the negative sentiment and the backlash will how it will be channeled because it’s possible that everybody sort of like that there’s a rejuvenation of let’s say union membership and you know people realize that we need to sort of like retake power to have more agency over our data and I think there’s also like you know especially in a world where there is so much AI that can manipulate which and the thing that I think is actually the best the one use case of AI where it performs the best against humans is persuasion more so than coding or writing or anything like that. Mhm.

And so so if you think about okay well how do you educate the public to a place where they are now at least interacting with the changes in a way that if they don’t they may not have total agency but you have more agency if you at least can make decisions and so for me it was like well where do you start? Well you need like we actually started out as the American AI forum because this idea was like we need to educate everybody. Yeah.

And then and then I quickly re realized that schools weren’t teaching kids. And I was like, "Okay, well that’s that’s an obvious place to start. The future workers, they’re at least making a very clear decision that’s going to be impact." Like you should even if you say I really want to be a, you know, a truck driver or, you know, drive Uber, you should know that there are companies that literally are planning to displace those jobs, right?

And it’s it’s not the entire solution. And so, you know, when you talk about like regulation, like we really don’t we don’t advocate for regulation. I mean, we have an opinion about what guard rails should be in place.

But I think I think it’s the hardest part. Yeah. Like the regulation is going to require a lot of thought and and strategy, but like reaching real people, especially people that are not have never heard of Next Computer.

Yeah, a lot of those people part of my hope for this podcast or this YouTube channel, whatever we’re going to call it, is connect some of these thinkers to educators to funders that are right now I think understandably kind of like very focused on the ball in front of them. Yeah, they have to be in some senses because it’s it’s like a fire. They have to pay attention to it.

Yeah, that was like a house that was already on fire. Yeah, it’s not like different fire but Right. So now they now the question is how do we how do you house it?

Yeah. Anyway, so I this is helpful. It’s interesting for me.

I appreciate the thank you so much invitation coming and regaling us. Glad to do it.