WEBVTT - Let’s Imagine the Tech Without the Tech Companies - The Story

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<v Speaker 1>Welcome to tech stuff. I'm as Voloscian if my LinkedIn

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<v Speaker 1>feed is to be believed. There are two kinds of

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<v Speaker 1>people in the world. Those who think AI is the

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<v Speaker 1>future of everything and will save us all, and those

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<v Speaker 1>who see it as a harbinger of the apocalypse that

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<v Speaker 1>will destroy society as we know it. But how are

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<v Speaker 1>real people actually using AI? What is it good for

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<v Speaker 1>when we think of it as a tool and not

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<v Speaker 1>some kind of demigod. Josh Sharangel is a journalist at

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<v Speaker 1>The Atlantic, a documentary producer, and the author of a

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<v Speaker 1>new book where he tries to answer that very question.

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<v Speaker 1>It's called AI for Good. How real people are using

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<v Speaker 1>artificial intelligence to fix things that matter? Josh, Welcome to

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<v Speaker 1>text Stuff.

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<v Speaker 2>Thanks so much for having me.

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<v Speaker 1>What made you want to write this book?

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<v Speaker 2>I started doing ar reporting as a columnist for the

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<v Speaker 2>Wahian Post, right and so I was sort of immersed

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<v Speaker 2>really right after the debut of ch GPT three point five,

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<v Speaker 2>which is the moment most people think of as the

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<v Speaker 2>kind of a rival of modern AI. And I've found

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<v Speaker 2>myself entertained but not enlightened by the conversation, by which

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<v Speaker 2>I mean, you know, I spent a lot of time

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<v Speaker 2>in the valley, and people were telling me basically this

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<v Speaker 2>sort of like almost routine level of repetition about how

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<v Speaker 2>AI is going to make everything better and we're going

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<v Speaker 2>to cure cancer and we're going to mitigate climate change,

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<v Speaker 2>but we really have to go go, go to make

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<v Speaker 2>those things happen, right, and so on the one hand,

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<v Speaker 2>it's like, oh, it's very interesting. And then there was

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<v Speaker 2>this other side, as you mentioned in the intro, of

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<v Speaker 2>people who are like, you have no idea what is

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<v Speaker 2>about to come for us? Come with me if you

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<v Speaker 2>want to live like all the apocalyptic stuff. And as

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<v Speaker 2>a columnist when you're just trying to like get through

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<v Speaker 2>a couple of cycles, you're like, well, this is great.

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<v Speaker 2>You have these two very clear narrative sides. The stakes

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<v Speaker 2>are very high. You're talking to very smart people, by

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<v Speaker 2>the way, like these are uncommonly smart but also uncommonly

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<v Speaker 2>egocentric people who have.

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<v Speaker 1>Been uncommonly rewarded for their activities, which is not the

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<v Speaker 1>best thing for your sense of a reality check.

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<v Speaker 2>I guess, yeah. No, they're very richly rewarded for what

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<v Speaker 2>they say. They think quite highly of themselves, which is

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<v Speaker 2>again not to undercut the accomplishments or the intelligence. But

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<v Speaker 2>at some point you're like, Okay, I've heard enough of this.

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<v Speaker 2>I don't actually care. Like, you know, they start telling

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<v Speaker 2>you about their fundraising and it's like, do you know

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<v Speaker 2>how little I care about like your latest round? Like

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<v Speaker 2>I don't care at all. That's great for you, but

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<v Speaker 2>you got the wrong guy. And so at some point

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<v Speaker 2>I was just like, what is this good for? And

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<v Speaker 2>so I was having this conversation with a guy named

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<v Speaker 2>Danny Hillis, who for people who don't know, Danny is

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<v Speaker 2>the creator of cloud computing. And if you were to

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<v Speaker 2>make a sort of Mount Rushmore of computer science, he'd

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<v Speaker 2>definitely be on there, and he would be kind of

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<v Speaker 2>in the Teddy Roosevelt spot of just like broad shoulder

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<v Speaker 2>and bearded and fun. And you know, I said, like, Danny,

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<v Speaker 2>what the fuck is this good for? And he just

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<v Speaker 2>kind of laughed at me, and then he said, look,

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<v Speaker 2>you have to imagine the tech without the tech companies.

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<v Speaker 1>I mentioned the tech without the tech companies?

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<v Speaker 2>Was there? Yeah, And I'll be honest with you, I'm

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<v Speaker 2>a little embarrassed that it took that long for the

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<v Speaker 2>thought to occur to me. But it does say something

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<v Speaker 2>about the lockdown that these big companies have on the tech.

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<v Speaker 2>And what Danny really meant by that was, if you're

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<v Speaker 2>only listening to them, they have already predetermined the sales point,

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<v Speaker 2>the revenue they need, and the practization that is required.

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<v Speaker 2>But this is a wildly important technology. Go find people

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<v Speaker 2>who are using it and experimenting it in ways that

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<v Speaker 2>may not be immediately profitable, but will help us do

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<v Speaker 2>things that we care about in the world. And so

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<v Speaker 2>that really opened my eyes. And from there, you know,

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<v Speaker 2>I just started exploring largely around things that I cared about. Right,

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<v Speaker 2>So can AI be useful in government? I care a

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<v Speaker 2>lot about the future of the republic and the competence

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<v Speaker 2>of government services healthcare, both in providing healthcare but also

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<v Speaker 2>making the experience of healthcare better, education, personal connection. So

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<v Speaker 2>that was really the genesis for the book. And I

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<v Speaker 2>owe Danny, you know, a ton of credit for kind

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<v Speaker 2>of shaking me by the shoulders and just being like,

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<v Speaker 2>you got to get away from the big companies.

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<v Speaker 1>Yeah, this was a debate you know, we had on

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<v Speaker 1>tech stuff recently, which is like, even if you accept

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<v Speaker 1>that that may be a financial bubble around AI right now,

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<v Speaker 1>and that financial bubble may crash. It seems like the

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<v Speaker 1>resids you will nonetheless be intensely valuable, unlike say Web

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<v Speaker 1>three or other kind of tech type bubbles.

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<v Speaker 2>Yeah. I think that's a very important point of discernment,

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<v Speaker 2>which is that it is not going to go away.

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<v Speaker 2>Like I'm a true believer that this is the most

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<v Speaker 2>powerful software we've invented, which is not to say it's

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<v Speaker 2>instantly miraculous. It's just very powerful. I can understand why

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<v Speaker 2>people are like, oh, it's a bubble. Well, no, the

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<v Speaker 2>finances are well, technologies real.

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<v Speaker 1>How have you found your own views evolving on this

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<v Speaker 1>through writing the book? Did you have a significant change

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<v Speaker 1>in going from thinking has had no value to being

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<v Speaker 1>very persuadive? Is value? And what was the kind of

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<v Speaker 1>well the turning points along the way of your Josh

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<v Speaker 1>journey on this topic.

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<v Speaker 2>I mean, it's a great question. I started to see

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<v Speaker 2>patterns in the challenge of implementing AI that really opened

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<v Speaker 2>my eyes to kind of the pace of implementation, the

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<v Speaker 2>pace of change. And really the most interesting one of

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<v Speaker 2>those challenges is human beings. Like we kick up a

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<v Speaker 2>lot of friction, right and for good reason. So like

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<v Speaker 2>we you know, when you talk about AI, to change

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<v Speaker 2>government like man. Government is a political system, thousands and

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<v Speaker 2>thousands of inputs and small rules and regulations. It has

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<v Speaker 2>human beings who just want to work an eight hour day.

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<v Speaker 2>It has other human beings who have political imperatives. And

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<v Speaker 2>so what you see is like the tech doesn't just

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<v Speaker 2>arrive and rule itself out and all of a sudden

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<v Speaker 2>everything solved. Tech still needs human beings to implement it

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<v Speaker 2>and refine it and mess with it, and they're really

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<v Speaker 2>like in every aspect that I was dealing with, what

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<v Speaker 2>you would see is like, you know, the tech almost

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<v Speaker 2>arrives in this kind of heroic caravan of like we're here,

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<v Speaker 2>we're going to fix it, and then everybody's like, mm

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<v Speaker 2>hmmm are you because our problems are not the problems

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<v Speaker 2>you think we have, right, And so what I got

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<v Speaker 2>out of that is, first of all, like, okay, human beings,

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<v Speaker 2>we're not going anywhere anytime soon. And two, we are

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<v Speaker 2>actually really really good at diagnosing how we want technology

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<v Speaker 2>to work and hammering away at it until it works

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<v Speaker 2>the way we want. It just takes time.

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<v Speaker 1>This is one of my big questions about the drone moment,

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<v Speaker 1>because obviously there's been so much investment in anderil and

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<v Speaker 1>healsing in Europe and then in Iran and Ukraine, Like

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<v Speaker 1>people are actually cobbling together drones and making them for

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<v Speaker 1>combat situation. So to your point about like the devil

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<v Speaker 1>is in the deployed and the environment of making stuff

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<v Speaker 1>when you needed to save your life maybe much more

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<v Speaker 1>fertile for innovation than the environment when you have tensive

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<v Speaker 1>billions of dollars of venture capital.

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<v Speaker 2>Well, I think that's right. I would say that there's

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<v Speaker 2>one distinction with drones that's pretty important, which is what

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<v Speaker 2>does success look like? There's only two phenomenon. Did you

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<v Speaker 2>hit the enemy did you avoid the ally? That's what

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<v Speaker 2>drone success is. It's pretty binary. And so I actually

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<v Speaker 2>think that in a way, as shocking as impressive as

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<v Speaker 2>some of that technology is, it's actually quite simple from

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<v Speaker 2>a decision tree point view. It's very different when you're saying, Okay,

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<v Speaker 2>we're in a hospital and we're trying to create an

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<v Speaker 2>AI program that can predict accurately whether a patient is septic.

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<v Speaker 2>You're dealing with massive amounts of variation, which is not

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<v Speaker 2>to say there isn't variation in drone technology. You've got

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<v Speaker 2>to know the terrain, you got to know the weather,

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<v Speaker 2>you got to know lots of different stuff. But the

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<v Speaker 2>unpredictability that can be kicked up by human beings internal

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<v Speaker 2>organs is vastly larger, and you don't necessarily get this

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<v Speaker 2>binary outcome. Each one of these problems, which is what

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<v Speaker 2>I learned, has a very distinct, unique kind of application,

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<v Speaker 2>and you have to pay attention to all that. It's

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<v Speaker 2>not as simple as hey, here's GPT, go solve it,

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<v Speaker 2>or hey, Claude code will agentify your world. That's kind

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<v Speaker 2>of meaningless. The devil is in the details that a

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<v Speaker 2>patent shape. You're thinking, I mean, it's such a wild company.

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<v Speaker 2>I mean, and I think you know. I'll go back

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<v Speaker 2>to the beginning for listeners who may not know this,

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<v Speaker 2>but basically it was created by these two guys, Alex

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<v Speaker 2>Karp and Peter Tiel. And Peter Tiel most people have

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<v Speaker 2>heard of largely because he is very successful Silicon Valley entrepreneur,

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<v Speaker 2>early investor in Facebook. Also, his politics are libertarian to

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<v Speaker 2>very very hard right. He's an ally of Donald Trump.

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<v Speaker 2>Alex carp'si opposite. He's a half black caf jew New Yorker.

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<v Speaker 2>Socialists gave money to Hillary Clinton and Kamala Harris, and

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<v Speaker 2>the two of them met in law school and they

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<v Speaker 2>decided very early on that artificial intelligence and software could

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<v Speaker 2>order the world if properly deployed. And so they got

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<v Speaker 2>money originally from inq Tel, which is the investment arm

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<v Speaker 2>of the CIA, and you know Pollunteer. They named it

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<v Speaker 2>after the mysterious Stones and the Lord of the Rings,

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<v Speaker 2>and the company has this mystique. What's hilarious about it

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<v Speaker 2>is it does the most boring thing in all of technology.

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<v Speaker 2>Their chief architect was like, I don't know what to

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<v Speaker 2>tell you. We are the mole people of Silicon Valley.

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<v Speaker 2>We deal with plumbing. And that's basically what they do

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<v Speaker 2>is that they take data pipelines, which are very unglamorous

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<v Speaker 2>things to build and to maintain. They hook into large

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<v Speaker 2>data sets that are active. They improve the performance of

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<v Speaker 2>those data sets, and then they pipe them into a

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<v Speaker 2>useful interface so that you can play a system like

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<v Speaker 2>a video game, and so that system could be for example,

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<v Speaker 2>you know, think Lows and Home Depot used Palenteer to

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<v Speaker 2>manage your inventory. Cleveland, Clinton, which I've spent a lot

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<v Speaker 2>of time reporting in, uses Pollenteer to manage their hospital events,

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<v Speaker 2>so they know like when new patients coming in and

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<v Speaker 2>when they're going out, which is critical to getting even

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<v Speaker 2>the minuscule margins that a hospital system has. And so

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<v Speaker 2>on the one hand, like obviously they're now a very

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<v Speaker 2>politicized company, and on the other they build infrastructure so

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<v Speaker 2>that you can actually live in a sort of more

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<v Speaker 2>enlightened digital world. And I kept running across them in

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<v Speaker 2>various places because in a lot of ways, you know,

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<v Speaker 2>people would run into problems and pollunteer would be able

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<v Speaker 2>to bail them out, not because what they build is

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<v Speaker 2>so beautiful or so elegant, but because it's very useful.

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<v Speaker 2>It's very bare bones. They're often much cheaper than their

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<v Speaker 2>competitors for reasons that we can get into. So, yeah,

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<v Speaker 2>I have a much more nuanced view of that company

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<v Speaker 2>than I think a lot of people do. You know.

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<v Speaker 2>I've certainly people on Twitter have certainly come after me.

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<v Speaker 1>We took a lot of criticisms, kind of holding up

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<v Speaker 1>as an example of a company that was making helpful

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<v Speaker 1>AI deployment.

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<v Speaker 2>Yeah, and listen, I'm not suggesting that you should agree

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<v Speaker 2>with their decision making to help the Trump administration with ICE,

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<v Speaker 2>but they're very clear it's not like they're hiding from it.

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<v Speaker 2>Alex Krp is very open about the fact that he

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<v Speaker 2>believes if you work at Palenteer, you should want to

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<v Speaker 2>make the United States government have the best possible software.

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<v Speaker 2>And what I saw time and time again is like,

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<v Speaker 2>they make really good software and so you know what,

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<v Speaker 2>people can can misinterpret that in any way they want,

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<v Speaker 2>But like, that's what I found.

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<v Speaker 1>Talk about the kind of human stories that you grounded

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<v Speaker 1>your book in. There was a school superintendent in Indiana

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<v Speaker 1>called Peggy Buffington who had kind of an interesting story.

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<v Speaker 2>Yeah, I mean, look, we'll talk shop just for a second.

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<v Speaker 2>But if you are going to write a book about AI,

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<v Speaker 2>the very first consideration you have is like, well, how

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<v Speaker 2>is this not going to be obsolete? Before I start,

0:11:56.600 --> 0:11:58.200
<v Speaker 2>and one of the things I sort of thought about

0:11:58.360 --> 0:12:01.120
<v Speaker 2>is like, well, you know, I've done enough reporting to

0:12:01.160 --> 0:12:03.839
<v Speaker 2>know that this stuff doesn't roll itself out. The human

0:12:03.840 --> 0:12:06.560
<v Speaker 2>beings are really going to be critical, and also that

0:12:06.600 --> 0:12:09.080
<v Speaker 2>there is a type of human being who is successful

0:12:09.760 --> 0:12:12.000
<v Speaker 2>at using and implementing AI. They're just it is, and

0:12:12.000 --> 0:12:16.280
<v Speaker 2>they're usually stubborn. They're usually really wetted to a problem,

0:12:16.320 --> 0:12:19.559
<v Speaker 2>like they really care about it. Almost irrationally. I kept

0:12:19.640 --> 0:12:23.360
<v Speaker 2>running into people who are much more dedicated to solving

0:12:23.360 --> 0:12:26.240
<v Speaker 2>the problem in their own domain than they are to AI,

0:12:26.880 --> 0:12:30.240
<v Speaker 2>and they've been so frustrated by the inability to make

0:12:30.280 --> 0:12:32.480
<v Speaker 2>progress that they're like, well, what the hell is this?

0:12:32.920 --> 0:12:35.160
<v Speaker 2>Is this AI thing worth a shot? Many of them

0:12:35.160 --> 0:12:38.480
<v Speaker 2>have no technical backgrounds, but they are just wedded to

0:12:38.640 --> 0:12:43.280
<v Speaker 2>either like improving health care quality, improving the way students learn,

0:12:44.000 --> 0:12:46.720
<v Speaker 2>and they start dabbling. And so Peggy's a really good example.

0:12:46.800 --> 0:12:49.640
<v Speaker 2>Is Peggy Actually you know, she's in a town called Hobert,

0:12:49.960 --> 0:12:53.199
<v Speaker 2>small town, it's like thirty thousand people. She's the school superintendent,

0:12:53.320 --> 0:12:56.640
<v Speaker 2>and she is a force of nature. She's very small,

0:12:56.760 --> 0:13:00.720
<v Speaker 2>but you can tell like she's the celebrity of this city.

0:13:01.679 --> 0:13:04.720
<v Speaker 2>And so when AI came along, she got a call

0:13:04.760 --> 0:13:09.640
<v Speaker 2>from Salcon. Salcon was aware that this was a person

0:13:09.640 --> 0:13:12.360
<v Speaker 2>who with a sort of force to her, and Con

0:13:12.360 --> 0:13:15.800
<v Speaker 2>Academy had done this collaboration with Open AI in secret

0:13:16.440 --> 0:13:20.440
<v Speaker 2>to build an AI bot, basically a tutoring bot. And

0:13:20.480 --> 0:13:23.679
<v Speaker 2>so they call Peggy and Peggy's like, yeah, I'm in.

0:13:24.120 --> 0:13:27.400
<v Speaker 2>We'll test it here, will beat the crap out of it,

0:13:27.960 --> 0:13:30.240
<v Speaker 2>and we're going to involve students, We're gonna involve teachers.

0:13:30.320 --> 0:13:32.720
<v Speaker 2>We're gonna give you the unvarnished truth about this bot

0:13:32.720 --> 0:13:35.640
<v Speaker 2>so we can understand it. And so the reason she's

0:13:35.679 --> 0:13:38.080
<v Speaker 2>so important is that what she really understands is school

0:13:38.640 --> 0:13:44.079
<v Speaker 2>she's in Indiana. Indiana is a politicized educational environment in

0:13:44.120 --> 0:13:47.720
<v Speaker 2>which they want high quality education, good scores, and they

0:13:47.800 --> 0:13:50.959
<v Speaker 2>want it cheap. And so Peggy's like, well, let's try it.

0:13:51.000 --> 0:13:53.720
<v Speaker 2>And what I saw from her was this incredible dedication

0:13:54.400 --> 0:13:57.439
<v Speaker 2>to get the stuff in the right kids and the

0:13:57.520 --> 0:14:00.840
<v Speaker 2>right teacher's hands, and then lots of pfational development with

0:14:00.880 --> 0:14:04.880
<v Speaker 2>teachers teaching them how to use it. And she's just relentless.

0:14:04.920 --> 0:14:08.040
<v Speaker 2>And in the end, I mean, the kind of interesting

0:14:08.080 --> 0:14:10.959
<v Speaker 2>thing about the entire open ai Con Academy experiment is

0:14:11.000 --> 0:14:14.440
<v Speaker 2>like didn't really work much for students. You know, they

0:14:14.480 --> 0:14:18.400
<v Speaker 2>went to the trouble of engineering this incredible tutoring bod

0:14:18.960 --> 0:14:22.560
<v Speaker 2>and the younger students were just like kind of ignored it,

0:14:22.600 --> 0:14:26.440
<v Speaker 2>weren't really all that enthusiastic. The older students, oftentimes, you

0:14:26.480 --> 0:14:28.440
<v Speaker 2>were very angry about it because they thought it was

0:14:28.480 --> 0:14:31.920
<v Speaker 2>an attempt to get between the relationship between them and

0:14:31.920 --> 0:14:34.360
<v Speaker 2>their teachers. What it was able to do. And this

0:14:34.400 --> 0:14:37.880
<v Speaker 2>is what Peggy was specifically so good at was it

0:14:37.920 --> 0:14:42.200
<v Speaker 2>really helped teachers reinvent the way they interact with their students.

0:14:42.560 --> 0:14:44.920
<v Speaker 2>So they made a bunch of teaching tools in con Migo,

0:14:45.000 --> 0:14:47.640
<v Speaker 2>which is the name of the bod. And it turns out,

0:14:47.680 --> 0:14:50.240
<v Speaker 2>like a lot of teachers post COVID, we're in this

0:14:50.360 --> 0:14:53.760
<v Speaker 2>environment where they were doing the you know the thing

0:14:53.760 --> 0:14:55.800
<v Speaker 2>that you and I grew up with, which is they'd

0:14:55.800 --> 0:14:58.720
<v Speaker 2>stand in front of the class. They had a parcel

0:14:58.760 --> 0:15:01.000
<v Speaker 2>of information that over the or so forty minutes they

0:15:01.000 --> 0:15:04.440
<v Speaker 2>were going to teach the class. Eventually the students would

0:15:04.440 --> 0:15:08.360
<v Speaker 2>be graded on their ability to synthesize and repeat the information.

0:15:08.960 --> 0:15:11.320
<v Speaker 2>And the model is just dead. What the teachers told

0:15:11.360 --> 0:15:13.840
<v Speaker 2>me is like post COVID, like what are we even doing?

0:15:14.000 --> 0:15:15.880
<v Speaker 2>I know they're looking at their phones, I know they

0:15:15.880 --> 0:15:18.680
<v Speaker 2>have multiple tabs open, and I'm up there kind of

0:15:18.760 --> 0:15:21.800
<v Speaker 2>bombing basically, And so what they were able to do,

0:15:21.840 --> 0:15:24.520
<v Speaker 2>and I saw this in a couple instances, but there's

0:15:24.520 --> 0:15:27.040
<v Speaker 2>one teachers just great at it. She basically was like,

0:15:27.080 --> 0:15:29.640
<v Speaker 2>all right, I gotta change, and so she would use

0:15:29.800 --> 0:15:33.120
<v Speaker 2>the bot to input the lesson planned the curricular points

0:15:33.120 --> 0:15:36.280
<v Speaker 2>she needed to hit. And then she would say, help

0:15:36.360 --> 0:15:39.920
<v Speaker 2>me take this lecture and make it a lab. Help

0:15:39.960 --> 0:15:42.760
<v Speaker 2>me take this test and make it a lab. And

0:15:42.800 --> 0:15:47.640
<v Speaker 2>what I saw was that the kids basically were just active.

0:15:47.880 --> 0:15:50.160
<v Speaker 2>They were always moving, they were talking to each other,

0:15:50.640 --> 0:15:53.440
<v Speaker 2>they were talking to her. She could scaffold the learning

0:15:53.520 --> 0:15:55.800
<v Speaker 2>so that, you know, there's this phrase in teaching called

0:15:55.800 --> 0:15:58.920
<v Speaker 2>teaching to the middle, where you're basically trying to account

0:15:59.160 --> 0:16:01.320
<v Speaker 2>not for the top kids and not for the bottom kids,

0:16:01.640 --> 0:16:04.520
<v Speaker 2>but for this imagined median, so that as many kids

0:16:04.520 --> 0:16:08.080
<v Speaker 2>as possible. Following along which she discovered, this teacher and

0:16:08.120 --> 0:16:11.720
<v Speaker 2>many others was like, actually, you can teach to multiple

0:16:11.720 --> 0:16:15.520
<v Speaker 2>groups in differentiation if the BOT is telling them and

0:16:15.640 --> 0:16:18.080
<v Speaker 2>helping them understand when they need to call me to

0:16:18.120 --> 0:16:21.000
<v Speaker 2>come over and help. And so I saw a lot

0:16:21.040 --> 0:16:23.720
<v Speaker 2>of promise out of that. But it just never would

0:16:23.760 --> 0:16:27.840
<v Speaker 2>have happened without Peggy Buffington, who kept the Indiana Department

0:16:27.880 --> 0:16:30.920
<v Speaker 2>of Education at bay. Didn't tell them this was happening

0:16:30.920 --> 0:16:34.160
<v Speaker 2>because she knew they would squash it. Who was relentless

0:16:34.160 --> 0:16:37.280
<v Speaker 2>with teachers, who then would take the successful teachers and

0:16:37.440 --> 0:16:39.600
<v Speaker 2>make them talk to the unsuccessful teachers.

0:16:39.760 --> 0:16:42.040
<v Speaker 1>This to your point is about making it technology useful

0:16:42.040 --> 0:16:44.239
<v Speaker 1>within a system rather than about technology.

0:16:44.680 --> 0:16:47.400
<v Speaker 2>Yeah is it? Like, yes, the software is cool, but

0:16:47.640 --> 0:16:50.960
<v Speaker 2>change is hard in any realm, and if you want

0:16:51.000 --> 0:16:53.640
<v Speaker 2>the software to actually make the changes that we want

0:16:53.680 --> 0:16:55.400
<v Speaker 2>to see in the world, it's not going to happen

0:16:55.440 --> 0:16:59.760
<v Speaker 2>without multiple advocates who are stubborn and grumpy and willing

0:16:59.800 --> 0:17:01.520
<v Speaker 2>to get head in your face if you're standing in

0:17:01.560 --> 0:17:04.359
<v Speaker 2>a way. And so Pegy just stood out to me.

0:17:04.440 --> 0:17:08.000
<v Speaker 2>It's like, oh yeah, like this thing would be dead

0:17:08.040 --> 0:17:11.720
<v Speaker 2>as a doornail without her. And again, every place I went,

0:17:12.160 --> 0:17:16.080
<v Speaker 2>I would found some version of Peggy, and without it,

0:17:16.160 --> 0:17:20.840
<v Speaker 2>you just having an unsuccessful producting unsuccessful implementation.

0:17:39.040 --> 0:17:41.720
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<v Speaker 1>Vital Proteins. What do you make of these examples like

0:18:53.680 --> 0:18:58.560
<v Speaker 1>Peggy's example, which you're ultimately fairly heroic characters. How do

0:18:58.680 --> 0:19:01.280
<v Speaker 1>those kind of singular exsessles they ladder up to like

0:19:01.320 --> 0:19:04.440
<v Speaker 1>a new paradigm for how we may interact with AI

0:19:04.560 --> 0:19:07.639
<v Speaker 1>or how AI may diffuse through society? Like what do

0:19:07.720 --> 0:19:11.080
<v Speaker 1>these what do these examples coalesce into for you?

0:19:11.680 --> 0:19:13.600
<v Speaker 2>Well, there's it's a good question. I mean, I think

0:19:13.640 --> 0:19:15.440
<v Speaker 2>there's a there's a couple of ways to approach it, right,

0:19:15.840 --> 0:19:17.760
<v Speaker 2>we only have a couple of years to figure out

0:19:18.160 --> 0:19:20.439
<v Speaker 2>what we want from AI before it gets kind of

0:19:20.560 --> 0:19:23.960
<v Speaker 2>too late, and companies and governments have dictated what we're

0:19:23.960 --> 0:19:25.399
<v Speaker 2>going to get from AI because.

0:19:25.160 --> 0:19:27.159
<v Speaker 1>To your point, we're not able to interact with the

0:19:27.200 --> 0:19:28.960
<v Speaker 1>text ever from the tech companies anymore. It's kind of

0:19:28.960 --> 0:19:30.000
<v Speaker 1>locked in. It's captured.

0:19:30.320 --> 0:19:32.399
<v Speaker 2>Yeah, And so a lot of the response to that

0:19:32.440 --> 0:19:35.440
<v Speaker 2>has been for people to say, no, I don't want

0:19:35.480 --> 0:19:37.720
<v Speaker 2>it to do this, and that's an important response, but

0:19:37.800 --> 0:19:41.399
<v Speaker 2>I think an equally important response is, but I would

0:19:41.480 --> 0:19:44.080
<v Speaker 2>like it to do that. And so part of what

0:19:44.119 --> 0:19:48.560
<v Speaker 2>they're doing is showing up and demonstrating that, like, actually,

0:19:49.440 --> 0:19:52.240
<v Speaker 2>it can improve certain aspects of our lives. It's not

0:19:52.240 --> 0:19:54.600
<v Speaker 2>gonna be easy, but with the right person and the

0:19:54.640 --> 0:19:56.719
<v Speaker 2>right funding and the right understanding, what we're trying to do,

0:19:56.880 --> 0:20:00.880
<v Speaker 2>like this is very useful stuff in lat of different realms.

0:20:00.880 --> 0:20:03.600
<v Speaker 2>And so I think there's a couple lessons from it. One,

0:20:03.760 --> 0:20:06.000
<v Speaker 2>as I said, is like why don't we show people

0:20:06.040 --> 0:20:08.719
<v Speaker 2>what we want? Why don't we demonstrate there's a market

0:20:08.800 --> 0:20:13.960
<v Speaker 2>for like, actually, good positive implementations that help our civilization advance?

0:20:14.600 --> 0:20:17.239
<v Speaker 2>And on the other you know, look, I do know

0:20:17.320 --> 0:20:19.800
<v Speaker 2>that there are lots of companies and lots of cities

0:20:19.840 --> 0:20:21.919
<v Speaker 2>and lots of organizations that are like, how are we

0:20:21.960 --> 0:20:25.080
<v Speaker 2>going to do this? And if their example is kind

0:20:25.080 --> 0:20:28.440
<v Speaker 2>of a roadmap, which I think it is, that's helpful too.

0:20:28.960 --> 0:20:30.840
<v Speaker 2>I've been a business journalist for a long time and

0:20:30.880 --> 0:20:34.280
<v Speaker 2>when I talk to CEOs, they don't really know much

0:20:34.320 --> 0:20:37.040
<v Speaker 2>about their software for the most part, Right.

0:20:37.000 --> 0:20:39.400
<v Speaker 1>And the mandate is use AI is ensure this.

0:20:39.320 --> 0:20:41.919
<v Speaker 2>Is use AI, right, And then most recently, the mandate

0:20:42.000 --> 0:20:45.680
<v Speaker 2>is stop using AI. It's too expensive. Right. And part

0:20:45.720 --> 0:20:49.000
<v Speaker 2>of this is like, actually, guys, it's not about the expense,

0:20:49.240 --> 0:20:53.400
<v Speaker 2>and it's not about this broad brush. It's about asking yourself, Hey,

0:20:53.520 --> 0:20:55.720
<v Speaker 2>is there a problem that we're really trying to solve here?

0:20:56.240 --> 0:20:59.440
<v Speaker 2>And if so, who's the right person to lead that?

0:20:59.600 --> 0:21:02.560
<v Speaker 2>So when I was at the Cleveland Clinic, they had

0:21:02.600 --> 0:21:06.040
<v Speaker 2>recently hired a new CTO chief technical officer who'd come

0:21:06.080 --> 0:21:10.399
<v Speaker 2>from outside healthcare. And what he realized is that there

0:21:10.440 --> 0:21:13.320
<v Speaker 2>are lots of problems in a hospital system today I

0:21:13.359 --> 0:21:16.000
<v Speaker 2>would be good for But he wasn't going to start there.

0:21:16.359 --> 0:21:18.800
<v Speaker 2>He was actually going to start by finding people in

0:21:18.840 --> 0:21:23.560
<v Speaker 2>the organization who he thought had the goods to be

0:21:23.880 --> 0:21:28.520
<v Speaker 2>essentially product managers, and so he would find people who

0:21:28.560 --> 0:21:32.960
<v Speaker 2>he saw were like super prustrated by a problem, and

0:21:33.000 --> 0:21:36.680
<v Speaker 2>then he would start to reverse engineer, like, oh, this

0:21:36.800 --> 0:21:39.280
<v Speaker 2>might be a good partner if I wanted to do X,

0:21:39.400 --> 0:21:41.600
<v Speaker 2>Y or Z, right, And I said, I was sitting

0:21:41.640 --> 0:21:44.480
<v Speaker 2>across from row Hit at lunch and he was telling

0:21:44.480 --> 0:21:47.639
<v Speaker 2>me all this just in the most like down fraud

0:21:47.720 --> 0:21:50.080
<v Speaker 2>and eyre kind of way, and I was like, Hit,

0:21:50.119 --> 0:21:54.480
<v Speaker 2>it's an eighty thousand person organization, Cleveland Clinic. How many

0:21:54.520 --> 0:21:59.159
<v Speaker 2>people have you found? He's like eight. Ten. It's like,

0:21:59.200 --> 0:22:02.080
<v Speaker 2>I don't know about the but eight or ten. Now

0:22:02.320 --> 0:22:06.239
<v Speaker 2>I think he was under selling a little bit. But

0:22:06.359 --> 0:22:08.439
<v Speaker 2>the point is taken, which is that this stuff is

0:22:08.520 --> 0:22:12.400
<v Speaker 2>actually really hard. You need this domain expertise, but when

0:22:12.400 --> 0:22:15.560
<v Speaker 2>you have it and the tech is calibrated to work,

0:22:15.800 --> 0:22:18.040
<v Speaker 2>you get pretty incredible results.

0:22:18.880 --> 0:22:22.800
<v Speaker 1>The New Times reviewed your book and sort of pointed

0:22:22.840 --> 0:22:26.119
<v Speaker 1>to an irony about surfacing these optimistic case studies at

0:22:26.119 --> 0:22:28.199
<v Speaker 1>a time when quote companies are laying off tens of

0:22:28.200 --> 0:22:30.560
<v Speaker 1>thousands in the name of automation. How do you respond

0:22:30.560 --> 0:22:30.920
<v Speaker 1>to that.

0:22:31.720 --> 0:22:35.600
<v Speaker 2>Well, I'm glad you asked. So. I actually wrote a

0:22:35.640 --> 0:22:38.440
<v Speaker 2>cover story for The Atlantic in February about AI and

0:22:38.480 --> 0:22:41.840
<v Speaker 2>the future of employment. Here's the deal. At this moment,

0:22:42.560 --> 0:22:45.840
<v Speaker 2>we have seen no impact. Like in the data, we

0:22:45.920 --> 0:22:49.560
<v Speaker 2>have yet to see a significant impact of any kind

0:22:50.359 --> 0:22:54.000
<v Speaker 2>suggesting that AI is compromising the future of employment.

0:22:54.160 --> 0:22:57.800
<v Speaker 1>We have seen mass layoffs at tech companies with AI

0:22:57.840 --> 0:22:58.520
<v Speaker 1>as a proviso.

0:22:59.480 --> 0:23:03.360
<v Speaker 2>Yeah, and by the way, like from a narrative perspective,

0:23:03.359 --> 0:23:05.640
<v Speaker 2>i'd love for that to be the case. It's much easier, right,

0:23:06.040 --> 0:23:08.680
<v Speaker 2>But I haven't seen it bear out. And a lot

0:23:08.720 --> 0:23:11.040
<v Speaker 2>of times when people were laying off engineers, what they

0:23:11.040 --> 0:23:14.800
<v Speaker 2>discovered is they actually needed those engineers. So how that

0:23:14.840 --> 0:23:17.080
<v Speaker 2>plays out is largely a function of speed. There will

0:23:17.119 --> 0:23:19.040
<v Speaker 2>probably be some job loss, like I have no doubt

0:23:19.080 --> 0:23:22.080
<v Speaker 2>about that. The question is does it happen in three years,

0:23:22.119 --> 0:23:24.679
<v Speaker 2>five years, or thirty years. If it happens in thirty years,

0:23:25.160 --> 0:23:27.879
<v Speaker 2>we'll be fine. The labor markets have a natural rate

0:23:27.920 --> 0:23:32.199
<v Speaker 2>of adjustment. They always have. General purpose technologies usually come in,

0:23:33.160 --> 0:23:36.560
<v Speaker 2>they eliminate some jobs, they create new categories, and the

0:23:36.600 --> 0:23:40.600
<v Speaker 2>economists are very sanguine about this. In my reporting, the

0:23:40.640 --> 0:23:43.440
<v Speaker 2>thing that I found most disturbing and most worthy of

0:23:43.480 --> 0:23:46.640
<v Speaker 2>alarm is that there's really no conversation about the what ifs,

0:23:47.320 --> 0:23:50.880
<v Speaker 2>and that companies are feeling like they have a financial issue,

0:23:50.960 --> 0:23:53.000
<v Speaker 2>as we discussed earlier, which is they've made all this

0:23:53.119 --> 0:23:58.200
<v Speaker 2>investment in AI and are not yet seeing massive productivity gains. Therefore,

0:23:58.600 --> 0:24:01.480
<v Speaker 2>if they're under pressure, they may have to cut jobs. Therefore,

0:24:02.000 --> 0:24:04.440
<v Speaker 2>what's going to happen to all those people? And politically,

0:24:04.520 --> 0:24:07.159
<v Speaker 2>there's almost no answer other than Gina Romondo, who's made

0:24:07.160 --> 0:24:09.800
<v Speaker 2>a couple of suggestions about ways to do job retraining,

0:24:10.320 --> 0:24:13.959
<v Speaker 2>like it's a completely ignored issue by the political class

0:24:14.600 --> 0:24:17.080
<v Speaker 2>that said, like to the Times critique, like, well, what

0:24:17.080 --> 0:24:20.879
<v Speaker 2>about the jobs. I'm not saying it's not significant, But

0:24:20.960 --> 0:24:23.640
<v Speaker 2>the premise of the book is not let me adjudicate

0:24:23.680 --> 0:24:27.159
<v Speaker 2>whether AI is going to be good or bad. Writ large,

0:24:27.440 --> 0:24:29.760
<v Speaker 2>I don't know. I don't know how anyone can know.

0:24:30.440 --> 0:24:32.720
<v Speaker 2>I think that what I'm trying to restore is some

0:24:33.080 --> 0:24:35.440
<v Speaker 2>agency to all of us who may feel a little

0:24:35.440 --> 0:24:37.600
<v Speaker 2>bit helpless about it. And it goes back to this

0:24:37.960 --> 0:24:41.640
<v Speaker 2>point that LIKE doesn't have to be what the tech

0:24:41.680 --> 0:24:44.800
<v Speaker 2>companies want it to be. If we can demonstrate what

0:24:44.960 --> 0:24:47.840
<v Speaker 2>we want from the technology, and we like what we

0:24:48.000 --> 0:24:51.280
<v Speaker 2>demand from it. That is a form of agency, So

0:24:51.560 --> 0:24:54.400
<v Speaker 2>is not using it for the things that are terrible? Right,

0:24:54.520 --> 0:24:57.320
<v Speaker 2>And like, you know this very well, and I'm guessing

0:24:57.359 --> 0:25:00.440
<v Speaker 2>your listeners do too, But like, the the best way

0:25:00.440 --> 0:25:02.520
<v Speaker 2>to cast your vote with the technology company is in

0:25:02.720 --> 0:25:06.479
<v Speaker 2>your user behavior. If you use Google a certain way,

0:25:06.800 --> 0:25:10.320
<v Speaker 2>Google knows and then moves in that direction. If we

0:25:10.480 --> 0:25:14.639
<v Speaker 2>use AI in ways that replace human labor, in ways

0:25:14.640 --> 0:25:17.760
<v Speaker 2>that contribute to the sort of slopification of information, like,

0:25:18.320 --> 0:25:20.680
<v Speaker 2>we're going to get more of it. If we use

0:25:20.720 --> 0:25:23.520
<v Speaker 2>AI in other ways, we'll get more of that. Like

0:25:23.560 --> 0:25:27.720
<v Speaker 2>we're not without power here. I don't think that power

0:25:27.760 --> 0:25:29.840
<v Speaker 2>is going to last forever. This stuff is going to

0:25:29.880 --> 0:25:33.119
<v Speaker 2>settle quick. And so part of my belief is, like

0:25:33.160 --> 0:25:36.199
<v Speaker 2>we do have an obligation to show and report on

0:25:36.240 --> 0:25:38.120
<v Speaker 2>what we want to see from them that that will

0:25:38.160 --> 0:25:40.919
<v Speaker 2>help other people see that too. So like, listen, I've

0:25:40.920 --> 0:25:43.960
<v Speaker 2>written book reviews too, Like I totally know you're kind

0:25:43.960 --> 0:25:46.040
<v Speaker 2>of like you got to make your points, but I

0:25:46.480 --> 0:25:48.159
<v Speaker 2>don't think that's what my premise was.

0:25:48.920 --> 0:25:50.919
<v Speaker 1>Coming back to this idea that we don't have to

0:25:51.840 --> 0:25:54.719
<v Speaker 1>entirely align on how we perceive the tech with how

0:25:54.720 --> 0:25:56.960
<v Speaker 1>we perceive the tech companies. What is the road of

0:25:57.000 --> 0:25:58.800
<v Speaker 1>the tech companies in all of this, and how much

0:25:58.840 --> 0:26:02.560
<v Speaker 1>blame do you think they can for the anger you've described,

0:26:02.560 --> 0:26:04.920
<v Speaker 1>And what do you think the output of that anger

0:26:04.920 --> 0:26:07.000
<v Speaker 1>will it? Will it be the election of politicians who

0:26:07.080 --> 0:26:10.960
<v Speaker 1>essentially regulate this into as much of a moratorium as

0:26:11.000 --> 0:26:13.000
<v Speaker 1>can be, And what could be the harms of that?

0:26:13.080 --> 0:26:17.080
<v Speaker 1>I mean, why do you see this interaction between how

0:26:17.119 --> 0:26:20.960
<v Speaker 1>we live our daily lives, these trillion dollar IPOs and

0:26:21.760 --> 0:26:23.879
<v Speaker 1>the political winds blowing?

0:26:24.359 --> 0:26:27.760
<v Speaker 2>Yeah? I mean, on the one hand, these companies deserve

0:26:27.840 --> 0:26:32.840
<v Speaker 2>credit for creating pretty incredible technology, and on the other

0:26:32.960 --> 0:26:35.919
<v Speaker 2>they deserve a ton of blame for our overall relationships

0:26:35.920 --> 0:26:39.919
<v Speaker 2>with technology. Right, So, I'm thinking specifically Google and Meta

0:26:40.000 --> 0:26:42.480
<v Speaker 2>and the ones that have been around all centuries selling

0:26:42.520 --> 0:26:45.680
<v Speaker 2>as social media or search in ways that have gradually

0:26:45.720 --> 0:26:50.360
<v Speaker 2>deteriorated our ability to trust information, our connection with other

0:26:50.440 --> 0:26:53.440
<v Speaker 2>human beings like we've seen it. I think it's not

0:26:53.560 --> 0:26:56.640
<v Speaker 2>unfair to say that there are similarities to tobacco companies.

0:26:57.200 --> 0:26:59.240
<v Speaker 2>We get it. If you're not anger, you're skeptical, you

0:26:59.280 --> 0:27:02.320
<v Speaker 2>haven't been paid. The thing that stands out to me

0:27:02.480 --> 0:27:06.040
<v Speaker 2>is that there's no ambiguity about what people want when

0:27:06.040 --> 0:27:09.239
<v Speaker 2>it comes to a regulatory regime from AI. So this

0:27:09.320 --> 0:27:11.440
<v Speaker 2>is a slightly older poll now, I think it's only

0:27:11.640 --> 0:27:13.920
<v Speaker 2>it's maybe two three months old. But Fox News polling,

0:27:14.080 --> 0:27:17.119
<v Speaker 2>which is different than Fox News, eighty one percent of

0:27:17.119 --> 0:27:19.919
<v Speaker 2>people said it was a very urgent or urgent priority

0:27:20.000 --> 0:27:23.120
<v Speaker 2>to regulate AI, that they wanted that from the government.

0:27:23.480 --> 0:27:26.840
<v Speaker 2>Problem is we have people in our elected offices who

0:27:26.840 --> 0:27:30.280
<v Speaker 2>buy and large don't know anything about technology. And there's

0:27:30.320 --> 0:27:34.960
<v Speaker 2>this gap that has existed forever, which is the people

0:27:34.960 --> 0:27:38.439
<v Speaker 2>who make technology are generally uninterested in public policy. The

0:27:38.480 --> 0:27:41.840
<v Speaker 2>people who make public policy are generally uninterested in technology. Right,

0:27:42.800 --> 0:27:46.760
<v Speaker 2>tech always moves faster than the law always does. And

0:27:46.800 --> 0:27:50.399
<v Speaker 2>in American particular, we're sort of famous for regulating after

0:27:50.480 --> 0:27:54.280
<v Speaker 2>the catastrophe. You know, Europe may regulate to prevent opportunity.

0:27:54.440 --> 0:27:59.440
<v Speaker 2>We regulate after the catastrophe. We only have one institutional

0:27:59.440 --> 0:28:01.919
<v Speaker 2>way to fix it, which is how we vote. And

0:28:02.000 --> 0:28:05.640
<v Speaker 2>so another reason that I'm so interested in talking about

0:28:05.680 --> 0:28:08.360
<v Speaker 2>this now is that we have a data election coming up.

0:28:09.240 --> 0:28:12.560
<v Speaker 2>We ought to demand that our elected representatives have a

0:28:12.560 --> 0:28:15.680
<v Speaker 2>point of view about what kind of regulation they want

0:28:16.000 --> 0:28:19.679
<v Speaker 2>of these companies, whether it's some sort of international collaboration,

0:28:20.160 --> 0:28:23.000
<v Speaker 2>like you know, International Atomic Energy Commission, which has done

0:28:23.359 --> 0:28:27.800
<v Speaker 2>pretty well at regulating nuclear proliferation, whether it's state by state,

0:28:28.000 --> 0:28:31.040
<v Speaker 2>whether it's data centers. But we're not having like a

0:28:31.119 --> 0:28:35.080
<v Speaker 2>mature conversation about what we want and what we don't

0:28:35.119 --> 0:28:39.600
<v Speaker 2>want from the tech. Usually we reserve that for our lawmakers.

0:28:40.120 --> 0:28:42.960
<v Speaker 2>I would hope that at town halls and in debates

0:28:43.040 --> 0:28:45.080
<v Speaker 2>that that is a pretty big issue. My hunches it

0:28:45.120 --> 0:28:48.440
<v Speaker 2>will be. And I'm generally not an optimist about this stuff,

0:28:49.120 --> 0:28:51.920
<v Speaker 2>but I think the data center issue, while a little

0:28:51.920 --> 0:28:55.280
<v Speaker 2>bit misplaced, it's the only thing people can point to

0:28:55.360 --> 0:28:58.720
<v Speaker 2>physically and say I hate this except for flow cameras,

0:28:59.160 --> 0:29:02.560
<v Speaker 2>except for flockcamp. It's right, it's kind of time. I

0:29:02.600 --> 0:29:04.360
<v Speaker 2>don't you know. I know that this will be a

0:29:04.360 --> 0:29:07.360
<v Speaker 2>massive issue in twenty twenty eight, no question, but it's

0:29:07.360 --> 0:29:10.959
<v Speaker 2>already massive. So like, could we get to a place

0:29:11.040 --> 0:29:14.960
<v Speaker 2>because in the end, I think the company CEOs, many

0:29:15.000 --> 0:29:18.000
<v Speaker 2>of whom I've spoken with, they may posture, they may bluff,

0:29:18.040 --> 0:29:20.480
<v Speaker 2>they may say they want this, they want that. Pretty

0:29:20.560 --> 0:29:22.800
<v Speaker 2>much all of them will tell me privately, like, yeah,

0:29:22.840 --> 0:29:24.880
<v Speaker 2>it be a real relief to have some regulations, we

0:29:25.480 --> 0:29:28.000
<v Speaker 2>stop having to talk about this, and we want the

0:29:28.040 --> 0:29:30.360
<v Speaker 2>right kind of regulation for us. Of course they do,

0:29:30.880 --> 0:29:32.840
<v Speaker 2>but I haven't met any of them who are like,

0:29:32.920 --> 0:29:35.520
<v Speaker 2>don't regulate it at all. They don't want to spend

0:29:35.560 --> 0:29:38.960
<v Speaker 2>time wasted here. But it's about this like gap we have,

0:29:39.160 --> 0:29:41.280
<v Speaker 2>and it's you know, my hope is we can make

0:29:41.280 --> 0:29:42.720
<v Speaker 2>progress on that in the midterms.

0:29:43.640 --> 0:29:46.360
<v Speaker 1>The book is AI for Good, how real people are

0:29:46.400 --> 0:29:50.280
<v Speaker 1>using artificial intelligence to fix things that matter. Josh, thank

0:29:50.280 --> 0:30:08.640
<v Speaker 1>you my pleasure for tech Stuff. I'm os Voloshin. This

0:30:08.760 --> 0:30:11.920
<v Speaker 1>episode was produced by Eliza Dennis and Tyler Hill. It

0:30:11.960 --> 0:30:15.280
<v Speaker 1>was executive produced by me and Julian Nutter for Kaleidoscope

0:30:15.480 --> 0:30:19.240
<v Speaker 1>and Katria Novel for iHeart Podcasts. Jack Insley makes this

0:30:19.320 --> 0:30:22.760
<v Speaker 1>episode and Kyle Murdoch rotar THEMESI a special thank you

0:30:22.800 --> 0:30:25.760
<v Speaker 1>to all our listeners. Please rate, review, and reach out

0:30:25.760 --> 0:30:28.680
<v Speaker 1>to us at tech Stuff podcast at gmail dot com.

0:30:28.680 --> 0:30:29.680
<v Speaker 1>We love hearing from you.