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One of the most depressing things I see

when I talk to boards of very large companies

and to C -level leaders is a lot of them have come to believe they just can't change, right?

So I show them what AI can do and they're like, well, I guess we'll ride this out.

And I'm like, that's an insane answer.

You have 100 ,000 people working for you.

The smartest people in the world are working for your company.

You have customers and resources.

The idea that you can't be flexible compete seems to be overly baked in right now.

Ethan Mollick, professor at Wharton and author of Coexistence, welcome to the IdeaCast.

Thanks for having me.

Yeah.

So you've been closely tracking advances in AI and constantly experimenting with all the new tools.

What is different today than even a few months ago in terms of how new developments should guide the way

that leaders are thinking about strategy in particular?

One is the exponential curve continues.

AI just keeps getting better.

There isn't really a sign of that stopping right now.

And the second is that agents are real,

which are AI tools that can go off and do work on their own autonomously and do real process work, real important things. And

that has become a viable approach to work in the near past

it seems like a lot of companies right

now are thinking of ai as really a productivity exercise you know you're automating tasks you're saving time you're reducing costs

how does you know a genuinely strategic ai transformation strategy differ from

that what do you think companies should be doing now i think

it's actually pretty urgent to do a few things one is viewing

this purely as a productivity tool gets you in trouble in a lot of different ways.

People are getting ROI from AI, right?

They're getting ROI from that productivity gain.

Productivity gains are real.

The problem is, is that those productivity gains get eaten up by the organization, which is if you don't change any part of the process,

AI is mostly a single player entity and you end up with more of everything.

So if you don't change your KPIs, people can produce more PowerPoint, they produce more code.

That's not actually what you want.

So leadership needs to decide what this thing is.

And the second is AI is getting very capable.

And I worry that if our default is automation, we end up with a situation where we just keep handing tasks to AI

and don't think hard enough about where humans fall into this process.

And that leads to a lot of danger as well.

Is your argument

that leaders need to move from asking sort of where can we use AI in this existing business alongside humans

to a question that's more like, you know, how does AI change our business going forward?

At this point, we have ample evidence

that AI can regularly do the kind of work

that would take a human two to 18 weeks in a reasonable amount of time, a reasonable amount of money with high success rates.

That implies a lot of different things.

It implies that what your organization does has to change.

How you think about the boundaries between jobs is changing.

I talk to C -level leaders all the time.

And one of the first effects they see of AI, and we saw this also in our research, is that jobs start to blur.

Coders do design work.

Designers do coding.

Product managers do everything.

How do you start thinking about what the boundaries of your organization are?

What's the product you're trying to produce?

Is it an interim product of a PowerPoint?

What was the point of the PowerPoint in the first place?

You start to run into really fundamental questions about what the goal of the organization is,

how you accomplish that, what are your values for getting there?

And they're urgent.

This isn't sort of theoretical, you know, let's sit down in an academic way.

It is your organization is already changing from the inside.

What do you do about that?

So what are some best practices

that you recommend to leaders who are grappling with these issues right now

that need to start having those really urgent conversations and not just have them,

but sort of make them progress enough so that they can actually start making some real decisions?

There's some individual feedback and there's some organizational feedback.

On the organization side, the model I've seen works best is sort of like a meta model, which is leadership lab and crowd.

So you need three things to succeed.

You need the leadership team to actually lead in this, right?

That's not just say, use AI.

They have to make decisions about how AI is used, what the organization does with AI, how to incentivize people.

The crowd means giving people access to these tools we're at a point now we're giving organizations inferior ai access

because either just

because you haven't bothered to improve the most recent models leads

you to trouble like you'd actually become very deluded about what ai can

and can't do

and then you need a lab

you need people doing 24 7 work who are not primarily technical by the way there'll be technical people there

but also organizational people who are thinking about how do we restructure the organization around ai

they're benchmarking what systems could do building systems of the future, taking ideas from the crowd and ideas from leadership and helping build them.

So you need to be doing this as a whole organization response.

At the individual level, the factor that I'm seeing that differentiates leadership that succeeds at AI use

and organizations that succeed in those that don't is leaders who are willing to eat risk, who are willing to say,

look, nobody knows what's going to happen here, but unless we do active experimentation, we're not going to know anything.

And if you just let everybody in the organization have vetoes over AI use because it might be risky,

it is, but not doing anything is risky too.

And that also means actively using it yourself.

So leaders who don't use the latest AI systems, who wait for reports from other people, who are waiting for training some week,

fall behind those who just are experimenting.

So I think the last time that you and I talked about this,

you sort of had this model for human -AI collaboration that was either a centaur, you're half using your human skills,

and then half using AI or a cyborg, where you're going back and forth between the two.

Talk about how that is evolving to one that you now describe as conjurer work,

that you think leaders should not only be doing themselves as individuals, but also enabling in their organizations as part of their strategy.

The biggest change, if you really look at the sweep of the last year, was that in every independent study we have,

basically, AI went from regularly being able to do a half hour of work, 45 minutes of work,

to you can get 16 plus hours of work out of a single AI instruction with good chance of success.

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And that means you don't work next to the AI anymore.

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You don't work back and forth with it in the same way you did before.

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You are actually assigning the AI to tasks almost like your team you're assigning and it does the work.

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So a conjurer is really about conjuring a team into existence when you need it.

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It's almost like writing an RFP for a project you need done

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or SOP or any of the other standard TLA three -letter acronyms that we use in business to assign work.

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And you send that out and the AI just does that work.

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That's a very different situation than I prompt the AI, look at the results, I prompt it again.

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This is a very different, you know, action -oriented field.

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How does that change how leaders approach, you know, the structure of the organization, what it even means to be a manager?

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So everyone's a manager to some degree right now.

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Everyone has AI to manage.

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And AI skills look like management skills.

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In fact, there's a great study out of Anthropic that shows

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that the people who are the most successful coders at the first pass of coding are actually managers, not coders

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because they do a good job specifying what they want and the AI executes on that.

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So that is the big problem, right?

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Your organization is something capacity increases.

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And I think organizations that don't have imagination are going to retreat to automating away the work.

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And automation is a well -worn playbook in the business world.

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I think it's a recipe for disaster.

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I mean, it's a disaster for all of us.

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If all of our work becomes automated or we just become verifiers of AI content, that's like not a good situation to be in.

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But also it removes all the differentiation.

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Like leaders need to be building companies that blend AI

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and human work together with process and with culture in ways that accomplish things that neither could do alone.

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If you just move to automation, you're going to have the same Claude -based company everyone else does.

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Well, that's an interesting point.

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So, you know, when we're talking about strategy, we're also talking about finding competitive advantage.

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So if every company has access to the same frontier models,

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where exactly can leaders differentiate their organizations from competitors?

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that's that's where the key kind of comes in you are

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probably not using ai enough inside your organization not trusting enough

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but you then want to bring people back in i mean

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we're already seeing as the the most extreme version of this

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right in terms of organizational change are things like software dark factory

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so dark factory it's factory all the work is done by robots

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you don't need humans in it you don't need lights um software dark factories i know one company strong dm

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that has been shipping product to customers and uh there's no human involved in

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that their rules are no human can look at code, no human can write code.

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It is 100 % AI automated.

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I think that's a dangerous approach because you're just letting the code do the work.

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I think we need to think about building twilight factories,

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situations where the AI proactively reaches out to people and brings them in because they have variants and views, because their process or expertise matters,

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because their sign -off approvals need it, or because they find the work interesting.

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And I think we need to start restructuring work around the idea

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that these systems really can accomplish many things and will be able to accomplish more in the near future.

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So it's integrating it with humans at the right places at the right time

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and process that's going to give you a differentiator.

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Where are the specific ways in which you think that as fast as AI is evolving,

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humans still have a role and particularly leaders still have a role?

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One thing is you can't really be in the loop in the same way you were, right?

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It was easy to be in the loop when it was a chatbot.

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It's not easy when, I mean, I literally have projects where I have a swarm of 100 agents solving a problem for me.

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Like I have the report out to me like I'm a manager.

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So I am in the loop to the extent that,

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you know, they give me a PowerPoint about their progress and they show me demos of what they're building, but that's very different than,

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you know, I'm approving every action.

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Right.

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And so leaders actually, one of the outputs of AI is leaders are super empowered.

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I keep seeing organizations where leadership basically takes back control over their company because they can now directly ask the AI for,

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you know, for the information they need.

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I have talked to people who are like C -level leaders who've actually diagnosed problems of plants

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that no one brought up to them

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because they could just literally query all of the information at their company's disposal

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and ask questions and have the AI go off and do tasks for them.

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So it's a really interesting change in the span of control if you set this up right,

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which lets leadership actually.. think more about things rather than hearing things through three or four different layers of leadership.

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So would you say then that AI agents are making strategic decisions on behalf of leaders and organizations with the right context,

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you know, given to them?

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I mean, they already are.

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If you have an AI agent that is doing any long running work, right?

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Epoch, which is a AI benchmarking company, was reporting that they could get regularly sort of between two

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and 18 weeks of engineer's work out of a single prompt to Claude for a couple hundred dollars, right?

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That involves tons of strategic decisions and judgment calls and creativity calls.

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They may not be your judgment or creativity, but we can't say anymore the AI isn't making strategic decisions.

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It is.

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The question for you is, where do you want to make decisions and where not?

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And just like any team, you can give it those instructions.

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You could say, listen, I want you to come with the five best options, your defenses for each of them,

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surface those, pressure test them for me, and then deliver so I can pick between them.

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And this is the format I want them in.

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Or you could say, just go ahead and execute, right?

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You're hooked up to code, build a prototype for it and test it with a few people.

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Those are now options you have available to you.

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So some of this is reconstructing what we've already done with humans.

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So what's the process by using humans and AI together that they get a working idea that's much better than ideas before?

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How does that get done much faster and better?

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Where exactly though, does a leader know where they add the most value and where they don't?

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Like, what are the points at which they should be involved absolutely every time?

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I think part of this is you have to make decisions

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now about how to best accomplish what your company wants to accomplish.

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And you obviously don't want to give this all up, right?

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You don't want to give it up because the AI is not as smart as you at what you're best at.

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Because the AI doesn't have enough variation.

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Because it doesn't actually absorb your culture in a deep way.

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You know, you can give some of that as context, but that's part of process design.

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I mean, we do this all the time.

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What are the limits of your product engineering group versus your marketing group versus your legal team?

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We have to think about the same kind of process design here, which is where are those limits and limitations?

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I think the danger is that by skipping doing that, you either move to an all -automation world where all those choices are made by AI,

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or you underestimate what these systems can actually do.

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So it is actually disciplined decision -making that gets you there.

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And what are the things, the aspects of strategic leadership that you don't think can ever be delegated or outsourced?

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So I'm always careful about bright lines where we say something can be never done, right?

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Never, ever.

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But the systems are jagged.

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They're good at a lot of stuff and bad at stuff you wouldn't expect.

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And that means, by the way, there's different things the jobs do.

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Think about coding, right?

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You are hiring coders to be the best at writing elegant lines of code.

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Then AI could write the code.

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So then it became about sort of systems engineering could you see the bigger picture

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now ai is increasingly doing some of the systems engineering work in some ways it's very upsetting

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if you're a coder because the nature of the job is changing in ways

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that are good or bad all the time right

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and what you learned is crap no longer is i think

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we're going to see the same thing about leadership like right

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now your maybe your judgment calls are about uh

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you know about the details of pricing strategy the ai might be better pricing video

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you won't know until your lab tests it for you i can't give you the answer in your industry

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or not you only know by experimentation and you don't have to do

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that all alone right that's where your lab and your crowd comes in.

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Your crowd is surfacing use cases and values of AI.

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You don't have to do this all yourself.

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But I think a big danger of companies is they think someone's solved this problem for them.

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Someone's figured this out.

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We're all faking it.

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Nobody knows anything.

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Like the AI labs don't know anything.

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The vendors are all making this up.

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This is a three -year -old technology.

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So you have to sort of seize control to some extent

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of this yourself to figure out what you want to use it for.

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Are there capabilities that you suggest your students, your MBA students, your kids,

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aspiring leaders that you suggest they develop because you believe they'll be increasingly important in an AI -dominated world?

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So I think there are four things that are very useful.

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They're not the only things.

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One of those is deep knowledge, which is the ability to actually understand a field really well, right?

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So we're on this podcast, and you know for the beginning of my question or not, before I even say like a few sentences,

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whether this is going to be an interesting answer or not, I hope this is one of them,

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and whether you need to redirect things or not.

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That comes from deep expertise, right?

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It's hard to make that judgment call if you don't have the expertise.

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Similarly, in your industry area, you can look at a spreadsheet and know whether something is wrong.

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You don't have to read those 400 pages of AI content because you know right away because you're an expert.

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It's one of the reasons why junior people often struggling now.

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They don't have that expertise.

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Second thing you need is wide knowledge.

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As people start to cross field boundaries,

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knowing a little bit about many things helps you give the AI better feedback and ask for the things you want.

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The third thing you want is taste you want to be able to like pick among things and

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that could be company taste too what strategy feels like the

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kind of strategy our company would do are these many designs the ai come up with

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which is the right one selecting among many options

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and i think we develop taste and then the fourth one is agency the willingness to take action under uncertainty.

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And I think that's a little bit harder to teach.

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We try and do that.

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I'm an entrepreneurship professor.

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But I think giving people the leeway to engage in experimentation is important.

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How are you encouraging the sort of old guard of leaders

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who aren't accustomed to such uncertainty to become more comfortable with it?

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I think there's a danger of people overstating the generational aspect of this technology.

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Like it certainly was true that digital natives were a thing.

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Like you didn't grow up with the internet.

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There's a lot of weird stuff about the internet you don't understand as an internet user.

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It doesn't work the same way with AI because AI doesn't have the same sort of innate background.

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It's sort of a single player setup at this point.

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And because it rewards expertise.

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In our early studies with some of my colleagues at Harvard and my teenagers at Warwick,

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we found that like BCG consultants with less experience were worse at using AI.

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And there's other studies that show less expertise results in worse AI outcomes, including philanthropic itself.

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And so I think one of the dangers you have is you're like, oh, I need these, like,

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you know, there's some group people who gets this.

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Nobody gets this, right?

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So as a senior leader, you actually have the best tools available because you have that expertise and judgment to make decisions about things.

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And you probably have a thrive under surgery too.

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If you've been around for 20 years or you've been through financial crisis,

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you've been through wars, this is going to be a time of a lot of change.

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That doesn't mean that you're inflexible because you're a senior leader.

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In fact, often the best senior leaders who got where they are, are good at deciding how to make changes.

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Have you seen companies or leaders that you know really successfully use AI in strategy development itself?

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I've talked to C -level leaders at very large funds who've done this.

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I've spoken to senior leaders in large financial services organizations, certainly in software.

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Yes, there are people doing this all the time, right?

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And in fact, I would argue you're probably making a mistake if you're not getting a second opinion from the AI.

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Like the models are very smart right now.

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It's just like if you're getting medical advice, absolutely see your doctor.

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But every piece of evidence is you should absolutely be feeding that into the AI system, too, right?

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I would do the same thing with legal advice.

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Listen to your lawyer first, but why not get a second opinion?

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Second opinions are cheap right now.

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And the best models, and again, I can't emphasize enough, you need to use one of the frontier models, which at the time of the recording,

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that means, you know, Claude Fable 5 .1 of whatever it'll be in the next couple of days and GPD 6, right?

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And, you know, maybe Google comes up with the model again.

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But you need to use one of these frontier models that you have to pay for.

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And you should be asking a question like the only way you're going to find out what this is good

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or bad is by using it

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so by all means use

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it for everything during the day take its opinions decide where it's dumb

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that's a useful set of information for you yeah

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when it comes to strategy does ai do you believe it ultimately favors large companies with more resources

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and data um and those experienced leaders that you were talking about

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or you know does it allow much smaller companies to compete compete, you know, with capabilities that they couldn't access before.

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Both of those things.

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One of the most depressing things I see

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when I talk to boards of very large companies

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and to C -level leaders is a lot of them have come to believe they just can't change, right?

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So I show them what AI can do and they're like, well, I guess we'll ride this out.

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And I'm like, that's an insane answer.

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You have 100 ,000 people working for you.

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The smartest people in the world are working for your company.

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You have customers and resources.

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The idea that you can't be flexible and compete seems to be overly baked in right now.

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And like I'm a business school professor, I understand we talk a lot about,

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you know, how difficult it is to survive disruption and how much inertia companies have.

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But it's also a very weird decision because you have all the resources

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because who's going to figure out how to use AI in your organization is going to be people in your company.

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Experience has returns, right?

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The startup doesn't have any of the same barriers you do, but they don't have the experience anymore.

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And that experience matters.

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It's no longer a 20 -something knows everything about this.

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The more experience you have, the more you know about AI in your field.

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And everyone's using the same AI models.

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It's not like the startup you're talking to is going to have a better model than, you know, than Claude.

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So I worry a little bit that people are sort of giving up.

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On the startup side, it's never a better time to be a startup company.

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Startups are all about that you're the top 0 .1 % at something

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and everything else you're terrible at and you hope that doesn't destroy your company.

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Now we have a tool that makes you good at everything you were good at.

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Now it makes that stuff all feel a little bit similar to each other, but that's okay.

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You know, I'm somebody who, when I was in my first startup company, we had to do payroll

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and I didn't realize you could pay a payroll company a couple cents per payroll period to do taxes for you.

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So I would do taxes by hand that excel for every person of the company.

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It took like eight hours every week before I realized the better one.

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Like AI will just tell me I didn't have to do that.

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It could take care of it for me.

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There's so much that it could help solve.

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So it's accelerating everybody, right?

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And we haven't talked enough about the individuals in your company.

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They're also accelerating.

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The question is, what do you do with that?

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I have to ask about sort of all the fear mongering

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now that's going on about sort of unleashing agents in your organization

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and having them create chaos or start doing whatever they want to, you know, without listening to what leaders want.

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So tell me your reaction to sort of all the recent news.

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with OpenAI and Anthropa calling for maybe a slowdown in AI development?

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So I think we need to differentiate between future model capabilities, the un -guard railed version of models that are being tested now, and current capabilities.

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So I think there is enough smart people worried about future model development, and we're still in this exponential curve,

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that if they're worried about a slowdown, government action or other action to pace the frontier

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and figure out what the right thing to do is and give people time,

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like, so that we are in the loop as deciding what happens seems reasonable, right?

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What form that takes is up for grabs.

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How important, how risky it is really is kind of up for grabs.

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But like, there's no reason not to have some, you know, to have policy to prevent existential risk.

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I think we're in for a cybersecurity environment that is apocalyptic in the near term.

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You're going to have, you know, it might be solvable.

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Defense could beat offense.

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But I think we are in a world where when we get unguard railed models, cybersecurity is going to be a real issue.

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Within organizations, using guard railed models, there just isn't the same kind of risk.

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So I think we need to differentiate between those things.

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But how should organizational leaders be thinking about that environment, which you said might become apocalyptic for,

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you know, rogue agents from outside thwarting their competitive strategy because they're stealing information or causing disruption?

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I think that you should think about it not so much as your competitors unleashing things on you to cause trouble.

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I think it's going to be that you're going to have to deal with security environments where agents,

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for many, many reasons, are going to be attempting to penetrate your security.

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I think that when I talk to CSIOs,

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I mean, OpenAI pulled 20 % of their staff off of research and 20 % of the resources to hardening their systems.

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I have talked to leaders of large financial institutions are pulling 50 % plus of their staff and try, everyone's trying to figure what to do.

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At the same time, defense has increased.

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The amount of patching you need to do has increased, which also creates problems.

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So like, we don't know how this goes through,

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but I think we're going to have a very disruptive period where the cybersecurity environment is going to get kind of intense.

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And that will look very, you know, like crazy from the outside.

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I think that thinking about how you're going to deal with that now is really important.

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I think you're probably underestimating how difficult that situation is going to be based on the experts I talk to.

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I'm not a cybersecurity expert.

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So I don't think it's

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so much attackers bothering

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you as much as every part of your system exposed to the internet is going to be a target for agents.

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Maybe accomplishing completely random benign tasks, right?

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If I ask an agent swarm to price products, it might decide to really get good pricing for Christmas because you wanted to buy a sweater.

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I really need to get inside of this wool mills systems

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because that's the only way can find out the pricing I need and relentlessly attack that will mill.

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That wasn't a malicious attack.

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That was a, I just need to find the price per wool that you have in the futures contract market.

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And I've become obsessed with this.

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So it's going to get very complicated.

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We're dealing with kind of alien technology to some extent.

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What's the biggest strategic mistake

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that you see companies making right now as they're trying to figure out what AI means for their business?

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So either, there's two giant ones.

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One of them is fixing on the ability of AI right now.

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I have this list I have of what the hot thing is in AI, and every three months it changes it.

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Everyone goes all in.

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All of these things are because we're anchoring on what the capabilities of AI are today.

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So the hot thing that has been recently is everyone's spending too much on tokens.

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Well, guess what?

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Once you start thinking about multiplayer AI, where many people in your organization are using it, token costs become more reasonable.

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All the AI companies are now reducing the costs of models

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and having models use dumber agents to do work that are cheaper.

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Everyone who went all in on cost was the main issue.

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That may or may not be the concern a few months from now.

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I mean, I can't tell you.

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It depends on your organization and the choices you're making.

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So I think there's a danger in fixating on what you have now

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and satisficing based on the systems and capabilities you have today and not realizing that we're still in the exponential.

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That is by far the number one problem, right?

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The second problem is trusting that other people have got this nailed down.

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Like every vendor you talk to is also making this up as we go along.

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Every consultant you talk to does not have 17 successful cases.

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And if they have successful cases, it's for GPT -4, a model that's obsolete and no one would use for anything anymore.

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And your phone's model is more powerful than that.

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So you have to build the capacity to make your own solutions in -house.

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That doesn't mean write all your code or anything else yourself, but it does mean build that capacity internally.

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So let's talk about that first problem or mistake that you see.

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How do you teach yourself to embrace the idea of exponential advancement

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and be ready sort of for where the puck is going?

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So to take an idea from Harvard, from Mike Tushman and company, it was the idea that you would need ambidexterity.

419

You need to be able to do two things at once.

420

You need to be able to exploit what's happening today, but realize you're still exploring.

421

That's why the lab is so important.

422

Without your own internal flexible team that's building impossible things for the future, you get stuck.

423

so there's a few questions i ask organizations whenever

424

i talk to them the first is what have you stopped

425

doing as a result of ai ai has made something in your organization trivial what is the trivial thing

426

that used to be really important

427

but it's no longer important you have to stop doing

428

it ai just does it second thing is what are you building

429

it's impossible you should be building at least one thing

430

that is unbelievable and that would change the industry

431

if you're not building that you're not being ambitious enough even more

432

so you should be building something doesn't work yet because you're betting

433

that exponential curve will keep growing and at some point it will work.

434

And then the fourth question is, do you have leadership lab and crowd in place?

435

So I think if you have those four questions answered, you're in much better shape for being ambidextrous.

436

Yeah.

437

And how do you staff your entire organization with the talent that's able to execute on everything you're talking about?

438

It's also a really hard question.

439

Everything from psychological safety to how do we build the right structure?

440

All of that now is you're cashing all of that in.

441

If you have a good structure and a good organization and a good culture and the right reward systems,

442

people in your organization are discovering use cases and they're telling you about it.

443

If you're not, then everyone's hiding all their uses

444

and nothing you could do can change that because they're scared

445

if they tell you they'll get fired or they won't get respected, they won't get rewarded.

446

And why would they ever tell you about how they're using these systems?

447

So you need expertise in your organization, finding this and people willing to talk about that.

448

And that comes from having a good culture, structure, reward system, openness, good management.

449

Those are more urgent than ever.

450

You talked about sort of the first questions that you ask leaders

451

when you're beginning to advise them on how they should be managing this.

452

As you sort of get down the line and they sort of face challenges, what are sort of the most common hurdles

453

you see to prevent them from sort of fully moving forward into this new world of sort of AI led strategy?

454

I mean, I think that your danger is in a lot of organizations making this a normal technology, right?

455

Like there's a desperate desire to make this like any other technology

456

that we can swallow and we'll turn into a project that has X dollars associated with it.

457

And we have a six month RP process

458

and then an eight month discovery process that like that is not going to work.

459

You're going to experiment.

460

You're going to have to fail.

461

Like nobody has answers your question here.

462

So if you try and do this as a completely de -risked thing,

463

you end up in a position where you end up replicating other large technology projects.

464

And that is going to be obsolete the moment you launch it.

465

And you haven't built the dynamic capability to change what you change.

466

Making sure that you stay hungry for the future is the real problem here.

467

And keep up with that exponential.

468

And I only have so much good advice.

469

It's a hard problem to solve.

470

Yeah, absolutely.

471

So it's not sort of an AI adoption or even transformation plan.

472

it's really revolutionizing the way you think about what your business is designed to do

473

the one thing i've gotten most wrong about ai in the

474

last year is i thought agents would be harder to pull off than they are i thought

475

you would to organize teams of agents i thought we need to use all of our organizational design knowledge

476

and it turns out we don't that there are 100 000 agent swarms

477

that are self -organizing i didn't expect that to happen

478

but it makes sense when you think about the reason we have organizations and they're complicated is People have limited knowledge.

479

People have limited, have different interests in mind.

480

They have conflicts.

481

They don't have the same goal.

482

AI agents share all those things.

483

So suddenly coordination is not as hard as we thought the problem was going to be.

484

Like coordination turns out to be an easier problem than we thought.

485

And as a result of all of that, right?

486

Like suddenly you can get things done with AI in the way you'd expect.

487

So the problem becomes now in the leadership side, what do I want the organization to do?

488

And if your KPIs and other material are all baked around the idea of human outputs, You don't want 100 times more PowerPoint.

489

You don't want 100 times more code.

490

You want different outputs that are better.

491

So you need to sit back and think about what those outputs

492

and what those goals are and be aiming to maximize those goals.

493

Otherwise, you have internal things and you end up just producing 100 times more code, 100 times more PowerPoint.

494

I mean, the one thing, if there's one lesson I learned from my MBA program before I got my PhD,

495

it's that people do what they're incentivized to do.

496

And you need to think really hard about the incentives in your organization.

497

So the incentive to your organization are minimize the impact of AI,

498

use AI as a productivity tool to do more of what my KPI tells me I should do.

499

It's not transformation.

500

Let's zoom out and talk about a few bigger picture issues.

501

First, you know, obviously there's a fear that this will lead to massive unemployment.

502

So what do you see as sort of the future labor market in this new AI -led world?

503

That is the biggest question and debate among economists about this topic and we don't have easy answers, right?

504

So you can easily theorize that like the junior market gets squeezed because junior employees don't have the expertise.

505

Why use them when you can use AI?

506

And there's some indications of that, but there's also some indications there may be more hiring because I mean,

507

you know, now you can use coders to do things that organizations didn't have coders now can write their own code.

508

So you start insourcing and now everybody's doing their own marketing firm.

509

So maybe you lose jobs at like large centralized firms, but inside the organization, jobs grow because there's more demand it's really hard to know right?

510

You can imagine, and the AI labs, to be clear, are aiming for the jobpocalypse.

511

They want AI to replace all human labor.

512

That is their explicit goal.

513

I think there's reasons that, A, they might be wrong about this, and B, we don't want them to win,

514

so we shouldn't let that be the future that we get.

515

But that is their goal.

516

So I don't know what the outcome is in the long term in terms of employment per se,

517

what the impact on jobs is going to be, and nobody does.

518

So we're watching that closely, and everything is very confused right now in terms of the indicators

519

we don't really have an answer as a technology advances so quickly

520

and we don't even know what's going to happen with it as you said

521

you know sort of you have to be developing your strategy dynamically

522

you have to be prepared for change you have to experiment

523

and fail you have to be ready for exponential advancements how do we get better at predicting not just the opportunities,

524

but also the really terrifying risks?

525

I mean, things are accelerating quite quickly.

526

I think it is hard to bounce those things out.

527

I think that this is a general purpose technology.

528

It's many different things at once.

529

There are existential risks associated with it potentially.

530

There are certainly near -term risks.

531

I worry a lot about, again, automation of jobs instead of augmentation of jobs.

532

I worry about the people being taken out of all the interesting parts of their job

533

and their only job is verifying the results of AI content and be fired if it's wrong.

534

It seems like a terrible way to live.

535

I worry about impacts on our information environment.

536

I mean, at the same time, I'm worried about as an instructor that people are cheating with AI and then they don't learn as much.

537

At the same time, we know AI is also an incredibly good tutor,

538

so it can transform teaching for the positive just like it can for the negative.

539

I think that it's a great freeing aspect for people who are stuck, that they can get more stuff done before.

540

We certainly know it gives good medical advice, legal advice, things like that.

541

Still consult your doctor and lawyer.

542

I'm not trying to tell you not to do that.

543

But I think that it's a mixed bag.

544

So how do we react to that?

545

We react with policy to encourage good uses and mitigate bad ones.

546

We encourage it by being meaningful about how we're using these systems and not doing them blindly.

547

We do that by as leaders making choices about how these systems are used

548

and working in concert with our team and our organization to make sure that we're not disempowering people on math.

549

And I think good leadership will start to show through in these kinds of circumstances.

550

Well, Ethan, thank you so much for wrestling with all these really tricky issues with us.

551

I've learned a lot about AI from you over the years, and especially a lot from your new book.

552

Thanks so much for being with me today.

553

Thank you for having me.

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このレッスンの語彙とスピーキングのポイント

このスピーキングレッスンは、動画「How AI Is Changing Strategy—and 4 Skills Leaders Need Next」を教材にしています。繰り返し出てくる語は次のとおりです：organization, leader, Ethan, Mollick。この動画には、シャドーイング用の文が483文、単語が6952語あります。音声の長さは32:32です。話す速さは速く、1分あたり約214語です。音のつながりや弱く発音される音が多くなります。単語の84%は英語の頻出3,000語に含まれます。残りは練習の前に確認しておきましょう。

この動画の重要語彙

動画に出てくる覚えておきたい単語15語を、発音と意味つきで紹介します。

単語	発音	意味
expertise 名詞	/ˌɛkspɜːˈtiːz/	専門的知識
capability 名詞	/ˌkeɪ.pəˈbɪl.ɪ.ti/	能力
strategic 形容詞	/stɹəˈti.dʒɪk/	戦略的な
productivity 名詞	/ˌpɹɒdʌkˈtɪvəti/	生産性
judgment 名詞	/ˈd͡ʒʌd͡ʒ.mənt/	判決, 裁き
startup 名詞	/ˈstɑɹtˌʌp/	スタートアップ, ベンチャー
automation 名詞	/ˌɔ.təˈmeɪ.ʃən/	自動化, オートメーション
output 名詞	/ˈaʊtpʊt/	出力
experiment 名詞	/ɪkˈspɛɹ.ə.mənt/	実験
loop 名詞	/luːp/	ループ
urgent 形容詞	/ˈɜː.d͡ʒənt/	緊急の, 急を要する
frontier 名詞	/ˈfɹʌntɪə/	境界, 国境
extent 名詞	/ɪkˈstɛnt/	程度
encourage 動詞	/ɪnˈkʌɹ.ɪd͡ʒ/	勇気付ける, 奨励する
solve 動詞	/sɒlv/	解決する

動画に出てくる句動詞

単語	発音	意味
find out 動詞		見出す, 調べる
eat up 動詞		平らげる
fall behind 動詞		遅れる
give up 動詞		降服する
go ahead 動詞	/ˌɡoʊ əˈhɛd/	どうぞお先に
go back 動詞		戻る, 帰る
zoom out 動詞	/ˌzuːm ˈaʊt/	ズームアウトする, 縮小する

繰り返し練習したい文

動画に出てくる短く完結した文です。日常会話でそのまま使えます。

What do you think companies should be doing now?
What's the product you're trying to produce?
What do you do about that?
How do we incentivize people?
Second thing is, what are you building that's impossible?

この動画の文法

話し手がよく使っている文型を、動画の実際の表現とともに紹介します。

文型	動画での表現
現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している	have come · you've been · has become
受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる	be overly baked · is used · is done
関係詞節 who / which + 節 — 人や物について情報を加える	organization, which is · model, which is · people who are

注意したい発音

話し手はyou're, don't, they'reなど、短縮形や弱形を186回使っています。聞こえたとおりの短い形で発音しましょう。

「sh」と「zh」の音: accomplish /əˈkɑm.plɪʃ/, organizational /ˌɔː(ɹ)ɡənɪˈzeɪʃənəl/, automation /ˌɔ.təˈmeɪ.ʃən/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/, differentiate /dɪf.əˈɹɛn.ʃi.eɪt/
長い単語（アクセントの位置に注意）: capability /ˌkeɪ.pəˈbɪl.ɪ.ti/, productivity /ˌpɹɒdʌkˈtɪvəti/, organizational /ˌɔː(ɹ)ɡənɪˈzeɪʃənəl/, automation /ˌɔ.təˈmeɪ.ʃən/, experiment /ɪkˈspɛɹ.ə.mənt/

日本語話者が苦手な音：

/r/ と /l/ の区別 — /l/ は舌先を歯茎につけ、/r/ はどこにもつけない: organizational /ˌɔː(ɹ)ɡənɪˈzeɪʃənəl/, payroll /ˈpeɪˌɹəʊl/, accelerate /ɪkˈsɛl.əˌɹeɪt/, incredibly /ɪŋˈkɹɛdɪbli/, trivial /ˈtɹɪv.i.əl/
/v/ — /b/ にならないように、上の歯を下唇に当てる: productivity /ˌpɹɒdʌkˈtɪvəti/, solve /sɒlv/, curve /kɚv/, versus /ˈvɝsəs/, creativity /kri.eɪˈtɪv.ᵻ.ti/
/f/ — 「フ」ではなく、上の歯と下唇で出す: frontier /ˈfɹʌntɪə/, feedback /ˈfiːdˌbæk/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/, flexible /ˈflɛk.sɪ.bəl/, differentiate /dɪf.əˈɹɛn.ʃi.eɪt/

この動画での練習方法

まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
まず0.75倍速で一文ずつシャドーイングし、慣れてきたら通常の速度に戻します。
自分の声を録音して元の音声と比べます。expertise, capability, strategicなどの単語に特に注意しましょう。

シャドーイングとは？英語上達に効果的な理由

シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。

シャドーイングのやり方: ステップ別の完全ガイドを読む →

シャドーイング練習: How AI Is Changing Strategy—and 4 Skills Leaders Need Next - 動画で英語スピーキングを学ぶ

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