Yep, yep.

But if you think about, for example, something like, um, uh, social media, uh, the malicious use of, of, of social media for, say, for example, like misinformation, right? Or, or, um, that's just compounded, right, by AI, because AI is very good at provide- producing misinformation or deepfakes or the kinds of things that already we worry about can be done much more quickly and more effectively.

So that's sort of an example there.

Or if we think about the environment, um, AI uses a lot of energy, right? And so the more that we use AI, in some sense, we're just multiplying the use of energy that sort of raises challenges there, right? And you sort of see the whole thing about data centers, the need for water.

Um, so again, just as we grow, uh, that problem is compounded as well.

Or if we think about inequality, right? Um, uh, both in terms of, say, uh, loss of jobs, but also in terms of concentration of ownership of the benefits of AI, uh, that kind of inequality is likely to grow.

Um, and w- and one that we don't think about as much, I think, is actually the question of, uh, linguistic access, right? So you mentioned earlier, right, that LLMs are basically trained on what exists, but they're also trained on a few languages.

So that quote, that statement, that sentiment, what is decently in an AI-driven organization?

Okay, first, Ninh Ha Sha on what happens when a machine starts doing things no human ever thought to try.

So the first thing is maybe we are finally at the moment where the things that are routine, mm, boring, dangerous about work can be eliminated and done by machines.

Uh, because if you think about it, every, uh, generation of technological progress has promised that we will be freed from drudgery, for example, right? You go back to Oscar Wilde back in the 1850s, he was talking about how machines would liberate us from sort of drudgery and allow us to pursue the arts and do the kinds of things that we really wanted to do.

But if you think about it, that really hasn't come to pass.

Uh, if you think about, um, kitchen appliances, uh, laundry machines, right? Those are supposed to take away some of the drudgery and time that people spent doing those tasks, but apparently people still spend the same amount of time doing a lot of household work.

So maybe [laughs] we're at a moment where this technological development will allow people to be freed up to do the kinds of things that they would wanna do in their creative time.

So that's sort of one thing that could be good.

Thank you.

Go ahead.

But if you think about, for example, something like, um, uh, social media, uh, the malicious use of, of, of social media for, say, for example, like misinformation, right? Or, or, um...

That's just compounded, right, by AI because AI is very good at provide- producing misinformation or deepfakes or the kinds of things that already we worry about can be done much more quickly and more effectively.

So that's sort of an example there.

Or if we think about the environment, um, AI uses a lot of energy, right? And so the more that we use AI, in some sense we're just multiplying the use of energy that sort of raises challenges there, right? And you sort of see the whole thing about data centers, the need for water.

Um, so again, just as we grow, uh, that problem is compounded as well.

Or if you think about inequality, right? Um, uh, both in terms of, say, uh, loss of jobs, but also in terms of concentration of ownership of the benefits of AI, uh, that kind of inequality is likely to grow.

Yeah

Here's Nien-he Hsieh.

So if it's a mid-career person, I would ask them first to reflect upon what they believe it is that has enabled them to succeed thus far in the context of their work.

Let's just lay out in a very simple way, either sort of at a very concrete level, whether it's certain skills, whether it's certain resources, whether it's a network, whether it'sThings that they're good at, to a much more abstract level, sort of thinking about, "Well, actually, how is it that I got to where I am, and sort of what is it about me, right, that I bring to the table?" And my hope is in that kind of exercise.

Each of us will find, in some sense, those things that are distinctive and unique to us, right? Some of those may be hard skills, but other things may be things about what we do or how we act or how we lead and engage an organization.

And my guess is a lot of those things that have enabled people to succeed thus far, if we focus on those, we'll find in there, right, things that are distinctively not just unique to us, but also sort of more on the human side that couldn't easily be replicated by AI.

So that...

And then sort of double down on those.

... you're talking about AI responsibility.