Birju: How do you, sitting in your position, actually having a direct interaction from these lenses with these leaders, hold compassion in your heart as you're engaging with them?

Randima: First correction, I haven't sat opposite Mark himself, but Cheryl, yes. What I would say, I think the general thing you're asking is, I wanted to correct this in the beginning, too, because it's not that there, when we say it's the system, not the people, it doesn't mean there aren't bad people or people making bad choices, right? There are some bad people. I think it's hard when you see examples where Facebook is a good example, where there are so many, there's a lot of evidence, mainly from whistleblowers, where things were raised, and they went up to the top, and the choice was to not fix it.

The choice was to not fix it. Repeatedly.

Birju: Yeah.

Randima: The point, I think there's an important point, which is whoever is at the top would make that choice again and again and again. So, that is very common, right? It's very... the incentive structures really lock people into making bad decisions, but in some cases, some of these choices were really just bad choices.

They would not even cost that much revenue, right? Most of them do cost something. And what happens is, in the end, in the future... In theory, they should be paid out, right, when the legislation catches up, or the litigation catches up.

The problem is that the general model is paying a few billion dollars in fines is fine. It's actually not a problem. It's part of the calculus. And that's what I think people miss.

It's intentional. And so the compassion piece, I don't do as well on that. That's... that's...

I mean, I know the... I know the right way to do it, and I don't do it that well, which is... The reality is, from a conditions-based lens, people always have their conditions, and why they end up doing what they do. Even the worst choices are perpetuated by the conditions that lead to the mind that leads to that moment when you make the choice.

I get that. It's still hard. It's... it's...

I do struggle with that, because I expect... I just expect more, and there's this really funny thing where a lot of these tech leaders, and I won't name names, but they're extremely smart and very capable, and when you have interview topics that are inconvenient, they suddenly become very obtuse. As an example, and again, won't name names, but if you build automation technology, AI is designed to automate. So, therefore, by design, by definition.

It automates cognition, and robotics automates labor. That is their purpose, to make the other parts zero, right? And so, to get rid of all the human jobs. And so then, funnily, when you talk to them about the jobs, they're like, yeah, I don't know!

I don't know, I really don't know what's gonna happen. Maybe? Maybe there'll be some impacts, but it's probably gonna be some new stuff. Okay, but you built automation technology, which automates cognition, right?

And you train on every new thing that we happen to… maybe if we find a new thing. You'll train on that, too, and you make sure it's good at that, too. So, let's just be real about… there's a different conversation, which is… Just admit, and be like, look, yeah, we're building, we're building on the mission technology. And in this new era, we're gonna need to redesign everything, because our assumptions are all… we're violating all the assumptions of the systems we built.

So let's talk about that. That would be a much more interesting conversation than saying something like, well, who's to say? I really don't know. I don't know what this automation technology's gonna do.