Topics / Decision making

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Decision making

How leaders set priorities, evaluate uncertainty and define progress.

2 people · 2 sources · 3 viewpoints

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2 people · 2 sources · 3 viewpoints

Jensen Huang

Reasoning aloud lets colleagues challenge intermediate steps

Jensen Huang says he reasons through problems aloud in meetings so colleagues can challenge specific reasoning steps rather than only his conclusions. He describes the resulting interaction as a collective path-searching method.

Supporting evidence

Original excerpt

The nice thing about reasoning through things and letting people interact with it is that they don’t have to disagree with your outcome. They can disagree with your reasoning steps. And they could pull me in different directions, and then we can reason forward.
Jensen Huang on AI agent safety, leadership and trust
Context

And when I’m wrong—when I’m wrong or it didn’t turn out that way or, you know, I mean, most of the things that I say outside I’m fairly certain about. And the reason for that is because it’s gonna impact somebody else and I want to be quite concerned about that and quite circumspect about that. For stuff that I’m reasoning about inside a meeting, you know, a lot of things could turn out differently. And so, but it doesn’t ever stop me from reasoning. The way that I manage and lead, I’m constantly reasoning in front of people. And even when I’m talking to you, you can kind of see me reasoning through things. And I want to make sure that you understand what I’m saying not because I told you- Yeah, you have this way about you of … When you’re explaining stuff, I can feel you actually reasoning on the spot about it with a constant open-mindedness where you could … I could feel like I could steer your thinking. And that’s a—that’s really beautiful that you’ve been able to maintain that after so many years of success, and pain. I think sometimes pain closes you down a bit. And I think to maintain-

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Starting from trust despite sometimes being taken advantage of

Jensen Huang says he starts from confidence in human kindness, generosity and a desire to help others. He acknowledges sometimes being taken advantage of, but says this does not change his starting assumption, which he believes is repeatedly borne out.

Supporting evidence

Original excerpt

I start with always that people want to do good. People want to help others. And vastly, I am proven right. Constantly proven right. And often it exceeds my expectations.
Jensen Huang on AI agent safety, leadership and trust
Context

Well, from a fan perspective, given your extremely enormous positive impact on civilization, of course, I hope you keep going. But also it’s just fun to watch what NVIDIA is doing, you know. It’s just the rate of innovation. And I’m a huge fan of engineering. There’s so much incredible engineering continuously being done by NVIDIA. It’s just fun to watch. It’s a celebration of humanity, a celebration of great builders, a celebration of great engineering. So, it represents something special. So I hope you and NVIDIA keep going. What gives you hope about this whole thing we got going on, about humanity, about the future of humanity? When you look out, when you think about the future quite a bit, when you look out 10, 20, 50, 100 years from now, what gives you hope? And that there’s so many things that we wanna solve. There’s so many problems we wanna solve. There’s so many things that we wanna build. There’s so many good things that we wanna do that are now within our reach, and within the reach of my lifetime. You just can’t possibly not be romantic about that. You know what I’m saying? O-

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Dario Amodei

Capability extrapolation suggests 2026-2027 arrival

Informally extrapolating recent AI capability improvements—from high school to undergraduate to PhD-level performance—suggests powerful AI could arrive by 2026 or 2027. However, this estimate remains uncertain due to potential derailments like data scarcity, cluster scaling limits, or geopolitical disruptions.

Supporting evidence

Original excerpt

if you just eyeball the rate at which these capabilities are increasing, it does make you think that we’ll get there by 2026 or 2027. Again, lots of things could derail it. We could run out of data. We might not be able to scale clusters as much as we want.
Dario Amodei, Amanda Askell & Chris Olah on AI Development and Safety
Context

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These are individual perspectives, not a measure of consensus. Source material stays in its original language.