Shadowing Practice: Building Anthropic | A conversation with our co-founders - Learn English Speaking with Video
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Why are we working on AI in the first place?
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I'm just going to arbitrarily pick Jared.
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Why are you doing AI at all?
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I mean, I was working on physics for a long time, and I got bored, and I wanted to hang out with more of my friends, so.
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Yeah, I thought Dario pitched you on it.
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I don't think I explicitly pitched you at any point.
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I just showed you results of AI models
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and was trying to make the point that they're very general and they don't only apply to one thing, and then just at some point after I showed you enough of them, and you were like, oh,
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yeah, that seems like it's right.
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How long have you been a professor when you started?
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I think like six years or so.
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I think I helped recruit Sam.
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I talked to you, and you were like, I think I've created a good bubble here.
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My goal is to get Tom to come back, and then it worked.
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And did you meet everyone through Google when you were doing the interpretability stuff, Chris?
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No. So I guess I actually met a bunch of you when I was 19, and I was visiting the Bay Area for the first time.
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So I guess I met Dario and Jared then, and I guess they were postdocs, which I thought was very cool at the time.
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And then I was working at Google Brain and Dario joined and we sat side by side actually for a while.
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We had desks beside each other and I worked with Tom there as well.
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And then of course, I got to work with all of you at OpenAI when I went there.
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So I guess I've known a lot of you for like more than a decade, which is kind of wild.
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If I remember correctly, I met Dario in 2015
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when I went to a conference you were at
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and I tried to interview you
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and Google PR said I would have read all of your research papers that you needed to.
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Yeah, I think I was writing Concrete Problems in AI Safety when I was at Google.
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I think you wrote a story about that paper.
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I did.
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I remember right before I started working with you, I think you invited me to the office to come chat and just tell me everything about AI,
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and you explained.
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I remember afterwards being like, oh, I guess this stuff is much more serious than I realized, and you were probably explaining the big blob of compute
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and parameter counting and how how many neurons are in the brain and everything.
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I feel like Dario often has that effect on people.
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This is much more serious than I realized.
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Yeah, I'm the bringer of happy tidings.
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But I remember when we were at OpenAI, where there was the scaling law stuff and just making things bigger, and it started to feel like it was working.
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And then it kind of kept on eerily working on a bunch of different projects, which I think is how we all ended up working closely together,
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because it was first GPT -2 and then scaling laws and GPT -3, Yeah, we're the plop of people that were making things work.
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Yeah, that's right.
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I think we're also excited about safety because in that era, there was sort of this idea that AI would become very powerful,
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but like potentially not understand human values or not even be able to communicate with us.
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And so I think we were all like pretty excited about language models as a way to kind of guarantee
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that AI systems would have to understand kind of implicit knowledge that...
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And RL for human feedback on top of language models, which was the whole reason for scaling these models up was that, you know, we couldn't do,
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we couldn't, the models weren't smart enough to do RLHSF on top of.
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So that's the kind of intertwinement of safety and scaling of the models that we, you know, still believe in today.
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I think there was also an element of like the scaling work was done as part of the safety team
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that Dario started at OpenAI because we thought
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that forecasting AI trends was important to be able to have us take them seriously and take safety seriously as a problem.
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Correct.
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Yeah, I mean, we took, I remember being in some airport in England sampling from GPT -2
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and using it to write fake news articles and slacking Dario and being like, oh, this stuff actually works and might have like huge policy implications.
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I think Dario said something like, yes.
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It's a typical way.
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But then we worked on that a bunch, as well as the release stuff, which was kind of wild.
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Yeah, I remember the release stuff.
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I think that was when we first started working together.
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That was a fun time, the GPT -2 launch.
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But I think it was good for us, because we did a kind of slightly strange, safety -oriented thing all together.
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And then we ended up doing Anthropic, which is a much larger, slightly strange, safety -oriented thing altogether.
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So I guess just, like, going back to the concrete problems, because I remember, so I joined OpenAI, like, 2016, like, one of the first 20 employees or whatever with Udario.
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And I remember at that time, the, like, concrete problems in AI safety seemed like it was, like, the first, like, mainstream, like, AI safety paper.
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I guess I don't really know if I ever asked you what the story was for, like, how that came about.
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Chris knows the story because he was involved in it.
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I think, you know, we were both at Google.
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I forget what other project I was working on, but like with many things, it was my attempt to kind of procrastinate from whatever other project I was working on
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that I've now completely forgotten what it was.
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But I think it was like Chris and I decided to write down.
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what are some open problems in terms of AI safety
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and also AI safety usually talked about in this very kind of abstruse, abstract way.
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Can we kind of ground it in the ML that was going on at the time?
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I mean, now there's been like six, seven years of work in that vein, but there was almost a strange idea at the time.
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Yeah, I think there's a way in which it was almost a kind of political project where at the time, a lot of people didn't take safety seriously.
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So I think that there was sort of this goal to collate a list of problems
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that sort of people agreed were reasonable, often already existed in literature,
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and then get a bunch of people across different institutions who are credible to be authors.
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And I remember I had this whole long period where I
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just talked to 20 different researchers at Brain to build support for publishing the paper.
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In some ways, if you look at it in terms of the problems and a lot of things it emphasized, I think it hasn't held up that well in that I think it's not really the right problems.
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But I think if you sort of see it instead as a consensus -building exercise, that there's something here that is real and that is worth taking seriously,
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then it was a pretty important moment.
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I mean, you end up in this really weird sci -fi world where I remember at the start of Anthropic, we were talking about constitutional AI.
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And I think Jared said, oh, we're just going to write like a constitution for a language model and that'll change all of its behavior.
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And I remember that sounded like incredibly crazy at the time.
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But why did you guys think that was going to work?
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because I remember that was one of the first early big research ideas we had at the company.
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Yeah, I think Dario and I had talked about it for a while.
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I think simple things just work really, really well in AI.
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I think the first versions of that were quite complicated, but then we whittled away into just use the fact
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that AI systems are good at solving multiple choice exams and give them a prompt that tells them what they're looking for.
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That was what we needed.
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Um, and then we were able to just write down these principles.
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I mean, it goes back to like the big blob of computer, the bitter lesson or the scaling hypothesis.
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If you can identify, you know, something that you can give the AI data for, and that's kind of a clear target, you'll get it to do it.
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So like, here's this, here's this set of instructions.
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Here's this set of principles.
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AI language models can like read that set of principles
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and they can like compare it to the behavior that they themselves are engaging in.
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So like you've got your training target there.
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So once you know that, I think my view and Jared's view is there's a way to get it to work.
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You just have to fiddle with enough of the details.
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Yeah.
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I think it was always weird for me, especially in these early eras, because I was in physics and then coming from physics.
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And I think now we forget about this because everyone's excited about AI.
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But I remember talking to Dario about concrete problems and other things.
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And I just got the sense that AI researchers were very, very kind of psychologically damaged by the AI winter,
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where they just kind of felt like having like really ambitious ideas or ambitious visions was like very disallowed.
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And that's kind of how I imagine it was in terms of talking about safety.
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In order to care about safety, you have to believe that AI systems could actually be really powerful and really useful.
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And I think that like there was kind of a prohibition against being ambitious.
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And I think one of the benefits is that physicists are very arrogant.
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And so they're constantly doing really ambitious things and talking about things in terms of grand schemes.
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And so, yeah.
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I mean, I think that's definitely true.
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Like, I remember in 2014, it was like there were just like, I don't know, there were just like some things you couldn't say, right?
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But I actually think it was kind of an extension of problems that exist across academia, other than maybe theoretical physics,
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where they've kind of evolved into very risk -averse institutions for a number of reasons
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and even the industrial parts of AI had kind of transplanted or forklifted that mentality.
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And it took a long time.
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I think it took until 2022 to get out of that mentality.
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There's a weird thing about what does it mean to be conservative and respectful, where you might think one version you could have is
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that what it means to be conservative is to take the risks
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or the potential harms of what you're doing really seriously and worry about that.
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But another kind of conservatism is to be like, taking an idea too seriously and believing that it might succeed is sort of like scientific arrogance.
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And so I think there's like kind of two different kinds of conservatism or caution.
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And I think we were sort of in a regime that was very controlled by that one.
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I mean, you see it historically, right?
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Like
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if you look at the early discussions in 1939 between people
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involved in nuclear physics about whether nuclear bombs were sort of a serious concern,
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you see exactly the same thing with like Fermi resisting these ideas because it just seemed kind of like a crazy thing.
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And other people like Zillard or Teller are taking the ideas seriously because they were worried about the risks.
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Perhaps the deepest lesson that I've learned in the last 10 years, and probably all of you have learned some form of it as well,
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is there can be this kind of seeming consensus, these things that kind of everyone knows.
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that, I don't know, seem sort of wise, seem like they're common sense,
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but really they're just kind of herding behavior masquerading as maturity and sophistication.
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And when you've seen, the consensus can change overnight.
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And when you've seen it happen a number of times, you suspected, but you didn't really bet on it.
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And you're like, oh man, I kind of thought this, but what do I know?
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How can I be right and all these people are wrong?
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You see that a few times, then you just start saying, nope, this is the bet we're going to make.
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I don't know for sure if we're right, but just ignore all this other stuff, see it happen.
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And I don't know, even if you're right 50 % of the time, being right 50 % of the time contributes so much.
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You're adding so much that is not being added by anyone else.
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And it feels like that's where we are today with some safety stuff, where there's a consensus view that a lot of this safety stuff is unusual or doesn't naturally fall out of the technology.
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And then at Anthropic, we do all of this research where weird safety misalignment problems fall out as a natural dividend of the tech we're building.
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So it feels like we're in that counter consensus view right now.
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But I feel like that has been shifting over the past, even just like 18.
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We've been helping.
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We've definitely been.
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Publishing and doing research.
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Constant force.
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But I also think just like world sentiment around AI has shifted really dramatically.
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And, you know, it's more common in the user research that we do to hear just customers,
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regular people, say, I'm really worried about what the impact of AI on the world more broadly is going to be.
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Sometimes that means jobs or bias or toxicity, but it also sometimes means, is this just going to mess up the world?
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How is this going to contribute to fundamentally changing how humans work together, operate, which I wouldn't have predicted that, actually.
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For whatever reason, it seems like people in the ML research sphere have always
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been more pessimistic about AI becoming very powerful than the general public.
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Maybe it's a weird form of humility or something.
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When Daria and I went to the White House in 2023, in that meeting, Harris and Raimondo and stuff basically said,
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paraphrase, but basically said, we've got our eye on you guys.
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AI is going to be a really big deal and we're now actually paying attention, which is...
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And they're right.
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They're absolutely right.
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But I think in 2018, you wouldn't have been like, the president will call you to the White House to tell
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you they're paying close attention to the development of language models yeah it's just like a crazy one thing
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that i think is interesting too just uh is like i
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guess like all of us kind of got into this
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when it didn't seem like there was like like we thought like we thought we thought
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that it could happen
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but yeah it was it was like like fair me being
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like skeptical of the atomic bomb it was like he had he was just a good scientist
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and like there was some evidence that it could happen
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but there also was a lot of evidence against it happening
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and he I guess decided that it would be worthwhile because
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if it was true then it would be a big deal
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and I think for all of us it was like yeah like 2015, 2016, 2017 like there was some evidence and increasing evidence
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that this might be a big deal
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but like I remember in 2016 like talking to all my advisors
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and I was like I've done startup stuff like I want to help out with the with like AI safety
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but like I'm like not great at math I like don't
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know don't know how to don't exactly know how i can do it um
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and i think at the time people were like either were
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like well you need to be super good at like decision theory in order to help out
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and i was like that's probably not gonna work
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or they were like uh like it doesn't really seem like we're gonna get some crazy ai thing um
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and so i had uh only a few people basically
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that were like like yeah okay
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that seems like a good thing to do i remember in 2014 making graphs of image net results over time
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when i was a journalist and trying to like get stories published about them
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and people thought i was completely mad
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and then i remember in 2015 trying to persuade bloomberg to let me write a story about nvidia
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because every ai research paper had started mentioning the use of gpus
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and they said that was completely mad and then in 2016
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when i left journalism to go into ai i had these
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emails saying you're making like the worst mistake of your life which i now occasionally look back on
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but it was like it was seemed crazy at the time from like any many perspectives to go and take this seriously, that scaling was going to work,
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and something was maybe different about the technology paradigm.
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You're like Michael Jordan and that coach that didn't believe in him in high school.
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How did you actually make the decision though?
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Did you feel torn or was it obvious to you?
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I did a crazy counter bet where I said, let me become your full -time AI reporter and double my salary, which I knew that they wouldn't say yes to.
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Then I went to sleep and then I woke up and resigned.
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It was all fairly relaxed.
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You're just a decisive guy.
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In that instance, I think it's because I was like going to work, reading archive papers and then printing archive papers off and coming home and reading archive papers,
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including like Dario's papers and from the Baidu stuff and being like something like completely crazy is happening here.
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And at some point I thought you should bet with conviction, which I think everyone here has done in their careers is just betting with conviction that this is.
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gonna work.
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Yeah.
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I definitely, I definitely was not as decisive as you.
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I spent like six months like, like flopping, like, okay, like, should I, should I do it?
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Like, should I try to do a startup?
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Should I try to do this thing?
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But I also feel like back then there were, there wasn't as much talk of engineers and the impact that an engineer can have in AI, right?
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That feels so natural to us now.
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And we're at the same sort of talent raise for engineers of all different types.
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But at the time it was like, you're a researcher and that's the only people that can work on AI.
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So I don't think it was crazy that you were spending time thinking about that.
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Yeah, yeah.
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And I think that that was basically the thing that got me to join OpenAI was, like, I, like, I messaged the people there and they were, like, yeah, we actually think that you can help out by doing engineering work.
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Yeah.
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And, like, that you can help out with AI safety in that way, which I think there hadn't really been an opportunity for that.
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So that was what got me there.
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That's right.
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You were my manager at OpenAI.
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I was.
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That's right.
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I think I joined after you'd been there for a while.
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A little bit.
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I was at Brain for a bit.
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Yeah.
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I don't know if I ever asked you like what it was that got you to join.
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Yeah.
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So I had been at Stripe for about five and a half years and I knew Greg.
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He had been my boss.
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He was my boss at Stripe for a while.
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And I actually introduced him and Dario because I said when he was starting OpenAI, I was like the smartest person that I know is Dario.
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You should, you would be really lucky to get him.
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So Dario was at OpenAI.
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I had a few friends from Stripe that had gone there too.
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And I think sort of like you, I'd been thinking about what I wanted to do after Stripe.
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I had gone there just because I wanted to get more skills after working in, you know, nonprofit and international development.
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And I actually thought I was going to go back to doing that.
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Like essentially, I had always been working.
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I was like, I really want to help people that have, you know, less than I do.
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But I didn't have the skills when I was doing it before Stripe.
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And so I looked at going back to public health.
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I thought about going back into politics very briefly.
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But I was also looking around at other tech companies and other sort of ways of having impact.
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And OpenAI at the time felt like it was a really nice intersection.
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It was a nonprofit.
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They were working on this really big lofty mission.
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I really believed in sort of the AI, you know, potential because, I mean, I know Dario a little bit.
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And so he was - They needed management help.
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They definitely needed management help.
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That is a fact.
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And so there was, I think that felt, it felt very me -shaped, right? i was like oh there's this non -profit
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and they like there's all these really great people with these like really good intentions
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but it seems like they're a little bit of a mess um
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and that was that was that felt really exciting to me to get to come in
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and even you know just i was i was such a utility player right i was running like people
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but i was also running some of the technical teams yeah the scaling
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or i worked on the language team i took over i
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worked on policy we worked on some policy stuff i worked with chris
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and i felt like there was just so much goodness in
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so many the employees there and i felt a very strong desire to come
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and sort of just try to help make the company a
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little more functional i remember towards the end after we've done
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gpt free you were like have you guys heard of something called trust
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and safety yes i said you know i used to run some trust
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and safety teams at stripe there's a thing called trust
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and safety that you might want to consider for a technology like this
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and it's funny because it sort of is the intermediary step between, you know, AI safety research,
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right, which is how do you actually make the model safe to something just much more practical.
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I do think there was a value in saying, you know, this is going to be a big thing.
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We also have to be doing this sort of practical work day to day to build the muscles for
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when things are going to be a lot higher stakes.
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That might be a good transition point to talk about things like a responsible scaling policy
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and how we came up with that or why we came up with it and how we're using it now, especially given how much trust and safety work we do on today's models.
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So whose idea was the RSP?
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Yeah, it was me and Paul first talked about it in late, Paul Cristiano in late 2022.
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First, it was like, oh, should we cap scaling at a particular point until we've discovered how to solve certain safety problems?
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And then it was like, well, it's strange to have this one place where you cap it, and then you uncap it.
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So let's have a bunch of thresholds.
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And then at each threshold you have to do certain tests to see
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if the model is capable and you have to take increasing safety
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and security measures originally we had this idea
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and then the thought was just look like you know this will go better
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if you know
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if it's done by some third party like we shouldn't we
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shouldn't be the ones to do it right it shouldn't come from one company
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because then other companies are less likely to adopt it so paul actually went off and designed it and and you know, many, many features of it changed.
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And we were kind of on our side, working on how it should work.
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And, you know, once Paul had something together, then pretty much immediately after he announced the concept, we announced ours within a month or two.
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I mean, many of us were heavily involved in it.
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I remember writing at least one draft of it myself, but there were like several drafts of it.
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There were so many drafts.
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I think it's gone through the most drafts any doc
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which makes sense right it's like like i feel like it is in the same way that like the U .S.
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treats, like, the Constitution as, like, the holy document, which, like, I think is just a big thing that, like, strengthens the U .S.
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Yes.
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And, like, we don't expect the U .S to go off the rails in part because just, like, every single person in the U .S is, like, the Constitution is a big deal, and if you tread on that, like, I'm mad.
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Yeah.
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Like, I think that, like, the RSP is our, like, it holds that thing.
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It's, like, the holy document for Anthropic, so it's, like, worth doing a lot of iterations getting it right.
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Some of what I think has been so cool to watch about the RSP, like development at Aetheropic too, is it feels like it has gone through so many different phases
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and there's so many different skills that are needed to make it work, right?
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There's like the big ideas, which I feel like Dario and Paul and Sam and Jared and so many others are like, what are the principles?
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Like, what are we trying to say?
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How do we know if we're right?
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But there's also this very like operational approach to just iterating where we're like, well, we thought that we were going to see this at this, you know, safety level and we didn't.
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So should we change it so that we're making sure that we're holding ourselves accountable?
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And then there's all kinds of organizational things, right?
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We just were like, let's change the structure of the RSP organization for clearer accountability.
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And I think my sense is that for a document that's as important as this, right?
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I love the constitution analogy.
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It's like, there's all of these bodies and systems
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that exist in the U .S to like make sure that we follow the constitution, right?
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There's the courts, there's the Supreme Court, there's the presidency, there's the you know the both houses of of you know congress
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and they do all kinds of other things of course
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but there's like all of this infrastructure
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that you need around this like one document
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and i feel like we're also learning
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that lesson here i think it sort of reflects a view we a lot of us have about safety which is
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that it's it's a solvable problem it's just a very very hard problem that's going to take tons
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and tons of work yeah all these institutions
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that we need to build up like there's there's all sorts
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of institutions built up around like automotive safety build up over many, many years.
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But we're like, do we have the time to do that?
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We've got to go as fast as we can to figure
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out what the institutions we need for AI safety are
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and build those and try to build them here first but make it exportable.
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It forces unity also because if any part of the org is not kind of in line with our safety values,
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it shows up through kind of the RSP, right?
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The RSP is going to block them from doing what they want to do.
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And so it's a way to remind everyone over and over again, basically to make safety a product requirement, part of the product planning process.
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And so, like, it's not just a bunch of kind of like bromides that we repeat.
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It's something that you actually, if you show up here and you're not aligned, you actually run into it.
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And, like, you either have to learn to get with the program or it doesn't work out.
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The RSP has become kind of funny over time because we spend thousands of hours of work on it.
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And then I go and talk to senators and I explain the RSP and I'm like, we have some stuff that means it's hard to steal what we make and also that it's safe.
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And they're like, yes, that's a completely normal thing to do.
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Are you telling me not everyone does this?
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And you're like, oh, okay, yeah.
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It's the exact truth that not everyone does this.
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But that kind of, it's amazing.
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We've and so much effort on it here.
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And when you boil it down, they're like, yes, that sounds like a normal way.
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Yeah, that sounds good.
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That's been the goal.
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Like Daniela was saying, like, let's make this as boring and normal.
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Like, let's make this a finance thing.
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Yeah, imagine it's like an audit.
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Yeah, yeah.
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No, boring and normal is what we want.
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Certainly in retrospect.
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Well, also, Dario, I think in addition to driving alignment, it also drives clarity because it's really, it's written down what we're trying to do
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and it's legible to everyone in the company
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and it's legible externally what we think we're supposed to be aiming towards from a safety perspective.
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It's not perfect.
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We're iterating on it.
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We're making it better.
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But I think there's some value in saying like, this is what we're worried about.
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This thing over here, like you can't just use this word to sort of derail something in either direction, right?
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To say, oh, because of safety, we can't do X or because of safety, we have to do X.
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We're really trying to make it clearer what we mean.
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Yeah.
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You can't, it prevents you from worrying about every last little thing under the sun, right?
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Because it's actually the fire drills that damage the cause of safety in the long run.
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I've said, like, if there's a building and, you know, the fire alarm goes off every week, like, that's a really unsafe building.
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Because when there's, like, actually a fire, you're just going to be like, oh, it just goes off all the time.
374
So it's very important to be calibrated.
375
Yeah.
376
A slightly different frame that I find kind of clarifying is
377
that I think the RSP creates healthy incentives at a lot of levels.
378
So I think internally, it aligns the the incentives of every team with safety, because it means that if we don't make progress on safety, we're going to block.
379
I also think that externally, it creates a lot of healthier incentives than other possibilities, at least that I see, because it means that, you know, if we at some point have to take some kind of dramatic action,
380
like if at some point we have to say, you know, our model, we've reached some point and we can't yet make a model safe, it aligns that with sort of the point where there's evidence that supports that decision.
381
And there's sort of a pre -existing framework for thinking about it and it's legible.
382
And so I think there's a lot of levels of which the RSP, I think in ways that maybe I didn't initially understand when we were talking about the early versions of it.
383
It creates a better framework than any of the other ones that I've thought about.
384
I think this is all true, but I feel like it undersells how challenging it's been to figure out what the right policies
385
and evaluations and what the lines should be.
386
I think that we have and continue to iterate a lot on that.
387
I think there is a question also that's difficult.
388
You can be at a point where it's very clear something's dangerous or very clear that something's safe, But with some technology that's so new, there's actually like a big gray area.
389
And so I think that has been like all the things that we're saying were things that made me really, really excited about the RSP at the beginning and still do.
390
But also, I think, enacting this in a clear way
391
and making it work has been much harder and more complicated than I anticipated.
392
Yeah, I think this is exactly the point.
393
The gray areas are impossible to predict there's
394
so many of them like until you actually try to implement
395
everything you like don't know what's going to go wrong
396
so what we're trying to do is go and implement everything
397
so we can see as early as possible what's going to go wrong
398
so yeah you have to you have to do three
399
or four passes before before you really you really get it right like iteration is just very powerful
400
and you know you're not going to get it you're not going to get it right on the first time
401
and so you know
402
if the stakes are increasing you want to get your iterations
403
in early you don't want to get them in light you're also building the for like internal institutions
404
and processes so the specifics might change a lot
405
but building the muscle of just doing it is the really
406
valuable thing i'm i'm responsible for like compute at anthropic that's important
407
so thank you so like i think
408
that like for me for me like i guess we have
409
to deal we have to deal with external folks yeah
410
and different external folks kind of are on different spectrums of
411
the like how fast do they think stuff is going again
412
and like I think that's also been a thing where like I started out like not thinking stuff would be
413
that fast and have changed over time and
414
so I have some I have sympathy for that and
415
so I think the RSP has been extremely useful for me in communicating with people who think
416
that things might take longer
417
because then we have a thing where it's like we don't
418
need to do extreme safety measures until stuff gets really intense
419
and then we can be like
420
that they might be like I don't think stuff will get intense for a long time
421
and then I'll be like okay yeah we don't have to do extreme safety measures, and so that makes it a lot easier to communicate with other folks externally.
422
Yeah, yeah, it makes it like a normal thing you can talk about, rather than something really strange.
423
Yeah.
424
How else is it, like, showing up for people?
425
Evals, evals, evals.
426
Good.
427
It's all about evals.
428
Everyone's doing evals.
429
Like, the training team is doing evals all the time.
430
We're trying to figure out, like, has this model gotten enough better that it has the potential to be dangerous?
431
So, how many teams do we have that are evals teams?
432
Frontier red team, there must be, I mean, there's a lot of people.
433
Every team produces evals, basically.
434
And that means you're just measuring against the RSP, like measuring for certain signs of things that would concern you or not concern you.
435
Exactly.
436
Like it's not, it's easy to lower bound the abilities of a model, but it's hard to upper bound.
437
So we just put tons and tons of research effort into saying, can this model do this dangerous thing or not?
438
Maybe there's some trick that we haven't thought of, like chain of thought or best event
439
or some kind of tool use that's going to make it so it can help you like do something very dangerous?
440
It's been really useful in policy
441
because it's been a really abstract concept what safety is and
442
when I'm like we have an eval which changes whether we deploy the model
443
or not and then you can go and calibrate with policymakers
444
or experts in national security or some of these CBRN areas
445
that we do to actually help us build evals that are well calibrated and that counterfactually just wouldn't have happened otherwise.
446
But once you've got the specific thing, people are a lot more motivated to help you make it accurate.
447
So it's been useful for that.
448
The RRSP shows up for me for sure.
449
Often.
450
I actually think the, weirdly, the way that I think about the RRSP the most is like what it sounds like.
451
Just like the tone.
452
I think we just did a big rewrite of the tone of the RRSP
453
because it felt overly like technocratic and even a little bit adversarial.
454
I spent a lot of time thinking about how do you build a system
455
that people just want to be a part of, right?
456
It's so much better if the RSP is something that everyone in the company can walk around and tell you, just like with OKRs like we do right now, what are the top goals of the RSP?
457
How do we know if we're meeting them?
458
What AI safety level are we at right now?
459
Are we at ASL 2?
460
Are we at ASL 3?
461
That people know what to look for because
462
that is how you're going to have good common knowledge of if something's going wrong, right?
463
If it's overly technocratic and it's something that only particular people in the company feel is accessible to them.
464
It's just like not as productive, right?
465
And I think it's been really cool to watch it sort of transition into this document where I actually think most, if not everybody at the company,
466
regardless of their role, could read it and say, this feels really reasonable.
467
I want to make sure that.. we're building AI in the following ways
468
and I see why I would be worried about these things.
469
And I also kind of know what to look for if I bump into something, right?
470
It's almost like, make it simple enough that if you are working at a manufacturing plant and you're like, huh, it looks like the safety seatbelt on this should connect this way,
471
but it doesn't connect that you can spot it.
472
And that there's just like healthy feedback flow between leadership
473
and the board and the rest of the company and the people that are actually building it.
474
Because I actually think the way this stuff goes wrong in most cases is just like the wires don't connect
475
or like they get crossed.
476
And that would just be like a really sad way for things to go wrong, right?
477
It's just all about operationalizing it, making it easy for people to understand.
478
Yeah, the thing I would say is none of us wanted to found a company.
479
We felt like it was our duty, right?
480
I felt like we had to.
481
Like we have to do this thing.
482
This is the way we're going to make things go better for the AI.
483
Right.
484
Like, that's also why we did the pledge.
485
Right.
486
Yeah.
487
Because we're like, the reason we're doing this is feels like our duty.
488
I wanted to invent and discover things in some kind of beneficial way.
489
That was that was how I came to it.
490
And that led to working on AI and AI required a lot of engineering and eventually AI required a lot of capital.
491
But what I found was that if you don't do this in a way where you're setting the environment,
492
where you set up the company, then a lot of it gets done.
493
A lot of it repeats the same mistakes that I found so alienating about the tech community.
494
It's the same people.
495
It's the same attitude.
496
It's the same pattern matching.
497
And so at some point, it just seemed inevitable that we need to do it in a different way.
498
When we were hanging out in graduate school, I remember you had kind of this whole like program of
499
trying to figure out how to do science in a way that would sort of advance the public good.
500
And I think I think that's like pretty similar to how we we think about this.
501
Maybe you had like this like Project Vannevar or something to do that.
502
I was a professor.
503
I think basically I just looked at the situation and I was convinced that AI was on a very,
504
very, very steep trajectory in terms of impact didn't seem like because of the necessity for capital that as a physics professor, I could continue doing that.
505
I wanted to work with people that I trusted in building an institution to try to make AI go well.
506
But yeah, I would never recommend founding a company or really want to do it.
507
I mean, yeah, I think it's just a means to an end.
508
I think that's usually how things go well, though.
509
If you're doing something just to enrich yourself or gain power, you have to to actually care about accomplishing a real goal in the world,
510
and then you find whatever means you have to.
511
Well, something I think about a lot is just a strategic advantage for us.
512
It sounds really funny to say, but just how much trust there is at this table, right?
513
I think that's not—Tom, you were at other startups.
514
I was never a founder before, but it's actually really hard to get a big group of people to have the same mission.
515
Right.
516
And I think the thing that I feel like the happiest about
517
when I come into work and probably the most proud of at Anthropic is how well
518
that has scaled to a lot of people.
519
It feels to me like in this group and with the rest of leadership, everyone is here for the mission and our mission is really clear.
520
And it's it's very pure.
521
Right.
522
And I think that is something that I don't see as often to Dario's point in sort of the tech industry.
523
It feels like there's just a wholesomeness to what we're trying to do.
524
I agree.
525
None of us were like, let's just go found a company.
526
I felt like we had to do it.
527
It just felt like we couldn't keep doing what we were doing the place we were doing it.
528
We had to do it by ourselves.
529
It felt like with GPT -3, which all of us had touched or worked on, and scaling laws and everything else, we could see it in front of us in 2020.
530
It felt like, well, if we don't do something soon altogether, you're going to hit the point of no return.
531
You have to do something to have any ability to change
532
the environment i i think building up danielle i do i do think
533
that um there's just like a lot of trust in this group i think like each of us knows
534
that we we got into this
535
because we want to help out with the world yeah um
536
we did the like 80 like the 80 pledge thing and
537
that was like i think
538
that everybody everybody was just like yes obviously we're going to do this it was it was
539
and yeah i i yeah i i do think that the
540
that the the trust thing is a special thing that's that's
541
extremely rare yeah i credit daniella with like keeping the keeping
542
the bar high i i credit you with the fact
543
that we scaled people say how nice people are here
544
which is actually uh a wildly important thing i think anthropic is really low politics
545
and of course we all have a different vantage point than average
546
and i try to remember it's because of low ego but But it's low ego, and I do think our interview process and just the type of people who work here, there's almost an allergic reaction.
547
to politics and unity unity unity is so important the idea
548
that the product team the research team the trust
549
and safety team you know the go -to -market team the policy team like
550
that this the safety folks they're all trying to contribute to
551
kind of the same goal the same mission of the company right i think it's i think it's dysfunctional
552
when different parts of the company are are think they're trying to accomplish different things think the company is about different things,
553
or think that other parts of the company are trying to undermine what they're doing.
554
And I think the most important thing we've managed to preserve is, and again, things like the RSP drive it, this idea that it's not,
555
you know, there's some parts of the company causing damage and other parts of the company trying to repair it, but that there are different parts of the company doing different functions,
556
and they all function under a single theory of change.
557
Extreme pragmatism, right?
558
But, you know, the reason I went to OpenAI in the first place, you know, it was a nonprofit.
559
It was a place where I could go and focus on safety.
560
And I think over time, you know, that maybe wasn't as good a fit.
561
And there were some difficult decisions.
562
And I think in a lot of ways, I really trusted Dario and Danielle on that.
563
But I didn't want to leave.
564
That was like something that I think I was actually like pretty reluctant to go along with.
565
Because I think, for one thing, I didn't know that it was good for the world to have more AI labs.
566
And I think it was something that I was pretty reluctant for.
567
And I think, as well, when we did leave, I think I was reluctant to start a company.
568
I think I was arguing for a long time that we should do a nonprofit instead and just focus on safety research.
569
And I think it really took pragmatism and confronting the constraints
570
and just being honest about what the constraints implied for accomplishing that mission that led to Anthropic.
571
I think just a really important lesson
572
that we were good about early on is like make less promises and keep more of them, right?
573
Like try to be calibrated,
574
be realistic, confront the trade -offs because trust and credibility are more important than any particular policy.
575
It is so unusual to have what we have and watching like Mike Krieger defend safety things of,
576
like, reasons why we shouldn't ship a product yet, but also then to watch Vinay sort of say, like, okay, we have to do the right thing for the business.
577
Like, how do we get this across the finish line?
578
And to hear, you know, people, like, deep in the technical safety org talking about how it's also important
579
that we build things that are practical for people and hearing, you know, engineers on inference talk about safety, that's amazing.
580
Like, I think that is, again, one of the most special things about working here is everybody with that unity is prioritizing the pragmatism, the safety, the business.
581
That's wild.
582
I think about it as spreading the trade -offs from just the leadership of the company to everyone, right?
583
I think the dysfunctional world is like, you have a bunch of people who only see a big, you know, safety is like, we always have to do this.
584
And product is like, we always have to do this.
585
And research is like, you know, this is the only thing we care about.
586
And then you're stuck at the top.
587
You're stuck at the top.
588
You have to decide between.
589
You don't have as much information as either of them.
590
That's the dysfunctional world.
591
The functional world is when you're able to communicate to everyone.
592
There are these trade -offs we're all facing together.
593
The world is a far from perfect place.
594
There's trade -offs.
595
Everything you do is going to be suboptimal.
596
Everything you do is going to be some attempt to get the best of both worlds that that, you know, doesn't work out as well as you thought it was.
597
And everyone is on the same page about confronting those trade -offs together.
598
And they just feel like they're confronting them from a particular post, from a particular job as part of the overall job of confronting all the trade -offs.
599
It's a bet on race to the top, right?
600
It's a bet on race to the top.
601
Like it's not a pure upside bet.
602
Things could go wrong.
603
But like, we're all aligned on like, this is the bet that we're making.
604
And markets are pragmatic.
605
So if the more successful and profit becomes as a company, the more incentive there is for people to copy the things that make us successful.
606
And the more that success is tied to actual safety stuff we do,
607
the more it just creates a like gravitational force in the industry that will actually get the rest of industry to compete.
608
And it's like, sure, we'll like build seatbelts and everyone else can copy them.
609
That's good.
610
It's like good world.
611
Yeah, this is the race to the top, right?
612
But if you're saying, well, we're not going to build the technology, you're not going to build it better than someone else,
613
that in the end, that just doesn't work because you're not proving that it's possible to get from here to there.
614
Where the world needs to get, never mind the industry, never mind one company, is it needs to get us successfully through from this technology does, doesn't exist.
615
to the technology exists in a very powerful way and society has actually managed it.
616
And I think the only way that's going to happen is
617
that if you have at the level of a single company and eventually at the level of the industry, you're actually confronting those trade -offs.
618
You have to find a way to actually be competitive, to actually lead the industry in some cases, and yet manage to do things safely.
619
And if you can do that, the gravitational pull you exert is so great.
620
There are
621
so many factors from the regulatory environment to the kinds of
622
people who want to work at different places to even sometimes the views of customers
623
that drive in the direction of if you can show that you can do well on safety without sacrificing competitiveness, right?
624
If you can find these kind of win -wins, then others are incentivized to do the same thing.
625
I think that's why getting things like the RSP right is so important, because I think that we ourselves,
626
seeing where the technology is headed, have often thought, oh, wow, we need to be really careful of this thing.
627
But at the same time, we have to be even more careful not to be crying wolf, saying that innovation needs to stop here.
628
We need to find a way to make AI useful,
629
innovative, delightful for customers, but also figure out what the constraints really have to be
630
that we can stand behind that make systems safe, so that it's possible for others to think that they can do that too, and they can succeed, they can compete with us.
631
We're not doomers, right?
632
We want to build the positive thing.
633
We want to build the good thing.
634
And we've seen it happen in practice.
635
A few months after we came out with our RRSP, the three most prominent AI companies had one, right? interpretability research, that's another area we've done it.
636
Just the focus on safety overall, like collaboration with the AI safety institutes, other areas.
637
Yeah, the Frontier Red team got cloned almost immediately, which is good.
638
You want all the labs to be testing for very, very scary risks.
639
Export the seatbelts.
640
Well, Jack also mentioned it earlier, but customers also really care about safety, right?
641
Customers don't want models that are hallucinating.
642
They don't want models that are easy to jailbreak.
643
They want models that are helpful and harmless, right?
644
And so a lot of the time what we hear in customer calls is just we're going with Claude
645
because we know it's safer.
646
I think that is also a huge market impact, right?
647
Because our ability to have models that are trustworthy and reliable, that matters for the market pressure that it puts on competitors, too.
648
Maybe to unpack something that Dario said a little bit more.
649
I think there's this narrative or this idea that maybe the virtuous thing is to almost like nobly fail, right?
650
It's like you should go and like put safety, you should go and put things, you should sort of demonstrate like in an impragmatic way so
651
that you can sort of demonstrate your purity to the cause or something like this.
652
And I think if you do that, it's actually very self -defeating.
653
For one thing, it means
654
that you're going to have the people who are making decisions be self -selected for being people who don't care
655
and for people who aren't prioritizing safety and who don't care about it.
656
And I think on the other hand, if you try really hard to find the way to align the incentives
657
and make it so that if there are hard decisions, they happen at the points where there is the most force to go
658
and support making the correct hard decisions and where there's the most evidence, then you can sort of start to trigger this race to the top that Dario is describing,
659
where instead of going and having the people who care get pushed out of influence, you instead pull other people to have to go and follow.
660
So what are you all excited about when it comes to the next things we'll be working on?
661
I think there's a bunch of reasons you can be excited about Interpreal.
662
One is obviously safety.
663
But there's another one that I think I find at an emotional level, you know, equally exciting or equally meaningful to me,
664
which is just that I think neural networks are beautiful.
665
And I think that there's a lot of beauty in them that we don't see.
666
We treat them like these black boxes that we're not particularly interested in the internal stuff.
667
But when you start to go and look inside them, they're just full of amazing, beautiful structure.
668
You know, it's sort of like if people looked at biology and they were like, you know, like evolution is really boring.
669
It just like it's a simple thing that goes and like runs for a long time and then it makes animals.
670
And like instead, it's like actually, you know, each one of those animals that evolution produces
671
and I think that it's an optimization process like training a neural network.
672
They're full of incredible complexity and structure and we have an entire sort of artificial biology inside of neural networks.
673
If you're just willing to look inside them, there's all of this amazing stuff and I think
674
that we're just starting to slowly unpack it and it's incredible and there's so much there.
675
But it's just.. so much to be discovered there.
676
We're just starting to crack it open.
677
And I think it's going to be amazing and beautiful.
678
And sometimes I imagine, you know, like a decade in the future, walking into a bookstore and buying, you know, the textbook on neural network interpolability, or really like on the biology of neural networks
679
and just the kind of wild things that are going to be inside of it.
680
And I think that in the next decade, we're going to, in the next couple of years even, we're going to go and start to go in and really discover all of those things.
681
And it's to be wild and incredible.
682
It's also going to be great that you get to buy your own textbook.
683
I'm excited that a few years ago, if you had said governments will set up new bodies to test
684
and evaluate AI systems and they will actually be competent and good, you would have not thought that was going to be the case, but it's happened.
685
It's like governments have built these new new embassies almost to deal with this new kind of class of technology
686
or like thing that Chris studies.
687
And I'm just very excited to see where that goes.
688
I think it actually means that we have state capacity to deal with this kind of societal transition.
689
So it's not just companies.
690
And I'm excited to help with that.
691
I'm already excited about this to, you know, to a certain extent today.
692
But I think just imagining the future world of what AI is going to be able to do for people is, it's impossible to not feel excited about that.
693
Dario talks about this a lot, but I think even just the sort of glimmers of Claude being able to help with,
694
you know, vaccine development and cancer research and biological research is crazy, like just to be able to watch what it can do now.
695
But when I fast forward, you know, three years in the future or five years in the future, imagining that Claude could actually solve so many of the fundamental problems
696
that we just face as as humans just even just from a health perspective alone even
697
if you sort of take everything else out feels really exciting
698
to me just like thinking back to my international development times it would be amazing
699
if Claude was responsible for helping to do a lot of the work
700
that I was trying to do a lot less effectively when I was like 25.
701
I mean I guess similarly I'm excited to build Claude for
702
work like I'm excited to build like I'm excited to build Claude into the company
703
and into companies all over the world i guess i'm excited
704
just for uh i guess like personally like i like using
705
claude a lot like i definitely uh there's been increasing amounts
706
of like home times with like me just like chatting with
707
john claude about stuff um i think the biggest recent thing
708
has been um uh code where like six months ago like
709
i didn't use claude to do any coding work like our teams didn't really use Claude
710
that much for for coding
711
and now it's like just face difference like i i gave a talk at yc like uh week before last
712
and at the beginning i just asked like okay
713
so like how many how many folks here use Claude for for coding now
714
and literally 95 of hands like all the hands in the room
715
which just like is totally different than how it was four months ago
716
or whatever yeah so when i think about what i'm excited about i think about places where, like I said before, where there's this consensus that,
717
again, seems like consensus, seems like what everyone wise thinks, and then it just breaks.
718
Places where I think that's about to happen and it hasn't happened yet.
719
One of them is interpretability.
720
I think interpretability is both the key to steering and making safe AI systems, and we're about to understand.
721
And interpretability contains insights about intelligent optimization problems and about how the human brain works.
722
I've said, and I'm really not joking, Chris Ola is going to be a future Nobel medicine laureate.
723
I'm serious.
724
I'm serious because a lot of these, I used to be a neuroscientist, a lot of these mental illnesses, the ones we haven't figured out, right?
725
Schizophrenia or the mood disorders, I suspect there's some higher level system thing going on and
726
that it's It's hard to make sense of those with brains
727
because brains are so mushy and hard to open up and interact with.
728
Neural nets are not like this.
729
They're not a perfect analogy, but as time goes on, they will be a better analogy.
730
That's one area.
731
Second is related to that, I think, just the use of AI for biology.
732
Biology is an incredibly difficult problem.
733
People continue to be skeptical for a number of reasons.
734
I think that consensus is starting to break.
735
We saw a Nobel Prize in chemistry awarded for alpha fold, remarkable accomplishment.
736
We should be trying to build things that can help us create 100 alpha folds.
737
And then finally, using AI to enhance democracy.
738
We worry about if AI is built in the wrong way, it can be a tool for authoritarianism how can AI be a tool for freedom and self -determination.
739
I think that one is earlier than the other two, but it's going to be just as important.
740
Yeah.
741
I mean, I guess two things that at least connect to what you were saying earlier.
742
I mean, one is I feel like people frequently
743
join Anthropic because they're sort of scientifically really curious about AI
744
and then kind of get convinced by AI progress to sort
745
of share the vision of the need not just to advance the technology, but to understand it more deeply and to make sure that it's safe.
746
And I feel like it's actually just sort of exciting to have people that you're working with,
747
like kind of more and more united in their vision for both what AI development looks like
748
and the sort of sense of responsibility associated with it.
749
And I feel like that's been happening a lot due to a lot of advances that have happened in the last year, like what Tom talked about.
750
Another is that, going back really to concrete problems, I feel like we've done a lot of work on AI safety up until this point, a lot of it's really important.
751
But I think we're now, with some recent developments, really getting a glimmer of what kinds of risks might literally come about from systems that are very,
752
very advanced so that we can investigate and study them directly with interpretability, with other kinds of safety mechanisms,
753
and really understand, like, what the risks from very advanced AI might look like.
754
And I think that that's something that is really going to allow us to sort of further the mission in a, like, really deeply scientific empirical way. And
755
so I'm excited about sort of the next six months of
756
how we use our understanding of what can go wrong with advanced systems to characterize
757
that and figure out how to avoid those pitfalls.
758
Perfect.
759
Finn.
760
I did it.
761
Good job.
762
This is the only time we ever get to catch up.
הורד אפליקציה
כל מה שאתה צריך כדי לדבר בשטף
הגיית AIציון לכל משפט
תרגול IPAשלוט בכל צליל
אוצר מיליםבנה את מאגר המילים שלך
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אוצר מילים והערות הגייה לשיעור הזה
שיעור הדיבור הזה ברמה C1 מבוסס על הסרטון “Building Anthropic”. בסרטון הזה יש 762 משפטים ו-10613 מילים לתרגול Shadowing. הדיבור נמשך 51:48. הדובר מדבר מהר, כ-205 מילים בדקה, אז צפו לצלילים מחוברים ומקוצרים. 86% מהמילים נמצאות בין 3,000 המילים הנפוצות ביותר באנגלית; כדאי ללמוד את השאר לפני שמתחילים.
אוצר מילים מרכזי בסרטון הזה
12 המילים המתקדמות ביותר בסרטון, עם ההגייה והמשמעות שלהן:
| מילה | הגייה | משמעות |
|---|---|---|
| stripe שם עצם | /stɹaɪp/ | פַּס |
| confront פועל | /kənˈfɹʌnt/ | הִתְעַמֵּת |
| ambitious שם תואר | /æmˈbɪʃ.əs/ | שאפתני |
| incentive שם עצם | /ɪnˈsɛntɪv/ | תמריץ |
| calibrate פועל | /ˈkæl.ɪ.bɹeɪ̯t/ | כייל |
| legible שם תואר | /ˈlɛd͡ʒəbl/ | קָרִיא |
| bore פועל | /boɹ/ | שִׁעֲמֵם |
| archive שם עצם | /ˈɑɹˌkaɪv/ | אַרְכִיּוֹן |
| textbook שם עצם | /ˈtɛkst.bʊk/ | סֵפֶר לִמוּד |
| decisive שם תואר | /dɪˈsaɪsɪv/ | החלטי |
| startup שם עצם | /ˈstɑɹtˌʌp/ | סטארטאפ, חברת סטארטאפ |
| accomplish פועל | /əˈkɑm.plɪʃ/ | בִּצֵּעַ |
Phrasal verbs שתשמעו
| מילה | משמעות |
|---|---|
| figure out פועל | הֵבִין, מצא תשובה |
| write down פועל | רָשַׁם |
| come back פועל | שָׁב |
משפטים שכדאי לחזור עליהם
משפטים קצרים ושלמים מהסרטון שאפשר להשתמש בהם בשיחה יומיומית:
- How did you actually make the decision though?
- Should I try to do this thing?
- How do we know if we're right?
- Are you telling me not everyone does this?
- How do we know if we're meeting them?
דקדוק בסרטון הזה
המבנים שהדובר משתמש בהם הכי הרבה, עם המילים המדויקות מהסרטון:
| מבנה | בסרטון |
|---|---|
| הווה מושלם ממושך (Present perfect continuous) have/has been + -ing — פעולה שהתחילה בעבר ועדיין נמשכת עכשיו | has been shifting · We've been helping · there's been increasing |
| משפטי תנאי (Conditional sentences) if + clause, will/would + verb — תנאי ותוצאה שלו | if it was true then it would be · if you had said governments will set |
| “Used to” used to + verb — הרגל או מצב שהיה נכון בעבר וכבר לא קיים | used to run · used to be |
| הווה מושלם (Present perfect) have/has + past participle — פעולה שקרתה בעבר ועדיין יש לה חשיבות או השפעה עכשיו | have you been · I've created · I've known |
הגייה ששווה לשים לב אליה
הדובר משתמש ב-226 קיצורים וצורות מקוצרות, כמו we're, you're, don't. אמרו אותם בצורה הקצרה, כפי שאתם שומעים אותם.
- הצלילים של “th”: anthropic /ænˈθɹɒp.ɪk/, altogether /ɔl.tuˈɡɛð.ɚ/
- הצלילים של “sh” ו-“zh”: ambitious /æmˈbɪʃ.əs/, gravitational /ˌɡɹævɪˈteɪʃənəl/, iteration /ˌɪt.əˈɹeɪ.ʃən/, optimization /ˌɑptəmaɪˈzeɪʃən/, accomplish /əˈkɑm.plɪʃ/
- מילים ארוכות — שימו לב להטעמה: pragmatism /ˈpɹæɡmətɪzəm/, analogy /əˈnæləd͡ʒi/, conservatism /kənˈsɜːvətɪzəm/, gravitational /ˌɡɹævɪˈteɪʃənəl/, iteration /ˌɪt.əˈɹeɪ.ʃən/
צלילים שדוברי עברית מתקשים בהם:
- /æ/ — פתוח יותר מה-“אֶ” העברית, לא כמו “a” ב-“cup”: anthropic /ænˈθɹɒp.ɪk/, eval /ˈiː.væl/, ambitious /æmˈbɪʃ.əs/, calibrate /ˈkæl.ɪ.bɹeɪ̯t/, pragmatism /ˈpɹæɡmətɪzəm/
- /r/ — הלשון לא נוגעת בשום מקום, לא ה-“ר” הגרונית: anthropic /ænˈθɹɒp.ɪk/, stripe /stɹaɪp/, neural /ˈnʊɹəl/, confront /kənˈfɹʌnt/, calibrate /ˈkæl.ɪ.bɹeɪ̯t/
- עיצור קולי בסוף מילה — /b/, /d/, /g/, /z/, /v/ בלי להפוך אותם לאטומים: incentive /ɪnˈsɛntɪv/, archive /ˈɑɹˌkaɪv/, blob /blɑb/, prioritize /pɹaɪˈɒɹ.ə.taɪz/, bind /ˈbaɪ̯nd/
איך לתרגל עם הסרטון הזה
- הקשיבו לכל הסרטון פעם אחת בלי לדבר, ורשמו את המילים שאתם לא מכירים.
- התחילו במהירות 0.75×, תרגלו Shadowing משפט אחר משפט, וחזרו למהירות רגילה כשזה מרגיש קל.
- הקליטו את עצמכם והשוו למקור, תוך תשומת לב למילים כמו stripe, confront, ambitious.
מהי טכניקת השאדואינג?
שאדואינג היא טכניקת למידת שפה מבוססת מדע שפותחה במקור להכשרת מתורגמנים מקצועיים ופופולרית על ידי הפוליגלוט ד"ר אלכסנדר ארגואלס. השיטה פשוטה אך עוצמתית: אתה מקשיב לאודיו באנגלית ילידית וחוזר עליו בקול רם מיד — כמו צל העוקב אחרי הדובר עם עיכוב של 1–2 שניות בלבד. בניגוד להאזנה פסיבית או תרגילי דקדוק, שאדואינג מכריח את המוח והשרירים בפה לעבד ולשחזר דפוסי דיבור אמיתיים בו זמנית. מחקרים מראים שזה משפר משמעותית את דיוק ההגייה, האינטונציה, הקצב, הדיבור המחובר, הבנת השמיעה ושטף הדיבור — מה שהופך אותו לאחת השיטות היעילות ביותר להכנה לIELTS Speaking ולתקשורת באנגלית בעולם האמיתי.