Chamath
speaker
939 appearances
12 recordings
2 series
first heard Jun 2024
last heard 3 Jun
Chamath’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 8 in all, peaking in Nov 2025 with 2.
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And you can go to Mearsheimer.ai and ask it questions. And it will tell you the answers in his voice because we cloned his voice using Resemble AI. Anyway, so AI can do voice now. And it can be trained extremely well on large data sets to give you answers to questions, which is pretty much what customer support is.
So I think it's now becoming clear that I think within the next two to three years, you're going to see a massive disruption in that industry.
I agree with that massively. And I think there's another underreported story, which is People don't like to call and talk to a customer service agent, like an actual human, if they can avoid it. They would much rather go on YouTube and say, how do I fix this? Or ask ChatGPT, how do I fix this? It's like, I don't want to waste another person's time. Just give me the answer as quick as possible.
And AI will give you the answer quicker. YouTube will give you the answer quicker. I've had so many times where I have people who work for me who are like, I don't know how to do that. And I literally would walk up to their computer and load YouTube and type in, how do I blank and there's a video there, watch it on two speed, you can do it.
That's what's, you know, gonna also kill this, like, I don't want to talk to a human, just change my flight, just You know, answer my question.
Yeah, I mean, you talk about disruption. Call centers are a very big part of the economy in certain geographies. Denver, Salt Lake, I mean, parts of Florida. Yeah, exactly. It's a really big deal. If like half the cost gets ripped out of those call centers.
Where would you move those people?
If you had your choice, could they move to sales? Well, I think sales will be the one that's disrupted after customer support. But I don't know. I think it's going to be very disruptive. One of the reasons I think this is, in the early days of LLMs, people were saying that legal services would be disrupted. And you saw some very highly valued startups rocketing up based on that.
I think the problem with that is the error rate. So when you think about AI applications, you have to think about what is the tolerable error rate that the industry will allow? Because we know that AIs get things wrong, they can hallucinate. And you're never going to be able to make it perfect. I mean, you can improve the quality, but it's still going to have some errors.
And when you're dealing with like legal services, for example, you just can't have mistakes. This is not tolerated. However, customer support is different. Customer support is already organized into levels. Level one, level two, level three, based on difficulty. And there's already, in a sense, a mechanism for failover.
If like the level one customer support person can't answer the question, they kick it up to level two. So... There's a place for... LLMs to start in customer support, which is replacing all the level one and then working their way up the chain to level two as they get better and better.
And so what I'm saying is that the level of accuracy now, especially with the new PhD level reasoning models is good enough. We don't need to wait for like some perfect LLM model. And I think this is why this is going to be a big, big disruption. Millions of people potentially are going to have their jobs disrupted or at least transformed.
Well, it could be the end of the entire career as well, Chamath, if you were to look at this four by four sort of quadrant chart that Sachs is describing, which is the cost of an era, you know, and the actual complexity of the job, perhaps, or the cost of the job. How do you look at this? I know you're working on software that kind of does this with your startup as well.
Yeah.
It's really... Have you guys worked with the O1 preview yet? I just literally have been using this new reasoning engine that... OpenAI released, and it is extraordinary. And it's kind of thinking about the next three or four prompts you would do. And I literally just got this while we're on the show. I've hit the limit for my paid account because this thing is so intense on compute, I guess.
Right.
One of the reasons why I'm bullish on this customer support use case is because there's a very large data set to train on. You've got all of the product documentation that companies have already created. You've got all of the previous email support. And calls. And calls, yeah. The calls have been recorded, so you can now train the AI on that. So there's a very large...
body of data to train the AI model on. And it's not necessarily the most proprietary. It's not like dealing with people's medical records or even confidential legal documents, something like that. So the data is readily available. And then the foundation models are getting really good.
I think there's a big question here about value capture, which is there's a number of startups now that are becoming very highly valued that are chasing this disruption, this sort of customer support agent disruption. And they're getting into very high valuations, even unicorn valuations already.
And the question is, well, wait, if the foundation models are advancing at such a rate, like a year from now, why couldn't a developer, just a startup of a few guys, take next year's model and train it and then commoditize the
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