Okay, that was an excellent set of presentations. And I think there are questions embedded in those presentations for all the presenters. So let me start by asking Jack, you said very clearly you were talking about the upside, and the others talked about some of the downsides. So let me point to a potential downside that so many people are worried about, especially young people: will they find a job, and will they be able to keep them as AI gets better and better? What's your response?
So maybe three things here. One, we agree that early graduate hiring is a thing where you need support, you need to try novel things. Anthropic has funded an initiative called Claude Corpse where we're trying to create a thousand jobs for early graduate students that we deploy into nonprofits and NGOs around America. We pay their salaries because the best way to get people onto the career ladder is to get them a first job. There we need to run many experiments like this, and then obviously the AI industry would need to show up with the wealth to pay for that at scale. That's one.
Number two, we can study occupations by their exposure to AI and what happens to employment in those occupations. There are occupations where high AI exposure may lead to a job loss. I think call services and customer service agents are an example. There that becomes a matter of policy, a matter of discussion. And there are jobs where it seems like the application of AI might have a null to positive impact on jobs. We see this in some of the scientific disciplines. You add AI, you make them more productive, maybe you even raise employment in those areas. We need to share that data. Anthropic shares data from its own platform and works with governments to join that with local data to their country, so we can know that.
And I think finally, the bigger thing here is what I talked about is my model. It can't predict jobs that might be added from these new industries. It does predict potentially very disruptive employment impacts. And the purpose of my speech is actually to say, just very frankly, that that might happen. The implication of changing all of science and technology and how innovation happens is you may change pretty fundamental things about how employment markets work. And I don't say that lightly or like it's going to be easy, but rather that's a serious issue that we actually need to work on together. So I'd say that in response.
Professor Shen talked about some ways that what you said could be implemented when she talked about the Chinese medical system and using AI to help expand medical access. I mean, that's clearly a place where the world needs far more.
And so combining AI with people can actually do more. But you know, turning to China itself...
Can I add one tiny thing only just because you mentioned it? You said firms should disclose hiring and training. We happen to publish a post today about hiring and training practices at Anthropic, including employment trends about different employees and their seniority, where again it comports with the data. I think that we absolutely should just mandate that firms share that stuff because that's the telemetry that lets you make decisions. Sorry.
Absolutely. So, but to your point about China, you're talking about it exacerbating some of the problems, for example pricing and the fact that companies are not able to make profits as much. But has the technology implementation, where China is ahead of many other countries, you know, you talked about some job creation. Are the youth in China also as worried as they are elsewhere in the world? And how do you see that playing out? Are there new initiatives to...
Yeah, true. So it's both sides. So I do hope that the positive side can dominate in the future. So we did a field trip in both this summer and last summer to look at the enterprises. So I do observe that it has been used widely, not only in traditional firms. For example, we visited energy production firms. So what they do is that they use predictive AI widely in their system, so for forecasting, for risk controlling, and so on. So it's a listed firm and they are profitable. That's number one, to make their existing business more standardized, more controlled.
And number two is the new part that I just mentioned about health care, like making the health care a much better situation. And number three is we observe new platforms that can help to find new demand, for example for young people. So one day there's a social post that a young lady complained that her boyfriend never separates the socks with other clothes. And then what happened is that that post was delivered to a machine producing factory. So they invented this new washing machine that you do one load, but this one load can do three separate washings with whatever you want to separate. So that means that you can cultivate.
So we do observe it's not only the trend is changing from production to demand is becoming so-called consumer, we also observe that some of the production is done by voting. So the producer will put a note and then the commentators will vote which one they want and then it will then be produced. That's number three. And also we observe it has been used in areas that we don't think it can be standardized. So I give one example about milk tea. So we visited a milk tea firm. It was created only less than three years. It already had 15,000 branches in China, 15,000, and their goal is to expand to 20,000 this year.
Okay. So what they do is that they produce this tea fresh and standardized within two days. So that standardization, you know, milk and fruits and so on, that cannot be simply done without an AI system. So that is we observe that it has been used widely, and there we observe something called a premium of engineer. And so there's a teapot effect in one sense because of AI, some of the engineers lose their job from IT industries, but then it creates this premium demand for service industries so that they can use these engineers with a relatively cheaper price. That's what the owner told me. They told me that you know they can hire an engineer with about 200,000 RMB, but compared with the US it's going to be much more expensive.
So that will be something that could probably be used because China is graduating 5 million STEM graduates this year, if we think about this teapot effect for the service industry. So there's still great hope there.
Absolutely. Dr. Nageshwaran, there are many countries that are not at the frontier, unlike the US and China. India is one of them. How do you see the role of AI in India, and how will that play out? You gave us a few examples, but is it a real problem not being at the frontier, or are there opportunities that actually emerge because of that?
No, I think considering the very capital-intensive nature of being at the frontier, perhaps it is a blessing in disguise, and considering the way the large models are currently beginning to unravel, so probably it's okay to be a second or third mover in terms of being out of the frontier. So that's one. The second, I also noticed that in the Indian startups, the edge AI or the frugal AI computing models are at different stages of development, which will probably make it more cost-effective and will help in diffusion, because even now enterprise-wide adoption, even though per-token cost is falling, the aggregate costs are still formidable for many enterprises, but these edge AI models may probably make it easier. So that's the second part.
The third one I think is that, I mean as she mentioned in the case of the dishwasher example, which is very interesting, in health care I also learned that diabetic-induced blindness early detection would help, and apparently AI and Aravind Eye Care is piloting it, apparently it is doing a very good job of coming up with this detection much more quickly. So that, and also both in remote learning and in education in remote parts of the country, I'm sure AI can substitute where humans are not willing to go. So these are all the developments that are happening from the Indian perspective.
And last but not the least, as I mentioned, it is probably making it more urgent for a country like India, which is labor-rich and has to create about 8 million livelihoods a year, to focus much more on labor-intensive skills and labor-intensive services. So in that sense, the fact that we are not at the frontier is probably concentrating minds on where we should be concentrating.
Great. Yeah. Jack, I'm going to come back to you, but I want the others to also answer this question, which is, you know, Anthropic has been in the forefront of talking about the existential risks that AI development poses, and at the same time you have a reluctance of countries to regulate for fear of other countries getting ahead. Now, is humanity doomed because we fear other humans more than we fear extinction? What regulations can we cooperate on, and what is needed to start this process? And I want you both also to come in on this.
I'll just be brief. No, humanity is not doomed.
Good. Good. Yeah, I think especially that there's a pattern where people will try everything but the actual hard thing until the very end, and then they will do the hard pivotal act when the crisis arrives. Our view and the disposition of Anthropic is we would like to avoid the crisis. So we are trying to talk at length about what we see in front of us because we think these are choices that not only companies make but are actually matters of policy, and not national policy but international policy.
I think that the most confusing part of all of this is that anytime we talk about regulating AI, it is as if we are talking about needing to invent general relativity or something. And yet we talk about that in offices that are highly regulated, and we took a car there that was highly regulated, and then we go home and eat food that is highly regulated, and then we're like, it is a total mystery how to regulate technology. It's really confusing to me, and we're surrounded by regulation. And I think it's just simply having the recognition of the seriousness of this technology that we apply those basic safety testing standards, transparency standards, disclosure standards, and be able to price the risk of this appropriately. So I think we can do it.
Because I'm thinking more about the regulation from the finance side, because I'm also working on the Institute of Digital Finance. So the creation of agents, I think probably we'll need to pay more attention, especially for the financial market, about, you know, we could have more market risk, we can have more operational risk, and so on. That will not be something that one country can do. So in China there's always this dilemma that if you are tolerating... So there's always a balance between how we are advancing technology innovation as well as controlling the risk.
China's perspective often happens is that when we are encouraging technology, then there will be a lot of wild things and create risks. And for this AI technology nowadays, because of the black box in terms of its characteristics of calculation, I do call for more international collaboration in terms of financial systems. That's something probably least political, but that may be something that we can work on. Other things maybe, you know, it's difficult to discuss, but maybe we can work on something that's least political, and finance, please.
Yeah, well thankfully finance is non-political, so it should be useful, but...
