Reid riffs on AI adoption, NVIDIA, and AI’s impact on entry-level jobs

0:29I'm Reid Hoffman. about this new MIT study. And it said that 95 % of these AI pilot programs and companies actually don't lead to the scaled impact we're looking for. And they argue that it wasn't necessarily the technology wasn't there, but there are organizational hurdles, people aren't adopting, there's internal policies. And so that sort of the uptake, especially for Fortune 500 companies, for big companies who are used to doing things a certain way, is going to be much slower. So my question for you is, what do you make of this? Does this high failure rate mean that AI adoption is going to slow down across the board, especially because some organizations are still on the fence about AI adoption?

1:07Well, let's start with a couple of very broad themes. Surprising to everyone, I know. This is the way that I would open up my answers. But the first is, you know, part of the key thing about the AI revolution, the current one is it's scale compute to scale with scale learning systems. on scale data done by scale teams. And then it's scale adoption. And the societies, the industries, the companies will be the ones that kind of adopt that scale. And by the way, blitzscaling was obviously going to play in here as well in time, which will be the massive beneficiaries. And they'll be the beneficiaries the same way that England wasn't the country, Britain wasn't the country that invented the Industrial Revolution but embraced it early.

1:52And that's part of the reason why this relatively small island had a global empire for centuries. And I think that's part of the adoption thing is really key. Now, it doesn't surprise me that the way that, you know, most traditional enterprise companies say, well, the way we adopt technology is we assign a group of people, call it three to five or something, and we say we buy a pilot program and we test something and then we go, oh, look, that's that. And that's not really working as a format because this is the kind of just as it's a transformation of how individuals work, it's a transformation of how companies work as well.

2:27And, you know, when I glanced at the at the MIT study, I was like, well, the next study that should be done with this is compare and contrast startups. Right. Because my guess is the startups will be 95 percent are finding, you know, great acceleration or integrating it and all the rest in the ways of doing it because they're building their work process from ground up. And it's one of the things that I love about the work I do as an entrepreneur, a technology inventor, an investor in Silicon Valley and Greylock and all the rest of this because of this exact thing. And so, and you know, you know, one of the things that I also say is that to every individual is if you're not fundamentally discovering something by which AI helps you do your work better today, you're not trying hard enough.

3:14And it's part of the reason why, you know, we love all the stuff that Ethan Mullick and a number of other folks are doing. This is, and why, you know, why we're at super agency to try to, try to get this, this message and going out there. And so, and why we do this podcast, all of that is engage and find the ways it's helping you. Doesn't mean it'll help you on everything. Doesn't mean it'll take over your entire job. Actually very, very few jobs can it take over the entire job of as of today. But for every human being who is using language in how they work. If you don't speak at all, then maybe AI is not ready for you, but it still might be.

3:47But if you're using language at all, AI is helpful. Right. I mean, you often say that for any problem we have, technology is somewhere between 30 and 80 percent of the solution. And I wouldn't say this is sort of a problem. This is how can we enhance productivity and sort of in this instance, do our work lives better. Do you think more of the improvement in AI use and productivity over the next, let's call it, year or two is going to come from advancements in AI, like actually the technology, or more sort of advancements in OB, advancements in how humans and organizations use it and integrate it?

4:19Well, actually, you're critically going to need both. But one of the things that exists today is kind of an underuse of the capabilities we have. Absolutely. So part of the reason why your question is, well, A or B is like, well, we've got a bunch of capabilities we're not actually using. Right. And if we don't get the use of the deployment, then the capabilities won't make a big difference. Now, that being said, you know, at different timescales and the timescale of this one will likely be much faster than earlier technology adoption, because even when you started with the personal computer or the mobile phone, not only, you know, the tech gets built, and then eventually it gets deployed into things.

4:58And it's always a lag of deployment. Sometimes that lag of deployment is very, very short and sometimes long. I think part of the reason why people get surprised about the AI one is because, you know, there's so much drum rolling about how transformative it is. And so they think you just turn it on and it starts working. And it's actually, there's a whole bunch of things to happen. I mean, this is among the reasons why there's a whole range of great technology investments in AI, whether it's Greylock or others, you know, in terms of doing this and why that, you know, kind of building applications is worth.

5:32And, And, you know, I'm not of the belief that it will just be the, well, once we have the one model, that one model will just be doing everything. I think there's a lot of fabric that goes into this. And what's more, the best model will undoubtedly be extremely expensive to run. And a bunch of the thing gets to the kind of the inference compute side of how does it become where intelligence gets added to everything with the same fluidity that electricity gets added to everything. And it's just the electricity of powering of intelligence that upgrades everything that you're doing. And I think that's one of the things that is so, you know, kind of fundamental of what this revolution needs to be.

6:13So adoption is a central part of it. And actually, as you know from various conversations you and I have had, it's one of my worries about where democracies can screw themselves up because the democracy could lead to an impedance in the adoption of these technologies. I think probably the leading democracies with the leading impedances will probably be the Europeans because, you know, they're kind of AI act and all the rest in terms of this. And it's one of the reasons why I try to help them in various ways to say, no, no, don't don't fumble the cognitive industrial revolution in this because the adoption will actually be very important in terms of doing this.

6:52And so that adoption actually, in fact, really matters. So perfect segue, because I wanted to talk actually about sort of the global scene again. So recently, Donald Trump reversed policy and said that NVIDIA could sell their chips to China, potentially in exchange for 15 percent of the resulting Chinese revenue. But then the Chinese government said to their companies, Bydance, Alibaba, Tencent, that they had to suspend their purchase of NVIDIA chips because of data security concerns. And so according to the information, DeepSeek, which we know is one of China's leading AI developers, they have begun training some of their next generation models on Huawei's Ascend chips, which is a shift away from NVIDIA.

7:32And so while DeepSeq still uses NVIDIA for its largest models, the partnership with Huawei signals a strategic turning point where China is trying to build a self-reliant AI ecosystem that can compete globally, even as U.S. companies like NVIDIA warned that the competition has undeniably arrived. So that is what the information has said about this sort of global back and forth. So my question for you is, does this DeepSeq pivot towards essentially their own chips, towards Chinese chips away from NVIDIA? Like, does that mean the end of NVIDIA dominance? Like, where does this further split the East and West in terms of technological power?

8:08Well, you know, part of the reason that the chip exports to China were constrained by previous U.S. policy was a combination of economic and national security, you know, rationale for, you know, kind of saying, hey, we should get the appropriate benefits to both U.S. economic interests and U.S. national security interests in terms of doing this. And the first thing is it's completely incoherent to say, well, we'll undercut those things by just taking a 15 % tax the U.S. Treasury. Like it doesn't have any intellectual property. All the people who are, you know, kind of theorists about American business and trade success and economic success should all abhor this.

8:58Now, one could argue that one should allow a certain set of chips to get out. Like you say, hey, the most current chips we'll hold on to and the other chips we will then more broadly provision in which we should just do that. We shouldn't be imposing a 15 % cut on doing it because there's no particular reason to be doing that because it's like, well, you know, if we charge 15%, then we don't have our national security concerns anymore. It's like it's just, you know, really unrelated. Yeah, quite bizarre. But by the way, of course, if you're forking the availability, then it is creating incentives for the for the Chinese to to to accelerate their own chip industry.

9:40I do think that chip industry is a strategic power, a strategic capability that is, you know, kind of on the level of nuclear or energy or I think we fumbled over the decades in doing this in the U.S. And I think it's super important to regain it in various ways. And of course, as we put in pressure to actively limit the Chinese, that creates an incentive for them to build their own, which then could lead to a decoupling, could lead to ultimately them solving problems. And part of the in-depth of thing that's happening with the Huawei chip is actually a different mathematical model that's kind of underlying it.

10:28And they say, well, maybe that'll be different or maybe that'll be better or maybe that'll be. And that creates all those risk factors. This is a complicated policy area for doing that. And I do think there is a very strong technological competition between China and the West. I said, do you think there's a strong economic competition between China and the West and the West and the US in these cases? And I think that competition is good. I think it's also cooperation is also good. And decoupling is generally speaking, while having competition, decoupling is one of the things that can actually lead to further conflicts and further issues.

11:07And one has to be very careful on those things. It's a strategy that can be executed, but with competence and care, not by, you know, governance by tweet or, you know, other kinds of things that we are too much in the weeds of right now. And I'm, you know, generally speaking, a very strong voice in favor of the West's economic and national security concerns here. But I don't think decoupling is a good idea. Now, the Chinese, I think, are very smart, and I think part of their concern, I don't think they have data security concerns, but I do think that part of what they're trying to figure out is say, well, okay, we do not want to be dependent upon the same kind of things we, the U.S.

11:55recognize. We don't want to be dependent upon supply chain here that can make a difference. And, you know, I think part of the thing that the current federal government is learning is to say, well, we have dependencies upon the Chinese supply chains too. And I think part of the background story of this, which you can see from various slips in Lutnik and others' statements is like, well, actually, this actually more ties to rare earths and other kinds of things that we have as a concern with maybe papering over a 15 % tariff in terms of how this operates. Like this is the kind of thing where quality governance and quality activity matters.

12:35And I think we're being a little bit, well, I'm being charitable. We're being incompetent about how we're navigating this stuff. And I think we should up our level of competence on this. Yep, absolutely. Saying one thing has different implications, saying they're doing it for one reason, affecting another thing, and then also just sort of not sticking to their capitalist roots. I would say. Some news that I was really excited about from last week came from OpenAI. Obviously, you know OpenAI well. You led the first investment round in OpenAI. You were on the board for a number of years. It's a company that we're really excited for all sort of the good that they can do on the AI side.

13:16And so last week, they announced a$50 million fund, and they want to help NGOs use AI for education, healthcare, economic opportunity, community organizing, all of the things that you and I often talk about that we think AI can be really fantastic for. One of the things I liked about this grant program in particular is they said they were launching it in early September. It closes in early October and they're going to give these grants out by the end of the year. So it is quick$50 million out the door to organizations, both old and new, that can really use AI to, you know, create better wellbeing for all Americans.

13:48And so is this something that you think all of the AI labs should be doing or should this be governments? Should this be foundations? Like, where should the responsibility to sort of use this AI for social good lie? So, probably not surprising to you, since I tend to be in these kind of major things, I tend to be inclusive. So, the short answer is everyone. So, yes, the Frontier Lab should do it. Yes, it's a great thing showing kind of open AI's leadership in its being a humanist organization and caring about what happens with human society and human individuals. I think everyone should be doing this sort of thing within the commercial, but I think it also means that governments and NGOs and all the rest should also, of course, be doing this because what will happen is we'll be going through rapid news cycles because just like we were talking about the MIT study, it was like, oh my God, everything's going to change.

14:41Wait, nothing changed in the last three to six months. This is all overblown. It's all fictional, et cetera. And what frequently happens here is that the discourse overpredicts the next one, two, three years, and under predicts 10 years. And so it's kind of like, look, the reason why they get in this, to be experimenting, to be doing things, whether it's individuals, other things, is because that's important. So for example, one of the things that just like we did earlier in our podcast, what is AI going to mean for education in various ways? And AI's impact for education is going to be very important.

15:16Getting it in deployments is very important. Obviously, there's going to be a bunch of different democratic and other institutions that are going to resist that, which will be bad for American children because they don't want to change their work processes. And you even see like, you know, universities doing that. It's not just a, you know, a K-12 thing. Everyone's kind of like, oh, I don't want to, I'm used to how I teach my syllabus and what I do for. Sure. I'm used to testing. I have my tests. I have my curricula. Why would I change it? Sure. Yes. And it's part of the reasons why, you know, what like, you know, Larry Kerman, London School of Economics are doing, you know, it's kind of like providing the funds for them to kind of restructure professors' curriculums.

15:50You know, using AI as part of it's great leadership by them. There's others as well. You know, Michael Crow at ASU is always doing amazing things. But I think it's really, really important to do this stuff. And I think it's great that OpenAI is establishing this and saying, hey, we have partial responsibility to help here too. Awesome. So another study that came out last week led by one of our great friends, the researcher and professor at Stanford, Eric Brynjolfsson. And what he and his team looked at is they actually looked at ADP data from thousands upon thousands of employees in the U.S. He also actually they partnered with Anthropic and Cloud Code to do this and saw that if you looked at entry level jobs in particular for 22 to 24 year olds, those dropped 16 percent.

16:36in those professions that you would say would most be sort of vulnerable to AI replacement. So customer service, computer engineering, things like that. Whereas when you looked at professions in entry level that weren't sort of vulnerable to AI, nursing, in-person, trucking, they actually didn't see a drop. So they looked through a lot of reasons this could be. And their hypothesis is that AI is directly contributing to the 16 % drop in jobs for 22 to 25-year-olds. So my question for you is like, this is not good. This is what we've all been like, ah, is it going to hit the entry-level folks first because we don't need those entry-level folks because we're substituting out for AI?

17:13And, you know, their study evokes the canaries in the coal mine. Is this going to be something worse down the road? Could this just be aberrance in the data? Or is this something we need to prepare for over the coming years that AI will actually negatively affect that very first rung in the career ladder? Well, I thought that Eric and Ren Yolson always does great work, and I'm on the advisory board for his digital economy lab, so I should just advertise that, given that I'm about to say a bunch of very positive things about his work. And I think it's great, solid work. Now, I've personally thought that the first place that we're going to see a bunch of job replacement versus job transformation is in customer service.

17:53You know, it's one of the reasons why I've invested in, you know, some customer service companies. I think it's kind of like job transformation the same way that like moving agriculture to urban as the industrial revolution is actually, in fact, a good transformation. There may ultimately be a different form, you know, maybe some forms of customer service will follow accounting. Like everyone thought when the spreadsheet was created, accounting would just simply go away and instead it transformed into scenario analysis and, you know, kind of other kinds of detailed kind of risk planning and mitigation and kind of economic planning.

18:26And maybe there will be a similar parallel where customer service becomes more of a strategically planned thing where it's customer engagement and has a bunch of different things and that there is a new set of human jobs that go into it. Any place where you're trying to get a human being to act like a robot, ultimately the robot will be better and customer service jobs tend to go through these weird scripting systems and all the rest. one of the things we're seeing, you know, in terms of, you know, anecdotally, and I don't know what the percentages are, but you get customer people calling into human customer service agents saying, please stop, you know, get me away from the AI and have me talk to you to a human being because the human being is trying to follow a script.

19:01And they're like, oh, this is so fucked up. This must be an AI, you know, as a way it's doing it, even though it's like, no, no, I'm a human. It's like, no, that's what I would say, you know, so it does not surprise me at all. Now, the computer engineering one was an interesting one to me. I still believe very strongly that there will still be essentially unlimited jobs in the kind of computer science and engineering thing, in part because I think all human knowledge work, all human information work will have a software co-pilot for doing it. And so therefore people who also think in terms of how computational programs work will be naturally enhanced in this.

19:41And I think there'll be a much broader base of it. Now, in the first part of it, it may be that the, hey, entry-level coding jobs, we don't know how to engage them yet. One of the other natural places to start using it is within engineering, all highly good technical organizations are now actually, in fact, really going deep about what kinds of ways can they be using AI co-pilots and other amplifications in order for me doing doing code. And that may be kind of like getting them into an unknown era when it gets to it. Now, my general advice for organizations and also for these students would be is like, well, really embrace the boldest edges of vibe coding and all the rest.

20:24And to be using that to what you would bring to corporations in terms of how you operate, because by the way, organizations are slow to adopt. That's something you could bring. Organizations should look for it. This is part of my second book, The Alliance, in terms of hiring entrepreneurial folks, and they should be engaging with that. Now, the fact they may not be doing it yet, it may be a slower transition. Maybe that's what's going on. Maybe that's what the hypothesis that Eric is discovering. I don't think it's a law of physics that plays out that way. And I'm still bullish that way, but it does cause me to go look at it, recheck my theories of the world, what needs to happen in order to get there.

21:03Always love, you know, quality work from Eric and others in these fields. Awesome. Reid, pleasure to talk to you. Thanks so much. Always.

21:33and Mulea Agudelo. Jenny Kaplan is our executive producer and editor. Special thanks to Surya Yalamanchili, Sayida Sepieva, Ian Alice, Greg Beato, Parth Patil, and Ben Rallis.