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In an interview with India Today, cognitive scientist and author Gary Marcus discussed the immediate and long-term risks surrounding artificial intelligence, calling current extinction-level warnings exaggerated while emphasizing tangible short-term threats. Marcus noted, "Right now, I think AI is probably a net negative for society," pointing out that current large language models and autonomous agents are unreliable and fundamentally unable to follow rules reliably. Addressing global governance and technology access, Marcus highlighted issues of weaponization, tech equity, and the US-China chip rivalry, arguing that nations are competing over the wrong resource rather than solving architectural limitations in AI. He proposed an FDA-style regulatory framework requiring transparent cost-benefit analyses by independent scientists before AI models are deployed, alongside technical redesigns. Marcus urged global leaders to move away from viewing AI development as a zero-sum geopolitical race and instead adopt cooperative international standards to prevent widespread cybercrime, misinformation, and economic disruption.

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00:00Let me bring in Gary Marcus, cognitive scientist, and he's joining me from Vancouver, Canada.
00:07Gary on X says that he is the original OG Gen AI skeptic. So, Dr. Marcus, welcome to this broadcast.
00:20You have said, Dr. Marcus, that it is preposterous that humans will be extinct in five years
00:27and equally preposterous that everything is going to be fine.
00:31The two most dominant narratives are entirely wrong. Those are your words.
00:35If both extremes are wrong, what exactly do you place Jacob, where do you, I should say,
00:43where do you place Jacob Coxon's warning that the anthropic researcher who quit saying that labs are, in fact, gambling
00:53with our lives?
00:56I think the specifics of what he has said are preposterous, and he is kind of like doubled down in
01:02a series of interviews.
01:04So, he is very much on the side of like, we might all die in five years, and I think
01:08that's ridiculous.
01:09But I think that there are serious warnings. People in the field talk about P-Doom, the probability of extinction.
01:16I think that's pretty close to zero. You know, if the robots come for us, we will fight back.
01:23We're geographically diverse. We're genetically diverse.
01:26AI is not going to wipe out the entire population.
01:28But there's also what I call peak catastrophe, the chance that something serious might happen.
01:34If something serious means, for example, electrical grids being knocked out by cybercrime,
01:39maybe by malicious individuals using these technologies, or from disinformation causing accidental wars,
01:47I think that that's very serious.
01:48I think that we probably will have some very serious side effects.
01:53We're not going to literally have extinction.
01:55People don't need to, I think, have nightmares about that.
01:58I mean, eventually, you know, the sun will explode or something in the very long term.
02:01But in the near term, we don't have to worry about those things.
02:05But we do have to worry, excuse me, my voice is giving out because I've done so many interviews.
02:10We do have to worry about the consequences of insecure AI, basically.
02:17Another way I would put it is, like, a lot of AI is fine.
02:20Like, GPS navigation systems are not going to kill anybody.
02:24You know, people basically know how to make that software.
02:27But we have this new kind of software called AI Agents, where you take large language models that are not
02:33reliable,
02:33you hook up a lot of them, and you connect them to the Internet.
02:37And sometimes they do bad things.
02:38The OpenAI Hugging Face Incident was an example of that, where a set of AI agents couldn't follow basic rules,
02:46like don't hack into other people's systems.
02:48And that inability to follow rules is where the danger really comes from.
02:52It's not because AI is too smart.
02:53It's because current AI is not that smart.
02:56It can't follow rules.
02:57That means it can be exploited by bad actors.
03:01And it means that it might just misunderstand instruction and do something bad.
03:06Not extinction level, but serious.
03:09So, like, Jacob Coxon, I think, is way overstating the case.
03:14But there is something to be worried about.
03:16We can't just sit here and have no regulation and hope for the best, as some world leaders may have
03:21suggested.
03:22Okay, Dr. Marcus, I'm going to move away from what Jacob said to BRICS summit here in New Delhi.
03:30Prime Minister Modi told BRICS that weaponization of technology can hinder shared progress, calling for inclusive access.
03:39Is the danger you see the same one Prime Minister Modi is naming nations using tech access as leverage?
03:47Or is the more urgent weaponization happening elsewhere, perhaps in how AI capability itself is being marketed?
03:59I mean, there's a lot of different things going on.
04:01It's hard to say, like, which is most important.
04:04There is, though it's not really what I was talking about, issues about equitable access to these technologies, about how
04:11the countries other than U.S. and China are going to have access and what that's going to look like.
04:17So there's a whole set of issues about economic distribution and things like that.
04:24There's another set of issues, which is really one that I'm talking about, which is that current AI is not
04:29very well controlled, or people use the technical term aligned.
04:32And that can mean it does things we don't want it to do, or that bad actors can exploit it
04:37to do bad things.
04:38And that's a different set of risks than the economic set of risks.
04:42And there's still other risks, like non-consensual deepfake porn is yet a third category of risks that is, you
04:50know, starting to cause a lot of problems in high schools and so forth.
04:54The thing about AI is it's not one thing, but many.
04:57There's many different underlying technologies with different capabilities.
05:01There's all kinds of social and political things that are factoring in.
05:04So we can't just say, like, this is the one thing you should worry about.
05:08The truth is it's like a hydra with many different heads.
05:10There are also many advantages, but there are many disadvantages we need to worry and to rein in.
05:15Then are you worried more about disadvantages than advantages, given the pace of AI and the concerns being raised now
05:23by the big, you know, the tech giants themselves?
05:30I'm pretty worried at this point.
05:32I think I'm long-term positive in the sense that I think that there's a possible path to an AI
05:39that is really good for society.
05:41But short-term, I'm worried.
05:42I think the current technology is unreliable.
05:45And I think that unreliability causes problems.
05:48And I think that the companies that are running it are mostly pretty greedy and not really mindful of the
05:53consequences.
05:54And I think the governments are mostly letting those greedy companies do what they want at the expense of humanity.
05:59So right now, I think AI is probably a net negative for society.
06:03I don't think it has to be that way, but I think that's kind of where we are right now.
06:07And we need to up our regulation game.
06:09And we need to have better technical tools as well.
06:12So what can be these regulation and technical tools?
06:16What's responsible AI then?
06:19Or should I say, how do you make AI responsible?
06:24On the governance and regulatory side, I think the number one thing that we need is that every country needs
06:31to do something like what the U.S. does for drugs with the FDA, which is to say, we require
06:36a cost-benefit analysis.
06:37You have to show that the value of your drug offsets the consequences, the risks.
06:43You know, lots of drugs have side effects.
06:45That doesn't mean we don't put them on the market.
06:47But you have to make the case that the benefits outweigh the risks.
06:50And you have to have independent scientists weigh in on that.
06:54You have to make an argument that what you have is good, and they may send you back to the
06:58drawing board or ask you to do more experiments or come up with a way of dealing with something.
07:03We need that for AI.
07:04That's the number one thing that we need on the regulatory side is some kind of screening procedure.
07:10It needs to be transparent.
07:11It needs to have independent scientists in the loop.
07:15Secondly, on the technical side, we need an AI that can follow instructions.
07:19That might sound trivial, but it's actually pretty hard given the starting point we have now.
07:24So the starting point that we have right now is these large language models that try to predict essentially what
07:31a person would say in a given context.
07:33And it does that by looking at the statistics of how people talk across a whole lot of different kinds
07:38of text and so forth.
07:39That system is not built from the ground up to follow instructions.
07:43It often follows instructions, but it's not guaranteed to do so.
07:47And that's a real problem.
07:49And I think we need to go back to the drawing board for better technology.
07:52So, Professor Marcus, China here is pushing open source AI models to the global south.
07:58And BRICS itself has just unveiled an open source AI ecosystem framed as tech equity.
08:05Is there a genuine or do you see this as genuine democratization or a subtler form of the same weaponization
08:14that Prime Minister Modi has warned against?
08:20It's probably both.
08:22You know, there's some value in having open source AI for the world.
08:25And it's China pushing its particular advantage.
08:29They're not as good on the closed source models.
08:31They're better on the open source models.
08:32And they want, you know, what we call vendor lock-in.
08:35They want people to use their models.
08:37So it's a bit of both.
08:39It's complicated.
08:40Okay.
08:40So the U.S.-China chip war is fought over who controls the physical layer AI runs on.
08:46If scaling is really a dead end, as you argue, has the entire chip war and China's open source counter
08:55push been fought over the wrong resource then?
09:02I think that people have been fighting over the wrong thing.
09:05So right now, what matters maybe is who has more chips.
09:09But that's not going to last.
09:11What's really going to matter in the long run is who builds the better AI.
09:15And I think there's actually much more room to improve AI than people realize.
09:18We still have massive problems with planning, massive problems with these systems hallucinating, with reliability, and so forth.
09:26I think that the real advantage comes from solving those technical problems.
09:30On the chip side, I think that China is going to catch up.
09:33The price of chips is going to go down.
09:36And people are actually going to lose a lot of money on chips when they depreciate.
09:39And you have, you know, more chips than you really need.
09:41The other thing is that people are going to make more and more efficient systems.
09:46They're optimized to use fewer and fewer chips to run locally rather than on the cloud.
09:51And so we're going to wind up with all these data centers we don't really need.
09:55And the advantage is not going to be in the data centers, I think, but in developing a better technology.
10:00But everybody is wedded to large language models right now and maybe not thinking outside the box enough.
10:05Okay, Professor Marcus, can a country then build real autonomy in AI by adopting China's open source models?
10:13Or does that just trade American dependency for Chinese dependency?
10:21This is, again, a complicated question.
10:25I don't know that any country can build autonomy at this moment except by building a better and maybe more
10:34creative technology.
10:36There's going to be some dependency right now.
10:39I think there's a lot of talk about building sovereign AI.
10:42And I think more and more countries are going to do that because they don't want those dependencies.
10:47The positive thing for countries that might want to do that is that the general techniques that people are using
10:53are pretty well known.
10:55And so it is possible for other countries to build their own.
10:59And as efficiencies come in place, I think the advantage of having giant clusters of GPUs will diminish.
11:06You won't need quite as many in order to do most of what you want to do.
11:10I have two more questions here, Dr. Marcus.
11:13After Jacob Coxon was who we saw, Dario Amadei and also Sam Altman both spoke about slow down,
11:21that how there is a need to slow down the pace of growth.
11:25Where is the future then, according to you?
11:28I mean, it's hard to predict the political future.
11:32It's kind of changing literally by the hour.
11:35In one minute, Dario says, we should have a pause.
11:37And Sam says, great.
11:38And then five hours later, Sam posts something on Twitter saying, well, we're not really stopping.
11:43We're just trying to do things a little bit more carefully or something like that.
11:48So there's a lot of back and forth about what these guys mean.
11:51There's back and forth about what the U.S. government wants to do.
11:54One minute they're saying they don't want to regulate anything.
11:57The next minute they say, we've got regulation.
11:59And that regulation is, you know, something secret that we don't want to tell anybody about.
12:03And so the kind of political winds on this are changing constantly.
12:09And I think are pretty unpredictable.
12:12You know, in the United States, there's a lot of pressure against data centers.
12:15A lot of people are now concerned about so-called runaway AI.
12:20You know, Trump could change his mind on all of this on a dime.
12:23And then we'd be in a different place.
12:25So I feel comfortable making technical predictions about what particular technologies can do.
12:30But on the political side right now, I don't think it's ever been more unpredictable than it is right now.
12:34And I don't think it's ever been more consequential either.
12:37Like, for example, whether or not China and the U.S. come to any deal about slowing down would have
12:42profound consequence.
12:43Maybe they'll do that.
12:44They probably won't.
12:45There's a good argument that they should.
12:48These decisions that Xi and Trump are going to make in the next several weeks are super important but very
12:55hard to predict.
12:56Okay, so let's try and sum this up, sum this conversation up, Dr. Gary Marcus.
13:01Because you are saying that it's difficult to predict on the political side.
13:06What would you want the countries to do then?
13:11I think that the best thing that we could do is to stop thinking about AI as a zero-sum
13:16thing and start to think about AI as how we could collaborate and make the world a better place if
13:22we work together.
13:23That's not really how people are thinking about it now.
13:25They're all thinking about it as a race.
13:27But that race has not really helped anybody but NVIDIA.
13:30It hasn't really helped individual people.
13:32It's put economies like the U.S. perhaps in jeopardy.
13:35It's raised the level of tension in the world.
13:37Maybe we need a different paradigm that's much more cooperative.
13:40All right, Dr. Gary Marcus, pleasure having you and getting your thoughts on what is appearing to be a very,
13:48very fast-changing world even in the space of AI.
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