Stop the world.

Welcome to STOP THE WORLD.

I'm David Roy.

And I'm Olivia Nelson.

Today Live is pure awesomeness.

We have the wonderful Alice friend talking on artificial intelligence.

Alice is Google's global head of AI and emerging tech policy, and she's a big brain on questions like how do we regulate AI?

How do we encourage the best out of it while avoiding the problems?

How do we integrate it into our economies?

Alice views AI as a normal technology in the sense that like electrification or the Internet, it's something that we are creating with an exist within an existing set of rules and standards.

It'll be adopted gradually over time, and ultimately it'll be something we can control.

Yeah, she's not a utopian.

She's not a I, on the other hand, one or the other depending on what day it is, so it makes for an interesting conversation.

We covered the idea of embodiment as being necessary to achieving a general form of AI, often known as artificial general Intelligence or AGI.

The meaningfulness of ideas like super intelligence that would far outstrip us at any cognitive task.

And of course, what it means to win or lose the global AI race.

Alice also talks about the policies that governments can put in place to encourage the uptake of AI into our economies and societies, the impact on jobs and the value of building sovereign AI.

What she calls having the keys to your own data, your own computing power and your own AI capabilities.

Noting we are a strategic Policy Institute, it's worth remembering that AI capability is likely to determine the future of nations and therefore is very much a strategic technology.

Yeah, so it's not just a frolic on my part, it's it's very relevant to to our core project here at Aspy.

We don't often quote Vladimir Putin on this podcast, but as Putin said of AI in 2017, the one who becomes the leader in this sphere will be the ruler of the world.

I rarely agree, but but in this instance I do.

And based on the effort that many countries are taking, that view seems to be prevailing at the moment.

Alice is great on this because she can philosophise with the best of them, but she also has practical groundedness that comes with being fully immersed in AI policy.

We hope.

Would you enjoy listening to Alice friend?

I'm here with Alice, friend.

Alice, thanks for coming on Stop THE World.

Oh, thanks for having me, David.

I want to start with where AI is going now.

On the one hand, we have optimists telling us it's going to solve every problem on the planet.

On the other hand, we have pessimists saying it's going to destroy us all.

Investors are giddy, even though the big labs aren't close to making a profit.

The best impartial guess, it seems to me, is, is that it's going to be transformative.

But it's very much up to us how we manage the ups and downs of that.

What are the key features you feel reasonably confident about projecting forward, say a decade from now, when you look at the current trajectory that we're on with AI?

Yeah, I think you summarised that really very well.

I think the next 10 years are really going to be about the votes that humans cast about how we adopt AI, how we use it, how we deploy it throughout our economies, how we use it in our personal lives and in our professional lives.

But you know, I think if history is any guide in the history of previous general purpose technologies, it really is about, you know.

How?

Do people incorporate this technology into the many, many facets of our lives?

And I think what we know is that this technology is very powerful.

It's very broadly applicable.

It can be integrated into many, many areas in our lives.

And that we are going to need to go through the kind of process we've gone through with other profound technologies in our history to understand its limits, to understand its benefits and to understand how we can best use it to our benefit and then also understand how we control it most effectively.

And I think that's what the next 10 years are really going to look like.

And I, I'm somebody that subscribes to the AI as normal technology school.

I think it is much more likely that we are going to see that process that is full of friction, full of human choices, you know, full of unpredictable moments for sure.

But I think at every major juncture, human beings are really going to be the ones making the decisions about, about how we take AI on in our lives.

And so I think that's, that's what the the future is going to bring us.

And I think we're actually living that right now, right?

We're real time gathering data about how are people using AI, you know, how are they using different AI applications?

How are they responding to it as it's being integrated in their work lives?

Sort of what?

We in industry would call the enterprise level.

And then how are sort of everyday folks using it for their own reasons, You know, how are they using it to to learn information, to learn new skills, to think through thorny problems?

How are they using AI as something that can augment their own their own productivity again, both at work, but also in their in their lives as well.

And they're just sort of a million different ways that people can think of to use it.

And as people explore the technology, we'll learn all all the different places and ways in which that exploration sort of leads us to the ways it's going to also change the way we do all these things.

So it's a very exciting time in my view, but also a time that I think we'll be, you know, much more under our control than than I think a lot of folks are concerned about.

I can already tell we're going to have a fascinating conversation because I don't subscribe to the normal technology view.

I, I, I got to say I, I think I've gone through the sci fi wormhole probably further than you might have, but I, I actually regarded as sort of categorically different.

So I suppose we'll sort of test that and perhaps at some stage you can persuade me.

But let's just start with a couple of the, I mean, the projections forward that we tend to hear about our artificial general intelligence and super intelligence, both of which I think have at their heart are kind of an assumption that this is not going to be like previous technologies for various reasons.

But let's let's look at those one at a time.

And one look, I mean, there are so many different ways to come at it that I'm going to pick one, which is a post that you put on LinkedIn recently.

I mean, frankly, the, the video that accompanied that messed me up for about 12 hours afterwards because it showed 2 humanoid robots playing ping pong with extraordinary ability.

And your point about it was that, I mean, this is real progress in the robotics sense.

But the point you drew from it was that to achieve general intelligence, you probably need some kind of embodiment of the intelligence so that it's actually sort of doing things in the real world, interacting with the real world.

And you feel that that is somehow fundamental and conditional for intelligence to become sort of generalised in that sort of general purpose way.

Just explain why you think that is the case.

Yeah.

So that post was about artificial general intelligence and embodiment and me putting my cards on the table, which is that I have always felt that we aren't going to be able to achieve AGI, which has various definitions by the way.

But the the one that I think is kind of the most useful is, is saying cognition that is essentially the same as the kind of cognition that the human mind is capable of.

I don't think we're going to achieve AGI until AI is embodied, until it it's in some kind of physical system that can interact with the world.

And the reason I think that is because that is so much of how human cognition not only develops in each individual Organism, but evolved over human history.

So much of the human mind is about being a mind and a body in the world, interacting with other minds and bodies in the world and with the physical world and all of the laws of physics and the laws of biology that we encounter.

And so much of our learning and so much of our understanding is in that physical world.

And, you know, this is a, this is a big area of debate among artificial intelligence specialists, among neuroscientists, folks that sort of, you know, focus on cognition.

So I have a lot of humility about, you know, what any of us really know.

But it's always just struck me.

And, you know, and I also won't claim I got this out of nowhere.

Early in my own studies.

I read Terry Winograd on this point, who wrote a book called Understanding Computers and Cognition, and he makes this argument about biological structures and how part of learning and understanding is really an interaction with the structure of the mechanism that does the learning and the understanding right.

And then, you know, also, I'm a parent.

I've, I've watched small kids sort of figure out the world.

And it just strikes me that if what we're trying to build is something human like, then you need to give it as human like conditions as you can.

And so I just come back to, you know, you and I Co create our intelligence in some way because we're social and our minds evolved to be part of a social system.

And the way we create meaning is social as well.

And so when we get into these really philosophical debates and AGI about what is understanding, what is the nature of reality anyway?

How can we know reality?

LLMS are incredibly powerful tools, but they are limited to the abstract representations of language.

And so I've just always felt that you're going to need, you know, first multi modality, which is where frontier models have gone in recent, you know, in recent time.

But you're going, I think to need to get to to embodiment before we really start to see something that's.

That's AGI like.

We can also then debate measuring it.

How will we know we got to?

It, but that's a whole other question.

Sure, sure.

Okay, well, look, I mean that makes a lot of sense.

And I mean quite apart from the accuracy of it as a truthful statement.

It also seems to me just on a values basis, it's healthier if the intelligence that we are creating understands the world and the way that it engages in the world in approximately the same way that we do.

And that embodiment is part of that.

Otherwise, I mean, just to take a really basic example, if it doesn't understand moving as a three dimensional object through space, it might well not think about barrelling over a an old granny on the footpath.

If it doesn't understand that it is a a physical being in a physical space, that's probably a pretty lame example.

But I mean, if you sort of extrapolate outwards from that, certainly it's going to be a healthier creation in terms of the way that it exists in the world that we actually physically inhabit if it is embodied in that sense, I suppose, and look at respond to that as much as you would like to.

But one other thought that I have is that it's possible then for very powerful intelligence to be created that isn't embodied and therefore is doesn't have that sort of generality that's attached to human intelligence in sharing that experience of the world through embodiment, but is nonetheless very powerful to the point of super intelligence.

But it's going to be kind of alien by comparison with a, a general intelligence that's more attached to human style cognition.

And that is where we could actually get into danger.

Does that resonate at all for you?

And does the concept of super intelligence sort of resonate for you in some meaningful way?

Yeah, I mean, I think.

Super intelligence is fascinating as a concept.

I think your use of the word alien is quite correct because I think whereas AGI again grounds itself on this human like cognitive ability, you know, perhaps as smart as the smartest human ever to live or is as smart as a combination of all the smartest humans ever to live, right.

We're all we're all sort of circling around that kind of definition.

But in any case, it's comparable to human capacity.

ASI is about a speculated version of AI that is not like human cognition that, you know, I think some would say exceeds it and by every measure, but also maybe just departs from it.

And so alien is a great word to use because I think that is where we get a bit into the realm of science fiction or at least theoretical possibilities.

And it is hard to see what the empirical evidence is for us getting to that other than, you know, sort of a logic chain as it's been described by Nick Bostrom and others who who sort of think through.

What would ASI?

Even look like and I think a lot of analysts say, you know, we're actually, we actually don't know.

We don't know what.

It would look.

Like, because part of the point is this recursive self improvement goes in directions that humans wouldn't think of, couldn't think of.

And then you get into all kinds of weird conversations about would we even understand what it was doing?

Would it still be able to understand us?

Would we be relevant to it?

Would it be relevant to us?

What kind of limitations might be on it?

We don't know of anything in nature that is limitless, right?

That is capable of all things.

So, you know, I think ASI is sort of a fascinating thought experiment.

And I also think that there are very serious people that that think about it as a serious possibility.

But again, getting back to AI's normal technology, I tend, and I think it's because I'm a social scientist, I tend to think of the friction in the world and the hurdles to, you know, actual technology development and adoption as as really meaningful friction, sometimes in ways that we intend and design and sometimes in ways that we don't intend and that frustrate us.

So you know, the direct answer your question is I think ASI is a useful and coherent concept, but you know I think a lot harder about the near term kinds of AI that we're fairly confident is going to obtain as opposed to ASI that I think the probabilities are a lot lower.

Yeah, I find the chain of logic though, fairly convincing and compelling.

The idea that something that is very smart, say at coding can actually build smarter versions of itself in order to move ahead very, very quickly, for instance, or even just on the, you know, the human engineering trajectory that we're on at the moment.

You know, the improvements in large language models, for instance, or you know, through reinforcement learning, as we saw with A0 as an example, where it basically just played against itself millions and millions of times until it it was able to beat the, the world Go champion.

Those sort of trends seem to me even before we get to ideas like recursive self improvement, which for, for the benefit of the listener just means basically machines that will build machines smarter than itself.

And so I mean, the, the, the phrase kind of explains itself, but even without recursive self improvement, it seems to me that the improvement that we've seen over the last say, well, 15 years is such that it could, we could actually quite easily get to a, you know, a state of super intelligence sometime in the next decade.

I mean, do you find this sort of chain of logic, I suppose of it compelling?

I think the.

Chain of logic is compelling, but again, it also has to make a lot of assumptions about what's available to the system.

It assumes compute at the necessary scale.

It assumes energy at the necessary scale.

It assumes, you know, infinite inference capacity.

I think it's making a lot of assumptions about what's available to the system in order to do this.

Not only recursive self improvement to the point of obtaining ASI, however you might measure it, which again, is very fuzzy, right?

But then also I think part of the concern about such a scenario is sort of the sustained operation of such a thing.

And I think again, there's just a lot of friction between here and there, not just in terms of sort of there are physical limitations on every system that we've ever observed, but also, you know, humans again, will still have a vote and there will still be human limitations imposed on the system as well.

Which is one reason that thinking about these kinds of things now is very useful.

Because I think part of being a responsible actor and developing AGI is thinking very carefully about how do we monitor these systems for capabilities beyond what we're comfortable with And how do we design mitigations?

How we, how do we design systems, You know, in the case of heading towards ASI, for example, how do we make it so that it's incrementally improving as opposed to the evolutionary leaps, right?

I think there's a lot of engineering and design still very much available to us.

And so these, these kinds of speculations are useful for that reason.

We can imagine a world that we do not want to get to and then we can work backwards and say, OK, well, what do human beings have to do to enact those mitigations?

And I, I just, I tend to have a lot more confidence in people.

I think despite all of us, I, I think that we have in fact, over time managed quite well figuring out.

How to work?

With and live with very powerful technologies.

I suppose to me, and I will get us back on track in a moment, but the the difference for me, I suppose is that the prospect of something that is cognitively and intellectually more capable than us, which I think is a unquantifiable but real possibility, means that it is categorically different to any other technology that has come before.

Even general purpose technologies like electrification or computing and so forth.

The prospect of something being smarter than us is something that has literally never happened in world history and, and to the best of our knowledge, history of the universe, we to the best of our knowledge, we are the smartest things in the universe.

And, and the the idea that there might be something smarter than us just means that it's it is in a different category.

Does that?

How does that sit with you?

You know, I'm not an evolutionary biologist, but I, I am sceptical of that argument that we are the smartest thing that has ever happened.

I mean, first of all, we have no idea for the smartest thing that's ever emerged in the universe throughout all time, right?

So we can't, we cannot make that claim with any confidence.

I also don't know that that isn't an overly anthropocentric view that our intelligence is superior to every other form of intelligence that has ever happened before in our planet's history.

I think there are many different kinds of intelligence.

I also think, again, back to this idea that our intelligence is very social.

A lot of the power of human intelligence has been our ability to come together socially.

And to combine minds.

And so I think the the notion of sort of mimicking one human brain and measuring against sort of the the uniqueness of a human intelligence is also a little bit missing the point too.

I think part of the success of our species, if you will, has to do with the, the socialness of our intelligence.

Now you can think about systems that, that mimic that pattern as well for, for certain.

But I, I just, I tend to be wary of bold claims about nothing's ever been smarter than us.

And therefore and making any sort of prediction about we therefore have any confidence about what will happen if something smarter than us arises.

You know, we, we, we just can't have that, that kind of confidence, I think.

All right.

Thanks for indulging me there, Alice.

Let's talk about how AI can be and is being used in our economies and societies.

Now the excitement tends to be around large language models.

Every time a new model comes out, it's front page news, etcetera, everybody gets very breathless.

Is increasing commentary, though, saying that really it will be about the way individual countries and economies adopt and integrate AI at A at a much more basic, prosaic and and potentially dull level, rather than always being about those frontier models.

It's very relevant for a country like Australia, which probably isn't going to be building frontier models anytime soon.

But we do have a high educated population and A and a relatively advanced economy that can benefit very greatly from AI.

How does that sort of commentary resonate with you?

I mean, it certainly seems to to fit with a lot of what you've been saying that it really in terms of the geopolitical and the economic race around AI, it will be countries that that adopt and integrate it, that get ahead.

Yeah, I, I mean, I, I do think especially at at this point, you know, the story of AI is going to be about how we adopt it in the, you know, near to medium future.

I do think we need to, to continue to enable innovation and R&D, you know, fundamental research about AI, you know, continue pushing on the frontier.

So I think it's important for that work to keep keep happening, but I also think that, you know, a technology like that in its raw form doesn't really do us that much good as a society.

What does us good as a society is using it for beneficial purposes.

And as I said earlier, you know, that takes a lot of work that doesn't just happen naturally.

That takes, you know, on the the individual level, on the firm level, on the government level and on the societal level, you know, figuring out useful applications of AI.

And again, there there's just because it's a general purpose technology, there's a lot of different ways that human beings can use it.

And I think we're still very early days in that story.

I mean, just in Australia, I know Public First did a study that came out in February that found that only about one in 10 Australians feel like they really understand how to use AI tools.

I'm sure that number is better now because February and AI time is like ancient history.

But, you know, you could probably cite similar statistics in other parts of the world as well.

People are just starting to figure out how do I incorporate this into my workflow?

What do I find useful?

What's actually speeding up my day?

What's actually helping me?

And then of course, across industry, we're all sort of constantly introducing new tools, new features, new ways that we're trying to figure out that users appreciate it.

There's a real race among industry players, you know, to figure out what do users like and how, how will users use AI?

What's an intuitive interface for AI?

What's an intuitive way for folks to use it?

And to what end?

And again, I think we're early days and figuring out what those things are.

And some of the earliest, you know, big leaps we've made are on the scientific discovery side.

They're for folks that are doing sort of deep research using AI tools that involve lots and lots of data that involve being able to to work through extremely complex and formerly time consuming calculations.

But when it comes to how is this going to affect our economies?

How is this going to affect the work in companies?

How is this going to augment the average worker?

How are people going to use it again to make their their daily lives better, easier?

That's a a long, you know, detailed again, person by person, you know, sub organisation by sub organisation process.

I do think that's where we're going to really.

See.

The benefits of AI manifest, right?

So there's a lot of predictions out there about kind of what is this going to mean for the economy.

That same Public First survey predicted about $240 billion in economic value could be added for Australia alone.

And there are lots of predictions about global GDP increasing by, you know, significant percentages.

And so if this is really, you know, what we have in the offing, then figuring out how do we obtain that I think is the next big challenge.

And that's an an adoption story.

It's not just the raw technology itself.

So the Australian government has recognised this, it seems to me, and I mean, they're talking about this is one of the big solutions to our productivity problem, which like, I mean, like a lot of economies right now, Australia has pretty flat lining productivity and has had four, at least a couple of decades outside of, you know, a couple of sectors that do explain those gains that have been made.

And so our treasurer and our, our government has been talking about AI as a, as an obvious solution to this.

I just want to quickly say, I absolutely recognise what you're saying there about things like scientific discoveries and solving those big mathematical problems that are just time consuming.

I look at something like Alpha Fold, which is solved protein folding, paving the way for all sorts of drug discoveries and, and health discoveries and it makes a chat bot just look like a, you know, a cheap toy.

Really.

Those sorts of things are really where a lot of the the huge improvements to, to human welfare are going to come from.

What do you think are the key things that governments can be doing to bend their their economies and their societies more towards productive, healthy use of AI?

Yes, there are a lot of things governments can be doing right now.

I think one of the most important ways for for governments to engage on AI and to really seize the opportunity of AI is to recognise all the preparation we need to be doing now, particularly in things where governments excel, where they can invest at a scale that's really beneficial to their whole societies.

And that includes investing in skilling the labour market as well.

Something that we recognise at Google is that, you know, there probably will be a labour transition.

And so the important way to get everyone through the transition well is to really put a lot of money into reskilling, upskilling the labour market, to really be able to to leverage AI and its ability to boost productivity and contribute to economies in in some of the ways I described earlier.

You know, there's also big.

Infrastructure investments that governments can make.

You know, there's a lot of talk right now about AI and energy.

And it is true that if we're going to do not just training at scale, but inference at scale, you know, increasingly that's the story of AI will be especially if we in fact drive enough adoption, you know, inference use of compute is is just going to go up.

And so thinking through all the ways that we can add to the energy grid, you know, certainly we at Google have been thinking about that hard and making our own investments so that in fact, it is additive.

But, you know, having a modern way of powering these systems is going to be really essential.

And I think governments around the world need to be working.

Hard on that, that set of challenges.

And then, you know, there's also an entire set of questions around how do you regulate this technology and how do you do?

So in a pro innovation way, that balances concern about risks with guaranteeing that you can leverage all the benefits.

So there's plenty of things that governments can be doing now to really sort of shepherd adoption in ways that are that are the most beneficial for their own societies.

You mentioned the labour transition.

I'm, I'm glad you brought that up again, that that's one area where they're optimists and pessimists in terms of what effect it will have, how disruptive it'll be on the workforce, how permanent that disruption will be.

Again, I'm, I'm slightly on the pessimistic side in the sense I, I, I just wonder what the value of human labour will be in a world in which there are machines that can do everything or almost everything more capably and without getting tired and without wanting breaks and without forming unions and all these sorts of things.

So yes, I think new work will be created as we demand new products and services.

I mean, obviously there's no reason there should be a ceiling on demand.

It should be infinite.

But what I'm not convinced about is why the jobs created to meet those demands necessarily have to be filled by human beings.

What are your thoughts on that?

So, you know, labour is one of those topics where we have to have a lot of humility about what we know and what we don't know and what we have empirical data for and what we don't.

And we just don't have a lot of empirical data right now, if we're honest.

And So what we do have is historical precedent.

And so if you accept the premise that, again, AI is a general purpose technology that is akin to past general purpose technologies, which have also been very profound and have been disruptive, but in the end, humanity not only adapted to them, but in fact use them in really beneficial ways, then you can at least look to those historical precedents.

And you can think about, you know, electricity.

You can think about steam.

You can think about particular inventions like the car, like the aeroplane.

You can think about the Internet itself and the disruption but also creation associated with that invention.

I can never remember the pure statistic, but it is something like the overwhelming majority of jobs on the market in the United States today.

Did not exist before 1940.

Five.

And if you had asked someone in the United States in 1900 about, you know, my job, for example, they would not have understood what it even meant, right?

They wouldn't have been able to sort of contextualise or conceive of such a thing because so many things have also happened alongside it.

So it's very hard for us, I think practically to predict.

But again, if history is a guide, then it is more likely that AI is going to augment some jobs and create a lot of new jobs and possibly make some jobs obsolete the way past technologies have done with job categories in the past.

Another thing that a lot of labour economists focus on as the technology is today is that it tends not to replace whole job categories, because job categories are actually a large collection of tasks.

What it does tend to do is automate tasks, and a lot of those tasks are the kinds of things that make people's jobs really boring.

And in fact, people that can automate the boring stuff in their jobs find a lot more meaning in their labour after being able to adopt AI because it moves them up the value chain.

It means they can focus more of their time on those meaningful things.

So one study that a lot of folks point to was of call centre workers.

And the really interesting thing about that study was if you were a top performer in the call centre, the AI assistant didn't help you that much, but if you were a lower performer, it dramatically improved your performance.

So the other thing about AI that we've observed is that again, it augments human labour.

It actually upskills people in the same role, but it makes them better at their job, which is a pretty exciting thing actually, because it actually brings people up into more meaningful work.

And I think that's a huge part of our, our conversation right now around labour, particularly in industrial economies, is around the meaning of work and finding things to do that are more meaningful.

And if AI is going to help with that, you know, I think that's a thing we should embrace.

But also, again, you know, track really closely and, and, and gather that empirical data so that we'll have a better sense of the direction this is trending.

I think that's a really important point about, I suppose, being prepared to broaden and redefine as necessary what we regard as constituting valuable use of human time and endeavour.

And interestingly, I mean, if, if you went back 100 years and told a farmer, for instance, that their great grandchild was going to be a prompt engineer, not only would they not understand it, but they would probably look at it with some scorn saying, well, that's not, that's not work, you know, that's not producing food, you know, so that's not using your grit and your muscle and all the, I mean, I'm sorry, I'm typecasting farmers here.

But, you know, not only might we not understand future use of human time and labour, but we might almost look at it from a judgmental point of view that it's not, you know, valuable or it's not a justifiable use of human time.

So I, I think it's a really important idea that we're going to have to get used pretty quickly to the idea of reorientating the way we see human effort and, and maybe even start to move away from some of the economic models that we have at the moment about, you know, simply, you know, salaries in return for labour and, and start to even think about, you know, other ways of recognising the value of what people do with their lives.

Does that make any sense?

Yeah.

I also think, you know, it is unlikely that the pace of change is going to be uniform across all sectors of the economy, right, that that's unlikely.

What's much more likely is that particular sectors will have faster uptake of AI than other sectors do.

Again, as AI itself evolves, as we sort of invent better or different models and systems, there will be increasing use cases for it.

But the context matters so much in AI deployment that I think that's the other thing that tends to get a little bit lost in some of the discussion right now about labour and jobs is, well, it sort of depends on the the sector you're talking about or the category of job, you know, how AI as we have it today is affecting that category, you know, and what we might be able to foresee in the future.

So yeah, I just think.

There is a lot that.

We are again back to my theme about.

There's just a lot of.

Points of human decision, and a lot of points where things aren't going to be necessarily or naturally faster than we are able to think them through.

I think everybody needs to take the message that it really is up to us how we implement policy over the next 1020, fifty years to, to make this work for us because it should be the best thing that's ever happened to us.

And it shouldn't.

I mean, it will, yes, it will be disruptive, but we should be able to manage that disruption.

And so let's let's all please agree not to screw it up.

I think that's hopefully one of the messages that when you and I, yeah, that we drive home to people.

So let's just talk about regulation then.

And you've touched on it already.

This is another one where it seems to me, and again, I'm coming from that different premise that that we're talking about a categorically different technology here.

But it seems to me that we don't have great historical paradigms to work with when it comes to regulation.

So I'm thinking about, you know, templates for legislation, general approaches that we have.

Intended to take.

In the past, it feels to me like there aren't existing models that we can just sort of copy and apply to something that is as wildly different as I regard AI as being.

One way to to ask you the question, I suppose most directly is what do you see as the main basic principles of regulation that a generic country should be following and, and do do you give any credence to what I was just saying about the idea that, you know, maybe we have to come up with completely new paradigms?

Yeah, let me take those in order.

So again, I think because it is a general purpose technology and will be integrated in diverse ways across sectors, the way to approach thinking about regulation of AI is not to think about one law that will work to regulate AI across all of those different use cases.

The far wiser course is to start where most governments already are with that sectoral regulation.

And the benefit of doing that is that sectoral regulators are already quite steeped in that sector in those contexts.

So the example I always use is, you know, in medicine, there are usually, there's usually a regulator for pharmaceuticals and that that community knows vastly different things than the regulators that do aviation.

You see AI integrated in both places.

You see AI integrated into drug discovery right now.

You can see AI sort of an avionics systems, right, But totally different use cases, totally different risk scenarios and risk surfaces.

You know, both of those are also can be high risk activities, but also very high reward.

And so that surfaces a lot of things about why you want to go sector by sector with AI and start with your existing sectoral regulation and go through a process where you you examine that regulation and AI and try to determine if there are gaps in what you already have.

And, you know, it's my view, both professionally and personally, that most regulation that exists in these sectors tends to cover AI already.

And if there is something novel about AI, then indeed you need new regulation for that.

But it's important to discover that first, something that we say at Google a lot is if it's illegal without AI, it's still illegal with AI.

If it's regulated without it, it's still regulated with it.

And those sectoral regulators are going to have the expertise to understand, well, how might we need to adapt to this?

Now, what is often helpful for governments is to have some kind of hub of expertise in the technology of AI to support those sectoral regulators.

We sort of traditionally called that the hub and spoke approach, but that, you know, I'm a former policy maker myself.

That strikes me as the most pragmatic way to to go about it.

You don't have to sort of reinvent government.

You don't have to invent a large new department.

If you already have a Ministry of Technology, certainly that's a good place for that AI expertise to reside in your government.

I mean, if you have a Minister of Health, that Minister of Health should still be able to adapt their regulations to AI.

So I think that's like that's a good starting point.

Another really important thing that we like to point regulators at is the evolving landscape of AI standards.

You know, the international standards organisation, standardisation organisation has a robust agenda on developing AI standards.

And the more the entire ecosystem, policymakers, you know, industry players, civil society can be working off the basis of those technical standards for AI, the more we'll be able to create interoperability across the globe on how we understand safety, how we understand performance, how we understand management of this technology.

And the more interoperable it is, the fewer gaps and seams there will be, which is a really good thing for AI safety as well.

You don't want an uneven safety standard across the world.

So ISO is well on its way.

Again, it's got that robust agenda.

They've already promulgated several AI standards.

They've got a lot more in the pipeline.

So we're also always pointing to that process and encouraging governments to think through ways for their the local regulations to really take on board that what's in those ISO standards.

It's a really interesting, it feels almost like a reversal in 2025 where you actually have international global standards and, and well, not governance, but certainly standards almost kind of leading the way for individual nations that that feels so.

But you feel that the the ISO is in a place where it's done enough work that individual countries can actually look to that global picture and say, OK, that's we can take our cues from that.

Yeah, and there's still more to go again, that they have a pipeline of, of things that they're working on.

But you know, the, the great thing about standards bodies is that standards are developed by technical experts.

So it, it really is about sort of, you know, the standard measures for these technologies, standards for how to build these technologies securely, standards for deployment, standards for management.

The first ISO standard was 42,000 and one which is about how organisations can manage AI.

So yes, I, I think that is a very healthy direction for governments to look in for a lot of specifics about, you know, how can we use AI, approach AI, measure AI again, in a way that's consistent with how others across the world are measuring it as well.

OK, All right.

Now I've taken this down so many rabbit holes and apologies for that, but we haven't covered everything that I wanted to cover, but I'm conscious of time.

But there's one, one more thing and it's almost the flip side to what you were just talking about, and that is this question of sovereign AI and the value of sovereign AI, which countries are understandably looking at given the geopolitical picture that they're facing.

I mean, I always think of South Korea as a very interesting example.

At the moment it's building its own sovereign large language models.

Language is an issue that makes sense for the Koreans because they, you know, if they, they can train and build their their models around the Korean language, which has advantages.

But then you look at a country like Australia, which we share a language with the United States and therefore that's not one of the barriers, but we are nonetheless.

Well, there are a couple of projects being proposed or at least under development of first sovereign Australian models.

They're not going to rival Gemini or GPT 5 or Claude or anything like that.

But you know, they will be our own.

They they can be trained on some of our own data and therefore kind of refined and and fine tuned in a way that works for our, for the purposes that we would like.

Tell me from your point of view, what is the the value of sovereign AI models, you know, in economic terms, in strategic terms, given geopolitics again, maybe even cultural?

Terms as well.

And is it worth the effort for a country like Australia to, you know, to pursue that perhaps in a, in a public private partnership kind of way?

Yeah.

I think the important thing to think about when you're thinking about sovereignty and AI is really about, you know, do countries have a short control over the way AI is used domestically, over the data and information that is sovereign and also over do they have a resilient system?

And I think what I would caution is not to think of all of those things as having to be purely territorially domestically assured, particularly that resilience piece.

Our own Google Cloud assures resilience in a variety of ways.

We do offer national solutions for customers that prefer it, but a lot of sort of cloud resilience has to do with being able to distribute in different locations and being able to have some redundancy in those locations.

And so, you know, I have a background in international relations.

Sovereignty and territoriality are very linked concepts.

And I think when you're thinking about AI, you have to think a little bit more about, you know, not kind of whose flag is in the server, but who has the keys to it and who has the assured control over the information and the data that's in it.

That's the key question, not so much sort of the geolocation per SE, because again, you're going to want that resilience.

And that redundancy built into the system.

So I think that's the kind of question that countries leaders should ask themselves as they're thinking about sovereign AI solutions.

Language concerns are sort of building an LLM that really sort of reflects a country's uniqueness.

I think is, you know, a reasonable aspiration and a fair critique.

But you yourself pointed out, you know, with many foundational models, there's a lot you can do to fine tune them on information.

So I think there's just a lot of ways to assure sovereign AI control that just require a little bit of shifting of that traditional sovereignty mindset to make sure that control and that resilience stays with with a particular country.

All right, Alice, look, as you can probably guess, I can talk about this all day, but I think.

We've covered a lot.

Let's catch up sometime soon because there are 1,000,000 questions that I wanted to ask but haven't had time to.

But look, you've been really generous, Alice.

It's been great to talk to you.

Thanks so much for coming on the.

Podcast, thanks so much for having me.

That's it for today, folks.

Please join us again next week for another episode of Stop the World.