Azeem Azhar

Well, I mean, crowding out risk is real.

And the reason it will be happening is because the rates of return you can get to build a data center are just much higher than the rates of return you could get from building an apartment block.

But these things can and often do level out.

I mean, what you really want to see is a demand stimulus.

I mean, if we take a look at America's energy infrastructure for 25, 30 years, Americans have under-invested in their electricity grid, in their sources of power generation, and that's broken a very long-term relationship in our economies that more energy is more welfare, it's more prosperity, it's more income, it's more innovation.

And that there are rich companies putting in a kick to say, we need more power, is probably going to be net-net a good thing over the medium term.

But of course, to get there, there is this difficult squeeze right now.

And the beauty of building is that there's a lot of learning...

processes, practices that get developed that are transferable across into other types of building as and when the time comes.

So I agree that there is this problem right now, this problem of focus.

But the question is, when we look out over three or five years, will we start to see benefits emerging from it?

So it turns not less to be a black hole and more to be an M-class star.

Well, I think they'll make back the money if the revenues show up.

So we can talk about revenues later in the discussion, but you've put your finger on one of the uglier parts of all of this, which is that, you know, of the three, $400 billion that will go into data centers this year,

About 50% to 60% will be in those GPUs.

So we'll call it a couple of hundred billion.

And the companies themselves mostly keep them on their books for six years.

So that's a long time, but it's not a canal or a railroad.

And as we know, within three years, they have to move out of frontline service.

So I think that there is a bit of an ugliness in the way that this is accounted for.

It is something that we should keep our eye on.

Now, they may argue, well, the GPUs make money and we're paid back within two years, which is what it seems to be the case.

And they still have some useful life in 6.

But it feels like it's a bit of a thin argument.

And this is one area where I think we could...

Ask for, hope for, but we won't get better rules around how GPUs should get depreciated, the kind of tax incentives companies should get because they are effectively operational expenses and greater transparency.

Somebody sent me an email about this and they said that...

In China, the accounting rules are such that computer chips are effectively depreciated over a three year maximum five year period.

So I think this is a you know, this is one of those ugly pimples of this picture that we should just stare at and say this could turn into a problem.

Well, I mean, it is worrying, but it's just not where other things were.

I mean, in the case of the dot-com, we were...

an order of magnitude practically away from that in terms of the capital that had gone in and the revenues that were being generated.

And the question is really how long can you sustain yourself at that point?

The thing to note here is that

We had no-gen AI revenues three years ago, and our estimates and the calculations that we've done are quite conservative.

We've come in at about $60 billion for 2025.

One of the big investment banks has suggested it's closer to $153 billion for 2025.

In truth, it's probably somewhere in between there.

So we're quite conservative.

And I would add to that, Derek, that that $60 billion is just a rough measure to cover the capital expenditure.

It doesn't involve margin.

It doesn't involve profit.

It doesn't involve a whole set of other things that you might need in order to justify this long term.

But when you look at the build-out of...

technologies during this phase, they are generally behind on the revenue curve.

It's not like building a hotel, which you expect to have 80% occupancy the day you finally open it.

So that's the kind of historical nature of all of this, that it's a technology, it's got to catch up.

16% coverage for now is okay.

If we were still there in a couple of years, I'd be a bit nervous, to be honest.

It's worrying for now, but I will explain the way that I think about this, which is that, you know, imagine you're taking off from the runway in a plane I have to fly on on Sunday.

Halfway down the runway, you're at 80 miles an hour and you're praying the pilot doesn't pull back on the stick because that's going to be really, really messy.

And so...

if we're 80 miles an hour, 90% down the way of the runway, we're going to hit the brick wall at the end of it.

And that's going to be horrible.

So that's why I put this in the amber, because we need to, in a sense, see what's happening with acceleration and see where that takes us.

But this is the piece that really makes a difference.

Ultimately, real revenues from real customers who are not being forced to pay, who understand what they're buying, is a way of

paying for any investment that you make.

And that, I think, is the biggest bet that's being taken.

I mean, they'll need a lot.

They will really, really need a lot.

We're talking about 100% a year for a couple of years and then perhaps lower than that after that as the baseline rises.

And where is that revenue going to come from?

It'll come from probably three areas.

One will be

consumers and businesses paying for services and buying up or eating up all those tokens or paying an application that is doing something for them.

Maybe it's managing their accounting or their logistics or their supply chain.

The second thing where they'll start to get their revenue will be on return on advertising spend because AI can target ads far better and Meta has given some evidence for this.

And the final thing will be

can productivity benefits from AI actually reduce their costs or allow them to produce better products?

And that works in two ways.

So in one way, it might be that you save money because you can ship a better product with the same number of developers.

It might work another way, which is that in order to keep your product relevant in the market, you've now got to pay someone

to provide AI services for it.

And I think a lot of business services companies with software on the web are finding this at the moment.

In order to stay relevant, they have to provide an AI add-on.

They're maybe charging $20 a month for it, but it's costing them $25 a month.

But if they don't do it,

they will lose customers.

So that's the threefold way in which we get to this number.

And I think it's also worth knowing that these are big numbers today already.

The amount that goes in online advertising and buying software is well over a trillion dollars a year, and it's already growing at 14% a year.

So I think part of the question is, how much could AI increase that growth rate?

And how much would it displace

existing companies who are not using AI.

And so there is a journey.

I think one of the big investment banks talks about a trillion dollars of revenue a year in 2030, which, you know, it's not a shoo-in, but it's not completely pie in the sky.

You know, it's somewhere in between.

Wow.

Well, I mean, that would be really grim.

It would be a little bit like

feudal Russia, only the Tsar is even richer and the serfs are even poorer.

So, I mean, when you paint it that way, without there being really significant policy interventions, you'd essentially have a giant vacuum cleaner in San Francisco sucking up all of the money in the

AI systems and even companies that are operating with their own products will be paying so much economic rent to the AI companies that they'll have the slimmest of margins, just like sharecroppers on a 17th century English farm.

And I think that that direction

absent any policy interventions, and if the technology worked out the way you described it, would probably be a direction that we would end up traveling.

Now, my bet is that the AGI vision won't play out the way people think it will.

I think it'll take much longer to get this technology in the economy.

I think...

It'll just be harder to get that robot that can do anything and everything.

And I also think that at some point, as we saw in Caledonia, Wisconsin, people would stand up and say, hey, we need to change this script.

We need to push things in a different direction.

the technological rapture, as we might call it.

It is quite odd to feel that sense of messianic belief that comes from some of the bosses and some of the people who work for them.

But reality has a rude way of interrupting dreams.

It just does.

And things end up being messier than we might expect.

And I think we could already see that with

with generative AI today.

So it is true that businesses have adopted it faster than previous technologies, faster than the internet and faster than the PC.

It's also true that they're starting to see results pretty quickly and that many of them are struggling with it.

But that doesn't mean that getting this technology, getting that imaginary all-powerful AI into our economy, into our hands, across the whole of the US and beyond, will be as simple as pushing an update to an iPhone.

And we've got quite a few steps before we get there.

And what I start to see when I talk to

business people.

I was in Las Vegas a few weeks ago, and I did a show of hands with 300 IT bosses.

And half of them said, listen, we're not getting results now, but we should get results in a couple of years.

And when you talk to them, it's really challenging, practical stuff that is not far off trying to unblock a sink.

Because companies are difficult.

You don't just drop chat GPT and

Costco or Walmart and snap your fingers.

It takes time.

There's a lot to do.

And I think that that has been part of the story of how rapidly technologies diffuse and deploy across economies.

And so I take a view that these systems will get better, they'll get better quicker, that we have to get prepared for them to be really good.

But reality is always more messy than the spreadsheet model.

I think it's quite different to the dot-com bubble, because while there was a lot of circular supplier sub-financing, that was provided as debt rather than equity, which has technically a different categorization, and the spending was often sham on the way back, and nobody was using those fibers.

I mean, we didn't start to use those fibers that were laid until 2012, 2013, when YouTube was really, really kicking off.

So the differences here are that this is done in equity.

A lot of these things are tranched.

That means that they're dependent on people reaching specific milestones.

And businesses across America and across the world are demanding this technology like we've never seen before.

Now,

Is it ugly?

I'm actually looking on my screen at the moment at the spider's web map that you talked about, and it's really ugly.

And you should look at that and say, this doesn't feel right.

But let me give you one other example where perhaps...

We're familiar with it, and it worked for a long time in vendor financing, which is in about 1919, General Motors set up something called GMAC, and they said dealers are finding it hard to get finance to set up dealerships, and Americans have got stable jobs but can't buy the car up front.

So what we will do is we've got the best balance sheet, we've got the most cash, and we will finance both dealers and dealers.

American homeowners.

And GMAC still exists today.

But within a decade, the loan book of GMAC was the equivalent of half a percent of US GDP.

Wow.

So vendor

financing doesn't always have to end badly what it does do is it creates a new set of risks for the participants to behave badly and then we so we need some transparency right to see what's going on between these these relationships uh how real are they are there things that are being hidden we've got that experience from enron we've got that experience from worldcom uh and we're

For me, this is exactly an area where we need the kind of inspection that journalists have been doing.

But right now, when I look at it, I think, well, yeah, it could go bad, but it hasn't.

And it seems like it's got a little while to go before it might.

I think it's going to present all sorts of risks that we can't map and that we will be behind the curve as we were during the global financial crisis on bond rating agencies not rating these buckets of terrible mortgages correctly.

The thing that strikes me about a lot of these deals is that

They are at this point still relatively transparent.

There is a little bit of economic logic.

It's not so much about NVIDIA saying, you're going to buy my chips from me and then I'm going to give you money to do it.

What's happening here is that NVIDIA has the strongest balance sheet and it also has the monopoly on these chips and it really wants to sell a lot.

So in a way, this is a...

almost a logical way of them doing this, particularly as OpenAI, which is the one that needs most of these, is such a young company, it's massively loss-making, and it doesn't have the kind of balance sheet that lends itself to buying these off the cap.

Now, look, that is a very, very positive, Pollyannish reading of what's going on.

But it also is one that you might end up with if you were trying to think,

How do we make sure we don't run out of fuel to meet our customer demand?

I mean, I'm not sure what else you would do if demand is as hot as people claim it is.

Right.

Look, it's quite messy.

The big companies who've had time to build profits and get cash are obviously not needing to do this.

But the younger ones, the Corweaves, which is a new hyperscaler, or OpenAI, need to be able to fund this growth that they're seeing.

And of course, they're going to find more and more spicy ways of doing it.

At this stage, again, just thinking about where we are in the cycle,

These things look exotic rather than poisonous at the moment.

But the question, I think, will be what happens thereafter?

And is there enough transparency?

Can we see what's going on in these SPVs?

Can we really see how far the GPUs have depreciated?

Because if we can't, then that opacity allows all sorts of shenanigans to take place.

And when you look at the way in which these booms turn to bubbles and bust, and I did do some analysis on this, out of the 18 busts that I had enough data to really drill into, the funding quality was the trigger in nine of them.

It was the stressor.

that broke in half of them.

And sometimes it was out and out illegality.

Sometimes it was just that you couldn't see where the risk had gone, as we found with a global financial crisis.

So this is definitely one of the gauges that we need to keep an eye on and hope that the capital markets will do that at this moment where there's probably no one else to look to to keep everyone honest.

I think that's a very, very reasonable summary.

The gauge that matters most is revenue growth.

It is the signal that people want this.

It's the signal that we can monetize it.

And that's the one I'm trying to keep my eyes on and all the little ways that we can, because of course, these are mostly private companies.

And if revenue growth doesn't show up, then we are in a pets.com or a

telecom bubble moment where we've built infrastructure that might one day be useful that we can't fill today.

And that's often the story of these infrastructure build-outs.

In a funny way, we might be grateful for it.

Of course, there will be stock market prices going down, but what will have happened is that there will be a lot of GPU infrastructure that is out there, computing infrastructure that's out there that companies, organizations with less money could pick up at fire sale prices.

And those assets will go to those smaller players who might have newer approaches.

They may prefer open source.

They may decide they don't want to chase after the machine god.

They may decide that pricing needs to be more sensible and there's more financial discipline.

We might even see faster innovation alongside democratization with diverse teams living on this affordable infrastructure.

So there is perhaps some light outside of the doom that a bust might otherwise present us.

Well, what is a bubble?

I mean, that's the $10 trillion question.

And I would say I probably agree with a lot of what Paul said to you.

In my mind, a bubble needs to be defined very, very crisply.

It's not just vibes.

So...

I say it has to have two components.

Number one, there needs to be a significant market correction.

It needs to be beyond the 20% bear market.

It has to be 40%, 50%.

And one that sustains for a long time, the dot-com bubble market correction sustained for 15 years, the housing bubble for seven or eight years.

But the second thing to distinguish this from just speculators getting too excited is

is that the productive capital investment that drove the bubble in the first place also has to decline significantly.

And what we've seen in previous bubbles is it really needs to be 50% or more.

So I'm looking for two tests, a decline in market valuations by at least 50% for a few years, and productive capital for this technology dropping by about 50% again for a few years.

Well, I lived through both of those.

And I remember during the dot-com bubble, somebody broke into my office by climbing up the fire escape to pitch us their business to look for investment.

I mean, so that is a moment that I hope will not be repeated.

During the

housing crisis, I had to get to know the face of Angelo Mazzillo, who was the CEO of Countrywide Financial and a subprime lender with a great tan.

And for some reason, he was on my TV screen every single day.

It was absolutely spectacular.

And unfortunately, the reverse was true of his mortgages.

You see these characters, you see these moments.

The similarities with the dot-com are that this is being driven by venture capital, by Silicon Valley, by the promise of a new technology that will reinvent the world.

We thought we might create a new nation in cyberspace back in 1999.

But what's really different is that back then,

nobody was really using these sites and these services outside of Yahoo and CNET and eBay.

these things were empty.

The line was from the Kevin Costner film, Field of Dreams, build it and they will come.

And people built it, built these things and nobody came.

I mean, pets.com spent $150 million to make $600,000 a month in sales.

I mean, it was really, really confused.

So I think that's where the similarity to some extent ends, which is that

what we are seeing with generative AI is that most of us are using chat GPT and many companies are, and there's billions of dollars being spent.

So that feels distinctly different to where we were back in 2000.

Well, I think Thinking Machines is a really great example.

And I felt my Spidey sense tingle as well.

It's really just a hard thing to piece together.

No business plan, no product and that kind of valuation.

But those things happen in...

you know, the private venture capital market from time to time, and they don't really spill over in the real world.

I mean, three years ago, venture capital fundraising, prices investors were willing to pay was really, really crazy.

That stopped.

Prices normalised.

They're getting expensive now.

And, well, that does feel like it's heading towards that moment of bubbledom, if that's the right word.

But the other side of that is...

even these startups are growing like absolute topsy.

We've just heard that Cursor, which makes a tool to automate coding, has reached a few hundred million dollars of revenue and this company is only three years old.

invested in an email tool that uses AI to help you answer your emails.

And I remember being really cross with the founders when I looked at their pitch, where they said, we'll get to $10 million in revenues in the first year.

And I was saying, that's just ridiculous.

That's not going to happen.

And this is the thing that I don't like about your pitch.

They got to 17 million in revenue after about nine months and they're growing faster than ever before because customers want to pay for this.

And we shouldn't forget that by the end of this year, ChatGPT, that weird experiment that spilled into our lives three years ago,

we'll be making about $10 billion annualized at the end of this year.

And again, that's real money.

And that's money that's been made faster than Facebook got to that milestone or TikTok got to that milestone.

So of course, you get these moments of exuberance.

But they're also examples where real customers are spending real money on products that they really like.

Well, I've looked historically at 18 bubble instances back to the canals in the 1790s and where I could get the data.

What I found was that the threshold where it gets really sticky is around 2% of GDP and really problematic at 3%, which is where the railroads got to in the 1870s.

And the reason seems to be, and it's particularly true in a big and complex economy like America's, that it's a big and complex economy and it can absorb that sort of level.

But I'd also add that

It's actually quite a good thing at this moment where without the build-out of these data centres, the American economy might be heading towards recession.

And the build-out involves things that America has stated and authors like you and your colleague have stated need to happen, which is getting back to building.

When you build a data centre, you pour concrete.

I was speaking to the leadership team of a 140-year-old American engineering firm that

built many of the famous old bridges that you've probably driven across.

They're busy building data centers right now, and it's electricians and project managers and HVAC engineers who are there.

And that feels to me like it's probably quite a good thing to be happening right now.

So I would say that that's...

feels at the economic level pretty interesting.

But let's also recognize there are some other consequences of it.

So everywhere where the data centers are being built right now, we're seeing electricity prices rise.

So that cost is falling on people who are not benefiting, frankly, from the large venture capital rounds in Silicon Valley.

And we are starting to see communities push back.

So just this week, a town in Wisconsin called Caledonia rejected a Microsoft

data center.

And they said, look, we don't like turning farmland into this data center.

And I think that that is quite an interesting tension that's starting to emerge around where will the pressure point of this economic strain fall?

Will it really be purely that it's just too much of the economy?

Or will there also be a political dynamic to it that forces these companies to behave differently?