Azeem Azhar's Exponential View
What it will take for AI to scale (energy, compute, talent)
10 Dec 2025
Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years. Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic. To keep up with the Exponential transition, subscribe to this channel or to my newsletter: https://www.exponentialview.co/ --- In this episode, I look at the next 24 months of AI. The technology is improving rapidly – so what could hold back widespread transformation of how we work and live? I dig into the real constraints, from electricity shortages to institutional inertia, why mid-2026 matters for enterprise AI, and why so many people remain uneasy about a technology they use every day. I cover: (00:03) Predicting AI's next two years (01:50) How life changing are chatbots, really? (03:36) Our current biggest AI constraint (07:58) The remarkable increase in token efficiency (10:43) Why mid-2026 is a crucial turning point (13:01) Do we actually want AI in our lives? (15:28) Should organizations wait to jump in? (16:39) How is OpenAI reckoning with Gemini? (18:41) The market's reaction to OpenAI's code red (19:32) Where will value accrue in the supply chain? (20:51) What's the best strategy for middling powers?Where to find me: Exponential View newsletter: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azhar/Twitter/X: https://x.com/azeem Production by supermix.io and EPIIPLUS1 Production and research: Chantal Smith and Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
Chapter 1: What predictions are made for AI in the next two years?
What I'd like to do today is talk about what we need to look out for over the next 24 months with the AI build out, with all of the things that are going on in the deployment of AI, how people are feeling about it. all of the tensions, all of the potential crises and the potential wins. Now, two years is a particularly difficult time to shoot for.
So I am really hanging myself out because everyone can predict the next week and you can kind of predict the next 20 years. But I'm going to be brave and talk about those two years. Readers will know that I've talked a little bit about this in the newsletter. So go back and look at those essays. In short, this is all about absorption.
This is all about the extent to which AI can be absorbed in the economy, in our world. Are companies absorbing artificial intelligence fast enough? Can the electrical and power systems absorb all the new demand from the data centers? Is the economy absorbing any benefits at all?
And do people, do society, do us, do we want to absorb AI, all the things it brings with us at the speed with which it is emerging? So let's just step through each of those questions of absorption. On the firm, I've written a lot about this over the last few months, weeks and months.
Chapter 2: How are chatbots changing our daily lives?
Are companies really making use of this technology at all? And I think we can even step back a layer and say, are people really using these technologies in any meaningful sense? We talk about 800 million people using ChatGPT. We talk about a couple of billion using these chatbots. globally.
But are we using them the way that we've used other technologies when we talk about them being deployed, like the flush toilet or electricity? Is it really use of one of these technologies if you just put the odd query into it rather than Google, but your life remains largely the same?
I mean, it's almost trivially easy to put a product in the hands of tens of thousands, hundreds of thousands, millions of people because of the App Store, because of iPhones. And perhaps when we think about where we are in absorption, we need to go beyond someone downloaded the app and played with it a little bit.
And I'm sure within those 2 billion people, there are many people like me for whom our ways of working and ways of living have changed because of these tools. But we need to be a little bit thoughtful about that. And that also, I think, reflects back on companies. Companies have a lot to do when they want to transform their businesses. API access is the easy bit.
It's the institutional metabolism that is quite hard, very hard in some cases. That's why you hear stories of, and I speak to bosses a lot of the time, they're very happy largely with how their AI deployments are going, but they also recognize that really deep and meaningful change is going to take quite a lot of time.
Now, I don't think this is going to be a multi-multi-decade process that it was with electricity. I just think companies are more adaptable, people understand the technology better. We've spent years thinking about how to manage large-scale change, that horrible management consultancy word, transformation. And firm appetite is very, very clear.
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Chapter 3: What is the biggest constraint on AI development currently?
And the adoption will get easier now that there are standard operating procedures, there are playbooks over the last couple of years that at least the leading firms have developed. But there is a real constraint, and that real constraint is electricity. It's the physical limitations of this software and whether existing systems can absorb the demands being put on them.
Now, a data center can be built in a couple of years, maybe a bit faster if you're Elon Musk. But getting that grid connection, as we know, can take several years. So you can have a data center, but you can't power it up. And Satya Nadella a few weeks ago said that they had that very situation.
Chapter 4: Why is mid-2026 a crucial turning point for enterprise AI?
So there is a scramble for getting energy into data centers. I've spoken to data center developers in the last few weeks. And what I'm hearing are really quite staggering stories, multi-billion dollar offtake deals for electricity, not just in the US, but in Europe as well.
The demand is really high that the hyperscalers are somewhat price insensitive in order to be able to build capacity to meet that demand. This isn't just about large language models. This is actually also about the shift of businesses, processes into computation, into digital processes. It's also about the growth of digital services.
You know, earlier this year, AWS, which is Amazon's hyperscaler business, they put in tens of billions of dollars, nearly $100 billion in CapEx, this year, had to turn down business from Fortnite, a multi-million dollar contract. I didn't even know Fortnite was still a thing. It's that game where people fly around and wear skins.
They had to turn down that deal because they didn't have the capacity. And if you follow the news and the analysis, you'll see that that story comes up time and time again. So this is a long-term cycle. This is not just about large language models. This is not just about
Chapter 5: Do people really want AI integrated into their lives?
whether open AI can grow and can become profitable. This is a fundamental shift in the economy, as fundamental as going from 1880, when nobody was really using electricity in the economy, to the 1930s when the US, it was the prime move of the bulk way in which factories and offices were getting their power.
More and more economic activity will move into computational systems, even when LLMs look completely long in the tooth and who would use an LLM in the same way that not many of us use penny-farthing bicycles to get to work. And so it's really fascinating. again to see how that is changing the narrative. Now, some of this, I think, is just expediency.
It's just, let's take advantage of the changes. But some, I think, is real. We know, for example, that Google has done a deal with Commonwealth Fusion Systems for a 400 megawatt power tranche when their fusion reactor goes live in a few years. And Helion Energy, which is another fusion company, has ties with Microsoft to power data centers. So this is a really, really significant problem.
It's a big issue in the US, much less of an issue in China, where they've mastered the ability to deliver clean electrons at scale. There's also this squeeze coming in between inference, which is the bit of the AI activity that makes money, and training, which is when you're doing your product development for your next model.
Chapter 6: Should organizations wait before adopting AI technologies?
Model companies will be battling between where do they put their resources into training the next model or into serving customers for revenues today. They have lots of resources, but even those resources are not infinite. And earlier this week, Brookfield which is an asset manager lined up with our estimates that in a few years, about 70 to 75% of compute cycles will be used on inference.
Chapter 7: How is OpenAI responding to competition from Gemini?
So that's going to be a tension, right? Do we pay bills today or do we build the next big thing in some different way? And you see the labs. I mean, I think the contrast between Anthropic and OpenAI is most marked in how they approach that, right? Anthropic appears to be rather more focused in thinking through the economics of that particular trade-off between training and inference.
There are levers to address that. Efficiency gains being one that is an obvious approach. We are starting to see more and more companies routing requests to cheaper, which means models that use less electricity and cost less models in within their application.
So you put in the query and the query figures out, oh, maybe I should send this to DeepSeek rather than to an open AI model to get the result. And we've made real technical progress both across the algorithms and across the chips that serve them over the last few years. If you look at a sort of GPT-4 level class of inferencing back in 2022, it would take one watt hour to generate 50 tokens.
So what is a watt hour? If you've got a 10 watt LED bulb, one watt hour is sort of leaving that on for six minutes to get your 50 tokens. Today with the latest NVIDIA chips and more efficient optimized language models, we're getting to about 600 tokens per watt hour. So that's a 20X improvement just over four years. Of course, the amount of tokens we want has increased significantly.
And so on the other hand, you have these new reasoning models that might burn 10 to 100 times more tokens per query. So what you have there is these firms, the hyperscalers who are really hungry for compute, they're hungry for compute, not just for AI, but for other workloads. And they're hungry for that because we as consumers and as businesses want those types of services.
So you've got those on one hand, the grids can't keep up, the GPUs are being rationed between training and serving. This is a short-term squeeze is my sense and that over as the industry matures, the trade-offs will become much more apparent. We'll get through some of the blockages around providing power.
We often see that in markets that you get these squeezes and ultimately the market industries take one or two years to reconfigure and to be able to deliver what is required. It's just not going to happen tomorrow. I think the third thing is about the economic engine and the question about whether we are going to see results from all of this.
Now, I went into this in some detail in last week, but there's just one thing that I wanted to come back to. You know, there's a lot of uncertainty in this market right now. And it's amazing that such huge decisions are being made in the face of such uncertainty.
One good example is that Arvind Krishna, who is the CEO and the chairman of IBM, was speaking last week and he said, look, it costs about $80 million per megawatt because in the last year, tech companies have started to think of their computing in terms of watts and megawatts. So $80 million per megawatt for a data center. Now, that's quite a high number.
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Chapter 8: What strategies should middle powers adopt in the AI landscape?
And that two-year clock will start to be reached in the early summer through to late summer and early fall of 2026. And at that point, we should start to see more and more companies talking about the results they're getting. If they don't talk about them, they might still be getting results and just don't want to share, which is not unheard of in this market. So that's the third layer.
Then the fourth layer is really about the politics of absorption. So are we really able to absorb this politically? And there are complexities. You know, the idea of sovereign AI, sovereign technology, which I wrote about in my first book as well, is becoming incredibly, incredibly real. It's a race for the US and for China, but for Middle powers, which is really everyone else, right?
If you're not US or China, you're kind of bunked in as middle powers. It's a challenge, right? How much control are you going to actually have on this absolutely critical, critical infrastructure? And it creates this strategic dilemma for states. There are concrete signals of these smaller companies doing things, of course, the UK, of course, the Gulf.
Brazil and India both have new AI data center projects running into the tens of billions. But it's going to be really complicated for those middle powers when they think about not so much the models, because you can always get an open source model, but they think about the chips, they think about ultimately the serving infrastructure.
And that is going to play out significantly, certainly over the next couple of years. But the final one, I think, is this. And this is the most paradoxical part of this AI wave where 2 billion people are using these tools. People like nano banana and they like image generation and they like Sora and they like other things.
And yet we know from surveys, Edelman Trust Barometer being one that I use as a barometer, frankly, showing that roughly 70 to 75% of Americans are pessimistic about what AI might bring to them while they even use the tools. It's almost like you're forced to use them somehow because you need to participate. It's an uncomfortable place to be.
And I think that that problem is going to become more and more acute. We're already starting to see resistance in the US to the build-out of data centers.
There's an analyst pressure group that tracks this, and they had identified 142 data center projects of total value over $64 billion stopped in the US since 2023, whether that was happening in Virginia or the Midwest or Pennsylvania or the South. These groups are organizing and trying to resist the build-out of this infrastructure. And it's fanatically bipartisan.
So you've got traditional landowners and rural communities connecting up with environmentalists to ensure that the I's get dotted and the T's get crossed, but also at some point that you can resist the build-out of this. And that, to me, is going to be a really interesting and important tension that will grow over the next year or two.
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