Where to for the AI datacentre boom? Transformational utilities, and their bubbles.

Prediction: The AI datacentre industry will be another example of a recurring pattern I’ll call a “transformational utility”: an industry which is capital intensive, massively disruptive, and soon indispensable to the rest of the economy, but also undifferentiated. And therefore, for early equity holders, often disappointing.

The old playbook, again

AI is obviously transformative, but it’s not the first technology to rewire society. Let’s look at previous innovations such as canals, railways, steel, electricity, fibre internet, and mobile phone networks to see what we can learn about capital-intensive, society-changing inventions.

When societies reorganise around new infrastructure, the story tends to rhyme:

Breakthrough + capex. A new invention arrives with vast promise, but equally vast capital requirements.
Early scarcity. Capacity lags because capital projects take time to execute.
“Bubble” phase. Those in the lead enjoy massive valuations, as they promise to dominate the revolution.
Commoditisation. The buildout catches up with demand; the lack of differentiation in the underlying product exposes an inability to sustain high prices.
Real growth continues. The sector keeps getting bigger and more valuable to society.
Multiple compression. But the early players cannot maintain pricing power, valuations tend back down, and many early investors lose despite the sector’s real-world success.

Canals, railways, steel, electricity, fibre backbones, and mobile networks have all walked this path: The railway barons, US Steel, Edison Electric (later GE), Cisco, and most mobile networks enjoyed boom valuations at some point. Then returns normalised, even as their industries grew to multiples of their prior size.

Steel is a particularly interesting example. It sat at the centre of USSR and later Chinese industrial strategy. But as raw steel capacity become abundant, the USA’s path showed that long term economic leadership came from differentiated offerings downstream. (There is a geopolitical angle to steel which is re-emerging now: more on that below.)

Of course, there are high-valuation industries (and bubbles) that are NOT transformational utility bubbles, for example:

Tulips. Some bubbles centre on things with trivial enduring utility. AI compute isn’t that.
iPhones (or Rolexes). Some products sustain premium margins through differentiation and brand. Raw compute is not that either. For example, the mechanical-watch industry (Rolex, etc.) is worth more than ever before, because it has reframed itself as that of strongly-branded status symbols for men, not merely timekeepers.

Why AI data centres behave like utilities

The test for utility economics is interchangeability. If buyers view your product as equivalent across providers, price drifts towards (operating cost + cost of capital). Higher prices just attract new entrants, who can gain share until prices converge to the threshold for new entrants.

Compute is globally tradeable over networks. A data centre is just a building with electricity, cooling and connectivity, in which lots of matrix multiplications can be done. It may, in fact, be the most tradable of all the transformational utilities, as there are no technical reasons why we couldn’t put all our compute in one place on the planet.

The major structural reason to deviate from this gravity is geopolitics (data sovereignty, national security, sanctions, energy policy). Governments can and will localise capacity; they can also tax or subsidise it. But that’s political risk, not durable product differentiation, and it drives subsidies, not big financial returns.

So where’s the differentiation (and the excess return)?

Think of where differentiation exists in the AI stack:

Chips (e.g., NVIDIA). Moderate. Real technical edge and speed-of-innovation moats, but they’re cyclical and not guaranteed (ask Intel).
Data centres / cloud compute. Low (outside geopolitics). Scale and operations matter, but sameness dominates pricing power in the long run.
Models (LLMs, core algorithms). Moderate now, lower over time. Capabilities diffuse fast; weights leak; papers ship; open models improve. Most use cases allow users to freely swap between several LLMs.
Applications. High variance, real moats available. This is where long-term margin lives — exactly as electricity’s wealth accrued to the things using it, not the grid itself.

What this implies

I’m not predicting a dramatic bubble “burst” tomorrow. Scarcity can continue longer than sceptics expect, and AI is likely to be capacity constrained for a long time. But multiples for compute-heavy businesses should compress as capacity catches up. The companies will be fine, some shareholders won’t. (Cisco still exists; 1999 buyers are still unhappy.)

Infrastructure bets need a clear theory of longevity. OpenAI (and others) tying valuation to data-centre buildout only makes sense if controlling compute during the next few years catapults them into a leading position in a new post-AI world, in a way that didn’t happen to any of the previous darlings of transformational infrastructure. This might happen (AI is unusual enough to keep minds open) but it’s a high-conviction, high-timing bet.

Finally, to be really clear, I’m not predicting that the AI revolution will underwhelm. Far from it! Just that the actual buildout of data centres is something you might want to leave to someone else.

The AI singularity: Situational Awareness vs the Societal Speed Limit

It’s a good time of year to look back at the bigger questions facing us. So … AI it is! Here some of my current thoughts, mostly so that I can look back in five years time and laugh at how terribly naive I was / we were.

The paper Situational Awareness – The Decade Ahead paints an extraordinary picture of the next decade, one where AI transforms almost every aspect of society at a breakneck pace. It’s incredibly breathtaking in scope and implication, and well worth a read, as well as provoking the question as to whether change can really happen as rapidly as it claims. Let’s ask that question, and propose a “Societal Speed Limit” which I think will be the ultimate decider of the pace of AI-driven change.

Three points from the paper stood out most strongly for me:

1. Credible projections of an incredibly fast pace of improvement

The paper forecasts AI capabilities to advance at an astonishing rate, driven by improvements in hardware availability (compute), algorithms, and deployment methodologies (e.g., agentic tools). Together, this could give up to 10 orders of magnitude (i.e., ten billion times more capable AIs) over less than a decade—a staggering figure. Given that AI already exceeds human-level capabilities in many narrow-defined areas, this would inevitably change the world.

2. The Adoption Curve: Slow, Then Sudden

AI tools today often stall at the proof-of-concept stage, requiring integration and adaptation by organisations. But the emergence of human-level agents that can directly use existing tools without integration effort could act as a tipping point: “hire” an AI in the same way, using the same tools, as a human hire would use. This would immediately make most PoCs irrelevant, and make far more human roles open to AI improvement / replacement.

3. The Geopolitical Frame

The paper spends a lot of time on U.S.-China competition, arguing that AI leadership could define not just economic success but also military dominance. While this might be geopolitically accurate, it feels to me a bit sad that the focus moves so quickly to this specific great power competition of this point in time, given AI’s broader historical importance. This is possibly a pivotal point in the history of our species, or even life on earth! It’s a bit like imagining that the invention of a usable numerical systems was primarily about ancient Sumerian-Babylonian competition.

Where I agree

No ceiling in sight: Some suggest that AI is plateauing. This feels incredibly ambitious to claim, given that we’re barely more than two years into the post-ChatGPT world, and already far beyond the capabilities of ChatGPT 3. Every week is still bringing breakthroughs.
Cost as a non-constraint: Yes, AI is (arguably) expensive. But, for example, the costs of specific OpenAI capabilities have come down by ~99% over the last two years. This is Moore’s law of steroids. Barriers to adoption are unlikely to be economic, short-term corrections notwithstanding.
Surprises ahead: We cannot imagine all the impacts AI will have, and we will be surprised. Looking back, the experts expected it to take decades to make the progress we’ve seen in the last five years, and few expected how current AI turns out to be really good at creative work (writing, art) in particular.

Where I disagree: the pace of change on the ground

Technical Roadblocks? Yes (but it doesn’t matter)

Technically, I think we’ll hit some roadblocks. My current opinion is that Situational Awareness underestimates the architectural challenges we still need to overcome.

Current LLMs are built on “attention” as the simplifying breakthrough. But this architecture inherently has limited internal mental state, likely crucial for persistent goals and nuanced understanding of their environment, such as noticing when they’re stuck in a non-productive loop. Addressing this may require significant architectural changes. In particular, having a persistent mental state makes training difficult, as the model’s output is no longer deterministically produced just by its input, but also the broader model context. It might be that the “world models” approach provides a manageable way for AIs to understand the context of their inputs and outputs. I worry, though, that we need to invent a more self-organising approach to training, probably including recursive connections, i.e., output looping back to input within the model’s neural network. However, this removes much of the massive training gains we won with the attention mechanism.

The paragraph above may be hopelessly naive (I’m not an expert), and anyway doesn’t really matter: the current models, with conservative extrapolation, are quite enough to completely change society. So, will they?

Societal uptake: Why it will be slower

1. Deploying new technology is never instant

History is full of examples of groundbreaking technologies taking far longer to reshape society than expected. For example, electricity: it’s fantastic, but still is from from universally available across the globe. To achieve its economic advantages, electrification needs an ecosystem: infrastructure, supply chains, capabilities, demand. You can’t use electricity in a factory until you have an economic context with known opportunities and demand, input materials, logistics networks, trained staff, conducive regulations, etc etc. This is why rebuilding an economy (e.g., Germany in 1945) is often far easier than creating economic growth from scratch: people remember how the networks worked and can reimplement them, rather than needing to solve all the pieces from scratch.

AI will face similar challenges. It can’t just be “dropped in” to most organisations or systems, even in agent form. If we think of AI today as a vast pool of really smart, low wage university graduates (with amnesia, though that maybe solved in coming years), then the challenge is clear: most organisations cannot productively absorb a big pool of such graduates, as there are bottlenecks elsewhere.

AI plus robotics can be argued to undermine this argument: just use robots to build the ecosystem too. But even this needs time: to build the robots, to build the factories that build the robots, to build the mines that provide the materials to the factories, etc.

2. AI will replace people bottom-up

The way AI replaces human labor will likely follow a bottom-up trajectory, starting with junior roles and tasks. To be clear though, not only (or even primarily) low-skill roles, but rather junior roles that can be done with a computer. That’s a lot of roles! But, starting at entry-level positions.

Why? Obviously, leaders rarely automate themselves. But beyond self-preservation, senior roles often involve judgment, relationships, and high-stakes decisions that stakeholders are reluctant to entrust to AI. For example, in a law firm, it’s easy to imagine junior associates being replaced by AI for drafting contracts or due diligences, but much harder to envision clients trusting AI with high-stakes negotiations typically handled by partners. Likewise CEOs: even if AI would probably do a better job … who would be brave enough to make that call?

Additionally, it’s easier to replace, for example, 50% of the seats in a standardised role, than 50% of a job done by a single person (i.e., a leader).

I expect we’ll see junior positions vanish faster than senior ones, hollowing out traditional career progression.

3. The “societal speed limit” on the rate of producing “losers”

Perhaps the most significant constraint on AI adoption will come from society itself. Disruption creates “winners and losers”, and the pace of that disruption matters. If AI displaces workers faster than society can absorb the shock, the resulting inequality could create enormous political and social backlash.

Let me suggest a principle:

Society has an “immune response” to fight against change that produces lots of people who feel that their future prospects are deteriorating.
The greater the rate (percentage of people per annum) at which which people are experiencing change that results in deteriorating prospects, the stronger the response.
The response escalates from pressure on governments to regulate, to voting out those governments in favour of others that promise to act more firmly, all the way to destructive protests and ultimately revolution.

That is, society will “fight back” against change producing too large a share of people with deteriorating prospects, by finding leaders or actions that will successfully slow down the rate of change.

The “societal speed limit” isn’t just a concept—it’s a reality we’ve seen time and again. From the Luddites to modern protests against globalization, society resists changes that leave too many people behind. With AI, this principle will likely shape the pace of adoption as much as the technology itself.

The challenge isn’t just economic; it’s also generational. What happens when young people entering the workforce find fewer paths to meaningful employment? Youth unemployment could lead to disengagement, frustration, and instability, creating long-term societal challenges far beyond the immediate economic impact.

So where to?

To summarise:

The paper Situational Awareness – The Decade Ahead paints in picture of extraordinarily disruptive and rapid change.
It may underestimate some of the technical challenges, but the projections are so extreme that even a far slower technical pathway requires us to ask how, and how fast, society can change.
Social and economic change will be slower than the paper expects, for three reasons:
- Deploying any technology requires networks, and any “silver bullet” from AI cannot instantly create the ecosystem for instant change.
- Change is likely to start bottom-up in the economy, affecting the youth first.
- Society has a “speed limit” for how rapidly change can produce people with deteriorating personal prospects. Exceed the speed limit, and society will force actions to slow the pace of change.

We are in for one hell of a ride in the years to come! Change will come incredibly quickly in some areas. For the rest, I believe it will come faster than most expect, in unexpected ways, but still slower than the Situational Awareness paper projects in its extreme scenarios.

It will affect the youth more quickly, and risk leaving parts of the world with less developed ecosystems even further behind.

The “societal speed limit” may slow the pace of change, but we should not expect this process to be comfortable, as that slowing may come from huge societal unrest. And through it all, we need to avoid a catastrophic AI-safety failure where AIs attack humanity, and avoid a superpower war.

Easily summarise PDFs (with AI)

Next chapter in learning and deploying AI tools: a tool to summarise arbitrary-length PDFs. Very useful for dry, long reports, legal judgements, etc. You could use it on a book, but it will be rather … dry?

Code at https://github.com/langabi/RecursiveSummarizer. It’s a simple (and not particularly elegant) Python script, but also a MacOS finder shortcut (right click on any PDF to summarise it, like a super-power on my computer!) Needs minor technical skill, and a (free) OpenAI.com API key.

The main point of this is to explore how to work around one of the key constraints of current Large Language Models like GPT-3: the limited size of the prompt. GPT-3 current version can, at time of writing, handle around 4000 tokens, or around 3000 words in the combined input and output. So, if you have a document bigger than this, chunking and recursive summaries are one way to go. It produces a decent result, at the risk of the summary losing a little of the overarching theme of a document.

More generally, a key technique to avoid AI models from, well, making things up, is to enrich the prompt with information relevant for an accurate answer to the prompt question. The limited prompt size is a constraint here too — you might find yourself using AI techniques like embedding to find exactly and only the needed content.

But certainly, I expect that there will be a lot of innovation in the next few months in how to give AIs working memory (long and short term), through things like architectural changes (bigger prompt windows), embedding-driven long-term memory, and maybe using the bots themselves to create a summary of a current conversation, that is repeated at the start of each new prompt window. And no doubt much more!

AI: The “spreadsheet” for copywriting

We just reduced new product copywriting time from 40 minutes to 15 minutes, with our first internal AI-powered tool. Here’s how I’m thinking about AI as the “spreadsheet for copywriting”.

Technically, the implementation relies on:

Enriching the “prompt” by replacing product- and order-specific references with all the information we have available, before passing the (much bigger) prompt to GPT-3, so that the AI is working on complete, current product and order information. Currently we’re just matching product/order IDs, but a more complete implementation will also used named entity extraction to find products, and then traditional search or embedding-based search to match and retrieve the matching products
Adding some boilerplate to the prompt about tone of voice, and instructing it to respond “I don’t know” when it is not confident in being able to answer the prompt.

Without this, we found (as have many others) that the AI makes up very plausible but completely fictional details.

For now, we’ve released the tool in an internal “chat bot”-style interface, but are looking at a Google Sheets function, to allow generating text for each of a list of products in response to a standard prompt — very useful for newsletter creation. Regardless, human review and editing is critical for all our use cases.

So far, we’re seeing fastest adoption by our copywriters, especially for listing new products. Faithful to Nature’s team, for example, spends hours reviewing each and every ingredient with our suppliers, before listing a product. This isn’t changing, but the process to convert that information into a standard, compelling product description, has dropped from 40 minutes to 15 minutes per product.

So where to from here? (I’m asking, and so, to be honest, are our copywriters).

I keep returning to a podcast by Planet Money on the impact of the spreadsheet on finance and accounting. Tl;dr:

spreadsheets used to be physical paper, calculated with hand calculators, laboriously
digital spreadsheets (Lotus 1-2-3, Excel) completely changed this, and away went all that human work
but, since then, finance employment has grown substantially
because spreadsheets changed from a reporting tool, to a scenario planning tool: what would happen if we opened another store? Changed our margin? So finance itself become far more strategic.

This is how I’m thinking about these AI copywriting tools: by replacing the “hand-held calculator” of having to write every single word by hand, we will achieve two things:

Unlock opportunity for copy, that was previously marginal. Ideally we’d have a page on our site for every possible combination of interests and questions for every small segment of customers, but that’s a lot of work. If the work halves, a lot more become possible — and indeed, we’re already working on newly-opened opportunities.
Make copywriting a strategic “what if” function. Currently, the marketing flow is linear: come up with a campaign concept, choose a tone of voice, identify copy requirements, write the copy, send to customer. This can become iterative: if 90% usable copy is generated instantly, we can experiment with different concepts based on how they turn out, before finalising the brief.

Clearly, elevating copywriting from execution to what-if strategy is very applicable elsewhere, for example, in the legal industry.

If you’ve deployed AI tools yet, what have your experiences been?