POD! On YIMBY Pod this week, Martin and I do our best impression of Tim Harford on More or Less, debunking some questionable Guardian reporting on data centres. Then we speak to James Howat from ThinkLabour, who argues that we can build more infrastructure if we bring back municipal bonds. Listen here!

GAME! How many MPs can you remember? Try my stupid little MP memory game to find out!

My partner asked me what I want for Christmas the other day.

This is a surprisingly difficult question. We’re middle-class and childless, and though we’re not particularly wealthy in any meaningful way, this does mean that if there’s something reasonably priced that we want, like a book or a video game… we just buy it. We don’t wait for a special occasion, because why bother?

So I sat there thinking for more than a few moments, my mind blank, when all of a sudden something depressing hit me. Could she buy me some more AI tokens?1 Does ChatGPT sell gift cards?

As bizarre as it may seem to most people, a lack of AI compute is, with only a little exaggeration, one of the biggest problems in my life. ChatGPT has proven so useful to me as a knowledge engine, software development platform and work assistant, that I’ve recently found my supply of AI tokens running dry, to the point where, even though I pay for the $20/month ‘Plus’ tier, it eventually refuses to respond to my queries for several hours – or even days.2

From a business perspective, this makes total sense for OpenAI. Though an app on a computer screen may feel weightless to the user, the computation to make the magic happen is extremely resource-intensive. If you prompt ChatGPT, in order to compute the answer, something roughly equivalent to an entire rack of computers in a data centre briefly dedicates itself to your query. That’s why AI energy usage is a significant environmental concern, as well as a bottleneck for development – it’s a non-trivial expense that the AI companies need to pay for.

But from my perspective, knowing this does not make it less annoying. Though there is a way around this – I could simply pay more. ChatGPT would love me to upgrade to the $100/month tier for a 5x increase in the number of AI tokens I can use.

However, here I hit a different roadblock. I can’t really afford to spend $100/month on AI compute. I mean, I suppose I could – but at that level it becomes a middle-class spending trade-off, where I’m forced to choose between my precious tokens and, say, subscriptions to Netflix, Amazon Prime, and a myriad of other subscriptions. And I should probably continue to pay my half of the mortgage, for the sake of my relationship and having a house to live in.

Anyway, it was when I was experiencing this great pain that I realised something important. Though my dilemma was simply annoying, I think in the not too distant future it could become an important challenge for society.

Because what happens when this problem hits people who are not as fortunate as I am? If the most optimistic predictions about AI are true, and it really is world-changing in its utility, and if compute remains a constrained and expensive resource – what will limitations on AI usage mean for the poorest people in society? Won’t it be unfair if the rich can buy all of the compute they need, and the poor are left without?

In other words… Am I crazy to think that AI could soon become a social justice issue?

Subscribe now

Power to the processors

I wrote recently about how AI has broken the link between human brains and cognition.

Though we can debate at exhaustive length about exactly which tasks AI can perform, or the limits of its ‘intelligence’, it’s increasingly true that human-like cognition can be spun up on demand with a credit card.

This is a weird analogy, but it’s a bit like the transition to cloud computing in the mid-2000s. In the before times, companies and individuals needed to make huge capital outlays on computing equipment – essentially placing bets on their future needs.

But then cloud arrived and smoothed the correlation between demand and costs. When more people want to use your app, the cloud platforms automatically spin up more computers to meet the demand, and when fewer people use it, it scales down again.

And we’re arguably already witnessing the same happen with ‘intelligence’. The amount of smarts you will have to dedicate to any given task will be the sum total of both your brain and however much AI compute you have access to.

In other words, there will be a similarly smooth correlation between intelligence and how much you can afford to pay.

So wind the clock forwards and the implications of this are scary. The rise of AI could end up having significant distributional impacts, and could become a source of significant inequality.

Now, I know what you’re thinking. “Finally O’Malley gets it! That naive AI booster is conceding that the hectoring critics have been right all along!”

So let’s be clear. I’m not talking here about the potential AI impact on jobs, intellectual property or any of the other well-worn criticisms of the technology. Those are different debates for other pieces.

What I’m talking about specifically is access to AI intelligence – especially if the most optimistic Silicon Valley predictions about AI turn out to be true.

This is because if AI is as transformative as the Tech Bros hope, the technology will inevitably be woven into all aspects of our lives. If intelligence is available on tap, it’s plausible that we’ll all essentially have AI agents running in the background of our lives and this will be completely normal – a bit like how today it is basically impossible to function in society without a smartphone.

What will these agents be doing? It would be silly to speculate on what specific products might emerge. Though, for the sake of giving you something to picture, imagine a virtual personal assistant that is like Siri on steroids. Something that can proactively work on your behalf, without needing prompting. It could manage your money, organise your calendar, negotiate with the government over your benefits, or perform ten billion other tasks basically autonomously to reduce the friction in your life.3

Whatever conveniences end up being invented will require compute resources – and those resources will be subject to the same dynamic: Those with more money will have access to more compute, and therefore will be able to do more stuff, further entrenching a society of haves and have-nots. And unless we plan for it, it could turn out that not having access to compute is just as debilitating as living without a phone is today.

Uncertain assumptions

Obviously, my argument here is highly speculative. It rests on several assumptions about the future coming true. Perhaps the AI sceptics will be proven right.

But even if the technology does prove maximally useful, there are other examples where analogous to this where any problems have simply disappeared.

For example, when my parents first got a dial-up internet connection in the 90s, access to the internet cost a penny per minute, which meant that time online was severely rationed. I couldn’t spend an evening wasting my life, scrolling listlessly because there was a permanent ticking clock running, and it could add up to serious expense.4

But eventually, this problem mostly disappeared as the market shook out so that instead today everyone pays a flat fee, in exchange for internet usage that is effectively unmetered, no matter how long we spend on Geocities, or whatever it was we did in the 90s.

The same happened to mobile internet usage too. At the beginning of the iPhone era, phone contracts severely limited everyone’s data allowance, but eventually mobile infrastructure caught up and the cost of data collapsed, which is why today most people can afford a package that is either unmetered, or large enough for data limits not to really matter.5

So there is a strong argument that AI will likely go the same way. The reason you can easily hit a hard limit on ChatGPT and Claude today is that the availability of compute is severely limited. As soon as the world builds more data centres (as is happening), the theory is the price of compute will fall and AI use will be effectively unmetered.

And then there’s the related argument that in the future, most AI compute will happen locally – your AI data crunching won’t happen in a distant data centre, but on the chip inside your phone or computer. That would mean no need to pay a subscription, and you can use AI as much as you like for free.6

I readily concede that, maybe, this is a possible future too. However, I must admit, I’m a little sceptical in the medium term, because of the huge gulf in capability between local models, designed to run on home devices, and the latest frontier models that run in the cloud. Maybe I just lack the imagination, but given the hardware and energy requirements, I’m not sure how all of that can be shrunk down into a smaller model that can work just as effectively on significantly less capable hardware.7

But anyway, my point is that though these are two possible futures – there is a third. And this is where the danger lies. What if the cost of compute doesn’t fall, or if the price equilibrium lands somewhere inconvenient and out of reach of many people?

We got lucky with broadband and mobile – it’s so cheap that most people can afford a home internet connection and a SIM card with a reasonable data allowance.

But what if AI is so unbelievably useful to the rich and to corporate users that the cost of compute for people on low incomes, or small businesses just starting out, is just too out of reach? So that when rich and poor compete for access to this new, scarce resource, there’s only one winner?

I don’t think this is as hard to imagine as it might seem, as we’ve already seen a microcosm of what this might look like, and the victims are some of the least sympathetic people on Earth.

I’m talking, of course, about gamers.

A significant consequence of the AI boom is that because data centres require similar chips, RAM and other components to the computers in our homes, manufacturers have dedicated their capacity to providing for data centres – sending the cost of home computing spiralling.

For example, earlier this year Apple raised the price of the entry-level MacBook Air from £1,099 to £1,299, and amazingly the cost of a new Playstation 5 today is £569.99 – 27% higher than when the console was first released six years ago.

This is completely unprecedented. Typically what you’d expect to happen is for consoles, phones and computers to get cheaper over time as devices age and components become cheaper to make. But cold business logic dictates that spending on AI is just much more lucrative, which has left everyone else paying over the odds for hardware.

So my point is – what if we end up in a similar position in terms of access to compute? What if the marginal value of a little bit of extra compute is much higher to a large corporation to the point where it makes compute unaffordable for regular people? And what if this isn’t a problem that goes away quickly like we might expect as supply catches up to demand?

Similarly, I don’t think this is as unlikely as it might sound.

Though the current AI build-out is often described as a bubble, the record investment could simply be a reflection that this really is a world-historic, valuable technology. And perhaps we are starting to see the first signs of this. Anthropic is now forecasting that it expects to bring in upwards of $100bn in the next year. That’s not hype – that’s cash from customers who want to buy the AI capabilities that the company is selling.8

And I think demand for compute could still conceivably go even further too. As I wrote previously, this is a new general-purpose technology – there are countless tasks to which this new resource of cognition-on-demand can be applied. And as Ben Thompson has argued, what will drive this demand for compute – or ‘inference’9 – isn’t just humans typing queries, but AI agents working autonomously – compute begetting more compute without human intervention:

Today’s agents are fancy answer inference; in the future true agentic inference will be work done by computers according to dictates given by other computers, and the market size scales not with humans but with compute.

Given that agentic computing is already exploding, I think it is at least plausible that we will reach a point where it is far more valuable for corporations to pay a premium for marginal compute, creating the market failure I describe above. And it’s not a given that just because demand will be there from individuals, supply will catch up – because building isn’t without friction, as data centres are hugely controversial and subject not just to market forces, but the arbitrary whims of planners and other political interests.

And if this sounds familiar, it’s because though I’m describing a hypothetical problem for AI, there’s another sector where the corporations and individuals are already locked into a competition for the same scarce resource that is subject to the same dynamic: energy.

As things stand, if electricity was a pure free market, we’d build vastly more energy infrastructure to reduce price and service demand, but energy infrastructure is subject to other constraints. And likewise, if domestic energy prices were set by the market, they would be significantly higher, and the prices would disproportionately punish the poor.

That’s why, in the case of energy, the government created the energy price cap. Though it doesn’t completely delink household energy costs from the market, it does attempt to mitigate it to reduce the burden.

So is it that crazy to imagine something similar being required to ensure AI compute is shared fairly too?

Left behind?

So that’s my wild speculation on how access to compute could conceivably, in the not-too-distant future, become a social justice issue. Like I say, perhaps it won’t happen – perhaps one of the other futures will emerge, and availability of compute will be so commoditised this simply doesn’t become an issue.10

But if this is what the future looks like, and AI does live up to the hype, if you care about social justice and poor people, then this is something everyone – particularly those on the left of politics – is going to have to think about.

And so far, I don’t see much thinking along these lines. Much of the left-of-centre response to AI so far has been either outright denial of its capabilities11 or has been stuck in the intellectual dead end of “do we even want this?”, as though the LLM genie can somehow be stuffed back into the bottle. Then more credibly, there are more serious leftist critiques that are worth taking seriously, such as the impact AI will have on jobs, or how it will shift power from labour to capital.12

But what has been missing from all of this is any discussion of how to manage AI if the most optimistic expectations are right. We’ve invented this incredible new technology, capable of so many potentially positive things – but how can we ensure that the fruits of artificial intelligence are fairly shared?

I suspect the reason for this blind spot is mostly because, for people on the left, it is faintly embarrassing to talk about such a sci-fi idea, and not to mention politically awkward to contemplate given other ideological commitments. To entertain the idea that compute is a social justice issue is to implicitly concede that the hated Tech Bros will have created something good for the world.13

But I do think issues like this have a habit of seeming like a joke until suddenly they are very much not.

It wasn’t so long ago that you would have sounded mad for worrying about the moderation policies of a social platform for university students – but now those same rules help decide elections. Likewise, the micro-blogging platform where you used to post jokes and memes has helped smash the cordon sanitaire between the mainstream and fascist right. And that funny video app where people post dancing videos and comedy skits? It’s handed the Chinese Communist Party the tools to shape the minds of younger generations across the West.

So if AI does turn out to be as transformative as many people expect it to be, we should start thinking now about how to ensure that access to compute does not further entrench inequality in society, and so that everyone can share the benefits of this new technology.

I’d ask ChatGPT how to get started with this, but I’m all out of tokens.

Subscribe now

Your usual newsletter essay will be arriving later this week, but in the meantime I couldn’t resist sending you my latest creation – a memory challenge to see how many MPs you can remember in five minutes.

It’s surprisingly tricky – I managed 44, and I’m completely brain-poisoned by Westminster politics. The best score I’ve seen so far is 61.

If you want to try it, click here. And don’t forget to share your results on social media for bragging rights/pity.

Anyway, enjoy!

James

On YIMBY Pod this week:

• We try to channel the spirit of Tim Harford and debunk some rather strange numbers in the Guardian’s data centre reporting.

• Then we speak to James Howat from ThinkLabour about how bringing back municipal bond markets to get Britain building again.

James wrote up his Guardian debunking here, and covers two other bad data centres stories too.

Martin’s Substack is here, and his YouTube channel is here.

Don’t forget to tell your friends about the pod – and leave us a nice review on Apple Podcasts!

YIMBY Pod is available on Apple Podcasts, Spotify, YouTube, Substack and hopefully wherever you get your podcasts!

You can find James on Bluesky here, and Martin here.

In 1979, the city of Shenzhen in southern China was little more than a fishing village. Today, however, it is a city of 17 million people, and the centre of the global electronics industry.

Sure, most software we use is written in the United States, and Silicon Valley is where the most important hardware is designed. But most of the actual production lines that turn blueprints, raw materials and components into finished devices are found in Shenzhen. In fact, there’s a good chance the phone or laptop you’re reading this on right now was made there.

If you want to read about this in detail, I strongly recommend the fantastic Apple in China by Patrick McGee. It is, of course, a book about Apple, but it also tells a broader story of how this one Chinese city came to dominate tech manufacturing.

It’s a story into which Apple is tightly woven – the smartphone boom that fuelled the city’s growth is all downstream of Tim Cook’s mastery of supply chains and outsourcing. But it’s also a story of how, by satiating the West’s desire for cheap, reliable manufacturing, China has steadily built up its own domestic giants. This is what enabled China to ascend from the bottom of the ‘value chain’, manufacturing basic widgets, to the top, as a home for extremely high-end manufacturers.

It’s why today China is the best country in the world at everything from drones to humanoid robots to electric cars, and the stereotype of the cheap Chinese knock-off is almost as out-of-date as when people used the same slur against Taiwan and Japan.

But how exactly did this transformation happen?

In one of the most striking passages in the book, McGee talks about how, ironically, despite the globalised nature of modern tech supply chains, agglomeration really is critical to Shenzhen’s success:

Indeed, China sports a level of technical sophistication that multiple experts struggle to even comprehend. The layperson often believes Foxconn could just open a factory in a different country; but the Foxconn hubs in China are surrounded by hundreds of sub-suppliers all ready to compete for the next major order. “For Apple to give that system up is tricky,” says Jay Goldberg, founder of D2D Advisory, a tech consultancy. If Foxconn, for example, needs to install sonic welders-a process to merge different metals or plastics with ultrasonic energy-it can call up any number of firms to run the line and hire the labor.

“There’s all these subcontracted, specialty niche firms, and nowhere else does that exist,” Goldberg says. What China offers, in other words, is not simply labor, but an entire ecosystem of processes developed over more than two decades.

This brings me to Britain – and Europe more broadly.

As much as we are beneficiaries of decades of cheap manufacturing in China, there has been an implicit trade-off. We have lost almost all of our ability to manufacture and build at home – a fact that has been made stark by the war in Ukraine, which was fought at the beginning with almost exclusively Chinese-made consumer drones. Even today, though Ukraine has rapidly developed a sophisticated domestic drone industry, many of the underlying components, like motors, control chips and magnets, are still sourced from China.

This, then, represents a huge strategic vulnerability. Which is why one recurring theme in our politics is the need to reindustrialise Britain, to protect ourselves from the more volatile global situation. And slowly the government is moving in this direction.

Just this week, Andy Burnham visited the motorsport company McLaren to trumpet the firm’s plans to invest in domestic manufacturing – as domestic manufacturing is not just good for high-minded security concerns, it’s good for creating vocational jobs too.

But even though he visited an F1 team, I’m worried that Burnham is not moving fast enough.

Don’t get me wrong – the noises being made are all of the right ones. Politicians from Burnham down clearly recognise the problem that if Russia invades a NATO ally, or if China moves on Taiwan,1 it’s not going to matter all that much that Britain is a global hub for TV production or corporate legal services.

However, I also think that if we take the geopolitical threats seriously, then we need to be more ambitious. Which is why today, I want to sketch out, on the back of a proverbial envelope, something bigger – a real attempt to get Britain manufacturing again.

Because though there is a lot of talk about the opportunity for a ‘knowledge corridor’ in the Oxford-Cambridge arc, which will provide new homes and graduate jobs in the London commuter belt, Europe also needs a powerful manufacturing ecosystem of its own.

And if we know this is something our continent needs, why can’t we build it ourselves? Which is why this week, I’m going to explain why – and how – we should turn East Anglia into Europe’s Shenzhen.

Read more

Hello! It’s time for Odds and Ends, your semi-regular round-up of the most interesting links I’ve seen lately, plus some shorter takes.

This time I’m featuring:

• Some sobering facts about the plan to build near train stations

• The ultimate Treasury Brain case study

• Mark Carney showing Andy Burnham how to communicate

• Burnham acting like a human on camera

• This one weird trick could help Britain build tonnes more infrastructure without spending more money

• The sporting event I’ve found most compelling recently

• Some intriguing data journalism about the final tracks on albums

• And a pre-9/11 blast from the past that feels extremely weird now

Read more

On YIMBY Pod this week:

• The government has discovered a new weapon to fight the British state’s sluggishness – an angry letter.

• And we speak to policy consultant Adam Tyndall about the latest on Heathrow’s third runway, and just how close we are to it actually happening.

James recently wrote about data centres again, picking a fight with the Guardian in the process.

Martin’s Substack is here, and his YouTube channel is here.

The letter we discussed on the show is here.

Don’t forget to tell your friends about the pod – and leave us a nice review on Apple Podcasts!

YIMBY Pod is available on Apple Podcasts, Spotify, YouTube, Substack and hopefully wherever you get your podcasts!

You can find James on Bluesky here, and Martin here.

POD! On YIMBY Pod this week, Martin finally takes the Claude pill and I try to persuade him of the need to build data centres – and we speak to the excellent Jonn Elledge about his new book, on the 31 Inventions That Build Our World! Listen here, or wherever you get your pods.

I’ve fallen into a self-destructive feedback loop.

No, I haven’t started drinking or experimenting with drugs. I’ve been doing something much worse: I’ve been clicking links to news articles on Facebook.

We should all know by now that this is a deeply unwise thing to do, because of how the algorithm works. It doesn’t select what to show you based on what is important or useful to your life, and it has long given up showing us things our friends have posted. Instead, the algorithm optimises for engagement – whatever keeps us clicking and scrolling the longest.

And unfortunately for me, that means a constant slew of articles and comments complaining about proposed data centres, usually published in the Guardian.

So now my morning routine is wake up, pick up my phone, load up Facebook, and find myself raging mad before I’ve even got out of bed.

Though in fairness to me, this isn’t just because I have a deeply dysfunctional relationship with my phone. It’s the Guardian’s fault too, as some of the claims the paper has been making about data centres are not exactly wrong… but they are lacking significant context in a particularly annoying way.

And this is something worth getting a little mad about. I wrote a couple of weeks ago about how we need more data centres for reasons of national security, and if you buy my argument, then we need to be rational about the trade-offs involved in data centre construction.

So this week, I want to focus on three recent Guardian data centre stories, and why it’s actually a little more complicated than they first appear.1

I can only do this sort of mildly unhinged deep-dive with your support, so please consider subscribing (for free) to get more of this sort of thing direct to your inbox. Or if you really value my work, please consider taking out a paid subscription.

Subscribe now

1. Flights of fancy

Let’s start with a story that the Guardian ran last month, which was originally headlined “Proposed London datacentre will have annual carbon footprint of 27,000 flights to New York”.

The piece focuses on the proposed East Havering Data Centre Campus (EHDCC) in North Ockenden, on the outer edges of London, and it claims that:

The 1.2m tonnes of CO₂e the EHDCC expects to emit would amount to 26,795 long-haul flights from London Heathrow to New York’s John F Kennedy international airport, according to the UN’s International Civil Aviation Organization’s carbon calculator.

Unfortunately though, it appears that someone screwed up the calculation – as the story was later revised and it instead puts the estimated emission at the equivalent to 8,480 flights instead, which the Guardian notes in a correction.

However, this isn’t actually my problem with this story. Flight emission calculations are always going to be shaky, as calculators necessarily make a bunch of assumptions that can steer numbers in wildly different directions. For example, the UN calculator linked in the excerpt clearly does a distance calculation, as it asks for departure and arrival airport – but it isn’t concerned about the type of aircraft that actually flies the route, even though that too can make a significant difference.2

But anyway, none of this matters, because what I’m more bothered by is the claim that sits behind that: the idea that the new data centre campus will lead to an additional 1.2m tonnes of CO2 emissions every year, because once you dig into how it was calculated, things start to get extremely weird.

So where did the 1.2m come from? Surprisingly this wasn’t (or doesn’t appear to have been) provided to the Guardian by an anti-data centre activist group, but it’s a number taken from the actual planning documents provided by the actual developer.

So, case closed, right? Well, I don’t think so – and to explain why, we need to do some maths.

Summing up the problem

In total, the completed EHDCC is expected to be a 600MW campus in total. Data centres are measured in terms of energy usage rather than, say, processing power, as ultimately that is what matters more. The chips will be replaced every few years with something a little faster, so what really matters is how much energy is used by the site, which is more fixed.

To start then, if you look at Technical Appendix 14.1 of the planning documents, you’ll discover that the developers started by taking the technical characteristics of a representative 50MW data centre and basically scaled it up. So all of the sums were done for 50MW to start with.

Then we reach the first choice the modellers have made. There’s basically an implicit assumption throughout the calculation that the data centre will be working at 100% capacity 24/7 throughout its lifetime. This is something the government has disputed in response to criticisms of other data centre projects, but I think this assumption is fair enough to make, at least when modelling energy usage. If AI is as transformative as it appears to be, the demand for inference is going to be almost limitless.

So we start with 50MW of usage – but in reality a ‘50MW’ data centre will actually use more than 50MW of energy, as the headline figure refers only to the energy used by the computers crunching the data, and not the supporting infrastructure like air conditioning, lights on the ceiling, or the microwave in the canteen.

So to account for this, in the modelling the 50MW figure has been multiplied by 1.326 – what is known as the “Power Usage Effectiveness” ratio. This multiplier just comes from the reference figures for an equivalent data centre. So really, according to the reference data, a 50MW data centre should expect to use 66.3MW of electricity.

And then we have to multiply it again to turn that into a prediction of annual energy usage. So we multiply 66.3MW by 24 hours a day and 365 days a year, which gives us approximately 581GWh of electricity usage in any given year. Phew!

But we’re not done yet – now we have to work out how all of the energy usage translates to emissions.

The technical appendix says that to work this out, the developers have used a figure of 0.14kg of carbon emissions per kWh, from the government’s Standard Assessment Procedure (SAP 10.2), which is, well, a standard used across the construction industry for estimating emissions.

So next we have to do a bunch more multiplication, to come up with an emissions estimate:3

Now, mathematicians reading will note that the final figure – roughly 1m tonnes when rounded up – is still less than the scary-sounding 1.2m tonnes figure. So what about that last little bit?

Unfortunately there isn’t a clean calculation page in the planning documents, but the developers do say that an “uncertainty factor of 15% has been applied to the carbon intensities”, which would bring us to around 1.12m tonnes per year if this is what has been done here.

Then finally, on top of this there are a few extra components added on to the calculation. The developer’s model also takes into account ‘embodied’ emissions from construction, but on the flip side it also negates some emissions by taking into account the fact the build-out of the campus will be phased and that the full site won’t be up and running until 2043.4

And that’s what gets us to approximately the average figure of 1.2m tonnes per year figure published by the Guardian. Phew.

Insane assumptions

Now, this sounds super comprehensive, right? I’ve described an incredibly detailed model of the emissions, running to dozens and dozens of pages in the planning documents. It certainly has the appearance of being thorough and thoughtful.

But did you spot the deeply strange assumption that has been baked into this calculation?

That’s right, it’s that 0.14kg of carbon dioxide per kWh. For the sake of the calculation it is treated as immutable, even though it really shouldn’t be.

And the technical appendix inadvertently makes it clear why this, in my view, is a massive flaw in the maths. It says that:

“Future Energy Scenarios which predict future decarbonisation of the electricity grid have not been applied.”

In other words, this entire carbon calculation is based on the idea that over the next six decades Britain will not decarbonise any further than it has now. Which would obviously be an absolutely mental assumption to make.

If the 0.14kg stays constant, it assumes that Britain will not reduce the carbon load on the grid by building any more wind farms or solar panels. It also assumes that two major nuclear plants, Hinkley Point C – and Sizewell C – will not come online, even though the former is half complete. It seems to assume that we will not increase the proportion of renewable energy on the grid from now until I am 99 years old in the year 2086.5

In other words, what’s happened is that for some reason the developers have deliberately modelled what they call in the documents a “reasonable worst-case scenario”, and the Guardian has run with it, without mentioning this worst-case caveat, even though the odds of it actually happening are basically zero.

So what will EHDCC’s emissions be instead? It depends on how aggressively we decarbonise the grid – but I’m very confident that it will be significantly less than the worst case scenario, especially if the government continues to achieve its decarbonisation goals and (more importantly) the costs of solar and battery storage continue to dramatically fall.6

Now, I don’t blame the developers for doing the calculation in this way. Presumably they had no choice7 – otherwise you’d expect developers to pick the most optimistic numbers possible.

But once you step back and take the time to think about it, as the Guardian probably should have, it strains credulity. And to pretend otherwise is not going to help readers form an accurate picture of the world.

Subscribe now

2. Water use

Now let’s turn to a story the Guardian published in July, titled “Not enough water for UK’s datacentre plans, trade body says”.

Water UK, the trade body that represents water companies in Britain and Northern Ireland, said in a publicly available written briefing to MPs that the government’s water forecasts were “fatally flawed” and “explicitly exclude” datacentres.

It wrote that the government “makes not a single mention of water” in its policy for delivering AI growth zones, its AI for science strategy or its AI opportunities action plan.

There are plenty of legitimate reasons to be sceptical or even mad about the AI buildout. The implications of the technology are uncertain, and despite the nonsense in the first section, it is true that AI data centres use a non-trivial amount of electricity in their operations, which we need to plan for.

But there’s also a lot of bullshit too. Water usage for cooling data centres is one such example – a fear that has been consistently debunked by people like Andy Masley.

So that’s what makes this story so interesting – it’s the water industry itself seemingly, er, wading in, with its concerns about building more data centres. Perhaps it suggests there is something to worry about after all?

However, I think what is missing here is some pretty vital context, which should change our perception of just how big a problem this is.

But before we get to that, what do we know about AI water usage specifically? As Water UK notes in the briefing for MPs linked above, there are two factors that can dramatically change how much water a data centre uses.

First, whether or not the building uses an ‘open-loop’ or ‘closed-loop’ cooling system matters. An open-loop system ingests water, cools the servers and then the heated water evaporates in a cooling tower back into the atmosphere. This means it requires a constant supply of new, fresh water to operate.

Closed-loop systems, however, recycle the water they use. The water is heated by the computer equipment, but it is retained and cooled so that the same water can be used over and over again. This is obviously a much better way of doing it, as fresh water is only occasionally used to top up the system. The data isn’t great, but it appears that most UK data centres are thought to use this sort of cooling system – or have no water cooling system at all.

Then we get to the second reality of data centres in the UK, which Water UK also notes in its briefing. Water usage is highly dependent on climate. A data centre built in a desert will require far more water cooling than a data centre built in a rainy and grey Scotland. And in fact, this means that in Britain, water cooling systems simply won’t be switched on for much of the year anyway. According to TechUK, 64% of surveyed data centres use less than 10,000m³ every year, which the organisation says is less than a typical leisure centre.

Though to be clear, this doesn’t mean there isn’t a small, legitimate challenge here. According to Water UK, the problem is that even if demand for water cooling only kicks in on hot days, that’s exactly the same time when demand peaks more broadly, as everyone wants to keep cool by consuming water at the same time. So more data centres will conceivably exacerbate the peaks.

So what’s missing? Why am I complaining? Doesn’t this all seem pretty reasonable to report? On one level, perhaps – but it also has a severe lack of context.

I really recommend reading this excellent piece by Rowland Manthorpe,8 who has been investigating water usage in British data centres for some time.

In his piece, he looks at two scary-sounding stats on water usage – one of a Scottish data centre that reportedly uses 27m litres per year, and an estimate published by The Times that data centres in the Thames Water region use 10bn litres per year. That sounds dramatic, but it works out as just 0.005% and 0.2% of the water supply respectively.

So even if we multiplied the number of data centres and the water they were using many times over, it would still be a microscopic proportion of overall water usage.

And this is ultimately the problem with the Guardian piece too: it doesn’t mention the denominator. To go back to Water UK’s briefing, it says there are 14 billion litres of water supplied every day and data centres currently account for 6.6 million of those litres – so around 0.047% of Britain’s daily water usage. So this is all to say that ultimately, compared to everything else, data centre water usage is basically a drop in the ocean.9

3. What the FOI?

The final story I want to look at is the most recent – as it’s very, very silly.

“UK government does not know what datacentres are being used for, FoI request shows”, reads the Guardian headline, and the story sounds pretty damning:

When asked in a freedom of information (FoI) request who was using the largest sites and what for, the Department for Science, Innovation and Technology said it did “not hold the requested information”.

Sounds bad, right? But the reality is that this appears to be hilariously tenuous.

The story begins over a year ago, on the 25th August 2025, when Oliver Hayes from a charity called Global Action Plan submitted the following FOI request to the Department of Science, Innovation and Technology (DSIT):

Dear Department for Science, Innovation and Technology,

Please confirm whether you hold information on the companies, organisations or bodies currently using the UK’s largest (more than 15MW of rated IT capacity) data centres and the purposes for which they are used.

If this information is held, please provide a list of these businesses and purposes. If it is not possible to provide details on individual companies, please try to provide a breakdown of the types of companies, organisations or bodies accessing the UK’s data centres and the proportion of the UK’s data centre capacity that these types of industry currently utilise.

Thank you in advance for providing this information,
Oliver Hayes on behalf of Global Action Plan

The department then responded on the 12th September last year, confirming that it does not hold the requested information.

And what you might not realise unless you’re the sort of weirdo who sends FOIs regularly is that this is actually a pretty narrow response.

The way the Freedom of Information Act works is that anyone can request information from a public body or government department – but only if a document containing that information already exists.

In other words, all this response is really saying is that there is no document that exists in the department which contains a list of data centres in a table alongside the businesses that use them and for what purpose.

So it’s less of a damning response, and arguably more Hayes encountering a deliberate friction hidden within the law. Yes, information is free, but you have to know what you’re looking for first.10

And as much as it pains me to say, as someone who regularly sends FOI requests, this makes a lot of sense. If any random member of the public could write in and essentially task civil servants with a homework assignment to put together a new document for them, the business of government would quickly become unworkable.

But anyway, let’s be more generous and assume that DSIT doesn’t actually hold the information at all, in any form. My next question is… so what? I can’t quite work out what response Hayes was expecting it to reveal or why it would be particularly useful for the government to know that, say, Netflix is using capacity in one of Amazon’s data centres, or OpenAI is using capacity in one of Microsoft’s.

So I think what this story really is more than anything is just a spectacular PR success on the part of Global Action Plan. With an extremely thin justification – an FOI with a boilerplate non-response, sent over a year ago – they’ve managed to get their talking points published as a big story in a major news outlet, on the strength of basically nothing. Bravo!

Facts x Importance = News

So there are three stories about data centres and why they make me mad. Like I say above, none of them are false, exactly. But in each case, scary-sounding numbers and facts become significantly less scary once you add a little context – and the reason I’ve spent several thousand words banging on about this is because I think this really matters.

As has become clear from the coverage, data centres are a new, salient dividing line in politics, and are fuel for the next round of the culture wars. If we’re going to navigate the build-out successfully, we’re going to need to be working from a common set of facts about the world – and we should want news coverage to clearly explain the real trade-offs involved in both building and not building.

And I should probably spend less time on Facebook getting mad.

Phew, another long one this week. If you enjoyed reading this then make sure you subscribe (for free) to get more like this in your inbox. Or take out a paid subscription if you really value my work. And if you’re from the Guardian, please don’t break my legs.

Subscribe now

Follow me on Bluesky

On YIMBY Pod this week:

• We talk about the security implications of the latest AI advances – and how the British government should respond to threat. And more importantly, James claims victory in his argument with Martin over AI.

• Then we speak to the brilliant Jonn Elledge about his new book, 31 Inventions that Built Our World.

James recently wrote about the national security case for building data centres, and how to fix Top Gear.

Martin’s Substack is here, and his YouTube channel is here.

Don’t forget to tell your friends about the pod – and leave us a nice review on Apple Podcasts!

YIMBY Pod is available on Apple Podcasts, Spotify, YouTube, Substack and hopefully wherever you get your podcasts!

You can find James on Bluesky here, and Martin here.

ICYMI: I got so annoyed by bad data centre takes last weekend that I decided to remove the paywall from my recent post on Britain’s looming AI national security crisis. Go and read it if you missed it!

I’ve also written about the slightly less important issue of how I picked a fight with Jeremy Clarkson (which he then went on to win).

POD! On YIMBY Pod this week, Martin and I talk about a Cambridge new-build estate where it is literally illegal to recycle rainwater – and we speak to Sunny Hundal about his new campaign to make energy both clean and cheap. Listen here, or wherever you get your pods.

Hello! It’s time for Odds and Ends, your semi-irregular round-up of the most interesting links I’ve seen recently, plus some shorter takes.

This time I’m featuring:

• How the UK consensus on Net Zero has broken down

• Why globalisation can’t be stopped

• The case for municipal bond markets

• Andy Burnham not being serious about Freeview switch-off

• A tiny sign the Democrats are taking winning seriously

• A great TV drama about feminism in the 1970s

• Can AI reason itself to constitutional government?

• An amazing four hour podcast about the history of electric grids

• And a sign the future could entail a whole new season of Star Trek: The Next Generation (!).

Now let’s get started!

Read more

BIG NEWS! I got so annoyed by bad data centre takes last weekend that I decided to remove the paywall from my recent post on Britain’s looming AI national security crisis. Go and read it if you missed it!

POD! On YIMBY Pod this week, Martin and I talk about a Cambridge new-build estate where it is literally illegal to recycle rainwater – and we speak to Sunny Hundal about his new campaign to make energy both clean and cheap. Listen here, or wherever you get your pods.

Jeremy Clarkson once brutally humiliated me.

Read more

On YIMBY Pod this week:

• It turns out it is literally illegal to recycle rainwater and use it to flush your toilet – and this is causing problems in Cambridge.

• And we speak to Sunny Hundal about his new campaign to make energy both cheap and clean at the same time!

The Guardian reporting on Cambridge’s grey water problem is here.

The Cheap Clean Energy Alliance is here.

This week James wrote about the national security case for building data centres.

Martin’s piece on ‘picky bits’ and national decline is here, and his YouTube channel is here.

Don’t forget to tell your friends about the pod – and leave us a nice review on Apple Podcasts!

YIMBY Pod is available on Apple Podcasts, Spotify, YouTube, Substack and hopefully wherever you get your podcasts!

You can find James on Bluesky here, and Martin here.

POD! On YIMBY Pod this week, we talk about the government’s latest efforts to make it easier to build near train stations and densify our cities. Then we speak to Nick Maini about why Hammersmith Bridge has been closed to public transport for six years – and how autonomous pods could fix it. Listen here, or wherever you get your pods.

I swear I don’t mean to keep writing about AI, but it’s the biggest story in the world, and I think this is particularly important…

Every so often, our lives are punctuated by shocking events that change everything.

9/11 is an obvious example. I was 14 when it happened, and though I didn’t fully understand it at the time, I still remember having the visceral feeling in the pit of my stomach that nothing was ever going to be quite the same again.

I felt something similar a few weeks ago. I was watching two OpenAI employees give a presentation at the DefCon security conference, detailing what happened in the now-infamous cyberattack that OpenAI’s AI models autonomously carried out against Hugging Face, another AI company.

In case you missed it, the technical details are complicated but the story is simple. An AI model was being evaluated internally. It had essentially been tasked with an assignment to solve a particular digital security puzzle. But due to an error on the part of the testers, the AI was unable to access some of the documents it was supposed to use to find the solution.1

However, instead of giving up and declaring the task not possible, something extraordinary happened. Over a period of a couple of months, the AI explored alternative solutions to the problem it had been tasked with, and essentially discovered that it could create a 'message board’ where it could interact with other AI models inside the company. Then astonishingly, unbeknownst to any human, the AIs figured out how to work together to not just break containment and access the internet – but they reasoned to themselves that the best way to solve their problem was to hack Hugging Face. Which they did successfully.2

I’m assuming that after this, the examiners were not too worried about the examination, as this whole incident was an absolutely spectacular demonstration of just how sophisticated AI has become in such a short space of time. Give a sufficiently powerful model a task and it will pursue its goal relentlessly until it finds a solution.

This brings me to Britain.

Though we are only a middle power, and the ‘frontier’ models like ChatGPT and Claude are being developed elsewhere, we have so far carved out a rather successful niche in the AI present. The AI Security Institute (AISI) was created by Rishi Sunak, and in less than three years has proven itself seriously credible in the eyes of both the tech industry and foreign governments.

AISI has assembled a team of world-leading experts who, when new AI models are created, can credibly evaluate the threats they may pose. When Claude Mythos, a powerful new model, was created by Anthropic earlier this year, AISI was granted early access to test the model – and it published a warning about its capabilities, which was taken very seriously.

However, as effective as this new institution is, following the Hugging Face incident I’m not convinced it is enough if we want to protect Britain and our allies from AI threats in the future. 9/11 was a clear indication that global peace and stability were about to be upended, and Hugging Face is the same. And unless Britain acts now, it won’t be ready for the threat that is rapidly emerging.

So this week, please allow me to explain the emerging problem – and what we can do about it.

I’ve got so annoyed about the discourse on this, I’ve now removed the paywall on this post. So if you find it useful or informative, please consider subscribing.

Subscribe now

The new security threat

As Ben Thompson has observed, the next few years are going to be a security nightmare.

However optimistic I may be about the potential good things that AI will make possible, there is going to be an awkward period of adjustment as sophisticated AI models pick over the code that humans have written over the last half-century. AI will reveal bugs and vulnerabilities at an unprecedented scale – in software that we use every day, and in code that underlies the infrastructure of the modern world.

This isn’t because the code is bad, per se. It is because writing code is inherently an imperfect process. An operating system like Windows or MacOS, for example, is the combined work of tens of thousands of people. It is so bewilderingly vast and complicated, that it simply isn’t possible to ensure that it is impervious to threats.

Think of it like an airport. We can add layers of checkpoints, bag searches and x-ray machines to reduce the security risks, but it is impossible to make such a complicated building completely safe.

So since the dawn of computing, cybersecurity has been a permanent fact of life. The bad guys find vulnerabilities, and the good guys update the code to make it safe again. This is a never-ending process, and it is the reason why with annoying regularity Windows will insist on installing updates when you power down, or MacOS will randomly reboot to update.

For a long time, the game of cat and mouse has just about held – but now AI is getting involved in finding vulnerabilities. And we’ve already seen what that looks like – it’s overwhelming.

When Claude created Mythos, it let the makers of some of the world’s most critical software use the new model to look for vulnerabilities in their own code and the results were striking. Cloudflare, which provides important internet infrastructure, found over 2,000 bugs (including 400 critical security issues), and Mozilla, which makes the Firefox web browser, found 271. And in addition, over 3,900 high or critical security vulnerabilities were found in an analysis of various open source projects.

And it is not just Mythos that is the problem. Earlier this summer, OpenAI used its latest model, GPT-5.6 to find numerous bugs in apps like Chrome and Safari, as well as in the kernels of Linux and FreeBSD3 – two operating systems that run the vast majority of the server infrastructure across the internet.

Pandora’s box

Perhaps you can see the problem. We’ve now got highly sophisticated AI tools that can detect vulnerabilities at unprecedented scale, far faster than they can be patched by traditional methods – and they will relentlessly pursue the goals they have been given. This is not a hypothetical future – this is the world we live in today. And right now is the worst they will ever be.

In the early days of the post-ChatGPT AI explosion, some serious people talked about how governments could set rules to slow or regulate AI progress. This was basically one of the reasons the AI Security Institute was created in the first place – to give the government a role in deploying this potentially dangerous new technology.

But now it is clear that the brakes cannot be applied.

If we lived in a world where OpenAI and Anthropic were the only companies that had this technology, it might conceivably be possible. But unlike other dangers like, say, nuclear or biological weapons, we can’t control AI models with treaties or restrictions on materials. The technology runs on commodity computer hardware and the models are ultimately just large text files full of numbers.

And even if we could, somehow, control these things, then there’s the problem that other countries are working on AI too, including most notably, China. And this makes clear why we now face an unprecedented security threat.

So far, China’s AI progress has lagged the US, but it has consistently only been behind by months. When it was first released last year, DeepSeek caused a minor freakout as it was almost as sophisticated as the best ChatGPT model available at the time, and shockingly it ran much more efficiently. Then more recently, another Chinese model, Kimi K3 has done the same. Though comparing AI models like-for-like is difficult, on some benchmarks it is only narrowly trailing the capabilities of GPT-5.6 Sol and Claude Fable.

This is to say that the genie on this technology is already out of the bottle.

Perhaps if Chinese companies had pursued an approach to development similar to OpenAI and Anthropic, it might be possible to just about imagine a modern equivalent to an arms control treaty between the major powers. Maybe.

But the problem with this though is that China is pursuing a completely different AI strategy, and its government and businesses have wildly different incentives.

As China is currently trailing US capability, most Chinese models are open source and/or open weight – meaning the raw code and data used to create the models have been published freely for anyone to use.4

Why would Chinese companies do this, given that Anthropic and OpenAI have both received astronomical valuations in part by keeping their models closed? Isn’t that the ‘secret sauce’ that makes the companies so valuable? The difference is that because China is trailing on raw capability, it has decided to compete instead in the ecosystem around the models. (This is a strategy that has come all the way from the top, with Xi Jinping himself calling for this.)

So the hope is that people and companies will choose to use Chinese models because open models can work out cheaper to run. Openness also creates an incentive across the companies that use them to work out how to run them even more efficiently, reducing the need for cutting-edge Nvidia AI chips, which are subject to export controls that have been imposed on China by the West.

And though this is great from an AI-ecosystem perspective, arguably for everyone in the world except Anthropic and OpenAI, openness does also have one difficult geopolitical consequence: As anyone can use them, it really does mean that anyone can use them.

That could mean that rival powers like China and Russia, rogue nations like Iran and North Korea, or hackers and terrorist groups. They can take these open models and do with them, well, whatever they like. Unlike closed models, where usage runs through OpenAI and Anthropic’s servers, if you have enough compute capability you can run these models without restrictions on nefarious uses.5

So this is why I think we’re hurtling towards an AI security crisis. Though the open-weight models are still not as capable as GPT-5.6 Sol, or Claude Fable, if history is any guide they are not far behind. And when they catch up, there will be no effective limitations on their use – we will have to assume that the worst possible person or group, with the worst intentions, will make use of these models too.

And that’s why we need to prepare for that world now.

A war of resources

Let’s go back to Britain.

Choose your own ominous metaphor: It’s 1938 and Germany is rapidly rearming. It’s 2001 and a CIA memo is warning that Bin Laden is determined to strike the United States. It’s 2013 and Vince Cable is signing the paperwork to sell off the Postcode Address File. It’s 2020 and there are reports of a strange virus in Wuhan.

In the not-too-distant future, it is incredibly likely that we will be living in a world where sophisticated AI models, which cannot be controlled or regulated out of existence, can be deployed to relentlessly and autonomously assault every digital system in our lives by actors unknown.6

How does global power work in this world? And how can Britain protect itself?

In terms of the mechanics of defending, the old paradigms of cybersecurity are dead. Any human-led efforts to patch and update software will simply move too slowly. They will not be able to keep up with the AI assault.

So the weapon we will need to use to defend ourselves is, well, AI. Just as AI can find vulnerabilities at speed, it can also conceivably patch them just as quickly too.

In other words, the only way to stop a bad guy with an AI, is a good guy with an AI.

If you’re a traditional IT security person reading, this might sound like a heresy. What I’m arguing here is that it simply won’t be possible to have a human in the loop, reviewing code before it is patched in. We will need to defer authority to defensive AI systems to update critical systems to defend them. There will be no other way to do this.

And the consequence of this fact is something quite profound, I think, as it essentially reshapes global power.

Power has always been a function of hardware. The number of tanks, missiles and (increasingly) drones that a country can deploy defines its ability to defend itself.

But in a post-Hugging Face world, the hardware we need has changed again. Simply put, power in the new world will be a function of how much compute a nation has at its disposal. If every digital system is under constant attack by autonomous AI agents, we will need to have the capability to stand up to such an assault.

And, sadly, I’m just not convinced that Britain is appropriately readying itself for the battle ahead.

Protecting British interests

So how is the British government supposed to respond to the new reality?

If we want to put ourselves in a strong strategic position, there are essentially three core interventions we could make in the AI ‘stack’: on models, compute, and energy.

On models, if we had our own frontier models, it would put us in a strong position. Our AI could literally outsmart the other AI. This is how we get away with having relatively few soldiers – we rely on our missiles and our planes being better than those of our enemies.

But sadly, in the case of AI we do not have our own frontier models – and developing one would be extremely difficult.

And it’s worth dwelling on this to understand exactly how difficult it is. We’re not just one extra commitment casually thrown out by Andy Burnham away from making it happen, as catching up with the best American and Chinese models would be almost impossibly expensive.

For example, Meta – which is desperate to catch up with OpenAI and Anthropic – has reportedly already sunk over £100bn this year alone into its ‘superintelligence’ lab, and it still appears to be nowhere close to developing a similarly top-tier model. And for context, that’s equivalent to around half of the NHS’s annual operating budget. So in terms of public spending, building our own model is a non-starter for obvious political reasons.

Instead, our only option model-wise is to hope that, despite the Trump of it all, the United States continues to let Britain access the most sophisticated models. This is why having AISI is such a useful bargaining chip as America likes its credibility too.

However, assuming we have a sophisticated model, we need more than this too – we need the physical hardware to run it on. And this is where I do think we can do something useful.

If power is a function of compute, then we can simply build more data centres, and generate more energy with which to power those data centres. The more AI tokens we can crunch, the stronger our defences will be against the threats to come.

And to an extent, this is something the government is already working on. There’s no indication that Andy Burnham intends to change the AI Growth Zones plan, and there’s still a pipeline of renewables coming online (along with Sizewell C and Hinkley Point C). So in due course, we’ll certainly have more data centres and energy generation to power them.

But my concern is that given the new security paradigm I describe above, this still isn’t fast or large enough.

For a start, there’s a huge misalignment in terms of timelines – data centres and power plants currently take years to build, and yet AI progress, and the lag between the frontier and Chinese open source models can be measured in months. It will be surprising if China doesn’t have a model as powerful as GPT-5.6 or Mythos by Christmas.

And this wouldn’t be so bad if AI was just an economic nice-to-have. If we don’t have the latest technology the fastest, it merely means sluggish economic growth and a loss of competitiveness to our peers.7

But viewed from a national security perspective? Suddenly that capability gap feels a lot more urgent.

However, there is one reason for optimism here. As urgent as the need to build our AI infrastructure is, it need not be costly to the public purse. Unlike building a frontier model, which would require enormous upfront capital spending, data centres and new sources of electricity are economically useful in their own right. They can both help the economy and act in a dual-use capacity for our cyber-defences, should we require it.

So this means that new data centres can pay for themselves. In fact, given the AI boom, the private sector is pretty desperate to build out capacity. All the government needs to do is, well, let them build.

And this is why, finally, I want to end by proposing a policy that is simultaneously deeply important if we want to protect Britain’s national security, while also perhaps the single most electorally unpopular idea anyone has ever proposed.

I’m proposing that the government needs to create ‘Nightingale’ data centres.

These are, of course, named with reference to the conference centres full of hospital beds that were spun up in the early days of the pandemic. What’s remarkable wasn’t that they were not actually all that useful (we didn’t know that at the time) – what matters is that we built them quickly.8

AI data centres are more technically demanding than a room with hospital beds. And I’m not proposing we start setting up server racks in the ExCeL Centre. But we should take that same urgent spirit and apply it to the data centres we’ll need in a new world that has arguably already emerged.

At a minimum, plans for data centres and energy infrastructure should be rapidly called-in by the government and even more rapidly approved. Process should be vastly curtailed, and every official should be driven by the need for rapid delivery. Like during Covid, we correctly decided that now wasn’t the time for layers of consultation and review – the need for compute should be approached the same way.

I still believe that, ultimately, AI will do incredible things to improve our lives. But the Hugging Face incident was a warning, like the Bin Laden memo sent a few weeks before 9/11, and we should act now to avoid the inevitable security crisis once the Chinese models catch up.

Subscribe now

On YIMBY Pod this week:

• The government’s latest planning rules are making building near train stations easier – hurrah!

• And we speak to Nick Maini about his proposal to build a new autonomous transport system to cross the beleaguered Hammersmith Bridge.

The Sam Dumitriu post on the NPPF we talk about is here.

Barnes Hammersmith Electric Light Transit are here.

This week James wrote about principles and the death of Jason Arday.

Martin’s piece on ‘picky bits’ and national decline is here, and his YouTube channel is here.

Don’t forget to tell your friends about the pod – and leave us a nice review on Apple Podcasts!

YIMBY Pod is available on Apple Podcasts, Spotify, YouTube, Substack and hopefully wherever you get your podcasts!

You can find James on Bluesky here, and Martin here.

POD! On YIMBY Pod this week, Martin and I tried to sort fact from fiction in the case of Dobby’s grave, and we speak to London Assembly member Neil Garratt about his fantastic London travel times map, and his work figuring out why building infrastructure is so damn expensive. Listen here, or wherever you get your pods.

ESSAY: In case you missed it, my essay this week was offering some thoughts following the death of Jason Arday. Read it here.

Hello! It’s time for Odds and Ends, your regular round-up of the most interesting links I’ve seen lately, plus some shorter takes.

This time, I’m featuring:

• Why we’re going to get lots of new flats near train stations (hopefully)

• A new way to think about big, heavy industry

• How to have more safe, clinical innovation

• Can you help save a library under threat?

• Has there just been a ‘ChatGPT moment’ for robotics?

• How I predicted Amazon’s latest AI training trick

• Why Doomerism is bad

• And did Armando Iannucci predict the future 20 years ago?

Now let’s get started!

Read more

This is a tragedy

It goes without saying that the death of Jason Arday is a tragedy. Though an official cause has not been revealed, the timing and unexpected nature of his death strongly suggest that he took his own life. And whatever problems there were with his scholarship or the plausibility of his story, do not make his death any less awful.

The journalistic scrutiny of his academic record and background were justified

Many commentators on social media, such as Jolyon Maugham, have already called for a public inquiry following Arday’s death. Others have advocated reigniting the “Leveson 2” inquiry – a planned, but long-shelved follow-up to the 2011 Leveson Inquiry following the phone hacking scandal. The clear implication is that an inquiry would eventually lead to stronger regulation of the press.

This would be absolutely the wrong response.

The problem is that the journalistic scrutiny that Arday received was broadly justified. He was not a random member of the public, unfairly picked out of obscurity for a punishment beating. He was a senior academic at the University of Cambridge, the author of a high-profile memoir, and a public figure who had been building his profile with numerous media appearances over the years.

Given this, no wonder the Times Higher Education (THE) originally sought to investigate claims of plagiarism. And no wonder the wider press took an interest when a high-status public figure was revealed to be a fantasist with a wildly implausible backstory.

And though his death is tragic, it does not retroactively change these underlying facts.

An uncomfortable source does not render true information invalid

What made the Arday story initially controversial was that the story of his alleged plagiarism was originally published not by THE, which had been investigating, but by Nathan Cofnas, a former academic. Cofnas describes himself as a ‘race-realist’, and he once argued that without ‘DEI’ (Diversity, Equity and Inclusion) hiring policies, there wouldn’t be any black academics.

Whatever motivated Cofnas to investigate Arday’s work (I suspect it isn’t hard to guess), the evidence of plagiarism he revealed was seemingly real, or at least worthy of further investigation.

And this is a very awkward situation to be in, because what is the right thing to do when important, real information comes to light via a source we don’t like?

The closest analogue I can think of to the current situation is the Russian hack of various Democrats’ email accounts in 2016, including those of the Hillary Clinton campaign and the personal email of her campaign manager – which were subsequently released online just before the election.

In that case, the hack didn’t turn up any significant revelations beyond John Podesta’s risotto technique, and the contents were mostly just embarrassing. But imagine if the hack had exposed some genuinely noteworthy scandal that could change the election? Would it have been right to report it?

As frustrating and unfair as it is process-wise, I think that it probably would be. And I think the same is true in the Arday case – just because I don’t like the guy who alleged the plagiarism, it doesn’t make his reporting untrue.

The difficult proportionality question

This brings me to what makes the Arday story particularly complicated, because racism is clearly an important part of explaining why the story captured so much attention. As Stephen Bush wrote yesterday, there was plenty of coverage of Arday that was not just about a ‘professor accused of plagiarism’, but a ‘black professor accused of plagiarism’.

So even though I think the scrutiny of Arday was legitimate journalism, I think it is also true that he was subjected to disproportionate attention because of the colour of his skin, or what he represented in the eyes of racists.

As has been pointed out by Stephen and others, this phenomenon is perhaps best illustrated by comparison with Professor William O’Reilly, a Cambridge Professor who faced plagiarism allegations in 2023, which didn’t become a massive story.

Given this, I can totally understand why black academics, or academics from other minority backgrounds, may feel they are on the receiving end of wildly disproportionate scrutiny, relative to their white colleagues. There’s definitely a question of proportionality here.

To use another analogy with American politics, it’s a bit like how Trump has attempted to persecute his political opponents by investigating them for mortgage fraud, which has turned up real cases, by virtue of applying disproportionate attention to a specific group of people.

So this is arguably part of what happened to Arday, and more generally, I can imagine how this same problem could conceivably be weaponised against other groups too.

For example, imagine if controversial right-wing professors were to receive intense, disproportionate scrutiny from lefty activists? Would it be wrong to report on claims that they used ChatGPT to write their book?

Or to use the single most contentious hypothetical, what if pro-Israel activists carefully picked over the work of academics who are vocally on the Palestinian side of that conflict, or vice versa? What happens if real misconduct is found along the way?

However, here’s the difficult part. Though I can certainly recognise the proportionality problem as a Bad Thing, I’m not sure what we’re supposed to do about it. Like, though many people have raised the issue in the wake of Arday’s death, I’m not sure what is supposed to be done about it. What rules could be written, or norms could be developed to mitigate it? What do people actually want to happen?

If the media is to blame, so is the public

According to an organisation called NewsCord, 249 stories were published about Arday in the 22 days leading up to his death, which is supposedly more than there were about Peter Mandelson in the weeks following the publication of the Epstein files.

I’m not sure how real those numbers are,1 but what’s clear is that there were certainly a lot of Arday stories in the British press, and it isn’t hard to imagine what toll that level of negative attention can take on someone’s mental health.

But why were so many stories written? The answer isn’t just cartoon villain newspaper editors with a racist axe to grind – it’s because stories about Arday were clearly drawing significant audience attention. The news media is not a profitable business at the best of times, so when a story hits, there’s a strong incentive to cover it more.

So this is what frustrates me about the knee-jerk attacks on the media coverage. It’s not just the journalists – to a significant extent they are serving audience demands for more. The public are complicit for clicking, reading, watching and talking about it.2

Plagiarism isn’t what made the story viral

What makes the Arday story difficult to untangle is that there were essentially two different stories compounded into one.

The first was a relatively narrow story about academic misconduct and plagiarism allegations. This is where the comparison with William O’Reilly broadly holds.

But I do not think this is why the story captured so much public attention. What really propelled the story was the unravelling of the non-academic parts of Arday’s life, and the many fantastical aspects of his backstory – because the sheer volume of mad claims was objectively funny.

I made this observation myself before his death, rather unfortunately writing last week that I found the story “tremendously entertaining”.

He was a guy in a high-status position, who variously claimed to have been non-verbal until he was 11, who claimed to have suffered and recovered from numerous serious illnesses, who claimed to have run multiple back-to-back marathons to an extent that would make him one of the world’s greatest runners, and who claimed to have played football to a semi-professional level. He also claimed to have raised £5m for charity, but that his co-fundraisers had signed a non-disclosure agreement, and that a severed pig’s head was sent to his family.

This is self-evidently ludicrous – and over the last few weeks, every new story that emerged revealed that he made even more wildly implausible claims about his own life.

So this is why I don’t think citing simply racism and drawing a sober comparison with other alleged academic plagiarists fully explains the story. Though racism clearly played a part, he was still a fantasist in a high-profile role. A better model to understand why the Arday story ‘cut through’ is perhaps previous similarly bizarre stories, like ‘canoe man’ John Darwin, ‘Cat Bin Woman’, or even an incident like Dominic Cummings’s trip to Barnard Castle, where more than any technical political story about Covid policy, in that case the public could smell bullshit and it came wrapped up in a compelling story.

The organisations and institutions that elevated Arday have some difficult questions to answer

It is, of course, deeply unwise to speculate why Jason Arday appears to have taken his own life. You never know fully what else is going on inside someone’s head.

But what is clearer is that the Arday story would not have blown up if he hadn’t been failed by multiple institutions over the last few years. And these institutions should reflect on their role in elevating him.

For example, why did the University of Cambridge hire someone whose backstory is almost self-evidently absurd? Did nobody ask about the marathons during the interview?

And according to Retraction Watch, concerns about his scholarship were first raised in May 2023, two months after he was first appointed. Why wasn’t more done to scrutinise or investigate the allegations then? If Arday had lost his job back then, the story would have quickly disappeared.

Then most recently, why didn’t the publishers of his new memoir raise any questions about the wildly implausible claims he makes in the book? This was, after all, supposed to be a work of non-fiction. If there had been any scrutiny of his manuscript before publication, the book would never have been published. Given this follows not long after the Salt Path controversy, doesn’t this suggest the publishing industry needs to get better at vetting and fact-checking the authors it publishes, even if they have a great story to tell?

I also think the Good Law Project should reflect on its role in the controversy. It was quick to defend Arday after the initial coverage, launching a petition calling for people to ‘stand in solidarity’ with Arday, which was co-signed by a number of prominent people. But in effect, it was making a similar error to Cambridge – of elevating Arday. It turned Arday from an academic with questions over his record into a political fight over what he represented. Which, of course, added more fuel to the fire and drove more attention.

The ‘DEI’ angle deserves interrogation

One of the reasons Arday was seized on by the right is not just because he was black. It’s also because of how his appointment has been linked to ‘DEI’ – an Americanism that stands for ‘Diversity, Equity and Inclusion’.

Had Arday, the implication was, been elevated by Cambridge not on the merits of his scholarship, but in an act of positive discrimination? If this is the case, the fact that Arday turned out to be a fantasist and alleged plagiarist would be a serious mark against ‘DEI’3 hiring policies.

Now, to be clear, I’m extremely sympathetic to institutions proactively supporting greater diversity. We should want our universities – and our politicians, publishers and Hollywood adaptations of Ancient Greek myths – to better reflect the modern world, and we should want shared institutions in which everyone feels like they have a stake.

But it appears that good intentions may have gone badly wrong in the case of Arday, as clearly there is still a balance to be struck between someone’s personal characteristics and their actual ability to do their job.4

As things stand, the extent to which ‘DEI’ weighed on Arday’s appointment still isn’t clear. In fact, before his death, Cambridge announced plans for an independent inquiry into his appointment.

But there is some circumstantial evidence that these motivations may have played a role here. For example, Cambridge has a set of policies designed to increase the diversity of its workforce. Then when Arday was appointed, the university made his background a triumphal part of the announcement, highlighting the challenges he claims to have faced growing up, and the fact that he is the youngest black person ever appointed to a professorship at the university.

And more broadly, his appointment in 2023 was at a time when issues of race and racism were extremely culturally salient, particularly in left/liberal-ish institutions. It was still the post-George Floyd ‘woke’ moment culturally, and there was significant political energy directed towards performative diversity, particularly in academia, politics and the media.

So you can imagine why, given that context, Cambridge may have been persuaded to hire Arday – not just because he was black, but because his particular specialism was “improving the representation of Black, Asian and Minority Ethnic people in higher education”. Then add on a compelling backstory that intersects with other ‘vectors of oppression’, and it adds up to a compelling case – even though there are presumably many more eminently qualified black academics with less exciting backstories, who haven’t plagiarised, who could have been given the job instead.

Nobody knows what principles apply, or should take precedence

If there is a through-line in many of the above thoughts, it is that it is unclear on what principles many of the reactions to Arday were operating, or which principles should take precedence when there is a clash.

I was struck by reading this short reflection on the Arday story by Dmitry Grozoubinski. I agree with his frustration at what he calls the ‘grifter-right’, which uses its megaphone to amplify culture war fodder like the Arday story in bad faith – and at the frustration of trying to operate with principles when your opponents do not.

Dmitry writes:

• If we treat every allegation as credible we create incentives for terrible people to bury us in them, paralysing us with investigations the results of which will be weaponized regardless of outcome as either ‘gotchas’ or ‘conspiracies’.

• If we give a right wing outrage oxygen, the fire behind it grows, often vastly out of proportion to any alleged wrongdoing - and with consequences far beyond the formal.

• Yet if we ignore the hints of smoke and do not ever search for fire, we sacrifice doing what’s right for the sake of the broader culture war and give up our own integrity and that of our institutions.

• I suppose the answer could be that we must exist as Caesar’s wife, above reproach, but the world is vast and full of potential targets, full of humans, and full of sins.

But what’s striking about the above is that you could substitute the word “right” for “left” above, and you’d have an almost word-perfect critique of ‘cancel culture’ that could be levelled at the left over the last decade.

And I think this illustrates exactly why it’s important for good-faith operators to not give up on having principles.

A while back, I wrote about why the reason I’m not a fan of the Good Law Project is that despite what the name implies, it does not appear to be guided by any particular philosophy or doctrine on what is considered ‘good law’. Instead, it uses the law as a tool to pursue somewhat arbitrary political goals.

But the truth is, if we want to make sense of a terrible tragedy like the death of Jason Arday, we need to understand the principles on which we’re working. Only then can we design institutions, norms and rules about what allegations are considered acceptable to scrutinise, or proportionate to cover.

Subscribe now

On YIMBY Pod this week:

• We try to uncover the truth about the electricity interconnection that was supposedly diverted by Dobby’s grave.

• And we speak to London Assembly Member Neil Garratt about his fantastic London travel times map, and his Euston, we have a problem: Mind the funding gap report on why infrastructure is so damn expensive.

The Energy Revolution podcast we talk about is here.

Dan Davies’s post about Dobby’s grave is here. Chris Curtis’s is here.

This week James made the case for why the AI ‘bubble’ narrative might be nonsense.

Martin’s piece on ‘picky bits’ and national decline is here, and his YouTube channel is here.

Don’t forget to tell your friends about the pod – and leave us a nice review on Apple Podcasts!

YIMBY Pod is available on Apple Podcasts, Spotify, YouTube, Substack and hopefully wherever you get your podcasts!

You can find James on Bluesky here, and Martin here.

POD! On YIMBY Pod this week, Martin and I dig into Andy Burnham’s devolution agenda, talking about integrated transport ticketing, making the lines on the map more consistent, and much more. Then we speak to Jamie Airnes from Cornish Lithium about his attempt to turn Cornwall into a lithium superpower. Listen here, or wherever you get your pods!

If you’re a smart opinion-haver on the internet, there’s one position on AI you can hold where you can’t really go wrong.

AI, you tell everyone, isn’t snake-oil, like the loudest detractors claim. The people who cling on to phrases like “fancy autocomplete” and “stochastic parrots” are clearly missing the bigger picture at this point. According to OpenAI, a billion people use ChatGPT at least once a month. AI is clearly doing something useful for them.

However, having conceded this, you’re supposed to turn and caveat your view, pointing out that despite this, it can still be the case that the current AI moment is a bubble. The unprecedented capital investment, which has now reportedly topped $1 trillion since early 2023, will ultimately prove unsustainable.

There are plenty of weird-looking, circular-ish deals that would seem to evidence this too. For example, last September Nvidia pledged to invest up to $100bn in OpenAI – and in return OpenAI pledged to use the cash to buy huge numbers of Nvidia chips. And last November, Microsoft and Nvidia pledged to invest up to $15bn into Anthropic, in exchange for the company behind Claude committing to spend $30bn on Microsoft Azure cloud computing capacity that uses Nvidia chips.

There are other oddities too. In recent days, Nvidia has announced a new plan to work with institutional investors like pension funds to invest in chips as a new asset class.

So I can understand why seeing this, it might feel as though something strange is going on – and that it feels like at some point, the bubble is going to have to burst.

And as a narrative, this makes total sense. After all, this is the exact story of the original dot-com crash. The underlying ‘internet’ technology was far from worthless, but billions of dollars were wiped out nonetheless.

Anyway, this is all to say that I think the bubble hypothesis is a completely reasonable analysis of the current situation. It might even be correct. I am very open to the idea that this wildly hyped new technology might not work out in the short to medium term, as this is a reasonable default assumption, similar to predicting that Spain will do well in a World Cup, or that a Christopher Nolan film will receive positive reviews.

But here’s the thing that’s awkward to admit. The more I see of what AI is now capable of, and the more I experience these new capabilities for myself, the more I’m sympathetic to the Silicon Valley perspective that this is not a bubble – that this technology is really – rapidly – changing everything. And that, despite the ungodly sums of money spent fabricating chips and pouring concrete to build data centres, there might not be a bubble at all.

So this week, I thought I’d explain how I’ve ended up here – and make the bull case for the AI investment boom, and explain why the bubble explanation might not be true.

Read more

POD! On YIMBY Pod this week, Martin and I dig into Andy Burnham’s devolution agenda, talking about integrated transport ticketing, making the lines on the map more consistent, and much more. Then we speak to Jamie Airnes from Cornish Lithium about his attempt to turn Cornwall into a lithium superpower. Listen here, or wherever you get your pods!

Hello! It’s time for Odds and Ends, your semi-regular round-up of the most interesting links I’ve seen this week, plus some shorter takes.

This week, you’ll have to indulge me as I’m going to tell you about an interesting place I visited recently – though I think you’ll find it interesting. Then we’ve got some fun and scary links on what’s going on in AI – and whether you like it or not, I’m going to tell you what I think of the Jason Arday controversy.

Now let’s get started!

My trip to Europe’s craziest border

Every so often someone will confuse me and Jonn Elledge. I can sort-of see why1 – we both write nerdy Substacks that often touch upon similar topics, especially as Jonn is one of the people who radicalised me on housing back when he was the editor of CityMetric.

Read more

On YIMBY Pod this week:

• We talk about Andy Burnham’s big plans to devolve power across the country – and focus on the most important issues, like integrated transport ticketing and whether or not the boundary lines are consistent.

• And we speak to Jamie Airnes, CEO of Cornish Lithium, about his ambitious plan to make Cornwall central to the battery supply chain.

This week James wrote about why people are arguing about Net Zero badly – and what the real debate over the policy is about.

Martin’s piece on ‘picky bits’ and national decline is here, and his YouTube channel is here.

Don’t forget to tell your friends about the pod – and leave us a nice review on Apple Podcasts!

YIMBY Pod is available on Apple Podcasts, Spotify, YouTube, Substack and hopefully wherever you get your podcasts!

You can find James on Bluesky here, and Martin here.

POD! On YIMBY Pod this week, Martin and I tell the respective stories of why we both decided to get solar recently – and Martin risks cancellation with his opinions on electric cars. Then we speak to Ben Garratt from Logistics UK about why, well, logistics, is an underrated part of getting Britain growing. Listen here or wherever you get your pods!

“Net zero scepticism is not climate-change denial,” wrote Kemi Badenoch at the end of last week, in a blog post.

It was in response to a frankly surprising column by Ambrose Evans-Pritchard1 in the Telegraph, criticising her purge of prospective Conservative candidates who don’t share her desire to repeal the Climate Change Act, and other measures that have been put in place to tackle climate change.

Leaving aside the fact that ‘prospective Conservative candidate’ sounds like a position about as enviable as Trump Administration ethics advisor, it was a striking moment. Not just because it barred well-known figures like former cabinet minister Michael Green Grant Shapps from standing for the party, but because it seems like the end of an era.

Wherever the party goes now, it seems a long way from David Cameron’s ‘hug a husky’ moment, Theresa May’s passage of the 2050 Net Zero target into law, and Boris Johnson hosting the COP climate change summit in Glasgow.

Needless to say, then, Badenoch’s response has proved extremely controversial. Many people on my timeline were quick to jump in, snarking at Badenoch for questioning the wisdom of Net Zero during a historic heatwave, when the stakes of the climate change debate have been made clear by nature itself.

But there was something that bothered me about the response on my half of the political spectrum. I’m worried that many people on my side – the lefty side – aren’t quite grasping the contours of the debate. Because it’s much easier to argue about the principle of Net Zero, where the answer is unambiguous – obviously we should decarbonise – than it is to engage with the actual policy choices that Badenoch is disputing.

Because here’s the worst thing about Badenoch’s Net Zero criticism: she might have a point.

Now, I’m not saying that I agree with her diagnosis, or her prescription. But what’s clear is that if you take Net Zero seriously, under the high-level argument about principles there are actually some difficult trade-offs we need to navigate.

So let’s dig in and find out what they are.

If you like nerdy stuff like this, support my work by subscribing for free:

Subscribe now

The complexity of energy pricing

The reason the Net Zero debate often defaults back to abstract principles is that understanding energy pricing is unbelievably complicated. But let’s run through some of the basics to make sure we’re all on the same page.2

First, the price of electricity is always changing and is pretty volatile. In principle, the price is set by the amount of energy being supplied in the system and how much demand there is for that energy at any one time.

Secondly, because the plug sockets in your house don’t care whether the electricity came from a gas plant, wind turbines or a solar farm, the cost of the energy that reaches your home is heavily influenced by the price of power needed to meet demand at any given moment – which is often gas. This means that even if gas is only supplying 14.6% of the grid – as it is right now at the time of writing – if gas prices rise or fall, it also influences how much money renewable suppliers can earn selling electricity they have generated too.

The consequence of this is that it makes it pretty difficult if you’re an investor thinking of building, say, a wind farm. If you put millions of pounds into building turbines, whether you make back your money is basically entirely out of your control – it depends on what happens with gas. So why risk putting up the cash to build the turbines in the first place?

This problem is why during the Coalition years, the government started a scheme called Contracts for Difference (CfD). The idea was that the government holds a reverse auction where renewable developers bid for contracts guaranteeing a fixed “strike price” for the electricity their project generates.

The developer then still sells their electricity to the wholesale market like normal, but if the market price for their energy is £60/MWh, and their agreed strike price is £90/MWh, the CfD makes up the £30 difference. And what’s smart is that the contracts also work in reverse. If the market price rises above the agreed strike price, the generator pays back the difference.

In the years since, this scheme has basically been wildly successful in supporting Britain’s energy transition. Our electricity is now over 50% renewable. It’s a genuinely huge transformation of Britain’s energy system that has taken place.

But the CfD system does have certain trade-offs embedded within it.

Top-up cash

The benefit of CfDs is that they take the risk away from the renewable generators, which is good for building out renewables. But one consequence of them is that the corresponding gains and losses have to be accounted for elsewhere in the energy system.

To continue the example from above, who is paying the extra £30 to top-up what the generator gets paid, if the market price is only £60/MWh? If the generator was promised £90/MWh, the extra cash has to come from somewhere.

So in the short term, the top-up cash to make up the difference is paid by energy suppliers like British Gas and Octopus, who are charged a levy on the energy they buy from generators. And this – usually – leads to an increase in energy bills as the costs are passed on to consumers.3

For example, the Q3 price levy for this year has been set at £10.559/MWh – or just over a penny per kWh. So for a typical household using 2,500kWh of electricity every year, that works out at around an extra £26 per year.

Ed’s gamble

I know what you’re thinking - £26 per year isn’t that much money. So why did so many people get so mad at Ed Miliband when he was Energy Secretary?4

Ignoring the hysterical know-nothing frothing, the serious critique is that he’s basically placed a big bet that might not pay off, and that his choices in his previous job have stored up more energy bill pain at a time when there’s already a cost of living crisis.

This is because so far any levy that has increased energy bills so far is actually a consequence of earlier CfD auctions – those made under previous Conservative governments. The real-world impact of Ed’s choices hasn’t actually been felt yet.

So what exactly did he do? When he took office in July 2024, he inherited the sixth round of CfD allocations, known as AR6, and he chose to pile in an extra £530m to projects representing 9.65GW of generation capacity, much more than had been planned by the Tories.

Then, between August last year and February, the seventh allocation round took place – AR7. This increased the offshore wind budget by £890m, taking it to £1.79bn, and increased the total guaranteed generation across all technologies to 14.7GW.

In other words, he’s been signing contracts that could conceivably lead to an even larger premium on energy bills in the future, depending on future wholesale energy prices.5

And because of his more aggressive approach, it’s also conceivable that the hit on bills could go higher still. CfD top-ups only pay for the cost of the energy generated, but we will also need to build more infrastructure to actually use it, such as new pylons and substations to take energy to where it is needed. And if we build more renewables more quickly, more supporting infrastructure will be needed more quickly too. So it will probably lead to an increase in standing charges on energy bills too.

Of course, all that said, it could turn out that Ed’s bet will pay off, and that bills won’t increase by all that much as a consequence of these choices. The start of the Iran war in the spring certainly makes his commitments during AR6 seem less crazy. Because if we’re now facing years of structurally higher energy prices, the wholesale price of energy will be higher – meaning fewer top-up fees that need paying and thus a lower levy. Which still won’t be great for bills in the short term, as the underlying cost of fossil fuels has shot up, but it will make the idea of more rapidly building out renewables seem smarter in the long term.

However, this is, ultimately, a bet that has been placed with some degree of uncertainty. And this leads us to something else worth noting about the downside risk if it doesn’t pay off. Which is that though we mostly think about energy costs in terms of household bills, the levy affects businesses too. And this means that any increase in bills could have a larger downstream economic impact in terms of competitiveness, productivity, investment and so on too.

Zero vs Cheap

So what is the real, serious debate at the heart of Net Zero? It’s not about the principle of Net Zero – but a question of speed.

When he was Energy Secretary, Ed Miliband chose to optimise for speed, in order to meet the Labour manifesto’s Clean Power 2030 commitment – which pledged to have a 95% Net Zero electric grid by the end of the decade.6

And as per the wildly complicated explanation above, what this essentially meant in practice was a commitment to front-loading more of the costs of decarbonising now, instead of spreading that commitment out over a longer period of time.

This is why the policy has been repeatedly attacked by the Shadow Energy Secretary, former YIMBY Pod guest Claire Coutinho. She instead argues that it is more important to optimise for making energy as cheap as possible – which implies a slower transition as it means fewer risky bets on CfDs, and fewer top-up fees put on energy bills.

And what I think is interesting about this debate is that there isn’t really a clean answer here.

On the one hand, if we optimise for ‘speed’, we can choose to decarbonise the grid quickly. This would increase bills in the short term, but it would pay off in the long term, when we would have more, cheaper, clean energy – not to mention the political benefits of a more resilient energy system that is less exposed to international turmoil. We’d also be setting an example internationally, and would further reduce Britain’s contribution to global emissions.

But on the other hand, if we optimise for ‘cheap’, we could help people with the cost of living in the short term, and reduce the cost burden on businesses – but at the cost of leaving us more exposed to Putin, and pumping more carbon into the atmosphere.

Details, not principles

At risk of giving you an unsatisfying conclusion, I think both sides of the debate make strong arguments. There are real trade-offs involved, and I say that even though I’m a solar panel guy who cares deeply about climate change and the principle of Net Zero.

But there are obvious challenges to the speed approach. For example, ultimately Britain’s actual contribution in terms of carbon emissions is tiny. We’re responsible for less than 1% of global emissions, so what we do with our grid doesn’t really matter all that much, whatever we do.

That’s also why I think Tony Blair is right that the most effective thing we can actually do is use our scientific and financial muscle to support India and China’s development needs by pushing them in a cleaner direction. What those two countries do is basically going to entirely determine whether the world mitigates climate change or not.

So if our scientists can, say, invent solar panels that are 1% more efficient and their innovation is deployed globally, that will matter far more than the exact proportion of renewable energy on our grid.

It’s also why – and I hate that I’ve reasoned myself to this view – I’m not that fussed if Andy Burnham decides to allow more North Sea drilling. The emissions impact would be small,7 and it would, more importantly, neutralise a dividing line between Labour and Reform, making it more likely Labour will be reelected and that we’ll actually still have people who believe climate change is real in power.8

But despite this, and though I am a staunch advocate of cheap energy abundance, I also want it to be clean. And that’s why I think the “cheap” advocates have a flaw in their argument.

Despite the current debate, I still haven’t really heard an argument from Kemi Badenoch or any other sceptic of the current policy about what a path to Net Zero actually looks like if we dump the current approach. Like, what should the revised timeline look like?

And this is why despite recognising the criticisms, I’m sympathetic to the fact that the Ed Miliband approach actually grapples with the need to decarbonise. If there is one iron rule of British politics, it is that politicians love to kick the can down the road – so is there not something to be said for actually doing something?

So even if we slow the Net Zero transition now because of our current economic woes, at what point will it ramp back up? This question matters, because if the world doesn’t mitigate climate change, that too will not be costless in terms of global population displacement, crop yields and increasingly catastrophic weather.

And this is ultimately why I think that despite the difficult trade-offs, on balance I’m on Team Speed. Well, perhaps more accurately Team Tony. But more importantly, instead of just snarking about abstract principles, I’m happy to admit that when you look at the details the choices become rather complicated.

Phew, you made it to the end! If you enjoy deep dives on policy and politics, then make sure you subscribe (for free) to get more like this in your inbox!

Subscribe now