Central Banks are Doing the Right Thing for the Wrong Reasons

January 17, 2023 in Essays

Imagine you’re planning to start a new small business. You’re going to open a gym to serve your local town. Before you can start charging clients, you’ll need to get a premises, to paint it and fit it out with equipment and then fill it with trained staff.

Eventually you plan to pay the staff and the landlord with the money you make from membership fees, but you won’t have many members while you’re just starting out, so you need to find other ways to bridge the gap between starting-up and breaking even.

With all new business, some of this happens through sheer force of will – convincing a landlord to give you a space for a few months for free, or for people to volunteer their time and energy to the cause. But it’s rare for this to bring you all the way to breakeven.

For most new businesses they get some sort of “credit” to bridge the gap – an overdraft or a term loan or investment.

A good model for thinking about the role this credit plays is that it helps new businesses pull resources from their existing employment and put them to new use.

You might use your credit to outbid other tenants looking to put the premises to different use, like a bakery or a café. You’ll pay wages to convince people to switch from being a barista, or an accountant or a self-employed personal trainer to becoming a trainer in your gym.

The big unknown here, for both you and the bank, is how likely your gym is to succeed? Will you use the money to assemble people, property and equipment in a productive way that earns you money and pays back the loan?

You care about the absolute answer here – will the business succeed, yes or no? But in some ways you also care about the relative answer – will my gym make MORE productive use of the inputs than a bakery or a cafe – because (in part) the rent and the wages you pay needs to outbid those other businesses, and you can only do that over the long term if you can put them to more productive use.

The same question can be asked at a wider, more macro level too. Is the general level of credit in the economy encouraging more productive uses of our land, time and equipment?

Central Banks try to influence and manage the answer to this question. They think about the availability of credit broadly within an economy, for businesses to start, grow and increase productivity. This is a key role Central Banks play in economic progress and productivity growth.

It’s not that simple, of course. The problem is that not all new or expanding business make *more* productive use of people’s time, our land and available technology. Capitalism is a game of trial and error, much like evolution, where new business models are created and tested and those that make the best use of the factors of production can scale. 

While your new gym faces the risk of not getting to break-even, the risk the Central Banks face is that people are put to work in equivalent or less productive businesses and industries. Are they making credit too easily available for chocolate teapot factories and NFT exchanges?

In times of low employment, like we faced after the global financial crisis, that risk was very low. The “current use” of property was vacancy. Diverting people from unemployment to any employment was likely to be a more productive use of their time.

Nobody was worried about the risk of funding too many stupid businesses, because anything was better than nothing. So Central Banks used the tools at their disposal to make credit more freely available. Lots of good businesses were given credit to start and expand, and probably some bad ones too.

This post-recession reality didn’t last forever, and by the time we entered 2022 we found ourselves in the exact opposite situation. Rents are exploding from competition and workers are (almost) all employed. We have staff shortages in almost every sector. Commodities are in short supply and prices are high and climbing.

So this is definitely a time that Central Banks can afford to foster more discernment. If we remain in a low interest rate world, with a higher tolerance for risk and a lower level of discernment, it is more likely that more and more worker hours would be spent in unproductive businesses.

So the central banks are tightening up. They are making credit more restrictive.

You could think of this as a sort of a stress test which less productive businesses will fail. It’s not a very precise test, many productive business who are over-reliant on borrowing or going through a short-term rough patch might also fail too.

In my opinion, this is largely positive. If you believe that too long of a period of loose credit can lead to the creation of jobs that shouldn’t really exist (and I do), then you should want that period to end before the impact from tightening gets too big. The longer it runs, the bigger the eventual job losses will be when it ends.

There is no better time to do this than the present. In most developed countries, unemployment is low and commercial rents are high. Every business I visit can’t find staff. We don’t have enough teachers, nurses, truck drivers… you name it.

So if the European Central Bank is restricting credit to make it harder for a crypto startup to raise a big round of funding, so that some people it might have recruited might instead go an become teachers … I don’t think that’s a bad thing.

The problem, however, is that this isn’t how some Central Bankers are describing why they’re doing this.

Many Central Bankers (but not all) and economic commentators are expressly aiming to *increase unemployment* to reduce aggregate demand. Sometimes with comical lack of self awareness: Here’s Larry Summers, former United States Secretary of the Treasury, saying the US Central Bank (Fed) “recognize that there’s going to need to be increases in unemployment to contain inflation”…. from a sun lounger on a tropical island?


As a civilisation, we’ve still very early into figuring out how to manage economies, so it feels easy to predict that our descendants will consider this approach barbaric in the not-too-distant future. Like the way we look back on the medical practice of blood letting.

When faced with times where all our resources are being put to full use and our productive capacity is stretched, our Governments and Central Banks should want to reduce employment in unproductive businesses (e.g. Bitcoin startups) and increase employment in productive businesses.

This is a more humane approach than trying to reduce the burden on our productive capacity by making a chunk of people unemployed and unable to afford things. It also makes more common sense. Selfishly, we don’t want people idle and twiddling their thumbs all day, we want them to have the opportunity to be active and doing their bit to increase the productive capacity of our shared economy.

Luckily, so far, and despite the intentions of some, that’s what’s happening!

Is 2023 the Year of AI?

January 5, 2023 in Essays, Weekly Newsletter

In 1987, economist Robert Solow famously said that the computer age was “everywhere except in the productivity statistics.” While computers were clearly becoming more prevalent in the economy, their effects on productivity were not yet showing up in the data.

The problem wasn’t that computers were inherently unproductive, but rather that it takes time for businesses and organizations to adapt to new technologies and figure out how to use them effectively. This is true for all new technologies, not just computers.

Some technologies are transformative, but many are just useful, or interesting or fun. It’s difficult to predict where new technologies will fall along this spectrum.

Electricity was a revolutionary technology that became widely available in the late 1800s, but its impact on productivity took decades. Edison built his first power plant in 1881, but 20 years later less than 5% of mechanical drive power in American factories was coming from electric motors.

For factory owners, swapping their factory’s single, big steam engine for a big electric engine had a high cost, but not a huge benefit. One big engine at the core of a factory powered all movement, and switching the source of that energy wasn’t hugely beneficial.

It wasn’t until they started rethinking their processes and business models that they were able to truly take advantage of the new technology and boost their productivity. Steam was very inefficient at small scales, but electrical motors could be any size you wanted. Electricity allowed power to be delivered exactly where and when it was needed, and the use of multiple small electric motors allowed for the organization of factories around the logic of a production line, rather than being centered around a single drive shaft. This allowed for more efficient and flexible production.

This pattern can be seen throughout history. Whenever a new technology emerges, it takes time for businesses and organizations to figure out how to use it effectively. We’re slower to adopt new technologies at first, until we get the hang of it. Once we do, however, the benefits can be enormous.

So how should we think about current AI technologies and the path they will take to becoming felt in the productivity statistics?

Machine Learning, a subset of AI, had its first big breakthroughs in the early 2010s, but it hasn’t yet made noticeable impact on general productivity.

So far, most of the benefits of machine learning have been seen in consumer-facing applications, such as auto-predict to complete sentences, vastly improved voice recognition, and better digital recommendations. This was Predictive AI. But in 2022, we saw some quantum leaps in another branch of Machine Learning – Generative AI.

The first wave of Predictive AI took large amounts of data, analysed patterns and used them to make predictions – the next word in a sentence, the next song on a playlist, if an image contained a cat, if a mole was cancerous. The next wave of Generative AI is using the same analysis to generate new patterns. Patterns of text (i.e. sentences and paragraphs), images, music and more.

For example, Large language models (like GPT-3) are AI systems that are trained on vast amounts of text data, which allow them to generate patterns of text which match the patterns of real-world text.

Generative AI is already helping developers write code, assisting lawyers in drafting basic legal documents and producers generate music.

So how long will it take before we see the effects of Generative AI in GDP numbers, if at all? And how should we think about the impact it might have on jobs and the future of work?

To answer that question, we need to understand the inherent capabalities and limitations of both Predictive and Generative AI.

Currently, there are certain tasks that Generative AI is decent-but-not-great at, but it is likely to rapidly improve over time. It writes like a 16 year old, but soon that will be college student, then intern, then junior employee. Any area where humans can set a goal, the AI can generate a pattern or a prediction, which can the be rated against that goal. Defined fields like coding, law, medicine or translation all seem like prime candidates as code either works or it doesn’t, a diagnosis is either correct or it isn’t.

On the other hand, there are certain tasks that Predictive and Generative AI may never be able to do as well as humans, or may never be able to do at all. It can draft a legal document, but we still need humans to review and approve it, and of course to set it the right task in the first place.

Once we have a better understanding of what Generative AI is capable of, we can then think about the jobs and tasks within those jobs that it may be able to do better than humans. This will help us think about the potential impact on employment.

Lastly, we need to get an accurate idea of the time horizons we’re looking at. Not just of when the technology will be capable, but how long it will take before new, more productive models of work are built around it. Technological capability looks set to advance dramatically over the next five years – will the widespread productivity impact take a further 5, 10 or 20 years to materialise?

All of this thinking might also help us generate some ideas about how we can speed this process up. What if someone in 1881 had done the analysis and developed conceptual frameworks to show that modularisation was a key benefit of electricity? Could it have pulled the electric age 10 years forward? Can academics, business and policy makers make that same acceleration for AI?

That’s a key topic I’ll be exploring in the newsletter in 2023, alongside the usual political economy topics. I’m excited. Happy new year!

What Should Ireland’s Data Centre Strategy Be?

August 14, 2020 in Essays

Last week TikTok announced it would be investing €420m in new Data Centres in Ireland, and creating up to 100 jobs in the process.

The IDA announced this as a big win for Ireland, but the announcement sparked a bit of pushback and public debate, with many questioning the wisdom of our industrial strategy that encourages Data Centres to locate here.

The main concern with Data Centres is their power usage. Eirgrid, which manages our national electricity grid, predicts that Data Centres alone will account for 29% of our total energy demand in 8 years time.

“The demand forecast in Ireland continues to be heavily influenced by the expected growth of large energy users, primarily Data Centres. These need a lot of power and can require the same amount of energy as a large town.”

We have signed up to aggressive carbon emission reduction targets, which we’re already at risk of missing, so an ever increasing number of power hungry Data Centres will surely worsen our performance, costing us both financially and ecologically.

Is it fair that we are giving our beef farmers grief one day, but celebrating new Data Centres the next?

The other main critique is that, given all of the power Data Centres demand, they give back very little in terms of jobs. The initial build creates a flurry of construction work, but after that the maintenance is minimal.

The Government’s policy on the benefit of Data Centres also feels weak. The Government ‘Statement on The Role of Data Centres in Ireland’s Enterprise Strategy’ says:

“Data centre presence in Ireland raises its visibility internationally as a technology-rich, innovative economy. In turn, this places Ireland on the map as a location of choice for a range of sectors and activities that are increasingly reliant on digital capabilities including manufacturing, financial services, animation, retail and global business services.”

But does that really hold up? Just because your Data Centre is in Athlone, does that make you more likely to locate your UX design team in Grand Canal Dock? I’m always skeptical of any strategy that has “raising awareness” as it’s main KPI.

TikTok’s dual announcement of HQ and Data Centre jobs this week adds weight to their claim, but overall I’m still skeptical.

Taking this all into account, however, I still think a strong case can be made for Data Centres in our strategic industrial policy.

They don’t create many jobs, sure, but that shouldn’t be entirely negative. They create a large amount of economic value, but don’t take much human intervention. So we need to ensure that we capture that value for our wider society. This means ensuring the Corporate Tax rate is enforced and paid in full.

An important factor of their energy consumption is that it is all Electricity, and every year more and more of our Electricity is generated by renewables. We’re approaching 40% in 2020, the vast majority of which is Wind.

You can’t plug a cow into a windmill, but you can a data centre.

It’s also worth asking “if not here, then where?” Due to our cool climate, Ireland could be one of the most carbon efficient places to host data centres. We’re not going to stop watching Netflix and making Zoom calls, so are we just pushing the servers behind those activities to a more carbon intensive environment?

In a simplistic view, one can imagine each new Data Centre built here being connected to a state owned Wind Farm, financed by zero interest bonds, paying large amounts of recurring revenue for its energy and paying its corporate taxes each year.

This would take a courageous industrial policy, attracting Data Centres because we know Ireland is a great place for them, and charging and regulating them accordingly.

The current Government strategy doesn’t do this. It’s far too optimistic on the positive externalities, talking about Data Centres as if they’re akin to University R&D Labs, rather than warehouses full of servers, and it’s too light on the measures we need to counter the negative externalities and push for net-zero carbon emissions. The only concrete proposals it contains are about removing local communities from the planning process, to streamline it.

I do think a stronger, more ambitious policy is possible, which continues to pitch Ireland as the home of Data Centres in Europe, but mandates a goal net-zero carbon emissions, ties in a strategy of state owned renewable generation and ensures that local communities have a say in how they benefit from these businesses.

In this model wind and cold weather can become Ireland’s new natural resource. It’s our new gold and Data Centres are how we mine them.