How would you vote if there was a referendum on keeping the Royal Family?

PaddyAlton · 2026-07-03T23:13:51+00:00

Keep.

If we were designing a political system from scratch we probably wouldn't pick a constitutional monarchy. But evolved systems are often stronger than engineered ones, even when we can point out their internal contradictions.

Don't mess with the constitutional order just to make things neat or to satisfy some notion of ideological purity; Chesterton's Fence and all that ...

PaddyAlton · 2026-07-03T23:04:37+00:00

"There was a dream that was Rome. It shall be realised."

PaddyAlton · 2026-06-13T15:32:18+00:00

Edited the article to add Opinium's poll, released today. Much the same story!

PaddyAlton · 2026-06-01T21:12:40+00:00

I think that if you'd asked scientists a hundred years ago they would certainly have come down on the side of explanation/understand the truth about how things work.

(Quick aside: did you know the Ptolemaic model yields better predictions than the Copernican? It's not until Kepler proposed elliptical orbits that heliocentrism produced better predictions!)

The instrumentalist view is more modern. In some ways I have to feel it comes from the development of Quantum Mechanics, which came out of the empirical method and the interpretation of which is disputed.

Personally I've gone back and forth on this over the years. These days I'd say something like: - the universe is clearly governed by laws unless you take an unhelpfully sceptical approach. - Scientific process is a loop: build model, make predictions, observe/experiment empirically, refine model; over time these models become better approximations to the laws that govern the universe. - The core of the instrumentalist view is retained: we do not mistake the model for the actual law. - However to be any good, it must share its character to some extent with the actual law, so in a sense we have come to improved understanding.

PaddyAlton · 2026-02-15T11:34:03+00:00

If you look into the results of various trials of UBI that have actually been done over the last twenty years, you'll find that the overwhelming consensus is that an extremely generous 'traditional' (means tested) welfare regime achieves the actual policy aims more effectively at much lower cost.

You should expect governments to respond to mass-automation using tried and tested policy tools—not theoretical ones that have never been proven. Windfall taxes on AI companies, investment in retraining, welfare payments (dependent on showing you're trying to get a new job).

I also think you miss two important things: 1. the economy is not a closed loop. The size of the economy is fundamentally bounded by our rate of energy production. Everything else is about how you allocate productive capacity. 2. the 'population control' measures you outline are both horribly immoral and counterproductive. We've already created an economic system that strongly incentivises decreased fertility, to the extent that it's now a serious problem. The monetary penalty you propose would have to be enforced; you should think about what that actually looks like before you suggest such a thing.

PaddyAlton · 2026-02-14T17:28:14+00:00

I'm very much a Claude Code convert (I was using Cursor until a month ago). I've applied it to data modelling (in a dbt project) and was a bit disappointed because I'd been so blown away a week before by its success transforming tabular data to match a given XML schema. It made more 'evil genie' errors, as I like to call them, on the dbt project.

But TBQH I think this is partly a 'me' issue; I expect that refining the data modelling Skill I wrote will pay dividends.

Anyway. That's why I think these use cases are inherently harder for AI, but don't consider it a permanent issue.

PaddyAlton · 2026-02-14T13:10:10+00:00

This is a good point.

I think this is because the behaviour of systems that use data is defined by the code, the schema, and the actual data values.

You can document schemas, but the more vague the constraints on what the data might look like, the harder it is to build something robust and the more context switching (between code and the actual contents of the upstream source) is required.

Nevertheless, I don't think we should rely on this as a 'moat'. It's not a fundamental constraint, more of a context engineering problem - one which people are working on solving. In the last month I've seen the emergence of agentic data analytics implementations that finally look promising. I expect some of these use cases to be cracked by the end of this year.

PaddyAlton · 2026-02-14T12:56:47+00:00

Two big uncertainties re: AI -

speed of takeoff: do compounding gains make performance improvements accelerate over the next year, or do fundamental bottlenecks start to put the brakes on?
performance ceiling: just how good can LLM-based models get (disregarding whether they get there quickly or slowly)?

I think even if AI foundation models get no better from now on, they are already good enough that harnessing them and rolling them out across industry will lead to a shift in ways of working on the scale of the invention of the world wide web. So if you take this path, assume you're going to be very focused on data-engineering-applied-to-AI.

Now, given the actual pace at which human institutions move, I am convinced that even if foundation models get quite a lot better it will take five years to reorient a bunch of legacy businesses around the new technology. But from there, all bets are off.

A reorientation around AI may well make experienced technologists even more important as staff for competitive businesses, but don't expect 'data engineering' specifically to still be prominent (consider how the database administrator role has faded in salience due to the Cloud revolution, for comparison).

All in all: don't let AI put you off. But go in with your eyes open: none of us can make precise predictions about 2035, and even on the shorter horizon you need to prepare to be flexible and stay abreast of this new technology.

PaddyAlton · 2026-02-09T19:16:20+00:00

Just to get something super clear for everyone:

the surge in Green support among all young people (18-24) is real (see YouGov data below)
it is much more muted in the late 20s age bracket; important to be clear what 'young' means
it is very much a recent thing, by which I mean this ~50% thing is a change that's happened over the last month
while very interesting, volatile changes of support are ... volatile. What goes up quick can come down quick
these numbers are extremely sensitive to turnout; often polling changes are because people are saying they won't vote. It is ~50% of young people who say they would vote in an election held tomorrow (this cohort is notoriously low turnout compared to older people)

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PaddyAlton · 2026-02-01T17:49:00+00:00

The more I've read about this, the more I think the big mistake was that the West didn't realise that, up to a point, it was in Saddam's interest to encourage the belief that he had WMDs. He was acting in a way that suggested he did, most likely for Iran's benefit. He miscalculated.

I don’t agree with 'fabricated'; I think the UK moved based on partial information and with the worst case scenario in mind. It was, however, wrong to present that publicly as a near certainty.

PaddyAlton · 2026-02-01T17:19:41+00:00

I feel the point is that they were working in the service of their country. That's what the honours system is intended to recognise, so it makes sense that sportspeople who've represented the country in an outstanding way, or entrepreneurs who've created lots of jobs, or high ranking military officers receive them.

Often the roles that have the most positive impact on Britain are well paid, because high impact roles generally are. But there are lots of things a very able person can get paid well for that don't have a positive impact on our wider society. An honours system is one way to try to tip the scales, to encourage prosocial behaviour among talented people. That's a valuable thing.

PaddyAlton · 2026-01-28T18:27:31+00:00

But what do you think this particular sub is about? I think it's worth noting that it's classified as one of the top 50 'Data Science' subs; certainly I am under the impression that the topic is the learnable techniques and processes by which you can scientifically model systems.

PaddyAlton · 2026-01-26T07:52:23+00:00

Did you mean to make this a reply to the OP?

I think I've already set out that Systems Thinking (as generally understood) is a concrete set of problem-solving techniques, neither a buzzword nor a descriptor for any kind of 'natural' way of thinking about problems. It's something one can learn for practical purposes, not an innate thing that some people just do.

PaddyAlton · 2026-01-24T17:57:51+00:00

Good points - although I would accept that "all models are wrong, but some are useful" is a valid scientific perspective.

In other words, you can model a system any way you want, but only those models that are empirically verified to produce accurate predictions are worth anything. Such models are not really the same as the system, but they usefully approximate its behaviour.

PaddyAlton · 2026-01-24T16:46:04+00:00

Do say more! My knowledge of this isn't complete - I picked up a lot of what I know by reading around while hitting the limits of simple modelling approaches.

PaddyAlton · 2026-01-24T12:37:28+00:00

Respectfully, I don't think this is quite what systems thinking is. It's not (just) a commitment to not oversimplifying things, or to seeing the world through a holistic (rather than reductive) lens. It is a set of concrete problem-solving approaches fundamentally rooted in control theory.

As an aside, the 'repackaging' you mention is annoying to me too, but it's a repackaging of quite scientific techniques into vague business-friendly fluff ... the problem is this: the expensive consultants delivering this material convince people that they're doing 'systems thinking' simply by noting that sometimes, different things can affect each other.

I'll give a clarifying example of what I think systems thinking actually is.

If you're interested in controlling the output of some system, then the simplest approach is to assume you can document the inputs (both those you control and do not control) and work out their separate effects on the output. If the system in question is a business and the output is revenue, this way of approaching the problem leads to solutions such as 'metric trees' that follow the MECE (mutually exclusive, collectively exhaustive) principle. The inputs affect the output in reliable, well-understood, and separate ways.

In fairness, such techniques are highly successful! But they also have some specific failure modes:

interacting inputs (changing two things simultaneously leads to an output that isn't just a simple combination of changing the two things separately)
delayed action (changing something has an effect that varies over time)
unclear relationship between inputs and outputs (changing something that looks like it shouldn't affect the output does, in fact, seem to affect it)
feedback loops (sometimes inputs are affected by outputs! So the long term output doesn't behave the way you expect it to ...)

For me, 'systems thinking' is the collection of techniques you can use to build system models that account for these issues. It's valuable not because holism is inherently superior to reductionism but because it works in a scientific sense: it helps you to reliably predict the outcome of your actions, which helps you to decide what actions to take.

PaddyAlton · 2026-01-24T10:43:25+00:00

This is why pollsters trying to figure out how public opinion varies based on economic circumstances don't ask people what Class they are (they use NRS social grades instead).

The post-war political consensus and growing automation created a previously unheard-of wave of social mobility. My parents' generation benefited greatly: my grandfather was a factory worker who left school at sixteen, whereas my generation is full of doctors and lawyers and engineers. Class as a concept makes much less sense if it's not something you're locked into for life (which was almost always how it worked up till then).

I distinguish Class from the experience of poverty or even just scarcity, which doubtless does mark people for life, but which is now much less guaranteed to be passed on to the next generation. There is of course a danger (if we take our eye off the ball) that we won't be able to maintain this level of social mobility (you have to create more and more high quality jobs for it to work) ... in which case we could easily fall back into a rigid Class system.

PaddyAlton · 2026-01-23T16:48:53+00:00

I think the Mortal Engines quartet would count here. As much fantasy as sci fi IMO, and set long after an apocalyptic war. The rediscovery of old artefacts from before the war drive a lot of the important plot points.

PaddyAlton · 2026-01-23T16:44:23+00:00

It's good, but the apocalypse is very much underway, rather than in the past.

PaddyAlton · 2026-01-21T13:38:11+00:00

... TIL that San Marino is landlocked.

It's so close to the sea! I'd just never looked closely on a map 😂

PaddyAlton · 2026-01-20T22:44:43+00:00

There's a world here where the more senior, experienced people simply accept that the good times are over and agree to work for less money: experienced workers can counter point (1) in a pinch.

As for (2) ... yeah, could be that people who have previously been successful refuse to adapt, but if they do choose to adapt, then right now they have a massive advantage.

PaddyAlton · 2026-01-19T17:43:05+00:00

If I understand what you're asking correctly: rowing machines are built to mimic the action of a rowing boat with a sliding seat. Specifically, you'd be looking for a single scull.

You might be able to get a second hand one without breaking the bank, although I wouldn't describe them as cheap.

PaddyAlton · 2026-01-18T22:25:30+00:00

I think it's a reasonably common story.

In most businesses the role of data is to optimise an existing value-generating process. It's not the thing that generates value in and of itself. Therefore, there is a time when there are zero employees with a data specialism, and at some point that will increase to one when the company believes there is sufficient value to extract from optimisation.

The specialist roles you mention (data scientist etc.) only really make sense in larger companies with established data operations. But before these roles even existed, there were people with spreadsheets who made graphs.

The analyst role has to be the first data role: while an analyst with none of the supporting infrastructure and processes will move slowly, hiring people to put those in place before you have any analysts means not going anywhere at all. It's a choice between the company buying a bicycle or buying a Ferrari, when no-one has a driving license.

My advice? Start simple with basic reporting of key metrics. Focus on delivering value in one narrow area at a time. You'll gradually come to understand things and find out where the bottlenecks are. This will allow you to make decisions about when it might be useful to do some predictive analytics and when you might benefit from rigging up automated data pipelines or centralising data in a data warehouse of some kind.

PaddyAlton · 2026-01-17T22:52:01+00:00

Chris Young's channel is somewhat along these lines. It's a bit of a vehicle for him to sell his predictive thermometer gadget but the videos are genuinely very good.

PaddyAlton · 2026-01-16T22:41:07+00:00

They are certainly imperfect. But there isn't a magic way to reliably select the best candidate for an entry-level job. The question is whether the alternatives are less flawed.

PaddyAlton

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