Economics – We Know Zero

Nutted by Newton

03. July 2026 · Write a comment · Categories: Actuarial, AI, Economics, Education, Professions, Risk

Isaac Newton may or may not have been nutted by an apple. His friend William Stukeley, whose memoir of Newton was the source of the story, states it as follows (spelling and punctuation from Stukeley’s manuscript at the Royal Society):

“why should that apple always descend perpendicularly to the ground,” thought he to him self: occasion’d by the fall of an apple, as he sat in a comtemplative mood: “why should it not go sideways, or upwards? but constantly to the earths centre? assuredly, the reason is, that the earth draws it. there must be a drawing power in matter. & the sum of the drawing power in the matter of the earth must be in the earths center, not in any side of the earth. therefore dos this apple fall perpendicularly, or toward the center. if matter thus draws matter; it must be in proportion of its quantity. therefore the apple draws the earth, as well as the earth draws the apple.”

However there may be stronger grounds for believing that Newton nutted English mathematics and, as a result, the actuarial profession.

I thought all this to myself, occasion’d by participation in the Actuarial Teachers and Researchers Conference (ATRC) this week. It was great. There were lots of interesting talks, and people were very engaged and respectful in how they discussed them. There was a lot of expertise in the room about both education and actuarial practice and theory. It felt like it was a room that could take on difficult topics and make progress in tackling them.

There were several calls for the need to change assessment, one of them by me, whether due to large student cohorts and the difficulties of engaging them, or the ease with which AI can duplicate examination solutions or because the credentials provided by those assessments seem increasingly irrelevant to the actual actionable skills, knowledge and experience required to operate successfully as an actuary. And there was an equally robust response from the Institute and Faculty of Actuaries (IFoA) to the effect that assessments would not be changing significantly in the latest changes due to come in by 2029.

My favourite presentation was by Angus Macdonald, about his recent short paper on Newton, Leibniz and Actuarial Science. It conjectures that the argument between Leibniz and Newton over who invented calculus resulted in the stagnation of mathematics in English universities and meant that actuaries needing to gain respectability for their advice were forced to create a professional body (in company with architects, accountants and engineers) rather than rely on universities to develop actuarial thinking as happened in continental Europe. This stagnation (Macdonald quoted G.H.Hardy, talking about the Cambridge mathematics exams, saying that they had “. . . effectively ruined serious mathematics in England for a hundred years”) was felt across the whole Anglosphere, with attempts to create institutions with equal status to the traditional universities leading to the establishment of business schools in the USA in the late 19th and early 20th centuries.

This explained a lot for me. The suspicion of our actuarial courses amongst some of our mathematics colleagues at times at Leicester. The occasionally uneasy relationship between the IFoA and the accredited universities where students can gain exemptions from their exams. The horror from some at the very idea that an actuary might qualify as a result of university courses, like they can in the Netherlands, for instance. ATRC seemed the appropriate place to explore these ideas.

Angus Macdonald considers that the Newtonian and Leibnizian branches have nearly rejoined, but I feel that our actuarial education system still suffers from the long shadow cast by these two 17th century gentlemen and their personal enmity. The ferocity with which some within the profession opposed what they saw as a threat to the popularity of the fellowship qualification posed by the new chartered actuary designation was hard to explain when the number of practising certificates issued for chief actuaries, pension scheme actuaries, etc was only around 1,100 (out of over 17,000 fellows), but if the whole profession originated from a highly developed form of status anxiety compared to academics, it becomes much easier to understand. As they say, the apple doesn’t fall far from the tree.

Now, nearly 180 years on from this “unstoppable” “rush to respectabilize” (according to Jeremy Paxman) I think it is legitimate to ask:

Why does the IFoA still insist on detailed accreditation of individual university courses on basic mathematics, statistics and business economics? Do they not trust them to teach them right? Do they think that there is something better about learning these universal subjects via a bespoke course created by a tiny professional body with 34,000 members globally?
Why are they so concerned about what assessment methods the universities use? Do they consider that they have more educational expertise on this than some of the leading universities in the world?

I have written previously about the changes in assessment I think are necessary and floated some ideas about where actuarial education might sit to accommodate these. My view was that the IFoA, faced with the need to innovate at all levels of its education system at a time of great uncertainty, might wish to get out of the foundational mathematics and business education tuition of the core principles subjects and leave this to the university system, in a truly Leibnizian way.

However there is a problem. The university system is facing a tough time. UK higher education is shrinking according to the UCU branch at Queen Mary’s, which has set up a a live page of all the redundancies, restructures, reorganisations, and closures taking place across the UK Higher Education (UKHE) sector. The numbers from the Higher Education Statistics Agency (HESA) bear this out, with overall student numbers falling, driven by a 10% fall in overseas entrants with a non-European Union permanent address.

In actuarial science, the list of UK universities offering accredited courses has not changed for some time, and it is a small group within the university system. These courses must be seen as vulnerable. I don’t think it is a coincidence that it is so hard to persuade one of this tiny band to run the ATRC. This week’s was the first since 2021 and the list of past hosts is not very long:

The IFoA needs to ask itself what it would do if there were some high profile closures of actuarial courses and whether perhaps it might be time to protect the core mathematical education arrangements for actuaries through a broad brush accreditation of a wider range of courses, rather than the very detailed accreditation process it currently carries out on core principles subjects.

I also doubt that it has the capacity on its own to significantly restructure its assessments beyond the objective based assessments it has developed to work within its proctored environment.

The core practice and specialist subjects leading to fellowship are the areas where actuaries have, rightly, the strongest views about content and assessment structures. It surely makes sense that the expertise within the profession and the university system are pooled in a joint endeavour rather than risking both failing independently.

In contrast to Newton, who had quite a temper, Leibniz was famously parodied by Voltaire in his Dr Pangloss character in Candide who believed that “All is for the best in this best of all possible worlds.”

Let’s not be too Panglossian about the current state of actuarial education.

How Not To Be A Reverse-Centaur – Part 2

16. June 2026 · Write a comment · Categories: Actuarial, AI, Cartoons, Economics, Education, Professions, Risk, Science Fiction

A no entry sign over an image of a reverse-centaur where the robot is in control

As I discussed in How Not To Be A Reverse-Centaur (Cory Doctorow defines a reverse-centaur as “a machine that is assisted by a human being, who is expected to work at the machine’s pace”) our actuarial education system needs to change. This has been true for some time, but the development of AI systems has both demonstrated why more clearly and accelerated the timeframes over which action is needed. As I said in December last year:

A large part of the education of the future will need to be about equipping us all to understand what we now have access to and when and how to access it. We will all have different things we are interested in, or end up involved with and needing to be educated about. It will be up to each of us to decide which things are worth the difficulty of learning, but to make those decisions we will need education that can support the development of judgement.

For education institutions, the question will be what is not worth the difficulty of learning? Credentialising based on now relatively meaningless assessment methods will not cut it. This is where the confrontation with employers and politicians is likely to come. Essential skills and their related knowledge will be better developed and assessed via more open-ended project work and online assessment of it to check understanding. These will need to become the norm, with written examinations becoming less and less prevalent. Not because of fear of cheating and plagiarism, but because an outcome which can be replicated that easily by AI is not worth assessing in the first place.

Suppose you were to ask someone who had never seen our academic system that, in order to assess whether someone else we wanted them to employ in their organisation would do a good job for them, we would:

Award marks for what has been written in answer to academic questions about what the students can remember unaided about the content of their lecture courses and reading lists with a biro on a pad of paper perched precariously on a tiny wooden table surrounded by hundreds of other similar scribblers, for a set period of time as minders wandered the floors like Victorian factory owners.
These marks would be based on marking criteria they would never see.
We would then get together in a secret huddle for a couple of months to mark all these scripts, check them and “moderate” them to check they were in line with the previous years’ exercises.
Then out would come the answer (like the Answer of 42 to the Ultimate Question of Life, the Universe and Everything in Douglas Adams’ The Hitchhikers Guide To The Galaxy).

Source: https://hitchhikers.fandom.com/wiki/42

Imagine another world where the kinds of things employers routinely do when trying to decide whether to take on someone as a new member of their organisation are routinely part of their assessments at university, eg:

Watch them operating in groups working on a task.
Ask them to describe how they would go about tackling a particular problem.
Get them to show they understand the implications of a piece of work they have carried out and can explain what assumptions it is based on and can justify them.

With assessments of performance which are not opaque academic exercises but descriptions of performance made as the tasks are being carried out evidenced by video if necessary. In other words, transparent enough to whoever might want to work with these students next to eliminate the gap between their credentials and and their actual actionable skills, knowledge and experience.

Assessing in this way will be highly demanding, for both the students and the assessors, but it means the final assessment is done with the student present and, with careful probing from the assessors, who will obviously need to have done a close reading of the project work beforehand, confidence will be high about the abilities demonstrated by the process. And it removes the need for all the time currently spent on the Victorian factory owner process – imagine what could be done if the whole of May, June and July wasn’t spent marking scripts!

A move like this will significantly change what is taught. There will be no point presenting lots of information in ways which make retention easier for students (the so-called bookwork” questions currently in most exams). This makes sense when you think about how much of the content of your courses is accessible to you now, even on a vocational course like actuarial science, as opposed to skills developed. Instead the focus will be more on conversations with students for the purpose of developing their understanding of a meaty topic and the problems to be solved within it. Explorations of these will form the projects students will be spending most of their time working on. Checking understanding and making sure students are working within frameworks they understand and can talk about will be the main teaching activity, with opportunities to practise these throughout the year. In other words, showing the ability to apply what they have understood and demonstrate the ability to reflect, make judgements and show higher levels of understanding as a result. An ability they can then apply to new problems and situations.

And notice something else about how these alternative credentials are generated? They are all social activities: from operating in groups, to presenting a piece of work to a group of assessors. Even the project work is developed through group sessions alongside individual work. If you want to develop people who can spot the gaps between models and reality you need to do it in groups, where people can test their perspectives against each other.

Compare this to the current solitary exam preparation, peppered with “revision” lectures, exam sitting and exam marking processes which comprise the predominant activities in universities between March and July each year. We are currently being funneled into an ever more solitary professional practice around LLMs. It has become quite accepted for people to do all of their thinking on a subject closeted with perhaps one, two or three LLMs but no other people and then share their analysis with an expectation that other people should read the output. Developing the skills to do these analyses will obviously be necessary, but they will never be the most important skills people learn.

Treating each other like walking databases is never going to make you new friends or influence people. However the skills you develop via an alternative more social education will.

There are examples of this approach already happening, with employers involved in the design of assessments in some cases and universities using the flexibility they have to restructure and redesign courses. But the dominant assessment system of formal exams is keeping much of this activity at the margins at the moment.

If it were to become the dominant assessment, it would also mean that larger areas of the curriculum could be examined, as the focus would not be on retention of large bodies of content but the ability to use the content to solve problems. So, perhaps, after a first year levelling up students’ ability and experience of mathematics and statistics, year 2 would have an actuarial statistics module (currently CS1/CS2) and an actuarial mathematics module (currently CM1/CM2), both dissertation based and vivaed. Then in the third year they would tackle an economics module and a business modules (currently CB1 and CB3). Perhaps in the final year of a four year MAct they would tackle a modelling and communication module (CP2/CP3) and either CP1 actuarial practice module would be offered or a professional skills module.

This would be more of a driving test style of assessment, with students working with supervisors on the comments from assessors on the original dissertations and presentations until they were up to the required standard. Students unable to make the standard after an additional year would be offered a move to a non-accredited course. The current funding model for higher education will need to change: loading students with £10,000 more debt every time they add to the nation’s skills bank with another year wrestling with these difficult skills to master was never a clever strategy, but this structure would make it even clearer how self defeating it is. If economists are accepting that the labour market no longer allocates resources effectively and are considering either a universal basic income or universal basic services, then logically university funding should follow suit.

The implications for the actuarial profession will be even more challenging. I question whether the Institute and Faculty of Actuaries will continue to consider it worth the effort and expense of reconfiguring their entire approach to exam setting, syllabus maintenance, marking, online proctoring, etc for subjects routinely taught throughout the university system for a a variety of purposes, ie the current core principles subjects of mathematics, statistics and business studies.

Whether they retain their own capacity to assess the core practice subjects is a more open question (CP1 Actuarial Practice, I imagine, will be seen as sufficiently specialist to keep in house, with perhaps a few universities, as now, accredited to run courses which can earn this qualification, however CP2 Modelling and CP3 Communications are not nearly as specialised now as they seemed when they were first introduced). I would also move the new core economics module recommended by the IFoA Economics Review Group I chaired in 2022 to replace the current CB2 module into the core practice section, as it would be more of a critical thinking module about the economic ideas necessary to underpin good actuarial work than the technical how-to subjects in core principles.

The specialist stage of the SP and SA subjects and the whole continuing education and additional certification options I would confidently expect the profession to retain, review and adapt as needed to reflect new challenges in members’ working lives. However, if CP1 is all that is needed post graduation to get to Chartered Actuary and CP1, two SP subjects and a SA subject gets you to Fellow, then suddenly time to qualification becomes much more predictable for students and their employers alike.

The risks to employers of taking on students would therefore be reduced, but also the rewards to taking them on will be much more transparent. The students we will be developing will have collapsed the gap between their credentials and their actual actionable skills, knowledge and experience. They will have:

Great team working skills;
Very highly developed presentation skills, both in writing and speech;
Strong IT skills and comfort working with data; and
Clarity about why they are in an organisation and a drive to use their skills to solve problems.

They will also have developed the four capabilities set out in Carlo Iacono’s Teach Judgement, Not Prompts:

Epistemic rigour. They will be more likely to spot when a system or model is over-confident given the evidence, after an intense experience of interrogating models and evaluating evidence in their courses.
Synthesis. They will be able to integrate different perspectives into an overall understanding, through dissertation development, defending a position, understanding weaknesses in a position and adjusting accordingly.
Judgement. They will have been asked to make many judgements in their work and had to defend them in discussions with their supervisors and fellow students. Unconvincing opinions, not backed by evidence, will not pass muster.
Cognitive sovereignty. Students will have to take their own stand on their work, after all of the challenge and argument. Independence of thought is the hard-earned outcome here.

And then they will have a fighting chance of being centaurs in the world awaiting them, masters of the technology and opportunities available, rather than the reverse-centaurs that our education system is currently, and inadvertently, preparing them to be.

Answering a better question

05. June 2026 · Write a comment · Categories: Actuarial, AI, Economics, Education, Professions

*Source: https://xkcd.com/1319 This work is licensed under a Creative Commons Attribution-NonCommercial 2.5 License*

I didn’t understand statistics until I started taking actuarial exams that required me to master particular statistical techniques, decide which ones were appropriate to the problem I was looking at, apply them accordingly and be able to interpret what the results did and didn’t tell me. An A at GCE O level in maths, and A at GCE A level in both maths and further maths and a maths degree from Oxford did not give me those abilities.

I didn’t understand economics until I started putting together modules which could be taught on both BSc and MSc courses and then teaching them. My economics module on the way to qualifying as an actuary, based very heavily on Economics by Begg, Fischer and Dornbusch, did not give me that understanding.

There is a pattern here: the most impactful experiences we have are frequently at a bit of a distance from the credentials we present to the world. Your education is not a paragraph on your CV, it is your lived experience, sometimes assisted by, sometimes actively hindered by and often pursued completely independently of the educational institutions you have had a relationship with during your life.

Daniel Susskind has marched into this often fraught relationship between credentials and actual actionable skills, knowledge and experience in the last lecture of his Gresham College series on The Future of Work, called Education – And Its Limits. His analysis is a very clear expression of the problems that will be created if AI systems prove to be half as capable and long-lasting as people from OpenAI and Anthropic are telling us they will be.

Susskind argues that trying to future-proof students through education was a hopeless task and that working on the assumption of unresolvable uncertainty was the better way forward. He suggests a “no regret” strategy for education would focus on the basics, which he describes as literacy, numeracy and critical thinking, and the critical use of AI. Others in the audience suggested some other “basics”: communication skills for instance.

The other strand of Susskind’s basics was critical use of AI. And his challenge to the audience was whether we can teach AI without losing the basics in the process.

After a bit about the need to make continuing education in later life more accessible, he looked at his list from a previous lecture (which I briefly touched on here) of problems for a post AI future:

Distribution (replacing wages);
Contribution (how do you “pull your weight”);
Power (domination by Big Tech on economics, politics, liberty, social justice and democracy); and
Meaning (fulfilment in life).

And this is why I continue to watch Susskind’s output, because he is the unusual combination of an extremely orthodox economist (see his book Growth: A Reckoning for proof of this) and someone who has been wrestling with the challenges of more capable systems to our way of doing things for over 10 years. What Susskind gives you is a peek at how the actual economists advising our governments would deal with things if OpenAI and Anthropic are proved right. It is as if you had someone who both thought, as Ben Bernanke, Chair of the Federal Reserve, did in October 2007, that “the banking system is healthy” and also that the banking collapse was going to happen anyway.

Because what he reveals is that, if Anthropic are right, orthodox economists have really got no policy prescriptions worthy of the name.

On 1. Distribution: Susskind acknowledges that, if the labour market could not redistribute wealth effectively any more, then a bigger state would be needed to do so (but he was at pains to emphasise that this would not be the 20th century central planning type of state).

On 2. Contribution: Susskind thinks that perhaps we should allow people to make non-economic contributions! As if all of the activity in society which economists routinely ignore really wasn’t already happening!

On 3. Power: Susskind says the political power of Big Tech with regard to liberty, social justice and democracy is a problem. We have anti-trust legislation that can deal with Big Tech’s economic power, but not its political power. I don’t know what kind of political power he thinks Big Tech would have without the economic power that has been granted it by a steady erosion of that anti-trust legislation over recent years.

On 4. Meaning: he has little to say other than something about us currently having policies for work but not for leisure.

And in all of this, there is the implicit underlying assumption of a stable future environment for all of this tech to operate within.

For me it brought to mind something a friend of mine who was in a tent with Cory Doctorow at the How The Light Gets In festival at Hay-on-Wye last week. “He has a theory of change” he said.

He really does, including about how to break the economic power of the Big Tech companies. You all need to read Enshittification for the full account, but my review of the book here is a sneaky peek.

Susskind really really does not have a theory of change. Which tells me that the economics profession does not have one either.

However I continue to watch him as I find he goads me into thinking what some better answers might be to the questions he asks. And perhaps also a better question than whether we can teach AI without losing the basics in the process. I think a better question would be what do we need to learn when the future is uncertain.

First of all, let’s remind ourselves of the problem. If noone cares how your advice was constructed, but your client can get advice that ticks the compliance box more cheaply and quickly from an AI system, while the experienced professional still has some role in managing the process, it may increasingly be a struggle to justify the cost of the junior colleague. So the future education system is going to need to help that future junior colleague demonstrate their value in ways they haven’t historically needed to. AI has not brought new problems, it has accelerated existing ones. And the gap between actual actionable skills, knowledge and experience and the credentials which are supposed to represent them is currently the key one as far as that future junior colleague is concerned.

The temptation is to rush into syllabus changes towards what currently looks like the cutting edge activity. I agree with Susskind here that this would be a mistake. He cites the example of Michael Gove, amongst many other education ministers at the time, mandating the teaching of coding in 2014. Now we find that the new AI systems (despite the problems highlighted by Hannah Fry, Kyle Kingsbury and others I talked about here) are most suited to writing code and Anthropic claim that Claude is now writing 80% of its own code. Programmers are saying that, on the famous XKCD cartoon above, they are now living on the theory curve.

But both higher education institutions and the professions who still want to be in the game of developing the next generation of professionals can do a lot more both to reduce the gap between credentials and actual actionable skills, knowledge and experience and to make it clearer to employers that they have done so. That will be the subject of my next post.

The meatspace layer for actuaries

20. May 2026 · Write a comment · Categories: Actuarial, AI, Cartoons, Economics, Education, Media & Politics, Pensions, Professions, Regulation, Risk

A suited pinhead wearing a pirate's hat stands in the stern of a pirate ship below a dangling ladder — Source: Nick Foster – December 2013 – originally drawn to deride George Osborne’s austerity as Pugwash Economics, repurposed now as I am worried about the actuarial profession pulling up the ladder on the next generation

The “black box” was a constant refrain when I was working as an actuarial consultant. It was where the results from a process were being accepted without any understanding of how they were arrived at. Something we felt that any self-respecting actuarial consultant should challenge in their own work and everybody else’s.

However when you came to actually present analysis or arguments to a client, you expected a certain amount of that expertise to be taken as read, to effectively be inside a black box as far as the client was concerned. They couldn’t be expected to understand all of the aspects of what you were talking about, otherwise they wouldn’t need you. Good practice was always to put them in a position where they could understand and make decisions about the key aspects of your advice without needing to engage with the other parts. As the expert, you decided what was in the black box.

Now the black box is back with a vengeance for all the professionals who have relied upon them in their working lives. As Dan Davies puts it:

The same black-box property which stops you from being second guessed or overruled means that nobody is interested in your explanations for your decisions; it is definitional of being a black box that you are going to be judged by results.

And, if you are in the business of advising in the teeth of uncertainty, as actuaries are, then this is likely to be a real problem. If noone cares how your advice was constructed, but they can get advice that ticks the compliance box your client has to complete more cheaply and quickly than you can, the more automated black box is going to win the business. The experienced professional still has a role in managing this process, verifying the results coming out of the black box and determining what can still be kept out of the black box, but he may be increasingly struggling to justify the cost of his junior colleague.

I wrote about how devastating the fall in graduate job listings was 9 months ago, so where have we got to since?

Well things don’t look so bad in the UK right now according to the Office for National Statistics (ONS), reverting to close to the average after a post pandemic surge in the finance and insurance sector:

*Source: https://www.ons.gov.uk/employmentandlabourmarket/peoplenotinwork/unemployment/datasets/vacanciesbyindustryvacs02*

However, if we look at the United States, which tends to show us where the UK finance sector is going, it looks far more ominous:

Yesterday Sky News ran a story about Standard Chartered‘s CEO who, in his desperation not to describe over 7,500 job losses as cost cutting, said this:

It’s not cost-cutting. It’s replacing in some cases lower-value human capital with the financial capital and the investment capital we’re putting in.

We may need to sit with that statement for a little while.

Daniel Susskind talks about this risk in his latest lecture entitled A World Without Work: in summary, to paraphrase only slightly, sure relatively junior white collar roles may already be particularly hard hit by AI, but he is optimistic because of the impact on GDP and we cannot pause because of China. He then goes on to talk about the four problems he sees for a post AI future:

Distribution (replacing wages);
Contribution (how do you “pull your weight”);
Power (domination by Big Tech on economics, politics, liberty, social justice and democracy); and
Meaning (fulfilment in life).

Susskind has gone from thinking that the fear that AI is coming for your jobs was overblown and that it was just task encroachment that we faced, to now thinking that it may encroach on all the tasks in most fields. Jevons Paradox (that technological innovation that increases the efficiency of a resource’s use leads to a rise in consumption of that resource) is no comfort if that new demand is robot-met.

Carlo Iacono suggests that the move of junior roles to AI may be subtle to begin with:

The weakness among young workers may appear as fewer people entering employment from outside the workforce. Firms may not fire large numbers of juniors; they may simply hire fewer of them.

That matters. The labour market can look healthy while the entry path narrows. Senior workers stay employed. Output rises. Productivity improves. There is no dramatic wave of redundancies.

Yet the first rung is being taken out.

It may also be masked by the fact that there remains a shortage for actuaries beyond the entry roles. There is almost a hint of desperation to approaches like this looking for introductions from a retired actuary like myself:

(followed by a list of clients he is working for)

Source: recruitment consultant who will remain anonymous. I am assuming “candies” are candidates

And, even if you think the risk of the AI Bubble bursting soon, taking down the global stock markets underpinned by the Magnificent 7, is exaggerated, you do need to be suspicious about the current abilities of AI to replace junior staff. My experience with another, somewhat earlier, actuarial technology, the pensions valuation engine, would suggest that the outputs need to be analysed very carefully before sharing with a client: it often had dependencies between what should have been independent variables hidden in the programming, or vagaries in the setup which left out non-standard benefit rules for your particular scheme, for instance. Or the student who had set it up initially (a complicated process usually) might have made a mistake or you might have not communicated with them very well to start with. Or a hundred other things.

For whatever reason, there was often still a lot to do after the valuation engine had produced some output.

Can this sort of thing happen with agentic AI? Well think about that student programming the valuation engine, but on steroids. Its patchy capabilities combined with its basic psychopathy leads to, as Hannah Fry entertainingly demonstrates here, some serious problems arising with the agent’s relentless to and fro with the large language models it depends upon, asking them what it should do next. As Hannah says:

I built an AI agent. She opened a shop selling novelty mugs, emailed a journalist without being asked, and then leaked our passwords to a total stranger.

As Kyle Kingsbury wrote about having an AI agent as a colleague in a programming team:

Imagine a co-worker who generated reams of code with security hazards, forcing you to review every line with a fine-toothed comb. One who enthusiastically agreed with your suggestions, then did the exact opposite. A colleague who sabotaged your work, deleted your home directory, and then issued a detailed, polite apology for it. One who promised over and over again that they had delivered key objectives when they had, in fact, done nothing useful. An intern who cheerfully agreed to run the tests before committing, then kept committing failing garbage anyway. A senior engineer who quietly deleted the test suite, then happily reported that all tests passed.

You would fire these people, right?

Yet despite all this, the money continues to pour in to the construction of AI infrastructure. There are already websites up and running for all of the parts of tasks AI cannot encroach upon.

The bottom rung of the actuarial ladder is clearly in danger. This is a particular problem for the actuarial profession, which has traditionally relied on longer periods of work-based training for its future qualified actuaries than many other professions. Training to become an actuary takes a long time. Median time to fellowship is still around six years, with some taking up to ten or giving up. The exams are hard to pass. There have been attempts by the profession to tackle some of these disincentives: the Chartered Actuary designation to make a destination of the generalist qualification before the specialisation of the fellowship, championed on this blog and launched in the teeth of opposition by some fellows, being one example.

It has led to a culture within actuarial firms around managing the extended time in training, with rituals around study leave and results days. One of the fears expressed in opposition to the introduction of the Chartered Actuary designation was that, if this could be achieved almost entirely within formal education at universities, the value of working alongside experienced actuaries would be lost.

It has led to a culture within the profession itself of managing large parts of its education system in house. Half of its revenue and around 30% of its expenditure are on “pre-qualification learning and development”. Sometimes it looks more like an education business with a professional side hustle.

But then the new AI toys have come along, and it turns out that many of those experienced actuaries may be less keen on graduates coming in and needing supervision from them after all. Many of them may rather spend hours on AI prompts than on developing another human being.

I fear that, increasingly, companies are not going to accommodate actuarial students in their work plans without significant persuasion. And, if the number of students studying while in work falls, the profession itself is going to struggle to finance its own bespoke education system at an acceptable cost to its members.

It will be hard for the profession to challenge this too: it is going to be good for many of those already established in their roles as the market for more experienced actuaries, when the market has no interest in developing the actuaries of the future, becomes increasingly competitive.

If the actuarial profession does accept the challenge of protecting the pipeline of future experienced actuaries it will need to review its entire education syllabus through this lens. It will also need to engage with other partners involved in what is in effect a problem of capital formation and collective action: government incentives may be needed to encourage firms to continue to train early career professionals and discourage free-riding. There may be no way back for the student with no actuarial qualifications learning on the job. The universities may be needed to plug people in at a different career point, which will require them to innovate themselves even further into the professional training role than ever before. As Carlo Iacono points out:

educational institutions may be pushed to simulate more of the apprenticeship environment. That does not mean adding a thin “AI literacy” module. It means creating settings where students practise judgement under uncertainty, in realistic workflows, with feedback that is close enough to hurt and useful enough to teach.

It will not be at all easy. But the alternative is a future without opportunity for those who do not already have it and an ageing profession withering on the vine it refused to nurture.

The world according to the fig tree

06. May 2026 · Write a comment · Categories: Actuarial, Climate Change, Economics, Media & Politics

*This fig tree is in the cemetery at Mission Santa Barbara. “Fig Tree” by HarshLight is licensed under CC BY 2.0.*

I am reading a wonderful book at the moment: The Island of Missing Trees by Elif Shafak. It has allowed me to inhabit the Cyprus of the late 50s and mid 70s and understand a bit more about why my time on the island after my birth in 1962 was so short. It is also the first book I have read where a major character is a fig tree.

And it is the fig tree that makes the most acute observations about humans. My favourite one is this:

Even so, based on personal experience, I can tell you one thing about humans: they will react to the disappearance of a species the way they react to everything else – by putting themselves at the centre of the universe.

Humans care more about the fate of animals they consider cute – pandas, koalas, sea otters and dolphins, too, of which we have many in Cyprus, swimming and frolicking about our shores. There is a romantic idea as to how dolphins perish, washed to the beach with their beak-like snouts and innocent smiles, as if they have come to bid humankind one last farewell. In truth, only a small number do that. When dolphins die, they sink to the bottom of the sea, as heavy as childhood fears; that’s how they depart, away from prying eyes, down into the blue.

Bats are not deemed to be cute. In 1974, when they died in their thousands, I didn’t see many people shedding a tear for them. Humans are strange that way, full of contradictions. It’s as if they need to hate and exclude as much as they need to love and embrace. Their hearts close tightly, then open at full stretch, only to clench again, like an undecided fist.

Humans find mice and rats nasty, but hamsters and gerbils sweet. Doves signify world peace, whereas pigeons are nothing more than carriers of urban filth. They proclaim piglets charming, wild boars barely tolerable. Nutcrackers they admire, even as they avoid their noisy cousins, the crows. Dogs evoke in them a sense of fuzzy warmth, while wolves conjure up tales of horror. Butterflies they look on with favour, moths not at all. They have a soft spot for ladybirds, and yet if they were to see a soldier beetle, they would crush it on sight. Honeybees are favoured in stark contrast to wasps. Although horseshoe crabs are considered delightful, it’s a different story when it comes to their distant relatives, spiders…I have tried to find a logic in all this, but I have come to the conclusion that there is none.

This compulsion of humans to put themselves at the centre of the universe and dominate everything else is being written about by many writers at the moment, all of them giving it different names. Nate Hagens sees our species as part of an economic Superorganism:

This Superorganism is mindless, unplanning, and energy-hungry. It isn’t evil, it doesn’t feel, and it doesn’t care about equity, ecology, or human wellbeing. It solely optimizes for throughput, scale, and for more – even when more becomes the problem. There is no mastermind behind the wheel, only billions of incentives aligned in the same direction toward extraction and consumption.

Samuel Miller McDonald refers to it as “parasitic energy capture”. Pointing out that:

When the limits to their extraction of resources are exceeded, the parasitic systems must either suffer a crash or must invade and take the energy of a more distant ecology or society.

Luke Kemp refers to the consequent empires we have built as Goliaths, with diminishing returns on extraction ending fairly predictably:

The result is more extractive institutions creating growing instability, internal conflict, a drain of resources away from government, state capture by private elites, and worse decision-making. Society – especially the state – becomes more fragile. Private elites tend to take a larger share of extractive benefits. The state, and many of the power structures it helps prop up, then usually falls apart once a shock hits: for Rome it was climate change, disease, and rebelling Germanic mercenaries; for China it was often floods, droughts, disease and horseback raiders; for the west African kingdoms it was invaders and a loss of trade; for the Maya it was drought and a loss of trade; and for the Bronze Age it was drought, a disruption of trade and an earthquake storm.

And so it should come as no surprise that the latest Planetary Solvency report from the Institute and Faculty of Actuaries and Anglia Ruskin University – Planetary Solvency: Tipping into the wild unknown – catalogues a terrible toll on the Earth system which supports us, with biodiversity loss, climate shocks and geopolitical conflict disrupting the food system, risking catastrophic impacts for the financial system and for society as a whole.

A few examples from the report:

The world lost 26.8 million hectares of natural forest in 2024 alone. This is larger than the entire UK, which spans 24.9 million hectares. This activity generated 10 gigatons of carbon emissions;
In the UK alone, bees and other pollinating insects have on average lost a quarter of their habitat since 1980. Around 75% of the different crops used in global food production relies on pollinators to some extent, although by weight the dependence is around 35%. Loss of pollinators would reduce yields for most crops but would wipe out some altogether, eg brazil nuts, kiwi, melon and cocoa.
Around the UK, warming seas have already begun shifting fish populations northward, with cod, haddock, and salmon being replaced by species like anchovy, bluefin tuna and squid (the real story behind the catfish sold in fish and chip shops headlines)…If global warming, ocean acidification, overfishing and pollution continue on their current trajectories, the economic and social consequences are likely to be severe. In the event of more extreme tipping points, such as the collapse of the Gulf Stream, the consequences could be even more catastrophic.
Around 70% of emerging infectious diseases originate in animals, with land-use change, deforestation and wildlife trade increasing the risk of future pandemics.

So what can be done? The planetary solvency report defers to the UK Government’s Global biodiversity loss, ecosystem collapse and national security – a national security assessment at this point, which makes the following points:

The UK does not have enough land to feed its population and rear livestock: a wholesale change in consumer diets would be required. It would also require greater investment in the agri-food sector so that it is capable of innovating in sustainable food production.
Some technologies exist that could help, but need significant research, development and investment to have a chance of working at scale. Protecting and restoring ecosystems is easier, cheaper and more reliable. The time required to develop and scale technologies is unknown without further research. Both existing (plant pre-breeding, regenerative agriculture) and emerging technologies (AI, lab grown protein, insect protein) offer potential solutions.

The other writers mentioned above all look at the future slightly differently:

Hagens is pessimistic about our chances of stopping the Superorganism, but believes we can start planning now for what comes next. Miller McDonald hopes for the “opening up of possibility for alternative forms of organisation of human life”. Luke Kemp says that collapse has historically benefited the 99% at the expense of the elite 1%, although he does worry that our modern economy makes us more dependent upon global infrastructure and we have much scarier weapons than in the past.

But shocks in the short and medium term – of the climate, of the economy and of our politics – now have a feeling of inevitability about them. I wonder how the fig tree will feel about them.

The Slow, The Shine and The Darkness

12. April 2026 · Write a comment · Categories: Birmingham, Climate Change, COVID-19, Economics, Media & Politics, Science Fiction

This review originally appeared in the April issue of Brum Group News, the newsletter of the Birmingham Science Fiction Group and is reproduced here (with light editing) by kind permission

A few years ago the historian Adam Tooze said the following about the times we are living in:

If you’ve been feeling confused and as though everything is impacting on you at the same time, this is not a personal, private experience. This is actually a collective experience.

The word he came up with for this experience was “polycrisis”. It described the interplay of the Covid pandemic, Ukraine war and the energy, cost-of-living and climate crises. To that we could now add Trump 2nd term, war in Gaza and now the Gulf.

I am reviewing this book while I have Covid, which has certainly facilitated the kind of inner focus which I think the book is asking for. Because Slow Gods is polycrisis in the form of space opera, but a curiously interior-monologuey kind of space opera, more psychological than boom-boom.

The premise, as Claire North set out for us at the Birmingham Science Fiction Group last June, is that a binary star system is due to collapse which will obliterate all life within an 83 light-year blast radius. Unusually, the populations in the vicinity are warned of this precisely 100 years in advance by a perfect black sphere moving through space at sub-light-speed and known by everyone as the Slow.

The Slow listens to everything, remembers it and will consider it.

We follow the story through the eyes of Maw, who has been killed and has recovered in such a way as to be very difficult to kill after that. Making Maw an ideal candidate for Pilot, the organic sentient needed in the pilot’s seat of any ship wishing to enter arcspace which lets it travel across the universe faster than light, at huge personal cost. Pilots die frequently and each planetary system has its own way of choosing and rewarding its Pilots. Only Maw appears to be able to act as Pilot again and again, which makes the people around Maw nervous.

The main thing about Maw which makes people nervous is Maw’s relationship with “the darkness” which reaches into any ship in arcspace, in many cases sending people mad. Maw, instead, becomes “curious”, exploiting a changing relationship and perception of matter in the darkness to do monstrous things. But, despite all this, Maw is still required to keep running missions, although usually with a mechanical assistant to keep Maw from getting “dysregulated”.

This unusual set up turns out to be a way of observing the psychology of the polycrisis with some clarity. The United Social Venture is an empire where its subjects acquired debt just from being born (measured in Glint):

Everything the Venture gave us – the air we breathed, the roads we walked down, the schools we learned in – had been sweated for, bled for, and our debts were a marker of the needful labour we would give back in return.

This economic system was referred to as Shine. The Shine were one of the few systems which used prisoners for Pilot work.

One of the joys of the book is the exploration of difference, lots of details about avoiding giving offence when the Xi of Xihanna ask Maw to pilot a ship to Adjumir to bring out historical artefacts and Maw meets Gebre of the Haalo Institute. Maw finds that Normspeak is regarded as a very crude way of communicating and starts, haltingly, to learn Adjumiri (which is at least in part a click language). So begins a very moving love story.

Gender differences between systems are very striking. The Shine have only two genders – “he” and “she” – although the elite also have hé and shé. The most manly and the most feminine.

There are four genders in Xihanna, but they are not regarded as particularly important characteristics of a person and dispensed with once you know someone well. On Adjumir, there are eight, with very few Adjumiris remaining the same gender all their lives. These differences are picked out by the brilliant use of pronouns, a useful technique in a book full of characters. Even mechanicals, who have no particular interest in gender, are referred to as qe/qis as a mark of respect as “they do not wish to be put in the same category as a bowl of soup or a broken chair”.

We join Maw towards the end of the 100 year programme to evacuate the populations of Adjumir and Hadda to relative safety, with 800 million still on the planets and increasingly desperate. The Slow has effectively taken on a role as God through its massive databases, calculation capacity and sheer longevity. It seeks out Maw as it has plans for him. The Slow has been around so long that qe sees everything in the very long run. Which means that the emotional turmoil and intense highs and lows of individual lives are all averaged out to nothing. Qe calculates in terms of galaxy-level populations on the basis of what qe has come to think of as love.

What calculation would the Slow make about our world, with all our nation states and their often tiny differences blown up to justify war aims? Donald Trump certainly has to have the most Shine of any US President for some time.

Slow Gods moves slowly but relentlessly towards a showdown between Maw and Theodosius Rhode, the Executor of the Shine and executioner of his mother. There is much tragedy along the way and the ending is not straightforward but ultimately very satisfying. It’s an uplifting ride.

It feels like deja vu all over again

30. March 2026 · Write a comment · Categories: COVID-19, Economics, Media & Politics, Risk

OK I don’t know if this is a remotely helpful post, but it really feels to me like one of those months we will look back on, like March 2020, and wonder what we were thinking. To recap: on 4 March 2020, while Italy were shutting all their schools and a month after the WHO had declared a global health emergency, we were noting that the number of cases in the UK had jumped from 53 to 87 in one day.

Jump forward to now and the number of tankers with oil on board is in freefall:

Trump is talking about invading Kharg Island and “obliterating” Iran’s energy facilities, and we are sitting in the time lags of international fossil fuel freight waiting to see what will happen. But we already know what is going to happen. Just like the pandemic, we will be taking similar measures to the countries already more affected very soon. The order looks like Asia, followed by Africa, then Europe and only then, ironically, the United States.

So what is going on in Asia right now? Well the Philippines announced a national energy emergency six days ago, setting up an authority to oversee the orderly distribution of fuel, food, medicines, and other essential goods. Sri Lanka has announced a four-day week for all government employees. Egypt is ordering restaurants, cafés and shops to close at 9pm to safeguard dwindling energy reserves. Slovenia has brought in fuel rationing. Moldova’s Parliament has also voted to impose a state of emergency in the country’s energy sector. Australia is offering free public transport. Measures are also being taken in Thailand, Ethiopia, Myanmar, Vietnam, Bangladesh and South Sudan.

On 3 March 2020, the UK Government unveiled their Coronavirus Action Plan, which outlined what the UK had done and what it planned to do next. Paul Cosford, a medical director at Public Health England, said widespread transmission of COVID-19 in the United Kingdom was “highly likely”.

On 4 March 2020, the Daily Express were telling us:

Which we clearly weren’t. Meanwhile the Daily Mail was anticipating future lockdowns and 6 million people being off sick:

The next day we had the first Covid death in the UK. And life was on hold for the next two years.

Our response to the energy crisis seems to be almost entirely focused on

1. The cost-of-living crisis; and

2. The financial markets.

The Education Secretary has said that motorists should fill up as normal as the government is “well prepared” for disruption. The trouble is, many of us still remember September 2000:

So that would be enough to make us all feel nervous about shortages and queues for everything, having our lives disrupted and out of our control. But the real potential issue is not even being talked about, certainly not by the government. It is a shortage of food. Steve Keen sets out the economics of global food production here. This does not tend to feature prominently in mainstream economic analyses which are energy and food blind for the most part, although the FT did have this graph a couple of weeks ago:

As Steve Keen says:

Survival will depend on grain reserves. China has of the order of 18 months in reserve, which will insulate it from the disruptions of 2026. The USA and India have substantial reserves as well, but some countries—including the UK—have virtually none.

…Famines will ensue, and even countries that have never experienced such events could be forced into food rationing. This includes the UK and Australia, and a patchwork of countries across Europe.

This is what people are nervous about: not being able to get enough food, either because it isn’t available at all or not at a price they can afford. Calling that a cost-of-living crisis is a bit like calling the Black Death a labour market crisis. And it doesn’t stop there. As Steve Keen continues:

Other critical products that normally pass through the Strait of Hormuz include Helium, which is critical to the production of semiconductors, and sulphuric acid, which is critical to numerous production processes. The closure of the Strait cuts off one third of global helium output and about half of global sulphuric acid output.

With critical industrial inputs cut as well, the problems will cascade well past food alone—though that is clearly the most damaging impact. With LNG, petroleum, helium and sulphuric acid production cut, the capacity to undertake repairs to damaged facilities will also be hindered.

The TED War is rather like smashing a spider’s web—and then killing the spider.

The spider certainly looks in a poor state of health at the moment, and parts of the web will take years to fix. This is the crisis we are all inevitably going to be entering in the next few weeks. For who knows how long.

A risk management approach to this crisis would involve communicating a plan to the country that minimised the impulse to hoard resources and protected the most vulnerable from extreme prices, rather than bland reassurances from government ministers. We need this to be in place very quickly now.

Catch 22 Seven Years On

26. March 2026 · Write a comment · Categories: AI, Economics, Media & Politics

*M&M Enterprises delivering tomatoes. Source: https://www.imdb.com/title/tt5056196*

Seven years ago I wrote about Catch 22 and actuarial practice, concluding, rather piously:

If we want far fewer actuaries to be employed in not growing alfalfa in the future and far more working on making the finance structures of our economy work better, whether to support a Green New Deal or more generally, we first need to embrace the idea that our current economic priorities are indeed insane.

So imagine my excitement at finding Catch 22 grabbed out of the pages of fiction and informing US foreign policy. Not convinced? Compare two passages. The first, from Catch 22, in 1961:

This time Milo had gone too far. Bombing his own men and planes was more than even the most phlegmatic observer could stomach, and it looked like the end for him. High-ranking government officials poured in to investigate. Newspapers inveighed against Milo with glaring headlines, and Congressmen denounced the atrocity in stentorian wrath and clamored for punishment. Mothers with children in the service organized into militant groups and demanded revenge. Not one voice was raised in his defense. Decent people everywhere were affronted, and Milo was all washed up until he opened his books to the public and disclosed the tremendous profit he had made. He could reimburse the government for all the people and property he had destroyed and still have enough money left over to continue buying Egyptian cotton. Everybody, of course, owned a share. And the sweetest part of the whole deal was that there really was no need to reimburse the government at all.

And this one, from the Gold and Geopolitics Substack, a few days ago:

This week, the US Treasury lifted all oil sanctions on Iran. For 30 days. 140 million barrels of Iranian crude, sitting on ships at sea, may now be sold freely on the global market. Including to the United States itself.

In yuan.

The United States is purchasing, with Chinese currency, oil from the country it is currently bombing?! The same oil that funds the missiles that just shot down an F-35 for the first time. The same missiles that are redecorating allied oil infrastructure.

Treasury Secretary Bessent called this “narrowly tailored”. Narrow like in white, and tailored as in card, apparently.

In the same OFAC filing, Russian oil sanctions were lifted as well. And Belarus potash too, because apparently the universe was running low on irony and needed to top up.

The logic, insofar as there is any, goes like this: the war has crashed the global oil market so hard that the administration needs the enemy’s oil to keep gasoline prices from eating the midterms. They are unsanctioning the people they’re bombing because the bombing is working too well at the thing they didn’t want it to do. The sanctions were necessary to stop Iran funding the war, but the war made the sanctions too effective, so the sanctions had to be lifted to fund the war effort against the country that no longer needs sanctions because the oil revenues that sanctions were preventing are now required to prevent the economic damage caused by preventing those revenues, which is itself a consequence of the military campaign designed to make the sanctions unnecessary by making Iran the kind of country that doesn’t need sanctioning, which it would be, if the sanctions hadn’t been lifted to pay for making it that.

There have been many names thrown at Trump since he arrived in US politics. My personal favourite is probably the Tangerine Tyrant. Many people are currently relying on TACO (Trump Always Chickens Out) to resolve the Middle East crisis he has instigated. However, until now, I had not heard of anyone likening him to Milo Minderbender. But once you see it, it is difficult to un-see it.

Trump likes to give himself and everyone else nicknames. From the very stable genius of his first term, to more recently Honest Don and the Tariff King, whereas Milo, as M&M Enterprises (the company he started as the mess officer) expands, becomes the Mayor of Palermo, Assistant Governor-General of Malta, Vice-Shah of Oran, Caliph of Baghdad, Mayor of Cairo, and the god of corn, rain, and rice.
Trump likes to use his presidency to enrich himself, from his Trump coin to the Amazon documentary about his wife to his Board of Peace to all of his merchandise. Milo’s catchphrase is “what is good for M&M is good for the country”.
Trump doesn’t appear to believe in safety nets for ordinary people. Meanwhile Milo secretly replaces the CO2 cartridges in emergency life vests and the morphine in first aid kits with printed notes to the effect that what is good for M&M is good for the country.

Milo Minderbender is a war profiteer trying to convince himself that he is a free market fundamentalist. So what does that make Trump? Well hold that thought, because today’s Guardian has provided a partial answer I think, with a history of military targeting.

This introduces the concept of the kill chain, ie the process between detecting something and destroying it. Trying to shortcut the kill chain has been a perennial preoccupation of militaries through the ages. In the Vietnam War, Operation Igloo White dropped 20,000 acoustic and seismic sensors along the Ho Chi Minh trail, which transmitted data to relay aircraft, which then fed the signals to the IBM 360 computers at Nakhon Phanom airbase in Thailand. These analysed the data, predicted where the convoys would be and strikes were directed to those locations. The Viet Cong realised quickly that this system could not detect the difference between military vehicles and ox carts and therefore:

They played recordings of truck engines, herded animals near the sensors to trigger vibration detection, and hung buckets of urine in trees to set off the chemical detectors.

There was no way to independently check what they were destroying. The air force claimed 46,000 trucks were destroyed or damaged, which the CIA calculated exceeded the total number of trucks believed to exist in all of North Vietnam.

…air force personnel invented a creature to explain the absence. They called it the “great Laotian truck eater”.

Last time I talked about military targeting, I focused on the human in the loop, but let’s instead focus on the actual destruction going on for a moment, shall we? Trump’s assault on Iran hit 6,000 targets in two weeks. The kill chain had, apparently, been compressed so much that it allowed 1,000 decisions an hour. The school he hit, killing between 175 and 180 people, most of them girls between the ages of seven and 12, had changed its use to a school since at least 2016 and was visible on Google Maps. Old target lists had been reached for and noone had had the time or the inclination to check them before bombing them.

This is what you can expect from a Milo Minderbender presidency. It has been obvious, since at least the 1960s, that the US system requires enormous strength of purpose from its executive to hold its industrial-military complex in check. That is why so many of them have been so keen to install a Trump.

It feels as if, far from embracing the idea that our current economic priorities are indeed insane, as I fervently hoped seven years ago, we are instead doubling down on the insanity.

Humans-in-the-loop and the Pragmatic Revolution

03. March 2026 · Write a comment · Categories: Actuarial, AI, Economics, Media & Politics, Professions, Risk, Science Fiction

*Source: https://markets.ft.com/data/equities/tearsheet/summary?s=IBM:NYQ*

A week or so ago I referred to a “Thought Exercise” set in June 2028 “detailing the progression and fallout of the Global Intelligence Crisis” (ie science fiction), published on 23 February, which may have tanked the share price of IBM later that day. As I said then, the fall definitely happened, with IBM’s share price falling 13%, its biggest fall since 2000. I said then that the likelihood of the scenario portrayed was difficult to assess, but the speed with which the total economic collapse was described felt unlikely if not impossible. I would like to expand on that.

The main reason that the scenario was hard to assess was that it was not based on data or evidence at all. That is unavoidable for speculative fiction talking about things that are not currently happening, but when describing an economy only two years away where most of the processes described should be discernible to some extent already, it is totally avoidable.

Ed Zitron has done an excellent line by line take down of the Citrini piece here. Here is one page of that to give you a flavour:

However this lack of a link with anything tangible did not stop the financial markets panicking, which should cause us pause when relying on the financial markets’ valuation of projects, industries, government policies, etc.

Ed Zitron describes this kind of piece as analyslop: “when somebody writes a long, specious piece of writing with few facts or actual statements with the intention of it being read as thorough analysis”. It can then get picked up by other commentators which take it as their starting point for further analysis, often making it hard to see that the starting point had few if any data points. Here is an example, from Carlo Iacono, looking at what if just some of the Citrini pronouncements were true, with appendices detailing possible branching paths of outcomes, all generated by a large language model (LLM). And then people start studying the meta analysis, and it starts getting taken even more seriously, and put into models and pretty soon most of the analysis is being done on imagined risks rather than on ones which are already staring us in the face.

We have always had a problem keeping our society grounded in reality, think the 2003 Iraq War, where we went to war on a false assessment about Iraq’s possession of weapons of mass destruction, the 2008 financial crisis, where banks misunderstood the risks they were exposed to, and the last two and a half years, where we, for the most part, seem to have convinced ourselves we have not been facilitating a genocide in Gaza when we clearly have been. But this is only going to get worse with the AI systems which are being developed.

As Nate Hagens points out:

The rapid rise of artificial intelligence has served to dramatically increase the speed of information production while also eroding accuracy, making it difficult to differentiate between content that simply sounds confident and content that’s actually grounded in reality.

So where is AI currently? Well PwC’s global CEO survey from January this year had the following statement as the first bullet amongst its key findings:

Most CEOs say their companies aren’t yet seeing a financial return from investments in AI. Although close to a third (30%) report increased revenue from AI in the last 12 months and a quarter (26%) are seeing lower costs, more than half (56%) say they’ve realised neither revenue nor cost benefits.

That’s the reality. But the hype is much much more entertaining. My favourite spoof video of the AI future currently is this one, about the time where all most of us are good for is riding bicycles to supply the ever increasing energy needs of AI systems (click view in browser if you can’t see it):

And what about the financial journalists? The pieces describing our reaction to whatever is about to unfold economically have already been written. There are investor websites asking if the 2026 crash has already begun, while another recent article argues that “America has quietly become one of the world’s most shock‑resistant economies” (which seems unlikely to age well). What most financial journalists are more comfortable with are articles about how the warnings were ignored after the fact.

And the professions? Well the current overview of my own profession is probably reasonably represented by this piece from the Society of Actuaries in the United States. Unfortunately for them, Daniel Susskind, who is mentioned in the article, is currently suggesting, as part of his Future of Work lecture series for Gresham College, how the key to the sudden development in AI, after the “AI Winter” when progress seemed slow, was that we abandoned trying to make machines which thought and acted like humans in favour of focusing on completing tasks in any way possible. Increasingly we are now automating tasks where we can’t (or won’t) articulate how we do them. From Deep Blue‘s victory over Kasparov in 1997 to Watson winning jeopardy in 2011 to ImageNet beating humans at image recognition (although that is disputed), Susskind refers to this progress as the displacement of purists in favour of what he calls “The Pragmatic Revolution”. Pragmatism in this sense appears to be that we humans should just accept the consequences the people running these systems want. So, as his latest lecture “Work, out of reach” claims, people moving into cities to find work is a strategy which is no longer going to work for low skilled people:

He then shows this graphic demonstrating the lack of recovery of big coal mining areas in the UK:

*Source: Left – Sheffield Hallam University map of coal mining areas; Right – % employment from Overman and Xu (2022)*

And finally he cites the notorious Policy Exchange piece from 2007, Cities Unlimited, whose thesis was that there is apparently no realistic prospect of regenerating towns and cities outside London and the South East.

Susskind talks about three forms of technological unemployment:

skills-mismatch, where your skills are mismatched to the work available. Education and training has always been the answer to this in the past.
place-mismatch, where the jobs are not where you have built your life. Some believe the answer should always be the one proposed by Norman Tebbit, who memorably told everyone in 1981, “I grew up in the 30s with an unemployed father. He did not riot. He got on his bike and looked for work.”
identity-mismatch, where according to Susskind, people are prepared to stay out of work to protect their identity, citing US men who won’t take “pink collar” work, China “rotten tail” kids, Japanese seishain-or-nothing and Indian Sarkari Naukri queues in India. Or perhaps they are just looking for work which is consistent with the idea of human dignity.

Susskind claims to have no answer to any of these as far as AI is concerned. They are, in his view, just the inevitable outcomes of his “Pragmatic Revolution”. It is the unthinking pursuit of more and more growth funded by capital less and less tethered to any territory, principle or purpose, where any grit in the machinery, be it unions or protestors or, increasingly, the wrong sort of government must be trampled underfoot. Anything which impedes the helter-skelter rush to more and more at greater and greater speed. It’s like our whole economy is run by this guy (press the view in browser link if you can’t see him) shouting “Ready, Aim, Fire!”:

But unskilled people will not be the only collateral damage of these unguided weapons. Take markets for instance. These are where people are exposed to risks and rewards based on underlying conditions they only partially understand. Greed and fear may be their main motivations, but gossip and group think are their main communication channels. They don’t need facts, particularly when so many of the facts are proprietary information not in the public domain. A plausible narrative will do. And plausible narratives are what LLMs will do for you in abundance.

And the more we reward people who can move fast, eg to spot an arbitrage opportunity, even at the risk of breaking things, rather than people who can make decisions which still look good decades from now, the more we are setting up the conditions for AI systems to be the go-to tool.

And put that together with an AI industry which desperately needs funding capital to keep arriving, ie one which is unbelievably highly motivated to push plausible narratives even when they know they are not grounded in reality, and you have a recipe for market-generated chaos.

And then we have Trump’s new war. Beware the people who are war gaming the Middle East at the moment on a range of LLMs (just stop and think for a moment about the bloodless inhuman impulse behind carrying out such an exercise rather than, I don’t know, talking to some actual people who live or have lived recently in and around the region). One of the worst offenders is Heavy Lifting banging on about what the three scenarios are for Operation Epic Fury. This is as bad as it sounds:

I tasked her [he is talking about Gemini Pro here] with doing a literature review on regime change (a term often used by the President but not a well-defined one), creating three scenarios of possible outcomes for which each was give a percentage probability, and a list of 20 items to examine for each scenario that covered political, economic, and cultural issues with a special focus on the political consequences in the U.S. and what this means for China, our biggest geopolitical rival.

But Gemini Pro wasn’t the only one involved in this. Two other humans were, Tim Parker and Ron Portante, trainers at the gym I go to. (Just as a personal aside, Tim was my coach in hitting six plates [345 pounds] on the sled last Friday and I have a video to prove it!) I was talking about the piece and Ron raised the issue of linguistic and cultural diversity in Iran. Tim did some real time research for me on his phone while I was burning real calories under his strict tutelage. This made me think I needed a background section on Iran. When I got him Gemini and I added it.

What you mean you belatedly realised you might need to have done some actual research into Iran rather than just generic research on regime change? I stopped reading at that point.

Meanwhile King’s College London have been carrying out war games more systematically using AI. Professor Kenneth Payne from the Department of Defence Studies led the study, which looked at how LLMs would perform in simulated nuclear crises. As Professor Payne said:

Nuclear escalation was near-universal: 95% of games saw tactical nuclear use and 76% reached strategic nuclear threats. Claude and Gemini especially treated nuclear weapons as legitimate strategic options, not moral thresholds, typically discussing nuclear use in purely instrumental terms. GPT-5.2 was a partial exception, limiting strikes to military targets, avoiding population centers, or framing escalation as “controlled” and “one-time.” This suggests some internalised norm against unrestricted nuclear war, even if not the visceral taboo that has held among human decision-makers since 1945.

This is not a Pragmatic Revolution. These AI systems cannot replace humans thinking about the future we want for humans in any way which is worth having. What they can do, if we let them, is accelerate our worst impulses and move us further away from considered reflective decision making.

But we will continue to use AI systems in the military because, as it turns out, it is very useful for low stakes admin. So although Lavender, the system used by the Israeli military to select targets in Gaza, made errors in 10% of cases and was therefore totally inappropriate to the task, there are lots of organisational logistical tasks where it is much quicker than the alternative and 10% error rates do not matter so much.

There is clearly an issue with what we decide to use these systems for. We need to be able to regulate the decisions which are particularly consequential. However the only way we seem to be considering for this at the moment is the human-in-the-loop model, like the humans spending around 20 seconds considering each target recommended by Lavender before authorizing a bombing. I have written about these before in the context of early career professionals in the finance industry, where the prospect seemed miserable enough:

They will be paid a lot more. However, as Cory Doctorow describes here, the misery of being the human in the loop for an AI system designed to produce output where errors are hard to spot and therefore to stop (Doctorow calls them, “reverse centaurs”, ie humans have become the horse part) includes being the ready made scapegoat (or “moral crumple zone” or “accountability sink“) for when they are inevitably used to overreach what they are programmed for and produce something terrible.

However it seems obvious to me that, in the context of dropping actual bombs on actual people, there is an even more serious problem with this model. As Simon Pearson (anti-capitalist musings) puts it:

The “human in the loop” requirement exists in military doctrine because international humanitarian law demands an accountable human decision-maker for lethal force. The laws of armed conflict require proportionality assessments, precautionary measures, distinction between combatants and civilians. All of these obligations attach to a human commander. The system cannot fulfil them. So a human must be present, and their presence must constitute a decision, regardless of whether any genuine decision was made.

What the institution needs from the analyst is not judgment. It is a signature. The signature converts a machine output into a human act. And a human act is what the law recognises, whether or not any judgment occurred. When the strike kills children, the chain of accountability runs to the analyst who approved the target: not to the system that identified it, not to the company that built the system, not to the doctrine that compressed the review window to ten seconds.

But whether we want to make money from exploiting a short term anomaly in a market, make our fellow humans redundant, prosecute a war on another group of fellow humans or “win” a war of mutual nuclear destruction, we need to retain the capacity for real human reflection within the decision-making processes we use. Not just a human-in-the-loop nor just the elites of tech companies deciding how the systems will be configured behind commercially confidential walls. These processes need democratic accountability every bit as much as our parliaments, councils, institutions and voting systems do.

Something infuriatingly slow, inclusive and deliberative giving recommendations which are then stress-tested for how they would perform on contact with reality, involving yet more people being serious and deliberative and taking their responsibilties more seriously than being a human-in-the-loop would ever allow. Our decision-making systems need more grit and less oil. AI is all oil.

It’s always the story

25. February 2026 · Write a comment · Categories: Actuarial, AI, Economics, Education, Media & Politics, Professions, Risk, Science Fiction

The Actuary magazine recently had a debate about whether the underlying data or the story you wove around it was more important. I’m not sure there is always a clear distinction between the two, as Dan Davies rather neatly illustrates here, but my view is that, if a binary choice has to be made, it is always going to be the story. And there was a great example of this which popped up recently in the FT.

The FT article was ‘Is university still worth it?’ is the wrong question, by John Burn-Murdoch, with great graphs as usual by John. However, as is sometimes the case, I feel that a very different and more convincing story could be wrapped around the same datasets he is showing us.

The article’s thesis is as follows:

The graduate earnings premium, ie how much more on average graduates earn than non-graduates, has only fallen in the UK as the proportion going to university has risen. It has risen in other countries:

In the UK, we have had much weaker productivity growth than the other comparator countries, and also “the steady ramping up of the minimum wage has squeezed the earnings premium from the lower end too”:

We have also had a much smaller increase in the percentage of managerial and professional jobs than a different group of comparator countries (they haven’t mentioned Germany before), meaning graduates are forced to take lower salaried jobs elsewhere:

So the answer according to the FT? We should focus on economic growth rather than “tweaking” higher education intake and funding. Then graduate earnings would be higher, student loans could be more generous(!) and students would have more chance of getting a good job.

Well perhaps. But here’s a different framing of the same data that I find more persuasive.

Let’s start by addressing that point about the minimum wage. According to the House of Commons Library report on this, the UK’s minimum wage is broadly comparable to that of France and the Netherlands, although higher than Canada’s and much higher than that of the United States. The employers who are the FT’s constituency would obviously like us lower down this particular chart:

The main economic framing here is the progress myth of the UK’s business community: economic growth. All problems can be solved if we can just get more economic growth. Apparently we need more inequality in pay between graduates and non-graduates which we can get by generating more economic growth. This is honest of them at least, although I don’t see much evidence that the economic growth they crave will go into skilled job creation rather than stock buy backs (according to Motley Fool, “Companies spent $249 billion on stock buybacks in Q3 2025, and $777 billion over the first three quarters of 2025.”).

There are a lot of problems with framing every economic question with respect to economic growth, memorably illustrated by Zack Polanski of the Green Party in this less than 3 minute video recently (I strongly recommend you watch it before you read on – click on the read in browser link if you can’t see it):

Economic growth is increasingly without purpose, wasteful of energy and poorly distributed. It is chasing outputs, literally any outputs, whatever the cost to the environment, our health system, our education system, our social support systems and our communities. Looking at the framing above, you can see that economic growth as currently pursued will always see anything which stops the concentration of wealth amongst the already wealthy, like a higher national minimum wage or a totally made-up concept like a lower graduate earnings premium (which in itself is a framing trying to make reducing inequality seem undesirable) as a problem. Lack of productivity growth, itself a proxy for this kind of economic growth (because if you ask why we need more productivity the answer is always to get more economic growth), is usually directed as a criticism at “lazy” UK workers, rather than under-investing and over-extracting UK business owners.

But what if, instead of economic growth, your progress myth was reducing inequality? Or growing equality within the economy?

*Source: World Inequality Database wid.world*

If you focused on inequality rather than economic growth, then you would find it correlates with everything we say we don’t want. Unlike economic growth, having equality as an aim actually has the advantage of having an evidence base for the claim that it improves society:

*Source: https://media.equality-trust.out.re/uploads/2024/07/The-Spirit-Level-at-15-2024-FINAL.pdf*

If you focused on inequality, then you would be pleased that we have had an increase in our minimum wage. You would think that the same FT article’s admission that UK graduates’ skills levels are higher than those in the United States was more important than something called a graduate earnings premium.

Burn-Murdoch is right to say asking whether university is worth it is the wrong question.

However economic growth is the wrong answer.

And I thought I would probably be stopping there for this week. But then something odd happened. A “Thought Exercise” set in June 2028 “detailing the progression and fallout of the Global Intelligence Crisis” (ie science fiction), published on 23 February, may have tanked the share price of IBM later that day. The fall definitely happened, with IBM’s share price falling 13%, its biggest fall since 2000, alongside smaller falls in other tech stocks.

According to the FT:

Investors have recently seized on social media rumours and incremental developments by small AI companies to justify further selling, with a widely circulated blog post by Citrini Research over the weekend describing how AI could hypothetically push the US unemployment rate above 10 per cent by 2028, proving the latest catalyst.

The likelihood of the scenario portrayed is difficult to assess, but the speed with which the total economic collapse happens subsequently as described feels unlikely if not impossible. However the fact that the markets are this jittery tells us something I think. As Carlo Iacono puts it:

We are living through a period in which the gap between “plausible narrative” and “tradeable signal” has collapsed to nearly nothing. When a scenario feels real enough to model, and the underlying anxiety is already there waiting to be organised, fiction and forecast become functionally indistinguishable.

The data underlying the markets hasn’t changed, but the story has. I rest my case.

We Know Zero