Telling a different story

07. October 2026 · Write a comment · Categories: Actuarial, Cartoons, COVID-19, Economics, Education, Health, Prediction, Professions, Regulation, Risk

We shall not cease from exploration
And the end of all our exploring
Will be to arrive where we started
And know the place for the first time.

T.S. Eliot – Four Quartets

I feel like I am having a bit of a T.S. Eliot week. I have been writing about the OBR since the beginning of this blog in 2013, with their repeated failures as a forecaster forming the banner for the blog for many years.

What has brought me back to the OBR, as if for the first time, is recalling during the recent Funding the Future conference the words of the very good and wise friend of mine who was there with me. I had invited him to talk to one of my undergraduate groups at the University of Leicester during a professional skills session a few years ago. We were looking at past disciplinary cases of the Institute and Faculty of Actuaries and discussing what the actuaries involved could have done better.

In one of the case studies we considered, an actuary had produced a quarterly reserving report for a company, estimating the reserves on a ‘high’ basis of £74.5 million, a ‘low’ basis of £36.9 million and a ‘best’ basis of £52.1 million. The company then produced financial statements for the year, approved by the board of which he was a part, containing a reserve figure of £15.5 million. My friend told the students that the way to challenge in a situation like this is not to focus on the numbers, but instead see that each different number reflects a different story being told. So you need to look at what the story would need to be for the £15.5 million to be an appropriate figure and take issue with the things that don’t make sense within that story.

It was great advice then and still is, and it is definitely the way to look at the OBR I think. We find a story unsatisfactory if the characters are shallow, do not behave in a credible way, and the plot oversimplifies a much richer situation (something which every story must do to some extent to be manageable to the reader) in a deeply unsatisfying way.

Several other commentators have talked about the inability of the OBR to forecast anything (including me) – this is the equivalent of insufficiently fleshed out characters behaving in unbelievable ways. Richard Murphy, for instance, pointed out how ridiculous the proposed character plotline for inflation was at the last budget:

Whereas in fact of course the downward trajectory predicted at the last budget didn’t look remotely likely even then. The Consumer Prices Index (CPI) rose by 3.1% in the 12 months to August 2026, up from 2.9% the previous month.

However it is Dan Davies’ recent couple of Substack posts (one and two) which get to the heart of the problem in my view. Dan Davies’ contention is that it is the plot oversimplification which is the main problem here. As he says:

The system we have set up only seems to be able to communicate two states though – the signal is either “COMPLIANT – WAR CHEST” or “NONCOMPLIANT – BLACK HOLE”. It would be a significant improvement even for the OBR to add some measure of urgency or non-urgency to its press release – a sentence like “this surplus is well within the forecast error, however, so it should not be used to justify a change in policy”.

But, in his view, it is even worse than that:

In general, most things which a government spends money on will tend to have effects on the economy which go well beyond the five year forecast. The OBR uses an information set which contains only a small and quite arbitrary subset of government decisions – it is, literally, not capable of representing the system it is meant to control at an acceptable level of accuracy.

The justification for ignoring the future consequences of current spending seems to be no more sophisticated than what we pejoratively call “Treasury Brain” – the belief that a) spending departments always claim that their budget should be seen as an investment, b) that they systematically overestimate the long term benefits of current costs and c) that the best solution to a) and b) is to completely ignore any future benefits from investment or future costs caused by noninvestment. It’s just not good enough.

It is this dog’s breakfast that apparently tells us whether we are on track to meet the plotline the government has set for itself. Its three fiscal rules:

Rule 1. The current budget should be on course to be in balance or surplus by 2029/30 (‘stability rule’);

Rule 2. Net financial debt should fall as a share of the economy in 2029/30 (‘investment rule’); and

Rule 3. Some types of welfare spending must remain below a pre-specified level (the ‘welfare cap’).

And if that gruesome threesome doesn’t set your heart racing about the country’s future story, you are not alone. Because the people banging on about fiscal space will always push for governments to spend less, regardless of the circumstances. For instance, at the height of the pandemic in December 2020, Moody’s were saying this (with excess deaths already estimated at over 30,000 in the UK by Public Health England at that point):

However, compared to the government’s March budget (that was quickly overtaken by events), there are some initial signs that fiscal policy outside of investment is likely to be less expansive than previously announced. What remains unclear is whether this ambition will be able to withstand the political pressures that seem to be inevitable given the government’s previous commitments. Even before the Spending Review, longer-term spending commitments for health, education, and defence had already been announced. Together, these three areas account for around 60% of total expenditure.

One of the themes from the Funding the Future conference was, to quote from Educating Rita:

“There must be better songs to sing than this”.

I agree. One possibility is proposed by the New Economics Foundation, who suggest categorising policies by their level of multiplier effect (ie how much economic activity is increased by an increase in spending) and replacing fiscal rules with a “fiscal referee” who can make a more holistic judgement addressing many of the omissions Dan Davies complains about. This would then lead to a discussion rather than the compliant/non-compliant cliff edge we currently have, and allow the government to make its case. And it would not automatically incentivise cuts like the current system does.

Other possibilities exist. As Dan Davies describes it:

any accounting system is a mental prison of some kind, and one which needs to be periodically escaped from

In my view, a jailbreak is long overdue.

The IFoA’s AI Manifesto – a response

01. October 2026 · Write a comment · Categories: Actuarial, AI, Cartoons, Pensions, Professions, Regulation, Risk, Science Fiction
A no entry sign over an image of a reverse-centaur where the robot is in control

Last month the Institute and Faculty of Actuaries (IFoA) launched what it calls its “AI Manifesto“. I thought I would take a look.

The Manifesto is admirably short and to the point. Its pitch can be summarised by this final line of its Introduction and context:

For the benefit of society, we encourage those responsible for developing and deploying AI systems to ensure that there is an actuary in the loop.

Think of it as a massive “Gissa job” plea to those companies currently investing in AI systems. But what sort of jobs are we pleading for?

The idea is that actuaries have been working with models which use machine learning for many years, that we have statistical expertise, are comfortable working with data, used to stress-testing systems and also to looking at the risk appetite of a business as a whole.

I think the first hint that there might be a problem here is when the Manifesto moves onto ethics and the public interest. As it says:

Actuaries help teams balance commercial aims with societal expectations, offering constructive solutions rather than imposing limits.

It is difficult to square this with Anthropic‘s recent IPO filing, where they warn its technology may pose “existential risks to humanity”. I am pretty confident that Society expects not to be put at existential risk. So if Anthropic thinks its technology is that dangerous, there is no balance to be found between commercial aims and societal expectations here. Societal expectations are paramount. And limits will need to be imposed rather than “constructive solutions”.

But, in my view, the main problem here is what “real world” the IFoA feel they are serving. This comes through in the role for “professionalism” they envisage:

Projects with sound validation, honest documentation and clear accountability clear governance more quickly, avoid late-stage rework, and fail in testing rather than in production. Professionalism is what lets an organisation say “yes” to AI with confidence – and protects it from the adverse outcomes that would otherwise force it to say “no”.

This, remember, is in an environment where the latest large language models like Anthropic’s Claude Opus 5.5 or OpenAI’s GPT-6 Astra, are proprietary models where validation, documentation, accountability and governance are all just what they let you see. A framework which allows companies to say yes with misplaced confidence is unlikely to be serving the public interest. And translating a capacity to apply a regulatory model to an insurance company where you have all the data and can make informed judgements about the things that you don’t know (Actuaries are used to working with large, imperfect, transactional datasets subject to audit and regulatory scrutiny. We clarify what data is needed, what quality level is acceptable, and how data should be checked, reconciled and monitored over time) is emphatically not the same as working with a model where you don’t know precisely how it works, cannot accurately predict the errors it will make and do not have enough time to look in all of the places you need to.

Take bias for instance. The Manifesto says:

We examine how a model behaves for different groups and sub-groups, test whether training data is representative of the people the system will affect, and help manage the biases we find responsibly through fairness metrics for predictive models, and output audits and red-teaming for generative systems.

Nita Farahany in the law and policy course documented in her Thinking Freely Substack demonstrates the limits of that bias management in practice, and this is Cory Doctorow on red-teaming (from Red Team Blues):

That’s the problem with blue teaming it—you need to be perfect, while—The red team only has to find a single error

Yes, that’s right, the IFoA haven’t even understood that they are on the Blue Team when it comes to AI errors, ie the one supposed to be stopping as many of them as possible. The Red Team breaks into systems by exploiting a single vulnerability. That is comparatively easy. The Blue Team needs to reassure its clients that it has removed ALL vulnerabilities.

That will be the role of any “actuary in the loop”. And the chances are it won’t be possible. The three examples given by the IFoA are as follows:

A generative AI customer assistant
An insurer wants an LLM-based assistant to answer customer queries. An actuary designs the evaluation
framework: a curated test set of realistic queries, explicit tolerances for error, escalation rules for high-stakes topics, and live monitoring of answer quality. The project clears internal governance at the first attempt because approvers can see exactly what “good enough” means and how it is evidenced.

A machine-learning decision model
A lender deploys an ML model to score applications. An actuary tests behaviour across customer sub-groups,
identifies a data artefact that disadvantages one segment, and works with the data science team to correct it. The documentation of that judgement becomes the centrepiece of a constructive conversation with the regulator.

AI risk across an organisation
A Chief Risk Officer faces AI adoption in a dozen business units at once. An actuary helps articulate an enterprise AI risk appetite, maps where the organisation depends on the same foundation models, and builds board reporting that distinguishes experimentation from deployment. The organisation accelerates adoption in low-risk areas precisely because it now knows where the high-risk areas are.

None of these examples look unreasonable in isolation, but, to justify the eye-watering expense of this technology, it will need to increase speed and capacity significantly. So, rather than a single generative AI customer assistant, imagine a client relationship management role where a junior actuary or senior student manages 10 times the current expected number of clients at the same time, because the generative AI customer assistant makes that possible?

Will the skills of an actuary versed in the intricacies of Solvency 2 or pensions regulation be so revered by your client that you would be given more than enough time to rubber stamp a system serving 30 or 40 clients? How long do you think you’d need to check the output on all of them properly? I think it unlikely you will be given that long.

This is the risk of becoming a reverse centaur, where you work for the assistant rather than the other way around. And my view is that the actuarial education system is positively encouraging the development of reverse centaurs amongst our current students.

It gets worse. The early evidence suggests that working with AI systems encourages rubber stamping. In one study of AI-generated audits with an auditor in the loop, 150 of the 160 reports were submitted without any changes made by the human. The performance of humans in the loop may also be adversely affected by working with AI systems, leading to “agency decay”:

Agency decay is the gradual erosion of a person’s ability and willingness to observe carefully, think independently, choose deliberately, and act responsibly. It does not arise because AI is inherently harmful. It arises when convenience becomes the default setting for cognition. A tool that first helps us think can begin to think around us, then for us, then without us noticing what has been weakened.

There are lessons to be learned from other professions who have been working for much longer with expert systems. The aviation industry for example. As Craig Bright, Co-Chief Operating Officer at Barclays has posted:

So the test shouldn’t be whether a human appears somewhere on the process map.

It’s whether the system remains understandable, controllable and recoverable when the agent is wrong, uncertain or unavailable.

Safety doesn’t come from leaving a human in the loop.

It comes from designing a system the human can still control.

The IFoA’s position appears contradictory. On the one hand, they clearly don’t believe that AI is as powerful or as dangerous as Anthropic are claiming in order to push up the IPO share price, or I assume they would not be pushing so hard to get an actuary on every team “developing and deploying AI systems”. I think they are right not to believe it, for a number of reasons, but Naomi Alderman’s AI predictions: seven ways you can tell useful thinking from sci-fi fantasies gives probably the most entertaining ones.

On the other hand, if this is just another technology for which the price is going to need to adjust to reality at some point, we need to be very careful as a profession not to be part of the mob urging their clients to adopt it at a faster rate than they would otherwise. Particularly if it is damaging our own professionals in the process.

AI in some form will survive the inevitable crash, but the shape and scope of the wreckage that will be left behind is currently hard to predict. Let’s be careful what we wish for.

Three names for the same idea

15. September 2026 · Write a comment · Categories: Actuarial, AI, Cartoons, Education, Professions
Source: https://xkcd.com/2347 This work is licensed under a Creative Commons Attribution-NonCommercial 2.5 License

Last week I suggested that the period of disruption we have entered would be likely to impact the relationship between society and its institutions and professions, and likened those institutions and professions to the models behind the models we normally focus on. I suggested that this has serious implications for actuarial education, amongst much else.

This week I want to expand a little on what I meant by that.

First, the impact of AI. My view of AI is that I don’t believe most of the claims coming out of OpenAI and Anthropic when noone else gets to see the data and they clearly have a vested interest in playing up the capabilities of their systems. And I certainly don’t believe the fantasy economics the Magnificent 7 are currently indulging in is sustainable (see Ed Zitron’s Hater’s Guide to Circular Financing for more on this). But I do believe that there is a lot of political power with an increasingly out of control stake in it continuing, which makes the quote often attributed to Keynes (“Markets can remain irrational longer than you can remain solvent”, although it may have been A. Garry Shilling in 1999) even more relevant here.

However, what cannot be denied is that the existence of AI is accelerating conversations which we should have been having a long time ago. Last month, MIT issued a report called AI and Education, which turns out to be more a philosophical musing about what we should be trying to achieve in universities, and what expectations we should have of both staff and students. It’s not a long report, but for the time poor amongst you there are two passages from this that I’d like to highlight here.

First, on the expectations we should have of students:

Leaning into learning means creating a new “social contract” between teachers and students. All of us who teach at MIT will need to be prepared to help students understand both that the process of education is necessarily a productive struggle, and that the most important product of their education is not a GPA or a diploma but themselves: their personal growth and intellectual maturity and the development of their own imagination, insight, and judgment.

Instilling these attitudes needs to become a central task for every educator, so that our students know not only what they should learn, but also how they should learn and why learning matters. We need to help them develop metacognitive abilities to think about thinking, to engage in reflective practices, and to enhance their sense of personal agency. This will require both dedication and fresh preparation on the part of instructors.

Remember that social contract idea, because it will be coming up again.

Second, on what kinds of assessments we need to move to in anything we still want to be able to refer to as education:

Rather than simply “AI-proof” current methods of assessment, instructors need to revisit what they really want students to know and devise assessments that foster, or even include, the kind of productive struggle that builds durable understanding and mastery.

We urge instructors to consider forms of assessment that are less vulnerable to AI, and more valuable for learning, such as oral exams, semester portfolios, and out-of-class assignments paired with in-class conversations. This likely means resources such as TAs and class time will become more central to evaluation.

and this:

To match the shift towards assessment methods that aren’t vulnerable to AI, instructors need to increase the role of experiential and project-based learning. To encourage this kind of creative teaching, MIT should support the development of teaching skills and practices for all instructors, and recognize contributions in this domain.

As someone who has already spent some time advocating just such a move in higher education, I feel quite vindicated. But back to that social contract point. I am in a WhatsApp group called Actuarial Change in Education (A C E), where we have been discussing this point for a while, but couldn’t agree what to call it. Culture was one proposal, but I think we all agreed on the MIT point that it should be aiming to ensure:

our students know not only what they should learn, but also how they should learn and why learning matters

And then, like buses, a third take on this point came from, of all people, Seth Godin in Two schools of thought about school:

One kind of school is about culture, compliance and community. We indoctrinate kids, for better or worse, in what it means to be in this world we’ve built, and how to do it together. We teach them how to be kids, how to be citizens and hopefully, how to be adults.

And the other kind of school is about the content. All the ‘R’s. The software that goes with the operating system we taught in the other school.

The first paragraph is what MIT and my WhatsApp group are referring to. It is the bit which has been assumed we shared in common for too long, but is the most important bit. A shared culture or social contract would allow us to meet and discuss things within a shared set of assumptions about how we can get agreement and take action. It is what has been holding back COPs and peace talks and politics more generally for my whole lifetime. It is also how we decide what we put in the second paragraph, which is necessarily stuff which will be being tweaked constantly and be much more short-term in focus..

The second paragraph is what the Institute and Faculty of Actuaries respond with when you ask them about education (I am only picking on them as this is the one I am most familiar with). Curriculum. Syllabus items. Suggested number of hours study. Past papers to practise for the traditional written exam.

Seth Godin calls this software. It is content. It is largely ephemeral, as the needs of the model we operate in and the tools we are given to do so change all the time. Little of it will survive a decade let alone a career. It does not encourage looking up and wondering if there is a better way. It is full of short-term deadlines and urgency and immediacy. Which tends to get confused with importance.

But the software will never surprise you or deal with situations noone has ever met before. If it is not in the underlying model, it will not turn up in the software. The important education is in the operating system. That is what needs to be reformed.

As I said, there is a WhatsApp group discussing such questions regularly. We are a small group currently, but would welcome new members interested in these questions. If you are, then respond to me in the comments section below or via a LinkedIn message if you are reading it there.

A little more information about A C E is set out below:

What is A C E?

Actuarial Change in Education. A place to discuss where change is needed in how actuarial students learn and the changing landscape they are preparing themselves for. A place to argue about how we teach, and how we get students to care about why they are in education and question the assumptions underlying it. A place to share thoughts and ideas you have had or seen about education, actuarial or otherwise. A place to collaborate with others equally concerned about the future of actuarial education. A place to plan and deliver communications with the wider actuarial community.

What are A C E’s aims?

To have an impact on actuarial education by providing a place for developing actionable ideas about education more generally and how they can be applied to actuarial studies in particular. The hope is that these will then be a useful resource for when the need for alternatives gains a wider acceptance.

Who can join A C E?

Anyone interested in the development of actuarial students.

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:

  1. 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.
  2. These marks would be based on marking criteria they would never see.
  3. 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.
  4. 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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.

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:

  1. Distribution (replacing wages);
  2. Contribution (how do you “pull your weight”);
  3. Power (domination by Big Tech on economics, politics, liberty, social justice and democracy); and
  4. 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.

Source: https://rentahuman.ai/

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.

Fine wines, disappointing brandy and petrol prices

13. February 2026 · Write a comment · Categories: Actuarial, Cartoons, Climate Change, Economics
The disappointing brandy scene from Goldfinger (1964) https://youtu.be/I6COBucJQfE?si=saiV5f80ISSB3FGY

Politics is a bit depressing this week, so I thought instead I would focus on the asymmetry of our attitudes towards different high octane liquids.

I remember when I first got interested in wine. It was the early noughties and I was out at a restaurant in Cardiff called Le Cassoulet (no longer trading under that name I understand) with my then boss who liked to hit his expense account pretty hard from time to time. The sommelier seemed to know him quite well and scurried off to get him some particularly old claret to accompany the meal. I think it was from 1972 or thereabouts. I remember noting that it had a different colour (brown) from the red wine I was used to drinking and, when sipped, there were a lot of different flavours and smells competing for my attention. Something which I later heard described as “complexity”. From then on I realised that wine drinking could involve something a bit more than just something nice in a glass to accompany a meal.

The journey of alcoholic drinks from drinks to luxury consumer items and assets is nicely illustrated by the Bond franchise. There are a number of movies we could choose but let’s go for Goldfinger, shall we?

In the disappointing brandy scene from Goldfinger, we have this exchange between M, Bond and the Governor of the Bank of England, Colonel Smithers:

Smithers: “Have a little more of this rather disappointing brandy.”

M: “What’s the matter with it?”

Bond: “I’d say it was a 30-year-old Fine indifferently blended, sir…with an overdose of Bons Bois.”

M: “Colonel Smithers is giving the lecture, 007.”

Now first of all, that is clearly not what the Governor of the Bank of England looks like. As readers of this blog already know, he looks like this:

That scene is also notable for including a brief discussion of how the relative value of gold held at the US and British central banks at the time was used “to establish respectively the true value of the dollar and the pound”. In 1964 this would have been via the London Gold Pool, running between 1961 and 1968, by which a group of eight central banks including the United States Fed and the Bank of England agreed to cooperate in maintaining the Bretton Woods System of fixed-rate convertible currencies and defending the gold price. Ian Fleming’s book, written in 1959, predated this arrangement, but the anxieties about the gold market which led to its creation would have been very much around. So we still have the Governor (meeting Bond alone rather than with M) saying (during a lecture which went on for 10 pages):

We can only tell what the true strength of the pound is, and other countries can only tell it, by knowing the amount of valuta we have behind our currency.

Valuta is a rare word, from American English, for the value of one currency in terms of its exchange rate with another, and perhaps an odd one for the Governor of the Bank of England to use. But it is clear that Bond is sent after Goldfinger primarily for economic reasons (finding a way to smuggle large amounts of gold across borders threatens the Bank of England’s cosy little gold club) rather than because (spoiler alert) Goldfinger thinks nothing of murdering people (quite a lot of people in the case of Operation Grand Slam) who get in his way, cheating at golf, employing butlers with lethal bowlers, slicing through things with gold lasers and planting nuclear devices in Fort Knox. Released shortly after Ian Fleming’s death, it was the last Bond movie he saw in production.

It is the same film in which Bond obsesses about getting his favourite champagne (Dom Perignon 1953 – Bond was also someone not afraid to hit his expense account pretty hard from time to time) chilled to 38°F (3.3°C) before he gets bashed on the back of the head and the girl he is with (Goldfinger’s assistant, Jill Masterson, played by Shirley Eaton) gets sprayed from head to toe with gold paint. Perhaps more than any other brand, Bond linked luxury and high octane liquids of various kinds.

Skip forward a few decades and some of it has clearly stopped being something to drink at all, but instead a, very fragile, status asset for the very rich to demonstrate their status to each other. Here are the top prices achieved by wine at auction from one website, 8 of the 10 of them pre-dating both me and Goldfinger:

Source: vinovest https://www.vinovest.co/blog/25-most-expensive-wines-in-the-world-2026

Contrast this with the way we have treated fossil fuels. As Luke Kemp points out in Goliath’s Curse:

We tend to forget that fossil fuels come primarily from long-dead plants and animals. These organisms died between 360 and 286 million years ago during the Carboniferous period, after capturing sunlight through photosynthesis or other means. It is that fossilised energy that we are consuming. According to one estimate, it would take 400 years of global photosynthesis to power the modern world for one year. It takes ninety-eight tons of organic matter buried during the Carboniferous to become just five litres of petrol. We are now a high-energy Goliath, powered by dead matter.

According to a petrol price checker from earlier this week, the garage closest to me currently sells unleaded petrol for £1.29 a litre. So 98 tones of organic matter curated for 300 million years retails for £6.45. That’s less than half the price of a sausage bap and a coffee from Costa via UberEats:

But apparently it’s still not cheap enough.

Most of the content from this article recommending eternal vigilance despite the cheapest prices for 5 years and the claims that “petrol is still 6p too high at the pumps” comes from Howard Cox, founder of FairFuelUK. Whose website includes this picture with a not-too-presumptious-claim-at-all below it:

Even if you weren’t concerned with climate change or the health effects of petrol fumes in the air, this seems like a strange hill for anyone to be dying on. And dying we are. According to the 2025 Global Report of the Lancet Countdown average global heat-related mortality has now reached 546,000 pa, up 63% in just over 20 years:

And that’s just heat. A recent report from the Royal College of Physicians: A Breath of Fresh Air estimated 30,000 deaths from air pollution each year, of which car emissions form an important component.

By the time even the Bond franchise had started worrying about environmental concerns in 2008 with Quantum of Solace, a Somerset Maughamish short story converted into an attempt by a sinister organisation to become the water monopoly in Bolivia through underhand means, the iconic shot of the woman covered in gold had become a female consular employee (Strawberry Fields, played by Gemma Arterton) drowned in oil:

Source; http://007magazine.co.uk/factfiles/factfiles_trivia5.htm

We currently pay between £3.12 and £7.09 per litre in duty on wine, depending on strength, and £0.53 per litre in duty on petrol.

Our attitude to different types of high octane liquids has clearly been nuts in all kinds of ways for a long time. But it is just part of our political frostbite at the moment: we allow our living organisations and institutions to remain frozen in time because we have always done things that way, regardless of the living tissue we are killing in the process. From the endless cycle of public inquiries and ignored recommendations to our use of economics to rationalise things we have already decided to do to batting on with traditional exams: it seems we are just going to do what we are going to do. And freezing fuel duty now looks like it needs to be added to that list.

We can laugh at Trump for accepting an award of “undisputed champion of beautiful clean coal” by the Washington Coal Club and legislating that black is now white by revoking the Environmental Protection Agency’s scientific ruling from 2009 about the harms of climate change. But Trump does at least think he needs a reason to support the fossil fuel industry, even if he needs to make one up. We are just doing it because our politics has gangrene.

Why the traditional exam is finished

06. January 2026 · Write a comment · Categories: Actuarial, AI, Cartoons, Education, Professions, Regulation
Source: https://xkcd.com/2415/ licence at: https://creativecommons.org/licenses/by-nc/2.5/

Happy new year all! New year, new banner, courtesy of my brilliant daughter who presented me with a plausible 3-D model of my very primitive cartoon of a reverse-centaur over Christmas. And I thought I would kick off with a relatively uncontentious subject: examinations!

“Back to normal!” That was the cry throughout education when the pandemic had finally ended enough for us to start cramming students into rooms again. The universities had all leveraged themselves to the maximum, and perhaps beyond, to add to the built estate, so as to entice students in both the overseas and the uncapped domestic market to their campuses, and one by-product of this was they had plenty of potential examination halls. So let’s get away from all of that electronic remote nonsense and get everyone in a room together where you can keep an eye on them and stop them cheating. This united the purists who yearned for the days of 10% of the cohort turning up for elite education via chalk and talk rather than the 50% we have today, senior management needing to justify the size of the built estate and politicians who kept referring to traditional exams in an exam hall as the “gold standard”.

So, in a time when students have access to information, tools, how to videos of everything imaginable, the entire output of the greatest minds of thousands of years of human history, as well as many of the less than great minds, in short anything which has ever caught anyone’s attention and been committed to some form of media: in this of all times, we want to sort the students into categories for the existing job market based on how they answer academic questions about what they 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 wander the floors like Victorian factory owners.

And for institutions that thought the technology we fast-tracked for education delivery and assessment in the pandemic would surely be part of education’s future? Or perhaps they just can’t afford to borrow half a billion or have the the land available to construct more cathedrals of glass and brick to house more examination halls? Simple! We just create the conditions for that gold standard examination right there in the student’s own bedroom or the company they work for!

There are 54 pages to the Institute and Faculty of Actuaries’ (IFoA’s) guidance for remotely invigilated candidates. It covers everything from the minimum specification of equipment you need, including the video camera to watch your every movement and the microphone to pick up every sound you make, to the proprietary spying software (called “Guardian Browser”) you will need to download onto your own computer, how to prove who you are to the system, what you are allowed to have in your bedroom with you and even how you need to sit for the duration of the exam (with a maximum of two 5 minute breaks) to ensure the system has sufficient visibility of you at all times:

These closed book remote arrangements replaced the previous open book online exams which most institutions operated during the pandemic. The reason given was that the exam results shot up so much that widespread cheating was suspected and the integrity of the qualifications was at risk. The IFoA’s latest assessment regulations can be found here.

The belief in examinations is very widespread. A couple of months ago I was discussing the teacher assessments which replaced them briefly during the pandemic with a secondary business studies teacher. He took great pride in the fact that he based his assessments solely on mock results, ie an assessment carried out before all of the syllabus had been covered and when students were unaware it would be the final assessment. But still in his mind more “objective” than any opinion he might have of his own students.

If a large language model can perform enormously better in an examination than your students can without it, what it actually demonstrates is that the traditional examination is woefully unprepared for the future. As Carlo Iacono puts it:

The machines learned from us.

They learned what we actually valued and it turned out to be different from what we said we valued.

We said we valued originality. We rewarded conformity to genre. We said we valued depth. We measured surface features. We said we valued critical thinking. We gave higher marks to confident assertion than to honest uncertainty.

So now the machines produce what the world trained them to produce: fluent, confident, passable output that fits the shapes we reward.

And we’re horrified. Not because they stole something from us. Because they showed us what the systems were selecting for all along.

The scandal isn’t that a model can imitate student writing. The scandal is that we built an educational and professional culture where imitation passes as competence, and then acted shocked when a machine learned to imitate faster.

We trained the incentives. We trained the rubrics. We trained the career ladders.

The pattern recognition which gets you through most formal examinations is just too cheap and easy to automate now. It is no longer a useful skill, even by proxy. It might as well be Hogwarts’ sorting hat for all the use it is in a post scarcity education world. If the machines have worked out how to unlock the elaborate captcha system we have placed around our gold standard assessments, an arms race of security measures protecting a range of tests which look increasingly narrow compared to the capabilities which matter does not seem like the way to go.

What instead we are doing is identifying which students are prepared to put themselves through literally anything to get the qualification. Companies like students like that. They will make ideal reverse-centaurs. The description of life as a reverse-centaur even sounds like the experience of a proctored exam:

Like an Amazon delivery driver, who sits in a cabin surrounded by AI cameras, that monitor the driver’s eyes and take points off if the driver looks in a proscribed direction, and monitors the driver’s mouth because singing isn’t allowed on the job, and rats the driver out to the boss if they don’t make quota.

The driver is in that van because the van can’t drive itself and can’t get a parcel from the curb to your porch. The driver is a peripheral for a van, and the van drives the driver, at superhuman speed, demanding superhuman endurance. But the driver is human, so the van doesn’t just use the driver. The van uses the driver up.

Source: Cory Doctorow, Enshittification

And, even if you are OK with all of that, all of these privacy intrusions don’t even work to prevent cheating! The ACCA, the world’s largest accounting professional body, has just announced it is stopping all remote exams after giving up the arms race against the cheats, facilitated in some cases seemingly by their Big Four employers lying about what had gone on.

Actuarial exams started in 1850, only 2 years after the Institute of Actuaries was established (Dermot Grenham wrote about them recently here). This pre-dated the establishment of the first examination boards by a few years (1856 Society of Arts, the Society for the encouragement of Arts, Manufactures and Commerce, later the Royal Society for the encouragement of Arts, Manufactures and Commerce (Royal Society of Arts); 1857: University of Oxford Delegacy of Local Examinations (founded by the University of Oxford); and 1858: University of Cambridge Local Examinations Syndicate (UCLES, founded by the University of Cambridge)), so keen were actuaries to institute examinations. However it was the massive expansion of the middle classes as the Industrial Revolution disrupted society in so many ways that led to the need for a new sorting hat beyond the capacity of the oral examinations that had previously been the norm.

Now people seem to be lining up to drag everyone back into the examination hall. Any suggestion of a retreat from traditional exams is met by howls of outrage from people like Sam Leith at The Spectator about lack of “rigour”. However, in my view, they are wrong.

Yes of course you can isolate students from every intellectual aid they would normally use, as a centaur, to augment their performance, limit the sources they can access, force them to rely on their own memories entirely, and put them under significant time pressure. You will definitely reduce marks by doing that. So that has made it harder and therefore more rigorous and more objective, right?

Well according to the Merriam-Webster dictionary, rigorous is a synonym of rigid, strict or stringent. However, while all these words mean extremely severe or stern, rigorous implies the imposition of hardship and difficulty. So promoting exams above all as an exercise in rigour reveals their true nature as a kind of punishment beating in written form, for which the prize for undergoing it is whatever it qualifies you for. Suddenly the sorting hat looks relatively less arbitrary.

The problems of traditional exams are well known, but the most important ones in my view are that they measure a limited range of abilities and therefore are unlikely to show what students can really achieve. Harder does not mean more objective. It is like deciding who can act by throwing students out, one at a time, in front of a baying mob of, let’s say for argument, readers of The Spectator. Sure, some of the students might be able to calm the crowd, some may even be able to redirect their anger towards a different target. But are the people who can play Mark Antony for real necessarily the best all-round actors? And has someone who can only stand frozen on the spot under those circumstances really proved that they could never act well?

It also means that education ends a month or more before the exams, to allow the appropriate cramming, followed by engaging all of the teaching staff in the extended exercise of marking, checking and moderating 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 wander the floors like Victorian factory owners. But what if instead the assessment was part of the teaching process? What if students felt that their assessment had been a meaningful part of their educational experience? What if, instead of arguing the toss over whether they scored 68% or 70% on an assessment, students could see for themselves whether they had demonstrated mastery of their subject.

One model of assessment which is getting a lot of attention at the moment, one I am a big fan of having used it at the University of Leicester on some modules, is something called interactive oral assessment, where students meet with a lecturer or tutor, individually or in a small group, and answer questions about work they have already submitted. It is a highly demanding form of assessment, 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, you can be highly confident of the degree to which the student understands the work they have submitted. It also allows the student to submit a piece of work of more complexity and ambition than can be accommodated by a traditional exam. And it needn’t take any more time if the interviews are carried out online when set against the exam marking time of the traditional exam. Something which all the technology we developed through the pandemic allows us to do, without the need for spyware.

There are other models which also assess the technological centaurs we wish our students to become rather than the reverse-centaurs we are currently dooming too many to become. It is looking like it may be time to start telling students to stop writing and to put down their pens on the traditional exam. And perhaps the actuarial profession, who led us into the era of professional written examinations so enthusiastically 175 years ago, might now want to take the lead in navigating our way out of them?

Enshittification and Cory Doctorow

04. December 2025 · Write a comment · Categories: AI, Book reviews, Cartoons, Economics, Health, Legal, Media & Politics, Professions, Regulation, Science Fiction

So this is my 42nd blog post of the year and the 8th where I have referenced Cory Doctorow. Thought it was more to be honest, so influential has he been on my thought, particularly as I have delved deeper into what, how and why the AI Rush is proceeding and what it means for the people exiting universities over the next few years.

Yesterday Cory published a reminder of his book reviews this year. He is an amazing book reviewer. There are 24 on the list this year, and I want to read every one of them on the strength of his reviews alone.

I would like to repay the compliment by reviewing his latest book: Enshittification (the other publication this year – Picks and Shovels – is also well worth your time by the way). Can’t believe this wasn’t the word of the year rather than rage bait, as it explains considerably more about the times we are living in.

I have been a fan of Doctorow for a couple of years now. I had had Walkaway sat on my shelves for a few years before I read it and was immediately enthralled by his tale of a post scarcity future which had still somehow descended into an inter-generational power struggle hellscape. I moved on to the Little Brother books, now being reenacted by Trump with his ICE force in one major US city after another. Followed those up with The Lost Cause, where the teenagers try desperately to bridge the gap across the generations with MAGA people, with tragic results along the way but a grim determination at the end “the surest way to lose is to stop running”. From there I migrated to the Marty Hench thrillers, his non-fiction The Internet Con (which details the argument for interoperability, ie the ability of any platform to interact with another) and his short fiction (I loved Radicalised, not just for the grimly prophetic Radicalised novella in the collection, but also the gleeful insanity of Unauthorised Bread). I highly recommend them all.

I came to Enshittification after reading his Pluralistic blog most days for the last year and a half, so was initially disappointed to find very little new as I started working my way through it. However what the first two parts – The Natural History and The Pathology – are is a patient explanation of the concept of enshittification and how it operates assuming no previous engagement with the term, all in one place.

Enshittifcation, as defined by Cory Doctorow, proceeds as follows:

  1. First, platforms are good to their users.
  2. Then they abuse their users to make things better for their business customers.
  3. Next, they abuse those business customers to claw back all the value for themselves.
  4. Finally, they have become a giant pile of shit.

So far, so familiar. But then I got to Part Three, explaining The Epidemiology of enshittification, and the book took off for me. The erosion of antitrust (what we would call competition) law since Carter. “Antitrust’s Vietnam” (how Robert Bork described the 12 years IBM fought and outspent the US Department of Justice year after year defending their monopolisation case) until Reagan became President. How this led to an opening to develop the operating system for IBM when it entered the personal computer market. How this led to Microsoft, etc. Then how the death of competition also killed Big Tech regulation ( regulating a competitive market which acts against collusion is much easier than regulating one with a small number of big players which absolutely will collude with each other).

And then we get to my favourite chapter of the book “Reverse-Centaurs and Chickenisation”. Any regular reader of this blog will already be familiar with what a reverse centaur is, although Cory has developed a snappy definition in the process of writing this book:

A reverse-centaur is a machine that uses a human to accomplish more than the machine could manage on its own.

And if that isn’t chilling enough for you, the description of the practices of poultry packers and how they control the lives of the nominally self-employed chicken farmers of the US, and how these have now been exported to companies like Amazon and Arise and Uber, should certainly be. The prankster who collected up the bottled piss of the Amazon drivers who weren’t allowed a loo break and resold it on Amazon‘s own platform as “a bitter lemon drink” called Release Energy, which Amazon then recategorised as a beverage without asking for any documentation to prove it was fit to drink and then, when it was so successful it topped their sales chart, rang the prankster up to discuss using Amazon for shipping and fulfillment – this was a rare moment of hilarity in a generally sordid tale of utter exploitation. My favourite bit is when he gets on to the production of his own digital rights management (DRM) free audio versions of his own books.

The central point of the DRM issue is, as Cory puts it, “how perverse DMCA 1201 is”:

If I, as the author, narrator, and investor in an audiobook, allow Amazon to sell you that book and later want to provide you with a tool so you can take your book to a rival platform, I will be committing a felony punishable by a five-year prison sentence and a $500,000 fine.

To put this in perspective: If you were to simply locate this book on a pirate torrent site and download it without paying for it, your penalty under copyright law is substantially less punitive than the penalty I would face for helping you remove the audiobook I made from Amazon’s walled garden. What’s more, if you were to visit a truck stop and shoplift my audiobook on CD from a spinner rack, you would face a significantly lighter penalty for stealing a physical item than I would for providing you with the means to take a copyrighted work that I created and financed out of the Amazon ecosystem. Finally, if you were to hijack the truck that delivers that CD to the truck stop and steal an entire fifty-three-foot trailer full of audiobooks, you would likely face a shorter prison sentence than I would for helping you break the DRM on a title I own.

DMCA1201 is the big break on interoperability. It is the reason, if you have a HP printer, you have to pay $10,000 a gallon for ink or risk committing a criminal offence by “circumventing an access control” (which is the software HP have installed on their printers to stop you using anyone else’s printer cartridges). And the reason for the increasing insistence on computer chips in everything from toasters (see “Unauthorised Bread” for where this could lead) to wheelchairs – so that using them in ways the manufacturer and its shareholders disapprove of becomes illegal.

The one last bastion against enshittification by Big Tech was the tech workers themselves. Then the US tech sector laid off 260,000 workers in 2023 and a further 100,000 in the first half of 2024.

In case you are feeling a little depressed (and hopefully very angry too) at this stage, Part 4 is called The Cure. This details the four forces that can discipline Big Tech and how they can all be revived, namely:

  1. Competition
  2. Regulation
  3. Interoperability
  4. Tech worker power

As Cory concludes the book:

Martin Luther King Jr once said, “It may be true that the law cannot make a man love me, but it can stop him lynching me, and I think that’s pretty important, also.”

And it may be true that the law can’t force corporate sociopaths to conceive of you as a human being entitled to dignity and fair treatment, and not just an ambulatory wallet, a supply of gut bacteria for the immortal colony organism that is a limited liability corporation.

But it can make that exec fear you enough to treat you fairly and afford you dignity, even if he doesn’t think you deserve it.

And I think that’s pretty important.

I was reading Enshittification on the train journey back from Hereford after visiting the Hay Winter Weekend, where I had listened to, amongst others, the oh-I’m-totally-not-working-for-Meta-any-more-but-somehow-haven’t-got-a-single-critical-word-to-say-about-them former Deputy Prime Minister Nick Clegg. While I was on the train, a man across the aisle had taken the decision to conduct a conversation with first Google and then Apple on speaker phone. A particular highlight was him just shouting “no, no, no!” at Google‘s bot trying to give him options. He had already been to the Vodaphone shop that morning and was on his way to an appointment which he couldn’t get at the Apple Store on New Street in Birmingham. He spotted the title of my book and, when I told him what enshittification meant, and how it might make some sense out of the predicament he found himself in, took a photo of the cover.

My feeling is that enshittification goes beyond Big Tech. It is the defining industrial battle of our times. We shouldn’t primarily worry about whether it is coming from the private or the public sector, as enshittification can happen in both places: from hollowing out justice to “paying more for medicines… at the exact moment we can’t afford to pay enough doctors to prescribe them” in the public sector, where we already reside within the Government’s walled garden, to all of the outrages mentioned above and more in the private sector.

The PFI local health hubs set out in last week’s budget take us back to perhaps the ultimate enshittificatory contracts the Government ever entered into, certainly before the pandemic. The Government got locked into 40 year contracts, took all the risk, and all the profit was privatised. The turbo-charging of the original PFI came out of the Blair-Brown government’s mania for keeping capital spending off the balance sheet in defence of Gordon Brown’s “Golden Rule” which has now been replaced by Rachel Reeves’ equally enshittifying fiscal rules. All the profits (or, increasingly, rents, as Doctorow discusses in the chapter on Varoufakis’ concept of Technofeudalism) from turning the offer to shit always seem to end up in the private sector. The battle is against enshittification from both private and, by proxy, via public monopolies.

Enshittification is, ultimately, a positive and empowering book which I strongly recommend you buy, avoiding Amazon if you can. We can have a better internet than this. We can strike a better deal with Big Tech over how we run our lives. But the surest way to lose is to stop running.

And next time a dead-eyed Amazon driver turns up at your door, be nice, they are probably having a worse day than you are.

Wallace and Gromit and AI’s Distribution Problem

30. October 2025 · Write a comment · Categories: AI, Cartoons, Economics, Media & Politics, Professions, Regulation
Wallace & Gromit: Vengeance Most Fowl models on display in Bristol. This file is licensed under the Creative Commons Attribution-Share Alike 4.0 International license.

I have been watching Daniel Susskind’s lectures on AI and the future of work this week: Automation Anxiety was delivered in September and The Economics of Work and Technology earlier this week. The next in the series, entitled Economics and Artificial Intelligence is scheduled for 13 January. They are all free and I highly recommend them for their great range of source material presented.

In my view the most telling graph, which featured in both lectures, was this one:

Original Source: Daniel Susskind A World Without Work

Susskind extended the usual concept of the ratio between average college and university graduate salaries to those of school leavers to include the equivalent ratio of craftsmen to labourers which then gives us data back to 1220. There are two big collapses in this ratio in the data: that following the Black Death (1346-1353), which may have killed 50% of Europe’s 14th century population, and the Industrial Revolution (which slow singularity started around 1760 and then took us through the horrors of the First World War and the Great Depression before the graph finally picks up post Bretton Woods).

As Susskind shows, the profits from the Industrial Revolution were not going to workers:

Source: The Technology Trap, Carl Benedikt Frey

So how is the AI Rush comparing? Well Susskind shared another graph:

Source: David Autor Work of the Past, Work of the future

This, from 2019, introduced the idea that the picture is now more complex than high-skilled and low-skilled workers, now there is a middle. And, as Autor has set out more recently, the middle is getting squeezed:

Key dynamics at play include:

  • Labor Share Decline: OECD data reveal a 3–5 percentage point drop in labor’s share of income in sectors most exposed to AI, a trend likely to accelerate as automation deepens.
  • Wage Polarization: The labor market is bifurcating. On one end, high-complexity “sense-making” roles; on the other, low-skill service jobs. The middle is squeezed, amplifying both political risk and regulatory scrutiny.
  • Productivity Paradox 2.0: Despite the promise of AI-driven efficiency, productivity gains remain elusive. The real challenge is not layering chatbots atop legacy processes, but re-architecting workflows from the ground up—a costly and complex endeavor.

For enterprise leaders, the implications are profound. AI is best understood not as a job destroyer, but as a “skill-lowering” platform. It enables internal labor arbitrage, shifting work toward judgment-intensive, context-rich tasks while automating the rest. The risk is not just technological—it is deeply human. Skill depreciation now sits alongside cyber and climate risk on the board agenda, demanding rigorous workforce-reskilling strategies and a keen eye on brand equity as a form of social license.

So, even if the overall number of jobs may not be reduced, the case being made is that the average skill level required to carry them out will be. As Susskind said, the Luddites may have been wrong about the spinning jenny replacing jobs, but it did replace and transform tasks and its impact on workers was to reduce their pay, quality of work, status as craftsmen and economic power. This looks like the threat being made by employers once again, with real UK wages already still only at the level they were at in 2008:

However this is where I part company with Susskind’s presentation, which has an implicit inevitability to it. The message is that these are economic forces we can’t fight against. When he discusses whether the substituting force (where AI replaces you) or the complementing force (where AI helps you to be more productive and increases the demand for your work) will be greater, it is almost as if we have no part to play in this. There is some cognitive dissonance when he quotes Blake, Engels, Marx and Ruskin about the horrors of living through such times, but on the whole it is presented as just a natural historical process that the whole of the profits from the massive increases in productivity of the Industrial Revolution should have ended up in the pockets of the fat guys in waistcoats:

Richard Arkwright, Sir Robert Peel, John Wilkinson and Josiah Wedgwood

I was recently at Cragside in Northumberland, where the arms inventor and dealer William Armstrong used the immense amount of money he made from selling big guns (as well as big cranes and the hydraulic mechanism which powers Tower Bridge) to decking out his house and grounds with the five artificial lakes required to power the world’s first hydro-electric lighting system. His 300 staff ran around, like good reverse-centaurs, trying to keep his various inventions from passenger lifts to an automated spit roast from breaking down, so that he could impress his long list of guests and potential clients to Cragside, from the Shah of Persia to the King of Siam and two future Prime Ministers of Japan. He made sure they were kept running around with a series of clock chimes throughout the day:

However, with some poetic irony, the “estate regulator” is what has since brought the entire mechanism crashing to a halt:

Which brings me to Wallace and Gromit. Wallace is the inventor, heedless of the impact of his inventions on those around him and especially on his closest friend Gromit, who he regularly dumps when he becomes inconvenient to his plans. Gromit just tries to keep everything working.

Wallace is a cheese-eating monster who cannot be assessed purely on the basis of his inventions. And neither can Armstrong, Arkwright, Peel, Wilkinson or Wedgwood. We are in the process of allowing a similar domination of our affairs by our new monsters:

Meta CEO Mark Zuckerberg beside Amazon CEO Jeff Bezos and his fiancée (now wife) Lauren, Google CEO Sundar Pichai and Elon Musk at President Trump’s 2nd Inauguration.

Around half an hour into his second lecture, Daniel Susskind started talking about pies. This is the GDP pie (Susskind has also written a recent book on Growth: A Reckoning, which argues that GDP growth can go on forever – my view would be closer to the critique here from Steve Keen) which, as Susskind says, increased by a factor of 113 in the UK between 1700 and 2000. But, as Steve Keen says:

The statistics strongly support Jevons’ perspective that energy—and specifically, energy from coal—caused rising living standards in the UK (see Figure 2). Coal, and not a hypothesised change in culture, propelled the rise in living standards that Susskind attributes to intangible ideas.

Source: https://www.themintmagazine.com/growth-some-inconvenient-truths/

Susskind talks about the productivity effect, he talks about the bigger pie effect and then he talks about the changing pie effect (ie changes to the types of work we do – think of the changes in the CPI basket of goods and services) as ways in which jobs are created by technological change. However he has nothing to say about just giving less of the pie to the monsters. Instead for Susskind the AI Rush is all about clever people throwing 10 times the amount of money at AI as was directed at the Manhattan Project and the heads of OpenAI, Anthropic and Google DeepMind stating that AI will replace humans in all economically useful tasks in 10 years, a claim which he says we should take seriously. Cory Doctorow, amongst others, disagrees. In his latest piece, When AI prophecy fails, he has this to say about why companies have reduced recruitment despite the underperformance of AI systems to date:

All this can feel improbable. Would bosses really fire workers on the promise of eventual AI replacements, leaving themselves with big bills for AI and falling revenues as the absence of those workers is felt?

The answer is a resounding yes. The AI industry has done such a good job of convincing bosses that AI can do their workers’ jobs that each boss for whom AI fails assumes that they’ve done something wrong. This is a familiar dynamic in con-jobs.

The Industrial Revolution had a distribution problem which gave birth to Chartism, Marxism, the Trades Union movement and the Labour Party in the UK alone. And all of that activity only very slowly chipped away at the wealth share of the top 10%:

Source: https://equalitytrust.org.uk/scale-economic-inequality-uk/

However the monsters of the Industrial Revoution did at least have solid proof that they could deliver what they promised. You don’t get more concrete a proof of concept than this after all:

View on the Thames and the opening Tower Bridge, London, from the terraces at Wapping High Street, at sunset in July 2013, Bert Seghers. This file is made available under the Creative Commons CC0 1.0 Universal Public Domain Dedication.

The AI Rush has a similar distribution problem, but it is also the first industrial revolution since the global finance industry decoupled from the global real economy. So the wealth share of the Top 10% isn’t going back up fast enough? No problem. Just redistribute the money at the top even further up:

What the monsters of the AI Rush lack is anything tangible to support their increasingly ambitious assertions. Wallace may be full of shit. And the rest of us can all just play a Gromit-like support role until we find out one way or the other or concentrate on what builds resilient communities instead.

Whether you think the claims for the potential of AI are exaggerated; or that the giant bet on it that the US stock market has made will end in an enormous depression; or that the energy demands of this developing technology will be its constraining force ultimately; or that we are all just making the world a colder place by prioritising systems, however capable, over people: take your pick as a reason to push back against the AI Rush. But my bet would be on the next 10 years not being dominated by breathless commentary on the exploits of Tech Bros.

The AI Rush

17. October 2025 · Write a comment · Categories: Actuarial, AI, Cartoons, Education, Pensions, Professions

To Generation Z: a message of support from a Boomer

So you’ve worked your way through school and now university, developing the skills you were told would always be in high demand, credentialising yourself as a protection against the vagaries of the global economy. You may have serious doubts about ever being able to afford a house of your own, particularly if your area of work is very concentrated in London…

…and you resent the additional tax that your generation pays to support higher education:

Source: https://taxpolicy.org.uk/2023/09/24/70percent/

But you still had belief in being able to operate successfully within the graduate market.

A rational functional graduate job market should be assessing your skills and competencies against the desired attributes of those currently performing the role and making selections accordingly. That is a system both the companies and graduates can plan for.

It is very different from a Rush. The first phenomenon known as a Rush was the Californian Gold Rush of 1848-55. However the capitalist phenomenon of transforming an area to facilitate intensive production probably dates from sugar production in Madeira in the 15th century. There have been many since, but all neatly described by this Punch cartoon from 1849:

A Rush is a big deal. The Californian Gold Rush resulted in the creation of California, now the 5th largest economy in the world. But when it comes to employment, a Rush is not like an orderly jobs market. As Carlo Iacono describes, in an excellent article on the characteristics of the current AI Rush:

The railway mania of the 1840s bankrupted thousands of investors and destroyed hundreds of companies. It also left Britain with a national rail network that powered a century of industrial dominance. The fibre-optic boom of the late 1990s wiped out about $5 trillion in market value across the broader dot-com crash. It also wired the world for the internet age.

A Rush is a difficult and unpredictable place to build a career, with a lot riding on dumb luck as much as any personal characteristics you might have. There is very little you can count on in a Rush. This one is even less predictable because as Carlo also points out:

When the railway bubble burst in the 1840s, the steel tracks remained. When the fibre-optic bubble burst in 2001, the “dark fibre” buried in the ground was still there, ready to carry traffic for decades. These crashes were painful, but they left behind durable infrastructure that society could repurpose.

Whereas the 40–60% of US real GDP growth in the first half of 2025 explained by investment in AI infrastructure isn’t like that:

The core assets are GPUs with short economic half-lives: in practice, they’re depreciated over ~3–5 years, and architectures are turning over faster (Hopper to Blackwell in roughly two years). Data centres filled with current-generation chips aren’t valuable, salvageable infrastructure when the bubble bursts. They’re warehouses full of rapidly depreciating silicon.

So today’s graduates are certainly going to need resilience, but that’s just what their future employers are requiring of them. They also need to build their own support structures which are going to see them through the massive disruption which is coming whether or not the enormous bet on AI is successful or not. The battle to be centaurs, rather than reverse-centaurs, as I set out in my last post (or as Carlo Iacono describes beautifully in his discussion of the legacy of the Luddites here), requires these alliances. To stop thinking of yourselves as being in competition with each other and start thinking of yourselves as being in competition for resources with my generation.

I remember when I first realised my generation (late Boomer, just before Generation X) was now making the weather. I had just sat a 304 Pensions and Other Benefits actuarial exam in London (now SP4 – unsuccessfully as it turned out), and nipped in to a matinee of Sam Mendes’ American Beauty and watched the plastic bag scene. I was 37 at the time.

My feeling is that despite our increasingly strident efforts to do so, our generation is now deservedly losing power and is trying to hang on by making reverse centaurs of your generation as a last ditch attempt to remain in control. It is like the scene in another movie, Triangle of Sadness, where the elite are swept onto a desert island and expect the servant who is the only one with survival skills in such an environment to carry on being their servant.

Don’t fall for it. My advice to young professionals is pretty much the same as it was to actuarial students last year on the launch of chartered actuary status:

If you are planning to join a profession to make a positive difference in the world, and that is in my view the best reason to do so, then you are going to have to shake a few things up along the way.

Perhaps there is a type of business you think the world is crying out for but it doesn’t know it yet because it doesn’t exist. Start one.

Perhaps there is an obvious skill set to run alongside your professional one which most of your fellow professionals haven’t realised would turbo-charge the effectiveness of both. Acquire it.

Perhaps your company has a client who noone has taken the time to put themselves in their shoes and communicate in a way they will properly understand and value. Be that person.

Or perhaps there are existing businesses who are struggling to manage their way in changing markets and need someone who can make sense of the data which is telling them this. Be that person.

All while remaining grounded in which ever community you have chosen for yourself. Be the member of your organisation or community who makes it better by being there.

None of these are reverse centaur positions. Don’t settle for anything less. This is your time.

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