Two events recently have made me think about the last 55 years in a different way. The first was the last Birmingham Science Fiction Group meeting, which we had dedicated to a 55 year anniversary celebration for the group (the 50th anniversary having been pandemicked). We had a cake, illustrated by David Hardy’s Stratus Hotel, and we showed a film of interviews from members across the years, again featuring some of David Hardy‘s artwork, with stories about guest speakers from Asimov to Frank Herbert to Iain Banks to Jack Cohen and very frank recollections from our current honorary president, Alastair Reynolds. If you want to see the film, you need to join the Group, which you can easily do by emailing us at contact@brumsfgroup.org.uk. Seeing some of the original organisers talking about some of these meetings like they had just happened made me reflect on how we often attribute significance to things after the event and tend to downplay things happening around us now. Things are both never as good or important as they used to be and, of course, as we prove to ourselves over and over again, they also are.
The other event was a visit with my brother and his wife to where we used to live, in the officers’ married quarters of an RAF station in Yorkshire, 55 years ago, when I was eight. Just standing on this patch of grass, with a field of wheat still growing the other side of the fence (to the right), I got a very strong sense of another way of getting from there to here. Obviously there was the one I had taken, with countless house moves, school moves, marriages, careers, children, holidays, illnesses and death and grief and so much joy and laughter in the very fortunate and privileged life I have lived.
But what if I had just stood here and the 55 years had just sped past me instead. My Liverpool football kit would have been a bit tight, with the number seven sewn on the back by my Mum (actually that might have been the following year, when the full magnificence of Kevin Keegan had become apparent). Or, much more spookily, if the eight-year-old just found themselves on the same patch of grass 55 years later in a Wells’ The Time Machine kind of way. It all suddenly felt just as possible and plausible, as if the 55 year old football-and-everything-else pitch was still there within touching distance somehow.
The thing is, I could understand the world of 1971, once I had been reminded of a few things no doubt. A world of cash (just decimalised so no shillings and old pence), phones fixed to houses and telephone boxes, smaller cars filled with leaded petrol, three channels on the black and white TV. I had just driven up there in a car with bodywork comprising mostly plastics, with no spare tyre and no wallet. Just a device known as a mobile which can not only make calls, but send and receive text messages, carry out all my financial transactions and navigate me to my brother’s house. My eight-year-old self would recognise very little about this world beyond the patch of grass he stood on and the field beyond the fence where he had parachuted his mother’s childhood teddy bear.
And this is the terrifying thing about life: we are all that eight-year-old facing the incomprehensible future, and so much of what we spend our time doing is trying to deal with that fear and lack of understanding with often meaningless structures and practices and customs to ward off the sense of being totally out of control. Trump is scary because he appears to have taken a lot of the guard rails off the path we are on, with little sense that he understands what he is doing. AI is scary because it is putting its foot on the accelerator before we have a clear idea of where we are going. Climate change is scary because we no longer look like we are headed where we thought we were.
And we try and stick some grit in the wheels to slow down the disruption to our own little bit of the world, whether to our sense of ourselves, or to how we think life should be lived. And sure we need to question whether we need to change things which are working perfectly well, but often the driver for resisting change is fear of what we don’t know is coming and finding familiar things irresistibly reassuring.
And when we realise that it is not just us with our, certainly in my case, fairly small and unimportant lives (although obviously very important to us!), but also the people who seem to be shaping the world around us that feel these insecurities and uncertainties, the possibility of working together to make it easier to adapt to the very weird future that is coming suddenly doesn’t seem so outlandish after all.
I think this – what we resist, what we create and what we could nurture in the face of this terrifying future – is what I am going to be writing about for the rest of this year, as we all rattle on down the road in the back of someone’s van.
What We Can Know by Ian McEwan is a difficult book to categorise. Some might call it “cli-fi”, as it sets itself in a future significantly altered by human actions and the landscape’s response to those. The Sunday Times appears to think that it might have created a new genre entirely. I think it is best seen as an Oxford novel, even if many of Oxford’s landmarks are submerged for large parts of it. Radiohead even gets a mention. As the petty snobberies and clandestine affairs of academics play out on the much-changed landscape of 2119, earnest academics Tom Metcalfe and Rose Church explore the petty snobberies and clandestine affairs of 2014 and the quest for the long-lost poem from that year. “A Corona for Vivien” certainly feels like a poem which belongs in a past of barn conversions and dinner parties.
For these academics, The Derangement – which is how they refer to the developing crisis mindset between 2015 and 2030 – is just a backdrop to what they regard as the important drama of their lives. It is followed by the first of several climate wars in 2036 involving nuclear weapons, ironically causing a cooling before the Inundation of 2042 causes the deaths of 200 million, with the UK’s population halving and life needing to be eked out on a cluster of small radioactive islands.
I set this out because the book, while occasionally referring to the eeriness of things, carries on with the work obsessions and love lives of its characters pretty much regardless. If What We Can Know is to be believed, academia carries on much as before, with lectures and seminars and essay marking, despite all of this. For humanities departments currently under the cosh despite the absence so far of a nuclear war, this may seem unlikely.
My grumpiness about all this may be partly due to the fact that I am writing this during a series of record breaking heatwaves and unbudgeable heat domes, but I feel like I have had to make more accommodations to the climate – cancellation of plans, a whole new routine at home with the single aim of keeping the thermometer from moving up any further – than McEwan’s characters seem to be doing in their post-not-quite-apocalyptic lives. Whereas one visit to the swimming pool last month was reduced to bobbing about in water along with a couple of dozen others in a manner reminiscent of the wet bulb event at the start of Kim Stanley Robinson’s The Ministry for the Future, McEwan’s only concession to a future world that can still apparently support an academic entirely focused on a 105 year old poem which has never been seen since, seems to be that journeys anywhere are now more difficult and dangerous.
And yet Tom then travels to Snowdonia (where the Bodleian is now housed) fairly easily. And he and Rose then travel to the flooded valleys of the Cotswolds to attempt to retrieve the poem. The only difficult journey described is once they get on to the target island and have to navigate through thick vegetation without satnav (GPS is no more) or even rudimentary hiking skills between the two of them. At one point we are told the internet is on the way out and at another that there is a National Artificial Intelligence which remembers everything you have ever told it.
This book seems as complicit in the “crisis of realism in fiction” as McEwan, via Rose, accuses everything else written between 2015 and 2030 of being. However it is an entertaining Oxford novel, with tense conversations in parked cars and breathtaking egomaniacs everywhere you look.
What we can’t know is what Tom and Rose will make of it in 100 years’ time.
Isaac Newton may or may not have been nutted by an apple. His friend William Stukeley, whose memoir of Newton was the source of the story, states it as follows (spelling and punctuation from Stukeley’s manuscript at the Royal Society):
“why should that apple always descend perpendicularly to the ground,” thought he to him self: occasion’d by the fall of an apple, as he sat in a comtemplative mood: “why should it not go sideways, or upwards? but constantly to the earths centre? assuredly, the reason is, that the earth draws it. there must be a drawing power in matter. & the sum of the drawing power in the matter of the earth must be in the earths center, not in any side of the earth. therefore dos this apple fall perpendicularly, or toward the center. if matter thus draws matter; it must be in proportion of its quantity. therefore the apple draws the earth, as well as the earth draws the apple.”
However there may be stronger grounds for believing that Newton nutted English mathematics and, as a result, the actuarial profession.
I thought all this to myself, occasion’d by participation in the Actuarial Teachers and Researchers Conference (ATRC) this week. It was great. There were lots of interesting talks, and people were very engaged and respectful in how they discussed them. There was a lot of expertise in the room about both education and actuarial practice and theory. It felt like it was a room that could take on difficult topics and make progress in tackling them.
There were several calls for the need to change assessment, one of them by me, whether due to large student cohorts and the difficulties of engaging them, or the ease with which AI can duplicate examination solutions or because the credentials provided by those assessments seem increasingly irrelevant to the actual actionable skills, knowledge and experience required to operate successfully as an actuary. And there was an equally robust response from the Institute and Faculty of Actuaries (IFoA) to the effect that assessments would not be changing significantly in the latest changes due to come in by 2029.
My favourite presentation was by Angus Macdonald, about his recent short paper on Newton, Leibniz and Actuarial Science. It conjectures that the argument between Leibniz and Newton over who invented calculus resulted in the stagnation of mathematics in English universities and meant that actuaries needing to gain respectability for their advice were forced to create a professional body (in company with architects, accountants and engineers) rather than rely on universities to develop actuarial thinking as happened in continental Europe. This stagnation (Macdonald quoted G.H.Hardy, talking about the Cambridge mathematics exams, saying that they had “. . . effectively ruined serious mathematics in England for a hundred years”) was felt across the whole Anglosphere, with attempts to create institutions with equal status to the traditional universities leading to the establishment of business schools in the USA in the late 19th and early 20th centuries.
This explained a lot for me. The suspicion of our actuarial courses amongst some of our mathematics colleagues at times at Leicester. The occasionally uneasy relationship between the IFoA and the accredited universities where students can gain exemptions from their exams. The horror from some at the very idea that an actuary might qualify as a result of university courses, like they can in the Netherlands, for instance. ATRC seemed the appropriate place to explore these ideas.
Angus Macdonald considers that the Newtonian and Leibnizian branches have nearly rejoined, but I feel that our actuarial education system still suffers from the long shadow cast by these two 17th century gentlemen and their personal enmity. The ferocity with which some within the profession opposed what they saw as a threat to the popularity of the fellowship qualification posed by the new chartered actuary designation was hard to explain when the number of practising certificates issued for chief actuaries, pension scheme actuaries, etc was only around 1,100 (out of over 17,000 fellows), but if the whole profession originated from a highly developed form of status anxiety compared to academics, it becomes much easier to understand. As they say, the apple doesn’t fall far from the tree.
Now, nearly 180 years on from this “unstoppable” “rush to respectabilize” (according to Jeremy Paxman) I think it is legitimate to ask:
Why does the IFoA still insist on detailed accreditation of individual university courses on basic mathematics, statistics and business economics? Do they not trust them to teach them right? Do they think that there is something better about learning these universal subjects via a bespoke course created by a tiny professional body with 34,000 members globally?
Why are they so concerned about what assessment methods the universities use? Do they consider that they have more educational expertise on this than some of the leading universities in the world?
I have written previously about the changes in assessment I think are necessary and floated some ideas about where actuarial education might sit to accommodate these. My view was that the IFoA, faced with the need to innovate at all levels of its education system at a time of great uncertainty, might wish to get out of the foundational mathematics and business education tuition of the core principles subjects and leave this to the university system, in a truly Leibnizian way.
In actuarial science, the list of UK universities offering accredited courses has not changed for some time, and it is a small group within the university system. These courses must be seen as vulnerable. I don’t think it is a coincidence that it is so hard to persuade one of this tiny band to run the ATRC. This week’s was the first since 2021 and the list of past hosts is not very long:
The IFoA needs to ask itself what it would do if there were some high profile closures of actuarial courses and whether perhaps it might be time to protect the core mathematical education arrangements for actuaries through a broad brush accreditation of a wider range of courses, rather than the very detailed accreditation process it currently carries out on core principles subjects.
I also doubt that it has the capacity on its own to significantly restructure its assessments beyond the objective based assessments it has developed to work within its proctored environment.
The core practice and specialist subjects leading to fellowship are the areas where actuaries have, rightly, the strongest views about content and assessment structures. It surely makes sense that the expertise within the profession and the university system are pooled in a joint endeavour rather than risking both failing independently.
In contrast to Newton, who had quite a temper, Leibniz was famously parodied by Voltaire in his Dr Pangloss character in Candide who believed that “All is for the best in this best of all possible worlds.”
Let’s not be too Panglossian about the current state of actuarial education.
As I discussed in How Not To Be A Reverse-Centaur (Cory Doctorow defines a reverse-centaur as “a machine that is assisted by a human being, who is expected to work at the machine’s pace”) our actuarial education system needs to change. This has been true for some time, but the development of AI systems has both demonstrated why more clearly and accelerated the timeframes over which action is needed. As I said in December last year:
A large part of the education of the future will need to be about equipping us all to understand what we now have access to and when and how to access it. We will all have different things we are interested in, or end up involved with and needing to be educated about. It will be up to each of us to decide which things are worth the difficulty of learning, but to make those decisions we will need education that can support the development of judgement.
For education institutions, the question will be what is not worth the difficulty of learning? Credentialising based on now relatively meaningless assessment methods will not cut it. This is where the confrontation with employers and politicians is likely to come. Essential skills and their related knowledge will be better developed and assessed via more open-ended project work and online assessment of it to check understanding. These will need to become the norm, with written examinations becoming less and less prevalent. Not because of fear of cheating and plagiarism, but because an outcome which can be replicated that easily by AI is not worth assessing in the first place.
Suppose you were to ask someone who had never seen our academic system that, in order to assess whether someone else we wanted them to employ in their organisation would do a good job for them, we would:
Award marks for what has been written in answer to academic questions about what the students can remember unaided about the content of their lecture courses and reading lists with a biro on a pad of paper perched precariously on a tiny wooden table surrounded by hundreds of other similar scribblers, for a set period of time as minders wandered the floors like Victorian factory owners.
These marks would be based on marking criteria they would never see.
We would then get together in a secret huddle for a couple of months to mark all these scripts, check them and “moderate” them to check they were in line with the previous years’ exercises.
Then out would come the answer (like the Answer of 42 to the Ultimate Question of Life, the Universe and Everything in Douglas Adams’ The Hitchhikers Guide To The Galaxy).
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.
Epistemic rigour. They will be more likely to spot when a system or model is over-confident given the evidence, after an intense experience of interrogating models and evaluating evidence in their courses.
Synthesis. They will be able to integrate different perspectives into an overall understanding, through dissertation development, defending a position, understanding weaknesses in a position and adjusting accordingly.
Judgement. They will have been asked to make many judgements in their work and had to defend them in discussions with their supervisors and fellow students. Unconvincing opinions, not backed by evidence, will not pass muster.
Cognitive sovereignty. Students will have to take their own stand on their work, after all of the challenge and argument. Independence of thought is the hard-earned outcome here.
And then they will have a fighting chance of being centaurs in the world awaiting them, masters of the technology and opportunities available, rather than the reverse-centaurs that our education system is currently, and inadvertently, preparing them to be.
It all started for me in May 2017, with the challenge from Daniel and Richard Susskind in their book “The Future of the Professions”, which set out two possible futures for the professions. Either:
They carry on much as they have since the mid 19th century, but with the use of technology to streamline and optimise the way they work; or
Increasingly capable machines will displace the work of current professionals.
Their research suggested that, while these two futures would exist in parallel for some time, in the long run the second future would dominate. The actuarial profession was going to be particularly vulnerable. As the Susskinds wrote:
Accountants and consultants, for example, are particularly effective at encroaching on the business of lawyers and actuaries.
I stood in the Institute and Faculty of Actuaries Council elections that month, on a platform saying that we needed to urgently respond to this challenge. I didn’t get elected.
However, at the University of Leicester we pursued a curriculum transformation programme in response to this challenge aimed at developing actuaries of the future who had:
Highly developed presentation skills, both in writing and in speech
Great team working skills
Strong IT skills – comfortable with working with data
Clarity about why they are there and the desire to use their skills to solve problems
The 2017 post also talked about emerging trends which had hardly started at all yet:
The end of reserved roles for actuaries
Different ways of communicating advice
Online self-help for users of actuarial advice
The advance of roboactuaries and their assistants
A paper called 2036: An actuarial odyssey with AI written for the Society of Actuaries in July 2016 by Dodzi Attimu and Bryon Robidoux discussed the possibilities for robo actuaries and robo actuarial analysts.
They estimated that robo actuarial analysts would be with us in 5-10 years and would provide:
A system that has limited cognitive abilities but can undertake specialized activities, e.g. perform the heavy lifting in model building (once the specification/configuration is created), perform portfolio optimization, generate reports including narratives (e.g. memos) based on data analysis, etc.
Whereas a robo actuary was more like 15-20 years away and which they also helpfully described:
We mean a software system that can more or less autonomously perform the following activities: develop products, set assumptions, build models based on product and general risk specifications, develop and recommend investment and hedging strategies, generate memos to senior management, etc.
Then, in 2018, there was the whole Bullshit Jobs argument posed by the late David Graeber, discussing Keynes’ prediction in 1930 that:
In quite a few years – in our own lifetimes I mean – we may be able to perform all the operations of agriculture, mining, and manufacture with a quarter of the human effort to which we have been accustomed.
Graeber, in Bullshit Jobs, pointed out that this never happened, despite pretty much all of the technological developments and income increases which Keynes predicted. He suggested that the future which the Susskinds were predicting is already happening in terms of needing fewer people to fill the meaningful roles within organisations but that, rather than employing fewer people, we are either creating “bullshit” jobs which even the people doing them can see no point to or bullshitizing existing roles for which the meaningful need has passed. It was as if the organisations themselves have attempted to maintain the outward appearance of the same structures by disguising the hollowing out of so many of their functions with simulated business.
Why? One of the reasons he thought the situation had been allowed to develop was that noone believed that capitalism could produce such an outcome. Graeber gave the example of the creation of Obamacare, where Barack Obama “bucked the preferences of the electorate and insisted on maintaining a private, for-profit health insurance system in America” in order to protect jobs in health insurance.
Then we had the pandemic, and the painful return to work which found that people were not necessarily keen to return to populating those office empires, preferring to work remotely. Some of the attempts by the captains of industry to get them back were a little desperate.
Now nine or ten years on from the initial challenge, we are deluged in articles about how AI is impacting different areas of actuarial work, whether it is already replacing graduate roles and what actuarial students need to do to make themselves employable. And now the blinkers also seem to have come off about capitalism not producing the need for fewer jobs.
Historically, accountancy firms have typically had a pyramid structure – wide base, heavy graduate recruitment. Firms are now starting to talk about a ‘diamond model’ with a wide middle tier of management because, ultimately, AI is not sophisticated enough yet to make those judgment calls.
But hang on a moment. Now there is something called an “AI boomerang”. Sam Altman of Open AI and Dario Amodei of Anthropic have both been backtracking on their predictions of job lay offs due to AI. As the Gold and Geopolitics Substack puts it:
Two-thirds of the companies that ran AI-driven layoffs last year are now rehiring. One in three spent more on the rehiring than they saved on the original cuts. Robert Half calls them “AI boomerangs” – which is the name a consultant invents when there’s a market in unwinding what the last consultant sold you.
The last few years of AI-generated headlines (in both senses of the phrase) have been quite the rollercoaster.
I do not want to add to the deluge, as currently you would need a LLM summarising reports for you 24/7 just to keep up with it as it is.
And there are good reasons for not rushing to judgement on this. It is only six weeks ago that the Bank of England’s Head of Financial Stability warned that global stock markets are overvalued. As she said:
The thing that really keeps me awake at night is the likelihood of a number of risks crystallising at the same time – a major macroeconomic shock, confidence in private credit goes, AI and other risky valuations readjust – what happens in that environment and are we prepared for it?
Since then, the economics of the major AI players has only got more bonkers. In this environment, there is considerable uncertainty about what students should be learning to prepare them for the world they will need to rebuild from the rubble of the current one.
Daniel Susskind reckons a “no regret” strategy for education would focus on the basics, which he describes as literacy, numeracy and critical thinking, and the critical use of AI. Carlo Iacono talks about the work which will always be hard to automate as follows:
The work that depends on reading a room. The work that relies on institutional memory. The work where the important fact is not in the document. The work where the answer is political, ethical or relational as much as technical. The work where being right is not enough, because someone has to be accountable for the consequences.
But you have to get into the organisation first before you can develop much of this. I remain reasonably comfortable with our prescription from 2017 in terms of broadly what the curriculum should be trying to achieve. Curriculum changes take time and, tempting though it is to pile into syllabus changes aimed at incorporating the latest cutting edge technological developments, the likelihood is that you will be arriving at the wrong fire. As a social media post this week put it:
The danger is that, as Agentic AI in particular looks so much like the predictions of 2016, we declare it the new messiah and bet everything on this being the future. I certainly think that 2036 will look very different to now, but I am not convinced that we have the shape of it yet. This may just be another bullshit alternative, waiting for the next crisis to mutate into something else.
What we can say with certainty is that the future for early professionals is as uncertain as any of us can remember. And, in my view, the best way to support our young people starting their professional lives (or whatever is going to replace professional lives) is to make the value they add much clearer to anyone who might want to work with them in the future.
Future education systems are going to need to help their graduates demonstrate their value in ways they haven’t historically needed to. AI has not brought new problems, it has accelerated existing ones. And the gap between actual actionable skills, knowledge and experience and the credentials which are supposed to represent them is the key one I believe as far as those graduates are concerned.
Because currently the companies who might want to take them on have to guess what they can do to a large extent. What does a 1st mean? A 2.1? Do the differences matter to their employer? Should they interrogate the details of student courses? How would they incorporate that into a manageable recruitment process? Some people see a future of AI generated applications by the thousand per student doing battle with AI powered triage systems operated by potential employers.
This is already an issue in academia. The conclusions of one academic paper on submissions to one major academic journal were stark:
Submission volume has risen 42% since the late 2022 release of ChatGPT, while writing quality has declined. The rise in AI-generated writing accounts for nearly all of these trends.
On the other hand, researchers complain about the increasing use of AI peer review to try and cope with these increased volumes, and some have tried to game the LLMs they believe they are dealing with.
I think we can do better than this in actuarial teaching and learning.
I didn’t understand statistics until I started taking actuarial exams that required me to master particular statistical techniques, decide which ones were appropriate to the problem I was looking at, apply them accordingly and be able to interpret what the results did and didn’t tell me. An A at GCE O level in maths, and A at GCE A level in both maths and further maths and a maths degree from Oxford did not give me those abilities.
I didn’t understand economics until I started putting together modules which could be taught on both BSc and MSc courses and then teaching them. My economics module on the way to qualifying as an actuary, based very heavily on Economics by Begg, Fischer and Dornbusch, did not give me that understanding.
There is a pattern here: the most impactful experiences we have are frequently at a bit of a distance from the credentials we present to the world. Your education is not a paragraph on your CV, it is your lived experience, sometimes assisted by, sometimes actively hindered by and often pursued completely independently of the educational institutions you have had a relationship with during your life.
Daniel Susskind has marched into this often fraught relationship between credentials and actual actionable skills, knowledge and experience in the last lecture of his Gresham College series on The Future of Work, called Education – And Its Limits. His analysis is a very clear expression of the problems that will be created if AI systems prove to be half as capable and long-lasting as people from OpenAI and Anthropic are telling us they will be.
Susskind argues that trying to future-proof students through education was a hopeless task and that working on the assumption of unresolvable uncertainty was the better way forward. He suggests a “no regret” strategy for education would focus on the basics, which he describes as literacy, numeracy and critical thinking, and the critical use of AI. Others in the audience suggested some other “basics”: communication skills for instance.
The other strand of Susskind’s basics was critical use of AI. And his challenge to the audience was whether we can teach AI without losing the basics in the process.
After a bit about the need to make continuing education in later life more accessible, he looked at his list from a previous lecture (which I briefly touched on here) of problems for a post AI future:
Distribution (replacing wages);
Contribution (how do you “pull your weight”);
Power (domination by Big Tech on economics, politics, liberty, social justice and democracy); and
Meaning (fulfilment in life).
And this is why I continue to watch Susskind’s output, because he is the unusual combination of an extremely orthodox economist (see his book Growth: A Reckoning for proof of this) and someone who has been wrestling with the challenges of more capable systems to our way of doing things for over 10 years. What Susskind gives you is a peek at how the actual economists advising our governments would deal with things if OpenAI and Anthropic are proved right. It is as if you had someone who both thought, as Ben Bernanke, Chair of the Federal Reserve, did in October 2007, that “the banking system is healthy” and also that the banking collapse was going to happen anyway.
Because what he reveals is that, if Anthropic are right, orthodox economists have really got no policy prescriptions worthy of the name.
On 1. Distribution: Susskind acknowledges that, if the labour market could not redistribute wealth effectively any more, then a bigger state would be needed to do so (but he was at pains to emphasise that this would not be the 20th century central planning type of state).
On 2. Contribution: Susskind thinks that perhaps we should allow people to make non-economic contributions! As if all of the activity in society which economists routinely ignore really wasn’t already happening!
On 3. Power: Susskind says the political power of Big Tech with regard to liberty, social justice and democracy is a problem. We have anti-trust legislation that can deal with Big Tech’s economic power, but not its political power. I don’t know what kind of political power he thinks Big Tech would have without the economic power that has been granted it by a steady erosion of that anti-trust legislation over recent years.
On 4. Meaning: he has little to say other than something about us currently having policies for work but not for leisure.
And in all of this, there is the implicit underlying assumption of a stable future environment for all of this tech to operate within.
For me it brought to mind something a friend of mine who was in a tent with Cory Doctorow at the How The Light Gets In festival at Hay-on-Wye last week. “He has a theory of change” he said.
He really does, including about how to break the economic power of the Big Tech companies. You all need to read Enshittification for the full account, but my review of the book here is a sneaky peek.
Susskind really really does not have a theory of change. Which tells me that the economics profession does not have one either.
However I continue to watch him as I find he goads me into thinking what some better answers might be to the questions he asks. And perhaps also a better question than whether we can teach AI without losing the basics in the process. I think a better question would be what do we need to learn when the future is uncertain.
First of all, let’s remind ourselves of the problem. If noone cares how your advice was constructed, but your client can get advice that ticks the compliance box more cheaply and quickly from an AI system, while the experienced professional still has some role in managing the process, it may increasingly be a struggle to justify the cost of the junior colleague. So the future education system is going to need to help that future junior colleague demonstrate their value in ways they haven’t historically needed to. AI has not brought new problems, it has accelerated existing ones. And the gap between actual actionable skills, knowledge and experience and the credentials which are supposed to represent them is currently the key one as far as that future junior colleague is concerned.
The temptation is to rush into syllabus changes towards what currently looks like the cutting edge activity. I agree with Susskind here that this would be a mistake. He cites the example of Michael Gove, amongst many other education ministers at the time, mandating the teaching of coding in 2014. Now we find that the new AI systems (despite the problems highlighted by Hannah Fry, Kyle Kingsbury and others I talked about here) are most suited to writing code and Anthropicclaim that Claude is now writing 80% of its own code. Programmers are saying that, on the famous XKCD cartoon above, they are now living on the theory curve.
But both higher education institutions and the professions who still want to be in the game of developing the next generation of professionals can do a lot more both to reduce the gap between credentials and actual actionable skills, knowledge and experience and to make it clearer to employers that they have done so. That will be the subject of my next post.
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.
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:
However, if we look at the United States, which tends to show us where the UK finance sector is going, it looks far more ominous:
Yesterday Sky News ran a story about Standard Chartered‘s CEO who, in his desperation not to describe over 7,500 job losses as cost cutting, said this:
It’s not cost-cutting. It’s replacing in some cases lower-value human capital with the financial capital and the investment capital we’re putting in.
We may need to sit with that statement for a little while.
Daniel Susskind talks about this risk in his latest lecture entitled A World Without Work: in summary, to paraphrase only slightly, sure relatively junior white collar roles may already be particularly hard hit by AI, but he is optimistic because of the impact on GDP and we cannot pause because of China. He then goes on to talk about the four problems he sees for a post AI future:
Distribution (replacing wages);
Contribution (how do you “pull your weight”);
Power (domination by Big Tech on economics, politics, liberty, social justice and democracy); and
Meaning (fulfilment in life).
Susskind has gone from thinking that the fear that AI is coming for your jobs was overblown and that it was just task encroachment that we faced, to now thinking that it may encroach on all the tasks in most fields. Jevons Paradox (that technological innovation that increases the efficiency of a resource’s use leads to a rise in consumption of that resource) is no comfort if that new demand is robot-met.
Carlo Iacono suggests that the move of junior roles to AI may be subtle to begin with:
The weakness among young workers may appear as fewer people entering employment from outside the workforce. Firms may not fire large numbers of juniors; they may simply hire fewer of them.
That matters. The labour market can look healthy while the entry path narrows. Senior workers stay employed. Output rises. Productivity improves. There is no dramatic wave of redundancies.
Yet the first rung is being taken out.
It may also be masked by the fact that there remains a shortage for actuaries beyond the entry roles. There is almost a hint of desperation to approaches like this looking for introductions from a retired actuary like myself:
(followed by a list of clients he is working for)
Source: recruitment consultant who will remain anonymous. I am assuming “candies” are candidates
And, even if you think the risk of the AI Bubble bursting soon, taking down the global stock markets underpinned by the Magnificent 7, is exaggerated, you do need to be suspicious about the current abilities of AI to replace junior staff. My experience with another, somewhat earlier, actuarial technology, the pensions valuation engine, would suggest that the outputs need to be analysed very carefully before sharing with a client: it often had dependencies between what should have been independent variables hidden in the programming, or vagaries in the setup which left out non-standard benefit rules for your particular scheme, for instance. Or the student who had set it up initially (a complicated process usually) might have made a mistake or you might have not communicated with them very well to start with. Or a hundred other things.
For whatever reason, there was often still a lot to do after the valuation engine had produced some output.
Can this sort of thing happen with agentic AI? Well think about that student programming the valuation engine, but on steroids. Its patchy capabilities combined with its basic psychopathy leads to, as Hannah Fry entertainingly demonstrates here, some serious problems arising with the agent’s relentless to and fro with the large language models it depends upon, asking them what it should do next. As Hannah says:
I built an AI agent. She opened a shop selling novelty mugs, emailed a journalist without being asked, and then leaked our passwords to a total stranger.
As Kyle Kingsbury wrote about having an AI agent as a colleague in a programming team:
Imagine a co-worker who generated reams of code with security hazards, forcing you to review every line with a fine-toothed comb. One who enthusiastically agreed with your suggestions, then did the exact opposite. A colleague who sabotaged your work, deleted your home directory, and then issued a detailed, polite apology for it. One who promised over and over again that they had delivered key objectives when they had, in fact, done nothing useful. An intern who cheerfully agreed to run the tests before committing, then kept committing failing garbage anyway. A senior engineer who quietly deleted the test suite, then happily reported that all tests passed.
You would fire these people, right?
Yet despite all this, the money continues to pour in to the construction of AI infrastructure. There are already websites up and running for all of the parts of tasks AI cannot encroach upon.
The bottom rung of the actuarial ladder is clearly in danger. This is a particular problem for the actuarial profession, which has traditionally relied on longer periods of work-based training for its future qualified actuaries than many other professions. Training to become an actuary takes a long time. Median time to fellowship is still around six years, with some taking up to ten or giving up. The exams are hard to pass. There have been attempts by the profession to tackle some of these disincentives: the Chartered Actuary designation to make a destination of the generalist qualification before the specialisation of the fellowship, championed on this blog and launched in the teeth of opposition by some fellows, being one example.
It has led to a culture within actuarial firms around managing the extended time in training, with rituals around study leave and results days. One of the fears expressed in opposition to the introduction of the Chartered Actuary designation was that, if this could be achieved almost entirely within formal education at universities, the value of working alongside experienced actuaries would be lost.
It has led to a culture within the profession itself of managing large parts of its education system in house. Half of its revenue and around 30% of its expenditure are on “pre-qualification learning and development”. Sometimes it looks more like an education business with a professional side hustle.
But then the new AI toys have come along, and it turns out that many of those experienced actuaries may be less keen on graduates coming in and needing supervision from them after all. Many of them may rather spend hours on AI prompts than on developing another human being.
I fear that, increasingly, companies are not going to accommodate actuarial students in their work plans without significant persuasion. And, if the number of students studying while in work falls, the profession itself is going to struggle to finance its own bespoke education system at an acceptable cost to its members.
It will be hard for the profession to challenge this too: it is going to be good for many of those already established in their roles as the market for more experienced actuaries, when the market has no interest in developing the actuaries of the future, becomes increasingly competitive.
If the actuarial profession does accept the challenge of protecting the pipeline of future experienced actuaries it will need to review its entire education syllabus through this lens. It will also need to engage with other partners involved in what is in effect a problem of capital formation and collective action: government incentives may be needed to encourage firms to continue to train early career professionals and discourage free-riding. There may be no way back for the student with no actuarial qualifications learning on the job. The universities may be needed to plug people in at a different career point, which will require them to innovate themselves even further into the professional training role than ever before. As Carlo Iacono points out:
educational institutions may be pushed to simulate more of the apprenticeship environment. That does not mean adding a thin “AI literacy” module. It means creating settings where students practise judgement under uncertainty, in realistic workflows, with feedback that is close enough to hurt and useful enough to teach.
It will not be at all easy. But the alternative is a future without opportunity for those who do not already have it and an ageing profession withering on the vine it refused to nurture.
The Wetherspoons pub The Mary Shelley in Bournemouth
A few months ago I decided to read Mary Shelley’s Frankenstein for the first time. I also watched Guillermo del Toro’s Frankenstein, on a big screen, despite, according to The New Yorker, it having been “Netflixed down to size”.
Shelley’s book is largely monologues of interior thoughts of Frankenstein and his creation, with wildly careering emotions and death, death, death everywhere – perhaps unsurprising from an author whose mother died 10 days after giving birth to her, who lost a child and whose half sister died by suicide while she was working on Frankenstein, with much more tragedy to follow after its publication. There is a word Mary Shelley uses more than I have read in any other book: variants of sympathy/sympathise/sympathies turn up 32 times. Because of course one of the many things the book is all about is mutual incomprehension of the creator and the created.
Last week I was in Bournemouth as a last minute substitute for Lanzarote, something I may come back to at a later date, and I stumbled across the churchyard of St Peter’s Church in which Mary Shelley was buried, along with the cremated heart of her husband Percy Shelley, at the age of 53. There is also a pub in Bournemouth named after her (above) but whose sign depicts the monster from her most famous piece of writing.
As we enter another time of mutual incomprehension of the creator and the created, I have been reading the surprisingly-difficult-to-access paper by Kyle Kingsbury (the systems engineer, not the MMA guy) called The Future of Everything is Lies, I Guess. I will put a link to an X account which shared it here, as going to the aphyr.com site to read it seems to generate this message:
Once you can read it though, it starts to sketch out a likeness of our current monster and chip away a little at the human side of the mutual incomprehension. I am talking, of course, about what people are currently calling “AI”, which Kingsbury defines as:
…a family of sophisticated Machine Learning (ML) technologies capable of recognizing, transforming, and generating large vectors of tokens: strings of text, images, audio, video, etc. A model is a giant pile of linear algebra which acts on these vectors. Large Language Models, or LLMs, operate on natural language: they work by predicting statistically likely completions of an input string, much like a phone auto-complete. Other models are devoted to processing audio, video, or still images, or link multiple kinds of models together.
The article sets out how this is a technology where nobody really understands why it has been successful or how to make it better, which falls into strange loops or attractors, has odd gaps in its capabilities and is highly sensitive to slight changes in its formatting. It is a technology which is simultaneously highly capable and an idiot. And Kingsbury worries that our culture is not ready for such a technology. As he says:
As LLMs etc are deployed in new situations, and at new scale, there will be all kinds of changes in work, politics, art, sex, communication and economics. Some of these effects will be good. Many will be bad. In general, ML promises to be profoundly weird.
Buckle up.
He continues:
Most people seem concerned with conscious, motivated threats: AIs could realize they are better off without people and kill us. I am concerned that ML systems could ruin our lives without realizing anything at all.
There follow extensive examples of the problems the various ML applications are already starting to cause and some speculation about where things may be going in various areas of our lives before we get to the chapter on work. And the subject of hiring “AI employees”. This is probably my favourite bit:
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?
Kingsbury sees the two extremes of the possible range of outcomes as:
ML systems continue to hallucinate, cannot be made reliable, and ultimately fail to deliver on the promise of transformative, broadly-useful “intelligence”. Or they work, but people get fed up and declare “AI Bad”…a lot of ML people lose their jobs, defaults cascade through the financial system, but the labor market eventually adapts and we muddle through. ML turns out to be a normal technology.
In the other extreme, OpenAI delivers on Sam Altman’s 2025 claims of PhD-level intelligence, and the companies writing all their code with Claude achieve phenomenal success with a fraction of the software engineers. ML massively amplified the capabilities of doctors, musicians , civil engineers, fashion designers, managers, accountants, etc, who briefly enjoy nice paychecks before discovering that demand for their service is not as elastic as once thought, especially once their clients lose their jobs or turn to ML to cut costs. Knowledge workers are laid off en masse and MBAs start taking jobs at McDonalds or driving for Lyft, at least until Waymo puts an end to human drivers. This is inconvenient for everyone: the MBAs, the people who used to work at McDonalds and are now competing with MBAs, and of course bankers, who were rather counting on the MBAs to keep paying their mortgages. The drop in consumer spending cascades through industries. A lot of people lose their savings, or even their homes. Hopefully the trades squeak through. Maybe the Jevons paradox kicks in eventually and we find new occupations.
In the following chapter Kingsbury speculates on what some of those new occupations might be:
Incanters. People who can prompt LLMs into getting what is wanted.
Process Engineers. People who help catch LLM errors. They build quality control processes – training people, identifying where more intense review is needed, assessing the cost-benefit trade offs of automating tasks, etc
Statistical Engineers. People who try and measure, model and control variability in ML systems.
Model Trainers. This will become increasingly difficult as the amount of false content or “slop” increases across the internet.
Meat Shields. People who are accountable for the errors of the ML systems they supervise.
Haruspices. People responsible for going through the model inputs, outputs and internal states of a ML system which has done something terrible to try and give a plausible reason for its behaviour.
But ultimately Kingsbury concludes that we should just stop using these systems. To return to the original analogy, the monster cannot be understood. There is often nothing actually there to understand. And it is certainly not in the business of understanding you. Although it may be very very good at convincing you otherwise.
On balance I think my view is currently at the muddle-through-with-ML-as-a-normal-technology end, which still looks likely to cause a disruption considerably bigger than 2008. My main reason is the already collapsing trust in many of the Big Tech companies. Trust which is going to be required even if their technology really can do some of this stuff. It is the scenario where we all get fed up and declare “AI Bad”. Like when we read about the people running Meta showing nowhere near the social responsibility commensurate with their current level of market power.
Or when, as last week, we have days and days of breathless commentary about Anthropic’s Mythos and Project Glasswing, and how its immense capabilities caused the company not to release it, sparking a meeting of central bankers to discuss the threat such technologies posed to financial systems. Only to finally read an account of attempts to verify any of what Anthropic have been saying. It is quite a technical piece, which I by no means understand all of, but the final paragraph is fairly arresting:
The most important thing in the Mythos release is not the model. It is the precedent. Anthropic has established, without discussion and without pushback, that a private company can unilaterally classify a capability as too dangerous for the public, grant selective access to the largest incumbents in the affected industry, and construct a parallel disclosure regime outside any democratic accountability structure. That precedent is exclusivity for abuse. It will be used by companies with worse judgment than Anthropic and narrower definitions of “partner” than the Glasswing consortium. The time to object to the shape of this thing is while it is still being built, not after it has removed all transparency and accountability.
How might Claude or ChatGPT respond to being designated “AI Bad”? Well Mary Shelley’s monster put it this way:
Once I falsely hoped to meet with beings who, pardoning my outward form, would love me for the excellent qualities which I was capable of unfolding. I was nourished with high thoughts of honour and devotion. But now crime has degraded me beneath the meanest animal. No guilt, no mischief, no malignity, no misery, can be found comparable to mine. When I run over the frightful catalogue of my sins, I cannot believe that I am the same creature whose thoughts were once filled with sublime and transcendent visions of the beauty and the majesty of goodness. But it is even so; the fallen angel becomes a malignant devil. Yet even that enemy of God and man had friends and associates in his desolation; I am alone.
Seven years ago I wrote about Catch 22 and actuarial practice, concluding, rather piously:
If we want far fewer actuaries to be employed in not growing alfalfa in the future and far more working on making the finance structures of our economy work better, whether to support a Green New Deal or more generally, we first need to embrace the idea that our current economic priorities are indeed insane.
So imagine my excitement at finding Catch 22 grabbed out of the pages of fiction and informing US foreign policy. Not convinced? Compare two passages. The first, from Catch 22, in 1961:
This time Milo had gone too far. Bombing his own men and planes was more than even the most phlegmatic observer could stomach, and it looked like the end for him. High-ranking government officials poured in to investigate. Newspapers inveighed against Milo with glaring headlines, and Congressmen denounced the atrocity in stentorian wrath and clamored for punishment. Mothers with children in the service organized into militant groups and demanded revenge. Not one voice was raised in his defense. Decent people everywhere were affronted, and Milo was all washed up until he opened his books to the public and disclosed the tremendous profit he had made. He could reimburse the government for all the people and property he had destroyed and still have enough money left over to continue buying Egyptian cotton. Everybody, of course, owned a share. And the sweetest part of the whole deal was that there really was no need to reimburse the government at all.
This week, the US Treasury lifted all oil sanctions on Iran. For 30 days. 140 million barrels of Iranian crude, sitting on ships at sea, may now be sold freely on the global market. Including to the United States itself.
In yuan.
The United States is purchasing, with Chinese currency, oil from the country it is currently bombing?! The same oil that funds the missiles that just shot down an F-35 for the first time. The same missiles that are redecorating allied oil infrastructure.
Treasury Secretary Bessent called this “narrowly tailored”. Narrow like in white, and tailored as in card, apparently.
In the same OFAC filing, Russian oil sanctions were lifted as well. And Belarus potash too, because apparently the universe was running low on irony and needed to top up.
The logic, insofar as there is any, goes like this: the war has crashed the global oil market so hard that the administration needs the enemy’s oil to keep gasoline prices from eating the midterms. They are unsanctioning the people they’re bombing because the bombing is working too well at the thing they didn’t want it to do. The sanctions were necessary to stop Iran funding the war, but the war made the sanctions too effective, so the sanctions had to be lifted to fund the war effort against the country that no longer needs sanctions because the oil revenues that sanctions were preventing are now required to prevent the economic damage caused by preventing those revenues, which is itself a consequence of the military campaign designed to make the sanctions unnecessary by making Iran the kind of country that doesn’t need sanctioning, which it would be, if the sanctions hadn’t been lifted to pay for making it that.
There have been many names thrown at Trump since he arrived in US politics. My personal favourite is probably the Tangerine Tyrant. Many people are currently relying on TACO (Trump Always Chickens Out) to resolve the Middle East crisis he has instigated. However, until now, I had not heard of anyone likening him to Milo Minderbender. But once you see it, it is difficult to un-see it.
Trump likes to give himself and everyone else nicknames. From the very stable genius of his first term, to more recently Honest Don and the Tariff King, whereas Milo, as M&M Enterprises (the company he started as the mess officer) expands, becomes the Mayor of Palermo, Assistant Governor-General of Malta, Vice-Shah of Oran, Caliph of Baghdad, Mayor of Cairo, and the god of corn, rain, and rice.
Trump likes to use his presidency to enrich himself, from his Trump coin to the Amazon documentary about his wife to his Board of Peace to all of his merchandise. Milo’s catchphrase is “what is good for M&M is good for the country”.
Trump doesn’t appear to believe in safety nets for ordinary people. Meanwhile Milo secretly replaces the CO2 cartridges in emergency life vests and the morphine in first aid kits with printed notes to the effect that what is good for M&M is good for the country.
Milo Minderbender is a war profiteer trying to convince himself that he is a free market fundamentalist. So what does that make Trump? Well hold that thought, because today’s Guardian has provided a partial answer I think, with a history of military targeting.
This introduces the concept of the kill chain, ie the process between detecting something and destroying it. Trying to shortcut the kill chain has been a perennial preoccupation of militaries through the ages. In the Vietnam War, Operation Igloo White dropped 20,000 acoustic and seismic sensors along the Ho Chi Minh trail, which transmitted data to relay aircraft, which then fed the signals to the IBM 360 computers at Nakhon Phanom airbase in Thailand. These analysed the data, predicted where the convoys would be and strikes were directed to those locations. The Viet Cong realised quickly that this system could not detect the difference between military vehicles and ox carts and therefore:
They played recordings of truck engines, herded animals near the sensors to trigger vibration detection, and hung buckets of urine in trees to set off the chemical detectors.
There was no way to independently check what they were destroying. The air force claimed 46,000 trucks were destroyed or damaged, which the CIA calculated exceeded the total number of trucks believed to exist in all of North Vietnam.
…air force personnel invented a creature to explain the absence. They called it the “great Laotian truck eater”.
Last time I talked about military targeting, I focused on the human in the loop, but let’s instead focus on the actual destruction going on for a moment, shall we? Trump’s assault on Iran hit 6,000 targets in two weeks. The kill chain had, apparently, been compressed so much that it allowed 1,000 decisions an hour. The school he hit, killing between 175 and 180 people, most of them girls between the ages of seven and 12, had changed its use to a school since at least 2016 and was visible on Google Maps. Old target lists had been reached for and noone had had the time or the inclination to check them before bombing them.
This is what you can expect from a Milo Minderbender presidency. It has been obvious, since at least the 1960s, that the US system requires enormous strength of purpose from its executive to hold its industrial-military complex in check. That is why so many of them have been so keen to install a Trump.
It feels as if, far from embracing the idea that our current economic priorities are indeed insane, as I fervently hoped seven years ago, we are instead doubling down on the insanity.
A week or so ago I referred to a “Thought Exercise” set in June 2028 “detailing the progression and fallout of the Global Intelligence Crisis” (ie science fiction), published on 23 February, which may have tanked the share price of IBM later that day. As I said then, the fall definitely happened, with IBM’s share price falling 13%, its biggest fall since 2000. I said then that the likelihood of the scenario portrayed was difficult to assess, but the speed with which the total economic collapse was described felt unlikely if not impossible. I would like to expand on that.
The main reason that the scenario was hard to assess was that it was not based on data or evidence at all. That is unavoidable for speculative fiction talking about things that are not currently happening, but when describing an economy only two years away where most of the processes described should be discernible to some extent already, it is totally avoidable.
Ed Zitron has done an excellent line by line take down of the Citrini piece here. Here is one page of that to give you a flavour:
However this lack of a link with anything tangible did not stop the financial markets panicking, which should cause us pause when relying on the financial markets’ valuation of projects, industries, government policies, etc.
Ed Zitron describes this kind of piece as analyslop: “when somebody writes a long, specious piece of writing with few facts or actual statements with the intention of it being read as thorough analysis”. It can then get picked up by other commentators which take it as their starting point for further analysis, often making it hard to see that the starting point had few if any data points. Here is an example, from Carlo Iacono, looking at what if just some of the Citrini pronouncements were true, with appendices detailing possible branching paths of outcomes, all generated by a large language model (LLM). And then people start studying the meta analysis, and it starts getting taken even more seriously, and put into models and pretty soon most of the analysis is being done on imagined risks rather than on ones which are already staring us in the face.
We have always had a problem keeping our society grounded in reality, think the 2003 Iraq War, where we went to war on a false assessment about Iraq’s possession of weapons of mass destruction, the 2008 financial crisis, where banks misunderstood the risks they were exposed to, and the last two and a half years, where we, for the most part, seem to have convinced ourselves we have not been facilitating a genocide in Gaza when we clearly have been. But this is only going to get worse with the AI systems which are being developed.
The rapid rise of artificial intelligence has served to dramatically increase the speed of information production while also eroding accuracy, making it difficult to differentiate between content that simply sounds confident and content that’s actually grounded in reality.
So where is AI currently? Well PwC’s global CEO survey from January this year had the following statement as the first bullet amongst its key findings:
Most CEOs say their companies aren’t yet seeing a financial return from investments in AI. Although close to a third (30%) report increased revenue from AI in the last 12 months and a quarter (26%) are seeing lower costs, more than half (56%) say they’ve realised neither revenue nor cost benefits.
That’s the reality. But the hype is much much more entertaining. My favourite spoof video of the AI future currently is this one, about the time where all most of us are good for is riding bicycles to supply the ever increasing energy needs of AI systems (click view in browser if you can’t see it):
And what about the financial journalists? The pieces describing our reaction to whatever is about to unfold economically have already been written. There are investor websites asking if the 2026 crash has already begun, while another recent article argues that “America has quietly become one of the world’s most shock‑resistant economies” (which seems unlikely to age well). What most financial journalists are more comfortable with are articles about how the warnings were ignored after the fact.
And the professions? Well the current overview of my own profession is probably reasonably represented by this piece from the Society of Actuaries in the United States. Unfortunately for them, Daniel Susskind, who is mentioned in the article, is currently suggesting, as part of his Future of Work lecture series for Gresham College, how the key to the sudden development in AI, after the “AI Winter” when progress seemed slow, was that we abandoned trying to make machines which thought and acted like humans in favour of focusing on completing tasks in any way possible. Increasingly we are now automating tasks where we can’t (or won’t) articulate how we do them. From Deep Blue‘s victory over Kasparov in 1997 to Watson winning jeopardy in 2011 to ImageNet beating humans at image recognition (although that is disputed), Susskind refers to this progress as the displacement of purists in favour of what he calls “The Pragmatic Revolution”. Pragmatism in this sense appears to be that we humans should just accept the consequences the people running these systems want. So, as his latest lecture “Work, out of reach” claims, people moving into cities to find work is a strategy which is no longer going to work for low skilled people:
He then shows this graphic demonstrating the lack of recovery of big coal mining areas in the UK:
Source: Left – Sheffield Hallam University map of coal mining areas; Right – % employment from Overman and Xu (2022)
And finally he cites the notorious Policy Exchange piece from 2007, Cities Unlimited, whose thesis was that there is apparently no realistic prospect of regenerating towns and cities outside London and the South East.
Susskind talks about three forms of technological unemployment:
skills-mismatch, where your skills are mismatched to the work available. Education and training has always been the answer to this in the past.
place-mismatch, where the jobs are not where you have built your life. Some believe the answer should always be the one proposed by Norman Tebbit, who memorably told everyone in 1981, “I grew up in the 30s with an unemployed father. He did not riot. He got on his bike and looked for work.”
identity-mismatch, where according to Susskind, people are prepared to stay out of work to protect their identity, citing US men who won’t take “pink collar” work, China “rotten tail” kids, Japanese seishain-or-nothing and Indian Sarkari Naukri queues in India. Or perhaps they are just looking for work which is consistent with the idea of human dignity.
Susskind claims to have no answer to any of these as far as AI is concerned. They are, in his view, just the inevitable outcomes of his “Pragmatic Revolution”. It is the unthinking pursuit of more and more growth funded by capital less and less tethered to any territory, principle or purpose, where any grit in the machinery, be it unions or protestors or, increasingly, the wrong sort of government must be trampled underfoot. Anything which impedes the helter-skelter rush to more and more at greater and greater speed. It’s like our whole economy is run by this guy (press the view in browser link if you can’t see him) shouting “Ready, Aim, Fire!”:
But unskilled people will not be the only collateral damage of these unguided weapons. Take markets for instance. These are where people are exposed to risks and rewards based on underlying conditions they only partially understand. Greed and fear may be their main motivations, but gossip and group think are their main communication channels. They don’t need facts, particularly when so many of the facts are proprietary information not in the public domain. A plausible narrative will do. And plausible narratives are what LLMs will do for you in abundance.
And the more we reward people who can move fast, eg to spot an arbitrage opportunity, even at the risk of breaking things, rather than people who can make decisions which still look good decades from now, the more we are setting up the conditions for AI systems to be the go-to tool.
And put that together with an AI industry which desperately needs funding capital to keep arriving, ie one which is unbelievably highly motivated to push plausible narratives even when they know they are not grounded in reality, and you have a recipe for market-generated chaos.
And then we have Trump’s new war. Beware the people who are war gaming the Middle East at the moment on a range of LLMs (just stop and think for a moment about the bloodless inhuman impulse behind carrying out such an exercise rather than, I don’t know, talking to some actual people who live or have lived recently in and around the region). One of the worst offenders is Heavy Lifting banging on about what the three scenarios are for Operation Epic Fury. This is as bad as it sounds:
I tasked her [he is talking about Gemini Pro here] with doing a literature review on regime change (a term often used by the President but not a well-defined one), creating three scenarios of possible outcomes for which each was give a percentage probability, and a list of 20 items to examine for each scenario that covered political, economic, and cultural issues with a special focus on the political consequences in the U.S. and what this means for China, our biggest geopolitical rival.
But Gemini Pro wasn’t the only one involved in this. Two other humans were, Tim Parker and Ron Portante, trainers at the gym I go to. (Just as a personal aside, Tim was my coach in hitting six plates [345 pounds] on the sled last Friday and I have a video to prove it!) I was talking about the piece and Ron raised the issue of linguistic and cultural diversity in Iran. Tim did some real time research for me on his phone while I was burning real calories under his strict tutelage. This made me think I needed a background section on Iran. When I got him Gemini and I added it.
What you mean you belatedly realised you might need to have done some actual research into Iran rather than just generic research on regime change? I stopped reading at that point.
Meanwhile King’s College London have been carrying out war games more systematically using AI. Professor Kenneth Payne from the Department of Defence Studies led the study, which looked at how LLMs would perform in simulated nuclear crises. As Professor Payne said:
Nuclear escalation was near-universal: 95% of games saw tactical nuclear use and 76% reached strategic nuclear threats. Claude and Gemini especially treated nuclear weapons as legitimate strategic options, not moral thresholds, typically discussing nuclear use in purely instrumental terms. GPT-5.2 was a partial exception, limiting strikes to military targets, avoiding population centers, or framing escalation as “controlled” and “one-time.” This suggests some internalised norm against unrestricted nuclear war, even if not the visceral taboo that has held among human decision-makers since 1945.
This is not a Pragmatic Revolution. These AI systems cannot replace humans thinking about the future we want for humans in any way which is worth having. What they can do, if we let them, is accelerate our worst impulses and move us further away from considered reflective decision making.
But we will continue to use AI systems in the military because, as it turns out, it is very useful for low stakes admin. So although Lavender, the system used by the Israeli military to select targets in Gaza, made errors in 10% of cases and was therefore totally inappropriate to the task, there are lots of organisational logistical tasks where it is much quicker than the alternative and 10% error rates do not matter so much.
There is clearly an issue with what we decide to use these systems for. We need to be able to regulate the decisions which are particularly consequential. However the only way we seem to be considering for this at the moment is the human-in-the-loop model, like the humans spending around 20 seconds considering each target recommended by Lavender before authorizing a bombing. I have written about these before in the context of early career professionals in the finance industry, where the prospect seemed miserable enough:
They will be paid a lot more. However, as Cory Doctorow describes here, the misery of being the human in the loop for an AI system designed to produce output where errors are hard to spot and therefore to stop (Doctorow calls them, “reverse centaurs”, ie humans have become the horse part) includes being the ready made scapegoat (or “moral crumple zone” or “accountability sink“) for when they are inevitably used to overreach what they are programmed for and produce something terrible.
However it seems obvious to me that, in the context of dropping actual bombs on actual people, there is an even more serious problem with this model. As Simon Pearson (anti-capitalist musings) puts it:
The “human in the loop” requirement exists in military doctrine because international humanitarian law demands an accountable human decision-maker for lethal force. The laws of armed conflict require proportionality assessments, precautionary measures, distinction between combatants and civilians. All of these obligations attach to a human commander. The system cannot fulfil them. So a human must be present, and their presence must constitute a decision, regardless of whether any genuine decision was made.
What the institution needs from the analyst is not judgment. It is a signature. The signature converts a machine output into a human act. And a human act is what the law recognises, whether or not any judgment occurred. When the strike kills children, the chain of accountability runs to the analyst who approved the target: not to the system that identified it, not to the company that built the system, not to the doctrine that compressed the review window to ten seconds.
But whether we want to make money from exploiting a short term anomaly in a market, make our fellow humans redundant, prosecute a war on another group of fellow humans or “win” a war of mutual nuclear destruction, we need to retain the capacity for real human reflection within the decision-making processes we use. Not just a human-in-the-loop nor just the elites of tech companies deciding how the systems will be configured behind commercially confidential walls. These processes need democratic accountability every bit as much as our parliaments, councils, institutions and voting systems do.
Something infuriatingly slow, inclusive and deliberative giving recommendations which are then stress-tested for how they would perform on contact with reality, involving yet more people being serious and deliberative and taking their responsibilties more seriously than being a human-in-the-loop would ever allow. Our decision-making systems need more grit and less oil. AI is all oil.
