We Know Zero

The AI Rollercoaster

11. June 2026 · Write a comment · Categories: Actuarial, AI, COVID-19, Education, Professions

*Source: https://xkcd.com/249/ This work is licensed under a Creative Commons Attribution-NonCommercial 2.5 License*

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.

Ian Pay of the ICAEW’s quote from last year was just one example:

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.

And this will be the subject of my next post.

Answering a better question

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Katabasis – following your PhD supervisor into Hell

27. May 2026 · Write a comment · Categories: Birmingham, Education, Science Fiction

*This review originally appeared in the May issue of Brum Group News, the newsletter of the Birmingham Science Fiction Group and is reproduced here (lightly edited) by kind permission*

This book is so many things: a work of fantasy, a literature review of every major work about the journey into hell, a love story, a wicked academic satire, a philosophical musing on the meaning of life and a love letter to Cambridge. Or perhaps the title itself, which means both a retreat to the coast, in this case to the banks of the River Lethe, as well as a descent into Hell. Like its Oxford counterpart before it – Babel – its central leap is that magic (here referred to as magick, ie the academic discipline stretching back to the alchemists and beyond) sits alongside the other subjects at Oxbridge colleges. The magic we see practised, taught, researched and dissertationed feels very mathematical. So we are unsurprised to hear that the mathematicians hate the magicians.

Alice Law is the Chinese-American PhD student of the great Jacob Grimes, who (accidentally?) sends him to Hell and then, with her once-friend-now-mortal-enemy and Grimes’ other PhD student, Peter Morgan, sets out to bring him back. So that Grimes can pass their dissertations, because that’s a good enough reason to journey to Hell and sacrifice half of your future lifespan. And so the quest begins.

There is quite a bit of mathematical fun had despite Alice not really knowing any mathematics, including an Escher Trap, a Penrose Staircase and a hyperbolic geometry which makes the quest very heavy going at times. And so many logic puzzles. Comedy and total horror are nicely juxtaposed throughout, as Alice and Jacob get to understand each other better and start to wonder whether Grimes is worth it after all.

The constant side swipes at the life of a junior academic are often hilarious. Magicians in training are apparently told by all their professors that they should consider careers in other fields or “alt academia”, as they called it:

“…no one really meant it when they said alt academia was just as prestigious (or, more commonly, that there was no shame in it, really). They meant it even less when they emphasized that alt academia paid better, had kinder hours, was less stressful, gave you better job security, made you happier. Oh, magicians do really well in consulting, they said. Employers like critical thinking and problem-solving skills, they said. Fewer people die in industry, they said.”

The most enigmatic character is their not-quite-constant companion, Archimedes the cat, who guides them when he feels like it. All the way through all eight of the Courts of Hell, alongside and across the Lethe and finally to King Yama’s Domain, on a journey which threatens to destroy Alice’s very sense of self. Her catechism, which she repeats at stressful moments:

I am Alice Law I am a postgraduate at Cambridge I study analytic magick

Alice has always felt that if she could just hang on to the delusions which had got her this far until the end of her PhD all would be well, but these turn out to be precisely the things she needs to confront if she is ever going to get out of Hell.

The meatspace layer for actuaries

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Yet the first rung is being taken out.

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

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

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

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

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

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

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

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

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

You would fire these people, right?

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

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

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

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

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

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

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

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

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

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

Emotional Truth

11. May 2026 · Write a comment · Categories: Media & Politics, Science Fiction

*Shakespeare appearing in the play he has written in order to say goodbye to his dead son*

“You are not saying what you think you are saying” was what Ray Nayler said to the Birmingham Science Fiction Group on Friday night, as part of a wider conversation about the mutual misunderstandings that result from cultural differences. He had landed up with the Peace Corps in Turkmenistan 20 years ago, “The worst place to live in the world”. It ripped away his sense of stability and the fixed nature of life he had developed growing up in San Francisco and made him realise that everything is arbitrary. His new book Palaces of the Crow is out next week, about a group of escapees on the run in a forest trapped between the German and Soviet armies in World War 2, with only a murder of intelligent crows as allies. I will be buying it.

And so to a different forest.

Last night I could not speak for half an hour. My face ached from the effort of holding myself together and tears were running down my face. No I wasn’t in the back of an ambulance on my way to Good Hope Hospital. I had just watched Hamnet for the first time.

I am peculiarly sensitive to father-son depicitions in art. I can’t remember when a film affected me as deeply as Hamnet did, but I do remember the last book that had me in floods of tears (The Road by Cormac McCarthy when (spoiler alert) the father of the boy dies. Suggest you don’t read it on a train like I did). Why should I cry for you by Sting also tends to have me in bits.

However Hamnet was still like nothing I have ever experienced before in a movie. It snuck up on me, this story of the fight to make a family and then keep it alive in a way that certainly didn’t feel over 400 years old before hitting me with the final scene which was, ultimately stagey for goodness’ sake. I felt connected – to the forest, to the plague-beset 16th century characters, to everyone who has ever lost a child, to everyone looking for connection to help them through their day. I have watched so much Shakespeare in my life, but I have never felt the urgency that must have lain behind the plays quite like this before.

This was just great art. Not in a way that impresses you but leaves you cold, but in a way that you realise has expressed the driving forces of life directly at your central nervous system.

And how close the film was to what really happened doesn’t matter. Any more than the plot accuracy of any of Shakespeare’s plays matters. It was emotionally true and believable and mourned the death of a child as every child death should be mourned. It made nearly every other movie I have ever seen seem trite by comparison, including the hugely entertaining but ultimately much less full Oscar rivals this year. This is the movie you stick on the next gold disc sent out on a probe into deep space to explain humanity.

And it immediately started me thinking about how infrequently I experience emotional truth outside my friends and family. Is this the missing component from public life?

Keir Starmer certainly wasn’t passing any auditions this morning. He was not saying what he thought he was saying. He thought he was saying something about training young people, being “at the heart of Europe” and nationalising British Steel. What he was actually saying was that he has no idea why he lost 1,496 English council seats over the weekend but, despite this, was going to hang on until someone removed him forcibly from office. And he is guessing, perhaps rightly, that the Labour Party does not have the determination to do so. It was the precise opposite of emotional truth or, as John Elledge posted:

“You are not saying what you think you are saying” is unfortunately true for nearly all of us nearly all of the time. Until it isn’t. And those moments when it isn’t are moments of enormous power.

And to think I still have Maggie O’Farrell’s novel to read. Or possibly the audiobook read by the great Jessie Buckley, Agnes Hathaway herself. May be hard to resist.

The world according to the fig tree

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

A few examples from the report:

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

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

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

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

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

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

Healthy life expectancy is unwell

28. April 2026 · Write a comment · Categories: Actuarial, COVID-19, Demographics, Health, Media & Politics, Pensions, Regulation

*Source: https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/healthandlifeexpectancies/datasets/healthstatelifeexpectancyallagesuk*

Yesterday an extraordinary thing happened: the news story about the UK’s falling healthy life expectancy led the BBC News for a while, ahead of the King’s visit to the US in the wake of the assassination attempt on Trump’s team and the latest twists in the Mandelson affair. And so it should: over the decade 2012–14 to 2022–24, healthy life expectancy in the UK fell by about 2 years, to 60.7 years for males and 60.9 years for females.

And that is just the average. As we can see from what I felt was the most informative graphic from the Health Foundation’s report, some of the local authority areas have seen precipitous falls over the same period. Merthyr Tydfil has fallen from 57.6 years to 50.1 years. North Lanarkshire has fallen from 58.3 years to 52.3 years. And in England, Sandwell has fallen from 57.7 years to 51.3 years. In the 2012-14 data, only one region had no local authorities with a healthy life expectancy below the state pension age. By 2022-24, most regions have a healthy life expectancy below 66 years.

Healthy Life Expectancy (HLE) is defined as the number of remaining years that an individual can expect to live in “very good” or “good” general health. Rates of “very good” and “good” general health by sex and five-year age band are captured from the following survey general health question on the Annual Population Survey (APS) and in the Census 2011 and Census 2021:

How is your health in general; would you say it was…

Very good?
Good?
Fair?
Bad?
Very bad?

I last wrote about HLE in 2017 in response to John Cridland’s review of the State Pension Age. My view at that time, when healthy life expectancy was plateauing rather than falling like a stone, was that it was time to consider a universal basic income model. Then only the poorest decile was going to be condemned to 18 years of working in poor health until they could claim a state pension. Now the overall averages in some local authorities have moved down to join them, this consideration appears rather more urgent.

In 2014, I was concerned about what happens if the healthy life expectancy doesn’t increase in line with the planned increases to the State Pension Age and, towed along 10 years behind it, Normal Minimum Pension Age (NMPA). Well here we are: 26 of the little local authority blobs are at or below the current NMPA of 55. This nearly doubles to 49 local authorities (assuming the fall in HLE doesn’t continue, which feels like a heroic assumption at the moment) when the NMPA is due to rise to 57 in April 2028.

As the Health Foundation report says:

While healthy life expectancy has declined, life expectancy has remained broadly stable for the UK overall, indicating that the deterioration is not primarily driven by changes in mortality. However, in more deprived areas, life expectancy remains below pre-pandemic levels, suggesting mortality plays a greater role in reducing healthy life expectancy in these areas. Worsening self-reported health remains a key factor throughout the UK, highlighted by a falling proportion of life spent in good health and by wider evidence of declining health among the working-age population.

Other countries have not experienced this, illustrated by the UK sliding down the international comparison tables:

*Source: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/gho-ghe-hale-healthy-life-expectancy-at-birth*

In the complete table the UK is sandwiched between Puerto Rico and China, with (from World Bank data) GDP per capita respectively of $39,344 and $13,303, compared to the UK’s GDP per capita of $53,246 (all from 2024).

Andrew Mooney, The Health Foundation’s principal data analyst, said: “The UK has the highest levels of obesity in western Europe and there has been a surge in mental ill health, especially among young people.”

Perhaps, instead of obsessing over GDP growth, we should be focusing on what countries like Iceland, Norway, Australia and New Zealand have been doing in recent years to tackle population health. I think it would make us all feel better.

The monsters we create

24. April 2026 · Write a comment · Categories: AI, Education, Media & Politics, Prediction, Risk, Science Fiction

*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:

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.

The Slow, The Shine and The Darkness

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Talking Terraforming in Birmingham

08. April 2026 · Write a comment · Categories: Actuarial, Birmingham, Climate Change, Media & Politics, Risk, Science Fiction

*Front page of the April 2026 issue of Brum Group News*

Three and a half years ago, I wrote a piece likening the rapid climate change on Earth to the fairly well-established science fiction concept of terraforming, but in reverse. So what has happened since? Well last summer, according to researchers at Imperial College and the London School of Hygiene and Tropical Medicine, two thirds of the 24,400 heat deaths from June to August across Europe were due to human-made global heating. And a study published last month has suggested that the pace of global warming has nearly doubled since 2015.

It this point I would like to suggest rehabilitating an old word to describe this process, in the opposite direction to terraforming (which is action designed to make a planet more habitable). Barrenize means to make barren or sterile and was used between the mid 1600s and the early 1700s according to the Oxford English Dictionary, originally in the context of animal husbandry. I think it’s time to bring this word back.

In a week when a US President has threatened, variously, “blowing everything up and taking over the oil” and that Iranians would be “living in Hell”, to last night saying that “a whole civilisation will die tonight”, unless they opened the Strait of Hormuz, it certainly sounds like a commitment to barrenization to me, only at a faster pace than the global warming he is already doing everything possible to accelerate further.

On Friday this week, the Birmingham Science Fiction Group will have Oliver Bettis as its guest speaker. Oliver has been a leading actuary in the field of sustainability for many years. He is one of the authors of a series of publications by the actuarial profession in collaboration with the University of Exeter in recent years.

Climate Scorpion shows how we need to develop a best guess about the worst-case scenarios and make policy on that basis, given our lack of knowledge about extreme climate risk and tipping points.

Planetary Solvency – finding our balance with nature sets out an approach to civilisational risk management which attempts to address the fact that the severity and frequency of extreme events are unprecedented and beyond current model projections.

Parasol Lost, which we will be discussing in particular this Friday, focuses on the cooling effect of aerosols: a side-effect of pollution from fossil fuel burning. Without aerosol cooling the global temperature would be around 0.5°C higher than the 1.4°C increase above pre-industrial temperature that we have today. It is critically important to recognise that, as air pollution is cleaned up, this may ironically lead to a short-term increase in warming through the loss of aerosol cooling. The question must be asked, can we afford to lose this cooling and if not, should this be replaced by working with nature, using technology or both?

This will allow us to tap into the rich history of science fiction literature on terraforming (and dealing with the threat of barrenization) and whether this can allow us to look at this question in a new way. It should be a lively discussion.

This event will be held in-person at the Friends of the Earth Warehouse, 54-57 Allison Street,
Birmingham B5 5TH and simultaneously on Zoom, with online access opening from around 7.45 for an 8 pm start.

Ticket prices for non-members are £8 for in-person attendance and £6 for Zoom attendance. For members it’s £4 in-person attendance and free Zoom attendance.

Tickets can be purchased on the door or via the Eventbrite link below:

https://www.eventbrite.co.uk/e/1985958692911

And if this whets your appetite for more science fiction and you think you might like to join the group, just email us at contact@brumsfgroup.org.uk. Hope to see you there!