Well, it’s been a long road since I raced the dinosaurs at a New Year’s Day parkrun. I’ve managed various niggles (knee, glute, achilles… yawn). I’ve run around the amazing Ladybower Reservoir, across St Mark’s Square, even chased Alistair Brownlee down Regent’s Park Canal. (He is faster than the dinosaurs.)

And tomorrow: it’s my first marathon. Quite possibly my only marathon. I’ve done the training, watched the videos, eaten the pasta, digested the data. I’m fairly terrified but I know it’s a privilege to be able to run in this event – for everyone who gets to run, about 20 people don’t get their chance. So I’m going to enjoy it. Or at least, try to appreciate it.

I feel lucky to have the opportunity to run in memory of those who do not. In particular I’m thinking of my cousin Winnie, who died last April at the age of 20.

Winnie was always positive and upbeat despite enduring enormous suffering with little hope of a happy ending, so I think I should be able to cope with a few hours of sore legs. People have been so, so generous – but if you feel inclined to support the Teenage Cancer Trust, please do. The TCT was a huge support to Winnie and her family and your donations will help them support other families facing a very difficult, lonely road. Thank you.

As for me, the road tomorrow will no doubt pose a few challenges but it certainly won’t be lonely. And since I’m now up to £650 per mile run I have plenty of incentive to keep going.

Dick and Mac are content with their lives: they enjoy making burgers by day and stargazing by night. Ray Kroc is a workaholic chasing success at any cost. When the brothers’ folksy charm collides with Kroc’s ruthless ambition it will birth one of the best known brands in the world. 

This is the story of two very different approaches to making hamburgers – and two very different approaches to making money.

This episode was previously released to members of the Cautionary Club, and Pushkin+ subscribers. For ad-free listening, a monthly video chat, newsletter and full-length episode – and to support Cautionary Tales – please consider becoming a member of the club.

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Further Reading

This script relied on Ray Kroc’s autobiography, Grinding it Out, and two books on the two burger franchises – McDonalds: Behind the Arches, by John F Love, and Flameout: The Rise and Fall of Burger Chef, by John McDonald – as well as the textbook An Introduction to Franchising, by Robert Webber.

The bare facts of the matter are these. In 1999, Bonilla was past his prime, but under his contract, the Mets still owed him just under $6mn. Bonilla agreed that instead of $6mn immediately, he would accept almost $30mn deferred. It would be paid in 25 annual instalments of over $1mn, every July 1, starting in 2011. Each July 1, Mets fans grumble or joke about Bobby Bonilla Day, the saddest holiday in the year. Bonilla retired a quarter of a century ago — and the Mets still owe him 10 more million-dollar-plus paydays.

It is not only Mets fans who hate this. Business Insider described it as “the worst contract in sports history”. Yet anyone with an economics training would shrug. As Planet Money points out, anyone who managed to invest $6mn at a 10 per cent rate of return in 1999 would have nearly $19mn by 2011. At that point, the investment pot would generate more than enough income to pay Bonilla his 25 annual instalments, leaving the principal sum to grow further. In other words, if the Mets could find a 10 per cent return on their money, they could shake hands with Bonilla, invest the $6mn, pay every penny of the $30mn they owe him, and have tens of millions of dollars left over in 2035 when the agreement expires.

So there was nothing stupid about the Mets agreeing to the deal. Maybe Bonilla was the one being stupid? Probably not. The deal with the Mets locked in an 8 per cent return for him at very low risk. Not bad; good enough, anyway.

The first lesson here is that most people do not understand the power of compound interest. Outraged Mets fans feel that their team got their faces ripped off by Bonilla and his agents; they didn’t. It just seems contrary to all logic and reason that $6mn now could possibly be worth $30mn later, but a few decades and an annual return in the high single digits will do wonders. (Many FT readers will already know the simple rule of thumb: divide 72 by the growth rate, and that is how many years your money will take to double. Seven per cent will double in about 10 years; 10 per cent will double in about seven. I mention this only because I am endlessly surprised at the number of mathematically gifted and trained people who don’t know this cognitive shortcut.)

The second lesson is about the psychological pain of debt. One of the reasons that Bobby Bonilla Day seems so egregious to the Mets fans is that Bonilla is still receiving cheques such a long time after he retired. This is, of course, literally how a pension works — but it also illustrates how annoying it can be when some shiny purchase-on-credit is gathering dust, yet the payments come through month after month. Some things are worth borrowing to pay for, but it’s also worth thinking ahead.

The third lesson is that even in what seems to be a zero-sum negotiation, there are often gains from trade to be found. The Mets wanted to pay as little as possible, and Bonilla wanted to be paid as much as possible, but there was still room to make both sides happy. The Mets urgently wanted the $6mn, while Bobby Bonilla didn’t. Professional baseball players are generally rich and young, have no particular skills in investing and are vulnerable either to sharks or to their own worst impulses. Bonilla didn’t want to bankrupt himself trying to invest his $6mn somewhere. He just wanted to retire and relax, knowing that he had a regular income locked in. It suited both Bonilla and the Mets to agree to defer the payments.

A fourth lesson is that some risks cannot be made to disappear, or are so costly to insure that few people would bother. Bonilla’s deal exposes him to three of those risks: longevity, inflation and counterparty risk.

Longevity risk is simply that while the payments expire in 2035, Bonilla probably will not. If he dies before the payments stop in 2035, he won’t get to enjoy the benefits of a contract that could have paid him in full in 1999. Conversely, he might easily live until 2045 (when he will be 87). That would mean scraping by for a decade without those nice cheques every July 1.

Inflation risk might not have seemed worth worrying about when Bonilla agreed the deal in 1999, but it is real. A cheque for $1mn today buys about as much as a cheque for $500,000 in 1999 — and that is after subdued inflation for (most of) the last quarter century. If the 2010s had been a rerun of the 1970s, with inflation typically between 5 and 10 per cent a year, the purchasing power of Bonilla’s annual cheques would have spectacularly shrunk by now. The moral of the story is that any long-term contract agreed in nominal terms contains a hidden bet on the inflation rate.

Bonilla also faces counterparty risk: the risk that the Mets somehow can’t or won’t pay. Thankfully, Bonilla has a plan B: he’s been collecting $500,000 a year from the Baltimore Orioles since 2004.

It’s good to see Bonilla being held up as a case study in retirement planning. Compound interest seems very abstract. What makes it real is seeing Bonilla turn $6mn into $30mn by the simple exercise of deferred gratification.

I must confess that the particular maths of Bonilla’s contract did strike close to home. He agreed to wait 12 years in exchange for receiving a further 25 years of annual income. I’m 52, so such a deal would pay me between the ages of 64 and 89, which sounds pretty much perfect as far as retirement plan timing goes.

Regrettably, nobody owes me $6mn. But if I could invest an extra £6,000 now — and earn an 8 per cent return somewhere — that would boost my retirement income by £1,000 a year. Fifty-two is later than ideal to be planning for retirement, yet still not too late.

What’s that, I hear you say? You were promised five lessons?

Here is the fifth: the Mets spent their $6mn on a new pitcher and reached the World Series; then they reinvested all the proceeds of their success. The fellow they put in charge of the investment was Bernard Madoff, the most famous Ponzi fraudster since Ponzi.

All that compound growth looks great in the spreadsheet, but in investment — as in life — nothing is certain.

Written for and first published in the Financial Times on 25 March 2026.

I’m running the London Marathon on 26 April in support of a very good cause. If you felt able to contribute something, I’d be extremely grateful.

Until the 1960s, women couldn’t compete in Olympic events any longer than a sprint – and commentators declared that a marathon would kill them, or leave them unable to have children. Rubbish, of course. But when Kathrine Switzer signed up for the 1967 Boston Marathon, it wasn’t the distance that bothered her – it was the enraged race officials trying to assault her.   

Thanks to pioneers like Kathrine, women have made huge strides in long distance running – and are now challenging the times of men in the very races they were banned from for so long.  

This episode was first published in July 2024. I’m running the London Marathon next week – 26 April – to raise money for the Teenage Cancer Trust, and am grateful to everyone who has supported this very worthy cause.

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Further reading

On Bobbi Gibb

Ailsa Ross, “The Woman Who Crashed the Boston Marathon” JSTOR Daily 18 March 2013

Olivier Guiberteau, “Bobbi Gibb: The Boston Marathon pioneer who raced a lie” BBC Sport 29 August 2023

Brigit Katz “The incredible story of Bobbi Gibb, the first woman to run the Boston Marathon” Women in the World, New York Times, 20 April 2015

On Kathrine Switzer

Kathrine Switzer “The Girl Who Started It All” Runners World – excerpt from Kathrine Switzer Marathon Woman;

The Spirit of the Marathon by Gail Waesche Kislevitz Breakaway Books, 2002

“I ran with the men and changed history” BBC Outlook

On The Spine Race

Nick Van Mead “Montane Spine Race: 268 miles of pain”

Dave Lee “Spine Race 2013”

“Spine” (Amazon Prime documentary)

Jasmin Paris “Spine Race”

Episode 6 – Spine Race 2019

BBC Sport

BBC Scotland

Other sources

Roger Robinson “Eleven Wretched Women” – What really happened in the first Olympic women’s 800m. Runner’s World 14 May 2012 

Colleen English “Not a Very Edifying Spectacle”: The Controversial Women’s 800-Meter Race in the 1928 Olympics 08 October 2015

A Marathon Legend Revisited

Natalie Angier “2 Experts Say Women Who Run May Overtake Men” The New York Times 7 January 1992

Run Repeat State of Ultra-running

BBC More or Less

“We settled on a goal of 1,000 different photographs of every single object category,” she writes in her autobiography The Worlds I See. “One thousand different photographs of violins. One thousand different photographs of German shepherds.”

The database, ImageNet, was released in 2009, and Li started a competition for researchers to build the best image-recognition algorithms. A few years later, a graduate student named Alex Krizhevsky, advised by AI pioneer Geoffrey Hinton, trained a neural network on ImageNet — and blew the competition away.

Neural networks had been languishing for decades: a clever idea, but the computers had been too slow and the datasets were too small. But Li’s dataset was different. It had seemed foolishly, grandiosely, uselessly large; it turned out to be the perfect input for a neural net. This was an indication of the power of data combined with the power of neural nets. It was also vindication for Li’s idea of using human judgment to apply millions of labels to a vast collection of images. The lesson: if you can measure it, you can automate it.

But image recognition neural nets proved brittle in unexpected ways. A 2015 paper, “Deep Neural Networks are Easily Fooled”, asked a state-of-the-art system to classify example after example of pure static. “Robin,” said the network, with more than 99.5 per cent certainty, as it looked at random noise. “Armadillo.” “Peacock.” The problem was that the network had only ever seen meaningful images, and confidently identified meaning where there was none.

This is an example of the “jagged frontier” of AI capability, a term referring to the fact that AI models can be stunningly good at one task and then gravely disappointing at another, as with neural nets confronted with static.

That jagged capability is not a problem in itself. “All technologies are good at some things and bad at others,” says Joshua Gans, economist and co-author of Prediction Machines. It’s best to use can openers to open soup cans and hammers to drive nails into walls, and not the other way round. But, adds Gans, “the difficulty is that with AI, we don’t know which is which”.

This raises the question: how do we know that the AI is doing a good job? It was easy to see the problem when a neural net was labelling static an armadillo. But how impressive is the response to that request to create an image of Joan of Arc in the style of Edward Hopper? Did that agent actually make a restaurant reservation, or did it reserve nothing except a space in my calendar? Are the business plan and pitch deck I requested persuasive, or full of holes, or — perhaps the worst case — persuasive and full of holes?

The most problematic cases are the ones where it is hard to know whether the AI has done a good job, and expensive if it turns out that it has not. If AI writes buggy code or clumsy prose, that can be spotted and fixed. If the code contains hidden security vulnerabilities, the prose is packed with fabricated facts or plagiarised phrases, or the structural engineering calculations seem fine but the building will collapse in the first storm, that is a problem. It is still a problem even if the mistakes are rare and the average quality excellent. These difficulties only become more acute as AI becomes more capable, because more challenging tasks are often more difficult to evaluate.

Two new working papers address the tricky issue of verifying quality. In “Some Simple Economics of AGI”, Christian Catalini, Xiang Hui and Jane Wu (assisted, sometimes gratingly, by generative AI) propose the inevitable 2×2 matrix in which economic activity can be easy to automate, easy to verify, both, or neither. Automatable, verifiable output is the stuff that computers do for us. The non-automatable stuff remains reassuringly artisanal.

The difficult quadrant is where tasks seem easy to complete but are hard to check. Catalini, Hui and Wu call this the “runaway risk zone”. It is not a reassuring label and it is not meant to be. The problem of verifying quality is not a new one: think about building contractors, second-hand cars or a restaurant in a tourist hotspot. In such contexts, low quality often takes over the market like knotweed, because the best providers struggle to prove that they are the best.

Solutions include reviews, word of mouth, or long-trusted brands. (Not for nothing do familiar brands such as Durex and Trojan dominate the market for condoms. Nobody wants an unpredictable condom.) In big projects with high stakes, it can help to have the option to sue some counterparty with deep pockets. But none of these solutions is ideal, and the danger is that AI produces such vast vats of plausible slop that they outpace our capacity to check. Create enough hallucinated legal arguments, flawed engineering calculations and backdoor-ridden code, and the slop vats fill faster than our capacity to tell good work from bad.

In the second paper, “A Model of Artificial Jagged Intelligence”, Joshua Gans offers an analogy in which asking AI to perform a task is like trying to cross a river over a network of planks supported by occasional pylons. The jagged frontier is represented by the fact that some planks are long and wobbly, while others are short and sturdy. Problem one: even if the planks are typically sturdy, the wobbly planks will require most of your time and attention. Problem two: if you can’t predict in advance which planks will let you down, you may quite sensibly prefer to eschew the AI entirely and row yourself across the old-fashioned way.

As Gans rightly points out, Silicon Valley’s AI firms have mostly been trying to raise the average performance of AI systems — that is, to make all the planks sturdier. It might be better, instead, to focus on stiffening the wobbliest ones. But that assumes you know which they are, which points to a third approach: improve the predictability of the system. If you know in advance where the wobbly planks are, they’re not nearly as dangerous.

If.

Written for and first published in the Financial Times on 18 March 2026.

I’m running the London Marathon in April in support of a very good cause. If you felt able to contribute something, I’d be extremely grateful.

For ad-free listening, monthly behind-the-scenes conversations, our newsletter, and monthly bonus episodes including this one, please consider joining the Cautionary Club.

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Further reading

This episode relied on the books The Lysenko Affair by David Joravsky, The Rise and Fall of T D Lysenko by Zhores Medvedev, Lysenko’s Ghost: Epigenetics and Russia by Loren Graham, Stalin and the Scientists: A History of Triumph and Tragedy by Simon Ings, and Our Daily Bread: The Essential Norman Borlaug by Norman Vietmeyer.

Cancer is brutal for anyone. For teenagers and young adults, it can be isolating in a particular way: while friends are setting off on the adventures of early adulthood, they’re navigating a life-changing – and sometimes fatal – illness. TCT’s specialist nurses and teams work within and alongside the NHS to make sure young people feel safe, seen and supported. They advocate for them in systems that don’t always know what to do with a 19-year-old having chemo, bringing expertise and sensitivity when it’s needed most.

I’m running in memory of Winnie – a cousin and a childhood friend of my daughter. Winnie died of cancer on 22 April 2025. She was 20. Through countless challenges she was upbeat and determined. She kept studying for a law degree throughout her ten months of treatment, despite her university’s initial reluctance to support the idea. Her degree will be awarded posthumously – a testament to Winnie’s courage.

Throughout her treatment, Winnie and her family were able to rely on TCT clinical nurse specialists for support, advice and advocacy. Her family are enormously grateful to TCT, and when I asked which charity I should run for, they didn’t hesitate.

I’m grateful for the chance to try to complete the London Marathon, and I don’t plan to waste it. If you’re able, please donate to the Teenage Cancer Trust. Your support will help TCT’s nurses be there for more young people like Winnie.

—

If you’re curious about how my training is actually going… thanks! It’s been… fine. I’ve learned a lot. The toughest lesson, although perhaps not surprising, is that while having a marathon hanging over you can be a great motivator, it can also be a source of misery. It turns out I don’t really need a marathon to motivate me – but I might have had a bit more fun without the little voice in my head telling me I can’t do it…

I wrote about fitness trackers and algorithmic training programs here – all their amazing strengths and dangerous seductions.

And if you want to see me doing stupid stuff on a treadmill, on Facebook – it is the place for all displays of Unc Energy after all – the link is here.

In 1860, police officer Robert O’Hara Burke plans an expedition to map the ‘ghastly blank’ in the centre of Australia. Joining him is scientist William Wills, and a ragtag team of hires. Burke falls out with virtually everyone around him, and demonstrates an uncanny ability to make catastropically bad choices – from the equipment he brings to the route he takes. But even as the mission unravels, one final, simple decision could still save him.

For ad-free listening, monthly bonus episodes, monthly behind-the-scenes conversations, our newsletter, and more, please consider joining the Cautionary Club.

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Further reading

This script relied mainly on two books: Cooper’s Creek by Alan Moorehead, and The Dig Tree by Sarah Murgatroyd. The more recent Burke and Wills by Peter FitzSimons is a richly detailed but lightly novelised account. Thomas Schelling discusses coordination games in his book The Strategy of Conflict.

Since Hardy was writing in 1940, there was a touch of the humblebrag about this claim. Chemist Fritz Haber had created chemical weapons for use in the first world war. Engineers had produced artillery, tanks and strategic bombers. Oppenheimer and the other physicists would soon create the atomic bomb. There was a comfort in Hardy’s protestations of uselessness — but perhaps a false comfort.

In the 1970s, some basic ideas in supposedly useless number theory were deployed by Ron Rivest, Adi Shamir and Leonard Adleman. They developed the RSA algorithm, which enables public key cryptography, without which there would be no ecommerce. Cryptography is hardly valueless to the military, either. One never knows when useless knowledge will be useful after all.

Hardy’s number theory was not alone in being accidentally useful. In a famous article published around the same time — “The Usefulness of Useless Knowledge” (1939) — the head of Princeton’s Institute for Advanced Study, Abraham Flexner, made the case for apparently useless research. Flexner started with the radio and the radio telegraph — remarkable inventions for which many people thanked Guglielmo Marconi, the Nobel Prize-winning engineer.

Flexner argued that the “real credit” should go to James Clerk Maxwell and Heinrich Hertz, who had done the fundamental research. “Neither Maxwell nor Hertz had any concern about the utility of their work,” wrote Flexner, adding that Marconi contributed “merely the last technical detail . . . now obsolete”.

Some more recent examples have been gathered by the American Association for the Advancement of Science for its Golden Goose awards. Ten years ago, the awards recognised the Honey Bee Algorithm, which began with biologists painting tiny numbers on the backs of chilled (and thus immobile) bees, and then tracking the individual bees to figure out how they contributed to the hive’s search for nectar. Why? Because they wanted to know.

A couple of engineers became intrigued, figuring that maybe the bees had evolved a smart mechanism which the engineers might use to . . . well, do something. Perhaps they could use it to smooth the flow of traffic or suchlike. The bees had indeed evolved a clever approach, but the engineers couldn’t work out how to use it.

Finally, a computer scientist (Oxford, IBM) got in touch with the engineers, speculating that he had a problem to which they might have a solution. He was right. The honey-bee foraging system was adapted to spread viral and ever-shifting internet traffic across many different servers.

The Golden Goose awards also recognised the microbiologists who poked around in the geysers of Yellowstone Park to understand how some bacteria managed to thrive at very high temperatures. The scientists discovered heat-resistant enzymes — polymerases — that could survive near boiling point. This, quite unexpectedly, paved the way for the polymerase chain reaction — a way of amplifying genetic information made all too famous by the PCR test of Covid-19 fame, but one which has many other applications.

The Golden Goose awards do not exist in a political vacuum: they are explicitly designed to showcase the unexpected benefits of federally funded research in the US, and were meant as a rebuke to the earlier Golden Fleece awards, in which US senator William Proxmire would mock what he considered wasteful government spending — often on strange-sounding scientific projects.

Proxmire was not wholly wrong: some government projects are a waste of money, and some academics produce research of little value. But the lack of value is generally not because the research is “useless” but because the research is sloppily or even fraudulently done. Superficially interesting claims congeal on the surface of a steaming vat of confusion.

Unfortunately, politicians are not well placed to venture an informed opinion on the value of scientific research. The fact that research sounds silly or strange is no guide to its value. My own hunch — and it is just a hunch — is that it’s the research that seems obviously useful that is most likely to be polluted by bad science. The merely odd, purely curiosity-driven research is less likely to be tainted. Incestuous as it might seem, the people best placed to hand out funding for basic scientific research are other scientists.

This is not to say that society should just write a blank cheque to researchers. There are plenty of useful ways to guide scientific research.

One possibility is the use of innovation prizes, where funders specify a goal, and research teams are rewarded for achieving it. Examples range from the longitude prizes of the 18th century to the advanced market commitments that have been used to subsidise vaccine doses in the 21st century. Darpa’s grand challenge of 2004 and 2005 helped jolt life into the field of autonomous vehicles for a few million dollars in prize money.

Another possibility is to explicitly favour long-shot research with a high chance of failure but a real prospect of creating a major breakthrough. The economists Pierre Azoulay, Joshua Graff Zivin and Gustavo Manso compared grants made by the Howard Hughes Medical Institute against the more cautious approach of the National Institutes of Health. They found that both organisations got what they were asking for: a higher success rate for the NIH, and a mix of failures and breakthroughs for the HHMI.

A healthy scientific ecosystem needs both. And perhaps most of all it needs the odd-sounding, curiosity-driven research that no venture capitalist would dream of funding. The Nobel Prize-winning physiologist Archibald Hill once gave a public lecture at which a grumpy member of the public challenged him to explain what possible practical value there might be in his research.

“To tell you the truth,” replied Hill, “we don’t do it because it is useful but because it’s amusing.” That’s the spirit.

Written for and first published in the Financial Times on 11 March 2026.

I’m running the London Marathon on 26 April in support of a very good cause. If you felt able to contribute something, I’d be extremely grateful.

As the Victorian era dawns, modernisation erodes the old ways of life and poverty rises. In the unrest, an unlikely hero emerges, capturing the imagination of the countryside’s working class. He claims to be the new Messiah, and promises a better future. Despite his unconventional appearance and strange claims, his message resonates with the people of Kent, many of whom are willing to follow him into bloody battle.

For this Cautionary Conversation, Ian Breckon – author of Mad Tom’s Rising: The Revolutionary Mystic Sir William Courtenay and the Last Battle Fought on English Soil (US) – joins Tim to discuss a  forgotten folk hero and the dangerous power of belief in desperate times.

For ad-free listening, monthly bonus episodes, monthly behind-the-scenes conversations, our newsletter, and more, please consider joining the Cautionary Club.

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Further reading