This episode is available exclusively to members of the Cautionary Club, and Pushkin+ subscribers.

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Further Reading

The Admiralty Regrets by Lt. Charles Warren and Sub Lt. James Benson.

Mike Lynch was often lauded as Britain’s answer to Bill Gates. Born into a working-class family, Lynch’s incredible intellect and passion for computers led him to become a billionaire tech entrepreneur.But behind the scenes, Lynch was a bully who couldn’t bear criticism and was prone to creative accounting. When computer giant Hewlett Packard bought his company, Autonomy, it triggered one of the biggest fraud scandals in Silicon Valley history. Tim talks to Katie Prescott, Technology Business Editor at The Times and author of the book The Curious Case of Mike Lynch, about the lessons we can take from a story no one could have predicted.

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What sort of signal? First, and most obvious, the signal to consumers to cut back. Anything with oil in the supply chain — from petrol to plastic to fertilised crops to package holidays — will become more expensive. The signal is to reduce when you can, because carrying on as usual will cost money. Maybe holiday nearer home this year; maybe find someone to share car journeys with. Maybe pull on a cardigan and turn down the central heating.

Or maybe none of these things. Unlike ration books or speeches by Jimmy Carter, price signals don’t tell anyone what to do; they change the incentives and we are all free to act, or not, depending on our own circumstances and preferences.

A second signal is to producers to look for ways to save energy in their production process. The low-hanging fruit will probably have been plucked already, but higher oil prices shift the calculus. Energy-saving measures that once seemed too difficult may now make sense. These measures are often simple reflections of the trade-off involved in using expensive energy, such as bundling deliveries together to save fuel, or switching off the patio heaters in the pub garden.

The third signal is to substitute away from oil and towards other energy sources. Disruption to the flow of oil through the Strait of Hormuz is good news for the makers of batteries and solar panels . . . and coal miners. If it lasts, or recurs, it may even be good news for the builders of nuclear power stations.

These energy-saving and oil-saving substitutions will, in the short-run, simply involve picking existing technologies and techniques off the shelf. But the same signal will also reach the world of science and technology. In 2002, the economist David Popp published a study of “induced innovation”, tracking the response by inventors to the oil shocks of the 1970s.

The oil price leapt in 1973 and surged further in 1979, before sliding lower throughout the early 1980s. Popp found that patent activity tracked the oil price — for example, there were 10 successful patent applications in the field of solar energy in 1972, but more than 100 in 1974 and about 300 a year in the late 1970s. As the oil price fell back, so did patent activity, with fewer than 50 successful solar patents a year from the mid-1980s onward.

Popp found that a similar story could be told for batteries (a natural complement to solar energy), and patent applications for deriving liquid and gaseous fuels from coal. In each case, the few years of high oil prices led to a few years in which oil-saving patent activity was also high.

Today’s high oil price sends more signals: to find oilfields outside the Gulf region; to build new pipelines and tanker ports that are further from harm; to find ways to defend vulnerable shipping. In fact, there are far too many to list, and that is the point: a price signal — which, of course, is also a monetary incentive — is an invitation to everyone, everywhere, to do things a little differently.

One result of all these signals twitching across the nervous system of the global economy is that catastrophic shocks are often less catastrophic than they first seem. We can adapt quickly when we have to. The 2008 banking crisis is a salutary counter-example, but we have seen many examples of apparently grievous economic harm — from earthquakes to typhoons to regional wars to Covid-19 — in which the damage was cushioned by smart operators swiftly finding profitable workarounds and alternatives.

Please indulge me in delivering this extended public service announcement, because while it will not surprise many readers of the FT, it may be news to the next person you talk to. Certainly, if governments around the world are any guide, the lesson that prices are signals to watch rather than evils to suppress has not been repeated quite often enough. All too frequently, the governmental instinct when voters are leaning on a hot stove is to inject a dose of anaesthetic rather than help them leap to safety.

The most infamous example is President Richard Nixon’s decision to freeze wages and prices in the US in the summer of 1971. Although many prices were liberalised again after 90 days, some were not — and the price of gasoline remained under government control for years. Some of the consequences were obvious: artificially cheap fuel meant long lines at the pump, and people wasted fuel as they drove around looking for more fuel.

Other consequences of price caps were obvious only in hindsight. Chicken farmers faced a price cap on the chickens they sold, but the price of chicken feed was anything but. This turned every chick into a lossmaking asset. Farmers smothered newly hatched chicks by packing them into airtight barrels, telling journalists that “the more we produce, the more we lose”. Did the price of chicken burgers fall as a result of this grim waste? Of course not.

Another unexpected problem, highlighted in a new working paper from economists Brian Albrecht, Alex Tabarrok and Mark Whitmeyer, is that in 1974 gasoline was in short supply in the big cities but “more than abundant” in rural areas. As Albrecht and colleagues point out, this is a natural consequence of constraining the price system. Since fuel sells at the price cap everywhere, why bother to pay the additional cost of delivering it to an urban area? Only when the gas stations near oil refineries are drowning in more petrol than they can sell will the tankers head to more distant markets.

Closer to home is the vast sum Liz Truss pledged to prevent energy bills from rising in late 2022, estimated at the time to be not far off the annual budget of the NHS. Households were encouraged to burn scarce gas, and the UK lives with the fiscal consequences.

Prices are the nervous system of the global economy. That sharp pain we are all feeling is the response to a series of injuries that few of us cared to risk, but which were inflicted on us anyway. That’s annoying — and for some, more than merely annoying. But now we need to clean the wounds and stop the bleeding, not beg for enough fentanyl to end the pain.

Written for and first published in the Financial Times on 1 April 2026.

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.

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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.