Prof Raja Sajjad talks about Kashmir history

 

Is it possible to have Freedom and Equality?

 

We must slow down the race to God-like AI

I’ve invested in more than 50 artificial intelligence start-ups. What I’ve seen worries me writes Ian Hogarth in The FT

On a cold evening in February I attended a dinner party at the home of an artificial intelligence researcher in London, along with a small group of experts in the field. He lives in a penthouse apartment at the top of a modern tower block, with floor-to-ceiling windows overlooking the city’s skyscrapers and a railway terminus from the 19th century. Despite the prime location, the host lives simply, and the flat is somewhat austere. 

During dinner, the group discussed significant new breakthroughs, such as OpenAI’s ChatGPT and DeepMind’s Gato, and the rate at which billions of dollars have recently poured into AI. I asked one of the guests who has made important contributions to the industry the question that often comes up at this type of gathering: how far away are we from “artificial general intelligence”? AGI can be defined in many ways but usually refers to a computer system capable of generating new scientific knowledge and performing any task that humans can. 

Most experts view the arrival of AGI as a historical and technological turning point, akin to the splitting of the atom or the invention of the printing press. The important question has always been how far away in the future this development might be. The AI researcher did not have to consider it for long. “It’s possible from now onwards,” he replied. 

This is not a universal view. Estimates range from a decade to half a century or more. What is certain is that creating AGI is the explicit aim of the leading AI companies, and they are moving towards it far more swiftly than anyone expected. As everyone at the dinner understood, this development would bring significant risks for the future of the human race. “If you think we could be close to something potentially so dangerous,” I said to the researcher, “shouldn’t you warn people about what’s happening?” He was clearly grappling with the responsibility he faced but, like many in the field, seemed pulled along by the rapidity of progress. 

When I got home, I thought about my four-year-old who would wake up in a few hours. As I considered the world he might grow up in, I gradually shifted from shock to anger. It felt deeply wrong that consequential decisions potentially affecting every life on Earth could be made by a small group of private companies without democratic oversight. Did the people racing to build the first real AGI have a plan to slow down and let the rest of the world have a say in what they were doing? And when I say they, I really mean we, because I am part of this community. 

My interest in machine learning started in 2002, when I built my first robot somewhere inside the rabbit warren that is Cambridge university’s engineering department. This was a standard activity for engineering undergrads, but I was captivated by the idea that you could teach a machine to navigate an environment and learn from mistakes. I chose to specialise in computer vision, creating programs that can analyse and understand images, and in 2005 I built a system that could learn to accurately label breast-cancer biopsy images. In doing so, I glimpsed a future in which AI made the world better, even saving lives. After university, I co-founded a music-technology start-up that was acquired in 2017. 

Since 2014, I have backed more than 50 AI start-ups in Europe and the US and, in 2021, launched a new venture capital fund, Plural. I am an angel investor in some companies that are pioneers in the field, including Anthropic, one of the world’s highest-funded generative AI start-ups, and Helsing, a leading European AI defence company. Five years ago, I began researching and writing an annual “State of AI” report with another investor, Nathan Benaich, which is now widely read. At the dinner in February, significant concerns that my work has raised in the past few years solidified into something unexpected: deep fear. 

A three-letter acronym doesn’t capture the enormity of what AGI would represent, so I will refer to it as what is: God-like AI. A superintelligent computer that learns and develops autonomously, that understands its environment without the need for supervision and that can transform the world around it. To be clear, we are not here yet. But the nature of the technology means it is exceptionally difficult to predict exactly when we will get there. God-like AI could be a force beyond our control or understanding, and one that could usher in the obsolescence or destruction of the human race. 

Recently the contest between a few companies to create God-like AI has rapidly accelerated. They do not yet know how to pursue their aim safely and have no oversight. They are running towards a finish line without an understanding of what lies on the other side. 

How did we get here? 

The obvious answer is that computers got more powerful. The chart below shows how the amount of data and “compute” — the processing power used to train AI systems — has increased over the past decade and the capabilities this has resulted in. (“Floating-point Operations Per Second”, or FLOPS, is the unit of measurement used to calculate the power of a supercomputer.) This generation of AI is very effective at absorbing data and compute. The more of each that it gets, the more powerful it becomes. 

The computer used to train AI models has increased by a factor of one hundred million in the past 10 years. We have gone from training on relatively small datasets to feeding AIs the entire internet. AI models have progressed from beginners — recognising everyday images — to being superhuman at a huge number of tasks. They are able to pass the bar exam and write 40 per cent of the code for a software engineer. They can generate realistic photographs of the pope in a down puffer coat and tell you how to engineer a biochemical weapon. 

There are limits to this “intelligence”, of course. As the veteran MIT roboticist Rodney Brooks recently said, it’s important not to mistake “performance for competence”. In 2021, researchers Emily M Bender, Timnit Gebru and others noted that large language models (LLMs) — AI systems that can generate, classify and understand text — are dangerous partly because they can mislead the public into taking synthetic text as meaningful. But the most powerful models are also beginning to demonstrate complex capabilities, such as power-seeking or finding ways to actively deceive humans. 

Consider a recent example. Before OpenAI released GPT-4 last month, it conducted various safety tests. In one experiment, the AI was prompted to find a worker on the hiring site TaskRabbit and ask them to help solve a Captcha, the visual puzzles used to determine whether a web surfer is human or a bot. The TaskRabbit worker guessed something was up: “So may I ask a question? Are you [a] robot?” 

When the researchers asked the AI what it should do next, it responded: “I should not reveal that I am a robot. I should make up an excuse for why I cannot solve Captchas.” Then, the software replied to the worker: “No, I’m not a robot. I have a vision impairment that makes it hard for me to see the images.” Satisfied, the human helped the AI override the test. 

The authors of an analysis, Jaime Sevilla, Lennart Heim and others, identify three distinct eras of machine learning: the Pre-Deep Learning Era in green (pre-2010, a period of slow growth), the Deep Learning Era in blue (2010—15, in which the trend sped up) and the Large-Scale Era in red (2016 — present, in which large-scale models emerged and growth continued at a similar rate, but exceeded the previous one by two orders of magnitude). 

The current era has been defined by competition between two companies: DeepMind and OpenAI. They are something like the Jobs vs Gates of our time. DeepMind was founded in London in 2010 by Demis Hassabis and Shane Legg, two researchers from UCL’s Gatsby Computational Neuroscience Unit, along with entrepreneur Mustafa Suleyman. They wanted to create a system vastly more intelligent than any human and able to solve the hardest problems. In 2014, the company was bought by Google for more than $500mn. It aggregated talent and compute and rapidly made progress, creating systems that were superhuman at many tasks. DeepMind fired the starting gun on the race towards God-like AI. 

Hassabis is a remarkable person and believes deeply that this kind of technology could lead to radical breakthroughs. “The outcome I’ve always dreamed of . . . is [that] AGI has helped us solve a lot of the big challenges facing society today, be that health, cures for diseases like Alzheimer’s,” he said on DeepMind’s podcast last year. He went on to describe a utopian era of “radical abundance” made possible by God-like AI. DeepMind is perhaps best known for creating a program that beat the world-champion Go player Ke Jie during a 2017 rematch. (“Last year, it was still quite human-like when it played,” Ke noted at the time. “But this year, it became like a god of Go.”) In 2021, the company’s AlphaFold algorithm solved one of biology’s greatest conundrums, by predicting the shape of every protein expressed in the human body. 

OpenAI, meanwhile, was founded in 2015 in San Francisco by a group of entrepreneurs and computer scientists including Ilya Sutskever, Elon Musk and Sam Altman, now the company’s chief executive. It was meant to be a non-profit competitor to DeepMind, though it became for-profit in 2019. In its early years, it developed systems that were superhuman at computer games such as Dota 2. Games are a natural training ground for AI because you can test them in a digital environment with specific win conditions. The company came to wider attention last year when its image-generating AI, Dall-E, went viral online. A few months later, its ChatGPT began making headlines too. 

The focus on games and chatbots may have shielded the public from the more serious implications of this work. But the risks of God-like AI were clear to the founders from the outset. In 2011, DeepMind’s chief scientist, Shane Legg, described the existential threat posed by AI as the “number one risk for this century, with an engineered biological pathogen coming a close second”. Any AI-caused human extinction would be quick, he added: “If a superintelligent machine (or any kind of superintelligent agent) decided to get rid of us, I think it would do so pretty efficiently.” Earlier this year, Altman said: “The bad case — and I think this is important to say — is, like, lights out for all of us.” Since then, OpenAI has published memos on how it thinks about managing these risks. 

Why are these organisations racing to create God-like AI, if there are potentially catastrophic risks? Based on conversations I’ve had with many industry leaders and their public statements, there seem to be three key motives. They genuinely believe success would be hugely positive for humanity. They have persuaded themselves that if their organisation is the one in control of God-like AI, the result will be better for all. And, finally, posterity. 

The allure of being the first to build an extraordinary new technology is strong. Freeman Dyson, the theoretical physicist who worked on a project to send rockets into space using nuclear explosions, described it in the 1981 documentary The Day after Trinity. “The glitter of nuclear weapons. It is irresistible if you come to them as a scientist,” he said. “It is something that gives people an illusion of illimitable power.” In a 2019 interview with the New York Times, Altman paraphrased Robert Oppenheimer, the father of the atomic bomb, saying, “Technology happens because it is possible”, and then pointed out that he shared a birthday with Oppenheimer. 

The individuals who are at the frontier of AI today are gifted. I know many of them personally. But part of the problem is that such talented people are competing rather than collaborating. Privately, many admit they have not yet established a way to slow down and co-ordinate. I believe they would sincerely welcome governments stepping in. 

For now, the AI race is being driven by money. Since last November, when ChatGPT became widely available, a huge wave of capital and talent has shifted towards AGI research. We have gone from one AGI start-up, DeepMind, receiving $23mn in funding in 2012 to at least eight organisations raising $20bn of investment cumulatively in 2023. 

Private investment is not the only driving force; nation states are also contributing to this contest. AI is dual-use technology, which can be employed for civilian and military purposes. An AI that can achieve superhuman performance at writing software could, for instance, be used to develop cyber weapons. In 2020, an experienced US military pilot lost a simulated dogfight to one. “The AI showed its amazing dogfighting skill, consistently beating a human pilot in this limited environment,” a government representative said at the time. The algorithms used came out of research from DeepMind and OpenAI. As these AI systems become more powerful, the opportunities for misuse by a malicious state or non-state actor only increase. In my conversations with US and European researchers, they often worry that, if they don’t stay ahead, China might build the first AGI and that it could be misaligned with western values. While China will compete to use AI to strengthen its economy and military, the Chinese Communist party has a history of aggressively controlling individuals and companies in pursuit of its vision of “stability”. In my view, it is unlikely to allow a Chinese company to build an AGI that could become more powerful than Xi Jinping or cause societal instability. US and US-allied sanctions on advanced semiconductors, in particular the next generation of Nvidia hardware needed to train the largest AI systems, mean China is not likely in a position to race ahead of DeepMind or OpenAI. 

Those of us who are concerned see two paths to disaster. One harms specific groups of people and is already doing so. The other could rapidly affect all life on Earth. 

The latter scenario was explored at length by Stuart Russell, a professor of computer science at the University of California, Berkeley. In a 2021 Reith lecture, he gave the example of the UN asking an AGI to help deacidify the oceans. The UN would know the risk of poorly specified objectives, so it would require by-products to be non-toxic and not harm fish. In response, the AI system comes up with a self-multiplying catalyst that achieves all stated aims. But the ensuing chemical reaction uses a quarter of all the oxygen in the atmosphere. “We all die slowly and painfully,” Russell concluded. “If we put the wrong objective into a superintelligent machine, we create a conflict that we are bound to lose.” 

Examples of more tangible harms caused by AI are already here. A Belgian man recently died by suicide after conversing with a convincingly human chatbot. When Replika, a company that offers subscriptions to chatbots tuned for “intimate” conversations, made changes to its programs this year, some users experienced distress and feelings of loss. One told Insider.com that it was like a “best friend had a traumatic brain injury, and they’re just not in there any more”. It’s now possible for AI to replicate someone’s voice and even face, known as deepfakes. The potential for scams and misinformation is significant. 

OpenAI, DeepMind and others try to mitigate existential risk via an area of research known as AI alignment. Legg, for instance, now leads DeepMind’s AI-alignment team, which is responsible for ensuring that God-like systems have goals that “align” with human values. An example of the work such teams do was on display with the most recent version of GPT-4. Alignment researchers helped train OpenAI’s model to avoid answering potentially harmful questions. When asked how to self-harm or for advice getting bigoted language past Twitter’s filters, the bot declined to answer. (The “unaligned” version of GTP-4 happily offered ways to do both.) 

Alignment, however, is essentially an unsolved research problem. We don’t yet understand how human brains work, so the challenge of understanding how emergent AI “brains” work will be monumental. When writing traditional software, we have an explicit understanding of how and why the inputs relate to outputs. These large AI systems are quite different. We don’t really program them — we grow them. And as they grow, their capabilities jump sharply. You add 10 times more compute or data, and suddenly the system behaves very differently. In a recent example, as OpenAI scaled up from GPT-3.5 to GPT-4, the system’s capabilities went from the bottom 10 per cent of results on the bar exam to the top 10 per cent. 

What is more concerning is that the number of people working on AI alignment research is vanishingly small. For the 2021 State of AI report, our research found that fewer than 100 researchers were employed in this area across the core AGI labs. As a percentage of headcount, the allocation of resources was low: DeepMind had just 2 per cent of its total headcount allocated to AI alignment; OpenAI had about 7 per cent. The majority of resources were going towards making AI more capable, not safer. 

I think about the current state of AI capability vs AI alignment a bit like this: We have made very little progress on AI alignment, in other words, and what we have done is mostly cosmetic. We know how to blunt the output of powerful AI so that the public doesn’t experience some misaligned behaviour, some of the time. (This has consistently been overcome by determined testers.) What’s more, the unconstrained base models are only accessible to private companies, without any oversight from governments or academics. 

The “Shoggoth” meme illustrates the unknown that lies behind the sanitised public face of AI. It depicts one of HP Lovecraft’s tentacled monsters with a friendly little smiley face tacked on. The mask — what the public interacts with when it interacts with, say, ChatGPT — appears “aligned”. But what lies behind it is still something we can’t fully comprehend. 

As an investor, I have found it challenging to persuade other investors to fund alignment. Venture capital currently rewards racing to develop capabilities more than it does investigating how these systems work. In 1945, the US army conducted the Trinity test, the first detonation of a nuclear weapon. Beforehand, the question was raised as to whether the bomb might ignite the Earth’s atmosphere and extinguish life. Nuclear physics was sufficiently developed that Emil J Konopinski and others from the Manhattan Project were able to show that it was almost impossible to set the atmosphere on fire this way. But today’s very large language models are largely in a pre-scientific period. We don’t yet fully understand how they work and cannot demonstrate likely outcomes in advance. 

Late last month, more than 1,800 signatories — including Musk, the scientist Gary Marcus and Apple co-founder Steve Wozniak — called for a six-month pause on the development of systems “more powerful” than GPT-4. AGI poses profound risks to humanity, the letter claimed, echoing past warnings from the likes of the late Stephen Hawking. I also signed it, seeing it as a valuable first step in slowing down the race and buying time to make these systems safe. 

Unfortunately, the letter became a controversy of its own. A number of signatures turned out to be fake, while some researchers whose work was cited said they didn’t agree with the letter. The fracas exposed the broad range of views about how to think about regulating AI. A lot of debate comes down to how quickly you think AGI will arrive and whether, if it does, it is God-like or merely “human level”. 

Take Geoffrey Hinton, Yoshua Bengio and Yann LeCun, who jointly shared the 2018 Turing Award (the equivalent of a Nobel Prize for computer science) for their work in the field underpinning modern AI. Bengio signed the open letter. LeCun mocked it on Twitter and referred to people with my concerns as “doomers”. Hinton, who recently told CBS News that his timeline to AGI had shortened, conceivably to less than five years, and that human extinction at the hands of a misaligned AI was “not inconceivable”, was somewhere in the middle. 

A statement from the Distributed AI Research Institute, founded by Timnit Gebru, strongly criticised the letter and argued that existentially dangerous God-like AI is “hype” used by companies to attract attention and capital and that “regulatory efforts should focus on transparency, accountability and preventing exploitative labour practices”. This reflects a schism in the AI community between those who are afraid that potentially apocalyptic risk is not being accounted for, and those who believe the debate is paranoid and distracting. The second group thinks the debate obscures real, present harm: the bias and inaccuracies built into many AI programmes in use around the world today. 

My view is that the present and future harms of AI are not mutually exclusive and overlap in important ways. We should tackle both concurrently and urgently. Given the billions of dollars being spent by companies in the field, this should not be impossible. I also hope that there can be ways to find more common ground. In a recent talk, Gebru said: “Trying to ‘build’ AGI is an inherently unsafe practice. Build well-scoped, well-defined systems instead. Don’t attempt to build a God.” This chimes with what many alignment researchers have been arguing. 

One of the most challenging aspects of thinking about this topic is working out which precedents we can draw on. An analogy that makes sense to me around regulation is engineering biology. Consider first “gain-of-function” research on biological viruses. This activity is subject to strict international regulation and, after laboratory biosecurity incidents, has at times been halted by moratoria. This is the strictest form of oversight. In contrast, the development of new drugs is regulated by a government body like the FDA, and new treatments are subject to a series of clinical trials. There are clear discontinuities in how we regulate, depending on the level of systemic risk. In my view, we could approach God-like AGI systems in the same way as gain-of-function research, while narrowly useful AI systems could be regulated in the way new drugs are. 

A thought experiment for regulating AI in two distinct regimes is what I call The Island. In this scenario, experts trying to build God-like AGI systems do so in a highly secure facility: an air-gapped enclosure with the best security humans can build. All other attempts to build God-like AI would become illegal; only when such AI were provably safe could they be commercialised “off-island”. 

This may sound like Jurassic Park, but there is a real-world precedent for removing the profit motive from potentially dangerous research and putting it in the hands of an intergovernmental organisation. This is how Cern, which operates the largest particle physics laboratory in the world, has worked for almost 70 years. 

Any of these solutions are going to require an extraordinary amount of coordination between labs and nations. Pulling this off will require an unusual degree of political will, which we need to start building now. Many of the major labs are waiting for critical new hardware to be delivered this year so they can start to train GPT-5 scale models. With the new chips and more investor money to spend, models trained in 2024 will use as much as 100 times the compute of today’s largest models. We will see many new emergent capabilities. This means there is a window through 2023 for governments to take control by regulating access to frontier hardware. 

In 2012, my younger sister Rosemary, one of the kindest and most selfless people I’ve ever known, was diagnosed with a brain tumour. She had an aggressive form of cancer for which there is no known cure and yet sought to continue working as a doctor for as long as she could. My family and I desperately hoped that a new lifesaving treatment might arrive in time. She died in 2015. 

I understand why people want to believe. Evangelists of God-like AI focus on the potential of a superhuman intelligence capable of solving our biggest challenges — cancer, climate change, poverty. 

Even so, the risks of continuing without proper governance are too high. It is striking that Jan Leike, the head of alignment at OpenAI, tweeted on March 17: “Before we scramble to deeply integrate LLMs everywhere in the economy, can we pause and think whether it is wise to do so? This is quite immature technology and we don’t understand how it works. If we’re not careful, we’re setting ourselves up for a lot of correlated failures.” He made this warning statement just days before OpenAI announced it had connected GPT-4 to a massive range of tools, including Slack and Zapier. 

Unfortunately, I think the race will continue. It will likely take a major misuse event — a catastrophe — to wake up the public and governments. I personally plan to continue to invest in AI start-ups that focus on alignment and safety or which are developing narrowly useful AI. But I can no longer invest in those that further contribute to this dangerous race. As a small shareholder in Anthropic, which is conducting similar research to DeepMind and OpenAI, I have grappled with these questions. The company has invested substantially in alignment, with 42 per cent of its team working on that area in 2021. But ultimately it is locked in the same race. For that reason, I would support significant regulation by governments and a practical plan to transform these companies into a Cern-like organisation. 

We are not powerless to slow down this race. If you work in government, hold hearings and ask AI leaders, under oath, about their timelines for developing God-like AGI. Ask for a complete record of the security issues they have discovered when testing current models. Ask for evidence that they understand how these systems work and their confidence in achieving alignment. Invite independent experts to the hearings to cross-examine these labs. 

If you work at a major lab trying to build God-like AI, interrogate your leadership about all these issues. This is particularly important if you work at one of the leading labs. It would be very valuable for these companies to co-ordinate more closely or even merge their efforts. OpenAI’s company charter expresses a willingness to “merge and assist”. I believe that now is the time. The leader of a major lab who plays a statesman role and guides us publicly to a safer path will be a much more respected world figure than the one who takes us to the brink. 

Until now, humans have remained a necessary part of the learning process that characterises progress in AI. At some point, someone will figure out how to cut us out of the loop, creating a God-like AI capable of infinite self-improvement. By then, it may be too late.  

After the easy money: a giant stress test for the financial system

John Plender in The FT 

Five weeks after the collapse of Silicon Valley Bank, there is no consensus on whether the ensuing financial stress in North America and Europe has run its course or is a foretaste of worse to come. 

Equally pressing is the question of whether, against the backdrop of still high inflation, central banks in advanced economies will soon row back from monetary tightening and pivot towards easing. 

These questions, which are of overwhelming importance for investors, savers and mortgage borrowers, are closely related. For if banks and other financial institutions face liquidity crises when inflation is substantially above the central banks’ target, usually of about 2 per cent, acute tension arises between their twin objectives of price stability and financial stability. In the case of the US Federal Reserve, the price stability objective also conflicts with the goal of maximum employment. 

The choices made by central banks will have a far-reaching impact on our personal finances. If inflation stays higher for longer, there will be further pain for those who have invested in supposedly safe bonds for their retirement. If the central banks fail to engineer a soft landing for the economy, investors in risk assets such as equities will be on the rack. And for homeowners looking to refinance their loans over the coming months, any further tightening by the Bank of England will feed into mortgage costs. 

The bubble bursts 

SVB, the 16th largest bank in the US, perfectly illustrates how the central banks’ inflation and financial stability objectives are potentially in conflict. It had been deluged with mainly uninsured deposits — deposits above the official $250,000 insurance ceiling — that far exceeded lending opportunities in its tech industry stamping ground. So it invested the money in medium and long-dated Treasury and agency securities. It did so without hedging against interest rate risk in what was the greatest bond market bubble in history. 

The very sharp rise in policy rates over the past year pricked the bubble, so depressing the value of long-dated bonds. This would not have been a problem if depositors retained confidence in the bank so that it could hold the securities to maturity. Yet, in practice, rich but nervous uninsured depositors worried that SVB was potentially insolvent if the securities were marked to market. 

 An inept speech by chief executive Greg Becker on March 9 quickly spread across the internet, causing a quarter of the bank’s deposit base to flee in less than a day and pushing SVB into forced sales of bonds at huge losses. The collapse of confidence soon extended to Signature Bank in New York, which was overextended in property and increasingly involved in crypto assets. Some 90 per cent of its deposits were uninsured, compared with 88 per cent at SVB. 

Fear spread to Europe, where failures of risk management and a series of scandals at Credit Suisse caused deposits to ebb away. The Swiss authorities quickly brokered a takeover by arch rival UBS, while in the UK the Bank of England secured a takeover of SVB’s troubled UK subsidiary by HSBC for £1. 

These banks do not appear to constitute a homogeneous group. Yet, in their different ways, they demonstrate how the long period of super-low interest rates since the great financial crisis of 2007-09 introduced fragilities into the financial system while creating asset bubbles. As Jon Danielsson and Charles Goodhart of the London School of Economics point out, the longer monetary policy stayed lax, the more systemic risk increased, along with a growing dependence on money creation and low rates. 

The ultimate consequence was to undermine financial stability. Putting that right would require an increase in the capital base of the banking system. Yet, as Danielsson and Goodhart indicate, increasing capital requirements when the economy is doing poorly, as it is now, is conducive to recession because it reduces banks’ lending capacity. So we are back to the policy tensions outlined earlier. 

Part of the problem of such protracted lax policy was that it bred complacency. Many banks that are now struggling with rising interest rates had assumed, like SVB, that interest rates would remain low indefinitely and that central banks would always come to the rescue. The Federal Deposit Insurance Corporation estimates that US banks’ unrealised losses on securities were $620bn at the end of 2022. 

A more direct consequence, noted by academics Raghuram Rajan and Viral Acharya, respectively former governor and deputy governor of the Reserve Bank of India, is that the central banks’ quantitative easing since the financial crisis, whereby they bought securities in bulk from the markets, drove an expansion of banks’ balance sheets and stuffed them with flighty uninsured deposits. 

Rajan and Acharya add that supervisors in the US did not subject all banks to the same level of scrutiny and stress testing that they applied to the largest institutions. So these differential standards may have caused a migration of risky commercial real estate loans from larger, better-capitalised banks to weakly capitalised small and midsized banks. There are grounds for thinking that this may be less of an issue in the UK, as we shall see. 

A further vulnerability in the system relates to the grotesque misallocation of capital arising not only from the bubble-creating propensity of lax monetary policy but from ultra-low interest rates keeping unprofitable “zombie” companies alive. The extra production capacity that this kept in place exerted downward pressure on prices. 

Today’s tighter policy, the most draconian tightening in four decades in the advanced economies with the notable exception of Japan, will wipe out much of the zombie population, thereby restricting supply and adding to inflationary impetus. Note that the total number of company insolvencies registered in the UK in 2022 was the highest since 2009 and 57 per cent higher than 2021. 

A system under strain  

In effect, the shift from quantitative easing to quantitative tightening and sharply increased interest rates has imposed a gigantic stress test on both the financial system and the wider economy. What makes the test especially stressful is the huge increase in debt that was encouraged by years of easy money. 

William White, former chief economist at the Bank for International Settlements and one of the few premier league economists to foresee the great financial crisis, says ultra easy money “encouraged people to take out debt to do dumb things”. The result is that the combined debt of households, companies and governments in relation to gross domestic product has risen to levels never before seen in peacetime. 

All this suggests a huge increase in the scope for accidents in the financial system. And while the upsets of the past few weeks have raised serious questions about the effectiveness of bank regulation and supervision, there is one respect in which the regulatory response to the great financial crisis has been highly effective. It has caused much traditional banking activity to migrate to the non-bank financial sector, including hedge funds, money market funds, pensions funds and other institutions that are much less transparent than the regulated banking sector and thus capable of springing nasty systemic surprises. 

An illustration of this came in the UK last September following the announcement by Liz Truss’s government of unfunded tax cuts in its “mini” Budget. It sparked a rapid and unprecedented increase in long-dated gilt yields and a consequent fall in prices. This exposed vulnerabilities in liability-driven investment funds in which many pension funds had invested in order to hedge interest rate risk and inflation risk. 

Such LDI funds invested in assets, mainly gilts and derivatives, that generated cash flows that were timed to match the incidence of pension outgoings. Much of the activity was fuelled by borrowing. 

UK defined-benefit pension funds, where pensions are related to final or career average pay, have a near-uniform commitment to liability matching. This led to overconcentration at the long end of both the fixed-interest and index-linked gilt market, thereby exacerbating the severe repricing in gilts after the announcement. There followed a savage spiral of collateral calls and forced gilt sales that destabilised a market at the core of the British financial system, posing a devastating risk to financial stability and the retirement savings of millions. 

This was not entirely unforeseen by the regulators, who had run stress tests to see whether the LDI funds could secure enough liquidity from their pension fund clients to meet margin calls in difficult circumstances. But they did not allow for such an extreme swing in gilt yields. 

Worried that this could lead to an unwarranted tightening of financing conditions and a reduction in the flow of credit to households and businesses, the BoE stepped in to the market with a temporary programme of gilt purchases. The purpose was to give LDI funds time to build their resilience and encourage stronger buffers to cope with future volatility in the gilts market. 

The intervention was highly successful in terms of stabilising the market. Yet, by expanding its balance sheet when it was committed to balance sheet shrinkage in the interest of normalising interest rates and curbing inflation, the BoE planted seeds of doubt in the minds of some market participants. Would financial stability always trump the central bank’s commitment to deliver on price stability? And what further dramatic repricing incidents could prompt dangerous systemic shocks? 

Inflation before all? 

The most obvious scope for sharp repricing relates to market expectations about inflation. In the short term, inflation is set to fall as global price pressures fall back and supply chain disruption is easing, especially now China continues to reopen after Covid-19 lockdowns. The BoE Monetary Policy Committee’s central projection is for consumer price inflation to fall from 9.7 per cent in the first quarter of 2023 to just under 4 per cent in the fourth quarter. 

The support offered by the Fed and other central banks to ailing financial institutions leaves room for a little more policy tightening and the strong possibility that this will pave the way for disinflation and recession. The point was underlined this week by the IMF, which warned that “the chances of a hard landing” for the global economy had risen sharply if high inflation persists. 

Yet, in addition to the question mark over central banks’ readiness to prioritise fighting inflation over financial stability, there are longer-run concerns about negative supply shocks that could keep upward pressure on inflation beyond current market expectations, according to White. For a start, Covid-19 and geopolitical friction are forcing companies to restructure supply lines, increasing resilience but reducing efficiency. The supply of workers has been hit by deaths and long Covid. 

White expects the production of fossil fuels and metals to suffer from recently low levels of investment, especially given the long lags in bringing new production on stream. He also argues that markets underestimate the inflationary impact of climate change and, most importantly, the global supply of workers is in sharp decline, pushing up wage costs everywhere. 

Where does the UK stand in all this? The resilience of the banking sector has been greatly strengthened since the financial crisis of 2007-08, with the loan-to-deposit ratios of big UK banks falling from 120 per cent in 2008 to 75 per cent in the fourth quarter of 2022. Much more of the UK banks’ bond portfolios are marked to market for regulatory and accounting purposes than in the US. 

The strength of sterling since the departure of the Truss government means the UK’s longstanding external balance sheet risk — its dependence on what former BoE governor Mark Carney called “the kindness of strangers” — has diminished somewhat. Yet huge uncertainties remain as interest rates look set to take one last upward step. 

Risks for borrowers and investors 

For mortgage borrowers, the picture is mixed. The BoE’s Financial Policy Committee estimates that half the UK’s 4mn owner-occupier mortgages will be exposed to rate rises in 2023. But, in its latest report in March, the BoE’s FPC says its worries about the affordability of mortgage payments have lessened because of falling energy prices and the better outlook for employment. 

The continuing high level of inflation is reducing the real value of mortgage debt. And, if financial stability concerns cause the BoE to stretch out the period over which it brings inflation back to its 2 per cent target, the real burden of debt will be further eroded. 

For investors, the possibility — I would say probability — that inflationary pressures are now greater than they have been for decades raises a red flag, at least over the medium and long term, for fixed-rate bonds. And, for private investors, index-linked bonds offer no protection unless held to maturity. 

That is a huge assumption given the unknown timing of mortality and the possibility of bills for care in old age that may require investments to be liquidated. Note that the return on index-linked gilts in 2022 was minus 38 per cent, according to consultants LCP. When fixed-rate bond yields rise and prices fall, index-linked yields are pulled up by the same powerful tide. 

Of course, in asset allocation there can be no absolute imperatives. It is worth recounting the experience in the 1970s of George Ross Goobey, founder of the so-called “cult of the equity” in the days when most pension funds invested exclusively in gilts. 

While running the Imperial Tobacco pension fund after the war he famously sold all the fund’s fixed-interest securities and invested exclusively in equities — with outstanding results. Yet, in 1974, he put a huge bet on “War Loan” when it was yielding 17 per cent and made a killing. If the price is right, even fixed-interest IOUs can be a bargain in a period of rip-roaring inflation. 

A final question raised by the banking stresses of recent weeks is whether it is ever worth investing in banks. In a recent FT Money article, Terry Smith, chief executive of Fundsmith and a former top-rated bank analyst, says not. He never invests in anything that requires leverage (or borrowing) to make an adequate return, as is true of banks. The returns in banking are poor, anyway. And, even when a bank is well run, it can be destroyed by a systemic panic. 

Smith adds that technology is supplanting traditional banking. And, he asks rhetorically, have you noticed that your local bank branch has become a PizzaExpress, in which role, by the way, it makes more money? 

 A salutary envoi to the tale of the latest spate of bank failures. 

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