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Scientists urge swift action to prepare for next pandemic

Credit: Dan Addison | UVA Communications An international team of researchers led by a University of Virginia School of Medicine professor is warning that scientists must better prepare for the next pandemic – and has developed a plan to do just that….




Credit: Dan Addison | UVA Communications

An international team of researchers led by a University of Virginia School of Medicine professor is warning that scientists must better prepare for the next pandemic – and has developed a plan to do just that.

Noting the “avalanche” of scientific data generated in response to COVID-19, UVA’s Wladek Minor, PhD, and colleagues are calling for the creation of an “advanced information system” (AIS) to help scientists integrate, monitor and evaluate the vast amounts of data that will be produced as researchers reveal the molecular architecture of the next pathogen posing a big biological threat. This information on the shape, structure, and function of a pathogen is essential to the development of medications, vaccines and treatments. For example, the COVID-19 vaccines now available target the “spike” protein on the surface of the SARS-CoV-2 virus.

Their heavily cited online resource for COVID-19 ( demonstrates the usefulness of their approach and can be used as a foundation for the new research strategy, they say. The site includes carefully validated 3-D structural models of numerous proteins related to the SARS-CoV-2 virus, including many potential drug targets.

“Structural models and other experimental results produced by various laboratories must follow a standard evaluation procedure to ensure that they are accurate and conform to accepted scientific standards,” said Minor, Harrison Distinguished Professor of Molecular Physiology and Biological Physics at UVa. “Standardized validation is important for all areas of biomedical sciences, especially for structural models, which are often used as a starting point in subsequent research, such as computer-guided drug docking studies and data mining. Even seemingly insignificant errors can lead such research astray.”

Battling a Pandemic

One important role of AIS would be to identify structures that can be refined and improved, the researchers say. They were happy to note that inspection of the molecular blueprints produced for components of COVID-19 and deposited in the Protein Data Bank online database suggests that most were very good. Less than 1% needed significant reinterpretation and less than 10% could be optimized by moderate revisions.

Still, good buildings require good blueprints. The same is true with vaccines and disease treatments. It’s critical, the researchers say, that the structural and other data for pathogens are as accurate as possible, and that scientists from various fields are speaking the same language when discussing and using them. The proposed AIS would help ensure conformity across disciplines.

“Almost 100,000 COVID-19-related papers have been published and over a thousand models of macromolecules encoded by SARS-CoV-2 have been experimentally determined in about a year. No single human can possibly digest this volume of information,” Minor said. “We believe that the most promising solution to information overload and the lack of effective information retrieval is the creation of an advanced information system that is capable of harvesting results from all relevant resources and presenting the information in instructive ways that promote understanding and knowledge.”

The researchers acknowledge that implementing their proposal would be a major undertaking. Other resources that sought to offer similar benefits on a smaller scale have already come and gone. That’s why it’s so important, the scientists say, that we act now. “Creating an AIS will undoubtedly require the collaboration of many scientists who are experts in their respective fields, but it seems to be the only way to prepare biomedical science for the next pandemic,” the researchers write in a new scientific paper outlining their proposal.

“In the history of humanity, the COVID-19 pandemic is relatively mild by comparison with the bubonic plague (Black Death) that killed a hundred times more people,” the researchers conclude. “We might not be so lucky next time.”

New Approach Outlined

The researchers – from UVA, the National Cancer Institute, Poland and Austria — have detailed their plan in an article in the scientific journal IUCrJ. The article is featured on the journal cover. The research team consists of Marek Grabowski, Joanna M. Macnar, Marcin Cymborowski, David R. Cooper, Ivan G. Shabalin, Miroslaw Gilski, Dariusz Brzezinski, Marcin Kowiel, Zbigniew Dauter, Bernhard Rupp, Alexander Wlodawer, Mariusz Jaskolski and Minor.

In their paper, the researchers gratefully acknowledged the financial support of the National Institutes of Health’s National Institute of General Medical Sciences, grant R01-GM132595; the Polish National Agency for Academic Exchange, grant PN/BEK/2018/1/00058/U/00001; the Polish National Science Center, grant 2020/01/0/NZ1/00134; the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research; FWF (Austrian Science Foundation), grant P 32821; and the Polish National Science Centre, grant 2018/29/B/ST6/01989.

Minor and his longtime collaborator Zbyszek Otwinowski, PhD, of the University of Texas Southwestern Medical Center, were recently awarded the Tadeusz Sendzimir Applied Sciences Award by the Polish Institute of Arts and Sciences of America for their efforts to develop and promote software for biomedical applications in the structural biology field.

To keep up with the latest medical research news from UVA, subscribe to the Making of Medicine blog at


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This startup is setting a DALL-E 2-like AI free, consequences be damned

DALL-E 2, OpenAI’s powerful text-to-image AI system, can create photos in the style of cartoonists, 19th century daguerreotypists, stop-motion animators…



DALL-E 2, OpenAI’s powerful text-to-image AI system, can create photos in the style of cartoonists, 19th century daguerreotypists, stop-motion animators and more. But it has an important, artificial limitation: a filter that prevents it from creating images depicting public figures and content deemed too toxic.

Now an open source alternative to DALL-E 2 is on the cusp of being released, and it’ll have no such filter.

London- and Los Altos-based startup Stability AI this week announced the release of a DALL-E 2-like system, Stable Diffusion, to just over a thousand researchers ahead of a public launch in the coming weeks. A collaboration between Stability AI, media creation company RunwayML, Heidelberg University researchers, and the research groups EleutherAI and LAION, Stable Diffusion is designed to run on most high-end consumer hardware, generating 512×512-pixel images in just a few seconds given any text prompt.

Stable Diffusion sample outputs.

“Stable Diffusion will allow both researchers and soon the public to run this under a range of conditions, democratizing image generation,” Stability AI CEO and founder Emad Mostaque wrote in a blog post. “We look forward to the open ecosystem that will emerge around this and further models to truly explore the boundaries of latent space.”

But Stable Diffusion’s lack of safeguards compared to systems like DALL-E 2 poses tricky ethical questions for the AI community. Even if the results aren’t perfectly convincing yet, making fake images of public figures opens a large can of worms. And making the raw components of the system freely available leaves the door open to bad actors who could train them on subjectively inappropriate content, like pornography and graphic violence.

Creating Stable Diffusion

Stable Diffusion is the brainchild of Mostque. Having graduated from Oxford with a Masters in mathematics and computer science, Mostque served as an analyst at various hedge funds before shifting gears to more public-facing works. In 2019, he co-founded Symmitree, a project that aimed to reduce the cost of smartphones and internet access for people living in impoverished communities. And in 2020, Mostque was the chief architect of Collective & Augmented Intelligence Against COVID-19, an alliance to help policymakers make decisions in the face of the pandemic by leveraging software.

He co-founded Stability AI in 2020, motivated both by a personal fascination with AI and what he characterized as a lack of “organization” within the open source AI community.

Stable Diffusion Obama

An image of former president Barrack Obama created by Stable Diffusion.

“Nobody has any voting rights except our 75 employees — no billionaires, big funds, governments or anyone else with control of the company or the communities we support. We’re completely independent,” Mostaque told TechCrunch in an email. “We plan to use our compute to accelerate open source, foundational AI.”

Mostque says that Stability AI funded the creation of LAION 5B, an open source, 250-terabyte dataset containing 5.6 billion images scraped from the internet. (“LAION” stands for Large-scale Artificial Intelligence Open Network, a nonprofit organization with the goal of making AI, datasets and code available to the public.) The company also worked with the LAION group to create a subset of LAION 5B called LAION-Aesthetics, which contains AI-filtered images ranked as particularly “beautiful” by testers of Stable Diffusion.

The initial version of Stable Diffusion was based on LAION-400M, the predecessor to LAION 5B, which was known to contain depictions of sex, slurs and harmful stereotypes. LAION-Aesthetics attempts to correct for this, but it’s too early to tell to what extent it’s successful.

Stable Diffusion

A collage of images created by Stable Diffusion.

In any case, Stable Diffusion builds on research incubated at OpenAI as well as Runway and Google Brain, one of Google’s AI R&D divisions. The system was trained on text-image pairs from LAION-Aesthetics to learn the associations between written concepts and images, like how the word “bird” can refer not only to bluebirds but parakeets and bald eagles, as well as more abstract notions.

At runtime, Stable Diffusion — like DALL-E 2 — breaks the image generation process down into a process of “diffusion.” It starts with pure noise and refines an image over time, making it incrementally closer to a given text description until there’s no noise left at all.

Boris Johnson Stable Diffusion

Boris Johnson wielding various weapons, generated by Stable Diffusion.

Stability AI used a cluster of 4,000 Nvidia A1000 GPUs running in AWS to train Stable Diffusion over the course of a month. CompVis, the machine vision and learning research group at Ludwig Maximilian University of Munich, oversaw the training, while Stability AI donated the compute power.

Stable Diffusion can run on graphics cards with around 5GB of VRAM. That’s roughly the capacity of mid-range cards like Nvidia’s GTX 1660, priced around $230. Work is underway on bringing compatibility to AMD MI200’s data center cards and even MacBooks with Apple’s M1 chip (although in the case of the latter, without GPU acceleration, image generation will take as long as a few minutes).

“We have optimized the model, compressing the knowledge of over 100 terabytes of images,” Mosque said. “Variants of this model will be on smaller datasets, particularly as reinforcement learning with human feedback and other techniques are used to take these general digital brains and make then even smaller and focused.”

Stability AI Stable Diffusion

Samples from Stable Diffusion.

For the past few weeks, Stability AI has allowed a limited number of users to query the Stable Diffusion model through its Discord server, slowing increasing the number of maximum queries to stress-test the system. Stability AI says that over 15,000 testers have used Stable Diffusion to create 2 million images a day.

Far-reaching implications

Stability AI plans to take a dual approach in making Stable Diffusion more widely available. It’ll host the model in the cloud, allowing people to continue using it to generate images without having to run the system themselves. In addition, the startup will release what it calls “benchmark” models under a permissive license that can be used for any purpose — commercial or otherwise — as well as compute to train the models.

That will make Stability AI the first to release an image generation model nearly as high-fidelity as DALL-E 2. While other AI-powered image generators have been available for some time, including Midjourney, NightCafe and, none have open-sourced their frameworks. Others, like Google and Meta, have chosen to keep their technologies under tight wraps, allowing only select users to pilot them for narrow use cases.

Stability AI will make money by training “private” models for customers and acting as a general infrastructure layer, Mostque said — presumably with a sensitive treatment of intellectual property. The company claims to have other commercializable projects in the works, including AI models for generating audio, music and even video.

Stable Diffusion Harry Potter

Sand sculptures of Harry Potter and Hogwarts, generated by Stable Diffusion.

“We will provide more details of our sustainable business model soon with our official launch, but it is basically the commercial open source software playbook: services and scale infrastructure,” Mostque said. “We think AI will go the way of servers and databases, with open beating proprietary systems — particularly given the passion of our communities.”

With the hosted version of Stable Diffusion — the one available through Stability AI’s Discord server — Stability AI doesn’t permit every kind of image generation. The startup’s terms of service ban some lewd or sexual material (although not scantily-clad figures), hateful or violent imagery (such as antisemitic iconography, racist caricatures, misogynistic and misandrist propaganda), prompts containing copyrighted or trademarked material, and personal information like phone numbers and Social Security numbers. But Stability AI won’t implement keyword-level filters like OpenAI’s, which prevent DALL-E 2 from even attempting to generate an image that might violate its content policy.

Stable Diffusion women

A Stable Diffusion generation, given the prompt: “very sexy woman with black hair, pale skin, in bikini, wet hair, sitting on the beach.”

Stability AI also doesn’t have a policy against images with public figures. That presumably makes deepfakes fair game (and Renaissance-style paintings of famous rappers), though the model struggles with faces at times, introducing odd artifacts that a skilled Photoshop artist rarely would.

“Our benchmark models that we release are based on general web crawls and are designed to represent the collective imagery of humanity compressed into files a few gigabytes big,” Mostque said. “Aside from illegal content, there is minimal filtering, and it is on the user to use it as they will.”

Stable Diffusion Hitler

An image of Hitler generated by Stable Diffusion.

Potentially more problematic are the soon-to-be-released tools for creating custom and fine-tuned Stable Diffusion models. An “AI furry porn generator” profiled by Vice offers a preview of what might come; an art student going by the name of CuteBlack trained an image generator to churn out illustrations of anthropomorphic animal genitalia by scraping artwork from furry fandom sites. The possibilities don’t stop at pornography. In theory, a malicious actor could fine-tune Stable Diffusion on images of riots and gore, for instance, or propaganda.

Already, testers in Stability AI’s Discord server are using Stable Diffusion to generate a range of content disallowed by other image generation services, including images of the war in Ukraine, nude women, a Chinese invasion of Taiwan, and controversial depictions of religious figures like the Prophet Mohammed. Many of the results bear telltale signs of an algorithmic creation, like disproportionate limbs and an incongruous mix of art styles. But others are passable on first glance. And the tech, presumably, will continue to improve.

Nude women Stability AI

Nude women generated by Stable Diffusion.

Mostque acknowledged that the tools could be used by bad actors to create “really nasty stuff,” and CompVis says that the public release of the benchmark Stable Diffusion model will “incorporate ethical considerations.” But Mostque argues that — by making the tools freely available — it allows the community to develop countermeasures.

“We hope to be the catalyst to coordinate global open source AI, both independent and academic, to build vital infrastructure, models and tools to maximize our collective potential,” Mostque said. “This is amazing technology that can transform humanity for the better and should be open infrastructure for all.”

Stable Diffusion Zelensky

A generation from Stable Diffusion, with the prompt: “[Ukrainian president Volodymyr] Zelenskyy committed crimes in Bucha.”

Not everyone agrees, as evidenced by the controversy over “GPT-4chan,” an AI model trained on one of 4chan’s infamously toxic discussion boards. AI researcher Yannic Kilcher made GPT-4chan — which learned to output racist, antisemitic and misogynist hate speech — available earlier this year on Hugging Face, a hub for sharing trained AI models. Following discussions on social media and Hugging Face’s comment section, the Hugging Face team first “gated” access to the model before removing it altogether, but not before it was downloaded over a thousand times.

War in Ukraine Stability AI

“War in Ukraine” images generated by Stable Diffusion.

Meta’s recent chatbot fiasco illustrates the challenge of keeping even ostensibly safe models from going off the rails. Just days after making its most advanced AI chatbot to date, BlenderBot 3, available on the web, Meta was forced to confront media reports that the bot made frequent antisemitic comments and repeated false claims about former U.S. president Donald Trump winning reelection two years ago.

BlenderBot 3’s toxicity came from biases in the public websites that were used to train it. It’s a well-known problem in AI — even when fed filtered training data, models tend to amplify biases like photo sets that portray men as executives and women as assistants. With DALL-E 2, OpenAI has attempted to combat this by implementing techniques, including dataset filtering, that help the model generate more “diverse” images. But some users claim that they’ve made the model less accurate than before at creating images based on certain prompts.

Stable Diffusion contains little in the way of mitigations besides training dataset filtering. So what’s to prevent someone from generating, say, photorealistic images of protests, “evidence” of fake moon landings and general misinformation? Nothing really. But Mostque says that’s the point.

Stable Diffusion protest

Given the prompt “protests against the dilma government, brazil [sic],” Stable Diffusion created this image.

“A percentage of people are simply unpleasant and weird, but that’s humanity,” Mostque said. “Indeed, it is our belief this technology will be prevalent, and the paternalistic and somewhat condescending attitude of many AI aficionados is misguided in not trusting society … We are taking significant safety measures including formulating cutting-edge tools to help mitigate potential harms across release and our own services. With hundreds of thousands developing on this model, we are confident the net benefit will be immensely positive and as billions use this tech harms will be negated.”

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A Tale Of Two Recessions: One Excellent, One Tumultuous

A Tale Of Two Recessions: One Excellent, One Tumultuous

Authored by Charles Hugh Smith via OfTwoMinds blog,

Events may show that there are…



A Tale Of Two Recessions: One Excellent, One Tumultuous

Authored by Charles Hugh Smith via OfTwoMinds blog,

Events may show that there are no winners, only survivors and those who failed to adapt.

Some recessions are brief, necessary cleansings in which extremes of leverage and speculation are unwound via painful defaults, reductions of risk and bear markets.

Some are reactions to exogenous shocks such as war or pandemic. The uncertainty triggers a mass reduction of risk which recedes once the worst is known and priced in.

Far less frequently, structural recessions are lengthy, tumultuous upheavals that can set the stage for excellent long-term expansion or unraveling and collapse. In these structural recessions, 10% to 20% of the workforce loses their jobs as entire sectors are obsoleted and jobs that depend on excesses of debt and speculation go away.

In the U.S. economy of today, this would translate into a minimum of 14 million jobs vanishing, never to return in their previous form and compensation.

The old jobs don't come back and new jobs demand different enterprises, training and skills. Unemployment remains elevated, spending is weak and productivity is low for years as enterprises and workers have to adjust to radically different conditions. If the economy and society persevere through this transition, the stage is set for the reworked economy to enjoy an era of renewed prosperity and opportunity.

If an economy and society can't complete this transition, stagnation decays into collapse.

I've annotated a St. Louis Federal Reserve chart of U.S. recessions since 1970 to show the taxonomy described above. The stagflationary 1970s / early 1980s were a lengthy, tumultuous structural upheaval; the 1990-91 recession was triggered by the First Gulf War; the Dot-Com Bust in 2000-2002 was largely the unwinding of speculative excesses in the technology sectors (similar to the radio-technology bubble of the 1920s); the Global Financial Meltdown (aka the Global Financial Crisis) was the structural reckoning of unregulated global financialization excesses, and the 2020 recession was the result of policy responses to the Covid pandemic.

Chart courtesy of St. Louis Federal Reserve Database (FRED)

Each of these resulted in a multi-quarter decline in GDP, the classic (though flawed) definition of recession.

Recessions that cleanse the system of financial deadwood are necessary and yield excellent results. Per the Yellowstone Analogy ( The Yellowstone Analogy and The Crisis of Neoliberal Capitalism (May 18, 2009) and No Recession Ever Again? The Yellowstone Analogy (November 8, 2019), the deadwood of excessive speculation, leverage, fraud and debt issued to poor credit risks must be burned off lest the deadwood pile up and consume the entire forest in a conflagration of the sort that was narrowly averted in 2008-09 when fraud and risk-taking had reached systemically destructive extremes.

The problem with letting deadwood pile up so it threatens the entire forest is the policy reaction creates its own extremes. The coordinated central bank policies unleashed in 2008 and beyond established new and unhealthy expectations and norms, the equivalent of counting on central bank water tankers to fly over and extinguish every firestorm of excessive risk-taking and fraud.

Those emergency measures create their own deadwood, distortions and risk, and are not a replacement for prudent forest management, i.e. maintaining a transparent market where excessive risk is continually reduced to ashes in semi-controlled burns.

When systemic changes in the economy and society demand structural transitions, the resulting tumult can either creatively rework entire sectors, weeding out what no longer works in favor of new methods and processes, or those benefiting most from the old structure can thwart desperately needed evolution to protect their gravy trains.

If change is stifled as a threat, the entire economy enters a death-spiral to collapse. Some eras present an economy with a stark choice: adapt or die. Adaptation in inherently messy, as new approaches are tried in a trial-and-error fashion and improvements are costly as the learning curve is steep.

Sacrifices must be made to achieve greater goals. as I outlined yesterday in A Most Peculiar Recession, in the 1970s the U.S. economy was forced to adapt to three simultaneous structural changes:

1. The peak of U.S. oil production and the dramatic repricing of oil globally by newly empowered OPEC oil exporters.

2. The pressing need to reconfigure the vast U.S. industrial base to limit pollution and clean up decades of environmental damage and become more efficient in response to higher energy prices.

3. The national security / geopolitical need to encourage the first wave of Globalization in the 1960s and 1970s to support the mercantilist economies of America's European and Asian allies to counter Soviet influence.

Coincidentally, this last goal required the U.S. expand the exorbitant privilege of the U.S. dollar, the primary reserve currency by exporting dollars to fund overseas expansion of U.S. allies like Japan and Germany and running permanent trade deficits to benefit mercantilist allies.

These measures created their own distortions which led to the Plaza Accord in 1985 and other structural adjustments. Ultimately, the U.S. managed to adapt to a knowledge economy (Peter Drucker's phrase) and a more efficient means of production, resulting in a much cleaner environment and a leaner, more adaptable economy.

The deadwood of hyper-financialization and the distortions of hyper-globalization have now piled up so high that they threaten the entire global economy. Those who have feasted most freely on hyper-financialization and hyper-globalization must now pay the heavy price of adjusting to definancialization and deglobalization.

Those who have been living on expanding debt and soaring exports are in for a drawn-out, wrenching structural adjustment to the reversal of these trends and the fires sweeping through the deadwood that's piled up for the past two decades.

There will winners and losers in this global structural upheaval. Mercantilist economies that feasted on 60 years of export expansion will be losers because there is no domestic sector large enough to absorb their excess production, and those who feasted on the expansion of debt to inflate asset bubbles will find their reluctance to conduct controlled burns of their speculative debt-laden deadwood will exact a devastating price when their entire financial system burns down.

Those who didn't rely on exports for growth will find the transition much less traumatic, as will those who maintained a regulated, transparent market for credit that limited excesses of leverage and high-risk debt.

Those who adjust to structurally higher energy costs by becoming more efficient and limiting waste via Degrowth will prosper, all others will sag under the crushing weight of waste is growth Landfill Economies. I explain why this is so in my new book Global Crisis, National Renewal.

Recessions which are allowed to clear the deadwood and encourage adaptation yield excellent results. Those which don't lead to the entire forest burning down. Economies optimized for graft, corruption, opacity and benefiting insiders will burn down, along with those that optimized speculative extremes of debt and those too rigid and rigged to allow any creative destruction of insiders' skims and scams.

Events may show that there are no winners, only survivors and those who failed to adapt and slid into the dustbin of history.

*  *  *

My new book is now available at a 10% discount this month: When You Can't Go On: Burnout, Reckoning and Renewal. If you found value in this content, please join me in seeking solutions by becoming a $1/month patron of my work via

Tyler Durden Fri, 08/12/2022 - 12:20

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How to fix the pensions triple lock but still protect pensioners from high inflation

The reintroduction of the pensions triple lock means the increase in weekly payments could vastly outpace earnings growth



The triple lock increases some benefits payments by inflation, earnings or 2.5%, whichever is highest. Max_Z / Shutterstock

Plans to increase state pension payments in line with inflation have been reinstated by the UK government and are supported by both of the contenders for the Conservative party leadership. But even if inflation was not always at the 40-year high we are currently seeing, a more sustainable way of calculating pensioners’ state income is needed.

The pensions triple lock was first introduced in the June 2010 budget. It means annual increases in payments are made in line with the highest out of earnings growth (6.2% as of May 2022), price inflation (currently 9.4%) or 2.5%.

The triple lock was suspended for one year in April 2022 as the end of the COVID-19 furlough scheme inflated average earnings growth. The government is now bringing it back in time for the annual update in pension and other state payments, which will come into effect in April 2023. The annual increase will be set by the government in the autumn. With inflation high and rising (the Bank of England expects it to reach 13% by October), it will be the measure used for the increase.

Inflation of more than 10% will see the value of a full basic state pension climb past £155 a week, while that of the new state pension – available to those reaching the state pension age since April 2016 – will increase to more than £200 a week. Since earnings are currently growing less quickly than inflation, a rise in pension income will be greater than any increase in average earnings. In other words, people receiving state pension payments will typically see stronger income growth than those relying on earned income.

As a result, the current period of higher growth in prices than in earnings has brought the triple lock into question. This is because it protects the value of state pensions when earnings growth is weak (as it is now) but will also continue to increase with any subsequent recovery in earnings.

A recent report from the Office for Budget Responsibility (OBR) shows why this approach is unsustainable. While inflation is spiking at the moment, the OBR believes it will average 2% over the long term and that average earnings growth will be around 3.8%. But it also thinks the triple lock will imply an average annual increase of 4.3% for pensions. This is because of volatility in the two sets of figures: while often earnings will grow faster than prices, on occasion that is not the case.

Unexpected expense

As such, maintaining the triple lock would see the value of the basic state pension and new state pension continue to grow faster than average earnings, pushing up government spending on state pensions. Overall, the OBR report projects that state pension spending will increase from 4.8% of national income in 2021–2022 to 8.1% in 50 years time, an increase of 3.2% of national income, which is equivalent to more than £80 billion a year in today’s terms. This is despite further rises in the state pension age. And the use of the triple lock will be a key driver of this increase, not average earnings growth.

When the triple lock was first introduced in the June 2010 Budget it was not expected to be this expensive. If the triple lock had been used over the 19 years prior to its launch, from 1991 to 2009, it would only have been more generous than increases in line with average earnings growth on three occasions. And so, overall, it would have caused state pension increases averaging just 0.1% a year more than if it was calculated using average earnings indexation.

In contrast, over the 12 years from 2010 to 2021, since the policy was first implemented, triple lock indexation would have been more generous than average earnings indexation on eight occasions, according to my calculations based on ONS figures. This would have caused state pension increases averaging 1% a year faster than average earnings indexation.

As such, the triple lock has already been significantly more expensive than expected. It was initially estimated to have cost £450 million in 2014–15, but subsequent OBR analysis suggests that it actually cost six times more – or £2.9 billion. This is clearly not sustainable, particularly amid the current economic downturn.

Older man at laptop with phone
There are more sustainable ways to calculate state pension payments in the current economic environment. astarot / Shutterstock

Finding more sustainable solutions

One solution put forward in the Conservatives’ 2017 general election manifesto was to move to a double lock, where the pension would increase by the greater of growth in prices or earnings. So the 2.5% underpin would no longer exist. In recent years inflation has been greater than earnings or 2.5%, and sometimes both earnings and inflation have been below 2.5%. So the triple lock has been more generous than earnings indexation, and a double lock would also have been more generous than earnings indexation (but not as generous as a triple lock).

But over the period from 2010 to 2021, a double lock still would still have seen the state pension increase by an average of 0.7% a year more than average earnings growth, according to my calculations. So while it would not be as expensive as the triple lock, it’s still not fiscally sustainable over the longer term.

Another option is to move to directly link pensions to average earnings. This was legislated by the Labour government in 2007 following the recommendations of the Pensions Commission. Such a policy could be fiscally sustainable over the long term, if implemented alongside state pension age increases due to rising longevity. But it would mean that in periods where earnings growth was running below inflation (such as now) there would be a real squeeze on pensioners’ incomes.

There is an alternative that would both be as generous as (but not more generous than) earnings indexation over the long term, but that would also preserve the real (inflation-adjusted) value of state pensions in years in which earnings were not keeping pace with prices. Instead of a triple lock, the government could set a target level for the state pension relative to average earnings – let’s say that pensions should be worth 25% of average earnings every year. If this target was 10% more than current pension payments, for example, the government could set a longer-term strategy for meeting that target by increasing payments in smaller annual increments. If prices grow faster than earnings one year, the government could make pension payments price-indexed and then adjust in subsequent years to remain on track for the target, if needed.

This would preserve the real value of state pensions without locking in unsustainable increases at times when earnings are growing faster than prices (as happens under a triple or double lock). It would protect pensioners from inflation while following a target. For whoever ends up being chancellor in the autumn, this could be a way to help improve long-term public finances.

The support of the Economic and Social Research Council (ESRC) is gratefully acknowledged (grant reference ES/W001594/1), as co-funding from the Centre for the Microeconomic Analysis of Public Policy (ES/T014334/1) at the Institute for Fiscal Studies. Over the last three years, I have also received research grants from the following parties, who may be interested in the topic and findings but who have had no material interest in this work nor any engagement with it: • Centre for Ageing Better • Department for Work and Pensions • Social Security Administration • Nuffield Foundation • As part of a consortium of funders of research into retirement and savings: Age UK, Aviva UK, Association of British Insurers, Association of Consulting Actuaries, Canada Life, Chartered Insurance Institute, Department for Work and Pensions, Interactive Investor, Investment Association, Legal and General Investment Management, Money and Pensions Service, and Pensions and Lifetime Savings Association. I am Deputy Director at the Institute for Fiscal Studies. In addition I am a member of the Social Security Advisory Committee and of the advisory panel of the Office for Budget Responsibility.

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