Autonomous AI warfare – have we silently slipped into a new and frightening world

It was only a matter of time.  The world has entered a new age of warfare and, like the dropping of the first atomic bomb, it will be hard to draw back.

In 2016, an open letter with 33,783 signatories including Stephen Hawking, Elon Musk, Stuart Russell and Steve Wozniak, called for a ban on “offensive autonomous weapons beyond meaningful human control” [FLI16]. Successive UN Secretary Generals have declared lethal autonomous weapons systems to be “politically unacceptable and morally repugnant“ [UN26].  A few weeks ago, the Pope’s encyclical Magnifica Humanitas argued that “the development and use of AI in warfare must be subject to the most rigorous ethical constraints, to guarantee respect for human dignity and the sanctity of life and to avoid a race to develop such arms” [PL26].

Over recent years, we have edged ever closer with drone warfare in the Ukraine [Be23] and the use of AI target identification systems such as ‘The Gospel’ by the Israeli forces in Gaza [DM23] and the implication of Maven in the US strike that killed 175 schoolchildren in Iran [Ba26].  Of course, ultimately humans deploy these systems, and humans in principle are okaying the target selection, no matter how perfunctorily.

However, according to Channel 4’s Substack blog, barely a week after Pope Leo XIV’s Encyclical, we have crossed the Rubicon.  It reports that Ukraine are deploying fully autonomous Hornet drones, which are sent to a ‘kill zone’ and then strike anything that the onboard AI identifies as a potential target [Hi26].  Russia will, no doubt, catch up soon and then NATO and the US will feel they need to have such weapons battle ready.

Photo. US Department of Defence, as used in [Co26]

Arguably hitting anything within a kill zone is no different from a poorly aimed Russian artillery shell, low-accuracy ballistic missile or evacuation orders such as used by the US in Fallujah and more recently by Israel in Gaza and Lebanon.  However, the use of AI for this is widely considered to be of a different ethical order, as voiced in the various international statements above, not least because of the dangers of a full-on AI arms race.

This feels as though it should be front page news given the prominence of AI stories generally.  It is possible that Channel 4 have jumped the gun, confusing the use of AI GPS-resistant terrain-following guidance [DE26], as used by early cruise missiles, with statements by a UK-Ukraine startup describing near-future scenarios [MD26].  The BBC simply refer to the Hornet as an “AI drone” without giving details [Co26]; however, France 24 also carries the story of fully autonomous operation [Mr26].

If this are accurate, it may be that the press do not want to foreground stories that reflect badly on an ally. Certainly, the UK seems to be softening its stance on fully autonomous weapons with recent statements by the UK armed forces minister, Al Carns, that while there “must be a human in the loop”, it must also (self-contradictorily) be possible to “take the human out of the loop when required” [Cl26] and the David Omand, the ex-head of ex-GCHQ, has changed his previous view that such autonomous weapons could not comply with international humanitarian law and now believes that they can be programmed with a “moral code” [MD26b].  Only a few months ago such statements would have sounded untenable, not least with AI exhibiting gung-ho tactics in nuclear war simulations [Pa26] and growing numbers of examples where AI has ignored its guardrails causing real-world damage to businesses and individuals [Al26,Bo26].

The pragmatics of warfare are fast overtaking the ethics of humanity.

References

[Al26] Tom Allen (2026). AI coding agent goes rogue, deletes company database in nine seconds. Computing, 29 April 2026. https://www.computing.co.uk/news/2026/ai/ai-coding-agent-goes-rogue

[Ba26]  Kevin T Baker (2026).  AI got the blame for the Iran school bombing. The truth is far more worrying. The Guardian, Thu 26 Mar 2026. https://www.theguardian.com/news/2026/mar/26/ai-got-the-blame-for-the-iran-school-bombing-the-truth-is-far-more-worrying

[Be23] Samuel Bendett (2023). Roles and implications of AI in the Russian–Ukrainian conflict. Russia Matters, Harvard Kennedy School (20 July 2023).  https://www.russiamatters.org/analysis/roles-and-implications-ai-russian-ukrainian-conflict

[Bo26] Robert Booth (2026). Number of AI chatbots ignoring human instructions increasing, study says. The Guardian, 27 Mar 2026. https://www.theguardian.com/technology/2026/mar/27/number-of-ai-chatbots-ignoring-human-instructions-increasing-study-says

[Cl26]  Charles Clover (2026).  UK military looks at allowing lethal strikes without human approval. The Financial Times, May 30, 2026.  https://www.ft.com/content/a21607ce-c25b-40ab-bd9c-e0262d344c8c

[Co26] Thomas Copeland and Paul Brown (2026). Ukraine using AI drones to strike vital convoys supplying Russian troops. BBC Verify, 30 May 2026. https://www.bbc.co.uk/news/articles/cdjp0n7rn41o

[DM23] Harry Davies, Bethan McKernan, and Dan Sabbagh (2023). ‘The Gospel’: How Israel uses AI to select bombing targets in Gaza. The Guardian (1 Dec. 2023).  https://www.theguardian.com/world/2023/dec/01/the-gospel-how-israel-uses-ai-to-select-bombing-targets

[DE26]  Defence Express (2026). What’s Special About Hornet UAV Ukrainians Using to Destroy russian Logistics, Where Did It Come From, What Are Its Key Features.  Defence Express, May 24, 2026  https://en.defence-ua.com/weapon_and_tech/whats_special_about_the_hornet_uav_that_ukrainians_using_to_destroy_russian_logistics_where_did_it_come_from_and_what_are_its_key_features-18597.html

[FLI16] Future of Life Institute (2016). Autonomous Weapons Open Letter: AI & Robotics Researchers. Published 9 February, 2016  (33,783 signatories including Stephen Hawking, Elon Musk, Stuart Russell and Steve Wozniak).  https://futureoflife.org/open-letter/open-letter-autonomous-weapons-ai-robotics/

[Hi26]  Lindsey Hilsum (2026).  How drones and AI are turning the tide in Ukraine war.  Channel 4 News Blog, May 23, 2026.  https://channel4news.substack.com/p/how-drones-and-ai-are-turning-the

[Mr26]  Guillaume Maurice (2026). Ukraine: How a kamikaze drone partially operated by AI is attacking Russian convoys. France 24, 01/06/2026. https://www.france24.com/en/europe/20260601-ukraine-kamikaze-drone-partially-operated-ai-attacking-russian-convoys

[MD26b]  Dan Milmo and Aisha Down (2026).  Future AI weapons such as drones should have moral code, says former UK spy chief.  The Guardian, Wed 3 Jun 2026.  https://www.theguardian.com/science/2026/jun/03/ai-weapons-drones-moral-code-former-uk-gchq-chief-david-omand

[MD26]  Dan Milmo and Aisha Down (2026).  Can autonomous AI-powered killer drones take morality onboard? The Guardian, Wed 3 Jun 2026.   https://www.theguardian.com/world/2026/jun/03/can-autonomous-ai-powered-killer-drones-take-morality-onboard

[Pa26]  Payne, Kenneth (2026). AI Arms and Influence: frontier models exhibit sophisticated reasoning in simulated nuclear crises. arXiv preprint arXiv:2602.14740. https://arxiv.org/abs/2602.14740

[PL26] Pope Leo XIV (2026).  Encyclical letter: Magnifica Humanitas of His Holiness Pope Leo XIV on Safeguarding The Human Person in the Time of Artificial Intelligence.  Holy See 15 May, 2026. https://www.vatican.va/content/leo-xiv/en/encyclicals/documents/20260515-magnifica-humanitas.html

[UN26]  UNODA (2016). Lethal Autonomous Weapon Systems. United Nations Office for Disarmament Affairs. Accessed 1/6/2026.  https://disarmament.unoda.org/en/our-work/emerging-challenges/lethal-autonomous-weapon-systems

 

 

 

 

 

The Abomination of AI – part 8 – summary and recap

This final post recaps what we’ve learnt about the runaway nature of the AI industry, how it undermines free markets, and how we can make a difference. The core question is not what can AI do, but what should AI do?

This is the last of the series of blogs based on my keynote “The abomination of AI” at ICoSCI 2026.  Each has an accompanying segment of the video and slides from the talk as well as detailed notes and references.  Section numbers refer to the full report which will be released in the final blog.   The slide thumbnails in the text correspond to the slides in the navigation panel below.  The presentation can be played below, or opened full screen. The full length video, complete slides and further information can be found at: https://alandix.com/academic/talks/ICOSCI-2026-abomination-of-AI/

Previously …

§1.  Every industry is driven by profits and power, but there is something about the nature of AI itself, which interacts with the nature of market forces in the world that is problematic and is different from other technologies.

§2.  Can any technology be neutral?  AI can be used for good purposes, such as advances in healthcare.  It can also have bad outcomes such as bias in the criminal justice system or online exploitative pornography.  Perhaps most often it is creating the frivolous or even ugly.

§3.  The obvious impact of AI is in the things it does directly. Some technologies also change the very nature of society, affecting even those who do not use them. Cars are an obvious example.  AI is also such a technology.

§4.  Doomsayers worry about the point when AI becomes sentient, outgrowing its creators.  The real danger is more insidious: the massive financial and human impacts of AI seem almost obscene.

§5.   Network externalities, the way one person’s use of AI and digital tech changes its value for others, creates positive feedback loops, leading to runaway growth and emergent monopolies, the nemesis of free markets. This the very nature of digital technology and AI breaks free markets leading to runaway inequality, even with the best intentions of industry … but some tech companies further exploit these effects.

§6.   Runaway growth of AI is not painless – opportunity costs of investment and human costs of lost jobs.  Gains may be transitory – buy-now-pay-later tech risk tying users into spiralling costs.

§7.   It all seems too big, requiring national and international responses.  But we can make a difference using appropriately chosen small AI (including none). Plus, this good use of AI is good for business too.

8.  In summary

So, in summary,  AI can do amazing good things, but often also bad things.

More crucial is how AI is shaping society.  We have to think explicitly about this, because AI has its own dynamic.  That dynamic is not good by its nature, so we have to control it to make it serve society. Some of this needs action at a governmental and intergovernmental level, because this is so big compared to most countries.

However there are things you can do.  You can choose to sometimes not use AI or use small AI, but always to try to use AI appropriately rather than just throwing it at a problem and wiping your hands of the wider impact.

The core question is not to think solely about what can AI do?  While it still does some things frighteningly badly, AI is getting better and better at doing more and more.

But the big question, the Big, BIG question is what should AI do?

And that’s the question we need to ask ourselves continually both in our individual work and at societal level.

AI will be an abomination, but only if we let it be.

Updates

It is now four months since I gave the talk at ICoSCI 2026 on which this blog series has been based.  In that short time there have been many changes, some strengthening the arguments and some challenging them.   In addition, I’ve had helpful feedback from several people, especially extensive comments by Mark Bernstein; so many thanks to Mark and others who have engaged with this series.

I’ve written updates at the end of several blogs.  In part 3 “A different kind of apocalypse” recent reports of agentic AI ignoring guardrails make Terminator-style AI devastation, seem less distant.  Of course, since those updates the publicity around Claude Mythos’ ability to find bugs in established codebases meant that Anthropic deemed it too dangerous to release without allowing selected partners to use it first to check their own security [An26a].  While this may be in part a PR exercise, it is being taken seriously by government and pan-government organisations [AISI26,Go26].  Furthermore, as well as these external threats, Anthropic are also monitoring for the potential of AI ‘sabotage’, that is:

when an AI model with access to powerful affordances within an organization uses its affordances to autonomously exploit, manipulate, or tamper with that organization’s systems or decision-making in a way that raises the risk of future catastrophic outcomes”  [An26b]

Updates at the end of part 6 “should we worry?“ reinforce the difficulty of switching AI models and the way OpenClaw has emphasised the under-pricing of AI use plans, and hence the way that these might adjust (upwards!) over time, just as we’ve seen with other forms of digital technology.  At the end of part 7 “what can we do?” there is a lovely example of really smart AI, combining AI and plain old computing to achieve better and cheaper outcomes.

In addition to these updates, recent developments (since the updates!) and comments have raised a couple of issues that I’d like to address.

Size matters

Since giving the talk in January, there have been further developments in reducing the costs of AI, not least DeepSeek’s V4 release, which has focused on making model training and execution more efficient [DS26].  Nvidia have released open source models designed to run on local small-scale (as in not more than a few $1000 Nvidia chips) installations [Ca26,Br26], some of these are designed for specialised applications, but some more general purpose; it could be that they are positioning themselves to spread their market beyond the small number of AI software mega-corporations, but in the process may weaken the emergent monopolies of these software players.  However Nvidia’s own near monopoly AI hardware position is being challenged by DeepSeek’s use of Huawei chips [CC26].

This seems to suggest several potential scenarios:

  1. The big players (OpenAI, Anthropic) see off the cheaper, but less powerful alternatives, maybe using market dominance to retain near monopoly positions as discussed in section 5 (blog part 4 and part 5)
  2. The lean, mean models become powerful enough to open up the market fully, so that the current mega companies and their investors lose their ‘bet’ on market dominance, leading to massive drops in their market values, and potentially a major stock exchange crash.
  3. The cheaper models become viable alternatives, but do not immediately compete on sheer power and corporate commitment leaving the mega AI corporations strong, but less all encompassing, and making the individual solution strategies in part 7 easier.

The impact of (2) on the global economy would be pretty disastrous, especially following the massive hits of US-Israel/Iran and Ukraine/Russia wars, so, on balance, the softer movement of scenario (3) feels like the best outcome.

Perversely, the big AI companies would be likely to weather the storm of (2) as the investment already committed provides a cash buffer. This was certainly the case during the dot-com period in which tech companies with second/third round investment before the crash often survived, including Lastminute.com, the IPO of which triggered the market re-evaluation of tech in 2000.  The founders and early funders will see paper devaluations, but otherwise still be in control of huge businesses; the smaller, more recent investors will lose however, including many global pension funds.

Cats and Consummate Consumerism

Section 3 (blog part 2) is, in part, quite dismissive of the vast volume of ‘frivolous’ use of generative AI.  Later, I hope that is clarified (e.g. the example of the Doctor’s Kitchen app) that this does not mean criticising all personal use of AI — appropriate use of AI in can be very beneficial, in particular allowing far more individualised access to digital technology.  Indeed, LLMs are already democratising access to many forms of professional advice that are beyond the reach of individuals and small businesses [Fu26].

However, that does leave the cats.   If that is what people want to create and view, surely that is their business?

In some ways these uses of AI are the ultimate form of consumerism — like the boxfuls of unused plastic toys, kitchen appliances that lie in the dark recess of cupboards, the 1.6 billion items of clothing in UK wardrobes that have never been worn [BBC22] — but now all digital, thrust before us by the relentless algorithms of social media.  Items we never knew we wanted instantly become essential, produced apparently for free and provided in precisely the quantity and kind that makes us want more.

Is this a choice, when the algorithms know how to nudge and channel us [HS26], when LLMs have learnt the lessons of the confidence trickster, and when the content itself is addictive [KK25]?  Is this a free market equivalent of Opium Wars?

For individuals many of the costs are effectively hidden, especially at the point of use.  Just as no fleece wearer or takeaway coffee drinker deliberately chooses to put microplastics in breast milk, the environmental and social impacts of digital and AI products are often physically and temporally distant and in many cases suffered by others [Ma24].

This distancing is in part due to digital communication and in part the diffuse relationship between the loci of production and use, especially when a large proportion of cost is in training.  However, the distancing is in part deliberate, not least the under-pricing of services to build reliance, a trick that has been part of digital products almost since their onset and very much in the playbook of the neighbourhood drug dealer.

One reason for listing the almost unbelievable facts and figures of AI growth in part 3 (§4,2) is to force us to face these choices explicitly.

The speed of change …

As is evident things are moving rapidly. This said although the details are changing, many of the large-scale impacts of AI on society and economics outlined in this series build on longer term trends in digital technology that have been evident at least since the turn of the Millennium.

With so many technologies in the past the societal impacts have only become apparent in hindsight.  With AI there are surprises, especially in terms of its spurts of almost unimaginably rapid progress, but also we are increasingly aware of the dangers and pitfalls.  The issues describe here are part of this conversation, aiming to ensure that we enter this exciting and dangerous time with eyes wide open.

Coming soon …

If you are interested in these issues, look out for the book AI or Social Justice, which Clara Crivellaro and I are currently working on.  The book website already includes a growing collection of resources including case studies and videos. .

References

[AISI26] AI Security Institute (2026) Our evaluation of Claude Mythos Preview’s cyber capabilities. AI Security Institute, Department of Science, Innovation and Technology. Apr 13, 2026. https://www.aisi.gov.uk/blog/our-evaluation-of-claude-mythos-previews-cyber-capabilities

[An26a]  Anthropic (2026).  Project Glasswing: Securing critical software for the AI era. Accessed 4th May 2026. https://www.anthropic.com/glasswing

[An26b]  Anthropic (2026).  Sabotage Risk Report: Claude Opus 4.6. Accessed 4th May 2026.   https://anthropic.com/claude-opus-4-6-risk-report

[BBC22]  BBC News (2022).  UK wardrobes stuffed with unworn clothes, study shows.  BBC News, 7 October 2022. https://www.bbc.co.uk/news/science-environment-63170952

[Br26]  Kari Briski (2026).  NVIDIA Launches Nemotron 3 Nano Omni Model, Unifying Vision, Audio and Language for up to 9x More Efficient AI Agents.  Nvidia blog.  April 28, 2026.  https://blogs.nvidia.com/blog/nemotron-3-nano-omni-multimodal-ai-agents/

[CC26]  Caiwei Chen (2026).  Three reasons why DeepSeek’s new model matters: The long-awaited V4 is more efficient and a win for Chinese chipmakers.  MIT Technology Review, April 24, 2026  https://www.technologyreview.com/2026/04/24/1136422/why-deepseeks-v4-matters/

[Ca26] Bryan Catanzaro (2026). NVIDIA Launches Open Models and Data to Accelerate AI Innovation Across Language, Biology and Robotics.  NVIDAI Blog, October 28, 2025.  https://blogs.nvidia.com/blog/open-models-data-ai/

[DS26]  DeepSeek-AI (2026).  DeepSeek-V4:Towards Highly Efficient Million-Token Context Intelligence.  Accessed 29th April 2026.  https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/DeepSeek_V4.pdf

[Fu26]  Maria Ines Fuenmayor (2026). The Privilege of Refusing AI. Codified, Medum,  Apr 22, 2026.  https://codifiedai.substack.com/p/the-privilege-of-refusing-ai

[Go26]  Gordon M. Goldstein (2026). Six Reasons Claude Mythos Is an Inflection Point for AI—and Global Security.  Council on Foreign Relations, April 15, 2026. https://www.cfr.org/articles/six-reasons-claude-mythos-is-an-inflection-point-for-ai-and-global-security

[HS26]  Kali Hays, Nardine Saad and Regan Morris, (2026).  Campaigners welcome Meta and YouTube’s defeat in landmark social media addiction trial.  BBC News, 25 March 2026.  https://www.bbc.co.uk/news/articles/c747x7gz249o

[KK25]  Kooli, Chokri, Youssef Kooli, and Eya Kooli (2025). Generative artificial intelligence addiction syndrome: A new behavioral disorder?.  Asian Journal of Psychiatry 107:104476.  https://doi.org/10.1016/j.ajp.2025.104476

[Ma24]  Murgia, Madhumita (2024). Code Dependent: How AI Is Changing Our Lives. Picador.

 

The Abomination of AI – part 7 – what can we do?

It all seems too big, requiring national and international responses.  But we can make a difference using appropriately chosen small AI (including none). Plus, this good use of AI is good for business too.

This is the seventh of a series of blogs based on my keynote “The abomination of AI” at ICoSCI 2026.  Each has an accompanying segment of the video and slides from the talk as well as detailed notes and references.  Section numbers refer to the full report which will be released in the final blog.   The slide thumbnails in the text correspond to the slides in the navigation panel below.  The presentation can be played below, or opened full screen. The full length video, complete slides and further information can be found at: https://alandix.com/academic/talks/ICOSCI-2026-abomination-of-AI/

Previously …

§1.  Every industry is driven by profits and power, but there is something about the nature of AI itself, which interacts with the nature of market forces in the world that is problematic and is different from other technologies.

§2.  Can any technology be neutral?  AI can be used for good purposes, such as advances in healthcare.  It can also have bad outcomes such as bias in the criminal justice system or online exploitative pornography.  Perhaps most often it is creating the frivolous or even ugly.

§3.  The obvious impact of AI is in the things it does directly. Some technologies also change the very nature of society, affecting even those who do not use them. Cars are an obvious example.  AI is also such a technology.

§4.  Doomsayers worry about the point when AI becomes sentient, outgrowing its creators.  The real danger is more insidious: the massive financial and human impacts of AI seem almost obscene.

§5.   Network externalities, the way one person’s use of AI and digital tech changes its value for others, creates positive feedback loops, leading to runaway growth and emergent monopolies, the nemesis of free markets. This the very nature of digital technology and AI breaks free markets leading to runaway inequality, even with the best intentions of industry … but some tech companies further exploit these effects.

§6.   Runaway growth of AI is not painless – opportunity costs of investment and human costs of lost jobs.  Gains may be transitory – buy-now-pay-later tech risk tying users into spiralling costs.

 

7.  What can we do?

These issues all seem too big, frighteningly so.   So what can you do?

You might be a policy maker, or on a government committee that’s advising governments.  If so, you might be in position to make changes at that scale.  Most do not have such high-level influence, but there are changes you can make within your own spheres to help ameliorate some of these potential dangers.  I’ll focus on the UX designer or AI developer, but some of the ideas are ones you might be able to adopt in your own personal use or within an organisation.

 

7.1  No AI

One option is to simply, say “no” to AI.

If you are a designer, ask, “do I need AI at all in my project?”  Of course, everybody now expects every product to say ‘AI powered’, so you may not be able to avoid AI altogether, but it could be very simple AI.  But do ask whether you need it at all, if you don’t, why are you feeling you need to use it?

 

7.2  Small AI

If you do decide to use AI, you can opt for small AI.

If you are using language models or other generative AI, you might use smaller models, the kind that have been deliberately designed to be able to run on less powerful hardware. There are many good reasons to do this.  Indeed, Apple have been encouraging smaller AI because they want the AI to run on people’s personal devices, not just in the cloud.  This is because privacy is a strong part of the company brand.

Where it is appropriate you could use traditional AI, which is usually much smaller in terms of memory and computation.

Purely from a technical perspective, there are some really interesting research challenges in this area, both in terms of human computer interaction (see my 2024 talk on ‘Patient Interaction’ [Dx24]) and also pure technical AI.

Images: [Di22,Sa23,Dx25,DS25]

You’ll have seen some of the modifications of algorithms that are transforming this landscape including open LLaMa [ZD22,TH23], LORA [HS22] and LiGO [WP23].  DeepSeek [DS24,DS25] made waves when US export restrictions on NVIDIA chips forced Chinese innovators to adopt a far leaner and smarter approach to LLM development [LF24].  Debatably DeepSeek’s learning might have piggybacked off some of the other LLMs [We25b], but certainly at execution time it used far less resources than other LLMs at time. Now other LLMs have adopted lessons from DeepSeek, and all are looking to perform more efficiently, so there is small shift in thinking away from a simplistic ‘bigger is better’ approach [Hi20].

 

7.3  When to use AI

There is also a choice about when to use AI.

The most obvious use of AI is at execution time in a user interface or delivered application as part of the service provided.  This can of course be small AI or no AI at all even.

But you can also use AI at design time.  You might use big AI to create small AI for the delivered system, for example using techniques to compress the model.  You can also use AI as part of the UX process to critique a user interface, create rapid prototypes, or propose design ideas [Dx26b].  In addition, AI-based coding tools can create AI-free (or low-AI) systems.

Crucially, if you use big AI to help create a (smaller) product, it effectively gets reused again and again and again and again.   So it’s less expensive – both moneywise expensive to a company, but also less expensive in terms of its impact on the environment and society.

In fact, this is really powerful use of AI. For instance, one of the things I argue elsewhere is that AI critiques of UIs will be far better for accessibility than even the best designers.  This is in part because it is really hard for us to think about even obvious diversity such as what’s it like to be blind or deaf, or have a physical disability, an automated design tool can check a concept or prototype against vast numbers of different types of perceptual and physical abilities as well as combinations.  Even more important, it is almost impossible for us to imagine what it’s like to be somebody who thinks differently, for example somewhere distant from ourselves in a neurodivergent space .  I don’t think AI will be good at this, but I think it will be better than we are.

 

7.4  How to use AI

Finally, if you are using AI think carefully about the kind of AI you are going to use, and how to incorporate it into a system.  For many years I’ve talked about appropriate intelligence, most often in relation to AI error and the need to design human–AI systems that together are robust and effective, not focusing in the AI accuracy alone [DB00,BD23].  However, the same lesson can be applied more broadly.

Often, we think about human interaction with AI, but it can be useful to think of a three-way interaction with human(s), AI and plain-old computing – that is hand coded algorithms or classic AI. Now look at each kind of AI that you are thinking of using and ask what is it good for?

What kind of things do I mean? One of the problems with traditional AI is that it was good with hard-nosed rules, but much more problematic with fuzzy things.  There are various techniques such as Bayesian methods and fuzzy logic, but they require you to formalize the fuzziness into probabilities or similar functions.  Amongst other things this limited various forms of natural language understanding and common-sense reasoning

Of course, large language models are really good at dealing with the nuances of language, but LLMs are less good when they try to be very precise, not least because they keep hallucinating!

So as you design for AI, ask what is it good for, how can I use it most appropriately?

As an example of the appropriate use of AI,  my wife uses an app from “The Doctor’s Kitchen” (https://www.thedoctorskitchen.com) to help keep track of the health value of food.

You take a photo of a plate of food before you eat it and the app creates a report on its nutritional value: how much fibre and protein it contains and its inflammation index.  Is it likely to be good for you or bad?

You could imagine doing this by writing a complicated prompt to an LLM or train a deep learning algorithm with lots of plates of food and hand-curated reports.  The app does not work like that.

What it does is to use image processing AI to analyse the plate and work out what food is on it.  Indeed, you can press an edit button to see what it thinks you’ve got on your plate, and, if it’s got it wrong, edit it.  One assumes that a log of these edits helps to further train the image processing AI.

So the AI has been used for the fuzzy part of the task, working out that there are crisps on the plate but a no cake. It even manages to recognise hummus and estimate how much.  It is amazingly good, but does sometimes get things wrong in terms of the volume or even what is there; however, when that happens you can easily see and correct it.

So this is using AI for the fuzzy bit.

This table of contents will then go into a standard algorithm that uses tables of nutritional values to look up how much protein is in, say, 10 grams of almonds, add this up for the plate and hence generate the final nutritional report.

AI and traditional computing together — combining the two using the best aspects of each.

Note that this is more explainable, you know, what’s going on.

It is also more flexible in terms of you can choose to enhance different components and change others.

There is also less vendor tie in.  This is not removed entirely as you need a new AI to be retrained.  However, it is easier to swap just the food recognition part than if the whole system were in a single AI.

This is good from a business point of view, but it also means you are using less large-scale AI with its environmental, financial and democratically damaging effects, when you could be using simpler computation.

Coming next …

Part 8 – summary and recap

This final post will recap what we’ve learnt about the runaway nature of the AI industry, how it undermines free markets, and how we can make a difference. The core question is not what can AI do, but what should AI do?

 

Update

Since the talk in January I read about A.T.L.A.S. (Adaptive Test-time Learning and Autonomous Specialization) an AI coding system built by a business student Johnathon Tigges wanting to challenge the assumption that “only the biggest players can build meaningful things” [Ti26] .  It is able to outcompete the big coding agents by being clever – rather than just throwing a problem into a big code-optimised LLMs and asking for a solution, it uses AI to generate lots of potential code fragments and tests them, using the best to further refine the AI model … all on a consume GPU.  A lovely example of smart use of AI!  For a more detailed description see Sebastian Buzdugan’s Medium story about it [Bu26].

References

[BD23] Alba Bisante, Alan Dix, Emanuele Panizzi, and Stefano Zeppieri (2023). To err is AI.In Proceedings of the 15th Biannual Conference of the Italian SIGCHI Chapter, pp.1–11. https://dsoi.org/10.1145/3605390.3605414

[Bu26] Sebastian Buzdugan (2026). Why a $500 GPU Can Beat Claude Sonnet on Coding Benchmarks. Medium. Mar 28, 2026. https://medium.com/@sebuzdugan/why-a-500-gpu-can-beat-claude-sonnet-on-coding-benchmarks-6c8169ffe4fe

[DS24]  DeepSeek-AI (2024).  DeepSeek-V3 Technical Report. arXiv preprint. 27 Dec 2024. https://arxiv.org/abs/2412.19437

[DS25]  DeepSeek-AI (2025).  DeepSeek-V3. GitHub Repository. Release v1.0.0. 27 Jun 2025. https://github.com/deepseek-ai/DeepSeek-V3

[Di22] Dickson, B. (2022). Can large language models be democratized? TechTalk,-May 16, 2022. https://bdtechtalks.com/2022/05/16/opt-175b-large-language-models/

[DB00] A. Dix, R. Beale and A. Wood (2000).  Architectures to make Simple Visualisations using Simple Systems.  Proceedings of Advanced Visual Interfaces – AVI2000, ACM Press, pp. 51-60.  https://www.alandix.com/academic/papers/avi2000/

[Dx24] Alan Dix (2024). Patient Interaction – for well-being, productivity and sustainability. FUSION 2024, Kuala Lumpur, Malaysia, 28 Sept. 2024. https://www.alandix.com/academic/talks/FUSION2024/

[Dx25]  Dix, A. (2025). Artificial Intelligence – Humans at the Heart of Algorithms, 2nd Edition, Chapman and Hall.  https://alandix.com/aibook/

[bibitelm name=Dx26b] A. Dix. (2026). AI for Human–Computer Interaction. CRC Press, in press. https://alandix.com/ai4hci/

[Hi20] Hinton, G. (2020). Extrapolating the spectacular performance of GPT3 into the future suggests that the answer to life, the universe and everything is just 4.398 trillion parameters. Twitter (now X), Jun 10, 2020. https://x.com/geoffreyhinton/status/1270814602931187715

[HS22]  Hu, E. J., Shen, Y., et al. (2022). LoRa: Low-rank adaptation of large language models. ICLR, 1(2), 3. https://arxiv.org/abs/2106.09685

[LF24] Liu, A., Feng, B., et al. (2024). Deepseek-v3 technical report. arXiv preprint arXiv:2412.19437. https://arxiv.org/abs/2412.19437

[Sa23] Sajid, H. (2023).  Artificial Intelligence: Can You Build Large Language Models Like ChatGPT At Half Cost? Unite.ai, May 11, 2023.  https://www.unite.ai/can-you-build-large-language-models-like-chatgpt-at-half-cost/

[Ti26]  Johnathon Tigges (2026).  A.T.L.A.S. – Adaptive Test-time Learning and Autonomous Specialization. GitHub. https://github.com/itigges22/ATLAS

[TH23] Touvron, H., Martin, L., et al. (2023). Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288. https://arxiv.org/abs/2307.09288

[WP23] Wang, P., Panda, R., et al. (2023). Learning to grow pretrained models for efficient transformer training. arXiv preprint.  https://arxiv.org/abs/2303.00980

[We25b] Werner, J. (2025). Did DeepSeek Copy Off Of OpenAI? And What Is Distillation? Forbes, Jan 30, 2025. https://www.forbes.com/sites/johnwerner/2025/01/30/did-deepseek-copy-off-of-openai-and-what-is-distillation/

[ZD22]  Zhang, S., Diab, M. and Zettlemoyer, L. (2022). Democratizing access to large-scale language models with OPT-175B. Meta Research Blog, May 3, 2022. https://ai.meta.com/blog/democratizing-access-to-large-scale-language-models-with-opt-175b/

 

 

 

The Abomination of AI – part 6 – should we worry?

Runaway growth of AI is not painless – opportunity costs of investment and human costs of lost jobs.  Gains may be transitory – buy-now-pay-later tech risk tying users into spiralling costs.

This is the sixth of a series of blogs based on my keynote “The abomination of AI” at ICoSCI 2026.  Each has an accompanying segment of the video and slides from the talk as well as detailed notes and references.  Section numbers refer to the full report which will be released in the final blog.   The slide thumbnails in the text correspond to the slides in the navigation panel below.  The presentation can be played below, or opened full screen. The full length video, complete slides and further information can be found at: https://alandix.com/academic/talks/ICOSCI-2026-abomination-of-AI/

Previously …

§1.  Every industry is driven by profits and power, but there is something about the nature of AI itself, which interacts with the nature of market forces in the world that is problematic and is different from other technologies.

§2.  Can any technology be neutral?  AI can be used for good purposes, such as advances in healthcare.  It can also have bad outcomes such as bias in the criminal justice system or online exploitative pornography.  Perhaps most often it is creating the frivolous or even ugly.

§3.  The obvious impact of AI is in the things it does directly. Some technologies also change the very nature of society, affecting even those who do not use them. Cars are an obvious example.  AI is also such a technology.

§4.  Doomsayers worry about the point when AI becomes sentient, outgrowing its creators.  The real danger is more insidious: the massive financial and human impacts of AI seem almost obscene.

§5 .   Network externalities, the way one person’s use of AI and digital tech changes its value for others, creates positive feedback loops, leading to runaway growth and emergent monopolies, the nemesis of free markets. This the very nature of digital technology and AI breaks free markets leading to runaway inequality, even with the best intentions of industry … but some tech companies further exploit these effects.

6.  Should we worry?

6.1  Jobs and power

Image: Scottish Government, CC BY 2.0. https://commons.wikimedia.org/wiki/File:One_of_the_typing_pools_%283829002585%29.jpg

Does this matter?  So what if a small number of companies have notional multi-trillion balance sheets and are engaged in runaway development in the digital realm, so long as it doesn’t affect the real world.  But, of course, it does; the digital domain is leaking into the physical domain.

Of course, technology and automation have long had massive impact on society, with a gradual shift from human expertise to financial capital.  This certainly dates back to the 19th century or late 18th century with the rise of the industrial revolution.  Of course, at that time, humans were still needed, but they went from being the expert weavers and spinners, to the those (including young children) who merely tended the machines, monitoring and knotting broken threads, and occasionally losing arms in the moving parts.  So it wasn’t that the humans were unnecessary, but they merely fed the machines.

Moving into the 20th century, machines replaced humans more completely with fully automated production lines and industrial robots, although of course still with humans cleaning up between them.  In many parts of the global north skilled manual work has all but disappeared, with a combination of automation and out-sourcing.

To some extent the impact of automation initially hit traditional male jobs, but in the latter half of 20th century, from about the early seventies on this also hit clerical roles.  Until then every big organisation would have had a typing pool.  My own mother was for many years a typist at first in the War Department throughout the Second World War, and then the Inland Revenue. These typing pools, consisted of ranks of people, usually women, typing sometimes from dictation and shorthand, and sometimes other forms of handwriting.  Word processors basically destroyed the typing pool. Whereas previously, managers would dictate letters and reports to a secretary who would then type it up,  with the word processor, despite initial resistance, they would type directly themselves.  Of course, after initial diversity, Microsoft Word soon became dominant – another emergent monopoly, although now matched by Google Workspace, with about 90% world share between them.

So, in general, skilled working class jobs have been destroyed by automation leaving a growing underclass with minimum wage jobs and gig work.

What we’re seeing now is that the mid-range intellectual work is starting to be eaten by AI [BSI25,].

You may have seen the MIT report that found that while many companies were investing heavily in AI, around 95% of the projects were considered to be failing or underperforming [CP25].  So, effective job substitution is not yet universal, but in some areas such as computing many of the lower range of the roles, typical graduate first jobs, are being replaced by AI.  Until recently the expert developer would have several junior developers who do the grunt work; now this is done by AI.  Similar pictures are emerging in advertising, aspects of finance, and some of the large management consultancies [Ko25,Sw26,IPA26,KM26,Pa26].  In the UK, and even more so in other parts of the world, there are strong pushes to use AI more extensively within government, not least on the assumption that it will improve efficiency [GUK25,Dx26].

There’s a critical issue about who’s in control.  Think about the road network.  In the UK there are some private roads and also some toll roads, but the majority of roads, including almost all in urban areas, are owned by the local authority or central government.  That is, the vast majority of the road network is local in terms of its maintenance and control.  Imagine if the road network was instead owned by two or three major companies based in the west coast of America.  Imagine if every road in Malaysia, every road in Indonesia, as well as every road in UK was owned by those two or three companies  half the world away.  If there’s a pothole in the road, it is those companies to whom you have to complain.  Perhaps they decide to charge you to use the road outside your house or decide to remove the roads entirely if they’re in dispute with you or your government.

That’s exactly the direction we are moving with AI and public services.  Even assuming the best intention of the big AI players, this does feel worrying.  And, of course, this isn’t a choice you can make or not.  Just like cars and roads, once AI is embedded into public service everything orients around it.

Returning to the changes in employment, once we lose the entry stage jobs, there’s a clear problem for the people who would have had them.  All the graduates from our universities, who would have been going into those jobs, are being hit, and, in many countries, on top of large student loans [DoE25,Pa25,Pa26].  This is creating a class of people who are underemployed, inexperienced, and quite likely disaffected with society.  Think of this in the light of the rise of extremism across the world.  Often this is dismissed as a problem of the uneducated, but here we are adding a vast number of highly educated people, who are disaffected in society, further spreading those extreme messages.

 

6.2  Locked into AI

This is also a problem within an organization.  If you are not employing those early career people, what happens in five or ten years’ time as your more experienced employees want to move up the organization?  How do you fill in those gaps if you haven’t been training people?

This might be something we need to address as universities, training people effectively to higher and higher levels so that they can jump in at that point.

Or the organisation can simply find they need more AI – what they certainly can’t do is just turn off the AI because they haven’t got the people with the experience in order to do the jobs anymore.  They have become locked in as a company to the use of AI.

This is also true of data.  Microsoft have a guide entitled, “Prepare your data for AI” [Ms26].  The use of AI is not coming for free, but needs a rearrangement of data for it.  One does wonder if the same effort making data ready for AI could be better spent making it ready for simpler statistical algorithms.

However, let’s assume you have put effort into reorienting your whole data around AI. Your systems rapidly become AI dependent – your recent information and new data has become deeply embedded into the AI itself in ways that are often opaque.

Once you have bought into an AI system, you can’t just say, “well, let’s just swap to something else”.  It’s difficult even to swap vendors once it is that embedded.

 

6.3  Buy now … pay later

If you have a loan with interest, you know you have to pay for it eventually, but things can be less obvious.  When I was little, my mum had a Kays catalogue, a sort of the 1960s  equivalent of internet selling [WA17].  Its pages were full of big colour pictures of clothes, white goods, toys, etc. …it was usually the toys I was looking at.  You could buy things from the catalogue and could pay over 20 weeks with no interest, but of course the things cost more than if you had the ready cash buy them at a shop.  So effectively you were paying extra.

AI currently is in that ‘buy now pay later’ mode, both globally and locally for individuals.  AI growth is funded by massive investment (as we discussed absolutely huge) possibly more than ever before except perhaps the South Sea Bubble.  However, the income doesn’t in any way cover the costs, and the ratio between expected income and investment is way out of kilter for what you’d expect even for a digital company, let alone for a physical one.

So how do the books add up?

If you’re an accountant in the company or if you’re an investment manager, what are you thinking about as, as you see these figures?  Why don’t you sound the alarm?  The reason is you are thinking that in the future you will have more money from that stream.  In early digital companies, like Amazon, you did that because you assumed you were going have a bigger market, the number of people who would use it would grow.

But AI already has lots of users, so instead you have two options.  The first is to find ways to make what you produce more cheaply, which is happening to some extent already. However, you don’t want it to get too cheap otherwise competitors can enter the market.  The alternative, and your only real option, to recoup your investment by charging more or getting the same customers to use more. Either way, it is the customer who pays in the end!

This is no secret.  Fortune magazine said that OpenAI’s business plan relies on “what amounts to a bet on dominance” [Sm25].  That is, in putting in all that investment, what investors are hoping is that the company will become the AI company in an area that everybody is tied into.  And then of course they can charge pretty much what they like: a buy now – pay later world. We’re using AI now, but the cost is going to come later on.

 

Coming next …

Part 7 – what can we do?

It all seems too big, requiring national and international responses.  But we can make a difference using appropriately chosen small AI (including none). Plus, this good use of AI is good for business too.

 

Update.

Since the talk, I read about a woman who had developed a close relationship with a chatbot hosted on a version of ChatGPT that is due to be retired [He26]. While she could probably export her chat history and use that to reinitialise the new version of the software, it would not be the same.  We will soon start to hear similar stories for business and public systems as tech companies have not had a good record of backward compatibility, and this is all but impossible with current LLMs.

Also, in late January, OpenClaw was released [OC26].  This highlighted the way current payment models do not reflect the actual cost of use. OpenClaw (originally called Clawdbot) is an open-source GitHub project that used the Claude API to create an automated assistant coordinating web and desktop resources.  Within days of the launch Anthropic enforced a long-standing, but unenforced, restriction on third-party use of its API and blocked OpenClaw for most user accounts including its $200 Max account.  This was because these accounts come with monthly usage limits, and OpenClawd encouraged full use of those limits.  However, the business model of even premium accounts depends on users NOT using their monthly allowances. OpenClawd encouraged full use of those limits, thus exposing.the true cost of the full use vastly exceeded the subscriptions [Ba26] .

 

 

References

[Ba26] Novy Baf (2026).  Anthropic Pushed Its Most Loyal Developers Straight Into OpenAI’s arms. OpenAI Didn’t Even Have to Ask.  The Nov TEch, 2nd Mar 2026.  https://www.thenovtech.com/p/anthropic-pushed-its-most-loyal-developers

[BSI25] British Standards Institution (2025). Evolving Together: AI, automation and  building the skilled  workforce of the future.  https://www.bsigroup.com/en-GB/insights-and-media/insights/whitepapers/evolving-together-flourishing-in-the-ai-workforce/

[Dx26] A. Dix. (2026). Beyond the Algorithm: Designing Human-Centric Public Service with AI. Talk at Service Design for Public Sector Spotlight Seminar series of challenges and opportunities between Design Cultures and Public Sector, Sapienza, University of Rome + Online, 4th February 2026. https://alandix.com/academic/talks/Rome-Seminar-Feb-2026/

[DoE25] Department of Education (2025). The impact of AI on UK jobs and training. November 2023.  https://www.gov.uk/government/publications/the-impact-of-ai-on-uk-jobs-and-training

[CP25]  Aditya Challapally, Chris Pease, Ramesh Raskar, Pradyumna Chari (2025). The GenAI Divide: State of AI in Business 2025. MIT NANDA, July 2025. https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf

[GUK25] Gov.UK (2025). AI to power national renewal as government announces billions of additional investment and new plans to boost UK businesses, jobs and innovation. Press release from Department for Science, Innovation and Technology, HM Treasury, Wales Office, The Rt Hon Liz Kendall MP, The Rt Hon Rachel Reeves MP and The Rt Hon Jo Stevens MP.  20 November 2025. https://www.gov.uk/government/news/ai-to-power-national-renewal-as-government-announces-billions-of-additional-investment-and-new-plans-to-boost-uk-businesses-jobs-and-innovation

[He26]  Stephanie Hegarty (2026). Rae fell for a chatbot called Barry, but their love might die when ChatGPT-4o is switched off. BBC News, 14 February 2026. https://www.bbc.co.uk/news/articles/crl43dxwwy9o

[IPA26] IPA (2026). IPA Agency Census 2025 shows workforce declines while diversity improves.  Institute of Practitioners in Advertising. 11 February 2026. https://ipa.co.uk/news/agency-census-2025/

[KM26] Lucy Knight and Sumaiya Motara (2026). The big AI job swap: why white-collar workers are ditching their careers. The Guardian,  11 Feb 2026. https://www.theguardian.com/technology/2026/feb/11/big-ai-job-swap-white-collar-workers-ditching-their-careers

[Ko25] Saskia Koopman (2025).  Big Four slash graduate jobs as AI takes on entry level work. City AM, 23 June 2025. https://www.cityam.com/big-four-slash-graduate-jobs-as-ai-takes-on-entry-level-work/

[Ms26] Microsoft (2026). Prepare your data for AI. Dated 20/1/2026.  https://learn.microsoft.com/en-gb/power-bi/create-reports/copilot-prepare-data-ai

[OC26] OpenClaw (2026).  OpenClaw — Personal AI Assistant. https://github.com/openclaw/openclaw

[Pa25] Joanna Partridge (2025). Gen Z faces ‘job-pocalypse’ as global firms prioritise AI over new hires, report says. The Guardian,  9 Oct 2025. https://www.theguardian.com/money/2025/oct/09/gen-z-face-job-pocalypse-as-global-firms-prioritise-ai-over-new-hires-report-says

[Pa26] Joanna Partridge (2026). More than a quarter of Britons say they fear losing jobs to AI in next five years. The Guardian,  25 Jan 2026. https://www.theguardian.com/business/2026/jan/25/more-than-quarter-britons-fear-losing-jobs-ai-next-five-years

[Sm25]  Dave Smith (2025). OpenAI says it plans to report stunning annual losses through 2028—and then turn wildly profitable just two years later . Fortune, November 12, 2025. https://fortune.com/2025/11/12/openai-cash-burn-rate-annual-losses-2028-profitable-2030-financial-documents/

[Sw26] Mark Sweney (2026). UK ad agencies undergo their biggest exodus of staff as AI threatens industry. The Guardian,  13 Feb 2026. https://www.theguardian.com/media/2026/feb/13/uk-ad-agencies-biggest-annual-exodus-of-staff-ai-threatens-industry

[WA17]  Worcestershire Archive and Archaeology Service (2017).  Christmas and Kays.  Explore the Past. 19th December 2017. https://www.explorethepast.co.uk/2017/12/christmas-and-kays/

 

 

 

 

The big stories buried beneath the headlines

In news stories this morning about pet abduction and sustainable fashion, the most critical parts are buried deep in the article: a chance remark that gives away the bigger story.

During the lockdown there has been a steep rise in the cost of dogs and other pets, and this has led to an increase in pet abductions. The most high profile example was when Lady Gaga’s dog walker was shot during the theft of her bulldogs in Los Angeles, but the BBC reports that there are over 2000 pet thefts in the UK alone last year.

Stock image of a person stealing a dog

Pet abduction to be made new criminal offence in thefts crackdown – BBC News

In principle pet theft is a crime covered by the UK Theft Act, but the use of this evidently does not reflect the emotional harms of pet abductions, hence the need for the new law. Reading further the article says:

Although offences under the Theft Act 1968 carry a maximum term of seven years, ministers say there is little evidence of that being used, because the severity of the sentence is partly determined by the monetary value of the item taken.

It was this that caught my eye.  The most severe penalties under the Theft Act are for the most valuable items.  If the second-hand car of a pensioner near the poverty line is stolen, it will attract a less severe sentence than the trophy Porsche from the millionaire’s collection.  This sounds like a law made in the 17th century, but is in fact from 1968 and applies today.

The lesson is clear, if you are poor then even the criminal law does nothing for you.

The second story is about Molly-Mae, ex-Love Island contestant and social media influencer, who has just been recruited as creative director of Pretty Little Things with a particular focus on sustainable fashion.

 

Molly Mae

Molly-Mae: “I’m not just an influencer anymore”

Reading further there is a section entitled “Wearing the same dress twice”, that has the following quote from Molly-Mae:

“I even captioned one of my Instagram pictures the other day saying ‘PSA it’s ok to wear the same dress twice’ – it’s a bad habit us girls have got into, like if you put it on Instagram it means you can’t wear it again.”

Although I did know some of the figures for this before, it still shocked me to hear that “wearing the same dress twice” is regarded as a significant message.

Sadly, this does reflect the previous figures I’ve seen suggesting that the median number of times a garment is worn is indeed one, with something like 20% of clothes never worn at all once bought.  This all has to be added to around 1/3 of fashion clothing that is shredded or otherwise disposed of without ever being sold, due to end of season, returns, or other reasons.

The fashion industry is estimated to contribute 10% of all global carbon emissions, not to mention plastic micro-fibres, chemical, water and other environmental impacts, as well as being built upon near slave-labour conditions across the world.

Given this, even wearing clothes twice could be a major benefit.

However, just imagine how the statement sounds to someone who lived through the second world war, or even anyone over 50.  This is reflected in figures for environmental action by age group: awareness is greatest in the younger age groups, but in nearly all areas life-style action is greatest in the older ones.  Perhaps influencers such as Molly-Mae can help turn this round.

So as you read the news, do look beyond the headlines, the most hard-hitting parts are often buried deep.

dog digging

Image: jimbomack66, CC BY 2.0, via Wikimedia Commons

Covid-19, the impact of university return

For many reasons, it is important for universities to re-open in the autumn, but it is also clear that this is a high-risk endeavour: bringing around 2% of the UK population together in close proximity for 10 to 12 weeks and then re-dispersing them at Christmas.

When I first estimated the actual size of the impact I was, to be honest, shocked; it was a turning point for me. With an academic hat on I can play with the numbers as an intellectual exercise, but we are talking about many, many thousands of lives at risk, the vast majority outside the university itself, with the communities around universities most at risk.

I have tried to think of easy, gentle and diplomatic ways of expressing this, but there are none; we seem in danger of creating killing zones around our places of learning.

At the very best, outbreaks will be detected early, and instead of massive deaths we will see substantial lockdowns in many university cities across the UK with the corresponding social and economic costs, which will create schisms between ‘town and gown’ that may poison civic relationships for years to come.

In the early months of the year many of us in the university sector watched with horror as we watched the Covid-19 numbers rising and could see where this would end. The eventual first ‘wave’ and its devastating death toll did not need sophisticated modelling to predict; in the intervening months it has played out precisely as expected. At that point the political will was clearly set and time was short; there was little we could do but shake our heads in despair and feel the pain of seeing our predictions become reality as the numbers grew, each number a person, each person a community.

Across the sector, many are worried about the implications of the return of students and staff in the autumn, but structurally the nature of the HE sector in the UK makes it near impossible even for individual universities to take sufficient steps to mitigate it, let alone individual academics.

Doing the sums

For some time, universities across the UK have been preparing for the re-opening, working out ways to reduce the risk. There has been a mathematical modelling working group trying to assess the impact of various measures, as well as much activity at individual institutions.  It appears too that SAGE has highlighted that universities pose a potential risk [SN], but this seems to have gone cold and universities are coping as best they can with apparently no national plan. Universities UK have issued guidance to universities on what to do as they emerge from lockdown [UUKa], but it does not include an estimate of the scale of the problem.

As I said, the turning point for me came when I realised just how bad this could be. As with the early national growth pattern, it does not require complex mathematics to assess, within rough ranges, the potential impact; and even the most conservative estimates are terrifying.

We know from freshers’ flu that infections spread quickly amongst the student community.  The social life is precisely why many students relocate to distant cities.  Without strong measures to control student infections it is clear that Covid-19 will spread rapidly on campuses, leading to thousands of cases in each university. Students themselves are at low (though not zero) risk of dying or having serious complications from Covid-19, but if there is even small ‘leakage’ into the surrounding community (via university staff, transport systems, stay-at-home students or night life), then the impact is catastrophic.

For a mid-sized university of 20,000 students, let’s say only 1 in 20 become infected during the term; that is around 1,000 student cases. As a very conservative estimate, let’s assume just one community infection for every 10 infected students. If city bars are open this figure will almost certainly be much higher, but we’ll take a very low estimate. In this case, we are looking at 100 initial community cases.

Now 100 additional cases is already potentially enough to cause a handful of deaths, but we have got used to trading off social benefits against health costs; for any activity there is always a level of risk that we are prepared to accept.

However, the one bit of mathematics you do need to know is the way that a relatively small R number still leads to a substantial number of cases. For example, an R of 0.9 means for every initial infection the total number of infections is actually 10 times higher (in general 1/(1-R), see [Dx1]).  When R is greater than 1 the effect is worse still, with the impact only limited when some additional societal measure kicks in, such as a vaccine or local lockdown.

A relatively conservative estimate for R in the autumn is 1.5 [AMS]. For R =1.5, those initial 100 community cases magnify to over 10,000 within 5 weeks and more than 600,000 within 10 weeks. Even with the most optimistic winter rate of 1.2, those 100 initial community infections will give rise to 20,000 cases by the end of a term.

That is for a single university.

With a mortality rate of 1% and the most optimistic figures, this means that each university will cause hundreds of deaths.  In other words, the universities in the UK will collectively create as many infections as the entire first wave.  At even slightly less optimistic figures, the impact is even more devastating.

Why return at all?

Given the potential dangers, why are universities returning at all in the autumn instead of continuing with fully online provision?

In many areas of life there is a trade-off to be made between, on the one hand, the immediate Covid-19 health impacts and, on the other, a variety of issues: social, educational, economic, and also longer term and indirect mental and physical health implications. This is no less true when we consider the re-opening of universities.

Social implications: We know that the lockdown has caused a significant increase in mental health problems amongst young people, for a variety of reasons: the social isolation itself, pressures on families, general anxiety about the disease, and of course worries about future education and jobs. Some of the arguments are similar to those for schools except that universities do not provide a ‘child minding’ role. Crucially, for both schools and universities, we know that online education is least effective for those who are already most economically deprived, not least because of continued poor access to digital technology. We risk creating a missed generation and deepening existing fractures in civil society.

Furthermore, the critical role of university research has been evident during the Covid crisis, from the development of new treatments to practical use of infrastructure for rapid production of PPE. Ongoing, the initial wave has emphasised the need for more medical training.  Of course, both education and research will also be critical for ‘post-Covid’ recovery.

Economic situation: Across the UK, universities generate £95 billion in gross output and support nearly a million jobs (2014–2015 data, [UUKb]).  Looking at Wales in particular, the HE sector “employs 17,300 full-time members of staff and spending by students and visitors supports an estimated 50,000 jobs across Wales”. At the same time the sector is particularly vulnerable to the effects of Covid-19 [HoC]. Universities across the UK were already financially straitened due to a combination of demographics and Brexit, leading to significant cost-cutting including job cuts [BBCa].  Covid-19 has intensified this; a Wales Fiscal Analysis briefing paper in May [WFA] suggests that Welsh universities may see a shortfall due to Covid-19 of between £100m and £140m. More recent estimates suggest that this may be understating the problem, if anything. Cardiff University alone is warning of a £168m fall in income [WO] and Sir Deian Hopkin, former Vice Chancellor of London South Bank and advisor to the Welsh Assembly, talks of a “perfect storm” in the university system [BBCb].

Government support has been minimal. The rules for Covid-19 furlough meant that universities were only able to take minimal advantage of the scheme. There has been some support in terms of general advice, reducing bureaucratic overheads and rescheduling payments to help university cashflow, but this has largely been within existing budgets, not new funding. The Welsh government has announced an FE/HE £50m support package with £27m targeting universities [WG], but this is small compared with predicted losses.

Universities across the UK have already cut casual teaching (the increase in zero-hour contracts has been a concern in HE for some years) and many have introduced voluntary severance schemes.  At the same time the competition over UK students has intensified in a bid to make up for reduced international numbers. Yet one of the principal ways to attract students is to maximise the amount of in-person teaching.

What is being done

To some extent, as in so many areas, coronavirus has exposed the structural weaknesses that have been developing in the university sector for the past 30 years. Universities have been forced to compete constantly and are measured in terms of student experience above educational impact. Society as a whole has been bombarded with messages that focus on individual success and safety rather than communal goals, and most current students have grown up in this context. This focus has been very evident in the majority of Covid-19 information and reporting [Dx2].

Everything we do is set against this backdrop, which both fundamentally limits what universities are able to do individually, and at the same time makes them responsible.  This is not to say that universities are not sharing good practice, both in top down efforts such as through Universities UK and direct contacts between senior management, and from the bottom up via person-to-person contacts and through subject-specific organisations such as CPHC.

Typically, universities are planning to retain some level of in-person teaching for small tutorials while completely or largely moving large-class activities such as lectures to online delivery, some live, some recorded. This will help to remove some student–student contact during teaching. Furthermore, many universities have discussed ways in which students could be formed into bubbles. At a large scale that could involve having rooms or buildings dedicated to a particular subject/year group for a day.  At a finer scale it has been suggested that students could be grouped into social/study bubbles of around ten or a dozen who are housed together in student accommodation and are also grouped for study purposes.

My own modelling of student bubbles [Dx3] suggests that while reducing the level of transmission, the impact is rapidly eroded if the bubbles are at all porous.  For example, if the small bubbles break and transmission hits whole year groups (80–200 students), the impact on outside communities becomes unacceptable. For students on campus the temptation to break these bubbles will be intense, both at an individual level and through bars and similar venues.  For those living at home, the complexities are even greater, and crucially they are a primary vector into the local community.

Combined with, or instead of, social/study bubbles some universities are looking at track and trace. Some are developing their own solutions both in terms of apps and regular testing programmes, but more will use normal health systems.  In Wales, for example, Public Health Wales regard university staff as a priority group for Covid-19 testing, although this is reactive (symptoms-based) rather than proactive (regular testing).

Dr Hans Kluge, the Europe regional director for the World Health Organization and others have warned that global surges across the world, including in Europe, are being driven by infections amongst younger people [BBCc].  He highlights the need to engage young people more in the science, a call that is reflected in a recent survey by the British Science Association which found that nine out of ten young people felt ignored by scientists and politicians [BSA].

As of 27th July, the UK Department for Education were “working to” two scenarios “Effective containment and testing” (reduce growth on campuses and reactive local lockdowns) and “On and off restrictions” (delaying all in-person teaching until January) [DfE].  Jim Dickinson has collated and analysed current advice and work at various government and advisory bodies including the DfE report above and SAGE, but so far there seems to be no public quantification of the risk [JD].

What can we do?

I think it is fair to say that the vast majority of high-level advice from national governments and pan-University bodies, and most individual university thinking, has been driven by safety concerns for students and staff rather than the potentially far more serious implications for society at large.

As with so many aspects of this crisis, the first step is to recognise there is a problem.

Within universitiesacknowledge that the risk level will be far higher than in society at large because the case load will be far higher. How much higher will depend on mitigating measures, but whereas general population levels by the start of term may be as low as 1 in 5,000, the rate amongst students will be an order of magnitude higher, comparable with general levels during the peak of the ‘first wave’. This means that advice, particularly for at risk groups, which is targeted at national levels, needs to be re-thought within the university context. This means that advice that is targeted at national levels, particularly for at risk groups, needs to be re-thought within the university context.  Individual vulnerable students are already worried [BBCd]. Chinese and Asian students seem more aware of the personal dangers and it is noticeable that both within the UK and in the US the universities with the greatest number of international students are more risk averse. University staff (academics, cleaners, security) will include more at risk individuals than the student body. It is hard to quantify, but the risk level will considerably higher than, say, a restaurant or pub, though of course lower than for front line medical staff. Even if it is ‘safe’ for vulnerable groups to come out of shielding in general society, it may not be safe in the context of the university. This will be difficult to manage: even if the university does not force vulnerable staff to return, the long-term culture of vocational commitment may make some people take unacceptable risks.

Outside the universities, local councils, national governments and communities need to be aware of the increased risks when the universities reopen, just as seaside towns have braced themselves for tourist surges post-lockdown. While SAGE has noted that universities may be an ‘amplifier’, the extent does not appear (at least publicly) to have been quantified.  In Aberdeen recently a cluster around a small number of pubs has caused the whole city to return to lockdown, and it is hard to imagine that we won’t see similar incidents around universities. This may lead to hard decisions, as has been discussed, between opening schools or pubs [BBCe] – city centre bars may well need to be re-thought. Universities benefit communities substantially both economically and educationally. For individual universities alone the costs of, say, weekly testing of students and staff would be prohibitive, but when seen in terms of regional or national health protection these may well be worthwhile. Although this is a ‘for example’ it could well be critical given the likelihood of large numbers of asymptomatic student cases.

Educate students – this is of course what we do as universities!  Covid-19 will be a live topic for every student, but they may well have many of the misconceptions that permeate popular discourse.  Can we help them become more aware of the aspects that connect to their own disciplines and hence to become ambassadors of good practice amongst their peers? Within maths and computing we can look at models and data analysis, which could be used in other scientific areas where these are taught.  Medicine is obvious and design and engineering students might have examples around PPE or ventilators. In architecture we can think about flows within buildings, ventilation, and design for hygiene (e.g. places to wash your hands in public spaces that aren’t inside a toilet!). In literature, there is pandemic fiction from Journal of the Plague Year to La Peste, and in economics we have examples of externalities (and if you leave externalities until a specialised final year option, rethink a 21st century economics syllabus!).

Time to act

On March 16, I posted on Facebook, “One week left to save the UK – and WE CAN DO IT.” Fortunately, we have more time now to ensure a safe university year but we need to act immediately to use that time effectively. We can do it.

References

[AMS] The Academy of Medical Sciences. Preparing for a challenging winter 2020-21. 14th July 2020. https://acmedsci.ac.uk/policy/policy-projects/coronavirus-preparing-for-challenges-this-winter

[BBCa] Cardiff University to cut 380 posts after £20m deficit. BBC News. 12th Feb 2019.  https://www.bbc.co.uk/news/uk-wales-47205659

[BBCb] Coronavirus: Universities’ ‘perfect storm’ threatens future.  Tomos Lewis  BBC News. 7 August 2020.  https://www.bbc.co.uk/news/uk-wales-53682774

[BBCc] WHO warns of rising cases among young in Europe. Lauren Turner, BBc New live reporting, 10:05am 29th July 2020. https://www.bbc.co.uk/news/live/world-53577222?pinned_post_locator=urn:asset:59cae0e7-5d3d-4e35-94ec-1895273ed016

[BBCd] Coronavirus: University life may ‘pose further risk’ to young shielders
Bethany Dawson. BBC News. 6th August 2020. https://www.bbc.co.uk/news/disability-53552077

[BBCe]  Coronavirus: Pubs ‘may need to shut’ to allow schools to reopen. BBC News. 1st August 2020.  https://www.bbc.co.uk/news/uk-53621613

[BG]  Colleges reverse course on reopening as pandemic continues.  Deirdre Fernandes, Boston Globe, updated 2nd August 2020.  https://www.bostonglobe.com/2020/08/02/metro/pandemic-continues-some-colleges-reverse-course-reopening/

[BSA] New survey results: Almost 9 in 10 young people feel scientists and politicians are leaving them out of the COVID-19 conversation. British Science Association. (undated) accessed 7/8/2020.  https://www.britishscienceassociation.org/news/new-survey-results-almost-9-in-10-young-people-feel-scientists-and-politicians-are-leaving-them-out-of-the-covid-19-conversation

[DfE] DfE: Introduction to higher education settings in England, 1 July 2020 Paper by the Department for Education (DfE) for the Scientific Advisory Group for Emergencies (SAGE). Original published 24th July 2020 (updated 27th July 2020).  https://www.gov.uk/government/publications/dfe-introduction-to-higher-education-settings-in-england-1-july-2020

[Dx1]  More than R – how we underestimate the impact of Covid-19 infection. . Dix (blog).  2nd August 2020  https://alandix.com/blog/2020/08/02/more-than-r-how-we-underestimate-the-impact-of-covid-19-infection/

[Dx2] Why pandemics and climate change are hard to understand, and can we help? A. Dix. North Lab Talks, 22nd April 2020 and Why It Matters, 30 April 2020 http://alandix.com/academic/talks/Covid-April-2020/

[Dx3] Covid-19 – Impact of a small number of large bubbles on University return. Working Paper. A. Dix. July 2020.  http://alandix.com/academic/papers/Covid-bubbles-July-2020/

[HEFCW] COVID-19 impact on higher education providers: funding, regulation and reporting implications.  HEFCW Circular, 4th May 2020 https://www.hefcw.ac.uk/documents/publications/circulars/circulars_2020/W20%2011HE%20COVID-19%20impact%20on%20higher%20education%20providers.pdf

[HoC]  The Welsh economy and Covid-19: Interim Report. House of Commons Welsh Affairs Committee. 16th July 2020. https://committees.parliament.uk/publications/1972/documents/19146/default/

[JD]  Universities get some SAGE advice on reopening campuses. Jim Dickinson, WonkHE, 25th July 2020.  https://wonkhe.com/blogs/universities-get-some-sage-advice-on-reopening-campuses/

[SN]  Coronavirus: University students could be ‘amplifiers’ for spreading COVID-19 around UK – SAGE. Alix Culbertson. Sky News. 24th July 2020. https://news.sky.com/story/coronavirus-university-students-could-be-amplifiers-for-spreading-covid-19-around-uk-sage-12035744

[UUKa] Principles and considerations: emerging from lockdown.   Universities UK, June 2020. https://www.universitiesuk.ac.uk/policy-and-analysis/reports/Pages/principles-considerations-emerging-lockdown-uk-universities-june-2020.aspx

[UUKb] https://www.universitiesuk.ac.uk/policy-and-analysis/reports/Pages/economic-impact-universities-2014-15.aspx

[WFA] Covid-19 and the Higher Education Sector in Wales (Briefing Paper). Cian Siôn, Wales Fiscal Analysis, Cardiff University.  14th May 2020.  https://www.cardiff.ac.uk/__data/assets/pdf_file/0010/2394361/Covid_FINAL.pdf

[WG]  Over £50 million to support Welsh universities, colleges and students.    Welsh Government press release.  22nd July 2020.  https://gov.wales/over-50-million-support-welsh-universities-colleges-and-students

[WO] Cardiff University warns of possible job cuts as it faces £168m fall in income. Abbie Wightwick, Wales Online. 10th June 2020.  https://www.walesonline.co.uk/news/education/cardiff-university-job-losses-coronavirus-18393947

 

 

 

 

 

 

Who not to feel sorry for facing an uncertain Brexit

map of areas with high indices of multiple deprivation in CornwallReading Julia Rampen’s “11 things I feel more sorry about than Cornwall losing money after Brexit” in the New Statesman, I agree with all the groups she cites who are going to suffer the effects of Brexit, but did not vote for it.

However, it is clear that, when considering Cornwall, which voted 56.5% to leave, where affluent second home owners and retirees are cheek-by-jowl with post-industrial poverty and decay, and which is now worried about the loss of regional funding; Rampen’s sympathy is, to say the least, muted.

This made me ponder other groups for whom I have little sympathy as we face the uncertainty, and quite likely utter disaster, of Brexit.

  • those, like myself, academics, professionals, who have reaped the benefits of open markets and labour, but ignored the plight of those who have suffered because of it
  • those who have accepted the narrative that those who suffered most were in some way to blame
  • the newspapers that lampooned and vilified Gordon Brown for his overheard private comment about a “bigoted woman“, but now happy to label leave voters who hold similar views as racist.
  • those that passively accepted the implicit racism of the leadership debates in the 2010 general election without comment
  • those who have accepted EU rules as an excuse for government inaction … such as happened with Port Talbot steel just before the referendum election
  • the governments and administrations of multiple hues over many years, who have applied EU regulations in ways that no other country would regard as reasonable

Some of these ‘unwilling vicitims’ will suffer from Brexit despite voting to remain. However, many of those who will suffer worst will have voted to leave, but only because remaining in offered them no hope either.

After a generation of closing our hearts to those suffering on our doorstep, we now demonise them yet again.

 

 

slaughter of the innocents – human shields or collateral damage?

By Huynh Cong Ut (also known as Nick Ut), image from Wikipedia

From the ‘Napalm Girl‘ in Vietnam, to Alan Kurdi’s body on a Turkish beach in 2015 and endless images of White Hat’s pulling children from the rubble in Aleppo, it is easy to become inured to the death of innocent children around the world.

In the church calendar, December 28th1 is the Feast of the Innocents or Childermas, a day to remember the children killed by King Herod as he sought the baby Jesus.

In Matthew’s Gospel we read:

 When Herod realized that he had been outwitted by the Magi, he was furious, and he gave orders to kill all the boys in Bethlehem and its vicinity who were two years old and under (Matt. 2:16, NIV).

However, for many it is the words in the Christmas carol, “Unto us a boy is born“, which is most familiar:

All the little boys he killed at Bethlehem in his fury.

Mary and Joseph had already fled, refugees to Egypt, so the babies were not simply slaughtered, but slaughtered in vain, an action missing its true target, like the bombs that killed Gaddaffi’s children and grandchildren in 1986 and 2011.

I’ve been reading Simon Garfield’s “Timekeepers‘ (a Christmas gift).  Garfield describes a meeting with Nick Ut, the photographer of ‘Napalm Girl’2.  The common story is that the US attack on the village from which Phan Thi Kim Phuc was running was a mistake, but Ut describes how in the village there were many dead Viet Cong, so that the mistake was more likely inadequate intelligence that the villagers had fled (Timekeepers, p.168).

A few weeks ago a BBC reporter in Yemen was visiting a school, which Saudi air strikes had repeatedly hit.  This was one example of many such incidents targeting schools during this conflict3. The reporter talked of how the school kept on working and pupils kept attending, despite the damage and the danger.  However, the report also showed the Houthi rebel arms dump next to the school.  “Can’t you move the school away from this?”, asked the reporter. “They would simply move the dump to follow us”, replied the headmaster.

Again this is a story we have heard so many times before: missiles fired from hospital grounds in Gaza, Ukraine keeping its air corridors open whilst in the midst of its air campaign against separatists4, ISIS preventing civilian evacuation from Mosul, or the South Korean artillery firing into disputed areas from a populated island.

In some cases civilians are deliberately put in the way of danger (as with ISIS); in others fighting in built up areas makes civilian presence inevitable (Aleppo, Gaza).  In some cases terror is the main aim of an attack or the civilians are seen as legitimate targets (as with ISIS attacks in Europe); in others terror is a deliberate secondary war aim (as with Dresden or Nagasaki). In some cases attackers seem to show flagrant disregard for civilian life (as in Gaza), and in others all care is take, but (often substantial) civilian deaths are accepted as collateral damage (probably the case with US drone extrajudicial killings).

Whether you blame those on the ground for using human shields or those attacking for disregarding human like, often depends on which side you are on5.

In most conflicts the truth is complex, especially where there are mismatches of firepower: Hamas in Gaza, anti-Assad rebel groups in Syria, or ISIS in Iraq would all have been slaughtered if they fought in the open.  And for the attacking side, where does the responsibility lie between callous disregard for human life and justifiable retaliation?  How do we place the death of children by bombs against those of starvation and illness caused by displaced populations, siege or international sanctions?

If the events in Bethlehem were to happen today, how would we view Herod?

Was he despotic dictator killing his own people?

Was the baby Jesus a ‘clear and present danger’, to the stability the state and thus the children of Bethlehem merely collateral damage?

Or were Mary, Joseph and God to blame for using human shields, placing this infant of mass disruption in the midst of a small town?

It is worryingly easy to justify the slaughter of a child.


Some organisations that are making a difference:

  1. The date varies in different churches, it is 28th December in most Western churches, but 27th, 29th Dec, or 10th January elsewhere[back]
  2. The ‘Napalm Girl’ recent obtained fresh notoriety when Facebook temporarily censored it because it showed nudity.[back]
  3. Another BBC report,amongst many, “Yemen crisis: Saudi-led coalition ‘targeting’ schools” documents this.[back]
  4. Before MH17 was shot down a Ukrainian military transport and other military planes had been shot down, and the first messages following the destruction of MH17 suggest the rebels thought they had downed another military aircraft.  Instead of re-routing flights the flying ceiling was raised, but still within distance of ground-to-air missiles, and carriers made their own choices as to whether to overfly.  Some newspapers suggest that the motives were mainly financial both for Malaysian Airways, and for the Ukrainian government decisions, rather than Ukraine using civilian flights as a deliberate human shield.[back]
  5. Patrick Cockburn’s comparison of Aleppo and Mosul in The Independent argues this is the case for the current conflicts in Syrian and Iraq.[back]

the educational divide – do numbers matter?

If a news article is all about numbers, why is the media shy about providing the actual data?

On the BBC News website this morning James McIvor‘s article “Clash over ‘rich v poor’ university student numbers” describes differences between Scottish Government (SNP) and Scottish Labour in the wake of Professor Peter Scott appointment as commissioner for fair access to higher education in Scotland.

Scottish Labour claim that while access to university by the most deprived has increased, the educational divide is growing, with the most deprived increasing by 0.8% since 2014, but those in the least deprived (most well off) growing at nearly three times that figure.  In contrast, the Sottish Government claims that in 2006 those from the least deprived areas were 5.8 times more likely to enter university than those in the most deprived areas, whereas now the difference is only 3.9 times, a substantial decrease in educational inequality..

The article is all about numbers, but the two parties seem to be saying contradictory things, one saying inequality is increasing, one saying it is decreasing!

Surely enough to make the average reader give up on experts, just like Michael Gove!

Of course, if you can read through the confusing array of leasts and mosts, the difference seems to be that the two parties are taking different base years: 2014 vs 2006, and that both can be true: a long term improvement with decreasing inequality, but a short term increase in inequality since 2014.  The former is good news, but the latter may be bad news, a change in direction that needs addressing, or simply ‘noise’ as we are taking about small changes on big numbers.

I looked in vain for a link to the data, web sites or reports n which this was based, after all this is an article where the numbers are the story, but there are none.

After a bit of digging, I found that the data that both are using is from the UCAS Undergraduate 2016 End of Cycle Report (the numerical data for this figure and links to CSV files are below).

Figure from UCAS 2016 End of Cycle Report

Looking at these it is clear that the university participation rate for the least deprived quintile (Q5, blue line at top) has stayed around 40% with odd ups and downs over the last ten years, whereas the participation of the most deprived quintile has been gradually increasing, again with year-by-year wiggles.  That is the ratio between least and most deprived used to be about 40:7 and now about 40:10, less inequality as the SNP say.

For some reason 2014 was a dip year for the Q5.  There is no real sign of a change in the long-term trend, but if you take 2014 to 2016, the increase in Q5 is larger than the increase in Q1, just as Scottish Labour say.  However, any other year would not give this picture.

In this case it looks like Scottish Labour either cherry picked a year that made the story they wanted, or simply accidentally chose it.

The issue for me though, is not so much who was right or wrong, but why the BBC didn’t present this data to make it possible to make this judgement?

I can understand the argument that people do not like, or understand numbers at all, but where, as in this case, the story is all about the numbers, why not at least present the raw data and ideally discuss why there is an apparent contradiction!

 

Numerical from figure 57 of UCAS  2016 End of Cycle Report

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Q1 7.21 7.58 7.09 7.95 8.47 8.14 8.91 9.52 10.10 9.72 10.90
Q2 13.20 12.80 13.20 14.30 15.70 14.40 14.80 15.90 16.10 17.40 18.00
Q3 21.10 20.60 20.70 21.30 23.60 21.10 22.10 22.50 22.30 24.00 24.10
Q4 29.40 29.10 30.20 30.70 31.50 29.10 29.70 29.20 28.70 30.30 31.10
Q5 42.00 39.80 41.40 42.80 41.70 40.80 41.20 40.90 39.70 41.10 42.30

UCAS provide the data in CSV form.  I converted this to the above tabular form and this is available in CSV or XLSX.

the rise of the new liberal facism

Across Europe the ultra-right wing raise again the ugly head of racism in scenes shockingly reminiscent of the late-1930s; while in America white supremacists throw stiff-armed salutes and shout “Heil Trump!”  It has become so common that reporters no longer even remark on the swastikas daubed as part of neo-Nazi graffiti.

Yet against this we are beginning to see a counter movement, spoken in the soft language of liberalism, often well  intentioned, but creating its own brand of facism.  The extremes of the right become the means to label whole classes of people as ‘deplorable’, too ignorant, stupid or evil to be taken seriously, in just the same way as the Paris terrorist attacks or Cologne sexual assaults were used by the ultra-right to label all Muslims and migrants.

Hilary Clinton quickly recanted her “basket of depolarables”.  However, it is shocking that this was said at all, especially by a politician who made a point of preparedness, in contrast to Trump’s off-the-cuff remarks.  In a speech, which will have been past Democrat PR experts as well as Clinton herself, to label half of Trump supporters, at that stage possibly 20% of the US electorate, as ‘deplorable’ says something about the common assumptions that are taken for granted, and worrying because of that.

My concern has been growing for a long time, but I’m prompted to write now having read  ‘s “Welcome to the age of anger” in the Guardian.  Mishra’s article builds on previous work including Steven Levitt’s Freakonomics and the growing discourse on post-truth politics.  He gives us a long and scholarly view from the Enlightenment, utopian visions of the 19th century, and models of economic self interest through to the fall of the Berlin Wall, the rise of Islamic extremism and ultimately Brexit and Trump.

The toxicity of debate in both the British EU Referendum and US Presidential Election is beyond doubt.  In both debates both sides frequently showed a disregard for truth and taste, but there is little equivalence between the tenor of the Trump and Clinton campaign, and, in the UK, the Leave campaign’s flagrant disregard for fact made even Remain’s claims of imminent third world war seem tame.

Indeed, to call either debate a ‘debate’ is perhaps misleading as rancour, distrust and vitriol dominated both, so much so that Jo Cox viscous murder, even though the work of a single new-Nazi individual, was almost unsurprising in the growing paranoia.

Mishra tries to interpret the frightening tide of anger sweeping the world, which seems to stand in such sharp contrast to rational enlightened self-interest and the inevitable rise of western democracy, which was the dominant narrative of the second half of the 20th century.  It is well argued, well sourced, the epitome of the very rationalism that it sees fading in the world.

It is not the argument itself that worries me, which is both illuminating and informing, but the tacit assumptions that lie behind it: the “age of anger” in the title itself and the belief throughout that those who disagree must be driven by crude emotions: angry, subject to malign ‘ressentiment‘, irrational … or to quote Lord Kerr (who to be fair was referring to ‘native Britains’ in general) just too “bloody stupid“.

Even the carefully chosen images portray the Leave campaigner and Trump supporter as almost bestial, rather than, say, the images of exultant joy at the announcement of the first Leave success in Sunderland, or even in the article’s own Trump campaign image, if you look from the central emotion filled face to those around.

guardian-leaver  telegraph-sunderland-win
guardian-trump-1  guardian-trump-2

The article does not condemn those that follow the “venomous campaign for Brexit” or the “rancorous Twitter troll”, instead they are treated, if not compassionately, impassionately: studied as you would a colony of ants or herd of wildebeest.

If those we disagree with are lesser beings, we can ignore them, not address their real concerns.

We would not treat them with the accidental cruelty that Dickens describes in pre-revolutionary Paris, but rather the paternalistic regard for the lower orders of pre-War Britain, or even the kindness of the more benign slave owner; folk not fully human, but worthy of care as you would a favourite dog.

Once we see our enemy as animal, or the populous as cattle, then, however well intentioned, there are few limits.

The 1930s should have taught us that.