午後の電車は空いていた。 窓の外には田んぼが続いている。

その田んぼの向こうに、どこまで来たのかよく分からない景色が広がる。 遠くまで来た感触だけが先に立って、距離が掴めない。 空いてる電車は確かに心地良いけれど、今どこを走っているのかは友人に聞けば教えてもらえる。 たぶん、聞けるから俺は自分で確かめようとしないだけだ。

電車の揺れに合わせて、俺はさっき買ったコーラのプルタブを開けた。 マイナーなメーカーのやつで、コカ・コーラとは少し違う味がする。 その違いが、なぜか今日はちょうど良かった。

炭酸の刺激が喉に残っているうちに思い出した。 昔から自分は、今どこにいるのかにあまり興味がなかった。

位置の話をしていたからか、友人が窓の外を指す。 さっきの木はお酒を飲んでたんですね、と言う。 木の根元にビール瓶が置いてあったのだろう。

その言葉に続けて、ああいうさりげない場所が会場になるんだね、と友人は言う。 こんなの団地の人みんな喜びますよ、とも言った。

団地という言葉から、なぜか髪をしばるゴムの話を思い出した。 たくさん持ってて良かったという声と、私を縛ってたゴムは無くなったみたいという声が、誰のものかも判然としないまま浮かぶ。

その言葉が指先に引っかかるような気がして、窓のガラスに映る自分の手を眺めた。 すると、卵にヒビを入れる力加減で警戒された記憶が浮かんだ。 卵を割るだけで、あんなに距離を取られるとは思わなかった。

視線を手元に戻すと、友人が飲みかけのペットボトルを差し出していた。 その手つきを見て、水筒の渡し方に愛がなかった日を思い出した。 あの渡し方は、たぶん、何かを伝えたかったわけじゃない。

駅をひとつ過ぎたあたりで、友人がまた話し始める。 さっきの実は体に電気が走りましたよ、と。 さっき自分の影に閉じ込められた人は大丈夫なの、とも言う。

影に閉じ込められるという言葉が耳に残る。 そのままの空気で、友人がぽつりと呟く。 そっかあ、スマホなくてもそうなんだ、と。 忘れるなら石に書いておくといい、とも言う。

忘れる話を聞いて、実家に帰ると暗い箇所をみつけてしまうことを思い出した。

暗い場所にばかり気づくのはおかしな話だ。 それなのに、昔、電話ボックスの中に置いてあった日記のことが頭に浮かんだ。 誰のものかも分からないノートに、誰かの文字が詰まっていた。

視線を窓の外に戻すと、電柱に3人の女子高生が残した落書きの形跡があった。 その電柱には虫がたかっていて、薄くなってよくわからない車の標識が立っている。

その標識を目で追いかけるのをやめて、目を閉じた。 音だけはずっと耳障りがよく、風が体内を抜けていった。

コーラの缶はもう空だ。 炭酸の抜けた空洞が、腹の底に残っている。 その感じが、今の自分の位置を教えてくれているような気がするけれど、それが正しいのかは、たぶん、分からないままだ。

Again, I can’t sleep after midnight for unknown reasons, i went to the rooftop and just stared at the moon for so long, wondering if you did as well, but i watched your house. you didnt, you werent watching the moon, so i was just staring at it by myself. i smoked a cigarette. watching your house, i was very close you could never notice, you would never know how close i was, i was just staring. until I got interrupted by a cat who was staring at me, thought it was you, telling me that I wasn’t staring by myself but thats just me being delusional with my own thoughts. My cigarette finished so i just went downstairs, heavy heart, heavy legs. i almost fell, i took a cold shower, i dont like cold showers but i did it because it felt like your hands, cold heart, with cold hands. Wondering if you’d ever notice how cold it is. you were too warm in my dreams i would wake up sweating, but your hands are still so cold to reach or even to hold. im leaving your city soon, this morning or tomorrow. i had fun, being so close, breathing the air you breathe, living close to an angel that I can’t reach, I can’t hold, weighted with my sins. would i ever be able to hold the hands of an angel if my hands were bloody and dirty? , i dont know. honestly, i dont even remember why i started talking about this, but its so pathetic to watch a yearner, yearning for someone who they wish to hold. but again, a part of me is wondering what got me to love in the first place. i dont think i was suppsed to be loved, or love someone much, because ill end up like this, writing and not knowing what i was even writing about, and perhaps thats what yearning does to people. it strips every coherent thought from your head until all that’s left is fragments. questions with no answers, and you start writing about moon. Cigarettes, then cold showers, then somehow convince yourself you’re talking about anything but the person you’re actually writing about. And it’s exhausting, you’d think id eventually grow tired of it. Still, i havent, and thats embarrasing ill admit it it is embarrassing, not that i miss you but that i still find new ways to miss you, different rooftops, different nights. I wonder how many times I’ve knowingly stood beneath the same sky as you. how many times we’ve breathed the same air without ever sharing the same moment. Probably countless, you’ll never know. some things are prettier from a distance (angels included). i still yearn for her, i love her as well, in the quiet way that never seems to leave me. she’ll never see this, and thats the thing i like about it. it can stay here, safely hidden, where it wont have the chance to embarrass either of us.

Tomrrow me is going to read this and look confused like I didn’t write it, we did write it. i always get too honest in these notes, like there’s no consequence. Maybe i like it that way, i like putting myself in trouble. id get bored without, even if it was just with myself. Still, I won’t delete it. i never do i just leave it here and pretend I’m above it.

Sincerely, future confusion

When a company tells the world that artificial intelligence has made your role redundant, it hands you a story about yourself. The story is tidy and modern and faintly heroic in its fatalism: the future arrived, the machine learned to do what you did, and there was nothing anybody could have done. You were not failed by your employer or by the economy. You were simply standing where the tide came in.

The trouble is that, on the evidence now accumulating, the story is frequently untrue. And a false story about why you lost your job is not a harmless thing. It is a map. It tells you which skills have become worthless, which industries to flee, and what to retrain into if you want to eat next year. If the map is wrong, every decision you make from it is wrong too. You will run from sectors that were never under threat, abandon skills that were never obsolete, and spend scarce money and scarcer time learning things that will not save you. You will also, very often, blame yourself.

This is the quiet scandal underneath the loud one. The loud scandal is that companies are firing people and pointing at AI. The quiet scandal is what that pointing does to the people on the receiving end, and whether the law or our collective ethics recognise any difference at all between being replaced by a machine and being made redundant by a spreadsheet wearing a machine's clothes.

The data that broke the story

The framing began to wobble in earnest at the start of 2026. On 7 January, Fortune published an analysis built on research from Oxford Economics that landed with the force of a deflating balloon. The headline finding was blunt: firms do not appear to be replacing workers with AI on any significant scale. The macroeconomic data, the consultancy argued, simply did not support the idea of a structural shift in employment driven by automation.

The numbers told the story. AI was cited as the reason for nearly 55,000 US job cuts in the first eleven months of 2025, a figure that accounted for more than three-quarters of all AI-attributed cuts reported since 2023. That sounds dramatic until you set it against the whole. Those 55,000 cuts represented just 4.5 per cent of total reported job losses. Redundancies blamed on ordinary market and economic conditions ran four times higher, at roughly 245,000. And every month, in the normal churn of the American labour market, somewhere between 1.5 and 1.8 million workers lose their jobs. Against that ocean, the AI cuts were a puddle.

Oxford Economics offered an unsentimental reading of why companies might reach for the AI explanation anyway. Attributing staff reductions to automation, the firm noted, conveys a more positive message to investors than admitting to weak consumer demand or, more awkwardly still, to having over-hired during the cheap-money years. As the analysts put it, they suspected some firms were trying to dress up layoffs as a good news story rather than a bad one. A redundancy is a confession of error. An AI transformation is a strategy. Same severance cheque, very different press release.

The consultancy also proposed a test that is hard to argue with. If AI were genuinely replacing labour at scale, productivity growth should be accelerating: fewer people producing the same or more output is, by definition, a productivity gain. Generally, it is not accelerating. The machines that have supposedly displaced all these workers have left almost no fingerprints on the output statistics.

The paradox that economists keep digging up

That absence has a name, or at least a precedent. In February 2026, the National Bureau of Economic Research published a survey of around 6,000 chief executives, chief financial officers and other senior managers across the United States, the United Kingdom, Germany and Australia. The result, reported by Fortune in an article by Sasha Rogelberg, was startling in its flatness. Nearly 90 per cent of firms said AI had made no impact on either employment or productivity over the previous three years.

This was not a survey of sceptics. Around two-thirds of the executives said they used AI. But that usage amounted to roughly 1.5 hours per week, and a quarter of respondents reported not using AI at work at all. The people running the companies, the ones with every incentive to talk up their digital transformation to shareholders, were quietly admitting in an academic survey that the revolution had not yet arrived in any measurable form. They still expected it to: forecasts pencilled in a 1.4 per cent productivity gain and a 0.8 per cent output gain over the next three years, alongside a 0.7 per cent cut to employment. The gains were always just over the horizon.

To economists, this had a familiar shape. In 1987, the Nobel laureate Robert Solow made an observation that has haunted every technological boom since. Despite the spread of computers through the economy, productivity growth had actually slowed, falling from 2.9 per cent in the post-war decades to around 1.1 per cent afterwards. You could see the computer age everywhere, Solow remarked, except in the productivity statistics. The gap between the visible presence of a technology and its invisible economic contribution became known as the Solow productivity paradox.

The parallel is not lost on the people watching the data now. Torsten Slok, the chief economist at Apollo, captured the present moment with a near-direct echo of Solow: AI, he observed, is everywhere except in the incoming macroeconomic data. There are signs the picture may be shifting. The Federal Reserve Bank of St Louis noted in November 2025 a productivity increase of around 1.9 per cent since ChatGPT's launch in late 2022, and the MIT economist Daron Acemoglu has projected a more modest gain of around half a per cent over a decade. But a half-per-cent productivity bump over ten years is not the sound of a labour market being demolished. It is the sound of a useful tool being slowly absorbed, the way spreadsheets and email and search engines were absorbed before it.

The vocabulary of the redundancy letter

If the productivity gains are not there, the language certainly is. By March 2026, the disconnect between AI's omnipresence in corporate communications and its near-absence from corporate output had become a recurring theme in technology writing, including at HackerNoon, which through its March coverage tracked how the rhetoric of machine intelligence had saturated the language of management while the efficiency it promised stayed stubbornly theoretical. AI had become the foundation on which policies, training programmes and strategic announcements were built, even where the underlying work had not changed at all.

The Wharton management professor Peter Cappelli put his finger on the sleight of hand. Companies, he has pointed out, announce layoffs that they never actually carry out, harvesting the favourable stock-market reaction to a leaner-sounding workforce. And on the AI claims specifically, he noticed something telling in the wording. The headline says it is because of AI, he observed, but when you read what the companies actually say, they tend to say they expect that AI will cover this work. Expect. Future tense. The work has not been automated. It has been earmarked for automation, at some unspecified point, by some unspecified system, and in the meantime the humans who did it are already gone.

This is the heart of what has come to be called AI washing, the workforce cousin of greenwashing. The term migrated from financial regulation, where it described companies overstating the role of AI in their products to attract investors, into the language of redundancy, where it describes companies overstating the role of AI in their cost-cutting to soften the blow and burnish the brand. By early 2026, compliance specialists were warning that AI washing carried real legal and reputational risk, and that it had arguably overtaken greenwashing as the corporate communications hazard of the moment.

The most striking confirmation came from inside the industry that has the most to gain from the displacement narrative. At the India AI Impact Summit in February 2026, Sam Altman, the chief executive of OpenAI, was asked about the wave of AI-attributed layoffs. He did not reach for the triumphal line. There is some AI washing, he conceded, where people are blaming AI for layoffs that they would otherwise do, and then there is some real displacement by AI of different kinds of jobs. He could not say what the exact percentage was. But the man whose company sells the picks and shovels of the AI gold rush was openly acknowledging that some of the gold was fake. Within weeks, by late May 2026, Altman was going further still, telling interviewers he had been pretty wrong about the speed of AI's economic impact, a notable reversal of his earlier warnings that entry-level roles were in serious jeopardy.

There is a complicating truth here, and the better analysts have insisted on it. Andy Challenger, of the outplacement firm Challenger, Gray and Christmas, made a point that cuts through the binary. Regardless of whether individual jobs are being replaced by AI, he noted, the money for those roles is. Capital that companies might once have spent on hiring is being diverted into AI infrastructure: the data centres, the chips, the licensing deals, the eye-watering capital expenditure that the hyperscalers have committed to. Through April 2026, AI was cited as justification for nearly 50,000 US job cuts, according to Challenger data. By late May 2026, technology-sector layoffs for the year had passed 142,000, and reporting noted that many of the firms doing the cutting were profitable companies trimming headcount to help fund AI infrastructure spending running into the hundreds of billions of dollars. A worker can be a casualty of AI spending without ever being replaced by an AI system. The job did not go to a machine. It went to the bill for the machines somebody else is building.

There is a further wrinkle that should make anyone pause before accepting the displacement story at face value. Even where firms have genuinely rolled out AI tools, the productivity returns have been ambiguous and sometimes negative. A Boston Consulting Group study of nearly 1,500 American workers found that productivity rose when people used one to three AI tools but fell sharply once they were juggling four or more, with workers reporting a kind of brain fog and an uptick in errors. The picture this paints is not one of clean substitution, a human swapped out for a more efficient machine. It is messier and more human: tools half-adopted, workflows half-rebuilt, gains that arrive in one place and evaporate in another. A labour market being smoothly automated would not look like this. It would look like the productivity statistics climbing while headcount fell. Instead, headcount is falling while the productivity statistics barely twitch, which is precisely the pattern you would expect if the cutting were driven by cost and capital allocation rather than by machines actually doing the work.

What a false map does to the person holding it

For the individual worker, these distinctions are not academic. They determine the shape of the next several years of a life.

Consider what the AI explanation actually communicates to the person receiving it. It says: the specific thing you were good at can now be done by software, therefore it has no future value, therefore you should retrain into something a machine cannot do. That instruction sounds responsible. It is the standard advice handed to displaced workers in every wave of automation since the power loom. But it is only sound advice if the premise is true. If your role was eliminated because your employer over-hired in 2022, or because a private-equity owner wanted to juice margins before a sale, or because demand for the product softened, then the skill you possessed has lost none of its market value. The job that used it has simply moved, or shrunk, or relocated to a cheaper labour market. Retraining away from that skill is not adaptation. It is a self-inflicted wound, dressed up as foresight.

The research on displaced workers is unforgiving about how costly these wrong turns are. Workers whose skills lie in declining industries already earn less even after they find new work, because their old competencies are hard to transfer. Studies of American retraining schemes have found that participants in some programmes remained underemployed and earning slightly less than comparable non-participants even four years after losing their jobs. Retraining is not a magic bridge across the labour market. It is a slow, expensive, uncertain crossing, and the single most important factor in whether it succeeds is whether the worker is retraining away from something genuinely obsolete and towards something genuinely in demand. A false map corrupts that calculation at its root. It can send a perfectly employable person sprinting away from a skill the market still wants, towards a future the market has not actually promised.

The damage is compounded by timing. Retraining decisions are made fastest in the weeks immediately after a job loss, when redundancy money is fresh, anxiety is highest and the instinct to do something, anything, is strongest. That is exactly the window in which the company's explanation has the most power, because it is the only authoritative-sounding account the worker possesses. If the leaving manager said the role was automated, that sentence becomes the seed of every subsequent choice: the course enrolled in, the sector written off, the contacts not called because that line of work is finished. By the time the worker discovers, months later, that a cheaper replacement was quietly hired or that the team was simply folded into another department, the money is spent and the new direction is half-travelled. The cost of the false map is not paid all at once. It compounds, quietly, in the form of a recovery aimed at the wrong target.

Then there is the damage that does not show up in earnings data. The psychology of job loss has been studied for decades, and the findings are consistent and grim. Unemployment inflicts stress, a collapse in perceived control, loss of self-esteem, shame, loss of social status, and a grieving process that resembles bereavement. Work, the research repeatedly finds, supplies purpose and identity as much as income; its removal produces feelings of helplessness, isolation and worthlessness.

How a person explains their job loss shapes how much of that damage they absorb. There is a well-documented divide in how people attribute redundancy. Those who blame themselves tend to feel worse about who they are, but stay oddly optimistic about their ability to learn new skills and recover. Those who blame the system suffer less self-reproach but feel more trapped, more convinced that nothing they personally do will change their situation. The AI redundancy narrative does something peculiar and corrosive: it manages to deliver the worst of both attributions at once. It is systemic, in that the machine is presented as an unstoppable historical force, which breeds the fatalism of the external-blame group. And yet it is intimately personal, because the message is that you, specifically, have been rendered obsolete by a technology, that your particular abilities have been surpassed. The worker is invited to feel both powerless against the tide and personally outdated. It is difficult to imagine a more demoralising combination, and it is built on a premise that, in a great many cases, is simply false.

This is the specific human cost the displacement narrative imposes when it is misapplied. It is not only that people lose jobs. People lose jobs in every downturn. It is that they are handed an explanation that misdirects their recovery and corrodes their sense of self, and they are handed it precisely because it was the most convenient thing for someone else to say.

Does the distinction carry legal weight?

If being told AI took your job is materially different from being made redundant by cost-cutting, you might expect the law to take an interest. The answer depends enormously on which side of the Atlantic you are standing.

In most of the United States, the doctrine of at-will employment means an employer generally needs no reason at all to end an employment relationship, provided the real reason is not an illegal one such as discrimination on the basis of a protected characteristic. There is no legal requirement to accurately state why a worker is being let go, and certainly none to disclose whether AI was involved. The principal federal protection, the Worker Adjustment and Retraining Notification Act, requires larger employers to give sixty days' notice of mass layoffs and plant closings, but it is a notice law, not a justification law. It governs the timing of the bad news, not its honesty. There is no federal requirement that an employer disclose whether a layoff is genuinely AI-driven, a gap that has not gone unnoticed; a proposed overhaul of WARN, introduced as the Fair Warning Act in early 2026, would represent the first significant rewrite since 1988, but the core architecture remains a question of notice rather than rationale. In the American legal frame, the AI explanation is largely a public-relations choice with little statutory consequence. A company can say almost anything about why it is shrinking, because in most states it does not have to say anything at all.

The United Kingdom is a different country in more than the obvious sense. Here, redundancy is one of a small number of potentially fair reasons for dismissal under the Employment Rights Act 1996, and the law cares a great deal about whether the stated reason is the real one. A genuine redundancy exists where an employer has ceased the business, or no longer needs employees to do work of a particular kind, or needs fewer of them. Crucially, if an employer dismisses someone and then immediately hires a replacement to do the same job, that is not a genuine redundancy at all. It is potentially an unfair dismissal. The role has to have actually disappeared, not merely changed hands.

This is where the AI framing becomes legally consequential rather than merely rhetorical. An employer in England or Wales can lawfully make staff redundant because it has introduced automation that genuinely removes the need for a role. But the reason has to withstand scrutiny. Employers may be required to explain, in clear and human terms, how an automated system has actually changed staffing levels or work design, and if that explanation cannot be coherently justified, defending the dismissal in a tribunal becomes considerably harder. A dismissal based purely on an automated recommendation, without proper assessment of the individual or genuine consideration of alternative roles, risks being found unfair. British employers also carry obligations to consult, to apply fair selection criteria, and to consider redeployment before reaching for redundancy. From 6 April 2026, the financial stakes rose: the maximum protective award for failing to comply with collective consultation obligations doubled from ninety to a hundred and eighty days' pay, sharply increasing the cost of getting the process wrong in a large-scale restructure.

So in the British context, the distinction the question asks about does carry weight, though perhaps not the weight one might hope. The law does not punish dishonest framing as such. There is no statutory offence of AI washing a redundancy. But the framing can become a liability, because a worker who suspects the AI story is a cover can challenge it. If a tribunal finds that the role did not really vanish, that a replacement was quietly hired, that the automation was aspirational rather than actual, or that the process was a pretext for getting rid of a particular person, the AI narrative collapses and the dismissal may be unfair. A worker has, in principle, three months less a day from the date of dismissal to bring such a claim, and the remedies can include compensation for lost earnings on top of statutory redundancy pay. The convenient story, in other words, can become the thread that unravels the whole decision if it does not match the facts on the ground.

That said, the protection is uneven and easily evaded. It applies to employees with sufficient qualifying service, not to the growing population of contractors, gig workers and the recently hired. It requires the worker to recognise that something is amiss, to absorb the cost and stress of a legal challenge, and to gather evidence about internal decisions they were never shown. The asymmetry of information is total. The employer knows whether the AI story is true. The worker can usually only guess. And a guess, however well-founded, is a thin basis on which to stake a tribunal claim while also trying to find a new job and pay the rent. The legal distinction, in short, exists in Britain and barely exists in America, but even where it exists it favours the party with the documents, the lawyers and the institutional memory, which is never the person who has just been shown the door.

The ethics of the convenient explanation

Strip away the legal scaffolding and an ethical question remains, and it is sharper than the legal one. Is it wrong for a company to attribute a layoff to AI when the real driver is something more ordinary, even if doing so breaks no law?

The case for leniency runs roughly as follows. Companies have always smoothed the language of bad news. Restructuring, rightsizing, streamlining, synergies: the corporate lexicon is a museum of euphemisms for sacking people, and AI transformation is merely the newest exhibit. Workers, the argument goes, know to read between the lines. No real harm is done by a gentler framing, and the alternative, brutal honesty about over-hiring or declining demand, might be worse for the morale of those who remain and the share price that funds everyone's pension.

The case against is more persuasive, and it turns on the specific nature of this particular lie. Most corporate euphemisms obscure the fact of the decision while leaving its meaning intact: everyone understands that streamlining means job cuts. The AI explanation is different in kind, because it does not merely soften the news. It actively misinforms the worker about the cause, and the cause is precisely the information the worker needs to plan a recovery. Telling someone they were streamlined leaves their understanding of the labour market undamaged. Telling someone they were replaced by AI, when they were not, plants a false belief about the value of their own skills, the safety of their own profession, and the direction in which their future lies. It is a lie that keeps working long after the person has left the building, steering their retraining, their job search and their self-image down a path laid by someone else's convenience.

There is also a collective harm that compounds the individual one. Every false AI redundancy adds to a public narrative of inevitable, accelerating, machine-driven displacement, a narrative that the productivity data does not currently support. That narrative has consequences well beyond the firms telling it. It shapes how governments think about retraining budgets and which sectors they prioritise. It influences which degrees school-leavers choose and which they avoid. It feeds a generalised anxiety about the future of work that the actual evidence, for now, does not justify. When companies AI wash their layoffs, they are not only misleading their own former employees. They are subsidising a public misunderstanding of the economy, and doing so for the narrow purpose of a better quarterly story.

The deepest ethical objection is about dignity. A worker who is made redundant for ordinary reasons retains a true account of what happened to them. They can be angry at the right target, grieve the right loss and plan around the real facts. A worker who is falsely told a machine surpassed them is denied even that. They are made to carry a story about their own obsolescence that is not true, told to them by people who knew better, for reasons that had nothing to do with them. There are few more basic things one person owes another than an honest account of why they are being harmed. The convenient explanation withholds exactly that, and calls the withholding progress.

Reading the map for what it is

None of this means AI will never displace workers. It almost certainly will, in some roles, on some timeline, and the real cases deserve real attention and real policy. Altman himself was careful to say that alongside the washing there is genuine displacement, and the diversion of capital from payrolls to AI infrastructure is reshaping hiring in ways that hurt people whether or not a model ever touches their old tasks. The point is not that the machine is innocent. The point is that the story has run far ahead of the evidence, and that the gap between the two is being filled with the cheapest available narrative.

For the worker handed that narrative, the most valuable instinct may be a sceptical one. The map you were given was drawn by someone with an interest in how it reads. Before you flee a sector or abandon a skill, it is worth asking the questions the company would prefer you did not: Did the role actually disappear, or was someone hired to do it? Is there a working system that does what I did, or only a slide deck that says one is coming? Did the firm over-hire, lose a contract, change owners, or simply decide its margins should be fatter? The honest answer to those questions is the real map. It tells you what is genuinely obsolete and what is merely inconvenient to keep paying for, and those are not the same thing at all.

The companies have learned that AI is a comfortable thing to blame, because it is no one's fault and everyone's future. The least we can do for the people on the wrong end of that sentence is to insist on the difference between the technology that took the work and the spreadsheet that took the worker. One of those stories is sometimes true. The other is just easier to tell.

References and sources

1. Lichtenberg, Nick. “AI layoffs are looking more and more like corporate fiction that's masking a darker reality, Oxford Economics suggests.” Fortune, 7 January 2026. https://fortune.com/2026/01/07/ai-layoffs-convenient-corporate-fiction-true-false-oxford-economics-productivity/

2. “Evidence of AI-driven job losses remains limited, says Oxford Economics report.” Workplace Insight, January 2026. https://workplaceinsight.net/evidence-of-ai-driven-job-losses-remains-limited-says-oxford-economics-report/

3. Rogelberg, Sasha. “Thousands of CEOs admit AI had no impact on employment or productivity, and it has economists resurrecting a paradox from 40 years ago.” Fortune, April 2026. https://fortune.com/article/why-do-thousands-of-ceos-believe-ai-not-having-impact-productivity-employment-study/

4. “A Huge Survey of CEOs and Other Execs Just Found Something Damning About AI's Effects on Productivity.” Futurism, February 2026. https://futurism.com/artificial-intelligence/survey-ceos-ai-workplace

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6. Rogelberg, Sasha. “OpenAI CEO Sam Altman warns 'AI washing' is real.” Fortune, 19 February 2026. https://fortune.com/2026/02/19/sam-altman-confirms-ai-washing-job-displacement-layoffs/

7. “Sam Altman and Dario Amodei are both walking back their AI jobs apocalypse prophecies as they eye blockbuster IPOs.” Fortune, 26 May 2026. https://fortune.com/2026/05/26/sam-altman-dario-amodei-walking-back-ai-jobs-apocalypse-prophecies-ipo/

8. “Sam Altman says some companies are 'AI washing' by blaming unrelated layoffs on the technology.” TechRadar, 2026. https://www.techradar.com/pro/sam-altman-says-some-companies-are-ai-washing-by-blaming-unrelated-layoffs-on-the-technology-but-admits-things-may-get-worse-soon

9. “Who the AI Works For.” HackerNoon, 16 March 2026. https://hackernoon.com/who-the-ai-works-for

10. “The HackerNoon Newsletter: Who the AI Works For (3/17/2026).” HackerNoon, 17 March 2026. https://hackernoon.com/3-17-2026-newsletter

11. “2026 Operational Guide to Cybersecurity, AI Governance and Emerging Risks.” Corporate Compliance Insights, 2026. https://www.corporatecomplianceinsights.com/2026-operational-guide-cybersecurity-ai-governance-emerging-risks/

12. “Tech Layoffs Reach 142,000 in 2026: Profitable Companies Cut Jobs to Fund $700B AI Infrastructure.” TechTimes, 29 May 2026. https://www.techtimes.com/articles/317392/20260529/tech-layoffs-reach-142000-2026-profitable-companies-cut-jobs-fund-700b-ai-infrastructure.htm

13. “The toll of job loss.” American Psychological Association, October 2020. https://www.apa.org/monitor/2020/10/toll-job-loss

14. “AI labor displacement and the limits of worker retraining.” Brookings Institution. https://www.brookings.edu/articles/ai-labor-displacement-and-the-limits-of-worker-retraining/

15. “The interplay between structure and agency in shaping the mental health consequences of job loss.” National Center for Biotechnology Information. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3573919/

16. LaLonde, Robert. “Retraining Displaced Workers.” The Hamilton Project. https://www.hamiltonproject.org/assets/legacy/files/downloads_and_links/10_displaced_workers_lalonde.pdf

17. Cakali, Samira. “I've Been Made Redundant Due to AI, Can I Claim Compensation?” Winston Solicitors. https://www.winstonsolicitors.co.uk/blog/ive-been-made-redundant-due-ai-can-i-claim-compensation

18. “AI and Redundancy: Is UK Employment Law Keeping Pace?” Bellevue Law. https://www.bellevuelaw.co.uk/insights/ai-and-redundancy-is-uk-employment-law-keeping-pace/

19. “Can I Replace Staff With AI and Make Them Redundant?” Pearce Legal. https://pearcelegal.co.uk/blog/can-i-replace-staff-with-ai

20. “UK Employment Rights Act 2025: What's new from April 2026.” Bird & Bird. https://www.twobirds.com/en/insights/2026/uk/uk-employment-rights-act-2025--whats-new-from-april-2026

21. “Unfair dismissal.” Acas. https://www.acas.org.uk/dismissals/unfair-dismissal

22. “What Is the WARN Act? Employee Rights and Layoff Notice Requirements.” FindLaw. https://www.findlaw.com/employment/losing-a-job/what-is-the-warn-act-employee-rights-and-layoff-notice-requirements.html

23. “Plant Closings and Layoffs.” US Department of Labor. https://www.dol.gov/general/topic/termination/plantclosings

24. “Congress Proposes Major Overhaul of WARN: What Employers Need to Know About the Fair Warning Act.” Law and the Workplace, January 2026. https://www.lawandtheworkplace.com/2026/01/congress-proposes-major-overhaul-of-warn-what-employers-need-to-know-about-the-fair-warning-act/

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

Listen to the free weekly SmarterArticles Podcast

Although my eyes were open they might just as well have been closed. I miss that stare I used to have as the one who looks at the world as though I’m seeing it for the first time. There’s this thing about me and I know I taste like the real thing. It does not often happen, but it happened to you loving it. How could you know?

I used to feel like seen for the first time when you would look at me. As if somehow whatever I am was crafted in the first night of the creation, in between stars and thunderstorms. In between taste and smells and memory.

And I know, I know there may be nothing above this sky. That all that is, is nothing but a trail of what once was.

/jul 26

In Summary: * Another Wednesday draws to a close. This morning was nearly exciting. The power went out here at approx. 10:00 AM. I saw my across-the-alley neighbor out in the alley looking up at the big transformer on the pole behind his house, so I went out back and asked if his power had gone out, too. “Yes,” he said. His had just gone out as mine had. We both decided the only thing to do was report our outage to the power company. When I made my report I learned that there was a big outage in our side of town. About an hour later the power came back on. I've not yet heard why it went out, but I'm sure glad it came back on!

I've still got to wrap up the day's prayers, then I'll head to bed. That's my plan, anyway.

Prayers, etc.: * I have a daily prayer regimen I try to follow throughout the day from early morning, as soon as I roll out of bed, until head hits pillow at night.

Health Metrics: * bw= 230.05 lbs. * bp= 146/84 (71)

Exercise: * morning stretches, balance exercises, kegel pelvic floor exercises, half squats, calf raises, wall push-ups, BP breathing exercises, pilates

Diet: * 07:30 – crispy oatmeal dunkin' cookies, cup of cold milk * 08:35 – 1 seafood salad and cheese sandwich * 13:00 – 3 boiled eggs * 15:15 – 1 pb&j sandwich

Activities, Chores, etc.: * 06:00 – listen to local news talk radio * 06:30 – bank accounts activity monitored. * 06:50 – read, write, pray, follow news reports from various sources, surf the socials, nap * 08:00 – stock newly arrived groceries * 11:45 – listening to general sports talk on 105.3 The Fan, DFW's #1 Sports Station, ahead of this afternoon's Rangers / Guardians game. * 15:15 – and Cleveland wins this one, 9 to 4. * 16:00 – listening to relaxing music

Chess: * 18:30 – moved in all pending CC Games

Die Hitzespannung steigt weiter: 25. Juni

Wir kommen langsam an einen Punkt, an dem das Arbeiten tagsüber immer schwieriger wird. Es fühlt sich irgendwie seltsam an, in der Wettervorhersage 40 Grad zu sehen. Und das in Deutschland, nicht in Spanien! Heute sollen es „nur“ 35 Grad werden. Aber selbst das setzt uns schon ziemlich zu.

Wie ich bereits erzählt habe, hat unser Hotel keine Klimaanlage. Das Gebäude ist alt und bleibt zwar lange kühl, aber wenn es sich einmal aufheizt, dauert es auch entsprechend lange, bis es wieder abkühlt. Noch schlimmer ist allerdings, dass unser Badezimmerfenster keinen Vorhang hat. Es zeigt nach Osten, und schon gegen acht Uhr morgens fühlt sich das Badezimmer wie eine Sauna an. Ich musste deshalb meine Cremes und andere Pflegeprodukte ins Zimmer bringen, weil sie dort bereits ganz warm geworden waren. Ich bin mir nämlich nicht sicher, ob sie dann noch die gleiche Wirkung haben. Was ist, wenn sie plötzlich genau das Gegenteil bewirken und ich statt weniger Falten am Ende noch mehr bekomme? Natürlich nur ein Scherz. Das wäre vermutlich noch mein kleinstes Problem – zumal ich sowieso kaum in den Spiegel schaue. Trotzdem wird es immer schwieriger, das Zimmer kühl zu halten. Den Ventilator lassen wir inzwischen fast den ganzen Tag laufen, aber viel hilft das auch nicht. Zum Glück haben wir Vorhänge im Zimmer, die wir tagsüber geschlossen halten. Das Fenster können wir allerdings nicht schließen, weil man sonst kaum noch Luft bekommt. Übrigens: Gestern haben wir unser Zimmer nach der zweiten oder dritten Nachfrage tatsächlich sauber vorgefunden. Die Bettwäsche und die Handtücher waren frisch, aber der Teppich und der Badezimmerboden ... na ja, eher nicht. Da fragte ich mich schon wieder, ob vielleicht wegen unserer Hunde gar nicht gesaugt wird. Meine Freunde lachen inzwischen über mich, aber heute habe ich tatsächlich einen Staubsauger bei Amazon bestellt. Eigentlich wollten wir ohnehin schon länger einen kleinen Akkustaubsauger fürs Auto und für unsere Winter in Spanien kaufen. Also wird er auf jeden Fall nützlich sein. Wenn ich allerdings jemandem erzähle, dass wir im Hotel wohnen und ich mir dafür einen Staubsauger kaufe, schaut man mich meistens ziemlich verwundert an. Na ja... Eigentlich ist das Hotel gar kein Loch. Sagen wir einfach: Es ist ein bisschen ... künstlerisch. 😉

Was die Arbeit betrifft, habe ich dagegen wirklich Glück. Unser Büro bleibt angenehm kühl. Auch das ist ein altes Gebäude, aber es heizt sich längst nicht so stark auf. Zumindest nicht der Raum, in dem ich mit meinen Kolleginnen arbeite. Ehrlich gesagt gehe ich zwischendurch manchmal sogar kurz nach draußen, um mich ein wenig aufzuwärmen. Nach drei Stunden am Schreibtisch fühlen sich meine Hände richtig kalt an. Mindaugas stellt jedes Mal fest, dass ich kalte Hände habe, wenn er mich gegen vier Uhr abholt. Bei der Arbeit habe ich also überhaupt keine Probleme. Viel schwieriger ist es für meinen Mann. Er arbeitet tagsüber meistens im Erdgeschoss des Hotels, weil es dort deutlich kühler ist als in unserem Zimmer. Gleichzeitig muss er aber darauf achten, dass unsere beiden Assistenten Pipiras und Begemotas ruhig unter dem Tisch bleiben. Sie sind nämlich ziemlich neugierig. Nach einer Weile wird ihnen langweilig, und dann möchten sie am liebsten mit allen vorbeigehenden Menschen Freundschaft schließen.

Heute habe ich zusammen mit Kai noch die fehlenden Voice-over-Texte aufgenommen und anschließend ein paar Routineaufgaben erledigt. Unsere Gespräche im Büro drehen sich übrigens oft darum, meinen deutschen Wortschatz zu erweitern. Heute stand das Thema Regen und Gewitter auf dem Programm. Heutzutage ist das Lernen wirklich einfach geworden. Selbst wenn ich die Wörter falsch aufschreibe, hilft mir später mein Freund ChatGPT dabei, sie richtig einzuordnen. Also, wie gesagt, heute haben wir über die unterschiedlichsten Arten von Regen gesprochen – nieseln, tröpfeln, pladdern, schütten, gießen und vieles mehr. Natürlich auch über Gewitter und andere Wetterbegriffe – fast so, als könnten wir mit diesen Wörtern selbst ein Gewitter heraufbeschwören. Am besten gefallen mir allerdings immer die Wörter, mit denen man jemanden liebevoll beschimpfen kann. Deshalb finde ich Begriffe wie Nulpe, Flitzpiepe oder Knilch besonders nützlich. Hoffentlich habe ich sie alle richtig aufgeschrieben. Ich habe nämlich eine ganz typische Angewohnheit: Ich kenne die Wörter eigentlich, verwechsle aber ständig die Vorsilben oder Endungen. Dann sage ich zum Beispiel “anreichern” statt “erreichen”. Oder ich frage: “Was steht drin?“”, obwohl ich eigentlich “Was ist drin?” meine. Oder – noch besser – ich sage einfach “Gummi”, obwohl ich “Kaugummi” meine! 🙈 Ach ja... In meiner Dolmetschprüfung habe ich sogar einmal “Kühlung mit “Erkältung” verwechselt. Zum Glück sind meine Kolleginnen und Kollegen sehr geduldig.

Zum Schluss noch eine Beobachtung. In Berlin gibt es unglaublich viele Wildtiere! Das hat mich wirklich überrascht. Bei unseren Spaziergängen begegnen wir regelmäßig Füchsen, Dutzenden von Kaninchen und unzähligen Vögeln. Meine Freundin erzählte mir außerdem, dass hier sogar Wildschweine, Rehe und noch viele andere Tiere leben. Zuerst dachte ich, das liege einfach an unserer Wohngegend. Schließlich wohnen wir direkt am Tierpark und ziemlich am Stadtrand. Aber nein. Sogar mitten in Berlin gibt es Füchse und Kaninchen. Mindaugas erzählte mir, dass diese Füchse überhaupt keine Angst vor Menschen hätten. Als wir zum ersten Mal einem begegneten, dachten wir sogar, er sei vielleicht krank. Er stand einfach auf der anderen Straßenseite, schaute uns an und blieb völlig ruhig stehen, obwohl wir mit unseren Hunden vorbeigingen. Mit den Kaninchen ist es ganz ähnlich. Sie sitzen gemütlich im Gebüsch, beobachten, wie Pipiras und der Hund meiner Freundin völlig verrückt werden, und denken gar nicht daran wegzulaufen. Offenbar wissen sie ganz genau, dass die Hunde an der Leine sind und sie ohnehin nicht erreichen können. Heute haben wir bei einer solchen Begegnung sogar noch ein kleines Drama erlebt. Pipiras ist an solche Begegnungen noch überhaupt nicht gewöhnt. Aber ehrlich gesagt gilt das auch für den Hund meiner Freundin. Deshalb wäre es hier viel zu gefährlich, Hunde frei laufen zu lassen. Man weiß schließlich nie, wann sie plötzlich den Jagdinstinkt entdecken.

1. Anne of Green Gables by L.M. Montgomery, 321p: I had fun reading this book. I've heard of it before. I knew it was a classic from Canadian literature. I had the chance to visit Prince Edward Island, so I decided that the trip was the perfect moment to read this book. The protagonist, Anne, is the joy of the book. She is always looking at the bright side of things, she inspires courage and joy. And she is such a relentless creative soul. I loved her vivid imagination and her curiosity. Overall, I was glad I spent some time with Anne and her friends, it was a comforting read that brought the landscapes of Prince Edward Island to life right before my eyes.

2. Shady Hollow (A Shady Hollow Mystery #1 ) by Juneau Black, 208p: I enjoyed most of this book, I thought it was cozy and interesting at the beginning. I began to lose interest past halfway through because the resolution to the mystery seemed very obvious to me. It's cute, but at some moments I had difficulty suspending my disbelief with the anthropomorphic animals. It didn't grab me enough for me to continue the series.

3. The Long Way to a Small, Angry Planet (Wayfarers #1) by Becky Chambers, 404p: I re-read this one for my local Book Club. I first read it back in 2017, and I remember I was far too harsh on this book. This time around I enjoyed it more because I am in a place right now where I can appreciate cozy, lower stakes stories. It is really low stakes, there are some tense moments, but conflicts are easily resolved, and you get back to just hanging out with this found family spaceship crew. It reminded me a lot of The Expanse series, but without the whole complex world building and political shenanigans. I could not stop visualizing the Wayfarer’s captain, Ashby, as James Holden. The book's positive points still hold up beautifully: diverse characters representing different sentient species with all types of biologies and cultures, interesting discussions on different types of relationships, and exploration of Artificial Intelligence rights and sentience. It actually works well as a comforting, character-driven space opera.

#readinglist #books #reading

As you can imagine, today has served as a recovery day. I slept in because we got in really late, and I don’t have a job to get to. In a way, this was a bit of a pro at least in this situation. I don’t have any plans so I will simply take it easy. Once I am better rested and recovered, I can get back to making a game plan for whatever comes next.

I continue to check for updates on my situation, but there have been absolutely no changes. I am getting anxious that this will last longer than I originally planned, however it remains outside of my control.

Thank you for your message. I am currently out of office with no set return date. I will get back to you when the time is right.

This heat wave is awful. Our Airbnb’s AC can’t keep up and we are suffering through it. It is our last day here but we are not heading back home until very late in the night. We have a concert to attend this evening! Our group split up into two for the afternoon and it was so fun, some of us wanted to do different things, so we figured it would be best to split up and reconvene before the concert.

Packing up and getting ready was a bit stressful but I am glad we got through it and in time to make it to the venue early. My parents watched the youngest and my dog while the rest of us went to the concert. It was an incredible night and one that I won’t forget. I wish our trip was a bit extended, but I am glad we are heading back tonight before my dog gets any worse. In a way, it was the perfect trip with her and I would not change a thing.

Thank you for your message. I am currently out of office with no set return date. I will get back to you when the time is right.

Today was a super fun day. It feels so odd to be out enjoying myself, while simultaneously obsessively checking in on her through my pet camera and wondering if today will be the day. We had an aquarium visit, pizza dinner and a special shopping trip. There is a crazy heat wave right now, so we tried finding indoor activities. It was super a packed day and my dog kept strong through it all. I am thankful for special moments like these with my family and with her.

Thank you for your message. I am currently out of office with no set return date. I will get back to you when the time is right.

We arrived at our Airbnb super late in the night, so we all slept in a bit and took it easy. My dog did great on the ride and seems to be doing just fine. I looked up some emergency vet locations near us just in case, but I really hope she makes it back home with us. We are only here for a few days and we brought all her favorite things to keep her comfortable.

I was able to meet up with a friend for a few hours before catching up with my family for dinner. It was a short day due to the late night driving, but we have a better plan for tomorrow.

Thank you for your message. I am currently out of office with no set return date. I will get back to you when the time is right.

For sci-fi adventure fans, Novelette 3 (19,700 words) of The Package trilogy series is finally published. It’s $3 for both EPUB and PDF versions on Gumroad.

Click on the Gumroad link here: https://ernestortizwritesnow.gumroad.com/l/thepackagesovereign

The Package (Novelette 1) is also available. Click on the Gumroad link here: https://ernestortizwritesnow.gumroad.com/l/thepackageone

The Package: Foul Run (Novelette 2) is also available. Click on the Gumroad link here: https://ernestortizwritesnow.gumroad.com/l/thepackagefoulrun

I’m currently making the paperback version. All three stories will be on one single book. I will keep you updated.

Let me know what you think. Thank you for your support!

#adventure #gumroad #epub #novelette #PDF #sciencefiction #scifi

Game 3 of 3.

This afternoon's MLB Game once again has the Texas Rangers playing the Cleveland Guardians. This is the third in a set of three games these two teams are playing against each other. The Rangers won the first game by a score of 6 to 3, and the second game by a score of 4 to 2. Today's game is scheduled to start at 12:10 PM CDT. As I usually do, I'll follow the game's score and stats in real time via MLB's Gameday Service where I'll also find a link to the radio-call of the game.

And the adventure continues.

Photo by: Ailbhe Flynn via Unsplash

I was into photography many years ago (like over 40 years ago), and in all this time there have been several significant changes. The single biggest is the change from analog film based photography to digital photography. This one change has completely changed aspects of photography that cannot be understated.

For example, photographers today will never know the joys and horrors of working in a darkroom. Dealing with all the chemicals for developing film and prints. Having to heat up or cool down the developer, and having to be cautious with the timing involved in developing film or prints.

But, while switching to digital tools for “developing” and editing photographs will take some getting used to, that's not the part I want to focus on here. There is something more fundamental in the process of actually taking photographs that has changed. It's something I feel is overlooked if you have experience using film.

This is the ISO setting on the camera. It's easy to understand what this is in digital photography: you are defining / setting the light sensitivity of the sensor in the camera. It's a great feature that leaves the photographer with a lot of flexibility.

But, this quite different from old school film-based photography. Back in those days light sensitivity was defined by physical properties of the film being used. Typically, your camera was locked to the ISO of the film that you were shooting on. Yes, you could change the ISO setting, but then you were choosing to deliberately overexpose or underexpose your film.

There were exceptions in which this was desirable. But, you also had to remember that if you overexposed or underexposed the film you would likely need to adjust your development process to account for it.

Being able to adjust the ISO of the sensor has knock-on effects, since ISO sensitivity is inextricably linked to the range of shutter speeds and aperture settings the photography can select. And this can have other effects, like changing the depth of field.

Digital photography changes the rules in this way. You aren't locked into the physical properties of film anymore. You are, in fact, far more likely to change your ISO based on lighting conditions now than you were in the past. And this takes getting used to, and it changes the way you think about taking a photograph in fundamental ways.

This can make it more interesting in terms of being able to account for other things like aperture, shutter speed, and depth of field. Unlocking the ISO setting has fundamentally changed the way photographers think about the relationship between light and their camera.

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Categories: #Features #Tags: #photography, #technology, #education, #learning

Med en hverdag i større og større grad preget av AI, blir behovet for kreativ utfoldelse større.

Følelsen av kreativitet er absolutt til stede, men med AI går den på høygir, og om man ikke stadig får påfyll av den gode kreative flyten i det samme høye tempoet kjenner man fort på at noe mangler. At ting går for sent, at man blir sulteforet. Har man først drukket fra kreativitetens springflod blir man fort uttørket når den reduseres til en liten piplende bekk, om enn bare for en periode.

Jeg har den siste tiden kjent på at jeg har et større behov for å søke andre måter å få kreativ utfoldelse på, i et mer nøkternt tempo. Et tempo som ikke står i fare for å trene refleksene mine til å forvente samme kreative motorvei som AI legger opp til.

Og dette er egentlig positivt, tror jeg. Det tvinger meg til å se på nye veier, nye ting å ta meg til, som kan fylle tomrommet etterlatt etter at trafikken på AI-motorveien har passert, frem til neste rush tid. Jeg kan finne mye nytt å nyte den stillheten med i mellomtiden.

Akkurat derfor spår jeg at mer fysiske, kreative hobbyer og håndverk vil få en ny renessanse når AI-hverdagen stabiliserer seg. Folk vil både ha tid, men også et sterkere behov for å utfolde seg kreativt der jobben kanskje tidligere gav mer enn nok.

Kan det kanskje til og med føre til et løft for kunst og kultur, heller enn at AI skal være dens dommedag?