In January 2026, Kristalina Georgieva, the Managing Director of the International Monetary Fund, stood before an audience at the World Economic Forum in Davos and offered a statistic that landed with the quiet brutality of a footnote in a corporate restructuring memo. The number of translators and interpreters at the IMF, she said, had dropped from 200 to 50. The cause was not a budget crisis or a policy realignment. It was technology. The fund had simply decided that machines could handle most of the work that humans used to do.

Georgieva presented the figure as evidence of a broader transformation. Forty per cent of global jobs, she argued, would be transformed or eliminated by artificial intelligence, with that figure climbing to 60 per cent in advanced economies. But it was the specificity of the translation example that stuck. This was not a hypothetical projection or an economist's forecast. It was a headcount. Real people, with real expertise in the precise rendering of financial policy across languages and cultures, had been replaced by systems that could approximate their output at a fraction of the cost.

The IMF is not alone. Across the global translation industry, now valued at an estimated 31.70 billion US dollars according to Slator's 2025 Language Industry Market Report, a similar pattern is playing out. Large language models and neural machine translation systems have not simply made human translators obsolete. They have restructured the profession from the inside, converting skilled practitioners into quality controllers for text they did not write. The question this raises is not whether AI can translate. It demonstrably can, often to a standard that passes casual inspection. The question is what happens to a profession, and to the cultural knowledge it carries, when the market decides that “good enough” is good enough.

The Numbers Behind the Quiet Collapse

A 2024 survey conducted by the United Kingdom's Society of Authors, which polled 787 of its 12,500 members, found that 36 per cent of translators had already lost work to generative AI. Forty-three per cent reported a decrease in income as a direct result of the technology. Over three-quarters, some 77 per cent, believed that generative AI would negatively affect their future earnings. Eighty-six per cent expressed concern that the use of generative AI devalues human-made creative work. These are not projections. They are reports from working professionals describing what has already happened to their livelihoods.

The income data from individual translators is more granular and more alarming. Brian Merchant, writing in his newsletter Blood in the Machine, documented cases across the profession in mid-2025. One technical translator with 15 years of experience reported earning just 8,000 euros in 2025, down from six figures in previous years. A French-English translator based in Quebec described a 60 per cent income decline in 2024, with projections suggesting an 80 per cent drop from peak earnings by the end of 2025. An Italian-English translator in Rome reported that work requests had ceased entirely for the month of June 2025, after years of working 50 to 60 hours per week. An English-Portuguese translator documented that post-editing rates had collapsed from 0.04 euros to 0.02 euros per source word, halving the already modest compensation for correcting machine output.

In the United States, Andy Benzo, president of the American Translators Association, told CNN in January 2026 that many translators were leaving the profession entirely. Benzo noted that the risks of using AI translation in “high-stakes” fields remain “humongous,” yet the exodus continues regardless. Ian Giles, chair of the Translators Association at the UK's Society of Authors, confirmed the same pattern, noting that translators were seeking retraining “because translation isn't generating the income it previously did.” The exits are not dramatic. There are no picket lines or public protests. People are simply disappearing from a profession that can no longer sustain them.

The scale of this workforce is not trivial. There are approximately 640,000 professional translators globally, and three out of four are freelancers. It is this freelance majority that has borne the brunt of the disruption, lacking the institutional protections and guaranteed workloads that might have cushioned the blow.

A study published in 2025 by Carl Benedikt Frey and Pedro Llanos-Paredes at the Oxford Martin School quantified the scale of displacement with unusual precision. Analysing variation in Google Translate adoption across 695 local labour markets in the United States, the researchers found that a one percentage point increase in the use of Google Translate corresponded to a 0.71 percentage point reduction in translator employment growth. The cumulative effect, they estimated, amounted to more than 28,000 fewer translator positions created over the period from 2010 to 2023. And that figure captures only the impact of a single, relatively crude machine translation tool that preceded the large language model era. The arrival of systems like GPT-4, Claude, and Gemini has accelerated the process enormously, because these models do not just translate. They handle idiomatic expression, register, and contextual nuance at a level that earlier statistical systems could not approach.

In July 2025, Microsoft researchers published a study examining which occupations were most exposed to generative AI capabilities. Translators and interpreters ranked first on the list, with 98 per cent of their work activities overlapping with tasks that AI systems could perform with relatively high completion rates. The study analysed 200,000 real-world conversations between users and Microsoft's Copilot system to arrive at its rankings. The researchers were careful to note that high exposure does not automatically mean elimination. But the practical effect has been unmistakable. Employers have used the availability of AI translation as justification for cutting rates, reducing headcounts, and restructuring workflows around machine output.

From Translator to Post-Editor

The restructuring of translation work follows a pattern that is becoming familiar across AI-affected professions. The human does not vanish. Instead, they are repositioned downstream in the production process, tasked with reviewing and correcting output that a machine generated in seconds. In the translation industry, this workflow is known as Machine Translation Post-Editing, or MTPE, and it has rapidly become the dominant model for commercial translation work.

According to Slator's 2025 survey of the language industry, 60 per cent of all respondents were using machine translation, with adoption reaching 80 per cent among language service providers. Among those using machine translation or large language models, between 90 and 98 per cent performed some level of post-editing on AI-generated content. Eighty-four per cent of language service integrators reported that clients had specifically requested human editing services to review AI-generated translations. The human, in other words, has not been removed from the process. But the nature of their involvement has been fundamentally altered. They are no longer creating. They are correcting.

The compensation reflects this downgrade. Post-editing rates typically fall between 50 and 70 per cent of standard translation rates, with some agencies offering as little as 25 per cent of what a full human translation would command. Industry data from 2025 indicates that MTPE work commands between 0.05 and 0.15 US dollars per word, compared with 0.15 to 0.30 dollars per word for standard human translation. One translator documented by Equal Times, an international labour news platform, described pre-translated segments paying just 30 to 50 per cent of original rates, while fully automated platforms paid up to seven times less than standard. The economic logic is straightforward. If the machine does 80 per cent of the work, the reasoning goes, then the human should be paid for only 20 per cent. What this calculation ignores is that post-editing often requires comparable time and cognitive effort to translation from scratch, because the translator must not only identify errors but also understand the systematic patterns of how the AI fails and where its confidence is misplaced.

The workflow itself has been transformed in ways that strip autonomy from the translator. Texts no longer arrive as clean source documents to be rendered thoughtfully into a target language. They arrive pre-segmented, with machine-generated suggestions already populating each segment. The translator's task becomes one of triage: deciding which suggestions are acceptable, which need modification, and which must be discarded entirely. Automated platforms distribute this work via alerts that give translators minutes or even seconds to claim individual segments, creating a piecework dynamic more reminiscent of a fulfilment warehouse than a skilled profession. Some platforms threaten automatic disconnection for translators who dispute corrections imposed by quality-assurance algorithms.

Jean-Jacques, a 30-year veteran translator quoted by Equal Times, described the shift bluntly. “It's not really a matter of translating anymore,” he said, “but revising and correcting the segments proposed by the machine.” Another translator, identified as Alina, captured the paradox at the heart of the arrangement. “AI is both a tool and a threat,” she said. “We ourselves are teaching it how to translate, how to improve.” Each correction a post-editor makes feeds back into the training data that will make the next generation of AI translation marginally better, and the human's role marginally less essential.

This dynamic, in which skilled workers are conscripted into training their own replacements, is not unique to translation. It has appeared in content moderation, coding, and legal document review. But in translation, the irony is particularly sharp, because the expertise being extracted is precisely the kind that AI systems struggle most to develop on their own: cultural sensitivity, tonal awareness, and the ability to navigate the space between what a text says and what it means.

What Machines Cannot Feel

The case for human translation has always rested on something more than accuracy. It rests on the claim that translation is an interpretive act, a creative negotiation between two linguistic and cultural systems that requires not just knowledge but judgement. Jhumpa Lahiri, the Pulitzer Prize-winning novelist who has written extensively about translation, describes the process as “a radical act of reshaping text and self.” In her essay collection Translating Myself and Others, published by Princeton University Press in 2022, Lahiri argues that “a translator restores the meaning of a text by means of an elaborate, alchemical process that requires imagination, ingenuity, and freedom.”

This is not the language of quality assurance. It is the language of craft, of a practice that involves the translator's full intellectual and emotional engagement with a text. Emily Wilson, the first woman to translate Homer's Odyssey into English, has spoken repeatedly about the impossibility of separating linguistic from cultural knowledge in translation. The hardest part of translation, she has argued, is not understanding the original but “figuring out how to create it entirely from scratch in a totally different language and culture.” Wilson's translation of the Odyssey was widely praised precisely because it made choices that no algorithm would make: tonal decisions, rhythmic choices, and interpretive framings that reflected not just the Greek text but Wilson's own understanding of what the poem means to contemporary English-speaking readers.

Gregory Rabassa's English translation of Gabriel Garcia Marquez's One Hundred Years of Solitude is perhaps the most celebrated example of translation as creative achievement. Marquez himself reportedly said that he considered the English translation a work of art in its own right, a remarkable statement from an author about a rendering of his own novel. Edith Grossman, the acclaimed translator of both Marquez and Cervantes, described Rabassa as “the godfather of us all,” crediting him with introducing Latin American literature to the English-speaking world in a way that preserved not just meaning but spirit.

These examples belong to the domain of literary translation, which remains relatively insulated from AI disruption. Literary commissions have continued to flow to human translators, in part because publishers recognise that the qualities that make a literary translation valuable are precisely the qualities that machines lack. But the insulation is narrower than it appears. The vast majority of professional translation work is not literary. It is commercial, legal, technical, medical, and administrative. And it is in these domains that the restructuring has been most severe, not because the cultural stakes are lower, but because the market has decided they are.

Consider the translation of a medical consent form from English into Tagalog for a Filipino patient in a London hospital. The document is not literary. It will never win a prize. But the accuracy of its translation has direct consequences for a person's understanding of what is being done to their body. A machine translation might render the words correctly while missing the pragmatic force of the language: the way a particular phrasing might sound reassuring or threatening, the cultural assumptions embedded in notions of consent, the difference between informing someone and making them feel informed. These are not edge cases. They are the bread and butter of professional translation, and they are the first tasks being handed to machines.

Or consider immigration proceedings, where a mistranslation can determine whether an asylum seeker's testimony is deemed credible. The translator in that context is not merely converting words. They are mediating between legal systems, cultural frameworks of narrative and evidence, and the emotional register of a person recounting traumatic experiences. The difference between “I was afraid” and “I feared for my life” is not a matter of synonymy. It is a matter of legal consequence, and navigating it requires the kind of situated cultural judgement that no statistical model possesses.

The Hybrid Illusion

The industry's preferred narrative for this transition is “human-AI collaboration.” The framing suggests a partnership: the machine handles the heavy lifting, and the human provides the finishing touch. But the power dynamics of this arrangement are radically asymmetric. The machine sets the terms. The human adjusts.

This is not collaboration in any meaningful sense. It is supervision, and it is supervision of a peculiarly degrading kind, because the supervisor is being paid less than they would earn if they were simply doing the work themselves. The translator who once sat with a source text and crafted a target text from scratch, making hundreds of micro-decisions about register, idiom, rhythm, and cultural resonance, now sits with a machine-generated draft and decides, sentence by sentence, whether it is wrong enough to fix.

The cognitive experience of post-editing is qualitatively different from translation. Several translators have described it as more fatiguing and less satisfying than original translation work. The machine's output creates a kind of gravitational pull. Even when the translator knows a better rendering exists, the effort required to override the machine's suggestion and compose something from scratch can feel disproportionate to the compensation. Over time, this produces a phenomenon that linguists and labour researchers have begun to call “anchoring,” in which the translator's own instincts are gradually subordinated to the machine's defaults. The result is not a blend of human and machine intelligence. It is machine intelligence with a human stamp of approval.

A 2025 survey of translators found that a majority, some 66 per cent, acknowledged that MTPE can be useful but still requires substantial human intervention. Roughly half of respondents refused to offer discounts for post-editing work, arguing that the effort required is routinely underestimated by clients and agencies. Among those who did discount, the most common reduction fell between 10 and 30 per cent, far less than the 50 to 75 per cent cuts that many agencies impose unilaterally.

Rosa, a translator quoted by Equal Times, described the economic logic with characteristic directness. “Profit is the only thing that matters,” she said, “and translation has become like a commodity that they extract from us at the lowest possible price.” The commodity metaphor is precise. What was once a craft, defined by the individual translator's knowledge, taste, and cultural fluency, has been reframed as a raw material to be processed at industrial scale.

The Structural Incapacity Argument

There is a version of this story in which what is happening to translators is tragic but temporary, a painful adjustment period that will eventually stabilise as the technology matures and the market finds a new equilibrium. In this version, AI translation will continue to improve until the quality gap between machine and human output narrows to insignificance, at which point the remaining human translators will occupy a small, highly specialised niche: literary translation, diplomatic interpreting, and other domains where the stakes are too high for automation.

But this narrative assumes that the qualities human translators bring are merely a matter of degree, that machines are doing a slightly worse version of the same thing, and that incremental improvement will close the gap. There is a competing argument, advanced by translators, linguists, and cognitive scientists, that the gap is not quantitative but structural. That what human translators do when they translate with cultural sensitivity and emotional intelligence is not a more refined version of pattern matching. It is a fundamentally different cognitive operation.

A study published in Nature's Humanities and Social Sciences Communications in 2026, examining AI performance in literary autobiography translation, found that while AI models could produce grammatically correct and largely accurate translations, they consistently failed to capture the emotional texture and cultural specificity of the original texts. The researchers concluded that human translators brought interpretive capacities that were not simply absent from AI systems but categorically different in kind. AI models could identify the surface layer of meaning but failed to recognise cultural allusions and deeper emotional context, elements that are essential not just to literature but to any communication that carries weight beyond its literal content.

This distinction matters because it determines whether human translators are a temporary patch or a permanent necessity. If translation is ultimately a pattern-matching problem, then machines will eventually solve it. If it is an interpretive problem, requiring the kind of embodied cultural knowledge that comes from living inside a language and its associated worldview, then machines will not solve it, regardless of how much training data they consume. The patterns they learn are drawn from existing translations, which means they can only reproduce what human translators have already created. They cannot originate the kind of interpretive leap that makes a translation feel alive.

Poetry, with its reliance on rhythm, rhyme, and figurative language, remains a particularly formidable challenge. A machine can translate the denotative content of a poem. It cannot translate its music. It cannot decide, as Emily Wilson did with the Odyssey, that the opening word of an epic should be “Tell me” rather than “Sing to me,” and understand the cascade of interpretive consequences that follows from that single choice.

The Market Does Not Care About Craft

The structural incapacity argument, however compelling, runs into a problem that is not technological but economic. The market for translation services is not optimised for craft. It is optimised for throughput, cost reduction, and acceptable quality at scale. And by this measure, AI translation is already good enough for the vast majority of commercial applications. The Slator survey found that while 72 per cent of respondents cited accuracy concerns with machine translation and 68 per cent cited quality concerns, adoption continued to accelerate regardless. Trust grew slowly, but adoption grew fast. The concerns are real. They are also, from a procurement perspective, manageable.

This is the uncomfortable truth at the centre of the translation crisis. The question is not whether AI can match human translators in quality. It demonstrably cannot, particularly in contexts requiring cultural nuance, tonal sensitivity, or interpretive judgement. The question is whether the market values those qualities enough to pay for them. And the evidence, from rate compression to headcount reduction to the restructuring of workflows around machine output, suggests that it does not.

The AI-enabled translation services market, valued at 5.18 billion US dollars in 2025 according to Precedence Research, is projected to reach 50.69 billion by 2035, expanding at a compound annual growth rate of 25.62 per cent. These are not numbers that suggest a market hedging its bets. They describe an industry that has made a decisive bet on automation, with human involvement reduced to the minimum necessary to maintain an acceptable error rate. Software platforms already dominate the market, holding nearly 73 per cent of 2025 revenue, and they are growing faster than any other component as enterprises embed AI-driven localisation into core workflows.

The parallel to other creative and knowledge-work professions is instructive. Journalism, graphic design, customer service, and legal research have all experienced similar dynamics: AI systems that produce output of variable but often adequate quality, followed by a restructuring of human roles around review, correction, and oversight rather than creation. In each case, the same rhetorical move occurs. The technology is presented as a tool that augments human capability. In practice, it becomes a ceiling that constrains it. The human is not empowered. The human is made cheaper.

What Gets Lost When Languages Lose Their Interpreters

The consequences of this restructuring extend beyond the economic fortunes of individual translators. Languages are not neutral containers for information. They are living systems of meaning, shaped by history, geography, power, and culture. A translator who has spent decades working between English and Arabic, or Mandarin and Portuguese, or Hindi and German, carries within them a form of knowledge that is not reducible to a bilingual dictionary or a statistical model trained on parallel corpora.

The Frey and Llanos-Paredes study at Oxford Martin documented an additional finding that received less attention than the employment data but may be more consequential in the long term. Areas with robust Google Translate usage saw job postings demanding Spanish fluency grow by about 1.4 percentage points less than in other regions, with similar declines of roughly 1.3 and 0.8 percentage points for Chinese and German respectively, and measurable dampening even for Japanese and French. The adoption of machine translation, in other words, is not just replacing translators. It is reducing the perceived value of knowing another language at all.

This is a feedback loop with serious cultural implications. As machine translation becomes more capable and more widely adopted, the incentive to invest in human language skills diminishes. Fewer people pursue translation as a career. Fewer organisations invest in in-house linguistic expertise. The pool of human knowledge about how languages relate to one another, how cultural contexts shape meaning, and how texts function differently across linguistic boundaries gradually shrinks. And the AI systems that replace this knowledge are trained on the output of the very translators they displace, creating a closed loop in which the training data grows stale as the human source of fresh interpretive insight dries up.

Ian Giles, in his capacity as chair of the Translators Association, has raised precisely this concern, questioning whether “the demand for subtlety and craft from enough readers and publishers” will “save highly skilled individuals from becoming mere AI post-editors.” The word “mere” carries the weight of the entire argument. It acknowledges that the role of post-editor exists. It questions whether the role is sufficient to sustain the expertise it depends upon.

The problem is compounded by the pipeline effect. If experienced translators leave the profession and aspiring translators are deterred by collapsing incomes, the next generation of human translators simply will not exist in sufficient numbers. The craft knowledge that takes years to develop, the intuitive feel for how a sentence should land in a target language, the awareness of cultural registers that no textbook teaches, is not the kind of knowledge that can be stored in a database and retrieved on demand. It lives in people. When those people leave, it leaves with them.

The Canary and the Coal Mine

Professional translators have long occupied a peculiar position in the knowledge economy. Their work is invisible when done well. A reader who encounters a beautifully translated novel does not think about the translator. A patient who reads a clearly rendered medical document in their own language does not consider the person who bridged the linguistic gap. This invisibility made translators vulnerable long before AI arrived. It meant that their expertise could be devalued without anyone noticing, because the beneficiaries of their work rarely understood what it involved.

What is happening to translators now is therefore not just a story about one profession. It is a preview of what happens when AI is deployed not to eliminate human workers but to restructure their role in ways that extract their expertise while diminishing their authority, autonomy, and compensation. The translator who becomes a post-editor is still needed. But the nature of the need has changed. They are needed not for what they can create but for what they can catch. Not for their vision but for their vigilance.

Georgieva's statistic from Davos, those 150 translators who lost their positions at the IMF, represents one institution's calculation that the cultural and interpretive knowledge those individuals carried was worth less than the cost savings achieved by replacing them with technology. That calculation is now being replicated across every sector that relies on translation, from international law to pharmaceutical regulation to immigration services. In each case, the logic is the same. The machine produces output that is adequate for most purposes. The remaining humans clean up whatever the machine gets wrong. And the expertise that once defined the profession gradually atrophies, because there is no economic incentive to develop it and no structural pathway through which it can be transmitted to the next generation.

The question, then, is not whether AI translation will continue to improve. It will. And it is not whether human translators will survive in some form. They will, at least for a while, as post-editors and quality reviewers and specialists in the narrow domains where machine output remains unreliable. The question is whether a society that systematically devalues the ability to translate with feeling, with cultural awareness, with the full depth of human interpretive intelligence, will eventually discover that it has lost something it cannot rebuild. Not because the technology failed, but because the market decided that what translators knew was not worth preserving.

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References and Sources

1. CNN. “Meet the translation professionals losing their jobs to AI.” CNN Business, 23 January 2026. https://www.cnn.com/2026/01/23/tech/translation-language-jobs-ai-automation-intl

2. TIME. “The IMF's Kristalina Georgieva on the AI 'Tsunami' Hitting Jobs.” TIME, January 2026. https://time.com/collections/davos-2026/7339218/ai-trade-global-economy-kristalina-georgieva-imf/

3. Slator. “Five Ways AI Reshaped the Translation Industry in 2025.” Slator, 2025. https://slator.com/five-ways-ai-reshaped-translation-industry-2025/

4. Slator. “Slator 2025 Language Industry Market Report.” Slator, 2025. https://slator.com/slator-2025-language-industry-market-report/

5. Society of Authors. “SoA survey reveals a third of translators and quarter of illustrators losing work to AI.” Society of Authors, April 2024. https://europeanwriterscouncil.eu/soa-survey-uk-ai-2024/

6. Merchant, Brian. “AI Killed My Job: Translators.” Blood in the Machine, 2025. https://www.bloodinthemachine.com/p/ai-killed-my-job-translators

7. Equal Times. “Artificial intelligence, dehumanisation and precarious work: translators on the frontline of tech-induced job degradation.” Equal Times, 2025. https://www.equaltimes.org/artificial-intelligence?lang=en

8. Frey, Carl Benedikt and Llanos-Paredes, Pedro. “Lost in Translation: Artificial Intelligence and the Demand for Foreign Language Skills.” Oxford Martin School, March 2025. https://www.oxfordmartin.ox.ac.uk/publications/lost-in-translation-artificial-intelligence-and-the-demand-for-foreign-language-skills

9. CEPR. “Lost in translation: AI's impact on translators and foreign language skills.” CEPR VoxEU, 2025. https://cepr.org/voxeu/columns/lost-translation-ais-impact-translators-and-foreign-language-skills

10. Fortune. “Microsoft researchers have revealed the 40 jobs most exposed to AI.” Fortune, July 2025. https://fortune.com/article/what-are-the-jobs-most-exposed-to-ai-microsoft-research/

11. CNBC. “These 10 jobs are the least AI-safe, according to new Microsoft report.” CNBC, 5 August 2025. https://www.cnbc.com/2025/08/05/these-10-jobs-are-the-least-ai-safe-according-to-new-microsoft-report.html

12. Precedence Research. “AI Enabled Translation Services Market Size 2025 to 2035.” Precedence Research, 2025. https://www.precedenceresearch.com/ai-enabled-translation-services-market

13. Lahiri, Jhumpa. Translating Myself and Others. Princeton University Press, 2022. https://press.princeton.edu/books/hardcover/9780691231167/translating-myself-and-others

14. Princeton University. “Jhumpa Lahiri champions the writerly art of translation.” Princeton University News, 4 September 2020. https://www.princeton.edu/news/2020/09/04/jhumpa-lahiri-champions-writerly-art-translation

15. Wilson, Emily. Conversations with Tyler, Episode 63. “Emily Wilson on Translations and Language.” https://conversationswithtyler.com/episodes/emily-wilson/

16. Nature. “Exploring AI's performance in literary autobiography translation: how closely do AI models match human translation.” Humanities and Social Sciences Communications, 2026. https://www.nature.com/articles/s41599-026-06630-4

17. Washington Post. “AI is taking on live translations. But jobs and meaning are getting lost.” Washington Post, 26 September 2025. https://www.washingtonpost.com/business/2025/09/26/ai-translation-jobs/

18. The Bookseller. “A third of translators report losing work to generative AI systems, SoA survey reveals.” The Bookseller, 2024. https://www.thebookseller.com/news/a-third-of-translators-report-losing-work-to-generative-ai-systems-soa-survey-reveals

19. World Economic Forum. “Putting a figure on it: Davos 2026 in numbers.” WEF, January 2026. https://www.weforum.org/stories/2026/01/davos-2026-in-numbers/

20. GTS Translation. “The State of Machine Translation Post-Editing (MTPE) in 2025: What Translators Think.” GTS Blog, 7 April 2025. https://blog.gts-translation.com/2025/04/07/the-state-of-machine-translation-post-editing-mtpe-in-2025-what-translators-think/

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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

人の行動は、すべて外的な事象に対する反応、もしくは体調の変化など内的な要因に対する反応によって生まれるものだと考えている。 つまり、純粋な能動的な行動というものは人間には存在しない。

火事や地震が起きたとき、身体が自動的に防衛反応を示すように、あらゆる行動は何かしらの刺激に対する応答である。それらは日常の中で小さく、視覚的に分かりにくくなっているだけで、本質的にはすべて、受けたものに対するカウンターだ。

部屋が汚いから掃除をする。 お腹が空いたから食事を作る。 体が冷えたから服を着込む。 これらはすべて、能動的に見えて実際には受動的な反応である。

努力という言葉がある。 努力は能動的な行動ではなく、それができること自体が才能だ、という意見がある。 自分も概ねその意見には賛成だが、どちらかというと、行動回数というのは「事象に反応するスイッチが入る回数」だと考えている。

会社でバリバリ働いている人は、一見すると主体的に努力しているように見える。 しかし、人間の行動をすべて受けたものへの反応と捉えるなら、それは例えば、貧しい生活への危機感に対する応答とも言える。 つまり、その人が能動的に動いているのではなく、状況に対して反応しているだけと解釈できる。 では、不幸な人間だけが行動するのかというと、そうではない。 「大切な人に美味しいものを食べさせたい」とか、「愛する人が病気なら治療費を出したい」といったように、人が動く理由は無数にある。

要するに、人は「受け取った刺激の回数」に応じて行動する。 そして、その刺激に関心を持つかどうかが個性になる。

どれだけ感受性があるか。 どのような刺激に反応するか。 それに対する応答のパターンをどれだけ持っているか。 それらが人の違いを形作っている。

ここまで考えると、人を動かすには「どれだけ刺激を与えるか」という話になる。 ただし個性がある以上、何に反応するかは人それぞれであり、特定することは難しい。 だからこそ、多様な刺激を、繰り返し与えるしかない。

しかし人は経験的に「つらいことが含まれているもの」には手を出さなくなる。 そのため、自力では到達できない領域が多く存在する。

そこで他人の存在が必要になる。 人は、他人からの刺激を待っている。

ただし、他人が自分に刺激を与える明確な理由は基本的にない。 だから、それは頻繁には起こらない。

ではどうするか。 自分が他人に刺激を与えれば、結果としてそれが自分にも返ってくるのではないか。

そう考えると、他人から刺激を受けたいなら、自分が先に与えるしかないという結論に至る。 これは、自分が動くための一つの理由になる。

人間の行動がすべて外的要因への反応の連続であるならば、 その中であえて自分が他者に刺激を与えにいくという行為は、どこか矛盾を含んでいるようにも感じる。 それでも、その矛盾が結果として自分を動かす理由になるのであれば、ここまで考えた意味はあったのだと思う。

ここで一度、思考を止める。 次は「では、何を相手に与えるべきか」を考えたい。

In Summary: * Listening now to the Diamondbacks Sports Network for the Pregame Show ahead of tonight's game between the Arizona Diamondbacks and the Baltimore Orioles. I'll stay with this station for the radio call of the game. When it ends I'll wrap up the night prayers and head to bed.

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. Details of that regimen are linked to my link tree, which is linked to my profile page here.

Starting Ash Wednesday, 2026, I've added this daily prayer as part of the Prayer Crusade Preceding the 2026 SSPX Episcopal Consecrations.

Health Metrics: * bw= 233.9 lbs. * bp= 157/93 (61)

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

Diet: * 07:00 – 1 banana, coffeecake * 09:25 – snack on cheese * 11:45 – meat oaf, white bread and butter, fresh mango * 16:40 – 1 fresh apple * 17:00 – 1 dish of ice cream

Activities, Chores, etc.: * 05:00 – listen to local news talk radio * 06:30 – bank accounts activity monitored. * 07:00 – read, write, pray, follow news reports from various sources, surf the socials, nap. * 11:45 to 14:15 – watch old game shows and eat lunch at home with Sylvia * 15:30 – listening to The Jack Riccardi Show * 16:30 – listening to sports talk on ESPN 620 AM, Phoenix, AZ

Chess: * 09:40 – moved in all pending CC games

With the benefit a week off work I visited Cardiff on Thursday. At the Oxfam Books and Music shop on St. Mary Street I bought two classical albums. Spinning them later that day I took a shine to one; a slight dislike to the other. The latter was a 2-LP collection of Paul Hindemith's piano music. Aside from the 2nd piano sonata, a piece I already knew and liked, the other works on it left me cold.

The more successful purchase was the Complete Works For Harpsichord – Vol.2 (Book One, Deuxième Ordre) of François Couperin, played by Kenneth Gilbert. Though lacking the discrimination of a connoisseur, I am nevertheless quite fussy about how my Couperin is served up. Luckily I found Gilbert's renditions to my liking. Certain of the pieces' titles seem, as with many of Couperin's compositions, as if they're referring to individuals: for example 'La Flatteuse' and 'La Voluptueuse'.

I already own an LP of the Cinquiême Ordre, another of the suites from Couperin's First Book (1713) of keyboard compositions, performed by Blandine Verlet. Oddly enough it came from a visit to same Oxfam shop six years ago. I'm not sure whether Kenneth Gilbert persevered in recording all of the 27 Ordres from Couperin's four books – but it appears he did enough of them at least to fill sixteen LPs.

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In Monmouth the following day another pair of vinyl purchases, but in quite a different musical vein. With a view to expanding my funk horizons I picked up My Radio Sure Sounds Good to Me by Larry Graham and Graham Central Station and Ultra Wave by Bootsy Collins. Again, there was one I liked rather better than the other. While the Larry Graham record had its moments (especially in the closing number ‘Are You Happy?‘) it was Bootsy's album I preferred: it had me smiling throughout. Try 'It's a Musical' by way of an example track.

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On the train to Cardiff I finished Jan Neruda's Prague Tales, a set of engaging narratives from mid-19th-Century Bohemia, some of them like freshly-served slices of life, others with a hint of urban legend about them. An informative introduction by Ivan Klíma provided useful context. There's much to savour in these pieces, in which Neruda's amiable tone grates only occasionally – such as in the moments revealing the baked-in antisemitism and sexism of his milieu.

That same evening I got to the end of Olga Ravn's short novel The Wax Child. The setting is early seventeenth-century Denmark, where a noblewoman finds herself accused of witchcraft. The tragic story is related in eerily evocative prose which vividly animates the protagonist and her world. While the flavour of the book is very different to the only other novel of Ravn's I've read (The Employees), one could argue there are nevertheless some intriguing parallels between them.

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The cheese of the week has been Hafod, an idiosyncratic Welsh-made organic cheddar. They make both pasteurized and raw milk variants, of which I'm sampling the latter. It has a yielding texture with hints of sharpness and vaguely mineral-like notes over mellow, buttery underpinnings, making for a blend of flavours that lingers for a very agreeable while on the palate.

Last weekend was Cables of Resistance, a conference I've been organizing together with 20-something other people since last September. The goal was to bring all the Berlin and German movements fighting against Big Tech in the same venue for cross-pollination, strategic coordination, and simply to discover more about each other.

For me, it was a chance to do something again in Berlin, the city where I live, after two years focusing on Tech Workers Coalition Global, which is primarily an online affair. The element of grounding and relationship-building, which underlined the conference, was for me a personal and emotional need before a political one.

I was skeptical at first: not being a Leftist, the organizing groups and the target crowd felt and still feel distant in culture, language, and identity. For a long time, I felt like a guest, suppressing this sentiment, as I often do, to pursue the organization of the conference, a necessity I agreed with.

Now that it is over, I want to look back and offer some insights that speak to the historical moment we are going through.

Let's start with some math.

Originally, we were targeting 300 participants. We booked what at the time felt like an oversized venue. We sold out all the tickets in less than a week, basically doing a single post on social media. This was months before the conference.

Wait, that's just not how it works: it was the first event for us, and possibly the first of this kind in decades in Germany. It wasn't targeting the general public, but people who were already politically active. Why was it so easy?

We had to sell more tickets. We sold more tickets. More participants coming required more volunteers, and in the end, more than 200 people took shifts to help us.

Comes the day of the event, and the venue is packed. Bodies are squeezed into every hall. People lining the walls of the seminar rooms. More people show up asking to volunteer to join the event. We struggle to count who's coming in through the door. In the end, probably more than 1000 people joined us across the three days.

How many were left out? Most of my friends couldn't get a ticket, which we stopped selling because, at some point, we were afraid of endangering people. All of this with pretty much no effort to try to sell the tickets. I like to speculate that we could have sold 3000 tickets if we had made different choices.

It may sound self-congratulatory, and it is. As I said, I'm not a Leftist: I like to win, and this result is a win worth of celebration, even if just instrumental to more impactful wins. But I'm sharing these numbers because they suggest a lot more is moving than we can see. The interest in the event surprised every single person involved, including me. I believed I had a sense of the technopolitical scene: I discovered I don't.

The numbers don't add up: we counted ourselves, and we are many more than we thought. We inherited from the tech industry the sentiment of always leaving on the bleeding edge, the fetishism for the new. Like Amazon still calling itself a “startup”. The numbers don't match the narrative, hence the narrative has to change. None of this is young and new: the movement is becoming adult.

Let me talk about the Saturday workshop. Since the program felt a bit too academic for my taste, I tried to bring something else to the table. Yeah, we know big tech is bad. Now what? Knowing things doesn't change things. Let's spice things up, I thought.

Some weeks before the conference, by chance, I met Nala at a party after a long time. We danced. We talked about Rodrigo Nunes. We talked about the conference. “What's your strategy to scale up this effort after the conference is over? What's the expected outcome? Where will you funnel the people involved? What do you want to get out of it?”

I didn't know.

As I said before, the conference for me was fulfilling primarily an emotional need rather than a strategic one, and I grew comfortable with the limited clarity on long-term clarity that motivated what in the end was a first event from a heterogeneous group of organizations with very different theories of change, perspectives, and motivations to join. I was so concerned with the short-term execution that I forgot to keep the focus on the next move.

Fuck. I'm getting sloppier.

In the end, I managed to squeeze in a Strategic Mapping workshop of the anti-big-tech organizations in Germany. Nala would facilitate. The slot is not great: 7:30 PM – 9:00 PM, in parallel with the dinner and a couple of other sessions, and a live performance. It's the end of a long day of conferencing, and it's a Saturday evening in Berlin. Only the more motivated will come, but it's ok. “I guess max 20 people will show up, plan for that, Nala.”

Five minutes before the time of the workshop, there are already 30 people in the room. “Simone, close the doors and let me think how to adapt the workshop.” Nala shuts down for a couple of minutes, eyes closed. “I got it”, she says.

People keep coming in. Lesson learned: if you place a sign saying “Full” on the door at an event full of Leftists, it won't achieve any effect. More people join. In the end, there will be around 60 participants in the room. Run around, grab post-its from every room in the venue, run back.

Nala replanned the workshop on the fly and gave me a master lecture on the ineffable art of the “It is what it is.” As a 3° Dan political facilitator, I was impressed by what a 6° Dan could do. I still have a long way to go. The workshop involved different exercises that culminated in the production of a collaborative map, documenting all the relevant organizations in Germany fighting against Big Tech.

The most interesting bit is that most people didn't know most of the actors and organizations that other participants were bringing up. Neither did I, despite having done similar mapping exercises before. You can see the results in the photo. Hopefully, soon the exhaustion from the conference will fade, I will regain control of my limbs, and be able to transcribe and systematize the results.

A second important insight, which was the input for the reflection I'm writing, is that when the participants were asked which actors are building the narrative we need, very few, and underwhelming, actors came up. Solarpunk and Lunarpunk were mentioned. Then Cory Doctorow. Big up for Cory, who always promotes Tech Workers Coalition, but I don't think his shoulders are broad enough to carry this burden. Where is the equivalent of Fridays For Future or Extinction Rebellion in the fight for democratic technology? There's nothing like that. Nobody is filling that ecosystemic function.

The dust still has yet to settle after the event. We have to deal with the consequences of German political repression. We haven't had a meeting yet, but we are already thinking about what comes next. It's clear this is not going to end here.

The intensification of the psycho-digital loops makes the whole society more nervous: Cables of Resistance is but an itch that got scratched.

The shakes provoked by the acceleration of Imperial collapse leave bigger and bigger cracks in the concrete, where the tendrils of a new technology probe around, looking for attachment, nourishment, and Sunlight.

We did what we did not because it was easy, but because we thought it was easy. We are going to do it again.

今日も電車には新入社員が溢れていて、乗れるだけ人が詰め込まれている。 なぜか今日は早足のグルーヴで歩いていて、乗り換えのタイミングで次の電車に乗ろうとしたら、自分の足が速すぎて、いつも乗っている一本前の電車が目の前に到着した。

なぜそんなに早足だったのかはわからない。 視力を良くしようと思って、電車の窓から見える家の色を見ては、その色を頭の中で言葉にしていたからかもしれない。 そして、その屋根に色は、意外とすぐに出てこない、絶妙な色が多かった。

一本早い電車に乗れたと思ったが、それは鈍行で、出社時間に間に合わないことに気づく。 見たことのない駅で降りて急行に乗り換えたが、結局いつもより遅い電車になった。 車内では綿菓子みたいな匂いがして、少し気分が悪くなった。

帰りの電車も混んでいた。 やたら体の存在感が強くて硬い外国人が隣にいて、急ブレーキでよろけたときにその人の肘に当たった。 手すりにぶつかったのかと思うくらい痛かったが、その人は当たったことにも気づかず、まったく動かなかった。

夕飯は生姜焼きだった。 平日にこんなにちゃんとした食事ができるのは、ただただありがたい。本当に嬉しい。

風呂に入る。 昔から、ときどき自分が誰かに話しかけているイメージが勝手に浮かんでくる。
漫画が好きだった頃は、読んでいるだけで自分が描いているような気になっていたと、その中の自分は豪語していた。 そのあとも、自分が楽しそうに話しているのに、音だけがあって、具体的な言葉はなかった。

新品で買ったパンツが傷んでいくのが嫌で、メルカリでスラックスを買った。 中古で安くて生地の良いブラウンのスラックスは手に入るが、ブラックはなかなか見つからない。 特にタイトなものは。

明日はそれを履いていく。 この子もきっと、好きになれる形をしている。