Know your God

In shadows deep where ox-wagons creaked, A boy from Natal chased the sun’s gold gleam, From Maritz kin, through dust and rivers bleak, Came willow plates, a trekker’s dream.

A godmother, with eyes like flame, Bestowed a watch, gold-ticked from distant shore, One hundred fifty years to chant the name, American heart in every chime it bore.

From Bruyns’ hearth, ‘neath the dragon mountain’s throne, A Spode service, blue as Italy’s wave, Dishes that whispered of the winds they’ve known, Heirlooms that danced from cradle to the grave.

Dad unearthed from attic gloom, A German train, clockwork from trenches’ roar, Just after Great War’s thunder shook the tomb, It chugged through peace on iron tracks once more.

Then youth awoke on Eddy Merckx’s steed, Through Argus miles around the Cape he flew, The bicycle that crowned his burning need, With sweat and glory, victory gleaming true.

To Ireland’s cliffs, where Waterford glass sang, Champagne flutes caught the foam of emerald seas, In toasts to lovers, wild and bittersweet, They sparkled bright as captured memories.

Black Forest pines, where cuckoos carved their call, He traded coin for time in wooden wing, That clock would herald joy, or mark the fall, Of hours fleeing on relentless spring.

Margerite, with paintbrush dipped in light, Gave Dutch girl portrait, cheeks like ripened peach, Her eyes held homelands lost to endless night, A canvas bridge no ocean could impeach.

In Mexico’s sierras, under sombrero’s brim, A hat he won from bandits in the fray, It shaded quests through canyons wild and grim, A trophy bold from freedom’s fierce array.

Oh, treasures gleam, they warm the fleeting breast, Yet hearts grow cold ‘neath gold and crystal’s fire! What joy in dust, what peace in iron chest? True riches burn in heaven’s pure desire!

The years rolled on like trains without a track, Sailing through storms, through love’s unyielding gale, Amassed a hoard, yet something pulls back— An empty ache no trophy could assuage.

One thunderous night, as pain racked the body’s frame, The treasures circled ‘round the bed, Watch ticked its dirge, the cuckoo wailed the name, The bicycle stood sentinel o’erhead. The willow cracked, the Spode in shards did weep, Flutes shattered shrill, the painting tore in twain, Train derailed in silence, cold and deep, Mexican hat blew lost in howling rain.

“What worth your gold?” a voice cried, “These chains of earth that bind the wandering soul? They sparkled once, but now in darkness died— Seek light eternal, make the spirit whole!”

Kneeling in ruin, treasures turned to rust, And whispered prayers to reach beyond the stars, “Forgive my chase through glory’s hollow dust, I know Thee now— my heart forever Thine!” A peace descended, sweeter than the wine In Waterford’s lost flutes, or victory’s crown; No watch could measure love so pure, divine— Smiling: “I’m happy, come down!”

Oh, treasures fade, they warm the fleeting breast, But your own God alone the frozen heart revives! In knowing, all wanderings find rest— Eternal joy where true fulfillment thrives!

~Roald Bartholomew

Ronald Bartels | LinkedIn | Instagram

Creation

Every summer, when the highveld heat starts to shimmer above the Witwatersrand, they come. Thousands upon thousands of Belenois aurota, the brown-veined whites, rising out of the south west like a living prayer. They pour over the Magaliesberg in pale rivers, drifting across the tar roads, settling on the dirt and trees, lifting again in scattered slow-motion clouds that catch the light and turn it into something almost holy.

No one stops. Vehicles hiss past without slowing. Cyclists hammer by in their bright jerseys, eyes fixed on power meters and heart-rate straps. Hikers stride along the trails staring at their boots or their phones. The butterflies swirl around them all, brushing cheeks and handlebars like gentle ghosts, and no one looks up.

But I do.

I pull over on eddy, feet on the ground, sweat cooling on my back, and I just watch. They move without hurry, without noise, without ego. Small, fragile-looking creatures that have out-survived everything. They were here when the first hominids stumbled out of these very valleys, blinking at the same sun. They were here when the giant reptiles shook the earth, when the Karoo was a vast green forest, when the continents themselves were still deciding where to settle. Dinosaurs rose and fell; the butterflies kept flying.

We think we’re the pinnacle. The late-arriving masters. Yet here, in the Cradle of Humankind itself, these delicate beings remind me how temporary we are. They carry the memory of deep time in their wings. When the last human voice has gone silent, when our cities have crumbled back into grass, K’aalógii will still be drifting across this landscape on warm summer winds.

The Navajo knew. They placed the butterfly in the sky first. K’aalógii, before Coyote got impatient and flung the rest of the stars into chaos. A single, perfect constellation to say: this is where life begins, in quiet transformation, in fragile strength.

I stand there on the side of the road, a lone man with a bicycle, surrounded by a storm of white wings, and I feel something ancient acknowledge me. Not the other way around. They were here long before us. They will be here long after.

Time slows like it did in the beginning and I can feel creation.

Ronald Bartels | LinkedIn | Instagram

Fresh Produce Supply Chain: From Wholesale Markets to Commercial Kitchens

Fresh fruit and vegetables go through several stages before reaching a restaurant kitchen. From growers and wholesale markets to storage facilities, distributors and final delivery, every stage plays an important role in maintaining product quality and availability.

For restaurants, hotels, cafés and catering businesses, having access to fresh produce is essential. Chefs depend on consistent ingredients to prepare meals, maintain menu standards and deliver a good customer experience. However, fresh produce is perishable, which means delays, unsuitable handling and poor storage can affect its quality before it reaches the kitchen.

A well-managed fresh produce supply chain connects suppliers, wholesalers, transport providers and hospitality businesses through an organised purchasing and distribution process. Understanding how this process works can help restaurant managers make better sourcing decisions, reduce avoidable waste and maintain a more reliable supply of ingredients.

What Is a Fresh Produce Supply Chain?

A fresh produce supply chain is the network of activities involved in sourcing, handling, storing, transporting and delivering fruit and vegetables from their place of production to the final customer.

In hospitality, this process usually begins with growers or importers and continues through wholesalers, distributors and commercial buyers.

A typical fresh produce supply chain may include:

1. Growing and harvesting: Fruit and vegetables are cultivated and collected when ready.
2. Initial sorting and grading: Produce is checked for quality, size, condition and suitability.
3. Wholesale sourcing: Products are purchased, consolidated and prepared for distribution.
4. Storage and handling: Produce is kept under appropriate conditions to maintain quality.
5. Transportation: Orders are moved from suppliers or wholesale markets to distribution points and customer locations.
6. Commercial delivery: Restaurants, hotels and catering kitchens receive and inspect their orders.
7. Kitchen storage and preparation: Ingredients are stored, prepared and used according to operational requirements.

Each stage affects the condition, availability and final purchasing cost of fresh produce.

Why Fresh Produce Supply Chain Management Matters

Fresh fruit and vegetables require more attention than many non-perishable products. Their condition can change during storage and transportation, and different products have different handling requirements.

For hospitality businesses, supply chain management is therefore about more than receiving an order. It involves ensuring that the right products arrive in the right quantities, at the right time and in a suitable condition.

1. Maintaining Freshness and Product Quality

Freshness is one of the most important considerations when buying fruit and vegetables for commercial kitchens.

Poor handling, unsuitable storage temperatures, excessive delays and physical damage can reduce product quality. Leafy vegetables may lose their crispness, berries can become damaged and delicate herbs may deteriorate quickly.

A reliable supply chain considers product requirements at every stage, from harvesting and grading to packing and delivery.

Restaurant buyers should establish clear quality specifications with suppliers so that deliveries meet the kitchen's expectations.

2. Improving Supply Reliability

A restaurant cannot easily maintain its menu if essential ingredients are unavailable.

Shortages of tomatoes, potatoes, onions, salad leaves or other frequently used ingredients can disrupt food preparation and create pressure on kitchen staff.

Reliable suppliers and organised delivery schedules help businesses plan their purchases and reduce the risk of unexpected shortages.

For products that are particularly important to daily operations, having suitable alternative suppliers can provide additional security.

3. Controlling Purchasing and Distribution Costs

Fresh produce passes through several stages before reaching a commercial kitchen. Handling, storage, packaging, transportation and intermediary services can all contribute to the final price.

Restaurants should therefore look beyond the quoted product price when evaluating suppliers.

Delivery charges, minimum order quantities, product quality, usable yield and potential spoilage all influence the actual cost of purchasing.

Reviewing the complete supply arrangement helps hospitality businesses make more informed procurement decisions.

4. Reducing Product Damage and Waste

Fresh produce can be damaged by rough handling, unsuitable packaging, poor stacking or excessive storage periods.

These problems may result in rejected deliveries, reduced usable quantities and unnecessary replacement orders.

Good handling procedures help protect products throughout the supply chain. Suppliers and hospitality buyers should also communicate clearly about packaging requirements, delivery conditions and acceptable product standards.

Reducing avoidable damage can help businesses get better value from every order.

From Growers to Wholesale Markets: How Fresh Produce Is Sourced

The journey of fresh produce often starts with farmers, growers or importers.

Growers harvest products according to their maturity and intended market requirements. Some produce is sold through wholesale markets, while other products move through specialist distributors or direct supplier arrangements.

Wholesale markets play an important role because they bring together different products, suppliers and buyers in one trading environment.

For hospitality businesses, wholesale suppliers can provide access to a wider selection of fruit and vegetables without requiring individual restaurants to purchase directly from numerous growers.

The Importance of Grading and Quality Selection

Not all produce is suitable for every commercial kitchen.

A restaurant preparing premium salads may require visually consistent salad leaves, tomatoes and cucumbers. A kitchen producing soups, sauces or cooked vegetable dishes may have different size and appearance requirements.

Grading helps classify produce according to relevant quality characteristics, including:

• Product size and consistency.
• Ripeness and maturity.
• Appearance and physical condition.
• Freshness and firmness.
• Suitability for the intended use.

Clear specifications help suppliers understand what buyers require and reduce misunderstandings when orders are fulfilled.

Choosing the Right Wholesale Supplier

A suitable wholesale produce supplier should understand the needs of commercial kitchens.

Restaurant buyers should consider product range, sourcing arrangements, delivery reliability, order flexibility, communication and quality control.

A supplier that offers a wide selection of fruit, vegetables, herbs, chillies, garlic and salad products may help businesses coordinate more of their fresh produce purchasing through one relationship.

However, businesses should always assess whether the supplier can meet their actual volume, quality and delivery requirements.

Fresh Produce Handling and Storage Before Delivery

Once produce has been sourced, careful handling becomes essential.

Fruit and vegetables can deteriorate if they are exposed to unsuitable conditions or handled incorrectly. Some products are particularly sensitive to temperature changes, moisture loss and physical pressure.

Appropriate Temperature Management

Different fruits and vegetables have different storage requirements. Some leafy vegetables benefit from refrigeration, while certain tropical products can suffer chilling injury if stored too cold.

A suitable storage process should take account of the product type, maturity, intended storage period and supplier guidance.

Businesses should avoid assuming that every fresh produce item can be stored at the same temperature.

Proper Packaging and Handling

Packaging helps protect produce during storage and transportation. Suitable crates, cartons and containers can reduce crushing, contamination and physical damage.

Products should be packed and stacked according to their characteristics. Delicate berries, for example, need more protection than firm root vegetables.

Careful loading and unloading are also important because repeated handling can increase the risk of bruising and damage.

Managing Ripeness and Shelf Life

Not every fruit or vegetable should be purchased at the same stage of ripeness.

Some products may need to be ready for immediate use, while others can be purchased slightly earlier in their ripening process, depending on the kitchen's requirements and storage arrangements.

Restaurant buyers should consider expected consumption, delivery frequency and remaining shelf life when placing orders.

This helps avoid purchasing quantities that cannot be used before quality declines.

Transportation and Distribution: Getting Produce to Commercial Kitchens

Transportation is one of the most important parts of the fresh produce supply chain.

Even high-quality produce can lose value if it is transported under unsuitable conditions or delivered too late.

Suppliers and distributors need to coordinate vehicles, delivery routes, loading procedures and customer schedules.

Temperature-Controlled Transportation

Some fresh produce requires temperature-controlled transportation to help maintain quality during transit.

Vehicles should be appropriate for the products being carried, and loading arrangements should reduce unnecessary exposure to unsuitable temperatures.

It is also important to recognise that different products may have different temperature requirements. A single temperature setting is not suitable for every fruit and vegetable.

Delivery Scheduling

Restaurants often work with limited storage space and fixed kitchen preparation schedules.

Receiving produce too early can create storage pressure, while late deliveries may delay preparation and service.

Suppliers should coordinate delivery windows with the customer's operational requirements.

For example, a restaurant that prepares ingredients early in the morning may need its produce delivered before kitchen preparation begins.

Clear ordering deadlines and regular communication can make these arrangements easier to manage.

Last-Mile Delivery

The final stage of distribution involves transporting produce from the supplier or distribution facility directly to the restaurant or hotel.

This stage is particularly important because it connects the supply chain with the actual kitchen operation.

Accurate order picking, suitable packaging, careful loading and reliable arrival times all contribute to a successful delivery.

A well-coordinated last-mile process helps ensure that the products received match the quantities and specifications requested by the customer.

How Restaurants Can Manage Fresh Produce Deliveries

Hospitality businesses also have responsibilities once the produce arrives.

A structured receiving process helps identify problems early and ensures that stock records remain accurate.

Check Product Quality on Arrival

Staff should inspect deliveries for visible damage, freshness, ripeness and suitability for the intended use.

For example, salad leaves should be checked for signs of wilting or deterioration, while berries should be inspected for crushing and spoilage.

Compare Deliveries With Purchase Orders

The delivery should be checked against the original order to confirm that the correct products, pack sizes and quantities have arrived.

Missing items, incorrect substitutions and damaged products should be reported to the supplier promptly.

Store Products Correctly

Once accepted, produce should be transferred to appropriate storage areas without unnecessary delay.

Staff should follow product-specific storage guidance and maintain clean, organised refrigeration and dry-storage areas.

Maintain Stock Rotation

A suitable stock rotation system helps kitchens use older stock before newer deliveries where appropriate.

Regular stock checks also help managers identify products that need to be used quickly and adjust upcoming orders accordingly.

These simple procedures can improve inventory control and reduce avoidable produce waste.

Common Challenges in the Fresh Produce Supply Chain

Fresh produce procurement involves several challenges that hospitality businesses should consider.

Seasonal availability: Certain fruits and vegetables may become harder to source at different times of the year. Restaurants should remain flexible and discuss suitable alternatives with suppliers.

Price fluctuations: Weather conditions, harvest volumes, transport costs and market demand can influence wholesale prices. Regular price reviews help buyers understand changes.

Supply interruptions: Transport delays, crop problems and supplier shortages can affect availability. Alternative sourcing arrangements can reduce dependence on a single supply route.

Inconsistent quality: Differences in grading, ripeness and product specifications can lead to rejected deliveries or additional preparation work. Clear product requirements help address these issues.

Delivery coordination: Poor communication between buyers and suppliers can result in incorrect quantities, missed delivery windows and unnecessary urgent orders.

Addressing these challenges requires cooperation between suppliers, wholesalers, distributors and hospitality purchasing teams.

How Technology Can Improve Fresh Produce Supply Chain Management

Digital tools can help businesses organise purchasing information and improve communication across the supply chain.

Depending on the system, restaurants and suppliers may use digital platforms to manage:

• Purchase orders and delivery schedules.
• Supplier details and product specifications.
• Current prices and order histories.
• Inventory levels and replenishment requirements.
• Delivery confirmations and discrepancies.
• Purchasing expenditure and supplier performance.

For example, a restaurant manager can use purchasing records to identify which ingredients are ordered most frequently and whether delivery quantities match actual kitchen consumption.

Better information can help businesses plan orders more accurately and communicate requirements more clearly.

Technology is most useful when records are kept up to date and employees follow consistent procedures.

The Role of Fresh Produce Wholesalers in Hospitality Procurement

Fresh produce wholesalers connect commercial kitchens with a wider network of growers, importers and distribution services.

They help hospitality businesses source different types of produce, coordinate purchasing requirements and arrange regular deliveries.

For restaurants, hotels, cafés and catering businesses, working with a suitable wholesale supplier can simplify purchasing and reduce the need to manage numerous individual sourcing relationships.

When choosing a wholesale partner, businesses should assess:

• The range of fresh fruit and vegetables available.
• Product quality and grading standards.
• Sourcing transparency and product origin information.
• Delivery frequency and geographical coverage.
• Order accuracy and flexibility.
• Communication and issue-resolution procedures.

The right supplier relationship should support the kitchen's operational requirements while providing dependable access to suitable fresh produce.

How ES Connect Supports Fresh Produce Procurement

Managing fresh produce sourcing involves more than finding products at wholesale prices. Hospitality businesses need to consider supplier reliability, product specifications, delivery arrangements, quality and overall purchasing value.

A structured procurement approach can help businesses review these requirements and organise their supplier relationships more effectively.

ES Connect's fruit and vegetable wholesale sourcing for hospitality businesses is relevant to restaurants, hotels, cafés and other hospitality operators looking to manage their fresh produce purchasing.

By focusing on supplier sourcing, purchasing requirements and supply chain coordination, hospitality businesses can work towards a more reliable process for obtaining the ingredients their kitchens need.

Conclusion

The fresh produce supply chain involves several connected stages, from growing and harvesting to wholesale sourcing, handling, storage, transportation and final delivery.

Every stage can influence product quality, availability and purchasing costs. For restaurants and other hospitality businesses, understanding this process makes it easier to identify potential problems and work with suppliers more effectively.

By choosing suitable wholesale partners, establishing clear product specifications, coordinating deliveries and managing kitchen stock carefully, businesses can improve the way they purchase and use fresh fruit and vegetables.

A reliable supply chain is not simply about moving produce from one location to another. It is about ensuring that suitable ingredients reach commercial kitchens in the right condition, quantity and timeframe to support consistent hospitality operations.

Washing dried, flight over, sat at my desk in a different location carrying the seeds of a different stance, I re-read the piece I wrote above.

I have written before on Defining Sentences that device that literary critics (and AI too now it seems) love to use; to identify a phrase or a sentence to which they can point and declare “this is what the story, essay, polemic, whatever, revolves around. And I have explained my own rebellious nature and how I seek to de-focus this. Because it is one thing to be focussed in the written expression of the thought. But that does not mean that the thought itself is focussed. And that very lack of focus needs to be protected. There is frequently a reason for it. Even now I know there is another note destined to follow this on “Anchor Points”.

Never-the-less I am nothing if not an essayist and must confirm that the first person educated (if I may use the word) by my writing, is me. So today I walked back through the note on “the mimicry of uncertainty” and here is what I found:

The note is not about AI trawling my work, but about intellectual honesty and the apparent performance of it. That's why Orwell entered my head so readily, occupying my attention because he represents a competing account of what intellectual honesty looks like when confronted by disappointment.

Orwell has been much on my mind lately, not because I disagree with his commitment to honesty, but because I am increasingly persuaded that disappointment can masquerade as honesty. To lose faith in a possibility because one has witnessed its failure is not necessarily realism. It may simply be another form of injury. An essayist must remain open to what remains unresolved. As I have written elsewhere there is always value in negative results

The whole piece seems to revolve around this: It remains an unresolved ache. The whole piece appears to turn on that idea. Elsewhere I say ...to endure a life of open wound and not collapse into cynicism. That feels like one of the underlying propositions of Essayism. The cynic closes the wound. The essayist keeps it open. Not because suffering is noble, but because closure too quickly becomes blindness. The essayist remains susceptible to surprise, disappointment, contradiction, wonder and connection. There is a cost to that susceptibility, but there is also insight.

The machine cannot live with an unresolved ache. The road-layer must pave it. The formulaic essay must convert it into a takeaway. The cynic must explain it away. The essayist, by contrast, carries it forward.

That's why I suspect Orwell and the AI essay ultimately occupy the same place in my argument. Both are, in different ways, examples of the urge to escape uncertainty. One escapes through disenchantment, the other through optimisation. The essayist's task is to remain present to it.

Overall, the note reveals something rather interesting: my criticism of AI is not primarily technological. It is ontological. The machine's failure is not that it lacks information. It lacks a lived history through which one thing unexpectedly becomes another thing. It cannot stand beside a washing machine and find Leicester, Mondragon, worker ownership, Orwell and the nature of essayism already waiting there.

That, I think, is the deeper argument beginning to emerge beneath the surface of this possible essay.

So the Notebook is becoming more than the sum of its parts. (Now my brain is off exploring the quintessential. STOP! Get back to the task in hand.)

When I began, I was simply looking for a friction-free surface where I might assemble a few notes on Essayism, in public. The first reason was that, after more than a decade or two of essaying, I had finally given myself permission to describe my disposition publically, as that of an essayist. The second was that there is a peculiar accountability in sharing one's thoughts with others. I “own” them. I become much more attentive to the care with which I express them. It sharpens my performance of good faith. The words chosen better represent the thought than the sloppy scribble in a Moleskine while sitting at the back of a bus.

In the early days, I found my posts attracting three or four readers. So I did not distress myself when I returned to a note to amend it or add an addendum. It did not seem significant. Now it has become a habit. And I was about to make a significant amendment to an early post when I noticed that my posts now receive anything between 150 and 200 readers. Should the quantity of readers matter? Probably not. But the notion of repeatedly presenting an incomplete thought has nagged at me a little. So, some changes.

1: There is now a FAQ. If you have arrived here recently, you can now take a look at a few short paragraphs that I hope address the inquiries of curious minds as to what I am doing here.

2: I've created a Google search box. The menu item for it sits both in the header, alongside the FAQ, and in the footer. At the moment, the search incorporates both this site, The Essayist's Notebook, and the wider Essayism.org. At some point in the near future I may have to separate them. But the point is that, for now, you can type in a keyword, a concept, or a fragment of a title and probably find something somewhere across the two sites.

3: Amendments and Updates. This is a significant structural change, and the search option facilitates it well. Rather than add to, or silently expand, an existing note, I will create a new note that links to the original. It will flow into the wider broadcast mechanism and readers such as yourself will be alerted: there has been an update to the thought.

That's it. A bit of housekeeping that shows some courtesy to you, the reader, whom I have to thank for providing the accountability I was seeking.

It's been a while, and it's also been a recurring theme in my life recently. It seems I am very good at starting things, but then I lose interest in them after a short while. Maybe my mind is just like that: starting new things, but never finishing them.

I do want to do better here and keep writing what's on my mind. However, maybe not make it a daily thing, or try to convince myself it should be. Putting pressure on myself for things that make no sense to pressure myself for is senseless, if you think about it.

This isn't only something I am dealing with in writing, but also in a lot of other aspects of life. I pick up hobbies it seems like every single week, but then I never see them through. I lose interest almost just as fast as I gain it. Learning to play guitar, a new language, some AI coding stuff, or an idea that pops in my head. Been there, done that, a million times.

I must not be the only one dealing with this, though; I guess in today’s day and age, it’s also very easy to get constantly distracted by something new and shiny. Then, after a couple of days, something new and shiny pops up again, and the cycle continues.

Today S, T and I Planned to play the new overwatch drives. We got hard shit on every single game. We changed roles. We changed heroes everything. We ended up like two wins eight losses. S finally called it and got off to go to bed, and then T and I went on an eight game win streak. S wasn’t doing bad at all, it is honestly pure luck and we kept playing with the same people and we had really good synergy, but it’s so fucking funny to me.

An American Conference Game.

My Friday's game of choice finds the (3-4) Tulane Green Wave football team traveling to the University of North Carolina at Charlotte to play the (1-6) Charlotte 49ers in an American Conference game scheduled to start at 6:00 PM CT. I should be able to listen to at least the first half, and maybe the whole game before sleep takes me away.

And the adventure continues.

Meer en meer zijn het Graaiend in afvalbakken Ieder blikje telt.

i didnt write yesterday because i have a big surprise to tell you, well, if im being honest. ive always come up with random surprises that shock us both. but this one is rather unexpected. i’ve fixed my sleep schedule. yes. you read that correctly. my sleep schedule. fixed. im just as surprised as you are, believe me. its quite amusing when i think about it. a few days ago, i was here complaining about insomnia, then sleeping at nine in the morning and waking up somewhere around seven in the evening, generally behaving like a creature that has no business being awake during daylight. id spend the night staring at the ceiling, then spend the day sleeping through whatever unfortunate responsibilities i had. a rather impressive arrangement, really. one id been committed to for quite some time. but now? gone. fixed. usually, fixing my sleep schedule requires more effort than im willing to put into most things. but, miracles do happen. actually, now that ive written it down, im less excited about it. thats disappointing. i had this lovely little surprise sitting in my head, and now that ive spilled it all over this page, it feels rather ordinary. maybe i should have kept my mouth.. well, in that case. my hands shut. a rare much rare opportunity wasted on an audience that doesnt even pay admission. anyway, i guess thats enough writing for today. i have nothing left to report.

Sincerely, Who might ruin his sleep after this.

For as long as automation has had a sales pitch, the pitch has been the same. The machines will take the drudgery. They will fill in the forms, file the reports, chase the invoices and reconcile the spreadsheets, and in doing so they will hand back to human beings the parts of work that are interesting, creative and their own. Strategy, design, ideas. The good stuff.

A paper presented at the 2026 CHI Conference on Human Factors in Computing Systems, held in Barcelona from 13 to 17 April, suggests that the pitch may have the order of events backwards. Its title asks the question bluntly: “Are We Automating the Joy Out of Work? Designing AI to Augment Work, Not Meaning”. Its authors are Jaspreet Ranjit and Swabha Swayamdipta of the University of Southern California, Ke Zhou of Nokia Bell Labs and the University of Nottingham, and Daniele Quercia of Nokia Bell Labs and the Politecnico di Torino. Their central finding, in the words of the abstract, is this: “Worryingly, we find that tasks that workers associate with a sense of agency or happiness may be disproportionately exposed to AI.”

Not the tedious tasks. The ones people like.

The authors go further. A central risk of AI, they argue, is not mass unemployment but what they call mass demoralisation: “a loss of meaning and ownership in day-to-day work”. As models generate more of the early-stage output, they write, the visible creative steps can start to feel machine-made, while the human contribution becomes rushed, under-resourced and difficult to see.

That is a different kind of warning from the one that has dominated the debate about AI and jobs. It is not about whether people will have work. It is about what work will be like for the people who keep it. And it raises two questions that most organisations deploying AI have not yet seriously asked. What happens to a person's relationship with their craft when the parts of the job they found most fulfilling are the first to be handed to a machine, and what is left is the emotional and relational upkeep? And if the danger is demoralisation rather than displacement, who should decide which parts of a job get automated?

How the researchers mapped meaning onto ten thousand tasks

The study starts with the US Department of Labor's Occupational Information Network, known as O*NET, which breaks every American occupation into short task statements. The researchers narrowed this universe to tasks performed mainly on a computer, carried out daily or weekly, and ones that workers on the research platform Prolific were highly familiar with. That produced a representative sample of 171 tasks across 22 occupations.

They then surveyed two groups. The first was 202 workers who actually performed those tasks. Each rated their tasks against dimensions drawn from a scoping review of research on meaningful work: whether a task felt pointless or valuable, whether it called for novel ideas, gave them freedom or made them happy, and whether it built relationships or required emotional awareness or in-person contact. The second group was 197 developers who build AI systems, who were asked what traits they would design into an AI tool for each task.

To scale the analysis beyond 171 tasks, the team prompted language models to simulate the ratings, checked how closely the simulated ratings matched the human ones, and, finding reasonable agreement, generated annotations for 10,131 tasks across 512 occupations and 19 sectors. They report that only 0.87 per cent of tasks showed a large gap, of two points or more on a five-point scale, between model and human ratings, and that their main patterns held when they reran the analysis using only the human ratings.

To decide which tasks counted as “exposed” to AI, the researchers used a patent-based measure developed by Ali Akbar Septiandri, Marios Constantinides and Quercia, published in PNAS Nexus in 2024, which matched descriptions of tens of thousands of AI patents filed with the US Patent and Trademark Office against O*NET task statements. Tasks above a threshold on that measure were classed as likely to be exposed. Restricting the analysis to computer-based work left 3,179 tasks in the likely-to-be-exposed group and 2,349 in the comparison group.

The parts of the job that machines reach first

The comparison between those two groups is where the paper becomes uncomfortable. Tasks likely to be exposed to AI were more strongly associated by workers with novelty and creativity, with positive feelings and with autonomy. The researchers say directly that this “challenges the usual narrative that automation mainly targets routine tasks”. Tasks less likely to be exposed were associated with emotional awareness, in-person interaction, relationship building and social connection.

The sector picture sharpens the point. The heaviest exposure of creative, high-agency work fell in Arts, Architecture and Engineering, Computer and Mathematics, and the Life, Physical and Social Sciences. The examples the paper draws from O*NET are telling: an art director “formulating basic layout design”; an actuary “constructing probability tables for natural disasters”. These are first passes, the authors note, of the kind that models can now credibly generate, with humans refining the output afterwards. Meanwhile, in Education, Sales, Community and Social Services and Healthcare, the work that remained less exposed involved activities such as guiding students through tangled academic and personal problems, or “presenting offers while preserving relationships”.

The authors read this as a reversal of the expected trajectory. Generative systems, they observe, already draft text, suggest layouts, start campaigns, run simulations and infer likely emotional responses. In their data, these uses line up with tasks that workers describe as meaningful precisely because they reflect taste and authorship. AI is not confined to chores. It is becoming entangled with the moments where people feel they add something of themselves.

There is a second finding that matters for anyone building the tools. Workers and developers disagreed about what an AI system should be like. Workers wanted systems that were straightforward, tolerant and practical. Developers said they would design systems that were polite, strict and imaginative. The single biggest gap was between straightforwardness and politeness, and it was widest in Production, Architecture and Engineering, and the sciences, where vagueness can mean wasted materials or flawed data. “In high-risk contexts,” the authors write, “the ethical choice may well be the blunt one, and the useful thing to say may be short.” Developers, they suggest, tend to romanticise creativity as a frontier capability, while many workers in structured, routine jobs simply want a tool that spots anomalies and sticks to the rules.

Put the two findings together and the problem comes into focus. The AI that is arriving is being built to be imaginative, and it is landing on the imaginative parts of the job.

Why exposure is not the same as replacement

It is worth being precise about what the study measures. “Exposure” does not mean a task has been automated, or even that it will be. The authors define it as tasks that current or near-term AI could plausibly perform or substantially speed up. It is a statement about technological reach, not about deployment decisions inside any particular firm, and the paper separates automation, where AI performs a task end to end, from augmentation, where humans keep primary responsibility.

The researchers are also candid about their limitations. The study covers only US occupational tasks. Prolific participants tend to be more technologically literate than average, so low-wage and non-digital work may be underrepresented. O*NET task statements are short summaries that different workers may picture differently. The scaling to 10,131 tasks relies on language-model ratings, which the authors treat as an approximation of human responses rather than as ground truth, and which they found to be more uniform and optimistic in some sectors. Even the creativity finding has a wrinkle. When the analysis was rerun on human ratings of the original 171 tasks alone, humans and models agreed on six of the seven significant dimensions; the exception was “requires novel ideas or creativity”, where the authors suggest human annotators tended to underestimate the novelty of tasks.

None of this undoes the core pattern. But the study is best read as a map of where pressure is likely to fall, not a ledger of what has already been lost. What actually happens to people's work is decided later, by managers, vendors and product teams.

From mass unemployment to mass demoralisation

Why should the location of the pressure matter so much? Because meaning in work is not spread evenly across a job. It clusters.

The sociologist Richard Sennett, in his 2008 book The Craftsman, described craftsmanship as the basic human impulse to do a job well for its own sake, and argued that this impulse extends far beyond the workshop, to programmers, doctors and artists. Craft, in that sense, lives in the moments of making: the first sketch, the opening paragraph, the elegant solution to a modelling problem.

If those moments are exactly where AI exposure is concentrated, then a job can be automated in a peculiar way. The worker keeps the job title, the salary and most of the hours. What disappears is the part that made them want the job in the first place. In the paper's own examples, the basic layout and the first probability table are precisely the drafts a model can now produce; what is left to the human is refinement. This is the scenario Ranjit and her colleagues have in mind when they describe a loss of ownership. The creative steps that others can see now appear to come from the machine, while the human's contribution moves into the background, squeezed for time and harder for anyone to credit.

There is already experimental evidence that this dynamic has psychological costs. In a 2025 paper in Scientific Reports, Suqing Wu, Yukun Liu, Mengqi Ruan, Siyu Chen and Xiao-Yun Xie of the School of Management at Zhejiang University reported four online experiments with a total of 3,562 participants. Working with generative AI improved immediate performance on professional tasks. But that performance boost did not carry over when people subsequently worked alone, and the switch from AI collaboration to solo work was accompanied by significant decreases in intrinsic motivation and increases in boredom, even as participants' sense of control rose. The authors' summary is succinct: collaboration with generative AI “enhances immediate task performance but can undermine long-term psychological experiences of human workers.”

Creative workers are already reporting effects outside the laboratory. A survey by the Society of Authors, the UK trade union for writers, illustrators and translators, received 787 responses in early 2024. A quarter of illustrators, 26 per cent, and more than a third of translators, 36 per cent, said they had already lost work to generative AI. The survey was self-reported and drawn from a membership sample, so it cannot measure the market as a whole. But it captures the sense, among people whose identities are bound up with a craft, that the craft itself is being lifted out of their hands.

What the remaining work asks of people

If the creative core of a job is thinned out, what is left? According to the CHI study, the tasks least exposed to AI are those built on emotional awareness, in-person presence and relationship building. That might sound like a reassuring residue. Machines draft, humans connect. But it is worth looking at what those remaining tasks involve.

In 1983, the sociologist Arlie Russell Hochschild of the University of California, Berkeley, published The Managed Heart: Commercialization of Human Feeling, which introduced the idea of emotional labour. Studying flight attendants and bill collectors, she described public-facing jobs in which managing feelings was part of what the employer was paying for. Flight attendants had to be, in the phrase used by her publisher, “nicer than natural”; bill collectors had to be “nastier than natural”. Hochschild's concern was that this kind of work, done for commercial purposes, could estrange people from their own feelings.

Relational work is not inherently draining. For many teachers, nurses, social workers and salespeople, the relationships are the point of the job, and the CHI data suggest that workers do regard these tasks as meaningful. The danger lies in the mix. A job that once combined making things with tending to people can become one that is mostly tending to people, with the making delegated. The remaining human work then has a distinctive profile: smoothing over the AI's output with clients, absorbing complaints, reassuring colleagues, maintaining relationships that the automated parts of the workflow no longer support. That is emotional labour, and much of it is invisible in the way that a finished design or a well-built model is not.

The Stanford University researchers behind the WORKBank database, Yijia Shao, Humishka Zope, Yucheng Jiang, Jiaxin Pei, David Nguyen, Erik Brynjolfsson and Diyi Yang, found a related signal in a 2025 study of 1,500 workers across 844 tasks in 104 occupations. Their results, they wrote, offer early signals that AI agents may reshape core human competencies, “shifting from information-focused skills to interpersonal ones.” The CHI paper adds a warning that this shift may not be benign. Among its design examples is the risk that teachers, managers and clinicians who use AI for canned replies and templated feedback end up making their communication with students, staff or patients feel more generic. If AI pushes people towards relational work while also hollowing out that relational work with templates, the worker is squeezed from both sides.

When the most fulfilling parts of a job are automated first, then, a person's relationship with their craft is put at risk in two ways. They lose authorship of the work that once expressed their skill and taste. And they are left with a larger share of work whose value is real but less visible and more emotionally costly. Whether that produces demoralisation depends on what else changes, and here a very different body of evidence becomes useful.

Lessons from people who stopped working

In September 2026, four researchers at Google DeepMind, Stephanie C.Y. Chan, Adam Bales, Katherine L. Hermann and Iason Gabriel, posted a review titled “Work, Wellbeing, and Choice: Empirical Lessons for AI Futures”. Their question was not what AI does to work, but what decades of research reveal about people who live without it. They drew on psychological, sociological and economic studies of the unemployed, retirees, lottery winners, financially dependent spouses and citizens of Gulf states with guaranteed public-sector employment, covering OECD countries, China, India and the Gulf.

Their findings are stark in places. Involuntary job loss harms life satisfaction, mental health, physical health and even mortality, and the damage can last: some studies find psychological “scarring” that persists for years after people find work again. Yet the vast majority of lottery winners, between 85 and 89 per cent across studies of American and Swedish winners, keep working, typically using their windfall to buy flexibility rather than to quit. And retirees, who are also not working, often report high and sustained wellbeing.

The authors draw on the work of the social psychologist Marie Jahoda, whose 1982 book Employment and Unemployment, published by Cambridge University Press, argued that a job provides “latent” benefits alongside its “manifest” purpose of earning a living. Jahoda identified time structure, social contact, status and identity, regular activity and collective purpose. The DeepMind review finds that both income and some of these latent benefits matter, and adds cognitive health: retirement has been causally linked to declines in cognitive ability.

To explain why the unemployed and the retired fare so differently, the review identifies three factors that mediate the relationship between work status and wellbeing. The first is agency and choice: whether the exit from work was voluntary, and whether people retain agency over the long term. The second is the availability of alternative sources of work's latent benefits, such as volunteering, hobbies or state-provided employment. The third is social and systemic context, including cultural norms around work and the strength of safety nets.

The evidence on each is striking. Between 20 and 30 per cent of retirees perceive their retirement as forced, and across countries those who retired voluntarily report significantly better physical and mental wellbeing than those who did not, even after controlling for health, income and other factors. Volunteering confers benefits at remarkably low doses, with gains for older adults observable from about one hour a week. One study cited in the review found that plant closures produced an almost four-fold greater increase in depression among American workers than among European workers, who have stronger safety nets. And the psychological cost of losing work tends to fall harder on men, which the authors attribute to stronger social pressure to conform to norms about working.

“Many policy discussions about AI futures focus on the availability of work or the availability of resources,” the authors write. “However, these discussions overlook the fact that it matters deeply whether people get to choose their path.” Their summary of the policy lesson is equally direct: purely material interventions such as universal basic income “are valuable but highly insufficient”.

Applying the exit evidence inside the job

The DeepMind review is about people who leave work altogether. But its three factors translate remarkably well to what happens when people stay in work and lose part of it. Demoralisation, in the CHI paper's sense, is a kind of partial exit: a withdrawal not from employment but from the meaningful core of a role. Read that way, the review's factors become a diagnostic for when task automation is likely to hurt.

Start with agency. A critical determinant of wellbeing in the review is not whether people work, but whether they chose their situation. Inside a job, the equivalent question is whether workers chose which of their tasks were handed to AI. The Society of Authors survey hints at the distinction: alongside those who chose to use generative AI, 5 per cent of illustrators and 8 per cent of translators said they had used it because their publisher or commissioning organisation asked them to. The CHI paper notes that, in practice, managers, vendors and technical teams, not workers, usually decide which tasks are exposed to AI and how far automation goes. “Managers, vendors, and technical teams make these decisions before workers can consent or take part, especially in roles with low bargaining power,” the authors write. That is the workplace version of involuntary exit.

Next, alternatives. Retirees do well when they find other sources of structure, status and purpose. Workers who lose the creative core of their job need somewhere else for those things to come from: new responsibilities with real authorship, protected time for the work they value, recognition for the judgement involved in steering and correcting AI output. If the time freed by automation is simply filled with more volume, the latent benefits drain away without replacement.

Finally, context and safety nets. The review shows that the same loss lands very differently depending on norms and institutional support. Inside an organisation, the analogue is culture and protection: whether people who push back on a deployment are heard or penalised, whether those whose roles shrink are offered retraining or redeployment, and whether the organisation measures anything other than throughput.

This is an analogy, not a finding. Neither paper tested it, and evidence about the unemployed cannot be transplanted wholesale onto people still in work. But the convergence is hard to ignore. Working from inside jobs, one paper finds that agency, ownership and meaning matter; working from outside, the other finds that choice, alternatives and context matter. Both suggest the damage depends less on what is taken away than on how.

Workers already say what they want automated

The good news is that workers are not, in general, opposed to automation. They are opposed to the wrong automation.

The WORKBank study asked its 1,500 workers which of their tasks they would like AI agents to take on. For 46.1 per cent of tasks, workers expressed positive attitudes towards automation, even after being prompted to reflect on job loss and on how much they enjoyed the work. Their main motivation was to free up time for higher-value work. The researchers then compared those desires with experts' assessments of what AI could currently do, sorting tasks into four zones: a “Green Light” zone where workers wanted automation and the technology could deliver it; a “Red Light” zone where the technology was capable but workers did not want it; an R&D opportunity zone where workers wanted help that the technology could not yet provide; and a low-priority zone.

Their most pointed finding concerned where the money was going. Mapping the tasks targeted by Y Combinator start-ups, they found that 41.0 per cent of company-task mappings were concentrated in the Low Priority and “Red Light” zones. A large share of the industry's effort, in other words, was aimed at tasks workers either did not care about or actively wanted to keep.

Combined with the CHI paper's developer-worker mismatch, the pattern is clear. Workers do not resist AI. Their preferences are simply rarely the input that shapes what gets built.

A test for choosing which task clusters to automate

If the risk is demoralisation, then the usual test for automation, which asks whether a task can be done faster or cheaper by a machine, is incomplete. It needs a second question: what does this task mean to the people who do it? Drawing on the two papers and the evidence around them, an organisation could reasonably weigh at least four considerations before automating a cluster of tasks.

The first is meaning. Ask the people who do the work which tasks they find meaningful, enjoyable or expressive of their skill, and which they find pointless. The CHI study shows that this can be measured at scale with straightforward survey instruments. WORKBank shows that workers can distinguish clearly between tasks they want help with and tasks they want to keep. Tasks that workers rate low on meaning and high on tedium are the obvious first candidates. Tasks rated high on creativity, autonomy and happiness deserve a heavier burden of justification.

The second is order. The CHI authors propose that, for creative work, the sequence matters as much as the division of labour. Their design heuristics include starting creative fields empty, so that a human sketch, prompt or outline exists before AI suggestions appear; offering several alternatives rather than a single finished replacement; letting workers accept or reject AI content sentence by sentence rather than through a single “replace all”; and allowing a worker to tick a box marked “I want to do this part myself”. The question for an organisation is not simply whether AI touches a creative task, but whether the human still makes the first move.

The third is what replaces the time saved. The DeepMind review's evidence on alternative sources of meaning suggests that automation which frees time is only beneficial if that time goes somewhere that sustains structure, status and purpose. For relational work, the CHI paper suggests using AI to prepare briefs and summaries so the human arrives better informed, assigning it to silent roles such as note-taking during live interactions, and reserving protected time for direct human engagement. Its proposed measures of success are telling: workers reporting that AI helps them “show up prepared” rather than “speak for me”, and that it does not “take away the good parts” of the job.

The fourth is measurement. The CHI authors recommend tracking, during deployment, whether workers still feel in control and whether the output “still feels like my work”, alongside conventional measures of speed and quality. They also suggest logging the level of assistance AI provides, from suggestion to draft to execution, and recording who made the final decision. Over time, they argue, workers' self-reported skills and confidence in their core craft should remain stable or improve. If those indicators fall while productivity rises, the organisation has learned something its dashboards would otherwise hide.

None of this means creative tasks should never be automated. Some workers will welcome help with the very tasks others prize, and the CHI data show wide variation between sectors. The point is that meaning should be treated as a real cost in the decision, not as a sentimental afterthought.

Giving workers a binding say

That leaves the second half of the question: what voice should workers have? The CHI paper's answer is explicit. Beyond designing interfaces that let individuals retain control within a task, the authors argue that organisations should give workers some say over which task clusters are candidates for automation. Without such mechanisms, they warn, “AI exposure risks deepening existing power imbalances.” They point to remedies from research on algorithmic management: direct involvement of workers and their representatives through formal consultation and collective bargaining over AI deployments, worker or union technology representatives with access to information about how systems allocate and evaluate work, and transparency tools that help workers understand and contest automated decisions.

There are working precedents. The most prominent came from Hollywood. The 2023 Minimum Basic Agreement between the Writers Guild of America and the major studios established that AI cannot be considered a writer and that material it produces cannot be treated as literary material. Under its terms, a writer may choose to use AI with the company's consent, but the guild's own summary states plainly that “the company can't require the writer to use AI software (e.g., ChatGPT) when performing writing services.” Companies must disclose when material given to a writer was generated by AI. In effect, the writers bargained over which parts of their craft would remain theirs. That agreement has since been succeeded by the 2026 contract, ratified on 24 April 2026 with 90.38% of votes in favour and running to 1 May 2030, which “preserves all of the protections negotiated in 2023”. It adds one more: companies must give the guild written notice when they license writers' work to train a commercial generative AI system, after which the guild can request a discussion about the licence, including any payment to writers. It does not establish payment for training.

European law offers a thinner but broader floor. Article 26(7) of the EU Artificial Intelligence Act, adopted in 2024, requires that, before a high-risk AI system is put into service or used at the workplace, “deployers who are employers shall inform workers' representatives and the affected workers” that they will be subject to it. That is a duty to inform, not to consult or obtain consent, and it applies only to systems classed as high-risk. It is also not yet in force for most workplace tools: the Digital Omnibus on AI, Regulation (EU) 2026/1744, which took effect on 27 July 2026, deferred the high-risk obligations for Annex III systems, a category that includes employment and worker management, from 2 August 2026 to 2 December 2027. In Germany, works councils have long held co-determination rights over the introduction of technical systems capable of monitoring employees' behaviour or performance, a provision that lawyers widely expect to cover many AI tools. The limits are visible too: in January 2024, the Hamburg Labour Court rejected a works council's application concerning employees' use of ChatGPT, partly because staff used it voluntarily through private accounts in a web browser rather than as an employer-installed system.

These mechanisms share a weakness when it comes to demoralisation. Most were designed to address surveillance, pay or credit, not meaning. The EU provision is triggered by risk classification, not by what a task means to the person who does it. The German right is anchored in monitoring. Even the WGA's protections, including the 2026 notice requirement on training licences, focus on credit and compensation, although by keeping AI out of the definition of a writer, they also protect authorship. A workplace that took the CHI findings seriously would go further: it would give workers a formal role in deciding which task clusters are automated, informed by evidence about which tasks they find meaningful, with a right to revisit the decision once the effects are known.

That voice matters most precisely where it is weakest. The CHI authors note that the decisions about exposure are made before workers can consent, especially in roles with little bargaining power. The DeepMind review cites evidence that, where safety nets are weak, pre-existing wealth becomes a key buffer against the shock of job loss. A model of AI deployment that consults only those with strong unions would protect screenwriters and leave behind most of the people whose tasks appear in the CHI data.

What a craft needs to survive

It is tempting to treat all of this as a soft concern, a matter of morale to be fixed with a workshop. The evidence suggests otherwise. Meaning is not a perk attached to work; it is part of what makes people willing to invest skill and care, and part of what keeps them well. The DeepMind review shows how much is at stake when people lose work altogether. The CHI paper suggests a quieter version of that loss may already be underway inside jobs that look perfectly secure.

The two papers point to the same remedy from opposite directions. What protects people is not the absence of change but the ability to shape it: to choose, to find meaning somewhere else when the old sources move, and to be supported while they do. For organisations, that means treating which tasks to automate as a decision with human costs, made with the people who bear them, rather than a technical question settled by whatever a vendor's model can do.

Ranjit and her co-authors end on a note of possibility. The outcome they describe, they stress, is shaped by design choices, which can in principle be revised: systems could speed up exploration and drafting while people keep authorship, make the final judgements and sustain the relationships that make work meaningful. That is a more humane division of labour than the current pitch implies. But it will not arrive by default. Left to the logic of capability alone, AI will keep flowing towards the tasks it can reach, and those turn out to be the ones people love. Keeping the spark in work will take a deliberate decision about who gets to say which parts of a craft stay human.

References and Sources

1. Ranjit, Jaspreet; Zhou, Ke; Swayamdipta, Swabha; Quercia, Daniele. “Are We Automating the Joy Out of Work? Designing AI to Augment Work, Not Meaning.” Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI '26), Barcelona, Spain, 13-17 April 2026. DOI: 10.1145/3772318.3791845. Preprint arXiv:2603.14963, 16 March 2026. https://arxiv.org/abs/2603.14963 (full text: https://arxiv.org/html/2603.14963)
2. Chan, Stephanie C.Y.; Bales, Adam; Hermann, Katherine L.; Gabriel, Iason. “Work, Wellbeing, and Choice: Empirical Lessons for AI Futures.” Google DeepMind. arXiv:2609.11019, 10 September 2026. https://arxiv.org/abs/2609.11019 (full text: https://arxiv.org/html/2609.11019)
3. Septiandri, Ali Akbar; Constantinides, Marios; Quercia, Daniele. “The potential impact of AI innovations on US occupations.” PNAS Nexus, 3(9), pgae320, September 2024. DOI: 10.1093/pnasnexus/pgae320. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11421150/
4. Wu, Suqing; Liu, Yukun; Ruan, Mengqi; Chen, Siyu; Xie, Xiao-Yun. “Human-generative AI collaboration enhances task performance but undermines human's intrinsic motivation.” Scientific Reports, 15, article 15105, 29 April 2025. DOI: 10.1038/s41598-025-98385-2. https://www.nature.com/articles/s41598-025-98385-2
5. Shao, Yijia; Zope, Humishka; Jiang, Yucheng; Pei, Jiaxin; Nguyen, David; Brynjolfsson, Erik; Yang, Diyi. “Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce.” arXiv:2506.06576, 6 June 2025 (revised 1 February 2026). https://arxiv.org/abs/2506.06576
6. Hochschild, Arlie Russell. The Managed Heart: Commercialization of Human Feeling. University of California Press, 1983 (third edition 2012). https://www.ucpress.edu/books/the-managed-heart/paper
7. Jahoda, Marie. Employment and Unemployment: A Social-Psychological Analysis. Cambridge University Press, 1982. ISBN 978-0-521-28586-5. https://ideas.repec.org/b/cup/cbooks/9780521285865.html
8. Sennett, Richard. The Craftsman. Yale University Press, 2008. https://yalebooks.yale.edu/book/9780300151190/the-craftsman/
9. The Bookseller. “A third of translators report losing work to generative AI systems, SoA survey reveals.” April 2024. https://thebookseller.com/news/a-third-of-translators-report-losing-work-to-generative-ai-systems-soa-survey-reveals
10. European Writers' Council. “SoA survey UK AI 2024.” April 2024. https://europeanwriterscouncil.eu/soa-survey-uk-ai-2024/
11. Books+Publishing. “A third of translators report losing work due to AI.” 15 April 2024. https://www.booksandpublishing.com.au/articles/2024/04/15/250317/a-third-of-translators-report-losing-work-due-to-ai/
12. Writers Guild of America West. “Know Your Rights: Artificial Intelligence.” https://www.wga.org/contracts/know-your-rights/artificial-intelligence
13. Writers Guild of America. “Summary of the 2023 WGA MBA.” https://wgacontract2023.org/the-campaign/summary-of-the-2023-wga-mba
14. Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act), Article 26: Obligations of deployers of high-risk AI systems. https://artificialintelligenceact.eu/article/26/ (Official Journal: https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng)
15. Littler Mendelson. “Germany: Does the Works Council Have a Say When Employees Use ChatGPT? No, According to the Labor Court Hamburg.” 2024. https://www.littler.com/publication-press/publication/germany-does-works-council-have-say-when-employees-use-chatgpt-no
16. Bird & Bird. “First Judgement on the Rights of Works Councils when Employees use AI Systems.” 2024. https://cm.twobirds.com/en/insights/2024/germany/erstes-urteil-zu-rechten-des-betriebsrats-bei-einsatz-von-kuenstlicher-intelligenz
17. Writers Guild of America. “WGA Ratifies 2026 Minimum Basic Agreement With AMPTP.” 24 April 2026. https://www.wga.org/news-events/news/press/wga-ratifies-2026-minimum-basic-agreement-with-amptp
18. Writers Guild of America. “Summary of the 2026 WGA MBA.” https://www.wga.org/contracts/contracts/mba/summary-of-the-2026-wga-mba
19. Regulation (EU) 2026/1744 amending Regulation (EU) 2024/1689 (Digital Omnibus on AI), in force 27 July 2026. https://artificialintelligenceact.eu/ai-act-explorer/digital-omnibus/

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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

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