Typical Day in the Life of an Average American- Part VI, Deepseek Edition

Deepseek has this for us:

“6:32 AM – The alarm sounds from a smartphone on the nightstand. He silences it with a thumb swipe, blinks at the ceiling, and checks the lock screen: weather (72°F, partly cloudy), three emails (two work, one retail promo), and a text from his sister. He sits up, swings feet to the carpet, and drinks a glass of tap water from the bathroom sink. He brushes his teeth with fluoride paste, flosses, and showers—six minutes, shampoo and body wash, water shut off while soaping.

7:05 AM – In the kitchen, he pours 16 ounces of black coffee from a programmable drip machine into a ceramic mug. He toasts two slices of whole-wheat bread, spreads peanut butter on both, and eats standing at the counter while scrolling a news aggregator. He packs a lunch: leftover grilled chicken, brown rice, and broccoli into a plastic container, adds an apple and a granola bar into a reusable grocery bag.

7:30 AM – He puts on jeans, a cotton polo shirt, and sneakers. He transfers wallet (ID, two credit cards, $40 cash), keys, and work badge from a bedside table to his pockets. He locks the front door of his single-family home, starts his 2018 sedan, and backs out of the driveway.

7:52 AM – He merges onto a four-lane suburban arterial. Traffic slows to 15 mph for 1.2 miles due to a school zone and a merging ramp. He listens to a talk-radio segment on local property taxes. At 8:17, he pulls into the asphalt lot of an office park, parks in Row C, and walks 200 yards to a three-story glass-front building.

8:22 AM – He swipes his badge at a turnstile, takes the stairs to the second floor, and sits at a cubicle with dual monitors. He logs into a Windows desktop, opens Outlook, and reads 14 new messages. He replies to three, deletes five, and flags six for later. He opens a spreadsheet and updates inventory figures from an overnight warehouse report.

9:45 AM – He joins a 12-person conference call via headset. The discussion covers shipping delays, vendor pricing, and Q3 projections. He speaks twice—once to confirm a delivery date, once to ask about a billing discrepancy. He takes handwritten notes on a legal pad.

11:10 AM – He walks to the breakroom, refills his coffee mug, and eats the granola bar. He chats with a colleague about weekend plans (lawn mowing, a nephew’s birthday party) for four minutes. He returns to his desk and drafts a three-paragraph email to a supplier, attaching a PDF of corrected purchase orders.

12:30 PM – He eats his packed lunch at his cubicle while watching a 10-minute YouTube tutorial on Excel pivot tables. He takes a 15-minute walk around the office park’s perimeter sidewalk, passing two other walkers and a delivery truck. He returns, checks personal email on his phone, and pays a $78 utility bill via banking app.

1:15 PM – He resumes work: a 45-minute video call with a remote team member in Chicago, screen-sharing a project timeline. He edits a shared document, adds three action items, and assigns deadlines. He prints a 6-page contract, reads it, and initiales two changes with a pen.

3:00 PM – He drives 8 minutes to a FedEx store, drops off a return package (pre-labeled), and stops at a gas station—pumps 11.2 gallons of regular unleaded, pays at the pump with a credit card. He returns to the office and spends 40 minutes entering expense receipts into an online reimbursement system.

4:20 PM – He clears his desk, shuts down the computer, and walks to his car. He calls his wife via Bluetooth on the drive home; they discuss dinner (she’ll pick up pizza) and his daughter’s dentist appointment scheduled for tomorrow.

4:55 PM – He arrives home. He changes into shorts and a t-shirt, takes a garbage bag from the kitchen to the outdoor bin, and checks the mailbox—one utility bill, one credit card offer, and a neighborhood association flyer. He waters three potted plants on the back patio with a hose.

5:40 PM – His wife and daughter arrive. He helps unload groceries (milk, eggs, lettuce, tortillas) and sets the table for pizza. They eat together in the dining room, discussing school, work, and an upcoming vacation rental booking. He clears plates, loads the dishwasher, and wipes the counter with a sponge.

7:15 PM – He sits in the living room and watches a 42-minute episode of a crime drama on a streaming service, with two commercial breaks (he does not skip them). During the show, he folds a load of laundry from the dryer—three towels, five t-shirts, assorted socks.

8:10 PM – He helps his daughter with 20 minutes of math homework (fractions and decimals) at the kitchen table. He then reads a chapter of a paperback novel (historical nonfiction) on the couch while his wife watches a home renovation show on a tablet with earbuds.

9:30 PM – He takes a second, shorter shower. He lays out tomorrow’s clothes—khakis, a button-down shirt, socks—on a chair. He sets the coffee machine’s timer for 6:00 AM. He plugs his phone and smartwatch into chargers on the nightstand.

10:15 PM – He gets into bed, sets the alarm for 6:30 AM, and reads news headlines for 12 minutes on his phone. He turns off the bedside lamp, adjusts the pillow, and lies on his right side. He closes his eyes.

10:48 PM – He falls asleep.”

Interesting its always a guy. Even though women make up 50.5% of the population. Anyway, this nameless man also works in an office, and also eats granola bars. Four references to his phone. No pet. Wife and daughter. The fact that this man spent his lunch break in his cubicle watching a “ 10-minute YouTube tutorial on Excel pivot tables” is absolutely soul crushing... but probably accurate. Again, I feel like these average Americans eat much better and have far higher executive functioning than a real average American. It took this guy 33 minutes to fall asleep at least, that's some sort of speed bump.

Typical Day in the Life of an Average American- Part V, Claude Edition

Here is what Claude has for us:

“Jason Kowalski's alarm goes off at 6:15 a.m. on a Tuesday in March. He is thirty-eight years old, lives in a three-bedroom rental house on the outskirts of Columbus, Ohio, with his wife Amanda and their two children, ages seven and eleven. He silences the alarm on his phone, checks it for eleven minutes before getting up — two texts from his sister, a work email he doesn't open yet, a headline about gas prices.

At 6:30 he showers. By 6:50 he's in the kitchen making coffee in a drip machine while Amanda gets the kids moving. He eats a bowl of cereal standing at the counter and packs leftover pasta into a plastic container for lunch. The kids eat toaster waffles. There's a brief argument about whether the eleven-year-old can wear shorts; she loses.

He leaves the house at 7:35, backs his 2019 Honda CR-V out of the driveway, and drives 14 miles to a distribution center where he works as a shift supervisor. The drive takes 28 minutes, mostly on the interstate. He listens to a sports radio show. He stops for gas once, filling up about two-thirds of a tank for $31.

His shift runs 8:00 a.m. to 4:30 p.m. He spends the morning checking inbound shipments against manifests, resolving a staffing gap on the loading dock by pulling someone from packaging, and sitting through a 20-minute stand-up meeting. At 10:30 he takes a break, eats a granola bar, and scrolls his phone for six minutes. Lunch is at 12:15 in a break room — the pasta from home, eaten in about fifteen minutes while he watches a video on his phone. The rest of the afternoon involves more of the same: two minor equipment issues, a conversation with a vendor on the phone, paperwork.

He clocks out at 4:32 and drives home, this time in slightly heavier traffic — the trip takes 35 minutes. He picks up his son from an after-school program on the way, a five-minute detour.

At home by 5:20, he changes clothes and helps his son with a math worksheet while Amanda, who works part-time and got home earlier, starts dinner — tacos. They eat together at 6:15, the four of them at the kitchen table, though the daughter is reading a book propped against a cereal box. Conversation covers a school project due Friday and a coworker's video Jason wants to show Amanda but decides to save for later.

After dinner, Jason clears the table while Amanda helps with homework. At 7:00 the kids get thirty minutes of tablet time. Jason pays two bills online — an electric bill and a co-pay from a doctor's visit — and replies to the work email from that morning. At 7:45 he and Amanda have the kids start getting ready for bed: brushing teeth, laying out clothes for tomorrow.

The kids are in bed with lights out by 8:30, though the eleven-year-old reads with a flashlight for another ten minutes before her mother notices. Jason and Amanda watch two episodes of a television show on a streaming service, sharing a bowl of pretzels. Around 9:40, Amanda goes upstairs; Jason stays down another twenty minutes to watch highlights from a basketball game and text his brother about a fantasy football league that's technically already over for the season.

He goes upstairs at 10:05, brushes his teeth, and gets into bed. Amanda is already on her phone. They talk briefly about a plumber they need to call about a slow drain. Jason sets his alarm for 6:15 the next morning, plugs in his phone, and reads four pages of a paperback novel before his eyes get heavy. He turns off the lamp at 10:34 p.m. Amanda's light stays on a few minutes longer. By 10:45, the house is dark and quiet.”

So our typical American actually has a first and last name, and his wife has been given a name as well. Six references to his phone. Two sons, but no pet. Jason, like Michael, eats granola bars. Overall of the four summaries we've looked at so far, I think this one is the most fleshed out. Still painfully normative, but at least approaching something of a human existence. I think texting his brother about the fantasy football league that's already over for the season pushed it over the edge in this regard.

Typical Day in the Life of an Average American- Part IV, Gemini Edition

Here is what Google has for us with its AI, Gemini:

“At 6:30 AM on Tuesday, October 14, 2025, Alex’s smartphone alarm rang on the bedside table. Alex reached out, silenced the alarm, and spent five minutes scanning incoming text messages, local weather updates, and overnight news notifications before getting out of bed.

By 6:40 AM, Alex completed personal hygiene, dressed in business-casual attire, and walked into the kitchen. Alex brewed a cup of drip coffee, toasted two slices of whole-wheat bread, scrambled two eggs, and ate while checking the morning traffic report on a tablet. After eating, Alex rinsed the plate, packed a leftover turkey sandwich and an apple into a lunch bag, grabbed house keys, and locked the front door.

At 7:25 AM, Alex backed the sedan out of the driveway and joined the morning rush-hour traffic onto the highway. During the 35-minute drive, Alex listened to an episode of a news commentary podcast while navigating routine slowdowns near the highway interchange.

Alex arrived at the suburban office park at 8:02 AM, parked, and walked into the office. After logging into the workstation, Alex spent the first 45 minutes answering urgent client emails and reviewing the daily calendar.

At 9:00 AM, Alex attended a 45-minute hybrid team meeting in the main conference room to review project milestones for the quarter. From 10:00 AM to 12:00 PM, Alex worked continuously at the desk, updating spreadsheets, drafting operational reports, and responding to internal team messages.

At 12:00 PM, Alex retrieved lunch from the breakroom refrigerator and ate at the workstation while watching a ten-minute online video clip. Afterward, Alex took a fifteen-minute outdoor walk around the office complex grounds before returning to the desk.

The afternoon work block began at 12:45 PM. Alex filled a water bottle, spent two hours reviewing vendor contracts, and conducted two phone calls with regional coordinators. At 3:15 PM, Alex walked to the office kitchen for a second cup of coffee and a brief conversation with a coworker. Alex spent the remaining hour and a half processing final administrative tasks, clearing the email inbox, and filing daily logs.

At 5:05 PM, Alex logged off the computer, packed up, and walked out to the parking lot. The evening commute took 40 minutes due to heavy traffic. En route home, Alex made a ten-minute stop at a neighborhood grocery store to purchase milk, fresh spinach, and a carton of berries.

Alex arrived home at 6:05 PM, brought in the groceries, and spent 20 minutes sorting mail and feeding the pet cat. At 6:30 PM, Alex began cooking dinner, making pasta with sautéed vegetables and ground turkey.

Alex sat down to eat dinner at 7:15 PM while watching an episode of a streamed television series. After finishing the meal at 7:50 PM, Alex loaded the dishwasher, wiped down the kitchen counters, and took out the trash and recycling bins.

From 8:15 PM to 9:30 PM, Alex folded a load of laundry from the dryer, placed the clothes in drawers, and spent the remaining time reading a book on the couch.

At 9:30 PM, Alex started the bedtime routine: washing face, brushing teeth, and putting on sleepwear. Alex got into bed at 9:50 PM, set the alarm for 6:30 AM, and spent twenty minutes scrolling through social media feeds on the phone. At 10:15 PM, Alex plugged the phone into its charger, turned off the bedside lamp, closed eyes, and fell asleep shortly after.”

Alex may not have a family, but he does have a cat! The first pet. Four references to his phone. He indeed eats and works in an office. I do have to say I'm astounded by how high the executive functioning is of the average American. Where is the four hours of laying in bed, doom scrolling and eating cookie butter out of a jar? Also, I don't think the average American eats as healthy as these summaries. I'm three in and this feels more like government propaganda for what the average citizen should be doing.

Typical Day in the Life of an Average American- Part III, Copilot Edition

So here is what I got from Copilot:

“A Day in the Life 6:27 a.m.
Their phone alarm vibrates on the nightstand. They reach for it, silence it, and lie still for a moment before sitting up. They check the weather, skim overnight texts, and glance at the news headlines. They head to the bathroom, use the toilet, wash their face, and brush their teeth. A shower follows — quick, warm, routine.

6:52 a.m.
They dress in clothes laid out the night before: a clean shirt, comfortable pants, socks from the top drawer. In the kitchen, they start the coffee maker, pour cereal into a bowl, and eat while scrolling through social media. They pack a lunch, grab keys, wallet, and phone, and step outside.

7:18 a.m.
They drive to work. Traffic is steady; they listen to a podcast and occasionally glance at the clock. They park, walk across the lot, and badge into the building.

8:03 a.m.
At their desk, they wake the computer, open email, and begin responding. A coworker stops by to ask a quick question. They attend a morning meeting, take notes, and return to their workstation to work through a set of tasks — spreadsheets, messages, small decisions.

12:11 p.m.
Lunch happens in the break room. They heat up leftovers, chat lightly with a colleague, and scroll through their phone again. After eating, they refill their water bottle and head back to their desk.

1:02 p.m.
The afternoon is a mix of focused work and interruptions: a call from a client, a software update, a brief walk to stretch their legs. They complete a few lingering tasks and send off a final email.

4:37 p.m.
They pack up, leave the building, and drive home. Traffic is slower now; they listen to music instead of a podcast. Once home, they drop their bag on a chair, change into comfortable clothes, and check the mail.

5:22 p.m.
Dinner prep begins. They chop vegetables, heat a pan, and cook a simple meal. They eat while watching a show, pausing occasionally to respond to a message or check something online.

6:41 p.m.
After dinner, they wash dishes, wipe the counter, and take out the trash. They spend the next hour on personal time: browsing online, reading, or playing a game. Later, they call a family member for a short conversation.

8:19 p.m.
They go for a short walk around the neighborhood. The air is cooler now; they pass familiar houses and nod at a neighbor. Back home, they shower again or simply wash up.

9:03 p.m.
They settle on the couch and watch another episode of their show. They snack lightly, scroll intermittently, and eventually turn off the TV.

10:11 p.m.
In the bedroom, they plug in their phone, change into sleep clothes, and brush their teeth. They set an alarm for the next morning, turn off the lights, and lie down.

10:27 p.m.
They close their eyes. The day ends quietly, without ceremony, and tomorrow will begin much the same.”

Interestingly this one did not give them a name, a family, or any sort of specifics about their job. Felt more autism coded than the ChatGPT one. Referenced the phone 4 times though, and the person has an office job and do indeed eat food. They got some exercise which is good. Still no pet. Despite this one being more anemic, it felt less soul crushing. Maybe Chat gave enough specificity to make it real enough to feel bleak. Anyway, on to the next.

Typical Day in the Life of An Average American Part II- Chat GPT Edition

So I submitted the prompt to Chat GPT as my first test. Here is what it spit out:

“At 6:47 a.m., the alarm on Michael’s phone sounds beside his bed. He silences it, remains in bed for several minutes, then checks the time, weather, text messages, and notifications that accumulated overnight. He gets up at 6:56, uses the bathroom, brushes his teeth, showers, applies deodorant, and gets dressed in jeans, a shirt, socks, and shoes.

At 7:22, he enters the kitchen. His wife is already awake, and their eight-year-old daughter is getting ready for school. Michael pours a cup of coffee and eats a bowl of cereal while standing at the kitchen counter. He puts his dishes in the sink, fills a travel mug with the remaining coffee, checks that he has his wallet, keys, and phone, and leaves the house at 7:48.

He gets into his six-year-old crossover, connects his phone to the car, and backs out of the driveway. The drive to work takes 31 minutes. He listens to music and a podcast, stopping at several traffic lights and spending about eight minutes in slow traffic near the highway interchange. He arrives at 8:19, parks, walks across the lot, and enters the building.

Michael works in an administrative position for a regional company. He turns on his computer, fills his water bottle, and begins reviewing email. Most of the morning is spent at his desk. He responds to messages, updates spreadsheets, speaks briefly with coworkers, and participates in a 35-minute meeting beginning at 10:00. At approximately 10:50, he uses the bathroom and buys a granola bar from a vending machine before returning to his desk.

At 12:14 p.m., Michael takes lunch. He heats leftover chicken and rice in the break-room microwave and eats at a table with two coworkers. They talk for about 20 minutes. He spends the remainder of his lunch break looking at his phone, reading several news stories, checking social media, and responding to a text from his wife. At 12:52, he returns to work.

The afternoon follows much the same pattern as the morning. Michael works at his computer, makes two phone calls, answers email, and spends part of the afternoon preparing material for a project due later in the week. At 2:43, he gets another cup of coffee. At 4:15, he checks the time and begins finishing the tasks he wants completed before leaving. He shuts down his computer at 4:58 and walks to his car.

Traffic is heavier on the trip home. At 5:18, he stops at a grocery store because his wife texted him asking for milk, bananas, and hamburger buns. He also buys a bag of chips and a six-pack of sparkling water. He pays by credit card and leaves at 5:31.

Michael arrives home at 5:49. He carries the groceries inside, changes out of his work clothes, and spends several minutes talking with his wife about their respective days. His daughter shows him a worksheet she completed at school. He checks the mail and puts a utility bill on the kitchen counter.

At 6:12, Michael begins making dinner. He cooks hamburgers on a grill outside while his wife prepares a salad and heats frozen French fries in the oven. The family sits down to eat at 6:42. They finish around 7:10. Michael clears the table, puts leftovers in the refrigerator, loads the dishwasher, wipes the kitchen counter, and takes a bag of garbage outside.

At 7:31, he sits on the couch and watches television with his family. His daughter sits nearby using a tablet for part of this time. At 8:05, Michael helps her get ready for bed. She changes clothes and brushes her teeth, and he reads with her for about 15 minutes. He leaves her room shortly after 8:30.

Michael returns downstairs. He and his wife watch another television program. Both intermittently use their phones while watching it. At 9:24, Michael gets a bowl of ice cream from the kitchen and eats it on the couch. At 9:50, he checks his personal email, looks at his bank account, and pays the utility bill that arrived earlier.

At 10:12, Michael turns off the television, checks that the exterior doors are locked, adjusts the thermostat, and turns off the downstairs lights. He goes upstairs, uses the bathroom, brushes his teeth, changes into clothes for sleeping, and plugs his phone into its charger beside the bed.

At 10:29, he gets into bed. His wife joins him several minutes later. Michael spends approximately 20 minutes looking at his phone, moving between social media, a news site, and a weather forecast for the following day. At 10:51, he sets his alarm for 6:45 a.m., places the phone on the nightstand, and turns off the lamp.

He talks briefly with his wife and then lies quietly in the dark. By approximately 11:05 p.m., he is asleep.”

Well, it successfully fulfilled the prompt, I suppose. God there was something incredibly depressing about the entire thing. Kind of modernity in a nutshell. I did like that it referenced his phone 9 separate times in a short summary. That really points to the ubiquity if not outright domination smart phones have over our daily lives. What struck me as most absent from this was anything weird. It was aggressively normative, which I guess is what the prompt asked for. I think if I was writing it, I'd insert something that is slightly less generic but still very much within the bounds of a typical person. Maybe he works on a wood carving in his garage, stairs at a blank screen for a novel he plans on writing and keeps putting off, or scares off a possum in his backyard. Though, maybe most people don't have anything like that on their average day. I also found it striking Michael did not have a pet since I think the majority of Americans do. I suppose that perhaps even if he did have a pet, his wife could have taken care of it such that it did not register in the summary of his daily activities. Anyway, let's see what soul crushing banality the next AI Chatbot has to offer.

Hi blog friends! What’s new or fascinating for you this week? Let me know in the comments, a text, message or email if you like.

My aches and pains have been medium to high this week, so I haven’t done much except sip warm creamy cups of coffee with extra milk, watch YouTube videos and classic TV shows and get the basics done. Videos about the history of CompuServe, the power of working under constraints and adorable chickens and guinea pigs (YouTube short) were the most interesting videos I saw this week. 

See you again next Tuesday!  FREE Cute Snail  coloring pages are still available in our Payhip store if anyone would like one. We have fall adventures, back to school adventures, everyday adventures & more. Great for kids and adults 3 and up who love snails, smiles and coloring.

❤️ Juliet

IMAGE ID: A close up picture of my little aloe vera plushie. I was looking at it while I enjoyed some simple yummy Great Value beef and bean burritos for breakfast today. 

Chapter 1: The Quiet Place Where You Finally Admit It

You turn off the light, set your phone beside the bed, and lie there staring into the dark. The day is over, but your mind has not gotten the message. You think about what you forgot, what someone said, what still needs to be done tomorrow, and how strange it feels to be this tired without knowing exactly what would make you feel better. Maybe that is why Christian encouragement when you feel emotionally numb and exhausted can feel so personal. Sometimes you are not looking for inspiration. You are simply trying to understand why you do not feel like yourself anymore.

You may have gotten very good at hiding it. You answer, “I’m fine,” because explaining would take more energy than you have. You laugh at the right time. You return the call. You finish the work. You keep showing up for people because that is what you have always done. But somewhere underneath all of that, finding faith when you are tired of carrying everything becomes a much quieter struggle than most people realize. You are not necessarily questioning whether God exists. You may simply be wondering why you cannot feel much of anything right now.

There is something lonely about being tired in a way nobody can see. If you had a broken arm, people would notice. If you were in the hospital, they would understand why you needed rest. But emotional exhaustion can leave you walking around looking completely normal while everything inside you feels dimmed. You can be sitting across from someone you love and realize that you are listening without really being there. You can open your Bible and want the words to reach you, yet feel as though they are landing somewhere just beyond your reach.

That does not make you a bad Christian.

It does not mean you have stopped loving God.

It may simply mean you are tired.

We do ourselves damage when we turn every difficult emotion into a spiritual accusation. Sometimes we are so afraid of appearing weak that we begin correcting our feelings instead of listening to them. We tell ourselves we should be more grateful. We should trust more. We should pray harder. We should stop feeling this way.

But the heart does not always respond well to being ordered around.

Think about the person who sits on the edge of the bed after getting dressed for work and stays there a little longer than usual. Shoes on. Keys ready. Coffee cooling on the dresser. They know they need to leave, but for one minute they just sit. There is no dramatic crisis. They are simply gathering enough of themselves to stand up and begin another day.

God sees that minute.

I think we sometimes imagine that God only pays attention to the moments when our faith looks impressive. The long prayers. The big decisions. The courageous stand. But Jesus repeatedly noticed people in ordinary human need. Hunger mattered. Weariness mattered. Grief mattered. Fear mattered. He never treated people like machines that should function perfectly because they believed in God.

That matters when you feel numb because numbness can make you suspicious of yourself. You may wonder whether your faith is fading because your emotions are quiet. But faith is not measured by how intensely you feel God every moment.

Sometimes faith is getting out of bed and whispering, “Jesus, stay with me today.”

Sometimes faith is admitting, “I do not have much to give You right now.”

Sometimes it is sitting silently because you do not have the words.

You may be surprised by how much relief there is in stopping the performance.

You do not have to enter God’s presence as the person everyone else expects you to be. You can come as the tired person. The confused person. The person who does not know whether they need sleep, a good cry, a long conversation, or just a few hours when nobody expects anything from them.

That is still you.

And Jesus has not lost interest in you because you are not at your best.

Maybe tonight you do not need to figure out your entire emotional life. Maybe you do not need a breakthrough. Maybe you only need enough honesty to say, “God, I am tired of pretending I am not tired.”

There are prayers that sound small but open enormous doors.

That may be one of them.

Chapter 2: When Rest Does Not Reach the Inside

You are standing in the grocery store looking at something you have bought a hundred times, and for a few seconds you cannot remember why you are there. The cart is half full. Someone is waiting behind you. Your phone buzzes in your pocket. Nothing is actually wrong, but your mind feels crowded and strangely far away. You move on, finish the shopping, load the bags into the car, and keep going because that is what you know how to do.

This kind of tiredness can make ordinary life feel harder than it should. Small decisions take more energy. A harmless question can irritate you. A simple task can feel heavier than it did a month ago. Even things you enjoy may start to feel like obligations. You may begin wondering why a weekend off did not fix it or why getting more sleep has not brought you completely back.

Sometimes the body rests before the heart does.

There are pressures you can carry while sitting perfectly still. Worry about someone you love. Regret over something you cannot change. Financial uncertainty. A job that keeps taking more from you. A relationship that feels strained. The fear that you are disappointing people. The private pressure to keep being the person everyone believes you are.

You can lie on the couch for an afternoon and still carry all of that.

This is why simply telling yourself to relax may not work. You may need something deeper than physical rest. You may need permission to stop solving everything for a little while.

Jesus gave people that kind of permission through the way He lived. He did not treat every problem as though it belonged entirely on His shoulders at every moment. There were times when He stepped away. There were times when He slept. There were times when He went somewhere quiet to pray.

If Jesus could make room for quiet, you do not need to feel guilty for needing it too.

But quiet can be uncomfortable when you have been busy for a long time. Once the television is off, the work is finished, and nobody is talking, you may finally hear what has been sitting underneath the noise.

“I am scared.”

“I am angry.”

“I miss who I used to be.”

“I do not know how much longer I can keep doing this.”

Those thoughts can feel frightening, but they can also become honest prayer.

God already knows what is underneath the surface. You are not protecting Him by hiding it. You are only making yourself carry it alone.

Maybe one evening you sit at the kitchen table after everyone has gone to bed. There is a cup in front of you that has gone cold because you forgot to drink it. Instead of turning on another show or checking your phone, you stay there for a few minutes and tell Jesus what you have not told anyone else.

You do not organize it. You do not make it sound faithful.

You just tell the truth.

That kind of prayer may not produce an immediate rush of peace. You may still wake up tired tomorrow. But something important has happened. The burden is no longer completely hidden.

You have let God into the room where you have been carrying it alone.

And sometimes coming back to yourself begins there, quietly, before you feel any different at all.

Chapter 3: You Do Not Have to Return All at Once

The morning comes, and you are standing by the window before anyone else is awake. The sky is beginning to lighten. Nothing in your life has been completely repaired overnight. The same responsibilities are waiting. The same questions may still be unanswered. But for the first time in a while, you are not asking yourself to become your old self by breakfast.

That matters.

Coming back from emotional exhaustion is often quieter than we expect. You may notice that you laugh at something without forcing it. You may enjoy a meal again. You may pray for two minutes and realize you were actually present for those two minutes. You may tell someone you trust that you have not been doing as well as you looked.

Jesus can work inside those ordinary moments.

You do not have to prove that you are getting better quickly. Give yourself room to be human. Rest when you can. Accept help when you need it. Take seriously what your mind and body are telling you. If the numbness stays, deepens, or begins interfering with daily life, reaching out for professional support can be a wise part of caring for the life God has given you.

You are more than the strong version of yourself.

You are still worthy of love when you are quiet, tired, uncertain, and rebuilding. Jesus is not standing somewhere in the distance waiting for you to catch up. He is with you here, even if today all you can manage is one small prayer and one honest step.

Your friend, Douglas Vandergraph

Explore the complete Douglas Vandergraph Master Index: https://douglasvandergraph.com/douglas-vandergraph-master-index/

Watch Douglas Vandergraph’s faith-based videos on YouTube: https://www.youtube.com/@douglasvandergraph

#gyushuaabo22yl

Dua puluh dua kali musim semi telah silih berganti semenjak Joshua Hong menikahi Kim Mingyu di kapel kecil kala itu. Sang Alpha yang dulu gagah dan tampan, kini rambutnya diselimuti uban dan garis-garis keriput mulai menghiasi kulit warna tembaganya. Sang Omega yang dulu begitu muda dan penuh gairah, kini jauh lebih tenang dengan wajah cantiknya tergurat jelas oleh kedewasaan. Namun, yang tak berubah dari mereka adalah bagaimana mereka masih saling mencintai bahkan ketika masing-masing telah menginjak usia 60 dan 40 tahun.

Cinta yang semakin erat saat topik menggelisahkan akan lamaran muncul kembali ke permukaan.

Mingyu menghela napas dalam-dalam. Di pangkuannya terbuka surat yang berasal dari amplop bersegel kerajaan. Tanda bahwa surat tersebut resmi dikirim dari istana. Di sisinya, Joshua setia menemani sang suami, mengelus-elus lembut bahunya untuk menenangkannya.

“Sayang...,” gumamnya. “Jangan terlalu dipikirin...”

“Bagaimana aku tidak memikirkannya, mereka meminta tangan Ri...,” ketakutan terbesar Kim Mingyu akhirnya terwujud juga. Semenjak putri semata wayangnya itu menarik perhatian dua pangeran kerajaan sekaligus, hati kecilnya gusar berkepanjangan. Ia sungguh tidak sanggup untuk memberikan tangan sang putri ke pasangannya nanti di altar pelaminan.

“Mingyu, kamu ini...,” helaan napas Joshua tidak kalah berat. Benar-benar deh, suaminya ini, padahal sudah tua tapi malah bertingkah mirip anak-anak. “Ri udah gede, Gyu. Dia udah dewasa.”

“Ri masih tujuh belas,” sanggah sang Alpha.

“Terus siapa yang pas aku tujuh belas dulu udah cium-cium bibirku, ha?” sambil menunjukkan ekspresi tidak terkesan, Joshua langsung membalas, membuat suaminya gelagapan, tak sanggup membantah, dan kedua pipinya memerah. Joshua sih hanya memutar bola mata akan reaksi (menggemaskan) itu. “Yeah, thought so.”

“Te-tetap saja aku tidak setuju kalau Ri menikah semuda itu. Seharusnya Ri masih menikmati hidupnya, menapaki dunia, meraih mimpi-mimpinya. Kondisi sekarang dan saat kita menikah dulu juga sudah berubah pesat,” merajuk dengan begitu keras kepalanya. Separuh hati Joshua ingin menjitak suaminya, separuh lagi ingin menciumi cibiran bibirnya itu sampai egonya luluh lantak.

Alih-alih, Joshua menangkup pipi Alphanya. “Mingyu. Kamu inget nggak? Kamu bilang kalo aku bisa ngelakuin apapun yang aku mau bahkan setelah kita nikah. Apa hal yang sama nggak berlaku buat putri kita?” ingatnya akan ucapan sang Alpha sendiri.

“Tapi Yido putra mahkota—”

“Kamu putra mahkota.”

Mingyu spontan menggeleng. “Aku hanya... kebetulan berbagi sedikit darah dengan Kak Cheol.. Berbeda dari Yido yang benar-benar penerus kerajaan...,” kemudian Mingyu melanjutkan. “Keluarga kerajaan itu ada tata krama, ada adat yang telah turun-menurun dilakukan. Aku...takut Ri akan terkekang meski telah menikah sekalipun...”

Joshua rasanya kepingin ketawa pakai hidung. “Mingyu! Ini anak Yoon Jeonghan lho!” terlepaslah tawa yang lugas dari sang Omega. “Mana mungkin Yido perlakuin putri kita kayak gitu. Ibunya aja mau kabur pas hari pernikahan dia sendiri.” Aah~ jadi mengenang masa lalu deh~ “Udahlah, kamu tenang aja. Tuan Raja kan cuma ngasih tau kalau Yido mau secara resmi deketin Ri di surat itu. Belum tentu juga Ri mau dipinang Yido.”

“Be...tul juga sih...”

“Soalnya firasatku bilang putri kita udah punya orang yang dia taksir dari dulu nggak sih...?” 🤔

...........

“Maksudmu...?”

“Kibum, lho, Kibum,” Joshua pun mencubit gemas pipi suaminya.

Kwon Kibum. Anak dari Kwon Soonyoung dan Wen Junhui. Rival sejati Choi Yido, anak dari Choi Seungcheol dan Yoon Jeonghan. Berdua, mereka sama-sama memiliki takhta di tangan mereka, meski nasib mereka sungguhlah berbeda. Bila Yido adalah penerus kerajaan yang terkukung singgasana, maka Kibum bebas berkelana ke manca negara, melihat dan mempelajari segala hal yang ia minati. Namun, keberadaan Kibum yang tercatat adalah dua tahun yang lalu. Sejak itu, mereka tidak lagi mendengar akan kabarnya.

“Kwon? Bukankah anak itu menghilang?” kedua alis Kim Mingyu terangkat. “Kudengar dia dinikahkan dengan Omega di negara kelahiran ayahnya.”

Joshua meringis, “Yah, rupanya itu rumor aja. Kebetulan ada 'burung kecil' yang nginfoin aku kalo Kibum lagi menuju sini, rumah kita. Expect to see him in a week, katanya.”

“Rumah kita? Untuk apa?”

“Menurutmu?” Joshua malah balik bertanya. Nadanya jahil seperti kilau di mata indahnya. “Putri kita mau 18 sebentar lagi, Gyu.”

Ketika pengertian jatuh menimpa Kim Mingyu, raut wajahnya kembali tegang. “Oh tidak...,” keluhnya. “Mereka serius dengan Ri? Yang satu telah meminta tangan Ri di surat, yang satu akan muncul di sini? Oh tidak. Tidak, tidak bisa...”

“Hei, hei, hei, don't crash out on this, Gyu.”

“Tapi katamu Ri mungkin menyukai Kibum!” Mingyu semakin tidak bisa menahan frustrasinya. “Kalau begitu, putri kita akan dibawanya pergi. Kita tidak bisa melihatnya lagi! Sebagai ayahnya, aku tidak mengijinkan putri kita diboyong ke istana atau dibawa keliling dunia! Langkahi dulu mayatku!”

Berderap penuh emosi, sang Alpha kemudian menyingkir ke ruang kerjanya, berniat mengurung diri di sana. Joshua menghela napas lagi. So dramatic. Semua ini hanya karena Mingyu tidak ingin kehilangan putri satu-satunya. Mungkin seharusnya dulu Joshua mengikuti saran Jeonghan untuk memiliki anak lagi.

(Nope, cukup sekali ia merasakan sakit seperti itu, never again).

Entahlah apa yang akan terjadi ketika Kibum sudah memijak kediaman keluarga Kim dan Yido yang mengetahui kedatangan rival abadinya itu. Chaos ensued, probably.

There is a particular kind of phone call that has been going out across the American and British labour markets since the spring. It comes from a number the recipient recognises. It is made by someone who, eighteen months or two years ago, sat across a table and explained that the role was being eliminated because the company was moving to an automated workflow. The tone of the new call is warm, slightly sheepish, careful not to say the word sorry in any way that could later be quoted. The substance is simple. We need you back.

For the person receiving it, the call is not simple at all. It arrives carrying a piece of information they were denied at the moment they most needed it: that the thing they were told about themselves, that a machine could do their job, was not true. It was a forecast dressed as a fact, and the forecast was wrong, and in the interval between the forecast and its correction they lost a salary, a title, a pension contribution, a professional identity and, in a good many cases, a settled sense of what they were for.

The reversal is now large enough to have its own vocabulary. Fast Company has called it the great AI rehire; others call it the AI boomerang. Whatever the label, the underlying phenomenon is a corporate class discovering, at scale and at expense, that the gap between what artificial intelligence was sold as capable of and what it can actually be trusted to do in production is wide enough to swallow a quality programme, a customer service function, an editorial operation, or a bank's call centre. What is far less examined is the cost of that discovery, and specifically who paid it.

The graybeards Ford had to buy back

The most instructive case is not a technology company. It is a car manufacturer.

In late June 2026, Ford disclosed that it had leaned too heavily on artificial intelligence in vehicle quality control and had spent roughly three years bringing back around 350 veteran engineers to repair the consequences. Some of them were former Ford employees. Others had drifted to suppliers. Internally and in the coverage that followed, they were referred to as the graybeards, a term that carries both affection and a certain institutional embarrassment.

Charles Poon, Ford's vice president of vehicle hardware engineering, gave an account of the failure that is unusually candid by the standards of corporate communications. The company, he said, had mistakenly thought that by just introducing artificial intelligence and ingesting the design requirements it had, that would produce a high-quality product. Elsewhere he put it more plainly still: artificial intelligence is a fantastic tool, but it is only as good as the information you use to train it.

What the returning engineers actually did is the detail that matters most, and it is routinely skipped. They did not replace the AI. They rebuilt the data pipelines feeding it, ran weekly design reviews to catch failure points before anything reached the factory floor, mentored junior staff, and functioned as internal auditors of the automated systems. For the 2026 Expedition alone, Ford added around 1,200 new inspections and 203 new inspectors at its Kentucky Truck Plant. The chief executive, Jim Farley, later described the cumulative effect of the quality overhaul as literally hundreds and hundreds of millions of dollars of a tailwind for Ford on cost. In the summer of 2026 Ford topped the J.D. Power Initial Quality Study among mainstream brands for the first time since 2010.

So the story has a happy ending, if you are Ford. Read it from the other end and it looks different. A company removed the people who held the tacit knowledge required to make its automated quality system work, discovered that the system could not function without them, and then spent roughly three years and an unknown sum reassembling that knowledge from the labour market. The engineers who came back are now, in effect, being paid to train their replacement, having previously been dismissed on the assumption that the replacement was already trained.

The account emerged first in Bloomberg on 25 June 2026, reporting that Ford had been rehiring quality inspectors after AI fell short. Kumar Galhotra, the company's chief operating officer, told journalists that Ford had been relying more and more on automated quality systems and not getting the results it wanted. TechCrunch carried Poon's remarks three days later.

Where the fifty-five per cent actually comes from

Every article about the AI rehire leans on a single statistic: 55 per cent of leaders who made AI-driven cuts now regret them. It appeared in Inc. in late July 2026 under the byline of Bruce Crumley, and was widely syndicated from there. It has been attributed variously to Forrester, to Orgvue, and in at least one widely shared instance to Gartner. That last attribution appears to be simply wrong.

There are, in fact, two distinct 55 per cent findings, and their coincidence has caused a good deal of laundering. The first comes from Orgvue, a workforce design software firm, whose survey was run by the research agency Vitreous World between February and March 2025 and published that April. It covered 1,163 C-suite and senior leaders across eight countries in North America, Europe and Asia Pacific. It found that 39 per cent of business leaders had made employees redundant as a result of deploying AI, and that of those, 55 per cent admitted they had made the wrong decision.

The second comes from Forrester's Predictions 2026 report on the future of work, published in October 2025, which found that 55 per cent of employers regretted technology-driven staff reductions and predicted that half of AI-attributed layoffs would be quietly reversed. Forrester's analysts were specific about the form the reversal would take, and the specificity is not flattering: much of this work, the firm predicted, will be given to lower-wage human workers, offshore or at lower salary.

Neither of these is a July 2026 survey. The headline that broke in late July 2026 was, in substantial part, research from spring and autumn 2025 recirculating with fresh urgency because the anecdotes had finally caught up with it. That does not make the finding false. It does mean the reversal has been visible in the data for well over a year while the layoffs continued, which complicates the tidy story of a sudden corporate awakening.

Two genuinely newer datasets do exist and are more useful. Careerminds, an outplacement firm, surveyed 600 HR professionals in February 2026. Of those whose organisations had made AI-led cuts, 32.7 per cent had already rehired between a quarter and a half of the roles they eliminated, and 35.6 per cent had brought back more than half. Add those together and you get 68.3 per cent, which is almost certainly the origin of the widely repeated claim that 68 per cent of firms are rehiring. The same survey found that 52.1 per cent had rehired within six months and 17.8 per cent within three, that 32.9 per cent of HR leaders said their organisations had lost critical skills, and that 28.1 per cent reported remaining staff lacked the capability to fill the resulting knowledge gaps.

The finance is the part that should make boards uncomfortable. Nearly 31 per cent of respondents found that rehiring costs exceeded the initial savings outright. A further 42.4 per cent said savings and rehiring costs roughly cancelled each other out. Only around 27 per cent were financially ahead. That is the actual return on a strategy that was announced to markets as transformation.

The second newer dataset comes from Robert Half, which surveyed more than 2,000 hiring managers in April 2026 and found that 32 per cent had eliminated a role primarily because of AI and later rehired for the same or a similar position. Dawn Fay, Robert Half's operational president, summarised the reason without embellishment: the communication, the judgement, the oversight, the institutional knowledge that employees have, the technology, up until this point, is just not able to replace all of that.

The number that does not say what it is quoted as saying

One figure in circulation deserves direct correction, because it has been assembled from two unrelated statistics and reads as reassurance when the underlying data is not reassuring at all.

The claim is that AI-attributed job cuts in 2026 have exceeded 165,000, and that the pace is 40 per cent slower than in 2025. The 40 per cent figure is real, but it does not describe AI cuts. According to Challenger, Gray and Christmas, whose monthly job cut reports are the standard reference, employers announced 443,604 job cuts in the first half of 2026, down 40 per cent from the 744,308 announced in the first half of 2025. That is total announced cuts across all stated reasons, heavily distorted by the extraordinary government-related reductions that inflated the 2025 baseline.

AI-attributed cuts went in precisely the opposite direction. Challenger recorded 101,743 cuts citing AI through June 2026, roughly 23 per cent of all cuts. The comparison point is 54,836 for the whole of 2025. In other words, in six months of 2026, AI-attributed cuts ran at nearly twice the entire previous year's total. AI was the leading stated reason for workforce reductions for four consecutive months. In May alone, AI was cited in 38,579 cuts, 40 per cent of that month's total and the highest monthly figure since Challenger began tracking the category in 2023. Technology sector cuts through June reached 139,156, an increase of 83 per cent on the same period in 2025.

Andy Challenger, the firm's chief revenue officer, put it flatly when the June report was released on 1 July 2026: tech remains the epicentre of this year's cuts, and AI is the dominant force as companies restructure around it, automate roles and reallocate budgets towards new capabilities.

So the honest framing is this. The rehire is real, and it is happening simultaneously with an acceleration of AI-attributed displacement, not after it. Both trends are running at once, in the same economy, sometimes inside the same organisation. Anyone reading the boomerang coverage as evidence that the danger has passed is reading it wrong.

What the machines could not be told

The reason the reversals cluster around certain functions is not mysterious, and it was described in economics literature a decade before the current wave.

In 2014, the MIT economist David Autor published a working paper for the National Bureau of Economic Research titled “Polanyi's Paradox and the Shape of Employment Growth”. The paradox he named belongs to the chemist and philosopher Michael Polanyi, who captured it in a sentence: we can know more than we can tell. Much skilled work consists of judgements the person making them cannot articulate as rules. Autor's argument was that automation requires exactly the sort of explicit specification that tacit knowledge resists, and that the tasks proving most stubborn to automate are precisely those demanding flexibility, judgement and common sense. He also warned, presciently, that commentators consistently overstate machine substitution while ignoring the complementarities between humans and machines.

Every case in the current reversal is a demonstration of that paradox meeting a quarterly earnings call.

At Ford, the tacit knowledge was in the heads of engineers who could look at a design requirement and know which failure modes it implied. The company assumed those requirements were the knowledge. They were the documentation of the knowledge, which is a different thing, and the AI trained on the documentation inherited the gap.

At IBM, the company's AskHR system handled roughly 94 per cent of routine human resources requests. Arvind Krishna, IBM's chief executive, confirmed in 2025 that AI agents had replaced several hundred people in the HR function. The residual 6 per cent turned out to include the ethically complicated, the legally sensitive and the genuinely distressing, which is to say the part of human resources that is actually the job. IBM's overall headcount rose regardless, because the savings were reinvested in software engineering, sales and marketing, which is a useful reminder that displacement and growth can coexist inside a single organisation.

At Klarna, the Swedish payments firm, the collapse was public and fast. In February 2024 the company announced that an OpenAI-powered assistant was doing the work of 700 customer service agents, handling more than two-thirds of conversations, resolving issues in under two minutes against eleven for humans, and on track to add 40 million dollars in profit. By May 2025 the chief executive, Sebastian Siemiatkowski, was conceding that the drive for cost and efficiency had produced lower quality. The AI handled volume. It could not handle complexity, emotional charge, or multi-step resolution. Klarna began recruiting humans again.

At the Commonwealth Bank of Australia, the failure was almost comic. In July 2025 the bank announced 45 redundancies in its call centre, replaced by an AI voice bot which it said had cut call volumes by around 2,000 a week. The Finance Sector Union disputed the figure, arguing that volumes were in fact rising, that staff were being offered overtime and that team leaders were being directed onto the phones. The union took the matter to the industrial tribunal. In August 2025 the bank reversed the decision, conceded that its redundancy assessment had not properly considered business needs, and offered affected staff the chance to stay, move within the bank or leave. It was not the end of the matter.

Four organisations, four different industries, one shared error: mistaking the codifiable portion of a job for the whole of it.

Coming back is not the same as never leaving

Here is where the boomerang narrative turns from corporate comedy into something closer to a labour story, because the terms of return are not the terms of departure.

Forrester's prediction was explicit that reversal would often mean rehiring offshore or at lower salary. Klarna's approach is the clearest illustration. The company did not simply reinstate its former customer service staff. It launched a pilot recruiting remote agents under what it described as an Uber-type model, targeting students, professionals and entrepreneurs, offering what a company spokesperson called competitive pay and full flexibility, with the eventual aim of replacing its outsourced customer service workforce with this arrangement. Flexibility is a word that does a lot of load-bearing work in that sentence. A permanent contact centre job with a rota, sick pay and a pension is not the same product as a flexible remote gig, even if the tasks performed are identical.

The countervailing evidence deserves its weight. Some reporting suggests returning workers are landing hybrid roles demanding data literacy, prompting skill and change management capability, at pay above the jobs the AI was meant to eliminate. Caroline Castrillon, writing in Forbes on 26 July 2026, advised treating a reversal offer as an entirely new proposition: negotiate higher base pay, signing bonuses, upgraded titles and guaranteed severance, and use the employer's admission of error as leverage. That is sound advice, and it describes a real bargaining position for a particular kind of worker: senior, scarce, holding knowledge the employer has just proved it cannot buy elsewhere.

The graybeards at Ford are in that position. A veteran quality engineer whose absence cost a manufacturer three years and a J.D. Power ranking can name a price. A contact centre agent whose absence produced longer hold times cannot, because the employer's alternative is not to rebuild the function but to source it more cheaply somewhere else. The reversal, in other words, redistributes bargaining power extremely unevenly, and it does so along exactly the lines that already determine labour market power. The scarce get leverage. The substitutable get a flexible contract.

There is also a body of research suggesting that rehiring is not the clean restoration employers imagine. A study published in the Journal of Management in 2021 by John Arnold, Chad Van Iddekinge, Michael C. Campion, Talya Bauer and Michael A. Campion examined 30,714 employees at a large retail organisation, including 1,318 boomerang employees rehired into management roles. Performance before and after rehiring tended to stay flat rather than improve. Boomerang managers performed comparably to internal and external hires in the first year, but both other groups improved more over time. Rehires were also more likely to leave again, and when they did, they tended to leave for reasons similar to those that prompted their first departure.

Read that finding against the current moment and it is faintly ominous. The reason for the first departure was that the employer decided a machine could do the job. That reason has not been retracted so much as postponed.

What the sentence does to the person who receives it

The literature on job loss is old, large and consistent, and it does not support the idea that unemployment is a neutral transition between positions.

Karsten Paul and Klaus Moser's 2009 meta-analysis in the Journal of Vocational Behavior pooled 237 cross-sectional and 87 longitudinal studies and found an average effect size of 0.51 for the impact of unemployment on mental health. Among the unemployed, an average of 34 per cent showed psychological problems, against 16 per cent of the employed. The effects ran across distress, depression, anxiety, psychosomatic symptoms, subjective wellbeing and self-esteem.

The physical consequences are equally documented. Daniel Sullivan and Till von Wachter, writing in the Quarterly Journal of Economics in 2009, matched administrative employment records for Pennsylvanian workers in the 1970s and 1980s to Social Security death records through 2006. For high-seniority male workers, mortality in the year following displacement ran 50 to 100 per cent above expectation. Job loss in a mass layoff, they estimated, reduces life expectancy by one to one and a half years. Effects were still detectable two decades later.

These are the baseline costs of any redundancy. Whether being told that a machine could do your job adds something on top is a separate question, and the answer is more interesting than intuition suggests.

In 2019, Armin Granulo, Christoph Fuchs and Stefano Puntoni published a paper in Nature Human Behaviour reporting eleven studies and surveys involving more than 2,000 participants across Europe and North America. Their finding ran counter to expectation. When people considered other workers being replaced, they preferred those workers to be replaced by humans rather than robots. When they considered their own replacement, the preference reversed: people would rather be replaced by a machine than by another person. The mechanism the authors identified was self-threat. Being displaced by a human invites a direct comparison of worth. Being displaced by a machine does not, because the machine is not a rival in the social hierarchy.

Which suggests that, at the moment of the layoff, the AI explanation may genuinely soften the blow. It removes the sting of having been judged inferior to a colleague. It replaces a personal verdict with a technological inevitability.

And that is exactly what makes the reversal so corrosive. The protective effect depends on the story being true. If you accept that a machine surpassed you, you have absorbed a loss without the humiliation of comparison. When the employer then returns to say that the machine did not surpass you after all, the protective framing collapses and something worse is exposed underneath. You were not replaced by a superior system. You were removed on the basis of an unverified claim about a system, by people who had not checked, in service of a narrative about the company's future that was being told to investors rather than to you. The comfort was borrowed and the loan has been called in.

There is a further injury that has no name in the psychological literature but is obvious to anyone who has lived it. The rehire is an admission of error that arrives without an apology, because an apology would be an admission of liability. The worker is asked to return to an organisation that has demonstrated, in the most concrete way available, that its assessment of their value was not merely mistaken but unexamined. Trust, in the ordinary employment sense, is a bet that the employer's judgement about you is made in good faith and with due care. The reversal is documentary evidence that it was not. Coming back means working inside that knowledge daily.

The demo, the pilot and the shop floor

None of this would have happened if capability claims had been tested before headcount decisions were made rather than after. The record on that testing is poor across the whole ecosystem.

In August 2025, preliminary findings from MIT's Project NANDA, drawing on more than 300 enterprise deployments, 52 case studies and 153 leadership interviews, reported that roughly 95 per cent of enterprise generative AI pilots produced no measurable return, despite tens of billions of dollars of investment. The figure has been misread and overstated in circulation, and the researchers themselves emphasised that the failures were organisational rather than a verdict on model quality, but the direction is not in dispute.

Orgvue's more recent research points the same way, finding that 92 per cent of organisations had invested in AI while 78 per cent reported projects either failed outright or remained stuck in pilot, with around a third saying they still did not understand how to make AI work.

The most methodologically careful evidence, and the most honest about its own limits, comes from METR. In July 2025 the research organisation published a randomised controlled trial in which sixteen experienced open-source developers completed 246 real tasks, some with AI assistance and some without. The developers expected AI to cut completion time by 24 per cent. Afterwards, they estimated it had made them 20 per cent faster. Measured against the clock, they were 19 per cent slower. The gap between perceived and actual productivity, in the group with the strongest professional incentive to judge accurately, was around 39 percentage points.

METR has since revised the picture, and intellectual honesty requires reporting the revision. In February 2026 the organisation published follow-up work covering 57 developers, ten of them veterans of the original study, across 143 repositories and more than 800 tasks. The raw results pointed the other way. Among the newly recruited developers AI produced an estimated 4 per cent speedup, with a confidence interval running from 15 per cent faster to 9 per cent slower, against the original 19 per cent slowdown whose interval ran from 2 to 39 per cent slower. METR also disclosed a selection problem, though not the one it is commonly reported as having. Developers were not refusing to enrol. Between 30 and 50 per cent of them said they were choosing not to submit particular tasks because they did not want to do those tasks without AI, which quietly stripped from the sample the work where the uplift would have been greatest. A rising share also said they would not want to do half their work without AI at all, even at 50 dollars an hour. METR now describes its own headline result as historical and cautions that its newer data is only very weak evidence for the size of any improvement.

The perception side of that gap has meanwhile widened. In May 2026 METR published a survey of 349 technical workers, conducted between February and April, in which the median self-reported change in the value of work produced with AI tools was between 1.4 and 2 times. Respondents put it at 1.3 times for March 2025, 2 times for March 2026, and forecast 2.5 times for March 2027. METR flagged reasons to doubt the magnitude, noting that its own staff returned the lowest estimates of any subgroup, which it attributed to their familiarity with the evidence on the gap between perceived and actual gains.

That sequence is a model of how capability claims ought to be handled: measured, published, revised, hedged. It is also the exact opposite of how they were handled in the decisions that removed 101,743 people from payrolls in the first half of 2026. Those decisions were made on vendor roadmaps and board-level enthusiasm, not on randomised trials. The research community spent a year arguing about whether AI made sixteen developers slightly faster or slightly slower. Corporate management, working from the same underlying technology, concluded that entire functions could be eliminated and acted on it within a quarter.

Why firms cannot stop even when they know

The most uncomfortable analysis of this cycle argues that the reversals will not prevent the next round, because the incentive structure that produced them is intact.

In March 2026, Brett Hemenway Falk of the University of Pennsylvania's Department of Computer and Information Science and Gerry Tsoukalas of Boston University posted a paper to arXiv titled “The AI Layoff Trap”, subsequently revised in June. Its opening premise is the familiar one: if AI displaces human workers faster than the economy can reabsorb them, it risks eroding the very consumer demand firms depend on.

The paper's actual contribution is sharper than that summary, and worth stating accurately. Falk and Tsoukalas show that knowing this is not enough for firms to stop it. In a competitive task-based model of a transitioning economy, each firm captures the full cost saving from automation but bears only a fraction of the demand loss it creates, with the remainder falling on rivals. That demand externality traps rational firms in an automation arms race, displacing workers well beyond what is collectively optimal. The resulting loss, they argue, harms both workers and firm owners. More competition and better AI amplify the excess rather than correcting it. Wage adjustment, free entry, capital income taxes, worker equity, universal basic income, upskilling and Coasean bargaining all fail to eliminate it. Their proposed remedy is a Pigouvian automation tax, priced to make firms internalise the demand destruction they cause.

The relevance to the boomerang is direct. If the model is right, the firms conducting reversals are not being corrected by the market in any way that will change their future behaviour. They are absorbing an idiosyncratic operational failure, learning a narrow lesson about one function, and returning to the same competitive pressure that produced the original decision. The rehire fixes the quality problem. It does not touch the externality.

This is a theoretical model, recently posted and not yet through peer review, and its conclusions are contingent on its assumptions. But it offers something the anecdotes do not: an explanation of why sophisticated, well-advised organisations keep making a decision that most of them subsequently regret.

A correction, or a slower version of the same thing

The honest answer is that it is both, and which one dominates depends on where you sit in the labour market.

The genuinely corrective signal is IBM's. In mid-February 2026, at Charter's Leading with AI Summit, the company's chief human resources officer, Nickle LaMoreaux, announced that IBM would triple its United States entry-level hiring in 2026, explicitly for roles that, as the company put it, we are being told AI can do. Entry-level job descriptions were redesigned away from tasks AI automates well, such as routine coding, and towards customer engagement and judgement. LaMoreaux's rationale was strategic rather than sentimental: the companies three to five years from now that are going to be the most successful, she said, are those that doubled down on entry-level hiring in this environment.

That matters because the entry-level damage is the least reversible part of this whole episode. Data from the Burning Glass Institute cited in Forrester's analysis shows the share of postings that are entry-level falling between 2018 and 2024 from 43 to 28 per cent in software development, from 35 to 22 per cent in data analysis, and from 41 to 26 per cent in consulting. Youth unemployment for bachelor's degree holders aged 20 to 24 rose from 5.2 per cent in 2018 and 2019 to 6.2 per cent. You cannot boomerang a graduate who never got hired in the first place. There is no former employee to call.

Set against IBM's example is the pattern Forrester actually predicted, which was not restoration but relocation: work returning to humans, at lower cost, offshore or on worse terms. Klarna's flexible remote model is that pattern in practice. The Careerminds finding that only around 27 per cent of firms came out financially ahead suggests the cost discipline that drove the original decision has not gone anywhere, and will simply be pursued through a different mechanism.

The Commonwealth Bank has since supplied the clearest available test of that prediction, because it is the same employer. In July 2026, less than a year after conceding it had got the redundancies wrong, the bank cut hundreds of customer service chat roles held by contractors supplied by Nutun, a Johannesburg outsourcing firm, as it wound back the contract, and confirmed a further 276 redundancies across technology, operations and human resources. The Finance Sector Union puts the total at around 800 Commonwealth Bank roles over the preceding year, including 176 technology and engineering positions, and alleges that some of the eliminated roles were subsequently advertised through the bank's India-based subsidiary. By May 2026, under a chief AI officer appointed at the start of the year, the bank's Hey CommBank chatbot, running on a messaging platform built with Microsoft, was resolving almost nine in ten customer conversations without human assistance. The union has lodged a formal dispute at the Fair Work Commission and challenged the bank to state its real rationale, given a half-year net profit of 5.44 billion Australian dollars. Forty-five onshore jobs were restored with a public admission of error. Several hundred offshore contractor roles went without one, because a contract that is quietly not renewed requires no consultation and generates no headline.

The Washington Times reported on 10 March 2026 that complaints from frustrated customers had prompted e-commerce and financial technology firms to quietly rehire content writers, software engineers and customer service workers replaced by AI systems, and that IBM, Salesforce, Google and Meta had added undisclosed numbers of workers in redefined roles. The operative words in that reporting are quietly, undisclosed and redefined. A reversal conducted quietly, at undisclosed scale, into redefined roles is not the same thing as a retraction.

What an apology would have to include

For the individual worker holding the phone, the practical questions are narrow and answerable. Is the role permanent or a contract? Is the pay above or below where it was, adjusted for two years of inflation? Is there written severance protection this time? Has the organisation changed how it makes automation decisions, or only which decision it reached about your function? Robert Half's Dawn Fay is right that judgement, oversight and institutional knowledge could not be replaced. The question worth asking the employer is whether they now have a process for finding that out before the redundancy consultation rather than three years afterwards.

The broader accounting is harder, because the costs and the benefits landed on different people. Ford's shareholders got a J.D. Power ranking and hundreds of millions in cost tailwind. The engineers got three years of disruption. Klarna's investors got a widely admired efficiency story in 2024 and a widely admired humility story in 2025. The 700 agents got neither. Across the Careerminds sample, roughly seven in ten organisations rehired, and roughly seven in ten found that the exercise had made them no money at all. The severance was paid, the recruitment fees were paid, the re-onboarding was paid, the quality failures were paid for by customers, and the ledger came out flat. The only unambiguous transfer was from the workers to nobody in particular.

What the cycle reveals about the gap between AI rhetoric and AI capability is not that the technology is useless. Ford still uses it. Klarna's assistant still handles two-thirds of conversations. IBM's AskHR still resolves 94 per cent of routine requests. The gap is not between AI working and AI failing. It is between a claim about what a system will be able to do and evidence about what it currently does, and the fact that in 2025 and 2026 a great many organisations treated the first as though it were the second, then priced human beings out of their livelihoods on the strength of it.

The workers who are being called back were not defeated by a machine. They were removed by a forecast. Those are different things, and the difference is the whole of the injury.

Sources and References

1. Crumley, Bruce. “55 Percent of Leaders Regret AI Layoffs, and a Major Hiring Reversal Has Begun.” Inc., late July 2026. https://www.inc.com/bruce-crumley/55-percent-of-leaders-regret-ai-layoffs-and-a-major-hiring-reversal-has-begun/91380901
2. Forrester Research. “Predictions 2026: The Workforce Muddles Through Ambient Disruption.” Forrester, October 2025. https://www.forrester.com/blogs/future-of-work-predictions-2026-whats-coming-for-work-and-the-workforce
3. Orgvue. “55% of Businesses Admit Wrong Decisions in Making Employees Redundant When Bringing AI Into the Workforce.” PR Newswire, 29 April 2025. https://www.prnewswire.com/news-releases/55-of-businesses-admit-wrong-decisions-in-making-employees-redundant-when-bringing-ai-into-the-workforce-302440611.html; and “92% of Organizations Have Invested in AI but 78% Say Projects Have Either Stalled or Failed.” Orgvue, 2026. https://www.orgvue.com/news/92-of-organizations-have-invested-in-ai-but-78-say-projects-have-either-stalled-or-failed/
4. Careerminds. “AI-Led Layoffs: What HR Leaders Wish They Knew Before Making Job Cuts.” Careerminds, 2026, survey of 600 HR professionals, February 2026. https://careerminds.com/blog/cost-of-ai-layoffs
5. “Employers Who Laid Off Workers Citing AI Are Already Starting to Regret It.” CNBC, 1 July 2026, reporting Robert Half data. https://www.cnbc.com/2026/07/01/employers-who-laid-off-workers-for-ai-are-reversing-their-decisions.html
6. Castrillon, Caroline. “Your AI Layoff Was Reversed. Should You Go Back?” Forbes, 26 July 2026. https://www.forbes.com/sites/carolinecastrillon/2026/07/26/your-ai-layoff-was-reversed-should-you-go-back/
7. “The Great AI Layoff Is Turning Into the Great AI Rehire.” Fast Company, 2026. https://www.fastcompany.com/91571824/the-great-ai-layoff-is-turning-into-the-great-ai-rehire
8. “Companies Rehire Workers After AI Replacements Fail.” The Washington Times, 10 March 2026. https://www.washingtontimes.com/news/2026/mar/10/ai-layoff-reversal-companies-rehire-customer-roles-eliminated/
9. “Ford Has Been Rehiring Quality Inspectors After AI Fell Short.” Bloomberg, 25 June 2026. https://www.bloomberg.com/news/articles/2026-06-25/ford-has-been-rehiring-quality-inspectors-after-ai-fell-short
10. “Ford Rehires 'Gray Beard' Engineers After AI Falls Short.” TechCrunch, 28 June 2026. https://techcrunch.com/2026/06/28/ford-rehires-gray-beard-engineers-after-ai-falls-short/
11. Challenger, Gray and Christmas. “Challenger Report: June Layoffs Cool to 45,849, Down 53% From May; AI Leads Reasons for Fourth Consecutive Month.” 1 July 2026. https://www.challengergray.com/blog/challenger-report-june-layoffs-cool-to-45849-down-53-from-may-ai-leads-reasons-for-fourth-consecutive-month/; and “Job Cut Announcement Report, May 2026.” June 2026. https://www.challengergray.com/wp-content/uploads/2026/06/Challenger-Report-May-2026.pdf
12. Autor, David H. “Polanyi's Paradox and the Shape of Employment Growth.” NBER Working Paper 20485, September 2014. https://www.nber.org/system/files/working_papers/w20485/w20485.pdf
13. Falk, Brett Hemenway, and Gerry Tsoukalas. “The AI Layoff Trap.” arXiv:2603.20617, submitted 21 March 2026, revised 3 June 2026. https://arxiv.org/abs/2603.20617
14. Paul, Karsten I., and Klaus Moser. “Unemployment Impairs Mental Health: Meta-Analyses.” Journal of Vocational Behavior, vol. 74, 2009, pp. 264 to 282. DOI 10.1016/j.jvb.2009.01.001. https://www.sciencedirect.com/science/article/abs/pii/S0001879109000037
15. Sullivan, Daniel, and Till von Wachter. “Job Displacement and Mortality: An Analysis Using Administrative Data.” The Quarterly Journal of Economics, vol. 124, no. 3, August 2009, pp. 1265 to 1306. https://academic.oup.com/qje/article-abstract/124/3/1265/1905153
16. Granulo, Armin, Christoph Fuchs and Stefano Puntoni. “Psychological Reactions to Human Versus Robotic Job Replacement.” Nature Human Behaviour, vol. 3, no. 10, October 2019, pp. 1062 to 1069. DOI 10.1038/s41562-019-0670-y. https://www.nature.com/articles/s41562-019-0670-y
17. Arnold, John D., Chad H. Van Iddekinge, Michael C. Campion, Talya N. Bauer and Michael A. Campion. “Welcome Back? Job Performance and Turnover of Boomerang Employees Compared to Internal and External Hires.” Journal of Management, 2021. https://journals.sagepub.com/doi/abs/10.1177/0149206320936335
18. METR. “Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity.” 10 July 2025. https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/; and “We Are Changing Our Developer Productivity Experiment Design.” 24 February 2026. https://metr.org/blog/2026-02-24-uplift-update/
19. METR. “Measuring the Self-Reported Impact of Early-2026 AI on Technical Worker Productivity.” 11 May 2026. https://metr.org/blog/2026-05-11-ai-usage-survey/
20. “MIT Report Finds Most AI Business Investments Fail, Reveals GenAI Divide.” Virtualization Review, 19 August 2025, reporting MIT Project NANDA “The GenAI Divide: State of AI in Business 2025”. https://virtualizationreview.com/articles/2025/08/19/mit-report-finds-most-ai-business-investments-fail-reveals-genai-divide.aspx
21. “Klarna Changes Its AI Tune and Again Recruits Humans for Customer Service.” CX Dive, 9 May 2025. https://www.customerexperiencedive.com/news/klarna-reinvests-human-talent-customer-service-AI-chatbot/747586/
22. “Commonwealth Bank Reverses Job Cuts Decision Over AI Chatbots.” Bloomberg, 21 August 2025. https://www.bloomberg.com/news/articles/2025-08-21/commonwealth-bank-reverses-job-cuts-decision-over-ai-chatbots
23. “AI Drives Fresh CommBank Job Cuts.” Information Age, Australian Computer Society, 30 July 2026. https://ia.acs.org.au/article/2026/ai-drives-fresh-commbank-job-cuts.html; and “CBA Axes Contractor Call Centre Jobs After AI Rollout.” Human Resources Director Australia, 29 July 2026. https://www.hcamag.com/au/specialisation/hr-technology/cba-axes-contractor-call-centre-jobs-after-ai-rollout/584060
24. “IBM Plans to Triple Entry-Level Hiring in the US in 2026.” Bloomberg, 12 February 2026. https://www.bloomberg.com/news/articles/2026-02-12/ibm-plans-to-triple-entry-level-hiring-in-the-us-in-2026
25. “IBM Replaced Hundreds of HR Workers With AI, According to Its CEO.” Entrepreneur, 2025. https://www.entrepreneur.com/business-news/ibm-ceo-ai-replaced-hundreds-of-human-resources-staff/491341

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Tim Green UK-based Systems Theorist & Independent Technology Writer

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

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

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

Listen to the free weekly SmarterArticles Podcast

S.N.I.t.

Van Voorbijgaande Aard beseft het eerder op tv geziene ook nog niet helemaal.

Wij staan hier nog altijd op het ere metaal winnaar meet & greet de journalist van eigen land en taal point bij Jan Ongeveer gouden medaille winnaar van het onderdeel 2500 meter slinger sprong. Jan je hebt zojuist je persoonlijk aanloop record op de 2500 meter verbeterd, met een kogel in je hand, deze na de aanloop maar liefst 5 meter verder geworpen dan tijdens de vorige aluminium meeting waarop je maar tweede werd en daarna sprong je maar liefst 16 meter en 54 cm vanaf het slingerpunt, meer dan genoeg om alle andere deelnemers te kleineren, en ruim voldoende voor je eerste gouden plak ooit op een internationaal kampioenschap dit jaar. Wat gaat er door je heen.

Ik kan er met mijn hoofd niet bij, hier werk je maanden naar toe en je hoopt er het beste van als de strijd begint en dan winnen, van al die andere strijders, super atleten, ik besef het nog niet helemaal.

Oké, bedankt. Is het goed dat we daar dan morgen op terug komen?

Eh ja, oké.

De volgende dag, exact 24 uur later

Jan leuk je weer te zien. Besef je inmiddels wat je hebt gedaan gisteren.

Nee, nog altijd niet, ik heb mijn best gedaan maar vooralsnog besef ik het niet. Vraag me morgen nog maar eens.

Doen we

De volgende dag 25 uur later na de laatste ontmoeting, meer dan twee dagen na het goud.

Jan, welkom terug bij ons meet & greet point. Gaat het inmiddels volledig door je heen. Is het daar, het besef?

Het is nakende denk ik, maar voor zover ik nu durf te zeggen denk ik niet dat ik het alvoor de volle honderd procent deed, beseffen wat ik eerder heb gedaan en hoe.

We komen hier morgen op terug Jan.

De volgende dag, 27 uur later na gisteren.

*Hoi Jan, hier en nu net nadat ook Elsebed Oeverloos en Maritine Meervoud goud wonnen op het onderdeel Speer overwerpen, waarbij ook Elsebed het eerst maar moeilijk kon beseffen maar Maritine meteen, zij kon dat besef direct overdragen op Elsebed en dat kon je ook heel goed op tv zien en horen maar hoe is het met het jouwe Jan.

Op zich beter, ik had het besef maar even later verloor ik het alweer. Toen ik het wederom kwijt was brak ik in tranen uit, meteen daarna heb ik contact opgenomen met mijn mental coach omdat ik beter wil worden in omgaan met behaalde titel besef. Dit is al het vijfde toernooi waarop ik ere metaal behaal en bij geen van die eretitels had ik erna echt diepgaand besef. Ik kan zo niet doorgaan, beste, lieve, vragende mensen bij de meet en greet met mij, ik ga vanaf nu deelnemen aan het leer traject besef basis training zodat ook ik doordrongen zal zijn van het door mij behaalde en niet langer zomaar wat doe! Dit moet en zal de laatste keer zijn dat ik te weinig besef ervaar zowel hier bij jullie als naast de baan, in mijn hotel kamers, bij mijn collega's. Eindelijk ben ik me ervan bewust dat ik ondanks alle trainingen, het bloed, zweet en de tranen dit ene kleine onderdeel horende bij het bereiken van elk podium niet beheers. Ik schaam me hiervoor. Hopelijk kunnen jullie van de pers, de sponsoren, mijn familie en het publiek mij hierin steunen zodat ik wanneer ik alweer op het podium sta meteen kan zeggen dat ik daar en dan meteen besef wat ik op de baan heb gepresteerd meestal hetzelfde als waarvoor ik ook altijd alles op zij heb gezet, titels, prijzen in de kast, een plek boven op het podium.

Bedankt Jan Ongeveer, sterkte gewenst met het behalen van besef behorende bij successen dan komt het beste vast helemaal bij je boven en kun je net als ons echt heel veel van al die top prestaties genieten, telkens weer.

Where the wheels pause, the world waits.

Tire Shop Purgatory – A Poetic Exploration

Midmorning on a Friday. A trip to the local landfill left a leaking hole in one of my tires. The tire shop (a national chain) is ringed with worn red chairs. Decades old and sun bleached.

Mostly middle-aged men hunch over their phones, scrolling Facebook or news. The lone woman is thread-worn but clinging to youth—blouse a size too young, short skirt revealing legs that are billboards for her love of the sea: turtles, octopuses, tribal tides of ink.

Her skin seconds her love of the sea.

I wish women would not tattoo their legs (or arms or chests). They are such beautiful features, and I have never seen appendages improved by ink.

A subtle mark in a quiet place can be alluring—even evocative. But women are creatures of beauty; their bodies are art. Covering them in tattoos feels like graffitiing a masterpiece.

Men? Snips and snails and puppy-dog tails... so I guess... whatever!

At the counter, the clerk is quietly explaining to a pretty young mom that she needs two tires, when two is two more than she can afford. I think I sense his trepidation at trying to upsell her, but he keeps going. It’s his job.

Two old men laugh about the old days.

“Mike!” one says, extending a hand.

“Ron,” the other corrects while shaking. “Mike’s my fatter, less handsome brother.”

They roar with laughter.

Car-lot camaraderie, I suspect.

A second young mom enters with her toddler. Workout clothes, immaculate hair, painted-on leggings, and a magenta strap of fabric for a top—form over function. Her right shoulder blooms with a sleeve of flowers and a circular logo in the middle. I stare too long, trying to decipher its meaning.

What’s clearer are the horse tracks climbing her spine. Barrel racer? Bronc buster? Eight-second rides memorialized in ink?

Hahaha! Do women break horses? Of course, I know they do, we dined at a horsewoman’s home during the workshop we attended a few weeks ago. I don’t recall ever seeing that at rodeos however.

Then—shock—a woman in a long skirt and brown tank top is reading a book. How pleasant to see a reader among the scrollers.

I want to stand up and declare, 'Hear ye, hear ye! This blessed soul is transferring thoughts and intention long-considered, drafted, edited, rewritten, then set and printed into physical form. We shall laud her adherence to REAL reading!!'

And follow with,

'By the way, I'm not like you lemmings, I'm using my device to MAKE ART. About you, ironically.'

But I do not. I just smile to myself, finish this brief and go back to my Jack and Mara story. He has just discovered he has arrived to late to see this very special woman.

I wonder if THEY will ever be indelible enough to be in ink.

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2025-10-10 13:00:30

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#tireshop #essay #travel #penandink #100daystooffset #writing

In Summary: * Listening now to the pregame show ahead of tonight's MLB game between the Guardians and the Tigers, and looking forward to tomorrow morning when a/c contactors will be here to, hopefully, get the a/c in this house back in working order. I am SO looking forward to that!

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

Health Metrics: * bw= 225.53 lbs. * bp= 128/76 (67)

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

Diet: * 05:20 – 1 banana * 06:10 – 2 HEB bakery cookies, 1 ham and cheese sandwich * 08:10 – 1 small milk cheese cake * 10:30 – 2 more cookies * 12:15 – meat loaf, mashed potatoes and gravy, green beans, whole kernel corn, fresh mango

Activities, Chores, etc.: * 04:00 – listen to local news talk radio * 04:45 – bank accounts activity monitored. * 05:05 – read, write, pray, follow news reports from various sources, surf the socials, nap * 10:45 to 11:45 – yard work, cleaning weeds out of the flower bed in front of the house, got bug bit * 12:15 to 13:15 – watch old game shows and eat lunch at home with Sylvia * 15:45 – now listening to WTAM 1100 Cleveland's News, Talk & Sports Station, for general sports talk ahead of tonight's MLB Game between the Guardians and the Tigers * 17:10 – MLB has just activated their audio feed bringing me WTAM's Guardians pregame show ahead of tonight's game.

Chess: * 16:15 – moved in all pending CC games

#fiction #books #fantasy

Warning: Contains spoilers

Fantasy has a subgenre that asks itself “what if history, but with magic?”, and within that subgenre there is a peculiar penchant for novels set during the British Regency era with its High Society romances and the thrill of the Napoleonic Wars. Bridgerton with Dragons, if you will. Because after all, what is more dashing than a well-bred aristocrat wizard wearing the uniform of His Majesty’s Royal Navy?

Susanna Clarke’s Jonathan Strange & Mr Norrell falls within this genre, but takes a deeper approach than adding magical creatures to a romance or adventure story. Set during the Napoleonic Wars, it tells the story of the two titular magicians and their efforts to restore magic, specifically English magic, to the world. The novel’s 900+ pages of high society salons, the Peninsular War and expeditions to elven lands are quite possibly less about magic, and more about what does or does not define Englishness. Clarke’s novel almost feels like an incantation itself, a magical tome containing an intricate spell to conjure up a lost England for the reader.

Powerful spells require excellent concentration and superb command of one’s ingredients however, and this is where Jonathan Strange & Mr Norrell does not always have the wizardry required. It is a daunting undertaking to sustain pace and dramatic tension across hundreds of pages, and at times Clarke does not succeed at this. But it is the ending where for me the story unravelled and the spell broke. There is much to enjoy in Jonathan Strange & Mr Norrell, in particular for fans of the English classics, but the finale did not deliver the magical fireworks that its premise promised.

Jonathan Strange & Mr Norrell is built on an almost dialectical series of interconnected juxtapositions that start with the contrast between the two mages and spirals out from there. We are introduced first to Mr Norrell, a deeply insecure, narrow-minded and vindictive member of the minor gentry set outwith Society by virtue of both his status and personality. His deep-seated anxieties drive his contradictory impulses of wanting to restore English magic, while simultaneously sequestering any magical books he can find away in his private library lest anyone else might read them. Norrell is a man who craves intellectual companionship with the same strength with which he fears potential competition. It is a rare moment indeed when he overcomes his caution and takes on Jonathan Strange as his apprentice, who is psychologically the complete opposite of Norrell, the yang to Norrell’s yin. Equally riven by contradictory impulses, Strange oscillates between the insouciant and carefree nonchalance that only comes from being securely born as an aristocrat into high society, and obsessive fixation on whatever interest or task he has set himself. Strange takes up magic on a whim, but his unthinking self-confidence means ends up drawn far deeper into the mysteries of the art than Norrell.

Unbeknownst to themselves, both magicians find themselves in conflict with The Gentleman with Thistledown Hair. A faery with no regard for morality, honour or good manners, he represents the dark and wild side of English magic that Norrell seeks desperately to excise from his modern approach to magic, and to which Strange becomes fixedly attracted. Yet it is Norrell who first sets the Gentleman on a course against the magicians, by compelling him to assist in the resurrection of Lady Pole, a young woman, to secure Norrell’s access to the highest levels of government.

Together with various other characters orbiting the three magicians, Lady Pole represents a third juxtaposition within the novel, between the three mages and their collateral damage. For what unites Norrell, Strange and the Gentleman is a callous disregard for the consequence of their actions. Even though the three mages are constantly at odds with one another, they share a deep carelessness about other people: Norrell because he cares only for himself, Strange because he does not care for others, and the Gentleman because he cares not at all.

The characters hurt by the three mages represent various oppressed groups in Regency England: women, black slaves, the poor and working classes. Norrell’s bargain with the Gentleman to restore life to Lady Pole sees the latter lead a double life in the Faerie, leaving her exhausted and seemingly mad, to eventually be secluded away for the sake of propriety. Strange’s neglected and ignored wife Arabella is similarly enchanted, as is Stephen Black, black butler to the Pole household who is neither slave nor free man. And poor, working and even middle class magicians are excluded from the revival of English magic as it established as a gentlemanly pursuit.

And therein lies the final contradiction, for while Norrell, Strange and their Government Ministers dream of a thoroughly English magic for the modern age, the magic itself stubbornly refuses to conform to their aristocratic sensibilities. Magic was brought to England a thousand years earlier by the Raven King, a human foundling raised by faeries who conquered Northern England to establish his own kingdom. The Raven King’s magic is indeed English, but it is the magic of gnarled oaks in dark winter forests as much as of the green rolling hills in the imaginations of Norrell, Strange, and the past and present English upper classes, ensconced in their stately homes in the Home Counties. There is an obvious implied argument here that it is not just the magic, but Englishness itself that is much darker, older and wyrder than the national myth of the green and pleasant land and the people that own it. That England’s blasted moorlands, mountain crags and snow-covered forests, and the people that dwell there, have an equal claim to Englishness.

Breaking open the dominant notion of Englishness to make it more inclusive has merit, but the risk of an argument about the cultural self-conception of a specific part of a small island nation on the periphery of the Eurasian continent is that it becomes, well, insular. The outsized cultural influence of the Anglosphere notwithstanding, Jonathan Strange & Mr Norrell does not escape this inherent provincialism, and instead rather leans into it. In part this is because the debate at the heart of the book implies that magic is something uniquely English, there being no evidence or engagement with foreign magical practices past or present throughout the novel. On top of that, the style and format of the novel further reinforce its profoundly inward-looking nature. To pay homage to English Romance novels, Jonathan Strange & Mr Norrell is written like a contemporaneous account of the novel’s events (footnotes included!), but the price of this is that despite Clarke’s possible misgivings about the English upper class, they still dominate the pages and provide our lens on the story’s events. Unable to escape their inbred sense of superiority, Jonathan Strange & Mr Norrell is unfortunately suffused of the infamous “Heavy Fog in Channel – Continent Cut Off” mentality.

The centrality of upper class characters is also one of the reasons why Jonathan Strange & Mr Norrell did not fully work for me as a narrative that attempts to bring marginalised groups back into the story, though I don’t dispute this is a possible reading. It is undoubtedly the case that the female and working class characters have more common sense and see things more clearly than either of the two titular magicians or the upper class characters who are frequently depicted as either incompetent, venal, or both. But with the exception of Mr Norrell’s manservant Childermass and the vagrant prophet-magician Vinculus, most of the marginalised characters have their agency curtailed either directly (as in the case of Lady Pole who is enchanted by the Gentleman) or indirectly (as in the case of Stephen Black who has no standing on account of his race). While they act as a foil for the arrogance of the wizards and their class, they do not exert their own power on events until the final part of the book, and even then their role is instrumental, but not directive.

This is is because as we approach the novel’s finale in the third act, we discover that Clarke chose to play with that most dangerous magic of all: prophecy. Jonathan Strange and Gilbert Norrell may believe they are directing the return of magic to England, but gradually it is revealed that they are mere ingredients in someone else’s spell. A spell by none other than John Uskglass, the Raven King himself, to restore his magic to England.

It is an intriguing development, falling somewhat short of a twist by virtue of serious foreshadowing, but it has the effect of collapsing the dramatic tension just as we approach the conclusion, For as soon as we know that events are preordained, the actions, agonising and arguments of the protagonists cease to be compelling. The outcome of the story may well be interesting, but it is so in the way of a game of chess: we might be invested in the outcome and the play, but nobody will enquire after the moods or motivations of the pieces.

For this reason I was ultimately also not persuaded by the thesis that Jonathan Strange & Mr Norrell emancipates its marginalised characters by moving them from periphery into the centre of the story. Because the centre of this story is empty, a void spun around the absent figure of the Raven King who turns out to have been pulling everyone’s strings all along. In a way, Clarke does manage to equalise the standing of her upper class magicians and her other characters, but it is done by reducing the former rather than elevating the latter.

As an argument about who magic belongs to, the denouncement of Jonathan Strange & Mr Norrell may work very well, but as a story it fizzled out for me. By this point, the reader has spent hundreds of pages of sometimes laboured prose in the company of two unpleasant magicians, whose idiosyncrasies, inability to communicate and failure to notice crucial events perform precisely as the plot demands. It may have worked if Clarke had also intended Jonathan Strange & Mr Norrell as a pastiche of a comedy of errors, but I doubt that is the case. In a way the ending retroactively justifies these odd lapses of judgement and the fact that the cause of English magic ever came to rest on the shoulders of two men so ill-equipped to bear it, but satisfying it is not.

Jonathan Strange & Mr Norrell is an ambitious, at times audacious work. It mixes evident affection for fantasy and the English classics with a critique of both genres, taking its story from what feels like a familiar beginning into an unexpected direction. But at over 900 pages it also feels drawn out, with great lapses where nothing seems to happen and the reader is left waiting for events to unfold. Since it was published in 2004, fantasy has seen much innovation and a proliferation of protagonists from diverse backgrounds, and by comparison Jonathan Strange & Mr Norrell can feel dated. It should certainly still appeal to readers with a love of the ‘historic Britain with magic’ subgenre, in particular those who are intimately familiar with the English classics. But beyond that it is somewhat too much like its wizards: learned but long-winded, adroit but unfocused, and ultimately just not in control of its magic.

Notes & Suggestions

• There were times when Jonathan Strange & Mr Norrell reminded me of Gormenghast, probably because of the similarities in length, the drifting plot and the constant sense of something ominous in the background. Where Peake uses Gormenghast Castle as the structure that contains his protagonists, Clarke could be said to use the whole of the English landscape. But England is much larger than Gormenghast, and we never spend enough time outside of London to give it the same dominating presence as Peake’s Castle.
• ‘Men failing to communicate’ is also central to the plot of Akira, and I suppose it is generally a common trope. Akira’s sheer energy and much shorter runtime mean it gets away with it in a way that Jonathan Strange & Mr Norrell does not.
• I enjoyed putting this review in dialogue with a 2008 review of Jonathan Strange & Mr Norrell by Elizabeth Hoiem, published in Strange Horizons magazine. Although I eventually tracked down the original, I initially accessed it using the Wayback Machine, which seeks to preserve web content even after the original pages expire. A laudable aim that always values support.

What is it you would like to say?
 Say it, for this is the world’s end.

A poem about death shortly after the anniversary of losing my wife's closest friend.

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How could I worry about me?

I am now struck— Struck by how much I loved her, And how through all the years, I never once said, I love you.

I laughed with her. Supped with her. Took her cookies. Listened to her. Suffered next to her. Dreamed of her.

But—like my flesh and blood, I took her for granted— This woman who gathered me in, Through three decades and five, Sometimes as son, Sometimes as brother, Sometimes as a wise one.

—

Now, it's 3:30 a.m., Adrift in the emptiness of a parking lot 
I hear the distant whistle Of a night train crying Through the wind.


The clack-clack-clack Rolling in the dark Carries me back Where these sounds once Wove themselves into her walls.

Vibrations signaling I was trapped— Not in fear, But in refuge Walled from a worried world For just a few minutes more.

How I wish now— I wish I could be trapped one more time, Just for those moments. Not long— Just long enough to say:

Thank you.

I love you.

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#poetry #death #confession #storytelling #write #100daystooffload

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2025-04-26 09:39:17