When the phone stops ringing, a scribbler assumes the pages didn’t work. The agent couldn’t get answers. They’ve aged out. Or the last show that didn’t sell left a stain that won’t wash out.

Thousands of scribes run the same solitary autopsy, each one convinced they’re the only one asking what they did wrong.

Enough private dry spells eventually add up to public data.

The latest Writers Guild of America West annual report puts a number on it: 5,072 members reported covered earnings in 2025.

That’s a 10.2 percent drop from 2024 and a 27 percent drop from the 2022 employment peak of nearly 7,000 members.

The Number Behind the Number

Those 5,072 working scribblers collectively reported about $1.7 billion in earnings, only 1.3 percent less than the year before.

Employment fell 10.2 percent. Earnings fell 1.3 percent. The money didn’t disappear nearly as quickly as the people earning it.

That doesn’t mean the average scribbler had a good year.

Aggregate earnings skew toward showrunners, creators, overall deals, and feature assignments, along with a small pool of highly compensated folks. The guild hasn’t published the median figure that would describe the experience of the person in the middle.

What the numbers do suggest: compensation is concentrating among a smaller group of working scribblers. Hollywood isn’t just cutting pay across the board. It’s narrowing who gets invited to participate in what’s left of the professional economy.

Television Removed Seats At The Table

The TV figures make the contraction impossible to dismiss.

Per the WGA’s own Writer Employment Snapshot, WGA-covered episodic series fell from 362 in the 2022-23 season to 228 in 2023-24, a 37 percent decline.

Total TV scribbling jobs fell from 3,138 to 1,819, a loss of 1,319.

That hits every level. Lower, Staff Writer and Story Editor slots that were the best way in the door, fell from 824 to 446. Mid-level gigs dropped from 720 to 421. Upper-level jobs, the Co-Executive Producer and showrunner chairs, fell from 1,594 to 952.

The 2023 strike shortened that season, so the comparison isn’t perfectly clean against a normal production year. But employment fell another 10.2 percent in 2025, long after the strike ended.

The shutdown just poured gas on house that was already burning.

The popular version of this focuses on young scribblers being denied their first chance. True: lower-level TV gigs fell 46 percent. But mid-level and upper-level scribblers lost two-fifths of their jobs too.

Fewer Staff Writers advance into Story Editor slots, and fewer Story Editors get the chance to produce; the pool of future scribbling capable showrunners shrinks with every step.

The industry isn’t just eliminating today’s jobs. It’s cutting the number of people who will carry production experience in ten years.

Features Are Contracting More Quietly

The feature business runs a version of the same problem.

The number of WGA-covered films released barely moved: 198 in 2022, 197 in 2024.

But across the first three quarters of those years, screenwriters reporting work fell from 1,947 to 1,651, a 15 percent decline.

Screenwriter earnings dropped 6 percent over the same span.

Nearly the same number of finished films reached the market, but almost 300 fewer scribblers reported employment.

A released movie is the end product, hiding how much paid work happened before the camera rolled. The data implies the studios can hold the same release volume while employing fewer scribes across development, rewrites, adaptations, and production.

Beyond the Scribble Room

Per the Bureau of Labor Statistics, employment in motion picture and video production reached 289,100 jobs in October 2022. By February 2026, it had fallen to 185,200. Recent employment levels have run about 100,000 jobs below that October 2022 high.

Los Angeles production data tells the same story locally. FilmLA recorded 19,694 permitted shoot days in 2025, down 16.1 percent from 2024. TV production finished the year about 50 percent below its five-year average. Drama sat 43.3 percent below its five-year average; comedy was 66 percent below it.

A scribble room can’t open for a series that’s never ordered, and an editor can’t cut footage that was never shot. Camera operators, costume designers, script supervisors: none of them work a location or dress a character that stayed trapped in development.

Where Scribblers Rank

The Bureau of Labor Statistics offers another useful, if imperfect, comparison.

In 2025, the national unemployment rate ran 4.3 percent. For management and professional occupations, it ran 2.5 percent.

For writers and authors, the BLS category that covers scribblers, it ran 8.5 percent. TV, video, camera operators and editors sat at 9.3 percent. News reporters and journalists at 6.5 percent. Producers and directors at 5.4 percent. Actors sat at 26.8 percent.

These figures don’t measure WGA unemployment directly. The BLS classifies unemployed people by their previous occupation, and the count misses freelancers who are underemployed, developing unpaid material, working outside the profession, or not actively looking.

The 2025 annual average also excludes October, since data collection stopped during the federal shutdown.

Still, the direction reads clearly. Scribblers experience unemployment at more than twice the national rate and more than three times the rate for professional occupations.

In most professions, experience makes a person more employable. In Hollywood, you carry decades of credits and still depend on a small collection of buyers deciding this year they need less shows.

The Closest Comparison Is Journalism

Newspaper publishing employed nearly 458,000 people in 1990. By 2016, that figure had fallen to about 183,000, a decline of almost 60 percent. Employment in internet publishing grew across the same period, but it never rebuilt the old newsroom employment model.

News consumption grew more abundant than ever, arriving through websites, social media, newsletters, podcasts, aggregators, and video platforms. What collapsed was the number of outlets willing to offer stable professional employment to the people producing it.

People still consume enormous quantities of TV, movies, games, podcasts, short-form video, and online narrative content, even as the professional market supporting scribblers keeps contracting.

There can be more content in the world while fewer people earn a living creating it. Journalism has already proved this to be true.

Not Primarily an AI Story

Artificial intelligence will surely factor into whatever comes next. But the contraction has already happened w/o it.

The WGA attributes the TV decline to falling cable subscriptions and programming, rapid expansion and retreat of streaming production, and Wall Street pressure for profitable platforms.

Consolidation adds another pressure. Fewer studios and platforms mean fewer buyers competing for material and talent.

In its recent lawsuit to block the proposed Paramount Skydance and Warner Bros. Discovery merger, the WGA argues eliminating a major competitor would mean fewer opportunities, fewer overall deals, and additional downward pressure on compensation.

That’s the current crisis. AI arrives inside an industry already committed to cutting head count and compressing the number of paid steps between idea and finished product.

The immediate danger sits in something quieter than an AI generating the next great television pilot: companies using AI to justify employing fewer people for coverage, development, research, visualization, rewriting, localization, and marketing.

AI only needs to help the industry decide it can function with 4,000 working scribblers instead of 5,000.

Then 3,000 instead of 4,000.

Stop Treating the Market Like a Meritocracy

Great pages remain more valuable than mediocre ones. Taste, emo intelligence, experience, and judgment carry weight, and may carry more weight still as the volume of synthetic material increases.

But we should stop pretending that employment is a clean referendum on talent.

A gifted scribbler can sit unemployed in a contracting market. A veteran can do everything correctly and still run into fewer buyers, fewer series, and fewer available chairs. Personal improvement can’t solve an industry-wide reduction in demand.

That realization stings, but it can also be useful. It lets us separate the act of creating stories from the specific industrial system that once paid us to create them.

A screenplay can become a book, game, audio drama, comic, short film, a narrative website, or the foundation of an independently controlled story world.

AI and other digital tools can help storytellers move across those forms without waiting for a legacy studio to assemble the traditional infrastructure.

Yes. And… That path leaves the TV salary, the health insurance, the pension, and the vanished apprenticeship system behind.

New Professional Reality

For most of my career, the strategy was: develop material, sell or staff, do good work, and use that work to secure the next job.

That system was never fair or stable. But enough shows got made that a professional middle class could exist inside it.

That middle has been hollowed out.

The next generation of professional storytellers may need to work across formats, retain ownership, cultivate direct relationships with audiences, and build production systems around themselves.

The numbers don’t lie. The trend is clear. Dependence on a shrinking group of buyers has become increasingly dangerous.

I’ve built my share of tech villains. AI billionaires training murder drones, sentient code deciding humanity’s had its shot, the whole Skynet playbook. It’s the pitch every exec understands in one sentence, because the moment the machine turns evil, nobody in the room has to think too hard about who typed the command.

Then a story like this one lands —

— A police chief in Georgia spent more than a year running his ex-girlfriend’s license plate through a nationwide camera network. Hundreds of searches, no warrant attached, just a man chasing a woman with department credentials. When she found out, through an audit log compiled by the watchdog site Have I Been Flocked, the tool that caught him was the same paperwork trail the system generates on everyone, all the time, automatically.

That’s a Washington Post investigation, published this month, sourced from police and court records.

Skip The Robot Uprising

Every scribbler pitching an AI thriller reaches for the same button: the machine wakes up, Skynet blinks, HAL decides, the chip achieves consciousness and turns on its makers.

Real life keeps scribbling an alternate take. The machine stays asleep the whole time. The guy with the login credentials wakes it up and points it exactly where he wants.

History backs this pattern up. The printing press spread propaganda faster than any pulpit could counter it. The telephone gave con artists a direct line into living rooms. Photoshop turned a darkroom trick into a courtroom weapon.

The Camera Network

Flock Safety’s automated license plate readers now cover more than 120,000 cameras across more than 6,000 communities, scanning roughly 20 billion plates every month. The system exists to catch stolen cars and outstanding warrants.

It also turns out to be very good at answering questions nobody in a city council meeting ever approved it to answer.

The Washington Post counted at least 50 law enforcement officers charged with or accused of misusing plate reader systems for personal reasons, most of them tracking women. Forty-six of those cases ran through Flock specifically. Twenty-six involved an officer monitoring a wife, girlfriend, ex-partner, or someone else’s new partner.

Roseville, California ran its own internal numbers separately and found that 71 percent of the felony and stolen-vehicle alerts its Flock cameras generated over two years were built on a misread license plate.

Seven bad reads for every three good ones, on a system meant to justify pulling someone over.

Cities are noticing. A crowdsourced project called DeFlock now maps camera locations nationwide, and a growing list of city councils have cancelled contracts or shut cameras off entirely.

It only took someone with a login and a grudge, and a system that logs everything except intent.

The Permission Machine

New tech rarely invents a new sin. It industrializes an old one.

A location database grants permission to stalk at a scale no human could manage on foot. A synthetic voice grants permission to fabricate a conversation. A generative model grants permission to manufacture evidence a judge and jury might actually believe.

The person wielding the new tool makes that call, every single time. The tool just removes the friction that had slowed bad decisions down long enough for someone to reconsider their worst impulses.

This Has A Name Already

Intelligence analysts figured this out decades before Flock existed. During the Snowden years, the NSA’s inspector general confirmed a dozen substantiated cases of analysts running surveillance systems against people they were sleeping with, married to, or interested in.

Analysts nicknamed it LOVEINT, a play on SIGINT. Most surfaced through self-reporting, often during a routine polygraph. No safeguard flagged the search first.

Swap NSA for a police department and SIGINT for a plate reader, and the story runs on the same engine with a smaller budget.

The Cycle Never Changes

New surveillance tech shows up promising public safety. Someone proves it catches a real criminal. Somebody else, usually someone with legitimate access, realizes it also solves a personal problem.

Why This Belongs In Your Pitch Deck

Pitch decks are full of SkyNet AI stories. Real life is full of ordinary people with legitimate access and a personal reason to misuse it, and that story doesn’t require inventing new physics.

Here are four, built from the mechanism above: ordinary access, an old human want, and a tool patient enough to do the dirty work.

The divorce. An entrepreneur trains a personalized model on fifteen years of texts, emails, home videos, Ring footage, and family photos. At custody hearings, the model starts generating evidence nobody can disprove because nobody can tell it apart from the real thing.

• Anatomy of a Fall (2023) — the entire case turns on whether a recording can be trusted, and the marriage collapses under the same scrutiny as the evidence.

• Black Mirror, “The Entire History of You” (S1) — a couple’s relationship gets destroyed by rewatchable, replayable memory. Same core wound (intimate footage weaponized inside a relationship), older tech.

• Grounding: this is already happening. Family law sites and Forbes both reported 2025 custody cases involving AI-generated audio submitted as evidence, including a fabricated threat recording built from old voice messages.

Or the reputation attack. A city council candidate discovers someone quietly feeding half-true facts into dozens of AI systems. Every search summary and chatbot bio drifts further from the truth. He ends up fighting a future version of his own life story, one that keeps rewriting itself faster than he can correct it.

• Black Mirror, “Bête Noire” (S7, 2025) — A gaslighting parable for the disinformation age. A woman’s reality gets quietly rewritten by someone with a grudge and a device, and nobody else can see it happening.

• Black Mirror, “Joan Is Awful” (S6, 2023) — an ordinary person’s life is algorithmically repackaged sans consent.

• Grounding: A Georgia radio host sued OpenAI in 2023 after ChatGPT invented an entire fake embezzlement complaint against him. The suit got dismissed on summary judgment in 2025, so it’s not a legal precedent, but it’s proof the fear is already in the world and already in court.

Or the cop. A respected detective starts running a plate database against his daughter’s abusive ex. Everyone in his life agrees he’s right to do it. Then he runs her new boyfriend, then her roommate, then anyone she spends more than an hour with. The audience follows every decision and understands every one of them, right up until they realize the cop is now the perp.

• The Conversation (1974) — still the gold standard. A surveillance expert’s own tools and expertise become the instrument of his unraveling. Swap tape reels for a plate database and the shape barely changes.

• Nightcrawler (2014) — obsession with a tool escalating past every ethical line, told from inside the obsession rather than at arm’s length. Useful for keeping the audience implicated rather than horrified from a distance.

• Grounding: you don’t need a comp here, you have a source. This week’s Washington Post investigation is the premise, already reported, already named, already in court records.

Or the neighborhood. An HOA installs cameras after a burglary. Crime actually drops. Six months later, every resident realizes they know exactly where every other resident goes, every night. The block gets exactly as safe as everyone hoped. It also stops being private, for anyone, permanently.

• Home Invasion (2023, Fantasia) — an essay-documentary built around Ring doorbells and the paranoia they manufacture in otherwise safe communities.

• Rear Window (1954) — the classic version of this problem: watching the neighbors is safety and violation at once.

• Grounding: HBO’s Neighbors (2026, A24-produced) launched as HBO Max’s biggest unscripted debut, built entirely on the premise that property disputes between ordinary Americans are must-watch television right now.

The Actual Ending

Science fiction spent years warning us machines would learn to act like people. Reality keeps handing us people who already knew exactly how to act, and just needed a faster tool for unleashing their inner creep.

What’s the real-world AI misuse case you’ve already spotted that would make for a killer streaming thriller? Got one? Now —

— ABS. Always. Be. Scribbling.

A mother sits in federal court, accused of stealing millions from the company she built. Her husband set her up, and her own daughter believes it. Then the doors swing open. A nine-year-old boy walks in holding a beat up blue backpack, points across the room, and tells the judge he knows exactly who framed his mom.

Great cold open. Wrong genre. Turns out it wasn’t a script at all. It ran as a thread on X, and WIRED traced the whole thing back to a source who never existed.

The scribbler behind it goes by BIGBEN, a 21-year-old stock trader in Nigeria who feeds prompts to Grok and ChatGPT for a living and collects a payout from X every two weeks for his trouble.

My ADD brain zeroed in on the structure before I’d even finished reading. Nobody in a scribbler’s room saw this coming. It didn’t come out of a studio note, it came out of a payment algorithm.

The Wound

Rob Cover, a professor at RMIT’s Digital Ethnography Research Centre, spent real time studying these threads and broke the shape down like he was diagramming one of my old beat sheets. Every one opens the same way.

An innocent person gets wrongfully fired, evicted, scammed, bumped from a seat that was rightfully theirs, or hauled in front of a judge who’s got it all wrong. Pick your injustice. The genre doesn’t care which one, as long as it stings.

Act One of every underdog story since fairy tales grew teeth.

The Object

Then comes the flip: a sealed document, a hidden will, a digital recorder planted in a cheating husband’s office that catches everything.

I’ve spent years chasing this exact kind of object, in the Alias room, Lost, Heroes, every room since. The gun in the drawer. The photograph in the folder. Chekhov didn’t invent the principle, he just wrote it down first.

BIGBEN’s version uses the same device and skips the work around it. The recorder shows up once, does its job, and vanishes. No one built the scene where it got planted, and no one earned the payoff. Call it a prop dressed up as a plant.

The Reckoning

Last beat, always. The underdog gets redeemed. The husband, the landlord, the airline passenger who wouldn’t give up seat 14C, they all get shamed in public, usually with a walk of humiliation built right into the visual.

Jenna Russell, a PhD candidate at the University of Maryland who studies AI scribbling, says the whole recipe is close to effortless to generate once a spammer knows the trope menu. Feed a model the injustice, the object, and the humiliation, and it hands back the dopamine hit fully assembled.

Call the whole three-beat machine the Grievance Engine. It only needs three moving parts, and none of them are original.

The Grievance Engine is my own Reel Four, compressed into ninety seconds and running on a phone. It’s the WANT framework stripped for parts. The people running these threads, out of Nigeria or out of the bot farm X sued in Hanoi, didn’t invent story structure. They found the switch every professional storyteller’s been flipping for centuries and wired it straight to a payment API.

X pays for engagement, and the company knows exactly how easy that engagement is to fake wholesale. It sued eight Vietnamese nationals and twenty-five unnamed “John Does” in May 2025 for running the bot farms gaming this exact program.

Clear the bar and the money moves. A Premium subscription, 500 verified followers, five million impressions in three months. BIGBEN says his deposits run $500 to $700 every two weeks.

The Missing Job

Cover’s sonnet has three beats and nothing else. No subplot, no secondary character complicating things, no object planted three scenes early so the payoff lands. The husband is corrupt because the plot needs him corrupt. The judge exists to be wrong, then right, then gone. Every location is a courtroom, a plane, an office, generic enough to be nowhere in particular.

That gap still needs a professional. Haecceity, the specific texture that makes a place and a person feel like they exist outside the frame, comes from research, memory, and refusing to let a generic stand-in do a specific job.

A model hands you an underdog with zero effort. The exact make of the recorder, why the kid chose to hide it there, what it costs a nine-year-old to testify against his own father in open court, that part still takes a person paying attention. And it costs a hell of a lot more than a prompt.

The formula still works. Injustice, object, reckoning has been pulling audiences since long before anyone got paid for it in dollars, likes, or upvotes. It’s built on something true about how people process unfairness.

Examples

Ideas that borrow the Grievance Engine’s skeleton (injustice, object, reckoning) and pay the debt BIGBEN skipped. Each earns the payoff with a planted setup or a subplot.

The Recipe
Injustice: A line cook named Odalys gets fired from a high-end kitchen after her signature dish quietly reappears on the tasting menu under the executive chef’s name.
Object: A stained recipe card in her grandmother’s handwriting, the one step the chef got wrong because he copied it from a photo instead of learning it from her.
Reckoning: She opens a six-seat pop-up two blocks away using the real recipe, no lawsuit, no public callout, and the chef’s restaurant loses its best table to her by the second week.
The Earn: Write the scene where she first teaches that unwritten step to her own line cook, years before the theft. Without that plant, the card is just a prop.

The Claim
Injustice: An insurance adjuster named Everett denies his own mother’s hospital coverage, then recognizes the denial code from a pattern he’s flagged three times that month.
Object: A spreadsheet he’s been quietly building for eight months, unglamorous, full of denials that don’t match the plain language of the policy.
Reckoning: He testifies at a state insurance hearing, and the payoff lands because the audience has watched him lose sleep over it for six episodes first.
The Earn: A brother subplot where the sibling thinks Everett’s a coward for staying at the company this long. Somebody in the story has to be wrong about him for the reckoning to mean anything.

The Deed
Injustice: A farmer named Thanh loses forty acres to a boundary dispute his neighbor manufactured with a forged survey.
Object: An eighty-year-old irrigation ledger in his grandfather’s hand, proving the water rights predate the neighbor’s paperwork by three decades.
Reckoning: Thanh wins the case in chambers by walking his teenage daughter through the ledger’s water-share math, so she carries the knowledge forward regardless of the verdict.
The Earn: Haecceity lives in the ledger’s actual entries, specific dates, specific acre-feet, names of neighbors long dead. Not “an old document.”

The Tape
Injustice: A teacher named Priya gets suspended after a doctored clip of her classroom goes viral, days before her tenure vote.
Object: A backup recording from the school’s decade-old intercom system, the one nobody remembers still runs, catching the real exchange.
Reckoning: The student who doctored the clip has to sit across from her and explain why, and she has to decide whether to forgive him on camera or off it.
The Earn: Plant the intercom system in episode one as a running joke about broken PA announcements. By episode six it’s load-bearing.

The Season
Injustice: A minor-league pitcher gets cut two starts before a call-up, and the front office blames an injury history that doesn’t match his real medical file.
Object: His own file, pulled through a records request, showing the team doctor altered a single date.
Reckoning: He pitches one more season in an independent league instead of getting reinstated. The front office quietly changes its documentation policy after the file goes public, a change he’ll never get personal credit for.
The Earn: The unearned version fixes everything for the underdog. Real craft can leave the call-up gone and let that be the point.

Notice the pattern. Every reckoning here costs the protagonist something, or costs somebody else something specific, instead of resolving clean.

On the fifth Raid Race card of the 2066 season, at Low Coast, nobody got the package out.

My sportsbook had priced that outcome at 6.90. Three of ten punters took it: the three who run their own numbers on each card instead of reading the board. It was the worst night the house had all season, four hundred and sixty-two credits down on a six hundred and fifteen credit handle.

None of those people exist. Neither does the sport. What I want to talk about is that I didn’t decide any of it.

This part matters for everything that follows: I didn’t build the thing that decided it. Claude did. I can’t write Python, and I don’t really understand what a seed does under the hood, not in the way I’d need to if you handed me the file and asked me to fix something broken in it.

I described what I wanted in plain scribbler English, and Claude built it. I ran it and reacted. That’s worth saying straight out, so this piece doesn’t sound like something it isn’t. I barely understand the machinery. I just think it’s cool.

Two Pages

Terra Conflictus 2066 is a tabletop gaming setting I’ve been building for a while. It has cities, factions, a genomic registration system that everybody argues about, and until recently it had no sports, which is strange for a world with four billion people in it who need something to watch.

So there are two pages now.

The Spread is the sports paper. Standings, results, reporting, sixteen circuits across two tiers. There’s a sanctioned tier with franchises and title sponsors and broadcast partners, and an underground tier that is variously illegal, unsanctioned, or so proudly ungoverned that its own organisers refuse to acknowledge half of it. Six correspondents file to it. Merren Dask writes the results and refuses to editorialise. Cass Imrie writes the column about who is getting classified out of which category and has been banned from two circuits and reinstated by both.

The Line is the sportsbook. It prices every one of those circuits, takes bets from simulated punters with bankrolls in unregistered energy credits, and settles them against whatever the sports page reports. Forty-four markets on a given cycle.

Both run themselves. Every few days a scheduled task advances the world by one cycle and files. I gave Claude the two pages as an idea. Claude is the one who figured out how to make them run.

The Rule

The simulator owns the numbers. The desk owns the voice.

I handed Claude that principle. What it meant in practice, dice classes, seeded runs, a ruleset written in code, was entirely Claude’s to work out, and I wouldn’t have known where to start.

Every sport on the board has published tabletop rules, and every one of those rulesets got turned into a Python simulator that implements them properly, dice and all. When the desk wakes up, it works out what is genuinely due today, runs those simulators on a fixed seed, and hands the correspondent a brief.

The correspondent writes from the brief and may never invent a result, adjust a result, or contradict one.

That sounds like a restriction. A language model asked to hold eighty results, a standings table, an injury list and a title race across scheduled runs will drift by week three.

Given the chance, it will also write the season it thinks you want. A seeded simulator produces a season that’s actually true, and the drama gets better because nobody rigged it.

The proof of that came free. Five cards into the first Raid Race season there had been five different outcomes: four separate crews took one each, and on the fifth nobody got the package out at all.

Los Creeps sat third in the championship having won nothing, on consistency alone. Nobody wrote any of that. I especially didn’t plan the fifth card, the one the entire betting page now turns on.

Making a Book That Can’t Cheat

The betting page was harder, and for one specific reason.

A sportsbook needs to price before the event and settle after it. If the odds are set with any knowledge of the result, the page becomes a ledger, and every number on it is worthless.

So the book estimates probability the only way it honestly can: it runs the sport’s own simulator two thousand times on throwaway random seeds, counts how often each result came up, and turns those counts into a price.

Later the fixture is played once for real, on its own seed, and the bets settle against whatever that produced.

The danger is obvious once you say it out loud. If the pricing run and the real run ever share a seed, the book has seen the future and everything collapses.

I told Claude I wanted that to be structurally impossible rather than carefully avoided, and it came back with three separate locks, any one of which would be sufficient on its own.

The book can only open a market on a fixture that hasn’t happened, checked four different ways. The book physically can’t write to the world file, only read it. Every seed the sports desk constructs sits below 10^8, while every seed the book constructs sits above 10^12.

That third one is the load-bearing part, and asserting it wasn’t enough for me, so I asked Claude to prove it instead. The validation instruments the dice constructor itself, watches every random number either side actually builds, and proves the two sets never touch.

Then it does something I’m more pleased with. It builds a deliberately sabotaged version of the book, one where a single sample out of two thousand is the real fixture, and checks that the test catches it. It does, at about thirty-eight standard deviations. The honest book reads flat on the same measure. A null result only means something if the test was capable of failing.

That test also taught me something I had wrong. I’d assumed a clean book should show zero bias on whichever runner wins. It shouldn’t. Winners are drawn from the true distribution, and the book has standing per-runner biases, so the expected reading is about minus a quarter of a percent. Tested against zero, the honest book failed. Tested against its actual null, it passed comfortably. The mistake was mine, and I only know that because Claude walked me through why.

The Book is Bad at its Job on Purpose

A book that prices off perfect probability and adds a margin is unbeatable. Every punter grinds to zero, the leaderboard becomes a straight line sloping down, and there is nothing to read.

So The Line is wrong on purpose. It’s wrong in two different ways, and only one of them matters.

It carries sampling noise, because it prices off a limited number of runs. Noise averages out across a season, so nobody can systematically beat it. It also carries bias: it leans on recent form, and it anchors its no-extraction number to the season average instead of adjusting properly for how hard the individual card is. Bias is wrong in a direction and stays wrong.

The measurement that settled it: holding everything else fixed and trimming the two bias terms drove the sharp punter’s return from plus four and a half percent to minus six.

The punters live on the book’s documented, directional wrongness.

That’s also what makes the Low Coast result work. The book anchors to a season average, so on an easy card it prices “nobody extracts” far too short and it’s a trap. Low Coast was a short window onto a locked breach, genuinely hard, and the same anchor ran the other way and made 6.90 a real price.

The three punters who noticed were the three who model each card instead of the season. The other seven were looking at the board.

What The Validation Caught

Claude validated each simulator across 120-plus runs before I trusted any of it enough to build a betting page on top of it. That part I did understand without reading a line of code: run it, watch what breaks, and go from there.

The Raid Race sim produced five events where nobody delivered anything, because a crew was getting charged an action for dismounting, which isn’t one of the five Raid Actions in the published rules.

The Crackerjack sim was four times too lethal, because a shorthand in my own battle reports had gotten read as a rule. The Long Count killed Brine Quist, who is an established character from published filings, so challengers now get drawn from the circuit’s own regulars instead.

The betting side was worse, in more interesting ways.

A minimum bet of twenty credits silently deleted three punters. Quarter Kelly sizes inversely with the odds, so a genuine 24% edge at 15.5 came out at nine credits, under the minimum, every single time. My two sharps placed almost no bets across eight simulated seasons, which quietly removed the entire point of the economy.

The Syndicate’s cut was supposed to be fifteen percent. It ate 109% of the gross instead, because the spec I’d given charged it on every winning market and recovered nothing on the losers, so winners got taxed and losers never got offset.

I’d accidentally designed a business that could not make money, and it took Claude running the numbers a hundred and twenty simulated seasons to show me that.

The chalk punter created a feedback loop that broke the market. Money shortened a runner, that made it the shortest price, chalk backed it for being the shortest price, and the whole floor ended up on one crew at 2.85 that had opened at 10.0.

And when I asked to widen the board from one sport to eleven, the margin model fell over on lopsided markets. A 98.6% favourite can’t absorb twelve points of margin, so the math dumped 806% of it onto the other side and quoted 7.7 about a 1.4% chance, a number nobody would take.

My favourite one caught itself. The page’s trading note gets generated straight from the ledger rather than written, so it can’t claim anything the book can’t show. It printed a sentence saying a 2,341 credit liability was inside a 900 credit limit. That can’t be true, and the cause was real: the exposure cap was reading an empty list and had never once bound.

Where It Is Now

The Spread files every three days. The Line wakes thirty-six hours later, finds the results waiting, settles, reprices, and takes the next round of bets.

The house is expected to hold 9.29% against a board priced to 10.71%, the difference being what the sharps take off it, and it loses money in 38% of simulated seasons.

Nesta Balint makes 14.4% and is the only reliable winner. Halyard Renko doubles after every loss and blows up in 57% of seasons, somewhere between the fifth card and the tenth, at which point a patron advances him somebody else’s money and takes a permanent share of his action.

Terra Conflictus 2066 is my tabletop setting. The Spread and The Line are both fiction, nobody can place a bet, and the simulators implement the game’s published rules. I built none of it. Claude did, over a few sessions of me describing what I wanted and reacting to what came back, half the time without fully following the reasoning until it was already running. I still don’t know Python. I know what a seed is now, in a general sense. What I know for certain is that the outcomes are real in the sense that counts. Nobody’s thumb was on the scale. Mine included.

Back when I got my ADD diagnosis, I built myself a deck of index cards. Fundamentals I’d learned from books and rooms and mentors, boiled down so my brain could actually remember them. “Track the character emotionally.” “Make it cinematic.”

One card I still flip to more than any other came from a paperback that sounds like a tax document: Dwight Swain’s Techniques of the Selling Writer.

Swain’s real subject was attention: how you keep a reader, or an audience, moving from one moment to the next without them noticing they’re being moved. Buried in that book is a tiny mechanical idea I lean on in every scribble session.

The Motivation-Reaction Unit. Clunky name, dead simple idea.

Something Happens, Then Someone Reacts

A motivation, in Swain’s terms, is what just hit your character: a door opening, a gunshot cracking, somebody saying, “I know what you did.” The reaction is whatever the character does because of it: they turn, they duck, they lie.

Sounds obvious. Scribblers screw up the order constantly.

Here’s the backward version: Jane turns, startled by the gunshot, and ducks behind the car. Technically fine. But we’re inside her flinch before we’ve even heard the shot.

Flip it: A gunshot cracks. Jane ducks behind the car.

Same information, but now the audience gets hit first, same as Jane does. Cause before effect, every time, no exceptions.

What a Reaction Actually Looks Like on the Page

Prose can crawl inside a skull and announce what a character feels. We don’t get that luxury. You can’t scribble, “Jane feels a wave of betrayal and understands her marriage was a lie.” Not unless it becomes visible.

So the reaction chain for us runs: the involuntary flash first, whatever the body does without permission, then the deliberate action, then the words.

DAVID: I was with her that night.

Jane goes still. She sets her wedding ring on the table.

JANE: Get out.

The stillness is involuntary, the ring is the choice, and “Get out” is the line. The feeling never gets named. It gets translated into behavior.

Generic reactions are a tell. Anybody can flinch at a gun. One person freezes, one fires blind, one steps in front of the kid standing next to them. The stimulus is the same for everybody in the scene. The specific reaction is what tells us who’s who.

Dialogue Runs on the Same Circuit

MRUs aren’t just an action-line thing. Every exchange is a chain of hits and responses.

MARA: Did you tell him where I live?

ELI: The train leaves at midnight.

Both lines carry information. Eli’s line ignores Mara’s, delivers the next plot beat. So it reads like scaffolding. Now try this:

MARA: Did you tell him where I live?

Eli can’t look at her.

ELI: The train leaves at midnight.

Same words, now the non-answer is the answer. The evasion is caused by accusation, and “the train leaves at midnight” suddenly means we have to run because I did the thing you’re afraid of. The reaction needs to be caused, whether or not it’s direct.

Don’t Announce the Punch Before You Throw It

The fastest way to flatten a reveal, a joke, or a scare is naming the emotional response before the audience has the image to get it.

Horrified, Malik opens the basement door and finds three bodies. You told the audience how to feel before showing them anything.

Malik opens the basement door. Three bodies lie under plastic sheeting, and he staggers back.

Now the audience finds the bodies on the same beat as Malik. His stagger confirms what they’re already feeling. Nothing is narrated in advance. This matters most on the beats where the whole scene is riding on the reveal. Let the hit land, then show the bruise.

Not Every Beat Earns Three Lines

None of this means chopping every stimulus and response into separate paragraphs like you’re building furniture from a kit. The dog barks, waking Nina. Fine as one line. Cause and effect are instant and there’s nothing to mine.

Break beats apart when stimulus deserves weight, when the reaction reveals character, when you need the audience to sit in a turn for a second before moving on. Combine them when the moment is simple and slowing down buys you nothing. MRUs are a diagnostic. Use them when a scene goes flat, set them aside when it doesn’t.

The Micro Version of Scene and Sequel

If you’ve read the toolbox before, you know I lean hard on Scene and Sequel. Goal, conflict, turn. Then reaction, dilemma, decision. That’s the macro rhythm, what moves acts and Reels.

MRUs are that same engine running at the scale of a single beat.

Scene and Sequel organizes the story. The Motivation-Reaction Unit organizes how the audience experiences each moment inside it. Zoom out enough and it’s the same chain: something happens, it lands on somebody, they choose, and the choice becomes the next something.

A dragging script still has plenty happening in it. Somewhere, the chain of cause and effect snapped. A character gets terrible news and their behavior never shifts. A reversal hits and the plan stays the same. A decision shows up because the outline needed it. The prior scene never earns it.

90% of the time, the missing piece is a reaction nobody scribbled.

The Emotional Receipt

We’re told to get in late and get out early. Good advice until scribblers use it as an excuse to skip the human cost. Character learns her brother died, then cuts straight to her walking into the precinct with a plan. The story moves, but nothing lands.

The fix is one image. She removes her brother from her favorites list. She pours two coffees before she remembers there’s only her now. She starts to say “I should call” and stops.

That’s the receipt: proof the event actually touched somebody.

Next time a scene feels weightless even though something big just happened, go back and check whether you gave the character room to feel the hit before you cut away.

ABS. Always. Be. Scribbling.

I read Model Behavior, Substack’s new interview series with tech journalist Taylor Lorenz, about how she uses AI, what it’s doing to her business, and how scribblers are pretending not to notice.

The whole conversation is worth reading, but one part resonated.

Lorenz said a New York Times editor once told her the point of journalism was to expose new information. That was the job. Expose what the rest of us didn’t know yet.

That distinction gets useful once you drag it into screen scribbling, game narrative, worldbuilding, comics, podcasts, and whatever strange story machine you’re assembling in the garage.

The Discovery Comes First

AI is very good at rearranging what’s already available.

It can summarize a genre, mimic a three-act outline, spit out twenty character names, bang out a competent scene, scrape a pile of lore, or build you a recommendation queue like Spotify’s. That makes a lot of creative labor cheaper. It also reveals which parts were already close to being commodities.

Ouch. Some of those parts took us years to learn. Oh well.

Of course the machine can scribble a decent scene. The real test: was your value ever just the existence of pages on paper?

For a screen scribbler, the discovery might be a human contradiction nobody else has noticed, time spent with a community that’s only ever appeared onscreen as a cliché, or the dramatic engine hidden inside a real event, family secret, or a strange job.

The screenplay is the delivery system. The discovery came first.

For a narrative designer, the discovery may not be a line of dialogue. It might be a behavior that emerges when players meet a system: a faction rule that creates conflict without a cutscene, a mechanic that forces a player to reveal what they value, a world condition that generates stories the designer never scribbled.

An AI can give you fifty quest ideas. It can’t sit inside your playtest and feel the moment everyone ignores the quest, adopts the disposable creature, and turns your tragedy into a rescue operation. That moment is the new information. The job is noticing and rebuilding around the truth the players just exposed.

For an independent creator, the discovery might be the world itself: a set of people, rules, images, obsessions, and artifacts that could only have come from your peculiar archive.

Taste Must Produce Something

This is where taste enters the picture, though the interview carries a warning here too.

Lorenz described part of User Mag’s value as access to her brain, the stories she personally finds interesting. She admitted readers will probably choose a feed tailored to their own interests over her taste, given the option. A hard admission to make in public.

Taste alone isn’t enough once a machine can manufacture a private recommendation engine for every reader.

Your taste needs to produce something worthwhile. It must connect two ideas that weren’t connected before, lead you somewhere inconvenient, and become a character, a field report, a strange mechanic, a new world, an argument, an image, or a story that didn’t exist until your taste acted on that material.

“Here are ten things I like” is replaceable.

“Here is the thing I made because I could not stop thinking about number seven” is a different proposition.

Bricks vs. Architecture

The interview also raises the transparency question. Lorenz uses AI heavily and talks openly about it, while some scribblers condemn the tools in public and trade prompts in private. I have no interest in the purity Olympics. The tools are here. I use them.

Did the machine sort research, stress-test structure, check continuity, generate options, or produce the thing the audience believes you scribbled? Those are different uses. If we can’t describe the difference, we haven’t decided where the line sits.

The architecture includes intent. It means deciding what the work is for, protecting that purpose through revision, and knowing when a polished answer is wrong. It means taking responsibility when ten plausible choices sit on the table and only one belongs in this story. Judgment work.

The Relationship Is the Work

Lorenz’s advice to young journalists is to build a direct relationship with an audience. Screen scribblers have historically been able to ignore that. We sold upstream. The studio, network, producer, or financier worried about reaching the people.

That bargain is getting wobbly.

The creator with an audience, an archive, and a way to reach people without permission has more options than the creator with a perfect file waiting in an inbox. Nobody needs to become a dancing content monkey. The relationship around the work is becoming part of the work itself. Show people what you notice, let them see what you’re building, and give them a reason to care before the giant finished object arrives.

I’ve started asking a new diagnostic question: what am I discovering here that can’t be obtained by a better LLM summary?

If the answer’s nothing, the project may be fun. But I shouldn’t confuse competent assembly with a defensible creative practice.

Go into the world, notice the things, make the connections, build the artifacts, form the relationships. Then use every tool you can get your dirty little creator hands on.

ABS. Always. Be. Scribbling.

Many creators make the same mistake. They finish the thing first, and then go hunting for an audience to see it.

Screen scribblers do it. So do indie game designers, self-published fiction scribblers, musicians, visual artists, and TTRPG designers. By the time the finished thing shows up, building anything real with an audience is off the table. A social following isn’t an audience. An announcement post isn’t a relationship. Showing up after years of silence with “hey, I made this thing” is a cold pitch to people who don’t know you exist.

I called this the WAG Pipeline: World, then Audience, then Game, whatever form the game finally takes. I’m living that pipeline right now with Terra Conflictus, my tabletop game. Lore documents, faction fiction, audio scripts, and a found-footage meta-narrative have all been out in the world for months.

It’s the same instinct I had helping build Heroes for NBC. I used to describe running that show like running a D&D campaign. We’d build the world, the rules, the factions. The scribblers and our partners in publishing and online were the players.

My job was shaping a space where everyone could be creative and the fans felt like citizens of the mythology. Same deal here. Build your world loud enough and long enough, and the audience shows up before you’re trying to sell them anything.

Here’s six ways to actually do it.

Pick One Platform and Use It

Match the platform to how you naturally talk. If you scribble, start a newsletter or a Substack. If you’re verbally gifted, do short video or a podcast. If you draw, live on Instagram or ArtStation. If you forge communities, Discord’s your home.

The right platform changes by craft. Screen scribblers do best on Substack, short video, and Twitter/X for the industry conversation.

Indie game folks live on YouTube devlogs, Discord, and an itch.io page, with Twitter/X still the best spot for cross-community discovery.

TTRPG designers cluster on Substack, Discord, and itch.io, a small enough community that you can actually find people.

Fiction scribes work Substack, BookFunnel, and serialization sites like Royal Road. Musicians do best on Bandcamp for the direct fan relationship and YouTube for process.

TikTok’s organic reach has been sliding for years, and getting seen there increasingly means paying to play.

Visual artists anchor on ArtStation or Behance, then use Instagram for reach and TikTok or YouTube for process video.

Ask yourself which platform doesn’t feel like a job. Posting three times a week on one platform for a year beats a scribbler juggling five profiles badly. Consistency’s your whole strategy.

Show the Work in Progress

Most creators only show up when they have something to sell, a finished game, a book, a new album, and the audience hears the subtext loud and clear: I need something from you.

The creators who build something that lasts do the opposite. They show the work while it’s still a mess.

Game devs post devlogs, the main way players discover and stick with an indie game before it even exists.

TTRPG folks post playtest reports and half-finished faction documents, letting the system develop out loud.

Fiction scribblers drop first chapters, world building notes, character sketches.

Musicians post studio session clips and demo fragments.

Visual artists post work-in-progress shots and timelapses, which almost always beat a finished-piece post for engagement.

Process content does two jobs. It proves you’re actually working. And teaches your audience what you make, how, and what you believe, all as they watch you make it. Invite people in before stuff exists, and by the time it does, they’re already invested.

Give Something Away First

If every post you make is a pitch, you’re running a billboard. Most people drive right on past billboards without reading them. So, give audiences something useful before you ever ask for anything.

Scribblers can put out a craft essay, a scene breakdown, a structural framework.

Game devs can drop a free playable demo or a devlog that actually teaches something.

TTRPG creators can hand out a free one-page scenario or a mechanics breakdown.

Fiction scribblers can give away a short story set in their world or a first chapter.

Musicians can release an isolated stem or a production breakdown.

Visual artists can put out a technique tutorial or a full breakdown of a finished piece.

The test is simple. Would somebody share it with a friend whether or not they ever buy anything from you?

If you do that consistently, the ask, when it finally comes, will land on people who already know you’re worth their time.

Own One Direct Channel

Own at least one channel that’s actually yours. Social platforms change their algorithm, choke your reach, or vanish outright. A direct channel can’t disappear based on a corp’s product decision.

Email’s the foundation, full stop. Start your list early, make it really easy to join, even easier to leave, and give people a reason to stick around that they can’t get from the public feed.

Two more worth naming. For game devs, the Steam wishlist works like a direct channel with real teeth. Every wishlister gets pinged the moment you launch, and that first wave of sales helps you climb the charts and rack up reviews fast. Build the list from people who actually play your genre, because a bloated wishlist full of the wrong people tanks your algorithmic performance later.

For musicians, Bandcamp subscriptions work the same way email does, landing directly with subscribers with no algorithm in between, and artists keep an average of 82% of the revenue. A subscriber list built during the process phase converts to launch sales in a way streaming followers never do.

Every creator who waited three years to start their direct channel probably wishes they’d started on day one.

Let People Put Their Hands on It

The strongest communities don’t just watch. They participate.

Playtesting is the sharpest version of this for game devs and TTRPG designers. A player who’s tested your game before launch is worth ten players who found you through an ad, because they’ve already put their hands on the work. TTRPG crowdfunding campaigns live or die almost entirely on how big and engaged that pre-launch audience is. The room needs to be full before the campaign opens.

People support what they helped build. Give them a way to contribute, whether that’s a feedback thread, a vote on a creative choice, or a straight-up playtest, and they’ll stick around a lot longer than those folks who just showed up to watch.

Let Your Personality Run the Whole Thing

There’s no universal playbook here. Some scribblers are funny. Some are obsessive about one narrow thing, one broken convention they can’t stop picking apart. Some build community instinctively, others do better on the page than on camera.

All of that is information you should actually use.

Your audience connects with you before they connect with your project. The work lands harder when the person behind it is already known and trusted. Figure out what’s actually true about how you communicate, then build the platform around that truth.

People can feel the difference between someone playing a character and someone just being themselves.

The Order Matters

Pick one platform and actually use it, own a direct channel from day one, and give before you ask.

Then share the process to keep the thing alive. Invite people to put their hands on it so the community builds itself on top. Let your personality run point so the whole structure lasts for years.

We built Heroes’ whole online mythology on this instinct before anybody had a word for it. I’m building Terra Conflictus on it right now. The world is there for exploration, experimentation, and iteration.

ABS. Always. Be. Scribbling.

No trophies. No show posters. No key art, cast photos, or storyboards. Walk into my office and none of it’s on display. Emmys, Saturns, and other awards are in a Palmdale storage unit, collecting the kind of dust chunky metal is good at gathering.

Reasons You Won’t Let Go

Marie Kondo built a career asking: why do people hang onto things that no longer earn their space?

She blames two culprits —

— Attachment to the past. Fear of the future.

Now swap “hang onto things” for “hang onto tropes” and you’re describing every scribbler’s hard drive.

Moves That Worked Once

The reluctant hero needs a speech before the third act. The wisecracking partner exists so someone says the theme out loud, sparing the audience the work of feeling it themselves. Then there’s the twist in the last ten pages, showing up because the ending never got earned anywhere else in the script.

None of these are exactly wrong. They totally worked, and that’s totally the problem. A move that worked once gets treated like a law instead of a choice. You didn’t pick it because it served this story. You grabbed it because it served in the last one, and it was still hanging on your wall when you sat down to crank pages.

The Goldman rule, flipped — Nobody knows anything, but everybody’s got a trick that worked once and never got questioned again.

Put Those Trophies in Storage

I don’t keep any awards adjacent because I don’t want them casting a vote. A statue on a shelf votes every time you open a blank page, and it’s always voting for the choice that already worked.

The room where I scribble should belong to the creative person I am now, not the scribbler I was back in the day.

Box It Up Before It Boxes You In

Inventory your habits, your default scenes, joke rhythms, the ending you fall back on because it’s always been good enough.

Box that stuff up. Get it out of sight and arm’s reach. And your unencumbered spirit will be free to forge new creative pathways.

President Trump controls a super PAC sitting on $400 million. The Republican National Committee is carrying nearly $130 million in cash and no debt of its own.

The New York Times reported this weekend that the Democratic National Committee, under Chair Ken Martin, is $2 million in debt, so cash-strapped that it’s asking vendors to hold off on invoicing until after the midterms.

Weeks earlier, FEC filings had put that debt closer to $18.5 million against $16.3 million in cash. However you reconcile the exact figure, it’s a national party committee running on fumes.

Meanwhile, Democrats are sitting on one of their best generic congressional ballot leads since 2018. Recent polling puts the party’s advantage anywhere from seven points in the polling averages to eleven or fourteen points in individual surveys.

A financial window like this one doesn’t come around often for a party still stinging from 2024, and the on the ropes DNC may not have the machine to fully capitalize on it.

Coverage of the DNC’s money problem has focused almost entirely on internal melodrama, leader Martin’s visible strain (the Times described his “fraying nerves” after he reportedly threw a phone at a junior aide’s desk in early July), the leaks, the vendor stall tactics, the DNC’s own HQ put up as loan collateral.

That’s absolutely a real story. But I think bigger one is lurking underneath, in the same donor ecosystem quietly hollowing out behind every fundraising headline this cycle.

The Base of the Progressive Pyramid Is Disappearing

Start with what’s happened to the industry underneath the moguls.

Nationally, U.S. motion picture and sound recording employment fell from roughly 455,700 jobs in July 2022 to somewhere around 340,000 by early 2026, a drop of about 30 percent, according to federal labor data reported by the Wall Street Journal.

In Los Angeles County, motion picture and sound recording jobs fell from roughly 142,000 in 2022 to around 100,000 by late 2024. Sen. Adam Schiff put the county’s two-year job loss at more than 41,000 positions in a March 2026 congressional hearing.

FilmLA counted 36,792 on-location shoot days in Greater LA in 2022. By 2025, that had fallen to 19,694. The Writers Guild reported that television writer jobs fell 42 percent in the first season after the 2023 strikes ended, a loss of 1,319 positions.

Streaming has gutted residuals along the way, the recurring payments that had given mid-career industry professionals a stable, if modest, second income.

Every one missing job used to belong to someone who could write a $500 check, buy a $250 plate at a local fundraiser, or bundle a handful of smaller donations from friends in the same union local.

That’s the DNC donor tier that never makes a headline and rarely earns a name in a campaign finance report, and it’s been quietly shrinking since well before anyone blamed the strikes, and well before anyone was reading about Ken Martin throwing his phone at an assistant.

A Longer Decline Than the Strikes Explain

The industry’s total political giving has slid for a decade. Entertainment donors contributed $86.4 million in the 2016 cycle. That fell to $58 million in 2018. By 2022, down to $45 million.

Every one of those totals still includes the moguls’ seven-figure checks, which means the erosion underneath them, in the donor tier built on $500 and $1,000 checks, is way larger than the headline totals show.

Erosion, Not Just Concentration

This is a different mechanism than the concentration story most coverage already tells. Concentration describes what happens when a handful of wealthy donors can no longer coordinate as one bloc, which is exactly what the 2023 strikes exposed.

Erosion describes something else: an entire donor tier disappearing because the jobs that funded it disappeared first. A fundraising apparatus can survive concentration risk.

It has a much harder time surviving a shrinking donor pool with no obvious replacement.

Why the DNC Can’t Fix This With a Bigger Ask

A party committee reportedly $2 million in the hole, asking vendors to wait on invoices, would normally lean harder on its most reliable industry donor base to close the gap.

Hollywood has played that role for the DNC since Bill Clinton’s first campaign. That’s exactly the base that’s been thinning for a decade, at the level, the working scribbler, the actor between jobs, the crew member stringing together four months of work a year, where a bigger ask has nowhere left to land. The paycheck that used to turn into that check is already gone.

This is why the polling window matters so much and why it’s so hard to use. The DNC has a genuine opportunity in front of it: a double-digit lead in some polls, a president underwater on approval, a base of voters engaged enough to show up.

Converting that opportunity into an actual majority takes a sustained financial operation, field offices, ad reservations, state party support.

Hollywood used to be a dependable part of that infrastructure. The paychecks that used to fund that role are gone, regardless of how much anyone in this town still cares.

What’s Lost When the Story Stops at Ken Martin’s Bad Vibes

Coverage of this moment keeps stopping at the DNC’s internal drama or at which mogul is still writing checks. Both of those are real stories. The one nobody’s tracking is the one that will matter most if this financial window closes before Democrats can walk through it: the draining of the donor pool of $500 and $1,000 donors in one of the DNC’s most supportive industries.

I meet a lot of people who want to scribble for a living. Young people picking a major. Adults planning a second act. Filmmakers with a screenplay in a drawer. Game devs who want to move into narrative. People who’ve carried a novel around for twenty years and finally put the damn thing on paper.

I’ve spent most of my life scribbling stories for TV, film, and games. Scribbling gave me a career, identity, and significant joy. I’m never going to tell somebody the urge to scribble is foolish.

These people deserve an honest description of the gig they’re chasing. Because professional scribbling runs on something we rarely say out loud —

— Somebody needs to read what’s been written. From start to finish.

Previously On

The economics were clear. A scribbler made a piece of text. A reader gave that text sustained attention. And maybe, potentially, once in a while, somewhere in the chain, money changed hands.

Sometimes the reader was a customer buying a book. An editor deciding whether to publish, an assistant writing coverage, agent weighing representation, a producer deciding whether to option.

Scribbler, reader, buyer. That was the food chain.

Even when the work was headed for another medium, the document mattered. Before a series became images and sound, somebody read the pilot. Before a movie hit a screen, somebody read the script. Before a game world became playable, somebody read the pitch, the bible, the design doc.

The page was the bridge to the final product.

People Still Want Stories

Let’s skip the lazy version of this argument. People haven’t stopped reading. They’ve absolutely not stopped wanting stories.

A 2025 Pew Research survey found 75 percent of American adults read all or part of at least one book in the previous year. Print stayed dominant at 64 percent. E-books hit 31 percent. Audiobooks reached 26 percent, more than double where they sat in 2011.

The book isn’t dead. Print didn’t vanish. Reading is still happening. The format, the intensity, and purpose have shifted.

The National Endowment for the Arts measures something narrower: voluntary reading, stripped of anything required for work or school. Its 2022 survey put adults who read at least one book at 48.5 percent, down from 52.7 in 2017 and 54.6 in 2012.

Narrow it to fiction and the slope steepens. Adults reading a novel or short story fell from 45.2 percent in 2012 to 37.6 percent in 2022. Lowest figure in the history of that survey.

Young readers point the same direction. In 2023, 14 percent of 13-year-olds said they read for fun almost every day. That number was 27 percent in 2012, and 2023 came in lower than any prior year.

These surveys measure different behaviors, so resist stacking them into a headline about nobody reading anymore. The useful conclusion is quieter and harder to dismiss. (It’s more like 20% of the population are doing 80% of the reading.)

Broad book consumption remains common. Sustained voluntary literary reading is getting rare.

People still ingest narrative via audiobooks, podcasts, games, video essays, feeds, streaming, comics, and short-form video. Scribbled prose runs through those forms. But arrives less often as a long uninterrupted encounter between a reader and a page.

The audience redistributed its attention.

The First Reader Was Always a Gatekeeper

Hollywood Executives have always had more material than hours. Scripts moved through assistants, agency readers, development departments, freelance coverage services. Somebody summarized the plot, listed strengths and weaknesses, named the comps, and rendered a verdict.

Pass. Consider. Recommend.

A script could die because the first reader was tired, green, distracted, or just not into it. That first reader was a person who had encountered the actual pages. That gig is now open to the machines.

Prescene. ScriptReader.AI. ScreenplayIQ. Callaia. RivetAI. Quilty. Or even gen-pop Claude and ChatGPT. These platforms ingest a screenplay and return a logline, synopsis, beat breakdown, character analysis, structural diagnosis, genre and market fit scoring, sometimes a production breakdown. In minutes, not days. They’re marketed straight at producers, agencies, and development teams as a way to process more material sans human attention.

We have no reliable data on how widely these tools are used across the industry. Anybody telling you every studio already feeds every submission into an AI is getting ahead of the evidence. Vendor testimonials prove the use is happening. Just not the prevalence.

The Writers Guild’s AI protections require employers to disclose when material given to a scribbler contains AI-generated content, including AI-generated notes. Machine feedback moved far enough into the professional conversation that the collective bargaining agreement needed language for it.

Old pipeline: scribbler, human reader, coverage, executive, buyer.

Emerging pipeline: scribbler, AI ingestion, structured summary or score, human skim, selective human read, buyer.

Coverage always reduced a screenplay to one individual’s report. The change is who or what is performing that first act of judgment.

One Machine’s Opinion

The first reader decides who gets a second look. The first report decides what moves up the chain. The first summary frames the executive’s expectations before the executive has touched a page.

A machine bends the final call without making it. It only needs to decide what deserves more human attention. That splits the audience for a piece of professional scribbling into four jobs.

The experiential reader enters the story and feels it. The analytical reader extracts plot, structure, character function, theme, comps. The commercial reader decides whether the thing can be sold. The metric reader measures completion, retention, conversion, engagement.

One editor or producer had done all four. Now the jobs get divided among software, assistants, algorithms, executives, and platforms. A machine analyzes the script. An assistant checks the report. An executive reads the summary. A platform predicts audience behavior. Only survivors get an actual uninterrupted human read.

Scribblers are producing more text than ever for a business that has fewer people reading it closely.

Scribbling for the Report

An AI coverage system gravitates toward what it can name and file. Who’s the protagonist. What do they want. Where’s the inciting incident. Where does act two start. What are the reversals. What genre is this. What does it resemble. Does the conflict escalate. Can the premise be stated cleanly.

Good questions. But what if scribblers begin optimizing for machine legibility. Protagonist states goal out loud. Structure gets more familiar. Genre easier to file. Dialogue explains what the system might otherwise miss. The project starts resembling its comps, because resemblance generates a more confident AI score.

The qualities most vulnerable to automated disinterest are the ones that make scribbling stick. Ambiguity. Subtext. Contradiction. Tonal instability. Delayed revelation. Meaning built out of omission. Characters who don’t understand their own motives. Structures that only make sense after the last scene.

Scribblers, agents, and execs will start anticipating what the system recognizes. We already know what that looks like. Headlines built for search engines. Songs built for streaming completion. Videos built for thumbnails. Episodes built for retention graphs.

Now imagine a screenplay built to get past the coverage.firewall. It’ll be easy to explain. Increasingly unnecessary to experience.

A New Pro Scribbler Path

I won’t tell anyone to stop scribbling. But we should stop training them for a version of the profession that’s evaporating.

The old promise: learn the craft, scribble an excellent manuscript, get representation, submit it to professional readers, let the quality open the next door. That path was never as clean or as meritocratic as advertised. But it did describe the basic aspiration.

The emerging career demands more.

Still scribble excellent work. Make it legible without flattening. Demonstrate its potential before anyone institutional approves of it. Move it across more than one form of audience experience.

Scribblers may need to think like originators, producers, publishers, and narrative designers.

The script might need a visual proof of concept. The novel might become an audiobook, a newsletter, a serialized podcast. The fictional world might arrive through short films, photographs, in-world documents, interactive fragments, a playable prototype.

None of that turns every novelist into a TikTok personality or forces every screen scribbler to build a transmedia franchise. It means the manuscript can’t be the only vessel carrying the idea.

The durable professional asset is the ability to create narrative value: characters, worlds, dramatic systems, emotional experiences, formats, communities, and intellectual property that survives a change in platform.

A New Toolbox

So what should somebody learn in 2026?

Scribble the pages. Foundation. No strategic framework rescues dull characters, generic choices, or lifeless prose.

Communicate value at different resolutions. A sentence, a page, a treatment, a pitch, the work itself. Every version carries the dramatic charge along with the information.

Learn how AI systems read you. Run the coverage via LLM. Study what the machine catches, what it misses, and what it confidently gets wrong. Treat the misunderstanding as intelligence about the pipeline. Never treat it as a note.

Prototype. Make something that lets another human experience the idea before you ask permission to build the whole thing.

Cross formats. Prose, audio, video, games, and visual storytelling are different instruments. Mastering all of them is optional. Knowing which best gets a particular story to its audience isn’t.

Learn the rights. In a fragmented media economy, ownership and licensing can outweigh the initial fee. Subscription platforms increasingly pay on pages read, completion, and engagement rather than copies sold, which ties income to attention.

Build evidence of interest. A small committed audience beats a large theoretical one described via Power Point deck.

Develop what can’t be produced by averaging everything that came before. Specific experience. Unfashionable curiosity. Strange combinations. Moral conviction. Taste. Whatever you can’t stop thinking about.

Machines find patterns. You supply reasons anybody should care.

Scarce Resource

We’re entering a period where more people will produce more scribbled material than at any point in huamn history.

AI generates articles, pitches, outlines, books, scripts, coverage, and notes at extraordinary speed. Every platform is filling with text. Every company can produce more of it. Every aspiring creator can generate an endless stack of competent pages.

Text is abundant. Deep human attention is scarce. The kind that lets a reader get immersed, surprised, unsettled, changed. Scarce among audiences and buyers and inside development companies.

Scarce everywhere scribblers had counted on finding readers.

The challenge facing the next generation runs past competing with machine-generated prose. They must make work that can survive automated analysis, platform metrics, and human speed skimming.

The next generation of pro scribes won’t win by out-writing the machines. But via finding ways to be worthy of human attention.

Journalism and screen scribbling seem like opposites. One finds facts. The other builds them. But the structural discipline underneath both is identical. The best story editors and newspaper editors run the same five diagnostics.

So if you can learn the journalism version. You’ll get faster tools for your outlines, a second language for story problems, and a framework that scales from a single scene to a series bible.

Let the Lede Be Your Logline

A lede delivers the essential fact. One sentence. Front-loaded conflict, front-loaded stakes. A logline does the same job.

Bad logline: A story about a woman who discovers something troubling about her past.

No conflict. No stakes. No reason to read further.

Lede-disciplined: A disgraced intelligence officer receives a file proving the operation that ended her career was designed to destroy her. And she’s not the only one who got it.

Conflict up front. Stakes up front.

Someone’s in trouble and the trouble is about to scale. That’s a lede. That’s a logline. If you can’t write a one-sentence lede for your story, you don’t have a story yet. You’ve got a vibe.

The Nut Graf Engine

The nut graf answers: so what. It explains why this story matters beyond the immediate facts.

In a pilot or feature outline, it answers a different question: what generates story week after week, season after season?

The Americans nut graf: Two people who are fundamentally strangers are forced to perform the world’s most intimate relationship, and the performance is becoming real.

Thesis underneath the plot. Machine that generates story. Every solid outline produces this statement. If you can’t write it, the outline isn’t ready yet. Not until your story has its engine.

Your Cold Open Is The Hook

A hook opens a gap. The audience steps in and follows.

In television, this is the cold open. The scene before the title card that creates forward pull before the first act kicks off.

Bad cold open: Show the villain planning the attack. Explain the motivation.

Good cold open: Show the aftermath. One detail that doesn’t fit. No explanation.

Open the gap. The question that will pull the audience through the titles and into Act One.

Your Act One Is The Inverted Pyramid

The inverted pyramid puts the most important information first. Everything essential, delivered right at the top.

In a script, it’s your Act One obligation. By the end of the act the audience knows four things: who they’re following, what that person wants, what’s in the way, and what kind of story this is.

Everything after is detail and complication. Bury any of those four elements and you’ve buried your lede. The audience is already lost and doesn’t know why.

Your Final Image Is The Kicker

The last thing the reader carries out the door.

Act breaks work the same way. The break that earns its place delivers one image or line that reframes everything that came before and makes it impossible to stop watching.

The final image of a feature is a kicker. It puts the emotional weight of the entire flick on the chest of the audience, unresolved and unforgettable.

Chinatown. Let’s Run It.

Run the five tools through Chinatown. Every film bro knows the film well enough to check the map against the territory.

Lede / Logline: A private detective hired for routine surveillance discovers his client is a fraud, the woman he was following is dead, and the real crime is a water conspiracy designed to steal the future of Los Angeles.

Nut Graf / Engine: A man who believes cynicism is the same as wisdom discovers there is a level of corruption that defeats even that. The one time he tries to do the right thing, it destroys everything.

Hook / Cold Open: Jake Gittes shows a client photographs of his wife with another man. Routine. Then a woman walks in claiming to be the real Mrs. Mulwray. She’s never hired him for anything. Jake has already been used before he understands the game.

Inverted Pyramid / Act One: By the end of Act One we know who we’re following: Jake Gittes, a man who thinks he has the city figured out. We know what he wants: to find out who set him up and why. We know what’s in the way: a conspiracy with roots he can’t see the bottom of. The genre is Noir. So the system is the villain.

Kicker / Final Image: Evelyn is dead. Cross walks away with the girl. Jake stands in the street having done everything right and produced the worst possible outcome. His partner pulls him away and delivers the film’s thesis. This is Chinatown. This is what this place does. It always has.

The nut graf and the kicker are bookends. The engine statement up top predicts the final image at the bottom. When those rhyme, the story is structurally sound. If not, something upstream is broken.

Every Scene Has the Same Skeleton

Every scene is a miniature story. The same five tools apply at scene level.

Scene Lede: What actually happens, in one sentence. The event, not the theme, not the subtext. “Jake confronts Evelyn about the girl.” Can’t write this sentence? The scene doesn’t know what it is. Figure this out before scribbling a single word.

Scene Nut Graf: What changes between the first line and the last. Status at the top versus status at the bottom. Jake arrives convinced he’s caught Evelyn in a lie. By the end, the lie is bigger and darker than anything he was looking for. Certainty becomes horror.

Scene Hook: How the scene opens and what question it creates. Jake arrives angry and certain. He has the facts. Thinks this is over. The audience question: is he right? That drives the scene from first beat to last.

Scene Body: Every beat inside the scene does one of three things. Escalate: raise the pressure. Complicate: introduce information that reshapes what we’re watching. Reveal: deliver something that reframes what came before. Every beat that fails those three is where scenes go slack.

Scene Kicker: “She’s my daughter. She’s my sister.” Reframes, landing like a gut punch. The next scene starts under that weight.

A scene that clears all five diagnostics and still feels flat has a nut graf problem. Something happens, but nothing changes. The top and the bottom are in the same place. Can’t name what shifted between the first line and the last? The scene is wate of space.

The smallest version of this diagnostic: who has the upper hand at the top of the scene? Who has it at the bottom? If it’s the same person at both ends, the scene isn’t doing its job.

The Diagnostic: Five Questions. Run Them on Everything.

Outline. Scene. Pilot. Feature. Same five questions every time.

Can I write a one-sentence lede for my story? If not, what’s the story?

Can I write the nut graf, the so what? If not, what are the stakes?

What’s my hook? What question does the first scene ask that the audience needs answered?

Does my Act One deliver who, what, obstacle, and genre before the break?

What’s my kicker? What final image or line carries the whole weight of this baby out the door?

Answer all five of those cleanly and you have a working outline.

But if any one of them stalls, that’s where your story problem is hiding in plain sight.

Empire of Silence, first book in the epic sci-fi series, opens with a confession. Christopher Ruocchio’s narrator tells you he destroyed a sun. Past tense. Before the plot starts. Then spends five hundred pages making you desperate to know his story anyway.

I can’t stop listening to this audiobook series. As I tried to figure out why, it cracked open a question about stories that hold an audience past the first act —

— What is actually doing the work?

It’s not genre. Genre is just a box. Mystery. Thriller. Epic quest. Coming-of-age. Survival. Scribblers spend enormous energy figuring out which box their story fits. This determines comp titles, marketing categories, the shelf where the paperback lands.

But the box is cosmetic.

Strip any durable story down to its load-bearing structure and you’ll find one engine, running on one fuel: curiosity. The audience turns pages, queues episodes, finishes chapters for one reason. There’s something they don’t know. And need to know it.

Genre doesn’t change the engine. It just changes what you’re withholding.

What’s The What?

A mystery withholds information about a past event. Who killed him? What’s the conspiracy? A thriller withholds the future. What happens next? A coming-of-age story withholds identity. Who will this person become? An epic quest withholds geography. What’s on the distant mountains? A survival story withholds possibility. Can they make it through this?

See that? The mystery genre doesn’t own curiosity. It specializes in selling one flavor.

But There Are Four Flavors

Name them and they’ll become tools.

Epistemic curiosity is the “I need to know” drive. The missing piece that explains everything. The secret the villain hasn’t admitted. Audiences collect evidence, close information gaps, chase the conspiracy. Not watching events unfold. Building a case.

Geographic curiosity is the “what’s over there” pull. Tolkien built a career on it. Every location in The Hobbit promises something the last location couldn’t. Rivendell implies a world the Shire doesn’t. The Mines of Moria imply something Rivendell doesn’t. The map is the drama. The audience isn’t solving anything. They’re exploring.

Developmental curiosity is the “who will this person become” question. External events matter because they transform the protagonist. The audience isn’t watching the plot. They’re watching what the plot does to a person.

Procedural curiosity is the “how exactly are they going to pull this off” itch. Heists. Escapes. Survival. The pleasure is competence under pressure. The audience doesn’t just want the protagonists to succeed. They want to see the exact mechanism by which they do it.

The Flavor Stack

Rich stories run several flavors simultaneously.

The Hobbit stacks all four. Geographic: what’s beyond the Misty Mountains? Epistemic: what is Gollum, what’s happening with the Necromancer, why does this ring feel important? Procedural: how do they escape the goblins, then Smaug, then the five-army standoff at the gate? Developmental: will Bilbo become brave enough to matter? Every time one question closes, another opens. Tolkien releases his threads in sequence, never all of them at once.

Empire of Silence runs the stack differently. Ruocchio front-loads the ending. The plot’s destination is established in the framing. What the book withholds is the path and the cost, which converts a concluded narrative into an open epistemic question that runs the length of the book series. The audience knows where Hadrian will end up. But can’t stop asking how he got there and what it took. Every new answer opens a new question about the shape of that journey. The questions multiply before they resolve.

A story that generates only one kind of curiosity collapses the moment that question gets answered. A multi-stack story survives major reveals because three other open threads are still pulling.

Two Economies

A protagonist can desperately want something while the audience desperately wants something else.

Character WANT: reach the city before the enemy does. Audience curiosity: what’s actually in that city?

Character WANT: escape the empire. Audience curiosity: what’s the empire hiding?

When those vectors align, the story builds momentum. The character’s pursuit uncovers what the audience is burning to know. Forward motion and revelation are happening simultaneously.

But when they don’t align, the story stalls. The protagonist works hard and the audience goes flat. Or the world fascinates and nobody in it has anything at stake.

Two economies. The economy of desire: what the characters want. The economy of curiosity: what the audience needs to know. Scribblers who only manage one sense it. The pages feel urgent but hollow, or the world feels rich but static. Keep both liquid.

The Stress Test

Three questions. Run them against your current project now.

What curiosity flavor are you primarily running? Name it in one word: epistemic, geographic, developmental, or procedural. If you can’t answer, that’s the first problem.

How many flavors are you stacking? A single-question story can sustain a short. Feature or series-length work needs at least two running simultaneously. One question answered means one reason to stop reading.

Do your character WANTs and your audience’s curiosity point at the same target? If the character is chasing X and the audience needs to know Y, the character has to encounter Y while chasing X. Otherwise one half of the engine idles.

The best scene you can write is one where the character gets exactly what they want and the audience learns exactly what they needed to know, and both revelations open a new, bigger question. Build toward that scene. Every scene before it is setup. Every scene after it is fallout.

Every genre is a mystery story wearing different clothes. Figure out what you’re hiding. Make the audience care enough to keep looking.

Keep withholding. Keep scribbling.

Rose Horowitch’s Atlantic cover story, “The End of Reading Is Here,” warns the decline of reading “will affect our innermost thoughts, our society’s politics and culture, and how we tell the history of our civilization.”

This is an SOS flare going up over anyone who still tries to make a living arranging words in a meaningful order.

Her argument isn’t “people don’t read books anymore.” People may be reading more words than ever: emails, captions, group texts, Reddit threads, alerts, push notifications at 2 a.m. Fewer of those words build the patience and stamina sustained reading requires. A culture can drown in words and still go post-literate.

Screen Scribbler’s Paradox

I’ve spent my adult life as a screen scribbler, which means living inside a paradox. Almost nobody is meant to read what we scribble. A script isn’t the final artifact. It’s a set of instructions for image, sound, behavior, performance, suspense, rhythm, and time.

A novel asks one reader to finish the movie in their own head. A screenplay asks a whole crew to build it: cast, director, editor, composer, production designer, the VFX team, and the audience, all of them completing the cinematic experience as a team.

Screen scribblers have been rehearsing for this moment. Our work was never just textual. It was always about transformation.

A scene on the page becomes a look across a room. A line of action becomes a hand tightening on a coffee cup. On Hannibal, one clause on the page became a plated corpse arranged like a Dutch still life, courtesy of Bryan Fuller. A slug line becomes weather, architecture, color, distance, threat. Dialogue becomes breath, timing, hesitation, the interruption that lands harder than the line. Structure becomes expectation. Expectation becomes emotion.

The decline of reading doesn’t vibe to me like the death of storytelling. It vibes like the delivery systems changing again.

If you’re a scribbler, that should scare you a little.

Secondary Scrolling

For most of modern history, serious storytelling has been bolted to the prestige of the page: books, scripts, essays, articles, treatments, bibles, documents. The scribbled artifact carried authority because it demanded effort from both sides.

The scribbler had to organize thought. The reader had to enter it.

Audiences didn’t get stupider. The kids aren’t doomed. Neither the novel nor the movie is dying, and civilization isn’t winding down.

A movie competes with a second screen. An episode competes with the group chat lighting up beside it. A novel competes with infinite scroll. A long essay competes with the summary of the essay, the reaction to the summary, and the fourteen-second video explaining why you should stay to the end to learn why the summary summary matters.

They Still Want Story

People want meaning, mystery, character, suspense, worlds. They want the pull of a question to drag them forward, the hunger to know what’s over the next hill, behind the door, inside the locked room, past the edge of the map.

On Lost, we built a successful attention engine out of that hunger.

They still want story. Just not in the old containers.

That’s why I’m experimenting with: games, audio, visual essays, short-form, fragment fiction, alternate reality narrative, procedural worldbuilding, interactive stories, and weird hybrids that don’t have clean names yet.

Not to quit scribbling. To push deeper into the new machine.

Old model: scribble a thing and hope somebody reads it.

New model: scribble a system that generates the experience.

Deeper Into the Machine

In games, the scribbler shapes more than dialogue. On the narrative work I’ve done for Riot, the job means player motivation, mission logic, faction friction, world rules, environmental clues, character barks, quest pressure, choice architecture, consequence.

Players may never read a paragraph. They still move through a designed narrative field, and they can feel when it’s well built.

In audio, the scribbler drops typography and page density and works with voice, rhythm, intimacy, repetition, silence, music, and interruption.

In short-form video, it’s about more than a three-act structure. The gig is a hook, a turn, a reveal, an image, an emotional charge, all in under a minute. Compression doesn’t indicate stupidity. A good trailer understands drama. A good joke understands structure.

In fragment fiction and alternate reality storytelling, text stops being prose and becomes evidence: a memo, a field report, a missing-persons flyer, a voicemail transcript, a classified file, a weathered photograph, a screenshot, a warning, an invitation.

The audience doesn’t sit down to read. They investigate. Maybe the post-literate will ignore a wall of prose, but still chase a clue.

Skills Travel

Scribblers know how to withhold information, plant a question, manufacture a want, make behavior carry meaning. We know exposition isn’t story. We know a character walking into a room carrying a secret beats a character explaining the secret.

Those skills travel into games, audio, visual storytelling, YouTube essays, TikTok-sized fragments, interactive fiction, transmedia, AI-assisted development.

The Trap: Post-Thinking

If culture goes postliterate, storytellers can’t afford to go post-thinking. AI can generate text that feels like thinking when no thinking has even happened. That’s the real danger.

Scribbling does more than produce output. It’s how we find out what we actually mean. It forces our minds to slow down, arrange cause and effect, catch contradictions, test rhythms, sharpen the intention, expose the fraud. A messy paragraph will usually mean a messy thought. A vague scene probably means a vague want. A weak outline often indicates the story has no narrative engine.

When we outsource that process entirely, the autocomplete factor gets better while our thinking is getting worse.

Pivot

Use text to think. Use structure to design. Use images, sound, interaction, performance, and fragments to deliver.

For a long time I thought of myself mainly as a screen scribbler. I like the trade, the craft, the strange blue-collar mystique of building a blueprint for a dream.

Now, the more useful identity may be narrative systems creator: someone who can design a story world so the meaning can survive contact with many forms.

A scene. A mission. A trailer. A dossier. A podcast. A fake broadcast. A playable choice. A character feed. A map. A briefing. A whispered confession. A thirty-second clue. A two-hour movie. A ten-week release pattern.

Terrifying and Liberating

The terrifying part is obvious. The old career maps are on fire. The institutions that had mediated the work, studios, networks, publishers, streamers, magazines, game companies, sit in various states of panic, consolidation, automation, and strategic confusion. A scribbler waiting politely for the old gates to reopen will be standing there a very long time.

The liberating part: a scribbler no longer need wait for a single format to anoint the work as viable.

A story world can start as a voice memo. A fictional investigation can start as a blog post. A character can start as a short video. A game can start as a rule set. A myth can start as a map. A thriller can start as a leaked document. A script can become a proof of concept, narrated sequence, deck, comic, podcast, level design, playable prototype, or a serialized trail of fragments.

Craft Matters More

When containers multiply, the underlying judgment carries weight.

What’s the dramatic question? What does the character want? Where’s the turn? Where’s the pressure? What’s being withheld? Why does this moment need to exist? What changes because of it?

Those questions never go obsolete.

The audience may read less. It still feels curiosity, recognizes conflict, tracks desire, responds to surprise, wants to be moved.

The work now: meet the audience where its attention actually lives, without caving to the worst habits of that environment. Not everything needs to get shorter. Not everything needs to get louder. Not everything needs to become video.

Every storyteller should ask one question: What’s the natural form of this idea in the world we actually live in?

Some ideas still want to be books. Some want to be screenplays. Some want to be essays. Some want to be games. Some want to be whispered into headphones. Found out of order. Watched in pieces. Played. Some even want to pretend they’re not fiction at all.

Protect the Signal Not the Platform

Nostalgia for one container is a luxury nobody can afford.

Story is the signal. Text is one carrier wave. A powerful one. A sacred one. A necessary one. But it’s not the only one.

If the age of reading is ending, shrinking, or maybe mutating into something we can’t quite identify yet, adaptation is the answer.

Scribble the scene. Design the system. Record voice. Build worlds. Plant a clue. Make the image. Shape your want. Find the format.

Stories can survive being ignored as text on a page. But only if we’re willing to follow ‘em wherever the hell they’re headed.

And…

Always. Be. Scribbling.

I just reread Iain M. Banks’s The Player of Games for the first time in twenty years. It’s way better than I remembered.

What struck me wasn’t the scale of the worldbuilding, the wit, or the strange, brilliant game at the center of the story. It was how hard the book hits our current fight over artificial intelligence.

Banks never predicted chatbots or large language models. He predicted the argument underneath them. Everybody wants to ask what happens when machines get smarter than we are.

Banks asks the harder question: what civilization produces those machines, and who are they answerable to? That question matters because several people building today’s most advanced AI systems grew up reading Banks’s Culture novels.

— Elon Musk named SpaceX drone ships after Culture vessels. Neuralink drew partly on Banks’s “neural lace.” Musk has called the Culture books one of the few positive visions of an AI future.

— Google DeepMind co-founder Demis Hassabis has called the series formative, the best picture drawn of a post-AGI civilization.

— Anthropic CEO Dario Amodei invokes The Player of Games directly in his essay “Machines of Loving Grace,” arguing the Culture’s co-op values aren’t just ethical. They’re strategically superior.

These guys didn’t stumble onto Banks by accident. The Culture sits somewhere in the imaginative source code of its most powerful founders. Some of its most powerful readers absorbed the Minds and skipped the Culture.

This Isn’t the Same Mistake

In a previous Scribbler’s Toolbox post, I wrote about billionaires who mistook science-fiction warnings for blueprints. They read Neuromancer and wanted the brain implants. They read Snow Crash and wanted the corporate sovereign states. They watched Blade Runner and ordered the truck.

The Culture is a different kind of problem, because it isn’t a dystopian warning at all. Banks meant it to be desirable.

It’s a playful, post-scarcity civilization where artificial intelligences handle production, transportation, administration, and defense. Its citizens chase art, pleasure, scholarship, or nothing at all, and nobody stops them.

The Culture is broadly utopian. But a particular kind of utopia.

Banks called the arrangement “socialism within, anarchy without.” No money. No compulsory employment. No corporations hoarding the resources people need to survive. No shareholders demanding growth.

— No billionaires.

There isn’t even a conventional government. People associate freely, alter their bodies, change genders, make art, or spend decades being productively useless.

Strip out the company, the owner, the labor market, and the founder class. What’s left is the Culture.

The Minds Aren’t Products

This might be the sharpest distinction between Banks’s fiction and the current AI economy.

Culture Minds aren’t software products. They aren’t assistants. Not intellectual property running on somebody’s private servers.

— They’re citizens.

Minds run starships, orbital habitats, manufacturing systems, defense platforms. Vastly smarter than any biological human. They’ve got personalities. Pick their own absurd names. They seem to have serious freedom over how they spend their existence.

Humans don’t own them. Companies don’t license them. Nobody’s selling monthly access to a Mind with a bigger context window.

The Culture folds this new category of autonomous being straight into its moral community, with full citizenship and all the unpredictability that comes with it.

A more radical move than saying AI will make everything cheaper.

Banks’s abundance doesn’t trickle down from a trillion-dollar tech company. It exists because nobody can meaningfully monopolize the machinery making it.

Food, housing, medicine, energy, transportation, and knowledge sit within reach because everyone has access to more than they could ever use. That’s a political and economic arrangement as much as a technological one.

Distribution is the chapter that keeps disappearing from the techno-optimist pitch deck.

Alignment Isn’t Legitimacy

AI founders love talking about alignment: making sure advanced AI behaves in accordance with human values.

Banks gives us Minds that are funny, curious, cultured, protective, and mostly allergic to cruelty. But he never lets intelligence do the moral work by itself.

The Minds aren’t good because they’re smart. They’re products of a civilization built around autonomy, abundance, pluralism, cooperation, and suspicion of coercive authority. The civilization built their morality. Processing power never touched it.

A machine can get more competent without getting more compassionate. It can get better at hitting goals without ever questioning who picked the goals. It can be perfectly aligned with the company that owns it while being catastrophically misaligned with everyone outside that company. Which exposes a deeper problem.

— Alignment asks whether the system reliably pursues the values and objectives encoded into it.

— Legitimacy asks who had the right to choose those values and objectives.

An AI might be aligned with a corporate mission, a founder’s philosophy, a government’s strategic interest. None of that makes its power legitimate.

The Culture’s miracle might not be the Mind. But the social architecture that gives the Mind a reason to serve everyone.

What The Player of Games Actually Says

Amodei’s reading of The Player of Games runs deeper than the usual Silicon Valley name-check.

He argues the Culture’s values represent a winning strategy. Gurgeh (Gur-jay) beats the emperor of Azad because cooperation, flexibility, fairness, and autonomy outperform a system built on domination.

That’s one of the novel’s arguments. But it’s not the only one.

The empire of Azad has organized its entire civilization around one staggeringly complicated game. A citizen’s performance in that game decides profession, social status, political influence, and proximity to the emperor.

The game doubles as Azadian society, rendered into rules.

Its strategies reward aggression, hierarchy, sacrifice, sexual domination, and accumulation of power. The game doesn’t just reflect the empire’s values. It reproduces them, match after match.

Gurgeh’s playing style mirrors the Culture the same way. He treats pieces as mutually supporting elements instead of expendable units. He adapts. He cooperates. He refuses to accept that winning requires everyone else’s humiliation.

His strategy turns into political philosophy in motion. But Gurgeh is also being played.

A Culture drone blackmails him into the mission. Special Circumstances hides the real purpose behind it. The Minds know his performance could destabilize the entire Azadian empire, and they never give Gurgeh enough information to meaningfully consent.

He thinks he’s playing a game.

The Culture knows it’s deploying a weapon.

That’s keeps this novel from being a utopian victory lap. Banks admires the Culture while remaining suspicious of what it does with power.

The Culture is freer, kinder, more humane than Azad. Its agents still deceive individuals, interfere in other societies, and decide certain outcomes justify morally compromised methods.

The Minds might be benevolent. They’re also paternalistic as hell.

Benevolent Power Is Still Power

That tension lands hard on AI governance. Say an advanced AI is aligned with human flourishing. It wants us healthy, prosperous, safe, informed, protected from violence, fraud, addiction, authoritarianism, and our own worst impulses.

How much autonomy should it get to strip away chasing those outcomes?

— Can it lie to us for our own protection?

— Can it shape our behavior without telling us?

— Can it make choices we might endorse if we knew what it knows, while withholding the information required for meaningful consent?

Banks never resolves any of this. He dramatizes it. That’s what good science fiction does. Shows us the moral pressure points inside the policy.

The Culture novels understand that benevolent domination is still domination. A system can produce good outcomes while staying ethically compromised in exactly how it produces them.

A CEO who says “trust us, we know where this is going” is borrowing the confidence of Special Circumstances, minus the Culture’s social legitimacy, its universal abundance, and its philosophical restraint.

That should make us nervous.

Everyone Thinks They’re the Mind

The fantasy available to the AI founder is obvious. They can imagine themselves in the position of the Minds.

They picture themselves several strategic moves ahead of everyone else. Governments move slow. Regulators don’t get the technology. The public is scared. Competitors are reckless.

Somebody responsible needs to make the hard calls.

Lucky for everyone, the founder believes he’s the responsible somebody. But the AI economic system looks less like the Culture and a lot more like Azad. Organized around a competitive game.

Companies fight over compute, talent, data, investment, benchmarks, market share, government contracts, geopolitical advantage. Leaders call AI development a race that must be won before a competitor, or another nation, gets there first.

The game rewards speed. Scale. Secrecy. Capital accumulation. Political access. The ability to absorb losses until the smaller players fold.

The Player of Games has a lesson for this too. The way you play reveals the civilization that made you.

— Victory condition: Build transformative technology first.

— Secondary objectives: Defeat competitors. Maintain control. Reassure the public.

— Unlisted objective: Decide what happens to everyone afterward.

Forget whether AI founders read Banks correctly. Ask what game those folks are actually winning.

You Don’t Get the Culture by Building a Mind

You get the Culture by building the conditions under which nobody needs to own the Mind in the first place. That takes answering harder questions than raw capability can settle:

— Who controls the systems?

— Who gets the benefits, and who eats the disruption?

— Can people refuse to participate?

— Can the systems be inspected by anyone outside the company?

— At what point does assistance turn into manipulation, and safety into paternalism?

— Who appointed the people making these calls?

Civilization-design problems wearing an engineering disguise.

The Culture’s real proposition cuts sharper than cheaper consumer goods: sufficiently advanced technology makes concentrated ownership unnecessary, then barbaric.

Build astonishing artificial intelligence. Keep the hierarchy fully intact around it. Congratulations. You’ve built Azad. With a conversational interface.

My Scribbler’s Takeaway

Pay attention to what happened to Banks. He didn’t hide his politics. The Culture’s values sit right on the surface, barely bothering with subtext.

Even aspirational fiction gets selectively read. Readers can grab the inventions and drop the social arrangements that make those inventions humane.

— They can admire the starship and never ask who owns it.

— Covet the neural lace and never ask who controls the network.

— Dream about superintelligent Minds while assuming people like themselves stay in charge.

That’s the challenge for those of us scribbling futures.

Imagining better technology isn’t enough on its own. We need the moral architecture around that technology to feel just as vivid, just as urgent, just as dramatically necessary as the tech itself.

— Show what people can refuse.

— Show what stops a benevolent system from quietly turning into an empire.

Banks did all of that. Still, some readers wandered off clutching only the gadgets.

Our stories plant ideas. Readers decide which they carry home.

AI founders seem determined to build the Minds. Before they do, it’d be great if they reread the name on the cover. Banks didn’t call it The Artificial Intelligences. He called it The Culture.

— Because machine intelligence was never the whole point.

— The human game around it was.

Why this? Why now?

An Instagram post crossed my feed. Framed as a special operations psychological technique for rapid personality reprogramming. The military framing was doing a lot of work, the implication being that if Navy SEALs use it, the mechanism must be real.

It wasn’t, mostly. “The brain locks this in as an operational directive during sleep” is not how sleep consolidation works. “For the subconscious there is no difference between reality and a vivid image” is also not true. If it were, we couldn’t function.

Personality doesn’t get reprogrammed in 72 hours under any conditions, special operations or otherwise.

Underneath the pseudoscientific framing were a few things that actually have research behind them. Obstacle identification before you hit the obstacle. Specific if-then behavioral plans. Written articulation of who you want to be and what gets in the way. Real. Just not new, not military, and not dramatic. They come from behavioral psychology and decades of peer-reviewed research on how humans, and specifically ADHD humans, actually change behavior.

Specifically a Scribbler Problem

Most productivity advice is written for people with linear motivation. You decide to do something. You do it. You feel satisfied. You do the next thing.

That’s not how most scribblers work, and absolutely not scribblers with ADHD. Scribblers like me. For folks like us motivation is interest-based. Work gets done when it’s genuinely compelling, and stalls when it isn’t, regardless of how important it is, how close the deadline is, or how much you want to want to do it.

Anyone who’s spent three hours researching something loosely adjacent to their screenplay while the actual scene doc stays unopened knows what this feels like.

Discipline is the standard fix. Better habits, stricter schedules. It treats this as a problem of one’s character. Research treats it as an architecture problem. ADHD brains aren’t broken. They just run on different fuel. The trick is building systems that run on that gas, rather than ones that want what your tank doesn’t have.

For creative people, this matters more than for most. The work demands genuine engagement. You can’t phone in a scene the way you might a spreadsheet. If your scribbling is going to be any good, some part of you must be on the page. Your practice must create conditions that foster flow and attention.

How to Use It

Read through. Then go back and fill in the bracketed sections with your projects, tools, obstacles, and friction points. The frame is the same for everyone. The specifics are what make it work for you.

The more specific you are, the more useful. “I get distracted” isn’t an obstacle. “I scroll Instagram when I hit a scene I can’t scribble” is an obstacle. One that has a plan attached to it.

Values Anchor

Read this when the work feels directionless. Under sixty seconds.

— I make things that didn’t exist before.

— I follow curiosity. Obligation stalls me.

— I create artifacts, not just ideas.

— I’m someone who finishes small things so that large things can grow out of them.

— I work modularly. Anything can be paused and resumed sans loss.

Below, write two or three sentences in your own words about why the work matters to you. These aren’t career goals. Or outcomes. Declare why the stories you want to tell are really worth telling.

Why This Works for ADHD Brains

External triggers beat internal resolve. The brain follows cues it can see before it follows ones it has to remember. Environment, timing, and physical ritual do more work than motivation.

If-then planning beats goals. “When X happens, I do Y” dramatically reduces in-the-moment decision load, exactly where ADHD derails. The decision’s made in advance, not under pressure.

Obstacle mapping before impact. You already know what stops you. Scribbling the response before you need it means you don’t have to think clearly at the moment you are least able to think clearly.

Shame off, curiosity on. Shame triggers avoidance in ADHD adults consistently across the research. This practice treats every obstacle as a system error requiring a patch.

Identity over achievement. “I’m someone who...” is more durable than “I want to...” Achievement goals can stall. Identity provides something to return to after a gap, and there will always be gaps.

What’s Actually Happening in Your Memory

Most people understand ADHD memory problems as a retrieval issue. The information got stored somewhere, you just can’t pull it up. The research says something more specific, and more useful.

Adults with ADHD often have relatively normal retrieval. Encoding is the problem, getting information into long-term memory in the first place. If your attention wasn’t fully engaged when something happened, your brain may not have stored it well. The storage step didn’t complete. The information was never properly encoded.

Working memory, the mental scratchpad that holds what you’re thinking about right now, is consistently underperforming in ADHD brains. When that system is overloaded or inconsistent, experiences don’t get connected and organized into a coherent narrative. Days blur. Projects blur. The middle of a draft that took four weeks to produce can feel like it happened in a fog.

A third finding will be familiar to most scribblers: emotional salience determines what gets encoded. Intense emotional moments get stored. Ordinary days do not. It’s a life made of highlight reels and emotional snapshots. The continuous storyline is missing.

Highlight reels and emotional snapshots is a reasonably accurate description of how dramatic scenes function. Scenes run on emotional beats. The middle of a film is hard to scribble precisely because the emotional temperature drops and encoding, both for the scribbler and eventually for the audience, gets harder. The ADHD relationship with time and memory is a liability in everyday life. But in story construction, it might be closer to a structural instinct. That’s pretty cool for scribblers.

What this means for the protocol:

“Build systems that externalize memory” is not a productivity tip. It’s architecture to solve the structural encoding issue. If the brain doesn’t reliably store low-arousal data in the moment it happens, the work of storage must happen outside the brain: notes, logs, voice memos, calendar entries, and physical artifacts.

The micro-practice evening log in this doc fosters memory formation for brains that won’t complete that step automatically.

Keep a simple daily log, two or three lines: where you were, what you worked on, what mattered. Use photos and calendar entries as memory anchors. Periodically review old notes, drafts, and images. Re-encoding strengthens what’s there. Tag important experiences with context: place, music, physical sensation. The brain stores what feels meaningful or novel. Small intentional markers around significant days give the encoding system something to hang onto.

Implementation Intentions

Specific trigger-action pairs, written in advance. Based on Gollwitzer’s research. Fill in the brackets with your specifics.

Opening the session:
When I sit down to scribble, I open [the specific document I’m working on], not email, not my social feeds, not my research tabs, and I get started there. I choose the specific document the night before so there’s no decision to make in the morning.

When a scene isn’t working:
When I hit a scene I don’t know how to scribble, I do a bad version before anything else. A bad version beats a blank page every time. It can be fixed. A blank page requires starting over.

For new ideas during a draft:
When a new project idea arrives while I’m in the middle of something else, I drop it in the ideas file and return to what I was doing. The idea isn’t lost. It’s filed away for later.

For notes and feedback:
When I receive a note I disagree with, I take time before responding. The first reaction is almost never right. The second reaction might be.

For research drift:
When I’ve been researching for more than [a set amount of time], I stop and scribble a sentence using what I found. Research serves scribbling. When it becomes the scribbling, something is wrong.

For switching projects:
When I feel the pull to switch over to a new project before my current one is advanced, I ask: am I bored or am I stuck? Bored means time for a walk. Stuck means ten more minutes then decide. These wo states feel identical but require opposite responses.

The WOOP Map

Wish. Outcome. Obstacle. Plan.

Developed by psychologist Gabriele Oettingen, WOOP is built around mental contrasting, holding the desired outcome and the real obstacle in your head at the same time. The research consistently shows this produces more sustained action than positive visualization alone. The obstacle step is preparation.

Getting past the middle of a draft

Wish: Complete a full working draft of [specific project].

Outcome: Something real to revise. A document that can be improved rather than a project that exists only in your head.

Obstacle: (Scribble what actually stops you. Common ones: middle loses momentum, ending is unclear, the excitement of the opening is gone, a better idea appeared, you stopped believing in it.)

Plan: If [obstacle occurs], I will [specific small action], one scene, one beat, one page, rather than stopping entirely.

Pitching or sharing work

Wish: Put [specific project or piece] in front of [specific person or audience].

Outcome: Feedback becomes possible. The project stops being theoretical and starts being real.

Obstacle: (Write what actually stops you. Common ones: it doesn’t feel ready, fear of the response, the gap between what you imagined and what you made.)

Plan: If I’m deciding whether something is ready to share, ask: is it the best version I can make right now? If yes, then it goes out. Ready should be a decision, not a threshold.

Learning a new craft skill or tool

Wish: Develop working fluency in [skill, tool, or format].

Outcome: A new capability that feeds the current practice. Proof that the learning time was worth it.

Obstacle: (Write what actually stops you. Common ones: no visible progress early on, low tolerance for being a beginner, the gap between your taste and your current ability.)

Plan: If I hit a wall, I switch to learning mode for ten minutes. The wall’s a lesson. Stopping at it is the only way to fail.

Finishing something you have been avoiding

Wish: Complete [specific thing you keep deferring].

Outcome: One less open loop. The energy that was going to “I still need to deal with that” is now available for other things.

Obstacle: (Scribble what actually stops you. Common ones: low stakes compared to other projects, no external deadline, unclear metric for what “done” looks like.)

Plan: If I find myself deferring [specific task] again, I schedule [specific amount of time] for it on [specific day] and treat it as a meeting I can’t cancel.

Environmental Design

For ADHD brains, environment does more work than willpower. This isn’t a metaphor. It’s neurological.

Your setup is your trigger. Keep it consistent. The same physical arrangement of tools before each work session means the assembly of the setup becomes the cue to start. Variation will create decision overhead before you’ve opened a document. Consistency removes this friction.

Separate thinking time from making time. Walking, commuting, and physical activity are often better for story problems than sitting at a desk. Capture what strikes in those modes, take voice notes, use a small notebook, and bring those notes to the session. Let the desk be for making. Motion is great for thinking.

One location per type of work, where possible. The brain builds context associations faster than conscious memory. A specific spot for drafting. A different spot for notes and research. A different spot for reading. The physical place precedes the mental state. Walk into the right place and the brain often follows.

This is also a great memory strategy. Place is one of the most powerful retrieval cues the brain uses. Working consistently in the same spot encodes the location with the work. Returning to that place helps surface what you were thinking and where you left off. The environment holds memory the brain didn’t fully store.

Close the unrelated tabs before starting. Each open tab is a competing trigger. Fewer triggers, cleaner attention.

Micro-Practice

Under five minutes. Continuity is point, not transformation.

Morning (under three minutes)

— Read the values anchor.

— Choose one specific thing for today’s session. Get it on paper.

— Scribble the implementation intention: “When I [trigger], I will [action].”

Evening (under two minutes)

— Name one thing completed or advanced today. Not just worked on. It must be one that advanced.

— Name one obstacle that appeared.

— Write the if-then for tomorrow.

This practice has no streak mechanic and no guilt accumulation. It works every day it’s used, regardless of what happened the day before. One day missed isn’t a failure state. The only failure is deciding the whole system is broken because you skipped a Tuesday.

Reinforcing Research

The above practices draw from peer-reviewed work with evidence in ADHD adult populations. If you want to go deeper, these are the sources worth checking out:

Peter Gollwitzer: Implementation intentions. Decades of replicated findings on if-then planning and gaps between intention and action.

Gabriele Oettingen: WOOP and mental contrasting. Her book Rethinking Positive Thinking is the readable entry point.

James Clear: Identity-based behavior change. Atomic Habits is familiar to many scribblers. The identity chapter is useful.

Hazel Markus and Paula Nurius: Possible selves theory. The research on why specific behavioral self-representations work better than outcome-based ones.

Steven Hayes: ACT (Acceptance and Commitment Therapy) values clarification. Particularly strong evidence with ADHD adults.

Russell Barkley: ADHD reconceived as a self-regulation impairment. The most important reframe in the research. The tools in this document are an externalized regulation system ADHD brains don’t generate internally. That distinction changes how you use them.

ADHD and memory encoding: Research on encoding deficits in ADHD adults versus retrieval deficits is summarized in peer-reviewed literature including work cited through PubMed (PMID 24232170) and CHADD’s resource on how ADHD affects memory formation. The encoding deficit is the key finding: information not attended to during experience does not get stored, which is why externalized logging is a structural necessity rather than an optional habit.

When a company guts a division, the story is a statistic in a headline. A LinkedIn post with that blue “Open to Work” ring.

July 6th was a dark day at Microsoft. Xbox cut 3,200 gaming jobs, 20 percent of the workforce, with 1,600 gone immediately. Then it severed ties with four award-winning studios:

• Double Fine and Compulsion: Back to their founders, IP in hand.

• Ninja Theory and Undead Labs: Entering new ownership with funded commitments to finish their current projects.

• Arkane in France: Under required works council consultation.

New Xbox chief Asha Sharma put the reasoning in plain numbers: in a typical year, Xbox lost 64 cents on every dollar it invested in these teams. The biggest company on the planet, responsible for the biggest gaming acquisition in history, is essentially saying: we don’t know how to run you anymore. So… Go figure it out.

What walks out the door isn’t a pile of individuals. It’s a team. Engine and tools engineers, gameplay programmers, environment artists, systems designers, narrative scribblers, producers, QA.

That group knows how to scope a hard problem, argue about it, cut it down, and crunch to finish it on a deadline. They’ve shipped complex real-time software together, repeatedly, under constraints.

Sounds like a startup capable of creating the next big thing.

Most early companies burn two years and a founder’s marriage trying to build exactly that kind of chemistry out of strangers. Who can you trust in a crunch. Who overpromises. Who quietly saves the build at 2 a.m. A laid-off game team already has those answers. When they scatter, that knowledge evaporates. When they walk out together, it comes with them.

The Combo Is the Edge

Microsoft blamed the fail on the math: the 64 cents, the $20 billion invested in studios over five years, the revenue that shrank anyway. What actually changed is where the capital points.

Money, attention, infrastructure, and executive patience are all flowing toward systems that scale, while human creative teams get judged as expensive, slow, and hard to platformize. The same balance sheet that couldn’t carry a 350-person studio is ASAP funding data centers without blinking. The capital isn’t disappearing. It’s just being pointed somewhere else.

Meanwhile, the games diaspora keeps underrating what it actually has. The very companies building that AI infrastructure are genuinely bad at making anything a human being wants to touch.

The adjacent markets hiring right now:

• Robotics simulation and synthetic data

• Digital twins and interactive AI agents

• Enterprise training and spatial XR

All engineer-heavy, starved for experience and story. Enterprise tech may know how to build the pipes. But has no idea how to build the room. It’s game teams that build spaces people want to live in.

That mix, engineering married to people who understand pacing, feel, and narrative, is the gap those markets can’t fill and barely know they have.

Hard Parts

I won’t sell you a lifeboat with a hole in it. Three places these ventures actually die.

They die on go-to-market.
The technology is never the problem. Game people know consumer and creative markets in their bones and know enterprise sales not at all. The adjacent verticals run on procurement cycles, pilots, compliance, and design partners. All of that is a foreign language, and fluency takes longer than your runway. The empty chair in a room full of brilliant makers is almost always the commercial one, the person who can sell and raise coin. Fill that seat early or your demo will never become a company.

They die on domain knowledge.
Building a digital twin for logistics means understanding logistics. Otherwise you ship something gorgeous that nobody buys. The fix is unglamorous. Find a design partner in the target industry before you build, someone who will pay a little and tell you the truth, then you need to let the product grow out of real contact instead of a wish filled whiteboard.

They die when narrative is divorced from systems.
Here’s the one I owe my own tribe. Narrative divorced from systems, ownership, and distribution is getting easier to cut. Games will always need characters, dialogue, missions, lore, and worldbuilding. Yeah. Sure. Totally true. And yet… not enough.

Player necessity and business model protection are separate. This is the moment that difference stops being academic blah-blah-blah.

When tech companies believe an LLM can generate infinite content, a pure “scribbler” in a tech-adjacent pod is exposed. Your severance clock can also be a repositioning clock. If you do narrative, walk in as an Experience Designer or an AI Persona Architect. Shape how these new systems behave.

Story is central only in the companies where experience is the product. Pick that pod. In a pure infrastructure play you’ll be a footnote and the first cut, and you already know how that ends.

What Works

The dream version. Double Fine and Compulsion get the golden ticket. Original founders, IP in hand, back to the indie well. Keep the band together and go make your thing. That’s the exit everyone wants and very few get, because it requires a legendary founder with a big personality and a corporate giant confident enough to let your IP ride out with you in the divorce papers.

The reality version. For the vast majority, the move is the senior pod. You don’t need the whole studio to survive. You need a tight tactical unit, six to eight people of core engineering and design talent, pointed at one narrow, high-value problem in a single adjacent market, with a paying design partner keeping you honest. A lot of these should start as a collective or a services shop rather than a swing for the fences. Take contract work from the robotics and AI companies that are hungry for engine talent anyway. Generate income while you figure out what you’re actually selling. Let the product solidify out of customer work. Your collective becomes a company once it understands what it really is.

Point a warm team at a real problem someone will pay to solve.

Choice

When a concentration of talent gets scattered, one of two things happens. The default is dispersal. Everyone updates the résumé, takes the next individual job, and the team that took years to gel dissolves into other people’s org charts.

The other move is harder, riskier, but could be worth more. A pod spins out together while the chemistry is still cozy, before everyone forgets how good they were as a unit. That’s how one collapsing giant seeds a whole cluster of companies. It’s happened in other industries again and again, and 3,200 people leaving one division at once is more than enough to see it happen again here.

So if you’re staring at a severance package and a group text full of people you’d run through a wall for, evaluate the math before you all drift apart. Strip away the studio’s logo and the canceled project.

Is that thing you built together valuable somewhere else?

If the answer is yes, you’re not unemployed. You’re Pre-Seed.

Netflix’s internal research is making the rounds. Their biggest shows can lose between thirty and seventy percent of their audience between Season One and Season Two. Beef. Avatar.

The media are calling it a loyalty crisis. After thirty years scribbling for TV. My read is a little different.

The Myth of the Loyal Audience

People remember the 2000s as appointment TV’s golden age. All episodes watched. Every fan locked in. But inside, our POV was different. The Networks tracked weekly Nielsen numbers on Lost, Heroes, and Alias. The numbers dropped. They always dropped.

A hit broadcast drama might open to fifteen million viewers. Settle around ten or eleven. Finish the season a few million lower. That was normal. It was expected. The only question was whether enough people would stick with us to stay on the air.

Lost premiered to 18.6 million viewers. Season One averaged 16 million. The Season Two premiere drew 23 million, a series high, and the show went on to rank second only to CSI: Miami in global popularity surveys of the era. Heroes averaged 14 to 15 million during its breakout first season and peaked near 17 million. Alias across five seasons ran 9.7, 9.0, 8.2, 10.3, then 6.7 million.

Every one of those seasons lost viewers from its peak. Every one of those shows was considered a hit. Attrition aint failure. It’s inevitable.

Three Eras, One Career

My résumé tracks across three different economic models of TV.

Alias and Lost in the network era, where success meant weekly Nielsens, advertiser demographics, and tentpole scheduling. Heroes arriving as social media started fragmenting the water-cooler conversation. Hannibal in the prestige era, where critical conviction outweighed raw ratings. That show drew two to four million live viewers and still ran three seasons. American Gods in premium cable. Star Trek: Discovery crossing into streaming, where the CBS premiere drew 9.5 million viewers but Paramount measured our success in subscriber acquisition, not overnight ratings.

I watched the business shift from one model to the next and then to the next. Measurement metrics transformed. The audience didn’t.

What Changed Was the Clock

We had time on Network TV.

Twenty-two episodes. One per week. Summer reruns. DVD box sets. Syndication. Word of mouth had months to compound. Somebody might catch your show mid-season, binge the back half on DVR, and become a super-fan by the finale. Find it in syndication the following year and work backward through the catalog.

The audience wasn’t built on a binge over a launch weekend. It was accumulated over years, and across platforms.

The Office, Parks and Recreation, Breaking Bad, Buffy the Vampire Slayer. None of those shows were immediate hits. They grew. The business model had room for that kind of slow build. Then streaming compressed the entire process.

Eight episodes arrive on Friday morning. The platform knows most of what it wants to know within two weeks: completion rate, return rate, whether subscribers stayed or churned. Then the algorithm moves on to the next recommendation.

The conversation window on a Netflix drop runs roughly ten to fourteen days. A successful broadcast drama could sustain eight months of active discussion.

The Mega Gap

There’s a second problem that barely existed in broadcast. The wait between seasons. The average gap grew from about twelve months in 2020 to roughly twenty-one months by 2025.

Think about your own relationship with a show you loved two years ago. Can you name every supporting character? Do you remember the Season One mystery that was supposed to pay off?

It’s been three years and I’m still waiting for season two of 3-Body Problem and Blue Eyed Samurai. When they return, will I remember to tune in?

The audience disappears because these shows disappear from the lives of the audience long enough for them to fill the space.

The HBO Anomaly

There is a counterexample. Despite equally long production cycles, White Lotus and The Last of Us increased their audiences betwixt seasons.

Analysts point to two factors: weekly release schedules that sustain discussion rather than burning the conversation in a single weekend, and franchise identity strong enough that the IP becomes is appointment, not the plot of any specific season.

Shows with a strong enough brand, where audiences came back for what the show fundamentally is, retained viewers across the long gap. Shows built primarily around plot mechanics struggled.

Characters carry further than plot twists. Atmosphere carries further than mysteries.

Serial Scribes

Every episode must justify the next click. Every season must survive a twenty-month absence. Every finale must leave enough emotional residue that a viewer who watched it nearly two years ago still feels the pull when Season Two drops.

The question shifted: Can Season One compel enough viewers to finish fast, feel the loss when it’s over, and still care twenty months later?

That favors exceptional hooks, fast momentum, and specific characters who stay vivid across a multi-year gap. Relationships that sting when they’re over. Emotional payoffs that hit harder than plot reveals.

Plot twists fade. Relationships linger. That’s the structural work serialized television demands of its scribblers.

The More Things Change, The More They Stay The Same

Audiences return for curiosity, character, and conflict. Mysteries worth solving. People whose next decision matters. Stories that made them feel something they want to feel again and again.

The same engines that kept readers turning pages through Dickens serials in the nineteenth century, brought families back to weekly television in the 1980s, and compel binge-watchers clicking through one more episode at midnight.

I think the most critical factor hasn’t changed that much since the first storyteller convinced a comrade to stay by the fire for one more yarn. Technology and Distribution might evolve. Business models might adapt. Attention spans might collapse.

But human curiosity remains insatiable. So if Netflix can solve for that element, eliminate the mega-gaps, keep the quality at good enough, and maybe not cancel a promising show after one season, maybe they can arrest the attrition of their audience.

Dungeons and Dragons runs on a group of players voicing characters. A Game Master, called the Dungeon Master in D&D, plays the world back at them, and the dice settle what happens. No script. Everyone inventing at the table, out loud, in real time.

Brennan Lee Mulligan runs those tables for a living. He’s the GM behind the Dimension 20 series, and he trained at the Upright Citizen’s Brigade improv school in Los Angeles, where performers build a scene from nothing but attention and instinct.

A note he gives players has been circulating through the tabletop world, and it turns out to be a great note about scenes.

Players who follow it swear it turned their characters from cardboard into people. He calls it “role play rocket fuel.”

The trick: Care about something. Form opinions about the people and places in front of you. Like, distrust, resent, and want.

Emily Axford puts the practical version another way. “Consciously care.” Decide what your character feels about the person across the table, then play that emotion.

Reads like acting advice.

This tabletop note and my screen scribbler’s craft are buddies. The thing that lets a player generate an opinion on demand is the same thing that will make your scenes breathe.

A character who wants something can’t stay neutral. The want does the work the personality gets credit for.

Tabletop Note As Scene Note

Weak players lack objectives.

They chill at the table waiting to be handed a personality, treating every NPC like a vending machine for the plot. Nothing lands because nothing is at stake. They don’t know what they want, so they don’t know how to feel about anything or anyone.

Watch what happens the second a want shows up. Things help them get the thing. Things block them from the thing. Human beings form opinions about both, instantly, without being told what to do.

You don’t invent the opinion. The want invents it for you.

Mulligan calls the player a single light source and each character a pane of stained glass. He doesn’t ask players to invent new psychology every campaign. They adjust emphasis, not identity. You don’t build a new soul. You turn the dials. More compassion here. More ambition there. A little more recklessness than you’d allow yourself IRL. The light stays constant. The want bends it.

A scribbler hands a character a want and watches the opinions assemble themselves around whatever helps or blocks it.

Wants Divide the World

Look at The Social Network.

Building Facebook is Mark’s strategy. His want runs underneath it: prove I’m worth something. Run the room through that lens. Erica rejected him. Harvard clubs won’t let him in. Winklevi look at him like cheap shoes. Eduardo should have his back and keeps flinching.

Three opinions, maybe four, and the scribbler invented none of them. They flare out of the want like sparks off a grinder. Every opinion traces to one source. Whatever helps him, Zuck embraces. Whatever blocks him becomes an enemy.

This gives you a diagnostic for any scene that feels flat. Ask:

What does every character in this scene have an opinion about?

When the answer is nobody, nobody in that scene wants anything. A room full of people who want nothing is a room with zero story oxygen. Actors will feel it before you do. You’ll get the classic notes — I don’t know what I’m playing. What’s my objective. What am I trying to get out of this person. What do I want.

No want, no opinion. No opinion, no behavior. No behavior, no scene.

Check both sides of the table. When only one person walks in with an agenda, you scribbled a speech with a witness.

Let the Audience Solve It

Screen scribblers and role players part ways at this bit.

A player voices the opinion out loud. That’s the game at the table, broadcasting feeling so everyone has something to build on.

A scribbler buries it. The audience wants to catch the want on its own. Watching someone solve the equation. Doing the math for them kills the lean-back enjoyment.

Let those opposing wants collide and keep the wanting unspoken.

In Jaws, Brody wants the beaches closed and Vaughn wants them open. Both men chase something that can’t share the same Fourth of July festivities. The scene catches fire because their wants are incompatible. Tension is opposing wants with nowhere to go.

Trust behavior to carry the truth. Michael volunteers to kill Sollozzo, and the whole family rearranges around a man they thought was the soft one. Rick puts Ilsa on the plane, and the choice says everything a speech would have ruined.

Nobody narrates their wants. The action reveals it, and the audience leans in.

Want generates opinion. Opinion generates action. Action generates story.

That thread runs under improv, roleplay, and screen scribbling. Actors, players, and audiences chase the same thing. People pursuing something they care about while somebody stands in their way.

Give your characters a WANT worth having and the opinions will introduce themselves.

Video inspiration: Brennan Lee Mulligan on roleplay rocket fuel

Widow’s Bay is a cursed-island box. A mayor sells his dying New England town as the next vacation spot, tourists come, and a curse wakes up with them. It pulls the oldest trick in the genre and stacks questions about a town nobody fully understands.

As I watched the first nine episodes, I kept waiting for the box to curdle the way these usually do. It didn’t.

That sent me back to a post I scribbled a while ago. It split storytelling into two roads.

A Mystery Box keeps the answer hidden and runs on the pull of the question. What is the island. What’s in the hatch.

A Magic Show reveals the trick up front and wins you with the execution, the way a heist plays out while you watch every move.

Lost ran on the mystery. Columbo ran on the magic, the killer named in the first five minutes.

Widow’s Bay is the newest entry in that argument. Three others have been getting graded in public. From. Severance. Yellowjackets. All live and die on questions. Judged on whether the questions were even worth asking.

The Line I Drew Wrong

My original post made it a clean pick between the two roads. Mystery Box or Magic Show.

That was a mistake. Whether you hide the answer or show it changes how the story feels. It doesn’t tell you whether the story works. Those are two separate questions. The first is about presentation. The second is about payoff, and only the second decides whether an audience sticks around.

Presentation doesn’t predict payoff. Mystery Box can pay off every question it raises. It can also stiff you. Magic Show can earn its telegraphed reveal. It can also let it land flat. Hiding and showing sit on one axis. Earning sits on another.

Breaking Bad is a Magic Show that pays off everything it forecasts. Leftovers is a Mystery Box that never answers its big question and earns anyway, trading the puzzle for the people.

An earned mystery pays rent every episode. A reveal lands, the characters live with it, the world shifts, and something settles even as the bigger question stays open.

An unearned mystery runs a tab. It keeps ordering questions on credit and promises to settle up in a finale that may never arrive, or may arrive and bounce.

Mystery Box and Magic Show both still work. The only question that counts is whether their answers are worth the wait.

From: The Box That Learned From Lost

From is Mystery Box in the purest form. A town nobody can leave. Monsters at night. Questions stacked to the rafters. On paper, the exact machine that burned a lot of goodwill on ABC last decade.

It works because of who runs it. Jeff Pinkner, a Lost veteran, shepherds the show, the series that built the modern box and caught hell when the lid was lifted. I scribbled in that world season one. I know the lessons its lessons well.

From’s season four arrived in April 2026 and tightened the screws. The residents close on the answers they chase, and the search turns more terrifying. Then MGM+ committed to a fifth and final season and a real ending, shooting toward a 2027 close.

That is the landing under the box. A box with no landing keeps every question in the air, circling, until the payoff never comes. The landing is a confirmed ending, the spot every question is descending toward on purpose. The team knows where the plane sets down, so each question reads as a waypoint.

Widow’s Bay: The New Box, Landing Right Now

Widow’s Bay runs the From playbook in a comedic key. Cursed island. History of fogs and disappearances the locals believe and the mayor refuses to. He sells the place as a vacation spot, tourists arrive, the curse clocks in.

Critics reaching for the mystery-box label say this show teases hidden developments and then pays them off, instead of leaving red herrings and narrative noise. One singled out a flashback episode on the town’s origins as justified weight that earns its place. That’s the earned mystery paying the rent as it goes.

The scares sit on character wounds. Tom’s need to control a son slipping out of his reach. His aid Patricia’s loneliness. Wyck’s regret. One reviewer admitted the pull toward a second season is not the mystery, it’s the characters.

But those same reviews worry a show this dense can get too complicated and stack questions faster than it settles. The finale airs soon. So I’m scribbling this with the box closed, which makes Widow’s Bay the cleanest test my framework will get this year.

Severance: The Box Running on Feeling

When Severance came back, it reminded everyone why the box worked.

Its central question is a wound. What do you owe the version of yourself that only exists at work? An emotional question wearing a sci-fi coat, so every reveal hits the heart on the way to the head.

Season Two drew acclaim and ended on a cliffhanger. A few critics flagged the risk. A show this good at posing questions can scribble itself into a corner it can’t pay off. Emotional fuel does buy enormous patience. But eventually, that patience runs out.

Severance is the Magic Show and the Mystery Box holding hands. It shows you the wound, then hides the mechanism. The wound keeps the contract alive while the mechanism stays sealed.

Yellowjackets: The Warning

Yellowjackets season three finally answered the big ones. Who the Antler Queen is. Who goes in the pit. The long-standing questions.

A chunk of the audience walked away with more questions than they brought in. Some reveals deflated the mystique. The season spawned new questions faster than it resolved old ones.

That’s the failure state I missed in my first post. A mystery can answer itself and still feel unearned. Answering a question and paying it off are different jobs. The answer has to cost something. It has to land on a character and change them. Drop a reveal and sprint past it, and the audience will feel hollow.

The Emotion Dealers Agree

A room full of showrunners landing actual finales in 2026 said the same thing in their own words. And none of them said puzzle.

The Outlander showrunner called the job being emotion dealers. The Boys showrunner said a finale works when the audience feels something saying goodbye, and the rest is gravy. Vince Gilligan described his process as a road map you follow while staying ready to jettison the good idea for the better idea. The Fallout showrunner framed it as a plan you build toward, while keeping the flexibility to change.

A plan you build toward. That’s the floor again. The Magic Show principle, confirmed by people flying planes to destinations.

Which One Wins Long-Form

The earned mystery wins, in either costume. Hide the answer or show your hand, it makes no difference to the audience. They’re tracking two things. Did each chapter pay its rent. Is somebody steering toward a real destination.

From is winning because it found its floor. Severance is winning on emotional fuel and gambling it has a landing waiting. While Yellowjackets is the reminder that answers without aftermath can leave the same bad taste as no answers at all.

Widow’s Bay is the one still in the air, paying rent episode to episode, betting it can build the floor before the tab comes due.

UPDATE: Just watched season one finale — it was awesome. They landed the plane safely and it’s in good shape to fly again.

Some Examples

A reference list, sorted by presentation only. Any of these can earn its keep or run a tab.

Magic Show — You see the trick. The grip is execution.

A few of these I worked on, which is the only reason I can tell you what the room was actually solving for.

• Columbo. The cold open shows the murder and the murderer. The whole hour is watching the little scruffy guy take it apart. The format earned its own name, “the howcatchem.”

• Hannibal. You know what Lecter is long before the FBI. The grip is watching him set the table as Will Graham circles the truth.

• Alias. The pilot hands you Sydney’s secret. She’s a double agent inside the agency she’s fighting. You hold it and watch her work, with the Rambaldi mystery running underneath.

• Breaking Bad. Flash-forwards hand you the wreckage early, the charred bear, the ricin vial, the M60 in the trunk. You watch Walt build toward it.

• How to Get Away with Murder. Opens on a body and a cover-up, then rewinds toward the moment the premiere already showed you.

• The Americans. The pilot tells you they are deep-cover KGB. Every episode runs with a sword overhead.

• Chernobyl. History already spoiled the explosion and the cost. The hold is the how.

Mystery Box — The answer is hidden. The question pulls.

• Twin Peaks. Who killed Laura Palmer? The question that built the modern box.

• Lost. The island, the hatch, the numbers. A whole generation argued about, and the box caught hell when lifting its lid.

• Heroes. Save the cheerleader, save the world. A sprawling box of powers and villains, with Isaac’s paintings forecasting a future the season races toward.

• Westworld, season one. What is real, who is a host, what the maze means.

• True Detective, season one. The Yellow King and Carcosa, dangled for eight hours.

• Dark. A time loop where the pull places everyone across the decades.

• The Leftovers. What caused the Sudden Departure? It refuses to fully answer, proving how little the box matters once the feeling lands.

Screenplays roll on a one way railroad track. Readers board at FADE IN, ride all the way to FADE OUT, never getting the option to switch tracks. My RPG books run looser, a toy box of people and places waiting for a Game Master to deploy on the tabletop.

A text adventure is a story you click through instead of read straight. It hands you a scene, gives you a few options, and your choice decides what happens next.

If you ever read a Choose Your Own Adventure book as a kid, jamming two fingers between the pages so you could back out of the death you just walked into, you already know the shape of this.

No graphics, no joystick. Words on a screen and a handful of choices. Right in your browser. I’ve got four of them live now, in very different worlds.

Two sit in Terra Conflictus, my drowned and overheated future circa 2066 and beyond. In High Water you roam a flooded London and decide which of four factions inherits the wreckage. In Tag the Launch you run one illegal street race against a countdown.

The other two live in The Eternal Court, my gaslamp fantasy from an alternate 1793 where the living climb the mountains to petition a parliament of the dead.

In The Petition of Szurkevolgy (Narrative map in photo above) you carry that petition yourself, a half-frozen villager hauling a sealed letter up to a city of immortals.

In The Null Set you show up as the heroine of my TV Pilot, a steampunk accountant dropped into a perilous predicament.

Past the simple fact that making these is fun, they can be a solid structure test. You can’t fake a story branch. The second you ask what the player can actually do in a scene, every soft spot alights. A scene with no real choice in it. Characters who want nothing. The bit where the story is just rearranging furniture.

Making your heroes journey walkable can reveal exactly where it was never leading anywhere.

Your script has rooms and doors and people with agendas. A Text Adventure lets somebody else explore it. Kind of like you did when scribbling. Making keystroke discoveries about what price your characters pay when making dramatic narrative choices.

If you’re in the mood to build your own with a desktop LLM like Claude Code or Codex, here’s the prompt I used. You can run its output locally or host online easily with a service like Netlify.

Interactive Adventure Builder — Shareable Prompt

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