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Writing while the World is on Fire

Episode - 676

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February 18, 2025 Psych 101, Scriptnotes

How do you keep doing creative work when the world is falling apart around you? To sift through the despair and doubt, John welcomes back legendary Scriptnotes guest, writer-turned-psychotherapist Dennis Palumbo. They discuss the many feelings that catastrophic events can bring up in artists, the personal narratives that often inform those feelings, and how to keep moving forward when you feel like the band on the Titanic.

We also follow up on AI, and answer listener questions on competing with brain trusts and how to support a friend embroiled in controversy.

In our bonus segment for premium members, Dennis guides us through the best examples and worst mistakes of portraying therapists on screen.

Links:

  • “Am I Just Fiddling While Rome Burns?” by Dennis Palumbo for Psychiatric Times
  • Scriptnotes 99 – Psychotherapy for Screenwriters
  • ShotDeck
  • River Runner Global
  • At the Existentialist Café by Sarah Bakewell
  • Get a Scriptnotes T-shirt!
  • Check out the Inneresting Newsletter
  • Gift a Scriptnotes Subscription or treat yourself to a premium subscription!
  • Craig Mazin on Threads and Instagram
  • John August on BlueSky, Threads, and Instagram
  • Outro by Spencer Lackey (send us yours!)
  • Scriptnotes is produced by Drew Marquardt and edited by Matthew Chilelli.

Email us at ask@johnaugust.com

You can download the episode here.

UPDATE 2-19-25: The transcript for this episode can be found here.

Something’s Coming

January 6, 2025 Film Industry, Geek Alert, General, Psych 101, Tools

Last week, Dwarkesh Patel put words to an uneasy feeling that resonated with me:

I think we’re at what late February 2020 was for Covid, but for AI.

If you can remember back to February 2020, both the media and the general public were still in normal-times mode, discussing Trump’s impeachment, the Democratic primaries and Harvey Weinstein. Epidemiologists recognized that something big and potentially unprecedented was coming, but the news hadn’t yet broken through.

One of the first front-page articles I can find in the NY Times about Covid is from February 22nd, 2020.

image of NY Times front page, with covid story on left edge

Just three weeks later, markets had crashed and schools were closing. The world was upended. Covid had become the context for everything.

Patel foresees a similar pattern with AI:

Every single world leader, every single CEO, every single institution, members of the general public are going to realize pretty soon that the main thing we as a world are dealing with is Covid, or in this case, AI.

By “pretty soon,” I don’t think Patel believes we’re three weeks away from global upheaval. But the timeframes are much shorter than commonly believed — and getting shorter month by month.

Wait, what? And why?

This post is meant to be an explainer for friends and readers who haven’t been paying close attention to what’s been happening in AI. Which is okay! Technology is full of hype and bullshit, which most people should happily ignore.

We’ve seen countless examples of Next Big Things ultimately revealed to be nothing burgers. Many of the promises and perils of AI could meet a similar fate. Patel himself is putting together a media venture focused on AI, so of course he’s going to frame the issue as existential. Wherever there’s billions of dollars being spent, there’s hype and hyperbole, predictions and polemics.

Still — much like with epidemiologists and Covid in February 2020, the folks who deal with AI for a living are pretty sure something big is coming, and sooner than expected.

Something big doesn’t necessarily mean catastrophic; the Covid analogy only goes so far. Indeed, some researchers see AI ushering in a golden age of scientific enlightenment and economic bounty. Others are more pessimistic — realistic, I’d say — warning that we’re in for a bumpy and unpredictable ride, one that’s going to be playing out in a lot of upcoming headlines.

The sky isn’t falling — but it’s worth directing your gaze upwards.

The world of tomorrow, today

Science fiction is becoming science fact much faster than almost anyone anticipated. One way to track this is to ask interested parties how many years it will be before we have artificial general intelligence (AGI) capable of doing most human tasks. In 2020, the average estimate was around 50 years. By the end of 2023, it was seven.

chart showing decline from 30 years to 8 years, with dashed lines indicating further declines

Over the past few months, a common prediction has become three years. That’s the end of 2027. Exactly how much AI progress we’ll see by then has become the subject of a recent bet. Of the ten evaluation criteria for the bet, one hits particularly close to home for me:

8) With little or no human involvement, [AI will be able to] write Oscar-caliber screenplays.

As a professional screenwriter and Academy voter, I can’t give you precise delimiters for “Oscar-caliber” versus “pretty good” screenplays. But the larger point is that AI should be able to generate text that feels original, compelling and emotionally honest, both beat-by-beat and over the course of 120 satisfying pages. Very few humans can do that, so will an AI be able to?

A lot of researchers say yes, and by the end of 2027.

I’m skeptical — but that may be a combination of ego preservation and goalpost-moving. It’s not art without struggle, et cetera.

The fact that we’ve moved from the theoretical (“Could AI generate a plausible screenplay?”) to practical (“Should an AI-generated screenplay be eligible for an Oscar?”) in two years is indicative of just how fast things are moving.

So what happened? Basically, AI got smarter much faster than expected.

Warp speed

Some of the acceleration is easy to notice. When large language models (LLMs) like ChatGPT debuted at the end of 2022, they felt like a novelty. They generated text and images, but nothing particularly useful, and they frequently “hallucinated,” a polite way of saying made shit up.

If you shrugged and moved on, I get it.

The quality of LLM’s output has improved a lot over the past two years, to the point that real professionals are using them daily. Even in their current state — even if they never get any better — LLMs can disrupt a lot of work, for better and for worse.

An example: Over the holidays, I built two little iOS apps using Cursor, which generates code from plain text using an LLM.

Here’s what I told it as I was starting one app:

I’ll be attaching screen shots to show you what I’m describing.

  1. Main screen is the starting screen upon launching the app. There will be a background image, but you can ignore that for now. There are three buttons. New Game, How to Play, and Credits.

  2. How to Play is reached through the How to Play button on the main screen. The text for that scrolling view is the file in the project how-to-play.txt.

  3. New Game screen is reached through the new game button. It has two pop-up lists. the first chooses from 3 to 20. the second from 1 to 10. Clicking Start takes you into the game. (In the game view, the top-right field should show the players times round, so if you had 3 players and five rounds, it would start with 1/15, then 2/15.

  4. the Setup screen is linked to from the game screen, if they need to make adjustments or restart/quit the game.

Within seconds, it had generated an app I could build and run in Xcode. It’s now installed on my phone. It’s not a commercial app anyone will ever buy, but if it were, this would be a decent prototype.

Using Cursor feels like magic. I’m barely a programmer, but in the hands of someone who knew what they were doing, it’s easy to imagine technology like this tripling their productivity.1 That’s great for the software engineer — unless the company paying them decides they don’t need triple the productivity and will instead just hire one-third the engineers.

The same calculation can be applied to nearly any industry involving knowledge work. If your job can be made more productive by AI, your position is potentially in jeopardy.

That LLMs are getting better at doing actually useful things is notable, but that’s not the main reason timelines are shortening.

Let’s see how clever you really are

To measure how powerful a given AI system is, you need to establish some benchmarks. Existing LLMs easily pass the SAT, the GRE, and most professional certification exams. So researchers must come up with harder and harder questions, ones that won’t be in the model’s training set.

No matter how high you set the bar, the newest systems keep jumping over it. Month after month, each new model does a little better. Then, right before the holidays, OpenAI announced that its o3 system made a huge and unexpected leap:

chart showing o3 performance and cost, both vastly higher

With LLMs like ChatGPT or Claude, we’re used to getting fast and cheap answers. They spit out a text or image in seconds. In contrast, o3 spends considerably more time (and computing power) planning and assessing. It’s a significant change in the paradigm. The o3 approach is slower and more expensive — potentially thousands of dollars per query versus mere pennies — but the results for certain types of questions are dramatically better. For billion-dollar companies, it’s worth it.

Systems like these are particularly good at solving difficult math and computer science problems. And since AI systems themselves are based on math and computer science, today’s model will help build the next generation. This virtuous cycle is a significant reason the timelines keep getting shorter. AI is getting more powerful because AI is getting more powerful.

When and why this will become the major story

In 2020, Covid wasn’t on the front page of the NY Times until its economic and societal impacts were unmistakable. The stock market tanked; hospitals were filling up. Covid became impossible to ignore. Patel’s prediction is the same thing will happen with AI. I agree.

I can imagine many scenarios bringing AI to the front page, none of which involve a robot uprising.

Here are a few topics I expect we’ll see in the headlines over the next three years.

  • Global tensions. As with nuclear technology during the Cold War, big nations worry about falling behind. China has caps on the number of high-performance AI chips it’s allowed to import. Those chips it needs? They’re made in Taiwan. Gulp.

  • Espionage. Corporations spend billions training their models.2 Those model weights are incredibly valuable, both to competitors and bad actors.

  • Alignment. This is a term of art for “making sure the AI doesn’t kill us,” and is a major source of concern for professionals working in the field. How do you teach AI to act responsibly, and how do you know it’s not just faking it? AI safety is currently the responsibility of corporations racing to be the first to market. Not ideal!

  • Nationalizing AI. For all three of the reasons above, a nation (say, the U.S.) might decide that it’s a security risk to allow such powerful technology to be controlled by anyone but the government.

  • Spectacular bankruptcy. Several of these companies have massive valuations and questionable governance. It seems likely one or more will fail, which will lead to questions about the worth of the whole AI industry.

  • The economy. The stock market could skyrocket — or tank. Many economists believe AI will lead to productivity gains that will increase GDP, but also, people work jobs to earn money and buy things? That seems important.

  • Labor unrest. Unemployment is one thing, but what happens when entire professions are no longer viable? What’s the point in retraining for a different job if AI could do that one too?

  • Breakthroughs in science and medicine. Once you have one AI as smart as a Nobel prize winner, you can spin up one million of them to work in parallel. New drugs? Miracle cures? Revolutionary technology, like fusion power and quantum computing? Everything seems possible.

  • Environmental impact (bad). When you see articles about the carbon footprint of LLMs, they’re talking about initial training stage. That’s the energy intensive step, but also way smaller than you may be expecting? After that, the carbon impact of each individual query is negligible, on the order of watching a YouTube video. That said, the techniques powering systems like o3 involve using more power to deliver answers, which is why you see Microsoft and others talking about recommissioning nuclear plants. Also, e-waste! All those outdated chips need to be recycled.

  • Environmental impact (good). AI systems excel at science, engineering, and anything involving patterns. Last month, Google’s DeepMind pushed weather forecasting from 10 days to 15 days. Work like this could help us deal with effects of climate change, by improving crop yields and the energy grid, for example.

So how freaked out should you be?

What is an ordinary person supposed to do with the knowledge that the world could suddenly change?

My best advice is to hold onto your assumptions about the future loosely. Make plans. Live your life. Pay attention to what’s happening, but don’t let it dominate your decision-making. Don’t let uncertainty paralyze you.

A healthy dose of skepticism is warranted. But denial isn’t. I still hear smart colleagues dismissing AI as fancy autocomplete. Sure, fine — but if it can autocomplete a diagnosis more accurately than a trained doctor, we should pay attention.

It’s reasonable to assume that 2027 will look a lot like 2024. We’ll still have politics and memes and misbehaving celebrities. It’ll be different from today in ways we can’t fully predict. The future, as always, will remain confusing, confounding and unevenly distributed.

Just like the actual pandemic wasn’t quite Contagion or Outbreak, the arrival of stronger AI won’t closely resemble Her or The Terminator or Leave the World Behind. Rather, it’ll be its own movie of some unspecified genre.

Which hopefully won’t be written by an AI. We’ll see.

Thanks to Drew, Nima and other friends for reading an early draft of this post.

  1. Google’s CEO says that more than 25% of their code is already being generated by AI. ↩
  2. DeepSeek, a Chinese firm, apparently trained their latest LLM for just $6 million, an impressive feat if true. ↩

Movies We Haven’t Seen

March 30, 2024 Film Industry

I’ve seen 54 out of the 100 films on AFI’s list of all-time greatest American films. Of the ones I’ve missed, there are a few I do genuinely want to see. But will my life or career be negatively impacted if I never see Intolerance (1916)? I doubt it.

In episode 637 of Scriptnotes, Craig and I discuss which movies screenwriters “should” see. That is, of all the movies out there, which ones are most likely to come up in meetings, or be relevant to projects we’re writing in the 2020s?

Inevitably, one’s viewing is going to be greatly affected by when you were born. Craig and I were both born in the 1970s. Is it realistic or necessary for a screenwriter born in 2000 to have the same breadth of 1980s cinematic knowledge?

Preparing for the segment, Scriptnotes producer Drew Marquardt and I went through online lists of the 100 best movies for past four decades. Some of the lists were from Rolling Stone, others from IMDb.1 Drew and I marked which films we’ve never seen.

Key: John hasn’t seen | Drew hasn’t seen

Movies of the 1980s

  1. Do the Right Thing
  2. Videodrome
  3. Raging Bull
  4. Blue Velvet
  5. Ran
  6. Shoah
  7. Blade Runner
  8. Stranger Than Paradise
  9. The Thin Blue Line
  10. Raiders of the Lost Ark
  11. Sex, Lies, and Videotape
  12. Come and See
  13. The Thing
  14. Brazil
  15. Die Hard
  16. The Shining
  17. Raising Arizona
  18. Say Anything
  19. Something Wild
  20. Blow Out
  21. Stop Making Sense
  22. An American Werewolf in London
  23. The Right Stuff
  24. Paris, Texas
  25. RoboCop
  26. The King of Comedy
  27. E.T.
  28. The Terminator
  29. This Is Spinal Tap
  30. Elephant
  31. Repo Man
  32. Fast Times at Ridgemont High
  33. They Live
  34. Wings of Desire
  35. Risky Business
  36. Fanny and Alexander
  37. Star Wars: Episode V – The Empire Strikes Back
  38. The Elephant Man
  39. My Neighbor Totoro
  40. Reds
  41. The Decalogue
  42. Pee-wee’s Big Adventure
  43. The Times of Harvey Milk
  44. Mad Max 2
  45. After Hours
  46. Once Upon a Time in America
  47. The Blues Brothers
  48. She’s Gotta Have It
  49. Koyaanisqatsi
  50. Fitzcarraldo
  51. Aliens
  52. Thief
  53. Sophie’s Choice
  54. Roger & Me
  55. My Beautiful Laundrette
  56. Big
  57. Modern Romance
  58. Purple Rain
  59. My Dinner with Andre
  60. Bull Durham
  61. Cutter’s Way
  62. Evil Dead II
  63. Broadcast News
  64. Akira
  65. Back to the Future
  66. The Long Good Friday
  67. Desperately Seeking Susan
  68. Blood Simple
  69. Caddyshack
  70. The Killer
  71. Possession
  72. 48 Hrs.
  73. Ghostbusters
  74. Drugstore Cowboy
  75. Vagabond
  76. Heathers
  77. Women on the Verge of a Nervous Breakdown
  78. Police Story
  79. Full Metal Jacket
  80. Sweetie
  81. River’s Edge
  82. Hollywood Shuffle
  83. The Little Mermaid
  84. Midnight Run
  85. Bill & Ted’s Excellent Adventure
  86. Withnail & I
  87. Atlantic City
  88. The Brother from Another Planet
  89. Amadeus
  90. The Cook, the Thief, His Wife & Her Lover
  91. The Vanishing
  92. Airplane!
  93. Near Dark
  94. Who Framed Roger Rabbit
  95. Matewan
  96. Scarface
  97. Miracle Mile
  98. The Decline of Western Civilization
  99. Gregory’s Girl
  100. Testament

Movies of the 1990s

  1. Pulp Fiction
  2. Goodfellas
  3. Fargo
  4. L.A. Confidential
  5. The Big Lebowski
  6. Saving Private Ryan
  7. Fight Club
  8. The Silence of the Lambs
  9. Magnolia
  10. American Beauty
  11. Unforgiven
  12. Se7en
  13. The Shawshank Redemption
  14. Forrest Gump
  15. Heat
  16. Sling Blade
  17. Out of Sight
  18. Dazed and Confused
  19. American History X
  20. Election
  21. Miller’s Crossing
  22. Boogie Nights
  23. Groundhog Day
  24. Schindler’s List
  25. Good Will Hunting
  26. True Romance
  27. The Usual Suspects
  28. Being John Malkovich
  29. Rushmore
  30. Reservoir Dogs
  31. Braveheart
  32. JFK
  33. Ed Wood
  34. Waiting for Guffman
  35. Dances with Wolves
  36. Kingpin
  37. Dumb and Dumber
  38. Clerks
  39. Mallrats
  40. Jackie Brown
  41. Boyz n the Hood
  42. Get Shorty
  43. Jerry Maguire
  44. Bottle Rocket
  45. Rounders
  46. The Matrix
  47. Malcolm X
  48. Quiz Show
  49. Titanic
  50. The Rainmaker
  51. Terminator 2: Judgment Day
  52. As Good as It Gets
  53. Barton Fink
  54. Toy Story
  55. Dead Man Walking
  56. Jurassic Park
  57. Dead Man
  58. Toy Story 2
  59. The Sixth Sense
  60. The English Patient
  61. Edward Scissorhands
  62. The Fugitive
  63. Donnie Brasco
  64. Three Kings
  65. The Thin Red Line
  66. Glengarry Glen Ross
  67. South Park: Bigger, Longer & Uncut
  68. The Green Mile
  69. Trainspotting
  70. Scent of a Woman
  71. In the Name of the Father
  72. Scream
  73. The Last of the Mohicans
  74. Leaving Las Vegas
  75. The Lion King
  76. Apollo 13
  77. Short Cuts
  78. Aladdin
  79. The Grifters
  80. Beauty and the Beast
  81. Philadelphia
  82. Wag the Dog
  83. Wayne’s World
  84. The Player
  85. My Cousin Vinny
  86. The Truman Show
  87. There’s Something About Mary
  88. Lock, Stock and Two Smoking Barrels
  89. Léon: The Professional
  90. Office Space
  91. Thelma & Louise
  92. The Insider
  93. Nobody’s Fool
  94. Swingers
  95. A Few Good Men
  96. The People vs. Larry Flynt
  97. Chasing Amy
  98. Lone Star
  99. The Fisher King
  100. 12 Monkeys

Movies of the 2000s

  1. Gladiator
  2. The Dark Knight
  3. Slumdog Millionaire
  4. The Departed
  5. The Lord of the Rings: The Return of the King
  6. Pan’s Labyrinth
  7. Blood Diamond
  8. City of God
  9. Finding Nemo
  10. No Country for Old Men
  11. Cinderella Man
  12. V for Vendetta
  13. There Will Be Blood
  14. Donnie Darko
  15. Sin City
  16. Mystic River
  17. 300
  18. Let the Right One In
  19. A Beautiful Mind
  20. Munich
  21. Up
  22. Memento
  23. The Lord of the Rings: The Two Towers
  24. The Prestige
  25. WALL·E
  26. Requiem for a Dream
  27. Into the Wild
  28. The Pianist
  29. Inglourious Basterds
  30. The Lord of the Rings: The Fellowship of the Ring
  31. Lost in Translation
  32. The Hurt Locker
  33. Eternal Sunshine of the Spotless Mind
  34. Crouching Tiger, Hidden Dragon
  35. American Psycho
  36. Kill Bill: Vol. 1
  37. Road to Perdition
  38. Walk the Line
  39. The Last Samurai
  40. Million Dollar Baby
  41. O Brother, Where Art Thou?
  42. Downfall
  43. Black Hawk Down
  44. Hotel Rwanda
  45. The Curious Case of Benjamin Button
  46. Eastern Promises
  47. Little Miss Sunshine
  48. The Incredibles
  49. American Gangster
  50. Gran Torino
  51. Zombieland
  52. The Wrestler
  53. Big Fish
  54. Crazy Heart
  55. Doubt
  56. 28 Days Later
  57. Thank You for Smoking
  58. The Assassination of Jesse James by the Coward Robert Ford
  59. The Bourne Identity
  60. Taken
  61. Snatch
  62. Casino Royale
  63. The Bourne Ultimatum
  64. Almost Famous
  65. Letters from Iwo Jima
  66. Gangs of New York
  67. Children of Men
  68. The Pursuit of Happyness
  69. Tears of the Sun
  70. Avatar
  71. Collateral
  72. Batman Begins
  73. Kill Bill: Vol. 2
  74. The Aviator
  75. Saw
  76. Kung Fu Panda
  77. Ocean’s Eleven
  78. Superbad
  79. Man on Fire
  80. Minority Report
  81. Seven Pounds
  82. Traffic
  83. United 93
  84. The Bourne Supremacy
  85. Monsters, Inc.
  86. Shrek
  87. The Boy in the Striped Pajamas
  88. Catch Me If You Can
  89. Iron Man
  90. Cloudy with a Chance of Meatballs
  91. Training Day
  92. Sunshine
  93. 21 Grams
  94. 3:10 to Yuma
  95. District 9
  96. The Others
  97. Anchorman: The Legend of Ron Burgundy
  98. In Bruges
  99. Crash
  100. Shaun of the Dead

Movies of the 2010s

  1. Parasite
  2. Mad Max: Fury Road
  3. Django Unchained
  4. Three Billboards Outside Ebbing, Missouri
  5. La La Land
  6. Dunkirk
  7. Whiplash
  8. The Irishman
  9. Your Name.
  10. Avengers: Endgame
  11. Inception
  12. Spider-Man: Into the Spider-Verse
  13. Birdman or (The Unexpected Virtue of Ignorance)
  14. Blade Runner 2049
  15. 1917
  16. Avengers: Infinity War
  17. Locke
  18. Calvary
  19. The Hunt
  20. Interstellar
  21. Once Upon a Time… in Hollywood
  22. Guardians of the Galaxy
  23. The Hateful Eight
  24. Logan
  25. X-Men: Days of Future Past
  26. Captain America: Civil War
  27. Captain America: The Winter Soldier
  28. Rogue One: A Star Wars Story
  29. Inside Llewyn Davis
  30. Baby Driver
  31. Marriage Story
  32. The Social Network
  33. The Dark Knight Rises
  34. Her
  35. Manchester by the Sea
  36. Knives Out
  37. Confessions
  38. Arrival
  39. Gone Girl
  40. A Silent Voice: The Movie
  41. Ford v Ferrari
  42. A Quiet Place
  43. Nightcrawler
  44. Fruitvale Station
  45. Prisoners
  46. Skyfall
  47. Warrior
  48. Thor: Ragnarok
  49. The Avengers
  50. Joker
  51. Wind River
  52. Frank
  53. Green Book
  54. Deadpool
  55. Spider-Man: Far from Home
  56. Jojo Rabbit
  57. Guardians of the Galaxy Vol. 2
  58. Deadpool 2
  59. Nocturnal Animals
  60. Kick-Ass
  61. The Grand Budapest Hotel
  62. Gravity
  63. Room
  64. 12 Years a Slave
  65. Inside Out
  66. Toy Story 3
  67. Argo
  68. Moonrise Kingdom
  69. Moonlight
  70. Burning
  71. A Star Is Born
  72. The Great Beauty
  73. Harry Potter and the Deathly Hallows: Part 2
  74. 13 Assassins
  75. Coco
  76. Hell or High Water
  77. The Farewell
  78. The Wind Rises
  79. The Martian
  80. Tinker Tailor Soldier Spy
  81. Star Wars: Episode VII – The Force Awakens
  82. Get Out
  83. Toy Story 4
  84. Black Swan
  85. Sound of Metal
  86. Dallas Buyers Club
  87. Black Panther
  88. Midnight in Paris
  89. 127 Hours
  90. The Big Short
  91. Sicario
  92. True Grit
  93. The Revenant
  94. Drive
  95. Zootopia
  96. The Wolf of Wall Street
  97. Bridge of Spies
  98. Masquerade
  99. Rush
  100. Weathering with You
  1. IMDb lists reflect an individual user’s preferences, which is why there’s occasionally a film you’ve never heard of. Still, this is probably better than just looking at the Oscar-nominated films from each year, which can include entries that don’t hold up. ↩

A few thoughts on Sora

February 16, 2024 Film Industry, Geek Alert, WGA

Yesterday, OpenAI announced Sora, a new product that generates realistic video from text prompts.1 The examples are remarkable.

A TV writer friend texted me to ask “is it time to be petrified?”

I wrote back:

I don’t think you need to be petrified. It’s very impressive at creating video in a way that’s like how Dall-E does images. A huge achievement. For pre-viz? Mood reels? Incredible. We’ll see stuff coming out of it used in commercials first.

For longer, narrative stuff, there’s a real challenge moving from text generation (gpt-4 putting together something that looks like a script) to “filming” that script with these tools to resemble anything like our movies and television.

Writers, directors, actors and crew have a sense of why they’re doing what they’re doing, and what makes sense in this fictitious reality they’re creating. I don’t think you can do that without consciousness, without self-awareness, and if/when AI gets there, stuff like Sora will be the least of our concerns.

With a night to sleep on it, I think there are a few larger, more immediate concerns. Writers (and humans in general) should be aware of but not petrified by some of the implications of this technology beyond the obvious ones like deepfakes and disinformation.

  1. Video as input. Like image generators, this technology can work off of a text prompt. But you can also feed it video and have it change things. Do you want A Few Good Men, but with Muppets? Done. Need to replace Kevin Spacey in a movie? No need to reshoot anything. Just let Sora do it.

  2. Remake vs. refresh. Similarly, any existing film or television episode could be “redone” with this technology. In some cases, that could mean a restoration or visual effects refresh, like George Lucas did with Star Wars. Or it could be what we’d consider a remake, where the original writer gets paid. What’s the difference between a refresh and a remake, and who decides?

  3. Animation vs. live action. How do we define the video material that comes out of Sora? It can look like live action, but wasn’t filmed with cameras. It can look like animation, but it didn’t come out of an animation process. This matters because while the WGA represents writers of both live action and animation, studios are not currently required to use WGA writers in animation. We can’t let this technology to be used as an end-run around WGA (and other guild) jurisdiction.

  4. Reality engines. In a second paper, OpenAI notes that Sora could point to “general purpose simulators of the physical world.” The implications go far beyond any disruptive effects on Hollywood, and are worth a closer look.

It seems like a long way to go from videos of cute paper craft turtles to The Matrix, but it’s worth taking the progress they’ve made here seriously. In generating video, Sora does a few things that are really difficult, and resemble human developmental milestones.

Like all models, Sora is predictive, making guesses about what just happened and what happens next. But it feels different because it’s doing this in a 3D space that largely tracks with our lived experience. It remembers objects, even if they’re not on screen at the moment, and recognizes interactions between objects, such as paintbrushes leaving marks on the canvas.2

Sora makes mistakes, but the results surprisingly good for a system that wasn’t explicitly trained to do anything other than generate video. Those capabilities could be used to do other things. In a jargon-heavy paragraph, OpenAI notes:

Sora is also able to simulate artificial processes — one example is video games. Sora can simultaneously control the player in Minecraft with a basic policy while also rendering the world and its dynamics in high fidelity. These capabilities can be elicited zero-shot by prompting Sora with captions mentioning “Minecraft.”

Sora “gets” Minecraft because it’s ingested countless hours of Minecraft videos. If it’s able to create a simulation of the game that is indistinguishable from the original, is there really a difference? If it’s able to create a convincing simulation of reality based on the endless video it scapes, what are the implications for “our” reality?

These are questions for philosophers, sure, but we’re all going to be faced with them sooner than we’d like. Sora and its descendants are going to have an impact beyond the cool video they generate.

  1. Sora is a great name, btw. It doesn’t mean anything, and doesn’t have any specific connotation, yet feels like something that should exist. ↩
  2. Not to dive too deeply into theories of human consciousness, but the ability to internally model reality and predict things feel like table stakes. ↩
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