Studio Guidebook: AI and Screenwriting
Part I: AI Guidebook - AI & Screenwriting Pre and Post Crossover
Steve Newcomb is a tech entrepreneur, AI strategist, and filmmaker focused on the future of AI-driven storytelling. He co-founded Powerset, the pioneering natural language AI startup that became Microsoft Bing, and has deep expertise in AI, machine learning, and NLP. He has also funded two of the world’s largest open-source projects and has a track record of building high-impact startups. His Substack is tailored for CTOs of film and television studios, offering in-depth analysis of AI’s disruptive impact on storytelling, business models, and the future of the entertainment industry.
When contemplating the role of AI in Screenwriting, it’s not a matter of “can AI write a screenplay for me?” - it’s much more complex than that. CTOs must navigate complex technical, cultural, legal, and business issues.
…and what’s more - it’s all going to be about timing.
In my article Film Studios and the Innovator’s Dilemma I wrote extensively about the crossover point - the moment when AI-generated scripts and storytelling reach a quality level indistinguishable from traditional human writing - and how that should inform our decision making of CTO’s and their Head of AI (a role that is coming to studios soon).
In summary…
Once the crossover point happens, the world changes, the rules change, and technology, workflows, and people that were once essential become obsolete.
Prior to the crossover point, CTOs should invest (e.g. spend time/money on internal development or external partnerships) in two types of AI technologies and avoid a third. I’ll provide example for all of these in the article.
It is OK to invest in AI technologies that will NOT be useful AFTER the crossover point, but only if the benefits of investing in these throwaway technologies create a return on investment that justifies their existence.
DO invest in AI technologies that will be useful AFTER the crossover point
DO NOT invest in AI technologies that are long term, expensive, and risky and are NOT useful after the crossover point.
This raises the critical question: when will the crossover point arrive? If it’s imminent, investing in hybrid human-AI systems is a losing bet, as those workflows will soon become obsolete. But if the crossover is still decades away, then a human-AI hybrid approach makes far more strategic sense, allowing studios to gradually integrate AI while retaining human expertise.
So how do we know when the crossover point is going to happen?
The Asymptotic Genre Adoption Axiom: Different genres will reach parity with human writing at different speeds, dictated by their structural complexity and reliance on human nuance.
Supporting Principles:
Genres with simple, repetitive structures will cross over first. Children's content, with its predictable emotional beats and formulaic storytelling, will be the earliest to transition, as AI can already generate stories within this space with reasonable accuracy.
Procedural and genre-driven storytelling will follow soon after. Action films, romantic comedies, thrillers, and procedural television rely on established tropes and conventional story arcs, making them relatively easy for AI to replicate and refine.
High-concept, character-driven storytelling will cross over last. A24-style indie films, experimental narratives, and deeply personal dramas demand an understanding of subtext, human psychology, and ambiguity—elements that AI still struggles to master.
Studios must invest based on the expected crossover timeline. AI investments should align with how soon a genre will transition, prioritizing areas where AI has immediate utility while avoiding premature bets on AI replicating complex human storytelling.
AI screenwriting will not be a clean disruption, but a creeping evolution. Rather than an overnight shift, AI will asymptotically approach human-level writing, gradually improving across different genres at different times, creating an uneven but inevitable transformation.
The Turing-Screen Test
I am currently working on the formation of a standardized screenwriting test that will determine whether AI has reached the crossover point in storytelling. Just as AI has been tested in chess matches, mathematical proof solving, and protein folding, we need a rigorous, repeatable method to evaluate AI’s capabilities in screenwriting. This test, which I propose calling The Turing-Screen Test, would establish a detailed set of indices measuring AI’s ability to generate compelling, structurally sound, and emotionally resonant narratives.
For screen writing, these indices would evaluate:
Character depth and consistency across a full-length screenplay
Narrative coherence and structural integrity (three-act structure, foreshadowing, payoff)
Thematic depth and ability to weave complex subtext into a script
Emotional engagement and human relatability
Originality and avoidance of derivative storytelling
Ability to create satisfying and unpredictable yet logical plot developments
Dialogue quality, including subtext, character voice differentiation, and natural flow
For decades, chess was seen as a pinnacle of human intelligence—too complex for computers to master. Early AI chess programs in the 1950s and 60s could play at a basic level but were nowhere near human grandmasters. The breakthrough came in 1997, when IBM’s Deep Blue defeated world champion Garry Kasparov, marking the crossover point where AI could compete at the highest levels of human play. This wasn’t an overnight achievement—Deep Blue had lost to Kasparov just a year earlier, and AI researchers had spent decades refining machine-learning techniques and brute-force search algorithms to improve performance. Once AI reached parity, progress didn’t stop; today, engines like Stockfish and AlphaZero vastly outperform any human and are used as essential training tools by grandmasters. This pattern—a slow, iterative climb followed by a sudden, irreversible tipping point—is exactly how AI in screenwriting will evolve.
With a structured evaluation framework in place, every major AI model or variant update could be subjected to the Turing-Screen Test, generating an objective measurement of its storytelling capabilities. This would allow us to establish a velocity curve—a measurement of how quickly AI is closing the gap on human storytelling. By mapping the knock-down list of remaining deficiencies AI must overcome before reaching parity, we could then plot an asymptotic trajectory that gives us real visibility into when the crossover point will likely occur. This isn’t just about assessing where AI is today—it’s about forecasting its inevitable trajectory and ensuring the industry is prepared before the crossover point arrives.
Prior to the Crossover Points
Above, I described two types of AI investments that are valid before the crossover point:
Those that provide enough immediate return on investment to justify their short-term value, even if they eventually become obsolete.
Those that will remain useful after the crossover and
The first type ensures long-term positioning, preparing studios for the inevitable AI-driven industry, while the second type allows for incremental efficiency improvements without committing to expensive, risky, or soon-to-be outdated technologies. Below are examples of each type.
Short term investments that pay-off before they become obsolete
In the short term, some AI investments offer immediate value by enhancing human-driven writing workflows, even though they will become obsolete once AI reaches full storytelling parity. These technologies help streamline script development, improve efficiency, and reduce costs, but they are fundamentally designed to assist human writers rather than function in a fully AI-driven industry.
The key investment question here is: Does this AI technology create enough short-term value to justify its cost, even if it won’t survive the crossover point? If the answer is yes, then it is a valid, albeit temporary, investment.
AI-assisted script formatting and cleanup – Tools that automatically apply screenplay structure, ensuring compliance with industry standards.
AI-driven dialogue enhancement – Systems that suggest alternative lines or improve the flow of dialogue based on audience engagement data.
AI-powered beat and pacing analysis – AI tools that analyze scripts for structural integrity and recommend adjustments to improve engagement.
AI-generated story prompts and brainstorming assistants – AI-powered tools that help human writers overcome writer’s block by suggesting plot twists and themes.
AI-enhanced script summarization tools – AI-generated one-page treatments and pitch-ready synopses based on full-length scripts.
AI-driven character consistency checks – Tools that analyze a script for inconsistencies in character dialogue, tone, or behavior across multiple drafts.
AI-powered market alignment tools – AI-driven software that suggests minor adjustments to scripts based on audience trend analysis.
Automated pre-production script breakdowns – AI tools that extract production details, budgets, and shooting schedules from screenplays to improve efficiency.
AI-assisted screenplay comparison tools – Software that evaluates scripts against similar past box office successes, providing insight into potential market viability.
AI-driven rewrite optimization – AI-generated alternative scenes that allow human writers to refine drafts faster by choosing from multiple AI-suggested variations.
AI investments that will remain useful after the crossover
In a world where AI is fully capable of writing screenplays, the only AI technologies that remain useful are those that continue to serve AI-driven storytelling, rather than augment human writers. If a tool’s primary function was to assist human screenwriters, it becomes obsolete once AI reaches full parity.
The key question for investment is: Does this AI technology still provide value when AI is writing everything? If the answer is yes, then it’s a valid long-term investment. Below are 10 AI technologies that meet this criterion.
Automated real-time content testing – Large-scale, AI-driven A/B testing platforms that iterate and refine AI-generated scripts before release.
AI-driven audience prediction models – Systems that analyze global audience data to predict which AI-generated story concepts will resonate best before they are even produced.
AI-powered localization engines – Fully automated tools that translate and adapt AI-generated scripts for international markets while preserving cultural nuance.
AI-generated transmedia expansion – AI systems that automatically generate spin-offs, interactive narratives, and franchise extensions across multiple media formats.
AI-enhanced virtual production tools – AI-driven workflows that seamlessly transform AI-generated scripts into virtual production-ready assets.
AI-based post-production optimization – AI tools that refine AI-generated dialogue, cinematography, and pacing dynamically to enhance storytelling effectiveness.
AI-driven monetization and distribution platforms – Systems that optimize how AI-generated films are distributed, marketed, and monetized based on engagement metrics.
Autonomous story evolution engines – AI platforms that allow continuously updating, evolving narratives that respond to real-time audience interaction.
Automated IP protection and content originality detection – AI models that track and protect AI-generated IP, ensuring originality while preventing unauthorized replication.
AI-generated adaptive storytelling – AI-driven tools that modify a film’s plot, dialogue, and pacing in real time based on audience reactions, creating personalized storytelling experiences.
AI investments to avoid
One of the biggest mistakes a studio can make in preparing for AI-driven filmmaking is investing in the wrong AI projects—ones that are expensive, time-consuming, and ultimately irrelevant before their full value is realized.
Question 1 for avoiding bad investments is: Does this investment prepare the studio for the AI-dominant future, or does it make change management even harder down the line? If the answer is the latter, it could be a mistake.
Question 2 for avoiding bad investments is: Will this AI investment still be relevant by the time it is fully developed and its return on investment is realized? If the answer is no, it’s a mistake.
These mistakes typically fall into three major traps.
Building AI internally instead of letting the market compete– Major AI advancements are being driven by startups that are rapidly iterating in the open market. Trying to build a proprietary AI filmmaking system in-house burns time and capital at a slower pace than external AI startups, which are constantly improving. The smarter move is to watch, wait, and acquire or partner with the right players once the battle produces a clear winner.
Embedding the wrong cultural DNA – Studios that focus on AI integrations designed to assist traditional filmmaking risk training their teams to think about AI incorrectly. If the studio becomes too dependent on hybrid human-AI workflows, they will face an even larger cultural and organizational shift when AI eventually reaches full parity. For genres where the crossover point is immanent, avoid investing in hybrid AI/Writer DNA, but if it’s further out, it may be worth it.
Expensive AI retrofitting of old technology – Upgrading legacy systems to be AI-enabled sounds good in theory, but often locks a studio into an outdated framework that AI-first companies will simply bypass. In some cases, a well-planned upgrade can extend the life of an existing system, but more often than not, it becomes an expensive distraction from investing in AI-native tools and workflows. The key is to be extremely cautious of any "AI upgrade" projects that don’t have a clear competitive advantage after the crossover point.
Putting it all together.
A CTO’s strategy for AI in screenwriting should be adaptive, genre-specific, and focused on maximizing both short-term efficiency and long-term competitive advantage. The key is to understand when each genre will reach the AI crossover point and position the studio accordingly. For genres where AI will achieve parity soon, the best strategy is to monitor, partner with, or acquire emerging AI-first studios early. For genres with a longer AI horizon, studios should leverage AI for immediate efficiency gains while avoiding risky, long-term investments that won’t be useful after the crossover point. Below is a breakdown of how this strategy applies to each genre.
The adoption of electricity was not a single, overnight transformation—it followed a staggered, industry-specific trajectory, much like how AI will unfold in screenwriting. Some industries, like street lighting and telegraph systems, embraced electricity almost immediately because the benefits were clear and the transition was simple. Others, like manufacturing, took decades to fully shift, as companies first attempted incremental integrations before realizing they needed to redesign entire workflows around electricity. Meanwhile, consumer adoption lagged behind even further, as most homes weren’t fully electrified until the 1930s and 40s. The key lesson? Misestimating the timing of a paradigm shift—or failing to understand its staggered nature—can be fatal. Companies that moved too early wasted resources on immature technology, while those that moved too late were left behind by faster, more adaptive competitors.
One of the most famous failures in this transition was Pullman Palace Car Company, which dominated the railroad sleeping car industry in the late 19th and early 20th centuries. Pullman built its empire on steam-powered railcars and assumed that steam technology would remain the backbone of passenger transport indefinitely. While competitors began electrifying their rail systems and adopting modern electric heating and lighting, Pullman resisted, believing that minor upgrades to steam-powered cars were enough to stay competitive. But as electric rail technology advanced and passengers demanded modernized travel experiences, Pullman’s once-luxurious cars quickly became outdated. By the time the company finally attempted to pivot, electrification had already transformed the industry, and Pullman was too slow to catch up. The company struggled to remain relevant and was ultimately broken up in 1947, its slow adaptation to electric technology playing a major role in its decline.
Some context for each genre (note: I put dates here, but only as a guide, the appropriate method of assigning dates would be conducting the Turing-Screen Test over multiple years.
Children’s Content (Crossover: 2026) – Since AI will master this genre early, CTOs should start scouting AI-first studios now. Get to know the landscape, track the leading AI-driven animation and scriptwriting companies, and prepare to partner or acquire the strongest players before they dominate the space.
Procedural TV (Crossover: 2027) – AI will soon be capable of generating highly structured narratives for crime dramas, medical shows, and other procedural formats. Studios should establish relationships with AI-driven content generation startups and begin small-scale experimental collaborations to prepare for an AI-driven transition.
Romantic Comedies (Crossover: 2028) – AI will quickly master the repetitive structures and dialogue patterns of rom-coms. Studios should focus on identifying AI-powered screenwriting tools that can generate drafts, test audience response, and iterate efficiently while keeping an eye on emerging AI-first production studios.
Action Blockbusters (Crossover: 2029) – Given the reliance on big-budget spectacle and formulaic storytelling, AI-generated action scripts will mature faster than most. CTOs should invest in AI-driven pre-visualization and automated scene generation tools while also identifying potential AI-first competitors that could disrupt the space.
Science Fiction (Crossover: 2030) – AI can generate high-concept sci-fi ideas, but world-building complexity slows full automation. The strategy should focus on AI-assisted story ideation and visualization tools for short-term gains, while monitoring AI-native studios working on sci-fi content.
Horror (Crossover: 2031) – Since horror depends on psychological tension, atmosphere, and cultural context, AI will take longer to master it. Studios should use AI to enhance marketing and audience testing rather than investing in AI-generated horror scripts too soon.
Indie Dramas (Crossover: 2033) – These stories are deeply human, subtle, and emotionally complex, making them some of the last to cross over. The focus should be on using AI for development efficiencies—such as script analysis, dialogue improvement, and thematic refinement—rather than expecting AI to generate market-ready indie screenplays.
A24-Style Films (Crossover: 2035) – AI will struggle the most with nuanced storytelling, subtext, and artistic experimentation. CTOs should maximize short-term AI efficiency gains but avoid long-term bets on AI-generated indie storytelling. Human creativity will remain dominant here for the foreseeable future.
Positioning strategies to acquire the winners
For studios to successfully navigate the AI-driven shift in filmmaking, timing is everything. The closer an AI film studio gets to the crossover point—where AI-generated content achieves parity with traditional production—the more valuable it becomes. Waiting too long means paying a dramatically higher acquisition price or, worse, losing ground to competitors who moved earlier, or even worse - miss the opportunity altogether to acquire it and go out of business. The goal is to identify promising AI studios before they fully cross over, track their trajectory, and be in a position to acquire or partner with them at the optimal moment. Below are key strategies to achieve this.
Yahoo! serves as a cautionary tale of what happens when a company waits too long to acquire disruptive competitors—only to watch them become unstoppable giants. In 1998, Google’s founders, Larry Page and Sergey Brin approached Yahoo! looking to sell their fledgling search engine for just $1 million. Yahoo! declined, believing its curated directory approach was superior. By 2002, after Google had gained serious traction, Yahoo! reconsidered and offered $3 billion—but Google now saw its own potential and rejected the deal. The same mistake happened again in 2006 when Yahoo! attempted to buy Facebook for $1 billion, only to lower its bid to $850 million after its stock dropped, prompting Mark Zuckerberg to walk away. Meanwhile, in 2008, Yahoo! failed to even participate when Microsoft and Google competed to acquire Powerset ( I know this because I was a co-founder of Powerset), a cutting-edge semantic search engine. Microsoft secured the deal uncontested, helping to create Bing, which now holds a significant portion of the global search market and generates billions in revenue annually—solidifying Microsoft's relevance in search while Yahoo! faded into irrelevance. By the time Yahoo! realized the importance of search and social media, it was too late—Google and Facebook had already cemented their dominance. Yahoo! ultimately sold to Verizon in 2017 for just $4.5 billion, a fraction of what it could have been worth. For comparison, Google and Facebook, two companies it could have acquired prior to the search and social crossover points, were worth $729 billion and $512 billion respectively in 2017. This is precisely the risk that studios face today—if they wait too long to identify and acquire AI-first film studios, they will either be forced to pay an astronomical price or, worse, be locked out of the future of filmmaking entirely.
Establish AI Startup Watchlists – Create an internal team dedicated to tracking emerging AI film startups, monitoring funding rounds, leadership changes, and product developments. Knowing which companies are making real progress and which are hype-driven ensures early access to potential acquisition targets.
Build Relationships with AI Investors and VCs – AI startups often rely on venture capital and private investment. By partnering with investors who fund AI filmmaking companies, studios can gain early insights into which startups are showing promise before they hit mainstream awareness. Did you know that Yahoo! (wisely) partnered with Google in 2000 in order to key an eye on it - but failed to execute when it came time to acquire.
Run AI Film Contests and Innovation Labs – Hosting AI-driven filmmaking competitions attracts top talent and startups, giving studios a firsthand look at the most advanced AI storytelling technologies while creating an opportunity to test potential partners in a controlled environment.
Develop Strategic Partnerships Before the Crossover Point – Entering low-risk partnerships with AI-first studios allows traditional studios to work alongside emerging AI talent without fully committing to an acquisition. This helps gauge compatibility, evaluate AI-generated content in real production environments, and lock in early partnership agreements before valuations skyrocket.
Monitor Crossover Velocity Through AI Benchmarking – Regularly test AI-generated content using industry-specific benchmarks, such as the Turing-Screen Test, to measure how close different startups are to the crossover point. Having quantifiable data on AI's trajectory helps determine when an acquisition needs to happen before a startup’s value surges.
Conclusion
The transition to AI-driven screenwriting is not a question of if, but when—and for studio CTOs, navigating this shift is all about timing and strategic positioning. Investing too early in the wrong technologies can result in wasted capital, while waiting too long can mean losing competitive ground to those who moved first. The key is to align investments with the asymptotic genre adoption timeline, ensuring that studios are prepared for the crossover points in the genres that will transition first while avoiding costly dead-end projects.
The studios that thrive in the AI era will be the ones that embrace short-term AI efficiencies where they make sense, position themselves for long-term AI-native storytelling, and track the right AI-first startups for acquisition at the optimal moment. Those who miscalculate—either by overinvesting in doomed hybrid workflows or by underestimating the velocity of AI adoption—risk irrelevance. The window of opportunity is shrinking. The next decade will separate the studios that proactively shape the future of AI filmmaking from those that struggle to keep up.