What is a Head of AI
Part II: How the Head of AI can solve the Innovator's Dilemma for film studios
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.
In my last article, Film Studios and the Innovator’s Dilemma, I outlined the crossover point is coming—when AI filmmaking reaches parity with traditional production, but at 100X lower cost and 300X faster speed. With AI rapidly reshaping the film industry, studios can no longer afford to treat it as just another technology experiment. AI isn’t just a tool—it’s a paradigm shift, and how studios handle it will determine their survival in the face of The Innovator’s Dilemma.
Enter the Head of AI, a role that doesn’t exist yet in most studios—but perhaps more than any other role, could determine a studios fate.
But there is a dragon hiding in the weeds here - and that’s the perception that a Head of AI is tech position - a lab-bound role where someone runs AI experiments, presents findings to an indifferent executive team, and ultimately burns through R&D budgets without making real business impact.
And this has happened before…
In the late 1990s and early 2000s, brick-and-mortar retailers faced a choice: treat the internet as a gimmick, an incremental add-on, or recognize it as a paradigm shift that would redefine commerce. Many, blinded by their own success, chose the former—and paid the ultimate price.
Consider Borders, once one of the largest and most respected booksellers in the U.S. Instead of embracing e-commerce, Borders saw the internet as a side project—something that could augment its in-store experience but never replace it. In 2001, rather than investing in its own online store, Borders outsourced its entire e-commerce business to Amazon. It was a catastrophic mistake. By the time Borders realized that online retail wasn’t just an add-on but the future of retail itself, Amazon had already cemented its dominance, and Borders had no digital infrastructure, no e-commerce expertise, and no way to catch up.
Borders went bankrupt in 2011.
Why? Because Borders hired for the internet like a side project. Instead of appointing a Head of Digital Transformation, they hired Heads of Web, people with no real authority, often trapped in lab-bound, experimental roles—running websites that executives barely cared about. These teams presented findings, ran pilot projects, and experimented with online retail—while the real game was happening elsewhere, outside the walls of Borders’ boardroom.
Conversely, Walmart saw the shift coming and took an entirely different approach. While many retailers treated e-commerce as a novelty, Walmart recognized that if they didn’t master online sales, Amazon would eat them alive. They built their own digital infrastructure, invested billions in e-commerce, and aggressively expanded online operations. In 2016, Walmart acquired Jet.com for $3.3 billion, signaling their commitment to competing head-on with Amazon. Today, while many legacy retailers have faded, Walmart remains a dominant force—because they took the internet seriously, rather than treating it as an afterthought.
Walmart’s market cap today → over $400 billion up nearly 300% since 1998.
Our 1998 Moment. Thus, now is the time for studios to properly define the role, mission, and function of a Head of AI inside a major studio. Those studios who nail this will do what Walmart did - those who don’t may become the next Borders.
The Head of AI needs to be a business transformation leader that understands both business, technology, and how to lead.
The Head of AI is a strategic leadership role responsible for navigating the most significant disruption in the history of filmmaking: the AI revolution. This role is not about running lab experiments—it is about solving The Innovator’s Dilemma, defining the studio’s AI strategy, and ensuring the company thrives at the crossover point where AI filmmaking surpasses traditional methods.
The DNA of the Head of AI
Has been a CEO and understands all aspects of a business
Has founded companies
Has led large organizations
Understands Artificial Intelligence
Understands Films Studios
A Seasoned Leader that’s been through many transformations
A Level-Headed, hard to convince of anything, financially responsible leader
A partner to the CEO and a team player with the executive team
What you do NOT want
An AI-fan boy
Someone who promises the world
A tech-nerd only
A person with no real experience
This is a business-critical position that spans corporate strategy, change management, financial discipline, legal and regulatory affairs, labor relations, marketing, and external AI investments. The right candidate is not an AI fanboy but a pragmatic, bottom-line-driven executive with the technical and business acumen to separate real opportunity from hype.
Key Responsibilities
Solving the Innovator’s Dilemma → Develop a corporate strategy that enables the studio to navigate disruption and emerge as a leader in AI-driven filmmaking.
Change Management → Lead cultural and operational shifts, ensuring that AI is successfully integrated into teams, workflows, and budgets while managing resistance.
Strategic Decision-Making → Balance technical expertise with business intelligence, identifying when and where AI investments generate real ROI.
Regulatory & Legal → Address IP rights, copyright concerns, compliance, and the evolving regulatory landscape of AI-generated content.
Union Engagement → Develop and execute a proactive AI labor strategy, ensuring the studio’s position is clear, strategic, and sustainable in labor negotiations.
Marketing & Branding → Position the studio as an AI-forward leader without alienating talent, audiences, or the creative community.
External AI Strategy → Identify and create pathways for the studio to participate in pure AI filmmaking plays, ensuring the company is on the offense, not defense, as AI filmmaking scales.
Talent & Workforce Planning → How will AI impact hiring, training, and upskilling inside the studio? The Head of AI needs to redefine roles and ensure the company has the right mix of human and AI-assisted talent.
AI Partnerships & M&A Strategy → Studios will need alliances with AI tech companies and may need to acquire or invest in AI startups to stay ahead. This role should include corporate development responsibilities.
Technology Infrastructure → What internal AI tools and platforms should the studio build vs. license? The Head of AI should ensure the company has a clear roadmap for AI technology adoption.
Risk & Crisis Management → AI presents reputational and operational risks. The Head of AI must have a playbook for handling backlash, IP lawsuits, or failures in AI-generated content.
Financial Impact & Cost Models → How does AI change production economics? The Head of AI should be able to quantify AI’s impact on cost structures, revenue streams, and long-term financial planning.
But let’s start with the elephant in the room—the Innovator’s Dilemma — the one I mentioned in my last article, Film Studios and the Innovator’s Dilemma. Cliff notes, below.
The Innovator’s Dilemma for film studios is this: AI-first startups are holding AI constant—locking in its massive cost and speed advantages while rapidly improving quality—while traditional studios are holding quality constant, integrating AI only incrementally as it matures. This means that while studios move slowly, constrained by unions, legacy workflows, and anti-AI resistance, AI-native startups are moving exponentially, iterating and improving until they hit the crossover point—where AI filmmaking matches traditional quality but remains 100X cheaper and 300X faster.
At that cross-over point, studios that treated AI as a side project, or an integration project will be structurally unable to compete. Their budgets, production timelines, labor agreements, and even creative workflows will be built for a business model that no longer makes sense.
So how do you solve this problem.
How Netflix Solved The Innovator’s Dilemma—And Won
In the early 2000s, Netflix faced the same Innovator’s Dilemma that Hollywood studios face today—a powerful, entrenched industry (Blockbuster and DVD sales) dismissing a disruptive technology (streaming) as a niche, low-quality alternative. At the time, streaming was a terrible product—slow buffering, low-resolution video, and a limited content library made it inferior to physical DVDs in almost every way. Blockbuster, the dominant player, saw streaming as an incremental add-on at best, not a replacement for its core business.
But Netflix didn’t try to integrate streaming into its existing DVD rental business the way Blockbuster might have. Instead, it bet everything on streaming, understanding that while quality was low today, it would inevitably improve—and once it hit the crossover point, where streaming quality equaled or surpassed physical media, the old model would collapse instantly.
Netflix solved The Innovator’s Dilemma by:
Spinning up streaming as its core business, not a side project—it didn’t just offer streaming; it designed the company around it.
Investing in the future of internet bandwidth, compression, and on-demand technology before they were fully ready.
Making hard strategic choices—even cannibalizing its profitable DVD business to push customers toward streaming.
Scaling aggressively so that when streaming quality improved, Netflix was already the dominant platform.
By the time Blockbuster realized streaming wasn’t just an add-on but the future of entertainment, it was too late.
Netflix had already won the market by 2010 and Blockbuster filed for bankruptcy.
… and their not done yet - they are re-inventing themselves again right now.
Right now, Netflix knows that streaming isn’t a forever strategy - and it’s solve the innovator’s dilemma again - this time in gaming. Netflix’s early gaming offerings—mobile games bundled with subscriptions—aren’t widely played, and critics argue that Netflix can’t compete with industry giants like PlayStation, Xbox, and Steam. This skepticism is exactly what Blockbuster executives said about streaming in 2007—that DVDs were still dominant and streaming quality wasn’t good enough to replace them.
But Netflix isn’t reacting to today’s gaming industry—it’s positioning itself for the future crossover point, when interactive entertainment, cloud gaming, and AI-generated experiences reshape what people expect from content. Just as internet speeds and on-demand technology paved the way for streaming, Netflix is betting on a future where cloud-based, subscription-driven gaming becomes dominant—and when that happens, they want to be the Netflix of games before anyone else gets there.
Here’s how they’re solving The Innovator’s Dilemma again:
Building their gaming infrastructure now, not later—before it’s “obvious” that streaming gaming will take over.
Buying studios and talent aggressively to prepare for a world where gaming and entertainment merge.
Leveraging AI to disrupt traditional game development, just as they disrupted traditional TV production with data-driven content.
Playing the long game—investing in a new paradigm rather than focusing only on short-term revenue.
Solving the Innovator’s Dilemma: a Film Studio Imperative
Film studios must adopt a two-track strategy to navigate the AI disruption successfully:
Protecting your Money Maker: incremental AI adoption inside existing workflows (e.g. preserve an improve the existing business
Betting on the Future: a bold, AI-first strategy that positions them in the disruptive startup landscape.
The first track ensures they stay competitive within their current model, leveraging AI to cut costs, improve efficiency, and gradually introduce AI into creative processes. However, the second track is where true survival lies—actively participating in the AI-native filmmaking revolution rather than being blindsided by it.
Track 1: Incremental AI Adoption – Evolving Without Breaking the Model
Now, for those of you who are reading my posts carefully, you’re thinking… but wait—he said before this was a mistake—a deadly decision. Well… there’s a way to do this, when properly paired with the second strategy, that creates a strategic handshake between the old business and the new business at the crossover point. It involves actively participating in AI integration and investment in certain areas, while also deliberately avoiding AI integration and investment in others—and it’s this second one that I really want to dig into.
How to Get This Right
Invest in integration AI with the crossover point in mind - …and by this I mean when greenlighting any AI investment in technology, workflow, or infrastructure there is one question that should drive all of your decision making.
Is this AI technology, workflow, or infrastructure something that we will use AFTER the crossover point?
If it’s not… don’t do it.
Examples of Good AI Investments
Automate the Invisible → Use AI in post-production, localization, and workflow automation—areas where improvements are clear, but disruption is minimal.
Example: AI-driven dubbing and subtitle generation can cut localization costs by 50%+ without affecting creative teams.
Rule of the Road: If no one notices the AI, but it saves money—it’s a good first step.
Enhance Pre-Production & Development → AI can help storyboarding, previsualization, and script analysis without replacing creative decision-making.
Example: AI-powered previs tools help directors test shots before expensive production begins, improving efficiency without changing the core filmmaking process.
Rule of the Road: Use AI to give creatives more tools, not replace their decisions.
Improve Marketing & Personalization → AI-generated trailers, audience segmentation, and ad targeting can make marketing campaigns more effective without touching creative teams.
Example: Netflix’s AI-powered thumbnail selection increases engagement by up to 30%, and studios can apply the same concept to poster variants, trailers, and promotional material.
Rule of the Road: Use AI to make films more successful, not to make the films themselves.
Invest in AI-Driven Audience Insights → AI can help predict what audiences will engage with, without dictating creative choices.
Example: Warner Bros. used an AI-powered platform to forecast box office performance, helping execs make smarter greenlighting decisions.
Rule of the Road: AI should inform decisions, not make them.
Develop an Internal AI Talent Pipeline → Studios should train internal teams on AI tools rather than replacing them.
Example: Offer AI upskilling programs for editors, VFX artists, and marketers to future-proof the workforce.
Rule of the Road: AI should empower existing teams, not threaten them.
How to Get This Wrong
One of the biggest AI investment mistakes studios can make is pouring capital into AI integrations designed to enhance existing workflows, teams, and software tools but ARE NOT transferrable after the crossover point—especially when those investments come with high costs, long timelines, and massive organizational resistance. These are the worst types of AI investments because they share three fatal flaws:
They require massive capital investment upfront. Studios may spend hundreds of millions on AI-driven production software, proprietary AI training models, or internal AI R&D without a clear pathway to short-term ROI.
They won’t deliver meaningful benefits until after the crossover point. Many AI-integrated tools—such as AI-powered editing suites, real-time AI animation tools, or AI-enhanced production pipelines—are still years away from maturity. By the time they work well enough to justify their cost, AI-first studios will have already leapfrogged traditional production models, rendering these slow-moving integrations obsolete before they pay off.
They require massive change management that also won’t mature in time. AI integration at the workflow level demands organizational buy-in, retraining, and cultural acceptance—all of which take time. If that process won’t be fully adopted until after the crossover point, then the studio has effectively wasted resources building AI into a system that will soon be irrelevant.
Examples of Bad AI Investments:
Spending years modifying Maya, Blender, or Houdini to incorporate AI-powered animation, rigging, or rendering—only for AI-native tools to emerge that completely bypass these legacy software workflows. When the crossover point happens, many, if not all, of these tools will become irrelevant.
Developing AI-assisted editing features inside traditional NLEs like Premiere Pro or Avid instead of preparing for AI-native editing environments that will redefine post-production. Ditto to the above.
Building proprietary AI tools inside legacy rendering engines instead of realizing that AI-generated video will eventually remove the need for traditional 3D rendering workflows altogether. Do you have a proprietary system you spend $200 million developing - well think twice about upgrading it with AI.
Retrofitting AI into decades-old pipeline automation tools (e.g., Shotgun, Nuke, Resolve) rather than preparing for AI-first production systems that operate with entirely new, cloud-native infrastructures.
…and as you might predict, there are some really good examples here of the getting it Wrong.
Kodak’s Digital Blind Spot: The $500 Million Bet That Became Worthless
In the early 2000s, Kodak saw the rise of digital photography and knew it needed to adapt. But instead of fully embracing digital cameras, the company poured hundreds of millions into modifying its film-based business—trying to merge old technology with new.
Kodak’s biggest bet? Advancing film-based digital hybrids—like the APS (Advanced Photo System) film format, which allowed for easier digitization of film prints. The idea was that customers would still shoot on film, but Kodak would use AI-enhanced scanning and printing technologies to convert images into digital files. Kodak spent over $500 million optimizing this half-step solution, thinking it could transition gradually rather than going all-in on digital.
But while Kodak was busy modifying its film ecosystem, the real disruption was happening elsewhere. Companies like Sony and Canon leapfrogged Kodak entirely by going full digital, making film-based systems instantly obsolete. By the time Kodak realized that consumers didn’t want film-to-digital hybrid solutions—they just wanted pure digital cameras and online photo sharing—it had already lost the market.
Kodak’s investment became a sunk cost, and worse, it delayed their pivot to true digital adoption. In 2012, Kodak filed for bankruptcy, crushed by the very disruption it had seen coming but failed to react to correctly.
…ok deep breath.
Track 2: AI-First Strategy – Betting on the Future
…And to do this, every major studio needs to ask one fundamental question:
What will the AI-first film industry look like at the crossover point—five years from now?
The AI-first film industry won’t emerge in a smooth, linear fashion—it will be a chaotic battlefield of rapid innovation, failed experiments, and massive content overload. As AI filmmaking reaches technical and creative parity with traditional filmmaking, thousands of startups and platforms will compete to define the new landscape. Most will fail. However, just as we’ve seen in every technological revolution before, a few key players will rise from the ashes to dominate the industry. The sheer volume of AI-generated content will explode, but winning in AI filmmaking will not be about who generates the most content—it will be about who controls distribution, audience engagement, and franchise-building in an AI-first world.
What the AI Film Industry Will Look Like at the Crossover Point:
The Battlefield Will Be Littered with Dead Startups → Tens of thousands of AI filmmaking startups will emerge and fail. The cost of entry will be near zero, meaning everyone will try, but only a handful will have the business model, execution, and market positioning to survive.
Many AI Film Technologies Will Hit a Brick Wall → Some AI production pipelines will appear promising at first, only to collapse under scalability issues, legal roadblocks, or creative limitations. Many technologies that seem cutting-edge today won’t make it past the experimental phase.
AI Film Studios Will Outnumber Traditional Studios 10,000 to 1 → AI filmmaking will be so cheap and accessible that tens of thousands of AI studios will emerge—producing an unfathomable amount of content. However, the majority of it will be low-quality, forgettable, and buried under the noise.
Despite the Volume, There Will Be Fewer than 5 True Winners → AI filmmaking will follow the same pattern as every previous disruptive industry—consolidation will occur as a handful of studios establish dominance. These winners will own AI-native IP, AI-first distribution models, or massive, engaged fanbases that give them leverage.
Only a Few AI Films Will Become True Blockbusters → Just as today’s industry is dominated by a small handful of major box office hits, the AI-first industry will be no different. No matter how much content is generated, most will be ignored, some will break through, and only a few will truly matter. Owning audience trust and brand identity will be more critical than ever.
…and history helps us drive home the point here as well.
The dot-com boom and bust of the late 1990s and early 2000s followed the same pattern that AI filmmaking is about to experience—a flood of startups, a period of chaos, and eventual consolidation into a few dominant players. Nowhere was this more evident than in the search industry. In the mid-1990s, AOL (founded in 1985) and Netscape (founded in 1994) dominated internet access, and early search was built around human-curated directories like Yahoo! Directory (1994), Lycos (1994), and Excite (1995)—each trying to index the web manually. As search demand grew, hundreds of search startups emerged, including Altavista (1995) and Ask Jeeves (1996), all competing to structure the chaotic new internet. However, these early platforms failed to scale, and by 2000, most of them collapsed under their own inefficiencies—either going bankrupt or getting absorbed into larger companies. Then, in 1998, Google launched with an entirely new approach—an AI-driven PageRank system that automated search rankings based on link authority. At first, it was just another player in a crowded field, but by 2003, Google had clearly won the battle for search, leaving the early competitors obsolete.
… and then of coarse there was Powerset which became Microsoft Bing - the company that I founded. What made it important was that it marked the beginning of the next era in search - NLP, ML, and AI. While Powerset was the first, we are now seeing companies like OpenAI, for the first time, challenge Google’s search dominance.
So what should a studio do?
The best AI-first strategy for film studios isn’t about blindly betting on early AI filmmaking tools or rushing to build internal AI labs. It is about strategically positioning the studio to acquire and partner with the inevitable winners once the industry stabilizes. Instead of wasting capital on unproven technologies, studios should act as market observers, thought leaders, and strategic partners to AI startups and investors. This ensures they have front-row access to the best innovations without overcommitting too soon. By letting the AI film startup ecosystem battle it out, studios can wait for the strongest players to emerge and, when they do, be ready to invest, acquire, or partner at the perfect moment.
How to Get This Right
Be like Netflix and bet big. Establish a new AI-First Studio that is completely separate. In other words, partner with an established technology player who wants to be the engine behind this studio, who has a ton of money, and can partner with you to put billions in. Note: this strategy means joining the fray of the battlefield - the difference here - you have the brand and the IP and the tech companies - and their investors have the money. Imagine raising $10 billion from Alibaba or Tencent in partnership with some US Private Equity firms and VCs, then taking something like Wan2.0 (e.g. an open source competitor to Runway) and just go for it - your Head of AI could be the CEO of this company.
If not Netflix, then partner, or perhaps make fund investments, with VC firms that are funding AI filmmaking startups. This provides insight into the AI landscape and allows the studio to track the most promising companies without taking on the risk of direct investment too early.
Position the studio as a thought leader in AI filmmaking. Establishing educational partnerships, research initiatives, and executive panels ensures that AI startups see the studio as a valuable industry ally rather than an outdated incumbent → but don’t spend a lot here - this is all about having a Steve Jobs like leader as your Head of AI.
Partner with leading AI tech companies to run AI film contests. Collaborating with platforms like Midjourney, Runway, and OpenAI to host competitions will create a structured way to identify emerging AI filmmakers, track new technologies, and build a comprehensive database of the most promising players in the space → this is how you create your acquisition list.
But more important than any other thing…
Position yourself to be ready to know who to acquire, how much to pay, and to get the call to be on the list once the AI filmmaking winners begin to emerge. And just as importantly, don’t be afraid to pay up - the winners here will win everything. When the dust settles, there will be a handful of category-defining AI platforms, tools, and studios—the ones that have cracked the code on scalability, quality, and audience engagement. The moment this becomes clear, the studio must act decisively to secure its place in the AI-first industry.
Two great examples…
Adobe has mastered a long-game acquisition strategy that allows it to avoid risky bets on early-stage technologies while still owning the winners once the dust settles. Instead of trying to innovate internally at every turn, Adobe lets startups battle it out in emerging markets, watching as VCs fund and validate the most promising players. Throughout this process, Adobe positions itself as a thought leader, maintaining deep relationships with venture capital firms, startup founders, and industry insiders—giving them a front-row seat to the evolution of new technologies. This strategy ensures that by the time a clear leader emerges, Adobe has already built relationships, understands the technology deeply, and can acquire the winner with confidence. Case in point: Figma. Adobe let the design collaboration market evolve organically, observed how Figma disrupted traditional design workflows, and then acquired it for $20 billion in 2022 when it became clear Figma was the dominant player. This same approach can be seen with Adobe’s acquisitions of Frame.io (video collaboration), Substance (3D texturing), and Behance (creative networking)—all of which started as independent startups that Adobe waited to absorb once they proved their market position. By taking this patient, VC-aligned approach, Adobe minimizes risk while ensuring it always owns the category-defining tools of the next generation—a playbook that film studios should seriously consider in the AI era.
and
In the aftermath of the iPhone's release in 2007, the tech landscape saw an explosion of startups aiming to revolutionize mobile photo-sharing applications. Amidst this frenzy, Facebook strategically observed the evolving market and, in April 2012, acquired Instagram for approximately $1 billion in cash and stock. At the time, critics questioned the hefty price tag for a company with a relatively small user base. However, this investment has proven astute; as of 2025, Instagram's valuation has soared, contributing significantly to Meta Platforms' market capitalization, which now exceeds $900 billion. This acquisition exemplifies the potential long-term value of identifying and investing in emerging platforms within a crowded startup ecosystem.
The lesson to learn: When you find a winner during a paradigm shift, pay up - it’s worth it.
How to Get This Wrong
When a paradigm-shifting technology emerges, it follows a predictable pattern: the short-term impact is overestimated, while the long-term impact is underestimated (Amara’s Law). AI filmmaking will be no different. In the early years, hype and panic will drive reactionary decisions, leading to misplaced investments, failed initiatives, and wasted capital. The industry will be littered with startups, platforms, and technologies that burn bright and then collapse. The studios that survive and lead will be those that navigate the chaos with discipline, avoiding short-term traps while quietly positioning themselves for long-term dominance.
The Worst Mistakes Studios Can Make in the AI Battle:
Rushing into AI investments based on hype, not fundamentals. With thousands of startups vying for dominance, many will promise revolutionary breakthroughs that won’t scale. Jumping in too soon risks tying up resources in technology that fails before it matures.
Building an AI lab without a strategic purpose. Many studios will pour millions into AI R&D only to realize too late that they lack a clear plan for monetization, integration, or competitive advantage. Without a business-first strategy, AI labs become expensive distractions.
Underestimating the pace of AI-generated content saturation. AI filmmaking will produce 10,000 times more content than the industry does today. Studios that invest in low-effort AI content instead of AI-native premium storytelling will be buried in the noise.
Overestimating how quickly audiences and unions will accept AI. Many studios will assume AI filmmaking will be mainstream within a few years and overcommit to AI-led projects too early—risking backlash, regulatory pushback, and audience rejection.
Spreading AI investments too thin across multiple startups. Instead of betting aggressively on a few high-potential winners, many studios will scatter their capital across dozens of startups—ensuring they lose money on most of them.
Ignoring AI’s impact on distribution and monetization. The real revolution isn’t just AI making films faster—it’s how AI will change content discovery, audience personalization, and revenue models. Focusing only on AI filmmaking while ignoring AI-powered distribution is a fatal mistake.
Signing long-term AI partnerships that lock the studio into the wrong technology. The AI landscape is evolving too quickly to commit to exclusive deals with a single AI platform. Studios that lock themselves into the wrong ecosystem will be stuck when better technology inevitably emerges.
Focusing on speed instead of storytelling. AI will make production faster and cheaper, but volume isn’t the goal—winning the battle for audience trust, IP ownership, and premium storytelling is.
Assuming AI filmmaking will mirror traditional Hollywood. AI filmmaking will evolve in unexpected ways, and trying to force AI into old industry structures will lead to failure. Studios that adapt to new storytelling formats, audience engagement models, and AI-native creators will thrive.
=== that’s it for now ===
So of the 12 key responsibilities of the Head of AI - this article covered just the synopsis of 1 of those responsibilities. My hope is that studio executives that read this can see just how critical this role will be.
… FYI, if you noticed Clayton Christensen wrote a second book - The Innovators Solution - don’t buy it… While his first book was novel - his second was a turd.
→ In the next few articles I’ll begin the flesh out the other 11 key responsibilities as well as dig deeper into specific projects I would execute now.