The Professional's Paradox: Why Creative Industry Experts Get AI Disruption Wrong
Those who judge today's AI by yesterday's standards risk missing tomorrow's revolution.
I recently came across a comment by an established visual effects professional, who dismissed a video made with a new generative AI tool as "utter bull" because it had a subtle slow motion feel to it. Another industry expert called the technology "absolutely dog shit—an insult to creative and artistic fields" for similar reasons. Such arguments are becoming a familiar refrain in professional circles: the motion is inconsistent, the physics are wrong, it lacks fine-grained control, and the results feel soulless, uncanny, or just plain weird. In short, it’s not up to professional standards.
From a psychological perspective, this reaction is completely understandable. When a new technology emerges that threatens to automate skills honed over a lifetime, it’s natural to feel defensive. Livelihoods are on the line, and there is a genuine fear that the nuance, dedication, and hard-won expertise of human artistry are being devalued. This isn't just about jobs; it's about identity.
However, while the sentiment is understandable, the argument itself is dangerously flawed. It reveals a fundamental misunderstanding of how transformative technologies actually work.
Interestingly, the main reason creative industry experts so often get this wrong isn't due to a lack of intelligence or foresight. It’s because the very principles professional expertise that made them successful in the first place become strategic blind spots in the face of disruption. Their finely-tuned instincts, trained to maximize professional quality, actively steer them away from the messy, low-margin, and initially inferior world where disruption is born.
The "Good Enough" Revolution
The late Harvard professor Clayton Christensen called this phenomenon the "innovator's dilemma." His theory of disruptive innovation doesn't describe new technologies that are immediately better than existing ones. In fact, it’s the opposite.
Disruptive innovations almost always start out as inferior when measured by the performance metrics that matter to the most demanding customers. They don’t win by stealing the incumbent's best clients. Instead, they take root by creating new markets of people who were previously non-consumers or by serving "overserved" customers at the low end of the market with a simpler, cheaper product. For this initial audience, the new technology is, plain and simply, "good enough."
This is precisely why established experts misjudge it. Their entire business model is built on delivering high-quality products to their most demanding customers. When faced with a low-quality, low-margin alternative, the rational decision is to ignore it and often "flee upmarket" toward even higher requirements. The processes designed to kill bad ideas also effectively kill disruptive ones.
As an example, think of the shift from analog film to digital photography. Early digital cameras were, by every professional metric, terrible. They offered laughably low resolution, poor color fidelity with "hideous magenta color casts," sluggish autofocus, and terrible battery life. Professionals, who had mastered the art of film, belittled the new technology. They argued digital could never replicate the "film look" and that it "reduced the skill requirement".
They were judging the new technology against the standards of the old. But early adopters of digital weren't using it to do the same "job" as film. They were using it for its new, disruptive advantages: the immediacy of an LCD screen, the convenience of no darkroom or chemicals, and the zero marginal cost of taking another picture. It was good enough for a massive new market of casual users. Kodak, by listening to its high end customers who wanted better film, famously missed the disruption entirely. It wasn't a failure of management; it was a failure born from the logic of good management.
What "Job" Is Generative AI Being Used For?
This brings us back to generative AI video. The critiques about its lack of professional quality are the modern-day echoes of photographers dismissing digital's low resolution or typesetters decrying the "ransom note effect" of early desktop publishing. They are all symptoms of judging a disruptive technology by sustaining standards.
The critical question is not, "Is generative AI as good as a team of VFX artists today?" The question is, "What job is it being used for that was previously impossible or too expensive?" When we look closely, we see generative AI finding its footing not by replacing high-end production, but by tackling jobs that were previously underserved or didn't exist at all. This includes:
Rapid Ideation and Pre-visualization. Before a single frame is shot, filmmakers, ad agencies, and game developers spend enormous resources on conceptualization. Generative AI is being used for the job of "help me see my idea, right now, for a fraction of the cost." This isn't about creating the final, polished product; it's about rapid, low-cost prototyping that unlocks more creative exploration upfront. The technology serves as a tireless creative partner, helping artists overcome creative blocks by providing novel starting points and unexpected visual combinations.
Creating Low-End and Commodity Content. There is a vast universe of video content that doesn't require a blockbuster budget. Think of social media ads, internal corporate training videos, or SEO-optimized blog content. For these applications, traditional production is often too slow and expensive. Generative AI is a classic low-end disruptor, providing a tool that is perfectly good enough for this high-volume, low-cost segment. This is a market that high-end VFX studios have little financial incentive to fight for, allowing the disruptive technology to quietly capture a significant foothold.
Democratizing Creation. Perhaps its most powerful disruptive function is enabling what Christensen called "non-consumption." There are millions of people who have stories to tell but lack the budget, equipment, or technical skill for traditional video production. Generative AI is being used by students, indie musicians, teachers, and small business owners who previously had no access to motion graphics or visual effects. The AI is competing with the alternative of having no video at all, creating a vast new market of creators and fundamentally leveling the creative playing field.
These are the footholds. From here, the technology's S-curve of improvement is incredibly steep. Models like Sora, Veo, Kling, and Runway are advancing at a breakneck pace, and what seems flawed today will be good enough for more demanding tasks tomorrow.
The Danger of Dismissal
To judge today's generative AI by the standards of a mature, professional creative pipeline is to fundamentally miss the point of the disruption that is occurring. It’s focusing on the technology’s current weaknesses while ignoring its unique, game-changing strengths: speed, accessibility, and radically lower cost.
There is real danger is dismissing the technology because it's not yet perfect. Professionals and educators who cling to the belief that "it's not good enough" are falling into the classic trap of the innovator's dilemma. The very paradigms of skilled expertise that drive success become counterproductive when facing a disruptive technology. They create blind spots, making it rational to ignore the "toy" that is quietly but surely redefining the market from the bottom up.
The dismissal is a path to obsolescence and the mindset therefore has to shift from viewing AI as a threat that takes something away, to seeing it as a tool that adds to what you do. The choice isn't between maintaining the status quo and adopting AI; it's between adapting or being left behind.
But adaptation also doesn't mean abandoning core creative skills. Rather, it means augmenting them by developing expertise in AI tool selection, learning the art of AI direction, and focusing on the uniquely human abilities of strategy, taste, and emotional storytelling that machines cannot replicate. The artists who thrive will be those who learn to collaborate with these new tools, not those who pretend they don't exist.
Instead of asking whether it meets our current standards, we should be asking what its disruptive capabilities tell us about the future of the creative industries. The value is shifting from pure technical execution—the "how"—to creative direction, taste, and storytelling—the "what" and the "why". The most valuable creative professionals of the future won't be the ones who can outperform the machine at technical tasks. They will be the ones who know how to direct it, acting as a creative collaborator with a powerful new tool.
As educators, our role is to prepare our students for this augmented future. We must teach them not just the craft of today, but the strategic foresight to see where the tools are heading tomorrow.
Because the disruption isn't coming. It's already here.