Soderbergh’s Lennon Film: A Case Study in Selective AI Adoption
Steven Soderbergh’s documentary “John Lennon: The Last Interview” has become a touchstone in the debate over AI in cinema production. Built around a long, previously unreleased radio interview with John Lennon and Yoko Ono, the film leans heavily on more than 1,000 archival photos and video clips. Only about ten percent of its visuals are generated with Meta’s AI video tools, and even those are deliberately surreal rather than literal: abstract circles of light, a black rose turning into choreography, and painterly diptychs stand in for images that were, as Soderbergh put it, “impossible to shoot.” Crucially, there are no AI deepfakes of Lennon or Ono. Soderbergh treated AI as a last-stage finishing tool when time and resources had run out, subjecting every AI shot to a strict test of necessity. His approach frames AI as a targeted instrument, not a wholesale replacement for traditional craft.
Transparency Battles: AI Disclosure Becomes a Creative Flashpoint
The reaction to Soderbergh’s AI-assisted documentary at Cannes underlined how contested AI in cinema production has become. Soderbergh openly described himself as “my own whistle blower,” insisting on full transparency about where AI appears in the cut and why it was used. His stance contrasted sharply with polarised commentary at the same festival: Guillermo del Toro angrily rejected generative tools, while Peter Jackson expressed openness to AI-enhanced performances, including digitally resurrected actors, provided guardians of their legacy approve. Inside the industry market, discussions shifted to procedure and ethics, with directors like Darren Aronofsky stressing AI as a tool in a long historical line of technological shifts, from sound to portable cameras. The emerging consensus is not about banning AI but about disclosing it—ensuring audiences, collaborators, and performers know when algorithms have shaped the image, and preventing AI from quietly manipulating viewers without their knowledge.
Why Hybrid Beats Pure AI: Protecting Craft, Enhancing Production
Across both features and branded content, filmmakers AI tools are being woven into hybrid video production pipelines rather than used as end-to-end replacements. Soderbergh argues that most on-set jobs “cannot be performed by this tech and never will be,” pushing back against narratives that AI will erase human crews. His use of generative images for “dream space” sequences left core directing, editing, and storytelling untouched. In advertising, Aagey Se Right AI Studio has reached a similar conclusion: pre-production and post-production stages such as story development, screenplay, storyboarding, editing, and sound remain firmly human-led because they demand context, judgement, and emotional nuance. AI steps in at specific pressure points—mainly production—where cost, logistics, and timelines typically restrict experimentation. This AI filmmaking hybrid model keeps the aesthetic and emotional quality grounded in human craft, while treating algorithms as accelerators that expand what can be tried, tested, and refined.
Inside the Hybrid Studio: Aagey Se Right and the Kuku TV Experiment
Aagey Se Right AI Studio’s collaboration with Kuku TV illustrates how hybrid video production can fundamentally change creative workflows. For the “Bas Do Minute” project, the team started with a behavioural insight about quick, impulsive content consumption. Instead of compressing that idea into a single, high-risk film, they used AI-backed production to generate multiple executions: different storylines, tones, and audience hooks, all orbiting the same core concept. Human creatives handled the strategic and narrative heavy lifting—developing treatments, scripts, and edits—while AI made production faster and more flexible, removing traditional constraints that usually force agencies to pick just one route. In parallel, a separately produced campaign featuring MS Dhoni, executed through conventional means, provided a comparison point. The experiment shifted decision-making from assumption to validation, allowing teams to see which stories and emotional beats genuinely resonate before committing fully to a single direction.
The Future: AI as Partner, Not Director
Taken together, these examples show why pure AI filmmaking is struggling to win over directors, while hybrid approaches are quietly becoming the norm. Fully synthetic, end-to-end AI films often feel hollow because they lack the lived experience, nuance, and ethical accountability that human storytellers bring. By contrast, AI in cinema production thrives when it is boxed into specific roles: generating abstract visuals for otherwise unshootable moments, accelerating test content, or widening the range of ideas that can be explored within tight schedules. Directors retain authorship of tone, structure, and performance, while AI handles tasks that are repetitive, technically complex, or logistically impractical. As tool chains mature, the most sustainable path forward will likely mirror Soderbergh and Aagey Se Right’s practice: AI as an enhancement layer that respects human vision, not a substitute for it, keeping cinema grounded in craft even as it embraces new technology.