AI actor Tilly Norwood releases a musical video arguing that artificial intelligence can expand creativity in film
Updated
March 13, 2026 2:18 PM
AI Actor Tilly Norwood. PHOTO: INSTAGRAM@TILLYNORWOOD
As Hollywood prepares for this weekend’s Oscars, a different kind of performer is stepping into the spotlight — one that doesn’t physically exist.
Tilly Norwood, described as the world’s first AI actor, has released her debut musical comedy video, Take the Lead. The project arrives at a moment when artificial intelligence has become one of the most contentious topics in the film industry.
The message of the song is simple. AI should not be seen as a threat to actors. Instead, it can become another creative tool. The release also offers a first look at what Norwood’s creators call the “Tillyverse”. It is envisioned as a cloud-based entertainment world where AI characters can live, interact and perform.
Behind the character is actor and producer Eline van der Velden. She is the CEO of production company Particle6 and AI talent studio Xicoia. Van der Velden created Tilly as a way to experiment with how artificial intelligence could be used in storytelling.
The timing is not accidental. The entertainment industry has spent the past few years debating the role AI should play in filmmaking and acting. Questions about digital replicas, automated performances and creative ownership continue to divide artists and studios.
Norwood’s musical video enters that debate with a different tone. Instead of warning about AI replacing actors, the project suggests that the technology could expand what performers are able to do.
The video itself also serves as a technical experiment. The song Take the Lead was generated using the AI music platform Suno. The video was then produced using a combination of widely available AI tools and Particle6’s own creative process.
One of the newer techniques used in the project is performance capture. Van der Velden physically acted out Tilly’s movements and expressions so the digital character could mirror a human performance. But the production was far from automated. According to Particle6, a team of 18 people worked on the video. The group included a director, editor, production designer, costume designer, comedy writer and creative technologist. In other words, the project still relied heavily on human creativity.
“Tilly has always been a vehicle to test the creative capabilities and boundaries of AI,” van der Velden said. “It’s not about taking anyone’s job”. She added that even with powerful tools, good AI content still takes time, taste and creative direction.
The project also reflects how quickly production technology is evolving. Tools that once required large studios are now accessible to smaller creative teams experimenting with AI-driven storytelling.
For Particle6, the character of Tilly Norwood acts as a testing ground. Each project explores how AI performers might be developed, directed and integrated into entertainment. Whether audiences embrace digital actors remains an open question. Many in the industry are still wary of how AI could reshape creative work.
But projects like Take the Lead show another possibility. Instead of replacing performers, artificial intelligence could become part of the creative process itself. In that sense, Tilly Norwood may represent something more than a virtual performer. She is also an experiment in how humans and machines might collaborate in the future of entertainment.
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The hidden cost of scaling AI: infrastructure, energy, and the push for liquid cooling.
Updated
January 8, 2026 6:31 PM
The inside of a data centre, with rows of server racks. PHOTO: FREEPIK
As artificial intelligence models grow larger and more demanding, the quiet pressure point isn’t the algorithms themselves—it’s the AI infrastructure that has to run them. Training and deploying modern AI models now requires enormous amounts of computing power, which creates a different kind of challenge: heat, energy use and space inside data centers. This is the context in which Supermicro and NVIDIA’s collaboration on AI infrastructure begins to matter.
Supermicro designs and builds large-scale computing systems for data centers. It has now expanded its support for NVIDIA’s Blackwell generation of AI chips with new liquid-cooled server platforms built around the NVIDIA HGX B300. The announcement isn’t just about faster hardware. It reflects a broader effort to rethink how AI data center infrastructure is built as facilities strain under rising power and cooling demands.
At a basic level, the systems are designed to pack more AI chips into less space while using less energy to keep them running. Instead of relying mainly on air cooling—fans, chillers and large amounts of electricity, these liquid-cooled AI servers circulate liquid directly across critical components. That approach removes heat more efficiently, allowing servers to run denser AI workloads without overheating or wasting energy.
Why does that matter outside a data center? Because AI doesn’t scale in isolation. As models become more complex, the cost of running them rises quickly, not just in hardware budgets, but in electricity use, water consumption and physical footprint. Traditional air-cooling methods are increasingly becoming a bottleneck, limiting how far AI systems can grow before energy and infrastructure costs spiral.
This is where the Supermicro–NVIDIA partnership fits in. NVIDIA supplies the computing engines—the Blackwell-based GPUs designed to handle massive AI workloads. Supermicro focuses on how those chips are deployed in the real world: how many GPUs can fit in a rack, how they are cooled, how quickly systems can be assembled and how reliably they can operate at scale in modern data centers. Together, the goal is to make high-density AI computing more practical, not just more powerful.
The new liquid-cooled designs are aimed at hyperscale data centers and so-called AI factories—facilities built specifically to train and run large AI models continuously. By increasing GPU density per rack and removing most of the heat through liquid cooling, these systems aim to ease a growing tension in the AI boom: the need for more computers without an equally dramatic rise in energy waste.
Just as important is speed. Large organizations don’t want to spend months stitching together custom AI infrastructure. Supermicro’s approach packages compute, networking and cooling into pre-validated data center building blocks that can be deployed faster. In a world where AI capabilities are advancing rapidly, time to deployment can matter as much as raw performance.
Stepping back, this development says less about one product launch and more about a shift in priorities across the AI industry. The next phase of AI growth isn’t only about smarter models—it’s about whether the physical infrastructure powering AI can scale responsibly. Efficiency, power use and sustainability are becoming as critical as speed.