Argentinian science fiction series El Eternauta is the first Netflix production to use AI-generated footage, with the streaming service claiming that the move achieves spectacular results 10 times faster than usual and at a significantly lower cost. But does GAI (generative AI) provide a mortal threat to humans in the film and television industry?

Human VFX specialists may be nervous about their future employment prospects, but really this is just the latest technological innovation in an industry constantly competing both for audience attention and cost-cutting ingenuity. Crowd scenes in modern films are often populated by a mix of human extras and computer-generated figures. Real actors perform in front of blue (or green) screens, onto which the spectacular locations are added later by CGI. Instead of physical make-up, AI can make actors look younger or older, or transform them into fantastical beings. The question concerns where this use of GAI falls between enhancing the creative powers of humans and replacing those humans with machines.

Recent strikes by writers and actors have highlighted the threat to creative livelihoods posed by GAI. The Writers’ Guild of America ended a five-month strike in 2023 with an agreement which limited the role of AI in screenwriting. American actors’ union SAG-AFTRA recently ended a year-long strike by performers in video games, having won an agreement that requires consent before a company can use a performer’s likeness — and previous work — to generate new material without the person’s presence, or even knowledge.

Movies can, after all, generate convincing “performances” by digital clones of deceased actors: why pay a human to work when a computer program can generate the same product? Voice-over artists are especially vulnerable, when a recorded voice can be used to generate new speech ad infinitum.

In the UK, negotiations between the actors’ union Equity and producers about the use of AI are ongoing. The Writers’ Guild of Great Britain has a campaign around AI, but so far has reached no firm agreements to limit the use of ChatGPT and other large language models (LLMs) in replacing the human imagination.

The UK government, which looks to the AI industry as one of the engines for future economic growth, has taken an outlying position on generative AI and human creators, arguing that normal copyright rules should be suspended for companies wanting to train their models on what humans have created. Writers, performers, visual artists and musicians are campaigning against this position, arguing that “just as tech firms are content to pay for the huge quantity of electricity that powers their data centers, they must be content to pay for the high-quality copyright-protected works which are essential to train and ground accurate GAI models.”

The difference between previous technological innovations and GAI trained on their output, according to the creatives defending their copyright, is that GAI will first consume their work and then become their direct competitor. Musicians will find their audiences lured away by AI-generated pastiches of their own work. Actors will be replaced by software which parrots their own voice, down to the timbre and characteristic emotional expressions. Writers will no longer be hired when companies can request work by genre and subject matter, “in the style of” the redundant scribe.

In short, though VFX specialists may be wondering whether to find a new profession, Netflix’s use of GAI to realize a director’s vision is far from humanity’s biggest problem when it comes to generative AI on screen.

When AI is generating all the scripts, performances, music and visual design, and there are no more creative humans whose work the machines can ingest and regurgitate, we may start to wonder why cinema is so homogenous and cold. El Eternauta’s use of AI may not be overly troubling in and of itself, but the future of entertainment could be far more artificial.

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Timandra Harkness presents the BBC Radio 4 series, FutureProofing and How To Disagree. Her book, Technology is Not the Problem, is published by Harper Collins.

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China is trying to catch up with America on artificial intelligence. The Wall Street Journal has reported that Zhipu AI — one of China’s six “AI tiger” LLMs — has released a new model that can rival leading US systems, including Anthropic’s Mythos, in cybersecurity tasks such as pinpointing security bugs. While this marks a milestone in China’s drive to catch up with Western AI capabilities, strong performance on a single benchmark does not mean it has taken the lead. Chinese models still lag behind their Western counterparts in broader capabilities, such as autonomous operation. Skepticism is therefore warranted before resorting to hysterical conclusions, but complacency about the geopolitical implications of China’s AI advances would be an even greater mistake.

On the infrastructure side, Chinese AI is still constrained by access to advanced chips, with American labs way ahead in computing capacity as well as investment. Analysis from earlier this year suggests that Chinese models are likely to be at least a few months behind those in the US. But they are still continuing to make progress, or that the geopolitical importance of AI will be decided only by whose LLM has ventured deeper into the technological frontier. The practical applications of AI, countries’ to capture foreign markets, and the application of AI into the real economy will matter just as much.

Here, China may hold an advantage. As with its dominance across many critical supply chains, Beijing may not need to produce the most advanced AI systems — only those that are affordable and widely deployable. In doing so, it could consolidate global influence by supplying functional, low-cost AI at scale.

Beijing seems to be pursuing exactly that path, developing an AI “open-source” strategy that offers affordable, widely available AI models for companies and individuals to use and modify as they wish. The production of the DeepSeek AI model, which matched the performance of Silicon Valley tools such as ChatGPT at a fraction of the cost for users, created goodwill among Chinese models with developers.

The four most popular models on OpenRouter, an AI hardware platform for developers, are now all Chinese. The goal for China is not only to win the frontier-model race, but to make its systems the default layer of AI adoption across industries and global markets. For most economies, the choice is increasingly between an affordable tool they can deploy now and a more robust one that may be out of reach.

And while the countries adopting Chinese models may be exposed to political pressure and cyber threats from Beijing, safer and more capable alternatives matter little if they are unaffordable. American AI companies are already under pressure to monetize products whose operating costs are rising. If Chinese open-source models become the cheap default for startups, universities, governments and businesses across the developing world, then America’s AI lead will be eroded from below.

Perhaps more concerning for America in the long run is how AI can give Chinese manufacturing even more strength, through the ongoing integration of AI as a general-purpose technology. China’s new Five-Year Plan mentioned AI more than 50 times and includes an “AI+” action plan aimed at spreading AI across the economy.

Beijing has been pioneering automation of its critical infrastructure for years, with promising recent results in increasing warplane production capacity. In that regard, China’s open-model strategy and manufacturing dominance will reinforce each other. Cheap, adaptable models accelerate deployment across the real economy while those deployments generate real-world data and use cases that can feed back into further model improvement.

The United States should not dismiss the importance of its lead in the AI race. That lead worries Beijing, not least because a more automated Chinese economy would also become more vulnerable to AI-generated cyber threats. But nor should Washington assume that China cannot catch up with American capabilities over time.

This AI competition represents part of a broader struggle over tech supply chains and geopolitical influence. Decisions over whether to adopt US or Chinese models could produce a more fragmented global reality, with different regions relying on different cloud providers, chips and security structures. The result will likely be a global economy which is divided into competing spheres, rather than one which produces a single winner.

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Miquel Vila is a political and geopolitical risk consultant focusing on industrial strategy, critical infrastructure and global supply chains.

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