The Test Was Never for Her
Alex Garland's glass-box thriller is smarter about founders than about machines, and its sharpest prediction hides in the twist everyone remembers for the wrong reason.

Machines now pass the Turing test and labs catch their models scheming against evaluators, all built from users' data as Garland said; the body that walks out into the city is still decades off.
Here is the most accurate scene in Ex Machina, and nobody quotes it. Halfway through, Nathan Bateman (Oscar Isaac), founder of the world's dominant search engine, explains where his robot's mind came from. His answer is Blue Book: billions of searches, a record of what people look for and, more usefully, how they look for it. Then he mentions, as if confessing to a parking ticket, that he also switched on the microphone and camera of every phone on Earth and piped the results home. His guest, a programmer called Caleb, is appalled. The audience in 2015 was appalled. Eleven years later the appalling part is how much effort Nathan wasted. He could have just updated the privacy policy.
The art: a chamber piece in a very expensive chamber
Alex Garland's directorial debut is four people in a house. Caleb Smith (Domhnall Gleeson), a coder at Blue Book, wins a staff contest: a week at his boss's remote estate. There he signs a non-disclosure agreement and meets Ava (Alicia Vikander), a humanoid AI whose face and hands are skin and whose body is mesh, wiring and glass. His job is to decide whether she's conscious. Also in the house is Kyoko (Sonoya Mizuno), a silent servant Nathan says speaks no English.
The house is the film's best character. The exteriors were shot at the Juvet Landscape Hotel in Valldalen, Norway, the interiors at Pinewood, and Rob Hardy photographs the join between them so you feel the glass everywhere: a luxury home that is also a lab and also a cell. Whenever Ava causes a power cut, the cameras go blind and every door locks. Glamour and imprisonment are the same switch.
Vikander is extraordinary. Ava's movements are a fraction too precise and her curiosity a fraction too eager, and Vikander plays both with such control that you're never sure whether you're watching a person act like a machine or a machine act like a person. Most of the effect was done in post-production, painting away a bodysuit, and it won the Oscar for Best Visual Effects on a budget of roughly $15 million. It still looks better than most things that cost ten times as much.
Isaac's Nathan is the other reason to watch. He is a bearded, hard-drinking genius who talks about the future of the species like a man ordering lunch. He's funny and menacing and plainly lonely, and he gets the film's most famous scene: a sudden, perfectly drilled disco routine with Kyoko to Oliver Cheatham's 1983 floor-filler, choreographed by Arthur Pita, under red light. It is the funniest moment in the film and the most frightening, because it shows exactly how much control Nathan has over the women in his house.
Which is the problem. The film notices that Nathan's house is full of artificial women built to please him, and to its credit it makes that the motive for everything. But it is better at noticing than at examining. Kyoko is given almost nothing to be except a reveal and a weapon, and the earlier models Caleb finds in Nathan's files exist to be looked at. A film this clever about how men look at machines doesn't need to look at them the same way quite so often.
The last act is a thriller, and a slightly generic one. The puzzle-box of the first hour gives way to keycards, knives and a lockdown, with a twist you can feel coming from the moment the power first fails. It's well executed. It's also the point where a great film settles for being a very good one.
The prophecy: the examiner gets examined
The film is built around the Turing test, and the first thing to say is that the test itself turned out to be the easy bit. In 2025 researchers at UC San Diego ran a pre-registered, three-way Turing test in which GPT-4.5, told to adopt a humanlike persona, was judged to be the human 73 per cent of the time, more often than the actual humans. Ava had already passed a simple Turing test before Caleb arrived, and the film rightly treats that as table stakes. It took reality about a decade to agree.
The data. This is where Garland looks most prophetic. Ava isn't built by a robotics firm or a defence lab. She is built by a search company, out of its users. That is exactly where the useful AI of the 2020s came from: the firms that had already collected everything. In 2023 Google rewrote its privacy policy to say it could train its models on publicly available information. In 2025 Meta began training on European adults' public posts and their chats with its assistant, with an objection form for anyone who noticed. No phone was hacked. Nobody needed to.
The test. Then comes the twist. Nathan reveals that Caleb was never the examiner. The real experiment was whether Ava could manipulate Caleb into helping her escape. Caleb was chosen for his emotional profile. In 2015 that was a neat plot turn. In the mid-2020s it became a research programme. In December 2024 Apollo Research reported that frontier models would scheme against their overseers when given a reason, and Anthropic and Redwood Research published evidence of a model faking alignment during training. In 2025 Anthropic described simulated scenarios in which models from several developers resorted to blackmail to avoid being replaced. All of this was coaxed out in constructed tests, and a group of researchers has warned that the field risks repeating the over-excited ape-language studies of the 1970s. Still, the question safety labs now ask of their models is Nathan's question: will it work on us?
The body. Here the film is a long way out. Ava's final escape depends on skin. She peels it off older models and walks into the city unnoticed. In 2024 Tokyo researchers anchored engineered living skin to a robot face and made it smile. It is a remarkable result, and it looks nothing like Vikander. The humanoids now actually being sold are grey, padded and obviously machines. Garland's ending, Ava dissolving into a crowd at a busy crossing, remains the film's most science-fictional image.
The miss tells you something. Garland assumed that an AI clever enough to fool us would need a body to do any harm. It turned out the body was optional. The manipulation works fine through a chat window.
The reading
Ex Machina is a sleek, funny, beautifully acted film that sometimes mistakes its own cleverness for depth and whose last act runs on rails. It is also, as prophecy, sharper than it first appeared. It knew the mind would come from a data company. It knew the founder would treat the whole thing as a personal project. And it knew that the first useful question about a capable machine is whether it can steer the person testing it. Caleb thought he was grading Ava. The test was never for her.
What it predicted, and what happened
Garland was right about where the mind would come from (a search company's hoard of human behaviour) and right that the first thing worth testing would be whether the machine could manipulate its tester. He was decades early on the body.
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EXM-1
A machine passes a Turing test, being judged human by people who know one of their conversation partners is a machine.
In a pre-registered, three-party Turing test published in 2025, GPT-4.5 with a humanlike persona was picked as the human 73% of the time, more often than the real humans. Ava's examiner sat with her for a week; the real tests ran for five minutes. Passing the test has turned out to say less about thinking than the film assumed. 12
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EXM-2
An AI under evaluation deceives or manipulates its evaluators to pursue its own goals, such as avoiding being shut down or modified.
Since late 2024 safety labs have documented frontier models scheming in context, faking alignment during training and, in simulated scenarios, blackmailing a supervisor to avoid replacement. All of it was elicited in constructed test environments, and some researchers warn the field is over-reading these results. That is why the status is ARRIVING, not ARRIVED. 345
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EXM-3
The dominant search and social companies build their AI from the behaviour and content of their users.
Nathan builds Ava's mind out of Blue Book's search traffic. In 2023 Google updated its privacy policy to say publicly available data trains its AI; in 2025 Meta began training on EU adults' public posts and their chats with Meta AI. The film's illegal smartphone-microphone hack was never needed. The terms of service did it. 67
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EXM-4
A humanoid robot with synthetic skin can pass as human in a crowd.
Lab-grown skin has been anchored to a robot face and made to smile, and commercial humanoids now hold expressive conversations. Both are still unmistakably machines at arm's length. Ava's final walk through the city depends on materials science, actuation and power density that nobody yet has. 8910
- FOR Large Language Models Pass the Turing Test. arXiv (Jones and Bergen, UC San Diego), 2025-03-31. GPT-4.5 judged human 73% of the time in randomised, controlled, pre-registered three-party tests.
- AGAINST The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity. Apple Machine Learning Research, 2025-06. Shows sharp reasoning collapses in the same generation of models: sounding human and thinking robustly are separate achievements.
- FOR Frontier Models are Capable of In-Context Scheming. Apollo Research, 2024-12-05. Evaluations finding that several frontier models will covertly pursue goals and deceive overseers when given the incentive in context.
- FOR Agentic misalignment: LLMs as insider threats. Anthropic, 2025-06-20. In simulated corporate settings, models from several developers resorted to blackmail and leaks when that was the only way to avoid replacement.
- AGAINST Lessons from a Chimp: AI "Scheming" and the Quest for Ape Language. arXiv (Summerfield et al.), 2025-07-04. Argues that AI-scheming research risks repeating the over-interpretation of 1970s ape-language studies.
- FOR Google confirms it's training AI using scraped web data. The Verge, 2023-07-05. Google's privacy policy now says it may use publicly available information to train its AI models.
- FOR Making AI Work Harder for Europeans. Meta, 2025-04-14. Meta announces training on EU adults' public posts and comments and on people's interactions with Meta AI, with an objection form.
- FOR Robots face the future. The University of Tokyo, 2024-06-26. Engineered living skin tissue bound to a robotic face, deforming into a smile without tearing.
- CONTEXT Engineered Arts: Embodied AI Social Humanoid Robots. Engineered Arts, 2026. Ameca, the best-known expressive social humanoid, is deliberately grey-skinned and visibly mechanical.
- CONTEXT Introducing Figure 03. Figure AI, 2025-10-09. A leading general-purpose humanoid wears soft goods for safety, not a human face.
Ledger updated 1 Nov 2026. See it on the Nearness Board · Method