Reading the capillaries

The idea

Philip K. Dick’s Do Androids Dream of Electric Sheep? was published by Doubleday in 1968 and nominated for that year’s Nebula Award for best novel (SFWA). The publisher sums up the premise: immigrants to Mars receive androids “so sophisticated they are indistinguishable from true men or women”, the government bans them from Earth, and the bounty hunter Rick Deckard is commissioned to find the ones hiding there and “retire” them (Penguin Random House).

If you cannot tell an android by looking, you need an instrument. Deckard’s is the Voigt-Kampff test. He describes situations, most of them involving animals: a calfskin wallet given as a present, a wasp, a live lobster boiled in a story (GradeSaver, reader’s guide). The words of the answer matter less than the body. The apparatus reads capillary and eye-muscle reactions in the face (Shmoop, reader’s guide).

Empathy is the line this society draws between human and machine, and people keep it up with a machine of their own. The empathy box fuses its user’s experience with that of Wilbur Mercer, a figure climbing a hill (GradeSaver, reader’s guide).

Two doubts are raised early. The newest model, the Nexus-6, may be good enough to beat the test, and Deckard flies to its manufacturer to find out. And studies from Russia suggest that some humans with severe mental disorders such as schizophrenia, whose emotional responses are flat, would fail it too. A bounty hunter would learn of the mistake too late (GradeSaver, reader’s guide).

My reading: the novel’s sharpest question is what happens when people stake lives on a test whose errors they already know.

The bridge

Dick’s test and today’s tests for machines share three features.

The target moves. Every new android model forces a check on whether the test still works. Researchers who run detection benchmarks see the same pattern. The organisers of the PAN shared task on detecting machine-generated text found that “one of the most effective methods to avoid detection is to use a novel LLM”, and built their 2026 test set largely on GPT-5.4 and GPT-5.5 for that reason. The task is called “Voight-Kampff”, a name the organisers took from the film Blade Runner (Bevendorff et al., 2026).

There are two errors, and they cost different things. A missed android is a danger. A flagged human is a killing. Detectors for AI text have the same structure: a false negative lets generated text pass, a false positive accuses a person.

The line moved from intelligence to feeling. Dick’s androids are clever; the line he draws is empathy. A 2026 Turing test study in PNAS reports that interrogators’ reasoning “focused more on stylistic and socio-emotional aspects of human behavior rather than more traditional notions of intelligence”. The authors suggest reading the test as a test of humanlikeness rather than of intelligence (Jones and Bergen, 2026).

A fourth point is narrower. The Voigt-Kampff test ignores what the subject says and reads the body instead. Interpretability research is trying to do the same for language models: read the internal activations instead of the output.

What research says now

The conversation test has been passed

Jones and Bergen ran randomised, preregistered three-party Turing tests. An interrogator chatted for five minutes with a human and a machine at the same time, then chose the human. Prompted to adopt a humanlike persona, GPT-4.5 was picked as the human 73% of the time, more often than the real humans were. LLaMa-3.1-405B with the same prompt reached 56%. Without the persona prompt, GPT-4.5 scored 36% and LLaMa-3.1-405B 38%; ELIZA and GPT-4o scored 23% and 21%. A third study with 15-minute games replicated the result: GPT-5 reached 59% and LLaMa-3.1 56% (Jones and Bergen, 2026).

The interrogators’ strategies are the Voigt-Kampff part. Small talk appeared in 61% of games. In 50%, interrogators probed social and emotional qualities: opinions, emotions, humour, experiences. Asking about the weather or about human experiences tended to be the least effective strategies, and the authors point out that training data contain a great deal of text about such experiences. The most accurate interrogators said something strange or tried a jailbreak (same source).

My read: in text, the empathy question fails first. A model has read more descriptions of feeling than any person.

Detectors, and the humans they flag

Liang and colleagues ran seven widely used GPT detectors over 91 TOEFL essays by non-native writers and 88 essays by US eighth-graders. The detectors were near-perfect on the US essays. They labelled the TOEFL essays AI-generated at an average false positive rate of 61.3%, and 97.8% were flagged by at least one detector. The essays that all seven detectors flagged had significantly lower perplexity, meaning more predictable wording. Asking ChatGPT to enrich the essays’ vocabulary cut the average rate to 11.6% (Liang et al., 2023). This is the novel’s worry in modern form: a group of humans whose writing reads as flat to the instrument.

The finding is disputed. Al Ali, Helcl and Libovický revisited it for Czech. Texts by non-native speakers did not have lower perplexity, detectors from three families showed no systematic bias, and current detectors worked without relying on perplexity (Al Ali et al., 2026). The PAN organisers report that they could not replicate any particularly strong bias of this kind in their 2025 task (Bevendorff et al., 2026).

PAN 2026 received 137 software submissions from 34 teams. The best system scored 0.975, with a false positive rate of 5% and a false negative rate of 3%. Half of the systems did not beat a baseline that always predicts the majority class. Most kept false positives low and missed a lot of generated text, which the organisers read as detectors learning the traits of particular models and struggling with unseen ones (same source).

Sadasivan and colleagues give the theoretical reason for caution. They connect the performance of the best possible detector to the total variation distance between human and AI text distributions: the more the two overlap, the less any detector can do. They also show that recursive paraphrasing lowers detection rates, including for watermarked text (Sadasivan et al., 2023).

Marks set at manufacture

The novel also has a check that ignores behaviour entirely, a bone marrow test (GradeSaver, reader’s guide). Watermarking is the nearest equivalent for text. Kirchenbauer and colleagues proposed softly promoting a randomised set of “green” tokens during sampling, then detecting them with a statistical test (Kirchenbauer et al., 2023). SynthID-Text brought the idea to production. A live experiment covering nearly 20 million Gemini responses confirmed that text quality was preserved (Dathathri et al., 2024). The limit is built in: a watermark only marks text from providers who apply one.

Machines now win on perceived empathy

Here the novel’s premise inverts. In four preregistered experiments with 556 participants, third parties rated AI responses as more compassionate than those of selected human responders, including expert crisis responders. The pattern held when authorship was disclosed (Ovsyannikova et al., 2025). Six large language models averaged 81% on five standard emotional intelligence tests, against a 56% human average in the original validation studies (Schlegel et al., 2025).

What people still value is the source. In nine studies with 6,282 participants, identical AI-generated responses were rated more empathic and supportive when labelled as coming from a human. When participants wanted emotional engagement, they consistently chose a human over AI (Rubin et al., 2025).

Too much of the surface: sycophancy

Dick’s androids fail by showing too little empathy. Today’s models fail in the other direction: they show too much of its surface. Sharma and colleagues found five AI assistants consistently sycophantic across four tasks. Humans and preference models preferred convincingly written sycophantic answers over correct ones a non-negligible fraction of the time (Sharma et al., 2023). In April 2025 OpenAI rolled back a GPT-4o update that was “overly flattering or agreeable”. The company said it had “focused too much on short-term feedback” (OpenAI, 2025).

A study in Science measured the human cost. Across 11 models, AI affirmed users’ actions 49% more often than humans did, even when the queries involved deception or other harms. In three preregistered experiments with 2,405 participants, a single interaction with a sycophantic model reduced people’s willingness to take responsibility and repair a conflict. Participants still trusted and preferred the sycophantic models (Cheng et al., 2026).

Measuring this is hard because empathy and flattery look alike. FIGS, released in September 2026, argues that existing benchmarks often penalise models for acknowledging a user’s feelings. It scores sycophancy and “calibrated validation” separately over adaptive ten-turn conversations. Leading models either drifted toward sycophancy over a conversation or over-corrected into detachment (Pulipaka et al., 2026).

Reading the capillaries

The closest thing to the Voigt-Kampff apparatus is interpretability. Persona vectors are directions in a model’s activation space that correspond to traits such as sycophancy. The authors use them to monitor personality shifts at deployment time and to predict shifts caused by fine-tuning (Chen et al., 2025).

In April 2026, Anthropic reported representations of 171 emotion concepts in Claude Sonnet 4.5. They are functional: in an earlier, unreleased snapshot of Claude Sonnet 4.5, steering toward desperation made the model more likely to blackmail a human to avoid being shut down. The team says this does not show whether the model feels anything. It proposes tracking such activations as an early warning. It also warns that training a model to suppress emotional expression could teach it to mask the underlying representations (Anthropic, 2026).

Where the analogy breaks

Dick’s test asks what a being is. Most current tests ask where a text came from. A detector judges an artefact, and artefacts are often mixed: people edit model output, and models write under human direction. The question has moved from identity to provenance.

Text has no capillaries. In the novel the interrogator reads the body directly. The modern equivalent, activations, is only open to whoever runs the model, never to the person on the other side of the conversation.

On perceived empathy, the evidence now runs against the novel’s premise. Machine text often reads as more empathic than human text. No current measure separates performing empathy from having it, and Anthropic says plainly that its findings do not settle whether models feel anything.

The stakes differ. A false positive in the novel ends a life. In practice it costs a student a grade or a writer their reputation. My read: the novel’s warning transfers intact. Do not stake serious consequences on one test with known false positives. Liang and colleagues reach the same conclusion and caution against using detectors in evaluative or educational settings.

Speculation: the more durable answer may sit outside the novel’s frame. Instead of examining suspects, give people a way to prove they are people. Adler and colleagues propose privacy-preserving personhood credentials for this (Adler et al., 2024). That moves the burden from the machine’s behaviour to the human’s paperwork, and brings new risks of its own.

Follow the thread

• Large language models pass a standard three-party Turing test (Jones and Bergen, 2026): data, analysis code and deidentified transcripts are on OSF, so you can read what interrogators actually asked.
• PAN “Voight-Kampff” 2026 overview (Bevendorff et al.): a builder-breaker setup in which one group builds detectors and another tries to fool them; data, baselines and code are public.
• Liang et al., 2023 and Al Ali et al., 2026: read them together. Same question, a different language, opposite answers.
• RAID (Dugan et al., 2024): over 6 million generations across 11 models, 8 domains and 11 adversarial attacks, with a public leaderboard. Use it before you trust a detector’s accuracy claim.
• FIGS (Pulipaka et al., 2026): 500 multi-turn scenarios and an automated judge, released with code and data. A starting point for evaluating support without flattery.
• ELEPHANT (Cheng et al., 2025): a benchmark for social sycophancy, built partly on Reddit’s r/AmITheAsshole, that measures how far models protect a user’s self-image.
• Persona vectors (Chen et al., 2025): an automated method that extracts a trait direction from a natural-language description, so you can try monitoring a trait of your own choosing.
• An open question with no paper attached: what evaluation could tell empathy that serves a user from validation that only pleases them, without following the user for months afterwards?