Will You Be AI’s Pet?

Will You Be AI’s Pet?

Professor Matt Jones

3rd February 2026

In the first lecture of this series, we confronted a widely shared fear: that we will be subjugated by artificial intelligence—outsmarted, outpaced, and ultimately rendered second-class citizens. The imagery was apocalyptic, fuelled by claims that machines would soon be smarter, more creative, and more empathetic than us. Yet as we saw, this vision of superintelligent takeover distracts from far more immediate dangers: deepfakes, disinformation, and the erosion of trust in everyday life.

The second lecture explored a different anxiety: not that we would be dominated by AI, but that we would be assimilated by it. Through constant interaction—searching, scrolling, prompting, ranking—we risk becoming shaped in its image. The danger is not simply using technology, but becoming technology: adapting our habits, attention, and even values to fit machine logics rather than human ones.

This third lecture turns to a quieter, more seductive possibility: domestication. Domestication does not arrive with threats or violence. It arrives with comfort, care, and convenience. It promises safety, efficiency, and peace of mind.

To explore this, I begin not with machines, but with animals—specifically, my two dogs, Molly and Pippa. They live extraordinarily good lives. They have high-quality food without needing to hunt, excellent healthcare without needing to pay, stimulating toys, affection, and a secure place in a stable social order. They live with another intelligence—mine—that designs, controls, and monitors their environment. This is what domestication looks like: care, provided by another.

And yet, dogs were once wolves. Wolves roam, hunt, take risks, and howl at the moon. Domestication traded freedom for security, unpredictability for comfort. It did not happen through force, but through proximity—through lingering near the campfire. Those animals that came close benefited from warmth and scraps; over generations, a new relationship formed.

This leads to a question that sits at the heart of this lecture: where are the campfires of AI—and who is choosing to sit beside them?

The lecture goes on to lay out a spectrum of animal lives and uses these as ways of thinking through different AI futures. Cats live alongside humans but retain independence; they can leave, refuse, and ignore. Reptiles inhabit structured environments where choice exists, but only within carefully designed boundaries. Birds are cared for and safe yet profoundly constrained by cages that cannot accommodate their capacities. Fish live in fully engineered worlds, healthy only so long as the system remains benevolent and competent.

Which of these lives would you choose?

Mapped onto human–AI futures, these metaphors reveal different forms of taming. A “Dog AI Life” offers perfectly tailored provision: groceries auto-ordered, health risks detected before symptoms appear, cognitive stimulation optimised, and social belonging algorithmically maintained. Life feels smooth, predictable, and safe. Scarcity disappears. So does effort. But with that comes a quiet trade-off: self-provisioning fades, choice narrows, and dependence deepens.

A “Cat AI Life” looks somewhat different. AI assists but does not supervise. Humans retain meaningful exit and refusal—like the cat that pushes through the back-door flap to spend the night hunting. The system coexists rather than encloses. A “Reptile AI Life” provides structured environments—freedom where it matters, but not everywhere. A “Bird AI Life” preserves creativity and intelligence but traps us inside systems too rigid to adapt. And a “Fish AI Life” represents total environmental engineering: life may be healthy, but only as long as nothing goes wrong.

None of these futures is science fiction. We are already being offered taming technologies across our lives, from cradle to great age. Systems now analyse baby cries, track employee sentiment, and monitor behavioural compliance and efficacy in workplaces. These are presented as supportive, caring technologies—but they also define what counts as normal, acceptable, and healthy.

Nowhere is this more evident than in the domain of ageing. There is intense commercial and research interest in technologies that allow older adults to remain at home while being continuously monitored for safety and “healthy” behaviour. Sensors track movement, detect falls, infer routines, and trigger interventions. On the surface, this is benevolent. Who would argue against such a safety net? Many of us with ageing parents are understandably drawn to these systems, designed to give peace of mind to families as much as to those being monitored.

Yet there is another way of thinking about ageing and technology. Yvonne Rogers and colleagues have argued that instead of treating later life primarily as a period of decline to be mitigated by surveillance, we should design systems that support people to flourish as they actively age. In the lecture, we illustrate this alternative through work from the EPSRC-funded Generation Project carried out by our team at Swansea University. Rather than monitoring decline and triggering external intervention, the system was designed with older adults to support reflection, conversation, and agency. Home-use data was presented back to occupants in playful, interpretable forms—heatmaps, physical artefacts, even AI-generated stories—allowing people to make sense of their own lives on their own terms. The shift is subtle but important: from being managed to being empowered.

At this point, the animals and pets that opened the lecture return with a second purpose. They do not just help us think about how AI might domesticate us; they also help us question what we mean by intelligence in the first place. Animals force us to confront intelligence as something embodied, situated, relational, and diverse—not simply abstract reasoning scaled up.

If AI is another kind of intelligence, then it is worth paying attention to the many non-human intelligences that already exist. Slime moulds solve mazes and optimise networks without neurons or brains—intelligence without thought. Octopuses distribute cognition across their bodies, with arms that act semi-independently. Crows are not generally intelligent; they are intelligently crows—deeply tuned to their ecological niche. Parrots like Alex demonstrated grounded understanding, using language to engage with the world rather than merely manipulate symbols. Dogs, meanwhile, excel not at abstract problem-solving but at social intelligence—reading gaze, tone, emotion, and intent.

These examples challenge the assumption that intelligence must look like human reasoning, only faster or more powerful. Yet much AI research remains fixated on general, human-like intelligence rather than embracing plurality, situatedness, and difference.

Ironically, many successful AI systems already resemble these alternative intelligences. Medical screening tools are highly specialised. Network optimisation borrows from ant colonies. Pattern-recognition systems operate more like slime moulds than minds.

This brings us to a final bit of animal-magic that can help us think through better was of designing and living with AI: Animal–Computer Interaction (ACI). ACI is not about novelty gadgets for pets; it is a rigorous design field that asks what happens when we take non-human intelligence seriously on its own terms. Researchers design technologies for beings with radically different bodies, senses, motivations, and capacities for action. The challenge is not to make animals behave more like humans, but to design systems that respect how animals already engage with the world.

In the lecture, we glimpse this through examples such as parrots given systems that allow them to initiate video calls with other parrots, and assistance dogs provided with interfaces designed around their abilities, enabling them to open doors and control environments. These systems work not because they are clever, but because they are restrained—because they meet intelligence where it already lives. And this is where the metaphor of re-wilding becomes useful. In ecology, re-wilding means being restrained, stepping back: fewer interventions, fewer controls, fewer attempts to optimise every outcome. Health and resilience emerge not from micromanagement, but from diversity, friction, and self-regulation.

Applying this thinking to future AI innovation, with a focus on “Human re-wilding”, means restoring and indeed enhancing agency, attention, and the capacity to choose—even when those choices are inefficient or unpredictable. It means resisting the temptation to turn care into control, and support into enclosure. It means taking the collar off and howling again at the moon.

So, as with all Gresham Lectures—it’s time to think.

Will you be AI’s pet?
Or will you be a re-wilder?

© Matt Jones 2026

Further Reading

Gavin Bailey, Krishna Seunarine, Carlos Valter Baptista De Lima, Matt Carnie, Zaid Haymoor, Martin Hyde, Benedict H Jones, Matt Jones, Jennifer Pearson, Thomas Reitmaier, Simon Robinson, Deepak Ranjan Sahoo, and Aelwyn Williams. 2024. Older Generation: Self-Powered IoTs, Home-Life and “Ageing Well”. In Proceedings of the 13th Nordic Conference on Human-Computer Interaction (NordiCHI ’24). Association for Computing Machinery, New York, NY, USA, Article 74, 1–14. https://doi.org/10.1145/3679318.3685410

Bekoff, M. (2007). The emotional lives of animals: A leading scientist explores animal joy, sorrow, and empathy—and why they matter. New World Library.

Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’21). Association for Computing Machinery, New York, NY, USA, 610–623. https://doi.org/10.1145/3442188.3445922

Nima Dehghani and Michael Levin. 2024. Bio-inspired AI: Integrating Biological Complexity into Artificial Intelligence. arXiv:2411.15243 (Nov. 22, 2024). https://doi.org/10.48550/arXiv.2411.15243

Marco Dorigo, Vittorio Maniezzo, and Alberto Colorni. 1996. The ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics—Part B (Cybernetics) 26, 1 (1996), 1–13.

Rebecca Kleinberger, Jennifer Cunha, Megha M. Vemuri, and Ilyena Hirskyj-Douglas. 2023. Birds of a Feather Video-Flock Together: Design and Evaluation of an Agency-Based Parrot-to-Parrot Video-Calling System for Interspecies Ethical Enrichment. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 534, 1–16. https://doi.org/10.1145/3544548.3581166

Clara Mancini, Sha Li, Grainne O’Connor, Jose Valencia, Duncan Edwards, and Helen McCain. 2016. Towards multispecies interaction environments: extending accessibility to canine users. In Proceedings of the Third International Conference on Animal-Computer Interaction (ACI ’16). Association for Computing Machinery, New York, NY, USA, Article 8, 1–10. https://doi.org/10.1145/2995257.2995395

Pepperberg, I. M. (2008). Alex & Me: How a scientist and a parrot uncovered a hidden world of animal intelligence—and formed a deep bond in the process. HarperCollins.

Yvonne Rogers, Jeni Paay, Margot Brereton, Kate L. Vaisutis, Gary Marsden, and Frank Vetere. 2014. Never too old: engaging retired people inventing the future with MaKey MaKey. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’14). Association for Computing Machinery, New York, NY, USA, 3913–3922. https://doi.org/10.1145/2556288.2557184

Yong, E. (2022). An immense world: How animal senses reveal the hidden realms around us. Random House.