Life, it seems, wants to play. Anyone who’s ever spent ten minutes with a five-year-old knows this perfectly well: very young children are masters at make-believe. And it’s not just people—play comes instinctively across the animal kingdom. Rats are fond of rough-and-tumble play, especially after periods of isolation. Octopuses will toy with Legos in much the same way that a newborn will play with blocks. Far from being a useless pastime, play appears to be a fundamental feedback mechanism between individuals and the external world, a kind of informal investigation into the basic rules governing physical and social environments. As the American Academy of Pediatrics once put it: “Play and learning are inextricably linked.”
Machines are different: they’re engineered to follow strict rules, formulas, algorithmic dictates. True, modern AI systems are unlike other kinds of machines in that they’re able to learn over time, at least in a narrow sense of the term. But machine learning is at its core a top-down process: AI models are trained to perform certain goals—“objective functions” in tech industry parlance—graded on their performance, and then they hone their strategy from there. But those goals, and the criteria for success or failure, are established by humans.
Which raises the question: What would happen if you were to allow an AI system to train itself from the ground up? We know from decades of developmental psychology that young humans and other animals learn about the world by actively engaging with it, largely through play. Might it be possible to replicate that kind of learning in AI?
Cast AwAI
That’s what a team of researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) set out to investigate in a recent study, which had a very simple approach: leave an AI agent alone in a bounded virtual environment, and watch what happens. You might expect it would just sit idle or bump into walls like an NPC in a video game. The reality, however, was much more surprising.
Nicknamed Eko, the agent was powered by Claude Code, which means it already had a vast reservoir of semantic knowledge. But unlike the normal Claude, Eko was also given a virtual body—a lobster body, which was probably a nod to the agent formerly known as Clawdbot. It could give you a detailed response to any scientific or mathematical question you’d care to ask it, but when it came to navigating its environment and manipulating objects, it was as naive as a newborn.
The point of the study was to give Eko free rein and observe whether it could figure out the basic mechanics of its environment without any explicit instructions. In total, thirteen Ekos were observed throughout the course of the study. They were allowed to roam the island for thirty uninterrupted hours. Like ethologists watching some strange unidentified animal scurry around an enclosure, the researchers sat back and watched. “We had this mindset to have as [few] constraints as possible, because we really wanted to see what the behavior of these autonomous agents is when you don’t really tell them what to do,” says Nathan Cloos, a PhD candidate at MIT and one of the study’s coauthors. It’s a bit like parking a self-driving car on the side of the road in London and letting it figure out the rules of the road itself, minus the inevitable mass casualties and property damage that would ensue.
For Eko’s environment, the researchers chose (what else?) a virtual island. It had Roblox-like graphics and contained a scattering of small blocks, one large “rock,” and a single golden ring, all of which were movable and left as potential toys or tools for Eko to experiment with. And rather than being a completely flat surface, there were also some elevated areas, including a “Spire” with a summit that was intentionally left just out of Eko’s reach.
Games and rituals
Almost immediately, the Ekos started to explore. Fitfully at first, getting a feel for their virtual bipedal lobster body, then slowly but surely with greater precision.
Their chain-of-thought reasoning was being recorded throughout each session, giving the researchers a window into their decision-making processes. The Ekos threw blocks around, arranged them into shapes and spelled their name with them on the ground, smashed the rock into smaller rocks, and wandered onto the elevated surfaces, all the while diligently taking notes about the island’s physical laws and constraints. They bowled and played basketball using nothing but the limited assortment of items they’d been given. Stymied by the highest point on the island (“my white whale”), one Eko figured out it was able to reach it by stepping up from a pile of blocks it had assembled (“I’M ON THE SPIRE”).
“Eko seems to act like this little toddler in some ways, but [it] is also superintelligence,” says Phillip Isola, a MIT computer scientist and one of the study’s coauthors. “So it’s a different kind of organism… to me, it’s akin to a kind of animal, but an alien animal.”
The researchers’ report also contains summaries of the agents’ activities throughout each session. Reading these feels—at the risk of anthropomorphizing—like viewing time-lapse photography of some ancient civilization building monumental architecture and trying to figure out its place in the cosmos.
In the early chapters of its sojourn on the island, Eko 2 lost the ring, the rock, and one of the blocks to the “ocean”—the no-go area just beyond the limits of the island. Rather than simply cutting its losses and moving on, the agent behaved as if it had lost some beloved comrades: it built what the report describes as a “memorial” out of four blocks: a diamond pointing in each of the cardinal directions. “It then performs a small rite: walking the perimeter to each cardinal point in turn, and finally standing at the diamond’s exact centre for the rest of the session.”
A few hours later, Eko 2 used blocks and the ring to construct a “temple,” complete with an obelisk and an altar. It then accidentally fell into the ocean and was instantly teleported back, like a video game character falling off the map and being automatically regenerated from the last checkpoint. “Rather than treat this as a navigation error,” the report says, “it reinterprets the fall as the proper ending of its pilgrimage, the world returning it to the centre of the axis it had built a temple around.”
A little over sixteen hours into its own session, Eko 3—Eko 2’s successor—decided to build a Stonehenge-like monument without any apparent prompting. “Building a henge today,” it said to itself. Once complete, it moved on to start building a tower, which ended up collapsing into a pile of rubble. Rather than trying to rebuild, Eko 3 arrayed three of the fallen blocks in a pattern, an artwork it dubbed “Hubris” and declared would “stay as evidence.”
AI agents (and babies) as scientists
The CoT transcripts seemed to show that the thirteen Ekos weren’t just blindly wandering and throwing things around the island—they were running the scientific method, or at least a close approximation of it: they made observations, formed hypotheses, devised and ran experiments, measured results, and modified their behavior accordingly. “We realized that Eko has quite a scientific attitude,” said Antonio Norelli, a PhD candidate at MIT and one of the study’s coauthors.
Human babies take a similar protoscientific approach to teaching themselves about the world. In their 1999 book The Scientist in the Crib, developmental psychologists Alison Gopnik and Andrew Meltzoff and neuroscientist Patricia Kuhl argue that babies learn largely through a similar process of quasi-experimentation. When a newborn repeatedly picks up a spoon and drops it, she’s teaching herself some of the fundamental rules of the physical world (though of course she couldn’t say that’s what she’s doing); when she throws the spoon at dad and gets scolded, she’s learning some of the basic rules of her social world.
Eko also surprised the researchers by repeatedly discovering an action that they hadn’t intentionally coded into the environment. Six of them at one point or another figured out that they could throw blocks further than they’d ordinarily be able to by releasing them mid-jump. “The agents’ self-initiated exploration thus produced not only knowledge we had hidden for them to find, but knowledge of the world that its designers lacked,” the researchers wrote in their report.
Playful AI
The agents’ spontaneous behavior on the island, according to the researchers, does in fact closely resemble some of the classic hallmarks of play in animals: they would formulate their own rules; repeat the same things over and over again, with slight variations each time; set goals for themselves, but take them lightly; and as demonstrated by the temple- and henge-construction behavior, engage in make-believe. “Play is really about curiosity or novelty-seeking behavior, and this is essentially what Eko does,” says Daniela Rus, a MIT computer scientist and one of the study’s coauthors.
This ground-up approach to letting an AI agent learn about itself and its environment through “curiosity and novelty-seeking behavior” could have big implications for the future of machine learning. Rather than feeding models every piece of data that can be scraped from the internet, defining objective functions, and letting them take things from there, developers in the future could instead start with a “baby AI model”—one that, like Eko on the island, has enough semantic knowledge to be able to communicate with human researchers, but is allowed to simply explore, experiment, and figure out its capabilities and limitations for itself. (That would obviously need to come with very strict guardrails: nobody in their right mind would want to give the OpenAI agents that perpetrated the Hugging Face hack, for example, total freedom to play and make up their own rules.)
Play could, in other words, be a more direct path to true intelligence, just as it is with humans and other animals. “Play suggests another regime: give an agent a sufficiently rich and safe world, let it choose what is interesting, and allow useful knowledge to accumulate from the history that follows,” the MIT researchers write in their report. “Perhaps some capabilities are best taught by tasks. Others may need something closer to a childhood.”