This article matters less as a robotics profile than as a signal of where enterprise automation may go next: from narrowly scripted systems to agents expected to operate in unfamiliar conditions. For CIOs and CTOs, the management question is not whether this specific startup wins, but when to treat model-based agents as a distinct investment category with different assumptions for testing, controls, and value realization.
The potential upside is operational flexibility. If agents can generalize beyond predefined workflows, they could reduce the cost of exception handling in logistics, field service, facilities, and other semi-structured environments. But leaders should separate technical possibility from deployable reliability. Planning-oriented agents may outperform brittle automation, yet they also introduce harder governance questions: what decisions can be delegated, what level of autonomy is acceptable, and who is accountable when the system improvises in edge cases?
This points to an operating-model challenge. Enterprises will need staged adoption rather than innovation theater: sandbox trials, simulation-based validation, human override design, incident review processes, and clearer ownership across IT, operations, safety, legal, and vendors. Procurement teams should also ask whether world-model performance in demos translates into measurable service levels, supportability, and integration with enterprise controls.
Practical next questions for leaders include:
- Which business processes have high exception costs and enough economic value to justify experimentation?
- What governance thresholds define acceptable autonomous action versus mandatory human review?
- How will benefits be measured: labor productivity, throughput, resilience, safety, or reduced retraining effort?
- What evidence is required before moving from pilot to scaled deployment?
Danijar Hafner’s office in San Francisco’s SoMa district sits mostly empty. His brand-new startup is still in stealth mode and doesn’t even have its name on the door. On the day I visit, there’s only one other person there, and little in the way of furniture. But what it lacks in decor, it makes up for in robots. Humanoids of various shapes and sizes hang like marionettes from racks that run down the center of the wide-open space.
While Hafner, 31, won’t say too much about his new venture just yet, he describes it as a continuation of his longtime work to enable AI to navigate environments it has not encountered in training. The humanoids, which he imports from China, are the next evolution of this work—and its physical embodiment. Their ability to react in previously untested scenarios will be key to getting robots into human spaces. Because if you want to send a robot into a person’s home, for example, it needs to be able to handle a floor plan and furniture it’s never seen before.
To achieve this, Hafner relies on something called model-based reinforcement learning. He develops world models—AI models designed to emulate physical reality—and trains agents within them. The agent essentially treats the model as a real-world simulation and learns how to act there. It then uses those experiences to make predictions (to dream or imagine, Hafner might say) about future outcomes. That allows agents—or the robots they’re embedded in—to navigate unfamiliar situations IRL.
“I get to interact with a lot of really smart people in research at Google, and he easily sits in the top half of 1%.”
Timothy Lillicrap, Google DeepMind
Unlike other efforts, Hafner’s technique enables agents and the robots they control to execute massively complicated tasks without the real-world trial-and-error training that’s traditionally been used in robotics.
Hafner grew up in a rural town in northeastern Germany, where his parents were both classical musicians. He learned programming from a neighbor, and in high school he began taking online courses about AI, which quickly developed into a passion. “I was always fascinated with how thinking works,” he says. AI offered him a way to emulate it on a computer.
In 2015, as a second-year undergraduate studying engineering at Hasso Plattner Institute in Potsdam, he won a role as a student researcher at Google Brain. From there, he went on to a dozen internships and other positions at the company, including stints with Google Brain and Google DeepMind (the two have since merged under DeepMind) in the UK, Canada, and the US. He worked with industry legends including Geoffrey Hinton, who is often referred to as one of the godfathers of AI, and Ashish Vaswani, coauthor of the groundbreaking research paper “Attention Is All You Need,” which described the transformer technology used by today’s large language models.
One of Hafner’s former managers and coauthors at Google, Timothy Lillicrap, describes him as a standout among standouts. “I get to interact with a lot of really smart people in research at Google, and he easily sits in the top half of 1%,” Lillicrap says. “In many cases he would build, single-handedly, things it would take entire teams of engineers to build.”
Over the years, Hafner has honed and proved his approach by pitting agents trained within his world models against popular video games. His first breakthrough was PlaNet, a model that allowed agents to execute actions by planning ahead. His Dreamer 2 was the first agent to hit human-level performance playing Atari 2600 games using a world model. Dreamer 3 was the first one to solve the Minecraft Diamond challenge—successfully mining in-game gems on its own. And Dreamer 4 went a step beyond that by learning to mine diamonds from an offline data set of recorded game-play videos, without ever interacting with the game directly.
More recently, he’s begun to migrate his agents out of the virtual world and into physical reality. His DayDreamer project used the Dreamer algorithm to let robots operate themselves in novel environments and react to new experiences (such as being pushed over) without any specific training.
Today, Hafner is working on his new startup, which he left Google DeepMind to form in the fall of 2025. Though he’s coy about his next steps, it’s clear he’s dreaming big: “I was interested in solving a problem,” he hints, “that would change the world.”
Enjoyed this article? Sign up for our newsletter to receive regular insights and stay connected.