Beyond Images and Video: AI Is Creating Worlds We Can Interact With
Imagine describing a place to an AI system, then being able to explore it and change what happens there. You move through the scene, ask for rain or another character, and watch the environment respond.
This is the idea behind Genie 4, the model Google DeepMind announced on October 27, 2026.
According to the company, the model can create interactive scenes that evolve as they run. Users can guide them through written requests in everyday language, with sounds and dialogue generated alongside the action. Access is currently limited to a research preview for selected testers.
What catches my attention is the user’s role. Instead of simply watching content, the user becomes part of the experience. Think of it as something like a game whose world you can explore and reshape through words.
How could this be useful?
From my perspective on technology and education, the value of this direction will become clearer when it helps us experience situations that are difficult to arrange in real life.
Imagine a student exploring a learning environment and asking questions along the way. Or a new employee practising how to handle an upset customer, then trying a different response to see how it changes the outcome.
These are possibilities I would like to see explored, rather than ready-made services I am claiming Genie 4 offers today.
The technology could also be useful for content creators. Exploring a scene and adjusting its camera angles and events as it unfolds could help a filmmaker or designer test an idea before investing time and money in production.
But the success of these uses will depend on more than attractive visuals. It will depend on how well we can control the experience and trust its results.
Does looking realistic mean being accurate?
For me, this is the most important question.
A scene can look convincing while still showing an illogical action or an inaccurate result. If we use it for education or training, a user could learn the wrong lesson just as easily as the right one.
When assessing tools like this, we should ask practical questions: Do they preserve the details of the environment? Do they respond consistently to instructions? Can their content be reviewed and corrected?
Google acknowledges that the model remains experimental. Some scene details can drift as a session continues, while spatial accuracy and complex physical interactions still need improvement.
For safety training, for example, realistic visuals would not be enough for me. I would need to check that the procedures and outcomes match the approved training material.
What matters now?
I do not think every new technology announcement requires us to subscribe or immediately replace our existing tools. Some announcements, however, deserve attention because they introduce a different way to work and learn.
As these tools develop, I believe specialists will have a role in designing the experience, deciding what users should learn from it, and checking its accuracy.
For me, the question worth following is this: When will these worlds become useful enough to help us solve a real problem?
When we can answer that with clear examples and practical experience, the technology will have moved beyond impressive demonstrations to an impact we can measure.
Source: Google DeepMind.