As an engineer who has been working in this field for over half a year, here are my thoughts.
Let me start with the conclusion: it is a good direction, but not everyone should jump in right now.
Why It’s a Good Direction
World models address a very fundamental problem: enabling AI not just to “see” the world, but to “understand” it.
Large language models have already demonstrated that when a model is large enough and the data is sufficient, strong capabilities can emerge. But language models understand the world of text, not the physical world. For robots to truly operate in real-world environments, they need to understand physical laws — gravity, friction, collisions, causality. These things cannot be learned from text data alone.
The core idea behind world models is quite intuitive: have the model learn “if I take this action, how will the environment change.” Once it learns this, the robot can train its policies in “imagination” rather than through endless trial and error in the real world. This is hugely significant for robotics — real-world trial and error is expensive, slow, and risks damaging hardware.
DreamerV3 (2023, Danijar Hafner et al.) has already demonstrated that this approach works. With the same set of parameters, it achieved strong scores across 55 Atari games, and on robotic manipulation tasks its sample efficiency was 10–100x higher than traditional RL.
In 2024–2025, world models have also exploded in the video generation domain. Sora, Kling, Vidu — these video generation models are essentially learning “how the world changes.” Although they currently remain at the visual level and haven’t been integrated with physical control, the direction is the same.
Why Not Everyone Should Jump In
First, this direction is still early-stage and far from large-scale commercialization
World models perform well in simulation environments, but Sim-to-Real transfer remains a major challenge. Policies trained in simulation typically see a 30–50% performance drop when deployed on real robots. Domain randomization and system identification can help mitigate this, but the problem is far from solved. If you’re an entrepreneur looking for quick returns, this direction may be too slow.
Second, the compute resource barrier is not trivial
Training a decent world model requires at least multiple A100s running for days to weeks. Unlike building a web app where a laptop suffices, individual researchers or small teams need to budget carefully.
Third, academic competition is fierce
Top-tier papers in this direction come primarily from institutions like Stanford, CMU, Berkeley, and DeepMind. If you don’t have that kind of background, producing influential work purely through self-study is extremely difficult.
My Advice
If you want to pursue research, do a PhD, or join a major tech company’s AI Lab, world models are absolutely worth investing in. They are one of the core technologies for embodied intelligence, and there will be sustained research demand over the next 5–10 years.
If you’re looking to start a company or build a product, I’d suggest focusing on the application layer of world models — for example, using world models for rapid task adaptation of industrial robots, or building sim-to-real transfer toolchains. These directions are closer to revenue, and the big companies haven’t fully covered them yet.
If you’re just an engineer with a technical interest, my advice is: first understand the principles, get the open-source code running (DreamerV3 has an official implementation), then practice on a specific task. You don’t need to train a large model from scratch — learning to use it, modify it, and deploy it is already very valuable.
I myself have taken the last path. I’m not an academic heavyweight, nor do I have big-company resources — just an engineer passionate about technology. I write blogs and publish articles to clarify my own understanding, and hopefully help others who want to enter this field.
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