Let me start with the conclusion: it is a boom for world models, but not a boom for everyone.
In 2025-2026, world models have undeniably heated up. Video generation models like Sora, Kling, and Vidu are all essentially learning “how the world changes”; DreamerV3 has demonstrated the sample-efficiency advantage of world models in robotic control; and a 2026 survey from the Chinese Academy of Systems Science lays out the four major technical paradigms clearly, marking the field’s transition from “scattered efforts” to a “systematized” stage.
But the word “boom” needs to be unpacked. I’ll discuss it across three levels.
Technical Level: A Genuine Explosion
Several signals are clear:
Paper counts have doubled. Top-venue papers on world models in 2024-2025 more than doubled compared to the two years prior, and the four major paradigms (observation-level generation, latent-space modeling, reinforcement-learning-driven, and object-centric representation) have crystallized.
Big tech has entered the arena. DeepMind, OpenAI, and Meta are all making moves. OpenAI’s Sora, while positioned as video generation, overlaps heavily with the world-model technical roadmap. Meta’s V-JEPA series has been advancing rapidly on latent-space world models.
Application scenarios are broadening. From the earliest work in game simulation, the field has expanded to robot manipulation, autonomous driving decision-making, scientific discovery simulation, and GUI agents. Each scenario is spawning new technical demands.
Viewed through the lens of the technology maturity curve, world models are roughly in the early stage of the “peak of inflated expectations” — the technology holds real value, but market expectations may have run ahead of actual capability.
Industry Level: Still a Distance from Large-Scale Commercialization
This is the part that calls for a clear-eyed view.
Sim-to-Real remains unsolved. World models perform well in simulation, but deploying them on real robots leads to a 30-50% performance drop. This bottleneck has persisted for eight years without a breakthrough, and a fundamental solution is unlikely in the near term.
Compute costs are too high. Training a competitive world model requires multiple A100 GPUs running for weeks. For startups or individual researchers, that barrier is non-trivial.
No killer app has emerged yet. Large language models have ChatGPT; image generation has Midjourney. The world-model “ChatGPT moment” has not arrived. The closest contender today is robot manipulation, but it is still a long way from a consumer-grade product.
So from an industry perspective, world models look more like “a boom two to three years out” rather than “a boom you can monetize right now.”
Individual Level: Who Should Enter Now
Based on my own observations and practice, here is how I would break it down:
Well-suited to enter:
- Researchers, PhD students, and those joining big-tech AI labs — this is a core technology for embodied intelligence, with sustained demand over the next 5-10 years.
- Engineers with a robotics/automation background — the application layer of world models (task adaptation, transfer toolchains) is closer to revenue, and big tech has not fully covered it yet.
- Teams with compute resources — if you can afford to run training, there is an opportunity to produce influential work.
Proceed with caution:
- Entrepreneurs seeking quick monetization — this direction is too slow for short-term return expectations.
- Pure software backgrounds with no physical-world experience — the core difficulty of world models lies in Sim-to-Real, which requires understanding robotics, control, and hardware.
- Individual researchers with limited compute — you can learn the principles and run open-source code, but making original contributions will be difficult.
My Assessment
World models represent a critical step in AI’s journey from “understanding language” to “understanding the world.” The value of this direction is certain, but the timeline may be longer than most people expect.
Short term (1-2 years): Technology will continue to advance, but commercialization cases will remain limited — primarily driven by big tech and research institutions.
Medium term (3-5 years): The Sim-to-Real problem will be partially solved, and industrial scenarios (robot assembly, warehouse logistics) will begin to see large-scale applications.
Long term (5-10 years): If the leap from “statistical fitting” to “mechanistic understanding” can be achieved, world models could become the cornerstone of artificial general intelligence.
For individuals, entering the field now is not too late — but patience is required. Do not expect to see returns within six months; give yourself at least 2-3 years.
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