Dr. Seth Dobrin puts a name to the next step beyond today’s language and multimodal models: world models. Instead of predicting what sounds right, these systems aim to simulate how the world behaves with physics and causality in the loop. He sees early impact arriving fast–in the order of three to six months–and ties that pace to where AI is headed next: robotics, autonomous vehicles, and other settings where intelligent systems have to act safely in environments that change in real time.

That shift also changes what “AI-ready” means inside companies. Dobrin is blunt that embodied AI is not a far-off timeline; even human-adjacent robots in controlled environments, he suggests, are a couple years out. For enterprise workflows, his emphasis is less on bigger models and more on usable economics and control: smaller, domain specific models where the scope is clear, the cost is tolerable, and governance is realistic. The deciding success factor for organizations is whether they can align incentives, decision rights, and leadership support.

Key takeaways:

• World models move AI toward simulation and cause and effect: They aim to model how environments behave, which becomes critical when systems have to act autonomously in the physical world.
• Human-safe robotics depends on software catching up: The hardware is largely here; the missing layer has been systems that can simulate and adapt in real time around people.
• Small models make sovereignty and ROI practical: For many business workflows, tightly scoped models can be cheaper to run, easier to govern, and easier to keep under enterprise control.
• AI readiness is organizational follow-through: Incentives and trusted leadership support determine whether pilots scale into durable capability.