Five inventions needed to ground AI in reality
Large Language Models have achieved a remarkable feat: they compress a vast fraction of written human knowledge into a next-token prediction engine. Yet the very nature of this success exposes a structural gap. Language describes the world, but it is not the world. As a result, today’s LLMs often exhibit “patchy” common sense: they know many rules because they have seen them phrased, not because they have learned them through interaction, internal modeling, memory of experience, and causal explanation. This is why the gap will not close by simply scaling datasets or building larger GPU clusters. What is needed is a paradigm shift from computational linguistics to computational cognitive science, where the central objects are world representations, action planning, learning from experience, and causal reasoning.
Invention one: training through embodiment, not only through text. Embodied AI argues that grounding emerges when an agent learns regularities of physics via action, either in a robotic body or in high-fidelity physics simulators. Recent surveys emphasize the complementary roles of physical simulators and learned world models: simulators provide safe, controllable training and evaluation; world models provide internal predictive representations that support planning and generalization beyond immediate sensory input.
Invention two: architectures that predict world states rather than words. Yann LeCun’s JEPA vision is precisely a move away from symbolic text completion toward predictive modeling in abstract continuous representation spaces. For autonomy, this matters because planning requires an internal model that can roll forward hypothetical sequences of actions and evaluate outcomes. Predicting pixels or tokens is often intractable or unnecessary; predicting the right abstractions of the world state is the key.
Invention three: built-in deliberation loops before answering. Chain-of-Thought prompting showed that eliciting intermediate reasoning steps can improve performance on complex tasks. The more recent “inference-time scaling” line of work generalizes this into a compute budget spent during inference for search, self-checking, refinement, and the use of verifiers or feedback. Done properly, this is not verbosity. It is functional System-2 style deliberation that trades latency and compute for reliability on hard problems.
Invention four: dynamic knowledge bases and continual learning without full retraining. Static training snapshots are misaligned with a changing world. Continual learning for generative models focuses on how to incorporate new information while mitigating catastrophic forgetting, using architectural strategies, regularization, replay, and memory mechanisms. In practice, robust systems will likely separate “competence” (general modeling) from “knowledge” (updatable, provenance-tracked facts), enabling real-time learning from new experience without rebuilding the entire model.
Invention five: integrating probabilistic graphical models and Causal AI into Transformers. Pure correlation is fragile under interventions, distribution shift, and counterfactual queries. Causal approaches aim to separate cause from coincidence and support reasoning about “what would happen if we changed X.” Work such as InferBERT and newer causal-transformer designs illustrate ways to embed do-calculus-inspired mechanisms or causal discovery components within transformer pipelines, enabling models to reason about interventions rather than merely extrapolating patterns.
A frequently overlooked accelerator sits underneath all five inventions: the sensory data layer. World models and physical agents need massive streams of visual and multimodal data, and the cost is often dominated by data movement and preprocessing rather than tensor math. Arguments for compute-aware, hierarchical data formats suggest that “retrieve and decode only what is needed,” for the right region of interest and level of quality, can reduce pipeline waste and make real-time embodied learning economically viable. This is where open standards and open implementations become strategic: they reduce lock-in, enable reproducibility, and allow European and Greek ecosystems to build sovereign, auditable infrastructure for physical AI.
Sources for this article:
A Survey: Learning Embodied Intelligence from Physical Simulators and World Models (Long et al., 2025): Survey of simulator-driven embodied training and internal world modeling for grounded autonomy. https://arxiv.org/abs/2507.00917
A Path Towards Autonomous Machine Intelligence (LeCun, 2022): Position paper proposing JEPA-style predictive architectures as a path beyond text prediction. https://openreview.net/pdf?id=BZ5a1r-kVsf
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022): Establishes intermediate reasoning traces as a lever for improved performance. https://arxiv.org/abs/2201.11903
Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead (Microsoft Research, 2025): Systematic study of scaling reasoning effort at inference time, including limits and gains. https://arxiv.org/abs/2504.00294
A Comprehensive Survey on Continual Learning in Generative Models (Guo et al., 2025): Comprehensive map of continual learning methods for LLMs and generative models. https://arxiv.org/abs/2506.13045
InferBERT: A Transformer-Based Causal Inference Framework (Wang et al., 2021): Transformer-based causal inference integrating do-calculus concepts. https://www.frontiersin.org/articles/10.3389/frai.2021.659622/full
Symmetry-Aware Transformers for Asymmetric Causal Discovery in Financial Time Series (CausalFormer, 2025): Example of embedding causal inference mechanisms within transformer blocks. https://www.mdpi.com/2073-8994/17/10/1591
Can “world models” fix AI’s blind spots? (The Economist, 11 Feb 2026): Discussion of LLM limitations in physical reality and the rise of world models. https://www.economist.com/podcasts/2026/02/11/can-world-models-fix-ais-blind-spots
AI’s Trillion-dollar Blind Spot: Why Compute-Aware Data Formats are the Missing Pillar for World Models and Physical AI (V-Nova, 2025): Argues that data formats and pipelines are a core bottleneck for visual/world-model AI. https://v-nova.com/articles/ais-trillion-dollar-blind-spot-why-compute-aware-data-formats-are-the-missing-pillar-for-world-models-and-physical-ai/