What Comes After LLMs? The Post-LLM Bet Explained

What comes after LLMs in real AI systems?

What comes after LLMs will probably look like a layered stack, with LLMs handling language while other models handle planning, memory, and environment prediction. That's the crux. LeCun has said for years that predicting the next word can't, on its own, produce human-like understanding, and he repeated that view in talks tied to Meta's AI roadmap and his broader push around world models. His research line suggests systems that learn abstract representations of the world, predict how states change after actions, and plan over those internal models instead of only autocompleting tokens. We can already spot pieces of that path in robotics labs, autonomous driving stacks, and game-playing agents. Think DeepMind's AlphaGo line. Or Tesla's end-to-end autonomy work, where action and state estimation matter more than polished prose. We'd argue the future probably isn't post-language; it's post-language-only. That's a bigger shift than it sounds. And it's a far stronger claim than the clicky line that LLMs are dead.

Why are investors funding world models vs LLMs now?

Investors are funding world models vs LLMs because text fluency by itself doesn't always turn into dependable enterprise results. Money follows bottlenecks. According to PitchBook, generative AI funding stayed strong through 2024, but investors increasingly favored startups claiming better reliability, vertical depth, or agentic execution instead of one more general chatbot. A company pitching representation learning, planning, or simulation can offer something buyers actually care about: lower hallucination rates in specific tasks, tighter control loops, and systems that act in environments rather than merely describe them. That's compelling. Especially in logistics, robotics, industrial automation, and defense, where an elegant sentence means almost nothing if the system can't predict consequences. Nvidia, Microsoft, and Amazon have all pushed software stacks for AI agents and simulation-heavy training, which points to where infrastructure buyers think demand is headed. Worth noting. We'd argue the $1 billion-plus story matters because capital markets rarely back pure contrarianism at that scale. They back a wager that the next big margin pool sits beyond chat.

How do world models work, and why do they differ from next-token prediction?

World models work by learning compressed internal representations of environments and then predicting state transitions, rewards, or outcomes after actions. That's very different. In a standard LLM, the training objective focuses on predicting the next token from prior context, which creates astonishing linguistic competence but not necessarily grounded causal understanding. In a world-model setup, the system tries to infer latent variables, model dynamics, and choose actions that maximize long-term goals. FAIR, DeepMind, and UC Berkeley have all explored nearby ideas, from self-supervised representation learning to model-based reinforcement learning. Here's the thing. LeCun's public comments often point to the need for systems that can reason about the physical world the way infants do, through observation, persistence, and abstraction rather than trillions of text snippets. That's why the limits of next token prediction keep resurfacing in this argument. Text prediction can imitate reasoning. But it doesn't always carry out reasoning in a form you'd trust for embodied or high-stakes action. We'd say that's consequential.

Where will ai beyond large language models outperform LLMs?

AI beyond large language models will likely outperform LLMs on tasks that depend on planning, long-horizon control, physical prediction, and structured decision-making. That's where the gap opens. A warehouse robot, for example, needs to estimate trajectories, avoid collisions, and adapt to changing layouts; language is useful, but physics and control matter more. Covariant and Boston Dynamics have made that pretty plain. Embodied AI gets its value from perception-action loops, not just dialogue polish. The same pattern shows up in industrial monitoring, supply chain optimization, and scientific simulation, where models need to reason over states, constraints, and counterfactuals. Early agent systems built on LLMs can draft plans, yet they often break when execution gets messy or environments shift without warning. We think this is where post-LLM architectures earn their keep. Simple enough. When the world pushes back, prediction over reality beats prediction over text. That's a bigger shift than it sounds.

Will world models replace LLMs or sit on top of them?

World models will probably sit on top of LLMs, beside them, or under them, but they usually won't replace them outright in customer-facing products. That's the practical answer. Enterprises still need a natural interface for employees, customers, and developers, and LLMs remain the best general-purpose interface layer we've got. OpenAI's tool use, Anthropic's computer-use work, and Microsoft's Copilot design all hint at the same pattern: language orchestrates, while tools and external systems execute. A post-LLM stack may rely on an LLM to parse intent, retrieve context, explain results, and summarize plans, then pass actual decision-making or environment simulation to other models. That's how buyers think. This hybrid design also fits enterprise purchasing because it keeps familiar chat and workflow surfaces while improving what happens underneath. So yes, world models vs LLMs is a meaningful technical comparison. But in products, the more consequential comparison is orchestration versus substitution. We'd argue that's the real commercial story.

Frequently Asked Questions

Sources

1. 1The Man Replacing LLMs — And He Has $1 Billion to Prove It — via Medium