Demis Hassabis likens current AI models to someone blurting out the first thought they have. To combat hallucinations, models must develop a capacity for 'thinking'—pausing to re-evaluate and check their intended output before delivering it. This reflective step is crucial for achieving true reasoning and reliability.

A classical, bottom-up simulation of a cell is infeasible, according to John Jumper. He sees the more practical path forward as fusing specialized models like AlphaFold with the broad reasoning of LLMs to create hybrid systems that understand biology.

Instead of building a single, monolithic AI agent that uses a vast, unstructured dataset, a more effective approach is to create multiple small, precise agents. Each agent is trained on a smaller, more controllable dataset specific to its task, which significantly reduces the risk of unpredictable interpretations and hallucinations.

Startups and major labs are focusing on "world models," which simulate physical reality, cause, and effect. This is seen as the necessary step beyond text-based LLMs to create agents that can truly understand and interact with the physical world, a key step towards AGI.

When multiple AI agents work as an ensemble, they can collectively suppress hallucinations. By referencing a shared knowledge graph as ground truth, the group can form a consensus, effectively ignoring the inaccurate output from one member and improving overall reliability.

Purely agentic systems can be unpredictable. A hybrid approach, like OpenAI's Deep Research forcing a clarifying question, inserts a deterministic workflow step (a "speed bump") before unleashing the agent. This mitigates risk, reduces errors, and ensures alignment before costly computation.

To make genuine scientific breakthroughs, an AI needs to learn the abstract reasoning strategies and mental models of expert scientists. This involves teaching it higher-level concepts, such as thinking in terms of symmetries, a core principle in physics that current models lack.

Instead of replacing entire systems with AI "world models," a superior approach is a hybrid model. Classical code should handle deterministic logic (like game physics), while AI provides a "differentiable" emergent layer for aesthetics and creativity (like real-time texturing). This leverages the unique strengths of both computational paradigms.

Current LLMs fail at science because they lack the ability to iterate. True scientific inquiry is a loop: form a hypothesis, conduct an experiment, analyze the result (even if incorrect), and refine. AI needs this same iterative capability with the real world to make genuine discoveries.