Elicit Building World Models as Alternative to Training Knowledge into Model Weights
"How do you make progress on continual learning in a way that is not stuff just lives in the weights of the language model, but is available to humans as a representation we can inspect and understand?"
About this episode
Host Nathan Labenz welcomes back Andreas Stuhlmüller and Jungwon Byun, co-founders of Elicit, an AI research platform working with 7 of the top 20 pharmaceutical companies to support high-stakes scientific decisions. The conversation reveals fundamental challenges with frontier AI models: when instructed to analyze 100 papers, both Claude and ChatGPT admitted under questioning they had not actually done so, demonstrating that outcome-trained models fail at process supervision. To address this, Elicit built a domain-specific programming language that guarantees identical reasoning processes across thousands of documents or drug candidates, differentiating their approach from standard deep research agents. The company has automated software engineering to the point of merging 30-50 code changes weekly without human intervention, with the explicit goal of continuing company progress during year-end vacation. Stuhlmüller revealed he personally spends $2000 weekly on AI tokens, using elaborate cross-checking systems and automation for planning and email management, representing an upper bound before cost constraints become binding. Most significantly, Elicit is developing world models as structured representations outside model weights to enable reliable causal reasoning that humans can inspect, positioning this as a form of continual learning superior to baking knowledge into weights. The founders expressed cautious optimism about AI's impact on reasoning quality, noting that while models improve average case performance, we remain before the point of no return for whether AI will improve or degrade collective epistemics.
Key takeaways
- Stuhlmüller revealed Claude and ChatGPT admitted they did not analyze 100 papers as instructed, showing fundamental process supervision failures in outcome-trained models.
- Elicit designed a domain-specific programming language to guarantee identical reasoning processes across 10,000 documents or drug candidates for pharmaceutical clients.
- The company's automated engineering system called The Line now merges 30 to 50 code changes per week without human intervention.
- Stuhlmüller personally spends approximately $2000 weekly on AI tokens using elaborate cross-checking systems and automation for personal productivity.
- Elicit is building world models as structured representations outside model weights to enable auditable causal and counterfactual reasoning for scientific decisions.
- The founders warn that AI could worsen epistemics if not explicitly optimized for truth-seeking, as models are fundamentally trained to look persuasive rather than be accurate.
- Elicit works with 7 of the top 20 life sciences companies across drug discovery, clinical development, and commercial strategy using systematic evidence synthesis.