From Black Box to Control Room: Startups Bring Engineering Rigor to LLM Training

As AI models become increasingly powerful, their inner workings remain opaque and difficult to control, making it hard to eliminate flaws like hallucinations. Mechanistic interpretability tools like Silico now allow engineers to peer inside LLMs during training and adjust parameters with precision—transforming AI development from trial-and-error empiricism into disciplined engineering. This represents a fundamental shift away from the industry's dominant 'more compute equals better AI' mindset toward a more transparent, controllable approach to building safer and more reliable AI systems.