We live in an era where generative AI can draft complex legal agreements in minutes, design plausible marketing campaigns in seconds and translate between dozens of languages on demand. The leap in capability from early machine learning models to today’s large language models (LLMs) — GPT-4, Claude, Gemini and beyond — has been nothing short of remarkable.
It’s no surprise that business leaders are asking: If an AI can write a convincing research paper or simulate a technical conversation, why can’t it run scientific experiments? In some circles, there’s even a whispered narrative that scientists — like travel agents or film projectionists before them — may soon be “disrupted” into irrelevance.
As someone who has spent over two decades at the intersection of AI innovation, scientific R&D, and enterprise-scale product development, I can tell you this narrative is both dangerously wrong and strategically misleading.
