AI's Next Frontier Is a Reasoning Revolution

If a machine can reason, whose perspective and whose logic does it represent? With greater complexity comes opacity

- Abhishek Baxi

Arun Mayya, a Bengaluru-based entrepreneur building things at the intersection of generative AI and content creation, believes that the real world is messy and full of problems that require multi-step solutions and creative thinking. He wants AI that understands and can work with this. "Reasoning AI models can infer cause and effect. If it's raining outside, for example, then they can infer that the ground is wet because of the rain, whereas vanilla models can get confused and assume that the events simply appear together and are not related," explains Mayya.

The difference becomes apparent when the questions themselves are less like trivia and more like conundrums. Imagine, for instance, asking: "Evaluate the feasibility of launching a vegan cloud kitchen in Gurgaon, with a break-even point within 9 months, based on current delivery and food-tech trends, along with regulatory risks." A chatbot that simply lists vegan recipes or gives a dated market estimate would be worse than useless. What's needed is a well-reasoned response that weighs demand and supply data, costs, consumer sentiment, policy shifts, and even the likely impact of next product pivot of food delivery apps. And now we have reasoning AI models that can do this, an evolution piggybacking on the rapid adoption of generative AI tools like OpenAI's ChatGPT, Microsoft's Copilot, and Google's Gemini along with a slew of AI experiences from innovative AI startups.

Over the past couple of years, millions of individuals and businesses started interacting with a seemingly intelligent system using natural language, sparking a wave of experimentation and speculation about a productivity revolution. These early models were impressive content generators, capable of drafting emails, writing code snippets, and summarizing articles with astonishing speed.