"But reasoning models have changed the game, Midha said, referring to the new generation of AI systems designed to "reason"problems step by step, mimicking logic and reflection rather than predicting the next word in a sequence. These models can evaluate their own outputs better, break complex tasks into sub-tasks, and learn from feedback, potentially bringing AI closer to complex, real-world problem-solving."
""Reinforcement learning as a new paradigm is working so extraordinarily well, especially on mission-critical problems," Midha said. "If you can define the reward model correctly, which startups are really good at doing when they embed themselves inside an industry-they go deep, they go vertical, and they end up understanding the customer's problem end to end-you can build entirely new, multibillion-dollar companies doing full end-t"
Reasoning models solve problems step-by-step by mimicking logic and reflection rather than predicting the next word. These models can evaluate their own outputs, decompose complex tasks into sub-tasks, and learn from feedback. Reinforcement learning performs exceptionally well on mission-critical problems when reward models are defined correctly. Startups embedded deeply within industries can specialize vertically, understand customer problems end to end, and build full end-to-end solutions. Such vertically focused companies can scale into entirely new, multibillion-dollar businesses rather than remaining limited to tiny niche components of larger systems.
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