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AI can solve Olympic-level math problems... and still fumble basic arithmetic. So what gives? According to Dhruv Batra, the answer lies in the “jaggedness” of intelligence—how AI can excel in some areas while completely breaking down in others. Dhruv, co-founder and Chief Scientist at Yutori, joins Hannah Clark to unpack the cognitive dissonance users feel when a model dazzles one moment and disappoints the next.
They explore how user expectations—shaped by decades of intuitive UI patterns and human conversations—often collide with the underlying limits of AI systems. From browser agents and automation to long-term feedback loops and trust-building, this conversation is a candid look at what today’s AI can actually do (and where it’s still bluffing). If you’re building with AI or trying to scope what’s possible, this one will recalibrate your expectations—in a good way.
Resources from this episode:
By Hannah Clark - The Product Manager4.8
66 ratings
AI can solve Olympic-level math problems... and still fumble basic arithmetic. So what gives? According to Dhruv Batra, the answer lies in the “jaggedness” of intelligence—how AI can excel in some areas while completely breaking down in others. Dhruv, co-founder and Chief Scientist at Yutori, joins Hannah Clark to unpack the cognitive dissonance users feel when a model dazzles one moment and disappoints the next.
They explore how user expectations—shaped by decades of intuitive UI patterns and human conversations—often collide with the underlying limits of AI systems. From browser agents and automation to long-term feedback loops and trust-building, this conversation is a candid look at what today’s AI can actually do (and where it’s still bluffing). If you’re building with AI or trying to scope what’s possible, this one will recalibrate your expectations—in a good way.
Resources from this episode:

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