In this thought-provoking episode, hosts Leslie D’Monte and Jayanth N speak with Anurag Sahay, VP & Global Business Unit Head – AI and Data Sciences at Nagarro, about why AI hallucinations might not be flaws, but indicators of an evolving intelligence paradigm. They explore how most people—and enterprises—still think in linear, deterministic ways, and why that mindset may no longer serve us in the GenAI era. Anurag explains how startups are often better positioned to take bold creative leaps, while enterprises struggle with embracing stochastic systems. The conversation also dives into why observability, not perfection, is the key to scaling AI—challenging conventional assumptions about accuracy, failure, and the true nature of intelligence.

In this episode, we dive into the hype, the reality, and the shifting ground beneath the world of technology, AI, and business strategy. From the microservices vs. monolith debate to the Codex CLI and GenAI frenzy, we break down the tech buzzwords enterprises love—and sometimes misuse. We unpack the role of developer insecurities, resume-driven engineering, and the fleeting nature of tech trends (remember Pokémon Go and the Oscar selfie?). As India's startup landscape sees an AI-first pitch from nearly every corner, we reflect on what truly defines deep tech, why traditional machine learning is far from obsolete, and how enterprises should resist the urge to jump on every shiny new tool. At the end of the day, the question remains: what business problem are you solving?

In this episode, we unpack the evolving landscape of generative AI — from the delay of GPT-5 and the release of OpenAI’s O3 and O4 Mini models to the explosive rise of Chinese innovations like DeepSeek and Mistral. We dig into why calling GPT-5 the 'holy grail' of AI is a flawed narrative, and how the global AI race is being reshaped by open-weight models, MoE (Mixture of Experts) architectures, and aggressive Chinese strategies.
We also explore the growing buzz around 'white coding' — where non-programmers use AI to generate code — and the infrastructure shocks that follow. Why are API bills skyrocketing? What happens when blindly deployed AI code meets real-world production? From misleading claims to misunderstood models, we break it all down.
Tune in as we question hype, bust myths, and offer a grounded take on where AI is really headed.