Small Models, Big Impact: Why the Future of AI Isn't Trillion-Parameter

Episode Summary

Most AI conversations start with parameter counts. This one doesn’t.

In this episode, we go inside the origin story of smallest.ai, a company built on the contrarian belief that true intelligence can be achieved with compute-constrained, smaller models — especially when the goal is real-time speech intelligence that can run actual workflows in production.

Sudarshan shares how his background in self-driving vehicles shaped his thinking on reliability, active learning loops, and why 90–95% of the work lives in data and labeling, not model training. We then zoom into real-world enterprise use cases like collections, outbound calls, and multilingual customer support, and talk through how CIOs can actually start with voice AI in a messy legacy stack.

In the second half, we switch gears into his founder journey: using LinkedIn and Discord as core distribution and learning channels, building the largest voice AI community, and his unfiltered advice on cold outreach, selecting whose advice to listen to, and running asset-light experiments before raising large rounds.

If you’re a founder building AI for the enterprise — or an executive trying to separate hype from deployable systems — this episode will give you a grounded way to think about small models, agents, and voice AI.

Key Topics

- Origin story of smallest.ai and the shift from self-driving to speech AI.

- Why “small vs large models” is the wrong framing — and how to think in terms of specialized vs general-purpose agents instead

- Building one of the world’s fastest text-to-speech and speech-to-speech systems

- Emotional information in audio vs traditional speech-to-text → LLM → TTS pipelines

- Handling multilingual, code-switching conversations (Hinglish and Spanish/English) in real-world deployments