The Future of AI Agents
Exploring open source profitability and small language models – Aditya Gautam
Aditya Gautam has built his career at the intersection of AI research, large-scale deployment, and public discourse. With experience at Google, Meta, and leading academic conferences, he works on large language models, AI agents, and responsible AI at scale.
In this episode, Aditya explores the debates around open-source AI, the economics behind LLMs, and the barriers enterprises face when adopting AI agents. He also shares his perspective on the rise of small language models and what these shifts mean for the future of AI.
We plan to cover:
Open-source AI: democratization and risks
The economics of LLMs and the challenge of profitability
Why enterprises struggle to adopt AI agents in practice
The role of small language models in efficiency and cost reduction
Emerging trends in AI research and deployment
About the Guest
Aditya Gautam is an AI researcher and engineer whose work spans industrial innovation, academic research, and AI policy. He has held roles at Google and Meta, working on recommendation systems, integrity, and large-scale generative AI deployment. His research covers topics including misinformation, multi-agent systems, and LLM evaluation, and he has published in top-tier conferences such as ICWSM while serving as a peer reviewer for venues like NeurIPS, ICML, and AAAI.
Aditya is also an active voice in the AI community: he speaks at industry events such as the Databricks Data + AI Summit and Analytics Vidhya, contributes to policy discussions around regulations like the EU Digital Services Act, and shares insights on the economics and practical adoption of LLMs and AI agents. He holds a Master’s degree from Carnegie Mellon University.
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