Arjun Kharpal
Showing 1–3 of 3 transcripts.
- RAISE Summit18 min
AI's Dropout Problem: Why the Best Agents Never Stop Learning | Decagon | RAISE Summit 2026
Ashwin Sreenivas, Arjun Kharpal, Alan Palleringham, Seth Vargoe
Decagon delivers an outcome-based platform for regulated enterprises that enables large organizations to collaboratively build and safely deploy AI agents across voice, chat, and text channels. The company differentiates itself through a robust deployability layer featuring a testing suite, real-time production monitoring, and a learning loop that automates improvements based on escalated human interactions. By prioritizing infrastructure, compliance, and model cost-efficiency over raw capability, Decagon targets high-stakes sectors like finance and telecommunications to minimize brand risk while driving revenue growth.
- RAISE Summit19 min
The Democratization of Engineering | Sassine Ghazi, Synopsys | RAISE Summit 2026
Synopsys CEO Sasan Ghazi outlines a strategic pivot toward full silicon-to-systems co-design following the ANSYS acquisition, enabling cross-domain physical optimization for complex AI and robotics applications. As the industry shifts from monolithic architectures to heterogeneous designs, the company leverages autonomous L4-level AI agents to accelerate chip workflows while addressing severe wafer capacity constraints projected to impact the market by 2030. This approach prioritizes high-fidelity digital twins to reduce prototyping costs and secure supply chains against the surging demand for physical AI infrastructure.
- RAISE Summit30 min
RAISE Summit 2025: The AI Evolution Open Source, Fast Inference, and the Agentic Revolution
Tony Kim, Thomas Wolf, Rodrigo Liang, Arjun Kharpal
The AI sector is rapidly pivoting from foundational model development to agentic inference and production, driven by a strategic race for full-stack superiority where proprietary models currently retain a six-to-twelve-month performance lead. Enterprises are increasingly adopting open-source alternatives to ensure data privacy and cost efficiency, prompting a hardware shift focused on energy-efficient inference rather than raw training power. While consumer assistants and automated SaaS applications are expected to mature within two years, the industry faces critical bottlenecks regarding gigawatt-scale energy demands and the potential for data security breaches.