Tech

The Foundations of Scalable AI

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 22/10/2026, 19:20:23 UTC.

Briefings

The evidence describes a scalable AI system requiring convergence of data, engineering, platforms, operating models, and governance to function effectively. It references a session at the MIT Technology Review EmTech Future conference focusing on building this foundation in a regulated environment. The speaker is Jing Wang, Principal at Vanguard, who leads the Center for AI and Insights. She holds licenses in Series 7, 24, and 65 and joined Vanguard in 2009 after a career at Boston Consulting Group. The session aims to demonstrate how responsible implementation can create value for investors.

Key points

• A scalable AI system requires the convergence of data, engineering, platforms, operating models, and governance to function effectively.
• Jing Wang, Principal at Vanguard, leads the Center for AI and Insights and holds Series 7, 24, and 65 licenses.

Context

This article discusses the foundational requirements for building scalable AI systems, specifically addressing challenges in a highly regulated environment. It highlights the role of responsible, trustworthy implementation in turning emerging capabilities like agentic systems into lasting value for investors.

Limitations

The evidence is limited to a single session from the MIT Technology Review EmTech Future conference. It does not provide specific dates for the event, the number of attendees, or the specific outcomes of the session. The evidence does not contain information about the broader market impact of the discussed topics or the specific regulatory frameworks applied.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 22/10/2026, 19:27:27 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

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