Antoine Buteau

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Lessons from Chris Olah

Chris Olah is an AI researcher and Anthropic co-founder who popularized mechanistic interpretability. By isolating neural circuits instead of accepting black boxes, he maps how models form concepts and advances the effort to decode large language models.

Lessons from Peter Gilgan

Peter Gilgan founded Mattamy Homes, a major privately owned homebuilder, and rethought suburban design. His work connects market cycles, modular housing, and philanthropy to a question: how can capital drive systemic change across healthcare, education, and community infrastructure?

Lessons from Ling Tang

Ling Tang, a sociologist, University of Melbourne lecturer, and author of Burnout Market Feminism, studies how elite Chinese businesswomen navigate authoritarianism and hyper-competitive capitalism, with chronic exhaustion often marking the cost of modern economic success.

Lessons from Sherry Brydson

Sherry Brydson is Woodbridge’s largest shareholder and directs Westerkirk Capital toward aviation, hospitality, and local media while remaining intensely private. Her stewardship contrasts substantial family wealth with feminist activist roots and efforts to repatriate sacred Indigenous art.

Lessons from Changpeng Zhao

Changpeng Zhao, known as CZ, founded Binance and scaled it through speed and a flat organization. His path traces the tension between mainstream crypto liquidity, federal compliance scrutiny, and a later focus on free global education.

Daily Digest - 2026-06-10

As AI capability accelerates, institutions and companies must replace passive experimentation with explicit safeguards, outcome-based economics, and operating models built around accountable deployment rather than raw intelligence alone.

Daily Digest - 2026-06-09

More compute and longer horizons are turning models into autonomous workers, shifting the constraint from instant intelligence to careful management of goals, loops, trust, and evaluation under real operating conditions.

Daily Digest - 2026-06-08

Production agents need more than capable models: layered infrastructure, explicit boundaries, compact context, and engineered feedback loops let teams scale autonomous work without allowing complexity to outrun reliability or control.

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