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In Episode 162, I connect two ideas leaders keep discussing separately, even though they are converging quickly: what happens when AI collapses the cost of building and experimenting, and what happens when enterprise AI shifts from impressive demos to governed deployment at scale.
I start in education with AI-native student entrepreneurship and applied innovation. Not as an elective or a marketing play, but as a strategic redesign of the school experience that turns students into builders through rapid prototyping, real feedback loops, and tangible outcomes.
Then I shift to the enterprise, using the Snowflake–Anthropic partnership expansion as a signal for where agentic AI is heading: into real platforms, real workflows, and real production constraints where governance, trust, and accountability matter.
I close with plain-language takeaways and one practical question you can bring into your organization immediately.
By Michael Ianni-PalarchioIn Episode 162, I connect two ideas leaders keep discussing separately, even though they are converging quickly: what happens when AI collapses the cost of building and experimenting, and what happens when enterprise AI shifts from impressive demos to governed deployment at scale.
I start in education with AI-native student entrepreneurship and applied innovation. Not as an elective or a marketing play, but as a strategic redesign of the school experience that turns students into builders through rapid prototyping, real feedback loops, and tangible outcomes.
Then I shift to the enterprise, using the Snowflake–Anthropic partnership expansion as a signal for where agentic AI is heading: into real platforms, real workflows, and real production constraints where governance, trust, and accountability matter.
I close with plain-language takeaways and one practical question you can bring into your organization immediately.