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Ben and Andrew record in person in Madison before a Brewers game and set up a discussion of Big Tech earnings.
Microsoft's stock surged after earnings, but the company has abandoned the AI frontier.
Ben lays out a framework comparing Amazon, Microsoft, Google, and Meta on cloud, frontier position, and AI threat.
The company was up 15 and added 450 billion in value in a single day last week, biggest single-day jump in market history according to Bloomberg.
🤯— The sheer scale of a single-day market value gain reveals how dramatically the market rewards Microsoft's middleware pivot.
What we're good at is being big. And actually being big is useful because you're kind of good at everything. But you're not really great at anything.
💡— This overturns the assumption that companies must be best-in-class at something to win, reframing bigness itself as the competitive advantage.
Microsoft's middleware strategy mirrors IBM's 1990s playbook: being big and good at everything beats being best at one thing.
Map your company's position on the integration-versus-modularization curve and identify whether your current advantage is durable or about to be commoditized.
The threat gradient runs from physical (safest) to digital (most threatened), with Amazon safest and Microsoft most exposed.
Assess your business model's physical-versus-digital ratio and write down one concrete way AI could disintermediate your core offering within three years.
Using frontier model harnesses gives away metadata about how your company actually works, training your own disruption.
Audit which AI tools your team uses this week and identify what workflow metadata is being shared with model providers.
As models improve, they internalize harness functionality, making over-engineered harnesses wasted effort.
Test the latest model version on a task you previously solved with a custom harness and measure whether the harness still adds value.
Транскрипция и инсайты создаются автоматически и могут содержать ошибки. Точность зависит от качества звука и чёткости речи дикторов — если что-то выглядит неверно, исходная запись всегда остаётся главным источником.
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