AI didn’t suddenly make data and AI architecture harder.
It made it legible.
In 2026, systems are easier to inspect, reason about, and question than ever before. AI copilots, automated reviews, and architectural analysis tools don’t just help teams move faster—they surface decisions that were previously buried under complexity.
That shift quietly changed what defines a successful career. — Read More
Daily Archives: March 1, 2026
How to Think About the Anthropic-Pentagon Dispute
The Pentagon wants AI that can fight wars — without limits. One of the United States’ leading AI companies says there are lines it won’t cross. And this week, that standoff turned into an all-out confrontation.
To discuss the implications of the dispute between Anthropic and the Pentagon, including the determination that the company represents a supply chain risk, I spoke to two experts:
— Kat Duffy, senior fellow for digital and cyberspace policy at the Council on Foreign Relations, and
— Amos Toh, senior counsel in the Liberty and National Security Program at the Brennan Center for Justice.
— Read More
What The AI Bubble Talk Misses: The Declining Marginal Cost of Additional Use Cases
The AI bubble is often compared to the early days of the railroad or telecom industries to draw parallels between capital expenditures and eventual revenues from those investments. That comparison is misleading, because in railroads and telecom, the expense was incurred to connect things. Every new rail route required steel, labor, land rights, and years of construction. Telecom required trenching fiber across continents. Revenue scaled linearly with physical deployment — every new mile was expensive.
In AI, it’s the opposite. Developing our AI engines is expensive. Connecting things to our AI engines is cheap, and getting cheaper. A new data pipeline. A prompt template. An API integration. An MCP Server. You’re not digging trenches — you’re copying software. This means the capex-to-revenue curve should look fundamentally different from railroads or telecom. Those industries needed decades of physical buildout before revenue caught up. AI needs months. — Read More
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