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For thirty-six years, Richard Lugar proved that pragmatism and decency work—not only in Washington but all over the world. Michelle and I send our warmest sympathies to his family and all those who had the privilege of knowing this American statesman.

A framework to understand how value accrues across the AI stack. This is a blueprint for understanding what builds AI into its pragmatic parts: what each layer is, where it ends, and where value is accrued. So here’s how you can think about it: 1. Layer 1 - Infrastructure Before any AI model trains or any robot moves, an industrial foundation must exist. Land, energy grids, cooling systems, critical minerals, and fabrication facilities. Infrastructure is the constraint that all the other layers depend on. 2. Layer 2 - Chips Transistors that are etched onto silicon wafers using extreme ultraviolet light. This is what allows both physical and digital AI to take an input, process it, and return a predictive output. The more transistors that fit on a chip, the more computation it can perform. 3. Layer 3 - Data Both digital and physical models train on data. Digital models train on text, code, and images; physical models train on gravity, friction, depth, and sensor streams. The more accurate the data, the more accurate the output. 4. Layer 4 - Models A model is a system that learns from examples. Feed it enough examples of inputs paired with correct outputs, and it adjusts its internal structure until it can predict correct outputs on inputs it has never seen before. LLMs represent a specific class trained on text. They learn by processing billions of examples of human language, developing the ability to write, reason, summarize, and generate code. 5. Layer 5 - Execution This is what lets models take actions on behalf of users. The execution layer lets models pursue objectives through sequential action: observing the environment, reasoning about the next step, acting, and looping until the goal is reached. 6. Layer 6 - Application All of the AI Stack’s revenue originates at the application layer, then goes to the layers below. Every dollar paid for AI is paid for an outcome, a task completed, and an answer delivered. Nobody wants H100s for their own sake. They want H100s because someone, somewhere, wants to run an application. These are the different layers that make up the entire ecosystem of AI. We did a full study on the AI stack. If you want to read about it, head over to my Substack (

Our GC @xethalis sat down with @ChairmanSelig at @TheBitcoinConf. The Chairman is a pragmatic and principled leader. I look forward to working as a member of his Innovation Advisory Council and seeing what he accomplishes to move the industry forward.

As president, I saw Chancellor Merkel lead through crises with her wise pragmatism, good humor, and unrelenting moral compass—and I feel lucky to call her a friend. Grateful for the chance to visit the @NMAAHC today, a reminder that America is a constant work in progress.

At #BNBMiami#, @valgui1, General Manager of @xStocksFi, shared what makes the BNB builder community stand out. Pragmatism is what sets this ecosystem apart and why hackathons here won’t just produce projects, they’ll produce solutions based on real needs that actually get used. "Ultimately, composability unlocks more use cases, and that's where I get really excited."

Had 30+ in-person meetings, lunches, and dinners over the last 4 days across Hong Kong and Singapore. - Conversations with regulators were very positive: highly knowledgeable, pragmatic, and encouraging. - Banks are feeling real pressure. Even institutions that were historically the most anti-crypto are now forced to lean in. - I came away with a much stronger appreciation for the role of regulated exchanges like @HashKeyGroup in the ecosystem. - The food, in-person culture, and respect for relationships are genuinely refreshing :)