Thank you @FT for citing four of @silicondata token indices. The falling unit costs can actually lead to more total consumption — especially as AI agents move from answering individual prompts to running long, compute-intensive workflows. It's exciting to see these datasets increasingly become part of the conversation around how the AI economy actually works.

🚨With CME launching futures on our indices this October, we’ve seen tremendous interest from market participants looking to use Silicon Data’s H100 and B200 indices for block trades, swaps, and other OTC transactions. To help accelerate adoption, we’re opening up our index APIs and offering complimentary licensing for firms using the indices as reference prices for OTC trading. If you’re trading or structuring compute products and want access, just ping us. We’ll provide the data access and a straightforward licensing agreement. Excited to support a deeper, more liquid compute trading ecosystem. Licensee may use Silicon Data’s H100 and B200 Indices solely as reference prices for bilateral OTC transactions, including block trades, swaps, forwards, pricing, settlement, and transaction-specific marks. The Indices may not be redistributed, resold, sublicensed, published, or used for broader risk management, portfolio valuation, benchmarking, regulatory reporting, or creation of other financial or data products without Silicon Data’s prior written consent. If interested, please reach out to contact@silicondata.com

. @Silicon_Data and @computeexchange were both built after the ChatGPT moment. But I still wouldn’t call either company truly AI-native—yet. Being founded in the AI era doesn’t automatically make an organization AI-native. Giving every employee access to ChatGPT certainly doesn’t. I’ve been thinking about the organizational structures of both companies, and the exercise has made me realize that AI-native organizations will not all look the same. @Silicon_Data is organized around building the independent reference layer for the compute economy: data infrastructure, indices, benchmarking, research, product commercialization and market adoption. @computeexchange is organized around creating liquidity: sourcing, verification, pricing, matching, contracting and settlement. Agents can transform both companies—but differently. At @Silicon_Data, agents can accelerate data analysis, research, product development, content production and customer intelligence. At @computeexchange, they can automate inventory normalization, provider onboarding, RFQs, matching and transaction workflows. This has also changed how I think about organizational design. Traditional companies are built around people, roles and reporting lines. Knowledge is distributed across individual brains, inboxes, documents, Slack channels and meetings. In that sense, a human organization is web-based: every person is a node, and work moves through the relationships connecting those nodes. An agent organization may be fundamentally different. It is Brain-based. Instead of every agent holding a fragmented version of the company, agents can operate from a centralized institutional Brain containing shared knowledge, history, decisions, priorities, permissions and real-time operating context. Each Brain sits a task-ownership system. Instead of asking, “Whose job is this?” the organization asks: What needs to be accomplished? What context and authority does it require? Should a human, an agent or a human-agent team own it? What constitutes completion? Who remains accountable? Humans continue to operate through networks of relationships, judgment, negotiation and trust. Agents operate through centralized knowledge, shared context and structured task ownership. The task layer tells you what needs to happen, who—or what—owns it, and whether it has actually been completed. To me, becoming AI-native means continuously redesigning this boundary between people, agents, knowledge and work. We are still experimenting. I’ll share what works, what fails, and how the two organizations evolve.

Another major citation for @Silicon_Data 📈 @Reuters is using our LLM Token Expenditure Index to examine one of AI’s biggest questions: Can AI labs ever turn a profit? Our index is increasingly becoming an industry metric for the economics of AI. And we’re going deeper: we’ve now launched Open LLM + Closed LLM Token sub-indices to show what’s driving the headline number

One of my favorite AI market paradoxes right now: 📈 Silicon Data GPU indices = bullish signal 📉 Silicon Data Token indices = bearish signal But they’re not contradictory. Compute is getting more valuable while intelligence is getting cheaper. Scarce infrastructure + rapidly improving model efficiency + fierce competition = higher GPU prices, lower token prices. The picks and shovels are appreciating while the output gets commoditized. Welcome to AI economics.