Don’t let your laptop tell you you’re doing too much. With #IntelCoreUltra# Series 3 you can create and edit and record and upload and stream and game, all in one thin, light laptop. #AICompute# #Processor# #CPU#

PAN is back with market notes. AI compute is becoming more than a resource. It is becoming a market. As inference demand grows, compute will need clearer ways to be measured, priced, verified, and settled. For Season 1 participants, participation records have been preserved internally. We’ll continue sharing notes on AI compute markets, inference, metering, settlement, and on-chain receipts. #OnChainAI# #ComputeMarketplace# #DePIN# #AICompute#

AI Bubble Burst? Bitcoin to Zero? Hedge Funds Wrecked? Sounds Like a Bottom is Near In this week's video: On many measures this is one of the largest hedge fund equity deleveraging events in the last 30 years without economic deleveraging. For equities parallels to the 2007 Quant Quake are striking. AI positions built over 18 months are being unwound in 75 days. This is concentration rather than factors. But here's what matters: Corporate profits are still growing ~20% YoY. AI compute demand continues to exceed supply. The S&P 500 has barely moved despite the liquidation. Contagion has spread into credit, multistrats, and European bonds. This is hedge fund deleveraging, NOT economic deleveraging. And I believe based on positioning we're in the final innings. I also cover why AI is accelerating breakthroughs in healthcare, why cybersecurity could drive tokenization, and why the convergence of AI and crypto is just beginning. The world is changing faster than traditional leverage and risk management can handle. The real bubble may have been in the assumption that investors still had time.

AI compute infrastructure stocks ($CRW, $NBIS) have taken a beating of late. But compute demand continues to grow. This is an opportunity to add exposure to the AI compute layer. Read more on The Stack:

AI CPUs: An $11.5B Wafer-Revenue Opportunity Morgan Stanley's 2027 model puts two CPU platforms at $11.52B in potential foundry wafer revenue: • $NVDA Vera: 5.75M CPUs → $6.23B • $AMD Venice: 4.41M CPUs → $5.29B Together, that's roughly 21% of MS's $55.23B modeled AI-compute wafer revenue pool. The read-through for $TSM: CPUs could become another engine of advanced-node manufacturing demand as AI infrastructure scales. These are MS estimates for wafer revenue, not NVIDIA or AMD CPU product sales or company guidance. Platform adoption, production volumes and wafer pricing will determine how much of the opportunity materializes. CPU Supercycle Introduction on Substack:

AI compute is being tokenized today on Arbitrum to unlock the credit markets needed to finance one of the world’s fastest-growing productive assets For @0xZergs, the programmable economy creates an opportunity to build entirely new financial infrastructure for AI Through @USDai_Official, the team is turning GPUs into collateral, giving AI infrastructure companies a new path to access flexible capital This is what the future of finance holds

AI at the edge isn’t necessarily a threat to centralized data centers — it’s a shift toward hybrid computing. Routine, low-latency and privacy-sensitive workloads can move to phones, PCs, vehicles and enterprise devices, reducing some cloud inference and bandwidth demand. But frontier-model training, complex reasoning, agentic workloads, massive context and advanced inference still require centralized compute. The bigger picture: AI compute is being distributed, not eliminated. Edge AI can actually act as a pressure valve for power- and grid-constrained data centers, allowing scarce centralized capacity to focus on increasingly demanding workloads. Cloud + Edge > Cloud vs. Edge. I don’t view edge AI as an immediate existential threat to hyperscale data-center demand. I view it as an evolution of the architecture. The cloud will increasingly handle the workloads that require enormous amounts of compute. The edge will handle workloads where latency, privacy, bandwidth and cost make local processing more efficient. And the boundary between the two will continue to move. The long-term AI infrastructure story therefore may not be about cloud versus edge. It may be about cloud + edge + increasingly intelligent distribution of compute. The winners may ultimately be the companies enabling that entire transition — from silicon and memory to networking, data centers, power and the intelligent devices sitting at the edge

AI scales with energy.....and Tesla is building the battery infrastructure to support that scale Megapacks are becoming the foundation for energy abundance Tesla is now building them at Megafactories in California, Texas and Shanghai, while deployments have expanded across 65+ countries worldwide The scale is already massive: • 77+ GWh of Megapack storage operating globally • 99.3% fleet uptime • Lathrop Megafactory: ~40 GWh/year capacity • Texas Megafactory: designed for ~50 GWh/year of Megapack 3 • Tesla deployed another 13.5 GWh of energy storage in Q2 alone As AI compute, data centers and electrification keep scaling, generating energy is only half the problem You also need to store enormous amounts of it and deliver it exactly when the grid needs it That is where Megapack becomes critical The future is electric