I think the market ultimately has no choice but to go sell memory, long optical in the "short term." Actually, some hedge funds already seem to have this position on. There are three main reasons. 1. With Korean leveraged ETFs effectively dead, LPs are in a redemption rush, which could bring out additional sell on flow. 2. Nvidia is nerfing Rubin Ultra's HBM and responding with optics, tying multiple racks together, so that even if Rubin Ultra's per rack performance is not superior to Rubin, at the cluster level optics let the Rubin Ultra cluster hold an edge over the Rubin cluster. This holds even if Rubin Ultra's HBM nerf is a supply problem rather than a demand problem. 3. Consensus is forming that memory prices will peak within the next two quarters. Medium to long term I am still a memory bull, but short term I am somewhat bearish on memory. I currently have no memory position.

🚨 BREAKING: The European Union has just PASSED a measure to surge mass deportations across the region — its officially law of the EU LFG! WAKE UP and take your countries back, don't slow down and keep pushing! 🔥 President Trump has been clear: NO MORE 3rd world mass migration, or Europe will no longer be Europe! 🇪🇺🇺🇸

New reports that $AMD is scrambling for CW laser supply. And is negotiating large-scale purchase orders for CW Lasers to ensure its production capacity is not constrained by $NVDA (Trendforce) Obvious CW laser beneficiaries: - $SIVE (AMD went to GFS for CPO, Sivers reference laser level) - $AAOI (Rosenblatt analyst checks) Lumentum/Coherent are kinda booked out way into 2028 as well. Lumentum is especially constrained for CW capacity already from existing EML contracts (so they probably are buying from Sumitomo/Furukawa and co). Maybe Macom and Japanese giants still have spare capacity. (disclosure, own aaoi/sivers). I predicted this last year and said hyperscalers should go more upstream to secure capacity... at laser levels, epiwafer levels, or even inp substrate levels. To not get bottlenecked by Nvidia.

$AMD could reach $2 Trillion Market Cap FY2027 🧵 W/ $TSM to solve @OpenAI @AnthropicAI Tokenomic Crisis Not Financial Advice! DYOR! “As these agents do work, they spawn more CPU tasks... We certainly see the movement towards where in the past the CPU to GPU ratio was primarily just as a host node in like a 1:4 or 1:8 configuration, now changing and getting closer to a 1-to-1 configuration or even... you can even imagine if you get lots and lots of agents that you could have more CPUs than GPUs.” — Dr. Lisa Su, AMD Chair and CEO Dr. Lisa Su’s observation captures the seismic shift now driving the tokenomic crisis, the transition from GPU-centric training to CPU-intensive, always-on agentic AI. What was once a supporting role for CPUs has become a primary bottleneck, colliding with severe supply constraints and fueling unsustainable inference costs for enterprises. 1. Primary Causes of the Tokenomic Crisis A.Explosive Agentic AI Demand and Token Multiplication Agentic workflows with multi-step reasoning, tool calling, orchestration, retries, and long-horizon autonomy consume 5–100x more tokens per task than traditional prompting. Frontier models like Anthropic’s Fable 5 (premium pricing + verbose outputs) and OpenAI’s advanced variants amplify this effect. Projections show global token demand potentially rising ~24x by 2030, with enterprises reporting budgets exhausted in weeks rather than years. Anthropic Fable 5 (Mythos-class): $10 / $50 per million input/output tokens explicitly ~2x their prior flagship (Opus 4.8 at $5/$25). It's positioned as a premium agentic/reasoning model. This is unsustainable, hence we are seeing companies crying about Tokens bills. OpenAI GPT-5.5 (current flagship): $5 / $30 per million, more affordable on a direct sticker-price basis than Fable 5. They also offer cheaper tiers like GPT-5.4 ($2.50/$15) and mini/nano variants for cost-sensitive work B. CPU Supply Crunch as the Hidden Bottleneck Traditional AI favored GPU-heavy ratios (1 CPU : 4–8 GPUs). Agentic systems reverse this dynamic to 1-5 CPU to 1 GPU,where CPUs manage orchestration, data loading, state management, external calls, and parallel task spawning, often leaving expensive GPUs idle while waiting on the host. The industry is rapidly shifting toward higher CPU ratios (or AMD new Agentic AI Rack). Server CPUs from AMD (EPYC) and Intel are largely sold out for 2026, with lead times stretching weeks to months and prices rising 10–35% in tight markets. TSMC’s heavy focus on AI accelerators has further squeezed CPU wafer capacity, raising overall cluster costs that flow directly into higher token pricing. Luckily, EPYC Venice has arrived and currently in mass production. C. Power, Networking, and Systemic Inefficiencies Poor utilization, high power draw, and legacy networking overhead compound the problem. Hyperscalers and enterprises now demand balanced, power-efficient rack-scale solutions to scale sustainably without exploding operational expenses. Surging consumption meets hardware scarcity → elevated marginal costs → provider pricing pressure and enterprise ROI scrutiny. 2. Why Increased TSMC 2nm Allocation for AMD Will Significantly Ease Token Costs AMD has secured strong early access to TSMC’s 2nm (N2) process and significant allocation, positioning EPYC Venice as one of the first major HPC products to ramp on it, with production already advancing in Taiwan and Arizona. This allocation advantage delivers immediate relief on supply, performance, and efficiency. Bringing token cost at GW Scale to as low as $0.0003-$0.0005/M Tokens. TSMC is ramping up 2nm Fabs at the faster pace than prior 3nm expansion, up to 12 2nm/1.4nm Fabs by 2027/2028 to service $AMD Agentic AI demand. ~Core Density & Performance Up to 256 Zen 6 cores per socket, substantial compute uplifts, and higher memory bandwidth directly alleviating the orchestration bottleneck in agentic workloads. ~2nm GAA transistors provide major improvements in performance-per-watt, critical for power constrained data centers and lower energy-per-token economics. ~Accelerated volume ramp (with Verano as the follow-on) will ease the 2026–2027 crunch, allowing hyperscalers like OpenAI and Meta to deploy more balanced clusters faster than allocation-constrained competitors. 3. The AMD Agentic AI Rack and Helios: Purpose-Built for the Agentic Era AMD is addressing the exact gap Dr. Su highlighted with dense EPYC Venice “Agentic AI Racks”, high-core-count CPU-optimized racks positioned between traditional servers and full GPU racks. These deliver massive orchestration capacity for fleets of agents. ~Optimized CPU:GPU Balance, Designed for 1:1 to 3–5:1 ratios that eliminate idle time and maximize end-to-end throughput. ~Rack-Scale Density, Current EPYC Turin racks already exceed 27,000 cores; Venice pushes beyond 36,000 cores per rack, delivering up to 3.30x rack-level throughput versus NVIDIA Vera baselines in agentic workloads. ~Efficiency Gains, Lower power per token through superior density, liquid cooling compatibility, and system-level optimizations. Projections show potential inference costs dropping to $0.0001–$0.0005 per million tokens (or even lower with Verano). H2 2026–2027: Venice-powered Agentic AI Racks and initial Helios deployments (already secured with major hyperscalers) relieve CPU constraints and lower underlying infrastructure costs, supporting token price relief. Pretty much CPUs are sold out for the next 3-5 years. Conclusion: While much of the industry chased raw training FLOPs and GPU scarcity narratives, Dr. Lisa Su positioned AMD early and decisively around inference nearly 4 years ago, the real long-term driver of token economics. She recognized that agentic AI would fundamentally invert workloads, making efficient, abundant CPUs the key to sustainable scaling rather than GPU-only supremacy. By doubling down on high-core-density EPYC platforms, rack-scale systems like Helios and dedicated Agentic AI racks, and securing leading TSMC 2nm allocation, AMD is not merely reacting to the tokenomic crisis, it is actively solving it. This strategic clarity enables hyperscalers and enterprises to deploy balanced, power-efficient infrastructure that drives down real $/token costs, improves utilization, and restores economic viability to large-scale agentic deployments. As Dr. Su anticipated the problem years in advance, AMD’s execution is now delivering the antidote. The result will be lower token prices, broader AI adoption, and a more balanced ecosystem where CPUs reclaim their central role AKA the brain. In the agentic era, the companies that solve the orchestration and efficiency bottlenecks, not just the matrix multiplications will define the winners. AMD, under Dr. Su’s leadership, is well-placed to be biggest TSMC customer in term of wafer as early as 2028. Not Financial Advice! DYOR!

Kevin Warsh's view is clear: AI will eventually force interest rates lower because it will be highly deflationary. "AI is going to make almost everything cost less. We're at the front end of a productivity boom." The problem is that today's economy is telling a different story. Inflation is at 4.2%, its highest level in three years. Tensions with Iran continue to threaten oil supplies. The labor market remains strong. AI may be deflationary in the long run, but the Fed has to deal with today's inflation first. I don't expect a single rate cut before the end of 2026.

🚨 BREAKING: SOFTBANK CEO MASAYOSHI SON JUST SAID: "I THINK AI IS MORE THAN 10X, PROBABLY 50X BIGGER THAN DOT-COM." HE IS FORBES #1# BILLIONAIRE IN ASIA WHO MADE OVER $100.7 BILLION INVESTING HE DEFINITELY KNOWS SOMETHING!!