Jordan Nanos

Jordan Nanos × Dylan Patel × Jordan Schneider

NVIDIA’s leadership shock has turned alleged chip diversion into an export enforcement crisis, though evidence that Jensen Huang knew remains circumstantial.Patel says U.S.-controlled GPU rentals could preserve a remote cutoff over Chinese compute while earning providers millions per megawatt; Schneider questions whether U.S. frontier labs would gain more value from scarce capacity.The panel supports MATCH restrictions to constrain China’s chipmaking scale, while NVIDIA’s outlook depends on executive retention and Rubin roadmap execution.

Jordan Nanos × Max Kan × Andrew Megalaa

GDPX targets domains Max Kan expects to capture the first $1 trillion of AI revenue, not routine work.Its 14-provider, mostly private benchmark found some models solved materials-science tasks costing $40–$50 for about $10.Finance and legal remain far from reliable autonomy, while Gemini 4 Carbon trails Astra and Opus, with OpenAI and Anthropic flagships expected soon.

Jordan Nanos × Doug O'Laughlin × Jeremie Eliahou Ontiveros × Joey Brookhart

S-1 disclosures of two profitable quarters, margins above 10%, 85% inference gross margins and 41% compute margins challenge the subsidy thesis.Joey cites ninefold existing-customer spending growth; Jeremie says prospective resources above $200 billion frame $500–600 billion of compute commitments.Whether revenue can monetize 2027 capacity remains unresolved, alongside 25% revenue concentration among the top five customers.

Kimbo Chen × Wenyao Gao × Jordan Nanos

GLM-5.3’s sparse attention eases bandwidth pressure, not KV-cache capacity needs, while cheaper serving could lift HBM and bandwidth demand.AMD’s MI355 reportedly matched B200 cost at 150 tokens per second per user within weeks, underscoring software-driven cost volatility.Post-training reportedly lifted agentic benchmarks by more than 10%, though GLM-5.3 is not Mythos-class and technical disclosure remains limited.

Alex Kantrowitz × Dylan Patel × Jordan Nanos

SemiAnalysis estimates next-year US AI infrastructure spending near $2 trillion, or 5–6% of a $30-trillion-plus economy, including capacity for overseas inference demand.Hyperscaler capex grew 116% year over year in Q3 2026, but demand exceeds supply; returns depend on six-year GPU and 15-year data-center lives, while neo-cloud security and internal model use remain risks.

Dan Nishball × Jordan Nanos

SemiAnalysis estimates $11.3 trillion in cumulative AI IT and data-center CapEx for 2024-29, implying roughly $7 trillion of debt by 2029 and $1 trillion of net funding this year.Hyperscaler and NVIDIA backstops, five-year offtakes, and frontier API gross margins near 90% could support financing, but lenders still face residual-value, churn, and execution risks.

Max Kan × Jordan Nanos × Andrew Megalaa

Anthropic’s $200 subscription delivered roughly $12,000 of API-equivalent usage in testing, but workload mix—not headline token counts—determines its economics.At roughly 96% cached reads priced at 0.1x or less of fresh input, model ownership may support 40–70% subscription gross margins, while OpenAI’s 50% limit cut and migration to open models remain risks.

Jordan Nanos × Cam Quilici × Bryan Shan × Alec Ibarra

Engrams co-design model architecture and memory hierarchy, retrieving 2-gram and 3-gram embeddings from DRAM or SSD.Hybrid Engram-MoE layers outperform either endpoint, while AgentX traces show 100,000 input tokens at P50 and cache-hit rates above 98%.DRAM offload matters for hundreds of agents at 40–60 tokens/s; TPU V7's early Pareto position still leaves software, utilization, pricing, and demand unresolved.

Sam Harshe × Pratt Bhatt × Jordan Nanos

ClusterMAX 3.0 reshuffles 77 providers: Nebius joins CoreWeave in Platinum, Google Cloud joins Oracle in Gold, while Azure, AWS, Crusoe, and Together fall.Endpoint X finds 75% versus 99% cache hit rates can double an identical bill, while NVIDIA’s backstop universe forecast above $2T by end-2031 leaves SLA discipline, security, and hosted RL’s unproven market as risks.

Jordan Nanos × Doug O'Laughlin × Max Kan × Joey Brookhart

“Pacing” would slow Anthropic’s capability progress without halting training or compute purchases, potentially weakening its strongest internal model.Near-term scarcity and safety workloads keep compute demand elevated, while semiconductor signals increasingly depend on frontier-lab ARR and capacity premiums.Bank hacks, data leaks, or AI-assisted biological attacks could accelerate regulation.