Hey friend. It's Friday, December 26, 2025
--- THE INDUSTRIALIZATION OF INTELLIGENCE ---
Hyperscalers are hiding $120 billion in debt to build the next generation of compute.
A small 1-billion parameter model outperforms human clinicians in a hospital setting.
The dominant hardware player is licensing challenger technology to secure the market.
📈 Market Pulse: Market Watch: NVDA
Nvidia is cannibalizing potential rivals early to prevent an inference hardware split. The company is licensing Groq technology to solidify its LPU market lead. Efficiency is the new priority. Expect Nvidia to dominate low-latency inference by late 2026. General-purpose chips are failing. Hardware consolidation is accelerating.
Sentiment: 🟢 Bullish
"I've been allergic to AI for a long time."
🔍 The Deep Dive
Hyperscalers are channeling over $120 billion into Special Purpose Vehicles to fund data center expansion. This keeps massive debt off main balance sheets. It allows for aggressive scaling. Equity markets remain unspooked by the leverage. Infrastructure needs are simply exploding.
Tech giants are betting that future token revenue will outpace hidden interest obligations. It is a high-stakes gamble on demand. Monitor the debt-to-equity ratios of SPV-heavy firms to identify hidden liquidity traps in the AI sector.
The Alpha: This is the 'industrialization' phase of AI. By decoupling debt, tech giants are betting that future token revenue will outpace hidden interest obligations. It is a high-stakes gamble on demand.
⚡ Rapid Fire
🏗️ The Infrastructure War
Nvidia: The company secured a $20B deal for Groq technology while absorbing its leadership.
Tsinghua University: This institution has outpaced all US universities in total AI patent filings since 2009.
AI Power: Rising compute demand drives a resurgence in gas and coal usage to meet energy needs.
OpenAI: Market share dropped 20 percent this year as Gemini and Anthropic gain enterprise traction.
The US Military: National security forces officially integrated Grok into their primary defense arsenal.
🤖 Agentic Evolution
Vercel: Reliability improved significantly after the team removed 80 percent of agent tools.
Mandate: This framework treats AI agents as economic actors with specific runtime authority policies.
Google: The company released a comprehensive handbook to standardize development practices for agents.
Pane: Visual interfaces now allow agents to move beyond simple text chat for collaboration.
FailCore: Execution-time safety runtimes block unsafe agent side-effects to protect private networks.
🧠 Research Corner
This architecture predicts features rather than pixels to improve efficiency for real-time applications.
Use Case: Deploying high-performance vision systems on edge devices with limited power.
Fused Triton kernels shrink models from 331MB down to 25MB while maintaining reasoning.
Use Case: Running sophisticated LLMs on low-cost IoT sensors.
Models spot their own errors by monitoring internal activation patterns instead of using external judges.
Use Case: Building automated quality-assurance loops for high-stakes coding tasks.
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Cheers, Teng Yan. See you tomorrow.