I Was Right About AI

The Rise of Local AI Hardware
🚀 Major hardware manufacturers, including Apple, Google, HP, Microsoft, and NVIDIA, are shifting focus to develop computers optimized for local Large Language Model (LLM) deployment.
💻 New reference platforms, such as the AMD Ryzen AI Halo, are capable of running models with up to 200 billion parameters entirely offline.
⚙️ Advanced systems are being designed to handle 1 petaflop of AI compute, enabling complex tasks like 3D rendering and multimodal workflows without requiring an internet connection or cloud API keys.

Storage Crisis and Data Center Demand
💾 Solid-state drive (SSD) prices have surged by 1,000% since October, with 4TB drives reaching $2,000 and 8TB models hitting $4,000 due to massive pre-emptive bulk buying by data centers.
📈 Components like RAM and SD cards are becoming increasingly scarce and expensive, as data center demand is outpacing global manufacturing capacity.
🏢 The proliferation of massive data centers, some equivalent to the size of 60 football fields, is driven by the need to train large-scale models and store vast amounts of surveillance data, such as facial recognition and license plate information.

Public Sentiment and Cultural Resistance
🚫 A significant anti-AI movement is emerging among younger generations and college students who express strong resentment toward AI-generated content, including AI music and "AI slop."
🗣️ While industry leaders, such as former Google CEO Eric Schmidt, frame AI as the "next industrial revolution," there is growing public pushback against the normalization of AI and constant automated surveillance.
📉 The trend suggests a future where consumers may prioritize private, locally-run AI tools to maintain autonomy, potentially undermining the long-term necessity of centralized, massive cloud-based data centers.

Key Points & Insights
➡️ Decentralization is the next phase: Just as the music industry moved from expensive professional studios to home-based computer recording, AI usage is migrating from massive cloud data centers to high-performance local hardware.
➡️ Local LLMs offer privacy: Running models locally on your own hardware, such as via MLX, allows users to perform 90% of typical AI tasks without external connectivity or data exposure.
➡️ Monitor hardware trends: The extreme inflation in storage costs is a direct indicator of corporate infrastructure expansion; if you rely on high-capacity storage for professional work, prepare for ongoing supply chain volatility.

📸 Video summarized with SummaryTube.com on Jun 19, 2026, 09:44 UTC