Author: US Stock Investment Network
1. What business logic has the proliferation of AI fundamentally changed?
Past = Ideas are cheap + Execution is very difficult.
Present = Ideas are everywhere and cheap + Execution is extremely easy (via AI).
This means → Only truly high-quality ideas are worthy of dedicating computing power to execute. In plain terms, execution is no longer a moat; the focus of capital and teams must shift to "how to choose the right idea" and "how to sell the output produced by AI."
2. What is the driving force behind enterprises' massive purchase of AI Tokens? What are the consequences if they don't follow suit?
Core driving force = Extreme leverage of efficiency. For example, a person spending a few thousand dollars on Tokens can accomplish in a few weeks what used to take a hundred-person team a year (e.g., chip reverse engineering analysis, nationwide power grid modeling).
Result = Creation of "Phantom GDP," where actual output increases significantly but costs plummet, distorting traditional GDP statistics.
If not following suit → Inevitable dimensional reduction attack. If you don't consume more Tokens to create and capture excess value, you will become a "permanent underclass in the AI era," quickly commoditized and eliminated by faster-moving peers.
3. Where exactly is the current supply bottleneck for AI computing power?
On the surface = Nvidia GPUs are in short supply, and the service life of older cards is significantly extended (from 5 years to 7-8 years), pushing up cloud providers' gross margins.
Deep Bottleneck 1 → Memory (DRAM): Capacity expansion is extremely slow, with new absolute supply not available until 2028, meaning memory prices will double or triple again.
Deep Bottleneck 2 → CPU: The scoring mechanisms in reinforcement learning environments and the execution of large amounts of AI-generated code require vast numbers of CPUs, leading to CPUs being completely sold out.
Deep Bottleneck 3 → TSMC and Edge Materials: TSMC's capital expenditure may surge to $100 billion by 2028, and inconspicuous edge supply chains like copper foil and PCB glass fiber are also fully loaded, with the entire industry scrambling for capacity using "sky-high prepayments."
4. What trends are emerging in the competitive landscape of the large model layer and Token economics?
Current status = Anthropic leads temporarily with Opus 4.7 and its internal "Mythos" model, even controlling risk by restricting release cadence, with extremely high profit margins (>72%).
Computing power battle = Anthropic is limited by total computing power, while OpenAI attempts to overtake through aggressive fundraising and amassing computing power (partnering with Microsoft and Oracle, etc.) using scale.
Core Conclusion → Token demand far exceeds the carrying capacity of the infrastructure. Even second-tier and third-tier large model makers will be "sold out" due to insufficient top-tier computing power. Essentially, as long as you can produce high-quality Tokens, the market will absorb them all.
5. Why is it predicted that there will be large-scale anti-AI protests in the short term?
Cause = The massive business restructuring brought by AI will make ordinary people feel fearful. The public tends to blame long-standing social issues on AI.
Catalyst = The poor public communication strategies of AI giants (like Sam Altman and Dario), combined with frequently grand narratives of "AI will change the world/replace jobs," intensify the public's anxiety.
US Stock Investment Network's recommendation → The industry must stop hyping future terrifying capabilities and instead demonstrate AI's positive empowering effects in the present. Otherwise, public anger will soon be weaponized by politicians or influencers, triggering a massive backlash.
Dylan Patel Background:
Chief analyst at research firm SemiAnalysis (Dylan Patel) deeply analyzes GPUs (especially Nvidia)
Analyzes AI computing power supply and demand (who lacks chips, who is hoarding)
Tracks the supply chain (ASML equipment → TSMC → Cloud providers → OpenAI, etc.)
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