The AI Bubble Is About to Burst — Why The Silver PRICE Will Skyrocket!

Key Concepts

• AI Infrastructure: The physical and software layers (GPUs, data centers, LLMs) required to power artificial intelligence.
• Full-Stack AI Adoption: The methodology of utilizing AI tools (like Cursor) to perform tasks from A to Z without manual coding.
• Quant Fund: A quantitative investment fund that uses mathematical models and algorithms to trade financial assets.
• GPU Shortage/Abundance: The fluctuating availability of Graphics Processing Units, which are critical for training AI models.
• Psychological Resistance: The barrier experienced by skilled professionals when transitioning from traditional workflows to AI-integrated workflows.
• AI-Driven Productivity: The ability for a single engineer using AI to perform the work of 5–10 traditional engineers.

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1. Background and Professional Journey

Ahmed Shah Shadid, founder of the O Foundation, transitioned from running a quantitative crypto/stock trading fund to the AI infrastructure space. His entry into AI was driven by the 2022 market volatility and the need for predictive time-series models (XGBoost). He gained significant experience in infrastructure by repurposing GPUs from Ethereum miners during the transition from Proof-of-Work to Proof-of-Stake, which allowed for a 90% cost reduction compared to traditional cloud providers like AWS. He later founded IO.net, scaling it to a $4.5 billion valuation before exiting in June 2024.

2. The AI Bubble and Market Outlook

Shadid argues that an AI bubble exists but has not yet reached its peak. He suggests the bubble will likely persist until major players like OpenAI go public (IPO) at a trillion-dollar valuation. He notes that while energy and nuclear stocks are benefiting from data center demand, the market is currently vulnerable to two types of shocks:

• Nvidia Dependency: The market is heavily reliant on Nvidia; any disruption to their operations could trigger a broad market downturn.
• Workforce Displacement: Companies are being rewarded by the market for aggressive cost-cutting through AI adoption, leading to a structural shift in the labor market.

3. The "Job Crisis" and Workforce Transformation

A central argument presented is that the "job crisis" is not merely about AI replacing humans, but about the psychological resistance of employees to adopt AI tools.

• Methodology: Shadid implemented a strict policy at his firm where engineers were forbidden from writing manual code, requiring them to use AI tools like Cursor for all tasks.
• Efficiency Gains: By mandating full AI integration, he reduced his engineering team from 90 to 7, while maintaining the same output. This reduced monthly payroll from $2 million to approximately $110,000.
• The Human Element: He emphasizes that he is not firing people because AI can do the job, but because he needs employees who will use AI to scale productivity. He notes that only 4–6% of engineers globally are currently utilizing AI from "A to Z."

4. Notable Quotes

• "If you still doing work with your hand, you’re going to be fired." — On the necessity of adopting AI tools in software engineering.
• "The big risk comes actually from the people themselves. It’s not coming because AI is replacing people." — Highlighting that the primary barrier to progress is human resistance to changing established workflows.
• "I don’t need to fire anyone just adopt AI." — Expressing his preference for upskilling over termination, provided the employee is willing to adapt.

5. Impact on Commodities

Regarding the intersection of AI and precious metals (gold/silver), Shadid maintains that AI’s impact on the commodities market is negligible (single-digit percentage). He argues that 95% of the movement in the commodities market is driven by geopolitical risks, specifically China’s gold reserves, currency policies, and tensions in regions like Taiwan and Iran.

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Synthesis and Conclusion

The main takeaway is that the AI revolution is fundamentally changing the definition of productivity. Companies are moving toward a "lean and fast" model where a small, AI-proficient team can outperform large, traditional departments. The primary challenge for the workforce is not the technology itself, but the psychological hurdle of abandoning legacy skills in favor of AI-assisted workflows. While the market may be in a bubble, the structural shift in how work is performed is permanent and will continue to force companies to either adapt or face obsolescence.