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AI Progression Cycle in Full Swing

The AI Supercycle symbolizes a revolutionary reconfiguration of the global economy, unlike any previous technological change. Although recent advancements are evident, this transition will develop over a 30-50 year span, akin to how the microchip revolution alteredly impacted various industries...

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Artificial Intelligence Evolutionary Cycle
Artificial Intelligence Evolutionary Cycle

AI Progression Cycle in Full Swing

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The AI Supercycle, currently reshaping the global economy, stands out as a unique and transformative force compared to previous technological shifts. Unlike earlier cycles, the AI Supercycle is not just about scaling AI models with large pre-training datasets and compute power; it's about post-training compute optimization.

This new paradigm emphasizes fine-tuning, specialization, and iterative refinement after the initial training phase, unlocking far greater efficiency, cost-effectiveness, and rapid improvement cycles.

One of the key characteristics of the AI Supercycle is the exponential model improvements, not only in raw performance but also in self-enhancement capacities. This enables faster breakthroughs and benchmarks that surpass competitors within months, as seen with models like Elon Musk’s Grok 4.

The AI Supercycle also converges with other macro trends such as the rise of crypto and massive institutional capital flows. This combination creates a tech-crypto supercycle effect, where AI innovation drives demand for advanced compute hardware and crypto assets.

Investment and value creation in the AI Supercycle are shifting from mere scaling towards the sophisticated application of AI techniques. Supervised fine-tuning and direct preference optimization, for example, reduce reliance on expensive human-labeled data and enable faster AI iteration cycles at lower cost.

The emergence of new financial and computational models is another hallmark of the AI Supercycle. In these models, post-training costs dominate development budgets, as demonstrated by Meta’s Llama 3.1, which spent $50 million on post-training.

The AI Supercycle represents a technical and financial revolution, driven by increasingly specialized, efficient, and rapidly improving AI models, coupled with deep integration into other macroeconomic trends. This is unlike previous cycles primarily defined by scaling hardware or software capabilities alone.

Moreover, the hardware and application layers also show high concentration due to infrastructure barriers and strong network effects, respectively.

In the AI era, capital will be easily available to top players, with stronger winner-take-all effects compared to the web era. This bootstrap-first mentality leads to a winner-take-all scenario on the capital employment side, making it challenging for startups to compete in a venture funding perspective.

However, startups can leverage the inside-out nature of AI to create products with fundamentally superior value propositions, potentially achieving rapid distribution as a side effect.

The unique distribution advantage of AI allows for a more moderate distribution, enabling multiple winners per vertical due to industry-specific needs and specialization opportunities. This requires non-technical professionals to become specialized generalists, combining broad technological understanding with deep vertical expertise in specific industries.

For investors, a barbell strategy might yield the highest returns, with concentrated bets in foundation, hardware, and application layers, and more distributed investments in verticals.

In conclusion, the AI Supercycle is a fundamental transformation of the global economy, expected to mature over 30-50 years. It reshapes career trajectories, allowing technical professionals to accelerate their growth and potentially achieve senior positions in 2-3 years. The 100x value prop snowball effect also means startups can achieve significant distribution due to their innovative products. Understanding and navigating this unique landscape will be crucial for success in the AI era.

  1. The growth of the AI Supercycle is reshaping business landscapes, offering entrepreneurs unprecedented opportunities for startups based on AI models.
  2. In the AI Supercycle, the focus is no longer solely on scaling AI models, but also on optimizing post-training compute for greater efficiency and cost-effectiveness.
  3. The AI Supercycle is marked by exponential model improvements, which enable faster breakthroughs and improvements that surpass competitors within short periods.
  4. The convergence of the AI Supercycle with trends like crypto and institutional capital flows creates a tech-crypto supercycle effect, driving demand for advanced technology.
  5. Investment strategies in the AI Supercycle are shifting towards sophisticated applications of AI techniques, such as supervised fine-tuning and direct preference optimization.
  6. New financial and computational models are emerging in the AI Supercycle, with post-training costs becoming a significant part of development budgets.
  7. The AI Supercycle presents a technical and financial revolution, driven by specialized, efficient, and rapidly improving AI models that integrate into other macroeconomic trends.
  8. The hardware and application layers in the AI Supercycle exhibit high concentration due to infrastructure barriers and strong network effects.
  9. To succeed in the AI era, non-technical professionals must become specialized generalists, combining broad technological understanding with deep vertical expertise in specific industries.

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