Policy For a Rapidly-evolving age
The rapid evolution of artificial intelligence (AI) has surpassed traditional benchmarks such as Moore's Law, which predicts a doubling of price-performance every 18-24 months. This unprecedented acceleration is driven primarily by innovations in AI model architectures and training methodologies, leading to more efficient and powerful systems.
Capability density measures the ratio between a model's effective parameter size—the minimum number of parameters needed to achieve a given performance level—and its actual parameter count. An empirical trend dubbed the "Densing Law" reveals that the maximum capability density of LLMs doubles roughly every 3.3 months. This exponential growth means models rapidly become more efficient, achieving similar or superior performance using fewer parameters and significantly reduced costs. If this trajectory continues, we could see an improvement of approximately one million times in AI price-performance by 2030.
This rapid advancement is partially due to the adoption of Mixture of Experts (MoE) architectures. MoE models incorporate multiple specialized expert sub-models, selectively activating only those needed based on the specific input during inference. This sparsely-gated mechanism enables models to scale to trillions of parameters without proportionally increasing computational requirements, drastically enhancing efficiency.
DeepSeek, an emerging Chinese AI lab originally spun out of a quantitative trading group, has disrupted the AI landscape by releasing its V3 family of LLMs and subsequently introducing DeepSeek R1. The R1 model, characterized as a "reasoning-first" or "reasoning-heavy" model, matches or nearly matches OpenAI’s advanced o1 model on various coding, math, and logic benchmarks. Remarkably, DeepSeek achieved this at a fraction of the cost—reportedly under $6 million—dramatically undermining the assumption that developing a GPT-4-level model requires tens or even hundreds of millions in computing resources.
Perhaps more notably, DeepSeek R1 is open source, making both its architecture and weights publicly available. Its design leverages a large-scale Mixture-of-Experts configuration with over 600 billion total parameters, though only a subset is activated at any given time. Additionally, the model employs a specialized "simulated reasoning" training approach, including iterative reinforcement learning cycles focused on tasks such as math, coding, and puzzle-solving. This process reinforces careful "chain-of-thought" reasoning, significantly enhancing the model's logical consistency and reliability.
The rapid proliferation of R1 triggered considerable international attention and debate. Critics, notably Microsoft and OpenAI, have alleged that DeepSeek’s "student-teacher distillation" approach could amount to unlawfully leveraging proprietary knowledge from models like GPT-4, potentially infringing upon intellectual property protections. However, considering OpenAI’s own practices of extensively mining online data without explicit permission, the ethical strength of this argument appears debatable.
These intellectual property disputes are entangled with broader geopolitical concerns. Analysts suggest the Chinese government might strategically leverage open-source AI advancements like R1 to challenge or dilute American leadership in AI technology, significantly lowering the barriers to entry for building powerful AI systems globally. Additionally, R1's uncensored capabilities, such as openly answering questions about politically sensitive topics like Tiananmen Square, have intensified discussions around censorship and free speech. Consequently, there is speculation about potential actions from U.S. agencies, including partial bans or blacklisting, particularly if they view the model as violating U.S. intellectual property rights or circumventing export control restrictions.
Meanwhile, OpenAI's new "Operator" system illustrates a parallel—and equally transformative—development. Operator equips ChatGPT-like agents with comprehensive browser automation capabilities, enabling autonomous online interactions, from navigating e-commerce platforms to filling out complex forms. This functionality significantly extends beyond traditional question-answer interactions, potentially streamlining business workflows and creating new digital marketplaces.
OpenAI’s Operator system isn't the only browser-automation tool available, but it represents a meaningful advancement in practical applicability. Early use cases range from bill payments and travel arrangements to quality assurance testing for local software environments. The open-source community is rapidly following suit, developing advanced browser agents compatible with various GPT-like models, with specialized applications emerging in healthcare, finance, and government services. Despite initial inefficiencies, the trajectory is clear: these systems will quickly mature, transforming workflows and expanding AI-driven commerce.
These dual advancements—low-cost, open-source AI and integrated digital agents—demonstrate AI’s swift transition from theoretical research to practical, real-world automation. Reflecting Jevons Paradox, increased AI efficiency stimulates greater demand rather than reducing it. Lower per-token or inference costs paradoxically drive increased use, leading to greater investment in GPUs, data centers, and energy infrastructure.
This dynamic is exemplified by the U.S. "Stargate" program—a $500 billion initiative backed by figures like President Trump, SoftBank, and OpenAI. Stargate focuses on building large-scale GPU clusters, specialized hardware, and next-generation nuclear-powered data centers. Microsoft has already recommissioned nuclear plants like Three Mile Island for powering data centers, while Amazon and Google explore modular reactor solutions. UK analysis highlights the critical role of nuclear energy to sustainably power AI infrastructure, recognizing solar and wind limitations.
Europe faces unique regulatory challenges, with Brexit complicating AI governance. Particularly in the UK, different regulatory frameworks coexist, placing significant burdens on technology firms. In response, the UK government has significantly expanded its sovereign AI computing capacity, including plans for specialized "Compute Zones" and streamlined approval processes for data centers and nuclear plants. Additionally, proposed "rights reservation" frameworks aim to balance copyright protection with AI innovation.
For investors and industry leaders, these developments underscore a robust growth trajectory rather than a race to the bottom. Open-source breakthroughs complement rather than replace proprietary solutions, enabling wider experimentation and specialized applications across diverse sectors. These advances promise increased capital investment, driving growth in hardware, software, and energy infrastructure.
In general, the trend toward faster, cheaper, and broader AI deployment remains strong, supported by intensifying investment, geopolitical competition, and technological innovation. Far from indicating saturation or contraction, these developments point toward sustained, robust growth in AI markets. They are strong evidence that the AI sector will keep expanding, pulling in capital expenditures on infrastructure, advanced chips, safer data-center designs, and new commercial applications. The net effect is a more democratized, yet more energy- and capital-intensive, AI ecosystem—one that savvy markets can embrace.