AI’s Price Revolution

The same capability, a fraction of the cost. What that unlocks — and what it destabilises.

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 to 24 months. The acceleration is driven primarily by innovations in AI model architectures and training methodologies, producing more efficient and more 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.

The million-fold figure is not a separate prediction; it is what a 3.3-month doubling arrives at. Moore’s Law, doubling price-performance every 18 to 24 months, manages roughly one decade over six years. Maximum capability density doubling roughly every 3.3 months crosses all six, which is why, if this trajectory continues, we could see an improvement of approximately one million times in AI price-performance by 2030. Both rates are the essay’s own, and both are stated approximately: read the crossing as around 2030, not as a date.

This rapid advancement is partially due to the adoption of Mixture of Experts (MoE) architectures. MoE models incorporate multiple specialised 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, characterised as a “reasoning-first” or “reasoning-heavy” model, matches or nearly matches OpenAI’s advanced o1 model on various coding, math, and logic benchmarks. DeepSeek achieved this at a fraction of the cost (reportedly under $6 million), puncturing the assumption that a GPT-4-level model requires tens or hundreds of millions in compute.

More notably still, DeepSeek R1 is open source, with both 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 specialised “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.

R1’s rapid spread triggered 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. Given OpenAI’s own practice of mining online data without explicit permission, the ethical force of that argument is debatable.

These IP disputes are entangled with broader geopolitical concerns. The Chinese government may leverage open-source AI advances like R1 to challenge American leadership in AI, lowering barriers to entry for building powerful systems globally. Because R1’s weights are openly released, the model can be run locally without the filtering applied by DeepSeek’s hosted service, which deflects on politically sensitive topics such as Tiananmen Square; that gap has intensified discussions around censorship and free speech. 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 controls.

Meanwhile, OpenAI’s “Operator” system illustrates a parallel development. Operator equips ChatGPT-like agents with browser automation: navigating e-commerce platforms, filling out complex forms, acting autonomously online. It extends well beyond traditional question-answer interactions, with the potential to streamline business workflows and create new digital marketplaces.

Operator is not the only browser-automation tool available, but it represents a meaningful advance 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 following suit, developing browser agents compatible with various GPT-like models, with specialised applications emerging in healthcare, finance, and government services. Despite early inefficiencies, the trajectory is clear: these systems will mature quickly, transforming workflows and expanding AI-driven commerce.

Together, low-cost open-source AI and integrated digital agents mark AI’s transition from research curiosity to practical automation. Jevons Paradox applies: increased efficiency stimulates greater demand rather than reducing it. Lower per-token costs drive increased use, which in turn drives greater investment in GPUs, data centres, and energy infrastructure.

The U.S. “Stargate” program exemplifies the dynamic: a $500 billion initiative backed by President Trump alongside SoftBank and OpenAI, focused on large-scale GPU clusters, specialised hardware, and next-generation nuclear-powered data centres. Microsoft has already recommissioned nuclear plants like Three Mile Island for powering data centres, while Amazon and Google explore modular reactor solutions. UK analysis highlights the critical role of nuclear energy in sustainably powering AI infrastructure, recognising the limits of solar and wind.

Europe faces distinct regulatory challenges, with Brexit complicating AI governance. In the UK, competing frameworks coexist, placing real burdens on technology firms. In response, the UK government has expanded its sovereign AI computing capacity, including plans for specialised “Compute Zones” and streamlined approval processes for data centres and nuclear plants. Proposed “rights reservation” frameworks aim to balance copyright protection with AI innovation.

For investors and industry leaders, the picture is one of growth rather than a race to the bottom. Open-source breakthroughs complement rather than replace proprietary solutions, enabling wider experimentation and specialised applications across sectors. The net result is increased capital investment, driving growth in hardware, software, and energy infrastructure.

The trend toward faster, cheaper, and broader AI deployment remains strong, supported by intensifying investment, geopolitical competition, and genuine technical progress. These developments point toward sustained growth in AI markets, pulling in capital expenditures on infrastructure, advanced chips, safer data-centre designs, and new commercial applications. The net effect is a more democratised, yet more energy- and capital-intensive, AI ecosystem.