The Missing Public-Policy Ecosystem Behind Expensive AI Tokens

ZRainbow Aug 12, 2026 The Missing Public-Policy Ecosystem Behind Expensive AI Tokens Why the biggest institutional problem is not compute cost alone, but the missing mechanism that passes technological gains on to users. A note to readers: I am a native Chinese speaker, and this is my first time posting to this community. This essay was originally written in Chinese and then translated and adapted for English readers. I welcome corrections, especially where a Chinese institutional term has no exact English equivalent. Terminology note: I use “token,” “token price,” and “AI pricing” somewhat loosely when the argument does not depend on the distinction. A few days ago, I read Orange AI’s essay, AI Is Really Too Expensive. Its complaint about model prices—and about those prices squeezing the room available for applications—quickly resonated with a great many people. That is, quite obviously, a good thing. Since the recent OpenClaw “lobster” craze, more and more people have stopped being satisfied with asking a model a few questions. Programmers want it to build an entire project. Researchers want it to help them publish a few more papers. Office workers want it to get them home earlier. Companies want agents inside customer service, marketing, R&D, and internal operations. Even former multi-level-marketing hustlers and degree-mill elites have formed a sacred alliance, covering themselves in hackathon badges and “child prodigy” titles in preparation for getting rich. The more useful the model becomes, the more steps people want it to take. Every extra step shows up, with admirable clarity, on the token bill. No one seriously denies that AI is already useful. Nearly everyone agrees that the price is absurdly high. Hidden inside that complaint is a consensus: we expect AI to become a necessity of future life, something closer to water, electricity, and gas than to an optional piece of software. And that expectation is not unreasonable. China has been unusually explicit in recognizing data as a factor of production. Tokens—the metered output of AI systems driven by compute and trained on data—inherit some of the economic importance of those underlying inputs. Only four years have passed since 2022, yet AI has already entered ordinary homes and workplaces. Its role will only deepen. The rest of us office drones already bring AI to work; before long, working without it may feel nearly impossible. The parallel with the great global internet expansion is hard to miss, and AI may ultimately reach even further. Once “Internet Plus” became a national slogan, platforms sprang up everywhere, and the world acquired the excellent habit of panicking whenever a phone lost Wi-Fi. That suggests a kind of historical inertia. In the previous generation of general-purpose technology, the Chinese state learned how to organize infrastructure investment, constrain bottleneck operators, restructure tariffs, compensate universal service, and transmit technological cost reductions through the market—helping create a much larger and more dynamic economy. Generative AI is now displaying the classic features of a general-purpose technology. It improves continuously, enters a wide range of industries and production processes, and becomes a common input into research, writing, design, customer service, decision-making, and automation.1 Governments could do something similar again. So far, they have not. That is the biggest institutional reason AI tokens remain expensive. Beyond the Reach of Private Business When should the price of a product become a public question? The answer cannot simply be “when it is expensive.” Luxury goods may be expensive. Frontier products may be expensive. What calls for special institutional treatment is an input that enters nearly every industry, so that its price propagates along entire supply chains. That is why water, electricity, communications, and energy cannot be treated exactly like ordinary consumer goods. A small reduction in their unit cost does not merely sell more of one product. It increases society’s ability to reorganize production, exchange information, and create things that did not exist before. Across different countries, such inputs are therefore often supplied directly by the state or local public enterprises, or by private firms operating under strict franchises, price regulation, and universal-service obligations. Not every part of water, electricity, or gas supply is a natural monopoly. The clearest natural-monopoly characteristics usually lie in the network itself: transmission grids, distribution grids, water mains, and gas pipelines. These require immense upfront investment; duplicating them is expensive; and a single operator may be more efficient. Yet the same conditions leave users with few alternatives, making it easy for an operator to convert economies of scale into monopoly rent. Whether publicly or privately owned, these bottleneck layers are therefore rarely allowed to price themselves like ordinary goods. They face cost review, price caps, fair-access rules, continuity requirements, and universal-supply obligations. The point of state intervention is not merely to make one commodity a little cheaper. It is to stop the controller of a foundational network from capturing all the gains from technological progress, and to keep lower infrastructure costs flowing onward to factories, shops, households, and industries that have not yet been invented.2 This does not mean that the entire AI industry is a natural monopoly, or that every model should be government-priced. The question is narrower: are compute, foundation models, and data developing bottleneck characteristics of the same kind—and why has the state not yet built an equivalent mechanism for passing cost reductions through them? Once we separate the three central inputs, the answer becomes clearer. Compute is machine time that genuinely consumes electricity and scarce chips. A foundation model absorbs very large fixed costs in training, research, and deployment; later calls still consume inference resources, but the model does not have to be retrained for every new user. Data is stranger still: one party’s use generally does not prevent another party from using it. A token is the receipt issued to the user after these three inputs have been organized into a service. The production-input character of tokens does not arise magically from the word token. It comes from the scarcity, organization, and distribution of compute, models, and data, all of which are ultimately expressed through token-based metering and settlement. A shortage of chips, a contractual lock-in, another margin, or a monopoly at any upstream layer eventually becomes another line on a developer’s bill. AI is not yet like municipal water, with one pipe that the whole industry cannot avoid. But it has already grown sluice gates at several critical points. Advanced chips are concentrated among very few suppliers. Hyperscale clouds command enormous amounts of compute and enterprise demand. Leading model companies must continually purchase cloud capacity. Important datasets may be difficult or impossible to substitute. These layers can exhibit high fixed costs, economies of scale, switching costs, and vertical integration. The U.S. Federal Trade Commission’s study of major cloud providers and foundation-model companies found that their partnerships may affect other firms’ access to key inputs such as compute, while technical constraints, long-term cloud-spending commitments, and contractual arrangements can raise the cost of switching cloud providers or using multiple clouds.3 China’s recently issued antitrust guidelines for public utilities offer a useful way to think about the problem. They distinguish natural-monopoly layers from competitive layers and seek to prevent operators from extending control over a physical network or essential facility into adjacent markets through refusal to deal, exclusive dealing, tying, or discriminatory treatment. The guidelines cover water, electricity, gas, heat, and public transport—not AI. But the structural principle travels well: control the bottleneck, open access, preserve competition. Applied to AI, a company should not be allowed to control cloud capacity, chips, or a key model gateway and thereby decide who gets to survive downstream and who must buy at a prohibitive price.4 This is a problem socialist market economies have faced for a long time: infrastructure may be commercially operated, and competitive products may be market-priced, but bottlenecks that determine the productive capacity of society cannot be allocated solely according to the private profit-maximization of whoever happens to control them. And state intervention can, in fact, reduce the price of foundational services. That is not a political wish. History has demonstrated it repeatedly. From Installation Fees to “Faster Speeds, Lower Fees” The history of Chinese telecommunications pricing is an unusually good case of state intervention. The first thing it teaches, however, is that government does not naturally mean low prices. Older Chinese readers will remember the telephone installation fee and the mobile access fee. These were government funds established to overcome inadequate communications infrastructure and a shortage of construction capital. An OECD review found that installation-fee revenue equaled 35.7 percent of total industry investment during the Sixth Five-Year Plan, 30.3 percent during the Seventh, and 39.4 percent during the Eighth; it still accounted for roughly 30 percent during the Ninth. At a time when public finance and corporate financing were limited and telephone lines were acutely scarce, making new users prepay part of network construction genuinely helped form the capital base of China’s telecommunications industry.5 The historical role of that charge resembles the early stage of today’s AI industry. It stood like a high dam: expensive, exclusionary, but also supporting the initial accumulation of infrastructure. Fortunately, the construction-era charge was not allowed to live forever. As the network expanded, users multiplied, and equipment costs declined, the installation fee changed from a construction instrument into an entry barrier. In 2001, the Ministry of Finance and the former Ministry of Information Industry abolished local-telephone installation fees, mobile-network access fees, and related government levies attached to basic telecommunications charges. The official account was explicit: those fees had once promoted communications construction, but once capacity broadly met social demand, abolition was needed to reduce the burden on society and normalize the distribution of government revenue.6 The rise and fall of the installation fee establish an important principle: high prices may be justified during an early period of technical scarcity, but when that scarcity recedes, public intervention can and should bring prices down. A tollbooth erected to finance a road must eventually be removed after the road has been built. Otherwise, a mechanism of capital formation mutates into a mechanism of permanent rent extraction. The reform around 2001 was not a uniform percentage cut across every telecommunications charge. It was a tariff rebalancing. Some local fixed-line charges increased; long-distance calls, international calls, leased lines, and internet access fell sharply. Some international, leased-line, and internet-access prices declined by roughly half. Policy deliberately lowered the costs most likely to obstruct information flows, business connectivity, and the entry of new operators.5 The OECD’s judgment was direct. Because the government still intervened in the pricing of almost all basic telecommunications services, the price reductions following the abolition of installation and access fees came p [truncated for AI cost control]