Seventy years separate two documents that say more about artificial intelligence than any model it has ever produced. The first is the proposal that convened, for the summer of 1956, the Dartmouth College conference that gave the field its name. The second was published more recently, on July 13, and is just four sentences long. It is the statement “We Must Act Now” organized by Stanford's Digital Economy Lab and signed by more than 200 economists and AI researchers, including 16 Nobel laureates. The first document overflowed with confidence. The second exudes urgency. Between one and the other, the technology traveled a road its creators did not foresee and arrived at a destination its heirs admit they do not understand.

It is worth rereading the original proposal to measure the distance. John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon requested funding to gather 10 researchers for 2 months, proceeding from the conjecture that “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.” They believed a significant advance was possible in that single summer. The concerns they listed were all technical. How to make machines use language, form abstractions, improve themselves. Nowhere does the question appear of what would happen to human labor, income distribution, or economic power if the project succeeded. Not out of negligence. The political economy of machines was simply not part of the problem those men believed they were solving.

The trajectory that followed punished optimism methodically. The field went through its first winter in the 1970s, when promises ran far ahead of the capacity to deliver and funding dried up. It was reborn with expert systems, froze again in the late 1980s, changed in nature with machine learning, accelerated with deep neural networks starting in 2012, and exploded with the transformer architecture, which made the launch of OpenAI’s ChatGPT possible in 2022. In every previous cycle, the mismatch was the same: the technology promised more than it could deliver. The current cycle has reversed the sign. For the first time, installed capability is running ahead of our understanding of its consequences.

The industry is in a hurry

It is precisely this reversal that the Stanford letter registers in its telegraphic wording. The text states that AI may become radically more powerful over the next 10 years and drive an economic transformation “larger than the Industrial Revolution,” but compressed into a drastically shorter time frame. It acknowledges both the risks of large-scale job displacement and the possible increase in living standards and concludes that governments and industry must build, starting now, the incentives, guardrails, and institutions capable of steering the technology to complement human beings rather than replace them. Yoshua Bengio, a Turing Award winner and one of the signatories, added in a separate statement that the choices must be collective and democratic rather than “letting market forces play out and risking leaving most citizens behind.”

The context surrounding the publication explains the hurry. In October 2025, Amazon announced the elimination of about 14,000 jobs, months after its CEO acknowledged that some tasks would be reassigned to generative AI and agents. Recent American graduates face the tightest job market in years, and a study by the same Stanford group identified a 16 percent relative decline in employment among workers aged 22 to 25 in the occupations most exposed to generative AI, while employment among experienced professionals remains stable. The transformation the letter announces is not a research hypothesis. It is already showing up in the employment statistics of the country leading the race.

A few absences

There are, however, three absences in the document that deserve as much scrutiny as its presences. The first is that of those responsible. Among the signatories are executives and co-founders of the very companies pushing the frontier, including Anthropic, OpenAI, and Google. It is an ambiguous gesture. It can be read as the lucidity of those who know the power of what they build, but also as the outsourcing of the problem to “governments and institutions” while the commercial race continues at full speed. The letter calls for guardrails without saying who pays for them, calls for institutions without saying who controls them, and calls for immediate action by everyone without assigning a specific obligation to anyone. The diagnosis is collective; the responsibility, diffuse.

The second absence is geographic. The text speaks of “our economy” in the singular, as if the transformation were to be evenly distributed across the planet. It will not be. Computing infrastructure, frontier models, and the revenues of the new economy are concentrated in a handful of companies from two countries. Job displacement, by contrast, will be global, and it will hit with particular force the economies that export precisely the services AI agents are learning to perform. For the Global South, the risk is not only the technological unemployment the letter describes; it is taking part in the transformation on the cost side alone, supplying data, energy, minerals, and consumer markets, while the promised productivity gains pile up on the balance sheets of those who control the infrastructure. The institutions the letter demands, if they are born only in Washington and its allied forums, will organize the transition for the members of the club and leave everyone else managing the consequences.

Nothing illustrates this imbalance better than the calendar, which brings the third absence. The Stanford letter is signed predominantly by members of institutions in the United States and Europe. While Western experts were publishing it, China was finalizing preparations to open, on July 17 in Shanghai, the new edition of the World Artificial Intelligence Conference, which this year featured an opening address by Chinese President Xi Jinping himself and a high-level meeting on global AI governance with representatives from dozens of countries. The event boasted more than 1,000 exhibitors, 3,000 frontier technologies, and 300 products making their world debut, all part of an industry that surpassed 1 trillion yuan in 2025 and projects growth of more than 30 percent in 2026. At the beginning of the conference, China and 28 other countries created the World AI Cooperation Organization (WAICO) to address the issues related to AI global governance with a Global South vision and a multilateral approach.

At the same time, the United States is building its AI governance the way it builds most things these days—through executive power and corporate blueprints, with no treaty, no charter, and no vote. Over five weeks in June and July 2026, the chief executives of the country's three leading AI companies published what amounts to a governance regime in installments, and the proposals fit together like floors of a single building. Dario Amodei of Anthropic designed the hard domestic layer, modeled on the FAA: mandatory third-party testing of every model above a compute threshold across four risks—cybersecurity, bioweapons, loss of control, and automated R&D—with legal authority to block releases and fines levied on revenue. Demis Hassabis of Google DeepMind designed the operational layer, modeled on FINRA: a body funded by the industry itself that would receive frontier models up to 30 days before release, with submissions being voluntary at first and then mandatory once proven robust. Sam Altman of OpenAI designed the outer facade, modeled on the IAEA: a Washington-led international forum that would set global standards and condition access to the technology on adherence to them. The specialists' letter demonstrated demand for these initiatives by calling for institutions without naming them; the three manifestos stepped forward to fill this gap with institutions pre-designed by the regulated.

Meanwhile, Washington was raising the building faster than the manifestos could describe it. The June executive order that pulled a frontier model from the world market with the stroke of a pen has already produced Gold Eagle, a vulnerability clearinghouse run by the Treasury with CISA and the Department of Defense, using frontier models—including Anthropic's Mythos— to shield critical infrastructure; the same order schedules for August a process for reviewing frontier models before their release to trusted partners, converting the manifesto’s central clause into state policy. The result is what one analyst called an involuntary regime of prior licensing: a de facto governance architecture, fused to the security apparatus, in which the regulator is invited to become a shareholder of the regulated, and none of it voted on or ratified by any legislature, not even the US Congress.

Two standards, two initiatives

The juxtaposition of the two scenes sums up the moment. On one side, a four-sentence manifesto that calls for institutions without building them, signed by those who keep pushing the technological frontier while admitting they do not control its consequences. On the other, a state that treats AI as public policy, displays measurable results, and offers the world, in the same week, exactly the governance platform the letter finds missing. Read in this context, the Stanford letter is less a program of action than the confession of a vacuum. And vacuums, in geopolitics, do not stay empty for long.

The participants in the 1956 Dartmouth conference got the timeline wrong but the direction right. The machine that simulates aspects of intelligence exists. What they could not foresee, because it was not a question of their time, is that the decisive challenge would not be building it, but deciding who appropriates the value it generates and who bears the cost of what it destroys. The 2026 letter has the merit of placing that question at the center of the agenda with the authority of 16 Nobel laureates. It has the limitation of not saying who the subject of the action it demands is. Seventy years after the summer of 1956, the bill for that 2-month experiment has come due for everyone. It falls to each country to decide whether to negotiate the terms of payment or simply receive it ready-made, issued in dollars and written in English.