Sam Altman will appear before the United Nations Security Council on Wednesday, three years after the council first formally discussed artificial intelligence. France has convened the meeting around AI and international security; OpenAI, Anthropic and China’s DeepSeek are all expected to participate. At nearly the same moment, the United Nations is trying to change the way it operates. In March, the General Assembly adopted new rules for reviewing the mandates that determine much of what the institution does, after eight decades in which more than 40,000 resolutions and decisions accumulated alongside roughly 27,000 intergovernmental meetings each year.
The Charter gives the organization work to do: solve international problems, improve standards of life, coordinate governments that often have little reason to cooperate. UN80 has spent this year confronting how much machinery accumulated around that work. Silicon Valley’s better companies developed under a less forgiving discipline; projects have owners, products reach users early, evidence arrives quickly, and a bad result eventually has to change where the money and people go. Peacekeeping and humanitarian relief obviously aren’t software products, but an institution can still know what it is trying to accomplish, measure whether the work is getting there, close things that have outlived their purpose and move resources toward whatever is producing better results.
Forty Thousand Instructions
More than 85% of active Secretariat mandates contained no instructions for review or termination when the UN examined them. Renewal consequently became the ordinary course for a large share of the work, a phenomenon the mandate review describes, with some restraint, as “inertia.” Secretariat entities cited roughly 4,000 mandate sources while preparing the original 2026 program budget, 36% of them cited by more than one entity; around 50 citations referred to instructions that appeared outdated or inactive. Recurring resolutions often changed little from one session to the next, separate reports covered similar subjects, and individual entities sometimes planned work without a complete view of what the rest of the system was already doing.
Member states created those obligations over decades through resolutions and decisions, after which the Secretariat converted them into programs, meetings, reports, staffing and budget requests. Wars produced peacekeeping missions, famines produced relief programs, new diseases produced health initiatives and successive development agendas generated their own institutions and reporting requirements. A program could make perfect sense when adopted and remain difficult to reconsider years later, after the emergency changed, another organization entered the field or the original political bargain lost its relevance. Governments usually have constituencies urging them to create something new; considerably fewer organize around reopening an old diplomatic compromise merely to close an office.
UN80 is reducing staff positions by 21% in the Secretariat’s revised 2026 operating model, relocating posts from expensive duty stations and combining administrative systems that developed separately across the organization. The new mandate rules require clearer objectives for future work and give member states procedures to adapt, merge or retire what already exists; the 2027 program plans already cite substantially fewer mandate sources per Secretariat entity after an initial review for relevance and comparative advantage.
Four Years
OpenAI was founded in 2015 and released ChatGPT publicly in November 2022. Less than four years later, companies have reorganized work around large language models, governments have rewritten policy around them and the company’s chief executive is appearing before the Security Council because the technology has acquired national-security implications. A bad product can survive hype, internal politics and an impressive amount of venture capital, but users leave, competitors ship something better, benchmark scores move and eventually somebody has to explain why the expensive thing still does not work.
DeepMind’s AlphaFold moved from research project to ordinary scientific infrastructure on an even shorter timetable. The AlphaFold Protein Structure Database opened in 2021 and expanded the following year to more than 200 million predicted structures, covering nearly every protein then catalogued by science. More than three million researchers in over 190 countries now use AlphaFold, including more than one million users in low- and middle-income countries; the work behind the system received part of the 2024 Nobel Prize in Chemistry three years after the database became publicly available.
Researchers still have to determine whether a predicted structure leads anywhere useful, design compounds, test them, manufacture whatever survives the trials and get the resulting medicine to patients. AlphaFold removed a large amount of work from one part of that sequence without removing the rest. When a structure that might previously have consumed months of laboratory effort is already available in a database, scientists reach the next bottleneck sooner; laboratory capacity, clinical trials and manufacturing become more valuable accordingly.
The World Bank’s 2026 World Development Report argues that developing economies could use AI to accomplish in a decade what might otherwise take a century, particularly where progress has been limited by shortages of skilled people. Steam technology took about 80 years to diffuse to lower-income countries, electricity around 40 and the internet roughly 20; middle-income countries accounted for half of ChatGPT’s worldwide traffic within six months of its release. The report documents AI already being used for medical screening, agricultural and weather advice, education and government services, including smaller systems capable of working through basic phones and voice interfaces rather than the enormous computing infrastructure required to train a frontier model.
Expertise Without Waiting
Poor countries have spent generations with too few doctors, engineers, teachers, agronomists and experienced civil servants. Training more remains necessary and takes years; importing them is expensive and often temporary. Software that can reproduce parts of their expertise cheaply won’t eliminate those professions, although it can increase how much useful knowledge reaches places that previously had very little of it. A nurse with better diagnostic assistance, a farmer receiving a more accurate local forecast or a teacher generating material in a language poorly served by existing textbooks gains capability without waiting for an entire professional workforce to arrive.
Electricity and connectivity still determine how much of that capability leaves the screen. The World Bank identifies power, internet access, skills, data and institutional quality among the principal constraints on AI adoption in developing economies. Generation and transmission have to expand; broadband has to reach more communities; clinics and schools need equipment that works reliably; ministries need procurement systems capable of buying useful technology before it becomes obsolete. Better models increase the value of those investments because the connection increasingly provides access to expertise as well as information.
The development ecosystem that grew through the 1990s and 2000s learned to work through multi-year grants, implementing partners, consultations, monitoring frameworks and reports because the underlying scarcities usually moved slowly. Doctors still took years to train, power plants took years to build, laboratories were expensive and administrative capacity accumulated over generations. Those structures could coordinate difficult work across dozens of countries, although they also made activity considerably easier to document than completion. A program could hire staff, disburse its budget, hold its consultations, submit its reports and still leave the underlying constraint largely where it found it.
AI can distribute pieces of expertise more cheaply; biotechnology can compress parts of scientific discovery; better forecasting can reduce uncertainty; automation can increase what a small organization or government can administer. Cheap diagnostic assistance does little for a clinic without medicine, better agricultural forecasts still need to reach the farmer, and faster drug discovery raises the value of factories capable of manufacturing what scientists find. Electricity, broadband, laboratories, factories and competent procurement offices remain stubbornly relevant, although new technology gives development institutions more opportunities to remove bottlenecks instead of organizing indefinitely around them.
The Old Operating Model
The General Assembly responded to AI by establishing a Global Dialogue on AI Governance and a 40-member scientific panel. The first dialogue met in Geneva in July, another is scheduled for New York next May, and the panel will produce annual assessments and thematic briefs; a separate AI Governance for Humanity Lab in Valencia supports work across the system. None of those bodies can regulate AI directly.
An issue becomes a mandate; the mandate produces a panel, office or program; the new structure needs staff, meetings, reports and a budget; the governments that created it rarely include an expiration date. No single step has to be unreasonable for the accumulated result to become difficult to manage. UN80 is now sorting through tens of thousands of earlier instructions created by the same process, identifying work that overlaps, has already been completed or no longer needs to continue in its existing form.
Cash, customers and competitors interrupt that sequence more often in a startup. Teams put unfinished products in front of users and change them when the assumptions prove wrong; capital moves among approaches, employees get reassigned and companies disappear when the evidence remains bad for long enough. Large technology companies become bureaucratic too, obviously. Younger firms generally encounter the consequences sooner.
Peacekeeping missions, humanitarian operations and diplomatic negotiations will never produce feedback as clean as a product dashboard, nor should they. Their objectives can still be defined with more precision, duplicated functions can be consolidated, costs can be compared with results, and a program whose original purpose disappeared twenty years ago can eventually disappear with it. UN80 already accepts most of those propositions; otherwise there would be little reason to review the mandates in the first place.
A consultation can identify an obstacle, a report can establish what governments do not know and an NGO can provide expertise a state does not possess. Did more electricity reach the community afterward? Did the clinic treat more patients? Did farmers receive better information? Did the ministry become capable of performing the work itself? Did the new technology reach people who could benefit from it? The UN’s mission concerns those outcomes rather than the institutional forms used to produce them.
What Survives Review
The 2027 program plans already cite about 30% fewer mandate sources per Secretariat entity after the initial review. Payroll, travel, technology services and other administrative functions are being consolidated; posts are moving from high-cost locations; member states now have procedures for adapting, combining and retiring existing work. An organization with global ambitions still has to know who is responsible for something, what it costs, whether another office is already doing it and when the work is finished.
Sequencing the first human genome consumed years and billions of dollars through the Human Genome Project; protein structures that occupied laboratories for months can now be retrieved from a database; translation, coding and scientific analysis that were expensive enough to ration are becoming inexpensive enough to distribute much more widely. Faster discovery increases the need for manufacturing, cheap intelligence increases the value of electricity and broadband, and better tools become more useful when governments and institutions can deploy them quickly.
Technology companies revisit constraints because removing one can create an enormous economic reward. Some turn out to be stubborn physical limits, some merely move somewhere else, and some disappear after enough engineering effort and capital arrive. Development institutions have a different incentive structure, but the people they serve would benefit from the same willingness to reconsider problems that have gradually become accepted as permanent.
Altman will spend Wednesday answering questions from diplomats about technologies that barely existed when the Security Council first put AI on its agenda three years ago. Nothing in that encounter requires a larger UN role in governing his industry; UN80 has already supplied a sufficiently difficult problem inside the building. The organization is trying to consolidate overlapping work, shorten an enormous list of mandates, reduce administrative duplication and develop a mechanism for deciding when an activity has accomplished enough to end.
Under the new mandate rules, member states will spend the coming year deciding which existing instructions should continue, which can be combined and which have run their course. The Secretariat still has thousands of mandates and decades of accumulated machinery to work through.