AI Governance and The Limits to Growth

Lessons from 1972 in age of doomers and optimists

Tim West, Visualising AI by DeepMind

“If the present growth trends continue unchanged, the limits of growth on this planet will be reached sometime within the next hundred years.” That was the primary claim of the 1972 The Limits to Growth written by a team of MIT researchers including Donella H. Meadows, Dennis L. Meadows, Jørgen Randers, and William W. Behrens III. Commissioned by the Club of Rome, a nonprofit founded in April 1968 by Italian industrialist Aurelio Peccei and OECD Director-General for Scientific Affairs Alexander King, the report detailed the results of a simulation modelling the possibility of economic and population growth with a finite supply of resources.

The central thesis of The Limits to Growth was simple: if the world's consumption patterns and population growth continued at the same rates documented in the 1970s, the earth's ecosystems would collapse sometime within the next hundred years. The report echoed concerns introduced by American biologist Paul Ehrlich in his 1968 bestseller The Population Bomb, which predicted that world demand for resources would lead to starvation. Ehrlich’s commentary belongs to the tradition of Malthusianism, which proposes that human population growth acts as a check on the further rise in population by consuming resources.

While the methodology of The Limits to Growth was criticised at publication and beyond–though defended in recent years–the publication of the document has become a much cited case study for historians of science and technology interested in the circulation and stabilisation of values and their ability to shape the political environment.

The origins of the report can be traced back to the formation of the Club of Rome itself. Aurelio Peccei made a 1965 speech to an international consortium of bankers aimed at supporting industrialisation in Latin America, which was translated into English and read by the then Scottish head of science at the OECD, Alexander King. According to the Club of Rome’s account, “the two found that they shared a profound concern for the long-term future of humanity and the planet, what they termed the modern predicament of mankind.”

In the spring of 1968, Peccei and King hosted thirty scientists, educators, economists, humanists, industrialists in Rome’s Lincean Academy. Founded in 1603 and housed in the historic Palazzo Corsini baroque, the academy derived its name from the Italian for lynx, an animal whose sharp vision was deemed to symbolise the observational prowess required in scientific practice. The meeting marked the birth of the Club of Rome, initially conceived as an informal collective “to foster understanding of the varied but interconnected components that make up the global system.” Crucial to the formation of the group was an intention to “bring that new understanding to the attention of policymakers and the public globally; and thus, promote new policy initiatives and action.”

At the core of its philosophy was the concept of the problématique, which held that viewing the problems of humankind––be they environment, economic, or social––could neither be viewed nor solved in isolation. As a 1970 proposal from the group explained: "It is this generalized meta-problem (or meta-system of problems) which we have called and shall continue to call the "problematic" that inheres in our situation."

Shortly after the Club of Rome’s inception, MIT systems professor Jay Forrester offered to use computer models to study the web of complex problems represented by the problématique. Receptive to the proposal, the group commissioned a team of researchers from MIT to study the implications of global economic growth by examining five basic factors: population, agricultural production, non-renewable resource depletion, industrial output, and pollution. Led by Professor Dennis Meadows, the study aimed to define the physical boundaries of population growth and the limitations imposed by economic activities by examining what they characterised as a set of interconnected problems.

Conclusions and controversy

In 1972, The Limits to Growth was published. Selling millions of copies worldwide, the document sought to scrutinise problems including the paradox of poverty amidst abundance, environmental degradation, institutional distrust, runaway urban growth, and economic disruptions such as inflation. The team from MIT developed a computer model, known as World3, based on the work of Jay Forrester. This model, which aimed to understand the causes and consequences of exponential growth in the global socioeconomic system, led the group to conclude:

That if the present growth trends in population, industrialisation, pollution, food production, and resource depletion continue unchanged, the limits to growth on the planet will be reached sometime within the next one hundred years. The most probable result will be a rather sudden and uncontrollable decline in both population and industrial capacity…It is possible to alter these growth trends and to establish a condition of ecological and economic stability that is sustainable far into the future…The state of global equilibrium could be designed so that the basic material needs of each person on earth are satisfied and each person has an equal opportunity to realise his individual human potential.

Despite its popularity, The Limits to Growth provoked criticisms soon after its publication. American academic Karl Kaysen condemned its "familiar, indeed fashionable thesis” that he felt essay that he felt overstated the scale of the problem in his paper The Computer That Printed out W*O*L*F. Peter Passell, an economist, published a 1972 article in the New York Times stating that “The Limits to Growth is best summarized not as a rediscovery of the laws of nature but as a rediscovery of the oldest maxim of computer science: Garbage In, Garbage Out.”

These criticisms took issue with the central claim at the heart of The Limits to Growth: should aggregate demand for resources increase as the world’s population grows and per capita income rises, the world will eventually run out of these precious resources. As Matthew Kahn recently explained, “Economists have tended to be more optimistic that ongoing economic growth can slow population growth, accelerate technological progress and bring about new goods that offer consumers the services they desire without the negative environmental consequences associated with past consumption.”

This dynamic was central to a 1973 rebuttal by the Science and Policy Unit at the University of Sussex, which concluded that simulations in The Limits to Growth were extremely sensitive to a small number of key assumptions. By this account, even minor changes to important variables would lead to huge swings when extrapolated far into the future. As a result, they suggested that the MIT projections were unduly pessimistic because of faults in the underlying methodology and data on which they were based. In response, however, the MIT group countered that their own arguments had been either misunderstood or wilfully misrepresented. They argued that the critics had failed to suggest any alternative model for the interaction of growth processes and resource availability, and "nor had they described in precise terms the sort of social change and technological advances that they believe would accommodate current growth processes."

A figure from ‘The Limits to Growth,’ with consumption continuing at the 1970 rate known as the 'Standard Run'. Depletion of nonrenewable resources leads to a collapse of industrial production, with growth stopping before 2100

More recently, some have proposed that the models presented in The Limits to Growth deserve a second look. Graham Turner of the Australian Commonwealth Scientific and Industrial Research Organisation, for example, used data from the UN to claim that modern indicators closely resembled the so-called 'Standard Run' from 1972 (a 'business as usual' attitude with few modifications of human behaviour). According to Turner, while birth rates and death rates were both slightly lower than projected, these effects cancelled each other out to leave the growth in world population in line with the forecasts.

Futures, policy, and AI

This post is not a commentary on whether or not the predictions made in The Limits to Growth were accurate. Given the most severe effects of the model are borne out towards the end of the twenty-first century, it remains difficult to claim with any certainty that the full account of the predictions will or will not come to pass. Where The Limits to Growth is instructive, however, is in understanding the way in which predictions about tomorrow can be used to inform the policy environment today.

Futures are a powerful thing. They are generated through an unstable field of language, practice and materiality in which actors and agents compete for the right to represent progress and deterioration. In scientific practice, visions of potential futures are often deployed to stimulate a desire to see potential technologies become realities. Historians interested in the futurity of science and technology generally take one of two approaches. First, the ‘sociociotechnical imaginaries’ perspective, which suggests that futures play a generative role in shaping socioeconomic, scientific, and political life at the point of production. They permeate the sphere of public policy, reconfiguring the socioeconomic environment by determining which citizens are included—and which are excluded—from the benefits of scientific development.

Characterised as ‘futurology’, the second approach explores the role that forecasting plays in contesting sites of action. It examines the way in which prediction can be used to control or protest futures and the role that “fictive scripts” play when deployed by scientists to win and sustain patronage. Analysis of the way in which futures are used by governments to manage the economy, and of the processes by which socioeconomic models of the future become entangled with political ideology, is central to this approach. While both ultimately seek to describe what the future could look like and prescribe what the future should look like, The Limits to Growth is generally associated with the second group. It demonstrates the power of predictions, coarse-grained or not, as tools for reorienting the policy environment through forecasts and extrapolation.

AI is a technology deeply entangled with notions of the future, and nowhere is that more clear than with respect to conceptions of highly advanced models whose capabilities might in many domains surpass those of a human. Researchers, civil society and policymakers ask when this style of AI will arrive, what impact will it have on the social and economic fabric of society, what dangers it poses, and how likely they are to manifest.

The recent calls for international bodies such as an Intergovernmental Panel for Artificial Intelligence modelled on the Intergovernmental Panel for Climate Change or an International Atomic Energy Agency equivalent for AI draw into focus the urgency with which the issue is being considered. The current climate of deep interest in AI safety has provided a window for action to design, develop and deploy governance mechanisms for powerful models in a manner that minimises harm and maximises benefit. But we should remember that today’s action space may not last indefinitely.

Right now, many believe that timelines for extremely powerful models are short. What happens if such systems have not yet materialised in the coming years, for example, and those who predicted their arrival are accused of overestimating the rate of progress? Some have suggested that we may well inadvertently enter a period in which risks are higher than ever (and in which the will to action most necessary) but in which governance efforts at the national and supranational level are harder to secure.

But The Limits to Growth episode shows us that it is not only accuracy of predictions that matters. To predict is to influence, and to influence is to exert power. Just as it is unwise to avoid careful consideration of how best to use the planet’s resources because predictions about their rate of depletion may not stack up, ensuring that the right governance structures exist to manage a world with highly-capable models should not be dependent on when precisely they will arrive.