Integrated Report on Artificial Intelligence Developments: August 29–31, 2026
Artificial intelligence in the service of profit expansion: costs borne by labor, society, and the environment
Developments over the past three days once again showed that, within the current economic order, artificial intelligence is not simply a neutral technology or merely a tool for expanding human capabilities. Its direction is shaped by ownership relations, corporate competition, and the drive to increase profit. Companies use AI to raise productivity, reduce labor costs, capture new markets, and expand profitable infrastructure, while a large share of its social and environmental costs is shifted onto workers, consumers, and local communities.
Turning AI’s requirements into multibillion-dollar markets
Energy and industrial-services company SLB announced that it will acquire cooling-equipment manufacturer Kelvion for $3.4 billion while assuming approximately $700 million of debt. The central reason for the deal is surging demand for electricity and cooling systems in AI data centers.
SLB expects its data-center-related activities to generate roughly $4.5–5 billion in revenue and $700–800 million in operating profit by 2028. Its shares also rose after the deal was announced.
This example clearly shows how AI’s technical demand for computation, electricity, and cooling has become a new arena for capital accumulation. Every increase in energy and water consumption creates additional profit opportunities for infrastructure companies, while the costs imposed on power grids, water supplies, and the environment are largely carried by society. Report on the SLB–Kelvion deal
A return to natural gas to power data centers
The rapid growth of data centers has increased electricity demand so sharply that major technology companies are again turning to natural gas and to dedicated power plants and turbines. Elon Musk has said SpaceX will manufacture components for its own gas turbines to meet the rising electricity needs of AI infrastructure.
This trend is not limited to SpaceX. Amazon, Microsoft, Meta, and OpenAI have also shown greater interest in gas-based sources to supply their enormous data centers.
Here the contradiction between private profit and public cost becomes unmistakable. Companies need continuous and inexpensive electricity to capture the AI market more quickly and recover their vast investments, while nearby communities bear air pollution, greenhouse-gas emissions, noise, and pressure on natural resources. Higher technical efficiency does not necessarily reduce total resource use, because competition for greater profit drives companies to build larger data centers and consume still more energy.
Automation to reduce labor costs
Meta is testing robots in its data centers to connect cables, move equipment, reset servers, and perform some maintenance tasks. Microsoft, Google, and Amazon are pursuing similar programs.
The stated objective is greater speed and safety, but reducing human-labor costs is also a central motivation. Robots still cannot perform every human task, yet the direction is clear: converting more human activities into automated processes and reducing corporate dependence on labor.
This also calls into question broad claims about data centers creating large numbers of jobs. Construction can create temporary employment, but once operational, data centers are highly automated facilities that usually require relatively few workers. Now even those limited maintenance and operations jobs face further automation.
Under this model, rather than reducing working hours and improving life for everyone, AI is used primarily to lower labor costs, limit hiring, and increase corporate profits. Report on Meta’s data-center automation
Economic opportunities without guaranteed job security
Small businesses are also gradually adopting AI for programming, customer service, business analysis, and information security. These applications can raise productivity and simplify time-consuming work. Yet smaller firms have learned from the experience of large corporations that high computing costs, unreliable AI agents, and rushed replacement of employees can be damaging.
Some companies have used AI as a justification for layoffs or reduced hiring even where the technology’s actual role in the decision is unclear. A distinction must therefore be made between genuinely expanding productive capacity and using “AI” as a promotional pretext to weaken job security and workers’ bargaining power. Review of the experience of small and large businesses
AI expansion and new threats to financial stability
The chair of the Financial Stability Board, which is linked to the Group of Twenty, has warned that AI-enhanced cyber risks are now among the most urgent threats to global financial stability. AI can dramatically increase the speed, scale, and cost-effectiveness of cyberattacks.
The concentration of infrastructure and advanced models in the hands of a small number of large technology companies creates another vulnerability. If one company’s services are disrupted or its systems are attacked, the effects can extend far beyond that firm and affect large parts of the financial and economic system.
At the same time, very high valuations of AI companies and enormous investments in data centers raise a further question: if future revenues fail to match investor expectations, how much of this investment will turn out to be a financial bubble? Financial Stability Board warning on AI risks
The problem is not the technology itself, but its social direction
AI can expand knowledge, reduce dangerous and repetitive work, improve public services, and enable more conscious economic and social planning. Criticizing its currently destructive role therefore does not mean opposing technology.
- Higher productivity does not necessarily reduce working hours or improve wages; it can lead instead to layoffs and reduced hiring.
- Progress in automation does not necessarily free people from exhausting work; it can eliminate their economic security.
- Greater data-center efficiency does not necessarily reduce resource use; competition to expand markets can increase total consumption of electricity, water, and fossil fuels.
- The concentration of data and computing power in a few corporations creates major security and political risks in addition to concentrating wealth.
- AI profits are privatized while its environmental, employment, and security costs are socialized.
Conclusion
Developments from August 29 to 31 show that AI has become a new engine of profit expansion and capital accumulation. Multibillion-dollar acquisitions, the expansion of gas-fired power generation, data-center automation, and efforts to reduce human-labor costs are all parts of a single process.
Technology has the capacity to increase social wealth, but private ownership and control concentrate its benefits in the hands of a small group while distributing its destructive consequences across society. This is the fundamental contradiction between the socialization of production and the continued private ownership of profit and decision-making power.
The solution is not to halt technological progress but to expand social oversight and participation in determining its direction. Decisions about data centers, water and energy use, job automation, and the use of public data must not be made solely in corporate boardrooms according to shareholder profit. Workers, experts, consumers, and local communities must have a real and direct role in these decisions.
AI can become a liberating force only when its productivity gains lead to shorter working hours, economic security, broader public services, environmental protection, and greater popular participation in power—not merely to stronger tools for profit expansion, labor displacement, and wealth concentration.
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