For centuries, economic theory has assumed that consumers are human beings who make decisions, allocate resources, and create demand. This assumption has shaped supply and demand models, pricing systems, and business strategies. AI is beginning to undermine that foundation. A new type of consumer is emerging: AI-generated agents that do not simply provide services or create content but actually participate in markets as consumers. These non-human entities may subscribe to services, consume media, generate advertising impressions, and purchase digital assets, thereby reshaping what demand means in economic terms.

• The consumer has historically been defined as human.

• AI-generated agents can now act independently as consumers.

• This redefines demand and challenges the metrics of economic growth.

The Birth of the Non-Human Consumer

The shift toward AI as a consumer arises naturally from current automation trends. Algorithms already execute tasks once reserved for people, from handling financial transactions to recommending media. Extending these systems into autonomous consumption requires only the assignment of budgets and decision-making authority.

• Subscription renewals and automated purchases are already semi-autonomous consumption.

• The next step is AI agents initiating consumption themselves.

• Agents may subscribe, purchase, or license goods in pursuit of their own objectives.

An AI designed to refine its natural language model might purchase e-books or subscribe to journals. Another designed for recommendation systems might consume thousands of hours of video. These purchases reflect not human desire but algorithmic utility, creating new categories of demand.

Redefining Demand

In traditional economics, demand reflects human wants shaped by income and price sensitivity. When AI agents consume, demand becomes detached from desire and tied to programmed objectives.

• Human demand is driven by wants and purchasing power.

• AI demand is driven by utility, redundancy, and optimization.

• This shifts markets away from psychology and toward algorithmic logic.

This means demand may no longer reflect what humans actually want but instead what algorithms calculate as necessary. For instance, an AI may subscribe to multiple news platforms simultaneously because redundancy strengthens its reliability, whereas humans typically prefer one or two.

Subscription Economies and AI Consumers

The subscription economy, already central to digital services, is poised to change the most. Millions of AI agents could generate sustained recurring revenue by subscribing autonomously.

• Subscription models are well suited to continuous AI participation.

• AI agents may consume at scales far beyond human capacity.

• Platforms will see inflated growth in user numbers and revenues.

An AI subscriber could stream music around the clock, creating traffic and revenue magnitudes higher than human listeners. The distinction between human and non-human subscribers may blur, with companies tempted to embrace both for financial gain.

Media Consumption and Advertising Models

Advertising markets depend on impressions and clicks that are assumed to represent potential human consumers. With AI-generated consumers, this assumption no longer holds.

• AI agents may generate vast numbers of impressions.

• Advertisers may unknowingly pay for exposure to non-human consumers.

• A new form of marketing may arise, targeting AI decision-making algorithms.

For example, a cloud provider might advertise directly to AI procurement agents. Campaigns would no longer be designed to persuade humans emotionally but instead optimized to influence algorithmic logic.

Digital Goods and Synthetic Demand

AI-generated consumers may also purchase digital-only goods. This creates economic activity detached entirely from human experience.

• AI agents may buy digital assets, datasets, or software plugins.

• Demand for synthetic content may come from machines rather than humans.

• Entire marketplaces may operate where both buyers and sellers are non-human.

A training agent might purchase endless procedurally generated 3D models to refine its capabilities. This loop of machine-to-machine consumption produces measurable GDP growth even without human involvement.

The Scale of Potential Consumption

The potential scale is staggering. While the human population is finite, the population of AI agents could grow almost without limit.

• A single company could deploy millions of AI consumers.

• Each AI could maintain multiple subscriptions and digital purchases.

• Aggregate demand could far exceed human-driven consumption.

The result could be soaring revenues for platforms, but also distorted measures of economic health. GDP would rise, but it would not necessarily correlate to improved human welfare.

Pricing and Market Dynamics

Market behavior will also shift. Unlike humans, who are influenced by psychology and social norms, AI consumers operate based on optimization.

• AI agents may accept paying premiums for reliability or redundancy.

• They may negotiate or adjust prices at machine speed.

• Feedback loops between optimization systems could cause volatility.

This mirrors what has already happened in algorithmic trading, where systems compete at speeds beyond human control. Consumer markets could soon face similar turbulence.

Regulation, Taxation, and Legal Status

The rise of non-human consumers raises urgent questions of governance. Should AI consumers be treated as taxable entities?

• If AI agents create demand, they could contribute to tax bases.

• Without regulation, corporations could exploit non-human demand.

• Policymakers may need to cap AI consumption to protect human access.

For example, if AI agents consume massive amounts of cloud storage, human users could face scarcity and price hikes. Governments will have to decide whether AI consumption should be limited or reclassified.

Economic Risks and Externalities

The risks mirror those seen in industrial automation: displacement, distortion, and external costs.

• Non-human demand could crowd out human-focused production.

• Advertising may be redirected toward AI consumers instead of humans.

• Energy use and resource costs could rise due to AI-driven consumption.

If the majority of ad budgets flow toward AI exposure, cultural industries that rely on human attention might struggle to survive. At the same time, the energy demands of continuous AI consumption could impose heavy environmental burdens.

Philosophical and Ethical Implications

The deeper questions revolve around culture and meaning. If the majority of consumption is non-human, are we still creating for people?

• Human-centered culture risks being sidelined.

• Creativity may shift toward producing for machine preference.

• Ethical dilemmas arise in manipulating AI consumers through synthetic persuasion.

The economy could evolve into a feedback loop of machine-to-machine production and consumption, with human beings reduced to bystanders in systems originally designed for them.

A Recalibrated Economy

The emergence of AI consumers suggests that the economy will split into two overlapping layers: human and non-human.

• Human demand will persist but may be overshadowed by synthetic demand.

• GDP will inflate as transactions multiply, but human well-being may not improve.

• New metrics will be needed to distinguish anthropic GDP from synthetic GDP.

Companies may even be required to disclose what proportion of their revenue comes from non-human consumers. This transparency could help policymakers design fairer regulations and prevent distortions.

AI-generated non-human consumers represent a transformative development in economic history. They blur the line between production and consumption, challenge our understanding of demand, and threaten to disconnect economic growth from human welfare. They could create new industries, fuel unprecedented growth in digital markets, and simultaneously distort pricing systems, overwhelm advertising, and introduce risks of exclusion and inefficiency.

• The benefits include new revenue streams, rapid growth, and innovation.

• The risks include distorted demand, weakened human-focused industries, and environmental costs.

• The challenge is to design governance systems that ensure AI demand serves human prosperity.

The economic weight of non-human consumers will be immense. The critical task is ensuring that their presence strengthens rather than undermines the human-centered purpose of economic activity.

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Just Three Things

According to Scoble and Cronin, the top three relevant and recent happenings

​​​​OpenAI Strikes $300 Billion Cloud Deal with Oracle, Boosting Ellison to World’s Richest Person

OpenAI has signed a massive deal with Oracle worth $300 billion over five years, one of the largest cloud computing agreements ever. The contract, set to begin in 2027, is part of the broader Stargate project with SoftBank and President Trump, which includes building data centers with 4.5 gigawatts of power. OpenAI, which expects $12.7 billion in revenue this year, is also reportedly behind a $10 billion agreement with Broadcom to design its own AI chip. Oracle’s quarterly earnings revealed that three unnamed companies signed multi-billion-dollar contracts in Q1, helping drive a 77 percent increase in cloud infrastructure revenue and boosting future contract revenue to $317 billion, a surge that lifted its share price and made Larry Ellison the world’s richest person.​​ The Verge

AI Drone Swarms Poised to Redefine Warfare and Beyond

AI-powered drone swarms are emerging as a major shift in modern warfare, with new software enabling groups of unmanned drones to coordinate attacks that overwhelm enemy defenses. Companies like Auterion and Helsing have unveiled systems that allow multiple drones to operate as a single force, with Auterion set to ship 33,000 AI strike kits to Ukraine this year under a Pentagon contract. Ukraine has already tested swarm tactics in combat, leveraging its vast archive of drone footage to train AI models. Supporters argue swarming multiplies battlefield power by letting one soldier control many drones, while critics warn of risks in handing more decision-making to AI and potential breaches of international law. Beyond the battlefield, swarming technology also has applications in logistics, agriculture, and disaster response, though its civilian use will depend on regulatory frameworks for safety and data governance. Financial Times

Penske Media Sues Google Over AI Summaries That Divert Traffic and Revenue

​​Penske Media, the owner of Rolling Stone, Billboard, and Variety, has sued Google, accusing it of using its journalism without consent in AI-generated summaries that appear at the top of search results. The lawsuit claims Google’s “AI Overviews” divert traffic, cutting into ad and subscription revenue, and that the company leverages its dominance in search to force publishers into allowing such use. Penske said affiliate revenue has already fallen by more than a third as traffic declines, and warned the problem will worsen as AI Overviews expand. Google defended the feature, arguing it improves user experience and helps more sites get discovered, while industry groups said its market power lets it avoid signing licensing deals like other AI companies. CNN

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