In my previous essay, When Language Changes the Answer: Data Poisoning, GEO, and the Limits of LLM Judgment, I examined how language models are shaped by their information environments, training data, and retrieved sources.

I ended with a question: when a model has access to enormous amounts of information, can it distinguish reliable evidence from repeated versions of the same claim? And when new evidence contradicts an earlier conclusion, can the model revise its judgment?

This is not just a question about artificial intelligence.

People also live within information environments shaped by language, media, history, and social relationships. We reason from the evidence available to us, but we also experience risks, interests, and fairness emotionally. Someone can think seriously about a question and still reach a different conclusion because they have different information or interpret causation differently.

When the subject shifts from historical facts to wealth distribution, employment, and technological progress, differences in judgment can also influence real economic and political behavior.

The recent debate over an AI bubble has brought this question back to my attention.

What does AI prosperity actually mean?

On November 9, 2026, Anthropic introduced Claude Fable 5.2. Engineer Felix Rieseberg subsequently showcased Saltline, a browser-based shooter created by the model.

According to the developer's public account, the model worked for roughly eight hours without human feedback during the run. At API list prices, development cost was estimated at about $550. It processed approximately 445 million input tokens, many of them cached, and produced around 2.7 million output tokens.

That is a noteworthy technical experiment. A game prototype that might previously have required coordination among programmers, artists, and sound designers can now have a substantial part of its production handled by a model.

But the experiment also reveals a second issue.

The developer obtained a game by relying on a remotely hosted model. The process still required cloud computation, servers, electricity, and the continued operation of a provider.

Once the game is finished, independently saved source code and assets may allow it to run without the original model. If essential features rely on remote services, a network outage or the provider's withdrawal may affect the game. AI changes how software is produced; it does not eliminate the resources needed to produce it. Some costs move from human development time to model inference, infrastructure, and maintenance.

Open-weight models illustrate another part of the transition.

Kimi K3, released in 2026 with approximately 2.8 trillion parameters, inspired community experiments including kimi-k3-in-c. That project explores disk-streamed weights, lower-precision representations, and cache management to run a very large model without a GPU.

One performance record reports:

8 tokens in 261.5 s

The same test reported peak RAM usage of about 8.24 GB, but the full weights still occupied roughly 1.56 TB of storage, with additional space needed for prepared data. A low RAM figure does not mean that a typical laptop can run a trillion-parameter model interactively.

What makes the experiment interesting is the community's ability to investigate different resource trade-offs. Open weights give developers opportunities to study and improve deployment without binding every use to the original company's online API.

These are real technical advances. They do not, by themselves, establish how much profit firms will earn.

An automatically produced game still needs an audience willing to pay for it. A model that can technically run locally still needs hardware with adequate storage and processing performance. The economic value lies in work accomplished, actual costs reduced, and useful results that people can continue to use.

That distinction matters when financial markets continually raise their expectations for future earnings.

In its October 2026 Global Financial Stability Report, the International Monetary Fund warned that AI investment and profits falling short of expectations could trigger market adjustments amplified by leverage and concentrated holdings.

A technology may transform production while some infrastructure built around it turns out to have been overinvested in.

Whether AI works technically, whether firms generate sustainable profits, and whether society receives commensurate benefits are three different economic outcomes to evaluate.

When machines increasingly use what other machines produce

Something is changing on the internet.

AI can generate articles, images, code, video, and product descriptions. Crawlers and AI agents can then ingest those materials and turn them into summaries, recommendations, or yet more generated content.

Machines can also execute payments.

In July 2026, Visa and Artemis published Agentic Payments from the Ground Up, examining real transactions involving software agents.

The report identified approximately 109.6 million transactions through the x402 payment protocol since its launch in May 2025, with about $15 million in adjusted transaction value.

Visa described one category as:

payments that one piece of software makes to another

Machine-to-machine payments are no longer merely a proposed future capability. But executing a payment does not mean that software has independently created purchasing power outside human economic activity.

When an agent books a plane ticket for someone, the money comes from the traveler's budget. When an agent buys API calls or computation for a firm, the money comes from that firm's operating funds. A machine executes the transaction; a person or institution with resources and a purpose ultimately bears its cost.

A hundred million automated payments are therefore not a hundred million new instances of independent human consumer demand.

Consider content produced by one company's AI, read by a second company's AI, and then rewritten by a third service. This activity creates calls, usage records, and expenses. The important question is what it ultimately does for people.

If the articles convey useful knowledge, the software accomplishes a real task, or automation cuts business costs, the source of value is intelligible. If content exists chiefly to attract bot traffic and is repeatedly crawled and republished, growing volumes and requests cannot be treated as proportionate growth in social welfare.

That takes me back to generative engine optimization, or GEO, which I discussed in the previous essay. As content providers compete to be cited by AI search systems, their incentives change.

A document being cited by an AI does not prove it has been fact-checked. Thousands of crawler visits do not establish that thousands of people have read and benefited from an article.

As machines increasingly produce, read, and purchase digital services, we need to follow who ultimately pays and who actually benefits.

When companies save on labor, what happens to people's income?

If we discuss only transactions among AI companies, we remain inside the technology sector. I care more about what is happening to ordinary economic lives.

In September 2026, the US Census Bureau published Graduating into Disruption: Labor Market Outcomes for AI-Exposed College Majors.

Using administrative employment and earnings records for college graduates, the researchers compared fields of study with different exposure to generative AI. After statistical adjustments, graduates in the 10 percent most-exposed majors had about a five-percentage-point lower probability of securing their first job and about 13 percent lower earnings in their initial complete quarter of employment.

The abstract states:

full-quarter initial earnings declined by thirteen percent

The study also found that about half the earnings decline reflected lower earnings within affected industries, while the rest was associated with graduates moving toward lower-paying sectors such as food service and retail.

This is evidence about differences in outcomes among fields with different AI exposure. It does not establish that every observed change was caused by AI substitution. It does, however, connect technological change to actual earnings and employment experiences of some young people.

Another study sheds light on hiring.

In August 2026, Stanford's Digital Economy Lab updated Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence.

Using ADP payroll data covering millions of US workers, the study found that employment among workers aged 22 to 25 in highly AI-exposed occupations was about 19 percent below the level implied by the employment trend for same-age workers in less-exposed occupations.

The authors emphasized that this change was driven primarily by hiring, not a wave of departures among existing employees:

reduced hiring of young workers rather than increased separations

This is a development that aggregate unemployment figures can easily obscure.

When a firm stops recruiting junior programmers, design assistants, or copywriters, current employees may keep their jobs. The national unemployment rate need not move noticeably at once. But an entry point has disappeared for someone who was preparing to join that profession.

A graduate may have spent years studying and still need to start in a lower-paying job or spend longer searching. Meanwhile, rent, transport, food, and other everyday bills do not disappear while that person searches for suitable work.

We cannot turn the reported earnings differential directly into a percentage fall in household consumption. But the household budget constraint is real. When income falls, people must cut spending, draw on savings, seek help from family, or borrow to maintain their living standards.

After essential spending, travel, cultural activities, electronics, software subscriptions, and personal education may have to be reconsidered. The effects extend beyond the worker to businesses that rely on ordinary customers buying their products.

That is why the employment impact of AI cannot be measured only by the labor costs saved by employers.

Making a task cheaper does not mean the person who formerly earned a living doing it has already found a replacement source of income.

Who can keep paying for AI?

AI companies naturally want more people to buy their services. But consumers' ability to pay does not automatically rise when models become more capable.

In October 2026, the National Bureau of Economic Research released Competition in Generative AI: Evidence from Consumer Demand.

Using roughly 114 billion card transactions covering approximately 174 million consumers, the researchers examined actual spending on leading generative AI services in the United States.

By April 2026, they estimated that roughly 2.9 percent of American consumers paid for at least one major generative AI service. The overwhelming majority of paying customers used basic plans priced at $30 per month or less, while annual customer retention across major providers was around 20 to 45 percent.

The study covers a particular paid consumer market. It does not include every free user, business contract, or indirect benefit of AI. Still, it makes a clear point: increasing model capability and sustained household willingness to pay are not the same variable.

People buy an AI subscription only after considering what it does for them and what else they need to pay for. A wage-earning household must balance an AI subscription against groceries, rent, savings, and family expenses.

This creates a central tension in the AI economy.

Companies have growing opportunities to reduce human labor requirements, but the ability of ordinary people to purchase the resulting products still depends on the income available to them.

Consumption does not come entirely from wages. Business investment, public spending, capital income, and international trade also create demand. But labor earnings remain an important source of purchasing power for many households.

If automation reduces the income of some workers while a larger share of its gains flows to asset owners, that distribution can change household consumption patterns and market demand.

Making goods easy to produce does not automatically produce buyers for them.

When AI companies become each other's important customers

In October 2026, the Bank for International Settlements published Circular relationships among AI firms.

It found that 55.2 percent of the investment value flowing to AI firms during 2021–2025 came from other AI firms. Of investment transactions between AI companies, 46.4 percent by value also involved a commercial supply relationship between the investor and investee.

The researchers warned of greater opacity and potential macro-financial risks.

A concrete example is CoreWeave's cloud-capacity arrangement with NVIDIA, initially valued at $6.3 billion in September 2025.

In a Form 8-K filed with the US Securities and Exchange Commission, CoreWeave described NVIDIA's obligation, subject to agreed conditions, to purchase computing capacity not sold to other customers.

The filing uses the phrase:

residual unsold cloud computing capacity

That arrangement provides a measure of sales protection and further connects the two companies' commercial interests. But a supplier's promise to buy residual capacity is not evidence that an equivalent amount has already been bought by independent end customers.

When a model company uses investor funds to purchase cloud services, and a cloud provider expands a data center against expected orders, sustainable activity ultimately requires sufficient customer revenue or other dependable sources of cash.

That is a central economic question behind concerns about an AI bubble.

Large investments can accelerate development, construction, equipment purchases, and employment. Yet if lasting demand does not support operations, the expected return on those investments may fail to materialize.

The consequences would extend beyond stock markets. Data centers use land, electricity, servers, cooling equipment, construction labor, and ongoing maintenance. If financing pressures lead to project cancellations, equipment suppliers, builders, energy providers, and local workers can all be affected.

An assessment of whether the boom can last must therefore follow real cash flow and final demand, rather than only the orders circulating inside the industry.

How does concentrated wealth shape people's understanding of the economy?

That brings me back to Marx's Capital.

In discussing accumulation, Marx was concerned not only with how much wealth a society could produce, but also with who owned the means of production and how accumulation changed relations between workers and capital owners.

In the AI era, advanced models, chips, data centers, and platform networks are becoming important productive assets.

The firms that own them can earn revenue from computing services, software licensing, and other businesses. Many ordinary workers, by contrast, depend chiefly on wages, self-employment income, and limited savings.

In 2025, the IMF published AI Adoption and Inequality, using household microdata and an economic model to examine distributional effects.

The researchers found that AI could displace some highly paid jobs and alter wage inequality, while higher capital returns could benefit wealthier asset-owning households and widen wealth inequality.

This is a modeling result, not a measurement of an already completed global transformation. It nevertheless raises a specific question: will productivity gains consistently reach workers through real earnings?

In October 2026, the International Labour Organization's Making productivity growth work for people also discussed the persistent gap between productivity gains and workers' incomes, particularly in lower-income countries.

Technology alone does not decide how its benefits are distributed.

Concentrated wealth is therefore not merely an abstract question of fairness; it bears on the economic lives of ordinary people. When some workers see slower earnings growth while asset returns continue to expand, their dissatisfaction with economic institutions may arise from real experience.

To explain why wealth gaps emerge, however, we still need evidence about innovation, invested capital, market competition, inheritance, monopoly power, and institutions.

In March 2026, the American Economic Review published Zero-Sum Thinking and the Roots of US Political Differences.

Surveying 20,400 US residents, the authors found a significant association between zero-sum thinking and support for policies including government redistribution, as well as a relationship to experiences of social mobility within families.

Zero-sum thinking here refers to the tendency to believe that one person's gain must come at another person's cost.

The research does not show that people who favor redistribution are irrational, or that their views inevitably lead to radical political action. It shows something more useful: how people understand the creation of wealth is connected to their beliefs about how it should be distributed.

Someone who has worked hard without being able to improve their living standards may reasonably conclude that existing institutions restrict opportunity and call for reform.

The deeper problem emerges when dissatisfaction is turned into an assumption that all the wealth of an entire group must have come from depriving others. Complex economic relationships then become a simple judgment about social identity.

History offers concrete examples of that transformation.

The Ruijin land revolution: When economic explanations became action

The Central Soviet Area in southern Jiangxi and western Fujian between 1929 and 1934, especially the 1933 land investigation campaign around Ruijin, offers a case for study.

This history predates the founding of the People's Republic of China in 1949. It cannot be understood simply by importing the later history of land reform.

The Land Decree of the Chinese Soviet Republic, dated December 1, 1931, provided for the uncompensated confiscation of designated land and redistribution of land and associated productive assets.

Land distribution involved real economic interests. For landless and land-poor peasants, receiving land offered an important productive resource and an opportunity to improve family life.

But as class status took on a political role, landholdings began to affect people's positions within social and political organizations.

A 1933 Preliminary Summary of the Land Investigation Campaign recorded that the Rentian district of Ruijin identified 300 landlord and rich-peasant households over 55 days. The report also records the execution of 12 people labeled counterrevolutionaries.

According to the same report, ten mass trial meetings and three traveling court activities took place there.

These numbers come from the campaign organizers' own account and remain subject to corroboration against other archives. But the report itself documents that property redistribution was carried out alongside class investigations, mass trials, and penalties against individuals.

In The Soviet Revolution and Local Society: Reexamining the Origins of the Land Investigation Campaign, Meng Qingyan uses local records, lineage materials, and recollections to examine the interplay of the Ruijin campaign and existing social conflicts.

His research indicates that land investigations did not take place in a rural society without internal divisions. Lineage relations, changes in local officials, competing interests, and political mobilization interacted, allowing class classifications to play a role in reorganizing local power.

The historical record also needs evidence preserved by the Nationalist government and its military in Taiwan.

Materials published by Taiwan's National Archives Administration under Economic Measures During the Anti-Communist Campaigns describe the Nanchang headquarters' measures in 1933–1934, including sending harvesting teams into blockaded Soviet-controlled areas to cut and remove rice.

One archival phrase is:

掩護割禾隊搶割稻禾

That is, providing cover for harvesting teams to seize and cut rice crops.

This confirms that military seizure of grain and economic blockade were additional sources of hardship for the region. It would be inaccurate to attribute all agricultural difficulties around Ruijin to the Soviet authorities' land policy. The existence of the blockade, however, does not erase the recorded human suffering caused by class classification and political purges.

Each source has to be used for what it actually establishes.

When a person's economic identity becomes a basis for deciding property rights, political status, or even personal safety, redistribution is no longer merely a change in how land is allocated.

The history reminds me that economic dissatisfaction may be grounded in genuine interests. But when a theory of where wealth comes from is used as justification to punish an entire identity group, the consequences extend far beyond the initial distribution problem.

What if the people who make the next cake disappear?

I want to return to the distribution problem through a simple metaphor.

Society already has a cake. Some people consider the division unfair and want to divide it differently.

But somebody must still make the next cake. Doing so requires ingredients, equipment, technology, labor, coordination, and investment.

In an agricultural society, landowners are not necessarily the people doing the farming. Tenants, hired laborers, owner-farmers, and managers may all contribute. In a modern firm, shareholders, engineers, managers, and employees perform different economic functions.

Changing the ownership of assets does not automatically erase productive capacity.

The key questions are whether assets continue to be used effectively, workers have stable incomes, managers and investors have reasons to continue producing, and society preserves the knowledge and organizational capabilities needed to create more wealth.

We have empirical evidence of how expectations about ownership can influence production.

In 2017, Shuo Chen and Xiaohuan Lan published There Will Be Killing: Collectivization and Death of Draft Animals in the American Economic Journal: Applied Economics.

Using variation in the timing of collectivization across roughly 1,600 Chinese counties between 1955 and 1957, they estimated that expectations of property transfer contributed to losses of about 12–15 percent of draft animals, reducing grain output by about 7 percent through lower animal inputs and productivity.

This happened in the mid-1950s, not during the 1933 land investigation campaign in Ruijin. The estimates cannot be transplanted into a calculation of Ruijin's losses.

What the study does establish is an observable economic mechanism: expectations about future property rights change the way people treat productive assets.

For a farmer who expects to lose control of an animal, slaughtering it early may preserve immediate private value. Across many farms, however, the loss of draft animals reduces the productive resources available for future cultivation.

Individual choices can make economic sense within the constraints people face, while their aggregate effect reduces output.

This is a more precise explanation of how institutions affect production than simply saying that people divided the cake and forgot to bake another.

In the AI era, the question likewise extends beyond who currently owns assets.

If some workers lose earnings and the resulting capital gains do not translate sufficiently into new opportunities, real wages, or lower living costs, the channels through which ordinary people share technological progress may narrow.

If firms focus only on reducing their wage bills, overlooking that workers are also important customers in the real economy, productivity gains can become disconnected from household purchasing power.

At the same time, long-term production needs skills, knowledge, investment, and continued innovation. We should study how more people can access advanced productive tools and earn returns through work, enterprise, and other legitimate economic activity—not only how existing wealth can be divided.

The danger is not questioning a view, but refusing to let evidence test it

Returning to the AI boom, several developments are already observable.

Advanced models have completed software tasks that were previously difficult to automate. That does not demonstrate that every AI investment will meet earnings expectations.

AI firms have built extensive investment and purchasing relationships, sometimes supported by capacity-purchase commitments. Those contracts cannot substitute for measuring independent end demand.

US labor-market research has identified narrower employment entry points and lower initial earnings for some young workers in highly AI-exposed fields.

Paying consumers still choose AI services based on prices and usefulness; they do not automatically increase subscription spending every time a model improves.

Together these facts point to a question that requires continued investigation: while AI productive capacity and capital spending expand quickly, how much of the benefit reaches ordinary people's earnings and consumption?

A dangerous conviction can begin when that connection disappears from our understanding of the economy.

The earlier essay's local Qwen experiment showed that a model could change the information available to its user by omitting important material without inventing an outright falsehood.

Something similar can happen in accounts of economic prosperity.

If industry reports consistently emphasize model parameters, computing demand, capital expenditure, contract values, and transaction counts while seldom tracking wages, household purchasing power, and actual productivity, their individual figures may be correct while the economic picture they present remains incomplete.

No deliberate fabrication is required. Continual attention to one set of indicators, and neglect of another, can shape what the public regards as the natural explanation of economic development.

When a model completes a harder task, the event may become another reason to expect uninterrupted industry growth. A firm's larger investment plan may be read as evidence of expanding future demand.

Those interpretations still need to be tested against cash flow and actual economic returns.

Likewise, when people observe widening wealth gaps and fewer job opportunities, the mechanisms behind those outcomes still deserve examination. Economic identity alone does not establish that every member of a group bears collective responsibility.

The history of Ruijin shows how an economic interpretation, once built into class classifications, mass trials, and property decisions, can have concrete consequences.

People do not act on emotion alone. They can seriously assess interests and risks, and construct coherent explanations from the information available. But if the initial information is incomplete or the causal account is wrong, further reasoning will not necessarily correct it.

This returns me to the question left open in my previous essay.

A dangerous conviction need not look like an obvious lie. It may be an apparently complete and coherent worldview that explains many events while continually excluding evidence that contradicts it.

When that affects a single model answer, the error can sometimes be corrected by checking sources. When it shapes investment, hiring, public policy, or people's rights, the consequences can persist.

What must an AI bubble debate ultimately answer?

I increasingly think that predicting the precise date of an AI bubble bursting should not be our only objective.

Even a severe financial correction would not automatically erase software, models, and engineering knowledge already created. Technology can also continue to improve without commensurate improvement in ordinary living conditions.

We need to observe whether productivity gains turn into income, whether business investment is supported by sustainable customer demand, and whether people can participate in and benefit from new economic output.

If AI makes goods and services cheaper, we should be able to examine prices and purchasing power. If it saves workers time, we should look for higher income, better work arrangements, or new opportunities. If AI companies expand investment, we should trace how much durable output those expenditures create, rather than merely counting the funds circulating along the supply chain.

These questions ultimately concern human lives.

People are more than labor expenses on a firm's balance sheet or subscription accounts sought by an AI provider. They need income to live, opportunities to develop their abilities, and a chance to participate in creation rather than merely receive machine-made products.

The productive tools, ownership structures, and distribution systems of Ruijin's era and today's AI industry are radically different. Yet one economic question remains worth asking:

As society becomes more capable of creating wealth, do people still have opportunities to take part in production, earn an income, and share in what is created?

My previous essay examined how models understand the world. This one asks how we understand economic prosperity.

If our evaluation consists only of model parameters, compute capacity, machine transactions, and market valuations—while leaving out wages, purchasing power, and quality of life—then the story of prosperity has not yet answered what economic development is for.

And when an interpretation begins to decide which evidence deserves attention, excluding facts that cannot support it, we ought to examine our own judgment more closely.

Are we using evidence to understand the world, or are we allowing a worldview we already hold to decide what counts as evidence?