When AI Takes Over Work: Will Ownership Become More Important Than Money?

In an interview with The Economist in July 2026, Elon Musk made a remarkable prediction: “Money won’t matter in 2036.” In other words, money will no longer play a significant role in just ten years (The Economist, Interview with Elon Musk).

At first glance, the reasoning behind this seems quite plausible. If artificial intelligence can take over virtually all mental work and humanoid robots can do the same for physical labor, goods and services could be produced with very little human labor. Labor would drastically lose its importance as a factor of production. Production would become cheaper, prosperity would increase, and many things that are expensive today could eventually be available almost for free.

But does this really mean that money will disappear?

Probably not.

The more interesting consequence might be another: If human labor becomes less valuable, property could simultaneously become more important.

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What Musk Actually Means

Musk’s thesis is based on the idea of a nearly fully automated economy. In this scenario, AI doesn’t just take on tasks such as programming, translation, accounting, or medical diagnostics. Robots also transfer this intelligence to the physical world. They build houses, produce food, repair machines, transport goods, and may eventually even take over caregiving, cleaning, and large parts of the skilled trades.

This would amplify a trend already evident in many digital products: the marginal cost of an additional unit can be very low.

An additional copy of software costs practically nothing. AI services can also be scaled up significantly, though not for free, since computing power and energy still incur costs. A fully automated factory could manufacture products with an ever-decreasing amount of human labor.

Musk therefore speaks of a kind of “quasi-infinite economy”: an economy in which AI and robotics are expected to produce goods and services in very large quantities (The Economist).

In a world like that, the question does indeed arise: Why do we still work, and why do we still need money?

The key point, however, is this: Automation can greatly reduce the need for human labor. It does not automatically eliminate scarcity.

AI Could Actually Make Intellectual Work Very Inexpensive

In the realm of digital work, this trend is already evident today.

Generative AI can write texts, develop software, analyze data, generate images, review contracts, summarize scientific literature, or answer customer inquiries. It still often requires human oversight. But this boundary is shifting at an astonishing rate.

The research organization METR attempts to measure this development using what are known as Task Completion Time Horizons. This involves examining how long it takes a human expert to complete a task that an AI agent can already solve independently with a certain level of reliability.

In recent years, this time span has increased exponentially in the benchmarks studied. METR initially found that the time roughly doubled every seven months; in some software, mathematics, and scientific benchmarks, even shorter doubling times were observed at times (METR, Measuring AI Ability to Complete Long Tasks ; current time-horizon data). However, METR explicitly warns against directly applying these benchmarks to entire professions: The tasks are predominantly drawn from software development, machine learning, and cybersecurity and are more clearly defined than many activities in the real working world. However, METR expressly cautions against directly applying these benchmarks to entire professions: The tasks are primarily drawn from software development, machine learning, and cybersecurity, and are more clearly defined than many activities in the real working world. Nevertheless, the data show that AI is increasingly capable of handling longer, continuous tasks independently.

In a comprehensive analysis from 2025, the International Labor Organization (ILO) concludes that approximately 24 percent of jobs worldwide are at least partially exposed to generative AI. In high-income countries, this figure stands at 34 percent. This is an estimate of the technical potential for exposure, not of automation that has already taken place (ILO & NASK, Generative AI and Jobs: A Refined Global Index of Occupational Exposure).

What is noteworthy, however, is the ILO’s interpretation: Transformation is currently far more likely than complete replacement.

So an accountant will not necessarily disappear, but might need significantly less time to do the same work. A software team could achieve the same results with fewer people using powerful AI agents. A lawyer can have large volumes of documents pre-sorted and analyzed much more quickly.

This is precisely where the first major change is likely to take place.

A Possible Scenario by 2030: AI Becomes a Regular Coworker

For the second half of the 2020s, it seems plausible that AI will initially take over large portions of routine digital work.

Research, translation, standard programming, documentation, customer service, marketing, administrative tasks, parts of accounting, and many analytical tasks can increasingly be automated.

This does not necessarily mean that millions of people will become unemployed within a few years. A productivity boost is more likely at first: With AI, one person can do the work that previously required several people.

The immediate effect, therefore, might consist less of spectacular mass layoffs and more of something less conspicuous: companies hiring fewer people.

Entry-level positions, in particular, could be affected. The very tasks through which young employees have traditionally gained experience are often relatively easy to automate.

This marks the beginning of a trend whose economic consequences could be significantly greater than they initially appear.

A Possible Scenario for the Early 2030s: From Individual Tasks to Entire Workflows

The next step is crucial.

Today, we mostly use AI as a tool. We ask a question, verify the answer, and then continue working.

Future AI agents, on the other hand, could pursue goals independently: gathering information, preparing decisions, operating software, coordinating other AI systems, and monitoring results.

A tool is thus increasingly becoming a digital employee.

If this step is successful, automation will no longer affect only individual tasks within a profession. Entire process chains can be automated.

Then, for example, accounting, insurance processing, parts of administration, software development, or technical documentation could function with a fraction of today’s workforce.

However, the speed at which this will happen is highly controversial in scientific circles.

A survey of 2,778 AI researchers conducted in 2023 and published in 2025 found that, on average, machines could surpass humans in virtually all tasks by around 2047, reaching the 50 percent mark. By contrast, the corresponding 50 percent threshold for the complete automation of all human occupations was not reached until 2116 (Grace et al., Thousands of AI Authors on the Future of AI).

The difference between these two figures is revealing.

Automating intelligence is not the same as automating the economy.

Robotics Is the More Challenging Step

That is precisely why Musk’s integration of AI and robotics is so important.

Today, an AI model can analyze a complex text in seconds. But replacing a faucet in an unfamiliar old building, tidying up a messy kitchen, or responding flexibly to unexpected situations on a construction site is much more difficult for a robot.

The physical world is unstructured.

Nevertheless, robotics is also advancing rapidly. According to the International Federation of Robotics, approximately 542,000 new industrial robots were installed worldwide in 2024 alone. That was more than twice as many as ten years earlier (IFR, World Robotics 2025 – Industrial Robots).

However, these machines operate primarily in highly controlled environments.

The big leap forward would be an affordable, general-purpose robot that not only performs precisely programmed movements but can also flexibly handle a variety of tasks.

If this breakthrough is achieved, significantly more physical tasks could be automated in the 2030s and 2040s.

Manufacturing, warehousing, agriculture, and logistics are likely to be automated more quickly than skilled trades, caregiving, or work in changing and unpredictable environments.

It is not possible to give a precise year for this with any certainty. But it is precisely at this point that it will be decided whether AI will merely be another major productivity revolution or actually mark the beginning of a fundamentally different economic order.

What happens when labor costs almost nothing?

Let’s take Musk’s scenario seriously for a moment.

A company manufactures a refrigerator. Today, this process involves human labor throughout a long chain: raw materials are extracted, components are produced, appliances are assembled, factories are operated, goods are transported, sold, and repaired.

If, at some point, nearly every one of these steps can be handled by machines, labor costs will drop dramatically.

The same applies to many services.

An AI tutor could provide individualized tutoring to every child. An AI system could analyze medical data. Software could be developed for practically no cost. A robot could clean the house.

As a result, a significant portion of our current consumption could indeed become extremely cheap.

But not everything.

The crucial question isn’t about labor, but about scarcity

Suppose robots can build houses at virtually no cost.

Then, in theory, they could construct millions of houses.

But they can’t produce a second plot of land right on Lake Starnberg.

This is precisely where the problem with Musk’s thesis lies.

Money doesn’t exist just because people have to be paid for their work. Money and prices also fulfill another fundamental function: They help coordinate and allocate scarce resources.

Let’s imagine ten particularly attractive houses on a lake. At the same time, ten thousand people want to live there.

Even if building the houses costs nothing, land remains scarce.

Some mechanism must determine who gets it.

People could draw lots. The government could allocate it. There could be waiting lists.

Or people could bid against each other.

Then something emerges that, economically speaking, functions almost exactly like money.

As long as different people want to own or use the same scarce resources, a pricing or allocation mechanism will likely remain necessary even in a highly automated economy.

Material Assets Do Not Automatically Retain Their Value

At first glance, one might conclude that material things are generally becoming more important.

However, it’s not that simple.

Even raw materials may become less scarce due to new extraction methods, recycling, or substitution. Agricultural land could lose relative importance if precision farming, autonomous machinery, new breeding methods, or indoor farming massively increase productivity.

Even a house consists largely of reproducible materials.

If robots eventually become capable of constructing buildings very cheaply, the real value of the structure could even decline.

The non-reproducible part is, rather, the land itself—and especially its location.

Therefore, the distinction between digital and material would not be the decisive factor.

What would be decisive is:

What will remain scarce?

Property Could Become More Important Than Work

And this gives rise to a much more far-reaching consequence.

For many people today, the ability to exchange work for income over decades is their most important economic asset.

They do not necessarily own large factories, land, or businesses. Their income therefore depends largely on their own labor.

A highly automated economy changes precisely this relationship.

If a machine can perform the same task more cheaply, human labor loses relative economic value.

But the machine itself belongs to someone.

As a result, income tends to shift from labor as a factor of production to capital as a factor of production.

Daron Acemoglu and Pascual Restrepo use historical U.S. data to show that automation may have contributed significantly to increased wage inequality between groups since 1980. In their model, the effects of automation account for about 52 percent of the increase in this between-group inequality; this cannot simply be applied to AI, but it underscores how strongly the distributional effects of automation depend on labor market and property structures (Acemoglu & Restrepo, Automation and Rent Dissipation).

In a fully automated factory, it may eventually no longer matter who works there.

What matters is who owns the factory.

The same applies to robots, data centers, energy production, infrastructure, and AI systems.

The socially crucial question of the AI revolution might therefore eventually not be:

What kind of job do you have?

But rather:

What do you own?

Does this mean you should buy real estate now?

This line of thinking almost inevitably leads to the question of real estate.

If human labor becomes relatively less important while scarce assets remain, it seems logical at first glance to convert earned income into property as early as possible.

There is certainly a rational argument to be made here.

But it does not follow that every piece of real estate is automatically a good hedge against the AI revolution.

Economically speaking, a piece of real estate consists of at least two very different components: the building and the land.

The building itself is, in principle, reproducible. Robotics could even significantly reduce construction costs in the long term.

The plot of land is not.

A world with extremely cheap automated production could therefore, paradoxically, make the difference between good and bad locations even more pronounced.

If a house can be built for a fraction of today’s cost, what’s on it becomes less important. Where it stands could become all the more important.

An attractive location in an economically strong region, with access to nature, infrastructure, or a sought-after city, cannot be replicated at will.

This generally suggests that certain forms of real estate ownership could remain relatively valuable in an automated economy.

However, it does not mean you should buy real estate at any cost.

Demographics, regulation, taxes, financing costs, and regional appeal will not disappear just because of AI. A heavily mortgaged property in a shrinking region is something entirely different from scarce land in a location that will always be in high demand.

Stocks Are Also a Form of Ownership

Even more important is a second consideration.

Ownership isn’t limited to real estate.

A stock is also a form of ownership.

If, in the future, companies own millions of robots, operate AI infrastructure, produce energy, and control automated factories, these means of production ultimately belong to their shareholders.

Stocks could therefore represent an even more direct stake in an automated economy than real estate.

Real estate is primarily a stake in location and land scarcity.

Company shares, on the other hand, are a stake in productivity and capital.

Anyone who wants to protect themselves against a future in which labor loses importance relative to capital should therefore not necessarily try to predict exactly which individual asset will gain value.

The more robust idea is broader:

Ownership of scarce and productive assets could become more important than relying exclusively on one’s own earned income.

Musk’s prediction is by no means the scientific consensus

The actual extent of AI’s economic impact remains entirely uncertain at this time.

Daron Acemoglu, for example, arrives at a significantly more conservative estimate than Musk. In The Simple Macroeconomics of AI, he estimates that the additional increase in total factor productivity (TFP) resulting from the AI wave foreseeable at that time would amount to no more than about 0.66 percent over ten years (Acemoglu, NBER Working Paper 32487).

That would be significant, but a far cry from an economy of unlimited abundance.

On the other hand, there is the exceptionally rapid progress of modern AI systems and research such as that by METR, which shows that the length of tasks that can be solved autonomously is currently increasing at an astonishingly rapid pace.

Both can be true at the same time.

Today’s economic impact may still be relatively small, while technical capabilities are growing very rapidly.

That is precisely why long-term forecasts are so difficult at the moment.

The gap between Acemoglu’s more evolutionary change and Musk’s post-economic world is not a matter of a few percentage points, but rather two fundamentally different visions of the future.

What might the year 2036 actually look like?

A completely cashless society by 2036 seems extremely unlikely.

A plausible—but by no means certain—scenario, however, would be a significantly transformed world of work.

By then, AI could be as commonplace for many forms of intellectual work as the internet is today. A single person could perform tasks that, in the early 2020s, still required entire teams.

Many simple digital tasks could be largely automated. Some professions might have disappeared, while others would undergo fundamental changes. Entering many traditional knowledge-based professions is likely to become more difficult.

At the same time, robotics could have taken over large parts of industry, logistics, and agriculture. General-purpose robots could also be widespread in everyday life for the first time, without yet being able to replace every human activity.

As a result, many services and industrial products could become significantly cheaper.

But land in attractive locations would remain scarce. Natural resources would remain limited. Energy, infrastructure, and computing power would still need to be produced. People would continue to compete for certain goods.

Money would therefore probably not disappear.

But something else could happen:

The link between work and prosperity could weaken.

And what if, at some point, almost no one really has to work anymore?

At that point, at the latest, the technical question would become a political one.

A society would come under considerable political and social pressure if nearly all production were automated, yet the means of production were owned by only a small portion of the population, while the rest would be unable to earn much, if any, income from work.

The pressure for forms of redistribution or for broader participation in productive capital would then likely increase significantly.

This could take the form of an unconditional basic income. It could involve citizen funds whose members collectively own shares in the automated economy. Governments could tax capital more heavily or hold stakes themselves. Another possibility would be a kind of social dividend based on the productivity of AI and robotics.

Musk occasionally refers to such a future as “Universal High Income” (Forbes, April 2026).

Perhaps money would then actually be less important for basic needs.

But even then, there would likely still be things that not everyone can have at the same time.

And so scarcity would remain.

Conclusion

Elon Musk’s statement that money will no longer play a role as early as 2036 seems exaggerated.

However, the fundamental idea behind it is far more interesting than the specific prediction.

If artificial intelligence takes over intellectual work and robotics increasingly takes over physical labor, the costs of many goods and services could drop dramatically. Labor would then no longer be the central limiting factor in our economy.

But scarcity would not disappear as a result.

Land, prime locations, natural resources, energy, infrastructure, and ultimately ownership of the machines that produce this new prosperity will remain limited.

Perhaps that is why the AI revolution will not lead to ownership becoming unimportant.

Perhaps the exact opposite will happen.

For many people today, their labor is their most important economic asset. If the relative value of that labor declines while automated means of production continue to generate returns for their owners, ownership could play a greater role in the distribution of wealth than it does today.

However, this does not mean that one should rush out and buy any real estate as quickly as possible.

The far more robust conclusion is:

If we are indeed heading toward a highly automated economy, it might make more sense to convert a portion of today’s earned income into assets that are permanently scarce or productive—such as real estate and broadly diversified corporate equity.

The truly crucial question for the coming decades may therefore not be whether AI will take our jobs away.

Rather:

Who owns the world when machines do the work?