The Last Scarcity

The optimistic case for AI is straightforward: smarter machines make us more productive, reduce necessary work and improve living standards. With more time and fewer material worries, people would have more freedom to pursue a good life.

There is much to welcome in that prospect. Better access to food, medicine and shelter would relieve real suffering. Less time spent on necessary work could leave more for family, friends and interests.

But having more choices leaves open the question of what we choose.

Care, friendship, art, study and rest are all possible uses of free time. So are status competition, cruelty and efforts to control other people. More time gives us room for both. The optimistic argument needs to explain what would encourage the first set of choices.

Material security makes a good life easier to pursue. Deciding how to live still takes habits, relationships and institutions that help us use that freedom well.

The capability charts below show why this question has become pressing. Models improved quickly, organizations adopted them, and industry produced most of the strongest systems. We can take those changes seriously while examining the reliability gaps alongside them.

For the scenarios that follow, suppose machines become powerful enough to reduce a substantial amount of work. We can then ask who gains time, who controls the systems and what people do with the gains.

Suppose paid work shrank. Two hours less. Four. Eight. A block of the day would open. The question is not only whether that can happen. It is what fills the hole, and who decides.

Remove some work hours and choose where the time goes. The outer rings show published U.S. averages for comparison. Your choices describe a possible day.

A shorter workday creates an opening. What fills it will depend on the demands of a household, the habits of its members and the opportunities available to them. The same two free hours can mean very different things in different lives.

An image or a piece of code can be copied for many people at once. A person’s attention has to be divided. Prestige depends on standing relative to others. Trust takes a relationship in which both sides have something at stake.

That difference matters once machine intelligence makes the copyable things cheap. Social competition does not evaporate. It often moves toward the goods that still require scarcity, position, or another free person.

The chart illustrates a possible shift using a fixed total of one hundred units of competition. Turn the dial to move some of that competition from goods toward attention, status, exclusivity, relationships and power. These widths are assumptions you can explore.

Under those assumptions, cheaper goods leave people competing over recognition and influence. Abundance would change what people compete for as well as what they can afford.

We learn what to want partly by watching other people. An ordinary object can become desirable because someone admired owns it. A popular person’s attention can become valuable precisely because so many others want it.

AI adds a supply of praise, rumor and advice that can be produced endlessly. Step through the three scenes to see the essay’s proposed distinction between plentiful flattering messages and the limited attention of other people.

Beat 1: the tireless strategist floods the court with praise. Flattery is almost free, so it stops ranking anyone.

In this scenario, a willing audience matters more because its attention is freely given. The possibility of refusal is part of what makes recognition valuable.

A companion system can remember a conversation, reply patiently and be available at three in the morning. People may find that useful or comforting. The harder question is what kind of relationship it offers, and how it fits with relationships outside the app.

One survey of Character.AI users already complicates the pure success story. People who used the systems mainly for companionship reported lower well-being, with stronger associations under heavier and more disclosive use. That does not prove the apps caused the harm. It does puncture the idea that more intimacy with a machine is automatically more support.

Human affection matters partly because another person chooses to give it. If that choice is what we seek, an unlimited supply of simulated affection cannot fully satisfy the desire. It may instead make freely given attention more valuable.

Even when food is not the issue, people still organize around rank, insult, memory, and control. Hunger is one reason for conflict. It is not the only one. Fear, humiliation, sovereignty, revenge, and the pleasure of making someone yield can all keep working after material shortages ease.

These sliders illustrate the distinction. Lower material scarcity and the other pressures remain where you set them. Use the model to consider which problems greater production might ease and which would require another response.

If the earlier chapters are right, abundance leaves two jobs on the table. One is about character: how people learn what is worth wanting. The other is about power: who owns the systems, data, and infrastructure that make the abundance usable.

Both matter. Fair access to technology cannot decide how people use their freedom. Good intentions cannot by themselves overcome institutions that concentrate ownership and control. The two axes below let you consider those problems separately.

You have already made some choices on this page: how free hours might be used, which argument made sense, which institutional levers you pushed. If you turned on the local reading mirror, there is also a record of where you lingered.

If you enabled the reading record, compare where you spent time with the choices you made. A long pause may mean interest, confusion or distraction; use it as a prompt to reflect on your reading.

AI could make advice, entertainment and convincing conversation abundant. Another person’s consent, trust or affection would still be theirs to give. The prospect of abundance therefore asks more of us than finding new uses for free time. It asks how we learn to choose well and how we share the power to make those choices.