Topics | Luise Freese / A(I)mbivalence

AI Literacy Is Not Enough

AI literacy has become one of the most popular answers to the problems around AI. People are meant to understand how generative models work, what hallucinations are, which data they should not enter, how to check results, and, of course, how to write better prompts. There is little to object to in that. Anyone who works with a technology should know what it can do and what it cannot.

The problem starts where technical competence is confused with social understanding. One can understand very precisely why a language model hallucinates and still know nothing about the labor it depends on. One can write excellent prompts and never ask who owns the model. One can check every output carefully and still have no influence over whether the system is introduced at one’s workplace, which tasks it automates, or who receives the productivity gains it creates.

From Resistance to Organization

Criticism alone does not shift power. We can protest, boycott, demonstrate, or withdraw. That can be right. But the harder question is how many individual acts of resistance become something that can exert lasting influence on decisions.

For almost every structural problem, one question eventually appears: What can I do? And even among highly engaged people, that question quickly leads to personal rules or the decision not to use certain systems. These are real political decisions. But they do not yet solve the problem that people alone usually have very little power over companies, owners, and institutions.

How to Behave Right in the Wrong System

Adorno’s phrase that there is “no right life in the wrong one” captures a contradiction that reaches far beyond AI. We are asked to make the right decisions within conditions we did not create and often cannot leave behind.

This is especially visible in AI. Companies decide which systems they build, which data they use, under what labor conditions they are produced, and which social and ecological costs they are willing to accept. Only afterward does the debate begin about how we should use these systems responsibly. Check the output. Do not enter sensitive data. Watch for bias. Think about resource use. Consider carefully when the use is still defensible.

Resistance is futile?!

In Star Trek, the Borg say “Resistance is futile” just before they assimilate others into their own order. It should sound dystopian. Today, we encounter the same logic in a friendlier vocabulary: change is inevitable. Companies have to act. Employees have to adapt. Anyone who hesitates will fall behind.

The difference is mostly the wording. No one has to say that resistance is pointless if everyone is already convinced that the future is fixed anyway. Then the introduction of AI is no longer seen as an economic or political decision, but as something that simply happens. Providers sell. Companies invest. Leadership decides. And those who have to live with the consequences are then told how important openness, retraining, and adaptability are.

The Productivity Trap

The promises are familiar: AI takes over routine, speeds up processes, reduces effort, and makes workers more productive. What is rarely asked is what actually happens to the time that gets saved.

In most companies, it does not belong to the people who saved it. If a task that used to take two hours now takes thirty minutes, that usually does not lead to an earlier finish. The saved time is replanned, filled with more tasks, or used as the basis for new targets. What was celebrated as an efficiency gain yesterday becomes the new normal tomorrow.

Your topic here

Don’t see your exact topic? I’m happy to speak on related themes too, and I love developing new talks for the right audience.

Start a conversation