Luise Freese / A(I)mbivalence
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A(I)MBIVALENCE

We are told to use AI responsibly while the industry extracts labor, knowledge and resources, privatizes the gains, and hands the consequences back to everyone else.

A(I)mbivalence is about refusing to mistake that contradiction for a personal moral failure, and asking what becomes possible once we stop treating structural problems as individual ones.

Luise Freese speaks about the relations of power and ownership under which AI is built and deployed, who profits from it, and who is left to carry its social and ecological costs. She asks why structural problems are so routinely turned into individual obligations: use AI responsibly, educate yourself, adapt faster, stay employable. And she asks what critique has to become if it is to do more than register discomfort. Structural power is not countered by ever better individual choices. It is challenged when people organize, identify shared interests, draw lines, refuse, demand a say, and fight over who technology serves, how work is organized, and who gets the gains.

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Keynotes, panels, podcasts, conversations, and workshops in English and German.

Topics

5 ideas to invite your audience to start thinking differently about AI

For stages, panels, podcasts, and editorial formats that want to move beyond tools and trends and talk about work, ownership, responsibility, and power.

LITERACY

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.

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ORGANIZING

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.

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POWER

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.

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RESISTANCE

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.

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LABOR

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.

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What is A(I)mbivalence?

Ambivalence is not a comfortable middle ground, nor is it indecision. Cognitive dissonance creates pressure to make a contradiction go away: justify one side, minimize the other, look somewhere else. A(I)mbivalence does the opposite. It means holding space for conflicting truths and refusing to clean them up just because they are f*cking uncomfortable.

AI can be useful and still be built on exploitation. It can make work easier while concentrating power elsewhere. We can benefit from the tools and reject the conditions under which they are produced. None of these truths cancels the others.

Sitting with that discomfort is not passivity, and it is not resignation. It keeps the contradiction visible long enough to ask better questions: what can individual choices actually change, where do they reach their limits, who benefits from keeping responsibility individualized, and what becomes possible once people stop facing structural problems alone?

A(I)mbivalence starts by refusing the easy escape. But rest assured, it does not end there.

A(I)mbivalence

/ˌeɪ.aɪˈæm.bɪ.və.ləns/

noun.

from
Ambivalence: contradictory feelings toward the same thing &
AI: artificial intelligence.

The contradiction of finding AI useful while rejecting a system that extracts labor, knowledge and resources, privatizes the gains, externalizes the costs, and tells everyone else to adapt.

See also extraction, exploitation, resistance, collective agency

AI Slop

/ˌeɪ.aɪ ˈslɒp/

noun, derogatory.

Low-effort, mass-produced AI content released without enough human judgment, care or responsibility.

The problem is not that AI was involved. It is that generating became cheaper than deciding whether something was worth producing. The cost then moves elsewhere: somebody still has to read, check, correct and discard it.

See also Meat Proxy, Cognitive Deskilling, Attention

cheap to produce. expensive to receive.

Meat Proxy

/ˈmiːt ˌprɒksi/

noun, informal.

A human who passes questions to AI and forwards the output to another human without adding meaningful judgment of their own.

The person is still visible, and therefore still lends the message identity, trust and accountability. But cognitively, they have become little more than an interface around the machine.

See also AI Slop, Bot People, Cognitive Deskilling

human in the loop. judgment out of the loop.

Surveillance Wages

/sərˈveɪləns ˈweɪdʒɪz/

plural noun.

Wages personalized through data about individual workers, their behavior and their bargaining position.

Instead of asking what the work should pay, the system can ask how little this particular person is likely to accept. Surveillance does not merely observe labor. It becomes part of the machinery used to price it.

See also Algorithmic Wage Discrimination, Privacy, Bargaining Power

the algorithm is looking for your floor.

BITS

Thoughts between the chapters

Short, pointed notes from the work on A(I)mbivalence, before they grow into something larger.

New

Dependency Is Power

On why dependency on AI infrastructure turns an ordinary product relationship into a power relationship.

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Read all notes

Formats

Let’s get into a conversation

Not every conversation needs the same format. Sometimes a clear thesis is the right starting point. Sometimes disagreement is. Sometimes the subject needs more room than a short stage slot can offer.

FORMAT 01 / STAGE

Keynotes and talks

Clear, argument-driven talks with an actual point of view. No trend overview, no product pitch, and no motivational prelude to the next so-called transformation.

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FORMAT 02 / DISCUSSION

Panels and moderated conversations

For discussions that do not separate technology from work, politics, and power. Luise Freese brings together practical system experience and a political view of the conditions under which those systems are built and deployed.

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FORMAT 03 / LONG FORM

Podcasts and interviews

For longer conversations that do not have to flatten contradiction into a quick conclusion. About AI, work, ownership, productivity myths, individual responsibility, and what critique has to become if it is to do more than make us feel politically awake.

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FORMAT 04 / WORKING SESSION

Workshops and roundtables

For organizations, teams, foundations, unions, and other groups that want to move beyond abstract AI talk and work through concrete questions. What is actually being introduced here? Who benefits? Who carries the costs? And which decisions should be made collectively instead of being treated as technical inevitabilities?

not a workshop where everyone gets three sticky notes and calls it transformation

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