Chapter 1 — Introduction (plain-language version)

The puzzle

Here is a moment that captures the problem this thesis is about.

A junior consultant on a railway project in Portugal produces the risk section of a client presentation. The writing is polished. The structure is clear. The regulatory categories are named correctly. But the senior partner reads it and stops.

The section treats the main risk as a routine contract-renewal question — textbook, defensible, wrong. The senior partner had spent three meetings reading the signals from the Portuguese rail regulator: the real concern was about timing, specifically how a contract renewal would interact with a separate parliamentary inquiry the regulator couldn’t name openly but kept circling back to with its questions. The junior consultant hadn’t been in those meetings. He had used an AI tool to draft the section from the brief.

The output was good enough to survive a first read. It was not good enough to survive a read by someone who had actually been in the room.

The partner corrects the section, says nothing, and then spends two weeks thinking about what this means. The junior couldn’t have known what he didn’t know. The AI tool couldn’t have known it either. And the quality-control process had nearly missed it.

That moment is small. What it points to is not.

This thesis is about a gap between two ways of talking about what AI tools are doing to professional consulting work.

In the first version — the one you hear at conferences, in marketing materials, from management consultants — AI tools are mainly a productivity story. Drafting goes faster. Research gets done in minutes instead of hours. Slides get polished more efficiently. The tools are useful and manageable, and the main question is how to deploy them sensibly.

In the second version — the one practitioners talk about in less guarded conversations — something more unsettling is happening. The junior consultants coming up now produce better-looking work than their predecessors did at the same stage, but their grasp of the work is sometimes thinner than the polish suggests. Senior consultants worry about how the next generation is being formed. Clients are changing how they buy consulting services. And underneath all of it, something in the texture of the profession feels like it is shifting in ways the productivity story doesn’t capture.

This thesis takes the second version seriously. It argues that what practitioners are sensing is real, that it matters, and that understanding it requires a different kind of framework than the existing literature provides.

What the thesis is called, and why each word matters

The full title is: The Proletarianisation of Advisory: Judgment, Sovereignty, and Responsibility in European Infrastructure Consulting under AI Mediation.

Proletarianisation is a word from the philosopher Bernard Stiegler (who himself borrowed and reworked it from Marx). It doesn’t mean job losses here. It means something more specific: the gradual transfer of practical know-how, judgment, and professional identity out of the people doing the work and into technical systems they don’t control. A factory worker was “proletarianised” when the craft knowledge in their hands was transferred into machines they operated but didn’t own. The thesis argues that something analogous is happening to consulting — not by replacing consultants, but by absorbing into AI tools the specific activities through which consultants have historically developed their judgment.

Judgment, Sovereignty, and Responsibility name what’s at stake. Judgment is the kind of expertise a senior consultant has that an AI tool doesn’t — not just knowing the frameworks, but knowing when the frameworks are wrong, reading what a regulator is really worried about, sensing when a client is saying one thing and meaning another. Sovereignty is about identity and independence: whether consulting firms maintain their own distinctive way of seeing the world, or whether they gradually start thinking in the default patterns of whoever built the AI tools they’re using. Responsibility is about who the work ultimately answers to — including the people who aren’t in the room but whose lives are shaped by infrastructure decisions.

European Infrastructure Consulting is the specific context the thesis uses to develop and test its argument. Infrastructure — railways, energy, water, urban development — is chosen for particular reasons: it involves long time horizons, public money, regulatory oversight, and consequences for people who never hired a consultant. The stakes of getting it wrong are high and long-lasting. If the argument holds here, where it should be hardest to make, it is more convincing as a general argument about consulting work.

Under AI Mediation refers to a class of tools — large language models and the systems built on them — rather than any specific product. The thesis deliberately avoids naming particular AI tools because those tools are changing faster than a thesis can be written and examined. The argument is about what this class of technology does, not about any particular version of it.

The central claim

The thesis makes a claim at two levels.

At the general level: AI tools, under the conditions in which they currently exist, are hollowing out the kind of situated, answerable judgment that makes professional advisory work legitimate.

In European infrastructure specifically: AI tools, under current conditions, are doing this to infrastructure advisory in ways that carry particular consequences — because the judgment at stake here answers not only to clients but to the public, to regulators, and to the people who will live with the consequences of decisions made today.

The “hardest case” logic matters here. If this is happening in the context where AI substitution should be least attractive — where the stakes are highest, the time horizons longest, the public accountability most visible — then the general argument is stronger, not weaker, for being tested there.

The thesis is careful about what it is and isn’t claiming. It is not saying consulting was better before AI tools existed. It is not saying AI tools should be refused. It is saying that the conditions under which consultants are formed into good consultants, and under which firms maintain their distinctive expertise, are being structurally altered — and that the current conversation about AI in consulting doesn’t have the right framework to see this clearly.

Why this matters

Three reasons.

First, the existing research on AI in consulting looks at the wrong level. It asks: which tasks are well-suited to AI tools, and how do you govern their use? This is a useful question, but it misses what happens to the people who are supposed to be learning by doing those tasks. When an AI tool does the financial model, the slides get produced faster — but the junior consultant who would have spent two weeks wrestling with the model hasn’t learned what they would have learned. The thesis tries to see what’s happening at the level of formation, not just at the level of output.

Second, infrastructure decisions bind the future. A railway built today, a water tariff reform designed today, a grid interconnection commissioned today — these shape how people live for decades, sometimes generations. The advisory profession that supports these decisions has historically been embedded in institutions that take the public-interest character of the work seriously. If that profession loses the kind of judgment that earns trust — not just competent output but genuine, answerable expertise — the institutions it serves become less answerable too.

Third, the argument leads somewhere. The thesis develops practical recommendations for four groups of people: individual consultants, consulting firms, infrastructure clients (the organisations that buy consulting services), and European policymakers. The analysis generates specific things each group can do, not just a warning that things are going wrong.

Six things this thesis contributes

The thesis makes six specific contributions that haven’t been made before.

1. It applies a philosophical framework about deskilling to management consulting for the first time. The philosopher Bernard Stiegler argued that expertise gets displaced into technical systems under specific conditions. This has been applied to factory workers, to platform workers, to cultural producers — but not to management consultants. Applying it here requires developing an account of how consulting expertise is structured at three levels: the individual practitioner, the firm, and the professional culture.

2. It applies a philosophical framework about long-term responsibility to AI-mediated advisory for the first time. Hans Jonas, a philosopher who wrote about the ethics of technology, argued that the ability to act at technological scale creates obligations to the future — you become responsible for the conditions you create for people who don’t yet have a voice. This has been applied in environmental ethics and bioethics, but not to infrastructure advisory, where it fits very naturally.

3. It maps the professional qualification framework for management consultants against what AI tools can and cannot do, for the first time. The ChMC framework (Chartered Management Consultant) describes three stages of professional development: Foundation (learning the basics), Applied (managing delivery), and Chartered (senior, authoritative judgment). The thesis goes through each stage and identifies specifically what AI mediation does at each level. This hasn’t been done before.

4. It shows that the same pattern is already visible in two adjacent professions. Software development and law have had AI tools operating at scale for longer than consulting. The thesis looks at what happened there — not at whether headcount fell, but at what happened to the formation of junior practitioners — and finds a consistent pattern: routine tasks got absorbed, senior judgment became more concentrated, and the pipeline from junior to senior got disrupted. This comparative evidence strengthens the consulting argument.

5. It develops an account of what “sovereignty” means for advisory work, at two levels at once. Existing discussions of AI sovereignty focus mainly on data infrastructure and regulation. The thesis argues that the more fundamental sovereignty question is about identity: whether European advisory practice retains its own interpretive framework, or whether it gradually defaults to the cultural and analytical assumptions embedded in AI tools built elsewhere. This happens at the level of the individual firm and at the level of the European professional tradition simultaneously.

6. It constructs four future scenarios for European infrastructure advisory as thought experiments, not predictions. The scenarios don’t predict which future will happen. They describe four coherent possible futures and then identify what the analysis has to say about all four — the findings that hold regardless of which trajectory unfolds. This produces recommendations that are robust to uncertainty.

How this research was done

The thesis is reflexive practitioner research. The author is a practising consultant in the European infrastructure space. The puzzle that drives the research is one the author has lived, not observed from outside.

This position has advantages and risks. The advantage is access that an external observer can’t have: what it actually feels like to do this work, what senior consultants worry about in unguarded conversations, what the texture of a client relationship is, what it means when a junior’s work reads as fluent but somehow thin. These things are hard to see from outside.

The risk is obvious: it’s easier to mistake your own situation for the situation of the field, and vested interests are hard to see clearly. The thesis tries to discipline this by being explicit about where it is drawing on lived experience and where it is drawing on published evidence, by engaging with research that might complicate the argument, and by being honest about the limits of what reflexive practitioner research can claim.

One methodological choice needs explaining: the thesis is deliberately not about specific AI tools. It doesn’t name particular products or models, and it doesn’t try to catalogue what different AI systems can currently do. The reason is simple: those tools are changing faster than a thesis can be written. The argument is about a class of technology and what it does to professional formation — and that argument needs to be at a level of abstraction that survives the specific tools changing. Where particular products are mentioned, they appear as examples of practice, not as the object of analysis.

The thesis is conceptual and normative, not a systematic empirical study. It doesn’t include interviews, surveys, or longitudinal data. Its claims are about what is structurally at stake, not about measuring exactly how much of it has already happened. That measurement is identified as important further work.

How the thesis is structured

Nine chapters, each building on the previous ones.

Chapter 2 sets the scene: what consulting is, what European infrastructure advisory specifically looks like, and who else is affected by infrastructure decisions besides the client and the consultant.

Chapter 3 lays out the philosophical framework: the ideas from Stiegler, Jonas, and others that give the thesis its analytical vocabulary.

Chapter 4 describes how expertise is actually formed in European infrastructure advisory — at the level of individual practitioners, at the level of firms, and at the level of the wider professional domain.

Chapter 5 develops the political-economic argument: who owns AI tools, what cultural assumptions they carry, and what happens to the distinctiveness of European advisory practice as they become embedded in daily work.

Chapter 6 looks at what has already happened in software development and law — the professions where AI tools have been operating at scale longest — and identifies the consistent patterns.

Chapter 7 is the analytical core: it goes through each of the three stages of the professional formation pathway (Foundation, Applied, Chartered) and traces what AI mediation does at each stage.

Chapter 8 constructs four scenarios for the future and identifies what holds across all of them.

Chapter 9 draws the conclusions and makes specific recommendations to the four groups whose decisions will shape what happens: individual consultants, consulting firms, infrastructure clients, and European policymakers.

The puzzle is one the author has lived. The framework is offered to make visible what the productivity story misses. The chapters that follow develop it.