Expertise comes and is developed in various forms and flavors. Regardless of whether you work as a surgeon, a medical guideline developer, or an information specialist, there are commonalities in the implementation of your expertise. Some experts rely more on cognitive skills, while others draw primarily from their bodily experiences. Some experts excel in quick responses, whereas others are deep-divers in their specific niche field. There are experts focused on compliance and those seeking major breakthroughs. This diversity shows that experts have many different shapes in the real world.

An expert is usually a person that received extensive training or experience in a specific field and therefore has great knowledge and experience with this. An expert is typically defined as a person who has dedicated substantial time and effort to receiving extensive formal training, often through academic institutions or specialized programs, complemented by considerable real-world experience in a specific domain. Consequently, this individual possesses a profound depth of knowledge, sophisticated technical skills, and a practical understanding of the nuances and complexities within their field. This combination of deep theoretical knowledge and practical experience allows the expert to solve difficult problems, make informed judgments, and offer authoritative advice that non-experts cannot. Their expertise is generally recognized and valued by peers and organizations within that field.

While all experts use their brains and skills, the "Cognitive-Only" expert focuses on manipulating existing information, whereas the "Physical/Field" expert focuses on acquiring new data or directly applying skills in a physical environment. Cognitive-Only experts operate primarily in the realm of "Abstract Processing." Their value lies in their ability to synthesize data, predict trends, and navigate complex systems from a central office or digital environment. While the physical and field experts' expertise is inseparable from their physical presence. For some experts this is on a scale.

The other important difference between experts is this of the maintainer versus the disrupter. Maintainers are experts who provide the structural integrity of civilization. Their expertise is characterized by high-stakes reliability, consistency, and the management of existing complexity. If they do their job perfectly, you don't notice them; if they fail, the system collapses. The disrupter, on the other hand, operates at the edges of the known. Their expertise is characterized by the discovery of new "rules," the creation of new tools, (biological) compositions, drugs, or the dismantling of old paradigms. They often make the current "system" obsolete and change the world. You can imagine that the impact of AI on the work of the expert will be different for the different profiles. The cognitive-only, maintainer expert will see their work change and at a different pace than the Physical/Field-based disrupter expert.

Before we continue to the next chapter, decide for your own expert job whether what your profile is:

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Across all four expert archetypes - cognitive and field-based, maintainers and disrupters - a comparable shift is visible, although in different forms. AI increasingly takes over their production of knowledge: summarizing, generating, optimizing, monitoring, and proposing at a speed and scale no human can match. What it does not take over is responsibility. As AI systems become more capable, the role of the human expert does not disappear - it fundamentally changes. The expert of the future is no longer primarily valued for recalling information, applying known rules, or generating first-order outputs. Instead, their value concentrates on judgment, accountability, ethical reasoning, supervising, and contextual interpretation. Experts become the final arbiters: the ones who understand not just what the system recommends, but how it can be improved, when it should be challenged, and what the consequences of effectively acting on it will be. Whether interpreting a guideline, curating disruptive ideas, intervening in a live system, or deciding which innovations should move forward because of their effectiveness, the expert increasingly serves as the human boundary around automated and capable intelligence.

For Cognitive-Only Maintainers, AI systems can reproduce the formal characteristics of expertise by generating outputs that are coherent, structured, and ostensibly authoritative, yet without assuming the responsibility that human experts bear and will continue to bear over the coming decades. Although such systems perform well in terms of consistency and scale, they frequently lack the epistemic depth required for reliable interpretation and effective implementation. In particular, they may fail to account for the rationale behind specific exceptions, the historical evolution of rules shaped by prior failures and iterative learning, the deliberate preservation of unresolved ambiguities, or the ethical considerations that have informed necessary compromises. Consequently, while AI may approximate the appearance of expertise, it does not fully capture the contextual, historical, and normative dimensions that underpin expert judgment and institutional trust.

For example, an AI may draft a medical guideline that is statistically sound but overlooks why clinicians historically deviated from that recommendation in specific populations with valid argumentation and reasoning. Or it may generate a compliance interpretation that is logically consistent but legally risky because it ignores precedent, intent, or enforcement culture within a specific country. For this kind of expert, the future is not about competing with AI on speed or scale. It is about becoming the final interpreter, the person who can say: “This looks correct, but considering the importance of the context, it is not correct, and we need to decide something else and continue our investigation for the correct answers.” Their value increasingly lies in judgment, accountability, effectiveness, capability, and institutional memory, things that cannot be inferred from data alone.

While for Cognitive-Only disrupters, AI enters this space not as a replacement, but as an amplifier of excellence. It can generate alternative framings, explore counterfactual scenarios, recombine ideas across disciplines, and surface patterns or analogies that would be difficult for any single human to produce alone. It dramatically lowers the cost of exploration and makes it much more effective. Lines of thought that once required months of work can now be sketched, expanded, and compared in hours, mastering large amounts of information in an instant. Here too, the risks are subtle but significant. AI systems are capable of generating novelty at scale, producing ideas that are original, coherent, and intellectually compelling, yet often without the discernment required to evaluate their broader implications. Such outputs may fail to account for whether a society or institution is prepared for a conceptual shift, whether a new framing undermines trust or institutional legitimacy, whether seemingly elegant solutions introduce ethical risks that only emerge in practice, or whether apparent novelty reflects genuine insight rather than recombination of existing patterns. As a result, AI-driven innovations may appear persuasive, while lacking the evaluative judgment of experts that is necessary for responsible adoption and long-term societal impact.

For example, an AI machine may propose or generate an innovative economic framework that optimizes efficiency while eroding social cohesion or fairness. The ideas look convincing, even visionary, but lack an anchor in lived consequences. For this kind of expert, the future is not about generating more ideas faster. It is about becoming the curator and boundary-setter, the person who decides which ideas deserve serious attention, which require careful testing, and which should be rejected despite their intellectual appeal. Their value increasingly lies in judgment, cultural awareness, and ethical responsibility: qualities that cannot be derived from pattern synthesis alone. AI expands the space of what is imaginable. Humans decide which imaginations are worth turning into reality.

For Field- or Lab-Based Maintainers, AI systems operate primarily on learned patterns and predefined thresholds, performing exceptionally well under expected and stable conditions. However, their effectiveness diminishes in situations that deviate from trained data, where early warning signals, rare but high-impact edge cases, or context-specific trade-offs between safety, speed, and outcome become critical. In such moments, ethical considerations that arise under time pressure or uncertainty may not be adequately captured by automated systems, underscoring the continued need for human judgment in maintaining safety and reliability in real-world settings. 

A clinical decision-support system may recommend a protocol-compliant treatment that conflicts with a patient’s unique circumstances. An automated monitoring system may report normal operation while an experienced operator senses instability based on subtle cues. In these moments, the cost of error is not abstract; it is immediate and physical. For this kind of expert, the future is not about deferring judgment to machines. It is about becoming the responsible overseer, the person who knows when to trust automation and when to intervene, or use the automation in a different manner. Their value increasingly lies in situational judgment, accountability, and the ability to act decisively when models fail and are not capable, qualities that cannot be fully encoded or automated.

Finally, for Field- or Lab-Based Disrupters, AI systems can propose potential solutions or avenues for experimentation, but they lack the capacity to judge whether a prospective breakthrough should be pursued, whether it is ethically acceptable, socially beneficial, or likely to generate meaningful impact. Such systems are ill-equipped to assess how risks and benefits are distributed, particularly with respect to vulnerable social or cultural groups, or to anticipate long-term consequences, including negative externalities that may only become apparent over time. Moreover, AI cannot determine when caution or restraint constitutes the responsible course of action, especially in light of local, national, or European regulatory frameworks and strategic priorities, leaving these judgments firmly within the domain of human expertise. 

More specifically, an AI may identify a highly effective biological intervention without accounting for ecological impact or ethical acceptability. A materials optimization system may prioritize performance while ignoring environmental cost. The outputs are impressive, but the responsibility for consequences remains human. For this kind of expert, the future is not about accelerating experimentation alone. It is about becoming the moral and strategic decision-maker, the one who decides which discoveries are worth advancing and under what conditions. Their value increasingly lies in ethical judgment, long-term thinking, and responsibility for outcomes that extend well beyond immediate success.This shift comes with important and significant consequences. In the long run, we will likely see a decline in demand for experts whose roles rely almost exclusively on routine cognitive work. For many professionals, this means actively rethinking career paths and embracing change management rather than resisting it. A new core skill emerges across domains: the ability to supervise AI systems, whether through HITL or HOTL models. Experts must learn how to validate outputs, detect failure modes, understand model limits, and intervene responsibly.

At the same time, experts carry a new obligation: ensuring that the AI systems they work with are capable, effective, and governed properly. This requires robust AI governance frameworks, validation processes, and institutional oversight. We do not move forward by using AI blindly; we move forward by building AI systems that are trustworthy enough to carry real responsibility, under human supervision. Crucially, AI should not consume expert time; it should free it. By embedding AI into daily workflows, repetitive and preparatory work can be automated, allowing experts to focus on what humans still do best: resolving ambiguity, making ethical trade-offs, and pushing toward the next meaningful breakthrough. Automation and discovery are not opposites; they form a cycle.

In essence, the AI pipeline we need is one where human expertise remains central, while the path toward high-risk, high-impact, AI-assisted decision-making becomes faster, safer, and more effective and efficient. The age of the expert is not ending, but it is undergoing its most profound transformation yet. With AGI and ASI still distant prospects, the immediate reality is close collaboration with increasingly powerful narrow AI and capable systems. It is the responsibility of today’s experts to ensure those systems truly add value. When experts can confidently claim that the AI they use is effective and capable, they can let go of the repetitive activities that take away a large amount of their time and energy, and focus on what really matters and what they have been educated for. That is how we unlock progress. And ultimately, that is what we are all aiming for: to move forward, faster.