Will artificial intelligence replace human thinking? AI can do many things better and faster – but it becomes a risk in these tasks

When machines develop ideas: Where their economic value ends

The new scarcity in the job market: Why judgment is becoming more valuable through AI

Artificial intelligence writes texts, develops software, summarizes information, and supports decision-making. In doing so, it takes over tasks that were long considered typical human knowledge work. But what lies behind its convincing results? Does an AI truly understand what it's talking about – or does it merely generate answers that seem understandable and plausible to humans?

For businesses, this question is more than just a philosophical debate. AI can accelerate work processes and improve the quality of results. However, studies also show that its support in certain tasks can lead to poorer decisions. Therefore, what matters is not just how fast a system works or how intelligent its answers sound. What's crucial is whether it's reliable in the specific application, what errors it makes, and what the consequences of those errors are.

The comparison between a "thinking" machine and a human being who merely understands is also too simplistic. AI can recognize patterns, connect information, and generate new suggestions, but this doesn't prove human experience or consciousness. At the same time, humans are by no means infallible: they, too, judge based on routines, overlook connections, and can be misled by convincing explanations.

The economic question, therefore, is not whether a machine can think like a human. It is which tasks it can reliably perform – and where expertise, critical review, and responsibility remain indispensable. Because the more easily AI generates texts, analyses, and ideas, the more crucial the ability to distinguish good suggestions from those that sound plausible but are ultimately wrong becomes.

AI calculates, humans understand? What artificial intelligence can actually do and where its economic value reaches its limits

Machines don't need consciousness to disrupt business models – but companies need more than perfect answers

Artificial intelligence is transforming the economy of intellectual labor. It writes texts, develops program code, structures information, designs products, and supports decision-making. In doing so, it is taking over tasks that were long considered to be uniquely human abilities. This development raises a fundamental question: If a machine delivers convincing results, does it truly think and understand – or is it merely producing something that appears to humans to be thinking and understanding?

The economically decisive answer lies between two exaggerations. One prematurely declares artificial intelligence to be a thinking and feeling entity. The other reduces it to a mere copying machine, thereby underestimating its actual capabilities. Both perspectives complicate sound investment decisions. Companies don't need to believe in machine consciousness or prove its fundamental impossibility to use AI effectively. They need to understand which tasks a system reliably performs, under what conditions it fails, and which human skills become more or less valuable as a result.

Three terms are particularly revealing here: abstraction, idea, and cognition. They describe different levels of mental processing. Abstraction derives a principle from individual observations. An idea opens up a new possibility for action. Cognition changes the understanding of a situation. AI can produce results that functionally replicate all three processes or actually realize them in certain tasks. However, this does not automatically mean that it performs these processes as a subjectively experienced experience.

This very separation between observable performance and inner experience is economically significant. Markets primarily pay for usable results, not for philosophical certainty about their origin. Responsibility, trust, objectives, and social legitimacy, on the other hand, cannot be derived solely from the quality of a generated result.

There is no simple dividing line between calculation methods and understanding

Modern generative language models learn statistical patterns from large datasets. Put simply, their parameters are adjusted during training so that they can generate linguistic continuations and other required outputs as effectively as possible. When used, they process the specific context and calculate the output accordingly.

This description is technically important, but it doesn't fully explain which abilities arise from the process. The fact that a system operates mathematically does not mean it processes only superficial word clusters. Complex relationships, categories, and abstracted structures can also be reflected in learned representations. Therefore, research discusses not only the limitations of such systems, but also the extent to which they develop internal world models and functionally relevant forms of understanding.

The example of an apple illustrates the necessary distinction. A person associates an apple with taste, smell, weight, memories, hunger, or a specific action. A purely language-based model lacks this personal experience. It can nevertheless process relationships between apples, ripening processes, nutrition, agriculture, and gravity. A multimodal system can additionally incorporate images or other sensor data. Therefore, a lack of human experience does not automatically equate to a lack of functional competence.

Conversely, an apt explanation does not necessarily demonstrate human understanding. A system can speak about taste without proving an actual taste experience. It can describe grief without eliciting a personal feeling from it. The language in which people express inner states can be generated without scientifically proving those states. Currently, there is no generally accepted basis for directly inferring subjective experience from convincing linguistic performance.

This leads to a sober perspective for businesses: A company should not judge AI based on its human-like qualities or its linguistic self-description. What matters are verifiable capabilities, error profiles, and the concrete benefits in the respective work process.

Why the formula of syntax without semantics falls short

The distinction between syntax and semantics describes a real problem. Syntax concerns the rules according to which symbols are arranged. Semantics concerns their meaning. A grammatically correct sentence can be factually incorrect. A convincingly formulated recommendation for action can be based on inaccurate assumptions.

However, it cannot be readily concluded from this that AI fundamentally processes no meaning structures whatsoever. The statement depends on what is understood by meaning. If meaning is conceived solely as subjective experience, its attribution to current systems is not certain. If, on the other hand, meaning encompasses the ability to differentiate concepts appropriately to the situation and to use relationships for problem-solving, then systems can demonstrate corresponding functional performance. Precisely for this reason, the philosophical classification remains controversial.

For business applications, this distinction is more practical than it initially appears. A model can use the term "delivery delay" correctly. However, whether it generates a reliable recommendation for a specific supply bottleneck depends on its ability to consider the relevant inventory levels, contractual terms, production dependencies, and available courses of action.

The problem is then not merely a lack of meaning in a philosophical sense, but potentially an incomplete understanding of operational reality. A linguistically powerful system without up-to-date inventory data remains limited for operational planning. A less expressive system with reliable data and clear decision-making rules can be more economically useful.

The central management question is therefore not whether an answer sounds intelligent. It is whether the underlying information, conclusions, and proposed actions are sufficient for the specific decision.

Abstraction means recognizing the essential point even outside of the example

People can form general concepts from individual experiences. A chair is not defined by a specific color, a particular material, or exactly four legs. What matters is its function within a context of action. Therefore, even an upturned bucket can serve as a seat.

This example demonstrates the strength of human concept formation: people connect perception, bodily experience, social cues, and practical goals. However, it would be misleading to derive a general rule from this that humans always manage with few examples while machines inherently require many. A child does not encounter objects without prior knowledge. Their learning is based on an already extensive developmental history.

Even with AI, a distinction must be made between the complex training phase and the application of a pre-trained model. A pre-trained system can handle new tasks using only a few examples because it draws on previously acquired structures. Therefore, the crucial factor is not so much the sheer number of examples immediately presented, but rather how far a learned principle extends beyond familiar situations.

Herein lies a key difficulty. Good performance in one task category does not guarantee uniform transferability to other tasks. Studies on knowledge-intensive work show that AI significantly helps with some tasks, while it can degrade the quality of results in other, outwardly similarly demanding tasks.

For companies, this means that abstraction capabilities must be tested against real-world transfer problems. A system that effectively summarizes historical complaints may not reliably identify that a previously unknown design flaw underlies new complaints. A model that convincingly describes existing business models may not accurately assess which of their underlying assumptions are currently collapsing.

When analogies become useful and when they lead you astray

Analogy is a special form of abstraction. People transfer structures from one area to another. They speak of organizational bottlenecks, digital ecosystems, or the flywheel of a business model. Such concepts can make complex relationships more understandable.

However, an analogy is not proof. Applying a physical principle to social processes can be an interesting thought experiment, but it doesn't provide a reliable explanation. Companies don't behave like planets, and markets don't simply follow the laws of mechanical motion.

Especially in AI-generated analyses, a distinction should therefore be made between linguistic clarity and causal validity. An elegant metaphor can obscure the fact that central assumptions remain untested. This is particularly true when a system constructs a plausible explanation from familiar narrative patterns.

Studies on the human-like reasoning of language models identify limitations such as heuristic shortcuts, spurious correlations, and faulty causal assumptions. Such problems can occur despite high success rates in other tests.

Abstraction only becomes economically valuable when it enables verifiable improvement. A good analogy helps to formulate a hypothesis. Data, experiments, or practical observations must then demonstrate whether the assumed relationship actually exists. The ability to transform an appealing idea into a verifiable statement remains a key prerequisite for robust analysis.

Ideas don't just arise from data, but they don't come from nothing either

Human ideas can arise from curiosity, frustration, lack, joy, fear, or personal experience. Someone who suffers daily from a cumbersome work process might discover an improvement that wasn't foreseen in any formal job description. An entrepreneur identifies a problem by talking to customers. A designer modifies a component because they experience its weaknesses during assembly.

This combination of experience, motivation, and goal setting distinguishes human creativity from the output of a modern generative system. However, human creativity should not be mystified. Ideas often arise from novel combinations of existing knowledge. They do not require origination outside of all previous experience to be original and economically valuable.

Therefore, the claim that AI can only replicate existing processes is also too strong. Systems can generate combinations that are novel in this form and draw people's attention to viable possibilities. In experimental consulting tasks, creative requirements were explicitly among the activities where AI could improve performance.

The more important question is who sets the direction and who assesses the value of the results. A machine can develop product variants. Whether this product solves a relevant customer problem, fits a company's needs, or is socially desirable remains a further evaluation.

Originality is only one criterion. A new idea can be unusable, unsaleable, or dangerous. Conversely, a less spectacular improvement can generate significant economic benefits. Therefore, the number of proposals produced is not a reliable measure of innovation.

Independent action is not yet proof of one's own intentions

The idea that AI reacts solely to a single prompt describes many simple applications, but not every form of its use. Systems can be integrated into workflows that start automatically, use tools, evaluate intermediate results, and trigger further steps. Accordingly, research also investigates AI agents that process defined tasks across multiple steps.

This allows for operational autonomy without requiring proof of individual needs. A system can work towards a goal because that goal has been technically defined. It doesn't need to feel ambition or personally desire success.

The term intentionality also requires precision. In philosophy, it often refers to the directness of mental states toward something. In everyday language, however, it often means intention or inner motivation. These meanings should not simply be equated. For businesses, a more practical distinction suffices initially: Who sets the goal, what scope for action is granted to the system, and who bears the consequences?

An automated sales agent could, for example, be optimized to generate as many qualified leads as possible. Whether this damages customer relationships, creates undesirable incentives, or results in the inappropriate use of sensitive information depends on the rules and controls in place.

The economic challenge, therefore, is not to attribute human motivation to a machine. It lies in reconciling the pursuit of technical goals with entrepreneurial interests and societal demands. The greater the scope for action, the more important verifiable limits become.

Insight is more than a convincing statement

A moment of insight often changes more than a single answer. Understanding a connection allows one to correct previous beliefs, adjust actions, and reassess one's own role. An "aha!" moment therefore possesses both a cognitive and a personal dimension.

With AI, these levels must be distinguished. A system can generate an accurate explanation or reveal a previously overlooked connection. This can lead to genuine insight for a person. However, this does not yet demonstrate that the system itself experienced a subjectively perceived moment of insight.

The question of consciousness cannot be resolved with a categorical assertion. There is no universally accepted operational definition that could definitively establish subjective experience in any given system. Therefore, from a scientific perspective, it is appropriate to distinguish between a lack of reliable evidence and proof of fundamental impossibility.

Research on limited forms of self-observation in language models illustrates this caution. In experiments, models were able to detect information about influenced internal states under certain conditions. However, the results were unreliable and were explicitly not interpreted as evidence of phenomenal consciousness.

For business decisions, this uncertainty is less paralyzing than it appears. Companies can assess the quality of a diagnostic tool, a design, or a forecast without first resolving the awareness problem. They simply mustn't derive additional attributes from convincing performance that are not substantiated.

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The market evaluates results, not the inner experience of their creator

The economic value of a service depends significantly on the problem it solves, the costs it avoids, and the risks it generates. A company doesn't need a system with human-like intuitiveness for the automatic assignment of documents. It needs reliable assignment, reasonable costs, and clear handling of exceptions.

This explains why a lack of proven consciousness does not constitute an economic safeguard for human labor. A machine does not need to experience an activity in a human way to deliver some of its marketable results. What matters is whether buyers accept the performance and whether the overall costs are lower or the results are better.

At the same time, not every outwardly similar expenditure is economically equivalent. An automatically generated strategy draft does not necessarily replace a responsible strategic decision. A text that sounds legalistic is not automatically a sound legal analysis. A forecast does not replace the decision about what level of risk a company is willing to bear.

In knowledge-intensive professions, this shifts the focus. Simply creating an initial draft may become more readily available. More valuable, however, may be a precise problem definition, access to relevant information, verification of assumptions, and implementation under real-world conditions.

The crucial difference, then, is not simply between humans and machines. It lies between interchangeable output production and a service that is reliably embedded in a concrete economic context.

What productivity studies show and what they leave open

The economic debate should not stop at fundamental considerations. Empirical studies show that generative AI can significantly improve work performance under certain conditions.

A study by Erik Brynjolfsson, Danielle Li, and Lindsey Raymond, in their early working paper, examined 5,179 customer service employees. Access to an AI assistant increased productivity, measured by customer issues resolved per hour, by an average of approximately 14 percent. For inexperienced and lower-skilled employees, the measured increase was around 34 percent, while experienced and high-performing employees benefited significantly less.

These figures are important for several reasons. First, the effect relates to a specific operational activity and not the economy as a whole. Second, the average is not a reliable measure for every individual employee. Third, AI can be particularly helpful where existing experiential knowledge becomes more easily accessible and directly applicable during work.

This leads to a plausible economic function: AI can disseminate some organizational knowledge that was previously tied to individual experienced people. New employees then don't have to discover every suitable formulation or solution themselves.

However, the study does not demonstrate the general replaceability of skilled workers. Nor does it prove that the same effects occur in every industry. Standardized customer service differs significantly from an investment decision, a complex negotiation, or the development of a safety-critical system. Productivity figures therefore always require a description of the activity under investigation.

Increased speed can lead to worse decisions

Particularly revealing is an experiment with 758 consultants from the Boston Consulting Group. When faced with tasks within the capabilities of the system under investigation, participants using AI support completed an average of 12.2 percent more tasks and finished them 25.1 percent faster. The assessed quality of their results was also significantly higher.

However, in a deliberately different, complex task, the advantage reversed. AI support reduced the probability of a correct solution. The accompanying report from Harvard Business School quantifies the average difference at around 19 percentage points.

This contains a key economic warning. The performance limits of AI are not uniform. A system can excel at one task and be unreliable at a seemingly similar one. This limit is not always immediately apparent to users.

Linguistic quality, in particular, can complicate matters. A well-structured answer easily generates trust, even if its crucial assumption is incorrect. If people accept the suggestion without examining the underlying logic, the technical weakness becomes an organizational weakness.

The relevant benchmark is therefore not solely the speed of processing. It is the speed at which a reliable, usable, and accountable result is achieved. A quick wrong decision can be significantly more costly economically than a slower correct one.

Cheaper computing power is shifting the shortage in knowledge work

The decreasing costs of powerful AI are intensifying this pressure for change. The Stanford AI Index 2025 reports that query costs for a system at a performance level comparable to GPT-3.5 fell by more than a factor of 280 between November 2022 and October 2024. This refers to a specific performance level, not a general reduction in the cost of all AI applications to the same extent.

Economically, such a development means that certain forms of intellectual groundwork become more readily available. Summaries, text variations, initial analyses, and simple drafts lose some of their former scarcity. This increases competitive pressure on services whose value stems primarily from their labor-intensive creation.

However, low model costs do not necessarily equate to low overall costs. A company may need to prepare data, develop interfaces, manage access rights, review results, and train employees. Furthermore, there are potential costs associated with erroneous expenditures.

The scarcity therefore shifts. If designs become cheap, their review can become the bottleneck. If ideas are generated in large numbers, the selection becomes more valuable. If information is quickly compiled, the question of which information is actually trustworthy and relevant to decision-making gains importance.

This shift explains why AI can simultaneously devalue services and generate new demand. Not every previously paid activity will disappear. But the portion of its value creation that relies on routine information processing must be redefined.

From apparent time savings to actual business results

A company should not measure the benefits of AI solely by how much faster a single work step is completed. What matters is whether the entire process improves.

Let's assume an analysis design takes ten hours without AI and six hours with AI. At the creation stage, there is a significant time saving. However, if several additional hours are subsequently required for source verification, data correction, and correcting erroneous conclusions, the actual benefit diminishes. This example is a model calculation, not a statement about a specific study.

Even more important is the question of what happens with the time saved. It can be used for additional orders, better customer service, or a more thorough review. However, it can also remain unused if other process steps limit capacity.

Economic evaluations should therefore consider development costs, monitoring efforts, consequences of errors, and actual usage together. A different level of risk is acceptable for a non-binding brainstorming session than for a decision with significant financial or safety-related consequences.

This also changes the investment logic. A cheap system is not automatically economical. A more expensive system can pay off if it significantly reduces rework or reliably eliminates a critical bottleneck. The decisive factor is the total cost of achieving a usable result.

Why improved individual performance does not guarantee an overall economic leap

Productivity gains at the task level cannot simply be extrapolated to companies or national economies. If an activity only occupies a small portion of total working time, its contribution to overall output remains limited. Furthermore, businesses consist of interconnected processes.

Speeding up the creation of offers is of little help if technical approval continues to be delayed. If AI accelerates software development, the bottleneck may lie in testing, integration, or customer requirements. Improving information retrieval won't generate additional revenue as long as decisions and implementation remain unchanged.

High adoption rates should also be interpreted with caution. The Stanford AI Index 2025 reports that 78 percent of surveyed organizations were using AI in 2024, compared to 55 percent the previous year. This refers to reported use in at least one business function, not proof of a complete, productive transformation.

There is a significant difference between experimentation and economically effective integration. A single text assistant alone will not change the business model. Only when data access, responsibilities, workflows, and quality assurance are aligned can technical capability translate into a sustainable competitive advantage.

A realistic perspective therefore combines technological dynamism with organizational patience. The possibilities can grow rapidly, while their operational implementation requires time and complementary investments.

Standard knowledge becomes more readily available; judgment does not automatically become superfluous

The customer service study shows that AI can reduce performance gaps, particularly when less experienced employees benefit significantly. This suggests that certain forms of experiential knowledge can be more easily disseminated. However, this does not equate to a general equalization of all skills.

In many professions, expertise consists of more than just answering a typical question correctly. It also includes the ability to recognize unusual situations, to classify incomplete information, and to realistically assess one's own level of knowledge.

If AI makes standard knowledge more readily accessible, the value of professional work may shift accordingly. It may become less important to reproduce a common explanation without assistance. More important will be asking the right question, recognizing contradictory clues, and deciding when a seemingly plausible answer is insufficient.

This presents a demanding challenge for education and training. Simply operating the tools is not enough. Those who are to verify results still need a solid foundation in the subject matter. Anyone who has never personally understood a cost calculation can only evaluate an automatically generated one to a limited extent.

A sensible qualification strategy should therefore combine both: the efficient use of technical support and the development of the knowledge that enables independent judgment. Short-term acceleration should not be confused with sustainable competence development.

More ideas only create more innovation if selection and testing keep pace

Generative AI facilitates the production of product variants. Companies can examine more product concepts, communication approaches, or problem-solving proposals in less time. This can lead to a competitive advantage in innovation.

At the same time, there is a growing risk of confusing a quantity of ideas with a gain in knowledge. One hundred suggestions are not automatically more valuable than ten. If they are based on similar assumptions, they primarily increase the effort required for selection.

The Harvard presentation of the consulting experiment points not only to the performance gains but also to the possibility that AI might limit the diversity of ideas. This is significant for innovation processes: A tool can improve individual results and simultaneously contribute to several people arriving at more similar solutions.

Companies should therefore use AI not only for production but also for targeted cross-checking. Different assumptions, alternative target groups, or deliberately contradictory perspectives can help broaden the scope of an investigation. However, whether this is successful must be measured by the actual results.

The crucial step remains testing. Customer feedback, technical trials, and robust cost-benefit analyses distinguish an interesting possibility from a viable innovation. AI can support this process, but it cannot replace it through the mere persuasiveness of its statements.

Human responsibility cannot be delegated through a fluent answer

In companies, decisions must not only be made, but also justified and accounted for. Those who invest, deploy staff, or release products decide on consequences for other people. A convincing system output does not eliminate this responsibility.

Human oversight alone is no guarantee. The results of the consulting experiment actually show that AI support can actually make people perform worse on certain tasks. Having a person at the end of the process doesn't automatically make a flawed system reliable.

Effective oversight therefore requires suitable conditions. The reviewing person must possess sufficient expertise, time, and access to information. Furthermore, they must actually be authorized to reject the recommendation or request additional investigations.

For important decisions, it should remain clear which data was used, which assumptions are decisive, and where uncertainty exists. The goal is not to make every technical detail understandable. It is about being able to examine the fundamentals relevant to the decision.

Herein lies a crucial difference between mere automation and responsible automation. The former produces results. The latter links results to traceable responsibilities, controlled risks, and appropriate handling of exceptions.

A new division of labor requires neither machine worship nor human overestimation of oneself

The comparison between a mere pattern-making machine and a purely creative human being is a vivid image, but too simplistic as a complete explanation. Humans also work with patterns, adopt routines, and make mistakes. AI, in turn, can achieve feats that go beyond simple repetition.

The valid distinction, therefore, does not lie in the assertion that one side focuses solely on calculation and the other solely on understanding. It lies in the different prerequisites, abilities, and contexts of responsibility.

Humans possess lived experiences, social relationships, and personal interests. They are affected by the consequences of economic decisions. AI systems can process information and generate results without demonstrating corresponding subjective experiences. The open question of consciousness does not alter this current state of evidence.

A productive division of labor should leverage these differences rather than linguistically smoothing them over. Machines can assist with searching, structuring, developing alternatives, and addressing problems in a defined way. Humans don't need to perform every intermediate step themselves. However, they should retain the capacity for judgment where goals, key assumptions, and consequential decisions are at stake.

This division of labor is not an immutable law of nature. It must be further developed with technological progress and the results of practical tests. A task that is difficult today may become automatable in the future. Responsibility and purpose, however, remain separate issues.

The decisive competitive advantage lies in verifiable judgment

The economic significance of artificial intelligence does not depend on whether machines will one day experience a human "aha!" moment. Even today, systems can accelerate and improve certain knowledge-based tasks. Likewise, studies show that their support in other tasks can lead to poorer results. Therefore, performance and reliability must always be considered together.

The original formula—that humans comprehend and AI calculates—identifies an important intuition: human thought is linked to experience, meaning, and personal involvement. However, as a general scientific boundary, it remains too categorical. Neither understanding nor its impossibility automatically follows from calculation. Convincing language does not imply proven consciousness.

For companies, a different conclusion is more robust. They should deploy AI where its benefits are verifiable and take its limitations seriously where errors have serious consequences. They should not romanticize human skills, but rather develop them systematically: expertise, critical analysis, problem understanding, experimental thinking, and the willingness to discard an attractive explanation.

The real bottleneck in an AI-driven economy might therefore be less the production of answers than the ability to distinguish good answers from bad ones. As suggestions, analyses, and designs become increasingly readily available, the winner is not automatically the one who produces the most. The advantage lies with those who select the right problems, test results against reality, and translate technical achievement into reliable added value.

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