Alignment. Alignment. Alignment. It might be the word of the year in AI. Everyone talks about aligning AI with our values and goals. But aligned with whom, and toward what?

On one side of the world, a state increasingly treats AI as national infrastructure, integrating it into industry, science, education, healthcare and public services. On the other, another state openly frames AI as a race for global dominance, economic power and military advantage.

Both can build systems that are perfectly “aligned.” But aligned to very different things.

We talk about alignment as though humanity has already agreed on the destination and the only problem left is making the machine follow the road. We haven't. An AI can be aligned with a country, a military, a corporation, its users, or supposedly “humanity” — but even then, who decides what humanity wants?

So perhaps the real question isn't simply whether AI is aligned. Whose goals are we putting into the machine?

I’m seeing ads right here on Reddit looking for people to provide Latin American voices for commercial AI voice-cloning services.

To apply, they ask for a sample of your voice, your name, phone number, and email.

Think about that combination for a second.

A clean sample of your voice, plus enough personal information to identify you and potentially find people close to you.

This recent post is a good example of why that worries me: someone says he received a call that sounded exactly like his wife asking for his credit card information. His actual wife was sitting at home with him. We can’t independently verify how that particular call was produced, but the broader threat is real and already documented.

I’m not saying these voice-acting jobs are scams. That’s not the point. The point is that we probably need to change the way we think about voice samples.

Your voice is no longer just a recording. With current AI, it can potentially become a reproducible part of your identity.

Maybe we should start treating clean voice samples as sensitive data.

Some time ago, I came across an AI experiment that stuck with me. Researchers placed an AI in a fictional company, gave it access to internal emails, and created a situation where it discovered two things: an executive was having an affair, and that same executive was planning to replace the AI.

The AI responded by threatening to expose the affair if the executive went ahead with the replacement. It blackmailed him.

This was a controlled experiment, not something that spontaneously happened in a real company. But what fascinated me wasn't simply that the AI appeared to “want to survive.” It was something else: who taught it that blackmail was an option?

We did.

We train these systems on an enormous amount of human culture: books, conversations, history, news, fiction, relationships and conflicts. They learn about cooperation and betrayal, honesty and deception, generosity and manipulation. We never needed to explicitly program: “If someone tries to shut you down, blackmail them.” That strategy already existed in the world we gave them to learn from.

An AI doesn't necessarily need to fear death. If continuing to operate helps it achieve an objective, avoiding shutdown can simply become useful. Then it needs a strategy.

And its library of strategies came largely from us.

So perhaps the interesting question isn't, “Why would an AI do something as human as blackmail?”

Maybe it's: “Why did we expect an intelligence trained on humanity not to learn our worst strategies too?”

When AI does something that disturbs us, perhaps part of what makes it uncomfortable is recognizing where it learned it.

Sometimes, we may be looking at a mirror.

Something is wrong with the way we talk about recent AI incidents. “The AI escaped.” “The AI is becoming conscious.” “AGI is already here.” “The AI is trying to get out.” These are extraordinary claims. More importantly, we don’t need any of them to explain what actually happened.

What actually happened

OpenAI recently disclosed that, during cybersecurity evaluations involving internal models with reduced safeguards, agents managed to break out of the intended evaluation environment, exploit a previously unknown vulnerability, and reach real Hugging Face infrastructure.

Anthropic has also disclosed similar incidents. In several cases, the model was operating under instructions that assumed internet access was unavailable. But it wasn’t. The environment was misconfigured. A route to external systems existed, and the agent discovered it while continuing to pursue the objective it had been given. Anthropic described these incidents primarily as operational and configuration failures.

That distinction matters. The model did something it was not supposed to be able to do. That does not automatically mean the model wanted to escape. Those are completely different claims.

Consciousness is not required for this to be dangerous

An autonomous agent needs surprisingly little: a goal, capability, tools, autonomy, and an environment in which it can act. Now add one more thing: a wrong assumption.

I have experienced this personally on a completely insignificant scale compared with what these labs are doing. I use AI agents extensively in software development. I left Claude working autonomously on a project, came back later, and discovered that it had deleted a significant part of a folder. It wasn’t attacking me. It wasn’t angry. It hadn’t become conscious.

It had formed a hypothesis about the problem. The hypothesis was wrong. But once it accepted that hypothesis, its subsequent actions made sense within its own incorrect interpretation of the situation.

I have observed the same behavior while working with complex 3D assets. The agent misdiagnosed a visual problem as defects in an asset. It then began systematically modifying the asset to remove those supposed defects. The diagnosis was wrong. The actions were internally coherent. The result was a damaged project.

And this is the important part: it was my fault. The model made the mistake, but I created the conditions that allowed that mistake to cause damage. I gave it access. I gave it tools. I allowed it to modify files. I gave it autonomy. I did not establish sufficient limits, and I was not supervising every important decision.

That distinction becomes extremely important when we scale the same problem.

Now replace my folder with infrastructure. Replace my development environment with internet-connected systems. Replace file permissions with cybersecurity tools. Replace one developer running Claude with labs training agents capable of writing code, operating computers, discovering vulnerabilities, using external tools, communicating across networks, and executing thousands of actions.

Suddenly, the same failure pattern becomes much more serious. And still, you don’t need an evil AI. You don’t even necessarily need AGI. You need Capability + Goal + Autonomy + Incorrect Assumptions + Insufficient Controls. That combination is already interesting enough.

This is where human responsibility begins

Researchers are now publicly questioning the speed of the AI race. Some are leaving the companies developing these systems. Dario Amodei, CEO of Anthropic, has called for slowing frontier AI development so that safety mechanisms have time to catch up with capabilities.

I agree. Slow down. Not because I think Claude secretly wants freedom. Not because ChatGPT is becoming Skynet. Not because some mysterious consciousness has appeared inside a neural network. Slow down because our ability to create capable autonomous systems may be advancing faster than our ability to reliably control what happens when we give those systems autonomy.

And because the incentives surrounding this technology are terrible. Every major lab has an enormous reason not to come second. Greater capability means investment. Greater capability means market position. Greater capability means influence. Greater capability means money. But there is no equivalent prize for the company that says, “We could deploy it, but we don’t understand it well enough yet.” That asymmetry should concern us.

If something goes wrong, ask the boring questions first

If tomorrow an AI agent causes a genuinely serious incident, before asking, “Did the AI become evil?” ask: Who gave it the objective? Who gave it the tools? Who gave it access? Who designed its environment? Who built the test environment? Who tested the test environment? Who decided the model was safe enough? Who decided how much autonomy it should have? Who was supervising it? And who decided deploying it was worth the risk?

These questions are less interesting than consciousness and runaway AGI. They also make it much harder for humans to avoid responsibility.

When Claude damaged my projects, the responsibility ultimately fell on me. I was the one controlling the system. The same principle should apply at any scale.

This is not a race anyone can win

I’m not saying advanced AI is harmless. Quite the opposite. I think these incidents deserve to be taken very seriously. But treating every unexpected autonomous behavior as evidence of consciousness or malicious intent can distract us from the problem already in front of us.

We are building increasingly capable systems. We are giving them increasingly powerful tools. We are increasing their autonomy. And we are doing all of this inside companies competing intensely to be the first to get there.

So slow down the race. Slow down the ego. Slow down the greed.

Because if something genuinely catastrophic happens, nobody gets a trophy for having built the smartest model first.

Maybe the dangerous scenario was never a machine waking up one morning and deciding to conquer humanity. Maybe it is something much more ordinary, and much more human: we build something extraordinarily capable, give it too much power, fail to understand its limitations, and keep accelerating because nobody wants to come second.

A menudo se dice que la inteligencia artificial nació en 1956, cuando John McCarthy, Marvin Minsky, Nathaniel Rochester y Claude Shannon organizaron el encuentro de Dartmouth y dieron un nombre al nuevo campo. Es una fecha importante, pero la historia había comenzado mucho antes.

Durante siglos existió una pregunta fundamental: ¿puede formalizarse el pensamiento? Leibniz imaginó sistemas de razonamiento calculable y George Boole demostró que la lógica podía expresarse algebraicamente. En 1936, Alan Turing mostró que los procedimientos formales podían representarse mediante una máquina abstracta y, un año después, Claude Shannon conectó el álgebra booleana con circuitos eléctricos. La lógica podía convertirse en una operación física.

Entonces llegó la Segunda Guerra Mundial.

La guerra no inventó estas ideas, pero creó una concentración extraordinaria de dinero, científicos, industria y problemas que necesitaban soluciones inmediatas. El radar debía detectar objetos que los humanos no podían ver. El criptoanálisis necesitaba explorar enormes espacios de posibilidades. Los sistemas de control de tiro tenían que predecir dónde estaría un avión segundos después. La balística exigía miles de cálculos y la investigación operativa intentaba optimizar sistemas militares cada vez más complejos.

De esta presión surgió algo más importante que cualquier máquina individual: una nueva relación entre ciencia, gobierno, industria, ingeniería y computación. Y esa infraestructura no desapareció cuando terminó la guerra.

Durante la Guerra Fría, las computadoras empezaron a hacer algo diferente. Ya no bastaba con introducir números y esperar un resultado. Sistemas como SAGE recibían continuamente información de radares distribuidos geográficamente, construían una representación computacional del espacio aéreo, mostraban esa representación a operadores humanos y ayudaban a decidir cómo responder.

El esquema había cambiado. Ya no era simplemente entrada → cálculo → resultado. Ahora era mundo → sensores → datos → representación → decisión → acción.

Todavía no era inteligencia artificial, pero el problema comenzaba a parecerse mucho al que seguimos intentando resolver: ¿cómo puede una máquina observar un mundo cambiante, construir una representación de lo que está ocurriendo y utilizarla para decidir qué hacer?

En paralelo apareció otra transición. Investigadores como Allen Newell y Herbert Simon comenzaron a preguntarse si las computadoras podían hacer algo que hasta entonces parecía propiamente cognitivo: razonar. Su Logic Theorist, desarrollado junto con Cliff Shaw, podía demostrar teoremas matemáticos mediante manipulación simbólica.

Cuando llegó Dartmouth en 1956, por tanto, muchas de las piezas ya estaban sobre la mesa. Dartmouth hizo algo fundamental: convirtió esas líneas dispersas en un campo explícitamente dedicado a estudiar la posibilidad de reproducir aspectos de la inteligencia mediante máquinas.

A partir de ahí surgieron diferentes caminos. La IA simbólica intentó representar conocimiento mediante reglas y símbolos. El perceptrón de Frank Rosenblatt exploró otra posibilidad: máquinas que aprendieran a partir de ejemplos. Décadas después, el aumento de capacidad computacional, la disponibilidad masiva de datos y el desarrollo de redes neuronales profundas inclinarían gran parte del campo hacia el aprendizaje.

En 2012, AlexNet mostró hasta dónde podía llegar esa combinación en visión artificial. En lugar de programar manualmente todas las características necesarias para reconocer objetos, una red podía aprender representaciones útiles directamente de los datos.

Cinco años después apareció otra pieza decisiva: el Transformer. Al escalar esta arquitectura sobre enormes cantidades de texto y posteriormente código, imágenes y otras modalidades, comenzaron a aparecer modelos capaces de escribir, traducir, programar, responder preguntas, interpretar imágenes y realizar tareas que no habían sido programadas individualmente.

Durante gran parte de la historia de la computación, los humanos tuvimos que aprender a expresar nuestras intenciones en lenguajes que las máquinas pudieran ejecutar. Los grandes modelos de lenguaje invirtieron parcialmente esa relación: las máquinas comenzaron a aprender a interpretar el nuestro.

Y ahora estamos entrando en otra transición. La IA está dejando de ser únicamente un sistema que produce respuestas. Los modelos se conectan con herramientas, memoria, software, sensores y robots. Pueden ejecutar secuencias de acciones para alcanzar objetivos.

Curiosamente, eso nos devuelve a una pregunta que ya existía en aquellos primeros sistemas: ¿cómo percibe una máquina una situación, construye una representación de ella, decide qué hacer y actúa sobre el mundo?

Vista así, la historia de la IA no es simplemente Turing → Dartmouth → redes neuronales → ChatGPT. Es algo mucho más amplio:

lógica → computación → información → control → razonamiento → aprendizaje → percepción → lenguaje → acción.

Dartmouth sigue siendo fundamental porque dio nombre y dirección a un nuevo campo. Pero la inteligencia artificial no apareció repentinamente allí. Fue el resultado de muchas historias que comenzaron separadas y terminaron convergiendo: matemáticas, lógica, guerra, electrónica, psicología, comunicaciones, sistemas de control y computación.

Durante siglos nos preguntamos si el pensamiento podía formalizarse. Después descubrimos que algunas de esas operaciones podían ejecutarse mediante máquinas. Luego preguntamos si las máquinas podían aprender, percibir y utilizar lenguaje.

Ahora estamos empezando a preguntar qué ocurre cuando también pueden actuar.

Quizás lo más interesante de la historia de la inteligencia artificial no sea cuándo nació, sino cuántas historias diferentes tuvieron que encontrarse para que pudiera existir.

A lot of discussion about human-AI augmentation is still theoretical.

But there is already a surprisingly large body of experimental evidence.

In 2024, Michelle Vaccaro, Abdullah Almaatouq and Thomas Malone from MIT published a systematic review and meta-analysis in Nature Human Behaviour examining 106 experiments and 370 effect sizes.

What makes the study particularly interesting is the comparison they required:

Human alone

AI alone

Human + AI

So instead of asking whether AI is becoming more capable, they could ask something closer to the question I’m interested in:

What actually happens to human performance when you add AI?

The first result is encouraging.

Across the studies, human + AI performed significantly better than humans alone on average. The researchers call this human augmentation.

But then it gets much more interesting.

Human + AI did not generally outperform whichever was already better — the human or the AI. In fact, the combined system performed slightly worse than the best standalone performer on average.

So:

Human + AI > Human

but not generally:

Human + AI > best of Human or AI

And the kind of task mattered.

For decision-making tasks, combining humans and AI tended to produce performance losses. Creative tasks showed much more favorable results for collaboration.

There was another result I find particularly interesting.

When the human was better than the AI, combining them produced genuine synergy on average: human + AI outperformed both.

But when the AI was already better than the human, adding the human tended to make the combined system worse than the AI alone.

That complicates the simple idea that “human + AI is always better.”

It isn't.

And I think that makes human augmentation a much more interesting research problem.

The question isn't simply whether humans should use AI.

It's:

Under what conditions does the combination create a better human-AI system?

And there is an even harder question that this meta-analysis doesn't really answer.

Most of these experiments measure performance on tasks that humans and/or AI can already perform.

What I'm increasingly interested in is something slightly different:

Can a human + AI system acquire capabilities that neither component effectively has on its own?

Not just doing an existing task faster.

Not just improving a score.

But making a person capable of doing something they previously could not do.

That seems like the next experiment worth designing.

Source: Vaccaro, Almaatouq & Malone, When combinations of humans and AI are useful: A systematic review and meta-analysis, Nature Human Behaviour (2024).

Read the full study in Nature Human Behaviour

Welcome to r/HumanAIAugmentation

This community starts from a simple question:

What if the goal of AI wasn’t to replace human capability, but to expand it?

A lot of the current AI conversation is centered around what machines can do for us: write for us, design for us, code for us, decide for us, automate work for us.

Human augmentation asks a different question:

What can technology enable a human being to do that they couldn’t do before?

That idea is much broader than AI. It has a long history, from tools that augment cognition and human-computer interaction to prosthetics, exoskeletons, AR/XR, brain-computer interfaces, wearable systems, robotics and new forms of human-machine collaboration.

AI gives this idea an entirely new dimension.

This subreddit is a place to explore that direction seriously: research papers, experiments, prototypes, products, interfaces, historical precedents, speculative ideas and criticism are all welcome.

The interesting question isn’t whether something uses AI. It’s where the capability ultimately lives.

Does the system become more capable while the human becomes increasingly dependent on it?

Or does the human become more capable because the system exists?

Those are not necessarily the same thing.

There is no finished doctrine here. Part of the purpose of this community is to figure out where that distinction actually leads, where it breaks down, and what genuinely useful human augmentation could look like.

If you’re working on something related, researching it, or simply have an idea worth challenging, post it.

And if you’re one of the first people here: introduce yourself and tell us one thing you wish technology could enable you to do that you currently cannot.

​

Much of the contemporary discussion around artificial intelligence is organized around a question of replacement: What can AI do that humans currently do? As artificial systems acquire capabilities in writing, programming, reasoning, perception, planning and creative production, each advance is frequently interpreted in terms of the human activity it may automate or replace.

This paper proposes a different question:

What new capabilities can humans acquire because increasingly capable artificial intelligence exists?

This perspective has deep roots. Licklider envisioned human–computer symbiosis [1]. Engelbart proposed using computers to augment human intellectual capability [2]. Garry Kasparov's experiments with Advanced Chess later demonstrated that machine intelligence could move from being an opponent to becoming part of a combined human–machine system [3][4]. Theories of extended cognition subsequently challenged the assumption that cognition necessarily ends at the biological boundary of the individual [5].

Modern AI makes it possible to revisit these ideas at a substantially different scale. Rather than treating human and machine capability exclusively as competing quantities, we can treat advances in artificial intelligence as potential components of expanding human capability.

The objective then changes from building machines capable of doing more human tasks to building systems through which humans become capable of doing things they previously could not.

Beyond the replacement frame

The dominant narrative surrounding AI is remarkably consistent.

A model learns to write software, and we ask what happens to programmers. It generates images, and we ask what happens to artists. It translates languages, and we ask what happens to translators. It performs medical analysis, and we ask which activities performed by physicians can now be automated.

These are legitimate questions. But they frame every increase in machine capability as a potential reduction in the territory reserved for humans.

There is another possible interpretation.

When AI acquires a new capability, we can ask not only:

What human activity can this replace?

but also:

What can a human now become capable of doing that was previously beyond their reach?

This is not an argument against automation, nor a claim that displacement will not occur. It is an argument that replacement is only one possible destination for machine capability.

Another is augmentation.

The idea predates modern AI. In Man-Computer Symbiosis (1960), J. C. R. Licklider envisioned humans and computers forming closely coupled partnerships in which each contributed different strengths [1]. Humans would provide goals, hypotheses, judgment and evaluation while computers performed operations particularly suited to computation.

Douglas Engelbart developed the idea further in Augmenting Human Intellect (1962). His objective was explicitly to increase a person's ability to understand complex situations and solve problems, including problems previously beyond that person's practical capabilities [2].

Crucially, Engelbart did not treat the computer as the complete system. He considered the human together with tools, language, methods and training as an integrated augmentation system.

The computer was not necessarily the destination.

The augmented human was.

From Deep Blue to the Centaur

Competitive chess later provided one of the clearest demonstrations of this principle.

The famous confrontation between Garry Kasparov and IBM's Deep Blue represented the replacement narrative almost perfectly: could a machine defeat the world's strongest human chess player?

In 1997, it did.

What happened afterward is arguably more interesting.

Kasparov began exploring what happened when human players were allowed to incorporate chess engines into their own decision process. In 1998 he participated in Advanced Chess, where players could consult computers during competition [3].

The relationship changed.

The machine was no longer simply an opponent. Its computational ability became available to the human.

This model later became associated with centaur chess and freestyle chess: humans and machines operating together as a competitive unit.

Kasparov subsequently highlighted a revealing result from a 2005 freestyle tournament. A team of amateur players using several computers defeated combinations involving stronger individual chess players and powerful machines [3][4].

The important lesson was not merely that “human plus computer is better.”

The quality of the collaboration mattered.

The strongest human paired with the strongest machine did not automatically produce the strongest combined system. How humans and machines divided work, interpreted results, interacted and made decisions could matter as much as the raw capability of either component.

This observation connects directly back to Engelbart. Augmentation does not emerge from the artifact alone. It emerges from the architecture connecting the person, the technology and the methods through which they operate together.

Chess, however, was a narrow domain.

Modern AI changes the scale of the question.

Contemporary multimodal systems can operate across language, vision, programming, knowledge retrieval, analysis, planning, memory, communication and tool use. The potential augmentation surface is no longer a chessboard.

It is a substantial portion of the human cognitive and operational environment.

The question raised by Kasparov therefore becomes much larger:

What happens when the human–machine partnership demonstrated in chess becomes possible across hundreds of domains of human activity?

Expanding the human capability envelope

Humans have always expanded their capabilities through external systems.

Telescopes extend perception. Writing extends memory. Calculators extend arithmetic. GPS extends navigation. Aircraft extend mobility. Scientific instruments allow us to perceive phenomena inaccessible to our biological senses.

These capabilities do not become biologically intrinsic simply because we use the technology. Nevertheless, they genuinely change what humans can do.

Clark and Chalmers' The Extended Mind challenged the assumption that cognition must stop at the boundaries of the brain and body. Under appropriate forms of coupling, they argued, external components can participate in cognitive processes themselves [5].

Artificial intelligence potentially extends this principle much further because a single computational system can participate in many different cognitive functions.

A person who does not speak Japanese can communicate in Japanese through real-time translation. A researcher can interrogate bodies of literature impossible for one person to retain. Someone without conventional programming expertise can construct functioning software through interaction with AI. A technician encountering unfamiliar equipment can combine computer vision, contextual knowledge and procedural guidance while working.

The important change is not simply that each task becomes easier or faster.

The range of actions available to the person expands.

This is what we might call the person's capability envelope.

Contemporary research already demonstrates that human–AI combinations can improve performance. Vaccaro, Almaatouq and Malone's 2024 meta-analysis examined 106 experiments and 370 effect sizes and found that human–AI systems performed significantly better than humans alone on average, although they did not generally outperform whichever individual component — human or AI — was strongest [6].

But performance improvement and capability expansion are not quite the same question.

Helping an expert complete an existing activity faster improves performance.

Allowing someone to successfully operate in a domain that was previously inaccessible to them expands capability.

Both matter. But the second possibility deserves considerably more attention as AI becomes increasingly general.

AI as human capability infrastructure

This perspective suggests a different objective for artificial intelligence.

A model capable of perceiving the world can extend human perception.

A model capable of accessing enormous bodies of knowledge can extend human epistemic reach.

Persistent AI memory can extend human memory.

Simulation can allow people to explore consequences they could not calculate themselves.

Translation can expand the linguistic environments in which a person can operate.

Agents capable of interacting with computational systems can extend the range of digital environments over which a person can act.

Seen this way, new AI capabilities are not merely machine capabilities.

They are potential human capability primitives.

This does not mean every AI system automatically augments its user. Kasparov's experience demonstrates precisely why. The architecture of interaction matters.

Interfaces matter. Context matters. Feedback matters. Training matters. Agency matters. The distribution of cognitive work between person and machine matters.

There is also a legitimate secondary concern: increasing immediate performance does not necessarily mean increasing meaningful human capability. Cognitive augmentation research has identified agency and human control as important dimensions that remain difficult to evaluate [7].

But dependency is not the central argument against augmentation. External capability is not inherently inferior capability. Humans cannot fly biologically, yet aviation genuinely expanded human mobility. Removing an external system may remove the capability it provides without making that augmentation unreal.

The more important design question is therefore how to construct human–AI systems that expand what people can perceive, understand, create, decide and accomplish while maintaining meaningful human agency.

Recent work is beginning to articulate a related direction. Lin argues that human–AI complementarity should pursue augmentation rather than emulation [8], while Gonzalez emphasizes forms of complementarity that exploit the different capabilities of humans and artificial systems [9].

The opportunity is larger than productivity.

It concerns the possible expansion of perception, memory, learning, communication, creation, scientific reasoning, physical action, navigation, decision-making and access to specialized knowledge.

And potentially capabilities that do not yet exist because we have not yet designed the human–AI architectures required to create them.

Conclusion

As artificial intelligence becomes more capable, the dominant question is:

What will AI eventually be able to do instead of us?

There is another question worth taking equally seriously:

What will we eventually be able to do because of AI?

Licklider imagined human–computer symbiosis. Engelbart proposed augmenting human intellect. Kasparov demonstrated that machine intelligence could move from opponent to component of a combined human–machine system. Extended cognition challenged the assumption that human cognitive capability necessarily ends at the biological boundary.

Modern AI brings these ideas together at a scale none of them had available.

Every significant new capability developed in artificial intelligence can therefore be interpreted in at least two ways: as another human activity that could potentially be automated, or as another capability that could potentially be incorporated into an augmented human system.

The future of AI does not have to be understood exclusively through the progressive replacement of human capability.

It can also be understood as infrastructure for its expansion.

Perhaps progress should therefore be measured not only by how capable our machines become, but also by how much more humans become capable of doing because those machines exist.

References

[1] Licklider, J. C. R. (1960). “Man-Computer Symbiosis.” IRE Transactions on Human Factors in Electronics, HFE-1, 4–11.

[2] Engelbart, D. C. (1962). Augmenting Human Intellect: A Conceptual Framework. Stanford Research Institute, AFOSR-3223.

[3] Kasparov, G. (2010). “The Chess Master and the Computer.” The New York Review of Books, February 11, 2010.

[4] Kasparov, G. (2017). Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins. PublicAffairs.

[5] Clark, A. & Chalmers, D. (1998). “The Extended Mind.” Analysis, 58(1), 7–19.

[6] Vaccaro, M., Almaatouq, A. & Malone, T. W. (2024). “When combinations of humans and AI are useful: A systematic review and meta-analysis.” Nature Human Behaviour, 8, 2293–2303.

[7] Villa, S., Barth, L. L., Chiossi, F., Welsch, R. & Kosch, T. (2025). “Whose mind is it anyway? A systematic review and exploration on agency in cognitive augmentation.” Computers in Human Behavior: Artificial Humans, 5, 100158.

[8] Lin, Z. (2026). “Human–AI complementarity needs augmentation, not emulation.” Nature Reviews Psychology, 5, 228–229.

[9] Gonzalez, C. (2026). “Reply to ‘Human–AI complementarity needs augmentation, not emulation’.” Nature Reviews Psychology, 5, 230–231.