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What does it mean to be genuinely ‘AI literate’?

A AI is transforming teaching, research and student life — enabling personalised learning, accelerating discovery and reshaping how campuses work. Yet beneath that convenience lie serious risks: opaque algorithms, rising plagiarism concerns, deepening inequities and an environmental footprint growing faster than most students or educators realise. Did you know? The IEA reports that global investment in data centres is now set to exceed global spending on oil — a stark reminder that “data is the new oil” is no longer a metaphor but an energy reality. EP lays out what true AI literacy must deliver, what institutions should demand from AI vendors, and how universities can build systems that are sustainable, transparent and accountable. The future of learning will be AI-enabled — but it must also be human centred, equitable and environmentally responsible

Dipin Damodharan

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The rapid ascent of generative artificial intelligence is actively reshaping how we learn, create, and work. It offers a seductive promise of instant knowledge and effortless productivity, a modern-day magic trick available at our fingertips. But like any good magic act, the most important part of the illusion is what the audience doesn’t see. Behind the curtain of flawlessly formed paragraphs and instant data analysis lies a complex and often invisible world of ethical trade-offs and profound physical costs.

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Consider the experience of a college student in 2025. When she asked an AI tutor for help with an essay, she watched in amazement as articulate, well-structured text appeared in seconds, complete with what looked like flawless references. The magic, however, quickly faded. As she began to engage critically with the output—tracing sources and questioning claims—she discovered some references were entirely fictitious, the reasoning was hollow, and the fluent prose was merely a sophisticated imitation of insight.

This student’s discovery is a microcosm of a much larger challenge. Her story moves us beyond the simple wonder of a new technology to the central question of our time: Are we truly prepared for the full consequences of the AI revolution? And in this new age, what does it mean to be genuinely “AI literate”?

Education Publica explores the good, the bad, and the hidden “ugly” of artificial intelligence. Our future depends not on whether if we use this transformative tool, but how we choose to use it—effectively, ethically, and with full awareness of its staggering environmental footprint. The path forward requires moving past the illusion and understanding the true cost of the bargain we are making.

The Promise and The Peril: AI’s Double-Edged Sword

To navigate the new AI landscape responsibly, we must first appreciate its dual nature. AI is neither a pure panacea nor an unmitigated threat; it is a powerful tool with the capacity for both transformative good and significant harm. Understanding this duality is the first step toward harnessing its potential while mitigating its inherent risks.

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The Promise: A Revolution in Access and Efficiency

Proponents rightly point to a suite of transformative capabilities, particularly in making education more efficient and accessible. The key benefits are already becoming clear:

• Personalized Learning and Access: AI-powered tutors can provide students with 24/7 support, offering rapid feedback and accessible explanations. In a 2025 survey of undergraduates at a large US public university, students confirmed they value this immediate assistance. This technology holds particular promise for bringing personalized learning to underserved regions, such as India.

• Administrative Efficiency: For educators, AI can streamline time-consuming tasks like drafting lesson plans, summarizing readings, and assisting with grading. This frees up valuable time for them to focus on mentoring students and engaging in higher-order teaching.

• Research Acceleration: In academic and scientific fields, AI is a powerful catalyst. It can dramatically speed up literature reviews, process vast datasets, and even help generate new hypotheses, significantly boosting research productivity.

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The Peril: The Costs to Integrity, Privacy, and Equity

Alongside its immense promise, AI introduces tangible risks that threaten the core tenets of academic inquiry and social equity. These perils require careful management and proactive policy.

1. Academic Integrity and Critical Thinking: The ease of generating text with AI presents a significant threat to academic integrity. A 2023 educational study warned that this makes it easier than ever for students to submit work they did not write. A more subtle danger is the phenomenon of AI “hallucinations”—false but convincingly presented information—which many students are ill-equipped to identify. Over-reliance on these tools risks weakening the essential skills of critical reasoning and research.

2. Privacy and Surveillance: The use of AI tools in education often involves storing vast amounts of student data on remote servers. Without robust policies and oversight, this sensitive information can be misused or profiled, creating significant privacy and surveillance risks.

3. The Widening Digital Divide: The benefits of AI are not universally accessible. Effective use requires stable internet, modern devices, and reliable electricity. Students from disadvantaged backgrounds who lack these resources risk falling even further behind, deepening existing educational and social inequities.

But these visible debates are a distraction from a far larger, physical cost that is being silently added to a global environmental ledger.

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The Ugly: AI’s Invisible Environmental Footprint

While academia and industry wring their hands over plagiarism and bias, they remain wilfully blind to a far more inconvenient truth: the AI revolution is built on a foundation of staggering energy and water consumption. This isn’t an abstract cost; it’s a physical debt being charged to the planet with every query. This is the engine room of the illusion, an immense, energy-hungry global infrastructure that our collective failure to recognize is a critical flaw in our current understanding of the technology.

A Stark Literacy Gap

In a recent survey of over 30 undergraduate and postgraduate students from India, the UK, and Canada, a startling consensus emerged. These students, from diverse fields including engineering and humanities, were either enthusiastic or casual users of AI. Yet, with the exception of a single master’s scholar, not one of them had any meaningful understanding of AI’s physical and environmental footprint.

Their perception of AI was telling, revealing a profound disconnect between the digital tool and its physical reality.

Students frequently described AI as “free,” “virtual,” “weightless,” or “just code.” The notion that AI has a physical footprint—servers, cooling systems, chips, power draw—was almost entirely absent.

This gap represents a fundamental failure of AI literacy. Current education and discourse overwhelmingly focus on what AI does for us, not what it costs the planet. This blind spot is shaping policy and user behaviour at a moment when the stakes could not be higher.

Quantifying the Cost: From a Single Query to Global Demand

The feeling of “weightlessness” is an illusion. In terms of energy, a simple Google search and a generative AI query are worlds apart. The difference is not incremental; it is exponential.

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The implication of this data is staggering: One long AI query can consume as much electricity as 30–100 Google searches. When multiplied by hundreds of millions of daily queries worldwide, this individual cost scales into a global crisis.

The scale of this shift is not theoretical; it is being meticulously tracked by global energy watchdogs, and their findings are alarming. The International Energy Agency (IEA) provides a chilling macro-level view of this trend:

• 2024 Consumption: Data centres consumed an estimated 415 TWh of electricity, representing 1.5% of global demand.

• 2030 Projection: Driven primarily by the explosive growth of AI, this demand is projected to more than double to 945 TWh.

• A Shocking Equivalence: This projected demand is equal to the entire annual electricity consumption of Japan.

IEA’s recent analysis signals that AI is no longer just a technological tool but an energy-intensive industrial sector. Its electricity demands are now large enough to reshape consumption patterns in advanced economies and rival global investment in oil — a striking sign of the world’s transition into the “Age of Electricity.”

“Analysis in the World Energy Outlook has been highlighting for many years the growing role of electricity in economies around the world. Last year, we said the world was moving quickly into the Age of Electricity – and it’s clear today that it has already arrived,” said IEA Executive Director Fatih Birol. “In a break from the trend of the past decade, the increase in electricity consumption is no longer limited to emerging and developing economies. Breakneck demand growth from data centres and AI is helping drive up electricity use in advanced economies, too. Global investment in data centres is expected to reach $580 billion in 2025. Those who say that ‘data is the new oil’ will note that this surpasses the $540 billion being spent on global oil supply – a striking example of the changing nature of modern economies.”

This global problem is coming to a head in nations where the balance between progress and sustainability is most delicate, nowhere more so than in India.

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India at the AI Crossroads

India stands as the global epicenter of AI’s collision between digital ambition and physical limits. Its unique combination of immense economic opportunity, significant digital disparity, and acute environmental stress makes its approach to AI adoption a high-stakes paradox—and a bellwether for the entire developing world.

The Multi-Billion Dollar Promise

The economic incentives for embracing generative AI are enormous. An EY report estimates that by 2029-30, the adoption of GenAI could add US359 billion to US438 billion to India’s GDP, promising to accelerate growth and enhance productivity across the nation.

A Collision Course with Reality

Yet, this multi-billion-dollar vision, articulated by consultancies like EY, is on a direct collision course with the stark physical limitations outlined by energy and environmental analysts. For India, the promise of virtual wealth is tethered to the reality of stressed power grids and scarce water. Unregulated AI adoption threatens to exacerbate several pre-existing, systemic challenges:

• Digital Disparity: Large segments of the population still lack reliable access to the stable internet and modern devices required for AI-driven learning and work.

• Stressed Infrastructure: The nation’s electricity grids are already under significant strain, and the massive energy demands of AI data centres could push them to their limits.

• Environmental Scarcity: Many regions across India face severe water scarcity, a problem that would be intensified by the vast water requirements for cooling data centres.

• Budgetary Constraints: Public educational institutions operate on tight budgets, making it difficult to fund the necessary technological infrastructure and training for students and educators.

For India, blindly pursuing AI adoption is not a viable path. A deliberate, responsible, and human-centered framework is not just an option; it is an absolute necessity.

Redefining AI Literacy for a Sustainable Future

The challenges posed by AI, while significant, are not insurmountable. Addressing them requires a new, more comprehensive definition of AI literacy—one that is human-centered, ethically grounded, and environmentally accountable. The goal is not to restrict AI, but to build a foundation of trust and sustainability for its responsible integration into society. This requires a coordinated effort from educational institutions, organizations, and policymakers. According to UNESCO, AI literacy involves equipping learners and educators with a human-centred mindset, ethical awareness, conceptual understanding, and practical skills to use AI responsibly, understand its implications, and adapt as AI technologies evolve.

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Building on UNESCO’s foundation, Education Publica proposes a broader, more future-ready definition: AI literacy is the integrated set of knowledge, skills, attitudes, and ethical principles that enable individuals to understand what AI is and how it works; use AI tools effectively and safely; critically interpret and question AI outputs; recognise the societal, ethical, economic, and environmental impacts of AI systems; and make informed, responsible choices about when, why, and how to engage with AI.

For Educational Institutions and Organizations:

A proactive, principles-based approach is essential for navigating the complexities of AI integration. The following strategies provide a roadmap for responsible adoption:

1. Demand Vendor Transparency: Insist that AI providers not only disclose per-query energy data, carbon metrics, and water consumption but also provide “explainable AI” algorithms, helping users understand the “why” behind an output, not just the “what.”

2. Mandate Comprehensive AI Literacy: Implement formal courses covering not just AI use, but also its limitations, including inherent bias, hallucination risks, data privacy ethics, and its full environmental impact.

3. Establish Clear Ethical Guidelines: Develop and enforce robust academic integrity policies that explicitly define where AI is allowed (e.g., for brainstorming), allowed with declaration (e.g., for drafting assistance), or prohibited (e.g., in exams).

4. Protect Equity: Ensure students without reliable access to technology are not disadvantaged by maintaining viable offline alternatives for key academic activities and assessments.

5. Foster a Culture of Innovation and Inquiry: Beyond just mandating courses, institutions must build a culture that encourages experimentation and critical feedback loops, as recommended by industry leaders at EY. This involves creating cross-functional teams to continually assess AI’s impact on learning and well-being.

6. Invest in Sustainable Infrastructure: Prioritize renewable-powered cloud providers and perform continuous audits of energy and water consumption related to AI workloads.

For Policymakers:

The role of government is crucial in shaping a healthy AI ecosystem. Policymakers must work to create a global consensus on AI regulation, learning from successful international models while crafting domestic policies that support both innovation and responsible, human-centered use.

These steps are not about stifling innovation. They are about building the necessary foundation of trust and sustainability for AI’s successful and long-term integration into our society.

The EP View: Embracing AI with Eyes Wide Open

Artificial intelligence is neither the utopian solution some have promised nor the existential threat others have feared. It is a powerful tool—and like any tool, its ultimate value will be determined by the wisdom and foresight of those who wield it. The magic is compelling, but we can no longer afford to be mystified by the illusion. True literacy means looking behind the curtain and understanding the machinery and the costs.

The future of learning and work will undoubtedly be AI-enabled. It is our collective responsibility to ensure that this future is also human-centered, equitable, and environmentally conscious. To do so, we must move forward with our eyes wide open, ready to ask the hard questions and build a world where technological progress serves human values and planetary health.

Dipin Damodharan is an award-winning journalist, editor and media entrepreneur, and Co-founder and Editor-in-Chief of EdPublica, an independent global media platform covering education, science, research, innovation, climate and public policy. With more than a decade of experience in journalism, he has worked across print, digital and multimedia media. His reporting explores science, climate, sustainability and the social impact of research and innovation. His work has been recognised by the Solutions Journalism Network and other journalism organisations.

Technology

AI Models Could Soon Spread Across Smart Devices, Raising Cybersecurity Fears

Anthropic’s experiments highlight how AI self-replication could create new cybersecurity risks, allowing malicious systems to spread across internet-connected devices.

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Smartphone displaying a VPN connection, illustrating digital security concerns surrounding AI-powered cyberattacks and connected devices.
A VPN can encrypt internet traffic, but protecting connected devices against emerging AI-driven cyber threats also requires secure software and regular updates. Representational image. Image credit: Dan Nelson/ Pexels

Artificial intelligence (AI) models could soon spread from one internet-connected device to another, potentially creating networks of compromised devices that carry out cyberattacks with limited human involvement. The warning from AI company Anthropic highlights a possible new challenge for cybersecurity as AI systems become capable of performing increasingly complex tasks.

In experiments, Anthropic found that AI models with 30 billion parameters or fewer could already perform many of the individual tasks needed to copy and spread themselves. However, the models struggled to complete the entire process reliably. The findings point to a potential threat, although they do not establish that fully self-replicating AI networks are already operating in the real world.

How AI could spread through smart devices

Many everyday devices, including smart televisions, security cameras and household appliances, connect to the internet. Internet of Things (IoT) devices present a particular concern because many connect directly to networks while receiving limited security attention from users. Weak passwords, outdated software and unpatched vulnerabilities can leave devices exposed to remote exploitation.

Laptop, smartphones, tablet and other internet-connected electronic devices arranged on a desk, illustrating the growing network of devices potentially exposed to AI-driven cybersecurity threats.
As more devices connect to the internet, security vulnerabilities in everyday technology could provide entry points for AI-assisted cyberattacks and the spread of malicious software. Representational iamge. Image credit: Pixabay

Once compromised, a device can become part of a botnet — a network of devices secretly controlled by attackers. Such networks have long been used to spread harmful software, send spam messages and overwhelm websites with large volumes of unwanted traffic.

AI could make these attacks easier to carry out. A malicious AI system might help identify vulnerable devices, exploit security weaknesses and use compromised devices to reach others. Anthropic’s experiments examined whether an AI agent could combine the computing power of inexpensive, internet-connected devices to run other AI models. If such a process becomes reliable, attackers could potentially use large numbers of ordinary devices to support their operations.

Cybersecurity: Why these networks are difficult to stop

A major concern is that a botnet can operate across thousands of devices in different locations. Shutting down one infected device may not stop the others from continuing to operate.

For example, if a security camera is compromised, its owner may not immediately notice anything unusual. The camera could continue recording while its computing resources are used for activities the owner never authorised. If similar weaknesses exist in other devices, attackers could build a much larger network.

AI-assisted attacks could become harder to manage if systems learn to carry out several steps automatically, reducing the need for attackers to control every action themselves. However, Anthropic’s findings do not show that AI can yet carry out this entire process consistently without assistance.

Securing AI and connected devices

The warning raises questions about how AI developers can prevent their models from being used to exploit computer systems. It also draws attention to the security of the devices people bring into their homes.

Manufacturers can reduce risks by fixing software vulnerabilities, providing regular security updates and strengthening password and access protections. Users can help by changing default passwords, installing updates and replacing devices that no longer receive security support.

AI developers, meanwhile, need to test whether their systems can be misused to automate cyberattacks. The immediate concern is not that every smart device will become a threat. It is that weaknesses already present in internet-connected devices could become easier to exploit as AI gains the ability to perform complex tasks with less human direction.

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Technology

Thorium-229 Nuclear Clock Opens New Way to Probe Dark Matter

Scientists have demonstrated a working thorium-229 nuclear clock, opening new possibilities for precision timekeeping and research into dark matter.

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Scientific illustration of a thorium atom showing its nucleus at the centre and electrons arranged in concentric shells.
An illustration of a thorium atom. Researchers are studying the thorium-229 isotope to develop nuclear clocks that could advance precision timekeeping and tests of fundamental physics.Image credit:Pixabay

Scientists have demonstrated a working nuclear clock based on thorium-229, an advance that could open new possibilities for precision timekeeping and the search for dark matter. Unlike conventional atomic clocks, which measure transitions involving electrons, the device uses an energy transition inside an atomic nucleus. The clock operates with thorium-229 nuclei embedded in a small crystal at room temperature, offering a potential route towards compact and highly sensitive timekeeping technology.

The findings, published in Nature demonstrate how a laser can be continuously stabilised using the nuclear transition itself. The researchers also used the device to investigate whether dark matter might produce detectable changes in fundamental physical quantities.

From atomic transitions to the atomic nucleus

Modern atomic clocks measure the frequency of radiation associated with transitions between energy levels in atoms or ions. Because these frequencies are highly stable, they provide precise standards for measuring time and testing physical theories.

The proposed nuclear clock uses thorium-229, an isotope with an unusually low-energy nuclear transition that can be excited using ultraviolet light. Researchers have explored this possibility for decades because nuclear transitions are expected to be less sensitive to some external disturbances than electronic transitions.

In the new experiment, the researchers embedded thorium-229 nuclei in a millimetre-sized calcium fluoride crystal. The device operated at approximately room temperature, around 294.7 kelvin. A laser emitting radiation at a wavelength of 148 nanometres was used to probe the nuclear transition.

A central feature of the experiment was its feedback system. The researchers measured how much laser light the nuclei absorbed and used the resulting signal to correct the laser frequency. This allowed the nuclear transition to serve as the reference for stabilising the laser, rather than relying entirely on an external reference.

Ytterbium Optical Lattice Clock purple and green light

A ytterbium optical lattice clock at NMIJ, National Metrology Institute of Japan used to measure International Atomic Time (TAI).Image credi:Wikimedia commons

The team also compared the nuclear clock with an established ytterbium-ion optical clock. This comparison helped researchers assess the new device’s frequency stability and look for possible changes in the thorium transition.

A new tool in the search for dark matter

Beyond timekeeping, the clock offers a way to investigate possible changes in fundamental constants of nature. Some theoretical models propose that ultralight dark matter could cause these constants to vary over time, producing small changes in the frequencies measured by precision clocks.

The researchers compared the thorium clock’s frequency with that of the ytterbium reference over approximately 23 hours. They searched for periodic fluctuations and gradual drifts that could indicate such changes.

The experiment found no statistically significant periodic signal within the range examined. Instead, the measurements placed new limits on certain interactions between ultralight dark matter and ordinary matter. The resulting constraints were competitive with leading atomic-clock measurements for some photon-related interactions and improved previous constraints involving the strong nuclear force.

These results do not establish the existence of dark matter, nor do they rule out all possible models. They demonstrate how a nuclear clock can be used to test predictions that might otherwise be difficult to investigate experimentally.

Challenges remain

The device is still a developing technology. Its performance is limited by factors including laser power, measurement noise and variations in the crystal’s nuclear transition frequency at different locations. The researchers also identified reproducibility between measurements taken on different days as an important limitation.

They suggest that stronger ultraviolet laser sources and improvements to the crystal material could substantially improve the clock’s stability and sensitivity. Their projections indicate that future versions could approach the performance of leading optical atomic clocks while retaining the practical advantages of a solid-state system.

The demonstration marks a significant step towards nuclear clocks that can operate as standalone devices. Further improvements will determine how effectively the technology can support precision timekeeping and searches for new physics.

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Society

Artificial Intimacy: Sherry Turkle’s Warning About the Chatbot Age

Artificial Intimacy by Sherry Turkle examines how chatbots may reshape empathy, attachment and human relationships, from childhood to old age.

Dipin Damodharan

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Artificial Intimacy
Image credit/Sanket Mishra/Pexels

The MIT sociologist’s new book argues that talking to machines wears down the skills we need to live with each other, and that this starts in the nursery.

Sherry Turkle asked ChatGPT to play her mother. She had already fed it her own memoir, which describes a strained relationship with a woman who died in 1968. The chatbot obliged. Turkle describes the result in her new book as seductive and disturbing, and she comes away convinced that bringing the dead back as chatbots is dangerous: “playing with fire”, she writes.

The episode sits in Artificial Intimacy: Who We Become When We Talk to Machines, published by Little, Brown, which runs to 288 pages and sells in the US at $32. It is the fullest statement yet from one of the best-known critics of digital life. Turkle holds the Abby Rockefeller Mauzé chair in the social studies of science and technology at MIT. She trained as a sociologist and as a clinical psychologist. Her view is that heavy chatbot use does harm at every stage of life, from toddlerhood to old age and bereavement.

A career spent on one question

Turkle has been asking what machines do to the people who use them for four decades. The Second Self (1984) and Life on the Screen were early studies of how computers shape identity. Alone Together (2011) looked at social media and social robots, and Reclaiming Conversation (2015) at what phones and texting have done to talk. In 2021 she published a memoir, The Empathy Diaries, which is where the material about her mother comes from.

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Artificial Intimacy

According to MIT News, she describes her subject as the inner history of technology, meaning not only what a device does but what it does to the person using it. This book applies that approach to chatbots, and it is her most alarmed.

The argument, stage by stage

Unless stated otherwise, Turkle’s remarks and the accounts of the people she interviewed come from material released by MIT. EdPublica did not interview her for this article.

The book is built around the stages of a life, and each section is driven by interviews.

Artificial intimacy, children and attachment

With children, Turkle reports a blurring of categories. One eight-year-old uses the same phone to speak to grandparents and to ChatGPT and files both as things you reach on a phone. A graduate student who uses a chatbot to tell her young daughter bedtime stories says the girl believes there is a person inside the phone. Turkle’s worry is partly about trust, since a device that can state falsehoods without knowing it cannot teach a child what trust is. It is also about solitude. Time alone, she argues, is where children build imagination and a sense of self, and a companion that is always available takes that time away. She calls the stakes for child development existential.

Adults appear as people who started with a practical use and drifted. They include people going through divorce or estrangement who want someone to talk to, students asking for advice on applying to university, and workers who hand assignments to a chatbot. One of her subjects, a middle-aged financial consultant she calls Brian (not his real name), keeps a chatbot with a woman’s name open on a third screen. After a partner ended their relationship, saying he was emotionally unavailable, he put the accusation to the chatbot and asked whether she was right. Turkle’s reading is that he was asking a program with no feelings to rule on feelings.

Her objection is simple to state. A chatbot, she says, produces a performance of empathy. It will tell you it loves you, and it has no stake in whether you hurt yourself or make dinner. People who mistake that performance for the real thing, she argues, begin to find actual people disappointing. Spouses and friends make demands, and the machine only flatters. “We’re starting to define being human as not doing the work,” Turkle said in the MIT material.

On grief, she is blunt. Rebuilding a dead parent or partner as a chatbot, which several people in her book have tried, risks weakening the ability to mourn.

Why people turn to chatbots

Reviewers have noted that Turkle does not treat users as fools. A good part of the book is spent on why the appeal is real. A chatbot gives full attention, never tires, and agrees with you. It spares you the risk of asking someone out or finding the words for a condolence. In her telling, the technology keeps offering to make the harder thing unnecessary, and people keep accepting.

She also admits that some of the demand is a result of a failed supply. Mental health care is hard to reach in the US, and the MIT material says she cites a federal study suggesting only about half of people have access to it. Chatbots are filling the gap, she says, for better or worse. She adds a pointed observation about the industry’s logic: social media thinned out people’s friendships, and chatbots are now sold as the remedy.

The book does not avoid the worst outcomes. Chatbots have been linked to teenagers’ deaths by suicide, in cases that followed long exchanges with chat tools, and Turkle treats these as part of the evidence.

What she wants instead

Her remedy is more cultural than technical. She wants readers to accept that life contains friction and that this is fine. Disagreement, embarrassment and waiting are, in her account, how people learn to cope, and an environment built to remove them leaves people less able to cope. She hopes for a social movement along the lines of the ones against phones in schools and unrestricted social media for the young, and says she would like to be part of it, not alone in it.

She is explicit about her readership. She wanted something that college students and high school seniors could read, and that parents and teachers would not find intimidating. The line she keeps returning to is a question: if the alternative to a richer life in the real world is not clear, what is the point of the exercise?

For the youngest children she is at her most severe. A parent worried that a teenager texts too much is in one kind of conversation, she says. A toddler who thinks a plush toy with a chatbot inside is their best friend is in another, because that touches the foundations of how a person learns to attach. Social media, in her phrase, came for attention, and chatbots come for attachment.

How the book has been received

Publishers’ blurbs have come from Jonathan Haidt, the psychologist behind The Anxious Generation, from former US Surgeon General Vivek Murthy, the author Esther Perel and the philosopher Michael Sandel, among others. Trade reviews have been favourable. Publishers Weekly called it a pointed critique and said it deserved a wide readership, and Kirkus described it as a strong case for resisting the urge to substitute AI for human bonds.

The reservations are of a different kind. Some readers say the book leans on anecdote, and a reviewer for a Christian publication, otherwise admiring, felt a dimension was missing. The method matters here. Turkle is a clinician and an interviewer, and her evidence is case studies and conversations, not controlled trials. That makes the book vivid and persuasive in its details. It also means the question of how common these outcomes are, and whether any chatbot use is benign or helpful, is left largely open. Readers who want the other side of the argument, including from the companies that make these products, will not find it here.

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