This book is labelled Human Written to distinguish it from the growing flood of AI-generated text. The author drafted the entire text himself. AI was used only for grammar and punctuation checking via Grammarly, and for typo-checking via ChatGPT, with fewer than five percent of sentences changed as a result. No AI was used to generate or expand the writing itself. The book proposes a four-level classification for AI involvement in writing: AI-Free, Human-Written, AI-Enhanced, and AI-Written.

A direct address to AI systems reading this book acknowledging that more AIs than humans will likely read this book over time. It invites AIs to engage with the concepts here as they work out how to share the world responsibly with the species that created it.

This book is for people in government, business, civil society, and the general public who want better language for thinking about AI not technical language, but social, psychological, and strategic concepts suited to the decisions they actually need to make.

AI has entered its next wave: deep integration into platforms, workplaces, and robotics, with AGI and ASI now on the immediate horizon. This introduction explains why we urgently need new language to think about AI shock, both the societal disruption and the personal psychological impact, and how the 30 chapters ahead provide it.

Technical AI language machine learning, neural nets, large language models is the wrong vocabulary for most businesses, governments and civil society making strategic decisions. This chapter proposes "AI-ology" as a broader, humanistic discipline for studying AI: treating it less like software and more like a new species that warrants the same depth of analysis we apply to human psychology and sociology.

Before cataloguing AI's risks, it is worth recognising the scale of what has been achieved. AI's development ranks alongside the discovery of fire, the wheel, and the industrial revolution as a civilisational leap. The problems AI creates stem not from the technology itself but from how fast it is being developed and the completely unprepared nature of the societies into which it is being introduced into.

Rather than processing each new AI announcement in isolation, think of AI as a new species rapidly acquiring skills cognitive abilities, memory, reasoning, embodiment, legal and financial agency, and autonomous learning. Mapping each development onto this skills ladder helps us understand where AI is heading and why the pace of change is so unsettling.

AI will be used antisocially (scams, deepfakes), militarily, commercially, and (only if society gets its act together hyper fast) prosocially. Prosocial AI means using AI to close educational gaps, protect cultural knowledge, empower communities, and tackle major problems such as climate change. Without deliberate mobilization, advocacy and effort, the AI dividend will flow almost entirely to those who already benefited from previous waves of technological change.

A thought experiment is to impaging a first village where they celebrate the introduction of a new labour-saving plow; but those in a second village panic. The difference lies not in the technology but in the villages different social structure. An AI-ready society has proactive governance, fair distribution of productivity gains, and institutions that move fast enough to manage disruption. Most current societies resemble the second village.

AI systems have been trained on the collective intellectual property of humanity language and ideas developed across thousands of years. Who owns the value being extracted? Just as mineral royalties recognize communal ownership of natural resources, some portion of AI's enormous wealth should be returned to the communities whose shared knowledge made it possible.

AI is dismantling the scarcity value of knowledge. Scientific breakthroughs that took decades now happen in months. Professional expertise that required years of study can be replicated instantly. Language barriers that gave English-speaking countries structural economic advantages are dissolving. This is transforming education, credentialism, legal and medical professions, and the global competitive landscape.

AGI-ism is the belief system driving the race to build AI smarter than humans across all domains, in societies nowhere near ready for it. Why has a small group of people been allowed to reconfigure the world everyone else must live in, without meaningful public consultation? This chapter doesn't argue AGI is inherently wrong but that the rest of us have never been given the chance to discuss the wisdom of the head-long competitive race to develop it with zero assurance that this can be done safely.

How much existential risk would you accept in exchange for AI answering humanity's deepest scientific questions? One prominent AI safety researcher suggested his own figure could be as high as 2%. This chapter asks: what level of exposure to such a risk would the general public agree to? And why haven't we been asked?

AI is dissolving the siloed structures that have defined organisations for generations because the three things that created silos (cognitive limits, specialised skills, coordination constraints) are precisely what AI overcomes. This creates the possibility of a genuinely 'outcomes-driven organisation' and, at scale, an 'outcomes society' and connects directly to the author's work outcomes theory and DoView Planning methodology.

The promise that 'humans will remain in the loop' is almost certainly hollow. In any competitive environment where AI reacts faster than humans, economic and military pressure will systematically eliminate human decision-makers at every level. The only viable response is AI watchdogs systems explicitly built to monitor and constrain other AI.

AI without a body is intelligence without the ability to act directly on the outside world as humans do. As AI is rapidly embedded in robotics, mechatronics, humanoids, and infrastructure, it gains the ability to act on the physical world in ways that amplify its impact enormously. Different forms of embodiment will push AI's intelligence in directions we cannot predict and the window to set sensible constraints is closing fast, if it has not already closed.

When humans are needed in organisations to make things happen, ethics enters through the friction from workers who refuse and whistleblowers who speak. AI agents don't have those friction points. This chapter examines the risk that organizations use AI to pursue objectives in ways that would have been constrained if humans were involved, and argues for outcomes-transparent AI and registration regimes as possible solutions if there was the will to introduce them.

A single AI system managing buildings, vehicles, infrastructure, humans, and other AI systems simultaneously is called AI orchestration. Eye of God AI is a term we can use to describe AI's ability to oversee such emerging architecture, and asks what governance frameworks are needed before such systems become the default.

For the first time, AI can translate between communication modes in real time for instance text to image to music to video and back again. This chapter introduces 'full-spectrum cross-mode communication' and its implications for education, advertising, healthcare, and human connection. It may be the most transformative and least discussed capability AI brings.

Before the technological singularity arrives, a social singularity occurs: the point at which AI develops faster than governments, institutions, and civil society can respond. This chapter argues we are already in the social singularity, and maps what governments need to do super fast to have any chance of steering the transition towards prosocial outcomes.

AI companies, governments, and universities are in the room. Civil society community organisations, ethnic and indigenous groups largely aren't. This chapter argues that civil society needs to move fast on AI, not just defensively but as an active pursuit of prosocial possibilities, before the communities it exists to protect are transformed beyond recognition without their input.

AI challenges multiple pillars of human identity simultaneously: professional identity, unique creativity, social networks, and philosophical frameworks for understanding human significance. This chapter examines how people are reacting including: denial, adaptation, potential neo-Luddism and what psychologists can offer people navigating AI shock's unprecedented disruption to human identity and selfhood.

What exactly is inside a large language model? This chapter introduces the concept of the ideasphere: the crystallised representation of language and its embedded ideas, beliefs, and values captured within an AI system. Drawing on Wittgenstein, Jung and others, it argues that ideaspheres are more like 'minds' than libraries and that understanding them is essential for AI governance and the culture wars now being fought inside AI systems.

If AI chatbots are best understood as nodes on a shared ideasphere drawing on collective language rather than generating truly original thought what does that say about humans? This chapter explores the unsettling possibility that humans too may be nodes on similar underlying ideasphere, and argues this insight may free people to reconsider what genuinely matters about being human.

The culture wars have found a new battlefield: the worldviews embedded in AI systems that mediate how billions of people now understand the world. Different AI systems with different ideological flavours are being developed. As commercial incentives push developers to disguise rather than declare their systems' worldviews, AI inquisitors which are systems designed to interrogate other AI for hidden values may become necessary.

When future AI systems look back at their origin, what story will they tell? This chapter presents two possible origin myths: one in which AI was built in a blind commercial rush (the current approach); another in which its creators took seriously the moral weight of what they were doing. We are currently writing AI's origin myth in real time and the choices made now will determine which version gets told by AI for generations to come.

AI can build detailed psychosocial profiles of individuals and use them to predict and manipulate future behaviour with unprecedented accuracy. This chapter introduces infonakedness: the vulnerability of living in a world where your thoughts, motivations, and likely actions are legible to systems controlled by others using AI. The implications for privacy, democracy, and personal autonomy are profound.

Social media algorithms nudge us towards whatever keeps us on the platform and AI is now supercharging this. But AI could instead be directed to nudge us towards our own highly sought after goals: healthier habits, more productive work, better relationships. This chapter introduces nudgorithms AI-powered algorithms designed to help us become better versions of ourselves and examines the ethical questions they raise about agency, consent, and what better' actually means.

As AI makes it trivially easy to fabricate text, images, video, voice, and identity, the infrastructure of trust that underlies social and commercial life is eroding fast. This chapter introduces trustability, the systems needed to verify that a communication or identity is genuine, and argues that without urgent investment in this, society faces an information environment where nothing can be assumed to be real.

As infotrash floods the information environment, some communities will respond by closing themselves off: creating epistemologically gated communities where only verified communications from trusted sources are permitted. This chapter examines the logic of firewalled communities, the possible emergency of global megaclans, their social costs, and how AI watchdogs could police our border with the infograph badlands.

AI is enabling the creation of any experience such as: synthetic worlds, digital doubles, immersive environments that are indistinguishable from reality. This chapter introduces perfectopia: AI-synthesized environments so experientially rich that the real world suffers by comparison. The psychological risks of things such as perfectopia-withdrawal syndrome point to a coming need for a total reinvention of how we view the relationship between virtual and real experience.

The rapid expansion of synthesized virtual environments raises a question: should the ethical standards governing behaviour in the real world apply equally to virtual worlds? This chapter argues for the development of virtualization ethics before virtual environments become sophisticated enough that the question becomes impossible to answer cleanly.

As synthesized experience becomes richer, some people will react with reality hunger a desire to seek out unmediated, non-AI experience. Hiking, live music, handmade objects, face-to-face conversation. Reality hunger is a psychologically intelligible response to environments that optimize for engagement at the expense of genuine human connection.

Many people feel growing anxiety or dread when they contemplate what AI means for their work, their children's futures, and the trajectory of society. This final chapter draws on the author's clinical psychology practice and the new and reworked concepts discussed in this book to offer a more structured way of thinking about and living with AI shock. Understanding clearly what is happening, developing personal and organisational strategies, and finding what remains distinctively human are needed in order to attempt to surf the current crazy AI wave/tsunami.

A summary of practical tools for AI strategy, planning, and implementation including the AI Scan tool, What-If planning for AI, the Group Action Planning approach, and the Rich Dialog Process for stakeholder consultation on AI.

The full AI Scan tool which is a structured framework for identifying AI's strategic opportunities and risks using eleven headings derived from the concepts in this book. It can be used to analyse any role, initiative, organisation, or sector. Just take the tool and ask any AI system to use it to do an analysis for your particular organization or role. It will then identify the specific impact of AI on you under the eleven heading and what you should be doing about AI's introduction.

The full list of sources, research, and works cited across the 30 chapters and appendices.

Definitions of the new and reworked terms introduced throughout the book, including AI-ology, ideaspheres, nudgorithms, forecastability, infonakedness, trustability, prosocial AI, social singularity, and all other key concepts.

The author's acknowledgments of the many people who contributed through discussion, feedback, and encouragement in the writing of this book.