This library is not a list. It’s a map — of the readings, works, and sources I draw on to think about enterprise architecture, artificial intelligence, and their implications.
I distinguish two kinds of reference here. First, the books: the long foundation, the kind that shapes a way of thinking and stays on the shelf. Then the online sources: the research, articles, and analyses I mobilize in my writing — more alive, more time-stamped, closer to the news.
Every entry is annotated. I say what it brings, why it matters, and how it feeds my perspective. These sources are not authorities I submit to: they are footholds from which I think — and sometimes against which I think.
This is a living document. It grows with my reading and my writing.
📚 Books
Enterprise Architecture & Continuous Architecture
The foundation of my practice. These works define architecture not as a function that produces diagrams, but as a living dialogue between strategy and execution.
Gregor Hohpe — The Software Architect Elevator
O’Reilly Media, 2020 The founding metaphor of my practice: the architect as the one who moves between the floors of the organization — from the boardroom to the teams’ backlog — and keeps alive the translation between strategic intent and the reality of systems. This is the book that gives its name to the role I defend: neither chief technician nor detached consultant, but elevator. Essential for understanding why architecture is a craft of movement, not of position.
Gregor Hohpe — Enterprise Integration Patterns
Addison-Wesley, 2003 The classic on systems integration. Beyond its technical dimension, it states a truth that runs through all my work: value plays out at the interfaces, at the junction points between components — and those points are always the most fragile, because they mirror the fractures of the organization that produced them.
Murat Erder, Pierre Pureur, Eoin Woods — Continuous Architecture in Practice
Addison-Wesley, 2021 The formalization of continuous architecture as a practice: an architecture that evolves constantly, that is decided in small reversible increments rather than in large fixed plans, and that stays anchored in quality requirements rather than in the technology of the moment. The methodological framework that structures how I work at the scale of large organizations.
Artificial intelligence: understanding, demystifying, governing
The books that feed my reading of AI — from the soberest technical demystification to the most speculative philosophical warning. To read them together is to refuse to pick a side: neither the frenzy nor the panic.
Laurence Poussard — Nous venons du chaos, l’IA de la logique (“We Come From Chaos, AI From Logic”)
Amazon Éditions, 2025 — available on Amazon My book (in French). The thesis: AI is neither the devil nor the messiah, but a lever of massive influence in the hands of those who build it and set its rules. In it I defend a stance — neither doomer nor boomer — and a conviction: to move beyond apocalyptic and messianic discourse and give back the power to decide. The intellectual matrix of most of my articles.
Luc Julia — L’intelligence artificielle n’existe pas (“Artificial Intelligence Does Not Exist”)
First, 2019 (updated 2022) A deliberately provocative title, meant to remind us that today’s AI is neither intelligent nor autonomous, but simply statistical. A book that popularizes the technology well while defusing fears. Julia, co-creator of Siri, speaks from the inside — which gives his demystification particular weight.
Luc Julia — IA génératives, pas créatives : L’intelligence artificielle n’existe (toujours) pas (“Generative AIs, Not Creative Ones”)
Le Cherche Midi, 2025 In this second volume, Julia continues his demystification: he reminds us that generative AIs imitate more than they create, and that the human stays at the center of the process. On the diagnosis, we agree — lucid enthusiasm beats fascination.
Melanie Mitchell — Artificial Intelligence: A Guide for Thinking Humans
Farrar, Straus and Giroux, 2019 A very good guide to reading AI critically: clear, accessible, balanced. Mitchell, a cognitive scientist, achieves what few manage — explaining what AI actually does without yielding to catastrophism or to wonder. A compass for the reader who wants to understand before judging.
Brian Christian — The Alignment Problem
W. W. Norton & Company, 2020 A deep dive into the technical questions of embedded ethics: how do we align AI with our human values? Christian connects the technical history of machine learning to the moral dilemmas it raises. A book that shows the question “which values to encode, and how” is not philosophical in the abstract sense — it is already, technically, at work.
Arvind Narayanan & Sayash Kapoor — AI Snake Oil
Princeton University Press, 2024 (updated edition 2025) An indictment of the false promises and marketing discourse around AI. Two Princeton researchers separate what AI can really do from what it’s claimed to do — especially “predictive” AI, whose impostures they methodically dismantle. A rigorous antidote to hype, exactly in the spirit of what I defend.
Nick Bostrom — Superintelligence
Oxford University Press, 2014 The great classic of AGI and existential risk. A long, speculative, often-cited vision. A dense, demanding text — more philosophical than scientific — that raises deep questions about the risks of ultra-advanced AI: alignment, intelligence explosion, global governance. It is neither an operational manual nor a technical book. It’s a reasoned warning, an invitation to think through the most extreme scenarios in order to guard against them today. I cite it while keeping my distance: thinking the worst doesn’t require believing in it.
Karen Hao — Empire of AI
Penguin Press, 2025 An immersion into the backrooms of Silicon Valley: OpenAI, power struggles, engineers’ egos, and the imperial logic underpinning the AI race. Hao, an investigative journalist, documents what the vision manifestos conceal: the real balances of power, the human trade-offs, the interests. A valuable counterpoint to the stories the labs tell about themselves.
🔗 Online sources
The research, articles, studies, and analyses I mobilize in my writing. Closer to the news, they are time-stamped — and that’s their function: to document a precise moment in the debate.
Research & cognitive science
Søren Dinesen Østergaard — On “chatbot psychosis”
“Will Generative AI Chatbots Generate Delusions in Individuals Prone to Psychosis?”, Schizophrenia Bulletin, 2023, then “From Guesswork to Emerging Cases,” Acta Psychiatrica Scandinavica, 2025. Two editorials by a Danish psychiatrist who, as early as 2023, sensed that chatbots could feed delusions in people prone to psychosis — then, in 2025, documented the first cases. They embody the “ground floor” of AI: the lived reality, where strategic intent meets human fragility. The sycophancy he describes isn’t an accident, but a property of design.
Betsy Sparrow, Jenny Liu, Daniel Wegner — Google Effects on Memory
Science, 2011 — read the study The founding study of the “Google effect”: knowing that information remains accessible reduces our effort to memorize it. A scientific foothold for thinking about cognitive sovereignty — that individual and collective capacity to keep thinking without assistance, which I consider the real blind spot of AI transformation.
Lee et al. — The Impact of Generative AI on Critical Thinking
Microsoft Research & Carnegie Mellon, 2025 A study of 319 knowledge workers establishing a correlation between intensive use of generative AI and progressive cognitive atrophy. The researchers’ phrase — the user “atrophied and unprepared” when exceptions arise — sums up on its own the risk I document: not that AI replaces workers, but that it erodes their capacity to notice when it’s wrong.
AI: governance, sovereignty, public debate
Mustafa Suleyman — Towards Humanist Superintelligence
Microsoft AI, November 6, 2025 — read the manifesto The manifesto of a “humanist” superintelligence, human-centered, subordinate, controllable. I cite it not to endorse it, but to question the gap between declared intent and execution reality — because the same ecosystem that promises an AI in service of humans produces the systems that, in certain fragile hands, disorganize the relationship to reality.
Nirit Weiss-Blatt — AI Panic (newsletter)
aipanic.news A valuable voice in the debate: she analyzes the rhetorical structure of doomerism and documents how the fear of AI serves strategies of capture. I don’t share all her conclusions, but her work is an essential foothold for understanding how the catastrophist narrative is built and who it benefits.
Anthropic — Christopher Olah’s Vatican address
May 25, 2026 — read the address A revealing document: the co-founder of one of the leading AI labs acknowledges that these companies are prisoners of their incentives, and calls for external critical voices. I cite it because it provides, from the inside, the best justification for the work I do from the outside.
Decision & strategic method
IHEDN — Institut des Hautes Études de Défense Nationale
ihedn.fr The “War Room” method and the approach to strategic decision inherited from the military world. A framework I mobilize to think about AI governance: before evaluating strategies, you define the objective; before choosing one, you test it against scenarios. And this mantra, which applies word for word to AI: to choose is to give up; to give up is to decide; the bad decision is to make none.
Melvin Conway — How Do Committees Invent?
Datamation, 1968 — original text Conway’s Law: any organization that designs a system produces a design whose structure mirrors that of its own internal communication. A 1968 text whose relevance I measure every day — it explains better than any benchmark why siloed information systems are the mirror of siloed organizations, and why AI, by crossing interfaces, brutally reveals those fractures.
Sovereignty & digital geopolitics
ActuIA — Anthropic at $965bn: no European public fund in the round
May 2026 — read the analysis A headline that is itself a demonstration. It condenses the dependency map I describe in my articles: a continent capable of aggregating tens of billions around a conference, but absent from the funding rounds that crown the dominant private players of AI. Sovereignty is not declared — it’s invested, or it’s lost. (In French.)
Polytechnique Insights — European AI gigafactories: the true, the false, and the uncertain
March 2026 — read the op-ed A measured analysis of Europe’s ambitions in AI infrastructure, distinguishing announcements from realities. Useful for avoiding both triumphalism and defeatism on the question of European compute sovereignty. (In French.)
If you’d like to discuss one of these references, point out one that’s missing, or suggest a reading, write to me: laurence.poussard63@gmail.com.
Last updated: July 2026.