I don’t really know how to review this book. Rick Rubin’s The Creative Act: A Way of Being is a book that, early on, asks you to stop labeling and analyzing, to resist the instinct to categorize. And then here I am, highlighter in hand, kind of trying to do exactly that.

This is a book about creativity by one of the most influential music producers alive, and it contains almost nothing about music. There are no studio war stories, no tales of recording with Johnny Cash or the Beastie Boys.

Instead, Rubin has written something closer to a secular devotional text. There are 78 short chapters he calls “areas of thought,” delivered in very short sentences, with generous white space, structured less like an argument and more like a series of meditations.

There is a very clear core thread: creativity is something you are, not only something you do.

It’s a way of moving through the world.

From page two, Rubin expands the notion of what counts as a creative act to a conversation, taking a new route home, rearranging the furniture in a room. If you perceive, filter, and curate experience, you’re already creating.

In addition to basically all of us being creators, it was liberating for Rubin to spell out that it makes no sense to say you’re “not good at” being creative, any more than you can be “bad at” being a monk.

You’re either practicing or you’re not, and that’s it.

For someone trained to analyze (and I say this as a foresight professional whose job literally contains elements of studying and categorizing emerging patterns) this is a welcome but also slightly uncomfortable invitation.

Have I ever thought I am, or would be, ‘bad at art’?

Absolutely. All the time.

Rubin argues that as soon as you label something, you’re no longer noticing it; you’re studying it. Analysis is a secondary function. The awareness comes first. I know he’s right. I also know that every margin note I made was an act of gentle defiance against the book’s central request.

There’s a spiritual dimension here that some readers will welcome and others will find too untethered. Rubin writes about the Source, about connection, about faith in creative direction without needing to understand it. He’s careful to decouple this from organized religion and uses spirituality as connection rather than doctrine, but the language will be familiar to anyone who’s spent time with Buddhist or contemplative traditions.

There’s a connection to T.M. Luhrmann’s work in How God Becomes Real: both suggest that practice creates the experience, not the other way around.

You don’t create because you’re creative, or practice because you believe.

You become creative because you create, and believe because you practice.

Rubin’s observation that all work is collaboration — with the art that came before, the world you live in, the tools you use, the audience, and who you are today — is one of those insights that seems obvious after (but typically only after) someone says it.

There are unexpectedly sharp moments scattered throughout, almost casually, and in a variety of styles. His chapter on openness, where he notes that we build frameworks that give us reduced options and a false sense of certainty, could have come from a text on cognitive bias or foresight methodology. The passage on curiosity, about how it explores all perspectives, craves constant expansion, and pushes to expose falsely set boundaries, on the other hand reads like a manifesto for anyone whose work involves seeing what others don’t yet see.

Rubin observes that we are interpretation machines, that our explanations are guesses which become fixed as fact; that we are the unreliable narrators of our own experience. On spontaneity, he joins the now-common forces puncturing the myth of effortless genius: the story of spontaneity is misleading, because we don’t see the lifetime of preparation that primes the artist for the “spontaneous” moment.

And then there’s this advice on page 387:

“If you’ve written a book that’s over three hundred pages, try to reduce it to less than a hundred without losing its essence.”

Again, on page 387. Of a book that runs past 400 pages.

Which brings us to one tension that Rubin doesn’t resolve.

The Creative Act would be a more powerful book at maybe half its length. Many of the 78 chapters circle the same ideas in slightly different language. The white space and short sentences create a meditative rhythm that works beautifully for the first hundred pages and starts to feel like padding by the third hundred. The repetition may be intentional; Rubin might, probably correctly, argue that returning to ideas from different angles is itself a creative practice, that the book is meant to be dipped into rather than consumed cover to cover.

Fair enough. But a book that advocates ruthless editing probably shouldn’t need that defense.

I also notice what’s absent. There’s no engagement with the ways creativity is constrained by systems, economics, or power, the very material conditions that determine who gets to really “live as an artist“ to the fullest, and who doesn’t.

The book assumes a degree of freedom and access that is, for many people, more of a real barrier rather than their mindset. I don’t consider this a fatal flaw, but it leaves a gap where something interesting might have been.

This is an overlong, occasionally repetitive book that contains at least a dozen ideas I will continue to carry with me and remind myself of. I’m not sure what rating that deserves.

• Rating: 4 out of 5, give or take at least half a point

• Dog-ear index: 7.2

• Who is it for: Creators of all kinds. With a high likelihood that includes you, even if you don’t think of yourself as one. Especially if you don’t think of yourself as one. Also for anyone who has become so focused on output and analysis that they’ve forgotten creation is a way of being, not just a way of producing. Not for those who need practical creative techniques or step-by-step methodology; this is philosophy, not instruction.

[reminder: I highlight important parts of the books I read, and dog-ear the really important pages. The dog-ear index is simply the average number of dog-eared pages per 100 pages]

Product link for reference only; please support your local bookstore where possible: https://www.amazon.com.au/Creative-Act-Being-Rick-Rubin/dp/1838858636/

I need to be honest with you upfront: this book is confirmation bias on steroids. I love writing. I love words. I spent decades working in and around communication technology. I now work in AI & strategic foresight. Naomi Alderman has written a book that sits squarely at the intersection of all those things. As a result, my highlighter barely survived the experience, and the book looks a little ridiculous; just look at the up-there dog-ear index.

Throughout my reading experience, I tried my best to check my biases. Even so, take what follows with a bigger-than-usual pinch of salt, given I was unlikely to succeed in setting all of them aside.

Anyway, Alderman’s thesis is cleanly: we are living through the third great information crisis in human history.

The first was the invention of writing. The second was the Gutenberg printing press. The third is digital communications technology.

Each crisis brought enormous leaps in knowledge and understanding — and also prolonged periods of intense instability, violence, and social upheaval. If you’d known the name of your era, she argues, it would have given you a clue about what to prepare for.

The framework is compelling, and Alderman builds it well. She draws on Walter Ong’s work on orality and literacy, tracing how writing made us value older people less (they were no longer the sole repositories of knowledge), how it enabled independent thinking by freeing people from constantly rehearsing traditional knowledge, and how each new communication technology introduces what she calls a “war of interpretation” — conflict over the meaning of written words with people you’ve never actually met.

There are a lot of observations in the book that come across as fundamentally important. The idea that when people worked with sheep all day, they imagined God as a shepherd; now that we work with computers, we describe ourselves as “hard-wired” or “programmed.” The Borges parallel — The Book of Sand, an infinite, unfinishable book that becomes a prison, as a metaphor that’s too close for comfort for the infinite scroll of social media. The Elizabeth Eisenstein insight that when technology went to press, occult lore flooded out alongside scientific knowledge, and few readers could discriminate between the two.

Well, bugger. Here we are again.

Alderman brings out interesting points about forgetting. She distinguishes between the right to be forgotten — which data protection laws especially in the EU attempt to address — and the right to forget. The imperfect way our memory works, she argues, actually mitigates lasting conflict. When people say “time heals,” what they partly mean is that memories fade, becoming less sharp and less wounding.

Digital technology has stolen that from us. Memories in digital form never fade - on the contrary, they might end up being amplified.

Twenty years turns a shitposting fifteen-year-old into a responsible thirty-five-year-old, but the internet remembers every post. We need, she says, to find ways to say “I am not that person any more“ and to actually mean it, and to let others mean it too. I agree wholeheartedly.

Growing up in a small Finnish town in the early days of the Internet, I recognise what she describes about the pre-digital world: before the Internet, there were so many things people simply never talked about, because there was no space for it.

The internet made it possible to talk about them; to find your tribe globally. I found that exhilirating and liberating in the 1990’s, where IRC allowed me to connect with like-minded people globally. That ability, Alderman acknowledges, is both a liberation and a source of enormous friction. The discovery of how many radically different lives are being lived in parallel to our own is disorienting. It can push us toward empathy or toward our own enclosed private pews, shutting out everything that challenges us. Alderman calls this the “box-pew effect”: individualism paradoxically leading to more rigid think-alike communities.

There’s much practical wisdom scattered throughout, such as pausing before reposting when you feel a strong emotion and starting hard conversations with what you agree on. Her personal rule — never talk about a culture-war topic with anyone who only wants to talk to you about that topic — is the kind of heuristic worth adopting immediately. And her call to protect institutions like Wikipedia, public libraries, and the Archive as information services that aren’t trying to sell you something feels both urgent and underappreciated.

When reading, I agreed with so much of the book that my gut reaction was to give it 5/5 just for that. But there are limitations, and I want to be fair about them.

The book is light on hard evidence and meticulous referencing. Alderman is working as an essayist, not a researcher, and the Reformation parallels don’t necessarily always hold up under scrutiny. The analogy between Luther’s disintermediation and Uber’s is suggestive but not what I’d call rigorous. And Alderman herself acknowledges, in her afterword, that the AI dimension is underdeveloped: “we’re not in the midst of it yet,” she writes.

On that point, I disagree strongly. We are, and we were squarely “in the midst of it” in 2025 when this book was published.

As a more general point, here’s what I find somewhat unnerving about books that resonate strongly with me. Often — not always, but often — when they touch on a topic I know quite a bit about, I find them wanting. Alderman’s treatment of AI is a case in point. She’s used Claude, finds it useful, acknowledges its limitations. But she treats the default sycophantic behaviour as inherent rather than configurable. She frames AI companionship as a supernormal-stimulus risk — which is a fair concern — but doesn’t engage with how the technology is actually developing.

What’s my point? Where the book doesn’t touch my areas of expertise, it feels like brilliant 5/5 work. Where the book does touch on topics of my expertise, it’s not. It’s not terrible either by any means, but it’s not 5/5.

The point is that I suspect it would be a very strange coincidence if the analysis were less than rigorous only in the areas I happen to know deeply.

I’m choosing not to penalise the book for those assumed flaws, but it’s a pattern worth noticing, and it tempers what might otherwise be a higher rating.

What Alderman does well — and this is no small thing — is name what an information crisis feels like. The anxiety, the anger, the sense that we’re surrounded by cretinous vicious imbeciles (her words) when actually most people around us are careful, thoughtful people who may disagree with us for good reasons. The loss of shared consensus reality. The feeling that the ground is shifting under our feet.

Naming the era you’re living through doesn’t solve it, but it helps you stop blaming yourself for feeling destabilised by it.

The book also echoes something Melvin Kranzberg captured more concisely: technology is neither good nor bad — nor is it neutral. Writing enabled both the Axial Age’s moral philosophy and centuries of religious war. The printing press gave us the Enlightenment and the Holocaust’s foundational texts.

The internet gives us Wikipedia and QAnon. Alderman’s contribution is to trace this pattern across three crises and invite us to learn from the previous two. The invitation is worth accepting.

She ends with hope, but not naive hope. Brains evolved for small groups of a few thousand are trying to build moral consensus among billions. She argues that even as we write ourselves into all sorts of trouble — moral, spiritual, physical, psychological, intellectual — we can also use these same technologies to work ourselves out of them again.

It’s a warm, humane ending.

I’d like to share her optimism fully. I’m not sure I do.

• Rating: 4 out of 5 (so, still very good)

• Dog-ear index: 14.7 (i.e., ridiculously high)

• Who is it for: People who sense that something has gone deeply wrong with how we communicate and want historical context for the feeling. Writers and communication professionals will find the most to chew on.

reminder: I highlight important parts of the books I read, and dog-ear the really important pages. The dog-ear index is simply the average number of dog-eared pages per 100 pages

Product link for reference only; please support your local bookstore where possible: https://www.amazon.com.au/Dont-Burn-Anyone-Stake-Today/dp/1405981393

What if the most reliable thing about your memories is that they’re wrong?

I’ve spent a lot of time in my life thinking about how humans reconstruct events after the fact — in revisionist history of our personal lives, in incident investigations, in organisations trying to learn from failure.

Memory is always at the centre of it. We build narratives. We fill gaps. Even when we acknowledge that memory can be fallible, we are often supremely confident about our memory, and about the veracity of the details we’ve just fabricated wholesale. In interpersonal arguments, we’re quick to doubt the other person’s narrative of the events, while remaining confident about our view.

Mark Rowlands’ The Book of Memory takes that discomfort and runs with it, arriving somewhere a little different than your standard book about memory: a philosophical meditation on what it means to be a person at all, written as a love letter to his sons.

Rowlands is a professor of philosophy at the University of Miami, not a neuroscientist. This is a book that uses science — reconsolidation, flashbulb memories, protein synthesis — but in service of philosophical argument rather than as an end in itself.

If you want a more rigorous deep-dive into memory science, Charan Ranganath’s Why We Remember is your book. Rowlands is after something different: what the science means for our understanding of selfhood.

Rowlands introduces the concept of being “ontically fat” versus “ontically thin” — a philosophical distinction I hadn’t encountered before and felt a little weird about at first.

You and I are ontically fat: there are facts about us that exist whether or not anyone knows them. Sherlock Holmes, by contrast, is ontically thin — there is no fact about what he had for breakfast on April 24, 1888 unless Conan Doyle wrote one. What Rowlands argues, provocatively, is that our memories make us partly fictional. Every time a memory is recalled, it must be reconsolidated — rebuilt, essentially — and each rebuilding can introduce distortions.

As such, the memories that make you you are, to a significant degree, works of authorship rather than records of fact.

He builds this argument through different pieces. The chain of events leading to his son’s conception — stretching back through a rebound relationship, a disastrous trip to India, a case of dysentery from a restaurant in Connaught Circus, New Delhi — is a delightful illustration of contingency.

His treatment of metaphor as “an invitation — why don’t you think of things in this way?“ is the kind of simple insight that makes you stop and sit with it for a moment. And the chapter on Stendhal’s syndrome - dizziness, fainting, even hallucinations triggered by exposure to overwhelming quantities of art - was entirely new to me.

The science, when it appears, is mostly solid. The Neisser and Harsch study of Challenger explosion memories is a well-established classic: students’ accounts of where they were when they heard the news changed dramatically between 1986 and 1989, while their confidence in those memories remained stubbornly high. Eleven of forty-four subjects scored zero for accuracy but still expressed high confidence. Karim Nader’s work on reconsolidation — showing that blocking protein synthesis during memory retrieval can effectively erase the memory — is also well-replicated and forms the backbone of Rowlands’ argument that remembering is fundamentally an act of rewriting.

But I said mostly. Where I’m less convinced is the treatment of PKMzeta, a protein Rowlands presents as crucial to both the formation and retrieval of memories. The PKMzeta story has become considerably more complicated since the initial findings. Knockout mice lacking PKMzeta entirely still form and retain memories, and the inhibitor ZIP — once thought to specifically target PKMzeta — turns out to have broader, non-specific effects including suppressing neural activity altogether. The picture has been partially rescued by more recent work showing that a related protein (PKCiota/lambda) compensates for missing PKMzeta, but the neat narrative Rowlands presents is more contested than he lets on. For a book leaning on neuroscience to support philosophical claims, this matters.

Some of the most interesting moments come from Rowlands’ notion of an “existential style” — the idea that even when the content of memories erodes, such as with people with Alzheimer’s, what remains is something like a characteristic way of being.

He illustrates this through his father-in-law Patrick, whose dementia stripped away specific memories but left his essential character — the quiet storytelling, the gentle erudition — perfectly visible. It’s a moving observation which probably, unfortunately, doesn’t neatly generalize, for I have equally heard stories of people who change to something very different in similar circumstances.

Nevertheless, it raises a question Rowlands doesn’t fully explore but that I couldn’t stop thinking about: if so much of our book of memory is dominated by redaction — vast seas of black ink — and our selves are relatively shallow constructs built from sparse islands of recollection, does this explain why our technology, which knows only a thin slice of us, can feel like it knows us so well?

Rowlands also makes a fascinating case that externalising memories — telling people about them, writing them down — can stabilise them against the distortions of reconsolidation. But he immediately complicates this: stability is not the same as accuracy. You can lock in a memory by talking about it, but if your account was inaccurate to begin with, you’ve now fixed the inaccuracy in place.

This sent me down a rabbit hole about journaling. If writing freezes memories in their current (possibly already distorted) state, could the practice of journaling actually be harmful in some cases — cementing false unhelpful narratives about our own lives? (Noting that some false narratives can be helpful). Rowlands doesn’t go there, and I suspect it’s not quite that simple, but the question of the possibility lingers.

The book is ultimately structured as a bequest. Rowlands is writing for his sons, passing on memories that will make his being “no longer my own, but resting on your retrieval and subsequent rewriting.” It’s a philosopher’s version of inheritance, and there’s something tender about it. He compares himself to Sherlock Holmes — a fictional character whose being depends entirely on others — and finds liberation rather than loss in the comparison.

An incompletable being can never die.

The prose throughout is warm and conversational, if occasionally a bit meandering. Rowlands has a habit of circling back to the same ideas from different angles, which serves the philosophical method but occasionally tests patience - though at 139, it’s still pretty short.

• Rating: 3.5 out of 5

• Dog-ear index: 7.1

• Who is it for: Philosophers who want some science, or science enthusiasts who want some philosophy. People grappling with questions of identity, memory, and what remains of us when the facts fall away. Not for readers wanting rigorous neuroscience or a complete treatise of how memory works.

reminder: I highlight important parts of the books I read, and dog-ear the really important pages. The dog-ear index is simply the average number of dog-eared pages per 100 pages

Product link for reference only; please support your local bookstore where possible: https://www.amazon.com.au/Book-Memory-How-Live-Forever/dp/1803512644

On one hand these stupid things are a useful reminder; on the other hand, they’re performative catering to confirmation bias.

They weren’t wrong about the output; the AI did fail.

Or, rather, their AI failed.

As a result, they are wrong about what think they demonstrate. They’d only tested their particular configuration of AI — which, based on most of the screenshots, are free-tier models with no customisation, no context, and no instructions beyond a single prompt typed into a chat box.

That’s a bit like test-driving a bicycle, concluding that vehicles can’t exceed 25 km/h, and proceeding to laugh at people who think they can drive to Sydney in a day.

I see this constantly, from both sides.

The enthusiasts running agentic frameworks and custom workflows who can’t understand why everyone else doesn’t see it.

And the skeptics who dismiss it entirely are usually running the default models.

At least sometimes, they’re both reporting their experiences honestly. But they’re not talking about the same thing, so they talk past each other, and irrespective of that, things are a little bit more complicated than either end would want you to believe.

This is the problem I want to dig into today, because it has consequences that go well beyond social media arguments. When organisations make decisions about AI strategy — how much to invest, where to deploy it, what it can and can’t do — those decisions are shaped by the experiences of the people in the room. And since those experiences vary so wildly, depending on how the tool is configured, most statements about “what AI can do” are essentially meaningless without specifying the setup in great detail.

The answer to almost every question about AI capability is “it depends.”

The Six Layers

To make this concrete, let me walk through a single, common task and show how the output transforms as you move through six layers of configuration.

Note: I am not going to touch on experimental agents or frameworks like OpenClaw here. In my judgement, they’re not ready to be used by ‘normal’ people. What I have are six layers that are ready for prime-time - doesn’t mean it’ll always be only six layers.

But first, a note about something that cuts across every layer: how you prompt matters. The quality of what you ask — how specific you are, how much context you provide, whether you iterate on the first answer or accept it — affects the output at every level.

A thoughtful, detailed prompt at Layer 1 will outperform a lazy one-liner at Layer 3. Prompting is still a skill and it’s the one variable that compounds with everything else. I’ll come back to this, but keep it in mind as we go.

Layer 1: The Default

This is where most people’s experience begins and ends. You go to ChatGPT or whatever tool your organisation provides. You type something like: “Help me prepare for a meeting with a healthcare exec team about AI training.”

You get back… advice. Fine, generic, sensible, forgettable advice. Research the company. Review their annual report. Prepare an agenda. Think about what questions they might ask.

Duh.

It’s not that it’s wrong, it’s just not very useful if you’re not a complete newbie into the world of business. You could have found the same suggestions in any business article written in the last thirty years. The AI doesn’t know who you are, who the client is, what your expertise is, or what you’re trying to achieve. It’s working with nothing but your twelve-word prompt and whatever statistical patterns it learned during training.

(This is where prompting skill matters most, by the way. At Layer 1, it’s all you’ve got. A detailed, context-rich prompt — explaining your role, the client’s background, the meeting’s purpose — would already produce a noticeably better result. But most people never try this, because their frame of mind is in testing the tool, not actually using it.)

This is the experience that produces most of the skepticism. And honestly? The skepticism is fair — for this level and this configuration.

The mistake is assuming this is all there is.

Layer 2: Upgrading the Engine

The simplest improvement is one that a surprising number of people still don’t know about: paying for the tool and selecting the best model.

Every major AI platform offers multiple models. Some are faster but less capable. Some are specifically designed for complex reasoning — often called “thinking” or “reasoning” models — that will actually work through a problem more in a step by step manner before answering. In most cases, this is extremely helpful.

In all the three frontier tools (Claude, ChatGPT, and Gemini), you can select different models from a dropdown menu. Most people have never clicked that dropdown. Many don’t know it exists, and others don’t think it matters. I don’t blame them; the labs have traditionally, in technical terms, sucked at the UX of this and the model naming has been less than helpful to be charitable.

The paid tier also typically means a larger context window (the AI can hold more information in its head at once), access to better models, and fewer restrictions on usage.

Same meeting prep prompt, better model: you now get a more structured analysis, perhaps some industry-specific considerations for healthcare AI adoption, a more nuanced discussion of stakeholder dynamics. It’s noticeably better. But it’s still generic, because the AI still doesn’t know anything about you.

Layer 3: Fixing the Personality

This is where most people leave enormous value on the table.

Every major AI platform lets you set custom instructions — persistent context that the AI reads before every conversation. Most people either don’t know this exists or have written something vague like “be concise and professional.”

But custom instructions can do far more than set a tone. You can tell the AI who you are, what you do, how you think, what you value, and how you want to work together. You can tell it to challenge your assumptions rather than validate them. You can tell it to match your communication style, understand your industry context, and skip the generic preamble.

The difference this makes is profound and underappreciated. With well-crafted instructions, the AI stops being a generic assistant and starts being your assistant. It frames meeting prep through your professional lens. It knows you’re a consultant, not an employee. It knows your approach to client relationships. It adjusts its depth and tone to what you actually need rather than what an average user might want.

The default behaviour isn’t neutral, but rather calibrated for the widest possible audience, which means it’s optimised for no one in particular, and quite often for engagement and not being offensive. Fixing the personality is fixing a misalignment that most people either don’t notice or are actively pissed off about (like the models being sycophantic), but don’t realise how easy it is to fix.

Layer 4: Teaching It Your Method

Here’s where it gets more interesting. Custom instructions can tell the AI who you are. But skills — reusable structured workflows — tell the AI how you want specific types of work done.

Think of the difference between hiring someone smart and hiring someone smart who’s been trained in your methodology. Skills are what I used liberally in my exploration of using GenAI for foresight that I talked about in the Futures Dramaturg article.

A skill for your client meeting preparation might include a specific structure: stakeholder mapping, engagement history review, risk and opportunity identification, industry context, and a pre-drafted agenda with discussion prompts. Every time you run it, the output follows your process rather than improvising a new approach each time.

This matters because consistency is where compounding efficiency lives. A one-off clever answer is nice. A repeatable workflow (or, better, an automatically reporting workflow) that produces reliably excellent prep for every meeting, every time, following your standards — that changes how you operate.

Most AI users are improvising every interaction from scratch, so quite naturally they’re getting different formats, different depths, different structures each time. Skills reduce that variance. You design the process once, and then it gets executed.

Layer 5: Reaching Out for Context

Up to Layer 4, the AI is working with your instructions and whatever you type into the prompt. It’s smart, it knows stuff about you and is personalised, it has your methodology, but it’s still essentially imagining the situation based on what you tell it.

At Layer 5, the AI connects directly to your data: Your calendar. Your email. Your notes. Your files.

For our meeting prep example: instead of asking you who the meeting is with and when it’s happening, the system reads your calendar and knows. It searches your email for recent exchanges with this client. It checks your notes for the history of the engagement — what you proposed, what they said, what’s outstanding. It finds the proposal you sent them three months ago and the follow-up that went unanswered.

The brief it produces isn’t then based on generic advice or even your methodology alone. It’s grounded in actual information about this specific meeting with this specific client. Things you might have forgotten or never thought to mention, or that simply would’ve been a lot of work to manually add.

This is the layer that makes most of the “AI can’t do real work” crowd look like they’ve been reviewing a different product entirely, because they have been.

Layer 6: Adding Depth

The final layer I will be talking about here extends the AI’s reach beyond your personal data to external knowledge sources — academic databases, computational tools, verified research, real-time data.

For the meeting prep: the AI now searches peer-reviewed literature on the topics you’ll be discussing. It pulls current industry reports and regulatory updates relevant to the client’s sector. It can compute financial projections or model scenarios if the meeting involves quantitative decisions. It accesses specialised databases that you’d normally need to search manually — or more likely, wouldn’t search at all because you didn’t have the time.

As a result, you walk into the meeting with knowledge that neither you nor the AI had in isolation. You’ve been augmented in the truest sense — not replaced, not automated, but operating with a depth and breadth of preparation that simply wasn’t possible before.

This is what advanced AI use actually looks like today.

The Branching Problem

So here’s where this connects to the broader challenge, and where it gets worse than a simple ladder model might suggest.

The six layers describe form the vertical differences in what people have and use, but there’s a horizontal one too. Even two people operating at the same layer will have fundamentally different experiences, because every choice they’ve made along the way creates divergence.

At Layer 3, your custom instructions are different from mine. You’ve told your AI to be formal and cautious; I’ve told mine to challenge my thinking and skip the preamble. Ergo, different tool. At Layer 5, you’ve connected your Outlook calendar and SharePoint; I’ve connected Gmail, Obsidian, and a CRM, so we get different universes of context. At Layer 6, you might be pulling from a legal database; I’m pulling from peer-reviewed academic research and computational tools. Completely different intelligences that excel, and fail, at different things.

The result is a tree.

Every configuration choice is a branching point. Model selection, custom instructions, which skills you build, which data you connect, which external tools you integrate, and underneath all of it how well you prompt. The further you go, the more branches there are, and the more personal the tool becomes. Two people at Layer 6 might be running systems so different from each other that comparing their experiences is barely more meaningful than comparing one person’s Layer 1 to another’s Layer 6.

This is why “what AI can do” is becoming a nearly meaningless question.

It’s like asking “what can software do?” The answer depends entirely on which software, configured how, by whom, for what purpose. We don’t ask that question about software because we intuitively understand the space is too vast. We haven’t yet developed that intuition for AI, but we need to, quickly, because the decisions being made in its absence are consequential.

When a CEO tells their board “we’ve tried AI and it’s not that impressive,” what were they running? What layer? What configuration? What instructions? What connections?

Was it stock-standard Copilot?

When a consulting firm says “AI saves our team ten hours a week,” what does their tree look like?

When LinkedIn’s main character of the day dunks on a chatbot for getting a maths problem wrong, which branch were they even on?

The answer matters enormously.

We’re in a moment where organisations are making consequential decisions about AI based on experiences that span a staggeringly wide range of capability, and the people making those decisions usually don’t fully understand the range that exists. They think their experience is the experience. It isn’t. It’s their experience of their configuration - one branch, or leaf, of an enormous tree.

In aviation, mode confusion — where pilots misidentify or lose track of what mode the aircraft automation is in — can lead to automation surprise, where pilots are startled by unexpected automated behaviour.

The AI equivalent is happening right now at an organisational level. People are surprised by AI’s limitations or capabilities because they don’t understand what configuration they’re looking at.

The LinkedIn dunkers are the most visible symptom, but they’re not the real problem. The real problem is the executive who used the default for twenty minutes, formed a confident opinion, and is now shaping their organisation’s AI strategy based on the experience of seeing one fallen twig from a giant tree.

What To Do About It

One caveat before the practical advice: the point of understanding these layers is not that higher is always better. Warren Vanderburgh’s original Children of the Magenta thesis — the one this Substack is named after — makes the strong case that pilots need to know when to reduce automation.

The same applies here. There are moments when you should deliberately drop down a layer or more: when the AI’s context connections are pulling in noise instead of signal, when you need your own unfiltered thinking before the AI shapes it, when a simple prompt to a clean model will give you a better answer than a complex workflow that’s optimising for the wrong question.

Knowing your way around all six layers means knowing which one to reach for in any given moment, including Layer 0 of no AI whatsoever.

If you’ve read this far and suspect you might be operating at a lower layer than you could be, here’s the honest answer: moving up is not trivial. Each layer requires some combination of investment, configuration, and, importantly, a shift in how you think about the tool.

But you can start immediately:

If you’re at Layer 1, the single highest-impact move is upgrading to a paid tier and spending five minutes learning which model to select for which task. That alone will change your experience more than anything else.

If you’re at Layer 2, write proper custom instructions. Not “be concise.” Tell the AI who you are, what you do, how you think, and what you expect. Spend an hour on this. It pays for itself in the first week.

If you’re at Layer 3, start thinking about the tasks you do repeatedly and whether you can structure them into consistent workflows. This requires more thought than the previous layers, but the leverage is big.

If you’re at Layer 4 and beyond, you’re already way ahead of most. The returns from here come from deeper integration; connecting more data sources, adding specialised external tools (many of which, yes, you will need to pay for), and refining your workflows as you discover what actually saves you time versus what’s merely impressive. Keep experimenting. The frontier moves fast, and at this level, you’re well-positioned to see it first.

The point isn’t to make everyone a Layer 6 user. The point is to make sure that when you form an opinion about what AI can and can’t do, you know which branch you’re standing on, and can calibrate accordingly.

If you’re making decisions that affect your organisation, your team, or your career, you should probably explore a few more branches before you decide what you think of the view.

If you’re a senior leader who wants to move beyond Layer 1-2, I run a bespoke GenAI coaching programs designed to get you to the level where AI actually transforms how you work, not just how you talk about it in your exec or board meetings. Book a free initial call here to discuss.

Some disclosures before we begin. First, I know the author. Second, this book sits squarely in my professional domain. Third, the thesis aligns so closely with what this Substack has been exploring that my confirmation bias is probably doing a victory lap. I tell you this not to undermine what follows, but because intellectual honesty requires it, and because the book itself would demand nothing less.

Steven D. Green’s Pilot Competency and Capability is an academic text that takes on one of aviation’s deepest philosophical tensions: the gap between what we say the pilot is — the final authority, the capable and competent aviator — and what the industry has been systematically building the pilot into: a systems operator within a cybernetic control loop.

Green’s argument is built on margins. Not just the margins pilots think about every day like landing distances, fuel reserves, and obstacle clearance, but also on the deeper margins that make safe operations possible at all: the margin of residual attention, the margin between work-as-imagined and work-as-done, the margin between what can be specified in a procedure and what actually keeps an airplane safely on a trajectory through an open, complex, tempestuous environment.

Green argues these margins exist to absorb the unforeseen. They are not there so that operational optimization could find more things to shave time or cost off of. When we shrink them — whether to save fuel, to increase throughput, or to squeeze a few more seconds out of a turnaround — we are spending safety capital that was never ours to spend.

It is, to me anyway, very easy to see how these concepts generalize broadly to our society today, with the exception that the society at large has none of the defenses against that encroachment that the aviation industry has built over decades of literal blood, sweat, and tears.

This is not a new argument in safety literature, but Green grounds it with unusual depth. He traces the concept of prudence to its Latin root, providentia — foresight — and builds a framework around St. Thomas Aquinas’s structure of practical reason: take counsel, judge what you have learned, then execute command. Command, in Green’s telling, is the action that follows judgment. It is what people do with their free will. And it is precisely the thing that gets compressed when we proceduralize everything, because the cybernetic approach to automation treats standard operating procedures as lines of code and the human being as a machine executing them.

One could say I’m a fan of cybernetics, or at the very least drawn to some of its concepts, so when Green takes aim at it, I was on guard. For the cybernetic approach to work, Green argues, the operating environment must be a closed system.

Aviation is no such thing. One could argue very few things operating out there in the real world are. Life is no such thing.

The cybernetic approach to automation, he writes, is a digitization of Herbert Simon’s 1947 premise that two people with the same information will rationally decide upon the same course of action — a premise Green identifies as a definitive statement of determinism, invested in what Sidney Dekker calls naïve Newtonian scientism. The belief that if we can just specify enough variables, control enough inputs, and standardize enough outputs, we will achieve safety.

We won’t.

And Green’s own experience gives this weight. Early in the book, he recounts a 1978 crash in a New Hampshire forest that killed his best friend and very nearly killed him. Over forty years later, he still doesn’t know what caused the airplane to sink into the trees on a calm summer evening.

The hidden axiom of the capable-and-competent pilot is the unstated assumption that the pilot will always see the threat coming.

Tell that to Richard de Crespigny of QF32 or Kevin Sullivan of QF72.

The accident you’ll actually have is the one you won’t see coming, and the capability to manage that uncertainty is exactly what gets eroded when we replace judgment with procedure. Those who see automation as the path to safety should note that the tail of things that can and does go wrong is far longer than any system designer can specify.

Readers of this Substack will recognise the through-line. What Green describes as the margin of residual attention (the capacity that must be protected so the pilot can identify stuck programs, spot unruly trajectories, and shoot them down) is the operational face of what James C. Scott calls métis: practical knowledge that resists codification because the environments in which it operates are too variable for formal procedures. Green’s entire framework of prudence, command, and the ecology of action maps onto Scott’s argument about what gets destroyed when systems demand legibility above all else.

And it connects directly to what I called the grace margin here — the space between what a system prescribes and what a person actually does. Green’s observation that controlling and anticipating workers’ compliance naturally generates pushback from people who desire to retain an identity of craftsmanship is the aviation-specific version of a universal truth: when you proceduralize everything, you don’t get compliance. You get shortcuts, workarounds, and optimizing violations, because the people doing the work understand something the system designers don’t.

The book is dense. It is academic in tone and structure, rich with references to Amalberti, Morin, Weick, Reason, Dekker, and Hollnagel. It covers a lot of territory like ampliative reasoning, self-organized criticality, fat-tailed probability distributions, and cognitive dissonance theory, some or all of which will be unfamiliar to many people. Green doesn’t condescend, or explain to the lowest common denominator. He doesn’t simplify where simplification would distort. This is not a book for a casual audience, and it doesn’t pretend to be.

It is also, inevitably, a book rooted in American aviation regulation. The FAR references, the NTSB case studies, the FAA-centric regulatory framework are appropriate to the subject but will require some translation for readers operating under EASA, CASA, or other regulatory regimes. The underlying arguments transcend jurisdiction, but the direct examples don’t always.

Amidst all that complexity, it can be easy to lose some of the key messages. Thankfully, they’re driven home with more plain language as well, such as this:

If you read one sentence from this review, make it that one.

Green closes by returning to Saint-Exupéry’s elemental divinities (the mountain, the sea, the wind) and arguing that automation, like the sky itself, is an open system. We can, at best, debate on terms of equality with it. We must never assume superiority to it. And we must vest the pilot with the authority and the capability to manage that debate, because the responsibility is towering, moral, and it can reside nowhere else.

• Rating: 4.5 out of 5

• Dog-ear index: 12.3

• Who is it for: Aviation professionals (especially pilots, instructors, safety specialists, and human factors researchers) who want the philosophical and theoretical underpinning for why proceduralization and automation alone will not be sufficient. Also for anyone working in safety-critical systems who suspects that the industry’s faith in procedures and technology has outpaced its understanding of what actually keeps complex operations safe. This is not a casual read. It will reward those who come to it with some background.
  

[reminder: I highlight important parts of the books I read, and dog-ear the really important pages. The dog-ear index is simply the average number of dog-eared pages per 100 pages]

This first chapter of the book is published as Open Access, so it is freely available online at https://library.oapen.org/bitstream/handle/20.500.12657/62009/1/9781003369677_10.1201_9781003369677-1.pdf. For the full book, please support your local bookstore where possible (who are not likely to have it in stock, but should be able to order it for you); Amazon product link for reference: https://www.amazon.com.au/Pilot-Competency-Capability-Responsibilities-Strategy/dp/1032439742/

We teach obedience extraordinarily well. We teach it in homes, in schools, in workplaces, in militaries. The vast majority of the time we reward compliance and punish deviation.

And then, when someone follows orders into a catastrophe — financial fraud, medical harm, atrocities — we act surprised. We ask, “How could they do that? Why did they just go along with it?“

Ira Chaleff’s Intelligent Disobedience wants to address that gap. The title concept is borrowed from guide dog training: a guide dog must learn when to disobey a command that would put the team in danger. When a handler says “forward” at an intersection, but a car is coming, the dog must refuse. Learning not to obey, the trainers say, is a higher order of skill.

Chaleff argues we need to develop the same capacity in humans, and he’s right. But is this book, published over a decade ago, still an applicable guide for getting there?

I should disclose my angle here: I’ve spent a significant chunks of time around the aviation industry, where the problem Chaleff describes was identified, and substantially addressed, decades ago. Crew Resource Management (CRM) emerged in the 1970s and 80s precisely because junior officers were failing to challenge captains making deadly errors.

CRM was the aviation industry’s answer to exactly this problem: structured, trained, practiced authority-challenging within hierarchies. So when Chaleff arrives at this territory, I’m reading with the eyes of someone who has seen the solution work at scale, and who notices where the book falls short of it.

To be fair, the book covers a lot of ground well. The “algorithm of obedience” framework, quoted below, which lays out the conditions under which compliance is appropriate, and the parallel conditions that should trigger resistance is genuinely useful as a teaching tool. There is also a useful training list, with an emphasis on practicing disobedience with a strong voice and commending it when it occurs.

• I am receiving a rule or order from a legitimate source, not from a random direction.

• I understand the rule or order, what its goal is and what is expected of me in achieving that goal.

• The order is good, or at least neutral in terms of the impact it will have.

• Because no serious harm will result from implementing the order and no core value is being violated, I will obey the order.

The Milgram experiments, which form some of the backbone of much of the book, can get tiresome for anyone who’s read anything about this topic. Yes, we know: people obey authority figures even when told to administer electric shocks. If you’ve read any popular psychology in the last forty years, this is well-worn territory, and Chaleff’s extended treatment of the original experiments feels like it’s padding for the familiar.

However, then Chaleff covers some of the lesser-known variations of Milgram’s experiments. The finding that compliance dropped from 65% to 40% when the subject could see the victim, and to just 30% when forced into physical contact with them, tells us something important: distance enables obedience. It works both ways: when the authority figure left the room and gave orders by phone, compliance plummeted to 20%.

This matters enormously right now.

The most frightening variation of Milgram’s experiment isn’t about distance at all, it’s about role. When subjects weren’t the ones administering shocks but were assigned ancillary tasks like reading questions or documenting answers, compliance shot to 90%. They weren’t pulling the lever; they were just doing their bit within a system that happened to cause harm downstream.

They were comfortably distant from the harm.

As Chaleff observes, most of us today occupy the equivalent of the analyst’s role — we’re not directly causing harm, we’re processing data in a contracting office, cleaning up statistical noise in a drug trial, providing affordable school lunches that happen to contribute to juvenile diabetes.

We’re upgrading our phones to the newest user-friendly technology without dwelling on the factory conditions that produced them. The mechanism that neutralises our moral resistance is removal from direct causation. And in an age of drone warfare, algorithmic decision-making, and bureaucratic chains so long that no individual feels the consequences of their actions, the conditions for that kind of compliance have never been better.

That insight alone is worth the read. But it sits alongside material that hasn’t aged well. The case studies of teachers pressuring to cheat on standardised tests, police underreporting crime statistics, football players urged to deliver concussion-causing hits all probably felt illustrative in 2015 when this book was written.

In 2026, after everything we’ve collectively witnessed, they feel quaint and almost inconsequential. The book’s examples operate in a world that seems smaller and simpler than the one we’re navigating now.

There’s also a scope limitation that nagged me throughout. Chaleff is explicit that intelligent disobedience “works within a system rather than challenging the system itself.” He clearly distinguishes it from civil disobedience; intelligent disobedience accepts the legitimacy of the hierarchy while pushing back on specific orders.

I understand the distinction, and it’s useful as far as it goes. But the harder question goes largely unaddressed: what do you do when the system itself is corrupt, when the mission isn’t positive, when resistance within the system will just get you fired, or worse?

The book assumes a fundamentally sound structure with occasionally flawed commands. Today, that’s a highly optimistic frame, and in many of the situations where disobedience matters most, it’s an inadequate one.

The strongest chapters deal with education and development. Chaleff’s argument that we train obedience into children without simultaneously developing the judgment to know when disobedience is warranted is compelling and undersold. The observation that critical thinking programs teach the thinking but not the acting — not the competence to express dissent in the face of positional authority — is sharp.

We teach children to analyse; we don’t teach them to resist.

We should.

If you’re drawn to this topic — and you should be — I’d actually first point you toward Sunita Sah’s Defy: The Power of No in a World that Demands Yes, which I reviewed last year and rated significantly higher. Sah covers fundamentally similar ground but with sharper tools, more contemporary framing, and a five-stage model of defiance that better captures the psychological reality of why we stay silent when we shouldn’t.

That said, Intelligent Disobedience was ahead of its time in 2015, and the core message remains both important and urgent. We need people who can distinguish between legitimate authority and illegitimate orders. We need to teach resistance as deliberately as we teach compliance. And we need to reckon with the fact that in an increasingly automated, bureaucratised, algorithmically mediated world, we are more removed from the consequences of our decisions than ever, which means the conditions for blind obedience have never been better.

The guide dog metaphor is a good one.

But a decade on, we need more than metaphors. We need the training program.

• Rating: 3.5 out of 5

• Dog-ear index: 7.4

• Who is it for: People new to the obedience/authority literature who want an accessible introduction; educators thinking about how to balance discipline with independent judgment; leaders who say they want to hear dissent but haven’t built the structures to support it. If you’ve already read books like Sah’s Defy or have a background in CRM, human factors, or organisational psychology, much of this will be familiar ground.

reminder: I highlight important parts of the books I read, and dog-ear the really important pages. The dog-ear index is simply the average number of dog-eared pages per 100 pages

Product link for reference only; please support your local bookstore where possible: https://www.amazon.com.au/Intelligent-Disobedience-Ira-Chaleff/dp/1626564272

Most philosophy books pretend to be neutral while smuggling in their biases anyway. Michael Huemer’s “Knowledge, Reality, and Value: A Mostly Common Sense Guide to Philosophy“ does something more honest and more interesting: it tells you upfront that it aims to be fair, but not neutral. He’s going to tell you what he thinks, and argue for it, and if you don’t like that, he suggests you go get another book. He even names one.

I respect that. It’s a harder standard to hold yourself to than pretend to be “objective”, and it signals that Huemer trusts his readers to evaluate arguments rather than needing to be guided to the “right” conclusion. Whether he consistently meets that standard is another question, but the disclosure itself earns points.

The book is a wide-ranging introduction to philosophy — epistemology, logic, ethics, free will, philosophy of religion, political philosophy — filtered through Huemer’s consistently libertarian-leaning, intuitionist lens. The subtitle's 'mostly common sense' is doing real rhetorical work here, framing his fairly opinionated positions as something any reasonable person would think if they just thought carefully enough. Sometimes that’s the case; other times it may not be.

There’s a lot of good stuff here on critical thinking. The treatment of fallacies is great; Huemer walks through anecdotal evidence, base rate neglect, whataboutism, cherry picking, and selection effects with the kind of clear, example-driven writing that makes you wish more philosophers wrote this way.

His base rate neglect example involving a rare disease and a 90% accurate test is the sort of thing everyone should encounter at least once in their education. His treatment of whataboutism is sharp too, as he argues that the practice systematically prevents evils from being addressed, because for any wrong in the world, you can always point to some other, worse wrong elsewhere, deflecting attention indefinitely. I’ve seen whataboutism play out far too much in domains ranging from boardrooms to social media my entire adult life.

Huemer also offers one of the better insights I’ve encountered on the relationship between bias and knowledge: that the factors making someone biased about a topic are often the very same factors making them knowledgeable about it. A war veteran discussing war is likely both the most biased and the most informed person in the room. Discounting “non-objective” perspectives therefore risks throwing out the perspectives of the most knowledgeable people. That observation has implications well beyond philosophy, and is relevant to anyone who has ever dismissed an expert’s view because they were “too close to the subject.”

And then he turns the lens on you:

Yet while the vast majority of people are dogmatic, no one thinks that they are. You, reader, are probably dogmatic, but you think you’re not. That’s partly because the word ‘dogmatic’ sounds insulting, and hence it is unpleasant to entertain the hypothesis that one is dogmatic. To make it sound less bad, you can just replace it with the description, “systematically underestimates appropriate belief revision”. You probably systematically underestimate how much you should revise your beliefs when you acquire new information, because the vast majority of people do that, but you probably don’t realize that you do this.

All the greatest hits are here: Pascal’s Wager, the omnipotence paradox (can God create a stone so heavy he can’t lift it?), the tension between omniscience and free will, Nozick’s Experience Machine, deontology versus consequentialism. For readers encountering these for the first time, the coverage is clear and engaging. For those who’ve already wrestled with them, it reads more like a well-organized refresher.

The chapters on epistemology get complicated quickly — Huemer himself acknowledges this transition — and the later material on ethical theory is where his intuitionist framework does the most work and also where a critical reader might push back hardest.

I am probably not be the target audience for this book. Much of the terrain was familiar, and I found myself nodding along more than being challenged. Huemer’s writing style of dry humor worked well for me, so it was fine; pleasant, even. And the pre-existing knowledge is, of course, not a criticism of the book so much as an acknowledgment of audience fit.

For someone coming to these ideas fresh, like a high school or maybe a university student or others who haven’t yet thought systematically about what rationality means or why being irrational might be morally blameworthy, this would be an excellent starting point.

Huemer writes with personality and a directness that is rare in academic philosophy. His style reminded me at times of what I appreciated about Simon McCarthy-Jones’ “Freethinking”; both authors trust their readers enough to take clear positions and defend them openly.

The question I didn’t fully come to a conclusion about is whether “fair but not neutral“ earned its keep or whether this collapses into “persuasive essays with a disclaimer.” On balance, I think Huemer mostly earns it; the libertarian lens does run through so consistently that readers should be aware they’re getting a survey of philosophy from a particular vantage point, which is still kind of presented as the view from nowhere.

• Rating: 3.5 out of 5

• Dog-ear index: 4.3

• Who is it for: Philosophy newcomers who want a clear, opinionated, and engaging introduction to the big questions, and who are comfortable being told what the author thinks the answers are.

[reminder: I highlight important parts of the books I read, and dog-ear the really important pages. The dog-ear index is simply the average number of dog-eared pages per 100 pages]

Product link for reference only; please support your local bookstore where possible: https://www.amazon.com.au/Knowledge-Reality-Value-Mostly-Philosophy/dp/B091F5QTDS

I am not, by nature, a history buff, though as a foresight professional we need to be somewhat familiar with history as well. But nearly seventeen years into calling Australia home, Mark McKenna’s “The Shortest History of Australia” landed on my radar and I’m glad it did.

If you live in Australia, there’s a better than 50% chance that either you or one or both of your parents were born overseas. That’s pretty remarkable. For an immigrant like me, someone who chose this country and has been chosen by it in return, I feel we owe it to ourselves to understand the full picture of the place we’ve made our home, not just the brochure version or the one we engage with in our suburb, social circles, or workplace.

The Shortest History of Australia is the perfect tool for this. It will tell you, honestly, what happened, but more than that, it reframes how you see where you are. For a history book, it’s also mercifully short; noteworthy because they often tend…not to be.

McKenna covers an enormous amount of ground in a compact book, and he does it with an honesty that never tips into either jingoism or self-flagellation. The narrative begins not in Sydney Cove but in the north, with tens of thousands of years of Indigenous history; a corrective to the default European-arrival starting point that still dominates popular understanding.

From there, we get the colonial project with its Christian underpinnings (Cook’s claims were also theological, which was news to me), the shaping of national identity through war and migration, and the slow, painful, still-incomplete reckoning with what was done to First Nations peoples.

There are ugly details to be found not just among the First Nations affairs; Australia is the world’s worst country for mammal extinction. Only Australians could remember the abysmal defeat of Gallipoli as a victory. The learning curve on water and fire management - skills Indigenous Australians had refined over millennia - was, and in some ways still is, embarrassingly slow.

McKenna doesn’t flinch from the uncomfortable facts, and the book is better for it.

Unsurprisingly, the sections on migration and multiculturalism resonated deeply. McKenna captures a striking phenomenon: how migrant views of their home countries tend to fossilise at the point of departure, frozen in time while the country itself moves on. I recognise that pattern; even the language of those who migrated here half a century ago feel like live historical archives. I can only imagine how stupid I’ll sound in Finnish in another decade or two.

He also traces how Australia went from enshrining racial purity at the start of the twentieth century to embracing racial equality by its end, a transformation so dramatic it’s easy to underestimate. Whitlam’s decision to make multiculturalism official government policy in 1973 was pivotal, and the book makes a compelling case that Australia only truly began to feel like an independent country after his election in 1972. The sheer volume of progressive legislation his government passed in just three years is staggering.

Yet McKenna is equally clear-eyed about what remains unresolved. Australia has become one of the world’s most diverse, multicultural liberal democracies, but it is also one of the most property-obsessed nations on Earth, increasingly politically polarised, and, another one of those places where the belief that life will be better for future generations is fading.

One’s postcode remains one of the strongest predictors of opportunity, and levels of sexism, misogyny, and domestic violence remain stubbornly high. The suburbs, McKenna shows, are simultaneously where the miracle of multicultural coexistence is lived out daily and where inequality quietly deepens.

And then there’s the unfinished business. As Australia approaches the 250th anniversary of British arrival, it has yet to recognise First Nations Australians in a just and substantive way. An indifference remains, McKenna writes: a failure to imagine what history looks and feels like for those on the other side. While my broadly multicultural circle of friends I have here is one of the best things in my life, I don’t have many Aboriginal friends; I should. That’s my own small version of the larger pattern he describes.

This was fascinating, authentic, honest and, true to the title, short enough. McKenna manages to compress millennia into a book you can finish in a few sittings without it feeling rushed or superficial.For anyone living in Australia, whether born here or arrived yesterday, this should be required reading. It might help you understand it better, and understanding is where everything worth doing starts.

• Rating: 4.5 out of 5

• Dog-ear index: 7.3

• Who is it for: Anyone living in Australia who wants to begin to understand the place beyond the surface; especially fellow immigrants who, like me, owe it to themselves and their adopted home to know the full story.

[reminder: I highlight important parts of the books I read, and dog-ear the really important pages. The dog-ear index is simply the average number of dog-eared pages per 100 pages]

Product link for reference only; please support your local bookstore where possible: https://www.amazon.com.au/Shortest-History-Australia-Mark-McKenna/dp/1760643599

The TL;DR version: AI now produces genuinely good foresight analysis. Not “good for an AI”, just good, period. The profession’s value is shifting from analytical work to holding the arc of an engagement: keeping people in the room, literally and figuratively, when the futures get uncomfortable, framing the right questions, facilitating the conversations that matter, and shepherding insight into action.

I’m calling this the dramaturg model. The analytical middle of foresight can increasingly be done with AI. The need for human work is stronger at the ends; the room where people confront uncomfortable truths, and the organisational reality where change actually happens. That’s where practitioners need to invest.

This shift is underway now, and it will keep moving.

I’ve spent considerable time over the past few years using generative AI for strategic foresight work, constantly trying to keep my finger on the pulse of what these tools are capable of in my domain(s). Running full engagement cycles: signal scanning, driver analysis, forecasting, scenario development, consequence mapping, cross-impact analysis, resilience assessment, strategic transition frameworks, future personas, tangible artifacts.

The kind of work that typically involves a team, a timeline measured in weeks or months, and a substantial budget.

Recently, something has shifted, and I claim that the tools have silently crossed the ‘good enough’ threshold for so many things that we foresight professionals need to recalibrate what we are here for.

I’m not alone in noticing. A 2025 survey by the OECD and World Economic Forum found that two-thirds of foresight practitioners are already using AI in their work, with most reporting significant time savings and expanded analytical capacity. The shift is underway.

In short, the output is now good. Scenarios that are genuinely distinct. Cross-impact matrices that reveal systemic patterns I hadn’t consciously assembled. Personas that are emotionally compelling. Strategic recommendations that are specific, actionable, and internally consistent across a body of work that grows more interconnected with each exercise, with a coverage and structure that feels almost suspiciously complete.

What does this mean for us?

I am a foresight professional. This is a significant part of my professional identity and livelihood. And I have watched a machine do a version of my work - not all of it, but a substantial and valuable portion - at a speed and consistency I could never match alone. That excited me, but it also scared me a little. I think the fact that it scared me is important.

(In an earlier draft of the above paragraph, I also had the words ‘not the hardest parts’ in what parts of my work AI cannot do, but I’m conscious that may have been a comforting lie, a psychological crutch to still feel special. Maybe AI is doing the hardest parts.)

If you’re a foresight practitioner reading this, you’re likely in one of several positions. You may have experimented with AI tools for foresight work already and formed a view, in which case I’d urge you to revisit that view, because the capabilities are shifting faster than most assessment cycles can track. And when you do, check yourself for motivated reasoning — are you exploring honestly, or looking for confirmation of a conclusion you’ve already reached?

Specifically: the jump from Anthropic’s Claude Opus 4.5 to Opus 4.6 has been, in my experience, a deceptively large shift, and in my experience the single most significant threshold-crossing upgrade in AI capabilities for foresight work I’ve encountered. If you tried this even six months ago and found it wanting, your conclusions have expired. Try again.

You may not have experimented yet but hold a position on whether AI is relevant to your practice. If so, I’d ask you to hold that position lightly for the length of this article. This is a domain where views need reconsidering at a cadence most of us aren’t comfortable with, because like it or not, things are changing fast.

Foresight professionals of all people should know this. How often do we apply that knowledge to ourselves?

You may also have principled objections to AI; ethical concerns about training data, labour displacement, environmental cost, concentration of power. I respect that position. Those concerns are legitimate and they deserve serious engagement. Nothing in this article argues that you’re wrong to hold them.

What I would say is that the capabilities are arriving regardless of whether any individual practitioner adopts them, and that understanding what AI can do in your domain is valuable even, perhaps especially, if you choose not to use it.

Foresight has always been about understanding even the futures you might not want.

Or you may have already composed a mental list of reasons this doesn’t apply to you on the assumption that your work is somehow exempt. If so, I want to gently suggest that the ability to construct reassuring narratives about why uncomfortable futures won’t materialise is a failure mode we’re supposed to help our clients avoid.

We’re not exempt

There is some level of professional illusory exceptionalism that can be found from many, if not most, domains. In foresight work it goes: “Yes, AI will transform accounting, law, journalism, and customer service — but our work is too creative, too human, too nuanced for machines. This is the one domain where human insight is critically important.”

This story is seductive precisely because it contains a kernel of truth.

Parts of foresight work are deeply human. But the kernel is not the whole crop.

Henrik Skaug Sætra, in a recent preprint, argues that dismissing AI as “stochastic parrots” has become a professional comfort blanket, a way to avoid engaging with how these systems are actually reshaping work.

The critique that gave the phrase stochastic parrots its power was important and necessary. But as a default stance, Sætra argues, it risks becoming a way to not see what’s happening. I think foresight has its own version of this comfort blanket: the belief that our work is too creative, too contextual, too human for AI to meaningfully contribute.

It’s a comforting story.

It’s also increasingly wrong.

Nick Foster, in Could Should Might Don’t, critiques the shiny, consequence-free variety of futures work he calls “Could” futurism. His call for “mundane futures” — more time in the messy middle where people actually live — resonates deeply.

I want to make a parallel call: mundane practice. Less grand theorising about AI’s role in foresight. More honest accounting of what happens when you sit down and do the work with it.

Let me be specific. If you write about megatrends, macro forces, or driving forces of change for a living — AI already does this at least as well as you do for most contexts, and in many cases better. It synthesises across wider source material, maintains consistency across frameworks, and knows the client context better than any random foresight professional. This isn’t capability that’s coming soon. It’s here.

The barbell of human foresight engagement

What I discovered in running full foresight cycles with GenAI is that the human contribution isn’t evenly distributed. It follows a sort of a barbell, or U-pattern: heavy human involvement at both ends, AI-dense in the middle.

The opening end is orientation and framing. This is the work of getting a room of executives to admit what they’re actually afraid of, and for all of them to gain honest situational awareness. It’s about reading the room; noticing when the CFO checked out or the CEO leaned in, and choosing which provocation to deploy based on who flinched.

Some of the most powerful foresight tools work precisely because they happen in a physical room with actual humans. An orientation exercise can transform a group’s situational awareness in ways that no document, no report, and no AI-generated scenario can replicate.

When you watch a leadership team physically map out the forces shaping their world and their views of them, it becomes collective sense-making. People read each other’s body language, pick up on hesitations, build shared understanding through the friction of disagreement. The visceral discovery when you see your leadership team doesn’dest, in fact, agree on something everyone thought was a shared truth, or the quiet executive who suddenly speaks up and reframes the entire conversation are moments that don’t happen in a chat interface.

Physical presence still has power, and rooms can be efficient vehicles for mindset shifts. I’ve watched a team walk in thinking they were there to discuss a five-year technology plan and walk out realising they had a workforce crisis they hadn’t named. That shift is possible because someone was in the room facilitating, probing, creating the conditions for honest conversation.

AI can generate the analytical inputs that make those conversations richer. It cannot be the person standing at the whiteboard when the room goes quiet and someone finally says the quiet thing out loud.

Will AI-mediated facilitation eventually enter this space? Almost certainly. Immersive simulation environments are already a thing. Robots are coming. But I doubt we’ll be comfortable having machines run deeply human exercises like collective orientation and values negotiation for some time.

The technology may arrive before the trust does, and in facilitation, trust is the technology.

The middle is analysis, synthesis, and generation. Signals through scenarios through consequences through strategy.

From an effort perspective, this used to be the bulk of many foresight projects — and this is where AI excels. GenAI models can now maintain coherence across a massive body of interconnected analysis, a dozen-plus exercises in consistent relationship with each other.

The quality I could match, potentially even exceed, given enough time. What I couldn’t match was the quality at that volume and speed, sustained across every exercise in the cycle, with a consistency no team could replicate in the same timeframe.

And also, when is there ever ‘enough time’?

The closing end is action and implementation, where things get translated back into the physical reality of the organization. Building roadmaps. Catalysing change.

All this requires what the Greeks called metis — practical wisdom, the knowledge of how things actually work in this specific organisation with these specific humans and their specific histories, grudges, and ambitions. Who needs to be in the room to make this decision stick? Who will quietly sabotage it if they weren’t consulted? Which board member needs to feel like this was their idea? That knowledge lives in relationships and corridor conversations and decades of institutional memory.

No AI has access to all that, and providing it with enough context to be genuinely helpful here is itself a substantial act of judgment.

An important caveat: I don’t think the barbell is a permanent shape. It’s the shape of the work right now. The middle is already highly AI-capable; the ends are still more firmly human. But the boundary will keep moving. AI will get better at facilitating structured conversations. It will gain access to more organisational context. The human ends of the barbell will compress over time — not disappear, but narrow. Recognising that the barbell is a snapshot, not an equilibrium, is itself a foresight act. Plan for today’s shape; don’t assume it’s tomorrow’s.

Points of disagreement

I’m not the first or the only one thinking about the changing nature of foresight work, quite obviously. The growing body of work on AI in foresight is encouraging and, I think, incomplete in some important ways.

A recent case study from Queensland University of Technology, working with the Queensland Government, documented a 60-70% reduction in staff hours through AI-augmented scenario development. They doubled their scenario output. They completed analysis in weeks rather than months. These are real gains and they match my experience. Where I depart from the efficiency framing is this: the most important thing wasn’t going faster. It was going deeper. More exercises than time constraints typically allow. Branches I wouldn’t have explored. Connections I wouldn’t have seen.

Speed is the obvious benefit. Depth is the transformative one.

The OECD/WEF survey identifies three maturity levels for AI in foresight: basic analysis augmentation, creative sparring partner, and fully integrated systems. What strikes me is that this framework describes integration depth but not role transformation. You can be at the highest maturity level and still think of yourself as an analyst who uses AI tools. That’s not the shift I’m describing.

What I’m describing is something more fundamental: a shift in what the foresight professional is.

There’s also a strand of literature that frames AI’s contribution in expansive, almost breathless terms like “analytical supremacy,” “epistemological pluralism,” AI that dynamically models complex interdependencies and simulates emergent cascading effects. Some of this is real. But these accounts describe AI as upgrading everything more or less uniformly. My experience says the opposite. The upgrade is profoundly uneven; the jagged edge is real even in foresight work. The analytical middle leaps forward. The human ends remain stubbornly, beautifully resistant.

That unevenness is the most important finding, and acknowledging that it too will shift over time is part of honest practice.

A finding from the QUT study deserves particular attention: AI hallucinations occasionally served as creative provocations in scenario work. I’ve seen this too; in divergent ideation, we like telling people there are no bad ideas, but do we extend the same charitable principle to our AI tools? This only works if the practitioner has enough independent expertise to distinguish a genuinely interesting provocation from confident nonsense.

Sætra draws on Stephen Barley’s sociology of work to make a distinction that matters here: between substitutional and infrastructural technological change. Substitution is when a new tool replaces an old one in roughly the same role.

Infrastructure rewires how the work is organised.

Most commentary about AI in foresight treats it as substitutional; a faster way to do scenario analysis, a better research assistant. My experience suggests it’s infrastructural. It doesn’t just speed up the middle of the barbell; it changes the relationship between the parts. When the analytical work that used to take weeks happens in hours, everything upstream and downstream reorganises around that new reality: client expectations shift, engagement design changes, the skills that matter are different, the bottleneck shifts.

Sætra identifies a historical pattern that should concern us: when a bottleneck moves from human to machine, the work doesn’t vanish, but the power embedded in that work does. The typesetter didn’t disappear overnight when the Linotype arrived. But the bottleneck moved, and with it went the leverage.

If foresight’s analytical middle follows this pattern — and I think it’s beginning to — then the question isn’t whether the work gets done. It’s who controls how it gets done, and what happens to the practitioners whose expertise was built on doing it. (Only half-facetiously I would suggest one place the bottleneck shifts to is calendar-wrangling of the stakeholders)

This connects to the deskilling concern that bothers me more generally.

If AI handles the analytical middle, where do junior practitioners develop the pattern-recognition skills that make senior expertise valuable? It’s the same tension aviation discovered decades ago — hence the name of this newsletter — when autopilot made flying safer but slowly eroded the manual skills pilots needed when the automation failed.

I don’t have a clean answer. But naming it honestly is more useful than pretending it doesn’t exist.

And I think the honest next question is: could we stop this even if we wanted to?

Almost certainly not. Foresight has always been a tough sell. It’s been seen as expensive, slow, its value diffuse and hard to measure. No client is going to voluntarily pay for the slow human-only version of analytical work to preserve our apprenticeship pipeline.

That’s asking the market to subsidise practitioner skill development out of kindness. Markets don’t do that.

There’s a deeper reason too. The acceleration isn’t just a threat to practitioner skills; there’s an argument to be made that it’s a genuine necessity.

A year-long foresight project on AI-adjacent topics is already obsolete by the time it ships. The world, in at least some domains, is moving faster than traditional foresight cycles can track. The profession had a speed problem before AI arrived as a tool, and now the same technology causing the acceleration is also the only plausible solution to it. If we can’t keep pace with the rate of change, our work is just wrong.

And there’s a genuine good buried in the disruption. Foresight has been a luxury good, available primarily to large organisations with large budgets. If AI makes meaningful futures work accessible to a small nonprofit, a local government, or a startup that could never have afforded a team engagement, that’s not a downside with a silver lining.

The counterweight is that sense-making has a biological clock. You can generate a cross-impact matrix in minutes, but the leadership team still needs time to sit with what it reveals. The uncomfortable finding that your core business model is a first-curve asset heading for decline needs to marinate.

The conversation where someone finally names the thing everyone was avoiding can’t be scheduled for 2:15 PM between the scenario generation and the action planning. Human processing time isn’t a bottleneck to be optimised. It’s where the value is actually created. Speed the analysis, yes. But don’t mistake faster generation for faster understanding.

The Dramaturg

All of this points to a specific evolution in the foresight professional’s role. I’ve been reaching for the right word for it. Conductor, curator, orchestrator all come to mind, but I landed on dramaturg as the best option. The role has elements of all the three before-mentioned roles, but dramaturg captures something more.

In theatre, the dramaturg holds the arc of the production. They don’t write every scene, but they know which scene needs to land. They don’t perform, but they understand what the audience needs to feel and when. They’re the bridge between raw creative material and its impact on the people who experience it.

That’s what I was doing in my AI-assisted foresight work. Making curatorial decisions constantly. Which exercise next? Do we have enough pre-work for this one? Is the body of divergent work sufficient to make the convergent exercise meaningful? Those decisions drew on years of practice; knowing what good foresight output feels like, and knowing when something’s missing before I could fully articulate why.

The dramaturg knows that the cross-impact matrix is analytically elegant but the persona of a woman managing her husband’s heart failure from a trailer in rural Mississippi is what will actually change behaviour in the boardroom. They design the emotional journey from comfortable assumptions through productive discomfort to committed action.

That design sense doesn’t quite automate. Yet.

This is just a snapshot

I’m wary of making confident predictions with specific timelines, that would be the kind of shiny futurism this piece is arguing against. But I can describe the direction.

The analytical middle of foresight is moving rapidly toward AI competence. Signal scanning, driver analysis, scenario generation, consequence mapping, cross-impact analysis, these are already being done well, and the capabilities are improving visibly.

Two-thirds of practitioners already use AI for this work, and even within cautious government environments, the productivity gains are too significant to ignore.

The creative and communicative layer is shifting next. Personas, artifacts from the future, the narratives that make abstract foresight visceral, these were among the strongest outputs in my work. As more practitioners discover this, the portion of an engagement that requires direct human generation will shrink further.

What will remain human longest is the relational work at both ends: the initial framing and the closing action, because these depend on physical presence, institutional knowledge, and trust between people who share stakes.

The profession may bifurcate. Practitioners who can articulate what they add beyond analysis, the dramaturgs, will find their work more valued, not less. Practitioners whose value proposition was primarily analytical rigour will face increasing competition from clients who can do that themselves.

This is the same structural shift that has reshaped graphic design, copywriting, coding, and data analysis. Foresight isn’t exempt.

I don’t know exactly how far this goes. I’ve run full engagement cycles in single sessions that produced output that was genuinely suitable for real client work. That would have been inconceivable two years ago.

What’s inconceivable today may be routine in another two.

Foresight professionals, of all people, should know better than to assume the present pace is the permanent pace.

So what?

Stop treating AI as either saviour or threat. Start treating it as a tool you need to learn to use well, the way you learned to use scenario frameworks and driver analysis and futures wheels and cones.

Even if your ethical stance is not to use any AI tooling, understanding what it can do in your domain is prudent due diligence.

Run the tools with it. Notice where it surprises you. Notice where it falls flat. Build your own sense of where the barbell sits for your practice, your clients, your domains.

Invest in the parts of your practice that live at the ends: reading rooms, framing questions, designing emotional arcs, shepherding action through institutional resistance.

Get better at being a dramaturg.

Engage with the discomfort rather than narrating around it. If you’re a foresight professional and the idea of AI doing your analytical work well doesn’t make you at least a little uneasy, you either haven’t tried it or you’re not being honest with yourself.

That unease is a weak signal. Use it the way you’d use any other weak signal: as evidence that something is changing and your current model may need updating.

It hits different when the weak signal is about your work than the client’s business, doesn’t it?

Get used to it. You if anyone have the tools to deal with that discomfort.

And acknowledge that even the barbell model I’ve described here is a snapshot, not a destination. The human ends may narrow. The middle may expand. New capabilities may emerge that don’t fit neatly into any current framework.

The shape of foresight practice in five years will surprise us, and our ability to be surprised by it, and adapt, is probably the most durable professional skill we have.

There’s a question I haven’t addressed here: what happens when clients figure out this capability shift? When they arrive having already run the scenarios, already generated the personas, and they say they only need you for two days in the room instead of two months of engagement? That’s a different piece, but if the barbell model is right, it’s not the threat it first appears to be. More on that later.

None of us trained for this version of the profession. But then, nobody trains for the exact futures they get.

That’s always been the point.

nb. I am using the terms AI and GenAI somewhat interchangeably here for brevity. I am quite aware of the wildly different flavors that get clumped under the AI umbrella, but this isn’t the essay to sort that out.

PS. A necessary caveat: the results I describe didn’t come from lazy prompting. They came from sustained, systematic work, including carefully constructed Claude Skills, lengthy custom instructions, and iterative refinement. The tools are capable of genuinely good foresight work, but they don’t produce it by default. How you use them matters enormously.

Nick Foster’s Could Should Might Don’t is published by Particular Books (Penguin). My review is here.

Henrik Skaug Sætra’s “The Tyranny of the Stochastic Parrot: How AI Critique Became a Way to Not See What’s Happening” is a 2026 preprint from the University of Oslo. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6249318

World Economic Forum/OECD (2025). AI in Strategic Foresight: Reshaping Anticipatory Governance. https://www.oecd.org/en/publications/ai-in-strategic-foresight_aa573076-en.html

Picavet, E. et al. (2025). “Human–Machine Collaboration for Strategy Foresight: The Case of Generative AI.” Public Administration Review. Queensland University of Technology. https://eprints.qut.edu.au/261336/

Soeiro de Carvalho, P. (2024). “How GenAI Will Transform Strategic Foresight.” IF Insight & Foresight / Hong Kong Institute of Futurologists & Foresight Analysts. https://hkifoa.com/wp-content/uploads/2024/12/how-genai-transform-strategic-foresight.pdf

Every organisation you’ve ever worked for has tried to simplify you: your performance has been reduced to a number; your skills to a list of keywords that HR has built company-wide profiles on (usually failing to accomplish anything meaningful in the process); your potential to a box on a nine-grid; and so on.

If you’ve ever felt that the version of you that exists in a system bears only a passing resemblance to the actual you, if that, James C. Scott has 445 pages explaining why — and what happens when that impulse to simplify is backed by the full coercive power of a state.

“Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed“ is an ambitious, dense, and frequently brilliant examination of what happens when states try to make the messy complexity of real life legible - reducible to categories they can then measure, manage, and control. First published in 1998, it remains remarkably relevant, even as some of its examples have aged a bit.

Scott opens with an example that seems deceptively simple: forestry. When eighteenth-century German states looked at a forest, they didn’t see an ecosystem. They saw - surprise! - revenue. Specifically, they saw how much timber could be extracted annually.

Everything else about the forest - the berries, the fungi, the medicinal plants, the grazing, the habitat, the spiritual significance - was invisible to the fiscal lens. The forest-as-habitat disappeared and was replaced by the forest-as-economic-resource.

This, Scott argues, is what states do to everything they touch: they strip away the particular, the local, and the contextual in favour of standardised categories that can be counted, taxed, and administered.

From forests, Scott expands to measurements (did you know that in pre-modern Malaysia, the answer to “how far is the next village?” might be “three rice-cookings”? - a unit that conveys what the traveller actually needs to know far better than any number of kilometres), to surnames (which are a surprisingly recent invention, imposed by states for the purpose of making populations legible), to language standardisation, to the redesign of cities, and eventually to the grand disasters of high modernism: Soviet collectivisation, compulsory villagisation in Tanzania, and the architectural hubris of Le Corbusier’s planned cities.

The book’s argument rests on four elements that, Scott contends, must combine to produce truly tragic state-initiated social engineering: administrative ordering of nature and society; a high-modernist ideology with uncritical confidence in science and technology; an authoritarian state willing to use coercive power; and a prostrate civil society that lacks the capacity to resist.

It’s hard to read this list in 2026 without a chill of recognition, except that today’s high modernists have replaced uncritical faith in science with uncritical faith in a blinkered, biased, and demonstrably false worldview.

I found the book to be at its best when discussing cities, drawing heavily on the work of Jane Jacobs. The contrast between Le Corbusier’s sterile modernist vision and Jacobs’ celebration of messy, organic urban life is one of the book’s most compelling sections, but YMMV of course.

The concept of being on “sidewalk terms” with people in your neighbourhood — not friends, but acquaintances who recognise one another and who collectively form an intricate web of informal social order — resonated deeply with me, and also ties into points made in many other books, such as Joe Keohane’s The Power of Strangers. Jacobs saw that the busiest room in a house is the kitchen, and the busiest street in a neighbourhood functions for the same reason: it is the most versatile setting, a place of socialisation and exchange. Understanding this, Scott argues, is no more difficult than understanding why the kitchen draws people; and yet urban planners have spent a century trying to engineer it away.

The analogy Scott draws between city development and language development is particularly elegant. Both are the unplanned creation of millions of people over time. Both resist central planning; Esperanto and Brasília share the same fundamental problem.

And both, when allowed to develop organically, tend towards a rich, multivalent complexity that planned alternatives simply cannot match.

The book’s final chapters, on what Scott calls métis (a Greek term denoting practical knowledge that can only come from experience) are where I found the deepest current value.

Métis is the firefighter who reads a situation; the sea captain who senses a change in weather through the roll of the ship; the farmer whose knowledge of this particular piece of land cannot be reduced to generic agricultural principles. It is knowledge that resists codification precisely because the environments in which it operates are too complex and variable for formal procedures.

You can master every principle and still fail at the craft, because knowing how and when to apply rules in a concrete situation is métis - and it is precisely what our current AI models lack. They can ingest every principle ever written down; what they cannot do is develop the practitioner’s feel for when the principles don’t apply.

The concept has stayed with me. In fact, it became one of the triggers for a recent essay I wrote on what I call “the grace margin“ - the space between what a system prescribes and what a person actually does, where someone can look at the rules, look at the situation, and choose judgment over procedure.

The grace margin lives in métis; in the practical, informal knowledge that no system can fully capture and no algorithm can replicate. Scott showed me where that concept has its roots, and why its erosion matters far more than most people realise.

The observation that formal order is always and to some considerable degree parasitic on informal processes is one of those insights that, once seen, cannot be unseen. Scott illustrates this brilliantly with the work-to-rule strike, aka malicious compliance: when workers follow every rule and procedure to the letter, production grinds to a halt, because actual work depends on a host of informal practices and improvisations that could never be codified.

The formal scheme cannot create or maintain these processes; it can only exist because of them.

I do have some reservations. The book is not a quick read. The concepts are heavy, the language is heavy, and Scott’s academic thoroughness sometimes comes at the expense of pace. Some readers will find the extended case studies, particularly the chapters on Soviet collectivisation and Tanzanian villagisation, more exhaustive and potentially exhausting than illuminating, though I appreciated having a case study I’d never encountered before in the Tanzania material.

More significantly, there is a datedness issue that becomes harder to ignore as the book progresses. Scott’s framework is built almost entirely from twentieth-century examples, and some of the most significant mechanisms of legibility and control in our current era — social media, the internet, algorithmic governance, platform economies — simply don’t register.

In many ways, what has been done in agriculture alone in the past twenty-five years represents another layer of extraordinary change, much of it not for the better, but some also for the better (e.g. permaculture, regenerative agriculture).

The core arguments remain powerful, but the book would benefit from a contemporary companion that extends Scott’s framework into the digital age, where legibility is no longer imposed by census-takers and cartographers, but extracted by algorithms processing our every click, purchase, and movement.

The state still sees; but so, now, does everyone else with sufficient data and computing power, and potentially in much higher detail than the state does.

Despite this, “Seeing Like a State“ is a book whose central insights have only become more urgent. The impulse to simplify, to make legible, to eliminate the variance where human judgment lives — this impulse hasn’t weakened since 1998. If anything, it has been turbocharged, because we can now have so many data points it feels like we could finally quantify everything and make everything legible. I’m highly skeptical of that view.

Scott’s warning that high-modernist designs tend to diminish the skills, agility, initiative, and morale of their intended beneficiaries feels less like historical analysis and more like a forecast of what algorithmic management is doing to workers right now.

And his closing plea for métis-friendly institutions — organisations that are multifunctional, plastic, diverse, and adaptable — feels almost radical in an era that worships efficiency and consistency above all else.

We need more flexibility and we are, as Scott feared, designing it away.

• Rating: 4 out of 5

• Dog-ear index: 6.6

• Who is it for: Anyone who has felt ground down by bureaucracy and wanted to understand why; people interested in urban planning, governance, or the tension between efficiency and humanity; and anyone building or deploying systems — algorithmic or otherwise — who wants to understand what gets lost when you optimise for legibility.

[reminder: I highlight important parts of the books I read, and dog-ear the really important pages. The dog-ear index is simply the average number of dog-eared pages per 100 pages]

Product link for reference only; please support your local bookstore where possible: https://www.amazon.com.au/Seeing-Like-State-Certain-Condition/dp/0300246757

This review comes with a disclosure: foresight is a significant chunk of my professional identity. Reading a book that critiques how we think about the future was simultaneously validating and uncomfortable - like having someone narrate your therapy session while occasionally getting the details wrong, prompting the occasional “Hey wait that’s not how it went down!”

Nick Foster’s Could Should Might Don’t gives us, in its title, a simple taxonomy for understanding the different flavors of futures work.

“Could“ futurism is the shiny, techno-optimistic variety that dominates TED stages, conferences, and often corporate innovation labs. “Should“ futurism brings in values and ideology. “Might“ is a more rigorous strategic foresight territory of scenarios and probabilities, but with its own problems. And “Don’t“ - the hardest and usually, at least from an action-point of view the most neglected, branch - forces us to confront futures we must actively prevent.

The framework is, to my moderate surprise, genuinely useful, and Foster wields it effectively against the futures-industrial complex. Many of his views align with mine, which means arguably provocative descriptions like trade shows as “cacophonous orgies of energetic capitalism“ made me chuckle at the accuracy rather than take offense. Foster’s takedown of commercial Could futurism - all gleaming surfaces and conveniently absent consequences – also clearly articulates a lot of the frustrations I’ve felt for years.

The observation that “nothing on earth works the way they pretend“ in these polished visions cuts to the heart of why so much futures work fail to connect with reality – and, frankly, why so many products arising out of that style of futures work fails miserably when it comes to contact with reality.

Foster makes several important points. A few examples include that newness as an inherently positive attribute is a modernist mindset shift we rarely question; that we have an insatiable and largely unwarranted desire for predictions; that identifying trends is far more questionable than trend-spotters would have you believe; and that the most transformative technologies eventually become mundane, embedded invisibly into everyday life. The concept of “Endineering” - thinking about endings and full lifecycles rather than just shiny beginnings - deserves wider adoption.

The book’s call for “mundane futures” also resonates deeply. Most of us will live in the statistically swollen middle, not in gleaming smart cities or dystopian wastelands. So much of futures work either fundamentally misunderstands or willfully ignores such basics as how cities come into being in the first place. The characters in our future, as Foster notes, “will not necessarily need to save the world at every turn - most of them will simply live in it, quietly getting through each day.”

This is where futures work should spend more time, yet rarely does.

And yet.

Reading as an insider of sorts, I found myself in a recurring “yes, but...“ pattern. The critique Foster lays out sometimes feels like it hasn’t fully engaged with the better work being done in the field - the rigorous scenario work at places like IFTF, for instance – curiously, IFTF is not so much as mentioned even though RAND Corporation, its parent organization, is (full disclosure: I am a Senior Research Affiliate at IFTF).

While Nick to his credit fully acknowledges his own biases and blind spots, occasionally they raise their heads in a somewhat ironic manner: his anecdote about finding time for reflection on long flights immediately after discussing how futures thinking skews toward the privileged is a perfect, probably unintentional, demonstration.

More frustratingly, I finished the book asking “now what?“ The diagnosis is comprehensive and often brilliant, but the prescription feels thin.

We need to do better, demand better, be more critical consumers of futures work - yes, agreed, violently so. But for practitioners seeking concrete guidance on how… There’s not all that much there.

Which, to be honest, is not what the title or subtitle promised either. Offering a prescription was never part of the plan, probably. I get that. It’s still something I as a practitioner would want effective ideas or options for.

My perspective is skewed by proximity. For readers who are not foresight professionals - which is most people - this book offers something very valuable. Executives, policymakers, and people who consume futures work (this is pretty much everyone, if you pay attention to marketing) would benefit enormously from Foster’s framework for identifying when they’re being sold seductive nonsense.

The billions wasted on manifestly bonkers projects like The Line - that mirage monument to Could futurism that treats the desert as a blank slate for techno-utopian fantasy and, let’s face it, is never going to get built - suggest we desperately need more critical consumers of futures visions. This book helps create them.

For those of us already living in Might and Don’t territories, “Could Should Might Don’t” offers the bittersweet comfort of having our professional frustrations eloquently articulated - along with the sobering reminder that this more rigorous approach is harder to sell. It’s validation, but not transformation.

• Rating: 4 out of 5 (closer to 4.5 for non-foresight readers)

• Dog-ear index: 11.8

• Who is it for: Anyone who consumes futures work and wants to become a more critical reader of it; executives and policymakers who’ve been dazzled by shiny visions; foresight professionals seeking articulate validation of their frustrations (though perhaps not new tools to address them).
  
  Oh, and the Could-futurists who need a good hard look in the mirror!

[reminder: I highlight important parts of the books I read, and dog-ear the really important pages. The dog-ear index is simply the average number of dog-eared pages per 100 pages]

Product link for reference only; please support your local bookstore where possible: https://www.amazon.com.au/Could-Should-Might-Dont-Future/dp/1837263833

The pharmacist who knows you fills your prescription two days early because you’ll be traveling.

The police officer who lets you off with a warning after slight speeding.

A teacher rounds a grade up because a student’s been going through hell at home.

A landlord waives the late fee because you’ve never missed a payment in four years.

A border agent looks at your slightly-expired document, looks at you, and waves you through.

None of these are policy; most of them technically violate policy. And yet they happen constantly in life. They happen quietly, invisibly, without being recorded or reported.

They are the space between what a system prescribes and what a person actually does; they are the silent artefacts of the fact that the world really runs on exceptions.

I don’t know what the right terminology is for this space, so I will call it the grace margin: the built-in tolerance in human systems where someone can look at the rules, look at the situation, and choose empathetic human judgment over procedure or policy.

The grace margin (n.): the space between what a system prescribes and what a person actually does. It is the moment someone looks at the rules, looks at the situation, and chooses judgment over procedure. It functions as both a compassion mechanism and an error-detection system, and it cannot survive the elimination of human discretion.

Or, as someone once put it rather more elegantly: “I don’t want to break the rules, but I do like them all bendy.“

You have been the beneficiary of the grace margin more than you realize. Every time a process should have gone against you but didn’t, because someone exercised discretion. Every time a deadline was quietly extended, a document accepted despite a minor error, a rule bent just enough to accommodate reality.

Some cultures, some organizations, and some people are better at having a grace margin than others (in my experience, Australians have a slightly larger grace margin than Finns).

You may also have felt its absence: the insurance claim denied on a technicality; the parking fine that would have been waived if you could have spoken to a person instead of a machine; the automated system that couldn’t process your situation because your situation wasn’t one of the available options.

Computer says no.

The grace margin, I would argue, is load-bearing infrastructure of our society, and it’s under threat.

Every time a human inside a system breaks protocol, they are generating a signal: this rule doesn’t fit this situation. The call center agent who keeps waiving a particular fee is telling the organization that the fee is wrong. The nurse who regularly bends triage protocol for a certain type of case is telling the hospital that the algorithm is missing something. Not that they’re listened to enough, but exceptions are how human systems can learn that they’re broken.

Remove the exceptions, and you get a less compassionate system.

You also get a system that has lost an important mechanism for detecting its own failures.

But the grace margin has been shrinking for decades, long before anyone started worrying about AI.

Every time a company introduces a “this call may be recorded for quality and training purposes“, it is eliminating the space where someone could quietly do the right thing off-script.

Every time a process is “optimized” and human touchpoints are removed in favor of digital systems and portals and chatbots, the organization is systematically closing the gaps where judgment once lived.

Every KPI, every compliance framework, every brand guideline that dictates how an employee may (or may not) speak to a customer, these are all mechanisms for compressing the grace margin by design.

The logic is understandable. The grace margin is where compassion lives, but it’s also where corruption lives. The customs official who waves someone through. The loan officer who approves a friend. The regulator who looks the other way. Every argument for human discretion is also an argument for human bias, favoritism, and abuse. Organizations have legitimate reasons to constrain it.

It’s where inefficiency lives, too. The ten-minute conversation that could have been an automated approval. The experienced employee who “wastes” time understanding a situation that a system could have processed in seconds. The manager who lets a team member take an unscheduled day off because they can see something’s wrong, absorbing the productivity hit without logging it. Grace takes time. It is, by every metric we’ve built to measure organizational performance, waste.

The trouble is that organizations can’t separate the grace from the corruption or the inefficiency, so they’ve opted to eliminate all of it. The grace was collateral damage.

Here’s where it gets dark.

Through decades of process optimization, compliance enforcement, and the relentless pursuit of efficiency, many organizations have already compressed the grace margin almost to zero. The humans are still there, but they operate within such tightly constrained parameters that their capacity for independent judgment — for grace — has been effectively removed.

They follow scripts and policy. They apply rules. They escalate to systems that apply more rules. Somewhere, nominally, a human could intervene. But the organizational architecture makes that intervention so difficult, so career-threatening, so structurally discouraged, that it almost never happens.

The employees are still present, but the function they were supposed to serve, the error-detection, the compassion, the messy human judgment that keeps rigid systems from becoming cruel ones, that has already been hollowed out.

The body is there; the soul left years ago.

Which brings us to AI.

Not as a revolutionary force, but as an inheritance.

When people worry about AI replacing humans in organizations, they tend to imagine a dramatic transition: robots in, people out. The reality is far less cinematic and far more disturbing.

AI doesn’t arrive to destroy the grace margin.

It arrives to find the grace margin already gone, and it makes the absence permanent.

A human operating under rigid constraints might still, on a good day, choose to deviate. The possibility exists even when it’s suppressed.

AI doesn’t suppress the possibility. It doesn’t have it. There is no gap between what the system prescribes and what the system does, because the system and the actor are the same entity. The concept of an exception becomes architecturally impossible by default; not because it can’t be designed in, but because the incentive is never to do so. You can’t appeal to a process’s better nature, because it doesn’t have one.

And this is not a flaw of AI. This is what we are asking it to do.

When an organization implements AI to “improve efficiency” and “ensure consistency,” what it is actually implementing is the elimination of variance. And within that variance lies the person choosing to be decent when the system doesn’t require it.

Efficiency and grace are, in this framing, fundamentally opposed, and we have made our choice about which one we worship.

I want to be clear about something: this is not an argument against AI.

AI implemented with deliberate, designed space for exceptions, for escalation to genuine human judgment, for override mechanisms that are actually accessible and not merely theoretical, with resources specifically dedicated to handle those exceptions does not have to eliminate the grace margin.

It could, in principle, even expand it: by handling routine cases efficiently, AI could free humans to focus their judgment on exactly the cases that need it most. The pharmacist freed from repetitive dispensing tasks might have more time to notice the patient who needs an accommodation or additional guidance, not less.

But that requires a design philosophy that treats human judgment as a feature and a genuine resource, not an annoying inefficiency to be ironed out. It requires organizations to want grace in their systems.

The entire trajectory of corporate optimization for the past forty years has been in the opposite direction: judgment is risk, discretion is liability, exceptions are inefficiency.

AI didn’t create that philosophy, but it does codify and then ossify it if done wrong.

This incoming wave of cruelty won’t be dramatic.

It won’t look like a dystopian film; not at first, anyway.

It will be mundane and bureaucratic and wrapped in the language of fairness. The system will treat everyone “equally,” which means the person whose situation doesn’t fit the model will be treated identically to the person whose situation does. Consistency will be maintained. Efficiency will improve. And the outliers - the complicated situations, the edge cases, the people whose lives don’t fit neatly into available simplified categories - will find that there is no one left to appeal to. No one who can look at the rules and then look at them and choose to do the decent thing.

The world will, on balance, be more efficient. It might even serve people well, on average. But for the outliers, there is cruelty in store.

Everyone, every single one of us, will be an outlier eventually.

When you’re grieving and can’t meet a deadline. When the system has your details wrong. When your situation is the one the algorithm wasn’t trained on.

The grace margin is where someone catches you.

Codify its absence, and there is nothing left but the fall.

If these ideas resonate, I’d recommend three books that, read together, map the architecture of what’s being lost: Dan Davies’ “The Unaccountability Machine” on how organizations systematically eliminate accountability; Josh Bornstein’s “Working for the Brand” on corporate control over human agency and speech; and James C. Scott’s “Seeing Like a State” on what happens when complex, local, human knowledge is overridden by systems that demand legibility above all else. The grace margin lives in what Scott calls mētis; the practical, informal knowledge that no system can fully capture and no algorithm can replicate.

When in Finland recently, I noticed Robert Greene’s books were everywhere. This struck me as odd - none of them are exactly hot off the presses. Curious, I picked up The Daily Laws, hoping for a year’s worth of psychological insight in digestible daily portions.

What I got instead was a villain’s training manual dressed up as self-improvement literature. This isn’t a book about understanding human nature - it’s a handbook for exploiting it.

The renewed popularity makes more sense with the additional context I learned since: Greene’s “48 Laws of Power” has apparently found fresh relevance with observers noting how closely Trump’s conduct mirrors its precepts. The villain’s handbook has found its audience.

The format promises daily wisdom across Greene’s broad body of work: The 48 Laws of Power, The Art of Seduction, The Laws of Human Nature, and others. I, of course, read it ‘wrong’ in that I spent a few days on it, not a year.

Whether read over a Leap Year or in one sitting, the inescapable fact is that the 366 entries treat every human interaction as a battlefield and every relationship as a transaction to be won.

Let me be clear about my core objection: this book doesn’t have an “understand your fellow human“, or even “understand your enemy” energy. It has “here’s how to become the villain“ energy - and it’s pretty much explicit about it.

When Greene discusses “occupying the moral high ground,” he’s not talking about actually being moral; he’s talking about the strategic appearance of morality. When he discusses seduction, it’s conquest, not connection. The underlying philosophy treats manipulation not as something to avoid, or even to recognize and defend against, but as a skill to cultivate and deploy for personal benefit.

That doesn’t mean all of it is bad, or invalid. There are some genuinely useful psychological observations buried in here; the discussion of confirmation bias is accurate; the narcissism spectrum concept reflects legitimate psychology; “Resist Simple Explanations“ is sound cognitive advice.

These insight are like finding vegetables (maybe even quite a few vegetables) in a dumpster - technically nutritious, but the context ruins the appetite.

The predatory mindset Greene shines through loud and clear. On June 1, he advises readers to “Make your face as malleable as the actor’s, work to conceal your intentions from others, practice luring people into traps“. On April 29, he suggests you “Throw in a completely inexplicable move“ to keep people off-balance, cultivating an “air of unpredictability” so that others fear you.

Sound like anyone familiar?

The book’s problems compound from there.

First, the rules contradict each other constantly. One entry counsels authenticity; another teaches concealment. One advocates boldness; another preaches patience and misdirection. You cannot follow all 366 laws because they constantly cancel each other out – you probably couldn’t even follow 50 of them.

Second, there is relentless repetition. The same ideas circle back again and again without deepening. Themes and ‘laws’ appear repeatedly, sometimes nearly verbatim. In a format explicitly designed for daily consumption over a year, this padding feels especially cynical, especially when the repetition can come back-to-back. Could’ve at least tried to put a ‘week’ in between them so maybe it wouldn’t be so jarring - but clearly there wasn’t enough genuine material to fill a year’s worth of pages.

Third, Greene gets many things wrong. His dismissal of academics as “largely indoctrinated“ people who “can never get outside“ their training ignores the entire purpose of the scientific method: systematic challenge of assumptions. His envy typologies are presented as psychological categories but appear to be Greene’s own invention, and certainly not established research. The claims that you have a “Life’s Task” and a “destiny to fulfill” are standard self-help bullshit to make people feel special.

More fundamentally, the book’s entire premise - that human nature is adversarial, relationships are zero-sum, and manipulation reliably works - contradicts extensive research on cooperation, trust, and long-term relationship outcomes. Evolutionary biology shows cooperation as a successful strategy; organizational psychology demonstrates that trust-based cultures outperform fear-based ones.

This is pathological behavior presented as wisdom. Greene actively isolates the reader from genuine connection, warning them on May 17 to “Be extra wary around people who display emphatic traits,” suggesting that empathy is merely a mask for a “dark side“. By framing empathy as a threat, Greene is effectively coaching the reader into a paranoid, lonely existence where trust is a liability.

Significant parts of Greene’s worldview are just demonstrably false, and to base ‘laws’ on such views is dangerous if enough people start believing them.

There’s an audience for this book, I suppose: people who want permission to treat others as obstacles or resources, wrapped in the legitimizing language of “laws” and “strategy.” For anyone else - anyone who believes human connection involves actual connection rather than conquest - The Daily Laws offers little beyond a dispiriting tour of one author’s transactional worldview.

If Greene wanted to be a positive influence in the world, he would position these books as a defense against the dark arts - how to spot immoral behavior and fight it. Instead, he has written a very clear manual on how to be the darkness.

Anyone who wants to follow the rules of this book can fuck right off from my life.

• Rating: 2 out of 5 (not 1 only because I suppose it’s useful to know where some people take their guidance from)

• Dog-ear index: 3.4

• Who is it for: Aspiring Machiavellis and insecure middle managers who think they are on Game of Thrones. Everyone else should look elsewhere for daily wisdom.

[reminder: I highlight important parts of the books I read, and dog-ear the really important pages. The dog-ear index is simply the average number of dog-eared pages per 100 pages]

Product link for reference only; please support your local bookstore where possible: https://www.amazon.com.au/Daily-Laws-Meditations-Seduction-Strategy/dp/1800816286

Some books deliver their value in one reading. Others are more like tuning forks - you return to them periodically to recalibrate something.

This was my first reading, but I suspect that Natalie Goldberg’s “Writing Down the Bones“ belongs to the latter category, and the fact that it’s now in its 30th anniversary edition suggests I’m not alone in thinking so.

This is a book about writing as a practice. Not in the sense of “practice makes perfect,” but in the Zen sense of practice as a discipline, a meditation, even as a way of being.

Goldberg draws explicit parallels between writing and Zazen; both require showing up, staying present, and getting out of your own way. The monkey mind that really wants to chatter during meditation is the same one that tells you your writing is garbage before you’ve finished a sentence.

The repeating ethos of the book is just to get you to write; the practical heart is Goldberg’s “timed writing” method: keep your hand moving, don’t cross out, don’t worry about spelling or punctuation, lose control, don’t think, go for the jugular.

These rules sound simple, almost trivially so, until you actually try to follow them and discover how desperately your internal editor wants to intervene. The method is designed to bypass the censor and access what Goldberg calls “first thoughts” - the raw, unfiltered material that emerges before self-consciousness kicks in.

This does not mean that the raw material would necessarily be ‘good’ in any real sense of the word; but it does provide the raw ore to mine and refine from.

There’s something almost embarrassingly obvious about many of Goldberg’s insights, like writing about your obsessions, giving things their proper names, trusting your own weird mind - yet they’re the kind of obvious that needs periodic reminding.

We know these things, and then we forget them, and then we need someone to say them again in a way that makes us actually feel them. This is where we get to the ‘repeated exposure needed’ hunch of this book.

The book’s structure mirrors its philosophy: short chapters, loosely connected, more like a collection of dharma talks than a systematic treatise. This makes it easy to dip into but also means it lacks the cumulative argument-building of more structured works. This is not a highly structured “How to Write Great” manual with clear steps and a guaranteed process for excellence. Some readers will find this freeing; others may want more scaffolding.

For anyone who writes, or wants to write, and has ever felt paralyzed by their own perfectionism, this is essential reading. Not because Goldberg has magic answers, but because she has the right questions and gives, explicitly and repeatedly, the permission you didn’t know you needed. It’s also arguably a classic that I am way late in discovering, but better late than never!

• Rating: 4.5 out of 5

• Dog-ear index: 10

• Who is it for: Writers. If writing doesn’t interest you, skip it - unless you want to understand how to support the writers in your life. For those who do write: this is for anyone who needs permission to write badly on the way to writing truly; anyone who suspects writing might be a spiritual practice but hasn’t found the language for it; perfectionists who need to be told, repeatedly, to just keep the hand moving.

[reminder: I highlight important parts of the books I read, and dog-ear the really important pages. The dog-ear index is simply the average number of dog-eared pages per 100 pages]

Product link for reference only; please support your local bookstore where possible: https://www.amazon.com.au/Writing-Down-Bones-Natalie-Goldberg/dp/161180308X

Friction Points is a new series of short observations about things that shouldn’t be this hard, this stupid, or this annoying – but are.

I bought a pan. A decent pan – stainless steel All-Clad, the kind that’ll outlast me if I treat it right.

Inside the box, a card invited me to “Join the All-Clad Community.”

A community. For cookware.

I’m asked to text a photo of a camera icon to a five-digit number so I can “find out about exclusive deals and the latest innovations.”

This ‘community’ is a mailing list wearing a see-through costume.

Somewhere along the line, marketing discovered that humans are lonely and that belonging feels good. So now everything has a community.

Levi’s has a community of 38 million people.

This is so stupid.

A community isn’t a newsletter signup with better branding. Community is mutual obligation, shared identity, the messy work of actually caring about people you didn’t choose. What’s being sold here is the aesthetic of belonging without any of its substance or cost. The exchange is simple: you hand over your data and inbox; they send you ads and call it membership.

Keep calling mailing lists “communities” and eventually we’ll forget the difference.

Last year was another stellar year of quality books. I read 46 books last year, many of which were excellent, with so many standouts that I just could not bring myself to making a Top 5 - so here’s the Top 6 books of 2025, in no particular order:

• Meditations for Mortals (by Oliver Burkeman): Brief daily reflections that tear down lazy thinking about achievement and success while offering radical permission to embrace human limitations. Most memorable quotes: “You are free to do whatever you like. You need only face the consequence”, and that “The truth is that it’s almost never literally the case that you have to meet a work deadline, honor a commitment, answer an email, fulfill a family obligation, or anything else.”

• Working for the Brand (by Josh Bornstein): This will speak to the many consequences that do arise when you speak up. A searing exposé of how corporations sacrifice employees at the altar of brand protection, systematically suppressing speech through vague contract clauses while demanding absolute loyalty. Essential reading for understanding how organizational power curtails democratic discourse and worse.

• The Edge of Sentience (by Jonathan Birch): Forces you to redraw your moral map by examining which beings - from cephalopods to AI systems - might be or become sentient and thus deserve moral consideration. Proposes a precautionary framework for democratic decision-making on consciousness that we urgently need.

• The Status Game (by Will Storr): Shines a spotlight onto the fact how every aspect of human life involves playing status games; from hotel lifts to social movements and why recognizing which game you’re in matters more than pretending you’re above it all. The protection lies in diversification: play multiple games, never invest everything in just one.

• Superbloom (by Nicholas Carr): On how each communication technology from the telegraph to TikTok follows the same arc: utopian promises, corporate capture, social fragmentation, and now the realization that mass connection might be fundamentally incompatible with human psychology. We’ve built a world where reality has become a distraction from media, and that’s…not great.

• How God Works (by David DeSteno): Treats religious practices as “spiritual technologies” worthy of scientific study regardless of theological truth, revealing that thousands of years of religious practice have discovered psychological tools that demonstrably work. Dismissing all of it because you don’t believe in the particular metaphysics interpretations would be like refusing antibiotics because you don’t believe in the four humors.

What’s in store for 2026?

The to-read shelf has grown to a bit of an antilibrary with 43 books on it. Here’s just a small sample to show the variety that is ahead:

This book was basically designed to trigger me, and it succeeded - though not in the way the science deniers it catalogues would hope.

Having spent time deep in conspiracy theory research during 2020, much of the territory Mann and Hotez cover felt like returning to a crime scene I’d already processed. The bodies are still there; more keep appearing; and the perpetrators are still walking free.

Science Under Siege identifies five drivers of what the authors call the “war on science” - and they’ve got a thing for alliteration: Plutocrats funding disinformation, Petrostates weaponizing it (Russia gets deservedly extensive coverage), Professionals lending pseudo-intellectual cover, Propagandists amplifying the message, and a Press that’s either complicit or captured.

As is often the case with American dysfunction, the US offers one of the starkest recent versions of many of these forces, but the story unfortunately rhymes everywhere.

The diagnosis is compelling and thoroughly documented. The authors nail the fundamental asymmetry of the information war: “The basic idea is to throw so much mud on the wall that the other side (the scientists) spend all their time trying to scrape it off. A losing battle.” They’re equally sharp on why scientists shouldn’t debate charlatans: “Simply getting up on the same stage with a science denier signals to the audience that the denialist viewpoint is a credible one. You’ve lost the debate before it started.”

This point deserves emphasis. Professional scientists do not typically “debate” science - they publish papers, submit to peer review, and let evidence accumulate. The demand for public debates is itself a bad-faith tactic, the intellectual equivalent of challenging someone to a knife fight and showing up with a machine gun loaded with Gish Gallop ammunition.

I find all aspects of this profoundly infuriating and sad.

Let’s take media for example; “false balance” and performative neutrality are rife; the pathological need to present “both sides” on equal footing even when one side is demonstrably wrong has become a cancer in mainstream journalism. The fourth estate, Mann and Hotez argue, “is now mortally threatened,” with plutocrats purchasing media outlets to remove the final check on their power.

Murdoch, Australia’s dubious gift to humanity, and his empire get extensive and justified coverage.

I find few things more infuriating than so much of the media neglecting the very elements of journalism they were founded on. Much like with accurate information vs disinformation, there are still good media outlets and actors out there, but the zone is being flooded with…shit.

So anyway, there’s a lot of accurate information in the book about how we got here. None of it is pretty.

The subtitle promises to show how we can fight these forces, and I have to say it doesn’t quite meet that promise.

The authors offer sensible recommendations: inoculate people against misinformation before they encounter it, replace sticky myths with stickier truths, communicate science more effectively, pressure plutocrats, mend the media.

All well and good. The problem is that enacting them requires functional democratic institutions, and the book itself documents how thoroughly those institutions have been captured.

They also spell out something we’re all increasingly and painfully aware of:

The United States is now itself, for the time being, a petrostate and a bad actor in this space. It is therefore incumbent upon other Democratic nations such as the European Union, United Kingdom, Australia, and Japan to band together... to take whatever punitive actions are necessary against bad state actors—the United States sadly now included—to rein them in.

I don’t disagree with this. I like this plan. I just don’t like its chances of succeeding when all three branches of American government are aligned against science, and when the authors themselves acknowledge their solutions are “mostly all pipe dreams for now”.

A minor quibble: the authors maintain that limiting warming to +1.5°C remains achievable. I posit it’s not. NASA says 2024 was +1.47°C above the pre-industrial baseline; WMO says +1.55°C; NOAA says +1.46°C.

Even if we take a more recent baseline, we get to +1.28°C or so – then when we acknowledge the current emissions trajectories, the physics of building stuff, and the climate hysteresis, there is no realistic future in which we will limit the warming to +1.5°C. Acknowledging this reality doesn’t mean abandoning mitigation - it means being honest about where we actually are.

My major personal pet peeve that kept surfacing throughout: the idiots railing against science rely on it daily for their very lives. Your smartphone, your medicine, your food safety, your weather forecasts, the private jets you fly in - all products of the scientific method these people now distrust.

The cognitive dissonance would be fascinating if it weren’t so dangerous. I don’t know how they live with it.

Overall, Science Under Siege is important, well-researched, and deeply depressing reading. It’s a comprehensive catalogue of how we got here and who’s responsible. What it cannot quite provide - through no fault of the authors - is a convincing path out when the arsonists are running the fire department.

• Rating: 4 out of 5

• Dog-ear index: 6.7

• Who is it for: People who want to understand the coordinated forces arrayed against scientific truth; those who still use “both sides” unironically; and people who need ammunition for arguments with relatives who’ve fallen down various rabbit holes - though fair warning, the book will also explain why those arguments probably won’t work.

[reminder: I highlight important parts of the books I read, and dog-ear the really important pages. The dog-ear index is simply the average number of dog-eared pages per 100 pages]

Product link for reference only; please support your local bookstore where possible: https://www.amazon.com.au/Science-Under-Siege-powerful-threaten/dp/1761381660/

How can there be hundreds of pages about wind?

That was my initial reaction when seeing “The Breath of the Gods: the history and future of the wind” in a bookstore; the initial skepticism went out the window some microseconds later when I saw it was written by Simon Winchester, whose earlier works I have been a big fan of, so I picked it up without a second thought.

Turns out it’s remarkably easy and fascinating to have hundreds of pages about wind. When you think about it, wind does touches nearly everything: it shapes geography, carries disease, betrays secrets, powers civilizations, wreaks havoc, decides battles, and has inspired more vocabulary than you’d ever imagine.

Winchester, with his characteristic erudition and wit, takes us on an encyclopedic journey through the invisible force that surrounds us. The book ranges across an astonishing variety of wind-adjacent topics. We learn, for example, that Hawaiian has over six hundred recorded words for wind, making Finnish’s oft-celebrated thirty words for snow look positively impoverished.

We discover that our cardinal directions (North, South, East, West) are a Greek and Hebrew refinement of Sumerian mythology, where the Four Mesopotamian Winds were invented as sibling gods some two thousand years earlier. There’s the delightfully obscure 1915 Huntington Theory, which held that “the cleverest and most civilized peoples“ lived where weather was endlessly variable, essentially arguing that climatic monotony produced dullards.

There are unexpected stories, such as the engineering challenge of making the American flag appear to fly on the windless moon (solved by NASA engineers just days before Apollo 11 launched); there are wind-adjacent disasters, like covering the history of FDR’s response to the Dust Bowl, planting hundreds of millions of trees in shelterbelts from North Dakota to Texas.

And the way wind betrayed Chernobyl: unseasonal southeasterly winds carried telltale radiation to Swedish detectors when westerlies would have let the USSR deny everything for much longer, with potentially disastrous consequences.

Winchester’s prose shines when describing things we think we know. I was surprised to learn that tumbleweeds, that icon of the American West so beloved by Hollywood, are actually Russian thistle (Salsola tragus), accidentally imported in the 1870s and now reviled as invasive pests. Each iconic rolling sphere carries up to a quarter of a million seeds. Nothing, Winchester notes, is “so large, so obvious, so emblematically Western, and, in a perverse way considering how it is so universally loathed, so romantic” as this prickly Russian import.

The book naturally devotes considerable space to matters like sailing, to the Beaufort scale, to dune formation (complete with Arabic-derived vocabulary: seifs, draas, barchans, serirs), and to how wind wreaks havoc in myriad of ways. The chapter on inclement winds includes harrowing accounts of gliders being sucked into cumulonimbus clouds and shredded by forces “beyond imagination.”

As someone with a particular fondness for aviation, I appreciated the aviation connections throughout; from the accidental discovery of jet streams when B-29s were blown over their targets at 450 mph during WWII bombing runs, to the explanation of why the Nepal Himalayas are the one place on Earth where you can actually see a jet stream.

If there’s a weakness, it’s that the book doesn’t quite cohere into a single argument or narrative arc. At the start, it alludes the Great Stilling, an as-of-yet tentative story of average wind speeds globally declining with climate change and the potentially highly important consequences of that, but doesn’t really dive deeper into this emerging evidence or potential consequences. It’s more of a collection of fascinating stories organised around a theme than a sustained thesis. But for readers who enjoy Winchester’s style — learned, discursive, full of tangents that prove more interesting than the main road — that’s hardly a complaint.

“The Breath of the Gods” won’t necessarily fundamentally shift your worldview, but it will enlighten and deepen it and will certainly leave you noticing wind differently. You’ll think of Sumerian mythology when checking which way the breeze blows. You’ll regard tumbleweeds with newfound complexity. And you’ll appreciate just how much of human history has been shaped by something we mostly take for granted until it knocks a tree onto our roof – which, ironically considering the great stilling, it may do more of in the future.

• Rating: 4 out of 5

• Dog-ear index: 4.5

• Who is it for: Anyone who enjoys well-written popular science writing; readers who appreciated Winchester’s previous works; people who want to understand the invisible force shaping everything from desert dunes to human civilization, and who don’t mind taking the scenic route to get there.

[reminder: I highlight important parts of the books I read, and dog-ear the really important pages. The dog-ear index is simply the average number of dog-eared pages per 100 pages]

Product link for reference only; please support your local bookstore where possible: https://www.amazon.com.au/Breath-Gods-History-Future-Wind/dp/0008679509

Full disclosure: I was in the room at the Institute for the Future conference in 2018 when Jamais Cascio first introduced BANI. I know Jamais and Bob. I have written about the BANI concept multiple times over the years (here, here, here, and here for starters), and I genuinely love the concept. BANI captures our current moment in more visceral way that the older VUCA framing doesn’t quite reach.

For those unfamiliar with the term, BANI stands for Brittle, Anxious, Nonlinear, and Incomprehensible. It’s a framework for describing how the modern world feels to those living in it. Brittle systems appear robust until they shatter catastrophically. Anxiety pervades our collective psyche as we await the next crisis. Nonlinear means cause and effect are disconnected - that small actions can trigger outsized consequences, and vice versa. And incomprehensible captures the reality that we now depend on systems so complex that no one fully understands them.

Where the older VUCA framework (Volatile, Uncertain, Complex, Ambiguous) describes the environment we operate in, BANI is, to me anyway, more about the human, emotional experience of navigating that environment. That’s what made it resonate when Jamais first introduced it, and why I’ve found it useful ever since.

As a fan of BANI, I really wanted to like this book.

I mean I really, really wanted to like this book.

I don’t. Not really.

The core problem could be that BANI worked brilliantly as an article, or a series of articles, but stretching it to book length has led more unnatural-feeling padding than useful substance – but since it’s a short book, the additions feel like they haven’t been given due attention either, and start at weird places.

Several complex topics like AI, climate change, pandemics, and social media receive treatment so superficial it borders on counterproductive. Climate change is introduced as if readers might not have heard of it. The AI section contains simplifications that veer into inaccuracy. And some claims are simply wrong: global suicide rates, for instance, are not increasing as stated.

Some of the framing choices grated on me. The book suggests we should “farewell” VUCA in favor of BANI, which is nonsensical - they describe different things; VUCA is about the environment, BANI about how we experience it. They’re complementary, not competing – or at least that’s how I’ve always treated them. I challenge anyone who thinks the VUCA elements of Volatile, Uncertain, Complex, and Ambiguous don’t still apply today.

But this whole thing made me think of BANI in more fundamental ways, too, and I realized we shouldn’t let the authors - or BANI aficionados like myself - off the hook here. We need to question the very base premise of the framework: the idea that we are living through a somehow unique inflection point of maximum chaos.

Are we? As much as the book talks about the importance of history, there is a case to be made that the book suffers from historical amnesia, and that we’re just suffering from illusory exceptionalism. Wouldn’t be the first time.

Over 100 years ago in 1919, watching the collapse of physics, empires, and logic simultaneously, Paul Valéry captured an eerily similar sentiment in his essay Disillusionment, noting:

“The storm has died away, and still we are restless, uneasy, as if the storm were about to break. Almost all the affairs of men remain in a terrible uncertainty... We think of what has disappeared, and we are almost destroyed by what has been destroyed... We civilizations now know that we are mortal.”

As much as I want to believe that ‘this time is different’, and agree there is some data to support that notion, I would also have to concede that in many ways the “anxiety” and “brittleness” are just the baseline condition of modernity. Possibly to some extent humanity. By framing them as novel, we risk confusing a recency bias for a paradigm shift.

Setting aside these larger questions, there are also execution issues. The forced alliteration of “positive BANI” counterparts (Anxious-Attentive, and so on) feels exactly that - forced. Worse, “nonlinear” gets redefined from its actual mathematical meaning to “a more metaphorical concept,” which strips the term of some of the power that made it useful.

There are internal contradictions that editing should really have caught. The book accurately acknowledges the nonlinearity and hysteresis of climate change - that we’re locked into worsening conditions regardless of what we do now - and then on page 79 assures us that “we know how to keep it from getting worse than it is now.”

Which is it?

Similarly, pages 169-170 argue, within paragraphs of each other, that leaders who have experienced mental health challenges like bipolar disorder are better suited for BANI chaos - and then that leaders need to be “extremely mentally and physically healthy to perform at the highest levels of leadership.“

Which is it?

The confusion is compounded by a fundamental misunderstanding: though a very minor point overall, bipolar disorder isn’t something you “experience” and move past. It’s a chronic, lifelong condition - manageable, yes, but not curable. People have bipolar disorder; they don’t “go through” it. Someone with well-managed bipolar disorder can absolutely be an excellent leader but framing it as a past event they’ve overcome undermines whatever point the authors were trying to make.

The practical advice sections, which I appreciate as a concept because too often authors of high-concept books try to shy away from anything practical, prompted me to scrawl “HOW??!” in the margins more than once.

To give you a taste of the language: under “Nonlinear” leadership qualities, we’re told that:

Under “Incomprehensible,” leaders are advised to foster “gameful emotionally laden attention to deliver first-person growing experiences.”

What do these even mean? The charitable interpretation here is that the authors have been too embedded in this terminology to realize it sounds weird, and you can make some sense of them, but why make it sound so difficult?

We’re told leaders must “lower tensions and bridge polarities“ – and then it’s pretty much left at that. There is no meaningful level of ‘how’ to that guidance. Granted, such an answer would probably have taken five more books, but the sheer complexity of doing this is not acknowledged properly.

Retrofitting famous examples like Viktor Frankl’s survival story into paragraphs on “Viktor Frankl’s Anxious Turnaround / Brittle Turnaround“ frameworks feels reductive and, frankly, a bit icky (technical term). And reframing Netflix’s strategy as demonstrating “neuroflexibility and improvisation“ is business book 101 in a negative way: taking old stories and slapping new terminology on them.

Too much of that part reads like what it probably is: material rewarmed from Bob’s previous books, combined with concepts that haven’t developed much beyond their original articulation. Or maybe they have developed, but not in ways that I found compelling or very useful.

The result feels like a principles-based AI ethics framework - appealing words and concepts, things few people will on the face of it disagree with, but that are extremely hard to implement, with little guidance offered for that crucial part and as such, not very helpful.

To be fair, I am not the target audience. As a foresight professional who knew about BANI from day one, little to nothing here was new to me, and that inevitably colors my reading experience; readers encountering these ideas for the first time are likely to find the book a more useful introduction. The point that BANI operates at a more human, emotional level than VUCA remains valid and important. There are scattered insights worth preserving.

• Rating: 3 out of 5

• Dog-ear index: 4

• Who is it for: Those entirely new to the BANI framework who want an accessible introduction. Foresight professionals and those already familiar with the concept will likely find little new ground covered.

[reminder: I highlight important parts of the books I read, and dog-ear the really important pages. The dog-ear index is simply the average number of dog-eared pages per 100 pages]

Product link for reference only; please support your local bookstore where possible: https://www.amazon.com.au/Navigating-Age-Chaos-Sense-Making-Doesnt/dp/B0DVBG9FYR/

There was a certain irony in reading a book about status games while sitting in Economy class, knees in the back of the seat in front of me, wishing I would have a little bit more room and hoping for everyone’s sake the person in front of me doesn’t recline his seat.

Will Storr’s The Status Game arrived at precisely the right moment to make me excruciatingly aware of how often I, and all of us, play these games - and how futile our denial of that fact is.

We are all playing status games. We can quibble about the definition of how exactly both the words ‘status’ and ‘game’ are defined, but that’s mostly immaterial. You play status games; I play status games; we as a species play heaps of status games.

From the premodern societies of Papua New Guinea to the skyscraper forests of Tokyo and Manhattan, humans form groups and compete for standing within them. We play political games, religious games, corporate games, hobby games, social media games.

The variety feels infinite, and escape not only feels but IS impossible. We cannot escape playing games because that’s who we are.

This might sound cynical, but Storr argues we make a fundamental error when we tend to reflexively categorise our desire for status as shameful.

Status games have driven humanity to extraordinary heights - innovation, art, vaccines, moral progress. The game compels us to scheme and strive, but also to lift strangers in distant moral battles. The shame and the pride, the plummet and the high - they’re features, not bugs.

Storr identifies three distinct flavours of status game: dominance, virtue, and success. Dominance games are the oldest - status through force, intimidation, coercion. Virtue games award status for moral righteousness, holding sacred beliefs, being on the ‘right’ side. Success games reward competence, achievement, and skill.

Most groups blend these, but the mix matters enormously; healthier societies and groups tend toward success games, where you gain standing by being genuinely useful. Dangerous ones drift toward dominance and virtue, where status comes from defeating enemies and enforcing orthodoxy.

Recognising which game you’re actually playing (and we’ll come back to this later), not which game you think you’re playing, turns out to be critically important.

I am allergic to books that seem to fit the pattern “when all you have is a hammer, everything looks like a nail”, and at first I felt there was a bit of that here. But if you loosen up your interpretation of the exact semantics, it is remarkable how pervasive these games really are in our societies, and recognizing that is fascinating, useful, and confronting.

Storr traces the status game through virtually everything, and it’s actually helpful. Wars become more comprehensible through the lens of humiliation and national standing - even WWII looks different when viewed this way. The Catholic church’s prohibition on cousin marriage inadvertently laid groundwork for Western modernity. Reagan and Thatcher kicked off the neoliberal status game, creating a world where we see ourselves as failures even when providing a good living. Social media suddenly makes perfect sense, too: it’s simply the status game made frictionless and global.

Do status games explain everything? No, of course not. But I do believe this book will help us be less wrong.

I found myself uncomfortably implicated throughout. I recognized myself from the note about playing fleeting micro-status games in hotel lifts, for God’s sake - silently noting room numbers, making micro-judgments about floors. Petty? Yes. Silly? Absolutely. Pointless? Definitely.

But I still do it, and pretending otherwise is self-deception.

The book takes a darker turn when examining what Storr calls “tight games” - groups where the rules become rigid, dissent becomes betrayal, and the sacred beliefs cannot be questioned.

Cults are the tightest games, but the dynamic appears everywhere: the New Left and New Right both play virtue games that weave hostile dreams. As games tighten, anyone expressing doubts or disagreements is expelled. Nuance becomes suspect; complexity becomes betrayal. You’re either with us or against us, and “it’s complicated” is no longer an acceptable answer.

Sound familiar? It should - we’re watching this tightening dynamic play out across our societies in real time.

One of the most striking sections traces what happens when societies attempt – in vain – to abolish status games entirely. The path leads, with grim inevitability, through Lenin and Stalin to catastrophe. Humans are not natural seekers of equality, Storr argues, and argues well with evidence - a hard truth that progressive idealists (including, at times, myself) need to sit with.

Trying to eliminate the games just makes the games deadly.

So what do we do? Near the end of the book, Storr offers seven rules for navigating the status game wisely. One of the key insights isn’t to play fewer games - it’s to play MORE. The danger lies in over-investment:

People who appear brainwashed have invested too much of their identity into a single game. They rely on it wholly for their connection and status, the maintenance of which requires them to be filled up with its dream of reality, no matter how delusional. Not only does this put them at risk of committing harm to others, they risk catastrophic collapse themselves. If the game fails, or they become expelled, their identity – their very self – can disintegrate.

Hat tip to high-control groups with the above again, which aim to do exactly this – make their game the only one its adherents play. On the contrary, those individuals who play multiple diverse games, and have complex self-identities across multiple domains, tend to be happier, healthier, and more emotionally stable. The protection lies in diversification.

I won’t spoil all the rules here because the groundwork that comes before them is what makes them impactful, but one more ‘action item’ for all of us relates to recognising what kind of games we play, and when a game has turned dangerous:

It’s possible to sense what kind of game we’re in by observing the ways in which status is typically awarded. Tyrannies are virtue-dominance games. Much of their daily play and conversation will focus on matters of obedience, belief and enemies. Is the game you’re playing coercing people, both inside and outside it, into conforming to its rules and symbols? Does it attempt to silence its ideological foes? Does it tell a simplistic story that explains the hierarchy, deifying their group whilst demonising a common enemy?

I have strong opinions about how things in the world should be - opinions that often contradict how things demonstrably are. This book landed smack in the middle of that tension; there are lots of things in the status games that I don’t want to be true, but the weight of evidence is overwhelming that this is how the world is. The best we can do, probably, is try to harness our nature and steer us to play the better games while accepting human nature.

Storr doesn’t let us off the hook, but he doesn’t condemn us either.

• Rating: 5 out of 5

• Dog-ear index: 12

• Who is it for: Anyone who’s ever felt the sting of status loss or the rush of status gain (so, everyone); those interested in why social media is the way it is; people puzzled by political polarisation; and anyone who’s ever played a petty game in their head in a hotel lift and felt slightly ashamed about it afterward.

[reminder: I highlight important parts of the books I read, and dog-ear the really important pages. The dog-ear index is simply the average number of dog-eared pages per 100 pages. Product link for reference only; please support your local bookstore where possible: https://www.amazon.com.au/Status-Game-Will-Storr/dp/0008354677 ]