A deck has been circulated. The numbers look clean, the language is tight, the recommendation feels sensible. Someone says, “This makes sense,” and a few heads nod. The decision looks good in the room.

That is usually when I pause and ask one question:

“What would make this indefensible in front of a regulator, a customer, or on the front page?”

The shift in the room is subtle but immediate. Someone stops turning pages. Someone else leans back. Someone finally looks up from their screen. The energy changes. We stop talking about what works on the slide and start talking about what would actually hold up under scrutiny.

This has become my favourite prompt. I ask it myself before signing anything material. Then I give it to my LLM and ask it to answer plainly and without politeness. More than once, it has stopped us from approving something that looked solid in the room but would have been uncomfortable to defend months later.

But it is not the only question I use. When speed increases, I rely on three questions almost daily:

Read more

When the chair asked if we were comfortable proceeding, the silence felt practiced rather than hesitant. A few nods. Someone glanced at the clock. I felt the pause in my own body before I spoke, a small tightening across the shoulders, the sense that the moment had already passed.

I said yes.

Nothing in the room changed.

I read Dario Amodei’s essay later that evening, and that meeting kept replaying in my head as I did.

He describes our moment as a kind of civilisational adolescence. Power arriving early. Capability moving fast. Systems working smoothly while judgement struggles to keep pace. I recognised the feeling immediately, because I’ve been encountering it in rooms like that one.

Over the past year, decisions have started arriving sooner and moving faster. Ownership spreads across teams, papers, and process. Everything appears well managed. The organisation keeps doing what it is designed to do. And yet something subtle changes in how decisions are held.

What stood out to me in the essay was how easily governance turns into reassurance once speed becomes the default. More documentation. More testing. More structure. Each layer adds clarity and comfort. Each layer also allows the work to keep moving without anyone having to slow it down long enough to feel what is being set in motion.

I recognise this far beyond AI.

When pace increases, leaders lean toward what can be presented cleanly. Dashboards. Reviews. Frameworks. These bring order. They also pull attention away from the moment where a decision actually takes shape. The system continues forward with confidence, while leadership quietly shifts from deciding to overseeing.

EDGE grew out of noticing that change.

From watching how repeated actions teach systems what to expect. From seeing how direction forms over time without a single moment that feels decisive. From realising how easy it is to approve something reasonable and only later sense what it has begun to teach.

What stayed with me after reading the essay was the sense that this is a developmental phase rather than a breaking point. Adolescence has its own texture. Tools move quickly. Feedback loops shorten. The future arrives before there’s time to sit with it.

At that point, restraint has to be learned deliberately. Someone has to stay present long enough to feel the weight of a choice before it becomes habit.

Maturity, at this scale, looks like character under pressure. It looks like authorship when movement would be easier. It looks like someone willing to stay in the room a little longer, even when everything appears to be working.

EDGE is my attempt to name that human work, while there is still time to practice it.

Reading the essay sharpened my attention. It made the edge feel close, already shaping how decisions land and what they quietly teach.

Which leaves me carrying a question into rooms like that one.

If this really is a rite of passage, who is actually doing the growing?

Technology has stopped waiting.
It now sets the conditions work happens inside.

Models update before you’ve understood what the change means.
Work moves before you’ve worked out what to do.
Life carries the consequences.

That gap is the edge.

The edge is no longer exceptional.
It is the default condition of work.

The edge is the tech-driven terrain you are operating in.
Your edge is your capacity to operate there.

Across more than twenty-five years inside government, global enterprises, and frontier technology companies, I’ve worked through successive waves of technological change while the ground was still shifting. Decisions landed before clarity arrived. Responsibility travelled faster than explanation.

EDGE comes directly out of that lived work.

It is a field guide.
The book sets out the core terrain and operating logic.
This publication sits alongside it, extending the work into live conditions.

Here, I publish dispatches from the edge.

Some of these dispatches are Field Notes.
They are short, precise instruments written for moments when judgement matters more than information. Field Notes are cumulative, numbered, and available via paid subscription.

Subscribe now

Other dispatches are public.
They orient the work, mark shifts in the terrain, or surface conditions as they appear.

This is not a commentary feed or a discussion space.
It is a working surface.

These dispatches are written for people who carry responsibility, operate under constraint, and need language that sharpens judgement rather than soothes it.

What to expect here:

• Field Notes released as conditions emerge (paid)

• Occasional public dispatches for orientation and context

• Short, self-contained pieces designed to be returned to

• No hot takes or optimisation for frequency

If you want to receive Field Notes as they’re issued, you can subscribe below.

The roller door was already open.
The air smelled like paper and ink.
Old machines. Oil. Heat.

The kind of place where things are made, not presented.

For the past year, I’ve been writing about operating at the edge of tech-driven change while living it as a CEO in Australia. Building an AI-native operating model. Working day to day with my AI Chief of Staff. Making decisions at a pace institutions were never designed to match.

Most of that work happens on screens.
Prompts. Dashboards. Threads.
Drafts that can always be revised.

Standing there, that stopped.

I wasn’t choosing words anymore.
I was choosing paper.

Earthy recycled stock. Green, flecked, imperfect. I ran my fingers over it. Dry. Textured. Honest. It smelled faintly of cardboard and ink.

We talked weight.
80 or 90gsm for the interior, so the book would open and move properly.
209gsm for the cover, so it would hold its shape without becoming rigid.

Every choice had a consequence.

Too heavy and the book becomes stiff.
Too light and it feels disposable.

No option to iterate later.

That’s when it landed in my body.

This is what authorship actually feels like.

In my work with AI, friction disappears quickly. Outputs appear instantly. Systems move by default. Decisions can feel as though they’re happening on their own.

But authorship only shows up when something has to hold.

When a decision can’t be softened later.
When a choice excludes other choices.
When someone is accountable for coherence, not just contribution.

Paper makes that unavoidable.

The press prints exactly what you give it.
Not what you intended.
Not what you meant and can revise later.
What you chose.

Alongside the book, I’m ordering kraft folders to encase the Field Notes section. Deliberately temporary. Meant to be opened, worked with, replaced.

And for those who want something that holds over time, a leather cover. A permanent artefact. Not default. Chosen.

Even here, authorship shows up as a decision about what should endure, and what should stay workable.

EDGE is almost ready to leave my hands.

It now exists in paper, weight, and binding, not just in drafts and on screen. It’s being published with Aburra Press, alongside my publishing collaborator Barry, who has helped shepherd its final form.

If you want to go deeper, the book is titled:

EDGE: A Field Guide for Navigating Tech-Driven Change

The waitlist is open at edge.guide.

To the edge and beyond. See you out there!

Kate

I started writing it as a field guide to leadership in fast-moving environments. How to lead well in the Intelligence Age. How to make decisions when conditions keep shifting.

Partway through, it became clear that wasn’t the real problem.

The book needed to be about authorship.

The shift came from a concept in systems engineering called edge computing.

In technology, edge computing exists because systems became too fast for centralised decision-making to keep up. When data has to travel back to a distant cloud, decisions that should take milliseconds start taking seconds. By the time a response arrives, the moment that required action has already passed.

That delay is called latency.

So systems adapt. Intelligence moves closer to where data is generated and where consequences occur. Not because it’s elegant. Because delay becomes intolerable under acceleration.

What’s changed now is that human systems are under the same pressure.

Human perception operates in fractions of a second. We notice a shift in tone, a message, a signal, almost immediately.

AI systems now operate at similar speed. Outputs appear instantly. Actions trigger without pause.

Institutions do not.

Institutional decision-making still runs on minutes, hours, days. A signal appears. It’s logged. Reviewed. Escalated. Discussed. Deferred.

By the time a formal decision arrives, the moment that required judgement has already gone.

Nothing breaks. Everyone follows process.

But decisions don’t stop. They migrate.

They show up in Slack threads, shared documents, dashboards, prompts, defaults. Work keeps moving, but no one clearly decides what matters, what stops, or what consequence they are prepared to carry.

Subscribe now

This is the authorship problem.

An author decides what belongs and what does not. They are accountable for coherence. When something fails, they cannot say, “I was just responding.”

A reader reacts to whatever appears next.

Under accelerating, tech-driven change, many capable people begin reading their working lives instead of authoring them.

They stay busy. They respond quickly. They deliver.

But nothing is clearly owned.

AI doesn’t remove human agency. It reshapes the conditions under which agency is exercised. Systems act by default. Outputs compound without friction. Decisions accumulate whether anyone claims them or not.

The risk isn’t automation.

The risk is decisions compounding without authorship, just as their consequences accelerate.

Over the quiet weeks of the holidays, I’ve been finalising the manuscript and preparing it for print. The first copies of EDGE: A Field Guide to Operating at the Edge of Tech-Driven Change will be available in late January.

Before it leaves my hands, I keep returning to one question:

Where would it be easier to defer judgement than to own it?

Because the question is no longer whether decisions are being made at the edge.

They already are.

The question is whether those decisions are authored, or defaulted.

That’s the edge I’m writing from.

To the edge and beyond. See you out there!

Kate

She was exploring AI. Not the hype or the fear, but how humans and machines shape each other.

For the machine layer, we dismantled old phones and a keyboard. Wires, circuits, keys.
For the human layer, she chose materials with memory: paper, fabric, photographs.

As she worked, the boundary softened.

The machine elements began to feel organic.
The human materials formed patterns that looked almost computational.

I see the same thing every day in my work.

Not at the kitchen table, but inside organisations trying to move faster with AI than their human systems allow.

AI isn’t the hard part.
Human systems are.

Once you stop looking at AI in theory and start watching how humans and systems actually interact under pressure, certain patterns become impossible to ignore.

Across banks, platforms, regulators, boards, and scale-ups, I keep seeing the same six capabilities determine whether teams adapt or quietly stall.

Subscribe now

1. Leadership alignment and direction

Most teams believe they’re aligned. Until they’re under pressure.

I’ve sat in rooms of the most senior, highly paid executives in an organisation where everyone nodded, then walked out with different interpretations of what mattered most.

You know alignment is slipping when you ask three people what success looks like this quarter and get three different answers.

What changed things wasn’t more communication. Cascading decks made it worse.

What worked was writing direction on a single page and using it everywhere for a full quarter. We named what we would not prioritise. And we assigned one owner per outcome. No shared accountability.

Clarity followed behaviour, not messaging.

2. Decision systems

Most delays aren’t caused by missing data. They’re caused by hesitation and diffusion.

I’ve watched capable teams stall for weeks because no one was quite sure who had the pen.

You know it’s broken when decisions keep circulating, changing language slightly, and never quite landing.

More forums didn’t help. They just created better discussion.

What worked was documenting who decides, who inputs, and who executes in plain English. We agreed which decisions had to land within 24 hours. And we stopped reopening decisions unless new information genuinely changed the risk.

Momentum returned quickly.

3. Governance and risk controls

Good governance doesn’t slow teams down. It protects them when speed increases.

Weak governance often looks efficient right up until the moment it isn’t.

You know it’s fragile when people can’t articulate the red lines without checking a policy.

SOPs didn’t help under pressure. No one read them.

What worked was stating the red lines out loud, repeatedly. Separating sandbox work from production. And designing a simple escalation path so exceptions didn’t trigger panic.

Risk conversations got shorter and sharper.

4. AI-in-the-loop workflows

AI doesn’t fix broken workflows. It exposes them.

I’ve seen AI introduced with real promise, only to surface confusion about who owns judgement when outputs are wrong or incomplete.

You know it’s unclear when no one can say who owns the outcome once AI is involved.

We started with tools. That was a mistake.

What worked was mapping one real workflow end to end. Marking where AI could assist and where human judgement had to sit. And explicitly agreeing who owned the outcome when AI got it wrong.

That agreement mattered more than the model.

5. Cross-functional coordination

Coordination isn’t about meetings. It’s about coherence.

Some of the biggest risks I’ve seen didn’t come from bad intent. They came from teams optimising locally and damaging the system globally.

You know coherence is breaking when work looks “done” in one team and creates problems for the next.

Detailed reporting hid the real issues.

What worked was a short, weekly cross-functional check-in with a fixed agenda. Progress was shown visually. And when priorities conflicted, leaders resolved it on the spot instead of letting teams absorb the tension.

Friction dropped almost immediately.

6. Intelligence behaviours

This is the quiet one.

No tool compensates for overload, fuzzy judgement, or fractured attention.

I see teams drowning in activity while clarity steadily declines.

You know it’s a problem when everything feels urgent and very little is actually finished.

Adding tools made the noise worse.

What worked was capping priorities at three per person. Cutting meetings aggressively. And protecting one block of uninterrupted thinking time each week, defended by leaders, not individuals.

Clarity improved without adding anything new.

Technology is no longer scarce.
Judgement is.

The teams that upgrade these human systems move faster and safer. The teams that don’t struggle, not because AI failed them, but because their systems never caught up with the world they’re now operating in. That’s the work now.

To the edge and beyond. See you out there!

Kate

It wasn’t as simple as opening a chat window. It took coding, custom logic, and far more trial and error than I expected. I spent three weeks over Christmas learning enough Python to build an early version and understand how it needed to work.

It was never about automating the job.

I built it to see whether AI could give me a clearer internal vantage point to hold the volume, pace, and complexity of my role as a CEO.

The moment it clicked came on one of those days when everything arrived at once.

Within the same hour, I was preparing for a board discussion, dealing with regulatory questions, and responding to journalists on a breaking story. Context switching was relentless.

I used an early version of Sage, my AI Chief of Staff, to pressure-test my thinking before stepping into each conversation. She didn’t give me answers. She stripped away noise so I could hold the line with more clarity and communicate with consistency.

Before that, my attention was fragmenting. I was coping, but at a cost. Each conversation made sense on its own, but coherence across them took real effort to maintain.

That day, something shifted.

AI stopped being a tool and became the basis of an intelligence system that could support how I actually work.

Subscribe now

The Chief of Staff was one part of it. I also needed something to challenge my assumptions and keep me sharp on difficult days. That’s where the Performance Coach and Advisory Board came in.

Together, they became the early version of what I now think of as my AI leadership operating system.

As it evolved, I started using the term Executive Intelligence Engineering.

It’s the discipline of deliberately shaping how intelligence moves around a leader so clarity, judgement, and impact rise under pressure.

This idea sits at the centre of my upcoming book, EDGE. It’s a field guide for operating where pace is high, information is incomplete, and consequence is real.

What surprised me most is that you no longer need to code to build something similar.

You can now create a simple but powerful leadership operating system inside a trusted LLM by being deliberate about how it’s designed.

Here’s what shaped mine.

Start with the real work, not the tech

An AI leadership OS begins by understanding where your work gets heavy.

I wrote down the parts of my role that scattered my focus or slowed decision-making. That list became the design brief.

The work, not the tool, sets the architecture.

Make your leadership cadence explicit

Every leader has a rhythm, whether it’s written down or not.

I had to name how I prefer information, the principles that guide my judgement, when I need to slow down, and when pace matters. That context became the ground the system operates on.

Without this, AI amplifies noise. With it, AI reinforces clarity.

Design for roles, not one voice

Different parts of the work need different forms of support.

The Chief of Staff carries structure.
The Performance Coach holds consistency and energy.
The Advisory Board creates distance and challenge.

This is where AI starts to enhance human capability rather than flatten it.

Set rules that hold under pressure

The rules were simple, but non-negotiable.

Pause first.
Ask clarifying questions.
Frame the issue.
Explore options.
Weigh trade-offs.
Land the next step.

Consistency mattered most on the days when nothing else felt consistent.

Apply it to real work

The system only became useful once I trusted it with real decisions.

Board preparation.
Complex conversations.
Strategy notes.
End-of-day reflection.

I learn continuously, and the system learns alongside me.

What I’ve learned through this process is that AI should expand human capability, not narrow it.

Leaders who design how intelligence flows around them can carry more load without losing coherence. Those who don’t risk being pulled apart by pace, even while performing well on the surface.

That’s why Executive Intelligence Engineering matters.

Not because of the technology, but because judgement is now the scarce resource.

To the edge and beyond. See you out there.

Kate