“…it resolves itself into black and white.” — Chris Rose, How to Win Campaigns

Most of us came into public affairs believing, somewhere underneath it all, that the better argument is supposed to win. Assemble the evidence, make the case clearly enough and often enough, and reasonable people come around. It’s a comforting idea to hold onto. And it’s largely wrong.

In this week’s episode, you’ll hear me come back to a story that serves as such a clear case study of this, and the many other lessons I took from Chris Rose. The story involves a busload (literally) of grandmothers driving from London to Brussels carrying vials of their own blood, along with the lab results and photographs of their grandchildren. That was WWF, in the early 2000s, trying to shape a European law on industrial chemicals against an industry with more money, better lobbyists, and the technical argument sewn up. WWF won. The grandmothers, and those vials of blood are why.

For decades, “toxic chemicals in the environment” had been one of those issues nobody could hold in their hands. Too big, too technical, too grey — all of it steeped in the abstract. So WWF stopped having that argument. They tested people’s blood, and they reduced the whole sprawling thing to a question a grandmother could put to a politician across a table: these chemicals are in my body, and nobody can prove they’re safe — are you all right with that, yes or no?

That case study is the core of How to Win Campaigns. Rose’s image for taking abstract concepts and making them real, it is an aerial photograph of a city. From high up, the whole issue is grey. Zoom in on any one part, keep zooming, and eventually it comes down to black and white. In your blood or not. The pen signs or it doesn’t.

If you’ve spent any time with Walter Lippmann — the first book we did on this show — you already know why this works. Lippmann’s argument was that none of us act on the world directly. We act on the pictures of the world in our heads, assembled secondhand from fragments.

Reduction looks like simplification, and the principled part of us flinches at it. But what it really does is decide which picture of the issue people end up carrying around. If we don’t decide that, someone else will.

If we go back to the case study above, the industry’s picture in the chemicals fight was a single word — workability. If you let that be the question, the answer writes itself, because who could possibly know what’s workable except the people who make the chemicals?

By contrast, WWF’s picture was a bag of blood. Same grey issue underneath; two reductions, pointed at opposite conclusions. The whole fight was over whose black-and-white question the public would end up answering.

And sometimes, a black-and-white picture wins, even if it doesn’t contain accurate facts.

The campaign that made Rose’s name was Greenpeace against Shell over the Brent Spar oil platform in 1995, which involved a David-and-Goliath occupation complete with water cannons and consumer boycotts. Greenpeace won completely.

And Greenpeace was wrong. Their central claim about how much oil and toxic waste the platform held was overstated by a wide margin. They apologised to Shell that September, and the independent studies that followed suggested deep-sea disposal had most likely been the better environmental option all along. The campaigners were wrong on the facts and won. The company was right on the facts, and lost. That’s how powerful a campaign is when it finds the black-and-white frame, applies the “photo test”, chooses the right antagonist, and finds a victim with a voice. These are the basic conditions for a winning campaign, no matter the facts of the case.

Rose writes from the environmental NGO seat, and he defaults to its examples — polar bears, blood tests, Brussels. It would be easy to decide none of it applies if you do corporate or institutional work, where there are no grandmothers and no obvious villain. Don’t let that distract you. This book has traveled well for me, as it serves as a constant check on whether I am defaulting an abstraction, rather than find the human being at the centre of the campaign.

Almost everything we do, underneath, is advocacy on behalf of someone. Rose’s standing question — who is the victim here, and can they speak? — is one corporate teams may not ask enough.

Why This Book Matters

Most of the books on this show are books of ideas. How to Win Campaigns is a manual — its original subtitle was 100 Steps to Success — but strip away the dated tooling and what’s left is a small set of ideas about how the public moves that have held up through every platform shift:

• You win by showing rather than arguing,

• You start from where your audience already is rather than where you wish they were

• A campaign is a sequence you move people through and not a message you broadcast at them.

These don’t expire. They’re the reason I take this book off the shelf nearly every time I sit down to plan a campaign.

What We Cover in the Episode

• The Brent Spar, and why being right is so rarely the thing that wins.

• The core idea: taking a grey, unwinnable issue and reducing it to a single black-and-white decision an ordinary person can see, feel, and act on.

• Locating the decision, and Rose’s photo test — if you can’t photograph the win, you don’t have a campaign yet, you have a feeling with a logo.

• The point of irreducibility, and why finding it is hard, disciplined work .

• Choosing an antagonist, and the upside-down question that cuts through buck-passing: not who caused this, but who has the power to fix it and won’t?

• Sequencing a campaign backwards from the moment of the win — awareness, alignment, engagement, action — so that by the time you make the ask, the ground is built to hold it.

• Two lenses worth keeping: the speaking victim, and the manufactured antagonist.

• Where Rose gets misread, and what it takes to master his lessons rather than memorise his toolkit.

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Bonus: Ideas from the Book That Didn’t Make the Episode

A few things I marked up but left on the cutting-room floor that are worth your time if you go to the book itself.

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This episode’s book is one that had a significant impact on my approach to campaigns. While it was written years after I led already led several political digital campaigns, it nonetheless validated my approach to constant experimentation and innovation, which I first learned while volunteering on the 2008 Obama campaign. Of course, that campaign gets a lot of coverage in The Victory Lab, and its lesson hold true nearly two decades later.

“Most things are done with only one check. People’s guts.” — Steve Rosenthal, quoted in The Victory Lab

Those of us in public affairs explain things for a living. We are good at the story after the fact: why the campaign worked, why the policy landed, why the vote broke the way it did. What we rarely do is run the test beforehand that would tell us whether the story is true in the first instance. And there is a comfortable reason we don’t, one that Sasha Issenberg’s The Victory Lab surfaces at length: work that is never measured can never be shown not to work.

If you heard our last episode, on Joseph Napolitan’s The Election Game, the contrast will be obvious. Napolitan won by instinct, out in the field, a generation before the academic research caught up to what he already knew in his gut. The Victory Lab is the story of that research catching up, and then, in a few places, overturning the instinct.

The book runs in a few different directions: the data, the targeting, the operatives, decades of turf wars between consultants. However, the single biggest takeaway I got from this book, and have applied often since, came from medicine: the randomized field experiment.

In 1998, two Yale political scientists, Alan Gerber and Donald Green took a large group of voters, split it at random into a contacted group and a comparison group that got nothing, waited for the state’s record of who voted, and counted the difference. In other words, a clinical trial, with the voter standing in for the patient.

The results were extremely eye opening:

• Phone calls, the workhorse every campaign spent fortunes on, moved turnout by essentially nothing.

• The mail nudged it a sliver.

• The oldest, least impressive tool in the kit worked: a person on the doorstep making the ask in person. That moved turnout about seven points, more than the winning margin in most races that matter.

The most expensive, most modern tactics were the weakest, and the thing that moved people was presence: one person asking another. Three decades of polished broadcast had gone into replacing the very thing that worked best. And the method had a pull of its own. You can only cleanly isolate a one-to-one contact — you can’t show a TV ad to one neighbour and hide it from the family next door — so once the field got serious about measurement, it was dragged back toward the individual human being. The tool chose the subject.

Then there’s the experiment that is often cited in our world, but worth revisiting: A Michigan consultant named Mark Grebner, working with Gerber and Green, mailed people a letter that printed their own voting record next to their neighbours’, and promised an updated copy after the election so the whole street would see who had shown up.

Turnout in that group jumped to nearly 38 per cent, against 30 for the group left alone: about three times the lift of any mailer they had tested, at roughly two dollars a vote.

You may already know why it works. It is social proof, the mechanism Robert Cialdini lays out in Influence: our pull toward whatever the people around us are seen to be doing. But here’s the catch: Voters found the letter menacing, and no candidate would put their name on it. The blowback was way too intense for any political campaign to stomach. And so, the most effective tool anyone had measured is one nobody can use. How, Donald Green asked, do you take that static electricity and turn it into useful current? You take it down a notch, and stick to the principles, of course. Which is always easier said than done.

The book is more than a decade old now, and it shows. The firms and tools it celebrates have mostly been overtaken, and a reader in 2026 can’t help measuring its sunny middle chapters against what micro-targeting turned into after 2016 (the book has no idea what’s coming, which gives it a strange innocence in hindsight).

It is also a long parade of American campaign operatives whose names blur together by the back half, and the rivalries between data shops get deep into the weeds. None of that is a reason to skip it. The core idea — test what you believe, or admit you’re guessing — holds as well for a regulated-industry coalition in Ottawa as for a turnout drive in New Haven.

Why This Book Matters

Any field that runs on inherited belief instead of evidence pays a tax, in money spent on things that don’t work. The campaign that books the phone bank every cycle because campaigns book phone banks is paying it; so is the trade association that flies its members to the capital each spring because it did last spring. (I’ve helped plan a few of those trips. The cost is real, and invisible, because nobody runs the comparison group that would expose it.)

The fix is almost embarrassingly cheap. We report motion — meetings taken, op-eds placed, alerts sent — and call it results, when the job was to move a number. So next time you run an advocacy push, hold back a random slice of your list, send them nothing, and compare. That one move separates a team that can report its activity from one that can prove its effect. The catch is temperament — we’d rather not find out our best tactic does nothing.

This one cost me something. On a provincial campaign I worked on in 2011, we tried things that were new for a Canadian race, much of it drawn from my time on the 2008 Obama campaign. We lost, so we never sat down afterward to ask what had worked. Plenty of it had. But to the victor go the spoils, and the losing side’s innovations get thrown out with everything else. Issenberg describes a literal dumpster where a campaign’s hard-won data ends up. It’s real. I’ve watched it get filled.

What We Cover in the Episode

• The New Haven experiment, and why it was the first field experiment of its kind in 70 years

• The three mechanisms that make the book work: the experiment itself, micro-targeting, and turnout as a measurable craft

• Hal Malchow’s polling flip — more about who voters are, less about what they think — and Alex Gage’s “consideration score”

• The neighbours letter, social proof, and the line between what works and what you can decently use

• Three mental models: the Lore Tax, the Untestable Channel, and the Margin Game

• Four ways the book gets misread, and four ways to use it in public affairs

• The mastery lesson: why the best operators are the ones willing to be proven wrong

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“Television provided the stimulus, but personal reaction was the real content of the spot.”

— Joseph Napolitan, The Election Game and How to Win It

The TV spot Napolitan was writing about is the Muskie heartbeat ad from the 1968 campaign. The visual was a wobbling line and a beeping heart monitor. The voice-over asked:

“Muskie. Agnew. Who is your choice to be a heartbeat away from the presidency? “

And that was the entire spot.

You’ll notice, it contains no claim about Muskie or Agnew, nor does it contain statistics or arguments in favour or against the candidates.

And yet every voter who saw it understood what it was saying — and many of them concluded that Agnew, not Muskie, was the unfit choice.

What’s fascinating (to me, at least), is that the spot didn’t put that conclusion into the viewer’s head. It pulled that conclusion from the viewers mind.

That may seem counter-intuitive on first blush, but it makes so much sense if we are already familiar with Walter Lippman’s Public Opinion, and his view that we all have pictures in our heads, and our job is to draw from those pictures. And it’s a theme Joseph Napolitan turns to in his book, The Election Game & How to Win It.

We see this same mechanism at play in the more famous Daisy ad from 1964, where the closing line was Lyndon Johnson saying “we must either love each other or we must die” — something no reasonable person could disagree with. And yet every voter understood the ad was about Goldwater, and that it was saying he would get them killed. The fear was already there, sitting in every American adult’s head since the Cuban Missile Crisis two years earlier. The spot pulled the trigger.

Napolitan calls this the difference between received and perceived media. The spot doesn’t deliver a message to a passive listener. The listener completes the spot from inside their own head. The medium is merely the catalyst. The reaction is the content. And the practitioner’s job is to choose stimuli that create this reaction.

This sounds like a theory of political advertising. It is. But it’s also a theory of all communication, and one that we ought to bring to our work every day if we are in the business of communicating with the public.

Why This Book Matters

For most of my career, I have been operating on a working theory I’d absorbed from mentors on political campaigns, or with political campaign experience. I think if these mentors had written their own guidebooks, most would overlap with Napolitan, even if his 1972 book is dated in parts, and difficult to enjoy in others. (There’s a lot of name dropping of characters none of us know, and it sometimes makes you feel like Napolitan is writing for an old boys’ club…if you know you know..)

But it’s still worth the read, because it’s the first useful manual for running a political campaign, and because its central claim is still incredibly relevant today: that communication works by recall, not by argument.

It also contains a counter-intuitive argument that voters don’t vote on issues, and most campaigners make the mistake of campaigning on issues.

Napolitan cites private polling in which he finds that when you ask voters what they actually want in a candidate, they don’t tell you they want policy alignment. They tell you they want someone who is honest and who cares about their problems. By a margin of four or five to one over a candidate whose positions match theirs.

Voters vote for the person they trust. And the gap between what the practitioner thinks voters care about and what voters actually decide on is, in my experience, one of the most expensive mistakes in this business.

I also think this book is worth a read even if you’re not interesting in running a political campaign. And that’s because every business is in the business of politics, whether the CEO knows it or not. Regulators, employees, customers, governments, activists, investors — they’re all constituencies. They all form opinions. They all vote in their own way. The CEO who pretends otherwise is a political leader who doesn’t know they’re running a campaign, which usually means they’re losing one. And the discipline that would have helped them — message definition, channel selection, controlled versus uncontrolled communication, treating the work as a one-day sale — is the discipline Napolitan documents in this book.

That’s why this old, dusty book belongs in the canon.

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What We Cover in the Episode

Here’s what we get into:

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“Bad strategy is the active avoidance of the hard work of crafting a good strategy. One common reason for choosing avoidance is the pain or the difficulty of choice.”

— Richard Rumelt, Good Strategy Bad Strategy

I have produced the dog’s dinner more often than I’d like to admit.

Dog’s dinner is Richard Rumelt’s term for a specific kind of failure. It usually starts with the desire to demonstrate that the team has been thoughtful. The file is complex, the client is sophisticated, and we want them to see that we’ve done the work. So we hit them with appendices, stakeholder maps, decision trees, tables that cross-reference jurisdictions and influence vectors and timing windows. The deck swells from twelve slides to fifty-six. By the time we’re done, the document is a monument to how complicated the situation is.

The client signs off. They thank us for the rigour. We feel like we’ve delivered.

We haven’t. We’ve delivered a wall of complexity that proves the team has been busy and produces no decision the client can actually act on.

For a long time I told myself this was just the cost of doing complex work. In truth, the wall of appendices isn’t what the file required. It’s what I produced when I avoided the actual work.

The actual work is the kernel: diagnose what’s going on, decide what’s pivotal, refuse the rest, concentrate the result. That often takes a single page, not fifty-six slides.

Rumelt spent forty years inside boardrooms as a consultant and a UCLA Anderson School professor, watching organisations produce documents called “strategy” that he’d argue weren’t strategies at all. Good Strategy Bad Strategy, published in 2011, sits at the centre of a serious strategy reading list because it takes a structural question seriously: what makes something a strategy at all.

Most strategies fail, Rumelt argues, because senior leaders make small political accommodations that, in aggregate, replace the strategy work with something else. Something that satisfies more people in the room, hurts no one’s interests, something everyone can sign.

“Universal buy-in usually signals the absence of choice.”

Put another way: death by consensus.

Why This Book Matters

Almost every public affairs leader — including me — has produced what Rumelt would call bad strategy. A coalition assembles around a regulatory file. Five member companies, each with their own priorities. The strategy session runs. The deck grows. By the end, the document lists eight strategic objectives, twenty-three priority actions, and a stakeholder map covering forty entities. The internal political problem has been solved. The strategy problem has been avoided.

Strategy, in Rumelt’s framing, is scarcity’s child. Without the constraint of having to choose, there is no strategy — only a list of things the organisation hopes will happen.

The diagnostic test Rumelt offers is the kernel. Any real strategy has three irreducible parts:

• A diagnosis of what’s actually going on

• A guiding policy for how to deal with it

• A set of coherent actions that carry the policy forward

Pull one out and the strategy stops being a strategy. The piece most often missing is the first, because diagnosing means choosing — saying that one factor matters more than another, knowing the second factor has a constituency in the room who’ll be disappointed. Diagnosing creates losers. The room is full of people who don’t want to lose.

If your organisation isn’t producing good strategy, the first place to look isn’t the team or the framework. It’s the leader. Strategy work can be delegated. Choice cannot. Choice creates losers in the room, and only the leader has the political authority to absorb that cost. The leader’s job is to refuse the document everyone can sign and send it back. To inflict the pain the team will not inflict on itself.

The audiences we’re trying to influence — ministers, deputy ministers, regulators, board members — are drowning in information and short on time. Clarity is in short supply, and simplicity is the biggest gift you can give. The wall of appendices is what you produce when you want to be seen to have done the work. Simplicity is what you produce when you’ve actually done it.

What We Cover in the Episode

Episode 8 of Masters in Public Affairs is a deep walk through Rumelt’s argument and its application to our field. Some of what we cover:

• The core idea — a strategy is a coherent response to an important challenge — and why every word in that sentence is doing exclusionary work.

• The kernel as the minimum-viable shape of any real strategy: diagnosis, guiding policy, coherent actions. The kernel works as a structural test. Take any document calling itself a strategy and ask: where’s the diagnosis, where’s the guiding policy, where are the actions? If you can’t find all three, you don’t have a strategy. You have something else with the wrong label on it.

• The four mechanisms that translate most directly to public affairs work: the kernel itself, concentration and the threshold effect, the proximate objective (and Rumelt’s counter-intuitive case for shorter planning horizons under high uncertainty), and the asymmetry move — Andy Marshall and James Roche’s 1976 Pentagon memo and how it applies to industry-vs-NGO regulatory fights today.

• Four mental models: the kernel, the pivot point, the long clock, and the dog’s dinner.

• Four common misreadings of the book — the most consequential being the assumption that bad strategy is a failure of effort.

• Four modern applications, including the consultant’s dog’s dinner, pulsing in regulatory work, proximate objectives in the legislative cycle, and the asymmetry move under the long clock.

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Bonus: Four Ideas From the Book That Didn’t Make the Episode

The book has more in it than even a sixty-five minute episode could carry. Four ideas from my highlights that didn’t make the cut.

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We can’t be expected to recognize and analyze all of the aspects of each person, event, and situation we encounter in even one day. We haven’t the time, energy, or capacity for it. Instead, we must often use our stereotypes, our rules of thumb, to classify things according to a few key features and then respond without thinking when one or another of the trigger features is present.

— Robert Cialdini, Influence

That’s the book’s spine: the human mind cannot deliberate on everything. The environment is too complex, life is too short, and the brain has adapted by building a library of shortcuts — preset programs that fire when a trigger feature shows up.

Expensive things? Usually good.

Experts? Usually right.

Crowds? Usually onto something.

The shortcuts are adaptive most of the time. They have to be, because we rely on them constantly without noticing.

Cialdini calls this click-run. Press a button, the cassette plays. And this classic book is about what happens when someone figures out how to press the button without providing any of the substance the button is supposed to signal.

From that one idea, he builds a taxonomy:

Although there are thousands of different tactics that compliance practitioners employ to produce yes, the majority fall within seven basic categories. Each of these categories is governed by a fundamental psychological principle that directs human behavior and in so doing gives the tactics their power.

Seven principles:

1. Reciprocity

2. Commitment and consistency

3. Social proof

4. Liking

5. Authority

6. Scarcity

7. Unity.

More than forty years of critique haven’t dislodged this structure, which is remarkable on its own. And even more remarkable is how you see these principles everywhere after you read about them. I can’t look at an e-commerce site anymore, they all so desperately try to implement Cialdini’s principles. And as we learn, that’s a common mistake many practitioners make—to think they can apply these principles as a playbook. You can, but if you do, you’re an amateur.

Why this book matters

Most of what we do in public affairs assumes deliberation. We write briefs. We build evidence. We sharpen arguments. We respond to objections with better reasoning. And then we watch the campaign with the stronger case lose to the one with the stronger trigger.

And that’s a bit of a shift for me. I’ve long argued that the campaign with the most emotive case wins. I think that is largely true, but I had to re-read this book to realize I’ve been missing the whole bit on triggers.

Influence explains this gap. It says, plainly, that most influence happens below the level of argument. The receiver isn’t stopping to weigh the evidence. They’re running a preset program that was loaded before the argument arrived, and that will still be running after it’s forgotten. Whoever fires the program controls the decision.

The Petrified Forest case in the episode is a clean example of this. A park sign designed to reduce theft — written the way most of us have written similar PSA campaign content at some point — nearly tripled the theft it was meant to prevent. A different sign, making the same moral plea through a different trigger, cut theft in half. Nobody on either side was persuaded differently. A different preset program ran.

Read the book as a practitioner and you start seeing that mechanism everywhere. In coalition letters. In compliance dashboards. In the way regulators frame rollbacks. In the way stakeholders react when freedoms are withdrawn. Influence is more than forty years old, and it keeps getting more relevant, because the environment keeps getting noisier and the shortcuts keep mattering more.

What the episode covers

Episode 7 of Masters in Public Affairs goes deep on Influence:

• Why Cialdini wrote the book, and why his three years undercover inside compliance industries matter more than most readers realize

• The core idea: most influence works by pulling a trigger, not by making a case

• Four of the seven principles in depth — reciprocity, commitment and consistency, social proof, and scarcity — and how each one shows up in modern public affairs practice

• Three mental models drawn from the book: the ledger, the granted freedom, and the authority shortcut

• Three common misreadings, including the biggest one: treating the book as a playbook rather than a diagnostic

• Four applications for the work we do now — the modern lobbyist problem, signatory inflation, the dashboard problem, and the politics of withdrawn benefits

• The mastery lesson: why reading this book doesn’t immunize you against anything in it

This is the companion to Episode 5 on Pre-Suasion. Pre-Suasion is about what should happen before the ask. Influence is about what makes the ask land. You need both.

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Bonus: three threads from the book that didn’t make the episode

There’s more material in Influence than any single episode can carry. Three threads I left out that are worth their own time.

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For 13 years in a row, Avis lost money. Thirteen years. Then, in 1962, it ran a campaign that said: “Avis is only number two in rent-a-cars, so why go with us? We try harder.”

Which is when it began to make a lot more money.

However, some people take away the wrong lesson from this story.

They think Avis succeeded because they tried harder — that the message was about effort and hustle. Ries and Trout are clear that’s wrong. Avis succeeded because they acknowledged where they sat in the prospect’s mind and used that position to their advantage.

They didn’t claim to be better than Hertz. They didn’t pretend to be the leader. They said: we’re number two, and here’s why that’s a reason to choose us.

Here's the fascinating part: Avis didn't change the product. They didn't get better cars or friendlier staff or lower prices. They changed where they sat in the customer's head. They accepted the mental hierarchy that already existed and found a way to make their rung work for them.

And for some reason, then took that tremendous success and toss it out the window. After the company was acquired, Avis ran a new campaign: “Avis is going to be number one.” That’s advertising your aspirations — telling the market what you wish were true instead of working with what the market already believes. It didn’t work. Ries and Trout call it wrong psychologically and wrong strategically.

Why did the first campaign work and the second one fail? Same company, same product, same market. The difference was where they started.

The first campaign started with what already existed in the prospect’s mind — Hertz is number one, and everyone knows it — and built from there.

The second campaign started with what Avis wanted to be true and pushed it outward. The mind accepted the first and rejected the second.

That distinction is the entire argument of Positioning: The Battle for Your Mind, published by Al Ries and Jack Trout in 1981. Their conclusion:

“You look for the solution to your problem not inside the product, not even inside your own mind.

You look for the solution to your problem inside the prospect’s mind.”

The solution to your communication problem is not a better argument, a sharper brief, or a more compelling data set. The solution is understanding what already exists in the mind of the person you're trying to reach — and working backward from there.

Ries and Trout call this "positioning," and they argue it's the foundational discipline for anyone trying to communicate in an over-saturated information environment.

They wrote that in 1981, with three television networks and no internet. The environment has gotten worse. The mechanism hasn't changed.

Before you read another brief or walk into another meeting, ask yourself: What does the person across the table already believe about this issue, about my industry, and about the competitive landscape of stakeholders trying to influence this decision?

If you don’t have an answer, your brief is probably noise to them.

If you do have an answer, you’ve got a shot at a position.

Why this book matters

Most people file Positioning under marketing. It lives on CMO bookshelves, gets taught in business schools, and a lot of practitioners in our field have never picked it up because they assume it’s about selling consumer products.

But I think it belongs in the public affairs canon. So much of what we do is positioning — positioning an issue so it occupies a specific mental slot before a competitor gets there, positioning our organisation so a minister thinks of us first, positioning a specific policy ask so it’s hard to oppose.

We did a lot of that in the Prime Minister’s Office, making sure every piece of legislation had a title that would make it politically painful for the opposition to vote against. It works.

Ries and Trout’s core diagnosis is that the mind doesn’t receive information neutrally. It defends against it. It screens out what doesn’t match existing beliefs, ranks what gets through into simplified hierarchies, and anchors hard on whoever gets there first.

In 20 years of practice, the pattern I’ve seen more often than any other is teams that lead with their cognitive argument — strong data, sound logic, clear recommendations — and expect the quality of the evidence to carry the day. It almost never works the way we expect it to. The decision maker already has a picture in their head. If the brief doesn’t fit that picture, the mind doesn’t update. It filters.

Ries and Trout explain why, and they offer a disciplined alternative: start with the prospect’s mind, figure out what’s already there, and build your position around that reality instead of against it.

What We Cover in This Episode

• Why a 1981 advertising book belongs in the public affairs canon

• How the mind filters, ranks, and anchors information — and what that means for how we design campaigns

• Why leading with facts and cognitive arguments is the most common pattern in public affairs and the least effective

• Naming as a strategic weapon — lessons from the PMO on legislative titles and policy framing

• The coalition dilution trap: how the instinct to build consensus often destroys the sharpness of your position

• Why the public assesses your issues against their own self-identity before deciding whether to support you — a blind spot the book itself underexplores

• The Avis, Tylenol, Xerox, and Taster’s Choice case studies

• Connections to Lippmann’s stereotypes, McRaney’s identity-protective cognition, Luntz’s language discipline, and Centola’s threshold dynamics

• Four mental models worth carrying around: the Ladder, the Créneau, the Teeter-Totter, and Sacrifice

Listen and subscribe here:

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Bonus: five ideas from the book that didn’t make the episode

The episode covers the core architecture of positioning. But there’s more in the book worth knowing. Here are five ideas from my highlights that didn’t make the cut.

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Researchers at Stanford wrote a news article about a city dealing with a three-year rise in crime. The article was factual and cited real statistics. But they changed one word.

• Half the readers saw crime described as a ravaging beast.

• The other half saw the same article, with the same statistics, but crime was described as a ravaging virus.

After reading the article, the “beast” readers wanted to catch criminals and lock them up. The “virus” readers wanted to address root causes — joblessness, poverty, education.

One word had a 22% shift in policy preference. More than double the effect of gender. Nearly triple the effect of party affiliation.

One word, placed before the evidence, had more influence on people’s prefered policy solutions than whether they were liberal or conservative.

Why?

Not why did the word matter — that part we can intuit. “Beast” sounds like something you hunt. “Virus” sounds like something you treat. But that doesn’t explain the scale. Why would a single word, buried in the opening sentence of a news article, override the effect of someone’s political identity? Why would it outperform the thing we normally treat as the strongest predictor of policy preference?

You might think this is about word choice — about finding the right metaphor. It’s not. Or not exactly. Because if it were just about metaphor, you’d expect the effect to show up after the evidence, as a way of interpreting what was already read. But this word appeared before the facts. It set the lens through which every subsequent statistic was processed. The readers never knew it was happening.

That’s the puzzle Robert Cialdini spent thirty years trying to solve. And his answer reframes how I think about campaign design:

“The factor most likely to determine a person’s choice in a situation is not the one that counsels most wisely there. It is the one that has been elevated in attention, and thereby in privilege, at the time of the decision.”

— Robert Cialdini, Pre-Suasion

The factor most likely to determine what someone decides is the one that happens to be focal in their mind at the moment they decide. Not the most accurate factor. Not the most reasonable. The most focal.

“Beast” made animal-control associations focal — capture, cage, punish. “Virus” made disease-control associations focal — treat causes, prevent spread. The word didn’t argue for a policy. It activated a lens. And once the lens was in place, the facts arranged themselves around it.

Cialdini calls these windows “privileged moments” — brief, identifiable points in time when a person’s receptiveness is elevated because their attention has been directed somewhere specific. The frame is set. The soil is prepared. And whatever lands next has an outsized chance of taking root.

This is the book I think we need to understand before reading Cialdini’s more famous work, Influence. That’s because Influence answers what makes people say yes. Pre-Suasion answers the prior question: what do the best communicators do before they make the ask?

Why This Book Matters

I sound like a broken record, as I’ve said this often, but most of us over-invest in the quality of our argument and under-invest in preparing the moment of reception.

Cialdini spent years embedded in the training programs of top persuaders across industries. He watched their techniques, observed their pitches, studied what separated the best from the rest. And he noticed something that surprised him. The best performers didn’t spend their extra time refining the pitch. They spent it on what happened before the pitch.

They recognized that they were frequently in no position to change what they were selling. Someone else in the organization had designed the product, the program, the plan. What they could control was the sequence — what the audience encountered before they encountered the offer.

That’s the problem this book is solving. And it’s our problem too. We’re rarely the ones who designed the policy, the position, or the announcement. What we control is the context in which it arrives. Cialdini’s evidence — across decades and hundreds of studies — says that’s not a secondary consideration. It may be the primary one.

And here’s what surprised me most: he’s not just talking about long lead-time preparation. The beast-versus-virus study wasn’t a months-long conditioning campaign. It was a single word in an opening sentence. Pre-suasion operates at the scale of weeks and also at the scale of seconds. The question you pose. The image someone sees. The word you choose to frame the problem. All of it can be leveraged in the instant before your message arrives.

What We Cover in this episode

• Why the best persuaders spend more time on what happens before the pitch than on the pitch itself

• The focusing illusion: whatever is focal seems important, whatever is important seems causal, and whatever isn’t focal doesn’t seem to matter

• How that one-word shift — “beast” vs. “virus” — rewired policy preferences more than ideology did

• What I call the three-gear engine of pre-suasion: attention creates importance, association spreads the effect, commitment locks it in

• Why asking “Do you consider yourself a helpful person?” before a request raised compliance from 29% to 77%

• Four mental models worth carrying around: the focusing illusion, the association bridge, the commitment lock, and the authenticity filter

• Why detection of an influence attempt doesn’t just weaken it — it reverses it

• The Iraq War embedded reporter program as a case study in institutional attention management

• What may be the most powerful five-word persuasive communication in thirty years of research

• The difference between attention-grabbing and pre-suasion — and why fear without an action pathway generates avoidance, not behaviour change

• Why mastery in this work is in the preparation, not the performance

Listen and subscribe here:

• Spotify

• YouTube

• Apple Podcasts

• Substack

Bonus: six ideas from the book that didn’t make the episode

The episode runs thirty-five minutes and covers the core architecture. But there’s a lot more in Pre-Suasion that’s worth knowing. Here are six ideas from my highlights that didn’t make the cut.

1. “If-When” plans: simple follow-through tools

Good intentions translate to action only about half the time. The failure isn’t motivation — it’s that people don’t recognize the right moment to act, or they get derailed by competing demands.

Read more

“Successful social change is not about information. It’s about norms.”

— Damon Centola, Change

That sentence sits at the centre of one of the most important books I’ve read in the past decade. And it took me a while to fully absorb what it means.

We spend enormous energy in public affairs on information. Crafting the right message. Finding the right frame. Deploying the right spokesperson. Getting the word out. And all of that matters. But Damon Centola, a network scientist at the University of Pennsylvania, spent two decades running experiments that show the same thing over and over: getting the word out is the easy part. The hard part is getting people to act on it. And those are two completely different problems that require completely different approaches.

Here’s the distinction. Information spreads through casual contact. One person tells another who tells another. Centola calls this a simple contagion. News, gossip, awareness — one exposure is enough. You hear it, you know it, done.

But behaviour change — adopting a new technology, joining a coalition, supporting a policy, showing up at a protest — is what Centola calls a complex contagion. And complex contagions don’t spread through reach. They spread through reinforcement. People need to see multiple others in their network adopting before they’ll move. Not one influencer. Not one compelling message. Multiple independent sources, all pointing in the same direction.

That one distinction — simple vs. complex contagion — reframes almost everything we do. It means the viral model is wrong for the work that matters most. It means the influencer playbook is structurally flawed. It means awareness campaigns can actually backfire if they create visibility without adoption. And it means the most important variable in any change effort isn’t the quality of the argument. It’s the architecture of the network through which that argument travels.

That’s what this episode is about.

Why This Book Matters

I’ve been in public affairs for over 20 years. I’ve designed campaigns, built coalitions, run grassroots mobilisation efforts. And I’ve operated, like most of us, on a set of assumptions borrowed from marketing and epidemiology: maximise reach, find the influencers, make the message sticky.

Centola’s work challenges those assumptions head on. It shows, with controlled experiments, that they lead us to do the opposite of what works for complex change.

A few examples from the book that stuck with me:

Hybrid corn in Iowa was objectively superior to what farmers had, free to try, and desperately needed as the Dust Bowl approached. After years of aggressive marketing, adoption sat at 1%. The marketing itself strengthened the rumours working against it.

PrEP in Zimbabwe — a free daily pill that eliminates HIV transmission — was so ineffective that villagers who told doctors they were taking it had no traces in their bloodstream. Social stigma was more powerful than the fear of death.

Google Glass had a billion-user company behind it and massive awareness. But the visible gap between early adopters (Silicon Valley techies) and everyone else created resentment, not aspiration. The norm turned against the product.

In each case, the failure wasn’t in the message or the product. It was in ignoring the social network that would interpret it.

What We Cover in the Episode

This is a long episode — and intentionally so. There’s a lot of ground to cover and I wanted to do it properly. Here’s what we get into:

• The core distinction between simple and complex contagions, and why it matters for everything we do in public affairs.

• Fireworks networks vs. fishing net networks — why redundancy, not reach, drives adoption. And the finding that “laggards” who need multiple sources of reinforcement before adopting are 300 times more likely to stick than early adopters.

• Countervailing influences — why targeting the most connected, most senior people first is often backwards. A person with 500 contacts is roughly 10 times less likely to adopt than someone with 50, because the silent non-adoption of the majority drowns out the signal from the few.

• The 25% tipping point — Centola’s experimental proof that a committed minority of 25% can overturn an established norm, and why activism below that threshold looks like failure even when it’s accumulating toward a breakthrough.

• Three principles of relevance — when to use similarity (credibility and solidarity) and when to use diversity (legitimacy). Most of us default to similarity. When the barrier is legitimacy, that default hurts us.

• The polarisation experiment — where removing decorative party graphics from a screen was the only difference between zero learning and 90% accuracy among Democrats and Republicans looking at the same NASA data.

• China’s 50 Cent party — how every mechanism for creating social change can be run in reverse to prevent it. The dark mirror of the entire framework.

• And the mastery lesson: diagnose the infrastructure before you design the campaign.

Listen and subscribe here:

• Spotify

• YouTube

• Apple Podcasts

• Substack

Bonus: Four Ideas From the Book That Didn’t Make the Episode

I took a lot of highlights while reading this book. More than I could fit into even a long episode. Here are four ideas that didn’t make the cut but are worth your time.

1. Stigma as a Resource

In the 1990s, public health campaigns were failing to reduce HIV transmission among injection drug users. The problem was familiar — drug users didn’t trust mainstream healthcare providers and weren’t interested in advice from authority figures.

Read more

The problem is that the output is getting very good. With the right context, I’m getting clear writing, accurate data, and well-structured reasoning from my AI assistants. That’s all great. But while the data and the information is often right, the interpretation is sometimes off. Not wildly off. Just off in a way that would lead to a bad decision if I didn’t notice it.

For example, when I ask an AI to synthesize a regulatory landscape for me, it pulls the right signals, cites the right stakeholders, maps the right risks. But when I read the brief, something is lacking. The logic holds and the facts check out, but the framing points somewhere that doesn’t match what I know about the client, the political dynamics, or the way this particular issue actually moves.

When I dig in, the answer is almost always the same. The AI framed the situation the way most people would frame it, by defaulting to the average interpretation, the one that usually emerges when you synthesize a large body of information and land on the most common lens. But as we know in this business, looking at the average rarely helps you make sense of your options.

I’ve been struggling to come up with a term for this, but I’ve landed on the “Mean Frame.” Actually, who am I bullshitting…I asked AI to help me come up with that name. And it works well enough.

The concept is this: The better and more polished AI gets, with more accurate, well-sourced outputs, the harder it is to spot the deficiency in the underlying frame it used to interpret the information.

This got me thinking about a broader question: we spend real money on information quality in this industry. Monitoring platforms, media tracking, stakeholder databases, sentiment tools, polling. But what do we spend on judgment quality? Sure, we spend money on smart people— I’ll come back to that. I mean the actual systems and structured processes that ensure the lens we use to interpret all that information is the right one.

I think the honest answer, for most of us, is nothing. And I think AI is making that gap more dangerous. Because when the brief looks this good, when the data is accurate and the writing is sharp and everything is well-sourced, we risk failing to question the lens. Why would we when the picture looks so clear?

I’ve spent the last few months writing about how institutional decisions break down:

• How we freeze when we can’t see consequences.

• How we commit too early when we fail to pressure-test.

• How we self-censor because naming something outside the consensus window feels like career suicide.

I wrote those as separate failure modes and built a visual framework showing how they connect.

However, these challenges are symptoms of the same thing. Good people, with access to good data, lacking a structured way of challenging the lens through which all that data got interpreted. We rely on human judgement for that interpretation. Get the information right and the right decisions will follow, right? Wrong.

“That’s what our people are for. That’s why we hire experienced professionals.”

Well, consider this story I recently found in Damon Centola’s Change book:

Barack Obama is giving a lecture at MIT about how leaders make good decisions under uncertainty. He describes sitting at the table with his cabinet, with the staffers lining the edges. If you’ve ever sat in a political briefing, you know what this looks like. The data analysts, policy wonks, and the people with binders who actually do the work, sit behind their bosses.

Obama points out that those sitting at the table didn’t have time to look at the data. They skimmed summaries from senior staff, then — he said, only partially joking — explained them probably inaccurately. The real knowledge was at the edges of the room, with people who had been told by their bosses not to speak.

His fix was simple. He made a habit of calling on those peripheral staffers directly. They were terrified, but the president asked, so they answered — and they brought insight the polished summaries had compressed out of existence.

The President of the United States, backed by the most sophisticated intelligence apparatus in history — still had to contend with a judgment gap. The frame-challenging insight was right there in the room; it just had no way into the conversation unless Obama personally pulled it in. Every single time. That’s not judgment infrastructure. That’s one leader’s personal habit holding everything together.

We’ve all seen versions of this. I wrote about a client whose experienced team locked in a frame before the environment finished forming. I wrote about exceptional analysts who self-censored because the professional cost of being wrong outweighed the institutional benefit of being imaginative. Good people, real judgment, no system for surfacing it when it counts.

And the context keeps getting harder. The conversation where the frame gets set increasingly isn’t happening in a room — it’s a Slack thread at 9pm or a WhatsApp exchange between meetings, where a default interpretation slides through unchallenged before anyone’s had coffee the next morning.

If the most powerful office in the world needed a deliberate workaround to hear what was already in the room, what are the odds we’re nailing this by accident?

The Predecision — the technique I wrote about a few weeks ago — was my attempt at addressing this gap. When a recommendation feels ready, you force a collision; make the best case against your own position and keep the frame elastic long enough to genuinely test it. It works. But it works late.

By the time we’re in Predecision territory, the frame has already shaped everything: Which data got prioritized? Which scenarios made it into the deck? Which options the team even considered? If the frame was wrong from the start, the Predecision is just pressure-testing a conclusion built on the wrong foundation.

What’s actually missing is a discipline that operates upstream. Before the analysis runs, before the brief gets drafted, before anyone starts building toward a recommendation. A structured challenge to the frame itself — not the conclusions that flow from it.

The encouraging part? This discipline already exists. Just not in our world.

When a finance team produces a report, someone challenges the assumptions underneath the numbers — not whether the math adds up, but whether the model makes sense. Legal pressure-tests reasoning. Engineering runs QA against intent, not just against whether the code compiles. These functions figured out a long time ago that accuracy without the right frame is a well-organized path to the wrong answer.

I’m not sure we have anything like this in public affairs. The frame that shapes a campaign or a regulatory approach gets selected informally — usually by whoever speaks first with a coherent view, or by the most senior person in the conversation. Once it takes hold, everything downstream gets evaluated inside it. The data gets sharper and the analysis gets tighter, but the frame itself never gets questioned.

The fix is to take the same review discipline that already exists in those other functions and point it at the frame shaping our work, before that frame starts shaping the analysis.

In practice, that means asking a specific set of questions before the analysis starts, not after: What frame are we using, and who selected it? What alternative frames did we consider and discard? What would have to be true for a completely different lens to be the right one? What does this look like through the frame our opponents are using?

These questions should be asked explicitly, documented, and revisited when the environment shifts, the same way an auditor revisits assumptions when conditions change.

My theory is that when this discipline exists, the downstream difference show up fast. We walk into a briefing — or an async update — aligned on why we see things the way they do, not just what the data says. When the CEO pushes back, the response isn’t scrambling or folding. It’s explaining the frame, showing it survived challenge, and moving directly to what action to take.

That shift — from “is this analysis right?” to “what do we do about it?” — is where I’ve watched many clients burn weeks. The data was fine and the analysis was sound. Nobody could move because nobody had surfaced the frame underneath it, let alone tested whether it was the right one.

I’ve been building what I’m calling a capabilities index for teams that need to make better decisions under conditions that won’t wait for certainty. The previous essays each examined a specific failure mode — paralysis, premature commitment, self-censorship. This essay is about the structural absence that makes all three possible.

The capability is Judgment Under Uncertainty: the organizational discipline of challenging the frame before it shapes the analysis, not just the conclusion after.

We’ve built extraordinary systems for knowing more, and almost nothing for seeing clearly. Every dollar we invest in sharper AI-powered analysis without a corresponding investment in how we decide what it all means just widens the gap between how good our briefings look and how sound our decisions actually are.

The tools will keep getting better. That’s the easy part. Building the judgment to use them well?

Photo by Derick McKinney on Unsplash

Masters in Public Affairs goes back to the foundational books in this field and extracts the principles that the best practitioners return to again and again. One book at a time.

“You can have the best message in the world, but the person on the receiving end will always understand it through the prism of his or her own emotions, preconceptions, prejudices, and pre-existing beliefs. It’s not enough to be correct or reasonable or even brilliant. The key to successful communication is to take the imaginative leap of stuffing yourself right into your listeners’ shoes to know what they are thinking and feeling in the deepest recesses of their mind and heart. How that person perceives what you say is even more real, at least in a practical sense, than how you perceive yourself.”

That’s Frank Luntz, right at the top of Words That Work. And that passage should sting a little. Because most of us have been on the wrong side of it.

I’ve certainly had that moment. I craft what I think is a perfect message. Clear, accurate, well-supported. Send it out. And the response makes no sense. People heard something we didn’t say. They reacted to a meaning we didn’t intend. They filtered our words through beliefs we didn’t account for.

Luntz spent decades studying exactly this problem. His career was built on one observation: communication is determined by the receiver, not the sender. The quality of what you said is measured entirely by what the other person did with it after your words left your mouth.

He puts it in a way I keep coming back to: “The act of speaking is not a conquest, but a surrender.”

That’s a hard idea for people in our field. We think of communication as projection. We build messages, we push them out, we measure impressions and reach. Luntz is saying the opposite. The moment you speak, you’ve handed your words over to someone else’s brain. You’ve surrendered control of what they mean.

Why this book matters for practitioners

Words That Work was published in 2007. The political examples are dated. Luntz spends a lot of time on the 2004 election and Rudy Giuliani’s approval ratings — not all of which ages particularly well.

But the principles underneath those examples haven’t moved.

The receiver’s filter is still operating. People still process your words through their existing beliefs before those words arrive as meaning. Sequence still matters — the order you present information changes how people interpret it. Persona still determines credibility — what you demonstrate always outweighs what you claim. And single-word reframes still reshape entire categories of public perception.

What makes this book earn its place in the Masters in Public Affairs canon is where it sits relative to the other books we’ve covered:

• In our first episode, we looked at Walter Lippmann’s Public Opinion, which established that people respond to the pictures in their heads, not to reality itself.

• In our second episode, David McRaney’s How Minds Change showed us the cognitive science behind why those pictures are so resistant to change — and that questions work better than arguments for shifting them.

Luntz takes both of those insights and builds a practitioner’s toolkit. He’s the bridge between understanding how opinion forms and knowing how to work with that formation process in real time. Lippmann diagnosed the problem. McRaney explained the science. Luntz shows you what to do about it in the campaign office.

That said, the book has an honest limitation worth naming. Luntz built his career on a specific empirical method — dial testing, instant-response focus groups, real-time language testing. The book almost entirely skips that methodology. You get the proven outputs without the process for generating your own. He gives you the answers from his lab. He doesn’t give you the lab manual.

His ten rules are useful defaults. They are not a substitute for doing the listening work with your own audience.

What we cover in the episode

Here’s what we get into:

• The Kissinger story that captures the entire thesis in a single anecdote — how one word he didn’t choose undermined a decade of foreign policy.

• The core idea that everything else in the book hangs from: communication is a receiver-side phenomenon. Your intentions don’t determine what people hear. Their existing mental pictures do.

• The multi-layered mechanism Luntz describes — how words get filtered through the listener’s beliefs, how sequence changes meaning, how persona creates or destroys credibility, and how single-word reframes can restructure an entire industry’s public perception without changing the underlying product.

• Four mental models for practitioners: the receiver’s filter, results over process, education before motivation, and consistency with freshness.

• The common misunderstandings — including the honest limits of language, the line between reframing and spin, and why the ten rules are a starting point rather than a complete system.

• Modern applications, including why the silence-equals-guilt principle matters more in the age of social media than it did when Luntz wrote this book.

• And the mastery lesson: the best communicators are the best listeners. Your audience already has the language. Your job is to find it.

Listen and subscribe here:

• Spotify

• YouTube

• Apple Podcasts

• Substack

Bonus: Ideas from the highlights that didn’t make the episode

Every episode requires cuts. Here are a few ideas from my reading notes that didn’t make it into the final script but are worth your time.

Read more

So you can imagine how accomplished I felt last weekend when I used my bare hands to build an app.

I called it Earmarked. It pulls my podcast feeds, runs AI analysis on each episode, and surfaces an executive summary and key takeaways before I’ve finished my first coffee. I built the first functioning version in under an hour, using Claude to develop the brief and Replit to do the build. By Monday morning it had already changed how I started my day.

Fair warning, if you click on that link and choose to sign-up for the app, do so at your own risk. This isn’t a public-facing app, and I have no intentions of maintaining it beyond my own personal needs.

I’ve since thought about what that means — for me, for the practitioners I work with, and for anyone in public affairs who’s been watching the AI wave roll in and wondering where exactly they’re supposed to stand.

The idea came from a thread at the office. We’d been talking about how The New York Times built a custom application to aggregate content from the manosphere. One of my colleagues flipped it to me—I help oversee our in-house AI platform—and asked whether we could build something similar. I thought it was a brilliant idea and added it to our product roadmap.

Then I thought about what comes next. Getting something built means back and forth with our engineer—messages, calls, trying to describe in words something that doesn’t yet exist. While our engineer does amazing work with very little direction, I nonetheless decided this was a great opportunity to finally give vibe coding a try and build a prototype he could react to.

It didn’t come without a few hiccups here and there. One podcast was only serving up opening credits instead of a full transcript, so my Replit AI built a workaround that pulled YouTube transcriptions directly into the app. Longer episodes were inconsistent—the original build hadn’t planned for them. Each time something broke, I described the problem in plain language and watched the AI troubleshoot it in real time, sharing its thinking as it worked. That part, I’ll admit, still feels a little like watching something that shouldn’t be possible.

Before Earmarked, I was consuming podcasts in the margins of my day. Dog walks, grocery runs, the sideline at my kids’ soccer practice. Important episodes would land in the morning and I’d get to them in the evening, if at all. I often felt a step behind—and in public affairs, where the news cycle doesn’t wait, that gap matters.

Monday morning was different.

I logged in, and there it was. Executive summaries for every episode that had dropped over the weekend. Key takeaways, pulled cleanly, accurate enough that I could immediately tell which episodes were worth my full attention and which ones I’d absorbed enough of just by reading. The ones worth going deeper on? I saved those for my walk. I got to choose where to spend my time rather than trying to keep up.

I’ve been in this industry long enough to know that feeling informed at the start of the day isn’t a small thing. It changes the quality of every conversation you have before 9am (Pacific Time). It changes what you notice, what you flag, what you bring to the table. Earmarked gives me that. An app I built in under an hour, designed entirely around how I actually work, costing a fraction of what any off-the-shelf alternative would have charged me to do half the job.

There’s a lot of noise right now about whether AI kills SaaS. Whether the era of paying monthly subscriptions for software that does roughly what you need is coming to an end. I’m not going to pretend I know how that plays out across the whole industry.

But I have a view on the consumer end of it.

What I built over a weekend, for my specific needs, works better than anything I would have found on the App Store. It cost me almost nothing. Every time I want a new feature, I add it. No support ticket, no waiting for a product roadmap that was built for someone else’s use case. Just me, describing what I need, and an AI that figures out how to build it.

I think B2C SaaS — software built for individual consumers — is more exposed than most people are willing to say out loud yet. Not because vibe coding is perfect, and not because everyone will do this. Most people will try, hit a wall, and stop. For those users, well-built consumer software still has a role. But the ceiling on what a non-developer can build for themselves has moved dramatically, and the products that sit in that space are going to feel it.

Enterprise is a different conversation. Security obligations, diverse user bases, the complexity of building for scale. I wouldn’t put what I built anywhere near that territory. But for personal productivity? For solving a specific problem that only you have, exactly the way you’d solve it? With open-source information? That market just got a lot more complicated.

So here’s what I took away from the weekend, and it’s not about SaaS.

In building my little app, I now understand, in a way I didn’t before, what’s possible. Where the edges are. What AI can self-troubleshoot and where it needs a clearer brief. What a good prototype feels like versus what needs a professional to take it further.

That literacy matters more than the app itself.

In the last few months, I’ve begun arguing that every public affairs team needs a public affairs operating system — a custom-built infrastructure, sitting on a Public Affairs Cloud, designed around how your practice actually works. Not a generic AI tool bolted onto your existing process. Something built for the specific demands of your issues, your stakeholders, your intelligence needs.

Most practitioners hear that and don’t know where to start. The gap between “we should probably do something with AI” and “we have a functioning operating system” feels enormous. I think vibe coding is the bridge.

You won’t build the operating system yourself, nor should you try, given what’s at stake with your company, association, or agency. But going through the process of building something small, something real, something that actually works, gives you the literacy to have the right conversation with the people who can build the bigger thing. You stop describing what you want in the abstract. You start describing it in terms of what it needs to do, how it needs to behave, where it needs to connect. That’s a different conversation, and it leads to a better outcome.

The practitioners who figure this out first are going to have a significant advantage. Not because they’re better at technology. Because they’re better at knowing what to ask for.

I logged into Earmarked this morning. Every episode I needed was there, summarized, prioritized, waiting. I took my dog for a run and went deeper on the podcasts that deserved it.

So yeah, I can’t hang a picture frame. But I built something last weekend that made me better at my job this week. And the more I sit with that, the more I think the question for public affairs practitioners isn’t whether AI is going to change this industry—it already has—it’s whether you’re going to develop the literacy to shape what that change looks like for your practice, or wait for someone else to hand you a solution that mostly fits.

You have the tools. The barrier is lower than you think. Go build something.

“There is no superior argument, no piece of information that we can offer that is going to change their mind. The only way they are going to change their mind is by changing their own mind, by talking themselves through their own thinking, by processing things they’ve never thought about before, things from their own life that are going to help them see things differently.”

— Steve Deline, deep canvasser

That quote comes from a man who has had more than 15,000 recorded conversations trying to change people’s minds on contentious social issues. He’s an activist with a clipboard, not a psychologist or a political scientist. He and his team at the Leadership Lab in Los Angeles stumbled onto a persuasion technique that independent researchers later measured as 102 times more effective than traditional canvassing, television ads, radio, direct mail, and phone banking combined.

And his core insight, the one that everything else in their method hangs from, is that persuasion is something you help someone do to themselves.

This is the idea that sets up David McRaney’s entire book, How Minds Change: The Surprising Science of Belief, Opinion, and Persuasion. McRaney is a science journalist who spent years telling people there was no point in trying to change minds — that motivated reasoning, confirmation bias, and tribal psychology made it essentially impossible. Then the shift in public opinion on same-sex marriage broke his framework. In 2012, the majority of Americans opposed it. The very next year, the majority supported it. If minds can't be changed, how do you explain that?

The book is his attempt to answer that question. And the answer he found, across deep canvassers, street epistemologists, cognitive scientists, and conflict negotiators, is remarkably consistent: the techniques that actually work all share a common structure. They create conditions where people re-examine their own reasoning and discover its weaknesses themselves.

Blaise Pascal figured this out four hundred years ago: "People are generally better persuaded by the reasons which they have themselves discovered than by those which have come into the mind of others." Four centuries, and we're still designing campaigns around the information deficit model: the assumption that people disagree because they don't have enough facts, and that providing those facts will bring them around.

It won’t. And McRaney shows why.

Why this book matters

If your job involves shaping opinion, building coalitions, or moving decision-makers from one position to another — this book challenges the operating assumption underneath most of what you do.

The assumption is that better information produces better outcomes. That if we can just get the right facts in front of the right people, presented clearly enough, they’ll come around. This the information deficit model, and the evidence against it is overwhelming.

Political scientists Donald Green and Alan Gerber reviewed more than 100 published studies on voter persuasion. Canvassing, TV ads, direct mail, phone banking. None of them produced lasting attitude change. Zero.

McRaney documents techniques that do work and explains the cognitive mechanisms behind them, giving practitioners something we can build on.

The core reframe: this is a post-trust crisis, not a post-truth crisis. That distinction matters enormously. If the problem is post-truth, you solve it with better facts. If the problem is post-trust, you solve it with better relationships and a fundamentally different posture toward the people you’re trying to reach.

What we cover in the episode

This is the latest episode of Masters in Public Affairs, where we go deep on the foundational books in public affairs and extract the mental models that hold up over time. In this episode, we cover:

The information deficit model and why it fails. McRaney traces the assumption that facts change minds through centuries of well-intentioned failure — from 19th-century rationalist philosophers to Benjamin Franklin to Timothy Leary to the modern “post-truth” panic. Each generation believed the next information technology would resolve disagreement. Each was wrong.

The Redlwask experiment. Subjects exposed to a moderate dose of negative information about their preferred political candidate became more supportive — not less. Below a certain threshold, counter-evidence strengthens the position you’re trying to change. Above it, people accommodate. There is no gentle middle path. Half-measures inoculate.

SURFPAD and the construction of reality. Why reasonable people looking at the same information reach opposite conclusions — and why neither side experiences themselves as having made a choice. This connects directly to Lippmann’s pseudo-environment from Episode 1, and McRaney gives us the neurological mechanism underneath it.

Tribal psychology and the cost of changing your mind. Brooke Harrington’s line — “Social death is more frightening than physical death” — explains why people cling to beliefs that outside observers find absurd. They're clinging to the group, and the belief is just the badge.

Three field-tested persuasion methods. Deep canvassing, street epistemology, and the Smart Politics method. Three independently developed techniques that converge on the same principle: questions over arguments, stories over facts, the other person's reasoning over your own.

Network percolation. How opinion change scales through populations, and why you don't need thought leaders or elites to start a cascade. The key variable is the susceptibility of the network.

Listen and subscribe here:

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• YouTube

• Apple Podcasts

• Substack

Bonus: Ideas from the book that didn’t make the episode

There’s more in How Minds Change than fits in a single episode. Here are a few ideas from my highlights that are worth knowing, even if I didn’t have time to develop them fully.

Read more

• Spotify

• YouTube

• Apple Podcasts

• Substack

I’ve been staring at my last three essays and feeling like I owe you an explanation.

One essay argues that teams freeze because they can’t see consequences. Another argues they commit too early because they stop pressure-testing. A third argues they miss risks entirely because no one feels safe naming them.

Read in sequence, you could reasonably ask: so which is it? Are we moving too slow, too fast, or not at all?

The honest answer: all three. Sometimes in the same organization. Sometimes in the same meeting.

I spent part of this week trying to reconcile this in my own head. Drawing on paper, rearranging boxes, feeling mildly annoyed that the ideas I'd been so confident about individually didn't snap together as cleanly as I wanted.

Slowly, I began to realize that these aren’t competing problems. They’re layers of the same architecture—and the reason they feel contradictory is that most of us think about decisions as a sequence. First you gather information, then you analyze, then you decide, then you act. Linear. Tidy. Wrong.

Real decisions move through layers. And healthy institutions cycle through those layers repeatedly, in both directions.

So I built a visual to clarify this for my own brain, and hopefully for yours too:

At the bottom: Silent Risk Surfacing

This is the work of expanding what's sayable. I’ve seen exceptional analysts imagine extreme scenarios all the time—and then not put them forward for the group’s consideration. Naming something outside the consensus window feels professionally reckless. So people protect their careers, organizations get blindsided, and everyone pretends the scenario was unforeseeable. It rarely was.

In the middle: The Predecision

The stutter step before a recommendation hardens. I've watched teams lock in a frame before the environment finished forming, then spend months defending a position that stopped making sense weeks earlier. The sunk cost isn't money. It's ego. The predecision is the discipline of keeping interpretation elastic long enough to actually test it.

At the surface: Ripple Effects

This is where consequences become visible before outcomes are known. Teams freeze when they can’t see what follows their choices. When you can make downstream effects explicit, the conversation shifts from “are we right?” to “what exposure are we willing to carry?”

The arrows in the visual run both directions because that’s how this actually works. An insight about consequences sends you back to question the frame. A surfaced risk reopens a decision everyone thought was settled. You don’t move through these layers once—you cycle through them until you’re ready to commit.

Where to go deeper

Each layer has its own essay:

• The scenarios no one will put in a deck explores why analysts self-censor and what AI changes about permission.

• The Predecision examines how teams lock in too early and a technique for keeping frames elastic.

• The missing layer in institutional decision-making diagnoses why smart teams freeze and what it takes to create visibility into consequences.

Together, they’re the first three entries in what I’m building toward: a capabilities index for institutions that need to make better decisions under conditions that don’t wait for certainty.

More to come.

“The only feeling that anyone can have about an event he does not experience is the feeling aroused by his mental image of that event.”

That sentence is the entire book.

Walter Lippmann’s Public Opinion, published in 1922, is built around one observation: People don’t respond to reality. They respond to pictures of reality in their heads.

This sounds obvious. It isn’t.

If you take it seriously, if you follow Lippmann through the implications, it changes how you think about communication, persuasion, democracy, and your own judgment.

The setup

Lippmann opens with the outbreak of World War I. He describes people going about their business in the days before war was declared. Making plans. Buying goods. Starting careers. All based on a picture of Europe that had already ceased to exist.

Then he gives you the end of the war. November 1918. The armistice is announced. People celebrate. The war is over.

Except the armistice hadn’t actually happened yet.

And in the five days between the celebrated armistice and the real one, several thousand men died on the battlefields.

That gap between the picture in people’s heads and the reality on the ground is where consequences live.

Why this book matters

Lippmann was one of the most influential journalists of the twentieth century. He advised presidents. He helped shape American foreign policy. And he had watched, from the inside, how governments shaped public opinion during wartime.

He was writing as someone who participated in constructing those pictures. He knew how the sausage was made.

What he produced is the closest thing we have to a diagnostic manual for how public opinion actually forms.

The mechanism he describes hasn’t changed. The technology has. Dramatically. But the underlying process is the same now as it was a century ago.

If you work in public affairs, communications, advocacy, or government relations, this is foundational. It belongs in the canon.

What we cover in this episode

This is the first full episode of Masters in Public Affairs. We cover a lot of ground in half an hour…

• Why Lippmann wrote this book and what he observed during World War I

• The core idea of pseudo-environments and why we respond to fictions as powerfully as realities

• The mechanism of how public opinion actually forms, step by step

• Three mental models you can extract and use immediately

• Common misunderstandings and why this isn’t a license to manipulate

• Modern application and what this means for practice today

• The mastery lesson of diagnosis before action

Listen and subscribe here:

• Spotify

• YouTube

• Apple Podcasts

• Substack

Bonus: what didn’t make the episode

There’s more in Public Opinion than fits in a single episode. Here are a few ideas from my highlights that are worth knowing, even if I didn’t have time to develop them fully.

Read more

Most people working in public affairs didn’t learn the job in a classroom.

I certainly didn’t. And most of the best practitioners I’ve learned from didn’t either.

I learned by doing — by watching campaigns succeed and fail, by sitting in rooms where decisions were being made with incomplete information and real consequences. I wrote things that didn’t work. I read research I misunderstood. I slowly realized that being busy was not the same as being effective.

It took years to notice a pattern that should have been obvious from the start: public affairs rewards people who take the fundamentals seriously. Not just early in their careers, but especially after they’ve been doing the work for a while.

And yet, as a profession, we’re surprisingly bad at teaching ourselves.

There is no shared curriculum. No agreed-upon canon. No common understanding of what everyone should master before they start improvising.

So most people improvise from day one.

They borrow tactics from the last campaign. They reuse the same playbook in different contexts. They confuse access with influence and activity with progress. When things don’t move, they work harder instead of stepping back.

I’ve done all of that myself.

One belief has become clearer to me over the years: public affairs is about winning public support.

Voters — not politicians — are the ultimate constraint in this work. Elections force clarity. Public opinion sets the boundaries of what’s possible. Campaigns, not arguments, are what actually move outcomes.

Once you understand that, a lot of bad public affairs suddenly makes sense. It also explains why so many practitioners struggle early in their careers. Understanding how public opinion works, how framing shapes perception, how coalitions form, how strategy differs from tactics — these aren’t advanced skills. They’re basic. Most of us are never taught them properly.

After more than 20 years in this field, I’ve found myself doing something that might look counterintuitive.

I’m going back to the basics.

Elite athletes are famous for this. Kobe Bryant spent hours on fundamental drills long after he’d mastered them because he understood that fundamentals decay if you stop training them. The greatest players are never too good for fundamentals. They return to them with more intention than everyone else.

The same is true in public affairs.

Writing clearly. Reading research without fooling yourself. Defining what winning actually looks like. Understanding framing. Designing campaigns instead of delivering outputs. These are skills that need deliberate practice, not one-time exposure.

And they’re skills I had to teach myself.

Masters in Public Affairs is an attempt to build the kind of learning I wish existed when I was starting out — and the kind I still want access to now.

Each episode focuses on one core skill in the craft and uses a book that belongs in a modern canon as a training tool. Not as theory. Not as commentary. As something you can actually learn from and apply.

Some of the books are old. Some are uncomfortable. Some aren’t “public affairs books” at all. That’s intentional. We’re sticking to what works.

If you’re early in your career, this is a way to accelerate learning that usually takes years of trial and error.

If you’ve been doing this work for a long time, this is an invitation to sharpen the fundamentals again — without apology.

A note on how this connects to my other work.

Power Shifts exists to explore how institutions adapt — or fail — under continuous political, regulatory, and structural change. It looks forward.

Masters in Public Affairs is the practical foundation underneath that work. It reinforces the basics that make good judgment possible in the first place.

Think of it as the curriculum inside the institution.

This project is for people who are serious about the craft.

People who want to understand why campaigns work — and why most don’t.

People who believe this profession can be learned, and relearned, if you’re willing to invest the time.

If that sounds like you, welcome.

The fundamentals are waiting. Pick your preferred training court:

• Spotify

• YouTube

• Apple Podcasts

• Substack

And the first episode is waiting for you:

We were driving to the physiotherapist. He’d strained his hamstring playing soccer—the kind of injury that happens when you’re competing hard, six days a week, at a level that doesn’t leave much room for rest. It came at the end of an intense week of trials, and I made the mistake of saying, in hindsight, he should have dialed things back before it happened.

His response: “Well, Dad, I didn’t know I was going to be injured until it happened.”

He’s right, of course. And he’s also describing the exact problem that keeps organizations flat-footed when the world shifts beneath them.

The Greenland test

We’re watching this play out in real time with Greenland this week.

Over the past year, financial markets have shown a striking tolerance for Donald Trump’s rhetoric. While tariff threats and diplomatic provocations have triggered brief bouts of volatility, they have rarely produced sustained market disruption. Equities have largely recovered, volatility has remained contained by historical standards, and many analysts have treated political shocks as episodic rather than systemic. Over time, that pattern has sent its own signal: much of this risk is seen as manageable, already priced in, and unlikely—on its own—to be the thing that breaks the system.

Then Trump briefly refused to rule out military force over Greenland, and something shifted. Gold went up. Markets went down. Suddenly, there was heightened recognition that the operating assumptions no longer held.

But here’s the thing: this wasn’t unimaginable. Someone, somewhere, almost certainly thought about it. Analysts who track US foreign policy, researchers who study Arctic geopolitics, strategists who game out territorial ambitions—at least a few of them probably entertained this scenario.

They just didn’t say it.

The consensus window

The conventional wisdom is that crises catch us off guard because we fail to imagine them. Black swans. Unknown unknowns. The vocabulary of surprise has become so familiar that it functions as an explanation in itself.

But I’m not sure imagination is actually the problem.

The real gap is between what’s thinkable and what’s sayable.

Consider what happens when an analyst prepares a risk assessment for a publicly traded company, an industry association, a government ministry. They build a dashboard. They map scenarios. They assign probabilities.

And then they filter.

Not consciously. Not maliciously. But instinctively. That’s because the professional identity of a public affairs analyst—of anyone whose value comes from expert political judgment—depends on being credibly right. Not imaginatively useful. Credibly right.

So the scenarios that make the cut are the ones that other serious professionals wouldn’t fault you for including. They fall within the consensus window: the range of possibilities that, even if they don’t happen, won’t make you look foolish for naming.

Everything outside that window gets quietly discarded because it would feel professionally embarrassing to name.

This is why risk dashboards fail in ways that matter most.

A dashboard can track sentiment. It can monitor media coverage, political temperature, market signals. It can aggregate data and surface trends. What it cannot do is capture scenarios that no analyst would put in a deck for fear of looking like they’ve lost the plot.

The Greenland scenario was thinkable. It just wasn’t sayable—not without risking your credibility as a serious professional.

And so it lived in what I’ve started calling the silent zone: the space between private imagination and professional speech where ideas go to die because no one will sponsor them.

The usual responses to this problem aren’t working.

Red teams help, but they’re expensive and infrequent. Scenario planning works, but it tends to produce five to ten carefully curated possibilities—each one selected, at least in part, because it won’t embarrass the person presenting it. Pre-mortems can surface risks, but participants often stay in familiar ruts. The creative leap required to name something absurd rarely survives the social pressure of a room full of colleagues.

All of these approaches share the same structural limitation: they depend on humans being willing to say out loud what they think might sound ridiculous.

That’s asking a lot. Maybe too much.

What AI changes

Here’s where I think AI changes the equation—but not in the way most people are talking about.

The obvious use case is having AI generate more creative scenarios. Feed it the parameters, let it imagine possibilities you haven’t considered. That’s useful, but it’s not new. Quant teams have run Monte Carlo simulations for decades. Brute-force computation isn’t the breakthrough.

The breakthrough is what AI does to permission.

When a human analyst names an extreme scenario, they own it. Their judgment is on the line. Their credibility is attached to the claim. If the scenario sounds absurd, they look absurd.

When an AI simulation surfaces the same scenario, no one owns it. The analyst can bring it to the table without putting their reputation at stake. “The simulation flagged this” is a fundamentally different sentence than “I think this might happen.”

AI provides institutional cover—a mechanism for extreme possibilities to enter the conversation without anyone having to personally vouch for them.

I’ve started calling this surfacing silent risks. Running thousands of AI-generated scenarios, then deliberately extracting the ones that fall outside the consensus window. Not because they’re more likely. Because they’re the ones no human analyst would feel safe naming on their own.

A friend of mine is a data analyst at an English Premier League club. He told me the club runs 10,000 simulations of a match before kickoff. Not ten. Not a hundred. Ten thousand.

There’s something instructive in that number. You’re not looking to find the most probable outcome as much as you’re mapping the full distribution of possibilities—including the tails that seem unlikely but matter enormously if they happen.

Now imagine applying that same logic to geopolitical risk, regulatory change, or reputational exposure. On the surface, it may seem like you’re trying to predict what will happen. In reality, you’re trying to surface what could happen that no one is willing to say.

The axis that matters is distance from consensus.

Pre-processing the shock

What does an organization actually get from this?

Not prediction. No system can reliably tell you that a US president will threaten military action over Arctic territory and NATO ally. But you can do something almost as valuable: pre-process the shock.

Think about what happens when an extreme scenario becomes real. Organization A used a silent-risk surfacing system six weeks earlier. A simulation flagged “US attempts territorial expansion through coercion.” The C-suite read it. They had a 15-minute conversation. They moved on.

Organization B didn’t. They’re hearing this for the first time with everyone else.

On Monday morning, Organization A can begin planning immediately. They don’t need to waste a day—or worse, a week—convincing themselves this is really happening. They’ve already done the cognitive work of accepting the scenario as possible. They skipped the disbelief stage.

Organization B is still stuck in that stage. Not analyzing. Not planning. Processing.

That gap—the time lost to shock—is where institutions lose power long before they lose control.

I realize this might sound like a small advantage. It’s not.

The organizations that respond fastest to rupture aren’t the ones that predicted it. They’re the ones that had already made peace with the possibility. They’d held the scenario in their minds long enough that when it arrived, it felt like recognition rather than revelation.

That’s what surfacing silent risks actually produces. Not foresight in the traditional sense. Something closer to pre-processed shock. You haven’t planned for the scenario. But you’ve imagined it, discussed it, sat with it. And that’s enough to collapse your response time when the absurd becomes real.

From unthinkable to unlikely

There’s one more thing worth saying.

Running an AI simulation that surfaces extreme scenarios doesn’t obligate you to plan for any of them. It doesn’t mean you have to take every possibility seriously. Many organizations will look at what gets surfaced and reasonably decide it’s too remote to warrant action.

That’s fine. The point isn’t to turn every silent risk into a strategic priority. The point is to move those risks from the private imaginations of individual analysts into the shared awareness of the organization.

Once it’s been named, it’s no longer unthinkable. It’s just unlikely.

And “unlikely” is a much better starting point than “we never saw it coming.”

Photo by Jonny Gios, Unsplash

It’s already being called a doctrine, at least in my LinkedIn and Substack feeds. And while it’s a hell of a speech, I’m not convinced it’s a doctrine yet. It is, however, the first step required before a doctrine can stick: an admission that the operating system Canada and other middle powers has relied on doesn’t work anymore, and that rebuilding it will require trade-offs we’ve dodged for generations.

And that’s why it landed. For me, at least.

Yes, it also landed because of the context of the last week. Mostly, it landed because it said out loud what heads of states and heads of government have spent the last year avoiding saying so clearly and so publicly—that the world we organized ourselves around is gone, and pretending otherwise has become risky, if not naive.

PM Carney could have played it soft. He could have hedged, noted the obvious that the world is changing, that power dynamics are evolving. No, he said it plainly, without sugar coating it: “we are in the midst of a rupture, not a transition.”

As a former speech writer, I’m a sucker for a good line. And that one line says it all. He shuts the door on the idea that we’re moving from one familiar phase to another. Transitions imply continuity. Ruptures don’t.

The inheritance money has dried up

For most of Canada’s modern history, power arrived quietly. Geography helped. Alliances helped more. The combination let us grow prosperous without constantly proving our strategic relevance or rebuilding our institutions from scratch.

That arrangement did more work than we acknowledged. It shaped how we funded defence, how we regulated our economy, how slow we let ourselves become.

Carney named the break cleanly: the assumption that geography and alliance membership automatically deliver prosperity and security no longer holds. Once you accept that argument, a lot of familiar arguments collapse. You can’t organize a country around inherited advantage once the inheritance stops paying out.

Why this speech feels different

Some will say it was inevitable. They’re half right. Recent events made silence impossible—economic coercion deployed openly, tariffs as strategic weapons, alliances turned conditional, even territorial sovereignty discussed as negotiable. Any prime minister would’ve had to address that, given the week’s events.

What makes this different is that it wasn’t dragged out under pressure. It felt planned, considered, almost restrained. That matters because reactive speeches can age badly—they answer the moment that forced them. Prepared speeches linger, especially when the diagnosis applies beyond your borders.

Whether this becomes doctrine depends on what follows, particularly in Europe. I don’t believe doctrine is declared. It only emerges when behaviour changes. We’re still far from that. We have, a very good speech.

The cost of “Variable Geometry”

The most ambitious idea here is also the most fragile: “variable geometry”—different coalitions for different problems, based on interests and values.

This is realism. It’s also uncomfortable. Flexible coalitions mean values won’t always align neatly. Compromise won’t be theoretical. Decisions that make strategic sense will sometimes sit poorly at home.

That tension hasn’t hit yet. It will when Canada deepens relationships that matter economically but challenge our moral self-image. Saying you’re prepared for that is one thing. Living through it is another.

This is a capability speech

And living it out domestically won’t be easy either. We have some serious capacity-building to do at home, not matter how positive the case the PM made in his speech about how Canada has pivoted in the last year.

A country that can’t rely on automatic guarantees has to rebuild power the old way: through capacity, competitiveness, and speed. The kind of stuff we excelled at during, and after the two World Wars. A military sized for reality, not reassurance. An economy that attracts capital because it’s decisive, not merely stable. Institutions that move faster, regulate with intent, block less by default.

This is the unglamorous work that follows an honest diagnosis. It’s also the work most governments delay because the costs are immediate and the benefits take time. Yes, Carney’s government has taken many important steps in this direction. But given the scope of the problem, he’s barely hit the 1km mark in an Ultramarathon race in the desert.

After the Rules

The rules-based international order depended on enforcement. That enforcement came from the United States. Remove it, and rules become optional.

Even if Canada, Europe, and much of the Global South agree on new standards, the major powers won’t bind themselves to them. That doesn’t mean chaos—it means leverage matters more than legitimacy, and capacity matters more than consensus.

Carney didn’t offer comfort on this. He didn’t try to revive the old language. He acknowledged the environment as it is.

That’s what made the speech consequential. It plainly accepted that the old one no longer applies—and that rebuilding will be harder than preserving ever was.

We aligned on a two-part strategy. Part one: establish the frame early. Part two: deliver the proof that makes the frame credible.

The room loved it. Especially part one.

The group was so confident in the frame that when we pressure-tested it in the scenario drill—forcing them to imagine an effective counter-campaign—they got enthusiastic. Not defensive. Not worried. Enthusiastic. They saw the attack coming and believed it would only strengthen their position.

I pushed back. I explained why the frame couldn’t hold without the proof. They found a way to make it make sense. And honestly? In that room, I found their confidence convincing too.

Two weeks later, they moved forward with part one.

The proof never followed.

At first, I assumed it was a timing issue. That part two would come. But within days, it became clear: they’d doubled down on the frame without building the foundation underneath it.

By the time I realized the file had hardened, it was too late to change course. The strategy would only work as a complete system. Implemented partially, it created exposure we’d explicitly planned to avoid.

Within weeks, the surprise scenario we’d gamed out happened. Not because we failed to see it coming. We’d literally written it down and walked through it weeks earlier.

We got surprised because the client—and if I’m honest, I—had stopped treating the frame as provisional the moment it felt compelling.

The question everyone asks after is always the same: “Why didn’t we see this coming?”

But we did see it. We saw it in a conference room months before it mattered.

We just committed to a view before the environment finished forming.

When having a view becomes a liability

That's the pattern I keep seeing.

Last week I wrote about teams that freeze—smart people with good analysis who can’t move because they lack visibility into consequences.

This week is about the opposite problem: teams that move too fast, locking in a view before power finishes shifting.

Both failures erode influence. One through paralysis, the other through premature commitment.

Most legacy public affairs systems surface commitments. Where decision-makers sit. Who is aligned. Who might mobilize. What polling or sentiment suggests about public reaction.

This material creates comfort. The deck gets longer. The stakeholder map fills in. Someone says, “Okay, we know where everyone stands.” The file looks controlled.

The work shifts from pressure-testing interpretations to defending a position. The question moves from “what could break this?” to “how do we proceed?” And once our minds turn to that question, alternatives carry less weight. The person who had doubts stops voicing them. Or leaves them in Slack instead of bringing them to the meeting.

And so, even when the decision hasn’t been announced, or approved, it has hardened.

That hardening has less to do with confidence in the analysis and more to do with anxiety about indecision. Unresolved disagreement feels risky. A stable recommendation feels safer, especially when senior leaders want clarity and momentum.

Why more analysis doesn’t change the outcome

So teams add more structure.

More dashboards. More polling. More stakeholder mapping. More scenario work.

Scenario planning gets treated as the corrective. Multiple futures get explored. Risks are documented. Contingencies are discussed.

The exercise ends the same way. One scenario becomes dominant. A most-likely path emerges. A recommendation is selected. Alignment follows.

The futures may differ, but the lens used to evaluate them converges early. Once that happens, the scenarios start to feel manageable. "We know what to do in each case." But the uncertainty hasn't actually shrunk—just the willingness to acknowledge it.

I say this as a massive proponent of scenario planning. It’s one of the best ways to get teams to put themselves in someone else’s shoes. For exploring possible outcomes, it’s excellent. But even in the most effective sessions, the frame used to judge those outcomes stays largely intact.

The risk teams are actually managing

So when teams lock a decision in early, what are they actually avoiding?

The perception of indecision.

Indecision reads as drift. It invites scrutiny. It creates discomfort in rooms where the CEO asks, “So where are we on this?” and wants a clean answer. Holding competing interpretations open feels harder to justify than presenting a coherent plan.

So we end up in a situation where decisions stabilize in an environment that does not.

When the realities of that environment stop fitting the frame, changing course feels expensive. Someone has to go back to the CEO and explain why last month’s confident recommendation no longer holds. Surprises arrive.

It’s far from perfect, but I’ve developed a technique for dealing with this problem.

When a client agrees with our strategy too quickly—when I can feel the frame settling before it’s been genuinely tested—I force a collision.

I set it up with just enough warning to make what comes next land harder: “I’m going to openly disagree with my colleagues here, and even with myself, because you should see the full range of what we’re wrestling with.”

Then I make the best possible case against our own recommendation.

Not a token devil’s advocate performance. A real argument, delivered with conviction. The kind that makes my colleagues shift in their seats because they’re not sure if I’ve actually changed my mind.

Sometimes the client looks confused. Sometimes my own team does. But what it demonstrates is honesty. And thoroughness. And the fact that we haven’t drunk our own Kool-Aid.

Here’s why it works: if our frame is going to lose to a counter-argument, I would rather have it happen in this room, at this stage, than in a regulatory hearing or a media cycle six weeks from now.

The technique doesn’t eliminate premature agreement. But it does surface whether the client’s confidence comes from genuine conviction or from relief that someone has given them a clear answer.

When the frame holds up under the collision, the decision that follows feels earned. When it doesn’t—when the counter-argument exposes a weakness everyone suddenly recognizes—the file stays open long enough to fix it.

I absolutely hate hearing this in a post-mortem: "The signals were there. The data existed. The outcome made sense once it happened."

So can we develop a radar system for detecting power shifts before it's too late?

Power sensing as a decision discipline

Power sensing isn’t the same as awareness—knowing who matters, tracking what’s shifting, staying ahead of risk.

That framing treats power sensing as information gathering. The operational problem is different: it’s not whether you see the shift, it’s whether you can still act on it.

Power sensing shows up in how long teams keep decisions elastic.

It shows up in whether disagreement stays visible after a recommendation begins to feel ready. It shows up in whether early confidence is treated as confirmation or as a prompt for further scrutiny.

Teams with this capability intervene when decisions start to settle. They don’t rush to reopen debates later, when costs are higher and positions are entrenched.

That requires a mechanism that works before alignment sets in.

The predecision

The predecision is the part of the work most of us skip because it’s uncomfortable.

It starts at the moment when a recommendation feels ready.

Not announced.

Not approved.

Just ready enough that people stop pushing on it.

In the predecision, that feeling becomes the signal to slow down for a quick stutter step.

This is where the work changes shape. The goal is no longer to refine the recommendation or prepare to engage in next steps. The goal is to examine how the recommendation is being justified.

In practice, this means pausing the question everyone expects next—“So what do we do?”—and asking a different set of questions instead.

• What frame are we using to interpret this situation?

• What has to be true for this recommendation to work?

• Which explanations did we discard quickly, and why?

• What would make this plan collapse?

These are not rhetorical questions. They are asked explicitly, in the room, with the expectation that answers will be incomplete and uncomfortable.

I’ve seen this done well only a handful of times.

Two close friends of mine, Farhaan and Oren, do it instinctively. Any time I bring them a decision that feels settled, they refuse to talk about execution. They won’t help me optimize the plan. They won’t debate tactics.

They force me back into the frame.

They ask what assumptions I’m leaning on without realizing it. They ask which alternative explanations I stopped taking seriously once the story started to make sense. They ask me to argue the position I least want to be true, and then stay there longer than feels reasonable.

The experience is frustrating. It feels like regression. It feels like indecision.

What it actually does is surface the weak points while the decision is still elastic.

In organizational settings, this work often looks messy. People talk past each other. Confidence fluctuates. The room feels less aligned than it did twenty minutes earlier.

That’s the point.

The predecision keeps incompatible interpretations alive long enough to test the frame itself, not just the plan built on top of it. It creates space for disagreement before disagreement becomes politically expensive.

Once a team moves past this moment—once the recommendation becomes the thing everyone is preparing to defend—the opportunity closes. From that point on, pressure-testing feels like obstruction. Changing course feels like failure.

The predecision exists to prevent that lock-in.

It does not slow action.

It changes when commitment happens.

As I look back on some of the most effective teams I’ve worked with, they practice this consistently and still don’t eliminate surprises. And that’s counter-intuitively, the point. Instead, they recognize surprises earlier. And then the decisions that flow feel deliberate. Escalation feels proportional. And when things break, they break closer to the surface.

Which all increases the odds that the post-mortem won’t include that dreadful question, “Why didn’t we see this coming?”

Not because the future became clearer, but because the decision stayed open to challenge long enough for power to finish moving.

Where this sits in the capabilities index

I’ve started building a capabilities index for teams that need to make better decisions, quickly. It’s a work in progress, but I intend to use these as the basis for a new category of operating systems.

In that list of capabilities, I’ve identified Power sensing: Seeing where authority is moving before it becomes obvious.

Legacy systems track commitments and reward early decision lock-in. Power sensing depends on resisting that pull.

The predecision enables this capability by keeping frames under pressure before commitment. It treats disagreement as a signal, not a phase to resolve.

And as we explored last week, ripple effects make consequences visible so teams can act without false certainty.

The predecision keeps frames elastic so teams don’t commit before power finishes moving.

Together, they address both failure modes: never locking in, and locking in too early.

Photo by Sufyan on Unsplash

Tell me if this feels familiar:

A risk memo lands on Tuesday. The analysis is serious—your team has put in exceptional work, which means the implications are uncomfortable. You get the senior leadership team to clear time. By Thursday, you’re in the conference room with the CFO checking her phone, the General Counsel leaning back with crossed arms, and your CEO asking questions that feel less like curiosity and more like cross-examination.

The discussion stretches. It spills into email threads. Someone asks for a follow-up deck. Language gets tightened. Hedges get added. The PowerPoint circulates with track changes until the formatting breaks.

And still, no one decides anything.

Here’s why: no one knows what to do with the analysis, even when it’s brilliant. Because the “so what do we do now?”question rarely has an obvious answer.

I’ve watched this pattern repeat across public affairs, corporate strategy, and governance. We have monitoring platforms that catch signals earlier than ever. We’re drowning in data. Analysis arrives faster, sharper, more sophisticated.

And yet decisions stall in the space between awareness and action. That’s where influence erodes.

Where decisions actually break down

The post-mortems always sound reasonable:

“The data was incomplete.”

“The recommendation lacked clarity.”

“Sarah hesitated because Legal wasn’t aligned.”

“We needed more validation from the field.”

These explanations miss something more basic: even well-informed teams freeze. Smart people, good data, clear stakes—and still, nothing moves.

What actually happens is more specific.

Teams re-litigate the analysis. They stress-test assumptions. They ask for additional validation. Someone questions the source methodology. The discussion circles endlessly around: “But is that right?”

That question feels responsible. It protects your credibility. It delays exposure. Most importantly, it buys time.

Which means it also delays the decision that actually matters.

Because here’s the truth: we’re not really afraid of being wrong. We’re afraid of being wrong in a way that weakens our future influence.

If we act on a signal that later proves overstated, that mistake has legs. Next time we walk into the room with an urgent brief, someone will remember. “Remember when they said we had to act immediately on that regulatory thing? And then nothing happened?”

Your advice gets discounted. Your access narrows. The warning bells you ring start sounding like background noise.

Paralysis isn’t a failure of judgment. It’s a rational response to an environment where credibility compounds slowly and collapses in a single blown call.

Better to keep analyzing. You can’t get it wrong if you never commit.

Certainty has become a proxy for safety

Watch what happens in these meetings. Certainty is doing work it was never designed to do.

Truth-testing becomes a stand-in for decision safety. Accuracy becomes a substitute for defensibility. The entire discussion—hours of senior leadership time—gets absorbed into whether the analysis is “right” instead of what follows if it’s directionally correct.

This is where the process collapses. Teams lack a shared way to judge impact independently of certainty. Without that separation, every decision feels final and irreversible. Waiting starts to look prudent, even when it carries its own cost.

Meanwhile, the signal doesn’t disappear. It joins a growing backlog of unresolved alerts, each one quietly weakening the institution’s ability to move when movement matters.

I’ve seen organizations with twenty-seven open risk items, all colour-coded, all “under monitoring,” none with a clear path to resolution.

The question leaders actually need answered

Here’s what’s missing from these rooms: leaders don’t need absolute confidence before acting.

They need visibility into consequences.

What happens next if we move? What follows after that? Where can we adjust as reality unfolds? How do the options branch? Where does exposure accumulate? Which paths keep flexibility alive?

Without that view, decisions collapse into false binaries framed too early: Act now or wait. Go big or go home. Commit fully or stay silent.

These binaries invite debate. They also invite caution. And delay. And eventually, irrelevance.

Ripple Effects

What’s missing is a layer between alerts and action. A way to see what follows a decision before committing to it.

I’m calling that layer Ripple Effects.

It’s a shared way to see possible futures clearly enough that teams can act before certainty arrives—without losing credibility when reality shifts.

Let me be clear: this is not a prediction engine. It doesn’t forecast outcomes with false precision. It surfaces consequences. It makes downstream effects explicit.

What does second-order impact look like here? Where do decisions stay reversible, and where do they harden into commitments we can’t undo? What signals would trigger escalation? What would tell us to adjust course or exit entirely?

When this layer exists, discussions change shape.

The debate stops being about whether the analysis is true. It becomes about what exposure the institution is willing to carry. The conversation becomes evaluative instead of defensive.

And credibility shifts. It no longer rests on being right in advance. It rests on being explicit about implications—and honest when they evolve.

What changes when consequences are visible

I’ve seen institutions that can see ripple effects. They move differently. Often before certainty arrives.

They escalate earlier without overcommitting. When they brief up, they don’t just present the risk—they map the decision tree. “If we do X and Y happens, here’s where we adjust. If Z happens instead, here’s the exit.”

When assumptions change, the decision logic remains legible. No one’s scrambling to explain why last month’s memo contradicted this month’s stance.

Risk memos stop being verdicts. They become structured inputs into evaluating paths forward. Monitoring stops being a passive holding pattern—it becomes an active posture with clear triggers.

Most importantly, influence stabilizes. Leaders trust teams that can explain why they acted, even when outcomes diverge from expectations. The right to be believed stops depending on perfect foresight.

Why institutions keep missing this layer

Legacy public affairs and corporate strategy models were built for slower environments. Signals arrived late. Feedback loops were long. Decisions could wait for confirmation without significant cost.

That world is gone.

Signals now arrive earlier, faster, and in greater volume. Waiting for certainty increasingly means waiting past the point where options remain open. Institutions that cannot move between signal and action lose relevance even when their analysis is sound.

This failure stems from the absence of a decision layer designed for uncertainty.

A capability, not a trait

We often describe the ability to act without certainty as courage, or instinct, or “leadership.”

In practice, it’s infrastructural.

Institutions that hold influence under pressure don’t have braver executives. They have systems that make consequences visible before outcomes are known. They’ve built shared language for provisional action. They preserve credibility by making decision logic explicit and reviewable.

Ripple Effects names that missing layer.

In environments where certainty arrives late, institutional performance depends on how clearly leaders can see what follows their choices, not how confidently they defend their assumptions.

Photo by Logan Voss on Unsplash

I spent the weekend celebrating 19 years of marriage with my amazing wife doing the type of thing we fell in love doing: spending as much time in the mountains as we could.

When we were younger, that time was spent rock climbing. Three kids later, we’re not climbing as much as we’d like, so hiking fills the void until they leave the nest. We’ll return to the cliff/crag when our weekends aren’t spent driving them to countless soccer games and martial arts competitions.

But the weekend gave me, for the first time in a long time, a chance to reflect.

Of late, I’ve had a lot on the go. Too many projects. Too many commitments. Work. This newsletter. A podcast. Managing and playing in a football team. Coaching football. Writing a book. And much, much, more.

My brain has been, in hindsight, incredibly scattered as a result.

So, I need to trim back, focus on the core stuff I do, and see where we end up by year’s end. This project is one of the things I’m cutting back. The content creation game is a real treadmill. Especially in a very niche industry like ours. My audience growth has been limited, and spending time writing to a small audience is really an exercise in ego. And as Substack has evolved, it's become clear what I need to do to grow this newsletter: crank out 3-5 content items per day to grow. Even if I leaned into AI, this is a massive cognitive distraction. I’m choosing to hop off the treadmill, at least for now.

That doesn’t mean I’m going dark. You will still find me working on the Craft Politics podcast with my friends Percy and Holly. And while I’ll be reducing the volume of content I’m producing on LinkedIn, I’ll still post the occasional thought/observation there, and cross-post those thoughts on Substack Notes.

Otherwise, I’ll take stock of my commitments in late December.

Cheers,

Joseph