A caveat up front: The Value Web has always been a community effort, and these are just my personal reflections as we reach a new part of the journey.

I was proud to be a part of the Value Web when it was a scrappy, international collective of practitioners that had come together to apply their talents to tackling some of the world's most intractable problems. I was a part of the board when we changed the mission statement to "transforming decision-making for the common good". It felt right. It felt big. It felt like it mattered.

But things have changed. And if you haven't noticed, decision-making doesn't seem to be doing too well right now.

What I have come to believe, however, is that we have, among us, the tools that we need to make a fundamental shift in how we approach the problems of our times.

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Where We Came From

This all started out - at least for me - as a corporate thing. Capgemini Consulting, by way of Ernst and Young, had acquired a methodology from a small, obscure and boutique group called MGTaylor for a facilitated process of group decision-making.

I was, perhaps, too inexperienced to fully understand why we were doing what we were doing when I started...but I knew something felt right about it. In the crudest sense, as I understood it then, what we were doing was creating and facilitating a process by which a group could openly and collectively evaluate the possibilities of potential future strategies and collaboratively find a path forward together.

Using a clear method for process design along with a set of design principles and concepts for the creation of physical environments to foster collaboration, the method achieved significant scale across the world and spawned a global community of practitioners dedicated to supporting collective decision-making. I'm abbreviating rather substantially in this history, of course.

When I first got involved, what stood out for me was the openness and democratic nature of the process. I thrived on that. From the moment I entered the workforce, I wondered why good ideas mattered less than hierarchy; encountering a method for allowing the best ideas from a group to emerge - no matter who they were from - was a breath of fresh air to me.

I didn't really understand the real meaning of what we were doing until years later. It was enough for me that the work we were doing seemed to give meaning to groups within various companies that were tackling their own challenges. They seemed to gain inspiration and energy simply by being engaged in conversation about how to approach the challenges facing their companies.

And Then, The Value Web

Over time, I came to genuinely appreciate the work we were doing. Participants in our processes became deeply engaged in the difficult work of navigating what were - oftentimes - existential threats to the organizations they were working for. I learned that by designing a collective process around how people think, which is often messy and non-linear, groups would build ownership and intent around their work that I hadn't seen otherwise in the working world. When they were meaningfully engaged, they leaned in.

And there was method around all this. We would often say "trust the process"...and we would mean it, because the "process" would reliably produce results.

When I came across The Value Web, they were doing something that I found very interesting: they were applying the methods we had used in our consulting context for collective decision-making in non-corporate settings, most interestingly in settings where nobody in the group was from the same organization.

I can't stress how important this factor was in the evolution of our thinking and our work.

Collective decision-making and co-creation is comparatively easy when everyone in the process is obligated to be there, has the same interests in the outcomes, and might be fired if they don't meaningfully contribute. It's participatory, but with consequences. And the smart participants know what game is being played.

Collective decision-making when everyone is from a different organization, when they do not share accountabilities and when ownership of the outcomes is unclear, is a different animal entirely. This was the arena I found the Value Web playing in. Diverse groups from multiple sectors and segments of society trying to figure out solutions to intractable problems. Together.

What became abundantly clear was that there was a real gap in how to balance the inclusiveness required to involve all the necessary stakeholders with the decisiveness required to move things forward. And what we were doing appeared to be working.

What We Did and What We Learned

For years, we operated as a collective. Awkwardly. Somehow, we, as a group, found organizations to work with that needed support, and we created beautiful, immersive and transformative experiences for leaders facing critical challenges.

We worked with the World Economic Forum to reimagine its gatherings. We worked with UN agencies to find points of collaboration between agencies to tackle complex challenges. We tackled projects on climate, nature, public health, resource scarcity while also learning the fundamental principles of community design and coalition-building.

More than anything, I think that what we learned was that while having methods to productively and decisively engage individuals in big decisions was useful in large organizations, it was fundamental to making progress in settings where the stakeholders weren't beholden to the same "boss" but, nonetheless, had common stakes in a problem that none of them owned individually, but all of them were responsible for collectively.

Over time, we reflected on what we were doing and realized that in our efforts, there was something bigger at play.

Shared Intent and Collective Intelligence

It turns out that having a global community of people obsessing over how decisions get made results in some fairly significant insights. Over hundreds of projects, there was a very real validation that all of the factors surrounding HOW decisions get made are as significant as the decisions themselves.

By focusing on the human experience of collaboration, the emotional journey, the heuristics and shortcuts of human cognition and the labor of human trust and connection - all of which were considered unprofessional, irrelevant externalities in traditional decision-making methods - we were able to create deep and stable transformations of the groups we worked with.

We came to see that work in these systemic contexts focused around three design challenges - distributed intelligence, individual action and personal intent.

It was similar to the work we did in the corporate context, but exploded to a scale that required us to extend the tools and models we used.

At its heart, though, the problems were deeply human. Intent was everything. With loose ties, individual intent that became shared intent was the most potent element in driving change. And collective intelligence simply meant that with the size of the challenges, no individual fully understood every part of the problem, so a meaningful process to allow members of a group to come to shared knowledge based on collective input was the most reliable way to ensure that decisions were based on the best information and could account for the many potential consequences.

Through it all, we validated that a model-driven design process helped create structure in otherwise confusing and unruly circumstances, because the common denominator, regardless of industry or domain, was the human condition.

Reaching the Limits of a Model

To achieve all this, The Value Web walked an organizational tightrope for many years. As a "collective", it was made up of a group of more than 30 practitioners delivering work together under a common name. Most of those members either operated another company or worked somewhere else, and the common brand was used as a neutral space to collaborate on projects for the common good.

It always felt somewhat temporary and incomplete. It had enough structure that we could work together, but each time we attempted to formalize it, the changes risked upsetting the balance that allowed a group of people who might otherwise be competitors to work together. The energy - our shared intent - was always in delivering meaningful work together to try and make a difference, and that energy was always tested when we tried to evolve the structure.

We had validated that well designed, effectively supported decision-making processes could make a difference at the very highest levels, and with the most difficult and complex problems, but we had done so using a structure held together with chewing gum and duct tape.

And then we noticed a set of challenges emerging that caused us to re-evaluate the path forward.

First, the rise of Design Thinking muddied any kind of comprehensive understanding of deeper methods. The runaway popularity of the set of techniques around Design Thinking made it more difficult to articulate the important nuance of designing thinking. And our community was small, obscure, and not widely known.

Second, our extended community of practice was getting older. Although practitioners of our craft had, collectively and individually, achieved a remarkable degree of success, the obscurity that had always given it an edge now acted as an impediment to a generation of people who didn't even know these practices existed. Extinction didn't seem out of the realm of possibility.

Thirdly, our own practice could not evolve if it was not clearly defined enough to enter into conversation with other practices. There was no clear frame of reference outside of our community for what on earth we were doing.

Finally, and most importantly, there was no conceivable way that we could achieve our mission of transforming decision-making for the common good simply through scaling our service delivery. We could never grow enough or deliver enough projects to meaningfully change the broken processes that were steering our species off a cliff.

So the question became, given all that we've learned, how could we, as a community, change societal expectations of what constitutes "due process" in how critical decisions are made?

Ending the Old to Create the New

Svenja Ruger, Tanja Kerlo and myself took on the project of imagining what that might look like. First, the organization could no longer deliver projects itself, so that it would not be in conflict or competing with its own members. This was the most emotionally difficult part of the process, as we all had a connection to this dysfunctional but beautiful way of working together.

We came up with a minimal structure that would allow us to conduct research, establish the boundaries of a profession, and provide a learning path for new practitioners and to advocate for leading methods in complex decision-making. We call it Process Activism.

The most important element, however, was that it would take the time, intelligence and talents of our entire existing community of practice to come together and bring this into the world. With thoughtful practitioners spread all across the world choosing to move this forward together, it might just stand a chance of really changing things for the better. With this community as a start, and with an intent to learn with other communities of practice, it would be conceivable that collectively we could shift towards more inclusive, informed and robust collaborative decision-making.

We believe that we need to build community around key challenges, so there is a shared understanding among stakeholders not around what a given decision should be, but how we should be approaching those decisions.

One week ago, we made our case to 100 members of our community that had gathered for The Happening. We relinquished control of the organization to allow its rebirth as a community project.

What I witnessed was a group of people stand up and answer the challenge. What I felt in that room was the emergence of shared intent, and a genuine desire to build something together to ensure that we, as a species, can work towards a time when we can say that all decisions that affect our future were made in the best interests of all, and represented the very best of our knowledge and abilities.

Matt and Gail Taylor gave an incredible gift to this community when they assembled and nurtured this method. This community has now expressed the desire to honour what we’ve learned by extending it, sharing it and allowing something new to grow.

I am very excited to work together with this amazing community to create a future where we can not only say our decisions reflect our collective intelligence, but that, together, we have nurtured the ability to act with collective wisdom.

You should join us.

Context

In the midst of all that is going on in the world, Canada just had a federal election. Normally, Canadian politics isn’t something that gets the blood racing, but these are not normal times, and this was not a normal election.

In the course of my work, I find I am more routinely keeping an eye on the flow of global politics, but over the last couple of years I have started to focus a lot more on the threats facing Western democracies, both from the corrosive effects of digital platforms but also with the increasing intensity and impact of influence campaigns waged by autocratic states. I was getting increasingly worried that the underpinnings of our democracies were crumbling, and we seemed ill equipped to counter the challenges we were facing, and as a result, I tried to focus my work, where I could, on some areas that could contribute to our collective defence.

All the while, the Canadian government was staggering along, with a beleaguered administration that just never quite got its stride again after the pandemic. We have a politician here leading the opposition who had made denigrating Canada into a full time, 2 year project, baiting the increasingly unpopular Prime Minister and drilling into Canadians that our country was broken, despite our better-than-the-global-average recovery from the pandemic, global supply shocks and inflation. A lot of Canadians came to believe him, and even among those of us who didn’t, there was little enthusiasm to support a Prime Minister that seemed to be holding on long past his due date.

And the polls were grim. Support for the Liberal government was at historic lows, and while they held on in Parliament with the support of a coalition party, survey after survey showed that the next election would be an extinction-level event for the ruling party, with a crushing majority for the Conservatives forecast whenever the writ might drop.

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Honestly, for my part, while I found the Conservative remedies completely unconvincing, I was becoming somewhat resigned to the fact that they would take power, and I felt that, perhaps, it would be best for them to have a term in power if only to prove that they couldn’t just wave a wand to make the world’s problems disappear. I started to think it might be worth them winning just to prove to Canadians that the simple platitudes they were offering to fix our “broken” country had no substance to them, so we could move on.

But then, a few things happened. Trump got elected. The joke about making Canada the 51st state stopped being a joke. Vance travelled to Europe to tell the German military it should be okay with fascism. The US started slapping tariffs on Canada. And then the Liberal government experienced a rapid, unscheduled disassembly.

Suddenly the copy/paste of Trump’s talking points into the Canadian Conservative leader’s speeches felt like less like a sign of admiration, and more like a Manchurian candidate.

I decided on the night that J.D. Vance spoke at the Munich Security Conference that I wanted to be involved in some small way to contribute. The challenge was that, as a resident of downtown Toronto, any campaign door-knocking I could do would be preaching to the converted. I was aware, however, that the misinformation war was unfolding online.

The Plan

My partner, Beth - who has worked extensively in social media strategy - convinced me that if I wanted to reach a lot of people in a short amount of time, the only choice was to try TikTok.

Beth pointed me towards several accounts that had begun focusing on political content as the election drew near and had grown rapidly and achieved significant reach in very little time, which suggested it would provide considerably more reach than knocking on the doors of my liberal neighbours.

To say I was skeptical would be an understatement. In my view, TikTok was a democratic destabilization machine controlled by the Chinese state, which didn’t really make it a great candidate.

The alternatives, however, were not great.

The Platforms

The social media landscape in 2025 is a dumpster fire. No matter what reason you have for using social media these days, you are probably unsatisfied with the experience, and are most likely generally worse off for using it, whether that be for personal reasons or professional.

I had been experimenting with Substack for some time, and putting an ungodly amount of effort into researching, writing and recording posts. While the platform benefits from having great tools for writers and isn’t burdened (yet) with advertisements, I found myself topping out at around 130 subscribers, with every new subscriber a hard fought battle. It seemed to me that it was a great place to bring an audience, but not a platform where you could easily build an audience.

I decided early on that I would try to use my professional network on LinkedIn to try and direct people over to Substack, by posting about my new articles there. It was only then that I realized how hollowed out LinkedIn had become. Firstly, because LinkedIn tries to encourage a posting frequency in their algorithm that is unsustainable for thoughtful production, it has become a hellscape of self-serving humble bragging, with only the rare post rewarding the reader with any actual insight or value. The worst part is that we all seem to know it. Professionally, we know we should at least appear to engage, so there are a smattering of likes and performative comments, and nothing more. The engagement on my posts linking to my articles was shockingly low, with numbers that were only a fraction of the number of connections I had. But worse than that, the “click-through” rate was so low that at first I thought Substack’s analytics were lying to me. On a LinkedIn post with a decent number of likes and even a few comments on the topic, Substack’s analytics would show that almost no one had actually clicked the link.

Instagram doesn’t even seem to know what it is as a platform any more. In response to TikTok’s onslaught of content from people you don’t know, Instagram threw away what was previously its insurmountable competitive advantage: its social graph. Combined with aggressive efforts at monetization by Meta, it is simply a platform for scrolling through advertisements, interspersed with cross-posted TikTok videos from people you don’t know, with bubbles at the top where the few friends you have active on the platform post coffee pictures and conspiracy theories.

Twitter, which now has a name you can’t start a sentence with, is only useful for finding out what its owner is doing, and what other white supremacists think about what he’s doing, is totally unfit for any sustained effort, besides being harmful for your mental wellbeing.

I stopped using Facebook during the pandemic, when it proved to be ground zero for radicalizing its users and turning them against vaccines and democracy. After being attacked mercilessly by a mob after I suggested to an acquaintance that vaccines didn’t cause autism or allow Bill Gates to track us, I decided I was done on that platform. Zuckerberg’s decision to end fact-checking and ban actual news sources from the platform sealed the deal for me.

I had never really used TikTok, mostly because the combination of an addictive algorithm, its ability to “understand” you at a deep level and its connections with the Chinese state had always been incredibly problematic for me, but also, simply, that I didn’t believe it to be a platform where any serious content could exist.

Also, in the context of Trump’s announcement that annexation of my country was on the table, it was worth considering who owned each of the platforms and what their agendas were. All of the platforms were owned by adversarial governments. The CEOs of Meta, X and TikTok all attended Trump’s inauguration, and given Trump’s fixation with Canada and China’s ongoing feud with the Liberal government and documented attempts to interfere in Canadian democracy, one had to assume that there could be interference in political discourse on all of these platforms.

Finally, while I had friends and family on Instagram and Facebook (100-200 connections), an old Twitter account with about 150 followers and a LinkedIn network of around 1500 connections, I had exactly zero followers on TikTok, as I would be setting up an account for the first time.

But, I was determined. No matter how small the contribution to the discourse, I wanted to do something, even if it was only correcting some misinformation, to help in the election.

Oh, and one other note; I was, and am, fully aware that maybe the problem with engagement on the things I’ve written wasn’t an algorithmic problem or a platform problem. It was also possible that I’m just boring.

Playing to the Algorithm

Beth laid out a simple formula for me. She was adamant that if I followed the formula, I would see results and the algorithm would respond, but if I didn’t, and I deviated, or slacked off, the algorithm would be merciless.

She also suggested that because there was so much attention on the election, that the time was now: if I harnessed a national conversation in the moment, the impact would be multiplied.

The formula was simple: you need to post three videos per day, every day, connecting with issues and topics as they arise. You need to find the hashtags for your topics, and respond to every early comment on your posts as they come in, while posting comments on the posts of a few, related, creators around the same time that you post your own videos.

While this was obviously a difficult pace to maintain, I was determined to give it a shot.

Given my experiences on other platforms, my expectations of a new platform with zero followers were pretty low. My very first post, however, got over 300 views almost immediately. Coming from LinkedIn, I thought that was pretty good. My second post, later that day, got 2,500. I was flabbergasted. I had only gained 1 follower from the first post, so that caught me off guard. The numbers seemed to fluctuate up and down, but a few more posts in and I had another hit 3,300. Even getting view counts in the hundreds seemed pretty good to me. Here on Substack, where a single post takes me hours of effort, getting above 200 views is a good day for me. Things seemed to be going pretty well, and with every new post, I would pick up a few new followers, something that on this platform I was finding pretty difficult to do.

What shocked me from my early interactions on the platform was the level of engagement from the people that use it. The dynamic was fundamentally different from anything I’d seen on other platforms. Beth would roll her eyes, because, of course, she’d been telling me this for a very long time. But not only were people viewing political content, they were debating it, sharing, and shaping it. The comments would fill up and users would go back and forth on the issues. And these weren’t the usual LinkedIn comments of “Totally agree: ask me how I can help with this using my special formula!” but actual, genuine comments and engagement. I hated that this platform was where the “town square” had moved, but I couldn’t deny that this seemed to be where Canadians were coming to find out what was going on and to discuss it. Love it or hate it, this was where it seemed to be happening.

That’s when I came across Common Sense Carl and some of the conspiracy content swirling around the platform.

The Misinformation problem

What became apparent from the comments was that while this election, for some, was about trying to find a candidate that was best suited to guide Canada in a time of geopolitical and economic turmoil, for many others, it was about something completely different.

To summarize, there were many who believed that Justin Trudeau, and now his carbon-copy replacement, Mark Carney, were puppets of a globalist cabal led by Klaus Schwab of the World Economic Forum to confine us in our Hunger Games inspired 15-minute City districts while being tracked by microchips injected by Bill Gates and the WHO during an imaginary Plandemic that was concocted to break our spirits into submitting to the communist, totalitarian Liberal government that was using carbon taxes to crush Alberta by hobbling its oil industry, all while extracting their hard-earned oil money to fund the profligacy of a corrupt capital in Ottawa and a lazy, welfare province of Quebec. Or something like that. But it definitely, definitely, could all be traced back to the WEF and their master plan.

Having worked as a consultant and collaborator with the WEF for close to 15 years, I felt the need to set the record straight. I realized that, perhaps, the value I could add in this debate was to offer the perspective of someone who had actually worked with the WEF and the WHO, and hadn’t just heard about them on Facebook. So I made a short video explaining what they actually did, and why the rumours had no basis in reality, gave it the title “The WEF Conspiracy” and posted it.

The video got about 40 views, and stopped dead. The normal curve of engagement for even my least engaging videos didn’t emerge. It just stopped.

After some investigation, and using a tool they have for scanning your content, I realized that I had been flagged for misinformation. Being flagged means that your post is no longer “eligible for promotion on the for you page”, which means that the magic algorithm that makes TikTok run will ignore your content. It won’t delete it, it will just ignore it. I tried changing the text description, the hashtags, and re-uploading it, with no luck. Any mention of globalism, globalist, WEF, World Economic Forum or a host of other words would get you suppressed if you tried to debunk anything.

To add insult to injury, when I looked at TikTok’s “creator insights” tool, which suggests trending topics to act as inspiration for creators, along with scripts to follow and related videos. The script was full of false information, and the suggested videos spoke freely about the WEF and all of the conspiracy theories, with view counts in the hundreds of thousands, meaning that they were using the same keywords while still being promoted widely by the algorithm.

This was one of many moments where you could feel the hand of foreign interference tipping the scales. Want to spread division? No problem. Want to stop it? That’s misinformation.

Where the misinformation really thrived was in the comment sections, and pushing back on the increasing waves of crazy comments became a full-time obsession. Some of it was just crazy, but a lot of it appeared to be bad faith parroting of misleading talking points.

There were definitely bots at play, but I think what’s more concerning than that is that this method of consuming information seems to turn people into bots. People with only cursory knowledge of topics post short, punchy videos putting a spin on headlines that are taken out of context, which are then repeated robotically by viewers who take them as truth. You could spot them immediately, because the talking points would be recycled so consistently: any mention of WEF, WHO, no-new-pipelines, equalization payments, 15 minute cities, Jeffrey Epstein, globalists, Liberal corruption, Brookfield, Klaus Schwab, scamdemic, Jared Kushner a lost decade or a host of other scripted points let you know where a commenter was getting their information. Most of these things took no more than a 2 minute Google search to see they were false, but this was evidently a Google search too far for most.

What was most disturbing for me was the interplay between the Conservative candidate, Pierre Poilievre, and the online misinformation. Poilievre would consistently make references to the online conspiracies in ways that he knew would engage those who knew, while often flying under the radar of those who didn’t. This reinforcement by a national party leader only served to reinforce their credibility, and cement the loyalty of a political base that saw him as the only candidate speaking the truth.

The Polarization Problem

I quickly amassed a small but loyal following of a few hundred - which, again, was astonishing given my experience on other platforms - but the reach of the posts I made, thanks to the algorithm, would extend far beyond that. My posts would consistently get hundreds of views, with some intermittently spiking into the thousands.

At a certain point, I noticed that I was clearly being served up to those that the algorithm knew would disagree with my content, while at the same time being served content myself that I would consider inflammatory.

One day when I expressed my frustration to Beth that I was being served “crazy stuff” that the algorithm would have to know I wouldn’t like, she reminded me that that was precisely the point; disagreement is one of the strongest forms of engagement. This clearly was having the effect of increasing the levels of polarization among users, as you would be comfortably surrounded by opinions you agree with, then suddenly served only the most extreme examples from the political opposite.

This polarization could be seen in the aftermath of the election results; following the Liberal victory, many Conservatives had difficulty accepting that the election results were valid, because they hadn’t come across anyone online who agreed with the Liberals, except for the occasional case of the most unhinged leftist. How could the Liberals have won if EVERYONE said they were going to vote Conservative?

The Fact Checkers and the Trolls

There were some creators I came across that put in an incredible amount of work to counteract the waves of disinformation that poured out on a daily basis. One in particular, Rachel Gilmore, was doing near forensic-level investigation of some of the trickier pieces of disinformation throughout the election. One claim that stood out for me was a picture of Prime Minister Mark Carney with members of a pro-Beijing business lobby, with the assertion that he had an in-depth meeting with them, despite his protestation that no such meeting had happened. By piecing together a timeline, and photos from the same event (based on backgrounds, clothing and flooring, no less), Gilmour showed that Conservative accusations were baseless, and the photo had simply been taken at a large political event, sandwiched between photos with other attendees.

The cost for fact-checkers like Gilmour, however, were substantial. Female voices online are particularly targeted, and Gilmour amassed an army of trolls that would follow her to any online space she would go to, culminating in the withdrawal of an offer to do an on-air segment with Canadian broadcaster CTV, which didn’t want to deal with the hassle of right-wing trolls and extremists.

This level of organized harassment should be deeply concerning for anyone interested in having effective political coverage throughout our elections.

A Side Note on China

Remember the part about TikTok being controlled by China? While I am going to go through this in detail in a separate post, it is worth mentioning that my first “viral” posts that got above 10k were about…China. Their performance was suspiciously out of step with any of my other content up to that point. Nestled between two other posts that garnered my then-typical view-counts of 450-650 views, my two posts about China attracted just below 13k and 10k…with the second post being an incredibly lengthy and dry rehash of China’s brushes with colonial powers through history. Coincidence? When I saw the other pro-China content being boosted in my feed at the time, when Trump was rattling his sabre, it was different enough to catch my attention.

Final Results

My original intent was to test whether using an online platform like TikTok would be more effective that volunteering in the campaign to knock on doors in Toronto. Just based on the limits of physical movement, there is likely an upper limit on the number of doors I could have knocked on, and again, it would have been in an area that was already heavily leaning Liberal.

By contrast, in the four weeks leading up to the election and the two weeks following, when I have been countering misinformation on the validity of the election results, here is a snapshot of my reach, having started an account from zero.

What strikes me is not only has the content gathered nearly a million views, it also attracted over 24,000 comments. Twenty. Four. Thousand. On a six-week-old account.

Further, as a test, at a few points during the campaign, I would post the same video both to Substack and to TikTok at exactly the same time in order to see if it was the content itself that was different, of if it was the platforms that made a difference. Here is a side-by-side comparison of the performance between the two after they’d been posted for the same amount of time:

The lesson that I take from this, and for the very few people who read this Substack, that I hope you take as well, is that this, very much, seems to be where the conversation is happening. My account was not even close to being one of the more popular political accounts during the election - those would have all have significant multiples of engagement beyond this. As of today, this account has around 3,000 followers, whereas the other creators leading the discourse all had tens or hundreds of thousands of followers.

And this really matters. I was very uncomfortable to hear, partway through the election, in comments from a large number of people, that my content had become a go-to for election information and a reality-check on what was real and what wasn’t. While this was, of course, my intent; to offer a balanced voice and some sound analysis into the public discussion, it made me realize that for every one of me, there could be ten people spreading false information. Some of the most popular political creators in Canada seemed to be working full-time churning out toxic, misleading content. But no matter which political bubble your feed takes you into, the trust that is built on these platforms is significant, and those of us concerned about the health of our institutions and democracy ignore these platforms at our peril.

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The Canadian election has had all kinds of strange accusations thrown around, including many calling Mark Carney a socialist. This is strange, indeed, given that he is an alum of Goldman Sachs. But with driverless semi trucks hitting the roads in Texas and autonomous robots building, we might want to consider some of our options.

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There has been something bothering me for some time as I’ve watched public opinion swing wildly on some longstanding issues, but until the results for Canada’s election came in last night it has felt difficult to put my finger on it.

Now, my area of specialization is in decision making, and as I have focused more and more on decision making around critical societal issues, public opinion has become a critical component of what decisions are possible and how they can be made. But a few years ago, I started seeing some dynamics emerging that we hadn’t been accounting for in our work, and it was only by working in vastly different domains that I was able to better understand what was going on.

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One basic democratic concept in policy work and in politics is that for anything that you might want to do, whether it’s about tax codes, public health, labor rights or foreign policy, it can’t stray too far from what the general public finds acceptable. If you do something that is too unpopular with too many people for too long, you will get voted out.

There is actually an elegant model for this that has become known as the Overton Window, which describes the range of play that leaders might have on any given issue.

In a given society, at a given moment, there is a range of policies politically acceptable to the mainstream.

A key part of the concept is the role that different players in the system play both in responding to issues that are within the window, but also how they work to shift the window to reflect new perspectives.

Generally, the theory went, politicians will only propose ideas that fall within the window. It falls to think tanks (and others) to propose unpopular things outside of the window in the hope of shifting the window and making the previously unthinkable achievable.

There is an important framing in this model that I really like, and that is the spectrum of acceptability that it lays out, that they intentionally laid out as a vertical so that it didn’t map to the simplistic left/right binary we often use. The spectrum centres on “Policy”, which is something that is so normalized that it can be comfortably enshrined as policy by government, but then has degrees of acceptability that range from there until they fall outside of the window: policy, popular, sensible, acceptable, radical, unthinkable.

The idea goes that when we look at public discourse and public opinion, there will be a window within which a politician can play that can be broad or narrow, but that trying to make the unthinkable into policy won’t be workable.

The Internet Enters the Chat

There are two major factors that have changed the calculations around the Overton Window, in my opinion. The first is the collapse of any shared reality or mainstream, and the second is the effects of personalized media and algorithmic editorialization.

In Musa al-Gharbi’s fascinating book “We Have Never Been Woke”, he outlines a dynamic in which a swing towards a new set of norms in media towards progressive themes creates a response by those that feel left out of those themes to create an alternative information infrastructure. Basically, as media becomes more progressive, people on the political right begin to set up a parallel media environment. This is where we are now with the contemporary right wing and left wing media.

The result of this, however, is that over time, you no longer have a singular “mainstream” like we had in the 90’s, you have increasingly separate media universes with their own parallel realities.

While this is challenging enough, we are also no longer in a network broadcast world, but in a fragmented media landscape where our information diet has radically changed. Whereas your media diet in the 90’s might have been one or two large meals a day - a newspaper and the evening network news - the modern media diet has largely done away with meals and involves constant snacking. And the snacks aren’t necessarily healthy.

Where a proper meal might take a lot of preparation and attention to nutritional balance, the switch to media snacking often consists of highly addictive and over-processed content.

With algorithms and social media platforms, the delivery of this content can also be targeted and tailored to be more addictive to the individual.

Your Own, Personal Window

This fragmentation of the media landscape and the personalization of the media environment means that the idea of the Overton Window is now working with completely different dynamics.

Whereas in Overton’s time, there was a shared, collective conversation in society, where persuasion happened in something like “the public square”, the conversations now happen within increasingly small and tailored bubbles specific to the journey of the individual. Instead of the Window framing what is acceptable to society, it can focus on what is acceptable to you. With every post you like on Facebook, or every explainer video you watch on TikTok or YouTube, your media ecosystem reshapes itself around you accordingly.

This means that rather than public discourse that is being shaped in the political sphere, it is individual thought that is being influenced.

From Discourse to Radicalization and Polarization

Democracy, as countries like Canada practice it, involves periodically asking the population to make a choice of leaders, who will then hold office and push policy for several years before they have to face the electorate again. This means that the stakes are incredibly high for those seeking power to influence that choice at that moment. And because the medium for information is no longer constrained by borders or any traditional institutions, it means that the players in the space can range from individual activists to foreign intelligence agencies, lobbyists, hate groups and leaders of industry.

The idea of “red-pilling” - which uses the famous red pill/blue pill scene from the Matrix as a metaphor - represents the process in social media when acceptance of one piece of an online narrative leads the individual down a rabbit hole of loosely related theories that all “connect” through some shadowy conspiracy. This has gone from a fringe phenomenon to something very widespread. This dynamic is fuelled by recommendation algorithms, as well as the development of intentional radicalization strategies by those pushing the narratives.

An example of this was the “#SaveTheChildren” hashtag, which coopted activism by a real NGO to promote conspiracy on child sex trafficking rings. The strategy was to take an issue that any “normal” person would be concerned about, and to connect it with something more sinister. This was a classic red pill tactic. Once the unwitting victim took the bait on the first part, they would be exposed to progressively more extreme content.

The result is that the spectrum in the Overton Window becomes more like a triangulation challenge: how do you make the popular seem radical, and the unthinkable seem sensible?

Whereas traditional discourse and debate might be more driven by a set of values and principles, the strategy for the unprincipled pursuit of power is to decouple values and principles from individual issues and policies by transforming the mundane into the maniacal. Life-saving vaccines become “forced medical experiments”; walkable neighbourhoods in 15-minute cities become “walled districts in the Hunger Games”. The goal of these is not to propose an alternative policy, but to tie anyone supporting these formerly “mainstream” concepts to sinister networks intent on implementing the unthinkable.

For foreign governments and geopolitical adversaries, this has become a cheap and easy way of seeding deeply polarizing conflict and paralyzing disagreement into their democratic rivals. For politicians attempting to enact policies that would be otherwise totally unpalatable to the public - like slashing social services and funnelling wealth to the rich at the expense of the working class - it is the perfect tactic for building a political movement without having to declare your intentions: you might not vote for me if you knew what I was planning to do with your pension, but you will definitely vote for me if I promise to protect you from my opponent, who feeds on the blood of trafficked children and plans on imprisoning you in your neighbourhood so they can conduct forced medical experiments on you and your loved ones.

Short Term Gain

What I worry about beyond the short-term effectiveness of this strategy is that it is, in the long run, completely corrosive to, and incompatible with, a functioning democracy. Though many Americans recoiled at the violent insurrection on January 6th in Washington that tried to overturn their election, the uncomfortable part is that many of the participants in the insurrection were convinced that they were actually fighting against a very real threat to democracy. The path that we are on right now is one in which that absolute certainty in parallel realities will only become more common and more pronounced.

As we have seen in countries where the West has tried to impose democracy on populations with deep sectarian divisions is that democracy cannot function when different factions view each other as dangerous, existential threats. It is only through a concerted effort to rebuild some semblance of shared truth and reality that we can hope to stay as a functioning democratic society.

In the future, you will own nothing, and you will be happy.

I can’t remember when I first heard this line in the lead up to Canada’s election, but before long, I kept hearing it on repeat as proof that Liberal candidate Mark Carney was part of a shadowy globalist cabal intent on bringing tyranny to Canada.

After hearing it enough times, I felt the need to understand where this was even coming from, as it seemed like an unlikely quote from an ex-Goldman-Sachs/ex-central banker.

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The source, in case you haven’t heard this one, was a foresight essay written by Danish MP Ida Auken that was published on the website of the World Economic Forum. The post has been taken down by the Forum by now, likely because of the odd controversy surrounding it, but you can find an archive of it here. The point of the essay, which is really more of a fictional vignette, was to take the vague concept of “the sharing economy” and to explore what it would look like if the concept were to be expanded to its fullest extent.

Now, to my knowledge, Carney and Auken don’t even know each other (though maybe they do, who knows). The implication for those who were spreading this conspiracy, however, was that this extreme version of the sharing economy in which nobody owns anything is the official position and secret intention of the World Economic Forum (though I’m not sure why they would publish secret plans on their website) and, because the WEF “controls” leaders around the world, and Carney has attended WEF events, that this essay represents his secret plan for Canada.

This conspiracy has gotten enough traction, clearly, that the WEF has finally just pulled the essay down from their website, which is a shame, because it is rather well done as a thought experiment.

Now as we enter the final stretch of the election campaign, another piece of foresight work is making the rounds, this time, from Policy Horizons Canada. Policy Horizons is the home of foresight in the Canadian Federal government, and the piece in question is called “Future Lives: Social Mobility in Question”.

As the published piece states, in a manner that will be familiar to anyone who has done foresight work:

The scenario below paints a picture of Canada in 2040 in which most Canadians find themselves stuck in the socioeconomic conditions of their birth and many face the very real possibility of downward social mobility.

Now, importantly, as though it needs other be said, the report clearly states that:

While this is neither the desired nor the preferred future, Policy Horizons’ strategic foresight suggests it is plausible. Thinking about future scenarios helps decision-makers understand some of the forces already influencing their policy environment. It can also help them test the future readiness of assumptions built into today’s policies and programs. Finally, it helps identify opportunities to take decisions today that may benefit Canada in the future.

The Problem

The Conservative candidate, Pierre Poilievre, has quoted this paper as a clear projection of a terrifying and dystopian future, made all the more damning because it is “predicted” by the government itself. The paper is now making the rounds on social media as proof that the incumbent Liberal party is intent on the economic enslavement and impoverishment of the Canadian people.

What has happened in both cases is a deliberate misinterpretation of the purpose of the papers in order to provide evidence of a conspiracy. When weaponized in this way, the very elements that give good foresight work its power become a liability.

Forecasting vs. Foresight vs. Policy Direction

Though it shouldn’t really need to be said, these are very different types of work, with a very different intent. Forecasting is the projection of quantitative data into the future to make predictions, like weather forecasting, election forecasting and economic forecasting. It is a discipline of data analysis that identifies trends in past and present data to understand where things are going in the future. It is the business of identifying probable or likely futures.

Foresight might draw from forecasts and quantitative trends, but also has a much more qualitative, speculative flavour to it. It uses scenarios, or narratives, to flesh out what possible futures might look like under certain circumstances. Foresight is a critical component of strategy and decision making processes, because it forces decision makers to consider the full consequences of present decisions and trends when extended into the future, and is often used to force consideration of future possibilities. So while forecasting is all about probable futures, foresight is more about possible futures. While some might be wildly speculative, foresight groups like Policy Horizons a likely to skew more towards plausible future scenarios.

Now, importantly, neither forecasting nor foresight are meant to present what is desirable. That is to say, neither is in the business of making a recommendation. Their purpose, in a decision making process, is to flesh out all the aspects and implications of a possible future so that those making decisions can evaluate whether that future is desirable. If it isn’t, they can then craft a strategy for how to avoid that future. If it is desirable, they can make decisions that they think will make that future outcome more likely.

Policy direction, or policy recommendations, would be outlines of the strategies required to achieve certain outcomes. It’s the work that might come after a forecasting or foresight exercise.

Futures Literacy

Organizations like UNESCO have, for some time, been promoting the idea of “Futures Literacy”, or the idea of improving education around the importance of future considerations in planning and strategy. This would likely be a great addition to high school civics classes, as the spread of conspiracies online using foresight materials suggests a general misunderstanding of what these scenarios are, what they are for, and why they exist.

For my part, foresight is an important part of my practice in designing decision making, especially when the planning environment is as chaotic as it is now. Imagining a set of possibilities for the future, to me, is a critical component of any strategic process, because the context in the future might be very different that the one you are in now. I also use scenarios and foresight to create the space in a decision making process to consider the ethical dimensions of their current decisions when projected into the future or when brought to scale. “If we do this, that is likely to happen. Is that what we want?”

The Chill

What troubles me is that if foresight work is increasingly used as the basis for conspiracy theories, it might put a chill on futures work in decision making while also making organizations less likely to share the foresight work that they have done. I have already had one foresight exercise I have done “leaked” as proof of nefarious play, when in fact I had used it as a way of spurring a conversation on ethics. While the work wasn’t classified, when it was shared, it was presented as if it was, with the scenarios presented as intended outcomes, rather than the ethical dilemmas they were intended to be.

As we’ve seen above, the WEF pulled down Auken’s paper, despite the fact that it is thought-provoking, because the controversy and conspiracy have made it into a distraction.

But I think that given that this trend is being fuelled by a major-party leader in a G7 country should be a wakeup call that critical tools in good decision making and policy making are under attack. Consistent explanations in media about what these reports are, and what they are not, should be the norm, and effective messaging to dissuade leaders from disingenuous references needs to enter the discourse. And futures literacy? I think it’s now more important than ever.

Researchers in Artificial Intelligence often use Chain of Thought (CoT) prompting as a way of getting AI models to improve their reasoning by explicitly laying out the steps taken to answer a specific question. Not only does this focus on the reasoning process help researchers better understand how LLMs arrived at their decision, it also, as it turns out, results in better results.

Like contemporary AI systems, modern governments have reached a point where the decisions being made resemble early LLM responses: it’s impossible to tell what data is being used, the reasoning process is totally unclear and they seem extremely prone to hallucinations.

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My career, so far, has been focused on decision making; not on telling people what decisions to make, but, rather, on designing the processes by which they make those decisions. Through research and practice, I have seen that through good design and by controlling for a few variables, the quality and speed of decision-making can be vastly improved, especially as our research in psychology and behaviour continue to develop while the quality and quantity of data we can use to inform our decisions has eclipsed what was available to us in the past. There are also vast international communities of practice around a host of methodologies with evidence and practical tools for supporting decision making effectively.

Which is all to say, we have never, in our history, been better equipped than we are today to make excellent decisions that draw from historical experience, weave together the knowledge and perspectives across our many domains of knowledge and are informed by a richness of data beyond anything imaginable even a generation ago.

You would expect that, given all we know and have access to now, we would be in some kind of golden age, plotting a path into the future together with nothing but the laws of physics to constrain our progress.

That, clearly, is not the situation we find ourselves in.

One of the principles we use in our practice is the idea of “designing backwards from desired outcomes”. The idea is to start with the end in mind, then imagine the sequence of conversations and areas of inquiry what would result in the outcome you’re hoping for.

I’m also reminded of another quote by Stafford Beer, which is “the purpose of a system is what it does.” I like how agnostic this statement is; it’s a great analytical prompt because it asks us to ignore what we think a system is for, and to evaluate it based on the actual outcomes, not the intent.

If I were to evaluate our political and decision-making systems using Beer’s lens, our current system’s “purpose” might be characterized by optimizing for short-term decisions that emphasize sentiment over evidence and conflict over consensus.

If I were to approach from the angle of desired outcomes, however, I would imagine that we would like a system that could craft well-informed policies that work in a unified way towards a shared vision for the country and future generations.

There is a considerable gap between these two realities, and I believe that design can play a crucial role in creating the conditions for these kinds of outcomes. But the first step in dealing with a problem is being able to name it.

I believe that the ways in which we approach decision-making in Western democracies no longer represents the best of what we know about structuring decisions, and if we want to enjoy a better future, we need to improve how we make decisions.

We need to improve our infrastructure for establishing shared truths.

We need to build processes for arriving at shared priorities.

We need to establish new norms for accountability in political speech.

We need mechanisms for inter-agency collaboration on complex issues that don’t fall neatly within one category.

We need to develop plans and policies that are responsive to evidence and respectful of future generations.

We need to adapt how we engage in democratic discourse in a way that is open, but can manage threats of foreign interference and misinformation.

We need policy decisions to have the same burden of due process that we expect of verdicts in the justice system.

We need to do all of this in ways that build on fundamental democratic principles.

We already have the tools, techniques and practices to design all of these processes. What we need is the will to put in the work and the courage to experiment. This, to me, is the essence of Process Activism: the knowledge that the decisions we make are shaped by how we make them, and only by designing a better system can we expect to have better results.

Integrating artificial intelligence into your workflow requires you to not only evaluate where and how it can add value, but also, what ethical considerations arise with each new implementation and the policies you might need to put in place as you go. So far I have found that AI can offer a lot of value in collaborative processes, but there are a number of areas where it is easy to violate trust in ways that will harm adoption in the future.

In this post, I wanted to document some of the considerations that have come up so far, and the beginnings of a framework for approaching your own policies. I’ve boiled it down to a few design principles around five key areas: risk, power, privacy, ownership and value.

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Risk

Principle: Learn with low stakes

There are a lot of risks, both real and imagined, in adopting AI, but they can be difficult to identify until you start using it. There could be security exposure, legal issues, hallucinations or reputational damage, but sometimes those risks can be difficult to fully understand in advance. Each platform has its own risks, but so does every use case. Experimenting and prototyping with low risk data, low risk stakeholders in a low risk environment can help you shape a better understanding of what the possibilities are, and also a more realistic picture of the risks. Trying to design a perfect implementation up front makes it very difficult to understand the full picture; I’ve been playing with hardware, software, process and context in “safe” projects in order to get a better understanding of the pitfalls.

I now gravitate to hardware with failsafes, clear articulations of data retention policies, AI platforms with clears Terms of Service and processing of inputs rather than outputs as a result of this approach.

Power

Principle: Level, don’t amplify, power imbalances

It’s easy to imagine uses for AI that allow you to centralize a lot of control and to optimize, automate or monitor a wider and wider range of inputs. I believe, however, that the AI use cases that will get the most traction are the ones that rebalance power, as opposed to exacerbating existing imbalances. Imagine a call centre, for example. One approach that leans on an existing imbalance would be to deploy chatbots and voice agents that allow the call centre to have fewer staff, and can triage callers before they speak to an agent. If the optimization is purely for the benefit of the company, it will most likely result in even more frustration from callers. An approach that addresses the imbalance would be to have an AI that works as an agent on behalf of the caller, to minimize their time and to negotiate a solution before reaching back out to them.

In a collaborative process, AI can be used to provide more channels for more input and engagement from more people in a meaningful way. Use it to increase, not replace, engagement.

Privacy

Principle: Respect autonomy, earn trust and don’t be creepy

In the workplace, and especially in collaborative settings, it is now possible to process so many inputs that it is very easy to move from “capture” to “surveillance”. I believe that over time, processes that don’t respect the rights of the people who participate will struggle to get buy in, and even those that do will be biased by the behaviour of individuals that know they are being surveilled. Once trust is lost, it is very difficult to get it back, and, further, when this technology is being used in environments where there are low levels of trust to begin with, extra steps will have to be made to get buy in. If you are planning an approach that takes away the autonomy of users or spies on them in a way that wouldn’t otherwise be socially acceptable (would you do this to your family? Friends?) it is very likely to backfire.

While I now use microphones in breakout sessions, for example, I am crafting a clear privacy policy around retention and use of any recordings, and am iterating the system to have no human-in-the-loop so that comments are not traceable to individuals (Chatham House Rule).

Ownership

Principle: Ownership of inputs should correlate to ownership of outputs

Aggregating data to build a new value proposition can lead to the same issues that AI companies have been facing with copyright holders: they are selling the outputs of a model that was created using other people’s inputs. If you are planning on aggregating data, or profiting from the output of aggregation, you should do this in collaboration with those who create the inputs. This is not only the right thing to do, this is an evolving area of law, so protects you from unforeseen exposure in the future.

Value

Principle: Build generative, not extractive, value propositions

AI can be used to extract benefit from others, or it can be used to generate value for everyone involved. While extracting value might be profitable, I think longer term value is to be had with generative value propositions. In a collaborative setting, you might use AI to generate interaction data over time that has value, or build “lock-in” with groups because you hold their data, but I think this will be met with more and more resistance as people become more savvy with the technology. Using AI to build supports for groups that can speed their work and enrich their experience, I think, will get a lot more adoption over time.

In Conclusion

If there was a final principle I would use, it would be this: be willing to show your work. I think that transparency is the best test across the entire workflow. If you’re not comfortable sharing who benefits and how, what technology you’re using, how you’re managing the data, what the data can be used for and how you’re thinking about all the stakeholders in the process, then that should be a gut check that you should make some changes in your approach.

The debate in many Western democracies on what to do about TikTok hinged around a few, very real concerns. The first concern related to the danger of a foreign adversary being able to use very powerful algorithms to shape the discourse around any issue and amplify misinformation. The second concern was in having a foreign adversary gaining deep access to data profiles of so many citizens, as the algorithm is able to generate incredibly rich profiles of its users.

The root problem is one of trust. The debate circled around our growing awareness on how much power and influence modern technologies have over our work, our knowledge, our opinions and, by extension, our democracy and its institutions.

The concern was also not theoretical. We know what China could do with the platform because it is what we are already doing with western tech platforms - US Intelligence and the 5 Eyes currently have the capabilities we are talking about, but because we “trust” each other, those are compromises we live with. The worry is; what happens when an adversary controls key parts of your information infrastructure?

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I think we will look back at the current moment as the time that American technology dominance began its decline.

The moment that the US made its allies question their trustworthiness, the entire question of collaboration on technology platforms changed. American tech companies have benefited handsomely from global markets, with Microsoft, Google, Amazon, Meta and Apple providing technology used by individuals, governments and businesses around the world. Just as many NATO members grew lazy in investing in their militaries because American protection was already so much easier to rely on, not many countries bothered to create homegrown tech like cloud storage, AI processing, email or productivity software. After all, why would you? American tech is so great, and, how on earth could you compete with Microsoft and Google on core productivity tools?

As a Canadian, I was forced to ask myself, if the US decided to follow through on its threat to annex my country, what might the first step be in that process? Our entire government runs on Azure and Google Cloud - if the US decided to hit the kill switch, our country couldn’t even run payroll, let alone send out any communications.

This should be a wake up call for anyone outside of the US that uses American technology. I’m writing this on a Mac (US), publishing it to Substack (US), my company runs on Google Workplace (US), my AI tools are Anthropic (US), OpenAI (US) and Gemini (US) and my backup cloud storage is Dropbox (US) and Box (US).

Europe is already waking up to their dependence on American technology, with Poland questioning whether Starlink can be trusted as a connectivity partner in Ukraine, sending Eutelsat shares soaring. The threat of Musk deciding to switch off access at key moments was enough to start some serious soul searching, but I think set off some deeper questioning on what it means for unpredictable, untrustworthy parties owning your data access.

With all of the recent patriotic fervour in Canada, I hope that begins to translate into the tech scene, as this country needs to take a long, hard look at what it means to be entirely dependent on a country that is declaring itself as an adversary for all of our information technology. We need a coordinated response, both from our tech industry - where there is a ton of talent - along with industry and government. We need our own DARPA.

For those making decisions for their organizations on tech, it is worth running some scenarios on what the impacts would be of losing connectivity to all of your cloud services, as a very minimum consideration. Build a contingency plan in the short term, but in the long term, we need to build new infrastructure.

The tech market is never going to be the same.

TL;DR - The compounding consequences and constitutional violations by DOGE should be a call to action for practitioners of multi-stakeholder work, which needs to prove it can move quickly and decisively in complex environments in order to avoid future chainsaw approaches to government reform.

We seem to be in a moment where you have to work especially hard to find some cause for optimism, but in that spirit, today I want to look at the US Department of Government Efficiency, or DOGE, for a bit of a silver lining. While the whole thing might look like a train wreck, what I want to know is: can we learn anything from it?

Many Canadians like myself are watching in dismay as the administrative state of our neighbour to the South is being hacked to pieces, with the many checks and balances that have held them together for centuries are dismantled at a record pace.

While Elon Musk’s slash and burn tactics at Twitter caused some disappointment in the loss of what was - for some - a beloved platform, they were also a source of schadenfreude as the scattershot cuts destabilized the site, alienated users, repelled advertisers and violated a host of legal obligations. It was easy to sneer as Musk’s ham-handed tactics failed to account for the social, technical and legal complexities of running a modern social media platform.

But government is another animal entirely.

A few years ago I read Michael Lewis’s book The Fifth Risk, which stemmed from a column he had been writing about the various functions of US Government Agencies. Lewis began writing the column because he realized that at this stage, many Americans had no idea what agencies like the US Department of Agriculture or the Department of Energy actually did. His research happened to be ongoing when the first Trump transition took place, so he saw first hand as critical fumbles took place with the machinery of government took place, such as an employee finally going home with the nuclear codes when no one relieved him of duty.

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While in many Western countries there has been a narrative for years that government should be run more like a business, the fact is that no businesses exist that have both the obligation to service and the level of complexity that governments have. While business may have some practices and principles to be applied to elements of government, the idea of simply “running it like a business” is like saying you should run a railway like a bicycle - sure, they both have wheels and move things, but they exist for very different reasons.

At the same time, I think that it is important to recognize that political moments don’t just appear out of nowhere. The narrative of porting business into government, I think, comes in part from the view that businesses face a kind of Darwinian pressure to deliver that governments don’t, meaning that organizations that fail to deliver simply cease to exist. While this is debatable, I think it reflects a growing sentiment in Western democracies that governments are increasingly expensive, inept and incapable of change.

Enter Elon Musk

With his very subtle chainsaw metaphor, Musk has, in very short order, made more changes to the US government than, I think, anyone would have thought possible. Entire agencies shuttered, whole functions erased, and tens of thousands of employees sent packing. Clearly the business principle he is bringing in is “Move Fast and Break Things” - the early motto in Facebook used to experiment, iterate quickly and adapt based on results. In this case, cut everything, and if any of that causes a problem, switch it up and keep moving. Instead of debating what might happen, make the change and find out, then make more changes based on what you find out.

But as we’ve seen, the stakes are somewhat higher with the US Government than with, say, a micro-blogging platform. We’ve already seen programs to prevent ebola get briefly cancelled, workers who maintain nuclear weapons get furloughed and those responsible for fighting forest fires get sent home.

“Breaking things” on Twitter might cause some slow load times or a glitchy livestream. With the government, breaking things might mean nuclear disaster or a global pandemic.

Silver Lining

So, you may be asking; where is that silver lining I was talking about? What Musk has demonstrated is that, well, it turns out rapid change is possible in government. It also demonstrates one possible method for doing it, and a host of lessons that we are likely only just beginning to be able to learn from.

I’ll say up front that taking a chainsaw to a set of functions you don’t understand that affect the lives and wellbeing of millions of people is a terrible idea. It is almost inevitable that something very bad will happen.

I do think, however, that it is worth being specific about why it’s a bad idea, and also, if there’s anything that we can learn from it.

Why Chainsaws are Bad for Surgery

The four main issues with Musk’s approach are around risk, complexity, authority and purpose.

First, with risk, there does not seem to be any weighting to account for how much caution should be taken relative to the risk of the functions they are playing with, whether immediate - in the case of nuclear weapons - or long term, as in the case of kneecapping research. Generally, one would assume that the greater the risks involved with an effort, the more effort should be put into mitigating those risks. While juggling rubber balls and juggling chainsaws are both juggling, most would approach the latter with a bit more caution than they might the former.

Second, making changes becomes more fraught in complex environments where second and third order effects might not be immediately obvious. Governments in general, and the US Government in particular, are highly complex, navigating a wide array of responsibilities, legal requirements, security considerations as well as constitutional and treaty obligations. Given the status of the United States as the world’s pre-eminent superpower, there are also a web of geopolitical implications that extend far beyond the nation’s borders. Governments span a dizzying array of functions, with responsibility sometimes spanning across agencies, and systems of varying vintages supporting all kinds of mission-critical processes which are, in many cases, poorly documented, if at all.

Third, as is becoming increasingly clear, Musk’s group appears to lack the necessary authority to carry out their efforts. As he moves from agency to agency, cancelling contracts, seizing data and laying off staff, the justice system has been following, identifying a litany of legal, employment and constitutional violations.

Finally, one of the factors that is animating opposition the most with DOGE is the lack of any clearly defined purpose in their actions. While “efficiency” is often touted as an end in itself, it is actually simply a measure of the resources required to achieve a specific goal within a set of constraints. What is happening with DOGE, however, is the wholesale dissolution of functions because the goal is deemed unimportant, rather than finding the approach inefficient. Further, having a private citizen who is a recipient of significant government funding and whose activities are regulated by agencies he is now influencing creates - I’ll be generous - a lack of clarity on what the objectives, decision criteria and guiding principles of DOGE’s activities might be.

Speedrunning Complexity

When I first started in consulting at Capgemini Ernst & Young, I was in a group called the Accelerated Solutions Environment, which was based on the MGTaylor method, which I still use to this day. Most of the major consultancies still have some variation of this method somewhere in their mix of offerings - with notable investments by PwC, KPMG and Oliver Wyman building out their capabilities and Capgemini continuing with its own.

The conceit of the method, as we practiced it then, was to compress decision making in complex, high risk situations by gathering all the “right” people (decision makers, influencers and implementers) in a room, giving them access to all the information they would need, and running them through an intensive, three day, highly iterative process that would end in a decision and a plan to move forward.

There was obviously a lot of marketing jargon we built around the offering - “months or years of work in three days!” - but there was actually something to it. If you add up all the meetings, emails, working groups and conference calls, making a major policy or strategy change can take AGES. Not to mention that over time, groups tend to lose the plot and settle on the easiest answer. So, compressing that process, and making sure that everyone who either had knowledge or influence was in the room and fully dedicated to generating an outcome actually can allow for a significant amount of acceleration.

The challenge was that getting leadership teams to fully sign on to an intensive process where stakeholders of varying levels would roll up their sleeves side-by-side was always a tough sell. And in many multi-organizational processes, stakeholders would rather send subordinates to consult than to send decision-makers to commit.

What we’re seeing with DOGE, however, are the consequences of not being able to make meaningful change in complex environments on a meaningful timescale. What DOGE has succeeded in doing is making significant change non-negotiable and unavoidable, which is, actually, rather significant.

Due Process

This, to me, is where the idea of “due process” in decision-making is so important. The justice system accepts that in order to render judgement in a criminal proceeding, there are certain considerations that should be made, certain rules and procedures that should be followed, and certain types of evidence that should - or shouldn’t - be brought in to the decision making process if it is to be considered fair.

There is also the imperative built into the justice system that, however exhaustive the process might be, the end result will be a decision.

For anyone that has been involved in a “public consultation” or a “strategic review” in government, however, or even a multi-stakeholder process, there is not necessarily the same imperative. A lengthy process might end in a series of recommendations. Inter-agency talks might end up deadlocked. Inexpedient evidence might be ignored, and turf battles might undermine processes completely.

So, to learn from the DOGE example, and imagine how we might do things better to make change across our public services, the question becomes, how do we address the complexity of public services while at the same time running a decisive process that produces provably better results for citizens?

I fundamentally believe that a multi-stakeholder approach is the best way we know to address all of the interdependencies in a modern government. It’s not really rocket science to suggest that if you are going to make major changes to something like, say, forestry services, you should involve those responsible for leading them, but also those familiar with actually delivering those services, those who understand how they intersect with urban environments, how climate change might affect the future and so on. If a service is meant to solve a problem, it doesn’t seem crazy to consider all aspects of that problem to make a decision about how to address it. But complexity shouldn’t be a license to “agree to disagree.” At some point, a decision needs to be made, and that decision, ideally, should reflect the very best of our knowledge of the issue.

Those in my industry know that a well designed process with the right people in the room focusing on a problem uninterrupted, with the right information at hand and working through tight, iterative cycles can solve enormously complex problems in a matter of days, if properly supported. Which is to say, from a process perspective, it is absolutely possible to make good decisions very quickly about highly complex, interconnected issues, if certain conditions are met.

The complexity and risk elements that DOGE gets wrong are mitigated simply by ensuring that the right stakeholders, with direct knowledge, influence and decision-power over the issue are involved in the process.

Having a well defined purpose for the change is absolutely fundamental to the success of the effort, and is not only a major driver of what DOGE is getting wrong, but is also why so many change initiatives go off the rails. Clarity on the purpose, in this case, comes both from a definition of why the change is being made, but also rooted in why the function being changed exists in the first place. A lack of coherence in either of these areas is almost a guarantee of a poor decision. Why does NOAA exist? Why does the Department of Education exist? What outcomes are they designed to create? If we don’t know what, specifically, they are meant to do, it is very difficult to meaningfully change them, or even to make them more efficient. If cutting back on researchers doing weather modelling saves millions, but exposes us to billions more in damage from weather events, is that more efficient? Because there is a dissonance there, it also means everyone in the process becomes naturally suspicious of the purpose of the change effort itself - are we really working towards efficiency (achieving the same outcomes with fewer resources), or are we just not interested in those outcomes any more? Have we just decided that we’re better off when hurricanes and tornadoes are a surprise?

I cannot stress enough how important that clarity of purpose is - it is foundational to any kind of intentional change.

What DOGE almost gets right, and where a lot of multi-stakeholder processes fall apart or end up stuck is on the authority front. I say DOGE gets this almost right because, by simply pretending that they have the authority, DOGE has, indeed, been able to make changes at a scale and pace that were, to me, previously unimaginable. I also say almost, because it seems that they don’t actually have that authority, and are, more or less, violating the constitution.

But as a thought exercise, an interagency body tasked with change that has the authority to make final decisions is a powerful tool, if it were to exercise that power according to an acceptable set of processes. Similarly, having the necessary stakeholders in the process include decision makers that are obligated to make a decision - like a collaborative form of binding arbitration - could still preserve autonomy in such a process.

This, to me, is what caught my attention as Elon waded into the government with his chainsaw, and it is also what is so tragic about how the opportunity is being squandered by his haphazard approach. Whether it is in Crown-Indigenous negotiations, the creation of Digital Services in government or even changing policy on waste disposal, the proximity of authority in stakeholder processes prevents them from being decisive, and the removal of decision-pressure allows procedural drift and a lowering of expectations that becomes corrosive over time. If you know a process is just for show, or has no stakes, you know you don’t have to take it seriously.

Move Fast and Fix Things

The optimist in me hopes that we can use the shock of the current moment to put energy into really fixing things. The Trump administration only became possible in a timeline where frustration, stagnation and a lack of confidence in our collective ability to make things better have all been festering under the surface. Throughout Western democracies, our perceived inability to make change is giving space for leaders who gain power with little more than a promise to burn it all down.

We already have the tools and methods to move quickly; we just need to use them with courage, proving to ourselves that we can be decisive without being destructive.

I have spent the better part of my career working with individuals, organizations and groups to make better decisions. There are, of course, so many ways to make a decision, and there is also a lot of lore around decision making and, by extension, leadership.

As a consultant, I found over the years that a large part of the value I could bring as an outsider was in helping a group to test and challenge assumptions that they may have become blind to. It happens to all of us. But because of the nature of consulting, and the fact that people rarely hire a consultant when everything is going great, I found that actually the ability of a group to discern the facts in front of them and to make decisions accordingly became a pretty key indicator of whether they would succeed or not - especially when they were faced with changing or challenging circumstances.

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The difficulty, in many cases, is that there is rarely an immediate and linear relationship between a decision and the result. I’ve always loved John Sterman’s paper, Nobody Ever Gets Credit for Fixing Problems that Never Happened, for its explanation of why managers that spend their time putting out fires get more credit that those that prevent them, especially because of the role that delays play in that dynamic.

We are hardwired to discern cause-and-effect relationships; it is how, from infancy, we learn to navigate the world. Where we struggle is when the effects are complex, or, when they are delayed. The results of the decisions we make might not become apparent for some time, and when they do emerge, we might even attribute them to some other cause, or write them off as “unintended consequences”. This reminds me of another classic John Sterman line: “There are no side effects—only effects.”

In very large organizations, in protected industries and in governments, there might be enough of a buffer between us and the consequences of our decisions that we might spend an entire career working on faulty assumptions and still enjoy personal success. I have worked with many organizations that managed to do well enough with the status quo, and with individuals who just hoped to retire before the bill came due.

After many years of working with startups, I found a very interesting counterpoint to that dynamic. Because many startup companies are extremely resource-constrained, the inability to test your assumptions is generally seen as an existential threat, to the point where I would often consider it a negative for a young company to have too much funding; it would keep them from having to validate their assumptions for too long, and they would have more difficulty finding traction. Many startups now use variations on rapid hypothesis-testing frameworks to formalize their ground-truthing process, as the longer they spend tuning their approach, the more likely they are to go bankrupt.

Finding Truth

Now, there are two things happening in these cases that reflect two kinds of truth. First, in the case of startups, in the initial years as they try to build teams and woo investors, they are trying to create a shared narrative, or shared truth. Shared truth is an essential element of collective action - coordination and collaboration is almost impossible without some kind of consensus reality.

There is also the interaction with objective truth, or objective reality itself. History is littered with states, companies and cults that had done a great job of convincing themselves of a truth that, in the end, did not serve them well in navigating the real world.

As far as decision making is concerned, shared truths are what bind groups together in choosing certain actions, and the delays in feedback between those shared truths and objective truths are what allow them to persist over time when there is a misalignment.

I bring this up now because I believe that we are, as a species, in an existential crisis of mounting consequences, and in Western societies, at an inflection point in whether we are able to navigate the future successfully.

Fragmented Futures

Last year, I wrapped up a project with the United Nations Environment Programme (UNEP) to explore possible futures through a foresight process. As a science-based agency, UNEP typically pursues its planning through evidence-based forecasting, essentially, by taking objective measures of the current state of the environment, making projections into the future, and planning action accordingly. They decided to undertake a foresight process because of an acute awareness that there were a host of non-scientific factors that might influence whether or not we would take appropriate action to protect our biosphere from destruction. I say that with tongue in cheek as a massive understatement.

Scientists spend their lives in pursuit of objective truth, but decisions tend to dwell in the domain of shared truths.

The exercise we undertook was to explore four possible future scenarios and to flesh them out in detail in order to evaluate the probabilities involved, the desirability of each and the actions that might be required to prevent some of the worst from happening.

The scenario I focused on was “Post-Truth Division”, which we detailed with a somewhat harrowing dissolution of democracy and collapse of Western democracies. You can find the report here if you’re interested.

As my group detailed the all-too-plausible-and-already-underway collapse in social cohesion, we came, again and again, to a single root cause: the loss of shared truth.

Democracy works when enough people agree on a version of reality and a sense of shared truth. Polarization becomes problematic because what we are seeing is not a simple disagreement on policy, but a disagreement on reality, in which various factions hold their own truths that are not compatible with others.

While my work with social media companies had already made me suspect we had a problem, playing out this scenario made me very concerned about where this path would lead us, and that concern was solidified in my work with military and intelligence services.

Because democracy requires a shared truth, our sense of shared truth has become an attack vector for adversaries of democratic societies, as I outlined last year in a post about Open Source Intelligence (OSINT) and adversarial information environments.

It also helps to explain the approaches of many nascent political movements, which specifically attack traditional sources of knowledge, expertise and authority. Donald Trump’s MAGA movement has been tremendously successful because it has created a compelling and strong sense of shared reality among its adherents that has allowed them to achieve power through concerted, coordinated action across a very motivated base of supporters. It is no mistake that his own social media platform is called Truth Social.

Design, Neutrality and The Future We Want

I obviously hold my own, rather strong views on the direction that politics are going. But the designer and facilitator in me is more interested in building the tools we need to shape our decision-making and democracy to be fit for purpose in navigating current and future challenges. And because of my background in strategy, I have a bias towards frameworks that translate explicit intention into desired outcomes.

I believe that sustainable action comes from a group that is both bound together by a set of shared truths that are reliably connected to objective truths.

I am aware that as a species we can go along to construct our own reality, which, in very tangible ways can become “real". Yuval Noah Harari makes a compelling case around the role of imagined realities as a way of coordinating human action. Without going into a semantic rabbit hole, I would say these imagined realities largely fall within the “shared truth” category, but I recognize that things can get fuzzy in the political application of these concepts, as what is “true” in a certain case - like “tariffs won’t cause inflation” - exists within layers of our own creation. The distinction that matters for me here, then, is between the things that we say and believe to be true, and the actual tangible, verifiable outcomes and context for those beliefs.

With social policy and economics, even though our choices play out in a system that is also of our creation, there are, at some point, very real consequences. People have food or they don’t, have housing or they don’t. So while we are playing with abstractions within abstractions, eventually there is likely a real result.

With environmental and health policy, for example, we might be bumping up against reality in a more direct way; maybe your shared truth is that vaccines don’t work or that climate change isn’t real, but there are still actual viruses and your crops might fail.

So, if I were to withhold my personal opinion of something like MAGA and evaluate it from the place of professional neutrality, my questions would focus on how well the stated aims of the movement direct the actions of the Trump administration, how well the actions of the administration translate into outcomes that match the stated aims, and how well those outcomes serve the long-term interests of all parties involved.

Democracies are messy and imperfect, and it is always possible that they produce results that we don’t like. But the way that we are practicing democracy now seems like a kind of procedural malpractice.

Rebuilding the practice of collective decision making requires us to bolster, build and protect a few key functions.

Getting from Here to There

Making collective decisions about where we want to go requires us to achieve a few basic things:

* Agree on the current conditions we are in

* Agree on some general goals for how we would like things to be

* Understand how things could be given our constraints

* Agree on principles that should guide our choices

* Initiate actions that will get us from where we are to where we want to be

* Repeat in a cycle to evaluate that our actions change our conditions as expected

If this sounds overly basic, that’s because it is. But we are somehow in a place where we have general dysfunction in all of these areas. Our current political discourse:

* Chooses conflicting or false measures of our present conditions

* Obfuscates or generalizes goals to conceal priorities

* Avoids specifics on limiting factors

* Does not connect principles to actions

* Initiates action without context

* Undertakes evaluation primarily as an opposition exercise

While this is a simplification, my point is that where the above dysfunctions happen, there is no longer any sense of shock at the lack of coherence.

In a very basic model of getting from “here” to “there”, I think we need some first principles thinking applied to rebuilding function across a few areas. To know where we stand in the present, we should have a coherent approach to making observations, applying existing knowledge, designing experimental actions and interpreting results.

To define where we want to go, we should be defining probabilities of how current conditions might extend into the future, define possibilities that could be created through concerted action, and defining clear principles for what our expectations are of the outcomes of our actions.

I feel almost awkward writing this, as it seems terribly simplistic given the scope of the challenges that we’re facing. But if we accept that these are the basic building blocks of establishing shared truths in pursuit of collective progress, you can see how attacking the norms as well as formal and informal information infrastructure we have around these things puts us in an untenable position.

Attacks on basic science, defunding statistics, undermining academia, attacking media all work to erode the basis for any trust around the elements of knowledge required for complex decision making.

But just as we have established due process around judicial proceedings, I think we need to do the same for collective decision making and political process. A court case that involves false or inadmissible evidence or violates due process is declared a mistrial. What expectations should we have for truth and process in our politics?

As an exercise in design, then, that means building coherent process and infrastructure around each of these areas in ways that respect democratic principles. At a startup scale, a successful founder wouldn’t dream of ignoring these kinds of signals; bankruptcy would come quickly. But at the scale and complexity of governments and countries, the feedback loops and delays become so weak and long that only deliberate, multi-disciplinary, multi-stakeholder efforts can plot a way forward.

As a last thought, this brought to mind a conversation I had with Jimmy Wales, the founder of Wikipedia, many years ago as part of a project. I was creating a series of visual models that would express the essence of how various leaders were approaching change. I was intrigued by what Wales had defined as the core of his efforts; Wikipedia was intended as a trusted, shared source of truth meant to allow reasoned, informed debate in society.

I think that’s a start.

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