goal17 Podcast

Research and Analysis by Aaron Williamson

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18
Episodes: Monthly
Cadence: 2024
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About goal17 Podcast

Analysis and thought on system-informed strategy through analysis, foresight and investigation at the intersection of technology, politics, international affairs and social change. goal17.substack.com (https://goal17.substack.com?utm_medium=podcast)

Publisher: Research and Analysis by Aaron Williamson
Category: news · technology
Language: en
Explicit: No
First episode: 17 Jun 2024
Latest episode: 8 Jul 2026

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8 Jul 2026
Why I Created Knowledge Commons
For anyone that’s been watching this space, you might have noticed that I’ve been rather quiet for a while. Some of my last posts were exploring the impacts and uses of AI in consulting, collaboration and public engagement. My early experiments quickly convinced me that AI tools offered significant value in collaborative work but I didn’t see a way to keep it as a core tool in my practice with the options that were available. What I did see, though, was that with the right tools, we could finally overcome some long-standing challenges in collaborative work to enable new forms of collective action and complex decision-making. Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Through several decades of work in designing large-scale, complex, multi-stakeholder decision-making processes, there were several persistent problems we’ve been working to correct for that finally seemed within reach, and, to me, offered the possibility for a real leap forward in how we organize ourselves around complex decisions. Whether in large organizations, in projects or across coalitions, the quality of coordination and decision-making has always been a function of how quickly information gets where it needs to be, how widely distributed it is, and whether those in authority are aligned in their understanding. What became clear very quickly was that we could improve the speed of information passing through teams and networks, ensure that “edge insights” become more distributed and bring groups to shared understanding more efficiently. As I tested recording in breakouts and across sessions, I could see that assumptions we had held about the quality of what groups would report about their work paled in comparison to the richness of complete capture. I felt that if you could synthesize disjointed, distributed or parallel work quickly enough, at scale, that this would no longer be a question of documentation after the fact, it would completely change the speed and comprehensiveness of collaborative work. Consultations would be able to not only capture in minute detail the perspectives of constituents, but could adapt their focus - at scale - as unexpected themes emerged. Just off the top of my head: * Strategic conversations could merge the views across all stakeholders and document inputs. * Workshops could iterate quickly on emerging ideas and save time on lengthy reports between teams. * Conferences could become knowledge-producing hubs. * “Systemic” conversations and policy processes could assemble first-hand knowledge from more people than was ever practical before. I was convinced of the potential, and I remain convinced. Anyone in professional services that is involved with engaging and assembling knowledge, perspectives and intent across large groups will, I believe, fall behind if they don’t account for this step-change in capability. However, I am not a blind AI fanboy. Ethics and modelling outcomes have been central to my practice for years, and I have been a long-time critic of how the unprincipled development and application of technology has had predictably dire outcomes for society. Having previously delivered AI ethics and governance projects, I also knew that rolling out something like this would require making decisions with very real consequences. I am not going to go into detail here on how, exactly, I addressed each of these issues. Many are worthy of future posts. This is not even an exhaustive list. Suffice it to say, however, that in my view, AI platforms and any prospective users of those platforms, need answers to these questions and many more. During development, I’ve noticed a deep chasm between those with technical expertise and those who are shaping policy and engaging in discussions on ethics. Part of my motivation in developing this platform has been to create a kind of “thesis” project that is fully production-ready, that is a technical reflection of many of the more philosophical, ethical questions relating to this technology. It’s why my writing has lapsed in this space for a while: I was writing something else. Now on to some of the issues and questions I addressed in my design. Privacy Listening to conversations at scale creates a host of privacy concerns. If “collective intelligence” is the goal, then trust and the confidence of individuals to speak freely is a precondition. If they are suspicious of the recorder, they won’t share. * What happens with the recordings, and who can listen to them? * How will personal side conversations be managed? * Where are the recordings being processed, and what are the policies of the processor? * Are statements being personally attributed, and if so, who has access to those attributed statements? * Are my conversations being used to train models, build someone else’s product, or serve someone else’s secondary business model? Security If these conversations are confidential, proprietary, or otherwise sensitive, there are a host of questions that emerge end-to-end in terms of how data is gathered, transmitted, processed and stored. * Can the radio frequencies from any recording devices be intercepted in order to eavesdrop on in-person conversations? * Do recordings stay on any recording media between or after meetings? * If recordings are being transmitted, can the data be intercepted in transit? * Can AI models learn, retain or leak any of the materials being processed beyond the scope of a given group? * Is all original, processed or derived data stored securely? Cognition Artificial Intelligence, when applied to cognitive work, can either augment human performance or systematically degrade it. Especially when applied to difficult, strategic conversations about the unknown, AI can adversely impact the ability of humans to tackle thought-work. * Is AI being used to replace creativity, decisions or tasks requiring judgement? * Who is generating recommendations or options - humans or AI? * Is the “work” being done by the AI assistive, or authoritative? * Does the application of AI generate additional cognitive load through over-generation? Human Agency AI systems can be extractive and erode the importance of human roles, or can be assistive and extend human capability. * Is the role of AI generating new value in the work being done by humans, or replacing existing roles previously done by humans? * Does AI draw from outside or unverifiable sources that compete with qualifying knowledge and expertise of human operators? * Is AI being used to make judgements that carry liability or normally demand formal accountability? * Is the role of AI creating dependence that might diminish the critical faculties of human users of the system over time? * Who decides what is important, and are those decisions clearly and transparently recorded? Sovereignty The development of AI is taking place on the backdrop of renewed geopolitical conflict, and control of the technology, infrastructure and data involved is now subject to state surveillance, restriction and control. * Is any part of the AI system - from collection, to transmission, processing and storage - transiting a potentially adversarial jurisdiction? * Are any critical components of the system vulnerable to import or access controls that might render the system inoperable? * Does the system rely on the goodwill of foreign or adversarial governments? * Is any part of the system in a jurisdiction that can compel involuntary access or disclosure? * Is the system in a jurisdiction that will credibly enforce stated privacy and content protections? Trust Many of the leading AI labs have systematically violated existing copyright laws and are backed by governments that prioritize rapid growth and technological supremacy over human, labour and privacy protections. Further, frontier labs have opaque cost and pricing structures that do not appear to align with publicly available estimations on delivery cost. * Does the AI provider have a verifiable set of protections in place for secondary uses of customer data? * Has the AI provider ever been found to infringe on the use of data they do not have rights to? * Do the AI provider’s prices accurately reflect the cost of delivering services, such that current prices are a reliable predictor of future prices? * Has the provider ever been seen to misuse, mishandle or misappropriate private information? * What assurances do users have on the accuracy or provenance of information provided by the service? * Are the stated aims and objectives of the provider or its leadership aligned with the interests of its customers? And So… Where did this leave me? There was, somehow, nothing I could find on the market that could record 20+ conversations in a room simultaneously, and no provider for processing all of that information in a way that satisfied all of the conditions above (and many more). So, that is why I built Knowledge Commons (https://www.knowledgecommons.ai) and the Raven Recorders (https://www.ravenrecorder.ai). I could sit on the sidelines and complain about how the AI industry was taking the wrong path, or, I could build something that demonstrated what I thought the right path should look like. Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to Goal17 at goal17.substack.com/subscribe (https://goal17.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4)
6 Aug 2025
The Future of Decision-Making
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. Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. 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 (https://goal17.substack.com/p/intent-is-everything) 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 (https://goal17.substack.com/p/design-thinking-vs-designing-thinking). (https://goal17.substack.com/p/design-thinking-vs-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 (https://goal17.substack.com/p/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 (https://2025-happening.lovable.app). (https://2025-happening.lovable.app) 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. (https://thevalueweb.org) Get full access to Goal17 at goal17.substack.com/subscribe (https://goal17.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4)
20 May 2025
What I Learned Trying to Influence the Canadian Election
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. Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. 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 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. Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to Goal17 at goal17.substack.com/subscribe (https://goal17.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4)
5 May 2025
The Coming AI Policy Challenges
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. Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to Goal17 at goal17.substack.com/subscribe (https://goal17.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4)
29 Apr 2025
The Overton Window is Broken
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. Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. 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. Get full access to Goal17 at goal17.substack.com/subscribe (https://goal17.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4)
23 Apr 2025
Foresight has a Disinformation Problem
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. Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. 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 (https://web.archive.org/web/20161125135500/https://www.weforum.org/agenda/2016/11/shopping-i-can-t-really-remember-what-that-is). 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 (https://horizons.service.canada.ca/en/home). 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 (https://horizons.service.canada.ca/en/2025/01/10/future-lives-social-mobility/index.shtml)”. 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 (https://www.cbc.ca/news/politics/policy-horizons-report-2040-poilievre-1.7515683) 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. Get full access to Goal17 at goal17.substack.com/subscribe (https://goal17.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4)
16 Apr 2025
Process Activism
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. Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. 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. Get full access to Goal17 at goal17.substack.com/subscribe (https://goal17.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4)
31 Mar 2025
AI Principles in Collaboration
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. Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. 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. 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25 Mar 2025
Sovereign Tech Stacks
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? Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. 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. Get full access to Goal17 at goal17.substack.com/subscribe (https://goal17.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4)
21 Mar 2025
Move Fast and Fix Things
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. Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. 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. Get full access to Goal17 at goal17.substack.com/subscribe (https://goal17.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4)
6 Mar 2025
What Use Is Truth?
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. Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. 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 (https://www.researchgate.net/publication/3228201_Nobody_Ever_Gets_Credit_for_Fixing_Problems_That_Never_Happened_Creating_and_Sustaining_Process_Improvement), 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 (https://www.unep.org/resources/global-foresight-report) 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) (https://goal17.substack.com/p/osint-is-the-future-of-news)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. Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to Goal17 at goal17.substack.com/subscribe (https://goal17.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4)
27 Feb 2025
Listening as Problem-Solving
While it often seems that people are just speaking for the sake of it, I want to talk a bit about conversations that are directed towards a specific purpose, and how we can adjust our listening to support a group to come to an outcome through conversation. When we speak with others, we are (ideally) trying to create a kind of shared understanding and meaning between us, but because we are all approaching the conversation from our own perspectives, worldviews and biases, we can get lost long before anything coherent emerges. Having a few mental models that can help orient you in these situations can help to shape a conversation in a way where real progress can emerge, with the obvious caveat that trying to force-fit a conversation into the wrong frame can be as unproductive as using no model at all. A little nuance goes a long way with this. Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Here we address dialogue with the purpose of understanding a problem, or imagining a solution. As a listener, I have often heard groups talk past each other as they try to work through a problem, and the issue is often not just the way in which they were listening, but what they were listening for. I offer two models here from MGTaylor, which are often used to design conversations. I won’t go into great detail on the models themselves, but will instead focus on how they inform the structure of conversation, and how they can help us make sense of what we are hearing. I first came across the use of one of these models as a listening lens from Dan Newman, who used the Vantage Points (https://legacy.mgtaylor.com/mgtaylor/glasbead/vantgpts.htm) model to identify when participants were talking about different “layers” of the same system, and from Kelvy Bird, who would use the Creating the Problem (https://legacy.mgtaylor.com/mgtaylor/glasbead/problem.htm) model to create a visual structure as she recorded aspirational conversations. In all of these cases, you can use these models to guide your listening in a few different ways, depending on your role in the conversation. As a listener or participant in the conversation, they can help orient you to where the other participants in the conversation are coming from, and how you might come to alignment. As a facilitator, you can use these models to discern where a group might be sliding into dysfunction, or how you might need to shift the conversation to come to resolution. As a graphic recorder, you can use these models as a way of reflecting the deeper structures of the conversation in order to reveal to a group the gaps in their thinking. Vantage Points - Listening for Coherence The first of these models is the Vantage Points model, which is meant to express the connections between the purpose of an organization, the things that it ends up doing, and everything in between. In professional settings, so many of our conversations focus on how we should work and what we should be working on, and sometimes, why we’re working on what we’re working on (a simplification, sure). The answer may lie at one of those levels, or at all of them, but be different depending on what vantage point we’re using to frame the issue. So we can listen, we can focus on the person, we can make connections…but can we solve the problem? Are we actually talking about the same thing? Applied to these kinds of conversations, the Vantage Points model suggests that work unfolds across a number of layers…that there is a logical structure to what we do if all our decisions are in harmony and work as a system. An organization exists for a reason, organizes itself to do that work, sets rules to keep itself on track, charts a path for how it will achieve its goals, sets steps to achieve those goals, marshals its resources to take those steps and undertakes the day-to-day work to get it done. Each of the parts in that “stack” need to make sense for the whole thing to work (and if you missed it, they map to the layers in the model at the top of the post), and if the decisions and work at each level align with all the others, everything goes smoothly. Based on time, personality, complexity, size, and our own particular role, we might focus on one part of that structure, and lose sight of the others. Likewise, when the world changes and we need to adapt, small changes at one level might have implications at others. I might be focused on building a website for my company, and trying to make a simple decision on where to host my content. This is a tactical decision, it will affect the simple tasks I end up doing in the future (how easy will it be to post things?) but might undermine my strategic intentions (does it connect with the audience that matters most to me if I build it one place, or another?) or may conflict with the very purpose of my company (am I in the business of making content? Is that what we set out to do in the first place?). A simple conversation with colleagues about a website, then, might involve thinking about our shared enterprise at multiple levels at once. I want to talk about how hard it is to upload new content, but IT is thinking about the infrastructure, while marketing is worried about our brand standards and strategy is starting to wonder why we’re doing this at all, when we’re actually in the business of selling staplers. But the conversation is just about “website”. As a listener, try to tune in to see if individuals in problem solving discussions are actually all having the same conversation. The meeting might be about “Deciding where to host our content”, but one person might be talking about time management, another about user experience, another about customer acquisition and the fourth wondering about why we produce content at all. They all think they are having the same conversation, but because they are each speaking from a different vantage point, the conversation just seems to go in circles. This same dysfunction can happen within an individual. I might seek advice of my peers on which accounting software I should be using for my company. This could be a lively debate about which ones manage exchange rates and expenses effectively. Ok, not that lively. But I appear to my peers quite fixated on this problem. As they coach me through this choice, one might wonder, am I fixated on tasks and details I can control in accounting because I’m afraid to focus and spend my time on the harder question of how to bring revenue into my company? Through attuned listening, we can pick up clues as to where disconnects lie. Using the Vantage Points model as we listen can help us discern which level our conversation should be on. If we have collective clarity on one level, we can use that to tackle the next, but if I am talking about what while you are talking about why, we will both walk away frustrated. Defining the Problem Even when a group is engaging at the same level, a common struggle is to accurately define the problem that they are trying to solve. A typical back and forth might be between statements about how they wish things could be, and complaints about how things are now. These conversations can quickly begin to feel circular, as many of the points will often feel disconnected from one another. But just because someone starts a sentence with “Well, the problem is…”, doesn’t mean that what they say next is the problem. The model we can use to tune our listening in these situations is the “Defining the Problem” model (https://legacy.mgtaylor.com/mgtaylor/glasbead/problem.htm), from MGTaylor. This is not only a useful lens to use during a problem-solving conversation, but also when you’re scoping a project or trying to hone in on a set of objectives. The conceit of this model is that when we identify undesirable aspects of our reality, those elements we identify aren’t necessarily problems; they are conditions. They might not be conditions we are pleased with, but they are not necessarily the problem to be solved. That “problem” emerges when we define what we would like the conditions to be, that is, when we define a vision for that particular element of our reality. The problem, then, is whatever stands in the way of the conditions changing to meet our vision. When we tune our ear using this model, we can begin to sort what we’re hearing between the conditions that people are upset about, and the aspirations they have for how things might be. I’ve always felt that you can save a lot of energy by avoiding the trap of solving the wrong problem really well. Often, when someone in a group becomes fixated on a particular condition being “the problem”, it is because they have an unstated or undeclared idea in their head of what an ideal state for the issue might be. Someone else might find that condition to be equally problematic, but for entirely different reasons. When participating in a conversation that is meant to define the problem, it is worth splitting out - either in your head, or explicitly with the group - which conditions, in particular, people think are problematic, but also what the elements of a desirable end state might look like. Breaking the conversation up in this way can allow you to find where the actual problems lie - between the conditions we agree we are unhappy about, and the vision we share for the future. I have previously mentioned the importance of shared intent (https://goal17.substack.com/p/intent-is-everything) in group process, and I view the ability to effectively discern “the problem” as very central to finding that intent. A bug to you might be a feature to me. Being able to structure our listening around this type of model can help us navigate to some intent, and also keep us from getting hijacked by control statements like “…well the real problem is…” where participants try to capture the narrative. Treat each “condition” as a point of inquiry, not a definitive answer. Listen if there is agreement on the conditions. Ask about what “good” might look like, and if there is agreement there, but keep in mind that if either side of that equation is unclear or not shared, then you probably haven’t found “the problem.” And so… As you might have guessed, you might be using both of these models at the same time, and possibly others. There is a third model I will explore separately that can help navigate the question of intent, but two models, for now, should be plenty. Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to Goal17 at goal17.substack.com/subscribe (https://goal17.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4)
10 Feb 2025
Listening with Your Whole Self
Building on “Why We Should Listen (https://goal17.substack.com/p/why-we-should-listen)”, this is the first tool for tuning your listening in an intentional way, by clearing your head of mental models that can cloud your attention. When I was studying zen meditation at Kouun-ji Temple (the “ji” actually means temple, so this is like saying “Fedex Express”, but it sounds weird without it) my teacher, Yoshitomi-san, would tell me stories about the visiting classes of kindergarteners that would come to visit on field trip. I loved that in Japan, kindergartens would visit a temple and learn about zen, instead of the standard maple syrup field trips we would do in Canada. Though I do love maple syrup. Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. When explaining meditation to the little kids, he would talk about thoughts creeping in to your head, like an itch, or like a fly landing on your face. Suddenly all you can think about is the itch. You want to scratch it so badly, that it pulls you away from what you were doing, that it takes your whole attention. I think trying to listen can be like that. I know I asked you about what you’re doing, but now that you’re telling me, I realized I’m not sure what I’m making for the kids for dinner tonight. And I forgot to send that email. And I actually only wanted to talk to you because I’m hoping you could introduce me to that friend of yours who works at Apple. And I can’t believe how bad I look today. Didn’t I just get a haircut? How is my hair already sticking out again? There are a lot of things that get in the way of connecting with another human through conversation, and quite a lot of them play out in our own heads. Being conscious of those things can help us to “show up” in an intentional way. What gets in the way I’ve simplified this down to “what gets in the way.” Jeff Hawkins, in his 2004 book On Intelligence, wrote a lot about how our brains are actually outputting a huge amount of information as we try to perceive the world around us. We are constantly comparing, remembering and projecting as we look and listen, and sometimes more than we are directly perceiving. In this (super scientific) diagram, notice that while we’re speaking, there is the direct exchange of information in terms of what I’m saying, what I hear, and what I see, but there is a heap of possibly unrelated and possibly counterproductive activity happening in my head at the same time. My mind is more pre-occupied with who I think you are, who I think you think I am, the impression I want to make, what I imagine you’re going to say and what I’m pretty sure you don’t want to hear than it is with what you’re actually saying, and why. Nervousness, self-consciousness, social position, bias, prejudice, assumptions all play into this model, especially in settings where playing a certain role, achieving a certain outcome or making a certain impression are seen as important. Cultivating a listening practice means reflecting on how you show up in a conversation, and identifying the barriers and baggage you carry into each interaction. Do you want to be sure you’re seen in a particular way? Are you conscious of assumptions that others may hold about you? Do you have an expectation of how an interaction is going to go before it even begins? Is it based on what the other person is doing, what you think they might do, or what “others like them” have done in the past? Our brains are pattern-recognition machines. We can’t help but build models of the world to make it easier to navigate in the future. But when we’re speaking more to our mental models than we are to other people, we become incapable of connecting and learning. Whether you are part of a group, in front of a group, one-on-one, or trying to capture a conversation in notes, becoming aware of your mental “outputs” can help you tune in to start focusing on the “inputs” As a listener, then, I want to peel away is the following: * Who am I listening to, and what are my assumptions about that person, and what they would say? * What are they talking about, and what do I think I know about that topic? * How do I think others see my contribution to this topic or conversation? * What do I think is “supposed to happen” in this conversation? You can add many more questions to this list, but, in essence, what you want to surface in your awareness are your own expectations and projections of an interaction, so you can let go of them in order to dedicate more cognitive energy to what is actually being said. If you are facilitating a group, you might be worried that what is being said will mess with your plan, or that the group might turn on you at any moment. If you are in conversation with a colleague, you might be waiting to hear if they finally “get” what you’ve been trying to tell them. If you are a graphic recorder, you might be worried if you’re doing a good job of capturing or if people will judge you. In your next interaction, try to be conscious of the mental models you bring in, and try to consciously let them go. While you are listening, if something doesn’t completely make sense to you, resist the urge to fill in the blanks with what you guess they mean, and ask. Try to fully explore what you’re hearing, with no expectation. Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. 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7 Feb 2025
Why We Should Listen
This material is the result of a line of inquiry that started with me trying to learn the craft of graphic recording. Graphic recording involves listening to group conversations, then using visuals to model the content and connections as they emerge. Spending an inordinate amount of time in the workplace listening as a professional activity - and, in many cases, feeling as though I was the only person in the room that was actually listening - I found myself starting to wonder why some conversations were so easy and enjoyable to model, and why some were so frustrating and painful. As I tried to represent not just the content of the conversations, but also the meaning, I started to wonder about the structure of meaning, and how it gets created between people. Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Some conversations seemed so productive, with new and exciting ideas coming out of the back-and-forth, while others went in circles. I couldn’t help but wonder: why? What was the difference? As my practice shifted more towards facilitation, I found that the muscles I had trained in listening became one of the most valuable parts of my practice. A few years ago, I wrote a small guide on how to begin training yourself to be a better listener. It was too short to be a book, but it is too long to be a post, and has, instead, just languished in the deep recesses of my hard drive. I’ve chosen to post it all here as a short series, as I have never felt so acutely that we all might benefit from taking a moment to really listen to one another. Hearing each other is not good enough. We need to really listen. Why Listen Ever since the Cognitive Revolution, Sapiens has thus been living in a dual reality. On the one hand, the objective reality of rivers, trees and lions; and on the other hand, the imagined reality of gods, nations and corporations. Since large-scale human cooperation is based on myths, the way people cooperate can be altered by changing the myths - by telling different stories. Sapiens, Yuval Noah Harari Our success as a species has been built on our ability to create a shared understanding of reality - to craft a sense of meaning that exists only in our minds, and not in the world around us. Our capacity to communicate our thoughts and ideas with each other allows us, as individuals, to expand beyond what it is possible to experience in a single lifetime. Through listening we can assimilate the wisdom of others. We can absorb entire lifetimes of experience. We can experience parallel experiences, infinite “what-ifs” and variations on our own chosen path. Or not. We are taught to lead, to inspire, to tell compelling narratives, to “fake-it-till-we-make-it”, to add value, to engage, find our voice, win people over and take space. But what happens if we all do that? A group of people on broadcast-mode is trapped in a kind of social-dysfunction that undermines the very power that has gotten our species to where it is today. Listening enables us to extend beyond ourselves and tap into a collective conscience that is available to all of us, but accessible only to those who consciously choose to do so. Whether you believe neuroscientists, Daniel Kahneman or Gautama Buddha, we are locked in our own heads interacting with faded and faulty representations of the world around us, and only rarely the world itself. We live in our own mental models. Daniel Kahneman’s important work on mental function showed that our early life is spent building a set of representations of the world around us - mental models - that allows us to navigate the world in a very low-energy-consuming way of being that very much resembles auto-pilot. He calls this System 1 thinking. We spend most of our time in System 1 thinking, following our mental models’ idea of what the world is, rather than responding to the world itself. We only access System 2 thinking when we encounter dissonance with our expected results, at which point our full mental faculties are “switched on” to figure out what is happening. When we spend all our time talking, we generally stay locked within our own representations of the world. Reflexive listening is what we most often do - waiting for an auditory stimulus that triggers us to make a culturally, contextually and situationally appropriate response. “How are you today?”, “How will you be paying?”, “What are you up to this weekend?”, “So what do you do?” all trigger near-automatic responses. Try answering randomly - in many cases, people will often ask you to repeat yourself, as though they hadn’t actually even heard the response, or will become flustered, confused, or annoyed. This is what finding the edges of a mental model looks like. We live our lives in layer after layer of mental models, some conscious, many unconscious, from how we walk down stairs, to how we structure our tasks to who we believe ourselves to be. So why listen? Deeply listening to others can allow us to challenge the limits of our own assumptions, biases and mental models to learn from the experiences of others and become more than only ourselves. Deeply listening can also be a profound act of service to others, unlocking their own constraints and boundaries by creating a space between you that is more than either of you. Ways of Listening When we interact with others, we are aware that there is probably more to that person than just the interaction we are having. Sometimes we interact with just the representation people put out. Sometimes we interact more with the context we are in than the person - the roles we play, whether it is a server in a restaurant, or a colleague at work. The iceberg metaphor comes up so often because we know that beneath the surface, there is more than just the context, and more than just the representation or projection of a person in front of us. Let’s be honest; we use roles and mental models because accessing our full faculties all the time is exhausting. Part of the path of Buddhism is to free yourself of all the models - assumptions, beliefs, ideas of other and self - that shroud the the world around us from our view. But that’s kinda hard. One simple “hack” to start on the path of deeper listening is to substitute an explicit model for how to approach the conversation for all the implicit ones rattling around in our heads that we might not be aware of. Over the next few posts I will share some models that you can use as a way of thinking about why you are listening, as a way of tuning your ear, and directing your inquiry. It’s easy to get lost in the details of a conversation, or be in a rush, or be thinking of everything you have to do, and forget about what the purpose of the conversation is. And, when I say purpose of the conversation, I’m trying to set a higher bar. I’m advocating for a purpose that is beyond simply being transactional, and that is part of the assumptions built into the models I’ll be sharing. In a series of posts, which I will add links to here as they go live, we will explore the use of listening to transform a network, to build community, to shift a system, to build a relationship, to reframe a problem, or to create something new. Each has a different model you can use as a listening lens. Listening Model #1: Listening With Your Whole Self (https://open.substack.com/pub/goal17/p/listening-with-your-whole-self?r=khw0h&utm_campaign=post&utm_medium=web) Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to Goal17 at goal17.substack.com/subscribe (https://goal17.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4)
30 Jan 2025
Visual Practice Starter Pack
I’ve had a few requests on where to get started to develop a practice in graphic facilitation, so I thought I would write my response here instead of firing off a few links. I still remember walking into my first MGTaylor environment and seeing a krew member scribing on the whiteboards. The first thing I noticed was that she was writing in a font. She was capturing what a group was saying, as they were saying it. When I asked her afterwards if she knew what they were going to be saying beforehand, she just shrugged, and said she just listens and lets it flow. I was awestruck, and knew that if we got to choose which role we would do, I wanted to do that. Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. A lot of people seem to have the same reaction when they see graphic facilitation for the first time, but as I’ve seen the practice become more common, I’ve also seen a lot of drift, and a much wider range of practice into some that is more utility-focused, and some that seems mostly aesthetic. If you'd like some more context and background on what graphic facilitation is and where it comes from, I suggest you read this great post by Christopher Fuller (https://www.griotseye.com/insights/history-graphic-recording). I'm grateful that he wrote it, so I can avoid writing that context and get down to answering the question of how to get started. This post is for anyone hoping to take visual notes either for themselves, or in a group setting. While I use these techniques in workshops all the time now, as a side note, this is a skill I wish I had when I was in college. Oh, the notes I would have had! The most useful distinction I found early on in trying to learn scribing was made by Tony Buzan in "The Mind Map Book" (https://www.amazon.ca/Mind-Map-Book-Tony-Buzan/dp/0452273226/ref=sr_1_5?crid=2ZLDWL0JC73TU&dib=eyJ2IjoiMSJ9.B96sKyJihAweI7MTGBy8I8Qqj4g5yJ3e1yipRzM3-0gZj_YBA697gWZAg593v0Rknm7xiThaDr1zcDXqEs1RheV1aBRCJ_PAZKPXwQ5oCr26zRqR27XCxO1jk3vARlCnQpYwZc-3obaRlywdhDb3D207YE0q7pciACeUqBNtJ-p8IfFm2wikHqUxXtTflAXDrx2CJuMIrHFAoEUBjtW9Z4jQ-tqUpcANE2bBiA1yH-lDVJh54mspRb1I3tCw1UF8RZIyYivZBcRP4fERh8xMooIavLrnM1bl9YPbLHnG_VY.nK-b1ZnVUmDJlmCJ8g2MmeIMTj7ir6ZKYAZ7Ia-KC94&dib_tag=se&keywords=tony+buzan+mind+map&qid=1718804957&sprefix=tony+buz%2Caps%2C134&sr=8-5) in which he differentiates between note taking and note making. Note taking is capturing information uncritically, usually as it comes at you in time. It tends to be linear, and reflect the order in which information is presented. Note making, by contrast, requires the listener to reflect on the meaning, structure and connectivity of information before recording it in order to capture it in a way that reflects the information at a conceptual level, as opposed to a strictly sequential level. Whatever style you end up choosing for how you represent information, if you pay attention to this difference, you'll immediately get a few benefits: * Improved Memory: by taking the extra step of considering how pieces of information connect with one another, we switch our brains from passive consumption to active processing, which aids in our ability to recall that information later. * Pattern Recognition: Actively listening and recording connections over time allows you to spot patterns and structure in the information, which can give you a second order of understanding in the content. * Surfacing Meaning: Information on its own isn't necessarily useful, but as patterns emerge we can start to find new meaning in the flow. Depending on the amount of information and the length of time in which it is presented, that can easily outstrip our working memory - by visually structuring information as it comes, we can go beyond our cognitive "buffer" and find meaning in larger pools of information. * Useful Reference: By creating a distinct visual structure that we associate with a time, place or conversation, we create a handy reference that we can use for later recall. Well structured visual notes can allow you to replay a conversation months and years later, creating a strong association with the moment they were captured. These benefits apply both to when you use these techniques in your own notebook, but also when you use them to capture notes for a group on a whiteboard in a meeting. It is also worth noting that in a group setting, having a shared experience of those benefits can create a very positive dynamic on a collective conversation. I would also say that each of those benefits should be applied as a kind of design principle for whatever style you choose to develop in your scribing. As graphic facilitation has become more popular as a profession, there has been a swing towards more form over function, where scribes create beautiful murals with little informational density or usefulness. Always ask yourself what the purpose is for the record that you are making, and be sure that the way that you are making that record will serve that purpose. I have actually found that my style has become more austere over time, as I focus on adding just enough beauty to trigger interest, but with an emphasis on the ideas that emerge and creating a record that will live beyond the moment in its usefulness. So, on to where to get started. Accept that this is a journey, and that mastery will come slowly, but utility can come relatively quickly. There are a few dimensions you will need to consider as you develop, and I'll give some guiding questions for each in the interests of keeping this from getting too long. You'll need to work on technique, practice and structure. Technique There are some basic techniques involved in scribing, and these are what give learners the greatest amount of anxiety coming in. "But I can't draw!" is the usual refrain. While artistic talent never hurts, focusing on the basics will get you 80% of the benefit. First, you need good lettering. There are lots of resources out there, but I'm biased towards lettering from architectural drafting (https://artdepartmental.com/blog/architectural-lettering/#:~:text=What%20is%20architectural%20lettering%3F,are%20universally%20easy%20to%20read). I learned old-school drafting in high school, and the discipline of that style has informed my lettering ever since. I found a great video here (https://www.youtube.com/watch?v=eGUDQ1F5tco&t=231s), which you can use as a starting point, but developing a legible, consistent style is an important baseline. Along with great lettering, you’ll need some basic shapes, connectors and basic iconography. Graphic Facilitators Guillaume Lagane and Nicolas Gros did a great job of laying out hundreds of common objects in three levels of visual fidelity in this book (https://www.amazon.fr/Encyclopédie-visuelle-dessins-croquis-crobards/dp/2212562160), but you can find plenty of inspiration in comics and across the internet. If you’re in the “but I can’t draw!” camp, get some inspiration from books like Stickman Odyssey (https://www.penguinrandomhouse.com/books/305878/stickman-odyssey-book-1-by-christopher-ford-illustrated-by-christopher-ford/) (the entire story of Homer’s Odyssey, illustrated with stickmen) or classics like Ed Eberly’s “Drawing Book of Animals” (https://www.amazon.com/Emberleys-Drawing-Animals-Turtleback-Emberley/dp/1417734027?ref_=ast_author_dp) (did anyone else grow up with this?). When in Doubt, Copy There are so many talented graphic facilitators out there, it can help when you’re starting out to find someone with a style that you like and start out by trying to emulate their style. Some are illustration-heavy, some very text-based. Some use colour in novel ways and others are monochrome. Structure, hierarchy, layout all manifest differently in different styles, so it helps to look at what is out there and see what speaks to you. Some examples of practitioners that I admire (in no particular order, and also just based on who has some of their stuff online: Alicia Bramlett (https://www.pinterest.com/alicia_bramlett/graphic-facilitationillustration/), Kelvy Bird (https://kelvybird.com/portfolio/), Guillaume Lagane (https://www.pinterest.com/glagane/), Alfredo Carlo & Housatonic (https://www.housatonic.eu/projects/#graphic-facilitation), and Liisa Sorsa (https://www.behance.net/thinklink). There are so many others, but I’ve realized to my dismay that many of us don’t have much out there online - I’d be grateful if anyone sends me links to more that I can include here. Practice A common misperception is that good graphic recording is all about drawing. What many of us have found as we’ve tried to structure trainings around graphic recording, however, is that it is much more about listening and structuring information. I found a very old piece I wrote on the topic (https://aaronwilliamson.com/shiftandshare/?p=83) (2009!!) when I was working in parallel rooms with Sita Magnuson and Kelvy Bird. Between sessions, I took a peek at their work and realized that we were all representing what we heard in fundamentally different ways. It got me thinking about how we were engaging with what we heard, and how we structured that on the boards. I have a lot of other materials that I’ll be formatting on to this Substack soon on developing a listening practice. Kelvy Bird has also published a great book on her approach called “Generative Scribing” (https://kelvybird.com/generative-scribing/) that is worth a read. Other Stuff A few other resources that are out and about: * Christopher Fuller’s take on how to get started in graphic facilitation (https://www.griotseye.com/insights/how-to-practice-graphicrecording) * Lots of examples from Peter Durand at Alphachimp (https://www.alphachimp.com/blog) * Dave Gray’s Marks and Meaning (https://xplaner.substack.com/p/marks-and-meaning) * Graphic Facilitation (https://www.facebook.com/share/g/15gm5AKMiv/) Group on Facebook (I know, Facebook…remember that?) I am aware that I am leaving so much out of this - this post has been a classic case of having so much that I want to include in it, that I get overwhelmed and put it off until I have more time to get it all in. Which never happens. So, this is the starter pack. I welcome any other links or resources people want to send my way, and for my part, there are more detailed parts of this I’ll dive into at a later date, but here’s to a first draft seeing the light of day. 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29 Jan 2025
Deep Dive: Why Crypto is So Polarizing
Note: I started writing this before Trump and Melania launched their own memecoins, which really raised the stakes for everything I was trying to lay out - it validates for me how important it is for people to understand what is involved, and what is at stake, in the crypto space. Policy moves slowly, tech moves quickly and finance moves in the shadows. I am always fascinated by the intersections of these worlds, and by the difficulty in making good decisions in the places where they overlap. Having policy keep up even with something as straightforward as ridesharing is a challenge, and it took years for the world to understand what was happening under the surface on Wall Street with CDOs. Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. So crypto, which involves both a deep, obscure tech ecosystem wrapped in jargon and hype, and an intersection with financial services is ripe for policy chaos and poor decision making. I believe that having the right questions sits at the heart of good decision making, and that, as it stands, we are so conditioned to ask the wrong questions about crypto that we are sleepwalking into a crisis. The Problem with Crypto I’ve been meaning to write this for some time, and the push that has finally made me do it was this article by Paul Krugman - the public economist and (formerly) NYTimes columnist - called “Crypto is for Criming (https://open.substack.com/pub/paulkrugman/p/crypto-is-for-criming?r=khw0h&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true)”. It is infuriatingly elitist and dismissive, with a mocking image of Doctor Evil at the top driving home the point that this is a topic barely worthy of debate in polite society; it is, instead, the domain of “crypto bros” and criminals, rather than serious economists and investors. So, then, one wonders, why would a Nobel Laureate feel the need to comment on it at all? For context, I went “down the rabbit hole” on crypto and blockchain a few years ago when a colleague at a major tech company reached out because they were trying to evaluate whether blockchain technology would be a good choice for a particular challenge they were working on. She found that the answers she got from her peers were either cultishly supportive of blockchain, or vehemently dismissive, with little clear justification either way. I was intrigued, because there didn’t seem to be a clear way to evaluate the suitability of the technology for her particular use case, and the level of passion in the responses didn’t seem to correlate with any rational criteria. And you see this pattern reproduced in media and society more broadly - either people are fanatically supportive of “crypto”, or adamantly opposed to the “Ponzi scheme” that is only fit for money-launderers, drug dealers and gamblers. I believe that ignoring what’s happening in this space is increasingly dangerous, not least because backers of the technology are now taking over its regulation in the US, and we’ve spent the last 5-10 years deliberately avoiding the real issues surrounding it. Question #1: Who Wouldn’t Want to Use Crypto? In Krugman’s article, he can only think of three possible explanations for the rise of crypto: * Maybe it’s a digital asset, like virtual gold, so it’s uselessness is okay? (but he clearly doesn’t think so) * Maybe it’s “all speculation or gambling…largely driven by testosterone” * Maybe it’s just for “tax evasion…blackmail [and] money laundering?” Krugman, like many others, are limited in their thinking from their starting premise that this is all rather ridiculous. The persistence of crypto is so insane, that the only possible explanation for its continued existence is that it enables shadowy crime syndicates and knuckle-dragging, UFC-obsessed, Trump-supporting gambling bros. So, what is the original proposition of a cryptocurrency that is so insane? The original problem it was meant to solve? “How do I quickly, securely and efficiently send money or to someone I don’t trust, without a middle-man?” Have you ever tried to send money to someone? How was the experience? Was it quick? Was it cheap? Are you a criminal, or were you just trying to split a dinner bill? Have you ever tried to send money between countries? How did you like the fees? Why did it take days to clear? If I reframed cryptocurrency as “competition on financial infrastructure to allow open systems of peer-to-peer exchange”, then one thing becomes VERY clear; the people who wouldn’t want to use cryptocurrency would be those with a vested interest in the current financial infrastructure. John Cassidy, with The New Yorker, wrote a fabulous article years ago just after the 2008 financial crisis, titled “What Good is Wall Street? (https://www.newyorker.com/magazine/2010/11/29/what-good-is-wall-street?printable=true)” that is very instructive here. Going back to base-principles, he explores just what the role of financial services is supposed to be. When the banking system behaves the way it is supposed to…it is akin to a power utility, distributing money (power) to where it is needed and keeping an account of how it is used. Just like power utilities, the big banks have a commanding position in the market, which they can use for the benefit of their customers and the economy at large. Part of the thrust of the article explores how the financial services industry has gone from a public utility to a rent-seeker on the entire economy. If up to 25% of GDP is devoted just to a set of functions that could be automated…might there be some resistance? Question #2: What Does Crypto’s Persistence Really Mean? Suffice it to say, the regulatory environment for crypto has not been very easy. Bitcoin was routinely criticized by the Biden administration for its voracious appetite for energy, with threats to clamp down to protect the environment. When the #2 cryptocurrency changed protocols to make it more energy efficient (to the tune of 99% more energy efficient), the administration turned around and threatened its users because now it had become a security (it’s a long story). The Securities and Exchange Commission has routinely refused to provide any regulatory frameworks for crypto, but has instead approached the industry only through enforcement, that is, they would never say what was allowed, but would prosecute based on shifting versions of what wasn’t allowed. High profile disasters like the collapse of FTX were the predictable result of setting no regulations around an industry with billions of dollars flowing through it. Which is all to say, there has not really been any strictly legal ways to experiment with cryptocurrencies. I’m struggling to think of any example of an industry with no rules or regulations that has grown to the size of the crypto space, which is, by today’s count, standing at 3.64 Trillion US dollars. That’s right. Trillion. So what’s going on? What does that mean? If Krugman is right, are we really looking at $3.6T worth of testosterone and drug deals? Isn’t this all just a scam, and a Ponzi scheme? I think that the single biggest risk that policy makers miss when looking at young crypto investors is not that stupid young men have been tricked into a Ponzi scheme, it’s this: Most crypto investors don’t think that crypto markets are fair, just like the stock market; they believe it’s fixed, just like the stock market. The only difference is that everyone in crypto admits it’s fixed. What this represents is a colossal, generational, and entirely rational, loss of faith in the traditional financial system. Young people feel shut out of the stock market, knowing that a very small group is capable of manipulating markets in ways that work to their benefit at the expense of “the rest”. A lack of regulation, in that case, becomes a feature, not a bug, as regulators are seen only as protecting the interests and privileged access of the elite. This couples with a second risk to create a systemically volatile combination: Stagnant wages, high living costs and a bleak sense of the future means many young people don’t believe it is possible to enjoy the quality of life their parents had without some kind of game-changing windfall. The social contract between generations and classes is breaking down, and the “rules of the game” seem more geared towards protecting the old and the rich than they are to keeping free and fair markets. Elite economists and commentators will mock a young generation focused on get-rich-quick schemes, while missing the fact that many don’t see how they could ever afford a house or a family without some kind of breakout. An investment that gets a steady return of 5% over decades won’t pay back student loans, medical bills and next month’s rent, but a sudden 1000x return on investment for some imaginary coin named after a dog or a cartoon frog might just pull them back from the brink. Why else would someone invest their child’s college fund in a coin named after an oral sex sound effect? (https://www.vanityfair.com/hollywood/story/the-rise-and-fall-and-rise-of-hawk-tuah-girl) Just as many in the establishment were blindsided by Trump’s election victory because they missed out on how alienated and disenfranchised many in the country felt, they are once again going to miss the same dynamic playing out in financial markets. Ironically, crypto markets as speculative investments seem to have emerged largely because a lack of regulation has prevented cryptocurrencies from fulfilling their original use case: paying for stuff. In fact, wildly fluctuating, volatile markets make cryptocurrencies largely unsuitable as a medium of exchange. Instead of a relatively stable “coin” spreading through its use in new and interesting applications, the technology is spreading through proliferation into new speculative assets (as of today, there are more than 2.4 million cryptocurrencies being actively traded). Question #3: Does Crypto Signal Parts of a Future We Want? A big part of the reason the crypto world lined up behind Trump was a total frustration with the SEC under Gary Gensler and the Biden Administration, which seemed hell bent on the destruction of the industry. You can charitably say that the hostility towards crypto came from a genuine concern for investor rights and the integrity of the financial system. You could also, less charitably, argue that the Biden administration was acting primarily in the interests of the financial services industry, which is facing its Napster Moment. With the Trump administration coming in on promises of setting the crypto industry free of its shackles, it’s worth asking what the actual potential risks and benefits are, and what design challenges new regulations would have to solve for. What amazes me is that as we collectively mock cryptocurrency and ask “what is it good for?”, we don’t question why the financial sector soaks up a quarter of global GDP for what is, essentially, plumbing. If that was our entry point, you might start the inquiry with questions like “what would a financial system look like if it was designed for a globalized, digital age?” From that frame, and realizing that this January, we are going to start seeing some very rapid movements in this space, here is what I think we should be paying attention to: First, let’s accept that exchanges have all the dumb risks we’ve already solved over the last 250 years with banks and stock markets Let’s get the stupid questions out of the way first. It’s frustrating to watch the same risks play out that we’ve already sweat blood over. People wanted to buy and sell crypto easily, but it’s distributed. So some people made a centralized place - an exchange - to trade them. But there were no rules. Would you let the NYSE trade against the market, giving themselves an edge? Would you let a bank say it has your money, but actually spend it all? Would you let the bank bet all your money on something for itself? No. We’ve already been through all this, but we thought that somehow, with billions at stake, that “trust me, bro” would work just fine. This is almost a copy/paste on regulations. It is also no small irony that so much of the risks in crypto emanate from centralization in exchanges, when the technology itself is based on decentralization. Because the regulators have just been captured, we need to be clear on the stakes of the game…and what game we’re playing The debate so far has been successfully contained to “what would you even use the internet for?” Advocates of cryptocurrency have been so insufferable that everyone’s eyes glaze over when it comes up, but we are in a situation where Biden decided that the entire future of money and global financial flows was not worth talking about, so now all of those rules will be set by Trump. There are three possible, not-mutually exclusive futures with their own implications. The Bitcoin Future - Virtual Gold Standard & Digital Scarcity In this future we decide that cryptocurrency is best seen as an asset, or commodity to be traded and held as a store of value. Many in the crypto industry have been fighting for the classification of cryptocurrencies as a commodity, because the bar for regulation is much lower, and the requirements for disclosure and reporting are much less stringent. In this case, a digital asset is bought and sold with no implied contracts or obligations. Like gold, the asset is valuable because it is scarce and there are people who will pay for it. Scarcity, in this case, is created by the rules that are encoded in Bitcoin’s blockchain itself and the algorithmic limitations on how quickly new coins are added to the available supply. In its own way, the idea that a Bitcoin has value is no less ridiculous than saying a small piece of paper has value; it does because we agree it does. What we should be asking is, what does this allow that other methods of exchange do not? If we treat it as a prototype for a new system, we can evaluate its features, rather than just dismiss it completely for ideological reasons. Pros: It is digital, moveable, universal, decentralized (i.e. not controllable by any particular entity), scarce (value responds to demand), immutable (creates a permanent record), open. Cons: It is volatile (thus making it inefficient for exchange), inherently energy inefficient (scarcity created by increasing energy consumption), immutable (you can’t change it, even if you want to), open (as criminals have found out, all transactions are public). As you can see, some of the things that are features, can also be bugs. But they are all elements to be considered in the future we want. The MemeCoin Future - Snake Oil and Swampland in Florida The generous reading of the SEC’s enforcement posture to date is that they were trying to avoid this future (the less generous reading is that they have been in the pocket of the financial services industry, preventing open financial infrastructure from emerging). This is the version of crypto that everyone tends to think of - it’s a Ponzi scheme, a scam. Securities regulations have emerged over time as a way of protecting against scammers. While small, private companies can represent themselves as they please (up to a point - fraud is still fraud), once they attract a certain number of investors, there is a level of disclosure that we require of them to ensure that their claims are largely verifiable, so that the broader investing public can make informed decisions about where they put their money. At least, that’s the idea. As crypto projects began to emerge, it became clear that instead of offering shares on a public exchange, groups could offer tokens on crypto exchanges to raise funds, with none of the rigour or disclosure required by the stock markets. While there were many serious projects that did this to fund real efforts in creating new use cases, it also sparked a gold rush of memecoins that would hype a release then sell quickly to make a quick exit. In the stock world this was known as a pump-and-dump, and in crypto is referred to as a “rug pull”. There is a very classic shape to these graphs: The far left of the graph is the pump, and immediately after is the dump, where the project originators liquidate their holdings, take profit, and use retail investors as their exit liquidity. It means that the sellers make a fortune, and anyone who joins in even moments too late loses everything. The above graph, by the way, is $TRUMP coin. This is what the SEC has been trying to prevent. When I first started researching crypto, the Reddit forums for TerraLuna - which had just imploded in spectacular fashion - had suicide hotlines pinned as the top post. It was that bad. For the serious members of the crypto community, these are the embarrassing projects that tarnish the industry - hucksters and scammers hustling useless but catchy coins to the desperate, who are hoping to change their lives by catching the next big thing. At this stage, these “projects” make no claim to any kind of utility, but are acting as a kind of digital Beanie Baby. Melania Trump’s coin offers the following in its Terms and Conditions: THE MELANIA MEMES ARE DIGITAL COLLECTIBLES INTENDED TO FUNCTION AS AN EXPRESSION OF SUPPORT FOR, AND ENGAGEMENT WITH, THE IDEALS AND BELIEFS EMBODIED BY THE SYMBOL “MELANIA” AND THE ASSOCIATED ARTWORK (THE “ARTWORK”) AND ARE NOT INTENDED TO BE, OR TO BE THE SUBJECT OF, AN INVESTMENT OPPORTUNITY, INVESTMENT CONTRACT, OR SECURITY OF ANY TYPE. This version of the future is one where anyone can make any claim they like, and the onus is entirely on the buyer to ascertain whether a given project has any inherent value or not. The explicit classification here of digital coins as collectibles says it all, but also does nothing but reinforce scepticism about digital currencies. The Ethereum Future - The World Computer Ethereum represents an extension of the original logic that Bitcoin brought to life; if value could be stored and transmitted through a distributed, digital platform, then bringing that together with the many ways in which we exchange value in the digital world could allow us to collaborate and exchange in new ways. Put another way, the first wave of the internet was all about the decentralized sharing of knowledge, but nobody really knew how to share any way other than free. The next wave of the internet was all about extracting value, as advertising and surveillance incentivized centralization and control. This is also the wave that has brought endless subscriptions and user-lock-in as the model for rewarding work. Ethereum, as a distributed computing platform, brings in the possibility of incentive design - the ability to create and share value between people and groups in a peer-to-peer fashion without the intermediaries and tech giants that have crushed the open internet today. Web browsers that share the reward for viewing ads, or micro-tip the authors of content you read. Content networks that distribute benefit to creators. Business cooperatives that are governed by stake, and distribute benefit based on contribution. Music that auto-distributes to creators based on baked-in rights contracts. The vision is quite radical, and potentially quite disruptive to the many industries that are controlled by highly centralized organizations, like financial services, music, film, gaming, media, publishing and software. And so… It’s quite easy to see why there might be a considerable amount of resistance to blockchain and cryptocurrencies, given all that they potentially threaten in incumbent powers. Just in Canada, for music rights distributions, SOCAN collects $523 million (CAD) in royalties, and even after years of improvements, still only distributes $442 million…either by direct deposit, or by sending a cheque; a literal piece of paper in the mail. In 2025. Music rights on the blockchain could always and forever disburse automatically and instantly to all rights holders. The SWIFT network, Visa and other financial intermediaries would be potentially obviated as payments would settle themselves in real time. Which is all to say that the reason to pay attention to how all of this unfolds is not because, as Krugman says, crypto is only good “for criming”, but because crypto is good for everything that involves an exchange of value. So here are a few policy areas we should all be paying attention to: * Monetary policy: at scale, something like Bitcoin as a reserve currency affects a key monetary policy lever, as it involves a store of value that can’t be easily manipulated to address issues like inflation in a given economy. * Privacy: part of the reason crypto currency was appealing to criminals originally was the perception of anonymity. What they have since realized is that the blockchain is a permanent, public record of all transactions, which means future crypto currencies would need to balance the needs for privacy for individuals and the ability to mitigate illicit flows like money laundering and terror financing. * Securities: there has been no clarity on when crypto is a security and when it is a commodity. Initial coin offerings and similar efforts, to my mind, were clearly securities, but with Ethereum, a given contract on the network might be a security while the token is a commodity. There needs to be good faith, modern design applied to these rules to create regulations fit for a digital economy, not simply using old analogues for new paradigms. * Transparency: blockchains and cryptocurrencies should be uniquely good at creating transparency because they are public; failures of centralized entities like FTX are inexcusably stupid, because creating transparent proofs should be easy, and financial disclosure for things like ICOs can and should be hard-coded into the blockchain. Good regulation would set standards for these things that could be instantly verified. * Global Reserve Currencies: Again, at scale, this could represent a major rebalancing of how international finance works, and this comes at a time when global powers want to challenge the supremacy of the US dollar as a global reserve currency. Sanctions work, in part, because of that system, and the shift away from the US dollar to a universal digital currency would have a lasting impact on trade and geopolitics. * Security: there have been a number of high-profile cases where coding errors and various exploits have been able to siphon money out of various crypto networks. The more value that is stored in these networks, the more attractive they will be as a target (not to mention the threat of Quantum computing to cryptographic systems). If, for example, the US were to create a National Bitcoin Reserve, it would be nice to know that a 12 year old in Latvia couldn’t just walk away with it. So, while the previous US administration decided to just ignore this whole space and hope it would go away, we are now looking at the Trump administration setting the rules for, potentially, how all future trade and transactions will take place. I happen to think that’s worth paying attention to, and I’d love it if people like Krugman - a Nobel Prize-winning economist - would take it seriously enough to weigh in on the future. Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to Goal17 at goal17.substack.com/subscribe (https://goal17.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4)
26 Jul 2024
Design Thinking vs. Designing Thinking
During a recent training I delivered for a group focused on supporting multi-stakeholder processes, I realized that there were a few missing steps needed to get them in the right headspace to explore some of the concepts we would be going through. Everything we were going to explore would be largely useless if they didn't first accept that a lot of the decisions they would have to make were part of a domain of design, and if they accepted that, that they needed some clarity on just what type of design we were talking about. I think it's easy to accept that we can design a better car, a better app, or a better running shoe. When we can hold, touch and use something, we can imagine there was some care, some method to how someone designed all the elements that constitute the use and construction of that artifact. But when it comes to the intangible, I have found that there is a leap of logic required for people to accept that there is the possibility of applying method to the design of an idea, an agreement, or a decision. So, of late, I've found myself starting out any training by first making the case for design for those who find themselves in a situation where they work with other humans. The frame that I have found unleashes the most curious practitioners who follow down the most interesting paths of inquiry are those who imagine that their domain is not "design thinking," it is "designing thinking". Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. In making the case for design, there is a kind of red pill/blue pill choice to be made for the practitioner. What it boils down to is this: given what we know about how humans behave, and what we have learned from psychology, economics, biology, sociology, history, anthropology and all of their many sub-domains and combinations, are there any factors that make outcomes in groups more or less likely, more often than not? Are there any patterns that can be identified among variables that might be isolated? Does one environment vs. another create a different outcome? One thought pattern vs. another? Could these patterns be abstracted, or modelled? If you answered no to all of these things, and you believe that email is as conducive to deep dialogue as a mountain retreat, then you can take the blue pill. If you believe that, in fact, different conditions can produce different outcomes within individuals and groups, then you take the red pill, and this starts quite an interesting journey in exploring just which conditions might facilitate which outcomes. This inquiry might be guided purely by intuition. Indeed, I have met many who believe that while differences matter, method isn't possible - this is purely a matter of personal instinct and intuition. I happen to believe that method is possible; that our decisions about time, place, sequence and focus can, more often than not, create conditions in which certain types of human experience are more likely to emerge. In short, I believe that decisions, ideas, thoughts, communities, innovations can all be designed...but not directly. More specifically, I believe that the conditions for human outcomes can be designed, even if we do not know specifically what those outcomes will be. To design this way, we need to think about design a little differently based on a few variables related to design: the object of design, influence on the end product, and agency in the process. I'll illustrate the difference with three very crude examples, which I think are useful, because I've seen so many cases where they are used almost interchangeably. Classic Design: in our classic conception of design, the designer perceives a need in the world, and uses their knowledge, experience, skill and intuition to devise something to meet that need. The object of that design process is the artifact itself. The designer, as the visionary and expert, has absolute influence over its creation, and all agency in this process lies with the designer - the end users, in fact, might not even appear in the process at all. Human-Centred Design: Human-centred design disrupted this model by asking a radical question that now seems pretty basic: "before we make a thing for people, what if we, like, talked to them first?" In this model, the designers engage with the end users to understand their context and needs before they create the artifact. The object of the design process is still the artifact itself, and while users in this model now have some influence - largely as input, final influence still lies with the expert designers. A limited form of user agency enters this model, but only insofar as they are asked. Increasingly, you see situations where the products we are talking about might be something less tangible. As design filters into the sphere of collaboration or human interaction, the artifacts we start designing for tend to be more conceptual than strictly functional; creativity, new ideas, innovations, partnerships, learning, agreements and strategies. You can't sit on or drive the products of these kinds of design processes, but the outputs are, nonetheless, very much something that groups or organizations want and need. Which brings us to... Emergent Design: Emergent design focuses on methods to create novel outputs that are not present from any of the individual inputs in a group process; to create conditions in which the desired outcome can't, itself, be directly designed. Learning, innovation, creativity, decisions, agreement, alignment, excitement, understanding are all highly desirable outcomes, but can't be directly manipulated; they are emergent outcomes based on a series of inputs and conditions which either make their emergence more likely or less likely. The object of design, then, is focused on the conditions, as opposed to the outcomes or outputs themselves. The degree to which the outcomes represent the potential of the group depends on the level of influence the "users" have over their creation. The durability of the outcomes depends on how much agency the "users" have before, during and after the process. This is not intended as a good, better, best model of design, but rather a lens for deciding what approach to design one should use. If you are in the business of producing software, and it is ultimately you who must code, test and ship your product, emergent design is probably not the approach for you. In any of these situations, if the beliefs and behaviours of participants in a process as individuals or as a collective (or both) is instrumental to the success of the outcomes, that is a good sign you need an emergent design process. If their input is required, but not their actions, then a human-centered design processes might be a good fit. If neither their input, nor their actions are required...you can head to your basement and get to work. This is where agency comes in. Considering agency can help expose where you are, in fact, designing for emergence. Many of our failed processes result from a focus on centralized agency in situations where the coordinated action of a great number of individuals is required, or where the one holding the pen is not the one responsible for delivering the outcomes. Education often focuses on how to teach, rather than the conditions in which learning occurs. A strategy might focus on an objective opportunity, rather than the appetite of those required to achieve it. We focus on what makes a great leader, and less on how groups work with shared intent. Many of the groups I have been brought in to help over the years wrestle with very basic questions that, nonetheless, remain very difficult to answer, especially as organizations get larger and the context gets more complex. "Where do we go next as an organization?" "How can all of us work to solve this shared problem?" "How do we adapt our organization to changing circumstances?" "How do we have better ideas?" This brings me back to my recent training. Nowhere is this more true that in "multi-stakeholder" groups, where individuals from various organizations, all with their own pressures and motives, need to coordinate around a collective issue. Success, at an aggregate level, comes from each individual reaching a personal decision point an impetus to act that matches with enough of the other individuals in the partnership to create a change that all of them contribute to, but none of them control. Emergent Design is the set of principles, models, practices and tools that creates the conditions for that to happen. Get full access to Goal17 at goal17.substack.com/subscribe (https://goal17.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4)
17 Jun 2024
Intent Is Everything
When I first started doing facilitation and design work, it was almost always in the context of a single company or organization. They needed a collaborative process because they were generally large organizations, and the only way to make sure their strategy took into consideration all the necessary complexity they were facing was to bring in all the people with direct knowledge from the far-flung parts of the company. Not only did it make for more nuanced strategies, it also got a jump-start on rolling out the plan afterwards, as so many of the key people had a hand in building the strategy and were thus much more likely to be bought into it. And that was the key challenge in those processes; finding a strategy or idea that was sufficiently compelling to enough people that they would actually want to do it. We always talked about how important it was to find that shared intent. Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. What I didn't know then was how much easier it was to find shared intent in a process where most people were told they had to be there. Don't get me wrong; having your boss order you to be in a room doesn't automatically guarantee your buy in for whatever happens in that room. The reason so many large projects fail is that we consistently under-represent the impact of misalignment, misunderstandings, passive resistance and apathy. But as a starting point, having people who feel they at least need to show up, and feel they have at least something at stake in the outcomes is a decent place to start. It wasn't until I started doing multi-stakeholder work that I realized just how important intent was. For those who are unfamiliar, multi-stakeholder work revolves around the many areas where people or organizations who don't necessarily have any responsibility or accountability to one another find themselves exploring an area in which they all have a stake or an interest. That might sound like a vague problem space, but it's actually where many of the biggest challenges facing civilization lie right now; the growing spaces and cracks in our systems that no individual actor has the knowledge, influence, resources, ability or mandate to tackle on their own. Back in the easy days of working on smaller problems, bad faith actors in a collaborative process could face consequences from their boss. In a multi-stakeholder process, if a crucial stakeholder becomes bored and decides to drop out, there's no boss to compel them. They have to want to be there, or feel they need to be there. We used to say that a group needed a "burning platform" in order to really move to action, but more and more, I don't see that as being anywhere near sufficient. In fact, I think that when it feels like the whole world is on fire, a burning platform doesn't seem as urgent as it used to. Just looking at issues like climate change, we are still not compelled to decisive action despite having a very real, and not-so-metaphorical burning platform. That's why, these days, I've come to think that intent is everything. There is a magical moment when people move from a thought or a conversation of some possibility into a visceral desire to act, to bring that possible future into reality. You can feel it in yourself, when you move from indecision into action. You can feel it, as well, when the intent you hold connects with others. There are so many things in life, whether in our personal lives or our working lives that we do out of routine, obligation or inertia. We go along to get along, we don't rock the boat, we do things the way they've always been done, we do things because we have to, because we're supposed to. Sometimes we change because we're forced to, because our conditions change. Change is happening, but not by design. Making deliberate change requires intent, and while we may experience intent as spontaneous, as something that "is there or it isn't", I believe there is a process to it. Intent is always individual, even when it's collective. It springs up in the place between a problem and a possibility, when the problem can be seen clearly enough and the possibility is desirable enough to motivate action. Groups of individuals need to go through the same process to build intent that individuals do, with the added challenge of, well, being a group. For shared intent to emerge, that group needs to come to a collective understanding and definition of the problem they are solving, and a collective vision for how to solve it. Everyone comes to intent on their own, but individual intent that is reached as part of an emerging group intent is incredibly powerful. So, the design challenge is to create a container where a group can arrive at that place together. A few tips for finding group intent: * Remember that organizations aren't people: just because an organization has been involved, doesn't mean that the person representing an organization has any context. Focus on people. * Every new person resets the clock: every time people come and go in the process, you have to assume a reset. If they haven't been on the journey, you need to think how to take them through that journey...but the journey might change with each new person and perspective you add. * Take time to build language: we all see the world a little differently, and while sharing diverse perspectives can be powerful, moving forward on the assumption that we already share the same perspectives is a recipe for misunderstandings. Diverse viewpoints tend to only be valuable when participants make the differences explicit, and build a new synthesis together. * Go slow to go fast: taking the time to build shared understanding slowly. Sometimes a first attempt at coming up with a solution will change your definition of the problem; allow these iterations, as it is part of the process of a group making sense together. Solving the wrong problem really well doesn't help. * Relationships matter: shared intent is not only about a shared understanding of what we want to do, but also a shared understanding of each other. Trusting that others want something as much as we do can help us take the leap into a new reality. Remembering some of these tips allows you to move from a mechanical approach to movement building to a more organic approach. Scheduling a series of consultations and one-on-one meetings may fill a project plan and look like progress, but understanding that intent is a core design consideration means you need to design for emotion and experience. If we want a future by design, and not default, we need to design for intent. Goal17 is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Get full access to Goal17 at goal17.substack.com/subscribe (https://goal17.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4)

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