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Untangled

Charley Johnson

Untangled is a podcast about technology, people, and power.

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Saturday · 49 min
Why "Go Experiment with AI" Fails as an AI Adoption Strategy
Welcome back to Untangled. It’s written by me, Charley Johnson, and valued by members like you. This week I talked with Jen Briselli, co-founder of Topology and an adjunct professor at Massachusetts College of Art and Design. Jen is one of my favorite thinkers on complexity, and she’s written two essays in the last six months that I keep returning to: one on what hockey taught her about complexity, and one arguing that “uncertainty tolerance” doesn’t live inside a person. We discuss: * Why “skate to where the puck is going” is worse advice than it sounds. * What a triangle of three forwards can teach an organization about rules. * Why “you’re not in traffic, you are traffic” is the hardest lesson in complexity work. * Jen’s image of organizations amputating healthy limbs to make room for a prosthetic -- and why it’s an apt way to think about AI adoption. As always, please send me feedback on today’s post by replying to this email. I read and respond to every note. On to the show! Untangled HQ In our next Facilitators' Workshop, Kate and I are hosting an 'Ask Us Anything.' Bring the meeting that went sideways. Bring the group that won't stop talking, or the one that won't start. Bring the facilitation question you've been slightly embarrassed to ask out loud. We'll dig into it all together. Deep Dive Uncertainty tolerance is not a personality trait Jen wrote her essay on uncertainty tolerance because she was fed up with headlines telling leaders that their teams can’t handle ambiguity. She argues that people actually love uncertainty. We gamble, we play sports, and we get mad at anyone who spoils the end of a movie. What people resist is uncertainty that comes with high consequences and no influence over the outcome. If those consequences reach someone’s salary, Jen said, “I don’t blame someone for being a little bit uptight about it.” The point, as Jen argues, is to stop treating uncertainty tolerance as a trait that a person has or lacks. Tolerance emerges from the entanglement between a person and their context. The same person who bluffs happily at a poker table on Saturday can freeze during the announced reorg on Monday. Nothing inside that person changed over the weekend. The stakes changed, and so did how much say they have. Why employees hesitate to experiment with AI Now map this distinction on to being instructed by your boss to “go experiment with AI.” The generous read might be that they don’t know how to strategically incorporate it into a workflow, and they want to involve you in the process. A li’l experimentation can’t hurt, right? The less generous interpretation is that management is asking staff to figure out what parts of their job can be automated — and they’re being asked to fork over that information with no sense of how it’s going to be used. Read this way, your hesitation makes sense. Why would you experiment with AI if success means you may have just shown your employer how to automate part of your job? When leadership calls that hesitation “resistance,” they locate inside you something that you and they produced together. How individual AI experimentation becomes a team problem The other issue with this instruction is that “go experiment with AI” asks you to act. It says nothing about whether you’ll ever learn what your action set in motion — as an individual or a team. This cuts against one of the core ideas in Jen’s hockey essay: never act faster than you learn. Take actions that are, in her words, high in learning potential and low in disaster potential, and sense for what those actions changed. So if your experimenting faster than your learning, slow down. Now comes the hard part: doing this as a team. Because teams that don’t sense and learn together develop problematic patterns. It’s rarely because someone has bad intentions or doesn’t care about their impact on the group. It’s because everyone is moving fast and trying new things. It feels like they’re saving time and being more efficient. But it turns out that each individual, working alone, is making what seems like a small accommodation: deferring to a confident output instead of forming their own view, starting from the AI’s framing instead of their own, letting the time it saves get absorbed as more volume. Within teams, these small accommodations snowball. They build and build until they become a patterned dynamic — judgment that no longer gets exercised, disagreements that no longer get voiced, accountability no longer exercised. And no one notices, because action outpaced learning and the action felt productive. What hockey systems teach leaders about AI adoption Hockey has something to offer here, too: hockey teams operate in systems, not plays. As Jen explained, 1-2-2 forecheck tells almost no one what to do. It tells the first forward to pressure the puck, the two behind her which lanes to hold, and the two behind them what they own if the puck gets through. Everyone can see where everyone else is, and the decision belongs to whoever is closest to the puck. The first forward can skate hard at a risky puck because two layers behind her make losing it recoverable. Her uncertainty is tolerable because of where her teammates are standing. “Go experiment with AI” is the opposite of a system. It says nothing about where anyone stands in relation to anyone else or what layer is responsible for covering the person who takes the risk. So what’s a better approach than ‘go experiment’? First, say plainly what will and won’t happen to someone’s role if they find a way to automate part of it, and keep your word. Second, help people see the system: protected time where people from different teams compare what they tried, what they learned, what they’re sensing and observing, what they would change, etc. This isn’t a moment for individual experimentation in a vacuum. It’s a moment for collective sense-making and learning. The technology is moving faster than any one person can grapple with one their own. But we can put in place team and organizational processes that attend to emerging patterns and allow groups to adapt together. So the next time a leader says “go experiment with AI,” the right response is to ask, where do you want me to stand, and who’s behind me? Until next time, Charley Work With Me Here are 3 ways I can help: * Advising: I can help you navigate uncertainty, make sense of AI, and steward change in your system. * Organizational Training: Everything you and your team need to cut through the tech-hype and implement strategies that catalyze true systems change. (For either Stewarding AI or Systems Change for Tech & Society Leaders) * 1:1 Leadership Coaching: I can help you facilitate change — in yourself, your organization, and the system you work within. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit untangled.substack.com
September 12 · 45 min
What is an 'AI strategy'?
Welcome back to Untangled. It’s written by me, Charley Johnson, and valued by members like you. This week I’m sharing my conversation with Nick Pyati, a former senior strategy leader at Microsoft and the founder of Belari AI. Nick spent nine years doing strategy for a company whose ground shifted every few months, and then all at once when ChatGPT landed. We disagreed about how much of the work AI will eventually do but on what strategy is and why most companies are getting it wrong, we landed in the same place. I hope you enjoy it — I certainly did! Untangled HQ Get on the waitlist for the second cohort of Stewarding AI: How to Build Responsible Principles, Workflows, and Practices. Here's what one participant had to say about the first cohort: Deep Dive What is an ‘AI strategy’? Nick and I talk about a lot of things: * What Nick learned about setting strategy at Microsoft in the months after ChatGPT, when planning horizons compressed from a year to a few months. * The three approaches to AI Nick sees right now, and why the companies that have done nothing about AI may be best positioned. * Facilitation as a strategic practice, and what the human conversations do that most analytical approaches to strategy can’t. * What AI is doing to knowledge work, and how companies are commodifying what once set them apart. But the question we kept circling back to was this: when a company’s context changes, how does it rebuild the link between that context and the way it works? Every team works inside a context — the people it serves, the competitors it faces, the technology available to it, and all of the interpersonal dynamics and ways of doing things that amount to culture. And every team has a set of habits, beliefs, and capabilities that fit that context. But then the context shifts. A new competitor arrives, customers start wanting something else, technology changes, etc. The habits, beliefs, and capabilities that made the team successful stop making sense. Yet they keep doing the same thing! Why? Because, as Nick argues, it served them well for years. Re-establishing the link means first recognizing that the old way of doing things is no longer serving you. High performing teams across companies, non-profits, and government will paradoxically have the hardest time because they have so many positive examples validating their current approach. And here we are. AI changed the context for everyone everywhere all at once. Your staff are working differently. The people you serve, and the board you answer to, expect something different from you. Somewhere a competitor you haven’t heard of is rebuilding the work from scratch. A context that felt durable now feels more like quicksand. As Nick notes, every company in every industry now has the problem Microsoft had in the months after ChatGPT, when planning horizons shrank from a year to a few months and the historical trend line stopped being a guide to, well, anything. Strategy, then, is whatever it takes to re-establish the link between how a team works and the context it now sits in. But strategy is also a big bet. Yes, Microsoft is data-rich and analytical, but Nick says almost none of that data decided the big questions. When your context is regularly changing, forecasting or trend analysis isn’t much use. Strategy stops being an analytical question because the future can’t be known. Instead, strategy becomes more about imagination, conviction, and sense-making; about what the team can believe in, and how the team adapts together without fragmenting. I’ve written recently about imagination and sense-making, so what makes for a good big bet? Nick thinks of it as a Venn diagram. The bet has to be plausible — supported by what the market is doing, with a real story for why this team has a comparative advantage. The team has to be able to get conviction around it. They have to believe in it. And the bet has to be big enough to be worth the risk, because of the fifteen good ideas on the table, maybe three will ever pay off at a scale that justifies the investment of time and resources. Companies and organizations aren’t making big bets mapped to the future they want to bring about. They’re focused on the same question every company has asked of every IT rollout before this one: where can we plug this in? What can we automate? And when you ask these questions of a technology that can reshape how the whole business works, you get a scattering of small bets with no idea underneath them. I wrote about the mechanism behind this in The Copycat Economy. When a technology is poorly understood, organizations copy each other rather than think for themselves — DiMaggio and Powell called it mimetic isomorphism. Every company is doing what its peers are doing, which is why their ‘AI strategies’ all look roughly the same. When your ‘AI strategy’ maps the technology to existing work, another differentiation problem pops up. The models are the same for everyone. Unless a company is using them in some thoughtful, tailored way, whatever it produces with them is essentially what everyone else can produce with them. That model is what a company turns itself into when it automates the work its people were doing. The idiosyncratic humans on staff, with their peculiarities, big unconventional ideas, and their weird accumulated knowledge of how things get done here, were the entire source of anything novel the company had to offer. As I argued in an essay on why data-driven organizations are the least prepared for AI, this knowledge is tacit and relational — it lives in bodies, in trusted relationships, in a team’s accumulated sense of how decisions get made versus how they’re supposed to get made. The knowledge isn’t in the text, so it isn’t in the model. Every time a CEO or Executive Director highlights the ‘efficiencies’ generated by cutting their workforce, Nick said you should interpret that as: you lacked the imagination to know what to do with those people now that you have the capability. I agree! It means that they started from the question “what is our AI strategy” and simply bolted the technology on to what they already do, as the context changed beneath their feet. The better question is this: what future are we trying to create, what do the people we already have make possible, and where — if anywhere — does AI help? Answer that and you have a bet. More soon, Charley Work With Me Here are 3 ways I can help: * Advising: I can help you navigate uncertainty, make sense of AI, and steward change in your system. * Organizational Training: Everything you and your team need to cut through the tech-hype and implement strategies that catalyze true systems change. (For either Stewarding AI or Systems Change for Tech & Society Leaders) * 1:1 Leadership Coaching: I can help you facilitate change — in yourself, your organization, and the system you work within. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit untangled.substack.com
August 29 · 37 min
You can't make sense of the world alone with a chatbot.
Hi there, Welcome back to Untangled. It’s written by me, Charley Johnson, and valued by members like you. Help me make it better? This week I’m sharing my conversation with Marina Nitze — former CTO of the Department of Veterans Affairs, senior White House technology advisor, and co-author (with Mikey Dickerson and Matthew Weaver) of Crisis Engineering: Time-Tested Tools for Turning Chaos into Clarity. We talk about the book, sense-making practices, and what actually helps a system change. As always, please send me feedback on today’s post by replying to this email. I read and respond to every note. On to the show! Untangled HQ * Stewarding AI: The next cohort of Stewarding AI: How to Build Responsible Principles, Workflows, and Practices is coming up. Get on the waitlist and be the first to hear when it opens. * Ask Me Anything: Ask me about the knots in your own systems change work (”my funder wants a five-year plan but my system doesn’t work like that — what do I do?”). Ask me about stewarding AI inside your organization (”my board/boss keeps asking what our AI strategy is -- where do I start?”). Ask me about something in the news you want untangled. Or ask me something personal — what I’m reading, what I’ve changed my mind about this year, or what my high-school self — Dashboard Confessional blasting in his headphones — would make of this newsletter. No question is too small or too “I should already know this.” Just use this form. Deep Dive You can’t make sense of the world alone with a chatbot. Marina and I talk about a lot of things (watch it here or on YouTube), including: * How the Healthcare.gov crisis ended on a shared story rather than any change in the metrics, and what Marina took away from living through it. * Why nobody in a large org holds an accurate picture of the whole system, and what it takes for a group to see it together. * Why people can’t be argued out of a comfortable story with facts, and what actually lets a group take up a truer, stranger one. * How crises loosen what was stuck, why most orgs waste the opening, and what leadership looks like inside that window. * What AI adoption would look like if it took the author’s account of why organizational change usually fails seriously. * How a leader should weigh automating against sustaining the human adaptive capacity that automation tends to engineer away. * What AI does to an org’s ability to know itself when AI summarizes the reports and watches the metrics, and where sensemaking has to stay human. You should give the entire episode a listen / watch, but I wanted to zoom in on one thread we kept coming back to: what actually allows systems to change amidst a crisis? When Marina was CTO of the VA, the disability claims backlog kept growing, and the only tool the agency had was mandatory overtime. So she flew around the country and watched claims get processed from start to finish — which, at the time, nobody owned. People owned steps of the process. Nobody owned the process. And nobody had traced the entire thing. She sat in on medical exams. Doctors would examine a veteran head to toe and then write an eight-page medical essay. When she asked them why, their answer was consistent: I’m a medical expert, writing for an even better medical expert, who will read every detail and make a nuanced decision about this veteran’s compensation. The longer the essay, the thinking went, the better served the veteran. Except the essay didn’t go to a better medical expert. It went to a claim specialist with no medical training and a very specific form — a form that asked, say, for the number of degrees the veteran’s elbow could bend. Often the number wasn’t anywhere in the eight pages. So the claim got kicked back, and the veteran was sent in for another head-to-toe exam. Not great. The doctors believed they were serving veterans as thoroughly as possible. So did the claim specialists. Yet together they were grinding veterans through repeat exams and growing the backlog. What fixed this system wasn’t a fact-filled memo, it was sense-making. Marina brought everyone into one room — the doctors, the claim specialists, etc. — and had them demonstrate their part of the process to the other. They left understanding how their well-intentioned actions were contributing to the overall system. Sensemaking is about how we assemble what’s happening around us into a story we can act on. Marina and her co-authors lean on organizational theorist Karl Weick in the book, who contends that sensemaking runs on plausibility, not accuracy. Given the choice, we’ll take a familiar story that fits some of the facts over a strange or complicated story that fits all of them. The book uses the accident at Three Mile Island as an example: nearly fifty years on, there are still two incompatible stories of that accident — “we were lied to,” and “honest mistakes were made, no harm done.” Both stories are alive and well, and no fact is coming along to settle it. The sensemaking process didn’t simply offer new information to well-intentioned actors. It was a collision with reality that went deeper than the workflow. Right, the doctors didn’t care about writing the eight-page document. They were protecting their own identity, an image of themselves as the person who serves veterans thoroughly. And now they’re in a room showing them that their contributions were an obstacle to change. A new shared story can’t change the system unless the participant’s identity shifts alongside it. From I’m the expert who serves veterans to I contribute to a process that serves veterans. Turns out, facts are downstream from the shared stories we tell. And all of that is downstream from who we think we are. Near the end of our conversation, almost in passing, Marina said something I haven’t stopped chewing on since: “You’re not going to be able to sense-make with yourself and your LLM. That’s actually fairly dangerous.” A chatbot simulates a conversation but it doesn’t offer the friction required for you to update your picture of the world, or yourself. Your chatbot doesn’t hold a different map than you do; after a few exchanges it mostly holds yours because its incentives are set to find your story plausible. And there is no room for it to read — no raised eyebrows or dismissive smirks, no stake in the disagreement. Whereas sensemaking is fundamentally social. It travels through tone and body language and power dynamics, which is why Marina insists you cannot work a crisis over Zoom — everyone has to be in the same physical room, looking at the same reality, revealing and picking up on social cues, interpersonal issues, and all the messy things about being a human that cannot be automated away. Organizations are concerned with the ‘AI risks’ they hear about in the media. How can we minimize ‘hallucinations?’ How can we ‘align’ it with our values? If your a long-time Untangled subscriber, you know I’ve been poking holes in these frames for a long time. Because the real danger of making sense of the world with a chatbot was never that it tells you false things or contradicts your values. It’s how smooth your own story starts to feel. More soon, Charley Work With Me Here are 3 ways I can help: * Advising: I can help you navigate uncertainty, make sense of AI, and steward change in your system. * Organizational Training: Everything you and your team need to cut through the tech-hype and implement strategies that catalyze true systems change. (For either Stewarding AI or Systems Change for Tech & Society Leaders) * 1:1 Leadership Coaching: I can help you facilitate change — in yourself, your organization, and the system you work within. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit untangled.substack.com
July 18 · 46 min
She spent hours thinking with Claude. Then she couldn't explain any of it.
Hi there, Welcome back to Untangled. It’s written by me, Charley Johnson, and valued by members like you. Help me make it better? This week: my conversation with Helen and Dave Edwards of The Artificiality Institute on how AI affects your thinking, cognitive sovereignty, and staying the author of your own mind. As always, please send me feedback on today’s post by replying to this email. I read and respond to every note. On to the show! Deep Dive Today, I’m sharing my conversation with Dave and Helen Edwards, co-founders of The Artificiality Institute, which helps people stay human in the age of AI. They also happen to be happily married — a fact that mattered in our conversation. Because it starts with what Helen calls an unhappily married moment. She had spent hours generating frameworks with Claude, feeling productive and energized the whole time — until Dave asked where she’d gotten to, and she couldn’t explain any of it. What happened next anchors our entire conversation. Along the way, we cover a lot of territory: * Helen offers three changes to watch for in yourself: blending, bonding, and bending. Blending is when you can’t quite tell which ideas are yours. Bonding is when the tool starts shaping your identity. And bending is when your frameworks for meaning-making shift, when what you believe you can become starts to change. None of these is bad on its own, she argues. But bending governs the other two because when your meaning-making shifts, it starts deciding what you notice and choose before you’re aware anything happened. * Cognitive sovereignty, in their telling, is three practices: noticing, choosing, and showing up. Noticing is real hard — “we’re wired to automate,” as Helen put it, and metacognition is “a devil to teach.” Her practical entry point, which resonates with my own, is to notice which role you’ve put the AI in: are you asking it to frame, to catalyze, to collaborate, to take something off your plate entirely? Choosing is about keeping the stakes in view — Helen’s point is that we choose more actively when the stakes stay front of mind, and drift when they fade -- and showing up is about remaining accountable to one another. * People with intuition for each other can do what the machine cannot. Dave’s side of the opening story is my favorite part of it. He looked at Helen’s tangle of slides and saw, in minutes, that she’d already figured it out — three dimensions, each with a high and a low, “a two by two by two” — she just hadn’t assembled it. He could see that because he knows how her mind works, the context of the presentation, and the purpose of what they were there to do. The machine had none of that context. * Frictionlessness erodes collective intelligence. There’s nothing wrong with friction. As Helen reminds us, synthesis is thesis plus antithesis — it requires struggle, turn-taking, and what she calls “mental maps of who knows what.” Dave adds the organizational history: decades of believing all the answers were in the data created organizations that value data and technology and devalue fundamentally human work. Put individual AI silos on top of that culture and you get summarization dressed as synthesis. Not great. * The line that matters is reducible versus irreducible. A language model works on data that is already a representation of what humans do, so the real question is what parts of work can’t be turned into a representation without losing the thing itself. So they created a tool -- The Irreducible Complexity Index -- to help measure exactly this. I’ve been playing with it this weekend and have found it super interesting -- give it a look! For all the frameworks in this conversation — and Dave and Helen are wonderful framework-makers — every one of them resolves, in the end, into other people. Helen’s story of losing her thinking begins with a machine but turns on Dave connecting the dots. The machine had the content; Dave had the context. Even their diagnosis of what’s breaking in organizations comes down to the same thing — synthesis needs people willing to struggle with each other’s ideas, not ten tabs of AI output waiting to be summarized. The risk may live in each of our heads but the safeguard, it seems, lives between us. The other thing I appreciated is the spirit of their whole project: none of this is inevitable. So much AI commentary asks you to pick a lane — the machines are coming for everything, or the machines will save us all — and both lanes have the same flaw, which is that there’s nothing left for you to do in them. Dave and Helen are building a third thing: vocabulary, frameworks, and a community for people who want to stay the authors of their own minds while working with these tools, not despite them. If you want somewhere to start, borrow the test from Helen’s story: the next time you finish a stretch of work with AI and you’re feeling productive, try explaining what you made to someone close to you. Whatever happens next will tell you whether you were doing the thinking — and noticing the difference is the whole practice, in miniature. Helen’s book, Stay Human: Authoring Your Mind in the Age of AI, is free, you just have to register. And if you want to do this thinking in a room full of people doing the same, their summit is October 22–24 in Bend, Oregon. I went last year and it’s the good kind of mind-bending. Until next time, Charley Work With Me Here are 3 ways I can help: * Advising: I can help you navigate uncertainty, make sense of AI, and steward change in your system. * Organizational Training: Everything you and your team need to cut through the tech-hype and implement strategies that catalyze true systems change. (For either Stewarding AI or Systems Change for Tech & Society Leaders) * 1:1 Leadership Coaching: I can help you facilitate change — in yourself, your organization, and the system you work within. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit untangled.substack.com
May 30 · 39 min
We Don't Have to Build the Filter Bubble of One
Hi there, Welcome back to Untangled. It’s written by me, Charley Johnson, and valued by members like you. Help me make it better? Today I’m sharing my conversation with Angelica Quicksey, Managing Director of New_Public, about the rise of the agentic interface era, and how we might shape it. As always, please send me feedback on today’s post by replying to this email. I read and respond to every note. On to the show! Untangled HQ Big update: I’m getting married next weekend! So I’m going to take a short break from Untangled, and I’ll be back in your inbox on June 21. In the meantime, don’t forget to sign up for the next Untangled community event on See the System — a one-hour workshop where we start not with the tool but with the system it would enter. Bring a specific use case you’re weighing, and leave with a map, a vision statement, and a Proceed/Pause/Decline decision you can actually defend. Deep Dive We Don’t Have to Build the Filter Bubble of One This week I spoke with Angelica Quicksey, Managing Director of New_ Public, about their new report After the Feed: Trust, connection, and the next era of social technology — which argues that we’ve crossed into a new era of social technology, as consequential a shift as the move from newspaper editors to algorithmic feeds was fifteen years ago: the agentic interface era. Let’s dig in. New_Public’s whole orientation comes from urban planning — what physical public space can teach us about the digital kind — and early in our conversation Angelica described what algorithmic social media actually feels like: you wake up every morning in Times Square. Bright, loud, and engineered to separate you from your money and your attention. Even people who enjoy visiting Times Square don’t want to live there! And yet that’s the only public space the last fifteen years built for us — one deafening square, optimized to keep us standing in it as long as possible. The argument in After the Feed is that we’re being pulled out of the square, whether we like it or not. A few forces are doing the pulling at once. The first is that the feed is no longer where our social lives or our information diet actually live. People will still scroll — parasocial entertainment isn’t going anywhere — but the place we go to figure out what’s happening, what to think, what to do, is increasingly a chat with an agent. Think about that handoff for a second. It used to be Walter Cronkite. Then it was the algorithmically ranked feed. Now it’s a chat window built just for you, and nobody else. The second is that the big platforms are quietly falling apart anyway — not because anyone reformed them, but because AI broke the things holding them together. Harassment is happening at industrial scale. The genuine back-and-forth between people is drying up. Machine-generated slop is everywhere, and bots already make up the majority of internet traffic. The gardens are still walled, but the walls are crumbling from the inside. And the third is that, as engagement gets cheaper to fake, the metrics that used to signal real human attention stop meaning much of anything. Likes, followers, reviews — all gameable. So the scarce thing is no longer attention; it’s trust. New_Public has a nice term for what trust looks like once you try to make it operational: thick reputation. Not “10K followers,” but “contributed thoughtfully to this community for two years.” Not “verified,” but “vouched for by people I trust.” But being pulled out of Times Square is not the same as arriving somewhere good. Angelica named the failure mode hiding underneath the whole promise: the filter bubble of one. We leave the deafening square and we don’t get the online equivalent of parks and libraries; we each get an information world drawn so tightly around us that nothing is held in common anymore. The old filter bubble at least had other people in it. This one wouldn’t. And it’s the default outcome, not the worst case, if nobody designs against it. So the real question the report is asking isn’t what’s replacing the feed? It’s what do we want to build in the space the feed is vacating — before the defaults get set for us? And the hopeful part of New_Public’s answer is that the raw materials are suddenly cheap. The cost of building software has fallen off a cliff: a community platform for 500 people used to cost millions, and now you can stand one up for a few hundred dollars a month. The old logic that said a platform needs billions of users to be worth building simply stops applying. A neighborhood, a hobbyist group, a mutual aid network, a book club — each can finally have software built just for it. Thousands of small, purpose-built spaces, instead of one square for everyone. Which sounds lovely until you try to run one! Healthy communities don’t tend themselves; they’re held together by stewards — the people who set norms, welcome newcomers, manage conflict, keep the shared memory. It’s real labor, usually unpaid, and burnout is the most common reason these spaces collapse. So the obvious move is to hand the routine moderation work to an AI agent and free the human steward up for the hard stuff that really requires care. Perhaps, but Angelica pointed to research on call centers that complicates the whole thing. When you route the easy tickets to self-service and leave the humans only the hard ones, the humans burn out faster. It turns out the easy work wasn’t filler. It was rhythm. It was rest. Strip it away and you don’t always get a more strategic steward; you get an exhausted one. This is the question I keep finding underneath every “what can we automate?” conversation, and it’s the thread that ties the whole report together for me. We treat routine as fungible — the part we can safely lift out — when sometimes it’s exactly where judgment gets built, where a steward comes to know the texture of her own community. The friction wasn’t always a cost to be eliminated. Sometimes it was doing the work. So maybe the better question isn’t what can we hand off? It’s what is the rhythm quietly doing that we haven’t named yet? That, in the end, is what I admire about After the Feed. It isn’t a promise that things will work out. It’s that Angelica and her colleagues are doing the thing tech criticism has mostly refused to do for fifteen years: describing, in concrete terms, what it would look like if we got it right. Many small spaces built for actual communities, owned by their members, connected through open protocols so you can carry your history with you. AI working quietly in the background as a kind of shared memory, rather than running the show out front. Stewards supported, paid, and designed for. Parks and plazas and libraries — not one more Times Square. That’s a long way from where we are. But it’s worth knowing someone’s building toward it. Until next time, Charley Work With Me Here are 3 ways I can help: * Advising: I can help you navigate uncertainty, make sense of AI, and steward change in your system. * Organizational Training: Everything you and your team need to cut through the tech-hype and implement strategies that catalyze true systems change. (For either Stewarding AI or Systems Change for Tech & Society Leaders) * 1:1 Leadership Coaching: I can help you facilitate change — in yourself, your organization, and the system you work within. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit untangled.substack.com
May 3 · 46 min
The World They're Building Toward
Hi there, This week I’m sharing a conversation I had with Bo Young Lee, CEO of AI4All about Silicon Valley imaginaries, rational refusal, and the futures we haven’t been offered. As always, please send me feedback on today’s post by replying to this email. I read and respond to every note. On to the show! Untangled HQ * Wednesday, May 5: I’m hosting a workshop on how to trace what must stay human when implementing AI responsibly. It will double as a preview of my new course on stewarding AI. * Thursday, May 6: As part of The Facilitators’ Workshop, Kate and I are hosting a workshop on how to turn stuck meetings into breakthrough moments. * Tuesday, May 12: Aarn and I are hosting a workshop on the discipline of holding tension: how to name tension without personalizing it, slow the moment without stalling the meeting, and protect the disagreement that actually matters. Join us! Deep Dive The World They’re Building Toward Start with the bunkers. In the last several years, a number of Silicon Valley’s most powerful technologists have been quietly building survival infrastructure. Bunkers in New Zealand. Fortified compounds in remote locations. Escape hatches from the civilization their products are shaping. Bo Young Lee noticed this before most people were talking about it, and she asked the obvious question: if these are the imaginaries — the foundational visions of the future — animating the people building our most consequential technologies, what does that tell us about the products they’re building? And how does their imaginary constrain our imagination? An imaginary is not a fantasy. It’s the operative picture of the future that structures present decisions — the unstated assumptions about where the world is going that determine what problems are worth solving, what risks are worth taking, and what populations are worth designing for. Imaginaries are embedded. They show up in product decisions, in hiring, in what gets funded and what gets ignored. Bo argues that the dominant Silicon Valley imaginary is, at its core, a story about inevitability and survival. Civilization is fragile. Disruption is coming. The question isn’t whether things collapse but who gets to build what comes next. If that’s the picture of the future you’re working from — even unconsciously — you’re not going to prioritize safety, privacy, or good governance in the present. Those things just get in the way! As Bo explains, the products that follow are predictable. Why design for women when women don’t figure prominently in survival scenarios? Why prioritize people with disabilities when they’re among the first casualties of disaster-oriented futures? Why hold yourself accountable to the communities your technology harms when they’re not in the imaginary? This isn’t hyperbole. Bo is describing a logical coherence between worldview and product — a through-line from the bunker to the algorithm that becomes visible once you start looking for it. Take the supposed ‘AI gender gap.‘ The narrative goes something like this: women are underrepresented in AI adoption because they lack confidence, access, or awareness. All we need to close the gap is a li’l education, outreach, and encouragement! Bo argues that women’s skepticism about AI is rational. Not because women don’t understand the technology, but because they understand it clearly enough to recognize that it wasn’t built for them, doesn’t work as well for them, and in specific contexts actively harms them. Right, women face systematically harsher professional consequences than men for identical workplace errors — a well-documented asymmetry researchers call the “tighter world” phenomenon. Women are more likely to be fired for mistakes and less likely to find subsequent employment. When a high error rate tool like generative AI enters that context, the risks land differently. Men’s mistakes get absorbed as the cost of experimentation. Women’s mistakes land on a narrower margin. A woman who understands this and proceeds with caution is doing the math. Calling that a confidence problem is its own kind of imaginary! The “AI for good” movement is similarly trapped by the Silicon Valley imaginary, but they don’t see through it in the same way. As Bo argues, the AI for good world has largely accepted the imaginaries it inherited. Its animating question is how to reduce harm within the existing AI paradigm — how to make the technology that’s been built safer, fairer, less biased. For example, Bo describes a philanthropy that funded three separate organizations — at seven-figure grants each — to build AI agents that would coach and tutor low-income, first-generation college students. The goal was equity. But research shows that when you train LLMs to eliminate overt racism, the covert bias doesn’t disappear — it actually increases. Show the same model two pieces of writing, one in standard English and one in African American Vernacular English (AAVE), and the LLM will rate the AAVE writer as less intelligent and less educated. A coaching agent built on that model, deployed to help first-generation students many of whom communicate in AAVE, may well steer those students toward easier majors and less rigorous courses — without anyone noticing, without anyone intending it. This example starts from a present-tense imagination of what AI is and what it’s for, and works forward from there. To free ourselves from these constraints, we have to separate refusal of this AI from refusal of AI altogether. Because when we do that, we can ask the more generative question that rarely gets asked: what futures do we actually want — and what would it take to build toward them? Bo’s organization offers one path forward. AI4All trains the next generation of AI practitioners from underrepresented communities, asking them from the beginning to identify social problems they want to address and work backward to the role AI might play. Because changing the imaginaries requires changing who builds the technology and who gets to define what it’s for. A more diverse AI workforce is an epistemic necessity — different people imagining different futures producing genuinely different technology. We were not given these imaginaries. We don’t have to keep them. Tools for Weavers My conversation with Bo inspired me to distill a number of the articles I've written about imagination, building alternative AI futures, and mapping backwards from the future -- and turn them into a tool! Your strategy documents already contain a picture of the future. You probably haven’t named it. It’s embedded in your metrics, your hiring plans, your roadmaps — quietly nudging you toward a particular kind of future without anyone actively choosing it. Imagining Otherwise is a practice for naming that picture — and then building a different one. Backcasting, futures in plural, and the question most teams skip: what are we willing to stop? Working canvas included. The last page will make sense when you get there. “Remember to imagine and craft the worlds you cannot live without, just as you dismantle the ones you cannot live within.” - Ruha Benjamin Work With Me Here are 3 ways I can help: * Advising: I can help you navigate uncertainty, make sense of AI, and steward change in your system. * Organizational Training: Everything you and your team need to cut through the tech-hype and implement strategies that catalyze true systems change. (For either Stewarding AI or Systems Change for Tech & Society Leaders) * 1:1 Leadership Coaching: I can help you facilitate change — in yourself, your organization, and the system you work within. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit untangled.substack.com
March 28 · 54 min
Your data isn't exhaust. It's a belonging.
Hi there, Welcome back to Untangled. It’s written by me, Charley Johnson, and supported by members like you. Help me make it better? This week I’m sharing a conversation I had with Beth Rudden — founder of Bast AI, former chief data officer for a $34 billion division at IBM, and someone building a genuinely different vision of what AI could be. 🏡 Untangled HQ Coming Up * Stewarding Complexity: Our next session is about finding and using the agency you actually have — even inside institutions that weren’t designed for it. * Untangled Collective: Your expense approval workflow is making decisions. So is your classification system, your algorithm, and your org chart. This session gives you a map of all of it — and shows you where to actually push. * Stewarding AI: How to Build Responsible Principles, Workflows, and Practices will take place July 3, 10, 17, and 24. It will open to the waitlist tomorrow. Enrollment is capped - join the waitlist if you want dibs on signing up. 🧶 Deep Dive Your data isn’t exhaust. It’s a belonging. Even the tech CEOs with the most to lose from the narrative bubble popping are quietly conceding that the scaling law was never actually a law. We’ll eventually let go of the equally silly notion that intelligence — or AGI, or whatever we’re calling it this quarter — is simply an emergent property of scale. Probably around the same time we admit that attaching sensors to people’s extremities was not the path to ‘embodied intelligence.’ Anyway! In the meantime, the story props up the technology. And the technology keeps doing what it does — make up false information, encode historical biases as neutral truth, and generate a mix of sloppy and genuinely useful outputs. Because we’ve anointed a few tech CEOs as our AI-narrators-in-chief, they get to decide what the data represents and what it means. Knowledge! Intelligence! Truth! Beth is building an alternative system that allows meaning to form the old-fashioned way: through interactions between people and systems. The critique starts with a claim about data that sounds simple but isn’t: decontextualized data doesn’t contain meaning. It carries patterns and associations. This distinction is fundamentally about whose meaning and knowledge grounds the AI system. This might sound academic but it matters a great deal. Take health care as an example — as Beth notes, seventy percent of patients don’t fully understand their outpatient procedures. A caregiver asks “why is my husband acting weird after his accident?” The clinical record says “behavioral dysregulation.” The gap between those two descriptions is where comprehension lives — and it’s invisible to any system that treats both as equivalent tokens. When patients and caregivers interact with clinical information, they generate something that doesn’t exist anywhere else: a record of how humans actually try to understand medical knowledge, where they get stuck, what vocabulary they use, and what they’re really asking beneath the surface question. Beth calls this interaction data, and its where meaning lives. From this you can start to build an ontology — a formal map of what exists within a domain and how concepts relate to each other. Here are the concepts in this field, here is how they connect, here is where each piece of knowledge sits relative to everything else. Without something to understand against, AI systems simply produce statistical appropriation rather than understanding. They pattern-match from frequency with no principled sense of how the patterns relate. The ontology is what offers the system ground truth. This isn’t an approach without challenges. Every organization contains multiple competing ontologies. The C-suite has one map of how knowledge is organized. Frontline workers have another. These disagreements aren’t accidental — they reflect different positions in the power structure, different relationships to risk. When you formalize an ontology, you’re making a political choice about whose map becomes the standard. But I’d much rather make an intentional choice about what knowledge matters than no choice at all — and you can navigate through this complexity by triangulating across different perspectives representing different positionalities. Beth has long described data as an artifact of human experience — carrying the fingerprints of its making, the lineage of decisions. But during a recent museum visit in Vancouver, a curator explained how her institution approaches Indigenous collections: these aren’t artifacts in our care. As Beth explains, they’re belongings. Artifacts can be extracted, cataloged, and owned. Belongings require consent and ongoing relationship with their communities of origin. Data isn’t an artifact of human experience. Data is a belonging. The current AI economy is built on the opposite assumption — harvesting people’s data without consent, using poorly compensated annotators, treating the exhaust of human experience as raw material. I couldn’t agree more with the alternative vision Beth is articulating: people whose data contributes to AI systems get compensated. They choose whether to monetize their experiences. The lineage and provenance aren’t overhead. They’re the infrastructure. That’s a long way from where we are. But I left the conversation feeling hopeful knowing someone is building toward it. 🙏 Share & Earn Help me build this community of people thinking differently about technology and earn free rewards (e.g. 1:1 coaching sessions, even free entry into one of my courses). Just share your personal link far and wide. 💫 Work With Me Here are 4 ways I can help: * Facilitation: I can help facilitate your team through complex and fraught dynamics, so that they can achieve their purpose. * Advising: I can help you navigate uncertainty, make sense of AI, and facilitate change in your system. * Organizational Training: Everything you and your team need to cut through the tech-hype and implement strategies that catalyze true systems change. (For either Stewarding AI or Systems Change for Tech & Society Leaders) * 1:1 Leadership Coaching: I can help you facilitate change — in yourself, your organization, and the system you work within. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit untangled.substack.com
March 21 · 40 min
The Age of Algorithmic Deference.
Hi there, Welcome back to Untangled. It’s written by me, Charley Johnson, and supported by members like you. Help me make it better? This week, I’m sharing a conversation I had with Hilke Schellmann — Emmy Award-winning investigative journalist, NYU professor, and author of The Algorithm — about her recent reporting on AI in hospitals. If you read my newsletter applying the STEWARD framework to AI in health care, you know her work was the spine of that piece. This conversation builds off of that, and goes a li’l deeper. On to the show! 🏡 Untangled HQ This Week * WEAVER: I opened enrollment for Cohort 7 of Systems Change for Tech & Society Leaders. You can get 40% off through March 27 with the promo code UNTANGLED40. * Community: Kate and I hosted “Navigating Challenging Personalities at Work.” Join The Facilitators’ Workshop if you don’t want to miss the next event. * Help me, help you: I launched a short survey to help me improve Untangled. Complete it and get a free email course. (Most participants are completing it in under 2 minutes.) Coming Up * STEWARD: Next week I’m presenting my STEWARD framework to the Technology Association of Grantmakers Inclusion By Design Leadership Cohort. Be the first to hear when Stewarding AI launches. * Untangled Collective: Power is everywhere. In the org chart, yes — but also in the intake form nobody questions, the metric everyone optimizes for, and the meeting that always ends the same way. Learn how to map it and identify and what you can actually do about it. 🧶 Deep Dive The Age of Algorithmic Deference. In my conversation with Hilke Schellmann, we opened with the story that anchors her piece: Adam Hart, a nurse at St. Rose Dominican Hospital in Nevada, at the bedside of a patient flagged by a sepsis alert. An algorithm generated an order to administer intravenous fluids. Hart noticed a dialysis catheter and knew fluids would harm her. His charge nurse tells him to comply. He refuses. A physician overhears, steps in, and orders dopamine instead — raising her blood pressure without adding fluid volume. The patient was fine. Nobody in that room had ill intent. In fact, the system worked as it was designed -- and that’s the problem. What stayed with me from this part of the conversation was Hilke’s reflection that Hart’s actions took genuine courage. Because it did! The charge nurse treated the algorithm with legitimacy and neutrality, and the alert became a verdict. Hart had years of experience and judgement underpinning his conviction -- but what about nurses earlier career, less confident in their own judgment? Then there’s Melissa Beebe and the BioButton at UC Davis — a wearable chest sensor that tracked vital signs continuously and generated alerts Beebe found vague, way too frequent, and hard to act on. Beebe asked to understand why the device was producing the outputs it was. She was a union rep with seventeen years of experience asking a completely reasonable question. But because we live in a culture obsessed with innovation -- and not one obsessed with patient outcomes -- she was labeled as resistant to technology. Hilke and I talked about what she was actually raising and why it wasn’t heard — and about what happens when it isn’t. Tools arrive with press releases and fanfare, get piloted for a year, quietly get shelved. Nobody shares what went wrong. And, as a result, the next health system starts from scratch. Mount Sinai offered a different picture. They brought AI development in-house, stopped trusting vendor promises, and found that the real work shifted from algorithm selection to trust, adoption, and workflow fit. Their most successful tool — a wound-care prediction model — came from a bedside nurse who identified the problem, helped build the solution, and trained her own colleagues. The catch: this only works if you have deep pockets and in-house expertise. Smaller and rural hospitals don’t. As Hilke argued, a two-tier system is developing, and the most vulnerable patients are on the wrong side of it. We went back to Hart’s story to pull on something implicit throughout: the hospital system never trained staff on what these systems actually are and what they aren’t. Which led us into the question of what must remain human. Knowing a patient’s baseline. Reading the room. Catching the slurred speech that doesn’t show in the labs or on the monitor. These tools don’t have access to that data. Workflow was the final thread. In most of the cases Hilke documented, the AI was simply added to an existing practice rather than prompting a redesign. Nobody asked what should happen when the alert is wrong, who has the authority to override it, or what a legitimate override even looks like. Those questions need to be answered before deployment — not discovered afterward. We closed with what Hilke would change about how AI is being implemented in work contexts. Her answer: stop treating stakeholder participation as an afterthought. Start treating it as a design requirement. 🖇️ Some Links The myth of the crowd: People are now betting real money on who gets voted off Survivor — a show that was filmed months ago and exists entirely on a hard drive somewhere. The New York Times reports this is creating obvious incentives for “insider” information, which is a very polite way of saying: someone who knows a producer is about to become very wealthy. Whether that counts as market manipulation apparently depends on your definition of “market,” “manipulation,” and possibly “reality.” (More on prediction markets) Growth over kids: Meta knew. That’s the thing that should make you put down whatever you’re holding. Internal documents — surfaced during New Mexico’s lawsuit — show that Meta’s own people repeatedly flagged that Instagram’s recommendation and contact systems were steering teenagers toward predatory accounts and enabling serious harm. They documented it. They had meetings about it. And then they ran the numbers on what stronger safety defaults would cost in growth and engagement. They chose growth and engagement over the safety of young people — and they always will. Pro-worker AI: A new paper sorts technological change into five categories, only one of which — “new task-creating” — is unambiguously good for workers. The other four range from “fine, probably” to “you’re being replaced by a script.” The authors note that pro-worker AI is chronically underinvested, which will surprise no one who has noticed that “we built a tool that makes humans more capable and irreplaceable” does not slap the same way AGI hype does. (More on AI & labor.) 📧 Learn With Me My email courses break big, messy topics into small, digestible, actionable steps and practices -- everyone comes with practical tools and frameworks I’ve created that you can apply immediately. (Or just complete the short survey and get one for free!) 💫 Work With Me Here are 4 ways I can help: * Facilitation: I can help facilitate your team through complex and fraught dynamics, so that they can achieve their purpose. * Advising: I can help you navigate uncertainty, make sense of AI, and facilitate change in your system. * Organizational Training: Everything you and your team need to cut through the tech-hype and implement strategies that catalyze true systems change. (For either Stewarding AI or Systems Change for Tech & Society Leaders) * 1:1 Leadership Coaching: I can help you facilitate change — in yourself, your organization, and the system you work within. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit untangled.substack.com
February 7 · 41 min
What If We Regulated Chatbots Like Any Other Product?
Hi there, Welcome back to Untangled. It’s written by me, Charley Johnson, and valued by members like you. Today, I’m sharing my conversation with Ben Winters, Director of AI and Privacy at Consumer Federation of America, about The People First Chatbot Bill—model legislation for regulating chatbots that’s been endorsed by over 70 organizations. As always, please send me feedback on today’s post by replying to this email. I read and respond to every note. 🔦Untangled HQ The Untangled Collective held its third community event earlier this week. Here’s what one participant had to say: On Tuesday, I’m launching another community with Aarn Wenneckers: Stewarding Complexity. This one is for boards, CEOs, and organizational leaders who need to step outside formal governance structures and practice making sense of complexity in real time—together. Join us? 🧶 Chatbots Don’t “Just Happen.” Companies Make Choices. Tech companies have successfully made chatbots seem like mystical, uncontrollable entities while simultaneously claiming they can be trusted without regulation. Yet, as Ben points out, every aspect of a chatbot—from training data to interface design to what responses get blocked—represents a series of choices by companies. When those choices foreseeably lead to harm, companies should be held accountable. In our conversation, Ben and I dug into the key provisions in the Bill, including: * Product liability: The bill leverages centuries of product liability law to hold companies accountable for design choices, rather than treating chatbots as neutral tools. * Data minimization over consent: Instead of relying on checkbox fatigue, the bill prohibits using personal data from outside chatbot interactions. * Private right of action: Harmed individuals can sue directly, not just rely on overwhelmed state attorneys general. We also discussed how lessons from failed social media regulation informed this Bill —why content-neutral design matters, how consent-based models cement the status quo, and what it takes to overcome platform lobbying that claims regulation will “kill innovation.” But more than any specific recommendation, the Bill serves as a reminder of the kind of world we could live in. It articulates an alternative future that we could inhabit. And here’s the good news: we know how to get there and state legislators are increasingly receptive. As civil society organizations look for what policies to push, and as states face push back from companies saying regulation will stifle innovation or that chatbots are too complex or that China will win etc., I hope they pick up a copy of The People First Chatbot Bill. It’s a lot simpler than the mystique that surrounds these bots — we just need to treat them like the products they actually are. 👉Before you go: 3 ways I can help * Advising: I help clients develop AI strategies that serve their future vision, craft policies that honor their values amid hard tradeoffs, and translate those ideas into lived organizational practice. * Courses & Trainings: Everything you and your team need to cut through the tech-hype and implement strategies that catalyze true systems change. * 1:1 Leadership Coaching: I can help you facilitate change — in yourself, your organization, and the system you work within. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit untangled.substack.com
January 24 · 35 min
When Your AI Assistant Becomes an Advertiser
Hi there, Welcome back to Untangled. It’s written by me, Charley Johnson, and supported by members like you. This week I’m sharing my conversation with Miranda Bogen (Director, AI Governance Lab, Center for Democracy & Technology) about what happens when your AI assistant becomes an advertiser. As always, please send me feedback on today’s post by replying to this email. I read and respond to every note. Don’t forget to sign up for The Untangled Collective — it’s my free community for tech & society leaders navigating technological change and changing systems, and the next event is coming up! 🏡Untangled HQ 🔦NEW: I’m teaming up with Aarn Wennekers (complexity expert and author of Super Cool & Hyper Critical) to launch Stewarding Complexity, a private, confidential gathering space for boards, executive teams, and organizational leaders to step outside formal governance structures, speak candidly with peers, and practice making sense of complexity — together. If that’s you, join us! 🚨Not New, But Important: Every organization I speak with is facing the same two questions: How do we build strategy for uncertainty—and what should we actually do about AI? My course, Systems Change for Tech & Society Leaders provides a structured approach to navigating both, helping leaders move beyond linear problem-solving and into systems thinking that engages emergence, power, and the relational foundations of change. Sign up for Cohort 6 today! Because why not: here’s a free diagnostic framework I use in the course to help you assess how your organization understands and uses technology across its strategy, programs, and operations. 🖇️ Some Links How Certain Is It? I’ve written a lot about why embracing uncertainty matters. Chatbots do the opposite—they collapse uncertainty into confident-sounding responses, packaging blind confidence as a feature. But what if we designed these tools differently? What would it take to preserve uncertainty rather than erase it? A new paper tackles this challenge, arguing we need to protect the messier, harder-to-quantify forms of uncertainty that professionals navigate through conversation and intuition. Their proposed fix? Create systems where professionals collectively shape how different forms of uncertainty get expressed and worked through. Blackbox Gets Subpoenaed Job applicants are suing Eightfold AI, claiming its hiring screening software should follow Fair Credit Reporting Act requirements—giving candidates the right to see what data is collected and dispute inaccuracies. Eightfold scores job applicants 1-5 using a database of over a billion professional profiles. Sound familiar? It’s essentially what credit agencies do: create dossiers, assign numeric scores, and determine eligibility. The lawsuit argues: if it works like a credit agency, it should be regulated like one. As David Seligman of Towards Justice put it: “There is no A.I. exemption to our laws. Far too often, the business model of these companies is to roll out these new technologies, to wrap them in fancy new language, and ultimately to just violate peoples’ rights.” Threatening Probabilities Every time a chatbot threatens or blackmails someone, my inbox fills with “proof” of sentience. But a new paper shows these behaviors aren’t anomalies—they’re just extreme versions of normal human interaction: price negotiation, power dynamics, ultimatums. Our surprise comes from assuming chatbots should only reproduce socially sanctioned behavior, not the full spectrum of how humans actually act. Threats and blackmail don’t signal consciousness. They signal the model is drawing from the complete statistical distribution of human behavior—including the parts we don’t like to acknowledge. It’s probabilities all the way down, even when they’re uncomfortable ones. 🧶When Your AI Assistant Becomes an Advertiser OpenAI just announced it will start testing ads in ChatGPT’s free tier. The press release was carefully worded—reassuring users that “ads will not change ChatGPT answers” and that “your chats are not shared with advertisers.” But as Miranda Bogen, director of the AI Governance Lab at the Center for Democracy and Technology, pointed out in a recent conversation, these statements are misleading and miss the entire point. What’s coming is a fundamental shift in who these systems serve—and what that means for people, privacy, and inequality. To understand why this matters, we need to look at three things: how AI changes advertising signals, what “privacy” really means in this context, and why this could be harder to detect than anything we’ve seen before. The Signal Problem The question is: what happens when your AI assistant becomes an advertiser? Answering that question, according to Miranda, starts by recognizing that advertising is all about high fidelity signals of intent—data that accurately predicts what you want to buy or do. When an ad interrupts your experience on Facebook, it’s hoping that you’ll care; that perhaps something you clicked awhile back will still be relevant. That’s not a great signal. Searching offers a better signal. You’re typically using Google because you want something. But ChatGPT is different. You’re not searching for information. You’re often thinking out loud, revealing what matters to you, what you’re struggling with, what you’re planning or hoping for. Each conversational turn reveals deeper context about your intent—creating rich data for advertisers. Now, OpenAI wants those signals but, if you read the press materials, they’re clearly concerned about losing users. For example, they bend over backwards to say that your chats won’t be “shared with advertisers.” But according to Miranda, this is technically accurate but completely misleading. The platform doesn’t need to send advertisers a list of your conversations. That’s the whole point of advertising infrastructure—OpenAI will target ads on behalf of advertisers, shielding your specific data while making the connection happen anyway. The press release also promises you can “turn off personalization” and “clear the data used for ads.” But there are multiple layers of personalization happening simultaneously (e.g. raw chat logs, explicit memory stored about you, etc.) and it’s unclear what exactly OpenAI is referring to. Plus, even if you did turn off all personalization and erased all memory in the system, the amount of information a chatbot has about you in a specific context window offers plenty of signal for advertisers. The Relationship Problem On Facebook or Google, it’s clear you’re dealing with an advertiser. Your intent is your own. The experience is transactional. But as Miranda argues, when your AI assistant or AI co-worker starts subtly suggesting new products or services, something fundamentally different is happening. It’s closer to influencer marketing where paid recommendations come wrapped in the veneer of authentic social connection. But an influencer’s audience typically knows that they’re being paid to sponsor a product. With an AI assistant, the lines start to blur. It has been helping you draft emails, think through career decisions, process relationship struggles. You’ve built relational trust with it over months, so when it suggests a therapist, lawyer, or contractor, you might perceive it as trusted advice without knowing, of course, which providers paid to be in the pool the AI draws from. The persuasion is invisible, wrapped in the same helpful tone the AI uses for everything else. The Visibility Problem Personalized ads and privacy harms are a big albeit old problem. These tools will of course propagate discrimination, exploit people at vulnerable moments, reinforce stereotypes and biases, and shape what opportunities people see (and don’t!). But this evolution of the advertising model brings something new: these harms will be even harder to identify. Why? Because these systems are being built to connect with each other. AI agents will call other tools, connect with your bank and service providers, exchange information across an ecosystem of interconnected systems. There will be money and incentives flowing through this network in ways that are nearly impossible to track. As Miranda put it: “Even just tracking where any of this is happening, where exchanges of money and incentives are happening behind the scenes and where that might be shaping people’s experiences will just be even more challenging to keep up with over time.” If your inner monologue so far is “this all sounds very bad,” well, I get it. But we didn’t end the conversation without imagining alternative business models and policy solutions. Listen to the end for these, and hear what Miranda would do to shift power back to users if she were advising our next (fingers crossed!) President four years from today. 👉 Before you go: 3 ways I can help * Advising: I help clients develop AI strategies that serve their future vision, craft policies that honor their values amid hard tradeoffs, and translate those ideas into lived organizational practice. * Courses & Trainings: Everything you and your team need to cut through the tech-hype and implement strategies that catalyze true systems change. * 1:1 Leadership Coaching: I can help you facilitate change — in yourself, your organization, and the system you work within. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit untangled.substack.com
January 17 · 40 min
What Happens When Your Coworkers Are AI Agents
Hi there, Welcome back to Untangled. It’s written by me, Charley Johnson, and supported by members like you. This week, I’m sharing my conversation with Evan Ratliff, journalist and host of the thought-provoking podcast, Shell Game. As always, please send me feedback on today’s post by replying to this email. I read and respond to every note. On to the show! 🔦 Untangled HQ I launched The Facilitators’ Workshop, a community of practice for leaders who want to perfect the craft of facilitating groups through conflict and ambiguity—so they can actually achieve their purpose. Our event on January 23, ”From Conflict to Clarity & Connection,” will give you a structured process for diagramming conflict—a way to slow down, make invisible dynamics visible, and understand what’s actually happening before deciding what to do next. I’m spinning up a lot of new things that I’m excited to tell you about. The best way to stay up to date on upcoming events and workshops is by joining The Untangled Collective. In season 1 of Shell Game, Evan cloned his voice, hitched it to an AI agent, and then put it in conversation with scammers and spammers, a therapist, work colleagues, and even his friends and family. You can listen to that conversation here. In season 2 of Shell Game, Evan explores what its like to run a company with AI agents as employees. A real company building a real product with users and interest from venture capitalists. This is the future that Silicon Valley is actively trying to bring into existence. Sam Altman recently shared that some of his fellow tech CEOs are literally betting on when the first one-person, billion-dollar company will appear. Now, all the hype would make you believe that we should welcome this future with open arms. Productivity will skyrocket. Time will feel abundant. Work will become frictionless and maximally efficient. That’s the story, anyway. You won’t be surprised to find that the gap between the hype and reality is, uh, massive. Evan and I talk about that gap, but Shell Game helps us see around the corner to what it might actually feel like to work with AI agents. It’s a story about: * What’s lost when an organizational culture becomes sycophantic. * What its like when your colleague regularly make stuff up, commits it to memory, and then repeats that thing in the future as if its real. * Why words like ‘agent’ and ‘agentic’ belie the reality that these large language models don’t really do anything on their own. * The costs and complexities of anthropomorphizing agents, and how we’re voluntarily tricking ourselves. * What humans are uniquely good at, and what it means for automation and the evolution of work. * What Silicon Valley misunderstands about the world they’re creating and what’s at stake in confusing fluency and judgement. Shell Game is smart, thought-provoking, and really funny. I can’t recommend it enough. I hope you enjoy my human to human conversation with Evan Ratliff. 🧶Want to go deeper? If you finished our conversation thinking, “Okay… I need to think about this more,” let me help. * Flattery as a Feature: Rethinking ‘AI Sycophancy’ * There’s no such thing as ‘fully autonomous’ agents * It’s okay to not know the answer * AI isn’t ‘hallucinating.’ We are. That’s it for now, Charley This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit untangled.substack.com
Nov 9, 2025 · 48 min
The Universe Called. It Says Your Theory of Change Is Cute.
If you’ve sensed a shift in Untangled of late, you’re not wrong. I’m writing a lot more about ‘complex systems.’ To name a few: * What even is a ‘complex system’ and how do you know if you’re in one. * How to act interdependently and do the next right thing in a complex system. * Why if/then theories of change that assume causality are bonkers — and how to map backward from the future. * How do you act amidst uncertainty — if you truly don’t know how your system will respond to your intervention, what do you do? * How should we think about goals in an uncertain world? * Here’s a fun diagnostic tool I developed to help you assess how your organization thinks, acts, and learns under complexity. I am obsessed with complex systems because the world is uncertain and unpredictable — and yet all of our strategies pretend otherwise. We crave certainty, so we build plans that presume causality, control, and predictability. We know in our gut that the systems we’re trying to change won’t sit still for our long-term plans, yet our instinct to cling to control amid uncertainty is too strong to resist. And honestly, in 2025, this shouldn’t be a hard sell. Politics, climate change, and AI are laughing at your five-year strategy decks. Complexity thinking helps us see this clearly — that systems are dynamic, nonlinear, and adaptive — but it, too, has blind spots. First, it lacks a theory of technology. The closest we get is Brian Arthur’s brilliant book, The Nature of Technology: What It Is and How It Evolves, which explains how technologies co-evolve with economic systems. (Give it a read, or check out write-up in Technically Social). But Arthur was focused on markets, not on social systems — not on how technology is entangled with people and power. That’s where my course comes in. I’m trying to offer frameworks and practices for creating change across difference, amid uncertainty, in tech-mediated environments — approaches that honor both complexity and the mutual shaping of people, power, and technology. (And yes, Cohort 5 of Systems Change for Tech & Society Leaders starts November 19.) Second, complexity is hard to talk about simply and make practical (that’s why my Playbook turned into a 200 page monstrosity!) Every time I use the words “complex” or “system,” I can feel the distance between me and whoever I’m talking to widen. I’ve been searching for thinkers who bridge that gap — who write about systems with both clarity and depth — and recently came across the brilliant work of Aarn Wennekers, who writes the great newsletter Super Cool & Hyper Critical (Subscribe if you haven’t yet!) After reading his essay, Systems Thinking Isn’t Enough Anymore, I reached out and invited him onto the podcast. I’m thrilled to share that conversation — one that digs into the mindsets and muscles leaders need to navigate uncertainty and constant change, the need to collapse old distinctions between strategy and operations, and what it really means to act when the ground beneath us keeps shifting. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit untangled.substack.com
Nov 2, 2025 · 1 hr 4 min
"Autonomy or Empire"- Rethinking What AI Is For
This week, I spoke with Harry Law, Editorial Lead at the Cosmos Institute and a researcher at the University of Cambridge, about AI and autonomy. Harry wrote a terrific essay on how generative AI might serve human autonomy rather than the empires Big Tech is intent on building. In our conversation, we explore: * What the Cosmos Institute is — and how it’s challenging the binary, deterministic thinking that dominates tech. * The difference between “democratic” and “authoritarian” technologies — and why it depends less on the tools themselves than on the political, cultural, and economic systems they’re embedded in. * The gap between agency (Silicon Valley’s favorite word) and autonomy, and why that difference matters. * How generative AI can collapse curiosity — closing the reflective space between question and answer — and what it might mean to design it instead for wonder, inquiry, and self-understanding. * Why removing friction and optimizing for efficiency often strips away learning, growth, and self-actualization. * The need for more “philosophy builders” — technologists designing systems that expand our capacity to think, choose, and act for ourselves. * Harry’s provocative idea of personalized AIs grounded in our own values and second-order preferences — a radically different vision from today’s “personalization” built for engagement. The conversation around generative AI has gone stale. Everyone is interpreting it through their own frames of meaning — their own logics, values, incentives, and worldviews — yet we still talk about “AI” as if it’s a single, coherent, inevitable thing. It’s not. My conversation with Harry is an attempt to move beyond the binary — to imagine alternative pathways for technology that place human autonomy, curiosity, and moral imagination at the center. If you’re fed up with imagining alternative futures and want to do the hard, strategic work of changing the system you’re in, and set it — and you! — on a fundamentally new path, sign up for Cohort 5 of my course, Systems Change for Tech & Society Leaders. It kicks off in three weeks and there are still a few spots available. https://www.charley-johnson.com/sociotechnicalsystemschange Before you go: 3 ways I can help * Systems Change for Tech & Society Leaders - Everything you need to cut through the tech-hype and implement strategies that catalyze true systems change. * Need 1:1 help aligning technology with your vision of the future. Apply for advising & executive coaching here. * Organizational Support: Your organizational playbook for navigating uncertainty and making sense of AI — what’s real, what’s noise, and how it should (or shouldn’t) shape your system. P.S. If you have a question about this post (or anything related to tech & systems change), reply to this email and let me know! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit untangled.substack.com
Oct 12, 2025 · 36 min
'Be Curious, Not judgmental' or What AI Critics Get Wrong!
Today, I’m sharing the 15-minute diagnostic framework I use to assess an organization’s capacity to navigate uncertainty and complexity. Fill out this short survey to get access. The diagnostic is just one tool of 30+ included in the Playbook that will help you put the frameworks from my course immediately into practice. This one helps participants see how their current assumptions, decision structures, and learning practices align (or clash) with the realities of complex systems — and identify immediate interventions they can try to build adaptive capacity across their teams and organizations. Fun, huh? Cohorts 4 & 5 are open but enrollment is limited. Sign up today! Okay, let’s get to my conversation with Lee Vinsel, Assistant Professor of Science, Technology, and Society at Virginia Tech and the creator of the great newsletter and podcast People & Things. I try (and fail often!) to live by the line from an incredible Ted Lasso scene, “Be curious, not judgmental.” I was reminded of that phrase while reading Lee Vinsel’s essay Against Narcissistic-Sociopathic Technology Studies, or Why Do People USE Technologies. Lee encourages scholars and critics of generative AI — and tech more broadly — to go beyond their own value judgments and actually study how and why people use technologies. He points to a perceived tension we don’t have to resolve: that “you can hold any ethical principle you want and still do the interpretive work of trying to understand other people who are not yourself.” I feel that tension! There are so many reasons to be critical of the inherently anti-democratic, scale-at-all-costs approach to generative AI. You know the one that anthropomorphizes fancy math and strips us of what it means to be human — all while carrying forward historical biases, stealing from creators, and contributing to climate change and water scarcity? (Deep breath.) But Lee’s point is that we can hold these truths and still choose curiosity. Choosing curiosity over judgment is also strategic. Often, judgment centers the technology, inflating its power, and reducing our own agency. This gestures at another one of Lee’s ideas, “criti-hype,” or critiques that are “parasitic upon and even inflates hype.” As Vinsel writes, these critics, “invert boosters’ messages — they retain the picture of extraordinary change but focus instead on negative problems and risks.” Judgment and critique focuses our attention on the technology itself and centers it as the driver of big problems, not the social and cultural systems it is entangled with. What we need instead is research and analysis that focuses on how and why people use generative AI, and the systems it often hides. In our conversation, Lee and I talk about: * How, in a world where tech discourse is all hype and increasingly political, curiosity can feel like ceding ground to ‘the other side.’ * Where narcissistic/sociopathic tech studies comes from — and what it would look like to center curiosity in how we talk about and research generative AI. * How centering the technology itself overplays its role in social problems and obscures the systems that actually need to change. * The limits of critique, and what would shift if experts and scholars centered description and translation instead of judgment. * Whether we’re in a bubble — and what might happen next. This conversation is a wonky one, but its implications are quite practical. If we don’t understand how and why organizations use generative AI, we can’t anticipate how work will change — or see that much of the adoption is actually performative. If we don’t understand how and why students use it, we’ll miss shifts in identity formation and learning. If we don’t understand how and why people choose it for companionship, we’ll miss big shifts in the nature of relationships. I could go on — but the point is this: in a rush to critique generative AI, we often forget to notice how people are using it in the present — the small, weird, human ways people are already making it part of their lives. To see around the corner, we have to get over ourselves. We have to replace assumption with observation, and judgment with curiosity. Before you go: 3 ways I can help * Systems Change for Tech & Society Leaders - Everything you need to cut through the tech-hype and implement strategies that catalyze true systems change. * Need 1:1 help aligning technology with your vision of the future. Apply for advising & executive coaching here. * Organizational Support: Your organizational playbook for navigating uncertainty and making sense of AI — what’s real, what’s noise, and how it should (or shouldn’t) shape your system. P.S. If you have a question about this post (or anything related to tech & systems change), reply to this email and let me know! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit untangled.substack.com
Jun 29, 2025 · 48 min
"Empire of AI" w/Karen Hao
Today, I’m sharing my conversation with Karen Hao, award-winning reporter covering artificial intelligence and author of NYT bestseller, Empire of AI. We discuss: * The scale-at-all cost approach to AI Big Tech is pursuing — the misguided assumptions and beliefs it rests upon, and the harms it causes. * How the companies pursuing this approach represent a modern-day empire, and the role narrative power plays in sustaining it. * Boomers, doomers, and the religion of AGI. * Alternative visions of AI that center consent, community ownership, context, and don’t come at the expense of people’s livelihoods, public health, and the environment. * How to reclaim our agency in an age of AI. 👉 Tech hype hides power. Reclaim it in my live course Systems Change for Tech & Society Leaders. Links: - Check out the podcast, Computer Says Maybe This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit untangled.substack.com
Apr 6, 2025 · 8 min
There’s no such thing as ‘fully autonomous’ agents
I’m Charley Johnson, and this is Untangled, a newsletter and podcast about our sociotechnical world, and how to change it. Today, I’m bringing you the audio version of my latest essay, “There’s no such thing as ‘fully autonomous agents.’ Before getting into it, two quick things: 1. I have two part essay out in Tech Policy Press with Michelle Shevin that offers a roadmap for how philanthropy can use the current “AI Moment” to build more just futures. 2. There is still room available in my upcoming course. In it, I weave together frameworks — from science and technology studies, complex adaptive systems, future thinking etc. — to offer you strategies and practical approaches to address the twin questions confronting all mission driven leaders, strategists, and change-makers right now: what is your 'AI strategy' and how will you change the system you’re in? Now, on to the show! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit untangled.substack.com
Jan 5, 2025 · 42 min
Is tech a religion? Is Elon Musk a hungry ghost?
Today, I’m sharing my conversation with Greg Epstein, American Humanist chaplain at Harvard University and the Massachusetts Institute of Technology, and author of the great new book Tech Agnostic: How Technology Became the World’s Most Powerful Religion, and Why It Desperately Needs a Reformation. We discuss: * How tech is becoming a religion, and why it’s connected to our belief that we’re never enough. * How Elon Musk, Mark Zuckerberg, Jeff Bezos, and Bill Gates are hungry ghosts. * What ‘tech-as-religion’ allows us to see and understand that ‘capitalism-as-religion’ doesn’t. * My concerns with the metaphor and Greg’s thoughtful response. * How we might usher in a tech reformation, and the tech humanists leading the way. * The value of agnosticism and not-knowing when it comes to tech. Okay, that’s it for now, Charley This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit untangled.substack.com
Dec 20, 2024 · 35 min
Can we democratize AI?
Today, I’m sharing my conversation with Divya Siddarth, Co-Founder and Executive Director of the Collective Intelligence Project (CIP) about how we might democratize the development and governance of AI. We discuss: * The CIP’s work on alignment assemblies with Anthropic and OpenAI — what they’ve learned, and why in the world a company would agree to increasing public participation. * The #1 risk of AI as ranked by the public. (Sneak peek: it has nothing to do with rogue robots.) * Are participatory processes good enough to bind companies to the decisions they generate? * How we need to fundamentally change our conception of ‘AI expertise.’ * How worker and public participation can shift the short-term thinking and incentives driving corporate America. * Should AI companies become direct democracies or representative ones? * How Divya would structure public participation if she had a blank sheet of paper and if AI companies had to adopt the recommendations. That’s it for now, Charley This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit untangled.substack.com
Dec 15, 2024 · 29 min
Building healthy local communities online & off
Today, I’m sharing my conversation with Deepti Doshi, Co-Director of New_Public about what they’ve learned building local healthy communities, online and off. We discuss: * The problem New_Public is trying to address with their initiative, Local Lab. (Which I highlighted in my recent essay, “Fragment the media! Embrace the shards!”) * What Deepti has learned about what makes for pro-social conversations that build community on messaging boards and private groups. * Why it’s an oxymoron to call Twitter a ‘global town square’ and the relationship between scale and trustworthy information ecosystems. * The importance of ‘digital stewards’ in facilitating online community. * How the social capital people build online is translating into IRL actions and civic engagement. * What a future might look like if New_Public realizes the vision of Local Lab. That’s it for now, Charley This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit untangled.substack.com
Dec 8, 2024 · 31 min
Block, build, be
This week, I’m sharing my conversation with Anya Kamenetz, the creator of The Golden Hour, a newsletter about “thriving and caring for others on a rapidly changing planet. Anya and I announced a new partnership recently — now, when you sign up for an annual paid subscription to Untangled, you’ll get free access to the paid version of The Golden Hour — and we wanted to talk about it, and the work ahead. Along the way, we also discuss: * How we’re adapting our newsletters in response to the election. * Why mitigating harms isn’t sufficient, and a framework that can help us all orient to the present moment: block, build, be. * How we consume information — our mindsets, habits, and practices — and also, why ‘consume’ isn’t the right frame. * The difference between social media connections and email-based relationships. * How to talk to your kids about the election. * The fragmentation of the news media environment and why it’s a good thing. I couldn’t be more excited to partner with Anya and introduce you to her work. Enjoy! More soon, Charley This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit untangled.substack.com

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