Untangled

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

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

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

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

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.


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