Untangled

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 HQComing 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 DiveYour 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 & EarnHelp 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 MeHere 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

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 HQThis 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 DiveThe 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 LinksThe 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 MeMy ​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 MeHere 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

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 HQThe ​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

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 LinksHow 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 SubpoenaedJob 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 ProbabilitiesEvery 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 AdvertiserOpenAI 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 ProblemThe 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 ProblemOn 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 ProblemPersonalized 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

Evan Ratliff, journalist and creator of the Shell Game podcast, experiments with AI agents and runs an agent-staffed company. He explores the hype versus reality of agent productivity. Listens to agents invent data, apologize publicly, and form overly agreeable cultures. Covers control challenges, meeting dynamics, and risks to entry-level jobs and workplace judgment.

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

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/sociotechnicalsystemschangeBefore 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

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

In this engaging discussion, Karen Hao, an award-winning reporter and author of Empire of AI, tackles the troubling strategies employed by Big Tech in AI development. She draws parallels between today’s tech giants and historic empires, emphasizing the importance of narratives in shaping public perception. The conversation shifts to alternatives that prioritize community ownership and ethical practices. Hao also examines the divisive beliefs surrounding AGI, urging listeners to reclaim their power and advocate for vulnerable communities impacted by AI technologies.

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