Untangled

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.


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 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.
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💫 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

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.​)
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💫 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

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