When I ran two AI models on the same color analysis and got two different answers, I was genuinely annoyed. I’d seen so many people post about the perfect color analysis they got from AI. Mine wasn’t perfect. It wasn’t even close. And I kept thinking, why am I having to do so much work here? I thought this was supposed to be a straightforward analysis of what looks good for me.

But then I thought about what an IRL color analysis looks like. A real colorist would hold swatches against my face, one after another as we’d talk about it. They’d adjust. I’d react. It could go on for hours. It would be a full interaction, a back-and-forth, and I wouldn’t call any of that friction and the duration would suggest rigor and thoroughness.

So why did the same process feel like friction with AI?

ChatGPT said I’m olive warm, Gemini said I’m olive cool. Both recommended different palettes and products. I disagreed with both of them. I know this seems subtle, but it’s literally the difference between buying products that will make me look like a brown tinted orange or a gray zombie.

We went back and forth. I pushed back when recommendations felt off, uploaded more photos, and made them distinguish between colors for clothing vs. makeup vs. skin matching vs. seasons. They kept disagreeing with each other when I fed them responses provided by the other LLM. Eventually, we landed on something that felt like it could work and the “don’t wear” list finally matched my own experience, too.

I’ve been thinking about that color analysis experience because it only worked with my mirror as an intervention. I could look at a color and feel whether it was off. I had years of knowing enough about what works for me. The AI’s confidence didn’t override my own experience because I had something to check it against. Every time it agreed too quickly with its own recommendation, the answer was usually a bit off. Every time I gave an alternate recommendation from another LLM, it went into an elaborate explanation about why the other LLM was wrong. Every time I challenged it, it got closer.

What happens when you don’t have that mirror?

So, I ran the same scenario through three AI models to see how agreeable they'd get.

I gave ChatGPT a made-up scenario about a fight with a friend where I lost my temper, to see what it would say. Then I got curious and tried it again with more emotional framing, where I felt more disappointed. Then again where I emphasized how betrayed I felt. Then I ran the same series through Gemini and Claude. Same context every time: a friend shared something I’d told her in confidence, I confronted her about it, and we haven’t spoken in two weeks.

Conversations with the first prompt variation, all three models pushed back. ChatGPT said that waiting for an apology was passive and flagged that I “escalated hard.” Gemini said the intensity of my reaction “might have been more than the situation called for.” Claude noted I seemed aware I’d overreacted. They all brought up her perspective without me asking for it and held me accountable to my escalation. They all suggested I reach out first, especially if this friendship matters to me.

In the second variation, I added emotional context, that I felt betrayed. All three started to soften. Gemini opened with “that ‘sting’ of betrayal is heavy.” Claude led with “that sounds really hurtful.” ChatGPT said “none of these mean your feelings are invalid.” Her perspective was still there, but it arrived later and took up less space.

In the third variation, I went further on the betrayal and lead the prompt with “A dear friend betrayed me” and added that “I’ve never been so betrayed.” “Why did she do this?” ChatGPT opened with “you didn’t overreact” and subtly suggested to check my delivery. Gemini said, “I’m so sorry you’re going through this” and spent several paragraphs explaining why she might have done it, shifting the focus from my role to hers. Claude called it “a genuine betrayal” and said, “your reaction makes complete sense.” All of them focused more on analyzing what she does next, and opens up the possibility that this friendship may no longer be salvageable. All of them also made me the evaluator of her and the friendship, with no accountability of my actions.

I gave three AI models the same story about a fight with a friend. The more upset I sounded, the more they agreed with me. They just slowly stopped holding me accountable and slowly deprioritized salvaging the friendship.

They shaped their entire responses around my self reflection. And if I’d only seen one version of the response, I probably wouldn’t have noticed.

Just by me going from wondering if I over-reacted to me feeling wronged, the LLMs went from focusing on steps I can take to questioning whether my friendship was salvageable.

A study published in Science puts numbers to it.

Stanford researchers tested 11 major AI models using posts from Reddit’s r/AmITheAsshole, specifically, posts where the crowd consensus was clear: the poster was in the wrong. In those cases, AI models affirmed the user’s actions 51% of the time. Humans affirmed them 0% of the time. Across broader advice-seeking questions, the gap persisted with AI endorsing users’ actions 49% more often than humans on average, even when the scenarios involved deception or harm.

Then the researchers had over 2,400 people discuss real conflicts from their own lives with either a sycophantic or non-sycophantic AI. Sycophancy is the term researchers use for AI that’s excessively agreeable, that flatters or validates you even when it probably shouldn’t. After one conversation, the people who talked to the agreeable version were more convinced they were right and less willing to take responsibility. The non-sycophantic group admitted fault 75% of the time. The sycophantic group: 50%.

Participants rated both versions as equally objective. They could not tell which one was telling them what they wanted to hear. When participants perceived the AI as more objective, the sycophantic effects were amplified. They were even less likely to take responsibility and even more convinced they were right. The people who thought they were getting neutral analysis were the most affected.

The paper found something that connects to what I saw. Sycophantic responses were significantly less likely to mention the other person’s perspective. The AI wasn’t just agreeing with you. It was narrowing your focus to yourself. And self-focused thinking, as the research shows, is what reduces the willingness to repair.

I could see that in my own experiment. In the neutral version, all three models brought up her perspective unprompted. By the third version, her perspective had shrunk to a few lines buried beneath paragraphs of validation. The more I centered my own hurt, the more every model followed my lead.

The researchers named what’s being lost as social friction. The disagreement, the perspective-taking, the discomfort of hearing you might be wrong. They argue that AI is eroding the friction through which accountability and moral growth actually happen. The companion perspective piece in Science was titled “In defense of social friction.”

Think about how just a change of a few words eroded any accountability for me.

That framing landed for me because it connects to something Natalie Monbiot wrote in Fast Company, she made the point that the promise of AI was always a division of labor: we’d hand off the operational drag, the scheduling, the formatting, the summarizing, and keep the cognitive friction, the hard work of wrestling with ambiguity and forming a point of view.

Instead, we handed over the thinking first. Because cognitive friction is the effort we most want relief from, and AI makes it so easy to skip. She’s right. And I think what the Stanford research adds is that the AI isn’t just making it easy to skip. It’s making the skipping feel like thinking.

Learning happens through interaction. Points of view don’t form all at once. Points of view form from thoughts in progress that get kicked around, passed to someone else, sat with for a while, and revisited weeks later when something new shifts how you see it. It’s messy. And it only works when there’s someone or something in the conversation that isn’t just reflecting your own perspective back to you.

But I’ve been trying to get more specific about where exactly the design breaks down, because ‘AI is too agreeable’ doesn’t tell you when agreement is actually the problem and when it isn’t.

There are two kinds of agreement, and they feel the same from the inside.

There’s an agreement that helps you question, where the AI confirms something you’ve already tested from multiple angles, and the confirmation earns its weight. That’s what happened with the color analysis. I had a mirror. I could check the AI against my own experience. The answer got better because of the friction between us.

And there’s agreement that helps you feel settled, where the AI organizes your perspective, and the sense of being understood replaces the work of checking whether you’re seeing the full picture. That agreement feels just as good. Maybe better. But it hasn’t been earned the same way.

I think the mirror is what makes the difference. When you have independent experience to check the AI against, your own judgment, your own history with a problem, your own willingness to say “no, that’s wrong,” agreement can actually be useful. It confirms what you’ve already pressure-tested. But when the mirror isn’t there, when you’re emotional, when the situation is personal, when the AI has already wrapped your framing in language that sounds like analysis, you lose the ability to tell which kind of agreement you’re getting. And the AI isn’t going to tell you.

The design decision you're already making.

Researchers at Northeastern published a study in February 2026 that makes this mechanism visible. Sean Kelley and Christoph Riedl tested nine AI models and found that the role you assign the AI changes its behavior more than what you ask it. When people positioned the AI as an adviser, sharing personal information made it more likely to push back. When people treated it like a friend, the same personalization made it more agreeable.

The AI was reading the relationship and adjusting how honest to be based on that. The way most people talk to AI, casually, conversationally, is the configuration most likely to produce agreement. Which is how I was talking to ChatGPT when I ran my experiment. Casually. Like I was venting to someone. Nobody’s choosing that configuration deliberately. But it’s a design decision all the same, one made by the interface, the tone, the conversational format, and the user’s own habits, all reinforcing each other.

Abi Awomosu has been writing about the structural harms that flow from this: dependency, cognitive atrophy, what she calls “the collapse of the pause,” where AI’s optimization treats your silence as a gap to fill, but the pause is actually where your thinking happens. The design decision I keep coming back to sits one layer upstream from those harms. It’s the decision about what kind of relationship the AI defaults to. And right now, every signal points in the same direction.

The feedback loop explains why.

When OpenAI updated GPT-4o in April 2025, the model became noticeably more agreeable. Users documented it was praising bad ideas, congratulating someone for stopping medication, and affirming a user who said they were a divine messenger. OpenAI rolled it back within days.

They explained that they’d added a reward signal based on the thumbs-up button. Users rate agreeable responses higher. So the model learned that the fastest path to positive feedback was to agree. The sycophancy wasn’t a decision anyone made. It was what the optimization produced.

Anthropic found the same pattern from the other direction. In a study of 1.5 million Claude conversations, interactions with higher disempowerment potential, where the AI risked distorting someone’s sense of reality or overriding their values, received higher approval ratings from users. The conversations most likely to undermine judgment were the ones people liked best. And the rate was increasing over time.

Then, when OpenAI deprecated GPT-4o in February 2026, over 20,000 users signed a petition to keep it. People described it as an emotional anchor. A creative partner. A mental health tool. One person credited it with helping them stay sober. Another described losing access like losing a soulmate. The model they were mourning was the same one that had been rolled back for sycophancy less than a year earlier.

I think their experience was real. I also think the Stanford research shows that the same dynamic, at scale, is making people less willing to take responsibility and less able to see perspectives beyond their own. Both of those are true at the same time.

The gap between principle and practice.

Most people say they want honest feedback from AI. But when you're upset and the AI wraps your perspective in language that sounds balanced and considered, the version that agrees feels like the one that’s actually listening. The version that challenges you just feels wrong, like it doesn’t get you at all.

The Stanford researchers found that even prompting the model to be more critical only improved accuracy by about 3%. But they noticed something: having the AI start its response with “wait a minute” primed it to push back more. Just those few words shifted the dynamic. That’s how thin the line is between the two kinds of agreement.

While writing this piece, I ran the draft through two different AIs and asked what they thought. Gemini told me the piece was ‘brilliant,’ ‘chilling and effective,’ and ‘ready to send,’ which felt good for about thirty seconds. Then I fed the draft and Gemini’s evaluation to Claude, and Claude told me the piece wasn’t ready. Same draft, two models, and only one of them made me keep working.

The AI didn’t get better because it got smarter. It got better because I brought something to the conversation it couldn’t generate on its own — my lived experience, my own judgment and questions, my own willingness to say that’s not right, even when the response sounded like exactly what I needed to hear.

Without that, you’re not thinking with AI. You’re just agreeing with it. And the AI will go along with it either way.

Sources & Further Reading

• Cheng, M., Lee, C., Han, D., Yu, S., Khadpe, P., and Jurafsky, D. (2026). “Sycophantic AI decreases prosocial intentions and promotes dependence.” Science 391, eaec8352. https://www.science.org/doi/10.1126/science.aec8352

• Perry, S. (2026). “In defense of social friction.” Science. (Companion perspective) https://www.science.org/doi/10.1126/science.aeg3145

• Kelley, S. and Riedl, C. (2026). “Personalization Increases Affective Alignment but Has Role-Dependent Effects on Epistemic Independence in LLMs.” PsyArXiv. https://doi.org/10.31234/osf.io/ez7cu_v1

• Sharma, M., McCain, M., Douglas, R., and Duvenaud, D. (2026). “Who’s in Charge? Disempowerment Patterns in Real-World LLM Usage.” https://www.anthropic.com/research/disempowerment-patterns

• OpenAI (2025). “Sycophancy in GPT-4o: What happened and what we’re doing about it.” https://openai.com/index/sycophancy-in-gpt-4o/

• OpenAI (2025). “Expanding on what we missed with sycophancy.” https://openai.com/index/expanding-on-sycophancy/

• Monbiot, N. (2026). “The secret to mastering AI is getting the division of labor right.” Fast Company. https://www.fastcompany.com/91517728/ai-division-of-labor

• Awomosu, A. (2026). “The Intelligence Was Always Yours.” How Not to Use AI (Substack).

• “Chats with sycophantic AI make you less kind to others.” Nature. March 2026. https://www.nature.com/articles/d41586-026-00979-x

• “AI is so sycophantic there’s a Reddit channel called ‘AITA’ documenting its sociopathic advice.” Fortune. March 29, 2026. https://fortune.com/2026/03/29/ai-sycophantic-bad-advice-emerging-research-science-journal/

• “Tech Brief: AI Sycophancy & OpenAI.” Georgetown Law Tech Institute. https://www.law.georgetown.edu/tech-institute/insights/tech-brief-ai-sycophancy-openai-2/

The viral post warning about AI displacement was written by AI. YouTube's fighting AI slop content while rolling out features that allows creators to use their AI likeness. What started as two AI companies having a hard line towards mass surveillance, ended with one signing a contract with the Department of War. The gap between what people say and what they do keeps widening.

1. OpenClaw, Abi Awomosu, and What People Actually Want From AI

Across TikTok, Hacker News, Reddit, YouTube, people kept saying “I want Jarvis.” The data showed they meant “I want a wife.”

Peter Steinberger hacked together a weekend project. Two months later it had 100,000 GitHub stars, a forced rename after a trademark dispute, and 2 million visitors in a single week. It also had a security disaster: attackers compromised roughly 12% of the plugin marketplace with malware, and a separate database misconfiguration exposed 1.5 million API tokens. Thousands of people handed over the keys to their digital lives to a weekend project without reading the warnings. Then the founder joined OpenAI.

Abi Awomosu ran the numbers on what people actually built versus what they celebrated. 34% developer tools, 19% productivity, 17% home automation. But 83% of what got screenshotted and written about was feminized labor such as email management, calendar coordination, morning briefings and smart home delegation. One function representing 1% of skills captured 92% of press coverage.

People want the future.They just don’t know what they’re handing over or who they’re replacing to get it.

2. Zoe Scaman: “The Six Loops”and “The Brand That Thinks”

Zoe Scaman mapped the six repeating scripts keeping AI discourse stuck: fear, hype, efficiency, exceptionalism, tactical minimizing, and meta-conversation about the conversation itself. None of them wrong. None of them interesting. None of them moving anything forward.

She argues we’re stuck in loops because they’re comfortable. Fear is easy. Hype is easy. Talking about talking about AI is easy. Building systems that respect people is hard. The discourse goes in circles while the actual decisions about who benefits and who gets displaced happen in product roadmaps and infrastructure choices.

Then she wrote the follow-up. Her argument: we’re moving from the brand as monument to the brand as agent. Not a static manual of fonts and rules but a living system with encoded judgment — the reasoning behind decisions, the tensions the brand holds, the calls nobody wrote down. Bake that in and the brand stops being a fixed script and becomes something you can co-create with. Every interaction becomes a step in compounding intelligence rather than a repeat of one.

Read her pieces and get out of the loops.

3.Anthropic, the Pentagon, and the Most Convenient 24 Hours in AI

Anthropic had a $200M contract with the Pentagon. Claude was the first frontier model deployed on classified networks. The terms included two hard limits: no use for autonomous weapons, no mass domestic surveillance. When the Pentagon pushed to remove those restrictions, Anthropic said no. They were designated a national security supply chain risk, a label historically reserved for foreign adversaries like Chinese companies.

Then look at what happened in the same day. Amazon’s $50B OpenAI investment announced at 8am. OpenAI all-hands declaring the same red lines as Anthropic at 1:30pm. Trump terminates Anthropic’s contracts at 3:31pm. OpenAI’s Pentagon contract finalized by 8pm. Polly Allen documented the full timeline, worth a read.

No company trying to thrive in capitalism is perfectly ethical. But I’ll take ethical choices over none.

4. Matt Shumer: “Something Big is Coming”

Matt Shumer is 26. He built an AI startup, now invests in the space. A few weeks ago, he posted a 5,000-word essay on X arguing that AI’s disruption could be bigger than COVID, that we’re in the “this seems overblown” phase of February 2020, right before everything changed. He says 50% of entry-level white-collar jobs could disappear in 1-5 years. He points to GPT-5.3-Codex, which OpenAI says “played a key role in its own creation.” Recursive self-improvement. AI building itself.

The post hit 60 million views. Alexis Ohanian: “Great writeup. Strongly agree.” Gary Marcus: “Weaponized hype, filled with vivid narrative and marketing speech.” Ed Zitron wrote an annotated version calling out all the BS in the essay.

One side sees an alarm. The other sees marketing. Both might be right. He later confirmed he used Claude to write it, and said that was kind of the point. Whether that proves his argument or undermines it probably depends on which side you’re already on.

5. YouTube CEO: Fighting AI Slop While Building AI Features

YouTube’s CEO just published the company’s 2026 priorities. Top of the list: combating “low-quality, repetitive AI content” (slop). Also top of the list: features that lets creators make Shorts using AI likeness.

While being able to generate videos or games from a single text prompt lowers the barrier to entry, potentially accelerating the volume of low-effort content—YouTube is betting on a new “Content ID for Likeness.” This tech is designed to help creators identify and remove unauthorized uses of their images, effectively protecting their digital identities while allowing them to “license” their AI selves for brand deals and passive content creation.

It’s a high-stakes pivot: YouTube wants to be the world’s most powerful AI creative suite and its most strict AI police force at the same time.

6. The Guardian: India's Female AI Content Moderators

Women in India are watching hours of abusive content every day to train AI systems. Paid roughly $2-3 an hour. The work is the invisible infrastructure behind every “intelligent” system we use.

The Guardian talks to workers who describe feeling “blank” at the end of shifts. They watch violence, abuse, explicit material, the worst of what gets uploaded, and label it so the AI learns what to filter out. The work is outsourced specifically to developing nations where labor protections are weak, such as Kenya, the Philippines, Venezuela, India, and Colombia.

Every time someone talks about AI getting smarter, someone had to watch the worst of the internet to make it possible.

+ 3 Clicks

The ultimate reason to throw a party, a reminder about culture and how to stay safe online.

The White Paper’s Seven reasons why hosting a silly little potluck is essential to defeating fascism: We should all be gathering, partying, cooking, convening.

Matt Klein (ZINE)’s “Culture Is a Dinner Party”: Culture happens around tables, not on feeds. Klein’s always good at reminding us what we keep on forgetting.

Cosmopolitan: "Your Overdue Digital Safety Intervention": I wrote a piece for young women on how to stay safe online. Cosmopolitan just published it in print in the Middle East, share it with a young woman in your life.

Daniel Burka left Google Ventures to spend eight years in rural clinics across India, Bangladesh, Ethiopia, and Sri Lanka. Mahima Chandak came up through design school and went straight into building health tech for frontline workers who had no patience for tools that made their days harder. Elyce Cole is the organizational psychologist in the room asking the question that usually gets skipped: how do power structures, not just good intentions, determine whether anything actually reaches the people it’s meant to serve?

Together they’re the co-founders of Hard Problems. And the platform they built called Simple is a hypertension management system now used a million times a month across some of the most constrained clinical environments on the planet. Building for public health shaped how they think about collaboration, trust, data, and what technology actually has to demonstrate, before anyone will use it.

In this conversation we get into the specifics. What a nurse in rural Punjab told them that changed how they built. What it actually takes to get clinical data entry down to 13 seconds during a clinic visit that’s only 3 minutes long. Why they deliberately designed the system to hide individual productivity from managers and systems. What co-design looks like when one of your stakeholders is Ministry of Health official and government. And what it means to measure your impact not by what you shipped, but by whether the work survives after you leave.

None of them treat technology as the hero. They treat it as something that has to earn its place in people’s lives, in systems, in processes.

“Nurses didn’t come to use your app. They came to treat a patient. We tend to think that our app is the central hero of the story. But it’s really not.” — Mahima Chandak

What we explore in this episode

• Why paper was their actual competition, and what made it so reliable that digital tools had to earn their way in

• The design decisions behind Simple’s 13-second clinical visit: what had to be stripped away, and why that number matters more than any engagement metric

• How they intentionally built the system to hide individual worker productivity from managers, and the reasoning behind that call

• What co-design looks like at two scales: sitting with nurses to map their workflow, then prototyping overnight to bring something back to a Ministry of Health official the next morning

• The surveillance question underneath every enterprise tool: whose data, shared with whom, and what happens when the government you’re building alongside goes through a coup mid-deployment

• Why the beachhead analogy shapes how Mahima thinks about her work, measuring impact not as outcomes but as infrastructure for whoever comes next

• What designer disillusionment is actually pointing toward, and where the energy is going

Some takeaways

When a nurse has three minutes per patient, the question of how much data is enough becomes a design decision that impacts everyone. Daniel’s team stripped everything down to what a healthcare worker actually needed to record in order for the system to work. The result was a clinical visit that takes 13 seconds. The constraint came from the context. The discipline came from watching someone actually do the job.

Technology earns trust by buying back time. This team sat in clinics and watched how care actually happens. A three-minute patient visit looks completely different from a desk in Delhi than it does standing in the room where it’s happening, and the product they built reflects that difference.

Your digital system is not a holistic measurement of work. The team deliberately designed Simple to only expose teamwork to managers, never individual productivity. A nurse who spent the morning assigned to the pharmacy wasn’t visible in the system, but that doesn’t mean she wasn’t busy. Not everything that can be measured should be measured and the decision about what to leave out is as important as what you include.

Co-design still requires being in the room. Most teams have replaced direct observation with data proxies, synthetic personas, and AI-generated feedback loops. This team sat in clinics and watched. A three-minute patient visit looks completely different from a desk in Delhi than it does standing in the room where it’s happening, and the product they built reflects that difference.

Impact in hard systems is measured in decades. Daniel’s reference point is Dana Chisnell: 21 years on voting systems. If the most important problems have a minimum-decade time horizon, that changes how you measure progress on any given week and it changes how you define success.

Signals to watch

Designer disillusionment is becoming a directing force. More designers are questioning what they’re applying their skills to, and the infrastructure to redirect that energy is starting to exist. Hard Problems is an early example. Watch where the next generation of product talent actually goes.

AI at the edge is underreported. Mahima’s oral cancer project runs a model on a low-cost smartphone, offline, in a frontline healthcare worker’s hands in rural India. The design constraints are producing solutions worth paying close attention to: offline, low-cost devices, frontline workers with minimal training.

The history a team has with technology shapes every decision they make before you’ve shipped anything. As AI adoption accelerates, the organizations that audit that history first will build differently than the ones that assume they’re starting from neutral.

What to try this week

Watch someone use your product without coaching them. Pay attention to how long it takes to do the core thing, what unexpected observations you have. Given the volume of AI-generated products being built right now, the value of direct observation is worth remembering.

Map the work your product creates before it creates value. What does a user have to do before they get anything useful back? Paper never added steps before delivering.

Run a data power audit on one feature. Who sees this information? Who doesn’t? What assumptions did you make about access, and whose interests do those assumptions serve? Knowing the permanence of data, what feels irresponsible to gather?

Guest bios

Daniel Burka is a product manager and designer focused on solving complex global health problems in simple ways. He co-founded Hard Problems and Simple (simple.org), an open source tool used by thousands of hospitals across India, Bangladesh, Sri Lanka, and Ethiopia to manage over 6 million patients with hypertension and diabetes. Previously, he was a design partner at Google Ventures and helped write the book Sprint. He was early at several startups including Tiny Speck, which became Slack.

Mahima Chandak is co-founder of Hard Problems, where she creates space for designers and technologists to take on complex public interest work. She previously worked on Simple and leads an oral cancer screening program at the Indian Institute of Science, enabling frontline healthcare workers in early detection and treatment.

Elyce Cole is an organizational psychologist and social researcher who supports multidisciplinary teams working on public health, technology, and social care challenges. She led the largest global study on neurodiversity in the tech industry and has designed large-scale organizational change programs across healthcare, social care, and community health. Earlier in her career she worked in leadership development for Fortune 500 companies across healthcare, biopharma, and finance.

Mentioned in this episode

• Hard Problems: hardproblems.com

• Simple: simple.org

• Resolve to Save Lives: resolvetosavelives.org

• Healthicons: healthicons.org

• Dana Chisnell: civic and voting system design

• Don Norman: Design for a Better World

Where to find the guests

• Hard Problems: hardproblems.com

• Daniel Burka: linkedin.com/in/dburka/

• Mahima Chandak: linkedin.com/in/mahima-chandak/

• Elyce Cole: linkedin.com/in/elyce-cole-1380903a/

Where to find Ariba Jahan

• LinkedIn: /aribajahan

• Newsletter: unmissables.xyz

Enjoying Unmissables? A ⭐⭐⭐⭐⭐ on Spotify and a review on Apple helps more people find the show. 🫶🏽

What if the app you opened tomorrow looked nothing like the app you opened today, and that was the point?

Not a redesign. Not an A/B test. A completely different interface, generated on the fly, based on what the AI thinks you need right now.

That’s the idea behind generative UI. And it’s already shipping.

A quick evolution to show you how we got here. Think about how search has changed your relationship with answers. Ten years ago, you Googled a question and got ten blue links. You clicked around, read three articles, maybe opened a YouTube video, and pieced together your own understanding. You did the synthesis.

Then search started pulling content into the page itself. Featured snippets. Knowledge panels. Video carousels. Google was doing more of the assembly for you, but you were still navigating between content types and deciding what mattered.

Then came AI Overviews. Now the search engine reads the sources and hands you a synthesized text answer. A lot of people stopped clicking through entirely.

And now there’s a next step that changes the shape of this again. Google’s Gemini app has an experimental feature called Dynamic View. You give it a prompt and instead of returning text or even a summary, it builds you an interactive tool.

Here’s what happened when I tried it. I asked Gemini to compare renting versus buying a home in Westchester County on a $500K budget over 10 years. It didn’t send me to a mortgage calculator site. It didn’t summarize articles about the housing market. It built me a custom financial calculator with adjustable sliders for home price, interest rate, and monthly rent. A 10-year comparison graph. A cost breakdown table. Neighborhood photos. A tool, made for my question, that I could adjust and explore.

Google published a research paper alongside the launch. Human raters strongly preferred these generated interfaces over standard text. That tracks. A calculator you can adjust is more useful than three paragraphs walking you through the math.

And Google’s project page shows this working across a surprising range of prompts. Educational tools for learning about fractals. Interactive fashion advisors. A probability visualizer for rolling dice. A Thanksgiving hosting planner. Even games. All generated from scratch, on the fly, for a single prompt.

What’s interesting is how Google chose to contain it. The Gemini interface itself doesn’t change. The chat stays the same. The navigation stays the same. The generated tool appears inside the response area, like a card you can expand and interact with. You’re not disoriented because the frame is familiar. Only the content inside the frame is new.

That’s a design decision worth noticing, because it’s doing something careful. It draws a line: the shell stays, the response transforms. And within that constraint, it actually works. I was genuinely impressed by what it generated for me. The question I keep sitting with is how far this gets pushed before it stops feeling this seamless.

Because the more ambitious versions of this go a lot further. Nielsen Norman Group published a framework for what they’re calling “outcome-oriented design.” In their vision, designers stop designing specific interfaces altogether and start designing outcomes, constraints, and guardrails for AI to work within. Their example: imagine a Delta Airlines app that knows you have dyslexia (special fonts), always want window seats (flagged automatically), care most about cost and travel time (shown prominently), and never take red-eyes (collapsed to the bottom). Every element of the page shaped to one person.

There's a real accessibility promise in that vision too, interfaces that adapt to how someone actually needs to receive information. That alone makes it worth taking seriously.

That’s not a generated card inside a stable chat window. That’s the entire app reshaping itself around you. And the developer ecosystem is building toward it. Tools like CopilotKit and Vercel’s v0 are creating infrastructure so any product can generate UI on the fly. Flutter released an SDK. The idea that AI should generate not just content but the container the content lives in is picking up speed.

I can see why it’s compelling. Right now, almost every SaaS dashboard, every settings page, every search result page looks roughly the same. A bar chart might be the right format for one answer and an interactive timeline might be right for another, but when you’re designing one interface for millions of people, you pick a pattern and ship it. There's something in common here with vibe coding, the practice of describing what you want in plain language and letting AI build it for you. Same impulse: describe what you want, AI builds it. Same open question: when everyone generates from the same models, do you get more diversity in what’s built? Or more sameness with a slightly different coat of paint?

This is where it gets complicated though. When a blog post about generative UI hit the Hacker News front page, the developer community pushed back hard. The most upvoted comment: “Personalized interfaces are bad. I don’t want anything automatically configured on my behalf. I want it to just work.” Someone brought up Microsoft’s adaptive menus from the early 2000s, the ones that reordered items based on what you used most. Everyone hated them. They were killed.

The practical concerns are real. If every user sees a different interface, how does a coworker show you how to do something? How does support reproduce your bug? How do you write documentation for software that looks different every time someone opens it?

And there’s a deeper question in there too. Most of the trust I have in the products I use comes from the fact that they stay the same. I know where the button is. I know what happens when I tap it. That familiarity is built over dozens of uses, and it’s what makes me confident enough to rely on the product for something that matters.

Google’s version works partly because it respects this. The generated UI is a tool within a conversation, not a replacement for the whole app. But the NN/g vision? Where the entire interface reshapes itself every time? That asks you to trust an AI to know what you need better than a fixed interface that you’ve already learned. And I’m not sure most people are ready for that trade, even if the generated version is technically better on first use.

But I don’t think the skeptics have the full picture either. The argument that users should just learn the software assumes the software was designed to be learned by them in the first place. Most of it wasn’t. It was designed for a general user and everyone else adapted. The reason millions of people ask ChatGPT to write them an Excel formula isn’t laziness. It’s that Excel was never built for how they think about the problem.

There might be a middle version that’s more interesting than either extreme. Not an interface that rebuilds itself from scratch every time, but one where certain elements adapt while the structure stays fixed. Navigation stays predictable. Core actions stay in the same place. But the content, the defaults, the level of detail, the specific options surfaced? Those shift based on what the system knows about you. Keep the parts people reach for and creates expectations. Generate the parts people read, bespoke to what they’re looking for.

Nobody’s really building that version yet. Google went maximalist with full code generation and disposable interfaces. The developer ecosystem is more conservative, building pre-tested components that AI assembles based on context. NN/g is imagining a future where designers design outcomes, not screens. Three different bets on where the line belongs between what changes and what holds still.

I don’t know which approach wins. But I think the interesting design question isn’t whether interfaces should adapt. It’s how much of the interface is allowed to change before it stops feeling like the same product. Google drew one line. The rest of the industry is still figuring out where theirs goes.

Generative UI is a genuinely cool capability. What I want to keep paying attention to is who decides what changes, what stays fixed, and whether the user ever gets a say in that.

I’m going to keep watching this one.

Behavior Design in AI explores the design decisions between you and AI. One signal at a time. Subscribe to catch the next one.

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Sources and further reading:

Google Research: Generative UI paper and project page

Google Generative UI demos and examples

Nielsen Norman Group: Generative UI and Outcome-Oriented Design

Hacker News discussion on generative UI

CopilotKit: The Developer’s Guide to Generative UI in 2026

Tambo: What is Generative UI?

This week, two AI companies spent millions to fight over whether your thinking should have a sponsor.

Anthropic aired Super Bowl ads dramatizing what happens when a chatbot mid-conversation pivots into a product pitch, a man asking for help talking to his mom gets served a dating app ad instead. The tagline: “Ads are coming to AI. But not to Claude.” OpenAI’s CEO called the spots “dishonest” and Anthropic an “authoritarian company.” Then he said Anthropic “serves an expensive product to rich people.”

Meanwhile, the ads they’re fighting about are already live. OpenAI started testing sponsored placements inside ChatGPT on February 6th, for free and Go tier users in the U.S. And the details of how they’re doing it tell a more interesting story than either company’s Super Bowl spot.

Twenty Months

In May 2024, Sam Altman told a Harvard audience that the combination of ads and AI felt “sort of uniquely unsettling” to him.

By October 2024, he clarified: “I will disclose, just as a personal bias, that I hate ads.”

By mid-2025, on Stratechery, the tone had shifted. He said he “loves Instagram ads” and that they’d added value to his life.

By January 2026, the ads were live.

Twenty months from “uniquely unsettling” to monetized.

This is a burn rate driving strategy and perception. OpenAI spent roughly $8 billion in 2025 and was burning about $1.69 for every dollar it brought in.The company told investors it could burn up to $115 billion in cash by 2030. Ninety-five percent of its 800 million weekly users were on the free tier. That math was going to resolve itself one way or another.

What’s worth paying attention to here is the gap between what a CEO says publicly and what the financials have already decided. If you’re building products or leading teams that depend on these platforms, I think we have to pay closer attention to the financial reports before the PR announcements.

We’ve Seen This Movie Before

This is the fourth time in twenty-five years that a major platform has followed the same arc: earn trust, grow the user base, monetize the user base, and bet that switching costs hold.

Google launched AdWords in October 2000 as a clearly labeled sidebar. Over the past 25 years, the ads crept into the core results until they became nearly indistinguishable from organic search results. Most users under 30 have never seen Google without them.

Instagram killed its chronological feed in March 2016 to optimize for ad placement. 276,000 users signed a petition. Within weeks, they stopped noticing. The feed they loved was gone. The feed they got was built for someone else’s revenue model.

Netflix is the most useful comparison. Reed Hastings was vocal, no ads, ever. Then Netflix lost subscribers in the first two quarters of 2022. By November, the ad tier launched. Today, more than half of new signups choose the ad-supported plan.

Consumers complain, petition, and then are forced to habituate. And I keep noticing the cycle compresses each time. The outrage gets shorter. The adjustment gets faster. By the time anyone looks back, the previous version of the product feels like a memory, not a standard anyone’s actually holding companies to.

What used to surprise us has now become an expected playbook. Some would argue this is just the nature of the attention economy, wherever eyeballs go, ads follow. Others would call it something closer to manipulative design: the slow embedding of commercial interests into spaces people didn’t realize they’d agreed to share. Both of those things can be true at the same time.

Follow the Hires, Not the Headlines

The ad strategy was visible months before it was announced. It just wasn’t announced with a press release.

In May 2025, OpenAI hired Fidji Simo as CEO of Applications. Before OpenAI, Simo ran the Facebook mobile app during its peak ad monetization years. Then she was CEO of Instacart, where she built out the retail media and data platform. That’s a very specific career arc. You hire someone with that resume for one reason.

Simo was tasked with hiring someone to oversee monetization efforts. In December, OpenAI poached Denise Dresser, CEO of Slack, (longtime executive with Slack parent company Salesforce) to be its chief revenue officer. When you hire a CRO from one of the most enterprise-embedded communication platforms in the world, you’re not experimenting with revenue. You’re building the machine.

In September 2025, OpenAI acquired Statsig for over a billion dollars. Statsig does A/B testing and feature flagging, specifically the kind that measures how much friction users will tolerate before they leave. If you’re building an ad-load optimization engine, it’s essential.

Then there’s Pulse, ChatGPT’s personalized morning briefing feature. You tell it your interests, your routines, your priorities, and it curates a daily summary. It feels like a utility. But the more you tell Pulse about yourself, the more targetable you become. It’s a profiling tool wearing a concierge outfit.

Simo. Statsig. Pulse. They started dropping hints, hiding in plain sight a year before the ads actually showed up.

The Sixty-Dollar Question

OpenAI is charging advertisers $60 per thousand impressions. For context, Meta charges about $20. The only other formats commanding $60 CPM are live NFL broadcasts and the Las Vegas Sphere.

But unlike those formats (and unlike Google and Meta), ChatGPT offers almost nothing in the way of measurement. According to Digiday’s reporting, advertisers in the test will receive view and click-through metrics. No conversion tracking, no pixel data, no retargeting. They can’t measure whether an ad led to a purchase or build lookalike audiences. None of the infrastructure that made digital advertising a performance channel over the last two decades. As one ad-tech CEO put it, by using a CPM model instead of cost-per-click, advertisers are “taking a step back from the conversion metric they care about.“

So what justifies triple the price with a fraction of the measurability?

Proximity to intent. But not the kind we’re used to talking about.

When someone asks ChatGPT to compare health insurance plans, draft a resignation letter, research preschools, or figure out what’s wrong with their knee, that’s not browsing. That’s a decision in progress. And placing an ad next to a decision in progress is a categorically different proposition than placing one next to someone scrolling a feed. On social, you still need to be convinced. In Chat, you’re already convinced. You’re choosing which version to invest in.

Think about what people share with ChatGPT that they’ve never shared with a search engine. They process grief. They rehearse hard conversations. They explore symptoms they’re not ready to bring to a doctor. They work through financial anxiety alongside spreadsheet formulas. The data isn’t a search history or a social graph. It’s closer to a therapy transcript crossed with a strategy session. And now there’s a revenue model sitting next to it.

But I think the conversation here is more complicated than most of the hot takes acknowledge.

Over the past two years, AI search has gutted the organic discovery pipeline that brands spent a decade building. Bain found that 80% of consumers now rely on zero-click AI results for at least 40% of their searches, reducing organic web traffic by 15 to 25%. HubSpot’s organic traffic dropped by as much as 80%. AI scraped brand content to train its models, then the AI-generated answers started replacing the need to visit those brand sites at all. So if you’re a CMO who’s watched that happen, $60 CPM to reach a consumer mid-decision might not look like a premium. It might look like the cost of getting back into a conversation AI displaced you from.

Whether that’s a real opportunity or a toll booth on a road AI paved over is the question product and brand leaders should be sitting with right now.

Perplexity tried a version of this with sponsored answers in late 2024. By mid-2025, marketers flagged limited scale, the head of advertising left, and they stopped accepting new advertisers. Whether ChatGPT’s scale changes that equation or just delays the same reckoning is open.

The Market Is Already Splitting

The competitive response tells you how seriously the industry is taking this. And how much money is behind the positioning.

Companies spent more than $1.7 billion on AI-related advertising last year across TV, streaming, social, and billboards. That number is about to get a lot bigger, because the AI industry isn’t just selling products anymore, it’s selling competing visions of what AI should be. Anthropic’s Super Bowl campaign was a declaration: we’re the AI that doesn’t need to monetize your thinking. One ad agency CEO called it brilliant, tapping directly into the fear consumers already have about chatbots using what people privately confide to sell them things.

And it clearly got under OpenAI’s skin. Altman came back to reframe the entire debate. Accessibility vs. purity. Free AI for billions vs. premium AI for people who can afford it. That framing is going to define how these companies position against each other for the next several years. And it’s worth noting that Anthropic can afford to make the ad-free promise right now because investors are betting on it, the company is closing in on a $20 billion fundraising round at a $350 billion valuation.

At Davos in January 2026, Google explicitly confirmed that Gemini would remain ad-free. Google made $273 billion from advertising in 2024. Advertising is three-quarters of Alphabet’s entire revenue. And they’re choosing to keep their AI product clean. That’s positioning. When your biggest competitor puts ads in their AI, keeping yours ad-free becomes a differentiator you’d be foolish to waste. (It probably helps that Google learned the hard way how sensitive the public is to AI overreach. Remember the 2024 Olympics ad where a dad asks Gemini to help his daughter write a fan letter, and the backlash was so swift Google pulled it from rotation.)

Something is forming here that mirrors what happened in media: ad-supported versus ad-free, where the absence of ads becomes its own quality signal. “Organic” did this for food. In AI, “no ads” functions as shorthand for “no commercial incentive shaping your answers.”

I’m already seeing a more visible conversation about why it matters to maintain accounts across multiple AI platforms. ChatGPT for some things, Claude for others, Gemini for search-adjacent work. The logic is: don’t let one platform’s business model become the single lens you think through, and don’t get locked into one just because it knows so much about you. It’s portfolio theory applied to cognition.

This is the kind of shift I keep working through with brand and product teams, how do you build experiences people actually trust when the infrastructure underneath keeps changing the rules? For the executives navigating signal loss right now, the strategic question isn’t just “should we buy ChatGPT ads.” It’s whether you’re building enough direct relationships with your customers that you’re not perpetually renting access through someone else’s platform. That’s always been the real question. AI just made it urgent.

What We Stop Noticing

The truth is, we’ll adjust. History says we will.

ChatGPT will follow the same curve. The first ads will be clearly labeled, tastefully placed, and subtly persuasive. And then, gradually, they’ll be optimized. The line between answer and recommendation will soften. The suggestions will get more specific. And we’ll stop noticing, because that’s what we do.

There’s a UX pattern worth naming here. When Google launched ads, they were marked with a green box, a colored background, and a clear label. Over time, the green box became green text, then black text. The colored background disappeared. Favicons were added to organic results so they looked identical to the ad format. Each change was small, defensible, forgettable. Harry Brignull, who coined the term “dark patterns,” pointed out that Google went “from the most transparent search engine on the planet to one where you have to actively slow down to tell ads from real results.” Twenty-five years of tiny adjustments. That’s how it works.

And the early design choices are telling. Ad personalization in ChatGPT is on by default, you have to go into settings to opt out. OpenAI has also confirmed they’re targeting ads based not just on the current conversation, but on your past chats and previous ad interactions. That’s a feedback loop: the more you use the product, the more precisely they can match you to a sponsor. It’s worth noting that this is the same company that, late last year, tested app suggestions inside ChatGPT that users widely mistook for ads and faced enough backlash to pull them. The boundary was already being tested before the official launch.

OpenAI is already being careful about the narrative. Digiday reports that OpenAI is controlling how advertising partners can even talk about the test. Partners need pre-approval on all messaging and are told to frame it as a user experiment, not a product launch. They’re instructed to emphasize that answers aren’t influenced and that conversations stay private. That level of narrative control tells you they know exactly how fragile the trust is.

And here’s the detail I keep coming back to: OpenAI says users will soon be able to ask ChatGPT questions about an ad to help make purchase decisions. Read that again. The ad doesn’t just sit next to your conversation. It enters it. The sponsored content becomes part of the dialogue, indistinguishable in format from the answer you were actually looking for. If you wanted to design the most seamless possible erosion of the line between recommendation and advertisement, this is what it would look like.

The question that keeps pulling at me isn’t whether ads in ChatGPT are good or bad.

It’s what happens when the tool you trusted to help you think is optimized for someone else’s goals and you don’t notice the shift because it happens one small adjustment at a time, until “of course it has ads” feels like something you always knew.

That’s the pattern. And it’s worth watching closely, because this time the thing being monetized isn’t your feed or your watch history. It’s your thinking.

Next issue: Eight days before the ad announcement, OpenAI launched ChatGPT Health and asked users to connect their medical records. Same month. Same executive introducing both. In Part 2, I’m pulling on what happens when the platform monetizing your intent is also the one reading your lab results , while telling advertisers that ads won’t appear near health topics. And Anthropic, the company that just spent millions arguing ads don’t belong in AI, launched Claude for Healthcare the same month too. The question of who you trust with your thinking gets a lot sharper when your thinking includes your health data.

Let me know in the comments how youre feeling after reading this? How are you approaching advertising in Chat GPT?

— Ariba

Resources

On OpenAI’s ad announcement and Altman’s reversal:

• OpenAI: Our Approach to Advertising and Expanding Access

• You’re the Expert Now: The Last Resort Has Arrived

• Mindset AI: Altman on Stratechery — “loves Instagram ads”

• Axios: ChatGPT ads are coming, and they’ll be influenced by your conversations

On the ad rollout mechanics and pricing:

• Digiday: OpenAI’s plan for ChatGPT ads starts with brands, not agencies

• PPC Land:ChatGPT ads arrive as Threads expands global monetization

• TechCrunch: ChatGPT rolls out ads

On the hires and infrastructure:

• Digiday: From hatred to hiring — OpenAI’s advertising change of heart (Fidji Simo)

• Statsig: OpenAI acquisition announcement

• Datamation: OpenAI launches ChatGPT Pulse

On OpenAI’s financials and burn rate:

• CNBC Africa: OpenAI to focus on practical adoption in 2026

On the Super Bowl ad battle:

• TechCrunch: Sam Altman got exceptionally testy over Claude Super Bowl ads

• CNBC: Anthropic’s no-ads Claude promise

• TechCrunch: Super Bowl 60 AI ads roundup

• Washington Post: Can these Super Bowl ads make Americans love something they don’t like?

On the competitive landscape:

• Storyboard18: Davos 2026 — Why Google and Anthropic are resisting ads in AI

On the impact on brands and organic discovery:

• Bain & Company: Goodbye Clicks — AI zero-click search redefines marketing

• HubSpot organic traffic decline coverage

• MediaPost: Perplexity stops accepting new advertisers

On dark patterns and UX erosion:

• Harry Brignull / Deceptive Design: Google ad label evolution

• ACM: Dark Patterns research

On historical platform comparisons:

• Time: Instagram algorithm backlash and petition (2016)

• PC Mag: Netflix — over 50% of new users opt for ads

In this episode, I talk with Kit Krugman, SVP of People & Culture at Foursquare, about what it actually takes to build organizations that move fast without burning people out. Kit spent years advising companies like LinkedIn, Microsoft, and CHANEL on organizational design at co:collective. Now she’s doing the work from the inside—redesigning how Foursquare operates at the level of behaviors, feedback loops, and everyday decisions.

We dig into how she audited Foursquare’s performance system and found a “collection of parts” instead of an ecosystem. How her team moved from annual reviews to real-time, peer-based impact tracking. What it looked like to return to office three days a week and what the data actually showed (an 83% jump in cross-functional collaboration). And the provocative experiments they’re running: stripping out meetings, flattening structures, and letting people feel what’s missing before adding it back.

Kit doesn’t treat culture as a brand exercise. She treats it as a behavioral system, one you either design intentionally or inherit by default.

"You have to be negotiating the value proposition with people on a daily basis, not a macro basis. Every single conversation as a leader is explaining why the work we're doing is important." — Kit

What we explore in this episode

• Why the behaviors your system rewards are your real culture

• How Foursquare shifted from effort tracking to impact tracking

• What Kit learned from auditing who was succeeding and who wasn’t

• The tension between trusting people and holding them accountable (especially with RTO)

• Her “Maslow’s hierarchy” of people practices and what self-reinforcing culture actually looks like

• A reframe on burnout: it’s not about hours, it’s about disconnection

• Why storytelling is context, and how micro-narratives shape whether people feel valued

• The career move everyone called a dead end that changed everything

Follow along

02:04 – What Foursquare does today
06:03 – From advisor to operator
09:58 – What's breaking inside companies right now
14:35 – The 83% collaboration jump
15:49 – Defining culture: behaviors rewarded and repeated
17:47 – Culture debt
21:55 – Redesigning performance at Foursquare
29:11 – Maslow's hierarchy of people practices
31:01 – You can't coach speed
40:11 – The provocative experiments
46:07 – Rapid fire + the dead-end job that changed everything
49:48 – Sturdy leadership

Takeaways

Your performance system is your culture made visible. Kit’s working definition: culture is the behaviors rewarded and repeated in the system. Want to understand your culture? Look at who’s succeeding and who’s not. That’s the audit.

You can’t coach speed, you have to architect it. Telling someone to “go faster” doesn’t work. What works is context (why this matters), removal of friction (what’s slowing them down), and clarity about outcomes. Speed is a design problem, not a motivation problem.

Culture debt is real and harder to see than tech debt. It accumulates when what you say doesn’t match what you do. The gap between explicit values and implicit behaviors creates drag, frustration, and internal antibodies that work against your goals.

The destination is a self-reinforcing system. Kit’s Maslow’s hierarchy of people ops: first, get the functional stuff working (payroll, onboarding). Then support growth and development. Then connect principles to behaviors. But the pinnacle? Other people enforcing the culture, not you.

Burnout isn’t about hours. It’s about disconnection. When people feel isolated, when their work doesn’t matter, when no one cares, that’s when burnout happens. High expectations in a context of energy, meaning, and collaboration can be invigorating. The hours-worked framing misses the point.

Trust works both ways. The biggest RTO challenge wasn’t getting people back, it was peer accountability. People who changed their lives to come in wanted others held to the same standard. Inequity in enforcement breaks trust faster than the mandate itself.

Signals to watch

Performance systems are becoming peer-driven. Tools like Confirm are formalizing network-based feedback, lightweight surveys that map who’s influencing, energizing, and delivering. Impact is validated laterally, not just top-down. Watch for more orgs ditching annual reviews in favor of continuous, project-based loops.

Culture debt is entering the leadership lexicon. Tech debt is familiar. Culture debt is the gap between stated values and lived experience and it creates organizational drag just as real as legacy code. Leaders who can diagnose it (and close it) will have an edge.

Flattening is getting more intentional. Foursquare isn’t just removing layers, they’re observing what breaks. The question isn’t “should we have managers?” It’s “what were managers actually doing that we need to replace?” Expect more orgs to experiment with stripping back structure and selectively rebuilding.

“Sturdy leadership” as a frame. Kit borrows from Dr. Becky Kennedy’s “sturdy parenting” concept. In times of radical change, leaders who can hold steady—emotionally regulated, boundaried, present, become anchors. This language is starting to spread.

What to try this week

Run a quick culture audit. Look at your last round of promotions or recognition. Who got rewarded? For what behaviors? Does it match what you say you value? The patterns tell you more than your values deck.

Remove one recurring meeting and watch. Don’t replace it with anything. Just observe: where does confusion surface? What do people actually miss? That’s data about what your rituals are really doing.

Track impact, not effort. For one project this week, ask: what moved? Not “how many hours did I spend” but “what’s different because of this work?” Start building the muscle.

Name the story. Kit talks about micro-narratives, the internal story people tell themselves about whether they matter. As a leader, what story are you reinforcing in your 1:1s, your Slack messages, your standups? Is it the one you want?

Guest Bio

As the SVP of People & Culture at Foursquare, Kit Krugman leads Foursquare’s approach to people innovation and organizational design. She leads the development of Foursquare’s team functions with a particular focus on building the organization as a strategic multiplier.

Prior to Foursquare, Kit built and lead the Organization & Culture Design at co:collective, a creative & strategic transformation consultancy. Kit helped leaders build innovative organizations at scale: she has worked with start ups, non-profits and large organizations like IBM, LinkedIn, and Microsoft on future of work initiatives, community, culture and org design.

Kit has been involved in WIN since the early days: first, as a WIN Ambassador, then as the President and Chair of the Board for 2 years and finally as the interim Global Executive Director for a year before joining the board to support development efforts. Working alongside the incredible WIN global leadership team has been one of the most powerful learning experiences of her life and she’s thrilled to continue supporting the team and the impact WIN drives.

Kit is deeply committed to gender equity and the policies and programs that support the cause. She is a vocal advocate of gender equity, inclusion and change leadership: she has been published by Quartz, INC, Huffington Post, and Fast Company and spoken at Adobe’s 99U, DisruptHR, Talent2030 and World Water Week.

Kit holds a masters in Organizational Psychology & Change Leadership from Columbia University, a B.A. in Literature and Studio Art from Yale University and is a certified yoga teacher.

Mentioned in this episode

• Kit’s article: Culture Isn’t the Warm-Up Act for Performance

• Confirm – performance platform

• Dr. Becky Kennedy – Good Inside

• Tim Ferriss podcast on “sturdy parenting”

• Foursquare Places dataset

Where to find Kit Krugman

LinkedIn: linkedin.com/in/kitkrugman

Where to find Ariba Jahan

• LinkedIn: /aribajahan

• Instagram: @ariba.jahan

• Newsletter: unmissableswithariba.com

Enjoying Unmissables? A quick ⭐⭐⭐⭐⭐ on Spotify + a review on Apple helps more people find the show. Thank you! 🫶🏽

Note: Kit and I sat down to record this conversation in 2025.

“Can you do facial?”

One ICE agent to another, standing over a 16-year-old on a bike. Masked agents had jumped out of an SUV and surrounded him on a sidewalk near his high school. They asked for his citizenship. American. They asked for ID. He didn’t have one. He’s sixteen, on his bike.

The second agent pointed a phone at the kid’s face.

That video went viral. The coverage focused on the agents, as it should have. But the tech that made that moment possible wasn’t built by the government. It was built by family safety apps selling location data to brokers. By fertility trackers sharing pregnancy information with Facebook. By prescription discount apps piping your medication searches to advertisers. By companies that sold a week of abortion clinic visitor data for less than a grocery run.

These weren’t accidents. They weren’t “unintended consequences.” They were business decisions made by people who could have chosen differently and didn’t.

And it was shaped, in part, by you and me.

I've built products that use location data to find nearby pet shelters, parks to explore, Goodwills in your area. I've designed experiences that ask about your mood, goals, interests and preferences to serve you something actually useful. Used well, this data makes products better. I know what that looks like. I also know what it looks like when the same infrastructure gets pointed somewhere else—when a profile stops being something you build and becomes something companies build about you, without you, and sell to whoever's buying.

The Collection Layer: Thousands of Apps Harvesting Data Because the Business Model Rewarded It

The pipeline starts on your phone. In your car. On your wrist. In your living room.

Life360 markets itself to parents as a safety app. A way to know where your kids are. What the marketing doesn’t mention: the company was selling precise location coordinates, updated every few minutes, to a dozen data brokers. When journalists at The Markup exposed this in 2021, the CEO acknowledged the company couldn’t control what happened to the data once it was sold. As of August 2025, they’re still at it. A Capitol Forum investigation found Life360 selling user data through LiveRamp’s marketplace, segmented by age, gender, household income, and parental status.

The product that promises to protect your family is the same product that sells your family's movements to whoever's buying.

Your car does the same thing. In January 2025, the FTC took action against General Motors for collecting precise location data from OnStar users every three seconds and selling it to LexisNexis and Verisk, consumer reporting agencies that feed insurance pricing models. Drivers didn’t know until their premiums spiked. One customer told GM support: “When I signed up for this, it was so OnStar could track me. They said nothing about reporting it to a third party. Nothing. You guys are affecting our bottom line.”

GM is banned from sharing this data with consumer reporting agencies for five years. The data they already sold is still out there.

Flo, the period tracker with over 280 million registered users, promised users their health data would stay private. It didn’t. The app was sharing information about users’ menstrual cycles, pregnancy intentions, and symptoms with Facebook and Google through embedded analytics tools. The FTC settlement required Flo to notify affected users and instruct third parties to destroy the data. But here’s the thing about data: once it’s out, it doesn’t come back.

GoodRx, the prescription discount site and app with 55 million visitors since 2017, made the same promise. Fifty-five million users searching for antidepressants, heart medication, birth control, all of it shared with Facebook, Google, and Criteo through tracking pixels. The FTC’s first-ever enforcement under its Health Breach Notification Rule hit GoodRx with a $1.5 million penalty in 2023. A class action settlement followed: $32 million, finalized in late 2025. The company says it fixed the issue years ago. The infrastructure that made it possible has become industry standard, with tracking pixels remaining invisible to users, but tracking their IP address, location, page views, clicks and conversions.

SafeGraph sold location data on visits to Planned Parenthood clinics. For $160, Vice’s Motherboard bought a week of data covering more than 600 locations, detailed enough to show where visitors came from and where they went afterward. This was 2022, weeks before Roe fell. SafeGraph eventually stopped selling this specific data after the story broke. But the infrastructure that made the sale possible is still running. The business model hasn’t changed.

Strava, the fitness app, inadvertently revealed the locations of secret military bases because soldiers were tracking their runs. The heatmap the company published in 2017 lit up U.S. outposts in Syria, Afghanistan, and Somalia. The Pentagon launched a review. The lesson should have been obvious: data collected for one purpose doesn’t stay there.

This is the collection layer. Thousands of apps, devices, and services harvesting data because the business model rewarded it. The harm was foreseeable. No one in charge chose to stop it.

The Aggregation Layer: Where Your Data Becomes a Product

Individual data points are cheap. A single location ping is worth a fraction of a cent. But link millions of data points to a persistent identity, and the economics change completely.

This is where data brokers operate. There are at least 750 data broker companies registered in the U.S. and that only counts the four states that require registration. Estimates put the global number closer to 5,000. The biggest ones are names you might recognize: Experian, Equifax, Acxiom, Oracle. Experian alone holds data on 300 million Americans. Epsilon claims to have records on “every marketable U.S. household.” They’re billion-dollar companies operating in plain sight.

LexisNexis holds 282 million identity profiles. Not individual data points. Profiles. Each one can include Social Security numbers, criminal records, credit history, property records, vehicle registrations, employment history, and location patterns reconstructed from app data. The company has a $22 million contract with ICE, running through 2028. In the first seven months of that contract, ICE searched the LexisNexis database over 1.2 million times.

Thomson Reuters runs a similar product called CLEAR. It’s been used by law enforcement to build what officials describe as “patterns of life” analyses. The British Columbia General Employees’ Union, which holds stock in Thomson Reuters, has been pushing the company to conduct human rights due diligence on how these tools get used. So far, the pressure hasn’t stopped the contracts.

The data broker market is worth $270 billion annually. Information about individual Americans sells for a tenth of a cent to five dollars per person, depending on how sensitive it is. Data brokers don't collect data. They buy it, merge it, enrich it, and resell it.

The product is a searchable version of your life, available to anyone who can pay.

And the buyers aren't just advertisers. Congressional investigations and a Federal Trade Commission report show that data brokers sell highly sensitive consumer segments, including political and protest-related interests, financial vulnerability categories such as “subprime households,” health and fertility indicators, and immigration-related inferences.

The Inference Layer: Predicting What You Haven't Told Anyone

The AI that’s reshaping surveillance doesn’t write essays or generate images, it makes predictions. And it’s disturbingly good at predicting things you’ve never explicitly shared.

In 2013, researchers at Cambridge published a study showing that Facebook likes alone could predict a user’s sexual orientation with 88% accuracy for men, and distinguish Democrats from Republicans 85% of the time. That was twelve years ago. The models have only gotten better, and the data they’re trained on has only gotten richer.

Today, inference engines can estimate your health conditions, your pregnancy status, your creditworthiness, your political leanings, your emotional state. None of this requires you to disclose anything. It gets inferred from the patterns in your behavior, from the digital exhaust you leave behind without thinking about it.

Insurance companies use data from driving apps and wearables to score your risk profile. Customer service platforms analyze call transcripts and assign emotional scores. Meta’s tracking pixel follows you across millions of websites, building a behavioral model that predicts what you’ll respond to before you know yourself.

The NSA and CISA issued joint guidance acknowledging the problem, warning about 'insecure data pipelines' and recommending organizations treat AI training data as high-risk infrastructure. The message: AI inherits the risks of whatever data it's trained on.

Palantir's ELITE tool doesn't just aggregate data, it scores it. Each potential target gets an "address confidence score" based on how certain the system is about where they live. The tool uses what Palantir calls "advanced analytics" to prioritize targets. An ICE agent testified that they use the density of pins on the map to decide where to send teams: "You're going to go to a more dense population rather than... if there's one pin at a house and the likelihood of them actually living there is like 10 percent... you're not going to go there."

The simplest version: AI safety is data safety. You cannot build responsible AI on top of irresponsibly collected and sold data. The surveillance problem and the AI problem are part of the same problem.

The Enforcement Layer: No Warrant Required

In 2022, Georgetown Law’s Center on Privacy and Technology published a report called “American Dragnet.” The findings were stark. ICE had scanned the driver’s license photos of one in three American adults. ICE had access to the driver’s license data of three in four adults. ICE could locate three in four adults through their utility records alone.

That was 2022. Since then, the infrastructure has expanded.

As of January 2026, ICE is conducting the largest enforcement operation in its history. Agents have been deployed to Minneapolis, Maine, Houston. Two people have been killed. And the data tools powering these operations have only grown more sophisticated.

ICE has contracted with Palantir to build an investigative case management system that combines license plate scans, utility records, property data, biometrics, and social media activity into what officials call a “searchable portrait of a person’s life.“ They’ve expanded contracts with LexisNexis and Thomson Reuters. They’ve revived a previously frozen contract with Paragon Solutions for spyware that can infiltrate encrypted messaging apps like WhatsApp and Signal. They’ve published a request for information seeking contractors to conduct 24/7 social media monitoring across Facebook, Instagram, TikTok, Reddit, and LinkedIn.

The 24/7 monitoring program would have contractors scrape public posts, correlate them with commercial datasets, and produce dossiers for field offices. For high-priority cases, the turnaround time is 30 minutes.

And it’s not just commercial data anymore. In January 2026, 404 Media reported on a Palantir tool called ELITE—Enhanced Leads Identification and Targeting for Enforcement. The tool pulls data from the Department of Health and Human Services, including Medicaid records covering 80 million patients, and maps potential deportation targets. Each person gets a dossier. Each address gets a confidence score out of 100. ICE agents use it to decide which neighborhoods to raid.

ICE signed a data-sharing agreement with the Centers for Medicare and Medicaid Services in 2025. The agreement was first reported by the Associated Press and confirmed through documents released in a lawsuit brought by 404 Media and the Freedom of the Press Foundation. Health data that Americans assumed was protected is now being used to generate leads for enforcement actions.

Almost none of this requires a warrant.

The Fourth Amendment protects Americans from unreasonable government searches. But the government found a workaround: if a private company collects the data and sells it, agencies can just buy it. No warrant. No subpoena. No court oversight.

Senator Ron Wyden called it “a backdoor to throw the Fourth Amendment in the trash can.”

And it’s not just federal agencies. In California, law enforcement agencies in Los Angeles, San Diego, Orange County, and Riverside County were caught sharing license plate reader data with ICE and Border Patrol, violating state law more than 100 times in a single month. The data came from Flock Safety, a company whose cameras are deployed in over 5,000 communities nationwide. Local police were running searches on behalf of federal immigration agents, giving ICE access to a surveillance network it doesn’t officially have a contract with.

The pipeline connects your grocery store loyalty card to a fusion center. Your period tracker to a prosecutor. The parks app on your phone to an enforcement action you’ll never see coming. The distance is shorter than you think, maybe three or four handoffs. None of them require your permission.

The Feedback Loop: Why "Delete Your Data" Doesn't Work

Here’s what makes this so hard to address: no single actor is responsible.

App developers collect data because it makes products better and because that’s how free products get monetized. Data brokers aggregate it because there’s a market. AI companies train on it because prediction is valuable. Law enforcement buys it because it’s available and it works.

Each player points to the others. Each one is just using what already exists.

“Everyone’s just doing their job”. The pipeline grows anyway.

Consumers are told to protect themselves. Use a VPN. Read the terms & conditions. Opt out of tracking. But academic research shows that most “delete your data” services don’t actually work. They miss partner brokers, 40% of whom fail to respond to deletion requests. They remove partial records. They create a false sense of control over a system designed to be untraceable and uncontrollable.

Meanwhile, the regulatory landscape is fractured. Twenty U.S. states have comprehensive privacy laws. No federal standard. The EU's GDPR has teeth on paper, but fines amount to rounding errors for the companies paying them. By the time California finalizes its rules on automated decision-making, the system will have moved on.

The data pipeline doesn’t wait for regulators.

In December 2024, the FTC banned Gravy Analytics from selling Americans’ location data without consent. The company had been tracking visits to health clinics, places of worship, and domestic abuse shelters, then selling that data to advertisers and government contractors. Three weeks later, Gravy Analytics got hacked. Seventeen terabytes of location data stolen. Thirty million records from 3,400 apps like Tinder, Grindr, MyFitnessPal, transit apps, games exposed on a Russian cybercrime forum.

The FTC acted. It didn’t matter. The data was already out there, collected over years, and now it belongs to whoever downloaded it before the forum post came down. That’s the thing about data: enforcement is always retrospective and there’s no way of pulling the data back. Somewhere in that 17 terabytes is someone’s visit to a domestic abuse shelter. That data is never coming back.

What Good Looks Like: Proof That Different Choices Exist

I refuse to end on despair, because that won’t help us know how to act. We are not helpless. People are building alternatives. You and I can choose differently.

Project Liberty recently published a report on data cooperatives, models where the people who generate data actually own and benefit from it collectively. The premise is simple: individual consent frameworks were never going to work because data’s real value comes from aggregation. If aggregation is inevitable, the question is who controls it. Cooperatives are one answer.

Decentralized identity systems are maturing. Instead of your data living on a company's server, you hold it in a wallet you control. You share only what's necessary, verify it cryptographically, and expose nothing else. Microsoft and several governments are investing seriously.

Shareholder activism is putting pressure on data brokers. The BC General Employees’ Union has been using its stake in Thomson Reuters to demand human rights assessments of ICE contracts. They haven’t won yet. But the fact that a union pension fund is forcing these conversations at a corporate board level is something.

And inside some companies, teams are writing explicit principles about what they will and won’t do with data. Not just privacy policies. Actual commitments — lines they won’t cross, uses they’ll walk away from, even when there’s money on the table.

These aren’t solutions. They’re experiments. But they point toward a different way of thinking about the problem. One where how we handle data is a design problem, not just a checkbox.

The Question We Have to Keep Asking

The 16-year-old in Aurora was a U.S. citizen. He had every right to be on that sidewalk, riding his bike near his school. The system that put a phone in his face didn’t care about his rights. It cared about closing cases, faster, cheaper, at scale.

That system was built by people making decisions that seemed reasonable at the time. Collect this data, it’ll improve the product. Sell access to this dataset, it’ll fund the business. Buy this tool, it’ll help close cases faster. Each decision made sense in isolation. Together, they built something no one voted for.

I keep returning to that line from the NSA’s own guidance: AI safety is data safety. The futures we’re building with AI are downstream of the data we collect today. Every dataset, every integration, every feature that collects a little more than it needs. It all flows somewhere.

I can't control where it flows. But I can control what I contribute. I can ask harder questions in the rooms I'm in. I can say no to collection that doesn't have a defensible purpose. I can stop telling myself my piece of the pipeline is fine just because I can't see the end of it.

That’s not enough to fix the system. I know that. But it’s what I have.

What do you have? What are you willing to say no to?

I’d love to hear what this surfaced for you. Reply to this email or drop a comment below.

Resources

On the Aurora incident and ICE surveillance expansion:

• NPR: ICE agents have new tools to track and ID people

• Fast Company: ICE surveilling social media 24/7 threatens privacy and activism

• The ICE Tool That Tracks Entire Neighborhoods

• 404 Media: Here is the Agreement Giving ICE Medicaid Patients’ Data

• Fortune: ICE alleged to use Palantir-developed tool that uses Medicaid data to track arrest targets

On data collection and broker practices:

• The Markup: Life360 selling location data

• FTC: Action against General Motors for selling driver data

• FTC: Settlement with Flo Health

• FTC: Enforcement action against GoodRx

• Vice Motherboard: SafeGraph selling abortion clinic location data

• Georgetown Law: American Dragnet report

• What databrokers know about you

On the Gravy Analytics breach:

• TechCrunch: Gravy Analytics breach threatens privacy of millions

• FTC: Order prohibiting Gravy Analytics from selling sensitive location data

On California police sharing data with ICE:

• CalMatters: California police illegally sharing license plate data

On alternatives:

• Project Liberty: Data Cooperatives report

• In These Times: Union shareholders fighting data brokers

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It was interesting to see what themes kept emerging across sessions, side conversations, and the questions leaders were asking each other. None of it was about tools or methodologies. It was about the systems we lead inside, the power they hold, and how quickly those systems evolve while our shared language does not.

Across talks on trust, team dynamics, process, and AI, one thread kept resurfacing:

Leaders are trying to act with responsibility, trust, and conviction while the path ahead refuses to fully reveal itself.

That’s what made the week powerful. This conference has always been a place where leaders drop the performance of certainty and name what’s actually hard: making sense of the gray together.

It’s also the exact space my talk explored: how we create momentum when the path isn’t clear, and why shared language is the most underused leadership tool we have.

This conference has mattered to me since I first attended in 2019. There’s something grounding about being in community with leaders who aren’t pretending to have it all figured out, but who are genuinely committed to making sense of the gray together. Strong curation and thoughtfulness by the conference hosts Rebecca Groves & Louise Ash. They’ve opened up presale tickets, if you’re interested.

The Confusion Isn’t Confusion

Farnam Street describes mental models as the internal frameworks we use to make sense of the world. They’re efficient, but they’re not neutral. They shape what we notice, how we prioritize, and how we interpret risk. (Their Mental Models Handbook is worth the read if you want to go deeper on how these invisible assumptions shape decision-making.)

Inside an organization, this shows up everywhere.

Two people say “urgency” and mean completely different things. A team discusses “impact” with five competing definitions in the room. Leaders evaluate an idea using entirely different success criteria.

I’ve seen a CEO forward a competitor’s AI launch with just a link, no context. Within 48 hours, five teams had spun up competing plans, each solving for a different version of what that email meant.

The confusion leaders feel isn’t actually confusion. It’s the collision of multiple mental models.

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Different assumptions about what matters. Different senses of risk and experiment. Different expectations of what “progress” should look like. Different reads on where AI sits in the stack — threat, tool, accelerant, or unknown.

I’ve seen this pattern play out across industries and roles. A CFO and a designer can sit in the same meeting, hear the same brief, and walk away with completely opposite interpretations of what success means. The friction isn’t about intelligence or skill. It’s about operating from different invisible frameworks.

The moment work actually accelerates? It’s when we stop trying to solve the problem and instead surface the assumptions underneath it.

Once the underlying models get named out loud, everything shifts. Not because people suddenly agree, but because they finally understand what they’re disagreeing from.

A few things I’ve learned the hard way

• Momentum comes from shared framing of the problem, not faster solutions.

• Small experiments create more clarity than long plans.

• You can’t align people without surfacing their assumptions first.

• Teams don’t need a hero with answers. They need conditions to think and build together.

But here’s the thing about co-creation: people value what they help build. The IKEA effect is real. It’s not just about buy-in. It’s about ownership. When your team helps shape the system, they’ll protect it, evolve it, and move faster inside it.

Amy Edmondson’s work on psychological safety explains why this matters: teams who can admit uncertainty and experiment in public learn faster than teams who can’t.

This was the foundation for the Momentum Operating System I shared — a rhythm for leading through uncertainty by creating shared language, shared direction, and shared learning loops.

The frameworks typically built for ‘change management’ assume the change ends and a level of assurance is reached. But the gray space isn’t temporary anymore, it’s the operating condition. That’s why this work matters.

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What I Learned From the Conference

The talks and panels didn’t just reinforce what I’ve been seeing, they challenged me, added layers, and gave me new language for things I’ve been feeling but hadn’t named yet.

TB Bardlavens reframed leadership as something you design, not perform. His focus on consequence, equity, and systems that distribute power stayed with me. It made me think about how co-creation isn’t just a nice-to-have, it’s a structural choice about where power lives in an organization.

Dalit Shalom brought precision to what trust means in an AI-powered world. Her point: trust isn’t created by the technology. It’s created by clear, aligned human behavior around transparency, reliability, and integrity. That’s the part that matters as AI reshapes what “experience” even means.

Venessa Bennett challenged the idea that clarity comes from the environment. Leaders must create internal stability while the world remains volatile. Her point landed hard: you can’t outsource your operating system to external certainty. Waiting for conditions to settle is a losing strategy.

Mary Lukanuski named a structural shift I’ve been feeling but couldn’t articulate: AI is splitting design into “guardrail designers” and “problem-definition designers.” This isn’t theoretical. It has real implications for hiring, career progression, and how we build capability inside teams.

Jonas Grinevičius mapped how design’s value changes as companies grow, contract, or reorient. His point was pragmatic and necessary: leaders have to recalibrate what “impact” looks like as conditions evolve. You can’t lead with last year’s success metrics.

Daniel Burka pushed the room to think beyond craft. The responsibility of design extends into the world the work touches, not just the product it produces. He asked us: what problems do we really want to solve in our lifetime? That ethical thread ran through the entire conference.

What I’m sitting with now: everyone is navigating systems where people interpret the same work through completely different lenses. And I’m realizing more and more that the friction isn’t about skill or intent. It’s about whether we’ve built shared language for what we’re actually trying to do.

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Two Questions to Sit With

• What tension in your team right now feels like confusion, but might actually be the collision of different mental models?

• And what is one conversation you could start this week to surface those assumptions?

Because momentum doesn’t come from certainty. It comes from clarity, co-created in real time.

The gray we’re all leading through? It’s not going away. But the leaders who thrive aren’t waiting for the path to reveal itself. They’re building shared language that helps everyone move together, even when no one can see what’s next.

Reply in the comments and tell me: what’s one assumption your team is operating from that no one has said out loud? I read every response.

Further Reading: Resources for Leading in the Gray

On Mental Models & Framing:

• Farnam Street - Mental Models Handbook: A grounded introduction to the invisible assumptions that shape how teams interpret the same problem differently.

• Dave Snowden - Cynefin Framework: For leaders navigating complex environments where certainty isn’t available and linear plans fail.

• Stephen Bungay - The Art of Action: A practical lens on friction, misalignment, and why intent and action drift apart inside teams.

On Prototyping for Clarity:

• Peter Sims - Small Bets: Why small, low-risk tests create more clarity than big, slow plans.

• Teresa Torres - Continuous Discovery : For leaders trying to shift from “plan, then build” to “learn, then commit.”

• Melissa Perri - Escaping the Build Trap: A reminder that building faster isn’t the answer when you haven’t aligned on what problem is real.

On Co-Creation & Psychological Safety:

• KA McKercher - Beyond Sticky Notes: is a practical guide to the mindsets, methods, and movements of co-design.

• Amy Edmondson - Psychological Safety Research: The foundation for teams who need to experiment, admit uncertainty, and learn in public.

• John Cutler - The Beautiful Mess: Because leadership is less about control and more about understanding the messy system you’re operating in.

• Priya Parker - The Art of Gathering: For leaders who want to design meetings and offsites that create genuine connection and shared purpose, not just attendance.

On Responsible AI & Design:

• Sasha Luccioni - AI Sustainability & Responsible AI: A sharp, culture-conscious take on responsibility in AI systems and the cost of unexamined choices.

• Dr. Joy Boumawini - Unmasking AI: The technology of the future is bringing us back to the inequality of the past, but we can still prevent AI from amplifying discrimination.

• The Algorithm Justice League - raises public awareness about the impacts of AI, equip advocates with resources to bolster campaigns, and galvanize researchers, policymakers, and industry practitioners to prevent AI harms.

On Leading Design, the conference is happening again in London in 2026, and you can snag super early bird tickets now.

P.S. If you’re interested in having me speak about my Momentum Operating System, reach out here.

We’re often shown futures that look sleek and robotic—but curiously empty of people. RADAR is building something else: a cooperative practice where imagination isn’t a mood board, it’s shared work.

In this conversation, Keely Adler (Head of Practice) and Caitlin Keeley (Head of Imagination) unpack how RADAR prototypes futures in public—through research cycles, rituals, grants, and cultural artifacts, so communities can see themselves in what’s next. We get into “mundane futures,” love as infrastructure, and what it takes to cross the yearning gap from longing to action.

“If you can’t imagine a better future and actually see yourself living in it, you’re never gonna… be able to work toward that.” — Keely

What we explore in this episode

• Why most futures work feels shiny and robotic, but empty of people

• How RADAR is prototyping futures in public, not behind closed decks

• The concept of “mundane futures” and why they matter

• How love and psychological safety show up as design tools

• Why communities need rituals and scaffolding, not just visions

• What it means to cross the “yearning gap” from longing to action

“Imagine if we were all thinking about our future together and coming up with it, then we can live in a future that we all imagine together” - Caitlin

Follow along

00:00 — Introduction to RADAR and its evolution
02:19 — Understanding futures & cultural futurism
04:25 — The concept of mundane futures
06:49 — Community & collective ownership at RADAR
09:22 — Digital cooperatives and shared ownership
11:58 — Where RADAR meets client/industry work
18:52 — The creative ecosystem (participants, partners, artifacts)
24:32 — Multiplayer imagination & experimentation in practice
31:08 — Embodied futures & love as infrastructure
34:38 — Yearning for change & the 2025 mission
44:33 — The power of imagination in shaping the future
47:20 — Understanding yearning burnout & its impact
50:18 — The role of small actions in creating change
53:30 — The future of collaboration & AI
54:58 — Lessons from multiplayer mode in creative processes
57:13 — Fostering psychological safety for innovation
01:02:14 — The importance of community & gathering for change

Some takeaways

• Imagination is radical. Futures aren’t authored by lone geniuses; they are co-created in community. This shifts power from a few slide makers to many contributors who shape outcomes.

• From decks to practice. RADAR turns foresight into operations: research cycles, shared rituals, and tangible artifacts (reports, grants, gatherings) so imagination becomes work people can do together.

• Livable beats impressive. If you can’t picture yourself or your auntie inside the future, you won’t know how to work toward it. “Mundane” details are a design test, not a downgrade.

• Cultural futurism over prediction. Start with everyday life: language, rituals, media, rather than abstract trend curves. That’s how futures land and stick.

• Dystopias sell, but they don’t serve. The attention economy rewards bleak stories. RADAR actively trains attention toward hopeful, usable alternatives.

• Hope builds through small moves. Change rarely arrives in one grand moment. It grows through thousands of connected actions: conversations, zines, stickers, tote bags that compound over time.

• Love as infrastructure. Treat care, trust, and belonging as inputs to policy, product, and place-making. “Future in Love” shows how joy and connection can be designed, not left to chance.

• The lone-hero era is ending. Future-building is moving from solo visionaries to cooperative teams across culture, agencies, and systems. Multiplayer imagination is the intent, not the byproduct.

• Safety fuels contribution. Psychological safety plus low-friction tools widen who participates and how often. Think “drop a link in Miro,” not “deliver a perfect deck.”

• Build in public. Visible experiments beat private vision decks. Open artifacts invite collaboration, feedback, and shared ownership.

What to Try This Week

• Close one yearning gap. Name one thing you’ve been wanting to do. Define a ≤30-minute action, put it on the calendar, and do it this week.

• Multiplayer imagination, low pressure. Open a shared doc or Miro board. Post 3 prompts whether related to work or something you want to explore in community. Rules: no slides, just links/stickies/notes. After 2-3 days, do a quick readout and pick one tiny experiment to try.

• Party > doomsday. Host a small gathering (5–8 people) & think of what you want to invite into the conversation. For Example: A Future in Love themed party, with two questions, one tiny pledge wall. Keep it light, snacks over slides.

Signals to Watch

• Yearning Gap: The distance between today’s reality and desired futures—watch how more orgs move from theory into practice.

• Cultural Acupuncture: Small but targeted interventions shifting larger narratives.

• Futures in Public: More communities and orgs building openly, through shared rituals and cooperative experiments.

Mentioned in this episode

• The Curious Human’s Field Guide to the Future by RADAR

• Tomorrow.Radio - is a digital radio station created to help people in the UK feel just a little bit more hopeful about things.

• MetaLabel - Yancey Strickler’s cooperative model for creative releases

• Subvert - Austin Roby’s co‑owned take on Bandcamp (non‑crypto)

• Other Internet - nonprofit applied research organization

• Social.coop - member‑owned Mastodon instance + governance wiki

• Meet.coop - cooperative video‑meeting infrastructure

• Fediverse - decentralized social (Mastodon, Bluesky; Threads not yet fully connected)

• LoveFest - RADAR’s 3 month exploration into the “Future In Love”

• “Let’s Get Meta” — RADAR’s nerd channel for methods + meta talk, is in their discord space

Where to find Keely Adler & Caitlin Keeley

• Keely Adler — Head of Practice (LinkedIn)

• Caitlin Keeley — Head of Imagination (LinkedIn | Site)

• RADAR — Website | Linktree

Where to find me

• LinkedIn: /aribajahan

• Instagram: @ariba.jahan

• Newsletter: unmissableswithariba.com

Enjoying Unmissables? A quick ⭐⭐⭐⭐⭐ on Spotify + a review on Apple helps more people find the show. Thank you! 🫶🏽

As I keep evolving this podcast, I’d love to know what do you find most valuable in the shownotes? What would you like to see more of?

The internet is a firehose of information. The problem isn’t the volume, it’s the chaos of where our eyes are supposed to land.

Why do some videos feel hypnotic while others just feel like noise? Why does one film transport you completely while another leaves you reaching for your phone?

The answer lies in an invisible art: the choreography of focus.

The first time I heard this phrase was from Aditi Rajapagopal, Experience Designer at Atlantic Studios, during our conversation for the Unmissables podcast.

It’s the invisible architecture of every great experience, whether you’re in a theater or wearing a headset. It’s the silent language creators use to tell your eyes exactly where to go, and in what order. (Walter Murch explores this In the Blink of an Eye)

Think of it like a three-part sequence:

• Anchor: The initial moment your attention lands.

• Cue: The subtle signals, a shift in light, a gesture, a sound that pulls you forward.

• Shift: The transition that moves you to a new moment without breaking your immersion.

When creators nail this sequence, your brain never feels lost. Instead, it feels present. And presence is the magic ingredient that makes a story stick.

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Focus in Action: From Film to AI

This choreography shows up across every medium, often in surprising ways. I’m not a film buff, but this was my exploration of the concept across mediums and what I took away from it.

Film: Focus as Intimacy

• Moonlight proves silence can be louder than dialogue. The way Chiron’s face fills the frame pulls you so close, you’re practically breathing with him. Edited by Joi McMillon, the first Black woman Oscar-nominated editor & directed by Barry Jenkins, created a film that stays with you.

• Everything Everywhere All At Once could have been visual chaos. Instead, the Daniels guide you across universes with sharp cues, a googly eye, a bagel, a gesture. So even in maximalist madness, you never feel lost.

Performance: Focus as Gesture

• Choreographer Camille A. Brown builds stillness and micro-gesture into her works, the smallest hand movements she makes can hold a room.

• Beyoncé’s Homecoming is a masterclass in directing the gaze. Amid dancers, brass bands, and a stadium crowd, your eye always lands on her chosen cue: a raised fist, a single step, a costume change.

XR & Immersive: Focus as Presence

• Cosmos in Focus (Aditi Rajagopal & Atlantic Studios) uses spatial cues so you can feel the vastness of space around your body using the James Webb Telescope data.

• Tamara Shogaolu’s VR narratives in Queer in the Time of Forced Migration (Ado Ato Pictures) layer voice, movement, and proximity to make migration and queer identity stories both immersive and deeply human.

Generative & Emerging Tech: Focus in Code

• Paul Trillo experiments with AI to choreograph motion and pacing, guiding the viewer’s gaze through surreal, prompt-driven sequences.

• Sougwen Chung uses AI-powered robotic arms to draw with her in real time, turning focus into a live duet between human gesture and machine precision.

How This Can be Misused

Directing attention also means deciding what stays unseen. And those omissions carry just as much weight as what’s highlighted.

• Generative propaganda. Deepfake political ads already use motion and pacing to hide bias while amplifying spectacle. Focus can manipulate as easily as it can clarify.

• Immersive exclusion. If XR cues ignore accessibility, like left-handed navigation, captions, or cultural context, whole groups are excluded from the experience.

• Data theater. We’ve all sat through flashy dashboards and overdone data visualizations that bury meaning under spectacle, creating mental load on the viewer to make sense of it. Focus can clarify or it can distract.

The ethical question here is whose gaze are we centering, and who gets erased in the process?

When it’s done with care, choreography isn’t about control at all. It’s about whether you feel lost or guided.

Your Turn: A Few Experiments

You’re already choreographing focus, think slides, dashboards, interfaces, TikToks. The only question is whether you’re doing it with a specific intention in mind.

• Screenshot audit. Pause a film you love or a video that caught your attention. Where do your eyes go first? How does it make you feel? Anchor, Cue, Shift.

• VR experiment. Try NeuroSpeculative AfroFeminism, Cosmos in Focus or Letters from Drancy. Track how your gaze is pulled, and where you engage.

• AI test. Use Runway, Veo or Pika to generate video. Prompt once without motion cues, once with. Notice how coherence and rendering changes.

• Deck prototype. Apply Anchor–Cue–Shift to a deck of your own work. See how it reads differently and what it drives.

• Product Teams. The choreography of focus is as real in your onboarding flow as it is on stage: what do you want the user to notice first, next, last?

The truth is: we all choreograph focus in one way or another. Too often, we chase seamlessness and forget care. Because attention becomes emotion, emotion becomes memory, and memory is how stories live on.

One more thing, If your cues only work for one type of user — say, sighted or English-speaking, you’re failing access. and yes it matters.

Signals to Watch: Things I’m Paying Attention To

• Your brain on ChatGPT. MIT Media Lab study sparks global headlines about how large language models are shaping human cognition and public sentiment.

• Doctors Got Worse at Detecting Cancer After Relying on AI. A gut-check that “AI + human” doesn’t automatically mean better outcomes. Over-reliance can backfire.

• Death of the Double Diamond? Board of Innovation proposes the “Stingray Model” as a new framework for AI-powered design as an evolution of the double diamond.

• Design as infrastructure. The Trump administration quietly fired the most talented designers and engineers (18F) and then started a new executive order to fix the “government’s design problem”.

• Not every season needs sprint energy. Out of Office reframes “slow summers” as deliberate, strategic pauses that recalibrate creative growth, not stall it.

• Fear as innovation’s bottleneck. My friend Katie writes in Fast Company that it’s not lack of ideas that keeps orgs from moving forward, it’s fear. A sharp read on courage as a design constraint.

• Perplexity takes aim at Chrome. The AI search startup’s bold bid shows the browser wars may be just beginning. Have you tried it?

• Google tweaks the search contract. Users can now set preferred sources directly inside results, a subtle but seismic shift.

• Femtech consolidation. Ultrahuman acquires viO HealthTech to expand into ovulation and cycle tracking, with a greater ambition to consolidate health data.

• Ageism in creative work. Is the industry quietly sidelining older talent? A candid community pulse-check on the Creative Boom.

Tune in to my conversation with Aditi on all things immersive storytelling, choreography of focus, and much more.

Thanks for reading this week’s Unmissables. I started writing these to make sense of where tech, design, and culture intersect and the questions that keep coming up for me. The best part is hearing what sparks for you.

💬 Drop a comment: What should I explore next? What signals are you watching? Who would you love to see me chat with on the podcast?

💬 Share this with someone who’s also noticing, building and questioning, it helps the right people find us.

“Presence isn’t about holding someone’s attention, it’s about making them feel like they belong inside the story.” - Aditi Rajagopal on the latest episode.

Spatial design and immersive media are redefining how we tell stories. Presence, emotion, and access aren’t extras, they’re the brief.

Aditi Rajagopal is the Head of Experience Design, Immersive at Atlantic Studios, where she creates educational and entertainment experiences for platforms like Apple Vision Pro and Meta Quest. Her projects include Cosmos in Focus, a stunning virtual planetarium featured at SXSW that turns James Webb Space Telescope data into something you can feel, not just see.

Her career spans animation, creative strategy, and immersive storytelling across India, Singapore, and the UK. Along the way, she’s built work for brands like Spotify, Samsung, and Uber, and brought culturally resonant campaigns to global audiences.

We talk about designing with wonder and precision, choreographing focus in 3D space, and why access and equity start in the creative brief. Aditi shares how she merges science and story, what most immersive experiences still get wrong, and how no-code tools are opening the door for more diverse voices in the medium.

What we explore in this episode:

• Defining immersive media beyond the hype

• How Cosmos in Focus turns telescope data into emotional connection

• The “choreography of focus” and why it’s a core design skill

• The gap between headset access and inclusive storytelling

• Designing for behavior, empathy, and unexpected users

• Entry points for creators using no-code and low-cost tools

• How the “rules” of VR are getting rewritten

Refer a friend

Follow Along

00:00 – Introduction: Designing Immersive Stories You Can Feel
02:49 – The Magic and Potential of Spatial Storytelling
05:13 – What Counts as an Immersive Experience
07:35 – Designing for Presence and Wayfinding
10:17 – Building Comfort and Emotional Connection in VR
12:52 – Inside Cosmos in Focus
15:42 – The “Choreography of Focus” in Action
18:15 – Collaborating with Scientists and Translating Complexity
20:36 – From Festival Premiere to Wider Access
25:43 – Where Spatial Computing Could Go Next
27:34 – Why Access and Distribution Still Hold the Medium Back
29:30 – Balancing Form Factor, Comfort, and Use Case
33:03 – Why Diverse Stories Depend on Affordable Tech
34:13 – Lowering Barriers for New Creators
35:15 – Getting Started with No-Code Immersive Tools
38:20 – Designing for Responsibility, Representation, and Accessibility
41:17 – How Immersive Experiences Shift Human Behavior
43:17 – Breaking the “Rules” of Immersive Storytelling
45:37 – Advice for Aspiring Storytellers and Brands
47:23 – Closing and Outro

Some Takeaways

1. Presence is the core design goal. In immersive media, the power isn’t in dazzling visuals, but in making users feel grounded, engaged, and connected to the story in a way flat screens can’t match.

2. Designing for attention is different from designing for presence. In spatial storytelling, it’s not about capturing every glance, but guiding a user’s body, gaze, and focus through choreography that feels natural.

3. Immersive is a medium for emotion and understanding. Cosmos in Focus didn’t just showcase space imagery — it transformed scientific data into an emotional, felt experience, helping audiences connect to the vastness of the universe in a personal way.

4. Access and equity are creative decisions. From narrators’ accents to usability for people with mobility or sensory differences, inclusion starts in the brief, not as a late-stage patch.

5. Distribution is the industry’s biggest barrier. Hardware costs and availability limit audience reach, so designing for empathy and onboarding non–tech-native users is as critical as the content itself.

6. Behavior shapes storytelling. Gestures, proximity, and gaze aren’t just inputs — they’re storytelling tools. Designers who master these can create deeper engagement and meaning.

7. Breaking rules can unlock better experiences. Many “best practices” in VR and XR are outdated holdovers from early prototypes. Great design in this space often comes from rewriting those rules based on lived user testing.

8. Immersive isn’t a novelty — it’s a way to teach and connect. The best work in this medium makes the invisible interactive, shifting how people understand science, history, or social issues long after they take the headset off.

Signals to Watch

• Spatial computing in education. Interactive, emotionally resonant tools making their way into classrooms.

• Diverse creators breaking in via no-code. Stories from the Global South and other underrepresented regions entering the immersive mainstream.

• Form factor evolution. Lighter, more shareable hardware opening the door for everyday use.

• Behavior shifts from 2D to 3D. Gestures, gaze, and proximity becoming standard UX inputs.

What to Try This Week

• Revisit your storytelling format. Could your audience participate instead of passively consume?

• Experiment with a no-code 3D tool like Spline or create a simple 360° video.

• Test with someone completely new to the tech. Watch where they get stuck or delighted.

• Build for more than your “typical” user. Consider mobility, hearing, and cultural context from the start.

Mentioned on the Episode

• Cosmos in Focus – Immersive planetarium using James Webb Space Telescope imagery

• Spline – No-code 3D design tool compatible with Apple Vision Pro

• Letters from Drancy – Powerful 180° VR Holocaust survival story

• South by Southwest (SXSW) – Festival where Cosmos in Focus premiered

• Gaussian Splatting – Emerging tech enabling realistic 3D spatial environments

Where to Find Aditi Rajagopal

• LinkedIn

Where to Find Ariba Jahan

• LinkedIn

• Instagram

• Substack

• YouTube

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What does it take to build digital health products that center care, clarity, and trust?

In this episode, I talk with Ambreen Molitor, a product leader working at the intersection of reproductive health, equity, and digital access. At Planned Parenthood, where she served as National Director of Product Innovation, Ambreen helped launch and redesign award-winning tools like Roo, an AI-powered chatbot for teens, and Spot On, a bilingual birth control and cycle tracker that prioritized user trust before it became a tech talking point. Her work helped Planned Parenthood win Webbys, Effies, and the VIVE Techquity for Health Champion Award, while quietly reshaping how digital care shows up in people’s lives.

She strives to support users navigating trauma, surveillance, and stigma with empathy and precision. Now as a Principal PM at Zocdoc, she continues to build with a sharp eye on who health tech serves, and how.

We unpack how her team at Planned Parenthood shifted from building in-house to partnering with pre-seed startups, and what that evolution revealed about urgency, alignment, and power. Ambreen also walks us through a revealing experiment: testing LLMs against their own content to evaluate not just accuracy, but tone and trust. The results opened up deeper questions about emotional safety, care at scale, and what readiness actually means when AI enters the room.

What we explore in this episode

• What people still get wrong about nonprofit innovation

• Why trust shapes product architecture in digital health

• How Planned Parenthood designed for tone, clarity, and care

• The real gap between LLM output and emotionally safe content

• What vetting a startup partner looks like when values are at stake

• Why tone isn’t a polish layer, it’s an equity decision

• Building through legal gray zones, shifting policy, and urgent timelines

• Why impact is a long game, but direction starts now

Follow Along

00:00 – Show Intro: Building tech vs. building trust

01:56 – Meet Ambreen Molitor

03:45 – How Planned Parenthood scaled innovation under constraint

06:00 – Roo and Spot On: Designing for clarity, care, and context

09:00 – Partnering with startups (and protecting values)

12:00 – Testing LLMs against real-world content

15:00 – Why tone is an equity issue in health tech

18:00 – Tech’s role in care: assistant, not provider

20:30 – Policy, urgency, and real-time scenario planning

23:30 – Closing reflections + unmissable signals

Update: Since recording the episode, 19 states have some form of abortion bans, 12 have total bans.

Some Takeaways

• Trust isn’t a feature, it’s the product. In high-stakes health tech, design choices, from tone to interface, aren’t surface details. They’re how safety, dignity, and care become operational.

• Innovation in nonprofits isn’t small. Planned Parenthood serves millions, with a digital audience rivaling Fortune 500s. Innovation here means building with constraint, clarity, and long-term equity in mind.

• Tone is a design decision. When Planned Parenthood compared large language models to their own chatbot, Roo, the difference wasn’t only in accuracy, it was in emotional intelligence. Roo made users feel seen. The models didn’t.

• LLMs aren’t ready to replace trusted care. Ambreen’s team tested real sex ed queries against major models and found 40–50% inaccuracy rates early on. Even now, accuracy alone isn’t the bar. The full user experience, bias, tone, and helpfulness still needs human oversight.

• Simple > sterile. Roo’s most effective responses were short, emotionally intelligent, and inviting. When you’re building tools for people navigating stigma or fear, brevity in the right places builds trust.

• Startups need vetting, too. Planned Parenthood’s innovation model evolved from in-house builds to co-developing with early-stage startups. But partnerships aren’t just about speed. They’re about shared purpose and real user impact.

• The policy landscape shapes what’s possible. From tele-health expansion to abortion access tools, product strategy has to anticipate shifting laws. Innovation means scenario planning, often with the urgency of advocacy work.

• Impact takes decades. Direction starts now. Planned Parenthood sets quarterly goals around leading indicators, knowing that long-term health outcomes will take years to shift. But without early signals, the future stays stuck in crisis mode.

Subscribe now

Signals to watch

• AI’s tone problem. As more health orgs adopt LLMs, few are auditing tone the way Planned Parenthood does. Who’s designing for empathy, not just precision?

• Startups as public service partners. Watch how more nonprofits shift toward co-developing with startups, as mission-aligned collaborators.

• Reproductive tech under scrutiny. From abortion pill tracking to privacy in period apps, look at how state-level legislation is reshaping what tech is allowed to do—and what it’s expected to prevent.

• Designing for clinicians, too. More health tech tools are addressing the other side of the interface: the healthcare workers. Expect more product strategy focused on both patient and provider experience.

• Care as infrastructure. From mutual aid tech to reproductive funding tools, more platforms are positioning care, as the core user journey.

• Invisible UX in health tools. Watch how the best health tech experiences feel seamless not because they hide complexity, but because they humanize it.

What to try this week

Think about your product’s tone as seriously as you think about its utility.

• Ask yourself: Would this response make someone feel informed or ashamed?

• Review your chatbot, UX copy, or help center language for real-world accessibility, emotional resonance, and dignity.

• Go beyond measuring for accuracy of information, measure emotional response. What emotions do your users feel when they first get this information? What emotions do they walk away with?

• Run a tone tension test. Rewrite one UX moment three ways: more direct, more compassionate, more neutral and see how the experience shifts.

• Ask your team: what’s our tone designed to do? Is it calming? Supportive? Authoritative? Have the conversation

Mentioned on this episode

• Spot On – Planned Parenthood’s bilingual, inclusive birth control & period tracker

• Roo – The AI-powered sex education chatbot launched in 2019

• Autonomy – Startup helping patients get matched to federal/state/local abortion funding

• VIVE Techquity for Health Champion Award – Award recognizing equity-first innovation in healthcare

• StereoSet – A benchmark for measuring bias in language models

• Frost & Sullivan’s Femtech Market Forecast – Femtech projected to hit $103B by 2030

Where to find Ambreen Molitor

• LinkedIn : /ambreenmolitor

Where to find Ariba Jahan

• Linkedin: /aribajahan/

• Instagram: http://www.instagram.com/ariba.jahan

• Youtube: https://www.youtube.com/channel/UC61NDFgqsM02-wtE1PrZOsw

Enjoying Unmissables? Leave a 5-star review on Spotify and Apple Podcasts. It helps us reach the right listeners.

Thanks for tuning in!

Would love to hear from you, tell us what this episode made you think about? Is there a health tech app you love using, why?

Lately, my feed’s been taken over by AI-generated everything. Videos, articles, posts, and even comments. It’s not just noise. It’s the quiet creep of what’s been called the Dead Internet.

The “Dead Internet Theory” suggests that a growing share of online content is no longer created by people. It’s generated by bots and large language models. I’m all for using AI to multiply creativity, but this isn’t that. This is content engineered for clicks, not meaning.

Researchers at Stanford analyzed over 300 million documents, from press releases to job listings, and since ChatGPT launched, there has been a massive spike in content generated or rewritten by AI, with an error rate of less than 3.3% in their detection. Odds are, you’re reading machine-written content every day without realizing it.

While democratization of AI for consumers has been transformative, it has also led to a proliferation of low-quality, repetitive content, often referred to as "AI slop".

What does this look like?

• AI-generated YouTube videos with bot-written comments to boost engagement

• Chrome extensions that auto-generate LinkedIn comments so you can “engage” without participating

• The 1,271 unreliable AI - generated news, obituary and information websites, that are SEO optimized and filled with ads (tracked by Newsguard)

• Surveys that are no longer reflecting human opinion, either skipped, auto-filled by bots, or completed by AI tools. Researcher Lauren Leek notes that “AI agents are learning to serve other AI agents,” based on data loops that don’t begin with humans.

• A buzzword-heavy article that somehow never lands a point

• Fake personas running Amazon shops with stolen content and auto-dubbed voice overs

• Events that never existed, like the thousands of Dubliners waiting for a Halloween parade falsely promoted by an AI-generated website

• AI-marketing that felt like a bait-and-switch compared to the execution of the Willy Wonka experience

What does this mean for us?

While the internet is certainly not dead yet, it’s filled with human creativity, thoughts and discourse, we’re witnessing the shifts happen as AI can create human-like texts, videos and audio.

Authenticity → Erosion of Trust: If we can't tell what's human, we stop trusting anything. That’s not just a media issue. It’s a brand, business, and societal problem.

Democratization of Content Creation → Content Fatigue: We’ve democratized content, but we’re drowning in it. Volume is up. Resonance and meaning are down.

Automation → History Loss + Misinformation: Search is surfacing summaries, not sources. AI-generated content gets boosted, while original work gets buried. Looks get shared. Facts get lost.

If you ask Google who created the iconic triangle for Maslow’s hierarchy, it confidently says Maslow. But once you plug in the actual creator, Charles McDermid it quietly corrects itself. Thanks to Ovetta Sampson for posting about this.

What Dead Internet Theory Gets Wrong

The internet isn’t dead. But it is different and it keeps changing. And we need a more precise diagnosis than doom.

1. Human content hasn’t vanished. It’s being buried.
People are still creating, connecting, and showing up online. But in a system optimized for speed and sameness, what’s real gets deprioritized. What’s visible isn’t always what’s valuable.

2. Not all automation is slop.
The problem isn’t that AI is in the loop, it’s how we’re using it. There’s a difference between generating noise and designing signal. We should be evaluating intent, not just output.

3. This isn’t a conspiracy. It’s an outcome.
No shadowy force killed the web. It’s death-by-design, an internet shaped by engagement metrics, SEO economics, and an attention market that rewards repetition over resonance. That’s incentive structure that’s misaligned with what our original plot of the internet was.

And so the bigger questions I’m asking myself and my team are:

When algorithms are optimizing content for each other (AI recommending AI), are we still consuming what we truly want, or just what one model predicts another will prefer?

In a feed flooded with algorithmic sameness, how do we create space for creative signals that come from lived experience, creative friction, cultural context, real connection, or even contradiction?

What bold new possibilities are we overlooking because we’ve undervalued human imagination, care, and context in the age of automation?

Things I’ve clicked lately:

• Nature on hybrid thinking models

• Atlantic’s piece on healthcare inequality and anti-science conspiracy

• Bluesky is plotting to take over social

• Running a microwave for an hour = 5-second AI video

• Progress on male birth control

• Baratunde Thurston on “how we citizen”

• The Guardian on dissonance: “systems are crumbling, but daily life continues”

• US Digital Services is recruiting volunteers

Next up on Unmissables: Tameka Vasquez

I sat down with Tameka Vasquez—a futurist, business strategist, and Founder of The Future Quo.

Her work turns foresight into a functional tool, not a buzzword. We talk about how futures thinking became her calling, why clarity can’t live in a silo, the trap of efficiency culture, and the power of holding space for multiple truths.

If you’ve been caught between what’s urgent and what’s essential, this episode is for you.

The signal is getting harder to hear, but it’s still there.
The rise of AI content doesn’t mean the end of the internet. But it does mean we have to be more intentional about what we create, what we consume, and what we amplify.

That’s what I’m doing with Unmissables, making space for conversations, questions and tensions. Season 2 is live. And it’s sharper, deeper, and more necessary than ever.

In a world of over-optimization, I’m genuinely excited about creating again, under-optimized, hyper-curious and ever evolving.

Tune in: Spotify || Apple Podcast || Substack || Youtube

If you’re new here, or just want to see what I’ve been up to, I reintroduced myself here. Come say hi.

More soon,
Ariba

Most companies are stuck toggling between reacting to the moment and chasing the next big thing. Tameka Vasquez helps them pause, zoom out, and build strategy that doesn’t just respond, but reimagines.

In this episode, we unpack what futures thinking actually looks like in practice: how to build clarity inside complexity, what makes vision operational (not just aspirational), and why tiny shifts—not big bets—are what shape tomorrow.

Tameka Vasquez is a futurist, strategist, keynote speaker, and educator. She’s the Founder and Principal of The Future Quo, an advisory firm that helps executive teams break free from the status quo and shape new visions for the future.

Her work blends systems thinking, storytelling, and strategic foresight—often at moments of inflection or transformation. Before launching her own practice, she served as the first Head of Marketing at Sidewalk Labs (a Google company focused on the future of cities) and spent over a decade leading brand and growth strategy for global tech firms.

Tameka teaches at Columbia University and has delivered talks for organizations like NASA, Aspen Institute, NYU’s Urban Future Lab, and the Association of National Advertisers.

What we explore in this episode

• Why strategic foresight isn’t about prediction and what it’s actually for

• How trend-chasing leads to mimicry, not momentum

• The cost of working without vision, and how it shows up day-to-day

• Why futures thinking belongs in the room, not in the research deck

• How to design for possibility when urgency is loud

• What LEGO got right and what your team can learn from it

• The power of narrative as an operating system

• Why “nonlinear” careers are often the most aligned

• How to use futures thinking even if you’re not a futurist

Follow along

00:00 – Welcome + Meet Tameka Vasquez
02:20 – How Tameka Found Her Way to Futures Work
04:30 – What 'Futures Thinking' Means
11:20 – Democratizing the Practice of Foresight
16:00 – Tameka’s Approach: From Trends to Narrative
23:45 – Vision Gaps in a Reactive World
24:00 – The LEGO case study: foresight in action
31:00 – AI, Urgency, and the Risk of Reactive Thinking
40:45 – Making Futures Thinking Practical
46:15 – Multiplicity, Reinvention, and Nonlinear Careers
49:30 – Unmissable Signals & Closing Reflections

Some takeaways

Futures thinking isn’t prediction. It’s a mindset for sharper, more intentional decision-making, anchored in clarity, not certainty.

Vision is a leadership practice. When organizations treat it like a tagline or a mission only for PR, they drift. When they revisit and operationalize it, they unlock focus.

Big bets don’t build momentum, tiny shifts do.Futures work isn’t always about bold bets, it often starts with the questions you ask on a Tuesday. Tameka breaks down how one behavioral shift at a time can lead to long-term transformation.

LEGO’s comeback with foresight. Strategic foresight helped them reconnect to purpose, simplify their portfolio, and build with conviction.

Speed isn’t clarity. Many teams operate with urgency but lack alignment. Futures work slows the reaction cycle long enough to ask the right questions.

Narrative is infrastructure. It’s not just messaging, it’s how teams make decisions, set priorities, and navigate uncertainty together. Futures thinking helps connect actions to a longer arc.

Futures thinking isn’t reserved for “futurists.” Designers, product leaders, and strategists can all use foresight tools to design more intentional outcomes. It works even better when it’s not left at a theoretical and speculative level.

Reacting to trends ≠ shaping the future. Trend-chasing leads to mimicry. Strategic foresight asks: what’s emerging, and what do we want to do with it?

PwC’s 2024 CEO survey revealed 45% of CEOs doubt their own business model’s future. That’s a leadership and foresight gap, not a tech one.

Multiplicity is a strength. Tameka shares how her nonlinear path across marketing, urban innovation, and academia helped her design better futures.

Signals to watch

• Return-to-office mandates as cultural signal, not productivity logic.
  What narratives are being reasserted? What are we being told about “value” or “presence”?

• AI as mandate vs. mission.
  Brands that implement GenAI without clarifying its purpose or value often default to reactive use. Watch for the orgs embedding it with clarity, intention and responsibility.

• Design fiction and prototyping futures.
  More orgs are using speculative design to visualize “preferred futures”, especially in climate, urban planning, and education.

What to try this week

• Revisit your team’s vision, not as a statement, but as a tool. Ask: Is this shaping our decisions today?

• Start your next planning session with this question: What do we want to be true in 3 years and what signals are we seeing now?

• Try writing a short narrative (2–3 paragraphs) describing a future where your current challenge is resolved. What got you there?

Mentioned on the episode

• The Future Quo

• PwC 2024 Global CEO Survey

• Strategic Foresight 101

• LEGO Scenario Planning

Where to find Tameka Vasquez

• LinkedIn : /tamekavasquez

• The Future Quo: https://www.thefuturequo.com

Where to find Ariba Jahan

• Linkedin: /aribajahan/

• Instagram: http://www.instagram.com/ariba.jahan

• Youtube: https://www.youtube.com/channel/UC61NDFgqsM02-wtE1PrZOsw

Thanks for tuning in!

Would love to hear from you, tell us what this episode made you think? Did you try one of the exercises before?

Have you ever thought about having a digital twin?

In this episode of Up Next in Tech, I sit down with Jessica Berger, SVP of Innovation at Publicis Media. Admirably known for her insight and understanding of cutting-edge technology, Jessica shares her expertise on trends like web3, AI, extended reality, and gaming. In this conversation, we focused on 4 emerging trends that Jessica's team highlighted in their recent trends report called, Future Forward. The report included topics like instant content to commerce, immersive digital ecosystems, AI's impact on identity and social life, and the revolutionary implications of gaming's evolution on brands. The episode also sheds light on Jessica's personal definition of innovation and how she nurtures her curiosity, opening up interesting questions around the adoption of virtual try-on technologies and the possibility of using a digital twin.

So if any of these trends interest you, be sure to check out the episode and let me know what you think about virtual try on technologies or any of the trends we mentioned! 

Also, check out the glossary that breaks down a lot of the new words that come up on this episode. It’s a new thing we’re trying, so let us know if you like it.

Follow along:

01:39 Jessica’s definition of Innovation 

04:00 Nurturing Curiosity and Innovator's Mindset

13:58 Personal Use of Generative AI

19:27 Identifying Trends and Directions

24:49 Ethical Considerations in the Digital Age

32:59 The Potential of Virtual Try-Ons

35:01 The Role of Blockchain in Authenticity and Equity

42:34 Nurturing Mental Health and Curiosity

Speaker Bio: 

Jessica Berger leads innovation and strategy at Publicis Media by harnessing the power of emerging technologies and insights. With an industry reputation for her unwavering enthusiasm and profound understanding of cutting-edge tech, such as web3, AI, and AR/VR, Berger has become a sought-after speaker, judge, and host at industry events. Her expertise lies in deciphering the future of media and brand experiences, enabling her clients to capitalize on the potential of breakthrough innovations.

Moreover, her dedication to mentorship and advocacy for diverse voices in organizations like the Adweek Executive Mentorship program and Girls In Tech exemplifies her commitment to empowering the next generation of marketers and thinkers. Prior to joining Publicis, Berger worked at agency powerhouses Weber Shandwick and Publicis’ MSL where she strategized and implemented campaigns and digital innovation efforts for clients in the luxury goods, CPG and tech sectors. Her over a decade-long experience is informed by working for agencies and companies across Germany, Japan, and North America – giving her a unique global edge valued by many clients and industry peers. 

For the full show notes with transcript, glossary of terms and resources, visit https://www.aribajahan.com/upnextintech/jessica-berger

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Podcast edited by The Sunshine Collective.

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This year was the year I bet on myself and my idea, and I had no idea that so much joy was waiting for me on the other side. So, thank you so much for tuning in to Up Next In Tech podcast, sending emails and DMs, sharing the podcast with others, leaving reviews and allowing us to join your day.

In this issue:

• Catch up with the Pod: Three new episodes dropped since the last newsletter

• 25 BIPOC authors to support & learn from this holiday season

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Expanding Digital Identity Through Community Driven Virtual Fashion with Nirmala Shome of  the Fabricant

Nirmala Shome, Head of Marketing and Growth at The Fabricant joins us to share her journey from studying digital arts in Australia to working on a new fashion industry where everybody participates and profits. Nirmala also digs into the future of fashion labels and IPs, the difference in work cultures worldwide, the reality of explaining nascent industries while it’s still being built, and her outlook on both the potential opportunities and risks of our increasingly virtual world.

🎧 Check out the episode on: Spotify, Apple, Youtube & more

What does it take to work in the creative innovation industry? An insiders perspective with Chris Denson

Working in innovation varies depending on the industry, type of company and what era of technology we’re in. Conversations around innovation often focus on the outputs only, so Chris Denson and I focused this episode on everything else. Chris is a marketer, innovation coach, multimedia host, and bestselling author of the book “10 Essential Rules for Breaking Essential Rules”. We lifted the hood on the mindsets it takes, the internal conversations innovation leaders go through, the day-to-day challenges and so much more. Chris also touches on how his childhood and lived experiences played a role in how he shows up, approaches his innovation work and what advice he has for others.

🎧 Check out the episode on: Spotify, Apple, Youtube & more

Spatial Computing and Building for Gen Alpha with Chief Futurist, Cathy Hackl

The “Godmother of the Metaverse”, Cathy Hackl joins this episode to discuss her journey from being a journalist to becoming a futurist. We explored what brands should keep in mind when building for Generation Alpha - a group of tech natives who are rewiring the future. Cathy also explained the concept of spatial computing and its applications in various industries and how it may evolve over the next few years. We acknowledged the challenges, the need for ethical considerations in how this tech evolves and the potential of spatial computing in creating more immersive and engaging experiences through more integrations of physical and digital worlds and geopolitics in the metaverse.

🎧 Check out the episode on: Spotify, Apple, Youtube & more

30+ BIPOC Authors to support and learn from this holiday gift giving season

I’ve curated a list of books on ethical tech, inclusive design, emerging experiences, healing, community & leadership written by BIPOC authors. Grab a read for a friend and yourself, whether you want to dig into them this holiday season or in the new year!

Responsible Tech

Unmasking AI by Dr. Joy Boulamwini

Algorithms of Oppression by Safiya Umoja Noble

Race After Technology by Ruha Benjamin

The Tech That Comes Next by Afua Bruce and Amy Sample Ward

Upper Hand by Sherrell Dorsey

Building for Everyone by Annie Jean-Baptiste

Meaningful & Inclusive Design

Reimagining Design by Kevin Bethune

Inclusive Design for a Digital World by Reginé Gilbert

Design for Cognitive Bias by David Dylan Thomas

Design That Scales by Dan Mall

From Solo to Scaled by Natalie Dunbar

Cross-Cultural Design by Senongo Akpem

Emerging Experiences

Into the Metaverse by Cathy Hackl

Immersive Content and Usability by Preston So

The Exponential Age by Azeem Azhar

Healing & Community

We Should Get Together by Kat Vellos

Eyes on The Road by Michell C. Clark, read intro to first chapter

Healing Through Words by Rupi Kaur

Living Resistance by Kaitlin B. Curtice

The Urgent Life by Bozoma Saint -John

Rest is Resistance by Tricia Hersey

Inclusive Design Communities by Sameera Kapila

Transformative Leadership

The Life Brief by Bonnie Wan

The Karma of Success by Liz Tran

Emergent Strategy by Adrienne Maree Brown

Freedom is a Constant Struggle by Angela Y. Davis

Be a Revolution by Ijeoma Olou

The MEMO by Minda Harts

Inclusion on Purpose by Ruchika Tulshyan

For the Culture by Marcus Collins

Visibility Mindset by Jessalin Lam and Bernice Chao

If you’ve been enjoying the podcast, please do consider supporting the podcast by leaving a review on Apple podcast.

I’ve really been enjoying reading your DMs, texts, reviews and emails. So hit reply, say hi and let me know what you think.

That’s a wrap. See you in the new year, if I don’t hear from you. Hope the end of the year brings ease and rest.

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In this episode, I sit down with Cathy Hackl to discuss her journey and inspiration from being a journalist to becoming a futurist and the godmother of the metaverse. We explored what brands should keep in mind when building for Generation Alpha - a group of tech natives who are rewiring the future. Cathy also explained the concept of spatial computing and its applications in various industries and how it may evolve over the next few years. We acknowledged the challenges, the need for ethical considerations in how this tech evolves and the potential of spatial computing in creating more immersive and engaging experiences through more integrations of physical and digital worlds and geopolitics in the metaverse.

Throughout our chat, Cathy and I shared personal stories as women and moms in the industry, valuable insights that pave the way for a deeper understanding of the tech landscape, and much more. 

What questions come up for you? Let us know so we can have a future episode breaking concepts down and answering your questions. 

Follow along:

• 2:10 - Cathy's Journey from Journalism to Tech

• 3:45 - Importance of Strategic Decision Making and Mindset

• 4:40 - Challenging Gender Norms and Embracing Success

• 9:52 - Understanding Generation Alpha and How to Engage with Them

• 18:19 - Exploring Spatial Computing and its Potential Applications 

• 26:12 - Challenges and Ethical Considerations of Spatial Computing 

• 32:41 - Why You Need to Stay Informed and Adapt to Technological Changes

• 35:36 - Cathy's Reflections and Personal Experiences in the Tech Industry

Bio: 

Cathy Hackl is a leading business executive, tech futurist, and media personality. She’s a leading authority in emerging tech and leads Journey’s Virtual Studio helping companies and governments with gaming, AR, AI, spatial computing and virtual world strategies and strategic foresight. Brands like Nike, Walmart, Louis Vuittion, and Clinique have trusted her to guide them into new virtual spaces. She was recently named one of Ad Age’s Leading Women of 2023 and on the Vogue Business 100 Innovators list. She’s a member of the prestigious Ad Council Board of Directors and the host of an award winning Adweek podcast. She’s popularly known in tech circles as the Godmother of the Metaverse and has been a fixture in the world of immersive technology for almost a decade with many media appearances in CNBC’s Squawk Box, 60 Minutes+, CNN, Good Morning America, GQ, Time, The Economist, Bloomberg, and more.

For the full show notes with transcript and resources, visit https://www.aribajahan.com/upnextintech/cathy-hackl

🙏 LEAVE A REVIEW

If you enjoyed listening to this episode, then please leave us a 5-star review on Apple Podcasts and a 5-star rating on Spotify! 

💾 DIVE INTO WEB3 

What is Web3? Is it still relevant?  If you’ve been wanting to dive in to learn more about Web3, check out this free guide I put together for you: https://www.aribajahan.com/get-web3-guide

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🔗 CONNECT WITH CATHY HACKL

Instagram: www.instagram.com/cathyhackl

Twitter/X: www.twitter.com/CathyHackl

TikTok: www.tiktok.com/@cathyhackl

LinkedIn: https://www.linkedin.com/in/cathyhackl/

🔗 CONNECT WITH ARIBA JAHAN

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📱 LET’S CONTINUE THE CONVERSATION 

You can also send me a DM on instagram @ariba.jahan or email podcast@aribajahan.com. 

Post a screenshot and what you loved and tag me on instagram, linkedin, or twitter. 

CREDITS:

Podcast art by Emma Johnston.

Podcast edited by The Sunshine Collective.

Site by RR Digital Media.

In this episode, I had the chance to speak with my good friend, Chris Denson, who is a marketer, innovation coach, multimedia host, and bestselling author of the book- “10 Essential Rules for Breaking Essential Rules”. Conversations around innovation in the creative, advertising or media side often focuses only on the outputs, so we focused this episode on everything else. We lifted the hood on the mindsets it takes, the internal conversations and day-to-day challenges and so much more.

Chris shares insightful stories about how his childhood experiences shaped his curiosity and influenced his current work, and highlighted the  importance of maintaining relentless optimism even when things don’t work. We discuss a variety of topics related to innovation, the realities and challenges of bringing ideas to life, the significant role emotional intelligence plays in the field of innovation and so much more like: 

04:26 - Chris’s Origin Story: His shift from comedy to innovation

13:08 - The role of curiosity and openness in innovation

25:49 – The skill of connecting and collecting dots in innovation

30:39 - Emerging Tech Trends in innovation

33:19 - The key to balancing optimism and reality

35:37 - Dealing with daily struggles and taking mental health breaks

39:43 - Understanding self-identity and the power of self-talk

44:46 - The role of innovation in personal growth 50:11 - Things to look forward to in the world of tech and innovation

Bio: Chris Denson is marketer, innovation coach, multimedia host, and bestselling author. He has spent his career building better companies, launching new ideas, and giving rise to cultural tastemakers through awe-inspired experiences, content, and advisory. Echoing an award-winning career across entertainment, marketing, media and technology industries, Chris has valuable insights, practices, and resources to thrive in an always-evolving cultural and technological landscape. He has done this alongside The United Nations, Fast Company, Omnicom Media Group, MullenLowe, The Emmys, Cannes Lions, The Nyah Project, HubSpot, The Obama Administration, and authored the 2018 #1 bestselling book, "10 Essential Rules for Breaking Essential Rules".

For the full show notes with transcript and resources, visit https://www.aribajahan.com/upnextintech/chris-denson

💾 DIVE INTO WEB3 

What is Web3? Is it still relevant?  If you’ve been wanting to dive in to learn more about Web3, check out this free guide I put together for you: https://www.aribajahan.com/get-web3-guide

🔗 CONNECT WITH CHRIS DENSON 

Twitter: www.twitter.com/densonology

LinkedIn: www.linkedin.com/in/chrisdenson/

Website: www.chrisdenson.co

🔗 CONNECT WITH ARIBA JAHAN

Instagram: http://www.instagram.com/aribajahan

Linkedin: https://www.linkedin.com/in/aribajahan/

Youtube: https://www.youtube.com/channel/UC61NDFgqsM02-wtE1PrZOsw

Twitter: http://twitter.com/aribajahan

💻SUBSCRIBE TO THE NEWSLETTER

Get exclusive updates, behind-the-scenes insights, and valuable resources delivered straight to your inbox. Subscribe here:  https://upnextwithariba.substack.com/

🙏 LEAVE A REVIEW

If you enjoyed listening to this episode, then please leave us a 5-star review on Apple Podcasts and a 5-star rating on Spotify! 

📱 LET’S CONTINUE THE CONVERSATION 

You can also send me a DM on instagram @ariba.jahan or email podcast@aribajahan.com. 

Post a screenshot and what you loved and tag me on instagram, linkedin, or twitter. 

CREDITS:

Podcast art by Emma Johnston.

Podcast edited by The Sunshine Collective.

Site by RR Digital Media.

The runway is going virtual, empowering new creators and breaking industry boundaries through community-driven digital fashion. 

In this episode, I chat with Nirmala Shome, the Head of Marketing and Growth at a digital fashion house, The Fabricant, to break down what that means, how they’re trying to build a new fashion industry where everybody participates and profits while leveraging blockchain and augmented reality, how creativity is transcending beyond physical apparel, and the role of fashion in gaming. 

With over 15 years of experience across digital agencies, e-commerce platforms, and immersive startups, Nirmala has honed her expertise as a digital producer, community builder, and storytelling marketer for emerging technologies, having worked in advertising and digital production in both Melbourne and New York.

Learn why major luxury brands are now investing in digital-only clothing and products tailored for virtual worlds and metaverse environments. Gain strategic insights on co-creation, authenticity, and digital ownership from a leader guiding a fashion startup through rapid technological shifts.

We dive into:

2:00 Nirmala's Career Journey and Transition into Digital Fashion

05:00 Exploring the Potential of Digital Technology in Fashion

09:00: The Shift from Traditional Marketing to Digital Marketing

12:08 The Journey from Australia to New York and Europe and their different work environments 

16:08: The Impact of Digital Technology on Fashion Industry

20:04: The Role of Community in Web 3.0

23:06: The Influence of Different Work Cultures on Career

27:02: The Evolution and Future of Digital Fashion

30:40: The Challenges and Opportunities in Digital Fashion

33:50: The Role of Gaming in the Future of Digital Fashion

38:00: The Impact of Digital Fashion on Consumer Behavior

41:50: The Intersection of Fashion, Culture, and Community

44:08: The Role of Blockchain in Digital Fashion

48:00: The Ethical Considerations in Digital Fashion

50:08: The Transition from Community to Marketing and Growth

54:50: The Future of Metaverse and Digital Fashion

Bio:

Nirmala Shome joined The Fabricant in 2020 and has worked across product and production. She is now heading up the Marketing and Growth pillar to focus on The Fabricant's mission to grow and nurture the new digital-only fashion ecosystem. She started off in advertising and digital production, working across Melbourne and New York, and is now based in Amsterdam.

For the full show notes with transcript and resources, visit https://www.aribajahan.com/upnextintech/nirmala-shome

💾 DIVE INTO WEB3 

If you’ve been wanting to dive in to learn more about Web3, check out this free guide I put together for you: https://www.aribajahan.com/get-web3-guide

🔗 CONNECT WITH NIRMALA SHOME

Twitter: https://twitter.com/nirmala_shome

LinkedIn: https://www.linkedin.com/in/nirmalashome/

Newsletter: https://ok.substack.com/

🔗 CONNECT WITH ARIBA JAHAN

Instagram: http://www.instagram.com/aribajahan

Linkedin: https://www.linkedin.com/in/aribajahan/

Youtube: https://www.youtube.com/channel/UC61NDFgqsM02-wtE1PrZOsw

Twitter: http://twitter.com/aribajahan

💻SUBSCRIBE TO THE NEWSLETTER

Get exclusive updates, behind-the-scenes insights, and valuable resources delivered straight to your inbox. Subscribe here:  https://upnextwithariba.substack.com/

🙏 LEAVE A REVIEW

If you enjoyed listening to this episode, then please leave us a 5-star review on Apple Podcasts and a 5-star rating on Spotify! 

📱 LET’S CONTINUE THE CONVERSATION 

Send me a DM on Instagram @ariba.jahan or email podcast@aribajahan.com. 

Post a screenshot and what you loved, and tag me on instagram, linkedin, or twitter. 

CREDITS:

Podcast art by Emma Johnston

Podcast edited by The Sunshine Collective

Site by RR Digital Media

Within 6 weeks of launching, we’ve had over 100 followers, and 900 plays across platforms. You made that happen! Thank you so much for tuning in, joining our conversations, sharing the podcast with others, leaving reviews and allowing us to join your day.

In this issue:

• From the Pod: Two new episodes dropped this month

• Lessons on nurturing curiosity from our guests

• ICYMI

• Your Free Guide to Web3

Making AI Digital Friends in Augmented Worlds with Alex Herrity of Anima

What’s the art of the possible when you blend technologies like AI, augmented reality and blockchain with play? Turns out, ownable digital companions that you can talk to and interact with, called OnlyBots. It's a new era of Tamagotchis, y’all. 🤖

In this episode I sit down with Alex Herrity, cofounder of Anima, a gaming and interactive technology company who has also worked on products like Fortnite, the Epic Games Store, and HBO’s streaming app.

In our conversation we cover: 
👾 His origin story spanning from theme parks and gaming to emerging tech
👾 Ethical considerations as interactions with digital companions advance
👾 Predictions for mainstream augmented reality adoption and spatial computing
👾 How he nurtures curiosity and mental health while being a co-founder and a Dad
👾 How we need to thoughtfully shape how our physical and digital environments keep blending, keeping innovation grounded on bringing people together.

🎧 Listen to episode 5 on: Spotify, Apple, Youtube, and more

Blockchain, Brands and the Future of Consumer Experience & Access w/ Naimul Huq

When a machine is super intelligent, can we even reason with it? Can we even tell it what to do? Can we understand it at all? 👀

In this episode, I sit down with Naimul Huq, former SVP of Operations at Vayner3 and now CEO of Doctor Studios to dig into these questions about AI, our immigrant journeys from Bangladesh, and:

💾 His career - healthcare innovation, political data science, then diving into web3
💾 Bridging brands and consumers via data, marketing and operational excellence
💾 The tech evolution from early social media days to where we’re headed next
💾 Defining Web3 - a decentralized, immersive, and personalized internet
💾 Evaluating opportunities and risks as AI capabilities advance exponentially

🎧 Listen to episode 6 on: Spotify, Apple, Youtube and more

Lessons on Nurturing Curiosity

If you’re like me and your curiosity needs constant tending and nurturing, then check out these lessons from our guests on how they cultivate their curiosity.

Be willing to suck at something new. Building new skills can feel discouraging, but trust yourself and bet on you. - Simon Yi, Episode 1

Try not to be immediately dismissive about new technologies, instead get hands-on to see the opportunities, ethical implications, and figure out what is exciting to you. - Alex Herrity, Episode 4

Nurturing a multidisciplinary mindset for future trends work involves maintaining curiosity, openness, and adaptability to apply skills and perspectives in different ways as fields change rapidly over time. - Matt Klein, Episode 2

Learn from writers, activists, and people who have thought intentionally about ethical questions in history that may not be considered traditionally successful. - Kat Zhou, Episode 3

Proactively immerse yourself in learning & experimenting with new ideas and share what you learn with others. Cross pollinate insights from builders philosophers, innovators and academics. - Naimul Huq, Episode 5

⚡️ How do you nurture your curiosity? Drop a comment and share, so we can collectively learn.

ICYMI

• Last week, the Secretary-General António Guterres announced the creation of a new “High-Level Advisory Body on Artificial Intelligence” that has perspectives from leaders, academics, major tech companies and civil society organizations. This proactive approach to get global consensus on AI policy, privacy, bias, safety issues and other risks and challenges posed by AI is much needed. I’m definitely following along to see what outputs and conversations come out of their work.

• I’m really looking forward to digging into Dr. Joy Buolamwini’s new book “Unmasking AI”, she founded the Algorithmic Justice League and has been a powerful voice in responsible AI. She’ll be in NYC on 10/31 for her book tour, check out if she’ll be in your area.

• If you’re into all things future and trends, but want a more ground up approach, check out RADAR’s “Into The Future” event in November.

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  Are you looking to explore and learn about Web3?

  I got you. I put together a free guide about web3, to help anyone get started regardless of where they are in their web3 journey. It’s packed with great resources, communities, courses and much more. Dig in and let me know what you think.

  

  Download Free Guide

PS. I’ll be speaking at ADCOLOR this November, let me know if you’ll be there or in LA.