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Articles by Chandler
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Call it Ephemeral
Call it Ephemeral
We keep trying to turn certain problems into products. Fundraising.
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Managers Were Always the Workaround: What Happens When They Disappear?Apr 1, 2026
Managers Were Always the Workaround: What Happens When They Disappear?
For most of human history, the hardest problem in any large organization was coordination: how do you get tens…
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The Human Durability TestMar 12, 2026
The Human Durability Test
This is the second article on durability in the AI era. The first piece focused on economic asset durability.
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The Asset Durability TestMar 9, 2026
The Asset Durability Test
I spend my time evaluating what is worth building for the long term — as an advisor, as an investor, and as an…
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Software engineering isn’t dying. It’s having its fast fashion moment.Feb 27, 2026
Software engineering isn’t dying. It’s having its fast fashion moment.
Everyone sees AI writing code and declares the profession over. Wrong takeaway.
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The Small Business Advantage (in the Era of Human Agency)Feb 20, 2026
The Small Business Advantage (in the Era of Human Agency)
This is Part 4B in a series on the Era of Human Agency. Part 4 focused on the painful transition large enterprises must…
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The Transition (How Large Enterprises Move Into the Era of Human Agency)Feb 10, 2026
The Transition (How Large Enterprises Move Into the Era of Human Agency)
This is a transition problem. Incentives, culture, and identity lag economic reality.
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Principles for Building Things that Matter (in the Era of Human Agency)Feb 5, 2026
Principles for Building Things that Matter (in the Era of Human Agency)
As AI collapses coordination and execution into software, the teams that already know how to move fast, build…
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The Organization (in the Era of Human Agency)Feb 3, 2026
The Organization (in the Era of Human Agency)
For organizations, AI collapses the cost of coordination and execution, breaking the logic of headcount-driven…
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Entering The "Era of Human Agency"Feb 1, 2026
Entering The "Era of Human Agency"
We are entering a new era of work. Most people are framing it around what AI can do — the agents, the copilots, the…
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Activity
4K followers
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Chandler K. posted thisEveryone arguing about whether agents should pay for seats is solving the wrong problem. Seat pricing made sense when the thing consuming software was a human being with one login and limited hours in the day. The seat was a proxy for consumption, and as proxies go, it was reasonable. Agents don't map to that model. One agent can run around the clock, process thousands of records, operate across multiple systems at once. You can't count them like employees and call it a pricing strategy. Dan Shipper made a prediction worth repeating: we'll build software for humans and agents together, not just software that humans use and agents happen to tolerate. If that's right, the pricing model has to change too, because it was designed for a different kind of user. The companies that figure this out first won't be debating seat counts. They'll be picking units that actually track value — tasks, decisions, records processed, something that moves in proportion to what agents actually do. The seat was always a proxy for consumption. When the nature of that consumption changes, the proxy breaks. There's also a margin story here that doesn't get enough attention. Agents don't need onboarding or support, and they don't churn because the interface was confusing. If you price on value rather than headcount, the unit economics can get better. The per-seat model isn't going away because SaaS is dying. It's going away because agents broke the assumption it was built on. The vendors that see this early enough will redesign the model. The ones that don't will keep charging per seat and wonder why the math stopped working. Over time, does this change how we pay for humans too?
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Chandler K. posted thisNobody gets promoted for stopping a report. We measure what we add. Prompts sent. Reports generated. Tools adopted. Monthly active users. These go up and to the right. They look great on a slide. Eric Porres, Chief AI Officer at Logitech, puts it cleanly: "AI adoption is not a training problem. It's a deletion problem. The models are good enough. People are curious enough. The real bottleneck is who has permission to stop doing the old thing." That reframe is correct. And there's a deeper problem underneath it. Usage dashboards measure curiosity. Transformation shows up in what disappears — which reports stopped being written, which workflows collapsed from days to hours, which artifacts vanished because an agent replaced them. But organizations don't have a mechanism for valuing deletion. We have performance reviews, project trackers, launch announcements. None of them capture "I stopped 47 people from spending 90 minutes every Friday on a status email nobody read." That's not a KPI. That's not a career move. That's not something the dashboard shows. The AI dashboard is lying to you because the incentive structure underneath it rewards addition and ignores subtraction. Until leaders can be evaluated on what they had the courage to stop, the usage numbers will keep going up and the work won't change.
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Chandler K. posted thisUser research isn't listening if you don't change anything. Most companies do user research to confirm, not to learn. The tell is what happens after. If the roadmap doesn't move — if the findings get filed, discussed in a meeting, and quietly set aside because they were inconvenient — the research didn't matter. It was expensive validation theater. Real listening is uncomfortable. It surfaces things you didn't want to know. It forces decisions you weren't ready to make. It requires the conviction to act on what you heard even when what you heard contradicts what you built. We've industrialized the appearance of listening. NPS surveys, user interviews, quarterly research sprints. All the infrastructure of a customer-centric organization without the actual behavior change that makes it real. The gap between what users say and what users do is where product strategy lives. Users describe their past behavior and surface preferences. They can't describe their future needs or underlying motivations. They can tell you what frustrated them. They can't tell you what would delight them. The builders who get this right aren't the ones with the most interviews. They're the ones who know which signal to trust, which to discount, and which apparent contradiction is actually the insight. What's the last thing a user told you that you didn't want to hear — and what did you do with it?
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Chandler K. posted thisSome businesses survive not because they are hard to replicate. Because they are hard to abandon. We spend a lot of time on the first question. Moats, switching costs, network effects, proprietary data. The asset durability test — does this business own something competitors can't replicate? That's a good question. But it's not the only one. The harder question is different: what human commitments make this organization difficult to walk away from? For me, three forces answer it. Community — belonging that forms around the organization over time. Not customers in a database. Participants in a shared outcome. CrossFit isn't a fitness brand. It's a distributed network of local communities where people learn each other's names and come back day after day. You can copy the workout. You can't manufacture the belonging. Trust — relationships built through repeated cooperation that no competitor can manufacture overnight. Toyota spent decades building supplier relationships, sharing operational knowledge, supporting partners through downturns. That network is a structural advantage that doesn't appear on a balance sheet. Conviction — people who refuse to abandon a mission when the economics get hard. Independent bookstores were supposed to be finished. First by chains, then by Amazon. The ones that survived did so because the owners refused to leave — and reoriented around curation, community, local identity. Things a fulfillment warehouse cannot replicate. The American Booksellers Association has grown its membership for several consecutive years. That recovery wasn't driven by a shift in unit economics but by conviction that held through the period when the economics were worst. AI is making it easier to start things. It is not making it easier to sustain them. When execution becomes cheap, the advantage shifts from speed to endurance. What are you building that would be hard to abandon — and why?
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Chandler K. posted thisLinear doesn't run A/B tests. Decisions are driven by taste and conviction, not engagement metrics. When they launched, the conventional wisdom — validated by years of enterprise sales and user research — was that teams wanted customizable project management. Flexibility. The ability to configure their own workflows. Every data point said build for optionality. The research wasn't wrong. Teams did want flexibility. What it couldn't surface was that the flexibility itself was the problem. Teams didn't know what their workflow should be. They wanted someone who understood software development deeply enough to make that decision for them. The desire for customization was a symptom. The actual problem was the absence of a strong, opinionated answer. Linear built exactly that. One right way to manage work. No A/B tests. No consensus roadmap. Decisions made by people with deep enough expertise to know what good looks like before the data confirms it. $1.25B valuation. Profitable within two years. 17 people at Series C. $35K lifetime marketing spend. The simplicity of the product isn't a design achievement. It's a diagnostic one. It proves the founders understood the problem — the actual problem, not the stated one — completely enough to make decisions users couldn't make for themselves. Simplicity is proof you understood the problem. Complexity is proof you didn't finish diagnosing it. What's the most complex part of your product right now — and is it complex because the problem is hard, or because you haven't fully understood it yet?
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Chandler K. posted thisI asked dozens of executives, founders and operators to walk me through their best and worst decisions. One finding came back cleaner than anything else — and nobody was prompted toward it. When asked what makes a decision significant, *every* single participant arrived at the same answer independently: how hard it is to undo. Not the size of the decision. Not the stakes. Not how long it took. Whether you can reverse it. One participant put it directly: "Significant decisions are the permanent ones. The ones that close a door behind you." That convergence matters. It happened across founders, operators, executives, individual contributors — different industries, different contexts, different decision styles. Reversibility is the universal definition of significance. Here's what that finding unlocks. When you look at the decisions people were actually proudest of — the ones they'd make again — every single one looked wrong on paper. Pay cuts. Career pivots away from prestige. Moves away from family. Closed businesses. In every case, the conventional inputs — salary, stability, status, proximity to support — pointed the other direction. The signal that made them feel right wasn't data. It was values alignment. Purpose over pay. Growth over comfort. Health over advancement. Data informed those calls. It didn't drive them. This is the pattern that keeps getting missed. We've spent decades building decision frameworks that optimize for the legible — the data-supported, the defensible, the things you can put in a deck. But the decisions that actually compound in people's lives were made on a different signal entirely. The most irreversible decisions — the ones that close a door — are the ones least likely to have clean analytical support at the moment you make them. If you led with values on your best decision and data on your worst, you're not alone. That was the dominant pattern in the data.
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Chandler K. shared thisIMO, A lot of “failed” software categories weren’t failures. We just built them wrong. Stuff like Fundraising tools. Personal CRMs. Planning systems. We treated them like SaaS problems—persistent, compounding, system-of-record products. They’re not. They’re high-stakes, context-dependent, one-off decisions that never should have been persistent. They should have been ephemeral. Full write-up below.
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Chandler K. posted thisA few weeks ago I wrote about conviction versus confidence — acting before the signal is unambiguous. I've been very curious about this topic and decided to run a study to understand it more. I asked dozens of senior operators and founders to walk me through their best and worst decisions (thanks to those that participated!) — five modules of structured reflection across work and life. One pattern came back cleaner than anything else. The decisions people were proudest of looked wrong on paper. Pay cuts. Career pivots away from prestige. Moves away from family. Data informed those calls. It didn't drive them. The signal that made them feel right was values alignment — which is just another way of saying conviction. The decisions that broke down shared one failure mode: the decision-maker assumed alignment that didn't actually exist. They thought people were bought in. They weren't. And the most expensive part of the whole process — across the board — wasn't analysis. It was people. Coordinating, aligning, bringing stakeholders along after the call was already clear. One participant said it exactly: "The cost isn't in the decision — it's in alignment." This points to something that gets underbuilt. Decision support isn't just a thinking problem. Most capable people know how to think through a call. Where they want help is twofold: getting feedback from smart people to build the right context, and moving faster on alignment — cleaner communication, better pressure-testing, without scheduling six meetings. And most wanted some way to track how decisions played out — not just make them and move on. The opportunity isn't "make the decision for me." That's human. That's us. But there's lots of space around both "help me get to clarity faster" AND "bring the right people along without so much friction."
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Chandler K. posted thisIn response to a recent post of mine, Matt Holden suggested skills.md files are the React components of the modern technology stack. He's right and really smart on this topic. The depth he's gone AI native on building modern software is more than anyone else I've chatted with, and even he is underselling this. Skills.md files are the React components of the AI native organization. They're the atomic units of action, instruction, and organizational direction — the place where a DRI's judgment gets distributed without a meeting, without a Slack thread, without a one-on-one. Right now, enablement is a distribution failure wearing a management failure's clothes. Someone learns something. They share it. The team forgets. You repeat the cycle. This isn't about better content (and never has been). Skills files break the cycle. When instructions live in a versioned, pushable, machine-readable file — enablement becomes infrastructure. You don't train people on the new process. You push an update. The organizational intelligence adopts it. This changes what leaders actually produce. An AI-native founder / CEO told me recently that their CTO had set the company loose to build whatever needed to be built — with one guardrail: don't touch my context files. Those eng-context.md files hold the CTO's decision posture, their guardrails, their non-negotiables. Not a doc. Not a wiki. A versioned file the whole system executes against. That's the model. A CPO does the same for product bets and prioritization logic. A CFO for capital allocation rules. A CEO for culture and performance standards. This also radically extends founders reach early as the play many of those roles - build the context files with advisors and smart input, and let the system run. Organizational strategy stops being a slide deck. It becomes a composition of context files — authored by the people most accountable, distributed to every agent and human executing against them. The constraint here isn't the file format. It's articulation. This only works if executives can actually enumerate their thinking — not just the output, but the reasoning behind the output. The decision logic. The context that produced it. Most leaders have never been asked to do that. They've been asked to decide. Writing down how they decide is a different discipline entirely. This is also the only way your agentic workers can live into your values, mission, and expectations — instead of inventing their own. The leaders and founders who build this muscle first will have a structural advantage — not an executional one. And structural advantages compound in ways executional ones never do. What barriers are showing up to doing this? One I've identified is that there simply is not a good system for maintaining skills.md and org_context.md files today - versioned, trustable, shareable, etc. Ramp's internal system is the closest I've seen. Anything better out there?
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Chandler K. reacted on thisChandler K. reacted on thisToday, I’m thrilled to share that Natoma has entered into an agreement to join Snowflake. Since we started building the company in early 2024, we’ve gone through ups and downs, built an incredible team, served fantastic customers and seen a rapid increase in the pace of enterprise AI adoption. While much about the agentic future is uncertain, it’s clear that companies require easy, safe access to data and tools to fully see productivity wins. Natoma + Snowflake, together, are positioned to provide a world-class experience for organizations to securely enable their agents to access their most sensitive enterprise data. Starting Natoma has been one of the hardest and most rewarding journeys of my life. I’ve learned more than I ever thought I would in 2.5 years, worked with an incredible team and partnered with the best cofounders in the world. While this is a great milestone, the hard work really begins in earnest now. Our hour is now, and this opportunity is massive. Joining Snowflake will help us bring our vision to the best enterprises around the world. First off, thank you to Pratyus Patnaik, Paresh Bhaya, and Zachary Hart for embarking on this journey with me. Thank you to our amazing team for getting us to this point today- I can’t wait for the work we’ll do over the next few years. Thank you to our fantastic lead investors for believing in our team and our vision - Saam Motamedi @ Greylock Partners Partners, Shardul Shah @ Index Ventures Ventures, and Dave Zilberman @ Norwest In particular, thank you to Abby Hart, Dwane Hamilton, and Glen Evans for helping us land so many early key teammates. To my family, thank you for your support- without you, I wouldn’t still be in this role and we wouldn't be doing this today. Most importantly, thank you to my wife, Rosie, for believing in me and our team. You’ve sacrificed so much personally to let me live out this dream. You supported me on long nights, work trips, interrupted dinners and missed family events and have been there for me every step of the way. I love you! Onwards and upwards, and for my Natomies, let’s have some fun! 🚀 https://lnkd.in/dX2RMU3TSnowflake to Acquire Natoma to Bring Governed Agentic Access to the EnterpriseSnowflake to Acquire Natoma to Bring Governed Agentic Access to the Enterprise
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Chandler K. liked thisChandler K. liked thisAnthropic shipped Live Artifacts in Claude about a month ago, and it hasn't gotten the attention it deserves. The pitch: build an artifact once, and it pulls fresh data from your connectors every time you open it. A personal MRR ticker streaming from Stripe. A custom HubSpot pipeline view that beats the native UI. A read-only Linear dashboard your manager can actually use. What's interesting is Live Artifacts turn connectors into the live backend for vibe-coded products. The artifact is the front-end you wrote in a single prompt. The connector is the data layer that keeps it useful tomorrow. Without persistence, vibe-coded apps die the moment you close the chat. With it, they become tools. (I'd bet ChatGPT ships a version of this within a quarter. It's too obvious not to.) If you've been on the fence about building a Claude connector, the surface area just expanded.
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Chandler K. reacted on thisChandler K. reacted on thisToday, we’re announcing Humanly’s $25M Series B. When we started Humanly, we believed high volume hiring teams needed much more than another system to manage applicants. They needed a way to engage at scale, hire the right employees faster and build pipeline for the next requistion. While creating a better experience for candidates along the way. That belief feels even more urgent today. Employers are under enormous pressure to stay fully staffed with the best talent, while managing high application volume, recruiter burnout, and rising candidate expectations. Most hiring systems still weren’t built for that reality. Meanwhile companies are spending $100B+ per year to attract applicants with job ads and recruitment marketing, but only have the human time to engage with 5% of that demand. At Humanly, we’ve spent the last several years building conversational AI across chat, phone, and video to help companies engage every candidate, build talent pipelines, qualify talent faster, and move people from application to interview in seconds. We've also engaged with millions of job seekers and built relationships with them over time. And this is why I'm so excited about where we’re headed next. We’re expanding toward a Service-as-a-Software model where customers don’t just buy recruiting tools; they get hiring outcomes. That means a consistent flow of qualified candidates from Humanly, faster hiring, and fully staffed teams. Tools with candidate pipeline already built in! Huge thanks to our customers, team, and investors, including SEEK Investments, Drive Capital, Zeal Capital Partners, and MassMutual Ventures, for believing in this vision. And thank you to the millions of job seekers and candidates who’s interacted with Humanly along the way. We believe hiring should feel faster, more human, and more accessible for everyone involved. Read the full announcement here: https://lnkd.in/gVhcXJRSHumanly Raises $25M Series B to Scale AI Hiring for Frontline and High-Volume Recruiting | HumanlyHumanly Raises $25M Series B to Scale AI Hiring for Frontline and High-Volume Recruiting | Humanly
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Chandler K. reacted on thisChandler K. reacted on thisWhen you have some of the smartest people in your country creating and selling AI while warning you about the dangers of their own technology, that message resonates, and it’s scary. Conversely, when you have some of the smartest people in your country creating and selling AI while actively promoting its upside, that message does not resonate; it looks like an advertisement. People in both camps continuously disparage the other, and AI sentiment is incredibly low in the US. The tech community continues to fail to communicate AI’s upside and pragmatic potential. There’s a lot to look forward to, and there will be bumps. Below are my high-level thoughts about the future of jobs and opportunities as we enter the AI era.When Life Gives You Lemons and AI: The Future of JobsWhen Life Gives You Lemons and AI: The Future of JobsCole Winans
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Chandler K. reacted on thisChandler K. reacted on thisExcited to share that I recently joined Aura as their Chief Product Officer. Aura is building something genuinely interesting- an AI-powered platform that protects individuals and families across their entire digital lives. The product challenge is real and the market need is clear as individuals, parents, kids, businesses, and schools all face threats to safety and wellbeing. After speaking with CEO Hari Ravichandran and COO (and HBS classmate) Thomas Clayton about the company and role, I was drawn to the opportunity to help lead Aura's transformation into an AI-first product and engineering organization. We're early in deploying agentic development practices at scale, and I expect to learn as much as I contribute, but that combination of a product vision and a front-row seat to how AI is reshaping the way software and products get built was too much to pass up. Aura recently announced the acquisition of Australia-based Qoria and I got to a chance in the interview process to meet Tim Levy who has built an incredible mission-aligned business focused on digital wellbeing for schools and parents. The combination of Aura and Qoria is going to deliver an amazing platform for users worldwide. I'm also excited to have a chance to reunite with some of my favorite Tripadvisor engineering alums Rekha Singh, Erdinç Yılmazel, Bill Langenberg, Jay A. and Raksik Kim as well as get to know an entire group of new colleagues. Looking forward to sharing more in the weeks ahead. https://lnkd.in/ez8nz8Ad
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Chandler K. reacted on thisChandler K. reacted on thisDuring a period of worker strife, it can feel borderline insensitive to talk about something going well. I hesitated to post about this for that reason. But I also think it’s important to recognize the bright spots. Highlight that there are companies, leaders, and workplaces where employees feel genuinely rewarded for their loyalty, commitment, and impact. Places where policies exist not just to attract talent, but to support and sustain workers over the long term. At Guild, employees become eligible for a five-week paid sabbatical after five years with the company. Maybe more importantly, it is a benefit people actually use. It’s not treated as performative or quietly discouraged - in fact, over the past five years I’ve watched colleagues across all teams and levels fully step away, recharge, and return. Sabbaticals have been modeled for me as a real and respected benefit at Guild. So in mid-April, I began my own five-week sabbatical. I spent the entire time in Europe, mostly in France, visiting museums, sitting at cafés, reading books, seeing friends, and even taking a pastry-making course at Le Cordon Bleu Paris (photos below). I didn’t bring my laptop. I took Slack off my phone. And I can proudly say I responded to only three emails (I'm a work in progress!). But before I left, I had a surprising amount of anticipatory anxiety. Like many of us, my identity is tightly wrapped up in my work: the impact I’m making, the leader I’m becoming, the sense of purpose and self-worth I derive from it. I was worried I’d be bored, or lonely, or maybe worst of all... that I’d actually miss work? But after my first few days in Paris, I found myself slowly relinquishing control and settling into truly being off. I trusted my teammates, especially Bijal Shah, Sarah Sawatzky, Beth Myer, Lisa Boyd, Zoe (Weintraub) Barrett, Kathy Qu, Matthew J. Daniel, Brooke LaRue, and others to step in while I was away, and slowly I let myself be more and more present in the moment instead of mentally tethered to work. And in that space, I found an overwhelming sense of gratitude. Much of it was gratitude for landing at Guild in 2021 (h/t Allison Dulin Salisbury), and for working at a company that genuinely walks the talk when it comes to valuing employees and investing in their well-being. Now, just three days into my first week back, I’m heading straight into Guild Gathers Live in Denver, our annual all-company event, which I’ve been joking is basically my welcome home party. So to anyone still waiting on a response from me, thank you for your patience. I’m still adjusting back from the slower pace of Parisian life :) #EmployeeSentiment #EmployeeBenefits #Sabbatical #WorkCulture #WorkLifeBalance #TimeOff #CorporateAmerica
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Chandler K. reacted on thisChandler K. reacted on thisToday, The Institute of Evidence-based Policymaking (IEBP), a board I’m lucky to serve on. Announced that we are being renamed the Romer Institute of Evidence-based Policy in honor of my Grandparents, Roy and Bea Romer. We are also formalizing an affiliation with Metropolitan State University of Denver (MSU Denver) - the university that Granpa Roy championed into existence as a young state legislator more than six decades ago. As he once told me, it was one of the most As part of this affiliation, Granpa Roy will donate his papers to Metropolitan State University of Denver archives. The collection will be a valuable resource for research and study, spanning my grandpa's legislative career, three terms as Governor of Colorado, his tenure as Superintendent of the Los Angeles Unified School District, and his national work on education reform and democratic engagement. Grandpa built & lead institutions on the conviction that good decisions require honest evidence, and that common ground is more available than politics suggests, and that the people who govern communities deserve the best available information. The Romer Institute aims to carry that conviction forward. I'm grateful to see two great leaders, Shepard Nevel and Janine Davidson, Ph.D. leading this great partnership! https://lnkd.in/gv5V7ZaH
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Veronica Lopez
Qrema Beauty Products • 3K followers
💰 Most retail startups struggle to raise $10M. eyewa just closed $100M. Here's what caught my attention about this Series C round: eyewa isn't just another e-commerce play. They're building something bigger—a tech-enabled eyewear empire across the Middle East. The numbers tell the story: ✅ 150+ stores already operational ✅ Plans for 100+ new locations in 2025 alone ✅ Targeting 250 total stores ✅ Launching their own production facility in Saudi Arabia But here's the real insight: General Atlantic doesn't just write $100M checks for retail expansion. They're betting on eyewa's omnichannel approach and supply chain innovation. This isn't about opening more stores—it's about revolutionizing how an entire region buys eyewear. The Middle East retail market is exploding, and eyewa is positioning itself as the dominant player before the competition even realizes what's happening. Smart money follows smart strategy. And this move shows exactly why the GCC is becoming a hotbed for retail innovation. What's your take—is omnichannel retail the key to winning emerging markets?
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Scott Cara
Elevate • 7K followers
"Where can AI create compounding impact in my business?" That's the question every builder, operator and executive should be asking themselves. Here’s our answer at Elevate: 1) Coding We build with Claude Code and Cursor, though we’re moving fully to Claude Code (seems to be the trend). AI coding dramatically speeds iteration. I can’t imagine building a startup without it. 2) Data transformations We use OpenAI API calls for aggregation, cleaning, and enrichment of messy data (e.g., customer deduplication, revenue stream categorization, smart cleanup). This is the unglamorous work most platforms avoid, and historically required headcount. We improve our models as our context grows. 3) Analytics agent On top of clean data, we run a Gemini-based analytical agent that does a first layer of QA and lets operators ask questions conversationally and get answers they can use. We tightly scope what the agent can access and how it reasons to protect accuracy. A few observations: We use all three major model providers (Google, OpenAI, Anthropic). For our use cases, they’re close enough to interchangeable, but we’re constantly testing that hypothesis. We are not using AI for sales outreach. The work is too contextual, relationships matter, and our ACVs are too high. We do use AI for GTM research. The landscape is changing fast, so we share learnings every week in our All Hands to stay current, e.g., yesterday, Steve Mazzari shared his latest Claude skills stack. What else should we be doing?
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Peter Pezaris
3K followers
Founders are getting stricter with pilots There’s a noticeable shift happening. More founders want pilots tied to real outcomes. Adoption. Activation. Growth. Not just “let’s see how it feels.” Honestly, that’s healthy. It forces everyone to be clear about what success actually looks like. Curious how others structure pilots today. Do you define success upfront, or figure it out as you go?
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Elizabeth Kiehner
Adobe • 14K followers
What if an AI could adopt roles like “thoughtful mentor,” “direct analyst,” or “empathetic listener”—not just by tone, but by structurally encoded behavior? Anthropic’s latest research on Persona Vectors shows how Claude can shift its personality and communication style through vector-based prompting, rather than word-based instructions. This could reshape how we think about user experience, personalization, and safety in LLMs. Worth a read for anyone thinking seriously about human-AI interaction design. 🔗 Read more here: https://lnkd.in/eyNxG53k #AI #LLM #PersonaDesign #HumanCenteredAI #PromptEngineering #Anthropic #Claude #AIUX #ResponsibleAI #FutureOfWork
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'Nero' Oghenenyerhovwo Okwa
FC Urban • 2K followers
Lessons from the Last 3 Years in Product Management Day 19/35: Improve How You Work Together Some of my most rewarding work wasn't on the product—it was on how we worked together. At QB, we ran team norms sessions where everyone shared their working styles and needs. We scheduled monthly in-person days to build rapport. We created psychological safety where all questions were encouraged. We celebrated together—team dinners or karaoke after major releases. The little things. Most teams obsess over their product roadmap but never work on improving how they collaborate. Yet team dynamics directly impact product quality. The best products come from teams that work well together. What's one thing your team does to improve how you work together? Link to my full PM retrospective in the comments. You can get more insights like this at Business Notes by Nero Okwa (nerookwa.substack.com), it just might transform your career. The best is yet to come. #ProductManagement #Career #ProductToCEO #BusinessNotes #BusinessNotesNeroOkwa #35DayChallenge
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Max Ruderman
Harmonic • 11K followers
Ride the fractious horse 🐎 Had a blast talking with Harrison Chase from LangChain the other day to a great group of founders, builders and investors about building agentic systems. One learning I shared: our team builds effective agentic systems by spending way more time doing than speculating. 🏇 In other words, we "ride the fractious horse": New models drop daily. New frameworks emerge constantly. Best practices get declared and discarded overnight. By the time you survey the landscape and theorized about how today's reasoning models can serve your system, both the landscape and capabilities have shifted. So we write playbooks instead of adopting them. The only way to develop those playbooks? Build and experiment at the fastest possible rate. ✈️ Wilbur Wright laid a great analogy back in 1901: He'd just realized that existing "standard" measurements of lift and drag were wrong, and that solving flight meant spending serious time actually trying to fly (previous "pilots" had logged minutes, at most, of cumulative airtime across all experiments). From his Chicago speech (this guy was such a clear writer btw): "There are two ways of learning how to ride a fractious horse: one is to get on him and learn by actual practice how each motion and trick may be best met; the other is to sit on a fence and watch the beast awhile, and then retire to the house and at leisure figure out the best way of overcoming his jumps and kicks." "The latter system is the safest; but the former, on the whole, turns out the larger proportion of good riders." As for planes: "It is very much the same in learning to ride a flying machine; if you are looking for perfect safety you will do well to sit on a fence and watch the birds; but if you really wish to learn you must mount a machine and become acquainted with its tricks by actual trial." We're not inventing flight, but constantly evolving LLMs and probabilistic workflows won't behave as you hope on first contact. If you want to figure out how to build effective products around those fractious beasts, get on the horse (keyboard) and start riding.
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Jose Adrian Luna Maya
Official Moon Cookies • 5K followers
Innovation is often romanticized as a solitary act of genius—but the reality is collaboration. The startups in San Francisco that scale fastest are those that cultivate networks of trust, openly share knowledge, and embrace failures as collective lessons. Let’s break down silos and lift each other because the ecosystem is only as strong as its weakest link. #StartupEcosystem #Collaboration #InnovationCulture #SanFrancisco #Entrepreneurship #NetworkEffect
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Neil Tewari
Conversion • 19K followers
PSA to seed and early-stage founders: stop worrying about competition. I’ve spoken to hundreds of late-stage founders, and they all say the same thing. The biggest mistake they see at the early stage is founders obsessing over who else is in their space. When you are pre-PMF, comparing yourself to other buzzy AI startups is a distraction. They already have a defined GTM motion, some level of product-market fit, and a playbook to execute. Their “big new feature” might just be about expanding revenue or building parity. You’re still trying to land your first 10 paying customers. Here’s the truth: 1. Worry about competitors only when you actively lose a deal to them. THESE ARE THE COMPETITORS TO WORRY ABOUT. 2. When a customer chooses someone else, they will usually tell you why. Nine times out of ten it’s not the reason you think. It’s rarely a missing feature. More often it’s pricing, brand trust, or timing. 3. Competitors often move in completely different directions. OpenAI went after consumers, while Anthropic leaned into enterprise and APIs. Playbooks diverge fast. 4. The only real roadmap at the early stage is customer pain. Every conversation, every demo, every user insight. Talk to as many customers as possible. If you spend your time trying to copy what competitors are doing, you will always be a step behind. If you spend your time talking to users, you’ll know exactly what to build next. The path to PMF isn’t found on Twitter threads about “AI competitors.” It’s found in the feedback from the 5 people actually using your product every single day.
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Michael Ni
Constellation Research, Inc. • 6K followers
Funding: WisdomAI just raised $50M. The message behind it is bigger than the round. Not saying investors always get it right, but I see takeaways for data/AI leaders: 1. Analytics teams to extend from dashboarding to “decision readiness.” The BI/analytics category is moving toward real-time, context-aware decision support and increasingly, automation. Data leaders: Shift KPIs from “time-to-insight” to time-to-decision and evaluate tools on their ability to support agents with semantic modeling, workflows, and closed-loop learning. 2. BI isn’t going away-it's evolving. Dashboards aren't dying. BI/analytics insights are still selling, but insights give way to fast growth in decision loop solutions built on context that can be verified. Data leaders: As analytics move into governance, expect your BI/ Semantics / catalog ecosystems to converge. Plan your architecture accordingly. 3. Context is the make-or-break factor for AI. WisdomAI's Enterprise Context Layer is a key part of how they provide the queries to answer prompted questions. Data/AI leaders: invest in the semantic layer, unify metadata and policy-as-code to govern reasoning. This is where the next competitive gap will open. WisdomAI grew on the bet that context + trust will power the next generation of AI-driven action. The bigger story? We’re moving from AI that answers → to AI that actually understands and acts—safely. That’s why this raise matters. Read WisdomAI's release here: https://lnkd.in/gnkssiFU #DataToDecision #DecisionVelocity #EnterpriseAI #SemanticLayer #BITransformation #CDAO #AnalyticsModernization
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Golda H. Hartman, MD
BrightAI • 2K followers
Essential services are under more strain and complexity than ever—but most still operate with limited visibility, reacting to failure instead of anticipating it. At BrightAI, we’re changing that. With $51M in Series A funding co-led by Khosla Ventures and Inspired Capital, we’re scaling Physical AI to bring real-time observability to the physical world—so essential services can act before things go wrong, not after. We’ve spent the past few years proving what’s possible—now it’s time to scale. Thanks to Dina Bass for the thoughtful writeup in @Bloomberg: https://lnkd.in/gisEct4G Grateful to Upfront Ventures—and especially our board member Nick Kim—for being such thoughtful, steady partners in this work, and to BoxGroup, Marlinspike, and Rsquared VC for their participation in this round. Because when the systems that support daily life run with preventative monitoring, everything else grows stronger.
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Piotr Majdan
SCALEUP.house • 2K followers
The gap in this chart is the real story. Anthropic’s data shows a massive disconnect between what AI could do (the blue) and what is actually happening in the real world (the red). In sectors like Finance, Legal, and Tech, the theoretical capability is nearly hitting the outer rim, yet the observed usage is barely a fraction of that. It suggests that most organizations are still just "playing" with AI at the edges rather than integrating it into their core operations. The tools are ready; the workflows are not. With SCALEUP.report, you will be able to see your own observed AI coverage against the theoretical AI coverage within your field. It’s about knowing where you actually stand, rather than where the hype says you should be. SCALEUP.report
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Brett Wilson
9K followers
Lots of folks are talking about the new Stanford Digital Economy Lab study from Erik Brynjolfsson, Bharat Chandar, and Ruyu Chen that uses ADP payroll data to confirm something many of us already knew (or suspected): a 13% relative employment decline for 22–25-year-olds in the most AI-exposed jobs, with software engineering and customer service taking the biggest hit. One finding from the study that isn’t getting as much attention (but should): marketing and sales roles seem to be faring slightly better. Early-career headcount is still down, but by smaller amounts — with an upward trend over the past two years clawing back much of the post-ChatGPT dip. What’s going on? There are many factors, but it likely lines up with a principal finding of the study: entry-level employment declines when AI automates work, with muted effects when AI augments it. In GTM today, AI often augments. It spins one idea into dozens of testable variants, compiles account research into briefs, surfaces the right moment or quote from hundreds of call transcripts and pulls the right chart from an ocean of creative assets. AI speeds experimentation without deciding the message or the deal. Because feedback loops are immediate (CTR, replies, meetings booked) and the blast radius is small, humans stay pivotal. The pattern points to a future where the best roles go to the most leveraged — people who combine AI with domain expertise, customer understanding, and creative judgment. What these positions will be and how we handle structural changes impacting young workers, though, remain important open questions that demand serious study and attention (and more than just “universal basic income will fix it”). Expect more thoughtful takes from us as we mull on it and talk to entrepreneurs and folks in academia; would love to hear what you think!
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Marko Marais
The Pipeline Group • 2K followers
Leadership in 2025: Designing for Alignment, Not Control Many leaders continue to confuse alignment with control, leading to over-engineered processes and unnecessary layers of oversight that hinder momentum. The most effective leaders, including those at TPG, prioritize alignment around mission and outcomes. When the vision is clear and metrics are established, teams require space rather than micromanagement. This environment fosters creativity and accelerates execution. A recent article in Harvard Business Review emphasizes that the strongest organizations thrive not on tighter control, but on cultivating systems of trust and alignment that promote speed and resilience. Today’s leadership is about removing friction, clarifying communication, and trusting the systems in place. #Leadership #Alignment #StrategicOps #TPG #CapitalEfficiency
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Dana Bennett
Snowflake • 2K followers
Check this out! There’s something powerful about third-party validation arriving before the broader market fully catches on. Seeing MELD featured in The Lynx List feels less like a milestone and more like confirmation of what many of us have already seen firsthand: strong execution, clear market pull, and a team building with unusual intentionality. What stands out most is that this recognition wasn’t engineered. It was earned. For early partners and investors, moments like this matter because they signal growing visibility at exactly the stage where momentum compounds fastest. Excited to keep watching MELD build. The trajectory is becoming increasingly difficult to ignore, especially under Katie McCormick Lelyveld's leadership.
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