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In many situations, the "problem" is actually this question: "What has thwarted so many efforts to fix this obvious problem?" Everyone talks about PMs "defining the problem to solve." What you typically get is something so obvious. Sure that's a problem. It has always been a problem. But what is the *real* problem.... why have so many smart humans out there failed to solve this problem? The answer isn't "because our product didn't exist." The answer isn't because "oh, this feature was missing" (in most cases). Once you understand this, it opens up a whole world of product opportunities.

The new product management

Marketing isn’t becoming more technical. It’s becoming more cognitive. AI is accelerating execution. But execution was never the real bottleneck. The constraint has always been: • Misread signals • Misaligned teams • Assumption-based decisions The teams that will win in the next cycle won’t just “use AI.” They’ll do three things differently: 1. Signal over channel They’ll prioritize behavioral pattern detection over platform tactics. 2. Clarity over volume Fewer campaigns. Better decisions. Cleaner tradeoffs. 3. Empathy at scale AI doesn’t replace judgment — it sharpens it. But only if the underlying model understands real human hesitation, not just clicks. The advantage won’t come from more automation. It will come from turning fragmented behavioral data into aligned decisions across Marketing, Product, UX, and Lifecycle. AI-native strategy isn’t about speed. It’s about decision clarity. #MarketingStrategy #CustomerInsights #GrowthStrategy #AINative #DecisionMaking

Some talk about purpose as if it’s inspiration. In my experience, it’s orientation. Purpose isn’t a slogan. It’s a filter. It answers a harder question: What are we actually here to do and what will we not do? Early in a career, purpose feels like ambition. Mid-career, it feels like direction. Later, it becomes stewardship. The same is true for organizations. When markets are calm, purpose is easy to talk about. When volatility rises, today that would include AI shifts, geopolitical friction, capital pressure, purpose either tightens the system or the organization starts to drift. Drift doesnt typically look dramatic. It looks like incremental accommodation. A product that doesn’t quite fit. A hire made for speed rather than alignment. A partnership that generates revenue but erodes identity. Boards often confront drift late when performance softens or culture fragments. Purpose, when it’s real, acts as a constraint. It narrows options. It provides discipline for trade-offs. It reduces internal politics because direction is clearer. In the Compass framework that has been written about, Purpose sits in the West not because it’s aspirational, but because it stabilizes the system. Without it, clarity fades. Without it, trust erodes. Without it, resilience becomes reactive. Leaders don’t drift because they lack intelligence. They drift because they lack orientation. The question isn’t whether you have a purpose statement. The question is whether it meaningfully constrains decisions. #Leadership #Governance #Strategy #BoardLeadership #OrganizationalHealth #TheCenterOfTheCompass

We measure delivery rigorously. But product judgment? Not nearly as much. Velocity, burn-down charts, on-time releases, all visible. Structured. Trackable. Easy to report. But judgment is invisible. Especially when a feature is de-prioritized or removed from the roadmap, where is that captured? When we prevent the team from solving the wrong problem, which dashboard reflects that? Some of the highest-leverage product decisions never make it into a sprint. Strong product leadership often creates value through what doesn’t get built , and that value rarely shows up in metrics. So how do we make judgment visible? One starting point is this: what we do prioritize should always be tied to a clear hypothesis. Every major initiative should be able to state: If we build X, metric Y will improve by Z. Then we measure it. * Predicted impact vs. actual impact * Business outcome per unit of effort * Percentage of shipped work directly tied to measurable KPIs Over time, this creates a track record of decision quality. Another idea I’ve been thinking about: Measure the percentage of shipped features that actually achieve their intended KPIs (adoption, revenue, retention, efficiency). That success rate could serve as a proxy for the effectiveness of overall prioritization , including the opportunities we consciously chose not to pursue. It’s a working thought, and I’d genuinely love to hear how others approach measuring product judgment in their organizations. Because in the end, shipping faster only matters if we’re heading in the right direction. And sometimes the most valuable product decision is the one that never makes it into a sprint. #ProductManagment #ProjectManagement #DeliveryLeadership #ProductLeadership #ProductStrategy #ProductThinking #ProductDevelopment #DecisionMaking #Prioritization #OutcomeDriven #ValueCreation #BusinessImpact

Forget all of this "we're replacing the marketing team" with AI agents stuff. I want to know why no one is talking about replacing the CEO with an AI agent. That's the real question! I'm sticking up for the marketing team...

I have a fresh $100M+ to deploy in SaaStr Fund. I also run an eight-figure media business. I also vibe code 1.5-2 hours a day. That's my whole budget. And with that 1.5-2 hours, we've shipped 12 production apps used 1,000,000+ times. The constraint isn't skill anymore. It's time allocation.

A VP of Product I know was in a strategy review when the Chief Product Officer said, "Something feels off about this launch.” His team had spent three weeks on the roadmap. User research, adoption forecasts, technical feasibility, competitive analysis. Twenty-five slides. The CPO looked at it for maybe seven minutes. "I can't put my finger on it, but no. Not this quarter." The VP was puzzled. What was the point of all that research if decisions came down to feelings? Four months later, the customer segment they were targeting shifted priorities completely. Budget freezes, new compliance requirements, the whole landscape changed. If they'd gone ahead, it would've launched into a market that no longer wanted it. How did the CPO know? He didn't, not exactly. But he'd launched products into similar segments before. Years of experience had trained his brain to detect patterns faster than any analysis could. The tricky part is that from the outside, good product instinct and bad instinct look identical. Both move fast. Both sound confident. Both frustrate the teams that did the research. Here’s the lesson. Some leaders lead with data. Some lead with vision. And some lead with instinct. Great leaders don’t fight these differences. Because leadership is about expanding your range from gut-based to data-driven. Have you ever worked with a leader who managed “by gut”? What did you learn from that experience? ------- ♻️ Repost to widen your leadership style 🔔 And follow Monica Aggarwal for more.

last week, a 3x exited founder worth hundreds of millions told me my startup was destined for failure. not "here's some feedback." he said i was wasting my time. the call ended and i just sat there...chest tight, face hot, and rethinking all my life choices. i had two options: option a: dismiss him. tell myself he's out of touch and pretend the call never happened. option b: sit in the discomfort. separate his delivery from his message. i chose b, even though my ego would've massively preferred option a because when i actually forced myself to listen, i found real problems and some truth behind his words. so i made a move and brought on a fractional CPO who's helped dozens of teams find product-market fit. not because one guy told me i was wrong, but because when i got honest with myself, parts of what he said were true. i'm convinced the single most important trait in early-stage founders is obliterating your ego as much as possible. the founders who make it aren't the ones with the best ideas. they're the ones who can absorb brutal feedback without letting it break them... and without pretending it didn't happen. the call that sucked might've been the most important one i've had this year.

Claude, initially: “...the leader who makes a B+ decision today might consistently beat the leader with A+ product sense who takes a week longer.” Check out the logic below of how AI can correct itself, with better prompts. Reacting to the screenshot below with “well, AI will agree with anything you say” would be *entirely missing the point*. In the AI age, it is paramount to *evaluate the logic* of what AI returns, rather than shortcuts like “if it has an em dash, it must be AI generated”, “if it is AI generated, it is slop”, “I don’t believe this because AI will always agree with you”, etc. Such tendencies are the opposite of clear thinking — something that was always important, and is now even more important for those who want to stay relevant over the long-term.

if you think in technology - Project & Program managers add NO value... Try running a program with 5 teams, 11 dependencies, and 3 conflicting VPs >> Program Managers do NOT exist to write code...They exist because code is NOT the real problem coordination is! Here is what T/PMs ACTUALLY do (but is not listed in any job description)>> 1// Translate 5 different versions of “We are blocked” into solvable problems Engineering says X Design says Y Leadership wants Z >>T/PMs turn multiple demands into action 2// Create clarity where everyone thinks they are already clear The #1 cause of delays? Assumptions wearing confidence masks >> T/PMs force alignment before misalignment becomes expensive 3// Make invisible work visible Dependencies, risks, capacity, unknown unknowns… >> T/PMs surface the stuff no dashboard can show 4 // Keep humans from derailing the project Politics, egos, turf wars, last-minute changes — this is where 80% of programs fail >> T/PMs are the shock absorbers between reality & delivery 5// Build systems so engineering can focus on engineering If engineers are busy coordinating, following up, clarifying, re-scoping… who is building the product? >> T/PMs create the space for teams to do their actual job 6// Turn chaos into predictable outcomes Plans are guesses Projects are experiments Programs are controlled evolution >>T/PMs are the ones controlling the variables 7// AI world will change how T/PM becomes more productive × In the AI era, T/PMs are NOT disappearing ✓ They are becoming force multipliers > AI writes code → T/PMs manage interactions > AI predicts delays → T/PMs prevent the causes > AI optimizes tasks → T/PMs optimize humans >> AI will take over documentation. It will never replace judgment, context, or alignment lastly, If you think PMs add no value, try running a large-scale program without one. You will deliver faster… …just not the right thing, not at the right time, and definitely not without a few fatalities ps. #5 is how T/PM delivers most ROI in a Technology world, what do you think? - The Ordinary TPM

One thing founders rarely talk about: The moment growth becomes stressful. At first, growth feels amazing. New users. More revenue. Investors paying attention. Then something shifts. Support tickets double. Your team starts asking more questions. The systems that worked at 10 people start breaking at 30. Suddenly growth creates more problems than excitement. This is the phase where companies either level up…or stall. Because the founder’s job changes. You’re no longer just building product. You’re building an organization that can survive scale. And that’s a completely different skill. Founders who’ve been through this — when did scaling first feel real for you?

Those of us who are very deep on AI Agents are wildly more productive than before. Already. Today. It's not just engineers. What's less clear is if we can keep this pace up. It's a serious cognitive load to process so many more outputs per unit time. I'm already tired from all our AI Agents. They never stop. And it's only March.

✨Latest post: Why Product Sense is the only product skill that will matter in the AI age https://lnkd.in/gpJdqhQN

In 2008, Crocs stock fell from $75 to under $1. The company had 230 different shoe styles. The classic clog (the product everyone associated with the brand) accounted for just 16% of revenue. They were making sandals, boots, high heels, walking shoes. All using the same rubbery Croslite material, but none with the visual identity that made Crocs recognizable. Revenue was climbing. So was complexity. Sound familiar? By 2014, they were on the edge of bankruptcy. New leadership came in and did the opposite of what most turnaround playbooks recommend. They didn't diversify further. They cut. Closed 100+ stores. Eliminated 180 jobs. Killed most of the 230 styles and refocused everything on the clog. Then they made a bet that shouldn't have worked. Instead of trying to make people stop mocking the shoes, they leaned into it. CEO Andrew Rees: "Our goal is not to make the haters love the brand. It's to exploit that tension... because it creates PR, media, interest. It creates a whole lot that would cost you a fortune to buy in other ways." They partnered with Balenciaga. Post Malone. Bad Bunny. Put Crocs on high-fashion runways. Launched custom charms at $3.99 a pop that let people personalize the one product that actually mattered. Revenue went from under $1 billion to $4.1 billion. Operating margins hit 36% (industry-leading for footwear). The entire turnaround came from subtraction, not addition. Fewer products. Fewer stores. Fewer customers to chase. More investment in the one thing that was already distinctive. Most struggling brands assume they need to expand their way out of trouble. New categories. New markets. New audiences. Crocs proved the opposite. They stripped everything back to the single product people either loved or hated (and made that polarity the strategy). The brands that compound aren't always the ones that do the most. They're the ones that do the right things...with enough conviction to let everything else go. --- 🔥 I've scaled multiple brands to 9 figures and built an 8-figure marketing agency with multiple exits. 🔔 Follow for growth marketing & brand scaling ♻️ Repost if this was useful 💾 Save to send to your CMO later ✅ Sign up to my weekly growth-hacks newsletter for easy-to-implement marketing tactics every Sunday: https://lnkd.in/eGMgpwUA

"All we are doing is shipping the wrong stuff faster." 🚀 With AI features dominating roadmaps, product teams are falling back into feature factory mode. This article shows you how to break the cycle by bringing stakeholders along your discovery journey instead of battling their opinions. You'll learn how to transform stakeholder relationships from obstacles to partners: 🎯 Start with shared outcomes, not solutions 🗺️ Use your opportunity solution tree as a stakeholder management tool 💬 Invite contribution by asking "did we miss anything?" 📊 Share assumption tests and results, not just conclusions 🔄 Show your work continuously, not in big reveals ⚖️ Tailor communication detail to each stakeholder's needs Key insight: "You can't win opinion battles. But you can bring information your stakeholders don't have—insights from customer interviews, data from assumption tests, patterns in the opportunity space that they haven't seen." Check out the article: https://buff.ly/JLtny99 🤔 When stakeholders come to you with AI feature requests, how do you typically respond? Share your thoughts in the comments below.

You are close to the product, close to the decisions, close to every hire, and often close to every problem. That proximity builds speed in the beginning. But if you don’t evolve as the company grows, the behaviors that once fueled momentum quietly become the ceiling. The first sign you’re still leading like it’s Day One is that you’re in every decision. If approvals and escalations all route through you, that isn’t excellence. It’s a bottleneck. Scaling requires distributed ownership. If your team cannot move confidently without you, you’ve built dependence, not leaders. Ask yourself: What decisions could you permanently remove yourself from this quarter? If the answer is “not many,” that’s your growth edge. The second sign is caring more about the product than the people building it. Passion may have gotten you here. But growth shifts your responsibility from refining output to developing leaders. Products do not scale themselves. People do. If most of your energy still goes toward fine-tuning deliverables instead of strengthening capability, your leadership is misallocated. The third sign shows up in your one-on-ones. If they are mostly status updates and deadline reviews, you’re managing tasks, not building alignment. At scale, belonging drives performance. Sustainable growth happens when people understand how their work connects to who they are becoming. The fourth sign is reactivity. Early intensity can look like drive. At scale, it feels like instability. Your tone sets culture. If you process frustration in real time, you teach caution instead of confidence. Scalable leadership requires steadiness. The fifth sign is subtle. Your team executes, but the energy feels flat. Metrics are met, but connection is thin. That is rarely a strategy issue. It is a belonging issue. Here is the shift: Day One leadership proves the idea. Scaling leadership builds the people. If your company doubled tomorrow, would your team feel directed or deeply aligned? That answer tells you whether you’ve grown with your company. If this resonated, subscribe to the Limitless Leader newsletter for practical strategies on evolving your leadership as your company scales. https://lnkd.in/e6RjZyJ9 #productivity #strategy #virtualassistant #management #leadership #entrepreneurship #ceo #business #team #technology #inspiration #startups

We hit our number. The chaos never stopped. Product says the roadmap is right. Sales says the market is slow. Marketing says the message is off. Customer Success says expectations are wrong. RevOps says the data is messy. Same company. Five different diagnoses. Strong teams fail inside weak systems. It’s not a talent problem. It’s a model problem. The GTM model most companies still run was built around reporting cycles. Activity. Pipeline. Closed revenue. Sales owned the number. Everything else became support. That model produces outputs. Not defensibility. At least not anymore. State. Scale. System. Signal. Four forces determine whether a company is hard to beat. Not a funnel. A shape. Your defensibility profile. Execution Beats Theory. Every Time. 👋 Hi, I’m Tim. I help engineering-led startups and mid-market scaleups build performant GTM systems, unify revenue, and scale with Agentic AI. At Entry Point 1, we partner with engineering-led teams that operate intentionally and play to win. We’ve helped generate $1B+ in revenue. Entry Point 1 #gtm #agenticai #gtmarchitecture #gotimmarket

A CEO once told me they’d invested in engineering and sales and now it was time to add Product team. I asked: What does Product mean to you? He said: Someone to connect everything. That’s usually the mistake. Product isn’t there to connect things. It’s there to make decisions. When Product is unclear, engineering builds but impact is random. Sales promises things that stretch reality. Marketing pushes a story the product can’t fully support. Then leadership asks why growth is slow. Because no one owns the decisions behind the roadmap. Instead of starting with: Do we need a Product Manager? Start with better questions: • What decision keeps getting pushed? • What metric has no clear owner? • Where are teams misaligned? • What stage are we actually in? Sometimes you need a builder. Sometimes you need someone who can scale. Sometimes you need to fix ownership before hiring anyone at all. Product isn’t a support role. It’s a decision role. If no one owns the decisions, nothing really moves. #ProductManagement #Hiring #Recruitment

Strategy *is* a form of prioritization...just a slightly different form of prioritization. It isn't either/or. You are prioritizing what shifts to pay attention to, and what shifts you can safely ignore (for now). You are forging a mental model for causality (often without a lot of hard data to back it up, at least for the time being). You are making sense of windows of opportunity, where acting is required earlier rather than later. You are prioritizing which competitor moves you have to respond to (reactively), and where you'd prefer to change the game proactively.

AI made building an MVP 100x faster. It made building the wrong MVP 100x faster too. In 2023, building an MVP meant 3 months of coding, $15K in dev costs, and praying your Docker container didn't break on launch day. In 2026? A non-technical founder can vibe-code a working product between coffee and dinner. 21% of YC's Winter 2025 batch now ships codebases that are 91% AI-generated. But here's what the "just vibe code it" crowd won't tell you: AI didn't change WHAT makes an MVP succeed. It only changed how fast you can build the wrong thing. 📊 The data: • 35% of startups still fail because there's no market need • 62% of AI-generated code contains security flaws (Cloud Security Alliance) • Founders who talk to 10+ users before building are 3x more likely to find product-market fit The pattern: Every time building gets cheaper, validation becomes the bottleneck. In 2023, we spent 3 months building and 3 weeks learning we built the wrong thing. In 2026, we spend 2 days building and 6 months realizing nobody wants it. Three types of founders right now: 🚀 The vibe coders: "I shipped 12 features this weekend!" → Zero paying users, 100% churn 🐌 The perfectionists: Still writing user stories while competitors test live → Too slow, missed the window ✅ The validators: 2 weeks of customer conversations, 2 days of building, 1 feature that actually solves pain → Winning What actually matters in 2026: Validating a problem people will pay to solve (not assume, actually ask) Defining ONE feature that delivers 80% of the value (not twelve that deliver 20%) Setting up feedback loops and analytics before launch (not after) Planning for what happens when it works (scaling, security, support) The gap between "I can build anything" and "I should build this" is the entire point. Are you optimizing for shipping speed, or learning speed?

If AI is the future, why are the smartest AI founders or rather Fathers of AI spending nearly half their time on culture? Anthropic’s Dario Amodei reportedly spends 40% of his time on culture but not product. And not culture as perks or slogans, culture as strategic infrastructure. He reinforces it through radical transparency, regular “Vision Quest” conversations where strategy, risks, and direction are discussed openly. No corporate fog. No filtered messaging. Just clarity. -That’s not soft leadership. -That’s control of trajectory. Because he understands something fundamental: In the AI race, tools will converge, models will improve and access will democratize. What won’t equalize? The quality of human judgment directing those tools. And judgment does not scale by accident. It scales through shared context, clear standards, and disciplined thinking. Yet here’s the blind spot: Organizations invest millions in AI tools, thousands of hours in infrastructure and mandatory platform training. But almost nothing in human readiness during transformation. AI can generate infinite output but only humans decide what is responsible, valuable, and worth scaling. That’s where HRQ™ comes in, not AI fluency alone but the human capabilities that make AI strategically valuable: • Meta-learning : adapting as tools evolve • Adaptive resilience : staying steady under acceleration • Emotional intelligence : protecting judgment under pressure • Human-AI fluency : staying in charge of the change • Human-centric leadership : building shared clarity at scale The future won’t belong to companies with the most AI, rather it will belong to companies with the strongest human core guiding it. If 40% of leadership focus goes to culture at the frontier of AI (Anthropic), what are leaders of other organizations thriving on? Source: Fortune, Feb 26, 2026

If adding one more tool actually fixed operational problems, most scaling companies would be running flawlessly by now. But they’re not. Inside growing SMBs, we see the same pattern over and over: More dashboards. More SaaS subscriptions. More integrations. More Slack threads. And still… slow decisions. Still confusion. Still bottlenecks. The issue isn’t a tooling gap. It’s a decision architecture gap. Technology doesn’t create speed. Clarity does. A strong decision architecture answers three simple questions: 1. Who decides? 2. Based on which data? 3. Inside which system? Without those answers, every new tool adds complexity instead of momentum. It’s rarely a CRM problem. It’s a “Who owns pipeline decisions?” problem. It’s rarely a project management issue. It’s a “Where do priorities get locked?” problem. It’s rarely about reporting. It’s a “Which metric actually triggers action?” problem. From a UX and product perspective, this is critical. Great systems aren’t just usable. They reinforce how decisions flow across the organization. The companies that scale cleanly don’t necessarily use fewer tools. They use them within a clear structure: Defined decision rights Clear data ownership Agreed escalation paths Single sources of truth That’s what increases speed. That’s what drives accountability. That’s what reduces operational drag. Before investing in the next platform, ask: What decision will this improve? Who will own that decision? What changes if we do nothing? Operational excellence isn’t built on software. It’s built on clarity. And clarity scales. Complexity doesn’t. How does your organization ensure tools actually improve decision-making instead of fragmenting it? #UXDesign #ProductDesign #SaaS #Startups #DigitalTransformation #AI #ProductManagement #BusinessGrowth #UXCrafts

Everyone is layering AI into go-to-market right now. I’m not anti-AI. But AI is a multiplier. Multipliers don’t create signal. They amplify whatever is already there. If the ICP is loose, AI scales noise. If the value prop isn’t tight, it just accelerates rejection. If the forecast math is already optimistic, now you’ve got cleaner dashboards for the same soft assumptions. Activity goes up. That feels like progress. It’s not the same thing as execution. Order matters more than the tool. You sell the product deeply first. You sit in the deals. You understand why they stall. You pressure test pricing and positioning in the field. Then you automate. Skip that and you’re not scaling efficiency. You’re scaling fragility. Automation without operator discipline isn’t innovation. It’s value erosion dressed up as leverage.

One thing I’ve learned working on products is how often big decisions get made with surprisingly little direct evidence. Features get prioritized. Roadmaps shift. Designs get revised over and over. And everyone usually has a “reasonable” explanation for why. But if you look closely, those decisions are often based on a thin layer of signals: Some analytics. A handful of support tickets. Internal intuition. Things that feel “obvious.” Not because teams don’t care about users, most teams genuinely do. It’s just that understanding users well takes time, and product work rarely slows down enough to make space for it. So teams move forward the best way they can combining experience, assumptions, and partial information, hoping they’re close enough. And if they’re lucky that’s enough to build a product that works. But the difference between a product that works and one that really clicks usually comes from a deeper level of understanding. The kind you only get by speaking directly with users, observing how they move through the experience, and testing assumptions against reality. That’s where you see the hesitation. The workarounds. The confusion. The unexpected behavior. The things that rarely show up in dashboards or meetings. Truth is the best product decisions aren’t just about opinions or data. They come from getting close enough to reality that the right direction becomes obvious.

Most prioritisation frameworks I evaluated traded accuracy for subjective parameters that caused confusion. So I stopped using them. Here's what I built instead. After looking at MoSCoW, Cost of Delay / CD3, Kano and DVFE (Desirability, Viability, Feasibility, & Effort), I tried Impact/Effort - but it didn't stick either. The discussions it generated were more draining than productive. Most of you have heard the analogy about rocks, pebbles and sand in a glass container: fill it with sand first and you've got no room for the rocks. Start with rocks, add pebbles, then sand - and everything fits. That became the basis for our roadmap. Three swimlanes — rocks (large initiatives), pebbles (mid-size), sand (small ideas and improvements) — across a Now, Next and Later timeline covering at least a year. We used high-level estimates and our very loosely defined product strategy to place the rocks first, then pebbles, accounting for dependencies. That's cells A–F in the image. For sand, we kept a rough stack-ranking of the top few - weighted by dependencies, commercial interest and reach. I called these "the stocking fillers". Whenever we needed to fill a sprint, or were heads-down in discovery for an upcoming initiative, I'd pull from that list. That's cell G. Rocks and pebbles were on the external roadmap. The customers knew sand existed, but we didn't commit to specifics. We held firm on "Now" priorities (cells A and D), were more flexible on "Later" (cells C and F), and moved sand items freely - with minimal impact on the bigger picture. The Now, Next and Later format came from "𝘗𝘳𝘰𝘥𝘶𝘤𝘵 𝘙𝘰𝘢𝘥𝘮𝘢𝘱𝘴 𝘙𝘦𝘭𝘢𝘶𝘯𝘤𝘩𝘦𝘥". The prioritisation, in all its messiness, is probably mine. Have you found a prioritisation approach that actually stuck? What made the difference? #ProductManagement #ProductOwner #Roadmaps

The thing I love most about where I work? I challenge myself to never take the easy route. Every decision I make has to justify itself and that pushes me to use my creativity in ways I never expected. It traces back to a 1976 document written by Ingvar Kamprad, IKEA's founder. One line in it changed how I think about everything 👇 "Any designer can design an expensive desk. But only the most highly skilled can design a good, functional desk at the lowest possible cost." It wasn't about cutting corners. The constraint is the skill test. We often celebrate the big, complex build. We admire the long, detailed roadmap. We reward the feature-packed release. But what about the engineer who solved the same problem with less? The PM who said no to five features because two did the job better? The designer who made things simpler — not just different? That's the good, functional desk. And honestly? It's the harder one to build. 🪑 Software Engineering → Anyone can make something work. Making it work in a way that doesn't create tomorrow's problem, that's engineering. ⚙️ Product Development → Complexity in a product is easy to create and hard to undo. Simplicity is the one thing users never have to think about. 🎯 Design → Making something beautiful with unlimited resources is easy. Making it work for everyone, at scale, with constraints? That's a different game entirely. ✏️ "We have no respect for a solution until we know what it costs." The Testament of a Furniture Dealer, Ingvar Kamprad, 1976 #IKEA #ProductThinking #SoftwareEngineering #Creativity #Simplicity #Leadership #Innovation

Your roadmap looks too polished? 👀 You’re probably not iterating fast enough. There’s a quote often attributed to Marc Andreessen: “If you’re not embarrassed by the first version of your product, you shipped too late.” 🚀 I think the same applies to planning. Over-polished roadmaps usually mean: 🔒 Assumptions locked in too early 🧐 Decisions made without real feedback 🎮 Certainty prioritized over learning Work-in-progress roadmaps mean: 🚢 You’re shipping and adjusting 💪 You’re learning in public ⚡ You value momentum over optics Which one are you optimizing for? 🤔

You built it brilliantly. So why does it still feel like you’re convincing people to buy it? That gap has a name. Most product-led founders lead with the solution. The features. The roadmap. The differentiation. The clever bits. All impressive. But your buyer hasn’t fully felt the problem yet. So your brilliance lands as… interesting. Optional. Comparable. You’re selling it (ass) backwards. I say this, not as criticism. But as a reveal. Buyers don’t move because a solution exists. They move because a problem feels 𝘶𝘯𝘥𝘦𝘯𝘪𝘢𝘣𝘭𝘦. If you haven’t articulated: → The friction → The identity tension → The cost of staying the same → The commercial consequence. Then your product becomes one of many. Nothing is wrong with what you built. You’re just starting in the wrong place. Buyers move like this: Friction → recognition → urgency → solution. Skip the first three and you create indifference. (and indifference is expensive). The moment founders see this, there’s visible relief. Because it’s not a reinvention. It’s a resequencing. Market the problem so clearly your ICP feels seen. ↳ Make the old way feel untenable. ↳Then introduce the solution. Now you’re not persuading. You’re providing relief. Different game. Once you see it… You can’t unsee it. ______ P.S. Most product-led founders naturally lead with the solution. Drop a “P” if you lead with the problem. Drop an “S” if you lead with the solution.

Dream come true moment this week. In every company I've worked at, correlating behavioral data with engineering bugs with team task status with quarterly goals was a multi-day exercise — because everyone owned a piece of the truth. Analytics had the behavioral data. Engineering had the bugs. Product had the roadmap. Getting them into the same view was a project in itself. I built that view in an afternoon with Claude Cowork. One dashboard. Live. Pulling from every source my teams touch: - All open tasks and status updates across every workstream, in real time - Behavioral analytics: where users rage-click, where they drop off, how sticky the product actually is week over week - Actual performance data tied to KPI movement — not just the number, but the signal behind why it's moving - Bug correlation: many behavioral friction events mapped to exactly 6 open engineering tickets. That's the gap. That's where you focus. This is what true triage looks like. Not a meeting where everyone brings their own slide. A single view where the data connects itself. And here's what made it possible: data connected through native integrations — not pipelines I had to build, not Zapier flows I had to maintain, not APIs I had to negotiate access to. Just connections, skills, and plugins. The orchestration layer is already there. You just describe what you need. Game over for the middleware era. The age of "set up an integration to set up your integration" is done. For anyone running product engineering, AI, or cross-functional teams: the leverage is real. Not "AI helped me write a new integration" real. "I now see things I couldn't see before, and I can act on them today" real. MInd blown at the speed, simplicity, and execution.

Wonderful takeaway from growth architect Maria Anselmi on our new book: less gut-feeling, less boss-pleasing, more fact-driven customer focus in product development and growth strategy design. Get it now. Soon also as ebook. And yes, it’s the leading book on jobs-to-be-done #jtbd in German and Yann Wermuth and I are working on the English version.

Anthropic : A Scary Kind Of Company 👇 A manic new phase of the AI boom is sweeping through Silicon Valley, powered by autonomous "agents" capable of liquefying weeks of manual labor into minutes. For now, the frenzy is largely confined to software engineering. But inside that bubble, the shift feels seismic — deepening the gulf between AI builders and bystanders. Anthropic CEO Dario Amodei recently described the current state of software engineering as the "centaur phase" — a reference to the half-human, half-horse creature of Greek mythology. Just as a chess player aided by a computer could once beat any standalone machine, an engineer paired with an AI agent may now be the most powerful unit in tech. Amodei argues that this hybrid phase may be "very brief" — perhaps only a few years before AI systems can independently outpace even the best human-led teams. Major AI labs have spent the past year pitching "agentic workflows" as the industry's next frontier. That vision snapped into focus last month with the explosive rise of OpenClaw, an open-source tool that lets developers spin up AI agents to plan, code and ship software end to end. Unlike chatbots that live in a browser or an app, OpenClaw gives agents "hands" on a user's local machine — letting them autonomously manage files, run terminal commands and message teammates. The popularity of the project was supercharged by Moltbook, a viral, AI-only social network where OpenClaw agents "hang out" and post autonomously. OpenClaw became the fastest-growing repository in GitHub history, and its founder, Austrian developer 🦄 Peter Steinberger, was poached by OpenAI to lead its "personal agents" division. "[Steinberger] is a genius with a lot of amazing ideas about the future of very smart agents interacting with each other to do very useful things for people," said OpenAI CEO Sam Altman. "We expect this will quickly become core to our product offerings." Angel investor Jason Calacanis says his firm "offloaded about 20% of our tasks to OpenClaw in 20 days" — and is pivoting its investment strategy to focus exclusively on OpenClaw-related startups. The demand has been so intense that it's triggered a global shortage of high-memory Mac Minis, as developers scramble to build "always-on" agent servers. "This is the age of CEOs crushing 10 people's work with Claude Code in nights and weekends and I am so here for it," tweeted Y Combinator CEO Garry Tan. "The fire in your belly that got you here never really goes out and now we are all cooking 20 hours a day." Read more at: The Anthropic Hive Mind by Steve Yegge lnkd.in/gN-TxAct Quote from the article: Ar both early Amazon and Anthropic, everyone knew something amazing was about to happen that would change society forever. (And also that whatever was coming would be extremely Aladeen for society.) It is unmistakable: Anthropic : A Scary Kind Of Company A STRANGE BEAST IN THE SILICON JUNGLE Are you scared?? Prateep Misra

If you are scaling and your executive team is not fully aligned, hiring another full-time executive may not be the answer. I launched my practice as a Fractional CFO because finance is often the first function companies formalize. Runway, forecasting discipline, capital allocation, and investor communication are foundational in growth-stage businesses. What became clear, however, is that scaling constraints are rarely financial alone. Finance builds disciplined projections, but sales incentives, operational capacity, product decisions, and talent infrastructure are not always aligned to those numbers. The issue isn’t the model. It’s cross-functional coordination. Early-stage and lower middle market companies often face a false choice: hire full-time C-suite leaders too early and stretch burn, or remain underpowered at the leadership level. That insight is why FinUP Partners evolved from Fractional CFO to Fractional C-Suite. Enterprise value is created when strategy, capital, operations, and talent move together. Optimizing one function helps. Optimizing the system drives durable value. If your organization is entering a new phase of complexity, we may be able to help. https://lnkd.in/dsuSRsrK #FractionalCFO #FractionalExecutive #CSuite #ExecutiveLeadership #StrategicFinance #womenowned #enterprisevalue #cashforecasting #finupparnters

There was a client experience that reshaped how I lead my agency. I genuinely loved the brand. The ethos was strong. The product had real potential. It was the kind of mission I enjoy supporting. She hired us for social media support. But from the start, I could see the bigger issue was not content. Her website was not built to convert visitors into customers. There was no email system to follow up with people who showed interest. Her messaging shifted depending on the platform. The foundation needed work. I told her that. She was clear she only wanted social media. And I said yes anyway. That is its own lesson about trusting my intuition. We created strong content. Engagement grew. Traffic increased. But sales did not increase the way she expected. When that happened, the expectation was to tweak content, test new hooks, and adjust messaging again. Social was being asked to produce consistent revenue on its own. Each time, I came back to the same recommendation. Before pushing harder on visibility, we needed to strengthen the foundation. Clear positioning. A site built to convert. Email flows to retain customers. Over time, it became clear we were trying to solve a structural issue with a visibility tool. No amount of posting can replace clarity in the brand itself. After a few months, I chose not to renew our contract. Not because effort was lacking. Not because strategy was weak. But because without commitment to the foundational work, we were going to keep chasing symptoms instead of solving the root issue. That experience changed how I operate. Today, we do not separate strategy from leadership. The systems matter. The messaging matters. But the founder’s commitment to the foundation matters just as much. Strategy cannot compensate for hesitation. What makes Energy Media different is not that we “do social.” It is that we build digital ecosystems while helping founders anchor into clear positioning and confident decision making. We care about the systems. We care about the numbers. And we care about the leadership behind the brand. If growth feels frustrating, it is often not a content issue. It is a clarity issue. A systems issue. Or a conviction issue. Real growth is not about doing more. It is about building something solid internally and structurally, and then scaling from there.

Full disclosure: I'm a Bobby Moesta and Lenny Rachitsky fanboy. Not sorry about it. I assigned the Lenny/Moesta JTBD interview to graduate marketing students at Yeshiva University's Katz School. In this round, I read 32 reflections back-to-back. Here's what hit me. Nearly every student, independently, without prompting, arrived at the same uncomfortable place: 𝘆𝗼𝘂𝗿 𝗺𝗼𝘀𝘁 𝗱𝗮𝗻𝗴𝗲𝗿𝗼𝘂𝘀 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗼𝗿 𝗽𝗿𝗼𝗯𝗮𝗯𝗹𝘆 𝗶𝘀𝗻'𝘁 𝗮 𝗰𝗼𝗺𝗽𝗮𝗻𝘆. It's your customer deciding to live with the problem. We build war rooms to beat Company X. Meanwhile, "I'll just deal with it" quietly owns most of the market. A few other things that spoke to them: → Customers aren't lying to you. But they're rarely telling you the truth either. The brain cleans up the story after the fact. The messy, contradictory version is where the insight lives. → That well-intentioned feature your team just shipped? It might actually be making it harder for people to switch to you. More complexity = more anxiety = more inaction. → The "today's the day" moment isn't random. There's a timeline of building pressure behind it that most of us never bother to map. Here's what got me, though: these students went further in a single reflection than I've seen some marketing teams go in a full planning cycle. I think it's because they don't have a product to defend yet. No roadmap to justify. No last quarter's campaign to protect. They just looked at the framework and saw what it actually says. Bob, the framework is doing exactly the job it was hired to do. Lenny, thanks for producing the content that keeps us coming back for more.

V7. Scaling Differentiation Is the Real Test of Digital Maturity Early differentiation is easy to create. Sustaining it is harder. From a customer’s perspective, many digital platforms start strong, then gradually become inconsistent as they grow. New features dilute clarity. New teams introduce variation. Momentum slows. The organizations unlocking growth in 2026 understand that scaling differentiation requires discipline: - They codify experience standards without stifling innovation. - They scale design systems and content frameworks intentionally. - They protect core journeys as new capabilities expand. - They align expansion decisions to customer value, not internal demand. - They continuously measure whether growth reinforces or weakens identity. When digital products, services, and experiences scale with intention, differentiation compounds instead of fragmenting. Customers feel continuity. This is where digital maturity becomes a strategic advantage. Not because the organization moves fastest, but because it moves consistently, with clarity. As your digital footprint grows, is your differentiation getting stronger or thinner? #oneofone #digitalmaturity #digitalstrategy #customerexperience #businesstransformation

Companies that align Product and GTM grow revenue 2–3x faster. This isn’t about collaboration culture. It’s about capital efficiency. In Series A B2B SaaS, every roadmap decision is a capital allocation decision. When Product and GTM operate in silos, you typically see: • Roadmaps driven by feature requests, not win-rate data • Sales cycles extended due to unclear positioning • Marketing messaging disconnected from real product differentiation • Customer Success compensating for slow Time-to-Value • Net Revenue Retention volatility The result: revenue growth requires 𝗱𝗶𝘀𝗽𝗿𝗼𝗽𝗼𝗿𝘁𝗶𝗼𝗻𝗮𝘁𝗲𝗹𝘆 𝗵𝗶𝗴𝗵𝗲𝗿 𝘀𝗽𝗲𝗻𝗱. In contrast, high-performing companies structurally align Product and GTM around shared revenue drivers: 𝟭. 𝗪𝗶𝗻 𝗥𝗮𝘁𝗲 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 Product prioritization informed by lost-deal analysis and competitive gaps. 𝟮. 𝗦𝗮𝗹𝗲𝘀 𝗖𝘆𝗰𝗹𝗲 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 UX, onboarding, and clarity engineered to reduce friction in buying. 𝟯. 𝗧𝗶𝗺𝗲-𝘁𝗼-𝗩𝗮𝗹𝘂𝗲 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗶𝗼𝗻 Activation milestones defined collaboratively with Sales and CS. 𝟰. 𝗘𝘅𝗽𝗮𝗻𝘀𝗶𝗼𝗻 & 𝗡𝗥𝗥 𝗚𝗿𝗼𝘄𝘁𝗵 Roadmaps intentionally designed around usage depth and monetization levers. This means that Product owns the structural drivers of revenue. The companies that scale efficiently align Product and GTM around shared economic outcomes. The question for founders and boards becomes: Is your product strategy explicitly linked to revenue drivers — or only indirectly connected through output metrics? #saasfounder #saas #productmanagement #leadership #fractional #cpo

AI tools are strangling UX because the product delivery lifecycle is composed of service relationships, while AI's main value proposition is freedom from relationships. When designers champion AI tools, we are not making ourselves layoff-proof. We are reinforcing a system that frames us as unnecessary friction. Some will tell you that the next era of design is to serve as janitors for vibe prototypes, absorbing the brain fry that comes with checking more and more AI-generated slop. But the real work has always been social The next era of design will invest less in tools, and more in designing relationships. Read the article for this week's top links and the thinking that connects the dots between them. ⸻ Enjoyed the article? Subscribe to the Product Picnic and be the first to read what's going on in the world of UX, Product, and tech, and how to create functional strategy amid the chaos. ⸻ Thanks to Cameron Tonkinwise Nicole Alexandra Michaelis Anna Cook, M.S. Angelos Arnis Jolena Ma Avery Pennarun Tom Kerwin Corissa Nunn Frank Elavsky Matthias Ott Martin Wright Dave Rupert Ashley Rolfmore for the ideas featured in this week's Product Picnic! https://lnkd.in/ev9az7Ku

There's a phrase I never want to hear from a leader again: "I'm blocked." Blocked waiting on eng to scope it? Use Claude Code or Devin to prototype it yourself. Blocked on design? Spin up a Lovable prototype and bring options to the table. Blocked on a spec? Use ChatPRD to draft it in 20 minutes. None of those will be perfect. That's not the point. The point is: you're no longer allowed to sit and wait. AI doesn't just give individuals more leverage, it eliminates excuses. BUT--your org chart hasn't caught up yet. The leaders who get there first set the new standard. On April 18-19, Zach Davis and I are running a 2-day intensive for execs ready to actually change how their teams work. More building, less excuses. → https://lnkd.in/dFNeSUrH

So many founders who haven't reignited growth say: -- "We're doing the best we can" -- "We're controlling what we can control" -- "We just missed our lowered base plan" Be honest. You are a dinosaur in Age of AI. It is you. You have time. But the window is rapidly shrinking.  Stop with the excuses. Grab your top 10-20 folks, move them into a new building, don't let anyone talk to them. Their only job is to build the #1 AI Agent in your space. This may be your only, last, best, chance.

Steve Jobs paid himself $1 a year to build great products. You can easily pay yourself hundreds of thousands more. There's no denying that Apple has produced some of the most successful products on the market. And a lot of that is down to Steve Jobs. He spent years rebuilding the iPhone over and over, even toward the end of his life. But you don't need to be like him to build a successful product. Instead of sticking to one product your entire life, you can add, tweak, and iterate different offerings. And if something doesn't work... You're allowed to scrap it. Because as much as you'll love whatever you create, you need to remember one key thing: Your product has to be profitable. There are 3 main steps you need to follow: 📈 First off, build an MVP Your minimum viable product is your most valuable player. → Ask, "What's the simplest possible version of a solution to this problem?" → Create the solution people need. → It doesn't have to be dirt cheap, but you have to prove people will pay for it. 📈 Next, figure out your stack. This is where a shocking number of entrepreneurs go wrong. → Your product isn't isolated, it's part of an ecosystem. → Build a path that draws people deeper into your business.  → I put 3 main methods of building a strong stack in the image below. 📈 Thirdly, build a pricing model. Even a great product will fail if the pricing doesn't add up. → 98% of founders devalue themselves and their business.  → Price your MVP in a way that supports your profit. → Test it out in the market and adjust yearly. The majority of entrepreneurs start their businesses because they want to help people. And because of our good intentions, we're more likely to think about profit last. But the more profitable you are, the more you can do. Good pricing doesn't make you greedy. It'll allow you the freedom to do more good around you. I've talked about this topic more in-depth in my newsletter Growth Factor. If you don't want to miss out on more lessons to building your company... Sign up here for all the lessons I've learned from scaling and exiting 6 businesses: bit.ly/Growth_Factor Do y'all find building a product challenging? P.S. Follow Lise Kuecker for more posts about building and scaling businesses. ♻️ Repost this to help another founder who's creating a product.

This week on Aspire, I sat down with Kelly Wearstler to talk about the business of creativity and what it takes to scale a vision into an enduring company. Kelly is widely recognized as one of the most influential designers in the world, but what stood out most in our conversation was the discipline behind the creativity. Over time she has built a company that extends far beyond interiors into product, licensing, brand partnerships, hospitality, and media while maintaining a strong creative point of view. We discuss how she built and now leads a 60 person studio, why she believes growth should happen in sequence rather than speed, and the strategic thinking required to turn creative work into sustainable revenue streams. We also talk about leadership. How high standards shape culture, why structure becomes essential as companies scale, and how motherhood influenced the way she leads today. If you are building a creative-led company, scaling a team, or thinking about how to translate vision into long term enterprise value, there are real insights in this conversation. You can watch the full episode here: https://lnkd.in/gPNujz42

Head of Claude Code Boris Cherny: "Everyone's going to be a product manager. Everyone's going to code."

Most AI strategies sound like this: “Let’s automate. Let’s cut costs. Let’s do more with less.” But if that’s all you’re doing, you’re already losing ground. The real edge? Building empathy into your AI from Day 1. Here’s how to move beyond efficiency and build AI that actually connects: → 1. Map the Human Journey Don’t just map the workflow. Interview real people who use or are impacted by your system. Ask: “Where does the process create friction or frustration?” → 2. Program for Context, Not Just Output Your AI doesn’t operate in a vacuum. Feed in contextual data—environment, stress signals, user preferences. Let people override the system. → 3. Prioritize Feedback Loops Set up rapid feedback channels. Watch for emotional cues: confusion, hesitation, delight. Tweak your product every week, not every quarter. → 4. Measure What Matters Don’t just track uptime or throughput. Track user trust. Track adoption rates. Ask for emotional feedback—not just star ratings. → 5. Champion Empathy in Leadership If your leaders only ask about efficiency, you’ll never build a product people love. Make empathy a boardroom metric. Here’s the brutal truth: Anyone can build an efficient tool. Only the bold build something people want to use every day. If you want your AI to stick around, start by making people feel seen. #AIWithEmpathy #HumanCenteredAI #EmpathyInTech #AIInnovation #UserExperience #TechLeadership #AIForGood #EmotionalIntelligence #AIProductDesign #FutureOfAI #EmmaShad

We're entering the faster horse era of software. The founders who win won't build what customers ask for. They'll build what customers don't know they need yet. Building software is getting cheaper by the month. Small teams can now ship what used to take 30 engineers. But cramming a bunch of features into a product happens when you don't have a point of view. When models are being updated everyday, the three-year product roadmap is dead, you won’t even know what’s possible in 6 months. The founders I'm paying attention to are doing something different. They're spending serious time understanding what AI models are good at right now and translating that into product decisions no one else is making yet. But here's what separates the good from the great. The good founder looks at AI and thinks, "This could automate a few steps in our workflow." The great founder is asking a bigger question: what does this entire function look like in two years? If you're building sales software, the feature factory founder is adding AI note-taking to calls. The visionary founder is rethinking what the sales process itself becomes when AI handles research, outreach, qualification, and follow-up. They're not building for the workflow that exists today. They're building the product that leads the market to where it's going. That's taste. Most customers can't picture a product that doesn't exist yet. You, as the founder who lives at the intersection of this industry and what AI is becoming, have to build it for them. And this is where build vs. buy tilts hard toward buy. Your customer is not waking up every morning thinking about how to improve their internal tools. They have a business to run. They're dealing with hiring, revenue, operations, a hundred fires that have nothing to do with software. But you are. Every single day, your team is thinking about how the product gets better. You're tracking what the models can do now. You're making product decisions this week that your customer won't even realize they needed for another six months. That's the gap that compounds. A company that builds internally gets a tool that works on the day they ship it. A company that buys from a visionary founder gets a product that's better next month than it is today, and better again the month after that. No internal team is going to keep up with a founder who's obsessed with this problem full-time. Taste is the new moat.

Almost every early-stage founder falls into the same hiring trap. You have massive, gaping holes in functional expertise: finance, product, marketing, and operations. The standard advice is to go out and hunt for a "Head of X" or a VP. The math on this rarely works for a Seed or Series A startup. You either burn your budget on a seasoned, expensive executive who isn't ready to get their hands dirty. Or, you elevate a former Director to a VP title, only to realize they expect a hands-off managerial role with nobody below them to do the actual work. There is a better way to get C-suite strategy and tactical execution on a startup budget. I call it the Advisor + Athlete Model. Instead of trying to hire one expensive unicorn executive who can do it all, you unbundle the role: 🏃‍➡️ The Athlete (The 80%) Hire a hungry, junior operator with the raw skills and a willingness to put in the hours. They own the sheer volume of daily legwork required to execute. They scrub the data, write the copy, and run the SQL queries. 🦉 The Advisor (The 20%) Pair them with a battle-tested mentor who has truly done it before - someone who has managed a P&L through a downturn or built a product organization from scratch. They own the high-stakes strategy, define the KPIs, and provide trap-avoidance. Their job is to coach the Athlete, not do homework. The 80/20 division of labor is incredibly efficient. The Advisor makes sure the ladder is leaning against the right wall. The Athlete climbs that ladder as fast as possible. You get massive operating leverage without blowing up your cap table or your burn rate on premature executive hires. Don’t look for a savior executive. Find an Athlete, find them a great coach, and let them run.

My biggest takeaways from Jenny Wen (Claude design lead at Anthropic): 1. The traditional design process is breaking down. The classic discover-diverge-converge loop that designers have relied on for years doesn’t work when engineers can spin up seven coding agents and ship a working version before a designer finishes exploring options. 2. Design work is splitting into two distinct modes. The first is supporting execution: consulting with engineers as they build, giving feedback, polishing in code. The second is setting short-range vision, now scoped to three to six months instead of multi-year roadmaps. The vision work is still critical because when everyone can build anything fast, someone needs to point the team in a coherent direction. 3. Build trust through speed, not perfection. Anthropic ships products early, labels them research previews, and then iterates publicly based on real feedback. Jenny argues that what actually degrades a brand isn’t launching something rough; it’s launching something rough and then going silent. If you ship fast, respond to feedback visibly, and keep improving, users will trust you more, not less. 4. The most overlooked hire in design right now is the cracked new grad. Most companies are hiring senior designers with deep experience. Jenny argues that early-career people with blank slates, fast learning curves, and no attachment to legacy processes may be uniquely suited to this moment. They don’t carry baked-in rituals that are now obsolete, and their lack of expectations can actually be an advantage. 5. Chat as an interface isn’t going away. Despite expectations that chatbots were a temporary stop on the way to richer UIs, Jenny sees chat as a permanently valuable interface because it offers infinite flexibility. But she expects a hybrid future where models increasingly generate UI elements on the fly for specific tasks (like the interactive widgets Claude recently shipped) while chat remains the connective tissue between them. 6. Jenny went from design director (12 to 15 reports) back to IC. She questioned whether middle management had a safe future and wanted hands-on time during a period of rapid change. The IC time is giving her hard skills she wouldn’t have gained while managing. 7. AI will likely get better at taste and judgment. Jenny says designers may be holding onto “taste” as a moat too tightly. But someone still has to be accountable for what ships, the same way an engineer is accountable for AI-generated code. 8. Figma is still essential, but for different reasons. Jenny says Figma remains the best tool for rapidly exploring 8 to 10 different design directions on a canvas, something that coding tools handle poorly because they’re too linear and create investment bias toward one direction. For micro-level visual and interaction decisions, spatial exploration still beats sequential iteration. Watch our full conversation: https://lnkd.in/gunZXqq8

We’re getting to the point where you can vibe code anything — if you are willing to put in the time. To build it, to ship it, and importantly, to maintain it. What is going to save the software industry is 99.99% of us are too lazy to actually do that.

I'm not sure people realize how massively inefficient many of the "marquee" tech companies were even *before* 2/3x-ing their headcount during the pandemic. We imagine, because certain companies drifted into some of the most graceful extractive motions in history, that these companies were paragons of operational effectiveness. But they (or many) weren't.... even *before* 2x-ing or 3x-ing headcount, and certainly afterwards. Part of the story is engineering arbitrage via stock-based compensation. Companies could scale headcount dramatically while paying a significant portion of comp in equity. Lo and behold, this registers as a non-cash expense and sits in the equity section of the balance sheet. The result was strong cash metrics even as real economic costs accumulated through dilution. Operational inefficiencies were financially tolerable as long as growth and multiples held. So now, with higher rates, compressed multiples, and equity no longer acting as cheap operating capital, the gig is up. AI may deliver meaningful productivity gains at the margin, but those gains are small compared to the structural inefficiencies accumulated during years of capital-fueled expansion.

Product leaders - what if every roadmap decision was backed by verified customer truth? 🔎 AI-driven research 🧠 Insights flowing straight into roadmap initiatives 📈 Every priority traceable to real demand 🔁 A continuous loop between discovery and delivery No more disconnected tools. No more static roadmaps. No more guessing. If you’re a Product or IT leader who wants to build the right products — faster and with confidence — let’s connect. I’m offering no-strings-attached consultations to walk through what this looks like in practice. Schedule a consultation: https://lnkd.in/gb2vJs7k The future of product is connected intelligence. 🚀

As I complete a year at Meta, my biggest learning from moving from one tech giant to another that it isn’t just a change of badge; it’s like switching from a high-stakes chess match to an improvisational jazz session. Having spent time at both Amazon and now Meta, I’ve realized that while they both dominate the industry, the "heartbeat" of each company couldn't be more different. Neither is "better"—they just require different versions of yourself. Here is what I’ve learned about the two worlds: 1. The Strategy At Amazon, it’s all about working backwards. You dive deep, write the PR/FAQ, and obsessed over the "Six-Pager." You perfect the product architecture before a single line of code is written because at that scale, a mistake is costly. It’s a masterclass in Depth-First Thinking. At Meta, we move fast. It’s about Breadth-First Scaling. We build, we test, we ship. We’d rather see how 4 billion people interact with a feature today and iterate tomorrow than wait for perfection. It’s an environment that rewards "Building for the Future" by living in it right now. 2. The Culture Amazon is the ultimate meritocracy of ideas driven by Leadership Principles. It’s highly structured—you know exactly what the bar is, and you’re given the tools to hit it. If you love clarity, ownership, and a roadmap that feels like a mission, Amazon is your home. Meta is definitely Bottom-Up. Ideas often spark from a single engineer or PM and catch fire across the company. There’s a beautiful lack of "red tape" that gives you immense autonomy. If you thrive in the "unstructured" and love being the architect of your own day-to-day, Meta is your playground. 3. The Pace Amazon moves with the steady, unstoppable force of a glacier. It’s about durability. Meta moves with the speed of a startup that happens to have billions of users. It’s about velocity. Which one is for you? If you’re a "Day 1" thinker who loves rigorous logic, solving massive infrastructure puzzles, and having a clear "north star," Amazon is an incredible place to build a legacy. If you’re a "Hacker" at heart who wants to influence product direction, pivot quickly, and work in a high-autonomy, high-energy environment (yeah, that’s more like me), Meta is waiting for you. I’m incredibly grateful for the discipline I learned at Amazon—it made me a better strategist. And I’m loving the "Impact" focus here at Meta—it’s making me a faster builder. To my fellow techies: What’s the one thing that changed your perspective most? Let's chat in the comments! 👇 #Meta #Amazon #TechCulture #Engineering #ProductManagement #CareerGrowth #BigTech #WorkLife

Just 4 years ago I was head-down building my multimillion dollar tech company and we were scaling. Today I’m a solopreneur. By choice. Then: I developed a leadership team so the burden wasn’t on me (and my partner) alone. Now: I do everything. Mostly alone. - - - Then: We had a great brand and reputation and sold a tangible B2B product and service. Now: I sell me. It’s all me. I’m the brand, the service and the product. - - - Then: We had a senior dedicated team, many of whom worked with me for years and years. Now: I have a new VA and some contractors to help when needed. - - - Then: 50% monthly reoccurring revenue and a diversified client mix. Now: I mostly trade time for money. If I lose a client I feel it. - - - Then: I built a valuable asset. And I sold it. Now: When I decide I’m done I will have nothing valuable to sell. It’s just where I’m at in my life….and it works. I’m happy. My job is to help founders scale their companies but I don’t even do it for myself. Does that make me an imposter? Am I the cobbler with no shoes? Do you ever feel like an imposter?

In the age of AI, authentic leadership matters more — not less. A recent Harvard Business Review piece, “Have CEOs Lost the Plot?” by Adi Ignatius, features insights from Bill George on the evolving role of CEOs. George argues that many leaders have retreated into the narrowest definition of their jobs — staying in meetings, managing email, avoiding visible positions — instead of showing up for their employees and customers. During moments of crisis, CEOs stepped forward as human, empathetic leaders. Now, in a more polarized environment, some have pulled back. What struck me is this line: “In an era of AI, we need authentic leaders more than ever.” As technology becomes more capable, the distinctly human aspects of leadership — empathy, courage, judgment, presence — become more valuable. AI can optimize workflows and analyze patterns. It cannot build trust on the front lines, sense morale shifts, or inspire a team through uncertainty. For engineering and product leaders especially, it’s easy to fall into the meeting trap. But the leaders who stay connected to customers and teams — who understand what’s actually happening at the edges — are better positioned to make smart strategic decisions. Technology is accelerating. That doesn’t reduce the need for human leadership. It sharpens it. Read the article here: https://lnkd.in/d9XkM8VS #Leadership #AI #FutureOfWork #Engineering #Salesforce

A new MacBook at $599 with an A18 Pro chip is just the latest step toward AI becoming the default way people interact with the internet. Here's the chain that matters for your business: Faster, cheaper, AI-optimized hardware means more people running ChatGPT, Claude and Perplexity as daily tools. Not power users. Everyday people. Your customers. More people using AI daily means more searches starting in AI. More product research happening in AI. More purchases being delegated to AI agents. And more agents hitting your site. Every major hardware cycle in history has accelerated the adoption of whatever technology it was built around. The iPhone normalized mobile internet. The A18 Pro chip in this MacBook is putting AI at the center of mainstream computing. The businesses that understood mobile was coming and optimized early won the last decade. The businesses that understand AI search is coming and optimize now will win this one. (Want to know if your site is ready? Check here: https://lnkd.in/gRaXKJxi) What that means in practice: Your customers are increasingly starting their journey in ChatGPT or Perplexity, getting a recommendation, and then going to Google to validate and purchase. That means you need to be visible in both places: cited in AI search at the top of the funnel, ranking in Google at the bottom. That's exactly what SEO Stuff's Gold Plan is built for: https://www.seo-stuff.com/ 10 long-form articles built to rank in Google and get cited in AI search, like ChatGPT, Gemini, Perplexity. 3 DR50+ backlinks with an optimized anchor strategy. A full SEO + AI search visibility audit and roadmap. The Gold Plan has SEO Stuff's highest success rate for ranking in ChatGPT, Claude and Google AI Overviews, because it's built for the exact moment we're in right now. Every new device Apple ships with an AI chip is another signal that this is the direction everything is heading. The question is whether your business is positioned to be found when it gets there.

Most business users do not need to understand AI like an engineer. They need one simple aha: What kind of business problem does each layer of AI actually solve? That is why I made this visual. Because for most leaders, teams, and operators, AI still shows up as one giant fog bank called “advanced technology.” Useful in theory. Murky in practice. But when you break it into layers, it gets much easier to see: 1. Rules-Based & Knowledge AI (Classic AI) This is where AI follows logic, rules, and workflows. Think: approvals, routing, compliance checks, business rules. 2. Predictive & Optimization AI (Machine Learning) This is where AI helps you forecast and improve outcomes. Think: demand forecasting, fraud detection, dynamic pricing, segmentation. 3. Decision Core AI (Neural Networks) This is where AI gets better at handling more variables and more complexity. Think: finding patterns humans would miss in messy business data. 4. Pattern & Relationship AI (Deep Learning) This is where AI starts making sense of unstructured information. Think: documents, conversations, images, trends, signals across channels. 5. Content & Insights AI (Generative AI) This is the layer most people know now. Think: drafting, summarizing, product copy, knowledge discovery, campaign ideas. 6. Business Agent AI (Agentic AI) This is where AI starts taking action across workflows. Think: orchestrating tasks, using systems, carrying work forward, not just generating output. So the real shift is this: AI is not one thing. It is a ladder of capability. From: following rules to predicting outcomes to understanding complexity to creating content to taking action That matters because too many businesses are trying to jump straight to the top layer while the foundations underneath are still held together with spreadsheets, tribal knowledge, and polite panic. No judgment. Just facts in uncomfortable shoes. If you are a business leader, this is the better question to ask: Which layer do we actually need for the problem in front of us? Not every problem needs an agent. Not every team needs a custom model. Sometimes the win is much simpler: better rules better predictions better knowledge access better execution That is usually where the real ROI starts. The best AI strategy is not the most impressive one. It is the one your business can actually use. ♻️ If this clarified something, repost it. 🔔 Follow Ranjana Sharma for practical, human-first takes on AI at work.

LinkedIn recently admitted that AI-powered search cut their B2B traffic by up to 60%. Since AI Overviews started answering people's questions directly inside the search results, users got what they needed without clicking through to the source. LinkedIn watched their traffic evaporate while every traditional SEO metric told them they were winning. Their response was radical: They scrapped their entire SEO measurement framework and rebuilt around a new model: "Be seen. Be mentioned. Be considered. Be chosen." They stopped measuring clicks as the primary signal of content success. They started measuring citations (how often their content gets referenced inside AI-generated answers). They assembled a cross-functional AI Search Taskforce pulling in SEO, PR, editorial, product marketing, and paid media. This is a complete structural reorganization. This is LinkedIn. The company that basically invented B2B content marketing as a distribution strategy. If they can't make the old playbook work, nobody can. Searches that trigger AI Overviews show a zero-click rate of 83%. When AI summaries cite a source, clicks to that source still drop 34% compared to a traditional top-10 ranking. Brands that are still measuring success by organic click volume are reading the wrong scoreboard. The function of content changed. It's no longer just about driving traffic. It's about being the source the AI trusts enough to quote. If your entire discovery strategy depends on people clicking through to your site, the environment just shifted underneath you. And your rankings won't tell you it happened. --- 🔥 I've scaled multiple brands to 9 figures and built an 8-figure marketing agency with multiple exits 🔔 Follow for growth marketing & brand scaling ♻️ Repost if this was useful 💾 Save to send to your CMO later ✅ Sign up to my weekly growth-hacks newsletter for easy-to-implement marketing tactics every Sunday: <https://lnkd.in/eGMgpwUA>

I stopped babysitting my AI coding agents. Now they write code, open pull requests, and shut themselves down all unsupervised. Headless Claude is my new best friend. I've been building with agentic dev for over a year and kept hitting the same wall - I'd write requirements, then spend half my time orchestrating the agents through implementation. Context-switching between "what should we build" and "let me check what the agent just did" was eating the productivity gains. So I removed myself from the loop. The pipeline now: I write a requirements doc. Start the pipeline feeding it the doc. Claude breaks it into stories in Linear via MCP. Each story becomes a GitHub Issue. When labeled agent:ready, a GitHub Actions workflow spins up a headless Claude Dev agent that reads the issue, implements the feature, writes tests, opens a PR, and shuts down. Review agents pick up the PRs automatically and validate them, then they also shut down. I have four touchpoints total. Feed requirements. Review the plan. Check tasks. Read PRs - not diffs, but the agent's decision narrative. Every agent decision, every line of code, every review comment - it's all captured in the PR history. You get a complete audit trail of what the AI did and why, with human approval at the PR level before anything hits main. If you're in a regulated industry, that's not a nice-to-have, it's the thing that gets Legal to sign off. The part people miss - this isn't a coding pattern. It's a work pattern. Anything with a requirements doc and testable criteria can run through this. The dev agents are just doer agents. The review agents are just quality gates. And the PRs provide the audit history. If you’d like to try it out - I open sourced a GitHub template with the full setup. Link in the comments. My plug: I offer fractional CTO/CPO services focusing on regulated industries such as insurance, healthcare and finance. Let’s have a quick discovery call to see how I can help you or your teams. #ClaudeCode #AIEngineering

Most people, and most companies, do not understand the purpose or function of "goals." As human beings, our reality is goal-driven. The psychologist Dr. Roy Baumeister explained, “The self is not a thing but a process; it uses the future to organize the present.” Humans use "the future" to guide and shape the present. We do this through goals- whether implicit or explicit. Goals determine not only what you perceive and see (signal vs. noise), but also, the pathways you're able to identify and advance on. If you're 20 years old and want to retire by age 65, that will shape your path and process of life. If, instead, you're 25 and want financial freedom by age 25, that will change your path and process. Once you understand that goals shape human perception, identity, and pathways/systems, it changes the types of goals you set. You begin setting 10x bigger goals (with aggressive timelines) solely for the purpose of 1) eliminating the majority of options, and 2) for filtering and finding the best paths forward. "Aiming higher" was what Bill Gates saw as Microsoft's single biggest advantage when they were first starting out. As he explained, "Even before Paul Allen and I started the partnership, were saying, A computer on every desk and in every home. IMB and other people- with resources and skill sets way beyond ours- weren't aiming for that goal. They didn't see it as a possibility, so they weren't pushing as hard as they could to make it happen." In an interview with Steven Levy from Wired Magazine, Larry Page of Google explained: "Most people tend to assume that things are impossible, rather than starting from real-world physics and figuring out what's actually possible." Levy elaborated, "The way Page sees it, a 10% improvement means you're doing the same thing as everyone else. You probably won't fail spectacularly, but you are guaranteed not to succeed wildly. That's why Page expects Googlers to create products and services that are ten times better than the competition. That means he isn't satisfied with discovering a couple of hidden efficiencies or tweaking code to achieve modest gains. 1,00% improvement requires rethinking problems, exploring what's technically possible and having fun in the process." Page's 10x-thinking is illustrated by Astro Teller, director of Google X or The Moonshot Factory, "If you want your car to get 50 miles per gallon, fine. You can retool your car a little bit. But if I tell you it has to run on a gallon of gas for 500 miles, you have to start over." Patty Stonesifer, the former CEO of The Washington Post and board member of Amazon, similarly taught: "Until you set a really big goal, like vaccinating every child everywhere, you can't find out which lever or mix of levers is most important." Here's FREE access to The Science of Scaling - a book that's helping thousands of companies 10x in 3 years or less: https://lnkd.in/gtspndDm

Sure, you can vibe code but have you ever shipped so much with AI you literally break GitHub? That’s what Chintan Turakhia and the team at Coinbase did as they pushed the edge of engineering with AI. This week, Chintan and I chat about how to get 1000s of engineers cooking with AI in real, production scale environments. You’ll see - how to use Cursor to build a mini app to drive more cursor adoption - his video/voice -> Linear -> Claude process for speed running user feedback - a new model for eng leadership where there’s more code than meetings Bonus: we get his hack on finding great wines based on scribbled tasting notes. Thanks to our awesome sponsors 💼 WorkOS - Make your app enterprise ready today 🐶 Atlassian Rovo - AI that knows your business Watch now: https://lnkd.in/gyFtZAa9

Is your product engine built to scale, or just built to survive? ⚙️ Traction validates where you've been—it doesn't guarantee where you're going. For our March Startup Your Morning session, we’re hosting Brandon Leibowitz (Fractional CPO & Advisor, Leibowitz Consulting & Advisory) to help growth-stage founders fix their product engines. This isn't theory. It’s a hands-on workshop to help you redesign your roadmap for the stage ahead. 📌 Friday, March 27 | 9:00 AM |  In-Person (DMV) 👥 Limited to 20 Growth-Stage Founders Apply to join our DC Startup & Tech Week Startup Your Morning here: https://luma.com/fmum65en #DCSTW #StartupYourMorning #ProductStrategy #GrowthStage #ScaleWithDiscipline

Jeetu Patel leads 30,000 people as President & CPO at Cisco—a $300B giant at the heart of the biggest infrastructure buildout in history—making him one of the most consequential and least talked-about leaders in tech right now. In our in-depth conversation, we discuss: 🔸 Why large companies don’t fail at innovating—they fail at going all-in 🔸 The easiest way to spot a megatrend vs. a hype cycle 🔸 His communication framework for preventing “packet loss” across an organization 🔸 His “right to win” strategy framework 🔸 Much more Listen now 👇 • YouTube: https://lnkd.in/gHkxPzp7 • Spotify: https://lnkd.in/ge7QwugA • Apple: https://lnkd.in/gc-cQcsT Thank you to our wonderful sponsors for supporting the podcast:  🏆 Sentry — Code breaks, fix it faster: https://sentry.io/lenny 🏆 Framer — Build better websites faster: https://framer.com/lenny 🏆 Samsara — Saving lives with AI built for physical operations: https://samsara.com/lenny

AI will not fix your strategy. It will expose whether you have one. In his latest blog, Francois Cattoen, Product Leader, shares a hard truth: AI accelerates execution, but it does not replace judgment, accountability, or leadership. When PRDs and prototypes take minutes instead of weeks, there are no more hiding places. Weak priorities surface faster. Tradeoffs become unavoidable. False certainty becomes the real risk. The shift is clear. Product leaders spend less time authoring and more time deciding. Strong teams challenge AI output. Weak teams accept it. If you are scaling AI in product development, this is required reading. Explore the article and rethink where your teams should slow down on purpose. https://dy.si/s2nV3A2

Semantic layer for founders: why your UI breaks at scale Scalable products are built with rules, not redesigns. A simple truth founders discover late: UI breaks at scale when it has no semantic layer. What’s a semantic layer? It’s when your UI is named by meaning, not by appearance. Example: “blue button” is visual “primary action” is semantic “danger action” is semantic “success feedback” is semantic Without semantic naming, teams ship: 12 shades of “primary” 9 button styles inconsistent states different patterns for the same action Then every new feature becomes a mini redesign. With semantic rules, your product becomes: faster to build easier to maintain consistent for users scalable across teams and regions Founder checklist: If you can’t answer “What is primary in this product?” your UI will keep drifting. If you want a simple semantic layer starter guide (tokens + components + rules) Let's Discuss. artonest.design #artonest #sandipdhameliya #uiuxdesign #designsystems #saas #productdesign #uidesign #designtokens #uxleadership #scaling