Tag: outcomes vs output OKRs

  • Scaling With Heart: Self-Aware Leadership, Tough Calls, and 10x Team Performance

    Scaling With Heart: Self-Aware Leadership, Tough Calls, and 10x Team Performance

    I’ve spent enough cycles scaling product organizations to know that leaders grow—or their companies stall. In this reflection, I distill the practices I rely on to scale an org, develop myself, and raise the performance ceiling across teams, especially when the economic environment demands sharper focus and better decisions.

    To ground this discussion, I often point leaders to exemplary people-first operators. Jack Altman is the co-founder and CEO of Lattice, a people success platform for building engaged, high-performing teams. Lattice has raised over $330M, and was last valued at $3B. His work on culture and performance—captured in “People Strategy”—reinforces many of the principles I use daily.

    I start with self-awareness because it’s the keystone. If I can’t see my own patterns—when I’m avoiding conflict, over-controlling, or confusing activity with outcomes—everything else degrades. I cultivate self-awareness by writing brutally honest weekly retros, asking my staff for one piece of constructive feedback every month, and running periodic 360s to reveal blind spots. The goal isn’t comfort; it’s truth. When I improve my signal on reality, my decisions get faster and my team gains confidence.

    Difficult conversations are a gift to performance. I’ve learned to tackle them quickly, with empathy and specificity. I name the gap between expectation and outcome, share observable examples, state the impact on the team, and propose a clear path forward with timelines. If emotions run hot, I slow down, seek to understand, and stay on the behavior and results—not the person. Avoidance compounds culture debt; candor repays it with interest.

    Scaling a company introduces predictable failure modes. I’ve seen leaders confuse hiring errors with management errors; it matters which you’re facing. A hiring error shows up as persistent gaps in role fundamentals even after clear expectations, coaching, and time-bound support. A management error usually stems from ambiguous goals, poor context, or inadequate resources. I assume management error first and fix the environment. If results still lag, I revisit the hire.

    Delegation versus control is a healthy tension. Early, I’ll “micro-mentor” on critical work to teach quality, taste, and judgment—then expand autonomy as pattern recognition develops. My rule: delegate outcomes, keep ownership of standards and context. I never give up the responsibility to set the bar for the team and to protect the product vision; those are one-way doors that define the company’s trajectory.

    Building a product organization that compounds requires clarity and context. I ensure every product trio understands the strategy, customer segments, and constraints. We anchor on outcomes vs output OKRs, maintain a living strategy doc, and write decision memos that document trade-offs. When context flows, people need fewer approvals and produce better work, faster.

    On so-called micro-management, here’s my take: it’s a tool, not an identity. Early in a function or with a new leader, I may be intentionally hands-on to transfer judgment. The moment competence and trust are proven, I deliberately pull back. The mistake isn’t micro-managing; it’s forgetting to stop.

    CEO-level context setting is non-negotiable. I articulate the narrative behind the plan—the why, the constraints, the risks, and what we’re not doing. Transparency isn’t oversharing; it’s sharing the right information at the right fidelity so people can make aligned decisions. I model this with written updates, open Q&A, and by explaining how major calls were made.

    Some of the most valuable leadership work happens in uncomfortable conversations. I prepare by drafting the core message, testing it for clarity and fairness, and deciding what success looks like for the person and for the business. I also own the decision. When the stakes are high, I don’t outsource the final call or feedback to a proxy; accountability builds trust.

    Speed versus accuracy in decision-making is situational. For reversible bets, I bias to speed, time-box the experiment, and set clear kill criteria. For one-way doors, I slow down, increase the sample of perspectives, and pressure-test assumptions. I counter hidden biases in group discussions by starting with silent written proposals and independent scoring before we debate out loud.

    I’ve even experimented with removing myself from recurring meetings for a cycle. The outcome: decisions kept moving, and I learned where my presence added value versus created drag. Now I show up intentionally—for feedback on taste, to unblock cross-functional issues, or to deliver context—then get out of the way.

    Here are four practices that consistently pay off for me: protect deep work blocks for strategic writing, conduct weekly customer calls, review hiring quality monthly, and keep a running list of hard problems only I can solve. This keeps me oriented toward leverage, not busyness.

    Talking to customers is an art. I avoid solution-leading questions and ask about current workflows, pains, and the last time the problem showed up. I go five whys deep, quantify the value of a better outcome, and listen for language customers use to describe success. The best product discovery lives in those unpolished details.

    Great leaders are constant learners. I rotate through books, operator peer groups, product management leadership communities, and curated newsletters. I also treat my own organization as a learning system—post-mortems, pre-mortems, and lightweight experiments build institutional knowledge faster than any single playbook.

    To maximize employee performance, I use a simple model: Clarity x Capability x Motivation x Environment. Clarity means crisp expectations and definitions of done. Capability is skills and experience, which I grow via coaching and targeted practice. Motivation blends purpose, recognition, and meaningful goals. Environment covers tools, psychological safety, and focus time. If any factor is near zero, performance collapses; my job is to diagnose and raise the lowest one.

    When long-time employees stop scaling with the company, I address it early. Sometimes a role redesign or releveling unlocks success. Other times, a dignified, well-supported transition is the right call for everyone. Avoiding the issue erodes trust; handling it with clarity and care strengthens culture.

    Low-performing but well-liked employees create a leadership test. I separate likability from impact. If values are strong but performance lags, I set a time-bound plan with clear checkpoints. If progress doesn’t materialize, I act. Keeping someone in a role they’re not meeting hurts the team and the individual by delaying a better fit.

    When someone is let go, I’m thoughtful about what to share. I communicate the change promptly, state the role-level rationale without gossip, thank the person for their contributions, and reinforce the plan going forward. The aim is to honor privacy while maintaining clarity about standards.

    In today’s tougher macro environment, I refocus on capital efficiency, ROI-driven roadmaps, and slower, more deliberate hiring. I raise the bar for product bets, validate earlier with customers, and price for value. Constraints, when embraced, sharpen strategy and execution.

    Aligning career goals with company goals is ongoing work. I use growth frameworks, individual development plans, and quarterly conversations that link business outcomes to skill-building. When people see a path to mastery and impact, performance accelerates.

    Most leaders underestimate their team’s potential. I raise expectations with ambitious, outcome-based goals, ensure people have the context to operate like owners, and celebrate learning velocity as much as wins. When standards, support, and trust rise together, teams routinely outperform even optimistic forecasts.

    Resources I recommend: Jack’s book: https://www.amazon.com/People-Strategy-Culture-Competitive-Advantage/dp/1119717043. Jack’s company, Lattice: https://lattice.com/. First Round Capital’s Newsletter: https://review.firstround.com/newsletter.


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  • My playbook: Intuition vs data, big swings, and product-led growth lessons from Slack

    My playbook: Intuition vs data, big swings, and product-led growth lessons from Slack

    I get asked constantly how I decide when to trust my gut, when to lean on data, and when to take a big swing versus iterate. As a product leader, my answer has been shaped by hard-won lessons building B2B SaaS, product-led funnels, and enterprise features. Recently, I revisited Slack’s approach to decision-making, product reviews, and balancing product-led vs sales-led growth—and distilled a set of practices I use with my teams today.

    Noah Desai Weiss is the Chief Product Officer of Slack, and has an accomplished track record inside and outside of the company. He started Slack’s Search, Learning, and Intelligence division, led the Self-Service (SMB) Business, and led the Expansion and Virtual HQ product areas (responsible for Huddles, Clips, and more). Before joining Slack, Noah was the SVP of Product Management at Foursquare (raised over $390m), and was a Product Manager at Google.

    The throughline for me starts with a simple truth: not all decisions should be data-driven. Early in a product’s life—or when exploring a novel experience—data is often either unavailable or misleading. That’s where intuition, taste, and judgment come in. I treat intuition as a hypothesis generator and momentum maker, then instrument quickly to validate direction. This blend of “When to use intuition vs data to drive decisions” has saved me from overfitting to small datasets and from analysis paralysis when speed was the real advantage.

    I’ve learned that “Taste and judgment are learnable.” You can coach it. Review artifacts together. Run side-by-side comparisons of design explorations. Write down what “good” looks like and why. My teams keep a living gallery of exemplary UX patterns and empty-state copy that exemplifies our bar. Over time, this scales the craft of intuition across a larger org—just as “How Slack scales intuition across their product org” suggests.

    Of course, there are “Challenges of intuition-led product building.” The biggest are founder or leader overreach and survivorship bias. I mitigate this with timeboxed discovery: we commit to a clear decision date, capture our priors in writing, and express our confidence as a range rather than a point estimate. This sets up a healthy dynamic for “Managing pace vs accuracy in decision-making.” We move fast when reversibility is high, we move slower when the blast radius is large.

    Matching people to the work matters too. Some product problems are inherently ambiguous and benefit from researchers, designers, and PMs who derive energy from the unknown. Others are best led by optimization-oriented builders who light up when the metric moves. I’m explicit about “Matching people to data vs intuition-driven work,” and I rotate folks so they can build both muscles.

    In remote and hybrid environments, I’ve found the most underrated traits are proactive context-sharing, crisp written communication, and the ability to create signal in Slack and docs. “Underrated qualities for remote workers” aren’t just stylistic preferences—they are execution speed ups. I look for people who make everyone around them smarter asynchronously.

    On product process, I’m inspired by “How Slack runs product reviews.” My rubric: one problem statement, a tight narrative memo, the bet framing (assumptions, risks, kill criteria), and outcomes tied to “outcomes vs output OKRs.” We align on the decision owner, consent vs consensus, and the next irreversible checkpoint. This keeps reviews from becoming theater and pushes decisions to the right altitude.

    Culture shows up in small moments. “The importance of a team’s ‘vibe’” is tangible: Do we demo early? Do we celebrate learned negatives as much as wins? Do engineers, designers, and PMs feel joint ownership of the experience, not just their function’s slice? When the vibe is right, latency from idea to insight collapses—and that compounding is everything in product discovery.

    Portfolio balance matters. I aim for a mix that lets us keep shipping customer-visible improvements while reserving room for breakthroughs. “Balancing “big swings” with incremental improvements” requires explicit ring-fencing: 70/20/10 works well for many orgs. Big swings get stage gates and PR/FAQ-like artifacts; incremental bets get weekly ship cadence and tight measurement. When we miss, we run pre-mortems and decision journals, reinforcing “Rituals for good decision-making.”

    Go-to-market is where strategy meets friction. My guidance on “Advice on product-led vs sales-led growth” is to design the handshake up front. Let product-led growth do the land—self-serve activation, collaborative aha, bottoms-up virality—and let sales-led growth do the expand—security, compliance, procurement, multi-workspace governance. Instrument the handoffs, define eligibility heuristics, and ensure pricing doesn’t punish adoption. This is also where “Which products should focus on end-users versus executives” gets real; optimize early journeys for end-user success while giving executives the portfolio-level control and analytics they require.

    I’m continually impressed by “What Slack learns from Salesforce.” Enterprise trust, admin controls, and scalable GTM motions can coexist with consumer-grade product craft. That hybrid DNA is powerful. I’ve adopted similar patterns: build for end-user joy, layer enterprise-grade controls, and price to match value realization, not procurement theatrics.

    Speaking of pricing, “Pricing lessons from Salesforce and Marc Andreessen” pushed me to keep pricing simple enough for PLG while being flexible enough for enterprise. Seat-based pricing remains intuitive for collaboration products, but usage and “SaaS pricing” add-ons can map value to heavy features without overcrowding your price page. The key is to test willingness to pay early, avoid grandfathering yourself into a corner, and treat packaging changes like product changes—with discovery, rollout plans, and success metrics.

    Humility isn’t fluffy—it’s an execution advantage. “Slack’s humility and why it matters” resonates with how I try to lead: ruthlessly honest about what we don’t know, eager to learn from customers quickly, and unafraid to reverse course when the evidence changes. That humility turns into speed because we stop defending past decisions and start iterating toward truth.

    When working with a strong product voice at the top, “How to build product with a product-focussed founder” comes down to mutually agreed principles. Capture the founder’s taste in explicit heuristics, define the moments where their judgment should overrule the process, and codify how dissent and disagree-and-commit work in practice. This protects clarity without stifling creativity.

    Here are the topics I unpacked and continue to apply across teams: “When to use intuition vs data to drive decisions,” “The most underrated traits in a remote work environment,” “How Slack runs product reviews,” “The importance of a team’s ‘vibe’,” “Managing pace vs accuracy in decision-making,” “Balancing “big swings” with incremental improvements,” and “Advice on product-led vs sales-led growth.” Each one is a lever that compounds when used together.

    Curious to learn more about Slack? You can try Slack Pro and get 50% off using this link.

    Creative Selection – Inside Apple’s Design Process During the Golden Age of Steve Jobs: https://www.amazon.com/Creative-Selection-Inside-Apples-Process/dp/1250194466

    Salesforce acquires Slack: https://slack.com/blog/news/salesforce-completes-acquisition-of-slack

    Thinking in Bets – Making Smarter Decisions When You Don’t Have All the Facts: https://www.amazon.com/Thinking-Bets-Making-Smarter-Decisions-ebook/dp/B074DG9LQF


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  • Hard-Earned Lessons from Loom: Product Strategy, Alignment at Scale, and Hiring That Wins

    Hard-Earned Lessons from Loom: Product Strategy, Alignment at Scale, and Hiring That Wins

    I’m always looking for crisp, scalable ways to drive product strategy, organizational alignment, and cross-functional performance that actually ship outcomes. Studying Loom’s operating system—and the career arc behind it—offered a masterclass worth sharing. Anique Drumright is the COO at Loom, a video communication tool for streamlining workflows. Loom has raised over $200M, and was last valued at $1.5B. Anique has a proven track record across product development, executive leadership, and building high-performing organizations. Before joining Loom, Anique was the VP of Product at TripActions, where she scaled the team over 8x globally, and she has also held multiple roles at Uber. In this breakdown, I dig into best-practice product management, how to achieve alignment at scale, the mechanics of cross-functional performance, Anique’s approach to finding top organizational talent, how to hire for roles outside your area of expertise, the most common fail cases with internal and external recruitment, and the specific interview tactics that actually surface the truth. One theme I return to often is the transition from product management to executive leadership. As a PM, I optimize for customer insight, prioritization, and execution velocity. As an exec, I optimize for clarity, systems, and sustained energy across teams. The job shifts from owning a roadmap to owning the conditions under which many roadmaps thrive—organizing for outcomes, setting non-negotiable standards, and removing ambiguity. Storytelling sits at the center of launch excellence. I love how Loom anchors launches in a human narrative: define the painful “before,” demonstrate the transformative “after,” and spotlight one memorable capability that makes the switch inevitable. I pair this with a crisp narrative memo, a demo-first internal review, and a simple, outcome-oriented success metric—so product, marketing, and sales sing the same chorus. Managing cross-functional scope and performance requires ruthless role clarity and shared measures of success. I align on a single definition of the customer problem, agree on leading indicators we can move now, and assign one DRI per decision. When we use outcomes vs output OKRs, we unlock better trade-offs: fewer features shipped, more customer problems solved. Organizational alignment is both essential and fragile. What looks like misalignment is usually mismatched time horizons, unclear ownership, or different definitions of success. The antidote is explicit agreements: who decides, how we decide, and what “good” looks like this quarter. When in doubt, I over-communicate context, not tasks. I’ve seen at scale—Uber is a notable example—that alignment travels fastest through shared rituals, not longer documents. Weekly business reviews, lightweight decision logs, and a common operating cadence create a heartbeat the org can follow. The point isn’t ceremony; it’s repeatable clarity. My go-to alignment rituals are simple. A Monday priorities memo sets the narrative and the week’s must-win outcomes. Midweek, a cross-functional stand-up surfaces risks and unblocks dependencies. Friday, we close the loop with a red-yellow-green on outcomes and a short retro on decisions—not just results—so we compound learning. One-on-ones are performance multipliers when they’re designed well. My winning format: start with energy and focus (what’s giving or draining energy), review outcomes not activity, walk a single thorny decision to closure, and end with explicit asks in both directions. Over time, this builds trust and speed. When and how to help functional leaders matters. I jump in when a decision is high-impact and ambiguous, when speed has stalled, or when the problem crosses multiple functions. Otherwise, I coach on principles and expect leaders to own the path. If I’m often in the weeds, we have a structure or talent gap—not a diligence problem. Hiring outside my domain expertise starts with outcomes, not resumes. I write the first-90-day outcomes, name the decisions the role must own, and recruit with a structured case that mirrors the real job. I bring in a domain advisor to probe depth and run a work-sample test to reduce false positives from polished storytellers. For senior leaders, my favorite interview questions are simple and hard to fake: Tell me about the last time you changed your mind on a critical decision—what evidence moved you? Walk me through your operating cadence—meetings, artifacts, and decisions—in a typical month. Describe your hardest cross-functional miss and the system you changed to prevent a repeat. The specificity of answers reveals the operator from the commentator. I adjust the hiring process when I’m outside my depth: heavier emphasis on work samples, more structured rubrics, a domain expert panel, and reference checks that test for actual outcomes. When the role is pivotal, I’ll run a paid trial project with clear guardrails; reality is the best filter. Common patterns of failed external hires: they manage optics over outcomes, never rewire the system, and don’t create leaders beneath them. Failed internal promotions often show up as scope growing faster than judgment, a reluctance to reset standards with former peers, or success limited to a familiar domain. Avoid over-promotion by decoupling recognition from scope; celebrate excellence without inflating title or span prematurely. To get honest answers in interviews, I normalize candor and ask for receipts. I request artifacts—planning docs, dashboards, postmortems—and I probe for the counterfactual: what would you do differently if you had to do it again? In reference checks, I ask for moments of truth: the hardest feedback you gave them, a decision you disagreed with and how they handled it, and the exact conditions under which you would rehire them tomorrow. Sustaining energy is an executive’s quiet superpower. I watch team energy levels as closely as metrics. What inspires people in a company is progress they can feel, standards that mean something, and leaders who tell the truth. If we keep those three alive, performance follows. A month in the life of a COO (and frankly any executive operator) is a portfolio: setting the narrative and outcomes, running the operating cadence, calibrating talent, and clearing systemic blockers. The best leadership dynamics work because roles are explicit, trust is earned through delivery, and debates resolve into single-threaded ownership—not committee compromises. Resources for further exploration: Loom (https://www.loom.com/), Navan (formerly TripActions): https://navan.com/, Teach for America: https://www.teachforamerica.org/, Uber: https://www.uber.com/. Timestamps I mapped my notes to for quick scanning: [00:03:00] similarities and differences between PM and executive leadership roles; [00:06:53] storytelling in launches; [00:10:01] cross-functional scope and performance; [00:13:41] goal-setting with functional leads; [00:16:59] organizational alignment; [00:20:40] alignment at scale; [00:24:06] alignment rituals; [00:25:23] one-on-one format; [00:27:49] supporting functional leads; [00:29:13] hiring outside your expertise; [00:32:55] interview questions; [00:33:55] adapting the hiring process; [00:36:09] failed external hires; [00:37:40] failed internal hires; [00:39:05] avoiding over-promotion; [00:40:51] inspiration; [00:45:40] getting honest answers; [00:47:12] reference checks; [00:51:29] a month in the life of a COO; [00:52:52] energy levels; [00:54:53] leadership dynamics; [00:57:30] outsized career influences.
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  • Supercharge Your Engineering Org: Alignment, AI, and Productivity from Adobe to Etsy

    Supercharge Your Engineering Org: Alignment, AI, and Productivity from Adobe to Etsy

    I obsess over building high-velocity engineering organizations that ship meaningful outcomes. When I evaluate what reliably moves the needle—across startups and scaled enterprises—it always comes back to alignment, disciplined management, and a modern view of engineering productivity. Recently, I revisited a set of insights that crystallize these themes and translate them into practical rituals any leader can adopt.

    Kellan Elliott-McCrea is a Head of Engineering at Adobe, overseeing Frame.io, a newly acquired video review and collaboration platform. He is known for his experience and expertise as an engineering leader. He was previously a VPE at Dropbox, and CTO at Etsy where he built and led a team of 300 people, from tech and platform reboot through to IPO. Kellan also built and scaled teams at Flickr, and has a coaching and advising practice for companies looking to supercharge their engineering teams.

    Here’s what we dig into when we talk about world-class engineering orgs: how software engineering has changed in the last 10-15 years; the future of software engineering, and the impact of AI; the importance of alignment and tactics for achieving it; how to think about and enable engineering productivity; lessons on culture from Adobe, Dropbox, and Flickr; concrete tips for being a better manager; and rituals for building business literacy throughout an org.

    Let’s start with a reality I see in my own work: engineering teams are bigger than they were a decade ago, despite dramatically better tools and platforms. The reason isn’t inefficiency—it’s scope. Today’s products carry higher bars for reliability, privacy, security, compliance, and multi-surface experience. The coordination surface area has exploded. That’s why operating models must evolve: clear interfaces between teams, standardized decision-making, and reliable cross-functional rhythms are no longer nice-to-haves—they’re throughput constraints.

    Alignment, then, is the ultimate speed multiplier. I’ve learned the hard way that slow teams are rarely under-skilled; they’re misaligned. “Slow teams are misaligned teams.” To counter this, I anchor on a few tactics: articulate a clear strategic narrative (why now, why us, why this), commit to outcomes vs output OKRs, and institutionalize decision logs so debates don’t reset every sprint. When teams know the customer problem, the business bet, and how their work ladders up, the flywheel starts turning.

    On engineering productivity, I avoid vanity metrics and favor a portfolio: flow and focus (interruptions, WIP), system signals (lead time, deployment frequency, change fail rate), and outcome alignment (how progress maps to customer value and revenue impact). Tools matter—DX investment in CI/CD, observability, and paved roads—yet the largest gains usually come from simplifying priorities and reducing cross-team coupling. Fewer, better bets will beat “more tickets shipped” every time.

    The future of software engineering is inseparable from AI. In my practice, I treat gen ai and gen ai for product prototyping as core accelerators: copilots for code and tests, scaffolding services that convert specs to boilerplate, and retrieval-augmented knowledge that collapses the gap between tribal lore and action. The key is to measure impact at the team level—cycle time, defect escape, and learning velocity—so AI augments engineering judgment rather than creating hidden complexity.

    Culture is the compounding edge. Lessons on culture from Adobe, Dropbox, and Flickr converge on a few essentials: invest in psychological safety and clarity of purpose, operationalize blameless learning, and make information radically accessible. “How Complex Systems Fail, by Richard I. Cook, MD” is a touchstone here—complexity punishes organizations that rely on heroics and rewards those that build resilient systems and shared mental models.

    For managers, I return to a short, durable list. Schedule real one-on-ones that prioritize coaching over status. Write more than you speak; clarity scales through documents. Run crisp, time-boxed decision forums with pre-reads and owners. Close the loop on feedback—especially in moments of disagreement—by documenting trade-offs and naming the decider. These concrete tips for being a better manager build trust, accelerate decisions, and enable autonomy.

    Every high-performing engineering org I’ve led invests in business literacy as a first-class ritual. I recommend monthly “Finance 101” briefings, customer support ride-alongs, and deal reviews to connect engineers to revenue realities. Pair that with tactics and rituals for enabling effective teams—weekly written updates, demo-driven reviews, and pre-mortems—and you get sharper prioritization and far better cross-functional coordination.

    Why so few companies successfully go multi-product? Most underinvest in platforms, shared services, and explicit funding models for internal APIs. The remedy: treat platforms as products with clear roadmaps, SLAs, and customer empathy; align incentives so teams don’t fork capabilities in the rush to ship; and adopt technical governance that favors standardization where it compounds and freedom where it differentiates.

    For compensation and career architecture, I pressure-test common models by asking: does this design reward the behaviors we say we want? If we value outcomes, impact, and enabling others, the ladders should reflect it. When the incentives match the mission, the org learns faster and scales cleaner.

    Referenced:

    Adobe: https://www.adobe.com

    Dropbox: https://www.dropbox.com/

    Flickr: https://www.flickr.com/

    Frame: https://www.frame.io/

    How Complex Systems Fail, by Richard I. Cook, MD: https://how.complexsystems.fail/

    How Etsy Grew their Number of Female Engineers by Almost 500% in One Year https://review.firstround.com/How-Etsy-Grew-their-Number-of-Female-Engineers-by-500-in-One-Year

    Where to find Kellan Elliott-McCrea:

    Twitter: https://www.twitter.com/kellan

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

    Website: https://kellanem.com/

    Personal blog: https://laughingmeme.org/

    My bottom line: if you want to supercharge your engineering org, anchor on alignment, measure what matters, and leverage AI to elevate—not replace—engineering judgment. Do that, and you’ll turn coordination costs into compounding advantages that show up in customer value, velocity, and morale.


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  • Inside Rewind AI’s Playbook: PMF Breakthroughs, Bold Twitter Fundraise, and the Future of AI

    Inside Rewind AI’s Playbook: PMF Breakthroughs, Bold Twitter Fundraise, and the Future of AI

    I sat down with Dan Siroker to explore the product, fundraising, and AI strategy lessons behind Rewind AI’s rapid rise — and to reflect on what I would adopt in my own product management practice today. Dan Siroker is the co-founder and CEO at Rewind AI, a personalized AI powered by everything you’ve seen, said, or heard. Dan launched Rewind to an emphatic response on Twitter, and used a public pitch video to fundraise at a $350m valuation. Prior to starting Rewind, Dan co-founded Optimizely, which reached $120m ARR before being acquired by Episerver, a content management company. Dan was also the Director of Analytics for Obama’s first presidential campaign.

    What stood out immediately was Rewind’s journey to Product Market Fit and how deliberately the team instrumented learning loops. As a product leader, I pay close attention to how founders reduce ambiguity: narrow the target segment, ship thin slices, measure engagement cohorts, and iterate fast. Rewind’s early focus on utility and trust — not novelty — created the conditions for PMF while the team resisted the temptation to over-scope.

    I was especially interested in how Rewind works and how the team managed scope while building a category-creating product. By focusing on personalized recall powered by on-device intelligence and a clear privacy narrative, they avoided the common trap of trying to solve everything for everyone. My own rule of thumb is to enforce brutal prioritization around the highest-intent jobs-to-be-done, then earn the right to expand. That same discipline shows up in Rewind’s cultural mantra for shipping and validating fast.

    Lessons from Optimizely echo throughout. Being a second-time founder sharpens pattern recognition — from building high-clarity cultural values to operationalizing product-market fit. I’ve found that codifying operating principles early helps a team move faster with fewer collisions, and Dan’s approach to open feedback and public learning raises the bar for transparency.

    On product positioning as a category creator, the team leaned into outcomes over features, which is critical when the mental model is new. Rather than compete in a features arms race, they framed a compelling before-and-after: instant, searchable memory that augments cognition. In my experience, that level of narrative clarity drives founder-led GTM and accelerates word-of-mouth.

    We also dug into where to build in AI, and what makes a “wrapper” thin versus thick. My take: thin wrappers add shallow convenience on top of foundation models; thick wrappers integrate proprietary data, workflow depth, distribution advantages, and durable UX moats. Founders should aim for thick wrappers with unique data flywheels, not commodity interfaces easily displaced by platform shifts.

    Operationalizing Product Market Fit remains a craft. I routinely use leading indicators like activation rate, day-7/day-30 retention for key actions, and sentiment via structured PMF surveys. Rahul Vohra’s framework for measuring and optimizing Product Market Fit: https://review.firstround.com/how-superhuman-built-an-engine-to-find-product-market-fit is a proven playbook. Pair that with cohort-based instrumentation and tight audience segmentation to reveal the “sharpest edge” of value.

    On AI hype, we aligned on a pragmatic view: real value accrues where latency, accuracy, and privacy meet workflow depth. Apple’s Silicon: https://www.macrumors.com/guide/apple-silicon/ and on-device acceleration will keep unlocking new consumer experiences, while ChatGPT: https://chat.openai.com/ has reset expectations for natural interfaces. The cautionary tales of Google Glass: https://en.wikipedia.org/wiki/Google_Glass and Google Wave: https://en.wikipedia.org/wiki/Google_Wave remind me that timing, social acceptability, and use-case clarity matter as much as technical novelty.

    Data privacy is now a core buying criterion, not a checkbox. I see a clear trend toward local-first approaches, explicit consent, and user agency — especially for products that touch memory, identity, and personal archives. Framing value through Maslow’s Hierarchy of Needs: https://www.simplypsychology.org/maslow.html helps prioritize trustworthy utility over gimmicks.

    Dan’s one-of-a-kind Twitter fundraising strategy was a masterclass in founder-led GTM. By sharing a public pitch and engaging directly with early users and supporters, he compressed feedback cycles and aligned community, product, and capital. For reference, see Dan’s public Twitter fundraise: https://twitter.com/dsiroker/status/1646895452317700097 and Dan’s Rewind demo tweet: https://twitter.com/dsiroker/status/1638799931891920897. The transparency extended to leadership practice as well, with Dan publicly sharing his own 360 performance reviews: https://twitter.com/dsiroker/status/1689763756459675650 — a bold move that builds trust.

    I’m watching what’s next for Rewind with interest, particularly around thicker integrations, extensibility, and collaboration patterns. In the next decade, I expect assistive AI to become ambient, multimodal, and context-aware — an ever-present copilot that feels less like a tool and more like an extension of cognition.

    Referenced: Apple’s Silicon: https://www.macrumors.com/guide/apple-silicon/

    Referenced: ChatGPT: https://chat.openai.com/

    Referenced: Dan publicly sharing his own 360 performance reviews: https://twitter.com/dsiroker/status/1689763756459675650

    Referenced: Dan’s public Twitter fundraise: https://twitter.com/dsiroker/status/1646895452317700097

    Referenced: Dan’s Rewind demo tweet: https://twitter.com/dsiroker/status/1638799931891920897

    Referenced: Google Glass: https://en.wikipedia.org/wiki/Google_Glass

    Referenced: Google Wave: https://en.wikipedia.org/wiki/Google_Wave

    Referenced: Maslow’s Hierarchy of Needs: https://www.simplypsychology.org/maslow.html

    Referenced: Optimizely: https://www.optimizely.com/

    Referenced: Paul Graham: https://twitter.com/paulg

    Referenced: Rahul Vohra’s framework for measuring and optimizing Product Market Fit: https://review.firstround.com/how-superhuman-built-an-engine-to-find-product-market-fit

    Referenced: Rewind AI: https://www.rewind.ai/

    Referenced: Scribe (which morphed into Rewind): https://www.scribe.ai/about

    Where to find Dan Siroker: Twitter: https://twitter.com/dsiroker

    Where to find Dan Siroker: LinkedIn: https://www.linkedin.com/in/dsiroker

    Where to find Dan Siroker: Personal website: https://siroker.com/

    Where to find Dan Siroker: Blog: https://medium.com/@dsiroker

    My takeaway for founders and product leaders: obsess over segmentation, instrument for learning, and tell a crisp narrative that earns trust. Thick wrappers, privacy-first design, and founder-led GTM are how you win the next wave of AI.


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  • Intuition, White-Glove Support, and Relentless Execution: Lessons from Looker to Omni

    Intuition, White-Glove Support, and Relentless Execution: Lessons from Looker to Omni

    I’m constantly drawn to product stories where intuition, customer obsession, and raw effort compound into durable advantage. This conversation with Colin Zima crystallized that arc—from pioneering high-touch support at scale to balancing gut feel with data to ship what matters. The through-line for me: when you operationalize empathy and pair it with disciplined execution, you create momentum that’s almost impossible to copy. Colin Zima is the co-founder and CEO of Omni, a business intelligence tool that has raised over $26.9m. Prior to starting Omni, Colin was Chief Analytics Officer and VP of Product at Looker, which was acquired by Google for $2.6b. Colin was an early employee at Looker, and stood up its high-touch customer support arm, which turned into a cornerstone competitive advantage for the company. What resonated most with my own practice is how deliberate investment in white-glove customer support can become a product strategy lever—not just a service function. When you’re in a category-creating phase or displacing an entrenched incumbent, those high-touch loops are how you learn the truth fast, reduce onboarding friction, and convert early believers into reference customers. The trick isn’t whether to do it; it’s when, why, and how to sequence it so the economics still make sense as you scale. On scaling high-touch support, I look for three signals before pushing the gas: repeatability in the top 5 user pain patterns, a crisp path to tooling and self-service, and tight product feedback loops that turn today’s premium assistance into tomorrow’s default experience. That’s how white-glove support pays for itself—first as acceleration for adoption, then as inputs that harden the core product. I also emphasize role clarity and career ladders so support becomes a talent engine, not a cul-de-sac, which makes hiring for and hiring from customer support a strategic advantage. Colin’s intuition-based approach to product echoes a belief I hold closely: data is essential for validation and prioritization, but it rarely originates the leap. Intuition frames the bet; data sizes the risk; customers ground the narrative. I’ve seen the merits—speed, conviction, and differentiated UX—and the downsides when intuition goes unchecked—overfitting to edge cases or mistaking novelty for value. The balance is intellectual honesty: writing down the thesis, the counter-thesis, and the disconfirming evidence you’ll accept before you commit resources. I was especially struck by the operational rigor behind hitting goals for 24 quarters in a row. That kind of consistency doesn’t happen by accident; it comes from outcomes over output, sober forecasting, and the cultural discipline to cut or delay work that doesn’t ladder up. I coach teams to make the target visible, tie metrics to customer value, and then prune relentlessly—because the opportunity cost of “almost done” is usually invisible until the quarter slips. The founding story of Omni reminds me that category shifts rarely come from a single breakthrough. They’re the product of dozens of earned insights about where the market is going and what’s still too hard for customers today. I pay close attention to how founders maintain intellectual honesty as the narrative tightens—keeping a clear line between what we know from the field and what we’re assuming, and revisiting that line often. There’s also practical career wisdom here. When choosing which startup to join, I look for founder clarity on the core problem, the early design partners, and the distribution wedge. On founder-market fit, I care less about domain tenure and more about a pattern of shipping, learning, and adjusting fast. And Colin’s unpopular opinion on how to hire good PMs aligns with my experience: bias toward builders who can synthesize customer reality, technology constraints, and go-to-market timing—then communicate clearly and commit. If you’re building in data and analytics, these references are useful context for the ecosystem and buyer expectations: BigQuery: https://cloud.google.com/bigquery, Hotel Tonight: https://www.hoteltonight.com/, Omni: https://omni.co/, Tableau: https://www.tableau.com/. For those who want to go deeper with Colin’s thinking and product journey, you can find him here: Twitter: https://twitter.com/drinkzima?lang=en and LinkedIn: https://www.linkedin.com/in/colinzima/. My takeaway as a product leader: make white-glove customer support a strategic instrument, not a cost center; let intuition set bold direction while data governs scope; and cultivate the operational cadence that makes hitting your goals a habit, not a headline. That combination is how you compound trust with customers and ship products that stand the test of time.
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  • Open Source to Revenue: How GitLab Scales Transparency, Community, and Enterprise Growth

    Open Source to Revenue: How GitLab Scales Transparency, Community, and Enterprise Growth

    I’m endlessly fascinated by how modern platforms turn open source momentum into durable, enterprise-grade businesses. Ashley Kramer is the CMO and CSO at GitLab, a publicly listed DevSecOps platform. She started out in software engineering before becoming a product leader, and eventually, a marketer. Most recently, Ashley was the CPO and CMO at Sisense, a data analytics company last valued at over $1b. That multifaceted path mirrors the intersection I live in daily—where product management leadership, developer evangelism, and go-to-market strategy converge to drive sustainable growth.

    What stood out to me most was the precision with which GitLab layered a commercial model on top of open source roots. The nuance matters: the difference between open core and open source isn’t just semantic; it determines packaging, pricing, and how you balance a vibrant community with enterprise-grade requirements. The tensions of being a commercial, open source company are real—especially when you’re serving many different customer segments with distinct needs. From my seat, this is the essence of open source monetization: protect developer trust while building clear value for enterprises that justifies “consumption SaaS pricing,” security, and support.

    Transparency plays a starring role. GitLab’s culture shows the power—and the trade-offs—of working in the open. I’ve seen firsthand how openness accelerates alignment, speeds up product discovery, and reinforces outcomes vs output OKRs. But you must be deliberate. Examples, benefits, and downsides of a transparent company culture are on full display in their handbooks and public processes, which I frequently reference for my own teams. Why GitLab is transparent about their marketing and the 2 examples of GitLab’s uniquely transparent culture provide a blueprint for building trust at scale—while the downsides of being a transparent company remind us to design guardrails.

    On the marketing front, the role of marketing at GitLab underscores a systems mindset: define the customer problems, align with the product roadmap, and ensure tight collaboration with sales and community. GitLab’s main marketing metrics, combined with a clear model for how marketing collaborates with product, make the strategy both measurable and adaptable. I’ve applied a similar approach by anchoring campaigns in user outcomes, then instrumenting every touch—from content to conversion—to close the loop with product usage and retention.

    Structure supports strategy. The thinking behind GitLab’s org structure, in and around marketing, is a reminder that ownership beats approval chains. GitLab’s planning process and GitLab’s meeting structure and cadence reflect a discipline that’s hard to achieve without cultural scaffolding. In my experience, explicit planning rhythms and written decision logs are force multipliers for cross-functional execution and faster product-market fit lessons.

    Selling to enterprise as an open core company demands clarity on what’s free, what’s paid, and why. That’s where serving many different customer segments becomes both an art and a science. Developer love and enterprise readiness can coexist when you design the offer thoughtfully—feature gating that respects the open source ethos, security and compliance that satisfy a “CISO,” and pricing models that feel fair. For teams driving developer evangelism, the north star remains unchanged: remove friction, amplify community contributions, and provide a clear, upgrade-worthy path for enterprises.

    When it comes to campaigns, I took away a simple, durable lesson from an example of a recent marketing campaign: anchor the narrative in customer pain, tie it to measurable outcomes, and connect the dots—from awareness to activation to expansion—across product and marketing. An example of GitLab’s marketing in practice reinforces that even in highly technical domains like “DevSecOps,” the most effective storytelling is still about clarity and credibility.

    I also appreciate how Ashley’s background informs execution. Benefits of having an engineering and product background as CMO include crisper problem definitions, better partnership with product leaders, and the ability to translate complexity into value propositions that resonate with both developers and executives. It’s a competitive advantage I’ve leaned on throughout my own career as we scale platforms and craft founder-led GTM motions into repeatable engines.

    For leaders building in the open, a few resources are worth bookmarking—and I keep returning to them when refining strategy, process, and messaging.

    DevSecOps: https://about.gitlab.com/topics/devsecops/

    GitLab’s open core business model: https://handbook.gitlab.com/handbook/company/stewardship/

    GitLab’s open source employee handbook: https://handbook.gitlab.com/handbook/people-group/

    GitLab’s open source marketing handbook: https://about.gitlab.com/handbook/marketing/

    GitLab’s open source remote handbook: https://handbook.gitlab.com/handbook/company/culture/all-remote/guide/

    GitLab legal team’s SAFE framework: https://about.gitlab.com/handbook/legal/safe-framework/

    GitLab: https://gitlab.com

    E-Group: https://about.gitlab.com/company/team/e-group/

    CISO: https://www.cisco.com/c/en/us/products/security/what-is-ciso.html

    Sid Sijbrandij, CEO of GitLab: https://www.linkedin.com/in/sijbrandij/

    Tableau: https://www.tableau.com/

    Pulling it all together, here’s the playbook I see: make the open core boundary unmistakably clear, invest deeply in your developer community, operationalize transparency with documented processes, and build revenue with enterprise-grade features that map to real-world risk and scale. Do that well, and you earn the right to price for value—while staying true to the community that made the product possible.


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  • Goal-Setting for AI Products: How I Plan, Prioritize, and Confidently Ship in a Nonlinear GenAI World

    Goal-Setting for AI Products: How I Plan, Prioritize, and Confidently Ship in a Nonlinear GenAI World

    I build and ship AI products in an environment where the frontier changes weekly, so my planning system has to be adaptive, evidence-driven, and unapologetically outcome-focused. In this piece, I share the frameworks I use to set goals for generative AI, balance research with product execution, and scale responsibly — drawing sharp lessons from one of the most influential applied AI companies operating today.

    Consider Runway, an applied AI research company shaping the next era of art, entertainment, and human creativity. Runway has raised $237m and was one of Time Magazine’s “100 most influential companies” in 2023. Runway has been a persistent viral sensation in recent years, and is behind many of the most famous AI demos online.

    The earliest stages of an AI company often begin with research breakthroughs, scrappy prototypes, and clever distribution. In practice, that means leveraging containerization (https://aws.amazon.com/what-is/containerization/) and Docker (https://www.docker.com/) to package models reproducibly, showcasing work where practitioners already gather — Hugging Face (https://huggingface.co/), Hugging Face Spaces (https://huggingface.co/spaces), and Hugging Face Model Hub (https://huggingface.co/docs/hub/models-the-hub) — and tapping infrastructure like Replicate (https://replicate.com/) to get demos into people’s hands. Early, magical use cases — like the Green screen tool by Runway (https://runwayml.com/green-screen/) — teach us which problems are both technically feasible and viscerally valuable.

    I’ve learned to be cautious about “The limitations of being “customer-driven” when building in AI”. Traditional product discovery assumes needs are legible and solutions are relatively deterministic. In generative AI, user desire often follows model capability, not the other way around. The job is to triangulate: run tight user loops to validate perceived value, instrument objective model quality, and explore novel interaction patterns that customers can’t yet articulate. I treat this as a portfolio of discovery bets — some customer-led, some capability-led, all evaluated against clear outcome thresholds.

    Balancing research development with product development requires organizational design that prevents context-switching tax while preserving velocity. I pair research pods with product pods, supported by forward deployed engineers and domain PMs who translate evaluation metrics into user-visible milestones. Safety and content moderation sit on the critical path, not as afterthoughts — think policy definition, classifier tooling, abuse red teaming, and clear escalation playbooks. This balance is how you move from a great demo to a dependable product without losing momentum.

    Goal-setting amidst constant change in AI starts with outcomes vs output OKRs. I write OKRs in terms of user impact and model performance thresholds — for example, target ranges for latency, quality scores against a golden dataset, or creator retention — then let teams choose the highest-leverage outputs (data pipelines, fine-tuning, UX improvements) to get there. Why I don’t plan very far ahead: I treat the annual view as a vision and bet map, the quarterly view as a constrained slate of outcomes, and the 6–8 week cycle as the execution heartbeat. AI roadmaps are hypotheses; evaluation harnesses and launch gates are the truth.

    Community is a force multiplier. Forming a vocal community and fostering community requires real access and real listening: early release cohorts, office hours, and transparent changelogs. How they picked users for early release matters — diversity of use cases, sophistication of workflows, and willingness to give crisp feedback. Expanding past the first 100 users of Gen-2 demands readiness: evaluation parity across modalities, scalable infra, and safety coverage. Done well, this motion compounds learning while building authentic advocacy.

    For founders, my advice echoes the core lessons above. Start with a narrow, high-intent wedge and prove durable value fast; let founder-led GTM compress the feedback loop; instrument everything from day one; and resist the urge to over-plan features before you’ve nailed outcomes. Product-market fit lessons in AI often arrive via small, fast experiments — not grand, long-range plans. Ship thin slices that demonstrate unmistakable value, then iterate toward a system, not a single feature. When in doubt, shorten the loop and improve the evaluation harness.

    People often ask: Will AI replace video editors? My view is that AI will replace zero editors who master these tools — and many who don’t. The winners blend taste, storytelling, and generative leverage. The products we build should honor this reality: design for control, iteration, and co-creation, not just automation.

    If you’re mapping the progression of tech and use-cases, a few public references are instructive: Runway Gen-1 (https://research.runwayml.com/gen1) and Runway Gen-2 (https://research.runwayml.com/gen2) show how capability unlocks new workflows and demand. Runway’s 30 AI Magic Tools (https://runwayml.com/ai-magic-tools/) illustrates portfolio thinking — a suite of composable powers rather than a monolith.

    For builders focused on gen ai for product prototyping through production: keep your demo muscle strong, your evaluation stronger, and your outcomes strongest. Invest in community, treat safety as a feature, and let your OKRs steer what ships — not the other way around.


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  • Engineering Leadership That Scales: Strategy, Velocity, and Org Design from Carta, Stripe, Uber, Calm

    Engineering Leadership That Scales: Strategy, Velocity, and Org Design from Carta, Stripe, Uber, Calm

    I’m often asked how I translate lessons from hypergrowth engineering organizations into practical playbooks for product and platform teams. In this piece, I unpack the patterns I’ve seen repeatedly work—anchored by what I admire about Will Larson’s approaches at Carta, Calm, Stripe, and Uber—and how I apply them to build resilient, high-velocity orgs. Will Larson is a case study in modern engineering leadership. As CTO at Carta—an ownership and equity management platform—he helped guide the company after it raised at a $7.4b valuation in 2021. Before that, he was CTO at Calm, founded Stripe’s Foundation Engineering org, and led Uber’s Platform Engineering people and strategy. He’s also the author of Staff Engineer and An Elegant Puzzle, both essential reads for leaders leveling up from line management to org design. When I craft an engineering strategy, I start by writing down a small set of clear principles. This isn’t performative; it’s an alignment mechanism. Principles reduce decision thrash, make trade-offs explicit, and help teams navigate ambiguity without constant escalation. I’ve found the discipline of writing them down upfront pays off 10x in execution quality later. For the strategy document itself, I structure it so anyone can understand the why, what, and how in one sitting. A useful pattern: a sharp problem definition, a few guiding policies, and a concise set of coherent actions. That scaffolding keeps the strategy legible and actionable across functions—especially as it ladders into product roadmaps, platform investments, and talent plans. Every engineering strategy has two parts. First, compounding capabilities: the platform, tooling, and architecture that unlock future velocity. Second, targeted bets: focused initiatives that advance near-term outcomes. Neglect either and you either stall out later (too many quick wins, no compounding) or fail to ship value now (all compounding, no customer impact). Turning strategy into action requires ruthless translation. I map each guiding policy to a small number of initiatives with owners, milestones, and outcome metrics—not output. This is where outcomes vs output OKRs matter: measure the user or business result, not just the deliverable. It’s also where you surface dependencies early and avoid the Hidden Variable Problem that quietly derails timelines. I’m particularly intrigued by Carta’s unique “navigator” model, which blends technical leadership with cross-functional guidance to accelerate execution while preserving autonomy. In my experience, similar patterns work when leaders are explicitly accountable for both system health and product outcomes—reducing the gap between platform decisions and customer value. Engineering velocity is explainable, measurable, and optimizable. I anchor on DORA and the research from Accelerate (book), and I complement it with the SPACE (framework) to account for satisfaction and collaboration, not just delivery. The story I tell executives is simple: pick a few canonical measures, instrument them consistently, and then drive the feedback loops—branching strategy, CI/CD hygiene, change size, and operational excellence. Choosing the right metrics for an engineering org matters as much as the metrics themselves. I use a balanced set: delivery (lead time for changes, deployment frequency), quality (change failure rate, availability), and flow (work in progress, batch size). Then I pair these with narrative context so the numbers inform decisions rather than become a game to win. On policy, nuance beats orthodoxy. Great leaders define clear, default rules while acknowledging real-world exceptions. I’ve learned to document the policy, define who can grant exceptions, and track exception volume to spot design flaws. The goal isn’t rigidity—it’s predictable operations with a safe on-ramp for edge cases. Micromanagement is a symptom, not a root cause. Telling someone “don’t micromanage” is often counterproductive. Instead, I focus on what’s missing—trust, clarity, or visibility. If leaders can see the plan, the risks, the checkpoints, and the demo cadence, they don’t need to hover. If they still do, fix incentives and accountability, not just behavior. I avoid management anti-patterns by watching for early signals: policies without principles, roadmaps without strategy, meetings without decisions, or dashboards without actions. The best engineering executives pair systems thinking with crisp communication. They’re close enough to the details to ask sharp questions, yet disciplined enough to scale through managers and staff engineers. Executive communication is an asymmetric game. I tailor the message to the decision horizon: one slide for the ask, one for the trade-offs, one for the plan and risks. The Minto Pyramid (framework) helps—lead with the answer, then support it. In meetings, the fastest way to derail progress is to lack a clear owner, a time box, or pre-reads. Fix those and you reclaim hours every week. For presentation feedback, I’ve found a cadence that works: clarify the objective, highlight the single biggest risk, and eliminate anything that doesn’t move the decision forward. A bad sign with direct reports is when updates are status-only and insight-light; I coach toward “what changed, why it changed, and what you need.” For early-career engineers, the most durable advantage is compounding learning: pick hard problems, write more than you think you should, and seek out leaders who invest in your growth. For team development, I borrow a simple model: staff your keystones, instrument your systems, and build a culture where the best ideas win, not the loudest voices. If you want to explore the foundations behind these practices, start here. Accelerate (book): https://www.amazon.com/Accelerate-Software-Performing-Technology-Organizations/dp/1942788339 Good Strategy, Bad Strategy (book): https://www.amazon.com/Good-Strategy-Bad-Difference-Matters/dp/0307886239 DORA: https://dora.dev/ SPACE (framework): https://queue.acm.org/detail.cfm Minto Pyramid (framework): https://untools.co/minto-pyramid Carta: https://www.carta.com/ Calm: https://www.calm.com/ Stripe: https://www.stripe.com/ JavaScript: https://www.javascript.com/ KAFKA: https://kafka.apache.org/ Ruby on Rails: https://rubyonrails.org/ To go deeper on Will’s writing and perspective, these are great starting points. Twitter/X: https://twitter.com/lethain LinkedIn: https://www.linkedin.com/in/will-larson-a44b543/ Personal website/blog: https://lethain.com/ An Elegant Puzzle (book): https://www.amazon.com/Elegant-Puzzle-Systems-Engineering-Management/dp/1732265186 Staff Engineer (book): https://staffeng.com/book
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  • Inside Bard’s Playbook: How to Ship AI Fast, Build Ethically, and Outlearn Competitors

    Inside Bard’s Playbook: How to Ship AI Fast, Build Ethically, and Outlearn Competitors

    I spend a lot of time helping teams reconcile two pressures that define modern product management: ship fast enough to learn and compete, but slow enough to be safe, ethical, and useful. Studying Bard offers a crisp blueprint for navigating that tension and leveling up how we build with Generative AI. Jack Krawczyk is a Senior Director of Product at Google, building Bard. Bard is Google’s collaborative, conversational, and experimental AI tool that’s bridging the gap between humans and bots, while addressing ethical considerations around AI. After joining the project in 2020, Jack helped ship Bard in less than four years. Bard sources information directly from the web, and now enables users to inquire about and summarize YouTube videos. From a product management lens, the most valuable takeaway is the sequencing: problem definition → principled constraints → rapid public learning with clear guardrails. I’ve seen this order de-risk speed. When we anchor teams on a tight product thesis and ethical framework, we unlock faster iteration without drifting into feature theater. Shipping early—especially with a Large Language Model (LLM)—can feel risky. Yet the decision to open Bard to the public quickly reflects a disciplined bias toward learning velocity. In my experience, the longer we delay real-world feedback with LLMs, the more our internal assumptions calcify. Early exposure surfaces edge cases, calibrates safety systems, and drives better prioritization than any lab-only evaluation can. Ethics in AI is not a separate workstream; it’s a product requirement. I anchor cross-functional reviews on harm modeling, transparency, and user agency. Bard’s framing makes this explicit: collaborative, conversational, experimental—language that signals co-creation and responsible exploration rather than unfettered automation. That positioning matters for trust and sets expectations for both quality and limitations. Differentiation in AI assistants increasingly hinges on live context and modality. Bard sources information directly from the web, and now enables users to inquire about and summarize YouTube videos. In practice, this moves Bard beyond static Q&A toward dynamic sensemaking. I advise teams to ask: what fresh, authoritative context can our system responsibly ingest to reduce hallucinations and increase actionability? On development speed, I look for a culture that marries ambition with measurable risk reduction. That means small, end-to-end vertical slices; evaluation harnesses aligned to user outcomes, not model vanity metrics; and weekly red-teaming that actually changes the roadmap. Outcomes vs output OKRs are critical here—optimize for quality-adjusted learning per unit time, not just feature count. Early user research should be embedded, not episodic. I’m a proponent of forward deployed engineers paired with product and research to observe failure modes in the wild and close the loop quickly. With LLM-based experiences, qualitative signals (confusion, trust breaks, cognitive load) often precede quantitative ones; instrument both and let them inform each other. Deciding when to ship comes down to clear thresholds. I pressure-test launch criteria with two prompts: what would change my mind tomorrow, and what could break if we’re right but too early? For AI features, I also require recovery paths—explanations, undo, source attribution—so that small misses don’t become trust-ending moments. As for the competitive landscape—Bard versus ChatGPT, and others—users ultimately reward utility, reliability, and workflow fit. I encourage teams to pick a sharp use case, lean into their unique distribution or data advantage, and prove value in minutes, not weeks. “Generative AI” is table stakes; reliable outcomes in a real job-to-be-done is differentiation. Zooming out, I see three fronts shaping the future of LLM, Generative AI, and AGI: model capability, grounding and retrieval quality, and product ergonomics. Most teams overinvest in capability and underinvest in grounding and UX. The fastest wins often come from better retrieval, tighter prompts, and clearer affordances—not just a larger model. For aspiring AI developers, start narrow and instrument deeply. Pick a workflow with painful status quo, ship a thin slice, measure correctness and confidence, and iterate with real users. For non-LLM companies, the mandate is different: augment your core product where AI reduces friction or unlocks frequency—don’t bolt on a chatbot because everyone else did. For product leaders, AI changes the craft in two ways. First, prototyping is faster—use this to expand the option space early. Second, evaluation requires new muscles—build an experimentation and safety stack that blends qualitative red-teaming with quantitative reliability and cost controls. The leaders who thrive will combine taste with statistical rigor. If you want to go deeper, these references are useful: Bard: https://bard.google.com/; ChatGPT: https://chat.openai.com/; Duet AI: https://cloud.google.com/duet-ai; Free courses on machine learning by Andrew Ng: https://www.andrewng.org/courses/; Google Assistant: https://assistant.google.com/; Introducing Google Assistant to Bard: https://blog.google/products/assistant/google-assistant-bard-generative-ai/; Large Language Model (LLM): https://en.wikipedia.org/wiki/Large_language_model; Meena: https://blog.research.google/2020/01/towards-conversational-agent-that-can.html. In sum, the Bard blueprint reinforces a simple truth: ship with a thesis, learn in public with care, and let principled constraints accelerate—not slow—your path to product-market fit. That’s how we create value fast, build ethically, and stay ahead in the next era of AI.
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  • Master Modern Entrepreneurship: Build Lean, Start Young, and Obsess Over Customers

    Master Modern Entrepreneurship: Build Lean, Start Young, and Obsess Over Customers

    Modern entrepreneurship demands speed, clarity, and relentless customer focus. In my role leading product management and shipping category-defining features, I’ve learned that the fastest way to build enduring companies is to build lean, start young in our experiments, and study customers with scientific rigor. This is not about heroics; it’s about disciplined learning and making the market the ultimate arbiter of truth.

    The foundational playbooks still guide my day-to-day: the Lean Startup approach and the timeless lessons from The Four Steps to the Epiphany and The Startup Owner’s Manual. Even in 2025, these ideas remain remarkably relevant because they center on one principle we can’t automate away—deep, direct customer understanding.

    Why aren’t there more successful startups? Most teams conflate building with learning. They fall in love with solutions, optimize for output over outcomes, and skip the uncomfortable parts of customer discovery. Another pattern I see: teams ignore market type. The tactics for entering an existing market versus creating a new one are fundamentally different; using the wrong go-to-market playbook can erase months of runway.

    Improving entrepreneurship in the USA starts with how we teach it. We should normalize hypothesis-driven product discovery in high schools and universities, pair students with real customers, and fund lightweight experiments instead of polished business plans. Programs modeled after The lean launchpad at Stanford demonstrate that when we combine mentorship, evidence, and speed, we create founders who learn faster than the market changes.

    Lean Startup is also widely misunderstood. An MVP is not an excuse for low quality; it’s a vehicle for validated learning. The goal is to reduce uncertainty—not craftsmanship. The best teams run a cadence of testable hypotheses, instrument the product to capture evidence, and tie their roadmap to outcomes vs output OKRs so effort maps directly to measurable customer and business value.

    Curiosity is the meta-skill. The founders who win are addicted to understanding “why” customers behave the way they do. Instincts matter, but instincts sharpen with reps. I treat instincts as hypotheses: hold them lightly, test them aggressively, and let the data upgrade your intuition.

    Outlier founders often share similar traits: an early comfort with ambiguity, an almost irrational attachment to a future state, and a bias for action. That “irrational” conviction is a feature, not a bug—so long as it’s paired with a willingness to invalidate one’s own beliefs when the evidence contradicts them.

    Becoming a great founder CEO requires a personal pivot from maker to multiplier. Early on, be the chief learner and chief seller. As traction builds, invest in systems—hiring bar, decision frameworks, and operating rhythms—that scale beyond your own heroics. I’ve found that clear product strategy, crisp decision rights, and outcomes vs output OKRs create the scaffolding for autonomy without chaos.

    Why do some second-time founders fail? They overfit to their previous win, underestimate how much luck and timing played a role, or import a playbook that doesn’t match the new market type. The antidote is humility and fresh customer discovery—treat your new company like your first, and earn product-market fit again.

    Building in existing versus new markets demands different muscles. In an existing market, your edge is focus, speed, and a sharp wedge that exploits a neglected segment or workflow. In a new market, your job is category education, sequencing use cases to reduce friction, and architecting distribution while the value narrative is still forming.

    When I evaluate what makes a startup successful, I look for a learning velocity advantage: a team that runs more meaningful experiments per unit time than peers, converts insights into product changes quickly, and compounds those lessons into differentiation. Execution quality matters, but the compounding engine is the ability to discover truth faster.

    On leadership, I often point to Satya Nadella’s transformation at Microsoft as a case study in rewriting culture through a growth mindset and customer-centric innovation. It’s a reminder that the “founder mentality” can be cultivated at scale when leaders change incentives, narratives, and mechanisms in concert.

    The Four Steps to the Epiphany in 2023 (and beyond) still hold: Customer Discovery, Customer Validation, Customer Creation, and Company Building. I treat them as a continuous loop rather than a one-time sequence. Discovery never stops; validation is ongoing; creation evolves with channels and pricing; company building is the operating system that sustains the pace of learning.

    If you’re building today, start smaller, learn faster, and get closer to the customer than your competition thinks is necessary. The compounding effect of disciplined product discovery, evidence-based roadmapping, and founder-led storytelling remains the closest thing we have to an unfair advantage.


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  • The New PLG Playbook: Avoid the Trap, Win Enterprise, and Break the $10B Ceiling

    The New PLG Playbook: Avoid the Trap, Win Enterprise, and Break the $10B Ceiling

    Product-led growth is powerful, but it can stall when teams mistake bottom-up activation for durable enterprise value. I’ve seen high-velocity self-serve engines hit a ceiling and wondered why the graphs flatten just shy of breakout scale. This is my updated playbook for avoiding the stall, winning enterprise adoption, and compounding into category leadership.

    At the core, the differences between PLG and enterprise companies come down to how rigorously we translate user value into company value. Bottom-up adoption, time-to-value, and collaboration loops are essential — but without verified ROI tied to executive priorities, the engine sputters. That’s the “PLG trap”: a product everybody loves but a business that struggles to justify top-down investment.

    I often use the contrast between user value versus company value to explain this gap. It’s why brilliant collaboration tools can go viral yet struggle to close budgeted deals. One stark lesson: “Dropbox had almost no company value.” When you dig into “Why Dropbox is worth $10b, not $50b,” you see the cost of not turning bottom-up enthusiasm into executive-aligned outcomes, integrations, and measurable impact on the business.

    Transitioning to enterprise can feel like building two companies at once — and in many ways, it is. You must preserve the product-led engine while layering enterprise-grade capabilities, packaging, and proof. Think of it as “The playbook for transitioning into enterprise”: enterprise security and admin, deployment support, integrations into systems of record, and verifiable ROI. On top of that, you need a narrative that elevates from individual productivity to organizational outcomes.

    That’s where the relationship between OKRs and executive champions becomes pivotal. Enterprise buyers don’t purchase features — they purchase progress against objectives. I align our value narrative directly to executive OKRs, then instrument the product and the sales process to quantify that movement. Executive champions emerge when we reliably help them hit their goals and make them look like heroes in their orgs.

    On message, I’ve found the most effective positioning is deceptively simple: “selling clarity.” When you help leaders reduce chaos, align teams, and ship outcomes with less friction, your product stops being a tool and starts being an operating system for work. The shift from activity to clarity is what elevates company value.

    “How and when to build an enterprise sales team” depends on traction signals. I look for repeatable proof of company value (multi-team adoption, enterprise-grade requirements appearing in deals, measurable impact on OKRs) before hiring a small, senior team of sellers, solutions engineers, and post-sales partners who can navigate complex orgs. Early on, I keep this team tight and highly analytical, obsessing over learning velocity rather than coverage.

    To accelerate pipeline and expansion, I map power and influence using the champion tree framework. This forces clarity on the relationships between users, managers, and executive sponsors — and how value flows across that network. In parallel, a unique customer success team focused on activation, habit-building, and executive reporting turns early landings into durable, budgeted expansions.

    For GTM, I operationalize the seed, land, and expand framework. Seed with irresistible self-serve value; land with a clear enterprise package that meets security, governance, and integration needs; expand with measurable outcomes, executive dashboards, and a cadence that ties adoption to business results. This is how you respect the PLG motion while building enterprise muscles.

    There are strategies PLG companies should avoid. The most common mistakes include building horizontally too early, optimizing solely for signups instead of revenue-qualified usage, and treating enterprise as just another pricing tier. “Building horizontally may be a mistake” if it dilutes the wedge that wins executive mindshare. Depth beats breadth until the wedge reliably translates into company value.

    So, “How PLG companies can break $10 billion market cap”? Earn the right to expand. Nail one or two high-value enterprise use cases, prove ROI against OKRs, and turn that proof into a repeatable playbook across segments and industries. Then widen the surface area — compliance, data controls, workflow integration, cross-functional analytics — and sequence multi-product expansion as an outcome of credibility, not a substitute for it.

    Finally, “Why it’s difficult to emulate Atlassian, Slack or Salesforce.” Their eras, audiences, categories, and ecosystems were uniquely favorable to their playbooks. Emulation without context leads to cargo cults. Instead, adopt the principles: reduce time-to-value, monetize company value, build ecosystems intentionally, and design your GTM so self-serve fuels — not fights — the enterprise motion.

    If you’re a founder or product leader, my guidance is simple: define the smallest wedge that connects user value to executive value; measure it obsessively; and build a lightweight, senior enterprise motion the moment you see repeatability. From there, keep “selling clarity,” nurture executive champions, and let seed, land, and expand do the compounding.

    Referenced resources:

    Airtable: https://www.airtable.com/

    Asana: https://asana.com/

    Atlassian: https://www.atlassian.com/

    Bitbucket: https://bitbucket.org/product/

    Confluent: https://www.confluent.io/

    Daniel Shapero: https://www.linkedin.com/in/dshapero/

    Datadog: https://www.datadoghq.com/

    Dennis Woodside: https://www.linkedin.com/in/dennis-woodside-341302/

    Dropbox: https://www.dropbox.com/

    Dustin Moskovitz: https://www.linkedin.com/in/dmoskov/

    Jay Simons: https://www.linkedin.com/in/jaysimons/

    Jira: https://www.atlassian.com/software/jira

    Justin Rosenstein: https://www.linkedin.com/in/justinrosenstein/

    Kim Scott: https://www.linkedin.com/in/kimm4/

    Salesforce: https://www.salesforce.com/

    Slack: https://slack.com/

    The PLG Trap: https://www.linkedin.com/pulse/plg-trap-oliver-jay/

    The seed, land, and expand framework: https://www.endgame.io/blog/seed-land-expand-framework

    Zendesk: https://www.zendesk.com/


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