Tag: product discovery

Go Totally Asynchronous: Inside Sidharth Kakkar’s Remote, Autonomous Culture That Scales

I’ve spent years leading product teams across global B2B SaaS, and few topics spark more debate than building a remote team that operates totally asynchronously. Recently, I revisited the playbook of Sidharth Kakkar, founder and CEO of Subscript, a subscription intelligence platform that empowers B2B SaaS leaders to better understand their revenue. (Read more about the company in this Techcrunch article.)

Previously, he was the founder, CEO of Freckle, an education platform that grew to serve 10 million students and was acquired by Renaissance Learning in 2019. As a repeat founder, Sidharth picked up a ton of valuable lessons, particularly when it comes to company culture and management.

Right from the start, he knew he wanted to build Subscript to be global, distributed, and asynchronous. That’s why there are no internal company meetings. Everyone also operates autonomously, deciding what to work on for themselves.

In this analysis, I dive into both the philosophy behind this unique approach and the nitty gritty details of how exactly it works in practice. I’ll unpack how to share company updates asynchronously every week; advice on how to approach goal-setting and performance feedback while minimizing micromanagement; tips for improving transparency and documentation, plus details on Subscript’s running product/market fit journal; thoughts on how to assess asynchronous communication skills when hiring; and how this culture impacts a founder’s role and schedule.

On weekly updates, asynchronous communication shines when it is consistent, structured, and outcomes-focused. I recommend a lightweight cadence that combines a written executive summary, links to artifacts (roadmaps, PRDs, dashboards), and optional short Looms for rich context. Tie each update to outcomes vs output OKRs so teams self-calibrate without meetings, and make updates searchable so new hires can ramp themselves with a clear trail of decisions and tradeoffs.

For goal-setting and performance feedback, autonomy and clarity must coexist. Define clear success metrics upfront, timebox discovery, and use product roadmapping and sprint planning rituals that emphasize measurable customer outcomes over task completion. Replace micromanagement with transparent expectation docs, written performance narratives, and asynchronous 360 feedback—so individuals know what good looks like and can course-correct without waiting for a meeting.

Transparency and documentation are the backbone of a remote, autonomous culture. Centralize decisions in a single source of truth, standardize decision records, and maintain a living discovery log alongside Subscript’s running product/market fit journal. This practice compresses feedback loops, preserves institutional memory, and accelerates product discovery by making assumptions, experiments, and results easy to consume across time zones.

When hiring, I prioritize asynchronous communication skills as first-class selection criteria. Use written work samples, time-boxed take-home prompts, and collaborative docs to evaluate clarity, rigor, and empathy in writing. Look for signal such as strong structuring, crisp problem statements, thoughtful tradeoffs, and proactive documentation of risks and unknowns—capabilities that predict success on distributed teams.

This culture fundamentally reshapes a founder’s role and schedule. With deep work protected and status noise automated, leaders can spend more time on strategy, customer conversations, and coaching. Decision latency drops because context is captured in writing, and the organization scales through principles rather than approvals—exactly what you want in a high-trust, high-leverage product operating system.

There’s tons of food for thought in here, whether you’re a founder thinking about shaping your company culture, or a manager looking for some fresh ideas.

Inspired by this post on First Round.

October 21, 2025
Operations vs Algorithms: How I Scale Startups with Data Science, Team Design, and Pre-Mortems

I recently revisited one of the most practical conversations I’ve had about scaling: insights from Ian Wong, co-founder and CTO of Opendoor. Before founding Opendoor, Ian was Square’s first data scientist, where he developed machine learning models and infrastructure for fraud detection. That trajectory—building from operational muscle to algorithmic advantage—maps closely to how I’ve led product and data investments in fast-growing environments.

As Ian puts it, in the early innings it might make sense for your startup to be operations heavy. But as you start to scale, data science becomes a critical component for running a business with longevity in mind. This mirrors my experience: hands-on operations create the learning loops you need early, while data science turns those learnings into scalable systems, better forecasts, and tighter risk controls.

We dive into how both Square and Opendoor approached this transition. The progression is instructive—start with pragmatic operational workflows to validate demand and unit economics, then shift toward machine learning and automation once the process is stable, the data pipeline is trustworthy, and the cost of manual work begins to drag margins and responsiveness.

Along those lines, we discuss some of the early considerations for your fledgling data science team, including the type of folks to hire for the early team, like whether to look for generalists or specialists, and how to set up your interview loops. In my playbook, I bias toward T-shaped generalists first—people who can partner with product, analytics, and engineering—then layer in specialists (ML ops, causal inference, experimentation) as complexity grows. For interview loops, I ensure we evaluate for problem framing, data intuition, model-to-product translation, and stakeholder communication—not just model accuracy.

Ian also dives into his lessons on structuring the data science function so that it’s deeply integrated with the rest of the technical org. I’ve found embedded pods work best early—data scientists sit with product teams to accelerate discovery, instrumentation, and iteration—paired with a light central platform group to standardize data quality, experimentation frameworks, and model deployment practices.

Next, we dive into some of his biggest lessons as a first-time founder and CTO, including his practice with Opendoor’s leadership team of doing pre-mortems to predict why something might not work. He also encourages founders to run through a bi-yearly exercise of re-writing their job rec. I’ve seen both rituals raise the bar: pre-mortems surface hidden risks before launch, and re-writing the job rec forces leaders to shed responsibilities, prevent role drift, and keep the org structured for the next stage—not the last one.

Practically, my guidance to founders and product leaders is straightforward: define the decisions data science will improve (pricing, risk, routing, personalization), instrument for leading indicators, and ruthlessly prioritize the smallest models that deliver outsized business value. Avoid premature optimization—let operations teach you where the algorithm belongs. Use clear success metrics to track outcomes, not just output, and revisit them as your market and product expand.

Finally, remember that the goal isn’t to replace operations—it’s to make operations smarter. Pair human judgment with machine learning in the workflows that matter most, invest in trustworthy data foundations, and build hiring and interview loops that reward interdisciplinary problem solvers. That’s how you turn early operational grit into durable, data-driven advantage.

October 21, 2025
Airtable’s Journey to Product-Market Fit: Hard-Won Lessons on Building Horizontal Products

I recently dove deep with Andrew Ofstad, co-founder of Airtable, to unpack Airtable’s path to product-market fit and the realities of building a truly horizontal product. As a product leader, I’m always looking for patterns that translate across teams and stages — and this conversation offered a wealth of practical insights for founders and product managers alike.
We started with first principles: how the founders came together, their vision for the product, and what the initial prototypes looked like. That early narrative matters — it anchors positioning, keeps scope disciplined, and ensures your earliest builds reflect the core value proposition rather than a laundry list of features.
From there, we stepped through Airtable’s alpha, beta, and launch timelines, as well as their early traction. I pay close attention to these milestones because they reveal the learning cadence: how quickly teams validated hypotheses, which signals they treated as leading indicators, and how they balanced qualitative pull with quantitative adoption during product discovery.
We also explored the challenges of creating a horizontal product that can do many things, including identifying initial use cases and figuring out how to describe what they were building. In my experience, the key is to land a small set of canonical use cases, articulate crisp jobs-to-be-done, and build narrative clarity around the “why now.” Without this, even great products struggle to convert curiosity into active usage.
On commercialization, we dug into how to approach pricing and competition, as well as their early go-to-market strategy. For products with broad applicability, I’ve found that founder-led GTM is essential early on — it shortens the learning loop and informs SaaS pricing and packaging. Start with simple, customer-aligned tiers, validate willingness to pay against clear outcomes, and keep positioning focused on value, not feature parity.
We then looked ahead to what the next 3 years will look like for Airtable, and how they’ve navigated scaling while staying true to their vision. Scaling a horizontal product requires tight product roadmapping and sprint planning, strong messaging discipline, and an unwavering commitment to the core value — all while layering ecosystem leverage, templates, and community to accelerate adoption without diluting the product’s identity.
Whether you’re a founder validating your own idea, or a product leader looking for growth advice, there are tons of tactics here that go much deeper than the typical founding stories you hear. My takeaway: great horizontal products find product-market fit by starting narrow in use case, obsessing over teachability, and letting customer outcomes pull the roadmap forward.

Inspired by this post on First Round.

October 21, 2025
Inside Figma’s 5-Phase Community-Led Growth Playbook to Ignite Organic GTM

When I study companies that turn product-market fit into durable, compounding momentum, Figma stands out. In this breakdown, I walk through Figma’s five phases of community-led growth and share how I’d apply each step to build an organic growth engine. My lens is product management leadership paired with pragmatic GTM thinking, so the emphasis is on what to do, when to do it, and why it works.

Phase 1 centers on the lessons from years in stealth mode — specifically, how to start planting the seeds for a community when you don’t have a fully-formed product. I focus on the inflection point to emerge from stealth, what signals matter, and how to use early feedback loops for product discovery without over-rotating on feature requests. Quietly building is necessary; quietly engaging is essential.

In this early phase, my playbook prioritizes rigorous product discovery, crisp problem narratives, and a cadence that makes learning visible. I invest in relationship-building with credible early adopters, share prototypes thoughtfully, and shape outcomes over output through disciplined product roadmapping and sprint planning. The goal is to design a community that feels invited into the creative process while preserving the integrity of your vision.

Phase 2 is all about the launch playbook — from taking over design Twitter, to marketing to folks who tend to bristle at traditional SaaS marketing. Here, founder-led GTM matters. I pair zero to one B2B marketing with specific audience rituals, leaning into channels where the product can be experienced, not just described. The message avoids enterprise jargon and instead showcases feel, speed, and collaboration, so the community can instantly “get it.”

Phase 3 shifts the focus to activation via community gravity. The goal is to get folks to try the product, even if they weren’t going to switch over right away to designing in Figma full-time. This is where I formalize an evangelist strategy — spotlighting workflows, templates, and stories that reduce the cost of the first session. I think about developer evangelism patterns applied to designers: celebrate makers, distribute small wins widely, and build momentum with authentic, peer-to-peer proof.

The final two phases connect passionate individual users to an enterprise strategy. They didn’t layer in a sales team until four years after the product launched, and didn’t add a paid product tier until another two years after that. Those choices underscore a disciplined sequencing of PLG with a sales overlay — land with love, expand with proof, monetize with timing.

In practice, this means evolving from bottoms-up adoption to clear value narratives for team and enterprise tiers, while tuning SaaS pricing and packaging to customer maturity. I optimize the handoff from product-led signals to sales motions, align success metrics to outcomes (not just usage), and enable procurement-friendly paths without dulling what made the product magical. That’s the GTM trade-off: protect the community engine while scaling into enterprise.

If you’re building your own community-led growth engine, these phases offer a durable blueprint: learn in stealth, launch where your audience lives, activate with authentic evangelism, and scale with a thoughtful enterprise bridge. Done well, this approach compounds — it strengthens product-market fit, accelerates zero to one B2B marketing, and sets the stage for sustainable growth without overreliance on paid channels.

October 21, 2025
From Roadmaps to Sprints: Proven Tactics to Ship Software at Scale Without Chaos

I recently sat down with Snir Kodesh, Head of Engineering at Retool, a development platform for building custom business tools. Before joining Retool, he spent six years as a Senior Director of Engineering at Lyft. Coming from my vantage point leading product at HighLevel, I was eager to compare notes on what it really takes to ship software at scale without losing clarity, customer focus, or team morale.

We dug into the biggest differences between leading engineering teams for a consumer product versus an enterprise platform — and the patterns that hold true across both. Consumer surfaces demand rapid iteration loops and relentless UX polish; enterprise platforms demand configurability, security, reliability, and stakeholder alignment across buyers and users. In my experience, the constant across both worlds is crisp product management leadership: clear problem definition, tight feedback loops, and unambiguous ownership.

We pulled back the curtain on the software development cycle, starting with setting the product roadmap while balancing a diverse set of customer needs. On roadmapping, I ensure we explicitly identify who’s in the room to represent product, engineering and design, as well as customer-facing teams like support and solutions. The most effective sessions make trade-offs visible: we quantify impact, risk, and effort; we surface dependencies; and we align on outcomes before timelines. The result is not just a list of features, but a sequenced narrative that earns the right to build.

From there, we discussed how engineering takes that product roadmap and turns it into a concrete plan of action using the “try, do, consider” framework. I’ve found this framing incredibly practical: “try” creates space for low-risk experiments, “do” commits to high-confidence work, and “consider” tracks explorations that need more discovery. When sprint planning inherits this taxonomy, teams retain momentum without overcommitting — and leaders get a transparent view into where learning versus delivery is happening.

He makes the case for leaning on QBRs instead of OKRs. I agree that quarterly business reviews calibrate teams on real outcomes, not vanity metrics, and they naturally force prioritization around customer value. When we do use OKRs, we emphasize outcomes vs output OKRs so teams aren’t incentivized to ship volume over impact. In practice, QBRs keep us honest: what shipped, what moved the needle, and what needs to change next quarter.

We also tackled why scope creep gets a bad rap. In my experience, what’s labeled as “scope creep” is often legitimate learning uncovered through product discovery. The key is disciplined change management: time-box discovery, explicitly re-baseline when new information emerges, and separate must-haves from nice-to-haves. When done well, this turns surprises into strategic clarity rather than delivery risk.

On estimation, we shared practical tactics for getting better at estimating how long a feature will actually take to complete. I lean on reference-class forecasting (compare to similar past work), risk burndown charts, explicit buffers for integration and QA, and a habit of capturing deltas between estimate and actuals. Over time, this creates a trustworthy velocity signal and sharpens intuition across both product and engineering.

Translating the roadmap to sprint planning is where execution quality shows. We align on definitions of ready and done, maintain code review SLAs, budget a percentage for tech debt, and instrument everything so we can spot drift early. The “try, do, consider” framework maps cleanly to backlog hygiene, keeping discovery visible without derailing delivery. This is how we sustain speed and quality at scale.

Finally, we zoomed out to essential advice for engineering leaders — especially folks scaling quickly from leading a small team to a much bigger one. Shift from direct control to leverage: clarify decision rights, invest in Staff+ ICs, and scale communication through operating cadences, decision logs, and crisp narratives. Pair autonomy with accountability using QBRs, and keep product discovery tight to preserve customer empathy as you add layers. The goal is the same at ten people or a thousand: ship valuable software predictably, learn fast, and keep the team energized.

If you’re navigating the leap from product roadmapping to sprint planning, these patterns are battle-tested. Anchor on outcomes, use the “try, do, consider” lens to manage ambiguity, and treat scope as a living artifact informed by discovery. With the right rituals and metrics, you can ship software at scale — without chaos.

October 21, 2025
Scaling Your Co-Founder Relationship: Rituals, Decision Rights, and Trust Lessons from Labelbox

I’ve learned that a startup’s trajectory often mirrors the strength of its co-founder relationship. With that lens, I sat down to unpack how to scale trust, decision-making, and speed between co-founders as the company itself scales. Our guests are Manu Sharma and Brian Rieger, co-founders of Labelbox. In this interview, I take a microscope to their co-founder DNA, exploring the ins and outs of how they’ve made the relationship work over the years. We traced how Manu and Brian came together as co-founders and landed on the idea for Labelbox. That origin story matters: it reveals the early signals of shared conviction, complementary experience, and a clear problem thesis—foundations I look for when evaluating co-founder fit in any startup. Before writing a line of code, they intentionally aligned their skillsets, values and responsibilities. I emphasize this step in my own product management leadership practice: make the invisible explicit. Define roles, boundaries, and how decisions get made while the stakes are low, so you can move fast when the stakes are high. As the company grows, their rituals for spending valuable time together keep the relationship durable. They use thought-starter questions for deep discussions and even benefit from sharing an executive coach. I’ve seen this playbook consistently reduce friction: structured prompts surface misalignments early, while an external coach creates a safe container to reset, recommit, and keep momentum. We also dug into how they run the executive team at scale and sketch out decision rights. Clear decision rights accelerate execution, prevent rework, and protect the co-founders’ relationship from getting pulled into every operational knot. In my experience, codifying who decides, who contributes, and how dissent is handled is one of the fastest ways to boost operating cadence without sacrificing trust. Manu and Brian both offer practical advice to other founders—whether you’re in the early stages of looking for a co-founder or aiming to add a little magic to an existing partnership. If you’re building, apply their lessons: align on values early, institutionalize rituals, use an executive coach, and be explicit about decision rights. These are the simple, scalable mechanisms that preserve focus, speed, and resilience as you pursue product-market fit and scale your executive team. You can follow Manu at @manuaero and Brian at @RiegerB on Twitter.

October 21, 2025
Rethinking Quitting: Annie Duke’s Decision Science for Smarter Product Strategy

I recently sat down with Annie Duke, a retired pro poker player and First Round’s Special Partner focused on Decision Science. She’s also the author of the bestselling book, “Thinking in Bets.” Her latest work, “Quit: The Power of Knowing When to Walk Away,” offers a provocative lens on one of the most misunderstood skills in product management and startup leadership: the decision to stop.
In our world, quitting is often dismissed as failure. We celebrate grit, persistence, and the legendary founders who “just kept going.” But in my experience leading product strategy, I’ve learned that knowing when to walk away is frequently the hallmark of great judgment. Annie’s perspective reframes quitting as a strategic move that preserves focus, accelerates learning, and ultimately improves outcomes.
What struck me most is how our psychology conspires against sound walk-away decisions. We’re vulnerable to sunk cost fallacy, status quo bias, and identity-driven attachment to our bets. As product teams, that shows up as clinging to features that don’t move the needle, extending pilots that never convert, or chasing a go-to-market motion that looked good in a deck but stalls in reality.
“Quit: The Power of Knowing When to Walk Away” argues that quitting, done right, is not capitulation—it’s optimization. I see that play out in product discovery and portfolio management. The teams that outperform define clear kill criteria up front, tie decisions to leading indicators rather than vanity metrics, and treat each initiative as a reversible bet until product-market fit evidence says otherwise.
Practically, I’ve found a few tactics echo Annie’s approach: set timeboxed experiments with explicit stop-loss rules; pre-commit to thresholds that trigger a review; and separate decision rights so the person advocating for a project isn’t the sole decider on its continuation. Decision logs, red-team reviews, and base-rate comparisons help strip away narrative bias and keep us grounded in outcomes vs output OKRs.
Annie also offers guidance for advice-givers—those moments when you need to nudge a teammate or founder to change course. Lead with curiosity, not combat. Ask, “If we were starting from zero today, would we choose this strategy?” Frame quitting as a path to the original goal, not a retreat from it. And when necessary, be gentle, yet firm—clarity is kind.
For product leaders, the real unlock is weaving quitting into the operating system. Stage funding for initiatives, escalate the evidence bar as you invest more, and normalize regular “stop, pivot, or double-down” checkpoints. This keeps resources flowing to the highest-leverage bets and reduces the emotional tax on hard calls.
If you’re rethinking your roadmap, pipeline, or GTM motions, this is the moment to institutionalize better walk-away decisions. Align quit criteria to business outcomes, define decision cadence, and coach teams to see quitting as learning in action. It’s how you create velocity toward product-market fit while minimizing opportunity cost.
If this resonates, add “Quit: The Power of Knowing When to Walk Away” to your reading list. And if you want to keep up with the ideas behind decision science and strategic quitting, you can follow Annie on Twitter at @AnnieDuke.
Quitting isn’t the opposite of perseverance—it’s how we preserve our best bets. When we treat walking away as a disciplined product management capability, we build stronger strategies, ship better products, and protect the most precious asset we have: time.

Inspired by this post on First Round.

October 21, 2025
Finding Product-Market Fit Twice: Alma’s bold pivot and tactics to stay close to customers

Finding product-market fit is hard enough once; doing it twice is the rare pressure test that separates resilient product teams from the rest. As a product leader, I’m obsessed with how teams navigate pivots without losing sight of their users — and Alma’s journey is a masterclass in staying customer-obsessed while scaling.

In a recent conversation with Harry Ritter, I dug into how disciplined product discovery, clear decision-making, and founder-led GTM can accelerate learning cycles. Harry Ritter, founder of Alma, a membership-based network that helps independent mental health care providers accept insurance and build thriving private practices.

What stands out to me is that the Alma team essentially had to find product-market fit twice as they went from physical, co-working office spaces pre-pandemic, to quickly building out their virtual care capabilities. That’s an enormous shift in operating model, unit economics, provider experience, and buyer expectations — yet the throughline was a relentless focus on customer needs and outcomes.

Team building matters even more during moments like these. Approaching team building as a solo founder means deliberately constructing complementary skills around the founder’s strengths and gaps. I look for three early anchors: an execution-first product partner, a zero-to-one GTM lead who can validate demand with founder energy, and a metrics-driven operator to keep the system healthy as complexity scales. Values, operating cadence, and decision rights must be explicit, or a pivot will expose the cracks.

Refining the idea and getting more insights from your customers through structured interviews, using the technique doctors are trained on is a powerful way to de-bias signal. I’ve used this clinical-style approach — open-ended history, precise symptom probing, differential hypotheses, and a closing summary for confirmation — to improve customer interview quality. It transforms conversations from anecdote collection into consistent, comparable product data you can act on.

When the ground shifts, rallying your team through a pivot depends on clarity and cadence. I anchor on a simple narrative: why the environment changed, what we’ve learned from customers, where we’re going, and how we’ll measure progress. Staying competitor aware — not competitor obsessed keeps speed and focus on the user, while avoiding reactive roadmaps. And it’s crucial to internalize The difference between building a marketplace versus a platform. Marketplaces require liquidity, trust, and incentive design; platforms demand extensibility, APIs, and ecosystem health. Confusing the two leads to misaligned KPIs and misallocated resources.

My practical playbook for a “second PMF” includes: weekly founder-led customer calls until patterns converge; a structured interview template and central repository; a pivot memo with explicit kill criteria and success metrics; a liquidity or engagement model depending on whether you’re a marketplace or platform; and a tight KPI set (retention, activation, and quality-of-service indicators) to validate product-market fit beyond growth vanity metrics.

Whether you’re in the early stages of starting a company or going through a tough pivot, there are tons of helpful tactics here. The Alma story reinforces a timeless truth: proximity to customers is the ultimate unfair advantage — in discovery, in execution, and especially in moments of change.

You can follow Harry on Twitter at @harryritter1.

Inspired by this post on First Round.

October 21, 2025
How Retool Hit $2M ARR Pre‑Launch: My Playbook on Developer Focus, Product‑Market Fit, and GTM

I spend my days building and scaling B2B products, and Retool’s journey to $2M in ARR before launch is a masterclass in focus. It’s also a case study I revisit when coaching teams on developer evangelism and founder-led GTM.

Listening to David Hsu recount the early decisions made the strategy crisp: stay laser‑focused on developers, remove the boilerplate of internal tools, and earn trust with speed.

Retool, a low-code platform for developers building custom internal tools.

Today, Retool is valued at over $3 billion and has some of the biggest companies in the world building apps on its platform.

Early on, plenty of smart folks thought the idea for Retool would fail and that the product’s developer focus would sink the company. I’ve heard variations of this skepticism whenever a team doubles down on a specific persona—especially developers.

What struck me is the clarity around the target customer and the discipline to pursue language-market fit. When you get the words right for developers—their jobs-to-be-done, primitives, and constraints—you lower friction across product discovery, onboarding, and activation.

Equally instructive is how Retool nabbed its earliest customers (which includes Brex, DoorDash and a Fortune 500 BigCo) and the way the team prioritized creating incredibly tight feedback cycles with these early evangelists. That’s founder-led GTM at its best: sit with users, ship fast, instrument everything, and turn customer conversations into a roadmap.

On the surface, Retool’s path to product-market fit seems incredibly smooth. But as David tells it, there were plenty of bumps in the road — and he’s got tons of advice for early-stage founders that are finding their footing. I’ve lived those bumps, too; they’re signals to tighten the loop, not reasons to pivot away from your core user.

My takeaways for product leaders: start with developer empathy, not feature breadth. Use founder bandwidth to run high-frequency user sessions, shadow internal tool builds, and test copy until you hit language-market fit. Treat docs, templates, and examples as part of the product; they often outperform UI tweaks for time-to-value.

Operationally, stand up a lightweight, metrics-driven pipeline that connects discovery to delivery. I like a weekly cadence that pairs qualitative insights with activation, time-to-first-value, and expansion signals—classic product-market fit lessons that prevent local optimizations. When you see pull, lean into developer evangelism and zero to one B2B marketing, not paid acquisition.

If I were replicating this playbook today, I’d deploy a small, forward-deployed team to embed with design partners, capture real workflows, and ship improvements daily. Pair that with clear outcomes vs output OKRs so the team optimizes for customer outcomes, not just shipping velocity. That’s how you earn trust with developers and translate it into durable ARR.

Retool’s story reinforces a principle I teach often: conviction in the right user beats broad appeal every time. Focus wins, feedback compounds, and the market rewards teams that can turn skepticism into traction—especially when the users are developers.

October 21, 2025
Defeat Inertia: A Product Leader’s Playbook to Lower Barriers and 10X Adoption

Change is the job. I build and sell products to reshape behaviors and markets, yet I’m constantly reminded that change is hard. Going back to chemistry, catalysts don’t just create change by pushing harder or exerting more energy — they remove or lower the barriers to change. That framing has reshaped how I approach product strategy, go-to-market, and adoption.

One lens I return to is “The Catalyst: How to Change Anyone’s Mind.” It underscores a simple truth: the obstacle isn’t always the idea; it’s the friction around it. The book outlines 5 specific barriers to change, called REDUCE — which stands for reactance, endowment, distance, uncertainty, and corroborating evidence. I’ve found that when we diagnose which barrier is in the way, our product and GTM decisions get sharper, faster, and far more effective.

Do you really need a 10X better product? Sometimes yes—but not always. In practice, the biggest competitor I face isn’t another vendor; it’s inertia. Prospects cling to the status quo because switching feels risky, expensive, or cognitively heavy. My job is to make staying put feel riskier than moving forward. That means de-risking the decision, shrinking the perceived switching cost, and removing “homeostasis hooks” like entrenched workflows and sunk costs. When I design onboarding, migration tooling, and progressive rollouts, adoption climbs—even when the product advantage is 2–3X, not 10X.

Urgency matters, but pressure backfires. I aim for urgency that respects autonomy. Instead of “buy now or else,” I show windows of compounding value—why acting this quarter creates momentum, unlocks ROI, or secures outcomes that are harder to capture later. Time-bound pilots, seasonal use cases, or milestone-based pricing can motivate action without triggering reactance. The goal is momentum, not manipulation.

Freemium isn’t just for software. I apply the same principle—reduce uncertainty and upfront commitment—to physical products and services through pilots, limited-scope deployments, try-before-you-buy programs, refundable deposits, warranties, and modular packaging. The point is to let customers experience value with minimal friction. If it lowers uncertainty and builds confidence, it belongs in your arsenal.

On pricing and negotiation, I default to clarity, not concessions. I anchor on measurable outcomes, map tiers to value ladders, and use give-get rules so price changes are tied to scope or risk, not arbitrary discounts. This avoids signaling lower quality and keeps identity intact—buyers want to feel like smart stewards, not bargain hunters. Framing around business impact (“Here’s the cost of staying put versus the ROI of switching”) consistently outperforms feature recitations.

Identity and category creation are powerful accelerants. When a prospect feels an offer threatens who they are—or what team norms demand—adoption stalls. I reframe the story so the decision aligns with their identity (“modern operator,” “data-driven leader”) and, when needed, I redefine the category to reduce comparison shopping. If you’re a new category, you’re not asking buyers to replace an incumbent—you’re inviting them to adopt a better lens. That shift diffuses reactance and opens the door to new budgets and metrics.

Corroborating evidence matters most when stakes are high. I equip champions with proof that speaks to their peers: credible case studies, ROI models, third-party benchmarks, and pragmatic references. Multiple independent signals—especially from customers “like them”—shorten the distance between interest and commitment. I’ve seen adoption jump when we pair a hands-on pilot with peer validation at each gate.

Here’s the throughline I use with teams: don’t push harder—remove friction. Diagnose which barrier in REDUCE is at play, then pick a targeted tactic: restore agency to counter reactance, offset endowment with easy reversibility and migration, bridge distance with progressive steps, shrink uncertainty with trials and proof, and stack corroborating evidence at the right moments. When we build products and GTM motions around lowering these barriers, we don’t just sell better—we make change feel inevitable.

Inspired by this post on First Round.

October 21, 2025
Inside X1’s Pivot: The Playbook Behind a 600K Waitlist and a $15 Million Raise

I gravitate toward consumer fintech stories that show how product-market fit is earned through conviction, narrative, and execution. In that spirit, I dug into the journey of Deepak Rao, co-founder and CEO of X1, a consumer fintech startup that’s building a credit card for a new generation. The arc of this build is a masterclass in pivoting under pressure and orchestrating a high-velocity launch without losing sight of fundamentals.

Just last week, X1 announced a $15 million funding round. To understand how they got here, I rewound the tape and focused on the pivotal decisions that transformed uncertainty into momentum.

First, the human side of the pivot: “The emotional journey of how the pandemic forced them to abandon the initial idea for a personal loan product.” I’ve been through moments like this, and the lesson is consistent — you can’t cling to sunk costs when the world changes. Teams need psychological safety to grieve the old plan, and leadership must reframe the mission quickly so energy channels into discovery, not denial.

Second, the demand-side proof: “How the team validated demand for the new idea by focusing on the launch announcement and getting all of the branding exactly right — before building anything.” This is a powerful consumer move. Before a single line of code defines the product, the market should be able to tell you — via signups and sentiment — whether your story resonates. In practice, that means sharpening positioning, crafting a memorable name and visual identity, and pressure-testing the value prop across landing pages, press briefs, and social. Treat the narrative as an MVP.

Third, the launch mechanics: “The launch strategy that crashed X1’s website and built up a 600K long waitlist.” When a launch overpowers your infrastructure, it’s both a win and a wake-up call. The playbook here blends scarcity (invites, phased access), social proof (credible press, testimonials), and a tight referral loop that rewards participation. In my experience, the secret is sequencing: tease, announce, then escalate with proof points — all while instrumenting analytics, scaling infrastructure, and staffing support for the surge.

Finally, the strategic lens: “Why finding product-market fit is different for consumer companies, plus advice on fundraising in tough times.” Consumer PMF hinges on emotion and habit, not just utility; you measure it through retention curves, organic growth engines (word of mouth, referrals), and depth of engagement. For fundraising in tough markets, the bar is higher: show authentic demand (waitlist-to-activation conversion), defensible economics (unit economics, risk controls), and a credible path to moats (brand, data, network effects). Narrative clarity matters — investors respond when your story, metrics, and roadmap triangulate.

Whether you’re in the early innings of starting a company, going through a tough pivot yourself, or planning out your product’s launch there are tons of helpful tactics here. My takeaway: lead with story, validate with signal, operationalize for scale, and keep the team emotionally aligned through the change. That’s how you turn a pivot into a product-market fit flywheel.

You can follow Deepak on Twitter at @drao1.

October 21, 2025
Build Culture Like a Product: Anna Binder’s Asana Playbook for High-Performing Teams

I’ve long believed that culture deserves the same rigor we bring to product management. That view crystallized in a recent deep dive with Anna Binder, Head of People at Asana, where we explored what it truly means to build culture like a product — with clear goals, tight feedback loops, and iterative learning.

We revisited the earliest days when she first took on the role, zeroing in on how she prioritized the initial things to tackle as a new People exec and combed through a slew of opinions that bubbled up from other folks at the company. What stood out to me is how much this mirrors product discovery: define the problem precisely, gather qualitative signals, and validate with small, high-leverage experiments before scaling.

Translating that into my own operating system, I treat cultural work like a roadmap. I write crisp problem statements, hypothesize the behavioral change we seek, run lightweight pilots, and measure adoption and sentiment. I anchor success on outcomes vs output OKRs so we avoid mistaking activity for impact. This mindset not only accelerates learning, it also builds trust because leaders can explain the why behind each cultural bet.

Anna shared her tactical playbook for creating a culture of feedback for not just low-performers, but high-performers, too. That nuance matters. High performers often get praise but little developmental tension; I’ve seen careers plateau when strengths go unsharpened. My practice: institutionalize upward feedback, time-box “bright spots and blind spots” in 1:1s, and ensure managers are trained to ask for evidence and examples, not just opinions. It’s an essential step in the IC to manager transition as well, where modeling curiosity sets the tone for the entire team.

She also unpacked her methodology of conscious leadership, and how the best leaders always interrogate how the opposite might be true. I’ve adopted that as a mental circuit breaker when I feel certain: I write the opposite hypothesis and list evidence for it. This habit reduces ego, surfaces hidden risks, and leads to more durable decisions — a hallmark of product management leadership.

From working on Asana’s executive team for nearly 7 years, Anna emphasized building habits that keep the exec team a healthy nucleus at the center of the company. I’ve seen the same: meeting hygiene (clear intents, pre-reads, decision logs), decision-making cadences that separate debate from decide, and transparent communication that closes loops with the broader org. Treating the exec group as a high-trust product squad prevents thrash and models the behaviors we want everywhere else.

We ended with a rapid-fire exchange that maps cleanly to everyday leadership. On onboarding: design a 30-60-90 plan with explicit outcomes, shadowing for context, and early relationship-building across functions. On all-hands meetings: prioritize clarity over spectacle, celebrate learning (not just wins), and reserve time for unscripted Q&A to keep the dialogue authentic. On mentors: build a personal board of advisors with complementary strengths — operators for execution, coaches for reflection, and domain experts for sharp edges.

If you’re looking to uplevel your culture, start small and think like a product creator: define outcomes, run thoughtful experiments, and iterate in the open. The compound interest from these practices shows up in engagement, execution velocity, and ultimately, sustainable performance.

Inspired by this post on First Round.

October 21, 2025