Tag: IC to manager transition

Master Startup Compensation: Proven Tactics for Offers, Equity, and Retention at Every Stage

Compensation is one of the most emotionally charged and strategically consequential decisions a startup makes. I recently dug deep into this topic with Kaitlyn Knopp, founder and CEO of Pequity, which automates HR workflows to make compensation more equitable and scalable. Her perspective resonated with my own experiences leading product teams, where pay clarity, fairness, and speed can make or break hiring and retention.

Prior to starting Pequity, Kaitlyn built compensation programs and teams at companies like Instacart, Cruise, and Google — bringing a deep well of experience to this often complicated topic. That breadth matters: startup compensation strategy must evolve as you move from zero to one, to scale, and then to sustained growth.

For founders making their first hires, I emphasize the same traps Kaitlyn flagged: ad hoc offers, one-off exceptions, and over-indexing on negotiation. Instead, I recommend a lightweight framework anchored in broad levels and an initial comp philosophy. This early scaffolding doesn’t need to be heavy or bureaucratic; it simply needs to be explicit enough to guide consistent decisions and communicate how salary, equity, and performance will work for the next 12–18 months.

On offers, I take a balanced view of negotiating. There are pros and cons to negotiating offers, and over-negotiation can quietly erode internal equity and manager confidence. Rather than endlessly debating base salary, I lean on creative approaches outside of salary — such as the exercise window — to tailor offers within guardrails. And because many candidates (especially those who’ve never worked at a startup before) struggle to value equity, I always include a simple equity one-pager: how vesting works, potential outcomes, and what risk actually means in practice.

As the company grows quickly, new challenges appear. Retaining existing employees requires intent, not improvisation. Equity refreshes are a powerful tool when tied to impact and market realities. I also pay close attention to the psychology of bonuses — they can motivate or misfire depending on timing, frequency, and clarity. During periods of inflation and salary adjustments, I favor a transparent narrative that connects the market, our compensation philosophy, and the choices we’re making this cycle.

There’s a tempting trend toward highly individualized packages. While flexibility has its place, I’ve found that too much customization introduces hidden inequities and ongoing operational drag. The antidote is education. I invest in helping employees fully understand their comp — not just the headline numbers. That means straightforward walk-throughs of dilution and tax considerations, so folks see the real value and trade-offs over time.

Here’s how I operationalize this playbook with my team: we publish a clear compensation philosophy; define broad levels and salary bands; standardize equity grant guidelines with built-in refresh logic; and formalize an offer review process to prevent one-off exceptions. We also equip hiring managers with candidate-facing materials to explain equity, stock options, the exercise window, vesting schedules, and potential scenarios. This reduces confusion, accelerates decision-making, and builds trust.

For ongoing discipline, I treat compensation like any other critical product system: we set review cadences, define decision rights, audit for pay equity, and proactively monitor market signals. When we do run compensation changes (promotions, adjustments, bonuses), we pair them with simple, empathetic communication so employees understand the why behind the change — not just the what.

No matter your stage, the goal is consistent and comprehensible startup compensation: an initial comp philosophy you can defend, offers that reflect both market and mission, and retention mechanisms that honor impact. Do these well, and you’ll ship faster, hire better, and keep your highest performers engaged for the long haul.

Inspired by this post on First Round.

October 21, 2025
Stop Promoting Your Top ICs: “When They Win, You Win.” Lessons for Modern Managers

I’m often asked what truly powers a high-performing product organization. My answer starts with managers. That’s why I was eager to revisit the work of Russ Laraway, a seasoned leader who’s been at Google, Twitter, Candor Inc, Qualtrics, and is now the Chief People Officer for Goodwater Capital. His career arc mirrors the kind of product management leadership many of us strive to cultivate on our teams.
He’s written a new book, titled: “When They Win, You Win.” It’s a research-backed guide that resonated with me because it balances practical tools with the nuance required for the IC to manager transition inside fast-moving product teams.
One idea that immediately stood out is how broken the manager selection process often is. Too many companies default to promoting the highest performer, rather than looking for folks who explicitly demonstrate leadership chops. In my own teams, I’ve seen elite individual contributors struggle when asked to lead without preparation. We now assess for behaviors like an ability to set clear outcomes (not just outputs), coach consistently, give and receive actionable feedback, and create clarity during ambiguity—before offering the role.
Equally valuable are the raw ingredients Russ outlines to gauge whether someone’s truly ready for management—even if they weren’t the best individual contributor. I’ve learned to look for three signals in promotion and hiring loops: (1) a habit of elevating peers’ work, (2) structured thinking that translates strategy into weekly execution, and (3) a bias toward accountability paired with empathy. If you’re hiring managers from outside the company, build your interview plan to suss out the right hire. I like questions that probe how candidates set outcomes vs output OKRs, run 1:1s that compound performance, and handle underperformance without losing team trust.
The book synthesizes heaps of research into clear management frameworks I can put to work immediately. One takeaway is a practical list of the behaviors of highly-engaging managers. What’s worked for me: weekly 1:1s anchored on priorities and growth, explicit role clarity, lightweight career conversations every quarter, strengths-based recognition tied to outcomes, and crisp decision rights. When managers consistently do these basics well, engagement rises and product velocity follows.
There’s no shortage of management advice out there—often contradictory. What I appreciate here is the distillation into an essential, research-backed guide for the modern manager that cuts through the noise. The result is a repeatable playbook I can hand to new product leads and know they’ll have the foundations to build trust, set direction, and deliver business impact.
You can follow Russ on Twitter at @ral1.
His book, “When They Win, You Win.” comes out on June 7, 2022. For more details, see the Amazon listing: https://www.amazon.com/When-They-Win-You-Manager/dp/1250279666.

October 21, 2025
From PM to VP: Proven Tactics to Accelerate Your Product Career and Lead with Confidence

I’ve spent my career growing product teams and coaching product managers, and I’m continually drawn to leaders whose playbooks translate across company stages. One standout is Jiaona Zhang (she goes by JZ), whose journey offers an especially clear roadmap for moving from individual contributor to executive product leadership.
JZ is the VP of Product at Webflow. Before that, she was the Senior Director of Product Management at WeWork, a Product Lead at Airbnb, and a PM at Dropbox and at Pocket Gems. She teaches product at Stanford and mentors rising product leaders. You may also know her for the widely shared article, “Don’t Serve Burnt Pizza (And Other Lessons in Building Minimum Lovable Products).”
What resonates most with me is her framing of the product career path. Instead of a linear ladder, think of three distinct phases: contributing as a PM, managing PMs, and leading the function. I’ve used a similar model to guide my own teams, and I’ll walk through how I apply this framework in practice.
Phase 1 — The PM role: When you’re breaking into product, focus on environments that will compound your learning. I look for signs of strong product discovery, clear ownership of product roadmapping and sprint planning, and a culture that values outcomes vs output. In interviews, I ask how success is measured (OKRs, customer outcomes, adoption) and how PMs partner with engineering and design. Early mistakes are common: trying to own decisions without owning the problem, shipping features without a minimum lovable product mindset, and confusing velocity with value. To avoid these traps, anchor your work in customer problems, link every roadmap item to measurable outcomes, and practice crisp storytelling that connects strategy to execution.
Phase 2 — The managing phase: The IC to manager transition is a shift from doing the work to building the system that does the work. As you become more senior, zoom out from features to portfolios, from experiments to strategy. When hiring, I look for complementary archetypes across the team — the product creator who thrives in zero-to-one, the operator who scales repeatable playbooks, the analyst who brings rigor to prioritization, and the evangelist who aligns stakeholders. For first-time managers, my advice is to establish clear decision rights, define the bar for product quality, and coach toward autonomy. Balance mentoring with mechanisms: weekly product reviews, outcomes-driven OKRs, and lightweight rituals that reinforce clarity without micromanaging.
Phase 3 — The executive phase: At this stage, I treat the product organization itself as a product. Define a vision, clarify the customer (your CEO, exec peers, board, and of course end users), and build feedback loops. With the CEO, align on the narrative, business model bets, and the handful of company-level outcomes that matter most. With peers on the exec team, drive cross-functional planning so GTM, finance, and product are synchronized around impact, not just output. With the board, translate strategy into measurable progress and risk mitigation. The goal is to ship strategy: clear choices, intentional sequencing, and a portfolio that advances product-market fit and durable growth.
Whether you’re trying to break into product, grow into management, or step into the executive arena, this three-phase arc is a reliable compass. Invest in product discovery, tie work to outcomes, and develop the operating cadence that turns intent into impact. That’s how you accelerate from PM to VP — and lead with confidence at every step.

Inspired by this post on First Round.

October 21, 2025
Why the COO Role Is the C-Suite’s Most Fluid: Archetypes, No-Blame Culture, and CEO Guidance

I’ve long believed the COO seat is the most fluid role in the executive suite, and my perspective has been sharpened by learning from operating leaders who’ve scaled iconic companies. One conversation that stands out centers on Sara Clemens, most recently COO of Twitch and former COO of Pandora.

In this interview, we explore the nuances of the COO role, which can vary drastically across different companies. We cover:

The three main COO archetypes and which sorts of folks are best suited for those roles.

The tactical elements of being a COO, including Sara’s advice for what good strategy actually looks like, and how to truly create a no-blame culture.

Sara’s lessons on keeping pace as a company doubles in size, including her tips on sketching out “decision rights.”

Guidance for CEOs considering bringing on a COO to the executive suite.

From my vantage point in product management leadership, the variability of the COO mandate is a feature, not a bug. Great COOs adapt to the business model, stage, and CEO superpowers. The best partnerships I’ve seen start with explicit clarity: What outcomes matter most in the next 12–18 months? Which constraints are real? Where will product, operations, and go-to-market intersect—and who owns what?

On archetypes, I map product’s needs to the operator’s strengths. If we’re pursuing step-function growth, I look for a COO who is comfortable orchestrating ambiguous, cross-functional bets. When the priority is scaling reliability and margins, I align with a process- and systems-oriented operator. When the goal is organizational transformation, I look for a builder who can reset norms while protecting momentum. Getting this fit right improves execution, reduces decision latency, and clarifies how we measure progress.

On the tactical elements of being a COO and what good strategy looks like, I anchor on a few principles that have never failed me: strategy is a coherent set of choices, not a list of initiatives; it prioritizes outcomes over output and forces trade-offs. We translate those choices into a focused operating cadence—clear goals, crisp leading indicators, and reviews that separate signal from noise. In practice, that means elevating outcomes vs output OKRs, pressure-testing assumptions early, and linking roadmaps to measurable value creation.

Creating a no-blame culture isn’t soft—it’s operationally essential. Blame keeps teams defensive; learning keeps them fast. I’ve had success institutionalizing blameless postmortems, pre-mortems for high-risk launches, and a norm of writing down hypotheses before we run experiments. We fix the process, not the person. Over time, this builds psychological safety and enables the honest retrospectives that high-velocity product and operations teams depend on.

As companies double in size, complexity compounds. This is where “decision rights” become a force multiplier. I recommend codifying who is the decision-maker, who must be consulted, and who needs to be informed before work begins. Whether you prefer RACI, DACI, or RAPID, choose one, teach it, and use it consistently. Pair decision rights with single-threaded ownership for critical initiatives and you’ll reduce escalation churn, speed handoffs, and preserve accountability as headcount grows.

Keeping pace during hypergrowth also demands an operating rhythm that scales. I align quarterly planning with a lightweight monthly business review, ensure product roadmapping and sprint planning tie directly to company-level outcomes, and maintain a disciplined change-management channel so emergent priorities don’t derail committed work. When the cadence is consistent and the artifacts are simple, leaders can move fast without breaking trust.

For CEOs considering bringing on a COO to the executive suite, my guidance is straightforward: define the mandate in terms of outcomes, not tasks; be explicit about the seams between CEO, COO, and product; and decide how you’ll make decisions together before the first decision. Align on metrics, communication rhythms, and escalation paths. Hiring a great COO is not about finding a clone—it’s about designing a complementary partnership that compounds your strengths and closes your gaps.

The through line across all of this is clarity—of strategy, of responsibilities, and of learning. Get those right, and the natural fluidity of the COO role becomes your organizational advantage.

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
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
My Playbook for the First 10 Hires: Lessons from Steven Bartel on Gem & Dropbox

I sat down with Steven Bartel, co-founder and CEO of Gem, to go deep on what really works when you’re making the very first critical hires in a startup. As a builder and operator, I know how much early talent decisions determine product velocity, culture, and ultimately whether the team can execute founder-led GTM with confidence.

Before building the talent acquisition platform, Steven was an early engineer at Dropbox, where he spent 5 years working on analytics, Dropbox Paper, and hiring as the company grew from 25 to 1500 people.

This experience from Dropbox, combined with his lessons from building out Gem’s own team and talking to his customer base of recruiters makes Steven the perfect person to talk to about early-stage recruiting.

In our conversation we focus on how to make those fourth, fifth, or tenth hires — those really early days when your startup has zero brand recognition or recruiting help. Here’s a preview of his tactical advice, paired with my product leadership lens on what to actually do next.

A trick for sourcing second-degree network connections. I’ve used this same approach to turn lukewarm interest into warm intros by mapping mutual connectors across former teammates, investors, advisors, and early customers. The goal is to engineer serendipity: stack-rank warm paths, ask for specific intros, and close the loop quickly with crisp role requirements and a two-sentence value proposition.

The power of sending a “break-up” message in your candidate outreach. When a candidate goes quiet, a polite, time-bounded note that gives them an easy out often re-engages the conversation. It respects their time, signals high standards, and creates a natural moment for them to opt back in—very similar to enterprise follow-ups in founder-led sales.

How Gem brought candidates on to work with them in very structured trial periods before making a full-time offer. I’ve found structured trials invaluable for de-risking early hires: define a scoped project, align on success criteria, and ensure tight feedback loops. This mirrors a product discovery sprint—short, measurable, and collaborative—while giving both sides a realistic preview of working together.

Advice for working on your recruiting pitch and nurturing passive talent. Your pitch should evolve like your product narrative: lead with the mission, the unique wedge, and the precise problems a candidate will own in the next 90 days. For passive talent, sequence lightweight touchpoints (demo the product, share a customer story, invite a technical deep dive) to build trust long before you ask for a decision.

The similarities between early-stage hiring and founder-led sales. Both require targeted prospecting, tight messaging, and rigorous follow-through. The best founders and product leaders treat recruiting pipelines like revenue pipelines—measure response rates, iterate on messaging, and run structured, time-boxed cycles to convert high-signal candidates.

If you’re navigating your first hiring wave, these principles will help you build a repeatable recruiting engine: amplify second-degree networks, use respectful “break-up” nudges, validate fit with structured trials, sharpen your pitch for passive talent, and apply founder-led sales discipline to every stage of the funnel. Do this well, and your early team becomes a durable competitive advantage.

Inspired by this post on First Round.

October 21, 2025
Scale With Your Startup: Proven Lessons from Mike Boufford’s Decade at Greenhouse

Scaling a company is only half the battle; scaling your own career in lockstep is the harder, more enduring challenge. I’ve seen high-growth environments reward those who adapt early and often, which is why the arc of Mike Boufford’s journey resonates deeply with me as a product leader.

Mike Boufford, CTO of Greenhouse, an applicant tracking system and recruiting platform.

He wrote the first line of code at Greenhouse in May 2012, and he’s still there — over a decade later.

This isn’t the typical path of non-co-founding engineers, who usually get layered or leave to start their own ventures.

Drawing on his story, I zero in on how founders build an environment that makes early employees want to stay, and importantly, how leaders can build the career skills and self-awareness they need to succeed at a startup long-term. In my experience, that combination—healthy culture plus relentless personal development—is what keeps top talent growing rather than going.

How his own motivation changed over time and how he managed his relationship with the company’s co-founders. I’ve learned that motivations naturally evolve—from creation and ownership, to scale and stewardship, to legacy and leverage. Naming those shifts early helps you reset expectations with co-founders before friction builds. Practically, this means recurring check-ins on roles, decision rights, and the tradeoffs you’re willing to accept as the organization matures.

The techniques he used to prepare himself for every next phase of growth and how his role would have to change in 18-24 months. I encourage leaders to keep a running “future job description” and refresh it quarterly. Ask: What will break at our next order of magnitude? Which systems, skills, and successors must I develop now so that I’m qualified for the job I’ll have in two years? This future-back planning keeps you ahead of the curve as the startup compounds.

Why he read two books on every other executive’s area of the business when he joined the leadership team. That habit builds cross-functional fluency fast. In my teams, this kind of immersion reduces friction with peers, sharpens strategy, and anchors debates in shared constraints—exactly what product and engineering leaders need to operate credibly at the executive level.

For a nuanced perspective on retention and healthy team evolution, I recommend reading: Why This Engineering Leader Thinks You Shouldn’t Aim for Zero Regrettable Attrition. Embracing the right amount of change—especially at senior levels—can unlock growth for both the organization and the individual.

If you’re navigating startup leadership, product management leadership, or the IC to manager transition, take this playbook to heart: anticipate the next phase, invest in cross-functional competence, and renegotiate your role before the org structure forces it. That’s how you scale with your startup, not in spite of it.

Inspired by this post on First Round.

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
What I Learned from Don Faul on Leading with Radical Transparency in Hard Times

Leading teams through volatility demands more than strategy decks—it demands conviction and clarity. That’s why I was eager to learn from Don Faul, CEO of CrossFit, whose leadership journey spans a combat zone and a corporate board room. He spent 8 years as a platoon commander in the U.S. Marine Corps, then took on roles at Google, Facebook and Pinterest, the latter of which he served as the Head of Operations. Few leaders have stress-tested their principles across cultures this different, and that perspective is invaluable for product management leadership. One theme we explored head-on: whether micromanagement is always a bad thing. In my experience, it isn’t binary. In moments of genuine risk—customer incidents, safety-critical launches, or brand-defining bets—short, explicit periods of hands-on leadership can help a team move faster and learn safely. The key is to set clear exit criteria, communicate the why, and anchor on outcomes vs output OKRs so the team understands what success looks like—and when autonomy returns. We also dove into what it takes to build a long-term company vision that actually energizes people. A credible vision marries a bold, emotionally resonant narrative with a concrete path of near-term milestones. In my role leading product management at HighLevel, we anchor that narrative in the customer’s pain, make the outcomes measurable, and translate the vision into crisp, sequenced bets. When teams can see how this quarter’s work ladders to a multi-year north star, execution energy skyrockets. All-hands meetings are another place where leadership either compounds trust—or depletes it. The most common mistakes I see: status-report theater, a sea of vanity metrics, and avoiding the hard questions everyone is already whispering about. My playbook is simple: lead with what’s hard, be explicit about trade-offs, highlight real customer stories, and tie priorities back to outcomes vs output OKRs. Then make space for unfiltered Q&A and follow up with written decisions so the operating system stays transparent. We also discussed what it takes to lead when things feel like they’re going off the rails, which plenty of startup folks are feeling right now. In uncertain markets, I default to over-communication: weekly updates on goals, financial runway and scenario plans; decision logs that explain what changed and why; and repeated clarity on the next three most important priorities. When the path gets rocky, transparency isn’t a virtue signal—it’s an operating mechanism that preserves momentum and dignity. Don unpacked lessons on embracing transparency when things aren’t going well, and also shared his experience having to wind down a company. My own approach in that situation is to move quickly and humanely: communicate early, share the specific criteria behind the decision, offer as much support as possible, and be crystal clear on timelines and logistics. People can handle tough news; what erodes trust is ambiguity and delay. For anyone navigating the IC to manager transition, there’s a powerful throughline in these lessons: leadership is context-aware. Your job shifts from owning tasks to designing systems—communication cadence, decision frameworks, and coaching—so that outcomes persist without your constant presence. The earlier you learn to set vision, define outcomes, and create feedback loops, the sooner your team compounds value. If you’re building in this market, remember: radical transparency is not just about sharing everything; it’s about sharing the right things at the right altitude, at the right time. Clarity on vision, grounded metrics, honest all-hands, and humane leadership in adversity—these are the habits that keep teams inspired and resilient. You can follow Don on Twitter @donfaul

October 21, 2025
How I Evaluate Talent Like a Pro: Lessons from Nadia Singer, Figma’s Chief People Officer

I recently sat down with Nadia Singer, Chief People Officer at Figma, to unpack what separates good interviewers from truly remarkable talent evaluators. As I reflect on my own hiring philosophy in product management leadership, her approach sharpened my lens for identifying signal over noise, eliminating bias, and scaling culture with intention.

Nadia joined Figma in 2020 and has seen explosive growth in her own career alongside the collaborative design platform’s. Before Figma, Singer was a talent expert who has hired hundreds of talented folks at places like Quora, Facebook and Google.

In our discussion, we dove into the patterns that consistently predict excellence. What resonated most was a simple yet powerful idea from her recruiter playbook: study how a candidate reaches an answer, rather than what they say. I’ve found this especially impactful when hiring PMs and cross-functional leaders. Rather than celebrating the “right” conclusion, I push candidates to narrate their reasoning, make trade-offs explicit, and surface assumptions—revealing structured thinking, customer empathy, and learning velocity.

To operationalize this, I ask candidates to walk me through ambiguous product decisions: Which constraints did you prioritize and why? Where did you seek disconfirming evidence? How did you iterate when new data emerged? I’m listening for clarity of problem framing, the ability to quantify impact, and the rigor of decision-making under uncertainty. The outcome matters, but the method matters more.

We also explored tactics interviewers can use to avoid pattern matching and other biases. In my teams, that starts with a role scorecard that defines the core competencies up front (not resume proxies), structured interviews with consistent prompts, and independent scoring before any debrief. I’m deliberate about diverse panels, rotating interviewers to reduce shared blind spots, and separating signal (evidence-backed behaviors) from story (polish, pedigree, or charisma). In debriefs, the most senior voice speaks last, we anchor on evidence tied to the scorecard, and we explicitly call out potential biases when they appear.

Another theme was learning from early missteps in recruiting. I’ve made many of the common mistakes: over-indexing on pedigree instead of proof of outcomes, letting hypotheticals outweigh real-world execution, asking leading questions that telegraph the “desired” answer, and failing to define success criteria before meeting candidates. The fix is discipline: better prompts, deeper follow-ups (“tell me about a time…” with measurable results), consistent rubrics, and a higher bar for reference checks that validate how someone collaborates under pressure.

Finally, we discussed ways that Figma tweaked its approach to culture so it could scale alongside the company. My takeaway: culture scales when it’s operationalized. Codify a few non-negotiable principles, translate them into observable behaviors, and weave them into hiring rubrics, onboarding, performance management, and rituals like product reviews. As the organization grows, refine language—without diluting standards—so new teams can apply the same principles to different contexts.

If you lead hiring for product or adjacent functions, here’s the throughline I’m taking forward: raise the bar on reasoning, not rhetoric; design interviews that produce comparable evidence; and treat culture as a living operating system, not a poster. That’s how you consistently spot high-agency, high-learning talent—and build teams that compound value over time.

October 21, 2025
Hypergrowth Leadership: My Takeaways from Claire Hughes Johnson on “learning organisms”

I’ve been reflecting on what it really takes to scale a product organization through hypergrowth, and I keep coming back to the discipline and mindset modeled by Claire Hughes Johnson. Her approach to operating at scale, executive hiring, and leadership development aligns closely with the highest standards of product management leadership.

Claire joined Stripe as its COO back in 2014 and, over the course of her nearly seven years in the company’s executive suite, she oversaw rapid growth as Stripe scaled from under 200 employees to over 7,000. Prior to Stripe, she spent 10 years at Google leading various high-impact business teams. That arc of operating experience sets the context for her new book, “Scaling People: Tactics for Management and Company Building.”

One story that particularly resonates with me is the inside look at her lengthy, no-stone-unturned interview process with the Collison brothers for the COO role. I’ve learned that this level of rigor is not just about due diligence; it’s a signal of shared standards and cultural alignment. When I hire for critical roles, I mirror this depth: clarify the mandate, pressure-test values, and evaluate for the long arc of decision quality—not merely short-term execution.

Hiring exceptional talent demands systems thinking. Claire’s emphasis on doing reference checks the right way—structured, targeted, and focused on observed behaviors—maps to my own playbook. I’ve found executive hiring is hard because the signals are noisy, the roles are often ambiguous, and it’s tempting to over-index on brand or storytelling. The antidote is to define success as outcomes, not activities, and then assess candidates against those outcomes. This is where outcomes vs output OKRs become indispensable for preventing mis-hiring and aligning expectations.

Her personal backstory also underscores a foundational leadership trait: curiosity. The way her parents instilled deep curiosity and fierce independence at a very young age is more than biography—it’s a blueprint. In practice, it translates to cultivating an owner’s mindset across the org, which is crucial for anyone navigating an IC to manager transition and for leaders who must empower teams without micromanaging.

I also appreciate her belief that all high-performers are “learning organisms.” I’ve seen the best product leaders systematize learning with deliberate feedback loops, postmortems, and explicit mechanisms to turn insight into action. In product discovery, this shows up as rapid cycles of hypothesis, experiment, and synthesis—creating a culture where learning velocity compounds just as reliably as revenue can.

This is why I recommend “Scaling People: Tactics for Management and Company Building” to operators who want pragmatic tools, not just abstractions. For complementary perspective, the book she recommended from Fred Kofman titled “Conscious Business” pairs well with these themes of ownership, integrity, and clear commitments—essentials for leaders who manage complexity at scale.

If you’re looking to stay close to her work, you can follow Claire on Twitter at @chughesjohnson. I’ve found her ongoing reflections a useful calibration point for raising the bar on leadership systems, executive hiring, and operating rigor.

My key takeaway is simple but powerful: scale rewards clarity, discipline, and humility. Hiring is a product in itself. Culture is a system, not a slogan. And the leaders who keep compounding are the ones who choose to be “learning organisms,” building teams that do the same.

October 21, 2025