Tag: executive hiring

Why I Bet on First-Time Executives: Inside Figma’s Playbook for AI, IPO Readiness, and Scale

Founders should bet on first-time executives. I’ve seen it pay off repeatedly, and a recent deep dive with Praveer Melwani, CFO at Figma, reinforced exactly why. Praveer joined Figma in 2017 as the company’s first business operations and finance hire—when the team was around 30 people and not yet charging for the product—and stepped into the CFO seat in 2022, helping to lead the company’s IPO in 2025. His journey from IC to CFO isn’t just a career arc; it’s a blueprint for scaling leadership capacity in high-velocity environments.

What struck me first was the clarity of the step functions that took him from operator to “whole-company” leader. Early on, he optimized for doing the work—building driver trees, stress-testing go-to-market assumptions, and putting the basics of board management in place. As the business matured, he shifted from answering questions to defining them, owning capital allocation, and shaping the operating cadence. That evolution—from execution to orchestration—is exactly the arc I look for when I’m hiring first-time VPs.

Another takeaway: Figma started acting like a public company three years before its IPO. That wasn’t optics; it was operating discipline. Quarterly rhythms, tight controls, an audit-proof close, and forward-looking narrative management helped the company move faster, not slower. In my experience, this kind of public-company readiness clarifies trade-offs, compresses decision cycles, and strengthens cross-functional trust—especially between product, finance, and go-to-market leadership.

We also unpacked what separates world-class finance leaders from a traffic-cop CFO. The latter enforces rules and guards budgets; the former uses first principles decision making to direct resources toward asymmetric upside. World-class CFOs help the company understand risk in a post-ChatGPT world, design SaaS pricing that matches product reality, and build reliable instrumentation for outcomes—not just outputs. They’re partners in product strategy as much as stewards of the balance sheet.

On pricing, I appreciated the courage behind selling the exec team on AI consumption pricing. Consumption SaaS pricing introduces variance, but it also aligns value with usage and accelerates time-to-value—especially for AI-driven features whose unit economics evolve rapidly. It requires tight stakeholder management, robust telemetry, and a crisp value proposition, but when executed well it can unlock both growth and discipline.

One of the boldest moves: Figma intentionally cut its 90% gross margin to invest in AI. That’s a masterclass in capital allocation. The reflex to protect margins is strong, but durable advantage often comes from compounding learning loops, not short-term optics. Framed correctly, AI Strategy isn’t a cost center—it’s an option on multiple future S-curves. The key is to define decision guardrails, instrument usage, and keep a living risk register for AI risk management.

I was also intrigued by how AI is changing the CFO craft itself. Tools like Claude Code are now part of the financial leader’s toolbox—useful for scenario modeling, policy drafting, and exploring new domains without slowing down the team. Paired with strong data governance and controls, this is where FinOps meets executive leverage: faster cycles, tighter experiments, and better communication with product and engineering.

Leadership transitions can catalyze phase shifts. When a COO leaves or a company re-architects its operating model, great executives don’t just fill gaps—they redesign the system. That’s when clarity about swimlanes, escalation paths, and decision rights matters most. The lesson for founders: hire for adaptability, not just pedigree, and look for people who can turn ambiguity into momentum.

Hiring leaders in functions you don’t deeply understand is a common founder challenge. The best antidote is a first-principles test for hiring VPs: can the candidate map the business model, define success metrics, and explain trade-offs in plain language? Do they show how they’d build the team, not just run it? Can they teach you something new in 30 minutes? I use this pattern across executive hiring because it scales better than relying on domain buzzwords.

Another practice I recommend: build an internal board of peer CFOs and operators. Regular, no-agenda check-ins create a community of practice that shortens feedback loops and surfaces non-obvious risks. It’s one of the most efficient ways to de-risk capital allocation and sharpen strategic narratives ahead of real board meetings.

We talked about scope versus depth: how deeply in the details should a CFO be? My view aligns with what I heard here—be in the details often enough to validate the model and coach the team, but not so deep that you become the bottleneck. The executive job is to raise the quality of decisions at scale, not to personally make every decision.

There were personal lessons, too—from the nine-year working relationship with Dylan Field to foundational team-building insights from time at Dropbox. Strong teams are built on crisp roles, tight feedback loops, and a bias for writing things down. That muscle—organizational development through clarity—is what separates resilient companies from merely lucky ones.

If you’re a founder weighing whether to back a rising operator or recruit a “proven” exec, this story tips the scale toward the former. Bet on slope, not just intercept. Create the scaffolding—public-company behaviors early, transparent metrics, and a culture that rewards learning—and your first-time executives will scale with the business. Done right, it’s the highest-LEVERAGE people decision you can make.

May 14, 2026
Inside Artemis’ AI vs AI Security War: Hiring at Speed, PMF Signals, and Founder-Led Sales

I’m fascinated by how fast truly AI-native companies can move when the problem is urgent, the founders have deep domain credibility, and the culture is built around customer obsession from day one. Artemis, an AI-native security platform, just emerged from stealth with $70M in combined seed and Series A funding, assembled a 30-person team in seven months, and made a bold promise to “stay on a texting basis with every customer, even at scale.” As a product leader, I see this as a masterclass in AI Strategy, go-to-market focus, and disciplined execution in cybersecurity.

At its core, Artemis is operating in what I’d call an “AI vs AI” security war: increasingly, we’re defending against adversaries who leverage models just as aggressively as we do. That shifts the job from rule-writing to intelligence orchestration, threat detection and response at machine speed, and continuous evaluation. It also explains why AI-native companies are outperforming their AI-enabled counterparts—when intelligence is the product, the org must be built around model quality, data pipelines, and rapid iteration, not as a bolt-on.

Founder-market fit is the early signal I look for, and here it’s unmistakable. Shachar Hirshberg’s “AWS and Palo Alto” playbook and Dan Shiebler’s path “From Twitter to Abnormal” create a rare combination: deep infrastructure and enterprise security know-how paired with production-grade machine learning at scale. When those experiences intersect, you get crisp problem statements, faster learning loops, and credibility with the exact ICP that feels the pain first.

Timing the leap to build is more art than science, but I listen for three cues: customers describing the problem in quantified terms, a wedge that can deliver value within one buying cycle, and a data advantage that compounds. Artemis clearly identified a high-urgency buyer and ignored adjacent segments that would dilute focus—an underrated act of courage that accelerates product-market fit.

Hiring for AI fluency is a different exercise than traditional software roles. I don’t just screen for model familiarity; I screen for product thinking under uncertainty, a bias for eval-driven development, and the ability to explain tradeoffs to security teams. Practical prompts help: “How would you diagnose precision/recall tradeoffs under evolving threat patterns?” or “Show me how you’d design a red/blue evaluation harness for a new detection.” The best candidates can translate model metrics into business outcomes and customer trust.

Building a 30-person AI-native team in stealth requires ruthless clarity on the handful of roles that compound: forward deployed engineers who can ship with customers, solutions engineering that feeds learning back into the model, and product managers who treat data as the primary surface area. Culture-wise, I anchor on two rituals: weekly customer debriefs with actual artifacts (alerts, misclassifications, escalations) and a written log of hypotheses, evals, and next bets—so the entire team can reason from the same evidence.

AI implementation reshapes the dashboard. Beyond the usual business KPIs, I watch a second layer: model precision/recall by scenario, alert fatigue reduction, time-to-first-signal on emerging threats, drift and data freshness, and latency under load. When these improve, downstream product metrics—activation, expansion, NRR—almost always follow. Observability isn’t an afterthought; it’s the control center for trust in AI-driven cybersecurity.

ICP discipline is non-negotiable. Artemis focused on the segment with the highest urgency-to-adopt and the clearest data pathways, and deliberately ignored a seemingly attractive adjacent ICP that would slow learning. I’ve made that trade myself: it feels painful in the short term but pays off in faster cycles, cleaner roadmap decisions, and better founder-led GTM.

Closing the first customers is where the magic happens—and where the most surprising signals of early product-market fit emerge. It’s rarely about feature breadth. It’s about whether customers escalate, volunteer data, and invite your team into their workflows. In founder-led sales, the most valuable insights come from the objections you lose on. I document every “no,” cluster them by root cause, and turn the top two into experiments within a sprint.

I also believe the first product should make founders a little uncomfortable—just enough to prove the thesis in the messiest, fastest path possible. In AI security, that often means prioritizing the smallest end-to-end loop that can stop or downgrade a real threat, even if the initial UX is rough. If the loop works, you’ll earn the right to harden it.

Co-founder dynamics matter as much as the roadmap. I liked the question “Should we be arguing more?” because it reframes conflict as a system. My rule: disagree in writing with a time box, escalate only the principle in dispute (not the plan), and commit to the decision with a pre-agreed review point. This keeps speed without calcifying bad calls.

On structure, I’m convinced AI-native beats AI-enabled for this market. Organize around data, evaluations, and deployment rather than traditional feature teams. Blend product, research, and solutions into durable, customer-facing units. Consider forward deployed engineers who can ship safely in live environments and bring back the sharpest, most actionable learning. It’s the only way to keep pace with adversaries that iterate as fast as you do.

The broader landscape provides context and competition. I benchmark capabilities and go-to-market motions against players like Abnormal, CrowdStrike, and Palo Alto Networks, with respect for the automation lineage from Demisto (now Cortex XSOAR). Cloud scale and data gravity from Amazon Web Services (AWS) matter, while model innovations from OpenAI and Anthropic raise the offensive and defensive bar. And Artemis is staking a claim in that intersection—where security outcomes, model excellence, and frontline customer intimacy meet.

If you care about AI risk management, threat detection and response, and building empowered product teams that can win in this “AI vs AI” environment, the lessons here are clear: hire for AI fluency, not just titles; instrument the model like a business; let founder-led GTM shape your roadmap; and keep the customer close enough that you can text them—because that’s how you outlearn the market.

April 21, 2026
From 70 Employees to Dominance: My Playbook for Hypergrowth, Focus, and Top-Down Goals

Scaling a real-world marketplace from scrappy to dominant takes a different kind of product leadership. Reflecting on Christopher Payne’s decade leading DoorDash as President and COO — growing from roughly 70 employees to the dominant food delivery platform in the US — I’m struck by how much of that success hinged on mastering an atoms-based business while still operating with software-level rigor. As a VP of Product Management, I see the same patterns in my own work: relentless clarity on inputs, a bias for builder-executives, and a cadence that keeps leaders close to product details without becoming bottlenecks.

Running an atoms-based business versus a pure software company forces you to obsess over operational physics: unit economics, quality control, on-time reliability, and dense local liquidity. It’s precisely where traditional “bits” executives can stumble. What’s worked for me is a simple “plate spinning” framework for executive attention: identify the five or six plates that must never stop — customer experience, marketplace health, quality and safety, product velocity, platform reliability, and P&L — then schedule recurring deep dives to keep those plates spinning. If a plate wobbles, I drop in, fix the root cause, re-instrument the inputs, and zoom back out.

Hiring at hypergrowth speed only works when you bias toward a “builder mentality.” I look for executives who run toward fuzzy problems, write clearly, and can prove they’ve shipped value with incomplete information. Prior industry experience can be a liability when you’re reinventing the market; first-principles thinkers outlearn domain experts who try to port yesterday’s playbooks. In executive hiring, I’ve found structured work samples and narrative memos far more predictive than marathon interview loops — companies routinely spend too much time on job interviews and too little time evaluating how candidates think and execute.

Great executives never outgrow the details. Staying close doesn’t mean micromanaging — it means sampling the customer journey and instrumenting the system so you can feel where it hurts. In my own practice, I rotate through frontline touchpoints weekly: support transcripts, NPS verbatims, failed checkout sessions, and reliability dashboards. Small signals often reveal systemic issues. A single ciabatta bread moment — the kind of edge-case substitution that seems trivial — can expose broken handoffs, unclear policies, and misaligned incentives across the marketplace.

Top-down goal setting beats bottom-up when you’re aiming for category leadership. Bottom-up targets tend to regress to comfort; they calibrate to today’s constraints, not tomorrow’s possibilities. I set ambitious, top-down outcomes (not output), frame the non-negotiables, and map driver trees to clarify the input metrics that matter. Then I ask empowered product teams to pressure-test the plan, propose approaches, and own the how. This preserves ambition while unlocking creativity — a practical balance of clarity and autonomy that outcomes vs output OKRs were designed to achieve.

One-size-fits-all management is a myth. Early-stage teams need hands-on coaching and fast decisions; later-stage teams need mechanisms that scale: crisp PRDs, pre-mortems, and operating cadences that separate strategy, planning, and execution. The mark of a high-functioning executive team is not uniform style — it’s high candor, fast escalation paths, and visible commitment after debate. In tough moments, a little charisma goes a long way; in practice, that’s not theatrics, it’s steady optimism, simple language, and consistent follow-through that keeps people moving forward.

The hypergrowth skill stack for executives is surprisingly learnable: ruthless prioritization under uncertainty, narrative writing that aligns cross-functionally, structured delegation with clear “inspection points,” and a weekly rhythm that protects maker time. I leverage a cadence of business reviews (inputs > outputs), customer-scent checks, and decision logs so we can move fast without losing the thread. CEO and executive time management is the ultimate forcing function — if we can’t show where our attention maps to goals, the team won’t either.

Some of my enduring lessons echo the best of Amazon and eBay: customer obsession beats competitor obsession, input metrics beat lagging vanity metrics, and simple mechanisms beat heroics. From Jeff Bezos’s playbook I borrow the insistence on written narratives, single-threaded ownership, and clarity on what will not change. Those principles remain the backbone of platform scalability and resilient product strategy, especially when markets get noisy.

AI is about to flatten organizations. With agentic AI, retrieval-first pipelines, and AI workflows embedded into product development, managers can widen their span without losing fidelity. I see LLMs for product managers accelerating discovery, PRD drafting, and experiment analysis — while raising the bar on decision quality. The implication for leadership: fewer layers, more transparency, and even greater pressure to define sharp, top-down outcomes that teams can autonomously pursue.

If I had to compress this into a playbook, it’s this: set audacious, top-down goals; keep your “plate spinning” calendar sacred; write more than you talk; hire builders, not resume archetypes; sample the customer journey every week; and build mechanisms that make the right thing easier than the heroic thing. That’s how you scale product management leadership from dozens to thousands — in atoms, in bits, and in the messy, exhilarating space where they meet.

April 17, 2026
Inside the Most Politically Dangerous C‑Suite Role: Hard Truths on Culture, Layoffs, and Leadership

I’ve long believed the people function is a strategic engine, not a support lane. That conviction was only reinforced in a recent deep dive with Katie Burke, now COO at Harvey after joining as Chief People Officer. Before Harvey, she spent 11 years in HR leadership at HubSpot, helping build one of tech’s most distinctive cultures. In this piece, I unpack what resonated most for me as a product leader: a marketing-minded approach to HR, deliberate hiring from hospitality, and the non-negotiable case for culture as a core business strategy.

The first principle is simple and often overlooked: HR leaders should think like marketers. Employer brand is a product; your candidate and employee journeys are funnels; and your programs deserve the same rigor we bring to product—segmentation, positioning, channels, and continuous A/B testing. When we treat onboarding, performance, and manager enablement like iterative product launches—complete with activation metrics, retention curves, and NPS—we stop guessing and start compounding results.

One line has become a north star for how I approach executive leadership: “Don’t ask for a seat at the table. Build the table.” In practice, that means codifying the operating system—decision rights, principles, cadences, and accountability—so the organization isn’t improvising strategy in every meeting. Product, People, and Finance should co-own this OS; that’s how you scale clarity faster than headcount.

Transparency is the tax we pay for alignment, and it compounds trust. After an IPO, the impulse can be to close ranks. The better move is radical transparency with context: what changed, why it matters, and how decisions get made now. On my teams, that looks like publishing decision records, sharing tradeoffs explicitly, and using written docs to reduce rumor velocity—core muscles in stakeholder management as complexity grows.

I also loved the counterintuitive hiring bet: prioritize hospitality backgrounds alongside traditional corporate pedigrees. People who’ve thrived in service environments bring customer empathy, operational resilience, and a bias for proactive care—traits that elevate everything from onboarding to incident response. In product terms, they’re culturally accretive hires with high signal on service quality and consistency.

The trickiest part of the Chief People Officer role isn’t process—it’s politics. You are the executive team’s own HR business partner, which requires coaching, candor, and conflict mediation at the highest stakes. The goal is to “Be the Michael Jordan of your exec team”—the teammate who elevates standards, makes others better, and chooses the hard right over the easy familiar.

Layoffs create a culture debt that accrues interest. Expect a “2.5-year cultural hangover after a layoff”—in many companies, an inevitable two-year layoff hangover—unless you actively repay it. That repayment plan includes narrating the why with specificity, rebuilding trust through manager enablement, and re-anchoring on performance and values. Measure leading indicators (manager effectiveness, time-to-decision, psychological safety) alongside lagging ones (regretted attrition) to track the true recovery arc.

People leaders also need to create “graceful exits.” Doing this well preserves dignity for the person, protects the team’s morale, and safeguards the company’s brand. The bar is straightforward: clear rationale, fair process, useful feedback, generous support, and alumni pathways. A graceful exit signals that even when business realities bite, respect is non-negotiable.

Expectation-setting matters. Two truths cut through the noise: “The workplace shouldn’t be Disneyland” and “Our job is not to make you happy every day.” The promise is not perpetual happiness; it’s meaningful work, fair standards, growth opportunities, and leaders who tell the truth. When we set that contract clearly, engagement becomes an outcome of purpose and progress—not perks.

On feedback, I use the protein vs. sugar rule for employee feedback. Sugar feedback is pleasant and perishable; protein feedback is specific, sometimes uncomfortable, and growth-driving. Great cultures build a taste for protein—clear role expectations, crisp examples, and written follow-ups. Mechanically, that looks like structured 1:1s, decision retros, skip-levels, and manager training that demystifies “what good looks like.”

Being a Chief People Officer isn’t for the faint of heart. The role must be demanding by design—on executive hiring quality, performance management courage, and values enforcement. Moments like “Berry-Gate” are reminders that small symbolic issues can balloon when feedback loops are unclear. Close the loop fast, publish the rationale, and ensure there’s a predictable path for concerns to be heard and resolved.

When hiring, beware patterns that predict friction. That’s why “frequent flyers” are a new-hire red flag. Movement can signal adaptability—but weather-vein pivots and blame-shifting often repeat. Probe for ownership, learning moments, and sustained impact; you want people who compound value, not just sample it.

Clarity on scope prevents leadership whiplash. Which company decisions fall to the Chief People Officer? Think leveling frameworks, compensation philosophy and bands, performance calibration, manager standards, ER policies, and org design guardrails—always in lockstep with Finance and the CEO. Escalate when there are values collisions or systemic risks; otherwise, push decisions to the right altitude and owner.

Scaling exposes the same few failure modes on repeat: fuzzy decision rights, a thin manager bench, brittle processes that don’t flex, and inconsistent leveling that erodes trust. The antidote is an operating model that pairs clear principles with lightweight mechanisms—documented roles, regular calibration, and reviews that audit for both outcomes and operating behaviors.

Comparing a scaled SaaS like HubSpot with an AI-native company like Harvey surfaces important differences. The former optimizes for durable systems, predictable cadences, and governance; the latter optimizes for rapid learning loops, emergent org design, and a higher tolerance for ambiguity. The art is porting the right controls at the right time without crushing velocity.

AI is already changing the people function. GenAI can draft job descriptions, summarize performance notes, classify themes from engagement surveys, and power AI workflows that resolve common HR tickets. The human-in-the-loop remains essential for judgment, context, and ethics—especially around data governance and privacy-by-design. A pragmatic AI Strategy here frees HRBPs for higher-order coaching and organizational development work.

One practice I recommend widely: share your own performance reviews. Modeling openness normalizes growth and turns feedback into a shared craft, not a secret ritual. It also builds trust when you later ask the organization to lean into sharper, protein-rich feedback.

Finally, disagreements with the CEO are inevitable—and healthy. Handle them with pre-briefs, crisp written proposals, explicit tradeoffs, and a shared decision record. Argue like scientists, not politicians; once a call is made, disagree and commit. That combination of candor and alignment is what keeps executive teams high-trust and high-velocity.

The people leader’s chair may be the most politically dangerous role in the C-suite—but it’s also one of the most leveraged. Build the table, tell the truth, design for standards and dignity, and treat culture like the product that powers everything else.

April 10, 2026
Inside Zipline’s Wild Pivot: My Take on Hiring Heat-Seekers and Scaling to 5,000 Hospitals

I’m consistently drawn to stories where product strategy and operational grit collide to change real lives. Zipline, the world’s largest commercial autonomous delivery system, is one of those rare cases. Serving 5,000 hospitals across multiple countries and saving an estimated 17,000 lives per year, it embodies the kind of mission-driven execution I try to model in product management. The arc—from a near-dead home robot startup to a scrappy bet on drone blood delivery in Rwanda, to 135 million autonomous miles flown—offers some of the clearest lessons I’ve seen on hiring, leadership, and product-market fit under extreme constraints.

One principle that immediately resonated with me: why Zipline doesn’t hire for experience. The idea behind “Why Zipline hires teenagers over PhDs” isn’t a dismissal of expertise; it’s a commitment to learning velocity, ownership, and unteachable hunger. The best startup employees, as described here, are “heat-seeking missiles for pain”—people who chase the hardest problems, not the shiniest projects. In my org, I look for the same signal: candidates who can move from ambiguity to action, who find the bottleneck without being asked, and who care more about outcomes than optics.

I also appreciated the unapologetic stance that “blind references are a non-negotiable.” In high-stakes builds—especially in regulated or safety-critical categories—the cost of a mis-hire compounds. I routinely validate for two traits during references: intellectual humility and accountability. “Can candidates admit when they screwed up?” is a powerful filter. If someone can’t name a hard mistake and how they specifically changed as a result, they’re unlikely to scale with the organization.

Equally important is clarity about who not to hire. The employees Zipline doesn’t want are those who optimize for status, process theater, or low-friction work. In practice, that means pressure-testing for problem-finding, not just problem-solving. I often design interviews around messy, cross-functional constraints (regulatory, operational, and financial) to see who can integrate tradeoffs, not just ideate features. That’s how we build empowered product teams that ship consequential outcomes, not outputs.

There’s a reference to “Zipline’s secret leadership playbook,” and while the specifics remain private, the spirit is unmistakable: first principles decision making, ruthless focus, and a culture that rewards radical responsibility. Translating that to my product organization, I emphasize five behaviors: orient to the mission under uncertainty, run fast but close the loop with data, communicate constraints early and often, own the long tail of consequences (especially in safety and reliability), and scale judgment by teaching the why, not just the what. That blend of clarity and autonomy is the backbone of product management leadership at any growth stage.

On the other side of the culture coin is “Why you should always fire quickly” and “The brutal firing advice that shaped Keller’s leadership.” I’ve learned (sometimes the hard way) that slow decisions erode trust and team velocity. Moving quickly doesn’t mean being harsh; it means being fair, explicit, and humane—tight feedback loops, role clarity, and decisive action when the gap persists. If your bar is clear and your coaching is consistent, acting fast protects both the mission and the team’s energy.

Strategically, the origin story reads like a masterclass in choosing the right problem. The team moved “from toy robots to drone delivery: Zipline’s pivot,” then partnered deeply with Rwanda, where “How Rwanda’s health minister changed everything” is a pivotal moment. It wasn’t a linear climb—”How Zipline almost died – twice” and “Why Zipline’s launch was a ‘complete disaster’” underline a tough truth: breakthrough products rarely arrive fully formed. What matters is the operating cadence that turns early chaos into repeatable reliability—especially when the stakes are measured in minutes and lives.

Scaling from 1 hospital to 5000 required more than product brilliance; it demanded systems thinking across logistics, compliance, safety, and community trust. That’s stakeholder management at its highest level. The product lessons are durable: anchor on outcomes, not artifacts; build reliability as a feature; and practice founder-led GTM where your credibility is on the line with customers and regulators. This is where first principles decision making beats benchmarking—particularly in novel categories where there are no playbooks to copy.

There’s also a hard-nosed operational takeaway in “The 10x hardware cost rule every founder should know.” My read: assume total cost of ownership will balloon once you account for manufacturing variability, support, redundancy, maintenance, and compliance. In product strategy, I treat those multipliers as design inputs, not afterthoughts. If the unit economics can’t survive these realities, the idea isn’t ready—no matter how elegant the prototype looks in a lab.

Across all of this, a few product management patterns stand out for me: build teams around outcomes vs output OKRs; hire for slope, not just intercept; make continuous discovery routine with real users (in this case, clinicians and health systems); and treat operational excellence as a product surface. When a mission is this consequential, culture becomes a safety system—and every leadership decision compounds into either speed with quality or speed with regret.

For leaders building in complex domains, this journey is a blueprint: pick problems that matter, hire “heat-seeking missiles for pain,” keep blind references non-negotiable, lead with first principles, and scale with responsibility. Do that well and even a “complete disaster” launch can become the inflection point of a category-defining company that flies 135 million autonomous miles and saves 17,000 lives per year.

March 12, 2026
90% of CROs Will Fall Behind by 2028: Hard-Learned Lessons to Stay Ahead of GTM Change

I’ve been reflecting on why so many revenue leaders are at risk of falling behind, and the conclusion is stark: fewer than 10% of current CROs will thrive by 2028. That isn’t hyperbole—it’s a wake-up call for how quickly go-to-market strategy, organizational design, and AI-driven execution are evolving. From my seat leading product, I see the pressure building on the CRO role to orchestrate the entire revenue system, not just run a sales team.

One story that crystallizes this reality comes from the journey of Stevie Case, the CRO of Vanta, the trust management platform serving everyone from founders to Fortune 100 CISOs. A former pro-video gamer who stumbled into sales through a mentor’s bet, she exemplifies how unconventional paths can drive unconventional insight. Her trajectory underscores a bigger truth I’ve witnessed across companies: the best revenue leaders aren’t just great sellers—they’re builders who understand product, process, and people at scale.

Why do early revenue hires fail? In my experience, it’s rarely about raw talent. It’s about fit, scope, and time horizon. Early-stage teams often hire coin-operated closers to sprint for this quarter’s number, when what they actually need are long-term builders who can shape ICP clarity, pipeline math, and repeatable motion. The trap is simple: you hire for momentum before you’ve validated the motion. That misalignment shows up at 00:00 Why early revenue hires fail and again at 04:16 Coin-operated sellers vs. long-term builders—two ideas every founder-led GTM team should internalize before the first half-dozen sales hires.

What separates a VP of Sales from a top 1% CRO is scope and systems thinking. A true CRO owns the full revenue engine—marketing, sales, solutions engineering, customer success, pricing, channels, and post-sale activation—not just the new-business line. It’s a role defined by precision around 07:44 Metrics, confidence, and velocity and the courage to decide when to centralize vs. decentralize capabilities as you grow. Should CROs lead sales? At 12:04 Should CROs lead sales?, the nuance is clear: yes, if the motion is still coalescing; not necessarily, once the machine is humming and specialization unlocks scale. My rule of thumb: start consolidated for speed of learning; split functions only when interlocks are provably robust.

There’s a humbling lesson in 16:36 Learning to scale at Twilio and 19:58 Stevie’s scaling mistake at Vanta: copying another company’s operating system, even a world-class one, is an easy way to blunt your edge. Context is king. What worked at Twilio won’t automatically work at a trust management business. That’s why the line at 17:44 “There is no CRO playbook” resonates so deeply. There are principles—org design, segmentation, enablement, compensation, customer activation—but your playbook must be bespoke to your product, pricing, cycle time, and buyer power map.

22:16 Why Vanta stays 100% sales-led is a reminder that not every high-growth motion demands product-led growth. In categories where compliance, security, and risk shape buying behavior, a consultative, sales-led approach builds trust and shortens time to value—especially when solutions engineering, onboarding, and customer success are tightly choreographed. I’ve seen teams chase PLG headlines while ignoring the higher-ROI path right in front of them: nailing the sales-led experience, from first touch to first value.

Top CROs plan 24–26 months ahead. 23:16 The value of planning 24-26 months ahead isn’t about creating perfect forecasts; it’s about designing optionality. That means hiring with stage gates, building enablement before you feel “ready,” instrumenting activation and retention early, and pressure-testing your pricing and packaging quarterly. In my org reviews, I push for scenario modeling: what breaks at 2x volume, what centralizes again at 600 headcount, and what competencies must be grown vs. bought.

On judgment and decision quality, 29:54 When trusting intuition was the wrong call is a familiar leadership tax. Pattern recognition is powerful—until it isn’t. I’ve learned to pair intuition with a data backstop and a lightweight pre-mortem: what would have to be true for this to fail? It’s the same posture I take with AI in GTM. At 30:49 Do humans still have a place in the future of GTM? and AI vs. humans in go-to-market, the answer is yes—but augmented. Humans set narrative, negotiate ambiguity, and build trust; AI accelerates research, writing, discovery, and coaching. The winning motion fuses both.

I’m often asked which tools materially shift outcomes. For revenue intelligence and operational rigor, I look to systems that compound learning: Gong: https://www.gong.io/, Salesforce: https://www.salesforce.com/, and Cursor: https://cursor.sh/. To study benchmark operating models and developer-led growth infrastructure, Twilio: https://www.twilio.com/ remains instructive. And to understand why trust, security, and compliance can define the entire GTM architecture, Vanta: https://www.vanta.com/ is a useful case study.

Leadership non-negotiables matter more as you scale. 33:33 Stevie’s leadership non-negotiables reminded me to be explicit about standards: clarity over activity, customer outcomes over internal wins, and auditability over anecdotes. 36:36 The myth of hiring for industry expertise shows up again and again—I’d rather hire for learning velocity, systems thinking, and builder DNA than narrow domain familiarity. And at 40:00 What stays centralized in a 600-person company, remember: centralize what must be consistent (data, tooling, pricing guardrails, core enablement), decentralize what benefits from speed and context (segment plays, partner motions, field marketing).

If you prefer a structured digest, here’s the operating checklist I use with revenue and product peers: define your ICP and value proposition crisply; hire builders over coin-operated sellers; instrument the first 30 days post-sale (47:09 The hidden leverage of a customer’s first 30 days); align pricing, packaging, and onboarding to activation; model capacity and hiring plans on 24–26 month horizons; decide early what stays centralized; use AI to amplify discovery, coaching, and content while keeping humans front-and-center for trust-building; and cultivate an unvarnished CEO–CRO pact (01:02:30 Unpacking the CEO-CRO dynamic) that aligns on strategy, segmentation, and sequencing.

For those who want a few timeline highlights: 00:00 Why early revenue hires fail; 02:23 Who to hire at $5M in revenue; 05:57 What excellence looks like in the CRO role; 17:44 “There is no CRO playbook”; 22:16 Why Vanta stays 100% sales-led; 23:16 The value of planning 24-26 months ahead; 47:09 The hidden leverage of a customer’s first 30 days; 53:42 Why the CRO role will face enormous changes by 2028; 58:42 What leaders must do now to stay relevant.

The throughline is simple and urgent. 53:42 Why the CRO role will face enormous changes by 2028 isn’t a forecast—it’s a present-tense mandate. 58:42 What leaders must do now to stay relevant: build a revenue system, not a sales team; plan further out while executing faster; let AI handle the mechanical so your people can master the human. Those who internalize this shift will be the fewer than 10% of current CROs who thrive by 2028. The rest will be outpaced by change they could have anticipated—and designed for.

February 19, 2026
Why “Figma Is Not the Source of Truth”: My Playbook for Design Leadership That Scales

I keep a simple mantra front and center: Figma is not the source of truth. The customer is. In practice, that means the only thing that truly counts is what we ship, how it performs, and whether users come back for more. Mockups are hypotheses; production usage is evidence. When my teams adopt this lens, velocity improves, judgment sharpens, and quality rises where it matters most.

So what does design actually do in a software company? At its best, design builds leverage for the whole system—engineering, product, and marketing—by clarifying problems, raising the quality bar, and making complex decisions legible. The standard I hold is ancient and still essential: products must be useful, usable, and desirable — and above all, used. When we calibrate around “used,” debates about pixels give way to outcomes, and cross-functional partners feel the difference.

I often trace the roots of our craft back well beyond the digital era. The lineage from industrial design to software is real; constraints, ergonomics, affordances, and systems thinking didn’t start with screens. If you’ve ever mapped delight, performance, and reliability in a Kano Model, you’ve touched this lineage. The translation to software is simple: design the full journey, not just the interface—prioritize what improves time-to-value, reduces cognitive load, and earns habitual use.

One lesson I’ve learned the hard way: why design leaders who stop designing stop leading. I still sketch flows, write UX copy, and prototype when it unblocks the team or sets a decisive quality bar. The altitude changes constantly—one hour I’m in a strategic roadmap review, the next I’m in a critique or poking at a prototype. Great design leaders jump up and down in altitude to connect vision to details without becoming a bottleneck.

Over time, I’ve come to rely on four pillars every design manager must master: craft (raising taste and execution), product strategy (clarifying choices and trade-offs), people leadership (coaching, feedback, and hiring), and systems (processes, rituals, and design ops that scale). Neglect any one of these and either quality, speed, or team health will eventually falter.

Perfectionism is a double-edged sword. Over-indexing on quality can paralyze decision-making, but lowering the bar indiscriminately is worse. I’ve seen moments where relaxing standards to “go faster” actually cost the business—rework piled up, trust eroded, and customer value stalled. The answer is principled delegation: I define what “must be true” at each milestone, delegate ownership with clear guardrails, and reserve my veto power for moments where product integrity is genuinely at risk.

Measuring success as a design leader starts with outcomes vs output OKRs. I care about activation, retention, time-to-first-value, NPS verbatims tied to key journeys, and the operational metrics that earn the right to build the next thing. Design output is visible; design outcomes are durable. When trade-offs are needed, I optimize for the smallest shippable surface that still proves the core value proposition, then expand with data.

Scaling judgment is the multiplier. I build it through pattern matching—studying enduring product systems from companies like Airbnb, Amazon, Apple, Asana, Notion, Stripe, Nest, and others—to distinguish where polish compels usage versus where it’s ornamental. Strong opinions matter, but so does being easy to convince with new evidence. I encourage designers to articulate the pattern they’re invoking, why it fits the job-to-be-done, and how we’ll know it worked.

Operating cadence matters. My week is anchored around recruiting, crits, and staff meetings that actually make decisions. In critiques, I use the Do/Try/Consider framework to give actionable direction without micromanaging. On one-on-ones, the question isn’t “Should one-on-ones exist?” but “What are they for right now?”—coaching, performance, or clearing execution blockers. If a meeting doesn’t increase clarity or commitment, it gets redesigned or removed.

Execution-wise, I’ve taken inspiration from Rippling’s operating system—especially its emphasis on speed, precise ownership, and hard commitments. The lesson is timeless: go fast on the right things, make clear promises, and instrument your work so you can see reality quickly. When speed is paired with crisp decision rights and observable outcomes, momentum compounds rather than frays trust.

Hiring your first design leader? Look for someone who can set standards, scale judgment, and ship. They should be able to zoom from company narrative to interaction copy in a single afternoon, coach product trios, and build rituals that make taste and trade-offs explicit. Above all, they should have a point of view on where quality moves the business and where speed is the quality.

Here’s how my team’s approach differs from many: Figma is not the source of truth. We design in Figma, but we learn from production. We pair designers with engineering early, prototype in code when it reduces risk, and wire telemetry into every critical path. Product trios use discovery to validate “useful, usable, desirable — and used,” then commit to outcomes with clear, testable definitions of success. The result is faster iteration, fewer surprises, and experiences customers actually adopt.

If you want to deepen your own pattern library, study products and practices from leaders like Airbnb (https://www.airbnb.com/), Amazon (https://www.amazon.com/), Apple (https://www.apple.com/), Asana (https://www.asana.com/), CrossFit (https://www.crossfit.com/), Figma (https://www.figma.com/), Honeywell (https://www.honeywell.com/), Nest (https://store.google.com/category/google_nest), Notion (https://www.notion.so/), Retool (https://retool.com/), Rippling (https://www.rippling.com/), and Stripe (https://www.stripe.com/). Pay attention to how they balance versatility with clarity, defaults with flexibility, and speed with trust.

The throughline is simple and demanding: design for reality, not for the board. Keep your standards where they create business value, scale judgment with explicit patterns, and instrument everything so learning never stops. When teams embrace that, the work gets better, customers feel it, and the roadmap starts to pull you forward.

February 12, 2026
Mastering 30,000-Foot Vision and Ground-Level Execution: Systems That Decide Without You

Executive function, for me, is the art and discipline of building systems that make high-quality decisions without my constant involvement. The real unlock isn’t personal heroics; it’s institutionalizing judgment. When I do my job well, teams move faster, ambiguity shrinks, and the organization compounds learning even when I’m not in the room.

Operating simultaneously at 30,000 feet and ground level is the defining muscle of executive leadership. I deliberately switch altitudes. At 30,000 feet, I obsess over strategy, architecture, and resourcing. On the ground, I validate core assumptions with firsthand data, listen for weak signals, and spot process cracks before they widen. Altitude changes are not random; they’re triggered by variance from plan, critical customer moments, or leading indicators that deviate from expected ranges.

The leap from frontline manager to manager of managers is where many rising leaders stall. As a manager of managers, my primary value shifts from personal execution to system design. I move from answering questions to installing mechanisms that ensure questions get answered well by others. This includes clear decision rights, shared metrics, and repeatable, lightweight rituals that scale across teams.

What is an executive actually accountable for? Outcomes over output, talent density, and the clarity of the operating system. That means defining strategy, aligning resources, creating a cadence of review that exposes truth, and ensuring incentives reward the behaviors we want. My barometer: if I step away, do priorities hold, do metrics behave as expected, and do tradeoffs land where I would have landed?

Knowing when to dive deep versus when to step back is a craft. I dive deep when risks are existential, when metrics have no credible owner, or when narrative and numbers diverge. I step back when leaders demonstrate consistent judgment, metrics sit inside control limits, and learnings are documented. The principle I return to again and again: context is everything. Senior leaders operate on context, not control.

To scale judgment, I teach people how I think. I externalize my mental models: how I construct decision trees, how I stress-test assumptions, and how I weigh time horizons. I rely heavily on driver trees for metrics because they force causal clarity. If we can’t map how a top-line goal decomposes into controllable levers, we’re managing by hope, not design.

Creating a shared language across the business is a force multiplier. I standardize definitions for our core metrics, codify what “good” looks like, and make it easy to repeat the system. We align around outcomes versus output, and we use cadences like MBRs and QBRs to unify narrative and numbers. Shared language makes decisions legible across functions and reduces rework.

My COO playbook emphasizes owning the full customer experience end to end. When marketing rolls up under a COO in certain stages, the upside is coherence: one narrative from awareness to activation to expansion, one set of metrics, one growth engine. The point isn’t org charts; it’s removing seams customers can feel.

Demanding and supportive is not a contradiction. I set ambitious, unambiguous bars and back them with coaching, resourcing, and fast feedback. The combination builds trust: expectations are clear, and help is immediate. I expect leaders to bring problems paired with proposed solutions and to escalate early, not perfectly.

Inside my executive interview process, I’m assessing altitude agility, operating cadence, and taste in metrics. I use structured interviews and live case workshops to see how candidates frame ambiguous problems, build driver trees, and prioritize tradeoffs. The best prompts are simple and revealing: design the operating system for a 3x scale scenario; diagnose a broken funnel with incomplete data; align two teams with conflicting incentives. The workshop prompts that reveal everything surface thinking speed, humility, and the instinct to make context legible.

The common thread in failed executive hires is a mismatch between the company’s operating system and the leader’s default mode. Some leaders can’t stop doing the work themselves. Others stay too abstract and never build mechanisms. I look for demonstrated ability to change systems, not just run them—leaders who can both author and evolve the playbook.

On metrics, I practice the driver tree philosophy. I begin with the North Star, decompose it into controllable levers, instrument each node, and assign single-threaded owners. We design review cadences where deviations trigger targeted diagnostics, not thrash. Each tree has documented assumptions, data sources, and thresholds that prompt action. This is how teams learn to anticipate, not react.

High-functioning executive teams are visibly collaborative. We clarify decision rights, disagree and commit quickly, and conduct post-decisions to harvest learnings without blame. My favorite litmus test is simple: can 30 people operate as one team when it matters? When we get this right, information flows, execution accelerates, and customers feel consistency.

One of the most counterintuitive leadership lessons is working yourself out of a job. If the system cannot run without you, you have a key-man risk, not a leadership strength. I aim to build successors, codify judgment, and design mechanisms that make good decisions the default state. That’s how you create durable, compounding advantage.

And the review feedback you can’t unhear? Mine was brutally honest: my bar was high, but my mechanisms were implicit. Once I wrote them down—how I decide, what I expect, where I dive deep—the organization moved faster, and I actually became less central. If there’s a throughline to extraordinary leadership, it’s this: make your judgment teachable and your systems inevitable.

February 5, 2026