I recently dug into a conversation with Marco Zappacosta, co-founder and CEO of Thumbtack, who has spent the last 13 years building the company into a billion-dollar business — and it’s his first and only job after graduating college. As someone who lives at the intersection of product management leadership and company-building, I was struck by how deliberately he navigates the deluge of advice that comes with being a first-time founder.
What resonated most was the way he differentiates between moments that demand a return to first principles and those that benefit from a tested playbook. In my own practice, I’ve found that product strategy and organizational design often require first-principles thinking, while operational cadence and execution rituals tend to scale best with proven patterns. The key is recognizing which game you’re playing — invention versus optimization — and applying the right mental models to filter input without losing velocity.
Marco’s approach to parsing counsel as a first-time CEO is refreshingly pragmatic. Rather than treating advice as binary, he triangulates from multiple data points, looks for invariants, and pressure-tests assumptions against the company’s unique context. I use a similar lens: anchor on the problem, map potential solutions to risk/return, and calibrate decisions with base rates where possible. It’s a disciplined way to turn a mountain of opinion into actionable signal — especially when stakes are high.
He also connects this discipline to stakeholder management, particularly in how he runs Thumbtack’s board so quarterly meetings become a critical resource, not just a time suck — and why he shares the board deck with the entire company. I’ve found this level of transparency to be a force multiplier: it aligns teams on priorities, elevates product roadmapping and sprint planning, and empowers leaders to make trade-offs with clarity. When the narrative is shared, accountability scales.
Marco candidly reflects on Thumbtack’s COVID-related layoff last year, and what he specifically did as CEO to ensure the folks who remained still had confidence in the company and his leadership moving forward. In hard moments like these, consistent communication, explicit prioritization, and a clear framework for decision-making matter more than ever. Trust is built by showing your work — why choices were made, what changes now, and how success will be measured.
Finally, he opens up his playbook for choosing what to spend his time on as a busy CEO with only so many hours in the day — and perhaps more importantly, how he stays accountable for these priorities. I’ve learned to pair outcome-oriented OKRs with a ruthless weekly schedule audit: if the calendar doesn’t reflect the strategy, the strategy won’t happen. This discipline creates focus, accelerates learning loops, and keeps leaders from becoming the bottleneck.
For builders at any growth stage, there’s a powerful takeaway here: cultivate a repeatable way to distill advice, clarify when to use first principles versus a playbook, operationalize board relationships as strategic assets, and turn time into your sharpest instrument. The result is a more resilient company — and a leadership practice that compounds.
AI has changed the tempo of product management, but not the timeless fundamentals. I’m living that paradox daily: the technology reshapes how we plan, build, and ship—yet the way we find real customer value hasn’t budged. Here’s how I reconcile both truths in practice.
At their core, large language models predict the next token. That’s it. Consider the prompt, “The cat sat on the ____.” Mat is most likely. Chair is pretty likely. Floor, roof, piano—all possible, just less probable. When you give an LLM a prompt, it runs through a neural network—billions of parameters making calculations—to predict the most likely next word, then the next, then the next. That neural net represents not just facts, but relationships, patterns, context—and some might even argue reasoning capabilities—that all influence the probabilities of different outputs. So LLMs don’t just predict the next token. They predict the next token by drawing upon a vast amount of knowledge. LLMs are transformational.
Behind the magic, LLMs don’t “know”—they rank what likely comes next. This slide’s cat sentence with weighted options underscores how generative AI reshapes work while remaining probability-driven.
And yet, AI makes silly mistakes. The kind that make you wonder how it could be so smart and so dumb at the same time. No matter how hard I tried, I could not get ChatGPT-5 to fix the closing quote on an image. And it took 12 seconds of thinking to figure out that I asked for a meeting summary but didn’t include any meeting notes. Why do LLMs make these mistakes? Well, it’s because all the LLM is doing is predicting the next token based on patterns. And sometimes those predictions are wrong. And when it’s wrong, it can be confidently wrong. So what do we do?
A side-by-side visual pits an optimistic AGI rocket against a frowning skeptic with a thumbs-down, ending with the prompt 'Which is it?'—capturing the debate over whether generative AI is transformative progress or overhyped.
To build reliable AI features, I focus my team on four core skills: prompt engineering, context engineering, orchestration, and evaluation (evals). Together, they let us harness what’s powerful about generative AI while reducing unforced errors.
Generative AI isn’t magic—it’s inputs, training data, and models. This visual invites readers to examine how predictions are produced and why transparency, bias checks, and ethics shape responsible outcomes.
First, prompt engineering. With LLMs, the quality of the input determines the quality of the output. An LLM can’t guess what you want. You have to specify what you want in a language the LLM understands. In a chat, you can iterate with multiple turns. In a product, you get one shot. When we ship a meeting summarizer, for example, I design a production-ready prompt that assigns a role (“You are a chief of staff for a busy executive…”), specifies output format (e.g., a structured list of action items), and clarifies exactly what to include and how to structure it. This is prompt engineering. It’s like writing a really good product spec—but for an AI.
A simple sentence becomes a window into generative AI: a neural network weighs options and selects 'mat,' showing how models rank likely words to produce fluent text—and why outputs feel confident yet probabilistic.
Second, context engineering. LLMs know a lot, but not everything. Imagine a takeaway reads, “Send the pipeline report to John by Friday.” When is Friday? Which John? What is the pipeline report? The model lacks today’s date, the roles of participants, and the specifics of our reporting vocabulary. If you dump every possible detail into the prompt, the model gets noisy input and the quality drops. The key is to add only the context the LLM needs to do the task at hand and nothing more. This is where techniques like RAG (Retrieval Augmented Generation) shine: retrieve just the relevant snippets—e.g., attendee roles, the precise definition of “pipeline report,” and the calendar context for “Friday”—and include only those in the prompt.
A minimalist slide demystifies how LLMs choose words: given 'The cat sat on the ____', the model ranks options by probability, showing that AI outputs are weighted predictions, not facts, which matters for ethics and product decisions.
Third, orchestration. LLMs are better at simpler tasks. If you try to do everything in one prompt—identify action items, summarize the meeting, categorize by urgency, match to owners, check for clarity—the quality goes down. But if you break it into steps, each focused on one thing, the quality goes up. I design a workflow of multiple LLM calls that work together. For example: First call: identify action items from the transcript. Second call: categorize by urgency using lightweight project context. Third call: match to owners with a minimal team directory. Fourth call: generate calendar events via an API. Each step is simple. But together, they create something sophisticated.
From building apps by chatting to auto-cleaning video and translating comments, AI compresses complex tasks into simple prompts. This scene shows why it feels transformative—while nudging us to weigh its limits and ethics.
Fourth, evaluation (evals). Otherwise known as: Is my AI product any good? At the heart of evals is this question: Given the input, did we get the expected output? I use several approaches. Datasets: I collect 20–100 real examples, define expected outputs, run the prompt, and measure the success rate. Code assertions: I enforce rules the output must follow (for example, “Every action item must have a task, owner, and deadline”) and fail the test when they’re violated. LLM-as-Judge: I use a model to verify factuality (e.g., “Is each item in this summary in the meeting transcript? Yes or no.”). Human evals: I track whether users need to edit the output and by how much. Start simple; get more sophisticated as the stakes rise.
From “AI Changes Everything (And Nothing At All),” this slide contrasts bold promises with everyday glitches: optimism about LLMs and AGI on the left, and a meeting-summarizer that can’t find content on the right—reminding us AI still errs.
Here’s the hard truth: you can master all four skills and still ship the wrong thing. Why? Because you chose the wrong problem to solve. Who needs yet another meeting summarizer? The ease of building with generative AI tempts us to jump straight to solutions. Resist it.
A keynote slide from AI Changes Everything (And Nothing At All) urges teams to master AI by harnessing its power while minimizing errors, framing a practical, ethical approach to building trustworthy products.
Discovery matters more than ever. Before writing your first prompt, be explicit about the impact you want and set a clear outcome. Talk to your customers and ensure you understand their needs; choose the right opportunity before you chase a cool solution. Explore multiple options and use assumption testing to converge on what will actually move the needle.
Clear prompts shape better AI. This slide shows how explicit, well-structured instructions lead to stronger LLM responses, reminding builders that inputs drive outcomes—and that responsible prompting begins with clarity.
Prototype, test, and build iteratively. With AI products, it’s easy to get to a great demo and hard to get to a production-grade experience. I validated feasibility before I wrote a line of production code by experimenting directly in a model’s UI, pasting real transcripts, and shaping the system instructions and output format. Only after I had a repeatable prompt and useful feedback did I worry about deployment.
Designing prompts is product design. This visual pairs common meeting requests with a rigorous summary framework, reminding teams that when prompts are embedded in products, accuracy matters because you get one shot.
From there, I tested deployment paths with the smallest viable investment. I tried a custom chat in Replit and embedded it. It wasn’t the right interaction model for targeted feedback. I switched to a submission flow using a homework-style pattern: students upload their transcript, the system processes it, they get detailed feedback. Done. That worked—until automation reliability lagged. I wired it up in Zapier, then rebuilt it on AWS Step Function for robust retries and better error handling when scale introduced edge cases.
A reminder that AI needs context: a simple instruction becomes ambiguous without dates, roles, recipients, and report details, highlighting the limits of LLM knowledge and the value of precise, shared prompts.
Each iteration tested a different assumption. Iteration 1 (Claude): Can AI even do this? What makes good feedback? Iteration 2 (Replit chat): How should people interact with it? Iteration 3 (Zapier): Can I integrate this into the existing workflow with minimal engineering? Iteration 4 (Step Functions): How do I make it reliable at scale when things inevitably go wrong? I didn’t know the answers up front; I learned by building, shipping, and observing.
From AI Changes Everything (And Nothing At All), this visual reminds builders that curated, high‑quality prompts yield better LLM responses, while overloaded, noisy inputs derail reasoning and reduce reliability.
Ethical data practices are non-negotiable. Improving AI products requires inspecting traces—the user input, system prompts, tool calls, and LLM responses. For a coaching flow, that includes the uploaded transcript, the system prompts that define evaluation criteria, and the AI’s feedback. To fix issues, I look at traces where things went wrong—but only with explicit user permission. Too many AI products collect everything by default and review traces without clear consent. Don’t do this.
A visual explainer showing how structured inputs—documents, verified code, roles, metrics, and timelines—flow into a network to shape a single, clear LLM response, underscoring the power of context in generative AI.
In my products, users must explicitly grant permission for me to review their data. The consent is clear and specific. If they say no, I don’t see their data. Full stop. That forces me to rely on synthetic data for many tests and to engineer privacy into the architecture from day one rather than bolt it on later. It’s the right thing to do, and increasingly, it’s required.
A visual guide to orchestration: break a complex meeting recap into smaller LLM calls, gather action items, bullet points, and a detailed summary, then aggregate the outputs to boost accuracy and reliability.
Everything changes—and nothing changes. Yes, there are new skills you should master: prompt engineering to get the right output the first time; context engineering to give the LLM exactly what it needs; orchestration to decompose complex tasks into simpler steps; and evals to systematically measure quality. And the fundamentals still rule: solve the right customer problems with strong discovery; prototype and iterate to de-risk; and uphold ethical data practices as a design constraint, not an afterthought. The teams that win with AI will master both the new technical craft and the timeless product fundamentals.
A presentation slide visualizes AI orchestration: user prompts generate meeting summaries while a graph of RAG and LLM calls routes tasks to the best sources, underscoring that retrieving the right context drives better outputs.
If this resonates, pick one active workflow, apply the four AI skills end-to-end, and run a short discovery and eval cycle against a clear outcome. Then ship. You’ll learn faster than any slide deck could ever teach you.
This presentation slide maps how to assess AI: link inputs to outputs with datasets, code assertions, LLM-as-judge checks, and human reviews—centering the question, "Given the input, did we get the expected output?"
Building a startup is equal parts craft and discipline. In my product leadership work, I’ve honed a repeatable approach for going from raw idea to real traction—and I often cross-check that playbook against the battle-tested experience of leaders I respect. I frequently reference insights from Gagan Biyani, co-founder and CEO of Maven, a company that empowers the world’s experts to offer cohort-based courses directly to their audience.
After being early at 3 startups that achieved over $1 million in run-rate in their first six months of going live, Gagan has learned some valuable lessons and seen a wide range of outcomes — from Udemy going on to IPO in 2021, to Sprig shutting down in 2017.
When I’m generating startup ideas, I start with open-ended exploration and a rigorous “problem inventory.” I look for founder–market fit, persistent pain points, and market signals that indicate urgency and willingness-to-pay. I also study competition to spot under-served segments or a wedge where a differentiated product discovery approach can win. The most common mistakes I see aspiring founders make are solution-first thinking, overvaluing total addressable market over real problems, and staying in stealth too long instead of testing in the wild.
Validation is where discipline pays off. I rely on minimum viable tests to rapidly de-risk assumptions and avoid false positives. My process mirrors the spirit of his “Minimum Viable Testing Process.” I define falsifiable hypotheses, run one-channel traction experiments, test willingness-to-pay early, and favor concierge or manual workflows before writing heavy code. These tight, timeboxed sprints force clarity on product-market fit signals while keeping burn low and learning velocity high.
Once the signals look promising, execution becomes a game of thoughtful sequencing. I explore multiple business models in parallel (subscriptions, usage-based, hybrid) while keeping the core value proposition crisp. Early go-to-market is founder-led GTM by design; I talk to customers daily, tune messaging, and iterate on onboarding until activation and retention curves stabilize. On the product side, I prioritize outcomes over output, set clear guardrails for roadmapping and sprint planning, and instrument the product to learn from every user interaction.
Co-founder selection and operating cadence matter as much as the idea. I look for complementary skills, shared values, and a bias for transparent conflict resolution. Before committing, we pressure-test collaboration with small, high-stakes projects, align on decision-making frameworks, and codify roles, equity, and vesting. As the company grows, I revisit these agreements to keep pace with evolving responsibilities and minimize execution drag.
If you’re eager to hear even more on finding startup ideas from Gagan, he’s teaming up with The Hustle’s Sam Parr to run an Ideation Bootcamp on the Maven platform — learn more and sign up here by May 2nd if you’re interested.
My takeaway: winning startups don’t depend on a eureka moment. They emerge from a disciplined loop—curious exploration, fast and falsifiable validation, and focused execution. If you apply these principles with persistence and empathy for the customer, you’ll increase your odds of finding product-market fit faster—and building something that endures.
I recently sat down with Melissa Tan to unpack the nuances between two PLG businesses and how growth strategy changes for a more complex product like Webflow. As I reflected on our conversation through the lens of product management leadership, I focused on what it really takes to design a high-impact growth organization, set rigorous goals, and evolve pricing and packaging without losing sight of customer value.
I spoke with Melissa Tan, GM of Self-Service and Head of Growth at Webflow and formerly Head of Growth and Monetization for Dropbox Business. Her experience scaling PLG motions across very different product surfaces offered practical signals on where to double down, what to sequence, and how to balance experimentation with long-term strategy.
Designing and structuring a growth org starts with mapping ownership to the user journey. I anchor cross-functional squads on outcomes across acquisition, activation, conversion, and expansion, with a shared platform and data foundation. Clear swimlanes, crisp interfaces with core product and marketing, and a consistent experimentation cadence keep velocity high while avoiding thrash. For PLG, I also recommend an embedded analytics function and strong partnerships with sales-assist and support to translate signals from self-serve to sales-led opportunities.
The right way to tackle goal-setting is outcomes-first. I favor outcomes vs output OKRs that ladder to a North Star and a small set of controllable, leading indicators (for example, activation rate, time-to-value, or successful workspace creation). I pair these with guardrail metrics to protect user experience and brand trust. Weekly reviews focus on decision quality and learning velocity, not just hit rates, so the team compounds insight even when experiments miss.
How Webflow’s pricing and packaging has evolved is a reminder that complex products require value-based packaging that clarifies who each plan is for and what milestones justify upgrade. When complexity rises, I encourage teams to simplify the fences, align packaging to clear value axes (usage, collaboration, security, or advanced workflows), and ensure in-product prompts communicate that value at the right moment in the journey.
How to calibrate pricing feedback comes down to triangulation. I segment qualitative input by customer size and use case, balance loud feedback with behavioral data (conversion by plan, downgrade reasons, add-on attach), and validate with structured research and live price tests. The aim is not to chase every request, but to isolate willingness-to-pay drivers, reduce friction for the majority, and preserve premium features that genuinely anchor expansion.
In this piece, I cover the essentials: designing and structuring a growth org, the right way to tackle goal-setting, how Webflow’s pricing and packaging has evolved, and how to calibrate pricing feedback. My goal is to leave you with a practical blueprint you can adapt to your PLG startup—one that aligns teams, accelerates learning, and translates product value into durable growth.
Every startup has a few hidden levers that, when pulled at the right time, deliver outsized results. As a product leader, I focus on finding those levers fast—by zeroing in on what truly drives customer behavior and by structuring experiments that compound learning. In this playbook, I share how I identify the key drivers of startup success, apply the Growth Lever framework, and adapt tactics across different business models without wasting cycles on vanity metrics or premature paid acquisition.
First, I anchor the strategy in a simple truth: the customer’s mindset is the ultimate growth driver. When I deeply understand why someone tries a product, what outcome they expect, and when they decide to trust it (or churn), growth stops being guesswork. That’s why I obsess over the customer journey—awareness, activation, retention, revenue, and referral—and use it to pinpoint bottlenecks that matter.
To operationalize this, I apply the Growth Lever framework. I map each step of the journey, quantify drop-offs, and prioritize interventions where a small improvement unlocks big gains. Instead of scattering effort across dozens of tactics, I concentrate experiments at the tightest constraint—whether that’s a broken onboarding moment, unclear value messaging, or a slow path to first value. The impact comes from focus, not volume.
Case studies reinforce this approach. For instance, when I analyze companies like Popsa: https://popsa.com/ and Shopify: https://www.shopify.com/, I look for the same pattern: where did customers first experience undeniable value, how quickly did they get there, and what changed when friction was removed? The lesson is consistent—clarifying the value moment and accelerating time-to-value often moves the needle more than any top-of-funnel push.
Customer research is my unfair advantage. I run structured interviews to uncover language, triggers, anxieties, and “jobs to be done.” I avoid leading questions, ask for concrete stories instead of opinions, and probe for the exact moment of conversion or abandonment. Then I translate those insights into product copy, onboarding flows, and pricing tests that align with how customers already think and decide.
I also watch for the triple threat of founder failure modes: chasing growth channels before product-market fit, optimizing local maxima instead of diagnosing constraints, and delegating growth too early. Founder-led growth strategies counteract this by keeping discovery, positioning, and early sales close to the founder until the core motion is repeatable. It’s the fastest way to learn, and it prevents premature scaling.
Unlocking growth bottlenecks requires sequencing. I time interventions deliberately: first tighten the ideal customer profile (ICP), then sharpen the value proposition, then smooth activation, and only then scale acquisition. When the sequence is right, each next step amplifies the last. When it’s wrong, spend increases while conversion and retention stagnate.
On experimentation, I prefer simple, falsifiable tests that isolate a single hypothesis—especially around activation and pricing. I predefine success criteria, run lean tests, and document learning rigorously. The goal is to make decision quality compounding: over time, the team gets faster at seeing what works and why.
Early on, I rarely recommend paid marketing. Most startups don’t need it to find traction; in fact, it often masks core issues and delays the hard work. Instead, I rely on customer interviews, founder-led sales, direct outreach, and product-led loops to validate whether the core value resonates. Paid channels become multipliers only after the core engine is efficient.
For sales-driven companies, I treat the sales process itself as a lever. I tighten ICP, refine discovery questions, align messaging to the buying triggers, and shorten the path to a compelling demo. In B2B sales, this often means proving value with a wedge use case, anchoring on outcomes instead of features, and engineering fast wins that build internal champions.
One concept I return to repeatedly is finding customer “locksmith moments”—those specific instances when the product unlocks a stubborn pain with surprising ease. Once I isolate that moment, I redesign onboarding, messaging, and pricing to get customers there faster and more reliably. That shift alone can transform activation and retention.
The power law of business reminds me to prioritize ruthlessly: a handful of decisions and experiments will drive most of the growth. I measure aggressively, kill distractions quickly, and double down on what demonstrably works. Momentum comes from stacking these wins in sequence.
Referenced examples and resources that often inform my thinking include benchmarks and patterns from companies across categories:
Airbnb: https://www.airbnb.com/
Bold Commerce: https://boldcommerce.com/
Calm: https://www.calm.com/
Caribou: https://www.usecaribou.com/
eBay: https://www.ebay.com/
FATMAP: https://fatmap.com/
PayPal: https://www.paypal.com/
Popsa: https://popsa.com/
Shopify: https://www.shopify.com/
Sonic Jobs: https://www.sonicjobs.com/
If you’re leading product or growth, my advice is simple: get uncomfortably close to your customers, trace their journey moment by moment, and pull the few levers that change behavior at scale. When you do, growth stops being mysterious—and starts being methodical.
I’m endlessly curious about what really moves the needle in product management—what separates a good developer tool from a beloved platform, and a growing company from a market-defining one. Sentry’s story is a blueprint I return to often, because it proves that focus, speed, and a healthy disregard for conventional wisdom can compound into outsized outcomes.
David Cramer is the co-founder of Sentry, the leading open-source error monitoring tool used by over 90,000 companies. A self-taught engineer, he went from 9th grade high school dropout and Burger King manager to building one of the most widely adopted developer tools in the world — by working hard and rejecting conventional wisdom. As of 2022, Sentry is valued at over $3 billion. David now serves as Chief Product Officer, after previously holding roles as CEO and CTO.
Here’s how I translate that journey into a practical playbook for product leaders. The non-linear start matters. “Learning to code through gaming” and “Dropping out of high school” may sound like detours, but they underscore a truth I’ve seen repeatedly: capability compounds fastest when you bias to action. “Building infrastructure at Disqus” sharpened the instincts that later shaped Sentry’s architecture and developer experience. And the remark “Software is not that hard” isn’t flippant—it’s a provocation to simplify aggressively, ship faster, and let real usage drive prioritization.
The origin story is a masterclass in bottoms-up product discovery. “Early interest in open source” created a natural surface area for feedback and trust. “The birth of Sentry” traces back to “How an code snippet grew into a ubiquitous monitoring platform”—a reminder that the best products often start as specific, useful utilities that earn their right to expand. And yes, “Why open source is an underrated distribution hack”: developers discover, adopt, and advocate when the product solves a painful problem with minimal friction. In my own roadmap decisions, I anchor on the same sequence—useful utility, instant setup, visible value, and a path to depth without forcing it.
Founders and product leaders stumble in predictable ways. “Two common founder mistakes” I see and Cramer’s story echoes: chasing novelty over necessity, and over-complicating early scope. “David’s unwavering focus” and “Finding conviction in decisions” show up as disciplined pruning—saying no to adjacent opportunities to win the core use case first. Equally, “More confidence, less ego” and the candid truth that “You’re gonna mess up” are cultural guardrails. Build mechanisms for fast feedback, reversible decisions, and postmortems that tighten the loop between signal and response.
On growth and go-to-market, Sentry reinforces principles I rely on. “Sentry’s journey to venture backing” followed traction, not the other way around; financing amplified momentum rather than manufacturing it. “How Sentry found PMF” came from obsessive product quality and clear value, not a clever pitch. The debate “Is sales valuable?” is resolved by context: high-velocity PLG can coexist with targeted sales when the product already sells itself. “Money is not the hardest problem”—focus and prioritization are. And the timeless warning, “Marketing won’t fix a bad product,” keeps the team oriented around durable value creation. “What makes Sentry’s market unique”—a critical, always-on workflow with near-universal developer demand—meant that “Why brand will always matter” wasn’t about polish for its own sake; it was about trust, clarity, and credibility with developers. Finally, “Eliminating all competition” is less about adversaries and more about erasing reasons a user would choose anything else: lower time-to-value, better signal-to-noise, and relentless polish where it counts.
The broader ecosystem threads are instructive, too. Touchpoints with Heroku, Dropbox, Stripe, Datadog, Okta, Oracle, Uber, Y Combinator, VS Code, Cursor, WindSurf, Yandex—and the perspectives of leaders like Aaron Levie, Max Levchin, Omar Johnson, and Satya Nadella—map to a shared pattern: build for developers, reduce cognitive overhead, and compound trust through consistent execution.
My distilled playbook for product leaders: prioritize clarity of problem and ruthless scope reduction; ship fast to earn trust, then deepen the product only where usage demands; use open source or frictionless entry as an adoption wedge when the audience is developer-first; layer brand on top of truth—signal speed, reliability, and craftsmanship; and design an operating cadence that turns mistakes into momentum. These aren’t slogans; they are operating constraints that consistently convert attention into advocacy.
When I look at Sentry’s trajectory, I’m reminded that the most durable products are built at the intersection of utility and taste. Utility earns adoption; taste earns loyalty. Do both, and you don’t just acquire users—you build an enduring market position that compounds, one excellent decision at a time.
Generative media is no longer a curiosity on the edges of product roadmaps—it’s fast becoming a core capability. Watching one company sprint from uncertainty to undeniable traction reminded me how much a decisive pivot, a developer-first brand, and ruthless focus can bend a growth curve. This is a story about finding product-market fit in real time, scaling with intention, and staying lean while the category accelerates beneath your feet.
Gorkem Yurtseven is the co-founder and CEO of fal, the generative media platform powering the next wave of image, video, and audio applications. In less than two years, fal has scaled from $2M to over $100M in ARR, serving over 2 million developers and more than 300 enterprises, including Adobe, Canva, and Shopify. In this conversation, Gorkem shares the inside story of fal’s pivot into explosive growth, the technical and cultural philosophies driving its success, and his predictions for the future of AI-generated media.
What stood out to me first was the clarity of the pivot: “How fal pivoted from data infrastructure to generative inference.” The hardest decisions often feel like abandonment—of code, roadmap, and even identity—but the right pivot reframes everything around a higher-signal customer need. That decision, described as “The hardest decision that saved the company,” unlocked a new trajectory and set a crisp north star for the team.
Equally important was the market intuition. As they put it, “Why ‘generative media’ is a greenfield new market.” Greenfield means pattern-breaking strategy: prioritize outcomes over parity, embrace new workflows rather than retrofit old ones, and measure value in quality, latency, and unit economics—not just features. In my experience, this is where product teams win or lose: you either build the new default or get trapped perfecting the old one.
fal’s “explosive year” wasn’t luck; it was systems thinking applied to a developer platform. The team stayed small—”lean <50-person team” and “Staying nimble as a 45-person company”—and built a brand that feels genuinely for builders: “Building a brand that resonates with developers.” That shows up in everything from docs and SDKs to the cultural quirks that scale signal, like “Why fal has 500 Slack channels.” Velocity and clarity compound when communication is designed for ownership.
Early traction came from sharp use cases and fast feedback loops. I loved the transition arc from “The early adopters of the first fal product” to “The transition from toy to tool.” In a new category, the fastest path to durable usage is making something delightful and then relentlessly hardening it for production: uptime targets, deterministic APIs, transparent pricing, and repeatable performance. That’s how you move from demos to dependable workflows.
The timing call is bold and specific: “Why 2025 is the year of AI-generated video” and “Predicting AI-generated film in 2027.” If you build in gen AI, this matters. Video will force teams to optimize for cost per second, temporal coherence, and developer ergonomics across long-running jobs. The winners will combine model choice (OpenAI, Anthropic, Google DeepMind, Stability AI; “Stable Diffusion XL (SDXL)”, “Sora”, “DALL-E”, “LLaMA”) with world-class inference, smart caching, and autoscaling that feels invisible to the developer.
On the go-to-market side, I see a masterclass in founder-led GTM and developer evangelism. “Competing in a fast-moving, fragmented market” requires sharp messaging and distinctive ideas. The story behind “GPU Rich / GPU Poor” is a perfect example: a memorable narrative that encodes a real infrastructure advantage. Pair that with “fal’s greatest optimization wins” and you get a brand promise rooted in measurable performance, not just clever copy.
Culture and team design are the force multipliers. “How to build a world-class team” and “fal’s unique hiring philosophy” emphasize high-slope talent, ownership, and speed over headcount. The result is a product org that ships, learns, and iterates without bureaucratic drag. For technical founders, “Learning sales as a technical founder” is a reminder that the best sales motion often emerges from the same instincts as great product discovery: ask better questions, observe real workflows, and sell through outcomes.
Here’s how I translate these lessons into a practical playbook for product leaders working in gen ai and developer platforms: double down on developer experience (time-to-first-output, clear pricing, robust SDKs), make latency and reliability your product features, sequence the roadmap from delightful demos to dependable production tools, and stay lean enough to pivot as models and use cases evolve. Above all, treat “Why generative media is a greenfield market” as a call to invent the defaults others will copy.
Looking ahead, the path is clear: as AI-generated video normalizes in 2025 and professional-grade content follows by 2027, the products that win will combine inference excellence with a brand developers trust. If you’re building in this space, now is the moment to ship fast, optimize relentlessly, and meet creators and developers where they already work.
Some conversations stay with you because they surface the hard-earned truths that quietly shape our judgment as product leaders. This episode of All Things Product with Teresa Torres and Petra Wille is one of those. As I listened, I found myself revisiting my own inflection points—times when prioritization became survival, when loyalty met reality, and when user research humbled my assumptions. What follows are the moments and mindsets I believe every product creator and product management leader can learn from.
Listen to this episode on: Spotify | Apple Podcasts
In this episode, Teresa and Petra swap the stories that shaped their careers. From navigating the fallout of the 2008 recession as a startup CEO, to realizing the company won’t love you back no matter how loyal you are, to the first user interviews that cracked open a new way of seeing product work—these are the pivotal (and sometimes funny) moments that changed everything. As I reflected, I connected these stories to practical patterns we all face: capacity limits that force clarity, leadership under uncertainty, and the discipline of product discovery.
At [02:30], Teresa’s crash course as a startup CEO during the 2008 recession reminded me that there are seasons in product where perfect information doesn’t exist—only direction and conviction. I’ve been there. In those moments, we earn trust by making the next best decision, communicating trade-offs clearly, and moving. That’s leadership when the stakes are real.
By [11:20], the conversation reframed prioritization as survival, not just a backlog exercise. I’ve learned the same lesson: hitting the limits of your own capacity reshapes how you prioritize. It’s not about doing more—it’s about deciding what not to do. In practice, that means aligning roadmaps to outcomes, not output, and letting OKRs focus the team on the few bets that matter now.
At [18:45], Teresa shares the insight that unlocked her agency as a leader: “No one knows the answer.” That line is liberating. When we stop searching for the mythical right answer, we create space for informed bets, time-boxed experiments, and evolving product strategy. I’ve seen teams accelerate the moment they internalize this.
At [29:10], Petra’s story—why the company doesn’t love you back—hits close to home. Loyalty is admirable, but without boundaries it becomes burnout. As leaders, we protect both people and outcomes by setting explicit expectations, designing sustainable on-call and delivery cadences, and recognizing impact early—long before a too-late pay raise tries to fix a deeper problem.
The [42:05] moment about the pay raise that came too late is a textbook example of how retention is a lagging indicator. Compensation, growth paths, and recognition must be proactive. If you wait for exit interviews to learn, you’ve already lost institutional knowledge and momentum.
At [50:15], Marty Cagan and Petra’s first user interviews at Starbucks show how humble, early customer conversations transform practice. Product discovery is not a ceremony; it’s a habit. Even scrappy interviews, when paired with a clear research objective and rapid synthesis, can change a roadmap. I encourage teams to start with simple, recurring conversations and make insights visible in sprint planning.
By [01:02:00], the funny research fail—“close the window” taken literally—delivers the humbling reminder that you are not your user. Language is loaded. Tasks must be unambiguous. And when in doubt, ask one more clarifying question. Every usability study I’ve run has revealed at least one assumption I didn’t know I was making.
Here’s what I took away as a leader and operator: capacity constraints are a gift if we let them focus us; uncertainty is the job, not a blocker; boundaries prevent burnout and build better products; and early, continuous user interviews keep us honest about outcomes over output. If your roadmap isn’t informed by real user context every week, it’s time to change your operating rhythm.
Follow Teresa Torres: https://ProductTalk.org
Follow Petra Wille: https://Petra-Wille.com
Mentioned in this episode: The True Story of Struggles and Success Of A Startup CEO with Teresa Torres by Barry O’Reilly: https://barryoreilly.com/explore/podcast/the-true-story-of-struggles-and-success-of-a-startup-ceo-with-teresa-torres/?ref=producttalk.org
Mentioned in this episode: Petra’s work on coaching product leaders: https://www.petra-wille.com/?ref=producttalk.org
Mentioned in this episode: Marty Cagan: https://www.svpg.com/team/marty-cagan/?ref=producttalk.org
Mentioned in this episode: iPAQ: https://en.wikipedia.org/wiki/IPAQ?ref=producttalk.org
Have thoughts on this episode? I’d love to hear which moment resonated most with you and how it’s shaping your product practice. Share your perspective and let’s learn from each other.
When I first heard, “Welcome to In Depth, a new podcast from First Round Review that’s dedicated to surfacing the tactical advice founders and startup leaders need to grow their teams, their companies and themselves,” I immediately thought: this is the kind of operating wisdom I reach for every week. As a product leader who obsesses over product management leadership and the realities of scaling teams, I’m drawn to resources that move beyond inspiration and deliver concrete playbooks I can put to work on Monday.
The promise here is refreshingly pragmatic: “We’ll cover a lot of ground and a wide range of topics, from hiring executives and becoming a better manager, to the importance of storytelling inside of your organization. But every interview will hit the level of tactical depth where the very best advice is found.” That’s exactly where the hard problems get solved—whether you’re navigating the IC to manager transition, tuning your approach to product discovery, or tackling employee retention at startups when growth forces you to rewrite the org playbook.
From my vantage point, the most valuable conversations unpack the patterns behind great executive hiring, the cadence of outcomes vs output OKRs, and how storytelling shapes alignment across product, engineering, and go-to-market. I’m eager for insights that translate directly into product roadmapping and sprint planning, lessons on product-market fit that stand up under scale, and founder-led GTM tactics that keep teams focused on what matters.
I’m all in for discussions that get specific—what to ask in a VP interview, how to structure a 30/60/90 for new leaders, and the rituals that keep quality high without slowing velocity. If you’re building, leading, or leveling up your craft, this is time well spent.
I hope you’ll join us. Subscribe to “In Depth” now and learn more at firstround.com
I sat down with Ayo Omojola, VP of Product at Carbon Health, to unpack the craft behind building in regulated industries and the discipline of choosing the right problems. Previously, he was the founding product manager on the banking team for Cash App at Square, where he co-created the Cash Card and helped build out Square’s technical banking infrastructure. He’s also a former founder of a Y Combinator-backed startup and an active angel investor, which gives him a unique lens into finding and evaluating startup ideas.
As we explored his time across healthcare and financial services, I was struck by how methodically he untangles regulation to reveal “the opportunities where it’s easy to stop.” That mindset—paired with his insistence on going “unreasonably deep” when building early products—mirrors the rigor I expect from high-performing product teams. It’s a reminder that in complex domains, product discovery starts with understanding constraints so thoroughly that they become catalysts for innovation.
Ayo thinks a lot about problem selection and makes the case for putting more effort into choosing what to work on. I couldn’t agree more. In my experience, a clear and deliberate bet at the outset compounds through product discovery, roadmapping, and execution—reducing thrash and sharpening the signal on product-market fit. The best product outcomes often stem from the discipline to say no until the “why now” and “why us” are undeniable.
If you’re thinking about starting a company someday, or you’re a product leader who hopes to help a new product take shape, this conversation will resonate. We talk about balancing speed with diligence, aligning teams around crisp context, and turning ambiguity into tractable work without losing sight of the customer. These are the muscles that separate good product management from product management leadership.
Even if company-building isn’t your immediate goal, there’s a lot to learn from the frameworks Ayo absorbed from exceptional operators. We dig into how to get better at process, how to set context so decisions scale, and why “optimizing for the outstanding” creates leverage across hiring, execution, and outcomes. I share where I’ve seen these approaches pay off in practice—especially during zero-to-one product discovery and early product roadmapping and sprint planning.
We also cover his management and hiring philosophy, including why he loves to hire former founders. I’ve seen the same pattern: ex-founders bring end-to-end ownership, a bias for outcomes over output, and a resilience that’s invaluable when the path is unclear. In fast-moving environments, those traits accelerate learning loops and raise the bar for the entire team.
You can follow Ayo on Twitter at @ay_o. For reference, the leaders he gave a shout out to in the episode include Robert Andersen (the founding designer at Square), Dhanji Prasanna (who led engineering for Cash App), Jim Esposito (Operations Lead for Cash App) and Emily Chiu (who led strategic development efforts for Cash App).
I recently connected with Steve El-Hage, co-founder and CEO of Drop, an electronics company that creates products powered by feedback by a massive community of enthusiasts and experts. As I listened, I put on both my product management leadership hat and my operator lens to unpack what other product creators and founders can take into their own journeys.
Reflecting on his 8-year, heads-down grind since becoming a first-time founder at 22, Steve shares the lessons that he figured out the hard way, from revenue dropping off a cliff and painful pivots, to hiring blunders and severe burnout.
When I hear “revenue dropping off a cliff,” I immediately think about product-market fit lessons and the mechanics of founder-led GTM. In my experience, these moments are brutal but clarifying: they force a ruthless reordering of priorities, sharper customer segmentation, and tighter feedback loops. I’ve learned to ask two simple questions in crises like this: Which customers are truly pulling the product? What’s the smallest, fastest test that can validate our next bet without mortgaging the roadmap?
The phrase “painful pivots” resonates because high-quality pivots are really about excellent product discovery under pressure. I anchor teams on outcomes vs output OKRs to avoid thrash, ensuring we change direction with purpose rather than panic. For me, the discipline is simple: re-state the problem in customer language, identify the leading indicator that proves we’re moving the needle, and then run small, compoundable experiments until the signal is undeniable.
Hiring blunders are equally instructive. Early-stage teams often over-index on resumes and under-weight learning velocity, values alignment, and ownership. I’ve corrected this by designing hiring loops that test real work, not theater, and by being explicit about the IC to manager transition expectations from day one. Strong onboarding and clear career paths improve employee retention at startups, but it starts with hiring for trajectory, not just pedigree.
Severe burnout is the tax we pay when pace outruns process. I’ve found that setting non-negotiable operating cadences—weekly priorities, monthly retros, and quarterly business reviews alongside OKRs (QBRs vs OKRs)—creates healthy pressure without chronic overload. Protecting maker time, limiting work-in-progress, and celebrating disciplined “no’s” are not luxuries; they are system-level safeguards that keep teams creative and resilient.
What Steve’s journey underscores for me is both humbling and practical: the hardest-earned lessons become your operating advantages. If you’re navigating a pivot, rebuilding after a revenue cliff, or evolving your team through growth, lean on customer-validated insights, tighten your feedback loops, and operationalize focus. That’s how you turn hard ways learned into repeatable wins.
“Start with the story” isn’t a slogan for me—it’s a daily operating principle. As I guide product strategy and align go-to-market with product discovery, I’ve seen how a clear narrative unlocks focus, speeds decisions, and lifts execution. That’s why David Cancel’s perspective resonates so strongly: when you build from the story, you build momentum.
David has been a CEO and founder of multiple different companies throughout his career. He’s also been a software engineer, a serial CTO, and the Chief Product Officer at Hubspot, giving him a unique lens into company building and leadership at different levels. That combination of product, engineering, and executive leadership creates the kind of pattern recognition I rely on when coaching PMs and shaping product management leadership practices across teams.
In my experience, the fastest way to align product, marketing, and sales is to anchor everyone in the same narrative. David’s framing around storytelling at Drift, a conversational marketing and sales platform, crystallizes this. From screenplay writing inspiration, to how storytelling training is part of their onboarding, David shares how they teach storytelling and drive narrative internally at Drift. I’ve adopted a similar approach—story-first onboarding for product creators and PMs, so every spec, roadmap, and customer interaction reinforces the same promise and positioning.
The leadership muscle here isn’t just crafting a compelling story—it’s keeping altitude discipline. He also shares tactical advice for engaging with exec teams and getting better at zooming in and out as CEO, as well as some really tactical frameworks, including Charlie Munger’s practice of inversion, the weekly rituals Drift relies on, and how they use asynchronous video communication. I use these same moves: inversion to pressure-test roadmaps and risks, weekly rituals to reinforce priorities, and async video to scale clear, human communication without meeting sprawl.
On my teams, inversion clarifies trade-offs: before we pursue a bet, we ask, “If this fails, what will have been the likely causes?” That simple prompt improves product discovery, sharpens founder-led GTM motions, and accelerates product-market fit lessons. When we meet, we start with a crisp narrative—who the customer is, what their struggle looks like, and why our solution is the inevitable next step. Outcomes replace output, and teams rally around impact.
I’ve also found that storytelling is a coachable skill, not an innate talent. We run lightweight workshops where PMs deconstruct great narratives (including screenplay structures) and rebuild their own. The effect is immediate: clearer problem statements, tighter product roadmapping and sprint planning, and better executive readouts that move decisions forward instead of sideways.
If you want to dive deeper into David’s perspective, you’ll find practical resources worth bookmarking. You can follow David on Twitter at @dcancel. He also pens a popular newsletter called “The One Thing,” and hosts a great podcast called “Seeking Wisdom.” For reference, the books he mentioned in the episode include Jon Kabat-Zinn’s work on mindful meditation, and “The Passion Paradox” by Brad Stulberg.
To learn more about how Drift approaches storytelling, check out this article David wrote for Inc:
To learn more about Charlie Munger’s concept of inversion that David mentioned, check out this Farnam Street post: https://fs.blog/2013/10/inversion/
If you’re leading teams, think of story as a system: a shared language for product, marketing, and sales; a filter for priorities; and a mechanism for speed. It’s a must-listen for current founders and CEOs, and anyone looking to level up their leadership skills. When we start with the story, we don’t just communicate better—we build better.
