Tag: gen ai

Building Products in a Post-LLM World: Hard-Won Lessons, Skeptic Busters, and Team Playbooks

The ground rules for product development have changed in the post-LLM world. I’m sharing a practical, first-person playbook—lessons I’ve pressure-tested in my own product org—to help you build AI-native products with confidence, cut through hype, and deliver outcomes that compound.

Sprig is an AI-powered user insights platform that has raised over $88m. Today’s discussion features two key individuals in Sprig’s journey so far: Ryan Glasgow, Sprig’s CEO and founder; and Kevin Mandich, Sprig’s Head of Machine Learning. Before Sprig, Ryan was an early PM at GraphScience, Vurb, and Weeby (all of which were acquired), and Kevin was an ML Engineer at Incubit, and a Post-Doctoral Researcher at UC San Diego.

In today’s episode, we discuss: Key lessons from the Sprig founding story; Product development in the pre vs. post-LLM world; How to overcome AI skepticism; How to evaluate new models and how to know when to switch; Why you need an ML engineer; Sprig’s “AI Squad” team structure; How Sprig upskills all team members on AI.

Founding story takeaways I keep returning to: conviction compounds when paired with continuous discovery. Early on, prioritize direct customer signal over elegant architectures. I’ve seen the fastest learning loops come from a tight PM–ML partnership that prototypes quickly, validates with real users, and refactors only after signal stabilizes. The Jobs to Be Done Framework: https://hbr.org/2016/09/know-your-customers-jobs-to-be-done remains my favorite lens to separate what the model can do from what the customer actually needs done.

Pre vs. post-LLM product development requires a mindset shift. Pre-LLM, we wrote deterministic systems and pushed the edge with models like Google’s BERT model: https://en.wikipedia.org/wiki/BERT_(language_model). Post-LLM, we design probabilistic systems, treat prompts like code, and invest in evaluation harnesses from day one. I routinely prototype with Chat GPT: https://chat.openai.com and scaffold experiments with Langchain: https://www.langchain.com/ to compress discovery cycles. The key is shipping guardrails and UX affordances that make non-determinism feel trustworthy.

On AI skepticism, I don’t argue—I demonstrate. I target one painful workflow, build a narrow, high-precision solution, and expose transparent failure modes with a human-in-the-loop escape hatch. This reframes AI from magic to leverage. In customer-facing settings (think customer support ai strategy), we measure deflection and satisfaction together so automation never outpaces user psychology.

Evaluating new models—and knowing when to switch—demands a clear rubric: task quality (ground-truthed), latency at p95, unit economics, privacy/compliance, and operational reliability. I run shadow evaluations before swapping production dependencies, then phase changes behind flags with canaries and backstops. Tools like Auto-GPT: https://github.com/Significant-Gravitas/Auto-GPT are useful for ideation, but I never skip rigorous offline and online evaluation before a cutover.

Why you need an ML engineer: the fastest teams pair a product manager who owns the problem framing with an ML engineer who owns the feasibility frontier. This duo translates ambiguous jobs into measurable tasks, instrumented datasets, and iterative model/UX improvements. In my experience, this partnership reduces time-to-learning more than any single tooling decision.

Sprig’s “AI Squad” team structure mirrors what I’ve seen work: a cross-functional pod with a PM, ML engineer, data engineer/analyst, design, and platform partner. The squad ships thin slices end-to-end, owns their eval suite, and meets weekly to review errors, edge cases, and customer feedback. We track outcomes vs output OKRs to ensure velocity serves impact—not the other way around.

Upskilling the entire team on AI is non-negotiable. I’ve had success with lightweight rituals: weekly demo hours, prompt libraries maintained in Jira: https://www.atlassian.com/software/jira, red-team exercises to uncover failure patterns, and internal brown bags where engineers and PMs teach each other. Small, frequent exposure beats heavyweight training.

For deeper exploration and hands-on experimentation, I reference: Auto-GPT: https://github.com/Significant-Gravitas/Auto-GPT; Chat GPT: https://chat.openai.com; Google’s BERT model: https://en.wikipedia.org/wiki/BERT_(language_model); Jira: https://www.atlassian.com/software/jira; Jobs to Be Done Framework: https://hbr.org/2016/09/know-your-customers-jobs-to-be-done; Langchain: https://www.langchain.com/; Sprig: https://sprig.com/.

Timestamps: (02:50) Intro (04:57) What attracted Kevin to Sprig (05:53) Kevin’s background before Sprig (07:56) How Ryan gained conviction about Kevin (09:55) Key technical challenges and how they solved them (18:46) How to overcome AI skepticism (21:47) The early difficulties of building an ML-enabled product (25:06) Evaluating new models and knowing when to switch (35:09) Using Chat GPT (37:23) Product development in the pre vs. post-LLM world (39:53) The impact of AI hype on Sprig’s product development (45:36) Balancing AI automation with user-psychology (48:47) Do recent LLMs reduce Sprig’s competitive advantage? (51:00) The importance of “selling the vision” to customers (54:40) How Sprig structures teams (57:25) How Sprig upskills all team members on AI (60:25) 3 key tips for companies trying to navigate AI (66:05) Major limitations with LLMs right now (70:27) The future of AI and the future of Sprig

Three guiding principles I use daily: first, reduce surface area—start with one high-value job and earn trust with reliability. Second, treat evaluation as a product—version prompts, log failures, and continuously retrain on your own data distributions. Third, design for collaboration—pair AI with human judgment and transparent controls so users feel empowered, not replaced. Post-LLM success isn’t about chasing models; it’s about building resilient systems, teams, and learning loops.

October 21, 2025
Inside Rewind AI’s Playbook: PMF Breakthroughs, Bold Twitter Fundraise, and the Future of AI

I sat down with Dan Siroker to explore the product, fundraising, and AI strategy lessons behind Rewind AI’s rapid rise — and to reflect on what I would adopt in my own product management practice today. Dan Siroker is the co-founder and CEO at Rewind AI, a personalized AI powered by everything you’ve seen, said, or heard. Dan launched Rewind to an emphatic response on Twitter, and used a public pitch video to fundraise at a $350m valuation. Prior to starting Rewind, Dan co-founded Optimizely, which reached $120m ARR before being acquired by Episerver, a content management company. Dan was also the Director of Analytics for Obama’s first presidential campaign.
What stood out immediately was Rewind’s journey to Product Market Fit and how deliberately the team instrumented learning loops. As a product leader, I pay close attention to how founders reduce ambiguity: narrow the target segment, ship thin slices, measure engagement cohorts, and iterate fast. Rewind’s early focus on utility and trust — not novelty — created the conditions for PMF while the team resisted the temptation to over-scope.
I was especially interested in how Rewind works and how the team managed scope while building a category-creating product. By focusing on personalized recall powered by on-device intelligence and a clear privacy narrative, they avoided the common trap of trying to solve everything for everyone. My own rule of thumb is to enforce brutal prioritization around the highest-intent jobs-to-be-done, then earn the right to expand. That same discipline shows up in Rewind’s cultural mantra for shipping and validating fast.
Lessons from Optimizely echo throughout. Being a second-time founder sharpens pattern recognition — from building high-clarity cultural values to operationalizing product-market fit. I’ve found that codifying operating principles early helps a team move faster with fewer collisions, and Dan’s approach to open feedback and public learning raises the bar for transparency.
On product positioning as a category creator, the team leaned into outcomes over features, which is critical when the mental model is new. Rather than compete in a features arms race, they framed a compelling before-and-after: instant, searchable memory that augments cognition. In my experience, that level of narrative clarity drives founder-led GTM and accelerates word-of-mouth.
We also dug into where to build in AI, and what makes a “wrapper” thin versus thick. My take: thin wrappers add shallow convenience on top of foundation models; thick wrappers integrate proprietary data, workflow depth, distribution advantages, and durable UX moats. Founders should aim for thick wrappers with unique data flywheels, not commodity interfaces easily displaced by platform shifts.
Operationalizing Product Market Fit remains a craft. I routinely use leading indicators like activation rate, day-7/day-30 retention for key actions, and sentiment via structured PMF surveys. Rahul Vohra’s framework for measuring and optimizing Product Market Fit: https://review.firstround.com/how-superhuman-built-an-engine-to-find-product-market-fit is a proven playbook. Pair that with cohort-based instrumentation and tight audience segmentation to reveal the “sharpest edge” of value.
On AI hype, we aligned on a pragmatic view: real value accrues where latency, accuracy, and privacy meet workflow depth. Apple’s Silicon: https://www.macrumors.com/guide/apple-silicon/ and on-device acceleration will keep unlocking new consumer experiences, while ChatGPT: https://chat.openai.com/ has reset expectations for natural interfaces. The cautionary tales of Google Glass: https://en.wikipedia.org/wiki/Google_Glass and Google Wave: https://en.wikipedia.org/wiki/Google_Wave remind me that timing, social acceptability, and use-case clarity matter as much as technical novelty.
Data privacy is now a core buying criterion, not a checkbox. I see a clear trend toward local-first approaches, explicit consent, and user agency — especially for products that touch memory, identity, and personal archives. Framing value through Maslow’s Hierarchy of Needs: https://www.simplypsychology.org/maslow.html helps prioritize trustworthy utility over gimmicks.
Dan’s one-of-a-kind Twitter fundraising strategy was a masterclass in founder-led GTM. By sharing a public pitch and engaging directly with early users and supporters, he compressed feedback cycles and aligned community, product, and capital. For reference, see Dan’s public Twitter fundraise: https://twitter.com/dsiroker/status/1646895452317700097 and Dan’s Rewind demo tweet: https://twitter.com/dsiroker/status/1638799931891920897. The transparency extended to leadership practice as well, with Dan publicly sharing his own 360 performance reviews: https://twitter.com/dsiroker/status/1689763756459675650 — a bold move that builds trust.
I’m watching what’s next for Rewind with interest, particularly around thicker integrations, extensibility, and collaboration patterns. In the next decade, I expect assistive AI to become ambient, multimodal, and context-aware — an ever-present copilot that feels less like a tool and more like an extension of cognition.
Referenced: Apple’s Silicon: https://www.macrumors.com/guide/apple-silicon/
Referenced: ChatGPT: https://chat.openai.com/
Referenced: Dan publicly sharing his own 360 performance reviews: https://twitter.com/dsiroker/status/1689763756459675650
Referenced: Dan’s public Twitter fundraise: https://twitter.com/dsiroker/status/1646895452317700097
Referenced: Dan’s Rewind demo tweet: https://twitter.com/dsiroker/status/1638799931891920897
Referenced: Google Glass: https://en.wikipedia.org/wiki/Google_Glass
Referenced: Google Wave: https://en.wikipedia.org/wiki/Google_Wave
Referenced: Maslow’s Hierarchy of Needs: https://www.simplypsychology.org/maslow.html
Referenced: Optimizely: https://www.optimizely.com/
Referenced: Paul Graham: https://twitter.com/paulg
Referenced: Rahul Vohra’s framework for measuring and optimizing Product Market Fit: https://review.firstround.com/how-superhuman-built-an-engine-to-find-product-market-fit
Referenced: Rewind AI: https://www.rewind.ai/
Referenced: Scribe (which morphed into Rewind): https://www.scribe.ai/about
Where to find Dan Siroker: Twitter: https://twitter.com/dsiroker
Where to find Dan Siroker: LinkedIn: https://www.linkedin.com/in/dsiroker
Where to find Dan Siroker: Personal website: https://siroker.com/
Where to find Dan Siroker: Blog: https://medium.com/@dsiroker
My takeaway for founders and product leaders: obsess over segmentation, instrument for learning, and tell a crisp narrative that earns trust. Thick wrappers, privacy-first design, and founder-led GTM are how you win the next wave of AI.

October 21, 2025
Goal-Setting for AI Products: How I Plan, Prioritize, and Confidently Ship in a Nonlinear GenAI World

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

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

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

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

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

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

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

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

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

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

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

October 20, 2025
Inside Bard’s Playbook: How to Ship AI Fast, Build Ethically, and Outlearn Competitors

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

October 20, 2025
From Vertical Focus to Power Users: My Playbook for Product-Market Fit and Founder Mindset

I’ve learned that the fastest route to product-market fit blends ruthless focus, a tight-knit community of power users, and a clear-eyed understanding of founder psychology. As a VP of Product, I’ve seen how aligning strategy and self-awareness creates compounding advantages—especially when you commit to a vertical, build with your most advanced users, and make decisions faster than your market shifts.

Start narrow. Building for a specific customer forces clarity: one ICP, one core job to be done, one measurable outcome. That single-threaded focus removes ambiguity from product discovery, sharpens prioritization, and accelerates iteration. Only after unmistakable pull—retention, compounding usage, and customer-led expansion—do I widen the aperture to a broader customer base.

Clay is a lead-generation software that uses AI to scrape 50+ databases and help companies scale their outbound campaigns. When I evaluate products like this, I look for a crisp vertical wedge (for example, outbound sales teams or growth marketers), a clear “time-to-first-value” path, and strong affordances for advanced workflows. Winning a vertical creates a reliable beachhead for expansion without diluting the core value proposition.

Power users are the engine of product evolution. I actively identify and convene them—by analyzing power-law usage patterns, high-complexity workflows, and frequent integration touches—then invite them into hands-on feedback loops. I’ve found small, recurring sessions where we co-design in Figma and document patterns in Notion to be especially effective. These users don’t just validate; they reveal emergent use cases, inspire templates, and shape the roadmap. The result is a community that evangelizes organically and sets a high bar for everyone else.

Speed is a strategy. I front-load decisions with clear success criteria, kill-switch thresholds, and a cadence of two-to-four-week sprints. The discipline isn’t just “moving fast”—it’s committing to bounded bets, reducing work-in-progress, and measuring outcomes over output. Focus is often misunderstood as doing less; in practice, it’s doing the essential few things completely and letting the data make the hard calls. This mindset unlocks faster iteration cycles and cleaner OKRs that reflect real customer value.

My principles for product-market fit are simple but demanding: undeniable engagement (habitual use without prompts), concentrated love from a specific customer archetype, willingness to endure friction for core value, expansion motions that begin with usage not discounts, and a backlog shaped by customer pull rather than internal aspiration. When these signals converge, you don’t ask, “Do we have PMF?”—you ask, “How do we scale responsibly?”

Founder psychology shapes the product more than most admit. A company is the reflection of its founder’s personality, from appetite for risk to tolerance for ambiguity. I align my own psychology with the business through honest self-inventory, explicit constraints, and a cadence of reflection. I’ve found the life spiral framework helpful to contextualize growth phases, and techniques from Internal Family Systems to reduce reactive decision-making. The outcome is a calmer operating system that scales with the company rather than against it.

Translating this into a customer journey, I design for a sharp “aha” moment within minutes, a guided path to first successful workflow, and a clear unlock that turns a single task into a repeatable motion. From there, I build leverage: templates, automations, and integrations that accelerate outcomes for advanced users while remaining accessible to newcomers. This is how individual success becomes team adoption—and team adoption becomes the basis for expansion.

To ground strategy in practice, I often pair discovery and build cycles with tools that meet teams where they already work: Airtable: https://www.airtable.com/, Clay: https://www.clay.com/, Figma: https://www.figma.com/, Internal Family Systems: https://ifs-institute.com/, NetSuite: https://www.netsuite.com/, Notion: https://www.notion.com, Sailthru: https://www.sailthru.com/. The stack matters less than the behaviors it enables: rapid prototyping, transparent collaboration, and decision trails that survive scale.

If you’re moving from first-time to second-time founder (or leading product through that evolution), the mindset shift is profound: less attachment to ideas, more attachment to evidence; fewer bets, bigger conviction; and a deeper respect for focus as an accelerant. Lean into vertical excellence, invest in your power users, and do the inner work. That’s how you achieve product-market fit—and keep it.

October 20, 2025
The Secret Lever Behind Replit’s Hypergrowth—and the Product Playbook You Can Reuse

I study breakout platforms to refine how we build and scale product at HighLevel, and one story I keep returning to is how a modern dev tool can outpace entrenched competitors by reducing friction and amplifying distribution. Replit’s trajectory is a masterclass in both. As a product leader, I wanted to capture the strategic levers I see at work—and how any product creator can adapt them.

Replit is an online platform designed for collaborative coding in multiple programming languages. Replit boasts over 30m users, has secured $200M in venture funding, and was recently valued at $1.2B. These facts aren’t just impressive milestones; they’re signals of product-market fit compounding through sharp positioning, relentless iteration, and a distribution engine that turned usage into growth.

Here’s the core insight I take from Replit’s rise: the most durable advantage came from collapsing the distance between idea and software. By making it trivial to start, share, and iterate, the product converted curiosity into creation, and creation into distribution. In practice, that looks like zero-setup environments, multiplayer by default, and a UX that rewards shipping. When the platform itself becomes the marketing, you’ve found the secret lever.

AI is accelerating this shift. Integrating gen AI into the flow of work doesn’t just speed coding; it broadens who can build. I see this daily with product teams using AI for scaffolding prototypes, refactoring tricky edge cases, and translating intent (“what should this do?”) into working software. This is where the new “software creator” role emerges—part product thinker, part prompt engineer, part builder—unlocked by copilots and smart defaults rather than heavyweight toolchains.

For me, this reframes the strategy question from “How do we add features?” to “How do we lower activation energy?” The drivers of growth are then predictable: faster time-to-first-value, social proof embedded in artifacts users already share, and a distribution engine that compounds. Think of every project as a portable, runnable demo—content, onboarding, and virality in one.

There’s also a leadership lesson in the origin story: resilience and contrarian conviction often precede acceptance. The path included fundraising difficulties and multiple near-misses with Y Combinator—“Why YC almost rejected Replit four times” is a reminder that consensus is a lagging indicator. Credit where due, timely belief from people like Paul Graham can change the arc, but the throughline is persistence paired with user obsession.

On monetization, the strategy I favor—and see reflected here—is to let the free tier fuel creation and community, then monetize depth: private workspaces, performance, collaboration, compute, enterprise governance. In other words, price the power, not the curiosity. This aligns the business model to the distribution engine and avoids taxing the very behaviors that drive growth.

As AI reshapes engineering, I expect team topologies to evolve. I’m already deploying forward deployed engineers who sit with customers, use gen ai for product prototyping, and collapse feedback loops from weeks to hours. Combined with outcomes vs output OKRs, this makes room for velocity without sacrificing quality: ship thin slices, observe real behavior, let data and user value—not internal preferences—pull the roadmap forward.

If you lead product, here’s the playbook I’d reuse tomorrow: remove setup friction until “start” feels inevitable; turn every creation into content people naturally share; instrument for learning and iterate weekly; layer gen AI where it erases toil and unlocks new builders; and keep the monetization strategy aligned to usage intensity, not entry.

A few references that continue to shape my thinking—and that surfaced in this story—are worth bookmarking: 7 Powers: https://www.amazon.com/7-Powers-Foundations-Business-Strategy/dp/0998116319/; The Innovator’s Dilemma: https://www.amazon.com/Innovators-Dilemma-Technologies-Management-Innovation/dp/1633691780/; Mythical Man-Month: https://www.amazon.com/Mythical-Man-Month-Software-Engineering-Anniversary/dp/0201835959; On the Naturalness of Software: https://people.inf.ethz.ch/suz/publications/natural.pdf. For practitioners, I’d also keep an eye on OpenAI: https://openai.com/, Hacker News: https://news.ycombinator.com/, and ecosystems like Python: https://www.python.org/.

Summing it up: distribution is a product choice, not a marketing afterthought. When you design for creation, collaboration, and shareability from day one—and amplify with AI—you don’t just chase growth; you manufacture it. That’s the lever I’m pulling across my teams, and the mindset I’d recommend to every product creator aiming to build category-defining software.

October 20, 2025
Scaling Enterprise AI That Sells: Battle-Tested Playbooks for PMF, Champions, and Agentic AI

Enterprise AI is exhilarating and unforgiving. I’ve seen gorgeous demos fall apart under real-world constraints and seemingly modest pilots unlock outsized value at scale. In this reflection, I share the playbooks I rely on to build, scale, and sell generative AI in the enterprise—what actually moves deals, secures product-market fit, and sustains trust with the C-suite and the front line.

Why is it so difficult to scale AI products for enterprise? Because the bar is higher on every dimension: data governance, security, extensibility, integration depth, reliability, and measurable ROI. An enterprise-grade, full-stack generative AI platform isn’t just a model; it’s the surrounding system—observability, evals, policy, workflow, and human-in-the-loop—that makes outcomes predictable, auditable, and safe. The fastest path to adoption is simple: deliver on-brand, on-policy content and decisions using a customer’s first-party data, and prove that quality holds up under load.

My north star is dependability over demo magic. The number one challenge is making model output dependable across messy, high-variance enterprise inputs. I build an evaluation harness early, with gold datasets, task-specific metrics, and human adjudication. Every change ships behind guardrails and is measured against cost, latency, and quality SLOs. When governance, change management, and procurement show up (they always do), I treat them like first-class product requirements, not hurdles.

Champions are the secret to winning complex accounts. I map the org, find operators who feel the pain daily, and quantify that pain in dollars and hours. Then I define success criteria upfront—time-to-value in under 30 days, measurable uplift (e.g., deflection, conversion, cycle time), and a plan for scale. I deploy forward deployed engineers alongside the business to co-design workflows, refine prompts and evaluators, and document before/after outcomes. Champions don’t just approve pilots; they co-author the business case and defend it.

To win the enterprise, trust architecture matters as much as model architecture. I lead with clear answers on data residency, encryption, SSO, RBAC, DLP, and retention policies; I address whether customer data trains models, default behaviors, and opt-in controls. I offer flexible deployment (VPC or private networking when needed), transparent pricing, and SLAs with real teeth. I also integrate where work already happens—CRM, help desk, knowledge bases—so value shows up in the flow of work.

Signs of healthy product-market fit are unmistakable: pull from lookalike buyers, multi-threaded expansions, champions who present results internally without me in the room, and usage that moves from experimentation to business-critical. I watch for weekly active usage above pilot thresholds, POCs converting to multi-year deals, and adjacent teams (Support, Marketing, Legal, RevOps) asking to onboard with minimal push. PMF feels less like persuasion and more like coordination.

Scaling large language models for specific use cases requires ruthless focus. I constrain scope to tightly defined workflows, pair retrieval with structured knowledge, and mix model strategies (base models, fine-tunes, tools, and function calling) based on cost and latency budgets. I codify policy-as-code and deploy guardrails at the orchestration layer, not just the model layer. Continuous evaluation—both automatic and human—is the heartbeat of quality.

My advice to AI founders in 2024 is pragmatic. Start with outcomes, not demos. Establish outcomes vs output OKRs that tie directly to revenue, cost, risk, or customer experience. Use gen AI for product prototyping to shorten discovery cycles, but graduate quickly to instrumented workflows in production. Align early with InfoSec and Legal; your speed will be gated by trust, not code. And when in doubt, ship smaller, safer increments faster.

Healthy co-founder relationships look the same across winning companies: clear domains, fast escalation, and a shared appetite for “disagree and commit.” I keep a decision log, time-box debates, and make moments-of-truth visible to the team and board. You’ll know it’s working when you have more energy after hard conversations than before.

The future of agentic AI is deeply enterprise: multi-agent workflows that plan, act, and verify with human oversight where it matters. The winners will combine reasoning, tool use, retrieval, and policy with audit trails that satisfy compliance while keeping velocity high. Think of it as re-engineering business processes around AI-native steps, not sprinkling AI on top of legacy workflows.

Culture turns strategy into reality. I anchor my teams on “connect, challenge, and own.” Connect means obsess over the customer problem and internal alignment. Challenge means we red-team our ideas, run experiments, and measure impact. Own means we accept outcomes, not just output, and we iterate until the business moves. This is how a customer support ai strategy becomes a durable moat, not a slide.

If you’re a product creator or product management leader, the above playbooks are meant to be lifted and adapted. Start where the pain is loudest, quantify the win, and let champions carry the story. The compound interest of disciplined product discovery, strong governance, and relentless evaluation is a generative AI business that sells itself—and scales.

October 20, 2025
DevTools at Scale: Hard-Won Lessons on PMF, AI, and Culture from Apple, AWS, Microsoft

Building and scaling DevTools has taught me that world-class culture and relentless product focus are non-negotiable. Drawing on experiences across Amazon, Apple, and Microsoft—and hard-won lessons from startups like Unblocked and Buddybuild—I’m sharing the principles I rely on to ship great developer products at scale.

Why building for developers is different: developers are discerning, allergic to friction, and quick to churn if the DX isn’t exceptional. That means fast setup, clear docs, ergonomic APIs, sane defaults, and deep integrations with GitHub, GitLab, Bitbucket, Confluence, AWS, and Microsoft Azure.

I benchmark teams against gold-standard platforms like Stripe, Twilio, and Looker—tools that reward mastery, never bury the lede, and make success observable in minutes, not days.

From the early days of Buddybuild, the signal was unmistakable: remove toil from CI/CD, shorten feedback loops, and teams will expand usage without a sales nudge. The pattern holds across DevTools: when time-to-value approaches zero, the product sells itself.

Early signs of product market fit: organic team-to-team adoption, repeatable setup success, contribution from power users, and inbound demand you cannot keep up with. When these show up, “Why great product is everything” stops sounding like a platitude and starts reading like a P&L.

Monetizing product market fit is straightforward if you align value and pricing units. Seat-based maps to collaboration; usage-based maps to compute, API calls, or storage; hybrid models reduce edge-case friction. Keep the packaging simple and double down on “The power of positioning.”

AI is complicating product market fit. Gen AI accelerates gen ai for product prototyping, but it also introduces instability: model drift, hallucinations, and evaluation blind spots. I build an evaluation harness, human-in-the-loop review for risky flows, and a clear customer support ai strategy before scaling.

Being customer-obsessed is the moat. I embed forward deployed engineers with key customers to translate real workflows into product decisions, close the empathy gap, and validate behavior in production environments.

On decision-making, I blend product discovery with crisp documents and measurable bets: PRFAQs or design docs to clarify intent, guardrails in analytics, and outcomes vs output OKRs to keep teams aligned to impact.

Unblocked, a developer tool that lets you talk to your codebase, points toward a future where code search, context, and refactoring converge into conversational workflows. I’m bullish on the pattern, but I stay sober about failure modes and cost-to-serve.

Here’s my cautious take on AI: latency, privacy, and provenance matter as much as model quality. The best teams treat prompts as product, training data as liability, and evaluation as a first-class release gate.

Hiring is where many teams stumble. Don’t over-index on competency when hiring. I optimize for learning velocity, ownership, and kindness under pressure. Competency scales output; character scales organizations.

As a second-time founder and operator, I treat mental health like uptime. I schedule recovery, define non-negotiables, and surround myself with peers who normalize the hard days. Burnout is a systems failure, not an individual weakness.

I don’t do demos. I prefer self-serve trials with instrumented onboarding, sample projects, and guardrails that let the product do the talking. If a prospect can’t succeed in 15 minutes, we fix the product, not the deck.

On customer feedback, I separate noise from signal with cohorts and context. I prioritize requests that reduce time-to-value, unblock integrations, or meaningfully expand the surface area of successful use cases. That’s how to deal with customer feedback without losing strategic focus.

To build and scale DevTools, keep the bar high and the loop tight: ship small, watch usage, learn fast. Invest in platform reliability, rock-solid SDKs and CLIs, and a developer experience that earns trust release after release.

Resources and touchstones I revisit often:

Apple’s acquisition of Buddybuild: https://www.cnbc.com/2018/01/02/apple-agrees-to-buy-buddybuild.html

AWS: https://aws.amazon.com

Bitbucket: https://bitbucket.org

Confluence: https://www.atlassian.com/software/confluence

GitHub: https://github.com

GitLab: https://gitlab.com

Looker: https://looker.com

Microsoft Azure: https://azure.microsoft.com

Stewart Butterfield: https://www.linkedin.com/in/butterfield/

Stripe: https://stripe.com

Twilio: https://twilio.com

Unblocked: https://getunblocked.com/

If you’re building for developers, stay ruthless about simplicity, respectful of their time, and obsessed with proof in production. That’s how durable product-market fit is earned—and monetized.

October 20, 2025
A Masterclass in Founder Conviction: Gong’s $100m ARR, PMF Breakthroughs, and AI Sales

I’ve long believed that true product leadership is measured by conviction you can defend with data. That’s why the story of Gong resonates so deeply with me. Eilon Reshef is the co-founder and CPO at Gong, an AI-powered platform that tracks, records, and analyzes sales calls to drive revenue growth. In 2021, Gong raised $250M at a $7.25B valuation. Gong was one of the fastest SaaS companies to hit $100m ARR, and now has over 4000 customers. Before Gong, Eilon sold his previous e-commerce startup, Webcollage. Why does this matter to product creators like us? Because betting on recording sales calls wasn’t a popular opinion at the time—it was a bold thesis about conversation data as the primary system of record for revenue. The insight was simple and powerful: conversations are the most unstructured and under-utilized signal in B2B sales. Capture them end-to-end, analyze them with AI, and you unlock repeatable sales execution at scale. I was bullish on this category early for the same reason: recording sales calls converts ephemeral “tribal knowledge” into searchable, coachable truth. That enables better product discovery, sharper positioning, and tighter feedback loops between go-to-market and product—even more so as gen ai capabilities matured. Early product-market fit signals were unmistakable: persistent usage by frontline reps, managers organically building coaching rituals around insights, and executives tying outcomes to pipeline velocity and win rates. The emergence of “raving fans” wasn’t a vanity metric—it was the leading indicator that the product was changing behavior and embedding into daily workflows. Keeping the beta lean was crucial. Instead of building a feature buffet, the focus stayed on a few, high-utility workflows that consistently delivered value in the wild. In my own teams, we mirror this with forward deployed engineers and a tight set of design partners who are willing to co-develop, tolerate rough edges, and trade early access for tangible impact. Design partners, when chosen well, become your reality check and your accelerant. Their hardest problems guide prioritization; their workflows reveal where friction truly lives. This is where outcomes vs output OKRs matter—measuring behavior change and revenue outcomes, not just shipped features. The initial demo reactions often sounded like a referendum on change management: legal concerns about recording, rep discomfort, or doubts about AI accuracy. Strong founder conviction met these with data and empathy—clear consent frameworks, rapid improvements in transcription and modeling, and, most importantly, undeniable win stories that reframed risk as opportunity. Monetization followed the value. Pricing and packaging worked best when buyers could connect usage directly to measurable outcomes: faster ramp, better forecast accuracy, higher conversion rates, and more consistent deal execution. With a land-and-expand motion, teams saw success at the manager pod level before scaling across the org. I appreciated the disciplined approach to the roadmap. A unique product roadmap framework anchored on durable customer outcomes created internal clarity: which insights change coaching, which recommendations change behavior, and which automations remove repetitive work. This is classic product management leadership—create alignment with narrative, evidence, and a few high-conviction bets. The journey to multi-product was a natural extension of product-market fit. Start with conversation intelligence; expand to adjacent revenue workflows where the same data asset offers compounding value—forecasting, deal risk, enablement, and coaching. The throughline: one trusted data layer, many value surfaces. Having built AI products since 2015, I’ve learned to prioritize data quality, model reliability, and tight human-in-the-loop design. The best gen ai experiences pair high-recall analysis with opinionated UX that guides managers and reps to take the next best action. That’s how you turn insights into habits. Looking ahead, the future of AI in B2B sales efficiency is practical autonomy: assistants that summarize calls, draft follow-ups, update CRM fields, flag risks, and trigger playbooks—without adding workflow friction. The winners will combine precision models, secure data handling, and workflow-native delivery. Measuring success goes beyond dashboard vanity. What matters: adoption depth across roles, coaching frequency, deal cycle time, conversion lift, forecast accuracy, and the creation of “raving fans” who advocate internally and externally. When the product becomes the backbone of pipeline conversations, you’ve crossed the line from tool to system. I also see enduring relevance in foundational thinking like Crossing the Chasm. It explains why design partner fit precedes market fit, why early majority buyers demand social proof, and why operational excellence matters as much as product insight during hypergrowth. If you want to explore the broader ecosystem and resources mentioned, here are the references exactly as noted: Act-On Software: https://act-on.com/ Amit Bendov: https://www.linkedin.com/in/amitbendov/ BlueJeans: https://www.bluejeans.com/ Crossing the Chasm: https://www.amazon.com/Crossing-Chasm-3rd-Geoffrey-Moore/dp/0062292986 Gong: https://www.gong.io/ Mistral: https://mistral.ai/ OpenAI: https://openai.com/ Salesforce: https://salesforce.com/ Webcollage: https://www.crunchbase.com/organization/webcollage Webex: https://www.webex.com/ Zoom: https://zoom.us/ Where to find Eilon Reshef: LinkedIn: https://www.linkedin.com/in/eilonreshef/ For product leaders, the takeaways are clear: anchor on customer outcomes, cultivate design partners who become co-authors of your roadmap, use gen ai for product prototyping to accelerate discovery, and measure conviction not by opinions but by repeatable revenue impact. That is the essence of durable, product-market fit lessons you can operationalize today.

October 20, 2025
Developing Technical Taste: My Playbook for Next‑Gen Engineers, AI Strategy, and 2024 Scaling

When I think about the next generation of engineers and product creators, one capability consistently separates the great from the good: technical taste. It’s the intuition to choose the simplest viable path, the humility to iterate, and the courage to ask “what if” before everyone else. In this piece, I share how I frame technical taste, what it means for AI strategy, and how to scale software teams in 2024 without losing speed or product-market fit.

Sam Schillace is the CVP and Deputy CTO at Microsoft. Before Microsoft, Sam held prominent engineering roles at Google and Box. He has also founded six startups, including Writely, which was acquired by Google and became Google Docs.

In this deep dive, I explore themes like “Sam’s advice for future engineers,” “What’s next for AI,” “How to develop technical taste,” “The importance of asking ‘what if’ questions,” “Lessons on market timing,” and “Scaling a software company in 2024.” My lens is product management leadership at scale, with a bias toward clear decision-making, rapid learning, and compounding leverage.

On market timing, my experience echoes the principle that momentum compounds only after you align product insight with the market’s inflection point. “The Innovator’s Dilemma” reminds us that the very systems designed to protect current value can block new value. The smartest move I’ve seen is to treat disruptive bets like venture portfolios inside the company—small, time-boxed, outcome-driven, and shielded from legacy KPIs. That’s how you preserve execution excellence while creating space for the next S-curve.

Technical taste is developable. I look for three signals: first, engineers who reduce a problem to its essence and deliver a working slice quickly; second, product creators who anchor on outcomes vs output OKRs; third, teams who habitually ask “what if” questions to surface non-obvious constraints and new leverage. When this mindset meets strong product discovery, you get faster cycles, fewer dead ends, and clearer product-market fit lessons.

“Building Google Docs” is a case study in choosing the web as the platform before it was fashionable—an act of taste under uncertainty. It’s also a reminder that what looks inevitable in hindsight was controversial in real time. Discussions about “The decline of Google apps” are less about any one company and more about the drift that occurs when focus fragments; taste is how you steer back to the core job-to-be-done.

On “The Innovator’s Dilemma facing Microsoft” and “The differences between Google and Microsoft,” I’ve seen how culture shapes product motion. One optimizes for experimentation at massive consumer scale; the other, for enterprise trust and durability. The playbook to reconcile both: define two operating modes—explore and exploit—and make the seams explicit. Use forward deployed engineers to learn with customers, while platform teams industrialize the wins.

“How to build a winning product” in 2024 is straightforward to say and hard to do: shorten the distance between insight and impact. I prioritize gen AI for product prototyping to test feasibility early, pair it with real-user loops from day one, and instrument everything to learn faster than competitors. Ruthlessly prune scope to ship a lovable slice, then iterate. That’s how you scale software in 2024 without bloating teams or code.

On “Becoming an optimist,” I’ve learned optimism is a practice: assume better solutions exist, then run experiments to find them. “What makes a great engineer” and “One thing the best engineers do” often collapse into the same behavior—holding high standards while moving fast. The best engineers I know ask precise “what if” questions, surface edge cases early, and translate ambiguity into a plan the team can execute.

“Sam’s prediction about AI,” “Capturing the value of AI,” and “How you should think about AI” all converge on a few product truths. Co-pilots and agents will become table stakes; differentiation will come from domain-specific data, workflow depth, and trust. Value accrues where AI is closest to the decision or outcome—embedded in the flow of work, not bolted on. For customer support AI strategy, the win isn’t a clever bot; it’s reducing time-to-resolution with explainability, guardrails, and continuous learning from real tickets.

“Microsoft’s new leverage,” “Scaling software in 2024,” and “The future of AI across several sectors” point to a broader shift: platforms that combine distribution, identity, and compliance will set the rules of engagement. But even in that world, local excellence matters—tight loops with customers, forward deployed engineers, and outcome-centric roadmaps will out-execute feature factories. The teams that treat gen AI as a capability—not a feature—will capture durable advantage.

Referenced:

Amazon: https://amazon.com

Box: https://www.box.com/

Elon Musk: https://twitter.com/elonmusk

Google Docs: https://docs.google.com

Itzhak Perlman: https://itzhakperlman.com/

Microsoft: https://www.microsoft.com

Netflix: https://www.netflix.com

Tesla: https://www.tesla.com/

The Innovator’s Dilemma: https://www.amazon.com.au/Innovators-Dilemma-Clayton-M-Christensen/dp/0062060244

TurboTax: https://turbotax.intuit.com/

Uber: https://www.uber.com/

Walmart: https://www.walmart.com/

Workday: https://www.workday.com/

Writely: https://techcrunch.com/2005/08/31/writely-process-words-with-your-browser/

Where to find Sam Schillace:

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

Newsletter: https://sundaylettersfromsam.substack.com/

Twitter/X: https://twitter.com/sschillace

Timestamps:

(00:00) Introduction

(02:54) Lessons on market timing

(07:30) Developing technical taste

(09:51) Asking “what if” questions

(14:03) Building Google Docs

(19:32) The decline of Google apps

(20:57) The Innovator’s Dilemma facing Microsoft

(22:53) The differences between Google and Microsoft

(24:42) How to build a winning product

(27:46) Becoming an optimist

(29:12) Why engineering teams aren’t smaller

(32:00) Sam’s prediction about AI

(34:11) Capturing the value of AI

(37:43) How you should think about AI

(45:33) Advice for future engineers

(48:18) What makes a great engineer

(49:45) One thing the best engineers do

(51:37) Microsoft’s new leverage

(56:01) Scaling software in 2024

(59:50) The future of AI across several sectors

(64:28) What Sam and a violinist have in common

October 20, 2025
Inside Stripe, OpenAI, Retool: Hard‑Won Marketing Lessons on Brand, GTM, and Scale

I spend a lot of time studying how the best product-led companies translate world-class product thinking into durable marketing systems. When I zoom out on OpenAI, Stripe, and Retool, I see a repeatable pattern: deep customer empathy, a narrative grounded in real product value, and an operational cadence that scales taste without diluting quality. In this piece, I share what’s worked for me as a product leader, and how I apply these lessons to build brand, accelerate go-to-market, and make smart resource allocation decisions.

Here’s the roadmap for this deep dive: Marketing lessons from OpenAI, Stripe, and Retool. The 3 pillars of Stripe’s approach to brand. How to manage resource allocation as a marketer. Adapting marketing strategy to different business models. Advice for early marketing hires. I’ll keep the phrases and names intact where they are factual, and I’ll add my own practical commentary on how I use these ideas day to day.

The 3 pillars of Stripe’s approach to brand is a useful way to think about brand systems in any technical company. Even without enumerating those pillars here, the underlying method is what matters: codify the few non-negotiables (the taste bar, the voice, the promise), make them visible to everyone, and hold the line in reviews. In my teams, we operationalize this by creating a short brand playbook that fits on a single page, pairing it with exemplar assets, and requiring every new program to declare how it advances at least one pillar. Clarity beats cleverness when you’re scaling.

How to manage resource allocation as a marketer is a perennial challenge as products and teams grow. I’ve had success with a 70/20/10 model: 70% on proven programs with measurable ROI, 20% on emerging bets with leading indicators (pipeline quality, engagement from priority personas), and 10% on frontier ideas that can reset the curve. We anchor work to outcomes vs output OKRs—pipeline, activation, time-to-value, product-qualified leads—so we’re funding results, not activity. As context changes (new ICP, pricing shifts, platform launches), we rebalance quarterly rather than set-and-forget.

As Stripe scaled taste, it demonstrated that high standards don’t have to mean bottlenecks. Rigorous reviews can empower teams when the criteria are explicit and teachable. Were Stripe reviews micromanaging? The lesson I apply: reviews should audit for narrative clarity, customer truth, and craft—not rewrite. We front-load narrative memos and storyboards, use pre-reads to keep live reviews crisp, and separate “taste feedback” from “blocking defects” to keep velocity high without compromising quality.

Marketing under founders with strong marketing skills can be a superpower if you channel it. My playbook: align on the narrative spine early, invite dissent in draft form (not in launch week), and turn founder intuition into reusable artifacts—positioning docs, messaging matrices, and reference stories. The goal is to scale judgment across the org, not centralize it.

Marketing at Retool vs Stripe and Marketing horizontal vs vertical products both highlight an important reality: default motions differ by product architecture and buyer psychology. For horizontal tools, the challenge is framing—teach the problem space, lead with canonical use cases, and invest in education (docs, templates, workshops) that unlock fast time-to-first-value. For vertical solutions, prioritize outcomes, credibility, and proof: ROI narratives, customer stories with industry-specific metrics, and targeted channel plays that map to where those buyers actually spend attention.

Marketing to mid-market vs SMB vs enterprise requires instrumentation and patience tuned to each segment. For SMB, focus on self-serve journeys, clear pricing, and conversion velocity; for mid-market, emphasize solution fit, workflow integration, and multi-threaded nurture; for enterprise, lead with trust, compliance, partner ecosystem, and value engineering. I set segment-specific “north-star” outcomes (e.g., self-serve activation rate, opportunity-to-close rate, average deal cycle) and build program portfolios around those.

Marketing programs that had an outsized impact often share a few traits: they’re product-adjacent, community-forward, and inherently educational. Two great examples from Stripe that I keep in my mental model are Stripe’s “Capture the Flag” campaign and Stripe Press—both programs build brand by creating genuine value for developers and builders instead of pushing features. They demonstrate how product-led marketing can compound over years.

Lessons from OpenAI remind me that speed, clarity, and responsibility can coexist. The best teams tell a simple, credible story about how the tech helps people do meaningful work—then prove it in product. Inside OpenAI’s recent website relaunch, the big takeaways for me were reduction and focus: fewer pages, tighter flows, and a narrative that meets users where they are (from curious newcomers to advanced builders). That same discipline improves any product site: prioritize the jobs-to-be-done, reduce cognitive load, and surface the shortest path to value.

How OpenAI’s marketers use OpenAI tooling is a model I bring into my teams daily. We use generative AI for content prototyping (outlines, angle exploration, voice calibration), for product discovery (summarizing interviews, clustering themes), and for campaign iteration (subject line tests, message variants, landing page microcopy). The bar is still human editorial judgment; AI accelerates the draft, we own the craft. Outside examples—like the Coca-Cola AI-generated wish card campaign—show how brand and AI can partner when creativity, data, and distribution align.

Advice for early marketing hires is straightforward and hard-won. Be a product creator at heart: learn the product, sit with support, talk to customers weekly. Start with the shortest loops that drive real outcomes—docs that unlock activation, case studies that remove friction, templates that accelerate time-to-value. Build measurement into everything, but don’t let dashboards paralyze momentum. Above all, write clearly; strong writing is the highest-leverage GTM skill and a forcing function for clear thinking.

When to start hiring marketers depends on signal. I look for repeatable demand patterns (consistent activation sources, emerging PQL signals), evidence of product-market fit lessons (clear ICP, pain–solution fit), and content debt (PMs and engineers over-producing GTM artifacts). For the first hire, I screen for full-stack utility, narrative instincts, and cross-functional leadership. How to screen early marketing hires: working sessions on positioning, a live critique of a landing page, and a writing exercise that reveals judgment under constraints.

If you’re orchestrating a website relaunch, a segment shift, or a new product line, the throughline from these companies is simple: set a high taste bar, operationalize it with lightweight systems, and make the customer’s job-to-be-done the hero. Pair that with disciplined resource allocation, and you’ll earn brand, pipeline, and loyalty the hard way—by delivering real value.

Referenced:

Coca-Cola AI-generated wish card campaign: https://theprint.in/ani-press-releases/coca-cola-ignites-diwali-celebrations-with-unique-personalized-ai-generated-wish-cards/1840093/

Cristina Cordova: https://www.linkedin.com/in/cristinajcordova/

Gong: https://www.gong.io/

Greg Brockman: https://www.linkedin.com/in/thegdb/

Kenzo Fong: https://www.linkedin.com/in/kenzofong/

Retool: https://retool.com/

Stripe’s “Capture the Flag” campaign: https://techcrunch.com/2012/08/22/stripes-capture-the-flag-2-0-a-hands-on-contest-for-app-developers-to-test-their-security-know-how/

Stripe Press: https://press.stripe.com/

Stripe Sigma: https://stripe.com/us/sigma

Tanya Khakbaz: https://www.linkedin.com/in/tanya-khakbaz-a725732/

October 20, 2025
Inside Intercom’s Bold Reboot: Lessons in AI Strategy, Ruthless Focus, and Culture

I’ve been reflecting on a remarkable comeback story that offers sharp lessons for product leaders navigating AI disruption. Eoghan McCabe is the CEO and cofounder at Intercom, an AI customer service platform. Intercom has raised over $240M, and was last valued at $1.3B in 2018. After spending 9 years building the company, Eoghan left Intercom in 2020, but he’s since returned, reshaping Intercom and pioneering its pivot to an AI-first service. That arc—departure, return, and reinvention—captures a founder’s willingness to defy orthodoxy and act from first principles.

What stood out to me most was the unapologetic embrace of intuition. In high-variance environments like AI and customer support, best practices lag reality. Founder intuition vs. standard practice isn’t a cliché here; it’s a capability. I’ve seen teams overfit to playbooks and underweight the signals that matter—customer truth, product discovery signals, and outcomes vs output OKRs that force clarity on what actually moves the needle.

McCabe’s reflections since leaving Intercom highlight the value of distance. Stepping away often exposes where complexity crept in and where focus was lost. On return, the immediate moves were decisive: refocus the strategy, simplify priorities, and set a higher bar for cadence and quality. Those changes were anchored by first-principles thinking and a willingness to question everything, including sacred cows.

The productivity step-change is telling. How Eoghan increased Intercom’s productivity by 41% wasn’t magic—it was management. In my experience, that kind of shift comes from ruthless prioritization, removing low-leverage work, and consolidating teams around fewer, outcome-aligned bets. Tactically, think tighter operating rhythms, clearer decision rights, and forward deployed engineers who sit closer to customers to collapse feedback loops—especially critical in gen ai and customer support AI strategy.

Strategy-wise, the pivot to AI-first wasn’t about feature-chasing; it was about category leadership. AI and category disruption demand conviction. Why you can’t make small improvements in big categories is simple: customers reward step changes in outcomes, not incrementalism. In customer service, that means rethinking workflows end-to-end, not just sprinkling gen ai for product prototyping on top of legacy processes.

Hiring was another area where the guidance was crisp. Tactical advice on hiring top talent included raising the bar on slope (rate of learning) and ownership, biasing for product creators who thrive in ambiguity, and building an executive team that can scale the operating model, not just the org chart. I’ve found this is where product management leadership shows up most clearly—pushing beyond conventional resumes to find people who can compound execution and insight.

Culture carried equal weight. Crafting a culture of ruthless honesty and transparency isn’t about being abrasive; it’s about creating a system where truth travels fast. In practice, that looks like instrumented business reviews tied to outcomes, written decision memos that capture tradeoffs, and a shared language for escalation. It’s uncomfortable at first, then liberating—because it accelerates learning.

Brand came in for a reality check, too. Why software branding is in crisis resonates in an era where many products sound the same, look the same, and promise the same. The antidote is clarity: a point-of-view that’s inseparable from the product experience. How Intercom thinks about brand appears to lean into differentiated behavior—speed, quality, outcomes—rather than slogans. In crowded categories, that’s what earns attention and trust.

Under the hood, this story is a masterclass in product-market fit lessons. It reaffirms that PMF isn’t a one-time event; it’s a moving target, especially when technology paradigms shift. The companies that navigate the shift are those that re-baseline their bets, measure what matters, and ship faster with higher standards. That’s the compounding loop I try to build: focused strategy, outcome-centric execution, and continuous product discovery.

If you’re steering an AI transformation, a few prompts I use: Are we solving for an outcome that customers will feel in minutes, not months? Where are we making bold, non-incremental bets? Which processes can we kill to regain tempo? And do our leaders model transparency in a way that accelerates truth-telling across the org?

For further context and inspiration, here are some of the references mentioned: 37signals: https://37signals.com, Basecamp: https://basecamp.com, Brian Halligan (HubSpot): https://www.linkedin.com/in/brianhalligan, David Heinemeier Hansson (37signals, Basecamp): https://www.linkedin.com/in/david-heinemeier-hansson-374b18221, Intercom: https://www.intercom.com, Jason Fried (37signals, Basecamp): https://www.linkedin.com/in/jason-fried, Salesforce: https://www.salesforce.com, Marc Benioff (Salesforce): https://www.linkedin.com/in/marcbenioff, Zendesk: https://www.zendesk.com.

If you want to follow Eoghan directly: LinkedIn: https://www.linkedin.com/in/eoghanmccabe/ and Twitter/X: https://x.com/eoghan. I find it valuable to track leaders who are actively rewriting the playbook in real time.

October 20, 2025