Tag: product discovery

Inside a $15B Autonomy Powerhouse: Founder Grit, Multi-Product Strategy, and GTM Wins

I’m fascinated by how world-class founders translate deep domain expertise into durable products and category-defining companies. Qasar Younis is the co-founder and CEO of Applied Intuition, a leading vehicle intelligence platform that helps companies develop and deploy autonomous systems at scale. In June 2025, the company raised $600M at a $15B valuation. Before Applied Intuition, Qasar was the COO and a group partner at Y Combinator, and earlier founded TalkBin, which was acquired by Google. He’s also held engineering roles at General Motors and Bosch.
From my vantage point leading product teams, the throughline in Qasar’s journey is the disciplined fusion of vertical SaaS focus with an enterprise-grade product-led GTM. It’s a masterclass in choosing a hard problem, building undeniable technical leverage, and then scaling with operational rigor. Below, I unpack the ideas that stood out and how I map them to day-to-day product management leadership and product-market fit lessons.
Two founder traits Silicon Valley undervalues came up early. I read this as stamina and operational discipline — the unglamorous habits that compound into advantage. In practice, that looks like tight execution cadences, brutally clear roles, and a willingness to slow down to make faster decisions later. In my experience, these traits are the difference between momentum and motion.
On productivity, the goal is to gain 1–3 extra months of work every year without burning people out. I’ve seen teams unlock this by standardizing operating rhythms (weekly operating reviews, quarterly product strategy resets), protecting deep work time, and eliminating decision latency with crisp escalation paths. If you’re looking for a playbook, “High Output Management” and “Only the Paranoid Survive” remain gold standards for building repeatable management systems.
Founders should read outside the startup canon. Industrial history like “The History of the Standard Oil Company” teaches power, platform strategy, and regulatory dynamics in ways Twitter threads never will. When you’re building in autonomy, defense, or other regulated arenas, these mental models become execution tools.
From YC, the big lessons were pattern matching and clean feedback loops. Pattern matching helps you see when a problem is fundamental versus incidental. But it only works if paired with fast, unvarnished feedback from customers and the board. I encourage PMs to institutionalize this with pre-briefs and debriefs for every major customer interaction and launch.
Qasar’s battle-tested startup formula resonated: start with a hard, valuable problem; recruit top 1% technical talent; instrument the business like an operator; and make the market come to you by shipping undeniable value. The founding insight for Applied was that companies needed robust simulation, tooling, and infrastructure to safely accelerate autonomy development — not just a single application.
Applied’s playbook — “vertical SaaS, product-led GTM, and leveraging VC networks” — is a blueprint I’d happily hand to any B2B founder. Product wins the first meeting; credibility and references win the second; value-delivery speed wins procurement. How Applied expanded beyond automotive and why Applied went multi-product early show the value of building a platform surface area that compounds learning, data, and revenue resiliency.
Why co-founder fit is make-or-break is a reminder that alignment on pace, product philosophy, and customer promise matters more than complementary résumés. The moment you become a real founder often coincides with choosing the harder path when an easier, shinier option appears. How great founders master luck is straightforward: maximize surface area with smart bets, tighten the feedback loop, and keep fixed costs low so you can wait for the right wave.
I appreciated the contrarian takes on startup culture, compensation, and cost control. Why being cheap is a startup superpower isn’t about austerity — it’s about optionality. Every dollar you don’t spend buys time to learn. And on the myth of “competition doesn’t matter,” the truth is it absolutely does; it shapes your positioning, pricing, hiring narrative, and customer urgency. Track it like a core product metric.
The early scrappiness — the Sunnyvale house setup — is a great reminder that proximity and speed matter in the zero-to-one phase. One tactic I’ve found powerful in enterprise motion is deploying forward deployed engineers to collapse the distance between product, implementation, and value realization. It converts “pilot purgatory” into production faster.
Why domain knowledge is making a comeback is obvious in autonomy and defense. In complex, safety-critical spaces, credibility, toolchain depth, and integration expertise drive trust as much as UI polish. That’s also why a multi-product strategy, when grounded in a coherent systems view, can accelerate product-market fit across adjacent verticals.
The mentors who shaped Qasar underscore the value of learning from operators across eras. Names like Paul Graham, Sam Altman, Marc Andreessen, Elad Gil, Kyle Vogt, and builders at companies like Waymo and Zoox reflect a network that pairs ambition with practical judgment — a useful pattern for any founder assembling their own advisory bench.
Referenced resources worth exploring: Applied Intuition; Ansys; Bosch; General Motors; Waymo; Zoox; “Google’s Acquisition of TalkBin”; “High Output Management”; “Only the Paranoid Survive”; “The History of the Standard Oil Company”; and profiles of Bilal Zuberi, Elad Gil, Marc Andreessen, Paul Graham, Peter Ludwig, and Sam Altman. These links provide context across autonomy, simulation, company building, and the investor-operator network that helps compound advantage.
Where to find Qasar: LinkedIn: https://www.linkedin.com/in/qasar/

October 20, 2025
How Ignoring Silicon Valley Advice Fueled a $3B Fintech Breakout—Lessons I Apply Daily

I’ve learned the hard way that you shouldn’t copy-paste advice. What works in one company, market, or moment often collapses under different constraints. Listening to the story of a digital banking platform becoming the go-to financial infrastructure for startups reinforced this truth—and sharpened how I think about building enduring products in fintech. In my role leading product strategy, I gravitate toward what’s invariant: genuine customer pull, clear value exchange, and an operating cadence that compounds.

The most durable lesson mirrors what I’ve seen across serial product-building: the gap between a good idea and a great business is defined by behavior. Users don’t just praise a great business; they pull it out of you. They adopt quickly, they expand without being asked, and they complain loudly if anything breaks. That’s what strong product-market fit feels like. It’s trust earned through painful clarity about the job-to-be-done, not a clever feature checklist.

Culture is where this begins. Personality trumps culture playbooks. Slide decks don’t make decisions—people do. The habits you normalize early (how you debate, ship, and hold the bar) become the “DNA” that scales. Mercury’s unusual culture playbook – and why it works comes down to a small set of lived behaviors: write to think, default to clarity, ship to learn, and protect craft. It’s a system that rewards truth-seeking over politics and outcomes over optics.

Hiring then becomes the highest-leverage culture act. How to hire with intention: define non-negotiable values, design interviews that surface them, and hold the line when the candidate is strong but misaligned. I favor structured prompts, real working sessions, and backchannel references that probe for ownership, curiosity, and resilience. Cultural fit isn’t about sameness; it’s about shared standards and complementary strengths.

On the product side, I’m uncompromising about avoiding the trap of weak product-market fit. Weak PMF feels like constant push—heroic sales, marketing duct tape, and feature thrash to chase disparate demands. Don’t fall into the weak product-market fit trap. Instead, isolate a segment with extreme pain, deliver a 10x improvement on the one thing that matters, and measure pull, not noise: self-serve activation, organic expansion, and sustained retention.

I’m often asked how to evaluate startup ideas that scale. I look for four compounding drivers: frequent usage (habit-forming workflows), margin structure with room for pricing power, embedded distribution (network or platform leverage), and defensibility (data, network effects, or regulated moats). In fintech, the regulatory and integration surface area adds weight to all four—if you get them right, the moat is real.

Mercury’s unlikely origin story is a reminder that the best wedge often looks too narrow from the outside. Focus on an overlooked user with distinct needs, build an MVP that does the essential thing flawlessly, and layer expansion only when the core is undeniable. Building Mercury’s MVP meant shipping the must-have workflow end-to-end with ruthless prioritization, not an encyclopedic feature set.

Breaking into the fintech space requires both product taste and institutional fluency. You need great UX and resilient plumbing. That means precise integrations, clear risk posture, and an obsessive approach to reliability. The teams that win treat compliance, security, and operations as product surfaces—not afterthoughts. It’s how you keep promises at scale.

There’s also a mindset shift that separates enduring companies from short-lived ones: moving from “This is hard” to long-term gains. Most advantages in fintech compound quietly—ledger accuracy, reconciliation speed, dispute handling, partner trust. When you invest in these flywheels early, growth feels smoother later.

Rapid growth tests every seam. Navigating Mercury’s rapid growth phase wasn’t about clever hacks; it was about raising the quality bar as you scale headcount, maintaining a crisp roadmap narrative, and protecting speed without sacrificing safety. The teams that thrive operationalize strategy: crisp goals, transparent tradeoffs, and one source of truth for priorities.

I remind founders that Competition isn’t the reason you’re failing. In most cases, the real culprit is fuzzy positioning, an undifferentiated wedge, or a value proposition that doesn’t clear the 10x bar. If your best customers wouldn’t fight to keep you, competitors aren’t your issue—focus is.

Shipping under intense pressure during the SVB crisis underscored what great product leadership looks like in a storm: compassionate, clear communication with customers; a written decision log to prevent thrash; and small, high-confidence releases that reduce risk fast. Crisis management during the SVB collapse is a masterclass in operational readiness—runbooks, war rooms, and real-time telemetry tied to a single owner for every critical path.

For additional context and resources mentioned: Airbnb: https://www.airbnb.com/; Andreessen Horowitz: https://a16z.com/; Apple: https://www.apple.com/; Block: https://block.xyz/; Brex: https://www.brex.com/; Chime: https://www.chime.com/; Gusto: https://gusto.com/; Mercury: https://mercury.com/; Paul Graham: https://x.com/paulg; Plaid: https://plaid.com/; Stripe: https://stripe.com/; SVB (Silicon Valley Bank): https://www.svb.com/; True Link Financial: https://www.truelinkfinancial.com/; Varo: https://www.varomoney.com/; Y Combinator: https://www.ycombinator.com/

If you’re a product creator navigating fintech—or any complex, high-stakes category—anchor on behaviorally proven value, not borrowed wisdom. Build a culture that compounds, hire with intention, and chase unmistakable pull. When the market is truly with you, the work gets harder—and far more rewarding—in all the right ways.

October 20, 2025
Inside the AI‑First Web: Designing Agent‑Friendly APIs, Prioritizing Accuracy, and Scaling Trust

I’ve spent the last few years watching AI reshape product roadmaps, developer workflows, and customer expectations. One idea now feels undeniable: the web must evolve to serve a new primary user—AIs. That shift changes how we think about search, reliability, governance, monetization, and ultimately, how we design products that scale with trust.

Parag Agrawal is the co-founder and CEO of Parallel, a startup building search infrastructure for the web’s second user: AIs. Before launching Parallel, Parag spent over a decade at Twitter, where he served as CTO and later CEO during a period of intense transformation, as well as public scrutiny.

I was particularly struck by how crisply this frames the next frontier for product leaders: build systems that machines can consume at massive scale without sacrificing accuracy, provenance, or trust. In particular, I was drawn to the emphasis on “deep research,” where Parallel is tackling “deep research” challenges by prioritizing accuracy over speed, and the design choices that make their APIs uniquely agent-friendly. As someone who has shipped AI features into production, that trade-off resonates—speed gets demos; accuracy earns renewals.

Here’s how I’m synthesizing the most actionable takeaways for product, engineering, and go-to-market leaders. First, design for AI as the primary customer. That means structuring content and APIs so agents can reliably reason, verify, and self-correct. Agent-friendly interfaces need deterministic schemas, explicit provenance, stable latency envelopes, and predictable failure modes. If an agent can’t trust your contract, it won’t chain your service into complex workflows, and you’ll lose the compounding effects that make AI platforms defensible.

Second, bring a systems mindset to accuracy. “Accuracy over speed” isn’t a slogan—it’s an architecture choice. In my experience, that shows up as retrieval strategies tuned for recall and precision trade-offs, multi-pass verification, and human-in-the-loop escalation paths for high-risk queries. For deep research use cases, you need to make the cost of being wrong explicit in your design and your SLAs.

Third, expect your ICP to evolve as AI matures. Early adopters may be research-heavy teams and product creators building agentic workflows. Over time, as reliability improves, your ideal customer shifts toward operational teams that demand measurable outcomes—support deflection, conversion lift, cycle-time reduction. I map these stages explicitly in the roadmap and keep pricing, packaging, and onboarding aligned to each phase.

Fourth, consider business models that keep the web open for AI while aligning incentives. If AIs are the web’s second user, publishers need fair value exchange for structured access, provenance, and usage. In practice, that could look like tiered access, usage-based pricing, attribution requirements, or revenue-sharing tied to agent-driven outcomes. The key is ensuring that openness and sustainability are not at odds.

Fifth, build engineering teams that are both pragmatic and research-aware. On my teams, I look for a balance between high-potential builders who move fast with ambiguous specs and experienced hands who can productionize novel systems. Forward deployed engineers can be a force multiplier here—embedding with customers to surface edge cases, close the verification loop, and turn qualitative insights into productized patterns.

Sixth, recognize how the software engineer’s role is evolving in an AI-assisted world. Engineers are increasingly orchestrators—composing models, retrieval layers, tools, and policies—rather than only writing business logic. That requires better observability for prompts and agents, reproducibility for experiments, and contracts that make emergent behavior inspectable and testable. This is where “uniquely agent-friendly” APIs show their value—clear contracts enable safe autonomy.

Seventh, treat launch timing as a function of trust, not just velocity. Founders often ask when to ship. My rule: launch when you can document bounds, prove repeatability on critical paths, and explain failure semantics. In AI, your narrative is your control surface—fundraising frameworks and customer conversations both benefit when you can quantify reliability, not just showcase capability.

Finally, the long-term vision matters. If agents are finally becoming useful in production, the platforms that win will combine: machine-readable content at scale, accuracy-first retrieval and verification, agent-safe API design, and sustainable economics for an open web. That’s the blueprint I’m applying to my own product strategy: build for agents, measure for trust, and align incentives so the ecosystem compounds rather than fragments.

To product leaders navigating this shift: revisit your ICP, rewrite your API contracts for agents, and make “accuracy over speed” a first-class requirement. To engineering leaders: invest in evaluation harnesses, data quality pipelines, and forward deployed engineers who can turn messy customer workflows into reusable system capabilities. The AI era rewards teams that pair ambition with discipline—and that’s where the next wave of durable advantage will be built.

October 20, 2025
Inside Canva’s $42B Rise: Unconventional Growth Levers, Onboarding Wins, and PM Lessons

I’m endlessly fascinated by how products break out of niches and become part of daily life for millions. Canva’s story is one of those rare cases where smart product decisions, relentless iteration, and unconventional growth levers combined to unlock mass-market adoption. From a product management standpoint, it’s a case study in lowering barriers, scaling trust, and aligning vision with execution.

Cameron Adams is the co-founder and Chief Product Officer at Canva, the design platform valued at $42B as of July 2025, used by over 230 million people every month.

Before starting Canva, Cameron was a designer and engineer at Google and co-founded Fluent, an email startup.

In this deep dive, Cameron walks through Canva’s earliest days — from the remarkably fast courtship with co-founders Melanie Perkins and Cliff Obrecht, to the counterintuitive product decisions that helped Canva instantly resonate with users who thought they would never design anything.

From my vantage point leading product teams, I see a set of repeatable patterns here: choose the right first persona, compress time-to-value with intuitive onboarding, and design growth into the experience rather than bolting it on. Canva executed these with uncommon clarity—and the results speak for themselves.

“In this episode, we cover:”

“How Canva turned social media managers into early evangelists”

Choosing social media managers as an initial wedge was a masterclass in product discovery. This persona had an urgent, recurring need for on-brand visuals at speed, and a strong incentive to share output publicly—perfect conditions for organic, product-led growth. When I map early adoption paths, I look for exactly this intersection: high-frequency jobs-to-be-done, immediate value, and built-in distribution.

“Balancing a huge vision with scrappy execution”

Vision without sequencing is just aspiration. Canva kept the ambition expansive, but the execution ruthlessly focused: nail core templates, make editing feel magical, and remove friction everywhere. That balance is how you earn the right to pursue the bigger roadmap later—enterprise, collaboration, and advanced workflows—without losing momentum.

“Hard lessons from their near-silent launch day”

Quiet launches are not failures; they are feedback. The key is converting that signal into action. I’ve learned to treat launch as the start of systematic learning: instrument onboarding, watch activation cohorts, prioritize the sharpest drop-offs, and keep shipping until the curve bends. Canva’s trajectory highlights the compounding effect of that discipline.

“The two growth levers that changed everything”

Every breakout product eventually finds one or two levers that out-pull the rest. The trick is recognizing them early, doubling down with conviction, and being willing to refactor the product, pricing, or go-to-market around them. When we run growth reviews, I ask: which lever moves both acquisition and retention, and how do we amplify it inside the product experience?

“And much more…”

“Why onboarding was the unlock for retention”

Onboarding is where trust is earned and churn is decided. Canva’s approach underscores a timeless principle: shorten time-to-first-value, scaffold early wins, and keep the UI context-aware so users never feel lost. In my teams, we treat onboarding as a living system—measured weekly, tuned to personas, and tightly coupled to activation, engagement, and long-term retention.

“How word-of-mouth spurred early retention”

When your product becomes part of how people express themselves publicly, word-of-mouth becomes an engine—not a byproduct. Canva benefited from this virtuous loop: the more people shared their creations, the more others discovered the tool. That’s community-led growth baked into the product, not just the marketing plan.

“Targeting different user personas”

Expansion requires thoughtful layering of personas—adjacent use cases, then adjacent buyers. The art is sequencing: keep the core experience simple while introducing just enough depth for power users and teams. This is where product management leadership shows up in the roadmap: deliberate tradeoffs, clear positioning, and crisp UX boundaries.

“Building a community on social media”

Community is a force multiplier when it’s authentic. By showcasing templates, celebrating user success, and teaching design basics, Canva turned social channels into an education loop. That creates durable retention because users don’t just use the product—they identify with it.

“Why Canva should have gone mobile sooner”

Mobile is not a form factor choice; it’s a job context. When creation moves to the moment and place of need, you capture frequency and defensibility. The takeaway for PMs: if your customers’ work happens on the go, mobile-first isn’t a feature—it’s the product.

“What underpins Canva’s dominance today”

Foundationally, it’s relentless focus on accessibility and outcomes: templates that reduce blank-page anxiety, collaboration that feels native, and a platform that scales from the individual to the enterprise. That alignment across product-market fit, brand promise, and go-to-market is what compounds.

“Rebuilding for enterprise”

Winning the enterprise means rethinking identity, permissions, governance, brand controls, and performance—often from the ground up. The lesson I emphasize with teams: enterprise-grade is not a layer you sprinkle on top; it’s an architectural commitment.

“Lessons from Canva’s tough times”

Every scaling company hits turbulence—hiring, platform debt, or market shifts. The durable ones maintain clarity of purpose, instrument their bets, and keep shipping value. That resilience is a cultural choice as much as a product choice.

References:

Adobe: https://www.adobe.com/home

Atlassian: https://www.atlassian.com/

Campaign Monitor: https://www.campaignmonitor.com/

Canva: https://www.canva.com/

Cliff Obrecht: https://www.linkedin.com/in/cliff-obrecht-79ba9920/

Dave Greiner: https://www.linkedin.com/in/davegreiner/

Lars Rasmussen: https://www.linkedin.com/in/larserasmussen/

Melanie Perkins: https://www.linkedin.com/in/melanieperkins/

Mike Cannon-Brookes: https://www.linkedin.com/in/mcannonbrookes/

New York Stock Exchange: https://www.nyse.com/

Pinterest: https://pinterest.com/

Scott Farquhar: https://www.linkedin.com/in/scottfarquhar/

Where to find Cameron:

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

Timestamps:

(01:24) The birth of Canva

(04:32) Meeting Canva’s co-founders

(11:22) Building the first iteration of Canva

(15:26) The discovery that changed prototyping

(20:48) Why onboarding was the unlock for retention

(27:36) The anticlimactic launch day

(32:43) How word-of-mouth spurred early retention

(36:33) Targeting different user personas

(41:02) Building a community on social media

(43:38) Two impactful growth levers

(47:14) Why Canva should have gone mobile sooner

(48:12) What underpins Canva’s dominance today

(53:37) Rebuilding for enterprise

(58:38) Lessons from Canva’s tough times

October 20, 2025
Building an Education Giant in a ‘Bad Market’: Product Strategy Lessons from ClassDojo

Education is often labeled a “bad market”—fragmented buyers, long sales cycles, and entrenched systems that resist change. Yet that framing misses a powerful truth: when you build directly for the people who care most—teachers, students, and families—you can unlock extraordinary adoption and defensibility. That’s the core product lesson I drew from the ClassDojo story and one I return to often as a product leader.

ClassDojo is a multi-product education platform used in 95% of U.S. schools and over 180 countries globally to connect teachers, students, and families. The scale is impressive, but the path there is what matters: start with the consumer, design for delight, and let community power distribution. In a space where enterprise selling is slow and political, that decision to serve families first wasn’t just contrarian—it was strategically correct.

Why build for families, not schools? Because enterprise education is broken. District procurement often prioritizes compliance and consensus over usability and joy. By focusing on the “end customer” experience—teachers in classrooms, students eager to learn, parents seeking connection—ClassDojo built pull instead of push. The platform earned trust the hard way: one classroom at a time, one interaction at a time.

The origin story included false starts. A group-making tool didn’t land, and early skepticism about the education market was warranted. But meeting co-founder Liam Don at a hackathon and getting into Imagine K12 provided momentum and mentorship. This is where the founder mindset showed up clearly: relentless resourcefulness. Instead of forcing a PMF narrative, they iterated until they found a communication platform that teachers loved and families valued.

One inflection point I found especially instructive was the conversation with Reid Hoffman that changed everything. The takeaway wasn’t about advice for advice’s sake; it was about reframing distribution. If you want to reach more families, you need to build the network and community that carry your product forward. That means designing every surface for shareability, trust, and repeat use—so your users become your go-to-market.

ClassDojo grew entirely by word-of-mouth. That doesn’t happen by accident. It happens when the product is genuinely delightful, solving a real problem with minimal friction. As a product manager, I think about “designed virality” not as gimmicks, but as a byproduct of exceptional UX: faster onboarding, clear moments of value, and emotional resonance that makes people want to invite others.

The team waited seven years to launch the first monetization feature. That restraint isn’t common, and it’s not always advisable—but in this case, it compounded trust and created a broader surface area for durable revenue later. The principle is timeless: earn the right to monetize by compounding value. When you do, paid experiences can feel like natural extensions rather than distractions.

Market selection decisions were equally thoughtful. Start focused; go broad when the network is strong enough to support new products. The explosive expansion into the tutoring industry is a great example of a logical adjacency: serve an existing community with a new solution that aligns to core jobs-to-be-done. That’s not opportunism—it’s strategy built on distribution strength.

Creating safe online spaces for kids is non-negotiable. Beyond compliance, safety is a product and brand promise. You earn parent and teacher loyalty when you treat trust as a first-class feature—clear controls, default safeguards, and purposeful content environments. In education, this is a core differentiator.

Harnessing AI in education adds a new dimension. The opportunity isn’t to replace teachers; it’s to augment them and personalize learning at scale while preserving safety and transparency. For teams building in this space, the bar is higher: align AI features to measurable learning outcomes, ensure explainability, and keep humans in the loop. That’s how you turn “gen ai” from a buzzword into durable product value.

What’s the enduring playbook I take from ClassDojo? Build for consumers in a system that undervalues them. Pursue word-of-mouth with product excellence, not marketing spend. Sequence monetization after trust. Expand into adjacencies when your community is ready. And above all, practice relentless resourcefulness—keep learning, keep iterating, and keep showing up for the people you serve.

October 20, 2025
Saying Yes to Customers: How Samsara Scaled from Basement Hack to IoT Leader

I’m endlessly fascinated by companies that turn raw customer obsession into enduring advantage. Listening to the story behind Samsara’s rise, I saw a roadmap that every product leader can learn from: start with real problems in physical operations, build unreasonably tight feedback loops, and keep a startup mindset even as you scale. Kiren Sekar, the CPO of Samsara, has lived this playbook. Before Samsara, he was an early leader at Meraki, which was acquired by Cisco for $1.2B—a formative experience that shaped how he thinks about product quality, go-to-market, and culture.
What struck me most was how the company’s origin story moved from hardware hacking in a basement to a cross-industry IoT platform by rigorously following customer signals. Early on, they said yes to on-the-ground learning, iterated fast, and let mid-market operators guide their priorities. As someone who’s led product teams through rapid growth, I’ve learned that the discipline to be customer-centric—especially when the signal is messy—is what separates hopeful roadmaps from high-velocity execution.
The decision to start with the mid-market wasn’t accidental; it was a deliberate go-to-market strategy. Mid-market buyers often make decisions faster, adopt products with less friction, and generate clearer product feedback loops. That dynamic accelerates discovery, sharpens positioning, and creates a foundation for a scalable sales motion. I’ve seen the same pattern: when you nail “ease of use,” adoption compounds and sales efficiency climbs.
Several themes stood out to me as powerful lessons in product management leadership. Lessons from Meraki’s acquisition by Cisco inform how to keep product quality uncompromising while scaling. Hiring for intrinsic motivation ensures teams stay close to the customer, not just the metrics. Building for operations industries means embracing real-world constraints, where reliability and clarity beat novelty and complexity every time.
The early hardware prototype—and the Cowgirl Creamery insight—illustrate why field research matters. Early customer research even surfaced a failed fridge monitoring idea, a reminder that the right near-miss can be more valuable than a false-positive win. I’ve learned to treat these moments as the price of market truth: when a hypothesis fails, your search space gets sharper.
Balancing depth and breadth was a recurrent tension. Building broad vs. niche from day one requires a crisp POV about platform versus verticalization. Samsara chose a platform approach while still solving acute, industry-specific use cases. That choice made it easier to transition from founder-selling to a scalable sales motion—because the product could flex to multiple profiles without fracturing the roadmap.
Organizing product teams around revenue vs. experience is another area where I’ve felt the trade-offs firsthand. Revenue squads drive near-term outcomes; experience squads protect long-term usability. The best model is often hybrid: scorecards that hold teams accountable to both pipeline impact and customer satisfaction while preserving a single, coherent user journey. That’s how “ease of use” becomes a growth secret rather than a slogan.
Pricing strategies and market positioning evolved in lockstep with customer value. As product-market fit deepened, pricing clarity improved, and packaging aligned with outcomes rather than features. The throughline: when customers trust you to help them navigate change management, they’re more willing to expand into new modules and adopt new workflows.
It was also energizing to hear how Samsara uses LLMs and AI today. In operations, AI becomes practical when it reduces cognitive load: summarizing events, flagging anomalies, and automating routine decisions. My rule of thumb is simple—AI should be invisible when it’s working well, surfacing the right insight at the right moment, with humans always in control. That’s where LLMs shine in IoT at scale.
A few timestamped moments I found especially useful: (01:27) Meraki’s growth and acquisition by Cisco; (03:25) The “evaporating” exit strategy from Meraki; (04:42) Identifying the IoT market gaps; (07:38) The early keys to success at Samsara; (09:39) What does quality mean to Kiren?
More highlights worth revisiting: (10:54) Building a customer-centric roadmap; (17:34) Early customer research and the failed fridge monitoring idea; (20:57) How a cheese producer helped create Samsara’s first prototype; (28:06) Balancing depth and breadth in customer profiles; (33:45) Developing customer trust to build feedback loops; (40:27) How “ease of use” became a growth secret; (44:23) Pricing strategies and market positioning; (51:51) How Meraki influenced Samsara’s GTM strategy; (57:19) Helping customers navigate change management; (1:00:48) How Samsara’s team evolved during rapid growth; (1:04:03) What AI means for an IoT giant.
If you want to follow the operator behind these insights, here’s where to find Kiren: LinkedIn: https://www.linkedin.com/in/kirensekar/
References for further exploration: Cisco: https://www.cisco.com/ | Clay: https://www.clay.com/ | Cowgirl Creamery: https://cowgirlcreamery.com/ | IBM: https://www.ibm.com/ | Meraki: https://meraki.cisco.com/ | Microsoft: https://www.microsoft.com/ | Salesforce: https://www.salesforce.com/ | Samsara: https://www.samsara.com/ | Sanjit Biswas: https://www.linkedin.com/in/sanjitbiswas/ | Uber: https://www.uber.com/
My takeaway as a product leader: saying yes to customer truth—especially when it’s inconvenient—creates momentum you can’t fake. When you combine a customer-centric roadmap, a scalable sales motion, clear pricing, and an unwavering commitment to “ease of use,” you don’t just ship features—you build a durable IoT platform that compounds with every feedback loop.

October 20, 2025
From Yale Dorm Room to Lifesaving AI: How Prepared Disrupted 911 and Won an Axon Acquisition

I’m fascinated by products that earn their right to exist in the toughest markets, and Prepared is one of those rare cases. Michael is the co-founder and CEO of Prepared, the AI assistant for 911 calls that helps dispatchers capture information faster, translate emergency calls in real time, and deliver lifesaving context to first responders. Founded out of Yale in 2019, Prepared grew from a school safety app into a critical platform for emergency communications, disrupting a notoriously tough market. This mission-driven journey just reached a major milestone: Prepared was acquired by Axon, the global public safety technology company. From a product leadership lens, several choices stand out. The catalyst—tragically, school shootings—anchored the team’s conviction and sharpened their definition of value: every second saved and every bit of context delivered could change an outcome. That clarity enabled an unusual go-to-market motion for govtech: give away the first product for years to earn trust, validate workflows, and build a wedge that later expanded into an AI-driven suite. Counterintuitive? Yes. But in a market defined by risk, compliance, and procurement inertia, it was precisely the kind of strategy that compounds. I’ve spent years navigating complex buyers, and Prepared’s approach to government and public safety agencies is a case study in disciplined product discovery. When systems are “so outdated,” pushing a modern layer requires empathy for the incumbent stack, forward deployed engineers who embed with users, and a readiness to translate mission need into procurement-friendly outcomes. It’s also a reminder that in govtech, distribution is a feature: partnerships, integrations, and interoperability often unlock more value than any single UX improvement. One lesson I keep returning to is mission as competitive moat. Mission creates resilience during headwinds—endless rejections, long sales cycles, and the grind of security reviews—and it focuses prioritization when tailwinds arrive. Along the way, the team balanced conviction with customer feedback, asking not just “What did we hear?” but “Which signals matter?” That’s the only way to move from a wedge product to a robust platform without drifting into feature sprawl. A few moments from the story hit me personally. Staying mission-oriented under pressure is more than a slogan; it’s the muscle memory of teams doing the work when no one’s watching. Negotiating an acquisition from a hospital bed underscores how founder endurance and timing often collide in ways you can’t plan for. And the self-aware quip—“I want to be terrible at sales”—captures a product ideal: build something so indispensable that champions sell it for you. It’s not anti-sales; it’s pro-traction. On the AI front, Prepared’s evolution mirrors what I see across high-stakes operations: start with a narrow, high-value job-to-be-done and expand as trust accrues. Real-time translation and structured data capture are obvious force multipliers for dispatchers. Expanding the product surface area with AI requires rigorous guardrails, model performance transparency, and tight human-in-the-loop workflows—especially in public safety. That’s where gen ai earns its keep: augmenting judgment, not replacing it. For founders and product leaders, here are the takeaways I’m carrying forward. Use a wedge that maps to urgent, measurable outcomes; then earn the right to broaden. Consider free or subsidized entry when trust and standardization are prerequisites to adoption. Treat procurement like a product: reduce friction, de-risk the choice, and make integration paths obvious. Balance conviction with a learner’s mindset to keep the signal-to-noise ratio high. And build investor relationships early and often so capital is an accelerant, not a lifeline. If you’re exploring product-market fit in an enterprise or govtech context, ask the hard questions: How much should you listen to customers? Are you building in headwinds or tailwinds—and why? What partnerships both de-risk and differentiate? And when the mission is non-negotiable, how do you sustain it across phases—from first user to acquisition—without losing the soul of the product? Where to find Michael: LinkedIn: https://www.linkedin.com/in/michaelchime/ References: Axon: https://www.axon.com/ Dylan Gleicher: https://www.linkedin.com/in/dylan-gleicher/ March for Our Lives: https://marchforourlives.org/ Neal Soni: https://www.linkedin.com/in/neal-soni/ OpenAI: https://openai.com/ Peter Thiel Fellowship: https://thielfellowship.org/ Prepared: https://www.prepared911.com/ Sam Altman: https://x.com/sama Slack: https://slack.com/ Uber Eats: https://www.ubereats.com/ Yale University: https://www.yale.edu/

October 20, 2025
The AI Support Blueprint: From Zero Playbook to 75% Resolution and a Reimagined Team

Rolling out an AI Agent doesn’t just change how your team works – it changes who your team is.

I learned that in the crucible of a fast-moving launch. Before we launched Fin publicly, our Support team became its first alpha/beta tester and we had to move fast. No roadmap. No step-by-step guide. Just a powerful new technology, and a steep learning curve.

That experience is exactly what led us to create The AI Agent Blueprint – a resource we wish we’d had when we were starting out, and one we hope will give other support teams a clearer path forward.

Looking back, I won’t lie and say I was cool, calm, and confident about how to do this – I was nervous as hell. I had no idea how to implement an AI Agent and ensure it resulted in huge cost savings and stellar customer experiences.

We had older machine learning technology available to us (shout out to our first-gen chatbot, Resolution Bot), but as a complex software business, we really only used it for basic FAQs. In all honesty, we still had a way to go – both in using automation more effectively and in making the chatbot experience actually enjoyable for our customers.

So why the urgency?

When ChatGPT burst onto the scene nearly three (!!) years ago, Intercom’s Machine Learning team immediately spotted the opportunity and dived into building the world’s first (and objectively best) AI Customer Service Agent.

Suddenly, we were being asked to pilot this brand new technology with real customers and go all in ASAP. Because we were selling this powerful new functionality, we had to use it ourselves and show it off in the best possible light so customers would want to use it too. #nopressure

There was no playbook, just a lot to figure out. As a product management leader, I had to switch into rigorous product discovery while staying execution-minded.

Steady involvement, rising resolutions. From February to July, teams maintain a high 87–93 involvement range as resolution rates climb from 65 to 82—signaling how AI-driven workflows can boost support efficiency and outcomes.

How do we do a phased rollout, but scale very quickly?

How do we QA Fin’s responses and make continuous improvements?

How will we produce and manage all the content Fin needs?

What will we do about all the outdated content we already have?

What are the success metrics now? Should they be different to original Support KPIs?

Who’s responsible for the success metrics? Who manages this newcomer to our team?

It was daunting. We had to take a brand new technology, figure out how to use it, build a team around it, and move at breakneck speed to implement every new feature that rolled out. It was ambiguous, fast-moving, and a massive lift.

But we got there and the results speak for themselves: Fin is now resolving over 75% of our inbound support volume.

An isometric blueprint reveals how an AI agent powers modern support—from triage to resolution—linking chat, knowledge, and workflows so teams scale service without losing accuracy, context, or the human touch.

That outcome didn’t happen by accident. We embedded forward deployed engineers with Support, treated our AI Agent like a product creator in its own right, and used gen ai for product prototyping to tighten our iteration loops. We prioritized a customer support AI strategy that balanced containment with quality: containment rate, CSAT on AI-resolved conversations, first-response latency, and recontact rates became our core scorecard.

That success led to real change for me and my team: new roles, new responsibilities, and new career paths. I now run a whole new function that didn’t exist before: AI Support. We’ve created new and elevated roles like Conversation Designers and Knowledge Managers. Fin hasn’t just changed how we support customers – it’s transformed the structure of our team and the trajectory of our careers.

And now, we’re helping our customers do the same.

In all transparency, if I hadn’t been this close to the work, I might have waited to see how generative AI played out before committing. I might have waited for a blueprint for how to deploy and scale an AI Agent. I wish I had something like that when we got started, or even later when we had a solid foundation but needed to scale our AI strategy.

How much less scary would it be to implement an AI Agent if something like that existed?

Whether you’re just getting started or already using AI in some way, you’re not early anymore—and you shouldn’t have to figure it all out alone. Strong product management leadership, a clear change plan, and tight feedback loops are what separate experiments from outcomes.

That’s why we created The AI Agent Blueprint – a practical map for launching and scaling AI in support. It brings together everything we’ve learned from our own journey, and from working closely with our customers who are doing the same.

If you’re ready to operationalize gen ai in support, align on the right metrics, and redesign roles for the future, this blueprint will help you move from pilots to pervasive impact with confidence.

Inspired by this post on The Intercom Blog.

October 20, 2025
A Bold Bet on React: How Intercom’s Shift Unlocked Speed, AI Flow, and Developer Joy

Bold, pragmatic bets separate teams that merely deliver from teams that truly accelerate. As a product leader, I’m drawn to decisions that reduce friction, empower engineers, and compound over time. Intercom’s recent investment in a new frontend direction is a standout example of this mindset—and it offers lessons any product, engineering, or design leader can apply.

Over the past two years, Intercom made one of the most significant changes an engineering organization can make: moving its core frontend from Ember to React. That choice fits a clear pattern of high-agency decision making in service of speed, quality, and developer experience.

Back in 2014, Ember was the right call for their main application. Its strong opinions and “batteries-included” approach aligned with a strategy I respect: make big decisions once, enable teams to move fast, and spend energy on customer problems instead of endless architecture debates. The result was scale few achieve—more than two million lines of code and 100,000+ pull requests merged.

I’ve been in the room when constraints outgrow the original bet. By 2023, “local builds stretched beyond 90 seconds,” and they were stuck on older framework versions that blocked adoption of modern build tools. Even with deep community engagement and contributions to the Embroider Initiative, the cost of staying put was compounding. Something had to change.

What I admire is the rigor behind their pivot. They ran workshops, health checks, and set explicit trigger conditions—then honored those triggers. When the evidence crossed the threshold, they chose a new path and framed the work with a clear, galvanizing banner: “The Future of Frontend.” That’s the kind of governance and narrative clarity that de-risks large platform shifts.

React quickly emerged as the right fit—not because of hype, but because it met practical criteria at scale. “React was already a core technology at Intercom (powering Messenger, Help Center, and our marketing site),” backed by a robust ecosystem, strong documentation, and broad familiarity internally and across the industry. Most importantly, it integrates naturally with AI-driven developer tools—a non-negotiable for the next decade of engineering productivity.

Fast forward to today, and the momentum is clear. “React is now the default for new UI development at Intercom.” That single sentence says a lot about organizational alignment and execution readiness.

The outcomes speak for themselves. “Blazing fast feedback loops: React builds in under 10 seconds locally, with sub-1s rebuilds – much faster than our Ember app’s 90+ seconds.” That kind of drop in cycle time unlocks more iteration, tighter designer–engineer collaboration, and faster learning loops.

Speed without joy is a half-win. “Higher developer velocity: Engineers consistently report being faster, happier, and more effective, particularly when paired with AI tools like Cursor, Augment, and Claude Code.” I’ve seen similar effects: once teams feel flow again, quality and ambition both rise.

Adoption at breadth matters as much as depth. “Wider adoption: Since March 2025, 10+ Product teams have shipped React features, contributing over 840 pull requests.” That level of traction signals a platform shift that’s not just technically sound but operationally viable.

The AI-enabled developer experience is the real unlock. “AI synergy: React just “clicks” with modern AI tooling. Designers and engineers are using agents to write code, generate components from Figma, and even build design playgrounds themselves.” That’s the future: product creators working in shared, generative environments where ideas move from Figma to code in minutes.

One engineer captured the productivity gain perfectly: “The work I had predicted would take me a week to achieve took me two days”. That’s not a marginal improvement—that’s a step-change.

This story isn’t just about frameworks; it’s about preparing for a decade where velocity, AI-native workflows, and developer experience determine competitive advantage. The ambition to “double our productivity over the next 12 months” requires removing friction, leaning into AI, and standardizing on tools that compound learning across teams. React is a pragmatic enabler for that journey.

I also appreciate the organizational design behind the change. A small, focused group—Team Frontend Tech—partnered tightly with Product teams to shape the new stack, build a design system, and accelerate adoption. That model creates a high-trust bridge between platform and product, which is essential for landing a migration at scale.

For leaders navigating similar crossroads, the playbook is clear: set explicit trigger conditions, articulate the future state, pick a stack that compounds with AI, and invest in a cross-functional nucleus to shepherd adoption. For engineers and designers, this is an exciting moment—one where your tools finally catch up with your ambition.

The takeaway I’m carrying forward: make the bold call when the evidence is conclusive, optimize for feedback loops and flow, and treat AI as a first-class partner in the creative process. That’s how we keep shipping fast, raise the quality bar, and focus on what really matters—solving meaningful problems for customers.

Inspired by this post on The Intercom Blog.

October 20, 2025
From Backlog Admin to Product Creator: How I Build Impactful Products with GenAI and Discovery

I have been emphasizing that the heart of the product manager job is product creation.

The job is not about being a facilitator or cheerleader, it’s not about being a project manager, and it’s definitely not about being a backlog administrator.

Rather, the necessary role of a product manager is a product creator, working alongside…

In practice, that means I pair closely with engineering, design, data, and go-to-market partners to explore, prototype, and validate solutions during product discovery. I set a clear problem statement, define success metrics, and align on the smallest coherent release so we can learn quickly and de-risk the path to value.

When the problem demands deep context, I embed forward deployed engineers with customers so we can observe workflows, capture constraints, and iterate on generative AI prototypes in days, not months. Those in-the-field insights shorten feedback loops and expose edge cases that never surface in a conference room or a ticketing system.

GenAI lets me reduce the cost of learning: with lightweight agents, synthetic data, and prompt-driven scaffolding, I can run multiple experiments in parallel and converge on what truly delivers value. This approach turns ambiguity into testable hypotheses and transforms discovery from a meeting cadence into a hands-on, evidence-driven practice.

This is product management leadership in action—setting outcomes, defining success metrics, and aligning a cross-functional team on the smallest coherent release—instead of shuffling tickets in a backlog. The difference is night and day: we move from output to outcomes, from activity to impact.

My weekly cadence is simple: articulate the customer problem, frame hypotheses, build the thinnest possible prototype, put it in front of real users, and measure behavior against leading indicators. That loop creates momentum, builds credibility with engineering, and keeps us honest about whether we’re creating something customers will adopt and pay for.

If you’re feeling stuck in coordination mode, reclaim your time for discovery and creation: carve out maker hours, ship prototypes, invite engineers to customer calls, and let evidence—not opinions—steer the roadmap. The more you build, the more you learn; the more you learn, the better you lead.

The fastest teams I’ve led are the ones that treat product management as a hands-on craft, embrace generative AI for product prototyping, and maintain a relentless focus on learning, not merely launching. That is how we earn trust, create enduring products, and make product creator more than a title—it becomes our daily practice.

Inspired by this post on SVPG.

October 20, 2025
Mastering Product in the Generative AI Era: Proven Strategies to Transform Teams and Delivery

As generative AI continues to evolve, I’m focused on understanding its real impact on product teams and practices—and translating that into practical guidance you can apply today.

This page is my curated hub of insights on AI in product development, where I share frameworks, case studies, and field notes from leading teams through this shift. I’ll update it regularly with new perspectives on AI-related topics that matter for product leaders and builders.

From what I’ve seen across enterprise and startup environments, AI reshapes collaboration, decision-making, and the operating model of product teams. Product managers, designers, and engineers must now work alongside models, data pipelines, and evaluation systems. In particular, forward deployed engineers are becoming essential—bridging real customer problems with model capabilities, and validating value in the wild.

On the product discovery front, generative AI accelerates how we identify opportunities and reduce uncertainty. I use it to synthesize qualitative research at scale, pressure-test problem statements, and generate alternative solution concepts. The key is to maintain rigor: clear research questions, transparent prompts, and measurable outcomes that tie discovery work directly to product strategy.

Prototyping has changed as well. With gen ai for product prototyping, my teams can move from concept to interactive demo in hours, not weeks. We rely on lightweight LLM sandboxes, prompt versioning, and red-teaming to assess feasibility and risks early. This makes usability testing and stakeholder alignment faster, while giving us tighter feedback loops before we commit real engineering capacity.

Delivery now requires new practices for reliability and governance. We integrate AI evaluation harnesses into CI/CD, monitor model drift alongside product metrics, and establish guardrails for safety, privacy, and fairness. It’s not just shipping features; it’s managing living systems where prompts, fine-tunes, and data quality are part of the release surface area.

For product management leadership, the mandate is clear: set a strategy that balances innovation with responsibility, upskill the organization, and define standards for AI product management. That includes establishing decision rights, clarifying model ownership, and measuring value end-to-end—from discovery signal to production impact—all while building trust with customers and regulators.

Expect ongoing updates here: proven playbooks for AI product discovery, templates for evaluation and prompt governance, and actionable guidance on team structure, roles, and KPIs. My goal is to help you navigate the AI era with confidence, reduce ambiguity, and deliver customer outcomes that stand the test of time.

Inspired by this post on SVPG.

October 20, 2025
Mastering Intelligent Products: Proven Strategies to Transform Product Development with Gen AI

I’m focused on the future of the products we’ll build—and how we’ll build them. To see where we’re headed, I find it essential to reflect on the past four decades of product development, from on-prem software to cloud-native platforms, from waterfall delivery to agile and DevOps, and now to Generative AI reshaping how we imagine, design, and ship value.

Those cycles taught us a consistent lesson: when technology shifts, our product practices must evolve with it. We learned to ship smaller, measure better, and iterate faster. Today, we’re at another inflection point where the very process of product discovery, prototyping, and delivery is being augmented by intelligence.

Consider this quote: “Applying AI to the software development process is a major research topic. There is tremendous…”

That unfinished thought captures exactly where we are right now—on the cusp of tremendous potential. I see AI accelerating the full lifecycle: transforming ambiguous problems into testable hypotheses, turning research signals into prototypes within hours, and translating product intent into working code and test suites. Gen AI is becoming a collaborator in product discovery, a catalyst for engineering velocity, and a force multiplier for product management leadership.

When I talk about creating intelligent products, I don’t mean bolting on a chatbot. I mean systems that learn from real usage, adapt to context, and continuously improve outcomes. Intelligent products are instrumented end-to-end: they observe, predict, and personalize—while giving users clear control and transparency. They reduce cognitive load, anticipate needs, and create compounding value over time.

How we create these products must change too. In discovery, I pair structured customer interviews with gen AI summaries to surface patterns quickly. I use gen AI for product prototyping to explore solution spaces before we commit code. Forward deployed engineers work alongside PMs and designers to ship high-signal experiments into real environments, shortening the feedback loop from weeks to days.

Operationally, the playbook includes four foundations. First, a robust data strategy: clean pipelines, privacy by design, and event models that map to user value. Second, a model lifecycle: from prompt engineering and fine-tuning to continuous evaluation and rollback plans. Third, a product discovery cadence that treats experiments as first-class artifacts. Fourth, a design system that includes AI interaction patterns—confidence indicators, explainability, and safe defaults—so experiences feel trustworthy and consistent.

Intelligent products demand responsible guardrails. I define clear acceptance criteria for safety, bias, privacy, and reliability, and I use evaluation harnesses with real-world scenarios to test them. Human-in-the-loop checkpoints remain essential for sensitive decisions. Governance is not a blocker; it’s a quality system that protects users and the business while allowing teams to move fast with confidence.

If you’re getting started, focus your next 90 days on three moves. Identify one high-friction workflow where intelligence can remove toil or accelerate time-to-value. Stand up a lightweight experimentation pipeline that logs outcomes and quality signals by default. And empower a small cross-functional squad—PM, designer, forward deployed engineer—to ship a measurable improvement, not a demo.

The destination is clear: product creators who master intelligent capabilities will deliver outsized impact. The path is practical: blend rigorous product discovery with gen AI acceleration, build trust through transparency and safety, and keep users at the center of every decision. That’s how we’ll create intelligent products that compound value—and why I’m optimistic about what we’ll build next.

Inspired by this post on SVPG.

October 20, 2025