GTMnow

The average CRO tenure is 18 months. Chris Degnan stayed over a decade, scaling Snowflake from stealth startup to $4B ARR and a $100B IPO. In our latest The GTMnow Newsletter, we break down the 4 lessons that made him one of the top 1% GTM leaders in SaaS, and how to apply them to your own growth motion. 🟢 Early hires must build the system Your first salespeople can’t need infrastructure, they create it. Hire builders who’ve had to start from zero, not operators used to enablement decks and warm leads. 🟢 Operate off one shared truth Snowflake’s multiplier was alignment: sales, marketing, product, and CEO all working from the same data and goals. Shared truth beats siloed metrics every time. 🟢 Acquisition means nothing without activation Chris tied rep comp to consumption, not just bookings. Paid usage and accountable onboarding turned customers into real revenue engines. 🟢 Methodology and humility beat hero culture Longevity in hypergrowth comes from systems, curiosity, and coachability. Chris’ mantra: “Ask good questions. Take feedback. Act fast.” Snowflake’s rise shows what happens when GTM is built on discipline, alignment, and humility at scale. __ 📖 Full breakdown in the latest The GTMnow Newsletter: https://lnkd.in/gcsNExt6 💡Join 50k+ GTM leaders and founders in GTMnow by subscribing on the website or Substack.

As 2025 winds down, we’ve been tracking dozens of independent takes on where AI is headed next. Three stand out (from Jamin Ball, Des Traynor, and Cassie Young) because together they reveal something bigger taking shape across the ecosystem. A single truth is coming into focus: Most AI traction today is experimental, not durable. And the market is beginning to price that reality in. We’re entering an AI cycle defined not by adoption, but by retention. Not by growth, but by durability. 🟢 Jamin Ball: ERR vs. ARR Much of what’s called ARR is really “experimental run-rate”: pilots annualized without commitment. The real signal now is gross retention, not top-line growth. 🟢 Des Traynor: The Four Horsemen of AI Most AI companies show growth, but little of it is durable. Des outlines four filters (revenue backed by usage, usage tied to impact, deep differentiation, and positive margins) that separate real traction from hype. 🟢 Cassie Young: The Customer Success Renaissance With switching costs collapsing and churn accelerating, Cassie says the most durable AI companies are the ones that make customer success their core, compressing time-to-value and operationalizing value delivery. AI’s early growth is inflated by experimental revenue. The fastest-growing companies may not survive their first renewal cycle. The most durable ones will own the market that remains. __ 📖 Full breakdown in the latest The GTMnow Newsletter: https://lnkd.in/gyenQ-vV 💡Join 50k+ GTM leaders and founders in @GTMnow by subscribing on the website or Substack.

Distribution is the final moat. The best founders build it from day one, treating GTM like part of the product itself. The AI wave just exposed what’s been true all along: features get copied fast, hype fades faster, and the companies that scale are the ones that reach customers first and best. In our latest The GTMnow Newsletter, we break down why founders must build distribution as part of the product from day one, and how the best GTM teams are doing it. 🟢 A great product isn’t enough Gong didn’t win because of feature velocity: they won by owning awareness, trust, and category authority. Distribution turned them from competitor to category leader. 🟢 Distribution is the new defensibility Top GenAI startups are hitting $1M ARR in under 9 months, with burn multiples below 1.0. Speed + efficiency = defensibility. And defensibility today = distribution. 🟢 Start with one channel The best founders find one channel their customers actually live in and go all-in until they own it. 🟢 Build your GTM moat early The companies winning now treat go-to-market like product design: intentional, iterative, and embedded from the start. Your product gets you noticed. Distribution makes you unstoppable. __ 📖 Full breakdown in the latest The GTMnow Newsletter: https://lnkd.in/gPN3DH5R 💡 Join 50k+ GTM leaders and founders by subscribing on the website or Substack.

The first episode of our new VC Series on the GTMnow Podcast is live! He’s a must follow and the legend of S-1 teardowns. The episode kicks off with Paul Irving and me sharing what we’re seeing in the market right now and reflections on the episode, before I sat down with Alex C., GP at Meritech. Key takeaways: 1. Two metrics matter more than any dashboard: GAAP revenue and cash burn. No matter how complex your reporting gets, it all compresses down to these two truths: how much money is actually coming in (GAAP revenue), and how much it costs you to earn it (cash burn). ARR can be gamed with multi-year contracts or soft pilots. CAC payback, NRR, and gross margin are all useful, but ultimately show up in how efficiently you’re converting spend into real dollars. If you’re early-stage, obsess over these. If you’re growth-stage, you’ll be judged by them. In AI’s chaotic metrics era, they’re still the clearest signal. 2. Secondaries have gone from edge case to mainstream liquidity strategy. The IPO window is no longer the singular goalpost. With companies now staying private for 12–17 years, secondaries have scaled 5X over the last decade and, in many years, outpace IPO volume entirely. That shift reshapes the incentives for everyone: founders get partial liquidity earlier, employees can unlock life-changing equity without waiting a decade, and seed funds can return capital while still holding upside. We’re in a new exit economy. 3. The traditional 10-year venture fund is buckling under modern timelines. Fund structures built for a world where IPOs happened in 5-7 years are mismatched with today’s private-market dynamics. When iconic companies like Stripe, Databricks, and SpaceX compound for 15+ years without going public, GPs need more flexibility. That’s driving a surge in rolling funds, continuation vehicles, and creative secondaries – tools that let funds adapt to longer holding periods while still returning capital to LPs. If you’re building or backing long-duration winners, this structural shift matters as much as product or market. 4. AI-native startups are smashing the SaaS growth curve and changing what "good" looks like. In traditional SaaS, “triple-triple-double-double” was the gold standard. Now? Some AI-native companies are hitting $50-100M ARR in under two years. That level of velocity redefines expectations: investors are increasingly tolerant of imperfect churn or thin margins if the growth curve is steep and non-linear. More than revenue, it’s about speed, signal, and slope. Founders in AI need to recognize that benchmarks have shifted. Growth-stage investors are playing by different rules and your numbers need to be reframed accordingly. Thank you to our VC series partner AngelList , who have been instrumental in helping GTMfund scale and evolve since day one. Watch the full episode on YouTube or wherever you get your podcasts.

From zero to $4 billion ARR (!!) - one CRO, one epic run. He figured out something about building in an AI-native world a decade before AI existed - and if you don’t understand it yet, you’re already behind. I just spent an hour on the GTMnow podcast listening to Chris Degnan break down how he scaled Snowflake from literally zero to IPO - and one insight kept hitting me over and over. What he mastered back then isn’t just relevant now. It’s the ONLY thing that matters. Here’s what changed: When AI makes building software trivial, competitive advantage moves. Every startup can ship great product in weeks now. The bottleneck isn’t development velocity anymore. It’s distribution. It’s narrative. It’s GTM. And here’s what Chris figured out a decade early: He didn’t join Snowflake to sell a product. He joined to build GTM as THE product. Before they had anything to sell, Chris was: -Running distribution experiments with potential customers - Refining narratives based on real conversations - Validating ICPs and selling points in the market He inverted the entire priority order. And in an ai-native world? That inversion just became mandatory. The old playbook: Build product → Find customers → Scale GTM The ai-native reality: Build GTM → Validate demand → Build product Most founders are still running the old playbook and wondering why their GTM isn’t working. Here’s my question for you: Where are you actually investing right now - in building faster, or in distributing better? Because one of those just became commoditized, and the other became your only moat. p.s. If you haven’t checked out the GTMnow Podcast yet, do it. Sophie Buonassisi is doing one hell of a job.

Had a great time sitting down with Sophie Buonassisi on The GTMnow Podcast! I enjoyed reminiscing about what it really took to scale a company from zero customers to $4B in ARR: the go-to-market lessons, near-death moments, and leadership principles that shaped Snowflake’s journey. Watch or listen here: - YouTube: https://lnkd.in/gy2jMKqg - Apple: https://lnkd.in/gi6tTgGv - Spotify: https://lnkd.in/g8ET9rBu #makeitsnow #snowflake #sales makeitsnowbook.com

Had a blast listening to Chris Degnan (ex-CRO, Snowflake) with Sophie Buonassisi The GTMnow Podcast. He was the first sales hire and scaled Snowflake from 0 customers to $4B in ARR and an IPO! Takeaways: Be humble, take feedback, act in feedback, have a high sense of urgency, and the importance of sales x marketing alignment as one #GTM team. Link in the comments: