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Curated SaaS and tech insight from around the web repackaged for people to put to good use
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1. SaaS METRIC OF THE WEEK: Fundraising Metrics. Make your fundraising way less chaotic by getting these metrics dialed in. Unless you are pre-revenue, Investors will expect to see detailed ARR, CAC, LTV, retention rates, and engagement metrics. A strong data deck (or data room) can answer investors' questions and show a clear path to growth.
2. PRICING: Kicking off this new year right with some pricing upgrade levers that may actually work for ya. You can kill monthly billing to reduce early churn on agentic products; Remove seat limits but tighten usage caps tied to value; Raise entry-tier pricing to "encourage" upgrades. 3. CAPITAL: Oh! Is your AI stack quietly funded by junk bonds? Tom Tunguz breaks down how hyperscalers are leaning hard on debt to finance their AI capex - the AI biz model here is seriously mad, commodity-based products that need tens of billions in data center, GPU, and power financing- and what happens if growth slows before cash flows catch up? 4. BRAND vs DEMAND: In a recent survey, 168 B2B CMOs say brand matters, but the reality is that their budgets don't back that up. Actual spend sits at ~70% demand / ~25–30% brand, even though the ideal mix looks closer to 50% / 40%. 73% believe brand makes demand more efficient, yet only 28% can tie their brand to pipeline - so when cuts come, 55% protect demand and just 11% protect brand. Which brings us to the second part: reach is a brand-based challenge. Brand expands the pool; demand converts it - nice little sales tech-ism for the week. 5. EDUCATION: A follow-up from all of the above (2-4) - Clouded Judgement makes a sharp point most AI startups miss: education IS the go-to-market (see #4 above). Buyers don’t know what to build, let alone buy (see #3 for that). The winners teach the market which problems matter, how they should be solved, and why their worldview is right. Free tiers, sandboxes, fast time-to-value aren’t growth hacks - they’re education engines (see #2). Until customers learn by doing, demand stalls or churns. 6. LEADERSHIP: When you take a look around, most leaders perform with certainty. This article argues that it’s a risk. The best leaders run leadership similar to GTM teams: small experiments, fast feedback, public learning. Shorter cycles beat perfect plans, curiosity beats confidence (and IMO teams trust you more when you stop pretending you have the answers). 7. DEV: Has the cost of shipping software dropped due to AI? This piece makes a serious case that agentic coding collapses implementation time, not thinking time. Month-long internal tools now ship in days. Coordination overhead disappears. $50k builds become $5k problems. The real moat isn’t necessarily the code anymore - it’s domain knowledge plus taste. And don't forget - all those people with knowledge have to come up the ranks somewhere. 8. SYSTEM OF RECORDS: Another Clouded Judgement article, and this is my wheelhouse, so this spoke to me - sure AI is cool - but the harder automation gets, the more enterprises need a hard core (and un-cool) source of truth that agents can reliably read/write. Systems of record don’t disappear; they get embedded as the truth layers that govern agent workflows and outputs. 9. REQUIREMENTS: This is also my wheelhouse, and I'm sure it's the same for many of you B2B-SaaS operators. Most projects/implementations don’t exactly start with clarity, but they do start with intent. This product-focused article argues the real skill isn’t forcing certainty early, it’s moving forward without pretending (I call it holding hands in the fog). Progressive elaboration and rolling-wave planning let you extract the signals from complexity, commit where you can, and deliberately leave the rest provisional/in the backlog. It’s a great article to frame how you can create momentum without boxing yourself into poor decisions or plans. 10. CASE STUDY: Series B+ raises. Most of us should be so lucky to get to this stage, but how do you raise funds beyond Series B (roughly 15% that raise seed make it to Series B). Scale Ventures’ Stacey Bishop gives the lowdown. POD OF THE WEEK: Two enterprise AI operators who’ve shipped 50+ AI products (across OpenAI, Google, Amazon, and Databricks) break down what actually works when building and scaling AI. 1. SaaS METRIC OF THE WEEK: Rounding out the year with your holiday reading list - A Guide to SaaS Metrics from, and covers all the greatest hits and more (ARPA, LTV:CAC, Burn Multiples, etc).
2. NCT: Crack open your tech dictionaries, I have a new acronym to lob your way. OKRs are old school. Ravi Mehta’s NCT (Narrative, Commitments, Tasks) model simplifies goal setting. So instead of vague objectives, start with a clear Narrative explaining the “why” behind each goal. Next, set 3-5 measurable commitments for the quarter, with Tasks as actionable steps. The difference is that OKRs can be overly ambitious, but NCTs focus on achievable milestones that align closely with strategic priorities, more Agile in a way, as course corrections are easier (and it increases team accountability). 3. PRODUCT DISCOVERY: Does your discovery motion look good on paper but not so much IRL? This article breaks down four product discovery models your teams actually use can use - Dual-Track Agile, Continuous Discovery, Opportunity Solution Trees, and Outcome-Driven Discovery - and it also shows where each one fails in practice. Useful if your discovery motion looks good on slides but not in shipped outcomes. 4. B2B AI: SaaStr makes it painfully clear: “AI-enhanced” is not a biz model. There are only three ways B2B AI actually makes money now: replacing a full workflow; replacing human labor with agents; or selling AI infrastructure itself. Everything else gets priced to zero. 5. VENTURE: SaaStr again, this time they look at data from Vencap, which shows how unforgiving venture really is. Most VC funds don’t meaningfully outperform public markets, and outcomes hinge on one or two breakouts per fund. Useful read if you’re fundraising (or wondering why great VCs are rare). This article also backs up the basic premise that shit's overfunded. INNOVATION: Such an overused and poorly understood phrase, often synonymous with economic progress. This article argues that it isn't true. Treating innovation as a magical growth lever obscures strategy, and it’s uneven and one of several forces that actually drive progress. MICRO MANAGEMENT: It's bad, right? Depends. This article makes the case for being Pro - it’s a spectrum of involvement. Some forms of it (teaching, co-ownership, guiding on high-risk work) accelerate learning and alignment, especially early on or when trust isn’t fully built. The key isn’t rigid delegation vs control - it’s clear expectations, context, and honest signals about why you’re (overly) involved. BENCHMARKS: High Alpha’s 2025 SaaS Benchmarks are out (covering 800+ companies). Here are some highlights - but there are way more: top-quartile growth ~300% in <$5M ARR bands, upper NRR hitting 110%+, gross margins ~85% (at scale), and best-in-class ARR per employee near $350K–$400K+ on later stage teams. EQUITY (and other things): Mostly Metrics breaks down when employee equity stops helping and starts becoming a problem - it’s not just dilution math, but hiring incentives, refresh pools, and retention traps that can bloat cap tables with little upside. Suggestion: early option pools of ~10–15% and first-hire grants often <1%, but pushing too hard can distort incentives (and screw over future raises). CASE STUDY: Outbound ain't dead. Check out how Workflows.io scaled to $2M+ ARR by combining your old school classic cold outreach to an ICP list with automated email plus LinkedIn campaigns layered on AI-signals (website visits, founder connections, social engagement). POD OF THE WEEK: The New AI Benchmarks fromJanelle Teng at Bessemer Venture Partners. 1. SaaS METRIC OF THE WEEK: SaaS METRIC OF THE WEEK: CAC PAYBACK: The 'payback' period is the nuance of why we measure CAC. How long until we break even? Benchmark-wise, the negative trough is way longer than you think, so take a seat! New B2B customers, on average, take 2 years and 2 months to become profitable. This really highlights a deepening dependency on access to capital to fund a SaaS company's growth through these SaaS Cash Flow Trough. BONUS: Here are last week's CAC Payback benchmarks.
2. TESTING (MARKETING): According to this Reforge article, marketers often don't see the expected big returns from testing because they avoid major risks. Making bigger bets with strong business cases can lead to transformational success - it has some great IRL example bets from Groupon and Google, and there is also a "Big Bet Calculator" embedded in the article for you to use. 3. GOVERNANCE: Here is Mark Suster's series on his Medium Blog covering StartUp Boards. With a follow-up article that shows a board structure by stage! He also provides a blog post AND a 43-slide deck. 4. BUBBLE: The tech-ism cycle is generally always boom, bubble, bust, boom again (but a little more chill). Is AI going to be any different? Doubt it. Another good tech-ism is that we always overestimate short-term impact and underestimate long-term transformation. Crazy Stupid Tech breaks this down: hype comes first, usefulness comes later, and the real returns go to whoever survives the whipsawing. 5. AI MOATS: Fast follow-up from above, and this was discussed a few weeks back - Stratechery now also makes the argument that, when it comes to AI, the real moats aren't models - they're distribution and compute control. As of today, Google wins with integration, Nvidia wins with infrastructure, and OpenAI wins with velocity (for now). The takeaway for startups: your moat won't be "better AI," - side quest - here is a list of AI Startups that have raised $100m plus this last year. 6. TECH DEBT: Hey - we all have it. Everyone knows they'll have to pay down tech debt sooner or later - Hyperact takes a Product perspective and claims that tech debt is a product choice, not an engineering mistake. Worth the read, as we all need to consider tech debt further up the decision tree. 7. VERTICAL SAAS: I got this report from Stripe - Vertical SaaS is evolving and the category is maturing fast: 70% of companies now sell more than one product, fintech is the second product for nearly half of them, and AI adoption sits just over 50%. 8. ENTERPRISE SAAS: Gartner says global IT spend will break $6T in 2026, with enterprise software jumping 15.2%. But here's the kicker: most of that growth isn't new logos or usage - it's price hikes and AI add-ons. Budgets are rising, but tolerance probably isn't, so if you're selling into the enterprise, expect harder ROI scrutiny (even if those wallets technically "grow."). 9. FINANCIAL PLANNING: The end-of-year planning season is here, and SaaStr has a surprisingly useful AI benchmarking tool that builds a C60-style financial plan in seconds. Revenue, burn, CAC, payback, hiring - all benchmarked instantly against thousands of SaaS peers. 10. CASE STUDY: Super interesting article updating the classic startup playbook. MVP → PMF → scale is old school - unlocking hidden loops, data flywheels, distribution hacks, and agent-driven workflows are all part of the new playbook, along with design roles around outcomes and what it takes to be a leader in this new company design. POD OF THE WEEK: This one is for all you metric nerds (like me:-)) - Don't forget to allocate CAC between new and existing customers. This oversight leads to misleading KPIs, inaccurate CAC payback, flawed LTV-to-CAC ratios, and unreliable unit economics. 1. SaaS METRIC OF THE WEEK: Time to Profit - Probably one of the most important metrics in the post-COVID Civid/free money era. Most startups die not from bad ideas but from running out of cash before reaching sustainability. Shorter TTP forces discipline: fewer vanity bets, tighter PMF proofs, and faster elimination of anything that doesn't compound.
2. CONTRACTS: This is the report none of us knew we needed - but it's the unsexy stuff that matters as there are sone insightful gems: A guide on SaaS Contracts, complete with benchmarks such as customer signature roles (3/4 are Executives), AI and ML clauses (big increases in recent years), and the big one, time to sign - 3-5 days-ish (SMB to Enterprise). Have a read - I bet you will have some serious takeaways. 3. PMF: Product-Market Fit is a spectrum and a gradual one, moving through stages of demand, customer satisfaction, and efficiency. Success means balancing high customer need with scalable growth. Check out this dynamic scale - it's pretty interesting as it enables measures across multiple dimensions. 4. MARKETING AGENTS: Some AI-adventurous marketers (vibe marketers?) are swapping their ENTIRE teams for AI agents. Check out SafetyCulture, who use AI-powered lead enrichment. Their outbound and follow-up workflows drove near-100% lead coverage, 2× more opportunities, and 3× meeting-rate lifts - without growing headcount. 5. SPATIAL INTELLIGENCE: New phrase for your tech dictionaries. a16z argues the next big leap is world models - what they mean by that consultant-ism is that soon AI will understand space, physics, and cause-and-effect. This shift unlocks agents that can act in real environments, not just talk about them—use cases for robots, AR, self-driving, logistics. Words are the table stakes - real-world shit is the moat. 6. STATE OF AI: Another week, another State of AI report - this time McKinsey, who note that AI adoption has flat-lined at 47 percent, yet enterprise spend is going vertical. The bottleneck isn't the models - it's companies. Most are stuck in pilot hell, drowning in fragmentation, shadow apps, and zero governance. A key takeaway for everyone to understand is that real gains typically emerge when companies redesign processes around AI rather than simply layering new tools on top of existing workflows. Also, check this neat infographic on Agent use by industry and business function. 7. STATE OF AI (FOR REALZ VERSION): I have had this conversation multiple times this week, even before OpenAI announced its "Code Red" emergency; it's obvious they were under pressure. What's the long-term (and sustainable) business model for Agentic AI? It's a commodity expectation ($20/month for an account - sure!) - it's generating massive amounts of revenue, but it's MASSIVELY expensive to serve in its current state to run these models. Check this article for a deeper read on OpenAI's emerging challenges. 8. INSURANCE: My day job is running a sexy Insurtech, and the insurance we need to operate globally is eye-watering. Mostly Metrics has a great breakdown of what tech companies actually need for coverage – from cyber to EPLI to the stuff your broker forgets to mention. The (mostly ironic) surprise: most startups are underinsured in the risky areas and wildly overpaying everywhere else. 9. AGILE BRANDING: I love the good ol' marketing-ism of "50% of our marketing works; we just don't know which 50%." Well, hey - from a brand perspective - there is an Agile Framework to figure out how. Kantar's new research says most of us waste brand spend by guessing. And you can figure that out with fast, iterative testing of real consumer tensions, experiments, and (AI-assisted) creative screening. TL;DR: agile brand-building beats big campaigns and guesswork, and you can measure your way into relevance instead of hoping for it. 10. CASE STUDY: Everyone loves a good exit story, but First Round's Merger Playbook is an IRL story of Crossbeam's merger from its CEO, who breaks down a brutally honest playbook on merging with your fastest-growing competitor. It ain't pretty - 18 months of stalled talks, 70/30 equity fights, gun-jumping landmines, ripping out whole codebases, cross-boarding 30,000 customers, and firing half the C-suite. POD OF THE WEEK: Bit of a case study - from SaaStr, who have used an AI inbound agent where 71% of their closed-won sponsorship deals in Q4 came from AI-qualified inbound leads. 1. SaaS METRIC OF THE WEEK: GES: Growth Endurance Score is a metric that assesses a company's ability to sustain growth over time. GES measures this efficiency by factoring in both net retention and customer acquisition efficiency. A high GES correlates with long-term business health and resilience. This score provides valuable insights for businesses aiming for consistent, sustainable growth. Bessemer has drilled deeper into it and plotted ARR growth lost YoY, and found that the decay is fairly predictable at 30%. That's a benchmark - in other words, you should expect next year's growth rate to be 70% of the current year, as the stakes get higher.
2. AI BENCHMARKS: HubSpot benchmarked 500 startups' AI-GTM setups in this 3-part report (part 2 and part 3 here) - 37% say AI lowered CAC and 72% improved upsell/cross-sell. Those Startups dedicating 50%+ of their GTM tech stack to AI hit meaningful scale: higher ARR brackets, and outsized efficiency. Some major barriers remain for many of us: 23% cite high cost, 17% cite a lack of AI expertise. 3. GROWTH LEVERS: Fast follow from number 2 above, a lot of startups say AI growth is now cheap. Kyle Poyar's Growth Unhinged shows how top PLG teams spend less on new channels, yet sell more via AI-driven content, intent-outbound, and search. If you're not treating AI as your growth stack, you're probably falling behind. 4. MOATS: A16z revisits their 2020 classic: margins don't make great companies, moats do. In this new AI-infused era, defensibility still comes from the old pillars - network effects, brand, scale, switching costs, proprietary tech - and now, momentum reeeeaaallly matters too. High margins alone can be a red flag that your product isn't using AI. 5. WRITING: Here is a bit that flips on the AI panic: it's not writers getting replaced, it's readers. It's kinda like SEO - writers start optimizing for LLMs as their true audience, trading human readers for influence and speculative digital immortality....let the enshitifcation or dead internet theory flow! 6. HIRING: Hey founders - here is a pretty accurate techism: You will never be able to hire anyone better than you. Y'all set the talent ceiling, so curate your team like your product. Recruiting is the game. 7. NAME: Who knew that picking a company name could be complicated? Picking a company name looks simple - but most of us skip the hard parts. According to First Round Review's playbook, you should derive names from your positioning statement, brainstorm hundreds of options, test how your audience pronounces and remembers them, then vet for domain, trademark, and growth-fit. 8. AI WARS v2: I've talked about the Public Cloud AI wars underway in the past, and the Clouded Judgement blog has picked up on it too - Model scores don't rule anymore. With Gemini 3.0 that Google launched last week, Google isn't just chasing benchmark wins, it's bundling AI into Android, Chrome, Workspace, and more (like doubling AI capacity every 6 months!). Anyway, Google AI is all over my shit this week - here is how to turn it all off! The fight now is distribution, tools, and ecosystem -where we all touch AI every day. 9. VALUATIONS: There is a lot in here. The latest from Data Driven VC shows that top-tier private tech companies are losing ~20% of their value month over month; AI & crypto are still dominating; and EV/NTM revenue multiples have compressed significantly across sectors. 10. CASE STUDY: Check this deep-dive on Microsoft's AI strategy - which traces the "big pause" in datacenter build-out, how the Azure-OpenAI tie-up evolved, and why the real battleground now is tokens and infrastructure economics (see #8 above!). POD OF THE WEEK: Lenny's Newsletter breaks down the clearest coldstart guide to building with AI: how to scope an AI product, when not to use models, evaluation traps, and what real teams ship first. If you're building anything AI-adjacent in 2026, use this to get your brain started. 1. SaaS METRIC OF THE WEEK: Fundraising Metrics. For all of you hitting 2026 in fundraising mode, make your fundraising way less chaotic by getting these metrics dialed in. Unless you are pre-revenue, Investors will expect to see detailed ARR, CAC, LTV, retention rates, and engagement metrics. A strong data deck (or data room) can answer investors' questions and show a clear growth path.
2. GO TO MARKET: Go to market motions can be pretty specific, and your GTM motions can impact your marketing strategy and your org chart. Robert Kaminski has distilled GTM motions into five types in this article, based on number of use cases. The summary diagram at the bottom is excellent. 3. AI BUBBLE: It's been a rowdy week in the public markets, and Coatue, like me, loves some good charts (18 in this report) and makes the case that AI is early and not in the bubble zone. $250B in funding, $100B in revenue… and only 2% of enterprise workloads on GenAI today. The mismatch between investment and adoption actually looks quite massive (and I reckon quite unsustainable). 4. SEED: Seed VC is busted! In 2021, 55% of seed rounds had at least one institutional VC - in 2024, it's dropped to 38%. LPs are pulling back, seed funds are missing targets, and exits aren't coming fast enough or at all. NextView warns: many firms raised too much, too fast, and now lack the reserves or conviction to back winners through to liquidity. Sidebar, but even in the top quartile in venture, "Doing Great" isn't enough to raise money. 5. MARKETPLACES: How do Marketplaces work with AI in the mix? Answer - they go mutant. a16z outlines how AI-native marketplaces will flip the advantage from owning the supply to owning the brain in the middle. Search, summarization, recommendations, pricing - all through agents. 6. VALUATIONS: The latest from Multiples.vc: Some Public SaaS is back above 6x revenue (median), but still far from the pre-COVID/free money-era highs. Median EV/NTM revenue is 6.8x, with only 26% of companies above 10x. Dev Tools lead at 10.5x; PropTech drags at 2.7x. 21% of the index is still unprofitable. Market's not frothy - it's got selective. 7. BEZOS?: Jeff Bezos may be over his flying-phallic-rockets-era and back into operator mode as co-CEO of Project Prometheus, a new AI startup that's already hauled $6.2B in funding (Bezos is one of the funders). It's not chatbots - it's physical systems, aerospace, and hard AI. 8. SAME-SAME: Do you have a hard time with product discovery - there seems to be sooooo much out there. Well, according to David Kellogg, "The Sea of Sameness" is real (and IMO it's getting worse). AI and SaaS startups are flooding the market with lookalike products, identical landing pages, and copy-paste taglines. Kellblog lays out why most companies fail to position clearly: they focus on features, not meaning. If buyers can't tell what makes you different in seconds, too bad, you lose. His advice? Anchor your positioning in a deep understanding of why you exist and say something only you can say. 9. GROWTH LEVERS: Updated for 2025 - Growth isn't just about hacking anymore - check this deep dive from Kyle Poyar and data from Chartmogul on the "outliers" - which are the companies that made it to $20m ARR (this chart is awesome). The big difference is that Groth levers shift and adapt over time for the outliers - from activation and monetization to expansion and retention. They all compound and have an outlier impact. 10. CASE STUDY: Cursor is one of my new favorite apps (I've pivoted it into use on the Product and Operations side), and this case study goes deep inside Cursor and breaks down how the business operates and grew by targeting high-intent engineers, shipping relentlessly, and making copilots feel native to dev flows. POD OF THE WEEK: From the AI Engineer Summit, Sayash Kapoor cuts through the hype and shows why today's agents fail in the wild - good guidance on evals, failure modes, and how to build agents that friggin work. 1. SaaS METRIC OF THE WEEK: CAC - In a modern SaaS world that has maturing companies using both Sales Led Growth (SLG) and Product Led Growth (PLG) motions and teams, how do you calculate CAC? The answer is really that they should be kept separate.
2. MULTIPLIER EFFECT: Not all startups create equal impact. Some become engines. Endeavor's data shows that when a founder builds a breakout company, it often leads to dozens of new startups, hundreds of hires, and a cycle of reinvestment. 3. WEB ANALYTICS: Traffic is no longer a reliable metric. With 60%+ of Google searches now ending in zero clicks, and LLMs answering questions directly, your beautifully optimized landing page may never get seen. Growth Unhinged dives into why old-school analytics tools are failing and what teams are tracking now. How the hell will attribution even work anymore??? 4. AI PRODUCT STRATEGY: Everyone's layering/wrapping/whatevering AI onto their products - but OpenAI shows how to build product with AI at the core. This teardown outlines their real strategy: multi-modal UX, agent interfaces, and ecosystem dominance. Key takeaway? AI-native ain't necessarily AI-featured. 5. LAYOFFS: It's been a shitty start to Q4 if you work in one of the big tech companies. The AI wave isn't just triggering layoffs — it's redefining how those roles are backfilled. Saastr unpacks why many tech jobs aren't returning (well, at least not in human form). AI is stepping in, quietly reshaping org charts and headcount plans. 6. AI & JOBS: Fast follow from #5 above. Don't panic, not all jobs are being eaten by AI.......yet. Bloomberry crunched 180M listings and found the real risk is clustered: entry-level admin, data entry, and some basic CS roles. For now, the creative, strategic, and cross-functional skills still have some air cover. 7. MULTIPLES: Tomasz Tunguz has been pretty off-task lately, but he's back with a great post that dives into public SaaS comps and shows the growth premium is still alive - just narrower. In 2021, fast growers earned a 5x multiple bump. Today it's closer to 1.5x. Growth still matters, but efficiency is what gets you paid. 8. DELIVERY: A project isn't just a pile of tasks. Second Thoughts argues that viewing projects as task bundles kills real outcomes. Instead: start with the outcome, then shape your workstreams backward. 9. GTM: ICONIQ loves a good report, and their latest, the 2025 GTM report (PDF here), shows that the early-stage pipeline is slowing, while top performers are building multi-threaded motion: PLG signals, outbound rigor, and deep post-sale expansion loops. CAC payback medians jumped to 24 months, but NRR is still holding strong above 120% for best-in-class. 10. CASE STUDY: CHAT GPT - If you're wondering how AI tools like ChatGPT are really used day-to-day, check these 15 charts that explain how tech and venture are evolving from when access to money was cheap/free. From shrinking median round sizes to late-stage volume collapsing, this is a pretty great visual cheat sheet for understanding where we are at. ChatGPT retention/reach, and check chart 7, which matches 5, 6, and 7 above all in one chart. POD OF THE WEEK: Being an advocate for people's happiness is part of my day job and mentoring mindset set, so it's nice when McKinsey comes out with a podcast on why it's cool to be kind. 1. SaaS METRIC OF THE WEEK: ASP or Average Sales Price. ASP tracks the average price new customers pay when signing up. Unlike ARPA, which includes renewals and expansions, ASP spotlights initial deal size—a key signal for sales performance and pricing strategy. Track it by region, plan, or channel to optimize revenue growth.
2. UN-SCALE: I've talked about this a few times - we're all focused on building out that repeatable, scalable business model, but along the way, there are things that have to be done that are totally un-scalable. This article highlights three specific non-scalable things sales teams should be doing right now. 3. AI-WARS: With OpenAI's new Browser, 'Atlas', and Perplexity's Comet, a new AI war has started for the Browser - shots are being fired at the dominance of Chrome. Security nightmare? You betcha, but it's part of a bigger play to own the full stack: model, agent, and now interface. The Chrome vs. ChatGPT battle is officially on. 4. AI SECURITY: Another week, another AI report but this one complements the AI Security nightmare in #3 above, Aikido's 2026 State of AI Security has to be a bit of a wake-up call: 90% of AI/ML repos have unresolved security issues, and model-level risks (e.g., prompt injection, training data poisoning) are rising - not even bringing up the browser issues. Security needs to shift - again. 5. FUNDRAISING: Going into fundraise mode is a bit of a full-time job, so check this great free AI-powered fundraising kit. Includes investor matching, warm intro prep, and a due diligence checklist. (and built by founders, not VCs - with a sprinkle of AI on top). 6. LEADERSHIP: Conflict is not necessarily a bad thing, and avoiding conflict is the fastest way to stall your team. In that article, Hypergrowth Partners makes the argument that great companies normalize tension and use it as a growth lever. 7. MARKETING: In many circles, LinkedIn is seen as a bit cringe, but it compounds. MKT1 breaks down the "LinkedIn Flywheel" - how founders and operators can use lightweight posting, engagement, and repackaging to build real pipeline without becoming a <insert vocal fry> thoughtfluencer. 8. TASTE: In our ever increasingly overcrowded SaaS markets, taste can be a competitive advantage. Sam Tomlinson explains why it's more than just aesthetics, as taste can help teams pick the right problems, craft better products, and earn trust faster. 9. SALES: Outreach analyzed millions of sales activities to uncover what's working as we start wrapping up 2025. Top reps are now sending 20% fewer emails but book 25% more meetings. Personalization still wins, but speed to first touch and persistence look to be the real differentiators. 10. CASE STUDY: How Intercom built a $50MM ARR Empire using strategic Content Marketing and SEO. POD OF THE WEEK: Also a bit of a case study, and I've already shared this out a few times. Cursor is turning into a project manager's dream product-management system - featuring Dennis Yang from Chime. 1. SaaS METRIC OF THE WEEK: CARR - Contracted Annual Recurring Revenue. This is a forward-looking SaaS revenue metric that estimates the maximum revenue size of a SaaS company, measuring current recurring revenue from your SaaS P&L and future revenue that sits in newly won customer contracts.
2. PRODUCT-MARKET FIT: Finding Product-market fit isn't a one-and-done event—just as the product you ship, it's an ongoing process. This updated playbook breaks down the key signals, from retention curves to sales velocity, and the tactical moves to iterate faster. If you're still guessing, this is your roadmap. 3. UNDERSELL: If expansion fits into your growth strategy (it should) take a read of two-part series from Tomasz Tunguz and Bill Binch - part one is deliberately underselling as a sales strategy to minimize churn and increase upsell/expansion opportunities as a land and expand strategy and post 2 is an expansion of land and expand witch details how to structure a Startup sales team for optimal land & expand. 4. COMPENSATION: Early-stage comp is more art than science, and most founders are so hectic at this point that it's easy to get it wrong. This First Round piece offers tactical advice on what to break (like "market rate" rules), what to follow (like transparency), and how to build trust before you build out pay bands. A little more in talky format here. 5. PRODUCT: Here is a tech-ism I like - AI changes everything and nothing at all. Teresa Torres is here to remind us all that AI is great and all, but great product work still always starts with understanding user needs. (and here is how you find out what they care about). 6. MENTOR: SaaS founders don't need perfect advisors; we all need people who've broken shit and learned from it. This guide covers why mentorship matters, how to earn it, and how to keep it useful. Includes outreach templates and cadence tips that can work. 7. VENTURE: Good VC pulse check. This pod n' post breaks down the "Great VC Shuffle," what's driving TAM fatigue in AI (bubble anyone?), and why $1B ARR is still just getting started (broken liquidity pipeline). Good follow-up to the State of Software signals from last week's newsletter. 8. LIQUIDITY: Fast follow from #8 above. If IPOs stay rare and private rounds dry up, who buys the next crop of SaaS companies? Lemkin breaks down the likely acquirers (and what they're really after). Probably means some top-of-the-heap consolidation is on the way. 9. DECIDE: Good decision-making isn't just speed, it's perception, insight, and good judgment - which this Forbes article consultingly wraps up as discernment. It also has 10 sharp rules to upgrade how you make calls as a founder or operator. Not new ideas, just well-said reminders worth keeping as an easily accessible reminder. I prefer this analogy, too - A fire ain't no emergency if you are the Fire Department. 10. CASE STUDY: AWS and AI - AWS currently dominates Public Cloud (did you forget what happened last week?) - we all assumed it was supposed to dominate AI too. But bloat, bureaucracy, and missed bets are showing up and slowing it down. Bloomberg breaks down why Amazon's cloud empire is stalling while others race ahead. POD OF THE WEEK: No matter what Robbie says in this Podcast from Lenny's Podcast - Google Search IS changing (60% of Google Search is now unclicked). But launching AI Overviews was cool, and he has also scaled Stories and Reels at Instagram. 1. SaaS METRIC OF THE WEEK: Burn Multiples tells you how efficiently your startup converts cash burn into ARR. It's the clearest signal of whether your growth is efficient - or just an expensive way to burn money. This breakdown covers benchmarks by stage and why 1.5x is the new magic number for survival.
2. FINANCE: Think you understand your P&L? Probably not well enough. This teardown by OnlyCFO explains how each line of the income statement connects to growth, margin, and cash. Especially useful: benchmarks, red flags, and why you can't ignore gross margin math at early stages. 3. AI: The 2025 State of AI report just dropped from Airstreet, and it's (313 slides) huge. Training costs for frontier models are flattening, while open-source LLMs continue to close the quality gap. AI safety is (finally) growing up, with more labs publishing evals and investing in red-teaming. AI-first startups are now scaling into tens of billions in revenue (bubble anyone?), NVIDIA still dominates the hardware race, and power supply is fast becoming the new bottleneck. The PDF Version is here, as the Google Slides are a little annoying. 4. STATE OF SOFTWARE: This one plays hard into both #1 and #3 above. The ICONIQ State of Software 2025 is mandatory reading because IMO it's a reality check: the old playbook is officially dead. While the Rule of 40 is still the strongest predictor of valuation, median growth for SaaS companies has dropped to 19% YoY (down from 29% in 2022). AI-Native companies are rewriting what's possible, scaling 2-3x faster than traditional SaaS peers. A new (and bonkers) data point: they're hitting $100M ARR in just 1-2 years, often with fewer than 20 employees. Side note for #1 above - public SaaS firms with Burn Multiples below 1.5 are commanding a 40% valuation premium. 5. GTM: So, based on #4 above, traditional Go-to-Market isn't dead, but maybe it's dying? So shit gotta change. Jason Lemkin warns that mediocre, copy-paste GTM executions are failing faster than ever. AI hasn't replaced sales and marketing; it's made bad playbooks easier to scale, so only truly excellent, AI-augmented execution wins. Buyers want outcomes, not promises. 6. FACILITATE: A super fun and VERY bookmark-able resource this week. It's a library of tools available to facilitate your next session with people or a team, including team building, brainstorming, icebreakers, and check-ins. They are all there! 7. FUNDRAISING: What does it take to raise a Series A? How do you break through the $10M ARR barrier and secure growth capital? More Intelligent offers a comprehensive guide, along with must-hit fundraising milestones—from scaling your go-to-market motion to tightening unit economics and KPIs. 8. VIBE CODING: I give Vibe Coding a (deserving) amount of shit, but to its credit, the idea is maturing. This piece traces that shift - from building fast with AI-assisted tools. Platforms like Solid are bridging the gap with full-stack code and proper control. But the "vibe wall" is real: fast prototyping hits limits without control and scalability. 9. PRICING: Your pricing is broken (but that's kinda OK) - but hey, it's not totally because of your tiers. Kyle Poyar breaks down why most SaaS pricing fails: it's static, and it mashes with my commentary of pricing is like software - it's never done. The best teams iterate on pricing every quarter, not every couple of years, to track the real value. His post unpacks seven pricing traps (like sticking with usage-based just because it's trendy), and gives a roadmap for fixing your monetization blind spots. 10. CASE STUDY: The $2 Trillion Founder Playbook is all about how Larry Page quietly reclaimed control of Google after being sidelined. Page played the long game—shedding titles, backing bold bets like Gmail and Android, and redesigning Google's structure for scale. POD OF THE WEEK: The video version of number 5 above about the new Go-to-Market thinking needed for success in today's markets from SaaStr. 1. SaaS METRIC OF THE WEEK: Forecasting: Forecasting MRR is more art than science - but this post gives you both. A smart walk-through of how to project MRR using cohort-based logic, with a FREEEEE Excel template that looks like it might actually work well.
2. GTM: Tomasz Tunguz just dropped the GTM guide every startup needs. Learn when to hire your first AE (after 10 founder-led deals is a great tech-ism), how to pick your sales model (field vs inside vs self-serve or a hybrid), and why the best GTM teams layer focus before they layer headcount. Includes pipeline targets, comp models, and team structures from $1M to $100M ARR. 3. AI PRODUCT: There is a bit of OpenAI in this week's newsletter - so let's start here with an AI-ism. AI features aren't AI products. This teardown of OpenAI's strategy shows what it takes to go beyond a wrapper: latency-aware UX, agent loops, invisible value, and goal-driven design. But shit moves so fast - that even AI apps don't move as fast as the market. 4. OPENAI: OpenAI Dev Day 2025 happened last week, and a few bombs dropped: GPT-5.5, a native app SDK, and "Agents as a Service." But the bigger shift? Distribution. A new AMD chip deal, first-party hosting, and native monetization rails signal OpenAI's ambition to own the full stack - infrastructure to app store and operating system. 5. TALENT: Is there a tech talent shortage, or is it just a mismatch? The WSJ reports that while layoffs have flooded the market, companies still can't find the right skills for AI, security, and infrastructure. 6. MARKET: Check out the GTMfund's take on the State of the Market: 75% of seed-stage startups have <12 months runway, 60% of Series A+ aren't hitting growth targets, and 25% of sales teams are shrinking. Shheeeeeeeeet. Buyers are more risk-averse, and PLG is on the rise again - not for price, but for speed. 7. SALES 1: Early-stage selling is heavy on Founder-Led sales, but founders often get sales wrong because they confuse product belief with buyer readiness. This playbook is great and covers how to build a sales process from scratch, including market mapping, email scripts, handling objections, founder-led tactics, and why closing isn't the hard part - it's getting to the first meeting. 8. SALES 2: What do you do when growth stalls? Jason Lemkin lays it out for us: double down on sales efficiency, revisit churn (see 10 below), and stop assuming marketing will save you. TL;DR: The answer is almost always "more pipeline." 9. MULTI-LLM: I often run multiple LLM agents on one task just to verify output - I have a lot of hallucination trust issues emerging. Simon Willison explores the rise of using agents in parallel for coding - as it's not just faster, but safer. Think multi-threaded prompts, error-checking via ensemble output, and LLM diversity as a helpful hedge against those friggin hallucinations. 10. CASE STUDY: A two-year mission to reduce churn. This post breaks down what actually worked with onboarding tweaks, activation metrics, internal alignment (and what didn't). The big takeaway is that there are no silver bullets, just sustained efforts to figure it out. POD OF THE WEEK: Finishing it with a little more OpenAI, Reforge founder Brian Balfour on why ChatGPT is on track to become the next major distribution platform. 1. SaaS METRIC OF THE WEEK: AI METRICS AI ARR isn't always what it seems. MostlyMetrics breaks down how companies are stretching definitions to inflate AI-related revenue — bundling, attribution games, and vague "AI features." Timely reminder: inspect the footnotes, stay cynical!
2. ESOP: Employee Share Option Plans are a wonderful idea to incentivize and retain great staff, but under the hood, ESOPs are complex, especially with changing valuations, both positive and negative, in today's market. Check out Airtree Venture's best practices for communicating the value of ESOP to teams. This article also has a bonus financial model template (value calculator, salary package calculator, and vesting schedule). Check this cheat sheet for common ESOP terms. 3. BUYERS vs USERS: The person writing you a check is not necessarily the same person getting value out of your business. So take a read of this insightful article from HeavyBit on differentiating messaging based on this premise and the different profiles. 4. ESOP BENCHMARKS: A fast follow from above is this wonderful site that has compiled a set of Option benchmark data, comprising over 20,000 option grants from more than 1,650 startups across the US and Europe, sorted by Seed or Venture stage. Carta has also recently done the math and found that for seed-stage valuations ($1M to $10M), the median pool size is 12.9% 5. LINGO: Here is a mega Tech Dictionary update for you because AI is changing who writes what, and when, so we need new terms for all that: Vibe Coding, Spec-Driven Dev, Prompt Jail, and more. 6. AGENTIC: This article and also Slide Deck breaks down what the "Agentic Era" of software actually means IRL: autonomy, context awareness, and goal-driven UX. 7. VENTURE: Carta's Q2 report is out and shows early-stage VC is still holding steady - but overall LP appetite is cooling. Time to liquidity is also growing, and markups are slowing. 8. BUBBLE: Is AI in a bubble? One-hundred-friggin-percent it is! But maybe all bubbles aren't all bad? Azeem Azhar takes a look at how over-capitalized hype cycles can actually drive real breakthroughs, as long as excess gets channeled into things like infrastructure. 9. GROWTH HACKING: Hacks that work edition: Growth hacking isn't dead—it's evolving. Foundation shares some cutting-edge strategies to scale in competitive markets. From viral loops to leveraging micro-influencers, there is some true gold in there. 10. CASE STUDY: Complementing #9 above, real growth hacking is about rapid iteration across product, marketing, and data. This First Round piece unpacks what growth hacking actually means today, with examples from Airbnb, Dropbox, and more. POD OF THE WEEK: Growth by experimentation, Albert Cheng shares how his teams run 1,000+ experiments a year (!) to unlock growth. 1. SaaS METRIC OF THE WEEK: Churn. See #2 for more on Churn. According to CatchJS, though, we're all calculating churn rates wrong. If you love Statistics, the article is well worth reading, and it even gives some Python code to perform the more complicated probability-based equation they recommend. You can then check out this tool (as a handy Google Sheet) from Newfund as a way to analyze the strength of revenue streams for any B2B startup. A complementary article outlining the methodology behind the tool is here (and you should read it first).
2. CHURN: See #1 above; it's the ultimate leaky bucket. BVP's guide on tackling customer churn explains how to identify root causes and implement strategies to reduce attrition (that can be terminal). Analyzing churn data, improving customer onboarding, and enhancing product value to retain users are all in there. The guide also provides actionable steps for creating a comprehensive churn action plan to plug those leaks. 3. SALES: Do technical products need a different type of sales process vs traditional enterprise SaaS products? Check this guide on Tech SDRs. These Reps, understanding developer needs, are key to selling DevTools efficiently. 4. PRICING: Are AI subscriptions broken? This piece argues usage is too spiky, infrastructure too costly, and churn too high for any SaaS-originated flat-rate pricing to hold. Pay-as-you-go may be the only model that scales sustainably. 5. UNIT OF WORK: How the heck do we all quantify what a "unit of work" is with AI? In SaaS, we all know it pretty well - it's a user capacity or a workflow. In AI, it's waaaaay fuzzier. This post digs into how designing around discrete, value-linked units will make AI tools more usable (and billable). 6. AI PAYMENTS: Speaking of billable (see above if ya skipped it). Google just launched AP2: Agents to Payments. It's an open protocol for connecting LLM agents to tools, data, and real-world actions — including transactions and Units of Work ;-) It's kinda like Stripe for autonomous AI workflows. 7. OPERATIONS: Oh - this one is short but good (and bleeds into these two longer reads here and here)! High performing companies run on "accountability machines" - simple, repeatable systems that clarify ownership and drive consistent execution. 8. SEARCH vs ASK: As mentioned in a newsletter earlier this month, with 60% of Google searches ending in zero clicks, the shift from search to ask is accelerating. ChatGPT already sees 300M+ monthly queries, so how much of that traffic is search-like? This article reframes this as an SEO problem for an "Ask Engine" world — where ranking means getting cited by an LLM, not just showing up on Google - how do you even measure that?? 9. FUNDRAISING: Ouch - This article doesn't mince words: most investors don't care about your business. What they do care about is how fast it can grow and how big the outcome could be. If you're building slowly and sustainably, you're maybe not the product for them. 10. CASE STUDY: I love studies on what NOT to do, and AI is a great one - it can 10x your output, or blow up your credibility. This firsthand account of an intern using AI to write code, emails, and even Slack messages is equal parts impressive, amusing, and alarming. POD OF THE WEEK: Why AI will make everyone a manager, and how the same skills you use to manage people today will be essential for also managing AI. 1. SaaS METRIC OF THE WEEK - VC MATHS: IRR vs Return Multiple — they sound similar but measure totally different things. This post breaks down the math VCs actually use to rank funds, justify exits, and explain why speed sometimes beats size.
2. CHIP WARS: After the US government took a 10% stake in Intel last month, Intel and NVIDIA just announced a major partnership. Intel will manufacture NVIDIA's next-gen AI chips — a move that could reboot Intel's foundry biz and reshape the AI infrastructure race. Huge signal bringing together two of the biggest players in the industry. 3. M&A: Navigating some kind of acquisitions process is a relatively unexplored topic in this newsletter, so check this guide to running an M&A process as a Founder from First Round Review. 4. LANDING PAGES: Want to get better at creating effective landing pages? Scrapbook's SaaS landing page optimization checklist provides a guide to creating compelling headlines, using high-quality visuals, optimizing for mobile, indexing, and, of course, including strong CTAs to boost conversions. BONUS: How to write killer CTAs. 5. PITCH DECK: A great pitch deck resource from Alexander Jarvis, who hosts almost 540 of them (and 10,000 pages)! The cool feature here is that you can search/filter startup decks by stage, topic, and country. 6. SALARIES: Want to retain startup talent? Their pay still matters. This breakdown shows how overall compensation mix (cash vs equity) impacts retention, with really clear data points by role, stage, and geography. Bookmark this for your next comp review for sure. 7. DATA MOATS: The idea that data creates a durable moat? It may be overhyped. The Platforms Substack argues most pure "data moats" leak — fast. Unless you've got proprietary generation and tight reinforcement loops, someone else can out-model you. 8. BOTS: One-third of the internet is now bots (see the live feed of that here) — and we have some very good bots out there, but most of these are not the good kind. Vice digs into how fake traffic, scraped content, and automated engagement are distorting the web. It's not just noise, it's business models built on BS that's enshittingifying what we have. 9. MANAGEMENT: Ever feel weird running a startup like it's a friggin' classroom? If you nodded your head, you're not alone. This piece unpacks "educational trauma" — why many leaders default to teacher mode, and how to build an adult-to-adult culture instead. 10. CASE STUDY: OpenAI isn't just building models — it's building a distribution machine. This breakdown shows how ChatGPT's product, pricing, and plug-ins mirror some classic SaaS GTM… but it all moves faster than traditional SaaS ever could. And it's publishing how people use their products. POD OF THE WEEK: Complementing number 2 in this week's newsletter is from a16z on the Chip Wars. A Deep dive into the AI chip race, TSMC choke points, and why NVIDIA's real moat isn't just hardware. 1. SaaS METRIC OF THE WEEK: CLTV to CAC Ratio: This is the classic signal of go-to-market efficiency. Everyone quotes 3:1 (too low, likely not PMF, too high, you ain't spending enough on marketing) — but this breakdown goes deeper with benchmarks by sales motion (PLG vs enterprise), contract size, and stage. Bonus: includes a free model template to run your own numbers.
2. VENTURE: PitchBook's Q2 report is out: global VC fundraising is down 32% YoY, and new fund launches hit a decade low. But dry powder is very much still around (and remains high), and early-stage rounds are holding firm. The market is slow, but not dead. 3. BOTTOMS UP: Bottoms up adoption is hard to crack😉 (learn more about the bottoms up model here). For example, according to Gergely Orosz, Hashcorp has a total of 4,300 customers, but only ~800 of them generate 90% of their annual revenue. And here is how to forecast. 4. AI: Like the great SaaS Ponzi schemes I joke about — AI may be worse. When we all have 100 agents, who's actually doing the work? SaaStr explores what may happen next: consolidation, vertical focus, and agent collapse? 5. WORKFLOWS: Workflows are eating SaaS. Clouded Judgement breaks down why every SaaS company wants to be a system of action — not just record. Usage-based pricing, AI features, and margin pressure are all fueling the shift. 6. SCALING: Sure, software is scalable, but Startups don't scale by adding people — they scale by building systems. This breakdown covers how to layer ops, reduce founder dependency, and keep complexity from killing momentum. 7. LOOP MARKETING: Hubspot has a brand new term for your teh dictionary: Loop Marketing. It's all about ditching more traditional funnels we all know well and embracing real-time feedback loops designed to connect product, content, and community to drive growth. Sounds a bit froo froo, but this matters because now about 60% of all Google searches end in zero clicks - users are getting answers from AI overviews without ever visiting your website. Looping means getting cited there. 8. SOFTWARE 3.0: Bessemer takes a crack at mapping the future of dev tooling in the AI era and are calling it "Software 3.0," - which is eval-first workflows, agentic infrastructure, and tools that help devs manage hallucinations, kinda the new style of bugs. 9. PRIVATE MARKETS: Super interesting question: What if AI doesn't just disrupt how companies operate — but how they're valued? Colossus explores how AI could reshape underwriting, due diligence, and exits in private markets. 10. CASE STUDY: Do your profit margins need a tweak? Here are 40 strategies for improving your profit margins from Sameer Dholakia, with a case study on SendGrid (from high burn to growth and IPO) POD OF THE WEEK: From CJ Guftason, how much revenue do you need to IPO in 2025? 1. SaaS METRIC OF THE WEEK: Attribution - Marketing attribution models help marketers assess the data behind user touch points and conversions to understand the return on Investment and effort. Learn more here on Single-touch attribution models, Multi-touch attribution models, attribution tools, and more to make better data-driven marketing decisions.
2. HOMEPAGES: IS your homepage a barrier to sales, opportunities, value prop comms?? This article makes the case that it is (as potential users want to get their hands on the end product as quickly as possible) - it's a time to value play across PLG and classic Sales lead SaaS. 3. CONTRADICTION: Point Nine Capital discusses the importance of embracing contradictions in business and also uses one of my new favorite phrases, "Startup Advice Industrial Complex". Startup Leaders should balance conflicting priorities, such as growth vs. profitability and innovation vs. stability, to drive success. In theory, understanding and managing these paradoxes should lead to more resilient and adaptable companies (and teams). 4. DESIGN: For the first time ever, I got the most comments from last week's post based on the Pod of the week - focused on Design Thinking for non-humans (agents, cars, etc), so I figure I should up the Product/Design listings in this newsletter. Most SaaS software/tools today feel like Costco - designed for bulk use, not necessarily individual joy. This sharp piece explains why "good design" now means consistency, speed, and scale over delight or elegance. 5. VC LANDSCAPE: AI now powers 47% of all new VC-backed startups. The Median time from pitch to term sheet? Just 6 days!!!!! Whut?? Infra is hot (as I mentioned last week), fintech is cooling, and round sizes are holding steady. Always Charts galore with The Data Driven VC. 6. MARKETS: Silicon Valley Bank's H2 2025 report is out (yup - SVB is still very much around): flat funding, cautious investors, and a "slow burn" recovery - all the negative hits still on repeat. But signs of life in AI infra and B2B SaaS. Profitable growth remains in vogue - burn multiples are under the microscope. SaaStr also takes a deep look into the market and adds color - some startups are thriving, while others are circling the drain. 7. AI MARKET SIZE: SaaStr pulled some data from a bunch of different sources and made a bold call: AI will be bigger than cloud by 2030. (Despite being 15 years its junior). AI is scaling so much faster, embedding deeper, and monetizing more aggressively than cloud ever did (or could). 8. VENTURE: Anyone that's raised here has heard the dreaded phrases: "Too early," "too crowded," "not in our thesis" - VC-isms. This article breaks down the real reasons VCs say no. 10 rejection themes, decoded with some brutal clarity. A must-bookmark/read in prep for your next raise. 9. EARNINGS: Snowflake just posted $900M in Q2 revenue, growing 35% YoY. Tomasz Tunguz unpacks the numbers - usage-based pricing is working, AI workloads are rising, but headcount growth is flat. Efficiency remains the neo-scale. 10. CASE STUDY: SaaStr shipped 3 IRL apps using Vibe Coding (and lived to tell the tale). What worked: shipping fast with small teams. What didn't: vague specs and AI guesswork. Brutally honest, super helpful. POD OF THE WEEK: Benchmark's Miles Grimshaw drops gold on what actually makes startups venture-scale. Jason Lemkin (SaaStr) and Marc Benioff are also there and go deep on what makes SaaS great — and what breaks it. 1. SaaS METRIC OF THE WEEK: Growth Endurance Score (GES). This is a new one for me, but it's a keeper. It's a metric that assesses a company's ability to sustain growth over time (something I have been discussing quite a bit lately, as I try to maintain my own growth momentum). GES measures this efficiency by factoring in both net retention and customer acquisition efficiency. A high GES correlates with long-term business health and resilience. This score provides valuable insights for businesses aiming for consistent, sustainable growth. Bessemer has drilled deeper into it and plotted ARR growth lost YoY, and found that the decay is fairly predictable at 30%. That's a benchmark - in other words, you should expect next year's growth rate to be 70% of the current year's, as the stakes get higher.
2. PRODUCTS: Balancing the needs of existing vs new customers is a hard product act to balance, and that push and pull is nicely described in this article with some great analogies and tips on how to influence the product roadmap, along with an article from First Round Review with a list of things to avoid when building highly-technical products. 3. AI NOISE: This is a great article, sending a warning that the signal-to-noise ratio in AI markets looks to be collapsing. As AI accelerates all kinds of content and capital flows, genuine information is getting harder to parse, distorting valuations and decision-making. So, the inevitable upcoming market correction may be less about fundamentals and more about all that noise. 4. STATE OF AI: I love me a good Bessemer Ventures Report. Their AI report is out, and it's a beast. TL;DR: Infra is hot, agents are messy, and the winners will balance open-source velocity with enterprise-grade plumbing. And it's kinda adding onto #3 above, over 50% of Series A+ AI startups are building infrastructure (vs apps or agents) — infra is where the early-stage dollars are going (and they are less noisy). Agents still hallucinate. Plumbing, not magic, will win IMO. 5. PR: Getting good PR if you're an unknown startup is hard (and also can be seen as a low priority in the endless stable of things-to-get-done) - but it's not as hard as you think without a publicist. Here is a great 101 article from Point Nine Capital (they call it PR for dummies) on how to get great press coverage. ChartMoguls also has an article on PR for SaaS, complete with sample scripts. 6. OUTAGES: Now is always a good time to front-foot your architecture. For techies, Google actually has a great article on Architecting disaster recovery for cloud infrastructure outages. Here is a good DR Plan article, and here is a good template. 7. GROWTH: Testing new tactics of marketing growth takes a lot of resources, and most of us often don't have much time for running experiments. Check out this Google Doc from Dashly, where they've collected 100 growth marketing hypotheses tested by their experts. (includes advert retargeting, wait list for product launches, niche glossaries, etc). 8. API DESIGN: You know the good thing about APIs? They are pretty much boring AF. That's the point. Sean Goedecke breaks down why a "well-behaved" API should feel predictable, invisible, and dull — like a fav spoon, not a Swiss Army knife. 9. MARKETING: Why do some B2B SaaS ads actually land? Because they nail three truths: product (what you do), emotional (why it matters), and cultural (why now). Stripe and Slack get it. Most don't. 10. CASE STUDY: Slack took 8 years to hit $1B ARR. Zoom? Just 4. Jason Lemkin asking the ultimate question: Does it matter? Because, maybe not. Saastr breaks down why long-haul growth often leads to stronger fundamentals (and fewer layoffs). POD OF THE WEEK: Fascinating thought exercise on what design is (As a design nerd, trying to answer that question is the cool bit). In the SaaS world, we are so CX focused - so what, in this new AI world, if that Customer was a non-human/machine? What changes from a product design and build perspective (API Design is a start) - new acronym alert - AX (Agent Experience). 1. SaaS METRIC OF THE WEEK: The Magic Number measures how effectively your company generates $$ via front-end spend - basically, new revenue generated over a specific period with the expenses incurred on Sales & Marketing during that same time frame. Check the SaaS CFO for guidance on calculating the Magic Number, and refer to this article that deconstructs it, highlighting that it's a complex metric influenced by various factors, such as market conditions and company spending, making it difficult to pinpoint specific areas for improvement.
2. PRODUCT-MARKET FIT: A long-running general rule (or a Growth-ism) in the Startup world is that getting to $1m of ARR is a strong sign of Product Market Fit (PMF). Kaiitlyn Henry from Openview runs contrary to this Growth-ism, stating that there's no specific revenue indicator that defines PMF, but also continues to write about concrete signals of PMF available beyond a $$ amount and gut feel. Read more about all those signals here. This sits well with Brian Balfour's work, who wrote an amazing article on the subject that is now almost 10 years old and still incredibly relevant for figuring out what stage(s) you may be at. 3. USER LED GROWTH: ULG is when your existing users become your biggest advocates, driving leads straight into your funnel. It's not for every B2B SaaS company, but when done right, it can build a flywheel that slashes your CAC and ramp times. A classic example is Dropbox's referral program, where users earned extra storage by inviting others—a powerful way to turn customers into evangelists one GB at a time. 4. MARKETS: Carta's Q2'25 State of Private Markets is out and shows venture deal volume up 17% QoQ, but check sizes fell (Seed –9%, Series B –6%). Median Series A valuations slid to $37M (down from $42M in Q1), while Seed valuations held flat. Liquidity is still tight: tender offers down 32% YoY. 5. SCALE: This is a must download. Mark Roberge, the founder of Stage 2 Capital and member of the founding team at HubSpot, has this incredible playbook for scaling. In the book, Mark has defined different stages of scale, with quantifiable measures for each of these stages, structures of the sequences, and signals of when to move from one stage to the next, with the optimal go-to-market design for each. 6. CC/CD: New entry for the tech dictionary: Continuous Calibration/Continuous Development (CC/CD). Unlike CI/CD, AI products drift without constant tuning. This framework highlights why calibration is as critical as shipping for long-term performance. 7. IDEAS: Hunting for your next SaaS idea? Check out 1-star reviews. This is similar to scouring Google Searches in that buried in customer complaints are unmet needs and broken workflows, which in reality is a goldmine for your next thingy. 8. AI RISKS: A core problem in current AI is its ability to be so wrong so confidently. Which really erodes trust. Here's the fix (pretty please, AI companies). Just build models that admit uncertainty and show reasoning. 9. AI: Worth pondering the hype. Casey Handmer argues AI's economic impact will dwarf expectations. Think trillions in productivity gains, new industries unlocked, and labor markets reshaped far faster than past tech waves. 10. CASE STUDY: If it's true that the IPO markets are slowly starting to creak back to life, Canva is shaping up as the next big B2B one. The design giant has crossed $3.3B ARR at a $42B valuation, with 185M+ MAUs. Their playbook is Freemium growth, rapid expansion into enterprise, and a relentless product velocity that rivals the best in SaaS. POD OF THE WEEK: Adding onto 5 above, Funnel and revenue math kindly explained by Mark Roberge and Matt Plank of Rippling in the Science of Scaling podcast. 1. SaaS METRIC OF THE WEEK: The nag Metric, I'm bringing this one out of the archives, as I have been nagged a bit lately (and I'm also away this week;-)): A call to action within a site or customer journey is kinda like a parent trying to get their kid to clean up their mess. It just gets a bit naggy after a while and gets mentally filtered out. This can impact your brand/NPS over time or just irritate people into churn. The Nag Score - outlined in detail here - is an attempt to quantify this.
2. NCT: Crack open your tech dictionaries, I have a new acronym to lob your way. OKRs are old school. Ravi Mehta's NCT (Narrative, Commitments, Tasks) model simplifies goal setting. So instead of vague objectives, start with a clear Narrative explaining the "why" behind each goal. Next, set 3-5 measurable commitments for the quarter, with Tasks as actionable steps. The difference is that OKRs can be overly ambitious, but NCTs focus on achievable milestones that align closely with strategic priorities, more Agile in a way, as course corrections are easier (and it increases team accountability). 3. AI: Polytheistic AI certainly is a phrase to wrap your heads around. It means multiple powerful models coexisting and not one AGI "god.". As part of a top 10 list of thoughts on AI, the article calls today's AI "amplified intelligence," excelling in the middle of tasks while humans still handle prompts and verification. 4. MOATS: Euclid VC digs into why tech moats are harder to build (and keep) than ever. Market speed, AI, open source, and fast-followers erode advantages quickly, meaning execution speed and distribution now trump IP in defensibility. 5. MARKET: Shopify, Datadog, and Palantir are all re-accelerating, showing that in B2B SaaS, the best can still crush it despite the macro gloom. For top players like these guys, growth (and multiples) are back. 6. AI: In AI, marginal costs trend toward zero—so this article points out that if this is true, then pricing should shift from scarcity to abundance. Think access-based, outcome-based, or ecosystem-driven models, not per-seat limits. 7. SHIP IT: AI teams these days are often told to "just ship it," but as us oldies know, rushing shit leads to underbaked products. Leah Tharin breaks down why speed still needs guardrails—especially when trust, safety, and market fit are on the line. 8. MARKETS: Silicon Valley Banks H2 2025 report shows Series A ARR medians nearly doubled to $3M since 2021 (crazy right?), with the lower quartile growing fastest. Translation: "Meh" traction will probably no longer get funded; fewer rounds is a higher bar. 9. PRICING: I make the joke/tech-ism that "pricing is like software - it's never done", too often. But it's true, dammit! So check this great 6-Step Guide to Pricing (With Case Studies), which is good and agrees with my tech-ism about how hard it is. 10. CASE STUDY: Shopify's AI mandate is now "operational law" — no resource or headcount is approved unless a team can prove AI couldn't do the job. AI fluency is in every performance review. An internal LLM proxy + MCP infrastructure makes AI instantly available to the entire company. This is culture remade in some weird AI way. POD OF THE WEEK: How ChatGPT accidentally became the fastest-growing product in history. 1. SaaS METRIC OF THE WEEK: Headcount. Headcount accounts for 60-80% of early-stage burn, which means most burn starts with hiring. So check out this headcount planning module to track every hire and salary
2. SALES TRANSITION: On the mission of moving from founder-led to AE-led sales? This article from Bain Capital Ventures makes the case that it's all about the "MVP/ICP Handshake." Nailing the alignment between your Minimum Viable Product and your Ideal Customer Profile is a key step. Without this, bringing in AEs too early can stunt growth (which has been seen time and time again in startup land). Solidify the customer segmentations and product-market fit before even thinking of adding a sales force. 3. VIBE CODING: Those "Vibe coding" tools like Replit, Lovable, and Cursor blend code, creativity, and collaboration. The TAM? Jason Lemkin takes a look, and it's likely much bigger than it looks once you count non-devs building with AI. 4. AI MARKETS: According to Elad Gil, core AI markets like Code, Legal, and Customer Support now have clear leaders. But sectors like Compliance, Security, and Sales are still wide open—and ripe for disruption. A great map of where AI is and where it's going. 5. FULL BREADTH: You heard of full-stack devs - so how about Full breadth? A new one for your tech dictionaries. These are engineers who go beyond deep tech skills, adding product sense, design taste, and business insight, making them deadly effective in those early-stage teams. 6. AI INFRASTRUCTURE: A post on the new AI Data Centers I posted earlier this month was a popular one, so it's obvious that AI infra's second act is here—moving from just GPUs to the 3 E's: environments (realistic testing), evals (robust benchmarks), and experience (seamless dev tools). The next wave is about trust, usability, and real-world performance. 7. AGENTIC AI: Lots of agentic hype at the moment. In this paper, Deloitte outlines why Agentic AI is the (obvious) next leap beyond GenAI; autonomous, reasoning systems executing complex, multistep tasks with minimal oversight (what could go wrong?) Success hinges on process fit, tech readiness, governance, good QA, and upskilling teams for human-AI collaboration 8. VENTURE: According to this new report I've found (The Big Book of Venture Capital), $205B wass raised in H1'25 (up 32% YoY), mega-rounds up, valuations near 2021 highs, and M&A re-surging (driven by AI). But it's not really a comeback, more just a reshuffle favoring top firms, some AI plays, and strategic capital. 9. WINNERS: a16z breaks down why top startups pull ahead, they compound their advantages, build faster loops, and leverage market power. Success isn't just product or timing; it's how to build momentum that's hard to catch. 10. CASE STUDY: Check out how Carta built their own AI agent "Equity Concierge" to automate complex equity tasks for startups. The team focused on high-value workflows, some tight human–AI loops, and fast iteration, which delivered real actual ROI. POD OF THE WEEK: How the heck do you scale an insanely fast growth company like OpenAI's Sales Team? Check how Maggie Hott does it (along with three other Unicorns). |
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