1. SaaS METRIC OF THE WEEK: CRO - Conversation Rate Optimization, Banklinko has created a great web guide about what it is and how to design for CRO within a business.


2. MULTIPLES: Redpoint's 2026 Market Update puts it bluntly: public SaaS trades at 4.1x NTM revenue, the lowest multiple in a decade, with software down 20% YTD - dead last in the S&P 500. Horizontal SaaS is down 35% LTM, while vertical and infra held flat. Holy crap - This means that public SaaS is now the worst-performing sector in the S&P 500, down 20% YTD. Median NTM revenue multiple has collapsed to 4.1x - the lowest in a decade.


3. RETENTION: Fast follow from above - as this report is JUICY! Redpoint also surveyed 141 CIOs: 54% are actively consolidating vendors; 45% of AI budgets are replacing existing software spend; AI features are the #1 driver of software spend increases at 58%, so that spend is cannibalizing existing budgets (and SaaS), not expanding them. Customer service, finance ops, and project management top the replacement list. 46% of CIOs expect usage/outcome-based pricing to grow, while 29% say seat-based pricing will decline.


4. GTM EFFICIENCY: AI-native companies are hitting $10M ARR within 9 months of their first $1M! That's roughly 3x faster than the best SaaS benchmarks (per Kyle Poyar's interviews with founders at Clay, Gamma, HeyGen, and Fyxer). The playbook: delay AE hires until $2M ARR, pair forward-deployed engineers 1:1 with sellers, and keep lifetime burn below ARR - no big deal.


5. DEPLOYMENT: FDEs are a new term for me. Forward Deployed Engineers. The biggest bottleneck in AI right now isn't the models - it's getting them deployed. Lemkin flags the FDE shortage: enterprise customers with proper training hit 60-80% automation rates, while self-serve lands around 20%.


6. PRODUCTIVITY: Price's Law isn't a new concept - but always so relevant - the square root of your headcount produces 50% of output. AI-first startups are proving it at scale. As mentioned a few weeks back (#9), Tunguz did the org chart math: 150 people equals 11,175 communication channels; 30 people + AI equal only 435. (and Anthropic generates ~$5M revenue per employee vs $200-300K at traditional SaaS).


7. TASTE: The VC Corner argues that taste is now the only moat AI can't replicate. Times have changed, fast! Coinbase says 40% of its code is now AI-generated. Lovable ships working apps in minutes. When creation is being radically commoditized, a scarce skill is curation - knowing what to cut, not what to build. We're the hipsters!


8. TOOLS: Claude Skills are under-appreciated, and I've been customizing a bunch this month for my personal use (they avoid re-prompting, no copy-pasting context, and makes your token use more efficient). The AI Corner published a highly bookmarkable 25 copy-paste Skills for startup marketing covering content, SEO, CRO, email, and positioning.


9. VENTURE: Anyone who'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.


10. CASE STUDY: Anthropic has reportedly hit $30B ARR, passing OpenAI's $25B, per The AI Corner. The kicker: it's spending roughly 4x less on training costs and projecting positive free cash flow by 2027 - three years ahead of OpenAI. Over 1,000 enterprise customers now spend $1M+ annually, up from 500 two months ago.


POD OF THE WEEK: More on the pricing push mentioned in #3 above - price wars are heating up and hands are being forced by the utility and ubiquity of Anthropic, OpenAI et all. ​

1. SaaS METRIC OF THE WEEK: BURN MULTIPLES - See #9 below for some of the why, but in this market, operators are expected to find the balance between growth and efficiency. So it's time to brush up on those efficiency metrics in this 2-part post covering Burn Multiple and  Sales Efficiency metrics (see post 2). A Burn Multiple measures how much a startup is burning to generate each incremental dollar of ARR. The higher the Burn Multiple, the more the startup is burning to achieve each unit of growth. Here is how to calculate this metric, along with an example.


2. GROWTH: I love this article from First Round Capital. Mainly, as it justifies my sentiment around rigor, data, and insight, to quote: "Growth is about implementing a rigorous, customer insight and data-driven process with sustained effort to remove friction".


3. SALES: Deal mechanics! How can Account Executives who are getting the same amount of meetings booked by SDRs outperform their counterparts? According to this article - best-practice deal mechanics. It's about what also happens between meetings, using video, phone, and LinkedIn to engage all the decision-makers involved in complex enterprise deals, giving your prospects an out, and setting next steps. Jason Lemkin also swoops in this week with some top tips to help sales execs close more.


4. CUSTOMER SERVICE:  In this current age of the customer, this article makes the argument that Customer Success is actually part of the product. Want to start building out that product? Check the HubSpot guide on getting started with your customer team.


5. PERFECT: Perfection is the enemy of progress or something, and it's also the fastest way to look inexperienced. A startup is by definition searching for a scalable model - if you claim you've found it, you're selling theater. The tell is in the pitch: founders who say "we've solved everything, just wire the funds" are dreamin'.


6. SHIFT WORK: Here is a real interesting idea - invert the standard agentic coding model: own the day - write specs, architecture, think hard - all so agents can run autonomously overnight while you sleep. The framing is the cool bit (and I think the markdown files are hard): time and cognitive energy are expensive and constrained, no context switching; agent tokens are cheap and plentiful. By morning, you can wake up with a changelog, a commit history, and a bunch of work done.


7. GTM: Clay went from $1M to $100M ARR in two years. ElevenLabs hit $330M ARR without a traditional sales org. The numbers behind the shift: AI SDR platforms run $12-60K/year vs $139K fully loaded for a human, handle 1,000+ contacts daily vs 50-80, and drop cost per lead from $262 to $39. The AI SDR market reached $4.1B in 2025 and is projected to reach $15B by 2030! Most teams are still running the 2020 playbook, and as I mentioned a couple of weeks back (see #9), Small teams move faster because communication scales badly.


8. A FUTURE OF WORK: A three-person team (at StrongDM) built a Software Factory with two rules: code must not be written by humans, code must not be reviewed by humans. Agents write it, testing agents try to break it, and they loop until done. Each engineer budgets $1,000/day in tokens. Here is the article's take: we've moved from working with AI to managing it - and the organizations figuring out what that looks like are going to be setting the precedent for everyone else.


9. VENTURE: The 2026 fundraising reality check: median time from seed to Series A is up 30%+, getting tighter, diligence that took a week now takes two months, and what got you a Series A in 2021 is now just the baseline at seed. (plus AI captured 41.7% of all seed capital last year). Burn multiple below 1.5x is the new bar - above 3x and the conversation is gonna get uncomfortable fast.


10. CASE STUDY: Cloudflare, I'm a fan - Most B2B companies are decelerating, but they just posted 34% growth ($2.4B ARR, up 27% YoY. Q4 numbers: 37,000 net new paying customers in a single quarter, $1M+ customers (up 55% YoY), NRR at 120% (up 9 points in a year), new ACV growing ~50% YoY - fastest since 2021. The reason: they own critical infrastructure nobody can defer to; AI traffic has gotta pass through their network. (Also, they fixed go-to-market a couple of years ago and are harvesting.)


POD OF THE WEEK: Follow from #9 above, VC partner Keshia Theobald-van Gent at Beta Ventures makes two points - fundraising is a sales process, not a networking activity, and data is no longer a moat.

​1. SaaS METRIC OF THE WEEK: ARR - Kyle Poyar makes a case that ARR is just not that trusty a metric for AI-native companies and proposes gross profit per million tokens (see #6 below). Late last year, Tomasz Tunguz found that this metric tracks closely with AI company trading multiples (and also revenue per employee). The logic: In an 80% margin SaaS world, ARR was a great proxy for value. In a 20-60% margin AI world (with wildly variable cost structures), it ain't.


2. DATA: I don't think I have more to add than the title of this article: "You Cannot Be Data-Driven Without Experimentation," but the opposite of that is also a truth to hammer home "You Cannot run experiments successfully without being data-driven". We all overestimate our experimentation skills AND our Data collection skills.


3. VENTURE: Bloody hell - The Big Book of VC is a BEAST of a PDF: AI captured 51% of all global venture funding but only 30% of deal volume - capital concentrating into fewer, bigger bets. Secondaries hit $102B in H1 2025, overtaking IPOs as an exit mechanism for the first time. Only 25% of current unicorns are realistically IPO-ready, and 60% of 2025 IPOs are priced below the last private round.


4. BRAND: Ever wonder what the heck those ads are that have no meaningful call to action? System1 analyzed 195 US TV ads and found that ads using characters, dialogue, and expressive people drive sustained attention; product shots and voiceover lose it, even from interested buyers. For us operators: 94% of B2B buyers build their shortlist before contacting a vendor, and 77% buy from their original top choice. Being in memory before they're in-market can make you an easy part of the deal.


5. PRICING: Bessemer's AI pricing playbook is out and identifies the core structural shift: SaaS charged for access, AI must charge for outcomes. The problem is that outcome-based pricing requires knowing your cost-to-serve per transaction before you set a price - most teams don't. Get the unit economics wrong at pricing design, and every enterprise contract becomes a margin liability at scale.


6. PRICING 2: Clay just took an immediate 10% revenue hit to restructure pricing around a platform + tokens model - separating out data credits (cost pass-through at 50-90% reduction) from workflow actions (value). Kyle Poyar's read: this is where AI pricing is heading. Credit-based pricing surged 126% in 2025. The operator question is whether your customers can predict their bill, and whether your margin survives if they can't.


7. RETENTION: ChatGPT was the fastest-growing consumer app in history. Claude just became the fastest-growing enterprise product, growing from $1B to $14B ARR in 14 months. The twist: Anthropic built a one-click cancel button into the product. Lemkin's point lands harder for that reason: a 2024 FTC/ICPEN study found 76% of SaaS companies deploy dark patterns to trap churning customers. If the category winner doesn't need to, neither do you.


8. COMPREHENSION DEBT: Folks, there is a new kind of debt in town - comprehension. An Anthropic randomized trial found that AI coding assistance produced the same velocity but 17% lower comprehension scores - and developers using AI for passive delegation scored below 40% on comprehension tests vs. above 65% for those using it for active inquiry. The tool isn't the problem; how you use it is.


9. GOVERNANCE: Whoops! Some of Amazon's AI coding assistants contributed to four P1 incidents in one week! $6.3M in lost eCom orders and a 99% drop in order volume across North America - ouch. The response: a 90-day safety reset and mandatory two-person review for all code changes. Tomasz Tunguz's frame is the harder question: AI-generated code produces 70% more issues than human code, Utah has already eliminated the hallucination defence in law, and the company bears liability for every action its agents take.


10. CASE STUDY: NON-DILUTES: The VC Corner has compiled 80+ non-dilutive funding sources across grants, RBF, venture debt, prizes, and cloud credits. One founder stacked four sources to raise $1.2M at zero dilution. The stacking math is the hook: cloud credits alone (I'm a big fan) across three major providers are worth $800K+, government grant programs run 15-20% approval rates for Phase I at $50K-$500K per award, and the global RBF market hit $5.8B in 2024, growing at 70% annually.


POD OF THE WEEK: Chris Degnan scaled revenue at Snowflake from $0 to more than $3B ARR.

1. 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, (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. Here is an article that looks at ways to shorten CAC Payback, and this article takes an operator view on CAC Payback that includes (and celebrates) the value of net revenue retention.


2. PROMPTS: Something I learned last month - your prompts are portable, and this is one of the better things Lemkin has written lately. If customers can export the prompts that power an AI workflow (which a lot of us did last week), switching vendors becomes trivial - meaning "AI agent churn" is the real issue-du-jour and far higher than traditional SaaS lock-in ever allowed.


3. VENTURE: Who's funding what? We all know by now that AI is swallowing venture funding. Crunchbase data still shows the hottest startups now raise far larger rounds, far earlier - but the capital is concentrating into a smaller set of companies. The gap between AI winners and the rest of the startup market is widening fast. Fun fact, Tiger Global and SoftBank Vision Fund led the 2021 boom - now both have cut activity by more than 95% by deal count.


4. EMERGING OPPORTUNITIES: Venture capital isn't evenly distributing its bets anymore. PitchBook's 2025 analysis shows early-stage returns concentrating in a handful of sectors. Early-stage AI deal is up 40+% and AI median pre-money valuations hit $52.1M (up from $28.1M in Q1 23). SaaS still leads in exit probability, with a 72.1% successful exit rate. Defense tech is the quiet/not-so-quiet mover, with median early-stage valuations passing $100M.


5. EARLY-STAGE: Another report this week — this one from CB Insights, covering early-stage trends - flags agentic AI security as the category proven founders are racing to own: 10 of 21 tracked startups rank in the top 100 (and 18 of 21 average a 55% M&A probability).


6. DATA CENTRES: AI's infrastructure boom is colliding with physics and geopolitical BS. US data centers are facing all kinds of power/infrastructure shortages, grid constraints, and rising energy costs, and transformers now take 4 years to deliver! Amazon, Google, Meta, Microsoft, OpenAI, Oracle, and xAI just signed Trump's Ratepayer Protection Pledge to self-fund all energy costs. But guess what - it's non-binding. Side bar: Google plans on spending $185 billion on AI capex. ANNUALLY. FOR THE NEXT FRIGGIN DECADE! That's a lot of Gemini Subs.


7. VC SUBSIDIES: I have this running techism I tell anyone who will listen- enjoy new tech things as much as you can while the VC Subsidy exists: Uber, AirBnB, Netflix, Shopify, Dropbox, MealPal - all these things I got to enjoy at the VC's expense to allow blitz scaling to happen.  And of course, AI is running the same model. A $200/month Claude Code subscription might consume up to $5,000 in compute, according to the CEO of Cursor.


8. BRAND: 45% of online shoppers already use GenAI tools to compare products, and Alphabet now has 75M daily users in AI Mode, according to this Bain Survey. Brand is increasingly what the agent says it is, not what your website says - this means we've gotta design for both human users and machine decision-makers.


9. PRODUCTIVITY: Here are some incredible productivity data points - great lens. Small teams move faster because communication scales badly. Tomasz Tunguz runs the org chart math: a 150-person company has 11,175 potential communication channels; a 30-person (and AI-enabled) team producing equivalent output has 435. Anthropic generates ~$5M in revenue per employee; Cursor, $3.3M; traditional SaaS considers $200-300k strong.


10. CASE STUDY: Uber is quietly running one of the largest internal AI engineering experiments. 92% of developers use agents monthly, 31% of code is AI-authored, and 11% of PRs are opened by agents. Downsides too: AI-related costs are up 6x since 2024, and token optimization is now a dedicated priority.


POD OF THE WEEK: Design as we know it is dead? Jenny Wen (head of design at Claude) reckons so. ​

​1. SaaS METRIC OF THE WEEK: Here is a highly bookmarkable Guide to SaaS Metrics from equals.com that covers all the greatest hits and more (ARPA, LTV:CAC, Burn Multiples, etc).


2. OPENAI: Wow - has it been a crappy week for mainstream AI companies - especially for OpenAI. Benedict Evans (even before this week's accelerated demise) argues that OpenAI has no durable moat: 800-900M users, but only 5% paying; 80% sent fewer than 1,000 messages in all of 2025; and every mainstream model is now roughly equivalent. That platform flywheel Sam Altman describes doesn't actually show a flywheel.


3. AI 1: This is also a Benedict Evans Part 2 - A couple of times each year, Benedict Evans goes on an absolute blinder in PowerPoint exploring macro and strategic trends in the tech industry, and his latest version is just an updated deck of both of last year's versions (they are all called "AI eats the world").


4. AI 2: Hear me out, cynics: Agentic AI isn't a feature add - it's a wedge into existing B2B SaaS - it's certainly something I'm leaning into. This piece argues vertical software vendors can embed agents that execute workflows, not just surface insights, turning systems of record into systems of action and expanding ARPU without adding seats.


5. SALES: Checkmate SaaS Companies that are using AI for sales, AI Companies are hiring human SDRs, 39% of top AI companies with open GTM roles are hiring SDRs.


6. DEATH OF SAAS: Jason Lemkin argues that SaaS ain't dead - but the old playbook is wobbly. Growth is way harder to get, NRR is under pressure, AI-native products are killing seat models, and buyers expect more features for less. For example - PagerDuty at $500M ARR trades at 2x revenue with a customer count flat for four years. Bit of a Canary? Janelle Wade reviews past SaaSacres.


7. CAPITAL: Mo' money doesn't fix weak fundamentals. This breakdown shows how oversized early rounds distort hiring, inflate burn, delay product-market fit discipline, and lock founders into growth expectations the business can't support when the market gets tight (74% of high-growth internet startups fail due to premature scaling, and 93% of those that scale early never break $100K MRR).


8. ENGINEERINGIFICATION: Great term for something we're all already probably living on the daily: GTM engineers, design engineers, sales engineers - every role is picking up engineering identity as LLMs lower the barrier. The line is moving from "who is allowed to build" to "who has the ideas and dedication to actually build." If your job title still has zero overlap with engineering, it probably will soon.


9. LAYOFFS: It's back in the news with the huge layoffs at Block last week casually laying off the biggest percent of a Fortune 500 company ever (40%). Tomasz Tunguz asks the bigger question: Could you operate your company with half the people?


10. CASE STUDY: Brex famously made bus stop advertising cool again, but they actually 3x'd signups by reworking their packaging and pricing - simplifying plans, personalizing everything they could, clarifying value by segment, and removing friction in the self-serve flow.


POD OF THE WEEK: Adding onto #3 and #4 above, (and another Benedict Evans thing) - AI and SaaS - What does AI do to software?

1. SaaS METRIC OF THE WEEK: SaaS METRIC OF THE WEEK: Revenue & Burn per Employee. In this profits-over-growth new SaaS world, forget vanity metrics. This post makes the case for tracking Revenue and Burn per Employee as core performance metrics. Why? They reveal whether you’re scaling efficiently—or just adding bodies. It also includes great benchmarks across stages.

2. DUNNING: Time for your annual reminder of the real term with a weird-ass name - it's a phrase for involuntary churn (aka bad or failed payments). According to Baremetrics SaaS and subscription businesses lose around 9% of their MRR due to failed payments on average. Learn more about a successful dunning (and pre-dunning) process.

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. TEAM: Check this stat: VCs say startup success depends on your TEAM DYNAMICS (56% of them), then timing (12%) and tech (9%). The tech sector likes teams more (64%) than healthcare (42%).

5. SAAS IS DEAD? 70% of public SaaS now trades below 5x forward multiples, down from (an admittedly bonkers and frothy) 25x+ in 2021. The market isn’t killing SaaS, but it’s repricing hype, or moving the hype around a little. According to Blackbird Ventures (and please scroll, as Blackbird has an annoying full-page banner image that makes the page look blank), “AI vs SaaS” is the wrong debate. Winners will be those who reinvent distribution and workflow leverage, not those relabeling decks. I guess we all get valued on our relevance?

6. AI MARGIN: The article above in #5 got me thinking. One of the investable things about SaaS is the margin - it's high. And AI is an expensive endeavor to do well. So I did a bit of sleuthing. SaaS built its legend on 75–90% gross margins. AI often runs 40–60% because of the tokens and compute-based architecture. The SaaSCFO did the maths, and their model showed AI needs ~6x the revenue to match SaaS EBITDA at the same cost structure. The math hasn't necessarily changed - the inputs have. TAM and ARPA have to now carry what margin no longer does.

7. PRICING: Great new report from ChartMogul, Seat-based pricing still dominates SaaS revenue, but their latest data shows per-seat plans drive the majority of ARR across B2B, with usage-based models growing but not replacing seats. Hybrid pricing is rising fast - especially in mid-market and enterprise - blending predictability with expansion upside.

8. CUSTOMER SERVICE: This is a short, but pretty interesting post - sure, tech is great at creating jobs that don't exist yet, but dang! Look at the first chart in this a16z post showing the plunge in hiring of CS roles from Q4 '23 to Q3 '25. It's about 1/3 of what it was 2 years ago.

9. BUILD vs BUY: It's a great Tech-ism and debate - but has it changed much, given the Agent/Vibe-based ease with which some tools can be built internally? AI made building ridiculously easier. BUUUUUUT - It didn’t make owning software easier. Sure, your Vibe coding lowers the cost to prototype, but not the cost to maintain, secure, audit, and evolve, etc. So........ build if it’s core differentiation. Buy if it’s your plumbing. TL;DR: AI can change speed but not your TCO.

10. CASE STUDY: In a down-round exit - Brex was acquired for $5.15B after a $12.3B peak valuation. In today's current environment, is that a bad thing? Early investors likely did great. Late-stage investors and many employee option holders? Not so much. Reminder for us all: valuation is a debt, not a trophy. Entry price and cap table structure determine a lot about who the winners are in liquidity events.

POD OF THE WEEK: Adding onto #5 and #6 above - this podcast looks at inference costs (stuff like GPU/TPU compute time, energy, and infrastructure overhead for every user prompt) with AI companies, noting that companies like Cursor and Lovable treat compute as their primary growth investment, not their primary margin drag

​1. SaaS METRIC OF THE WEEK: SaaS METRIC OF THE WEEK: Not all ARR is created equal - and some is faster! This week’s breakdown from The SaaS CFO explains how to split AI ARR from traditional SaaS ARR—vital as more tools blend AI features, infra, services, and usage-based models.

2. MOSTLY METRICS: I like this newsletter a lot - SaaS isn’t dead, it’s just priced like everyone gave up doing the maths. Median EV/NTM revenue is now 3.9x (a 10-year low), despite retention metrics holding steady (ServiceNow at 98%) and Rule of 40 profiles improving over the last coupla years. The Bears are betting that AI will kill SaaS, and that incumbents can’t use their data moats.

3. PRODUCTIVITY: Tomasz Tunguz draws a sharp parallel between Ford’s assembly line innovation and AI-assisted coding in relation to productivity. Just as the Model T drove a 90% productivity gain and wiped out 80% of automakers, AI dev tools are cutting build time 55–81% in under five years. But the outcome flips: instead of capital consolidating power, AI commoditizes it. Cheaper, faster software creation means more builders, more startups, and massive second-order job creation, not fewer developers.

4. BUYERS 1: New report - 6sense’s 2025 Buyer Experience Report points out the obvious - buyers still decide before talking to sales. But overall, journeys due to AI are shorter; seller contact happens ~6–7 weeks earlier; 80% of deals still go to the pre-contact favorite; buying stakeholders average 11–14 people, but only 3–4 ever engage sellers. AI can pull sellers in earlier, but not really influence anything later.

5. BUYERS 2: A follow-up to #4 above - are your Funnels lying to you? This article reframes buying as a circular orbit, not a long pipeline. About 60% of buyers are insulated, and out-of-market, ~35% are evaluating, and just ~5% are validating. 80% of vendor shortlists are locked before sales contact. Broad-beam brand builds mental availability for future cycles; narrow-beam proof only works once your buyers re-enter your orbit. Miss day-one considerations, and you’re probably done.

6. UNFAIR ADVANTAGES: Gaurav Vohra's Unfair Advantages Framework is a new one for your tech dictionaries. It’s all about identifying unique, hard-to-replicate strengths:  Proprietary data, customer networks, logged industry experience, lets you leverage what others can't - it's a startup superpower moat.

7. STRENGTHS AND WEAKNESSES: Expanding on number 5 above, this article explores how to turn a competitor's strength into your own advantage: Reposition their wins as your opportunities to differentiate, pivot, and outpace - really critical in today's competitive SaaS markets (with some good examples)

8. FOUNDER QUESTIONS: What can drive success for all y'all at the early stages in 2025? Check out these 16 questions you can use and be prepared to answer around strategy, customer focus, scaling plans, etc.

9. SALES: PLG is all fun and games in the early days, but at some point, as companies scale, it has to grow up. The shift to Sales Led Growth (or a hybrid of) isn’t necessarily a strategic thing - it’s customer-driven. Signals to watch: enterprise feature requests (like SSO, security/ISO/SOC/GDPR stuff), organic team expansion, and inbound for bigger contracts. Winners don’t “add sales,” - build the PLG-SLG hybrid around PQLs, usage signals, and shared ownership across product, marketing, and sales (see Calendly and Asana and the case study below).

10. CASE STUDY: Following on from #9 above, I really like Supabase on the Company and product size, so take a look at this Case Study that shows what product and community-led growth actually looks like in practice. By going open-source first, Supabase lets developers experience value before buying, turning GitHub stars and Discord into a high-intent funnel. Content is written by builders, not marketers. I just love the integration simplicity personally (similar in vein to how Stripe focused on simplicity and integration).

POD OF THE WEEK: Going through a bit of a slump in growth? Check this podcast out on 5 questions to ask (when your product stops growing).

1. SaaS METRIC OF THE WEEK: Cap Table - I'm bending this week's post to make it fit - I just think this article is pretty cool, and your Cap Table is a definite metric, I'll fight you on this. Your Cap Table isn’t just an ownership spreadsheet - it’s used as a decision-making constraint. It defines control, dilution, hiring leverage, follow-on funding options, and exit outcomes. Messy early cap tables compound quickly, especially with SAFEs, friends-and-family deals, advisor equity, and uneven founder splits. Clean cap tables preserve optionality; broken ones can quietly kill deals (moral), and most importantly, future raises.

2. CO-FOUNDERS: Starting from an idea, but being non-technical often means looking for or finding a technical co-founder. This article makes the argument that non-technical founders stall by outsourcing progress. Be productive first - talk to customers, validate demand, ship scrappy versions, reduce market risk. TL;DR - Productive founders will attract productive co-founders and efficiently build product.

3. SOLO: Fast follow from #2 above. And according to this article (bit of a biased domain name, though, tbf) - going Solo is no longer taboo, and 1/3 of all startups are currently flying solo. Driven by better tools, a bucket ton of AI leverage, and lower operating costs. The tradeoff hasn’t disappeared - speed and control go way up, but resilience and perspective are the things that go down.

4. BENCHMARKS: Mostly Metrics kicks off some good benchmarks for 2026 - they surveyed 132 SaaS companies (≈50% >$25M ARR, ~25% >$100M). Median renewal rates sit around 91% (top quartile ~95%, bottom ~84%). CS headcount peaks at $10–50M ARR then compresses at scale. 56% of CS teams are paid on expansion, but 63% don’t control it. Median CS variable comp is just ~20%, and the data show that product quality and customer fit drive renewals more than comp plans or org structure.

5. LEAD: New term for you to ponder, "Leading from the front" isn’t just a military-ism for most of us in startup land, though - it’s the difference between high-trust teams and checked-out ones. Stay SaaSy breaks down how great leaders model urgency, own the hard stuff, and never ask for what they won’t do themselves.

6. GROWTH: Growth is now a trust problem, not a funnel problem? With SEO (see #7 below to question that), paid, and corporate social collapsing under AI pressure, Elena Verna argues growth shifts to trust-based systems: employee-led distribution, creator credibility, community, and product-led brand. Retention also follows the same logic: when features commoditize, customers stick with products they trust will keep delivering outcomes, not just efficiency.

7. VELOCITY: Fast follow from 6 above; If growth is now a trust problem, velocity is the new authority. Om Malik argues that modern networks don’t reward being right, deep, or durable - they reward momentum. What travels fastest wins: first take beats best takes, access beats independence, memes beat meaning. The algorithms look to be optimizing for speed (and that ain't necessarily).

8. SEO: Oh shit - turns out, I'm kinda wrong - search (and SEO) isn’t dead. Graphite + Similarweb data across 40k sites shows organic traffic is down just -2.5% YoY, not the -25% to -50% collapse everyone’s been yelling about. AI Overviews do hit CTR (-35%) but only show ~30% of the time, mostly on low-value informational queries. Commercial search still holds, and 90% of Google clicks remain organic. SEO is changing, not dying.

9. CHURN: AI is facing a retention reckoning we can all learn from. ChartMogul data across 3,500 companies shows AI-native apps have ~40% GRR and ~48% NRR (for perspective - that's worse than B2C and far behind B2B SaaS (82% NRR)). The issue is all those “AI tourists” - low-cost, easy-to-buy tools that are just as easy to cancel. Pricing matters: AI products >$250/month look like real SaaS (70% GRR, 85% NRR). Durable ARR comes from deeper workflows, higher price points, annual plans, and narrowing the gap between shipping AI and actual adoption.

10. CASE STUDY: Here is the start of a great 3-part article from Notion covering the challenges faced by (VC-backed) startups towards $100m in revenue. Fun (?) fact: Only 1.2% of us achieve this milestone.

POD OF THE WEEK: Reed Hastings, ex-Netflix CEO, breaks down how to scale trust, talent, and bold bets - without turning your company into the Hunger Games. No PIPs, no micromanagement—just clear values, adult treatment, and $100M risks like House of Cards with no-BS masterclass in culture.

​1. SaaS METRIC OF THE WEEK: Cash Runway - most founders don’t even know how many months of cash runway they have left, so here is a new, updated for 2026, Cash Runway model for y'all.

2. OUTCOME-BASED PRICING: Another term for your AI Tech Dictionaries is outcome-based pricing. It’s a pricing strategy tied directly to the value your product delivers to customers, very much a pricing model driven by the growth of AI.

3. AI RESEARCH: VentureBeat flags four new terms for your AI Tech dictionary this year: continual learning (models updating without retraining or catastrophic forgetting - looking at you ChadGPT)), world models (AI learning physical reality from video and interaction, not labels), orchestration layers (routing between models, tools, and agents to stop multi-step failures), and refinement loops (systems that critique and improve their own outputs, cutting cost while boosting accuracy).

4. STATE OF STARTUPS: Carta’s State of Startups report shows capital concentrating, teams are staying lean longer, and AI is eating the stack. Total US startup funding rebounded to ~$110B in 2025, but round counts stayed low. AI startups now capture about 44% of all VC dollars, including about 55% at Series E+. Median net retention sits at 108%, CAC payback is looooooooong - 36 months, and founders are delaying first hires to out. to about 3/4 of a year. The market isn’t back - super different - tighter, faster, and more selective.

5. OPEN APIs: Are Open APIs dying? Tomasz Tunguz highlights a new defensive posture from incumbents: Salesforce is restricting Slack’s API access, Datadog has cut off Deductive AI, and platforms are increasingly treating data and integration as strategic choke points. For many startups, “build on top” now comes with real platform risk in this climate.

6. DATA VIZ: Better charts don’t just change decisions; they can be monetized. This guide lays out some fundamentals of why charts matter (explore patterns, explain concepts, spread ideas) - and the rules that separate insight from chart visualization crimes (pick the right chart, label directly, use small multiples, make it standalone, avoid misleading axes).

7. SALES 1: Even though he hasn't, David Sacks’ “Rule of 10” for sales and revenue has aged well and is a tidy way to stop guessing comp - set AE quota at ~10x base (10% comms, 50/50 OTE), plan for ~70% quota capacity attainment, and give managers a quota at ~80% of team QC.

8. SALES 2: Fast follow from #7 above, Capacity ain’t headcount. Doubling your reps doesn’t double revenue. Capacity planning breaks when you ignore attrition, hiring lag, ramp time, sub-100% attainment, support ratios, seasonality, and mismatch.

9. MARKETING: A follow-up from a bunch of marketing-led articles last week that seemed to get a lot of clicks (though surprisingly no feedback), marketing has a dual mandate: emotional repetition builds mental availability for the ~95% not-buying-yet set, while rational proof converts the ~5% who are in-market with their hands on their wallets. Real quick takeaway - Activation wins quarters. Brand wins cycles.

10. CASE STUDY: This is interesting (aport from all the pop-ups), OpenAI’s early pitch deck, broken down.

POD OF THE WEEK: I like Cursor and use it for product-based work - so this is great for me - the non-technical PM’s guide to building with Cursor.

​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: 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.