The weekly top 10 for B2B tech operators · Every Friday
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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.
Testing new marketing growth tactics takes a lot of resources, and most of us often don't have much time to run experiments. So check this Google Doc from Dashly where they collected 100 growth marketing hypotheses tested by a bunch of their experts. (includes ad retargeting, wait list for product launches, niche glossaries, etc).
Don't be surprised if you land a sales rep from a big company who met quota at their last job but gets 0% with you - these kinda reps succeed on product-market fit, tight process, and real support. Take away any one of the three, and they close nada, because they'd have to invent the sales process deal by deal. The fix is in the article - you gotta click it!
John at SuccessVP hosted a dinner of CROs and CCOs in New York in September. They're still struggling to find the AI productivity gains. His read: the models aren't the bottleneck anymore; deployment is. Someone has to connect the context, build the evals, and define what done means.
The competition continues, and a16z's David George thinks the AI model rankings no longer matter. We can swap models task by task, so switching costs are gone and pricing is a race to the bottom. What's left is who creates new customer behavior and who owns distribution. (His bet is OpenAI on both behavior and distribution - I'm not sold on that).
A lot of comments on the meat puppet post last week. So follow up for all you Meat Proxies - do you still enjoy it? And on the positive side - good thinking here on saying goodbye to the hard parts that never really mattered with your job anyway.
Follow-on from # 6 above. As a former Systems-and-Design-Thinker-for-a-job (and Ecologist), I'll take any excuse to quote Donella Meadows. Tunguz gives me one: Donella says writing code by hand is no longer economically productive, so what's left is the system. Also - let's take the three properties of great system design, resilience, self-organization, and hierarchy, and apply them to AI coding. The code writes itself now. The job is designing the loop that decides whether it's right.
Cut a dollar of brand spend now, and BCG reckons it costs $1.92 to win back the lost market share later. Google calls brand cuts an expensive loan against future pipeline. 67% of CMOs trust their marketing mix model to predict sales. Only 40% of CFOs do, in a BCG survey of 81 execs. Google argues this gap is why brand budgets get cut first.
Metrics don't get you funded. Harris (ex-YC partner) has seen nine-figure rounds close at $200k in revenue and companies fail to raise at $10M ARR. Be strategically persistent - love the take that "prepared minds get built in casual conversations".
This article tracks six liquidity events, from first check to last share, then runs IPO against M&A.Snowflake's lead VC took 9.5 years from first check to seeing a return, and that's the best case. Rubrik's lead is 12.4 years in and 82% out. Wiz went from founding to $32B in cash in six years.
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Curated SaaS and tech insight from around the web, repackaged for people to put to good use — free.
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Always a good report - this bookmarkable Guide to SaaS Metrics from equals.com covers all the greatest hits and more (ARPA, LTV:CAC, Burn Multiples, etc.).
Want to retain startup talent? Their pay still matters. This breakdown shows how overall compensation mix (cash vs. equity) impacts retention, with clear data points by role, stage, and geography. Bookmark this for your next comp review for sure.
Copy the ticket into Claude Code. Don't read the output. Paste reviewer comments back in. Repeat. Who actually did the work here? You're just being a meat proxy, and if that loop works with your brain off, the company can run the loop without you.
A whopper guide (149 pages) that provides actionable insights for navigating the complexities of raising capital, covering investor relations, pitching essentials, market awareness, and the fundraising process.
Athyna's newsletter will do $750k to $1M this year - this ain't no side project because it also drives 12 to 15% of their pipeline. They call it negative CAC: while we all fight to lower acquisition cost, they friggin' get paid to fill the funnel!
Not the litigious kind - from CB Insights is a 67-page report covering the 11 laws driving success in tech, such as Amazon's 2-pizza rule, the 80/20 principle, and more.
Stock options are a necessary thing for hiring and retaining the best talent in this hyper-competitive startup land, so how can companies build an effective and compelling option plan? Check this wonderful site that has compiled a set of 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.
The metrics of AI Data Centers are crazy - $4T is the debt hyperscalers and data center operators raise over five years (to build about 70GW and servicing that is gonna need $1.2T to $1.5T in AI revenue (it's $100B to $200B today). His companion post: GPU prices doubled in six months while AI got cheaper, and the metric to watch is gross profit per GPU-hour. Meanwhile, GPU rentals doubled to $8.08/hour in six months, but AI got cheaper, and the metric to watch is gross profit per GPU-hour.
L: Who owns cross-sell at your place: sales, CS, or the AM? This article says the answer is none of them and all of them. It goes to whoever watches the data. For example, if it came from usage, whoever watches usage owns it. If it came from new budget or a new buyer, it's sales. If nobody's sure, it's the AM.
LOOPS: Top startups don't grow by accident; they figure out how run growth loops that compound effects. This playbook breaks down tactics from the likes of OpenAI and Pinterest.
$141,125 of ARR per employee is the 2026 median for private SaaS, which is up from $129,724 last year. Revenue per head climbs with company size at every step, and the whole curve shifted up a rung this year. Go find your row in the report.
The median public SaaS company trades at 4.2x NTM revenue. The top five trade at 36.4x. Clouded Judgment/Jamin Ball's latest markets review shows the gap between the two is wider than at any point since the 2021 peak, and the median is still below the 7.8x pre-Covid average. If your growth rate sits on the low side, that's a discount a buyer is gonna start from.
Stripe just launched a great new data source for us all - the Stripe SaaS Index. Markets wiped close to $1T off SaaS in February; revenue didn't notice, though - up 21% since January (vs 14% for H2 2025). Year-on-year growth is now above 30%. SaaSpocalypse was all hype. Another interesting tidbit in there - in January, mature SaaS businesses were five points more likely than sub-one-year ones to be using AI tooling. By April that had flipped, and young firms now lead by four.
Apparently code changes at Meta have risen 220% year on year and features users could actually see rose 36%. That's Project overtime - replace thousands of staff with AI agents, cut some teams by up to 60% - per Reuters' investigation. Incidents climbed 40% and firefighting time 70%. AI made the engineers faster. The organization, well, yeah - not so much. Same is true for Apps at large - the supply of apps has surged like crazy......but no one is really using them.
The article above at #4 is the teachable moment - but time to be honest with yourself - do you still read the code? Not skim the diff - read it. The test - You can't justify an implementation choice without a long think, and eventually the only way to find out what you built is to ask the model - with no way to check the answer. That's not a middle path. That's vibecoding by accident - looking at you Meta!
Check out these 4 GTM playbooks that pair the data only you hold - champions, closed-lost deals, website visitors, product usage - with the bought signals that tell you when to act. Each playbook has the full workflow, step by step.
Your AI agents are like a bunch of new grads: smart, tireless, and useless on day one. This article argues more context won't fix that.......but a scorebook will - what the agent decided, how your expert graded it, what actually happened. Run it across every customer and that expertise compounds. But hey, even Anthropic runs its own GTM on Salesforce, Gong, and Clay.
Statistically, your staff engagement has never been lower, but productivity is getting higher. And AI is the reason those two numbers no longer move together - reports get written, and the ticket still closes. None of that depends on somebody wanting to do it (or caring) - want some anecdotal evidence. Take a look at this Reddit thread for some real-world apathy.
Bit of a staff-based theme this week and a new term for us all - Entry-level roles in AI-exposed jobs are now seven times more likely to demand skills that used to take a decade to earn. PwC calls it seniorization. Everyone wants to hire for judgment - but where does it come from now that the junior jobs that used to produce all that experience are being cut?
A year ago agents created 3% of the work in Linear. Now it's 50%, installed in 95% of paid workspaces, and NRR held at 177% through this change. Crazy amount of cash at hand.
GROWTH -: Median growth for private B2B SaaS is 22%, down from 25% last year. SaaS Capital's 15th annual survey (across 1,000+ companies). Only 7.3% reported flat or negative growth, though - the market slowed, but it didn't stall. Equity-backed medians held at 25% while bootstrapped slipped to 20%.
Daniel Dines gave his CEO job up at UiPath to some hired exec. Four months later (after one guidance cut and a 35% stock drop), he took the role back. The founder at Workday just did the same, same over at Intercom - Lemkin calls it The Last Stand - with a pre-AI B2B SaaS company that's big enough to survive, too slow to matter, but nobody but the founder can survive the rebuild needed.
1% of OpenAI and Anthropic's customers generate 80% of their enterprise revenue (and it hasn't moved in three years) - seems like a bit of a risk. The median business spends $11.95 per employee per year on AI. The top 1% spends $7,400. The AI boom has a lot fewer buyers than the earnings calls suggest.
There seems to be a general worry out there that some vibe-coded upstart is coming for your SaaS renewals? SaaS Capital took a good look and says it basically isn't happening - buyers grumble about building it themselves, then pay up like they always did (at least for now). The (more important) real move they're seeing is in reverse: the incumbents are doing the vibe-coding, building that adjacent tool so the client never buys it from anyone else.
Fast and opposing follow-up to #4 above and taking the buyer's side. The "We'll build it ourselves" objection has more than doubled in a year. Before you panic: under $25K, it's a bluff, the win rates don't move. Over $100K, it cuts your win chance 80% - and 60% of those losses aren't losses to a build. A median vendor, according to this post (only 100 anonymized companies), has nearly 10% of lost ACV tied to it.
Would anyone notice if your competitor stole your homepage? It's brutal because you already know the answer. Most hero messages are interchangeable - It's category wallpaper.
Graduating into a severe recession job market typically costs someone about 10% in first-year earnings. But take a look at this data - If you are graduating in 2026 with a degree that is really exposed to AI - it costs 13% - plus a 5-point lower chance of even having a job at all.
Check these 64 creative growth tactics from Tom Orbach (who does this for a living at Wiz). Each of his ideas has been run at least once; most are niche. Heads up: it paywalls just past halfway - but that's 32 things - gotta be at least one for ya!
End of year is around the corner - so this is your annual notice that Annual planning season is open - so start with CJ Gustafson's Annual Planning Bible for 2027. Before you model your dinero, check five things: current headcount, open headcount, this month's P&L, rep attainment history, and 12 months of costs.
ElevenLabs went from $0 to $100M ARR in 20 months, then $600M+ by month 41 - it friggin accelerated as it got bigger. Also - when their AI agents close revenue on an account, the human who owns it still gets the commission. Also, sales quota is 20x base salary (traditional B2B is 5x).
Get the top 10, every Friday
Curated SaaS and tech insight from around the web, repackaged for people to put to good use — free.