The weekly top 10 for B2B tech operators · Every Friday
Done - check your inbox to confirm.
This is a new acronym for your tech dictionaries - MMM, or Marketing mix modeling, is used to measure the cross-channel impact of all the various marketing and non-marketing drivers on your startup's sales or business efforts and performance. Google's Meridian is its open-source take on it.
I love me some laws and principles - here is a new list of 10 of the lesser known ones. Hanlon's Razor is a goodie: rather than questioning people's intentions, question their competence.
Sarah Guo built Conviction around one bet: the biggest AI opportunities sit above the foundation models, not inside them. Her logic is simple - the labs become infrastructure, while startups win by owning the application, workflow, and customer.
Counterpoint to Sarah Guo above (#3). Akash Bajwa reckons model labs and agent companies are converging the stack, not separating - Anthropic is building products while Harvey is training models. The endgame may be owning the model, the application, the workflow, and the customer around it.
Last week Anthropic/Claude announced that it will now embed an invisible watermark into the text it generates - and it can survive copy-paste and some editing. The weird bit: even using Claude to proofread or translate your own writing can leave the mark. Heavy rewriting can break it, but a third-party detection tool is coming. (not necessarily by choice - this is part of Anthropic's EU AI Act compliance).
Stop asking if you're above average. This article argues the more useful benchmark question is whether you're improving faster than everyone or anyone else. Your own trend shows change; the market trend tells you whether that change was actually yours or just the tide coming in for everyone.
Speaking of Benchmarks, the SaaS Capital Spending Benchmarks for B2B SaaS Companies is out - bookmark this when it's time to benchmark your wallets y'all! Bootstrapped B2B SaaS companies spend 96% of ARR (hey - I do better than that!!), while VC-backed ones burn 101% (last year it was 95% vs 106%). Equity-backed teams in turn grow at 25% average vs 20% if your boots are strapped.
Meta laid off 10% of staff and reassigned 20-30% of software engineers to AI whatever - now they are reportedly offering $400K-$1M+ in equity to senior engineers trying to resign after layoffs/AI reassignments. Some still left for Google, Anthropic, and OpenAI.
Startup liquidity is quietly becoming its own market. Carta administered 71 tender offers worth $3B in H1 2026 - the highest first-half total it's logged in at least six years. With IPOs taking longer, tenders are becoming the liquidity pressure valve for employees and investors.
WISE: Wise's original Seed deck is basically the opposite of most pitch decks today. No giant TAM slide, no AI-generated design theatre. It explains the problem, shows the before/after economics, proves people are already using it, then asks for $1.3M.
Get the top 10, every Friday
Curated SaaS and tech insight from around the web, repackaged for people to put to good use — free.
Done - check your inbox to confirm.
This issue uses the long-form layout — read it on its own page →
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 complimentary article outlining the methodology behind the tool is here (and you should read it first).
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.
Hey founders - check yourself (at least your metrics) - Investors have an experienced nose for this stuff, and generally, legitimately good metrics only mean something in pairs. NRR next to GRR tells you if retention is real or propped up by your upsells. Burn multiple at 1.1x - nice, that's top quartile. Now look at Rule of 40 next to it, down from 38% to 22% in four quarters? That's not efficiency, that's a company that stopped spending and stopped growing at the same time.
Only 12% of employees reckon their company onboards them well, so check out these 7 steps for onboarding a new team member (and they start two weeks before day one). Real handy if your onboarding currently means a laptop, slap on the back, and a Slack invite.
Carta's VC Fund Performance Q1 2026 is out. $3.9B raised across 86 new funds, the strongest start to a year since 2022, and TVPI (Total Value to Paid-In) is up for nearly every fund vintage. All on paper though. The median 2017 fund has returned 0.31x to its LPs after nine years, and 2018 sits at 0.15x.
Computer-using agents went from 42% on OSWorld a year ago to 85% today (which is past the 72% humans score). Before you get outraged and/or excited - 85% means 15 of every 100 tasks failed, and a business process only finishes if every step does. $6-8 an hour is what a16z reckons it costs to run a computer-using agent, against $10 for offshore people and $30-45 for a US back-office worker.
Turns out the agent boom isn't only a GPU story - AWS engineers were told in May to cut CPU waste and now reportedly wait days for an EC2 instance that used to take hours - agentic workloads are making CPUs scarce. Apparently, Intel, AMD, and others say customers will take whatever they can get.
Your sales are flat so you post more, add a platform, say yes to another podcast......etc. Sylvia Huang calls it "the visibility treadmill" - more output, same revenue, tireder founder. The real question underneath: have enough of the right people actually seen this yet? Until they have, you can't tell a reach problem from a product problem.
Report time and ignore the Plaid bias evident in this - rest of it is good. 44% of Americans would let an AI agent execute trades, it goes up to 54% for Gen Z and Millennials. But on the other hand, 80% of Americans think companies should reimburse them when AI makes a mistake with their money, and 78% say AI should be held to the same accountability standards as a human advisor. Can you have your AI cake and eat it?
M&A -: Airtable just sold to Bending Spoons for $1.3B, and they last raised at $11.7B in 2021! CJ Gustafson pulled 10 lessons out of it, and they're all worth your time, but here's the one that stings: back-of-the-envelope math from 20vc says nobody who invested after March 2018 made money (that includes employees).
ARR per employee. This is a BIG one for 2026 - the median B2B SaaS company now makes $193K per head, up 29% in a single year, per Aleph and Benchmarkit. The SaaS CFO calls it the biggest one-year move he's seen - AI absorbing work that used to need headcount. R&D as a share of revenue dropped 8 points to 27% over the same stretch.
That 'fastest ever to $100M ARR' headline you keep seeing? Warp's Zach Lloyd says pop the hood and a lot of these AI startups are just reselling inference (and that's at close to zero or negative margin) - so of their $100M ARR, maybe $90M flows straight to Anthropic or OpenAI. Easy to grow, ugly to own, nasty to invest in.
YC's guide to seed fundraising is still one of the best on the why and how - SAFEs over notes, raise fast then get back to building, let the market set your price. Just ignore the prices. It pegs a seed at $500k-$1.5M around a $2-10M valuation. Carta's Q4 median seed post-money is now $24M. The playbook holds, the numbers doubled.
The rumors of SF/Silicon Valley being over/cooked have been talking points circulating for years (often measured by U-Haul rates, office vacancy, and Austin lovers): wrong metric, y'all. Per Carta's startup ecosystem leaderboard (measuring $124B of capital over the last 12 months), the Bay Area takes 53.4% of every AI dollar and 56.3% of every B2B dollar. Add New York and those two metros swallow 72% of B2B venture.
Good one for all us Math nerds - AI just disproved three fairly famous math conjectures in a matter of weeks - problems that had been open for decades (and one for a full century). The models aren't proving big theorems yet. They're finding counterexamples: the low-hanging fruit we stopped checking because we assumed they were true.
We've all done it - promise your best person a promotion, and they're happy for about a day before drifting back to baseline. Deliver it, and they tick up a little. Fail to deliver, and they bottom out for years, and it never stops feeling unfair. Stay SaaSy's rule: never let a happy promise leave your lips unless it's already certain.
Their AI division is spending all their money, but Starlink went from 6 to 12 million subscriptions in a year and $4.3B of quarterly revenue. Now SpaceX wants your phone bill too - building a ground network of towers to sit under the satellites, because AT&T, Verizon, and T-Mobile all refused to rent access.
Apparently it's a big-time seller's market if you've got the right story. Buyer's market for everyone else. That's GLC's Jim Williams' take on 2026 SaaS M&A. Most deals still clear 3-6x revenue - GRR above 90% and NRR above 100% is now expected (top dollar is more like 92-93% GRR and 110-120% NRR), plus $15M ARR, 40%+ growth, and profitable (or a clear path there).
5 social media strategies for all you early-stage founders, here is a good one to entice you to click: Someone replies to your post. Do not immediately offer them a demo.
$14 and a week is what it took SaaStr to move 10 years of data and 450,000 contacts off Marketo. Ten years as one of the first ten customers, ended by an agent. If switching costs were your moat - agents just repriced them. Migration isn't friction anymore.
Get the top 10, every Friday
Curated SaaS and tech insight from around the web, repackaged for people to put to good use — free.