The weekly top 10 for B2B tech operators · Every Friday
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REVENUE MULTIPLES: Enterprise value divided by next-twelve-months revenue, and historically 10x has been the premium tier. But the juicy revenue multiples only come while you're growing 30%+ (that's doubling in about three years). Slip under that and buyers stop paying for your trajectory and start pricing your EBITDA. CJ Gustafson's worst place to be: 20-30% growth with no profits.
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.
Check out these four layers of AI. If you build SaaS, then you're at the top of that stack, and your question should be whether users still need your interface or if agents just call you headless (or both?). Below you on the stack: who owns the AI harness, when the model training stops paying back, and whether credit, GPU, and power can keep up. Tunguz's point is they all move together.
Fast follow to #3 above but taking a customer view to that stack: every time the models get better, so does your customer's DIY option. If a straight prompt to some new frontier model beats your product, why keep paying your premium? a16z's three rules for earning it: run the workflow better than the customer can, make your context cheaper to rent from you than to rebuild, and prove the value before you price it.
AI gets you on the shortlist, but it ain't closing the sale. Only 5% of shoppers buy on the spot when an AI assistant recommends something. Most check details first - 33% head to the reviews and 22% compare prices (PartnerCentric) - and a third say shipping and return policies change what they buy. Modern Retail rounds up the holiday data.
Anthropic has filed for a US IPO that could value it at up to US$2 trillion, even making a lil' profit! Meanwhile, OpenAI has serious problems securing money in its next round.
New phrase for our Tech Dictionaries - Vitalik Buterin (who's the Ethereum guy) reckons AI can do for digital minimalism what the internet did for owning less stuff. His experiments: 100 days with his phone on airplane mode, ditching X's interface (but not X) for a client with no algorithm, and swapping web apps for command-line tools (some written for him by AI).
A meaty one from Primer on B2B paid ads - 379 advertisers across Google, LinkedIn and Meta, real spend data (not a survey) through August. The big finding: HOW you sell predicts your ad mix better than WHO you sell to. ABM companies put \~30% of budget into LinkedIn, PLG \~12%, and demand gen goes \~62% to Google. There's a lookup table to find your own row by motion and buyer, which is cool.
Fast follow from #2 above - if you sell to marketing or sales leaders, your champion might not be there at renewal time. CMOs and CROs last 1.8 years on average, the shortest of any exec role (Pave data, 14,000 execs), so about 1 in 3 turn over every year. SaaStr sees it with its own sponsors: around half the marketing leaders are gone by renewal time.
BRAZE: Week three of who's buying software companies (Bending Spoons on Oct 23, Long Lake last week). This time it's the classic strategic buyer: Salesforce is paying $5.66B for Braze, 6.8x revenue, when the market had it at 3.7x the day before. Lemkin pulls out 5 lessons, including how Braze grew revenue 26% while opex grew just 6% (and see #1 on why strategic buyers look past your EBITDA).
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Revenue & Burn per Employee. In this profit-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.
Reverse Trials are a play on freemium, where new users start with a time-limited trial of all your paid features, and at the end of the trial, they can either buy or downgrade to a fully free tier - this article also explains how Airtable does this well. The benefit here is that, emotionally, the user experiences loss aversion, where the pain of losing something is twice as powerful a motivator as the pleasure of gaining.
Not sure if you were impacted (I certainly was), but the PHPShell issues last week impacted some 70+% of the internet, but it wasn't just about the vulnerability itself; it was the speed at which the hacks are coming in- they are coming in thick and fast.
Amazon now runs over 1 million robots alongside 1.5 million humans. The robots took the heaviest, riskiest jobs, and severe injuries fell 40% in its robotic warehouses. But strain injuries from repetitive work rose 77%, and the professor who co-wrote that study says it's because the robot now sets the pace.
New term for you to ponder, "Leading from the front" isn't just a military cliché 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.
Most business plans fail due to a skill set of founders: over-optimism, as well as a lack of adaptability, and inadequate market understanding. Founders often overestimate their control and underestimate challenges, leading to misguided strategies. Recognizing these pitfalls and fostering a culture of flexibility and realistic planning are the big skills for success - own the hard stuff, and never ask for what they won't do themselves.
New one for our AI workers - Attention. Ever wondered how a model knows what 'it' refers to in a sentence? Every word gets checked against every other word to decide which ones matter. Natalie Wolchover's Quanta column explains queries, keys, and values in plain English (no maths degree needed).
Adding onto #6 above - Remember Meta's code changes up 220% and incidents up 40% (see #4 here). More code, more outages. AI agents have roughly doubled PR frequency at almost every tech company in a year, data shows out-of-hours pages nearly doubling too. So now there's a whole category of AI SREs promising to investigate the incident before anyone wakes up. SRE's read the alerts, logs, and dashboards and hand you a likely root cause in Slack. Stripe's in-house one averages 3 minutes for a quick investigation, 14 for a biggy.
Fast follow from #8 above - if agents are writing twice the code, who reviews it all? Anthropic's Claude Code team (Claude writes \~90% of it) sizes human review to risk: docs and flagged changes need no human, normal work gets one, the riskiest paths get two. And a bot's sign-off never counts as a person's. Here are their six layers of checks.
Following up from last week's Bending Spoons case study (see #10) - a different kind of holding company. Long Lake has raised $7.5B to buy services businesses (residential services, architecture, HR, and now Amex GBT in a $6.3B take-private) and put AI inside them. Their pitch: selling AI from the outside means fighting security reviews, adoption, and pricing, so own the business instead.
WIN RATE: This is calculated via closed-won deals divided by qualified opportunities. It's easy to track, and also easy to ignore while everyone stares at their pipeline. According to Jason Lemkin, Owner.com took theirs from 38% to 53% in 10 weeks by having AI score every sales call. Check this list of ways to move it runs from pricing and qualification to the deals that ghosted you.
One for the Digital Marketers - Your iPhone retargeting numbers might be about to look weird. AdExchanger reports iOS 27 is quietly blocking hundreds of programmatic data companies across every browser on the phone. Is Apple now a shadow ad regulator?
Corporates are now also on the Cap Table for almost every big AI round. About 88% of US AI deals have some kind of corporate (BUT - Nvidia wrote most of that). Looking at KPMG's Q3 Venture Pulse - corporate VC hit $532.8B at the end of Q3 - already a record year, and 14 deals above $1.5B. PitchBook's new note also prices market entry into the AI frontier at about $100B over five years.
85% of enterprises are working on agentic AI, but only 5% have one in production that pays its way. Uber's agentic coding usage went from 32% to 84% across \~5,000 engineers in a month. The year's token budget was gone by April. That's one of the cases in Ray Rike and Peter Buchanan's rundown of the six things stopping agentic AI from paying off.
This one is for you early-stagers - Sylvia Huang's guide to early-stage viability lays out what to watch at each step. Good stuff in here - from pre-revenue intent through retention, repeatability, and unit economics to tell them apart.
Tunguz's 2026 survey of 455 senior technical builders. Cloudflare hosts 62% of agent sandboxes. 45% route across models. 48% review AI logs with coding agents. And security is now the #1 headache, up from sixth last year.
If you're selling agents by the seat, your buyers have moved on. G2's 2026 survey of 1,019 B2B decision-makers - 57% now prefer pay-as-you-go (38% last year), 19% pay-per-outcome, and only 9% call per-seat the fairest model. And 30% would consider switching vendors for better agent capabilities.
Your next round may get crowded by investors you already have. Hunter Walk expects more pro rata conflicts - because early rounds are bigger, rounds land faster, Series B and C can still return 20-100x in the best cases, and have buyers ready to go in Secondary Markets.
The default fix for weak sales is more features. So check out Sylvia Huang's checklist, which tells you whether that's right: prospects who vanish after asking about price or stall-out while onboarding point to the sales process. Users who buy, skip the core workflow, and never renew point to the product.
Bending Spoons is basically a holding company for once-great tech that's been put out to pasture: Evernote, WeTransfer, Vimeo, Eventbrite, AOL, and now Airtable and Miro too (both bought for around 90% below their peak valuations). Lemkin pulls apart how they run them - about 60 people on Vimeo, and AI writing 90%+ of the code. All part of a weirdly profitable portfolio: Q2 revenue $704M, up 126%.
I bet if you asked any founder their growth rate, you would probably get it instantly. But if you ask how long it takes to earn back what they spent winning a customer.........crickets. But that's the number investors check first. Median B2B SaaS is 16 months, top quartile under 6.
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.
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.
Code review is DEAD! Here are 16 things Thorsten Ball (he literally wrote the book on building interpreters) believes about where software development is heading. A few that'll start a fight in your engineering channel: code review dies, unit tests might too, and most bugs won't be coding bugs - they'll come from asking for the wrong thing.
Think $250K ARR per head is great? According to this ICONIQ report, the top AI companies (at $100M+ ARR) do $655K, top quartile is $890K. That's a $200M company running with about 300 people vs. 800. Also, at $100M+ ARR, the median grows 115%, keeping 90% of revenue before expansion, as it does at $655K ARR per employee.
Tech word of the year probably - Tomasz Tunguz argues that AI inference is about to become the most important market in software, and that every app is about to become an AI reseller. Inference will hit about $350B by 2027, nearly 2x the database market. If AI usage starts eating half your revenue, what's left of your margin?. The main models are commoditizing though, prices are down 41%, and token use up 50% since July.
Fast follow from #6, here's that margin squeeze from the inside. If you're pricing an AI product right now? Read this - the real unit economics for the AI product Candor: $2.50 to run an interview, $7 to buy one, and a free trial that costs him $68 a signup. The whole pricing model is in a public spreadsheet (you can copy). Oh but here is the gotcha nobody budgets for - upgrading the model made the product better but burned 25-35% more tokens.
a16z's Top 100 Consumer AI Apps is out, and this time it adds actual US card-spend data - which is key! Only 4.5% of US consumers pay for ChatGPT, Gemini, or Claude. The top 1% of payers average \~$903/month and outspend the entire bottom half combined.
Another follow from #6 - if the models are commoditizing, what's left to defend? Dan Hockenmaier has a one-hour Moat test for ya! Find the compounding advantage - switching costs, network effects, economies of scale, or data. Speed, brand, a great team, and funding don't count (he calls those "fuel"). Then check what the team actually works on every day (not what the strategy deck says).
YOU SUCK - 12 companies whose first product flopped. YouTube was a video dating site, so few users that it turned to Craigslist. Slack was the internal chat tool inside a failed game called Glitch. Bill Kerr's roundup of first products that were dumpster fires.
Get the top 10, every Friday
Curated SaaS and tech insight from around the web, repackaged for people to put to good use — free.