The weekly top 10 for B2B tech operators · Every Friday
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ASP, or Average Sales Price. ASP tracks the average price new customers pay at sign-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.
SaaS P&Ls are structured in quite specific ways, as defining what goes into the cost of goods sold (COGS) section is important. So take a good read of what the SaaS CFO recommends to include for COGS, as well as how to structure a SaaS P&L. According to the author - 90% of them are structured incorrectly, 5% are close, and 5% are correct.
Another term for your AI Tech Dictionaries, 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.
After a rough October in the tech/Sec world, two AI bigwigs take on the topic. OpenAI's model found 948 Linux kernel exploits in ten days, and its advice is to treat any low-level account on an unpatched box as good as root for a well-resourced attacker. Anthropic's answer is proof instead of testing: Claude formally verified 123 open-source libraries and turned up hundreds of bugs on the way.
Fast follow to last week's four layers of AI (#3) - so who actually keeps the money? Tunguz follows the gross profit from the power plant all the way up to the app, and on his model the top three layers keep 92% of it: apps 40%, neoclouds 28% (before paying for their GPUs) and inference 24%. Power and data centers split the last 8%.
Raising soon? The median is about six months from the first investor opening your deck to announcing the round, per Papermark's data on 24,541 decks. Decks that closed got twice the reading time (36 minutes vs 18), and 24% of decks are still unopened three days after you hit send.
New acronym for our Tech dictionaries - B2A, business-to-agent. We've all spent years keeping bots off our sites, and now they're picking products on their user's behalf. Agents already drive nearly 40% of Stripe's docs traffic, and about 40% of GitHub PRs now start as an agent draft (up from 17% six months earlier). a16z on what changes when the agent is your customer.
It's a whole new world out there - our team completely replaced monday.com with a better, easy-to-maintain tool last month - which leads to the burning question of how can you be defensible when anyone can clone your products - check more on a perspective on that here.
Speaking of #8 - three of these 5 GTM plays are teams building their own tools instead of buying them. Clay's head of growth built deal attribution in about two hours, Vercel's marketing lead built an events agent that took them from 1-2 to 3-6 events a month, and incident.io's CMO swapped his ad agency for a skill he built in a few days.
This one is super useful (at least I'm well aware of it) if you ever find that you need to "warm up" a domain for bulk email or marketing purposes - Resend shares how they warmed up a brand-new domain for a product launch without landing in spam. Great tactics to pay attention to here, including double opt-in, splitting sends across subdomains, and a six-week volume ramp.
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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).
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%.
Get the top 10, every Friday
Curated SaaS and tech insight from around the web, repackaged for people to put to good use — free.