The weekly top 10 for B2B tech operators · Every Friday
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Time to value (TTV)- an old but re-emerging metric du jour (see why in #2 below): the gap between a customer signup and the first real product value, the Aha moment (like Dropbox's first shared file, or Slack's first channel message). There is also a corollary "trough of disillusionment" that your customers may need to navigate.
Is NPS dead? A panel of CS leaders from Content Square, LinkedIn, and Carta discussed this very question at SaaStr AI 2026 - their opinion? Yup! NPS has low response rates; non-responders are churners, and there is no correlation with gross revenue retention. One of them keeps it only because the board still asks for it. Replacement KPI: time to value - see #1 above.
Big report on AI pricing. 89% have exceeded their initial AI budget - 45% significantly, 44% moderately, only 9% on budget. Top reason: AI features drove more usage than expected (67%), usage scaled faster than expected (63%). Only 10% blamed vendors changing pricing post-sale. 67% name IT as the primary owner of AI cost risk, just 17% Finance. 55% find credit/token pricing harder to evaluate for AI than for SaaS.
Following on from #3 above, Gartner projects 40% of enterprise apps will ship task-specific AI agents by the end of 2026, up from under 5% in 2025. B2B consultant Jason Greenwood argues that embedding is the lock-in moat: switching providers means losing institutional context and breaking automations, not just swapping an API key.
This is a good one - I'm guilty of more than I care to admit - "The cemetery of failed startups doesn't give interviews." Giacomo Falcone has a rundown of 17 cognitive biases we can all admit to, covering survivorship bias, anchoring (whoever speaks first sets the negotiation ceiling), and the blind spot that the more sophisticated you are, the better you rationalize flawed thinking.
I haven't talked much about this in a while - and it comes with another quotable post - "Money lets founders scale noise," Oh and here is another quote (and also the title of the book referenced) "The Power of Broke" and I can certainly relate - A funded founder can spend months on brand, hires, and features before learning the offer is unclear, capital removes that pressure too early, letting founders buy activity before earning clarity.
Probably one of the most significant AI drama/stories yet. The US government ordered Anthropic to suspend Fable 5 and Mythos 5 over the weekend, citing "national security". The directive barred access by any foreign national, including Anthropic's own non-citizen staff, forcing Anthropic (a couple of hours later) to pull both models worldwide, including on AWS Bedrock. And then they had to go visit Trump in DC - coincidentally also on his birthday.
New working paper on AI productivity - Claude Code now signs over 5% of all public GitHub commits, Autocomplete raised developer commits 40%. Sync agents took the cumulative effect to 140%, async agents to 180%. New iOS apps roughly doubled to \~100K/month since agentic coding arrived, but total usage of new apps is flat across the App Store
That bonkers SpaceX $75B IPO last week (biggest in history) also paid a crazy $500M in underwriting fees, also the biggest ever - which sounds like a massive amount but is only 0.67% of the overall $$. Most IPOs pay 4-5%. Goldman Sachs and Morgan Stanley each took 20% of SpaceX's fee allocation, which is about $100M apiece.
The OG Cloud bro, Salesforce, reaccelerated subscription growth from 9% to 12% at $45B+ ARR. The catch in the new deck: seat-based core apps grew just 7%; the reacceleration came from a data/platform layer up 23%.
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Curated SaaS and tech insight from around the web, repackaged for people to put to good use — free.
Thanks — you’re on the list. The top 10 lands every Friday.
ARR alone doesn't tell you what kind of growth you're getting. Check this article (and dashboard) breaks MRR into New, Expansion, Contraction, Churned, Net New, and Ending. A company growing through expansion behaves very differently from one that depends entirely on new logos. Only the Excel bit is paywalled. The diagnostic logic: if trials are stable but activation drops, fix onboarding. If activation is good but the paid conversion flat, the value moment is unclear. If the active base grows faster than acquisition, retention is doing the work.
Pragmatic Engineer has a two-part State of the Software Engineering Job Market 2026 ( here for part 2). It's way less doom and gloom than we assume (see below for more). Meta grew software engineering 20% over two years, then laid off 10% last week. Apple up 10%, Google up 5%, Microsoft and Amazon flat. But Scaleups grew faster: Ramp +94%, Wiz +84%, Datadog +68%, Rippling +55%. AI engineering roles are way up - 50-100% YoY at large tech firms.
Benedict Evans following up on the doom and gloom bit above on why AI job-exposure analyses don't work. The test case: 50 years of financial automation (VisiCalc to ERP to cloud). If you'd done a "professions exposed to automation" chart anytime since the 70s, Accountants would have been at the top. Instead, the accountant headcount kept going up. Just look at the Big 4.
Is the SaaSpocalypse over? Infrastructure and consumption businesses are still looking to get a premium (DigitalOcean grew at 227%, Datadog 76%, CrowdStrike 55%). Seat-based application software, an AI agent could replace gets discounted, even when the numbers are fine (Klaviyo down 52%, Monday down 45%, Atlassian down 36%).
Great quote "If you can articulate what would change your mind, you have a hypothesis. If you can't, you have a story you're protecting." The difference between a $5M and $15M valuation often comes down to whether the founder can draw a straight line between what they believe and what they're testing.
And shit loads of it - how are we all not screwed? Google generates $174B in annual cash flow - literally the best business model on earth - and it's not enough for that hungry hungry AI. They're spending more this year on AI infrastructure than the world's best cash machine can produce - their solution - an $80B equity raise (the largest ever), $100B+ in total debt up from $25B a year ago, a 100-year bond at 6.125% (matures 2126) - WTF. Anthropic and SpaceX (now an AI company) and OpenAI are all listing REAL soon - so much cash about to be thrown around.
Interesting idea - Your VP Sales (hopefully) has a quota. Your VP Marketing probably needs one too. Check Jason Lemkin's classic 3-question hiring filter: 1) What was your lead/opportunity commit at your last company, and how was it determined? 2) I want to hit $Xm ARR by year-end - what do we do in the first 90 days? 3) How should sales and marketing work together to hit it? If they fumble on this, they prob never owned a number.
Another Lemkin's post: As discussed in prior weeks' posts (see #9 on Productivity), AI broke the link between revenue and headcount. When Google crossed $30B in revenue, it had 32,000 people. When Salesforce did, it had 79,000. Anthropic crossed it with 5,000. And the Time-to-$1T is even more crazy: Apple 42 years, Google 21 years, OpenAI \~10 years, Anthropic \~5 years.
Public AI/closed models are getting more expensive, BUT open models are getting cheaper and performing on par for a lot of day-to-day stuff. Some real-world examples - Coinbase: routing prompts to cheaper models. Lindy: switched 100% to DeepSeek v4 from Anthropic, saves millions, and performance is up on core use cases.
Cloud-first was never cloud-native. AI-first is the same. Anthropic's Head of Industries, Eleanor Dorfman, walked through their actual GTM stack at SaaStr AI 2026: Friggin. Salesforce, Clay, Gong, Slack, Jira, Intercom Fin, Snowflake, BigQuery, G Suite. Same stuff everyone else has. Side bar fun facts - 54% of 2026 enterprise logos came through self-serve. 87% of AEs hit the number on $270K-$445K OTE\*\*.\*\*
All of it! ChartMogul has compiled its ("ultimate") SaaS Metrics Cheat Sheet.
Some more analysis on seed stage deals this week. Pavel Prata looked at the 10 biggest VC funds ($10B+ AUM) across three VC/Tech industry "periods": the pre-pandemic SaaS years (2015-19), the cheap-money boom (2020-22), and the AI era (2023-26). Collective early-stage deals went from 140-150/yr to 370-400/yr. a16z: 16.6 to 48.7 to 75.3 deals/yr. General Catalyst: 15.2 to 31.7 to 61.5. Both absolutely sending it!
Railway gets about 10,000+ signups a day! To manage this volume, they killed their generic welcome sequence and went event-triggered. Old open rate: 27%. New: 50-70% in the first 24 hours. The strongest signal: trust center document downloads (SOC 2 reports) - got them about a 50% reply rate when sales reaches out.
Here's a cool concept for our tech dictionaries - work-streams that need heat! In the cold-start problem world of early-stage traction, we need the Venn diagram overlaps among build, distribution, and founder capacity. Founders normally sequence them, but the argument is that they should run in parallel. Distribution is the one left cold first - it can run weeks with nothing visible to show. By the time you need it, you've lost months of compounding momentum.
The flipside of the agentic coding surge is the 2026 bill shocks. The WSJ reports Uber already burned through its entire 2026 AI budget (set in late 2025, before any of this actually worked). Amazon pulled an internal leaderboard that was driving too much usage. Salesforce went the other way, and 10x'd its budget. Uber's COO on a podcast: spend is high, specific gains hard to point to. Yeah, we all collectively coulda told you that. But apparently, 94% will keep spending on AI even when it fails.
Exploding Topics surveyed 1,065 Americans on AI health use. 66% have tried AI for a health query. 25% report a "serious problem" after following AI health advice, another 20% report a minor problem. Among those who used AI for mental health or stress, 41.56% reported a serious problem. 82% feel "listened to" by AI vs 74% by human doctors.
Tomasz Tunguz maps out where the market is actually shorting AI. Median short interest across software, semis, data centers, and hyperscalers is up ~24% in the last quarter - because we all know that shit is expensive - as it's concentrated, these shorts are a bet against companies whose AI exposure still depends on future capital, future demand, or future operating leverage. AI cloud/neoclouds: 16.8% of float, which is up 60% in a year. Dev tools/infra software: 9.5%. Enterprise SaaS/AI apps: 8.9%. Hyperscalers: 1.1%. NVIDIA: 1.2%.
Another new entry for our Tech Dictionaries - it's a systems-engineering term for downstream refusing more work until the producer cleans their shit up. Lucas Costa argues it's the next frontier for AI-aided dev work. Right now, the human is the backpressure: reviewing, prompting, and copy-pasting feedback between the coding agent and the review bot. The fix is tests, types, benchmarks, review agents, and PR monitors that send bad patches back before they become a human's problem.
Been through this a bit recently - April Dunford's rule for company/product positioning in the AI era: ignore future competition in your positioning, but NOT in your roadmap. Position against the competitors actually showing up on your prospects' short lists today, not the fast-moving threats you're convinced will arrive. But also build the product that can win the future match-up. Hype the vision for markets and investors. Sell against today's reality.
AMAZON - Bloomberg took a deep dive on Andy Jassy (CEO at Amazon): since ChatGPT, Microsoft, and Alphabet have collectively booked $600B more in future business than Amazon. Jassy's response: $50B committed to OpenAI, $13B+$20B option to Anthropic (with Claude now live), $200B AI infrastructure spend this year (they are becoming the Amazon of AI), $25B Mississippi data center cluster, 60,000 layoffs, dozens of projects killed.
Headcount can account for 60-80% of early-stage burn (in the non-AI first world), which means most burn starts with hiring. So check out this headcount planning module to track every hire and salary.
New entry for the tech dictionary: Continuous Calibration/Continuous Development (CC/CD). Unlike CI/CD, AI products drift without constant tuning. This framework highlights why calibration is as critical as shipping for long-term performance.
Let's check in on this article in a year - see how it aged. OpenAI and Anthropic revenues alone have gone from 0% of GDP to ~1% on a run-rate basis in 18 months. SF1 expects 4 portfolio companies under 12 months old to cross $100M ARR this year. Valuations for the fastest-growing AI companies are cheaper than mediocre 2021 SaaS.
OKRs are sooooo 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 closely align with strategic priorities, more Agile in a way, as course corrections are easier (and it increases team accountability).
This is a densely packed summary - but with the top 10 private market companies, five or is it six (SpaceX and x.ai are counted separately) on the top of that list are now AI-first. Top 10 EV/NTM revenue multiples re-rated to 19.3x avg (16.3x median), up from 16.1x in April - jesus-bubbling-christ! M&A deals hit record average size ($260M) but at lower multiples. The 2022 correction looks complete - just not that evenly distributed if you ain't AI.
"My boss brags it will 10X me." - every time I hear this, a GPU dies and goes to silicon heaven. The Walrus editor (Carmine Starnino) spent a week with people being told to use AI to do their jobs. He found a bunch of pressure to adapt without a plan or any rationale. People adopting shit that no one fully understands- anyhow - he found a third state between believer and skeptic: "collective acquiescence" - a surprise entry to our tech dictionaries.
Bessemer has an eComm reframe for us all: brands now need to optimize two front doors. One for AI agents parsing structured signals - clean product data, transparent pricing, accurate inventory. The other is for a human actually walking through. Impulse purchases may be an endangered species, check this quote from the article: "The buyer arrives better informed than the seller." FYI - 15-20% of retailer referral traffic now comes from AI chat. McKinsey projects $1T+ orchestrated US B2C retail by 2030, $3-5T globally.
A fast follow from above on retailer traffic (that makes me cringe a bit) - 460M people/mo now discover products inside AI assistants. Similarweb's read in this report: 46% of ChatGPT users who opened with zero commercial intent developed buying signals before an ad appeared. 83% of ad-triggering queries would never have triggered a Google Shopping ad. Conversations don't replace the intent; they generate it.
You may have noticed Google Search being a little different this week - just got rebuilt for the first time in 25 years- here is why (and more) - at the Google I/O Conference last week, the CEO announced 2030 as the year we get AGI - he also made 24 other announcements in 35 minutes, AI glasses ordering DoorDash (live on stage), background agents inside Search and a universal cart across Search, Gemini, YouTube, Gmail (see #7 and #8 above for more)
OpenAI's enterprise lead over Anthropic collapsed from 41 points to 8 points in 12 months. Claude went 21% to 48% share. Gemini 27% to 40%. OpenAI peaked at 62% in Sep 2025, down to 56% - the first YoY drop. ETR pins the shift on coding assistants. Grok is still a rounding error at 7%
Get the top 10, every Friday
Curated SaaS and tech insight from around the web, repackaged for people to put to good use — free.