The weekly top 10 for B2B tech operators · Every Friday
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SPEND - 48% of equity-backed private SaaS companies are running at a loss, versus 17% bootstrapped. Crazy stat - VC-backed companies are spending 100% more on marketing and customer success, and 70% more on sales, to grow 25% a year against a bootstrappers' 20% average. The biggest single line is R&D at 22% of revenue, ahead of sales at 15% and marketing at 8%.
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.
Sure, AI can make you fast - but can it make your company faster? This article argues that it's because we are handing off the slow stuff to everyone downstream of you.
The AI Corner watched a 90-minute Jeff Bezos interview so you don't have to, and also pulled 10 ideas founders and investors should/could steal. Here's a good one - the engineer's job is not typing code; it's identifying problems, and that's the layer that survives.
Venture is on a boom. Global venture funding reached an all-time record of $510B in H1 2026 (ALL of 2025 was $440B). Sounds crazy, but the majority of that money is concentrated into some insane deals (OpenAI and Anthropic account for $217 B of that figure all by themselves). IPOs and M&A are also back.
It's getting competitive - Model prices are collapsing. a16z clocked a 100x drop, from $60 to $0.60 per million tokens in three years. But AI bills are going the other way - because token burn is going way up.
Check out this study of 1M+ high-volume keywords - about a 30% decline in search demand. But SaaS is a winner: its search volume is up 48% year-over-year, one of only three verticals beating the AI-driven drop-off. FinTech got hit hardest; it's down 37.7%. Also - crazy new data point - 18% of consumers have bought something on an AI recommendation without running a separate search
Want to read a 40-page paper on "The Impact of Google AI Overviews on Publisher Traffic and User Experience"? Didn't think so, so here is the TL;DR - When a Google AI Overview appears (on about 40% of searches), clicks throughs drop 39.8% and zero-click searches jump 34.5%. What is interesting here is that Ad clicks don't move, so Google's revenue is just fine, and the publisher eats the loss. Users were no happier with the summaries than without - so it's diverting traffic without really helping the searcher.
Everyone's future problem - AI is eating the bottom of the Dev career ladder, not the top. US software developers aged 22 to 25 are down 19% from their late-2022 peak, while the 41-to-49 cohort is up 14%.
Coca-Cola cut their marketing-investment decision cycle from two weeks to an hour using 'Fuel Light 360', their proprietary AI scenario-modeling tool. It lets teams model marketing spend scenarios in real time, which also shifted meetings from arguing over whose right to actual decisions.
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CAC PAYBACK: The 'payback' period is the nuance of why we measure CAC. How long until we break even? Benchmark-wise, the negative trough is way longer than you think, so take a seat! New B2B customers, on _average_, take 2 years and 2 months to become profitable. SaaS world changes that a bit - payback periods have accelerated in the - 5X increase in payback under 6 months (see report at #2 below for more on that) CAC payback still really highlights the deep dependency on access to capital to fund a Tech company's growth through these SaaS Cash Flow Troughs.
A genuinely meaty report from Mercury Fund - their 2026 Early-Stage GTM survey (125+ B2B leaders) covers funnel, unit economics (see CAC above), comp, org design, and AI.Win rates up from 29% to 33%, mid-funnel sliding (Lead-to-MQL 41% to 32%), 4:1 LTV:CAC from 0% to 14% of teams. ChatGPT adoption at 80%, Claude at 60%, Gemini at 50%, though that may have changed in the months since this survey was completed.
ChatGPT referral traffic to monitored sites jumped over 60% in May, and homepages went from 3.5% of those referrals to \~24%. The key takeaway: your homepage is now read by the LLM and clicked by the human, so it needs to serve both of 'em. Seven tactics in there from Clay, Rippling, Lovable, Ahrefs.
Agentic AI spending is gonna hit $201.9B in 2026, up 141%, and it overtakes chatbot/assistant spending in 2027 for the first time, according to Gartner's first dedicated AI forecast. Chatbots peak at $264.7B then decline; agentic grows at 119% CAGR to $752.7B by 2029. Talky Bots to Acty Bots.
The high-agency test - do you dare to try it? It's the 10x you. Picture a version of you ten times faster and braver, then do what they'd do today, the scary email, the project you'd close. The gap, apparently, isn't information, it's willingness to take discomfort now.
Check this report covering platform sales and revenue - The biggest gap between high and low performers in platform sales isn't how they sell, it's what happens after signature: on whether customers can see value post-go-live, top sellers scored 1.86 vs 3.23 (lower is better) - platform sale fails not because buyers aren't interested, but because sellers answer feature questions instead of the three buyers ask, can it be done, is it worth it, will we survive.
Nightmare VC story time - A Sequoia partner passed on Cloudflare early because he didn't think a woman could run a security infrastructure company (now worth \~$80B). A $5M VC commit was cut to $100K on closing day, A GP fell asleep during a Series A pitch, and nobody in the room woke him - way more cringe stuff in there!
If you even vaguely sell a wrapper, don't price AI like you are reselling electricity. Price it based on the work it gets done. If a customer brings their own API key and sees raw inference cost on their bill, will your pricing survive? Cost-plus margin breaks, value-based holds, because you're selling outcomes, not tokens. Good example in there - Sierra charges per resolved ticket (and $0 for failures).
Digging this one back from the archives - every feature you say yes to is maintenance, bugs, and onboarding cost you signed up for forever. So knowing when NOT to code is the skill most of us need to get better at or master.
Ascend COO Omar Ismail boosted ARR 38% in six months without hiring a growth team, using Claude Code to build and run the entire stack. Six months ago: $20M ARR, 95% from word-of-mouth, zero paid acquisition
Taking the week off next week - Happy 4th of July break y'all!
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%.
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\*\*.\*\*
Get the top 10, every Friday
Curated SaaS and tech insight from around the web, repackaged for people to put to good use — free.