Top 10 in Tech — A weekly top 10 for B2B tech operators: SaaS metrics, pricing, retention, GTM, fundraising and product strategy. Published every Friday.

SaaS METRIC OF THE WEEK

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.

GTM

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.

AEO

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.

BOT SPEND

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.

HIGH AGENCY

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.

PLATFORMS

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.

VENTURE

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!

PRICING

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).

NO!

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.

CASE STUDY

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!

SaaS METRIC OF THE WEEK

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.

NPS

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.

PRICING

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.

LOCK-IN

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.

BIAS

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.

BOOTSTRAPPERS

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.

AI KILL SWITCH

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.

AI CODE

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

SPACEX

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.

CASE STUDY

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%.

SaaS METRIC OF THE WEEK

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.

JOBS 1/2

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.

JOBS 2/2

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.

MARKETS

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%).

VALUATIONS

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.

CASH

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.

QUOTA

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.

GROWTH

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.

SUBSTITUTION

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.

CASE STUDY

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\*\*.\*\*

SaaS METRIC OF THE WEEK

All of it! ChartMogul has compiled its ("ultimate") SaaS Metrics Cheat Sheet.

SEED

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!

ONBOARDING

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.

BURNER

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.

TOKENMANIA

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.

AI DOCTORS

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.

AI SKEPTICS

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%.

BACKPRESSURE

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.

POSITIONING

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.

CASE STUDY

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.