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Hey y’all!
Crazy week yet-a-gain. Anthropic dropped its S-1, showing $16.3B in H1 revenue, up ~14X and its first profitable quarter (my 10 highlights from the S-1 here; 900% NRR is the wildest stat imho). Just insane.
Meanwhile, ServiceNow had its worst day ever (down 27%) on fears that AI eats seat-based software (is this SaaSpocalypse 2.0?), and Alphabet had $500B of market cap wiped as investors start to question where the AI capex will turn into cash. Intel is also down 27% today (largest loss since 1974). Over $850B wiped out from the US stock market at open today.
And to cap it off: yesterday, Binance lost $6.4B in the largest crypto hack in history. It reportedly started with one engineer’s compromised laptop (and currently suspected to be North Korea hackers).
AI is splitting the market and investors are looking for proof of cash, not just capex spend. Where are we in the cycle? I have no idea! So, I’ll just keep talking about GTM and figuring out how to play in this quickly changing landscape.
We’re back with another edition of The GTM Weekly Roundup: 1) a quick editorial, plus 2) the top posts from the week.
Let’s get into it.
AI Adoption is coming in waves: coding was first, support was second, and GTM is next
AI goes wherever the output is easiest to verify, so it hits coding first, support second, and GTM third.
Code can be tested. A support ticket gets resolved or it doesn’t. GTM is harder to verify because a deal takes months and has no “right” answer, which is why it lags. It’s also why it’s next—now that agents can work on longer, fuzzier loops.
AI Adoption is coming in waves: coding was first, support was second, and GTM is next.
Wave 1: Coding
Code either runs or it doesn’t, so you know in seconds. a16z called coding “an order-of-magnitude outlier” among enterprise AI use cases.
(Coding examples: Cursor, Claude Code, Codex, Lovable, et al)
Wave 2: Support
A ticket gets resolved or it doesn’t, so you know in minutes. Companies in this space grew fast because support has clear outcomes and a lot of repetitive work.
(Support examples: Fin, Decagon, Sierra)
Wave 3: GTM
GTM is a trickier domain because it’s more art than science. It’s much more subjective than code. That’s why sales and marketing are behind.
(GTM examples: the only real example at scale here, so far, is Clay—at $7.1B & ~$200M ARR. Other challengers are companies like Rox, Monaco, etc.)
Navin Chaddha (Mayfield) recently said coding’s scale is “30 to 50x” anything else, and “it has to happen in sales.”
My take: it’s going to happen, but it’s still so early for GTM tech.
Two ~anecdotal points:
I tracked 719 GTM podcast episodes over the last 24 months. ‘Claude’ went from showing up in 10% of episodes to 53%. ‘Agents’ went from 15% to 39%. (Hilariously... the biggest drop was ‘quota’ - going from 29% to 7%.)
Yesterday, I asked on LinkedIn for people’s favorite GTM tech, and the top answers were Claude, Grok Bot and Codex, ahead of any GTM point tool.
So, it seems clear to me that GTM teams are mostly borrowing the coding wave’s tools to start building our own (GTM) products.
But, I suspect GTM will have standalone, scaled solutions over time. The wave just hasn’t fully hit GTM yet.
Again, each wave is held back by how fast you can tell whether the AI did a good job: seconds for code, minutes for support, quarters for a deal. Whoever can shorten those loops in GTM will win the next wave.
2027 is going to be wild in GTM tech land. Get your popcorn ready.
Weekly GTM Roundup:
The calculus around whether to “build” or “buy” in GTM has changed a lot over the last 1-2 years. I subscribe to the idea of: “buy the infrastructure, build the brain.” But, there are a lot of mixed (and nuanced) opinions on this topic.
So I asked the best GTM operators how they think about “build vs. buy” in 2026 (at Clay’s conference, Sculpt—which was s-tier by the way). Here’s what they told me:
Nikko is the Head of GTM AI & Systems at Hightouch. (I wrote a piece on what he built: How a $100M ARR Company Runs RevOps on GitHub; Inside Hightouch’s Agent Repo.) He recently posted this:
Good tips from Will and the team at Lavender (via Nick on X):
Another reminder that building a prototype is the easy part. And once it has shipped, 99% of the work is ahead of you.
Scott Stevenson@scottastevensonThe last 10% is 99% of the work
ℏεsam @HesamationAlexandr Wang says Meta has been working on Muse since February. the prototype took 1-2 weeks. but they spent 7 months on this: > 100+ specific agent behaviors were tracked in a spreadsheet > each with its own eval and a launch-blocking threshold > it was only after Muse Spark1:15 PM · Oct 12, 2026 · 28.8K Views3 Replies · 9 Reposts · 235 LikesNot wrong:
In case you missed our deep dive last week on The Swim Lanes Between RevOps and GTM Engineering. Here’s the tl;dr (in two images) if you don’t want to read the whole thing:
The Signal’s latest Live Show was a two-parter:
Pt. 1: The “State of B2B GTM 2026” Report with Kyle Poyar (Growth Unhinged) & Maja Voje (GTM Strategist)
The study found that warm intros/referrals are the highest-converting channel in B2B, but also, the hardest to scale. So, the back-half of the show was:
Pt. 2: How to Scale Warm Intros & Referrals with Richard Purcell
Job of the week:
My former (Zoom) colleague, Chris, shot me a message that OpenAI is looking for a senior SDR leader to build out the office here in Austin (you love to see it). So, if you’re an SDR Leader (or SDR!), check out their jobs page and apply! (Let them know you found out about it here)
That’s it for today!
As always, thank you for your attention and trust. I do not take it for granted.
See you next week,
Brendan 🫡
PS: If you liked today’s post, send it to one person who would enjoy it.
GTM is a great next step for AI adoption since it's internally verifiable. You get a reply or you don't; you get a sale or you don't. The process of getting there and each of the micro decisions feels like art, but that's also what people used to say about mathematics...