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Thanks for reading this hand-picked, farm-to-table (human-written) newsletter.

Presented by:

Clay is the infrastructure GTM engineers build on. Start building for free.

& Supported by:

Attention → AI Agents for RevOps.

Clarify → The Autonomous CRM. Sell more, admin less.

Clearskies → The Context Layer for Revenue AI.

Nooks → Revenue infrastructure for reps and agents.

Rox → Revenue Agents for the Global 2000.

Sumble → Buying signals from tech stacks, people data, & active projects.

Terret → The answer-to-action engine for revenue teams.

​Come watch Eric Nowolawski build and launch an outbound campaign live (end-to-end) using Grok Bot and the Clay CLI. This Wednesday (Sep 16th) at 10am PST. Register here (if you can’t make it live, but register, I’ll send you the recording+recap afterwards).

I stumbled upon a year-old podcast episode of Jason Lemkin on Lenny’s podcast. The whole thing is worth listening to, but one idea stuck out to me and I wanted to highlight it today, before jumping into the Friday round-up.

The idea is something I believe deeply: the best sales reps are problem solvers.

Jason said this in the podcast episode:

Here’s the mistake that 99% of founders and sales reps make.

We’re not really selling in B2B. We’re solving problems.

Our job as sales reps in SaaS is to not sell a used car. We’re selling a Tesla Model 3 performance. It has competition. They might not need it this week, but it’s pretty darn good. “Let me help you get you into that Model 3 performance today. I’ve even got a special discount for the end of this month. Let me just help you. I’ve spent four calls answering all your questions and I’ve explained to you all the things and why the supercharging network is better than the regular one that doesn’t really work at the charger near your house. I’ve gone on Google and I’ve seen there’s no charging network near your house. There’s only superchargers.”

That’s the job of a SaaS seller because we’re not selling commodities.

I used to say the best reps are “sherpas” or “guides.” They help a buyer solve a problem, either with their software (or recommending another/better path if there is not a fit). They build trust, and have valuable expertise.

I was catching up with a founder recently, who is building in the sales space. And at one point he said something like “I mean, you know, salespeople are manipulative.” It wasn’t offensive, as much as it was shocking, how little he knew about the profession. But, that’s the general consensus still, even in B2B—that salespeople are mere manipulators that get paid a lot of money for playing golf in a vest during the day and put on a suit for their steak dinner that night. This is so obviously a misconception about the best sellers. They are curious, smart, problem solvers. And AI cannot automate them away.

To be a great problem solver, you have to be a great researcher/investigator (this is why “discovery” in sales is so important).

It's kind of ridiculous, but I actually like that some call recording tools show talk time percentage live on a call for a rep to see. Mine even has a BIG YELLOW BUTTON that appears when I talk for too long, saying “Monologue!”.

Ideally, a seller is speaking well under 50% of the time on a sales call.

My goal as a seller was always to pull, not push. “Pull” (information), not “push” (or, pitch, my product). In other words, maniacal focus on discovery. Which is downstream of curiosity, which is downstream of problem-solving.

It is only once you have this information that you can even assess if (or how) you might be able to solve their pain/problems (either with your solution, or recommending another path for them).

I’m bullish on sellers in the AI era. As more of knowledge work gets slurped into the labs’ models, and surrounding ecosystem, the value of human-led problem-solving is going to become more valuable, not less. It’s a great time to be a creative, thoughtful problem-solver. It’s a great time to be in sales.

Weekly GTM Round-up:

• My friend Eric Linssen runs Demand Collective, a newsletter and vetted slack community for Demand Gen practitioners. He’s hosting Demand Day: “a virtual summit with 9, 20-min sessions from real demand marketers sharing what’s working for them right now.”

  Save your spot! (Sep 24th at 10am PST)

• (ICYMI) Tuesday’s post was with a legendary revenue leader (who I used to compete with at the company I co-founded, Groundswell); I think Tim is the prototypical future CRO.

  Here’s my summary if you don’t want to read the whole thing (but you should read the whole thing :)).

  

• GTM Engineer role at Sequen (they announced a $90M Series B this week).

  I worked with Tim, their CRO, on a data/signal-based selling project last year. He’s smart and cool :) and I’m very bullish on what they’re building (they have crazy customers already, like Priceline, Wayfair, etc). There’s a ton of upside/opportunity. My favorite line from the job description: “Help shape how Sequen builds its GTM engine — the tooling, plays, and process are still being written, and you’ll hold the pen.”

• Miro was acquired by Bending Spoons for $1.35B this week (they also own: Airtable, Eventbrite, Vimeo, AOL, and more). Miro’s peak valuation was $17.5B, so it was bought at a 92% discount. I liked Jason Lemkin’s (one of the godfathers of saas/sales) take on it:

  

• Great, tactical read by Noah Adelestein of thegtmengineer.ai:

  How Lovable built an AI-native sales intelligence stack while scaling from a handful of reps to a global enterprise GTM org.

• Elon tweeting about sales (tech) was not on my bingo card for this year (see! “GTM Twitter” is totally going to become a thing! heh)…

  

• Clay announced their Series D at a $7.1B valuation. I commented on Varun’s announcement post saying “ser i thought claude code replaced yall?” (obviously, as a joke). But in reality, this raise is another datapoint showing Clay is still a critical piece of the AI-native GTM tech stack, and clearly their metrics internally show that if investors are still willing to pile money into the business.

  [Disclaimer: I own zero equity in Clay, but they do sponsor The Signal. Fwiw, I don’t have any equity in any gtm tech companies, or referral agreements; when it’s a sponsored post I say that explicitly.]

  With that said, here are two charts I put together (wild to see):

  

• For anyone who missed the tweet that broke the internet this week, here it is; 166M impressions is bonkers (I haven’t fully formed a strong opinion, but I’ll say this: we should be kind, gracious, open-minded and work together to build a wonderful future for humanity)

  

• Alright, that’s it. Enjoy the weekend!

  

As always, thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

PS: If you liked today’s post, send it to one person who would enjoy it.

Subscribe now

Thanks for reading this hand-rolled, pesticide-free, farm-to-table (human-written) newsletter.

Presented by:

Clay is the infrastructure GTM engineers build on. Start building for free.

& Supported by:

Attention → AI Agents for RevOps.

Clarify → The Autonomous CRM. Sell more, admin less.

Clearskies → The Context Layer for Revenue AI.

Nooks → Revenue infrastructure for humans and agents.

Rox → Revenue Agents for the Global 2000.

Sumble → Buying signals from tech stacks, people data, & active projects.

Terret → The answer-to-action engine for revenue teams.

I used to compete directly with Tim Geisenheimer (he was running Correlated, while I was building Groundswell, both in the crowded “product-led sales”/PLG CRM space). And Tim was, by far, the best salesperson of the batch of ~20 competitors. We’ve stayed in touch, and over the last few years, I’ve watched Tim successfully build a killer Revenue team as CRO of Hatch, ultimately leading to an acquisition to Yelp. He recently showed me some of the stuff he’s personally building, and was kind enough to share it with us today (he also shares his tips for any revenue leader who wants to go from AI-dabbler to Claude Code/Codex/Cursor-pilled).

Today, Tim’s 100+ person revenue org runs downstream of Tim’s Claude Code sessions. Across 8 custom internal apps, 20 AI skills, and a whole system of self-reinforcing tools, he’s pushed his org to level up how they source, track, and close deals.

Tim opened a terminal for the first time in his life over winter break, just 8 months ago. Opus 4.5 had just dropped and he was spending a little too much time on X, and he got curious. Kids asleep, movie on, laptop open, while his wife made fun of him (...I’ve been there).

A few months later, Hatch (a top AI lead management platform) was acquired by Yelp for $300M. At the time, Hatch was doing $25M ARR and growing 70%. Things have only accelerated from there: since the close, his revenue org has grown from roughly 17 sellers to about 100. Tim says that Claude is a big part of what made all of this possible.

I’ve been on the hunt for GTM leaders at *scale companies (*dozens or hundreds of reps, complex gtm systems, and several products+personas, etc.) who are hands-on-keyboard with this stuff, and the list is short. Last month, I broke down how Nikko Georgantonis, Hightouch’s RevOps leader, runs their revenue engine on GitHub. I called Kyle Norton the most AI-pilled CRO in SaaS. Tim is coming for the belt.

Tim walked me through the whole machine he built at Hatch. Here’s what we cover in this article:

• How Tim got started

• How his system is actually structured

• The first two things Tim built

• What Tim and his team are building next

• Where Revenue leaders should start with Claude Code

• Takeaways

Alright, let’s get into it.

How Tim got started

The entry point was a Teresa Torres video when Tim was scrolling X on a Saturday night. Teresa tweeted that she manages her whole life with Obsidian and Claude Code, and Tim figured he could build a chief of staff on top of the same setup. He pulled in his calendar, meeting notes, and Slack. From there, he caught the bug…

I just got addicted. I started building things, messing around, learning how far I could go.

The transition from personal hacking to building a real system for his day job happened fast—out of necessity. Right as Tim started tinkering, the Yelp acquisition closed and the revenue org started scaling from ~17 sellers toward 100 people, which created lots of scaling problems that needed to be answered (and fast). How do we ramp reps faster? Who needs coaching, and on what specific things? Why did bookings dip last month in xyz segment? Where will this month actually land?

Tim told me the old way of doing things would have been to procure a bunch of software to solve these problems. But then it hit him… “I bet I can point Claude at a bunch of these.”

For the record, Tim does not know how to code. Keep that in mind for everything that follows.

How his system is actually structured

Tim can start a fresh session and ask any question he wants. So, for example, he could type in (or Superwhisper) “2 weeks ago we were doing a closed-loss analysis, pull it up and turn it into 3 slides,” and it does. Here’s how his system is able to answer any question he throws at it:

On the data side, MCP connectors and APIs feed Claude Code from HubSpot (pipeline and deals), ChartMogul (MRR and churn), Attention.com (thousands of transcripts), BigQuery (product usage), plus Slack, email, and meetings.

A system like this only works if you feed it (and maintain) the right context. Tim added 18 curated context files describing how Hatch goes to market, 43 memory files of gotchas and decisions, and (one of my favorite artifacts in the whole system) a “Sales Bible” distilled from 409 calls of Tim’s #1 rep. He studied what makes Hatch’s best seller good and wrote it into the files every agent reads. That tacit knowledge is gold.

It all lives in GitHub. “GitHub is the memory,” Tim says. “Commits are just different sessions. Anything I work on gets committed back. I don’t open GitHub. It’s just there.”

And Clay sits under the hood of it all. Enrichment runs in Clay and lands as HubSpot custom properties, so Claude reads it through the CRM. Tim has also started poking at Clay’s new CLI and API.

The system turns a bunch of little tasks into automations.

What used to be my Sunday night is now a single automation.

Daily. Upsell signals get scanned overnight and posted to Slack by rep at 7am. Every closed-won deal gets a CS handoff doc written from its own call history, hourly. At 5pm a headless agent sweeps Slack, email, and meetings into Tim’s task list and DMs him the digest.

Weekly. Rep, manager, and SDR scorecards roll up into his Monday leadership brief. A midweek deal-level forecast ships with risk scores and the top-15 must-win pipeline list. The Friday sales all-hands deck refreshes itself from live data.

Monthly. On the 20th, a scheduled agent emails Tim that it’s time to run the end-of-month deal push. At month end, a “path to close today” gets written for every late-stage deal (from the CRM plus the deal’s own calls), and an MRR decomposition explains what grew, what churned, and why, customer by customer.

The first two things Tim built

The CRO Cockpit

Tim built his forecasting war room over a weekend. It was live for managers to use on Monday morning. Four months later, it’s the default screen for sales leadership. Roughly 39,000 lines of code, 188 commits, and Tim as the only author. All built by a CRO who couldn’t code before December.

He told me his starting prompt was basically just “look at state-of-the-art forecasting tools like Clari, review the documentation, then use our context files to build similar functionality tailored to our business.” (This was the Opus 4.8 era, pre-Fable/Astra.) Managers layer their commits and calls on top, so the forecast becomes a blend of AI and human input.

Tim shared with me that they recently created a new lead channel that generated hundreds of inbound leads. Because of the spike, most of them weren’t routed and followed-up with correctly. So, that night, with the help of Claude Code, Tim shipped a live tracker tab, plus a daily alert that showed untouched leads (and the rep’s name who was responsible for each one). He was then able to add several features that the managers wanted within 24 hours. This speed of iteration is a good example of what’s possible when the CRO owns an in-house build personally.

Within 48 hours, the majority of the leads had been touched, response time was down significantly, leading to almost 100 demos booked. (Tim proactively mentioned other contributing factors: manager oversight/accountability and a leaderboard everyone can see.)

Deck Studio

Tim was unimpressed with Gamma and Google Slides for building on-brand decks that also know the business. So he built his own workflow. Reps type a brief (or drop in a transcript) and get a finished, Hatch-branded customer deck in minutes, with real case studies and battle cards baked in. It runs on $20/month of hosting.

Thirteen weeks in, the team has produced 377 decks across 49 creators. Wins that used custom decks are closing at a roughly 30% higher relative rate and coming in 50–70% larger ACVs. One of the most compelling pieces of anecdotal feedback was the fact that even marketing adopted the tool. One colleague on the marketing team Slacked Tim, “Dropped the same prompt I’d put into Gamma earlier today. Ours came out so much better.”

What Tim and his team are building next

Tim feels like they’ve only scratched the surface. Here’s what’s on their roadmap.

Rolling out next → one rep’s churn-winback play that’s being replicated across every AE’s book. Churned accounts get scanned, ranked, and shipped to the CRM as worked lists with the play attached.

In pilot → a rep-facing Slack assistant opens the same brain to the whole team. Any rep asks about any account (billing, usage, upsell signals) and gets the answer in the thread.

And on outbound → Hatch sells to home services companies, where Apollo and ZoomInfo coverage is thin. So data providers now battle head-to-head on the same lead lists, and every test gets logged with a hypothesis, a cost, a KPI, and a verdict. A likelihood-to-win model trained on Hatch’s own closed deals is in build, feeding the forecast and eventually deciding which plays are worth a rep’s morning.

Where Revenue leaders should start with Claude Code

At the end of the conversation, I asked Tim where he’d recommend a CRO or RevOps leader who hasn’t fully gone down the (AI) rabbit hole yet, should start. Here’s what he told me:

Clean up your data first. Tim’s data was also in decent shape going in (his CTO from Correlated, Tim’s last startup, came with him to Hatch and keeps BigQuery clean). If your warehouse is a mess, that’s step zero.

Start with analysis. Connect your systems with read access only. No write permissions back to the CRM or your data warehouse. You should start with analysis; you don’t need to be messing with the core systems.

Don’t run it solo. Almost every CRO should do this alongside RevOps and the data team. Tim built most of v1 himself, but the Hatch RevOps team is taking on more of it, and they’re the right long-term owner.

Don’t give up. Tim estimates he’s spent “hundreds of hours, probably more” (mostly nights after the kids went down). My experience has been similar. I’ve spent 10+ hours a week for the last 18 months re-skilling myself. So if you spent a weekend trying to “learn AI” and didn’t ship anything to production yet, don’t quit.

Takeaways

Here are a few things I learned (and/or re-affirmed) after spending time with Tim:

Automate recurring questions. Start by turning one recurring question that eats into your week into your first skill (that’s how /demo-show-rate was born for Tim, for instance).

You don’t have to live in the terminal. Cowork, ChatGPT-for-work, and newly released Grok Bot (check out our Live Show from last week: Grok Bot for GTM) connect to the same systems over MCP or a virtual machine and skip the command line entirely. Tim defaults to the terminal because that’s the muscle he built. Do what you’re comfortable with to start. Just start!

Humans for the last-mile. It took me too long to reach this conclusion, so I’ll save you the time: over my 18 months of consulting, I saw that the “last-mile” should be done by a human. In other words, nothing sends itself without a human reviewing it. Keep humans on the last mile... At least for now.

Tim is the future CRO prototype. I think that in, let’s say the next 3 years, the CROs at the darling companies will be the ones who spent hundreds of hours in 2026 learning Claude Code (like Tim), not the ones who outsourced it to someone on their team (RevOps, GTME, whoever).

Reach out to Tim

If you’re a revenue leader who is curious about building these types of things (or already have built something), Tim wants to chat with you. You can connect with Tim on LinkedIn.

(Also, Hatch is hiring! So if you want to work with Tim at a truly AI-native revenue org, go check out their open roles.)

Thanks to Tim for the conversation and for sharing alpha with us today. You’re the man.

That’s it for today!

As always, thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

PS: We had 25 GTM nerds get together last week at a Sauna/Cold Plunge spot here in Austin (shout-out to Sumble for co-hosting!). I’m planning a couple of things during Sculpt (Clay’s conference in SF on Oct 8th) so let me know if you’ll be around, would love to say hello!

Related posts:

• How a $100M ARR Company Runs RevOps on GitHub; Inside Hightouch’s Agent Repo (Skills for every rep. A four-layer repo for RevOps. And three AI experiments that got pulled. With Nikko Georgantonis, Head of GTM AI & Systems at Hightouch.)

• How to Think About Build vs. Buy in the AI Era (“Build Your Intelligence. Buy Your Infrastructure.” With Kyle Norton, CRO of Owner.com.)

• Inside “ChatGTM”: Cursor’s Internal Sales AI Used by their 400+ Sales Org (SDRs are booking 3x qualified meetings and AEs are shaving ramp by 50%+. What Cursor’s Head of Enterprise Growth, George Hou, built. And whether you should build your own version.)

Subscribe now

Thanks for reading this hand-picked, pesticide-free, farm-to-table (human-written) newsletter.

Presented by:

Clay is the infrastructure GTM engineers build on. Start building for free.

& Supported by:

Attention → AI Agents for RevOps.

Clarify → The Autonomous CRM. Sell more, admin less.

Clearskies → The Context Layer for Revenue AI.

Nooks → All your outbound. One AI workspace.

Rox → Revenue Agents for the Global 2000.

Sumble → ML-powered data and account intelligence for AI-native GTM.

Terret → The answer-to-action engine for revenue teams.

Howdy y’all! 👋

Back with another Friday round-up: a bite-sized editorial by yours truly followed by a few links that are worth your time.

Let’s get into it.

Salesforce’s CEO, Matthew McConaughey, announced “Claudeforce” last week. It’s an integration between Salesforce and Anthropic/Claude (Salesforce owns 1% of Anthropic; turned $50M into $1B…so far; not bad!).

Okay, I know McConaughey is not Salesforce’s CEO (Uncle Benioff is). And Unc has been talking about going headless since April (impressively early, honestly) and this may seem like simply an extension of that vision. But, I think it’s a bigger tell.

Here’s the announcement video:

Here’s what they say in the launch video, in reference to the SaaSpocalypse:

It is not the end of software. It’s the end of software that makes humans do all the work.

So what’s replacing it? Software that can actually do.

Software stops becoming a destination, it starts becoming a teammate. Not another place to go. A presence right there with you. In the flow of work. At the moment work is happening.

He’s talking about Claude. Not Salesforce. People are no longer working out of Salesforce. Of course, if you talk to anyone in go-to-market, this has been the case for a decade. But now, they’re admitting it.

The launch of Claudeforce shows us four distinct lessons:

1. You have to meet reps where they’re actually doing the work today: in LLMs (where they have their context/preferences ‘saved’). In other words, work is happening in the LLM. Not the CRM.

2. With that said, sources of truth are still valuable in the AI era (not being vibe-coded away)—one could argue, more valuable.

3. The intelligence layer is increasingly moving out of your software stack and into the LLM (or custom harness, alongside models).

4. Software is moving from being a place of recording actions, to a place that takes actions (eg: agents). Here’s the line from the announcement video:

   Systems that don’t just record the business, but help run it. This is where software becomes action. This is where decisions become outcomes.

I’m not saying I think agentforce/claudeforce is going to be the biggest winner (maybe… though, I’m skeptical, and honestly haven’t heard a single person raving about using it). But I do think that a lot of people should be taking this as seriously as Uncle Benioff is. Whether you’re using software as a seller, buying software as a RevOps person, building something in-house as a GTM Engineer, or starting a company as a founder, this is a new paradigm. And you have to build in a different way.

I’m very bullish on companies building in this new way—Clarify is a great example; they’re building an autonomous CRM—where founders are okay admitting that work is evolving. They’re not trying to be the the cockpit where reps work 8-hours a day. That’s not how work happens anymore. You can’t jam an LLM into your product. Instead, you have to figure out how to jam your product into the LLMs. And I realize this is not easy… from a technical perspective (API/MCP/CLI), and/or from a business perspective (eg: it impacts pricing strategy, margins, marketing, account management, everything).

I certainly don’t have the answer. But I do know that the launch of Claudeforce is a warning shot to anyone in GTM. This is the beginning of a new way of working with software. One where agents and humans collaborate. The UI/UX changes. And we have the opportunity to rethink much of what we’ve held on as ‘true’ for the last decade. What a time to be in tech-land!

Other corners of the internet worth exploring this week:

• OAI dropped GPT-6 Astra, scoring a 99.9% on ARC-AGI 3 which is nerdspeak for what they later said about the model: “this is the start of the AGI era.” Sheesh.

• Anthropic is trying to “claudify” their go-to-market motion:

  

  So basically a GTM Engineer on steroids. If even Anthropic is hiring this role, every company will. Also… could this be a step towards the application layer (eg: building gtm tech that they commercialize? probably not, but I’ll be watching this closely!).

• NVIDIA acquired Hugging Face (a host for open-source AI models) for $12.9B (apparently an easter egg in the exact dollar amount lol). Speaking of hosting…

• The Signal hosted our first IRL event this week at a Sauna & Cold Plunge spot here in Austin. We had 25 gtm operators nerding out over tacos and beers before sweating and shivering together.

• Eric Nowoslawski tweeted this: “I know this’ll be hard to believe, but I can’t stand the content on LinkedIn anymore and thinking about stopping and just doing YouTube. The content quality on LinkedIn (maybe even including mine) is so bad. I spend all my time here and on YouTube.” I responded (see below) and now I’m starting to think… we should actually make “GTM Twitter” a thing. Peep the tweet to see who to follow on X (there were a ton of great suggestions).

  

• The whole OpenAI/Hugging Face story in plain English: The Rise and Fall of Agent Civilizations, by Dwarkesh Patel. Long read (or listen), but fascinating.

• Oh, and Cybercabs launched (including here in Austin, completely taking over). Wild to see this happen.

• Last, but not least… a big thank you to Krista and Eric for sharing how they’re using Grok Bot for GTM on this week’s Live Show (follow both of them on X). Here are my biggest takeaways from the conversation/demos. Watch the full show:

That’s it for today. If you enjoyed this post, I’d be grateful if you send it to one person you think would also like it.

As always, thank you for your attention and trust. I do not take it for granted.

Have a great weekend, gtm folks!
–Brendan 🫡

PS: Next week’s article is about a CRO who is personally using Claude Code to run his 100+ org. It’s wild. See you then. :)

Subscribe now

Thanks for reading this hand-rolled, gluten-free, farm-to-table (human-written) newsletter.

Presented by:

Clay is the infrastructure GTM engineers build on. Start building for free.

& Supported by:

Attention → AI Agents for RevOps.

Clarify → The Autonomous CRM. Sell more, admin less.

Clearskies → The Context Layer for Revenue AI.

Nooks → All your outbound. One AI workspace.

Rox → Revenue Agents for the Global 2000.

Sumble → ML-powered data and account intelligence for AI-native GTM.

Terret → The answer-to-action engine for revenue teams.

Hey y’all! 👋

I’m feeling energized after spending a few days in SF this week, for an event with Rox, OpenAI, Clay and XBOW, where I got to run some interviews :) I’ll share some of that content soon.

Today’s post explores the rise and fall of the SaaSpocalypse and the nuance of where we go from here. At the end of this post, I’m sharing a few links to things I’ve been exploring (tools, articles, podcasts) that I think are worth your time.

Alright, let’s get into it.

Six months ago, Anthropic shipped Claude Cowork (Jan 12). Notably, it wasn’t a new model. It was an agent for knowledge work.

Then, on January 30th, Ant shipped 11 Cowork plugins (sales, legal, finance, support, etc) and Skills as markdown files.

Four days later the S&P software index dropped 4%, wiping out over $285B in a day (ruh oh… that’s a lot of “churn”). By the end of that week, over $1T (yes, trillion) of software market cap evaporated. Jeffrey Favuzza said there was a “get me out” style sell-off. He went on to say the—now infamous—line: “We call it the SaaSpocalypse.”

The argument was logical. It went something like this: “anyone can vibe code something in a weekend and AI agents are the future.” Remember Satya Nadella’s SaaS eulogy in December 2024?

SaaS businesses are essentially CRUD databases with a bunch of business logic. The business logic is all going to these AI agents.

Even Chamath got back in the arena, launching a company promising to rebuild the enterprise stack at 80% of the features for 90% off. And Sam Altman said we were “entering the fast fashion era of SaaS very soon.” People cancelled a bunch of tools and vibe-coded replacements over a weekend.

Then, this week, this happened…

Turns out, what Bill McDermott (ServiceNow’s CEO) said two months ago was right:

The market thinks maybe I can do everything with a language model and build the software myself. But that’s not gonna happen.

Building software with LLMs is possible. Replacing mission-critical enterprise software at scale is a completely different story. Custom software costs more, takes longer, creates support challenges, and increases complexity. Companies like ServiceNow and Salesforce will integrate AI. They are not being replaced by it.

So maybe “source of truths” are safe. But… HubSpot had its worst day in a decade (down 19%) last week. And Datadog also dropped 19% (but is still up 72% on the year). And of course, Airtable sold to Bending Spoons at a discount (for $1.3B; previously valued at ~$12B).

So, it’s more complex than ‘all saas bad’ or ‘all saas good.’ Just last week, Atlassian also had its best day since the IPO (up 35%), Twilio popped 20%+, as did Cloudflare.

Guillermo Rauch (Vercel’s CEO) recently tweeted something that resonated with me:

The SaaSpocalypse is both understated and overstated. Over because the key systems of record and storage are still there (Salesforce, Snowflake, etc.)

Understated because the software we are generating is more beautiful, personalized, and crucially, fits our business problems better.

I have seen that “generating a first version of an app” is very different from “replacing a product people still want maintained.”

It’s a tale of two cities.

Spencer Skates (Amplitude’s CEO) summarized it well on TBPN:

…on SaaS, the key lesson: the software you’ve already built, that is no longer a moat. Because whether it’s someone vibe coding it, or someone going after it at a company, they can replicate it pretty quickly.

And so the new key moat is speed of innovation.

What the SaaSpocalypse has gotten right is - if you look at the median SaaS company, their innovation has actually slowed to a standstill. It’s crazy how little they ship in terms of net new products.

There’s only a moat if we’re able to deliver the most bleeding edge capability.

If 10 agents can do the work of 100 reps, you can’t sell 100 seats anymore, you can only sell 10. I like how Jason Lemkin put it: “SaaS is being starved, not killed.” And you could even take it a step further, as Tomasz Tunguz posed the question (way back in 2024, before anyone had a name for the crash): “What does a software seat mean when a human is no longer operating the software?”

That is a GTM problem.

Who is the user of SaaS in the AI era? The human or the agent? Both?

If your product only works when a human is clicking around, that is a problem. If your product is something an agent can call (API, CLI, MCP), you just inherited a new buyer. I wrote about this a couple months back: Your next buyer won’t be a human. In it, I wrote:

If your buyer is a person, you send cold emails. You run events. You build inbound funnels. You create content for humans to read, evaluate, and share internally.

If your buyer is an agent, none of those motions work the same way. You’re not going to send a cold email to an AI agent. But you are going to optimize for AEO (agent engine optimization, the next evolution of SEO). You are going to build support documentation that looks very different for an agent versus a person. You are going to build features and tooling designed for agent-first workflows rather than human-first workflows.

There isn’t a conclusion to the story yet. That’s the fun part of this moment in time! :)

We’ve never lived through something like this before. And if you’re you’re building, or taking products to market right now, there’s never been a more exciting time. I don’t know how it all unfolds. But, I do know everything is up for grabs. Everything is changing. Everything is computer AI.

It’s not about “get on the train or be left behind.” But more optimistically it’s “get on the train, it’s so much fun!” I’m bullish on AI. I’m bullish on software. I think the AI era ushers in the biggest boom in SaaS that we’ve ever seen. Not all SaaS will thrive (or survive, for that matter). But much of it will. And those who can navigate this nuance will be the winners of the next decade.

Other corners of the internet worth exploring this week:

• Grok Bot for GTM “This is how I use Grok Bot for Enterprise GTM at SpaceXAI.”

• 2024: Clay is the bleeding edge. 2025: n8n replaces Clay. 2026: Claude Code replaces Clay. 2027: Clay is the bleeding edge. More here and here (I’m writing something on this subject that I’ll publish soon).

• Cool data showing the backgrounds of GTM Engineers (post from Danny Chepenko):

  

• Rox gathered 75 RevOps and GTM Engineers in San Francisco Tuesday night, and I did some interviews (sneak peek here). Bullish on in-person events in GTM these days!

• How to stand out in the slopification of the timeline:

That’s it for today.

As always, thank you for your attention and trust. I do not take it for granted.

Have a great weekend, gtm legends.
Brendan 🫡

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Rox → Revenue Agents for the Global 2000.

Sumble → ML-powered data and account intelligence for Ai-native GTM.

Terret → The answer-to-action engine for revenue teams.

Hey y’all! 👋

I am testing out a new format on this fine Friday: A weekly roundup. This week, I’m explaining one of the hot topics in the GTM space (triggered by a Paul Graham tweet), plus a few links to things I’ve been exploring (tools, articles, podcasts) that I think are worth your time.

Let me know if you think I should do this each week, or if you’d prefer no round-up. (Reply to this email, I read each response).

Paul Graham, the godfather of startups, dropped 13 words that absolutely obliterated the timeline earlier this month.

I think there are pros and cons of the term “GTM” gaining traction.

The good

Traditional sales was one subset of the overall go-to-market function. And at a startup, sellers are doing more than just selling. They’re also doing their own sales engineering, demand gen/sales development, running event follow-up, building enablement/playbooks on the fly, etc. “AE” (or “sales rep”) is limiting to the actual role. So it makes sense that startups would call their founding sellers “GTM”. I agree with Chris Balestras (who places lots of early sales hires at startups):

And even in large companies, “go-to-market” is a broader umbrella term.

From Stripe’s guide on What is a go-to-market strategy? How to create a GTM strategy for startups:

“Go-to-market” (GTM) refers broadly to the overall commercial framework a company uses to bring a product or service to customers, though in practice it is heavily centered around sales and marketing functions. While sales and marketing execute the core tactics, a complete GTM strategy is broader and cross-functional. It connects product positioning, pricing models, distribution channels, customer success, and revenue operations into a unified plan to acquire and retain buyers.”

Here was my way of explaining this…

The bad

Some people are using the title of GTM because they’re embarrassed to call themselves a salesperson (or AE).

First, because it’s low status, I guess? (I don’t see it that way :)). And, secondly, because prospects don’t want to talk to a ‘salesperson.’ Same reason we came up with ‘Account Executive’ or ‘Sales Development Representative.’ No one wants to talk to the ‘slimy used car salesman.’ But modern sellers are not slimy used car salesmen at all. I like how Irene Madrid put it:

Modern GTM means understanding yoru customer’s business before you ever pitch a product. Thinking like the CEO of their company. Getting obsessed with solving real problems, not just signing the deal.

So, maybe some people see this rebrand as warranted to escape this stigma? (I don’t think that’s necessary).

And here’s the final angle: some agency bros hijacked the term. “Lead gen” (aka cold email services) has become synonymous with “GTM” in some circles. A lot of people doing “meeting booking” as a service pivoted to doing “GTM work.” You can charge 50% more overnight, with that one rebrand. But this is brutal for the space. It undermines the value of what GTM actually means. It’s definitely not another name for “outbound.”

Conclusion

No matter where you fall on the argument(s), there’s no denying that “go-to-market” as a term is being used more today than ever before.

And, while there are many definitions (and hijackers of the term), I think it’s helpful for everyone to, eventually, get on the same page and use the term the same way.

So, what do you think of this trend of calling salespeople ‘GTM’? Reply with your thoughts! I read all responses.

Other corners of the internet worth exploring this week:

• Grok Bot launched this month. I’ve been using it a ton every day (for LinkedIn triage, editing, research, light design, “Chief of Staff” mode, Substack analysis and more). It’s the wildest AI experience I’ve had since Claude Code in December. Think of it like Claude Cowork meets Slack (but instead of humans, it’s bots that you create, and each one has computer use).

• Clay instituted an official AI Writing Policy that I think everyone should adopt (or create their own version). It’s soo so good (9k+ likes on the post, so I guess a lot of others agree).

• Lot of founders and revenue leaders are trying to figure out what a GTME/Growth Engineer should do (and if they should hire one). Much of the things they’re doing are newly unlocked things, thanks to AI. Here are some examples: 21 things a growth engineer does.

• A rare, and fascinating, interaction with Dario on X (responding to Gavin Baker’s comment that “Anthropic believes they could be the ONLY company left in the world”) → “I don’t usually spend much time on social media but I wanted to engage here because it really brings out the heart of an important conversation.”

• Ben Thompson (on Invest Like The Best) says the real money in AI is being made by Meta, not OpenAI or Anthropic: “The biggest impact of the models, the biggest monetization right now is probably not in Anthropic or OpenAI. It’s the incremental gain that is happening for Google and Meta.” And says Zuck shouldn’t be ashamed of the beautiful business model of ads, and that OpenAI should have done ads to support their consumer business sooner. (The entire conversation is worth a listen, or wherever you listen to your podcasts)

• In a leaked letter to Stripe’s investors explaining its acquisition of OpenRouter ($7.5B), Stripe founders say the singularity started January 1st 🤯

• X/Twitter open-sourced the algorithm for the “For You” page.

• Last but not least:

  

That’s it for today.

As always, thank you for your attention and trust. I do not take it for granted.

And let me know if you liked this edition (reply to this email!) and consider forwarding it to one person, it means the world to me.

Have a great weekend, gtm nerds!
Brendan 🫡

PS:
(Bonus nugget; the most beautiful website I’ve come across in a long time):

Our lives and homes are filled with things that once inspired similar awe. I tried to collect those feelings, in quotes…from people for whom what is now ordinary was brand new. A friar who met the inventor of eyeglasses, a founding father on the new smallpox vaccine, a woman in Iowa on her family’s first indoor bathroom. To look at a modern apartment through their eyes:

Click through it here: Ordinary Abundance.

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Hey y’all! 👋

In November of 2024, I published an article titled “The Anti-AI SDR Company Coming Out of Stealth.” It was about Rox, led by Ishan Mukherjee (who exited his previous startup to New Relic). Fast-forward about two years, being anti-AI SDR turned out to be the right approach.

I wanted to follow up on that first deep dive that I wrote, especially in the context of so much that has changed across the GTM landscape, and get an update on what they’ve built since coming out of stealth. So, we’ll call this a deep(er) dive post. As a reminder, sponsored deep dives give me an excuse to go deep on products (and spaces) that I’m interested in writing about, even if they weren’t paying me (don’t tell them this ;)).

Rox is a revenue agent platform. They’re probably the fastest-moving company I’ve seen in the GTM tech space. “Cracked,” as the kids say. In under 24 months, Rox has gone from an unknown company to a force to reckon with. They’re building in SF and have raised over $80M across their Seed and Series A rounds from tier-1 investors (Sequoia, GV, and General Catalyst). And today, they’re launching Teams, the self-serve entry to their revenue agent platform, that any size team can use.

I’m excited to revisit what I wrote about ~20 months ago, unpack what Rox has been up to since then, and talk about where they’re headed from here.

Let’s get into it.

One of my favorite Paul Graham quotes is from a 2012 essay How to Get Startup Ideas. In it, PG says:

Live in the future, then build what’s missing.

Ishan and Rox are doing exactly this in the GTM world. They’re building for a world that does not yet fully exist, but is starting to emerge, and—if they’re correct in their bets—they’ll be perfectly positioned to support this new world with how they’re building Rox.

What I covered in my first deep dive

To recap, here’s what I said about Rox back in Nov of 2024 when they launched out of stealth:

1. Account-based agents. Rox’s central idea is account-based agents for quota-carrying reps. Traditionally, we think about task-specific agents or assistants (eg: an email-writing assistant, a research bot, a CRM plugin, etc). Rox flips this on its head. Instead of having agents for different tasks, Rox provides one dedicated agent per account. These agents scour public data, third-party data partners, and first-party tools like your CRM and data warehouse. Think: agents working 24/7 across every account in a seller’s book of business.

2. Software that gets smarter with time. I joke that gtm tech gets worse the more you use it, not better (looking at you, Salesforce… after several years of usage, it starts to look more like a 59-year old Mike Tyson trying to fight a 27-year old YouTuber… tired and past its prime). But, with Rox, it gets smarter the more you use it. Over time, the tuning kicks in and the more context you share with it, the better it will get.

3. Results, not “demoware.” I still remember what Ishan said when I congratulated him on their fundraise: “It doesn’t matter. What matters is: do reps find it useful? Does it make them more productive? And hopefully the productivity is significant enough where it changes the fundamental unit economics of the market. That’s pretty much our sole focus.”

4. Anti-AI SDR. My dumbed-down explanation of the product at the time was: “Perplexity/Claude Projects + Clay-lite + a slick workflow layer (plus… it uses dark mode—my kinda people).” It’s built for quota-carrying reps. Rox is explicitly anti-AISDR. It’s not trying to replace the human touch with automation, the way some are (and now walking it back). Their approach is to empower reps by helping them save time on: information gathering, wrangling, and all the other things that take time away from reps spending time IRL with customers. Fast-forward about two years, and this turned out to be the right approach.

5. Supercharge your best reps. Rox’s arbitrage is around the fact that reps don’t spend time in Salesforce anymore. And, maybe more interestingly, the data is not all in Salesforce anymore, but instead moving into the data warehouse (this is a very big deal). As the AI-era continues to unfold, I believe this data problem becomes even bigger, and the data architectural decisions will, therefore, become more important for GTM teams.

Building for humans AND agents

I was re-reading Rox’s Manifesto while doing research for this piece, and thought this excerpt was worth sharing to help (re)set the table today:

Our mission at Rox is to secure and grow the world’s revenue. We build revenue agents that either augment or automate the entire revenue life-cycle from inbound, outbound, deal management to renewals. Coding, Support and Legal agent categories are already maturing and are driving drastic productivity benefits. We believe Revenue agents will do the same.

Innovation in this vertical requires us to re-invent the way humans AND agents work from the ground-up across the data platform, the agent harness and the UI. As we explore the frontier, we will continue to post our learnings along the way for the community and incorporate our research into our product.

They even have a dedicated Research Program to help accomplish this audacious goal (seems their plan to deploy that $80M isn’t just on a bunch of google ads and SDRs), to solve the problem around why revenue agents are uniquely hard to build:

Revenue agents operate in one of the messiest data environments imaginable. A seller’s world spans CRM records, email threads, meeting transcripts, call recordings, LinkedIn profiles, company news, and dozens of other signals, all constantly changing.

What is Rox building?

Here’s how Rox describes themselves: “Rox is a revenue agent platform. Rox agents do the actual work of a sales team, like finding and researching prospects, running outreach, managing live deals, and handling renewals.”

Here’s my take: the old revenue stack is getting torn apart. And the best AI-native GTM teams are building a new stack to scale their revenue from $1M to $1T. This new stack makes legacy CRM and SaaS obsolete, designed for Revenue Agents to do more of the actual revenue work: everything from generating pipeline, helping execute deals, forecasting through renewals.

Anyone can hack together an agent over a weekend that demos well. But, deploying (and scaling them) into production in an enterprise revenue org is incredibly hard. To do it, you need three layers:

• Data: one unified foundation across every source

• Harness: makes agents reliable

• Agents: actually do the work for sellers

Let’s unpack each one and how Rox is tackling it.

Data (warehouse-native)

If you’re a GTM nerd (you’re reading this little newsletter, so you probably are), you’ll probably enjoy this video of Shriram Sridharan (Co-founder & CTO at Rox) explaining the history of the database (Siebel in the 90s), to cloud (Salesforce in 2005), to warehouse (today).

Shriram goes on to share the journey of modern GTM teams and how their data setup is impacted by layering AI into their system(s).

The whole (7 min) video is worth watching, but here’s the tldr:

1. The GTM data already migrated, out of the CRM and into the lake house.

2. That makes the lake house the system of record, the only place the full customer picture exists.

3. The lake house is now operational, which means you can run agents directly on your system of record instead of on a stale copy.

For teams setting up an AI-native revenue system, you have to rethink how your data is structured, or else none of the agentic workflows you layer on top will flop due to missing context or inaccuracies.

Harness

A revenue harness is necessary to make these magical models work for teams. They give you the right context about your business, while leveraging this powerful, alien technology that is LLMs.

Rox enables teams building their own custom revenue harness, by providing what’s missing with out-of-the-box technology. This enables teams to deploy agents reliably, safely, and at scale, by giving them the tools needed to do things like testing, governance, compliance, evals, and other (somewhat boring, but necessary) tasks to run agents in enterprise orgs.

As one example, I recently got a message from a (very impressive) GTM team who built a custom harness. They’re using Rox as a core piece of infrastructure to build on top of. This type of builder is more and more going to run GTM systems at the fastest-growing companies in the world. As Kyle Norton shared with us, you want to “build your intelligence and buy your infrastructure.” Rox is building said infrastructure, which means they’re well-positioned to slot into these types of teams/systems.

Agents

Rox is entirely focused on “revenue agents.” But, what does that actually mean in revenue orgs at mid-market and enterprise companies? Let’s jump into a couple of examples to show exactly how the best AI-native revenue teams are building agents using Rox.

Prospecting is dead as we know it. In Rox’s launch around outbound prospecting a week ago, Ishan said:

This isn’t a faster way to prospect, it’s the end of prospecting as a job that you used to do by hand. Your reps weren’t hired to click through contact lists, they were hired to engage with customers to close and grow them. So let the agents prospect, and live in Rox.

I don’t see this as AI replacing humans. That’s not what the best teams are doing. Instead, they’re using AI for certain tasks (like researching, admin tasks, etc) so that sellers have more time for creative and impactful work that AI cannot do. Agents enable (or superpower or augment) the best sellers on your team.

Another example of agents is having them run revenue end-to-end. “No SaaS, no CRM, no Enrichment. Just an agent.” This is a wild statement, but I think the right future (and apparently thousands of other people agree). Like I said earlier, Ishan and the team at Rox are living in the future, and pulling us into it.

Two years ago, Rox launched agents. And then, earlier this year, they hit an inflection point: their largest enterprise customers switched many of those agents from Copilot to Autopilot. Rox continues to help revenue teams build bleeding edge agents.

And (as of today) it’s all self-serve!

Rox has spent the last 2.5 years spending all of their time doing research and shipping products to solve problems at revenue teams in the world’s largest enterprises (AI-native companies like MongoDB, Ramp, WSP, Puma Energy, XBOW, and Together AI).

They’ve launched a dialer, live call coaching, AI sequences, custom apps for RevOps, automated workflows, org charts, inbound agent, account plans, deal review/forecasting, deck generator, mac app, voice mode, and (much) more. This company SHIPS.

And today, they’re opening up the entire platform to all, 100% self-serve. This is not a watered-down version of Rox. All you need to do is sign up, sync Salesforce, connect your data, and the agents are ready.

Give it a try (and let me know what you think if you do!) → Build your Revenue Agent stack today. Get a demo. Get started now.

That’s it for today! Let me know what you think of today’s post (I read all the replies).

As always, thank you for your attention and trust. I do not take it for granted.

See you next time*,
Brendan 🫡

*For the legends who made it all the way to the end, here’s a sneak peek of our next few posts:

• How a CRO manages his $30M revenue org through Claude Code

• A look under the hood of the GTM tool built by a Director of GTM Engineering at a $1B company

• The swimlanes between RevOps and GTM Engineers

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Hey y’all! 👋

Something interesting is happening inside revenue teams right now. They have access to the best AI that has ever existed: incredible models and harnesses (eg: the agent tooling on top like Claude and ChatGPT) that get better every month. And yet almost every team I talk to is stuck in the same place: they can’t get all that intelligence to do consequential work with their revenue data. The limiting factor isn’t the model anymore. It’s context. AI doesn’t come out of the box knowing your customers, your team, how you sell, or what winning looks like at your company. And no model release will fix that.

The teams that solve context are handing real revenue work to AI: deep research across every deal, analyses they can trust and repeat, workflows deployed to a whole team. And the work compounds, because the system remembers. Everyone else is getting confident answers built on fragments, and most don’t know it yet, because the answers sound right.

The most AI-forward companies have already figured this out. Teams like Cursor and Vercel treat context infrastructure as part of their GTM strategy, with dedicated engineers building and maintaining their context layers full-time. But most companies aren’t doing that. Most can’t—or, frankly, shouldn’t.

That gap is the problem Pouyan Salehi built Clearskies to close. It launches today, open to any GTM team.

I’ve known Pouyan for over a decade. I first met him in San Francisco when he was building PersistIQ, one of the early sales engagement platforms, from the same wave as Outreach and Salesloft (and eventually Gong). Then he and his co-founder, Cyrus Karbassiyoon, built Scratchpad, the tool that made updating Salesforce feel so buttery that reps actually did it, which is why it spread fast, and organically. Now they’re building Clearskies, which they describe as “The Context Layer for Revenue AI.”

Even six months ago, almost no GTM leader was talking about a “context layer” for their team. Now it comes up in half the conversations I have. My bet: over the next 6-12 months, context becomes one of the most-discussed problems in go-to-market. That’s why I was excited for today’s sponsored deep dive into the context layer Clearskies is building.

Here’s what we cover today:

• What a context layer actually is

• What a context layer unlocks, starting with the hard stuff: deep research and win-loss analysis

• Where connectors stop being enough (and where they’re still great)

• Build vs. buy in the AI era

• Governance, portability, and the no-seat model

• Where this goes from here

Alright, let’s get into it.

What a context layer actually is

When I caught up with Pouyan recently, he laid out four layers of context:

1. Customer context → the full story of each account: deals, contacts, activity, commitments, risk. Identities resolved across sources, timeline ordered, with what’s changed since you last looked.

2. Team context → who’s selling: roles, reporting structure, segments, territories, tenure, quota, expertise, how they’ve performed.

3. Process context → how you sell: stages, methodology, definitions, approval paths, exceptions, what good looks like.

4. Business context → products or service lines, segments, pricing and packaging, strategy, OKRs, priorities.

Reminder: this is that stuff that foundational models don’t know, out of the box.

Customer context is the hardest of the four to wrangle. The real record of a customer doesn’t live in Salesforce or HubSpot. Your CRM is the secondhand version: the rep’s summary, hazy and half-right. The primary source is the emails, calendar invites, Slack threads, tickets, and call transcripts scattered across a dozen tools (Clearskies’ integrations list keeps growing), and a lot of it lives only in people’s heads.

The obvious fix is to pull all of those sources into one place. But that’s just a database. Context is what you get when the pieces are actually connected: the Acme opportunity in Salesforce, the calls, the email threads, the support tickets, and the Slack channel resolved into one account. Touch points ordered into one timeline, with changes tracked across the account’s history. Your sales process understood, so “stalled at Stage 3” means something. That’s a context layer. The connected picture, across all tools, kept current, so any AI can reason across the full story instead of rebuilding it from scratch on each new query. Revenue infrastructure that hands any AI the full picture of your customer, your team, your process, and your business.

What a context layer unlocks

The unlock isn’t just better answers to quick questions. It’s consequential revenue work—analysis and workflows you’d never have trusted AI with, done reliably enough to hand to a whole team, compounding as the system learns your business.

Start with the thing that’s hardest to fake: deep research and analysis across all of your revenue data. This is the difference between “search my CRM” and “go through every enterprise deal we lost last year—every call, every email, every Slack thread, every support ticket, across every rep—and tell me the real reason.” Done properly, win-loss means pulling each relevant deal, inspecting what happened on it end to end, finding a real answer per deal, storing it, and then analyzing the whole set. For hundreds or thousands of deals, that’s not something you can fake with a sample of your data. Ask a model to do it cold and it’ll hand you a confident answer built on a handful of data points and never mention that’s what it did. In revenue, those errors turn into bad commits, missed forecasts, and churn.

That’s where a context layer becomes an obvious need. A real analysis isn’t just a prompt. It’s the prompt plus the context, plus the memory of what you’ve already learned, plus the technique to run it the same way each time. And when it works, the answer shows its work: how many records it read, which sources, the per-deal summaries you can browse, and where the gaps are.

Once that’s in place, the same infrastructure (basically, a revenue brain, or “System of Intelligence”) does consequential work for every role:

• CEO: voice of the customer, built from what buyers actually said (not a survey), and briefs across the whole funnel.

• CRO: rep performance with the why behind numbers you already have. The dashboard tells you who’s behind quota; the layer shows what’s driving it: thin discovery, single-threaded deals, stalled next steps. Process gaps and objection trends across the pipeline, with the evidence attached.

• RevOps: pipeline health with the why attached: what’s at risk, what materially changed this week, and the recurring conditions that show up before deals stall or die as no-decisions. Plus win-loss, run the way I described above, across the entire book.

• Marketing: competitive intelligence from your own calls (which competitors come up, in which deals, what objections they raise, how they impact the deals, and how your team responds) and market trends straight from the source.

Analysis is half of it. The other half is execution: pre-call briefs that pull from the full history, post-call workflows, CRM fields that update themselves, handoffs that don’t lose the thread. One layer, feeding both. (Clearskies’ use-cases page has more.)

And there’s another new use-case that context unlocks: custom applications. As more teams build their own revenue apps and agents, the context graph is the definitive source to power them. You don’t have connectors to the application you just built; you need a layer underneath it.

What struck me is how far this reaches. It’s the whole team, from CEO to seller. It’s across functions: product, finance, marketing. And it’s across the entire customer lifecycle, from first discovery through handoff, expansion, and renewal. Each function has been asking the same questions about customers and rebuilding the answer in silos. But when you put one shared layer underneath, everyone’s questions get answered (from the same source).

Where connectors stop being enough

Since December, Claude and ChatGPT adoption inside revenue orgs has gone parabolic. Most teams are still early. They’ve turned it on, or they’re about to once security and legal clear the enterprise version, and they’re still working out what to do with it. But they know they should be using it (a lot of FOMO on the timeline, some of it coming straight from the board).

The more advanced teams are wiring up connectors (MCPs). And connectors are good at what they’re for. This isn’t connectors versus a context layer. The question is where each one fits.

Connectors shine on narrow, low-stakes work. One deal, one call, one rep, the data sitting in a single tool, a human who can eyeball the answer. They stop being enough the moment the job needs the full customer picture across tools, across records, across time, and across your entire revenue team. At that point the model reconstructs the picture from scratch on every run: slow, expensive, and every rebuild can come out a little different. Ask for the same pipeline review on Monday and on Tuesday, and you can get two different numbers. You can’t trust it to repeat, and you can’t safely hand it to a whole team. And the answer can only be as complete as the fetch. Nothing tells the model that the contact in the CRM and the voice on last week’s call are the same person, and nothing flags the email thread it never pulled. That’s why connector answers feel fine right up until they’re wrong.

A connector only knows “right now.” It reads whatever is in the tool at the moment you ask, and nothing persists. It can’t tell you what changed, when, or why: how a deal actually progressed, where it stalled, what happened after the pricing call. For anything that depends on how a customer or a deal evolved over time, a read of the present isn’t enough. You need a layer that retains memory.

Connectors are the right tool when the work is in one place, the scope is small, the stakes are low, and a person can quickly check the result. You’ve outgrown them when the work needs the full customer picture across tools and history, and when the output drives a real revenue decision. At that point, the team has to trust the answer without re-running the analysis, and the job is to find patterns and track change over time, not fetch or update one thing.

You’ve probably heard the advice to dump all your revenue data into a GitHub repo and point the model at it. But revenue isn’t code. In code, the context sits in the repo. In revenue, it’s scattered across all the tools your team touches, and stitching it together on the fly is exactly where connectors break.

Build vs. buy in the AI era

Building got dramatically easier (everyone knows this). I’m personally shipping working tools without writing a line of code. But, it’s more accurate to say what got easier is not one-shotting end-to-end production-grade solutions, but prototyping. A prototype is the starting line, not the finish. Getting to something you can rely on takes the patterns you only learn by doing the work. I don’t buy that all SaaS is dead. I think there’ll be far more software, and the unglamorous work of context, paired with expertise and taste, becomes the differentiator.

The context layer is where I see GTM leaders underestimate this the most, because it’s an iceberg. Above the waterline, a prototype comes together on a weekend: wire up a few connectors, load a database, point Claude at it. But it’s misleading. A database with a little data in it feels like a context layer. Below the waterline is what it takes to make it real. Things like identity resolution that holds up across edge cases, semantic search across millions of records (the average revenue team is not standing up a vector database and tuning search infrastructure), memory that persists and improves, permissions that hold. And below that is keeping it alive. Things like new tools being added, schemas drifting, techniques evolving, playbooks and processes changing, and the whole thing has to stay fresh enough to trust your revenue with. It’s less of a one-time project, and more of a permanent allocation of engineering, product, and design to internal plumbing.

Even teams with serious technical firepower like Cursor (who built ChatGTM), Ramp, and Rippling poured enormous effort into building and maintaining the context layer, including the custom harness: the tooling, memory, and orchestration around the model. Build vs. buy is a tale as old as time. You didn’t rack your own servers, you’re not vibe-coding your CRM, and you probably shouldn’t build your own revenue context layer either.

I recently published a piece with Kyle Norton: How to Think About Build vs. Buy in the AI Era (”Build Your Intelligence. Buy Your Infrastructure.”). Clearskies is the textbook case of infrastructure you buy, so your team can spend its time building the agents and workflows on top (that’s the “intelligence” part).

Clearskies also wrote a great piece on how operators are rethinking build vs. buy, with tactical examples from Vercel, a16z, and Browserbase. Including advice like: keep agents small, start with one job (not a platform), and keep GTM in the build loop. It’s worth reading the whole thing.

Governance, portability, and the no-seat model

Governance has to be part of the design. Execs have sensitive conversations with customers, HR sends confidential emails, board-level information floats around. You can’t just pipe your executives’ inboxes into Claude and start one-shotting agents. A real context layer controls what enters the shared context, with permissions and an audit trail. That’s the difference between a demo and something you can deploy across an enterprise.

Portability is the piece I find most interesting, and I think it’s changing fast. Right now most teams wire their tools straight into a single harness. Models are leapfrogging each other every few months, and if you’re hard-wired to one, you can’t move to a better one without rebuilding everything. When you own the context layer, the joins and the history live outside the model, so your answers don’t change when your model does. Swapping in whatever harness and model tops the benchmarks next is a lightweight change, not a migration. You get to select the best performance instead of being locked to one provider. (There’s an IP angle too: you keep your own decision-making and institutional knowledge instead of handing it to a single vendor. But the day-to-day win is simply being able to move.)

Kyle Norton put the compounding effect well:

[The winning companies] will be extremely deliberate about the intelligence layer. They will know what context matters, what memory should persist, what workflows create proprietary learning, and what parts of the system need to remain portable.

That is how AI maturity compounds. One good workflow becomes reusable context for the next workflow. One evaluation loop improves the next model output. One proprietary memory layer makes every future agent better.

The companies that win AI-native GTM will not have the most tools. They will not be the companies with the flashiest demos. They will be the companies whose systems get smarter every week because they own the memory that matters.

Which is why I keep coming back to “memory is the moat.” Clearskies made the same case (every revenue team needs its own brain) and built their layer so your context stays yours and works with any model or surface.

You can see the philosophy in how they price it. No per-seat licenses: you either pay as you go or pay a flat rate for the infrastructure, and the whole team gets access. And for the team using it, there’s no new app to learn. You use it through Claude, ChatGPT, Slack, or whatever surface fits. The app is for the builders and admins setting it up. The layer clears a higher bar than connectors where it matters: complete and current, shared across the team, governed, repeatable, portable across models, and priced so your token bill doesn’t balloon each time context gets re-stitched.

Where this goes from here

I expect the model and the harness to decouple. Open-source harnesses show up, and teams route task-specific models through their own context layer. When you own the context layer, you can plug it into anything.

Pouyan’s read on the market right now is that most companies’ “AI strategy” is “we bought Claude.” Few have done anything meaningful with it yet, and even fewer see context as the actual bottleneck; most assume connected tools and better models will sort it out. Which means the teams that figure out context now will pull well ahead of the ones waiting.

The adopt-AI-at-all-costs phase is already sliding into the rear-view mirror. CFOs are starting to look at the Anthropic invoice and ask, “what did we actually get for this?” The teams with a context layer their GTM org (and the rest of the company) can build on are the ones whose results compound. Everyone else keeps shipping one-off AI workflows they have to rebuild each time. That’s the asymmetric bet Pouyan, Cyrus, and the Clearskies team are making. After years of watching them go one layer deeper with each product they build, it’s the most convincing version I’ve seen yet.

Context as infrastructure is the story of 2026.

Visit Clearskies and start building for free, or book a 1:1 with their team.

Or, if you just want to chat more about context strategy and what you’re building, Pouyan is happy to connect and share learning → reach out to him on LinkedIn. He reads the comments here too, and he told me he’s happy to compare notes with anyone working through this.

That’s it for today!

As always, thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

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Thank you—*all 10,000+ of you!!! 🤯—for reading this hand-poured, 100% organic, farm-to-table (human-written) newsletter.

Happy Friday, y’all! 👋

We hit 10,000 subscribers this week.

I close every newsletter with the same line:

Thank you for your attention and trust. I do not take it for granted.

And I really do mean it.

I got back from vacation with my family last night (hence the photos of beach scenes throughout today’s piece). And this morning, with sand still in my shoes and the smell of sunscreen on my hat, sat down, genuinely excited to start working. What a gift. To be doing what I love—and have found “pmf” doing it.

When I started writing this newsletter, I wasn’t trying to build “a media company.” I genuinely did not think I could (or wanted to) do this full-time. I was simply trying to learn in public. I wanted a place to document what I was seeing as the old playbook was starting to fail, and a new playbook was emerging (in large part due to the rise of “GenAI”). This newsletter was a place to share the conversations I was having with operators who were heads-down shipping, while the rest of us were still figuring out what the new playbook even was.

Somewhere along the way, it became “a real thing.”

Now, there are over 10,000 weekly readers. Public company CROs and heads of RevOps, founders, and GTM leaders at OpenAI, Cursor, Ramp, Rippling, Anthropic, Vanta, Snowflake, Datadog, Baseten, Navan, Stripe, and many more. Operators who write me after every issue to push back, add context, or share what they’re building.

If you’re reading this: thank you.
(Your) attention is the scarce resource.
And I do not take it for granted.

A few things I’ve learned getting here

Quality beats scale. I’ve said this before, and I mean it: if I have half a million subscribers in three years, I have failed. I am building for a small group of people—by design (“nichemaxxing”). The goal is that the right people find this useful, not that everyone sees it.

Free, forever. I believe information should be free. Including not requiring an email gate on the archives. Trust compounds when you don’t make people jump through hoops to read. It’s also my own PLG motion :) which I deeply believe in.

Organic is slower, but better. Growth has been from word of mouth, LinkedIn posts, and people forwarding issues to a teammate. I have not spent a dollar on paid acquisition, and don’t plan to. Tortoise > hare:

Going full-time was the unlock. I’ve been able to do more interviews, deeper research, stronger live shows, and spend more time interviewing operators who don’t have 12-15 hours to write up what they’re doing. The GTM playbook is changing, and I want to create the place that documents what’s actually working. That was the bet I made. And it seems increasingly valuable in the AI era.

Some stats. I have published 106 articles, viewed over half a million times, and written 172K words (…don’t worry, I’m actively trying to work on making posts shorter… it’s hard!). Over 3M impressions on LinkedIn so far this year.

84% of subscribers are GTM leaders and founders. Below is the split across personas:

If you’re new (or want a refresher)

Here are the top/my favorite articles:

🤖 AI GTM

• Inside “ChatGTM”: Cursor’s Internal Sales AI Used by their 400+ Sales Org

  (SDRs are booking 3x qualified meetings and AEs are shaving ramp by 50%+. What Cursor’s Head of Enterprise Growth, George Hou, built. And whether you should build your own version.)

• 9 Lessons From 11 Growth-Stage Companies That Built GTM Agents In-House (Vercel, Ramp, LangChain, ClickUp, Deel, Vanta & others: what they built, their tech stacks, and what we can learn from them)

• How a $100M ARR Company Runs RevOps on GitHub; Inside Hightouch’s Agent Repo (Skills for every rep. A four-layer repo for RevOps. And three AI experiments that got pulled. With Nikko Georgantonis, Head of GTM AI & Systems at Hightouch.)

• 6 Ways GTM Teams are using MCPs and Claude/Claude Code Today

  + Video demos of real examples from our live event

• The best AI-native GTM plays you’re not running [Growth Unhinged collab] Automation doesn’t have to mean spray-and-pray. Now it doesn’t have to mean manual, either.

• 6 Ways to Modernize Your GTM Motion in 2026 (Lessons from working with 14 Seed-Series C companies in 2025)

🛠️ GTM Engineering

• The Rise of the GTM Engineer

• When Should You Hire a GTM Engineer?

• How to Become a GTM Engineer (8 Resources)

• Most Companies Are Using GTM Engineers for “Automated Outbound.” The Best GTM Teams Aren’t Stopping There.

• 26 FAQs about GTM Engineering in 2026

🎯 Prospecting

• “Micro-Campaigns”

• The best automated GTM plays you’re not running

• 6 “Evergreen” GTM Plays (and the tools to run them)

• How to automate signal-based selling

• How to Source 7-Figure Deals on Outbound (AI can’t do this)

• 7 Ways to Generate Pipeline (in Q4 of 2024)

• Inside Eric Nowoslawski’s AI-Powered Cold Outbound Machine

🧰 GTM Tech

• Clay Just Drained Its Own Moat (On Purpose)

• GTM Tech’s Race for Power *and* Ease of Use

• The “System of Intelligence”

• The $100B+ Winner in GTM Tech Will Sell Labor, Not Software

• Should You Buy an AI SDR?

• ProspectingStack.com

📊 Research Reports

• GTM Tech Stacks of the Fastest-Growing Private B2B Companies

• 54% of the Fastest Growing B2B SaaS Companies have a GTM Engineer

Two small asks

1/ Forward this to one or two people who geek out on AI × GTM the way you do. A founder. A RevOps lead. A GTM Engineer on your team. Send them this email, or this link: Best of The Signal. It’s free and ungated.

2/ If you’ve gotten value from The Signal, go check out one of the partners who make it possible for me to spend 100% of my time on this.

Thank you to Clay, our presenting partner.

Bruno, Varun, and the team took a big bet on me and The Signal early on. And I’m very grateful. (The best part is, I was already singing Clay’s praises for over a year while I was consulting, without being paid a dollar, so it has been a no-brainer to partner with them).

And to our supporting partners:

• Attention → AI Agents for RevOps.

• Clarify → The Autonomous CRM. Sell more, admin less.

• Clearskies → The Context Layer for Revenue AI.

• Nooks → All your outbound. One AI workspace.

• Rox → Revenue Agents for the Global 2000.

• Sumble → Buying signals from tech stacks, people data, & active projects.

• Terret → The answer-to-action engine for revenue teams.

The Signal literally wouldn’t be where it is today without the support of these companies.

So, do me a favor and request a demo (or sign up for their free product) today if you’ve found value in the content that my caffeine-infused fingers frantically type each week. And let them know The Signal sent you their way; that will help me continue creating valuable content.

What’s next?

I don’t know where this journey will lead me. But I’m loving it for now, and plan to continue down this road until I can see what’s around the next curve. With the same goal as the day I first published in Nov of 2023: to separate the signal from the noise as the new GTM playbook emerges.

I’m grateful you’re here in this little weird little corner of the internet with me.

Thank you for your trust and attention—I truly do not take it for granted.

See you next week,
Brendan 🫡

PS: Photo credit to my wife, Britt.

Subscribe now

Thanks for reading this hand-poured, 100% organic, farm-to-table (human-written) newsletter.

Presented by:

Clay is the infrastructure GTM engineers build on. Start building for free.

& Supported by:

Attention → AI Agents for RevOps.

Clarify → The Autonomous CRM. Sell more, admin less.

Clearskies → The Context Layer for Revenue AI.

Nooks → All your outbound. One AI workspace.

Rox → Revenue Agents for the Global 2000.

Sumble → ML-powered data and account intelligence for Ai-native GTM.

Terret → The answer-to-action engine for revenue teams.

Hey y’all! 👋

Earlier this month, Clay shipped a public API and CLI. Something every GTM Engineer building at the cutting edge has been begging for for the last 6+ months. And the one thing almost everyone (myself included!) said they’d never be able to ship.

The consensus logic was simple: Clay’s stickiness lived in its UI. Open it up, and the moat drains. Adam Robinson polled “all of the smartest Clay pundits in the world” and they told him Clay would absolutely never do it (by the way, I told him the same thing when we talked on the phone last month).

They did it anyway (I was wrong), which makes Clay’s decision to go this direction all the more noteworthy. Today, I’m sharing my personal opinion of what it means for our space.

What Clay shipped

On July 8, Clay released an API, a CLI, and an Agent Plugin that bundles both (plus MCP and skills) into a one-line install for Claude Code, Codex, and Cursor. From Kareem’s announcement:

I found two things notable:

1. It’s available on every plan, including legacy self-serve. And work triggered through the API or CLI burns the same credits as work done in the product. No API tax.

2. The comments read like a pressure valve releasing. GTM engineers, growth leads, RevOps operators, and even CROs, who started building in Claude Code/Codex/Cursor 6+ months ago and have been switching to tools that work natively inside these coding agents.

   

Why everyone said they’d never do it

Back in April, I wrote out why I didn’t think Clay could (or would) release a public API without crushing their business (and even texted it to Adam). Clay’s moat had three layers.

Layer one: negotiated access+pricing to 150+ data providers (for more, see: aggregation theory). This allowed builders to work across multiple data providers in a single workflow/Clay table. And also enabled the—now ubiquitous—‘waterfall’ feature (which everyone is now copying).

Layer two: workflow lock-in. Your enrichment logic, your templates, your prompts, your team’s muscle memory, all living inside Clay’s environment. This second layer is the expensive to rebuild, which is the real switching cost.

Layer three: Their go-to-market motion. I think the most underdiscussed/underrated. They accidentally stumbled into it. Selling to agencies (“Claygencies”) who build content on their behalf, creating a magical, organic flywheel for Clay. I unpacked this topic back in January of 2025: Clay’s Moat (not a sponsored post).

A public API threatens both #1 and #2. Once an endpoint exists, someone builds an abstraction layer on top of it. Your workflow logic moves out of Clay’s system and into your own codebase, where it’s portable. Clay becomes the backend instead of the product.

But there was always a counterpoint: Clay charges per credit, not per seat, so the unit economics shouldn’t care about the delivery mechanism. However, an API doesn’t kill revenue directly. It kills stickiness, and lost stickiness kills revenue over time.

Adam Robinson heard the same thing that I outlined above:

All of the smartest Clay pundits in the world told me they’d absolutely never ever do it. What does THAT tell you about the state of the world?

My Take: You’re an idiot if you’re building UI for people.

…Leave it to Adam to make my point more crudely bluntly. :) (Hello if you’re reading this, Adam 👋)

Why they did it anyway

Demand. Again, Adam put it well:

This way of doing things is in such high demand that a $5B darling unicorn is willing to satisfy it, even though it’s very not good for the stickiness of their business.

Two things I’d add.

First, the users changed. The most technical (and often highest-spending) slice of Clay’s market now builds in Claude Code, Codex, and Cursor. I’ve seen this shift happen firsthand (myself included). GTM builders don’t want a UI. They/we want an endpoint. You can see examples of GTM teams building in Claude Code during an event back in April (which feels like a decade ago).

Second, Clay had every excuse to sit still. $100M+ ARR. 14,000 customers. A $5B valuation. Enterprise NRR above 200%. Clay shipped the feature its own moat argued against, in the fastest-shifting stretch software has ever been through. Most companies at that scale defend. Clay went on the offense. Adapting to the (quickly) shifting environment. Impressive to see.

This isn’t the UI dying, either. As my friend (and the person who first showed me Clay), Andreas Wernicke, said in the comments:

This and UI have to coexist.

It just stops being the only door in. Here’s an excerpt from a piece I published recently about building agents inside your CRM:

The pushback (and what it tells you)

This is just the start for Clay.

Matthew Quan (Product at Clay) responded to a common question in the announcement post:

Clay isn’t porting its UI to an API, feature by feature. They’re rebuilding the primitives around what agents do well: workflows as code, no row caps, and an agent that converts the old format into the new one.

For builders of GTM tech

An API, a CLI, and MCP support are no longer “nice-to-haves” in 2027. They’re “need-to-haves.” Your most technical users (RevOps, growth, GTM engineers, and increasingly even the SDR and sales leaders hacking in these tools) already build out of the terminal, and they won’t use products that can’t meet them there.

I understand that this isn’t just a simple feature. It’s a product decision, a pricing and packaging decision, an architecture decision, a positioning decision, and a go-to-market decision.

Building API/CLI/MCP-first forces the whole org to re-orient around it. And I realize it’s a big and scary decision. But if even Clay is doing it—and remember, this was supposed to kill their business model—you probably should too.

For buyers of GTM tech

When a vendor ships a real API, your workflow logic moves out of their UI and into your own repo, where it’s version-controlled and under your control (see: “AI sovereignty”). That vendor is choosing to win your renewal on capability every month instead of on sunk cost. Reward that choice with your budget, and be suspicious of the UI-only, seat-walled alternative.

Second, buying and building stopped being opposites. The old question was build vs. buy. The new motion is: buy the infrastructure (data, enrichment, execution, agents) and own the intelligence. Even if you’re building in-house tooling, and more teams are than ever, you should be buying infrastructure from Clay and other agent-native companies (like Rox, Sumble, Attention, Nooks, Clearskies, etc.).

Buyers used to ask “will my team actually log into this?” Now they’re asking “can my agents call this?” API, CLI, and MCP coverage moves from an afterthought to a first-order buying criterion, next to data quality and price.

Within 12 months, I think the vendors who treat the agent as a first-class user will win the most technical, highest-spending slice of the market—operators building in the terminal. And if you’re an operator in GTM, become someone comfortable in the terminal.

It’s unclear how it’ll unfold exactly (which makes this moment so exciting!). So, please reply to this email with what you think happens from here! I’d love to continue to evolve my thinking here. Regardless, it’s a blast to watch as the space shifts so fast, and we all figure out the new playbook together.

That’s it for today!

As always, thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

Related posts:

• Inside “ChatGTM”: Cursor’s Internal Sales AI Used by their 400+ Sales Org (SDRs are booking 3x qualified meetings and AEs are shaving ramp by 50%+. What Cursor’s Head of Enterprise Growth, George Hou, built. And whether you should build your own version.)

• How to Think About Build vs. Buy in the AI Era (“Build Your Intelligence. Buy Your Infrastructure.” With Kyle Norton.)

• 9 Lessons From 11 Growth-Stage Companies That Built GTM Agents In-House (Vercel, Ramp, LangChain, ClickUp, Deel, Vanta & others: what they built, their tech stacks, and what we can learn from them)

• 54% of the Fastest-Growing B2B SaaS Companies have a GTM Engineer (Research Report by The Signal)

Subscribe now

Thanks for reading this hand-rolled, 100% organic, farm-to-table (human-written) newsletter.

Presented by:

Clay is the infrastructure GTM engineers build on. Start building for free.

& Supported by:

Attention → AI Agents for RevOps.

Clarify → The Autonomous CRM. Sell more, admin less.

Clearskies → The Context Layer for Revenue AI.

Nooks → All your outbound. One AI workspace.

Rox → Revenue Agents for the Global 2000.

Sumble → Buying signals from tech stacks, people data, & active projects.

Terret → The answer-to-action engine for revenue teams.

Hey y’all! 👋

If you’ve spent any time on LinkedIn this year, you’ve seen a post claiming “your sales team needs to put all your sales context in a GitHub repo!” Routing logic, field definitions, playbooks, all versioned, all readable by agents. But, often, the person making this claim is a one-man agency owner. So, I’ve been on the hunt to find scale companies running this playbook. Today, we explore one such company.

I first met Tejas, CEO of Hightouch, when we both lived in the Mission district in San Francisco; I was working at Zoom Video, and he had a new little startup and was trying to sell me (I tried to buy, but couldn’t get through procurement with my dinky title). It’s been a blast to see them absolutely ripping over the last 6 years. Hightouch crossed $100M ARR in April (they’ve grown more than 100% in each of the past two years) and raised a $150M Series D at a $2.75B valuation in March.

I recently sat down with a legend, Nikko Georgantonis, the Head of GTM AI & Systems at Hightouch. What they’ve built is one of the more sophisticated GTM systems I’ve seen. And this is not an agency guy who did a Claude Code project over a weekend. This is what AI adoption looks like inside a revenue org that’s scaling fast enough to have real stakes.

Here’s what we cover in today’s post:

• The two-pillar setup

• Pillar 1: Skills for every rep

• Pillar 2: The four-layer agent repo

• Why a repo instead of a tool

• What’s working for them right now

• What they tried that got pulled back

• RevOps becomes the agent wrangler

Alright, let’s get into it.

Nikko describes his job in two parts.

Part one is adoption. He’s going to QBRs and teaching the revenue org what skills are and why they matter. The deliverable is a library of packaged skills that reps actually use.

Part two is automation. His team has historically owned the system admin work of RevOps (data cleaning, lead list uploads, field hygiene, answering “can you update this account” requests in Slack). He’s handing that work to agents.

What caught my attention is how differently he architected each pillar. The rep-facing stuff is deliberately simple. The RevOps stuff is where the sophistication lives.

The two-pillar setup

↳ Pillar 1: Skills for every rep

The skills pillar isn’t overly complex. The highest-usage skills are things like account research. What makes them work is how prescriptive they are. Every skill follows the same formula: Here’s where to look, what to do, what good looks like, what you shouldn’t do.

A few things stood out to me about how he runs this:

Skills are overlaid onto the customer engagement process. He’s not building random one-off prompts. Each skill maps to a stage of how Hightouch engages customers, and it covers the whole revenue org: AEs, SEs, post-sales.

Reps choose their tool. Skills run in Claude or Glean, whichever the rep prefers. Nikko manages both. Either way, the skill taps into Hightouch’s internal brain through Glean’s MCP, which gives it access to Gong calls and internal docs.

Forking is the point. He ships the prescriptive version, and reps fork it to fit their own workflow.

The BI unlock for leaders. Skills can call Omni (their BI tool) to pull live metrics. That opens up manager and CRO use cases: scheduled skills that send weekly pacing reports without anyone asking for them.

↳ Pillar 2: The four-layer agent repo

This is the part Nikko was most stoked about.

All of the logic for his RevOps agents lives in a GitHub repo. Execution happens in n8n. The repo has four layers:

1. Context. The documentation that used to live in Notion, made machine-readable.

Their customer engagement process. Lead routing logic. How territories get cut. Forecasting definitions. Every opportunity and account field, audited and defined. Any agent can reference any of it.

2. Coordination. The routing layer.

Example: Hightouch has an internal RevOps Slack channel where people ask for account updates, flag duplicate leads, request list uploads, etc. The number of possible request types is too large for one prompt. So the first agent is a router. It classifies the request and hands it to the right specialist agent, passing notes along with it. Longer-term, this layer is how lead routing and scoring move from rule-based to probabilistic.

3. Memory. Agent history.

The example that made this click for me: Nikko has an agent that sends sales managers a report on each AE’s milestones, how they’re pacing, and a recommendation for what to fix. When it runs again two weeks later, it reads the recommendation it gave last time and factors that in. Some memory belongs in the warehouse. Recommendation history belongs in the repo.

4. Prompts. The actual prompts.

Most people would let these live inside n8n. Nikko keeps a skeleton in n8n that calls the prompt files in the repo instead.

One important note: reps never touch the repo. They see the outputs (a Slack reply, a field in the CRM, a report, a human-review step in the sequencer/dialer). Skills are how reps build. The repo is how RevOps builds.

Why a repo instead of a tool

Notably, Nikko told me he can’t make tooling bets right now.

Is n8n the right workflow tool today? Yes. Will it be in six months? No idea. Eight months ago, his “skills” were Glean agents. Then the technology moved. He’s watched his own stack rotate underneath him fast enough that betting the logic layer on any single vendor feels reckless.

So the investment goes into the layer that survives tool churn. Context, coordination logic, memory, and prompts, written crisply enough that they can be connected to whatever executes next. When something better than n8n shows up, the migration is re-pointing an execution layer, not rebuilding a brain. This aligns with the thinking Kyle Norton shared on “owning your intelligence” (ie: you should not buy your gtm brain)—you want control, portability, and compounding advantage.

Because everything is documented in one repo, he can point an LLM at it and ask where the redundancy is, where logic is duplicated, and how he should restructure. The system becomes legible to the tools that maintain it. To me, this is the obvious/inevitable future. And why, even companies like Clay, are going API/CLI-first.

And the endgame, in Nikko’s words, is that his job just becomes Claude Code. n8n has an MCP now, so he can do the development in Claude Code and have the documentation write itself back to GitHub. Salesforce admin changes that document themselves. Agents checking the outputs of other agents. His team’s old admin backlog, from data cleaning to lead list uploads, handed off. He says they’re system is starting to compound.

(One aside for anyone who read our ChatGTM piece: Cursor skipped the repo entirely and fetches everything live. Different problem. ChatGTM serves reps operational data that goes stale in hours; Nikko’s repo holds business rules that should outlive any tool. Live data gets fetched. Business logic gets committed.)

What’s working for them right now

Territory planning on a digital maturity score. This one predates the agent repo and Nikko was almost dismissive of it (”it’s been around for a bit”), but it might be the most valuable thing he showed me.

He built a Clay workflow that enriches every account with a digital maturity score: does this company run a data warehouse, does it do omnichannel marketing, does it operate a loyalty program, plus other boolean attributes. “Financial services” is a broad industry, and the “digital maturity score” is the next layer down. This bespoke scoring system separates the Chimes of the world from a credit union down the street. And it’s how Hightouch cuts territories today.

The manager pacing reports. Covered above, but it’s a pattern you can steal ~pretty easily: scheduled skill, pulls defined metrics from BI, gives each manager a per-AE diagnosis and recommendation, remembers what it said last time.

Betting on AI-native vendors. One example: Hightouch is replacing a legacy sales engagement platform with Nooks’ sequencer. A Series D company swapping a legacy sales engagement platform for a smaller, AI-native vendor is atypical, but the reasoning maps to everything above: he wants tools with real MCP workflows built in, not APIs with MCP slapped in front of them. As Nikko told me, “It’s easy to tell the difference.”

What they tried that got pulled back

I asked Nikko what hasn’t worked (I find that knowing which potholes to avoid is sometimes more helpful than sharing an exact roadmap of what to build).

1. Black-box reports. Hightouch built agents for proactive analytics: check the dashboards, diagnose what changed, recommend what to do. That works. But, where it broke was mixing in net-new metrics that nobody had defined before. Users didn’t trust them. People are pessimistic toward AI output by default, and an undefined metric inside an AI-generated report reads as a black box. The lesson he took is that agents can diagnose metrics your team already trusts. They shouldn’t invent new ones.

2. The asset-building skill. The team at Hightouch shipped a skill that helps sellers build customer-facing assets, but he’s rolling it back (for now, at least). The failure mode is subtle. The skill does 40-50% of the work. Experienced sellers know exactly what bad looks like, so they finish the remaining half with no problem. But new hires can’t identify which half is theirs to do. So for the people who needed help most, the skill amplified slop instead of raising the floor.

3. Fully automated outbound. For enterprise sales, Nikko isn’t bullish on “automated outbound”, and Hightouch didn’t find success with it. Where automation does work for them is inbound: handling demo-request follow-ups. Nikko clarified this one:

100% automated outbound we are bearish on, but agent-assisted we are bullish (and why we bought Nooks).

RevOps becomes the agent wrangler

Nikko wrote in a recent post that the RevOps job is heading toward agent wrangling, and after seeing his setup, the phrase fits. Entire post below, I think it’s helpful to read:

Look at what’s actually left for his team once this system matures. The admin work is delegated to agents. The tribal knowledge is codified in the context layer. The reactive Slack-channel work gets triaged by a router. What remains is designing the system: deciding what context agents get, how they hand off, what they remember, and checking their work.

That’s a real answer to the question I keep chasing: who should own AI inside the GTM org?. At Cursor, it was a growth leader who built a product. At the 11 growth-stage companies I profiled, it was usually a GTM engineer, RevOps, or a technical founder. At Hightouch, it’s RevOps with engineering habits: version control, documentation, composability.

The tools will keep rotating. Nikko is planning for that instead of fighting it. Six months from now, the executor might not be n8n, and the retrieval layer might not be Glean, and for Hightouch, it won’t matter much, because none of the logic lives there.

RevOps is evolving. And I suspect the early-adopter hacker/tinkerer-type builders, like Nikko at Hightouch, will be the people running systems for AI-native GTM teams in the coming years. I’m excited to continue exploring the new playbook being built.

» » »

Thanks to Nikko for sharing the awesome stuff he’s building over at Hightouch (a terrific product/team).

Connect with Nikko Georgantonis on LinkedIn if you want to learn more from him (and he’s currently hiring a GTM Engineer!).

That’s it for today!

Thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

Other posts:

• The head of enterprise growth at Cursor showed me the internal Sales AI he built (they call it “ChatGTM”) that’s helping their SDRs book 3x qualified meetings + decrease AE ramp by 50%+. It’s another example of an awesome in-house build.

• Best of The Signal (my favorite articles) → broken out into categories across AI GTM, GTM Engineering, Prospecting, GTM Tech, & Research Reports)

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The go-to-market industry has spent decades getting very good at one half of the job. We can tell you why you lost the deal. We can tell you which rep is sandbagging, which region is slipping, and which competitor keeps showing up in your closed-lost. What almost none of it does is change what happens on the next call. Exposing a problem is much easier than solving it.

Justin Shriber, Terret’s CEO, has been thinking about this problem space his whole career. He started at McKinsey, where he watched clients drop suitcases full of cash for root-cause analysis, only to watch the deck get put on a shelf to collect dust. Later, he ran large sales orgs at Oracle and LinkedIn and saw the same thing from the other side. Finding the answer is the easy part. Getting a rep in a different time zone, who only talks to HQ 2x/year, to change behavior on a live deal is the hard part.

What Terret is building aims to close this gap—between the right answer and someone acting on it.

And that’s why I’m excited to explore this topic (which is even more relevant in the AI era) in today’s sponsored deep dive on Terret.

Here’s what we cover in today’s post:

• Background: building “Cursor for sales”

• F1 analogy: the analyst, the pit crew, and the driver

• The closed-loop system (and why MEDDIC is dead)

• Build vs. buy: why governance, not effort, is the real argument

Alright, let’s get into it.

Building “Cursor for Sales”

Terret didn’t start here. The company began doing forecasting and conversation intelligence, the same category as Clari or Gong. Somewhere in that work, Justin realized the forecast wasn’t the valuable thing they’d built. The valuable thing was the platform underneath it. Because they were born in an agentic world, they’d built an AI-native system that pulled data from every revenue-facing source and used inference to join and normalize it, without a six-month warehouse project. Today, people call that a context graph or a revenue graph. Terret had one running years before the term got popular.

Once you have this, forecasting becomes just one of many use cases. The same loop runs on whatever question you point it at: why the forecast slipped, why pipeline is stalling in a segment, why one rep’s deals progress twice as fast as everyone else’s, why a product line isn’t landing. Terret is more generalizable than any single point solution. In a recent conversation, Justin told me:

I wanted to build “Cursor for sales.” Cursor doesn’t have one narrow kind of code that you write. And that’s what I was trying to create. Get to the right answer, but then implement it to the point where you actually have a working solution that is delivering a result or an outcome.

Cursor doesn’t lock you into one narrow kind of code. It sits a layer above. Similarly, Terret wants to sit a layer above forecasting, coaching, or competitive intel, to take revenue teams from answer to action.

Terret can stand up a demo on a customer’s own data in 48 hours using just CRM and conversation intelligence, run a POC with real results in two weeks, and fully deploy in a month. Their goal is to show a CRO something about their business that they didn’t already know within those first 48 hours.

F1 analogy: the analyst, the pit crew, and the driver

Justin shared a metaphor to explain how they differentiate in the market.

1. The McKinsey Analyst (”Ask”)

Imagine dropping a McKinsey consultant into your revenue org who has read every email, every document, every CRM update, and listened to every call, with perfect recall. That’s the revenue graph paired with a trained LLM. Ask it why win rates in EMEA dropped last quarter and it can actually answer, because it can pull the lost deals from CRM, the call transcripts from your CI tool, the email threads stuck in individual inboxes, and the product usage from wherever it lives, then normalize all of it. CI tools can’t do that alone. CRM can’t. And building your data warehouse to do it will take half a year and still stumble over unstructured data.

The hard part isn’t the question. It’s that sales has a deterministic half and a non-deterministic half. Art and science. The numbers are deterministic. The why behind them is not. LLMs are great at the why and bad at the numbers. Ask one how much you closed in North America last quarter and it’ll give you a different figure each time, then fold the second you push back. (We’ve all questioned our LLMs and heard them say: “You’re absolutely right! Sorry about that, it’s 12% MoM.”).

Terret’s forecasting legacy is what let them solve this. Forecasting is a quantitative problem, so they already had a deterministic approach to generating numbers. They give the model prescriptive instructions, then run a series of checks to confirm it computed what it was told to. The result is a system that gets the number right every time and explains the why behind it.

2. The F1 Pit Crew (”Operationalize”)

The analyst tells you what to do. The pit crew makes it real for everyone else. This is where playbooks, rep scripting, and the rest of the artifacts get built and pushed out at scale, with no human assembling slides. An answer (even a great one) is worthless if the “insight” just collects dust in an Enablement Playbook in Notion. The pit crew turns it into a motion the whole team can run.

3. The Driver (”Activation”)

The first two get you a great answer and a living playbook. Activation is making sure the artifacts actually get used, which is where most of this falls apart. Hand a rep a script and you have no idea whether they followed it. Terret watches rep behavior against the script, grades it, and feeds specific, evidenced coaching to both the rep and the manager so everyone moves the same direction.

This is the end of enablement as we’ve known it, and the start of what Justin calls “micro-enablement” moments. I ran ops and enablement for a 100+ BDR org at Zoom Video a few years ago, and the model was brutal even then. You’d launch a new product into a new market, run a training, and maybe half the team absorbed it. And the information would go stale a month later, buried in a portal nobody opened again. Micro-enablement flips this on its head. Instead of one stuffy session at SKO with hungover reps in an 8 a.m. ballroom, you get a script before the call, a 30-second grade after it, and a coaching point from your manager that same week. A rep gets more reinforcement on one playbook in a week than the old model delivered in a year, and every touch builds on the last.

Other things you can build when you have an analyst, pit crew, and driver in one continuous loop:

• Run a win analysis on EMEA over the last four quarters, and 3 minutes later you’ll have a root-cause analysis and competitor battle cards of how to overcome them.

• A living playbook built from your best reps actual language that your top closers use, not language a product marketer invented.

• Before a call, the agent scripts an opener pulled from top reps, anticipates the objections she’s likely to hit, and adapts to where the deal is and who’s in the room.

• After the call she gets a scorecard (she scored an 8 out of 10) with evidence of what she did well and what to fix.

• At the end of the week her manager gets a coaching sheet across every deal.

[A side note worth mentioning: Justin thinks the CRO role is about to change shape completely, and he frames it as a shift from Leonard Bernstein to Miles Davis. The old great CRO was Bernstein. A score, a rehearsed orchestra, everyone hitting their notes, and the team that played the written music best won. The market moves too fast for a score now. Miles Davis ran a loose framework and improvised as the music moved under him. “If you were Lenny Bernstein, one of the greats, your career is done,” Justin told me. “You can’t compete in sales like that anymore.” Harsh, but I think this is directionally correct.]

The closed-loop system (and why MEDDIC is dead)

Years ago, when I was running a company, one of my taglines was: “Actions > Insights”. The last decade of GTM was the decade of smarter/better/more “insights.” Endless Looker and Tableau dashboards, scorecards galore, and endless Salesforce reports. All of it sitting there waiting for a human to notice and act. My company wanted to automate the action instead of it sitting in a Tableau dashboard. When I said this to Justin (Actions > Insights), he (graciously) corrected me: “Insights plus actions is the way to build a revenue system today.” He’s right. Insights alone are shelfware. Actions without the insight behind them are guesses. You want both, wired together.

That wiring creates a closed loop. Terret’s agent sits on the calls. When a new objection comes up that isn’t in the playbook, it captures how other reps handled it, and by the end of the day, that response is in the bank. The system knows which questions got asked in deals that closed and which got asked in deals that died, and it feeds that back into the playbook automatically. The playbook isn’t a document someone updates quarterly. It’s a living, breathing thing that learns from what actually works.

Justin believes MEDDIC is dead too. It’s the qualification framework that came out of PTC in the mid-1990s, the one John McMahon is known for, built in part because PTC didn’t have a real product yet and needed rigor to compensate. It’s a great framework. It’s also 30 years old. Why run a static rubric from 1995 when the system can build you a qualification model from what your own closers actually do? Terret can fully automate MEDDIC if you want it, score against it, and suggest the next move. The better version is your own MEDDIC, derived from your top performers, not from a consultant’s template or an enablement manager’s best guess.

Build vs. buy: why governance, not effort, is the real argument

The obvious question from a CRO or a founder is, why not just build this myself with Claude and a few MCP connections? Justin shared what he’s seeing and why building in-house is not the right choice for most companies.

The first problem is governance. Your revenue data lives in systems with completely different access models. Email is user-based. CRM is hierarchical. If you MCP a model directly into each of them, you’re pulling data across those boundaries and trusting the LLM to decide what any given person is allowed to see. That’s a bad bet to make with customer data and competitive data. Terret builds the revenue graph with permissioning baked in, resolved against your CRM hierarchy, so the governance holds.

The second problem is cost. Ask a model to stitch together data from five systems on the fly and you’ve asked it to rebuild a small data warehouse on every single query. That’s how you burn tokens and end up with an unpredictable bill. Terret pre-packages the exact slice of data a given question needs, so the model gets a micro-payload instead of everything. That’s also why Terret can charge a fixed price instead of consumption-based pricing, which is the line that makes the CFO lean in.

The third is concentration risk. Build your stack on one model provider and you’re betting that provider wins. Terret runs a cocktail of models on the back end, swaps in whatever’s best as it ships, and manages that risk for you. You’re not married to one vendor’s roadmap.

None of this means building is always wrong. Stripe builds. Cursor builds. Ramp builds. But there are maybe twenty companies on earth with the engineering depth to build a true in-house solution for their revenue team (and keep maintaining it). For everyone else, building a context graph, a governance layer, and a multi-model router from scratch is a bad use of the next nine months. For reference, doing the same thing on something like Agentforce is a 6-9 month project with dedicated engineers on permanent integration duty. Terret needs your CRM, your email and calendar, and one RevOps person to keep an eye on it.

(Read more here: How to Think About Build vs. Buy in the AI Era | “Build Your Intelligence. Buy Your Infrastructure.” With Owner.com’s CRO, Kyle Norton. And Terret is a great example of infrastructure you should buy to build on top of.)

The takeaway

Terret’s main goal is to be more powerful, cheaper, and faster. Powerful because the analyst, the pit crew, and the driver run as one loop instead of three disconnected tools. Cheaper because of fixed pricing and one vendor instead of <several others> + a warehouse + an integration team. Faster because a playbook that used to take a product marketer six months now takes three minutes (and updates itself).

The answer was never the hardest part in sales. What was: getting the answer into a rep’s hands at the moment they need it, knowing whether they used it, and if it worked. Terret is the first thing I’ve looked at that treats that as the actual product instead of an afterthought. I’m excited to continue to watch what Justin and the team at Terret build in the space.

PS: Below is a 4-minute walkthrough of Terret that I recorded:

If you want to learn more about Terret, reach out to their team (and be sure to let them know The Signal sent you).

That’s it for today.

Thank you for your continued attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

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Terret → The answer-to-action engine for revenue teams.

Hey y’all! 👋

Wild week in gtm-tech land. HubSpot acquired Warmly and Zoom acquired Common Room. This comes just after the acquisition of Pocus by Apollo.io. I suspect consolidation continues to happen in the space.

All the while, more companies are deciding to build certain parts internally. Cursor’s Head of Growth, George Hou, gave us a look behind the curtain of what he built at Cursor (one of the wildest in-house builds I’ve seen).

This week, we’re graced with the presence of another goat of gtm, Kyle Norton. Kyle is the CRO at Owner.com, runs The Revenue Leadership Podcast/Newsletter, and (according to my calculations) is the most AI-pilled CRO in all of SaaS. He recently published a piece titled “Don’t buy your GTM brain” (worth reading in full, if you haven’t already).

The “Build vs. Buy” question is on every GTM leader's mind right now.

So, after reading Kyle’s piece on it, I caught up with him to go a layer deeper on this topic (and how it works at Owner.com). I also share my own experience from the last two years of co-building GTM engines/AI workflows alongside internal teams.

In this piece, we cover:

• How AI changed the calculus in favor of Build>Buy

• The pitfalls of building (it’s not all sunshine and rainbows)

• The risk of outsourcing (buying) your intelligence layer

• Why you can’t outsource your AI strategy

Alright, let’s get into it.

How AI changed the calculus in favor of Build>Buy

For most of the last decade, build vs buy wasn’t much of a debate. Building was slow, expensive, and, unless you had spare engineers and a very specific need, you bought the tool and moved on.

AI changed the calculus. Since the model jump in late 2025 (the 4.6 moment), building went from “expensive, slow, probably not worth it” to a real option. You can spin up a working prototype in an afternoon. Things you would have paid a vendor six figures for, you can now build yourself and customize to your precise needs.

I’ve personally seen this up close. For 18 months, I embedded inside teams to build+ship AI-powered GTM systems (things like: data orchestration, signal-based selling, scoring, routing, ICP mapping, automated outbound, and more). The results of these systems were often magical. I saw the possibility of AI to create real leverage.

The ROI ceiling (with AI) is higher than it used to be. Old sales automation got you a 10-25% bump. The right AI build can double+ a team’s output.

Here’s what Kyle said:

The old build vs buy conversation usually centered on cost, implementation time, customization, and whether engineering had the capacity. Those still matter, but they are incomplete for AI.

The better AI build vs buy filter has 2 categories.

First, infrastructure. These are the parts of the stack where reliability matters more than differentiation. Dialers, core CRM plumbing, enrichment pipes, call recording, data warehousing, identity, permissions, security, and the underlying models in most cases. Buy these unless you have an unusually strong reason not to.

Second, intelligence. These are the parts of the stack where your company’s unique judgment matters. ICP definitions, lead scoring logic, account prioritization, territory design, pre-call research, deal strategy, customer context, coaching philosophy, messaging, qualification standards, and the evaluation loops that determine whether AI output is actually good.

That second category is where I get much more cautious.

At Owner.com, the point is not to build AI for the sake of building AI. That’s AI performance theater. The point is to build systems that make our GTM motion better because they encode what we know about our customer, our market, our reps, and our operating model.

That is why the line I used at SaaStr matters: buy your infrastructure, build or own your intelligence.

“Own” is important because it does not always mean build every component from scratch. You can use vendors and still own the intelligence if you control the data, understand the logic, can export the memory, can evaluate the outputs, and can move the learning if you need to. You can also build something internally and still fail to own the intelligence if it is a fragile one-off workflow nobody understands.

Ownership is not about ego. It is about control, portability, and compounding advantage.

We’ll come back to this point.

The pitfalls of building (it’s not all sunshine and rainbows)

Building is easier now. But also messier. Here are some pitfalls to avoid:

Swimlanes.

Inside a hyper-growth company, it’s never one team executing a tidy little roadmap. At Owner.com, Kyle has three different groups shipping production AI: an applied AI function under BizOps and data, the product org, and RevOps. Kyle shared an example where one team built a tool, but it was incinerating API calls into the CRM, wasn’t compliant with another piece of their stack regulations, and broke part of the rep workflow somewhere else. They understood the adjacent motion well enough to build for it, and not well enough to avoid breaking it. Kyle’s response wasn’t “stop.” It was “build it with RevOps, dedicate me engineers who work only on GTM tech, and do the requirements and roadmap properly.” You can’t have one person YOLO a tool that breaks other things downstream.

You need an actual engineer.

Not a “GTM engineer” in the LinkedIn sense, which has come to mean a Clay-table operator building lists and a few automations. Kyle calls his function applied AI on purpose, to draw that exact line. Once something has to run in production every day, you need real software engineers. Kyle’s discipline is to push everything into GitHub so a human can inspect what exists, and to not run tools whose outputs you can’t see.

Budget for maintenance.

When I was consulting, we would build the engine and then “pass the keys” to the team. But we would have problems after we left if there wasn’t a person (or team) in-house to maintain (and build on) the engine we deployed. Because the systems were complex (and in some cases brittle), a CRO of a Series A company is not going to open the workflow builder to rewrite ICP logic when the market shifts, or debug a chain that broke because something upstream changed. That isn’t a knock on them. It’s not their job, and they shouldn’t have to reverse-engineer a system they didn’t build. But you have to budget for this reality.

Things decay, and some should be killed.

Kyle revisits internal builds around the 90-day mark and asks whether each one is still worth it. They’ve deprecated things. They’ve even killed things and then brought them back. One example: their lead-routing system worked well, then the person who built it got pulled onto other priorities, it degraded, reps drifted back to assigning leads by hand, and they had to rebuild it to win them back.

You need evals.

Evals are how you know the thing actually works. They are often the difference between a V1 that demos well and a production tool reps use every day. Most of the work in closing that gap is boring. When Owner.com built AI pre-call research, the applied AI lead flew to Toronto and sat beside the reps for a week. They would hand a rep a batch of leads, watch them work it, hear “this is weird” or “this is wrong, here’s why,” go fix it, hand them a fresh batch, run it again. The feedback in those sessions becomes the product. Builders focused on these feedback loops are indistinguishable from an early-stage founder building alongside their first customers.

Most teams treat the launch as the finish line. In reality, it’s the starting line. The real cost of “build” isn’t the build; it’s the standing capacity to keep the thing alive after it ships. If you can’t fund that capacity, you should buy.

The risk of outsourcing (buying) your intelligence layer

Buy your infrastructure.

The dialer, the CRM plumbing, enrichment pipes, call recording, the underlying models. The places where uptime, reliability, compliance, and scale matter more than uniqueness. There’s no prize for vibe-coding a mediocre dialer that goes down during a call blitz on a Tuesday morning on the last week of the quarter.

Build your intelligence (or at least own it).

ICP definitions, scoring logic, account prioritization, pre-call research, deal strategy, coaching philosophy, messaging, and the eval loops that decide whether the AI’s output is any good. The places where your company’s judgment is the actual product.

The reason this matters more with AI than it did with old software is that AI systems learn from the workflow and compound. The risk is rented learning. Here’s how Kyle explained it:

The risk is not that AI vendors are bad. Many of them are excellent. The risk is that GTM leaders treat every AI decision like normal SaaS procurement. The normal SaaS procurement process asks: “does this tool solve the problem?”

AI-native procurement also asks: what does this tool learn, where does that learning live, and who controls it?

The worst version is a black box that owns both the workflow and the memory. You get productivity, but the learning compounds somewhere else. The vendor gets smarter across your usage. Your company gets dependent.

Again, that might still be worth it in some cases. But it should be a conscious trade, not an accidental one.

“Own” does not mean “build from scratch.” You can use vendors and still own the intelligence if you control the data, understand the logic, can export the memory, can evaluate the outputs, and can move the learning when you need to. The inverse is equally dangerous. You can build something fully in-house and still fail to own it, if what you built is a fragile one-off that nobody left in the building understands.

Why you can’t outsource your AI strategy

The intelligence layer has to stay yours, and the capability to build and maintain it has to live inside your office walls (digitally). You can’t buy your brain from a vendor, and you can’t outsource your AI strategy either.

Which is why the most interesting thing in my conversation with Kyle wasn’t the framework. It was him. He’s a CRO who gets his hands dirty and thinks like a product leader, and that combination is what future GTM leaders will look like, more and more.

I asked him whether a CRO should know how to build a product roadmap. Two years ago, he would have said, no. Today, he said, “Yes, or at least someone senior on the revenue side has to.” His path into systems thinking came from Shopify, where if you couldn’t speak Product, you were treated as lesser than. He learned the discipline to be taken seriously there, and then realized the frameworks themselves were the unlock as a CRO (customer discovery with reps to build requirements, user stories, something close to Agile inside RevOps, etc.).

Kyle’s results of owning the GTM brain in-house speak for themselves. At Owner.com, an AE at ~$150k OTE brings in roughly $3M ARR. A 20x comp-to-close ratio compared to their competitors running door-to-door motions.

Looking forward

Kyle shared where he lands on the quickly evolving debate of Build vs. Buy:

I am bullish on AI vendors. I am also bullish on internal GTM engineering. The answer is not one or the other. The winning companies will be pragmatic. They will buy a lot of infrastructure. They will use best-in-class vendors where vendors are clearly better. They will move fast.

But they will be extremely deliberate about the intelligence layer. They will know what context matters, what memory should persist, what workflows create proprietary learning, and what parts of the system need to remain portable.

That is how AI maturity compounds. One good workflow becomes reusable context for the next workflow. One evaluation loop improves the next model output. One proprietary memory layer makes every future agent better.

The companies that win AI-native GTM will not have the most tools. They will not be the companies with the flashiest demos.

They will be the companies whose systems get smarter every week because they own the memory that matters.

Don’t build everything but don’t buy your brain.

Buy your infrastructure, build your intelligence. But don’t build a brain you can’t keep alive, either.

That’s it for today!

Thanks to Kyle Norton for the conversation. Go read his original piece, “Don’t buy your GTM brain,” over at The Revenue Leadership Podcast and subscribe to his newsletter + podcast.

And, as always, thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

Related posts:

• Inside “ChatGTM”: Cursor’s Internal Sales AI Used by their 400+ Sales Org (SDRs are booking 3x qualified meetings and AEs are shaving ramp by 50%+. What Cursor’s Head of Enterprise Growth, George Hou, built. And whether you should build your own version.)

• 54% of the Fastest Growing B2B SaaS Companies have a GTM Engineer (Research Report by The Signal)

• 9 Lessons From 11 Growth-Stage Companies That Built GTM Agents In-House (Vercel, Ramp, LangChain, ClickUp, Deel, Vanta & others: what they built, their tech stacks, and what we can learn from them)

• Most Companies Are Using GTM Engineers for “Automated Outbound.” The Best GTM Teams Aren’t Stopping There. (21 real examples)

Subscribe now

Thanks for reading this hand-rolled, 100% organic, farm-to-table (human-written) newsletter.

Presented by:

Bring AI agents, enrichment, and intent data together and turn insights into relevant, timely action. Start building for free.

& Supported by:

Attention → AI Agents for RevOps.

Clarify → The Autonomous CRM. Sell more, admin less.

Clearskies → The Context Layer for Revenue AI.

Nooks → All your outbound. One AI workspace.

Rox → Revenue Agents for the Global 2000.

Sumble → Buying signals from tech stacks, people data, & active projects.

Terret → The answer-to-action engine for revenue teams.

Hey y’all! 👋

George Hou (Head of Enterprise Growth at Cursor) and I caught up recently, and he (sort of sheepishly) mentioned an internal tool he had built for the sales team at Cursor. I (not so sheepishly) asked him to share more. Before I knew it, he was screen-sharing and saying things like “I can’t show you that part,” which, of course, made me all the more curious. :)

I’ve gotten to know George, and a few of the folks at Cursor, over the last year or so, and I see them as some of the most impressive people in go-to-market. Not just by resume, but by how they think from first principles, how they ship, and how little ego they carry. It’s a vortex of talent over there.

George is a legend, and a kind supporter of the work I’ve been doing with The Signal. So, he was down to give me (and y’all!) a peek behind the curtain of exactly what he built (thanks for sharing with us, George 🫡).

ChatGTM is gaining crazy adoption within Cursor’s 400+ person sales org, and it’s helping their SDRs book 3x more qualified meetings and decrease AE ramp time by 50%+.

Here’s what we cover in today’s article:

• What ChatGTM actually is

• Why they built it

• But why not just use Claude Cowork, ChatGPT, or Glean?

• The question on every Revenue Leader’s mind: Build vs. Buy

• Who owns AI x GTM initiatives inside Cursor

• The results across their 400-person sales org

• Three lessons from George

Alright, let’s get into it.

What ChatGTM actually is

Reps describe ChatGTM as AI that already knows their accounts and how they sell. The easiest way to understand is to watch it work, so here’s a day in the life.

• 7:30am, on the subway: their morning digest is waiting in Slack: context on today’s meetings and who’ll be in them, overnight news across their accounts, and key territory trends: usage spikes, fresh signups, new exec hires.

• 8:00am, at the desk: 25 outbound messages are already drafted, each tied to a signal and paired with a landing page personalized for the contact’s tech stack and role. The rep just reviews, tweaks, sends. The send is native to Gmail or Outreach, so there’s no copy-paste required.

• 8:45am, prepping for a call: the brief flags that the exec hates a hard pitch from an article she just published, so the rep walks in prepped on how peers approached her problems.

• 9:00am, on the call: the exec lobs a curveball about migrating off legacy infrastructure the rep has never heard of. A quick question to ChatGTM answers it live. They leave with next steps booked and the exec says she doesn’t need a POC.

• Between meetings: every call already has its recap email drafted and waiting, plus custom follow-ups to the threads that went quiet.

• 3:00pm, prepping tomorrow’s exec meeting: the rep builds the whole business case in one thread: the three why’s, the value drivers, a CFO-ready one-pager and deck. Then he maps the org chart to see who else needs to be in the room and how to reach them.

• 4:00pm: a Slack digest pulls everything worth catching up on: product releases, account updates, DMs. Where a release is relevant to an account, it drafts tailored outreach for them to review.

• Thursday, before the forecast: ChatGTM scans every deal, its notes, stage changes, and next steps, then assembles the forecast and drafts the weekly update for their manager and account team.

• Start of a new quarter, strategizing a new book: the rep kicks off ChatGTM on a research job that would’ve taken weeks: every intent signal, usage trend, past Gong call, and Salesforce record, plus each account’s key initiatives and tech stack. It comes back with a stack-ranked plan for how to spend the quarter.

^{The morning digest reps work from: pre-drafted messages, each with a landing page tailored to the contact’s stack, role, and seniority.}

The feed (UI) is the visible part. Underneath it, ChatGTM goes directly to the sources (eg: Salesforce, Gong, data warehouse, LinkedIn, third-party data vendors, and the open web) and pulls context on demand. This is a very unique approach (more on this later).

Roughly 70% of usage is outbound and account research. The other 30% is more varied (and interesting). One rep describes ChatGTM as her “fleet of agents.” She described it as a manager who coaches her before an external sales call or even an internal forecast meeting (“What are things that my manager will stress test me on?”).

It also has memory: individual preferences (”I like shorter emails,” “I care about these signals, not those”), and team-level context (like an org chart one rep corrects on a call that then propagates to everyone else).

Why they built it

George runs enterprise growth at Cursor. The team’s mandate is to convert intent signals into pipeline, at scale, across a GTM team of almost 500. So George did the unglamorous thing and sat next to the SDR team to watch their actual workflows.

He noticed that before a rep could pick a single account to work, they were opening seven tabs. A Hex dashboard, Salesforce, Gong, LinkedIn, and a couple of others. 45 minutes of aggregating data, and then they’d sit there staring at a blank canvas, trying to figure out how to reach out.

He told me his initial thought was that if he could make SDRs 20% more productive, that would be a win.

He started small. First, custom SQL queries to pull everything into one table. Then a scoring formula to prioritize who to hit. Then drafting the copy. Then, long-running agents to do deep account research. The agents were robust and comprehensive, but slow, expensive, and not dynamic. When George showed that long-running agent output to the SDRs, they found it very valuable, but wanted it to refresh every single day.

Around the same time, customer success and a few AEs saw it and started asking ad hoc questions (”give me the talk track that resonated with FinServ customers last quarter”). That’s when the chat interface was introduced. Chat, plugged into powerful tools and context, enabled anyone to ask anything. This design gave space for flexibility in how reps used it. In other words, he made the users of the product (reps)—who are subject matter experts—the builders of the product. (We’ll come back to this important point.)

^{Account research in action: a point of view, org chart build, the signals worth acting on, and a first pass at the three why’s.}

But why not just use Claude Cowork, ChatGPT, or Glean?

I get this question often. And Cursor actually had every possible tool on the shelf. So I asked George: why build your own?

Going from no AI to one of these tools is a huge leap. Of course it feels like magic. But it stalls fast.

These tools are genuinely good at small, simple pieces of the job: call notes, recap emails, call prep.

But to help do a rep’s job end to end, the AI has to know your business cold: your product, your pricing, who you sell to, how you sell, your whole GTM motion. Then it has to carry the work the whole way: build the org chart, pull the one line that matters from hours of Gong calls, shape a point of view and the three whys, draft the CFO one-pager and deck, tee up the outreach. That’s the hard part, and where these tools crack.

As one rep put it, using Claude meant repeatedly uploading data and re-explaining context.

Cursor isn’t alone here. The same questions come up with most sales leaders George talks to.

Can you point to an efficiency gain? Not whether reps like it and are using it. What number actually moved? One VP’s honest reflection after a few months: no real efficiency gain beyond better-written emails. If it didn’t inflect pipeline, ramp, or win rate, it’s an expensive assistant.

When’s the last time it was wrong? A RevOps leader admitted she still catches inaccurate outputs regularly. For every one you catch, how many does a rep miss and send to a customer anyway? Turning on an MCP isn’t the same as pulling the right data, and one wrong number can cost a deal.

Does it scale past your best rep? Your power user will thrive with anything. But “just build a skill” isn’t a plan. Will the other 90% find it, remember it, and use it? In an open chat box, most never get past the basics.

Where are the guardrails? A blank chat box has none. Nothing guarantees it pulls from the right data every time, or runs the way you actually sell: MEDDICC, command of the message, the three why’s. So the playbook you drill at SKO stays a slide deck. Whether it shows up in the work is left to each rep to remember.

And what does it cost once it’s actually used? It runs away from you. Ask for something like LinkedIn research and it uses your browser for 30 minutes, torching a chunk of the month’s budget in a single task.

None of this is a knock on these tools. They’re genuinely great, and Cursor’s reps still reach for them for a few use cases. But answering a question and doing a seller’s job end to end are different problems. George was trying to tackle the latter.

The question on every Revenue Leader’s mind: Build vs. Buy

Cursor isn’t the first company to build a full-blown sales platform. Ramp built a similar system in-house (which I covered in my piece: 11 growth-stage companies that built GTM agents in-house). But, George explicitly told me:

I don’t think most GTM teams should build this.

And I totally agree with his reasoning. Like Ramp, Cursor’s team is very AI-pilled; they have the internal resources (people and money), they have a ton of first-party data to use, the technical infrastructure (data warehouse and connectors) and data team in place, and have a ‘build over buy’ culture.

Plus, there’s a lot of context-engineering work underneath, which Cursor happens to be unusually good at (because building Cursor required exactly that). George shared with me that the core of ChatGTM took about two months. But that sat on top of a pile of earlier failures, compounded learnings, and hard-won knowledge about agents and data providers. None of that is just sitting on a shelf for an eager CRO or GTM Engineer at a Series D company.

Should small teams build a one-off agent in-house (lead qualification, call prep, an ICP rebuilt over a weekend)? Absolutely.

But what George built sits at the far end of the spectrum. It’s a full go-to-market product that reps work out of throughout their day.

They also, notably, didn’t rip out the systems of record. They kept Gong. They kept Salesforce. They kept Slack. They kept Nooks for the SDRs’ parallel dialing. ChatGTM sits on top of those and pulls from them on demand.

The “you need a GitHub repo full of sales context” idea you see all over LinkedIn? They didn’t do that either. In other words, all that context is not sitting in a data warehouse or in a GitHub repo. Instead, ChatGTM queries Salesforce, Gong, etc., in real time, going to the data warehouse or wherever the data already lives. It’s fetched on demand (via live API/MCP tool calls), not pre-loaded into a repo someone has to maintain.

Who owns AI x GTM initiatives inside Cursor

One of my biggest questions about this project is who actually built it, who owns it, and who maintains it?

It didn’t come from RevOps. It wasn’t handed to a recent GTM Engineer hire. It came from growth.

George started it solo. Wilson, a product research engineer who’s done ambitious harness engineering work (incl. building a search engine and browser!), built the infrastructure that finds and feeds the AI the right information at the right time. And Emily, a growth engineer, helped build key integrations. That’s the whole build team.

But the more interesting answer to “who owns it” is: the reps.

Cursor built ChatGTM in a way that enables a non-technical seller to create their own skills (repeatable workflows) and automations (a scheduled skill) in natural language, inside ChatGTM. They are the subject-matter experts. They shape how the product is used and evolves, and have created 500+ skills and 1,000+ automations

Sales reps are just piling in skills and automations, and every time I chat with them, they light up. They’re so excited to screen-share what they’ve built. Our sales team describes this as their ‘Cursor moment.’ The first time they got real superpowers from AI, because the thing actually knew their context and how they liked to work.

The results across their 400-person sales org

They started with a tiger team of 8 SDRs. That cohort is the most mature set of data/users (they have been using ChatGTM for ~1.5 months at the time I spoke with George).

Results (what we’re all here for):

The SDR team is booking triple the qualified meetings. The top 2 reps are at 4x.

Remember, the initial goal George was aiming for was to improve their outputs by 20%.

Separately, sales leaders are saying AE ramp time has decreased by more than 50%.

They’re tracking success against four core metrics:

1. Pipeline generated

2. Rep ramp time decreased

3. Sales cycles shortened

4. Time saved.

Anecdotes:

• Re: Pipeline generated — SDRs describe not being able to live without ChatGTM at this point. Some half-joke that if ChatGTM has an outage, they might as well go home because they can’t effectively outbound at the same pace. And to be clear: this team was AI-pilled before ChatGTM, with full-built skills that took months to build.

• Re: Ramp time — a strat rep used to need more than a week to get up to speed on a single account (Slack archaeology, Salesforce, Gong, piles of context tracked nowhere). Now they’re ramped on an account in 30-45 minutes. It’s day and night. Sales leaders are seeing the same thing. One watched a rep in their third week sound like a year-long veteran on a call.

• Re: Sales cycle — one rep described ChatGTM as her “fleet of agents” that collapsed multiple people into one chat: a manager who stress-tests her before a call, an SE who builds a usage graph, an SDR who sources contacts, an analyst who pulls closed-lost reasons. A sales leader had a similar moment on a call with a head of global AI, who fired off tough questions. Normally that’s a “let me get back to you.” Instead he answered live with ChatGTM five weeks in and booked the next step right then.

• Re: Time saved — one leader pulled out his phone at his son’s lacrosse practice, fired off a few requests to ChatGTM between plays, and had detailed research briefs and drafted email replies waiting by the time the game wrapped. He calls it having unlimited agents in his pocket.

The rate of adoption proves ChatGTM is valuable in the stack. The vast majority of the GTM org are DAUs.

When the SDR manager rebuilt the new-hire outbound curriculum, he told his team they were only allowed to use three tools: ChatGTM, Nooks (for parallel dialing), and LinkedIn. They should not be logging in to anything else.

Three lessons from George

I asked George what he’d tell someone who was at the beginning of this journey. He gave three concrete tips.

1. Work backwards from the metric you’re accountable for.

Find the fastest path to inflecting this number, whether that means buying or building on top. Shiny demos are cool until leaders ask for the impact. It’s hard to debate the cold metrics.

2. Nail one burning problem first.

Their approach early on was to throw agents at the problem and hope. That doesn’t work. An agent without the right context won’t magically solve anything. Most of the work is figuring out what context it actually needs, one workflow at a time.

3. Your team is the context.

Agents don’t come knowing how you sell. Your reps and leaders do. The real unlock is making it dead simple for those subject-matter experts to pour in their judgment, then getting out of their way.

Most teams won’t build their own version of ChatGTM, and George would be the first to tell you they shouldn’t. But, I hope this helps you think through “the art of what’s possible,” the ever-evolving (and nuanced) Build vs. Buy decision for revenue leaders right now, and how AI x GTM is shifting.

Oh, and if any Revenue Leaders are thinking through this now, George is happy to chat and share learnings. So reach out to him on LinkedIn.

Thanks again to George for giving us a peek behind one of the wildest in-house builds I’ve seen. We’ll have to revisit ChatGTM again in 6 months to get an update!

That’s it for today. Hope you enjoyed this insider peek into how one of the big 3 labs is building its GTM engine. Absolutely wild to see.

As always, thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

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Hey y’all! 👋

In May of last year, I argued that the future of CRM is autonomous. Then, in December, I followed up with The Autonomous CRM: 6 Months Later, after Clarify shipped Rep and AI Fields and the “autonomous CRM” moved from an idea for an investor pitch deck to core infrastructure that founders run their business on top of. Today, that story continues to evolve, as Clarify shipped its agent builder!

It lets you spin up autonomous agents directly inside your CRM. You tell it what you want in plain English, and under 5 seconds it’s built (literally; I know because I built one live—video later in this post, it’s magic). I think Clarify should be part of the conversation for any early-stage founder or GTM hire at a Seed-Series B company. I’m excited to walk you through everything in today’s sponsored deep dive on Clarify’s agent launch!

Here’s what we cover:

• The context arms race (and the layer you already own)

• Why the CRM failed us

• Agents fix the input problem

• From system of record to system of action

• I built an agent live (in 5 seconds)

• A starter list of agents to run inside your CRM

• Bring more data in, not less

• How SaaS UI evolves from here

• Looking forward

Alright, let’s get into it.

The context arms race (and the layer you already own)

You’ve heard it by now: your agents are only as smart as the context they can see. A sequencing agent that can’t see the prospect already replied “circle back in Q3” is worse than no agent at all. So the market has gotten obsessed with context, and a land grab is underway to be the context layer across GTM teams.

It has become commonplace to think the CRM is no longer the best place for context. But, in a world where agents are constantly working on your behalf to populate your CRM with context (eg: when every call, email thread, meeting, deal, stage change, contact is actually populated correctly)… I would argue the CRM can be the best place to house your context. The CRM was always supposed to be the brain. And now that you can keep it updated correctly, building agents on top of the place where the rest of your customer information lives is the obvious move.

Why the CRM failed us

If your CRM requires humans to update it, it will always be out of date.

And every GTM function downstream (forecasting, territory planning, attribution, scoring, and now every AI tool you bolt on top) inherits that staleness.

Everyone nods along that context should live in the CRM. The honest follow-up is that it usually doesn’t, because the data gets entered by reps at 8pm from the scribbles on their notepad (I lived this as a founder and AE), or it doesn’t get entered at all. Revenue slips through the cracks not because the system of record is poorly designed, but because nobody has the time to feed it. The container was right. The contents were garbage.

Agents fix the input problem

This is the unlock, and it challenges my thinking over the last few years around the CRM as the false ‘source of truth’.

When agents handle capture, the rep stops being the bottleneck. Calls get transcribed and summarized. Emails get parsed. Contacts get enriched. Stages move based on what actually happened in the activity, not what someone claims happened in a Friday pipe review. The system of record becomes self-maintaining.

The second-order effect is what’s interesting to me. Once the CRM is actually current, you can start building agents on top of it that are incredibly powerful. Most CRM automation has historically been mediocre because it was reasoning over stale, incomplete data. Garbage in, (consistently) wrong agent-output out. Fix the input, and everything downstream gets sharper at once.

That’s the case for running agents inside the CRM rather than in a tool floating above it. The capture loop and the action loop share the same source of truth, and that source of truth is finally trustworthy.

From system of record to system of action

In my piece last year, I described Clarify moving the CRM from a “system of record that takes from reps” to a “system of intelligence that gives them leverage.” The agent builder is the next step in that arc. The CRM becomes a system of action: a place where things happen, not just where you store what happened.

This is the same Assistant-versus-Executive distinction I keep coming back to. An Assistant waits for you to ask (”summarize this call”). An Executive acts on your behalf (”this deal went single-threaded after the champion stopped replying, I drafted a multi-thread email to the VP, want to send it?”). Agents inside the CRM are the most natural home for Executive behavior, because that’s where the full deal context already sits.

I built an agent live (in 5 seconds)

I recorded a Loom the first time I opened Clarify’s agent builder. No prep, no second take, just me describing an agent in plain English and watching it build.

I asked it to scan every call, pull action items tagged to each person, surface objections to follow up on, write the next step, and post it all to Slack. It built in about five seconds, named the agent, wired the trigger, wrote the instructions, and set tool access (read call recordings, write to Slack) on its own. It even added the meeting title, which I never asked for.

Here’s a real example of a Slack message the agent sent me:

This is what I said out loud in the video, and I still believe: my imagination is the limit now, not the tooling. The constraint used to be “can I build this.” Now it’s “what’s worth building.”

A starter list of agents to run inside your CRM

Here’s a tiered list to get the gears turning, from hygiene to autonomous action. Some of these are pre-built templates you can use in Clarify. Others, you can fork an existing agent or create one from scratch.

Tier 1: Capture and hygiene.

• Post-call summary to Slack with action items tagged per attendee, objections raised, and the next step. (The one I built live.)

• Stage updater that advances or flags a deal based on real activity, not on what a rep remembers to click.

• Competitor-mention tracker that scans calls and emails and keeps a “competitors in play” field current.

Tier 2: Proactive alerts.

• At-risk deal flag to Slack that watches for stalled momentum (no next meeting, champion gone quiet, single-threaded) and surfaces it before you check.

• Missing-persona alert that notices the economic buyer was never on a call and drafts the multi-threading email.

• Inbound ICP scorer that reads the company, scores fit, and routes accordingly.

Tier 3: Autonomous action.

• Signal-triggered outbound off a competitor outage, funding round, or relevant exec hire, drafted for you to approve.

• Win/loss post-mortem pushed to Slack and CRM written from the actual final calls and emails (AI Fields doing the analyst’s job).

• Lookalike sourcing that identifies the use case that closed a deal and goes finds more companies like it.

• Product feedback across entire GTM org. Every day, scan Sales/CSM call transcripts for any mentions of product bugs, feature requests, customer praise, etc. Check in linear to see if there are any matching tickets for a bug mentioned (if not, add one), add a Task in Linear for a PM about the feature request, check Stripe about their subscription, and notify their Account Manager in Slack.

Tier 1 makes the data trustworthy, Tier 2 turns it into nudges, Tier 3 turns nudges into actions. You can’t do Tier 3 well until Tier 1 is solved, which is the whole argument for why this belongs inside the CRM.

Bring more data in, not less

There’s a deeper unlock here that data and RevOps people will appreciate.

I felt this pain firsthand when I was building Groundswell, a company helping teams operationalize product-led sales. Getting first-party event/behavioral data out of the warehouse and into the systems where GTM teams live was brutal: reverse ETL, custom scoring models, PLG tools on top of the warehouse. The overhead was high enough that most companies didn’t bother, so the data stayed locked up where reps never saw it.

Agents change the calculus. An LLM can read raw activity and reason about what it means without it being pre-modeled, scored, and cleaned first. Stream the events in (product events, support tickets, first-party signals), let the agent reason over them, and act. So flip the old instinct: bring more data in, not less. The agent can use messy, raw, unmodeled data that would have been useless to a rule-based workflow. That lowers the bar for real automation for everyone, not just teams with a data engineer to spare.

How SaaS UI evolves from here

The cool, contrarian, new thing to say is that software is “going headless,” or that “UI doesn’t matter in a world of agents.” I think that’s a lazy take. I have a different prediction about how SaaS UI will evolve.

As agents get good, the GUI collapses from the console you operate all day into the console you open only to make decisions that carry real risk. The CRM is a great example. Historically, the GUI was the work, which is the source of the decades-old adoption problem: reps avoid the CRM because data entry is friction, so the record is always stale. With agents, capture stops being the rep’s job, and the (system of) record becomes self-maintaining.

What’s left for the human GUI is narrow and high-stakes. Approve the agent’s proposed stage change on a seven-figure opp. Override its territory routing. Inspect the branching history of everything it did to a strategic account before a QBR. Set the ICP and guardrails the fleet runs under. Step in on the relationship-sensitive moment (an exec escalation, an at-risk renewal). Plus, a read-mostly forecast you increasingly query in plain language (”why did Q3 slip?”) instead of clicking through.

So the rep’s relationship to the CRM moves from operating it to supervising a sales-agent fleet through a thin cockpit. They go from living in it all day to approving exceptions and reading the roll-up.

Two implications for anyone building or buying GTM software.

1. The adoption problem doesn’t get solved by better UI. It gets solved by taking the human out of the data-entry loop. The GUI doesn’t improve, it shrinks and moves up the stakes ladder.

2. Pricing decomposes rather than collapses. Humans still get seats (for access, and increasingly for governing the agent estate), and those seats may even multiply. But the agent’s work is usage-shaped, so it gets metered: actions, messages, resolutions, outcomes. As agents do more and the human-seat base shrinks, the revenue mix tilts from seats toward meters. The model that wins is seat plus meter, not one instead of the other. (This is the same shift I wrote about in The $100B+ Winner in GTM Tech Will Sell Labor, Not Software: budget moves from SaaS spend to labor spend.)

One bet sits under all of it. The GUI a human needs shrinks in direct proportion to how much they trust the agents. And the CRM is the first system where that gets obvious.

Looking forward

18 months ago Clarify had a vision. In December they had Rep. This week they have a way for you to build your own agents on top of the cleanest customer context you own. The trajectory has been consistent: take work off the rep’s plate, keep the record honest without anyone babysitting it, and let humans spend their time on the decisions that actually need one.

If you’ve ever lost a deal to a stale field, or watched your team drown in CRM admin, this is the version worth trying. Sign up for Clarify, connect your email, calendar, and Slack. And watch the CRM build itself, then build an agent and see how fast your imagination becomes the only constraint. It’s a magical new world we’re living in. Have fun!

That’s it for today. Thank you for your continued attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

PS: Patrick writes an awesome newsletter about “the honest reflections on the ups, downs, and in-betweens of startup and founder life,” called Founder Therapy.

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Happy Sunday!

During a consulting project in 2024, I was trying to find companies using PyTorch, TensorFlow (and other open-source ML frameworks), plus at least one full-time AI/ML engineer in-house. The technographics solutions I was using weren’t giving accurate or deep enough results. I found Sumble and haven’t looked back since.

As I’ve gone deeper with Sumble (and gotten to know the team* behind it), what’s most compelling to me isn’t their features; it’s the underlying data itself. Most vendors can tell you a 70,000-person enterprise “uses Grafana.” Sumble specifically tells you it’s the Platform Engineering team, what they’re building right now, and who leads it. (You can also use Sumble directly inside Claude or ChatGPT). Getting to that level inside a large, messy org is genuinely hard, and spending time with the team made clear why they can pull it off: they treat this as a machine learning problem first and a GTM product second.

*the founders (Anthony and Ben) previously founded Kaggle (in the AI/ML space), which was acquired by Google.

But the core focus of today’s piece is on their newly launched Account Scoring Skill, arguably one of the most important jobs to be done in go-to-market (it determines which accounts to focus on and how to engage them, which is the whole game). It’s something that’s been around for a long time, but I’ve always had beef with “scoring”, because salespeople never trusted these “scores.”

The pitch made sense on paper: score every account and contact, rank them, throw ‘em over the fence to your reps, and expect the list to get worked. 6sense and Demandbase grew into 9-figure businesses off that promise.

But, the old model was a black box. The new one is transparent (which matters even more as you deploy agents).

This shift and the downstream implications are massive. And the RevOps (and GTM) leaders building Account Scoring in this new way are getting results that are compounding across their GTM orgs. This is why I’m excited for today’s sponsored deep dive with Sumble.

Here’s what we cover:

• The black box problem

• The old way of account scoring, and what has changed

• The new rules of account scoring

• Score → reasoning (proof) → action

• Why data matters more for GTM teams using automation and agents

• Lessons learned from the field

• Where this goes next

Alright, let’s get into it.

The black box problem

Account scoring has been broken for a long time, for three reasons.

1. Opaque

2. Lack of data/context

3. Out of date (only gets updated ~1x/year)

I’m going to steal a section from a recent Sumble blog post, which is worth reading by the way (Account Scoring Should Explain, Not Just Rank):

“A score earns its place only if a rep can read it, trust it, and act on it. A good score doesn’t hand a rep a grade, it points them at their first move. Every number Sumble provides is backed by the people, teams, and projects behind it.”

We’ve all seen a CRM that has an account score of “92” or “A3” as a custom field. But what is a sales rep supposed to do with that? Add them to the “A3 sequence”? No. And worse, because the rep doesn’t know what it means, they don’t trust it, so it doesn’t get actioned, and the scoring model does not improve over time. I’ve seen this vicious cycle countless times.

Tl;dr: GTM teams have been building Account Scoring that sellers don’t understand/trust/use, running on data that can’t separate a great account from a big one, only refreshed once a year.

The old way of account scoring, and what has changed

The thing that actually changed is the data you can build a model on.

The old models ran on the data we had, things like industry, location, employee count, and if you’re feeling fancy, latest funding. Those are blunt data points. They put JPMorgan, Apple, and every other big logo at the top of every list. Sometimes that’s right. But more often than not, it isn’t. Thanks to AI (and magical tools like Sumble), we can get—what I call—”rich” data points. They call it “derived data”.

Not “how many employees.” But instead, things like: how many SDRs, and is that number growing, and how fast year over year. Not “what’s their tech stack,” but which three technologies appear in combination, which tells you something a single tool never could.

The cadence of updating an account score has also changed. It used to be a project you did during annual planning, in a spreadsheet, and then mostly left to collect dust in a Notion folder. By Q2 it was stale. That cadence is part of why “account fit” got treated as a separate, frozen thing from “is this account in market right now?” (which is what sellers actually need). When you only re-score annually, fit is the only thing you can really model, because timing changes faster than your once/year planning. But a model running on live data re-scores continuously. So when a relevant signal fires, the account moves up the list on its own. You don’t wait for the next annual review to notice that one of your accounts just posted 10 reqs for SDRs. That collapses the wall between fit and timing, because timing is now just another input the model watches in real time. (Spicy take, I know).

The new rules of account scoring

Anthony, Sumble’s CEO, has looked at 40+ different companies’ approaches to account scoring. I was catching up with him recently, and he told me the same mistakes show up again and again. Here’s the playbook he’s shared that the smartest RevOps are running:

Show rank, not score. What does a 92 actually mean? Nothing a rep can use. But “your fifth-best account” means something immediately. A raw score is an abstraction. What sellers really want is a ranked ‘call-down’ list (with context).

One model, segments applied after. A common mistake is building separate models for SMB, mid-market, and enterprise. Don’t. Run one model across everything, then slice the ranked output into segments afterward.

Score size by role, not by headcount. Total employee count rewards the wrong thing. Walmart has 2M employees, and you almost never want to hand Walmart points for that. If you sell to sales teams, the size variable that matters is how many SDRs and how many AEs they have, not how many total people work there.

Use concentration, not just absolute counts. If you sell dev tools, 100 DevOps engineers at a 400-person company is a far more interesting account than 100 DevOps engineers at a company with 100,000 employees. The ratio tells you how central your category is to that business.

Pro-tip: Anthony’s preferred decomposition is three buckets: size, growth, and concentration. Size handles the JPMorgans. Growth surfaces the fast climbers. Concentration tells you how much the account actually needs what you sell.

Keep funding out of the model. People love putting funding in. But, it breaks things. Funding only applies to venture-backed companies, so JPMorgan scores a zero on it and every Series B startup gets a bump. The result is a model that artificially inflates venture-backed accounts. And the useful part of “they raised a lot” already shows up in variables that touch the whole universe, like growth rate. So, score growth and drop funding.

Calibrate against your golden accounts, then trust the eye test. Feed the model your closed-won list, or an aspirational target list, and tune it so those known-good accounts rank where they should. Sumble’s Skill sets the initial weights (out of the box) and fits it towards that golden set, then humans adjust from there. The adjustment is where trust gets built. That loop (model proposes, operator corrects) is how you turn the best rep’s tacit instinct into something the whole team can run.

If you want to build this yourself, you should check out this amazing (truly) resource Sumble put together: A step by step guide to world class account scoring in under two hours.

Score → reasoning (proof) → action

A score has to carry its own story. When an account ranks high, you should be able to click straight into why. If SDR headcount drove the score, you should be able to see the 36 SDRs by name. If the model says they use Marketo, you should be able to open the exact job description where that shows up. The score isn’t the end of the work. It’s the starting point for prospecting.

The Sumble team was showing me how it worked, and we came across a real example, in Sumble’s own account model: Sigma was ranking above Deel (a much larger account). On headcount alone, Deel should win. It has the bigger sales org. But Sigma’s reps were hammering the product, thousands of queries in the last few weeks, and 11 days ago Sigma posted a job to replace legacy SaaS tools and migrate logic into their data warehouse, naming Salesforce and Clay in the post. There’s a live project at Sigma that’s directly relevant to Sumble’s product. Deel may be the bigger potential ACV, but Sigma is the most likely buyer this week.

The old approach forced you to choose: a fit score that loved Deel, an intent score that loved Sigma, and a rep left to reconcile two numbers on her own. “A man with two watches never knows what time it is.”

The newer approach bakes both into one ranked answer, with weights you can see and move. Turn the size weight up and Deel climbs. Turn it down and Sigma does. The point is that the trade-off between “how big” and “why now” is made deliberately, in the model, once, instead of being silently dumped on every rep to have to decide dozens of times a day.

Old way: Sigma is an “84”.
New way: Sigma is “high usage, a live warehouse-migration project, sufficient sales team size, and here are the people to call.”

Why data matters more for GTM teams using automation and agents

Every CRO is trying to figure out how to scale GTM with AI & automation, instead of headcount.

In the age of agents, the data underneath your go-to-market motion matters more than it ever has. A rep can open with “saw you’re moving onto Databricks, here’s how we’re different” only if the underlying data is trustworthy. When a human sends that, a wrong guess is a little embarrassing. When an agent starts sending at scale, a wrong guess is your brand, multiplied across every account it touches.

Account scoring is the first branch in that decision tree. Picture 5,000 accounts in your ICP, ranked. Your “Tier 1s” get white-glove human attention, dinners, ads, and an AE who knows their name. “Tier 3s” should ‘only’ get automated outreach via agents. Both tiers are valuable, so both need to be right.

Data accuracy impacts so much downstream, things like:

• Multi-threading

• Health scores (expansion/retention)

• Best path into accounts

• TAM and whitespace modeling

• Territory design

• Messaging

• Champion monitoring

• Risk detection

• New markets

If you get the scoring layer wrong, every one of these inherits the error since it’s the first branch in the tree.

Lessons learned from the field

Anthony has been doing forward-deployed engagements with their enterprise customers (some of the best software companies in the world, like Nooks, Atlan, and others), partnering with internal RevOps teams to build robust Account Scoring systems.

In my opinion, the company that sells a thing should be the best at using it. For Sumble, one of those things is account scoring.

Here are two customer stories and the takeaways.

Nooks ran Sumble’s Account Scoring Skill to build a model that both the RevOps team and sales team trust and can action. In the words of Michelle Idertogtokh (Senior Manager RevOps & Strategy at Nooks):

We stood this up in days, not the weeks-to-months projects of this caliber usually take: ICP definition → enriched, segment-calibrated, evaluated, deployed, and written back into HubSpot, in a few working sessions using their Claude skill in Cursor.

Easily some of the highest-leverage GTM work I’ve done.

And because the model runs across all of Sumble’s data, not just the accounts already in their CRM, it surfaced their whitespace: roughly 300 high-ranking accounts that weren’t in their CRM at all. That’s about two additional reps’ worth of territory, found for free as a byproduct of scoring the accounts they already had.

Atlan wanted something custom. They were a data catalog moving toward a semantic layer for AI, so the accounts they cared about were companies doing heavy AI and coding-agent work, the ones where they could point an agent at the right data store. They didn’t want a generic fit score. They wanted to score accounts on “AI readiness.” The model handled it, because the inputs are rich enough to define readiness in a way a binary tech-stack flag never could.

The mechanic behind all of it is a Skill. You point Claude/OpenAI at Sumble’s open-source skills repo and complete a quick setup. After that, it interviews you: who’s your ICP, what’s your email domain, do you want to read from Salesforce or HubSpot or just pull the account universe from Sumble. Then it pulls the data, builds you a local app with sliders for every weight, and lets you watch accounts move as you tune. Every score deep-links back to the raw evidence. There’s a whitespace view for the accounts you’re missing, and an evaluation view that works as a sanity check. Set up your own account scoring in under two hours.

Where this goes next

Sumble has the data infrastructure and the technical team to pull off what’s required to build s-tier account scoring models for the AI-era.

I’ve talked directly with Sumble’s team a lot on this topic and have read all their internal documents/blogs. They’re thinking very deeply about this topic, and I believe have started to crack a new way of doing account scoring. (Note: they’re working on several other deep Skills—beyond the account scoring Skill we explored today—like CRM hygiene, contact enrichment, account research and more).

Account scoring is now a live system that sits at the front of every routing decision you make, human or agent. An explainable model, calibrated to the accounts you’ve actually won and the instincts your best reps already carry, tells you exactly where to point your people and where to trust your automation. That’s the difference between a ranked call-down list reps actually work, and another “92” or “A3” sitting in the CRM that nobody touches.

PS: If you end up trying the Skill, let me know how it goes! And if you want to learn how to improve your team’s account scoring, chat with the team at Sumble—they’re all smart/awesome humans.

Thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

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Presented by:

Bring AI agents, enrichment, and intent data together and turn insights into relevant, timely action. Start building for free.

& Supported by:

Attention → AI Agents for RevOps.

Clarify → The Autonomous CRM. Sell more, admin less.

Clearskies → The Context Layer for Revenue AI.

Nooks → All your outbound. One AI workspace.

Rox → Revenue Agents for the Global 2000.

Sumble → Buying signals from tech stacks, people data, & active projects.

Terret → The answer-to-action engine for revenue teams.

Happy Monday!

Last week was an absolute doozy 😵‍💫

> anthropic and openai filed to go public
> spacex went public in the biggest ipo in history
> apple shipped what siri should have been 10 years ago
> anthropic finally dropped mythos (fable 5)
> then the government shut it down
> something big about basketball (i don’t really know)
> world cup kicked off
> new zelda and GTA 6 (apparently a big deal?)
> social network sequel trailer came out

Let’s see if this week can top that.

In other news, last Wednesday, we held the “Anonymous GTM Tech Demo Day.” Almost 300 people joined us live (700 registered). I spent the last few days building out this post as a resource for anyone who wasn’t with us live to skim over the 22 startups* featured.

*Important note: all of these companies are “startups” (where lots of innovation is happening) defined as having less than $5M ARR and less than $50M in funding.

Final things worth noting:

• Format: Live, 3-minute demos from real customers, not the founder.

• None of these companies paid to participate.

• These are some of the coolest startups building at the bleeding edge of GTM & AI.

• Full Playlist (YouTube) of the 22 demos (this was a marathon!).

If you end up taking a demo of any of them, let them know you found them on The Signal! (I don’t get a referral bonus, just want to share the love!).

Let’s dive in.

Top of Funnel (9)

Signal Intelligence:

Alfa

⤷ Alfa finds champions for anything you sell.

Masumi Watanabe (VP Revenue Operations at Phocas) demo’d Alfa.

My favorite quote from their demo:

You’re not drowning in a bunch of filters anymore. You just talk to their agent about who you want to go after.

Watch Alfa’s demo here (3.5 mins).

Saber

⤷ Saber is real-time signals infrastructure that surfaces in-market accounts the moment they show buying intent, so sales teams reach out at exactly the right time.

Simon Hoffenzits Lønstrøm (Head of Revenue Operations at All Gravy) demo’d Saber.

My favorite quote from his demo:

[Our reps] set this up, run it on 40 accounts, and then they go grab a coffee. When they’re back, they have 40 accounts ready to dial.

Watch Saber’s demo here (3 mins).

Smoke Signals AI

⤷ Alpha signal GTM systems powered by people and a suite of AI tech.

Evan Dunn (Head of Marketing at TitanX) demo’d Smoke Signals AI.

My favorite quote from his demo:

Dynamic AI-powered nurture is a forgotten channel for most folks.

Watch Smoke Signals AI’s demo here (2.5 mins).

Syft AI

⤷ Syft finds companies actively struggling with the exact problem your product solves, then tells your sellers or ai agents exactly who to engage and why.

Brian Jennerich (Account Executive at ClearBlade) demo’d Syft AI.

My favorite quote from his demo:

Four of my eight deals this quarter were generated through Syft. They were all from companies I’d never heard of.

You miss 100% of the revenue that you’re not aware of.

Watch Syft AI’s demo here (3 mins).

Syft Data

⤷ Convert warm prospects into the pipeline with website, LinkedIn, and product intent signals.

AJ Alonzo (Director of Marketing at DemandDrive) demo’d Syft Data.

My favorite quote from their demo:

Knowing that an account visited our website and what pages they went to doesn’t tell us why we should be reaching out.

Motions go from “I saw that you were on our website” to “it looks like you just overhauled your entire go-to-market team, which tells me you need pipeline yesterday.”

Watch Syft Data’s demo here (3.5 mins).

WhiteWhale

⤷ Know which accounts to target every day and why.

Tom Grainger (Founder at Advanced Client) demo’d WhiteWhale.

My favorite quote from his demo:

We can build completely custom signals, things like hiring SDRs, a new RevOps hire, new sales leaders. Anything that’s out there is completely custom, and you can build it out with natural language.

Watch WhiteWhale’s demo here (3 mins).

Outreach:

Exportly

⤷ The Clay-powered Chrome Extension for AI-Native Sales Reps.

Meghan Prudencio (Senior GTM Engineer at Webflow) demo’d Exportly.

My favorite quote from her demo:

The best sales plays live in tools like Clay, but our reps are living in LinkedIn, Salesforce, Outreach. All that power never really reaches them in a form that they’re actually using.

Watch Exportly’s demo here (4 mins).

FirstTouch

⤷ Operationalize Social inside HubSpot so you can automate, track, and attribute on it like a primary GTM channel.

Adia Toll (Head of Sales Development at AskElephant) demo’d FirstTouch.

My favorite quote from their demo:

We have a 98% reply rate on things that start with social.

Watch FirstTouch’s demo here (2 mins).

skyp

⤷ AI-powered outreach for high-value B2B sales. High deliverability built in.

Scott Desgrosseilliers (Founder & CEO at Wicked Reports) demo’d skyp.

My favorite quote from his demo:

This is 250 people, 97 opened. That’s sometimes better than an email on a warm list.

Watch skyp’s demo here (3 mins).

Middle of Funnel (5)

Engage on the Website:

1mind

⤷ 1mind deploys autonomous AI Superhumans across your entire customer journey to engage, qualify, sell, and support in every channel so buyers get one seamless, expert and delightful experience from first touch through renewal.

Steven Birdsall (CRO at Alteryx) demo’d 1mind.

My favorite quote from his demo:

Think of [Annie] as your best SDR, Atlas as your best SE, and Adam as your best CSM.

Watch 1mind’s demo here (4.5 mins).

Docket

⤷ Docket is your Inbound Demand Capture Agent. It engages your website visitors in human-like conversations that turn inbound traffic into qualified sales pipeline.

Scott Henderson (Senior Digital Marketing Manager at Galileo Financial Technologies) demo’d Docket.

My favorite quote from his demo (he asked Docket’s agent whether it could support another country, and it switched languages on the spot):

Claro, también puedo ayudarte en español.

Watch Docket’s demo here (3 mins).

Knock AI

⤷ Knock AI replaces static forms and follow-up emails with instant conversations, so you can engage high-intent buyers anywhere they show up. It qualifies, enriches, and routes them, helping marketing teams turn demand into pipeline before buyers disappear.

Ranny Bloch (Growth at Guardz) demo’d Knock AI.

My favorite quote from his demo:

The other day I jumped in and the guy said: I know it’s nice that you’re human, but can I go back to the bot, please?

Watch Knock AI’s demo here (4.5 mins).

Relationships & Account Mapping:

Centralize

⤷ Centralize is a deal GPS that automatically maps every relationship across your accounts and tells reps the next turn to make.

Samantha Lee (Director of Commercial Sales at LangChain) demo’d Centralize.

My favorite quote from her demo:

It used to take me at least five hours to put together an org chart. I built one for a 10,000-plus-person semiconductor company within an hour.

Watch Centralize’s demo here (4 mins).

The Swarm

⤷ The Swarm curates a rich dataset of 500M profiles with daily job changes, 50M companies with fundraising data, and a unique relationship mapping AI engine for builders and investors.

Richard Purcell (Marketing at Built) demo’d The Swarm.

My favorite quote from his demo:

Referrals are the highest performing channel across verticals, but they’re the least predictable.

Within 24 hours of passing this list to one of our SDRs, they booked a meeting with an EVP at a $2 billion company.

Watch The Swarm’s demo here (3.5 mins).

Bottom of Funnel (3)

Win the Deal:

Ruby

⤷ Ruby is the first always-on sales agent team - automatically gathering the full picture, building memory, and delivering contextual coaching and content in every deal, so every rep closes like your best one.

Adam Lebel (VP Sales at Axya) demo’d Ruby.

My favorite quote from his demo:

30 agents, all autonomously working together. Reps are supported throughout the entirety of the sales cycle.

Watch Ruby’s demo here (4 mins).

Terret

⤷ Terret is the AI-native answer-to-action engine for sales teams – turning any question into instant analysis and automated action for every role, from leadership to rep.

Matt Durazzani (VP of Revenue Operations at Litify) demo’d Terret.

My favorite quote from his demo:

All of a sudden, we have this extension of leadership in every deal.

Watch Terret’s demo here (7 mins).

Coach & Ramp Reps:

Avarra

⤷ Scale your best coaches and accelerate rep performance with personalized coaching and AI-driven simulations, at a fraction of traditional enablement costs.

John Harper (Senior Sales Leader at Paychex) demo’d Avarra.

My favorite quote from his demo:

Our average first year run rate before using Avarra was about $22,000 ARR. After using Avarra, our average run rate for new hires is about $96,000 ARR.

Watch Avarra’s demo here (3.5 mins).

Foundation (5)

Cedar

⤷ Cedar is the AI-native workspace for Account Executives. We help AE’s close more deals by finding the winning playbook and helping them execute it.

Sam MacGregor, Head of Growth at Simcare.

My favorite quote from his demo:

I need to automate everything because I can’t be typing out emails, I can’t be doing customer support. Cedar just acts as my brain for everything: it knows our entire company playbook and gets all the information across all our inboxes.

Watch Cedar’s demo here (3.5 mins).

Clearskies

⤷ The context layer for revenue AI.

Patrick Severs (Head of Revenue and Human Data at G2i) demo’d Clearskies.

My favorite quote from his demo:

We can’t do any of this unless Clearskies is in the middle doing the context work.

Watch Clearskies’ demo here (5 mins).

Deepline

⤷ Go-to-Market SDK + CLI for Agents - run your GTM motion from Claude Code to production, every provider on one API key.

Sujay Choubey (Growth at Composio) demo’d Deepline.

My favorite quote from his demo:

Because it exposes as a CLI, we could control the waterfall and sequence enrichment vendors to optimize our costs.

Watch Deepline’s demo here (3 mins).

Octave

⤷ Octave is the real-time context engine that helps GTM teams build smarter agents faster.

Heath Barnett (VP, Revenue at MixMax) demo’d Octave.

My favorite quote from his demo:

Octave is basically the GitHub of our ICP.

Watch Octave’s demo here (2.5 mins).

Swan AI

⤷ Your AI GTM Engineer.

Brianne Thomas (VP of Marketing at Elastic Path) demo’d Swan AI.

My favorite quote from her demo:

Swan has been a huge, huge time-saver for our sales team. They’re obsessed with it. I’ve never launched a tool before that our sales team has actually, like, wanted to use, and Swan is definitely one of them.

Watch Swan AI’s demo here (4.5 mins).

Other portals of the web I explored last week:

• The AI paradox: More automation, more humans, more work (Dan Shipper on Lenny’s podcast) ← if there’s one piece of content you consume, it’s this one (summary of 10 things from the pod)

• A few observations from deep in the trenches of GTM AI land over the last couple months by Nikko Georgantonis, Head of GTM AI & Systems at Hightough

• Davide Grieco, Head of Growth at Clay said, “I’m here to tell you your tech stack doesn’t matter. The entire GTM world is obsessing over AI agents and workflows. Meanwhile, the best marketers are doing the opposite.” Spicy (and 4k likes, so clearly hit a nerve).

• The AI-native GTM playbook is not “replace everyone with agents.” Great conversation with Replit’s CRO on Kyle Norton’s podcast.

• Avoiding Death on the Yellow Brick Road (Why The App Layer Isn’t Dead)

• On Grindslop (Notes on founder hagiography and aristocratic virtue) by the goat, Will Manidis

• The Simple Genius of Rick Rubin I can’t get enough David Senra or Rick Rubin these days (so this collab was a dream come true for me).

• SpaceX Launches Largest Ever IPO | OpenAI Files to Go Public | Uber Cuts 23% of HR (20VC with Harry Stebbings, Jason Lemkin, and Rory O’Driscoll)

• Someone built an app that monetizes the wait time on LLMs lol genius

• I’m still thinking about this one…

  

That’s it for today.

Have a great week, GTM nerds.

Thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

Subscribe now

Presented by Clay.

Bring AI agents, enrichment, and intent data together and turn insights into relevant, timely action. Start building for free.

& Supported by:

Attention → AI Agents for RevOps

Clarify → The Autonomous CRM

Nooks → Agent Workspace for Intelligent Outbound

Sumble → Buying signals from tech stacks, people data, & active projects

11x.ai → Accelerate growth with a digital GTM workforce

Hey, y’all! 👋

Today’s piece is a collaboration with Patrick Thompson, who writes Founder Therapy and is the Co-founder & CEO of Clarify. More on his newsletter at the end.

As a founder, you’re under real pressure to set the pace on AI inside your company (and scale with AI & automation instead of headcount). Your team watches how you use it. Your board asks what you’ve automated. And it’s hard to tell a real workflow from demoware on LinkedIn.

The most AI-pilled companies I see have a very AI-pilled CEO. The pace starts with the founders.

So here are 27 specific ways founders can use AI today, across:

• Revenue and customer-facing

• Marketing and content

• Product and the feedback loop

• Running the company

• Hiring

• Fundraising

Alright, let’s get into it.

Revenue and customer-facing

1. Build your ICP from your calls.

Feed an agent your prospect and customer call transcripts and have it build a specific ICP and buyer-persona sheet from what buyers actually say, not what you guessed at an offsite.

2. Signal-based outbound, founder-led.

An agent watches your ICP for the moments that predict a deal (social listening, a key hire, a competitor in their stack, a telling job post), then hands you a short list + a pre-drafted email to review. You still do founder-led outreach, but the signal-watching agent runs the research for you. (More ideas here: the best AI-native GTM plays you’re not running.)

3. Champion tracking.

Your best next customer is the one who already knows and loves your product. This is especially true in the early days. So, when a past champion changes jobs, an agent flags it and drafts the note to reach them in their new seat (this play will often lead to your fastest new logos).

4. Lookalikes of your best customers.

An agent builds a target list that mirrors your closed-won accounts, so you start from who already works, not a search using finger-in-the-air firmographic filters.

5. Warm intros.

Point an agent at your collective network (LinkedIn, Gmail, calendar, plus investors, advisors, and team), have it find who can introduce you into a target account, and draft the ask.

[Pro tip: export your LinkedIn messages and have Claude score them for sentiment, and wire up the Google APIs for calendar and email. I’ve done both, and it’s magic.]

6. A pre-call brief on every inbound.

The moment someone books a demo, an agent turns their funding, stack, recent news, and anything else relevant for you, into a simple brief, so nobody is scrambling to research the account three minutes before the call starts (or three minutes into the call… we’ve all been there).

7. Post-meeting follow-up that moves the deal.

After a call, an agent drafts the recap and the explicit next step toward closing, sent within the hour instead of three days later.

8. Closed-lost, analyzed and reopened.

Monthly, an agent clusters why deals actually died and drafts the re-engagement note when something changes.

9. A battlecard that updates itself.

An agent watches competitors’ pricing, changelogs, and G2 reviews and keeps your battlecard current instead of stale since March.

(The person who builds and owns most of these is usually a GTM Engineer, and most teams scope that role too narrowly. Here’s why. But in the early days, the founders should personally be building these agents.)

Marketing and content

10. Build your founder brand (idea-miner and editor, not ghostwriter).

If your buyers live on LinkedIn or X, use AI to mine post ideas from call transcripts and internal docs, and as a custom editor on your drafts (I wouldn’t let it ghostwrite yet). The same setup turns one post, talk, or call into a week of content in your voice. (More here: How to build a founder brand with the Rick Rubin of LinkedIn, Alec Paul.)

11. Personalized assets and decks.

Use Claude Design, Gamma, etc to spin up tailored sales-demo decks, marketing and event assets, even investor decks and internal memos.

12. Case studies from your first customers.

The moment you have a couple of paying customers, interview them and have an agent turn the call into a personalized case study with pull quotes. If you want to go deeper here, my friends Noah and Adrian recently published a great walkthrough: Building a Social Proof Engine.

13. Partner activation on autopilot.

When a partner goes live, an agent builds their landing page and drafts the welcome email with the link and promo code. (Patrick’s team runs this inside Clarify, on Sanity and Stripe.)

Product and the feedback loop

14. Feedback into a ranked roadmap.

Every ticket, sales objection, and churn note gets tagged by feature request and ranked, so the roadmap follows aggregated data, not the loudest customer.

15. A PRD from a voice memo.

Talk an idea through on a walk, and an agent returns a structured spec with edge cases and open questions. (Capture it with something like Superwhisper and it’s close to free.)

16. Support triage and first-draft answers.

Conversations sync from your help desk hourly, are deduped, and an agent drafts answers from your docs for a human to approve.

Running the company

17. The morning brief.

A daily brief across your inbox, calendar, CRM, and Slack, so you show up prepared by default.

18. A daily “what did the agents do” report.

Each morning, a Slack summary of the last 24 hours of agent runs and time saved. Visible, repeated wins are what make AI adoption actually stick on a team. (As you scale agents, this step will become more important.)

19. Decision memos on demand.

Talk through a hard call and get back a structured memo with the options, the tradeoffs, and a recommendation to react to.

20. Board updates and the metrics narrative.

An agent assembles the monthly board update from your metrics, CRM, and product data, and you query the numbers in plain language (”why did net revenue dip?”) instead of clicking dashboards.

Hiring

21. Sourcing, not just screening.

Talk to Claude about the exact person you want (eg: the companies they came from, their current role) and have it run deep research to source specific candidates. You can do this instead of using a recruiter or to augment a recruiter’s efforts.

22. A work sample that allows requires AI.

Let candidates use AI in the coding round, and its equivalent for every role. You’re hiring for real problem-solving in 2026, not for who can pretend ChatGPT doesn’t exist. Hire AI-natives.

23. Role briefs and screening, drafted.

Draft the internal role brief that defines what great looks like, then have an agent screen inbound applicants against it for consistency.

Fundraising

(Note: Treat fundraising as sales and these map cleanly onto a sales workflow.)

24. Record the calls, sharpen the pitch.

After each investor call, an agent recaps what landed and drafts a tighter version of the pitch for the next one.

25. Cluster objections into a battlecard.

An agent groups objections across calls (valuation, market, traction, team, competition, moat) and maintains a living battlecard with your best responses and proof points.

26. Run the raise like a sales pipeline.

A lightweight pipeline of targets, status, and next steps, with a weekly summary of what you’re learning, so your pitch and process improve as you go.

27. Prep and follow up for every meeting.

Before the meeting: get info on current portcos, where the company is investing, the partner’s thesis, tailored talking points and questions. After the meeting: a follow-up draft with the materials and deadlines they asked for.

Where to start

The founders pulling ahead have moved a dozen of these off their plate, so the company runs when you’re heads down, on a flight, or out for a week.

Don’t build all 27. Start with three: the workflow you hate most, the one that always gets skipped, and the one closest to revenue. Watch them for a week, then add three more.

Once data capture stops being a human job, the interesting questions become: what is the human best used for, and what does your software even look like when agents do more of the operating? I think that reshapes the tools we live in, starting with the CRM. More on that in a post that will be published later this month (“How SaaS UI is evolving”).

If you got something out of today’s piece, half the credit is Patrick’s. He writes Founder Therapy, a weekly, honest read on what building a company actually feels like. It’s a phenomenal Substack (I read it). If you’re a founder, subscribe here:

Founder Therapy

A weekly newsletter by Patrick Thompson, co-founder & CEO of Clarify. Honest reflections on the ups, downs, and in-betweens of startup and founder life.

By Patrick Thompson

Thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

Subscribe now

Presented by Clay.

Bring AI agents, enrichment, and intent data together and turn insights into relevant, timely action. Start building for free.

& Supported by:

Attention → AI Agents for RevOps

Clarify → The Autonomous CRM

Nooks → Agent Workspace for Intelligent Outbound

Sumble → Buying signals from tech stacks, people data, & active projects

11x.ai → Accelerate growth with a digital GTM workforce

Hey, y’all! 👋

Today’s post is by my friend, the legend, Eric Linssen. He’s a great marketing operator, and now he runs a private community (Demand Collective) of 650+ demand gen leaders from cos like Glean, Cursor, Datadog, and ElevenLabs. (He’s also been instrumental to me as I’ve built The Signal into ‘a real media company’… I can hear him laughing as I type that.)

Demand Collective runs incredible free virtual summits, like the “Demand Day Summit” coming up on June 18th from 10am - 1:30pm PST. 8, no-fluff 20-minute sessions, from operators at companies like Ashby, Superside, and Sentry.

See the agenda & register (it’s free) here.

He has interviewed 75+ demand practitioners about what’s actually working for them. And from those conversations, he’s collected dozens of what he calls “hidden multipliers”. Small, simple tweaks that can get you more growth without more budget. Today, he’s sharing 11 of the most useful ones he’s found.

Let’s get into it (over to Eric).

TL;DR: 11 Hidden Demand Gen Multipliers

There are no silver bullets (in demand gen), only lead bullets.

Our marketing’s impact has a ceiling dictated by forces outside of our control (PMF, market trends), but these hidden multipliers are lead bullets we can put to work to get as close to that ceiling as possible.

I’m calling these “hidden multipliers” because they don’t take extra budget. They’ll help you multiply your impact, without spending any extra budget on growth.

1/ ICP: Closing the ICP translation gap so you waste less spend on non-ICP accounts.

• Problem: You might be wasting spend on non-ICP accounts. Because you’re either using native platform targeting, or account lists built w. firmographic filtering.

• Root Cause: These tools are blunt. They think Bumble and Cursor are both “software development companies”.

• Solution: Use a tool like Clay to score your target accounts with much more granularity, so you can massively decrease wasted spend on non-ICP accounts.

Example/Go Deeper: How Tyler Calder, CMO @ PartnerStack increased ACV 58%+, while decreasing cost per dollar of pipe by 35% by “closing the ICP translation gap”.

2/ Person-level targeting audit

• Problem: Even if you have the right accounts, if you’re using job title filters in LinkedIn, you might be wasting spend on the wrong people.

• Root Cause: LinkedIn job title filtering sucks. Meta’s is even worse (it has none lol).

• Solution: First, you can pull a demographic report to see if you’re wasting spend on the wrong titles. If so, you can use a tool like Vector to target by specific people, across every channel.

• Example/Go Deeper: How Airbyte improved pipe:spend from 1.5:1 to 4:1 by fixing their targeting.

3/ Fix your confusing website positioning.

• Problem: You could be spending money getting qualified prospects to your website, but they leave having no idea what you do. Make sure everyone coming to your website knows *exactly* what you do, for who, and why you’re better than alternatives.

• Root Cause: Who knows, but this is true for 80%+ of b2b companies.

• Solution: Follow Fletch PMM’s positioning playbook.

4/ Fix your broken content.

• Problem: If your content (ads, blogs, resources, webinars) isn’t converting, you’re wasting ad spend & energy distributing it.

• Root Cause: It’s probably one of the root causes above👆.

• Solution: Make sure your content “is good, product-focused, and distributed well”.

Example/Go Deeper: Mary Keough’s framework (w. examples).

5/ Manual bids on LinkedIn

• Problem: If you’re not running manual bidding on LinkedIn, you’re probably wasting spend and overpaying for impressions, so your spend won’t go as far & CPA will go up.

• Root Cause: LinkedIn’s maximum delivery (or manual bids that are too high) will over-bid because LINKEDIN LOVES YOUR MONEY!

• Solution: Either start w. Lower-than-recommended manual bids and inch up until you get delivery, or use an automated tool like Vector’s bid agent.

6/ Test “micro-campaigns” in your outbound

The ‘playbook’ Brendan calls “Micro-campaigns” (well… this is meta…):

• Timing is more important than anything else in outbound. A ‘mid’ message to the right person at the right time beats a perfectly crafted message to someone with no need.

• The smartest outbound teams are using AI to find these people (tiny lists of people with a strong reason they’re likely to take a meeting NOW).

• They send them hyper-personalized messages with a no-brainer offer.

• And they repeat every week to new, hyper-targeted lists as new prospects move “in-market”.

It’s worth testing micro-campaigns with a small % of outbound budget against control and see if you can improve total throughput.

It’ll probably work. The volume is low, so you won’t need to hire anyone new, just reallocate a small % of the rep time already doing outbound.

7/ Test incentivized demos on BOF campaigns

Incentivized demos get a lot of flak, but for lots of Demand Collective members I talk to… they work. Not cold for most companies, but if tested against existing BOF “Get a Demo” ads, they out-perform them (must make sure targeting is dialed in).

So it’s worth running a small test comparing incentivized demos vs. regular demo request/BOF ads.

8/ Showcase customers in organic channels

This one takes the most resources out of the 11, but it’s worth calling out. If you’re already spending money on organic, highlighting existing customers is probably the highest-leverage way you can improve the channel. You may be able to learn from what worked for Nooks, Userled, or Jess Cook:

• Nooks’s organic LinkedIn playbook.

• Userled’s customer advocacy campaign

• Jess Cook’s influencer playbook.

9/ Cut wasted spend on Google Search

Setting up keyword-level ROI tracking takes half an hour. Once you have it you can see which specific deals came from which keywords. And also, which keywords you’re spending on that aren’t driving any downfunnel results (so you can cut them).

Full breakdown here.

10/ Win other high-intent discoverability channels

Chris Shuptrine, VP Mktg at Torii, scaled Ent leads 90%+ QoQ through what he calls “High Intent Discoverability”. He:

• Chooses a “phrase” he wants his buyers to associate him with (for Torii, “SaaS Management for IT, Finance, and Security”)

• Bids/focuses on organic *everywhere* a target prospect would search for that phrase. Google, Bing, Reddit, Capterra, G2, Quora.

• All so Torii “is seen 5+ places within 5 minutes of research.

Even if it’s $5/day, those high-intent discoverability channels are the highest ROI money you’ll spend if you have any sort of mature category, because you’re only bidding on the highest-intent searches.

Hope you found these valuable!

Thanks again to Eric for this guest post! He’s a legend, a terrific operator, an appreciator of both AI—and human craft, and he runs the world’s best community for Demand Gen practitioners. Here are three ways to go deeper with Eric/Demand Collective:

• Subscribe to his newsletter: The Demand Collective Newsletter.

• Apply to join The Demand Collective community (it’s free, but you have to be spending $30K+/mo on ads).

• Register here to attend Demand Day Summit (June 18th).

Thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

PS: Still want more? Best of The Signal

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11x.ai → Accelerate growth with a digital GTM workforce

Hey, y’all! 👋

I ran Operations & Enablement for the Global BDR Org (~125 people) at Zoom Video in 2020-2021. But a lot has changed with Sales Enablement since then.

So, I was excited to catch up with Mercy Bell, who ran Enablement at Webflow and has spent 15 years in RevOps + Enablement orgs and consulting with large, complex teams at Meta, Outreach, Retool, and more. And is a Stanford alum. I believe that most (not all, but most) of the best Sales Enablement folks came up as sellers themselves (Mercy started as an SDR at Google, then AE at Kahuna).

She made an off-handed comment during our conversation that I haven’t been able to shake: we are putting more care into architecting environments for our AI agents than we are into developing the humans who still run our go-to-market motions.

That’s what we’re going to unpack today. Specific frameworks, best practices, and programs. All backed by science (yes, science! from psychology research papers and old textbooks). Here’s what we cover:

• What enablement actually is (retrieve, recall, resist)

• Why the 2010s Enablement playbook is dead

• The agent parallel

• Why sales teams will get incredibly small

• Self-determination theory and why it matters for GTM

• Four programs every GTM org needs to build for humans

Alright, let’s get into it.

What enablement actually is (retrieve, recall, resist)

Mercy defines enablement through a framework of three R’s.

The first is retrieve. Can your people find what they need? Information, content, competitive intel, whatever. This is the systems and tools question, and it’s the one most companies default to solving. It’s also the easiest. RevOps can own a lot of this.

The second is recall. This is harder. Can your reps, in the moment, pull up the right heuristic, match tone to context, read the room, etc.? As Mercy put it: “What they need is to be able to recall. The human brain takes in so many factors. The fact that when the call got picked up, they heard the tone of voice was a little low energy and they matched it. They understand that person’s seniority level and role means a certain kind of shifting of how you position a question.”

Recall is the stuff that separates your top performers from everyone else. It’s pattern recognition built through repetition, not a knowledge base article.

The third is resist. Judgment. Impulse control. Knowing when not to oversell, when to walk away from a deal that doesn’t fit ICP, when to shut up and listen. “Discernment and judgment is mostly about what we don’t do, not what we do,” Mercy said.

This framework matters because, in my observation, most enablement teams spend the majority of their time on retrieve and almost no time on recall or resist. They have it inverted.

Why the 2010s Enablement playbook is dead

If you built an enablement team in the last decade, it probably had a content person, a systems person, and a trainer. That structure made sense when the playbook was stable, the tools moved slowly, and compliance meant keeping an LMS fresh.

That world is gone.

There’s absolutely no way to apply the 2000s, 2010s model of enablement around compliance and documentation to what’s being built and deployed inside every go-to-market org right now.

When she was interviewing for in-house roles, she kept asking: what would I be coming in to do? The answer was always “activate the field.” But a layer deeper, it was everything. Systems. Behavior change. How humans work alongside the technology they’re now riding on top of.

The people who are still doing last-mile training and keeping content fresh? They’re going to get replaced. The ones who survive will go one of two directions: into systems and operations (GTM Engineering, RevOps), or into coaching, recall-building, and leadership development.

The era of the generalist enablement hire is over, and if you’re not sure whether you need an enablement person, the right move is to diagnose the root cause first (it might be a product marketing gap, ICP clarity, or managers who can’t coach) and then hire a specialist to solve that, not a generalist to cover everything.

The agent parallel

Every founder and sales leader wants to scale GTM with AI and automation. I write about this constantly. Mercy expanded my perspective here.

Think about how much care goes into building a good AI agent. You’re constructing context windows. Giving examples of what good looks like. Reviewing outputs one by one, grading them, explaining why one was good and another bad. You’re architecting the entire environment for that agent to succeed.

Now ask yourself: are you doing the same for the humans on your team?

“We’re applying all this compassion and diligence to architecting environments for agents to do their best work,” Mercy said. “We’re not doing the same for the human beings. Most people I know right now are burnt out, anxious, a little bit confused about what their world is.”

Execution is a sales team’s moat. So, leaders need to get honest about where their people actually are. Not just their AI strategy or tech stack. Their people.

Why sales teams will get incredibly small

Mercy thinks that in 5-7 years, sales teams will be incredibly small, and managers will be true player-coaches, still carrying a book but spending far more time developing their people. Investment in human skills will go way up, especially where deals are complex and relationship-driven. The art of the one-on-one will matter more than the dashboard.

“The renaissance of enablement is world-class enablement of frontline leaders to do things like the art of the one-on-one,” Mercy said. “That is actually an art form. And it’s also science.”

Self-determination theory and why it matters for GTM

If you accept that investing in humans matters, the next question is how. Mercy’s framework draws from research that’s been around since the 1960s (I told you we were going to be looking at old research papers today!).

Self-determination theory (SDT), developed by Edward Deci and Richard Ryan, identifies three universal psychological needs that drive motivation across cultures, generations, and roles.

Autonomy is the need for meaningful choice. Not doing whatever you want, but feeling like you’re directing your own work.

Competence is the need to feel effective. This one has a trap. “Everyone gets a sensation of competence when they use AI,” Mercy warned. “It’s addictive. But that feeling of competence isn’t the same as actual competence.”

Relatedness is the need for genuine human connection. This is the one Mercy is most passionate about. “All of this tech can feel so isolating.” Teams and managers that foster real connection outperform, and it’s not a fuzzy concept. It’s neuroscience. Dopamine. Serotonin. The real stuff.

SDT also provides a motivation continuum. On one end: amotivation (no drive). Then external regulation (I work to get paid or avoid punishment). Then increasing stages of internalization before you reach intrinsic motivation, where someone does the work because they love it.

“The companies that are crushing it right now are almost exclusively in that identified-to-integrated range,” Mercy said. “You can’t run your business on fear right now.”

So if those are the psychological needs, what do the actual programs look like?

Four programs every GTM org needs to build for humans

Mercy outlined a four-program architecture for developing the people running your GTM motion.

1. Ramp (not “onboarding”)

“Onboarding” implies someone hops on board, and then they’re done. Ramp is the gradual process of bringing a human from “can’t do anything” to “can hit the number.” Neuroscience shows us that when something is new, the brain is at its most open and receptive. That window closes quickly.

Mercy suggests to build ramp around the customer journey, not the product. Tell the story of the customer from lead to renewal. Frame everything from the customer’s perspective.

And don’t do role plays in the first two weeks. Instead, “train their ear.” Capture a ton (like 60-70+) great clips of real sales calls where just the prospect is speaking. Play it, stop it, ask them what they heard.” Build the recall muscle early. This is hard work. There is no agent for it. And it’s worth doing specifically at companies with “high-trust sales cycles,” where the stakes of getting it wrong are six (or even seven) figure deals being lost.

2. Product and solution depth

Most companies train on product at the same altitude for every role. An AE, an SDR, and a solutions engineer need to know different things at different depths. Get specific about who needs to know what.

3. Skill development

Develop the fundamentals. Discovery. Giving a great demo. Negotiation. These are muscles that need consistent repetition, not a quarterly drop-in from an outside trainer. “The human brain requires repetition and feedback. It’s got to be an always-on thing.”

4. Leadership development

This is the one that gets punted to L&D and then mostly ignored. But everything above gets delivered by frontline managers. If those managers aren’t being developed, none of the rest works. How do you give hard feedback? How do you coach in the moment? These skills come from ongoing, repetitive development, not some random, one-time certification.

In 18 months, the best GTM orgs won’t just have the best agent infrastructure. They’ll have the best human infrastructure, too. The same rigor they’re applying to context windows and evals, they’ll be applying to ramp programs, coaching systems, and leadership development. Sales orgs that invest in both layers (agents and humans) are going to run circles around the teams that are over-indexing on agent infrastructure and overlooking human infrastructure.

That’s the Renaissance of Enablement.

Thanks to Mercy Bell for sharing her frameworks and for being the kind of practitioner who reads academic papers for fun (legend). She’s spent 15 years inside RevOps + Enablement orgs. If you’re looking for guidance on how to build an Enablement program in 2026, you should reach out to Mercy, she’s an awesome human.

Thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

PS: Want to share The Signal with a friend? Send them this to skim: Best of The Signal

Subscribe now

Thanks for reading this hand-rolled, 100% organic, farm-to-table (human-written) newsletter.

Presented by Clay.

Bring AI agents, enrichment, and intent data together and turn insights into relevant, timely action. Start building for free.

& Supported by:

Attention → AI Agents for RevOps

Clarify → The Autonomous CRM

Nooks → Agent Workspace for Intelligent Outbound

Sumble → Buying signals from tech stacks, people data, & active projects

11x.ai → Accelerate growth with a digital GTM workforce

Hey, y’all! 👋

I’m trying a different format today.

Maybe this will become a recurring monthly (?) thing. I’m experimenting :) Let me know if you like it or don’t like it (my feelings won’t be hurt!).

GTM-tech funding/news

It’s been a wild month in GTM-tech land so far.

Gong passed $500M in revenue, while growth has accelerated (according to my quant, that’ll put them at $800M in 1 year, so IPO is clearly in sight; that’ll be a big win for GTM-tech).

Actively AI announced their Series B. We first wrote about Actively AI when they raised their Series A in April of 2024: The AI Research Company Building GTM Superintelligence.

Monaco announced their Series B ($50M) just 3 months after announcing their A. Bonkers. Before starting Monaco, Sam Blond (CEO) was the VP Sales at Zenefits, then CRO of Brex, followed by a brief stint as a capital allocator at Founders Fund. And notably, one of his co-founders, Abhishek, is the previous CPO of Apollo.io. They’re building an all-in-one sales platform.

I announced I’m officially full-time on The Signal. I’m building this into a proper media business :) Exciting and scary plunge (with three young kids). But yolo. Thanks for the support. Speaking of, I’ll be partnering with a limited number of brands for the next 6 months. We should talk this week if you’re interested (reply to this email or dm me on LinkedIn).

The Signal, featured recently:

• [Growth Unhinged by Kyle Poyar] → The best AI-native plays you’re not running (Our second collaboration)

• [30 Minutes to President’s Club] → How to Use AI to Identify the Perfect Time to Prospect

• [Topline] → GTM Engineer: The Most Misunderstood Role in Go-to-Market by Brendan Short [Topline] | A framework for revenue leaders deciding whether to build this capacity now, later, or not at all

GTM content of the month:

• Kyle Norton (CRO at Owner.com, and friend of The Signal) gave a kind shout-out to me/The Signal in his talk at SaaStr this week. Super cool to see from such a goat in our space. His entire talk is worth listening to: AI-native GTM 101: The 5 AI Decisions to Get Right

  

• Who’s actually hiring in GTM right now | The companies selling AI are the ones doubling their SDR headcount on Growth Unhinged with Kyle Poyar

  

• IC work is the new career flex | The rise of the High-impact Individual Contributor (HI-C!) by another one of the goats, Elena Verna. Here’s my take on this trend.

• Sell the Truth | On persuasion, charisma, dealmaking, and walking away (podcast episode by Naval)

• Firsthand: How I Bet On Clay (And It Bet On Me) | I joined Clay as one of its earliest employees and a reluctant marketing hire. This is how, over the last three years, we built a role around my strengths as a storyteller, scaled past $100M ARR, and created a brand that startups study. by Mishti Sharma

• Agentic workflow (by Jerry O’Shea) that does the heavy lifting before a rep touches an account using a bunch of tools connected together:

  

• Knowledge bomb by Todd Bussler (former Co-founder & CEO of Champify, recently joined Clay as an Enterprise Sales Leader) → “90 days into a new job, everyone loves to post about their 5 biggest learnings. I’m doing the opposite. Here is everything I had to UN-LEARN going from a sales leader at a traditional SaaS company to an AI-first company like Clay”.

• 10 memes (h/t Will Aiken) for you to Slack to your co-worker (instead of updating Salesforce before your forecast meeting later today)

  

Oldies, but goodies (from the archives)

The Signal has added ~2,000 new subscribers in the past 90 days (roughly 30% growth), so I thought it would be worth (re)sharing a few older posts from the archives.

• The “System of Intelligence” The biggest opportunity in GTM tech

  This was true in Oct 2024 when I wrote this piece. And even more true today, as AI has continued to ramp. There is still no clear winner (though, there are many attacking this problem). Excerpt:

  

  

• The Rise of the GTM Engineer (published Nov 2024). Excerpt:

  I am hopeful for the future of modern go-to-market. I think it will include more intelligence and creativity injected throughout it. And, no matter what this name of this new role ends up being, I think these new “jobs to be done” are going to grow in importance in the coming years. We’ll find out!

  …The question in my mind is simple: will this number be higher or lower this time next year? What about in two years? My bet: it will be much, much higher.

  Crazy to see this early data from back then (here’s what I said in the article:

  

• My AI Journey: Not One Lightbulb Moment, but 100 Sparks that Gave Me Deep Conviction (& why you’re not behind on AI) + My top 11 lessons from my AI journey so far. Excerpt:

  

• (Last week’s post) How Two of Rippling’s Marketing Pioneers are Building a GTM Engine from Scratch (I Was Surprised) | With Brandon Camhi & Noah Adelstein. Excerpt:

  

Alright, that’s it for today!

Reply to this email and let me know what you think of this (monthly?) format.

Thank you for your attention and trust. I do not take it for granted.

See you next time,
Brendan 🫡

PS: Want to share The Signal with a friend? Send them this to skim: Best of The Signal