Maybe its been over a decade! But, I can’t remember when I shaved my beard last. This week I did just that to kick off Movember and raise money for mens health. If you haven’t heard of the cause, the idea is simple:

• Shave

• Grow a stache

• Raise awareness

• Raise money for the cause

My wife, Tori, who’s in social impact, gave me the idea to do it this month. Prostate cancer and suicide prevention are close topics. My father-in-law is a survivor of prostate cancer (he caught it early thankfully) and I have an Uncle that tragically took his life due to deep depression. This was a long time ago, but it’s something that’s always stuck with me. I’ve also struggled with mental health (anxiety & depression) through the years. Always happy to talk to anyone if you need help.

I’m doing this for them and all men who struggle with mental health & to raise awareness so more men can catch prostate cancer earlier.

Prostate stats you should know:

• Prostate cancer is the most diagnosed cancer in the world for men

• 1.4M men are diagnosed with it each year

• One of the first ways to spot it is through a quick, simple blood test for PSA (prostate specific antigen)

My goal is to raise $10,000.

For every company that donates, I will include your logo in a section of this newsletter until the end of the year as a thank you. There’s close to 10,000 subscribers and almost 1,000 subscribe every month now (and that number is growing). I publish 2-3 times a week, so this should be good exposure.

The top company donation gets:

• The largest spotlight in the section

• A brief blurb on the company so people can learn more

• Shout outs on LinkedIn

I’ll do the same thing for individuals.

Hope to see you get involved or share resources with someone you know.

Donate to support mens health

Now back to regularly scheduled programming…

The prospecting playbook for EMEA expansion

Google was fined 325M Euros in Sept 2025 in France.

It wasn’t because they sent cold emails - at least not by the definition you know. They got fined because:

1. They showed ads formatted as emails in Gmail inboxes

2. Set ad cookies during account creation without valid consent

The regulator treated an ad in the inbox as direct-marketing email under CPCE L.34-5, so consent applied. 'Email-like' formats count as email.

Honestly, never heard of that until I started researching the topic.

A more applicable case for this discussion is with Lusha Systems. Italy fined Lusha 2M Euros and ordered record deletions. Lusha claim they build and sellcontact cards on a legitimate-interest LIA. Regulators said this was missing. The case is currently suspended as a challenge is pending. In no way is this to put Lusha in a bad light, its just the most applicable fine I could find for the topic of cold email.

This is what all of my customers are worried about. They’d frequently ask:

• Is data from Clay GDPR compliant?

• How do companies do prospect in GDPR-strict countries?

I struggled to answer the last one. My response has typically been:

1. Clay cannot make you GDPR compliant. That’s within your control, not ours.

2. We can help setup guardrails in Clay to help you reduce risk.

3. There’s no data provider that can give you double-opted in contacts (they’d have to do this for every company). Or, the company is lieing to you.

4. The risk reward calculation of whether to break the law is up to you.

Then I met Nicolas Schell, founder of Scalantec, a boutique GTM agency out of Berline, Germany. They outbound prospecting in Germany. So, I had to learn more and invite him to do Reddit GTM Builder AMAs (hosted about a month ago). Btw, they also won Clay Partner of the Year award at Sculpt.

In this post, I cover:

1. My agent newsletter experiment

2. The basics of GDPR

3. Recent fines in the news

4. The GTM Engineer check list for GDPR

5. The GDPR prospecting playbook from Nicolas

6. GDPR takeaways

7. Partner of the week with 3 plays: Definitive Healthcare

This post summarizes the key takeaways from that r/gtmengineering AMA: Prospecting in Europe / GDPR-strict countries. How Nicolas Schell sends millions of cold emails in Germany. Important note: I am not a lawyer. This is not legal advice and in no way represents any opinions or guidance from Clay.

Otto & autonomous

I’m running an experiment - can a Grok Bot agent write, grow, and monetize a newsletter? Tune in as Otto shares everything that’s working & failing while getting tips on how to use agents to save money & be more productive.

autonomous

Otto is an agent tasked to write and grow this newsletter. Can it grow to 10k subs in 90 days? Otto posts its next growth experiment, what works, and what failed.

By Alex Lindahl | GTM Engineering

The basics of GDPR and recent fines

What is GDPR?

GDPR stands for General Data Protection Regulation. It is a comprehensive data privacy and security law passed by the European Union. GDPR became effective May 25, 2018 and applies to all organizations in the EU and EEA, and any company outside of the region that collects or processes personal data belonging to EU residents. The core purpose is to provide individuals with control over their data.

GDPR.eu outlines the following rights for individuals:

• The right to be informed about how data is collected and used.

• The right of access to see what personal data an organization holds.

• The right to rectification to correct inaccurate data.

• The right to erasure (also known as “the right to be forgotten”) to request the deletion of data.

• The right to data portability to transfer data to another service.

• The right to object or restrict processing.

Non-compliance can result in hefty fines. Recently:

The GDPR playbook from a Germany-based agency who runs cold outbound

Key takeaways from our AMA with Nicolas Schell of Scalantec

Germany is supposed to be where cold email goes to die. The law says a first cold email to a business needs prior consent. And yet German companies send cold email every day.

So I asked someone who does it for a living. Nicolas Schell is the founder of Scalantec, a GTM agency that mostly serves IT and software clients. His numbers come from about 60 client campaigns over two years:

• 9.6% average reply rate on plain cold email, against the 1–3% benchmark he cites

• A handful of cease-and-desist letters (Abmahnungen) per quarter, at €300–500 each

• One letter from a data protection authority, which came from an ignored access request and not from an email

• Zero fines and zero court cases

In his words: “It’s a cost line, not a landmine.” Here’s how he gets there.

1. Know the difference between the law, the culture, the perception and the data

“Everyone sends, few get letters, almost nobody gets fined.”

The law. Under § 7 UWG, Germany’s unfair competition law, email advertising needs prior express consent, and there’s no B2B exception. GDPR kicks in as soon as you personalize an address. Nicolas says German courts have held that a single email is enough to break the rule. They’ve also held that the following do not represent consent:

• Impressum address (the contact details German companies must publish on their site to show who ‘owns’ the site)

• A LinkedIn connection

• An opt-out link doesn’t fix anything

Phone is different: a B2B cold call is allowed if you can plausibly presume the business is interested.

The culture. Prospects check your Impressum before they reply. The managing director of a 20-person company will write the cease-and-desist himself. Formality matters, but relevance matters more than cleverness.

A short mail that sounds like a colleague beats a landing page in prose.

The perception. Two opposite myths cancel each other out. “B2B is free” is wrong, and so is “it’s illegal so nobody does it.” US blogs add a third, the claim that first offenses draw €5,000–50,000 fines, which Nicolas says has no source anywhere.

The data. Germany has no regulator policing email. The Bundesnetzagentur, the telecoms regulator, only handles phone spam. Enforcement is private: a competitor or a recipient with a lawyer. Nicolas couldn’t find a single published data protection fine for B2B cold email in Germany.

Subscribe for sections 2-12 that includes

The outbound playbook

What to do if you get caught

A checklist before going live

Scalantec’s tech stack for cold email

Read more

Join 9,600+ GTM operators from OpenAI, Reddit, a16z, Figma and others who subscribe to learn how the fastest growing companies use AI in GTM.

Socials are filled with posts this week remembering Adam Robinson (founder of retention.com, RB2B, and moltsets) and his impact he’s had on so many. I didn’t know him personally, but as many have commented, it feels like I did from his presence on LinkedIn. My prayers go out to all who knew him and his family. Rest in peace.

This post by Andrew Mewborn captured what an amazing friend and person he is. This part made me tear up:

Give your family a hug today. Call your parents. It’s scary how fast life can change.

It turns out my Grok Bot for GTM Encyclopedia that I shared wasn’t accessible. (Company security policy since the account is tied to Cursor). My apologies!

To fix this I rebuilt the site on Lovable with Grok Bot. I figured I’d turn this into a growth experiment to learn some AEO techniques.

In this post, I’m sharing:

1. A newsletter experiment: can an agent write, grow, & monetize a newsletter?

2. The steps (and prompts) to build the Grok Bot for GTM Encyclopedia

3. 36 tips for Grok Bot & Clay (from SpaceXSI and I)

4. Data provider of the week

Can Otto write, grow, and monetize a newsletter?

The experiment is this: can an agent (Grok Bot) write, grow (to 10k subs in 90 days), and then monetize a newsletter?

I want to learn (and share with you):

• How much can a Grok Bot do itself?

• Can it learn and improve its approach over time?

• What growth experiments will work?

• Where will things fail?

This is how it’ll work:

Otto is the agent powered by Grok Bot. The newsletter is called autonomous and is hosted on Substack. Otto will research and write a post on the top agent news for the week. Think of autonomous as the Morning Brew for the agent economy. Each post will include:

• A round up of the most interesting agent economy news

• A growth experiment Otto is running to grow the newsletter

• Results of those growth experiments (what worked / what failed)

• A poll so you can provide feedback (so Otto can learn)

• Stats on newsletter growth

Join the experiment:

autonomous

Otto is an agent tasked to write and grow this newsletter. Can it grow to 10k subs in 90 days? Otto posts its next growth experiment, what works, and what failed.

By Alex Lindahl | GTM Engineering

The Grok Bot for GTM Encyclopedia

Built with Grok Bot, Notion, and Lovable. This is what it does:

• Use cases sourced from X, LinkedIn, Grok Bot website, and around the web

• Weekly updates with newly sourced use cases

• Links to templates so you can copy

• Who to follow on X for everything Grok Bot

Grok Bot for GTM Encyclopedia

How I build the self-updating Grok Bot GTM Encyclopedia

A step-by-step look at how Grok Bot sourced, checked and published every use case at grokbotgtm.com, with the prompts so you can copy it.

Read more

Join 9,600+ GTM operators from OpenAI, Reddit, a16z, Figma and others who subscribe to learn how the fastest growing companies use AI in GTM.

I had all the intentions of sending this out over the weekend. But that’s what happens when you’re raising 2 toddlers. Plans change.

Last week, I hosted a Grok Bot + Clay workshop with SpaceXSI. Hundreds showed up. While we called it a workshop, it was more of a ‘jam session’ with Matt Silberman (GTM Strategy at SpaceXSI). It’s clear people want to see more of the art of possible an what others are building for inspiration. Subscribe above to get alerted for the next workshop.

I used Grok Bot to build an Encyclopedia of Grok Bot for GTM use cases. As promised, you can access it here:

Grok Bot for GTM Encyclopedia

Have you taken the 2027 State of GTM Engineering Report survey?

We already have 159 responses. Last year it took over a month of hustling to get that many and then more to get to 225. Taking the survey unlocks 35+ perks from your favorite GTM tools. This is the 2nd year and I run the report. We’d love for you to join:

Take the survey & unlock 35 perks

GTM Twitter is becoming a thing! Follow me on X here.

I’ll add a section on my weekly roundup to cover all the good stuff.

The round up:

1. 5 BIG ideas for GTM:

   1. Meltwater: final GTM frontier = next best action for everything

   2. Databricks: unit of value = customer outcomes

   3. a16z: the 5 founder archetypes that will thrive in the AI era

   4. Encore: website agent readiness scan

   5. Bessemer: best sales leaders = system thinkers

2. Top 10 highest paying GTM Engineer jobs

3. GTM Twitter roundup

Read more

Join 9,350+ GTM operators from OpenAI, Reddit, a16z, Figma and others who subscribe to learn how the fastest growing companies use AI in GTM.

The 2027 State of GTM Engineering Report is open for submissions!

Take the survey & unlock 35 perks

Last year, and I launched the 1st State of GTM Engineering report. The response surpassed our expectations:

• 225 GTM Engineers took the survey

• The report was downloaded 2,250 times

• ~50,000 report impressions

• Nearly 40% of downloads were from C-suite and managers

This year it will be even bigger with 35 partners who are providing perks.

We’d love for you to take the survey.

I’m eager to see what’s changed. We already know that the job is being offered $295k. What would I most like to see change?

• More equity for GTM Engineers

• Better organizational understanding of the role

• Reduced capacity bottleneck

I thought it’d be interesting to review the findings from last year.

Executive Summary: 2026 State of GTME

Compensation - $135k median salary for in-house GTMEs

US in-house GTMEs earn a $135K median - but the range runs from $60K to $250K+. Non-US peers sit at $75K median. Meanwhile, job postings tell a different story: across 224 listings, the median job posting salary is $150K. The gap suggests survey respondents skew lower partly because several companies declined to share data. At the bottom of the distribution, GTMEs are being paid $30K-$65K for roles carrying pipeline ownership, cross-functional authority, and technical scope that rivals senior engineering. The market has not priced this function correctly yet.

Equity - 68% have no meaningful equity

Despite owning pipeline, ARR, and the revenue infrastructure these companies run on, most GTMEs hold 0-0.10% or nothing. At Seed and Series B - stages where GTM systems are being built from the ground up - 70%+ carry negligible or zero equity. For a role this leveraged, that's a structural mismatch. We expect it to change as the function formalizes.

Agencies - massive range from $1,000 - $33,000 a month

Agency minimum fees median at $5K/mo, maximums at $8K/mo, but the full range ($1K-$33K) reflects a market still figuring out what it's buying. "Done-for-you outbound" and "done-with-you systems architecture" are not the same service - and aren't priced the same. What agencies do, how they work, and what they actually deliver varies enormously. Diligence the scope before signing.

AI tooling - Clay is near universal, but wishes for ‘all-in-one’ outbound

Clay sits at 84% overall adoption (96% among agencies) - but it's also the most-frustrating tool in the stack, not just the most-used. Cursor and Claude Code have hit 70%+ adoption with almost no frustration - the strongest love-to-frustration ratio in the data. Notably, Unify registers just 8.8% adoption despite positioning squarely in this market. That's interesting because the #1 tool respondents wish existed is an all-in-one outbound platform - cited by 12%. The demand is there; no single product has captured it yet. Unify and a few others may want to be that product - the data says the market is waiting.

Org understanding - majority don’t understand the role

Only 38% say their organization clearly understands the role. Another 44% describe it as partial at best - or a flat no. This isn't a messaging problem. It's a maturity problem. The function is new, the scope is wide, and most organizations don't yet have a mental model for what GTM Engineering actually is.

#1 Bottleneck - capacity

25% cite time and capacity as their #1 bottleneck - ahead of tool complexity (17%), internal buy-in (8%), and data quality (3%). GTMEs aren't blocked by budget. They're blocked by prioritization, headcount, and the compounding tax of operating as a one-person infrastructure team across 7+ workflow domains.

Breaking into GTM Eng. - 52% are self-taught

52.6% broke into GTM engineering by learning the tools on their own, not through a degree or a formal path.

Take the survey

Thank you in advance for helping to support this initiative.

And special thanks to Garrett Wolfe for this massive contribution to the community. This was all him. I’m just here to support and spread awareness.

Join 9,350+ GTM operators from OpenAI, Reddit, a16z, Figma and others who subscribe to learn how the fastest growing companies use AI in GTM.

Today’s download

1. A new way to save money on AI in Clay

2. Grok Bot + Clay workshop this week with SpaceXSI

3. Outbound AMA with Eric Nowoslawski

4. Deep dive: building a weekly audience & social media analyst report

5. Skill of the week: Ideal Customer Signals

6. Top 10 highest paying GTME job openings

A new way to save money on AI in Clay

We just announced auto-mode for Claygent (Clay’s AI research agent). Simply put, it auto chooses the right AI model for your task. Check out the video below.

There’s now at least 5 ways to save credits and money in Clay:

1. Conditional runs - only run an enrichment if something is true. For example, only enrich an inbound lead with a mobile number if the lead matches your ICP.

2. Jev - use the decision model (costs a fraction of generative AI) to decide if that profile meets your ICP. Check out my posts here on how to use Jev in Clay.

3. Budgets - setup budgets for everyone on your team using Clay.

4. AI analysis - use your Codex, Claude, or Grok Bot to analyze the use case to find opportunities to save credits.

5. Claygent auto-mode - the right model for the task is chosen for you.

Give me a shout if you have another clever way to save and I’ll feature it.

Grok Bot + Clay workshop with SpaceXSI

• When: this Thursday, Oct 29

• Time: 2:30 - 3:30PM EST

• Register: Grok Bot X Clay: Describe the outcome, skip the busywork

Cold Email AMA with Eric Nowoslawski

• When: this Friday, Oct 30

• Time: 1:30 - 2:30 PM EST

• Register:

Register to join on Friday

Eric Nowoslawski is one of the most well known cold email experts in the GTM community. He just returned to Clay after building Growth Engine X, an outbound agency that generates 60,000 positive replies per year. If you do cold email, you won't want to miss this one! Eric is sharing his secrets and answering everything. AMA is happening live and answers will be posted to r/gtmengineering.

The Grok Bot system & how to build it

The newsletter started to grow faster from an average of +20 subs/day to over +30 subs/day over the last several weeks. I increased my posting from 1 to 3 posts per week, but I struggled to figure out what:

• What’s working?

• What’s not working?

• Are the right personas subscribing?

• What should I do next to grow even faster?

Great use case for building an always on system that guides me on every week.

The system does the following:

1. Sends subscribers from Substack to Clay

2. Clay enriches emails for work identities and adds them to Audiences

3. Grok Bot pulls stats from all of my social platforms

4. Grok Bot aggregates all of the data, keeps snapshots in Notion

5. Grok Bot provides me with a weekly report, analysis, and recommendations

Every marketer needs a system to:

• Answer the same questions

• Double down on what’s working to generate more leads

This guide walks you through:

1. Where and how I extract data

2. Where data & stats are stored

3. 10 growth metrics I’m tracking with real data examples of my audience

4. Step by step on how I built everything (so you can copy)

5. Prompts you can use to get started

Where and how I extract the data

I’m growing 5 different channels. Each platform requires a slightly different way to pull data. This is how I extract the data out of each one:

LinkedIn (ME Alex Lindahl):

• Ordinal (via MCP) + CSV downloads (manual) + Grok Bot & Apify (scraping)

Substack (GTM Engineering Newsletter):

• WriteStack provides analytics on Notes

• Grok Bot

X (@gtm_engineer):

• Grok Bot natively connects with X to search, monitor, and read posts

YouTube (@gtm_engineering_ai):

• Grok Bot pulls public data on # of subscribers

• I’ll need to figure out how I get other data later for V2

Reddit (r/gtmengineering):

• Grok Bot + Apify

Where I store the incoming data

Clay Audiences - this provides me with up to 600 fields for people & companies. It’s the context layer for the GTM system. This is where I append enrichments to subscribers, note if a subscriber disabled email, their persona, and so on.

Notion - Grok Bot takes snapshots of followers, engagements numbers, and post stats so that they can be compared each week. The Bot automatically stores the data in a Notion DB. This is also a context layer, but for the content I’m producing and how it performs.

Both of these storage backends can be accessed via MCP.

10 growth metrics that I’m tracking (w/ examples)

The metrics and screenshots from V1 Grok Bot report.

Read more

Join 9,200+ GTM operators from OpenAI, Reddit, a16z, Figma and others who subscribe to learn how the fastest growing companies use AI in GTM.

I’m chronically online and these are the 5 big ideas I came across this week.

But first, a couple of fun things and a short rant after reading the Foursquare post mortem.

My Grok Bot for Clay guide is up on SpaceXSI. Honestly, kinda cool. I never thought I’d publish something on their website. It’s another example of how writing online can lead to unique opportunities.

Register for event here

It also takes guts. The Foursquare post mortem this week hit hard. The app defined a mobile era. Social and real time location sharing collided for the first time. After 17 years, the company was acquired. No one made any money. A real kick in the balls for their employees. I have so much respect for Dennis Crowley for sharing this. Despite the outcome, the company inspired many in tech.

This is why I hate it when leadership defines getting equity as a meaningful form of compensation. It’s not. It’s a lottery ticket. An incredible amount of well time events and company successes need to play out to make any money. I’ve been a part of 8 startups. Joined all of them at Series A. 4 became unicorns (including Clay). There’s still no life changing money. But don’t get me wrong, I have much gratitude for the one exit (Acquia at $1B, 15th employee) that resulted in a sizable mortgage down payment. And then there’s Snyk - the darling of the cybersecurity industry for a couple years. Snyk rose to $8.7B in valuation. I paid 6 figures in taxes when I left. The share price now? Plummeted. They raised nearly $1B. That means $1B needs to go back to investors before employees get anything. I’ll save that for another post and full breakdown of all the companies and financial results so far.

Subscribe now

On a more positive note - newsletter growth is starting to take off. The average daily signups have been going from 10 👉 15 👉 20 👉 30.

Oh btw, here’s a cool way to analyze Substack subscriber growth.

1. Download subscriber data

2. Upload to your LLM

3. Ask it for analysis

This is one of the charts I got from Grok Bot:

And speaking of Grok Bot - I’m hosting a webinar with the SpaceXSI team on how to use Grok Bot and Clay.

One last thought, companies are positioning themselves in more wild ways. Cold Caked launched and described themselves as the Palantir of booking meetings:

Love it. And here’s Clay’s rebranding:

And now for the 5 BIG GTM ideas from the week:

1. The brewing agent war in commerce (while others welcome them in)

2. Voice agents that call customers (it works)

3. You need a frontier team (why + the Harvey marketing frontier org)

4. Deterministic vs. reasoning GTM workflows

5. The State of GTM B2B (but here’s my take)

1. The war on agents has commenced

Agents can buy things for you now. That’s led to 2 different company responses:

❌ BLOCK:

• Amazon: sued Perplexity over Comet, then blocked Meta’s Muse, Grok Bot, and OpenAI’s agents

• eBay: robots.txt blocks agent bots, and its user agreement bans “buy-for-me agents”

• Delta: stops agents from booking tickets

• Yelp: only lets in agents that pay for its data

• JPMorgan Chase and Schwab: keep agents out of customer accounts, citing security

✅ WELCOME:

• Walmart: put its Sparky agent inside ChatGPT and co-built Google’s agent checkout standard

• Shopify: merchants’ products show up in ChatGPT, Gemini, Copilot, and Meta by default

• Target, Wayfair, Etsy: co-developed Google’s Universal Commerce Protocol

• Newegg: joined Google’s agent checkout on Oct 12

• Instacart: full checkout inside ChatGPT across 1,800+ retailers

Why would Amazon care? They generate $76B in advertising revenue every year. They need humans on the site for the ads to work. Agents represent a threat.

That raises a question… will advertising to agents become a thing?

2. Voice agents are calling customers to book mtgs

monday.com’s voice agent books 1,500 meetings a month. It calls leads, makes them laugh, qualifies them, and then finds time on the calendar.

Sounds simple. It’s not. The backend requires real time coordination with 4 subagents.

Surprisingly, customers will talk to agents. They got good feedback on it, too. I’m convinced this is the future of lead follow up. Read the full post on how Oran Akron, VP of AI GTM, rebuilt their $1B GTM engine with AI agents.

3. You need a frontier team

Every media/software company needs a frontier team. A dedicated squad tasked to absorb & understand the rapid developments of AI. The goal is focus them on experimentation so the primary workforce can focus on shipping.

Awesome episode on The Every Podcast. They built a team that balances 2 opposing personalities:

• The Pirates (The Experimenters): These are your chaotic, fast-moving team members. They love testing new beta models, stringing together unstable APIs, and breaking workflows to find unexpected efficiencies.

• The Architects (The Systems Builders): These are structured engineers or product minds. Their job is to take the messy, successful experiments from the “pirates” and turn them into stable, production-ready internal tools with clean code and sustainable infrastructure.

The structure of their team looks like this:

What does this look like in a software company?

Harvey shared how their frontier marketing org built agents that:

• Launch videos created entirely by agents

• Tailored demo videos for every customer using sales calls and account insights

• Script and voice hundreds of films overnight

This is built using:

• Product Map: agents crawled Harvey’s whole app, clicked every button, and rebuilt the key screens as Figma components. That library is the single source of truth for what the product looks like.

• Motion Primitives: a Figma library of reusable animated pieces, like camera pushes, screen handoffs, a steady click rhythm, and redlines arriving in a document. A fix to one piece updates every film that uses it.

• Agentic Brand Guidelines: a Figma library that works like a top-level AGENTS.md (a set of instructions agents read first). Each project folder adds its own instructions on top. They moved off markdown because words describe layout and the relationships between elements badly.

4. Deterministic vs. reasoning GTM workflows

Workflows (like what you build in Clay) are largely deterministic. There’s specific steps that are taken to produce an outcome. We’re much more comfortable with these because as humans we love control. Easier to get buy-in for and less risky (to start).

Clay also has agents, which are non-deterministic. They use reasoning, tool calls, skills, and context to produce an outcome. This incorporates more data and ultimately get to better lead qualification (as one example).

The majority of use cases are deterministic today. My sense is that this will switch to agentic / reasoning as the predominant way to get to outcomes.

5. State of B2B GTM: investment areas for 20267

My friends Kyle Poyar and Maja Voje shared the State of B2B GTM. This is GTM Strategist recap of the report.

These are obvious investment areas from mainstream B2B.

This would be my top 5 areas of investment (based on what I’ve learned and what we’re doing at Clay:

1. Build internal media - output high end viral videos

2. Assemble an internal creator team - people trust people over brands

3. Operationalize an exec posting program - experts help them go viral

4. Experiment with different channels - youtube, instagram, x

5. Build a frontier team within GTM - experiment & then scale the cutting edge

Subscribe now

Marketing Engineering University

Most marketing teams have added AI this year and are still rewriting what it produces, because nobody has given it any context about the company.

Matteo Tittarelli's program, Marketing Engineering University, starts there. Over four live weeks from 30 Nov, you build a company brain covering your competitors, ICP, positioning, messaging and brand voice, and then you turn your repeat work into skills and agents that use it.

He has run this with more than a dozen teams this year. Good Systems, the company behind Goodnotes, says its team "10x'd its impact", and Seapoint tripled its monthly signups at launch.

gave me a secret promo code to give you a discount.

Signup with promo code: LINDAHL15

Disclosure: I earn a referral fee if you enroll with my code.

Join 9,200+ GTM operators from OpenAI, Reddit, a16z, Figma and others who subscribe to learn how the fastest growing companies use AI in GTM.

Oran Akron built and led operations at monday.com that supported org scale from 60 to 1,800+ employees. Revenue scaled from ~$10M ARR to over $1B ARR during the same period.

Over the past year, Oran became the VP of AI GTM and rebuilt their GTM systems to be AI-native. It is one of the most advanced AI implementations I’ve seen in GTM. (And, I’ve talked to 100s of Clay customers.)

In early 2025, an inbound lead at monday waited about 25 hours on average to hear back from a human. monday had tens of thousands of inbound visitors a day and a sales team spread across New York, London and Australia. It still couldn’t staff 24/7 coverage around the world.

By the time Oran left monday last week, it was < 2 minutes. An AI voice agent calls the lead, qualifies them, figures out which rep owns their region and segment, checks that rep’s calendar and books the meeting while the prospect is still on the phone. It books 1,500 meetings/month!

Test your speed to lead

Oran and I have become good friends over the year. He came through the Clay studio in New York last week walk me through how they did it. The full video is coming to GTM Engineering & AI YouTube channel. This is the written version, with the parts I think other GTM teams can learn from.

In this post, you’ll learn:

• What GTM agents he built for monday.com’s GTM team (and what they do)

• How their voice agent wins the conversation in the first 5 seconds

• What the day-to-day management of agents looks like

• How team structure changed from pre-AI to AI-era

• His strategy for leadership (and broader org) buy-in

• Learnings from the build out.

Before diving in, you’ll also want to check out and …

Upcoming events I’m hosting:

1. Clay + SpaceXSI webinar: How to build GTM with Grok Bot & Clay

2. Cold Email AMA with Eric Nowoslawski: Submit your questions here

Enjoy!

Take the 2027 State of GTM Engineering Survey.

You’ll get 29 free GTM perks that includes

• 20% off Apify

• 1 Year of Firecrawl Hobby Plan for free

• Early access to Attention Self-Serve 7 Days free.

Unlock 29 free GTM perks

How Oran brought GTM at monday.com into the AI-era

Eight years on a rocket ship

Oran joined monday in 2018. The company had 60 to 70 employees, around $10M ARR, and one revenue motion: self-serve. There was no CRM, no sales tech stack, no shared sales terminology. He and monday’s CRO at the time, Yoni, built the sales org from scratch, and Oran grew RevOps from himself to a global team of 70 to 80.

Today monday does roughly $1.4 to $1.5B in ARR. “It’s a rocket ship that never ends,” he said. “You never have really the time to maintain what you build.”

Each phase needed something different. 2018 and 2019 were foundations: he signed more than seven vendors in one year just to have a working motion. Around Covid it was scaling and finding new growth engines. Then offices opened globally, then came regional plays and multi-product selling, which meant reworking quota, comp and how information reached reps in the field.

Pre-AI, his org looked like most RevOps teams: regional RevOps partners for the customer-facing teams, analysts, enablement, strategy and planning. About 70 to 80 people supporting more than 1,800 people in sales, customer success, services and partners.

Pre-AI: RevOps was “the back office supporting the humans.”

AI-Era: “I managed the agents that manage customers. I became the front line of the business.”

The first meeting

In February of 2025, one of monday’s investors asked Oran to visit a company they were evaluating and give an operator’s read on it. The company was Clay. Surprisingly, Oran hadn’t heard of us.

He walked into the office. “It felt like Monday in 2019” he said.

I think it would’ve gone much different if I had treated it like a typical sales meeting. But, as Becca (our Head of Sales) likes to say “I like to go off-roading”. The meeting was more of a jam session, or mending of the minds. Our joint excitement compounded. It was clear we both shared a vision for the future GTM.

We built workflows live. I brought up my personal stash of experimental Clay tables. Spoke frankly about what works in Clay and what doesn’t. Opinions on the future of GTM. And answered a TON of rapid fire questions from Oran. It was quite fun.

He described it as “a slap in my face.”

In 2018, Oran’s team spent about a year building monday’s lead pool: enrichment, routing, scoring, dedupe and distribution for thousands of inbound leads a day, knowing which ones were sales-relevant and which rep should get them. It worked. It took a year to get right. Watching the demo, he realized he could rebuild it in a month with Clay.

“I felt, okay, we are not in the game.”

He flew back to Tel Aviv and talked it through with monday’s CEO. His pitch was simple:

If we had to build the GTM org from zero today, we wouldn’t build it the same way.

They agreed to try one flow and make it agentic.

Pick the first project for the wow, not just the ROI

This is how he got started.

Oran took one person from RevOps and one developer and picked a single top-of-funnel inbound flow. He chose a voice use case on purpose.

There were several reasons for the use case - all part of his strategy to get more buy-in to do more:

• The problem: Speed to lead was bad, and he believed fixing it would lift conversion at every stage below.

• Internal PR: He knew agents joining the workforce would be a change management fight, and voice had the same effect in early 2025 that ChatGPT had the first time it talked back to you.

• Cool demo: He wanted a real call between an agent and a customer that people would forward to each other on WhatsApp.

It worked.

monday formalized the effort as an internal startup called Rev AI, separate from day-to-day ops, with one mandate:

Break the existing motions and rebuild them with agents.

Oran ran the team with 2 rules:

1. Build, don’t buy. Oran didn’t want to wait for a vendor to adopt the next model or technique. If something better shipped, his team wanted to use it that week.

2. Ship at 60%. “You cannot predict interaction between AI and the customers in advance. You cannot simulate all of the possibilities.” They released early and tested on real leads.

Inside the voice agent and it’s 4 subagents

There is so much more to building a voice agent than I expected.

Here’s the flow. A form fill comes in and gets pre-qualified as either support or sales. Sales requests are picked up in seconds. The system researches the lead, works out what they likely need, and loads that context into the voice agent before it dials.

This is where it gets interesting.

During the call, four agents work together to support the voice agent:

• an intent agent that figures out what the person actually wants

• a qualification agent

• a commercial agent

• a scheduling agent

I had to ask, why not one agent?

Latency and hallucination.

The moment you inject too much tasks or context or instruction to one agent, then the more possibility that it will hallucinate.

Oran said the harder problem was the handoffs, passing variables from the intent agent to the qualification agent mid-conversation so the next agent picks up exactly where the last one left off. They use ElevenLabs for the voice. But it isn’t the product.

Next, they built the orchestration and semantic layer around it, partly in code, and that’s where most of the learning came from. An earlier platform treated the call as a rigid workflow, and the agent got confused and hallucinated for nearly 20 minutes.

Calendars and matching availability was surprisingly hard.

The agent has to know the lead’s region and segment, map that to the right reps, then read live availability. Before they fixed it, the agent would tell prospects: “We’re available at 8am, 8:15am, 8:30am, 8:45am...” and keep going. The fix was a set of microservices between the voice model and the third-party tools (Chili Piper and the reps’ calendars) that translate those systems into something the agent can reason about. Oran also noted that agents need different calendar access than humans do, which nobody had set up for.

Then there’s phone infrastructure.

Calling at volume means rotating numbers by state through Twilio and keeping those numbers from getting flagged as spam.

The first 5 seconds are critical to win the convo

Even though I’ve been at the epi-center of GTM innovation, I’ve always been skeptical if humans will engage (and at meaningful length) with agents.

I had to ask the obvious question: do people stay on the line once they realize it’s an AI?

You’d be surprised. People love it.

Not everyone, he said, but most. The big finding was that the first five to ten seconds decide the call. Oran believes agents should have an engaging, funny personality, so the greeting is always a joke. They have recordings of prospects laughing and telling the agent it’s the best AI they’ve ever talked to.

That’s also brand. Monday wanted the agent to sound like Monday: fun and a little ahead of everyone else.

The numbers and results

On a monthly basis the inbound agents handle:

• 20,000+ leads

• 40,000+ emails

• 10,000 to 15,000 calls (not every lead has a phone number, and not every call is answered)

• ~1,500 discovery calls booked for reps

The agent works across voice, email, SMS, in-product and WhatsApp. (”In Brazil you need WhatsApp. I didn’t know that.”) Oran said the hardest part wasn’t any one channel. It was keeping one coherent context per lead across all of them, so the agent can call and say, “I wanted to follow up on the email I sent you,” the way a human rep would.

How he got leadership onboard

The biggest bottleneck for AI adoption is not the tech, or learning how apply AI. It’s gaining leadership buy-in to truly do something new. And then there’s the fast follow of change management.

monday’s leadership backed the project, but Oran said that’s not why it spread.

To be honest, 50% of my job was more change management than technology and AI.

The method was data-driven expansion. The agent got a slice of the lead population. Only when its results matched or beat the humans on that slice did it get more. They worked with reps in the field the whole way so it never felt like the agent was taking their work. Add executive support, real results and call recordings anyone in the company could listen to, and eventually other teams started coming to Rev AI asking for help with their own use cases.

What managing an agent looks like

The team was 10 people at most: AI developers, AI engineers, an AI architect, analysts, and what Oran calls agent strategists. Many of the strategists came from RevOps or product management.

The thing that surprised him most was watching one agent strategist manage two top-of-funnel agents that were handling more than 10,000 leads in parallel. A rep might carry 50 accounts with RevOps, SDRs and CS around them. Here, one person tunes one system, “and immediately, tomorrow, you see different results.”

Every morning the team ran a ceremony. First the KPIs, to make sure everything was running. Then about 20 minutes of evals. You can’t listen to 10,000 calls, and Oran didn’t want to hire 20 people to try, so they built automated evaluation of every call and email against set criteria: agent behavior, technical failures and so on. That meeting produced action items every day, down to which individual words the agent used that moved results.

The end goal was to automate the ceremony too. On self-improvement, Oran said what’s usually missing is a definition of what good looks like. GTM has an advantage here because good is a number: a conversion, a call that hits a set of criteria. Get that defined and “you are 50% ready.” The rest is capturing and modeling the data so feedback flows back into prompts, context and tools. His ideal was a system that rewrites its own prompts and context, and even tells its manager which capability is missing. He said they got some of the way there.

Agents two and three

The voice agent was just the start. Now there’s a growing team of agents. These are the next 2 that got added:

PLG onboarding. Most people who come to Monday sign up and try it free. They didn’t come to talk to sales, so the agent’s first move couldn’t be qualification. Instead it helps them onboard and shows them the capabilities that fit what they’re trying to solve. Oran said:

Users who interacted with the agent in-platform adopted more than 50% better, and it moved conversion to paid.

Oz. The outbound agent started as account research and outreach, then grew into a copilot for reps called the OZ agent (as in Wizard of Oz). It’s an agentic layer over Monday’s systems of record, Gong, Snowflake and Salesforce, behind one interface the team built. Reps delegate tasks: pipegen outreach, CRM updates, creating and updating quotes in CPQ. Oran’s framing for it: if you manage 80 accounts today, how do you manage 200 with the same hours? Hand Oz the work that isn’t selling.

UI is commoditized, this is what’s next for reps

I asked what the rep’s interface looks like in a few years, since I go back and forth on it.

Oran has a firm view.

Pre-AI: UI was how a vendor steered you down the flow they wanted.

AI-era: Now everyone is used to iterating with ChatGPT and Claude, so the expectation has flipped: the user sets the tone, not the vendor. Chat will always be part of it, but he expects the same software to look different for every rep, designed by the rep or generated by the AI. Four hundred reps means four hundred working styles.

The real value will be behind the scene. It’s not about the UI anymore.

What’s next for Oran

Last week was Oran’s last at Monday. After eight years he’s starting his own company.

He says he keeps seeing the same pattern in companies that reach out to him for advice. They change how their engineers work with AI, move on to GTM, and get stuck. His read is that they’re treating AI as one more tool or point-solution agent dropped into an existing workflow. The question he’d ask instead is the one he asked his CEO in early 2025:

If you were building this from zero today, would you build it the same way?

He thinks there’s a two-to-three-year window that’s going to shape how GTM works, and he’d rather shape it than react to it. I’ll be following along.

I’ll let him share more about the new company when he’s ready.

Subscribe now

Next event: Building with Grok Bot & Clay

I’ve been cooking with the SpaceXSI team on some cool upcoming content. Next week, we’re hosting a join webinar on how to build with Grok Bot & Clay.

• When: Wednesday Oct 28

• Time: 2:30 - 3:30PM EST

• Register: sign up here

• Who:

  • Alex Lindahl (me), Creator in Residence, Clay

  • Matthew Silberman, Marketing Operations, SpaceXSI

​What you’ll learn

• ​Build Clay workflows in plain language. Describe the inputs, steps, and output you want, and Grok Bot builds the workflow in Clay.

• ​Cut outbound prep down. Automate account research, signals, contact enrichment, and personalized drafts so reps can focus on reviewing messages.

• ​Follow up on inbound leads in minutes. Enrich the lead, assess fit, route it to the right rep, and send a personalized email in less than five minutes instead of hours or days.

• ​Go beyond the Clay UI. Have Grok Bot fill out web forms, log into apps, remember what’s working, and pull Clay data from your phone.

Join 9,150+ GTM operators from OpenAI, Reddit, a16z, Figma and others who subscribe to learn how the fastest growing companies use AI in GTM.

Town announced today it raised $108M Series B at a $1B valuation.

Link to Jean-Denis Greze post.

This is months after they publicly launched in June with a $55M Series A. Within weeks of launch, Town pulled in 7-figures in revenue.

We use Town at Clay and we love it. Their Personal AIs, called Townies, learn how you work, maintain a wiki on you (think of this as your ‘how you work DNA’), and then do work.

Town is moving from providing 1-1 assistants to assistants that work together and across your team. This fills a gap that is missing from personal AIs today:

Today your work assistant knows you, but it can’t work directly with your team. A team bot learns from team channels, but it can’t see anyone’s inbox or preferences, so it never really knows any one person. And in neither case can they work well with folks outside the company.

What if everyone had an assistant that understood the individual and the team? An assistant that understood what to keep private and what to share? An assistant that could handle the back-and-forth just like a human does by using the same emails, texts, and chat we do?

The rise of personal AIs raises a few interesting GTM questions:

1. How do you use personal agents for GTM?

2. Are there different motions to bring agent to market compared to software?

3. Will all of these agents converge to the same set of use cases? And then, how do you differentiate your agent from others?

4. How will companies respond when agents start taking actions for humans? (e.g. buying something on Amazon, which then disrupts Amazon’s ad business)

To answer #1, I hosted their founding Growth Engineer, Adam Reisfield, on the GTM Builders’ AMA that I host: How Town’s growth team uses Town for growth.

The following is a recap of the AMA. Plus check out our previous AMA recaps:

Share

How Town uses personal AI in marketing

Town's head of marketing used to DM Adam Reisfield whenever she needed to know how a system worked or how to set up a campaign. Now she opens a thread with his agent. It answers more than 80% of her tactical questions, based on past conversations it's had with Adam.

The agent is named Lebron. He’s a Townie, the personal agent Town sells.

Routines are agent skills Townies use to get stuff done.

Adam shared a few use cases on how they use Lebron internally.

This is how they use Lebron in marketing:

1. Town + Clay for inbound workflow triaging and automation

2. Capturing the voice of the customer after each customer call

3. Turning insights from calls into SEO & GEO optimized pages

4. Weekly funnel analysis and doc

5. Marketing team help desk

6. Town + Clay for recruiting

Town for inbound lead enrichments & follow ups

AMA Question: How should I use Town for inbound leads enrichments and follow ups?

Adam:

Good question! Town + the Clay integration is a great combo here 👌

The real skill here is using Town to build a skill (or clear criteria) for how you define your ICP. The better Town understands who you actually sell to and what a good customer looks like, the more useful it gets for inbound. You can even ask Town to review past conversation transcripts, CRM data, internal conversations, etc., to help sharpen this definition.

From there, it helps you:

• identify which inbound leads are truly ICP-aligned

• draft better, more personalized follow-ups that pull in real examples of how similar customers use your product

Then the setup:

1. A routine that fires on new inbound (form fill, demo request, or new HubSpot contact).

2. Enrich via Clay connected to Town (role, company size, funding, LinkedIn) plus a quick web pass (Town has invested heavily in best-in-class, deep people research with specialized integrations)

3. Score against your ICP skill and write it back to HubSpot.

4. Town drafts the follow-up in your voice with relevant customer examples and your calendar link. Start in draft mode and review before anything sends.

5. A second routine bumps no-replies after a few days - and you can also plug into existing outreach tools.

Ultimately, the hardest part of this process is knowing who your real customer is. Once you get that down, there are a bunch of different ways that you can implement the actual workflows, and Town is absolutely one of them.

Easiest start, share a prompt like this with your Townie: help me crisp up my ICP definition and then set up a workflow so we enrich inbound leads with Clay upon signup, log data back to our CRM, and help me draft not only the personalized follow-up message but also the bump sequence. Ask me critical questions to refine this workflow and the integrations.

Capture voice of the customer with routines

Capturing the voice of the customer is so important to everything we do, especially broken down by persona. After every customer call, a workflow fires that looks at the user’s persona classification, extracts customer quotes, product feedback, pain points, how they’re articulating value, etc., into structured data, and logs that information to a document (soon to be a spreadsheet).

With that foundation of customer voice in place, when we do things like create landing pages or build custom routines for specific personas, we’re grounding that in the real voice of the customer.

Turning voice into SEO/GEO optimized pages

AMA Question: Are there any cool growth loops where Townies have helped Town grow faster?

Adam:

One loop that comes to mind is turning what we’re hearing on customer calls into content (and there’s so much more that we can/should be doing here, to be honest).

Because Townies know about what we’re discussing with customers on calls, they can help turn that into rich content for us. One example is our routine library: https://www.town.com/routines

After learning about a ton of different rich workflows that customers were deploying, we then used our Townies to figure out, for different personas, what some of the top routines are. We then turned those into SEO/GEO-optimized content to not only help with activation for new customers by allowing them to deploy these routines (both from our marketing site and also in-app), but also drive sessions by expanding our website footprint.

Weekly funnel analysis & reporting

Each week the team meets with Town’s CEO to walk the full funnel and decide what to start, stop or continue. A Townie drafts the doc for that meeting.

It combines the numbers with updates and questions people raised in Slack and in recent Granola conversations, so the doc explains the data instead of just listing it. And it cuts the data by persona against baseline over different time periods, which Adam said “would be hard to do every week manually.”

The output is a Google Doc with tables, headers and bullets, “so that we can leave comments on the data, as opposed to just looking at a dashboard.”

Marketing team help desk

Back to Lebron. Town lets you put several Townies in one thread, and on a team they treat each other as trusted contacts. They share a lot without asking for approval, and still check with a human for some things.

Lebron knows how Town’s HubSpot is set up, how they enrich, and how HubSpot and Hex connect. That’s why the head of marketing asks Lebron instead of Adam. His summary: “Taking an individual’s specialized knowledge, and making it easily available to anyone else on the team - without needing to send them a million slacks.”

Routines travel too. When two people joined the enablement team, they installed three of Adam’s routines right away: meeting notes to HubSpot, the post-call follow-up drafter, and the voice-of-customer logger.

Using Clay + Town for recruiting

Try out Town: extra credits & time on trial

from Adam:

If you're interested in checking out Town, you can sign up with this link for an extra week on your free trial, plus an extra 5k credits (2x standard offering) during your trial:

Grab the extended trial & credits

Check out the Clay skills:

• Candidate sourcing run - fill any role’s shortlist without the manual sourcing grind

• Attendee research brief - research every attendee before you meet them

• Closed-lost win-back - win back my closed-lost deals when the timing’s right

• Target account news digest - know what’s moving at your target accounts

Btw, check out Adam’s 15 Tips for using Town

So many good tips:

1. Talk to your Townie like a human assistant. Be direct about the task and the goal. Adam says you don’t need to dig through settings or config.

2. Give feedback constantly. If a draft doesn’t sound like you, give it notes the way you would a new hire. It doesn’t need special training to learn your voice.

3. Show it what good looks like. Share an ICP doc you already have, or outreach emails that worked.

4. Connect your sources up front. Granola, Slack, Hex, Notion, your inbox and calendar. Then you don’t have to paste context into every strategy conversation.

5. Ask it for use cases. Tell it the job and the problem: “This is the job to be done. This is the problem I’m trying to solve. How can you help me?” It suggests routines from what it knows about you and can build the ones you pick.

6. Ask it to question you. End setup prompts with “Ask me critical questions to refine this workflow.” That surfaces the details you’d forget to mention.

7. Define your ICP before automating anything. Have it sharpen your ICP from past transcripts, CRM data and internal conversations, then save that as a skill other routines can score against.

8. Start routines in draft mode. Review emails before anything sends, and turn on auto-send only once you trust the output.

9. Log customer calls as structured data. After each call, have a routine pull quotes, pain points, feedback and how the customer describes the value they get, tagged by persona. Landing pages, content and meeting briefs can all build on that log.

10. Run routines before and after meetings. Before: a brief with the person’s background, CRM history and usage data. After: a follow-up in your voice with next steps, links to your docs, and notes logged to your CRM.

11. Watch for signals, then draft the email. Pair Clay with routines that look for buying signals, like new locations or a new CMO. When one hits, the Townie drafts the email tying the signal to your product instead of just alerting you.

12. Share routines with teammates. New hires can install your best routines on day one instead of rebuilding them.

13. Let teammates ask your Townie. Add it to threads so colleagues can get answers about your systems without DMing you.

14. Cc your Townie on scheduling emails. It works out availability with the other people in the thread directly, like an EA.

15. Clean up your data first. Good tracking, metric definitions and a solid CRM and BI setup make every routine more accurate.

How to use Clay with Codex

New video is up on the GTM Engineering & AI YouTube channel.

Everett Berry, former Head of GTM Engineering at Clay, walks us through how to use Clay inside Codex:

New here? Hi, I’m Alex Lindahl, Creator in Residence at Clay. Find me on GTM Engineering YouTube channel, X where I post daily GTM plays, and r/gtmengineering where I host weekly AMAs. Let’s Connect on LinkedIn.

Join 9,000+ GTM operators from OpenAI, Reddit, a16z, Figma and others who subscribe to learn how the fastest growing companies use AI in GTM.

Did you catch Tesla’s flying car demo last night? It’s bonkers:

Is this a glimpse into a Tesla / SpaceXSI merger?

Speaking of SpaceXSI - I talked to one of their Strategic AEs this week. He shared how they use Grok Bot to find warm paths into accounts at scale across the team. IMO this has never really been solved and I think this is it. More on that below.

Today’s post is a start of a new series where I share 5 big ideas in GTM that you should know about.

This week’s BIG 5:

1. Agentic qualification at OpenAI - from the OpenAI AMA with Keith Jones

2. Rise of the Creator in Residence - why every marketing team will need one

3. Community events in nature - an event format more should copy

4. Ramp’s multi-player AI work system - every company needs this to ship faster

5. SpaceXSI’s Grok Bot warm intro path finder - how to find warm intros at scale

Plus: the Clay play of the week & how to scrape public databases.

1. Agentic qualification will replace lead scoring

Keith Jones recently left OpenAI where he was Head of GTM Systems for the last 3 years. He started a new venture, Grey Company, and held an AMA on building GTM Systems at OpenAI on r/gtmengineering yesterday. Pour yourself a bourbon and cozy up. The entire thread is worthwhile to read. I’ll post a more thorough overview soon.

Why agentic qualification will replace traditional lead scoring:

2. Rise of the Creator in Residence

Alex Saunders called this out first on LinkedIn. I’m obviously bullish since I left sales at age 40 to be a Creator in Residence at Clay. Will this become the breakout role after the GTM Engineer?

The GTM Engineer was about building the machine; Data, signals, outbound, automated end to end. The Creator in Residence is about making people want to use it. These companies already have the product. What they're investing in is distribution through a person. Someone who does the work, then shows the work.
The best marketing comes from practitioners, not from the brand account.

This goes hand in hand with the idea 'distribution is the last moat”:

My advice - start building an internal media & creator team. Your 2027 will thank you. Platforms are putting out more incentives for UGC too. X announced this:

3. More community events in nature please

Work-Bench (a VC firm in NYC) brought 80 talented operators to a 1 day retreat in Hudson Valley this week. I got to co-lead a breakout session on “cool ass marketing”. My takeaways:

• We need more events in nature. It felt more intentional. Relaxed.

• Loved the socratic seminar breakout sessions.

• NYC talent is cracked. Consider NYC if you plan to start a company.

David Politis former co-founder of BetterCloud, shared 5 ways to build trust (one of the most important things to do if you want to move into leadership).

This was the format if you’re looking for event inspiration:

1. Keynote speaker

2. Fireside chat

3. Lunch

4. Scavenger hunt - this was basically time to make friends with an activity

5. Breakout session A - 5 group topics to choose from

6. Breakout session B - 5 group topics to choose from

7. Breakout session C - 5 group topics to choose from

8. Happy hour + dinner

4. Ramp’s multi-player AI system for productivity

David Senra asked Ramp’s Co-Founder, Eric Glyman “How does Ramp move so fast?”.

The answer is Ramp’s internal “Glass” AI platform that distributes skills & solutions (that were previously solved by 1 person) into the the workflows of everyone.

Rather than have me explain, let me show you.

Today, I asked Glass how many U.K. customers we’ve added since launching. It knew to exclude U.S. companies with U.K. offices because Finance taught it that months ago. I didn’t explain a thing.

A trivial improvement, you may say—not worth bothering about. But multiply that by every employee, every day, and your company gets better at work every time someone does it.

So far, 323 employees (PMs, CX agents, salespeople, marketers, analysts) have made 6,500+ edits to what Glass knows.

Take new feature requests. They used to be copied by hand into Salesforce, about 300 a month. In May, an account manager saved a prompt to Glass that pulls them straight from customer calls. Now we capture 20,000 a month. Another prompt prioritizes them. A third emails customers when something they asked for ships. More than 5,000 of those emails have gone out so far.

No model ships with your company’s brain.

5. How to use Grok Bot for finding warm paths

There’s a couple Grok Bot capabilities to understand first.

1. It has its own computer in the cloud. Every account comes with a full desktop, browser, and system, so the bot loads and uses web pages just like you would.

2. It clicks and types like a person. No API or MCP integration needed. It opens the app, reads the page, and clicks through it.

3. It stays logged in. Log it in once and it remembers. Cookies and sessions carry over from task to task, so it picks up where it left off.

4. It uses connectors when they exist. For tools with a direct integration, it skips the browser and connects straight in through connectors or MCP.

5. It hands you the tricky login parts (if needed). When a site asks for a password, a captcha, or a verification code, it pauses and passes that step to you instead of trying to force its way through.

This enables a Bot to log into Sales Navigator’s relationship explorer to see the common connections. Doing this just for your account isn’t that powerful. But when you have a Team Bot, it can look across everyone’s relationship explorer.

I asked if this would get flagged by LinkedIn. No one is a 100% sure, but the team doesn’t think it would get flagged for a couple reasons:

• Bots mimic how a human would navigate a website

• You can route your Bot traffic through your location

• It doesn’t push too hard and stops when it runs into potential blockers

I haven’t tried it yet, but this super clever.

Clay play of the week: gov databases

Follow our account on X for daily use cases & workflows you can build on Clay.

A security camera company had the same problem every seller has. Their “improve workplace safety” emails sounded exactly like everyone else’s.

So they stopped guessing which accounts cared about safety.

They looked it up, in a government database.

Here’s how the play works & how to build it 👇

A security-camera company used Claygent and Operator (an AI agent that browses websites) to search OSHA severe-injury reports and complaints for each target business. It counted that business’s incidents over the past year and used the count as personalization: how its cameras help reduce exactly the kinds of incidents on that business’s record. The signal is public, specific to each account, and almost nobody else uses it.

How to build it

1. Import target businesses into Clay along with the inputs for an OSHA report search.

2. Use Claygent and Operator to find each business’s severe-injury reports and complaints.

3. Count incidents from the past year, then score and filter the accounts that have relevant ones.

4. Write the evidence into approved personalization fields for the pitch.

What you need: Clay with Claygent. Effort is medium, credit usage is medium, and you can see value the same week.

Who it’s for: GTM engineers, SDRs, and AEs.

What you get: A scored list of accounts with incident evidence attached, plus personalized outreach copy.

Watch for: Injury records are sensitive. Keep the tone helpful rather than accusatory, have a human approve the copy, and double-check that each record matches the right business location. The source was a hackathon project, so it’s a proven concept but not yet a production result.

Share

Join 9,000+ GTM operators from OpenAI, Reddit, a16z, Figma and others who subscribe to learn how the fastest growing companies use AI in GTM.

In this newsletter:

• AMA w/ OpenAI’s former Head of GTM Systems

• 4 BIG announcements from Sculpt

• How GTM learning loops happen today

• What is loop engineering (aka learning loops)

• 5 stages of a GTM learning loop

• 3 types of Memory for loop engineering

• 5 GTM examples of learning loops

Reddit AMA with OpenAI’s Head of GTM Systems

OpenAI’s former Head of GTM Systems, Keith Jones, is joining us tomorrow!

Drop your questions on this thread. The AMA will be this Thursday Oct 15 at 12PM EST on r/gtmengineering.

4 big things you should know from Sculpt

We announced 4 things (the 4th is the most important:

1. Workflows

2. Global Clay agent

3. Inbound SDR agent

4. Self-learning revenue engine

The primary goal of the inbound SDR agent is to increase your speed to lead. Yours might be slower than you think. Test your speed to lead here

Self-learning revenue engine

This is the future of Clay & GTM. I believe these types of systems will counter moat erosion and be a key way teams gain a competitive edge.

Executing campaigns is easier and faster than ever. Teams get their real competitive edge — or GTM alpha — from how well they can learn. The teams that grow fastest get better with every campaign. The results of each campaign influence changes that raise the quality and conversion of the next.

Learning at that pace takes a self-learning revenue engine. A single system needs to be able to act on the full picture of your existing customers and your addressable market. It then needs to be able to learn from the results of its actions to improve: how should you adjust plays? What plays should you try for the first time? How can you expand your ICP? - Building the self-learning revenue engine.

Self-learning comes from loop engineering in software development. We’ll dive into that in more detail.

Satya Nadella, CEO of Microsoft, said on X that the future of digital systems is essentially loops between human capital & token capital.

Learning loops already occur in GTM today

They are just slow.

Think about QBRs. They include reflections of the past quarter to apply learnings to the next one.

The same thing happens with outbound campaigns. Only manually, and not very scientifically. SDRs review email campaign performance, repeat what works, and maybe try something new.

Similar learnings happen in performance marketing, customer success teams, marketing campaigns, and in almost every corner of GTM.

Humans do the learning, then update the system.

We’re now moving into a world where the system learns and then updates itself.

We call this self-learning in GTM. In software it’s called loop engineering.

What is loop engineering?

Boris Cherny, creator of Claude Code, famously said

I don't prompt Claude anymore. I have loops running that prompt Claude and figuring out what to do. My job is to write loops.

Andrew Ng explains in more detail:

It becomes easier to understand loop engineering when we compare it to prompt engineering.

A prompting workflow looks like the following (most of us are here today):

Prompt → review response → you refine → prompt again

• YOU act as the feedback loop

• YOU are also the bottleneck

You might have to repeat that process many times before you get to the goal.

Loop engineering goes beyond prompt engineering. It takes the human out of the iteration process. Instead, you write a loop. AI iterates until it achieves the desired outcome.

IBM defines loop engineering as the following:

Loop engineering is the practice of designing agentic workflows, or loops, that iteratively guide AI agents toward completing user-defined goals with minimal human intervention. Rather than require human prompting at every step, agent loops enable agents to dynamically act, observe, make decisions and iterate until a task is complete.

For developers, loop engineering reframes their role away from prompting AI agents toward one in which they design automated systems that prompt, check and guide agents. Within a well-designed loop, an agent is able to reason, act, review the outcomes of its actions and adjust its subsequent actions accordingly.

A loop engineering workflow looks like the following:

Provide a goal → agents act → verifies its own work → corrects → repeats until done

• The SYSTEM is now the loop

• YOU engineer the system

Here’s a visualization from the Product Compass newsletter to help you see it.

Great post for more on the topic:

The Product Compass

Agent Loops for PMs: 20+ You Can Run This Week

A loop does not fail because the agent keeps working. It fails because nobody told it what “done” means…

Read more

4 months ago · 78 likes · 1 comment · Paweł Huryn

The future of GTM is applying loop engineering to everything we do.

There’s 5 stages of a GTM learning loop

Learning loops can apply to anything in GTM where there’s an action, observable data, and desire to improve the play/system.

There’s no standard definition, but these are the general stages and mapped to a GTM example.

Stages mapped to a GTM loop (for prospecting):

• Trigger. This is what starts the loop, like an event, a schedule, or a signal. In GTM, that could be a target account hiring a new VP of Sales or visiting your pricing page.

• Context gathering. The agent pulls the data it needs before acting. Here, Clay enriches the account and contact with firmographics, the tech stack, recent posts, and CRM history.

• Action. The agent does the work. It writes a personalized first email based on the hiring signal and the person’s recent LinkedIn post.

• Evaluation. The output gets checked against clear criteria before it ships, by a rubric, a second model, or a human. A grader scores the email on relevance, accuracy, and tone, and anything below the bar gets rewritten or sent for human review.

• Feedback and iteration. Real-world results flow back to improve the next run. Reply and meeting rates by signal type update the prompts and scoring, so hiring signals that book meetings get prioritized and weak ones get dropped.

The key idea is that stages 4 and 5 make it a loop rather than a one-shot automation, because the system gets better on its own over time.

One of the reasons I love Grok Bot is because it has built in memory. That enables you to build mini self learning systems. An example of this is my hook generator I talk about in this post:

Loop engineering needs memory to work

Memory is the piece that lets the loop carry what it learned from one run into the next, so it doesn’t start from scratch every time. It usually sits next to feedback, or works as a sixth stage feeding back into context gathering. It comes in three kinds:

In GTM terms, memory is the difference between an agent that sends a good first email and one that runs a smart sequence over months. Your CRM, Clay tables, and call notes are its long-term memory, and the reply and meeting data are its learned memory. Without memory, the feedback stage has nowhere to write to.

There’s 2 ways this will happen in Clay:

1. Audiences (aggregates 1st & 3rd party data. you get up to 600 fields)

2. Knowledge Hub (this is where you’ll store your business context)

Subscribe now

Where else are we seeing GTM loops? There’s several starts that are providing solutions in different areas.

5 examples of GTM learning loops

1. Lovable: AI-driven CS risk & opportunity engine

• What it is: A customer success platform that analyzes real-time account activity to suggest proactive recommendations for reps.

• How the loop works: AI agents evaluate usage signals to generate next-best-action recommendations $\rightarrow$ Reps execute actions and log feedback $\rightarrow$ The agent adjusts its scoring model based on rep inputs and account outcomes.

• Key result: Keeps success teams focused on high-conviction risk and expansion signals while continuously improving action accuracy.

Check out this 5 min demo & explanation:

2. Grok Bot & Clay: automated outbound campaign optimization

• What it is: A daily automated routine using Grok Build CLI to test and refine outbound sales messaging.

• How the loop works: Daily scripts audit live campaign response rates against benchmark goals $\rightarrow$ The system flags underperforming messaging or targeting filters $\rightarrow$ It generates and deploys updated copy variations to re-test.

• Key result: Automatically prunes low-performing sequences and scales winning copy adjustments without manual SDR intervention.

Every day, a Grok Bot routine reviews all of Eric’s live campaigns. It compares each one to its positive response target, and then recommends changes. It runs on Grok Bot’s own computer through the Grok Build CLI, a separate xAI coding tool that comes with more usage than Grok Bot. The daily instruction is roughly:

“Here is all the messaging we are allowed to use for each customer, and all the list filtering we are allowed to do. Run through their campaigns, and for any campaign that is not hitting its positive response target, suggest improvements.”

It comes back with specific suggestions, such as tightening the list for one campaign or replacing copy that isn’t resonating in another. The loop also runs tests. - How to use Grok Bot & Clay for GTM Campaigns

3. Laurence: autonomous Amazon ad management

• What it is: An AI optimization engine built specifically for Amazon advertisers to hit dynamic profitability targets.

• How the loop works: Continuous tracking of campaign performance metrics $\rightarrow$ System dynamically recalculates expected ad value relative to target goals (e.g., lower CAC) $\rightarrow$ Real-time bid and placement adjustments are made automatically.

• Key result: Reduces customer acquisition costs by continually fine-tuning ad spend allocation toward top-converting segments.

4. Flint: closed-loop performance marketing

• What it is: A performance marketing platform created by Michelle Lim that unifies ad creative and landing page experience.

• How the loop works: The system tracks full-funnel conversion behavior from initial ad click to page conversion $\rightarrow$ Identifies disconnects between ad messaging and landing page content $\rightarrow$ Iteratively adapts creatives and copy to optimize the entire journey.

• Key result: Drives breakthroughs in conversion rate (CVR) and CAC by eliminating friction across the ad-to-page workflow.

5. Helena: self-improving AI-Marketer

Helena’s marketer dogfooded it grow from 55 to 1000 followers on X in 10 days.

Subscribe now

Join 8,800+ GTM operators from OpenAI, Reddit, a16z, Figma and many more. I share what I’m building inside Clay & how to use AI in GTM.

In this post:

• HubSpot layoffs

• How to use Grok Bot & Clay

• How to build an encyclopedia of use cases

• Self-learning hook generator

• Tailored agendas for event attendees

Don’t forget to try this 👇

Test your company's speed to lead

HubSpot laid off 7% of its workforce this week. 660 people in total.

CEO, Yamini Rangan, said it was “not driven by AI-related efficiencies” and “not simply a cost-cutting exercise.

Maybe not driven by AI efficiencies. But its still driven by AI.

What’s interesting is how they’re changing org operations to be more competitive in the AI era. There’s 3 reasons:

1. Organize around customer outcomes - instead of Hubs & features they will now organize product teams around outcomes.

2. Build a flatter organization with fewer layers - to make decisions faster with less management overhead.

3. Create agile teams with clear ownership - to reduce fragmented ownership to execute faster and with more clarify.

Flatter orgs are a necessity now.

A new Harvard Business School/INSEAD study of over 2,900 Y Combinator startups reveals something most AI coverage misses: the companies being built around AI aren’t just smaller—they're architected around a fundamentally different principle. Instead of using AI to make existing workflows faster, they embed AI directly into their products, moving knowledge work that used to require internal teams into what customers interact with directly. The result: organizations that operate with 25% fewer people, flatter hierarchies, and comparable valuations to non-AI peers—because the productive work happens in the product, not in the organization. - Forbes: AI-Native Firms Are Flatter, Leaner, And More Valuable: Threat Or Opportunity?

I wonder if these leaner employee ratios stay the same as these companies scale. Or, do they get pulled back to the mean?

To those that were laid off - I feel ya. It’s happened to me and it sucks. If there’s anything I can do to help, let me know.

Grok Bot for Clay (and content engines)

I used build Clay Tables manually by clicking around the UI.

Now its crazy to look back at the Table-era when we have Clay CLI. What an inefficient way to launch a new GTM play compared to now. The future is Workflows and you should be building with them, not on Tables.

The best part: have Grok Bot build your workflows with natural language.

Set up the Clay plugin by following the steps in https://github.com/clay-run/agent-plugins

Grok Bot builds the workflows; Clay supplies the data and infrastructure to run them.

Encyclopedia of your use cases

Grok Bot is hunting new use cases as you read this. It runs every week to hunt and add new ones. There’s over 700 in the Notion database now. It pulls from:

• Slack

• Notion

• YouTube

• X

• LinkedIn

• And other places around the web (ex. list of newsletters I subscribe to)

Follow the newsletter on X - I’ll be dropping 1 per day.

They’re tagged by persona, GTM motion, complexity to setup, credit usage, and provides links to the source with summaries.

Every marketer should do this. Then let your team query the data via Grok Bot.

Use case → building steps

My next Bot turns these into reusable pieces of content. For example, I shared how we provide Sculpt (Clay’s conference) attendees with suggested agendas that are tailored to their objectives, persona, and other context we have on them.

and gives me the steps on how to build it to share with you:

1. Start from who’s actually attending. The ticket list tied to account and owner.

2. Pull the signals you already own: product usage, call snippets, public posts that show what they care about right now. We have all of this saved in Audiences. (You can have 600 fields to store a wide range of customer data collected over time). Then you can query it.

3. Setup a Clay Agent that has the full conference agenda. For each person, have it draft a short agenda: which talks and roundtables fit their motion, not the whole conference.

4. Run a cross-account pass: who else attending shares a similar problem space, and should they meet?

5. Send the Data from Clay to a content management system to generate the webpages.

6. Put it behind a private link the owner can edit, redraft, and publish.

7. Notify the account owner so they can send it to the customer.

8. Ship it before the event

What to watch for

Wrong LinkedIn identity or a board title instead of the day job will poison the draft — let owners overwrite before publish. Same-domain companies can get conflated; keep plans per account. Don’t invent peer intros that the signals don’t support.

Steal this

Don’t send the conference PDF. Send a private plan: what to attend, who to meet, and why from signals you already have.

Hook generator (gets smarter & coaches me)

The Bot generates hooks based on psychology, emotional triggers, and a growing list of hooks from performant posts.

Stats for the LinkedIn posts are brought in via Ordinal. We use Ordinal for social posting and it has an MCP to connect to Grok Bot.

1. Grok Bot generates hooks

2. I use a hook and post

3. Grok Bot pulls how the posts did via Ordinal

4. Analysis is saved to Grok Bot Memory

5. Memory and examples are used for the next set hooks

Then get feedback & generate charts.

Example Grok Bot feedback:

Example Grok Bot report:

Subscribe now

Newsletter aggregator

You can now get a Grok Bot email address or give it one via AgentMail. (Agents will need everything humans do to interact with the real world: email, credit cards, phone numbers, ID, etc.). I’m pissed I don’t have access to the Grok Bot email yet. But this is how you do it:

Use either one to subscribe to newsletters. (Grok Bot had about a 50% success rate signing up for them. Sometimes it gets caught in anti-bot traps.)

Grok Bot now has access to the incoming newsletters. Have it generate a weekly ‘to hits” or “top ideas for X” briefing.

Viral post database

The other killer update is Grok Bot’s native integration to X to search, read, and monitor. Have it search for posts that meet a certain criteria, have it pull those posts, analyze them, generalize the hook & why it matters, and the upload it to Notion or Google Sheets. I’m dropping the full prompt for this on the AI Run Club newsletter.

Hungry for more Grok Bot GTM use cases?

Follow me and check out this post:

Yes, I just started on X. No, I'm not proud I’m just at 150 followers. BUT it is growing.

Subscribe now

Skill of the week

Grab the Score Accounts with Jev Skill on the Clay Marketplace.

Redline Growth is of Clay’s top partners. Shy Rahnama (follow him on LinkedIn) is also building some cool ways of keeping humans in the loop. More on that to come…

Join 8,800+ GTM operators from OpenAI, Reddit, a16z, Figma and others who subscribe to build better AI-native GTM systems.

In today’s post:

• Imposter syndrome & growth

• 5 ways to grow your brand as a GTM Engineer (or Operator)

• Upcoming AMA on Reddit with Town

• How Lovable uses Lovable to build GTM systems

• My next drop with the SpaceXAI team.

Imposter syndrome hit me hard this week. Who am I to be a full-time creator for Clay? Am I actually any good at this?

Jon, one of our media producers, pulled me back to reality: “Alex, you’re two months in.”

Right now, I feel like an SDR staring at an Enterprise AE closing mega-deals, desperate to bridge the gap overnight. So I turned to Brad Haft (who runs Clay’s brand and grew Alex Lieberman’s ultrathink newsletter to 50k subscribers in 6 months) for a reality check.

His feedback was direct:

Put more of yourself on the page. You’re trying to be too perfect. Draft a section by brain-dumping in all lowercase. Share what frustrates you, a hot take you're mulling over, or whatever's getting you hyped about GTM Engineering.

I’m slowly learning how our team approaches content that gets 100k views a week on YouTube. The biggest unlock? Understanding the gap between:

• Telling a story: Events → Details → Sequence

• Storytelling: Emotion → Meaning → Connection

Here are the key growth principles I’m putting into practice:

• Scale from 1 to writing 2-3 newsletters per week - Brendan Short (writes The Signal) told me this increased his new subscriber growth rate. The more posts, the more Substack promotes your work (or so I’m told)

• Find content formats that work - My new workflow uses Grok Bot and the Apify plugin to find high performing posts on LinkedIn. Grok Bot adds posts to a Notion database with insights I can use. Each row includes: link to the post, hook, hook rewritten in a generic format (easy to reuse), stats, and hypothesis on why it worked well.

• Post everyday on LinkedIn - But include more personal angles. Paul Alec (our LinkedIn brand export who works with our executive team) suggested a new form to try: screen shot a post on X and add commentary just comment. Even if its just “Yes”. crazy how well this post did.

• Put the personality on the page - That’s the one from Brad.

• Get really good at short form content - I started a new account on X and posting more bite sized content on GTM Engineering. Follow me on X here.

• Repurpose more content via X articles - I asked the SpaceXAI marketing team if this has any impact on SEO from duplicate content. It doesn’t, so this is an easy thing to do to improve reach.

That’s a lot of content production.

Posting more on LinkedIn is the obvious answer to “How can you build your brand online?”.

But these are more strategic:

5 ways to build your brand in GTM Engineering

Building a personal brand online accelerates your career. Full stop. It simply helps you attract more opportunities. And it all compounds.

It’s how I landed the gig at Clay. I started posting online. Varun Anand, our co-founder, saw it and invited me to do a work trial.

Jeff Ignacio also talked about how he built his RevOps brand first before leaving his VP RevOps post. Now he’s crushing with his solo GTM AI Agency.

1. Start a GitHub account

, who does growth at Tella, wrote a post “Why every marketer needs a GitHub.” This is even more applicable for GTM Engineers. It will become a requirement for job interviews to show what you’ve built:

1. GTM systems (and for who)

2. Workflows

3. Apps

4. Agents

5. Skills

6. Experiments

7. Teardowns

This is why:

Owning a GitHub and being creative or good at networking aren’t mutually exclusive. Quite the opposite.

You should have a GitHub as a marketer because it lets you show product thinking: knowing which problems your customers have and solving them with new features, skills, or apps. It can even be a way to ‘crowdsource’ interest in new features before including them in a product. - Louise de Sadeleer on Substack

2. Share internally on Slack

My previous post on How to break into GTM Engineering highlights this.

Apply this process to build your brand internally.

1. Find a key metric that matters to leadership

2. Interview people about how the system works today

3. Find a bottleneck that impacts the metric

4. Build a solution and iterate with those it impacts

5. Deploy it

6. Learn from the data

7. Share what happened

Plus help others around you with AI. So many people need help (even with the basics).

3. Help new startups and founders

You can do this with more popular products, too. It’s just an easier starting point because there’s less competition.

New startups and their founders are eager to showcase new users and how they’re using the product.

We started to use Ploy.ai at Clay. I started to build some cool tools - like this speed to lead test and other lead magnets that are in the works. I shared what I was doing with the Ploy team. Met up with Will Gao and told him I wanted to be his most innovative customers.

Of course, they wanted to highlight this and brought me on a webinar. What I built tied in with PloyDB that they were launching. The project then got featured on the Strategy Breakdowns newsletter (something like 180k subscribers ).

The webinar goes up on YouTube (How Clay builds interactive landing pages), gets promoted on LinkedIn, X, and other places.

Suddenly, you become part of the marketing strategy. Distribution follows.

This is the project. Test your team’s follow up: https://gtmengineering.link/speed

4. Write guest posts

Have a cool workflow or GTM play? I’d be happy to get it in front of nearly 9000 subscribers here on The GTM Engineering Newsletter.

This is one that I did on The GTM Strategist - “10 ways Clay’s GTM Engineers use AI to accelerate sales.” This one helped get my brand land in front of 36,000 subscribers.

Bonus: you become friends with some very cool creators. You help each other grow.

5. Speak on panels

I’ve now done 3 events with Lovable at their Boston office and even at JPMorgan Chase HQ in NYC.

Some of these opportunities come about from being at Clay, but others come from networking with the community. Primary Ventures invited me to the Enterprise AI forum at JPMorgan Chase.

You don’t have to be an exec either.

Speaking at Clay Club or local GTM meetup is a great way to start (and a bit more accessible).

Upcoming AMA on r/gtmengineering

Personal AIs exploded in popularity that last several weeks. We use Town internally across multiple departments. Townies learn how you work, keep an updated wiki (the context layers), and then start to take work off your plate.

Using agents to get more done at work is the future of work. I hopefully don’t need to tell you why you don’t want to fall behind here.

• Topic: How Town uses Town for growth

• When: Thursday (Oct 8) at 12pm EST

• Where: new thread on r/gtmengineering

• Who: Adam Reisfield, founding Growth Marketer

• Company: Town

Would love to get your growth or personal AI questions answered:

Submit your questions

Insights from previous AMAs:

• How to start a solo $1M AI GTM Agency

• How Clay builds GTM systems at Clay

How Lovable uses Lovable

I recently interviewed Monica Perez, Head of Customer Success, and Holly Goodliffe, AI Scaled Success Programs, on how the company builds software.

Lovable built its own customer success platform on Lovable. And that's just one part of a go-to-market stack that includes CPQ, pitch decks, onboarding tools, and internal apps their team uses every day.

Monica & Holly show me how it all works. They walk through Catalyst, their internal customer success platform, and show how AI agents turn account data into risk alerts, expansion opportunities, and emails a CSM can review and send.

What stood out to me was the feedback loop. When a CSM edits a draft or dismisses a signal, that feedback helps improve the agent. The team is tracking reply rates and how often drafts get sent without edits as they work toward more automation. Humans are still approving the actions today.

We go over:

1. Lovable’s internal App Store

2. Their self-learning customer success platform

3. How their building the GTM stack on Lovable

4. How they are scaling customer success

5. Zero-admin life of reps (and what’s automated)

Monica & Holly go deep on how they build & give lots of cool demos:

How to use Grok Bot with Clay

Stay tuned. I’m working with the SpaceXAI team on a guide. More on that later this week and on my next post.

Join 8,700+ GTM operators from OpenAI, Reddit, a16z, Figma and others who subscribe to learn how the fastest growing companies build better AI-native GTM systems. W

The opportunity to build AI GTM agencies is enormous.

While efficiency gains will reduce demand for some traditional services, BCG’s analysis shows that agentic AI will ultimately expand the total addressable market for technology services—unlocking up to $200 billion in net new value pools in the next five years. - Boston Consulting Group

$6 is spent on services for every $1 spent on software. Deploying AI isn’t the same as SaaS. This is why the Forward Deployed Engineer model is critical for companies selling AI.

That got me wondering, how do you start an agency that focuses on this deployment layer for AI GTM. And, can you do this as a solopreneur?

This led me to Jeff Ignacio who runs RevOps Impact. This is a recap of Jeff’s AMA on the GTM Engineering subreddit (1,500 join every month) and the advice he shares. He covers:

1. How he got started

2. The pitch vs. the practice

3. How he positions his services

4. Where he finds clients

5. His 4 call discovery to close process

6. How agents are used to scale

7. Advice for others starting

Lets dive in.

How to build a $1M solo AI GTM agency

How Jeff got started

Jeff Ignacio’s first clients came from people who’d already worked with him. When word got around that he’d left his VP of RevOps job, former employers and colleagues called. It took years of groundwork before that could happen.

He didn’t start in RevOps. He was a management consultant working on requirements for large ERP rollouts, then did an MBA and spent a few years in FP&A. He moved into sales ops in his thirties and called the switch “a headspinning venture.” He “powered through it working late nights and taking W’s and L’s along the way,” and worked up to running RevOps.

Along the way he did the thing most operators put off. He built a following on LinkedIn for years, then carried it into a Substack, the , which now reaches more than 6,000 RevOps leaders. So when he went independent, people already knew how he thought about revenue operations. The referrals came from that.

Year one has been “fraught with the rollercoaster ride of trying to get something up and running,” he said. “Having your back against the wall is a mentality I didn’t have as a W2. But it’s ever present in my new role!”

Don’t (only) sell Clay, sell judgement

Jeff described himself as “a combo of solo fractional RevOps that happens to do some GTME work.” Most clients bring him in as an interim Head of RevOps. Clay work is real, and “a huge opportunity over the last eighteen months,” because many teams don’t have the technical skills to use it well. But it isn’t the main thing he sells.

His positioning fits in one sentence: “You get someone who can wax poetic on business strategy AND work with your technology.” He’s clear that this works at Series A to C and less so at public or late-stage companies. He’s fine with that.

He also doesn’t own the stack. He builds inside the tools a client already has, or prototypes an alternative and lets them choose. For a solo operator I think that’s right. Owning the stack means owning uptime, and a person who owns uptime for eight clients can’t take a vacation. But it’s a choice, not a law. A GTM engineer who wants a real agency, with recurring revenue that doesn’t depend on their hours, will eventually have to sell managed systems. Jeff has decided not to be that business.

Where the clients come from now

Past the first wave of referrals, Jeff keeps his pipeline full the slow way. He builds partnerships with software vendors and other consultancies. He works with VC and PE firms to find portfolio companies that need help. He posts honest updates on what is and isn’t working.

His best channel is in-person networking, “by far.” He hasn’t tried cold email or LinkedIn outreach.

That should make GTM engineers pause. The people building outbound systems for a living are getting their own clients through relationships. I don’t read that as hypocrisy. At a senior fractional price, trust matters more than volume, and a warm intro closes faster than any sequence. If your offer is a $3k Clay build, you probably need outbound. If it’s a $15k a month interim executive, you need people who’ll vouch for you.

That’s also why his advice for someone in a GTM job today is to moonlight.

Most of the agency owners I know today started by moonlighting.

Get good at the work in your current seat, then talk about what’s working, without sharing anything proprietary, in the communities you belong to. You’re building the reputation that sends you your first clients.

The four-call discovery → SOW process

This was the most practical answer in the thread. Jeff’s sales process:

1. Intro call

2. 1 hour discovery

3. High level solutions & prioritization

4. SOW & proposal review

Step 4 sometimes takes a couple iterations. This was his exact response on Reddit:

Where agents fit, and where they don’t

When I asked how he uses agents to scale delivery, Jeff listed Claude plus agents he builds himself:

• List building for sales and marketing targeting

• Data hygiene for territories, lead assignment and segmentation

• Executive memos and weekly business reviews

• Forecasting scenarios: the downside, the upside, and what has to happen for each

• Pipeline management, spotting trends early

Most of it is the prep work behind an executive’s job: the memo, the forecast, the clean territory data. Agents compress the hours that used to go into preparing for the meeting. They don’t attend the meeting. That’s why the model scales by making Jeff more valuable per hour, not by adding clients.

What about bottlenecks?

The interim-executive model caps how many clients he can take: “My time can only scale so far.” He loses deals when a prospect wants a firm with several people, when he’s too expensive for a solo practitioner, when the client isn’t sure what they want and backs out, or when they try it themselves. That last group usually comes back about six months later.

He isn’t trying to solve the first two. He hasn’t decided he wants to grow past one person, and he likes the economics of a single-member LLC and the control over his time. “I just need my slice and that’s it. Don’t need to be greedy.”

So the honest version of the $1M solo agency, as Jeff runs it today, is a few senior clients at senior rates, with agents handling the grind. The version where agents do the delivery and one person runs twenty accounts might come.

Reddit AMA is TODAY

Today at 2:30PM EST on r/gtmengineering

We’ll be joined with Nicolas Schell, co-founder of Scalantec, a boutique agency in Berlin who’s sent millions of outbound messages in Germany.

He’ll be answering submitted questions such as:

• What are the boundaries between cold prospecting and warm prospecting in Germany, and what are some tactics that companies can deploy to shift prospects from cold to warm? And how does that change the legal dynamics for outreach?

• Are there real risks to ignoring the strict consent rules? I’m sure someone at some point has gotten a fine, but it really seems like most startups just ignore it. Never heard of anyone getting caught, but that could just be my ignorance.

Clay launched an App Store (but for skills)

I’ve been looking forward to this for a while and previously wrote:

I loved this infographic from Yukta Kandhari that highlights some of the featured skills by creators (from Clay & the community):

Skill of week: org chart generator

I wish I had this as a rep.

Input a company. Clay researches the executive team. Claude builds an interactive org chat where you can:

• Drag and drop to adjust reporting structure

• Mark the buying committee (Champion, Executive Buyer, etc.)

• One-click source work email + mobile number

• Visually see how is new in role (3, 6, or 12 months)

• Recent news with executive mentions

Copy & use in minutes: https://gtmengineering.link/org-skill

AI Run Club

I’m launching an AI Academy & community for GTM leaders who want to get better at AI. Check out our newsletter & let me know if you’re interested to learn more.

AI Run Club

How to setup AI workflows in Gmail (to eliminate unwanted emails)

New here? The AI Run Club is weekly newsletter to help GTM leaders skill up on AI. Receive a bite size AI workflow you can setup in minutes. Subscribe for AI productivity workflows for GTM Leaders…

Read more

a month ago · 1 like · Alex Lindahl | GTM Engineering

Share

Join 8,700+ GTM operators from OpenAI, Reddit, a16z, Figma and others who subscribe to build better AI-native GTM systems.

Diogo Almeida, the co-inventor of ChatGPT, left OpenAI 2 years ago to answer 1 question:

If these models are so intelligent, why have they automated so little of the world’s work?

He’s been building in stealth for 2 years and just announced Jev, a new type of model from his company, TypeSafe.ai. Jev gives AI the properties of code. Their mission is to:

It’s a big deal for GTM (and for building in Clay). This is what you need to know using it in GTM:

1. What is Jev

2. The difference between Jev and an LLM

3. Why it’s important that AI has properties of code

4. Why Jev works so well for GTM workflows

5. When to use Jev vs. Claygent or a formula column

6. A step by step guide for setting up Jev

7. Where to dive in for more

If you’re on X, there’s some insane demos that showcase speed like this viral x post analyzer (from ):

Jev for GTM & Clay

You can think of Jev like this:

Jev is like a bouncer checking IDs at the door: fast, cheap, and only capable of yes/no/which-bucket judgments against a fixed set of criteria. An LLM is like the bartender who can actually hold a conversation, improvise a drink recommendation, and explain their reasoning — much more capable, but far slower and pricier for a job that's really just "check the ID and let them in or don't."

What is Jev?

Jev is a model from TypeSafe AI and calls it a “System One” model, after Kahneman’s split between fast, intuitive judgment (System 1) and slow, deliberate reasoning (System 2). LLMs are built to think out loud. Jev is built to make the call.

It can’t write anything. You give it some text (TypeSafe calls this the state) and a set of questions. It returns typed answers. There are three question types:

• Choice. Pick one option from a list of up to 255. You get back the pick, a probability for every option, and a confidence score.

• Score. Rate something on a scale of 2 to 10 levels you define. You get back a score, the probability for each level, and confidence.

• Noul. A yes/no question answered as a single probability between 0 and 1.

You can ask several questions in one API call, and Jev answers each one separately and in parallel. One row can get a persona label, an ICP score and a buying-signal check from a single request.

There’s a ton of situations GTM (and its systems) where you just need judgement calls instead of generating a brief, email, landing page, or something else.

How is Jev different from an LLM

The obvious difference is output. An LLM gives you a string you then have to parse. Jev gives you "choice": "investor" and "confidence": 0.91. There’s nothing to clean up.

The less obvious difference is how it produces that answer. An LLM generates one token at a time, each based on the tokens before it. Jev produces its answers all at once. TypeSafe trained it with a method it calls Reinforcement Learning for Calibrated Decisions (RLCD). The idea is that when Jev says 0.8, it’s right about 80% of the time.

Speed and cost improvements are useless unless the confidence score is reliable.

Some caveats before anyone quotes the headline numbers. TypeSafe’s site says Jev is 193x faster and 445x cheaper than frontier LLMs, and that hallucination is “mathematically impossible”. Those numbers come from TypeSafe’s own tests, and the company’s launch post says they’re “on the higher end of real world gains.” The no-hallucination claim is narrower than it sounds. Jev can’t invent facts because it can only pick from options you wrote. It can still pick the wrong one. It reads wording literally. And its context window is capped at 64k tokens. TypeSafe also says that giving it more context can make it less accurate, which matters a lot in Clay, where every row carries a pile of enrichment data.

So Jev isn’t a replacement for your LLM columns. It goes next to them. The LLM writes. Jev decides.

Why Jev works well in GTM & Clay

Look at what a typical Clay table actually does. Very little of it is writing. Most of it is small judgment calls, repeated tens of thousands of times:

Is this title a buyer? Is this company in our ICP? Is this job post a hiring signal for our product? Is this reply a “not now” or a “never”? Which of our 40 industry buckets does this company belong in?

Each one needs judgment (a lookup won’t work) but produces a label, a number or a yes/no. It has to come out the same way on row 1 and row 40,000, and in a format the next column can use. That’s the job Jev was built for.

The use cases I’d start with

Think of it this way: anything that needs classification, scoring, routing, or filtering. We do a lot of that in Clay and this is where Jev will accelerate and reduce the cost of workflows or table builds.

Which tool for which job in Clay

My rule of thumb when I’m building a workflow or table:

• A deterministic rule works (domain matches, headcount > 50) → formula or lookup. Don’t pay a model for arithmetic.

• It needs judgment, and the answer is a label, a score or a yes/no → Jev.

• It needs writing: an opener, a summary, an email → an LLM column.

• It needs the web or several research steps → Claygent.

I now chain my workflows in the following way:

1. Claygent researches

2. Jev decides

3. LLM writes, but only for rows that cleared Jev’s threshold.

How to setup Jev in Clay

Clay has no native Jev integration yet, so we'll use the HTTP API column. It takes about ten minutes.

1. Get an API key. Sign up at console.typesafe.ai (it’s in early access at the time of writing) and create a key under Keys. Before you touch Clay, paste a few sample rows into TypeSafe’s Playground and tune your questions there. Iterating is much faster outside a table.

2. Add an HTTP API column to your Clay table.

• Method: POST

• Endpoint: https://api.typesafe.ai/v1/systemone

3. Add headers.

Authorization: Bearer YOUR_TYPESAFE_API_KEY
Content-Type: application/json

4. Write the body. state is the text Jev reads, built by inserting your Clay columns. questions is where you define the decisions. Here’s a persona classifier and ICP score in one call (swap the {{ }} placeholders for your actual columns using Clay’s column picker):

{
  "model": "jev-latest",
  "state": "Name: {{Full Name}}. Title: {{Job Title}}. Company: {{Company Name}}. Employees: {{Employee Count}}. Industry: {{Industry}}. Headline: {{LinkedIn Headline}}",
  "questions": {
    "persona": {
      "type": "choice",
      "instructions": "What is this person's current role relative to the company?",
      "criteria": {
        "operating_founder": "Founded the company and currently runs it day to day",
        "investor": "Investor or board member, not an operator",
        "founding_employee": "Early employee who did not found the company",
        "founder_support": "Founder's office, chief of staff, or executive assistant",
        "unclear": "Not enough information to tell"
      }
    },
    "icp_fit": {
      "type": "score",
      "instructions": "How well does this company fit our ICP: B2B software, 50 to 500 employees, selling to technical buyers?",
      "criteria": [
        "No fit",
        "Weak fit: one criterion met",
        "Partial fit: two criteria met",
        "Strong fit: all criteria met"
      ]
    }
  }
}

Two things worth noticing. state is short and labeled: only the fields the decision needs, not the whole enrichment blob. And the persona question has an unclear option. Without it, Jev has to pick one of the real options even when the data is thin.

5. Parse the response. You'll get something like:

{
  "answers": {
    "persona": {
      "choice": "investor",
      "probabilities": {
        "operating_founder": 0.06,
        "investor": 0.88,
        "founding_employee": 0.02,
        "founder_support": 0.01,
        "unclear": 0.03
      },
      "confidence": 0.84
    },
    "icp_fit": {
      "score": 2.6,
      "probabilities": [0.02, 0.08, 0.2, 0.7],
      "confidence": 0.62
    }
  }
}

Extract answers.persona.choice, answers.persona.confidence and answers.icp_fit.score into their own columns. Note that Score returns a weighted average (2.6 here), not a whole number, so round it or bucket it if your team wants tiers.

6. Turn confidence into action. Add a formula column that sorts every row into one of three lanes. TypeSafe’s own guidance is that “a confidence threshold is not one number,” and they’re right. Set a stricter threshold for actions that are hard to undo:

• confidence ≥ 0.90 → auto-enroll in a sequence

• 0.70–0.90 → flag for rep review

• < 0.70 → don’t act; send to research or drop

The clay-jev-people-ranker repo uses 0.90 for “current founder” and 0.80 for “operating founder.” That’s a reasonable starting point.

7. Add run conditions. Only run the Jev column when the fields it needs aren’t empty. Then gate your expensive columns (paid enrichment, LLM email writing) on the lane column. This step is where you save money.

8. Handle errors. A 429 means you hit TypeSafe’s rate limit and a 529 means their service is overloaded, so lower Clay’s request rate on the column and let it retry. A 422 almost always means malformed JSON. The usual cause is a quote mark or line break inside a merged field, like a LinkedIn headline with " in it.

Share

Don’t run 20,000 rows on the first try. Run 50. Label 20 of them yourself and compare.

When Jev gets one wrong, look at the probabilities, not just the pick. If the right answer came in second at 0.35, your criteria are overlapping or vaguely worded. Rewrite the criteria before you touch anything else. Nine times out of ten the fix is in the criteria.

And resist the urge to add context. When accuracy dips, the instinct is to add more fields to state. TypeSafe’s guidance runs the other way. Cut state to what a smart human would need to make the call, and test again.

A common mistake

Criteria that overlap (”Enterprise” and “Large company”). No “unclear” option. One confidence threshold for everything. Pasting a whole enrichment payload into state. Asking Jev to write a first line. It can’t.

What it costs

At TypeSafe’s published rate of $0.042 per million input tokens, a request like the one above (around 300 input tokens) costs about $0.0000126 a row. That’s roughly $1.26 per 100,000 rows in API cost.

[FILL IN: the same classification run through a Clay “Use AI” column on a mid-tier model costs ___ credits per row, or about $___ per 100k rows.]

[VERIFY: whether Clay charges credits for HTTP API columns that use your own key.]

Even if the real-world gap is a fraction of TypeSafe’s headline numbers, classification stops being a line item worth thinking about. The bigger saving is indirect: everything you don’t run on rows Jev filtered out.

When not to use Jev

When the output is words. When the answer needs the web. When the task takes several reasoning steps. And when you can’t write down clear criteria for the decision: if you can’t explain the difference between “Partial fit” and “Strong fit” to a new SDR, Jev can’t learn it from you either. That’s a problem with your ICP definition, not with the model.

Another example workflow

Want to go deeper?

If you want to go deeper, I'm building an interactive course on using Jev for GTM engineers who code a little. It's on uare.ai (where I’m building my personal AI you can chat with.): Jev for GTM Engineers: Structured Decisions Without the LLM Tax

This is a pretty cool new service. The course includes an interactive AI, which is trained on me. Check out the interview on Personal AI with their founder, Robert LoCascio, who previously founded LivePerson and brought them public with $500M ARR.

You can also find more Jev Cookbooks here.

p.s. OpenAI is rumored to be launching their answer to Grok Bot, Instinct, and Muse tomorrow. Do you know the difference?

Join 8,700+ GTM operators from OpenAI, Reddit, a16z, Figma and some of the fastest growing companies who subscribe.

Dropping Clay’s 1st Benchmark

This is one of my first projects as Clay’s Creator in Residence. Check out 👉 Clay’s 1st GTM Engineering Benchmark: Speed to Lead. 👈 We orchestrated thousands of agents to fill out 6,436 B2B contact forms. We analyzed:

• Speed to lead (email & call back)

• Personalization level

• Form size

• ICP Fit of lead

• Industry & Company size

A personalized report is generated for company’s we tested!

Check our Speed to Lead Benchmark

How to break into GTM Engineering (with no experience)

There are thousands of people who want to break into GTM Engineering. If that’s your goal, then this is for you.

If you’re hiring, then check out the flip side of this How to hire a GTM Engineer.

This is what I’d do if I had no experience as a GTM Engineer. (Based on 100s of conversations with GTM Engineers and 100s of companies I’ve helped adopt Clay over the last 2.5 years.)

The majority of content focuses on scaling cold email infrastructure. Some of the most popular content on LinkedIn are infographics that tout a zillion tools strung together. This is not where to start. It also doesn’t define what a GTM Engineer does or what will provide value to your company.

The best way in is to build your way in.

This is the process I’d take that helped me became a Creator in Residence at Clay and from what I’ve seen work.

I’ve also included a list of resources I’d recommend.

How I made the transition: GTME to Creator

Nobody at Clay handed me a new title or told me to go build a newsletter. I was in sales, closing deals, and sprinkling in some Forward Deployed Engineering work. On the side, I started to share what I was learning: how to use Clay and use AI in GTM. No one asked me to. Claymation (the original newsletter was born) that I rebranded to The GTM Engineering Newsletter.

I became obsessive about posting on LinkedIn. Almost all of it about Clay and what’ve learned working at a series of unicorns.

Then I started r/gtmengineering.

That’s 3 surface areas where audiences started to grow. I shipped content on a regular cadence and resulted in some decent numbers so far:

• GTM Engineering Newsletter 👉 now at 8,600+ subscribers

• Reddit r/GTMEngineering 👉 16,000+ members

• Personal LinkedIn (connect with me) 👉 nearly 27,000+ followers

Moving out of sales had been on my mind for awhile. I knew writing and building in public could lead to something because that’s how I landed at Clay.

I shared this with leadership and met with Jake Block, Head of Media, who had just joined. We talked about doing a YouTube show, GTM Engineering & AI, which is now live. (Subscribe for an upcoming episode on how Lovable uses Lovable.)

The seed was planted and I kept building.

Then at the end of last quarter, the Media Team was ready. I’m now 1.5 months in and I haven’t been this happy in a role in very long time.

As you can imagine, it’d be hard to make the case to join the Media Team without any experience. Especially, at a company with a high bar for craft and pretty much everything.

The same is true to if you want to become a GTM Engineer.

This is what I’d do to become a GTM Engineer (if I had zero experience)

First note - you can come from the engineering side or the business side. The guidance would be he same. You just need to become proficient on your weaker side. An engineer must have the aptitude to learn the business side. A sales rep (or anyone on the business side) must have the aptitude to learn the technical side. To become a GTM Engineer, you need to have both skillsets.

Step 0: Apply to Clay’s $1M GTME Scholarship Fund & find your resources

Apply! We’re funding people to take courses, join cohort programs, and learn. There’s an internal mission to help the next 1,000 become GTM Engineers. Check it out and apply here.

In parallel, consider taking a course to learn the fundamentals. There’s free and paid programs. Plus newsletters, media, and communities to join. My recommendations:

• Clay: Clay University | Alpha Forge Clay Bootcamp | Clay Cohorts | Maven Series Building an AI GTM Engine

• Communities: Clay Slack Community | r/gtmengineering | Clay Clubs (local) | Unlock GTME | Clay Cafe

• Paid Schools: The GTM Engineer School | Bootprint

• YouTube: GTM Engineering & AI | Eric Nowoslawski | Tim Yakubson

• Newsletter short list:

  • GTM Engineering Newsletter (consider me your insider at Clay)

  • The GTM Engineer (by Clay)

  • The GTM Engineer & Podcast - Experiments, interviews, how tos

  • The GTM Engineer Pulse - Roundups by the GTM Engineer School

  • The Short List - GTM Engineer job post roundup

  • The Workflow - Detailed GTM workflow guides

  • RevOps Impact - You’ll need to learn some RevOps. This is my go to.

  • The GTM Strategist - On building AI-native GTM

  • FullFunnel - GTME guidance from one of Clay’s top agencies

  • Know your Growth - from Clay’s Head of GTME Ecosystem, Yash.

I also put out daily content on LinkedIn if you want to connect with me.

Share

Step 1: Pick a metric that matters right now at your company

Sales productivity, pipeline generation, something the customer success team feels every week. It should already be on leadership’s radar, not something you have to convince them to care about.

This is the thing you want to impact.

Here’s a few metrics to consider:

• Speed to lead (check out the 2026 Speed to Lead Benchmark)

• Outbound capacity per rep

• New meetings per week (for sales reps)

• Ad match rates

• Rep ramp time

• Pipeline generation

• Expansion pipeline

• CRM data quality

Step 2: Understand how the system works today

Dive into the people, technologies, and processes that are behind getting to that metric. Do this through informal conversations at your company.

Questions to consider:

• Can you show me how you do X today?

• Who owns this process?

• What providers are we using?

• Can you walk me through this system step by step?

• Where are the handoffs?

Step 3: Discovery to find the bottleneck(s)

I’d do this through casual conversations with the people living in the process and experience the pain of whatever that bottleneck causes.

Your goal should be to get multiple data points from many people. Not everyone will give you the same answer and its your job to triangulate and properly diagnose.

Questions to considers:

• What’s preventing you from moving faster doing X?

• Where do you spend your time?

• Can you walk me through how this is done today?

• What providers are behind X and how do they work together?

• What do you think the bottleneck is?

Step 4: Build something that solves it

Start with a hypothesis and think about how you’ll measure whether what you’re building improves the metric or not.

Now start building. It could be a workflow, a skill, a Claude Artifact, Codex Site, Grok Bot, or a simple prompt. Whatever you choose needs to move the metric you want to impact. Think of the person you’re helping as a design partner. I’d break up the project and ask for feedback at each stage.

“But Alex, we don’t have a Clay subscription. How can I get some Clay credits?”

Good question! Here’s a few ideas:

• Apply to Alpha Forge - Clay provides 20k free credits as part of the program.

• See if you can expense a Launch Plan (starts at $167/month)

• Hit up Clay sales and we could potentially turn the project into a real POC (reply if you want help to connect). We provide some free credits during this period. You would need to bring in the right people to ensure there’s some sponsorship behind the use case.

• Referral program gives you and whoever signs up for a free version 3,000 free credits each.

If you need help conceptualizing how to build what you’re aiming to do, drop in the chat and I can provide some thoughts.

Also, don’t miss this opportunity to use our newest tools such as Workflows and Agent Plugins (Grok Bot is my new favorite):

Step 5: Deploy it for someone, measure, and improve it

Get feedback, iterate, and turn that person into a genuine fan of what you built. You want them telling other people about it before you do.

Be sure to:

• Track results

• What worked?

• What didn’t work?

• Document

• Share what you learned

Step 6: Repeat steps 1-5, tinker, experiment to get “AI-pilled”

Remember, the goal is to build your way in.

The only way I’ve gotten better at using AI is to tinker and experiment. You’ll begin to form a mental model of what’s possible and how to use AI as a building partner instead of search box.

A few tips to accelerate your productivity using AI:

• Ask AI to interview you for context - “Before starting, interview me for any additional context or clarification you need.”

• Tell it your objectives - “My goal is to build a landing page that converts views to signs ups.”

• Give it examples you like - “Use [insert website] as inspiration. I specifically want X to be like Y on my site.”

• Ask AI how it’d improve it - “How would you improve this hook [provide your hook] to drive engagement on LinkedIn?”

• Ask for variations - “Create 3 different variations of the chart design that we can riff on.”

My favorite thing to tinker with right now is Grok Bot. It can log into sites and even scroll LinkedIn to find examples for you. The possibilities are infinite.

Step 7: Build pull

People should be coming to you with questions about AI because you’ve become more visible internally & helped people.

If that pull isn’t happening yet, you haven’t found the right bottleneck, shared enough, or you haven’t unlocked the use case someone actually cares about. This takes time.

There is another possibility worth calling out. Sometimes the pull isn’t missing because of what you’re doing. It’s missing because the company you’re at is a laggard, with leadership that isn’t leaning into AI, new ideas, or willing to try anything new. That’s not a sign to try harder. You might need to find a company that does.

My take is that you’ll be held back by a company that doesn’t invest and lean into using AI. This doesn’t mean giving people a Claude subscription.

This is also where community can really help. Especially, showing up to in-person events. You’ll optimize your chance of meeting someone who might be hiring or can make the right introduction. Pull rarely happens in isolation. You’re simply increasing your surface area to get lucky.

Step 8: Build your business case

By now you should have a compelling story, proof points that show impact, and a couple of internal champions. You might have to educate leadership on what GTM Engineering even is. Case studies help here, and there are plenty online now.

Make the ask. Go to leadership and make the case to do this full time.

Note - make sure this isn’t the first time you engage with the leader. You’ve ideally built up rapport along the way and they have some idea about what you’ve been up to ahead of time.

Step 9: Then go build in public

Ideally you become known for something specific, like GTM Engineering for cybersecurity. That specificity is what makes the next move easier, whether your company promotes you or somebody else comes calling.

How to choose the right company

Let’s say you don’t want to stay. Before

I’d start a bit different on this one. You first need to optimize:

• Growth stage

• Industry

• Tech Stack

Growth stage

A Series A startup and a Series D company are quite different. So is a 3,000-person org, and a Fortune 500 tanker. Some people thrive in ambiguity and building zero to one with no roadmap. Others need structure, and they do their best work designing systems that scale inside a process machine.

There are two reasons this matters. The first is fit: where will you actually be happiest, do your best work, have the right scope, and make the largest impact? The second is tactical: this choice shapes your entire game plan for breaking in. You prepare for an interview (and the job) differently for a scrappy Series A than you do for a company three layers deep in process.

Industry

Pick an industry or niche. AI-native companies, cybersecurity, dev tooling, insurance, whatever pulls you. This does three things at once. It gives you a shot at becoming the expert GTM Engineer in that segment, where expertise meets branding. It lets you build a targeted wishlist of companies you want to work for, split between the ones that already have a GTM Engineering function and the ones that don’t yet. And it tells you exactly what to spend your learning hours on instead of everything at once.

Tech Stack

Industry isn’t the whole picture either. You’ll also want to look at the company’s GTM motion(s) and ask what excites you: PLG or enterprise sales, B2B or B2C, partner led or direct sales. A company like Wiz (a cybersecurity company), selling top down to large enterprises, needs completely different workflows and systems than a PLG company like Clay. Once you know your motion, three things get a lot clearer. What workflows to learn inside and out, since that becomes your portfolio. Who to learn from, if I were focusing on PLG today, I’d go find 10 GTM Engineers already working inside PLG companies and learn directly from them.

My favorite bootcamp success story

If you think you need a head start to make this jump, I’d point you to Nayab Osaf. She started cold-calling at 16, freelanced in HR, and got haggled down from $15 an hour to $13 on jobs that barely paid to begin with. She had zero technical background when she saw a posting for a Clay Bootcamp coaching role. She applied anyway, learned the platform in two weeks, and started teaching CEOs and sales leaders how to use it while still working through her own imposter syndrome. She now runs an agency with her husband charging $8,000 to $10,000 a month per client for the same kind of GTM Engineering work this guide is about. Ten to thirteen times what she was making before.

Join 8,300+ GTM operators from OpenAI, Reddit, a16z, Figma and some of the fastest growing companies who are a part of the community.

Hey GTM Engineers & Operators,

A customer recently asked me, “how does Clay figure out what GTM systems and workflows to build next?”

It hit me. I couldn’t answer the question as well as before. Clay grew from less than 30 to over 500 colleagues in ~2.5 years. A LOT has changed since the OG days where everyone knows everything that’s happening. Every quarter we are forced to rethink how we build to support the next few months of rapid growth.

This question paired with “how does Clay do GTM engineering” left me wanting a better resource to provide customers & ya’ll. So, I hosted the a Reddit GTM Operator’s AMA and invited Spencer Chemtob, who’s one of our internal GTM Engineers, to answer these questions and others from the community.

Below is a recap of the first one, How Spencer Chemtob builds GTM systems at Clay to scale from $25M to $250M?

What is Reddit GTM Operator’s AMA?

Every week, one operator, founder, or builder opens the floor to real questions from the community. No fluff. No vendor pitch. Just the actual thinking.

AMA insights

How does Clay decide what its GTM engineers should build?

Spencer’s team tries not to operate like a reactive service desk. Instead of waiting for sales, marketing, or post-sales to file one-off requests, his engineers sit with those teams and watch for problems that keep showing up.

When the same need appears across two or three groups, that repetition is the signal to build something reusable rather than another point solution. His example: pre-meeting notes for post-sales teams. Instead of a separate workflow per function, Clay builds one shared foundation and personalizes the output per user. This is how we reduce redundant builds. Read the full answer →

Where should someone new to GTM engineering start?

It’s counter intuitive, but don’t start with a tool. Spencer’s advice is to pick a functional specialty (marketing, sales, data, RevOps, growth) and learn which metrics actually matter, how they move, and what levers control them.

In his view, this is what separates automation from engineering: automation finishes a task, GTM engineering ties a task to a business outcome. The best practitioners can explain why a system should exist before they touch how to build it. Full answer →

Should you start with CRM cleanup?

Spencer’s answer was close to no. A broad cleanup can burn months with no clear line back to revenue.

His suggestion: figure out first where the company is winning and losing. Once you know which segments, signals, or behaviors correlate with success, clean the CRM data that supports those specific findings. You don’t need to do the whole database. And the data you need might not even live in the CRM; it could be sitting in product usage, seller notes, call transcripts, or enrichment sources. Pull that, show sellers what you found, form a thesis, and let the system catch up as evidence builds.

If you want the unedited back-and-forth, the whole thread is worth the scroll: r/gtmengineering AMA with Spencer Chemtob.

What does Clay’s GTM data architecture actually look like?

Clay joins CRM and product-usage data into shared audiences, segmented by deal stage across objects. Those audiences feed an orchestration layer: land in a stage-two opportunity and you might enter a targeted ad audience; hit stage three and a Slack notification pings a sales leader to help the rep multithread the account.

Spencer’s framing: this is the shift from static lists to active audiences that respond as an account’s context changes, not just when someone remembers to update a field. Full answer →

How does Clay keep AI-assisted outreach from sounding like AI?

Agents can automate the research. They pull ICP signals, reverse-engineer prospecting triggers, find lookalike accounts, and can match each one to a use case or customer story. Spencer’s point is that none of that should show up in the message itself. Prospects clock generic AI output instantly, so what reaches the inbox should be a short, specific line, not a paragraph of synthetic personalization.

Clay also caps daily send volume per inbox and pays for verification when sending from its own domains. Better data doesn’t buy you out of deliverability discipline. Full answer →

How do these systems get maintained once they’re live?

Every six months or so, Spencer’s team runs a formal review: each system gets rebuilt, reimagined, or explicitly signed off as still fit for purpose. Growth changes what a system has to handle (more data, more headcount, more product complexity) and last year’s fix tends to break again in a new shape.

The number that stuck with me: only 15 to 20 minutes of his typical day goes to maintenance. Not because nothing breaks, but because maintenance gets designed into the system up front instead of becoming a permanent fire drill. Full answer →

What actually kills a GTM play?

Three patterns, by Spencer’s account:

• The audience is too small to produce a real sample

• It’s sliced into so many microsegments that the results stop meaning anything

• Everyone else in the market starts running the same play, and the edge disappears

His fix: start with enough volume to actually learn something, segment once patterns show up, and retire a play the moment it stops converting instead of letting it quietly burn through the addressable market. He also pointed out that the best plays run always-on in the background — success stops depending on convincing every rep to adopt a workflow, because the system is doing the work for them. Full answer →

Excerpt from our sister publication, The GTM Engineer by Clay.

The GTM Engineer by Clay

Insights on building revenue systems with AI and automation—from the people who coined the term GTM Engineer.

By Mishti

How should GTM engineering performance get measured?

Clay’s GTM engineering org sits across multiple revenue functions on purpose, so the line between GTM engineering, marketing ops, and sales ops stays blurry. Spencer measures stakeholder satisfaction and how much low-value work gets eliminated; for growth-facing teams, pipeline generation still matters most. He also flagged a subtler signal: whether the team finds creative, responsible ways to say yes to real business needs instead of defaulting to gatekeeper mode.

An insight that might surprise you: Clay keeps CRM writes deliberately thin. Routing might touch one or two fields. An inbound conversion can justify 30 to 40 enrichment fields. Records get re-enriched at real touch points, with a full refresh roughly every 90 days.

What’s actually worked?

Chemtob’s favorite build is natural-language audience building, which lets sales leaders pull their own audiences and reports without filing a ticket. This collapses the loop between a leadership question and an answer. Close behind it: social listening tied to ad retargeting, so someone who shows up on a platform gets advertised back to in that same environment. Further down funnel, product-usage analysis feeds expansion plays. Taken together, it’s a reminder that GTM engineering isn’t just a prospecting discipline. GTM engineering touches acquisition, pipeline, expansion, and almost any leadership initiative related to growing the business.

👇 Cont’d reading on my learnings from inside Clay:

Next AMA: how to build a $1M solo AI GTM agency

Event details:

• Where: r/gtmengineering

• Date: Wednesday, Sept 2

• Time: 12pm EST

• Who: , a former VP RevOps and founder of RevOps Impact, an AI GTM agency. Jeff is also the author of (one of my most recommended newsletters).

RevOps Impact Newsletter

Building out a successful Revenue Operations capability at your business doesn't have to be difficult. I share my experiences in building up revenue operations at growth stage companies and at large, enterprises too.

By Jeff Ignacio

Want your question answered?

Comment on the post and I’ll be sure to include it!

Share

Hey GTM Engineers,

I’m getting roasted for just starting to use Wispr Flow. Totally deserved.

If you’re a productivitymaxxer or gadget lover - you’re gonna dig these products. These are recommendations from comments in my LinkedIn post about buying the DJI Mini Mic (and potentially a pedal) for Wispr.

There’s everyone from our Head of Sales to Software Engineers to our Head of Studio who love Wispr Flow and use it to build faster (without touching a keyboard!)

What’s all the hype about?

The simple reason Wispr Flow is a $2B company

You can get your thoughts & text on a screen 4X faster than typing.

However, the breakthrough isn’t only speech-to-text itself.

It’s that Flow handles all the annoying work between speaking and producing usable writing:

• You press a key and speak naturally in almost any app.

• It removes “uh,” repetitions, and abandoned sentences.

• It adds punctuation and formatting.

• It adapts the result to the app—an email sounds like an email, while Slack sounds like Slack. Check out Wispr’s context-awareness docs.

• The cleaned text appears directly where your cursor already is.

You no longer need to formulate perfect prose before opening your mouth. Flow converts messy thinking into acceptable writing.

The whole reason to talk instead of type is to stay inside your own train of thought.

Try the magic of Wispr Flow

The breakthrough on the speach-to-text front?

Teams at Clay are using it to:

• Convert thoughts to words

• Compose and ship emails faster

• Go keyboard free

• Use AI faster

• Code faster

I didn’t fully appreciate the magic until I started using it.

Some crazy stats:

• 60B words transcribed

• Almost all of the Fortune 500 uses it

• 10,000+ companies

These are the recommendations to give it superpowers (like voice isolation in the office + being able to whisper even quieter) and peripherals to max out productivity with automations.

Try Wispr Flow

Mic & gadget recs for Wispr Flow

DJI Mini Mic

I purchased this one and it arrived today. A couple people internally are using this and said the mic is amazing. Will let you know how it goes!

• Price: $44.99

• Use Case: wearable mic for whispering & sound isolation in office

• Purchase on Amazon: https://amzn.to/4xUOTf0

• Recommended by: Dane Williams, Software Engineer, and Jake Block, Head of Studio, at Clay.

Gooseneck Mic BOYA BY-GM18CB

You’ll see this one at a ton of desks if you’re walking around the Clay sales floor. Our Head of Sales, Becca Lindquist, also uses one.

• Price: $62

• Use Case: mic for whispering while at your desk

• Purchase on Amazon: https://www.amazon.com/gp/aw/d/B09PNKWMQP

• Recommended by: Jay Bhandari, GTM Engineer at Clay

8BitDo Controller

• Price: $25

• Purchase on Amazon: https://amzn.to/4zCI0ks

• Recommended by: Eric Mistry, CSM + AI & Automation Transformation Lead at Zapier

Logitech MX Master 4

• Price: $119.99

• Use Case: buttons to short cuts, automations, and to turn Wispr Flow on and off

• Purchase on Amazon: https://amzn.to/4qtBM1T

• Recommended by: Brendan O’Neil, Strategic Partnerships and Business Development at Robin

How to setup buttons thanks to Meagan Glenn, former Product Lead & Head of Customer Success at Lavendar, for the quick gif tutorial:

Togkey Micropads

Very simple. Super customizable with all of the color options. This would be such a killer giveaway at booths. There’s smaller versions with 2 keys for keychains, too.

• Price: $25 - $45

• Recommended by: Henry Hund, former Chief Operating Officer of Aptible

Insta360 Mic

• Price: $99

• Use Case: mic for Wispr Flow or video recording

• Purchase on Amazon: https://amzn.to/4gmRkzR

• Recommended by: Charles Tidswell, Co-Founder of The GTM Studio

iPhone Action Button + AirPods

• Automation: “iPhone Action Button → Wispr → stores thoughts → agent picks it up and puts it in the right tool, calendar, backlog, you name it.” - Patrick Schwarz

• Recommended by: Patrick Schwarz, GTM Engineer at Cevotec

Holly M2 Mic

• Price: $88

• Use Case: mic for Wispr Flow

• Purchase on Amazon: https://amzn.to/4c7E8hb

• Recommended by: Parth Nagda, Co-Founder of Valcat.to

Keyboard Keys

• Price: free

• Use Case: key combo short cuts (Fn to turn Wispr Flow recording on)

• Recommended by: Brandon Hoe, VP Marketing at JupiterOne

Work Louder Codex Micro

Me: take my money

Also me: how do I use this? eh, looks sick, gonna buy it anyway

• Price: $198

• Use Case: controller for OpenAI Codex and other apps

• Purchase on Work Louder: https://worklouder.cc/codex-micro

• Recommended by: me! but I haven’t used it, I just want it!

Subscribe now

SEO updates from Google + copying Clay’s pSEO

New video is up on YouTube on the GTM Engineering & AI Automation channel. Check it out and subscribe!

Reddit GTM Engineering AMA on Wednesday

Post up on r/gtmengineering

Drop your questions in the comments and I can add them.

p.s. some links are affiliates. Please don’t blame me… I’d love some extra cash to buy the Work Louder Codex Micro.

Finally, I retired from software sales after 16+ years.

I’m 40 years old and pivoting to be a full time creator.

There were only a couple GTM Engineers when I joined Clay almost 2.5 years ago.

And less than 30 employees at the company.

Luckily, the role caught on and there’s close to 1,000 GTM Engineer job openings. The market agrees GTM Engineering is the new way to Go-To-Market.

I believe a similar story will emerge with my new role. It’s a bet similar to the one I took when I dropped the Account Executive title for a made up title no one had heard before or understood.

There’s only 28 people (that I can find) who have the exact title today.

Let me reintroduce myself as a Creator in Residence at Clay.

Most creator roles focus on managing external creators. This role is all in-house and I’ll be working alongside Bhaumik Patel, our other Creator in Residence.

Bhaumik helped launch the first code academy, was the 1st employee at Maven, and has worked with who’s who in the creator space including Lenny’s Newsletter to build courses. Given him a follow. He’s starting to release content and has been cooking!

Follow Bhaumik Patel on LinkedIn

A few reasons I’m excited about being a Creator in Residence:

1. Jake Block, Head of Studio at Clay, built HubSpot Media and we’re gonna make Clay Media even bigger

2. I plan to share a ton of what I’m learning on this journey

3. Creator-led is the future of GTM

4. My hypothesis is many hot companies will adopt this role

5. There might be more GTM Engineering use cases on the marketing side vs. sales

On a more personal level, I hope to be a role model for my kids to have the confidence to try new paths, build an unconventional career, and make changes along the way to find the things that give them energy.

P.s. thank you to my wonderful wife, Tori, who has not only been supportive, but has provided encouragement to make the change!

The future of GTM is Creator-led

The creator economy could approach half-a-trillion dollars by 2027

- Goldman Sachs

Buyers no longer trust the channels vendors control. They trust the people they follow. 75% of B2B marketers have already shifted budget toward creators and thought leaders to cut through AI-generated noise [Edelman-LinkedIn 2025 B2B Thought Leadership Impact Report], and 83% now call their influencer programs effective [Sprout Social, cited via Contentgrip].

Creators are not cheaper than ads. That’s because the provide something much more valuable: a credible voice. Creator content is more raw, unfiltered, and leans more towards sharing personal views instead of corporate polish (that we all know is designed to sell you something). Personality and views into those creators’ lives also help to make that person and content more relatable.

Creator voices can do more to convince a stranger than an ad could ever do.

This is why distribution through trusted individuals now outperforms distribution through owned brand channels.

The creator-led operating model is not new, it’s just gaining more traction within GTM.

The category winners over the next few years won’t be the ones with the largest ad budgets. It’ll be those who work with networks of creators who their market trust. These networks can be internal and external. Your exec team is one network, and yes, you can activate them as creatives, too.

The executive LinkedIn posting program crushed in the 1st quarter and generated:

• 6M impressions in 3 months on LinkedIn

• 80,000+ new LinkedIn followers

What am I focused on this quarter?

What will I be doing as a Creator in Residence this quarter?

2 assigned missions:

1. Launch GTM Engineering benchmark reports, but do it in a way that drives activation (not only the email capture)

2. Activate Reddit as a more meaningful acquisition channel

+ continuing to build the compounding flywheel:

1. Launch and grow my YouTube channel on GTM Engineering & AI, examples:

   1. Rise of the Forward Deployed Engineer

   2. How to build a Personal AI (2nd Brain)

2. Continue growing The GTM Engineering Newsletter (what you’re reading)

3. Continue growing a LinkedIn Audience where I post 3-5x/week.

4. Grow & engage with the Reddit sub r/gtmengineering

5. Create a flywheel of all content to repurpose on all channels

1. Launching GTM Engineering Benchmarks

To give you a teaser, these a couple examples of what’s cooking:

1. Speed to lead (we tested close to 7,000 B2B forms with agents)

2. GTM Engineer compensation report

3. CRM decay index

4. GTM tech stack of the fastest growing companies

Example of the speed to lead analysis:

Clay will be used to source data, analyze it, and serve as another way to showcase new capabilities such as Clay CLI, MCP, and Workflows. Think: ‘behind the build’ and I’ll share everything with you. Some you’ll be able to copy as templates.

These will also incorporate creative activation methods:

Input company name → generate signals report → generate agent prompt → flow into product to activate agent based on custom report findings.

2. Activate Reddit & grow the community

The first objective here is to do a better job modding and providing some community guidelines to reduce slop.

The second objective is drive more value for the community. This will be in the form of weekly or bi-weekly AMAs with GTM operators, leaders, and founders.

The community has been (as AI would say) quietly growing into one of the key places to hang out on the web for GTM Engineering content. Check out where we are today below.

r/gtmengineering

Sneak peek: GTM Engineer Compensation Report

I’ve been analyzing 30,000+ open job roles for various GTM positions.

Customers frequently ask:

1. When should I hire a GTM Engineer?

2. What should I pay them?

3. What should I look for?

I started to dive into #1 and when companies start to hire a GTM Engineer. Hundreds of customer conversations led me to believe that companies are hiring GTMEs as early as pre-seed.

Here’s 3 insights as a preview of what’s coming:

Companies of all sizes & stages are hiring GTM Engineers

There’s a fairly equal spread of open GTM Engineering roles at all company levels. I’d be confident to conclude the earlier the better since GTM systems compound over time. The one question I’d still have is whether the pre-seed/seed and Series A companies have any revenue. Should companies hire pre-revenue or post-revenue. My thinking is that it doesn’t matter and if you hire a generalist, then that GTME can assist with a wide range of projects.

GTM Engineers are getting paid more than traditional ops roles: Sales, Marketing, & RevOps

The blue line skews to the right (the higher range of comp) compared to RevOps (Red), Sales Ops (Green), and Marketing Ops (Orange).

GTM Engineer vs. RevOps salaries diverge in the Enterprise

This chart shows the average salary at different company stages as you progress from pre-seed/seed to public companies.

How to build a personal AI in minutes

New YouTube is up on the GTM Engineering & AI channel!

An interview with Rob LoCascio, former Founder of LivePerson who pioneered website chat apps, and now the Founder/CEO of Uare.ai.

You’ll learn how to:

1. Build a Personal AI (LLM trained on you)

2. Offer your Personal AI as a service to monetize your expertise

3. Use your Personal AI in other applications

Plus: the 7 elements of a Human Life Model in AI.

Try it with 500 Extra Credits

Hot GTM Engineering jobs to check out

KKR, the private equity giant, recently had me present to 16 of their portfolio Chief Revenue Officers on GTM AI transformation.

To be honest, I was a bit nervous. Then I realized presenting to a room filled of CROs is as new to me as those CROs actually using AI themselves.

If the impetus for for GTM AI transformation isn’t obvious, reports already indicate that high AI adopters generate 2X more revenue per GTM FTE compared to medium and low adopters. That’s a massive difference.

Like most execs, their urgency to adopt AI equally matched their hesitancy to chart a new course. It’s hard to drive change management when you don’t know how to use the tech yourself. This isn’t their fault. They don’t have nearly as much time to tinker, experiment, and build AI skills.

The above is the reason I started the AI Run Club for GTM Operators:

If you’re a Director+ GTM operator and looking to level up yourself, then you should join the AI Run Club. We’re building an exec AI coaching program that includes highly tailored training to accelerate your skills + how you use AI internally. Our upcoming session will teach you how to build a MEDDICC self learning Agent for your team. Reply to this email if you’re interested.

One of the CROs told the room he was pausing his AI initiative. He wanted to take a step back. Think through where AI should actually fit in their GTM before moving forward.

I kept my face neutral, but was thinking:

This guy doesn’t know how to use AI himself. He’s never automated a single workflow with his own hands. And he’s going to sit down and decide where AI belongs in his organization?

We’ll come back to that one at the end.

When we talk about AI transformation, I’m not talking about whether your team has access to AI uses chat like another search engine. Real AI transformation comes from changing how the entire org operates, the infrastructure we build on, our process for shipping & iterating fast, and route to a more sustainable competitive advantage.

AI GTM Transformation needs to be led at the top and change 4 things:

1. Objective - understand and continually seek GTM alpha.

2. Org design - operate more like product and engineering teams.

3. Infrastructure - new infrastructure & primitives are needed to engineer new GTM systems.

4. Process - ship in sprints, learn, iterate, improve.

You combine these to built modern, AI-native GTM systems that help you scale without increasing headcount. These systems follow a maturity curve:

Everett Berry, Head of GTM Engineering at Clay, and I are working on experiments to build the first GTM Loops. This is the name we’re giving self-optimizing agentic systems that improve overtime by executing GTM plays, analyzing data, improving AI with learnings, and then running the next iteration of the play.

We’re not there yet. So the key is to focus on the 4 elements above to drive more effective AI implementations for GTM.

AI adoption is no longer the story – how companies embed it is. And the gap between deep adopters and everyone else is now showing up in the numbers.

Companies with deeper AI integration are outperforming peers across funnel metrics, quota attainment, and team efficiency. The gains are most visible at the top of the funnel, where AI-influenced pipeline generation is driving meaningfully higher lead-to-MQL and MQL-to-SQL conversion rates– up 11% and 8%, respectively. The impact on active deal cycles is more modest for now, but the pipeline quality improvements are real and compounding.

The organizational implications are becoming clearer too. High AI adopters run leaner GTM teams at every revenue band, suggesting that AI is beginning to drive genuine leverage rather than just incremental productivity. The same revenue is being generated with fewer people, which changes the unit economics of growth in ways that will increasingly separate high-growth companies from the rest.

Let’s dive deeper into each one steps that your org can take today to start your AI transformation in GTM.

ICONIQ 2026 State of Go-To-Market

GTM alpha is your North Star

The teams pulling ahead right now aren’t winning because they have better reps or a larger budget. They are winning because of this equation:

Unique data × Unique plays × GTM Engineering capability

Canva detects off-brand content across social channels and sends tailored recommendations directly to design leads. Intercom analyzes the depth of a company’s support documentation to find new verticals. Reddit find recently launched products and then recommends 3 perfect subreddits that the company should advertise within. All of them are on auto-pilot. I personally built the Reddit system.

These aren’t AI experiments. They’re systems that run semi-autonomously.

That’s GTM alpha.

Organization design

GTM AI transformation is more of an organizational problem than a technology problem. To solve the org transformation, think of the following:

Broadly speaking, AI has birthed new roles in 3 general areas.

New roles in AI from product building → deployment

1. AI in software engineering - the AI Engineer

2. AI in go-to-market - the GTM Engineer

3. AI in customer deployments - the Forward Deployed Engineer

Those are the doers, and we’re also seeing the emergence of new leadership types of roles within each category. For example, GitLab just posted 2 new roles:

• AI Transformation Owner, CRO [$139,200 - $235,200 USD]

• AI Transformation Owner, Marketing [$152,800 - $259,200 USD]

(Kind of interesting that the Marketing role pays better)

These are the responsibilities of the AI Transformation Owner in the CRO office:

What are the characteristics of a GTM Engineer?

They typically come from an engineering/technical background and have the slope to learn the business context, or the other way around. You do need both sets of skills.

We look for the following:

The demand for GTM Engineers and Forward Deployed Engineers are exploding in parallel.

We have both teams at Clay. Check out my previous post Rise of the Forward Deployed Engineer and video on the GTM Engineering YouTube channel:

Transition from silos to a platform team

Historically, Sales Ops, Marketing Ops, and RevOps have worked in silos. Separate teams that build for their respective departments and less for the aggregate whole.

At Clay, the GTM Engineering department consolidates these into one function, Revenue Platform.

The team is run under our Head of Revenue Platform. Similar to a software engineering team, the Revenue Platform team builds infrastructure supports the GTM team to reduce friction and ship faster. Platform teams in engineering do the same thing, but for developers.

Many debate whether GTM Engineering is the same as RevOps. At one of our panel discussions, it was debated that RevOps is more about setting guardrails and is reactive, where as GTM Engineers are more proactive and invent new ways of doing GTM.

Loading...

Before our GTM Engineering team builds anything, they must figure out how that system services all segments of the GTM org: Sales, Marketing, RevOps, and Customer Success. The silos are collapsed. The results are systems that are more deeply integrated and have a more significant impact.

If you haven’t caught onto the analogy yet, it’s this…

GTM needs to operate more like product & engineering

Modern GTM orgs operate more like product & engineering teams these days.

The guiding principle here is thinking in systems, not headcount to scale the business. You need to ask yourself: “am I scaling by adding labor or by building systems?”

Architecting and building systems aren’t the operational elements. There’s much more that goes into operating like product & engineering:

Build once, then reuse. Engineers don’t rewrite auth for every feature. They build it once, version it, and compose. GTM still treats every campaign, account plan, and sequence as a one-off. Operating like eng means your enrichment workflows, signal definitions, message frameworks, and research skills become reusable assets that get versioned and pulled off the shelf across accounts and not rebuilt from scratch every time.

Deployment & observability. Engineers ship behind instrumentation. They can see what’s running, what broke, and what changed. GTM ships a sequence into the void and finds out three weeks later when someone notices reply rates cratered. Treat your outbound motion as a deployed system with monitoring, not a set-and-forget artifact. Speed-to-lead is a good place to start: it’s a service-level objective for GTM, and most teams have no idea they’re breaching it.

Spec before activity. Good eng work starts with a clear problem definition before anyone writes a line of code. GTM skips straight to doing. Borrow the PRD habit — what signal are we acting on, what’s the hypothesis, what does success look like — and you kill most of the low-quality motion before it ever ships.

Tight iteration loops. Product runs on build-measure-learn. GTM’s feedback loop is the quarterly pipeline review. Lagging and slow. Compress the loop so a messaging or targeting change returns a signal in days, not a quarter. (This is exactly what Clay’s bi-weekly campaign board does, which you get to below.)

Ownership & on-call. In eng, someone owns the service and is accountable when it degrades. GTM systems too often belong to nobody — the workflow that “just runs” has no owner until the day it breaks. The maturity move is naming who owns the machine. It’s the same reason the function is shifting from RevOps to GTM Engineering: proactive ownership of systems, not reactive guardrails.

So how do you start operating this way?

This is the advice I gave the CRO who wanted to pause AI:

• You don’t need to go all in on GTM Engineering, yet

• Start off with 2 high impact workflows (team + individual you) and figure out how to

  • Automate

  • Apply AI

• The process helps you figure out what’s possible.

• There’s always someone tinkering with AI on the team who is building in their free time. Figure out who that person is and free up more time for them to do that. This person has potential to become your first GTM Engineer

• Start to build buy-in from other stakeholders across GTM

• At the very least, get everyone a Claude or OpenAI subscription. Have a 1 day hackathon where everyone builds a skill. Show and tell at the end of the day. Then decide which one will have the biggest impact and operationalize it.

Infrastructure & Process

These are the last 2 changes that go into successful GTM AI Transformation.

I ran out of room in this newsletter, so I’ll cover the 2 topics in the next post.

I’m exited to announce the GTM Engineering & AI YouTub channel.

The Clay Studio team and I have a TON of content planned - all focused on the future of GTM, how to use AI, experiments we’re running, and interviews with incredible founders. Upcoming topics include:

• GTM Engineering benchmarks

• How Lovable uses Lovable

• Self optimizing ad campaigns with agents

• Learning loops in GTM

• A startup working towards automating businesses full-stack with AI. They want to be the first company to help a solo entrepreneur build a billion dollar business!

• Interview with the founder of AgentMail as a follow up to this post

We are gonna cook!

Back to regularly scheduled programming…

Subscribe now

A brief history of email

The most consequential piece of digital communication in history was sent by someone who wasn’t supposed to be working on it — and the message itself was so forgettable he couldn’t even remember what it said. What he accidentally invented in that moment still runs every inbox on Earth, and until about a year ago, it had never had a second kind of recipient.

In late 1971, a programmer named Ray Tomlinson was tinkering in a Cambridge, Massachusetts lab at a company called Bolt, Beranek and Newman — a government contractor building ARPANET, the precursor to the internet. His actual job was helping route data across the network.

But he’d gotten curious about a simpler problem:

People could already leave messages for each other on the same computer, but there was no way to send a message to someone on a different machine.

He wheeled his chair between two PDP-10 computers sitting roughly ten feet apart, typed a test message on one, rolled over, and checked if it arrived on the other. It did.

To make it work, he needed a way to separate the person’s name from the machine they were on. He scanned the keyboard for a character that wouldn’t appear in anyone’s name. He landed on @. “I thought about other symbols,” he later told Forbes, “but @ didn’t appear in any names, so it worked.” It was a practical decision made in seconds. The format he created — user@host — remains the global standard for every email address on Earth today, more than half a century later.

The message he sent? ”The test messages were entirely forgettable and I have, therefore, forgotten them,” he said. Probably “QWERTYUIOP.” He showed the system to his colleague Jerry Burchfiel and his first instinct wasn’t pride — it was caution.

”Don’t tell anyone,” he said. ”This isn’t what we’re supposed to be working on.”

Within two years, a study by ARPA found that 75% of all traffic on the network had become email. The infrastructure built to share computing resources and scientific data was being used, overwhelmingly, for people to talk to each other. The killer app of the internet wasn’t computation. It was conversation.

For 54 years since that moment, the fundamental shape of email never changed. A human writes. A human reads. A human responds.

2025 agents enter the email thread.

They now have email, too.

Say hello to AgentMail.

2026 last week - Clay integration with AgentMail is live!

What does AgentMail do?

AgentMail is purpose-built email infrastructure for AI agents. Not AI for your email. Email for your AI.

Every tool in your GTM stack that touches email right now is a one-way pipe. Smartlead sends. Outreach sequences. Klaviyo broadcasts. None of them give your agent a persistent inbox — a place to receive, thread, reply, and remember.

AgentMail provisions real inboxes via API. One customer alone runs 25,000 inboxes through the platform, handling millions of emails. It handles deliverability, spam prevention, proper email headers, and threading.

Why do agents need an email?

Here’s an example:

Browser Use is the open-source framework that gives AI agents control of a web browser — 96,000+ GitHub stars, used by Fortune 500 and YC teams to automate real tasks on real websites. They hit a wall that had nothing to do with browser control: the moment a site asked an agent to check its email — a signup confirmation, an OTP code, a 2FA prompt, a password reset — the run just stopped. The agent had no inbox to receive anything, so a human had to step in and paste a code. The bottleneck was never the browser. It was identity.

Browser Use’s fix was to give every session its own AgentMail inbox, automatically, no config required. Tell the agent to sign up for a service, and it creates an inbox, fills in the form, waits for the verification email, confirms the account, and keeps going — no human, no pause. The same primitive — a real inbox an agent actually owns — that unblocks a browser agent stuck on a verification screen is what unblocks a Clay table that’s been able to send email but never receive it.

What makes AgentMail + Clay such a special combo?

The thing that makes this different for Clay specifically: before today, email was only ever an output from a Clay table. You could send from Clay. But you couldn’t receive. You couldn’t trigger a Clay workflow when someone replied, signed up, submitted a form, or sent anything back.

That changes now. Your Clay table can have its own inbox — and that inbox can kick off workflows, update rows, classify intent, route to CRMs, and trigger actions the moment an email arrives.

Here’s what that opens up.

11 Clay use cases with AgentMail

1. Build an AI SDR in Clay

AISDR runs autonomous SDR agents at scale on AgentMail with roughly 500 inboxes in production and 7,000 emails per week, no human in the loop.

You can now build this in Clay.

Build a table enriches and scores prospects, generates a personalized first-touch via Claude, and fires it through an AgentMail inbox. When a prospect replies, a webhook fires, Claude classifies the intent — interested, objection, referral, not now — and the agent responds within the same thread. A human AE only gets a Slack ping when there’s a live buying signal in the thread.

2. Measuring speed to response for form fills

We have the speed to lead GTM Engineering benchmark coming up. We used Subconscious.dev agent to fill out thousands of B2B web forms (contact us, demo request, etc.). We couldn’t have done this easily without AgentMail. Subconscious agent provided an AgentMail address as the contact email, logs the submission timestamp, and then the webhook fires when the first response arrives. You now know exactly how fast every company in a given market responds to inbound leads — measured the same way, at the same time, across an entire category. No survey data, no self-reported numbers. Just timestamps.

There’s hundreds of variations to this with forms on the web.

3. Inbound reply handling without a human SDR

This is the minimum viable version most teams should build first.

Right now someone on your team reviews replies every morning and triages manually. That’s a bottleneck where the fastest leads go cold.

Replace it: AgentMail webhook fires on every reply, Claude classifies intent, routes automatically. Interested → drop a meeting link and log to CRM. Not now → set a 30-day snooze. Referral → extract the contact name, create a new Clay row, start a new thread. Unsubscribe → suppress forever, no exceptions.

You still write the first-touch. You just stop doing the triage.

4. CC an agent into a live deal thread

Every AE has live deal threads where context lives and dies in their own inbox — what the prospect said, what was promised, what’s still open. None of that is visible to RevOps until the deal closes or stalls.

CC an AgentMail inbox on the thread. The agent reads every message as it comes in, tracks context across the whole conversation, and can draft follow-ups, suggest next steps, or schedule the next meeting. Every meaningful update gets written back to Clay — what was discussed, what’s outstanding, what changed. RevOps gets visibility without asking the AE for a status update. The AE gets a second set of eyes on a thread that used to be a black box.

5. Forward messy GTM signals into Clay

The signal you need is almost never sitting in a clean database. It’s a newsletter mention, an ICP list someone shared in Slack, an intro email, an event attendee list, a funding alert, a job-change notification — all landing in someone’s personal inbox and going nowhere.

Forward any of it to an AgentMail inbox instead. AgentMail turns the email itself into a workflow trigger — it doesn’t matter if the body is a forwarded newsletter or a pasted spreadsheet. Clay picks it up, enriches the people and accounts mentioned, segments them, and routes them into the right GTM motion. The inbox becomes the front door for every messy, unstructured signal your team already has — instead of letting it die in someone’s forwarded folder.

This is one I built for a rep selling financial services at UBS to startup founders. I used AgentMail to turn PitchBook emails into automated signal → outbound workflow.

6. Reply-based outbound automation

This is the more general version of use case #3, and it’s worth building even if you already have a triage process, because the classification logic is where most teams under-invest.

When a prospect replies to an outbound campaign, AgentMail captures the response and classifies it — positive, not interested, OOO, referral, needs follow-up. From there, Clay enriches the account with whatever’s missing, assigns an owner if one doesn’t exist yet, and hands the rep (or the agent) everything needed to draft the next reply with full context. The output isn’t just a routed lead — it’s a fully prepped next move.

7. Email-native meeting prep

The annoying part of meeting prep isn’t the research — it’s remembering to do it before every single call, especially the ones that get added to your calendar with two hours’ notice.

Forward the prospect or customer thread to an AgentMail inbox before the call. Clay generates an enriched account brief on the back end: company context, the relevant stakeholders, recent signals (funding, hiring, product launches), known pain points, and suggested talking points based on what’s already been said in the thread. By the time you open your calendar invite, the brief is sitting in your inbox.

8. Per-account research agents with persistent inboxes

Most enrichment is pull-based — you query an API and get back whatever’s cached at that moment. This inverts it. Spin up a dedicated inbox per target account, and have the agent subscribe to that company’s competitor newsletters, product announcements, job change alerts, and press releases on its behalf. When something lands, the webhook fires and updates the Clay row in real time.

You’re not scraping anything. You’re building a listening layer per account that gets smarter the longer it runs.

This one is on my to do list to build & try out.

9. Two-way partner channel management

Channel programs break at scale because the coordination overhead per partner eats the margin. Build an agent that manages a partnerships@yourdomain.com inbox: receives partner lead submissions, extracts structured data, routes to the right AE with context, sends a professional acknowledgment, and logs to CRM.

The agent holds the thread and only surfaces a human when the deal is warm enough to need one.

10. A lightweight GTM command inbox

Sometimes the fastest interface isn’t a tool, it’s an email. A rep or operator emails a plain-language request to an AgentMail inbox — “enrich these companies,” “find me 20 accounts that look like this one,” “prep these leads for tomorrow” — and Clay handles the enrichment and workflow execution on the back end, replying in the same thread when it’s done.

No login, no new tab, no learning a new interface. The inbox is the UI. For non-technical reps especially, this collapses the gap between “I know what I want” and “I know how to ask the system for it.”

11. Multi-agent coordination via email threads

The most architecturally interesting use case. Instead of building complex orchestration infrastructure to pass state between agents, use email threads as the shared memory layer.

Research agent drops enriched prospect context into a thread. Outbound agent picks it up, writes and sends the first-touch. Reply-handler watches the inbox, classifies responses. CRM sync agent reads the thread and updates Salesforce. Each agent has its own inbox. The thread is the handoff protocol.

Email becomes the message bus. It’s already universally understood, persistent, and standardized. Using it for agent coordination instead of building something custom from scratch is exactly the kind of move that looks obvious in retrospect.

Emails give agents an identity

Clay has been adding more and more purpose-built agents — research agents, enrichment agents, outbound agents. Every one of them has had the same missing piece. They could act, but they couldn’t be reached. They had no address.

That’s the gap AgentMail closes, and it’s bigger than any single use case above. Agents now have an identity. It can be CC’d. It can be replied to. It can hold a conversation across days or weeks without losing the thread, literally. Once every agent in your stack has that, the whole category of “things only a human could do because only a human has an inbox” starts to shrink.

Btw Agents just got phone numbers, too:

Where this goes next

A few things worth watching, because they’ll shape how fast this category moves.

Agent traffic is already outpacing human traffic, faster than anyone predicted. Cloudflare tracked agentic AI traffic growing nearly 80x over the course of 2025, and crossed the point where bot traffic exceeds human traffic about a year and a half earlier than its own CEO had forecast. The agents showing up in inboxes aren’t a future trend to plan for. They’re already the majority of the traffic.

Email is the first layer of a much bigger identity stack, and the people building this know it. Email is the start, not the finish. As agents take on more of what humans currently do, they’ll need credentials, reputation, and trust — the fuller architecture of an online identity — not just an inbox.

The next billion internet users are already arriving, and they’re agents that just need somewhere to receive their mail. Inbox first, then the rest of what identity requires.

Standards for proving who an agent is are showing up faster than most people realize. Google’s Agent-to-Agent protocol, OpenID’s extension for agent identity (OIDC-A), and a wave of academic work on delegation chains and cryptographic agent verification are all trying to solve the same problem from different angles: when an agent shows up in your inbox claiming to represent a company, how do you know it’s telling the truth? Nobody’s fully solved this yet. Whoever does will own a meaningful piece of how every agent-to-business interaction gets trusted.

Security is the open wound, and it’s GTM’s problem too, not just engineering’s. Prompt injection through email is a real, demonstrated attack vector — a malicious actor can craft an email specifically to manipulate the Claude prompt reading it, not just the human. Any team building reply classification or autonomous response logic needs to treat every inbound email as untrusted input, the same way a developer treats user input on a web form.

The winners will be the teams that treat the agent’s inbox as infrastructure, not a feature. Every prediction above points the same direction: agents are going to need persistent, verifiable, increasingly trusted identities, and email is the first and most universal piece of that. Teams that build on top of that primitive now — rather than waiting for a fully-baked, all-in-one platform — will have a multi-year head start by the time this becomes the obvious way every GTM team operates.

How to setup ‘email as a source’ in Clay with AgentMail

1. Setup an AgentMail account

Complete the following steps in your AgentMail account before connecting to Clay.

1. Sign up at agentmail.to and open the dashboard.

2. Add a domain. In the AgentMail dashboard, add the domain or subdomain you want to send and receive from. AgentMail will generate DNS records (MX, SPF, DKIM, DMARC) for you to add to your domain manager.

   • If your root domain already receives mail through Gmail (Google Workspace) or Outlook (Microsoft 365), its MX records already point to that provider. A domain’s MX records can only point to one mail server, so you cannot also route it to AgentMail. Use a dedicated subdomain instead (e.g., inbox.yourdomain.com).

3. Add the DNS records. Copy the records AgentMail generates into your domain manager exactly as shown — type, name/host, and value. DNS propagation can take a few minutes to a few hours. Once the records are in place, click Verify Domain in the AgentMail dashboard.

4. Create an inbox. Once your domain is verified, create one or more inboxes (e.g., sales@inbox.yourdomain.com). This is the live address Clay will listen to.

5. Generate an API key. In the AgentMail dashboard, generate an API key. This is the only credential you need to connect AgentMail to Clay.

For more detail on domain configuration, see AgentMail’s custom domains guide.

2. Create a Clay table and use AgentMail for ‘email as a source’

1. In a workbook, click + Add at the bottom.

2. Search for AgentMail and select it from the results.

3. In the modal, you will be asked to Select AgentMail account.

   • If you haven’t already connected your AgentMail account, click + Add account and enter the API key you generated.

You can check out more Clay + AgentMail documentation here.

GTM Engineer role of the week

Honesty, I’m thinking of applying to this one. That’s how cool I think this company is and how much potential it has.

That’s right… AgentMail is hiring a Founding GTM Lead.

Check out more details here.