Alex Lindahl's LinkedIn Strategy, I help…

I've been an early GTM employee at 4 unicorns. The way Clay helps its employees with equity liquidity is extremely rare... This is our 2nd employee tender in 9 months. We just announced a tender al…

LinkedIn post image: I've been an early GTM employee at 4 unicorns.

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Equity Compensation7 months ago

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Dropping the 𝟭𝘀𝘁 𝗦𝘁𝗮𝘁𝗲 𝗼𝗳 𝗚𝗧𝗠 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 today w/ Garrett Wolfe & Maja Voje! It's packed with insights on salaries/comp, tooling, & predictions for where GTME is headed from 2…

LinkedIn post image: Dropping the 𝟭𝘀𝘁 𝗦𝘁𝗮𝘁𝗲 𝗼𝗳 𝗚𝗧𝗠 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 today w/ Garrett Wolfe & Maja Voje! It's packed with i

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GTM Engineering6 months ago

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Last night Chris Balestras and I hosted the 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗴𝗲𝗻𝘁𝘀 𝗶𝗻 𝗚𝗧𝗠 event at Clay. ~100 operators came to learn how 4 AI startups are building agentic systems to work alongside GTM…

LinkedIn post image: Last night Chris Balestras and I hosted the 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗴𝗲𝗻𝘁𝘀 𝗶𝗻 𝗚𝗧𝗠 event at Clay. ~100 operators ca

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AI Agents in GTM6 months ago

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Are you in GTM Engineering? You won't want to miss this.... Salary insights. Predictions. Org Design. Nearly 200 responses from GTM Engineers and GTM Leaders from around the world. GTM Engineering i…

LinkedIn post image: Are you in GTM Engineering? You won't want to miss this....

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GTM Engineering7 months ago

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The vibescaling & Clay AI in GTM event series has quickly become of one the hottest GTM events in NYC to attend as a GTM Leader, AE, SDR, etc. Last week 125+ came to learn about the future of Agents i…

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AI in Sales5 months ago

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Did you know you could build AI Apps on Clay data? I've been vibe coding internal tools (like this Territory Command) by combining Clay + Supabase + Lovable. The possibilities are really endless. B…

LinkedIn post image: Did you know you could build AI Apps on Clay data?

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Sales Automation7 months ago

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Topics & Content Focus

Primary Topics

Agentic AI for GTM execution (agents augmenting SDR/AE workflows, customer engagement, and internal operations)GTM Engineering as a new career function (comp benchmarks, tooling stack norms, skills premium, equity dynamics)Building internal GTM apps on top of enriched data (Clay + modern data/app builders for territory, ABM, and enablement)

Secondary Themes

Technographic and partnership data as a targeting/positioning advantage inside GTM stacksCommunity-led learning loops (NYC operator events, live demos, builder-to-builder AMAs)Career navigation during AI-driven role shifts (what to learn, how to pick companies, how to stay relevant)

Industry Focus

B2B SaaS Go-To-Market (Sales, RevOps, GTM Ops, Sales Enablement)AI-native GTM tooling ecosystem (Clay-centered workflows, agent platforms, AI coding tools)NYC venture/startup operator community (events, demos, early adoption patterns)

Content Categories

Event debriefs + ecosystem curationBenchmark/report-driven thought leadershipTactical build logs (internal tools, stacks, workflows)Partner/product education (data integrations, in-product use cases)Career framing for modern GTM roles

Performance Insights

113.6%

Avg Engagement Rate

STABLE

Performance Trend

Best Performing Topics

Data-backed GTM Engineering benchmarks (comp/tooling) with a clear participation CTADetailed agent use cases demonstrated through real workflows (demo, legal redlines, content generation, orchestration)Build-in-public internal GTM tools that reduce workflow fragmentation

Virality Signals

Quantified specifics (headcounts, salary numbers, adoption percentages, turnaround times)“First ever” positioning and category creation (GTME benchmark)High-status name-dropping (companies, YC, tools, recognizable operators)Comment-keyword mechanic that converts passive readers into participantsDemonstration storytelling (“I didn’t demo it, the agent did”) that’s easy to retell/share

Structure & Quality

420

Avg Length (Words)

HIGH

Depth Level

ADVANCED

Expertise Level

0.82/10

Uniqueness Score

Common Hooks

Social proof + FOMO opener (event turnout, “hottest event”, operator attendance)Novelty/authority announcement (“first ever” report/benchmark drop)Problem-pressure framing (too many tools, too much work, legal backlog, AI noise)“Did you know…” curiosity hook leading into a build/demo

Common Endings

Forward-looking tease (next quarter event / more to share later)Link-in-comments redirectExplicit interaction prompt (comment keyword to receive resource)Open question(s) about skills/career direction

Value Delivery Methods

Turns hype into deployable GTM mental models (what’s real, what’s working, what’s next)Provides concrete stacks/workflows, not generic AI adviceCreates original field data (survey/benchmark) that the audience can referenceBridges builders and operators (tooling + business impact + career implications)

Formatting Style

Section headers with bold/uppercase emphasisChecklist bullets (✅) and short punchy subpointsConcrete examples and named entities (tools, startups, speakers)Skimmable blocks separated by whitespaceOccasional emoji for mood/contrast (minimal, functional)

Audience & Tone

YES

Question Usage

0.18%

Response Rate

Detected Tone

Smart-casual operator who buildsEcosystem curator + connectorOptimistic realist (future-facing but acknowledges friction)Pragmatic, workflow-first technologistsemi-formalfirst-person

Interaction Style

Community rallying (events, surveys, shared learning)Peer validation loops (highlighting how operators are using tools)Resource exchange (reports, links, future participation)

Community Building Signals

Recurring event series as an owned distribution channelBuilder-to-builder positioning (AMAs, integrations, tooling roadmaps)Audience participation mechanisms (comment keyword + future survey invites)Ecosystem curation by spotlighting multiple startups in one narrative

Writing Style Patterns

Content Strategy

Hook: Social proof + FOMO opener (event turnout, “hottest event”, Tone: semi-formalCTA: Comment-gated CTA (e.g., keyword for report/survey

Writing style breakdown

The 𝗦𝘁𝗮𝘁𝗲 𝗼𝗳 𝗔𝗜 𝗶𝗻 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 report just dropped and the data is wild. We surveyed 500+ Ops leaders to see how they are actually deploying LLMs into their stack.

There's a lot of "AI washing" happening right now, but the real builders are heads down.

I've been chatting with folks like Stefan Kollenberg about this because the shift is happening faster than people realize:

✅ 65% of teams have replaced at least one manual data cleaning workflow with AI

✅ "Vibe coding" is becoming a legitimate internal tool strategy

✅ The gap between "AI curious" and "AI native" companies is widening

But here is the most interesting part...

Where is the budget actually coming from?

Is it new spend, or are we cannibalizing old SaaS seats?

Excited to dive deeper into this in the next newsletter...

Huge thanks to the teams that helped pull this data together

Garrett Wolfe from the GTME crew

Maja Voje for the European insights

The Kiln for the distribution support

Grab the full 40-page breakdown in the link in the comments.

Comment "OPS" if you want the raw CSV data to play with!

Alex Lindahl

Warm Analysis

Performance Overview

Top Posts by Engagement

Posting Patterns & Frequency

Best Performing Days

Best Performing Times To Post