Kamesh P.

I ran across another "founding GTM engineer" hiring position at a startup. This one person is supposed to: → Define ICP and target accounts → Build enterprise buying strategies → Create messaging and content → Configure CRM and marketing tools → Build AI agents and workflows → Run outbound campaigns → Analyze pipeline → Support enterprise sales cycles → Partner directly with founders to build the entire GTM motion That's product marketing, RevOps, SDRs, sales executives, campaign management, and tool expertise all rolled into one person! It seems these companies are betting that AI can do all of these functions and one strong GTM engineer with AI can now create incredible leverage. What is your reaction to this? Would you take on such a role? (My reaction: this person is in a "no-win" situation") I think the role itself is trying to solve the wrong problem. A VP of Engineering with AI coding assistants can build software faster. They still need to know what to build, why to build it, and how the system should be architected. GTM is no different. The future is not one GTM person replacing an entire function with AI agents. The future is a GTM leader building the system and using AI to multiply the impact. I wrote about what founders should do as they scale from early traction to repeatable growth. One hire like this "ain't gonna cut it". #GoToMarket #GTMStrategy #ArtificialIntelligence #B2B #SaaS #StartupGrowth #FounderLedGrowth

One recent "GTM Engineer" startup job posting received so many applications that the company literally shut down incoming submissions in just a few days. But as I was saying repeatedly in the past few months, this is an impossible role for anyone to be successful at. Can you honestly say you have all these skills and you can execute without burning out? (If you do, I would love to meet you. It would be an honor to meet superman!) - The Infrastructure Engineer: Deep research in Clay, waterfall enrichments, webhooks, REST APIs, and custom HubSpot automated plumbing. - The Outbound & Signal Specialist: Deliverability, signal-based LinkedIn outreach, and cold email sequencing across Instantly and Valley. - The Paid Media & Growth Marketer: Managing paid search, paid social budgets, conversion testing, and messaging experimentation. - The SEO & AI Content Strategist: SEMRush gap analysis, AI-driven content generation, and Answer Engine Optimization (AEO) for LLMs. - The Internal Product Guinea Pig: Acting as "Customer Zero" to build and test internal AI agents on an unreleased platform. - The Public Evangelist: Giving keynote speeches at events, hosting webinars, and presenting narratives on stage. When you context switch across 7 distinct domains, three things happen: 1. You spend all day putting out fires instead of building scalable pipeline. 2. You end up being mediocre at everything instead of elite at one end-to-end GTM motion that actually makes a dent into your…

I finally saw a "Founding GTM Lead" job posting that gets it right (mostly) I wrote last week about the mistake most startups make when hiring for this role. They are optimizing for automated execution with AI agents (ofc) of five different functions before they've proven a repeatable motion. This one asks for evidence. (more on what I mean below) What they got right: → Success is measured by "GTM learning velocity" - hmm interesting metric! → Every test needs a documented scale, iterate, or stop decision → "Structured curiosity" is a listed requirement, labeling assumptions, seeking disconfirming evidence → AI is framed as a way to accelerate research and structure ambiguous work → The goal is to ultimately narrow to a "concentrated set of target accounts" This is the first posting I've seen that's actually written for a company that knows it's still in search mode, not one pretending it's ready to scale. But a few things still need to be clear, and these matter enormously as they can make or break the success of this role: → "Qualified pipeline created" is still listed as a primary success measure, right alongside learning velocity. Hmm, is it possible to have both these together when one can often conflict the other? → Who has the actual authority to call "stop" on a channel or message or an offer? If the founders second guess before the "learning" actually happens, the GTM engineer loses. → Do the founders assume they have product-market fit? What makes them…

Most GTM Engineering job descriptions are a trap. I have been doing the breadown of these jobs for the past few months: Companies keep asking a single person to be a full-stack engineer, data architect, RevOps admin, and SDR lead, and evangelist all at once. it’s a recipe for burnout for the role and failed initiative for the company. Last week, I finally found a job posting from a fast-growing B2B AI company that got it 100% right. Their mandate was surgically precise: "Treat GTM as a software problem and ship software that does the work a sales rep used to do." No kitchen-sink wish list. Just a tight, highly effective scope built around removing mechanical drag from sales reps. I think I have narrowed down a clear operating blueprint for revenue leaders and candidates, which can be highly effective for most organizations. Check it out. #GTM #RevOps #SalesOps #GTMEngineering #GoToMarket

More and more startups are hiring for GTM engineering roles. Some established brands are hiring for it too. The problem is that everyone has a different definition of what the role is actually supposed to be. This is what I'm seeing: A "GTM engineer" posting with RevOps, sales, demand gen, and product marketing roles all mushed and AI-slapped together with a new name. A marketing ops hire suddenly expected to own sales enablement, product signals, and CS handoffs, with no authority over any of the four functions they're meant to connect. Give someone a system-level job with a function-level mandate, and they are bound to fail the same way the last marketing or sales leader did. I wrote how the mandate gets lost at every stage from $1M to $20M+ ARR, what's actually in scope for the role (and what isn't), and why AI is the thing that finally makes this job executable not necesarily by just one person. Full breakdown in the article. What's your GTM engineer actually responsible for? Curious how different this looks company to company. #GTMEngineering #GoToMarket #RevOps #B2BSaaS #StartupGrowth #RevenueOperations #GTMStrategy #SaaSGrowth #FounderLessons #ScalingStartups

Anthropic posted for an "AI Engineer, GTM Claudification" at up to $405K. On the surface that sounds like a GTM engineer for Anthropic eating their own dog food. But look at what the job actually asks for: 1/ Model Context Protocol (MCP) servers, wired directly into core CRM and data warehouses. 2/ Evaluation frameworks: seed scenarios, scoring rubrics, regression runs that prove agents are production-ready before they ever touch a customer. 3/ Guardrails: approval gates, deterministic handoffs, explicit escalation paths that keep human sellers in control. Anthropic can pay $405K for someone to own that system because the system already exists. Probably they have done a pretty good job of having clean data, otherwise the system will collapse at scale. Structured quote-to-cash. Revenue operations that function like an API. Their sellers aren't guessing, they're working inside a loop that's already proven. Now look at the typical $5M–$20M B2B startup. Should they even consider GTM "Claudification". There's a GTM team/founder manually routing leads from a Typeform. An AE arguing with marketing about what "qualified" means. A CRM that looks like a crime scene. Drop an agentic workflow into that environment and you haven't automated anything. You've just built a machine that mistakes noise for pipeline, faster. If you are thinking of using AI agents to fix your pipeline, answer the unsexy question first: if you had to write down, right now, exactly how you find the righ…

Over the past few weeks, I have been sharing my observations about modern GTM positions (Founding GTM Engineer, Founding GTM Lead, Director of GTM, and GTM Strategy Manager) that many companies are now hiring for. Here is a quick recap of the top articles and the core insight from each: 👉 AI can accelerate GTM. It cannot engineer your GTM system. AI multiplies execution speed, but expecting one "GTM engineer" to replace six entire sales and marketing functions without system architecture is a setup for failure. https://lnkd.in/gMDHf-Fr 👉 Founding GTM role definition done right (almost) Early-stage GTM roles should optimize for learning velocity and documented testing frameworks, not just automated AI execution across unproven motions. https://lnkd.in/gCnECDya 👉 A new B2B GTM role is emerging. Why is this happening now and why you may also need it These unicorn job postings exist because no one owns the connective workflows between Product, Sales, and CS—causing pipeline to stall while departmental metrics look "green." https://lnkd.in/gH5YMFrk 👉 Why do they need The "Director of GTM" role at a multi-billion dollar data infrastructure company You shouldn't need a six-figure executive hire to get departments to talk. They need a connected system designed and governed from week one. https://lnkd.in/gt7ZqY7V Whether you are scaling a startup or running GTM campaigns, the message is clear: Go-to-market is not a collection of isolated tactics—it must be engineered as…

For months I've torn down "GTM Engineer" job postings that ask one person to do five jobs. This week I read one that gets it right. And it still has a blind spot that prevent GTM engines from scaling. The role is scoped like a real system: → Map the TAM → Test signals against it → Buy vs. build the stack → Enable sellers and marketers with AI That's the beginning of a real GTM architecture, But every item does one thing: detect accounts i.e. the top of the funnel. Here's what the spec never mentions: → What a signal means to Sales vs. Marketing and who decides what to do with the signal → How a detected account becomes qualified pipeline everyone agrees on → Who owns the handoff when the AI-sourced account meets a human That's what I call the pipeline bridge. Without it, a GTM engine doesn't fix your growth. It fills the funnel faster with accounts your teams argue about. The best GTM systems don't start at the signal. They start at the definition of qualified pipeline — and work backward. I broke down the whole 5-step bridge in this week's newsletter. 👇 Real question for the GTM crowd: when a "high-intent" signal fires and Sales says "not ready" — who's right, and who gets to decide? #GoToMarket #GTMStrategy #GTMEngineering #RevOps #PipelineGeneration #B2BSaaS #ArtificialIntelligence #ProductMarketFit

I ran across a GTM architect job posting ($200k+ base) at a public software company with more than $1 billion in revenue, still growing around 30% a year. The company is rebuilding its marketing, sales and customer experience stack around AI agents. Not as an experiment. As its operating model. This one person is supposed to: → Own the architecture across CRM, marketing automation, sales engagement, enrichment and support platforms → Redesign lead-to-revenue, opportunity management, renewals and support handoffs around AI → Ship AI agents into production, not pilots → Write Salesforce code and build integrations by hand → Set data quality and governance standards → Build the reporting leadership relies on → Advise Marketing, Sales, RevOps, CX and executives → Review code, pair with engineers and mentor the team That's a systems architect, a Salesforce developer, an AI engineer, a RevOps lead, an internal advisor to five functions and an engineering manager, all rolled into one person! To be fair, the posting is honest about it. It says plainly this isn't a diagrams-only role. The bet is that one senior person with AI coding tools can design the new GTM system and build its hardest parts. What's your reaction to this? Would you take on this role? (My reaction: at this company, with all RevOps, Marketing, Demand Gen, Content seats filled, it could still fail given the unrealistic expectations. Copied into a startup, it's a complete disaster.) The difference come…