Say you're the GTM engineer at a 200-person SaaS company. You have Claude Code, a repo with a few dozen skills, a CRM that is mostly right and a backlog longer than the quarter. On Monday your CRO forwards a YC launch, "Frontrunner: Cursor for GTM", with one line on top: can we just buy this?

Read the launch and look at the competitor it names. It isn't Clay, Outreach or Salesforce. It's Claude: "Your nth project in Claude will not incorporate the meeting outcomes." Nex, from ex-HubSpot operators, makes the same case from the cost side: high-volume GTM work "would burn your entire weekly limit overnight on Claude or Codex." Swan calls itself "the AI GTM engineer your team needs, without the hire."

They aren't competing with your tools. They're competing with you, the person who would build it yourself. So I took the CRO's question seriously. Is it worth working with one of these products, or should you build it on your own? To answer it, I first went back a year.

How we got here

For a while I kept a habit. Every few months I read the latest Y Combinator launches, opened every sales and lead-gen startup, checked the pricing page and whether I could start without a demo, and posted a shortlist in my Telegram channel. Then I stopped for most of a year. This month I read all 895 launches YC published between January 8 and October 6, and rechecked the 33 companies from my old lists.

A year later: 33 YC sales startups I picked A grid of 33 squares, one per YC sales startup I picked a year earlier. 21 still sell to GTM teams, 7 left sales entirely, 1 was acquired (Ciro, by Reevo), 2 look dead (Kular and crmCopilot) and 2 could not be traced. Where the seven went: Lyra, an AI-native Zoom for sales, became fintech on stablecoins; Aside, a sales call assistant, an AI browser; Primer, an AI demo agent, software for law firms; Clice, an AI-native CRM, training data for AI labs; SixtyFour, a Bloomberg for sales, identity intelligence; Pickle, an AI double for calls, personal AI; Lopus, buyer intent from social, analytics. Checked October 6 to 9, 2026, from company sites, YC pages and news. A YEAR LATER 33 YC sales startups I picked 21 Still selling to GTM teams 7 Left sales entirely 1 Acquired (Ciro → Reevo) 2 Look dead (Kular, crmCopilot) 2 Could not trace WHERE THE SEVEN WENT Lyra AI-native Zoom for sales → fintech on stablecoins Aside sales call assistant → AI browser Primer AI demo agent → software for law firms Clice AI-native CRM → training data for AI labs SixtyFour Bloomberg for sales → identity intelligence Pickle AI double for calls → personal AI Lopus buyer intent from social → analytics Checked October 6–9, 2026: company sites, YC pages and news.

21 of the 33 are alive and still selling to GTM teams. Seven left sales entirely: Lyra became a stablecoin financial institution, Aside an AI browser, Clice a supplier of training data to AI labs. Two look dead, one was acquired (Ciro, by Reevo), and two I couldn't trace. Most casualties were thin AI SDRs, call copilots and AI CRMs. It is hard to sell what Claude with two MCP servers does out of the box.

The survivors and the new batch changed in three ways.

AI tools hired humans back. InstaAgent reached $1M ARR in ten months and then moved to an agency model, because "80% quality = 0% retention." Roger, an AI SDR from my old list, now sells the agent plus a dedicated human SDR. Revnu offers an agent for $99 a month with no sales call, and next to it a Managed tier with a "forward-deployed growth engineer".

Old companies relaunched as agents. Mutiny raised $72M on website personalization and then, in its own words, "killed the SaaS product and rebuilt the whole thing from scratch." OneSignal, founded in 2011, added an autonomy ladder that runs from Level 0, manual, to Level 4, full operator.

Sending stopped being an advantage. AiSDR published medians across its customers: about 1% positive replies, roughly what a decent manual program gets. Anyone can buy the send. The edge moved to what you know about the account.

Meanwhile, GTM engineers started building it themselves. "Anyone else running GTM workflows in Claude Code?" was a thread title on r/gtmengineering in January. Today Systems Lab catalogues 2,968 public agent skills for GTM across 158 libraries. When I read 2,628 of them in September, some were quietly selling to your agent. Still, nobody starts from zero anymore.

That's the gap the new platforms walk into. AI SDRs sold you an employee. The new wave sells you the engineer.

How we got here: from AI SDR to AI GTM engineer A timeline of who does the work. 2024 to 2025: the AI SDR sold you an employee (11x, Artisan, AiSDR), and the agent does the work. 2025 to 2026: AI SDR plus a human, because the agent needed a person to work (InstaAgent, Roger, Revnu), so the agent and their human do the work. 2026: build it yourself with Claude Code and 2,968 public GTM skills, as GTM engineers on r/gtmengineering do, so your GTM engineer does the work. Now: the AI GTM engineer sold you the engineer (Frontrunner, Nex, Swan), so the platform and their human do the work. Next: assemble, with rented parts, your harness and your memory. HOW WE GOT HERE From AI SDR to AI GTM engineer WHO DOES THE WORK 2024–25 AI SDR Sold you an employee 11x, Artisan, AiSDR The agent 2025–26 AI SDR + a human The agent needed a person to work InstaAgent, Roger, Revnu Agent + their human 2026 Build it yourself Claude Code + 2,968 public GTM skills GTM engineers on r/gtmengineering Your GTM engineer NOW AI GTM engineer Sold you the engineer Frontrunner, Nex, Swan Platform + their human Next: assemble. Rented parts, your harness, your memory.

Three analogies, three buyers

Each of them explains itself through a developer tool, and the analogy tells you who they think the buyer is.

Frontrunner (YC F26) calls itself "Cursor for GTM". Its founders ran GTM engineering at Console and Encord. It plugs into your CRM, email, calendar and call recordings, turns them into "a graph of your business", and runs a fleet of agents on top. Cursor is a tool for engineers, so the natural buyer already has someone who builds. There's no public price: you get started or book a demo.

Nex (YC S26, backed by HubSpot founder Dharmesh Shah) launched as "Claude Cowork for high-volume workflows, starting with GTM." Cowork is for people who work next to Claude without writing code. Nex's smartest idea is under the hood: the AI writes code for the predictable steps and saves model judgment for the hard ones, so a big run doesn't re-think every row. The agent builder is $49 a month plus usage.

Swan was pitched by its founder as "Lovable for GTM". Lovable is for people who want the thing built without building it. Plans run from $100 a month for one seat and 625 credits to $419 for five seats, and Scale is custom. Today the homepage leads with "Build a GTM brain": "a brain that remembers everything".

Cursor sells to builders. Lovable sells to teams without builders. Cowork sells to operators who've hit their limits. Three pitches aimed at three different people, with one shared promise: you won't have to wire this yourself.

Who is it really priced for?

$49 or $100 a month is a team lunch. Nobody runs a pipeline on a team lunch. So I looked at how these products actually get into a company.

Nex's pricing page now opens with custom agents and "pay for outcomes": book a free call with a "RevOps agent expert" and "get your first agent built on the call." Swan's Scale tier comes with a dedicated onboarding specialist. Its founder, Amos Bar-Joseph, wrote that the company once hit 267 demo calls a week, with the two founders "completely maxed out." One of Swan's case studies, Elastic Path, builds its plays with an embedded agency partner. Revnu sells the forward-deployed engineer as a tier.

Who is it priced for? The price page versus what makes it work Five rows compare what each option shows on its price page with what makes it work. Nex: $49 a month plus usage; an agent expert builds your first agent on a call, and custom agents are pay for outcomes. Swan: $100 to $419 a month plus credits; the Scale tier has a dedicated onboarding specialist, and a case study was built with an agency partner. Frontrunner: no public price; get started or book a demo. Revnu: $99 a month with no sales call; a Managed tier adds a forward-deployed growth engineer. Build it yourself: a Claude or Codex plan plus API; your GTM engineer's hours. Pricing pages, launch posts and case studies checked October 20, 2026. WHO IS IT PRICED FOR? The price page vs what makes it work ON THE PRICE PAGE WHAT MAKES IT WORK Nex $49/mo + usage → An agent expert builds your first agent on a call. Custom agents: “pay for outcomes”. Swan $100–$419/mo + credits → Scale tier: dedicated onboarding specialist. Case study built with an agency partner. Frontrunner No public price → Get started or book a demo. Revnu $99/mo, no sales call → Managed tier: a “forward-deployed growth engineer”. Build it yourself Claude or Codex plan + API → Your GTM engineer’s hours. Pricing pages, launch posts and case studies, checked October 20, 2026.

My bet: the self-serve price is the door, and the business is deployment. A person, theirs or a partner's, sits with your team, learns how you sell and wires the agents into your stack. That's forward-deployed engineering, the Palantir model, at GTM scale, and its best buyer is a company with no GTM engineer. Nobody publishes their revenue split, and the customers I could see have 50 to 500 people, so treat it as a bet. But it rhymes with last year. The AI SDRs hired humans back to make the product work. The AI GTM engineers are doing it one layer up.

The author of that r/gtmengineering thread said it more bluntly: "the human/services layer (FDEs, SEs) will matter too. 'click the button and get money' is dead."

Which leaves our GTM engineer with an awkward question. If the vendor's forward-deployed engineer is the real product, and you already are one, what exactly are you buying?

Five questions from the GTM engineer's chair

Here is how I'd evaluate any of them.

1. Does it plug into my stack, or become it? All three connect to the usual CRM, inbox and calendar, and write back. Swan goes furthest, with a waterfall across 40+ data providers on "one bill". Convenient. It also means your data spend, enrichment logic and account history now run through one vendor.

2. Who keeps the memory? This is the real product. Swan sells a "GTM brain". Frontrunner says "every run creates more context". Nex promises "your company's knowledge, ready for every agent". Context that compounds is exactly what you want. The question is whose database it compounds in.

3. Whose playbook is it? Frontrunner calls itself "an opinionated harness for GTM". Opinionated is great until the opinion isn't yours. A playbook tuned for the average customer is one-size-fits-all with your logo on it.

4. Am I paying a markup on my own tokens? Under the hood these products call the same frontier models you can pay for directly, and credits are a margin on top. Sometimes it's worth it: Nex's code-for-the-boring-steps design really does save tokens. But it's a design you can copy, not a moat.

5. If we leave, what stays with us? Workflows as code you can read? Your ICP and scoring rules in a format you can export? Or a login that stops working?

Practitioners split in a revealing way. I had Codex go through Reddit and Hacker News and pull every relevant post and comment it could open: 33 in total. 14 lean toward building, 5 toward buying, 14 sit in between, from a technical crowd that leans DIY anyway. The strongest case for building: "internal knowledge is the real moat. no external product can beat the internal feedback cycle." The strongest case for buying, from the same thread: once you count "keeping integrations alive, swapping data sources, handling edge cases, rate limits, failures," it's "basically a product team's job."

Both are right. That's why I don't think the answer is build or rent.

Not build, not rent: assemble

The best evidence came from Swan's own founder. Last month he posted on r/AI_Agents that he had been running about 30 agents for Swan, and "by the end of it most of my week was going into keeping the agents working rather than doing gtm." So he killed 27 of them and rebuilt around three: Claude Code for engineering, Swan for GTM, OpenClaw for everything in between.

That isn't building, and it isn't renting. It's assembling, and I think that's where most teams will land.

Five questions: build, rent or assemble A table comparing build, rent and assemble on five questions. Who wires the stack: build, you wire every integration; rent, they do and it becomes the stack; assemble, rented parts with your wiring. Who keeps the memory: you; the vendor's brain; you, in your own repo. Whose playbook: yours, from scratch; theirs, tuned for the average; yours, forked from public skills. Who pays for tokens: you, at cost; you, plus their markup; you, at cost, with data and sending rented. If we leave: everything stays; exports, if any; everything but the rented parts. The test for any vendor: does it read your memory, or keep it? FIVE QUESTIONS Build, rent or assemble BUILD RENT ASSEMBLE Who wires the stack? You wire every integration They do, and it becomes the stack Rented parts, your wiring Who keeps the memory? You The vendor’s brain You, in your own repo Whose playbook? Yours, from scratch Theirs, tuned for the average Yours, forked from public skills Who pays for tokens? You, at cost You, plus their markup You, at cost. Data and sending are rented If we leave? Everything stays Exports, if any Everything but the rented parts The test for any vendor: does it read your memory, or keep it?

Here's how I'd split it:

  • Rent the commodity. Data providers, email and LinkedIn sending, deliverability, high-volume runs. Nobody wins by building their own waterfall of 40 enrichment vendors. If a platform does this well, pay for it.
  • Own the judgment. Your ICP, scoring rules, plays and the record of what worked, kept in a repo as skills that people and agents can both read. Public skills are a fine starting point, but read them first, because some are written to sell to your agent.
  • Wire it on a harness you control. For one GTM engineer, that can simply be Claude Code or Codex plus your skills repo. For a team that needs scheduled runs, approvals and shared memory, use an agent framework. My current pick is Mastra, because it feels the most bleeding-edge: TypeScript, workflows that pause for a human's approval and pick up where they stopped, memory in your own database. It's one option of many, though. Agno, LangGraph and plenty of others do the job.

Then hold every vendor to one test: does it read your memory, or keep it? A tool that plugs into your context through MCP and permissions you set is a part you can swap. A tool that pulls your context into its own brain becomes the place your company remembers things. That's the point I made in "Who does your AI really work for?": a free tier and a $49 plan are the entrance fee to your memory.

And if you don't have a GTM engineer and won't hire one? Rent, honestly. The vendor's forward-deployed person becomes your GTM engineer for a while. Just make sure the skills and the context stay with you when they leave.

Where this goes

My prediction for 2027: the "AI GTM engineer" platforms split in two. One group ships its agents as skills and MCP servers that run inside the customer's own Claude, Codex or harness, and charges for data, volume and expertise. The other becomes a services firm with very good software. The GTM engineer doesn't disappear. The job moves from wiring tools to assembling them, and to owning the judgment inside.

Back to the CRO's email. My answer would be: we're not buying a GTM engineer. We'll rent the data and the sending, and we'll keep the brain.

What I'd do this quarter:

  • Put your ICP, scoring rules and best plays into a repo as skills before you evaluate any platform. You'll negotiate with something already built.
  • Rent data and sending. Don't rent memory.
  • Ask every vendor two questions. If we leave, what stays with us? And what are your medians, not your best case study?
  • If someone offers to build your first agent on a call, take the call. Then ask for the code.

P.S. Five from the YC batch I'd actually try

  • Mutiny: an agent that builds deal rooms, business cases and prospecting pages for each account. Free plan, Business $50 a month, self-serve.
  • CatchAll: trigger-based outbound when you need every matching event on the web, not the first ten links. From $0 pay-as-you-go, $50 for 6,000 credits.
  • Intuned: it writes a scraper as code once and repairs it when the site changes. A part you can assemble with. Free tier, paid from $25 a month.
  • Promi: simulated users built from your own product, billing and support data, to test copy and flows before real customers see them. $99 per seat, 14-day trial after a call.
  • COACH: the only coaching tool in the batch built for teams that sell face to face. By COACH's own count, 85% of the world's sales still happen in person. Demo only.

Companies, prices and pages checked October 6 to 20, 2026. Traction figures are self-reported.