# Jared Waxman

Jared Waxman writes the GTM Engineer Pulse and runs GTM Engineer School. Their work explains how businesses can build AI context layers and practical agent workflows for sales and marketing teams.

- Profile: https://b2binfluencer.marketing/creators/jared-waxman/
- LinkedIn: https://www.linkedin.com/in/jwaxman
- LinkedIn followers: 8,873
- Topics: artificial-intelligence, ai-agents, ai-adoption, go-to-market-strategy
- Based on 11 dated posts, latest 2026-10-08

## Summary

They are most useful for practical explanations of AI context layers and agent workflows for GTM teams.

- Best for: B2B SaaS company piloting GTM AI context layers and agent workflows.
- Not for: Broad sponsored content for a general B2B software buying audience.
- Check first: Confirm audience details and whether a paid talk or advisory format exists.

### Takeaways

- An agent withheld 15 customer-email replies because it could not find the company standard.
- A sales team used a context layer to help reps position a feature for different partners.
- Name which context layer you are building: brand, canonical or deterministic.

## Main ideas

- **The model is not the moat. Where the context layer lives is.**: Waxman makes the same argument from two angles. Models and agents improve on someone else's roadmap, so they cannot be a company's advantage; what matters is where the context that feeds them lives. No current option is neutral: GitHub suits agents but not humans, Notion suits humans but not agents, and a folder of files works until a sales team is in the field every day with years of history behind it. Then the feedback loop from every call has to stay on.
- **Name which context layer you are building: brand, canonical or deterministic**: Waxman's complaint is that most companies say "context layer" and mean one of three things without knowing which. Brand is the layer anyone in marketing can write to. Canonical is the governed layer for product and GTM strategy, and it carries a release cycle. Deterministic is the systems of record, answered through a semantic layer. Naming the layer matters because it determines who owns the content, how it changes and how much control it needs.
- **Builders and consumers are different people in the same company**: Waxman takes a specific position on adoption: a salesperson is never moving into Claude Code. The people who govern and build the context layer need engineering tools and a release process; everyone else needs a chat interface with the plumbing hidden. Treating them as one audience is what makes an AI rollout stall.
- **Pick one problem with a deadline instead of building a company brain**: Waxman relays the instruction not to make the project "build a company brain." Choose one problem with a deadline and start there; the tooling follows the problem, not the other way round. As Waxman puts it, all positioning is a theory, and the sales team is out there getting punched in the face testing it every day. A dated, real problem is the smallest unit that survives contact with a buyer.
- **Write the rule down, then decide where the human stays**: Waxman's lead item argues that an agent's ceiling is documentation, not capability. An agent that cannot find a written standard does nothing, and that can be the better outcome. Once rules exist, someone still has to decide which actions a person approves, and the field is split on how. One reading of the week's job postings found a company granting agents autonomy through their acceptance rate, while another kept a human on every send, arguing that the gate gets worse as the system gets better.
- **Learn GTM engineering by shipping one real workflow for one real company**: Waxman's priority is learning this work by building against a real problem, not by studying tools. They run GTM Engineer School from a folder of markdown files in a GitHub repository. Cohort 5, starting 26 October, puts 50 students each on one real workflow for one real company, with a 30-minute call a week. It is free, not consulting, and carries no promised deliverable; the median participant in Cohort 3 had 12 years of experience. Waxman also runs an hour-long recorded conversation format and a weekly roundup, GTM Engineer Pulse, which mixes interviews with items sent in by readers.

## Work to read

- [Swan AI's zero-SDR claim and where a context layer lives](https://www.linkedin.com/posts/jwaxman_swan-ai-has-no-sdrs-no-customer-success-activity-7503171706916794368-wYu7)
- [Three kinds of context layer, and why "build a company brain" fails](https://www.linkedin.com/posts/jwaxman_i-run-gtm-engineer-school-on-a-folder-of-activity-7506381788701913088-qaLq)
- [Astra's benchmark win was knowing when to send nothing](https://www.linkedin.com/posts/jwaxman_openais-newest-model-astra-was-handed-15-activity-7503818361311539200-NU8l)
- [What 18 months changed for one GTM team, and what the CRM vendors shipped](https://www.linkedin.com/posts/jwaxman_eighteen-months-ago-danny-chepenkos-team-activity-7508576202203262976-GaLM)

## Brand relationships

- **GTM Engineer School** (owned_business)
- **Swan AI** (mention)
- **Clay** (mention)
- **Anthropic** (mention)
- **OpenAI** (mention)

## Partnerships

Jared Waxman's four posts cover editorial and program-related topics: weekly GTM engineering roundups, a recorded conversation with a vendor founder, and a note about GTM Engineer School. They don't advertise a rate card, sponsored-placement format, branded-series offer, or inquiry route for paid partnerships. One adjacent program is Cohort 5, described as free and "not consulting, no promised deliverable," with buyers supplying a concrete problem and joining a 30-minute weekly call; entry is by commenting CAPSTONE. It's a participation program, not a sponsorship format, and its terms come from the creator rather than a negotiated paid engagement. Brands should ask directly about partnership availability, format, and terms; the posts provide no published route or menu.

## Alternatives

- Sara McNamara: Choose Sara when the buyer's main challenge is deciding who should own GTM AI adoption and how it fits into RevOps. Her post compares two emerging models: an AI-native RevOps leader who owns the operating model and builder teams, or a separate GTM AI leader alongside RevOps. She also describes the shift toward writing precise process specifications that agents can build from, including the need to define exceptions, guardrails, and tests. That makes her a closer fit for an organizational-design session or a working session to turn an existing process into a buildable specification.
- Brigitta Ruha: Choose Brigitta when the buyer's immediate priority is a practical AI-assisted lead-generation workflow. Her example lays out a sequence for selecting buying signals, checking accounts against a two-signal threshold, showing evidence for each qualification, and shaping outreach from real messages. That offers a direct starting point for a demand-generation team seeking a campaign or workflow session.
- Shane Spencer: Choose Shane if the buyer wants a critical discussion of AI-agency delivery or the implications of Salesforce's shift toward APIs, agent tools, and Slack-based workflows. His examples take a strong operator's point of view: one argues that service delivery matters more than agency funnels, while another describes building agents and skills around Slack and business systems. That could suit a session focused on delivery discipline or a specific Salesforce-and-Slack platform direction.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/