Show Notes
About our guest — Yash Tekriwal Yash Tekriwal is Head of Education at Clay, where he builds the programs, content, and partnerships that help GTM operators learn one of the most powerful — but complex — tools in the modern stack. Before running education, he was Clay's first founding GTM engineer, building out the core sales process, demo tables, and custom POC model that supported the company's back-to-back years of 6–10x revenue growth. A former high school computer science teacher, 2x founder (Lectureless, Radify Labs), and self-described expert generalist, Yash brings an unusual mix of classroom chops and operator instincts to
Core takeaways The Layered Grunt Work Problem: AI doesn't delete grunt work — it moves it up a layer of abstraction. What used to be "write your weekly update" becomes "read everyone else's updates," and that shift unlocks more interconnected team conversations downstream. The leverage is in what becomes newly possible, not just in hours saved. Computational Thinking Beats a CS Degree: Having written Python doesn't prove you can think computationally. The real test is transferability — can you port a skill from Claude to ChatGPT, or from Python to JavaScript, with light pointers? If not, you've been given fish, not taught to fish. In a toolscape that reshapes every 90 days, transferable skills are the highest-signal indicator of top talent. Three Personas Hit Three Different Walls: GTM-background operators get lost in computational thinking and burn credits before they learn. Ops-background people with no-code experience need to learn workflow thinking — where Clay fits vs. where it doesn't. Engineers are often the hardest persona because syntax knowledge doesn't guarantee systems thinking; the abstraction layer is the new challenge. Automate by Category + Time to Value: Every automation either replaces manual work with identical output OR unlocks capability that wasn't possible before. Prioritise by the 5–10 minute test: can you get 80% of the way there? If yes, ship it. Optimise the last mile of any automation only after you've covered the broad gains. Pareto Principle is always in effect.
Top quotes > "What I think AI is doing, which is still a step change forward, is it is moving the grunt work up one layer of abstraction." > "Just because you have a computer science degree does not mean that you know how to think computationally." > "We will probably enter the most entrepreneurial generation of the past couple of decades because you don't need all the things or the obstacles that were in your way of starting a business before." > "Take the 20% that you can automate to give you 80% of your returns on the things that you need. Do that for as many things as you can before you start to try and optimize the last mile of your automations."
Referenced tools and resources Sales & Revenue: Clay (Ads, Audiences in beta), Salesforce, Attention AI & Agents: Claude, Claude Code, OpenClaw (formerly Cloudbot), OpenAI Productivity & Ops: Notion (custom agents, meeting recorder), Dust, Slack Workflow Automation: N8N, Zapier, Airtable Learning: Clay University, Code Academy, Algorithms to Live By (book)
Timestamps (02:34) Welcome back to the GTM Engineer Podcast season two, and why Yash is the right person to open it (05:20) How GTM engineering has evolved in the last year, and the Andreessen parallel to software engineering titles (07:19) The evolution from software engineer to frontend to forward-deployed — and what that means for GTM engineering next (07:38) What Yash would miss most if GTM engineering disappeared tomorrow (08:48) AI doesn't delete grunt work — it moves it up a layer of abstraction (09:16) Where the hype is overselling and what still has to happen manually (11:29) The two-category framework for deciding what to automate first (14:37) The 5–10 minute time-to-value test for any new automation (16:00) Why we're entering the most entrepreneurial generation in decades (16:20) Why larger compani
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