Pivotal early lessons of today's best startups.
Welcome to the focal podcast where we go deep with some of today's best founders and operators on ONE crucial lessons from their early days.
This podcast is not the usual "highlight reel" startup podcast that goes one inch deep across 20+ topics. Rather, we ask the questions you’d ask if you were sitting across from them. No fluff, just the real, actionable insights you’d get if these founders were mentoring you 1on1.
We cover topics including:
- What worked and why.
- Costly mistakes and how they fixed them.
- Frameworks that truly made a difference.
- Tactics to move faster.
- What they wish they’d known sooner.
- And much more!
"Only a fool learns from their own mistakes. The wise learn from the mistakes of others."
The Clay Playbook for Hyper-Targeted Outbound | How to Turn Multiple Signals Into One Story | Why Your List Matters More Than Your Message | The "Magic Wand" Framework for Finding Your Best Customers with Osman Sheikhnureldin, Head GTM Engineering at Clay
Most founders think GTM engineering is just cold outbound done better. Clay's Head of GTM Engineering Osman Sheikhnureldin reveals why that mindset will cost you months of wasted effort.
In this episode, Osman walks us through exactly how to identify your ideal customers at the perfect moment, then demonstrates his framework live with two early-stage founders - showing you can't just wing GTM engineering, but when done right, the results compound.
Osman Sheikhnureldin is the Head of GTM Engineering at Clay, the company that invented GTM Engineering. He's helped hundreds of startups transform how they use data and technology to remove growth constraints. Joining him are Nilo Rahmani, Co-founder and CEO of Thoras AI (AI-driven cloud reliability and cost optimization), and Panos Papageorgiou, Co-founder of Keragon (HIPAA-compliant automation platform for healthcare).
In Today's Episode We Discuss:
01:51 - GTM engineering defined: Solving growth constraints with technology, not headcount
02:57 - Why your target list matters more than your message will ever matter
04:45 - Ditch static ICPs: The jobs-to-be-done framework that actually works
06:53 - The "magic wand" question every founder must answer before building workflows
08:49 - The account scoring workflow no human should ever do manually again
13:46 - How Clay built an ML model to predict contract value from enrichment data
19:18 - Vanta's genius GTM hack using AI screenshots to analyze brand consistency
21:30 - European food startup's signal stack: First US hire + ad spend + new landing pages
23:06 - Early-stage messaging must be hyper-specific—big company tactics won't work for you
26:54 - Founders who lived the pain have an unfair advantage in outbound messaging
28:59 - Counterintuitive truth: AI SDRs have failed—human taste matters more than ever
31:48 - Why hybrid LLM + human skeleton emails crush pure AI-generated copy
34:42 - Voice AI skepticism: Great for extraction, not ready for cold calls
37:18 - Three brutal truths: GTM engineering takes months, hard work, and real creativity
38:20 - "Earn the right to message someone"—the philosophy behind effective outbound
39:24 - Live teardown: Keragon's healthcare GTM using EHR migration as the trigger signal
47:08 - Finding EHR signals through PR announcements, patient portals, and RSS feeds
55:38 - Live teardown: Thoras AI's challenge—spotting cost-cutting triggers that signal growth
01:00:58 - Why "cutting costs" often means a company is scaling, not struggling
01:07:32 - Novel signal: Second product launch as the perfect moment to reach infrastructure teams
01:13:06 - The biggest misconception: GTM engineering goes far beyond cold outbound
11 Dec 2025
Why GTM Engineering is the Future | When to Hire Your First GTM Engineer | How to Treat GTM Like a Product | How Clay Scaled from PLG to Enterprise | Automate the Manual, Never the Important with Yash Tekriwal, Head of Education at Clay
Clay pioneered GTM engineering and went from $1M to $100M in ARR in 2 years.
I talked to the person who invented the role of GTM Engineer at Clay.
Yash Tekriwal, Clay's first GTM engineer - back when the $3B company was still figuring out what that even meant.
What started as one person drowning in too many jobs (RevOps + Sales + BDR + data analyst) has since become a new category that's now reshaping how startups think about go-to-market.
You’ll learn:
Why RevOps is "maintenance" but GTM engineering is a growth lever
The skills that define a great GTM engineer today (hint: it involves vibe coding)
What "treating go-to-market like a product" actually looks like in practice
Two org models for GTM engineering teams - and which to start with
"Automate the manual, but don't automate the important"
In Today's Episode We Discuss:
01:23 - The origin story of GTM engineering at Clay and why the term is polarizing
05:02 - GTM engineer vs RevOps: maintenance function versus growth lever
07:31 - Treating go-to-market like a product team, not an individual sport
10:42 - Three experiments every GTM team should run on inbound and outbound
15:24 - The essential GTM tech stack: CRM, enrichment, sequencing, and what actually matters
19:08 - Tools founders should consider when getting started—and the automation trap to avoid
22:12 - Zero to $1M: be thrifty on tools and process information manually
25:38 - What to look for in your first GTM engineering hire (hint: it's not technical skills)
28:43 - Signals that you need to hire a GTM engineer for outbound vs inbound motions
31:45 - Scaling past $10M: specialize fast and the hyperscaler dilemma
36:01 - Two org models for GTM engineering: centralized hit team vs embedded engineers
40:22 - The ideal GTM engineer profile: tinkerers, not traditional engineers
43:33 - Why engineers are not the ideal candidates for GTM engineering roles
45:19 - Can salespeople become great GTM engineers? The sales hacker archetype
47:26 - Resources to learn GTM engineering: Clay University, substacks, YouTube channels, and agencies
51:00 - Top three things founders must know about GTM engineering at any stage
52:16 - The most creative GTM engineering builds: satellite imagery, hospital capacity, and custom memes
56:24 - Personal lessons from scaling at Clay: ego death, pivoting, and balancing maintenance with big bets
59:42 - The one thing Yash would change: stop oscillating and let problems become obvious
1 Dec 2025
Why Horizontal Beats Vertical in AI Agents | The Compounding Error Problem Most Founders Miss | The Case For Research-Heavy Teams Win | How to Build AI That Actually Generalizes with Abhishek Das, Co-Founder & Co-CEO of Yutori AI
The Horizontal vs Vertical AI Debate: Why This Ex-Meta AI Researcher Is Betting Big on Horizontal Web Agents
Should you build narrow (vertical) or go broad (horizontal) in AI? This episode unpacks why one PhD researcher abandoned his working vertical product to chase a much riskier horizontal bet - and why VCs leaning heavily into vertical AI might be missing something.
Abhishek Das is the co-founder and co-CEO of Yutori, which has raised over $15 million from Radical Ventures, Felicis, and prominent angels including Ali Gil, Sarah Guo, Scott Belsky, and Guillermo Rauch. Previously a research scientist at Meta's FAIR lab, Abhishek holds a PhD from Georgia Tech where he pioneered work on AI agents that can see, talk, and act starting in 2016.
In Today's Episode We Discuss:
00:53 - Why how we interact with the web hasn't changed in three decades and what will break that
02:27 - The coming shift from manual browsing to AI assistants performing tasks in the background
05:57 - What "agents" actually meant in ML research before the term became overloaded
06:14 - Why 90% accuracy per step creates catastrophic failure rates over multi-step workflows
08:46 - The behavior pattern humans nail intuitively that machines struggle with: backtracking from errors
10:11 - The DoorDash experiment: building an end-to-end food ordering agent that never shipped
12:58 - Why training on sinle websites leads to memorization instead of generalization
13:03 - The dopamine problem: some tasks users don't want automated
15:08 - Why capability-scoped beats website-scoped: the pivot to read-only horizontal agents
18:05 - Three criteria that drove the horizontal decision: research, user value, and data strategy
24:18 - Scouts API launch: why different channels have different risk appetites for web agents
26:30 - Flying close to the sun: how Yutori competes with hyperscalers on horizontal AI
30:32 - What VCs should actually test for in horizontal AI teams beyond founder horsepower
32:10 - Why three-month roadmaps are the only reasonable planning horizon in AI today
33:05 - The dogfooding ritual: every team member rotates through user feedback weekly
34:50 - Why research and product can't be siloed and how ideas flow both directions
36:03 - The uncomfortable truth: end users don't care about your research breakthroughs
37:32 - The Nintendo Switch 2 problem: aggregating individual feedback into systemic fixes
39:35 - Reframing web agents as "buyer's agents" that filter the internet on your behalf
40:59 - The simulation bet: training agents on cloned websites for high-stakes irreversible actions
43:05 - Why initial team skepticism about Scouts' value proposition was completely wrong
45:01 - How scout reports contextualize results with reasoning and ingest feedback over time
47:52 - The core insight test: where does your instinct lie across research, market, and domain?
49:36 - The hiring trap: why preemptively hiring sales leadership to impress VCs backfires
51:18 - The 12-year-old advice that still guides him: "Be a sponge when entering a new space"
53:05 - Non-negotiables: walking the dog with podcasts and personally reading every user email
54:49 - What founders actually need from VCs: direct and timely feedback, not just capital
Who has been a guest on focal podcast
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Natasha Ratanshi-Stein
Guillaume Jacquet
Alexa Grabell
Han Wang
Santiago Suarez Ordoñez
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