Why I picked this
Great framing for functional emergence of AI in GTM
“The work RevOps does today is the work AI is best at eliminating. The only version of the role that survives is one that fundamentally transforms.”
Key takeaways
- RevOps faces existential choice: evolve into GTM system architect or be automated away by AI - no middle path exists
- Modern GTM is now a system with 106+ SaaS tools creating quadratic complexity (106 integration points per new tool) that humans cannot manually operate
- AI deployment sequence matters critically - most companies implement backward by automating tactical work before fixing underlying system architecture
- GTM system ownership is a CEO-level decision about who controls data architecture, workflows, agent layer, and feedback loops - not just vendor selection
- RevOps originated as CRM administration and expanded by absorbing complexity; AI represents the largest complexity jump yet, forcing role redefinition
Why this matters for operators: CEOs and RevOps leaders navigating organizational design in AI era; companies restructuring GTM ownership
I cover AI×GTM intelligence like this every Wednesday.
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- Public opposition to nearby data centers nearly doubled from 42% to 71% in just nine months
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Where we maybe previously paid the W-2 of a human to do this necessary thing for the business, that cost didn’t really go away. It just transferred from a W-2 to an inference provider.”
- GTM operators are entering a 'toolbox era' where bringing your own AI stack (like mechanics bring tools) becomes expected in FTE and fractional roles
- Running AI models locally (Ollama, LM Studio, Jan.ai) gives operators data ownership and independence from SaaS vendor terms of service and uptime
- GitHub repos are becoming the new resume for GTM operators - demonstrating technical capability and owned infrastructure matters more than traditional credentials
Exclusive: Office workers embrace OpenAI's Codex
Productivity and brain tax so real
- AI coding agents are rapidly expanding beyond developers - knowledge workers now represent 20% of OpenAI Codex users and growing 3x faster than technical users, with 4M weekly actives (5x growth since February)
- The mental cost of AI supervision is emerging as a critical adoption barrier - power users report 'AI psychosis' and cognitive exhaustion from managing multiple fast-moving AI workstreams, creating a new type of workplace stress distinct from traditional productivity fatigue
- Agentic AI is creating a workplace artifact integration layer - tools like Codex connect email, calendar, docs, Slack/Teams to surface context across siloed systems, with 60% of users now running concurrent AI tasks (up from <50% in April), signaling shift from single-task automation to orchestration workflows
This analysis was produced using the STEEPWORKS system — the same agents, skills, and knowledge architecture available in the GrowthOS package.