Engineering Leadership
8 posts on this topic — practical guidance from Shawn Livermore on fractional CTO, AI, and technology leadership.
Andrew Ng's AI Engineering Skills Map Gets the Priorities Right. Here's the Missing Layer.
Andrew Ng's AI Engineering Skills Map, published in The Batch on August 21, 2026, names building AI apps, software fundamentals, and coding agents as the top priorities. The enterprise view adds a fourth layer the map doesn't cover.
Read post →Andrew Ng's AI Skills Map Is Accurate. Most Organizations Have No Idea How to Use It.
Andrew Ng's The Batch Issue #366 maps what AI engineering competency actually looks like in 2026. The map is correct. The problem is that most mid-market organizations don't have the internal reference point to translate it into hiring criteria, onboarding, or performance benchmarks.
Read post →Meta's Reassignment Decision and the Engineering Culture Cost That Doesn't Show Up in the Quarterly Report
Gergely Orosz's Pragmatic Engineer documents a voluntary resignation wave at Meta following forced reassignment of engineers to AI data labeling. The mechanism isn't unique to Meta — and understanding why equity retainers aren't stopping it matters for any organization managing technical talent.
Read post →Senior Engineering Leaders Are Moving to Fractional Work. Gergely Orosz's 2026 Data Shows Why.
Gergely Orosz's Part 3 on the 2026 tech jobs market documents something most coverage missed: senior engineering leaders are deliberately moving to fractional roles. Here is the practitioner view of why that shift is structural.
Read post →The Tech Workforce Is Splitting in Two. That's an Engineering Leadership Problem.
Lenny Rachitsky's second annual survey shows burnout climbing to 55.7% while a parallel cohort reports feeling more capable than ever. The split is real — and leading it well is a technology leadership discipline, not an HR response.
Read post →Andrej Karpathy Named the Shift. Engineering Leaders Now Have to Manage It.
Karpathy's Sequoia Ascent fireside chat defined agentic engineering as the new professional discipline for software engineers. What it doesn't address — and what engineering leaders have to solve — is what happens when an entire team makes this shift simultaneously.
Read post →What Meta's Engineering Redeployment Reveals About AI Organizational Design
Gergely Orosz's reporting in The Pragmatic Engineer documents Meta redirecting roughly 6,500 engineers to data labeling and AI training work. The decision reflects a deliberate strategic bet. The organizational design questions it surfaces belong in every technology leader's planning conversation.
Read post →Enterprise Vibe Coding: Why Speed Without Governance Breaks at Scale
Individual AI coding assistance is a productivity story. The same capability running across 20 engineers in a shared codebase with security requirements and integration dependencies is a governance problem.
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