Why engineering leadership matters more than ever | Manoj Mohan

AI as an intern, the death of "Scale," and choosing clarity over cleverness.

The common narrative suggests AI will make engineering leadership obsolete, but history - and the Industrial Revolution - suggests the opposite is true. Engineering executive Manoj Mohan joins the show to argue that as code generation costs drop, the demand for high-level judgment and strategic oversight will only skyrocket. He breaks down why leaders must stop starting with models and start with customer pain points, utilizing his “3GF” framework to manage the risks.

Recorded live at the Engineering Leadership Conference.

---

1. The Federal push for AI consolidation

The White House has signed an executive order establishing a single national regulatory framework for artificial intelligence, overriding state-level regulations by leveraging $42.5 billion in broadband program funding. This mirrors the consolidation of the early web. With data centers, engineers, and users distributed globally, a federal approach via the Interstate Commerce Clause is the logical step to prevent a patchwork of state restrictions. The goal isn’t just regulation, but preventing a fractured landscape for development.

Read: Ensuring a National Policy Framework For Artificial Intelligence

2. Context is the new prompt engineering

The Linux Foundation has launched the Agentic AI Foundation (AAIF), backed by tech giants like Amazon, Google, and Microsoft. This brings projects like Anthropic’s Model Context Protocol (MCP) and OpenAI’s AGENTS.md under one roof. The real shift here is moving from “prompt engineering” to “context engineering.” In a perfect world, prompts remain simple while the underlying corpus provides the context needed for agents to interact with repositories autonomously. One prompt solves a single problem; context solves all problems.

Read: Linux Foundation Announces the Formation of the Agentic AI Foundation (AAIF)

3. The end of the “Scale” era

At NeurIPS 2025, leading figures admitted that simply making models bigger is no longer sufficient to achieve general intelligence. Marcus on AI argues this dawning realization aligns with industry data from MIT and McKinsey showing that 95% of companies are not realizing significant ROI from generative AI investments. The era of exponential gains from raw compute is fading. Future value won’t come from massive training runs, but from the practical application of existing technology: fine-tuning, efficiency, and solving discrete business problems.

Read: “Scale Is All You Need” is dead

“Scale Is All You Need” is dead

4. The missing link for Copilot & Cursor

Engineering leaders are doubling down on AI, but the big question remains: How do you actually measure the impact? With LinearB’s new Copilot and Cursor dashboards, you finally can.

Bring all your AI assistant metrics—adoption, acceptance rates, and engagement—into one view and connect them directly to delivery outcomes. Don’t just buy tools; understand how deeply they are integrated into your SDLC and where trust is growing. Turn your AI data into real engineering insights.

Read: Measuring the impact of Copilot and Cursor on engineering productivity

5. Your project name should explain its function, not your sense of humor

The industry’s obsession with “clever” names has become a cognitive liability. A new article argues that abstract naming imposes a high cognitive cost on developers, turning dependency lists into deciphering tasks. A better path is namespacing. By clustering projects semantically (e.g., agent-marketing, agent-copy), you don’t just help your team scan repos. You help LLMs understand your architecture better. Sometimes, boring is better.

Read: Programmers and software developers lost the plot on naming their tools