# career

AI can handle 80-90% of frontend work, but the remaining edges — depth, sensitivity to new platform features, and knowing when the stable default is not the best answer — are becoming the real moat. Fundamentals are not obsolete. They are compounding assets.

Phil Chen shares six years of career lessons — from his own startup through Helm AI, Scale AI, OpenAI, and Google: when agents can solve every well-defined problem, what stays valuable is finding problems, sprinting the last mile, and everything that cannot be graded by a loss function.

An NLP PhD at UW applied for industry research roles: 11 companies, 57 interviews. She lays it all out — how interviews break down, how to prepare, how to negotiate. The sharpest line: practice coding with AI assistance completely off, or you'll underestimate how much you rely on it.

Django co-creator Simon Willison gave Lenny's Podcast a broad AI status report: November 2025 as tipping point, coding agents burning him out by 11 AM, Dark Factories, mid-career risk, and the security pattern he calls the Lethal Trifecta.

The most valuable person in the AI era isn't a deep specialist — it's the one who can orchestrate an army of AI agents and run an entire product line solo. The shift from IC to Generalist Orchestrator is already happening.

The tweet says a 10-person team becomes 3 — and those 3 outperform the old 10. You pick which side you're on. This post uses that framework as a mirror to audit ShroomDog honestly — what's working, what's quietly falling apart, and the uncomfortable contradiction in the middle.

Alexey Grigorev compiled 200+ resources from major AI labs, engineering teams, practitioners, and community discussions into Awesome AI Engineering — a curated list that connects how companies build AI, how they hire, and what skills keep showing up.

How hard is it for a Data Engineer to become an AI Engineer? Alexey Grigorev says way easier than you think — because AI engineering is still engineering, and the hard parts are exactly what DEs already do every day.

Claude Code creator Boris Cherny declares coding 'practically solved,' predicts the 'software engineer' title will fade in 2026. He shares 3 team principles: let Claude do it, underfund to force AI adoption, and go faster.

Postlight's former CEO Paul Ford wrote in the NYT: projects he used to quote at $350K now take a weekend with Claude Code at $200/mo. Plus Andrew Ng's 'X Engineer' vision for the future of software jobs.

Bloomberg Beta investor Amy Tam maps AI-era career tradeoffs from a VC perch. Her thesis: work is shifting from execution to judgment, and the K-curve is widening as early movers compound while fence-sitters compound backward.

AI writing better code? That "Deep Blue" feeling, coined by Simon Willison & Adam Leventhal (Oxide & Friends), means IBM's chess computer & the color of sadness. It's not just a tech problem, but a psychological crisis for engineers.

Thoughtworks' AI in software retreat: Juniors more valuable, mid-level devs at risk, source code transient, AI agents on org charts. Humans too slow for AI's speed.

Andrew Ng: AI isn't mass unemployment. Teams shrink (8 eng + 1 PM -> 2 eng + 1 PM). Bottleneck shifts from 'how to build' to 'what to build' – the "PM Bottleneck."

A comprehensive cold email job guide. Core principles: keep it under ~200 words, no fancy words, have a clear ask, be specific, don't spam. These principles are really a universal framework for 'how to effectively communicate with busy decision-makers.'