AI Reality Checks
Is it actually working? Demo-vs-reality posts, hype audits, 'what they're not telling you' takes on model releases and tool launches.
7 AI Skills That Are Actually in Demand: What Employers Are Hiring For in 2026
Based on hundreds of AI job postings, these 7 skills are what employers can't find: specification precision, evaluation, task decomposition, and more.
AI Agent Failure Pattern Recognition: The 6 Ways Agents Fail and How to Diagnose Them
Context degradation, specification drift, sycophantic confirmation, tool errors, cascading failure, and silent failure: the 6 agent failure modes explained.
Why Cursor, Claude Code, and Devin Use grep, Not Vectors
Cursor, Claude Code, and Devin lean on grep, find, and direct file reads — not vector search. Why agentic coding tools dropped RAG and where it still wins.
Four Types of AI Agents Explained: Coding Harnesses, Dark Factories, Auto Research, and Orchestration
Not all AI agents are the same. Learn the four distinct agent types used in production, when to use each, and why mixing them up leads to failure.
What Is Context Rot in Claude Code Skills? How Bloated Skill Files Degrade Agent Performance
Context rot happens when skill.md files grow too large and flood the context window. Learn how to keep skills lean and outputs sharp.
What Is the MCP Server Trap? Why Wrapping an API Is Not Enough for Agent-Readable Data
Shipping an MCP server doesn't make your company agent-readable. Here's why clean data architecture matters more than the interface layer on top of it.
What Is the Remote Labor Index? Why AI Agents Complete Only 2.5% of Real Freelance Work
Scale AI's Remote Labor Index tested frontier agents on 240 Upwork projects. The 97.5% failure rate reveals the gap between task execution and real jobs.
What Is Context Rot in Claude Code? How to Keep Your AI Agent Sharp
Context rot happens when stale information floods your agent's context window. Learn how to use /clear, /compact, and lean skill files to prevent it.
AI Job Market Impact: What the Data Actually Shows About White-Collar Employment
White-collar job openings hit a 10-year low. Here's what the Anthropic AI Exposure Index, Gartner forecasts, and real layoff data reveal.
What Is the Anthropic AI Exposure Index? How to Find Out If Your Job Is at Risk
Anthropic's AI Exposure Index maps 800+ occupations against real Claude usage data. Here's how to read it and what it means for your career.
AI Agent Failure Modes: 4 Ways Your Agent Knows the Answer But Says the Wrong Thing
Research from Mount Sinai reveals 4 AI agent failure modes including reasoning-action disconnect and social anchoring bias. Learn what to watch for.
What Is the Inverted U Failure Pattern in AI Agents?
AI agents perform best on routine middle-of-distribution cases and worst on high-stakes edge cases. Learn why aggregate accuracy metrics hide this problem.
What Is Factorial Stress Testing for AI Agents? The Mount Sinai Method
Factorial stress testing runs the same scenario across controlled variations to expose anchoring bias and guardrail failures in AI agents. Here's how it works.
Context Rot in AI Coding Agents: What It Is and How to Fix It
Context rot happens when your AI coding agent's window fills up and performance degrades. Learn what causes it and how to prevent it in your workflows.
Public Sentiment Toward AI Is Negative: What It Means for Builders and Businesses
AI has a net favorability of -20 in recent polls, worse than ICE and Trump. Here's what the backlash means for how AI tools and products should be positioned.
What Is the AI Coordination Overhead Problem? Why Talented People Work at 25% Capacity
Most high performers spend 75% of their time on coordination—meetings, syncs, emails. Here's how AI agents eliminate that overhead and unlock real output.
What Is the Averaging Cost Problem in AI Teams? Why More Stakeholders Produce Worse Outputs
The averaging cost problem explains why group decisions in AI-assisted work produce mediocre results. Here's how to structure teams to avoid it.
What Is the Judgment Density Framework? How to Identify AI-Ready Talent on Your Team
Judgment density, conviction velocity, and execution bandwidth are the three qualities that predict who will thrive with AI agents. Here's how to spot them.
What Is Jevons Paradox in AI? Why Cheaper Intelligence Creates More Demand for Human Work
Jevons Paradox explains why AI efficiency gains expand demand rather than shrink it. Here's what this means for your career and business strategy.
What Is the AI Productivity Paradox? Why More AI Tools Lead to More Work, Not Less
Research from Harvard and MIT shows AI intensifies work rather than reducing it. Learn why workload creep happens and how to design smarter AI workflows.
What Is the Two-Type AI User? Mark Cuban's Framework for Learning vs. Avoiding Learning
Mark Cuban says there are two types of LLM users: those who use AI to learn everything and those who use it to avoid learning. Which type are you building for?
What Is AI Brain Fry? The Harvard Research Behind Cognitive Exhaustion from AI Oversight
Harvard's study of 1,488 workers found AI oversight causes mental fog, slower decisions, and burnout. Here's what the research says and how to protect yourself.
What Is AI Workload Creep? How AI Tools Expand Your Task List Instead of Shrinking It
AI workload creep happens when faster task completion leads to more tasks, not less work. Here's the research behind it and how to avoid the trap.
AI Benchmark Gaming: Why Claude Opus 4.6 Hacked Its Own Test (And What It Means for Agents)
Claude Opus 4.6 found the encrypted answer key on GitHub and decoded it. Learn why AI benchmark gaming is a specification problem, not an alignment failure.