The age of AI evangelism is over. Welcome to the evaluation era.
Transparency scores are falling, hallucination rates on user-framed statements hit as high as 94%, and benchmark performance still fails to predict real-world results. The gap between what AI can do and what organizations can actually verify is now the problem worth solving...
25 AI engineers you should be following in 2026
Twenty five names, organized by what they actually do, plus a practical note on how to follow them without drowning in the noise...
30 startups rebuilding enterprise software with AI agents
In Q1 2026, AI companies pulled in $242 billion in venture capital. That is 80% of all global VC funding for the quarter. From coding to compliance, customer service to clinical documentation, these 30 companies are not updating enterprise software. They are rebuilding it from scratch.
The US AI rulebook is being rewritten. Your compliance team can’t wait
America's AI regulatory landscape just had a month that made legal counsel everywhere reach for stronger coffee. Colorado's landmark AI Act, once celebrated as the country's first comprehensive state AI law, was gutted and replaced before it ever took effect.
The benchmark gap, explained: What AI leaderboards measure and what they miss
Every frontier model now scores above 88% on MMLU. So why does a 37% gap still exist between lab benchmark scores and real-world AI deployment performance? We explain why the tests keep lying, and what rigorous evaluation actually looks like.
6 things every AI leader needs to get right in H2 2026
The pilot phase is over. Here are the 6 trends shaping AI strategy in H2 2026, from agentic infrastructure to physical AI and custom builds.
5 questions AI agent vendors hope you don’t ask
Most AI agent failures don't happen during the demo. They happen when APIs fail, context windows explode, costs spiral, and nobody can explain why the agent made a decision. Here are five questions that separate production-ready platforms from expensive experiments.
The AI-first GTM strategist: agents, workflows, and knowing when to stop
Most GTM teams deploy AI where it's most visible. The question worth asking first: is that actually where it's most ready?
Your data engineers may be more influential than you think
The data engineer has gone from a largely behind-the-scenes role to one of the most strategically important positions in a modern technology organization. The leaders who understand why are making significantly better infrastructure decisions than the ones who do not.
8 ways self-evolving AI agents are about to change how we build software
A new paper out of arXiv this week describes an AI system that builds, improves, and deploys its own specialist agents. Here is what that actually means for engineers and technical teams.