From prediction to decision: Smaller models will reshape how we build AI
The bottleneck in AI systems isn't model intelligence anymore, but decision agility. Mallika Rao, engineering manager and former Netflix infrastructure lead, breaks down why smaller, task-specific models are outrunning foundational ones, and what that means for every team building at scale.
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.
Is your most capable AI agent also your biggest data leak?
A Microsoft and Huazhong University benchmark tested GPT-4o, GPT-5, Grok-3, and others on realistic enterprise data scenarios. Privacy violation rates hit 50.9%. More capable models made it worse, and the fix has nothing to do with model selection...
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.
Governed agents are here. Is your stack ready?
Microsoft Build 2026 didn't just announce products. It announced a philosophy: the era of the unmanaged AI agent is over.
Demystifying AI agents: going beyond the buzzwords
"Agent" is the most overused word in AI right now. But strip away the hype and what are you actually working with? Adobe principal scientist Deepak Pai breaks down the real building blocks of agentic systems and when they're worth reaching for.
Why smart companies don’t add AI everywhere
Boards want AI roadmaps. Competitors are shipping AI features. And 74% of companies still can't make it pay. This piece breaks down the eight-point framework that separates disciplined AI adoption from expensive noise.
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.
6 things to fix before RLHF turns your biases into features
Your reward model is learning exactly what your annotators prefer. The problem is that "better" and "unbiased" are two different things, and RLHF has no way to tell them apart.