What breaks after the pilot, why it breaks, and how to build AI systems that actually survive production.
A seven-year-old answers in a second. A camera and a tactile gripper took seven falsified measurement channels and a calibration plate to get halfway.
ReadGrad-CAM says the model is looking at the defect. But is it looking at the defect, or the lighting around the defect?
ReadThe build-versus-buy decision for AI systems has $500K–$2M consequences over three years. Most organizations make it in a single meeting.
ReadWe have world-class tools for monitoring databases and web servers. For AI inference? We're still flying blind.
ReadThe reason NVIDIA dominates AI infrastructure isn't just better software. It's 15 years of hard-won intuition that can't be scheduled on a Gantt chart.
ReadThe vision AI system that worked perfectly during installation will eventually fail during the night shift. The question is whether you built for that.
Read43% of employees are using AI tools you haven't approved. The risk isn't that they're using AI — it's that you don't know about it.
ReadFix the GPU bottleneck and you'll find the memory bottleneck. Fix that and you'll find the network bottleneck. Welcome to production AI.
ReadA $250 camera with 64 megapixels sounds like a bargain. Until you understand what those pixels actually measure.
ReadThe most dangerous AI failure isn't a crash. It's a slow, silent degradation that no alert will catch.
ReadEvery AI pilot looks brilliant in the conference room. Day Two is what happens when it meets the production floor.
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