Vision
Vision
The Morning Check That Stopped Happening
The verification script sits three commands up in the terminal history. Tuesday's run, or maybe last week's. The analyst scrolls past it to start Wednesday's work—competitive pricing analysis, market intelligence updates. The coffee is still hot when the absence registers. When did the morning check become optional?
Six months ago, the ritual was non-negotiable: scan the dashboard, spot-check records, verify authentication flows. Fifteen minutes every morning, sometimes longer when something broke. Now the analyst can't remember the last failure that required intervention. The script just sits there, unused. The crossing happened without anyone marking the moment.
The Morning Check That Stopped Happening
The verification script sits three commands up in the terminal history. Tuesday's run, or maybe last week's. The analyst scrolls past it to start Wednesday's work—competitive pricing analysis, market intelligence updates. The coffee is still hot when the absence registers. When did the morning check become optional?
Six months ago, the ritual was non-negotiable: scan the dashboard, spot-check records, verify authentication flows. Fifteen minutes every morning, sometimes longer when something broke. Now the analyst can't remember the last failure that required intervention. The script just sits there, unused. The crossing happened without anyone marking the moment.
The Economics
When Continuous Workloads Break Cloud Economics
Cloud economics assumed workloads would spike and scale to zero. Training runs, batch jobs, traffic surges—all episodic. By early 2026, inference consumed over 55% of AI infrastructure spending. Inference runs continuously, serving requests 24/7. When investors demand ROI in six months and infrastructure costs must grow slower than earnings, the continuous nature of inference workloads exposes limits in the elasticity model. The economics that worked for episodic compute face different constraints.
Why Cost Predictability Became a Reliability Problem
A system running at 99.9% uptime with costs swinging 40% quarter-to-quarter creates a reliability problem when CFOs need to model infrastructure spend. The dashboard shows green. The P&L shows chaos. Continuous inference workloads—the ones dominating AI infrastructure spending—revealed a gap: cost predictability matters as much as operational uptime. Reliability architecture had to account for structural dependencies that determine whether you can afford to keep running at the scale customers need.
Research Illuminating Tomorrow's Path
LLM Agent Maturity Model for Research Collaboration
Training scientists to work alongside AI that can execute entire research workflows independently.
Research becoming a partnership where humans pose questions AI can actually pursue alone.
Research Illuminating Tomorrow's Path
Capability Asymmetry Drives Human-AI Complementarity
Build AI that makes different mistakes than humans, not just different calculations.
First hard numbers showing capability mismatch itself generates team performance gains.
Research Illuminating Tomorrow's Path
AI Explanations Paradoxically Increase Over-Reliance
Knowing when to ignore persuasive AI, not just understanding domain expertise.
Well-explained recommendations slip past human judgment precisely because they sound so reasonable.
Research Illuminating Tomorrow's Path
Meta Agents Automatically Design Superior Agent Systems
Agent development shifts from human engineering to machines discovering what actually works.
Learning solutions potentially faster than methodically designing them by hand.