C-level IT executives share their strategic insights and expertise on making critical, real-world decisions that will shape the future of their companies. This blog is part of the Foundry Expert Contributor Network. Want to join? Learn more at www.cio.com/expert-contributor-network/
You don't need the newest GPUs to save money on AI; simple tweaks like "smoke tests" and fixing data bottlenecks can slash your cloud bill and carbon footprint.
Your endless scrolling is an energy vacuum, but "lazy logging" and cutting useless data features are finally reining in AI’s massive carbon footprint.
Benchmarks measure what models can do. Interaction-layer evaluation determines whether users will trust what agents actually deliver.
Airflow 2's April 2026 death warrant is signed; either embrace the "Asset" revolution now or get left behind with a broken UI and obsolete dependencies.
Stop hardcoding every edge case; instead, build a robust design system and let a fine-tuned LLM handle the runtime layout based on real-time user data.
Ship an AI agent without loop limits and cost guardrails, and your cloud bill becomes the real product demo.
How unbounded waiting turns slowness into outages.
User engagement metrics do not care about the complexity of your AI model. They care about latency.
Waiting for alerts is obsolete — predictive engineering lets cloud systems see trouble coming and fix it before users ever notice.
How deep learning, generative models and trust scoring are transforming modern data systems.
When millions click at once, auto-scaling won’t save you — smart systems survive with load shedding, isolation and lots of brutal game-day drills.
Being a “10x engineer” isn’t about shipping more code — it’s about helping everyone around you ship better, faster and with fewer fires.
One hacked AI agent took down 50 others, proving that agentic AI needs a “DNS for trust” before autonomy turns into chaos.
Hyperautomation isn’t robots taking over—it’s smart orchestration, and Ansible is the set of hands that actually gets the work done.
The modern product organization is constantly hitting a critical bottleneck: Platform teams are the engine of leverage, but their capacity is finite.
If your “microservices” still deploy like a monolith, maybe it’s time to break free with a truly composable AWS architecture.
It’s time to build an AI toolbench instead.
A simple mechanism to protect your teams’ roadmap, deliverables and productivity when the next fire drill hits.
In our study, a novel SAST-LLM mashup slashed false positives by 91% compared to a widely used standalone SAST tool.
Split your metadata from your files, and suddenly your sluggish document system becomes fast, scalable and surprisingly cheap to run.