Artificial intelligence is celebrated for its creativity, its speed, its uncanny ability to generate text, images, and predictions at scale. But behind the brilliance, a quieter question hums: can we trust it to work when it matters most?
For Ankush Sharma, that question has defined a career. With over twenty years in AI, cloud infrastructure, and Site Reliability Engineering, his work doesn’t chase headlines. It confronts what often gets overlooked the fragility of large-scale AI when exposed to real-world unpredictability.
Sharma’s book, Observability for Large Language Models: SRE and Chaos Engineering for AI at Scale, has become a reference point in this conversation. Rather than offering another “future of AI” vision, it lays out a framework for making today’s systems more dependable. What started as a technical deep dive has traveled far copies in circulation across North America, Europe, Asia, and South America; engineers and researchers in at least a dozen countries treating it less like optional reading and more like operational guidance.
The significance lies in its adoption. Doctor Droid, a Y Combinator–backed company developing AI agent tools for platform engineers, credits Sharma’s observability methods with reshaping their approach to reliability. “Fundamentally changed how we approach AI reliability,” notes its CEO, Siddarth Jain.
The endorsement doesn’t stop at startups. At Oracle in Australia, principal engineers report that integrating Sharma’s chaos engineering principles has led to a measurable reduction in downtime and silent model failures. For a global enterprise operating at scale, those improvements represent more than technical efficiency—they translate into greater operational resilience across critical systems.
These aren’t abstract compliments; they mark lived change inside environments that millions depend on every day.
And yet the scope is broader than a handful of endorsements. Sharma’s frameworks are already active in high-stakes sectors: hospitals where an error could affect a diagnosis, financial networks where downtime risks billions, and public infrastructure where analytics keep cities running. His influence shows up not only in conference talks or research citations but in the invisible stability of systems that, by design, draw little attention when they work as intended.
This blend of research and practice explains why his academic output continues to resonate. Peer-reviewed studies on generative AI in healthcare, sustainability in model training (what he calls Green AI), financial innovations, and resilience testing of large models attract more than 200,000 online readers annually. The readership is diverse scholars, practitioners, and policy advisers—and it reflects the widening audience that sees reliability as central to the future of AI.
Reliability doesn’t lend itself to dramatic storytelling. There are no viral demos for “systems that didn’t fail today.” But perhaps that is the point. Sharma’s work is part of a shift in AI where success is measured not by novelty but by trust. In the years ahead, as governments, enterprises, and startups embed AI deeper into healthcare, banking, and infrastructure, the frameworks he has put forward may well serve as the unacknowledged scaffolding beneath them.
The story of AI is often told through breakthroughs that dazzle. Ankush Sharma’s contribution reminds us that longevity in this field may depend on something less visible but far more consequential: the systems that keep those breakthroughs standing.
