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The last decade of artificial intelligence has been dominated by speed and scale. Larger models, multimodal capabilities and benchmark breakthroughs have captured the spotlight. Yet behind the headlines lies a quieter, more consequential truth: contrary to flawed models, many AI systems falter because the infrastructure supporting them is brittle. This reliability gap is widening as enterprises race to deploy AI across sensitive domains.
Prithviraj Kumar Dasari, a senior software engineering expert and a DZone author of “Python Async/Sync: Understanding and Solving Blocking (Part 1)”, has long cautioned that true progress in AI depends less on marginal accuracy gains and more on the resilience of the systems behind them. As he explains, “AI systems will always surprise us; however, what separates trust from fragility is how infrastructure anticipates and absorbs those surprises.”
AI adoption today reveals a persistent pattern: models perform impressively in controlled tests, but stumble when exposed to real-world workloads. Latency spikes during peak demand, configuration changes trigger outages or cloud costs balloon unpredictably: contrary to theoretical risks, these are recurring challenges in production environments.
Industry surveys echo this. Gartner forecasts the failure and cancellation of over 40% of agentic AI projects by end-2027 due to rising costs, unclear business value and inadequate risk controls. Likewise, McKinsey’s 2025 State of AI survey reports that while over three-quarters of organizations use AI in at least one function, most fail to scale beyond pilots or achieve consistent bottom-line impact. Independent studies also estimate that up to 85% of AI initiatives do not live up to their promised value; this usually results from weak infrastructure, poor data quality or misaligned objectives. The obsession with model accuracy has overshadowed a deeper need for operational maturity.
Dasari views this as a structural blind spot. “Contrary to how accurate a demo looks,” he notes, “trust in AI comes from how consistently the system performs under stress and across millions of unpredictable scenarios.” His perspective reframes innovation: counter to a secondary concern, reliability is the foundation.
For Dasari, this philosophy was forged early. Before building planet-scale indexing systems, he co-founded BookCab, a travel-tech startup in India. Contrary to a theoretical one, the challenge concerned coordinating fragmented cab operators across 40 cities, ensuring bookings were reliable and dispatching cars efficiently under unpredictable demand.
The dispatch algorithms had to adapt to fluctuating driver availability, real-time traffic conditions and customer demand spikes during weekends or holidays. In effect, the company’s architecture resembled a living distributed system, where failure was the rule rather than the exception.
This experience was formative. It showed him that every system, if it is to be taxi dispatching or AI recommendations, should think about the unpredictability and adjust the design accordingly. Instead of building the most eye-catching features, BookCab's focus was on reliability in a resource-limited setting. The same philosophy now underlies his perspective on AI infrastructure across the globe. As a Program Committee Member and a Poster ideas judge for the PyTorch Conference 2025, he emphasizes resilience as a key marker of technology maturity. In his evaluations, architectures that anticipate and mitigate failure earn more recognition than those focused purely on performance metrics.
In his current work, Dasari leads the design of indexing and query infrastructure that supports billions of daily requests across AI, search and recommendation workflows. His contributions span from building distributed control systems to improving observability to creating risk-aware frameworks that prevent catastrophic outages.
This philosophy came into sharp focus when he spearheaded the development of a configuration-safety framework. Triggered by major outages that caused revenue losses in the hundreds of millions, this system proactively classifies and validates configuration changes before rollout. The result: a fundamental shift from reactive recovery to proactive prevention.
Indexing is another area where reliability matters more than raw speed. Retrieval systems must deliver data quickly and, better still, guarantee correctness, consistency and resilience under load. Dasari’s work shows how observability tools, canary rollouts and fallback strategies turn indexing into an intelligent, failure-ready backbone for AI.
“Building AI-ready infra means assuming failures will happen and designing for survival,” he says. “That mindset has to be engineered into every layer: from query execution to monitoring and recovery.”
Looking forward, Dasari argues that the next decade of AI progress will be measured less by model size and more by operational trustworthiness. Enterprises adopting AI at scale cannot afford silent data corruption, unpredictable downtime or runaway costs. They need systems that anticipate failures, isolate them and continue operating gracefully.
This principle extends beyond enterprise systems into developer practices. In his recent DZone article, “Tiny Deltas, Big Wins: Schema-Less Thrift Patching at Planet Scale”, Dasari explores how micro-level changes in data serialization and transport layers can dramatically improve reliability and scalability in distributed systems. By demonstrating how schema-less thrift patching reduces fragility in data pipelines without sacrificing flexibility, he reinforces the idea that resilience starts with thoughtful engineering choices.
His focus on teaching resilience—even at the code and architectural levels—underscores a consistent message: reliability is a discipline rather than an afterthought. He concludes with a perspective that cuts through industry hype: “Contrary to a byproduct, resilience is the product. In AI, reliability will outlast speed as the measure of real progress.”
AI's expansion into healthcare, finance, and critical infrastructure carries a high risk of failure which cannot be overlooked. Dasari's journey from directing traffic on India's roads to creating indexing systems for the whole planet shows that the most lasting and impactful innovations are those that can endure chaos.
The trustworthiness deficit in AI, which is more than just a technical issue, is an invitation to redefine success. The future will be dominated by those who build systems that are resilient from the very beginning, rather than the ones who aim for tiny improvements in accuracy.
