ORLANDO, Fla. — AI cleared the adoption hurdle in 2026. Reliability may be the harder problem.
At NVIDIA’s GTC 2026 keynote, CEO Jensen Huang said the “inflection point of inference has arrived,” pointing to a new phase where the cost and reliability of running AI continuously matters as much as building the models themselves.
The shift is already showing up inside production systems. Companies are watching AI budgets climb, model failures reach customers and roughly half of AI-generated code still breaking in production after passing QA.
Ridhima Mahajan, a senior software engineer with more than a decade of experience in decision pipelines and backend architecture, sees many of today’s AI problems as familiar engineering challenges with new vocabulary.
What has changed now that the industry is calling it AI?
The model has moved from being a specialized component to becoming a dependency that other services build around.
In older machine learning applications, a model might score or classify something within a bounded workflow. With generative and agentic systems, model output can become the interface and mediator for downstream actions.
That changes the risk profile. A wrong answer in a single query is recoverable. A wrong answer that flows through a dozen downstream decisions before anyone notices is the million-dollar problem worth designing against.
Once AI enters production decision pipelines, the priorities become reversibility and provenance. The architecture has to limit how far a bad or uncertain output can propagate, and it has to be able to trace an output back to the inputs and logic that produced it.
What tends to break first when AI starts handling live customer traffic?
The input side, almost always.
Pilots often run on curated data someone has inspected recently. Production systems inherit messy and incomplete data from changing upstream sources. Those inputs can drift, and that drift can be flattened and made invisible by the time it reaches the model.
The dangerous part is that the system may not fail in an obvious way. The model may keep producing outputs that look superficially fine, even when the inputs no longer mean what it learned to expect.
Mature AI infrastructure monitors input distributions with the same seriousness as output quality. Teams watching only final accuracy or user-facing metrics are often catching problems after they have already reached customers.
Where does the cost actually leak?
The most common source of leakage is calling the model when it is not strictly necessary.
A surprising amount of production traffic can be served from cached outputs or smaller models. Routing requests to the cheapest layer that can answer them is still underdeveloped at many organizations.
Another source is inefficient routing. Teams often build one path where every request hits the same model layer, even when batched requests or simpler systems could handle many of them.
Large context windows can also drive costs higher. They are useful, but they are not free. More context can increase cost and latency without materially improving the decision.
The principle across all three is understanding that each inference call is often the most expensive option in the stack. A bill that scales linearly with adoption is usually flagging the architecture above the inference layer, not just the model itself.
Why do fallback paths matter?
Fallback paths are one of the most undervalued parts of production AI infrastructure.
AI systems can be unavailable, too expensive for a given request or uncertain in their output. When that happens, the product cannot drift into undefined behavior. A predictable, rule-based fallback gives the architecture a known safe path with a known result.
The right fallback depends on the workflow. A cached answer can work for non-time-sensitive requests. A rule-based calculation may work in other cases. For higher-risk workflows, a human review queue may be the safest option.
This matters most in workflows involving money, safety or user trust, where an unexplained AI output can be worse than a measured, predictable result.
What would you look for when evaluating an AI company?
A good starting point is how much engineering investment has gone into the platform around the model itself.
Companies should have strong data systems, observability, routing and fallback paths. Those are what make cost-aware inference and reliable production behavior possible.
A company that treats the model itself as the entire product may be missing the point. Models will keep changing and improving. The architecture that lets a company validate, trace and improve decisions over time is a more durable asset.
Sustainable production-grade AI is about controlled intelligence.
©2026 Cox Media Group
