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Here’s a clean, production-grade framing. No hype, no model worship.

Title options, in descending order of sharpness:

1. The Model Isn’t the Bottleneck. The Data Is.

2. Why AI Fails in Production Even When Metrics Look Great

3. Clean Metrics, Broken Systems: The Data Problem in AI

4. From Model-Centric to Data-Centric: Where Real AI Work Lives

5. AI Doesn’t Break in Production. It Was Never Trained for Reality

Podcast notes, structured for solo or interview use:

For years, AI progress has been framed as a model problem. Bigger architectures, more parameters, better training tricks. That narrative still dominates headlines, but it no longer matches reality in production systems.

When you talk to teams deploying AI in the real world, autonomous vehicles, medical imaging, robotics, industrial vision, the bottleneck is almost never the model. It’s the data. More specifically, whether the data actually reflects the environment the system is expected to operate in.

One of the most dangerous illusions in machine learning is clean metrics. Accuracy, precision, recall. They feel authoritative, but they only describe performance relative to the dataset you chose. If that dataset is biased, incomplete, or inconsistent, the metrics will confidently validate the wrong conclusion.

This is why so many systems perform well in evaluation and then quietly fail in production. The model didn’t suddenly break. It never learned the right thing in the first place.

As models leave controlled environments, small data problems compound quickly. Annotation guidelines drift. Labels encode human disagreement. Edge cases are missing. Sensors change. Data pipelines evolve. None of these are fixable with hyperparameter tuning or larger models.

These are structural data problems. Solving them requires visibility into what the data actually contains and how the model behaves across slices, edge cases, and failure modes.

For a long time, the default response was “collect more data.” That worked when data was cheap and abundant. In high-stakes or regulated domains, it isn’t. Data is expensive, sensitive, or physically limited. Adding more data often just adds more noise.

This is why the field is shifting toward a data-centric mindset. Improving performance now means curating datasets, refining labels, identifying outliers, understanding where and why models fail, and aligning data with real operating conditions.

The frontier isn’t bigger models. It’s better understanding.