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Barr Moses

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Articles

AI Is More Confident Than Ever. That Doesn’t Mean It’s More Reliable.

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By Seth Wilson Verified, Barr Moses
| Built In Verified
Image: Shutterstock / Built In Summary: AI reliability is lagging behind rapid model advancements, causing $4.4 billion in losses. To move from pilots to production, companies must implement end-to-end observability. Trust requires monitoring the entire loop — inputs, context and behavior — rather than just checking for hallucinations. more AI reliability is lagging behind rapid model advancements, causing $4.4 billion in losses.
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10 Data + AI Observations for Fall 2025

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By Barr Moses
| Towards Data Science
the final quarter of 2025, it’s time to step back and examine the trends that will shape data and AI in 2026. While the headlines might focus on the latest model releases and benchmark wars, they’re far from the most transformative developments on the ground. The real change is playing out in the trenches — where data scientists, data + AI engineers, and AI/ML teams are activating these complex systems and technologies for production.
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Is Data Quality Moving Upstream for AI?

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By Barr Moses
| Datafloq - Driving Innovation
Before we can talk about the new AI corpus, we need to look backward. For decades, data + AI teams have been trained to look downstream towards their analysts or business users for requirements. This is in part because data quality is specific to the use-case. For example, a machine learning application may require fresh but only directionally accurate data while a finance report might need to be accurate down to the penny but only updated once per day. But it wasn’t all pragmatic.
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