Me and My Shadow [audio] (www.thisamericanlife.org)

In the audio piece "Me and My Shadow," NPR senior editor David Kestenbaum examines a central, under-discussed question in the post‑ChatGPT era: are large language models merely sophisticated pattern‑matchers, or have they begun to exhibit something like human understanding of language, concepts and reasoning? Kestenbaum speaks with researchers at Microsoft who are actively probing this boundary—testing whether models genuinely grasp meaning and can reason about the world, or whether their apparent intelligence is a convincing illusion driven by scale and training data. This inquiry matters because the answer shapes how we evaluate, deploy and govern AI systems. If models have emergent conceptual understanding, that could expand their utility in complex tasks but also raise new safety, trust and accountability issues; if not, reliance on them requires stricter guardrails to avoid brittle failures and misinterpretation. Researchers are using behavioral tests, interpretability tools and targeted evaluations to tease apart surface fluency from deeper representations, looking for both strengths and systematic blind spots. The piece highlights that settling whether machines “understand” won’t be purely philosophical—it's a technical research frontier with practical consequences for AI development, evaluation and policy.