To maintain quality, 6AM City's AI newsletters don't generate content from scratch. Instead, they use "extractive generative" AI to summarize information from existing, verified sources. This minimizes the risk of AI "hallucinations" and factual errors, which are common when AI is asked to expand upon a topic or create net-new content.

Despite the hype, Datycs' CEO finds that even fine-tuned healthcare LLMs struggle with the real-world complexity and messiness of clinical notes. This reality check highlights the ongoing need for specialized NLP and domain-specific tools to achieve accuracy in healthcare.

The danger of LLMs in research extends beyond simple hallucinations. Because they reference scientific literature—up to 50% of which may be irreproducible in life sciences—they can confidently present and build upon flawed or falsified data, creating a false sense of validity and amplifying the reproducibility crisis.

AI can produce scientific claims and codebases thousands of times faster than humans. However, the meticulous work of validating these outputs remains a human task. This growing gap between generation and verification could create a backlog of unproven ideas, slowing true scientific advancement.

A key risk for AI in healthcare is its tendency to present information with unwarranted certainty, like an "overconfident intern who doesn't know what they don't know." To be safe, these systems must display "calibrated uncertainty," show their sources, and have clear accountability frameworks for when they are inevitably wrong.

Don't blindly trust AI. The correct mental model is to view it as a super-smart intern fresh out of school. It has vast knowledge but no real-world experience, so its work requires constant verification, code reviews, and a human-in-the-loop process to catch errors.

Unlike consumer chatbots, AlphaSense's AI is designed for verification in high-stakes environments. The UI makes it easy to see the source documents for every claim in a generated summary. This focus on traceable citations is crucial for building the user confidence required for multi-billion dollar decisions.

AI tools for literature searches lack the transparency required for scientific rigor. The inability to document and reproduce the AI's exact methodology presents a significant challenge for research validation, as the process cannot be audited or replicated by others.

Advanced AI tools like "deep research" models can produce vast amounts of information, like 30-page reports, in minutes. This creates a new productivity paradox: the AI's output capacity far exceeds a human's finite ability to verify sources, apply critical thought, and transform the raw output into authentic, usable insights.