What AI is actually doing well (and why it matters)

Continuing on from #449, our previous post explored why AI scepticism is understandable and historically consistent.
This post will act as a natural continuation, a grounded look at where AI delivers real value for thoughtful professionals who may still be sceptical.

We'll discuss:

1 . verified examples, moving past the hype, of AI making a meaningful contribution
For example

• Drug discovery (target identification, molecular generation)
• Medical imaging (diagnostic accuracy, radiologist throughput)
• Materials and climate science (faster exploration of chemical space)
• Code analysis (finding classes of bugs humans systematically miss) and code vetting

2. generalised capabilities that apply across domains, for instance:

• Information retrieval and synthesis (medicine, law, research, finance)
• Accelerating scientific workflows
• Augmenting expertise rather than replacing it
• Skill improvement
• Accessibility

Our article will strive to present an objective, justified, selective overview of areas where AI is already making meaningful contributions. In continuation with the AI discussion from the previous article, we'll aim to convey that AI can augment rather than replace jobs or take away the joy of problem solving.