I ran a LinkedIn poll asking software developers about their experiences using AI coding assistants with production code. The results were interesting 👇

Why production code? Developers spend most of their time writing it, and AI tools can handle non-critical activities like proof-of-concepts, solo projects, and quick experiments fairly well.

I received 50 responses – not a huge sample, but interesting trends emerged nonetheless. Also, LinkedIn polls aren’t anonymous (which I didn’t know!), so I could see who voted. It allowed me to exclude non-developers and gauge experience levels from LinkedIn profiles and add it to the data.

You can see the results broken down by answer and experience level in the chart attached.

What stood out to me?

– Only one respondent – an apprentice engineer – said they “automate most things.”

– 74% found AI assistants helpful, but 26% felt they’re more trouble than they’re worth.

– All who said AI assistants “get in the way” (24%) had 8+ years of experience.

– 80% of respondents had 8+ years’ experience (I know a few of them, and others’ profiles suggest they’re legitimately experienced engineers).

My thoughts? Unless they improve significantly:

1) They’re unlikely to shift productivity dramatically. From my experience, “task” work takes about 10-30% of a developer’s coding time. So, while helpful, they’re not game-changing.

2) That a significant portion – especially experienced engineers – find these tools intrusive is concerning.

3) Having heard stories of AI tools introducing subtle bugs, I was surprised not to see more votes for “considered harmful.”

4) The idea that AI tools might replace software engineers – or that even “conversational programming” is anywhere close – still feels a very long way off.

5) I worry about an apprentice software engineer getting an AI tool to do most of their work. Partly because more experienced developers are a lot more cautious and must have a reason for this, but mainly because they won’t be learning much by doing so.