Developers have never been more inclined to use AI, yet never less inclined to trust its output. In Stack Overflow’s surveys, the proportion of respondents using AI tools in development keeps climbing (44% in 2023, 62% in 2024, and 83% in 2026), but trust hasn’t kept pace.
Developers have never been more inclined to use AI, yet never less inclined to trust its output. In Stack Overflow’s surveys, the proportion of respondents using AI tools in development keeps climbing (44% in 2023, 62% in 2024, and 83% in 2026), but trust hasn’t kept pace.
Adoption, then, is running well ahead of confidence. This could well be someone’s chance to step in and repair the trust gap.
GitHub has an opportunity here, even when developers prefer someone else’s coding agent. Teams already use it to inspect, test, and review code. Helping them do the same with work from Claude or Codex gives developers more choice while making its own platform even stickier. It’s a remarkably clever jujitsu move, but will it work?
Back in 2023, I worried GitHub’s enthusiasm for Copilot could distract it from Git, particularly the visibility and collaboration that made the platform useful. There’s a better possibility now: AI could make those capabilities more valuable. As the number of tools proposing changes grows, companies need continuity in how they inspect, test, and accept them. AI, in short, could make GitHub more relevant than ever.
Just because we’re using AI to build faster doesn’t mean we necessarily trust it. The 2026 survey makes that distinction explicit. Its revised trust question asks about the conditions under which people rely on AI. And? Well, roughly 48% say they trust it but only when they can easily verify the output. Just 7% implicitly trust it for important work decisions. Those figures can’t be compared directly with the earlier accuracy question, as noted, but they do describe a practical bargain: People want the help, but increasingly, they also want evidence they can check because they worry about AI being “almost, but not quite, right.”
In context
- Topic: Inteligencia Artificial — IA aplicada al negocio: casos, límites, costos y gobierno.
- Source: InfoWorld
- Published: 13/10/2026
Continue reading at the original source →
Excerpt published automatically by the site radar. The full text belongs to its publisher and is linked above.
Why it matters
Every week there is an announcement that promises to change everything, and every week most organisations are still fighting the same battle: messy data, processes nobody documented, and expectations running faster than capability. This story reads better with that in the background.
The first thing I look at is not model capability but the data feeding it. Almost every project I have seen fail did not fail on the algorithm. It failed because the data lived in three systems with three different definitions and nobody wanted to be the one to fix that.
What usually goes wrong
The mistake I have seen repeat most is measuring nothing before starting. It gets deployed, everybody feels things improved, and when somebody asks by how much, there is no answer. Without a baseline there is no way to defend next year's budget, and that is where good projects die.
What to watch
- Where the data ends up and under what contract, especially when customer information is involved.
- Whether the organisation can change provider without rebuilding everything, which is the proof it did not get locked in.
- Which concrete process is touched and how it is measured before and after; with no baseline there is no result, only opinion.
How I read this entry
I would use it for a talent conversation rather than a purchasing one. The capability that needs installing is not operating a tool, which changes every six months, but framing the problem well and judging the answer. That one stays in the house.
This entry is an excerpt from the original source, selected by the site radar. The commentary above is the site's own and does not belong to the cited publisher.
Comments
No comments yet. Yours can be the first.
Living through this in your own team?
Open the chat and tell me how you're handling it. I'm interested in comparing notes.
