We surveyed over 1,000 engineers building with AI

Across the board, we’re seeing meaningful increases in usage in all modalities (especially outside of text, which basically everyone is using). Note that survey respondents are, by all accounts, a fairly AI-native group of people… and the majority of them still aren’t using most modalities. There’s such incredible growth yet to come.

The biggest jump from last year came from image generation. 2x as many (36% vs. 18% in 2025) say they’re using generative AI for images and it’s working well. Many of the best image gen solutions saw V2s over the past 6-9 months (e.g. NanoBanana 2, ChatGPT Images 2.0) and it seems to be paying off.

Which AI modalities are you actively building with at work?

One thing we’re all wondering: what’s going on with audio? Last year, we argued that audio was poised for a huge adoption swing; 37% who weren’t using audio said they planned to soon. There has definitely been growth since then – 24% are happily using audio vs. 17% last year – but it’s not the kind of growth you might have expected. Not only that, but the intent-to-adopt rate for audio is even higher than last year: 56% of those not using audio plan to use it soon.

There has been a lot written over the past few months about open weights. The data in the survey shows that they’re getting increasingly popular… but almost nobody is using them independently. Today open source is best thought of as a helpful companion to closed source but not yet a direct replacement.

Of the 54% using open-weight models — either out of the box (37%) or fine-tuned (17%) – >90% are also using closed models. Open vs. closed is actually only a primary consideration for 5%. The most important quality by far is… quality, followed by agentic capabilities and then cost (we’ll come back to this).

What are your top 3 considerations when choosing a model for production? · select up to 3

Only 20% put reliability / uptime in their top 3 considerations. This is an interesting data point when contrasted with the public X uproar every time Claude Code has an outage. It’s annoying when models aren’t working, and sometimes you just want to complain about it – but as long as they work most of the time, people don’t seem to really care.

The overwhelming majority (81%) are actively using multiple models together. 44% route by task type, 26% run multiple and compare outputs, and 11% route based on cost. But this might not last. The data imply that the Great Standardization may be coming: >50% say there is at least some standardization occurring on fewer AI tools within their org.

For a given task, how do you decide which model to use?

Thematically, 2025 was all vibes and pure usage while 2026 is shaping up to be the year where we started thinking about value. As model improvements flatten out and prices continue to go up, we are starting to see cost play a major role in how organizations make AI decisions.

40% say cost regularly shapes how ambitiously they use AI, and 36% say it sometimes does. All in all, three-fourths are adjusting their AI usage based on cost. This sounds… obvious, but if you can put yourself in your own shoes just 12 months ago, it was anything but.

Has cost ever made you dial back your AI ambitions?

If we zoom out to look at the AI engineering stack as a whole, there’s no widespread agreement on what the biggest challenge is. Evals, orchestration and agent logic, inference, and security are all within a few points of each other on the survey. In other words, we have a long way to go (a solid 4% think the stack is working well).

Which layer of your AI stack is currently your biggest challenge?

We asked respondents about how they’re approaching build vs. buy for different layers of the stack. They were able to mark whether they’ve built or bought for particular layers, plus whether they’re happy with that decision or are considering switching (e.g. have built fine-tuning, but are considering buying instead).

How have you approached each layer of the stack?

One of the most interesting takeaways is that inference / model serving is far and away the most commonly bought layer of the stack: 45% have bought (highest in the survey, 37% happy with that decision) and only 23% have built (lowest in the survey).

Prompt management is the opposite. Out of all layers of the stack, it’s the most commonly built and least commonly bought.

On the reverse side, we asked where AI has been the most impactful on their development stack. A surprising takeaway is that AI has impacted all parts of the SWE stack, not just codegen: almost half (48%) say planning and spec writing, and even 28% say backend / API.

Where in your development and deployment pipeline has AI had the biggest impact? · select up to 3

Finally, we asked everyone if they agree, aren’t sure, or disagree on some fundamental takes related to AI Engineering.

Rate each statement

We then did the same thing, but for 5 years out. Will agents be making purchasing decisions? Will SOTA models still be closed source?

Save for maybe AI-generated AI research ideas, the only commonality among these topics is that everyone is split on them. Interestingly, only 9% believe that Transformers are the final boss architecture (and yet 5 years into this, we still don’t have a viable alternative).

In 5 years…