AI Is Producing More Software. Why Isn’t It Being Used?

Artificial intelligence coding tools dramatically increase developers’ productivity, but much of those gains are lost before they translate into finished software because human bottlenecks persist later in the production process.

That’s according to a new study written by Leon Musolff, a Wharton professor of business economics and public policy, and MIT researchers Mert Demirer and Liyuan Yang. (Editor’s note: This paper was updated with new data after this article was written.)

When looking at the impact of AI tools on coding activity, the gains grew sharply with each new generation of AI tools. Autocomplete systems that suggest the next line of code increased coding activity by 40%. Adding “sync agents,” which edit code alongside developers in real time, lifted the cumulative increase to 140%; “async agents,” which work autonomously from a prompt, pushed it to 180%.

Yet even the biggest cumulative gain translated into only a 50% increase in software projects, and a 30% increase in software releases. “In software, the binding constraint appears to be shifting from writing code to reviewing, integrating, and ultimately distributing it,” wrote the authors in the paper.

What’s Blocking AI Productivity Gains?

The researchers tracked more than 100,000 developers on GitHub, the world’s biggest software development platform, comparing their productivity before and after they adopted the three successive generations of AI coding tools, from 2022 to 2026. They combined those public GitHub records with Microsoft data on developers’ use of the tools to identify when they first adopted the technology.

The findings suggest that AI can dramatically speed up individual coding tasks, but those gains will not automatically translate into more finished software — unless AI can also automate more of the work involved in reviewing, integrating, and releasing software.

Increasingly powerful AI coding tools have made it possible to generate working software from simple prompts, dramatically lowering the barriers to software development. That has helped fuel the recent “vibe coding” boom, allowing employees with limited programming experience to build applications in minutes. But the research suggests writing code is no longer the main block.

“If the world froze at today’s level of AI capabilities, these results would be a bit of a cold shower.”— Leon Musolff

Will AI Coding Tools Improve?

So what lessons should companies draw from the findings? “If the world froze at today’s level of AI capabilities, these results would be a bit of a cold shower,” Musolff said.

However, the tools are improving apace. “We studied these tools in a previous paper, and it’s night and day,” said Musolff. “A 30% increase in software releases — there are very few technologies you can invest in today that deliver those kinds of gains.”

The researchers also found that each new generation of tools is tackling a later stage of the software development process, so the gap between gains in coding productivity and gains in finished software could begin to narrow as the AI gets better.

The paper says that if the tech can produce higher-quality code that requires less human review, today’s bottlenecks may prove temporary.

Some tech companies are already trying to tackle that problem by developing AI tools that review machine-written code. But Musolff is unconvinced they can yet match human judgement.

“If the same AI that wrote the code also reviews it, that doesn’t really solve the problem. The review just isn’t of the same quality,” he said.

“If the same AI that wrote the code also reviews it, that doesn’t really solve the problem.”— Leon Musolff

Is User Adoption the Next Barrier?

Even once software is released, it still has to find traction with an audience. The research found AI is increasing the number of new software applications, but not user adoption.

The researchers studied the four biggest software marketplaces — Apple App Store, Google Play Store, Chrome Web Store and SourceForge — and found a broad surge in new software applications since mid-2025. But crucially, no increase in overall usage.

On Apple’s App Store, for example, monthly new releases rose from around 30,000 before AI coding agents arrived in early 2025 to roughly 100,000 per month by April 2026. Yet total usage remained flat or declined across the four major app stores.

“It could simply be that it’s much harder to discover new applications when there’s such a flood of them,” said Musolff. “Alternatively, even once you’ve shipped an app, there’s another skill involved: iterating with users.”

In other words: Getting software into users’ hands is only the start.

Source: View the original article at Knowledge@Wharton.