The way forward for gen AI-powered code development in 2025

This article is part of VentureBeat's special issue, “AI at Scale: From Vision to Viability.” Read more from the magazine here.

Three years ago the development of AI-powered code was straightforward Copilot GitHub.

GitHub's AI-powered developer tool surprised developers with its ability to help with code completion and even generate new code. Now, at the beginning of 2025, there are a dozen or more AI coding tools and services available from vendors large and small. AI-powered coding tools now provide sophisticated code generation and completion features, and support multiple programming languages ​​and usage patterns.

The new class of software development tools has the potential to completely transform how applications are built and delivered – or so many vendors say. Some observers worry that these new tools will end professional coding as we know it.

What is the truth? How are tools really influencing today? Where are they falling short and where is the market headed in 2025?

“This year, AI tools have become increasingly essential for developer productivity,” said Mario Rodriguez, chief product officer at GitHubsaid VentureBeat.

The promise of enterprise efficiency for code development powered by gen AI

So what can AI-powered code development tools do now?

Rodriguez said tools like GitHub Copilot can already generate 30-50% of code in some workflows. The tools can also help automate repetitive tasks and help with debugging and learning. They can even act as a thought partner to help developers go from idea to application in minutes.

“We also see that AI tools not only help developers write code faster, but also write better quality code,” said Rodriguez. “In our latest controlled developer survey we found that code written with Copilot is not only easier to read but also more functional – it's 56% more likely to pass unit tests.”

While GitHub Copilot is an early pioneer in the space, other newer entrants are seeing similar benefits. It is one of the hottest sellers in the area Repeatwhich has developed an AI-agent approach to accelerate software development. According to Amjad Masad, CEO of Replit, gen AI coding tools can make coding anywhere between 10-40% faster for professional engineers.

“Front-end engineers are the biggest beneficiaries, where there's so much boilerplate and repetition in the work,” Masad told VentureBeat. “On the other hand, I think it has less impact on low-level software engineers where you have to be careful with memory management and security.”

What is more interesting to Masad is not the impact of coding gen AI on existing developers, but the impact it could have on others.

“The most exciting thing, at least from Replit's perspective, is that non-engineers can become young engineers,” Masad said. “Suddenly, anyone can create software with code . This can change the world.”

Gen AI coding tools certainly have the potential to democratize development and improve the efficiency of professional developers.

That said, it's not a panacea and has some limitations, at least for now.

“For simple, isolated projects, AI has made incredible progress,” Itamar Friedman, co-founder and CEO of Qodo, told VentureBeat.

Excavation (formerly Codium AI) builds a suite of AI agent-driven enterprise application development tools. Friedman said that by using automated AI tools, one can now create basic websites faster and with more personalization than traditional website builders can.

“However, for complex enterprise software that powers Fortune 5000 companies, AI is still not capable of full end-to-end automation,” Friedman said. “It excels at specific tasks, such as answering questions on complex code, line completion, test generation and code reviews.”

Friedman argued that the main challenge lies in the complexity of enterprise software. In his view, the capabilities of a very large language model (LLM) alone cannot handle this complexity.

“Simply using AI to generate more lines of code could worsen code quality — which is already a huge problem in enterprise settings,” Friedman said. “So the reason we're not seeing mass adoption yet is because there are still more advances in technology, engineering and machine learning that need to be made for AI solutions to understand software quite a complicated enterprise.”

Friedman said Qodo addresses that issue by focusing on understanding complex code, documenting it, categorizing it and understanding organizational best practices to generate meaningful testing and code reviews. .

Another barrier to wider adoption and use is legacy code. Brandon Jung, VP of ecosystem at gen AI development vendor Tabninetold VentureBeat that he sees a lack of quality data preventing the wider adoption of AI coding tools.

“For enterprises, many have large code bases and that code is not understood,” Jung said. “Data has always been critical for machine learning and that's no different for gen AI for code.”

Towards fully agent-driven AI-driven code development in 2025

A single LLM cannot handle everything necessary for modern enterprise software development. This is why leading retailers have adopted an agent AI approach.

Qodo's Friedman predicts that in 2025 the features that seemed revolutionary in 2022 – such as self-complete and simple code chat functions – will be used.

“The real evolution will be toward specialized agent workflows—not one universal agent, but many specialized ones that excel at specific tasks,” Friedman said. “In 2025 we're going to see a lot of these special agents being developed and used until eventually, when there are enough of these, we're going to see the next tipping point, where can agents collaborate to create complex software. “

It's a direction GitHub's Rodriguez sees as well. It is expected that through 2025, AI tools will continue to evolve to help developers through the entire software lifecycle. That's more than just writing code; it also builds, deploys, tests, maintains and even repairs software. Humans will not be replaced in this process, they will be supplemented by AI that will make things faster and more efficient.

“This is going to be achieved using AI agents, where developers have agents helping them with specific tasks through each step of the development process – and crucially, an iterative feedback loop that the developer remains in control at all times,” Rodriguez said. said.

In a world where AI-powered coding will become increasingly mainstream in 2025 and beyond, there is at least one difference that will matter to enterprises. In Rodriguez's view, that's platform integration.

“To really succeed at scale, AI tools need to integrate seamlessly into existing workflows,” Rodriguez said.