The way forward for AI-powered code development in 2025

This article is part of VentureBeat Magazine’s special issue, “AI at Scale: From Vision to Feasibility.” Read more of the case here.

Three years ago, AI-powered code development was mostly abstract Github copilot.

GitHub’s AI-powered developer tool has amazed developers with its ability to help complete code and even create new code. Now, at the beginning of 2025, a dozen or more AI coding tools and services are available from vendors large and small. AI-powered programming tools now provide advanced code generation and completion features, and support a range of programming languages ​​and publishing styles.

The new category of software development tools has the potential to completely revolutionize how applications are built and delivered – or so many vendors claim. Some observers worry that these new tools will spell the end of professional programmers as we know them.

What is reality? How are tools actually making an impact today? Where are they lacking and where is the market headed in 2025?

“In the past year, AI tools have become increasingly essential to developer productivity,” said Mario Rodriguez, chief product officer at github,” he told VentureBeat.

The promise of enterprise efficiency with AI-powered code development

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

Tools like GitHub Copilot can already generate 30-50% of the code in certain workflows, Rodriguez said. Tools can also help automate repetitive tasks and aid in debugging and learning. They can also act as a thought partner to help developers go from idea to app in minutes.

“We’re also seeing that AI tools help developers not only write code faster, but also write better quality code,” Rodriguez said. “In our latest study of developers, we found that code written with Copilot is not only easier to read, it is also more functional – and is 56% more likely to pass unit tests.”

While GitHub Copilot is an early leader in this space, other more recent entrants are seeing similar gains. One of the most important sellers in this field is rewhich developed an AI agent approach to accelerate software development. According to Amjad Massad, CEO of Replit, generic AI-powered programming tools can make the programming process 10% to 40% faster for professional engineers.

“The biggest beneficiaries are front-end engineers, where there is a lot of modularity and redundancy in the work,” Massad told VentureBeat. “On the other hand, I think it has less impact on low-level software engineers where you have to be careful about memory management and security.”

What’s most exciting to Massad is not the impact AGI coding will have on current developers, but rather the impact it can have on others.

“The most exciting thing, at least from Replicate’s perspective, is that it can turn non-engineers into junior engineers,” Massad said. “Suddenly, anyone could create a program using code. This could change the world.”

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

However, it is not a magic cure and has some limitations, at least for now.

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

Kudo (formerly known as Codium AI) is building a series of AI agent-based enterprise application development tools. With automated AI tools, anyone can now build basic websites faster and with greater customization than traditional website builders, Friedman said.

“However, for the complex enterprise software that supports Fortune 5000 companies, AI is not yet capable of complete end-to-end automation,” Friedman noted. “He excels at specific tasks, such as answering questions about complex code, line completion, creating tests, and code reviews.”

Friedman argued that the fundamental challenge lies in the complexity of enterprise software. In his view, the capabilities of the Large Language Model (LLM) alone cannot handle this complexity.

“Simply using AI to generate more lines of code may actually worsen code quality — which is already a big problem in enterprise settings,” Friedman said. “So the reason we haven’t seen significant adoption yet is that there is still more advances in technology, engineering, and machine learning to be made in order for AI solutions to fully understand complex enterprise software.”

Qodo addresses this problem by focusing on understanding, indexing and classifying complex code and understanding organizational best practices for creating meaningful tests and code reviews, Friedman said.

Another barrier to wider adoption and deployment is legacy code. Brandon Young, VP of Ecosystem at AGI development vendor TibninHe told VentureBeat that he sees a lack of high-quality data preventing wider adoption of AI coding tools.

“For companies, many of them have large, legacy code bases, and that code is not well understood,” Young said. “Data has always been critical to machine learning, and this is no different with AGI for code.”

Towards developing fully artificial intelligence-based software codes in 2025

No single LLM can handle everything required for modern enterprise software development. That’s why major vendors have embraced an effective AI approach.

Qodo’s Friedman predicts that in 2025, features that seemed revolutionary in 2022 — like autocomplete and simple avatar chat functions — will be commoditized.

“The real evolution will be toward specialized agent workflows — not one global agent, but many specialized agents that each excel at specific tasks,” Friedman said. “In 2025, we will see many more of these specialized agents being developed and deployed until eventually, when there are enough of them, we will see the next turning point, where agents can collaborate to create complex software.”

It’s a trend that GitHub’s Rodriguez sees as well. He predicts that throughout 2025, AI tools will continue to evolve to assist developers throughout the entire software lifecycle. This is more than just writing code; They also build, deploy, test, maintain, and even repair software. Humans will not be replaced in this process, but will be enhanced by artificial intelligence that will make things faster and more efficient.

“This will be achieved through the use of AI agents, where developers have agents assisting them with specific tasks during each step of the development process – and, most importantly, an iterative feedback loop that keeps the developer in control at all times,” Rodriguez said.

In a world where AI-driven coding will become increasingly mainstream in 2025 and beyond, there is at least one difference that will be key for organizations. In Rodriguez’s view, this is platform integration.

“To be successful at scale, AI tools must integrate seamlessly into existing workflows,” Rodriguez said.

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