The way forward to the generation of AI-powered code development in 2025

Three years ago, developing AI-powered code was mostly simple Copilot on GitHub.

GitHub’s AI-powered developer tool has wowed developers with its ability to help with code completion and even generate new code. Now, in early 2025, a dozen or more generative AI coding tools and services are available from vendors large and small. AI-powered coding tools now provide advanced code generation and completion features and support a range of programming languages ​​and deployment models.

A new class of software development tools has the potential to completely revolutionize the way 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 the reality? How do tools actually make an impact today? Where are they falling short and where is the market headed in 2025?

“Over the past year, AI tools have become increasingly important to developer productivity,” Mario Rodriguez, Chief Product Officer at GitHubsaid VentureBeat.

The promise of enterprise-grade AI-powered code development

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

Rodriguez said tools like GitHub Copilot can now generate 30-50% of the code in certain workflows. Tools can also help automate repetitive tasks and help with debugging and training. They can even serve as a thought partner to help developers go from idea to app in minutes.

“We’re also seeing AI tools not only help developers write code faster, but to write better quality code,” Rodriguez said. “In our latest controlled developer study, 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 more recent entrants are seeing similar gains. One of the hottest providers in the space is Replitwhich has developed an AI-agent approach to accelerate software development. According to Amjad Massad, CEO of Replit, AI-based coding tools can make coding between 10-40% faster for professional engineers.

“The biggest beneficiaries are the front-end engineers, where there is so much pattern and repetition 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 with memory management and security.”

What’s more exciting for Masad isn’t the impact of next-generation AI coding on existing developers, but rather the impact it could have on others.

“The most exciting thing, at least from Replit’s perspective, is that it can turn non-engineers into junior engineers,” Massad said. “Suddenly, anyone can create software with code. It can change the world.”

Certainly, AI-powered coding tools 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 remarkable progress,” Itamar Friedman, co-founder and CEO of Qodo, told VentureBeat.

Dig up (formerly Codium AI) builds a series of tools for developing enterprise applications driven by AI agents. Friedman said that with the help of automated AI tools, anyone can now build basic websites faster and with more customization than traditional website builders.

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

Friedman argues that the main challenge is the complexity of enterprise software. According to him, the capabilities of a pure large language model (LLM) alone cannot handle this complexity.

“Simply using AI to generate more lines of code can actually degrade code quality — which is already a significant problem in enterprise settings,” Friedman said. “So the reason we’re not seeing much adoption yet is that there’s still more advancements in technology, engineering and machine learning that need to be made for AI solutions to fully understand complex enterprise software.”

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

Another barrier to wider adoption and deployment is legacy code. Brandon Jung, VP of Ecosystem at Gen AI development provider Tabninetold VentureBeat that he sees a lack of quality data holding back the wider adoption of AI coding tools.

“For enterprises, many of them have large, old code bases, and that code is not well understood,” Jung said. “Data has always been critical to machine learning, and Gen AI for Code is no different.”

Towards fully agentic, AI-driven code development in 2025

No single LLM can handle everything needed to develop modern enterprise software. That’s why leading vendors have adopted the agent-based AI approach.

Qodo’s Friedman expects that in 2025 the features that seemed revolutionary in 2022. – such as autofill and simple code chat features – will become commercialized.

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

It’s a direction GitHub’s Rodriguez sees as well. He expects in 2025. AI tools to continue to evolve to support developers throughout the software lifecycle. It’s more than just writing code; it’s also building, deploying, testing, maintaining, and even fixing software. Humans will not be replaced in this process, they will be augmented with AI that will make things faster and more efficient.

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

In a world where AI-powered coding will become increasingly mainstream in 2025. and then, there is at least one distinguishing feature that will be key for enterprises. According to Rodriguez, it’s platform integration.

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