Zencoder buys Machinet to challenge GitHub Copilot as AI coding assistant consolidation accelerates

Zencoder announced today the acquisition of Machinet, a developer of context-aware AI coding assistants with more than 100,000 downloads in the JetBrains ecosystem. The acquisition strengthens Zencoder’s position in the competitive AI coding assistant landscape and expands its reach among Java developers and other users of JetBrains’ popular development environments.

The deal represents a strategic expansion for Zencoder, which emerged from stealth mode just six months ago but has quickly established itself as a serious competitor to GitHub Copilot and other AI coding tools.

“At this point, there are three strong coordination products in the market that are production grade: it’s us, Cursor, and Windsurf. For smaller companies, it’s becoming harder and harder to compete,” said Andrew Filev, CEO and founder of Zencoder, in an exclusive interview with VentureBeat about the acquisition. “Our technical staff includes more than 50 engineers. For some startups, it’s very hard to keep that pace.”

The great AI coding assistant shakeout: Why small players can’t compete

This acquisition comes at a pivotal moment in the AI coding assistant market. Just last week, reports emerged that OpenAI is in discussions to acquire Windsurf, another AI coding assistant, for approximately $3 billion. While Filev maintains the timing is coincidental, he acknowledges that it reflects broader market dynamics.

“I think there’s going to be more to it, and I’m looking forward to it,” Filev said. “It’s a huge product surface. You have to support multiple IDEs, you have to integrate with multiple DevOps tools, you have to support different parts of software life cycle. There are 70-plus, 100-plus programming languages… There’s so much work there that it’s very, very hard for the smaller companies that only have like sub-10 engineers to compete in the long term.”

How Zencoder’s JetBrains strategy outflanks Microsoft-dependent rivals

One of the key strategic values of acquiring Machinet is its strong presence in the JetBrains ecosystem, which is particularly popular among Java developers and enterprise backend teams.

“JetBrains audiences are millions of engineers. They’re one of the leading providers for certain programming languages and technologies. They’re particularly well known in the Java world, which is a big chunk of enterprise backend,” Filev explained.

This gives Zencoder an advantage over competitors like Cursor and Windsurf, which are built as forks of Visual Studio Code and may face increasing constraints due to Microsoft’s tightening of licensing restrictions.

“Both Cursor and Windsurf are what’s called forks of Visual Studio, and Microsoft recently started tightening their licensing restrictions,” Filev noted. “The support that VS Code has for certain languages is better than the support that Cursor and Windsurf can offer, specifically for C Sharp, C++.”

By contrast, Zencoder works with Microsoft’s native platforms on VS Code and also integrates directly with JetBrains IDEs, giving it more flexibility across development environments.

Beyond hype: How Zencoder’s benchmark victories translate to real developer value

Zencoder differentiates itself from competitors through what it calls “Repo Grokking” technology, which analyzes entire code repositories to provide AI models with better context, and an error-corrected inference pipeline that aims to reduce code errors.

The company claims impressive performance on industry benchmarks, with Filev highlighting results from March that showed Zencoder outperforming competitors:

“On SWE-Bench Multimodal, the best result was around 13%, and we have been able to easily do 27% which we submitted, so we doubled the next best result. We later resubmitted even higher results of 31%,” Filev said.

He also noted performance on OpenAI’s benchmark: “On the SWE-Lancer ‘diamond’ subset, OpenAI’s best result that they published was in the high 20s. Our result was in the low 30s, so we beat OpenAI on that benchmark by 20%.”

These benchmarks matter because they measure an AI’s ability to solve real-world coding problems, not just generate syntactically correct but functionally flawed code.

Multi-agent architecture: Zencoder’s answer to code quality and security concerns

A significant concern among developers regarding AI coding tools is whether they produce secure, high-quality code. Zencoder’s approach, according to Filev, is to build on established software engineering best practices rather than reinventing them.

“I think when we design AI systems, we definitely should borrow from the wisdom of human systems. The software engineering industry was rapidly developing for the last 40 years,” Filev explained. “Sometimes you don’t have to reinvent the wheel. Sometimes the best approach is to take whatever best practices and tools are in the market and leverage them.”

This philosophy manifests in Zencoder’s agentic approach, where AI acts as an orchestrator that uses various tools, similar to how human developers use multiple tools in their workflows.

“We enable AI to use all of those tools,” said Filev. “We’re building a truly multi-agentic platform. In our previous release, we not only shipped coding agents, like some of our competitors, but we also shipped unit testing agents, and you’re going to see more agents from us in that multi-agent interaction platform.”

Coffee mode and the future: When AI does the work while developers take a break

One of Zencoder’s most talked-about features is its recently launched “Coffee Mode,” which allows developers to set the AI to work on tasks like writing unit tests while they take a break.

“You can literally hit that button and go grab a coffee, and the agent will do that work by itself,” Filev told VentureBeat in a previous interview. “As we like to say in the company, you can watch forever the waterfall, the fire burning, and the agent working in coffee mode.”

This approach reflects Zencoder’s vision of AI as a developer’s companion rather than a replacement.

“We’re not trying to substitute humans,” Filev emphasized. “We’re trying to progressively and rapidly make them 10x more productive. The more powerful the AI technology is, the more powerful is the human that uses it.”

As part of the acquisition, Machinet will transfer its domain and marketplace presence to Zencoder. Current Machinet customers will receive guidance on transitioning to Zencoder’s platform, which offers enhanced capabilities through its proprietary Repo Grokking technology and AI agents.

The new developer landscape: A rapidly evolving ecosystem

The acquisition of Machinet by Zencoder signals a turning point in the AI coding assistant market, as larger players absorb innovative smaller companies with specialized expertise. For enterprise decision-makers evaluating AI coding tools, the landscape is shifting from a question of whether to adopt these technologies to which platform will provide the most strategic advantage.

“Jokingly, I think like half of the Y Combinator batch is AI startups, and it’s just impossible to compete in this space with two engineers at this point,” Filev noted. “You’ve got to have some real resources, technical resources and market resources in order to succeed here.”

As industry titans like Microsoft and OpenAI deepen their investments in this space, companies like Zencoder are carving out distinctive positions based on integration flexibility, benchmark performance, and engineering philosophies that align with enterprise needs.

For developers watching this market consolidation unfold, one thing is becoming increasingly clear: the future won’t be about whether AI writes your code, but rather which AI becomes your preferred pair programmer when you return from that coffee break.