Code Metal, a Boston-based startup that uses AI to write code and translate it into other programming languages, just closed a $125 million Series B funding round from new and existing investors. The news comes just a few months after the startup raised $36 million in series A financing led by Accel.
Code Metal is part of a new wave of startups aiming to modernize the tech industry by using AI to generate code and translate it across programming languages. One of the questions that persists about AI-assisted code, though, is whether the output is any good—and what the consequences might be if it’s not.
Over the past two years companies Antithesis, Code Rabbit, Synthesized, Theorem, and Harness have all secured millions in backing from venture capitalists for their approaches to automating, validating, testing, and securing AI-generated code. These startups are selling the “picks and shovels” of the AI gold rush—tech tools that serve a larger industry. While some of the methodologies behind their technology remain unproven, investors are willing to gamble that at least a few will pan out.
Code Metal, which was founded in 2023, has focused its efforts on code translation and code verification for the defense industry. It boasts L3Harris, RTX (formerly known as Raytheon), and the US Air Force as early customers. The startup is also working with Japanese electronics company Toshiba and says it’s in talks with a large chip company to work on code portability across chip platforms, though the company declined to say which one.
The startup’s software platform translates code from high-level programming languages Python, Julia, Matlab, and C++ to lower-level languages or code that runs on specific hardware, Rust, VHDL, and chip-specific languages Nvidia’s CUDA.
Code Metal CEO Peter Morales, who previously worked at Microsoft and the MIT Lincoln Laboratory, says the market is starting to recognize “the big tentpole problems” in an industry that could, in the not-so-distant future, be propped up by AI-generated code. One of those problems is porting old code into new applications. If a government agency or defense contractor needs coding work done quickly, Morales says, but only has access to engineers who have specialized in a legacy programming language, that slows everyone down.
Morales cites a recent post on X from well-known AI researcher Andrej Karpathy, who observed the “rising momentum behind porting C to Rust,” among other things. Karpathy concluded: “It feels ly that we’ll end up rewriting large fractions of all software ever written many times over.”
“That is all of what we do in one ,” Morales says.
One of Code Metal’s investors, Yan-David Erlich, a general partner at B Capital, says the reality is that some of the code that controls essential communications infrastructure, and even satellites, “is old, it’s crufty, it’s written in programming languages that people might not use anymore. It needs to be modernized.”
“But in the course of translation,” Erlich added, “you might be inserting bugs—which is catastrophically problematic.”
That’s where Code Metal says its proprietary tech comes in. Morales says that at each step of translation, Code Metal’s software generates a series of test harnesses—a virtual container of data and tools—that evaluate the code and show customers along the way that it’s working. When asked about Code Metal’s error rate for translation, Morales says it depends largely on how difficult the code conversion is, but that for the pipelines Code Metal currently runs, “there’s no way to generate an error. The software will just say, ‘There’s no solution for this’ if we can’t complete the translation.”
The startup is skittish about sharing too many details about its methodology. One element of the business it’s not shying away from talking about, however, is its approach to pricing.
Code Metal, along with thousands of other AI startups trying to sell enterprise software, is coming of age at a time when the “per seat” sales model is growing stale. Enterprise software giants Oracle and Salesforce have long sold their services to customers based on how many employees that customer has. In the age of generative AI, when tokens—the units of data being processed—are expensive and “time d” has become a critical measurement, companies are instead trying to agree on new metrics of value.
Morales says that his company negotiates pricing with every customer individually and that the cost is typically based on one of three factors: the amount of time it takes to develop a kernel, lines of code translated (which is based on time to write code), or development time d. In other words, the company isn’t just charging for the raw cost of tokens but the actual time d by using the company’s software, which can get a little squishy. (Morales acknowledges that the process of determining value can “get murky” but says it seems to be working so far and that “every pilot [the company has] deployed ends up going to the next phase.”)
As part of its sales strategy, the startup has hired executive Ryan Aytay as its president and chief operating officer. Aytay was previously at Salesforce, where he was chief executive of the Salesforce-owned app Tableau. Laura Shen, previously the director for China at the US National Security Council, was brought in last year as the company’s executive vice president of growth.
With this latest $125 million funding round, which was led by Salesforce Ventures and includes Accel, B Capital, J2 Ventures, and others, Code Metal is now valued on the private market at $1.25 billion. The company claims it’s profitable, making it a unicorn in more ways than one—valued at over $1 billion and already generating positive cash flow. Whether that translates to long-term success remains a question for any startup looking to stake its claim in the new AI landscape.
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Wired.com
