Every day, 3 trillion dollars worth of transactions are handled by a 64-year-old programming language that hardly anybody knows anymore.
It's called COBOL (Common Business Oriented Language), and despite the fact that most schools and universities stopped teaching it decades ago, it remains one of the top mainframe programming languages used today, especially in industries like banking, automotive, insurance, government, healthcare, and finance. According to the International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), 43 percent of all banking systems are still using COBOL, which handles those $3 trillion daily transactions, including 95 percent of all ATM activity in the US, and 80 percent of all in-person credit card transactions.
The problem is that very few people are interested in learning COBOL these days. Coding it is cumbersome, it reads like an English lesson (too much typing), the coding format is meticulous and inflexible, and it takes far longer to compile than its competitors. And since nobody's learning it anymore, programmers who can work with and maintain all that code are a increasingly hard to find. Many of these "COBOL cowboys" are aging out of the workforce, and replacements are in short supply.
In the early days of COBOL development, programs were punched on a card and loaded into a specialized punch card reader, which would compile the code and load it as a program. Modern COBOL systems don't use punchcards anymore, but the programming language itself hasn't changed much since the 1960s (Credit: IBM)This puts us in a tricky predicament. We need to maintain and modernize the code that underpins so much of the business and finance worlds, but we don't have enough skilled workers we need to carry out those updates.
This is precisely the kind of problem that IBM thinks it can fix with AI.
Watson to the Rescue?
IBM’s approach is fairly straightforward: Rather than relying exclusively on a limited pool of human programmers to solve the problem, it built a generative AI-powered code assistant (watsonx) that helps convert all that dusty old COBOL code to a more modern language, thereby saving coders countless hours of reprogramming. In extremely simplified terms, the process is similar to feeding an essay written in English into ChatGPT and asking it to translate certain paragraphs into Esperanto. It allows programmers to take a chunk of COBOL and enlist watsonx to transform it into Java. But of course, it’s not quite that simple in practice.
“It might be 80 or 90 percent of what they need, but it still requires a couple of changes. It’s a productivity enhancement—not a developer replacement." - Skyla Loomis, VP of IBM Z SoftwareIBM’s Vice President of Product Management, IT Automation, Keri Olson, explains that watsonx is an end-to-end solution that involves a multi-step process to perform these kinds of complex code translation tasks. After IBM and the customer have a thorough understanding of the application landscape, the data flow, and the existing dependencies, “we help them refactor their applications,” she says. “That is, breaking it down into smaller pieces, which the customer can selectively choose, at that point, to do the modernization from COBOL to Java.”
Skyla Loomis, IBM’s Vice President of IBM Z Software adds, “But you have to remember that this is a developer assistant tool. It's AI assisted, but it still requires the developer. So yes, the developer is involved with the tooling and helping the customers select the services.” Once the partnership between man and machine is established, the AI steps in and says, ‘Okay, I want to transform this portion of code. The developer may still need to perform some minor editing of the code that the AI provides, Loomis explains. “It might be 80 or 90 percent of what they need, but it still requires a couple of changes. It’s a productivity enhancement—not a developer replacement type of activity.”
No Such Thing as a Sure Thing
If it proves successful, the watsonx code assistant could have huge implications for the future, but not everyone is convinced it's a silver bullet that IBM says it is. Many who remember IBM’s previous AI experiment, Watson Health, are hesitant to trust another big AI project from the company because the previous one failed so miserably and didn't deliver on its high-flying promises.
Gartner Distinguished Vice President and Analyst, Arun Chandrasekara is also skeptical because “IBM has no case studies, at this time, to validate its claims,” he says. “AI generation is an early-stage technology that takes time to perfect. I’m sure they have checks and balances in place to address this situation, but I prefer to take the ‘wait and see if it works’ approach.”
This clip showcases IBM's watsonx Code Assistant translating COBOL into Java (IBM)Even IBM admits that the technology is new and unproven, but remains optimistic about its future. “If you're asking about case studies specific to watsonx code assistant, Arun is correct,” says Olson. “We haven't published any case studies around that yet. However, if you look at our experience with Z computing and our customers on the mainframe, as well as our experience with AI; we're marrying these two things to provide a state-of-the-art AI experience. It’s true, we are in the early day in terms of bringing this to clients.”
So while AI code translation is certainly a promising idea, it still remains to be seen if it can actually be deployed successfully and make an impact in the real world.
Robots and Coders Working Side by Side?
If this all pans out though, it could have implications far beyond the COBOL conundrum. Updating and modernizing old code is just the tip of the iceberg when it comes to what's possible with AI-augmented code creation, and IBM isn't the only company racing to build a solution.
One 2023 report from Gartner claims that "By 2028, the combination of humans and AI assistants working in tandem could reduce the time to complete coding tasks by 30 percent," and that 80 percent of programmers will use AI in some way. Many believe this will happen much sooner as AI technology sweeps the globe with more companies investing in its development every day.
Now, as Gartner analyst Chandrasekara says, we just have to “wait and see.”
