Application programming interfaces gave millions of developers instant access to powerful and complex software services, changing the world. As generative artificial intelligence enters that world, we’ll see new forms of access to data and services, machines talking to people and other machines in new ways, and an explosion of activity, collaboration and creativity.

What should we expect? Plan for scale at a whole new level: More developers, researchers and companies involved. More complex programs and automation. More data moving around. More need than ever for API management systems with more visibility, operability, security and ease of use, at scale, and for people across the value chain. As with many big tech changes, expect new winners and losers, based on who best understands and manages opportunity and risk.

With that in mind, let’s talk about what APIs really mean for the world, what generative AI can and will do, and the best practices for ensuring we proceed in helpful and secure ways. (* Disclosure below.)

APIs, AI and modern software

APIs are an easy way for computer programs to call on someone else’s data or software services. Accessing airline schedules, using online checkouts, delivering comprehensive news, or securing and tracking international supply chains are just some of the thousands of online activities made possible by APIs. Combined with cloud computing, including services that manage millions of API transactions securely and easily, APIs enabled a software ecosystem of common components, producing the critical means of business engagement in online life. 

Until recently, AI, largely in the form of machine learning, has been great at spotting and leveraging patterns in big sets of data, enabling new efficiencies and better forecasting. Generative AI helps people interact with computers in natural speech, and programs can summarize large amounts of information, create novel text, images, music or even program themselves.

Putting it together

It’s easy to see how APIs, which play a critical role in access to data and services, matter for generative AI, which feeds on data and augments services. In turn, people without much training will adopt traditional developer skills through generative AI, conversationally asking machines to create programs.

This means a new set of digital workflows, often involving one or more APIs. There will also be an increase in today’s API collaboration, as people share knowledge, best practices and code snippets, fostering more innovation and yet more code. 

More savvy users, such as skilled programmers, will create complex software utilizing multiple APIs to solve tough problems. They may interrogate generative AI to suggest new things to code and programs to build. In many cases, generative AI works by utilizing specialized large language models, or LLMs, and APIs can augment an LLM with new or better data, programs and algorithms.

The AI itself may be offered back to the developer community as a simple API, creating even more powerful architectures through reusability. We’re already seeing ways that researchers are making this work.

The initial focus will be on eliminating repetitive work, such as scaffolding, documenting and creating tests, but over time the overall architecture will improve too. With AI-driven assistance, developers can easily design, mock, create, discover and share APIs more efficiently, with good quality and adherence to best practices.

New opportunities, new risk management 

This all sounds great, but it means lots of new relationships and dependencies among data, programs and organizations. And that’s before considering machine-to-machine API usage, brokered by generative AI. Unmanaged, it means conflicts and confusion with current privacy, risk and compliance frameworks, and hazards such as intellectual property leakage from poorly designed programs released in the wild. 

We’ll likely see a complete makeover in API discovery, the way people now find and secure data and programs. Companies will want their data and services to be found, since the cost of staying out of the new tech will be too high. We saw this when APIs became a core part of cloud and mobility services; now it will happen faster, with more use cases.

Organizations will need robust strategies for efficient management, security and compliance, even when the API call is between two machines, with none of the usual social contracts we’ve previously had in APIs, awareness, policy governance and enforcement. Developers will also need to understand the right use cases for their data to tune LLMs, the kind of outputs that are being created, and whether they are compliant with any regulatory guidelines, such as data privacy regulations, that arise. They will need to have guards against deployment when there is misplaced confidence in outcomes or generative AI “hallucinations,” the confident mistakes these systems can make.  

There is a critical need for even stronger predictive and reactive lifecycle management and governance. Security and governance must increase to ensure the protection of sensitive data and adherence to regulatory standards.

(Disclosure: Frank Weigel is Google Cloud’s vice president of business application systems, and Nils Swart is Google Cloud’s group outbound product manager for Google’s API management platform Apigee. They wrote this piece for SiliconANGLE.)

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