AI’s Open Origins and How it Affects us Now

Open-source technology has been one of the greatest contributors to tech innovation over the past 30 years. The ability to share R&D costs, reuse common code blocks, and accelerate proprietary applications fostered a technological boom that still shapes the digital ecosystem we occupy today.

Our modern web servers, operating systems, and everyday software are built on a foundation of open-source infrastructure amounting to trillions of dollars in economic value. The Linux Kernel, Wikipedia, Firefox, and GitHub are just a few of its countless offshoots. It’s the underacknowledged foundation beneath our modern tech boom. Despite the success, its story was defined by a perpetual struggle with Microsoft and Big Software.

Open source’s influence was resisted from the outset, and not until the 2000s did the movement and broader community get a permanent place in the tech world. Now, just as AI once looked on track to become the new crown jewel of the open-source resurgence, its future is being shaped by the exact same factors. But it may not have the same ending.

You may have heard that the AI movement was at a crossroads, and for a while it was. After ChatGPT’s release in 2022, OpenAI was flirting with a hybrid half-and-half model, but since their decision to go fully for-profit, the gates are closed and sealed shut. With all the incredible progress made by open-source AI, one could reasonably assume we’d be doubling down on this same approach although the reality is not so simple. Cooperation used to help AI researchers and enthusiasts advance the field and share their knowledge.

Now, even the slightest advancement could be worth billions in potential profits and IP value. AI models also now require astronomical quantities of data to train, much of which includes increasingly private and sensitive data. That’s not something the AI power players are willing to give up. The movement once built on cooperation, shared knowledge, and benevolent ideals has now become an arms race.

The differentiator, therefore, comes from being able to train these open models to supplement your organization’s needs. With high quality data, and a strategic knowledge of how best to utilize it, businesses can create their own personalized AI toolkit. You don’t have to match OpenAI’s computing power, nor its billion-dollar coffers. You just need to properly utilize the one commodity you have that they don’t—your data.

Too often, companies have viewed data as a reactive asset. Amid the AI explosion, there’s an over fixation on the tools and models themselves, and not what powers them in the first place. Now, data is squarely at the forefront. With curated, agile datasets, companies can create bespoke AI tools for their needs in real time. When generating valuable insights, streamlining workflows, and automating key processes, this can make all the difference, especially as competitors struggle to keep up.

The story of the open-source movement is inextricably tied to the broader history of programming, coding, and computer science. As personal computers first became widely available, a community of likeminded enthusiasts and experts came together to solve problems, advance the industry, and pool their collective knowledge. This became a deeply prosperous and productive movement, with a community that’s still active today. The fruits of its labor lead to the creation of open-source tools we still use, such as MySQL, Linux, and countless others. According to a recent HBS study, open-source infrastructure amounts to over $8 trillion in economic value. Without it, the study estimated that the average software purchase would be four times as expensive.