Why open source is key to the future of conversational AI

The idea behind modern conversational AI is that AI helps humans complete a variety of tasks, primarily through voice or text, from ordering products online to planning remote travel. Tech giants are investing heavily in conversational AI, but plenty of challenges remain. Even huge investments don't guarantee a breakthrough.

Open source is the key to faster development of conversational artificial intelligence. You cannot rely solely on tech giants to drive growth in this area. While they have the ability to create sophisticated products in-house, deploying more resources in R&D than any lab or startup, their efforts are not enough to unlock the full promise of conversational AI. Here's why.

1. The need for an environment that promotes innovation

Big tech companies stand shoulder to shoulder with other tech giants when it comes to ideas, people, and expertise. For example, in 1988, Apple sued Microsoft for stealing ideas from the Macintosh, even though both parties used ideas from Xerox PARC. Or take Dave Cutler, for example, who was hired by Microsoft from DEC to develop a new version of OS/2 for IBM PCs. Eventually, the product was rebuilt as Windows NT and evolved into Windows 11. Innovation requires an environment that encourages the free flow of ideas among organizations and experts.

2. Companies lock innovation within their ecosystems

In the late 21st century, Apple, Google, Amazon and other companies began to develop closed ecosystems to maximize the benefits from millions of revenue from customers. While constantly creating new products, improving existing products, and seeking innovation, they lock in their own breakthroughs. This is not surprising, as tech giants want to control and profit from the entire life cycle and future iterations of their products. But this need for control is antithetical to a decentralized Internet, one that even today is driven by protocols like HTTP.

3. Conversational AI still makes mistakes

Conversational AI is one of the most complex areas of research and product development. It's also one of the fastest. It requires rapid development, which of course requires making mistakes along the way. But public companies are risk-averse and tend to avoid mistakes, especially when it comes to new products. The result is the same thoughts over and over again.

Imagine a company working on an idea behind closed doors only to have something go wrong. But no one outside the company knew about it, and no one had access to what the problem was. Microsoft's Cortana is an example of a failure that enjoyed significant investment resources. The point is, has the larger AI community learned from its failures?

4. Collaboration at the core

Businesses need to collaborate on conversational AI. Amazon, Google, and other companies have dozens of truly massive projects. While one can name many collaborative projects, they are all superficial. For example, several collaboration examples of integrating multiple assistants into one device are known.

However, real innovation should happen at a deeper level, at the core. Currently, all core technologies are more or less at the same level and locked within large tech companies.

But if they collaborated more freely, together they could create something truly amazing. For example, when Linux was released as a free and open source operating system, many people hated it. Former Microsoft CEO Steve Ballmer saw it as a threat to intellectual property, calling it a "cancer" in 2001. It took Microsoft more than 10 years to change its attitude; Linux proved to be a unique phenomenon. The more types of industries that invest in it, the better it becomes. Linux is now everywhere, from Android phones to smart TVs to NASA equipment.

People believe it’s possible to build a “Linux” moment in conversational AI. Open source is the only way to significantly accelerate innovation and focus on different potential application areas. Having a core open to everyone and ensuring simple access to it can facilitate development and lead to new products in different application areas, from personal AI assistants in our home devices to complex systems in industry.

Unless cooperation occurs at the true core, progress in the most promising and fastest-growing areas will be slowed.

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