Even as late as December of last year, few were aware of generative AI. Then ChatGPT popped up, and Microsoft started putting it in everything including its developer tools. Now it’s currently the hottest thing in the market. It is also still immature, but it is working well enough that people are finding it surprisingly useful. This is very different than what happened with previous Microsoft products like Apple Newton and Microsoft Bob, both of which were released well before the underlying technology cooked enough for the general market.
Generative AI is a new way for people to interface with their technology, but it has some shortcomings.
Let’s talk about this from a developer’s standpoint, and about why, once generative AI becomes commonplace, we’ll likely have a very different group of companies like we did with the introduction of the Web.
Generative AI’s promise
The promise for generative AI is that you can use your natural, spoken language to ask the computer to do something and the computer will automatically do it. In Microsoft Office, the initial implementation is very sub-product-centric. For instance, you can request Word to create a document to your specifications, but you’ll have to go to PowerPoint or Excel if you want the tool to create a blended document. I expect the next generation of this Microsoft offering will bridge those apps and other products to allow you to create more complex documents just by putting in information the AI asks for to strengthen the piece.
This is going to make for a difficult evolution for firms that have apps that don’t currently integrate well because the user will want one interface, not multiple AIs that each require different command language or that use different language models.
The generative AI problem
While developing your own generative AI may help, long-term integration with the platform’s generative AI will quickly be a differentiator focused on user satisfaction and retention. I point out that last because users who get frustrated working with multiple generative AI platforms will likely begin preferring products that interoperate and integrate with a major generative AI solution so that the user doesn’t have to train and learn multiple generative AI offerings.
In short, one of the bigger problems is integrating the app with the generative AI most likely to be found on it. Neither Apple nor Google have a cooked generative AI model, and neither company is as good as Microsoft in terms of bringing partners on board to better address their lack of a generative AI solution.
The other big trend in generative AI is putting the technology into development tools that will allow the AI to become a coding accelerator. But with code, errors tend to proliferate. While this initial instance of generative AI is very fast, it’s anything but infallible. If you don’t want a lot of mistakes, the initial focus of any generative AI user needs to be on quality over quantity. The error-checking capability of generative AI is still very young and often makes mistakes. That means coders who use generative AI need to focus more on quality than they currently do. You’ll be training the tool while you use it, and if you train it to make a mistake, that mistake has the potential to proliferate and create additional problems. So, when using development tools that make use of generative AI, the massive increase in speed needs to be tempered with an increased focus on quality. Otherwise, your quality is likely to degrade badly over time.
Generative AI is a game changer. It allows people to increasingly interact with their smartphones, PCs, apps and cloud services as if they were people. To make this work optimal, applications will need to be able to integrate under a generative AI umbrella so that the user only needs to make a request and the relevant app(s) is launched to complete the request. With its announcements of generative AI for its developer tools and Office, Microsoft is arguably the farthest along this path, but we are still early days, and this leadership is likely to become dynamic in the future.
The path to success will be to adapt an existing generative AI tool tactically but work to create the hooks to better integrate your app with the platform’s most likely generative AI solution so that you can dictate once and the AI will move between tools to complete the task. We’re far from that point now, but that gives you time to figure out how to address it.
In short, we are at the front end of a massive generative AI change. Make your related decisions very carefully because you want to be standing when this AI trend reaches critical mass and users move from products that haven’t embraced it much like they did with GUIs and the Web.