“Tech startups are rushing to build the next game-changing innovation that will ‘change the world;’ while financiers are also rushing to fund each one in the hopes that they will own a stake in whatever world-leading companies emerge.”
This could just as easily describe the AI boom over the last few years and the Dot Com Bubble in the late 1990s. Both have a few major features in common: new technology produced a craze. In the Dot Com Bubble that was the internet, and now it is all the new AI technology. In the 90s, the hype caused people to overvalue tech companies, funding companies that never had a chance in the first place. This created a stock market bubble that eventually burst causing many overinflated companies to go belly up. The same will likely say the same thing about the current explosion in AI investment.
At the same time, the internet did significantly change the world. Among other things, it created new companies, including the corporate giants like Google and Facebook. Thus, investment, even significant investment, did make sense. The overinvestment may have created a bubble with many losers, but there were still many financial winners as well. The same likely could be said about this AI craze.
There is one very important difference, however. A difference that could significantly worsen how new AI technology develops and becomes implemented in our society:
The internet was created by a decade-long series of government-corporate sponsorships with the overall goal to decentralize communications (this book https://www.penguinrandomhouse.com/books/534709/the-code-by-margaret-omara/ is a fascinating detailed discussion of that history), but the recent AI technology has been mostly created by a handful of highly centralized large corporations, most often with the goal of creating a centralized “super-AI” or suite of AI products. This difference may prove crucial to how these technologies develop.
The internet was created to be decentralized, and decentralization became ingrained into the ethos of the technology. No single government or corporation uniquely controls what gets put onto the internet, and even as major companies/websites like Google, Facebook, and Amazon now dominate the internet ecosystem, they do not actively control or prevent people from contributing to the vast world that we call the internet. There are a lot of details about how to best implement this decentralization, as anyone who has sat through net neutrality or internet content debates knows, but the internet overall became a sprawling web that anyone could help create.
The latest AI systems, in contrast, tend to be created by one large company seemingly with the goal of becoming the AI platform everyone uses. Large portions of current funding has gone into creating massive, super-AIs to be able to be the first company to create the supposedly massively intelligent AI so that they can own and thus profit from whatever it can do. This goal encourages consolidation.
Now, AI technologies like large-language models have a certain amount of centralization almost baked into their development. For example, it takes a lot of people and resources to scour the internet and create a large-language model, and more still to run and maintain such a large computational system everyday. Thus this industry may tend towards a certain amount of consolidation, but that doesn’t seem to explain all of the centralization.
The internet also takes a tremendous amount of resources to be able to allow billions of people to be able to communicate on multiple devices simultaneously. Other similar communication systems (such as the system of phone lines connecting everyone by phone) historically tended to create natural monopolies because of how much resources it took to build a phone network system, and inherently, the internet is not that different. Yet the usage of the internet became different. We don’t see a supply side monopoly in the same way. (Overtime, monopsony has arisen, where a few social media companies disproportionately leverage their widespread usage to their advantage, but that is different.) The communications themselves became something everyone had access to.
This centralization seems to have had a tremendous impact on how the current AI technology has developed and will continue to develop. AI seems focused on the interests of big (mostly tech) corporations. One of these companies’ main goals seems to be to automate routine human tasks. This can have an appeal with regular people, but this is really a priority of “business management” interested in saving money by increasing efficiency. Further, many companies are using the hype around this “AI revolution” as a face-saving way to reduce headcount anyways. Finally, the AI interfaces are built to suck users in, to make them want to use it more and more, in ways pretty common on most apps and major websites today. This too tends to be a business management interest.
A new major technology, like say this current generation of AI technologies, usually provides different people with varying amounts of opportunities, helping some and hurting others. Who it helps or hurts is often rooted in who designed it and what their interests/goals lead to it being designed in the first place.
For example, it’s not surprising that agentic coding platforms like Claude Code are really useful for software engineers. It seems to transform their space. Originally, most of the software engineer’s time was spent on busywork instead of the “cool” parts of programming, and Claude Code more or less automates their busywork, allowing them to focus on the cool stuff. Software engineers and other techies were the main people who created Claude Code, so of course, it’s useful for that space.
Many in other professions seem noticeably more cautious about the introduction of AI into their fields. For artists, for example, AI image generation tools seem to allow others to make their art without them. It takes away some of the most interesting parts of making art. Instead, the tools process their past art and allow others to make it without them. Here the process was designed for regular people in mind who do not have the ability to create their own art and noticed how it benefits them over artists.
Thus, we should think more about who is involved in building the AI technology, because that will influence what kinds of problems it gets built to solve. Right now, it seems to favor the interests of business elites and large corporations. I am not necessarily trying to morally condemn these people and corporations (maybe some are good people, maybe some are bad), but I want to point out how slanted their interests involved in building this technology have been and suggest we may need to broaden who “leads the charge” in the development of AI.
So what? What can we do about this? The interests with which a technology is first developed sets a course for how that technology is conceived and what kinds of things people imagine it doing, but nothing is ever completely set in stone. Technologies often grow and evolve over history, becoming used for purposes very different from their original conception. But obviously, the sooner one does this the better. Given that the technology is still young, this is the best time to incorporate other forms of thinking. How it develops now will have cascading impacts on what AI looks like in the future.
How can we build AI in different ways and move beyond the specific interests that hog the AI field now? Exactly how, I don’t know. It’s worth thinking about. I wrote this article to help you think about the problem and get your juices going. Maybe together we can develop a way to build AI in a way that serves the interests of regular people.