How AI Will Disrupt Big Tech Giants

Public concern is growing over how generative artificial intelligence (AI) might obliterate established industries and professions, from lawyers to Uber drivers to accountants. However, what is often overlooked is that the first major victim of AI disruption will be the technology sector itself. AI is already starting to cannibalize established giants and reshape the profession of software engineering, with significant implications for research, antitrust policy, and safety regulation.

Since the invention of modern computers during World War II, technological progress has enabled us to make computers increasingly user-friendly. However, these systems still rely on rigid, highly structured programming and usage methods. This is true even for many consumer applications, such as searching for information, filling out forms, navigating screens, creating spreadsheets, and specifying document formatting. With the advent of AI, this is starting to change.

One of the most obvious victims of AI disruption within the tech sector is Google. Traditional Google Search has become a cumbersome process of sorting through too many links and ads. In contrast, with OpenAI’s ChatGPT, Anthropic’s Claude, or Perplexity, you simply request the information you want and get it. These AI models are still imperfect but are already vastly superior to the Google search model. Their usage is growing at about 10 percent per month, and this year ChatGPT alone is projected to exceed US$10 billion in revenue. Google has belatedly released AI Mode, but it is hard to see how the company can respond effectively without destroying itself. Google’s management is clearly aware of this and is thoroughly terrified, as they should be.

Something similar is happening across a significant portion of the tech sector. Current industry giants such as Microsoft, Apple, Salesforce, SAP, Nvidia, Amazon, Intel, and Dell were built on technologies that disrupted earlier incumbents, particularly IBM and its mainframes. However, these once-revolutionary technologies are now several decades old. AI may well supersede them, and it is already opening space for new disruptors.

Consider productivity applications like word processors and spreadsheets. Microsoft and others are adding AI “copilots” to their products, but these are burdened by historical constraints. They cannot change much because they must retain compatibility with all the documents and applications already built with them. The same is true for many other traditional applications that handle documents, emails, calendars, data retrieval, and presentation graphics.

As an experiment, before writing this article, I asked Perplexity to write it for me based on a paragraph of instructions. It did a pretty decent job, at least as a first draft, and I could have refined it into something usable. This process will become even easier in the future, with verbal instructions eliminating the need to type anything, and enabling us to do much more than conventional applications allow.

This is just the beginning. Suppose you wanted to find every AI start-up CEO in the San Francisco Bay Area whose company’s revenues were under US$10 million, write each of them a customized email, attach your CV, and automatically track the replies. Microsoft would try adding this capability to MS Office, but it would be messy and complicated. Wouldn’t it be easier using something designed from the ground up with AI in mind, a product that just did all that for you when you asked it to? Several AI start-ups, such as Clay and Paradigm, are already challenging Microsoft in this way. Similarly, FuseAI is challenging Salesforce, and several other start-ups are challenging LinkedIn.

Now consider personal devices. Current mobile phones, from iPhones to Samsung Galaxies to Google Pixels to those from Huawei and Xiaomi, use microprocessors, operating systems, and user interfaces whose architectures are decades old, none of them designed for AI. However, several start-ups, such as Etched, are working on new AI processors. Within five years, we will likely be able to run good AI models on a phone, which could spawn radically new operating systems and applications.

The shift is already underway. On May 21, OpenAI announced that it was buying Jony Ive’s start-up io for US$6.5 billion to develop new AI-first mobile devices, with the explicit goal of displacing Apple. While this particular venture has some doubts, the disruption will surely occur. Similarly, there will be AI challengers to Mac and “Wintel” desktop and laptop computers.

Online shopping, particularly on Amazon, is another area poised for profound change. Amazon’s market power derives from its unmatched logistics systems and software that helps you search for, evaluate, and purchase nearly anything. However, Amazon’s interface is cumbersome, similar to Google Search. What if you could just tell your AI to search the Internet, find the nearest store carrying a product, or the world’s best product for a specific need, and have it delivered? In such a world, Amazon’s advantages would erode quickly. We are not there yet, but many smart people are working hard to make it a reality. OpenAI has already introduced a product, and Perplexity has made a deal with Shopify to enable Perplexity searches to find and display products in online stores built with Shopify.

My sense is that this new generation of disruption, with AI rendering much of the current tech sector obsolete, could occur faster and more forcefully than earlier ones. AI technology is improving at a stunning rate, and nowhere faster than in the automation of software development itself. Thanks to AI tools such as Cursor, Windsurf, and Claude Opus 4, the pace of software development in leading-edge AI companies has increased sharply. I have seen and heard estimates ranging from a 25 percent improvement to a doubling of software productivity in the past year alone. In my AI investing, I now routinely encounter start-ups with only a few employees that developed their entire product within a few months, and sometimes within a few weeks. One subject of amused but serious conversation in the industry is when we will see the first one-person start-up unicorn. We might not have to wait long.

Many people involved with AI believe that software engineers might disappear completely. I do not think so, but the profession will change dramatically, and many older software engineers might find their skills rendered obsolete. Even software engineers at Google, a very elite crowd, are worried about losing their jobs, particularly as AI disruption of search starts to affect Google’s revenues.

For many observers, the humbling of the industry’s giants at the hands of a radically accelerated AI start-up sector could not arrive too soon. After all, the incumbents’ looming vulnerability comes after many years of astounding profits derived from monopolies, cozy oligopolies, and “walled gardens.” Several incumbents have clearly started squeezing their semi-captive users, degrading product quality to increase profits, a phenomenon that the writer Cory Doctorow has colorfully termed “enshittification.” (Google and Apple are often cited.) Even as the incumbents face potentially mortal threats, they also face antitrust actions and growing complaints from suppliers, application developers, and consumers.

Both phenomena—the threat of disruption and longstanding, extreme market power—are very real. The history of the tech sector is one of successive monopolies eventually eliminated by technological revolutions that produce new monopolies. Regulatory or antitrust actions have sometimes helped unleash innovation, such as the US Federal Communications Commission’s opening of long-distance telecommunications to competition in the 1970s, the breakup of AT&T in the 1980s, the Microsoft antitrust case in the 1990s, and prevention of several attempted acquisitions.

However, antitrust and regulatory systems, never swift and sometimes foolish, have been progressively crippled over the past several decades by a combination of worsening bureaucracy, obsolescence, and growing corruption. In both Europe and the US, it takes many years, and sometimes decades, to resolve antitrust cases. Defendants such as Microsoft, Google, and Apple hire the most prominent specialists in antitrust economics and law to oppose and delay everything. Corporate antitrust defendants often outspend the government by a hundred to one.

The incumbents also benefit from the fact that antitrust laws and legal precedents, some of them more than a century old, are woefully inappropriate to the realities of modern information technology. For example, Google avoided any antitrust scrutiny of its acquisition of Character.AI because it did not technically acquire Character.AI at all. It simply acquired everything important about Character.AI—its top talent and its technology—by paying its investors and employees US$3 billion. Microsoft did the same thing with another major AI start-up, Inflection AI, paying US$650 million, and Amazon did it, too, not acquiring Adept for at least US$439 million.

Some argue that, given the ease with which regulatory systems can be subverted, and given that technological revolutions eventually dethrone incumbents for us anyway, we should not even bother with antitrust or regulatory policy at all. Perhaps it would be more efficient and less subject to corrupt influence to let the market take its course. However, the broader implications of AI disruption, including job displacement, ethical concerns, and the potential for new monopolies, must be carefully considered and managed.

The disruption of the tech sector by AI is inevitable and could have far-reaching consequences. As AI continues to evolve, it will not only challenge the dominance of current giants but also open new opportunities for innovation and growth. The key will be to ensure that this disruption benefits society as a whole, rather than leading to new forms of inequality and control.