Many believe that multinational tech giants continuously create new business value through sheer technological breakthroughs and open sourcing their results, however, this perspective is only partially correct. The reason they are able to continuously create significant value is not just due to their technology, but also because of the "network externality", also known as the network effect, and the economic principle of "increasing marginal revenue".
"Network externality" has been fully understood by the industry since the rise of the digital economy. Simply put, a few technology companies have control over user scale and service entry points. In this case, not only does increased usage of a service lead to its increased value, creating a positive cycle, but even small improvements to the service can generate substantial new business value. In the classic book "Zero to One," Peter Thiel also emphasizes the importance of network externality, which is inherent to digital products.
Imagine, if Facebook or Google improves its computer vision technology by 1% in recognition rate, it may represent a new advertising value of over 1 billion or even up to 10 billion US dollars. But for a startup company, the added value would be relatively limited as the foundation and economies of scale for advertising is dominated by technology giants, rather than general start-ups.
"Increasing marginal revenue" is an inherent phenomenon in the knowledge economy. It refers to the fact that as more knowledge and technology are invested in a knowledge-dependent economy, output will increase and producer revenue will also trend upward. In contrast, traditional agricultural and industrial economies rely on material resources, which have a distinct exclusionary characteristic: their value can only be utilized by one user at a time. Furthermore, these resources are scarce, and as their usage increases, costs become higher, ultimately leading to a decrease in producer revenue. Knowledge-based resources are shareable, and the same knowledge can be simultaneously occupied and used by multiple people. Also, they are not consumed, but rather utilized, and generate new knowledge during use. Information and knowledge resources accumulate and develop with use, and their cost decreases with repetitive use, leading to increasing revenue.
Why do technology giants continuously open source their cutting-edge technology without reservation? Because open source code is not their core competitive advantage. Instead, the real competitive advantages lie in "network effects" and "increasing marginal revenue." Open-sourcing these code is to further utilize existing network externalities, to create a larger user base. A startup company that develops a framework like TensorFlow or PyTorch might carefully protect it as intellectual property, see it as a core asset, and even try to profit directly from it; but for big tech, TensorFlow or PyTorch is just like any other product, an amazing technological innovation, but its core purpose is to expand the network effects of its other products. These two business logic are fundamentally different.
With the combination of these two effects, the phenomenon of "big gets bigger" in the digital economy occurs. That's why the global technology giants can continuously attract top talents, continuously invest in new technology and knowledge-based products, and continue to grow year after year. Scholars from Harvard Business School have also summarized this economic phenomenon with a simpler term: "hub economy". This means that companies who control the entry points for users and services have a significant competitive advantage."
However, the emergence of ChatGPT has shaken the competitive advantage in the industry. Before November 2022, it was unexpected that a non-profit organization such as OpenAI would achieve such success with deep learning. With funding from Microsoft's investment, OpenAI was able to train and launch ChatGPT, resulting in its overnight popularity. Its applications might quickly expand into various fields, and may also have a significant impact on the core business models of technology giants.
Certainly, technology giants still have powerful product channels and network effects, and are actively deploying technology equivalent to ChatGPT into each of their products, to strengthen their own network effects and defend against the threat posed by ChatGPT. Should technology giants continue to share their advanced AI technology with nimble competitors, who may then use it against them? The industry has likely begun to take notice of this potential issue, which is not a technical challenge or a question of high-minded universal values, but a fundamental business problem.
On the other hand, some start-ups have built their business models directly on GPT-3, and their decisions and actions are already considered fast. However, OpenAI recently announced the launch of ChatGPT, which is based on GPT-3.5, and it is rumored that GPT-4 is on the horizon. This is significant and unpredictable for companies that have built their entire business model on GPT-3, akin to a black swan event. Nevertheless, this is the pace of technological evolution in the industry today.
We are now witnessing a historic transformation in the technology industry, both in terms of technology and business.