Economy of AI: How to Make Money with Machine Learning?
The AI ecosystem has generated a multi-billion dollar industry, and it all starts with data. Going up the value chain, there are chips (GPUs) that allow for the physical storage of Big Data (with NVIDIA being a dominant player).
This Big Data will need to be stored on platforms and infrastructures that SMEs cannot afford. That's where players like Google Cloud, Amazon AWS, IBM Cloud, and Azure come to the rescue.
At a larger scale, a few companies control the enterprise AI market, while nations like China, the United States, Japan, Germany, the United Kingdom, and France have heavily invested in it!
Yet with the development of new AI companies like OpenAI, the ecosystem has completely changed and is now developing around three layers of AI.
Understanding the AI ecosystem
Beyond all the buzz and media hype that come with new words entering the mainstream, AI is another one of those disciplines that have become essential in today's economic landscape.
Far from being in its infancy, the AI ecosystem has become a multi-billion dollar enterprise ecosystem, led by tech giants ranging from IBM to Google, Azure, Amazon, and many others.
This does not mean that there are no opportunities for new entrants. On the contrary.
The AI ecosystem revolves around a few key elements that can also be considered as the "AI toolbox:"
Data or megadata Infrastructure Algorithms Let's take a deeper look at each of these key elements for an AI ecosystem. But before we dive in, we need to understand who and how is making money with AI.
Who is making money with AI?
Billions of dollars have been invested in the AI ecosystem, especially by large tech companies.
This is good news because these tech companies have created an ecosystem that is there, ready to be understood so that you can make it your own business.
Indeed, understanding how this ecosystem works is the first step to making money from it.
And it all starts with data!
Keep in mind that the whole point of AI is to manage and be able to do something useful with a massive amount of data.
In short, even though we like to talk about AI and machine learning, these are technologies for themselves. In reality, the foundation of these technologies is data.
An organized data pipeline is a basis for an AI ecosystem to work in the first place.
Companies like Google, Wolfram Alpha, Amazon, and many others, spend billions to maintain and keep their data. If anything, we can say that for companies like Google, data is its main asset.
As already explained in the Blockchain Economy, in today's economic world built on digitization, the rule is to keep these data confidential. This made sense because it is these data that are ultimately monetized with multiple strategies.
Let's take some opposite examples of how data is monetized:
Google's freemium data: Google uses its exclusive data (collected from billions of user searches every day) to sell advertising. Apple's reverse data razor: iPhones know a lot about you, but Apple does not share this data with marketers. Instead, it monetizes it by selling expensive devices (the iPhone is the main one).
When data reaches a critical mass, we can talk about Big Data. There is no single definition of Big Data, and it can actually vary over the years.
Given that the more the AI industry develops, the less collecting and processing data will be expensive.
This, in turn, will allow for the management of an increasingly significant volume of data.
For the purposes of this discussion, and...
