Breaking Down AI’s Walls Training AI models require vast amounts of data.
That’s something that mostly only giants like Google, IBM or Microsoft are capable of.
This has led to a concentration of AI resources in the hands of a few corporations, which has resulted in siloed AI models.
Data is everything to AI. Data is what powers accuracy and reliability in the answers AI gives you.
High-quality, representative data is essential for building effective AI models.
Since these companies compete with each other, they don’t share data.
And of course, that makes sense. Generative AI alone is expected to bring in $1.3 trillion in revenues by 2032, and they each want the biggest share of this market as possible.  But that also means that Google’s AI model could come up with a good answer to one question but Microsoft’s AI could come up with a good answer to another.
But you don’t get to combine the best of both worlds.
Worse yet, this keeps us from taking a very easy step forward with AI development.
Think about it like this: what if you bring a problem to AI and it requires two steps of problem-solving?
One AI model could be great in the first step, but it might not be as good as another AI model in the second step.
This is where crypto offers an obvious solution.
Blockchains provide an infrastructure where a person or a group of people can develop an AI model and allow it to be used by others.
Then based on experience using the model, developers would have an idea of what it’s good at and what it’s not so good at.
Then in true collaborative spirit, they could combine that model with another one that overcomes the first one’s limitations.
There are already crypto platforms that do this.
They encourage and financially incentivize people to develop AI models and are working toward combining such models to produce the best outcomes.
In this way, crypto democratizes access to AI.
You don’t have to be one of these large tech companies to build or operate an AI model.
You could be an independent researcher who goes onto one of these platforms and with the help of other people’s work, come up with your own AI model.
And unlike those tech giants, the data that you use to develop your models will be transparent and easily accessible. Building Tomorrow’s Virtual Data Centers But data is not all you need.
You need massive computing power as well if you are to successfully build AI models.
This is another area that hyperscalers like Amazon and Google dominate.
These giants are racing to build out hyperscale data centers so that they can rent out the computing capacity to users who want to use it to develop AI models.
In fact, U.S. data center demand is expected to grow by 10% a year until 2030.  Building out physical data centers that can handle these types of tasks is impractical for everyone, with the exception of giant corporations that have massive amounts of money to spend.
High-performance GPU chips that run these AI workloads alone cost a minimum of $10,000 each, and then you have the costs of the rest of the computing infrastructure that makes use of that GPU.
Not to mention the land needed to build warehouse structures that can fit at least 5,000 servers and all the associated equipment that helps them run smoothly.
However, while a physical data center network is out of reach for most, a virtual data center network is not.
Imagine this — you have a computer with a top-of-the-line GPU or a server rack for personal use loaded with these GPUs.
And although you do use some of this GPU capacity, generally most of that capacity goes underutilized.
So, even with everything that you do, you will only ever use a fraction of this storage capacity — a pretty bad return on the investment you made buying that hardware in the first place.
But what if you could sign up that GPU capacity to a network that allows you to make that unused GPU capacity available to users of that network.
Now imagine there are thousands of people doing exactly the same thing you are.
Together, the network that you all create results in a massive virtual decentralized GPU-powered data center.
Users can then just rent out that GPU capacity for a fee that gets paid to those whose GPUs are being used.
Crypto projects already utilize the blockchain to build such virtual networks.
That means an AI researcher can rent out this virtual data center capacity at a fraction of what they would pay a hyperscaler.
This is yet another way in which crypto democratizes access to AI.
With more people around the globe able to easily access both the data and the computing power necessary, the more unique and diverse contributions to AI development.
And like with the Human Genome Project, the more collaboration that we have, the more likely we are to lead AI into its next evolution.
The steps that the world of crypto has already taken in this direction are why I believe we are on the cusp of a convergence.
The convergence of crypto and AI.
I have put together a report on the best way to benefit from this convergence.
You can check out that report right here.
Until next time, 
Ian King
Chief Strategist, Strategic Fortunes
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