The way we find information has always been changing

Wrote this some ago but for some reason, I didn't publish it. Well, here it is.

The progression of our relationship with machines has reached an interesting junction. Let's skip the industrial revolution and straight into the information age. Back in the day, there I go again, at the beginning of the internet before it became well known to the general public, there was a belief that the skill of finding information would be crucial. This is because the internet at that time was already full of information. However, the problem was finding the information. So they looked to the people who have the skills to find information, librarians. The prevailing thought back then was that Librarians and the library sciences would become important because there lies the skill to index the vast amounts of information on the internet. Then, the search engine was invented and that line of thinking hit a brick wall. Machines was taking over the function of librarians, going out and indexing information on the internet and and fulfilling our requests to provide the most relevant answer to what we were asking for.

So it shifted from knowing how to find stuff on the internet, to knowing where to go to to find stuff on the internet. Think of it this way, one is search for something. We Google it. The name of the search engine became a verb, that is how prevalent that paradigm is. We want to find something to buy, will you look it up at Amazon. You still Google it, though. And so on and so forth. 

It's now shifting again. We no longer need to know where to go to find the information we need. With artificial intelligence, and specifically the LLM technology of AI, we go to these information oracles and ask questions. These oracles spew out the answer to us. But not only does it give us the answer, it can also tell us what what we likely want, how we want it, and if we're even not sure, we can ask it in vague terms or we can ask it to build the description of what we want for us. 

No wonder Google is heavily investing in AI. It is their next iteration. It is not just finding the answer on the internet but it is building the answer for us. Granted, the answer is from the information of the internet. But we will soon not see that layer and just get the summary. In the end, we need to go to Gemini or ChatGPT for all our information needs. 

There is some discussion about the narrowing of creativity that since LLM uses pre-built information to appear to generate new results, the result will never be original. However, new stuff is always built on or from old stuff. This evolution is just another step.

There is something to say about this process of putting AI answers first before the information link and discouraging people from clicking them. Will it disincentivize content generation? Some content are not generated for income and those tend to hold original information and facts. So maybe we will less of the SEO optimized links designed to ride the current trend and more information sources coming to the front. And maybe that is better.

A play against Local AI?

A lot of people don't know that it doesn't take a lot of GPU power to run AI. You don't need a lot of computing power to use AI. However, building AI models does take a lot of GPU power but running them doesn't. Think about it this way: It takes a lot of effort to write, record, create music but it doesn't take a lot of effort to listen to them. AI works in a similar way. That's why I was surprised when all of these AI companies started building all these AI data centers and buying up all the GPUs and RAM. They said that they are building these data centers to run AI and AI related services to sell to customers. I get it that they want lots of customers. But running AI and AI services doesn't need the building of datacenters of that magnitude.

I also understood that it was partly a FOMO mentality, the fear of missing out, that was also driving this AI Datacenter boom. Everyone wants to be the next Google, a company that provides a service so essential that their name become the description to what it does. Like Google is a verb in "to Google". There have been other successful internet companies but none to the scale of Google. Some will rise to dominance but who will later fade away. Facebook is a good example. In social media, people have started to move away from Facebook to other services such as instagram and tiktok. Facebook bought Instagram and that's why they remain relevant as a company. It also copied Twitter, tiktok and Snapchat services to be competitive. But many people under 20 don't have a Facebook account. All of these AI companies who are competing with each other, want to be dominant and will take the steps to become so. That that means building datacenters.

So, what does this have to do with running AI and the data Center boom? The secret is right there: It doesn't take a lot of processing power to run AI. Specifically, it doesn't take a a high end GPU to run AI. In fact, Local AI is where you download AI models and run them on your own hardware. This is obviously a threat to the AI companies. They would be less relevant if more people can run AI themselves. What would prevent people from running their own AI? Everyone would need a GPU powered laptop or PCs fitted with the GPU card. This is compounded when you factor in that normally prices for new technology drives the price of existing tech down. Your want faster processors which means that slightly older CPUs would cost less. Same thing works for storage. New storage devices rolling out of the factories with higher capacities would drive down the prices of the product already on the shelf. So the companies thought, how can this be stopped? 

What if the data Center boom is actually a play against local AI? The construction of all of these new data centers gives a reason to buy out an entire year's inventory of GPUs and storage. That will drive up the prices of inventory in the market and make running local AI more expensive. If you can make running local AI more expensive than subscribing to AI Services, you would slow down the adoption of local AI by corporations and drive customers to your AI subscription services. And do you really need to buy all that storage and GPUs? Hardware that will become obsolete in the coming months? Or will this eventually lead to a situation where the consumer is forced to buy pre-owned GPUs and storage from the data centers? It is so important for these AI companies to build these data centers and buy all the GPUs and storage that they finance it all thru a circle-jerk financial and investment arrangement that will fold like dominos if something goes wrong, bringing down the entire stock market because investment firms are so into AI investments.

These companies can't stop local AI entirely because there are free and open source AI models. These models have become smaller and easier to run on consumer GPUs. But if you starve the market of GPUs, more people can't run Local AI immediately because the prices are so high. And maybe that's the point.