When it comes to learning and understanding, facts are easy. If I ask you how many bits are in an IPv4 address it’s a single answer. People memorize facts and figures like this all the time. It’s easy to recall them for tests and to prove you understand the material. Where things start getting interesting is when you need to provide context around the answer. Context is expensive.

Cognitive Costs

Questions with one correct answer or with a binary answer choice are easy to deal with cognitively. You memorize the right answer and move on with your life. IPv4 addresses are 32 bits long. The sun rises in the east. You like Star Wars but not Galatica 1980. These things don’t take much effort to recall.

Now, think about why those answers exist. Why does the sun rise in the east? Why are addresses 32 bits long? Why don’t you like Galactica 1980? The answers are much longer now. They involve nuance and understanding of things that are outside of the bounds of simple fact recall. For example, look at this video of Vint Cerf explaining why they decided on 32-bit addresses all the way back in the mid-1970s:

There’s a lot of context around a simple fact. For some of us it’s fun to learn the context and provide it at parties or when we’re trying to put someone to sleep with endless recitation of trivia. A lot of people won’t bother to care about the why and move on with their life.

You know who does care about the why? People building AI systems. When you think about it, answers that are facts are searchable. You would jump on Google or Bing or Duck Duck Go to find out where the sun rises or what time it rises today. But the answer to why is something that people building AI algorithms are working on. They want to be the search engine that provides the context. They want to overwhelm you with the reasoning and the justification behind something. And that increases cognitive load on the system.

If I asked you to explain why Star Wars is better than Galactica 1980 you could likely give me five reasons with justification quickly. But if I asked you to analyze each of those for underlying assumptions about science fiction and writing styles you’d take a little longer to come up with your answers. That extra level of reasoning increases our cognitive load. Now imagine a system working on all that cognitive load simultaneously. That’s where we are with AI right now.

Making Money

People know how to deal with overwhelming amounts of information. They can selectively discard what they don’t care about and focus on what matters. It’s why we can find the signal of a person’s conversation in the noise of a cocktail party. Our brains can filter when necessary to reduce cognitive load. AI isn’t as good at that right now which is why every piece of data included in a list of sources or a prompt has to be included somewhere. AI doesn’t know how to say “this is important” or “this is something we can forget about”. It’s better than it was before but it has a long way to go to behave a like intelligence.

The problem is that all of that processing costs resources. Power consumption, water consumption, and processing time are all used when a model is doing things. Models perform well when things are easy to produce, like with simple answers or with information that has already been generated. They fall down a bit when they have to do many things simultaneously. Just like with the human brain it can get overwhelming. Only the AI doesn’t do as good of a job as the brain of isolating the important parts.

The answer, at least according to AI researchers, is to just do it all. Burn a lot of those magical tokens to get every answer and every piece of context and just have it ready in case someone asks. It’s not unlike rehearsing a conversation in your head over and over again to get the perfect response to every question that could be asked. And yes, before you ask I’m the kind of person that does that. The cognitive load of exploring every conversation option is usually two or three times longer than the conversation itself. That’s time I’m not going to be get back.

The context around the answers incurs additional expense. We want to understand why and we’re willing to pay to get that detail. Right now it’s mostly free for us to play around with. The models are getting trained by what we ask and refining their ability to predict responses and understand how we think. What happens in the future when that model isn’t free any longer? Are we willing to pay to access the context? Are the providers going to force us to see ads to provider compensation for it?

Tom’s Take

I love context. I provide it all the time. Sometimes it’s not warranted or appreciated. But it’s always there. Because I want to know why something is the way it is. I’m the kind of person that is going to cause our modern systems to burn additional resources to sate my curiosity. Right now the model doesn’t look to be sustainable because we haven’t trained our algorithms to understand that sometimes the best part about being smart is knowing when not to be.