LLMs are essentially a text prediction engine. This can be used for basic reasoning tasks, however the LLM doesn't have much in the way of actual knowledge. However, it can use knowledge that exists to predict text better. For instance, if you plug a bunch of scientific articles into it, it will be really good at answering questions about the subject of those articles.
The problem is that context windows are very short. 200k-1M tokens or so. This means that the model needs to focus down on very specific information if possible. This is what makes tool using, reasoning, and agentic AI very powerful. The model can find the most relevant information it needs within its limited context and generate relevant answers to questions. The LLM pulls from web searches, documentation, long term memories in graph databases, and database queries to answer the questions using real information.
Raw dog Chat LLMs are pretty worthless. But run an agent with tool invocation and they get scary good. It's amazing how much reasoning is packed into the English language. Provide your model with enough information and it can pull some miracles out of thin air. It's not the "Replace humans" level yet, but you can automate a lot of stuff you wouldn't expect to be able to automate.
I think that this is doable. Similar to having a new employee shadow a more experienced one and observe, you could implement a sort of program where AI shadows experienced employees and asks questions when they do something it doesn't understand.
But this is difficult to implement since AI doesn't have a body to follow someone around and it would take immense amounts of compute to do so using telemetry and cameras. You would literally be spying on employees 24x7 for weeks at a time with the express goal of replacing them someday.
I think most of them were losses by attrition. Where they don't replace lost employees. That's usually the preferred method of downsizing if you can get away with it.
There are two kinds of knowledge. There is explicit knowledge which can be codified easily in markdown files or a wiki. Then there is tacit knowledge which is mostly encoded in the experience of an organization's individuals. Explicit knowledge is like the tip of a giant institutional knowledge iceberg.
In America it is a little different. Unfortunately California is still using 48% natural gas at this point. But they are moving in the right direction. Most states are continuing this trend. Driving in Texas you see wide load trucks hauling wind turbine parts almost every day.
I am not aware of a "System" line item on the accounting for cost. Do you mean the capital put forward to construct the system? That is included in LCOE.
Typically power is priced by Levelized Cost of Electricity (LCOE) where you amortize the total cost of ownership across the total power generated to get a per kwh cost which is what most grid operators care about. After all, large scale investors and governments don't care if a plant costs a billion dollars if it produces 20 billion in electricity.
What you are talking about is the Payback Period (PP) or Return on Investment (ROI) which is more important to homeowners adding a solar plant to their homes.
Both of these types of measure take into account both capital and operating expenses.
Solar/wind is the cheapest form of power generation by far. You just can't beat it because they don't have any fuel costs. Gas peaker plants will always make sense until we have enough grid scale batteries. They will hold on for now until the price of natural gas hits rock bottom. But with the current advances in low cost battery technology I see them becoming less and less necessary. They would probably already be dead if hydrofracturing hadn't propped up the cost of gas.
People underestimate how much hormones change their brains. We all think "I have free will!" Then the progesterone comes in and is like "The heck you do! Time to eat that cake!"
The person was sent home before the Ebola diagnosis. He would still have been sent home under this new protocol.
The USA, similar to other nations, is extremely well equipped to treat Ebola patients once they have been diagnosed. Doctors just don't expect it because it is so rare.
It isn't magic, it's just math.