> However, using a computer cursor to highlight the redacted text box in the report to DWEE, then copying and pasting it into a separate document reveals that *Agate LLC* is reporting 52.65 megawatts of electricity during peak electrical demand and 13.299 megagallons of water between cooling towers, evaporative systems and site operations in the last year. That totals out to 13 million gallons of water.

> For context, 13 million gallons is enough to fill around 20 Olympic-size swimming pools, and is less than half of what the City of Lincoln reports using on Sept. 29.

> The *six* data centers reporting as of Sept. 30 show a total use of 765 million gallons of water last year, enough water to fill 1,159.93 Olympic-size swimming pools.

> More improper redaction shows that the data center using the most water annually is *Fireball Group LLC*, the Google Data Center in Papillion. Fireball LLC reports 547.88 megagallons for their 2025 Annual Water Consumption.

Oh, are you talking more about the lack of continual learning across context windows? Gotcha if so, my error.

Could you explain why running without sensory input is relevant here? It strikes me as unrelated to how dangerous/hard-to-"kill" something is (sure, I could run without sensory input, but I'm not doin' anything anymore!) - what makes you feel differently (or am I misunderstanding you again?)

Yes and no, but mostly no, at least within a context window.

Mathematically, you could write a single step of LLM decode as a pure function from a list of past tokens to a predicted token (or a distribution over tokens, if you consider sampling separately).

But nobody actually implements this, because each token depends on state computed at past tokens in a way you can reuse.

So, in practice, inference computes a very rich vector of state- at each layer, for each token. And models do indeed use this to plan and track things over time (you can see this in interpretability results, e.g. with linear probes or natural language autoencoders).

> Stop the GPU, hit ctrl-c on the inference server, pull the power plug, cut the ethernet cable, send a kill signal, etc - any of these stop submitting new batches to the GPU and halt execution. That stops tokens from being generated. Stopping a "rogue" LLM is that easy. No input, no output.

This is also true about a human brain. My brain isn't going anywhere- it can't move by itself. It's also easy to kill (without the rest of my body, it dies in minutes!)

However, malicious human brains- especially powerful human brains, like leaders of countries- are often quite difficult to stop, because they're able to control systems that can see, speak, walk, run, fire a weapon, and so on.

One such system is the rest of the body, of course, but there are others (consider a UAV pilot, Perimetr, or a powerful leader who tells other humans what to do).

The brain being squishy doesn't make the thing easy to kill.

But, in the same way that one particle not being conscious doesn't necessarily mean some configurations of many particles- like humans- aren't, one logic gate not being conscious doesn't mean there can't exist configurations of many logic gates that are.

And yet humans, made of such particles, are!

So it seems like reductionism doesn't work here.

To be clear, the claim you're making is that because Twitter has their third corporate office in the same building as an AT&T switching center, and US intelligence used a room in AT&T's switching center for surveillance, then Twitter must have been controlled by US intelligence? And thus the Arab Spring uprising, where Twitter was used, was "fully a CIA/NSA operation"?

How is this different from "probabilistic pattern selection"?