The other outcome would be clearly inequitable: forcing the counter party to eat the loss for your irresponsible use of an AI agent.

If you don't like AI slop. Don't read it. But wasting your time generating human slop to complain about AI slop is so obviously futile that it immediately identifies the writer as lacking the capacity for reason or emotional clarity, or merely seeking attention for their self-promotion with clickbait.

Programming with AI enforces some good disciplines, which were always true, but could be avoided doing it by hand. Most importantly: you are shipping your tests. If you don't have reproducible automated tests then it probably doesn't work.

AI is not fake and it does work, but what I am saying is that from a pure systemic analysis perspective, you can do the numbers, and even if AI was complete fugazi, the benefits you get from the electrical generation capacity, and the ability to fund it through private markets, which bypasses Congress, and locks in commercial contracts (often with foreign governments) which will be almost impossible politically to reverse, would still make it optimal from a strategic perspective. That is my calculation, and to the extent that it is correct, I would assume that the US Military's strategic planning apparatus would arrive at the same conclusion.

AI compute has some unique characteristics that make it especially useful for grid management. Moving consumer compute to the cloud means that the electrical use of that compute can be centrally managed. In an emergency, you can cut electrical use for consumer AI by 50% or more, because chips run more efficiently at lower power, and you can shift workloads onto quantized models, reduce resolution for video output, etc, to reduce compute, which leads to minor service degradation but not interruption. AI datacenters are also adding massive amounts of battery storage capacity, which is an additional grid buffer. For every GW in capacity added by hyperscalers that is creating a dispatchable reserve capacity of 50% under completely normal circumstances (hyperscalers do this internally to optimize their own costs) and then that number goes up depending on the scale and duration of the emergency.

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This is my observation from using it without an specific context engineering to optimize for Deepseek's cache compression and sparse attention mechanisms. I am pretty sure that if you specifically structure your context to align to the cache compression boundaries you can significantly improve performance in the full 1M context, but there is not much reason to do this, because if you design your outer loop to work with shorter contexts that solution is portable and more efficient, so I haven't bothered with a optimizing for DS at this point.

For an example of my real token usage for a day with DS: Input (Cache hit) 530,949,760, Input (Cache miss) 7,875,004, Output 1,389,685 - it is still 1/5th the price of Baidu (the cheapest) and 1/7th-1/10th the price of US hosts.

Also, Deepseek platform is not the same thing as Deepseek open weights. There is a major misconception that the existence of an open weights model means that it is the same thing as the proprietary platform offering, but that is definitely not the case.

AI is really all about electricity. AI could be completely fake and yield zero value whatsoever and the US would do exactly what it is doing now because the AI bubble is what creates the market for building new electrical generation capacity, which is needed for re-industrialization. Also why our friends in UK/Europe/China are so busy pushing anti-AI propaganda to try to undermine this.

But on a personal level I would vastly prefer my children use even a crappy AI chat app like Deepseek than watch Youtube. If you have young children and pay attention it would be almost impossible to come to a different conclusion.

an LLM is a next token prediction function. of course it matters how you use it, and if you want to use it for an educational purpose, that has to be intentional, so i agree with you on that.

chatgpt and all the other ai powered scams and attention monetization apps are horrible, because they are just using improved technology to accelerate the exploitation of consumers (both adults and children) of media and app platforms.

the solution here is not to say the technology is bad but to use it to create competitive products that are actually beneficial.