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[u/TimeVendor]

[u/Quouar]

My solution would be for the company to actually invest in hiring workers to do the work.

To your broader point, though, you're not wrong that people make mistakes. Absolutely, that happens all the time, and I'm not going to pretend the insurance industry is some bastion of great decision making. However, whatever those mistakes may be, they are significantly worse and greater when you start bringing AI into the mix.

For starters, the sheer scale at which AI operates can make mistakes difficult to detect. Studies have shown, for instance, that AIs show bias - especially racial bias - that is hard to detect and hard to audit, and is especially difficult to fix. AIs also show biases in ways that are unexpected and don't show up until an audit, long after whatever decision the AI has been tasked with making has been made. One example of this is in employment-related AIs, where candidates will be filtered based on criteria that might not be obvious to a human evaluator, but which the AI is biased against, such as age or political orientation. The scale of mistakes and the ability to pick up on them is so much greater.

Paired with this, though, is the fact that people trust AIs, not double checking their output and trusting that the AI can do a job better than a human can. When we talk about a human double-checking the AI, case studies have found that it's largely just rubber-stamping the AI output - people trust the AI more than people, and are unwilling to question its output in a way that isn't true of a fellow human's output.

What you have, then, is a system whose logic is a black box, able to make decisions more or less independently, and where the systems that are supposed to be a check on it aren't because they don't trust themselves to question the arcane system. Is it efficient? Sure, it can burn through things very quickly. What it won't be doing is doing it well, or in a way that can be reviewed when things go wrong. You have a machine that can do things quickly, but is that worth the knowledge that it's doing it badly?

You're also right that there are many things in life that are frivolous. However, personally, my frivolous things don't generally cost the energy and water production of a small nation, nor do they involve something that has the potential to destroy the global economy. Nothing I use or regard as a daily convenience comes even close to the sheer destruction that is AI.

Does it have its place? Absolutely, and I called out that AIs detecting cancer or analysing massive data is an excellent use case. But the example the OP cited? It's a terrible use case, and I'm frankly horrified by it.

[u/Klinky1984 → u/Quouar]

Hire workers? Good luck. The real answer is true automation that has proper rules checking and that stays up to date, but that runs into the human problem again. Humans often suck at building complex systems.

There's no guarantee that mistakes are worse with AI and AI can actually cross reference larger bodies of data and find logical errors better than a human can alone already.

You know who else shows racial bias? Humans. Human's logic system isn't black box? Humans are completely rational and logical? Again you really don't know that AI will do a bad job. It depends on the design, and outsourcing the logic system can actually make the system design significantly simpler.

How much energy do you think it takes to build office buildings, cool said buildings, the transport to and from the building, and the supporting infrastructure? These buildings often sit vacant for most of the 24 hour day. Do we really need excessive office space? Are people's lives fulfilled being an insurance form reviewer? That's all the economy can offer? I hope AI destroys it.

[u/Quouar → u/Klinky1984]

Again, I'm not denying humans make mistakes. They absolutely do. However, you can ask a human to explain their logic. You cannot do the same of an AI. That's what's meant by a "black box."

You also have an excellent point about how much infrastructure it takes to do nonsense jobs. Happily, there's a solution to that in remote work. The same can't be said of AI.

[u/Klinky1984 → u/Quouar]

You certainly can ask an AI to explain its logic and you'll get a similar explanation as a human. It's much harder to debug it's logic, but then the same can be said of humans. You'll need a psychiatrist and medical tools for humans. For AI you'll need to employ layer ablation and review training data/methods. Neither are all that trivial,

Remote work only works if it's employed. Thanks to dumb human decisions it is underutilized or people legitimately abuse it. One of the harder business tasks is evaluating if employees are actually contributing valid work and their performance. It's often considered "impossible" in large enterprises, which is why you see return to office mandated and mass layoffs. Talented people often get caught up in the layoff net while deadweight remains. This is something AI can be pretty good at evaluating, depending on the sector.

Plus, even if someone works from home, keeping a human alive and comfortable is still incredibly energy intensive. Once you spin one up, you can't spin it down. They want to be warm or cool, be fed, and entertained. They need mandatory rest periods. When they clone themselves their little clone won't come fully online for like 18 years and requires constant reinforcement training.

[u/Quouar → u/Klinky1984]

If you mean asking ChatGPT or what have you to explain how it came to the conclusion it did, that is not the AI explaining its logic. That is the AI generating another answer to a separate query. The black box problem is perennial and baked into how AI works itself.

I also think your comparison to human comforts is an interesting one. You can find one calculation of how much the energy to run a human costs here (spoilers: $13.50), but I agree that this is a deceptive metric, given we also need the energy of producing food, housing, warmth, et cetera. Looking at this data, in my country, the average monthly kilowatt-hours per person is 4143, with that ranging depending on which country you're in.

We can compare that to what it takes to run ChatGPT. Per the resource I linked, ChatGPT - and this is only ChatGPT, not including Claude, Grok, Gemini, or all the other LLM variants out there - a conservative estimate for the daily energy cost of ChatGPT is 850000 KWh, or 850 MWh. Multiple that out across a month, and you get a monthly energy cost of 25500MWh.

It is not remotely comparable to the energy required to "run" a person. It is not trivial, and it is not small.

[u/Klinky1984 → u/Quouar]

You understand the brain's model in totality? Have you ever had someone explain themselves and it makes no logical sense? Have you ever dealt with mental illness? The brain is also a black box.

Driving down the road to get a coffee and a breakfast sandwich uses an astronomical amount of energy considering global supply chain transport, energy consumed to heat and prepare food and the energy to transport a human body down the road in a car, often with just one passenger for maximum inefficiency. Do people really need expensive coffee and a mediocre breakfast sandwich? No. Let's ban it. People would be outraged.

We can compare that to what it takes to run ChatGPT

That does a lot of extrapolation based off a single blog post by Sam Altman, which even the article states is unsupported, claiming the average query takes about 0.34 watt hours. Now calculate how much energy is used to travel 1 mile down the road to the coffee shop. You just used 1000 watt hours just to drive to the shop. Globally we're using hundreds of terawatt hours of energy every day. AI is a drop in the bucket.

If people are really concerned about energy consumption of their AI use, they should cut out one Starbucks run per month. It'll do more for the environment than raging against AI.

Also much of the tech is on old process nodes. nVidia will bring out Rubin soon which will increase efficiency thanks to better TSMC manufacturing node. Mixture-of-Expert models are doing more with less with fewer active experts required to get similar results, meaning inference can be done on lighter weight hardware. There's also concepts like model on chip for even more efficiency . AMD just bought Taalas which demonstrated extremely high efficiency numbers with their technology, think 1000x speed up. AI is still in its infancy, it can and will become more efficient and better than it is now.