Show HN: Why write code if the LLM can just do the thing? (web app experiment)

In my go example we have a human and an AI model competing at the same task. A good AI model will perform much much much better and probably win the game, but if we measure the energy input into either player the AI model will consume a lot more energy. However a game of go is not automation, it won’t save us any time. The benefits of the AI model is it helps human go players improve their own game, finding new moves, new patterns, new proverbs, etc. Because of go playing AI models human go players now play their games better, but nor more efficiently, nor faster.

In your LLM coding example you have a human and an AI model collaborating on a single task, both spend some amount of energy (taking your assumptions at face value, compatible amount of energy) and produce a single outcome. In the go example it is easy to compare energy usage and the quality of the outcome is also easy to measure (simply who won the game). In your coding example the quality of the outcome is impossible to measure, and because the effort is collaborative, splitting the energy usage is complected.

When talking about automation my game of go example falls apart. A much better examples would be something like a loom, or a digital calculator. These tools help the human arrive at a particular outcome much faster and with much less effort then a human performing the task without the help of the machines. The time saved by using these tools are measured in several orders of magnitudes, and the energy spent is at par with a human. It is easy to see how a loom or a digital calculator are more efficient then a human.

I guess if we take into account the training cost of an LLM model we should also take into account the production costs of looms and digital calculators. I don‘t know how to do that, but I can’t imagine it would be anywhere close to that of an LLM model.

And we have an LLM model we have increased the productivity of, not 5000x[1], but by 5%-30%. To me this does not sound like a revolutionary technology. But I have my doubts of even the 5%-30% figure. We have preliminary research ranging anywhere from negative productivity increase to your cited 5%-30%. We will have to wait for more research, and possibly some meta-analysis before we can accurately assess the productivity boost of LLMs. But we will have to do a whole lot better then 5%-30% to sufficiently justify the huge energy consumption of AI[2].

Personally, I am not convinced by your back of the envelope calculations. It fails my sniff test that 9 Wh of matrix multiplication will consistently save you an hour of using your brain to perform the same task adequately. I know our brains are not super good at the logic required for coding (but neither are LLMs), but I know for a fact they are very efficient at it.

That said I refuse to accept your framing that we can simply ignore the energy used in training, on the bases that it is equally invalid as considering the energy used for evolving into our species, or that we can simply stop training new models and use the models we do have. That is simply not how things work. New models will get trained (unless the AI bubble bursts and the market looses interest) and the energy consumed by training is the bulk of the energy cost. And omitting it makes the case for AI comically easy to justify. I reject this framing.

Instead of calculating, instead I’m gonna do a thought experiment. Imagine a late 19th century where iron and steel production took an entire 2% of world’s energy consumption[3] (maybe an alternative reality where Iron working is simply that challenging and requires much higher temperatures to work). But the steam train could only carry the same load as a 20 mule team, and would only do it 5%-30% faster on average then the state of the art cargo carriages at the time without steam power. Would you accept the argument that we should simply ignore the fact that rail production takes a whopping 2% of global energy consumption, when factoring the energy consumption of the steam train, even when it only provides you with 5%-30% productivity boost. I don‘t think so.

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1: I don‘t know how much the loom has increased productivity, but this is what I would guess without any way of knowing how to even find out.

2: That is, if you are only interested in the increased productivity. If you are interested in the LLM models for some other reason, those reason will have to be measured differently.

3: https://www.allaboutai.com/resources/ai-statistics/ai-enviro...

Here in Switzerland the bus is the deterministic choice. Just saying.

Sure, let's put some numbers on the thought experiment. Oversimplifying everything: say before AI, 100% of the world's energy enabled 100 units of productivity. Now, adding 2% more power for AI (which includes the training!), 102% buys us 105 units of productivity at the lower end? That's a good deal!

Of course, realistically speaking, the 5-30% boost is only across certain domains in knowledge work, not all work ever, so we're not gonna see a 105% boost globally. To get a fairer comparison, let's look at the US in 2024 where we have some broad data about AI power usage for and its overall productivity impact.

For power [1] indicates datacenters accounted for about 4.4% of all US power demand in 2024. From [2] it seems upto 20% of that was for AI workloads. That gives us 0.88% of total US power consumed by AI in 2024.

Now look at [2]. This is a St. Louis Fed study based on broad survey data which finds similar ~25% productivity boosts across multiple industries, which is comparable to other randomized studies on empirical data (some included in my previous links.) They estimate that current levels of AI usage in only the industries and roles exposed to it may already be improving total US labor productivity by upto 1.2%!

So we are probably already a bit better than breakeven!

Now consider that productivity will only keep increasing as people and companies learn to harness AI properly. As anecdotal evidence, notice how the sentiments about AI-based coding have shifted on this very forum; you see way more converts than skeptics.

Also consider that [3] found that people still use AI for a small part of their workday. As usage increases, so will productivity. Power consumption will go up too, but from all the above it looks very much like the tradeoff is very favorable to AI.

Yes we need more studies and data, but the ones we have so far are already sort of mind-blowing.

[1] https://www.pewresearch.org/short-reads/2025/10/24/what-we-k...

[2] https://www.wired.com/story/new-research-energy-electricity-...

[3] https://s3.amazonaws.com/real.stlouisfed.org/wp/2024/2024-02...

When I write to an unknown audience, unable to know in advance what terms they rely on, I tend to circumlocute to build emotional subtext. They might only get some percent but it may be familiar enough terms to act as middleware to the rest.

The words Man, father, and grapefruit aren't essential to existence of man, father, grapefruit. All existed before language.

What you mean by "human experience" is "bird song my culture uses to describe shared space". Leave meaning to be debated in meat space and include the current geometry of the language in the model. Just make it mutable.

The machine can just focus on rendering geometry to the pixel limit of the machine using electrical theory; it doesn't need to care internally if it's text with meaning. It's only represented like that on the screen anyway. Compress the information required to just geometric representation and don't anthropomorphize machine state manipulation.

The actual app is the set of processes.