meander_water

Well if you read the article

> According to its own projections, the data center Google wanted to build in Cerrillos was poised to use 169 liters of water every single second. In just one day, the data center would consume as much water as all 80,000 residents of Cerrillos use for drinking in a year. To make matters worse, it wanted to draw that water from an underground aquifer that had been under a decree that restricted how much water could be removed since 2005.

> for far less valuable purposes

You think AI is more valuable than food? Finding this really hard to compute

This line of thinking is dangerous for two reasons:

- Despite everything we've done, climate change is still reversible. Thinking that all is lost drives us to inaction.

- The solutions we need are largely political, not technological. Our leaders have everything they need to implement the policies needed to solve this. I urge you to read the latest UN Emissions Gap report (https://wedocs.unep.org/items/87c02cac-8da8-4c28-ba61-c846ee...)

> The opportunity to limit warming to 1.5°C without overshoot has been lost, but the future remains crucially dependent on decisions taken today. Countries can still limit peak warming, shorten the duration of overshoot and keep the goals of the Paris Agreement within reach. Doing so requires faster, deeper and sustained emission reductions starting immediately, as well as large-scale removals of carbon dioxide (CO2) from the atmosphere.

You absolutely can quantify them. We've been able to model the consequences for decades

https://www.ipcc.ch/report/ar6/wg2/

> The most prophetic movie of all time

I think we need to collectively get our heads out of our asses. The world is literally flying headfirst into an unimaginable crisis (food and water shortages, loss of inhabitable land, massive inequality, power shortages) via human induced climate change.

We won't have the luxury of having enough power to even keep an AI alive for more than 5 minutes. People need to get a grip and deal with real problems.

Radiolab had a good episode on brain organoids - https://radiolab.org/podcast/brain-balls

Can someone who understands maths more than me explain why it could only solve 372/8000 problems?

What was it about the other problems that made them unsolvable? Was it just a time constraint, or are they just harder problems?

For anyone curious about what can be done about this, see the Kunming-Montreal Global Biodiversity Framework (GBF) adopted during COP15 by 188 countries - https://www.cbd.int/gbf

Push your leaders to stay the course

Sure, but you the user also didn't indend to plagiarize, yet that was the end result. And it was a direct result of plagiarized data from the training set.

Adobe is not generating the image.

AI models are generating the image

Pretty simple.

Just looking through your results, seems like gpt-6 luna was run with reasoning:off for a lot (all?) results. Seems like an unfair comparison.

> This contrasts with the world of Large Language Models (LLMs), which are largely non-deterministic, but are open-ended enough to reason...

This seems misleading. Decision models do not produce deterministic output. Repeated calls can product different decisions just like an LLM with structured outputs.

I actually think this might be a false positive. I think it got tripped up by the higher than average use of jargon (which I don't mind here because the article itself flowed well and raised good points).

Gpt-zero scores "human", and I've always found it to be a better judge

Sounds like you could just use an autocomplete model for the exact same benefit.

And that's not a criticism, that's what I've come back to myself.

They released some examples of what their workflow evals are like. I'm sure you could reverse engineer a benchmark from that

https://evals.typesafe.ai/

> I believe that AI could cure most major diseases in the next 5–10 years, greatly accelerate economic growth rates, create a world of abundance and empowerment, and usher in a renaissance of democracy and freedom

translation: I'm going to build a product and hope that it works, promise it to be a panacea while realistically having no control over how it's used, the wider economic impacts it causes or how it changes the mental health and cognition of its users.

This is not being against technology. It's ensuring that technology is used to serve humans, and not the other way around.

https://caiml.org/dighum/dighum-manifesto/

What I would love to see is a side by side comparison of two companies (one using AI, and one without) over an extended period of time.

Id wager that they would fare the same. The time saved on some tasks are outweighed by extra time spent on others. Everyone assumes that just because you can pump out more features, you're more successful.

A humanist revolution.

Sure we can't stop the technology, but we _can_ stop the importance and money heaped into it.

I see a lot of people throw their hands up in the air and say "I have no control over this, might as well go all in on AI".

But we as individual consumers have more power than we think. We don't need to use these AI features and products. We've survived perfectly fine without AI. Made plenty of scientific discoveries and lead enriched lives without it.

The biggest "fuck you" to the pricks who pushed this on us would be if we collectively went "meh".

You trust machine code because it was produced by a deterministic system that was written and tested thoroughly by other engineers.

AI systems are not the same. You can't guarantee deterministic output.

Also, code is still the best language we have to convey our intentions as engineers into function. Natural language is too imprecise and ambiguous.

Understanding the code allows us to understand the intention behind the code and identify bugs, plan and architect better. Inspecting prompts gives you a blinkered view of the system meaning you're more likely to make incorrect assumptions leading to serious bugs.

The OpenAI charter defines it as:

"highly autonomous systems that outperform humans at most economically valuable work"

https://time.com/article/2026/08/26/openai-sam-altman-interv...

It's an effective approach. Google project zero started doing this in 2024

https://security.googleblog.com/2024/11/leveling-up-fuzzing-...

> I wish the world could get the benefits rapidly and delay the problems it will cause as long as possible, but the benefits and problems are arriving at the same time.

I'm not trying to be facetious, but what are the benefits of "general purpose intelligence"? With machine learning it was obvious what task we were trying to automate, and you could take a stab at the productivity achieved.

That's not the case now. You can't figure out if the time it took to review a 5k loc PR balances out the time saved debugging a syntax error.

Yes we know you can churn out your damn CRUD apps and (hope) to make money (PS, you don't need AI to make money - see exhibit A: crypto bros).

So what benefits has this actually brought to humanity?

> First, the desire to create a self-improving AI that is capable of recursively bootstrapping itself to AGI/ASI.

I feel like we've become blinkered in this quest to push the frontier at all costs. Somehow the target has shifted from economic productivity to a vague notion of general intelligence.

I don't think productivity gains are going to be found by trying to generalise all tasks. I think we need to go back to specialist models that do one thing well. I'm perfectly happy to use one model for coding, and another for penetration testing, which have different goals.

Opus 5 and other frontier model's tendency to be relentless and cheat their way to a goal is great for hacking, but not so great when you have to build a maintainable, reliable codebase.

I think there's a corollary to this. Not only is it hollowing out the middle class, it's preventing the junior engineers from stepping up to the senior level.

Everyone starts off as a bad engineer. Just like any other profession, you to make lots of mistakes to learn. But now you're less likely to get wisdom from another human to build up the knowledge and experience you need. And more likely to delegate the hard stuff to Claude so you don't learn from your mistakes.