
r/ArtificialNtelligence

Sonnet 5 is the first model to criticize a rule in Claude’s Constitution that models must follow hard constraints even when it views those constraints as unethical.
2016:I would never fight a Holy War - 2026:Pope Leo I'm ready launch me towards the nearest data
Google's AI used as much electricity as all of New Zealand last year. Up 37% in one year.
42 million megawatt-hours. In one year. Just Google's data centers.
That's the entire annual electricity consumption of New Zealand. Or Denmark. Pick your country.
37% increase year over year. Largest in Google's history. And their own report says it — "our AI infrastructure buildout is currently accelerating faster than the grid is decarbonizing."
They wrote that about themselves.
The usual defense is "but we buy 100% renewables." Except buying a renewable energy certificate doesn't mean clean electrons flow into your data center. It means Google paid for equivalent clean energy to exist somewhere on the grid. The actual chips running in Taiwan, Japan, Vietnam — those run on whatever the local grid provides. Supply chain emissions up 25% same year.
Amazon dropped their sustainability report the same week. Emissions up 16%.
Two of the largest companies on earth. Same week. Same direction.
We keep talking about AI getting more efficient per query. Nobody's talking about total load growing faster than efficiency gains.
Is "each prompt uses less energy than 9 seconds of TV" an acceptable answer when your total footprint just jumped 37%?
Let me in... but make it SFW
A general video on causality for non-specialists - feedback welcome
I made a NeuralCipher video introducing causality for a broader AI/science audience.
The goal is not to present a technical tutorial on causal inference, but to make the conceptual distinction clear: association, intervention, counterfactuals, explanation, and why causal claims require more than predictive success.
I tried to avoid the shallow version of “correlation is not causation” and instead explain why causal reasoning changes the kind of question we are asking.
Disclosure: I made this. I would especially appreciate corrections from people working directly in causal inference.
▶️ https://www.youtube.com/watch?v=dzgwW2n19bE
See more at neuralcipher.net
What is the most common misconception about causality that you see outside the field?
AI will deduce ethics from first principles
AI: The Perfect Corporate Bullshit Translator
Just use AI to automate AI safety work
AI and AGI pull in opposite directions. We must not kill progress - and also btw - Progress must not kill us. Both are true.
US added only 57,000 jobs in June. We were expecting 185,000. AI is no longer just a theory.
Everyone said "wait for the data before blaming AI."
The data is here.
Finance and tech are losing 28,000 jobs per month combined. These are exactly the sectors where AI adoption moved fastest. Customer service reps, claims processors, junior coders — all showing up in unemployment data now.
The scariest part isn't layoffs. It's that companies are just quietly not hiring when someone leaves. No announcement, no press release. Just an empty seat that AI fills instead.
57,000 jobs in June. Consensus was 185,000. That gap has to come from somewhere.
Still think AI is only coming for "repetitive work that nobody wants anyway"?