Worth calling out AI sentiment among young people is not nearly so rosy: <a href="https://news.gallup.com/poll/708224/gen-adoption-steady-skepticism-climbs.aspx" rel="nofollow">https://news.gallup.com/poll/708224/gen-adoption-steady-skep...</a>
> The report estimates that training the latest frontier large language models, such as xAI’s Grok 4, can generate over 72,000 tons of carbon-equivalent emissions.<p>That seems pretty trivial, relative to 38bn per year globally?
Also nobody will ever have a moat, so the graph of investor stupidity is going through the roof.
Of course they will. Tokens are valuable, you can always spend a finite budget on specialized tokens or fewer and higher quality tokens, size of user base and engagement gives you a flywheel moat that is difficult for newcomers to compete with. The market is complex and easy to oversimplify.
My new startup tokencoin will blah blah blah exchange rate, (something AI writes here), 3. profit (more AI), benefiting all human kind and helping our users scale up their productive intelligence!
It's hard and complex to enter any mature market. The vast majority of firms that attempt to enter a new market fail. LLM's have no more than this normal moat.
Isn’t capital and momentum a moat? Sure Chinese models use distillation but I don’t see them training models from scratch. At least not today. But maybe as chips get cheaper and they have Chinese made ones?
> Isn’t capital and momentum a moat?<p>Apparently not much of one. There are, what, 5 or more companies with frontier models? And open weights models like MiniMax are snapping at their heels
There are many markets where open source has been nipping at heels for a long time.<p>Obviously product areas differ for reasons structural and happenstance. But there is definitely a pattern that occurs, where open source fast follows commercial advances, benefiting from having a clear target to develop for.<p>Which is of course, a great service. Even if it never unseats the commercial version, it forces the owners to reinvest more in improvements, by undermining their moats. As well as providing a much better value alternative version for many people.
I’m not technically familiar but I remember someone saying that models like MiniMax basically skip the cost of training by using distillation to basically “steal” the models from OpenAI or Anthropic, and that these companies now have various defenses against this. What happens when MiniMax has to do the full work themselves?
>Chinese models use distillation but I don’t see them training models from scratch<p>Maybe because they don't have to. If someone is doing the heavy work and they can take output of that, it's a win for them.
> Training AI models can generate enormous carbon emissions<p>Sure, but what I'd really like to see is a graph for how much carbon is generated serving these models globally.
Stating "Software engineers are all-in on AI" because of an increase in github projects being created is hilarious. I didn't realise creating a github repo made someone a software engineer. If only I had known this I wouldn't have bothered learning all the other stuff!
Besides the lead in robotics for China, those Grok emissions charts are the thing that most leap off the page.
I still don't understand the State of AI in 2026.
[dupe]<p><a href="https://news.ycombinator.com/item?id=47758028">https://news.ycombinator.com/item?id=47758028</a><p>Source: <a href="https://hai.stanford.edu/ai-index/2026-ai-index-report" rel="nofollow">https://hai.stanford.edu/ai-index/2026-ai-index-report</a>
China’s robotics lead holy cow.
That's the lead in industrial robot installed. That lead is understandable because of manufacturing concentration in China. Here are 10 top robot makers, none of them are Chinese (*), and five are Japanese:<p><a href="https://manufacturingdigital.com/top10/top-10-industrial-robot-manufacturers" rel="nofollow">https://manufacturingdigital.com/top10/top-10-industrial-rob...</a><p>(*) Kuka was a top German maker who got acquired by Chinese company Midea recently
They also lead the world in EV production on paper, but in practice a large portion of those numbers might be driven by government pressure, not actual demand [1].<p>I’d personally take this data with a big grain of Goodhart’s law.<p>[1]: <a href="https://www.bloomberg.com/features/2023-china-ev-graveyards/" rel="nofollow">https://www.bloomberg.com/features/2023-china-ev-graveyards/</a>
The graph says "new industrial robots installed", which is a bit misleading. For example the newest BYD factories are still stuffed with German/Japanese robots.
China’s manufacturing lead in a graph
It striking, but says nothing about AI.
Don't they have ten times more people than the next highest country (Japan) though?
What's worse is that this the predictable result of a choice that America made decades ago and continues to make.<p>Outsourcing manufacturing capacity to China and letting domestic manufacturing skills atrophy and institutional knowledge die out was a choice that many people opposed but were ultimately helpless to stop because the people making the decisions ignored them and did it anyways for personal gain is how we got here.<p>You'd think that the supply chain shocks that we saw during COVID would be a wake up call that would have jolted people into action.<p>You'd think that Ukraine-Russia war would have been a wake up call that would have jolted people into action.<p>You'd think that the recent failures by the US military in Iran and the depletion of years of missile stockpiles would have been a wake up call that would have jolted people into action.<p>I'm at a loss to explain it. It's like the American oligarchs <i>want</i> to weaken America, or at least are willing to do so if it means that they have greater control over it. Maybe they don't care about manufacturing capacity because they know that America is ultimately a nuclear protected island and that even if things continue to decline they'll be safe to rule it like a king?
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The "China leads in robotics" seems to be unaffected by AI. The China line is basically on the same trajectory since 2012. The chart does no belong in the article.
Profits generated by AI: <not graphed><p>The absence speaks volumes.
> The report estimates that carbon emissions from models with the least efficient inference are over 10 times as high as those with the most efficient inference. DeepSeek’s V3 models were estimated to consume around 23 watts when responding to a “medium-length” prompt, while Claude 4 Opus was estimated to consume about 5 watts.<p>This makes absolutely no sense. I suppose they meant watt hours, and that's a weird way to explain carbon emissions...