For all its flaws and limitations in practice, you have far greater data privacy in the UK than the US. Largely because it seems you basically have none as a private citizen in the US.
> a) trivial to bypass by adding dither to the test transactions and
I know someone who worked in fraud detection of financial transactions. He told me that indeed lots of filters that are applied mostly test for anomalies. The thing is that most criminals are not insanely smart, and commonly don't have a lot of inside knowledge about accounting, banking, finance system etc., so criminals often have a bad intuition about more subtle things that are looked at for fraud detection.
But if you are a very dedicated criminal with lots of inside knowledge about, say, accounting, banking, finance system, ..., you could likely outsmart these filters. But these people typically have much better career options (even if they want a career as a "big fish criminal": just look at the history of accounting scandals, stock manipulations, Ponzi schemes, ...).
Regardless of one’s opinion about AI, from a product perspective this seems somewhat similar to the dev using his 48gb ram machine and latest iphone to test an app that will be used by consumers with entry-level devices
AI bros love hyping about their insanely inefficient token usage. It's become some sort of a dick-measuring contest. And if you work for OpenAI, of course you can claim insane measurements.
Just last week I saw a dude boasting about how they used their $20/month ChatGPT subscription to earn $15 (or similar trivial amount) in a bug bounty by running the model the whole day. Sam Altman replied to that tweet but not entirely positively.
OpenAI has been removing limits on token usage to take on Anthropic but I'm sure most of the users they are acquiring are these AI bros who are burning tokens for the sake of it. Massive price hikes are coming after OpenAI and Anthropic IPOs probably an order of magnitude larger than what happened to ride sharing.
Except we have flywheels to solve the power problem (single digit minutes of storage, extremely high power).
The reason why batteries are being talked at all is because we are missing the energy storage problem (hours, or even days. Some people want seasonal storage but that's impossible with our current levels of tech).
The so called lawnmower engine that stores energy slowly in the summer but delivers it slowly in Winter would be more useful than you might think. Especially because we have solutions for every other tier (capacitors/supercaps for seconds of storage, flywheels for small minutes of storage, batteries for large minutes of storage, pumped hydro / compressed air energy storage for hours of storage).
I guess there are two types of "sovereignty" people talk about here.
First is "data sovereignty", which is what the current (data) migrations are all about. As long as the data remains in place where it cannot be suddenly locked away by the US government, people don't care if the CPU was purchased from the US, as the government cannot suddenly disable those (as far as we know at least).
Second is "hardware sovereignty", which is what this article talks about, about the geographical locations where the hardware is designed and built. This is obviously much harder, but also less important at this very moment. That's why you're not seeing people suddenly rushing to fund EU fabs for silicon, there are more important things to focus on right now, with real implications.
The article kind of does everyone a disservice by mixing the two and not clearly separating which ones it's actually talking about. But to be fair, if they did that, then they've wouldn't have been able to publish this whole "Look how they aren't actually sovereign after all" article if they did so, here we are...
This is quite a misleading title because this is the raw API cost, but he (obviously) has unlimited usage as an OpenAI employee. Moreover, if you use e.g. the $200 Codex sub, you get about ~$5k-$6k monthly API usage if you spend every week of your usage, if not more, which shows that the raw API cost is not how much it (likely) costs to OpenAI, unless they're subsidizing all this.
He did clarify that it was with fast mode. Without fast mode it'd "only" be $300k in raw API cost, or ~60 $200 Codex subscriptions.
tl;dr Peter Steinberger shared a product demo for CodexBar [0] with a graph of OpenAI token usage. This graph shows one million spent, prefers gpt-5.5 and spent twenty thousand today.
However, I do not see a strong reason to believe that this is his actual, personal usage. It could be all openclaw usage or some subset of openai usage, given that he is inside them. I suspect it is far more likely to be fake data [1] that exercises the graph library in a visually satisfying way. Notice that it has no usage for a 'week' after April 15 (a Wednesday), but picks up a bunch later. As marketing copy it needn't have any basis in reality [2]. I should hope openai would put a procedure in front of their entrepreneur acquisition that prevents accidentally exposing trade secrets [3].
I think what has really happened is a re-weighting of the importance of a lot of software practices. I think basically all of scrum/agile is completely useless now, but tests, PR reviews, documentation, decision records, etc, are more important than ever.
Huawei fixed it in their Cangjie language. According to the docs [1][2], it throws an exception by default and you can use an annotation to get wrapping or saturation instead.
(Cangjie seems like a pretty nice language in other ways as well. Similar to Kotlin with some improvements and no Java. Bootstrapping the toolchain from source seems difficult though.)
That's what I'm saying. It's a misdiagnosis. Whether or not they have tantrums should not be a factor in whether or not they are high-functioning or low-functioning.
