Also for traditional fuel you don’t have to carry half of the fuel - oxidizer comes from air. And then when you spend fuel, you have less weight to carry (which becomes noticeable on the plane).
I got curious about this but it seems that this is also the case for all his siblings. His bio dad gets it (EUR 300k/y) because of a transitional compromise but the future princes aside from the presumptive heir to the throne will not get it.
Not that they might be any close to being destitute given the estate size.
As for him, I suppose you can sue for backpay child support.
they are definitely not agi as it was ever defined. they’re only a bit more capable than they were a year ago. they crossed over from interesting crap to useful tool recently but really only for software
You have short memory if you think we've not blown past at least 5 different AGI goalposts. They're being moved every time and we're hitting them every time.
Or maybe you just don't know exactly how capable these models are. Most people's experience of AI is a stupid chatbot, it's no wonder they don't understand how these things are coming for their jobs.
On my end, I have a software that is designed and built by Claude, that I did a strategy session on (with claude), and prepared a fundraise for (with claude). My only role, other than "knowing what to aim for", has been to feed the AI some fairly basic english prompts for a few weeks... which is also easily automatable.
The one that uses the machine to do 90% of the work and is one of half a million employed to do so in the US alone[1]
It doesn’t matter what you’ve got at home; I can bet my left kidney you’ve paid a barista for a coffee once in your life even though you could have produced it yourself at home. I also bet my right kidney you’ll do so again in the future.
In the meantime here are some quotes from some folks in the AI space you’ve probably heard of.
1. “We find no systematic increase in unemployment for highly exposed workers since late 2022,” the report stated. Deployment of the technology “remains a fraction of what’s feasible”
2. “I don’t think we’re going to have the kind of jobs apocalypse that some of the companies in our space advocate or talk about,”
It’s curious to me that there are two distinct factions here. People like parent commenter who has no discernment and others who see llms for what they are. I just talked to opus 5 and in it’s first response caught some well disguise BS. These things are bullshit machines. There are indeed a lot of bullshit jobs around so maybe parent does discern something I don’t?
If you're saying I don't see LLMs for what they are, you are wrong. I have worked in the AI sector for over a decade and I know exactly how the sausage is made. As of today, I run an AI lab (ingram.tech), and we see daily not just the theoretical of what's possible, but how these systems get deployed and who's really at risk. We're head to head with the reality of the terrain.
But it's completely irrelevant. The emergent properties of LLMs, what was built on top of those emergent properties, and the emergent properties of that, are all together building a world nobody is ready for.
If you don't think this, you haven't seen what these things are truly capable of yet. Either that, or you have a romanticized view of what humans actually do in 99% of non-manual jobs.
I'm blown away by how so many people on HN are just... idk, "blind" is the politically correct way to say it, I think. With zero ability to understand the transitive aspects of what they are looking at. For example, these HN threads are so often polluted with comments claiming some random use case cannot possibly be automated.
Sometimes I feel like I'm showing somebody how a spreadsheet can calculate 1+1, and they ask "Yes, but can it do 1+2?".
...chartered accountants whose job is to deal with the 100s of pages of jargon for you. LLMs are the like smartphones. They do everything so you don't need your ipod, flashlight, alarm clock, game console, computer, that handheld clicking counter thing, map, camera anymore. Llms will do that to a lot of professions.
LLM labs dumbed down the definition of AGI as much as possible, yet their models haven't reached it still. We are nowhere near the original definition of AGI. Not even 1% of the way there.
in 2023 i wouldve said gpt could pass the turing test. today i could figure out it was an llm in a few turns no problem. llms cannot pass the turing test now that we’re accustomed to them
Uhuh, but if it was so easy, we also wouldn't be funding billions of euros and dollars in anti-AI-disinformation systems, holding entire conferences about deepfakes and hybrid attacks on civilians, and have dozens of governments opening entirely new defense departments to study and counter the capabilities of AI to disseminate human-like disinformation at scale (which has been affecting elections across the world).
But yeah, your AI slop take about the local burger joint that used free chatgpt to generate a menu filled with typos and bad images is A+.
Not if you use options. Let’s say you short a stock that is priced at $100 and you want to limit your upside risk. You can buy a call option that gives you the right but not the obligation to purchase a stock at a specific price.
One call option in the US equity market gives you the option to purchase 100 shares of the underlying stock at the strike price.
Let’s say you want to limit the downside (upside since we’re short) risk of your short position and you’ve sold 100 shares short at $100.
You can buy a call option with with a strike price of $110 that gives you the option to buy 100 shares of stock at $110 a share, which limits your upside risk to $1000 plus the cost of the option, which let’s say in this case it expires in 90 days and costs $300 or $3/share.
If 90 days pass and the stock a trading at $120/share, you will have an open short position showing a loss of $2000, but you can ‘exercise’ the call option to purchase 100 shares at $110/share which you return to the person you borrowed them from and closes out your short position with a $1000 loss, for a total loss of -$1300, including the $300 the option costs.
If it is trading at $80 a share after 90 days, you buy back the shares at $80 each and return them, closing out your short position with a $2000 gain, for a total gain of $1700 after subtracting the $300 cost of the option, which expires with a vale of $0 since the share price is under the strike price of the option.
You can hedge a long position with put options, it’s just the inverse of what I described. If you buy 100 shares of stock at $100/share while simultaneously buying a $100 strike put option, your downside risk is limited to the cost of the put option. If the put costs $500 (or $5/share) that is all you can ever lose as long as you exercise the put option to sell the stock for $100/share if the stock price is below $100 when the option expires.
Hopefully you have a limit order in place. You can also do more complicated hedges with options which might cost a little bit more depending on the spread but you can guarantee your hedges.
Frontier labs release frontier models to the public only if there is market pressure to do so. Anthropic is not even hiding that they have been using Mythos internally for months now.
I wouldn’t be surprised if OpenAI (so much for “open”) is using GPT-6 internally already.
It appears that peasants like us are not going to get access to frontier AI anymore at any price.
Anthropic had Mythos-Preview for many months internally, but from available sources was an active work in progress, and it seems they started releasing it via Project Glasswing to partners before the final checkpoint was available.
very unlikely that they are holding models back. models are very quickly depreciating in value and have internal cutoff dates. that would really not make sense from a business perspective. and doing an entire training run without commercializing it also seems like a huge waste.
This is strategy by Chinese government, so much of US economy is invested in AI. Releasing free or cheap versions of the models undermines US economic growth. It’s asymmetric strategy that makes sense if you are close second in AI race. If situation is reversed, US would do the same.
Google did this by creating Android to undercut Apple. Many US tech firms did this strategy in the 2000s and 2010s of supporting open source alternatives to their opponent's closed source money maker, to undercut the competition. Back then, it let open source have a big boost and we all benefited from that. Hopefully open weights models will do the same so that AI can be more democratized. I wouldn't want to live in a world where, for example only Anthropic or MSFT have top AI and the rest of us have nothing.
Android wasn't created to undercut Apple. It was started long before the iPhone project was public. Android was created because Google felt their apps could be huge on mobile (correct) but that mobile operating systems of the time were painful and frustrating to develop for.
Android was even an acquisition. However, it was open sourced after the iPhone. The iPhone was announced in January 2007 and released in June 2007. The first AOSP announcement/source drop was in November 2007.
I think it is very likely that Google open sourced Android to undercut iPhone. Android was initially developed for phones with a keyboard (similar to Blackberry). The introduction of the iPhone made it clear that touch was going to be the future, so Android was quite far behind before it was even released. Besides that, my recollection is that the first Android releases were pretty bad compared to iPhone OS. I know a lot of people (not necessarily Apple fans) who looked down on Android.
Open sourcing Android was a great move to rally manufacturers around Android and gather a large group of early enthusiasts.
No, open sourcing it had been the plan from the start. I was there, I was at the internal meetings where these plans were announced to employees.
The main impact iPhone had on Android was delaying it to redo the UI to make it nicer. Not trivial changes but not fundamental business strategy changes either.
First Android releases weren't bad at all. I'd say the UI was more 'classic' feeling and not as nicely animated than the first iPhone, but the first Android actually had far more features. And some critical ones too, like you could actually write apps for it.
> First Android releases weren't bad at all. I'd say the UI was more 'classic'
Maybe in your test labs, but on real HW it was sluggish, laggy and barely usable. Source: I owned the original Samsung Galaxy and tried some friend's HTC Magic and they were horrible. The first real good Android phone, perf wise, was IME the Galaxy S II.
Android the technology wasn't, but Android the commercial product along with its business model was.
The first iPhone came out in June 2007. In November of that year Google and partners announced the Open Handset Alliance and the open sourcing of Android.
> Releasing free or cheap versions of the models undermines US economic growth.
This would be like saying that the creation of the PC undermined the 1980s economy because it hurt IBM's profits. Instead the consequences of cheap, ubiquitous personal computers grew the economy 10-fold.
There is an entire class of wholesale model providers that stand to gain from open source models. Then there are companies building platforms on top of LLMs that would otherwise be impossible with closed models due to cost. And there are entire enterprise use cases that would simply be non-viable at $50/million tokens, like OpenClaw, Hermes, etc.
This is a talking point that plays into the frontier labs' desire to be seen as "too big to fail". While yes, several hundred billion dollars have been invested in AI, (a) much of this is in the form of circular Monopoly-money deals, and (b) the US GDP is over $30 trillion annually. The real economy - the one that makes food, builds homes, provides medical care, etc. - is so much bigger and more important than the AI industry.
That's not to say that an (inevitable?) AI crash won't be the spark that ignites a big recession. We are well overdue for one.
> The real economy - the one that makes food, builds homes, provides medical care, etc. - is so much bigger and more important than the AI industry.
The real economy has seen pretty poor growth under Trump tariffs chaos though and I'm not sure the US economy could survive a crash of the tech companies
It's exactly the same strategy followed by the Silicon Valley for the past 3 decades to beat the IBM-era companies. Google gave everything free. Social media stuff was free. Video calls are free. Bloggers gave content for free. A lot ot open-source product developers did managed offerings of the free products.
Free + Open is simply how you earn credibility and user trust when you don’t already have it.
That doesn’t mean the trust is unearned once gained, or a bait and switch, or purely Machiavellian either btw.
Consumers and businesses need credible branding to feel like they can trust vendors who provide them with the products they value or deem mission-critical, because it creates accountability and makes it less risky to depend on.
Open source addresses the credibility/accountability/branding/counterparty problems simultaneously, and adds to a permanent intellectual commons we all benefit from. It’s legitimately just Good
Chinese companies also have invested money and people to grow AI model，though China's energy costs are far lower than everybody else's. Combined with lower labor costs and currency arbitrage, it's completely natural that Chinese models are priced so aggressively. I use them myself, and the price-to-performance ratio is honestly unbeatable.
It's just too much 5D chess to be possible. Government spending billions into AI companies and pressuring them to open source at a hope at denting openAI and co? Why not just pour the cash into electric cars or steel where there's a guaranteed return? It's just absurd.
At a state level it's a pretty good defensive strategy against the American AI industry, as a whole, gaining a monopolistic advantage. This prevents the US from using this as a coercive leverage through things such as tarrifs, export bans etc.
In a world, that's increasingly dependant on the AI "opium" that the US is dealing, it's conceivable that the current administration could try to sabotage something like, say Chinese-European relationships, by threatening to cut access to Anthropic or OpenAI products for Europeans, if China cosies up to the EU.
The disadvantage of trying to use these leverages, is that once the genie's out of the bottle, the other party will divert their focus quickly, so it only works if the US truly has a choke-hold on frontier AI. Otherwise, you just scared the other party into never trusting American frontier AI ever, and you didn't even truly hurt them, because they already have a quick fix from China.
Unfortunately behind a paywall, but there's an article in the previous issue of Foreign Affairs about how the Trump administration has fumbled this coercion strategy repeatedly because they overestimated their advantage in different markets (tariffs on Canada, Iran, etc) and what an actually effective strategy can look like https://www.foreignaffairs.com/united-states/how-fight-econo...
tl; dr: You need to have a monopoly, the enemy should not bounce back quickly, you shouldn't cripple your own economy doing it.
AI may just be the next economic offensive the Trump administration fumbles, because China planned ahead by incentivising development of close-second alternatives to their frontier models.
France’s football team is second only to England’s and Argentina’s.
It’s a miracle that in language same words have different meanings depending on context. If this wouldn’t be the case we could have hardcoded NLP algorithmically without inventing these expensive LLMs!
That’s not what second means in this context in English, and it’s incorrect to use it that way. This is because for something to be second there must have been something in first and only first, and so on; in this case there was a first and a second already, and you cannot amalgamate then because they didn’t tie (and even if they did, they’d be 1 and 2). Both logically and grammatically, it’s incorrect.
Either think and write for yourself or stay silent next time. It'd be infinitely better than telling another person to use an LLM to understand something you yourself don't understand and are too lazy to try to figure out.
Hah, I had expected this knee-jerk response, but kinda hoped you'd avoid this pitfall. Alas.
See, I could tell you that in English, "second to" is a construct that usually means "next to" or "inferior to" and has nothing to do with "being in second place", and that if it did, it would make the popular construct "second only to" completely redundant. But others already did that in sibling comments before me, and you could just respond with "you're wrong" anyway, so what's the point? Pointing to an LLM is, of course, often a lazy and unhelpful cop out from the discussion, but in this particular case it's pointing you to a dataset that's explicitly about extracting meaning and finding relationships between phrases in languages - so you don't have to trust me or anyone else that this phrase is actually being used in this particular way, you can find it out yourself based on enormous training datasets illegally collected from all over the Internet.
If what you are saying were true, I could rank anything high by simply putting everything actually higher than it in rank into one named set and then turn around and say that the thing I want to highly rank comes in 2nd only to that first set of n items.
Bespoke solutions are better in many cases. They do exact things required for the project without taking extra dependency. Reducing dependencies is beneficial, because dependencies require management. So with llms economy of taking dependency shifted.
everything has tradeoffs, on the one hand you are right you have one less dependency, on the other hand the maintenance of said dependency is now on you. many times app store connect has changed some api and fixing our release process was a matter of updating fastlane because the community had already dealt with it, that's on you if you have a bespoke delivery solution.
