I mean - I'd say electricity, agriculture, steam power, metallurgy, silicon computing (cmos), atomic power, the scientific method - these are _all_ very impressive - all lead to drastic changes for humanity. Not sure how I'd rank them.
I personally think AI will end up sitting in the top 3 of these - but that is an opinion. I do think it is obvious it is at least _somewhere_ in that list.
The EOD reconciliation (and corresponding inability to settle a position in milliseconds) is a feature - it allows "obvious erroneous trade" roll-back mechanisms, etc.
Very few people want the financial system to be a contractual suicide pact - they want it to be predictable, but when the unpredictable happens - they want the retail and institutional investor to be protected (the HFT players can go beat each other up - no one will really cry about them). And unpredictable can be anything from a power event taking out multiple exchanges in the NJ triangle (Sandy hurricane) to a cyber-attack (never happened yet) to a flash-crash driven by algorithms from multiple HFT driving each other nuts (happened at least once).
So, it is not EOD processes as such, but the ability to pause, assess the entire system holistically, and then correct it before it blows up the portfolios of everyone holding a 401k. So even though the exchanges _could_ got to 24/7 trading, I'd be surprised if we just went away from cyclical 24-hr based windows of settlement.
Right, but EOD also introduces credit risk on the clearing house/bilateral.
Also, I would say that probabilistic finality is one of the main issues that tradfi has with crypto (which also exists in the case of margined exchanges, for the reasons you mention). Market participants expect trades to be final, the idea that they can be rolled back is extremely unattractive.
The reason you don't need to stop the market in crypto is because you don't have EOD reconcliation. If everything settles immediately and the risk engine can keep up with the market then there is no credit risk (there have also been multiple solutions to this problem in crypto, none of them involved waiting until the end of the day to see what happens when they try to cross everyone). The reason they have market halts is to limit credit risk from the market moving in one direction and winners being unable to recover gains from the losers. It is fair to say crypto DEX haven't solved this with ADLs but they start from a better place and the higher level of competition means that innovation to invent new solutions is actually happening. The reason exchanges have shit tech is because there is no competition.
I feel like your comment is baiting because you surely know what happened at LME with trades getting cancelled because they would have caused LME insiders to lose money. Hunt Brothers caused massive issues for clearing house, HK government had to bail out clearing house...there are massive issues with the current system.
But it does allow these investors to participate in the markets without losing their shirts - and the lack of such liquidity would impact the market more so than the cost of the risk mitigation - which as you completely correctly noted is not free - both in first and second order terms.
Rumor says Hudson River Trading just ordered a bunch. So, the finance AI guys definitely. And they (AI finance - DeShaw, HRT, Citadel, G Research, XTX) have deployed about 15% of total GPU capacity, so not small fries.
Note that google cloud has an itar-compatible gemini pro and google drive / docs - so, people do talk to it - and google is of course contractually obligated to not export it, nor to learn from it.
This is very different that AWS fed-gov bedrock thingie - where AWS promises that the models are running on hardware dedicated to you, with no external logging, etc.
Maybe a better way to say it would be, no one is talking to AI that isn't on company serves, managed by that company personnel.
My overall point being, no one is submitting design files to ChatGPT for analysis or emailing their friends in China test reports to get a second opinion on the experimental results.
> google cloud has an itar-compatible gemini pro and google drive / docs - so, people do talk to it
A lot of aerospace engineering is touch and feel. Someone has a "sense" for when to do the next step, and how to finagle the part so it comes out a particular way. They can train someone, if they apply themselves intently. But they probably couldn't explain it in words if they tried.
The issue is not better - it’s better _AND_ fast enough. An agentic loop is essentially [think,verify] in a loop - i.e. [t1,v1,t2,v2,t3,v3,…] A model that does [t1,t2,t3,t4] in 40 minutes, if verify takes 10 min, will most likely do MUCH worse that a model that does t1 (decently worse) in 10 mins, v1 in 10 mins, t2 now based on t1 and v1 in 10 mins, v2 in 10 mins, etc..
So, for agentic workflows - ones where the model gets feedback from tools, etc…, fast enough is important.
It's interesting - imo we'll soon have draft models specifically post-trained for denser, more complicated models. Wouldn't be surprised if diffusion models made a comeback for this - they can draft many tokens at once, and learning curves seem to top out at 90+% match for auto-regressive ones so quite interesting..
So, this especially bites if your validation step (let’s say integration tests) take 1hr plus. The harness is just waiting, prefix caching should happily resume things with just a minor new prefill chunk of output from the harness, and bam - completely new prefill.
Still requires thousands of logical qubits, which would correspond to millions of physical qubits. And this machine isn't even fully there for the physical qubit part. It's like the first step to physical qubits.
I personally think AI will end up sitting in the top 3 of these - but that is an opinion. I do think it is obvious it is at least _somewhere_ in that list.
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