My agent is currently doing the work of ~6 senior engineers, based on ticket closures. These are not trivial tasks: all of them involve a mix of code, judgement, executing remote commands in a production environment, etc.
This is at a well-known tech company operating at massive scale (and resulting complexity).
L1/L2 tech support is completely dead within a couple years. The delay is only around how long it takes people to realize.
> 1. that closed source models are more efficient than open source
Not a reasonable assumption for a variety of reasons.
> 2. Deepseek is served at a profit and not a loss
Not a reasonable assumption either.
> Why do you need to know the architecture? Just compare Deepseek V4's performance with GPT 4 and treat internals as a blackbox.
Because the internals are what actually matter and what drives inference cost.
It would be entirely reasonable to expect that GPT-5.5 has some sort of optimizations or changes to the architecture to make it easier to train, or to make runtime ablation easier, or to better handle large batches, or whatever.
Those changes, particularly if they are non-public, can easily result in worse inference performance than a comparably sized model without those changes.
> It is borderline conspiratorial to believe it this way.
It's not any sort of conspiracy. It's how land-grab tech companies have always worked. To presume otherwise is silly.
There's no reason to think that the latest frontier models have similar inference costs to open source models.
It would be more surprising if the surrounding architecture hasn't significantly diverged. If it _hasn't_ significantly diverged, then given the performance difference it would imply that the frontier models have significantly greater param counts, which would result in a higher cost.
The price a company charges, _particularly_ a high growth VC-backed one, is a poor signal for their costs.
That blog post is not very compelling either. Without knowing details of the architecture, comparing the various frontier models to open models doesn’t make sense.
> It is in the same way that educated guessing is.
I guess (heh) it depends on your definition of 'educated guessing'? Looking at the problem, considering a solution, discarding it, trying another and testing, iteratively, is how most people would approach any tricky problem.
Brute force is substantially different. It would be saying that, other than maybe setting some basic bounds and heuristics, I'm going to try literally everything and test each. That's not at all what the LLM did here.
Open models, in actual practice, don't match up to even one or two generation prior models from Anthropic/OpenAI/Google. They've clearly been trained on the benchmarks. Entirely possible it was by mistake, but it's definitely happening.
Everytime I've tried a local model, and I have tried lots for a couple years now, they just seem like they were overtrained on benchmarks. They consistently perform dramatically worse than even older models from Anthropic/OAI/Google.
> Europe is behind because we do not have good leadership. The decisions taken by leadership, no matter what level you look at - local, company, national, supranational - are rarely in the best interest of Europeans. Our markets - housing, rental, labor, capital, pension - are broken and therefore the population does not find opportunities to express their talent completely and the more motivated migrate. Europeans lack well-paying jobs and pay is low because pay is not transparent.
Sounds like Europe is behind because Europeans are working less and taking more vacations. You just point to poor leadership as the cause.
> Saying "Accidents happen in war" is absolutely a way of saying "Accidents are acceptable in war".
Bridges fall down sometimes. I don't think it's acceptable. It's a statement of fact. There are always going to be mistakes, in every field and in pursuit of every goal. Your objection and implications aren't particularly charitable here.
> My "brilliant" plan would have been the negotiations that were happening where Iran agreed to pretty strict monitoring and stipulations on nuclear fuel development.
Iran was not complying with the monitoring requirements.
> The "Iran was getting nukes" rhetoric needs real evidence that was actually happening not "we think that might be happening because Trump said so."
Intelligence agencies under both Biden and Trump (and since at least the 90s) have repeatedly confirmed it.
This isn't really a question or doubt any reasonable person can have. There can be an argument about how close they are at any given moment, but they are actively pursuing nuclear weapons.
This is at a well-known tech company operating at massive scale (and resulting complexity).
L1/L2 tech support is completely dead within a couple years. The delay is only around how long it takes people to realize.