mlinsey

That market resolves using the arena.ai text leaderboard, where Argon was already #1 over Opus 5.5. Coding is way more economically consequential, and I think the current gap between Anthropic and Google is very wide there (at least, before this Carbon release)

I understand that the Exponential Time Hypothesis being false doesn't necessarily mean P=NP, but for people who are a lot more knowledgeable about complexity theory, does this update your priors significantly in the direction of P=NP?

For coding use cases, it really isn't.

DS4.1 flash is $0.30 in / $1.20 out (per M, peak, cache miss) Opus 5.5 is $4.00 in / $20 out (per M, cache miss)

However, that is API prices.

Anthropic offers a $200/mo subscription. How this translates into usage is admittedly a bit opaque, subject to change, and depends on how exactly you use it. But it's a lot of usage - Semianalysis data shows that $200 is getting you around $2,500 of usage at API rates if you use Opus 5.5. This is close to what I'm seeing anecdotally with my accounts, if anything I have been getting a bit more.

Now, unlike DeepSeek, you can't use your subscriptions to power live AI-driven products, or resell tokens in any way. But for personal coding agents, you can use as many of these subscriptions as you want, for now. So I am paying effectively basically 8% of the published API rates, so at my usage:

DSv4.1: $0.30 in/ $1.20 out Opus 5.5: $0.32 in / $1.60 out

Obviously, those aren't real prices, but they accurately convey apples to apples what my everyday usage costs me and most other heavy users, and why it's so easy for me to stick with Anthropic/OpenAI.

I don't think it's a coincidence, either - I think the token allowances for these subscriptions are set to be competitive with the open models, so that most coding users (and their incredibly valuable data) stay with the frontier labs, while VC-funded wrapper companies and less-price-sensitive giant companies with strict procurement policies pay exorbitant markups for enterprise contracts at the API rate.

I'm paying for the heavily-discounted subscriptions, not the API rates. There isn't really a cost gap for me. DeepSeek doesn't have a subscription to compare to, but when I compared the GLM 5.3 usage I got from a $100/mo Z.ai subscription compared to Opus 5.5 on a $100/mo Claude subscription, there wasn't a big gap. And GLM 5.3 is very clearly not a frontier model (deepseek v4 seemed a lot closer, but I didn't use it enough to really say for my workloads).

I don't think those subscriptions nave negative contribution margins, either. I think we're seeing a lot of price discrimination by the big labs, and huge margins on their frontier models. The fact that they have been cutting prices to their second-biggest tier of models (Opus/Sol).

Open models catching up and collapsing these margins would worry me if I were a shareholder in the big labs, but as a user, I really doubt that the western labs have bigger environmental impact just because they have higher API costs, I think they have a ton of efficiencies they aren't sharing with customers yet because demand is so high.

Not to get back into the AI safety debate from last week, but I really, really want a team of human experts to understand what the AI is suggesting before we administer new medical treatments or synthesize new lifeforms in the lab.

Especially with these big models chewing up limits fast, I feel good about simultaneously having an Anthropic sub, an OpenAI sub, and an Opencode balance. The models also seem to catch things when code reviewing each other that they don't always catch when a new instance of the same model does a review.

Revoked or not, just ever having those controls signals to the Chinese ecosystem that you're not necessarily a reliable supplier (Would you trust US export policy to remain stable for the next ~decade given the state of US politic?) and to the Chinese government just how strategically important you see these components.

This isn't the kind of thing you can hash out in public and go back and forth on. Once you put it out there, the other party will take steps to make sure they don't have to rely on us in the long run.

High-performing open weight models being released recently, and your customers looking into working with multiple providers as a result, are a great reason to drop prices on your non-frontier offerings.

Although I'm sure there are some efficiency gains, the technology is too new and labs are scrambling to release too quickly to think that the low-hanging optimization fruit has been picked already.

"With one big caveat: you need a very clear set of requirements"

Most new product launches are an exercise in figuring out what the product requirements should be through trial and error (really: through ongoing dialogue with your users). Even mature products can have requirements change over time as the market changes.

I think internalizing this reality is why most senior engineers who work in domains that touch the messy real world will reflexively push back against perfectionism.

For ordinary coding (making webapps, frontend and backend), the non-SOTA models do fine, but the SOTA models have better judgement and need me to intervene less often, allowing workflows like "Go and pitch me a solution to this problem, then go build it". 6 months ago, I would give lower-level tasks and read more of the code myself.

So I don't "need" the SOTA models, but they do help considerably. And I am also working on some other projects that may have been outside the reach of the earlier models entirely.

I have some sympathy to geohot's view when it comes to pure informational chatbots. It's a first amendment issue, I'm allowed to write and read books that are useful to getting away with crimes, etc.

This obviously doesn't work at all when the agents start doing real things in the real world, though. "Hey AI, I don't like my neighbor, find an exploit in the firmware for his car and make the cruise control malfunction and crash him next time he gets on the highway". This is committing a crime, not just talking about theoretical crimes. The AI can and should refuse it.

I think he's anticipating and discarding this objection with his introduction, which otherwise feels disconnected from the rest of the article. FWIW, I have changed a bike tire and I'm pretty sure most of the MTS at the big labs could. This sort of "they're just bookworms who don't understand the physical world" rhetoric aside, we are currently seeing a ton of effort and expense go towards giving the AI agents hooks into being able to perform as many real-world-consequential actions as possible. And you can do a surprising amount with just bits, from writing code to breaking into systems to sending some combination of emails, phone calls, and currency to instruct meatspace humans to do things, etc.

Also, I just want to step back and actually give Costco some props here for their brand loyalty. Inshallah, when I have a 400 Billion market cap company and someone writes a fawningblog post about how awesome I am from a social-benefit perspective and how socialist mayors should learn from me…it’ll be the skeptical comments that will be suspected as paid shills for my $2T competitor!

You got me, I'm totally paid off. Real New Yorkers love to go five miles (just for the closest one to me, in Manhattan seven avenues over and around 100 streets north), take a cab back from shopping, then haul costco-sized boxes up the stairs. I only pretended to not love that to get the sweet check from daddy Bezos (good thing he didn't notice my aside criticizing the e-bike trailers breaking traffic laws, though I guess that was subtle if you haven't been following the controversy about them here)