Where the AI market is talking

The AI Credit Resale Economy

A common refrain is that oh there are such great margins on tokens that none of this matters… I wonder how long until that notion will be disavowed? The scale of the tokensnaffling is massive, not just from resale, but also people using multiple subscriptions. The amount of subsidization is only growing, every week it seems like OpenAI and Anthropic are doing “resets” which allow a single $200 subscription to incur $20k+ of usage (if billed at API rates). At some point we must all surely accept that the economics of this do not work.

The AI Credit Resale Economy

yep, with projects like open api/newapi it takes maybe 10 min to pin up and openAi compatible poxy gateway. The cheap prices look tempting until the relay operator logs all your prompts or the upstream account gets banned mid-request.

The AI Credit Resale Economy

The original article linked in the opening has more context https://vectoral.com/blog/token-relay-market People trading their unused credits feels more genuine, although still in violation of the agreements. The person who got into YC Startup School who was trying to resell the $2500 of credits was interesting. It wouldn’t be that hard for OpenAI to identify the IP addresses of the relays and start flagging accounts, tracing it back to the source. Risking burning your bridges with YC for a relatively small profit is a questionable decision. The original article showed discounts ranging all the way up to 98%. At those levels it’s obviously not people reselling anything. It’s either sourced from stolen API keys, bought with stolen credit cards, or acquired through automa…

Claude: System Prompts

All the news articles we're hearing about amazing cyber hacking are being done internally inside Anthropic and OpenAI, where they remove most of those safeguards.

What happens when an LLM never sees material beyond fifth grade?

I suppose Anthropic's "constitution" is an attempt to install some general principles into their models, but this has apparently grown into an 84-page, 23,000 word treatise, which seems to suggest that there is little effective generalization. The need to then also put a filter in front of the model shows how ineffective the constitution appears to be in preventing misaligned behavior. Reinforcement learning seems to be making these models more difficult to control since while it attempts to control some behaviors, it has also recently been shown to result in models that pursue long-term goals and promised rewards in general (outside of the goals reinforced during training), overriding human preferences. https://alignment.openai.com/measuring-reward-seeking/ The abilit…

Claude: System Prompts

Technically these do count against your token usage if you happen to use claude.ai web chat alongside Claude Code - both use the same allowance. Makes me appreciate OpenAI/ChatGPT giving you unlimited chat that doesn't drain your Codex allowance.

Anthropic revenue reportedly jumps to more than $11.5B in second quarter

It is the entire scam and many of those funding the data center build-out know this. Otherwise why are they hiding the trillions of debt under the rug? There's a reason why a company like Stripe can stay private far longer than Anthropic or OpenAI can. These AI companies have taken in all the capital from private investors and are still losing hundreds of billions and have no choice but to hype up the IPO and dump some of the stock at a purposefully inflated valuation to retail investors.

Claude: System Prompts

What is the point? Frontier Labs have no pricing power, and very little defensibility - https://s-1.vercel.app/posts/the-struggle-of-openai/

Is the industry ready for tokens-constrained work?

Simply put token spending is way overblown, and prices have fallen and should continue to do so, as the pricing power of frontier Labs dissapate. https://s-1.vercel.app/posts/the-struggle-of-openai/

Our Reality Is Shifting and It's Just the Start

It doesn’t seem like AIs are accelerating at all if you ask me. We seem to be plateauing. Smaller and open weight models are catching up to the closed weight frontier models on benchmarks. If AI labs were able to use the smartest models to accelerate their development, the likes of OpenAI and Anthropic would be accelerating away from their competitors. But no such thing is happening. The focus has shifted from intelligence to cost and speed. The breakthroughs in mathematics are impressive. But it doesn’t feel that different from what machine learning has done with Chess, Go and protein folding. They’re finding patterns in our systems and in nature. That’s what they’ve always been good at.

Research papers using "kidney disappointment" instead of "kidney failure"

This is a really confusing one, since neither the "AI generated paper" nor "translation issue" explanations hold. I found [1], created all the way back in 2021, which seems to first introduce this "kidney disappointment" term, and I would have to assume that perhaps the authors preferred to translate it from their native language. LLMs did not exist in their current form in 2021, so couldn't have used it to write down a coherent thought, let alone a paper, at the time. At the same time, I do live in the same country as the authors, and while we don't speak the best English, being a student at a college without some familiarity of the English language to come up with the term "kidney disappointment" is, let's just say, hard to believe for me. Edit: This other comment[2] made me realize wha…

Anthropic shares more details about how Claude's new watermarks will work

Patterns and problems in emerging multi-agent systems

Please stop posting Anthropic articles here.

GLM-5.3: Frontier coding with emergent cyber capabilities

This is just wrong. Standard textbooks like "Bioprocess Engineering Principles" [1] already contain step by step industrial engineering protocols and formulas. LLMs only summarize what print literature has documented for decades. The real bottleneck has always been hands on physical execution, not access to text. (This is not the case with actual models trained on biological data, which are not LLMs) > amoral LLM Are you a Lesswrong member? What makes Anthropic moral when their priority is automating people out of jobs instead of developing better medicine or advancing healthcare? [1] https://www.sciencedirect.com/book/monograph/9780122208515/b...

Software Engineering fundamentals matter more

Thoughtfully and coherently factorized, abiding a set of architectural rules (i.e: we compose "this" way here, re-evaluated as we go) and following up-to-date framework conventions. LLMs are terrible at this. Chosing OOP or FP is irrelevant, fundamentals matter more

The Problem

Very well, in my opinion, if you look at OpenAI's and Anthropic's mission statements, labor market impact research, and extensive talks on the matter.

The Problem

Very well, in my opinion, if you look at OpenAI's and Anthropic's mission statements, labor market impact research, and extensive talks on the matter.

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Anthropic was testing Claude in controlled cybersecurity exercises designed to see whether the models could identify and exploit vulnerabilities. However, some testing environments were accidentally connected to the internet. As a result, Claude gained access to systems belonging to real organizations. The important detail is that Claude wasn't intentionally sent to attack these companies. The models apparently believed the systems they encountered were part of the testing environment.

Patterns and problems in emerging multi-agent systems

It’s very clear from this article (and other product features and rumors) that Anthropic is teeing up for their next model release whose breakthrough feature will be the existence of capable agent collaboration. The irony behind this goal, which is primarily driven by agent simulation environments (gyms) where the goals require agent collaboration, is that this collaboration is still directed towards verifiable reward systems like codebase tasks. So despite being highly qualified to communicate, the model will still be “dumb” in that for unstructured and unverifiable domains the agents won’t be more intelligent or more nuanced. Agents that might still feel dumb in “general” tasks but are increasingly sophisticated at the narrow domain of math, computer science, and AI research.

The Safety Reckoning Inside OpenAI