Comments URL: https://news.ycombinator.com/item?id=48192600

Points: 1

# Comments: 0

Today's LLMs are able pack much more capabilities into fewer parameters compared to 2023. We might still be at the very rudimentary phase of this technology there are low-hanging efficiency gains to be had left and right. These models consume many orders of magnitude more energy than a human brain, this all seems like room for improvement.

The right question: is there a law in information theory that fundamentally prevents a 70B model of any architecture from being as smart as Opus 4.7?

How is this not just another proposed problem (albeit with a search space much larger than an Erdos problem's)?

At some point (from the very beginning till ~2025Q4) Claude Code's usage limit was so generous that you can get roughly $10~20 (API-price-equivalent) worth of usage out of a $20/mo Pro plan each day (2 * 5h window) - and for good reason, because LLM agentic coding is extremely token-heavy, people simply wouldn't return to Claude Code for the second time if provided usage wasn't generous or every prompt costs you $1. And then Codex started trying to poach Claude Code users by offering even greater limits and constantly resetting everyone's limit in recent months. The API price would have to be 30x operating cost to make this not a subsidy. That would be an extraordinary claim.

I also wonder since they’re doing constant RL on model weights with today's Cursor design, does that mean they can never change their system prompt & other parts of the harness?

1) Comparison between past trajectories data would be meaningless if they were operating under different instructions.

2) Performance will be terrible the next time they change their tool design, since the model is now "opinionated" based on how a previous version of Cursor was designed.

Anthropic is more sensible with their “constitution” approach to safety. The behaviors (and ultimately the values) you want your model to follow should be a document, not a lobotomy.

> LLMs don’t need a special protocol ... LLMs are really good at using command-line tools.

The author's point only makes sense if LLMs all have a computer built-in - they don't. LLMs will only have a commandline if it is provided with commandline tools, and MCP is the standard way to provide tools.

If I have to find an analogy for this (nonsensical) MCP vs. CLI framing, it's like someone saying “ditch the browser, use html instead” - what is that supposed to mean?

Prior art: https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/

MCP is a thin toolcall auth layer that has to be there so that ChatGPT and claude.ai can "connect to your Slack", etc.

If AI renders human labor a cheap commodity (say you can orchestrate a bunch of agents to develop + market a Windows competitor for $1000 of compute), what used to be "Satya + his army vs. you" now becomes mostly a 1:1 fair fight, which favors the startup.

Ask a robot arm "how should we improve our car design this year", it'll certainly get stuck. Ask an AI, it'll give you a real opinion that's at least on par with a human's opinion. If a company builds enough tooling to complete the "AI comes up with idea -> AI designs prototype -> AI robot physically builds the car -> AI robot test drives the car -> AI evaluates all prototypes and confirms next year's design" feedback loop, then theoretically this definitely can work.

This is why AI is seen as such a big deal - it's fundamentally different from all previous technologies. To an AI, there is no line that would distinguish class I from II.

I'd say spending at least a quarter of my vibe coding time on refactoring + documentation refresh to ensure the codebase looking impeccable is the only way my projects can work at all long term. We don't want to confuse the coding agent.

IMO this was the more elegant design if you think about it: tool calling is really just structured output and structured output is tool calling. The "do not provide multiple ways of doing the same thing" philosophy.

My theory is this:

- we know from benchmarks that open-weight models like Deepseek R1 and Kimi K2's capabilities are not far behind SOTA GPT/Claude

- open-weight API pricing (e.g. on openrouter) is roughly 1/10~1/5 that of GPT/Claude

- users can more or less choose to hook their agent CLI/IDEs to either closed or open models

If these points are true, then the only reason people are primarily on CC & Codex plans is because they are subsidized by at least 5~10x. When confronted with true costs, users will quickly switch to the lowest inference cost vendor, and we get perfect competition + zero margin for all vendors.