adrianvi

Not only that, but scalable, pay as you go, easy to get solutions.

You could also pay for a ML product, but not so easily and cheap. This enables PoCs and small products.

There's eebench, but maybe it's not exactly what you were thinking

> That is a leap in logic

It is not, it doesn't matter if a brain's neurons or a prediction algorithm managed to get the solution if it has done so in a (sort of) reliable way.

https://en.wikipedia.org/wiki/Duck_test

Errors are made by both machines and humans, so that's not a differenting factor.

What is clear is that the human element will be more valued on art as time goes on.

Yes, the optimal solution won't be achievable, it will burn lots of compute and we need better simulations so that less errors are made (and will be made).

Still, if LLMs are capable of writing working code, art and solving math problems, they qre definitely capable of doing suboptimal routing (with some algorithmic aid)

I do think that slightly smarter LLMs and a good (although not perfect) autorouting algorithm could solve 99% of hobbyist's projects and simple industry boards, due to the repetition of those patterns in real life.

The LLM would learn the heiristics (example: data lines first, power lines later, etc.) and would request the autorouter to do that routing, then take the image output and request a different part (depth-first). If later no routing is posible with that configuration, after some retries it could try another way.

It wouldn't solve complex boards, and engineers are always needed for short comings (and even if not, for research), but I wouldn't say this is something different than poetry, music or pixel art, LLMs can imitate although most of the times in a uncanny way.

I can't speak for all cases, but the AI space is seeing improvements month by month, so it is beneficial to wait until it settles (a model becomes the standard in intelligence/price) before designing and mass producing an "LLM ASIC" of said model.

The big AI labs won't do that unless they are forced to, as they want you to spend more money on the big, expensive, frontier models (so they can live up to their valuation), so it's more likely that you will see this on smaller open weights models.

Important to note that both OpenAI and Anthropic do not allow the subsidized monthly subscriptions for enterprises.

Companies have to pay monthly for the harness app (codex, claude code) and the tokens are priced separately based on standard API pricing.

I'm not sure if your estimate is right. These summer months you will get 7000 AI credits (4000 from september, iirc). Each AI credit is 1 cent. Meaning, 100 AI credits = 1 $.

Then they have a price table that is the official public API prices for the models, but converted to their AI credits.

Currently, I'm averaging 15 credits (0.15 $) per request for some QnA questions with claude sonnet 4.6. It should be similar with gpt 5.4 (same output token price, similar input token price).

I think with the current pricing you can do light work with normal models (10-20 code completions/day), but vibe coding, heavy usage or using bigger models is not viable anymore.

It's hard to measure since I did not average my token expenditure per day.

So, the day arrived when some users (annual billing Pro and Pro+) would see their usage bar fill faster. I checked today and they modified it so you can't see the original page.

Unfortunately, nobody saved it on the wayback machine (I just did with today's page), but you can search "github copilot pricing multiplier" and still see on google images the cost increase x6 or x9 on some models.

I am on an Enterprise plan so it's different for me: I pay the subscription and I get credits, but the token pricing is essentially the same as the public API pricing.

I understand they needed to change it, but I don't like how they try to erase the old prices from the record.

step 1: create the problem, step 2: sell the solution, step 3: profit