cl42

Thanks, that makes sense. I wasn't sure why The Economist would make this distinction, though I can see why certain companies (e.g., 'poorer' non-hyperscalers) might opt for cheap/loud options.

Given your experience with data centers, what do you think of this quote from the article?

> In any case, even the noise worry is overstated: many viral videos of supposed AI data centre noise are really Bitcoin mines, not state-of-the-art hyperscaler facilities.

Are there really major differences between a hyperscaler, neocloud, and crypto mining data center in terms of noise pollution or other negative effects?

I'm a founder who went through YC (S12), raised VC funding, etc. Now working on other businesses. I also know a few other founders who raised tons of VC funding on their first go, some who have succeeded and some who shut down.

Anecdotally, I feel like the second-time founders see VC as a very specific tool and many prefer not to raise... They'd rather bootstrap and scale more slowly, retaining control, freedom, and flexibility. Granted, a lot of founders also have bad experiences with VCs.

I think your framing VC as a tool is the right one. Not sure if first-time founders are still like this, but ~15 years ago, it seemed like raising VC funding was validation in and of itself and you were perceived as successful due to the raise. The second-time folks know this is neither validation of your product nor is it any sort of guarantee of success... If you need to raise lots of money to capture a speculative market or build something that won't see revenue for a while, VC is great. If you want to build a decent business that grows 20% to 30% per year and compounds over decades, then maybe it's not for you.

Finally, VC is a broad category. Raising money from former founders or from strategic VCs who can legitimately mentor you is very different than getting a check from a fund that makes 100s of investments per year and simply manages you as an arms length portfolio entry.

I recently read a few articles about how the harness is a bigger factor to successful LLM usage and wish they discussed this here.

I use GLM-5.3, Qwen3.8, Claude (all of 'em), GPT Sol/Luna/Terra across direct API calls + local models where I can (128GB Macbook Pro)... The harness and whether the model or underlying system prompts know how to make the best use of iterative LLM calls makes such a big difference...

For example: one-off articles on a news topic (e.g., "Update me on the US-Canada relations") yields very similar results across all models... But "run a web search, write a draft perspective from three points of view, and structure data around it" will make everything but Claude + GPT struggle.