Yep. I've seen this personally. The ROI of a great engineer is bigger than ever.

This reads like a bad linkedin post...

Also consider using Cerebras' inference APIs. They released a voice demo a while back and the latency of their model inference is insane.

I tried to use Cerebras and it was unbeatable at first, but the client didn't want to pay $1300 a month and the $50/month or pay as you go was just not reliable. It would give service unavailable errors or falsely claim we were over our rate limit.

Also Groq is very fast, but the latency wasn't always consistent and I saw some very strange responses on a few calls that I had to attribute to quantization.

Cerebras is a totally different product though. They can (theoretically) run any frontier model provided it gets compiled a certain way. Like a wafer scale TPU.

This is using hardwired weights with on-die SRAM used for K/V for example. It's WAY more power efficient and faster. The tradeoff being it's hardwired.

Still, most frontier models are "good enough" where an obscenely fast version would be a major seller.

I've been using this for a few months now and it's amazing. Crazy fast and the vast majority of the time I get the result I want in one shot.

They have prebuilt binaries now for fff

The thing is, the barrier isn't near zero. The time to reach an MVP has just decreased. But you still very much need expertise, strategy, etc. to deliver something worthwhile. The bar has just increased.

You'll see on HN itself how many people want to work on this surveillance. How many people want all white collar work eliminated by AI. How many people want a quick buck at anyone's expense, the morality be damned.

Money and only money talks nowadays. It's sad.

This is easily the most spot-on comment I've read on HN in a long time.

The humility of understanding what you don't know and the limitations of that is out the window for many people now. I see time and time again the idea that "expertise is dead". Yet it's crystal clear it's not. But those people cannot understand why.

It all boils down to a simple reality: you can't understand why something is fundamentally bad if you don't understand it at all.

> Similar to the media, I've picked up on vibes from academia that have a baseline AI negative tilt.

The media is extremely pro-AI (and a quick look at their ownership structure gives you a hint as to why). You seem to be projecting your own biases here, no?

And how would those LLMs learn? How would you learn to ask the right questions that further scientific research?