If they optimize though - and this is coming at some point - local AI becomes possible, and their entire business case as a cloud monopoly evaporates. I think they know they're in a race between centralized control, and widespread use and control, and that is what is really driving this.
While I agree there’s a lack of attention for the impact of software engineers on near-term industry growth — rather the opposite with layoffs and agentic automation (attempts) et cetera; the mentioned Scott Gray is working at OpenAI now, so the human capital angle is I guess just flying under the mainstream radar.
There are a few "optimization startups". But in this context I find it a bit ironic that pretty much everyone is working with the same architecture, and the same hardware for the most part, so actually there isn't really that much demand for bespoke optimizations.
And when you have enough spending to account for 1%+ of revenue for the AI hardware companies?
You can get the engineers from those very hardware companies to do bespoke optimizations for your specific high load use cases. That's something a startup would struggle to match.
OP here, I didn't write the post, but found it interesting and posted it here.
> So i understand correctly, they spend more even thought They can, optimize and spend less
This is what I understand as well, we could utilise the hw better today and make things more efficient but instead we are focusing on making more. TBH I think both need to happen, money should be spent to make better more performant hw and at the same time squeeze any performance we can from what we already have.
I believe the author is making the point that the companies spending all this money on hardware aren't concerned at all with how the hardware is actually used.
Optimization isn't even being considered because its the total cost spent on hardware that is the goal, not output from the hardware.
> When I look around, I see hundreds of billions of dollars being spent on hardware – GPUs, data centers, and power stations. What I don’t see are people waving large checks at ML infrastructure engineers like me and my team.
That doesn't seem to be the case to me. I guess the author wants to do everything on his own terms and maybe companies aren't interested in that.
If they optimize though - and this is coming at some point - local AI becomes possible, and their entire business case as a cloud monopoly evaporates. I think they know they're in a race between centralized control, and widespread use and control, and that is what is really driving this.
While I agree there’s a lack of attention for the impact of software engineers on near-term industry growth — rather the opposite with layoffs and agentic automation (attempts) et cetera; the mentioned Scott Gray is working at OpenAI now, so the human capital angle is I guess just flying under the mainstream radar.
OTOH garage-startup acquisitions are acquihires.
This is just not correct. Also nobody is making optimization startups because if you cared you’d have an in house team working on it.
There are a few "optimization startups". But in this context I find it a bit ironic that pretty much everyone is working with the same architecture, and the same hardware for the most part, so actually there isn't really that much demand for bespoke optimizations.
And when you have enough spending to account for 1%+ of revenue for the AI hardware companies?
You can get the engineers from those very hardware companies to do bespoke optimizations for your specific high load use cases. That's something a startup would struggle to match.
It's good that you didn't give up ! So i understand correctly,they spend more even thought They can, optimize and spend less ?
OP here, I didn't write the post, but found it interesting and posted it here.
> So i understand correctly, they spend more even thought They can, optimize and spend less
This is what I understand as well, we could utilise the hw better today and make things more efficient but instead we are focusing on making more. TBH I think both need to happen, money should be spent to make better more performant hw and at the same time squeeze any performance we can from what we already have.
I believe the author is making the point that the companies spending all this money on hardware aren't concerned at all with how the hardware is actually used.
Optimization isn't even being considered because its the total cost spent on hardware that is the goal, not output from the hardware.
> When I look around, I see hundreds of billions of dollars being spent on hardware – GPUs, data centers, and power stations. What I don’t see are people waving large checks at ML infrastructure engineers like me and my team.
That doesn't seem to be the case to me. I guess the author wants to do everything on his own terms and maybe companies aren't interested in that.