Remember, the cost to the employer is roughly double the employee's salary, therefore the $250,000 per annum cost means the employee salary is $125,000 per annum. Not exactly top dollar.

Where did you get an 100% overhead for an employer?

I've done back-of-the-envelope estimates before and have come nowhere near that. At best, it's somewhere between 15-25% depending on employer 401(k)/etc matching, deferred comp, and office space pricing.

The worst I could find is 40% - http://web.mit.edu/e-club/hadzima/how-much-does-an-employee-... - and that uses a much different cost basis (not $125k).

Where in the world did you come up with that number?

Sure, everyone has a different number. For example, in the article you linked to, there is this metric: "the fully functioning managed employee costs about 2.7 times the base salary".

The person I was replying to specifically said "pay to an engineer". That is not the fully-loaded cost.

The person he was replying to was talking about cost to design the chip, which would imply employer costs. The error has to stop somewhere.

I think a lot of next generation chips are not just squeezing more compute into hardware, but examining whether we can change the nature of neural net compute ro better fit what we can do in hardware. That's Graphcore's pitch at least. And plenty of people are looking into whether you can do non-IEEE754 math in a way that works for compute, e.g. FP16, quantized arithmetic, etc.

You probably don't need the entire team to be familiar with the math, but someone has to be.

And as other comments have noted, Software is actually going to be a very big deal.

No one gets paid 250K because they know what fp16 minifloats are

At some point it wasn't widely known that you could do NN training with fp16. Now it's common knowledge, but what is the next thing?