>I believe companies can independently build their own TPUs at lesser than $60M within 2 years.

I think we have very different work history wrt. the size/agility/cost of doing things/etc. of the companies that we've worked at :)

50 employees * $250,000/annum * 2 years = $25M. At 3 years, it's $37M. Realistically, for $35M you should have a working, reliable TPU if you're already a semi company, like Samsung

>$250,000/annum

it is bare minimum that you'd have to pay to an engineer, and i'm still not sure that you can find somebody able to do hardware design in AI space on that kind of low money today.

Also - the actual cost of an employee is significantly higher than what you pay to the employee. Plus everything else you need to run R&D company, especially if it involves hardware prototyping, etc.

I mean i can believe that a hot startup where people would take equity and see good chances at good exit can probably reach some reasonable prototype (not actual product) stage on $60M. For any bigger established company - it would be several times more expensive with an order of magnitude higher chances to screw the project (due to incorrect product fit, internal politics, overall internal inefficiency, managers actually hiring a crowd of "AI hardware designers" at $250K :), etc.)

Look at Cisco - the company does understand that they can't do anything new in house, so they regularly spin-off a $100M wad of cash (with usually the same 3 guys attached) and buy it back 1-2 years later for $700-800M. If Cisco attempted to invest those $800M into new tech development in-house the chances for a good outcome would be much lower.

> >$250,000/annum

> it is bare minimum that you'd have to pay to an engineer, and i'm still not sure that you can find somebody able to do hardware design in AI space on that kind of low money today.

Maybe in SV. In Dallas, only top analog and digital designers are commanding that kind of salary. There's plenty of fab and board shop capacity there, too.

Also, I don't see why you specifically need "somebody able to do hardware design in [the] AI space". Experienced digital and mixed-signal designers should be sufficient.

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?

Hardware design with synthesis is much easier than you think and costs a lot less.