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Comment by pengaru | Hacker News Reader

> NVidia chips can do this job.

Presumably Tesla, at the time they decided to pursue this option, thought they'd potentially have a competitive advantage with in-house designs.

It's entirely possible that's just their hubris showing, time will tell if this was the right decision or not. After seeing the NVidia presentation announcing their latest datacenter-scale AI hardware, I'd be surprised if Tesla's in-house design is more than just a massive cost center vs. buying something from NVidia.

But sometimes you do things that appear irrational in part to keep your talented engineers from seeking work elsewhere. Just look at NASA's SLS, how much of that is a job program in part to prevent hoards of talented folks building rockets for competing nations?

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There's nothing irrational about it. It's a big, bold, costly bet. A risky bet but not irrational.

People doing this are not some bored Tesla engineers.

Both the FSD chip and Dojo are staffed by chip design veterans Tesla poached from AMD, Apple and others.

Just read those resumes:

https://www.linkedin.com/in/peterbannon/ : this is the guy leading FSD chip

https://www.linkedin.com/in/ganesh-venkataramanan-99272a3/ : this is the guy leading Dojo

People like this can only be poached with a combination of great project and great salary.

It's a team that was built from grounds up because Tesla is (also) an AI company and Musk is thinking 10 years ahead.

> People doing this are not some bored Tesla engineers.

The whole reason they're not bored is because they're doing this.

You do realize we're basically in agreement, right? The more impressive the resumes the more important it is that you give them Real Work to do.

But NVidia's state of the art in this space seems substantially better than Dojo. How much that matters in totality remains to be seen.

If Tesla wasn't designing chips, they wouldn't have any chip designers to get bored.

But once Tesla is designing chips for their in-vehicle inference needs, they need to keep those people interested and the large-scale training side is arguably more interesting to DIY.

> It's a team that was built from grounds up because Tesla is (also) an AI company and Musk is thinking 10 years ahead.

Well, also because Tesla has an unnaturally low cost of capital because of its meme stock status.

Tesla makes mad money. It's not a meme stock.

the biggest product of Tesla is its stock, there are no ifs and buts about it. this must change soon, since that mad money is barely enough to eek out a profitable quarter.

You clearly don't read SEC filings / financials.

For your convenience, and you will want to look mainly at 10K and 10Q forms: https://www.nasdaq.com/market-activity/stocks/tsla/sec-filin...

> After seeing the NVidia presentation announcing their latest datacenter-scale AI hardware

Did we watch the same presentation? NVidia knocked it out of the park.

Thread block cluster is obviously amazing. Routing between SMs / compute units will be far faster with this level of software abstraction, and it will be exceptionally easy to write code for. NVidia always impresses me with their advanced software techniques and clear understanding of the fundamental model of SIMD compute.

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Ignoring those software details... the important thing is GH100 will be TSMC 4nm, which is 1.5 nodes ahead of the 7nm Dojo. A significant process advantage, representing 60+% less power usage and 300% the transistor density of the older 7nm tech.

Even if NVidia's GPU had issues, there's something to be said about just being a raw process node (or 1.5 nodes) ahead.

> Did we watch the same presentation? NVidia knocked it out of the park.

Perhaps I worded it poorly, I agree with you.

My meaning was vs. NVidia's latest tech it seems like Tesla's in-house datacenter NN could be nothing more than a huge cost center without even offering an advantage over what NVidia could sell them.

But like I said, if you have a staff of folks capable of building such things you have to keep them satisfied with practicing their craft or they leave.

Gotcha. Misread for a sec.

This was the case when Google launched their TPU as well. Look where they are now

Where?

The person you were replying to agreed with you, that it seems like Nvidia is doing a great job in the data center.

> NASA's SLS, how much of that is a job program in part to prevent hoards of talented folks building rockets for competing nations

Zero. Because NASA has no problem if those engineers would work for other nation as long as it isn't Russia or North Korea and co. And that wouldn't happen anyway.

Those people would likely work in one of the huge amount of space startups or just go to the typical ULA, BlueOrigin, SpaceX and so on.

You make the totally wrong assumption that SLS has anything to do with rational thought. It really doesn't.

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