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Comment by setgree

16 hours ago

It's fun to see that even an extremely large company can find unexpected product market fit [0]. Per this article, "The company reportedly did not possess an engineering team dedicated to business customers or staff focused on developer relations, and lacked an enterprise AI strategy." That sounds insane in retrospect, but I think there's just inherent uncertainty in what people actually need and will use things for.

[0]https://pmarchive.com/guide_to_startups_part4.html: "In a great market—a market with lots of real potential customers—the market pulls product out of the startup... The product doesn’t need to be great; it just has to basically work."

You should listen to the podcast Acquired, specifically Nvidia and then Jensen Huang. They basically lucked into AI. Some researcher was using Nvidia gaming cards, and reached out to them about questions on CUDA. That email eventually turned them into a trillion dollar question.

  • What year are you talking about? When I was in grad school, around 2007, Nvidia was aggressively marketing GPUs for high performance computing. They would go to campuses, talk to professors, etc.

    Yes, the whole Deep Learning thing was luck, but as with most lucky things, they ensured they were positioned to capitalize on it.

    • Probably cerca 2014 as that's when AlexNet was released, demonstrating that neural networks could beat traditional ML models at image recognition tasks. I recall the researchers used Cuda to optimize their training setup.

      AlexNet kicked off a new wave of research around neural networks by demonstrating they could be scaled well and trained on GPUs.

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  • to their credit, there was a lot of work behind "luck". Jensen showed up in person in 2017 in NEURIPS and he and likely a lot of his top brass basically sat down and read the entire conference proceedings/abstracts; there was likely a lot of work behind the scenes to behind the ML research pivot.

    • And 2017 was _late_ in their pivot. They'd been active for much, much longer. Last winter break I sat down to watch every GTC keynote, going back to 2009[1]. Even then, he's talking about expanding to non-graphics workloads. Google's GPU paper[2] just slotted naturally into their existing narrative and were happy to support it. "fortune favors the prepared" as they say.

      [1]: https://www.youtube.com/watch?v=fYuH2Kl_b98 [2]: https://scholar.google.com/citations?view_op=view_citation&h...

    • Yeah, The NVIDIA Way goes into a lot of detail on how and why the pivot from graphics to AI happened. This is a prime example of “you make your own luck.” Jensen engineered an organization that was primed to recognize and pounce on the next big thing, and it ended up being AI. But they saw it coming WAY in advance (like 2011/2012, not 2017) because they were explicitly on the lookout.

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  • I might have believe this story, if not at the same time Intel had made an expensive bet on producing not-quite-gaming cards, later looked at the same trillion dollar question.. and then almost decided that this did not bring enough luck to keep spending.

  • In 2006. The next 20 years of cuda support weren't luck, as anyone trying to use AMD will know.

Maybe a bit of hindsight bias / the outside view here, but I feel like they're completely asleep if they didn't anticipate strong demand for this specific use case.

  • I think a reasonable story could have been told that goes like this: local models aren’t as good as frontier models with a $20/month subscription, and the hardware costs a lot. So only a few enthusiasts will buy Apple machines for this purpose.

    This story turned out to be false but I think smart, reasonable people a couple years ago could have believed it with conviction. It doesn’t really seem like “completely asleep” to me.

  • I don’t understand how that’s possible. They should have had a better idea of what was happening in the memory markets than pretty much any other entity.

    • Their universal RAM strategy is so obviously helpful for AI. (1) GPU/NPU <--> CPU RAM copies eliminated. (2) All (most) RAM available for GPU/Neural, when local models are typically kneecapped by limited GPU RAM sizes vs. the much larger RAM options for M/Max/Pro/Ultras.

      They have been taking NPU's seriously on their phones, tablets and laptops since the M1.

      Then they enabled fully-connected RDMA for 4 x 512GB MacStudio's = 2TB RAM. Perfect for a large Mixture-of-Experts model.

      It would be very strange if they didn't notice their product line had landed in a new sweet spot.

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  • Tim Cook has been touted as the greatest supply chain logistics person on the planet and revolutionizing Apple's product delivery, securing exclusive contracts years in advance, etc., etc.

    But "oops, we missed that people are interested in AI work on our machines" seems like a really fucking big myopia. But then again, Tim's off to retire on a bed made of cash this week, so...

Was this the case in the past?

My vibes were that Apple wound down the “actual work” side of their operations (including machines like Xserve), because Ives couldn’t handle the unsexiness and unpredictability of business requirements in hardware.

He was self-indulgent and only wanted to work on things that “vibed” with him, rather than what the customers needed. It’s easy to be creative when you get to do what you want to do, it’s hard when you have hard constraints.

  • I think Jobs was quite sceptical about courting enterprises. Personally this is one of the reasons I choose Apple over Microsoft.

  • Apple has not in the past three decades really courted the capital E Enterprise market. They'll definitely sell to Enterprise customers and have Enterprise sales teams for big customers. But they're not and never have been Dell or HP.

    Enterprise sales sucks. There's infinite amounts of politicking and glad handing and buyers will get all sorts of sweet brib..."sales dinners" then go with the cheapest option. Margins on hardware sucks and the only money is in support contracts. Apple instead invests in consumer sales/support primarily and all the other channels are side businesses.

    Stuff like the Xserve existed mostly for Apple internal purposes and ended up being sold externally to goose the scale enough to make them not a huge loss. At one point a large percentage of the offices on Bubb road were packed with Xserves running portions of the iTunes Music Store and the Apple online store. More offices were packed with Xserves doing media ingest and encoding for iTMS. Just about every building had racks of them as build and file servers.

It's also fun to see how many people here believed this was all some clear deliberate strategy in the first place rather than an accident.

  • They didn't "accidentally" add tensor units to the GPU cores in the M5 generation.

    However, I don't think they expected the level of Enterprise interest they saw.

No ‘staff focused on developer relations’ is entirely unsurprising based on what I see from the outside.

  • That raw statement is completely and totally false.

    “Not as fully staffed as some people might hope” or “Developer Relations isn’t as responsive as I’d like” are both at least not obviously false.

> "The company reportedly did not possess an engineering team dedicated to business customers or staff focused on developer relations, and lacked an enterprise AI strategy"

This is clearly a mis-statement, they have a whole annual conference for developers. Maybe they mean specifically AI devs.