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Even with unlimited budget and talent, overcoming 25 years of success is ... difficult. Nvidia employs it's own top-tier folks and now has massive margins to in

by lumost 3y ago

Even with unlimited budget and talent, overcoming 25 years of success is ... difficult. Nvidia employs it's own top-tier folks and now has massive margins to invest. If your goal is to sell an AI product to end-customers, choosing to pick up the R&D cost of building great AI chips as well as training gigantic models and the product R&D to make a product customers love ... is a tall order.

andrewstuart 3y ago
“Years of success” is not a moat. “Years of success” means zero in the technology world. Something better with zero years success can win instantly.
- lumost 3y ago
  I'd beg to differ, migrations are extraordinarily expensive in Tech. If you have a sky scraper, you don't tear it down and rebuild it when materials become 10% stronger. Big tech firms generally maintain market position for decades. Cisco still remains the networking winner, and IBM still dominates mainframes, Oracle is going strong. AI compute isn't something that snuck up on NVidia, they've built the market.
  - renonce 3y ago
    > migrations are extraordinarily expensive in Tech Is that really the case with Deep Learning? You write a new model architecture in a single file and use a new acceleration card by changing device name from 'cuda' to 'mygpu' in your preferred DL framework (such as PyTorch). You obtain the dataset for training without NVIDIA. You train with NVIDIA to get the model parameters and do inference on whatever platform you want. Once an NVIDIA competitor builds a training framework that works out of the box, how would migrations be expensive?
    - lumost 3y ago
      “Builds a training framework which works out of the box”. This is the hard part. Nvidia has built thousands of optimizations into cudnn/cuda. They contribute to all of the major frameworks and perform substantial research internally. It’s very difficult to replicate an ecosystem of hundreds to thousands of individual contributors working across 10+ years. In theory you could use google/AMD offerings for DL, but for unmysterious reasons no one does.
- Fnoord 3y ago
  Yep just look at SGI and Sun and all that.

by lumost 3y ago

andrewstuart 3y ago

“Years of success” is not a moat. “Years of success” means zero in the technology world. Something better with zero years success can win instantly.

lumost 3y ago
I'd beg to differ, migrations are extraordinarily expensive in Tech. If you have a sky scraper, you don't tear it down and rebuild it when materials become 10% stronger. Big tech firms generally maintain market position for decades. Cisco still remains the networking winner, and IBM still dominates mainframes, Oracle is going strong. AI compute isn't something that snuck up on NVidia, they've built the market.
- renonce 3y ago
  > migrations are extraordinarily expensive in Tech Is that really the case with Deep Learning? You write a new model architecture in a single file and use a new acceleration card by changing device name from 'cuda' to 'mygpu' in your preferred DL framework (such as PyTorch). You obtain the dataset for training without NVIDIA. You train with NVIDIA to get the model parameters and do inference on whatever platform you want. Once an NVIDIA competitor builds a training framework that works out of the box, how would migrations be expensive?
  - lumost 3y ago
    “Builds a training framework which works out of the box”. This is the hard part. Nvidia has built thousands of optimizations into cudnn/cuda. They contribute to all of the major frameworks and perform substantial research internally. It’s very difficult to replicate an ecosystem of hundreds to thousands of individual contributors working across 10+ years. In theory you could use google/AMD offerings for DL, but for unmysterious reasons no one does.
Fnoord 3y ago
Yep just look at SGI and Sun and all that.