| ▲ | kroaton 5 hours ago |
| I think it mostly shows that there is no moat and the only advantage the U.S companies have over the Chinese is more compute.
Qwen Max, Kimi K3, GLM 5.3 are really close to Opus/Sol/Fable/Astra and they are open weights. |
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| ▲ | davidguetta 4 hours ago | parent | next [-] |
| bringing the price down b.c. competition != no moat. There's not 100 frontier labs, it's not like airline companies |
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| ▲ | haldujai 2 hours ago | parent [-] | | About the same, 5-10, when you consider major (aka frontier) airlines. Actually not a bad comparison. Both burn massive amounts of up front capital to protect an oligopoly in the hopes their commodity product eventually pays off. |
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| ▲ | VirusNewbie 4 hours ago | parent | prev | next [-] |
| If there was no moat, nvidia and meta would have SoTA models too. |
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| ▲ | evilduck 2 hours ago | parent | next [-] | | Nvidia does have one of the best completely open models. Open weights are nice but Nemotron is open training data too. | |
| ▲ | seunosewa 4 hours ago | parent | prev | next [-] | | Meta is awfully close. | | | |
| ▲ | amazingamazing 4 hours ago | parent | prev | next [-] | | It is not in nvidia’s interest to be too good at model creation | | |
| ▲ | reilly3000 2 hours ago | parent | next [-] | | But it is in their interest that their customers can use their models as a base for post-training and LoRAs. | | | |
| ▲ | david-gpu 3 hours ago | parent | prev [-] | | Why not? Commoditize your complement, and all that. | | |
| ▲ | angulardragon03 2 hours ago | parent [-] | | And if they get too good, they risk harming or otherwise killing their golden geese (their customers), who they are heavily invested in. | | |
| ▲ | david-gpu 2 hours ago | parent [-] | | How? Imagine an open-weight model comes out that is somehow better than proprietary solutions. Now the marginal cost for the consumer is just the cost of renting the inference hardware, without having to pay the overhead of the owner of a proprietary model. And because it is cheaper, more customers want to use it, and Nvidia will sell the providers the inference hardware that they need. | | |
| ▲ | amazingamazing 34 minutes ago | parent [-] | | 1. No open ai and anthropic means no buying gpus to train. Now nvidia spends money on hardware training their own models. Opportunity cost plus expense. 2. Any open models created from this will not necessarily need their silicon, see apple mlx. |
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| ▲ | sensanaty 3 hours ago | parent | prev [-] | | [dead] |
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| ▲ | Razengan 3 hours ago | parent | prev | next [-] |
| The "moat" is the "harness", the app. For most people, the app IS the AI. And even for its wonkiness, ChatGPT has had the best UX/UI of them all. The way to win the AI wars in the eyes of the common folk is through the frontend, to be the Apple of AI, as it were. |
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| ▲ | tonyhart7 4 hours ago | parent | prev | next [-] |
| they don't have moat in hardware either Chinese counterpart like CXMT and Huawei is begin producing their own chip You cant block an entire nation level effort with tariff |
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| ▲ | astrobiased 4 hours ago | parent [-] | | I think the moat that China has is energy costs. It's taking learnings from the Bitter Lesson. If you role up scale and compute to the next level, it's energy resources. China has it and sharing open weight models is an effective means of removing the tech moat. This idea has been floating around for a bit now (I'm not taking credit for it). | | |
| ▲ | rgbrenner 3 hours ago | parent | next [-] | | It's not energy costs. The US produces about 70% more electricity per capita. Chinese households do pay less than half what US households pay for electricity, but that's because the NDRC sets prices below costs for households. They make it up by charging industry more, and the industrial electricity prices in China are roughly 34% higher than in the US. | |
| ▲ | spartacusnacho 3 hours ago | parent | prev [-] | | They also benefit from the commodification of software/knowledge work since they own manufacturing |
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| ▲ | keeganpoppen 4 hours ago | parent | prev [-] |
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