The part of this that doesn’t jibe with me is the fact that they also released this incredibly detailed technical report on their architecture and training strategy. The paper is well-written and has a lot of specifics. Exactly the opposite of what you would do if you had truly made an advancement of world-altering magnitude. All this says to me is that the models themselves have very little intrinsic value / are highly fungible. The true value lies in the software interfaces to the models, and the ability to make it easy to plug your data into the models.
My guess is the consumer market will ultimately be won by 2-3 players that make the best app / interface and leverage some kind of network effect, and enterprise market will just be captured by the people who have the enterprise data, I.e. MSFT, AMZN, GOOG. Depending on just how impactful AI can be for consumers, this could upend Apple if a full mobile hardware+OS redesign is able to create a step change in seamlessness of UI. That seems to me to be the biggest unknown now - how will hardware and devices adapt?
NVDA will still do quite well because as others have noted, if it’s cheaper to train, the balance will just shift toward deploying more edge devices for inference, which is necessary to realize the value built up in the bubble anyway. Some day the compute will become more fungible but the momentum behind the nvidia ecosystem is way too strong right now.
What has changed is the perception that people like OpenAI/MSFT would have an edge on the competition because of their huge datacenters full of NVDA hardware. That is no longer true. People now believe that you can build very capable AI applications for far less money. So the perception is that the big guys no longer have an edge.
Tesla had already proven that to be wrong. Tesla's Hardware 3 is a 6 year old design, and it does amazingly well on less than 300 watts. And that was mostly trained on a 8k cluster.
I mean, I think they still do have an edge - ChatGPT is a great app and has strong consumer recognition already, very hard to displace.. and MSFT has a major installed base of enterprise customers who cannot readily switch cloud / productivity suite providers. So I guess they still have an edge it’s just nore of a traditional edge.
> The part of this that doesn’t jibe with me is the fact that they also released this incredibly detailed technical report on their architecture and training strategy. The paper is well-written and has a lot of specifics. Exactly the opposite of what you would do if you had truly made an advancement of world-altering magnitude.
I disagree completely on this sentiment. This was in fact the trend for a century or more (see inventions ranging from the polio vaccine to "Attention is all you need" by Vaswani et. al.) before "Open"AI became the biggest player on the market due and Sam Altman tried to bag all the gains for himself. Hopefully, we can reverse course on this trend and go back to when world-changing innovations are shared openly so they can actually change the world.
Exactly. There's a strong case for being open about the advancements in AI. Secretive companies like Microsoft, OpenAI, and others are undercut by DeepSeek and any other company on the globe who wants to build on what they've published. Politically there are more reasons why China should not become the global center of AI and less reasons why the US should remain the center of it. Therefore, an approach that enables AI institutions worldwide makes more sense for China at this stage. The EU for example has even less reason now to form a dependency on OpenAI and Nvidia, which works to the advantage of China and Chinese AI companies.
I’m not arguing for/against the altruistic ideal of sharing technological advancements with society, I’m just saying that having a great model architecture is really not a defensible value proposition for a business. Maybe more accurate to say publishing everything in detail indicates that it’s likely not a defensible advancement, not that it isn’t significant.
I always thought AMZN is the winner since I looked into Bedrock. When I saw Claude on there it added a fuck yeah, and now the best models being open just takes it to another level.
AWS’s usual most doesn’t really apply here. AWS is Hotel California — if your business and data is in AWS, the cost of moving any data-intensive portion out of AWS is absurd due to egress fees. But LLM inference is not data-transfer intensive at all — a relatively small number of bytes/tokens go to the model, it does a lot of compute, and a relatively small number of tokens come back. So a business that’s stuck in AWS can cost-effectively outsource their LLM inference to a competitor without any substantial egress fees.
RAG is kind of an exception, but RAG still splits the database part from the inference part, and the inference part is what needs lots of inference-time compute. AWS may still have a strong moat for the compute needed to build an embedding database in the first place.
Simple, cheap, low-compute inference on large amounts of data is another exception, but this use will strongly favor the “cheap” part, which means there may not be as much money in it for AWS. No one is about to do o3-style inference on each of 1M old business records.
You are not taking into account why people are willing to pay exceedingly high prices for GPUs now and that the underlying reason may have been taken away.
Build trust by releasing your inferior product for free and as open as possible. Get attention, then release your superior product behind paywall. Name recognition is incredibly important within and outside of China.
Keep in mind, they’re still competing with Baidu, Tencent and other AI labs.
My guess is the consumer market will ultimately be won by 2-3 players that make the best app / interface and leverage some kind of network effect, and enterprise market will just be captured by the people who have the enterprise data, I.e. MSFT, AMZN, GOOG. Depending on just how impactful AI can be for consumers, this could upend Apple if a full mobile hardware+OS redesign is able to create a step change in seamlessness of UI. That seems to me to be the biggest unknown now - how will hardware and devices adapt?
NVDA will still do quite well because as others have noted, if it’s cheaper to train, the balance will just shift toward deploying more edge devices for inference, which is necessary to realize the value built up in the bubble anyway. Some day the compute will become more fungible but the momentum behind the nvidia ecosystem is way too strong right now.