What I expect will happen with Nvidia stock in next 6 months
1. US companies such as OpenAI, Anthropic, Google are all smearing DeepSeek in the media. They're saying how DeepSeek trained on OpenAI's outputs illegally. This is just lobbying for what's to come.
2. Trump administration will be in full force to protect AI companies in the US. Sam Altman is now buddies with Trump after giving him credit for Stargate announcement. Elon owns xAI.
3. Expect the US government to sanction Deepseek. This will prevent American companies such as Microsoft or Huggingface from hosting DeepSeek models.
4. Expect the US government to curtail Nvidia chips even more in China. Perhaps they'll do a full Nvidia ban. Perhaps they will restrict shipments to Singapore, where GPUs are then funneled into China.
5. Expect Chinese companies to put in huge orders for Nvidia chips right now. They're not stupid. They know it's coming. Q2 should be a blowout quarter for Nvidia because of a huge influx of Chinese orders before an even bigger ban on Nvidia exports.
6. Expect the ban to happen at the end of Q2 sometime.
7. Expect Nvidia stock to recover first when they announce earnings. Investors will see a huge future orders from American companies (Jevons Paradox) and huge sales in China (before the ban).
8. Expect stocks to sink a bit after the announcement of the ban.
Well, I have a bear call spread on NVDA and so far its not working super great. It all depends on timing and I seem to be pretty crappy at options. o well.
Deepseek's advancements are not going away, even if you ban them. Demand for NVDA chips will go down, and likely will take a long time before Jevon's Paradox kicks in.
>Demand for NVDA chips will go down, and likely will take a long time before Jevon's Paradox kicks in. reply
I expect Jevons Paradox effect to start immediately. Small businesses and enterprises are likely wanting to order DGX systems for internal inference. OpenAI and larger AI labs will want to put a bigger distance between themselves and Chinese AI models by using compute muscle and advantage. All AI labs see the same efficiency benefit so they will have to continue to compete on compute capacity.
Last week the moat was the large size or cost to generate a model. That has gone away. So there should be lots of competition now within the US from smaller companies.
That was never the moat.
Nvidia's moat is controlling compute for 80-90% of workloads.
Why do you think Steam has more RTX 4090 than 4080 in their survey?
Ever since RTX 2080TI you could buy multi server consumer GPUs for ML. We sell PCs with RTX cards regularly to customer for small local AI applications.
Project Digits is the next thing. It is not only a Nvidia GPU in some PC, it's the real AI PC. The real deal and we're already considering switchting to that as a system instead of a PC since it's size is perfect for our Vision application.
Do you think Nvidia cares if you buy 1 Blackwell DC GPU, 13x Digits or 20x RTX 5090? In the end it's all the same turnover for Nvidia.
Nvidia's goal is to spread and dominate workloads worldwide and that no matter if DC, enterprise or consumer, Nvidia HW is used.
I re-read the thread topic and it is in response to NVIDIA stock. My response was in response to LLM generators and their moat (not NVIDIA's moat). So, my point was the moat for LLM generators had diminished. Additional competition generating LLMs from new entrants may increase demand for HW. The HW may be used more efficiently but I am still waiting to see if LLM performance continues to improve.
New AI Lab: Trains a model using Deepseek's techniques on 2,000 GPUs
xAI/OpenAI/Anthropic/Google: Trains a model using Deepseek's techniques on 100,000 GPUs
I fail to see how this makes smaller companies competitive.
Depends on whether smaller companies can deliver sufficient results cheaper than the larger ones. There are some indications that suggest that there are diminishing returns on investing on ever more power.
It's like you don't need a 1000HP supercar to get around town, a 55HP sedan is fine for most folks.
Yep, that is my point. If the large scale LLMs are not sufficiently better than the new crop of startups, I suspect the large firms will need to acquire the startups (that would be their response due to a lack of a moat). Its hard to buy everything and to know where to place your bets.
So you’re worried that LLMs have stopped scaling even though the biggest breakthrough from DeepSeek is scaling RF learning without humans?
As I see it: I'm waiting to see improvements in LLM performance. What I see is an improvement in computational efficiency (less hardware needed)
If general LLMs do not show continued performance improvement, then there is a lot of excess HW that needs to be utilized somehow. If LLMs continue to show performance improvement, then the hardware can be used more efficiently.
So how does Deepseek change anything for your view? What you wrote was true before Deepseeek and their non-human RF breakthrough.