not financial advice, please do your own due diligence. not a professional investor/advisor either.

Alright so seeing all the sloppy analysis and crap-posts on this subreddit, I decided to give my attempt at stock/sector analysis. Feel free to disagree.

Nvidia as of now

Stock just hit 216/217 USD at a P/E (according to Google) of 43.94 with a forward P/E around 23-25. Market cap of roughly 5.23T USD.
Q4 2026 showed 75% growth compared to the previous year, showing strong growth.
Analysts from major investment banks predict an average price of $275-$280 in the future, which is roughly 25-30% growth from current prices. (STRONG BUY RATING)
TL;DR: They're looking good for the current quarter and probably for the short term, but this is a value investing sub where long term revenue and profits are prioritised over short term gains (or well at least officially).

Nvidia Bear Thesis

Nvidia's current value proposition is basically built on a few main foundations: (1) The continued demand for cutting edge GPU's to train frontier models like Claude and ChatGPT and (2) The politics of American semiconductors leading to China not being able to receive the parts needed to create cutting edge GPU's (such as EUV lithography supplied by ASML or HBM which is crucial to AI performance).
I will explain why I believe Nvidia is overvalued and why these two foundations are not as strong as the market would like to believe.

(1)

The continued demand for GPUS. With the release of Deepseek V4, a new open source (so anybody can download and tweak it basically, including for money) model, the Chinese companies have shown that getting 90-95% of the performance of western frontier labs' latest AI models such as Opus 4.6/4.7 and ChatGPT 5.4 and 5.5 but at roughly anywhere from 1/20th to 1/50th the price of western models.
For example, Deepseek V4 PRO is costs approximately $1.74/per million tokens and the V4 FLASH model (the younger, faster brother of the PRO) costs only $0.14/per million tokens.
While the numbers are very difficult to pinpoint exactly due to the different numbers given by multiple sources (I have seen from 2.8x to 50x in some cases) it remains clear that these models are significantly cheaper than Anthropic's and OpenAI's API costs which (1) are almost always rate limited due to high costs and frequency of requests and (2) Anthropic just removed Claude Code (arguably they're most prized product among the Claude family of products) from the Pro plan.
To further cement the idea of just how cheap Deepseek is, I have seen people on AI roleplay subreddits (strictly for research purposes) use the API's and managed to spend less than $5 on 15M-20M tokens. Source like NXCode report that for teams processing 10 million tokens a day, Deepseek V4 would cost $1,400 per year compared to one running Claude Code ($58,000) or GPT 5.4 ($40,000). That's an insane amount of money being saved on a model that can also be ran on and be fine tuned on your own PC (provided you meet the requirements).
This speaks volumes about Deepseek's engineering team, who have managed to build a model that is comparable to western ones with objectively worse chips (Huwaei chips), no EUV lithography, a smaller team (Deepseek is estimated to have around 150-200 employees compared to 8,000 at Open AI) and less money than a lot of it's western counterparts. I will concede though, that without EUV lithography it is limited, but there are early rumours of a prototype

(2)

Currently, China is lagging behind in mostly the hardware angle of AI. As mentioned before, they do not have access to ASML's EUV lithography which is needed to build hyper efficient chips the west does have access to. To compensate however, China has been **focusing on brute efficiency which is at the moment the opposite strategy of most of Silicon Valley which keeps buying more and more GPUs for less gains then Deepseek have shown are possible with software optimisation and mathematical breakthroughs. **
I believe we will see more and more non-western countries adopting Deepseek over western frontier labs, which hits future potential markets and growth. Regions such as South East Asia, Africa and a lot of East Asia (especially China and ASEAN) are incredibly price sensitive. I think this can be best demonstrated through record Linux (an open source OS) adoption - reaching 15% in India and 5% in the US and growing quickly globally as well. Companies like Anthropic and OpenAI cannot afford to lower subscriptions, which are already incredibly unprofitable, and they MUST buy more Nvidia GPU's unless they want to be thrown into irrelevancy. It's a trap, it's just the timing that we can't be sure of.
Don't get this analysis wrong, there will certainly be demand for Frontier Labs, though not the type the markets are pricing in right now. It will be Top 500 companies, high compliance and legal companies in the West mostly and a few corporations internationally that need the compliance and security labs such as Anthropic provide. It's just that for 90% of consumers, cheaper models that come from China, Google (already implementing Google AI for searching) and local models from say Deepseek/Qwen will be able to satisfy demand.
What I'm going to talking about next is heavy speculation, but I do believe that we will see Apple Intelligence soon. Let me explain. Currently Mac Mini's and Mac Desktops in general are being snatched up by AI hobbyists and while this doesn't seem so important so far for a market thesis, I do believe it shows a trend where we will start to see more beefy hardware on computers (which has been the case in the last 10 years, but more rapidly I'd say) to be able to run local models. **Apple has unified memory, which long story short means that it can ran larger AI models at it's price point compared to windows laptops. This I believe will also cut at Nvidia's revenue as if local users use their own GPU's instead of cloud ones, hyperscale's buy less GPU's directly affecting Nvidia revenue. **

Jevons paradox

AI nerds will inevitably talk about this and, yes, I will talk about it. Basically, Jevon's paradox is the dramatic cost reduction could lead to more total GPU demand as inference explodes. I don't doubt that. My main argument isn't that this wont' happen, but rather that it wont' benefit Nvidia or Western labs. As I mentioned before, something like 80% of world population (quick google search, the exact number is not that relevant) lives outside of the "West". $20 Claude subscriptions cost a lot more in India than say in America. A quick google search says around 1999INR which is about the same as what Americans pay but remember that it's more of their budget than in the West.
I think all this new AI demand for consumers and SME enterprises will inevitably be supplied by the likes of Apple (Mac Minis are selling like hot pies for AI inference and unified memory), Deepseek (Chinese eEnterprise, Indian start-ups downloading the models and fine tuning etc), which diverts profits away from Nvidia and western labs and towards the other organisations and companies.
The market share for AI labs such as Anthropic and Open AI is still fiercely competitive, even without Chinese models, from the likes of Google's Gemini and Gemma, Grok (as much as every redditor hates Elon Musk, he Grok is increasing in market share). The difference is that Gemini has the direct backing of Google, one of the most valuable companies in the world and first class access to the most used Search Engine (Google's monopoly) and Grok has X (Ragebait monopoly, though still incredibly valuable for studying human language which is exactly what an LLM needs). Anthropic and Open AI require funding from the other Big Tech companies, and have little ecosystems that aren't as deeply entrenched and are uniquely vulnerable.
There's also a Forbes article about how API and AI subscriptions are costing firms more than just hiring humans to do the same work. Deepseek is providing a direct answer for that in a lot of regions, as up to 90% less API costs actually make it viable to (yes here comes the moans and groans) to cut human workers. Anthropic and OpenAI simply can't cut it that much without losing massive profits. China quite literally has half the electricity costs, arguably better stability as well (solar panels + coal, not just reliant on oil unlike a lot of datacentre in America which use diesel generators).

Predictions

Don't get me wrong. Nvidia is still going to make bank in the future from selling GPUs to certain frontier labs, but they're moat is going to start softening. I'll make a rough estimate, and say within the next 5 years we will see alternatives to CUDA popping up (probably open source as well), other chip designers creating in-house AI chips that reduce the need for a third party creator like Nvidia.
As for consumer predictions, I predict the rise of local open source models, users increasingly using local/smaller models that require less compute for 90% of tasks but they run on the computer or on Google's chip or Apple's chips instead of Nvidia's chips. Hybrid workflows basically. It's already happening, it's just that I'm thinking this is going to become more common for your average joe.
Essentially, if less GPUs can do more, that directly affects Nvidia margins which is all about selling as many GPU's as possible.

TL;DR: **AI and Intelligence in general is becoming a utility, not a luxury. The market believes that hyperscaler demand for GPUs will continue, but I believe the rise of local models, open source models and cheaper Chinese alternatives will heavily disrupt Nvidia's market value as it's main revenue comes from a few concentrated players, not consumers. I am not saying Nvidia is worthless or should not be bought for short term gains (which I'm sure there still might be some), rather that it's long term value is diminished significantly and that as value investors buying Nvidia seems to be an incredibly risky move for 15-30 year gains. **

this is a qualitative analysis, do your own modelling to see if you agree or not before making a financial decision.

no AI was used in the creation of this analysis, only for research and refining.