10 Reasons Why Nvidia (NVDA) Stock Will Plunge to $200
There are about 100 public companies with market caps larger than $100 billion. Of these, Nvidia (NASDAQ:NVDA) is the only stock up more than 200% since the beginning of 2016. In fact, NVDA is up more than 600% in this period. The stock is acting as if execution risks do not exist.
But they do, and they are never more apparent than right now. When a stock sells off while analysts are not budging – you know something is wrong. These are the 10 reasons why NVDA is on its way to $200:
- A great valuation that already reflects a great company but ignores risks going forward: High multiple stock that is no longer beating and only meeting expectations. Since Q1 FY16, NVDA has beat on both revenue and EPS every single quarter (i.e., 13 straight quarters). However, Q2 FY19 revenue beat by less than 1%, while EPS only beat by <5%. This is disappointing for a company that is notorious for managing the Street’s expectations, and this quarter was by far the smallest beat on both metrics in the last 3 years (after beating on revenue and EPS by as much as 20% and 70%, respectively, in prior quarters).
- Competition from the most well-funded and respected tech companies in the world (e.g., Baidu, Alibaba, Tencent, Facebook, Google, Microsoft, Amazon etc.): Just two weeks ago Tencent led a $50 million round for AI chip maker Suiyuan Technologies. This company was founded in March and is just the latest in a long list of Chinese AI chip start-ups that capital is being rained on to address a problem that is not capital intensive. Not only are these tech giants future competitors, but they’re also current customers, a cut that could burn twice in the same stroke.
- A semi-seismic shift in tectonic plates: Already seeing a shift from GPU to TPU. Google’s TPU initiative means the world’s leading AI giant will be relying far less on GPU’s, but this is even worse when you consider that other large enterprises can avoid spending by renting this hardware. Moreover, just a few months ago, EBAY gave a presentation on how their in-house AI hardware -powered by NVDA GPU’s – was used to make EBAY image-recognition stronger. A few months later, we came across a blog post from an EBAY AI research
scientist that we expect to start turning heads. In short, EBAY’s AI team found that using Google’s TPU was significantly faster than using their in-house systems, and it also allowed EBAY to avoid large upfront capex. EBAY Case Study: (Source).
- Graphcore: When discussing competition, one must mention Graphcore whose name has recently been put in every conversation given the strength of their Intelligence Processing Unit. If you don’t know it and you own NVDA, it is time to read this.
- While NVDA’s Jensen Huang is a visionary, he is no short of hype: In its life span, NVDA has dropped 85% from its highs two times! This has largely been attributed to product upgrades not living up to hype, but when a mega-cap is up more than 600% in less than 3 years, small hiccups can have grave repercussions.
- Management changing the narrative: Jensen has promoted the crypto boom as a key driver of NVDA business over the last year but his optimism has quickly reversed. Just one year ago on the Q2 earnings call, CEO Jensen Huang was hyping up crypto: “Cryptocurrency and blockchain is here to stay. The market need for it is going to grow, and over time it will become quite large. It is very clear that new currencies will come to market, and it’s very clear that the GPU is just fantastic at cryptography.” (source) In March this year, Jensen told Cramer in an interview that cryptocurrency was an “important driver” for NVDA (source). When crypto was brought up on NVDA’s call last week the end impression was crypto was dead for good and would be “immaterial for the second half.”
- Autonomous is also not what we thought it was: On Tesla’s latest earnings call, Elon Musk explained that NVDA was no longer necessary as their own self-driving chip was more than 10x better: “It’s an incredible job by Pete and his team to create this, the world’s most advanced computer designed specifically for autonomous operation. And as a rough sort of [indiscernible] whereas the current NVIDIA’s hardware can do 200 frames a second, this is able to do over 2,000 frames a second and with full redundancy and fail-over.” (Source)
- The Glaring Problem: Bull case is now predicated on an upgrade cycle (can anyone say ‘Turing’).
- Real Time Ray-Tracing: A term that has been foreign to investors until now is the differentiating factor in Turing. NVDA released its new RTX 2080 chip yesterday. Although the hype was loud, the real experts are already voicing some serious concerns. NVDA failed to release a single traditional benchmark, leading many to believe the ray-tracing technology came at the cost of overall performance. Further, when you consider Jensen spent the entire presentation speaking about a single RTX 2080 feature (ray-tracing), these voices of concern grow clearer and louder.
- And one of our favorites – short sellers are on the sidelines (short interest is at an all-time low) Anytime NVDA has been hit with potential concerns (e.g., crypto, autonomous driving), the immediate analyst fall back is to raise price targets up because of AI datacenter. Despite the inevitable slowdown to the law of large numbers, short sellers have become exhausted in the name and that is usually a good time to get involved as short interest is at an all-time low – GREAT NEWS. We see many parallels with NFLX. When NFLX optimism was at its peak, we were seeing changes in the media industry that could not be ignored by any long-term investor. NFLX was trading around $390 when Citron said the stock would pull back to $340 or lower. As of today, NFLX is down almost 20% at ~$329.
Citron made a promise to donate $230k to the Humane Society if NVDA closes the year above $230 in April. As much as NVDA is one cute dog….this is the dog that finally caught the car. We will donate $100k when $NVDA closes below 200 in April.
NVDA remains a victim of its own success with competition mounting from every side. Execution risks are many, and, at peak multiples, NVDA’s margin for error is next to nothing.
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