For those worried about a bubble in AI stocks, the next level play is to “sell out of the NVDA ecosystem and buy into the GOOGL ecosystem”. 

 The world loves a binary framework, especially Wall Street. Cut through the noise, make a simple, can’t-lose, directional bet based upon a believable thesis. And, in the short run, it works—as long as the children follow the pied piper out of town. Most of these trades are variations on chasing momentum, just with a rationale provided by the first movers. 

 

After all, the GOOGL ecosystem is safe, rational, and underappreciated with a multi-layered AI opportunity—many shots on goal. While NVDA, along with OpenAI, the new black sheep of the AI trade, is engaged in questionable accounting and complex, Gordian knot-type financings that pump up and overstate sales. Cue up the short sellers implying nefarious intent and legal, but morally questionable, practices that put the financial markets at risk on the scale of Enron or the GFC. For good measure, remind everyone of the excesses of the internet bubble and how the early “winners” were profligate spenders who ultimately lost—after all, history always rhymes, right? 

A Caveat Before We Continue 

I realize this sounds like we are picking on GOOGL because we haven’t owned it for a while. Perhaps. But more importantly, I’m trying to make the case that nothing about the future of AI is easy. Will NVDA retain an 80-90% market share in chips for inference1? Unlikely. Will GOOGL maintain a 90%+ market share in search2? Unlikely. Was GOOGL undervalued relative to its peers and its opportunity set? Likely. Is GOOGL really a head-to-head competitive threat to NVDA? Not today. 

Not Apples to Apples 

The competition with NVDA on chips is not really apples to apples. Why? 

 

Architecture: GPUs are more flexible with thousands of smaller cores handling diverse parallel tasks. TPUs are optimized for the specific math patterns in neural networks—potentially more efficient for that narrow use case, but less versatile. 

Ecosystem: NVIDIA’s moat is CUDA software—its flexibility and a decade of developer tooling and use. TPUs require specialized software and work best within Google’s infrastructure (after all, they were built for that purpose). 

 

Most researchers and startups default to NVIDIA because of its universality and ease of use, optimized for the broadest possible range of workloads. 

 

They’re competing in the sense that every TPU Google deploys (for itself or for new third-party customers) is a GPU it didn’t buy from NVIDIA. But Google isn’t threatening NVIDIA’s core business for more generic workloads. The real competitive question is whether hyperscalers, already building and utilizing their own silicon, will gain additional third-party buyers for their chips, collectively reducing NVIDIA’s TAM over time. On the margin, I think so, but it’s worth watching closely. 

 

The Opportunity Question 

According to a CNBC interview with Stacy Rasgon, a senior tech analyst at Bernstein, “[w]e’re not at the point where we have to worry about who’s winning and who’s losing. It’s more a case of: is the opportunity in front of AI sustainable or not? If it is, they’re all fine, if it isn’t, they’re all screwed.” Well said. 

We will likely know when, and if, the competition becomes material to Nvidia when the company’s prized gross margins (currently around 73%3) begin to meaningfully slide, indicating that Nvidia is lowering prices to protect sales. Right now, the market is already pricing this in on some level and remains skeptical about future spending and speculative contracts and financings. 

 

As of 12/4/25, according to Bloomberg, NVDA’s current forward P/E multiple of approximately 24x sits roughly 60% below its 10-year average of 65x versus GOOGL at approximately 23x, roughly in line with its 10-year average of 26x. Further, NVDA’s multiple is down substantially from where it was when OpenAI was announced. Skepticism about the future is appropriate—it just needs to be evenly and rationally applied. 

Google’s Track Record 

For its part, GOOGL has spent money for years without much to show for it, in terms of revenue growth outside of its core business and bottom-line impact. Have we really already forgotten the “moonshots”? And, in fact, it continues to spend copiously on capex, leading the pack of its Mag7 peers in terms of CapEx as a percentage of market cap4. For sure, this spend has resulted in technological progress (Waymo, DeepMind, etc.) and some improvements and enhancements to its existing business (Tensor technology for its cloud infrastructure, discussed above). However, we have yet to see effective commercialization with meaningful revenue impact. Why are they suddenly better stewards of capital now? 

 

Maybe all the previous and current spend is about to pay off in a big way. Perhaps, but for my part I remain skeptical—NVDA has earned the right to benefit from “seeing around corners” based upon its track record in gaming, bitcoin mining, etc. GOOGL has not—have we already forgotten how they fumbled their LLM rollout? It’s worth remembering that Google was in the pole position long before OpenAI even existed and opened the door for new competitors. Now the market seems to think the tortoise will win the race. 

 

Early Innings, Intense Competition 

In any case, the AI race has just left the starting line and the field is very competitive. We note that fully six days after investors cheered the release and subsequent performance advantage of GOOGL’s Gemini 3 LLM (which juiced the stock another 8.4% for the week5), Anthropic released Opus 4.5 and GOOGL was knocked off its pedestal. According to Composio’s real-world coding tests, Opus performed better than Gemini on software engineering tasks, with Opus 4.5 achieving 80.9% on SWE-bench versus Gemini 3 Pro at 76.2%.6 Of course, performance on standardized tests versus real-world applications are often two different things, so we take all of these comparisons with a grain of salt—but the takeaway should be that LLM models are constantly evolving and immensely competitive. 

 

Don’t get me wrong—GOOGL is doing the right thing. This level of spend is directionally correct for these big, MAG7 cash flow machines—they must continue to compete on a constantly shifting playing field. In short, no MAG7 member has a pristine track record of return on spend, save NVDA perhaps. And no one has done less to reinvest and innovate than AAPL. That lack of spend is now considered an advantage. Once again, it is assumed that AAPL can sit on its hands and leverage the capital spend of its more foolish and panicky competitors at its own pace. I don’t think this can work indefinitely—just ask Intel. 

 

The Takeaway 

Reality is more nuanced than easy narratives and trades would suggest. Binary frameworks make for compelling storytelling, but investing is rarely that simple. The NVDA-to-GOOGL rotation trade assumes certainties that don’t exist: that NVDA’s dominance will crumble, that GOOGL’s spending will finally pay off, and that the competitive landscape will remain static long enough for the trade to work. 

 

I prefer to focus on what I can validate through our research process—identifying emergent points of inflection based on fundamentals, not momentum-driven narratives dressed up as insight. In a world where AI leadership can shift in less than a week, the only binary outcome I’m confident in is this: those who rely on oversimplified frameworks will eventually be surprised by the complexity they ignored. 

 

Footnotes 

 1  Vivek Arya (BofA), quoted in “Nvidia Dominates AI Chips: Analyst Sees 85% Market Share Amid Fierce Competition,” Yahoo Finance, February 2025.  

2 Dominic Reigns, “Google Search Market Share Statistics 2025,” About Chromebooks, October 27, 2025  

3 Andrew Kessel & Kara Greenberg, “Nvidia Earnings Live: Results Top Expectations on…,” Investopedia, February 26, 2025 

4 Sheraz Mian, “Mag 7 Members Report Strong Earnings, Double Down on CapEx,” Zacks Earnings Trends, February 5, 2025 

5  Bloomberg historical OHLC price data as of December 5, 2025 

6 Composio.dev, Blog:”Claude 4.5 Opus vs. Gemini 3 Pro vs. GPT-5-codex-max: the sota coding model”, Nov.28, 2025 

 

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The Validex Global Growth strategy and the Destra Multi-Alternative Fund that is sub-advised by Validus invest in NVDA.   

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