In April 2026, SpaceX secured an option to acquire AI coding startup Cursor for $60 billion. The rationale behind this acquisition is to strengthen SpaceX/ xAI’s vertical integration position.

But isn’t SpaceX a rocket company? Why are the buying an AI coding software?

For people who do not regularly follow developments around SpaceX and “Elon-verse”, let me provide you with some context.

xAI, which is an AI company similar to Open AI and Anthropic, acquired X (formerly Twitter) in March 2025 through an all-stock deal valuing xAI at $80 billion and X at $33 billion (or $45 billion including debt). Then, 11 months later, in February 2026, SpaceX acquired xAI. This all-stock merger valued the combined entity at $1.25 trillion, integrating xAI’s AI capabilities like Grok into SpaceX.

So, SpaceX is no longer just a rocket company. Depending on how you see, it can either be classifed as a conglomerate or a highly vertically integrated business. But let’s not get into this debate here.

Where will Cursor fit into SpaceX/ xAI’s stack?

As per Nvidia CEO Jensen Huang, the AI stack can be viewed as a “five-layer cake” consisting of energy, chips, compute infrastructure, models, and applications. Currently, SpaceX is strong in 3 out of these 5 layers i.e.

1. Compute Infrastructure (Colossus supercomputer in Memphis, Tennessee, which is currently one of the world’s largest AI training systems),

2. Energy (the above-mentioned Tennessee data center is powered by their own gas turbines); and

3. AI models (xAI/ Grok).

However, they are lacking in the other 2 layers i.e. Chips and Application. I won’t be covering their chips strategy here as it is beyond the scope of this discussion.

Here, I’ll keep the focus on how SpaceX/ xAI are planning to strengthen at the Application layer. Acquiring Cursor, which provides a front-end developing interface for coders, would fill this gap and allow them to get one step closer to a completely vertically integrated AI stack.

A primary driver for the deal is the need for independence from competitors. xAI reportedly had its access to Claude Code (an Anthropic tool) cut off because they were seen as a competitive threat. So, by owning Cursor, xAI secures a permanent front-end interface that will not be restricted by third-party suppliers like OpenAI or Anthropic, who are developing their own competing coding applications. On top of that they gain direct access to a large pool of developers who are already using the Cursor interface.

Are coding agents “Application layer” of the AI stack?

On first look, it might seem AI assisted coding platforms do not seem to fit into the Application layer of the 5 layered-AI stack. But it actually does. Let me try to justify it.

Cursor fits squarely into the application layer, which is the top layer of the “five-layer cake” (consisting of energy, chips, compute infrastructure, models, and applications),.

While it may not seem like a typical end-consumer application, this classification can be justified because Cursor is a “front-end developing interface” that allows users (coders) to interact with and utilize underlying AI models. In this framework, any software that sits on top of a model to provide a specific utility to a user is considered an application

So “application”, in this case, does not only mean as a mass-market consumer app like Instagram. Rather, it can be any user-facing software that translates the power of underlying models into specific, productive tasks. In the case of Cursor, its automated coding.

Potential Risks

[The key risk that I can think of is their ability to integrate the differnt layers. So far, all of Elon Musk’s companies have operated as separate entities.

But, building a truly vertically integrated company requires much deeper coordination across the different layers. It remains unclear how effectively SpaceX, xAI, X/Twitter, and Cursor can be integrated into a unified ecosystem without creating operational complexity or internal friction. If the integration fails, the company risks becoming a collection of loosely connected businesses rather than a cohesive AI platform.

Conclusion

Viewed in isolation, SpaceX acquiring Cursor may seem like an unusual deal. But when seen through the broader strategy of building a vertically integrated AI ecosystem, the rationale becomes clearer. Cursor potentially gives them direct ownership of a key application layer interface, reducing dependence on external platforms while deepening their connection with developers.

The other area that SpaceX would benefit from this deal is justifying their valuation ahead of their anticipated IPO in later this year (2026). SpaceX is planning to raise $75 billion at a $1.75 trillion valuation. But, as per Yahoo Finance, SpaceX generated about $8 billion in profit on $15 billion to $16 billion of revenue last year (2025).

According to an article from “The Information”, SpaceX’s 2025 pro-forma numbers (i.e. post-xAI acquisition) show a Total Revenue of around $18.7 billion with a net loss of around 5 billion.

Of the $18.7 billion revenue, $11.4 billion came from Starlink, $4.1 billion from launches, and $3.2 billion from xAI (Grok + X/Twitter).

As per the report SpaceX on its own made a modest profit, but it was still a profit. The net loss is due to xAI loosing around $5 billion, turning SpaceX’s standalone modest profit into a net loss.

These numbers make it hard to justify the $1.75 trillion valuation to investors outside of Elon Musk fans. But maybe this acqusition can give more weight to the argument of being able to monetize the AI models more effectively and turning xAI’s losses into profits.

Moving from just providing the infrastructure & AI model layer to owning the application layer allows for much higher returns on assets, potentially shortening payback periods from four years (for satellites) to as little as six to twelve months. Owning a high-revenue application like Cursor (which is reportedly doing $2 billion in revenue and doubling year-over-year) could help transform that compute power into massive cash flow.

We’ll have to watch and see how this unfolds.

SOURCES:

• SpaceX generated about $8 billion in profit last year ahead of IPO, sources say

• “Terafab”, Musk’s Biggest Scam Yet - YouTube

In terms of their technological prowess, the public perception around them saw a significant shift, from being questioned as to why they got left behind in the AI race, to being recognized as having the best AI model. I have written about this in details previously.

So, it will be interesting to see what Google does in 2026, in order to keep their position as the best performer.

Here’s an analysis of Alphabet’s performance over FY2025, so that we get a better, quantitative understanding of their performance, beyond the media and marketing hype.

I’ll start with the Income Statement first.

1. Revenue Analysis

Let’s start with the top-line numbers. There was a 18% YoY increase (from $96.5 billion in Q4 2024 to $113.8 billion in Q4 2025) and a 11% QoQ increase (Revenues increased from $102.3 billion in Q32025).

Now, you might be aware, if you follow Alphabet’s business, that revenues from advertisement make up the majority of their Total Revenues. But this is changing. In the next 2 charts below, you’ll see Revenue from Advertising having a downward slope. On the other hand, Revenues from cloud, driven by AI demands, is showing a consistent upward slope.

With all the CapEx (Capital Expenditures) on building AI Infrastructure, the question is whether its going to pay off? The growth in Revenues from Google Cloud shows that the investments are actually paying off.

In the next 2 images below, you’ll notice the Revenue from Google Cloud more than doubled over the past 2 quarters.

Now, let’s take a look at the QoQ growth trends of the Revenue, in the chart below.

We can clearly observe that Google Cloud (Green) shows the most robust positive momentum. Unlike the other segments, it never dips into negative growth. It starts 2025 with modest growth (+2.6%) but accelerates significantly throughout the year, ending with a massive +16.5% jump in Q4 2025.

Both Advertising and Subscriptions show a clear seasonal pattern. They both drop significantly in Q1 2025 (negative growth) after a strong holiday season (Q4 2024).

Subscriptions, Platforms & Devices Volatility (Orange) is the most volatile, with sharp peaks (e.g., +14.9% in Q3 2025) and deep troughs (10.8% in Q1 2025), likely driven by hardware launch cycles.

2. Profit Margin Analysis

Now, let’s take a look at the 4 profit Margins: Gross Margins, Operating Margin, EBITDA Margin & Net Margin.

Observations and Insights

From the chart above, we can observe 2 things:

1. Upward trend in Gross Margin.

The gradual upward trend in Gross Margin (reaching ~60% by late 2025) is primarily driven by a improved efficiency in cloud infrastructure.

Cloud revenue surged (e.g., +48% YoY in Q4 2025), but the cost to deliver that revenue did not grow as fast. What this means is that the variable cost to serve additional customers is less than the Revenue generated by that customer. The high fixed costs of data centers are spread across more customers.

In the 10-k form for FY 2025, Management noted that growth was “led by an increase in Google Cloud Platform (GCP) across enterprise AI Infrastructure... as well as core GCP products”. The segment’s operating income grew significantly faster than its revenue, demonstrating that the high fixed costs of data centers are being spread over a larger revenue base.

2. Operating, EBIDTA & Net Margin are fluctuating within a small range.

While Gross Margin has been steadily ticking up, the other margins are volatile. But these volatility is due to specific “one-time” charges and non-operating financial activities rather than core business health.

Operating margin trends would have been similar to Gross Margin trends if not for massive, irregular expenses recorded in the past 2 quarters, such as the ones below:

• Legal Fines: In Q3 2025, the operating margin dropped to 30.5%. This was directly caused by a $3.5 billion accrued fine from the European Commission. Excluding this fine, the Operating margin would have been 33.9% (30.5%). (The company accrued a $3.5 billion fine imposed by the European Commission regarding Google’s advertising technology business)

• Waymo Compensation Charge: In Q4 2025, operating income was hit by a $2.1 billion employee compensation charge related to Waymo. This is a non-cash expense related to the valuation of Waymo stock following its investment rounds, creating a temporary drag on reported margins.

3. Expense Structure Analysis

Let’s now move on to expenses.

Here’s the Break down operating expenses as % of revenue

• Total Operating Expenses (R&D + S&M + G&A): 27.6% of Revenue.

Observations & Insights

• Cost of revenues (COR) remains the largest component.

One of the primary reasons why COR is high because The Cost of Revenues includes the energy and electricity costs required to run the data centers.

According to the 2025 Annual Report (Form 10-K), the “Cost of Revenues” specifically includes “other technical infrastructure operations costs,” which includes “Energy” among other components

Now, isn’t this a contradiction? Previously, we found out that the Gross Margins have been increasing over the past 8 quarters. How could that be, if Cost of Revenue is also increasing?

This is possible if Revenue is growing faster than Cost of Revenue. And that’s what the scenario is.

Even though Cost of Revenue grew by 11% YoY, the Total Revenue grew at an even higher rate by +15%. That’s the reason why Gross Profit, and hence gross profit is positive and showing an upward trend.

As I have already mentioned this in the Profit Margin Analysis section, this means that the variable cost to serve additional customers is less than the Revenue generated by that customer. The high fixed costs of data centers are spread across more customers.

4. Operating Leverage Analysis

Here’s the trend of the Revenue Growth & Operating Income Growth over the past 8 quarters.

Here’s the Operating Leverage trend for the past 8 Quarters:

Observations

• Over the past 8 Quarters, Revenue growth has an upward trend.

• However, Operating Income growth has decreased over the past 8 quarters, from +46.3% in Q1FY24 to +16.0% in Q425

• Operating Income growth has caused the Operating Leverage to follow a downward trend too, from 3 in Q12024 to less than 1 in Q42025

Insights (making sense of the Observations)

Now, this decrease in Operating leverage, driven by decrease in Operational Income growth, might make you ponder “Is declining leverage due to strategic investments or inefficiency?”

This decline is definitely not because of operational efficiency. Rather, the recent decline is due to 2 main reasons: strategic investments and one-time charges. The company spent $91.4 billion on capital expenditures in 2025 to build AI infrastructure. This created significantly higher depreciation costs (21.1 billion) that weigh on current profits.

Furthermore, reported leverage was artificially lowered by irregular expenses that I have already mentioned earlier (a $3.5 billion legal fine in Q3 and a $2.1 billion compensation charge for Waymo employees in Q4). Without these specific items, the company maintained strict discipline over its regular operating expenses.

…..

The most important question about AI it whether it will be profitable enough, given the massive investments needed. Stated in a more formal way, Does the AI infrastructure business model support high leverage potential?

The answer is Yes, the business model supports massive leverage potential. Once the expensive “fixed” costs (data centers and servers) are paid for, adding new customers costs very little, allowing profits to soar. Google Cloud’s Q4 2025 revenue grew 48%, but operating profit exploded by 154%. This confirms that as the company fills the capacity of its new AI infrastructure, profits will grow much faster than Revenue, demonstrating the inherent scalability of the model.

5. Net Profit (Bottom-line) Analysis

The following table summarizes the Revenue, Basic and Diluted EPS alongside their respective growth rates compared to the same quarter in the prior year (YoY).

Across all eight quarters, we can observe that the EPS growth has consistently outpaced revenue growth, often by a multiple of 2x or more.

• Revenue Trend: Revenue growth has remained steady, fluctuating between 12% and 18%.

• EPS Trend: Earnings growth has been explosive, ranging from 22% to 62%.

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While profits growing faster than revenue positive for shareholders, such a large divergence often indicates that growth is being driven by non-operating factors or one-time events rather than core business leverage alone.

So, the question is, Is net income growing due to efficient operations or financial engineering?

The short answer is that it’s mostly due to Financial Engineering instead efficent operations.

The long answer is, the significant gap between moderate top-line growth and surging bottom-line earnings is driven by three primary factors:

1. Share Repurchases (Financial Engineering): Alphabet has aggressively bought back stock (e.g., 62.0 billion in 2024 and 45.4 billion in 2025). This reduces the “denominator” (share count), automatically boosting EPS even if net income stays flat.

2. Investment Gains (Non-Operating): Significant spikes in EPS, particularly in Q1 2025 (+49%) and Q3 2025 (+35%), were driven by massive unrealized gains on equity securities (e.g., an $8.0 billion gain in Q1 2025 and substantial gains in Q3).

   What does this mean?

   Alphabet (Google) acts like an investor. Besides selling ads and cloud services, it owns shares in other private companies (startups). In Q1 2025 and Q3 2025, the estimated value of some of these startups increased massively (by $8.0 billion in Q1 alone).

   This is profit that exists only on paper.Accounting rules require Alphabet to list this increase in value as “income” immediately, even though they haven’t sold the shares or collected the cash.

   Because this “paper money” is added to the company’s total profit for the quarter, the final profit number looks huge.

   As a result, EPS (profit per share) jumped 49% in Q1 and 35% in Q3. This huge jump didn’t happen because Google sold 49% more ads or cloud services. It happened because their investment portfolio had a lucky quarter. Therefore, it doesn’t reflect “core operating efficiency” (how well they run their actual day-to-day business).

Final Insights from Income Statement Analysis

Positives

1. Diversification of Revenue

Even though Advertisement is still the largest share of revenue, we can observe diversification, especially through Google Cloud’s impressive growth.

2. Investments in AI infrastructure is paying off

The most immediate return on AI infrastructure investment is seen in the Google Cloud segment, where customers pay to use the very infrastructure (TPUs, GPUs, data centers) Google is building.

The payoff is visible in the 48% Cloud growth, the 154% Cloud profit jump, and the sustained 17% growth in Search. The infrastructure is not sitting idle. It is generating high-margin revenue faster than the company can build it, necessitating an even larger investment for 2026. (2026 CapEx guidance: $175 billion – $185 billion).

So, this concludes the analysis of Income Statement.

To understand this story better, I think it will be better if I gave you a brief history, so that you’ll have a better context.

…..

After the first major wave of restrictions in October 2022, Nvidia created modified, less powerful, versions of its flagship chips specifically for Chinese customers.

• A800: A slowed-down version of the A100.

• H800: A slowed-down version of the H100.

These were allowed because they had reduced interconnect bandwidth (how fast chips talk to each other), which made it much harder to link thousands of them together into a massive supercomputer.

But, eventually the Chinese firms found a way to work around it. So, in October 2023, the US Govt. completely banned A800 & H100.

Nvidia responded by releasing the H20, a severely “neutered” chip with only about 15% of the power of the standard H100. This was the most advanced chip legally available to China throughout 2024 and most of 2025.

Then, in April 2025, the incoming Trump administration briefly clamped down even harder, revoking licenses for even the “compliant” H20 chips and creating blackout of Nvidia’s high-end AI hardware to China. Why did they do it? My guess is it was a power move.

Now, in January 2026, the US government officially shifted its policy to allow the export of Nvidia’s H200 AI chips to China. Now, the reason they have allowed it is because early in January 2026, at the CES, Nvidia revealed it’s newest architecture, the Blackwell series.

..

I think I should also mention Nvidia’s naming convention for the different architectures.

Nvidia typically releases a new architecture every two years, with data center chips (for AI) and consumer chips (RTX gaming) often sharing the same underlying tech under different names.

Now, Anthropic CEO Dario Amodei was not happy with this decision. Here’s what he said at the World Economic Forum 2026:

Anthropic Chief Executive Officer Dario Amodei said selling advanced artificial intelligence chips to China is a blunder with “incredible national security implications” as the US moves to allow Nvidia Corp. to sell its H200 processors to Beijing. “It would be a big mistake to ship these chips,” Amodei said in an interview with Bloomberg Editor-in-Chief John Micklethwait at the World Economic Forum in Davos, Switzerland. “I think this is crazy. It’s a bit like selling nuclear weapons to North Korea.” - (Source: Anthropic CEO Says AI Chip Sales to China Like Selling Nukes to North Korea - Bloomberg)

But this is why it makes strategic sense for Nvidia should sell its chips

Here’s Ben Thompson’s argument TSMC Risk:

The nuclear weapon analogy is an interesting one: a lot of game theory was developed to manage the risk of nuclear weapons, particularly once the U.S.S.R. gained/stole nuclear capability, ending the U.S.’s brief monopoly on the technology. Before that happened, however, the U.S. had a dominant military position, given we had nuclear weapons and no one else did. Perhaps Amodei believes the U.S. should have advanced AI and China should not, giving us a dominant military position?

The problem with that reality, however, is Taiwan, as I explained in AI Promise and Chip Precariousness. AI, in contrast to nuclear weapons, has a physical dependency in Taiwan that can be easily destroyed by Chinese missiles, even without an invasion; if we got to a situation where only the U.S. had the sort of AI that would give us an unassailable advantage militarily, then the optimal strategy for China would change to taking TSMC off of the board.

Given this dependency, my recommendations in the Article run counter to Amodei: I want China dependent on not just U.S. chips but also on TSMC directly, which is why I argued in favor of selling Nvidia chips to China, and further believe that Huawei and other Chinese companies ought to be able to source from TSMC (on the flip side, I would ban the sale of semiconductor manufacturing equipment to Chinese fabs). I think it’s a good thing the Trump administration moved on the first point, at least.

From a military perspective, a country having an advanced AI will give them a Dominant Military Position over a country that doesn’t have it, just like a country having nuclear weapons gives them a dominant military position over a country that doesn’t have it.

Now, to develop this advanced AI, Nvidia’s chips are the most advanced hardware. But as you might know, Nvidia only designs these chips, but it’s manufactured by TSMC.

Now, if China is denied access to Nvidia chips, this means they won’t be able to develop the advance AI and hence will lag behind AI. The military strategy in this case would be to destroy TSMC’s manufacturing facilities, in Taiwan, with a missile, thus destroying US’s capabilities to develop the hardware to train & run the advanced AI, thus levelling the playing field in terms of AI supremacy. (If I can’t have it, you can’t have it either. Thus, we are even). China won’t even have to invade Taiwan for this.

But you may say, “If China invades Taiwan and takes control of TSMC, instead of destroying TSMC’s facilities they can just use it to build the chips for themselves”.

There are a few things that makes it infeasible. First, as I mentioned earlier, Nvidia designs the chip architecture. So even if China gains control of the manufacturing capabilities, China does not yet have any company that matches Nvidia’s chip architecture design capabilities.

…….

But, if Nvidia sells their chips to China, then China is also reliant on TSMC (and indirectly on U.S. companies like Nvidia) for advanced chips, then Taiwan’s semiconductor industry becomes a strategic deterrent. Any attack on Taiwan would cut off China’s own access to these advanced chips. So, China won’t have the incentive to invade Taiwan or attack TSMC’s facilities.

This is the reason I think it makes sense for US govt. to allow sales of H200 chips to Chinese firms.

..

Insights for Investors

If this logic is solid, then Nvidia will get back access to one of the largest markets for its chips not just temporarily, but for the long term.

Sources referred & links for Further Research

• Anthropic’s CEO stuns Davos with Nvidia criticism | TechCrunch

Addendum

1. TSMC’s operations in China

TSMC has historically manufactured chips for Chinese chip design companies, but this relationship has been significantly restricted by U.S. export controls in recent years.

TSMC has served several major Chinese tech companies as foundry customers, including :

• Alibaba (for their AI chip designs)

• Baidu (AI and cloud computing chips)

• Huawei (various chip designs before restrictions)

• Tencent (chip designs)

• ByteDance (partnered with TSMC to develop AI chips to rival Nvidia)

In November 2024, TSMC was ordered by the U.S. Department of Commerce to halt shipments of advanced chips (7 nanometer and below) to Chinese customers used in AI applications . This was triggered after a TSMC-manufactured chip was discovered in a Huawei AI processor, apparently violating earlier U.S. controls.

What TSMC Still Does in China

TSMC operates manufacturing facilities in mainland China:

• Nanjing plant: Produces 16nm and other mature node chips (not advanced nodes)

• Shanghai plant: Another manufacturing facility

In January 2026, TSMC announced it received a U.S. license to continue importing chipmaking equipment to its Nanjing facility for these mature node operations, which accounted for approximately 2.4% of TSMC’s overall revenue in 2024

…..

So, TSMC does and has made chips for Chinese chip design companies, but:

• Advanced chips (7nm and below for AI/GPU): Currently restricted by U.S. export controls

• Mature node chips (16nm and above): Still permitted and manufactured at TSMC’s China facilities

• Tesla delivered 1.63 million vehicles globally in 2025, a 9% fall from 1.79 million in 2024

• BYD, on the other hand, delivered 2.26 million EVs in 2025

• The elimination of the $7,500 U.S. federal tax incentive that seems to have played a big role in the decline of EV sales in the U.S. in the fourth quarter, which obviously affected Tesla numbers negatively.

…..

Now, Tesla bulls justify this by saying that Tesla is more than an EV company. It’s transitioning to an AI and robotics company

But the bulk of Tesla’s income comes from its EV business. For instance, Tesla generated $28 billion in revenue in the third quarter, of which $21.2 billion came from selling EVs.

Again, I understand that this is simply a snapshot of the present, not what’s to come in the future.

…..

A logic that doesn’t make sense to me

Tesla has a data advantage for self-driving cars. But they do not have this advantage for Robotics. But the entire thesis/argument is about Tesla transitioning to AI and Robotics, but how are they expected to crack Robotics if they do not have the data advantage for Robotics i.e. they do not have any advantage over other Robot companies.

Now, if you say that, Robots can be trained in virtual/ simulated worlds, then my response is “If robots can be trained on simulated/ virtual worlds, then self-driving can also be trained in the same manner”. So, Tesla’s data advantage is not a good thesis.

So, either:

1. Tesla has a data advantage for self-driving car, in which case Tesla does not have a data advantage for humanoid robots (unless they have been collecting humanoid robot centric data for the last decade unknown to public knowledge). This means that Tesla will dominate autonomous driving, but there will be aggressive competition for autonomous humanoid robots, with no guarantee that Tesla’s Optimus will come out on top.

OR

2. Humanoid robots can be trained in simulated virtual worlds, in which case self-driving cars can also be trained in a similar manner in theory. In this case Tesla does not have the data advantage.

..

Personally, I lean into option 1, based on my knowledge of machine learning works.

SOURCES and FURTHER EXPLORATION:

• Global EV sales reach 20.7 million units in 2025, growing by 20% - Rho Motion

• Tesla Fourth Quarter 2025 Production, Deliveries & Deployments

ADDENDUM

1. EV Sales Data in 2025

• Global: 20.7 million, +20%

• China: 12.9 million, +17% (Around 63% of the total EV’s were sold in China)

• Europe: 4.3 million, +33%

• North America: 1.8 million, -4%

• Rest of World: 1.7 million, +48%

  

2. Tesla launched fully autonomous ride hailing services in Austin

Tesla has recently launched fully autonomous (unsupervised) robotaxi rides in Austin, Texas, without a human safety monitor physically inside the vehicle. This is part of a limited pilot program that began with safety drivers in June 2025 and expanded to public, driverless rides in January 2026 (Tesla launches robotaxi rides without safety monitors in Austin - Automotive News)

Over the years, we are witnessing more and more of the features of cars being paywalled. From a consumer’s perspective this sounds ridiculous. A car used to be something you bought once, outright. The only costs were the repair & maintenence (and the fuel, obviously).

If you’ve ever watched the YouTuber Louis Rossmann, you might be familiar that he strongly criticizes the trend of car subscriptions and features locked behind paywalls, viewing it as a push by tech and auto companies to shift from ownership to recurring revenue, similar to software, where features like horsepower, heated seats, or even suspension performance are rented monthly instead of being part of the initial purchase.

Now, starting February 14, Tesla is officially retiring the option to purchase its Full Self-Driving (FSD) suite outright for $8,000. Moving forward, the only way to access the feature will be through a subscription: $99 per month or $999 per year.

..

The deadline is set for Valentine’s Day. After that, the $8,000 “buy-it-and-forget-it” option vanishes. This move appears strategically timed to align with Elon Musk’s 2025 CEO Performance Award, which reportedly hinges on Tesla hitting a massive milestone: 10 million active FSD subscriptions over the next decade.

The scale of this challenge is enormous. As of late 2025, only about 12% of the Tesla fleet had opted for FSD. To hit 10 million, Tesla needs to transform FSD from a luxury add-on into an essential utility for nearly every car it produces.

..

At $99 per month, it takes roughly 81 months, nearly 7 years, to reach the $8,000 price tag of the current one-time purchase. If you opt for the $999 annual plan, the “break-even” point is exactly 8 years.

Given that the average car ownership cycle in the U.S. is roughly 6 to 8 years, many users might actually save money in the short term. However, the catch is the residual value. An $8,000 software package stays with the car (usually), potentially boosting resale value. A subscription disappears the moment you stop paying.

…..

This is a classic move to improve valuation multiples. Markets hate “lumpy” revenue, where you have to sell a new car every time you want to make a dollar. Markets love “recurring” revenue, because it provides the following:

1. Predictability: A subscription model provides a steady, predictable cash flow that allows for better long-term capital allocation.

2. Higher Multiples: Software companies (SaaS) trade at much higher Price-to-Earnings (P/E) multiples than hardware manufacturers. By forcing FSD into a subscription, Tesla is signaling to Wall Street that it is a software company first, and a car company second.

3. Lower Barrier to Entry: Convincing someone to try a $99/month service is significantly easier than asking for an extra $8,000 at the dealership. This “top of the funnel” expansion is critical for hitting that 10-million-subscriber goal.

Owning vs. Renting the Future

On the ground, particularly within the Reddit investment and Tesla owner communities, the sentiment is more divided. There is a palpable sense of “subscription fatigue.” We’ve seen this coming; businesses don’t do one-time sales anymore if they want to survive. They need you on the hook indefinitely.

For the Tesla owner who keeps their car for a decade, this is a net loss. There is a fundamental psychological difference between owning a product outright and paying for a “right to use.” When you own the $8,000 FSD package, you own an asset. When you subscribe, you are a tenant in your own car.

Furthermore, owners face the risk of future price hikes. Unlike an $8,000 purchase that is locked in, a subscription price can be adjusted with a single line of code. Many owners feel that the car they “bought” is slowly being turned into a rental, where features can be gated or priced at a premium at any moment.

..

If a customer pays $50,000 for a car, they expect the hardware and its primary capabilities to belong to them. Forcing a subscription model onto safety and convenience features feels like a “bait and switch” to those who bought into the brand’s early promise of disruptive ownership.

…..

Ultimately, this move is about ecosystem lock-in. Musk is betting that once a driver gets used to FSD, it becomes a “sticky” utility they can’t live without, much like an iPhone user and iCloud.

For shareholders, the news is a bullish indicator of margin expansion. Software has nearly zero marginal cost, meaning every $99 subscription is almost pure profit once the R&D is amortized. For owners, it’s a reminder that in the modern economy, “ownership” is becoming a legacy concept.

This called into question about Google’s future prospect. Because Google makes most of its money through advertisements, through Google search. And a decrease in google search traffic will reduce the advertising revenue, which will in turn affect is valuation, since there will be a decrease in the future cash flow. (Remember, the value of a company is the PV of sum of all cash flows).

But then, towards the end of the year, Google launched Gemini 3, and since then, it’s been considered to be the best AI Model. ChatGPT, which was the dominant LLM since it was release on 30th November 2022, saw its market share halved by end of October 2025.

(Source: Investing visuals on X: “OpenAI reportedly plans a potential $1 TRILLION IPO in 2027)

…..

Then, it was revealed that Gemini 3 was trained exclusively on TPU’s, which are Google’s own custom processors. Moreover, Claude 4.5, which was launched just a few days after Gemini 3, also used Google’s TPUs for its training.

Overall, they are now the only company that has a stake in every layer of the AI Value Chain.

(Source: Lin on X: “Google is the only company that owns the entire AI stack)

…..

Now, Google has begun 2026 in full gear. They are now focusing more on the application layer of the AI Value Chain. And that is crucial, since this is the only way in which the whole of AI Industry stabilizes itself and gets rid of the “bubble” tag.

Apple announced that the updated Siri will be powered by Google’s Gemini.

The other thing they revealed was the Universal Commerce Protocol.

So, the public narrative around Google has changed significantly over the past 1 year. And it has been reflected in terms of stock performance, Google rose by around 65% over the past year.

Their market cap also crossed the $4 billion dollar line for the first time in their history. In the process they became only the 4th company in history to achieve this feat. Nvidia, Apple & Microsoft were the other 3 companies. However, Apple & Microsoft have dropped down.

Currently, Google’s (Alphabet’s) valuation is second only to Nvidia, but in terms of Revenue & operating income, it is actually better than Nvidia.

(Source: GenAI_Now on X: “@Google is just $0.5T away from passing @nvidia)

Let’s see what 2026 brings in for Google & the AI industry.

In this post, I intended to provide an explainer about this deal for people who do not regularly follow the AI hardware industry. So if you are a regular follower of this industry, you might find this post a bit simplistic.

What Groq Does

Groq builds specialized AI chips called Language Processing Units (LPUs) designed exclusively for AI inference i.e. the stage where a trained AI model actually responds to user queries in real-time, such as whenever you chat with an AI chatbot.

To use an analogy, training an AI model is like teaching a student (computationally expensive, done once), while inference is like the student answering questions on an exam (needs to be fast and cheap, done millions of times).​

Groq’s LPUs excel at delivering ultra-fast responses with lower costs by using on-chip SRAM memory that’s 10x faster than the memory in traditional GPUs. This architecture makes them ideal for applications requiring instant AI responses, like chatbots, autonomous vehicles, and real-time analytics

Does Groq compete directly with Nvidia?

There’s partial overlap but different focus areas. Nvidia’s GPUs dominate AI training, the computationally intensive process of building AI models from scratch. However, Nvidia also competes in inference, where Groq specializes exclusively.​

The key difference is that Nvidia offers general-purpose GPUs that handle both training and inference, making them flexible but less optimized for inference speed. Groq’s LPUs are custom-built ASICs (application-specific chips) that sacrifice flexibility for dramatically faster inference performance at lower cost per query.

As AI shifts from “training new models” to “deploying existing models efficiently,” Groq’s inference-first approach directly challenges Nvidia’s inference business.

What does Nvidia get from this deal with Groq?

I’ll answer this in 2 parts. From a technology standpoint and from a Business standpoint.

1. Technology Benefits

Nvidia gains Groq’s revolutionary LPU architecture that uses on-chip SRAM memory instead of external HBM (High-Bandwidth Memory), delivering up to 100× faster data access and 80 TB/s memory bandwidth. This eliminates latency bottlenecks that plague traditional GPUs, achieving sub-millisecond response times for AI inference. Groq’s deterministic, single-core design avoids “wasted cycles” common in GPU scheduling, providing 2-10× faster inference performance.

Nvidia also acquires Jonathan Ross (Google’s former TPU inventor) and elite engineers who bring years of specialized low-latency chip design expertise.

This technology compresses what would have been years of internal R&D into immediate capability, filling critical gaps in Nvidia’s inference offering

2. Business Strategy (Eliminates a Rising Competitor)

Nvidia essentially neutralizes Groq before it becomes a serious threat. Groq’s LPU chips were gaining traction as a faster, cheaper alternative for AI inference, the exact market segment where Nvidia’s GPUs are being challenged. By absorbing Groq’s technology and talent, Nvidia removes a competitor that could have captured market share in the rapidly growing inference business.

Regulatory issues about the Nvidia-Groq deal

By structuring this as a licensing deal rather than a full acquisition, Nvidia avoids traditional antitrust reviews despite its already dominant 92% market share in data center GPUs. Groq remains nominally independent, continuing its GroqCloud service under new CEO Simon Edwards, which creates the appearance of ongoing competition.

On 21 Dec 2025, Investor Bill Ackman posted on X about a proposal about brining SpaceX to the public markets.

Bill Ackman on X: “.@elonmusk, what if we took @SpaceX public by merging it with Pershing Square SPARC Holdings, Ltd. (SPARC)

…..

Bill Ackman has proposed a unique way to take SpaceX public that skips the expensive “middlemen” of Wall Street. He wants to use a financial tool he created called a SPARC to merge with SpaceX. This method challenges the traditional Initial Public Offering (IPO) process, where investment banks typically charge massive fees, estimated at $400 million for a deal this big, to handle the paperwork and sale.

…..

What is a SPARC?

Let me start with SPAC first.

A SPAC (Special Purpose Acquisition Company) is a shell company that raises money from investors first, then looks for a private company to merge with and take public. It has no operations of its own—its only job is to find a target within a set time (usually 2 years). If it succeeds, the private company becomes publicly listed through the merger. If it fails, investors typically get most of their money back. SPACs became popular because they offered a faster, sometimes simpler alternative to traditional IPOs, but many deals ended up being poor-quality or overpriced.

A SPARC (Special Purpose Acquisition Rights Company) is a newer twist on this idea. Instead of taking investors’ cash upfront, it gives them rights i.e. options to invest later if they like the deal. Money only moves after a real company and terms are announced. That way, investors avoid having their cash locked up, and only commit if they believe the specific company (like SpaceX) and valuation make sense.

Some context behind SPARC

Bill Ackman invented this structure to fix the flaws of the old model. He received government (SEC) approval for it in September 2023, but it has been sitting “on the shelf” unused. If this goes through, it would be the first time in history a SPARC is actually used

The Deal on the Table

Ackman is offering this vehicle to Elon Musk for free. He won’t charge SpaceX the usual banking fees. In exchange, Ackman’s only demand is that he gets to buy about $4 billion worth of SpaceX stock for his own fund.​

You can notice that there are 2 big advantages here:

• Fairer Pricing: In a normal IPO, banks often set the price artificially low so their best clients can buy cheap and make a quick profit when the stock goes up. The SPARC removes the banks, letting the market set a fairer price for SpaceX.​

• Access for Regular Investors: Usually, hot new stocks are reserved for big hedge funds. This proposal includes a plan to give everyday Tesla shareholders a special chance to buy SpaceX stock early.​

But, there’s One Big Problem

The SPARC company is currently registered in Delaware. Because Elon Musk has had bad experiences with Delaware courts (which voided his Tesla pay package), he likely won’t agree unless the entity is moved to Texas. This legal move could delay the process by one to three months

…..

Ackman’s SPARC proposal is a genuine innovation in capital raising, but still unproven in practice, as it’s never been used before. If executed, it could set a new benchmark for founder-friendly listings and retail access, yet legal friction and structural novelty means if this actually happens then it’s going to be a high-uncertainty experiment.

I am not sure that its going to be very likely. My personal take is that I would give it a low probability of materializing, given that SPARC company is currently registered in Delaware. But if the entity is moved, then I’ll have to re-evaluate my probabilites

…..

Sponsor Section:

Get in touch here

1. Google’s TPU

For years, Nvidia’s high-end GPUs were the default for big AI training runs. This changed when Google quietly doubled down on internal silicon, culminating in Gemini 3, widely seen as a technical leap and the first “frontier model” not trained on Nvidia hardware. Instead, Gemini 3 and even Claude Opus 4.5 were engineered entirely on Google’s TPUs (Tensor Processing Units), which brings a massive cost-performance advantage because Google doesn’t have to “rent” expensive chips from external players.​

• Google now controls both the model and the hardware stack. They’re not at the mercy of GPU supply shortages, and their TPUs are now evidently strong enough to beat, or at least rival, Nvidia’s for AI workloads. Gemini 3, for example, has shown new capabilities in complex multimodal reasoning, like directly “watching” a video or analysing real-world sensor data, which is central to its edge over other models.​

• Meta (Facebook’s parent), a massive Nvidia customer, is rumored to be negotiating a switch to Google’s TPUs for its own generative AI push. If Meta moves, that’s a potential billions-worth customer loss for Nvidia. Google has been pitching its TPUs heavily and suggests capturing even 10% of Nvidia’s annual chip revenue from new deals with hyperscalers and financial firms.​

So, the takeaway is that Google’s TPU challenge looks real, especially if other hyperscalers follow Meta’s flirtation. If Google derives a moat from tightly coupling AI/ML model innovation with in-house silicon, Nvidia must work twice as hard to maintain ecosystem lock-in and keep customers loyal, on either software or absolute hardware performance.

2. Allegations over “Faked Revenue” and Nvidia’s Response

2 well-known investors, Jim Chanos and Michael Burry (of the “Big Short” fame) called Nvidia’s recent earnings into question.

Burry has claimed that Nvidia’s revenue boom is suspicious, drawing comparisons to historic bubbles like Cisco pre-dotcom and even pushing the argument that the company’s owner earnings are artificially inflated due to aggressive stock-based compensation.​

Jim Chanos, on the other hand has compared Nvidia to Lucent. Lucent Technologies, in the late 1990s, gave loans to struggling telecom startups so they could afford Lucent equipment. When the dot-com bubble burst and those startups collapsed, Lucent lost billions because the “customers” couldn’t actually pay. Chanos calls Nvidia’s strategy “Lucent 2.0” and warns that if AI demand doesn’t materialize as projected, orders could get cancelled en masse.

…..

BUT I want to focus some points highlighted by a little-known person, in his Substack Post.

During the earnings call, CEO Jensen Huang described demand as “insane” (aka high demand). But the numbers from the financial statements does not align with this. Receivables as well as Inventory, both went up. If the demand for chips is high, then these 2 metrics should go down, not up.

..

1. RECEIVABLES

Let’s look at Receivables first. Nvidia’s historical DSO (Days Sales Outstanding), averaged across fiscal years 2020 through 2024, stood at 46 days. But Nvidia’s third quarter of fiscal year 2026 is 53.3 days. If demand is rising, this should be going down, not up.

..

Receivables rose because Nvidia recorded sales where customers have not yet paid. When chips are shipped, the company books revenue immediately, but if payment is delayed, the owed amount is logged as accounts receivable instead of cash. This means Nvidia’s reported revenue is increasing without a matching rise in cash inflows. Normally, that’s not an issue. Receivables rise when healthy customers buy on credit and they pay it later.

But Jim Chanos argues that Nvidia is investing in or lending to money‑losing customers (like CoreWeave or Open AI), who then use that funding to buy Nvidia’s chips. So, Receivables is rising because Nvidia’s own money is cycling back through customers to book sales. This could be evidence of structural risk, where receivables reflect Nvidia funding its own sales rather than genuine market demand. This kind of circular financing inflate demand and revenue without independent cash inflows.

..

The hope from Nvidia’s perspective is that these companies that Nvidia has invested in i.e. CoreWeave, Open AI etc, are eventually profitable and can pay Nvidia back. But, if they fail, then Nvidia has to write down investments or restate financials and, as a result, the stock could fall dramatically. Perera’s model suggests a 60%+ drop to $71/share.

…..

2. INVENTORY

Now, looking at the Inventory, Nvidia reported inventory of $19.8 billion, up from $15.0 billion in the prior quarter. This is a 32% quarter-over-quarter growth. Again, if demand is rising, this metric should be going down, not up.

In short, Nvidia’s warehouses are filling up when they should be emptying, raising doubts about whether reported sales truly reflect real, cash-backed demand.

..

These 2 metrics raise valid questions about the true nature of demand for Nvidia’s chips.

3. Key Investors Head for the Exit

Some of Nvidia’s key Investors in Nvidia have recently exited their positions.

Peter Thiel, through his Thiel Macro fund, has completely liquidated his Nvidia holding, more than 537,000 shares, valued at nearly $100 million and formerly almost 40% of his entire portfolio. The exit came after consistent accumulation in previous quarters and coincides with broader AI bubble fears and profit-taking at likely all-time highs.​

SoftBank, which once owned a massive position, also revealed a full exit just before the Q3 rally.​

Notably, Thiel’s exit was paired with slashing Tesla and Vistra Energy as well, suggesting a rotation or de-risking strategy rather than a bear bet solely on Nvidia.

Some analysts point out that funds like Thiel’s routinely churn big positions and may have timed the run-up for profit. However, that both Thiel and institutions such as SoftBank exited concurrently does spark concern that “smart money” could see risks ahead, whether from new chip competition, AI regulation, or China uncertainty, worth locking in historic gains before another leg up.

4. China Market

Nvidia’s China story used to be simple: big growth, minimal geopolitical trouble. In 2025, that story looks very different:

• U.S. regulators barred Nvidia from selling its most advanced GPUs to China due to national security concerns, leaving only a watered-down H20 chip available. Chinese firms such as ByteDance tried to hoard inventory but new export bans, plus Chinese authorities banning top tech companies from further purchases, have cratered demand.​

• Concrete numbers underscore the impact: Nvidia generated just $50 million in revenue from China for its H20 chips during Q3, against expectations north of $8 billion. China now represents less than 5% of Nvidia’s revenue, down from over 13% a year ago. The “Blackwell” generation of GPUs may also be kept out of the market due to ongoing political negotiations.​

• Chinese competitors like Huawei and Alibaba are launching domestic GPU/AI chips, aiming to replace Nvidia in hyperscale data centers as fast as possible.​

• CEO Jensen Huang has admitted that Nvidia’s business in China is now “zero,” pending unlikely regulatory breakthroughs.​

Does this hurt Nvidia strategically? It does, but only if China keeps out Nvidia long-term. Losing one of the world’s largest AI infrastructure markets is material, even as the U.S. and Taiwan remain strongholds.

Further Reading:

• Nvidia says it isn’t using ‘circular financing’ schemes. 2 famous short sellers disagree.

• The Algorithm That Detected a $610 Billion Fraud: How Machine Intelligence Exposed the AI Industry’s Circular Financing Scheme

In this post, I want to look at its earnings, outlooks by management and then present my thoughts on what are my takeaways from this result, and in general, about Nvidia’s valuations and the broader AI market/ industry.

I want to take a look at whether the economics of this entire AI infrastructure spending spree make sense in the long run. Because if they don’t, Nvidia’s extraordinary growth will eventually hit a wall.​

First, let me walk through the numbers and what they tell us about the so-called “AI bubble” and Nvidia’s long-term value.

Earnings Summary

Nvidia’s Q3 results were nothing short of exceptional. The company reported revenue of $57.0 billion, up 22% quarter-over-quarter and 62% year-over-year. This beat analyst expectations of around $54.9 billion by roughly $2 billion. Non-GAAP earnings per share came in at $1.30, beating the consensus estimate of $1.26.​

The Data Center segment, which is the engine driving Nvidia’s growth, generated a record $51.2 billion in revenue—up 25% from Q2 and 66% year-over-year. This segment alone accounted for nearly 90% of Nvidia’s total revenue, and it beat analyst expectations by about $2 billion. Gaming revenue was $4.3 billion (up 30% YoY), Professional Visualization brought in $760 million (up 56% YoY), and Automotive generated $592 million (up 32% YoY).​

Nvidia’s gross margins remained at 73.6% (non-GAAP), and operating income hit $37.8 billion. The company also returned $37 billion to shareholders through buybacks and dividends in the first nine months of fiscal 2026, with $62.2 billion remaining under its share repurchase authorization.​

The company has now posted consecutive quarters of record revenue, and it continues to expand its dominance across multiple verticals, from cloud AI to autonomous vehicles to 5G/6G infrastructure.​

Outlook & Guidance by Nvidia

For Q4 fiscal 2026, Nvidia guided to $65 billion in revenue, which is significantly above the Street’s expectation of around $62 billion. The company also projected gross margins of 75%, which would be an improvement from the current quarter.​

Jensen Huang’s commentary during the earnings call was bullish across the board. He emphasized that “compute demand keeps accelerating and compounding across training and inference—each growing exponentially.” Nvidia also announced major partnerships with OpenAI (10 gigawatts of systems), Anthropic (1 gigawatt), and multiple hyperscalers including Microsoft , Amazon , Meta , and Alphabet .​

The company is also expanding into new verticals. Nvidia announced partnerships with Nokia and T-Mobile for AI-RAN infrastructure for 5G/6G networks, a collaboration with Uber to scale autonomous vehicle fleets, and partnerships with Toyota, Caterpillar, and other industrial players to drive physical AI and robotics.​

Nvidia’s next-generation Vera Rubin platform is expected to launch in late 2025 or 2026, and the company is already positioning it as the next leap forward for AI infrastructure. The rollout of the Blackwell architecture is also accelerating, with production ramping up at TSMC’s Arizona facility.​​

All of this sounds incredibly bullish. But it’s important to take a step back and ask: what are Nvidia’s customers getting in return for this massive spending?

………

Factors that might affect Nvidia’s future trajectory

Although there are a lot of dimensions that will affect Nvidia’s future trajectory, here I’ll limit myself into these 2 factors.

1. Impact of Custom Chips by Google, Microsoft, Amazon and Tesla on Nvidia

Over the past few years, the hyperscalers have all moved toward building their own chips: Google with its TPUs, Microsoft with Maia accelerators, Amazon with Trainium and Inferentia, and Tesla with its AI4 and AI5 chips. The reason is straightforward—reduce dependence on Nvidia , improve unit economics, and optimize hardware specifically for their own workloads.​

Let me start with Google. The company is no longer keeping its TPUs strictly for internal use. It’s actively placing them in smaller cloud providers like Fluidstack, and it’s willing to backstop up to $3.2 billion of capacity to make the economics work for those partners. This is a direct attempt to displace Nvidia racks at the margin and weaken Nvidia’s pricing power over time. Microsoft’s Maia 100 is tightly integrated into Azure, specifically for large language models running on Azure OpenAI. The entire stack—silicon, racks, networking, software—is tuned to run those workloads more efficiently than generic GPUs. Amazon’s Trainium2 is explicitly positioned as a way to reduce Nvidia dependency and offer cheaper training to AWS customers, though early feedback from some startups suggests it still lags behind Nvidia’s H100 in both latency and ecosystem maturity.​

Now, Tesla is an interesting case. The company has been designing its own chips for years. The AI4 chip, which is currently deployed in millions of Tesla vehicles, is optimized for real-time inference—processing a million video pixels in about a millisecond, according to Tesla’s AI chief Ashok Elluswamy. Tesla also had Dojo, a custom supercomputer designed specifically for training neural networks for autonomous driving. But in August 2025, Elon Musk announced that Dojo was essentially being shut down as an “evolutionary dead end.”​

Here’s what happened. Tesla had two separate chip lines: one for training (Dojo) and another for running the neural networks in the cars (the AIX series, which includes AI4 and the upcoming AI5). The new strategy is to unify these into a single architecture starting with AI6, which will be powerful enough for both in-vehicle inference and data center training. As Musk put it, “Once it became clear that all paths converged to AI6, I had to shut down Dojo and make some tough personnel choices, as Dojo 2 was now an evolutionary dead end. Dojo 3 arguably lives on in the form of a large number of AI6 SoCs on a single board.”​

But here’s the thing: the real reason for Dojo’s cancellation is that Nvidia really can’t be beat on the training front, and capacity has started to catch up, so Tesla isn’t as constrained anymore. Tesla continues to rely heavily on Nvidia’s H100 and H200 GPUs for AI training, with roughly 50,000 H100s in its Cortex data center at Giga Texas alone. Musk himself has been clear: “We’re not about to replace Nvidia, to be clear, but we do use both in combination.”​

So, are these custom chips true competitors to Nvidia? In one sense, yes—every internal chip that is “good enough” for a subset of workloads is one less GPU sold. Google, Microsoft, and Amazon are all explicitly trying to reduce their Nvidia spend for certain types of inference and training workloads. Tesla’s AI4 and upcoming AI5 chips allow the company to handle in-vehicle inference without buying Nvidia hardware for every car it ships.​

But in another sense, these chips mostly cap Nvidia’s upside rather than collapsing its volumes, at least for now. Nvidia still sits at the performance and ecosystem frontier, and the most demanding or latency-sensitive workloads continue to prefer its hardware. Amazon’s customers are complaining that Trainium isn’t as good as Nvidia’s chips. Tesla killed Dojo because Nvidia’s GPUs were simply better for training.​

Where this becomes a real risk for Nvidia is not in 2025 or 2026, but in the phase after the infrastructure build-out. If AI revenue growth normalizes and cloud capex slows, these companies now have credible in-house alternatives they can lean on to defend margins, and that will directly compress Nvidia’s volume growth and potentially its pricing power. In other words, custom chips are less about killing Nvidia today and more about limiting Nvidia’s ability to keep extracting outsized economics in a more mature AI market.

2. The uncertainty of the China Market

China is both one of Nvidia’s biggest opportunities and one of its most complex risks. The Chinese AI accelerator market is estimated at over $40 billion annually and growing north of 25% per year, but US export controls have blocked Nvidia’s most advanced chips like the H200 and Blackwell, forcing it to ship neutered variants such as the H20 instead. Those China-specific SKUs are less profitable and strategically weaker, and they open the door for domestic alternatives.​

And here’s where it gets messy: US policy has been all over the place. In late November 2025, there were reports that the Trump administration is considering allowing exports of H200s to China, and even hinting that current-generation Blackwell chips could be allowed once they are no longer the frontier node. At the same time, US authorities just charged four people for schemes to illegally export restricted A100 and H200 GPUs to China and Hong Kong, which shows how intense demand is despite the controls. Nvidia is stuck between a $40+ billion growth market on one side and shifting US national-security red lines on the other.​

The other dimension of risk is competitive. Beijing is explicitly pushing Chinese firms to transition to homegrown AI hardware. Huawei’s Ascend chips, combined with its software stack, are emerging as the flagship alternative to Nvidia in the domestic market. Those platforms are already being adopted by Chinese companies and government agencies, helped by both policy pressure and the fact that Nvidia’s best products are restricted. In the short term, this threatens Nvidia’s China revenue mix—more low-margin H20 units, fewer premium SKUs. Over a longer horizon, the concern is that a Chinese stack that matures under protection at home could start to compete credibly in emerging markets or even globally, especially in price-sensitive segments.​

The main counterweight is CUDA and Nvidia’s broader software ecosystem, which remain the de facto standard for most cutting-edge AI research and production workloads outside China. Porting entire toolchains and models to a new architecture is slow and expensive, and that inertia works in Nvidia’s favor. But if export rules keep forcing Chinese players away from Nvidia, the incentive to fully replicate that ecosystem domestically only grows. So China is a potential incubator for a geopolitical competitor that exists because Nvidia was locked out.

…………………….

FURTHER EXPLORATION:

• Elon: “Once it became clear that all paths converged to AI6, I had to shut down Dojo and make some tough personnel choices, as Dojo 2 was now an evolutionary dead end. Dojo 3 arguably lives on in the form of a large number of AI6 SoCs on a single board.” : r/teslamotors

• NVIDIA Corp Research - Alpha Spread

• How Nvidia GPUs Compare To Google’s And Amazon’s AI Chips

• Trump Administration Ponders Allowing Nvidia To Trade H200 AI Chips With China

I believe that with this acquisition Adobe is addressing two critical threats to Adobe’s long-term dominance: the structural disruption of digital discovery by generative AI and the persistent competitive pressure from Figma .

Let me explain my reasoning with the data.

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Adobe’s Current Performance

Before diving into the strategic rationale, let’s examine Adobe’s recent financial performance to understand the company’s position.

Adobe’s Q3 FY2025 results (reported September 2025) show a company that remains financially robust but faces growth deceleration. Total revenue reached $5.99 billion, up 11% year-over-year, beating consensus estimates of $5.91 billion (source). The Digital Media segment delivered $4.46 billion in revenue (+12% YoY), representing 74.4% of total revenue, with Annual Recurring Revenue (ARR) expanding 11.7% to $18.59 billion (source, source).

The Digital Experience segment generated $1.48 billion (+9% YoY), with subscription revenue at $1.37 billion (+11% YoY) (source). Adobe Experience Platform (AEP) ARR grew more than 40% YoY, and GenStudio now exceeds $1 billion in ARR, growing over 25% YoY (source).

Adobe’s Q3 financial performance (FY2025):

Source: Adobe Q3 FY2025 earnings (source)

These are impressive numbers. But they mask a critical vulnerability that becomes apparent when we examine the competitive landscape.

The Figma Problem

While Adobe’s overall performance remains strong, its position in the UI/UX design market tells a very different story. Figma has captured 80-90% of the UI/UX design market and achieved a $1.0+ billion annual revenue run-rate with 38% year-over-year growth in Q3 2025 (source).

Let me show you the numbers:

Figma’s Q3 2025 revenue reached $274.2 million, up 38% year-over-year, with net dollar retention at 131% (source, source). This represents an annualized revenue run-rate exceeding $1.0 billion (source).

The growth rate differential is stark. While Adobe’s Digital Media segment grows at 12% annually, Figma is expanding at more than triple that rate at 38% YoY. This threatens Adobe’s core creative software franchise.

The significance of Figma’s threat was confirmed by UK antitrust regulators, who revealed internal Adobe documents showing the company “regularly reference competing with Figma” and ultimately scrapped its own competing screen design tool shortly before announcing the failed $20 billion acquisition attempt in 2022 (source). This admission confirms Figma’s status as a “significant potential threat” to Adobe’s creative hegemony.

Adobe attempted to neutralize this threat through acquisition in 2022, offering $20 billion for Figma. That deal was blocked by regulators in 2023. Now, in November 2025, Adobe faces the same competitive pressure but without the option to simply acquire the threat away.

The SEMrush Acquisition

This is where the SEMrush acquisition becomes strategically significant. Since Adobe cannot neutralize Figma through acquisition, it must pursue an alternative strategy: expand into adjacent high-growth markets where it can establish dominance before competitors arrive.

SEMrush brings something Adobe desperately needs: proprietary capabilities in Generative Engine Optimization (GEO)—the ability to monitor and optimize how brands appear in responses from large language models including ChatGPT and Gemini (source, source).

Let me explain why this matters with data.

The GEO Market: A New Frontier

Anil Chakravarthy, President of Adobe’s Digital Experience Business, stated: “brand visibility is being reshaped by generative AI, and brands that don’t embrace this new opportunity risk losing relevance and revenue” (source). This reflects a fundamental shift in how consumers discover brands and products.

As AI chatbots, agents, and AI browsers increasingly mediate product discovery and commercial transactions, brand visibility is no longer determined solely by traditional search engine rankings. Companies need tools to optimize their presence in AI-generated responses, and SEMrush has built exactly that capability.

SEMrush’s financial performance validates this opportunity:

SEMrush Q3 2025 Performance:

• Revenue: $112.1 million (+14% YoY)

• Enterprise ARR Growth: +33% YoY

• AI Products: Contributed $10 million in net new ARR

• Customer Base: Includes Amazon , JPMorgan Chase , and TikTok

Source: SEMrush Q3 FY2025 earnings (source)

The 33% year-over-year growth in SEMrush’s enterprise ARR segment is particularly significant. It demonstrates that large enterprises are already allocating significant budgets to GEO and visibility optimization—precisely the customer base Adobe serves with its Digital Experience segment.

SEMrush’s Q1 2025 results provide additional context. The company reported ARR of $424.7 million (+20% YoY), with nearly 200 paying customers for its Enterprise SEO Solution at an average ARR per customer of approximately $60,000 (source). The number of customers paying over $50,000 increased 86% year-over-year to 388, while customers paying over $10,000 grew approximately 40% YoY to over 5,000 (source).

SEMrush management indicated their expectation that average ARR per enterprise customer will “approach and even exceed $100,000” as they scale their enterprise portfolio (source). This pricing power in the enterprise segment suggests strong product-market fit and limited competition in the GEO space.

SO, Is $1.9 Billion Justified?

Based on SEMrush’s Q3 2025 quarterly revenue of $112.1 million, we can project annual 2025 revenue of approximately $440-445 million. The $1.9 billion purchase price therefore represents approximately 4.3x projected 2025 revenue.

For context, Adobe itself trades at approximately 10x revenue (market cap ~$240 billion on ~$24 billion annual revenue). SaaS companies with 30%+ growth rates typically command 8-12x revenue multiples. At 4.3x revenue, Adobe is paying a reasonable—arguably discounted—multiple for a high-growth strategic asset.

The 77% premium to SEMrush’s trading price reflects:

1. Strategic scarcity value: Few companies possess proprietary GEO capabilities at scale

2. Competitive dynamics: Likely bidding from Salesforce or HubSpot

3. Integration synergies: Immediate cross-sell opportunities to Adobe’s installed base

How SEMrush could fit within Adobe’s ecosystem

Adobe’s installed base includes 99% of Fortune 100 companies already using Adobe’s AI tools (source). Integrating SEMrush’s GEO capabilities creates a unified workflow:

1. Create content in Creative Cloud (Photoshop, Illustrator, Premiere)

2. Optimize content for both traditional search and AI platforms through SEMrush

3. Distribute and measure through Adobe Experience Platform

4. Personalize and iterate based on performance data

This vertical integration addresses a critical pain point for enterprise CMOs: tool fragmentation. Rather than stitching together 15+ martech tools, Adobe can offer an end-to-end solution from a single vendor, reducing complexity and improving ROI attribution.

The cross-selling opportunity is substantial. If Adobe can penetrate even 20% of its Digital Experience customer base with SEMrush capabilities at an average $75,000 ARR per customer, this represents hundreds of millions in incremental revenue opportunity.

Adobe’s AI Monetization Problem, and How SEMrush Solves It

Adobe has invested heavily in generative AI through Firefly and GenStudio. GenStudio now exceeds $1 billion in ARR with 25%+ YoY growth (source). But investor pressure has mounted to demonstrate tangible returns beyond headline AI announcements.

SEMrush provides a clear monetization pathway. Rather than selling AI as a feature, Adobe can now package AI-driven marketing optimization as a standalone high-value offering. GEO represents a new budget category that didn’t exist 18 months ago—Adobe is positioning to capture it before competitors.

This is particularly important given Adobe’s Digital Experience segment growth of only 9% YoY—the slowest-growing part of Adobe’s business. SEMrush’s 33% enterprise ARR growth could materially accelerate this segment’s trajectory.

Figma, Adobe, and Market Positioning

The SEMrush acquisition doesn’t directly counter Figma’s design market dominance. But it represents Adobe’s acknowledgment that it must compete on multiple fronts:

1. Defend core creative tools (where Figma attacks)

2. Expand into adjacent high-growth markets (GEO, marketing infrastructure)

3. Leverage AI as a differentiator across both strategies

Adobe’s total addressable market (TAM) expansion through SEMrush is significant. The digital marketing software market exceeds $50 billion annually, with GEO representing a rapidly growing subsegment as AI adoption accelerates.

Meanwhile, Figma’s 38% growth rate and $1+ billion revenue run-rate confirm it will continue pressuring Adobe’s Creative Cloud franchise. But Adobe’s response isn’t to fight Figma head-on in UI/UX—that battle is largely lost. Instead, Adobe is diversifying its growth drivers to offset Creative Cloud deceleration.

Forward Outlook and Risks

Adobe’s FY2025 guidance projects $23.65-$23.70 billion in total revenue (source). The SEMrush acquisition isn’t reflected in this guidance but should contribute to FY2026 growth acceleration.

The key risks are:

1. Integration execution: Adobe must preserve SEMrush’s product velocity and culture

2. Customer retention: SEMrush’s core SEO practitioner base may resist “enterprise-ification”

3. Competitive response: Salesforce, HubSpot, or Oracle may acquire competing GEO capabilities

4. Technology risk: If GEO proves to be a transient opportunity, Adobe overpaid

However, I view the integration risk as manageable. Unlike the failed Figma acquisition (which would have required integrating a company with vastly different culture and positioning), SEMrush serves enterprise customers—Adobe’s core competency. The go-to-market alignment is strong.

My Conclusion

Adobe’s SEMrush acquisition represents a strategically sound response to the company’s two-front competitive challenge. While Figma continues to erode Adobe’s UI/UX market share with 38% revenue growth and 80%+ market dominance, Adobe is expanding into the emerging $50+ billion GEO market where it can establish first-mover advantage.

The $1.9 billion valuation (4.3x revenue) is reasonable for a high-growth strategic asset with 33% enterprise ARR growth and minimal direct competition. The integration case is strong, with immediate cross-sell opportunities to Adobe’s installed base of 99% of Fortune 100 companies.

This acquisition will not solve Adobe’s Figma problem. But it doesn’t need to. It creates a new growth engine precisely when Adobe’s core creative software faces structural headwinds. That’s sound portfolio strategy.

For investors, the key metric to watch is Adobe’s Digital Experience segment growth. If SEMrush integration drives acceleration from the current 9% YoY toward 15-20% growth rates, this deal will prove highly accretive. The data will tell us within 12-18 months whether Adobe’s bet on GEO was prescient or premature.

Riding this wave is Apollo Micro Systems, a key supplier of critical electronics for missiles, aircraft, and naval systems. While giants like HAL and BEL posted impressive gains, Apollo’s stock skyrocketed an astounding 139.6% year-to-date, signaling that the market sees immense potential in this specialized Tier-1 OEM.

This report unpacks the story behind Apollo’s stellar run and assesses its future prospects.

1. COMPANY INTRODUCTION

Founded in 1985 and headquartered in Hyderabad, Apollo Micro Systems Limited (AMS) has quietly become one of India’s most critical defense electronics suppliers. The company designs and manufactures ruggedized electronic and electromechanical systems that power the country’s most sensitive military platforms—from missiles streaking through the sky to torpedoes hunting beneath the waves.

AMS is involved in over 150 indigenous defense programs as a Tier-1 sub-system partner, providing the electronic “brains” for weapons systems across the Army, Navy, and Air Force. With CEMILAC certification, the gold standard for military airworthiness, Apollo serves a who’s who of Indian defense: DRDO, HAL , BEL , and even ISRO for space missions.

But Apollo isn’t standing still. In May 2025, it made a strategic leap by acquiring IDL Explosives, a 60-year-old ammunition manufacturer with ₹623 crore in FY24 revenue, for ₹107 crores. This transforms Apollo into an integrated weapons manufacturer capable of delivering end-to-end solutions. It’s a bold bet on India’s Atmanirbhar Bharat push, positioning Apollo as a rare full-spectrum defense OEM just as government spending accelerates.

2. BUSINESS AND PRODUCT PORTFOLIO

Apollo operates through two main business models. The first is “Build-to-Specifications” where customers tell them what they need, and Apollo designs and builds custom solutions. The second is “Build-to-Print” where customers give them the designs, and Apollo manufactures the systems. This flexibility helps them win contracts across different segments.

Their product range covers following main areas:

Defense Electronics: They make ruggedized electronics that can survive harsh military conditions. Think of circuit boards and control systems that need to work perfectly whether they’re in the desert heat, high-altitude cold, or getting bounced around in a tank.

Aerospace & Avionics: This includes systems for aircraft – things like integrated avionics modules, actuators (parts that control aircraft movement), and electronic control units. These go into both fighter jets and transport aircraft.

Missile Systems: Apollo provides electronics for India’s missile programs, covering everything from fuzing (the trigger mechanism) to fire control systems. This works across both tactical missiles (short-range) and strategic missiles (long-range).

Naval Defense: They specialize in underwater warfare electronics, including homing systems for torpedoes (both lightweight and heavyweight versions), and smart mines. They also make combat electronics for ships.

Space Applications: They supply electronic systems for ISRO’s launch vehicles and satellites. These need to be extremely reliable since you can’t fix them once they’re in space.

Beyond just making final products, Apollo has in-house capabilities for PCB fabrication, embedded software development, hardware design, and even emerging tech like drone control systems and torpedo tracking technology. This integrated setup means they can control quality and deliver faster than competitors who outsource these services.

3. FUTURE GROWTH DRIVERS AND SECTOR OUTLOOK

3.1 Structural Policy Tailwinds that will create Growth Opportunity for Apollo

I think the 2 initiatives mentioned below will create higher demand for Apollo Micro Systems’ products.

• Defense Budget Expansion

India’s defense budget reached ₹6.81 lakh crore ($78.3 billion) for FY 2025-26, marking a 9.5% YoY increase. Defense spending is projected at $543.1 billion during 2026-2030, with ₹1.8 lakh crore specifically allocated for capital expenditure emphasizing modernization of military equipment including drones, advanced weaponry, and light combat aircraft. All these are areas where Apollo operates. The sustained budget growth creates expanding revenue opportunities for domestic suppliers like Apollo Micro Systems

• Atmanirbhar Bharat Policy

This policy prioritizes domestic manufacturers over imports. The Defense Production and Export Promotion Policy (DPEPP) targets domestic defense manufacturing of ₹1.75 lakh crore by 2025. Import embargoes on various defense items and “Make in India” procurement preferences create a protected and expanding market for indigenous players. Apollo, positioned as a Tier-1 defense OEM, greatly benefits from this policy framework.

3.2 Customer Ecosystem Experiencing massive Expansion

Additionally, below are some of the developments happening in the Defense and Aerospace Industry/ Ecosystem in India and I believe Apollo Microsystems are in a good position to benefit from it.

- Armed Forces Modernization (₹2.5 Trillion+ Approved Procurement)

The Ministry of Defence declared 2025 as the “Year of Reforms” with comprehensive modernization plans. Indian Air Force aims to expand from 31 squadrons to 42 squadrons by 2035, requiring 300+ aircraft production (Tejas Mk-1A, Tejas Mk II, AMCA). IAF will deploy 450 fighter jets each along Pakistan and China borders and acquire autonomous drones. Indian Navy’s 15-year plan includes 10+ frigates/destroyers, 7 corvettes, and a new aircraft carrier with advanced launch systems. Indian Army is investing in Directed Energy Weapons, high-energy lasers, and integrated battlefield management systems. All these platforms require avionics, electronic control systems, and related components, which are Apollo’s core products, thus creating sustained multi-year demand worth hundreds of crores for the company.

Defense PSU Order Pipeline (₹8.5-9 Lakh Crore Over 5-7 Years)

HAL has a ₹4.7 lakh crore potential order pipeline, including a ₹62,370 crore deal for 97 Tejas aircraft (September 2025) and ramping production from 16 to 24 aircraft annually, targeting 30+ by FY27. HAL has completed 19 Tejas airframes awaiting engine installation. BEL has an order book of ₹74,859 crore and expects new orders exceeding ₹27,000 crore in FY26. India’s defense sector production is expected to nearly triple from ₹94,845 crore in FY22 to ₹3 lakh crore by FY29, representing 18% CAGR. As an established sub-system partner to HAL and BEL, Apollo benefits directly from this execution pipeline. Each Tejas aircraft requires ₹8-12 crore worth of avionics, creating immediate revenue opportunities for Apollo as production scales.

Private Sector Defense Manufacturers Scaling Aggressively

Adani Defence announced ambitious expansion, with the aim of doubling ammunition capacity from 150 million to 300 million rounds by December 2025, new large caliber facility producing 150,000 rounds annually, and medium caliber facility launching by Diwali 2026, all in the 500-acre UP Defence Corridor. Also, L&T and Tata are scaling defense manufacturing significantly under Atmanirbhar Bharat. Apollo recently secured ₹225.71 crore from Defense PSUs in November 2025, demonstrating its ability to capture orders as these private players expand their production and require electronic components and control systems.

DRDO and ISRO Programs

DRDO’s budget increased to ₹26,816.82 crore in FY 2025-26. Apollo secured ₹110.16 crore in orders from DRDO in November 2025. ISRO has major missions planned: Gaganyaan (2026) - India’s first human spaceflight, Chandrayaan-4 (2027) - lunar exploration, and Samudrayaan (2026) - deep ocean exploration. Each space mission requires specialised electronic systems, avionics, and control modules that Apollo specializes in, turning DRDO’s expanded R&D budget and ISRO’s ambitious mission schedule into direct contract opportunities, which means more revenue for the Apollo.

4. ESTIMATES ABOUT EXPECTED FY26, FY 27 REVENUE OF THE COMPANY

4.1 Looking at the past revenue trends

During the Q2 FY26 earnings commentary, held on 5th November 2025, Managing Director Baddam Karunakar Reddy projected 45-50% annual revenue growth for the next few years, with core business contribution expected to increase from 25-35% to 40-45%.

Below are 3 data-points from Apollo’s recent financial results that strongly supports this statement.

1. First, if we look at the results of FY24 and FY 25 and see the growth, we can see that the YoY revenue growth from FY24 to FY 25 is 51.24%

2. Next, if we look at the Revenue growth for the first half of FY 26 (April - September 2025), we can see a growth of 42.5%, which is very close to the projected annual revenue growth of 45-50%.

3. Finally, looking at the results of the most recent quarter, Q2FY26 (July- September 2025), and comparing it QoQ and YoY, we can see a QoQ growth of 68.6% and a YoY growth of 40.2%

Therefore, by looking at these 3 data-points, along with the growth opportunities mentioned in Section 3, it can be said that it is very likely that revenue will continue to grow at 45-50% for FY26 and FY 27.

4.2 Order book analysis

As of September 2025, Apollo Micro System’s total order book has now reached ₹785 crore (source). And there’s news that they have received additional order worth ₹340.96 crores, as of November 6-7, 2025. This would bring the total value of the order book to ₹1,125 crores. In May 2025, management stated they expected the order book to grow to ₹2,500 crores by December 2025. While Apollo Micro Systems has demonstrated strong order momentum with ₹375 crores secured in November alone, the ₹2,500 crore order book target for December 2025 appears unlikely to be met. However, an order book of ₹1,200-1,500 crores would still provide strong revenue visibility for FY26-FY27, supporting their 45-50% revenue growth guidance.

4.3 Final Words on Revenue expectations

From their financial performance over the past year and the growing order book, it seems very likely that revenue will continue to grow at 45-50% for FY26 and FY 27.

(NOTE: The content provided here is for informational purposes only and DO NOT constitute as Investment Advice)

SOURCES and REFERENCES:

• Goldman Sachs turns bullish on India’s aerospace, defence sector | IBEF

• Apollo Micro Systems Secures Orders Worth INR 340.96 Crore from Defense and Private Sectors

I’ll divide it into the following parts:

• In PART 1, I’ll briefly review the financials and Q3 2025 earnings.

• In PART 2, I’ll look at the arguments from both perspectives, the TESLA BULLS and the TESLA SKEPTICS.

• CONCLUSION, where I’ll give my concluding thoughts.

PART 1: A brief review of the financial results

Tesla posted record revenue of $28.1 billion in Q3 2025, representing 12% year-over-year growth, beating analyst estimates of $26.4 billion.

However, profitability deteriorated sharply with net income plunging 37% to $1.37 billion ($0.39 EPS on GAAP basis, $0.50 non-GAAP), missing consensus estimates of $0.54-$0.56 per share.​

However, the financial summary table above shows concerning “margin compression” a.k.a. decrease in profitability. As we can see in the visual above, Gross margin declined from 19.8% in Q3 2024 to 18.0% in Q3 2025, while operating margin collapsed from 10.8% to just 5.8%. Net margin similarly deteriorated from 8.6% to 4.9% over the same period.​​

Why did Gross, Operational and Net Margin decrease despite Record Revenue?

From what I found, Tesla’s margin compression stems from three primary factors.​

1. Aggressive Price Cuts: Tesla reduced vehicle prices significantly to drive volume, particularly to capitalize on demand surging before the $7,500 federal EV tax credit expired September 30, 2025. This strategy boosted deliveries to record levels but sacrificed per-unit profitability.​

2. Soaring Operating Costs: Operating expenses exploded 50% year-over-year to $3.43 billion, driven by massive investments in AI infrastructure (including Nvidia H100 GPUs), R&D for robotaxis and humanoid robots, and higher SG&A costs. Operating margin consequently halved from 10.8% to 5.8%.​

3. Structural Cost Pressures: Tesla faced unavoidable headwinds that squeezed margins despite record revenue.

   1. When certain models produce fewer units, factory fixed costs (rent, equipment, salaries) spread across fewer vehicles, raising per-unit manufacturing costs.

   2. Moreover, import tariffs added over $400 million in expenses, while an unfavorable sales mix—selling proportionally more lower-margin Model 3/Y instead of premium Model S/X—reduced overall profitability per sale.

   3. Most significantly, regulatory credit revenue plummeted 44% year-over-year from $739 million to just $417 million, eliminating a historically high-profit income stream.

      These structural factors combined to compress margins even as raw material costs decreased slightly.

……

Record Delivery of Vehicles

Tesla achieved record vehicle deliveries of 497,099 units in Q3 2025, though this was heavily inflated by buyers rushing to purchase before the federal EV tax credit expired at the end of September. The company delivered more vehicles than it produced, drawing down inventory to meet this artificial demand surge.

Current Price and Valuation

Tesla trades at a P/E ratio of 240.29 with the stock currently at $456.56. The company’s market cap of $1.47 trillion prices in massive potential future growth from AI, robotics, and Autonomus Robotaxi initiatives rather than its current automotive fundamentals.

This is where the investors are divided.

• On the one hand, there are the bulls, that are confident that these technologies will deliver, justifying the high PE Ratio

• The bears, on the other hand, remain skeptical of how much of these advance tech will really come to fruition.

PART 2: Comparing the Bull and Bear Arguments

The argument of the Tesla Bulls

Tesla bulls base their investment thesis primarily on three transformative technologies: Full Self-Driving (FSD), Robotaxis, and the Optimus humanoid robot. They argue that near-term profitability challenges are merely temporary headwinds compared to the massive market opportunities these initiatives represent. This is the fundamental difference between bears and bulls. The disagreement is not about current problems, but radically different expectations about future value creation.

Let’s look at these 3 of them in details.

1. Full Self Driving

FSD generated less revenue in Q3 2025 than Q3 2024, when it brought in $326 million. However, bulls argue this revenue decline is temporary and driven by Tesla’s strategic pivot toward higher-value monetization models. As of September 2025, FSD adoption has reached 50-60% among Model S and Model X owners, and 12-18% among Model 3 and Model Y buyers, a significant increase from single-digit adoption rates just 18 months prior.​

Bulls emphasize that current subscription revenue at $99 per month is nascent compared to the potential upside:

• Growth trajectory: With 13-19% fleet-wide adoption now, and Model 3/Y adoption entering the “teens” (historically considered the mass-market adoption threshold), subscription take rates are accelerating rapidly. If adoption reaches 30-40% fleet-wide over the next 3-5 years, monthly recurring revenue could scale to $1-2 billion annually.​

• Margin expansion: Unlike vehicle sales with compressed margins, FSD subscription revenue carries extremely high gross margins, estimated at 80%+ after server costs. This creates a powerful alternative to automotive profitability as the subscriber base grows.​

• Data compounding: Each FSD user generates data improving neural networks. Tesla reports customers have driven 3 billion miles on FSD as of January 2025, creating an irreplicable training dataset. More subscribers mean better AI, which justifies higher subscription fees and attracts new customers, a virtuous cycle.​

2. Robotaxis and Cybercab

Tesla launched its Robotaxi service on June 12, 2025 in Austin and the Bay Area, marking the first direct commercial deployment of autonomous vehicles against Waymo. This is where bulls see the true value creation.​

The Robotaxi strategy leverages the 3 billion miles of FSD data collected to date. Bulls argue that the raw miles driven on public roads, capturing edge cases, weather conditions, and unscripted human behavior, cannot be easily replicated by competitors. Tesla is using this dataset to train the neural networks powering Robotaxis, accelerating commercialization faster than Waymo or traditional automakers can achieve.​

The ultimate vision is the Cybercab—a purpose-built, steering-wheel-free vehicle designed exclusively for autonomous operation, expected around 2026-2027. Bulls highlight the economic difference:

• Current Robotaxis use modified Model Y vehicles with onboard safety equipment, limiting profitability.

• Cybercabs eliminate steering wheels, pedals, and redundant controls, reducing manufacturing costs and increasing margins significantly.

• If operational costs per mile drop below human-driver economics, the fleet economics become attractive to investors and operators.

Tesla already announced plans to produce 5,000 Optimus units in 2025, scaling to 50,000 in 2026, with Musk projecting unit costs under $20,000—cheaper than a Model Y. This manufacturing playbook mirrors Tesla’s historical approach: internal deployment first, then scaling to external customers.​

3. Optimus Humanoid Robot

Finally, bulls point to the Optimus humanoid robot as potentially dwarfing even Robotaxi economics. Musk himself stated that 80% of Tesla’s long-term value could come from Optimus rather than vehicles.​

The 1X humanoid robot (developed by a separate company but commercially available since 2025) demonstrates this market is becoming real. 1X deployed its Neo humanoid robots at BMW manufacturing facilities and has begun consumer deployments, validating demand for general-purpose humanoid robots. While 1X represents a competitor, it also proves the category is moving from theoretical to commercial.​

Bulls emphasize Tesla’s unique positioning in this market:

• AI infrastructure: Tesla’s FSD AI stack, which is trained on billions of driving mile, can be repurposed for robot cognition, requiring approximately 10x the compute power but leveraging existing neural network expertise.​

• Manufacturing scale: Tesla’s factory automation experience positions it to achieve unit economics competitors cannot match at scale.

• Addressable market: If humanoid robots achieve even 10% of the productivity gains promised, the TAM extends beyond warehousing into household, healthcare, and general labor, easily a $100+ billion market by 2030.​

The bull case rests on Tesla’s ability to execute this three-stage progression: FSD subscriptions fund robotaxi development, which generates data for Optimus development, which eventually becomes the largest profit driver. If all three succeed, today’s $1.47 trillion valuation could be justified by 2030-2035 revenues alone.

The Argument of the Tesla Skeptics

Tesla critics acknowledge that the company has accomplished remarkable feats in electric vehicles and battery technology. However, they fundamentally dispute whether the company’s core value creation thesis—that FSD, Robotaxis, and Optimus will generate the bulk of future profits—is realistic or achievable on Tesla’s claimed timeline. Bears argue that Tesla has repeatedly over-promised on autonomy while cutting corners on safety, that near-term margin pressures are structural rather than cyclical, and that competitors (both traditional automakers and startups like Waymo) are better positioned to capture the autonomous market even if Tesla eventually succeeds.

1. Full Self-Driving

Tesla’s FSD marketing significantly overstates what the technology can actually do, according to critics. The company’s website advertises that the system is “designed to be able to conduct short and long-distance trips with no action required by the person in the driver’s seat,” yet this capability does not exist. California’s Department of Motor Vehicles sued Tesla in 2022 for false advertising, arguing that vehicles equipped with FSD “could not, at the time of those advertisements, and cannot now, operate as autonomous vehicles.” A federal judge certified a class-action lawsuit in August 2025 covering FSD purchasers from October 2016 onwards, finding sufficient evidence of Tesla’s “overblown claims.” (source: Tesla’s Ability to Sell Cars in California Hangs in the Balance After Weeklong Hearing - Business Insider)

The safety record validates these concerns. Tesla’s own Autopilot director testified under oath that the company’s famous 2016 “Full Self-Driving Hardware” demo video was staged—the route was pre-mapped, and the vehicle had actually crashed into a fence during testing. Engineers had warned against removing radar sensors from the system, but Musk rejected their advice anyway, resulting in a spike from 34 phantom braking incidents in 22 months to over 100 in just three months after radar removal.

Moreover, customer surveys indicate that rather than driving adoption, FSD’s poor performance is actually damaging Tesla’s brand. One September 2025 analysis noted that “Tesla’s FSD initiative may actually be dampening demand” rather than generating enthusiasm. Current FSD adoption remains at only 13-19% fleet-wide, with even premium Model S/X customers showing only 50-60% adoption. This suggests customers are voting with their wallets, rejecting a product they perceive as unreliable and not ready for use. (source: Customers Don’t Like Tesla’s FSD. Should You Hit the Brakes on TSLA Stock in September 2025?)

2. Robotaxis

Tesla launched its robotaxi service in June 2025 in Austin, but the deployment is far more limited than the bull case suggests. The vehicles operate in a restricted geofenced area with safety monitors in the front seat, meaning they are not truly autonomous. This falls short of the regulatory definition of Level 4/5 autonomy and contradicts Musk’s prior promises of “hundreds of thousands of driverless taxis” by end of 2026.​

Critically, Tesla’s camera-only FSD approach faces fundamental technical limitations that experts argue cannot be overcome. Autonomous driving researcher Missy Cummings stated: “You cannot fix this with computer vision, at least not in today’s technology, and I don’t think in my lifetime.” The best that computer vision can achieve is 97% accuracy—meaning three out of 100 situations result in errors. Other autonomous vehicle companies use three complementary sensor systems (lidar, radar, and cameras), while Tesla uses only cameras, making it systematically more prone to environmental disruptions

Additionally, Waymo, operating in multiple U.S. cities with SAE Level 4 autonomy in defined areas, is valued at approximately $45 billion with only 1,500 vehicles in operation. Tesla’s current robotaxi operation with safety riders is not even Level 4, making direct valuation comparisons problematic. The bear case is that if Waymo’s genuine autonomous vehicles (which don’t require human monitoring) are worth that much with limited deployment, Tesla’s supervised Level 2 system is worth far less, not more.

3. Optimus Robot

Roboticists and engineers express deep skepticism about Optimus commercialization prospects. Owen Nicholson, CEO of Slamcore, observed: “A lot of companies work in the area of humanoids because it’s interesting for the public to see. But wheel-based robots and even drones are so much easier to control. The amount of work that goes into just getting a humanoid to stand upright is huge.” More bluntly, an expert analysis stated: “Optimus is pitched as a general-purpose robot, and I think we are very far away from a time when that will make sense. Possibly not in my lifetime.” This reflects industry consensus that general-purpose humanoids remain decades away from commercial viability.​

The technical challenges are substantial and unresolved:

• Balance and mobility: Two-legged walking on uneven surfaces requires years of tuning and remains difficult.​

• Fine motor control: Picking up fragile items, folding clothes, or operating tools requires sensors, grip adjustment, and environmental learning, capabilities not yet proven at scale.​

• Battery life: Musk targets full-day runtime, but this remains uncertain with continuous movement and processing.​

• Safety for human interaction: A 125-pound robot moving around children or elderly requires proven collision detection, force-limiting, and emergency overrides, not yet demonstrated.​

Tesla’s aggressive 5,000-unit 2025 production target appears increasingly unachievable. Reports from July 2025 indicated that “persistent technical challenges have delayed assembly,” with hand and forearm mechanisms cited as key constraints. Unlike the bull case’s optimism, Tesla hasn’t provided firm near-term production commitments, suggesting internal recognition of these problems.​

China’s rare earth metal export restrictions pose additional supply chain risks for Optimus servo motors and movement systems. Even with export licenses, this creates ongoing dependency.​

Finally, demand remains completely unproven. The 1X Neo has opened preorders but has not achieved mass deployment. Customer acceptance remains uncertain. Industry consensus suggests humanoid robots will remain niche applications for years, not a transformative billion-dollar business by 2030.

CONCLUSION

So, what’s my position on Tesla? If you’re someone who’s very much grounded in reality. Someone who’s “I’ll believe it when I see it”, then Tesla is not a company that you’d like to invest in.

But, this is where historical knowledge comes into play. Elon Musk has delivered in every single business that he’s in. You may not like him as a person, but objectively speaking, you can’t deny his capability as a business-person.

When Elon Musk took over Tesla, I was still enrolled in my MBA. My professor, someone with decades of experience, predicted that Twitter will go bankrupt in the next 6 months. But 3 years in, it’s still up and running.

So, here’s what I am doing. First, I am going to keep my position in Tesla. Second, I’ll spend more time trying understand the technology and potential market for both 1. FSD & Robotaxis and 2. Humanoid Robots to get a clearer picture on where these technologies, and by extension, Tesla is headed.

…..

My next post will most probably be on Tesla’s Robotaxi and Cybercab, where I’ll look into it in details.

Until next time.

NOTE: The content provided here is for informational purposes only and DO NOT constitute Investment Advice.

Here’s what happened.

1. The “round-trip” investment between the major AI players (There are multiple YouTube videos explaining this)

2. Open AI launched Sora 2, an invite-only “AI Slop” App.

3. Then, Sam Altman announced that they will allow generation of “erotica”, i.e. an AI generated adult content.

   

4. TSMC and ASML, 2 companies that are crucial for the production of the hardware that’s needed to train and run the AI models, reported their Quarterly earnings.

..

In light of all of this, there’s talk of an AI bubble forming. Let me spell out the definition of a bubble, because I have read other people mentioning the term bubble(AI Made Simple) where the writer does not seem to understand the concept of a bubble. “An economic bubble is when an asset’s price, such as a stock or a house, rapidly increases to a level far beyond its intrinsic/ fair/ fundamental value.” Notice, that a bubble does not mean that the asset has no value. It is worth something, but because of the hype around it, its price rises way above what its actually worth.

Now, from the data that I am going to provide in the image below, it seems that it’s not an opinion but a factual reality that there’s overvaluation of businesses operating in the AI industry.

Let me explain what this chart means. As per this chart, the overall revenue annually from AI products is $100 billion. The blue bar on the right says that by 2030, a total of $6 trillion dollars will be spent on AI infrastructure (which includes Data Centers, Chips, power systems etc.) At the rate of $100 billion per year, the total revenue will only add up to $600 billion, 10 times less than the amount spent on the infrastructure.

Here’s what happens with 50% year-over-year growth starting from $100 billion:

• 2025: $100 billion

• 2026: $150 billion

• 2027: $225 billion

• 2028: $337.50 billion

• 2029: $506.25 billion

• 2030: $759.38 billion

So, starting at $100 billion and growing by 50% year over year (btw, assuming that revenues will grow at this rate is an extraordinary assumption) for the next 5 years, the total revenue, which is $759.38 billion, comes nowhere near to cover the $6 trillion dollar spending.

…..

Open AI’s focus on creating AI-generated version of Tik Tok (via Sora2) and AI-generated adult content service completely makes sense from a business perspective, under the current rules of capitalism under which the world operates. The rule is: “Valuation depends on the present value of all future cash flows”. And how to maximize value as per this rule?

So, as per these criteria of valuation, using AI to cure cancer isn’t that “valuable”. But creating an AI version of a brain-rot app (Sora 2) and adult content is lucarative. Advertisements and adult content are 2 of the most successful means of monetization on the Internet. So Open AI are simply playing the game. But is it still enough?

What I am asking is, even after releasing these 2 products, will they still be able to generate the revenue to justify their valuations?

To objectively answer that, I would have to do a proper calculations and projections. I will probably have to look at Revenues and Profits from TikTok, which is what Sora2 is trying to replicate. And I would also have to look at annual revenue geneartion in the adult industry which is what Open AI is aiing for with AI generated adult content.

…..

But, if I have to make an estimate off the top of my head based on my knowledge and experience so far, I will say the AI industry is overvalued at the least, if not in a bubble.

by RhPt

Microsoft has committed over $33 billion to neocloud providers, including Nebius, CoreWeave, Nscale, and Lambda, as the company faces significant capacity constraints in scaling its AI infrastructure. This substantial investment represents a strategic shift in how Microsoft is managing its AI compute needs while navigating the challenges of rapid AI expansion.

Before I begin, a quick refresher on Neoclouds.

What Are Neoclouds?

Neoclouds are specialized cloud infrastructure providers dedicated exclusively to AI workloads, including training and inference operations. Unlike traditional cloud providers that handle storage and general software applications, neoclouds focus specifically on AI compute capacity. These providers include European players like Nscale and Nebius, as well as established names like CoreWeave.

Why Microsoft Is Turning to External Providers

The core reason behind this strategy is capacity constraints. Microsoft needs to get as many chips online as possible to serve three key constituents: its own internal AI development, its enterprise customers, and OpenAI.

Here are 2 points that I think are of note for investors.

1. Building their own proprietary AI Models

For investors, a key takeaway is that Microsoft is using this new spending to build its own AI technology, separate from its famous partner, OpenAI. Think of it as a backup plan.

Under Mustafa Suleyman, who came from DeepMind and joined Microsoft after the company acquired Inflection, Microsoft is building its consumer AI offerings. The first foundational language model being developed internally under Suleyman’s leadership is using CoreWeave’s assets repeatedly. In essence, Microsoft is accessing Nvidia hardware through CoreWeave rather than deploying it directly.

While the partnership with OpenAI has been great for Microsoft, relying too much on one partner is risky. What if their relationship changes in the future? By using these “neoclouds” to develop its own AI models, Microsoft is making sure it won’t be left behind.

Essentially, Microsoft is trying to move vertically in the value chain and reduce its dependency on Open AI. It means Microsoft’s future in AI doesn’t completely depend on another company. If something happens with the OpenAI partnership, Microsoft will have its own powerful AI ready to go. For investors, this makes Microsoft a safer long-term bet in the AI race and strengthens its competitive advantage.

2. Looking at it from a Financial Perspective

The other way to look at it is how Microsoft is managing its money. From an investor’s point of view, it’s a brilliant financial move. Here’s why:

• Buying vs. Renting: When a company like Microsoft builds its own giant data centers, it’s a huge upfront cost (called a Capital Expenditure, or CapEx). This looks like a massive expense on their financial reports. But by renting computing power from these neoclouds, the cost is treated like a monthly bill (an Operating Expense, or OpEx). This makes their spending look more manageable to Wall Street.

• Staying Flexible: The world of AI technology changes incredibly fast. The best computer chips today could be outdated in a few years. By renting, Microsoft avoids getting stuck with billions of dollars worth of old equipment. If a better, faster chip comes out, they can simply end their rental contract and switch to the new technology.

• Making More Money from Their Own Assets: This strategy also frees up Microsoft’s own valuable data centers. They can now rent that space out to other companies for high-margin AI work, which directly increases their revenue.

…..

So, the thing to note here is that the scale of investment in building proprietary models suggests Microsoft is serious about developing its own foundation models to compete directly with OpenAI and Anthropic, rather than relying solely on partnerships.

Written by: RhPt

I, too, wrote about the positives that could come out of this deal. You can read it below:

Let me briefly mention the details of the deal before I get into my commentary.

The details of the deal

The deal involves a significant financial investment and a strategic collaboration on product development. Nvidia announced it would invest $5 billion to acquire a stake of about 4% in Intel. The core of the partnership is the joint development of custom chips for both data centers and personal computers. For the data center market, Intel will design and build custom x86 processors that will be integrated with Nvidia’s GPU-based AI hardware. For the consumer PC market, Intel will produce new system-on-chips (SoCs) that incorporate Nvidia’s RTX GPU chiplets.

…..

Now, my commentary on this development.

The significance of the deal

I interpret Nvidia’s collaboration with Intel as evidence supporting my argument that I presented in my above-mentioned post. The US stake in Intel provided Nvidia the assurance it needed to enter into a partnership with Intel, a company that doesn’t have a strong moat in any aspect within the semiconductor industry. Which means there are questions about its long-term existence, and no company would want to enter into partnership with another company that cannot guarantee its existence. The US Government’s stake in Intel provided that stability that other companies would look for to enter into business with Intel.

..

Nvidia will not directly outsource the manufacturing of its chips to Intel in this partnership. Instead, it will be a “collabrative partnership” for lack of a better term. The partnership involves Intel designing and manufacturing custom processors specifically for the joint products they’re developing together. Intel Foundry will supply central processors for these collaborative products and will package chips from Nvidia for some of them, but this is distinct from Nvidia outsourcing its general chip production to Intel. Nvidia continues to rely on Taiwan Semiconductor Manufacturing Company (TSMC) for producing its flagship processors, including its AI chips.

However, this partnership can be viewed as a potential stepping stone toward future foundry services. Jack Gold from J.Gold Associates notes that “any relationship with Nvidia at this point, while not explicitly talking about the foundry services, should be seen as a possible extension of the partnership in the future”. (source) ..

This is similar to what I think. No one should expect Nvidia to use Nvidia’s manufacturing services straight away and ditch TSMC. I think Nvidia will test Intel’s manufacturing capabilities through this partnership, and based on their satisfaction, Nvidia might proceed to use Intel’s manufacturing services, diversifying some of its business for TSMC

..

Riding on this wave, there is also news about Intel having talks with Apple. I think this collaboration is going to be less about design collab, and more about manufacturing. For Apple, it would be about diversifying away from TSMC and using a US manufacturing partner and being on the good books of the US President from this partnership.

But the Important thing to keep in mind is that the conversations between Intel and Apple are in the preliminary stages, and there is no guarantee that they will result in a formal agreement.

…..

Overall, it sounds like good news for Intel, especially their manufacturing business. Now it will all depend on Intel’s competence to be able to deliver it.

Written by: RhPt

The majority of the commentary made on this deal were opposed to it. Below are 2 of the sources that commented against this move.

• Scott Lincicome on X: "A terrible decision bad for almost everyone. Six quick reasons why: 1. Bad for Intel's long-term viability, as politics, not commercial considerations, increasingly drive its decisions. (SOEs are notoriously slow, bloated, & unproductive.) Foreign govts might also target it." / X

• Intel government stake won't fix long-term woes | TechTarget

Stratechery’s post on this issue was the only one I found that supported this move, although to be clear he wasn’t supporting it as a socialist. He mentions it as the “least-worst option”, by which he meant that given the circumstances, it’s the best possible option available. A necessary evil, so to speak. And I agree with it. He analysed this deal by also including the geopolitical angle that other analysts seem to either have missed or overlooked, intentionally or otherwise.

…..

After going through the articles and videos that are opposed to this deal, I have found a few arguments that the analysts used.

The first argument against it is coming from the place of free-market capitalism and their argument is that this decision goes against this idea. This deal is a socialist move, which is a big no-no, a taboo word in the West, especially in the USA.

This argument is good for academic debate but not for practical concerns. In the practical world, the thing that should matter is what gives results, not ideological elegance.

..

The second, and a more practical, argument is that with a government stake, Intel will be viewed as a political instrument. That is, when the US government owns equity in Intel, it gains influence over corporate decision-making beyond normal regulatory oversight. Intel's leadership may face pressure to align business decisions with US foreign policy objectives, even when those decisions conflict with commercial interests. This transforms Intel from a purely profit-driven entity into one that must balance commercial and political considerations.

This argument also falls flat. Because regardless of ownership, the government can still impact the future of a business. Nvidia is a clear example of this. US has restricted the sale of its advanced chips to China which affected its future revenue streams. I have written more about this here:

…..

So far, I have provided counter arguments against the prevailing sentiment, attempting to convince why these arguments are so hold strict examination.

But that begs the question: What’s my logical argument for supporting this deal? That’s where I am headed next.

Here’s the step-by-step logic behind my argument:

• Right now, TSMC is the leading Semiconductor manufacturing company in the world.

• But, TSMC is located in a geopolitically uncertain region, i.e. Taiwan. If China were to attack and invade Taiwan, this will handicap US’s ability to procure Semiconductor chips.

• So, USA needs a domestic Chip Manufacturer to get over its reliance on TSMC.

• A chip manufacturing startup is practically impossible to create because of the decades of expertise and know-how required to start such a business.

• So, Intel, as bad as their situation is, is US’s best option for a domestic Semiconductor Chip Manufacturer

• But there isn’t much demand for Intel’s manufacturing services. Chip design companies like Nvidia, Apple prefer TSMC over Intel.

• So, Intel needs an external customer to make its foundry (a.k.a. Semiconductor Chip Manufacturing service) viable, but no external customer will go with Intel if there is a possibility that Intel Foundry will not be viable i.e. if Intel cannot guarantee that that their manufacturing business will still be operating in the long term.

• Intel alone can’t make this promise because as a profit-driven business, the better option for them would be to give up on manufacturing altogether and focus on chip design, as it’s a higher margin business. So, they might close down their manufacturing services.

• That would be good move for Intel and it’s shareholders but not for USA as a nation. Because that would mean, USA will be depended on its long term chip fabrication needs on foreign companies TSMC and Samsung, and the HQs of these companies are situated in countries close to China, even though these countries might be

• Therefore, U.S. taking a stake in Intel, is an implicit promise that Intel Foundry is not going anywhere.

Ultimately, this is also good for Intel investors. Intel prioritizing government interests, i.e. focusing on national security and the long-term viability of U.S. semiconductor manufacturing, over their fiduciary duties could be seen as Intel focusing on the long-term over the short term. Had Intel done just that over the last two decades they wouldn’t be in this position to begin with (source).

It is because of their short sightedness that they are in this position in the first place. In the 2000s, they were the leaders in PC CPU design. But then came the smartphones in late 2007 after Steve Jobs showcased the first iPhone. By the early 2010s, smartphone market was growing while PC demand was in a decline (Source). What made them good in PC CPU design is what made them lag in smartphone CPUs. Intel faced competition from companies like AMD and were lagging behind in smartphone CPU design. But, even though Intel was lagging behind as Chip Design company, they were a great Chip Manufacturing company. Had they accepted it and focused on expanding it’s manufacturing services back in the early 2010’s, they wouldn’t have been in this situation in the first place. But they were short-sighted and continued to focus on the declining market of PC CPU design, and they are facing the consequences today.

..

So, over the long-term future, if Intel prioritize the short-term gains over long term strategy, and decide to shut down its manufacturing operations, then U.S. Chip design companies will be completely dependent on foreign companies for the most important products on earth, and while everything may seem fine for the next five, ten, or even fifteen years, the seeds of that failure will eventually sprout, just like those 2007 seeds sprouted for Intel over the last couple of years(source: Stratechery). And once they exit manufacturing, there is practically no coming back, because of the difficult barriers to entry that I previously mentioned.

As a result, China will leap past US in semiconductor manufacturing capabilities. The repercussions of this failure will be catastrophic not for the USA geopolitically, but for Intel as a business too. Their chip designs are falling behind, too. So, there’s no guarantee that shutting off their foundry business and focusing on becoming a chip design company would guarantee results. Therefore, in the long term, may be in the next 20-30 years, Intel will become completely obsolete, probably even cease to exist. So, any investor holding Intel stock today, their stock wouldn’t be worth anything.

Below are the numbers for the past 4 fiscal years, where you can see that China was ranked 3rd in FY22 and FY23, but dropped to 4th from FY24 onwards.

As you can see in the chart below, over the past 3 fiscal years, Nvidia’s business footprint in China has decreased due to the geopolitical tensions between China and the USA. While absolute revenue from China has grown, its percentage share of total revenue has declined. In FY2023, revenue from China represented ~21% of total revenues. But in FY 2025, it dropped down to only 13% of total revenue.

Here’s the Revenue from China (including Hong Kong) for the past 3 fiscal years:

And if we look at the Quarterly data, we see the SAME pattern. Note that there’s a dramatic drop in Q2FY26, due to the restrictions on sale of H20 GPU’s to China that as imposed in April 2025. (I’ll cover this in details later in the article).

[ Data Sources: Nvidia Form 10Q Q2FY2026 (Page 22), Nvidia Form 10K FY2025 (Page 79)]

..

So, these data clearly tell that Nvidia’s business with Chinese customers has been decreasing. My aim here is to take a look at the status of Nvidia’s business in China and take a look at where its heading in the future. Let’s begin.

Status of Nvidia’s business in China

Let me set the context first:

USA and China are in competing to be in the lead for AI technology. To that end, USA is doing anything it can to prevent China from taking the lead. One such measure is to restrict the sale of high-performance GPUs by American companies to Chinese tech companies that are working on building AI models. Without the advanced hardware, it will be difficult for Chinese companies to work on the cutting-edge AI technologies.

This is good for USA as a nation, as it helps them establish themselves the leader in AI technology. But it hurts Nvidia’s business, because it’s cutting off access to a big market.

On October 2022, the US put a ban on export of high-end AI chips (Nvidia's A100 and H100) to mainland China and Hong Kong (source). So, Nvidia developed the H20 GPUs to get around this restriction. The H20 GPU is Nvidia's modified version of their H100 high-performance data centre GPUs, created to comply with U.S. export controls while still serving the Chinese AI market.

..

Nvidia sold a significant amount of H20 GPUs to China till Q1FY26 (Feb to April). However, these sales were halted abruptly due to new U.S. export licensing requirements that prevented the sales even these “low-powered” H20 GPUs, probably because even though H100s are much better for pre-training AI models, but the “low-powered” H20 chips are ~20% faster at AI inference. Until recently, AI inference was not a large part of frontier AI development. But recent breakthroughs have made AI inference — and therefore inference chips — critical to developing frontier models (source).

The last time Nvidia sold H20 GPUs to China was in the first quarter of fiscal year 2026, prior to the April 9, 2025 implementation of these new U.S. export licensing rules. Since that date, and through the second quarter of fiscal 2026, there have been no H20 sales to China-based customers

In the first quarter of fiscal 2026, Nvidia's sales of H20 products to China totalled $4.6 billion. These sales occurred before the U.S. government implemented new export license requirements on April 9, 2025

The sudden change in regulations had a significant financial impact on Nvidia. The company was unable to ship an additional $2.5 billion worth of H20 products that were already in demand, leading to a $4.5 billion charge in the first quarter related to excess H20 inventory and purchase obligations

..

The 15% Revenue sharing deal

In July 2025, the Trump Administration reversed its policy, and Nvidia received assurances that it would be granted licenses to resume sales to China. By early August 2025, the U.S. Commerce Department began issuing these licenses to Nvidia, officially clearing the way for the company to export its H20 chips to China.

Although the ban was lifted, THERE WAS A CONDITION.

..

The condition is that the licenses were granted based on the agreement that Nvidia would pay 15% of the revenue from these sales back to the U.S. government. While the principle of this deal has been agreed upon, the government has not yet published the official rules and guidelines for how this revenue-sharing plan will actually work.

Because there is no formal, legal framework for implementing the 15% payment (as of writing this, 01 Sep 2025), Nvidia cannot proceed with the sales. The company is essentially in a position where it has a license to sell, but the mandatory conditions attached to that license are not yet defined, preventing any transactions from being completed.

This is precisely why Nvidia reported zero H20 sales to China in its Q2 fiscal 2026 earnings and why it has not projected any potential H20 sales to China in its Q3FY2026 forecast.

..

So to answer the question, “Are they going to sell to Chinese customers in FY26?”, the answer is NO, for at least the next quarter. While Nvidia has received license to sell to select Chinese customers, they have not yet shipped any products under these new licenses due to ongoing geopolitical issues and a lack of a finalized agreement with the U.S. government. Hence, Nvidia has not included any potential H20 sales to China in its Q3FY2026 forecast.

So, what will happen moving forward? Will Nvidia resume its sales?

This is the trillion-dollar question. But that’s where the real fun lies for me. To be able to analyse a scenario and try to forecast its future trajectory. And that’s what I’ll attempt to do here. I do not claim that my forecast would be correct. My aim is, instead, to take in the relevant information, and use the tools of structured thinking and analysis to come up with an output that’s backed by strong logic or evidence.

With that, let’s move forward.

Let’s look at some of the factors that play an important role in deciding whether Nvidia will conduct business in China.

..

1. USA’s goal of being the leader in AI technology. So, if not Nvidia, then domestic Chinese companies will do it. So it might as well be American companies.

   The US doesn’t want China to have access to the advanced Hardware. But they don’t want to completely shut off exports to China too, because this would then allow China to develop their domestic hardware industry.

   Scott Bessent said that they do not want Chinese tech companies to have a “digital Belt and Road”. He’s warning against a scenario where Huawei (and by extension, Chinese tech firms) dominate global digital infrastructure, especially in AI chips, semiconductors, and networking standards. If Huawei and other Chinese tech firms sets the technical standards for digital systems worldwide, it could:

   • Exclude U.S. technologies from key markets and supply chains

   • Shift global norms toward Chinese-designed systems, protocols, and governance

   • Undermine U.S. strategic leverage in emerging tech sectors like AI and semiconductors

   So, the plan is to allow Nvidia to sell the lower powered H20 GPUs instead of the H100, so that China do not have the advanced AI chips to outperform USA, and at the same time preserve U.S. influence in China’s tech ecosystem.

   ..

   Now, you may say “But wouldn't this move prompt China and domestic Chinese companies to work on their own AI chip hardware, instead of relying on low powered American tech?”

   And the answer is that its already happening to an extent. Cyberspace Administration of China (CAC) cautioning domestic tech companies like Tencent and ByteDance against purchasing Nvidia H20 chips/ GPUs over concerns about “backdoors” and national security and instead prioritize domestic Chinese suppliers. Chinese regulators worry that even downgraded chips like the H20 could include remote shutdown mechanisms or tracking tech. (source).

   ..

   In terms of domestic players, Alibaba is developing a new AI chip designed for broader inference tasks, fabricated entirely in China (source). Huawei, SMIC, Cambricon, and MetaX are ramping up efforts to triple China’s AI chip output by 2026 (source).

   ..

   So, USA’s strategy to have a footprint in China’s AI chip market is a double-edged sword. The limited access may:

   • Preserve short-term influence, especially through CUDA ecosystem lock-in

   • But also accelerate long-term decoupling, as China builds its own stack: hardware, software, and standards.

   ..

   For me, the situation boils down to: how easy is it to replicate Nvidia’s technology?

…..

2. Nvidia’s technology is not easy to replicate.

From the various research reports that I studied, I have come to the conclusion that Nvidia has a strong Moat i.e. it will not be easy to replicate their products, at least in the short term.

Here’s what Brian Collelo, Senior Equity Analyst from Morningstar, writes:

GPUs perform parallel processing, in contrast to the serial processing performed by central processing units used to run the software and applications on PCs, smartphones, and many other types of devices.

..

Nvidia was an early leader and designer of GPUs, which were originally developed to offload graphic processing tasks on PCs and gaming consoles.

…

(Now, these GPU’s are also best suited to train all kinds of AI models, including AI models)

…

Moreover, Nvidia made shrewd moves to build and expand the Cuda software platform, creating and hosting a variety of libraries, compilers, frameworks, and development tools that allowed AI professionals to build their models. Cuda is proprietary to Nvidia and only runs on Nvidia GPUs.

…

This hardware plus software integration has created high customer switching costs in AI that contribute to Nvidia’s wide moat.

(source: Morningstar Equity Analyst Report | Report as of 28 Aug 2025 04:12, UTC, Nvidia Earnings: US Growth Outshines the Drama in China; Raising Fair Value to $190 From $170)

These statements highlight that Nvidia’s technology would be difficult to replicate because of the unique hardware + software integration that it has. So, it seems like it will be a while since Chinese AI Chip designers comes close to Nvidia.

…..

3. A potential source of revenue for the US Government

   It’s well known that the US Government debt is at an all-time high. The primary ways for a government to earn money is through taxes. But clearly, USA do not have enough economic activity to raise enough taxes to sustain itself. As the economist Richard Wolff says, “America is living beyond its means”. And so they need other ways to bring money in in order to repay it as well as pursue its other objectives. So far in 2025, USA increasing taxes has been throughout the news. But what the news doesn’t tell is the underlying logic behind it. I think it’s one of the alternate sources of revenue.

   And that is exactly what this revenue share model also provides. It is another alternate source of revenue for the US Government.

My forecast*

(*Forecast does not constitute investment advice. This is my attempt to make a prediction using the available information and using structured logical inference)

From the above points, I can infer that US has a strong incentive to let Nvidia do business in China. Because it achieves 2 objectives:

1. Maintains a footprint of US technology in China and prevents the development of domestic AI hardware companies in China.

2. An alternate source of revenue for the US Government, which it desperately needs.

From China’s perspective, they would certainly want Nvidia’s tech for the short term because it won’t be easy to replicate Nvidia’s technology in the near future. China would eventually want to develop and use their own AI hardware to overcome this reliance. But I conclude that it isn’t going to happen in the near future.

So, if Chinese tech companies working on AI models wait for domestic AI hardware to catch up to Nvidia, they will be left behind by the Americans. So, the logical option would be to continue with American Chip designers (Nvidia, AMD) at least for the short term.

..

Therefore, from the above-mentioned arguments, I conclude that there’s a high probability that Nvidia will be allowed to continue to conduct business in China.

by RhPt

0. TL;DR / Executive Summary

USA is ramping up its construction of Data Centers. But, Data Centers require lot of energy. US energy/ electricity production capacity has increased only slightly in the past 20 years. Data Centers electricity costs are being subsidized. Local Citizens bills have increased despite minimum increase in their personal usage. This is because their money is used to pay for the electricity/ energy used by these data centres. But that’s just one of the problems. There are 2 other concerns caused by these data centres are Public Health concerns and Environmental concerns. These above-mentioned 3 concerns (rising electricity bills, public health and environmental concerns) might prompt citizens to pressurize the government to cut the subsidies/ tax breaks for data centers. This could bring down the intrinsic value of the companies in the AI industry

1. USA is increasing its number of Data Centers

USA is ramping up its construction of Data Centers. The primary reason that’s driving this demand is to facilitate the development of AI technologies and AI-driven processes. You may ask why? Depending on who you ask you might get different answers. For me, I see to main motives: 1. At the Global Level, to maintain America’s technological supremacy over global competitors, particularly China. 2. On a Domestic Level, the aim is to drive Economic Growth and Re-industrialization

To that end, Project Stargate was announced by a consortium of private technology companies and unveiled publicly by U.S. President Donald Trump at a White House press conference on January 21, 2025. The objective of Project Stargate is to build a massive network of advanced data centers specifically for AI.

Moreover, spending in Data Centers in the US has increased from 1.8 billion U.S. dollars in 2014 to 28.33 billion U.S. dollars in 2024, an increase of 1,475% in 14 years. The US data centre construction boom has resulted in nearly quadrupling the number of facilities since 2010, with the most explosive growth occurring in 2022-2024 driven by AI demands.

…..

2. But… there are 3 problems could affect this expansion plan

Data Centers require lot of energy. And US electricity production capacity has increased only slightly in the past 20 years.

So, Data Centers electricity costs are being subsidized. This has increased Local Citizens’ electricity despite minimum increase in their personal usage. That’s because their money is used to pay for the electricity/ energy used by these data centres. But that’s just one of the problems. There are 2 other concerns caused by these data centres are Public Health concerns and Environmental concerns. (Take a look at this video by Vanessa Wingardh for more details on this).

…..

3 How these issues could affect the valuations of companies in the AI Industry/ value chain

This could potentially impact the valuation of AI companies that rely on these data centres. How? If we consider determining the intrinsic value of a company using the DCF method, the value of the company comes down to the discounted values of its future cash flows/ income (bottom-line). But these cash flows could be affected. Let me explain how.

The above-mentioned 3 concerns (rising electricity bills, public health and environmental concerns) might prompt citizens to pressurize the government to cut the subsidies/ tax breaks for data centers. If these happen, then there could be 2 scenarios:

1. These companies would decrease the expansion plans of the data centres. This would imply a smaller Serviceable Market for these companies, along with higher Operating Costs. This would, in turn, lead to lower income/ bottom-line for AI companies.

2. These companies could still move ahead with building these data centres with their own money. This will also increase their Operating Costs and hence would also lead to lower bottom-line for the AI companies.

Either way, the companies in the AI Industry would end up with higher Operating Costs. And these costs will trickle down throughout the value chain, hence decreasing the bottom-line/ cash flows of all the companies in the value chain, thus dropping their intrinsic value.

I am assuming that the valuation that’s being used in the funding rounds and acquisitions of these companies haven’t factored these yet.

On 31 March 2025, OpenAI raised $40 billion at a $300 billion post-money valuation (source: Open AI blog). As of August 2025, OpenAI is in early-stage discussions for a secondary share sale that would value the company at approximately $500 billion. This deal would allow current and former employees to sell around $6 billion worth of their stock to a group of investors. This is an increase in valuation by $200 billion in 5 months.

Anthropic (maker of Claude AI), one of OpenAI biggest competitor, raised $3.5 billion at a $61.5 billion post-money valuation, on 03 Match 2025. (source: Anthropic blog). And as of 30 July 2025, there are talks of Anthropic raising $5B at a $170B Valuation (source: Crunchbase). For context, they were valued at $18.5 billion in February 2024. That’s nearly a 19x increase in valuation in about 15-16 months.

…..

4. This section is a continuation of the argument from the previous section!

Now these valuations could be justified in terms of saying that there’s huge potential for diverse use-case and hence a large Total Addressable Market (TAM). But have these valuations considered the costs necessary to service these markets? Are the analysts valuing these companies ware of the real Operating Costs behind the developments of the AI models? If yes, then it’s all good. But it’s more likely that they aren’t and that it’s more of a sign of a bubble and people riding the wave. How else could one justify a $200 billion increase in valuation of OpenAI in 5 months, or Anthropic’s 9x increase in valuation in 15 months?

…..

5

So that’s my conclusion here. My aim with this post was to talk about the lesser-known Operating Costs behind the development of these AI models, which are being subsidized currently. But because of the issues they are causing, there might be a chance that these subsidies might go away and hence would impact the intrinsic value of the companies in the AI Industry.

…..

6

A future scope for research would be trying to quantify the probability of occurrence of this event (i.e. removal of subsidies/ tax breaks for data centers), using Quantitative and data-intensive models.

But intuitively and logically speaking, it seems less likely to happen. The US is in a fierce race against China for AI Supremacy, which has been aggressively investing in its own AI capabilities. By creating this massive, dedicated AI infrastructure, the U.S. aims to stay ahead in the global AI race, set international standards, and ensure that critical technologies for defense and intelligence remain under its control. (Read more about this here by Anand Ramachandran)

On a bigger picture, USA aims to be the leader in 3 critical technologies: AI, Semiconductors and Quantum computing.

Thus, slowing down construction of Data Centers wouldn’t make sense as it would be acting against their strategy. Any move to curb the construction of Data Centers would put USA at a competitive disadvantage against China. So, by that logic, they wouldn’t want to do anything that would slow down their pace of progress in AI technology.

..

So, on the one hand, at the External Order level, there’s the competition with China for AI Supremacy.

On the other hand, at the Internal Order level, there are concerns with 1. Rising Electricity bills, 2. Public Health Concerns; and 3. Environmental Concerns.

How will USA deal with this? What will be their strategy? And what does this say about USA’s state of External Order and Internal Order?

These topics go beyond valuations of AI companies and touch on Macro level themes. Hence, I will talk about them in the next article, where I’ll continue with this discussion on a Macro level.

by RhPt

Google’s moat

Google's moat has traditionally rested on two pillars:

1. Search engine dominance

2. Browser market control

1. Search engine dominance

In search, Google still commands 89.74% of the global market11, but this represents a decline from over 93% just two years ago1. More tellingly, Bing has nearly doubled its market share from 2.18% to 3.98% in the same period1. In the U.S., Google's desktop search share has plummeted from 88.10% in 2015 to 79.32% in 2025, while Bing has surged from 4.53% to 11.96%—a staggering 164% increase17.

The browser landscape tells a similar story of gradual erosion. Chrome maintains 66.6% market share globally2, but this dominance masks underlying vulnerabilities. Microsoft Edge has grown from 1.5% in 2020 to over 5% in 202512, while privacy-focused alternatives like Brave and DuckDuckGo are gaining traction among increasingly privacy-conscious users.

The emergence of ChatGPT created a fundamental paradigm shift in how users interact with information. While Google processes approximately 14 billion searches daily4, ChatGPT handles an estimated 37.5 million search-like prompts per day4. That might seem insignificant—a mere 0.25% market share—but it's growing exponentially and represents a completely different user behaviour pattern.

The critical insight here is that ChatGPT users aren't just searching; they're conversing with information. They're asking follow-up questions, requesting explanations, and engaging in iterative problem-solving that traditional search simply cannot provide. This isn't a marginal improvement. Instead, it's a category-defining innovation that makes Google's "ten blue links" feel as antiquated as a card catalogue.

Perplexity has taken this disruption further by processing over 100 million queries weekly5 with its AI-powered search that provides direct, cited answers rather than link farms. Their upcoming Comet browser represents the logical next step: integrating AI-native search directly into the browsing experience, potentially eliminating the need for traditional search engines altogether.

2. Browser market control

The browser market is experiencing its most significant upheaval since Chrome dethroned Internet Explorer. But this isn't just about market share redistribution—it's about fundamental reimagining of what a browser can be.

Microsoft Edge has emerged as the most credible Chrome alternative, leveraging its integration with Windows and AI features powered by ChatGPT7. The browser now includes Copilot assistance, vertical tabs, and productivity features that Chrome lacks, making it particularly attractive to enterprise users.

Perplexity's Comet represents something entirely new: an "agentic" browser that promises to automate tasks and conduct deep research autonomously5. Built on Chromium for compatibility, Comet aims to transform browsing from passive consumption to active assistance. Imagine a browser that doesn't just find flight information but actually books your trip based on preferences it's learned from your behavior.

Arc Browser and SigmaOS are reimagining browser interfaces entirely, moving beyond the traditional tab-based model to workspace-oriented designs that treat the browser as a productivity platform rather than a document viewer15.

Even OpenAI is reportedly developing its own browser8, which would integrate ChatGPT directly into web navigation, potentially offering ad-free experiences that prioritize user intent over advertising revenue.

3. Other factors that are hurting Google’s dominance

On top of new entrants challenging its moat, Google (along with other Big Tech Companies) is also facing legal and regulatory challenges in the United States as well as in Europe that are hurting them.

The Regulatory issues

Google's legal troubles are accelerating competitive pressure at precisely the wrong moment. The U.S. Department of Justice has argued that Google must divest Chrome entirely13, while European regulators have forced choice screens for Android users. These regulatory interventions are creating artificial openings for competitors that might not have existed in a purely market-driven environment.

The irony is palpable: Google built its moat by being genuinely better than alternatives, but regulatory action is now forcing users to consider options they might never have tried otherwise. When users are presented with choice screens featuring Bing, DuckDuckGo, or other alternatives, some discover they don't actually need Google's marginally superior results for most queries.

The Innovator’s Dilemma for Google

Google faces a classic innovator's dilemma: its most profitable products are being disrupted by technologies that threaten its core business model. The company has world-class AI capabilities through DeepMind and its own LLM development, but deploying these capabilities would cannibalize its advertising revenue.

Historically, Google's network effects worked perfectly in their favour:

• More users → More search queries → More data about user behaviour

• More data about user behaviour → Better search algorithms → More accurate results

• More accurate results → More users → More advertisers willing to pay premium rates

• More advertisers → More revenue → More resources for infrastructure expansion and R&D

This created a virtuous cycle where scale begat more scale, making Google nearly impossible to compete with using traditional search technology.

The paradox emerges because Google's massive scale works on a revenue model (advertising) that is different from how the new generation of AI-first startups are generating their revenue. So, far it’s been through subscriptions to their AI chatbots and APIs and at personal and enterprise level. There’s no advertising revenue, yet.

So, when Google integrates AI answers directly into search results, it reduces clicks to external websites, which reduces ad revenue. When competitors like Perplexity provide AI answers without this constraint, they can optimize purely for user experience rather than balancing user satisfaction against revenue protection.

This dynamic is visible in Google's cautious rollout of AI features compared to the aggressive innovation happening at OpenAI, Perplexity, and other AI-native companies. Google is trying to evolve its existing products rather than reimagining them from first principles.

This is exactly the Innovator’s dilemma that they are facing. They cannot unleash their creativity while innovating with AI. They have to remain structured and try to integrate the AI features systematically. Otherwise, they are in danger of damaging their own Revenue model.

My verdict on Google’s Moat: Weakening but Not Broken (yet)

Google's moat remains formidable but is undeniably weakening. The company still processes 373 times more searches than ChatGPT4 and maintains dominant market positions across multiple products. Chrome's 66.6% browser market share2 provides significant distribution advantages, while Android's mobile dominance creates additional defensive barriers.

However, the moat's effectiveness is declining for several reasons:

• User behaviour is shifting toward conversational interfaces that bypass traditional search entirely.

• New entrants like Perplexity and OpenAI are building AI-native products rather than trying to retrofit existing search paradigms.

• Regulatory pressure is forcing artificial competition that wouldn't exist in purely market-driven scenarios.

The most telling indicator might be Google's own behaviour. The company's aggressive push into AI, its development of Gemini, and its integration of AI features into search suggest internal recognition that the traditional search model is under existential threat.

The Path Forward

Google's response will likely involve 2 strategies: product innovation to match AI capabilities while preserving advertising revenue, ecosystem lock-in through deeper integration across its product suite.

The company's vast resources, talent pool, and existing user base provide significant advantages in this transition. The next 24 months will be critical for Google.

Citations and Sources:

1. https://www.impressiondigital.com/blog/bing-differ-google/

2. https://backlinko.com/browser-market-share

3. https://www.promarket.org/2025/04/22/why-googles-dominance-in-search-persists-and-how-to-fix-it/

4. https://searchengineland.com/google-search-bigger-chatgpt-search-453142

5. https://timesofindia.indiatimes.com/technology/tech-news/perplexity-takes-on-google-chrome-with-its-ai-web-browser-comet/articleshow/118560885.cms

6. https://www.digit.in/features/general/top-5-google-chrome-alternatives-to-use-in-2025.html

7. https://www.techradar.com/best/browser

1. https://efficient.app/compare/sigmaos-vs-dia

2. https://content.techgig.com/technology/revolutionizing-web-development-microsoft-edge-launches-cutting-edge-on-device-ai-apis/articleshow/121297784.cms

3. https://www.proceedinnovative.com/blog/search-engine-market-share-2025/

4. https://www.visualcapitalist.com/ranked-the-most-popular-web-browsers/

5. https://ddnews.gov.in/en/google-must-divest-chrome-to-restore-competition-in-online-search-doj-says/

6. https://writesonic.com/blog/comet-ai-browser

7. https://efficient.app/best/browser

8. https://learn.g2.com/best-browser

9. https://explodingtopics.com/blog/google-searches-per-day

10. https://gs.statcounter.com/search-engine-market-share

11. https://www.statista.com/statistics/216573/worldwide-market-share-of-search-engines/

12. https://gs.statcounter.com/search-engine-market-share/mobile/india

13. https://fi.money/guides/us-stocks/chatgpt-vs-googles-bard-what-is-their-impact-on-the-us-stock-market

14. https://www.pymnts.com/google/2025/analysts-google-search-traffic-stable-despite-surge-of-ai-chatbots-use/

15. https://www.socialmediatoday.com/news/google-search-dominance-ai-impact-infographic/739752/

16. https://zapier.com/blog/best-web-browser/

17. https://www.reddit.com/r/browsers/comments/1hrr4lb/browser_recommendation_megathread_january_2025/

18. https://www.stanventures.com/news/chatgpt-gains-4-33-in-search-threat-to-google-1295/

19. https://www.9rooftops.com/blog/search-in-2025-can-chatgpt-compete-with-google/

20. https://www.highspeedinternet.com/resources/best-web-browsers