Quick View: How did NVIDIA’s earnings call address AI bubble concerns?
Portfolio Manager Richard Clode summarises the main investor implications from NVIDIA’s most recent earnings report.
4 minute read
Key takeaways:
- NVIDIA’s quarterly results exceeded expectations, with reaccelerating growth and incremental deals reinforcing NVIDIA’s leadership in AI infrastructure.
- The company cited generative AI, agentic AI, as well as the end of Moore’s Law as structural forces fuelling long-term demand for accelerated computing.
- Management addressed fears around GPU lifespan, circular financing, and competitive threats, emphasising strong cash flow and ecosystem expansion in an effort to debunk investor concerns.
In recent months there has been rising market angst around circular financing and an AI bubble, with hedge fund investor Michael Burry’s short thesis around hyperscalers overstating earnings via understating depreciation, circular financing and recent customer deals with competitors adding fuel to the fire.
Yesterday’s earnings call provided an opportune moment for NVIDIA to set the record straight on what the company was seeing from their vantage point as the AI leader. NVIDIA head Jensen Huang gave an impassioned defence of what is driving a huge investment in new compute infrastructure, explaining how in his view, AI still had many years of growth to come:
3 key vectors driving AI infrastructure investment
1. The end of Moore’s Law
Existing compute infrastructure that underpins all data and software no longer scales at the pace it has previously, suggesting that Moore’s Law is finally approaching obsolescence. But rather than seeing the pace of change slowing, it is instead driving a wave of innovation, pushing the need to transition to accelerated compute to reduce cost.
2. Generative AI
The internet services that we interact with daily are built on search rankings, recommender engines and advertisement targeting. Large language models (LLMs) are much more effective in driving these engines, which in turn creates higher revenue via more user engagement and traffic. LLMs are also getting better at identifying commercial intent, leading to higher click-through-rates.
3. Agentic AI/Physical AI
These new revolutionary technologies are creating brand new, very large addressable markets. This is driving some of the fastest revenue increases we have ever seen. Jensen referenced AI safety and research company Anthropic, which has seen its annualised revenue increase from US$1 billion earlier this year to US$7 billion annualised last month.1
There’s been a lot of talk about an AI bubble. From our vantage point, we see something very different.
Jensen Huang, NVIDIA founder & CEO
3 main arguments from NVIDIA’s point of view to assuage recent concerns about the company
1. Useful life of a GPU
NVIDIA’s CFO made the point that its six-year-old Ampere GPUs are still available in the cloud and fully utilised today. This contrasts with Michael Burry’s assertion that the useful life of GPUs is only two-to-three years. While a six-year-old GPU would not be used to train a highly advanced, large-scale frontier model, it can be used for less intensive workloads. The CFO also made the point that the performance of an old GPU does not sit still as software upgrades can drive meaningfully improved performance over time (4-5x for the Hopper chip over its lifetime to date).
2. Circular financing
Recently announced investments by NVIDIA in OpenAI and Anthropic need to be put in context. NVIDIA generated US$22 billion in free cashflow last quarter, it has more than US$50 billion of net cash on its balance sheet.2
NVIDIA defended these deals as a means to expand its ecosystem with Anthropic committing this week to use NVIDIA GPUs for the first time. Such deals can help accelerate the growth plans of these new customers, as well as the potential future financial returns of taking stakes in companies NVIDIA views as being generational in their capabilities.
3. Competition
NVIDIA argued that its proprietary programming language CUDA as well as versatile architecture allow the company’s GPUs to have a much longer life versus the competition, which could be limited to a few years as model technologies evolve. It is worth noting that while rival AMD at their recent analyst day promised tens of billions of dollars of AI compute revenues in 2027, NVIDIA has reported a similar amount in one quarter.3
Earnings highlights: Growth acceleration and strategic deals
In terms of earnings results, NVIDIA delivered more than the usual quantum of upside versus expectations and reaccelerating growth. Having already laid out at its recent GTC Washington event a pathway to more than US$300 billion in data centre sales next year, we saw this week’s HUMAIN and Anthropic deals as incremental to that number.4
The sales figures for NVIDIA continue to include de minimis China sales, given the ongoing US-China impasse. But NVIDIA has not given up hope, emphasising the need to have a competitive product to sell in China and the importance of the US being able to compete globally. Buy-side forecasts currently continue to be well ahead of formal sell-side consensus, with upgrades overnight looking to be more of a catch up. Many investors view that NVIDIA’s stock still trades at a far from egregious valuation – this could be viewed as a strong retort to recent AI bubble concerns and comparisons with the dotcom market in 2000.
Source for all earnings call-related information is NVIDIA Newsroom; “NVIDIA Announces Financial Results for Third Quarter Fiscal 2026”; 19 November 2025.
1 DigitalAsia; “Anthropic reportedly poised to reach US$70 billion in revenue by 2028, driven by API sales surge”; 7 November 2025.
2 NVIDIA Newsroom as at 19 November 2025.
3 AMD.com; “AMD Unveils Strategy to Lead the $1 Trillion Compute Market and Accelerate Next Phase of Growth”; 11 November 2025. NVIDIA Newsroom; NVIDIA reported revenue of US$57 billion in fiscal Q3 2026.
4 Businesswire; “AWS and HUMAIN Expand Partnership with NVIDIA AI Infrastructure and AWS AI Chip Deal to Drive Global AI Innovation”; 19 November 2025. CNN Business, “The world’s most valuable public company just laid out its vision for the future”; 28 October 2025.
Agentic AI: An AI system that uses sophisticated reasoning and iterative planning to autonomously solve complex, multi-step problems. Vast amounts of data from multiple data sources and third-party applications are used to independently analyse challenges, develop strategies and execute tasks.
Balance sheet: A financial statement that summarises a company’s assets, liabilities, and shareholders’ equity at a particular point in time. Each segment gives investors an idea as to what the company owns and owes, as well as the amount invested by shareholders.
Buy-side/Sell-side: Buy-side is the side of the financial market that buys and invests large portions of securities for the purpose of money or fund management. Sell-side is the other side of the financial market, which deals with the creation, promotion, and selling of traded securities to the public.
Circular financing: Concerns that financing of AI infrastructure investment among mega caps is becoming unsustainable. Interconnected deals and investments within a small group of companies means companies are investing in each other, with the funding recipient using the capital to make purchases from the original investor. Some of these companies may have insufficient cash flows and it could lead to a bubble when company valuations become excessive, with broader market implications.
Depreciation: Refers to the measurement of how much value physical assets lose over time. It is an accounting method that allocates the cost of a tangible asset over its useful life to reflect its decreasing value through use and obsolescence.
Free cash flow: Cash that a company generates after allowing for day-to-day running expenses and capital expenditure. It can then use the cash to make purchases, pay dividends or reduce debt.
Generative AI: Refers to deep-learning models that train on large volumes of raw data to generate ‘new content’ including text, images, audio and video.
GPU: A graphics processing unit performs complex mathematical and geometric calculations that are necessary for graphics rendering and are also used in gaming, content creation and machine learning.
Hedge fund: A fund that employs a strategy that takes an offsetting position to another investment that will lose value as the primary investment gains and vice versa. These positions are used to reduce or manage various risk factors and limit the probability of overall loss in a portfolio. Various techniques may be used, including derivatives.
Hyperscaler: Companies that provide infrastructure for cloud, networking, and internet services at scale. Examples include Google, Microsoft, Facebook, Alibaba, and Amazon Web Services (AWS).
LLM (large language model): A specialised type of artificial intelligence that has been trained on vast amounts of text to understand existing content and generate original content.
Moore’s Law: Refers to Intel co-founder Gordon E. Moore’s (1965) suggestion that the number of transistors that can fit onto a microchip would double every two years. Thus we can expect the speed and capability of computers to increase every couple of years, and at lower cost. Another tenet of Moore’s Law asserts that this growth is exponential.
Valuation: The process of determining the fair value of an asset, investment, or firm. Among others, future earnings and other company attributes are used to arrive at a valuation.
4 minute read
Key takeaways:
- NVIDIA’s quarterly results exceeded expectations, with reaccelerating growth and incremental deals reinforcing NVIDIA’s leadership in AI infrastructure.
- The company cited generative AI, agentic AI, as well as the end of Moore’s Law as structural forces fuelling long-term demand for accelerated computing.
- Management addressed fears around GPU lifespan, circular financing, and competitive threats, emphasising strong cash flow and ecosystem expansion in an effort to debunk investor concerns.