Nvidia just dropped a number that made the entire market stop and pay attention.
During the second quarter fiscal 2027 earnings call, CFO Colette Kress delivered a projection nobody expected to see this soon: 70% revenue growth for fiscal year 2028, blowing past the average analyst estimate of 44% that Wall Street had on the table, according to LSEG data.
That is not a small deal.
Considering the consensus revenue projection of $396 billion for fiscal year 2027, which ends in January, sales the following year would reach a staggering $673 billion. If those numbers hold up, Nvidia will surpass both Apple and Alphabet in revenue, trailing only Amazon — which is still essentially a retailer — among the major U.S. tech giants.
And the detail that makes all of this even more impressive is that this growth could actually be even higher, if the supply chain could keep up with the real demand for the company’s technology.
But what is behind such a bold projection? And why did Nvidia decide, right now, to look this far ahead when historically the company avoided this kind of forecast? That is exactly what we are going to break down here. 👇
From niche chipmaker to the most valuable company in the world
It is worth pausing for a second to remember where Nvidia came from. Before the artificial intelligence boom, it was a relatively niche chipmaker, known mostly for graphics cards aimed at gaming and visual computing applications. Nobody imagined that this company would become, in just a few years, the most valuable company on the planet.
And the numbers keep telling a historic story. In the most recent quarter, revenue flat-out doubled compared to the same period the year before. We are talking about growth rates that are rare in any industry, let alone for a company of this size. It is exactly this kind of performance that put Nvidia at the absolute center of the AI revolution and now supports such an aggressive projection for the years ahead.
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What changed in Nvidia’s approach to forward guidance
For a long time, Nvidia was known for being extremely conservative when it came to long-term guidance. The company preferred to work with short-term forecasts, delivering quarter by quarter, without committing to numbers that far out. In recent years, CEO Jensen Huang did share some data points about expected AI chip sales within a two-year window, but a formal projection reaching this far into the future was not something common in the company’s history.
What changed now is the visibility Huang says he has into future demand. According to the CEO himself, Nvidia decided to provide this forecast because the company can already clearly see the computational power needs for the coming year. And there is a very practical reason behind it: Nvidia’s chips are driving so much infrastructure investment that the company wanted to keep all of its partners on the same page.
Everyone is putting a lot of resources into play, so we wanted to make sure everyone had the same set of information, Huang said during the analyst call.
Those partners include suppliers of complementary inputs for data centers, such as real estate and electricity. When you build an AI data center, chips alone are not enough — you need physical space, robust electrical infrastructure, and an entire coordinated chain. By giving visibility into its own numbers, Nvidia helps the whole ecosystem plan with greater confidence.
Why 70% growth is a number that turns heads on Wall Street
When we talk about 70% growth for a company the size of Nvidia, we are talking about something very different from an early-stage startup. Mature companies with revenues in the hundreds of billions of dollars rarely post growth rates at this level. The norm is large companies growing between 5% and 15% annually, with occasional peaks of up to 30% during periods of aggressive expansion. A number like 70%, at this scale, redefines what the market considers possible for an established big tech company.
To put it in better context, the average analyst estimate before the announcement was 44%, which on its own would already be impressive growth. The difference between 44% and 70% is not just numerical — it represents tens of billions of dollars in additional revenue, a direct impact on valuation, a revision of financial models at investment funds around the world, and a scramble to adjust forecasts that affect everyone from large asset managers to small retail investors.
On top of that, the impact of these projections goes well beyond Nvidia’s own stock. Companies that supply components, companies competing in the AI space, and even infrastructure players like data center builders and energy providers are all recalibrating their own strategies based on this number. The artificial intelligence market is a highly interdependent ecosystem, and when Nvidia signals it will grow 70%, it is indirectly telling the world that demand for AI is going to stay hot for much longer than even the most optimistic analysts imagined.
The bottleneck that could hold back growth: the supply chain
One of the most important points raised during the earnings call was precisely the supply chain limitation. Nvidia made it clear that the projected 70% growth does not represent the ceiling of real demand for its products, but rather the ceiling of what the company can deliver given the current pace of production. In Jensen Huang’s own words:
Our demand is much greater than 70%. Our supply allows us to deliver 70% with confidence, and we will continue working with our supply chain to increase that number.
One of the most critical bottlenecks lies in the shortage of components like memory, which is facing a global supply crisis precisely because of the AI infrastructure building race. The more data centers go up, the more memory components are needed, and the global supply simply cannot keep up with this accelerated pace of demand.
Scaling up production capacity at this level is not something that happens overnight. New equipment needs to be acquired, installed, and calibrated, and the average timeline to expand capacity in this segment can vary significantly. This creates a window of structural scarcity that, paradoxically, ends up benefiting Nvidia in terms of pricing power, but at the same time limits the amount of revenue the company can capture in the short term.
From a strategic standpoint, with demand consistently outpacing supply, Nvidia’s customers need to lock in their orders well in advance, which generates long-term contracts and revenue visibility for the coming quarters. The scarcity, interestingly enough, is helping Nvidia build an even more committed customer base around its platform.
Demand is spreading well beyond the hyperscalers
One of the concerns hovering over investors was Nvidia’s dependence on a handful of large tech companies, the so-called hyperscalers. These giants build massive data centers that were primarily serving a few frontier labs, like OpenAI, which need more computational power than virtually any other organization on the planet.
But that reality is changing, and fast. According to Huang, some of the earliest phases of this buildout were driven by, in his words, a single lab alone. Today, the picture is completely different.
Demand is accelerating. At this same time last year, a single lab was driving the buildout; today, we have a golden age of new AI labs and startups, multiple frontier labs scaling in parallel, a thriving ecosystem of open models, and physical AI coming online, with strong momentum in the United States and around the world, Huang said.
Huang also detailed a new customer category he calls ACIE, which includes regional AI companies, so-called neo-clouds, startups, and traditional enterprises. Nvidia’s technology is attractive to this group because the company can supply a good portion of everything a data center needs — not just the chips. According to the CEO, this type of customer was, until recently, practically invisible on the industry’s radar.
Earlier this month, Nvidia even announced a program to help these customers, beyond the frontier labs, access financing through six major financial institutions. The reason is straightforward: the company’s systems have become so expensive that businesses without a long track record have a hard time purchasing them without structured financial support.
This part of global computing is probably going to be larger over time than even what we are currently experiencing in the cloud, and I think the demand we see is driven by all of these factors, Huang concluded.
What this move means for the AI market going forward
Nvidia’s projections work as a barometer for the artificial intelligence market as a whole. When the most central company in this ecosystem signals accelerated growth, it is reflecting what its biggest customers have already communicated internally in their own planning cycles.
This indicates that the AI market is still far from saturation. Contrary to what some more skeptical analysts suggested — when they questioned whether demand for AI infrastructure was sustainable or just a speculative bubble — the numbers Nvidia presented show that technology adoption is deepening and spreading into new sectors. Healthcare, logistics, manufacturing, financial services, and even governments are starting to build their own AI capabilities, expanding the customer base well beyond the big tech companies that dominated the first waves of adoption.
For anyone closely following the development of artificial intelligence, Nvidia’s move represents much more than good financial news. It signals that the computational infrastructure needed to power large language models, computer vision systems, and real-time AI applications will continue to be built at a rapid pace. This has direct implications for how quickly new AI capabilities reach the market and for how companies of all sizes will need to position themselves in the coming years to avoid falling behind in a race that, by all indications, is just getting started. 🚀
