Why Nvidia Has Become the World’s Most Valuable AI Company

Quick Answer

Nvidia became the world’s most valuable publicly traded company because it supplies much of the essential computing infrastructure behind the artificial intelligence boom.

Its advantage extends beyond selling powerful graphics processing units, or GPUs. Nvidia has built an integrated AI platform that combines chips, networking equipment, systems, software, developer tools and industry-specific applications.

This has created a powerful business cycle:

Developers build AI applications using Nvidia’s software.
Cloud providers purchase Nvidia-powered systems to serve those developers.
More organizations adopt Nvidia’s platform because talent, tools and infrastructure are already available.
Nvidia reinvests its profits into faster chips, improved software and larger computing systems.

As of July 2026, Nvidia remains the world’s largest publicly traded company by market capitalization, although that position can change as share prices move.

TwikUp Insight: Nvidia is not valuable merely because it designed a popular AI chip. Investors increasingly view the company as the supplier of a complete computing platform for building, training and operating artificial intelligence.

Nvidia’s Rise Is About More Than One Successful Product

A few years ago, many investors primarily associated Nvidia with graphics cards used by gamers.

That description is no longer sufficient.

Nvidia’s technology now supports:

Generative AI models
Cloud computing
Scientific research
Autonomous vehicles
Robotics
Drug discovery
Industrial simulation
Recommendation engines
Cybersecurity systems
High-performance computing

The company did not abandon gaming. Instead, it expanded the use of the same fundamental technology—parallel computing—into much larger commercial markets.

Traditional central processing units, or CPUs, are designed to perform a relatively small number of complex tasks sequentially. GPUs can perform thousands of calculations simultaneously.

That ability became especially important as artificial intelligence models grew larger and required enormous numbers of mathematical operations.

Nvidia was positioned to benefit because it had spent years developing GPUs and the software required to program them.

Nvidia’s Growth in Numbers

Nvidia’s transformation becomes clearer when looking at its financial results.

For its 2026 fiscal year, which ended in January 2026, Nvidia reported:

Financial measure	Fiscal 2026 result
Total revenue	$215.9 billion
Year-over-year revenue growth	65%
Data Center revenue growth	68%
GAAP gross margin	Approximately 75% in the fourth quarter

The acceleration continued into the following fiscal year.

For the quarter ended April 26, 2026, Nvidia reported:

$81.6 billion in quarterly revenue
85% year-over-year revenue growth
20% sequential revenue growth
A GAAP gross margin of 74.9%

These results show that Nvidia is not being valued solely on the possibility of future AI demand. The company is already converting demand for AI computing into substantial revenue and profit.

A Simple Growth Illustration

Suppose a company produced $100 in annual revenue and then increased that amount by 65%.

Its new revenue would be:

$100 × 1.65 = $165

Maintaining that type of growth becomes increasingly difficult as a company gets larger.

Growing from $1 billion to $1.65 billion requires an additional $650 million.

Growing from $130 billion to approximately $216 billion requires roughly $86 billion of additional annual revenue.

That scale helps explain why Nvidia’s performance attracted so much investor attention.

However, extraordinary historical growth should never be assumed to continue indefinitely.

1. Nvidia Sells the Infrastructure Behind AI

Many companies are building AI applications.

Nvidia sells the computing infrastructure that makes much of that development possible.

This position is often compared with selling picks and shovels during a gold rush. The comparison is useful, but incomplete.

Nvidia is not simply selling individual chips. It offers components across the AI computing stack:

GPUs
Central processing units
High-speed networking
Interconnect technology
Complete server systems
AI development libraries
Enterprise software
Cloud services
Pretrained models
Simulation platforms

A customer building a large AI system may therefore purchase several connected Nvidia products rather than a standalone processor.

This increases Nvidia’s potential revenue per customer while making the overall platform more difficult to replace.

2. CUDA Created a Powerful Software Advantage

One of Nvidia’s most important assets is not a physical chip.

It is CUDA.

CUDA is Nvidia’s parallel-computing software platform and programming model. It allows developers to use Nvidia GPUs for tasks beyond traditional graphics processing.

Over many years, universities, researchers, startups and large corporations have built applications around CUDA and related Nvidia libraries.

This created an extensive ecosystem that includes:

Programming tools
AI frameworks
Optimization libraries
Documentation
Training resources
Cloud integrations
Developer communities
Existing production applications

A competing semiconductor company may produce a capable chip. But customers must also consider whether their software, employees and existing systems can use it effectively.

Changing hardware platforms can require:

Rewriting code
Retraining engineering teams
Testing model performance
Rebuilding infrastructure
Validating reliability
Interrupting production workloads

That creates switching costs.

Nvidia’s software ecosystem does not make the company impossible to challenge, but it provides a competitive advantage that hardware specifications alone do not fully capture.

TwikUp Insight: Nvidia’s moat is partly created by accumulated developer time. Every application optimized for CUDA can make the broader Nvidia platform more useful to the next customer.

3. Nvidia Turned Individual Chips Into AI Factories

Large artificial intelligence models are not normally trained on a single GPU.

They may require thousands of processors working together.

As the number of processors increases, communication between them becomes critical. A powerful GPU can sit idle when information cannot move through the system efficiently.

Nvidia addressed this problem by expanding into:

NVLink interconnect technology
InfiniBand networking
Ethernet networking
Grace CPUs
DGX systems
HGX platforms
Rack-scale computing
AI infrastructure software

Nvidia describes large, integrated computing facilities designed for AI workloads as AI factories.

The terminology is intentional. Traditional factories convert raw materials into physical goods. AI data centres convert electricity and data into model training, predictions, generated content and automated decisions.

This transition changed Nvidia’s economic opportunity.

Instead of competing only for a chip sale, Nvidia can participate in the design of an entire computing system.

4. Blackwell Expanded Nvidia’s Performance Ambitions

Nvidia’s Blackwell architecture was designed for accelerated computing and generative AI workloads.

According to Nvidia, Blackwell-architecture GPUs contain 208 billion transistors and use a high-speed connection between two dies so that they function as a unified GPU.

But transistor counts alone do not explain the business value.

AI customers generally care about several connected factors:

How quickly a model can be trained
How many user requests a system can process
How much electricity the system consumes
How much data can move between processors
How reliably the system operates
How much the complete workload costs

For cloud providers and AI companies, the cost per completed task may matter more than the purchase price of an individual processor.

A more expensive system can still be economically attractive when it trains a model faster, serves more customers or reduces the cost of producing each AI response.

This is why Nvidia increasingly markets complete system performance rather than only chip-level speed.

5. Nvidia Benefits From the AI Capital-Spending Cycle

Large technology companies are spending heavily on data centres, processors, networking, energy infrastructure and cloud capacity.

Major Nvidia customers include cloud-platform operators and companies developing increasingly sophisticated AI services.

The investment cycle can work in Nvidia’s favour:

More AI users
      ↓
More demand for AI applications
      ↓
More computing capacity required
      ↓
More data-centre investment
      ↓
More demand for GPUs, networking and AI systems

Nvidia does not need every AI application to become profitable immediately.

It can generate revenue when organizations purchase infrastructure to develop, train and operate those applications.

This is one reason Nvidia may benefit earlier in the AI investment cycle than some software businesses.

However, this relationship also creates a major risk. If large customers reduce capital spending, extend the useful life of existing equipment or develop more internal chips, Nvidia’s growth could slow.

6. Nvidia Has Become a Platform, Not Just a Semiconductor Company

Traditional semiconductor analysis often focuses on:

Chip performance
Manufacturing capacity
Average selling prices
Product cycles
Inventory
Gross margins

Those measures remain important for Nvidia.

But the company increasingly resembles a platform business because its products connect hardware, software and developers.

Consider the difference:

A chip company

A chip company designs a processor, sells it and competes largely through performance, price and manufacturing availability.

A computing-platform company

A platform company offers processors, networking, software libraries, development tools, systems and services that work together.

Nvidia increasingly fits the second description.

Its platform extends into multiple sectors:

Nvidia platform area	Potential application
Data-centre computing	Training and operating AI models
Omniverse	Industrial simulation and digital twins
DRIVE	Autonomous and software-defined vehicles
Jetson	Robotics and edge computing
BioNeMo	AI-assisted biotechnology research
DGX Cloud	Cloud-based AI development
Networking	Connecting large computing clusters

Not every business segment will grow at the same pace, and some may remain relatively small compared with data-centre revenue.

Still, the range of applications gives investors a reason to see Nvidia as more than a single-product company.

7. Nvidia’s Business Model Produces Exceptional Economics

Nvidia designs its processors but generally relies on manufacturing partners, particularly advanced semiconductor foundries, to fabricate them.

This is commonly called a fabless semiconductor model.

Nvidia does not need to own all the factories that manufacture its chips. That allows the company to concentrate substantial resources on:

Architecture
Software
networking
System design
Research and development
Customer relationships

The model can produce attractive economics when demand is strong.

Nvidia’s recent gross margins near 75% are unusually high for a company associated with physical computing equipment. Those margins reflect the perceived value of its technology, software ecosystem and limited availability of comparable AI systems.

But high margins also attract competition.

Customers and competitors have strong financial incentives to develop alternatives when one supplier captures an unusually large portion of industry profits.

8. Nvidia’s Lead Is Reinforced by Its Product Roadmap

AI infrastructure buyers make long-term planning decisions.

A cloud provider constructing a new data centre must think about:

Future chip availability
Cooling requirements
Electricity supply
Networking design
Software compatibility
Customer demand
Equipment replacement cycles

Nvidia has attempted to strengthen customer confidence by communicating a frequent product-development roadmap.

A predictable roadmap can encourage customers to build their infrastructure around Nvidia’s future systems rather than treat each GPU generation as an isolated purchase.

It also creates pressure.

Nvidia must continue delivering significant performance improvements while managing increasingly complex products, manufacturing arrangements and supply chains.

A delayed architecture or production problem could affect billions of dollars of planned customer investment.

9. Jensen Huang Helped Nvidia Anticipate Accelerated Computing

Nvidia co-founder and chief executive Jensen Huang has led the company since its creation in 1993.

Leadership continuity does not guarantee investment success, but it matters in understanding Nvidia’s strategic development.

The company invested in GPU computing, developer software and artificial intelligence before those areas became the centre of global technology spending.

Nvidia’s long-term approach included:

Developing programmable GPUs
Launching CUDA
Supporting academic researchers
Building specialized AI libraries
Expanding into data-centre networking
Creating complete AI systems
Investing in robotics and simulation

Many of these investments required years before producing their present commercial impact.

This is an important lesson for investors: Nvidia’s market position was not created overnight by the arrival of ChatGPT or one quarter of strong chip demand.

It was built through multiple product cycles and years of software development.

10. Market Capitalization Reflects Expectations, Not Certainty

Nvidia’s position as the world’s most valuable publicly traded company refers to its market capitalization.

Market capitalization is calculated as:

Share price × Shares outstanding = Market capitalization

For example:

$200 share price × 25 billion shares = $5 trillion market capitalization

Market capitalization is not:

The amount of cash Nvidia has
The company’s annual revenue
The price required to purchase the company outright
A guarantee of future profits
A government-certified measure of intrinsic value

It represents what investors collectively value the company’s publicly traded shares at during a particular moment.

As of July 2026, Nvidia’s market capitalization was around the $5 trillion level, placing it ahead of other major public companies. Because share prices move continuously, the exact valuation and ranking can change from one trading session to the next.

That distinction is important.

Being the world’s most valuable company does not automatically mean Nvidia is the best investment at every price.

Why Investors Are Willing to Assign Nvidia Such a High Valuation

Investors appear to be pricing in several expectations:

Global AI computing demand will continue growing.
Nvidia will retain a major share of that demand.
Customers will keep purchasing complete Nvidia systems.
The company will maintain strong pricing power.
CUDA and Nvidia’s software ecosystem will remain difficult to replace.
New markets such as robotics, autonomous vehicles and industrial AI will become commercially significant.
Nvidia will continue introducing more capable products.
AI spending will eventually generate sufficient returns for Nvidia’s customers.

When a company’s valuation becomes exceptionally large, its future results must support exceptionally high expectations.

Strong performance may not be enough when investors were already expecting extraordinary performance.

Nvidia’s Biggest Competitive Advantages

Full-stack integration

Nvidia can optimize the chip, networking, software and system together.

CUDA ecosystem

Developers have spent years building applications and expertise around Nvidia’s software.

Scale

Large revenue and cash generation allow Nvidia to invest heavily in research and development.

Customer access

Nvidia works with cloud providers, governments, research institutions, corporations and AI developers.

Brand recognition

Nvidia has become closely associated with modern AI infrastructure.

Product cadence

Frequent architecture updates encourage customers to remain within the platform.

High-performance networking

Nvidia’s networking technology helps large groups of processors function as a coordinated system.

Developer adoption

A large developer community can attract additional customers and encourage software support.

None of these advantages is permanent. But together they create a more difficult competitive challenge than simply designing a fast processor.

The Risks Investors Should Not Ignore

A company can be strategically important and still become an unsuccessful investment when purchased at an excessive valuation.

Before investing in Nvidia, investors should examine the risks as carefully as the growth story.

1. Valuation risk

Nvidia’s share price reflects expectations of substantial future growth.

If revenue or earnings grow more slowly than expected, the stock could decline even when the company remains profitable.

2. Customer concentration

A meaningful portion of AI infrastructure spending comes from a limited number of large technology companies.

Those customers possess considerable bargaining power and have the resources to develop their own chips.

3. Custom AI accelerators

Google, Amazon, Microsoft, Meta and other companies are developing or deploying custom processors.

These chips do not need to outperform Nvidia in every task. They may only need to operate efficiently within a customer’s specific workload.

4. Competition from other chipmakers

AMD, Intel and specialized semiconductor companies are developing competing AI hardware and software.

Competition could pressure Nvidia’s pricing, market share or profit margins.

5. Dependence on manufacturing partners

Nvidia depends on external companies for advanced chip manufacturing, packaging and memory.

Production disruptions, capacity shortages or manufacturing delays could limit supply.

6. Export controls

Advanced AI processors are affected by United States export restrictions and evolving national-security policies.

Changes in those rules may limit the products Nvidia can sell to particular countries or customers.

Nvidia disclosed in its regulatory filings that export controls can affect product design, market access and revenue opportunities.

7. AI spending could slow

Customers may eventually decide that they have built sufficient computing capacity or that AI services are not generating adequate financial returns.

That could lead to a slowdown in infrastructure orders.

8. Technological disruption

AI computing architectures may change.

More efficient models, alternative processors or new computing methods could reduce demand for Nvidia’s current systems.

9. Energy constraints

AI data centres require large amounts of electricity, cooling and physical infrastructure.

Power availability could limit how quickly new computing capacity is deployed.

10. Cyclical semiconductor demand

The semiconductor industry has historically experienced periods of shortages followed by excess capacity and weaker pricing.

AI demand may not eliminate that cyclicality.

Could Nvidia Lose Its Position?

Yes.

Nvidia’s position is powerful, but it is not guaranteed.

Its lead could weaken if:

Customers successfully shift workloads to internal chips
Competitors improve their software ecosystems
AI models require less computing power
Data-centre investment slows
Export restrictions expand
Manufacturing bottlenecks delay new products
Nvidia’s prices become economically unattractive
Open software reduces platform-switching costs
A new computing architecture becomes more efficient

There is also a difference between losing market share and losing revenue.

The overall AI computing market could grow rapidly enough for Nvidia’s revenue to increase even if competitors capture a larger portion of new demand.

Investors should therefore monitor both absolute growth and competitive market position.

What Investors Should Watch in Nvidia’s Financial Reports

Investors should avoid focusing only on the share price or headline revenue.

The following indicators may provide a more complete view:

Data Center revenue

This shows the scale of demand for Nvidia’s AI infrastructure.

Year-over-year growth

Growth rates help investors judge whether demand is accelerating or slowing.

Sequential growth

Quarter-to-quarter changes can provide early evidence of shifting customer spending.

Gross margin

Falling margins could signal higher production costs, stronger competition, changing product mix or weaker pricing power.

Inventory

Rapid inventory growth may be normal during a product launch, but it can also indicate slowing demand.

Purchase commitments

Large commitments may show confidence in future demand but can increase risk when market conditions change.

Research and development spending

Continued investment is necessary for Nvidia to protect its technological lead.

Customer concentration

Dependence on a few large buyers increases sensitivity to their budgets.

Supply availability

Advanced packaging, high-bandwidth memory and foundry capacity can affect Nvidia’s ability to fulfil orders.

Export-control effects

Regulatory changes can influence both current revenue and future product design.

Capital spending by Nvidia’s customers

Nvidia’s growth is connected to the infrastructure budgets of cloud and technology companies.

Nvidia Stock Is Not the Same as Nvidia the Business

This distinction is one of the most important concepts in investing.

Nvidia may continue to be an excellent business while its stock produces disappointing returns.

The outcome depends partly on the price an investor pays.

Consider two simplified scenarios:

Scenario A: Strong business, excessive expectations

Earnings grow by 30%.
Investors had expected 60% growth.
The valuation multiple declines.
The share price falls despite higher earnings.

Scenario B: Slower business, lower expectations

Earnings grow by 15%.
Investors expected only 5%.
Confidence improves.
The share price rises.

Stocks react to the difference between actual results and market expectations—not simply whether a company is growing.

This is why headlines describing Nvidia as the world’s most valuable AI company should not be treated as automatic buy signals.

Should You Buy Nvidia Because AI Is Growing?

Believing that artificial intelligence will grow does not automatically prove that Nvidia shares are attractively valued.

Investors should ask:

How much future growth is already reflected in the price?
What percentage of my portfolio is already exposed to Nvidia?
Do my ETFs already hold Nvidia shares?
Can I tolerate a substantial decline?
Am I investing for years or reacting to recent performance?
What assumptions must be true for the current valuation to make sense?
What would cause me to sell?
Am I depending on one company, sector or investment theme?

An investor who owns an S&P 500, Nasdaq-100 or global equity ETF may already have meaningful exposure to Nvidia.

Purchasing additional shares could create more concentration than the investor realizes.

For a broader comparison of popular index funds, read XEQT vs VEQT vs VFV vs VOO: Which ETF Is Best for Long-Term Investing in 2026?.

Nvidia and the Portfolio-Concentration Problem

Nvidia’s growth has increased its weight in major stock indexes.

That means investors can hold Nvidia through:

Individual Nvidia shares
S&P 500 ETFs
Nasdaq-100 ETFs
Technology-sector ETFs
Semiconductor ETFs
Global equity ETFs
Artificial-intelligence ETFs
Mutual funds
Pension investments

An investor may appear diversified across several funds while repeatedly owning the same underlying company.

For example:

Portfolio holding 1: S&P 500 ETF
Portfolio holding 2: Nasdaq-100 ETF
Portfolio holding 3: Technology ETF
Portfolio holding 4: Nvidia shares

All four positions may include direct or indirect Nvidia exposure.

This overlap is not automatically wrong. But it should be intentional.

Investors concerned about technology concentration can read Are You Overinvested in AI? The Truth About Tech Stocks and the Magnificent Seven.

Do Dividend Investors Need Nvidia?

Nvidia pays a dividend, but its investment case is generally based more on growth than dividend income.

A company can create shareholder value through:

Reinvesting in the business
Growing revenue and earnings
Repurchasing shares
Paying dividends
Making strategic acquisitions

Investors should not select a company merely because it pays a dividend—or reject it because its yield is low.

A high dividend yield can sometimes indicate that the share price has fallen because investors expect financial trouble or a future dividend reduction.

For a detailed comparison, read Dividend ETFs vs Growth ETFs: Should You Chase High Dividend Yields?.

Younger investors considering an income-focused strategy may also find Dividend Investing Before Age 30—Smart Strategy or Too Early? useful.

Could a Covered Call ETF Reduce Nvidia Risk?

Some investors consider covered call ETFs because they advertise regular cash distributions.

A covered call strategy may generate option income, but it can also surrender part of the upside when the underlying stocks rise sharply.

Therefore, buying a technology or Nvidia-focused covered call ETF is not automatically a safer or superior way to gain AI exposure.

Investors must examine:

The ETF’s underlying holdings
The percentage of the portfolio covered by options
Distribution composition
Management fees
Upside limitations
Downside exposure
Tax treatment
Long-term total return

Read Covered Call ETFs Explained: Passive-Income Strategy or Performance Trap? before evaluating these funds only by their advertised yield.

Avoid the Performance-Chasing Trap

Nvidia’s past returns may encourage investors to believe that buying after a major rise is safer because the company has already proved itself.

That reasoning can be dangerous.

A rising share price can indicate improving fundamentals, but it can also increase the expectations embedded in the valuation.

Investors often underperform their own investments because they:

Buy after excitement peaks
Sell during temporary declines
Frequently switch strategies
Chase recent winners
Ignore valuation
Concentrate without realizing it
React emotionally to news

Read Why Most ETF Investors Underperform Their Own ETFs for a deeper explanation of behaviour-driven underperformance.

Canadian investors should also consider account rules before trading aggressively. Review The Biggest TFSA Investing Mistake Canadians Make.

A Practical Nvidia Investment Checklist

Before purchasing Nvidia shares, consider completing this checklist.

Business

Do I understand how Nvidia earns money?
Do I understand why CUDA matters?
Do I understand the role of data-centre revenue?
Have I reviewed the company’s latest regulatory filing?
Do I know which risks could weaken Nvidia’s competitive position?

Valuation

Am I evaluating earnings and cash flow, not only revenue growth?
What growth assumptions appear reflected in the share price?
What happens to my return if Nvidia’s valuation multiple declines?
Am I relying on an optimistic analyst target?

Portfolio

Do my existing ETFs already hold Nvidia?
What percentage of my total portfolio would depend on Nvidia?
How much exposure do I have to technology and AI?
Could I tolerate a decline of 30%, 40% or more without panic-selling?

Time horizon

Am I prepared to hold through semiconductor cycles?
Is this money needed within the next several years?
Am I investing based on long-term analysis or a recent headline?

Risk controls

Have I defined a reasonable position size?
Am I using borrowed money?
Would a diversified ETF better fit my knowledge and risk tolerance?
Have I considered currency and tax consequences?

Frequently Asked Questions

Why is Nvidia so valuable?

Nvidia combines high-performance AI processors with networking, software, developer tools and complete computing systems. Strong demand for AI infrastructure has produced rapid revenue growth and high profit margins.

Is Nvidia the largest company in the world?

As of July 2026, Nvidia is the largest publicly traded company by market capitalization. Its precise valuation and ranking can change daily with its share price.

Does Nvidia manufacture its own chips?

Nvidia primarily designs its chips and uses external manufacturing partners to produce them. This is known as a fabless semiconductor business model.

What is CUDA?

CUDA is Nvidia’s parallel-computing platform and programming model. It allows developers to use Nvidia GPUs for artificial intelligence, scientific computing and other demanding workloads.

What is Nvidia Blackwell?

Blackwell is an Nvidia computing architecture designed for generative AI and accelerated-computing workloads. It is used across GPUs and larger integrated systems.

Is Nvidia only an AI-chip company?

No. Nvidia also operates in gaming, professional visualization, networking, automotive computing, robotics, simulation and cloud-based AI services. Data-centre computing, however, has become its dominant growth engine.

Is Nvidia stock safe?

No individual stock is guaranteed to be safe. Nvidia faces valuation, competition, customer-concentration, manufacturing, export-control and technology risks.

Can Nvidia continue growing this quickly?

It may continue growing, but maintaining extremely high percentage growth becomes harder as revenue increases. Investors should not automatically extrapolate recent growth indefinitely.

Do S&P 500 ETFs hold Nvidia?

Broad S&P 500 index ETFs generally hold Nvidia because the company is included in the index. Its portfolio weight changes with market capitalization and index rebalancing.

Is Nvidia suitable for a TFSA?

Publicly traded Nvidia shares may generally be held in a TFSA through eligible exchanges, but investors remain responsible for investment suitability, contribution limits and compliance with applicable tax rules. Frequent or business-like trading activity can create tax concerns.

Final Takeaway

Nvidia became the world’s most valuable AI company by turning decades of graphics-processing expertise into a complete artificial-intelligence computing platform.

Its lead is supported by several connected advantages:

Powerful processors
The CUDA software ecosystem
High-speed networking
Complete AI systems
A large developer community
Strong customer relationships
Rapid product development
Exceptional recent financial growth

But Nvidia’s business strength should not be confused with guaranteed investment returns.

The company must continue meeting enormous expectations while facing competition, export restrictions, customer concentration, supply-chain dependence and the possibility of slower AI infrastructure spending.

The most important investor question is therefore not simply:

“Is Nvidia a great company?”

It is:

“Does Nvidia’s future growth justify its current valuation, and does the stock fit within my diversified portfolio and risk tolerance?”

Investment Disclaimer

This article is provided for general educational and informational purposes only. It does not constitute financial, investment, tax, legal or accounting advice, a securities recommendation, or an offer to buy or sell any investment.

TwikUp and its contributors are not acting as your investment adviser, portfolio manager or financial planner. Company valuations, financial results, regulations and market conditions can change after publication.

All investments involve risk, including possible loss of principal. Individual stocks and technology-sector investments may experience substantial volatility. Past performance does not guarantee future results.

Canadian investors should independently verify TFSA, RRSP and other registered-account rules with the Canada Revenue Agency and consider consulting an appropriately registered financial professional before making investment decisions.

Why Nvidia Is the World’s Most Valuable Company: How It Built Its AI Empire