Jensen Huang – Will Nvidia’s moat persist?

Name: Jensen Huang – Will Nvidia’s moat persist?
Uploaded: 2026-04-15T16:42:32.000Z
Duration: 3 h 26 min 13 s

The Future of Software and AI: Insights from Nvidia

The Impact of AI on Software Valuations

The valuation of software companies is declining as the expectation grows that AI will commoditize software.

Nvidia's role in the software manufacturing process raises questions about its potential commoditization if software itself becomes commoditized.

Transformation Process: Electrons to Tokens

The journey from electrons to tokens involves significant artistry, engineering, and science, making it difficult to fully commoditize this transformation.

The complexity and ongoing evolution of this transformation suggest that it won't be easily replicated or replaced.

Nvidia's Ecosystem and Strategy

Nvidia aims to facilitate the transformation process by partnering with others for tasks outside its core competencies, creating a vast ecosystem.

This ecosystem spans across five layers of AI development, emphasizing collaboration while focusing on challenging aspects that remain unique to Nvidia.

Growth in Tool Usage and Agents

Contrary to expectations, the number of agents (AI tools users) is predicted to grow exponentially alongside tool usage itself.

Current limitations are due to insufficient capabilities of agents; however, advancements will lead to increased exploration in design spaces using existing tools.

Nvidia's Competitive Moat

Nvidia has secured substantial purchase commitments (upwards of $250 billion), which may serve as a competitive advantage over other companies lacking similar access to components.

These upstream investments are driven by strong relationships with supply chain CEOs who recognize Nvidia’s capacity for demand fulfillment downstream.

Building Relationships Across the Supply Chain

Nvidia’s ability to inform and inspire upstream partners leads them to invest in resources specifically for Nvidia’s needs.

Events like GTC showcase the interconnectedness between upstream suppliers and downstream developers, fostering collaboration within the AI ecosystem.

Supply Chain Dynamics and AI Growth

The Importance of Supply Chain in Scaling Business

The speaker emphasizes the significance of a robust supply chain to support future growth, indicating that their business could scale to a trillion dollars due to strong downstream demand.

They highlight the impressive revenue growth, noting a consistent doubling of revenue year over year while also tripling computational capabilities.

A concern is raised about how to maintain this growth rate when being a major customer for TSMC's N3 node, questioning if AI compute growth will slow due to upstream limitations.

Addressing Industry Bottlenecks

The speaker discusses the challenge of scaling production facilities (fabs), suggesting that current demand exceeds supply across various sectors.

They note that CoWoS technology has become mainstream after significant investment and attention, which has led TSMC to align its scaling efforts with logic and memory demands.

Strategic Partnerships and Investments

The speaker reflects on past predictions made five years ago regarding the AI revolution, crediting early investors like Micron for their foresight and partnership in developing memory technologies.

They mention proactive measures taken to identify bottlenecks in advance, including investments in companies like Lumentum and advancements in silicon photonics.

Shaping the Ecosystem for Future Demand

An entire supply chain was built around TSMC through partnerships aimed at innovating new technologies and workflows necessary for scaling production capacity effectively.

The discussion includes challenges related to skilled labor shortages (e.g., plumbers and electricians), warning against discouraging careers in software engineering amidst fears of job loss due to automation.

Overcoming Manufacturing Challenges

The speaker asserts that while EUV machines are critical bottlenecks for memory and logic manufacturing, they can be scaled up within two or three years given sufficient demand signals.

They express confidence that no bottleneck lasts longer than a few years as long as there is continuous improvement in computing efficiency.

Nvidia's Vision for Accelerated Computing and AI

The Importance of Energy in Manufacturing

Nvidia emphasizes the critical role of energy policies in reindustrializing the U.S., particularly in chip manufacturing, EVs, and AI factories. Without sufficient energy, creating a new manufacturing industry is impossible.

Challenges in Chip Capacity

The speaker notes that increasing chip capacity and CoWoS (Chip-on-Wafer-on-Substrate) capacity are projected to take 2-3 years, highlighting differing opinions on timelines from various experts.

Differentiation Between Nvidia and Competitors

Unlike TPUs (Tensor Processing Units), Nvidia focuses on accelerated computing which encompasses a broader range of applications beyond just tensor processing.

Diversity of Applications for Accelerated Computing

Accelerated computing supports various fields such as molecular dynamics, fluid dynamics, data processing, and AI. This versatility positions Nvidia uniquely compared to competitors focused solely on specific tasks like TPUs.

Market Reach and Ecosystem Strength

Nvidia's systems are designed for broad usability across industries. Their ecosystem allows diverse frameworks and algorithms to operate seamlessly on their hardware, making them accessible to many operators globally.

The Future of AI with Nvidia's Technology

Revenue Growth Driven by AI

The speaker discusses how unprecedented growth in AI technology contributes significantly to Nvidia’s revenue rather than traditional sectors like pharmaceuticals or quantum computing.

Flexibility vs. Specialization in Hardware

While TPUs excel at matrix multiplication due to their design, GPUs offer greater flexibility for varied tasks within AI development. This adaptability is crucial as AI evolves beyond predictable patterns.

Programmability as a Key Advantage

The ability to create new architectures and algorithms easily is vital for advancing AI. A programmable system allows researchers to innovate without being constrained by fixed designs typical of specialized hardware like TPUs.

Innovations Beyond Moore's Law

To achieve significant performance leaps (10x or 100x), fundamental changes in algorithms are necessary rather than relying solely on Moore’s Law advancements. Nvidia leverages innovative models like MoEs (Mixture of Experts).

Co-design Philosophy Enhancing Performance

Nvidia’s co-design approach integrates computation into its architecture effectively, allowing offloading tasks into the fabric itself or network components like NVLink and Spectrum-X for enhanced efficiency.

NVIDIA's CUDA Ecosystem and Its Impact on AI Development

The Role of Crusoe in Advancing AI Infrastructure

Crusoe enables simultaneous changes across processors, systems, libraries, and algorithms using NVIDIA’s platforms like Blackwell and Vera Rubin.

Their KV caching mechanism allows efficient computation across multiple users and GPUs, significantly improving performance metrics such as time-to-first token and throughput.

Access to advanced hardware is crucial for inference workloads; Crusoe provides a comprehensive solution without needing to switch clouds.

CUDA's Importance for Different User Groups

Traditional users like professors require CUDA for optimized frameworks like PyTorch, while hyperscalers can develop their own kernels for specific architectures.

Companies like OpenAI utilize Triton alongside GPUs to create custom kernels tailored to their needs.

The Richness of the CUDA Ecosystem

CUDA offers a robust ecosystem supporting various frameworks (e.g., Triton, vLLM), making it advantageous for developers to build on this foundation.

New reinforcement learning frameworks are emerging, indicating rapid growth in post-training techniques within the AI landscape.

Trusting the Foundation of Code

Developers prefer building on a reliable codebase where issues are more likely due to their code rather than underlying system bugs.

The programmability and capability of the CUDA ecosystem provide confidence in software development.

Install Base as a Key Advantage

A large install base ensures that developed software runs effectively across numerous devices, enhancing its value proposition.

NVIDIA's extensive presence in various cloud services solidifies its unique position in the market.

Future Considerations for NVIDIA's Market Position

As AI capabilities improve, questions arise about whether customers will continue relying on NVIDIA or shift towards self-built solutions.

Custom kernel development by hyperscalers may challenge NVIDIA’s pricing model despite its historical margin advantages due to the CUDA ecosystem.

AI Architecture and Performance Insights

The Unique Nature of AI Architectures

The speaker compares CPUs to Nvidia's GPUs, likening CPUs to Cadillacs (easy to drive) and GPUs to F1 racers (requiring expertise for optimal performance).

Nvidia's expertise in optimizing AI stacks can lead to significant performance improvements, often achieving speed-ups of 2x or more for their partners.

The speaker asserts that Nvidia offers the best performance per total cost of ownership (TCO), claiming no other platform matches this ratio.

Competitive Landscape and Market Position

Despite competition from TPUs and Trainium, the speaker challenges these companies to demonstrate their claimed advantages in inference costs.

Nvidia's customer base is primarily external, with a strong presence in major cloud services like AWS and Azure, which enhances its market reach.

The combination of a large install base, programmability, and a rich ecosystem makes Nvidia an attractive choice for AI startups.

Performance Metrics and Revenue Generation

Companies are encouraged to choose architectures based on abundance; Nvidia leads in installed base and ecosystem richness.

A one gigawatt data center built on Nvidia architecture maximizes revenue generation through high tokens per watt efficiency.

Market Dynamics Among Major Players

The discussion highlights the importance of understanding market structure amidst competition from various AI companies using different accelerators.

The speaker emphasizes that while there are many AI companies, most significant compute usage comes from a few major players like Anthropic and OpenAI.

Addressing Competition with Other Accelerators

A question arises regarding why some companies opt for alternatives like TPUs despite Nvidia’s advantages; the response indicates that such instances are not indicative of broader trends.

Anthropic is cited as a unique case driving TPU growth; without it, there would be minimal interest in alternative accelerators according to the speaker.

Building Better Than Nvidia: Challenges and Insights

The Challenge of Competing with Nvidia

Building an ASIC (Application-Specific Integrated Circuit) that surpasses Nvidia is a significant challenge, as they consistently deliver substantial advancements annually.

Competitors may aim for products that are "not more than 70% worse" than Nvidia's offerings due to high margins; however, the actual savings from ASICs compared to Nvidia's products are minimal.

ASIC margins are notably high, around 65%, which reflects the industry's pride in these profit levels despite being slightly lower than Nvidia's 70%.

Investment Dynamics in AI Labs

Companies like Google and AWS have made substantial investments in AI labs such as Anthropic, enabling them to utilize advanced computing resources that others could not afford at the time.

The speaker acknowledges a past oversight regarding the necessity of large investments in AI labs, realizing now that venture capitalists would be unlikely to fund such massive amounts without guaranteed returns.

Regrets and Future Directions

Despite previous missed opportunities to invest earlier in companies like OpenAI and Anthropic, there is a commitment to support their growth moving forward.

The speaker reflects on how early investments could have significantly changed the landscape for both Nvidia and its competitors if executed sooner.

Strategic Use of Capital

Questions arise about why Nvidia did not make earlier investments when they had the cash available; however, it is clarified that they acted as soon as feasible given their circumstances at the time.

There was a lack of understanding regarding the scale of investment needed for AI development; previously held beliefs about VC funding were proven incorrect.

The Role of Domain-Specific Libraries

Acknowledgment is made regarding OpenAI’s innovative approach which required different funding strategies than traditional VC models could provide.

Despite current financial success, there remains an ongoing discussion about how best to allocate profits within Nvidia for future growth and innovation.

Cloud Computing Opportunities

There is potential for Nvidia to become a cloud service provider themselves by renting out compute resources instead of just manufacturing chips.

This idea aligns with company philosophy—doing what is necessary while minimizing excess—to ensure essential projects get completed effectively.

Commitment to Innovation

Emphasis on building foundational technologies like NVLink and CUDA over two decades highlights a commitment to long-term innovation despite initial losses.

The creation of domain-specific libraries has been crucial for advancing various applications including ray tracing and structured data processing.

NVIDIA's Role in Accelerated Computing and Ecosystem Support

Commitment to Innovation

The speaker emphasizes that without NVIDIA's contributions, advancements in computational lithography and accelerated computing would not have progressed as they have.

A philosophy of doing "as much as needed but as little as possible" guides the company's approach to innovation and support for new technologies.

Supporting Neoclouds

NVIDIA's support for companies like CoreWeave, Nscale, and Nebius is highlighted; their existence is attributed to NVIDIA’s backing.

The company aims to connect AI with various industries globally, promoting a vision where AI is foundational across sectors.

Investment Strategy

NVIDIA does not focus on picking winners among foundation model companies; instead, it supports all players in the ecosystem.

Historical context is provided: when NVIDIA started, many 3D graphics companies existed, yet only NVIDIA survived despite initial missteps in architecture design.

Humility in Business Decisions

The speaker reflects on past failures and stresses the importance of humility by avoiding the practice of selecting potential winners prematurely.

Clarification on supporting neoclouds: they must demonstrate a desire to exist and possess a viable business plan before receiving assistance from NVIDIA.

Focused Support Over Financing

While acknowledging the need for investment in startups like OpenAI, the speaker clarifies that NVIDIA prefers not to engage directly in financing but rather support its ecosystem effectively.

Investments are made based on belief in a company's potential impact rather than an aggressive pursuit of financial gain.

Personal Experience with AI Tools

The speaker shares personal experiences building an AI co-researcher tool using Cursor’s Composer 2 model for enhanced productivity during writing tasks.

Emphasizes hands-on experimentation with coding tools while maintaining oversight over implementation processes.

Addressing GPU Shortages

Acknowledgment of ongoing GPU shortages due to increasing demand from advanced models; highlights how NVIDIA allocates resources strategically among emerging cloud providers.

Disagreement with characterizations of market fracturing; asserts that resource allocation decisions are made thoughtfully rather than arbitrarily.

Nvidia's Approach to Demand and Supply Management

Forecasting and Order Placement

Nvidia emphasizes the importance of accurate forecasting in managing demand and supply, as building data centers takes significant time.

The company prioritizes first-in, first-out (FIFO) order processing; if a purchase order (PO) isn't placed, customers may face delays.

A notable anecdote is shared about a dinner with Larry and Elon, clarifying that no begging for GPUs occurred—only the necessity of placing orders was discussed.

Business Practices and Pricing Strategy

Nvidia does not engage in price hikes based on demand fluctuations, believing it to be poor business practice; they maintain consistent pricing once quoted.

The speaker contrasts Nvidia's approach with other chip manufacturers who adjust prices according to market demand.

Long-term Relationships and Reliability

Nvidia has maintained a long-standing relationship with TSMC without formal contracts, relying on mutual trust built over decades.

The speaker asserts that Nvidia’s reliability allows customers to depend on consistent product availability year after year.

Commitment to AI Industry Growth

The speaker highlights upcoming products like Vera Rubin Ultra and Feynman, showcasing Nvidia's commitment to innovation in AI technology.

Customers can confidently place large or small orders with Nvidia due to their established capacity for production across various scales.

The Debate Over Selling Chips to China

National Security Concerns

The discussion shifts towards the implications of selling chips to China amidst concerns about national security.

An example is given regarding Anthropic’s Mythos model which possesses advanced cyber-offensive capabilities but remains unreleased due to potential risks.

China's Technological Landscape

It is noted that China already has substantial resources in chip manufacturing (60% of mainstream chips), along with a significant number of AI researchers contributing globally.

Creating a Safe AI Ecosystem: The Role of Dialogue and Collaboration

Importance of Dialogue with China

Engaging in dialogue with Chinese AI researchers is crucial for creating a safe world, rather than viewing them solely as adversaries.

Establishing agreements on ethical AI usage, particularly regarding software bug detection, is essential to ensure responsible development.

The Cybersecurity Ecosystem

The ecosystem surrounding AI cybersecurity and safety is rich and diverse, comprising numerous startups focused on enhancing security measures.

A thriving open-source ecosystem is vital for the development of formidable AI systems that can maintain safety and security.

Open Source vs. Closed Ecosystems

It’s critical to keep the open-source ecosystem vibrant; limiting it could lead to a dangerous bifurcation between foreign and American tech stacks.

Encouraging global developers to work within the American tech stack ensures advancements are accessible within the U.S. ecosystem.

Compute Power Disparities

Despite China's significant computing capabilities, estimates suggest they have only one-tenth of the flops compared to the U.S., impacting their ability to develop advanced models like Mythos.

The deployment scale of AI models matters significantly; having more compute resources allows for greater operational capacity against potential cyber threats.

Addressing Misconceptions about China's Capabilities

While China has many skilled researchers, their productivity is limited by compute resources; American labs currently have an advantage due to superior computational power.

Although China possesses substantial energy resources and infrastructure, there are challenges in chip production that may hinder their progress in developing competitive AI technologies.

AI Development and Hardware Limitations

The Energy-Chip Relationship

The speaker emphasizes that concerns about AI capabilities are misplaced, as advancements have already surpassed initial thresholds. They describe AI as a "five-layer cake," with energy being the foundational layer.

In the U.S., energy scarcity necessitates continuous architectural advancements by companies like Nvidia to maximize throughput per watt, given limited chip availability.

The speaker notes that while 7nm chips (Hopper generation) are sufficient for current models, the abundance of energy is crucial for performance. Manufacturing capacity remains a concern but is currently met.

Bandwidth and Memory Considerations

There are significant questions regarding logic and memory production capabilities, particularly bandwidth limitations in training and inference processes. HBM2 memory can vary greatly in bandwidth compared to newer technologies.

Contrary to assumptions, advanced HBM does not strictly require EUV technology; alternative methods exist for connecting compute resources effectively.

Algorithmic Advances vs. Hardware

The speaker argues that algorithmic improvements significantly contribute to AI progress, often more than hardware advancements. They highlight the importance of smart algorithms developed under resource constraints.

Innovations such as Mixture of Experts (MoE) and attention mechanisms exemplify how algorithmic efficiency reduces computational demands.

Competitive Landscape in AI Research

Acknowledging that most advances stem from algorithmic breakthroughs rather than raw hardware reinforces the value of skilled researchers in driving innovation.

Concerns arise over potential competitive disadvantages if models like DeepSeek optimize for non-American architectures, which could undermine U.S. technological leadership.

Global Implications of AI Model Development

The speaker contrasts perceptions of good news regarding software development on American tech stacks with the reality that many global models perform better on non-American hardware.

Evidence suggests American labs successfully run models across various accelerators; however, there’s a risk if foreign tech becomes dominant globally.

Future Risks and Strategic Considerations

If Chinese companies exploit vulnerabilities using Nvidia hardware before American counterparts do, it poses significant risks to national security and technological supremacy.

The discussion highlights critical years ahead where ensuring all global AI models are built on American technology is essential for maintaining competitive advantage against rapidly advancing foreign capabilities.

Conclusion: Urgency in Addressing Technological Gaps

The urgency lies in recognizing that while competitors may be behind now, their manufacturing prowess could lead them to catch up quickly if strategic actions aren't taken promptly.

AI Security and the Global Tech Landscape

The Role of AI Models in Cybersecurity

Discussion on how reliance on the American tech stack may not prevent advanced cyber attacks, emphasizing the need for early preparation.

Critique of prioritizing one layer of AI over others, stressing that all five layers must succeed, particularly AI applications which enable offensive capabilities.

Backdoor Techniques in Language Models

Overview of Jane Street's experiment with backdoors in language models, highlighting methods to adjust backdoor strength through weight interpolation.

Insight into challenges faced when verifying model behavior; only two out of three models successfully demonstrated the backdoor technique.

Compute Capacity and Global Competition

Commentary on China's chip production capabilities, noting their current limitations at 7nm while the U.S. advances to smaller nodes like 3nm and below.

Assertion that increased compute capacity in China poses a threat as it enhances their ability to train AI models.

U.S. Technological Leadership

Affirmation that the U.S. should maintain its technological edge due to superior compute resources and advanced technologies from companies like Nvidia.

Questioning policies that might lead to a loss of global market share for American technology firms, advocating for balanced regulations.

Analogies and Misconceptions about Technology Transfer

Comparison made between selling chips to China and historical analogies involving sensitive technologies; concerns raised about enabling adversaries.

Debate over whether AI can be equated with dangerous materials like enriched uranium; emphasis on responsible technology transfer practices.

Long-term Strategy for AI Development

Proposal for open dialogues with international researchers to mitigate misuse of technology while ensuring U.S. leadership in computing resources.

Recognition that success across all layers of AI is crucial for long-term competitiveness, including hardware development.

Market Dynamics and Competitive Edge

Argument against conceding markets as a strategy for long-term success; examples given from Tesla's experience in China illustrating potential pitfalls.

Acknowledgment of Nvidia's unique position within the industry compared to other companies, reinforcing its competitive advantages.

The Importance of Ecosystem in AI Development

The Role of Developers and Market Dynamics

The richness of the ecosystem is crucial for the company, emphasizing that 50% of AI developers are based in China, which the U.S. should not overlook.

There is a belief that selling Nvidia chips to China does not hinder American labs from utilizing other accelerators; innovation must continue as market share grows.

The speaker argues against a defeatist attitude regarding competition with China, asserting that computing ecosystems are complex and difficult to replace.

Competition and Innovation

Acknowledging the importance of maintaining a competitive mindset, the speaker rejects notions of conceding markets based on pessimistic assumptions about U.S. capabilities.

The argument centers around extremes; any marginal compute can enhance model training, thus benefiting American technology sales.

Export Controls and National Security

Concerns arise over whether AI technology could enable cyber offensive capabilities; export controls exist for advanced technologies but may need reevaluation for AI.

The discussion highlights the necessity to minimize China's access to cutting-edge technology while recognizing existing exports like DRAM and CPUs.

Market Share and Technology Leadership

Despite Huawei's success, there’s an acknowledgment that U.S. companies have lost significant market share in China’s tech industry, which poses risks to national security.

Conceding this market is viewed as detrimental to U.S. technological leadership and security interests.

Understanding Competitive Dynamics

The speaker addresses apparent contradictions in arguments about competing with Huawei while acknowledging their advancements without U.S. involvement.

Emphasizing that better technology leads to better outcomes, it’s noted that American tech leadership benefits from global diffusion of its innovations.

Conclusion: Advancing American Technology Leadership

Ultimately, advancing American technology through developer engagement ensures continued leadership in global markets amidst competition from countries like China.

The Impact of Telecommunications Policies on American Industry

Consequences of Policy Decisions

The speaker argues that current telecommunications policies have led to the U.S. losing control over its own telecommunications industry, suggesting this is a narrow-minded approach with significant unintended consequences.

Acknowledgment of the balance between potential benefits and costs in technology policy is emphasized, particularly regarding compute power as an essential input for training advanced AI models capable of offensive cyber capabilities.

Competitive Landscape in AI Development

The speaker highlights the importance of American companies achieving advanced AI capabilities first, arguing that if China had developed these capabilities earlier, it could have posed serious risks.

There’s concern about conceding critical markets to China, which could lead to a different optimization ecosystem for future AI models that may surpass American standards.

Open Source Contributions and Market Dynamics

The discussion points out that China is currently the largest contributor to open-source software and models globally, built on the American tech stack provided by companies like Nvidia.

Emphasizing the need for success across all layers of the AI tech stack, the speaker warns against creating fear around AI technologies that could deter talent from entering fields like software engineering or radiology.

Misunderstanding Job vs. Task in Healthcare

The distinction between jobs and tasks is made clear; while tasks can be automated (like reading scans), jobs such as patient care require human involvement and cannot be replaced by AI alone.

Future Implications for Technology Exportation

The speaker predicts a future where America will want to export its technology globally but cautions against policies that might cause it to concede important markets unnecessarily.

A call for nuanced approaches rather than absolutes in technology policy is made, advocating for maintaining technological superiority domestically while also competing effectively on a global scale.

Nvidia's Chip Architecture and AI Ecosystem

The Competitive Landscape of Chip Technology

The optimization of models for 7nm chips is currently more effective than running them on 1.6nm chips, indicating a significant performance gap.

Moore's Law is considered "dead," with Blackwell being approximately 75% more advanced than Hopper in terms of transistor technology over three years, emphasizing the importance of architecture in chip design.

AI development relies heavily on both the computing stack and underlying architecture, highlighting that flexibility in software stacks like CUDA contributes to its effectiveness.

Implications of Policy Decisions

The potential exit from China could be detrimental to the U.S. semiconductor industry, as it may accelerate China's internal chip development and AI ecosystem.

Despite advancements beyond 7nm, there is no significant performance difference between 5nm and 7nm; architectural considerations are crucial for future developments.

Future Directions in Semiconductor Manufacturing

As Nvidia approaches majority production at N3 and N2 nodes, there's speculation about reverting to older nodes like N7 due to high demand for AI capabilities.

Transitioning back to older process nodes would require extensive R&D investment that may not be feasible unless capacity constraints become permanent.

Architectural Strategies and Market Adaptation

Nvidia has the capability to explore multiple chip architectures simultaneously but chooses not to due to lack of superior alternatives based on simulations.

Recent market changes have prompted Nvidia to integrate Groq into their CUDA ecosystem, reflecting an adaptation strategy based on evolving customer needs for responsiveness.

Innovations in Token Economics

The emergence of differentiated pricing for tokens allows Nvidia’s software engineers greater productivity by offering responsive token options tailored to specific customer demands.

A new segment focusing on faster response times despite lower throughput indicates a strategic shift towards maximizing average selling prices (ASPs), showcasing innovation within the inference market.

What If the Learning Revolution Didn't Happen?

The Role of Nvidia in Accelerated Computing

Nvidia's focus would remain on gaming and accelerated computing, emphasizing that general-purpose computing has limitations for certain computations.

The integration of GPU architecture with CPU through CUDA allows offloading different algorithms to GPUs, significantly speeding up applications by factors of 100x or 200x.

Even without AI, Nvidia's growth is attributed to the fundamental shift towards domain-specific acceleration as general-purpose computing reaches its limits.

Applications and Impact of Accelerated Computing

Early domains benefiting from CUDA include computer graphics, particle physics, fluid dynamics, and structured data processing, showcasing a wide range of applications.

Nvidia aims to democratize deep learning by making advanced computational resources accessible to researchers and students globally, enhancing scientific discovery.

Future Prospects Without AI

Despite the absence of AI advancements, the foundational promise of accelerated computing remains unchanged; it continues to enable significant breakthroughs in various scientific fields.