The Large Data Center Revolution -- Shay Boloor -- November 24, 2024.

Locator: 44411TECH.

The biggest winners in the AI data center revolution. Note: data center revolution. Not "tech / AI revolution," but a specific component, the data center revolution. Link here. From Shay Boloor.

The rise of AI isn’t just a trend -- it’s a seismic shift, and its ripple effects are reshaping industries at a pace that feels almost chaotic. Beneath the surface of this transformation lies an intricate web of infrastructure players, all converging to power the seemingly insatiable demands of AI. These aren’t just companies providing products -- they are the architects of a new era, spanning cloud services, semiconductors, servers, networking, and data management -- their combined efforts are creating the backbone for the next generation of intelligence.

At the forefront of this revolution, cloud service providers are emerging as the lifeblood of AI. They deliver the scalability and computational power that AI models feed on. AMZN leads the way, not merely by size but by its ability to innovate. SageMaker, its machine learning platform, simplifies model training and deployment, making AI accessible to enterprises at scale.

Meanwhile, MSFT charges forward with its strategic partnership with OpenAI, embedding itself deeper into workflows that demand agility and cutting-edge AI capabilities.

And then there’s GOOGL, leveraging its Gemini AI and Vertex AI platforms to merge foundational model prowess with a strong infrastructure.

But it doesn’t stop there. Smaller players like DOCN cater to SMBs with affordable, focused solutions, while ORCL and IBM carve out niches in enterprise-specific and regional markets. Even BABA, often overlooked in Western narratives, is an undeniable force, serving massive AI applications in e-commerce and logistics across Asia.

Yet, cloud power alone isn’t enough to sustain AI’s appetite. The real magic happens within semiconductors -- the beating heart of AI infrastructure.

NVDA towers over its competition, with GPUs like the H100 becoming synonymous with cutting-edge AI training. Its role is foundational, nearly irreplaceable, as it powers data centers and fuels innovation across industries.

AMD, however, is not content to play second fiddle, pushing its MI300 chips into the fray. While formidable, it’s a race where $NVDA remains several strides ahead, especially with the release of its Blackwell architecture, designed to further cement its dominance in AI workloads.

INTC, once the crown jewel of semiconductors, is fighting to reclaim relevance with its Gaudi AI processors, betting on cost-sensitive segments to stage its comeback.

Meanwhile, AVGO, in its quieter but equally crucial role, ensures that the networking and connectivity hardware supporting these chips operate seamlessly, stitching together the fabric of AI systems.

This computational power must live somewhere -- and that’s where servers come into play.

While SMCI has distinguished itself by delivering cutting-edge features like liquid cooling for AI-specific workloads, the server market remains fundamentally a commodity business. Liquid cooling, which allows SMCI’s servers to handle the intense power requirements of high-performance AI applications more efficiently, has been a strong differentiator. However, as AI adoption scales and price competition intensifies, DELL and HPE are leveraging their size and resources to outmaneuver SMCI in the broader server market.

Meanwhile, networking giants like CSCO and ANET are ensuring the highways of AI data run smoothly. Without them, the high-speed, low-latency transfers AI depends on would grind to a halt. Their innovations are the unsung heroes of this ecosystem, quietly facilitating the explosive growth of AI applications. As AI demands scale, their role becomes ever more pivotal, ensuring that no bottlenecks slow the flow of intelligence.

Of course, the treasure trove of data driving AI must be organized, analyzed, and acted upon. This is where the likes of SNOW and PLTR shine. Snowflake's pay-as-you-go pricing model aligns perfectly with the unpredictable needs of AI, while tools like Cortex empower companies to scale seamlessly. PLTR, with its Foundry platform, dives deep into complex analytics, turning raw data into actionable insights for governments and enterprises alike.

MDB and ESTC add layers of flexibility, while DDOG and CFLT keep systems running smoothly by providing real-time monitoring and data pipelines essential to AI.

Looming over all of this are the titans crafting foundational AI models.

META Llama, MSFT OpenAI, AMZN Anthropic, and GOOGL Gemini are not just products -- they’re blueprints for the future of intelligence. These companies, each in their unique way, are shaping how AI interacts with the world, bridging cutting-edge research with practical applications. Their efforts ripple through every layer of the infrastructure, from cloud to chip to server.

AI isn’t just growing -- it’s accelerating, pulling its entire ecosystem along for the ride. The players in this space aren’t merely reacting to change; they’re driving it, forging a path forward with innovations that redefine what’s possible. As the interplay between these companies deepens, the race to dominate this landscape becomes more intense, more complex, and ultimately more transformative. What emerges is not just an infrastructure for AI but a blueprint for the future of industries worldwide -- a future where every data point, every process, and every decision is touched by intelligence.

We've talked about this before. This reminds me of the second industrial revolution, 1870 - 1914. And this is why I suggest the second industrial revolution began in 1870 and 1914.

And like the second industrial revolution, there were a handful of companies in which one could invest. One didn't have to select among 1,000 companies. 

Minimum: seven stocks.

Optimum, perhaps: 15 stocks.

Max: 20 stocks. 

Note: Apple is not mentioned above because this is about large data centers. Apple is alone in its sector and their sector is not large data centers. Unless, of course, one thinks about the Mac Minis that are optimal for large data centers.

At the link, one can see a graphic of the narrative.