The Cloud-Driven AI Growth

1. GenAI and NVIDIA: A Love Story with Speedbumps

AI, specifically Generative AI (GenAI), is projected to grow by 40% annually. Great, right? But there's a catch: the servers just can’t handle that much cooking. NVIDIA's planning to triple its GPU capacity in the next two years, and yet even that massive growth won't satisfy the total demand—not even close.

Here's where it gets weird: AWS is only one slice of NVIDIA's client pie. There's also the gamers, media companies, telecoms, automakers—the list goes on. So, when NVIDIA triples its capacity, that doesn’t mean AWS gets three times as much GPU power. Instead, it means that AWS gets... a little bit more pie, but it's far from a feast.

Imagine you’re at a buffet where there’s a seemingly endless array of premium options, but every serving plate is painfully small. In the world of cloud computing, NVIDIA provides immense processing power, but AWS and others often can’t fully leverage it due to resource constraints, rising demand, and fixed IT budgets. This juxtaposition reflects the core challenges facing AI's expansion in financial services.

2. IT Budgets: Champagne Taste, Beer Budget

Imagine this: you want to grow your business, use more AI, and push the boundaries of tech—but your wallet hasn't grown. The IT budgets for AWS's clients are the same, growing maybe 3-5% a year. But the need for capacity, infrastructure, and GenAI consumption is ballooning by 40% annually. The math doesn’t add up.

So, AWS suggests "design big, deploy small"—build an AI model that can handle everything, but when it comes to deploying, be selective. It’s like designing a mansion but deciding to only furnish the rooms you're actually using (like the kitchen and bathroom). This strategy helps the clients get started without completely breaking the bank, but it's still a temporary fix.

AWS’s adoption of Generative AI (GenAI) is booming at a projected growth of 40% annually. However, with NVIDIA’s current capacity expanding by 300% in the next two years, it won’t fully satisfy demand. This discrepancy illustrates the scaling limitations even the most advanced AI providers face due to finite resources shared with industries like gaming, telecom, and automotive.

3. Cloud Providers: It's Complicated

AWS, Microsoft, Google, and Oracle are all vying for a bigger piece of the GenAI action. But things are complicated—switching cloud providers isn't like switching phone plans; there's a lot more involved. Nobody is ditching AWS for Microsoft just because Microsoft is doing some cool things with GenAI. Why? Because it would cost more to switch than to just stick around.

Cloud computing for AI is also a game of latency—how quickly can your data get processed? This is especially true for tasks that require real-time analysis, like autonomous cars or financial transactions. If your GenAI chatbot isn’t faster than the current system, why would a company switch? NVIDIA might make GPUs with 75% better latency, but if it’s not affordable, it doesn’t matter.

4. Regulatory Frameworks: The Elephant in the Room

There's also the big, heavy weight of regulations. It turns out you can’t just launch an AI product into the wild and hope for the best. The legal system’s got to keep up. It’s like building a rocket but being told you can’t launch it until there’s a safety net for the entire planet—just in case it falls.

Financial services, insurance, healthcare—these industries are in the early phases of adopting GenAI, but they can't go full steam ahead until regulators say, "okay." In some places, like Boston, regulators even halted AI projects until all the rules were figured out.

While the appetite for AI continues to grow, with financial services demanding infrastructure expansion, budgetary growth only hovers at 3-5% annually. This fiscal disconnect challenges AWS’s financial clients to adopt models that fulfill immediate goals without straining resources.

5. Networking and the 800G Question

AWS's future depends not only on more GPUs but also on improving how networks work. There’s talk about moving networking to 800G (which is basically super-duper-fast Internet). But the problem isn’t with making the chips faster—it’s controlling the traffic, like having a Ferrari with no traffic lights in the city.

This bottleneck means that even if they get the 800G network chips out there, until they solve traffic control, the customer experience doesn’t improve. It’s like upgrading all highways to eight lanes but keeping the same outdated stoplight system.

AWS's growth relies not only on GPU expansion but also on network speed advancements. Migrating to an 800G network faces hurdles in controlling traffic, akin to expanding highways without synchronized traffic lights. This issue underpins the importance of synchronized investment in networking for customer satisfaction.

6. The Fragmented Future

The market for AI infrastructure is expected to get more fragmented as different players carve out niches. Some applications don’t need ultra-low latency, while others are useless without it. Imagine a gigantic store where each aisle has a different type of shelf designed for a very specific type of product. That’s where the AI hardware market is heading—a continuum of use cases that demand different hardware solutions.

The opportunity here is for niche players to come in and specialize—not everyone needs the fastest NVIDIA chip. Maybe AMD’s MI300 or some other startup’s design will do the trick just as well for a lower price..

As AI infrastructure fragments, various applications require different latency and performance levels. This trend invites niche players, enabling flexibility in workload management and reducing reliance on top-performing NVIDIA GPUs for less latency-sensitive tasks.

The future of GenAI in financial services—and cloud computing more broadly—is a story of ambition tempered by hard limits. From capacity bottlenecks to budget shortfalls to regulatory uncertainty, there are a lot of moving parts that have to line up just right for things to accelerate. But there’s still progress, and there’s still excitement. The AI growth buffet is open, even if the plates are small.

The big takeaway? It’s not about having everything at once. It’s about designing for the future, then deploying in a way that doesn’t break the system—or the budget—in the process.