NVIDIA has just delivered another record quarter. Revenue and profit are surging; expectations are beaten across the board. And yet, the market reaction is surprisingly muted. Why the grain of salt?

Behind NVIDIA’s impressive numbers sit two uncomfortable questions:

1. Are we inflating an AI bubble?

2. How healthy is an ecosystem where one company controls around 90% of the high performance AI chip market?

Let’s be real – and take a hard look at a simple but crucial choice: Do we really want to solve the future of AI mainly by buying more compute? Or should we first and foremost focus on making our models more efficient?

NVIDIA as indicator and single point of failure

NVIDIAs latest figures make one thing clear: the tech giant has become the central proxy for the entire generative AI boom. When NVIDIA grows, the market takes it as a sign that “AI is working.” When the wind changes, NVIDIA is the first place investors look for cracks.

On paper, the story is straightforward: Data center demand keeps exploding; AI workloads grow faster than anticipated. Capital expenditure plans from hyperscalers signal that billions more will be spent on GPUs in the coming years.

But if one company sits at the bottleneck of AI infrastructure, every miscalculation in AI revenue expectations and every shift in regulation gets amplified through that single point. What looks like a strength today can quickly turn into a systemic vulnerability tomorrow.

That’s why NVIDIA is not just an indicator. It’s the single largest technical and economic dependency in the current AI stack.

The race for AI chips

In high-performance AI accelerators, NVIDIA’s dominance is not a matter of opinion. Most large foundation models are trained and served on NVIDIA GPUs. The surrounding software ecosystem, from CUDA to libraries and orchestration tools, is deeply intertwined with this hardware.

At the same time, other tech giants are actively looking to reduce their dependency:

• Hyperscalers like Amazon are investing in their own chips.

• Google is pushing TPUs as a serious alternative for selected workloads.

• AMD is closing multi-billion dollar agreements with companies like OpenAI and Meta.

Yet, even in a world where AMD and custom silicon gain ground, one uncomfortable truth remains: as long as our answer to “how do we scale AI” is “buy more accelerators,” we’re just moving the dependency around, not solving it.

The two levers of AI performance: compute vs. efficiency

Strip away the hype and you face a simple reality. If you’re responsible for training or operating large models, you essentially have two ways to get more out of your system:

1. You buy more compute.

2. You make your models and more efficient.

Whenever workloads grow, the default response is to provision more GPUs, spin up larger clusters, and adopt the next generation of even more powerful chips. It’s straightforward: You keep your architecture, your code, and your processes mostly the same. You throw more hardware at the problem and get faster training runs, more experiments, lower time to market.

But the bill comes later.

Efficiency as a strategic lever

When people hear “efficiency,” they often think of micro-optimizations. In modern AI systems, the reality is different. More efficient models change the economics of AI:

• You need less hardware to reach the same quality.

• You spend less per training run and per 1,000 inferences.

• You can deliver better latency and throughput to users without exploding your infrastructure bill.

• You lower your energy footprint and make AI workloads easier to justify long-term.

What an AI compiler really does

An AI compiler is not just a translator from high-level to low-level code. Done right, it behaves more like an optimization agent that understands both your model and your hardware.

• It analyzes how your workloads behave on real infrastructure.

• It identifies compute bottlenecks.

• It rewrites and reorders operations to better match your target hardware.

In other words: The same hardware delivers more useful work per dollar. And you reach your target performance with fewer resources.

A way out of the dependency

If your models are tightly coupled to one hardware stack and barely optimized, moving to a different platform becomes a massive project. If, on the other hand, you invest in portability and efficiency, you can easily evaluate alternative accelerators without rewriting your entire stack. You can balance workloads across different vendors based on price, availability and latency, and treat hardware choices as a variable in your optimization problem.

In that sense, efficiency is not just a cost lever. It’s a way to regain control over strategic choices in your AI infrastructure – including how much you want your future to depend on NVIDIA’s roadmap.

At yasp, we believe the next wave of AI progress will not be defined by who can buy the most GPUs, but by who uses their compute the smartest. And we believe that requires a new kind of tooling – one that treats performance and efficiency not as an afterthought, but as top priority.

Beyond the NVIDIA boom

To make it clear: NVIDIA will likely remain a central player in AI infrastructure for years to come. The company has executed brilliantly and created real value. The point is not to be against NVIDIA. It’s about recognizing that building the future of AI solely on “more of the same hardware” is a fragile strategy – economically, technically and environmentally.

If you care about resilience, cost discipline and long-term independence, you need a second lever alongside raw compute: efficiency. That is where the real power shift in AI lies. Not away from accelerators, but toward teams who can turn every GPU hour into more insight, more product value, and more room to maneuver.

And this is exactly the future we’re building at yasp.