The Nvidia Moat
How Nvidia built its moat through chip-level efficiency, instruction-set richness, system-level scalability, advanced packaging, and more
For most of the last decade, Nvidia has been described as “the GPU company.” That label is now dangerously incomplete. The more useful way to see Nvidia is as a compounding machine: it compounds performance, developer attention, and system-level lock-in across hardware, software, packaging, and full-rack delivery. That compounding shows up roughly a 10-year, ~1000× leap in GPU-relevant performance.
The 1000× leap did not come from a single miracle. It came from stacking multiple “small multipliers” that each delivered 2×, 10×, or 30× improvements, then letting them compound over time.
Increasing Chip-level Efficiency
The march from FP32 to FP16 to INT8/FP8 (and now FP4-class formats in the Blackwell era) matters so much: it’s not only about doing more ops, but about doing them with radically less energy per useful token generated. And crucially, the precision reduction isn’t “just software.” It needs microarchitectural support and algorithmic scaffolding so that training or inference quality doesn’t collapse.
That detail is easy to miss if you only look at FLOPS charts. One of the barrier why “train FP16 then PTQ-quantize” is not the same as having native low-precision pathways that actually save compute and memory at the same time. That’s why Nvidia’s advantage isn’t merely that it “supports FP8,” but that it keeps moving the boundary of what is practically trainable/servable at lower precision without asking the ecosystem to rewrite itself each generation.
Instruction-set Richness
Next comes instruction richness, which is where “GPU” starts to blur into “domain accelerator.” Nvidia has accumulated “tens of thousands” of instructions, while competitors’ instruction set is still a fraction of what Nvidia has.
The moat, however, doesn’t survive on instructions alone. It survives because Nvidia has built a stable abstraction boundary between fast-changing hardware and the developer world. PTX can act as a stable mapping layer upward into CUDA, keeping the ecosystem programmable even as the underlying machine changes. PTX is a structure that doesn’t change, is upgraded for backward compatibility, and is positioned as the stable anchor that lets fine-grained programming persist across generations.
This is where Nvidia’s story becomes less like semiconductors and more like operating systems. When hardware changes every 1–2 years, you either preserve compatibility or you reset the ecosystem and lose all accumulated upstream value. Nvidia’s approach is to keep the programming stable. That is a profoundly different kind of advantage than “better transistor density,” because it means Nvidia can improve hardware rapidly without forcing the world to pay the full migration tax each cycle.
A subtle but important point is that many of Nvidia’s advancements show up first in open-source projects, research posts, and engineering writeups - which are tightly linked to low-level hardware behavior. That coupling matters because it makes copying harder: even if a competitor reproduces the high-level idea, the last-mile optimization often still requires CUDA-style programming, and migrating that work is painful. Researchers optimize on Nvidia because it is the most reliable platform; Nvidia then absorbs those algorithmic wins into libraries and kernels; then the platform becomes even more attractive.
System-level Scalability
Once you accept that Nvidia is a compounding machine, you naturally ask: where does the next “multiplier” come from when single-die scaling slows? The answer lies in systems: the performance frontier moves to cluster architecture efficiency and utilization. It is a technical moat expansion: when the bottleneck shifts to interconnect and collective communication, owning the fabric becomes as important as owning the compute.
The network angle is revealing because it shows Nvidia trying to enter into a class of optimization that used to live outside the GPU vendor’s control. If AllReduce-style workloads happen within the network, networking traffic can be reduced and efficiency will be increased. This is the kind of advantage that doesn’t show up in a GPU spec sheet but shows up in customer TCO. It also reinforces the “AI factory” framing: Nvidia is not merely selling chips; it is trying to own the throughput of the whole factory line. AMD GPUs can look similar at a high level, but real performance hinges on the compiler’s ability to orchestrate data movement across HBM, caches, and registers under messy real-world shapes and batches.
At the same time, Nvidia’s GPUs are evolving toward ASIC-like behavior rather than away from it. Nvidia can absorb ASIC-style algorithmic innovations into GPU-centric systems. Parts of the Transformer Engine that previously existed as “software over Tensor Cores” are being moved into hardware - this is ASIC-like behavior as it hs hardening a workload pattern into silicon.