AI math milestone boosts Nvidia, but rivalry intensifies

Nvidia remains the clearest near-term beneficiary of the AI capex cycle. Its shares have climbed back above the 200-day moving average and recently held near $208, with the stock still trading roughly 17% above its 50-day average, a sign that institutional money is treating any pullback as a buying opportunity. Conventional technical indicators also suggest momentum has stabilized after a sharp mid-year reset: the relative strength index is back above 60 and the MACD has turned positive again. Adalytica’s NVIDIA Earnings Sentiment gauge is at 100, flagged as Extreme Greed, reflecting just how stretched expectations have become.

That positioning is exactly why the competitive narrative matters. Nvidia’s problem is not demand destruction; it is that demand is increasingly strategic, contested and politicized. The IMO result adds to evidence that Chinese AI development is moving from imitation to elite problem solving, which may accelerate domestic chip investment, software optimization and model training efforts outside Nvidia’s preferred ecosystem. Even if export controls limit access to its most advanced accelerators, a faster Chinese model stack raises the odds of more custom silicon, alternative architectures and greater bargaining pressure on suppliers.

The market is already sketching that tension. Advanced chip peers have seen much stronger runs over the past year, with TSMC and AMD both reflecting investor enthusiasm for the broader AI buildout. But Nvidia’s near-term multiple still depends on the assumption that it will keep capturing the lion’s share of frontier spending. A world in which Chinese labs achieve world-class reasoning performance while U.S. hyperscalers pursue efficiency rather than brute-force scaling could change that mix, even if total AI capex keeps rising.

The bull case remains intact as long as the frontier keeps moving upward and training runs keep getting larger. A perfect math score by AI suggests that frontier models still have room to expand into scientific research, coding and engineering, all of which are GPU-intensive. The bear case is that as model architecture and optimization improve, the industry could shift toward more efficient inference, more heterogeneous chips and a slower growth rate in the incremental demand Nvidia enjoys today.

For now, the message to investors is that Nvidia is still the center of the AI economy, but it is no longer a story about scarcity alone. It is about a race in which technological breakthroughs, including from China, can both enlarge the market and erode the moat.