The Best Artificial Intelligence (AI) Growth Stocks to Buy on the Nasdaq as the Rally Heats Up

The article is bullish on AMD and Alphabet as AI beneficiaries, citing AMD’s inference and agentic AI positioning plus Alphabet’s custom TPU advantage as key competitive drivers. It highlights AMD’s partnerships with Meta Platforms and OpenAI for 6 gigawatts of GPU capacity and Alphabet’s lower-cost training/inference stack versus Nvidia, with added revenue potential through Broadcom. A separate geopolitical note flags Strait of Hormuz disruption as a supply-chain risk for helium used in advanced chip manufacturing, but the piece is primarily an AI stock-picking commentary rather than a direct catalyst.

Analysis

The market is increasingly separating AI beneficiaries into three layers: chip hardware, software orchestration, and cost-optimized inference. On that map, AMD looks like a second-derivative winner rather than a first-order one: its upside depends less on winning the headline model race and more on being the “good enough, cheaper, flexible” compute choice as inference volumes explode. The key nuance is that inference economics favor memory bandwidth, packaging, and CPU-GPU balance, which means AMD’s opportunity is not just GPUs but also the server CPU attach rate that could compound as agentic workloads proliferate.

Alphabet’s advantage is more structural than cyclical. Custom silicon plus vertically integrated cloud and model deployment creates a cost curve that hyperscalers buying merchant GPUs cannot easily match; that gap can widen if model usage shifts from training capex to persistent inference opex. The second-order implication is negative for pure-play AI infrastructure vendors at the margin: as large platforms internalize more of the stack, they squeeze pricing power in adjacent compute layers and force competitors to compete on specialization rather than general-purpose performance.

The contrarian risk is that the market may be overpaying for the inference narrative before utilization is fully proven. If agentic workloads remain experimental, CPU bottlenecks may not show up soon enough to justify multiple expansion, and any delay in hyperscaler capex decisions would hit AMD first. Conversely, the article understates geopolitical supply-chain fragility: even without a full shipping disruption, helium and broader specialty-materials headlines can create intermittent component lead-time risk, which matters most over the next 1-3 quarters rather than the full-cycle AI thesis.

The cleanest setup is a relative-value trade: long AMD versus short NVDA on the view that inference/CPU mix and customer diversification are underappreciated, while GPU training growth is more mature and widely owned. For lower beta exposure, Alphabet is the highest-quality AI hedge because its internal chip economics convert AI usage into margin rather than just revenue growth; that makes pullbacks driven by sentiment or macro the better entry point. Broadcom remains the stealth beneficiary if custom silicon adoption accelerates, but its upside is more second-order and depends on hyperscaler willingness to outsource design capacity.

Key Decisions for Investors

• Initiate a 3-6 month long AMD / short NVDA pair on the thesis that inference and CPU attach are underpriced relative to mature training-cycle exposure; target 15-20% relative outperformance, stop if hyperscaler capex re-accelerates toward training-heavy demand.
• Buy GOOGL on 5-10% weakness over the next 1-2 months; the risk/reward is favorable because custom silicon should support margin durability even if AI sentiment cools, with downside mainly tied to regulatory headlines rather than model economics.
• Add AVGO on any pullback as a medium-term optionality trade on custom chip proliferation; expect slower but steadier upside than AMD, with better downside protection if hyperscalers keep insourcing more design.
• For traders, consider AMD Jan-2027 call spreads to express upside from agentic AI adoption without paying full premium for near-term execution risk; best when implied volatility compresses after earnings.
• Avoid chasing NVDA here on a momentum basis unless checks confirm renewed training demand; the risk/reward is less attractive versus more incremental beneficiaries of inference spending.