The first boom in AI was hardware (especially GPUs, where NVIDIA dominates with CUDA, H100, etc.). But AI is a massive stack: data → infrastructure → models → applications. Other layers are starting to explode as well.

Here’s a breakdown of who could be the next big “booming” players after NVIDIA 👇

🔹 1. Alternative AI Hardware Makers

• AMD → With ROCm (open-source CUDA alternative) + MI300X (192 GB HBM3 memory), AMD is pushing as an NVIDIA alternative for training/inference.
• Intel → Gaudi3 accelerators (used in AWS EC2 DL1), plus oneAPI + Ponte Vecchio GPUs. Could grab enterprise market share.
• Google → TPUs (v5p, v5e) already power a lot of training in-house + Google Cloud.
• Specialized Startups:
  • Cerebras (Wafer-Scale Engine — one giant chip).
  • Graphcore (IPUs).
  • Tenstorrent (RISC-V AI processors; India + Canada).
  • SambaNova Systems (reconfigurable AI chips).

👉 These could boom if enterprises want diversity away from NVIDIA lock-in.

🔹 2. Cloud & Infrastructure Providers

• AWS, Azure, Google Cloud → Already massive, but their AI-specific instances (p4d, p5, TPU pods, ND-series) are set to explode as companies can’t buy GPUs easily.
• CoreWeave, Lambda Labs, RunPod, Vast.ai → “GPU cloud startups” renting out GPUs at scale. Big growth as demand for compute skyrockets.

🔹 3. Model & Framework Players

• OpenAI (ChatGPT, GPTs) → Already a giant, but could dominate as a “platform layer” like Microsoft Windows did.
• Anthropic (Claude models) → Positioning around safety + enterprise AI.
• Mistral AI → Open-weight models, smaller but high-growth.
• Hugging Face 🤗 → Like “GitHub for AI” — booming as open-source hub for models/datasets.
• Stability AI, MidJourney → In image/video models.

🔹 4. Application Layer (where $$$ explodes)

• AI Agents / Copilots → GitHub Copilot, Microsoft Copilot, Notion AI, etc. → Productivity AI is exploding.
• Healthcare AI → Drug discovery (Insilico Medicine, Recursion), diagnostics (Tempus).
• Fintech AI → Fraud detection, AI trading, robo-advisors.
• Enterprise SaaS AI → Companies embedding AI into CRMs, ERPs, DAM (like Wizbrand 😉).

This application layer is where AI value will explode 10x bigger than hardware, just like the internet boom wasn’t about routers, but apps like Google, Amazon, Facebook.

🔹 5. Data & Tools Layer

• Databricks, Snowflake, DataRobot → Data + MLOps platforms will boom, because everyone needs clean + managed data to train models.
• LangChain, LlamaIndex, Weaviate, Pinecone → Vector databases + orchestration frameworks powering retrieval-augmented generation (RAG).
• Scale AI, Labelbox → Data labeling & synthetic data generation.

🔮 ✅ My Prediction (AI Boom, Next Leaders)

1. Hardware: AMD, Intel, Cerebras (challengers to NVIDIA).
2. Cloud GPU Providers: CoreWeave, Lambda Labs, RunPod.
3. Model Hubs: Hugging Face, Mistral, OpenAI, Anthropic.
4. Application Layer: Productivity AI (Microsoft, Notion AI), Healthcare AI, Finance AI.
5. Data Platforms: Databricks, Snowflake, Vector DB startups.

⚡ NVIDIA was the “pick-and-shovel” winner in Round 1 (compute). Round 2 winners will be in platforms and applications — the Googles and Amazons of AI.