Recommend US companies focused on artificial intelligence.

Here are some U.S.-listed companies whose business models give you direct or highly leveraged exposure to artificial-intelligence (AI) demand. They’re grouped so you can decide which part of the AI value-chain you’d like to own. All tickers are listed on U.S. exchanges. 1. Core “picks-and-shovels” (semiconductors & accelerated computing) • NVDA – NVIDIA: dominant supplier of GPU and networking silicon used to train and run large models; >80 % share of the data-center accelerator market. • AMD – Advanced Micro Devices: MI300 Instinct accelerators shipping at hyperscalers; gaining share as the second source to NVIDIA. • AVGO – Broadcom: custom AI chips (Google TPU, Meta MTIA), high-speed optical and switch silicon. • MRVL – Marvell Technology: ASICs and optical DSPs used inside AI server clusters. • ACLS – Axcelis & LRCX – Lam Research (equipment play): tools that enable advanced node foundry capacity for AI chips. 2. Cloud & hyperscale operators (own or rent out AI infrastructure) • MSFT – Microsoft: Azure’s AI supercomputer (co-developed with OpenAI) plus Copilot software subscriptions. • AMZN – Amazon.com: AWS Trainium/Inferentia chips and Bedrock foundation-model service layer. • GOOGL – Alphabet: internally built TPU v5p clusters and a fast-growing Vertex AI platform. 3. Enterprise software & data analytics (AI embedded in workflow) • CRM – Salesforce: Einstein GPT and Data Cloud bring generative AI into CRM, marketing-automation and service desks. • SNOW – Snowflake: “Snowpark” and Snowflake Cortex let customers run fine-tuned LLMs on governed data. • NOW – ServiceNow: gen-AI modules across ITSM and HR workflow; heavy NVIDIA partnership. • INTU – Intuit: Gen-AI “Intuit Assist” adds conversational tax and bookkeeping features for SMBs. 4. Pure-play / focused AI software vendors • PLTR – Palantir Technologies: Gotham & Foundry platforms for data fusion; AIP (AI Platform) driving new DoD and enterprise wins. • AI – C3.ai: offers model-centric applications (predictive maintenance, fraud detection) on an enterprise PaaS. • PATH – UiPath: Robotic Process Automation (RPA) that now layers gen-AI “Autopilot” for no-code workflow building. • S – SentinelOne & CRWD – CrowdStrike: AI-native cybersecurity engines (behavioral detection vs. signature matching). 5. Domain-specific AI beneficiaries • TSM – Taiwan Semi. ADR (TSM): not U.S.-incorporated but trades on NYSE; the foundry capacity bottleneck for virtually every AI chip. • TSLA – Tesla: AI-training compute cluster (“Dojo”) for autonomous-driving stack; may monetize as a service. • META – Meta Platforms: building open-source Llama models and infra; leverages AI for ad-targeting and Reels recommendation. Smaller-cap names to keep on your radar (higher risk / higher upside): • VERI – Veritone (media-workflow AI), • BBAI – BigBear.ai (defense & logistics), • SOUN – SoundHound AI (speech), • UPST – Upstart (AI-driven lending), • INOD – Innodata (data labeling / RAG pipelines). How to narrow the list: • If you want near-term earnings power and cash flow, start with NVIDIA, Broadcom, Microsoft and Palantir. • Looking for “early-stage torque” instead? Consider UiPath, C3.ai or the smaller caps above—but monitor cash-burn and dilution. • Blend the layers: a chip name + a cloud platform + an application software provider can create a diversified AI basket. Always dig into valuation (P/E or EV/Sales vs. growth), competitive moats, and balance-sheet health before allocating capital. Let me know if you’d like a quantitative screen—e.g., “AI stocks with YoY revenue growth >20 % and positive free-cash-flow”—and I can run that scan for you.