AI Stocks & AI IPOs
AI Stocks coming to market are driving Immesnse growth in Industries such as Healthcare and Software, creating new opportunities for you to Invest In AI stocks.
HOW TO INVEST IN AI STOCKS?
Investing in AI Stocks
Investing in AI stocks requires careful research and understanding of these companies' unique characteristics. Focus on key metrics and use specialized tools to uncover promising AI stocks.
AI Stock Metrics
Consider metrics such as R&D spending, patent portfolio, partnerships, market opportunity, competitive landscape, and growth prospects. Revenue growth, gross margins, and customer retention rates provide insights into financial health and competitive position.
Best AI Stocks to Buy
Leverage a stock screener tool to filter AI stocks based on criteria like market capitalization, sector, and financial metrics. Customize search parameters to identify stocks that align with your investment strategy and risk tolerance.
How AI Stocks Differ from Traditional Companies
AI companies prioritize long-term growth over short-term profitability, investing heavily in R&D to stay at the forefront of rapidly evolving technologies. This focus can lead to higher volatility and valuation premiums. Success often hinges on attracting and retaining top talent and continuously improving algorithms and data processing capabilities.
By understanding AI stocks' unique characteristics, using specialized metrics and tools, and maintaining a long-term perspective, investors can capitalize on the AI industry's tremendous growth potential.
Potential Upcoming AI IPOs
Overview of companies that have the largest potential to coming to market as an IPO in the AI industry.
OpenAI
With its industry-leading ChatGPT, OpenAI is possibly the most-anticipated AI IPO. The company has raised billions in funding from the world’s most prominent tech investors, and commands a valuation that ranks it as one of the world’s largest private companies. However, OpenAI has a long road to an IPO, due to its unique structure, governance, and near-limitless private funding.
Anthropic
Another leading LLM provider, Anthropic, the company behind Claude, likely has a more conventional trajectory to public markets. The company has raised billions in funding, including from the likes of Google and Salesforce, and reportedly has revenues exceeding $1 billion. Anthropic has also built an executive team with members who have IPO experience, a sign it may one day pursue its own IPO.
CoreWeave
As the AI boom creates an extraordinary demand more computing power, data center provider CoreWeave has posted explosive growth. The company has won large multi-year contracts with a number of major AI players, and counts Microsoft as its top customer. CoreWeave has 32 data centers running more than 250,000 GPUs, and claims that its platform is purpose-built for AI applications and model training.
Databricks
AI-driven data analytics platform Databricks has been one of the most-watched IPO candidates for years. The company has raised well over $10 billion dollars in funding, and posts an annual run rate in the billions. Databricks’ CEO first teased an IPO in 2021, and has since pursued numerous avenues to provide liquidity to employees and other shareholders. The company also made multiple acquisitions in 2024. A hot IPO market may incentivize Databricks to make its long-awaited public debut a reality.
SymphonyAI
Enterprise software provider SymphonyAI, which provides AI solutions for a number of industry verticals, is in the midst of preparing to go public. The company has an annual run rate in the hundreds of millions, and serves the retail, healthcare, and financial services industries, among others. Its CEO said the company targeted a listing in late 2025, and was speaking with banks to make it happen. SymphonyAI also recently hired a CFO with extensive IPO experience.
Waymo
Autonomous vehicle giant Waymo has carved out a leading position for itself in the nascent field of robo-taxis. Originally spun out of Google, the company has since raised billions of dollars in funding, including from crossover investors who may look to public markets for liquidity. As Waymo continues to develop the AI technology used in its vehicles, and expands to more cities, it could tap public markets to offer investors a way to play the space.
Scale AI
As LLMs and other AI models explode in popularity, the time, energy, and resources needed to train these models has also surged. Enter Scale AI, which provides curated data to train AI models. The company works with OpenAI, Cohere, Microsoft, Meta, and numerous government agencies. Scale AI reportedly earns hundreds of millions of dollars in revenue, and has raised funding from a wide array of tech investors, including Amazon, Nvidia, and Y Combinator.
Perplexity
While many LLM developers are trying to do away with the search engine model, Perplexity is instead pursuing a different strategy: developing a search engine powered by AI. The company has had a meteoric rise, tripling its valuation in early 2024, only to triple it again later in the year with the help of investors Nvidia and SoftBank.
Other pre-IPO AI companies include: xAI, Cohere, DataRobot, Dataiku, Lambda, ElevenLabs, Lightmatter, Groq, Grammarly, Moonshot AI, Xaira Therapeutics, Safe Superintelligence, Figure AI, Inflection AI, Skild AI, Kore.ai, Magic
Discover AI Stock Investment Ideas by looking at the Value Chain
The rapid development of artificial intelligence (AI) is creating a wave of exciting investment opportunities. One powerful framework for identifying these opportunities is the AI Value Chain, which maps out the stages that bring AI from concept to real-world use. By understanding each link in this chain, investors can spot companies poised to create value and drive returns.
The AI Value Chain consists of six key elements:
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AI Infrastructure
This foundational stage includes the hardware, software, and cloud platforms necessary for AI development and deployment. Key players are established tech giants like Nvidia, Intel, and Google Cloud, but there are also opportunities in startups developing specialized AI hardware or cloud solutions.
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Data Collection and Preparation
AI models require vast amounts of high-quality data. Companies that provide tools and services for data gathering, cleaning, and labeling, such as Scale AI and Appen, are well-positioned in this stage. Growing demand for diverse datasets supports investment potential.
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Model Development
This is where core AI capabilities are built. It involves cutting-edge machine learning research at companies like OpenAI, DeepMind, and Anthropic. Investing at this stage offers high potential rewards if breakthrough models are developed, but also comes with higher risks.
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Model Deployment and Scaling
Making AI accessible and usable at scale is the focus of this stage. Companies like Databricks and DataRobot that offer platforms to streamline AI deployment are attractive as more businesses seek to implement AI. Solutions for efficient, scalable deployment have strong growth prospects.
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Application Development
This stage translates AI into industry-specific solutions. C3.ai, UiPath, and Veritone are examples of companies building AI applications across sectors like healthcare, finance, and marketing. With AI becoming crucial for competitiveness in many industries, demand for these solutions is rising.
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End-User Services
Delivering AI products directly to consumers or businesses is the final stage. This includes AI-powered assistants, chatbots, and content creation tools from players like OpenAI and Jasper. End-user services represent the front lines of applied AI and benefit from strong adoption rates.
The AI Value Chain reveals a spectrum of entry points for investors. Each stage has distinct market dynamics, growth trajectories, and risk profiles.
Infrastructure and end-user services may offer steadier growth, while model development has potential for outsized returns but also higher uncertainty. Data preparation and application development have opportunities for smaller players in specialized verticals alongside bigger platforms.
The connective tissue between stages is also fertile ground. As breakthroughs at one level enable advances in others, companies facilitating these connections can benefit. For example, innovations in semiconductors boost the potential of both model development and end-user services.
Diversifying investments across the AI Value Chain allows investors to tap into AI's overall transformative potential while mitigating risks in any single area. A balanced AI portfolio might include established semiconductor companies, rapidly growing application developers, and research-driven startups pushing the envelope on model sophistication.
The AI Value Chain is a road map for navigating the complex, fast-moving world of AI investing. By understanding AI's path from lab to marketplace, investors can uncover a rich mosaic of opportunities and build diversified portfolios positioned to benefit from this transformational technology. Whether infrastructure, applications, or AI-powered products, each link in the Value Chain offers possibilities for growth powered by the future of artificial intelligence.
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Important Recent AI IPOs
These IPOs either had significant industry buzz, large capital raises, or strong results. These stocks are a sample of companies that you can research further using our IPO Pro tools.
- UK-based chip designer for smartphones and other markets.
- IPO Date: 09/13/2023
- IPO Deal Size: $4,871
- Return from IPO: 111.4%
- Social media platform with message boards for interest-based communities.
- IPO Date: 03/20/2024
- IPO Deal Size: $748
- Return from IPO: 216.8%
- Provides identity governance software to enterprises.
- IPO Date: 02/12/2025
- IPO Deal Size: $1,380
- Return from IPO: -15.0%
Explore Important AI Stock Industries
- These companies manufacture the processing units (GPUs, TPUs, NPUs, and other AI accelerators) that power AI computations.
- Examples:NVIDIA, AMD, Intel, Qualcomm, Graphcore
- Investment Angle:High R&D costs but strong competitive moats due to advanced semiconductor technology and AI chips.
- Provides cloud-based AI platforms and computing power to AI developers and enterprises.
- Examples: AWS (Amazon), Microsoft Azure, Google Cloud, Oracle Cloud
- Investment Angle: Recurring revenue from cloud AI services; strong competitive position due to data center dominance.
- These companies focus on creating state-of-the-art AI models, foundational models, and frameworks (e.g., OpenAI’s GPT, Google's Gemini).
- Examples: OpenAI, DeepMind (Google), Anthropic, Cohere, Mistral
- Investment Angle: Harder to invest in directly, but companies that own proprietary AI models may have long-term value in licensing and partnerships.
- Provide AI-powered applications, APIs, and tools for businesses (e.g., AI chatbots, automation, cybersecurity, sales intelligence).
- Examples: C3.ai, Palantir, ServiceNow, Salesforce (AI Cloud), Databricks
- Investment Angle: Many SaaS companies are integrating AI into their platforms, leading to potential productivity gains.
- Companies specializing in AI-powered data processing, storage, and business intelligence.
- Examples: Snowflake, Datadog, Alteryx, Splunk
- Investment Angle: AI needs vast amounts of structured/unstructured data—companies with strong data analytics capabilities are well-positioned.
- AI-powered robotics and automation for industries like manufacturing, logistics, and self-driving cars.
- Examples: Tesla (Autopilot), Boston Dynamics, Waymo (Alphabet), Nuro
- Investment Angle: Long-term growth potential but capital-intensive; regulatory and technological challenges.
- Companies using AI for drug discovery, diagnostics, and healthcare automation.
- Examples: Tempus, Insilico Medicine, PathAI, IBM Watson Health
- Investment Angle: High-growth sector, but regulatory approvals can slow down commercialization.
- AI-powered trading algorithms, fraud detection, and financial risk analysis.
- Examples: Two Sigma, Renaissance Technologies, Kensho (owned by S&P Global)
- Investment Angle: Fintech companies integrating AI have strong data-driven advantages.
- AI-driven threat detection, anomaly detection, and automated response to cyber threats.
- Examples: CrowdStrike, Palo Alto Networks, Darktrace
- Investment Angle: Growing market due to increasing cyber threats and AI-powered automation.
- AI for image, video, and text generation (e.g., generative AI models, deepfakes, AI-powered creative tools).
- Examples: Adobe (Firefly), OpenAI (DALL·E), Stability AI, Midjourney
- Investment Angle: High-growth potential but concerns over ethical use and copyright.
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