Buying artificial intelligence (AI) stocks after the run they’ve had over the past few years may seem silly. However, the reality is that many of these companies are still experiencing rapid growth and anticipate even greater gains on the horizon.

By investing now, you can get in on the second wave of AI investing success before it hits. While it won’t be nearly as lucrative as the first round that occurred from 2023 to 2024, it should still provide market-beating results, making these stocks great buys now.

AI Hardware: Taiwan Semiconductor and Nvidia

The demand for AI computing power appears to be insatiable. All of the AI hyperscalers are spending record amounts on building data centers in 2025, but they’re also projecting to top that number in 2026. This bodes well for companies supplying products to fill those data centers with the computing power needed for processing AI workloads.

Two of my favorites in this space are Nvidia (NVDA -3.38%) and Taiwan Semiconductor Manufacturing (TSM -3.05%). Nvidia makes graphics processing units (GPUs), which have been the primary computing muscle for AI workloads so far. Thousands of GPUs are connected in clusters due to their ability to process multiple calculations in parallel, creating a powerful computing machine designed for training and processing AI workloads.

Inside these GPUs are chips produced by Taiwan Semiconductor, the world’s leading contract chip manufacturer. TSMC also supplies chips to Nvidia’s competitors, such as Advanced Micro Devices, so it’s playing both sides of the arms race. This is a great position to be in, and it has led to impressive growth for TSMC.

Both Taiwan Semiconductor and Nvidia are capitalizing on massive data center demand, and have the growth to back it up. In Q2 FY 2026 (ending July 27), Nvidia’s revenue increased by 56% year over year. Taiwan Semiconductor’s revenue rose by 44% in its corresponding Q2, showcasing the strength of both of these businesses.

With data center demand only expected to increase, both of these companies make for smart buys now.

AI Hyperscalers: Amazon, Alphabet, and Meta Platforms

The AI hyperscalers are companies that spend a significant amount of money on AI computing capacity for internal use and to provide tools for consumers. Three major players in this space are Amazon (AMZN -1.16%), Alphabet (GOOG 0.56%) (GOOGL 0.63%), and Meta Platforms (META -1.69%).

Amazon makes this list due to the boost its cloud computing division, Amazon Web Services (AWS), is experiencing. Cloud computing is benefiting from the AI arms race because it allows clients to rent computing power from companies that have more resources than they do. AWS is the market leader in this space, and it is a huge part of Amazon’s business. Despite making up only 18% of Q2 revenue, it generated 53% of Amazon’s operating profits. AWS is a significant beneficiary of AI and is helping drive the stock higher.

Alphabet (GOOG 0.56%) (GOOGL 0.63%) also has a cloud computing wing with Google Cloud, but it’s also developing one of the highest-performing generative AI models: Gemini. Alphabet has integrated Gemini into nearly all of its products, including its most important, Google Search.

With the integration of generative AI into the traditional Google Search, Alphabet has bridged a gap that many investors feared would be the end for Google. This hasn’t been the case, and Alphabet’s impressive 12% growth in Google Search revenue in Q2 supports that. Despite its strong growth, Alphabet is by far the cheapest stock on this list, trading for less than 21 times forward earnings.

AMZN PE Ratio (Forward) data by YCharts

With Alphabet’s strength and strong position, combined with a cheap stock valuation, it’s an excellent one to buy now.

To round out this list, Meta Platforms (META -1.69%) is another smart pick. It’s the parent company of social media platforms Facebook and Instagram, and gets a huge amount of money from ads. As a result, it’s investing significant resources into improving how AI designs and targets ads, and it’s already seeing some effects. AI has already increased the amount of time users spend on Facebook and Instagram, and is also driving more ad conversions.

We’re just scratching the surface of what AI can do for Meta’s business, and with Meta spending a significant amount of money on top AI talent, it should be able to convert that into some substantial business wins.

AI is a significant boost for the world’s largest companies, and I wouldn’t be surprised to see them outperform the broader market in the coming year as a result.

Dublin, Sept. 02, 2025 (GLOBE NEWSWIRE) — The “Big Data and Artificial Intelligence Market Report 2025” has been added to ResearchAndMarkets.com’s offering.

The big data and artificial intelligence market size has grown rapidly in recent years. It will grow from $385.89 billion in 2024 to $456.35 billion in 2025 at a compound annual growth rate (CAGR) of 18.3%.

The growth observed during the historic period can be attributed to several factors, including an increasing focus on ethical artificial intelligence, rising demand for data-driven decision-making, greater adoption of artificial intelligence, heightened efforts by tech giants, and a growing demand for automation.

The big data and artificial intelligence market size is expected to see rapid growth in the next few years. It will grow to $884.42 billion in 2029 at a compound annual growth rate (CAGR) of 18%. The growth projected for the forecast period can be attributed to several factors, including the increasing generation of data across industries, greater adoption of cloud computing, rising investments in artificial intelligence, growing cybersecurity threats, and the escalating volume of data generated.

Key trends during this period include the rise of edge computing, investments in advanced big data tools, technological advancements, strategic collaborations, and the expansion of artificial intelligence in cybersecurity.

The growth of the big data and artificial intelligence market is expected to be driven by the increasing volume of data generated. The amount of data is growing rapidly due to the rise of internet of things (IoT) devices, social media, digital transactions, high-resolution media, and real-time analytics. Big data and artificial intelligence play a crucial role in managing this data by automating data collection, improving real-time processing, and extracting insights from various digital sources.

This integration allows businesses to optimize decision-making, enhance efficiency, and foster innovation across different industries. For example, in September 2023, the International Telecommunication Union reported that 67% of the global population, or 5.4 billion people, had internet access in 2022, marking a 4.7% increase from 2021. This growth in internet access is contributing to the rise in data generation, driving the expansion of the big data and artificial intelligence market.

Companies in the big data and artificial intelligence market are focusing on developing services such as AI-driven retrieval systems to maintain their competitive edge. These intelligent systems use artificial intelligence to extract, organize, and present relevant information from both structured and unstructured data sources. For instance, in February 2025, Snowflake Inc., a US-based cloud data storage company, introduced Cortex Agents, a retrieval service designed to enhance AI-driven data access and decision-making for businesses. The agents help retrieve structured data from Snowflake tables and unstructured data from object storage such as PDFs. Additionally, Cortex Search improves the retrieval of unstructured data and claims to outperform OpenAI’s embedding models by at least 11% across various benchmarks. These innovations enable businesses to analyze large datasets more efficiently, improving AI-driven decision-making and governance.

In August 2024, Advanced Micro Devices Inc. (AMD), a US-based semiconductor company, acquired Silo AI for $665 million. This acquisition aims to accelerate the development and deployment of artificial intelligence (AI) models on AMD hardware, enhancing the company’s AI capabilities and expanding its position in the AI-driven computing market. Silo AI, based in Finland, specializes in providing AI services and utilizes big data to create customized AI-driven solutions.

Major players in the big data and artificial intelligence market are Amazon Web Services Inc., Microsoft Corporation, Dell Technologies, Intel, IBM, Cisco Systems, Oracle, Google, SAP, Hewlett Packard Enterprise Company, NVIDIA Corporation, Salesforce Inc., Adobe Inc., Infosys Limited, SAS Institute Inc., LTI Mindtree Ltd., Teradata Corporation, QlikTech International AB, ScienceSoft USA Corporation, Yalantis, Cyfuture India Pvt. Ltd., Feathersoft Info Solutions Pvt Ltd., and Addepto.

Key Attributes:

Key Topics Covered:

1. Executive Summary

2. Big Data and Artificial Intelligence Market Characteristics

3. Big Data and Artificial Intelligence Market Trends and Strategies

4. Big Data and Artificial Intelligence Market – Macro Economic Scenario Macro Economic Scenario Including the Impact of Interest Rates, Inflation, Geopolitics, and the Recovery from COVID-19 on the Market

5. Global Big Data and Artificial Intelligence Growth Analysis and Strategic Analysis Framework
5.1. Global Big Data and Artificial Intelligence PESTEL Analysis
5.2. Analysis of End Use Industries
5.3. Global Big Data and Artificial Intelligence Market Growth Rate Analysis
5.4. Global Big Data and Artificial Intelligence Historic Market Size and Growth, 2019-2024, Value
5.5. Global Big Data and Artificial Intelligence Forecast Market Size and Growth, 2024-2029, 2034F, Value
5.6. Global Big Data and Artificial Intelligence Total Addressable Market (TAM)

6. Big Data and Artificial Intelligence Market Segmentation
6.1. Global Big Data and Artificial Intelligence Market, Segmentation by Type, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

• Predictive Analytics
• Fraud Detection
• Customer Analytics
• Risk Management
• Supply Chain Management

6.2. Global Big Data and Artificial Intelligence Market, Segmentation by Deployment Model, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

6.3. Global Big Data and Artificial Intelligence Market, Segmentation by Technology, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

• Machine Learning
• Natural Language Processing
• Data Mining
• Data Visualization
• Deep Learning

6.4. Global Big Data and Artificial Intelligence Market, Segmentation by End Use, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

• Banking, Financial Services, and Insurance
• Healthcare
• Retail
• Manufacturing
• Telecommunications

6.5. Global Big Data and Artificial Intelligence Market, Sub-Segmentation of Predictive Analytics, by Type, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

• Machine Learning-Based Predictive Analytics
• Statistical Modeling
• Data Mining
• Forecasting and Optimization

6.6. Global Big Data and Artificial Intelligence Market, Sub-Segmentation of Fraud Detection, by Type, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

• Identity Fraud Detection
• Transaction Fraud Detection
• Behavioral Analytics
• Anomaly Detection

6.7. Global Big Data and Artificial Intelligence Market, Sub-Segmentation of Customer Analytics, by Type, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

• Customer Segmentation
• Sentiment Analysis
• Personalized Recommendations
• Customer Churn Prediction

6.8. Global Big Data and Artificial Intelligence Market, Sub-Segmentation of Risk Management, by Type, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

• Credit Risk Assessment
• Market Risk Analysis
• Operational Risk Management
• Compliance and Regulatory Risk Management

6.9. Global Big Data and Artificial Intelligence Market, Sub-Segmentation of Supply Chain Management, by Type, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

• Demand Forecasting
• Inventory Optimization
• Supplier Risk Management
• Logistics and Transportation Analytics

7-29. Big Data and Artificial Intelligence Market Regional and Country Analysis

30. Big Data and Artificial Intelligence Market Competitive Landscape and Company Profiles
30.1. Big Data and Artificial Intelligence Market Competitive Landscape
30.2. Big Data and Artificial Intelligence Market Company Profiles
30.2.1. Amazon Web Services Inc. Overview, Products and Services, Strategy and Financial Analysis
30.2.2. Microsoft Corporation Overview, Products and Services, Strategy and Financial Analysis
30.2.3. Dell Technologies Inc. Overview, Products and Services, Strategy and Financial Analysis
30.2.4. Intel Corporation Overview, Products and Services, Strategy and Financial Analysis
30.2.5. International Business Machines Corporation Overview, Products and Services, Strategy and Financial Analysis

31. Big Data and Artificial Intelligence Market Other Major and Innovative Companies
31.1. Cisco Systems Inc.
31.2. Oracle Corporation
31.3. Google LLC.
31.4. SAP SE
31.5. Hewlett Packard Enterprise Company
31.6. NVIDIA Corporation
31.7. Salesforce Inc.
31.8. Adobe Inc.
31.9. Infosys Limited
31.10. SAS Institute Inc.
31.11. LTI Mindtree Ltd.
31.12. Teradata Corporation
31.13. QlikTech International AB
31.14. ScienceSoft USA Corporation
31.15. Yalantis

32. Global Big Data and Artificial Intelligence Market Competitive Benchmarking and Dashboard

33. Key Mergers and Acquisitions in the Big Data and Artificial Intelligence Market

34. Recent Developments in the Big Data and Artificial Intelligence Market

35. Big Data and Artificial Intelligence Market High Potential Countries, Segments and Strategies
35.1 Countries Offering Most New Opportunities
35.2 Segments Offering Most New Opportunities
35.3 Growth Strategies

For more information about this report visit https://www.researchandmarkets.com/r/n4vrac

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• Big Data and Artificial Intelligence Market

Introduction: The Information Overload Problem

We’ve all been there. You’re researching a complex topic—a blog post, a business proposal, an academic paper. Your screen is a chaotic sprawl of open tabs: PDFs, articles, interview transcripts, and your own half-finished notes. The information is all there, but synthesizing it into a coherent, original piece of work feels like a monumental task. You spend more time searching, copying, and pasting than you do thinking and creating.

What if you had a research assistant? One that never sleeps, instantly understands every source you provide, and can answer your questions, generate ideas, and draft content based solely on the information you trust.

This isn’t a futuristic dream. It’s the reality offered by Google’s NotebookLM چیست , an experimental AI-powered notebook that is quietly revolutionizing the way we interact with information.

What is NotebookLM? Beyond a Simple Note-Taking App

It’s crucial to understand that NotebookLM is not just another note-taking app like Evernote or Notion. While those are excellent for organization, NotebookLM is built for comprehension and synthesis. It’s a “language model” (the “LM” in its name) grounded in the documents you choose to provide.

Launched as an experiment from Google Labs, its core premise is simple yet powerful:

You provide the sources. Upload PDFs, copy-paste text from documents, or add notes from Google Docs.

NotebookLM “reads” and understands them. It creates a personalized AI that is an expert on your specific material.

You ask questions and get answers. You can interrogate your sources, generate new ideas, and create content, all grounded in the information you supplied.

This “grounding” process is NotebookLM’s killer feature. It drastically reduces the risk of AI “hallucinations” (making up facts) because its responses are tied directly to your source material.

How to Use NotebookLM: A Step-by-Step Workflow

Using NotebookLM is intuitively simple. Its power lies in how you integrate it into your workflow.

1. Create a Notebook and Add Sources:

Start a new project and give it a name. Then, add your research materials. You can upload PDFs (research papers, reports), copy and paste text, or directly select Google Docs from your Drive. NotebookLM can handle up to hundreds of thousands of words per notebook.

2. Let the AI Index Your Content:

Within seconds, NotebookLM will process your documents. It doesn’t just store them; it builds a semantic understanding of the concepts, people, dates, and relationships within your sources.

3. Engage with Your Content:

This is where the magic happens. The main interface features a notepad on the right (your space) and a chat panel on the left (the AI’s space). Here’s what you can do:

Ask Specific Questions: Instead of skimming a 50-page PDF, you can ask, “What were the three main conclusions of this study?” or “List all mentions of ‘sustainable architecture’ in these interview transcripts.”

Generate Summaries: Request a summary of a single document or a comparative summary of multiple sources.

Create Outlines and Drafts: Command it to “create a blog post outline based on these three articles” or “draft an email to my client summarizing the key points from this report.”

Generate Ideas: Spark creativity by asking, “What are some counter-arguments to the points made in these sources?” or “Suggest five blog post topics based on this research.”

4. Pin and Refine:

As you chat with the AI, you can “pin” its most useful responses to your notepad. These pinned notes become building blocks. You can then edit them, combine them, and use them as a springboard for your own original writing.

Key Features and What Sets It Apart

Source-Grounded Responses: The most important feature. Every response includes citations back to the original source documents, allowing you to verify accuracy instantly.

The Dynamic Outline: When you pin a few notes, you can ask NotebookLM to generate a structured outline from them, providing instant organization for your nascent ideas.

Guided Questions: Upon adding sources, NotebookLM often suggests helpful questions you can ask to dive deeper, helping you overcome the “blank slate” problem.

Simple, Focused Interface: The design is minimalist and purpose-built, reducing distractions and keeping the focus on your content.

Powerful Use Cases: Who Is NotebookLM For?

Students and Academics: Revolutionize literature reviews. Upload a dozen research papers and quickly compare methodologies, extract key findings, and identify gaps in the research.

Content Creators and Writers: Research and outline articles, scripts, or books at an unprecedented speed. Transcribe and summarize interviews, then generate first drafts based on the key quotes.

Professionals and Analysts: Quickly digest lengthy industry reports, quarterly earnings calls transcripts, and competitive intelligence documents to prepare briefs and presentations.

Curious Learners: Anyone learning about a new topic can upload a variety of sources and use the AI as a personal tutor, answering questions specifically from the provided materials.

Current Limitations and the Road Ahead

As an experimental product, NotebookLM has limitations. It currently only handles text-based sources (no images, videos, or spreadsheets yet). Its ability to synthesize across a massive number of sources, while impressive, can sometimes be less nuanced than a human expert. Furthermore, as with any AI tool, its output should be viewed as a first draft—a phenomenal starting point that requires human review, editing, and critical thinking.

Google is actively developing the product, and we can expect tighter integration with other Google Workspace tools and more advanced analytical features in the future.

Conclusion: Augmenting, Not Replacing, Human Intelligence

NotebookLM is not meant to replace your critical thinking. Instead, it’s designed to augment it. It offloads the tedious tasks of information foraging and initial synthesis, freeing up your mental bandwidth for what humans do best: higher-order analysis, making creative leaps, and crafting compelling narratives.

It represents a significant step towards a future where AI acts as a true collaborative partner, helping us navigate the ever-expanding sea of information and empowering us to create work that is deeper, more insightful, and more informed than ever before.

The best way to understand its potential is to try it. Pick a project you’ve been putting off, gather your sources, and start asking questions. You might just find that your most powerful thinking partner has been waiting for you all along.