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The big players in AI: Who owns what and how it works

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Time to read: 13 min

Blue, orange, and purple tech wave lines

Artificial intelligence is evolving at a breakneck pace, and a handful of powerful companies have emerged as leaders of the industry, shaping not just the tools we currently use, but the directions in which future tools are headed. Understanding who owns these technologies and what they actually deliver is key to cutting through the hype and making sense of a rapidly expanding ecosystem.


This is the second blog in our AI series, so if you have not had a chance to check out the first one, take a moment to dive in! 

Demystifying Artificial Intelligence
What we cover:
What we mean by “AI” today The biggest companies in AI right now OpenAI Anthropic Microsoft Google Meta What’s powering AI models, and who’s behind it? Large language models Cloud computing GPUs Data centers What these AI tools actually do well right now Where they fall short Comparing the major AI tools Similarities Key differences Core strengths & use cases Context window limits Real-time information access Integrations Is there an AI bubble?

What we mean by “AI” today

AI, which stands for artificial intelligence, is a broad term that can evoke more science fiction than reality. But the artificial intelligence that exists today is much simpler than its fantastical connotations. What most companies call “AI” is actually a collection of statistical models trained to recognize patterns in massive datasets and generate outputs based on those patterns.


At the center of today’s AI boom are systems known as large language models (LLMs) and related machine learning tools. These models don’t “think” or “understand” in the human sense, but they do predict based on probability. Given enough training data—text, images, code, or audio—they can learn to generate responses that mimic human language, create visuals, or automate tasks (Glidden, 2021). 

The biggest companies in AI right now

A small group of companies dominates the current AI landscape. While their tools may look similar on the surface, each takes a slightly different approach, prioritizing creativity, safety, productivity, or integration.


Here are some of the most powerful players in the AI space today (Weinberg, 2026).  

OpenAI

Product: ChatGPT, “the flexible, all-purpose AI”


Founded in 2015, OpenAI is the company behind ChatGPT, the tool that brought generative AI into the mainstream when it launched publicly on November 30, 2022 (Marr, 2023). While earlier models like GPT-1, GPT-2, and GPT-3 were primarily available to developers, ChatGPT brought a refined version—GPT-3.5—into a conversational format for everyday users (History.com, 2025). GPT stands for generative pre-trained transformer, referencing the machine learning principles that made it possible (Stryker, Bergmann).


OpenAI’s tools are powered by advanced large language models (LLMs), enabling tasks like writing, coding, and summarization. It can brainstorm ideas, explain various topics in plain language, and draft everything from emails to long-form content and presentations (Copeman, 2023). 

Anthropic

Product: Claude, “the safety-first, thoughtful analyst”


Founded in 2021 by former OpenAI employees, Anthropic positions itself as a company focused primarily on AI safety and research (Henshall, 2023). Its primary product, Claude, launched in 2023 as a conversational AI assistant designed to be, in the words of Anthropic’s programmers, “helpful, harmless, and honest” (Lewis-Kraus, 2026). 


In the AI space, Claude’s niche is long-form analysis, writing, and coding tasks, and it is often favored by professional users who need more structured, careful outputs. Because its design philosophy prioritizes reducing harmful or misleading responses, Claude has become the primary AI used in high-stakes or research-driven environments (Alexis, 2026). 

Microsoft

Product: Copilot, “the workplace productivity layer”


Microsoft has taken a broad approach to AI by embedding it across its ecosystem. Its AI assistant, Microsoft Copilot, evolved from earlier tool GitHub Copilot (launched in 2021), which was then incorporated into the pre-existing Bing Chat in 2023.


Copilot focuses on productivity, helping users draft documents, summarize emails, analyze data, perform work tasks, and write code directly within the Microsoft software they already use. Much of its capability is powered by OpenAI’s models, while Microsoft’s Azure cloud platform provides the infrastructure that supports both its own tools and many third-party AI systems (Pazur, 2025).  

Google

Product: Gemini, “the information and search powerhouse”


Google entered the generative AI space with the launch of Gemini in late 2023. Like other conversational chatbots, Gemini can generate text and answer questions, but its largest differentiator from the other AI tools is its deep integration with Google’s ecosystem.


Gemini’s goal is to enhance everyday tools like Gmail, Google Docs, and YouTube, and is now being built into Android devices, including Pixel phones. Google has also introduced voice-based interactions through Gemini Live, making a push toward more natural, voice-driven interactions (Casserly, Tomala, 2025).  

Meta

Product: Meta AI, “the social and consumer AI”


Meta has woven AI throughout its family of apps—including Facebook, Instagram, WhatsApp, and Messenger—under the umbrella of Meta AI. These tools are powered by Meta’s Llama (Large Language Model Meta AI) family of models (Pazur, 2025). 


Beyond chat and content generation, Meta is investing heavily in AI-driven augmented and virtual reality, including integrations with its Ray-Ban smart glasses for hands-free photo and video-taking, voice commands to Meta AI, real-time visual searches, live translations, and more (Caswell, 2025).  

What’s powering AI models, and who’s behind it?

Behind every AI tool is a combination of software and physical infrastructure working together to make it all possible. From the models themselves to the systems that run them, these components form the foundation of modern AI. 

Large language models

Large language models (LLMs) are the engines driving today’s AI tools. Through vast amounts of data, they learn patterns in human communication that allow them to predict, generate, and interpret speech and text. Built on advances in natural language processing (NLP), LLMs are behind everything from conversational chatbots to automated coding assistance (Stryker).


Notable companies:  

  • OpenAI

  • Anthropic

  • Google

  • Meta

  • Microsoft

  • Cohere (Data Science Dojo Staff, 2024) 

Cloud computing

Cloud computing provides the scalability that AI systems require. Instead of relying on local machines, companies can access vast amounts of computing power over the internet. This makes it possible to train and deploy AI models at a scale that would otherwise be prohibitively expensive, while also allowing businesses to integrate AI into their own products and workflows (Erickson, 2024).


Notable companies and products:  

  • Amazon Web Services 

  • Microsoft Azure 

  • Google Cloud 

  • IBM

  • Oracle (Baggiony-Taylor, 2025) 

GPUs

Graphics processing units (GPUs) play a critical role in AI development. Originally designed to render images and video faster than a CPU alone, GPUs are highly efficient at performing the parallel computations required for training machine learning models. GPU companies have become central to the AI ecosystem because their hardware powers much of the world’s model training (Flinders, et al.). 


Notable companies:  

  • Nvidia

  • AMD (Advanced Micro Devices)

  • Intel

  • Samsung (Rizvi, 2025) 

Data centers

All this technology runs inside data centers—large, specialized facilities that house servers, storage systems, and networking equipment. These centers are carefully designed with cooling systems, power redundancy, and security measures to ensure consistent performance. They provide the physical backbone of AI, enabling models to operate reliably at a global scale (Susnjara, Smalley).


Notable companies:

  • Amazon 

  • Meta 

  • Microsoft 

  • Equinix 

  • QTX 

  • Digital Realty (Gergs, 2026) 

Infographic of what

Image: Infographic of what's powering AI models

What these AI tools actually do well right now

The AI tools that we have today would boggle the minds of people even just 10 years ago. But like all tools, they are great for some tasks, and not so great for others. They support human effort, but they don’t replace it.


Here’s where they excel:  

    • Content generation: Writing emails, summarizing documents, and generating reports

    • Language tasks: Translating, rewriting for different formats, making tone adjustments for different audiences

    • Data assistance: Performing basic analysis and navigating spreadsheets

    • Productivity augmentation: Taking meeting notes, transcribing audio or video, surfacing information through search 

Where they fall short

Despite their strengths, these tools still have notable limitations.

  • Hallucinations: AI may generate responses that sound confident but are factually incorrect.

  • Limited reasoning: AI is unreliable for tasks that require deeper logic, nuanced judgment, or high factual precision.

  • Dependence on prompt quality: AI depends heavily on how a prompt is written, meaning unclear or vague inputs can lead to weak results.

  • Lack of true understanding or memory: AI lacks true understanding (they don’t “know” information in a human sense) and their memory is limited or session-based rather than continuous.

  • Inconsistent performance: AI’s performance can vary significantly from one task to the next, making human oversight an essential part of using AI. 

Comparing the major AI tools

Similarities

At a foundational level, these tools are far more alike than they are different. Despite branding and feature variations, they’re all built on the same core idea: predicting and generating language based on patterns learned from massive datasets. 


Across the board, these tools: 

  • Are powered by large language models (LLMs) trained on vast amounts of text, code, and media

  • Use machine learning to interpret prompts and generate responses

  • Perform similar core tasks, including writing, summarization, coding, and question-answering

  • Are increasingly multimodal, capable of handling text, images, audio, and more 

Key differences

If the similarities explain why these tools feel interchangeable, the differences explain why users still develop strong preferences. Those distinctions tend to fall into a few key categories:

Core strengths & use cases

Each tool is optimized for slightly different types of work:

  • ChatGPT: The most versatile “generalist.” Strong across writing, coding, reasoning, and creative tasks.

  • Claude: Excels in long-form writing, document analysis, and nuanced reasoning, with a strong safety focus.

  • Microsoft Copilot: Best for productivity—deeply embedded in tools like Word, Excel, and Teams.

  • Gemini: Strong at research and information synthesis, especially with real-time data via Google Search.

  • Meta AI: Focused on social platforms and consumer experiences (messaging, augmented and virtual reality, wearable tech). 

Strengths of AI models

Image: Strengths of AI models

Context window limits

When you interact with an AI chatbot, everything you type and everything it responds with exists inside what’s known as a “context window.” This is the amount of information the model can process at once while maintaining coherence and continuity.


Context windows are measured in tokens (roughly 3–4 characters per token), and larger windows allow models to handle longer documents, more complex instructions, and extended conversations without losing track.


Recent models have expanded these limits dramatically. Some, like Google’s Gemini 3 Pro, can process inputs of up to 10 million tokens—approaching book-length documents—while others, including models from OpenAI and Anthropic, offer increasingly large, less computationally taxing, and more inexpensive context windows (Cravero, 2026).  

Real-time information access

AI models can only be as accurate and up to date as the data they can access. Earlier versions of these tools were limited to static training data, which quickly became outdated and led to gaps in accuracy (Sinha).


That’s changed. Many modern AI systems now include browsing capabilities or are directly connected to live data sources:  

  • ChatGPT, Copilot, and Gemini can all access real-time web information (with some usage limits depending on the version)

  • Copilot is tightly integrated with Bing search and can, if given permission, search a user’s or company’s data to improve its output

  • Gemini benefits from Google’s search ecosystem

  • Claude tends to take a more cautious approach, with more limited or controlled internet access (Vysotsky, 2025) 

This shift toward live data has made AI tools more useful for research and current events, but it’s also introduced new challenges around reliability, privacy, and source quality.

Integrations

Some companies have deeply embedded AI into their existing ecosystems, like Microsoft Copilot, which is integrated across Microsoft 365, Windows, and other enterprise tools. Gemini is built in Google Workspace, Android devices, and the Google search engine, while Meta AI lives inside Facebook, Instagram, WhatsApp, and Messenger. 


Other products remain more standalone with optional integrations. ChatGPT functions primarily as a solitary interface, but plugins can expand its capabilities. Claude follows a similar model but is increasingly finding its way into enterprise platforms and developer workflows.


Here, we begin to see two different approaches: AI as a destination (something you open and use directly), and AI as an embedded layer that enhances tools you already use.  

Is there an AI bubble?

When people talk about an AI bubble, they’re referring to the current economic phenomenon of asset values rising far beyond the intrinsic value of the technology itself. The challenge is that economic bubbles are notoriously difficult to spot while the market is in one—they’re usually only confirmed in hindsight, after the bubble has burst (Kenton, 2025). So, the short answer to the question of an AI bubble? Only time will tell.


When we look back at the relatively brief history of artificial intelligence, a pattern appears, one defined by cycles of intense excitement followed by periods of disillusionment. Breakthroughs often spark waves of hype and investment, but when the technology falls short of expectations, funding and public interest tend to pull back in what’s referred to as an “AI winter.”


Today’s surge in AI shows some of those same warning signs. The valuations (estimated worth) of AI companies are soaring, while those same companies pour massive amounts of resources into infrastructure, mainly more computing power. At the same time, many of these companies are still figuring out how to turn their investments into any kind of sustained profit (Yip, 2026).


Whether this moment represents another bubble or a long-term shift remains an open question, but the pattern of rapid acceleration paired with real-world limitations is not new. 

For all the complexity surrounding artificial intelligence, the current landscape is more straightforward than it first appears. A relatively small group of companies powers the vast majority of AI tools, and beneath the surface, those tools rely on the same fundamental technology: large language models.


When you know what these systems are, who makes them, and what they’re doing behind the scenes, it becomes much easier to see through the hype. AI is a powerful, evolving tool—one that can accelerate workflows, expand creative possibilities, and surface information—but it still requires human judgment, oversight, and context.


AI technology will continue to develop, but one thing won’t change: The better you understand how AI works, the better you’ll be able to use it.  

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