AI: Understanding Large Language Models
tl;dr
- Large Language Models (LLMs): The "behind-the-scenes" AI engines. These models power ChatGPT, Claude, and most business AI tools transforming operations today
- Market Leaders: Seven major companies produce LLMs: OpenAI (GPT), Anthropic (Claude), Google (Gemini), Meta (Llama), Microsoft (MAI), XAI (Grok), and DeepSeek (DeepSeek)
- Business Applications: LLMs use sophisticated pattern recognition that handles context, reasoning, and business problems across every department and industry
- Competitive Advantage: Understanding that LLMs are the foundation helps organizations make strategic AI decisions that serve actual business needs rather than following trends
- Enterprise Adoption: Business applications span customer service automation to strategic planning, with measurable productivity improvements across organizations
The transformation happening in business operations reveals itself in compelling numbers. In August 2025, ChatGPT reached 700 million weekly users, up from 500 million in March, a 40% increase in just five months1. This growth is more than user adoption; it supports that AI has become critical business infrastructure for organizations worldwide.
You and your organization don't need to understand neural network architecture (the math and logic behind the pattern recognition that makes AI what it is today) to make strategic AI decisions that provide competitive advantage. But, understanding Large Language Models, the foundational technology powering nearly every AI tool making headlines today, will help your business navigate the rapidly evolving landscape and make choices that move your organization forward.
Large Language Models are the sophisticated software that's running "behind-the-scenes" of all AI applications. Just as your organization benefits from cloud computing without understanding server architecture, you can leverage LLM-powered applications without technical expertise -- and make informed strategic decisions about which AI capabilities will address business challenges or accelerate business objectives.
What Large Language Models Actually Are
Large Language Models are AI systems trained on vast amounts of text to understand and generate human-like responses. (Training is like teaching someone to write by first having them read everything ever written, then showing them good examples of how to respond to questions, and finally giving feedback on their answers to improve.) But that definition doesn't capture what matters for your strategic planning: these are powerful engines that can incorporate context, follow complex instructions, and solve problems across virtually any business domain.
Think of LLMs as Your Most Capable New Hire
Imagine hiring someone who has read every business book, legal document, marketing case study, and technical manual ever written, then trained them to have thoughtful conversations about any topic relevant to your organization. That's essentially what an LLM brings to your business operations—except this "employee" never gets tired, works around the clock, and can handle thousands of conversations simultaneously across your entire organization.
And remember that you don't have to be a company to benefit from hiring an LLM. These tools will also support your personal life in probably anyway you could imagine.
When you ask ChatGPT to help draft a proposal, analyze customer feedback, or explain complex regulations, you're tapping into patterns learned from millions of similar business documents and conversations. The model recognizes patterns in your request and generates responses based on training that spans industries, functions, and business contexts.
Beyond Simple Automation
Traditional business software and automations follow logical rules: if this, then that.
LLMs work differently. They handle requests that don't fit the if / then conditions.
1) Invoice - Manager Review
If invoice is over $1,000, then require manager review.
-- but what happens if the invoice shows "$999 plus applicable taxes"?
Traditional software would see $999, and because it's less than $1,000, it wouldn't require manager review. However, an LLM would interpret "plus applicable taxes," and understand that the invoice likely exceeds $1,000 and therefore would require manager review.
2) Email Routing
If email contains "cancel," then route to the retention team.
-- but what happens if the customer writes "I'm not ready to cancel yet, but I'm really frustrated about the interface"?
Traditional software would see "cancel" and incorrectly route this to the retention team. However, an LLM would understand the full context and recognize that the customer wants help with the interface and route to tech support.
The Major Players and Their Business Strategies
Seven companies currently dominate the LLM landscape, each with distinct approaches that affect how you and your organization might use their technology.
OpenAI: The Pioneer Building an Ecosystem
OpenAI created ChatGPT and remains the market leader with the most recognizable brand. With 92% of Fortune 500 companies using ChatGPT in some capacity2, their strategy focuses on building a comprehensive ecosystem through multiple access points.
ChatGPT Plus and Enterprise offerings provide direct access to LLM functionality for your teams. Many tools your organization might use (like Microsoft Copilot for productivity tasks or Notion's AI features) often use OpenAI's models under the hood. GPT models power thousands of custom business applications. OpenAI's revenue reached $12 billion in annual recurring revenue as of August 20253, demonstrating significant business momentum that suggests platform stability for enterprise planning.
Anthropic: The Business-First Alternative
Anthropic built Claude specifically for professional use, emphasizing safety, accuracy, and business-appropriate responses. Claude now holds 32% of the enterprise AI market as of mid-2025, surpassing OpenAI's 25% share in business applications4.
Organizations choosing Anthropic often value Claude's more measured responses and lower tendency to hallucinate. Claude excels at document analysis, strategic planning discussions, and situations where accuracy matters more than creativity for your business outcomes. Anthropic projects $3 billion in revenue for 20255, representing significant growth from previous years.
Google: Integration Across Everything
Google's Gemini strategy differs fundamentally from standalone chatbots. Instead of building separate AI products, Google integrates Gemini across their entire business suite—Gmail, Docs, Drive, Search, and beyond. Gemini now provides business users with more than 2 billion AI assists every month6.
For organizations already embedded in Google's ecosystem, Gemini offers the most seamless experience for your teams. Rather than switching between applications, AI assistance appears contextually where your employees are already working. This integration strategy makes Google particularly attractive for organizations prioritizing workflow efficiency over cutting-edge capabilities. Gemini has reached 450 million monthly active users by July 20257.
Meta: The Open-Source* Powerhouse
Meta takes a fundamentally different approach with its Llama models, emphasizing open-source availability and integration across Meta's ecosystem of platforms. Llama models have been downloaded over 650 million times, with more than 85,000 derivatives created by the open-source community9.
Meta AI, powered by Llama models, has reached 1 billion monthly active users across Facebook, Instagram, WhatsApp, and Messenger as of May 202510. Unlike other providers, Meta integrates AI directly into social media workflows where billions of users already spend their time, making it highly accessible for organizations already using Meta's business tools.
The April 2025 release of Llama 4 introduced multimodal capabilities with Scout and Maverick variants, offering organizations the flexibility to run models on-premises or customize them completely for specific business needs. This open-source approach appeals particularly to companies with strict data privacy requirements or those wanting complete control over their AI infrastructure.
Microsoft: The Enterprise Integration Specialist
Microsoft has long relied on OpenAI's models to power its AI services, including Copilot and other enterprise tools through its $13 billion partnership. However, the company began developing its own alternatives with the Phi series of small language models, releasing Phi-4 in January 2025. Phi-4 is a 14-billion parameter collection of experts (CoE) model that outperforms much larger competitors in reasoning and mathematical tasks11.
Moreover, the strategic shift toward independence became more pronounced with Microsoft's recent unveiling of MAI-Voice-1 and MAI-1-preview—the company's first fully in-house AI models designed to reduce reliance on OpenAI. The company is also developing MAI-1, a larger language model series designed to compete directly with OpenAI and Anthropic's offerings for general-purpose business applications, enabling Microsoft to control its AI destiny while serving enterprise needs12. This dual approach allows Microsoft to serve both resource-constrained environments with efficient small models and high-demand enterprise applications with powerful larger models.
XAI: The Challenger with Real-Time Information
Elon Musk's XAI launched Grok with a focus on real-time information access and less restrictive responses. While newer to the market, Grok's integration with X (formerly Twitter) provides current events awareness that other models often lack.
Grok appeals to organizations needing AI that can discuss recent developments and current events with minimal content restrictions. However, its association with Musk's public persona and platform management style has influenced organizational adoption decisions. The smaller user base also means fewer third-party integrations compared to more established players.
DeepSeek: The Open Source* Alternative
DeepSeek represents the growing open-source movement in AI, offering models that organizations can run independently rather than relying on cloud services. This approach appeals to companies with strict data privacy requirements or those wanting complete control over their AI infrastructure.
While requiring more technical expertise to implement, DeepSeek and similar open models offer long-term cost advantages and complete customization possibilities that cloud-based alternatives cannot match.
Also of note, DeepSeek V3 cost approximately $5.5 million to train compared to GPT-4's estimated $100 million cost8, demonstrating the potential for cost-efficient AI development.
Understanding Platform Strategies for Business Decisions
These different approaches create distinct advantages for different organizational needs. OpenAI and Anthropic focus on general-purpose business applications with comprehensive cloud-based solutions. Google emphasizes seamless integration within existing workflows for organizations already using Google Workspace. Meta provides open-source flexibility with massive social media reach. Microsoft prioritizes enterprise software integration through familiar business tools.
Understanding these strategic differences can help you and your organization choose solutions that align with your existing infrastructure, data privacy requirements, and business objectives rather than following market trends without strategic purpose.
Why This Matters for Your Business Decisions
Understanding LLMs helps demystify the AI tools appearing across every business function in your industry. Most specialized AI applications are built on top of these foundational models, adding specific training, interfaces, and even traditional software logic. Understanding the LLM landscape helps your organization make better strategic decisions about AI adoption.
The Foundation-to-Application Pipeline
If a sales team is using an AI-powered CRM that writes follow-up emails, that CRM likely uses OpenAI's or Anthropic's models as its foundation. The CRM company adds sales-specific training data and creates an interface designed for its sales workflows.
This foundation-to-application approach explains why some AI tools hit the mark and others don't. The underlying LLM provides language and broad reasoning capabilities, however, it's the application's software layer that determines specific functionalities and constraints which affect the user experience.
Evaluating Tool Claims and Capabilities
When vendors demonstrate AI-powered features to your team, you can better assess whether their capabilities align with current LLM technology. Claims about sophisticated pattern recognition, understanding context, and helping your employees work through complex problems are realistic. Claims about perfect accuracy, emotional understanding, or replacing human judgment should raise questions about vendor credibility and implementation success.
Making Build vs. Buy Decisions
Understanding that most specialized AI tools are applications built on foundational LLMs helps clarify the build vs. buy decision. If traditional software tools handle your business processes effectively today and into the foreseeable future, purchasing ready-made solutions is likely the most cost-effective approach. However, if your organization has unique requirements that off-the-shelf solutions can't address like analyzing complex financial documents with industry-specific terminology, generating personalized patient care summaries from medical records, or creating technical documentation that incorporates your company's proprietary methodologies, building a custom solution on an LLM foundation might provide significant competitive advantage and justify the additional expense.
Planning for Future Capabilities
The rapid improvement in LLM capabilities means today's impressive AI tools could be obsolete within months, though it has not shown to be nearly that severe. However, planning your organization's AI adoption with this trajectory in mind helps prevent premature over-investment. Building small, targeted implementations, and leveraging your understanding of the underlying LLMs and their specific business strategies positions you and your organization to plan more strategically and adapt more quickly than others as capabilities evolve.
Final Thoughts
Large Language Models power virtually every AI tool making business headlines today. The seven companies producing them: OpenAI, Anthropic, Google, Meta, Microsoft, XAI, and DeepSeek, each take different approaches that affect how their technology works in your organization.
When evaluating AI tools, remember that most are applications built on these foundational models. The vendor adds specific training and interfaces, but the core capabilities come from the underlying LLM. This knowledge helps you assess vendor claims realistically and choose solutions that match your actual needs.
Start with small implementations. Test tools in specific workflows before committing to enterprise-wide deployments. As LLM capabilities improve rapidly, organizations that understand this foundation can adapt quickly while others will struggle with tools that don't deliver promised results.
* - Neither Llama nor DeepSeek disclose their training data or scripts. This absence of full transparency complicates their classification as fully open-source.
References
- OpenAI's ChatGPT to hit 700 million weekly users, up 4x from last year – CNBC
- Number Of ChatGPT Users In 2025: Stats, Usage & Impact – Backlinko
- OpenAI Hits $12 Billion in Annualized Revenue, Breaks 700 Million ChatGPT Weekly Active Users – The Information
- Enterprises prefer Anthropic's AI models over anyone else's, including OpenAI's – TechCrunch
- Now It's Claude's World: How Anthropic Overtook OpenAI in the Enterprise AI Race – MarkTechPost
- Google's AI Overviews have 2B monthly users, AI Mode 100M in the US and India – TechCrunch
- Google's Gemini AI app has 400M monthly active users – TechCrunch
- DeepSeek Revenue and Usage Statistics (2025) – Business of Apps
- The future of AI: Built with Llama – Meta AI
- Mark Zuckerberg says Meta AI has 1 billion monthly active users – CNBC
- Introducing Phi-4: Microsoft's Newest Small Language Model Specializing in Complex Reasoning – Microsoft Community Hub
- Microsoft reportedly develops LLM series that can rival OpenAI, Anthropic models – SiliconANGLE
