How OpenAI Is Working with Microsoft to Bring New Technology to the World

Martin Low
6 days ago
22 min read

OpenAI has become a leading force in artificial intelligence (AI) by developing powerful AI models and collaborating with major industry partners. In particular, OpenAI’s long-term partnership with Microsoft has accelerated the creation and deployment of new AI technologies. This post examines OpenAI’s various AI products and models, explains the art of prompt engineering (how to craft effective requests for AI), traces Microsoft’s investment in OpenAI since 2019, analyzes OpenAI’s technical infrastructure and business model, and compares OpenAI to its key competitors such as Google DeepMind, Anthropic, and Meta AI. Throughout, the focus keyword “OpenAI” is emphasized in headings and text to highlight the topic.

OpenAI’s AI Technologies

OpenAI has developed a diverse portfolio of AI products and models that span text, code, image, audio, and video. These tools are designed for different users: developers building software, enterprises adopting AI at scale, educators exploring AI in learning, and creative professionals producing art and media. Below is a primer on OpenAI’s major AI technologies, organized by the types of users who typically use them.

For Developers

GPT Language Models and API. OpenAI’s core product line is the GPT series of large language models (LLMs). These generative pretrained transformer models can understand and generate text, and they are accessible via the OpenAI API. Currently available models include GPT-4o (the latest multi-modal “omni” model), GPT-4.1 (a high-capacity text model), and GPT-3.5 Turbo. These models have very large “context windows” (the amount of text they can consider) – up to 128K tokens for GPT-4o and up to 1,000,000 tokens for the new GPT-4.1 family. Developers use the API to build applications like chatbots, summarizers, translators, and more. The GPT-4o model can process text, images, and audio inputs in a single request, making it suitable for multimodal applications. OpenAI also offers the GPT-3.5 series, which is optimized for chat and cheaper inference. Together, these models power OpenAI’s famous ChatGPT chatbot as well as countless custom apps built by developers.
ChatGPT Platform. ChatGPT is a user-friendly chat interface built on OpenAI’s GPT technology. Developers can integrate ChatGPT-like experiences into their own apps via the API, and OpenAI provides a Playground for experimenting with prompts and models. In late 2023, OpenAI also introduced “GPTs,” a feature that lets anyone create a custom ChatGPT instance by specifying instructions and knowledge bases. As OpenAI explains, “GPTs are custom versions of ChatGPT that users can tailor for specific tasks or topics by combining instructions, knowledge, and capabilities”. For example, a developer could build a specialized GPT for coding help, math tutoring, or travel advice. This lowers the barrier for building complex AI assistants.
Code Generation (Codex). OpenAI offers models specifically trained to generate computer code. The Codex model (based on GPT-3) can translate natural language descriptions into code, supporting many programming languages. It powers tools like GitHub Copilot (co-developed by Microsoft) and is available via the OpenAI API. Developers use Codex for programming assistance, code completion, and even building AI-driven coding tutors. Because it understands both text and code, Codex is often treated as a GPT variant specialized for software development.
Embeddings and Semantic Search. OpenAI provides embedding models that convert text or images into high-dimensional vectors. These embeddings let developers perform semantic search, clustering, and recommendation. For example, a developer might use text-embedding-ada-002 to turn user queries and a document corpus into vectors, then find the most relevant results. This enables building custom search engines or matching systems powered by OpenAI.
Whisper (Speech Recognition). Whisper is OpenAI’s open-source automatic speech recognition (ASR) model. It transcribes spoken audio into text, handling many languages and accents. Whisper is available via the API and as open-source code. In early 2025, OpenAI released even more advanced audio models in the API: new speech-to-text (“transcribe”) and text-to-speech models. These allow developers to build voice-enabled applications, such as transcription services or AI voices that speak user instructions. The updated models outperform previous solutions in accuracy, setting a new state-of-the-art for ASR. (The image below, from OpenAI’s blog, illustrates the new audio interface.)

Caption: OpenAI’s new audio models enable advanced voice applications. The API now supports state-of-the-art speech-to-text and text-to-speech (audio) models. (Screenshot of the OpenAI audio model interface with a waveform.)

DALL·E (Image Generation). DALL·E is OpenAI’s series of image-generation models. DALL·E 2, released in 2022, and DALL·E 3, released in 2023, generate photorealistic images from natural language prompts. Artists and developers use DALL·E to create artwork, illustrations, product mockups, and more. The DALL·E models can also perform “inpainting” (filling in or editing parts of an image). Access is via the OpenAI API or through integrated tools like the DALL·E website and the ChatGPT interface. By describing a scene, developers can get a corresponding image, enabling creative applications and design automation.
CLIP (Vision Embeddings). CLIP is a model that connects text and images by embedding them into the same vector space. It was developed by OpenAI (and Meta) and is used for tasks like image classification or retrieving images by text. While OpenAI’s current API focuses more on generation, developers can still use CLIP (open source) for vision+language tasks.
Sora (Video Generation). In 2023, OpenAI introduced Sora, a generative video model currently available to ChatGPT Plus and Pro users. Sora takes text, images, or video clips as input and produces new video content. It includes features like “Remix” (alter the mood or style of a video), “Loop” (make a video seamless), and “Blend” (combine multiple videos). Sora’s tagline is “Bring your imagination to life with text, image, or video,” highlighting its creative potential. Developers and creators can use Sora (via the ChatGPT plugin) to generate storytelling videos, marketing clips, or artistic animations. It represents OpenAI’s foray into multimodal AI beyond text and static images.
Customization and Safety Tools. OpenAI also provides tools to tailor and moderate AI. For example, there is an API for fine-tuning models on custom data (allowing specialized versions of GPT). OpenAI’s Moderation API helps filter unsafe or disallowed content from outputs. Additionally, the ChatGPT platform allows adding system messages to guide AI tone. These tools help developers ensure the AI aligns with their needs and safety requirements.

For Enterprises

Azure OpenAI Service. Microsoft’s Azure cloud offers the OpenAI Service, which gives enterprise customers API access to OpenAI’s models (the same GPT, DALL·E, and other models) through Azure. This includes enterprise-grade features like compliance, security, and large-scale deployment. Enterprises can build production systems on Azure that call OpenAI models behind the scenes. Microsoft has stated that Azure will be OpenAI’s “exclusive cloud provider,” powering all OpenAI workloads. In practice, this means OpenAI’s models run on Azure infrastructure (GPUs, supercomputers) while enterprises use Azure’s platform to integrate those models into their workflows.
ChatGPT Enterprise. OpenAI offers ChatGPT Enterprise, a paid plan for businesses. This includes unlimited access to GPT-4 through chat interfaces, priority availability, usage analytics, and advanced security. Features like single sign-on (SSO), data encryption, and admin controls make it suitable for companies. By subscribing, organizations can give employees an AI assistant for tasks like drafting emails, generating reports, or analyzing data. According to OpenAI, about 79% of ChatGPT Enterprise customers access it via the Azure-OpenAI partnership, indicating the close integration of OpenAI with Microsoft’s enterprise offerings.
ChatGPT Business/Teams. For smaller teams, OpenAI has intermediate plans (often called ChatGPT Teams or Business) that offer group management features, priority support, and similar capabilities as Enterprise.
Microsoft Product Integrations. Through the partnership, OpenAI models have been embedded directly into Microsoft’s own products. For example, Microsoft 365 Copilot (in Word, Excel, Outlook, Teams, etc.) uses OpenAI’s GPT models to help with writing, data analysis, and communications. GitHub Copilot (co-developed with OpenAI) uses the Codex model to assist programmers. Bing Chat uses a GPT-based chatbot for search. These integrations bring OpenAI technology to millions of Microsoft enterprise and consumer users. In effect, a portion of OpenAI’s innovation is commercialized directly through Microsoft’s products.
Dedicated Support and SLAs. Enterprise customers can purchase premium support, service-level agreements, and consulting from Microsoft and OpenAI. This ensures businesses have assistance in scaling AI securely and effectively. For example, Microsoft has built multiple AI supercomputing clusters specifically for OpenAI’s research and for Azure clients. Enterprises benefit from this advanced infrastructure.

For Educators and Students

Academic Access and Discounts. OpenAI has initiatives for education. For example, college students in the U.S. and Canada can currently claim free access to ChatGPT Plus (GPT-4) for two months. OpenAI also announced “ChatGPT for Education,” offering verified academic accounts and educational features to K–12 and higher-ed institutions. These programs let students and teachers use ChatGPT and other models for learning, tutoring, and research. The idea is to enable experimentation and learning while the students are enrolled.
Teaching Tools and Research. Some educators integrate ChatGPT into their classrooms to demonstrate AI. OpenAI and Microsoft have provided grants and resources to educational programs. While concerns (like academic integrity) exist, many schools use OpenAI tools as part of curricula on coding, writing, and even art. OpenAI’s documentation includes guides for teaching with AI. In short, OpenAI’s tools (especially ChatGPT) are available for educational use, often at reduced cost or free, to promote understanding of AI in learning.

For Creative Professionals

Image and Art Generation. The DALL·E models are especially popular with artists, designers, and marketers. By writing creative prompts, professionals can generate concept art, illustrations, product designs, or storyboards in seconds. For example, an illustrator could describe a scene (“a retro-futuristic cityscape at sunset”) and get a high-quality image. DALL·E can greatly speed up creative workflows or provide inspiration. The models are also used in video game asset creation, advertising, and fashion design (to generate patterns or outfits).
Video Production. OpenAI’s Sora enables video creativity. Video editors can generate or edit video sequences with natural language: changing styles, adding animations, or looping scenes. Marketing teams, filmmakers, and social media creators can use Sora to prototype videos quickly. For instance, one could input a short video clip and say “make this sunrise scene look like a watercolor painting,” and Sora would apply that artistic effect. This lowers the barrier for creating animated content and special effects.
Audio and Music. While not yet a full product, OpenAI’s advancements in audio generation hint at creative uses. The new text-to-speech models (part of the audio release) allow developers to create custom AI voices. Creators could use this to narrate stories, produce podcasts, or even generate character voices for games. For example, a podcaster could type out a script and have it spoken in a lifelike voice that they can control (speed, tone, accents).
Writing and Storytelling. ChatGPT itself is used by writers, journalists, and marketers. It can draft articles, brainstorm story ideas, or compose poems. A novelist might use it to explore plot possibilities; a screenwriter to get dialogue suggestions. Many creatives use ChatGPT to overcome writer’s block, by asking it to outline a scene or define characters. The AI can also translate text or improve style. In this sense, text generation models are creative tools for content creation and editing.
Music Generation (future). OpenAI has research in music (see OpenAI’s MuseNet), but as of 2025 it hasn’t released a consumer music product yet. However, it points to future creative tools: an AI that composes songs from simple prompts.

In summary, OpenAI’s product suite includes:

Type of Model/Tool	Products/Examples	Usefulness
Text (Language Models)	GPT-4o, GPT-4.1, GPT-3.5 (via API)	Text generation, chat, translation, summarization for devs
Code (Codex)	Codex (GitHub Copilot)	Code completion and generation in IDEs
Image (Generative)	DALL·E 2, DALL·E 3	Create artwork, design images from text
Vision (Embeddings)	CLIP (multimodal embeddings)	Image search, classification
Speech (ASR)	Whisper, GPT-4o-transcribe models	Speech-to-text transcription
Speech (TTS)	New GPT-4o text-to-speech models	Generate speech/audio from text
Chatbot Interface	ChatGPT (free/Plus), GPTs (custom bots)	Conversational AI interface for general users
Video (Generative)	Sora (video generation in ChatGPT)	Generate and edit videos from text or clips
Audio (Generative)	(Future music models, no public release yet)	Research in music and audio synthesis
Enterprise Services	Azure OpenAI Service, ChatGPT Enterprise	Scalable API access with enterprise support
Developer Tools	Playground, SDKs, Moderation API	Experimentation and safety tools for developers

Each of these tools serves a different user group. For example, an indie game developer might use the GPT API (developer category) to generate game dialogue, call the DALL·E API (creative category) to make concept art, and build everything on Azure (enterprise category) when scaling to thousands of users. An educator might have students explore ChatGPT (educator category) to learn about AI, while a marketing team uses ChatGPT and DALL·E (creative category) to draft slogans and images. In all these cases, the underlying OpenAI models are accessible in ways suited to the user.

Prompt Engineering with OpenAI Models

To get the best results from OpenAI’s models, one must use effective prompt engineering. A prompt is simply the input text (in natural language) that you give to a model, asking it to perform a task. Prompt engineering is the practice of carefully crafting these inputs so the AI produces useful and high-quality outputs. Because generative AI is very flexible, how you ask the question or give instructions can greatly affect the answer. Below, we explain prompt engineering in accessible terms and share strategies for writing effective prompts, with examples.

What is Prompt Engineering? Prompt engineering involves guiding a generative AI system to produce the desired result by choosing the right words and structure in your prompt. In simple terms, you give the AI clear instructions and context so it understands what you want. For example, instead of saying “Tell me about cats,” a more effective prompt might be: “Explain in three sentences what makes domestic cats different from wild cats.” Notice how the second prompt is specific about the answer format (three sentences) and topic focus (domestic vs. wild cats). By refining your prompt with details, the AI can generate a more precise answer.

Provide context and instructions. Always assume the AI only knows the text you give. If background information is needed, include it. For instance, “You are a friendly travel agent. A customer asks: ‘Find a train itinerary from New York to Boston.’ Please respond with three travel options, including departure times and ticket prices.” In this prompt, we tell the AI its role (“travel agent”) and exactly what information to include (three options, times, prices). Such explicit instructions help the model generate a relevant answer.
Specify the format. If you need the answer in a certain format (bullet list, table, code snippet, etc.), mention that. For example: “List five key features of the OpenAI GPT-4 model as bullet points.” By specifying “bullet points,” you shape how the response is written. Similarly, you might say “Answer with one paragraph” or “Use numbering for each step.”
Use examples (few-shot prompting). Sometimes it helps to show the model a couple of examples in your prompt (this is called few-shot prompting). For example, if you want the AI to write a joke, you might start with:“Example joke: What do you call a bear with no teeth? A gummy bear.Now you write a joke about computers.”Here we gave an example to guide the model’s style. Few-shot prompting can teach the model the format of the answer you want by example.
Refine iteratively. Prompt engineering often requires trial and error. You may run a prompt, review the result, then tweak the prompt’s wording and try again. According to OpenAI, “the AI language models are very powerful…and don’t require much to start creating content…not every type of input generates helpful output”. By refining the prompt – adding context, rephrasing the question, or instructing the model to “think step by step” – you guide the AI toward a better answer.
Manage model behavior. You can instruct the model to adopt a persona or tone. For example: “You are an expert environmental scientist.” Or give system-like instructions: “Answer in a formal tone.” This can help steer the style and content. OpenAI’s documentation notes that prompt engineers often experiment with different inputs to guide the AI “so an application’s generative AI works as expected”.

Example Prompt: Suppose we need a summary of this blog post. A good prompt would be: “Summarize the key points of this text in 3-4 clear sentences.” A poor prompt would be: “Summarize.” The first prompt clearly sets the context (“this text”), the task (summary), and the format (3-4 sentences). The second prompt is too vague.

Advanced Tips: Other techniques include setting the model’s “temperature” (a parameter controlling randomness) or using the “most relevant tokens” technique. But even without those, just using plain language carefully often yields good results.

OpenAI defines prompt engineering as guiding the AI with detailed instructions and trial-and-error to get high-quality output. In practice, this means being clear, giving context, and specifying how you want the answer. Effective prompts lead to more meaningful and relevant answers, as prompt engineers “continuously refine prompts until [they] get the desired outcomes”.

Microsoft’s Investment in OpenAI (2019–Present)

Since 2019, Microsoft has made multi-billion-dollar investments and strategic commitments to OpenAI. This partnership has shaped both companies’ trajectories and driven major advances in AI. Here is a timeline of Microsoft’s involvement and its strategic goals:

July 2019 – Initial $1 B Investment: Microsoft announced a multiyear, multi-partnership with OpenAI, including a $1 billion investment. The goal was to build an Azure-based supercomputing platform for AI research. Microsoft became OpenAI’s “exclusive cloud provider”, meaning OpenAI would run its services on Azure and Microsoft would help commercialize the resulting AI technologies. As Microsoft said, the companies would “jointly build new Azure AI supercomputing technologies” to advance AI and work toward artificial general intelligence (AGI). This initial deal set the stage for OpenAI’s growth: OpenAI’s breakthrough models (like GPT-2 and GPT-3) would be trained on Azure’s infrastructure, and Microsoft would integrate OpenAI tech into products.
2021 – Follow-on Investments: In 2021, Microsoft reportedly invested an additional $2 billion in OpenAI (details were not fully public). This further solidified Azure as the backbone of OpenAI’s compute. At this time, OpenAI’s models started powering Microsoft offerings (for example, GitHub Copilot launched in 2021 using OpenAI Codex, and DALL·E image generation was integrated into Azure). The partnership also included commitments to build new data centers and hire experts for AI development.
January 2023 – Major Multibillion Extension: On January 23, 2023, Microsoft announced the “third phase of our long-term partnership with OpenAI,” a multiyear, multibillion-dollar investment. This extension reiterated and expanded the 2019 goals. The announcement highlighted three pillars:
1. Supercomputing at scale: Microsoft would build specialized supercomputer systems (with thousands of GPUs) to accelerate OpenAI’s research and also provide customers with top-tier AI infrastructure on Azure.
2. New AI-powered experiences: Microsoft planned to deploy OpenAI’s models across its consumer and enterprise products (for example, Microsoft 365 Copilot, Azure AI services) and introduce new digital experiences. It specifically mentioned Azure OpenAI Service as the channel for developers to access OpenAI models with enterprise-grade support.
3. Exclusive cloud provider: Microsoft reiterated that Azure would “power all OpenAI workloads across research, products and API services”. This means OpenAI would continue relying on Azure for training and serving its models (ensuring deep integration between the companies’ technologies).
2023 – Subsequent Funding and Deals: Reports indicate that Microsoft’s total investment in OpenAI has reached around $13 billion by 2023. Part of this came from the 2023 deal (around $10 billion) and earlier phases. These funds are used not only for research but also for expanding data centers and reducing OpenAI’s costs. In return, Microsoft has negotiated favorable terms: sources suggest Microsoft will receive up to 75% of OpenAI’s profits (until it recoups its investment) and possibly 49% ownership in the company. Microsoft also secured rights to license OpenAI technology exclusively (e.g., using OpenAI’s models in Bing search and other services).
April 2025 – Renegotiation News: A Financial Times report (cited by Cointelegraph) notes that Microsoft is in talks to renegotiate the partnership to ensure continued access to OpenAI’s latest technology beyond 2030, when the original deal expires. Under the original terms, Microsoft’s exclusive licensing runs until 2030, so this renegotiation may involve Microsoft giving up some equity in exchange for a renewed deal. The report highlighted Microsoft’s ongoing commitment: as of early 2025, Microsoft has invested over $13 billion in OpenAI since 2019. This underscores how crucial OpenAI is to Microsoft’s AI strategy.

The strategic goals of this partnership have been clear. Microsoft wants to embed OpenAI’s leading AI into its products and cloud, gaining a competitive edge in the AI race. Indeed, Microsoft has already integrated OpenAI models into Bing, Edge, Office, GitHub, and more. At the same time, Microsoft gains groundbreaking AI talent and research (OpenAI’s CEO Sam Altman and chief scientist Ilya Sutskever were once co-founders). For OpenAI, Microsoft provides the enormous compute power needed to train large models and a guaranteed commercial channel (Azure and Microsoft apps). As Microsoft CEO Satya Nadella said, the goal was to “democratize AI as a new technology platform” so developers and organizations can build innovative applications.

OpenAI and Microsoft logos, symbolizing their collaborative AI partnership. The two companies have committed multibillion-dollar investments and shared goals in AI. (Image source: Forbes.)

OpenAI: Technical and Financial Analysis

Analyzing OpenAI as a company involves looking at its technology stack, infrastructure, innovation pipeline, and business model. Here we break down OpenAI’s technical capabilities and financial strategy.

Infrastructure and Technical Capabilities

OpenAI’s AI models require enormous computing power. Thanks to the Microsoft partnership, OpenAI runs on massive supercomputers built on Azure. Microsoft has reported constructing “multiple AI supercomputing systems at massive scale” for OpenAI. In fact, Microsoft once announced building “the world’s top five supercomputer” (by the Top500 list) at OpenAI’s request. These clusters often contain tens of thousands of NVIDIA GPUs. Recent reports suggest Microsoft is building facilities with 100,000+ GPUs and consuming gigawatts of electricity per data center to support AI training. This infrastructure allows OpenAI to train models like GPT-4 and the new GPT-4.1 (which has up to a million-token context window) in weeks or months.

OpenAI’s technical innovation is rapid. Over a few years, it progressed from GPT-2 (2019) to GPT-3 (2020) to GPT-4 (2023) and beyond, each time improving capabilities. The release of GPT-4o (mid-2024) introduced models that can handle text, images, and audio together. Soon after, OpenAI announced GPT-4.1 (late 2024), which raised code-writing and reasoning performance and extended the context window to unprecedented lengths. The company also expanded into new modalities: DALL·E 3 (images), Whisper (speech recognition), and Sora (video). Most recently, its audio models (released March 2025) brought text-to-speech at a new level of quality. In short, OpenAI’s pipeline is constantly releasing new model versions and capabilities.

Underpinning this is OpenAI’s research and safety efforts. OpenAI employs advanced training techniques like reinforcement learning from human feedback (RLHF) to align models with human values. It also shares research (e.g. papers on AI alignment and safety) that benefits the wider community. However, unlike fully open-source projects, OpenAI keeps its largest models proprietary to maintain a business advantage. This balance – research publication vs. commercial secrecy – is a strategic choice that affects the competitive landscape.

Performance and Scalability

OpenAI’s models are widely regarded as state-of-the-art. For example, Google’s blog reports that its Gemini 2.5 model (trained on similar principles) was “state-of-the-art on a wide range of benchmarks” and could even outperform other models in reasoning and coding tasks. Similarly, Anthropic’s Claude models (competitors to GPT-4) claim top-tier performance in certain categories. In practice, benchmark rankings (like LM Arena) often show GPT-4 variants near the top, followed by Google Gemini and Anthropic Claude variants. OpenAI continues to push performance: the GPT-4.1 series reportedly outperforms the earlier GPT-4o in code generation and instruction-following tasks.

Scalability is a challenge. Running ChatGPT’s millions of daily interactions, or a business application on the GPT API, requires handling huge traffic. OpenAI relies on cloud auto-scaling and Microsoft’s global infrastructure to serve these requests. However, costs remain very high. OpenAI has billions of dollars in expenses, primarily for compute. Analyst reports estimate that OpenAI generates roughly $3–4 billion in annual revenue (mainly from subscriptions and API usage) but also incurs about $5–6 billion in operating costs annually. For example, Chris McKay of Maginative reports that about $3.2 billion of OpenAI’s recent revenue came from ChatGPT subscriptions and $200 million from the Azure partnership. Yet he also notes that OpenAI projected a $1 billion operating loss for 2023. Foundation Capital’s analysis estimates even larger losses (about $5 billion loss on $3.7 billion revenue for 2024) and that OpenAI’s infrastructure costs alone could be $6 billion per year. These numbers highlight that OpenAI is still in heavy investment mode, spending on servers, power, and staff faster than it earns revenue.

Part of OpenAI’s strategy is to treat this high spending as a temporary phase. Unlike typical companies, OpenAI uses a capped-profit model: investors (primarily Microsoft) will get up to 100× return on their investment, after which OpenAI’s profits would flow to its nonprofit parent. This means OpenAI can invest huge sums now, as it is essentially obligated to pay back profits to investors rather than accumulating endless profit margin. The leadership, including CEO Sam Altman, has said that growing the technology is the priority, while making sure it can eventually sustain itself financially through subscriptions, API fees, and enterprise contracts.

On innovation, OpenAI is still strong. It attracts top talent (though some co-founders have departed) and invests in new research areas (like multimodal models and robotics simulators). The open questions are whether it can maintain its lead as models like Google’s Gemini or Meta’s LLaMA improve. For now, OpenAI’s scale – backed by Microsoft’s compute power – is a significant advantage. For instance, OpenAI’s models are used in high-demand scenarios like Bing search and Microsoft Office, showing that they can handle enterprise workloads. The fact that many top companies (including Apple) are integrating ChatGPT also shows strong demand.

Monetization and Business Model

OpenAI makes money through several channels:

Subscriptions and API Fees. The bulk of revenue comes from OpenAI’s own service and API. ChatGPT Plus (a $20/month subscription) gives users faster access to GPT-4. OpenAI reports having tens of millions of users on ChatGPT (including free tier and paid subscribers). Developer customers pay per usage for the API: for example, cents per thousand tokens of text. The combination of consumer subscriptions and business API contracts is OpenAI’s main monetization. As noted, roughly $3.2 billion of recent revenue was from ChatGPT subscriptions and developer fees.
Enterprise Licensing. OpenAI also licenses technology to enterprises. For example, Azure customers pay Microsoft for an Azure OpenAI instance, of which OpenAI earns a share. GitHub Copilot (co-licensed with Microsoft) is another example: developers and teams pay Copilot subscription fees. Large corporations use OpenAI models for internal applications (customer service bots, analytics, etc.) through Azure. These enterprise deals provide recurring revenue and often have higher margins due to scale. OpenAI’s partnership with Microsoft ensures that enterprises find it easy to adopt OpenAI via Azure’s existing channels.
Strategic Partnerships. Microsoft itself is a customer. The $1 billion “investment” in 2019 included both cash and Azure credits, meaning Microsoft paid OpenAI for compute while funding new research. OpenAI effectively “sells” compute time to Microsoft, which uses it internally (e.g., in Bing or Office). Other partnerships (like with Amazon or Salesforce) can work similarly via reseller agreements. These may not be huge revenue streams yet, but they deepen OpenAI’s market presence.
Future Products. OpenAI is exploring new revenue models. For example, it has started piloting business-focused features (custom knowledge bases, advanced security) at premium prices. The recent Apple announcement (April 2024) that ChatGPT will be integrated into Apple products likely involves an undisclosed agreement: this is a form of licensing or revenue share that could be substantial.

Despite the growth in revenue, OpenAI’s costs are high. Key costs include:

Compute and Cloud: Training large models (especially multi-billion-parameter models) requires massive GPU clusters. Serving millions of queries also uses hardware constantly.
Research & Development: OpenAI employs hundreds of engineers and researchers, including experts in ML, safety, and policy.
Data and Bandwidth: Curating datasets and moving data costs money at this scale.
Customer Acquisition: Marketing and partnerships (like Apple) have associated costs.

Experts note that unlike traditional software, each new user of an AI model has a variable cost (compute per query). This means scaling up revenue does not automatically generate profit in the same way: “each additional user costs money in compute… OpenAI’s costs are growing in lockstep with its revenue”. In fact, to reach profitability, OpenAI would need to continue an extremely high growth rate for several years, which is challenging. As Foundation Capital warns, OpenAI may be “piling up progressively larger losses” to achieve hyper-growth.

Nevertheless, OpenAI’s valuation (recently reported around $80–86 billion) reflects the belief that its future AI services could generate orders of magnitude more revenue than today. For context, The Information reported OpenAI’s valuation implies a forward revenue multiple of about 25×. In other words, investors expect OpenAI’s models and products to become extremely valuable, perhaps akin to how Google Search or Facebook social media became dominant. Microsoft’s large investment and potential profit-sharing deals show that the biggest question is not whether OpenAI can innovate, but whether it can turn that innovation into sustainable profit at scale.

Comparison of OpenAI with Key Competitors

OpenAI is not the only organization developing cutting-edge AI. Several companies and labs are racing to build the next breakthrough. Here we compare OpenAI to its key rivals:

Google DeepMind / Google AI: Google is OpenAI’s most formidable competitor. Google (including its DeepMind division) has deep AI expertise and massive resources. Google’s flagship models are Gemini and its predecessors (Bard is the chatbot interface). Gemini 2.5 (released in early 2025) is described by Google as “our most intelligent AI model,” introducing “thinking” abilities that let it reason step-by-step. Google has optimized Gemini in different flavors (Ultra, Pro, Nano) for different costs. Like OpenAI, Google offers Gemini through APIs (e.g., in Vertex AI) and through consumer products (Google Search, Bard).
In benchmarks, Gemini is often competitive with GPT-4. For example, Gemini 2.5 Pro was said to top certain leaderboards and outperform other models on reasoning and math. Google continues to train even larger models (e.g., Gemini 3) and integrate them into Android, Chrome, and Workspace. However, Google’s release cycle is slower (big new models every few years) and more closed compared to OpenAI’s rapid deployment. Google also emphasizes multimodal AI (images, video with models like Imagen and Veo) and has deep expertise in research. The key difference is that Google has a massive existing user base (Android, Search) to seed into AI, whereas OpenAI’s reach has come mainly through partnership with Microsoft and viral adoption of ChatGPT.

Anthropic: Anthropic is an AI startup co-founded by former OpenAI researchers. Its models (named Claude) are direct competitors to GPT. The latest Claude 3 series, released in 2024, comes in three sizes: Haiku (smallest/fastest), Sonnet (middle), and Opus (largest/most capable). Anthropic emphasizes alignment and safety, training Claude models with techniques like Constitutional AI. Performance-wise, Claude 3 Opus claims “best-in-market performance” on complex tasks, while Haiku targets fast response times. These models are available via Anthropic’s own API and through platforms like Amazon Bedrock.
In fact, Anthropic partnered with Amazon to offer Claude on AWS Bedrock. For instance, AWS announced that Claude 3 Opus would be available to enterprise developers for large tasks, while Haiku was offered for lower-cost, real-time use. Anthropic’s approach is more focused on enterprise and B2B usage; it does not have a consumer chatbot like ChatGPT (though they may offer one in the future). On pricing, Anthropic’s models are generally more expensive per token than GPT due to smaller scale of operations (as implied by an Anthropic chart showing higher cost vs intelligence for each Claude model).
A key advantage of OpenAI over Anthropic is product adoption: ChatGPT has built a huge user base and brand recognition. Anthropic has strong financial backing (including from Google and Salesforce) but is smaller. However, companies evaluating models sometimes choose Claude for its safety features or AWS integration. OpenAI leads in versatility, while Anthropic pitches itself on cautious, controllable AI.
Meta AI (Facebook/Meta Platforms): Meta has invested heavily in AI research. Its open-source LLaMA models (Large Language Model Meta AI) were released for research use, with up to 65 billion parameters. Meta built a “Meta AI” chatbot (launched Sept 2023) available on Facebook, Instagram, WhatsApp, and a standalone app, all powered by its latest Llama 4 model. This chatbot aims to compete with ChatGPT; Meta’s Reuters announcement describes Llama 4 as “designed to rival the latest AI models from OpenAI, Google, [and] Anthropic, with improved reasoning… and efficiency”.
Meta’s strategy differs: it open-sources many models to build community usage, and integrates AI tightly into its social media ecosystem. LLaMA-4 reportedly powers the Meta AI assistant and has multilingual capabilities. Benchmark-wise, Meta’s models (especially Llama 3 and 4) have been noted as high-quality for research, and Meta holds events like “LlamaCon” to showcase them. However, Meta’s AI lab has faced criticism for slow product releases (they were slow to respond to ChatGPT’s launch in 2022). The company is ramping up again with an open stance and by hosting developer events.
In terms of core comparison, OpenAI remains more focused on cloud services and partnerships, whereas Meta treats AI as part of its consumer internet ecosystem. Meta’s advantage is its huge user base for distributing AI features (e.g., AI-generated avatars, social bots). But it lacks the same emphasis on developer APIs and enterprise support that OpenAI/Microsoft provides.
Other Players: Several other organizations are relevant. Google’s DeepMind lab (part of Google) continues advanced research (e.g. AlphaGo, AlphaFold) and now works on general AI; it’s blending with Google Brain under the “Gemini” brand. Amazon has its Bedrock platform offering models (including Anthropic and models from AI21 Labs and Cohere), but Amazon’s own generative model efforts (e.g. CodeWhisperer for code) are less public. AI21 Labs (startup) has the Jurassic language model, focusing on open access and writing tools. Cohere offers large language and embedding models via an API (popular in Europe). In China, tech giants like Baidu (with Ernie models), Alibaba (TongYi model), and Tencent are rapidly developing their own AI systems, though they mainly serve Chinese-speaking users.

Despite the competition, OpenAI has key strengths: its early lead with ChatGPT gave it massive mindshare and a large user community. Its integration with Microsoft also ensures enterprise deployment. Competitors like Google can match OpenAI in raw capability, but Google’s more cautious release style means OpenAI often appears first in headlines. Anthropic and Meta offer alternatives with different priorities (safety, openness), but they have not displaced OpenAI’s popularity. Going forward, all these labs push the technology forward, and OpenAI must keep innovating to stay ahead. As the AWS News chart (below) suggests, there is a roughly linear tradeoff between cost and performance for these model families – companies will choose the model that fits their budget and needs.

References

Microsoft, “Microsoft and OpenAI extend partnership” (Jan. 23, 2023), blogs.microsoft.com.
Microsoft, “OpenAI forms exclusive computing partnership” (July 22, 2019), news.microsoft.com.
Cointelegraph, “Microsoft renegotiating OpenAI deal” (May 2025), cointelegraph.com.
Aranca (PDF), “The Microsoft-OpenAI Partnership” (2023).
The Information, cited via Maginative, “OpenAI’s Annualized Revenue Doubles to $3.4B” (June 2024), maginative.com.
Foundation Capital, “Why OpenAI’s $157B valuation misreads AI’s future” (2024), foundationcapital.com.
OpenAI Blog, “Introducing our next-generation audio models in the API” (Mar. 20, 2025), openai.com.
AWS News Blog (Channy Yun), “Anthropic’s Claude 3 Haiku model is now available on Amazon Bedrock” (Apr. 16, 2024), aws.amazon.com.
OpenAI Developer Docs, “GPTs: Create Custom GPTs” (2023), openai.com/help.
Google Keyword Blog, “Introducing Gemini: our largest and most capable AI model” (Mar. 25, 2025), blog.google.com.
Google DeepMind, “Gemini” (2025), deepmind.google.
Voicebot.ai, “Meta Introduces Large Language Model LLaMA as a Competitor” (Feb. 27, 2023), voicebot.ai.
Reuters, “Meta launches standalone AI assistant app” (Apr. 29, 2025), reuters.com.
AWS What Is Guide, “What is prompt engineering?” (2023), aws.amazon.com.