What is ChatGPT? OpenAI’s Flagship Model

ChatGPT is OpenAI’s generative artificial intelligence chatbot built on large language models (LLMs), algorithms trained on massive datasets. When prompted by users, it can generate text, speech, and images.

OpenAI’s chatbot was released in November 2022, and it reached 100 million users within its first two months, becoming one of the fastest-adopted consumer apps in history. Since then, it has experienced explosive growth. By 2025, ChatGPT was reported to have 700 million weekly users and to process over 2.5 billion prompts per day.

ChatGPT’s Origins and Purpose

OpenAI was founded in December 2015 by a mix of tech entrepreneurs and AI researchers, including Sam Altman, Greg Brockman, Ilya Sutskever, John Schulman, Wojciech Zaremba, and Elon Musk, among others.

From the start, OpenAI’s framing was ambitious: the organization’s mission is to “ensure that artificial general intelligence… benefits all of humanity.”

ChatGPT is OpenAI’s flagship interface to that vision: a conversational AI built to make advanced language models accessible to everyday users. Instead of requiring developers to call APIs or train models themselves, ChatGPT wraps the technology in a chat UI, lowering the barrier for interaction, experimentation, and content generation.

Actions speak louder than words, though.

While OpenAI talks up openness, safety, and broad benefit, its partnerships and policies hint at more commercial motives. The “open” in its name was originally more than branding: the organization launched as a nonprofit committed to open research and public access.

That changed in 2019, when OpenAI created a “capped-profit” LLC under nonprofit control to raise the massive funding needed for large-scale models. Investors’ returns were technically limited, but critics argue the shift, along with closed-door partnerships and licensing deals, shows commercial imperatives may now be steering priorities just as much as altruism.

Which is a good segue into the next section, the company finding itself in hot water, not one, but multiple times:

OpenAI’s Legal and Policy Controversies

OpenAI’s path hasn’t been free of legal missteps. Below are some of the most consequential controversies around copyright, regulation, and data policy.

Copyright Lawsuits and Training Data Disputes

OpenAI is tangled in multiple copyright suits from authors, publishers, and media organizations. In 2023, authors including George RR Martin and John Grisham sued, claiming their works were used without permission in training GPT models.

The New York Times, among others, filed claims in December 2023 accusing OpenAI and Microsoft of using Times articles wholesale to train their models.

Some defenses have succeeded.

In certain cases, OpenAI won motions to dismiss parts of claims for lack of proven harm. Still, the company is consolidating dozens of these suits into a multidistrict litigation (MDL), signaling that the courts expect a broader precedent to emerge.

Potentially motivated by previous legal action, newer plaintiffs are joining the fray. In April 2025, publisher Ziff Davis (which owns CNET, PCMag, etc.) sued OpenAI in Delaware, alleging systematic misuse of its copyrighted content to train models. Also, Canadian media outlets filed suit in late 2024, claiming unauthorized use of their news content in ChatGPT training.

Regulatory Pressure: EU, U.S., and Transparency Mandates

As the legal storm intensifies, regulators in the U.S. and Europe are stepping in. The European Data Protection Board (EDPB) in 2023 created a task force focused on generative AI, and enforcement officials across EU nations are scrutinizing how services like ChatGPT handle personal data.

In 2023, the Italian data protection authority banned ChatGPT temporarily, citing GDPR concerns. The service was reinstated only after OpenAI committed to changes, like allowing users to remove personal data.

OpenAI’s own transparency is changing. Its Privacy pages now reference compliance with the EU AI Act, the EU Digital Services Act (DSA), and other regional frameworks.

Still, critics point out that while OpenAI cites “publicly available and licensed data” in its Sora (video) tool, it won’t reveal which datasets or licenses were used.

Privacy Policy Shifts and Conversation History Risks

One of the trickiest issues: how ChatGPT stores and uses user interaction data. OpenAI’s privacy policy confirms that user inputs may be shared with third parties (e.g., vendors, service providers) and that de-identified or aggregated data may be used to improve models.

OpenAI’s updated EU privacy terms have triggered debate: they now frame some data processing as “necessary for our legitimate interests and those of third parties and broader society,” including model training. That’s a looser legal basis in GDPR terms and invites scrutiny.

Also, a U.S. court recently ordered OpenAI to preserve all ChatGPT conversations indefinitely, as part of ongoing litigation brought by The New York Times and others. That implies chats are not as ephemeral or private as users might assume.

Beyond legal orders, the accumulation of massive conversation logs raises broader concerns: extensive profiling, cross-session memory, and surveillance possibilities. Universities have flagged OpenAI’s “data hunger” as having profiling risks, especially if personal data or user behavior patterns are centralized at scale.

How ChatGPT Works: Under the Hood

ChatGPT is trained on a mixture of sources: public web text, licensed datasets, and data from partnerships (e.g., Stack Overflow, academic corpora, possibly news archives). The exact proportions and sources remain largely opaque.

Some documents and public statements note that OpenAI also uses data from human reviewers and user interactions (when users allow it) to improve performance.

Behind the scenes, there’s a tension between raw/unlabeled data and annotated or curated data. The base pretraining phase ingests large volumes of raw text to learn general linguistic structure, while later stages (fine-tuning, alignment) rely more on carefully labeled prompt-response examples.

Debate exists over how balanced, representative, or high-quality these datasets are, and how much bias or noise is baked into the foundation models.

Training Processes and Methods

The training pipeline typically follows:

• Pretraining: The model learns from massive corpora (public + licensed + scraped sources), optimizing for next-token prediction and general language understanding.
• Fine-tuning via RLHF (Reinforcement Learning from Human Feedback): Responses from the base model are ranked by human annotators; a reward model is built to distinguish better vs worse answers, and then the model is optimized accordingly.
• Alignment interventions: OpenAI uses moderation systems, red teaming (forcing adversarial prompts), and trust & safety overlays to reduce harmful or undesirable outputs.
• Contrast with Constitutional AI (Claude’s approach): Unlike Claude, which uses a written “constitution” for internal self-critique, ChatGPT’s alignment leans more heavily on human feedback loops and filter layers.

Safety, Bias, and Alignment

To prevent harmful or inappropriate responses, ChatGPT layers on filters and safety mechanisms. It is programmed to refuse certain content categories (e.g., illegal advice, hate speech).

However, bias remains a known issue. Some refusal patterns reflect political, cultural, or domain-specific sensitivities. In other cases, it may default to overly cautious or vague answers, especially in contentious topics.

Updates and Policy Changes

OpenAI has rolled out features and policies affecting how memory, data, and integration work:

• Memory / Context features: ChatGPT can remember user preferences or details between sessions. Users can delete saved memories or disable memory features.
• Sora / video generation: Sora has settings for data controls, and users can opt out of having content used to train future models.
• Training opt-out controls: By default, user inputs on ChatGPT (Free/Plus) may be used to train models, but users can disable that via a toggle (“Improve the model for everyone”) in settings.
• For business / API customers, data is not used by default for training unless explicitly opted in.

These updates reflect attempts to balance model performance with user privacy control, but also raise questions about transparency, persistence of data, and what “memory off” truly means in practice.

What Can ChatGPT Do? Use Cases and Capabilities

Okay, let’s get down to the crux of this thing: what does ChatGPT actually do?

Major Use Cases

ChatGPT is highly flexible. So much so, it’s being used across a broad spectrum of tasks:

• Content creation: It can draft blog posts, marketing copy, summaries, emails, social media captions, and more. The natural language fluency is often cited as one of its biggest draws.
• Problem solving and research: Users tap ChatGPT for legal reasoning, academic summaries, comparative analysis, strategic planning, and more. When combined with tools like ChatGPT Search and Deep Research, it can pull in current or cited sources to support those outputs.
• Coding and reasoning tasks: ChatGPT is used for code generation, debugging, algorithmic reasoning, logical puzzles, and technical writing.
• Multimodal capabilities (images, video, Sora): ChatGPT supports image input (letting users upload pictures or diagrams for interpretation) and image generation. More recently, OpenAI has integrated Sora, a video generation model, allowing ChatGPT users (on paid plans) to generate, remix, and edit short video clips from text prompts.

Performance and Strengths

ChatGPT offers several powerful advantages:

• Natural language fluency and adaptability: Its ability to understand context, maintain coherent dialogue across multiple turns, and adapt tone makes it feel conversational and “human.”
• Plugin and ecosystem advantage: ChatGPT has a growing ecosystem of integrations (plugins, external tools, APIs) that let it tap into external systems (e.g., databases, web search, code execution). This extensibility gives it an edge over models that are closed or less tool-aware.
• Network effects / massive user base: With a huge user base, OpenAI accumulates feedback, usage signals, and real-world edge cases faster than many smaller LLM providers. That helps the model evolve, detect errors, and improve iteratively.

Limitations and Weak Spots

No model is perfect. Here are key weaknesses to keep in mind:

• Hallucinations and outdated knowledge: ChatGPT can generate false or misleading statements (“hallucinate”), especially when asked about niche or recent events. Its base knowledge also has cutoffs, so it may not know about very recent developments.
• Domain-specific accuracy: In specialized fields (law, medicine, advanced mathematics), ChatGPT may lack depth or make errors more often than a domain-focused expert system.
• Privacy risks / data retention: If “chat history” or memory is enabled, conversation data may be stored or used (unless users disable those features). For sensitive topics, this raises trust and data security concerns.
• Performance vs cost trade-offs: More powerful capabilities (multimodal, long context, deep reasoning) often come with higher computational cost, slower response times, or limitations on free usage tiers.
• Bias and alignment edge cases: Even with filters, ChatGPT can reflect social, cultural, or representational biases. Some prompts push it toward unintended or politically / culturally sensitive outputs.

Performance Comparison with Other AI Models

When sizing up ChatGPT against models from Claude, Gemini, Meta, and other AI families, it helps to compare across benchmarks, real-world tasks, architectural trade-offs, and design philosophy.

Here’s how things stack up as of September 2025:

GPT-5 vs Claude (Opus / Sonnet / 4 family)

• In a coding comparison between GPT-5 and Claude Opus 4.1, testers found GPT-5 slightly ahead on coding benchmarks like SWE-bench but using fewer tokens, making it more cost-efficient. GPT-5 reportedly supports a 400K token context window vs. 200K for Claude Opus.
• A review comparing GPT-5 and Claude 4.1 noted their safety differences: GPT-5 introduces Safe Completions that attempt hedged answers rather than flat refusals, while Claude leans into stricter refusal responses under its Constitutional AI-style filters.
• Benchmark results show GPT-5 achieving ~74.9% on SWE-bench, edging Claude 4’s Opus baseline (~72.5%), though when Claude uses “extended thinking” modes, it sometimes closes the gap.

TL;DR: GPT-5 is competing neck-and-neck with Claude in coding and reasoning tasks, sometimes leading slightly, but Claude still holds advantages in safe refusals, deeper exploration, or consistency in some edge prompts.

GPT-5 vs Grok (xAI)

• In aggregated model comparisons, GPT-5 is often placed in the top tier across math, coding, and reasoning tasks. Grok 4 is competitive, especially in structured reasoning and real-time data domains.
• Some comparisons claim GPT-5 has a lower hallucination rate and stronger reasoning consistency than Grok 4 in open-source prompt sets.
• However, critics and developer commentary note that Grok’s “style” (some personalities, sarcasm, probing questions) sometimes makes it more engaging or creative in dialogue tasks where surface correctness is less important.

TL;DR:  GPT-5 may currently lead on raw reasoning and accuracy benchmarks over Grok 4, but Grok still holds appeal in conversational flair or real-time, data-driven prompts.

GPT-5 vs Gemini (Google / DeepMind)

• In broad comparisons, GPT-5 is often contrasted with Gemini 2.5 Pro as close rivals. Some reports suggest Gemini excels in handling huge documents (1 million token context) and multimodal integration, whereas GPT-5 is strong in core reasoning + coding.
• In another comparison, GPT-5 is credited with excellent logic and math, while Gemini 2.5 Pro’s advantage lies in ingesting long documents and performing multimodal tasks without losing context.
• A buyer’s guide comparing GPT-5, Claude, Gemini, Grok, and LLaMA suggests that Gemini is appealing when you need deep integration with Google’s ecosystem and a few cracks when handling mixed inputs (text + images + voice).

TL;DR:  The trade-off tends to be: GPT-5 pushes ahead on pure reasoning/coding, Gemini’s strength is scale, multimodal inputs, and tight ecosystem integration.

GPT-5 vs LLaMA / Meta AI Models

• In comparisons of GPT-5, LLaMA 3, and Gemini, GPT-5 is often positioned as the benchmark in reasoning and performance, while LLaMA (Meta’s open-weight family) is praised for transparency, customizability, and open research appeal.
• Meta’s Llama 4 (variants Scout, Maverick) has faced criticism for “benchmark gaming,” where the versions tested in public leaderboards differ from the versions released publicly. This raises concerns about how apples-to-apples those metrics are.

TL;DR:  GPT-5 tends to outperform for many benchmark tasks, but LLaMA’s open model philosophy and ability to be run locally still make it attractive in certain use cases.

Conclusion and Next Steps

There’s little room for argument; generative search is here to stay. Publishers that adapt early will capture more visibility, traffic, and leads.

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