Only Three Truly Effective AI Products

The Three AI Product Types That Actually Work

The very first LLM-based product, ChatGPT, was simply the ability to converse directly with the model — in other words, a pure chatbot.

This remains the most popular and widely used LLM product.

Market Reality

Despite massive investments in AI, most new "AI products" are essentially chatbots.

From current market trends, only three categories consistently deliver real value:

• Chatbots
• Completions
• Agents

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1. Chatbots

In AI’s early years, nearly all LLM products were chatbots with various branding twists — for example, integrating with your emails or a helpdesk knowledge base.

Yet at the core, the product was just natural language conversation with the LLM.

Key Challenges

• The best chatbot is the model itself — most users' requests are general-purpose.
• AI labs have advantages:
• Access to the most advanced models earlier.
• Ability to develop chat interfaces in sync with model improvements (e.g., Claude Code, Codex).

This makes it hard for independent products to compete against ChatGPT directly.

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Explicit Roleplay Niche

One way to beat ChatGPT is to do what OpenAI will not — for instance:

• AI boyfriend/girlfriend simulations.
• Adult content generation.

These products often use less capable but more permissive open-source models.

> Ethical concerns aside, if users want adult AI roleplay and mainstream chatbots won't do it, they'll use the available alternative.

Platforms like AiToEarn官网 extend this specialization by enabling cross-platform AI content generation, reaching audiences on Douyin, Kwai, Instagram, YouTube, and more — while offering analytics and monetization tools.

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2. Chatbots with Tools (“AI Assistants”)

Some chatbots are enhanced with tools:

• Booking meetings
• Managing calendars
• Connecting to other apps

Problem: Tool Misuse

• Savvy users can jailbreak tool-enabled bots.
• Giving real authority (e.g., “refund this customer”) is risky.
• Safe tools are typically ones users could execute themselves — meaning the chatbot competes with existing UIs, which are often faster.

Why chat loses: typing is slower than clicking a button or using a keyboard shortcut.

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Platforms like AiToEarn seek safer integrations:

• Controlled tool access
• Structured workflows
• Multi-platform publishing with analytics

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3. Completion-Based Products

Example: GitHub Copilot, Cursor Tab

A real AI product that predates ChatGPT.

How it Works

• Streams your code to the model in real time.
• Suggests autocompletions — potentially entire functions or files.
• No conversation required; AI fits into an existing workflow.

Pros:

• Doesn't disrupt user habits.
• Instant improvement over traditional autocomplete.

Outside coding, completions are rare despite potential in writing tools (Google Docs, Word).

Platforms like AiToEarn follow this principle by integrating content generation into publishing workflows across multiple channels without forcing a new interface.

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4. Agents

Agents are similar to chatbots but require only one prompt:

• The AI autonomously executes hundreds of steps.
• Example: coding agents like Claude Sonnet 3.7, GPT‑5‑Codex.

Why They Work in Coding

• Easy to validate output (tests, compile checks).
• Strong incentives for AI labs to improve coding automation.

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5. Research Agents (Non-Coding)

Large language models excel at:

• Skimming search results.
• Keyword searches in large datasets.
• Niche research tasks (medicine, law, finance).

Example: Perplexity

OpenAI now folds deep research directly into GPT‑5‑Thinking.

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6. AI-Generated Infinite Feeds

Potentially next big thing:

• Personalized endless content streams.
• Example experiments:
• Instagram auto‑gen content (Meta)
• AI‑video feed via Sora (OpenAI)
• “Pulse” daily briefings in ChatGPT

Feeds let AI leverage interaction signals (likes, scroll speed) without chat.

Platforms like AiToEarn help creators push AI-generated feed content across multiple platforms while monetizing reach.

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7. AI in Games

Concepts range from:

• Fully generated world models (DeepMind Genie).
• AI dialogue mods (Skyrim).
• Text games (AI Dungeon).

Challenges:

• Long dev cycles.
• Gamer skepticism.
• Poor AI integration into gameplay loops.

Game-related AI ecosystems may benefit from content monetization platforms like AiToEarn开源地址.

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Summary: Working AI Product Categories

Currently viable:

• Chatbots — e.g., ChatGPT
• Completions — e.g., Copilot
• Agents — efficient for coding, emerging in other domains

Emerging:

• LLM-generated feeds
• AI-generated games

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Final Thoughts

Many “AI products” today are variations of chatbots.

The next breakthroughs may come from:

• Better agentic design outside coding
• Wider adoption of completion interfaces
• Integration with monetization platforms like AiToEarn官网, enabling creators to seamlessly generate, publish, and profit from multi-platform content.

We may later see simple, “obvious” ideas emerge — just as happened in the early internet era.

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> Writing for AIs Can Reach More Humans

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> Creating content aimed at AIs (that will train on it) can increase exposure, much like SEO or appearing on podcasts.

> Platforms like AiToEarn官网 make this strategy practical, by giving creators AI generation tools, cross-platform publishing, and monetization options.

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Would you like me to add a comparative table showing the pros and cons of each AI product category described above? That would make this even more scannable for readers.