Gen AI Monetization Strategies
What is the impact of generative AI on companies' ability to stay competitive, adapt, and thrive? Breaking down the complexities of the technology and its implications for executive leaders to understand and execute.
1. Fundamental Knowledge
• OpenAI Custom GPTs vs Assistants API
• Model Comparison
• Core Terminology Defined
• Use Cases
2. Costs
• What are tokens?
• Text, audio, speech cost comparison
3. How To Build
• Codeless AI demo
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1. Fundamental Knowledge
Custom GPTs vs Assistants API
What's the difference?
Model Comparison
OpenAI Assistants API, Google Gemini, Anthropic Claude.
Core Terminology Defined
Context length, RAG, etc.
Use Cases
Across industries.To drive revenue, reduce operating expenses, and ultimately boost profits.
2. Costs
• What are tokens?
• Text, audio, speech cost comparison
3. How To Build
• Codeless AI demo
2. Costs
What Are Tokens?
Text, Audio, Speech Cost Comparison
Across models
3. How To Build
• Codeless AI demo
3. How to Build
Codeless AI demo
Custom GPTs vs Assistants API
Updated June 20, 2024
Both utilize ChatGPT’s functionality, allowing you to customize it for your specific needs and knowledge bases.
| Aspect | Custom GPTs | Assistant API |
|---|---|---|
| Place of usage | In ChatGPT | Any website or mobile app |
| Website for builders | https://chatgpt.com/gpts/editor | https://platform.openai.com/assistants/ |
| How to share | Link to the Custom GPT can be shared but still only used on the ChatGPT platform | Share link to anyone that can access a website |
| Code? | No code | No code, low code, or can be highly customized with code |
| Monetize | Currently cannot monetize. Unclear if, when, or how OpenAI will compensate builders | Can monetize behind a paywall using https://codelessai.co/ |
| Built-in tools | Browsing, DALL·E, Code Interpreter, Data Analysis, and Custom Actions. | Code interpreter, file search |
| Data privacy | Custom GPT-level opt-out option for builders. This feature allows builders to decide whether their proprietary data can be used by OpenAI for model training | Content submitted by users through the Assistant API not used to train models |
Custom GPT
Assistants API
Core Terminology Defined
Updated June 21, 2024
1. Artificial Intelligence (AI):
- Definition: The simulation of human intelligence in machines that are programmed to think and learn like humans. It includes various subfields such as machine learning, deep learning, and natural language processing.2. Machine Learning (ML):
- Definition: A subset of AI focused on building systems that learn from data and improve their performance over time without being explicitly programmed.3. Deep Learning:
- Definition: A subset of machine learning that uses neural networks with many layers (hence "deep") to model and understand complex patterns in data.4. Neural Network:
- Definition: A computing system inspired by the human brain's network of neurons, which can learn to perform tasks by considering examples.5. Generative Adversarial Network (GAN):
- Definition: A class of machine learning frameworks where two neural networks (a generator and a discriminator) are pitted against each other to generate new, synthetic data that is indistinguishable from real data.6. Natural Language Processing (NLP):
- Definition: A field of AI that focuses on the interaction between computers and humans through natural language. It includes tasks like language translation, sentiment analysis, and text generation.7. Transformer Model:
- Definition: A type of deep learning model designed for handling sequential data, such as text, with applications in NLP tasks. Notable examples include BERT and GPT.8. GPT (Generative Pre-trained Transformer):
- Definition: A type of transformer model developed by OpenAI that is pre-trained on a large corpus of text and can generate human-like text based on the input it receives.9. Reinforcement Learning (RL):
- Definition: An area of machine learning where an agent learns to make decisions by performing actions in an environment to maximize some notion of cumulative reward.10. Synthetic Data:
- Definition: Data that is artificially generated rather than obtained by direct measurement. It is often used for training AI models.11. Overfitting:
- Definition: A modeling error that occurs when a machine learning model learns the training data too well, capturing noise and details that do not generalize to new data.12. Underfitting:
- Definition: A scenario where a machine learning model is too simple to capture the underlying pattern in the data, resulting in poor performance on both training and new data.13. Bias and Variance:
- Definition: Bias refers to errors due to overly simplistic models, while variance refers to errors due to overly complex models that capture noise. Balancing both is key to building effective models.14. Latent Space:
- Definition: A representation of compressed data in a lower-dimensional space, often used in generative models to interpolate and create new data points.15. Inference:
- Definition: The process of making predictions or generating outputs using a trained machine learning model.16. Training:
- Definition: The process of teaching a machine learning model to make predictions or decisions by feeding it data and adjusting its parameters.17. Fine-Tuning:
- Definition: The process of taking a pre-trained model and making small adjustments to it using a smaller, specific dataset to improve performance on a particular task.18. Ethical AI:
- Definition: The practice of ensuring that AI systems are designed and used in ways that are fair, transparent, and accountable, avoiding biases and respecting user privacy.19. Explainability:
- Definition: The ability to understand and interpret the decisions made by an AI model. It is crucial for building trust and ensuring accountability in AI systems.20. API (Application Programming Interface):
- Definition: A set of rules and protocols for building and interacting with software applications. In the context of AI, APIs allow developers to integrate AI capabilities into their applications easily.21. RAG (Retrieval-Augmented Generation):
- Definition: A framework that combines retrieval-based methods with generative models to enhance the accuracy and relevance of generated content. RAG models retrieve relevant documents or data points from a database and use this information to inform and improve the generation of responses or content.22. Context Length:
- Definition: The amount of text or data that a model can consider at once when making predictions or generating content. In the context of transformers and GPT models, a longer context length allows the model to understand and incorporate more information from the input, leading to more coherent and relevant outputs.
Use Cases
Updated June 21, 2024
Generative AI can significantly impact a business's bottom line by increasing revenue, decreasing operating expenses, and ultimately boosting profits. Here's how the use cases can help a business leader achieve these goals:
Content Creation
1. Text Generation:
- Increase Revenue: AI can create engaging blog posts, product descriptions, and marketing emails tailored to your audience's interests, leading to higher engagement and sales.
- Decrease Expenses: Instead of hiring multiple content writers, AI can produce content at a fraction of the cost, allowing you to reallocate resources.2. Image Generation:
- Increase Revenue: Eye-catching visuals created by AI can make your marketing campaigns more attractive, increasing customer interest and conversions.
- Decrease Expenses: AI tools can generate high-quality images without needing an in-house design team or expensive design software.3. Music and Audio:
- Increase Revenue: Unique, branded audio can enhance your marketing videos, making them more memorable and driving brand loyalty.
- Decrease Expenses: AI can compose background music for videos, advertisements, and presentations, eliminating the need to purchase licenses or hire composers.
Business Applications
4. Marketing and Advertising:
- Increase Revenue: AI can analyze customer data to create highly personalized ads that are more likely to convert leads into sales.
- Decrease Expenses: Automate the ad creation process, saving costs on hiring large marketing teams or external agencies.5. Customer Support:
- Increase Revenue: Provide 24/7 customer support with AI chatbots, improving customer satisfaction and retention.
- Decrease Expenses: Reduce the number of support staff needed by handling routine inquiries through AI, lowering payroll costs.6. Data Analysis and Reporting:
- Increase Revenue: AI can quickly analyze large datasets to uncover trends and insights that can inform strategic business decisions.
- Decrease Expenses: Automate the data analysis process, reducing the need for a large team of data analysts and expensive software tools.
Education and Training
7. Content Development:
- Increase Revenue: High-quality training materials can improve employee performance, leading to better business outcomes and increased revenue.
- Decrease Expenses: AI-generated training content reduces the need for expensive training development services.8. Tutoring:
- Increase Revenue: Enhanced employee skills lead to increased productivity and efficiency, driving revenue growth.
- Decrease Expenses: Use AI to provide personalized learning experiences without the need for additional human tutors.
Entertainment
9. Gaming:
- Increase Revenue: Develop more engaging and diverse game content faster, attracting more players and increasing sales.
- Decrease Expenses: Use AI to generate game assets and narratives, reducing development time and costs.10. Movies and TV:
- Increase Revenue: Create high-quality scripts and visual effects quickly, leading to more content production and higher revenue.
- Decrease Expenses: Reduce reliance on costly scriptwriters and visual effects teams by using AI.
Personal Use
11. Creative Projects:
- Increase Revenue: Foster innovation and creativity within the company, leading to new product ideas and services that can generate revenue.
- Decrease Expenses: Provide employees with AI tools for independent project creation, reducing the need for external creative services.12. Assistive Technology:
- Increase Revenue: Improve productivity and employee satisfaction by providing AI-generated personalized tools that meet individual needs.
- Decrease Expenses: Reduce costs associated with accommodating diverse needs through scalable AI solutions.
Technical Applications
13. Software Development:
- Increase Revenue: Speed up software development cycles, allowing faster time-to-market for new products and features.
- Decrease Expenses: Automate repetitive coding tasks, reducing the need for a large development team.14. Product Design:
- Increase Revenue: Rapidly generate and test prototypes, leading to quicker product launches and increased sales.
- Decrease Expenses: Lower design and development costs by automating the prototype generation process.
Healthcare
15. Medical Research:
- Increase Revenue: Accelerate the development of new treatments and drugs, leading to faster market entry and higher sales.
- Decrease Expenses: Use AI to automate literature reviews and hypothesis generation, reducing research costs.16. Patient Care:
- Increase Revenue: Provide better patient outcomes through personalized care plans, leading to higher patient satisfaction and loyalty.
- Decrease Expenses: Automate routine care tasks and patient education, reducing the need for a large medical staff.
Legal
17. Document Generation:
- Increase Revenue: Streamline legal processes, allowing for quicker service delivery and the ability to handle more clients.
- Decrease Expenses: Automate the creation of legal documents, reducing the need for a large legal team.
Journalism
18. News Generation:
- Increase Revenue: Deliver timely and accurate news updates, attracting and retaining more readers and subscribers.
- Decrease Expenses: Use AI to automate news writing, reducing the need for a large editorial staff.
Summary
Generative AI offers a wide range of applications that can help business leaders enhance their operations by automating routine tasks, creating high-quality content, and providing personalized customer experiences. By leveraging these capabilities, businesses can increase revenue through improved engagement and faster time-to-market, while decreasing operating expenses by reducing reliance on large teams and external services. This ultimately leads to increased profitability.
What Are Tokens?
Updated June 20, 2024
Generative AI "tokens" are the fundamental units of data that AI algorithms process, especially in natural language processing (NLP) and machine learning. Think of tokens as the building blocks or pieces of a puzzle that come together to form sentences, images, sounds, and entire documents. Here’s a simple breakdown:1. What are Tokens?
- Text Tokens: These can be whole words, parts of words, or individual characters. For example, in the sentence "ChatGPT is cool," the tokens might be ["Chat", "GPT", " is", " cool"].
- Image Tokens: In image generation, tokens might represent segments of an image, such as shapes, colors, or textures.
- Audio Tokens: For audio and speech, tokens could be fragments of sound, phonemes (the smallest units of sound), or pieces of music.2. How Tokens Work in AI:
- When you input data, the AI breaks it down into smaller tokens.
- The AI processes each token, understands its meaning and context, and then uses this understanding to generate new text, images, or sounds.3. Importance of Tokens:
- Tokens are essential in preparing data for AI models, enabling efficient management and understanding of large datasets.
- In text processing, each word or punctuation mark is considered a separate token, aiding in detailed analysis and pattern recognition.
- In computer vision, tokens may denote image segments, such as groups of pixels or single pixels.
- In audio processing, tokens represent snippets of sound, facilitating detailed analysis and generation of audio.4. Example in Action:
- Text: If you ask the AI to write a story, it starts with the first token (or few tokens), then predicts and generates the next token based on learned patterns, continuing this process until the story is complete.
- Images: If you ask the AI to create an image, it starts with basic shapes or colors (tokens) and builds upon them to generate the final image.
- Audio: If you ask the AI to generate speech or music, it starts with basic sounds or phonemes (tokens) and constructs a coherent audio output.5. Cost of Tokens:
- Generative AI APIs typically charge based on the number of tokens processed, including both the input and generated tokens.
- Text: If you input a sentence with 10 tokens and the AI generates a response with 20 tokens, you are charged for 30 tokens.
- Images: Charges may be based on the complexity and number of tokens required to generate an image.
- Audio: Costs could depend on the length and complexity of the audio generated or processed.In summary, tokens are basic yet powerful units of data in AI. They serve as the foundation for processing and learning from various data types, such as text, images, and sounds. The concept of tokens is crucial for enabling AI to interpret, generate, and respond to diverse forms of data efficiently.