One specific, and particularly annoying, phrase for entrepreneurs building specialized AI has gained traction: “GPT wrapper.” With thousands of AI tools now available, many of them on platforms like the OpenAI Marketplace, it's becoming increasingly common for applications to be built as wrappers around powerful language models like ChatGPT. These wrappers represent a significant portion of the AI market, providing interfaces that leverage the pre-trained capabilities of GPT models.

Today, there are what feels like endless ChatGPT powered applications available, ranging from writing assistants to chatbots, and the number is only growing. According to OpenAI “over 3 million custom versions of ChatGPT” already exist. Great for them, maybe closer to “meh” for you and me.

While the on demand nature and surface-level utility of these wrappers are undeniable, there’s a growing need to differentiate between superficial wrappers and valuable AI applications with specific and tangible business value.

So, What is a GPT ( generative pre-trained transformer, which is a type of artificial intelligence language model that's used to create human-like text or content broadly) Wrapper really?

A GPT wrapper is essentially an application or tool that utilizes a pre-trained GPT model to generate text-based responses. It acts as a 'wrapper' by providing an interface or framework that interacts with the underlying GPT model, tailoring its responses for specific tasks or industries. Wrappers vary in complexity; some merely offer a new skin with little to no added value.

Some examples of focused use cases that benefit from wrapping (or at least at some point did most likely):

• Virtual Writing Assistants: Tools like Grammarly use AI to enhance grammar checking and writing suggestions.
• Content Creation Platforms: Apps like Copy.ai and Writesonic generate marketing copy, blog posts, and social media content.
• Customer Support Chatbots: Many chatbots on e-commerce sites that handle basic queries and provide product information are powered by GPT models.
• Email Automation Tools: Some CRM systems use GPT to draft personalized email responses and follow-ups.
• Language Translation Tools: Certain translation apps use GPT to enhance the accuracy and fluency of translations.
• Legal Document Drafting: Applications that draft basic legal documents or contracts often use GPT models to provide templates and suggestions.
• Education Tutors: AI-based tutoring apps can use GPT to explain concepts, answer questions, and even grade assignments.
• Medical Information Services: Some health apps use GPT to offer general medical advice and information, though not for diagnostic purposes.
• SEO Optimization Tools: Tools like SurferSEO use GPT to suggest keywords, meta descriptions, and content outlines.
• News Aggregators: AI-driven news platforms might use GPT to summarize articles and provide briefings on current events.

The Good and the Bad Wrappers:

Good Example: Jasper.ai

Jasper.ai is a (mostly) successful example of a GPT wrapper because it goes beyond merely presenting a GPT interface. Here's how it excels:

Customization: Jasper offers industry-specific templates that cater to marketing, sales, and content creation needs, providing more relevant and targeted outputs.

Tone and Style Adjustments: Users can set the tone and style of the content, allowing Jasper to produce text that matches brand voice or desired communication style.

Workflow Integration: It integrates with various content management systems and tools, fitting seamlessly into users' existing workflows, thereby enhancing productivity.

Collaborative Features: Jasper supports team collaboration, enabling multiple users to contribute and edit content in real time, which is crucial for marketing teams and content creators.

Bad Example: Basic Chatbot Implementations

In contrast, some basic chatbots merely act as a façade for the GPT model without significant customization:

Generic Responses: They often generate one-size-fits-all responses, lacking the ability to engage users with tailored answers or deep context understanding.

Limited Memory: These chatbots typically don't remember previous interactions or customer history, making follow-up conversations disjointed and less effective.

No (or very minimal) Domain-Specific Training: Without incorporating specific industry knowledge or context, these chatbots provide commoditized information that might not be accurate or useful for complex queries.

While GPT wrappers have their uses, they come with inherent limitations:

Lack of Deep Understanding: Wrappers rely on the surface-level capabilities of the GPT model. For example, a basic chatbot might provide general information about a product, but if a customer asks about a nuanced feature or compares it with a competitor’s product, the chatbot may struggle to provide accurate, detailed responses.

Dependence on A Pre-Trained Model: Wrappers often cannot function independently if the core GPT model changes or goes offline. For instance, during a temporary outage of the GPT model, a customer support chatbot relying solely on GPT would be rendered useless.

Inability to Adapt in Real-Time: Wrappers typically lack real-time adaptability. Consider a customer support chatbot that handles hundreds of different queries; if it’s merely a wrapper, it may not adapt its responses based on ongoing interaction history, leading to frustratingly repetitive or irrelevant answers.

Limited Context Awareness: Without integration of advanced memory or context management, wrappers can’t remember past interactions. For example, a returning customer might mention a previous issue, but the wrapper won't recognize this, resulting in a breakdown in customer experience.

Why GPT Wrappers Struggle as Sales Agents

While GPT wrappers can effectively handle superficial tasks like website qualification, they struggle to act as full-fledged sales agents for several reasons:

Superficial Engagement: Wrappers can gather basic information from visitors, such as contact details or initial interest. However, they often fail to probe deeper or adaptively respond to nuanced customer inquiries, which are essential for sales engagement.

Lack of Persuasion Skills: Real sales agents use emotional intelligence, read non-verbal cues, and adapt their pitches based on the conversation's flow. A wrapper lacks the ability to gauge customer sentiment and adjust accordingly, which can be crucial in closing a deal.

No Relationship Building: Sales is about building relationships over time. Wrappers, especially those without advanced memory and learning capabilities, fail to track customer history and preferences, making it impossible to nurture leads effectively.

Handling Objections: Skilled sales agents anticipate objections and counter them with relevant arguments. A wrapper might provide pre-scripted responses that fall flat in the face of complex or unexpected objections.

Comparing and Contrasting a Wrapper vs. Dedicated AI Sales Agent

A typical scenario: A potential customer visits a website looking to purchase an enterprise software solution.

GPT Wrapper Approach:

• The wrapper greets the customer and asks generic questions like, “What features are you looking for?”
• It provides basic product information and asks if the customer would like to schedule a demo.
• If the customer asks a complex question, such as comparing the software’s scalability against a specific competitor, the wrapper might offer a vague response or redirect the user to a generic FAQ page but most of the time just keeps hounding the person for some sort of contact information.  Not great.

Dedicated Agentic AI Sales Agent Approach:

• The AI agent begins by understanding the customer’s business context, industry, and specific needs, asking pointed questions that reveal specific pain points.
• It provides detailed, tailored responses, drawing from a database of competitive analysis and past interactions.
• The agent uses persuasive techniques, adapting its pitch based on real-time feedback, and proactively addresses potential objections by drawing on a vast repository of sales knowledge.
• It remembers previous interactions with the customer, building a profile that helps tailor follow-up conversations and emails.

Which experience do you want?  Exactly.

The best investors recognize that truly valuable AI goes beyond providing quick, shallow solutions. They look for AI systems that demonstrate depth, adaptability, and a clear understanding of the specific domain they are designed for. Instead of being swayed by flashy presentations or immediate but shallow results, these investors dig deeper, analyzing how the AI handles complex scenarios, integrates with existing workflows, and evolves with usage. They understand that the most successful AI will provide sustainable, specialized solutions that address specific pain points rather than offering broad, generic responses. By focusing on the long-term value and scalability of the AI, these strategic investors are better positioned to identify and support technologies that will truly transform industries and create lasting impact.

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