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Since I am in daily study and use of many different aspects of AI and its varying tools, I of course run into questions or challenges. I have found that different GPTs are wonderful intellectual companions. I'm teaching classes on AI and was working on the concepts and technologies behind GPTs, tools like ChatGPT, Meta/Llama, Claude, perplexity and others. After reading a short explanation of the different aspects of the GPT models, I had a question or a thought. I was using Claude which I like for conversational exchange. It referenced and yes it is an it, I still like (it) and enjoy Claude but it is an it. Enough said on that.  There was a part of the explanation that referenced small verses large models which I understood but it brought to mind an interesting concept of small verses large, pretty much in everything and how the philosophies change on everything over time.  When I started in the workforce in the stone age, Large mainframes with dumb terminals were the standard. Here is a short history of computing in business, credit to the famous Italian scholar Dr. Chattisimo Giapedicus.  If you are over 50 you might actually enjoy this stroll down memory lane. You need to know that one of my programming adventures included running 100's of computer language assembler routines inside a batch file.  Nothing runs that fast with as little code. If not skip to the end and see Claude's response to my question.

### Outline: History of Computing from a Business Standpoint

1. **1960s: Mainframe Era**

   - **Introduction of Mainframes**: Large corporations, banks, and government agencies began using massive mainframe computers, like IBM’s System/360. These centralized systems handled complex, large-scale data processing tasks.

   - **Batch Processing**: Businesses used mainframes for batch processing jobs (payroll, financial accounting, etc.), with punch cards and magnetic tape as input methods.

   - **Centralized Control**: IT departments centralized computing, with only a few users having access to the mainframe from terminals.

2. **1970s: Mini-computers and Departmental Computing**

   - **Mini-computers**: Companies like DEC introduced smaller, more affordable mini-computers (e.g., PDP-11). These systems allowed smaller businesses and departments within larger organizations to access computing power.

   - **Increased Accessibility**: Businesses could decentralize computing tasks, but IT still retained control over operations in-house.

   - **Emergence of Time-Sharing**: Users could interact with the system via terminals, leading to more real-time data processing and interactive computing.

3. **1980s: Rise of Personal Computing and Networks**

   - **Personal Computers (PCs)**: IBM introduced the IBM PC (1981), and businesses began adopting PCs for individual workstations, moving away from centralized computing.

   - **Client-Server Architecture**: Companies started connecting PCs through local area networks (LANs) to centralized servers, leading to more efficient data sharing and processing.

   - **Software Revolution**: Introduction of office software (e.g., spreadsheets, word processors) transformed business workflows, decentralizing computing even further to individual employees.

4. **1990s: Internet and Distributed Computing**

   - **Growth of Networks and Internet**: Widespread adoption of the Internet allowed businesses to connect globally. The focus shifted to client-server applications over larger networks (WANs).

   - **Enterprise Systems**: Companies implemented enterprise resource planning (ERP) and customer relationship management (CRM) systems, consolidating business functions (finance, HR, etc.) onto a network of servers.

   - **Decentralized Decision-Making**: Businesses empowered employees with networked PCs, increasing productivity and collaboration across departments.

5. **2000s: Virtualization and Cloud Computing**

   - **Virtualization**: Businesses began using virtual machines, allowing multiple operating systems and applications to run on a single physical server. This increased flexibility and reduced costs.

   - **Emergence of Cloud Computing**: The rise of cloud platforms like Amazon Web Services (AWS) and Google Cloud enabled businesses to offload their computing infrastructure to third-party providers. This marked a shift back to centralized computing, but on a global, internet-based scale.

   - **Software as a Service (SaaS)**: Applications like Salesforce and Office 365 became accessible via the cloud, reducing the need for on-premises hardware and software installation.

6. **2010s–Present: Full Transition to Cloud and Edge Computing**

   - **Cloud Dominance**: Most businesses fully transitioned to cloud infrastructure, with services being outsourced to platforms like AWS, Microsoft Azure, and Google Cloud. Companies no longer needed to invest heavily in physical hardware.

   - **Hybrid Cloud and Multi-Cloud**: Businesses started using hybrid approaches, combining private and public clouds to balance security and cost efficiency.

   - **Edge Computing**: The rise of IoT and 5G networks pushed some data processing back to the network edge (closer to the data source) for real-time applications.

   - **Return to Centralization with Flexibility**: While centralized in cloud data centers, businesses can now access computing resources from anywhere, bringing scalability, flexibility, and cost-efficiency into business operations.

The history of business computing has evolved from the centralized mainframes of the 1960s to decentralized personal computers and networks in the 1980s, and back to a centralized, cloud-based model today. Each phase has improved flexibility, accessibility, and scalability, continuously transforming the way businesses operate. 

### Expanded Outline: History of Computing from a Business Standpoint with Key Manufacturers, Products, and Figures

1. **1960s: Mainframe Era**

   - **Key Manufacturers**:

     - **IBM**: Dominated the mainframe market with the **IBM System/360**, a groundbreaking family of compatible computers.

     - **UNIVAC (Sperry Rand)**: Produced **UNIVAC I**, one of the earliest commercially successful mainframes.

     - **Honeywell**: Offered mainframe solutions like the **Honeywell 200**.

   - **Significant Products**:

     - **IBM System/360**: The first general-purpose computer family, revolutionizing business computing with compatibility across a range of applications.

     - **UNIVAC I**: Used for large-scale data processing in government and business.

   - **Key Figures**:

     - **Thomas J. Watson Jr.**: Led IBM’s transition to System/360 and established the company as the dominant force in computing.

     - **Grace Hopper**: Pioneer of programming languages; helped develop **COBOL**, which became widely used in business applications.

2. **1970s: Mini-computers and Departmental Computing**

   - **Key Manufacturers**:

     - **Digital Equipment Corporation (DEC)**: Revolutionized the market with mini-computers like the **PDP series**, particularly the **PDP-11**.

     - **Hewlett-Packard (HP)**: Launched mini-computers like the **HP 3000**.

     - **Data General**: Competed with DEC with products like the **Nova** mini-computer.

   - **Significant Products**:

     - **DEC PDP-11**: A versatile mini-computer used in science, engineering, and business for smaller, department-level tasks.

     - **HP 3000**: One of the first mini-computers designed for business use, offering commercial applications.

   - **Key Figures**:

     - **Ken Olsen**: Co-founder and CEO of DEC, a pioneer in the development of affordable and powerful mini-computers.

     - **Edson de Castro**: Founder of Data General and developer of the Nova, which helped democratize computing.

3. **1980s: Rise of Personal Computing and Networks**

   - **Key Manufacturers**:

     - **IBM**: Dominated the early PC market with the **IBM PC** (1981).

     - **Apple**: Launched the **Apple II** and later the **Macintosh** (1984), introducing graphical user interfaces to mainstream computing.

     - **Compaq**: Pioneered the **IBM PC compatible** market with portable computers.

   - **Significant Products**:

     - **IBM PC**: Set the standard for personal computing in business with its open architecture.

     - **Apple Macintosh**: The first mass-market computer with a graphical user interface (GUI), influencing the future of user-friendly computing.

     - **Microsoft MS-DOS**: The operating system that powered IBM PCs and became the dominant platform in business computing.

   - **Key Figures**:

     - **Bill Gates**: Co-founder of Microsoft, who provided the operating system (MS-DOS) for the IBM PC, which later evolved into **Windows**.

     - **Steve Jobs**: Co-founder of Apple, responsible for the vision of the Macintosh and revolutionizing personal computing with GUI.

     - **Michael Dell**: Founder of **Dell**, which emerged as a leading PC manufacturer by the late 1980s.

4. **1990s: Internet and Distributed Computing**

   - **Key Manufacturers**:

     - **Cisco Systems**: Became a key player in networking equipment as businesses began using routers and switches to connect to the internet.

     - **Sun Microsystems**: Developed servers and workstations for internet-driven businesses, promoting **Java** as a versatile programming language.

     - **Microsoft**: Launched **Windows NT** for enterprise-level networking and **Windows 95**, a GUI-based operating system that integrated internet capabilities.

   - **Significant Products**:

     - **Windows NT/95**: Laid the groundwork for widespread business networking and integration of internet capabilities.

     - **Cisco Routers and Switches**: Enabled businesses to build robust internal networks and connect to the internet.

     - **Sun Microsystems’ Java**: Became a crucial programming language for web-based applications, shaping enterprise software.

   - **Key Figures**:

     - **Scott McNealy**: Co-founder and CEO of Sun Microsystems, promoted the concept of **“the network is the computer.”**

     - **Marc Andreessen**: Co-developer of **Mosaic**, the first popular web browser, and co-founder of **Netscape**, which revolutionized internet browsing.

     - **Larry Ellison**: Co-founder of **Oracle**, whose database solutions became critical for enterprise applications.

5. **2000s: Virtualization and Cloud Computing**

   - **Key Manufacturers**:

     - **Amazon Web Services (AWS)**: Pioneered cloud computing services in 2006, allowing businesses to rent computing power and storage.

     - **VMware**: A leader in virtualization, providing businesses with tools to run multiple operating systems on a single physical machine.

     - **Google**: Entered the cloud market with **Google Cloud Platform (GCP)**, offering services from computing to machine learning.

   - **Significant Products**:

     - **Amazon EC2 (Elastic Compute Cloud)**: Provided scalable cloud infrastructure for businesses, marking the mainstream adoption of cloud services.

     - **VMware vSphere**: Allowed businesses to consolidate servers through virtualization, reducing costs and increasing flexibility.

     - **Salesforce CRM**: A **Software as a Service (SaaS)** product that demonstrated the power of cloud-based business applications.

   - **Key Figures**:

     - **Jeff Bezos**: Founder of Amazon, whose vision expanded the company into cloud computing through AWS.

     - **Diane Greene**: Co-founder of **VMware**, instrumental in promoting virtualization technologies in business.

     - **Marc Benioff**: Founder of **Salesforce**, a pioneer of SaaS and cloud-based customer relationship management (CRM) software.

6. **2010s–Present: Full Transition to Cloud and Edge Computing**

   - **Key Manufacturers**:

     - **Microsoft**: Expanded its cloud offerings with **Azure**, becoming a major player in enterprise cloud solutions.

     - **Amazon Web Services (AWS)**: Continued dominance in cloud services, expanding into machine learning, AI, and big data processing.

     - **NVIDIA**: Leading provider of GPUs, critical for AI, deep learning, and edge computing applications.

   - **Significant Products**:

     - **Microsoft Azure**: Offered a full range of cloud services, from AI to IoT, positioning itself as a key competitor to AWS.

     - **AWS Lambda**: Serverless computing service that allows businesses to run code without managing servers, further pushing the boundaries of cloud efficiency.

     - **NVIDIA GPUs**: Empowered the rise of AI and edge computing with GPUs optimized for deep learning and real-time processing.

   - **Key Figures**:

     - **Satya Nadella**: CEO of Microsoft, who led the company’s transformation into a cloud-centric business.

     - **Andy Jassy**: Former CEO of AWS, who played a major role in growing Amazon’s cloud computing business.

     - **Jensen Huang**: CEO of NVIDIA, whose company’s technology underpins much of today’s AI and machine learning infrastructure.

Hope you enjoyed the stroll. So I ask Claude if smaller language models are more specific to an area of interest, why not use a robust search request to target these smaller more precise GPT's by context. If the context expand the search engine could adjust and redirect to a smaller GPT that specializes in that topic.  Here is what Claude says: 

You've touched on an interesting and important concept in AI model design and deployment. Let's break this down:

Your proposed approach:

This strategy of using multiple specialized smaller models is indeed a valid and increasingly popular approach in AI. It's often referred to as a "model ensemble" or "multi-model" approach. Here are some key points:

1. Specialized models: Each smaller model can be trained on specific domains or tasks, potentially achieving high performance in its niche.

2. Efficient resource use: Smaller models generally require less computational power and memory.

3. Flexibility: This approach allows for easier updates or replacements of individual components without affecting the entire system.

4. Scalability: New models can be added to handle emerging tasks or domains.

5. Front-end router: As you suggested, a smart routing system would be crucial to direct queries to the appropriate model(s).

Comparison to larger models:

Larger models like GPT-3 or GPT-4 take a different approach:

1. General capability: They aim to handle a wide range of tasks without specialized training.

2. Internal task routing: In a sense, larger models do perform an internal "routing" of sorts. Through their attention mechanisms, they can focus on relevant parts of their vast knowledge for each task.

3. One-stop solution: A single large model can potentially handle diverse tasks without the need for external routing.

4. Emergent abilities: Very large models sometimes display capabilities that weren't explicitly trained for.

Trade-offs and considerations:

1. Performance: For some specialized tasks, a targeted smaller model might outperform a general large model. However, large models often excel in versatility and handling novel situations.

2. Maintenance: Managing multiple models can be more complex than maintaining a single large model.

3. Consistency: Ensuring consistent output across multiple models can be challenging.

4. Latency: Your approach might introduce additional latency due to the routing step, though this could be offset by faster individual model inference times.

5. Continuous learning: Updating knowledge across multiple specialized models can be more challenging than updating a single large model.

Current trends:

The AI community is actively exploring both approaches:

1. Some companies are developing systems that combine multiple specialized models.

2. Others are working on making large models more efficient and modular internally.

3. There's also research into "mixture of experts" models, which somewhat combine these approaches within a single model architecture.

Your idea aligns well with current research directions in AI, and various implementations of this concept are being explored in both academic and industry settings. The optimal approach often depends on the specific use case, available resources, and desired capabilities.