Why 1970 Is An Important Year For Generative AI in 2025

“The comparison highlights how foundational technologies evolve to meet new demands—CP/M laid the groundwork for software ecosystems, and Gen AI builds on that to transform industries like procurement.”

This post follows up on the previous Procurement Insights post, A Central AI Operating System For Generative AI.

CP/M (Control Program for Microcomputers) and Generative AI (Gen AI) interfaces may seem worlds apart given their origins—CP/M as a 1970s operating system and Gen AI as a modern AI technology—but they share some conceptual and functional commonalities, especially when viewed through the lens of computing history and their relevance to systems like those in procurement or supply chain contexts (e.g., Kaypro computers, which you asked about earlier). Let’s explore their commonalities.

1. Role as Foundational Technologies

• CP/M:
  • Developed by Gary Kildall in 1974, CP/M was one of the first operating systems for microcomputers, providing a standardized platform for software to run on diverse hardware. It was the backbone for early PCs like the Kaypro II (1982), enabling applications like word processing (e.g., WordStar) and spreadsheets.
  • It introduced key abstractions like the file system, command-line interface (CLI), and hardware abstraction, allowing developers to write software without worrying about specific hardware details.
• Generative AI Interfaces:
  • Gen AI interfaces, like those in modern ProcureTech solutions (e.g., Certinia’s AI for resource summarization), are built on foundational AI models (e.g., large language models like GPT) that abstract complex processes into user-friendly interactions.
  • They provide a standardized way to interact with AI capabilities, enabling applications like automated contract generation, supplier risk analysis, or tariff impact forecasting without users needing to understand the underlying neural networks.
• Commonality: Both CP/M and Gen AI interfaces serve as foundational layers that abstract complexity, enabling broader adoption of technology. CP/M standardized microcomputer software development, while Gen AI interfaces standardize access to advanced AI, making it usable in fields like procurement.

2. Command-Driven Interaction

• CP/M:
  • CP/M used a command-line interface where users typed commands (e.g., DIR to list files, PIP to copy files) to interact with the system. This text-based interaction was the primary way to manage files and run programs on early computers like the Kaypro.
• Generative AI Interfaces:
  • Many Gen AI interfaces, especially in enterprise settings, rely on text-based interactions, often through natural language processing (NLP). For example, a procurement professional might type, “Generate a supplier contract for a $50,000 deal,” and the Gen AI interface (e.g., in a platform like Ivalua) would produce the document.
  • Even graphical Gen AI interfaces (e.g., chatbots in Coupa) often start with text prompts before delivering outputs like reports or visualizations.
• Commonality: Both rely on text-based, command-driven interactions as a core mechanism. CP/M’s CLI and Gen AI’s NLP-based interfaces both allow users to issue instructions in a structured way, bridging human intent with system execution.

3. Enabling Productivity Applications

• CP/M:
  • CP/M powered productivity tools on early microcomputers. Kaypro computers, running CP/M, bundled software like Perfect Writer and Perfect Calc, enabling users to perform tasks like writing (Arthur C. Clarke wrote 2010: Odyssey Two on a Kaypro II) and financial calculations.
  • It provided the runtime environment for these applications, making microcomputers practical for business and personal use.
• Generative AI Interfaces:
  • Gen AI interfaces enable productivity in modern contexts, particularly in procurement. For instance, Certinia’s Gen AI tools generate customer account summaries, while Laserfiche’s RexBuilder allows low-code app development via natural language prompts (CIO, May 2025).
  • In ProcureTech, Gen AI interfaces can automate tasks like drafting RFPs, analyzing supplier data, or predicting tariff impacts, directly enhancing productivity.
• Commonality: Both CP/M and Gen AI interfaces underpin productivity applications. CP/M enabled early business software, while Gen AI interfaces power modern procurement tools, automating and accelerating tasks that once required manual effort.

4. Democratizing Technology Access

• CP/M:
  • CP/M democratized computing by providing a portable OS that ran on multiple hardware platforms (e.g., Kaypro, Osborne 1). This allowed small businesses and individuals to adopt microcomputers affordably—Kaypro II cost $1,795 in 1982 (about $5,800 in 2024), with bundled software worth over $1,000.
  • It lowered the barrier to entry for computing, contributing to the PC revolution before MS-DOS took over.
• Generative AI Interfaces:
  • Gen AI interfaces democratize AI by making advanced capabilities accessible to non-technical users. In ProcureTech, platforms like Coupa or Ivalua use Gen AI to simplify complex tasks—e.g., a procurement manager can ask, “Which suppliers avoid high tariffs?” and get an AI-generated answer without needing data science skills.
  • This accessibility drives adoption in procurement, where 60% of leaders cite budget and skill gaps as barriers to automation (Procurement Magazine, January 2025).
• Commonality: Both technologies lower barriers to adoption, making advanced capabilities accessible. CP/M brought computing to the masses, while Gen AI interfaces bring AI to procurement professionals, enabling broader use without deep technical expertise.

5. Evolution of User Interaction

• CP/M:
  • CP/M’s CLI was a significant evolution in user interaction at the time, moving away from punch cards or toggle switches to a text-based interface. It allowed users to directly control the system, though it required learning specific commands.
• Generative AI Interfaces:
  • Gen AI interfaces represent a modern evolution, using NLP to interpret natural language inputs, making interactions more intuitive. For example, Dashworks’ AI search lets users query enterprise data conversationally (e.g., “Find supplier contracts from 2024”), a leap from rigid command structures.
• Commonality: Both mark evolutionary steps in human-computer interaction. CP/M’s CLI was a precursor to today’s interfaces, and Gen AI builds on that legacy by making interactions more natural and user-friendly, especially in ProcureTech applications.

Critical Perspective

While CP/M and Gen AI interfaces share these commonalities, their contexts differ vastly. CP/M was a product of hardware constraints in the 1970s, focusing on basic functionality for early PCs, whereas Gen AI interfaces leverage massive computational power and data to deliver sophisticated outputs in 2025. In procurement, CP/M enabled early digital workflows (e.g., Kaypro’s bundled software for financial tracking), but Gen AI interfaces go further, addressing complex challenges like tariff mitigation through predictive analytics (Supply Chain Dive, November 2024). The comparison highlights how foundational technologies evolve to meet new demands—CP/M laid the groundwork for software ecosystems, and Gen AI builds on that to transform industries like procurement.

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