AI and the Monkey’s Paw: Why SaaS Will Endure
In W.W. Jacobs’ The Monkey’s Paw, a family’s wishes are granted in horrifyingly unintended ways. Each wish is fulfilled literally, but without understanding nuance or context, leading to outcomes that are technically correct but tragically wrong. The story underscores a timeless truth: poorly expressed or misinterpreted intent can lead to disastrous results. This cautionary tale feels especially relevant to the future of AI and SaaS. Some predict SaaS platforms will become obsolete as AI evolves to interpret user intent directly, dynamically generating workflows, tools, or code. Why rely on predefined systems when AI can seemingly create everything on the fly? It’s an exciting idea but overlooks a critical issue: human intent is rarely clear, and generative AI is inherently unpredictable. Without a structure to clarify and align intent, we risk the same problem as the Monkey’s Paw: systems fulfilling requests in ways that miss the mark — or worse, cause harm. This is why SaaS isn’t going anywhere. As automation takes center stage, SaaS provides cognitive frameworks — structured concepts that organize, validate, and align intent with action. These frameworks ensure technology delivers outcomes that match goals and values, even when intent is messy, ambiguous, or incomplete. Why the “Direct Intent” Model Falls Apart The idea of bypassing SaaS entirely sounds appealing: imagine asking AI to “find the most valuable customers” or “optimize system performance” and having it execute seamlessly. No SaaS tools, no predefined workflows — just AI accessing a database directly. The problem? Human intent is rarely straightforward. A request like “find the most valuable customers” leaves much open to interpretation. Does “most valuable” mean those who spend the most, have the longest relationships, or offer the best profit margins? Without clarification, the system will choose one interpretation, which might not align with your goals. This is the core flaw of a direct intent-to-database model: it assumes perfect clarity in communication, but humans rarely operate that way. As complexity increases, so does the risk of misaligned outcomes. Without structured systems to refine and guide intent, results can easily become misinterpreted, misaligned, or even harmful. Why SaaS Matters: Cognitive Frameworks SaaS platforms provide more than task automation or data management — they offer cognitive frameworks that shape and refine intent. Consider the spreadsheet. At first glance, it’s just a grid of rows and columns. But its real power lies in how it helps users structure and clarify goals like “track project budgets” or “analyze sales data” into actionable formats with labeled columns, rows of data, and formulas. Spreadsheets don’t just store information — they shape how people think about data, forcing users to define what’s needed, how it relates, and what outcomes to measure. More complex SaaS tools extend this principle, acting as intermediaries between messy human intent and precise machine execution. Cognitive frameworks refine intent by breaking abstract goals into manageable components, aligning them with best practices, and validating them against known rules. They foster shared understanding, ensuring teams and systems are aligned. Without these frameworks, workflows would devolve into chaos, and AI’s probabilistic outputs would be far less reliable. Why SaaS Will Endure SaaS platforms provide more than software — they offer scaffolding that bridges the gap between human intent and machine action. By refining, validating, and aligning goals, SaaS ensures systems deliver what users need, not just what they request. As AI becomes more integrated into workflows, SaaS will evolve to harness AI’s dynamic capabilities while retaining its core role as a reliable structure. Some who predict the end of SaaS may specifically mean the disappearance of manual coding in SaaS. While AI might generate code dynamically, this assumption overlooks a fundamental challenge. There are compelling reasons why code — especially prewritten and tested code — will likely endure. AI-generated code, by its very probabilistic nature, will always carry a degree of unpredictability. No matter how advanced AI becomes, it cannot surpass the consistency, stability, and reliability of code that has been rigorously tested and refined over time. When predictability and certainty are non-negotiable, traditional prewritten code provides a level of assurance that probabilistic systems inherently cannot match. The promise of AI is vast, but intent is often more ambiguous than we realize. Without frameworks to refine and structure it, we risk outcomes that technically fulfill our requests but deviate from our true goals or values — a modern-day Monkey’s Paw scenario. SaaS prevents this by offering clarity and structure, ensuring human intent is effectively translated into actionable and meaningful results. It’s not ju
In W.W. Jacobs’ The Monkey’s Paw, a family’s wishes are granted in horrifyingly unintended ways. Each wish is fulfilled literally, but without understanding nuance or context, leading to outcomes that are technically correct but tragically wrong. The story underscores a timeless truth: poorly expressed or misinterpreted intent can lead to disastrous results.
This cautionary tale feels especially relevant to the future of AI and SaaS. Some predict SaaS platforms will become obsolete as AI evolves to interpret user intent directly, dynamically generating workflows, tools, or code. Why rely on predefined systems when AI can seemingly create everything on the fly?
It’s an exciting idea but overlooks a critical issue: human intent is rarely clear, and generative AI is inherently unpredictable. Without a structure to clarify and align intent, we risk the same problem as the Monkey’s Paw: systems fulfilling requests in ways that miss the mark — or worse, cause harm. This is why SaaS isn’t going anywhere. As automation takes center stage, SaaS provides cognitive frameworks — structured concepts that organize, validate, and align intent with action. These frameworks ensure technology delivers outcomes that match goals and values, even when intent is messy, ambiguous, or incomplete.
Why the “Direct Intent” Model Falls Apart
The idea of bypassing SaaS entirely sounds appealing: imagine asking AI to “find the most valuable customers” or “optimize system performance” and having it execute seamlessly. No SaaS tools, no predefined workflows — just AI accessing a database directly.
The problem? Human intent is rarely straightforward. A request like “find the most valuable customers” leaves much open to interpretation. Does “most valuable” mean those who spend the most, have the longest relationships, or offer the best profit margins? Without clarification, the system will choose one interpretation, which might not align with your goals.
This is the core flaw of a direct intent-to-database model: it assumes perfect clarity in communication, but humans rarely operate that way. As complexity increases, so does the risk of misaligned outcomes. Without structured systems to refine and guide intent, results can easily become misinterpreted, misaligned, or even harmful.
Why SaaS Matters: Cognitive Frameworks
SaaS platforms provide more than task automation or data management — they offer cognitive frameworks that shape and refine intent. Consider the spreadsheet. At first glance, it’s just a grid of rows and columns. But its real power lies in how it helps users structure and clarify goals like “track project budgets” or “analyze sales data” into actionable formats with labeled columns, rows of data, and formulas.
Spreadsheets don’t just store information — they shape how people think about data, forcing users to define what’s needed, how it relates, and what outcomes to measure. More complex SaaS tools extend this principle, acting as intermediaries between messy human intent and precise machine execution.
Cognitive frameworks refine intent by breaking abstract goals into manageable components, aligning them with best practices, and validating them against known rules. They foster shared understanding, ensuring teams and systems are aligned. Without these frameworks, workflows would devolve into chaos, and AI’s probabilistic outputs would be far less reliable.
Why SaaS Will Endure
SaaS platforms provide more than software — they offer scaffolding that bridges the gap between human intent and machine action. By refining, validating, and aligning goals, SaaS ensures systems deliver what users need, not just what they request. As AI becomes more integrated into workflows, SaaS will evolve to harness AI’s dynamic capabilities while retaining its core role as a reliable structure.
Some who predict the end of SaaS may specifically mean the disappearance of manual coding in SaaS. While AI might generate code dynamically, this assumption overlooks a fundamental challenge. There are compelling reasons why code — especially prewritten and tested code — will likely endure. AI-generated code, by its very probabilistic nature, will always carry a degree of unpredictability. No matter how advanced AI becomes, it cannot surpass the consistency, stability, and reliability of code that has been rigorously tested and refined over time. When predictability and certainty are non-negotiable, traditional prewritten code provides a level of assurance that probabilistic systems inherently cannot match.
The promise of AI is vast, but intent is often more ambiguous than we realize. Without frameworks to refine and structure it, we risk outcomes that technically fulfill our requests but deviate from our true goals or values — a modern-day Monkey’s Paw scenario. SaaS prevents this by offering clarity and structure, ensuring human intent is effectively translated into actionable and meaningful results. It’s not just software — it’s the foundation for aligning intent with desired outcomes.
