In this post, I examine the differences between Excel spreadsheets and traditional programming, primarily Java, but with some references to Python. Other computer languages like C and Fortran could be included but that would make the analysis too complex. My major objective is to explore the unique symbolic computing capabilities of the spreadsheet.

This inquiry comes from my long history into inquiring into both areas of computing, mostly spreadsheets. But also my more recent experience in a program that stresses students learn Java and Python. It ends with some speculation about AI and its influence on both.[1]

Excel vs. Java/Python

When we talk about programming, it’s easy to imagine a single tradition — algorithms, variables, loops — the kind of logic you find in computer science textbooks. But spreadsheets operate in a parallel tradition. They use symbolic computing in a grid environment. And when you set Excel side-by-side with Java and Python, the contrasts reveal not just different tools, but two very different ways of thinking about computation, knowledge, and work.

Excel and Java are representatives of two cultural histories of computing. The ledger-turned-simulator, serving the micro-decisions of commerce and finance. And the algorithm-turned-platform, serving the macro-architectures of global software development. Both shape our world, but through different epistemologies, and in service to different forms of power.

Excel is declarative and reactive. You don’t tell Excel how to compute something step-by-step; you state the relationship between values — =SUM(A1:A10). Then let the spreadsheet engine figure out the rest. Users declare relationships between cells; the engine maintains them automatically. The primary metaphor for the spreadsheet is the ledger. Spatial placement and references produce meaning. The “program” is a living grid where changes ripple through automatically with updated information.

Java’s procedural lineage grew from Cold War software engineering. It is procedural and object-oriented. You write explicit instructions: declare variables, loop over arrays, branch with conditions. The “program” is a designed machine — it does nothing until you start it, and it runs in the sequence you prescribe. Likewise, Python is a multi-paradigm programming language that is imperative, procedural, and mostly object-oriented (with functional elements). Users specify step-by-step instructions in human-readable syntax. The primary metaphor is the script, a narrative sequence of commands to be executed.

Python was developed in the early 1990s in the tradition of general-purpose scripting languages like Unix shells and C. It was designed for readability, minimal syntax noise, and broad applicability — from system scripting to AI/ML. Python code runs from top to bottom, unless redirected by flow control. Scripts can be interactive (REPL/Jupyter) or batch-run, but they start from a blank state each run unless persisted.

Excel’s grid is both interface and execution environment. A cell like “B2” is a symbol in a two-dimensional space, not a memory address. Ranges (A1:C3) are symbolic collections, defined by their position in the grid. The layout is the program. In Java, memory is abstract and invisible. You construct arrays, lists, and objects, but they live behind the scenes. The UI, if you build one, is separate from the computation. The layout of your code is not the same as the data it processes.

In Excel, execution is continuous and event-driven. Change one value, and the dependency graph kicks in: only cells that rely on that value are recalculated. There’s no “Run” button for formulas — the sheet is always “on.” In Java, execution is discrete. You compile and run the program, it executes a defined sequence, and it stops. Event-driven behavior (like GUIs) is possible, but it must be explicitly coded with listeners and handlers.

In Excel a cell can hold a number, text, a boolean, or an error without predeclaring its type. This flexibility makes rapid modeling possible, but it also hides data-type errors until they matter. In Java, every variable must have a type before it can be used, and the compiler enforces it. This prevents certain categories of bugs but slows the fluid improvisation that spreadsheets enable.

The spreadsheet user thinks spatially — “This cell depends on that one; those three cells add up to this total.” Programming in Excel feels like maintaining a ledger or building a map — computation embedded in geography.

The Java programmer thinks algorithmically — “First I do this, then I do that.” Programming in Java feels like designing a machine — computation as a process unfolding in time, not space.

Economic and Cultural Lineages

Excel descends from accounting, tabulation, and ledgers. It carries the epistemology of the bookkeeper — numbers aligned in rows and columns, meaning attached to placement, relationships visible at a glance. Java descends from structured programming and software engineering. It carries the epistemology of the engineer — modularity, encapsulation, and abstraction layers designed to scale from small programs to global systems.

Why it matters? Spreadsheets turned computation into a cultural form accessible to non-programmers, embedding symbolic computing into the everyday life of business, government, and finance. Java, and languages like it, built the backbone of global software infrastructure. Both changed the world — but they changed different worlds.

Excel’s “gridmatic” logic empowers rapid prototyping and live simulation, perfect for domains like finance where decisions are model-driven and iterative. Java’s “narrative” logic excels in building robust, reusable systems where execution order and data integrity are paramount.

The Role of AI

Excel’s symbolic, reactive grid and Python/Java’s procedural, text-based scripts represent two mature modes of computation. AI — especially large language models (LLMs) and generative systems — introduces a third mode: probabilistic, pattern-driven inference.

Instead of Excel’s symbolic mapping (“Cell B2 = sum of A1:A10”) and traditional programming’s procedural instruction (“Loop through this array and sum the numbers”), we now get pattern completion (“Given these values and my training data, here’s the sum, the trend, and a prediction — without you explicitly defining the steps”). AI doesn’t just run code — it generates it, interprets messy inputs, and adapts behavior without explicit reprogramming.

Excel is already being transformed into a natural-language modeling surface with Copilot for Excel that lets you type “Show me sales trends for Q3” and get formulas, pivot tables, or charts without knowing the syntax. AI can generate whole spreadsheets from plain text or PDFs, eliminating manual cell-by-cell building. Predictive modeling (e.g., forecasting, anomaly detection) is moving inside Excel’s grid without the user writing formulas.

AI is turning Excel into “spreadsheet as conversation.” The grid remains, but much of the symbolic formula-writing is offloaded to an AI intermediary. Excel’s tabular form will survive because the grid is a powerful cognitive interface. But its internal programming layer will be increasingly AI-written rather than human-written.

For procedural languages, AI is already functioning as a code generator where LLMs write Python/Java from natural language prompts.
It also works in refactoring and as a debugging assistant — turning vague goals into structured, tested, deployable code. AI can be seen as an Integration orchestrator, pulling APIs, databases, and machine learning models together without the developer having to remember all syntax and library calls.

Python in particular is symbiotic with AI — it’s the dominant language for training and deploying models, and AI frameworks (PyTorch, TensorFlow) are Python-native. Java will continue in large-scale systems and enterprise software, with AI increasingly writing parts of it (e.g., data-access layers, boilerplate). Procedural coding won’t vanish — but the human role will shift from “writing code” to “specifying requirements, constraints, and reviewing AI output.”

Excel democratized computing for business users without replacing procedural programming. It just absorbed a huge chunk of modeling work. AI will do the same. It will democratize and accelerate both symbolic and procedural programming, not erase them.

AI will continue to replace the rote coding of standard programming patterns, but humans will still be needed to define problems precisely, decide trade-offs between performance, accuracy, and interpretability, and ensure compliance, security, and business alignment.

Conclusion

Spreadsheets like Excel embody a distinct symbolic computing tradition, separate from conventional programming languages such as Java. Excel is declarative, reactive, spatial, and loosely typed, with computation embedded directly in its two-dimensional grid. Java is procedural, algorithmic, temporal, and strongly typed, with computation separated from its interface.

These differences reflect their cultural and economic lineages. Excel emerges from accounting and tabulation, enabling rapid, accessible modeling for business and finance, while Java descends from structured programming, supporting large-scale, robust software systems. Both have profoundly shaped modern life, but in different domains and through different epistemologies.

Understanding these perspectives side-by-side isn’t just academic. It’s a reminder that “programming” isn’t one thing — it’s a family of practices, each with its own history, cultural lineage, and vision of what computers are for. Digital spreadsheets will continue to be used because it’s a human-readable canvas for understanding and auditing results.

Excel and Python/Java will increasingly become AI-mediated layers in a bigger computational infosystem. Excel will be the visual decision surface while Python/Java are the execution engines and integration layers. AI will be the translator and generator between human intent and machine implementation.

Notes

[1] I wrote my MA thesis on eurodollars and telecommunications deregulation but didn’t appreciate the importance of the spreadsheet until my PhD student days, and my personal experience running a small Center for Modern Media at the University of Hawaii in the summers during graduate school. My overriding purpose is to develop a more sophisticated understanding of the digital spreadsheet and its impact on finance and other social areas.
[2] Many thanks to my Stony Brook colleague at SUNY Korea Antonino N. Mione who teaches the Java course for reviewing this article. Any remaining mistakes are strictly mine. He made a interesting point that I have not integrated into the text yet. “You can write a spreadsheet application in Python or Java, but it would be very hard to develop a Python interpreter or Java compiler simply using an Excel application.”

Prompt: I used a very general ChatGPT prompt to get this conversation started. Describe the symbolic computing functions in spreadsheets and how they differ from traditional computer science concepts. Then I followed up with requests to include Java and Python.

Citation APA (7th Edition)

Pennings, A.J. (2025, Aug 12) Excel vs. Java: Two Languages, Two Worlds of Thought: Symbolic Grid vs. the Algorithmic Script. apennings.com https://apennings.com/meaning-makers/digital-spreadsheets-power-over-people-and-resources/

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Anthony J. Pennings, PhD is a professor at the Department of Technology and Society, State University of New York, Korea and a Research Professor for Stony Brook University. He teaches AI and broadband policy. From 2002-2012 he taught digital economics and information systems management at New York University. He also taught in the Digital Media MBA at St. Edwards University in Austin, Texas, where he lives when not in Korea.

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