AI models are not an immediate threat to Excel because they are designed for approximation, not the precise computation required for financial and data analysis. Their 'black box' nature also contrasts with a spreadsheet's core value proposition: transparent, verifiable calculations that users can trust.
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Previously, data analysis required deep proficiency in tools like Excel. Now, AI platforms handle the technical manipulation, making the ability to ask insightful business questions—not technical skill—the most valuable asset for generating insights.
For data-heavy queries like financial projections, AI responses should transcend static text. The ideal output is an interactive visualization, such as a chart or graph, that the user can directly manipulate. This empowers them to explore scenarios and gain a deeper understanding of the data.
Founders are consistently and universally wrong about their financial projections, particularly cash runway. AI tools can provide an objective, data-driven forecast based on trailing growth, correcting for inherent founder optimism and preventing critical miscalculations.
Despite hype about full automation, AI's real-world application still has an approximate 80% success rate. The remaining 20% requires human intervention, positioning AI as a tool for human augmentation rather than complete job replacement for most business workflows today.
Unlike deterministic SaaS software that works consistently, AI is probabilistic and doesn't work perfectly out of the box. Achieving 'human-grade' performance (e.g., 99.9% reliability) requires continuous tuning and expert guidance, countering the hype that AI is an immediate, hands-off solution.
AI can quickly find data in financial reports but can't replicate an expert's ability to see crucial connections and second-order effects. This leads investors to a false sense of security, relying on a tool that provides information without the wisdom to interpret it correctly.
AI struggles with tasks requiring long and wide context, like software engineering. Because adding a linear amount of context requires an exponential increase in compute power, it cannot effectively manage the complex interdependencies of large projects.
A new technology's adoption depends on its fit with a profession's core tasks. Spreadsheets were an immediate revolution for accountants but a minor tool for lawyers. Similarly, generative AI is transformative for coders and marketers but struggles to find a daily use case in many other professions.
Excel's market dominance stems from Microsoft's strategy of bundling it into the non-negotiable Microsoft Office suite. This made it impossible for enterprise customers to purchase software à la carte, effectively locking out competitors and making individual user preference irrelevant.
The existential threat from large language models is greatest for apps that are essentially single-feature utilities (e.g., a keyword recommender). Complex SaaS products that solve a multifaceted "job to be done," like a CRM or error monitoring tool, are far less likely to be fully replaced.