My ChatGPT Business Plan: A Cautionary Tale
▼ Summary
– ChatGPT makes errors in long-form conversations, especially with complex variables like financial forecasting, requiring extensive verification.
– The AI frequently forgets key assumptions, contradicts its own data, and provides incorrect calculations without explanation.
– These errors occur because large language models lack true reasoning and have memory limitations in their context windows.
– Despite these flaws, ChatGPT offers valuable assistance by generating content and maintaining conversational relevance over time.
– Users must remain vigilant, double-check all outputs, and may benefit from technical solutions like retrieval-augmented generation (RAG) for stability.
Crafting a detailed business plan with ChatGPT reveals both the promise and pitfalls of relying on generative AI for complex, multi-step projects. While the technology offers impressive support in generating financial models, tables, and strategic outlines, users must remain vigilant, errors frequently emerge, especially in extended conversations involving numerous variables. The experience underscores a critical reality: without robust verification, AI-generated business plans can lead to costly miscalculations.
During a recent experiment using OpenAI’s GPT-5 model, I worked with ChatGPT to develop a three-year growth strategy for a subscription-based newsletter. The goal was straightforward: scale from 250 to 10,000 subscribers while accounting for churn, ad spend, and revenue projections. ChatGPT proved capable of constructing detailed Excel tables, adjusting assumptions like subscriber acquisition cost, and visualizing cash flow trends. At first, the collaboration felt productive and intuitive.
But as the session progressed, inconsistencies began to surface. In one instance, the model claimed the business would reach profitability around month 43, despite its own spreadsheet clearly indicating a positive turn by month 10. When challenged, ChatGPT acknowledged the mistake cheerfully, “and that’s on me”, but similar errors soon followed. It later misidentified the break-even point, forgetting the foundational assumption that we started with 250 subscribers, not zero.
These weren’t isolated incidents. When calculating terminal value, the estimated worth of the business once growth stabilizes, ChatGPT cited two different subscriber counts within minutes: 10,228 and 9,200. There was no explanation for the discrepancy, only a quick apology. Each error required manual correction, pulling attention away from creative brainstorming and toward tedious fact-checking.
The root of the problem lies in how large language models handle memory and context. Unlike human collaborators, ChatGPT lacks true reasoning capabilities. It references a “context window” of prior dialogue but often fails to uphold consistency, especially when discussions grow long and layered. Key assumptions slip away, numbers change without warning, and tables suddenly omit critical values. In one frustrating sequence, the model used an incorrect subscription price not once, but twice, throwing revenue calculations entirely off track.
This phenomenon isn’t limited to financial modeling. Similar issues arise in other complex tasks, like translating multi-page documents or structuring legal drafts. In another test involving poetry translation, ChatGPT omitted entire stanzas and inserted content not present in the original, a clear sign that reliability remains a work in progress.
Still, it’s important to acknowledge what generative AI does well. ChatGPT excels at supplying formulas, clarifying concepts, and maintaining thematic coherence throughout a conversation. It eliminates the need to constantly look up terms or equations, and it adapts quickly to new instructions. For brainstorming and initial structuring, it’s a powerful tool.
But when precision matters, human oversight is non-negotiable. Without enterprise-grade solutions like retrieval-augmented generation (RAG), which anchors key data points in external databases, users must adopt the role of vigilant editor. Every output should be cross-checked, every assumption reconfirmed.
In the end, working with ChatGPT on a business plan felt like playing whack-a-mole: just as one error was resolved, another popped up. The time saved in drafting was often lost in corrections. For now, the most practical approach is to embrace AI as a collaborative aid, not an autonomous expert. Keep the coffee brewing, stay alert, and always, always, verify.
(Source: ZDNET)
