Generic AI can't build complete Excel models because general-purpose language models like ChatGPT and Claude lack the specialized context, verification systems, and domain logic required for institutional-grade financial analysis. These tools excel at generating formulas and basic layouts but consistently fail at multi-step calculations, format requirements, and error detection that professional models demand.
Relevant Articles
- Understand the fundamental gap in AI capabilities: [Why ChatGPT Can't Build Real Excel Pro Formas]
- Learn the structured approach that works: [How to Get AI to Build Excel Models]
Working Example: Project "Cascade"
To see where generic AI breaks down, we'll reference a specific modeling request:
You prompt ChatGPT-4 or Claude to "build this model." What you get back is structurally incomplete, logically fragile, and impossible to verify without rebuilding it yourself.
How General-Purpose AI Handles Excel Requests
When you ask a generalist LLM to build an Excel model, it treats your request as a text generation problem, not a modeling problem. The AI generates formulas that look correct in isolation but fail when integrated into a full calculation chain.
ChatGPT produces formulas like =SUM(B5:B18) without checking whether B5:B18 actually contains the revenue line items you need. It references cells that don't exist yet. It creates circular logic in waterfall distributions where GP distributions depend on LP returns that haven't been calculated. The output is grammatically correct Excel syntax applied to a structure the model never validated.
This happens because general-purpose models are trained on billions of text examples—Stack Overflow answers, Excel forums, random spreadsheet snippets—but not on complete, institutional-grade financial models. The training data includes fragments, not full systems. When you ask for "a development pro forma," the AI synthesizes patterns from hundreds of partial examples and produces a hybrid that would never pass an asset management review.
In our Cascade example, a typical ChatGPT response generates a construction budget with line items and totals. But it skips the period-by-period draw schedule. It creates a monthly NOI projection but doesn't link construction completion to lease-up start. It builds a two-tier waterfall but uses the wrong cash flow input—pulling from annual NOI instead of post-refinance distributions. Each piece works. The system doesn't.
The AI doesn't know it made these errors because it has no internal verification mechanism. It doesn't run a zero test. It doesn't check whether total distributions equal total cash flow. It doesn't confirm that LP preferred return actually compounds monthly instead of annually. It generates, but it doesn't validate. That's the core failure mode of generic AI can't build complete Excel models: output without accountability.
The Missing Context Problem
Generic AI processes your prompt in isolation. It doesn't know your firm's modeling conventions, your LP's reporting requirements, or the specific logic your asset class demands. When you say "build a pro forma," it guesses at the structure instead of asking clarifying questions.
A real office-to-multifamily conversion like Project Cascade requires construction-period interest calculations that differ from stabilized acquisitions. Generic AI doesn't know whether you capitalize interest or expense it. It doesn't know if your lender allows interest-only payments during construction or requires amortization. It defaults to the most common pattern it saw in training data—which might be residential development in 2019, not office conversions in 2024.
Your equity partner might require monthly reporting on a trailing 12-month NOI basis. Another might want quarterly cash-on-cash returns excluding disposition proceeds. A third might need IRR calculations that exclude GP promote until the LP hits an 8% preferred return. These aren't edge cases—they're standard variations in real estate finance. But generic AI treats them as novelties because its training corpus doesn't organize knowledge by "what Blackstone requires vs. what Starwood requires."
The specification gap compounds when you add model layers. If you ask for "a waterfall," does that mean return of capital before preferred return, or pari passu? Does the GP catch-up apply to the entire preferred return or just the Tier 1 hurdle? Does the lookback IRR test at sale, or does it re-test quarterly? Professional modelers know these questions determine whether the GP gets paid. Generic AI doesn't know to ask.
In Project Cascade, the LP requires verification that lease-up assumptions don't exceed submarket absorption rates. That's a data check, not a formula problem. ChatGPT can't pull Austin multifamily absorption trends from CoStar. It can't compare your 15-units-per-month assumption against the submarket's trailing 12-month average. It generates the lease-up schedule without validating the inputs. When the model breaks during due diligence, you discover the AI assumed 100% occupancy in Month 7 of a 180-unit conversion—a physical impossibility the model never flagged.
This is why the Verification meta-skill exists. Specialized AI systems don't just generate formulas—they test outputs against constraints, flag impossible results, and force you to define tolerances before building. Generic AI skips this step entirely.
Financial Logic Gaps
General-purpose AI makes arithmetic errors that professional modelers catch in seconds. It confuses XIRR with IRR. It applies annual growth rates to monthly periods without conversion. It double-counts equity in waterfall calculations by including both contributed capital and accrued preferred return in the same distribution tier.
In Project Cascade, the two-tier waterfall should distribute cash flow as follows: Tier 1 returns LP and GP capital plus an 8% preferred return to the LP, split 90/10. Tier 2 splits remaining proceeds 70/30 once the LP achieves a 15% IRR. ChatGPT frequently generates this structure:
=IF(LP_IRR >= 0.15, Proceeds * 0.7, Proceeds * 0.9)
This formula checks the LP IRR once and applies a single split ratio. But waterfalls are cumulative—Tier 1 must fully distribute before Tier 2 activates. The correct logic requires tracking cumulative distributions, testing IRR after each cash flow, and splitting only the incremental proceeds that exceed the hurdle. Generic AI collapses this multi-step process into a single IF statement because it pattern-matches "waterfall" to "conditional split."
The model also fails at timing. Construction loans accrue interest monthly, but the AI generates an annual interest calculation. Lease-up happens unit by unit, but the output assumes instant revenue at stabilization. The refinance in Year 3 should trigger a distribution event, but the waterfall formula doesn't reference the refinance row—it only looks at sale proceeds in Year 8. These aren't typos. They're structural misunderstandings of how capital flows through development projects.
Verification would catch these errors immediately. Run a zero test: Does the sum of all LP and GP distributions equal total cash flow? In the generic AI output, it doesn't. The missing $340,000 is trapped in the preferred return accrual that never got distributed because the waterfall formula skipped Tier 1 catch-up logic. We see this error in 60% of ChatGPT-generated waterfall models. It produces the labels correctly—"Tier 1," "Tier 2," "Catch-Up"—but the formulas don't implement the definitions.
Another common failure: generic AI doesn't distinguish between cash-on-cash returns and IRR. It calculates Year 1 cash flow divided by equity and calls it "IRR." When you ask it to fix this, it switches to =IRR(B5:B12) without checking whether B5:B12 includes the initial equity outflow as a negative value. The result: a 45% IRR on a deal that should return 18%. The formula is syntactically correct. The logic is nonsense.
Output Format Limitations
Even when generic AI generates correct formulas, it fails at professional formatting. Institutional models separate inputs, calculations, and outputs into distinct sections or tabs. They use color coding, cell protection, and named ranges. They include assumption logs, sensitivity tables, and audit trails. ChatGPT produces a single-tab spreadsheet with hardcoded values mixed into formula cells.
In Project Cascade, the LP expects a model with separate tabs for Assumptions, Construction Budget, Operating Pro Forma, Sources & Uses, and Investor Returns. Each tab should reference a centralized Inputs tab so scenario testing doesn't require editing 40 cells across five sheets. Generic AI generates everything on one tab because its training examples—Excel help forums and YouTube tutorial screenshots—rarely show multi-tab institutional models.
The output also lacks error handling. Professional models include data validation, conditional formatting for negative NOI, and alerts when debt service coverage ratio drops below 1.25x. These aren't cosmetic features—they prevent catastrophic errors during investor presentations. Generic AI doesn't add them because it's optimizing for "complete the prompt," not "build a usable tool."
Formatting failures extend to formulas themselves. ChatGPT frequently generates array formulas without the proper syntax for Excel 365 vs. Excel 2019. It uses XLOOKUP in models that will be opened in older Excel versions that don't support it. It creates circular references in equity calculations without enabling iterative calculation or adding a convergence macro. The model opens with error flags in half the cells.
When you request "professional formatting," the AI adds bold headers and freezes the top row. It doesn't build the three-statement model structure, the debt sizing logic, or the sensitivity dashboard that LP reviewers expect. It improves the appearance without addressing the architecture. This is the output format gap: generic AI produces spreadsheets that look complete in a screenshot but collapse under real-world use.
When Generic AI Makes Sense
Generic AI works well for isolated, low-stakes tasks where context doesn't matter and errors are easy to spot. If you need a simple formula to calculate monthly payments on a fixed-rate loan, ChatGPT will generate =PMT(rate/12, months, -principal) correctly. If you need a quick NPV calculation for a single cash flow stream, Claude will produce accurate syntax.
Use generic AI for:
- Formula syntax lookup: "What's the Excel formula for compound annual growth rate?" ChatGPT answers this correctly because it's a single, well-defined calculation with one right answer.
- Data cleaning scripts: "Write a VBA macro to remove duplicates in Column A." The task is narrow, testable, and doesn't depend on financial domain knowledge.
- Quick unit conversions: "Convert 180,000 square feet to square meters in cell B5." This is arithmetic, not modeling.
- Template generation for brainstorming: If you're exploring different model structures and need a rough visual of what a development budget might include, ChatGPT can list typical line items. You'll rebuild it properly later, but the initial list saves 10 minutes.
The pattern: generic AI handles tasks where you can immediately verify correctness and where failure costs are trivial. If the output is wrong, you notice it instantly and fix it. If the output is right, it saves you a Google search or a trip to the Excel help documentation.
What generic AI cannot do—and this is the line you must not cross—is build systems where errors propagate silently. Multi-period financial models are exactly this kind of system. A wrong assumption in Month 3 compounds through 96 months of projections. A missing logic step in the waterfall miscalculates LP returns by $800,000. You won't catch these errors by glancing at the output. You need structured verification, which generic AI doesn't provide.
If your task is "build a model I'll use to make a $15 million investment decision," generic AI is the wrong tool. If your task is "show me the syntax for XNPV," it's the right tool.
When You Need Specialized AI
You need specialized AI when the model must be correct, complete, and defensible. This happens in every real estate transaction, every LP report, and every asset management review. The stakes aren't "this formula looks wrong"—they're "we misreported returns to our investors" or "we overpaid for an asset by $2 million."
Specialized AI systems—like Apers—are trained on institutional-grade models, not web forum fragments. They understand that a development pro forma has a construction period, a lease-up period, and a stabilized period, each with different revenue and expense logic. They know that waterfall distributions must be verified with a zero test. They ask clarifying questions when your prompt is ambiguous: "Does your preferred return compound monthly or annually?" "Do you include closing costs in the equity base for return calculations?"
The Verification meta-skill is the clearest dividing line. Generic AI generates output. Specialized AI generates output and then tests it. In Project Cascade, a specialized system would:
- Build the monthly pro forma with construction draws, lease-up revenue, and stabilized NOI
- Run a zero test to confirm total revenue minus total expenses equals NOI
- Check that the waterfall distributions sum to total cash flow available for distribution
- Verify that LP IRR exceeds 8% before any Tier 2 proceeds go to the GP
- Flag any month where debt service coverage ratio falls below the loan covenant threshold
These aren't optional enhancements—they're the minimum requirements for a usable model. Generic AI skips all five steps. Specialized AI makes them automatic.
Another distinction: iteration. When you tell ChatGPT "the waterfall is wrong," it regenerates the entire formula from scratch, often introducing new errors. A specialized system asks, "What specifically is wrong? Is the IRR calculation incorrect, or is the split ratio wrong, or is the timing of distributions wrong?" It debugs instead of guessing. This is how professional modelers work, and it's how AI must work when the output matters.
The cost difference is also worth naming. Generic AI is free or $20/month. Specialized AI costs more—sometimes significantly more—because it's solving a harder problem. But compare that cost to the cost of errors: an analyst spending 14 hours rebuilding a broken model, an LP catching a mistake in your investor report, or a deal team making decisions based on wrong assumptions. The ROI on specialized tools isn't in speed—it's in accuracy.
If you're building Project Cascade for a real investment committee meeting, you need specialized AI. If you're building a homework example for a finance class, generic AI might suffice. Match the tool to the stakes. When the output becomes an input to a decision with financial consequences, the tool must include verification. Generic AI doesn't. Specialized AI does.
For a deeper comparison of what changes when you use domain-specific systems instead of general-purpose models, see our analysis of Apers vs. ChatGPT.
