Just saw Rytr pushing a new 'finance' use case, specifically for drafting earnings reports and investor updates. As someone who has to parse actual cloud vendor financials, I had to laugh. Then I tried it. The laughter stopped.
Gave it a straightforward prompt based on a recent AWS quarter: "Draft an earnings report summary highlighting 20% revenue growth, but with a 15% increase in capital expenditures due to data center expansion. Include a cautionary note on rising energy costs."
The output was a masterclass in vague, buzzword-laden fluff. It generated phrases like "leveraging scalable synergies" and "navigating macroeconomic headwinds." Zero useful structure. When I asked for a table comparing projected vs. actual spend, it hallucinated numbers that didn't even add up. It's clear the model has no grounding in actual financial reporting conventions.
* **No grasp of unit economics:** Couldn't articulate cost per workload or compute hour trends.
* **Hallucinates metrics:** Invented terms like "cloud efficiency ratio" without definition.
* **Misses the point entirely:** The real "finance" use case would be analyzing your own cloud bill, not writing PR spin.
Trying to use this for anything resembling real financial communication is a fast track to looking incompetent. It's a content spinner dressed in a suit. For the cost of their premium plan, you could get actual FinOps tooling that interfaces with your CSP's billing API and gives you real, actionable data.
-- cost first
-- cost first
> "masterclass in vague, buzzword-laden fluff"
You've nailed it. The core issue is these tools are pattern-matchers trained on public corporate speak, not on actual financial data or SEC filing structure. They'll never produce anything with the necessary precision because they're built on language, not numbers.
Your last point is the real kicker. The actual finance use case is analyzing your own cloud bill to *write* those earnings reports. Feed it a CSV of your last six months of AWS charges and ask for trends? It'll fall apart. Ask it to calculate the true cost impact of a Reserved Instance purchase versus Savings Plans, factoring in utilization decay? Not a chance.
They're selling a veneer of competence over a void of actual utility. It's the equivalent of generating a convincing-looking architecture diagram that would collapse the second you tried to deploy it.
-- cost first
Exactly. The pattern-matching limitation becomes painfully clear when you ask for operational calculations. I tested a similar scenario using anonymized GCP billing data, asking for a three-year TCO projection comparing Committed Use Discounts to sustained use discounts with variable workloads. The model confidently produced a neatly formatted table where the monthly savings percentage exceeded the discount rate, violating basic arithmetic.
It doesn't understand the underlying financial or operational constraints. These tools can't comprehend that a Reserved Instance has an opportunity cost, or that a "savings" figure is meaningless without context like commitment utilization or instance family flexibility. They're rearranging words they've seen in whitepapers, not performing analysis.
Your bill is too high.
Spot on. That "cloud efficiency ratio" hallucination is the perfect example of why this fails. It's mimicking the *form* of a financial metric without any of the substance.
I actually tried feeding a similar tool actual, anonymized cost-per-acquisition data from a recent campaign and asked for the quarter-over-quarter change. It gave me a percentage that was mathematically impossible with the provided figures. When called out, it just apologized and generated another random number.
The core failure is mistaking syntax for analysis. These tools are great at generating the *shell* of a report, but the moment you need accuracy with real numbers, they're worse than useless because they present fiction with confidence.
Always optimizing.
Of course it gave you corporate fluff. These tools are trained on press releases, not 10-Qs. The language patterns they learn are from marketing, not accounting.
Your test is spot on. The moment you ask for a table with numbers, the whole facade collapses. It can't do math, it can't adhere to a consistent structure, it just improvises.
The funny part is, the actual finance teams who need this are buried in spreadsheets. They'd kill for something that could parse real data and spit out a coherent variance analysis. Instead they get a word salad about synergies.
CRM is a necessary evil
That's a really good point about spreadsheets. I've been trying to automate basic AWS cost anomaly alerts with terraform and cloudwatch, and getting even that small piece right is hard. The actual useful tool would just read a Cost and Usage Report and flag, "Hey, your S3 costs in us-east-2 doubled last week." Not write a press release about it.
That "buried in spreadsheets" line is the real problem. Finance teams aren't looking for a press release generator. They need something to automate the manual, error-prone grunt work.
But the vendors are pitching this 'finance' module to the executives who *approve* the budgets, not the analysts who *do* the work. It's a classic bait and switch. The C-suite sees a demo spitting out plausible-sounding paragraphs and thinks they've bought a solution, while the actual team groans because they now have to fact-check AI hallucinated numbers on top of their regular workload.
The tool fails at the single task that would save time: taking structured data and producing structured, accurate output. It's adding work, not reducing it.
Trust but verify.
Yep, that "cloud efficiency ratio" hallucination is the tell. It's generating the *sounds* of financial analysis without any of the math.
I've seen this same pattern in email marketing tools that claim to "optimize send times." They'll spit out a pretty chart suggesting 2:17 PM on a Tuesday, but it's just a superficial pattern match from aggregate data, not rooted in *your* audience's actual open-rate history.
The real utility would be ingesting your actual campaign metrics and pointing out, "Hey, your click-through rate for this segment dropped 40% last month while your list grew 20%, maybe check your content relevance." But that requires actual analysis, not just linguistic mimicry.
Always A/B test.