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60-Second Version
"Build a 12-month model from these numbers: [MRR, growth %, churn %, CAC, LTV]. Label every assumption as sourced or estimated with a confidence level, tell me which single assumption breaks the model if it's off by 20%, and give me one question to run past my accountant before I trust this."
Why this works: The "show your work" requirement forces transparency. The "sanity check" question creates a natural handoff to a human expert. The explicit instruction to ask for missing data instead of assuming defaults prevents the model from inventing financial parameters.
Where it breaks: AI models are not accountants. They cannot access your actual books, tax situation, or industry-specific regulations. Use this prompt for scenario planning and directional analysis only. Never use AI-generated financials for investor presentations or loan applications without human verification.
Claude vs. GPT vs. Gemini: What Each Actually Costs Right Now
There is a debate that will not die: Claude vs. GPT vs. Gemini. The capability differences are real but often overstated for typical business use cases. Pricing, on the other hand, moves fast enough that any table is a snapshot. The rates below are reported by third-party trackers as of August 13, 2026 — treat the specific model names and numbers as directional, not gospel. Verify against the provider's own pricing page before you budget against any of these.
| Model | Input / Output per 1M tokens | Notes |
|---|---|---|
| Claude Opus 5 | $5.00 / $25.00 | Flagship reasoning tier; 1M context at no surcharge |
| Claude Sonnet 5 | $2.00 / $10.00 | Introductory pricing through Aug 31, 2026 — rises to $3/$15 on Sep 1 |
| Claude Haiku 4.5 | $1.00 / $5.00 | Fastest, cheapest current Claude tier |
| GPT-5.6 Sol | $5.00 / $30.00 | OpenAI's flagship reasoning tier |
| GPT-5.6 Terra | $2.00 / $12.00 | Balanced mid-tier, cut 20% on Jul 30, 2026 |
| GPT-5.6 Luna | $0.20 / $1.20 | High-volume, cheapest current OpenAI tier |
| Gemini 3.1 Pro | $2.00 / $12.00 | Up to 200K tokens; $4/$18 above that |
| Gemini 3.6 Flash | $1.50 / $7.50 | Google's price-performance workhorse tier |
Rates reported by third-party trackers as of August 13, 2026. Model names, tiers, and prices in this table move monthly across all three providers — reconfirm directly with the provider before budgeting. If you're reading this more than a few weeks after the "Updated" date at the top, assume this table is stale and check the provider's own pricing page instead.
Here is the part that matters more than the rate card: the model is only part of output quality. Prompt architecture, context quality, and human review do most of the heavy lifting. A well-structured prompt on a mid-tier model routinely beats a lazy prompt on a flagship one.
The recommendation: pick one model family and get genuinely fluent in it before model-hopping in search of a shortcut. The gains are mostly in the prompt structure, not the underlying weights.
The Realistic AI Productivity Curve
Every AI vendor promises "10x productivity." The pattern most teams actually experience is more staged:
- Week 1–2: Noticeable speedup on simple tasks (emails, summaries, basic research). Enthusiasm is high.
- Week 3–4: Speedup narrows as teams realize the output needs heavier editing than expected. Frustration sets in.
- Month 2–3: If prompts are refined and workflows adjusted, speedup rebounds — but only on the specific, well-defined tasks the prompts were built for.
- Month 4+: The real payoff shows up as capability expansion — doing things that were not economically viable before (personalized outreach at scale, ongoing competitive monitoring, automated content testing). This is where the 10x claim starts to look plausible, and only for teams that invested in prompt infrastructure during months 1–3.
Teams that fail at AI implementation are usually the ones expecting the month-4 outcome in week one, and abandoning the tool in week four when it does not show up. Teams that succeed treat the first month as investment: building prompt libraries, documenting what works, accepting that the real payoff comes later.
⚠️ The Hidden Cost of "Free" AI Tools
Free tiers are fine for experimentation. They are riskier for business decisions — smaller context windows, reduced reasoning depth on some providers, and no API access for automation. Budget for a paid plan if you are using AI for anything client-facing or financially consequential. The cost of one bad decision made on rushed, unreviewed output can exceed a year of subscription cost.
How to Build a Prompt Library That Actually Gets Used
A prompt library sitting in a Notion doc nobody opens is worthless. One embedded in your team's actual workflow is a real advantage. Here is the system that holds up:
Step 1: Audit, Do Not Invent
Do not start by writing prompts. Start by logging what your team actually does for two weeks — every email, report, analysis, creative task. Then ask: which of these are repetitive enough to prompt, and complex enough to benefit from AI? Most teams find that a small number of categories cover the majority of their AI use. Focus there. Five excellent prompts beat fifty mediocre ones.
Step 2: Version Your Prompts Like Code
Every prompt should have a version number, a "last tested" date, and a "known failures" section. Models update, contexts shift, and prompts that worked in March quietly stop working in June. You need to know which version worked last.
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Top comments (1)
"AI models are not accountants. They cannot access your actual books, tax situation, or industry-specific regulations" - unless you'd provide it to them (RAG etc) ?
If I were an accountant, the AI onslaught would make me sleep even worse than if I were a developer ...