Every pitch deck has a TAM slide. Most of them are wrong. Asking an AI for “the TAM of X” gives you a confident eight-figure number that traces back to a 2022 Statista blurb cited by a 2024 Medium post. A useful market-sizing workflow uses AI to pull both top-down and bottom-up estimates, then triangulates them — and the gap between the two is where the real argument lives. This tutorial walks the loop investors actually want to see.
What this covers
A two-pass sizing workflow: top-down (start from a large public number and narrow with reasoned filters) and bottom-up (start from unit economics and multiply up). Both with AI. Then a triangulation step that compares the two, names the gap, and forces you to pick the model you can defend. Output is a one-slide TAM / SAM / SOM with the assumption table beneath it.
Who this is for
Founders sizing a market for a seed deck, PMs writing a “should we build this” memo, strategy teams scoping a new vertical, and investors who want to sanity-check a deck before a meeting. Not for: pure academic market reports — those need primary survey data, not AI synthesis.
When to reach for it
When you have a defined product or category, a target geography, and 90 minutes to produce a credible sizing slide. Skip this workflow when the market is so emergent there are no public numbers — then you are bottom-up only, and the AI does much less. For broader category context first, run an AI industry research workflow pass.
Before you start
- Define the unit you are sizing. “Annual revenue from X tool sold to Y persona in Z geography.” Vague unit, vague number.
- Decide TAM versus SAM versus SOM upfront. TAM = global category, SAM = reachable subset, SOM = realistic 3-5 year share. Most decks conflate them.
- Pick your engines: Perplexity for cited public numbers, ChatGPT or Claude Deep Research for synthesis, a spreadsheet for the actual math. AI does not do arithmetic — you do.
- Have one bookmarked credible source for the broad category. You will use it as the anchor for top-down.
Step by step
- Top-down: pull the anchor number with Perplexity. “Global market size for [category] in 2025-2026 — list 3-5 estimates from named sources with publication dates.” Read the spread. If the high and low estimates differ by 5x, the category is poorly measured — note that, do not paper over it.
- Apply reasoned filters to get from the anchor to your TAM. Geography (US only? Multiply by a published US share — never guess). Segment (SMB only? Filter by employee bucket). Use case (paid users only? Filter by conversion benchmarks). Each filter is a row in your assumption table with a source link.
- Bottom-up: start from unit economics. Average revenue per user multiplied by addressable user count multiplied by adoption rate. Use Perplexity to pull each input separately: “Average ARPU for [SaaS category] SMB tier in 2026.” “Number of SMBs in [geography] with [employee range].” “Adoption rate of [category tools] in [segment].”
- Do the arithmetic in a sheet, not in the chat. AI math fails silently on multi-step multiplication. Put every input in its own cell with a source URL in the next column. Total at the bottom.
- Triangulate. Compare top-down TAM and bottom-up TAM. If they are within 2x, you have a defensible range. If they differ by 10x, one of the models is wrong — usually because a filter is over-claimed or an ARPU is stale. For deeper synthesis on this kind of analytic crosscheck, see the ChatGPT research tutorial.
- Write the SAM and SOM as derivations of the triangulated TAM. SAM = TAM filtered by your actual go-to-market (channel reachable, language, regulation). SOM = SAM multiplied by a defensible 3-year share assumption (cite a comparable company’s share trajectory).
- One slide, assumption table beneath. TAM / SAM / SOM as three boxes, the assumption table as a small font table beneath with every input, its value, its source, and the year of the source.
First-run exercise
Pick a market you have first-hand knowledge of — your current employer’s, or a hobby industry where you know real ARPU. Run the workflow end-to-end. Compare the AI-derived number to your gut. Most first runs come back 2-5x too high — usually because the top-down anchor includes adjacent categories. That is the calibration lesson; you will tighten the unit definition on every future run.
Quality check
- Top-down and bottom-up numbers are within 2x. If not, name the gap explicitly on the slide.
- Every input cell has a source URL with a 2024-2026 publication date. Older sources need a footnote justifying why they still apply.
- SAM is meaningfully smaller than TAM. If SAM equals TAM, you have not actually filtered for go-to-market reachability.
- SOM has a named comparable. “We will reach 5 percent” with no comparable is not a defense.
- The assumption table fits on one slide. If it does not, you are oversizing the model.
How to reuse this workflow
- Save the top-down filter list and bottom-up input list as a template. New product, new numbers, same skeleton.
- Build a personal database of recurring inputs: ARPU benchmarks per category, SMB counts per geography, adoption rates per segment. Each one with its source. Update quarterly.
- Re-run the sizing every six months on the same template. Sizing that does not update goes stale by the next funding round.
Recommended workflow
Unit definition → top-down anchor → reasoned filters → bottom-up inputs → spreadsheet math → triangulation → SAM and SOM as derivations → one-slide output with assumption table. Plan 90 minutes for the first pass, 30 minutes for refreshes.
Common mistakes
- Asking the AI directly for “the TAM of X.” You get a number, not an argument. Investors want the argument.
- Doing the math in chat. Multi-step arithmetic with AI is unreliable; use a sheet.
- Skipping the triangulation step — top-down alone overstates, bottom-up alone understates, the truth lives in the gap.
- Citing stale sources without a footnote. A 2022 number used unmodified in a 2026 deck is a red flag.
- Conflating TAM and SAM. Investors notice immediately.
- Hiding the assumption table because it makes the model look “less clean.” The table is the model’s credibility.
FAQ
- What if there is no public number for my category?: Bottom-up only. Be explicit about it on the slide. Investors prefer “this is bottom-up only because the category is too new” over “TAM is $10B with no traceable source.”
- Which AI engine for sizing?: Perplexity for input retrieval, Claude or ChatGPT for synthesis, a spreadsheet for math. Do not collapse the steps.
- How fresh do sources need to be?: 2024 or later for input numbers, 2025-2026 preferred for ARPU and adoption rates.
- Should I include growth projections?: Yes, as a separate row, with a named source for the growth rate. Do not bake projected growth into the TAM number itself.
- What is a “defensible” share assumption for SOM?: One backed by a comparable company’s actual share trajectory at the equivalent stage. Without a comparable, your SOM is a wish.
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