Writing
Notes fromthe numbers.
Long-form posts and the Know Thyself newsletter — startup finance, building iOS products, and the parts of entrepreneurship that do not make it into the success stories. By Erdem Gülen.
Blog
Long-form posts published here, in English. Roughly weekly.
- App Store Proceeds Are Not Revenue: Reconciling What Apple Actually Pays YouThe App Store dashboard, the payment report and the bank deposit give three different numbers for the same month. How one €39.99 annual subscription becomes a customer price, proceeds, recognised revenue and cash; the six sources of the gap — tax, commission tier, Apple's fiscal calendar, currency, refunds and payment thresholds; why annual plans make proceeds flatter a growing app; and a one-hour monthly reconciliation that ties all of it to the ledger and the bank.
- Built to Be Handed Over: How a Fractional CFO Engagement Should EndFractional CFO engagements are scoped around how they start and almost never around how they end. The three ways one ends — graduation, step-down and drift — why the exit triggers belong in the first conversation, what a handover pack actually contains (close runbook, definitions, model with an assumption log, control matrix, obligations calendar, company-owned access), why the first hire is often a controller rather than a CFO, and how to run a three-month overlap measured in closes rather than meetings.
- Bookings, Billings, Revenue, Cash: Four Numbers That Describe One SaleOne $120,000 annual contract produces four honest answers to “how much did we sell?”, on four different dates, in four different systems. Why bookings, billings, revenue and cash diverge on purpose, why annual prepayment is a financing decision with a cost of capital, the five traps founders fall into when they mix the numbers up, and a monthly bridge that keeps all four reconciled before an investor rebuilds them in diligence.
- Read the Term Sheet Backwards: The Clauses That Matter More Than the ValuationFounders negotiate the valuation and accept the rest, but a term sheet allocates money, control and downside, and the price speaks only to the first. Reading one from the back: how a participating preference turns a 20% stake into a third of a $30 million sale, how the option pool shuffle takes $20 million pre-money down to $17.5 million, what the board and veto list hand over, and why the exclusivity clock is the one binding clause — plus a way to compare two offers at four exit values.
- Forecast Error Is Data: How a Startup Learns From Its Own MissesA startup revises its forecast every month and almost never checks how the last one did. The forecasts that get better are the ones with a track record — and a track record takes one saved copy and ten minutes a month to keep.
- On-Device AI Runs Inside a Budget: Latency, Memory and Heat on an iPhoneAccuracy gets an on-device feature approved; latency, memory and heat decide whether it survives on a four-year-old phone. The three budgets every model-backed iOS feature draws on, why to write the limits down before choosing the model, where the cost really goes — input size, call frequency and how long the model stays resident — how to test the way users hold the phone, and how to degrade on purpose instead of letting the OS do it.
- The Five-Day Close: A Monthly Close Calendar Startups Can Actually Keep Up WithMost startups either skip the monthly close or treat it as bookkeeping catch-up, and every number downstream — the board pack, the runway model, the investor update — is waiting on it. A five-day close calendar with a named owner on each day, the four checks that catch most errors, and why a month that never really ends costs more than it looks like.
- Stacked SAFEs: The Dilution Founders Don’t See Until the Series AEach SAFE looks small on the day it is signed; the stack converts all at once at the priced round. Why post-money SAFE percentages simply add up and come entirely out of the founders, a worked example in which founders expecting 59% leave their Series A with 48% — early low caps and a pool top-up nobody modelled — and the pro forma cap table, dilution budget and four questions to ask before signing the next one.
- The Data Layer Comes Before the Dashboard: One Source of Truth for Startup FinanceAt one client, revenue existed as four different numbers, each right by its own definition — and the fix was not a dashboard. Why the first in-house finance tool to build is the data layer: nightly pulls that copy and never edit, one store, five canonical tables, a definitions file in which every reported number is a versioned query with a named approver, and tests that block the board pack when the bank and the ledger disagree. What it costs, what becomes cheap once it exists, and the five ways it goes wrong.
- The Minimum Viable Control Environment: Cash Controls for a Startup With No Finance TeamThe duplicate payment, the supplier email with new bank details, and the contractor still on payroll all arrive before the finance team does. The five cash controls a ten-person company can run on one page — two people between an invoice and a payment, bank-detail changes confirmed by phone, cards with owners and limits, payroll reconciled by someone who did not run it, and a bank reconciliation someone actually reads — what they cost, and when to add the next layer.
- The Hiring Plan Is the Budget: Headcount Planning for Early-Stage StartupsSort an early-stage budget by size and the top rows are all people; the rest is noise with a line each. Why hire timing, fully loaded cost and ramp are where the forecast error lives, why every planned hire needs a trigger rather than a start date, what the sequence of hires says about the real strategy, and why the round should be sized from the hiring plan — not the other way round.
- Shipping Is the Cheap Part: The Carrying Cost of an iOS AppEvery app left in the store is a standing claim on future hours — the OS cycle, dependency churn, support, and attention — and it looks profitable only because the developer prices their own time at zero. The ledger I keep for each app, the three tests that decide which ones stay, and how to retire one without hurting the people who still use it.
- AI Should Read the Ledger, Not Write ItThe expensive AI mistakes in finance come from putting a probabilistic model where a deterministic one belongs. The four jobs where a language model earns its place in a finance stack, the four where it does not, and the propose–validate–approve pattern that keeps every number reproducible, traceable, and signed by a person.
- The Investor Update Is the Cheapest Fundraising Tool Founders SkipMost founders write to investors only when they need money — exactly when the email is least useful. What a monthly update that actually gets read contains, why the flat month is the most important one to send, and the failure modes visible from the receiving end.
- The First 90 Days of a Fractional CFO EngagementFounders picture a CFO arriving and building a model in a week. What actually happens: an audit before anything else, a first reporting pack, the model built on data that has already been checked, and an operating rhythm — plus where both sides most often go wrong.
- What Twelve Paywalls Taught Me About Pricing an iOS AppIndie app pricing gets decided in the last hour before submission and never revisited. Why the subscription-versus-one-time choice follows from your cost structure, why trial placement moves revenue more than price does, and the lifetime-value maths almost nobody computes.
- Runway Isn't a Number, It's a ModelMost founders track runway as cash divided by last month's burn — a number that is already wrong the moment something changes. Why runway needs to be a live model with a cash bridge, committed-versus-discretionary spend, and a burn multiple, not a single division problem.
- What Investors Actually Check in a Data RoomMost startup data rooms are built to look complete, not to survive scrutiny. What an associate opens first, the documents that kill deals when they are missing, and how to build a room that speeds up diligence instead of inviting it.
- The 'AI-First' Redesign of the Finance FunctionBolting a copilot onto a legacy finance stack is not an AI-first finance function. What actually changes when controlling, reporting, and forecasting are designed around AI from day one — and why most "AI adoption" in finance stops at the chatbot.
- Forward-Deployed Finance: In-House Tools Instead of More SaaSEarly and growth-stage startups keep buying finance software that half-fits and calling it solved. What forward-deployed engineering — small, in-house tools built around the actual business — looks like, and when it beats another SaaS subscription.
- Why Startups Need AI-Native Finance ExecutivesA CFO who uses ChatGPT occasionally is not the same as an AI-native finance executive. What the distinction means in practice, why it now shows up in fundraising outcomes and burn multiples, and what to look for when hiring.
- What We Learned Building On-Device AI Into Twelve iOS AppsOn-device AI is harder than calling an API, and worth it more often than people assume. What actually changes when you process photos and video locally — the economics, the constraints nobody mentions, and the cases where you should still use a server.
- When Does a Startup Actually Need a Fractional CFO?Almost every founder hires financial help later than they should — usually mid-raise, when the numbers have already become the bottleneck. Six concrete signals that the finance work has outgrown the founder, what a fractional CFO does that a bookkeeper does not, and when you genuinely do not need one yet.
Newsletter
Know Thyself — essays on startup finance, investment and entrepreneurship, published in Turkish on LinkedIn. Subscribe .
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