Cap Table Calculator
Model your startup cap table with founders, investors, ESOP, and convertible notes. Enter values for instant results with step-by-step formulas.
Calculator
Adjust values & calculateDetailed Cap Table
Formula
Each stakeholder's ownership percentage is calculated by dividing their shares by the total number of fully diluted shares, which includes all outstanding shares plus all reserved or potentially convertible shares. Valuation equals total shares multiplied by price per share.
Last reviewed: December 2025
Worked Examples
Example 1: Post-Series A Cap Table
Example 2: Early Stage with Two Founders
Background & Theory
The Cap Table Calculator applies the following established principles and formulas. Large language models process text by breaking it into tokens, sub-word units produced by algorithms such as byte-pair encoding. In English, one token approximates four characters or three-quarters of a word on average, though this ratio varies considerably across languages and code. A 1000-word document typically requires around 1300 to 1500 tokens. Token count drives both context window constraints and inference billing, making accurate estimation essential for budgeting API usage. The capability of a neural network scales primarily with its parameter count. Parameters are the numerical weights adjusted during training via gradient descent. GPT-3 contains 175 billion parameters; larger models in the trillion-parameter range require correspondingly greater compute and memory. Training compute is measured in floating-point operations (FLOPs): the Chinchilla scaling laws derived by Hoffmann et al. in 2022 show that optimal training allocates roughly 20 tokens per parameter, meaning a 70B-parameter model benefits from approximately 1.4 trillion training tokens. Inference latency depends on model size, hardware, and batching strategy. Running a 7B-parameter model in FP16 precision requires roughly 14 GB of GPU VRAM (2 bytes per parameter), while INT8 quantisation halves this to around 7 GB with modest quality loss, and INT4 reduces it to approximately 3.5 GB. This quantisation trade-off between memory, speed, and accuracy is central to deploying models on consumer hardware. Perplexity measures how surprised a language model is by a given text corpus; lower perplexity indicates better predictive accuracy. Embedding dimensions determine the size of the dense vector representations used to encode semantic meaning. Models like OpenAI's text-embedding-ada-002 produce 1536-dimensional vectors, while compact models may use 384 dimensions. Context window size defines the maximum token span a model can attend to in a single forward pass. Extending context windows from 4K to 128K tokens enables document-scale reasoning but substantially increases memory requirements, as the attention mechanism scales quadratically with sequence length without architectural modifications such as flash attention.
History
The history behind the Cap Table Calculator traces back through the following developments. The mathematical neuron model published by Warren McCulloch and Walter Pitts in 1943 first proposed that logical functions could be computed by networks of simple threshold units, planting the seed of neural computation. Frank Rosenblatt's Perceptron, introduced in 1957 and implemented in custom hardware by 1960, could learn linear classifiers from examples and generated enormous public excitement before Marvin Minsky and Seymour Papert's 1969 book rigorously analysed its fundamental limitations, demonstrating it could not learn the simple XOR function. The first AI winter, roughly 1974 to 1980, followed as funding agencies in the US and UK grew disillusioned with unrealised promises. A second wave of interest during the 1980s produced rule-based expert systems deployed in medicine and finance, and saw the re-derivation of backpropagation by Rumelhart, Hinton, and Williams in 1986, making it practical to train multi-layer networks on real problems. A second winter from 1987 to 1993 followed as expert systems proved brittle and hardware remained insufficient for genuine deep learning. The deep learning revival crystallised at the ImageNet Large Scale Visual Recognition Challenge in 2012, when Alex Krizhevsky's convolutional network AlexNet slashed the top-5 error rate by nearly 11 percentage points compared to the prior year's winner. This demonstrated that deep networks trained on GPUs with large labelled datasets could achieve human-competitive image recognition. Subsequent years saw rapid advances in recurrent networks, sequence-to-sequence models, and the attention mechanism, culminating in the transformer architecture introduced by Vaswani et al. in 2017. OpenAI released GPT-1 in 2018, demonstrating that unsupervised pre-training on large text corpora followed by task-specific fine-tuning could transfer knowledge broadly across language tasks. GPT-2 in 2019 demonstrated surprisingly fluent long-form text generation. GPT-3 in 2020, with 175 billion parameters, showed that scale alone could unlock few-shot learning. Kaplan et al.'s 2020 scaling laws paper provided the theoretical grounding. ChatGPT launched in November 2022, reaching one million users within five days and igniting mainstream global awareness of large language models.
Frequently Asked Questions
Formula
Ownership % = Shares Held / Total Fully Diluted Shares x 100
Each stakeholder's ownership percentage is calculated by dividing their shares by the total number of fully diluted shares, which includes all outstanding shares plus all reserved or potentially convertible shares. Valuation equals total shares multiplied by price per share.
Worked Examples
Example 1: Post-Series A Cap Table
Problem: Two founders split 10M shares (60/40). Seed investors hold 1.5M shares, Series A investors hold 2.5M shares, ESOP is 1.5M shares, and convertible notes converted to 500K shares. Price per share is $2.
Solution: Total Shares: 6M + 4M + 1.5M + 2.5M + 1.5M + 0.5M = 16,000,000\nFounder: 6M / 16M = 37.50%\nCo-founder: 4M / 16M = 25.00%\nSeed: 1.5M / 16M = 9.38%\nSeries A: 2.5M / 16M = 15.63%\nESOP: 1.5M / 16M = 9.38%\nNotes: 0.5M / 16M = 3.13%\nTotal Valuation: 16M x $2 = $32,000,000
Result: Founders: 62.5% | Investors: 28.1% | ESOP: 9.4% | Valuation: $32M
Example 2: Early Stage with Two Founders
Problem: Two co-founders start with 5M shares each. They create a 2M share ESOP and raise $500K seed at $1/share for 1M shares. Calculate the cap table.
Solution: Total Shares: 5M + 5M + 2M + 1M = 13,000,000\nFounder 1: 5M / 13M = 38.46%\nFounder 2: 5M / 13M = 38.46%\nESOP: 2M / 13M = 15.38%\nSeed: 1M / 13M = 7.69%\nPost-money Valuation: 13M x $1 = $13,000,000\nPre-money: $13M - $500K = $12,500,000
Result: Founders: 76.9% | ESOP: 15.4% | Seed: 7.7% | Valuation: $13M
Frequently Asked Questions
What is a cap table and why is it important?
A capitalization table (cap table) is a detailed spreadsheet or document that shows the equity ownership structure of a company, including all shareholders, their number of shares, the type of securities held, and the percentage of ownership. Cap tables are essential for startups because they track how ownership changes through each funding round, option grant, and equity transfer. Investors require accurate cap tables during due diligence before making investment decisions. A messy or inaccurate cap table can delay or kill a funding round. Cap tables also determine who has voting control, how proceeds are distributed during an exit, and how much dilution founders experience over time.
What are the key components of a startup cap table?
A comprehensive cap table includes several key components. Common stock held by founders and employees represents the base equity layer. Preferred stock issued to investors typically includes liquidation preferences and anti-dilution protections. The Employee Stock Option Pool (ESOP) represents shares reserved for future employee grants. Convertible instruments like SAFEs and convertible notes are tracked on a pro-forma basis showing their potential conversion into equity. Warrants give holders the right to purchase shares at a predetermined price. Each component shows the number of shares or units, the price paid per share, the percentage of total ownership, and any special rights or preferences attached to that class of stock.
How do convertible notes and SAFEs appear on a cap table?
Convertible notes and SAFEs appear on the cap table as potential (pro-forma) equity rather than actual equity because they have not yet converted into shares. They are typically shown in a separate section or as a footnote indicating the amount invested, the valuation cap, and the discount rate. When modeling the cap table for a new funding round, these instruments convert into shares based on the lower of the capped valuation or the discounted round price. For example, a SAFE with a $5 million cap would convert at the $5 million valuation even if the Series A is priced at $10 million, giving SAFE holders twice as many shares per dollar invested. Properly modeling these conversions before a round is critical for accurate dilution calculations.
What happens to the cap table during an exit or acquisition?
During an exit, the cap table determines how proceeds are distributed among shareholders through the liquidation waterfall. Preferred stockholders (investors) typically have liquidation preferences that guarantee they receive at least 1x their investment before common stockholders receive anything. Some investors have participating preferred stock, which allows them to receive their liquidation preference and then participate pro-rata in remaining proceeds. After all preferences are satisfied, remaining proceeds are distributed pro-rata to all shareholders based on their ownership percentages. Understanding this waterfall is crucial because a company sold for $20 million might leave nothing for common stockholders if investors hold $20 million in liquidation preferences.
What is anti-dilution protection and how does it affect the cap table?
Anti-dilution protection is a provision in preferred stock that protects investors from dilution if the company raises a future round at a lower valuation (a down round). The two main types are full ratchet and weighted average. Full ratchet adjusts the conversion price of existing preferred stock down to the new lower price, creating maximum protection for investors but significant dilution for founders. Weighted average anti-dilution uses a formula that considers both the lower price and the number of new shares issued, resulting in a more balanced adjustment. Most modern term sheets use broad-based weighted average anti-dilution, which is founder-friendlier. These provisions can dramatically reshape the cap table during a down round.
How should I model my cap table for future funding rounds?
To model future rounds on your cap table, start by establishing the pre-money valuation, which determines the price per share for new investors. Calculate the number of new shares issued by dividing the investment amount by the price per share. Add any option pool expansion required by investors (typically refreshed to 10 to 15 percent before each round). Account for any converting notes or SAFEs. Then calculate post-money ownership for all stakeholders. Most founders should model at least two to three rounds ahead to understand their potential dilution trajectory. Use scenarios with different valuations and round sizes to see best, base, and worst case outcomes. Cap table management tools like Carta, Pulley, or Shareworks can automate these calculations.
References
Reviewed by Daniel Agrici, Founder & Lead Developer · Editorial policy