Safe Note Calculator
Calculate equity conversion from SAFE notes using pre-money, post-money, and MFN terms. Enter values for instant results with step-by-step formulas.
Calculator
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The SAFE converts at whichever method yields the lower price per share (more shares for the investor). Cap Price divides the valuation cap by existing shares. Discount Price multiplies the Series A price by (1 - discount rate). The investor receives shares equal to their investment divided by the effective price.
Last reviewed: December 2025
Worked Examples
Example 1: SAFE with $5M Cap and 20% Discount
Example 2: Multiple SAFEs Before Series A
Background & Theory
The Safe Note 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 Safe Note 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
Effective Price = min(Cap / Shares, Price x (1 - Discount))
The SAFE converts at whichever method yields the lower price per share (more shares for the investor). Cap Price divides the valuation cap by existing shares. Discount Price multiplies the Series A price by (1 - discount rate). The investor receives shares equal to their investment divided by the effective price.
Worked Examples
Example 1: SAFE with $5M Cap and 20% Discount
Problem: An investor puts $500,000 into a startup via SAFE with a $5M valuation cap and 20% discount. The startup later raises Series A at $10M pre-money with 10M existing shares.
Solution: Price per share at pre-money: $10M / 10M = $1.00\nPrice at cap: $5M / 10M = $0.50\nPrice at discount: $1.00 x (1 - 0.20) = $0.80\nEffective price: min($0.50, $0.80) = $0.50 (cap wins)\nShares from SAFE: $500,000 / $0.50 = 1,000,000 shares\nOwnership: 1,000,000 / 13,000,000 total = ~7.69%
Result: Conversion at cap price of $0.50/share yields 1,000,000 shares (~7.69% ownership)
Example 2: Multiple SAFEs Before Series A
Problem: Two SAFEs: $250K at $4M cap and $500K at $8M cap. Series A at $12M pre-money with 10M shares outstanding.
Solution: SAFE 1: Price at cap = $4M/10M = $0.40, Shares = $250K/$0.40 = 625,000\nSAFE 2: Price at cap = $8M/10M = $0.80, Shares = $500K/$0.80 = 625,000\nSeries A price = $12M/10M = $1.20\nTotal shares post-conversion: 10M + 625K + 625K + new round shares\nSAFE 1 ownership: ~5.2%, SAFE 2 ownership: ~5.2%
Result: Both SAFEs convert at cap prices, yielding 625,000 shares each with combined ~10.4% dilution
Frequently Asked Questions
What is a SAFE note and how does it work for startups?
A SAFE (Simple Agreement for Future Equity) is an investment contract created by Y Combinator that allows investors to provide capital to a startup in exchange for the right to receive equity at a future priced round. Unlike convertible notes, SAFEs have no interest rate and no maturity date, making them simpler and more founder-friendly. When the startup raises a priced equity round, the SAFE automatically converts into shares at a price determined by either a valuation cap or a discount rate, whichever gives the investor more shares. SAFEs have become the most common instrument for early-stage fundraising because they reduce legal complexity and negotiation time compared to traditional equity rounds.
How does the valuation cap protect SAFE investors?
The valuation cap sets a maximum company valuation at which the SAFE converts into equity, regardless of how high the actual valuation goes in the priced round. If a startup raises a Series A at a $20 million pre-money valuation but the SAFE had a $5 million cap, the investor converts at the $5 million valuation, receiving four times more shares than Series A investors per dollar invested. This mechanism rewards early investors for taking greater risk when the company was less proven. The cap essentially guarantees a minimum ownership percentage, ensuring that spectacular company growth between the SAFE investment and the priced round benefits the early investor proportionally.
How does the discount rate work in a SAFE note conversion?
The discount rate gives SAFE holders a percentage reduction on the price per share paid by investors in the qualifying priced round. A typical 20% discount means the SAFE investor pays 80% of what Series A investors pay per share, effectively getting 25% more shares for the same investment. The discount rewards early-stage risk without requiring a specific valuation estimate. When a SAFE has both a valuation cap and a discount rate, the investor receives whichever conversion method yields more shares. In practice, if the company does very well, the valuation cap usually provides better terms, while the discount is more favorable when the priced round valuation is closer to the cap.
How do SAFE notes affect founder dilution at Series A?
SAFE notes create dilution when they convert into equity at the priced round, and the total dilution depends on how many SAFEs were issued and their conversion terms. A common scenario involves founders holding 80% after SAFEs convert, with 15-20% going to Series A investors and the remainder to SAFE holders. Multiple SAFEs with low valuation caps can compound dilution significantly, sometimes surprising founders who did not model the cap table carefully. Using post-money SAFEs helps founders track dilution more accurately since each SAFE ownership percentage is fixed at the cap amount. Founders should use a cap table calculator to model various scenarios before accepting SAFE investments.
Can SAFE notes have both a valuation cap and a discount rate?
Yes, most SAFE notes include both a valuation cap and a discount rate, and the investor receives whichever term produces a lower price per share at conversion, resulting in more equity. This dual protection is standard practice because it covers different valuation scenarios at the priced round. If the company raises at a valuation significantly above the cap, the cap determines conversion. If the company raises at a valuation near or below the cap, the discount typically provides better terms. Having both terms does not mean the investor gets to apply both simultaneously. Only one conversion method applies per SAFE, determined by which gives the investor the more favorable price per share.
What happens to a SAFE note if the startup never raises a priced round?
Since SAFEs have no maturity date or interest accrual, they remain outstanding indefinitely if no qualifying priced round occurs. If the startup is acquired before a priced round, most SAFEs include dissolution provisions that either return the original investment amount or convert at the valuation cap for the acquisition, whichever is greater. If the startup fails and shuts down, SAFE holders are treated as unsecured creditors and typically recover nothing after debts and secured creditors are paid. Some SAFEs include a most favored nation clause allowing investors to benefit from better terms offered to later SAFE investors. This indefinite nature makes SAFEs riskier than convertible notes, which at least have a maturity date forcing a resolution.
References
Reviewed by Daniel Agrici, Founder & Lead Developer · Editorial policy