How to Actually Value That Startup Equity Offer
By
Samara Garcia
•
Feb 23, 2026
In 2026, senior LLM engineers are routinely asked to trade guaranteed cash for fractions of a percent. One offer comes with liquid RSUs you can price instantly. The other offers 0.25 percent of a Series B startup and the promise of upside that may never materialize. The real decision is not upside versus safety. It is whether that equity has a defensible value at all.
This piece is for AI and ML engineers who want to compare startup equity to cash using clear reasoning, not optimism. Based on patterns we see every week at Fonzi AI, it shows how to think about risk, dilution, and realistic outcomes so you can decide with confidence rather than hope.
Key Takeaways
Equity valuation for private AI startups requires different methods than public stocks: less data, more assumptions, and significantly higher uncertainty around outcomes.
Focus on percentage ownership on a fully diluted basis, realistic dilution projections (25-40% for pre-Series C), and exit scenarios across multiple timelines rather than just counting options.
Combine scenario-based DCF thinking, comparable startup multiples, and ownership-to-outcome mapping for a practical assessment, no 50-tab spreadsheet required.
The gap between 409A valuation (used for tax purposes) and investor valuation (what you see in press releases) can be 40-60%, and understanding this difference is crucial.
Core Concepts: What “Equity Value” Really Means in a Startup
When you own shares in a public company, determining their market value takes about three seconds: check the stock price, multiply by your shares, and done. Daily liquidity means you can sell whenever you want.
Private startup equity operates on entirely different rules. There’s no public market price. You can’t sell your shares on an exchange. The typical path to liquidity is 7-10 years, and that’s assuming the company succeeds at all. This fundamental difference explains why equity valuation for startups requires a different toolkit.
Common Equity Instruments in Offer Letters
Stock Options (ISOs/NSOs): The right to purchase shares at a fixed strike price. Incentive Stock Options (ISOs) offer favorable tax treatment up to $100k in options vesting per calendar year; Non-Qualified Stock Options (NSOs) are taxed as ordinary income on exercise.
Restricted Stock Units (RSUs): Promises to deliver actual shares on a vesting schedule. More common at later-stage companies. You receive shares, not options to buy shares.
Restricted Stock: Actual shares granted upfront, often with vesting restrictions. More typical for co-founders or very early employees.
Vesting Schedules and the Cliff
The standard vesting schedule is 4 years with a 1-year cliff. This means you own nothing if you leave before 12 months. After the cliff, you typically vest monthly or quarterly over the remaining 3 years.
Early departure, layoffs, or termination dramatically affect realized equity. If you leave at month 11, you walk away with zero. If you leave at month 25, you’ve vested roughly 50% of your grant. Always understand the acceleration provisions; some companies offer partial or full acceleration if they’re acquired.
Shares vs. Percentage vs. Exit Value
Three numbers that sound related but mean very different things:
Number of shares: Raw count, largely meaningless without context
Percentage ownership (fully diluted): Your shares divided by total outstanding shares plus all options, warrants, and reserved pool
Implied value at exit: Your percentage multiplied by exit value, minus any liquidation preferences that get paid first
The internal “paper valuation” you might see (like a 409A valuation) is often significantly lower than the headline post-money valuation used in press releases. A Series C startup announcing a “$500M valuation” might have a 409A valuation of $200M for common stock. This isn’t deception; it reflects the different rights attached to preferred shares versus common shares.
Key Equity Valuation Methods Candidates Can Actually Use
Investors use complex valuation models, but you don’t need a VC playbook to evaluate a job offer. As an engineer comparing roles, you can borrow the core ideas without building massive spreadsheets.
We’ll focus on a few practical methods: scenario-based DCF thinking, comparable company multiples, ownership and outcome mapping, and using 409A valuations as sanity checks. One caution up front: investor models optimize for portfolios, not personal career risk, so copying them blindly can mislead you.
The key is range, not precision. Think in outcomes from bust to breakout, not a single “correct” number. When you’re projecting years into the future of a fast-moving industry, precision is usually false comfort.
Method 1: Scenario-Based DCF for Startup Employees
A full discounted cash flow model estimating free cash flow over 10 years, calculating terminal value via Gordon growth model assumptions, and determining net present value with a risk-adjusted discount rate is overkill for most engineers. But the underlying logic, that money in the future is worth less than money today, and that uncertain outcomes should be probability-weighted, is genuinely useful.
A simplified DCF-inspired process:
Estimate potential exit values: Think in ranges. For an AI infrastructure startup, plausible exits might be $0 (failure), $300M (acqui-hire or distressed sale), $1B (solid outcome), or $5B+ (exceptional outcome).
Assign rough probability weights: Be honest. Industry data suggests 70% of startups fail entirely. A reasonable distribution might be: 70% chance of $0, 15% chance of modest exit, 12% chance of strong exit, 3% chance of outlier.
Apply your ownership percentage after dilution: If you own 0.25% today and the company raises two more rounds, expect 25-40% dilution. Your 0.25% might become 0.15-0.19% by exit.
Discount for time and risk: Use a higher discount rate than public markets; 10-20% annually is reasonable for early-stage startups. Over 7 years at 15% discount, $1 of future value equals roughly $0.38 today.
Translating to expected value per year:
If your probability-weighted outcome suggests your equity has an expected present value of $300,000 over 4 years of vesting, you’re looking at roughly $75,000/year in expected equity value. Compare that to the equity component of a Big Tech offer to make the trade-off tangible.
For very early pre-revenue startups where future cash flows are impossible to estimate, lean more heavily on ownership percentage and comparable transaction data. Treat any DCF-style output as highly speculative.
Method 2: Comparable Startup and Deal Multiples
The comparable approach works by benchmarking your startup against similar companies. If comparable AI companies are being valued at 12x revenue, and your startup has $20M ARR, a rough enterprise value estimate would be $240M.
How to gather comps quickly:
Public databases: Crunchbase, PitchBook summaries, and press releases often disclose funding amounts and valuations
S-1 filings: When similar companies IPO, their financials become public. Study AI/ML and infrastructure companies that have gone public recently.
Focus on relevant metrics: ARR (annual recurring revenue), revenue growth rate, and key multiples like EV/Revenue or EV/ARR
A worked example:
A Series B AI infrastructure startup doing $10M ARR is valued at $300M post-money, that’s a 30x EV/ARR multiple. Compare this to public cloud infrastructure companies trading at 10-15x ARR. The difference represents a “growth premium” that investors are paying for expected future growth.
Limitations to understand:
Market cycles matter enormously: 2021 multiples averaged 15x+ for AI companies; by 2024, they’d contracted to 8x; as of 2025, they’ve rebounded to roughly 12x amid LLM enthusiasm
AI hype premiums: Companies with “AI” in their pitch deck often command higher multiples regardless of fundamentals
Business model comparability: An AI dev tools company and an AI-powered consumer app aren’t really peers, even if both fall under “AI.”
Method 3: Ownership & Outcome Mapping (The Most Practical Lens)
For busy candidates, this is often the most useful method: focus on what percentage of the company you’ll own and what that translates to across realistic exit scenarios.
Step-by-step process:
Step 1: Convert shares to percentage ownership
Ask explicitly for the fully diluted share count. If you’re granted 40,000 options and there are 16 million fully diluted shares, you own 0.25% today.
Step 2: Assume plausible exit values
Based on sector benchmarks and comparable transactions, what are realistic exit sizes? For AI infrastructure, common exits range from $200M (acqui-hire) to $2B+ (strong strategic acquisition) to $10B+ (IPO of a category leader).
Step 3: Apply likely dilution
If the company is pre-Series C, expect an additional 20-40% dilution across future rounds. Your 0.25% becomes roughly 0.15-0.20% by exit.
Step 4: Map outcomes
Exit Scenario | Your Ownership (Post-Dilution) | Gross Value | Notes |
$0 (Failure) | 0.18% | $0 | ~50% probability |
$500M Exit | 0.18% | $900,000 | Decent outcome |
$1.5B Exit | 0.18% | $2,700,000 | Strong outcome |
$5B Exit | 0.18% | $9,000,000 | Outlier win |
This table makes the risk/return trade-off tangible. A $2.7M outcome sounds great, but if there’s only a 15% chance of reaching that $1.5B exit, your probability-weighted value is much lower.
Method 4: 409A Valuation vs. “Real” Equity Value
A 409A valuation is an IRS-compliant appraisal of fair market value, conducted by an independent firm, used primarily to set option strike prices. It exists for tax compliance, guaranteeing you’re not getting options at an artificially low price that would trigger immediate taxation.
409A vs. Investor Valuation:
Investor valuation (Series C post-money): This is the number in TechCrunch headlines. It reflects what the last investors paid for preferred shares with liquidation preferences, anti-dilution protection, and other benefits.
409A valuation: Usually 40-60% lower than investor valuation. It values common stock, which lacks those protective features.
How to use 409A information:
Sanity check on strike price: If your strike price is $2/share and the 409A is $2/share, that’s normal. If your strike price is $0.50 and the 409A is $5, something’s off.
Internal pricing signal: A 409A that’s very close to investor valuation might suggest the company is being aggressive; one that’s significantly lower indicates a more conservative (and standard) approach.
Critically: 409A ≠ guaranteed exit value. A low strike price doesn’t mean “cheap shares.” It means the independent appraiser believes that’s the current fair market value of common stock. Your eventual cash-out depends entirely on what happens at exit, which could be far above, near, or below the current 409A.
How VCs Value Equity vs. How You Should as an Employee
Venture capitalists and employees look at the same equity through fundamentally different lenses. Understanding this gap helps you avoid copying investor models that don’t fit your situation.
Model multiple scenarios across dozens of portfolio companies
Expect that a few investments return the entire fund (power law distribution, 80% of gains from 20% of deals)
Optimize for upside optionality and follow-on investments
Target 10x returns portfolio-wide, accepting that most bets fail
Institutional investors like private equity funds can diversify across many positions
The Employee Perspective:
Need reliable salary, benefits, and sustainable work-life balance
Can only work at one company at a time (concentrated, not diversified)
Equity should be treated as a risky bonus, not a retirement plan
Career risk is real; a failed startup affects your resume, network, and financial security
Where Misalignments Emerge:
VCs may encourage aggressive growth strategies and risk-taking to chase outlier outcomes. That’s rational for their portfolio math. But employees bear concentrated career risk. A company that “swings for the fences” and misses leaves you job-hunting, not just marking down one investment among many.
This is why you should demand fair cash compensation to offset equity risk. The intrinsic value of stability matters when you have rent, family obligations, or simply prefer not to gamble your income.
How Fonzi AI Uses AI in Hiring
AI in hiring can either reduce bias or reinforce it. The difference is transparency. Black-box systems that auto-reject on keywords or pedigree amplify existing inequities. AI that supports human judgment makes hiring faster and fairer.
How Fonzi AI uses AI:
Fraud detection to ensure real, high-quality candidates
Bias-audited assessments that enhance fairness, eliminate bias in recruitment, and reduce liability
Skill-based matching driven by real work and compensation expectations, not keywords
Fonzi doesn’t auto-reject candidates. Humans make final decisions while AI handles low-signal, repetitive tasks.
Salary and equity transparency is enforced upfront, so engineers focus on fit and tradeoffs—not vague job descriptions.
Summary
Startup equity is neither magic nor meaningless. Its value falls on a spectrum shaped by ownership, company quality, funding terms, and realistic exit paths. The goal is not to predict a perfect number, but to decide whether the trade-offs make sense for your career and finances.
Here are a few things to keep in mind:
Estimate your ownership across plausible exit scenarios and weight them realistically
Use simplified DCF thinking and comparable multiples as guardrails, not precision tools
Compare offers side by side rather than in isolation
Remember that 409A valuations and investor valuations answer different questions and imply different values
Fonzi AI curates serious AI startups, enforces early salary and equity transparency, and uses AI responsibly to reduce bias and noise rather than auto-rejecting candidates behind black-box systems.
If you are ready to put this into practice, apply as a candidate. The process is free, selective, and designed to move you from search to offer within a single Match Day. You will see real compensation ranges in a comparable format, so you can evaluate equity like an investor in your own career, not a gambler hoping for luck.
FAQ
What are the most reliable equity valuation methods for pre-IPO startups?
How do I use DCF (discounted cash flow) to value startup equity in my offer letter?
What’s the difference between 409A valuation and actual equity value?
Which equity valuation technique works best for early-stage vs late-stage startups?
How do venture capitalists value equity differently from employees?
