What Rachel Cruzes Salary Lens Actually Is
It is a compensation benchmarking framework that has been circulating in mid-market tech circles since about 2021. The core idea is simple enough on paper: take total cash plus equity, add in benefits valuation and tax impact, then run it through a model that predicts what a candidate's actual take-home will look like in a specific city. The model was originally built by a former comp consultant who worked at a few Series B to D companies, and it got picked up by the entrepreneur network around 2023. People started calling it Toughtalk because the output tends to be blunt and occasionally uncomfortable for founders who are used to looking at base salary numbers and ignoring the rest. I first encountered this when a hiring manager at a growth-stage fintech tried to extend a compensation package to a senior engineer. The base was competitive, but the equity grant was priced at a 409A value that turned out to be significantly below market. The engineer accepted, joined, and six months later asked why his stock options were underwater. The hiring manager had no answer because he had never run the numbers through the lens. That is exactly where this tool exists: in the gap between what you offer on paper and what the candidate actually receives after taxes, vesting schedules, and option strike price calculations. The model breaks down into four inputs. The first is base salary, which is straightforward. The second is equity valuation, and this is where most people make mistakes. You cannot just use the last 409A appraisal from eighteen months ago. You need to project the company's current valuation using recent funding rounds, revenue multiples, and comparable exits in the sector. The third input is location-based cost of labor adjustment, which accounts for the fact that a hundred thousand dollars in San Francisco is not the same purchasing power as a hundred thousand dollars in Columbus. The fourth is tax optimization modeling, which factor in state income tax, capital gains treatment, and ISO versus NSO election implications.
The output is not a single number. It is a range with confidence intervals. A well-calibrated result will show you something like a $185,000 to $210,000 total compensation range for a specific role in a specific city, with the variance driven primarily by equity value uncertainty and tax bracket assumptions. The Toughtalk branding comes from the fact that when you run the numbers honestly, many companies discover their offers are less competitive than they assumed, particularly at the senior level where equity makes up sixty percent or more of the package.
Where I Got It Wrong
Early on, I used the framework with a startup that had just closed a Series C. We ran the comp model for a VP of Engineering role, and the output was roughly $280,000 total comp. The founder looked at it and said that number was too high. He had not realized that the equity component was being undervalued because the board had not updated the cap table in fourteen months. When we corrected that input, the range jumped to $340,000. The candidate negotiated, the founder pushed back, and eventually they landed somewhere in the middle. The point is not that the model is right or wrong. It is that garbage in, garbage out applies even here. If your 409A is stale, your option strike price is stale, and your vesting schedule assumptions are based on a different company structure, the output will mislead you. Another edge case I ran into involved an international hire. The framework assumes US tax treatment by default, but when you bring in a contractor from Berlin or a full-time employee from Toronto, the ISO calculations fall apart. I found that swapping in the local tax jurisdiction and running the equity through a phantom stock or SAR structure instead of actual options changed the total comp estimate by nearly twenty percent. That is a material difference when you are trying to build a hiring plan that does not overspend or undershoot simultaneously.
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Practical Pitfalls to Avoid
The most common mistake is treating the model as a static calculator. It is not. Market data shifts quarterly, valuation updates happen irregularly, and tax law changes at the federal and state levels. A reasonable refresh cadence is monthly for base salary benchmarks and quarterly for equity valuations. If you are hiring frequently, you might run it weekly for the active roles and archive the rest. The other mistake is ignoring the psychological component. Candidates do not think in total compensation ranges. They think in base salary and equity number. When you present a $250,000 package that is one hundred twenty thousand base and one hundred thirty thousand equity, the conversation usually derails because the candidate hears one hundred twenty thousand and walks away. The fix is to reframe the presentation, which is where the Toughtalk name actually earns its keep. The model forces you to confront the disconnect between what you pay and what people hear. The framework does not work well for very early-stage companies with no external valuation. If your last funding round was three years ago and the cap table has changed hands multiple times through secondary sales, the equity input becomes speculative. You can still run the model, but the confidence interval will be so wide that the output is not actionable. In those cases, I recommend falling back on publicly available data from Levels.fyi or Glassdoor, then applying a manual adjustment based on your specific role and location. It is less elegant, but it is honest about the uncertainty. The second failure mode is for roles where equity is not the primary compensation lever. Sales positions, operations roles, and individual contributor engineering positions at the junior level often have comp structures that are ninety percent cash. Running those through the lens adds complexity without changing the outcome meaningfully. You are better off using a straight market rate benchmark and ignoring the equity component entirely. The model assumes a standard tech equity package, and when that assumption breaks, the output becomes noise.
How to Use It Without Breaking Your Hiring Process
Start by building a clean data set. Gather your last three 409A reports, your current vesting schedules, and your latest cap table. Run the model for your open roles first, then compare the output against what you actually paid in the last six months. The variance will tell you how much your internal benchmarks are drifting. Once you have that baseline, you can use the framework for new hires, renegotiations, and retention offers. The whole process for a single role usually takes forty five minutes if your data is organized, and about two hours if you are still pulling information from five different spreadsheets. Factor that time in, because founders who underestimate the effort usually halfway through and end up making offers based on gut feel anyway. There is no free tool that does this properly. The original framework circulated as a private document, and several vendors have tried to commercialize it. Most of them strip out the tax optimization module and present a simplified version that looks professional but misses the nuance. If you decide to build an internal version, expect to spend a few weeks on the initial setup and another few weeks refining the assumptions. The payoff is that you stop leaving money on the table during negotiations and you stop losing candidates to comp offers that looked good on the surface but fell apart under scrutiny.
