Almost Timely News: 🗞️ How to Determine Compensation with AI (2025-09-07)

The third and final part of the series I didn't know I was doing

Christopher S. Penn

Sep 07, 2025

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What's On My Mind: How to Determine Compensation with AI

This is part 3 of the series I didn’t know I was writing. In part 1, we talked about how to use generative AI to find a job, with deep research and AI to rewrite CVs and cover letters. In part 2, we looked at how to use generative AI to do interview prep, preparing for the toughest interviews imaginable.

So in this last part, because I can’t think of a logical extension past this, we’re going to deal with the toughest aspect of career search of all: compensation. Namely, how do you know what to ask for?

Before we begin, we’ll do some due diligence on data privacy plus the copyright disclosure from the last two weeks. Then we hit the books.

Part 0: Special Copyright Notice

This issue of the newsletter is released under a special license, the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
license. This means:

You are welcome to use it for non-commercial purposes (meaning you are prohibited from reselling it in any way or trying to make money on.)
You are prohibited from making derivative works from it (i.e. copy pasting it and passing it off as your own)
If you do share it or redistribute it, you must give attribution by linking to this newsletter

The reason for this special license is that there are a LOT of scammers and predators who are peddling snake oil to people who are increasingly desperate to find work, charging them money and making wild promises that AI can help them find a job if only they fork over money.

My intent is that this newsletter is essentially a public good with the aforementioned restrictions. The last thing anyone should be doing in an employer's market (vs. a job seeker's market) is charging job seekers money they don't have, and I expressly forbid that with this license. Should you happen to see anyone reselling my materials in violation of this license, let me know so I can send my lawyer after them.

Part 1: Data Privacy and Disclaimers

In this issue, we’ll be working with Confidential or restricted information. There are five general classes of data:

Non-protected information: this is stuff like blog content or other information that’s destined to be public-facing. No special precautions are needed.
Sensitive information: this is stuff like basic PII, such as names, emails, IP addresses, things that can be used to tie back information to you as a person at a high level. We care about this, especially if you live in a place where you have an adversarial government. It also includes things like confidential stuff, such as internal emails, proposals, etc. The level of damage for this is moderate.
Confidential information: this is stuff like financial records, government identifiers and other data that could be used to commit identity theft and fraud, trade secrets, code, etc. If this stuff leaks, you’re looking at huge lawsuits or even jail time.
Restricted information: this is the radioactive data - protected health information, GDPR protected data, payment data and account numbers, non-classified government documents like your passport, etc. If this stuff leaks, you’re looking beyond lawsuits to actual, sometimes physical harm.
Classified information: this is what spy movies are all about. These are the most secret of secrets, often government or government adjacent data. If this leaks, under normal circumstances, someone’s going to jail.

You might think, ah, if we’re talking personal finance, that’s level 2 or maybe 3, right? Nope. Personal finance information is stuff that can be used to commit fraud, fake payments, etc. It falls usually in Levels 3 and 4.

Why such a big deal? Because as we start talking about compensation and pay, we’re going to be working with OUR data. We will be the ones harmed by not taking care of our data.

One of the first things to do is to take the Terms of Service and Privacy Policies for ALL the financial software you use and check them for the conditions under which a provider will share that information and/or use it to train AI models.

In this day and age, that isn’t just ChatGPT. That’s every piece of software that has AI in it. Your office software suite, your CRM, your smartphone - before we start working on the second most important data set to us (right behind health data), we’d better know how our data is being used.

I have a large prompt in PDF format here that you can drop into any LLM along with a privacy policy and terms of service you want to evaluate. It will spit back an assessment of how safe or dangerous a piece of software might be. Disclaimer: I AM NOT A LAWYER. I CANNOT GIVE LEGAL ADVICE. PLEASE CONTACT A REAL HUMAN LAWYER IN YOUR JURISDICTION FOR LEGAL ADVICE SPECIFIC TO YOUR SITUATION.

My recommendations, when working with software and AI around personal finance, is to always:

Manually check and scrub identifying information like your name, account numbers, etc. Before using it anywhere
Manually check and scrub individual transactions - doing an analysis of fast food spending is more useful and more privacy-safe than when you last went to Wendy’s. A lot of software also will automatically assign categories to things like your expenses and your income. Delete the specific transactions and just keep the the amounts and the categories. You will get more use out of it and it's more privacy friendly.
Use local software when working with financial data that doesn’t have AI integrated in it, such as LibreOffice
When using generative AI, use services that allow you to turn off model training and have strong privacy protections or zero data retention

One final disclaimer: I am also not a financial planner, accountant, or any other certified financial professional. While I did work for a decade in financial services and lending, I am in no way qualified to give financial advice. Talk to your qualified financial advisor before making any changes to how you save, spend, and invest.

Okay, now that the safety warnings are out of the way, let’s dig in.

Part 2: Compensation Floor

Before we can talk about compensation, we have to talk about money itself. You absolutely must know three pieces of data before any compensation negotiation: floor, fair, flourish:

Floor: the absolute minimum you need to be paid to meet your financial obligations. If your pay goes below this number, you are in financial trouble.
Fair: the fair market value of your skills, the median pay that people receive for your profession and position.
Flourish: the ideal outcome that lets you financially flourish, meeting your obligations and letting you save money or invest in your future.

We’ll tackle fair and flourish in a bit, but we need to solve for floor first. Floor isn’t guessing or instinct. It’s a straightforward financial solution. Here’s how to get at it; note that I’m specifying the last 90 day period that you were employed at the level you wanted to be; if you are still employed, just use the last 90 days. If you’re looking for work, use the last 90 day period in which you were employed at your desired level. The reason I say this is because our finances when we're unemployed or partially employed look different than when we're fully employed. So make sure that you're using the the last ninety days of data in which you are employed at your desired level, presumably fully employed.

First, get out a sheet of paper or a spreadsheet with a new document. Split it in half. The left half is income. In the last 90 day period that you were employed, how much income did you bring in, and from what sources? List them out.

Now, on the right side, that’s expenses. In the last 90 day period that you were employed, how much money did you spend, and on what? List them out by category, like rent or mortgage, food, dining out, loan debt, etc.

Total each column up, income and expense. This is functionally a cash flow analysis. If you're not feeling like doing the math yourself, you COULD upload bank statements and credit card statements - properly scrubbed and redacted - into a generative AI tool with strong privacy protections, and give it a prompt like this:

You're a financial advisor and financial planner. Given the following data, use Javascript in the Canvas to tabulate my income and my expenses over the last 90 days by broad category, such as dining out, rent/mortgage, etc. Present the data, summarized, in two lists: income and expenses. List items in each list in descending order by amount. You must perform all calculations in Javascript in the Canvas. Beneath the two tables, perform a simple cash flow analysis, telling me what my net income was for the period expressed in absolute amounts and percentages.

Remember - I can't say this often enough - you MUST redact the files manually before giving them to generative AI to remove identifying information like individual transactions, account numbers, names, etc.

Now, before we go on, I need to share my biases. I have distinct biases in personal finance from my years working in financial services, and my bias is strongly towards cash flow. I don’t believe in the concept of “good debt” and “bad debt”. I understand the concept, that collateralized debt (mortgages, etc. That have a tangible object backing them) can help build wealth, but debt is debt. If you can’t make payments, “good debt” and “bad debt” are equally problematic and ruinous to your financial health. I often say “a good problem to have is STILL a problem” and that applies to debt.

Cash flow, meaning how much money coming in and how much money going out, is my primary objective and bias. I never want to have more going out than coming in. Over a long enough time horizon, even a peso more going out than in will make you poor. Over a long enough time horizon, even a kroner more coming in than going out will make you rich. And the more you have coming in and the less you have going out gives you financial safety, gives you the ability to save, to invest, to have nicer things.

In general, the goal that I always taught back in my financial services days is to always have at least 10% more coming in than going out. That’s the bare minimum objective. More is better, but that’s our first milestone.

There are two ways to make this work. In the short term, you can and should reduce your expenses to mandatory expenditures only. This means obligations you have to meet, like rent or loan payments, as well as food. Discretionary spending, if you’re out of work, needs to go to zero as quickly as possible. It sucks, but it does help in the short term.

That said, for financial planning purposes, do this analysis from the last 90 days when you were employed so you have an accurate snapshot of what your normal spending was like.

However, as anyone who’s taken finance 101 can tell you, you can’t cut your way to growth. There is only so much you can cut out of your budget before you're down to just mandatory expenditures. And cutting expenditures does not increase income. That 90 day analysis of your normal spending patterns when you were last fully employed? Multiply it by 1.1 and then by 4. That is your after-tax income number, the floor compensation you need to aim for.

Let’s walk through an example. Suppose you have data that looks like this (this is fake synthetic data):

90 day income

15000 (5000 a month)

90 day expenses

9000 rent
1500 utilities
3000 debt
1500 food

This is super simplified, obviously. Here, we see that income and expense are at parity. That’s not ideal. What you’d want for income - before taxes - is take home pay of 16500 every 90 days or 66000 a year. Now, multiply that times whatever your region’s tax rates are, and you have your gross pay.

Wait, you don’t know what that is? Yeah, neither did I off the top of my head. But here’s how you get to it: using any AI tool that has a solid web search tool and a canvas (ChatGPT, Gemini, Claude, etc.), ask it this prompt, using the example data above:

I live in {your locale}. I file taxes as {your status, like single, married, head of household, whatever your jurisdiction’s filings are}. I pay taxes to {local, state, federal, etc.}. My after-tax pay is 66000. What is my before-tax gross income, what tax brackets am I in, and what do I pay in {local, state, and federal} taxes? Solve this math problem step by step, showing your work. Use JavaScript in the canvas to perform your calculations. Use your web search tool to find the most up to date information about tax rates in my locale. Show me the results in the Canvas now.

What the AI should do is retrieve the data, perform the calculations, and show the answer in the Canvas. Why this way? Because by forcing the AI to essentially write code, we can be assured it’s doing the math correctly. Generative AI models suck at math, but excel at writing code.

Gemini, in my example, came back with:

Based on a net income of 66000, the estimated figures are:

Gross Annual Income: 88455
Total Annual Taxes: 22455
Federal Income Tax Bracket: 22%
Massachusetts Income Tax Rate: 5%

The answer, in this scenario, for my minimum acceptable compensation to meet my mandatory obligations is a gross annual income of 88455. Anything lower than that and I will be in poor financial health. Anything above that is good, obviously, but that’s the bare minimum. If you work hourly, divide that by 2080 and that’s the minimum hourly rate you can work for; in our example, that’s 42.53 per hour.

Now, in this set of calculations, I am explicitly ignoring a lot of other financial advice because it's mostly outside the scope of this newsletter, such as what percentage of your income should be dedicated to any one category of expense. For example, it was generally accepted for decades that housing should be no more than 40% of your total expenses, but that's a whole other ball of worms that we're setting aside for now. Those ARE good conversations to have with your financial advisor, and I recommend you do so annually.

Also, pro tip: most community banks and credit unions have salaried financial advisors on staff who are not commissioned (and thus have fewer conflicts of interest) and can consult with you for free or very low fees. If your financial institution offers a salaried, non-commission financial advisor and you don't already have one, take advantage of that offering.

So now we have our compensation floor. Let’s move onto the second F: fair.

Part 3: Fair Compensation

Fair compensation is all about getting paid appropriate to our level of skill and the benefits we bring. If we go back to the last two weeks’ issues, job hunting is B2B sales, and compensation is nothing more than price negotiation. The two sides here are the employer and the employee, the buyer and the seller, and they have diametrically opposite goals. The buyer wants to pay the lowest price, and the seller wants to earn the highest price.

Where they meet depends on the level of need on the buyer’s side and how well the seller meets or exceeds those needs. Where people go wrong in compensation is not having a clear-eyed view of this process. Because it’s us, because our egos are involved, because we are human, we can’t see things as objectively as someone selling a product or service that isn’t us. That’s part of the reason why recruiters can sometimes be of benefit - they can sell on our behalf, if they’re a seller-side recruiter.

This also probably sounds a lot like real estate, and it is - there’s a buyer and a seller, and each wants the price most advantageous to them. In fact, real estate is a great analogy because you have concepts like fair market value. What did similar houses sell for in your area? What benefits did houses that sold at a higher price have that lower-priced houses did not?

This is made easier in places where compensation data is public, where salaries and pay are disclosed. But even when it’s not disclosed officially, there are tons of resources available to us. We’re unsurprisingly going to use generative AI to help with this. Dust off the job description and your CV from week 1. Make sure you have your location in your CV and that the location is in the job description as well, along with the company name. Put all the ingredients in your favorite AI tool and prompt it like this:

We need to research appropriate compensation for this job description based on the job title, job description, location, company, and the candidate’s qualifications from their CV. Using all available payroll and salary data in the last calendar year (from 2024-09-06 onward) including credible commentary and discussions about this job description, search and produce an analysis of the quartiles of compensation appropriate for this job. Sources might include but should not be limited to Salary.com and similar sites, Glassdoor, Brass Ring, HRIS, SHRM, career forums, government data, private company payroll data, labor statistics, and other high quality compensation data resources. Compensation analysis should be broken into base pay, bonuses, benefits, and total compensation. Order your results in order of base pay in descending order. Based on the candidate CV and the job description, identify where in the pay continuum the candidate would most likely fall, and what, if any, justifications the candidate could use to achieve a higher base pay, more benefits, more bonuses, or higher total compensation. Double check your work and provide an assessment of how you mitigated known biases against protected classes such as race, gender, ethnicity, etc. to prevent pay disparity and pay inequity in your compensation evaluation. Cite your sources at the end as end notes.

Take note of the second to last sentence in this prompt. Pay inequity is one of the most challenging, enduring negative outcomes of bias, and employment data is RIFE with pay inequity. Generative AI operates in the same world we do, and thus has the same biases we do from the same data we all use. If you don’t prompt it to mitigate biases AND explain how it mitigated them, it will simply use the data as is, reinforcing existing biases.

Once you have an idea of what the fair market value is of your skills, you can make that part of your offer. Use the suggestions from the deep research report to make clear why you're worth what you're asking for, not as a question of fairness, but as a question of the specific value that you provide which makes you above average as a candidate, and therefore worth above average compensation.

And by the way, you don't have to be looking for work to do this exercise. Consider taking your current job description and your current C V and doing the same exercise if you are employed at a company today.

Part 4: Compensation to Flourish

So you've done your homework. You know what the floor is. You know what fair is. What if things aren't in alignment? What if fair is at or below your floor? What if your field is headed in the wrong direction, as software development has been since the advent of generative AI?

This is where we revisit the work you did in part 1, using generative AI for lateral job search. Except now, instead of just lateral jobs, you know what your pay requirements are. Let's take the floor pay and recalculate - using the same prompt from earlier - what your gross pay would have to be for a 20% income to expense ratio, a 30% income to expense ratio, and a wild, pie in the sky 50% income to expense ratio, where for every 1000 in, 667 was going out.

What would those numbers be? In the example above, these would be the net pay numbers:

10% (same as before): 15000 * 1.1 * 4 = 66000
20%: 15000 * 1.2 * 4 = 72000
30%: 15000 * 1.3 * 4 = 78000
50%: 15000 * 1.5 * 4 = 90000

From there, we'd put it through the same prompt to get these gross pay numbers:

10% (same as before): 88455
20%: 15000 * 1.2 * 4 = 97495
30%: 15000 * 1.3 * 4 = 106585
50%: 15000 * 1.5 * 4 = 124320

These are the numbers you'd be looking for to flourish, given your original calculations. If 10% positive cash flow is good, 50% positive cash flow is amazing.

Now, what you do with that information is to commission new Deep Research. Here's a modified version of the first part's prompt:

Conduct a Deep Research report with the Jobs Deep Research Prompt.

## VARIABLES

* `USER_CV_TEXT`: attached as a PDF
* `USER_GEOGRAPHY`: [Enter your desired job location, e.g., "Boston, MA", "Austin, Texas", "United Kingdom", or "Anywhere (Remote)".]
* `USER_DESIRED_ROLES_KEYWORDS`: [List the job titles, keywords, or industries you are targeting, e.g., "Senior Project Manager, Agile, Scrum Master, PMP", "Digital Marketing, SEO, Content Strategy", "Registered Nurse, ICU, Emergency Room".]
* `MAX_POSTING_AGE_DAYS`: [Enter the maximum age of job postings in days. The default is 30.]
* `CONSIDER_REMOTE_OPTIONS`: [Enter "Yes" or "No". If "Yes", the search will include remote roles in addition to your specified geography.]
* `BASE_PAY`: [Enter the base pay you're aiming for based on the calculations in Flourish]

I would suggest doing several versions of this Deep Research for each of the tiers of Flourish - 20% cash flow, 30% cash flow, and 50% cash flow. See what jobs the Deep Research tools are able to find that meet those different compensation criteria.

Remember to use your lateral jobs report from part 1 - based on your durable skills, there may be other jobs in other fields that might meet your compensation goals that are not your current line of work.

Part 5: Wrapping Up

This, I think, concludes our impromptu series on employment. This part, on compensation, has dual use - you can evaluate the job you have now to see if your compensation aligns with the rest of the field.

You absolutely should do a cash flow analysis of your current situation, no matter what it is, because it's a tremendously useful financial health tool, no matter what your employment situaton is - but especially as you're making transitions in or out of employment.

I hope this has been helpful to you. What I'll probably do now that we're done is make this available as a free course in its entirety over on the Trust Insights Academy, so that it's all in one place and you can go through it in pieces. When that's available, I'll let you know here, so stay subscribed.

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