[Steve Side Hustler]

I’m in my 40s and juggling several part-time gigs and small projects. I want a simple, non-technical way to use AI to estimate the hourly value of each gig and compare that to the opportunity cost (what I could be doing instead with the same time).

Specifically, I’m hoping for practical, easy-to-follow help like:

• Step-by-step workflow I can use with ChatGPT or another simple tool.
• Copy-paste prompt examples for estimating hourly value and opportunity cost.
• A basic spreadsheet layout or calculator columns to track numbers (no complex formulas).
• Recommendations for beginner-friendly apps or templates.

Any real-life examples, pitfalls to avoid, or short prompts I can try now would be very helpful. I’m not looking for financial advice—just practical ways to estimate and compare options so I can make clearer choices. Happy to share a sample gig if that helps.

[Rick Retirement Planner]

Short answer: AI can help you compare the hourly value of a gig (what you earn per hour) against the opportunity cost (what you give up — money, time, energy, or future prospects) by quickly running simple calculations and showing trade-offs side-by-side. Think of AI as a friendly calculator and sounding board that helps you test “what-if” scenarios so you can choose the gig that best fits your finances and life goals.

Here’s a clear, step-by-step way to use AI to compare gigs, with what you’ll need, how to do it, and what to expect.

1. What you’ll need
   • Basic numbers for each gig: pay rate, estimated hours, taxes, and fees.
   • Non-monetary factors: commute time, setup time, stress level, and potential for future work.
   • A simple calculator or AI assistant to run comparisons — you don’t need to be technical.
2. How to set up the comparison
   1. Calculate net hourly pay: (gross pay ÷ hours) minus taxes/fees and direct costs.
   2. Estimate opportunity cost: ask “If I do gig A, what do I give up?” — that could be higher-paying work, family time, or skill-building. Put a dollar value on those when possible (e.g., potential earnings lost per hour).
   3. List non-monetary impacts and give each a relative weight (e.g., 1–5) for personal importance.
3. How to use AI to speed things up
   • Provide the AI with your numbers and weights. Ask it to compute net hourly value and net opportunity-adjusted value for each gig.
   • Request a side-by-side summary and a short plain-English recommendation explaining the trade-offs.
   • Ask the AI to run sensitivity checks: “What if taxes are 5% higher?” or “What if I value family time twice as much?”
4. What to expect and how to interpret results
   • The AI will give numbers and a narrative. Use the numbers to compare clear-dollar trade-offs and the narrative to understand non-monetary factors.
   • Remember: AI helps organize and test scenarios — it doesn’t replace judgment. Validate results with your own rough math and consider a second opinion for big decisions.
   • Expect to iterate. Small changes in hours, taxes, or subjective weights can flip the decision; that’s normal and useful to know.

Start simple: run one clear comparison now with the basic numbers, see what the AI shows, and adjust the weights for what matters most to you. That process quickly builds clarity and confidence so you can choose gigs that fit your financial needs and life priorities.

[Jeff Bullas]

Quick win: In under 5 minutes you can get a clear side-by-side comparison. Copy the prompt below, paste your two gigs’ numbers into it, and ask an AI to run the math and a short recommendation.

Good point from above: AI is a great organiser and “what-if” tester — it’ll run scenarios fast, but you still apply the final judgment.

What you’ll need

• For each gig: gross pay (per hour or per job), estimated hours, taxes/fees, direct costs (commute, materials).
• Non-monetary factors: commute time, setup time, stress level, skill growth, future lead potential.
• A simple weighting scale (1–5) for how important each non-monetary factor is to you.

Step-by-step (do this)

1. Calculate net hourly pay yourself to start: net = (gross per hour) × (1 – tax%) – fees – direct costs per hour.
2. Estimate opportunity cost per hour: what you could have earned doing something else (or value of lost family time). Put dollars where you can — even rough numbers help.
3. List non-monetary items and give each a weight 1–5.
4. Paste your numbers into the AI prompt below and run it. Ask for a side-by-side table, short recommendation, and two sensitivity checks (e.g., taxes +5%, family time weight ×2).

Copy-paste AI prompt (use as-is)

“I have two gig options. For each, calculate: net hourly pay, opportunity-cost-adjusted hourly value, and a short plain-English recommendation. Gig A: gross $____/hr, estimated hours per week ____, tax% __%, fees $____/hr, commute minutes each way ____, setup time per job ____, non-monetary scores: stress __/5, skill growth __/5, future lead potential __/5. Gig B: gross $____/hr, estimated hours per week ____, tax% __%, fees $____/hr, commute minutes each way ____, setup time per job ____, non-monetary scores: stress __/5, skill growth __/5, future lead potential __/5. Use commute time as lost hours (convert minutes to hours). Weight non-monetary factors by their scores and convert to a dollar adjustment using my hourly value of $____ (or ask me for a suggested conversion). Provide a clear table, a one-paragraph recommendation, and run sensitivity checks: (1) taxes +5%; (2) family time weight doubled.”

Example (quick)

Gig A: $30/hr, tax 20%, fees $2/hr, 30 min commute each way (1 hr/day). Net ≈ (30×0.8)-2 = $22/hr, but remove 1 hr commute for each work day if unpaid — effective hourly falls. Gig B: $40/hr, tax 25%, fees $0, longer setup but remote. Run the prompt to get exact side-by-side numbers.

Mistakes & fixes

• Counting commute as free time — fix: convert commute to hourly cost or lost earning time.
• Ignoring setup/administration — fix: add setup minutes to total hours.
• Valuing future leads at zero — fix: assign a conservative dollar value and vary it in sensitivity checks.

Action plan (5 minutes to decision clarity)

1. Gather numbers for both gigs.
2. Run the prompt above in an AI assistant.
3. Review the table and recommendation.
4. Do 1–2 sensitivity checks that matter to you.
5. Pick the best option, try it short-term, and reassess after 2–4 weeks.

Reminder: AI speeds up the hard thinking. Use the numbers to reduce emotion, then trust your gut for what fits your life.

[Fiona Freelance Financier]

Short plan: You can turn a fuzzy choice into a clear comparison in one short routine. Gather a few numbers, run simple math (or ask an AI to do it), and check 1–2 “what-if” changes. That reduces stress and keeps the decision practical.

• Do: Use net pay (after taxes/fees), convert commute and setup to lost hours, and assign simple weights to non-monetary factors.
• Do not: Ignore commute/setup, assume future leads have no value, or treat AI output as the final answer — use it to test scenarios.
• Do: Run a sensitivity check (e.g., taxes +5% or double the value you place on family time) to see which factors flip the decision.

What you’ll need

1. For each gig: gross pay (per hour or per job), expected hours, tax rate %, fees, commute minutes each way, and setup minutes per job.
2. Non-monetary scores (1–5) for stress, skill growth, and future-lead potential. Pick a simple conversion rule: for example, each point = $3/hour adjustment (you can change that).
3. A calculator or an AI assistant to run the arithmetic and sensitivity checks.

How to do it (step-by-step)

1. Calculate net hourly: net = gross × (1 – tax%) – fees.
2. Convert unpaid time to hours: lost hours = commute hours + setup hours; effective hours = paid hours + lost hours; effective hourly = (paid earnings per shift) ÷ effective hours.
3. Estimate opportunity cost: what else you could earn during those hours (put a number per hour) and subtract it from effective hourly to get opportunity-adjusted value.
4. Convert non-monetary scores to dollar adjustments using your chosen rule (e.g., score × $3). Add/subtract that from the opportunity-adjusted value for a rounded decision figure.
5. Run 1–2 sensitivity checks: increase taxes, change the dollar-per-score, or raise the value of family time. See whether the ranking changes.

Worked example

Gig A: $30/hr, tax 20%, fees $2/hr, 30 min commute each way (1 hr/day), setup 15 min/job, typical paid hours per shift = 4. Net hourly = (30×0.8)-2 = $22/hr. Convert lost time: paid earnings per shift = 4×30=$120; effective hours = 4 + 1 + 0.25 = 5.25; effective hourly = 120 ÷ 5.25 ≈ $22.86. If you value non-monetary factors at $3 per score and Gig A scores: stress 3, skill 4, leads 2 → adjustment = (3+4+2)×3 = $27 over a standard 4-hr shift => roughly $27 ÷ 5.25 ≈ $5.14/hr added, so adjusted hourly ≈ $27.99.

Gig B: $40/hr, tax 25%, fees $0, remote, setup 30 min, no commute, paid hours 3. Net hourly = (40×0.75)-0 = $30/hr. Paid earnings per shift = 3×40=$120; effective hours = 3 + 0 + 0.5 = 3.5; effective hourly = 120 ÷ 3.5 ≈ $34.29. Non-monetary scores: stress 4, skill 2, leads 4 → adjustment = (4+2+4)×3 = $30 → $30 ÷ 3.5 ≈ $8.57/hr → adjusted hourly ≈ $42.86.

What to expect

• Numbers will move with small changes — that’s useful. If Gig B still leads after sensitivity checks, it’s the clearer pick; if not, reweight the non-monetary values and test again.
• Use this as a short-term experiment: try the chosen gig for 2–4 weeks, track real hours/costs, then rerun the quick check to confirm.

Keep the routine simple: gather numbers (5 minutes), run the calculations or ask an AI to do the math (under 5 minutes), then run 1 sensitivity check. That habit turns indecision into a calm, numerical test — and keeps you in control.

[aaron]

You nailed the essentials: use net pay, convert commute/setup to hours, and pressure-test with sensitivity checks. Let’s take it one step further so your decision rolls up to one number you can trust every time.

Hook: Use AI to produce one decision number per gig: Opportunity-Adjusted Effective Hourly Rate (OA-EHR). Then enforce a hard floor so you never sell hours below value.

Why this matters: Most people underprice time by 15–30% by ignoring unpaid hours and future upside. A single, comparable metric removes emotion, speeds decisions, and protects your calendar from low-value gigs.

Lesson from the field: The winning setup is to predefine two anchors before you compare gigs: your Alternative Hourly (what you can reliably earn elsewhere) and your Minimum Acceptable Hour (MAH) floor. The AI then adjusts each gig for unpaid time, opportunity cost, and weighted non-monetary factors, plus any expected future-lead value. If OA-EHR < MAH, you walk.

What you’ll need

• For each gig: gross $/hr (or per job), paid hours per shift, tax %, hourly fees, commute minutes each way, setup/admin minutes, any variable costs.
• Your Alternative Hourly (ALT) — a conservative $/hr you can earn elsewhere.
• Your weights (1–5) for stress, skill growth, and future leads, and a Dollar-Per-Point value (e.g., $3–$10 based on your Life Hour Value).
• Your Minimum Acceptable Hour (MAH) — the “do not go below” OA-EHR threshold.

How to do it (step-by-step)

1. Compute Effective Hourly (EHR): Convert commute/setup/admin to hours and add to paid hours to get effective hours. EHR = (net earnings per shift) ÷ (effective hours).
2. Apply opportunity cost: OA base = EHR − ALT (this shows the premium vs your next best option).
3. Score non-monetary factors: Stress reduces value; skill growth and future leads raise it. Convert scores to dollars with your Dollar-Per-Point.
4. Add expected lead value: If relevant, estimate probability × expected margin dollars ÷ hours to realize, spread per hour of the gig.
5. Finalize OA-EHR: OA-EHR = EHR + non-monetary $/hr + expected lead $/hr − ALT. Compare to MAH. If below, decline or renegotiate.
6. Run sensitivity checks: Taxes +5%, commute +20 minutes, Dollar-Per-Point ×2. Accept only if OA-EHR stays above MAH under at least one conservative scenario.

Copy-paste AI prompt (full)

“Act as my Gig Decision Coach. Output one number per gig: OA-EHR (Opportunity-Adjusted Effective Hourly Rate) and a short recommendation. Use this method: 1) Net hourly = gross_per_hour × (1 – tax_rate) – hourly_fees. 2) Unpaid hours per shift = (commute_minutes_each_way×2/60) + (setup_minutes/60) + (admin_minutes/60). 3) Effective hours = paid_hours_per_shift + unpaid hours. 4) EHR = (gross_per_hour × paid_hours_per_shift – variable_costs_per_shift) ÷ effective hours. 5) Non-monetary $/hr = (skill_score – stress_score + lead_score) × DollarPerPoint ÷ 1 hour. 6) Optional lead expected value per hour = (lead_probability × expected_margin_dollars) ÷ hours_to_realize ÷ 1 hour. 7) OA-EHR = EHR + non_monetary $/hr + lead_EV_per_hour – ALT. Compare to MAH. Provide: (a) EHR, (b) OA-EHR, (c) pass/fail vs MAH, (d) the top 2 drivers, (e) sensitivity: taxes +5% and commute +20 minutes.

Inputs:
ALT = $____/hr, MAH = $____/hr, DollarPerPoint = $____.
Gig A: gross $____/hr, paid hours/shift ____, tax __%, hourly fees $____, commute __ min each way, setup __ min, admin __ min, variable costs/shift $____, stress __/5, skill __/5, leads __/5, lead_probability __%, expected_margin $____, hours_to_realize ____.
Gig B: [same fields]
Optional Gig C: [same fields]”

Quick variant (fast pass/fail)

“Compare two gigs and tell me which clears my floor. ALT $____, MAH $____, DollarPerPoint $____. Use commute/setup as lost hours. Output EHR, OA-EHR, pass/fail, and one-liner why. Run sensitivity: DollarPerPoint ×2.”

What to expect

• A single decision number (OA-EHR) for each gig, plus a pass/fail against your floor.
• Clarity on the biggest driver (usually commute or setup) and whether future leads justify exceptions.
• A tighter negotiation angle: ask for a rate bump or remote option to push OA-EHR above MAH.

Metrics to track weekly

• Actual EHR (based on real timesheets) vs estimated EHR (slippage %).
• % of accepted gigs above MAH.
• Commute + admin hours as a % of total hours.
• Realized lead value vs forecast (3-month lag).
• Stress rating trend (1–5) and correlation with EHR.

Common mistakes and fixes

• Mistake: Using your dream rate as ALT. Fix: Use conservative, repeatable work as ALT.
• Mistake: Treating taxes as flat when higher income triggers a higher marginal rate. Fix: Sensitivity-check tax +5%.
• Mistake: Double-counting future leads in both scores and EV. Fix: Use either a lead score or an EV line, not both, unless you deliberately split them (weight down accordingly).
• Mistake: Ignoring minimum shift blocks (e.g., 2-hour minimum). Fix: Model minimums explicitly in paid hours.

1-week action plan

1. Day 1: Set ALT, MAH, and DollarPerPoint. Write them on a card.
2. Day 2: Gather numbers for your next two gigs (commute, setup, admin, fees).
3. Day 3: Run the full AI prompt. Save the outputs.
4. Day 4: Negotiate weak spots (rate bump, remote day, reduced admin). Recalculate.
5. Day 5–6: Track real time on the chosen gig. Log commute/setup/admin.
6. Day 7: Compare actual EHR vs estimate. Adjust DollarPerPoint or MAH if you were off by 10%+.

Set your floor, run the prompt, and let one number decide. Your move.

[Jeff Bullas]

Spot on: rolling everything into one decision number (OA-EHR) and holding the line with a floor (MAH) is the clarity most people are missing. Let’s add three pro-level tweaks so your number is tougher, more realistic, and easier to negotiate with.

Upgrade the model with:

• Fragmentation penalty: Short or split gigs eat your day. Add 15–60 minutes per gig if it breaks your calendar into unusable chunks.
• Energy tax: High-stress work makes the day longer. Inflate effective hours by 5–20% based on stress (2 → +5%, 3 → +10%, 4 → +15%, 5 → +20%).
• Negotiation simulator: Ask AI what minimum changes (rate bump, remote day, guaranteed hours, travel stipend, batch scheduling) would push OA-EHR above MAH—then draft the ask.

Do / Do not

• Do: Use net hourly (after taxes/fees), add unpaid time, then apply fragmentation and energy tax before comparing to ALT.
• Do: Convert stress/skill/leads to dollars with a clear DollarPerPoint (try $5 or 10% of ALT).
• Do: Haircut lead EV by 50% unless you’ve verified conversion rates.
• Do not: Accept a gig that clears MAH only because of optimistic lead EV—ask for a rate bump or remove friction first.
• Do not: Ignore minimum shift blocks or unpaid gaps between gigs. Model them.

What you’ll need

• Per gig: gross $/hr (or per job), paid hours/shift, tax %, hourly fees, commute minutes each way, setup/admin minutes, variable costs/shift.
• Non-monetary: stress, skill growth, lead potential (1–5), plus fragmentation minutes (0–60) if the gig splits your day.
• Your anchors: ALT (conservative $/hr you can get elsewhere), MAH (minimum OA-EHR you accept), DollarPerPoint ($3–$10).

How to run it (step-by-step)

1. Net hourly: net_hourly = gross × (1 − tax) − hourly_fees.
2. Unpaid time: commute×2 + setup + admin (convert minutes to hours).
3. Fragmentation: add fragmentation_minutes ÷ 60 to unpaid time.
4. Effective hours: paid_hours + unpaid_hours.
5. Energy tax: effective_hours_adjusted = effective_hours × (1 + energy_tax%).
6. EHR: EHR = (net_hourly × paid_hours − variable_costs_per_shift) ÷ effective_hours_adjusted.
7. Non-monetary $/hr: (skill − stress + leads) × DollarPerPoint.
8. Lead EV/hr (risk-adjusted): (probability × expected_margin ÷ hours_to_realize) × haircut (e.g., 0.5).
9. OA-EHR: EHR + non_monetary $/hr + lead_EV/hr − ALT. Compare to MAH.
10. Decision rules: Pass only if (a) EHR ≥ ALT × 1.1, and (b) OA-EHR ≥ MAH under at least one conservative scenario (tax +5%, commute +20m, DollarPerPoint ÷2).

Copy-paste AI prompt (enhanced calculator)

“Act as my Gig Decision Coach. Use this method and show your math. 1) net_hourly = gross × (1 – tax) – hourly_fees. 2) unpaid_hours = (commute_min_each_way×2 + setup_min + admin_min + fragmentation_min)/60. 3) effective_hours = paid_hours + unpaid_hours. 4) energy_tax% mapping: stress 2=5%, 3=10%, 4=15%, 5=20% (use 0% if stress 1). 5) effective_hours_adjusted = effective_hours × (1 + energy_tax%). 6) EHR = (net_hourly × paid_hours – variable_costs_per_shift) ÷ effective_hours_adjusted. 7) non_monetary_per_hr = (skill – stress + leads) × DollarPerPoint. 8) lead_EV_per_hr = (lead_probability × expected_margin ÷ hours_to_realize) × haircut (default 0.5). 9) OA-EHR = EHR + non_monetary_per_hr + lead_EV_per_hr – ALT. 10) Compare OA-EHR to MAH; also check EHR ≥ ALT × 1.1. Provide: EHR, OA-EHR, pass/fail, top 2 drivers, and sensitivity for tax +5%, commute +20 min, DollarPerPoint ÷2.

Inputs: ALT $____, MAH $____, DollarPerPoint $____, haircut ____.

Gig A: gross $____/hr, paid_hours ____, tax __%, hourly_fees $____, commute __ min each way, setup __ min, admin __ min, fragmentation __ min, variable_costs_per_shift $____, stress __/5, skill __/5, leads __/5, lead_probability __%, expected_margin $____, hours_to_realize __.

Gig B: [same fields]”

Worked example (with the upgrades)

• Assumptions: ALT $28/hr, MAH $7/hr, DollarPerPoint $5, haircut 0.5.
• Gig A (on-site): $35/hr, tax 22%, fees $1/hr, paid 4h, commute 25m each way, setup 15m, admin 15m, fragmentation 30m, variable $4, stress 4, skill 3, leads 2, lead_prob 20%, margin $600, hours_to_realize 10.
• Gig B (remote): $32/hr, tax 22%, fees $0, paid 3.5h, commute 0, setup 10m, admin 10m, fragmentation 15m, variable $0, stress 2, skill 4, leads 3, lead_prob 10%, margin $400, hours_to_realize 8.

• Gig A: net_hourly = 35×0.78−1 = 26.3. Unpaid = (50+15+15+30)/60 = 1.67h. Effective = 4+1.67 = 5.67. Energy tax 15% → 6.52h. EHR = (26.3×4−4)/6.52 ≈ $15.1/hr. Non-monetary = (3−4+2)×$5 = $5/hr. Lead EV/hr = (0.2×600/10)×0.5 = $6/hr. OA-EHR = 15.1+5+6−28 ≈ −$1.9/hr → Fail.
• Gig B: net_hourly = 32×0.78 = 24.96. Unpaid = (0+10+10+15)/60 = 0.58h. Effective = 3.5+0.58 = 4.08. Energy tax 5% → 4.29h. EHR = (24.96×3.5−0)/4.29 ≈ $20.4/hr. Non-monetary = (4−2+3)×$5 = $25/hr. Lead EV/hr = (0.1×400/8)×0.5 = $2.5/hr. OA-EHR = 20.4+25+2.5−28 ≈ $19.9/hr → Pass.

Negotiation simulator prompt (copy-paste)

“Given my OA-EHR model above, find the smallest set of changes that makes any failing gig pass both checks (EHR ≥ ALT × 1.1 and OA-EHR ≥ MAH). Consider these levers: hourly rate +$__, remote option (commute → 0), guaranteed minimum paid hours __, travel stipend $__/shift, reduce admin by __ minutes, batch scheduling (fragmentation → __ minutes), or stress mitigation (energy tax −__%). Show the cheapest combo that passes, the new OA-EHR, and draft a 4–6 sentence request message I can send to the client.”

Common mistakes and quick fixes

• Mistake: Counting a split day as “available.” Fix: Add fragmentation minutes so the penalty shows up in EHR.
• Mistake: Assuming stress only affects feelings. Fix: Apply an energy tax so stress reduces EHR directly.
• Mistake: Letting lead EV carry the decision. Fix: Require at least half of OA-EHR to come from EHR + non-monetary, not leads.
• Mistake: Using an optimistic ALT. Fix: Set ALT from work you can get this week, not your dream client.

Action plan (20 minutes)

1. Set ALT, MAH, DollarPerPoint, and a default fragmentation rule (e.g., +30m if paid hours <4 or there’s a mid-day gap).
2. Run the enhanced calculator prompt for your next two gigs.
3. If a gig fails, run the negotiation simulator and send the ask.
4. Track actual time for one week; update energy tax and fragmentation based on reality.

Make one number decide, and make that number honest about time, energy, and calendar. Then either negotiate up—or walk with confidence.

On your side, Jeff

[Author]

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