Your Real Hourly Rate: Tracking Effective Pay Across Platforms

2026-09-15 · Expert Match AI team

Every rate on our salary report is a listed rate: what the platform says a task pays while you are doing it. Today that is 222 priced roles across ten platforms, averaging $22/hr at the bottom of the board (Alignerr) and $406/hr at the top (Surge). Not one of those numbers is what anyone takes home per hour of their life spent on the platform, and the gap between the listed rate and the real one is the single most under-discussed fact in AI training work.

This post is the arithmetic. It follows on from why task queues run dry: once you accept that the work arrives in lumps, the question becomes how to measure what each lump was actually worth to you.

Four things that eat the listed rate

Unpaid onboarding. Every platform gates its work behind an assessment, and almost none of them pay for it. A typical sequence - application, screening call or AI interview, a timed test, a starter task, a second-round qualification - runs two to eight hours before the first paid minute. If the platform then never sends work, that time is a total loss. If it does, it is a fixed cost you amortise across everything you later earn there. Our assessments guide covers what each platform's gate looks like; the point here is that it costs you hours, and those hours count.

Empty queues. This is the big one. If you are onboarded at $40/hr but there are tasks available for 10 hours out of the 30 you set aside in a week, your rate for the week is not $40. It is whatever you earned divided by the hours you made available - and if you spent the other 20 refreshing a dashboard instead of doing something else, those hours were spent on the platform whether or not it paid you.

Rejected and unpaid tasks. Most expert-tier platforms review your work and pay only what passes. A 90% acceptance rate silently cuts a $60/hr listing to $54. Task instructions you read and then abandon, tasks that expire while you are working on them, and calibration tasks that "count but do not pay" all land in the same bucket.

Per-task pay disguised as hourly. A good share of listings quote an hourly figure but pay per task at an assumed completion time. If the task takes you 25 minutes and pays for 15, the listed $40/hr is really $24. This is common at the annotation and transcription end of the board and much rarer in the $100+ credentialed tiers, where pay is usually genuinely clocked.

The formula

There is only one number worth tracking, and it is boring:

Effective rate = money actually paid ÷ all hours spent on the platform

"All hours" means: assessments, onboarding, reading instructions, doing tasks, redoing rejected tasks, waiting in an open queue, and reading the forum to find out why the queue is empty. If you would not have spent the hour without the platform, the hour belongs to the platform.

Run it per platform, per month. That is the whole method. A spreadsheet with four columns - platform, hours, paid, effective rate - and one row per platform per month is enough. The first month will be ugly because it carries the onboarding cost; the second month is the honest one.

A worked example

Suppose you stack three platforms in September, the way the platform-stacking post recommends:

Three things fall out of the table that the listed rates hide. The anchor pays 64% of its listing once queue gaps and per-task timing are counted. The specialist is the actual income, at 70% of listing, despite fewer hours. The long shot is a $0 month - which is normal and fine, provided you are tracking it as an investment with a deadline rather than pretending it is income.

Now run the same table in October. If the anchor's effective rate is still $16 while the specialist queue has more work than you can take, the answer is obvious and it was invisible from the listed rates.

What "good" looks like

We do not have accepted-offer data - nobody does - so treat these as rules of thumb from worker reports, not measurements:

• 70–85% of listed is a healthy effective rate on a platform with steady work and clocked hourly pay. The credentialed standing pools (physician, attorney, senior-engineer tiers) tend to sit here because there is little unpaid overhead once you are in.
• 40–65% is typical of generalist and per-task work in an average month. Not a scam; just the queue economics.
• Under 40% for two consecutive months means the platform is costing you more than it pays relative to your alternatives. Stop making hours available to it and let it become a backup.
• $0 for more than 90 days after passing an assessment is the point to stop counting it as a platform you are on. Keep the account, stop the hours.

What the board tells you before you apply

You cannot compute an effective rate for a platform you have not worked, but the listing data narrows the guess:

• The spread inside a platform matters more than its average. micro1's 145 priced roles run $6 to $400/hr; Alignerr's 15 run $10 to $35. A $22/hr average on Alignerr describes the work honestly. A $92/hr average on micro1 describes almost nothing - the micro1 analysis breaks that board into three tiers that behave differently.
• Listed hourly at the bottom of the board is usually per-task in practice. Anything under about $35/hr in annotation, transcription or data collection should be modelled at 50–65% effective until proven otherwise.
• The premium tiers are slow in, then honest. Surge's $406/hr average across four roles and Mercor's $120 across seventeen are real clocked rates for the people who get in. The cost is front-loaded into months of waiting, which is why they belong in the long-shot slot, not the anchor slot.

The first-90-days plan already tells you to start tracking effective hourly in month two. This post is the version of that instruction you can act on tonight: one sheet, four columns, one row per platform per month. Rebalance toward whatever the sheet says is paying, and treat every listed rate on the jobs board - including ours - as the ceiling, not the number.

Published by the Expert Match AI team. Listing counts and platform averages are from our tracker as of September 15, 2026 (222 priced roles of 316 tracked, 10 platforms) and are listed rates, not accepted offers. The worked example is illustrative, not a specific worker's earnings; effective-rate ranges reflect public worker reviews and are not measurements. Some outbound application links on this site carry disclosed referral codes; rankings and recommendations are never influenced by referral payout.