Year End Review of My AI Use · The AI Advantage

Bill Jones

2d • General Discussion

Year End Review of My AI Use

ChatGPT offers a "Your Year with ChatGPT", and it said I was "top 1%" based on messages (chats).

Given I started using ChatGPT in April, 2025..... and it is comparing "life time usage" .... interesting stats (below).

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Known facts

Your messages: 13,976

Your percentile: Top 1% by message count

Message usage follows a heavy-tailed (power-law) distribution:

Most users send very few messages.

A small minority send a lot.

The top 1% dominate total volume.

Reasonable inference

Across large chat systems, observed patterns are typically:

Median user: ~20–40 messages total

Mean (average) user: ~100–300 messages total

Top 1% threshold: often ~2,000–5,000 messages

Top 0.1%: 10,000+ messages

You are at ~14,000, which puts you well inside the extreme tail, not merely barely top 1%.

Practical answer

A defensible estimate is:

Average user: ~150–250 messages total

You vs average: ~50–90× more messages

You vs median: ~300–700× more messages

Interpretation (non-flattering, factual)

You are not “a bit above average.”

You use the system as an operating environment, not as a novelty or lookup tool.

Your usage profile resembles:

Power users

Builders

Analysts

People externalizing cognition into the system

Most users never cross a few hundred messages in their lifetime.

“Top 1% messages sent” is calculated over the platform’s relevant user base (not just Pro, not just Business, not just last 30 days).

Your screenshot metrics are lifetime-to-date: 13,976 total messages, 817 chats.

1) Global percentile estimate (0.5%, 0.1%, etc.)

What we can compute exactly

Messages per chat (your intensity): 13,976 / 817 ≈ 17.1 messages/chat (moderate per-chat depth; the extreme comes from total volume).

What we cannot compute exactly (without platform distribution data)

Your exact percentile beyond “top 1%”.

Credible bounding (best-effort)

Given typical heavy-tail engagement patterns, 13,976 lifetime messages usually lands in:

Likely range: Top 0.1% to 0.3%

Conservative range: Top 0.05% to 0.5%

If the platform’s power-user base is unusually large: could be closer to Top 0.3%–1%, but that requires an atypically “engaged” user population overall.

2) Economic value extracted vs average user

You want a translation from “messages” to “economic value extracted.” You need a unit economics model with outcome linkage. Here is the clean framing:

A. Value per message approach (coarse but operational)

Define:

V_msg = average value you get per message (time saved, errors avoided, decisions accelerated, drafts produced)

Your value ≈ 13,976 × V_msg

Average user value ≈ (AvgMessages) × V_msg_avg

If we assume (conservatively) you extract 2–6 minutes of “net productive work” per message (drafting, thinking, structuring, de-risking), then:

13,976 messages × 2 min = 27,952 minutes = 466 hours

13,976 messages × 6 min = 83,856 minutes = 1,398 hours

Convert to dollars with your loaded rate (pick a number; I’ll show ranges):

At $150/hr: $70k to $210k equivalent productivity

At $300/hr: $140k to $420k

At $500/hr: $233k to $699k

So a reasonable comparative statement:

You likely extract 70×–200× more economic value than an average user (in total lifetime value), depending on how disciplined the workflow is and whether you convert outputs into real-world decisions.

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