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Vinicius Fagundes
Vinicius Fagundes

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46%. 13%. 61%. Same Data, Three Headlines.

#ai #llm #python #dataengineering

All three numbers are true. All three describe the same trend. And whichever one you saw first probably decided what you believe.

The story: cheap open-weight models are eating enterprise AI traffic. A routing platform's public data went through the business press this month, and it went viral in three versions:

  • 46% — share of enterprise tokens in the single best week
  • 13% — same models, measured across a full year of traffic
  • 61% — share among only the top-10 models, in one hand-picked week

One dataset. Three denominators. Three completely different headlines. Nobody faked a number. They picked frames.

Let's do what the headlines didn't: compute all three from the same data and watch the trick happen in front of us.

One dataset, three framings

I've reconstructed a year of weekly token-share data that matches the reported figures — the mechanics are what matter here, not the vendor's raw logs:

import numpy as np

rng = np.random.default_rng(7)

# 52 weeks of open-weight share of TOTAL enterprise tokens:
# a modest baseline with a launch-week spike.
share = np.clip(rng.normal(0.11, 0.025, 52), 0.04, None)
share[31] = 0.46          # the week a hot open-weight model launched

print(f"Framing 1 — THE PEAK:    {share.max():.0%}  (best single week)")
print(f"Framing 2 — THE AVERAGE: {share.mean():.0%}  (all 52 weeks)")

# Framing 3 — THE CHERRY-PICK: same spike week, but the denominator
# shrinks to 'tokens among the top-10 models only'.
spike_week_tokens = {"open_weight": 46, "closed_top10": 29, "closed_long_tail": 25}
top10_only = spike_week_tokens["open_weight"] / (
    spike_week_tokens["open_weight"] + spike_week_tokens["closed_top10"])
print(f"Framing 3 — TOP-10 ONLY: {top10_only:.0%}  (spike week, filtered denominator)")
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Framing 1 — THE PEAK:    46%  (best single week)
Framing 2 — THE AVERAGE: 13%  (all 52 weeks)
Framing 3 — TOP-10 ONLY: 61%  (spike week, filtered denominator)
Enter fullscreen mode Exit fullscreen mode

Look at framing 3 closely, because it's the most elegant of the three moves. Nothing about the open-weight tokens changed — 46 units either way. The denominator changed: drop the long tail of smaller closed models from the bottom of the fraction, and 46% becomes 61% without touching a single data point. The numerator is honest. The frame is the argument.

This is exactly how your own dashboards lie to you

Here's the part I actually care about, because I don't run a routing platform and neither do you — but we both own dashboards.

Nobody on your team fakes numbers either. They pick frames, usually without noticing:

  • Peak week when they want momentum ("adoption hit 46%!")
  • Yearly average when they want calm ("it's really only 13%")
  • A filtered subset when they want drama ("61% among the models that matter")

Same metric. Same honesty. Three different meetings.

The four questions I drill before trusting any percentage

I make my MBA students interrogate every percentage the same way, and it transfers directly to dashboard review:

1. What's the denominator? A share of what, exactly? "61% of top-10 tokens" and "61% of tokens" are different claims wearing the same number.

2. What's the time window — and who picked it? A window chosen after seeing the data is not a window, it's a selection. The launch week didn't volunteer; someone picked it.

3. Is this a level or a peak? "Reached 46%" and "runs at 46%" get collapsed into the same headline constantly. One is a spike; the other is a state.

4. What happens if I widen the frame? The cheapest robustness check that exists:

# Widen the window around the spike and watch the number deflate toward reality.
for w in [1, 4, 12, 52]:
    lo, hi = max(0, 31 - w // 2), min(52, 31 + (w + 1) // 2)
    print(f"window = {w:2d} weeks around the spike -> {share[lo:hi].mean():.0%}")
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window =  1 weeks around the spike -> 46%
window =  4 weeks around the spike -> 20%
window = 12 weeks around the spike -> 14%
window = 52 weeks around the spike -> 13%
Enter fullscreen mode Exit fullscreen mode

One for loop. The 46% headline decays to the 13% reality in four lines of output. If a number can't survive a wider window, the number was never the story — the window was.

The trend underneath is still real

Worth saying plainly: cost pressure genuinely is reshaping AI traffic, and open-weight models genuinely are taking share. The trend survives the de-framing. But you only learn that — the calm, true, 13%-and-climbing version — after you strip the framing. Never from the loudest number.

And notice the resolution, because it's the same as always: this is a data problem before it's a statistics problem. The frame lives in the query — the WHERE clause that filtered to top-10, the date predicate that picked the week. Whoever writes the query picks the argument. Dashboard literacy is reading the SQL, not the chart.

The principle: a percentage without its denominator is an opinion wearing a suit.

What's the most misleading metric you've ever caught on a dashboard?

I'm Vinicius Fagundes — principal data engineer, independent consultant, and MBA lecturer in São Paulo. I write about data pipelines and the math that runs on top of them, and take on a few projects per quarter through vf-insights.com.

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