Question 1

Aaron Francis
x.com/aarondfrancisUseful AI with human taste and verification.
How will AI change the world?
Across: their expressed Doom–Bloom outlook. Up: scale of transformation.
Doom–Bloom: 74 out of 100. Scale of transformation: 31 out of 100. Interpretation ranges: 74 to 75 horizontally, 23 to 52 vertically. These are interpretation coordinates, not event probabilities.
Not specified
There is not enough relevant evidence yet to estimate their view of catastrophic risk.
A central assumption
If agents keep forgetting essential details, fail under ordinary real-world complexity, or require so much human checking that they do not actually remove grunt work, I would lower my expectations substantially.Answer 2
If this assumption turned out differently, how would their outlook change?
What could change their mind
The biggest change would be evidence that AI cannot reliably move beyond impressive demos into sustained, context-rich work.Answer 2
What evidence would be enough, and in which direction would it move their view?
More details
Substantial benefits are expected, with important conditions or distribution limits.
68 / 100
Interpretation range 67 to 67 on the qualitative scale.
Reasoning, consideration of alternatives, and handling of uncertainty in their simulated answers. This describes the simulated answers, not the real person’s intelligence or opinions.
93 / 100
Interpretation range 86 to 100 on the qualitative scale.
A tentative estimate from your answers; the wider range shows other plausible readings.
52 / 100
Interpretation range 0 to 100 on the qualitative scale.
These interpretations keep their stated conditions. Benefits and harms can both be substantial. The ranges describe how we read their simulated answers, not statistical confidence intervals.
Simulated Assessment
Question 2
What discovery or event would most change your view of AI’s future impact?
Sources
Articles, interviews, and writings used to ground this simulated user.
In his opening responses, Francis emphasizes human taste, compares models adversarially and rejects both avoiding useful AI entirely and assuming no knowledge is needed to build software.

Includes 2026 agent workflows and practical AI building; titles are a recency index, not evidence of unstated views.
