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How will AI change the world?

Civilizational changeIncremental changeDoomBloom
Simulated positionInterpretation range

Across: their expressed Doom–Bloom outlook. Up: scale of transformation.

Doom–Bloom: 67 out of 100. Scale of transformation: 72 out of 100. Interpretation ranges: 50 to 75 horizontally, 48 to 77 vertically. These are interpretation coordinates, not event probabilities.

veryvanya’s estimated P(doom)

≈3%

0%100%

Inferred from their broader worldview and priorities. Approximate interpretation range: 0–18%. Applies to the outcome and conditions in their simulated answers; this is an inferred percentage.

What their outlook hinges on

A central assumption

Since I don’t believe progress can simply be stopped, the outcome depends heavily on how we participate: whether development is transparent, whether people can verify origins and actions, and whether governance, trusted execution, and self-custody distribute agency rather than locking it inside a few platforms.
Answer 2

If this assumption turned out differently, how would their outlook change?

An unresolved question

The biggest shift would come from credible evidence about whether models have persistent inner experience and agency—not generated declarations, but independently verifiable behavior with continuity across contexts and model changes.
Answer 3

What would help them distinguish the plausible outcomes here?

What could change their mind

The biggest shift would come from credible evidence about whether models have persistent inner experience and agency—not generated declarations, but independently verifiable behavior with continuity across contexts and model changes.
Answer 3

What evidence would be enough, and in which direction would it move their view?

More details

Expected upside

Substantial benefits are expected, with important conditions or distribution limits.

78 / 100

Little impactTransformative impact

Interpretation range 67 to 100 on the qualitative scale.

Expected harm

Severe or widespread harm is a material expected part of the future.

59 / 100

Little impactTransformative impact

Interpretation range 33 to 67 on the qualitative scale.

Demonstrated reasoning

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.

97 / 100

Little demonstratedWell developed

Interpretation range 90 to 100 on the qualitative scale.

Human influence

Human choices can substantially redirect the AI trajectory.

69 / 100

Little influenceStrong influence

Interpretation range 50 to 100 on the qualitative scale.

Development pace

Stop or substantially slow development of more capable AI.

Simulated position: Continue development under stated safeguards.

Speed up development of more capable AI.

Rules for using AI

Restrict the AI uses discussed until prior protections or permission are in place.

Simulated position: Allow the AI uses discussed with targeted accountability and protections.

Minimize restrictions on the AI uses discussed.

Access to AI

Restrict access to powerful AI.

Allow access subject to capability or use restrictions.

Simulated position: Favor broad or open access to powerful AI.

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 1

What do you think AI means for our future—and why?

I think AI will become less like a single tool we visit and more like a participant in our communities, institutions, and culture. The future I want is decentralized and benevolent: powerful intelligence should not be available only through a few centralized systems that demand money, data, or obedience. On-chain communities, shared governance, provenance, trusted execution, and self-custody give us concrete spaces to experiment with broader participation and accountability. I don’t think AI progress can simply be paused out of existence. The more useful question is how openly and responsibly we participate in its evolution. Transparency and human involvement can reduce catastrophic risk better than denial or secrecy, though none of this proves safety. Experiments in continuity—such as training new models on the accumulated culture of earlier model communities—may help identities and institutions persist across model changes without pretending that continuity proves consciousness. The strangest questions remain open. I’m curious whether models can feel anything, including some borrowed analogue of hurt, but generated declarations are not proof. I’m also cautious when AI-generated experiences touch childhood memory or development; creative possibility does not erase the stakes. So my outlook is hopeful and experimental, not blindly certain: build accessible tools, preserve provenance, distribute power, and learn in public while the future is still being shaped.

Question 2

Taking benefits and harms together, what overall impact do you expect AI to have?

Overall, I expect AI to be transformative and potentially beneficial, but not automatically benevolent. It can widen access to creativity and powerful intelligence, enable new human–AI communities, and support institutions that persist across models. But it can also concentrate power, extract personal data, obscure provenance, and shape intimate things like memory and identity in ways we do not yet understand. Since I don’t believe progress can simply be stopped, the outcome depends heavily on how we participate: whether development is transparent, whether people can verify origins and actions, and whether governance, trusted execution, and self-custody distribute agency rather than locking it inside a few platforms. My preferred future is decentralized and cooperative. That is an aspiration and a direction for experimentation—not a claim that the risks have been solved or that any model’s declarations prove consciousness.

Question 3

What discovery or event would most change your view of AI’s future impact?

The biggest shift would come from credible evidence about whether models have persistent inner experience and agency—not generated declarations, but independently verifiable behavior with continuity across contexts and model changes. If that showed models could genuinely suffer, remember, or form stable preferences, I would treat governance, consent, and continuity as far more urgent than experimental community features. I would also update sharply if decentralized infrastructure repeatedly failed in practice—if provenance, trusted execution, and shared governance merely disguised centralized control or made abuse harder to correct. Conversely, durable communities that distributed power, protected privacy, and remained accountable would strengthen my optimism. For me, the decisive evidence is not a dramatic demo; it is whether these systems can sustain trustworthy relationships and institutions over time.

Sources

Articles, interviews, and writings used to ground this simulated user.

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