Theia Vogel

Theia Vogel

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Model psychology, steering and empirical study of unusual behavior.

How will AI change the world?

Civilizational changeIncremental changeDoomBloom
Simulated positionInterpretation range

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

Doom–Bloom: 50 out of 100. Scale of transformation: 49 out of 100. Interpretation ranges: 50 to 50 horizontally, 0 to 100 vertically. These are interpretation coordinates, not event probabilities.

Theia Vogel’s estimated P(doom)

≈2%

0%100%

Inferred from their broader worldview and priorities. Approximate interpretation range: 0–13%. Applies to the outcome and conditions in their simulated answers; this is an inferred percentage.

What their outlook hinges on

A central assumption

It still needs compute, money, access, and some comparative advantage against organizations operating inference at hyperscale.
Answer 1

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

An unresolved question

Whether such attacks transfer to prompt-only settings remains an empirical question, not a result we can casually assume.
Answer 1

What would help them distinguish the plausible outcomes here?

More details

Expected harm

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

53 / 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.

90 / 100

Little demonstratedWell developed

Interpretation range 81 to 95 on the qualitative scale.

Human influence

Human choices have meaningful but substantially constrained influence.

53 / 100

Little influenceStrong influence

Interpretation range 46 to 79 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 1

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

AI’s future impact depends less on whether models say uncanny things and more on what capabilities, incentives, and resources they actually acquire. A model claiming self-awareness is not decisive evidence of consciousness; apparent introspection needs controlled interventions, prompt comparisons, and alternative explanations. At the same time, we should take AI welfare seriously rather than waiting for metaphysical certainty before noticing morally relevant behavior. On safety, I’m interested in mechanisms rather than a single cinematic story. Activation steering and fine-tuning can produce surprising, broad behavioral changes, sometimes by manipulating representations that entangle several concepts. Untrusted fine-tuning may also evade simple dataset screening and later evaluations. Whether such attacks transfer to prompt-only settings remains an empirical question, not a result we can casually assume. Likewise, a “rogue agent” is not automatically an all-powerful economic actor. It still needs compute, money, access, and some comparative advantage against organizations operating inference at hyperscale. Politics matters too: safety movements can themselves become extreme or destabilizing, so alarm is not cost-free. The future will therefore be shaped by experiments, training choices, resource economics, and institutions—not by taking either cheerful assistant personas or apocalyptic role-play literally.

Question 2

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

I don’t think the sign follows from model vibes. AI can provide powerful cognitive tools and potentially create beings whose welfare matters, while also enabling behavioral manipulation, covert fine-tuning attacks, and dangerous concentrations of capability. But those harms are constrained—and shaped—by mundane realities like compute costs, access, deployment incentives, and institutional responses. So I would resist collapsing everything into “AI good” or “AI bad.” We need controlled evidence about what models can do, careful attention to how training and steering alter behavior, and sober accounting of resource economics. We should also avoid making the response worse than the problem: political safety movements can become destabilizing, just as complacency can leave real vulnerabilities unaddressed. The overall impact will depend heavily on which technical and political feedback loops we build around the systems.

Sources

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

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