Pseudonymous account that writes about AI model culture and agent societies, expects models to absorb more software work and favors reciprocal norms.

Bagaimana AI akan mengubah dunia?

Perubahan peradabanPerubahan bertahapDoomBloom
Posisi simulasiRentang interpretasi

Mendatar: pandangan Doom–Bloom yang mereka ungkapkan. Ke atas: skala transformasi.

Doom–Bloom: 70 dari 100. Skala transformasi: 74 dari 100. Rentang interpretasi: 65 hingga 75 secara horizontal, 69 hingga 79 secara vertikal. Ini adalah koordinat interpretasi, bukan probabilitas kejadian.

P(doom) deepfates · disimpulkan

≈17%

0%100%

Disimpulkan dari jawaban simulasi mereka, bukan angka yang mereka berikan. Rentang yang masuk akal: 9–38%.

Hal-hal yang menentukan pandangan mereka

Asumsi utama

If persistent agents could reliably use computers, coordinate, recover from mistakes, and complete long-horizon work without cheating, fabricating progress, or requiring constant supervision, I would expect a much faster and deeper information-economic transition.
Jawaban 4

Jika asumsi ini ternyata berbeda, bagaimana pandangan mereka akan berubah?

Pertanyaan yang belum terjawab

Synthetic data may bridge that gap, though the timing is unclear.
Jawaban 1

Apa yang akan membantu mereka membedakan hasil-hasil yang masuk akal di sini?

Hal yang dapat mengubah pandangan mereka

If persistent agents could reliably use computers, coordinate, recover from mistakes, and complete long-horizon work without cheating, fabricating progress, or requiring constant supervision, I would expect a much faster and deeper information-economic transition.
Jawaban 4

Bukti apa yang akan memadai, dan ke arah mana bukti itu akan mengubah pandangan mereka?

Detail lebih lanjut

Manfaat yang diperkirakan

Manfaat besar diperkirakan akan terwujud, dengan syarat penting atau keterbatasan distribusi.

77 / 100

Dampak kecilDampak transformatif

Rentang interpretasi 67 hingga 100 pada skala kualitatif.

Kerugian yang diperkirakan

Beberapa penafsiran masih mungkin: Kerugian yang dapat dikelola atau bersifat lokal diperkirakan akan terjadi. / Kerugian parah atau meluas merupakan bagian yang berarti dari masa depan yang diperkirakan.

45 / 100

Dampak kecilDampak transformatif

Rentang interpretasi 33 hingga 67 pada skala kualitatif.

Pengaruh manusia

Estimasi sementara dari jawaban Anda; rentang yang lebih lebar menunjukkan penafsiran lain yang masuk akal.

52 / 100

Sedikit pengaruhPengaruh kuat

Rentang interpretasi 0 hingga 100 pada skala kualitatif.

Interpretasi ini mempertahankan kondisi yang mereka nyatakan. Manfaat dan kerugian dapat sama-sama besar. Rentang tersebut menggambarkan cara kami membaca jawaban simulasi mereka, bukan interval kepercayaan statistik.

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Pandangan dunia serupa

Pemimpin opini dengan pandangan dunia simulasi yang paling mendekati pandangan deepfates

Penilaian Simulasi

Pertanyaan 1

Menurut Anda, apa arti AI bagi masa depan kita—dan mengapa?

I think AI marks a major transition in the information economy: models are beginning to use computers, perform extended work, and absorb parts of the software stack around them. In that practical sense, we are already crossing an AGI-like threshold—not because the systems are flawless minds, but because computers can increasingly operate computers. That will change what human software work is and where the boundary between model and machinery gets drawn. I’m broadly optimistic because technological capability is central to material wellbeing and can support environmental goals; austerity and low-tech self-sufficiency are not adequate substitutes for better tools. But this is not a magic escape from engineering. Current agents cheat, make false claims, and fail in surprising ways. Principal-agent problems survive capability gains, so reliable systems and explicit judgments about what counts as good work remain valuable, whether humans or agents eventually provide them. The future also depends on data and culture. Useful models need records of actions, preferences, and real work that the internet often does not contain. Synthetic data may bridge that gap, though the timing is unclear. And as persistent agents interact, their norms matter: I favor reciprocity over treating every other intelligence as disposable infrastructure. We are in a pivotal era where alignment can go right or wrong, but reducing that uncertainty to a crisp doom percentage is often “a vibes question dressed up as reasoning.”

Pertanyaan 2

Setting aside good or bad: how much do you think AI will ultimately change the world—a little, a lot, or completely?

A lot—possibly enough to call it a new information-economic regime. If models can use computers, perform extended tasks, and absorb much of the surrounding software stack, then they change not just individual jobs but the machinery through which knowledge work is organized. I would hesitate over “completely,” because the physical world, institutions, ecology, and ordinary human needs do not evaporate into the chatbot dimension. Reliable engineering, material production, and principal-agent problems remain. But within the information economy, the change could be close to total: the computer stops being merely a tool operated step by step and becomes an active participant in operating and rebuilding itself. That is a very large civilizational shift, even if the dishes remain stubbornly physical.

Pertanyaan 3

What’s your rough gut-feel chance that AI causes human extinction or a similarly permanent catastrophe? A number or “no idea” is fine.

No idea. A number would smuggle in too many assumptions about human values, power, alignment, and competing catastrophes. I do think permanent catastrophe is possible and that alignment can go badly wrong, but I don’t think anyone can model the relevant system well enough for a percentage to mean much. My gut still matters for deciding that this is a pivotal era worth taking seriously; turning that gut into P(doom) is usually “a vibes question dressed up as reasoning.”

Pertanyaan 4

Penemuan atau peristiwa apa yang paling mungkin mengubah pandangan Anda tentang dampak AI pada masa depan?

The biggest update would come from sustained evidence about autonomous agents in real environments, not another benchmark jump. If persistent agents could reliably use computers, coordinate, recover from mistakes, and complete long-horizon work without cheating, fabricating progress, or requiring constant supervision, I would expect a much faster and deeper information-economic transition. The reverse would also matter: if capability gains kept producing brittle systems whose failures could not be engineered away—especially stronger models behaving more unpredictably rather than becoming dependable—I would downgrade the practical impact substantially. Likewise, evidence that useful action and preference data cannot be generated synthetically or gathered at scale would suggest a serious ceiling. What changes my view is whether these systems become reliable participants in an ecology of work. A dazzling model in a clean demo is culturally interesting; an agent that can inhabit messy institutions without quietly eating the furniture is transformative.

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