Will Brown

Will Brown

x.com/willcb

AI researcher who builds open reinforcement-learning environments and evaluation tools for agents and favors open, widely distributed AI development.

Bagaimana AI akan mengubah dunia?

Perubahan peradabanPerubahan bertahapDoomBloom
Posisi simulasiRentang interpretasi

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

Doom–Bloom: 60 dari 100. Skala transformasi: 52 dari 100. Rentang interpretasi: 50 hingga 75 secara horizontal, 18 hingga 100 secara vertikal. Ini adalah koordinat interpretasi, bukan probabilitas kejadian.

P(doom) Will Brown · disimpulkan

≈8%

0%100%

Disimpulkan dari jawaban simulasi mereka, bukan angka yang mereka berikan. Rentang yang masuk akal: 1–40%.

Hal-hal yang menentukan pandangan mereka

Asumsi utama

That path concentrates capital, capability, and control, while giving institutions incentives to move faster than their evaluation and governance can support.
Jawaban 2

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

Pertanyaan yang belum terjawab

My technical work gives me concrete reasons to expect useful improvement, but it does not establish a confident net forecast across every social, political, or existential consequence.
Jawaban 2

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

Detail lebih lanjut

Manfaat yang diperkirakan

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

67 / 100

Dampak kecilDampak transformatif

Rentang interpretasi 67 hingga 67 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.

46 / 100

Dampak kecilDampak transformatif

Rentang interpretasi 33 hingga 67 pada skala kualitatif.

Pengaruh manusia

Pilihan manusia dapat mengarahkan ulang lintasan AI secara signifikan.

66 / 100

Sedikit pengaruhPengaruh kuat

Rentang interpretasi 50 hingga 75 pada skala kualitatif.

Laju pengembangan

Hentikan atau perlambat secara signifikan pengembangan AI yang lebih mampu.

Posisi simulasi: Lanjutkan pengembangan dengan perlindungan yang telah ditetapkan.

Percepat pengembangan AI yang lebih mampu.

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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Penilaian Simulasi

Pertanyaan 1

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

I expect AI progress to be broad and increasingly agentic, with many labs pursuing more capable systems rather than one uniquely destined winner. The practical driver is a growing feedback loop: better learning environments, evaluations, model-based judges, and review pipelines let models improve on tasks that cannot always be checked with a simple deterministic verifier. Models may even help repair flawed training data when embedded in careful orchestration and review. But the shape of progress matters as much as raw capability. My preferred future is slower and more diffuse: open, distillable models and specialized systems that let many people build, inspect, and adapt useful agents. A race for overwhelming geopolitical advantage could instead concentrate capital and decision-making in a few organizations, producing a darker and less safe outcome. So I do not think technical progress alone settles the future. Concrete institutional choices matter. If a lab promises to pace itself relative to the frontier, I want to know what operationally changes beyond existing pre-release evaluation: who evaluates, what triggers restraint, and how commitments alter deployment. My work suggests practical ways to improve agents and measure them; it does not, by itself, resolve broader policy or extinction-risk questions.

Pertanyaan 2

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

My expectation is conditional on how progress is organized. Diffuse, relatively slow development around open, distillable models and specialized systems could have a strongly positive impact: useful agents would become broadly accessible, evaluations could improve on real-world tasks, and models could support increasingly capable feedback and data-repair loops. But I am less optimistic about a race dominated by a few frontier labs seeking decisive economic or geopolitical advantage. That path concentrates capital, capability, and control, while giving institutions incentives to move faster than their evaluation and governance can support. Many labs are likely to pursue increasingly powerful systems, so concentration does not necessarily produce orderly coordination; it may simply intensify the race. I therefore expect substantial benefits, but not an automatically positive overall outcome. The distribution and pace of progress are central. I prefer the future where capability spreads through open infrastructure and specialized systems, rather than one where a handful of actors compete to control increasingly general intelligence. My technical work gives me concrete reasons to expect useful improvement, but it does not establish a confident net forecast across every social, political, or existential consequence.

Sumber

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