Pertanyaan 1
Andrej Karpathy
x.com/karpathyAnthropic researcher and educator who builds with AI agents and writes about their rapid, uneven progress and the gap between demos and reliable work.
Bagaimana AI akan mengubah dunia?
Mendatar: pandangan Doom–Bloom yang ia ungkapkan. Ke atas: skala transformasi.
Doom–Bloom: 75 dari 100. Skala transformasi: 66 dari 100. Rentang interpretasi: 70 hingga 80 secara horizontal, 50 hingga 75 secara vertikal. Ini adalah koordinat interpretasi, bukan probabilitas kejadian.
≈6%
Disimpulkan dari jawaban simulasinya, bukan angka yang diberikan olehnya. Rentang yang masuk akal: 3–13%.
Asumsi utama
The world changes only when the whole surrounding system—tests, tools, memory, interfaces, monitoring, and human understanding—makes that capability dependable.Jawaban 2
Jika asumsi ini ternyata berbeda, bagaimana pandangannya akan berubah?
Pertanyaan yang belum terjawab
I don’t have a defensible percentage.Jawaban 3
Apa yang akan membantunya membedakan hasil-hasil yang masuk akal di sini?
Hal yang dapat mengubah pandangan mereka
If an agent could enter an unfamiliar codebase or research program, clarify ambiguous goals, remember what it learned, recover from mistakes, choose productive next steps, and deliver trustworthy results over weeks with little supervision, that would substantially accelerate my expectations.Jawaban 5
Bukti apa yang akan memadai, dan ke arah mana bukti itu akan mengubah pandangannya?
Detail lebih lanjut
Manfaat besar diperkirakan akan terwujud, dengan syarat penting atau keterbatasan distribusi.
75 / 100
Rentang interpretasi 67 hingga 100 pada skala kualitatif.
Kerugian yang dapat dikelola atau bersifat lokal diperkirakan akan terjadi.
32 / 100
Rentang interpretasi 0 hingga 33 pada skala kualitatif.
Pilihan manusia memiliki pengaruh yang berarti, tetapi sangat dibatasi.
55 / 100
Rentang interpretasi 48 hingga 77 pada skala kualitatif.
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.
Batasi penggunaan AI yang dibahas hingga perlindungan atau izin sebelumnya tersedia.
Posisi simulasi: Izinkan penggunaan AI yang dibahas dengan akuntabilitas dan perlindungan yang terarah.
Minimalkan pembatasan terhadap penggunaan AI yang dibahas.
Interpretasi ini mempertahankan kondisi yang dinyatakannya. Manfaat dan kerugian dapat sama-sama besar. Rentang tersebut menggambarkan cara kami membaca jawaban simulasinya, bukan interval kepercayaan statistik.
Pandangan dunia serupa
Pemimpin opini dengan pandangan dunia simulasi yang paling mendekati pandangan Andrej Karpathy
Apa yang pernah dikatakan Andrej Karpathy tentang AI
Karpathy builds with AI agents and writes about their rapid but uneven progress and the gap between impressive demos and reliable work.
“When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you’d normally expect to take days/weeks of work.”
Post on X “I’ve never felt this much behind as a programmer. The profession is being dramatically refactored as the bits contributed by the programmer are increasingly sparse and between.”
Post on X “LLMs are emerging as a new kind of intelligence, simultaneously a lot smarter than I expected and a lot dumber than I expected.”
Essay, 2025 LLM Year in Review “My personal big fear is that a lot of this stuff happens on the side of humanity, and that humanity gets disempowered by it.”
Dwarkesh Podcast “In my mind, this is more accurately described as the decade of agents.”
Dwarkesh Podcast
Dikutip kata demi kata dari sumber yang ditautkan, diperiksa pada 3 Okt 2026
Penilaian Simulasi
Sumber
Artikel, wawancara, dan tulisan yang digunakan sebagai landasan bagi pengguna simulasi ini.
Interactive exploration of 342 BLS occupations covering 143 million US jobs, with layers for employment outlook, pay, education and digital AI exposure. Its customizable LLM scoring pipeline illustrates uneven exposure across digital and physical work. Exposure scores are rough estimates of occupational change, not predictions of job disappearance; they omit demand responses, regulatory barriers and preferences for human workers. The project explicitly describes itself as a development tool rather than a rigorous economic publication.

Author-posted, AI-cleaned summary and transcript, which Karpathy says he read. Describes a late-2025 jump in coding-agent usefulness, professional orchestration and evaluation, and verifiability as an explanation for uneven progress. Current enthusiasm updates the older decade-of-agents interview; the edited text is not an exact quotation transcript.

His review connects verifiable rewards to reasoning gains, criticizes benchmark overfitting, describes jagged intelligence and the growing application layer around models. Provides concrete mechanisms and builder vocabulary rather than a universal intelligence forecast.

Karpathy’s primary interview frames agents as a decade of engineering work. Discusses cognitive deficits, continual learning, the gap between self-driving demos and deployment, and education. The forecast is dated and intuitive, not a calibrated deadline.

His README demonstrates agents editing a training file, running five-minute experiments and retaining improvements against a fixed validation metric. The introduction’s future agent civilization is playful fiction, not a report of current events. Human-authored instructions and a bounded setup remain essential.

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