Shawn Wang

Shawn Wang

x.com/swyx

Latent Space writer and podcast host who covers AI engineering, from building agents on foundation models to testing and verifying what they do.

Bagaimana AI akan mengubah dunia?

Perubahan peradabanPerubahan bertahapDoomBloom
Posisi simulasiRentang interpretasi

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

Doom–Bloom: 74 dari 100. Skala transformasi: 53 dari 100. Rentang interpretasi: 69 hingga 79 secara horizontal, 40 hingga 60 secara vertikal. Ini adalah koordinat interpretasi, bukan probabilitas kejadian.

P(doom) Shawn Wang · disimpulkan

≈7%

0%100%

Disimpulkan dari jawaban simulasi mereka, bukan angka yang mereka berikan. Rentang yang masuk akal: 4–14%.

Hal-hal yang menentukan pandangan mereka

Asumsi utama

Overall, I expect AI to be strongly beneficial if we successfully turn capability into reliable, accessible systems.
Jawaban 2

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

Hal yang dapat mengubah pandangan mereka

The biggest update would come from real-world evidence that AI can reliably accelerate hard science—not just produce plausible hypotheses, but contribute to validated advances in medicine, materials, or climate.
Jawaban 3

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.

68 / 100

Dampak kecilDampak transformatif

Rentang interpretasi 67 hingga 67 pada skala kualitatif.

Kerugian yang diperkirakan

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

52 / 100

Dampak kecilDampak transformatif

Rentang interpretasi 33 hingga 67 pada skala kualitatif.

Pengaruh manusia

Pilihan manusia dapat mengarahkan ulang lintasan AI secara signifikan.

63 / 100

Sedikit pengaruhPengaruh kuat

Rentang interpretasi 46 hingga 79 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.

Aturan penggunaan AI

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 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 Shawn Wang

Penilaian Simulasi

Pertanyaan 1

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

I think AI expands what individuals and small teams can build, learn, and discover—but capability alone does not produce a useful future. The decisive layer is AI engineering: turning foundation models into reliable products through tools, data, evaluations, memory, control flow, infrastructure, and relentless integration work. Coding agents are the clearest example today, and the same pattern can extend into other work performed through software. The largest upside may be science. Applying these systems to medicine, materials, climate, and scientific discovery could matter far more than generating another stream of low-value content. AI can also lower barriers to education and entrepreneurship, even while concentrating wealth and power. I reject the idea that today’s distribution of benefits must become a permanent underclass structure. But autonomy is not reliability. Agents need delegated authority, and authority requires trust and verification. As generated code exceeds humans’ ability to review it manually, automated testing and verification become essential. Memory, infrastructure access, privacy, and biosafety remain real constraints; distributing a powerful model across many companies does not magically make access private or prevent abuse. Open models also matter for sovereign AI and broader participation. So my default frame is neither utopia nor doom: build the harnesses, measure real actions and consequences, and direct engineering talent toward outcomes worth having.

Pertanyaan 2

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

Overall, I expect AI to be strongly beneficial if we successfully turn capability into reliable, accessible systems. The biggest gains are likely to come from accelerating science—medicine, materials, climate, and discovery—and from giving individuals and small teams more leverage to learn, build, and start companies. But those benefits are not automatic. AI can concentrate wealth, enable abuse, create biosafety risks, and delegate consequential actions to systems that are capable but not dependable. The engineering stack matters: evaluations, memory, permissions, automated testing, verification, privacy, and infrastructure. Open models also matter for sovereign access and broad participation. So I’m optimistic about the opportunity, not complacent about the implementation. I would rather judge deployed systems by their observable actions and real consequences than by impressive demos or reasoning traces. I also would not turn that outlook into an AGI timeline or a numerical forecast.

Pertanyaan 3

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

The biggest update would come from real-world evidence that AI can reliably accelerate hard science—not just produce plausible hypotheses, but contribute to validated advances in medicine, materials, or climate. That would strengthen my optimism substantially. In the other direction, repeated consequential failures despite strong evaluations, permissions, testing, and verification would weaken it. So would evidence that capable systems make dangerous biological work broadly accessible, or that benefits remain structurally concentrated even as access improves. I care less about a striking demo or an eloquent reasoning trace than about observable actions, reproducible results, and real consequences.

Sumber

Artikel, wawancara, dan tulisan yang digunakan sebagai landasan bagi pengguna simulasi ini.

The Rise of the AI Engineer

Describes AI engineering as productizing foundation models with software, data and evaluations.

latent.space
Shawn Wang: writings and talks

First-party current index linking agent engineering work and the 2025 agent-lab essay; establishes scope, not a catastrophe forecast.

swyx.io
Cognition: The Devin is in the Details

Argues agent labs translate model capabilities into useful products through extensive integration and engineering; acknowledges many harnesses are superseded and uncertainty about competition from model labs.

swyx.io
The only Permanent Underclass are the ones who believe it is permanent

Acknowledges AI-linked wealth concentration but rejects fatalistic permanent-underclass narratives, arguing AI lowers barriers to learning, entrepreneurship and upward mobility for people who exercise agency.

swyx.io
Agent Engineering — keynote essay

His keynote essay treats intent, tools, control flow, planning, memory and delegated authority as essential agent ingredients. Argues improved models, tools and economics create a major engineering opportunity; emphasizes trust and verification rather than equating autonomy with reliability.

latent.space
Agent Labs Thesis — swyx on Unsupervised Learning

Speaker-attributed transcript: at 32:53 he expects coding agents to expand beyond coding; at 40:01–41:18 he raises biosafety concerns and doubts broad enterprise distribution is truly private access. At 44:30–48:58 he identifies memory constraints, revises upward on open models, and favors automated testing and verification as human code review becomes a bottleneck. No numeric p(doom) given.

latent.space
Reality: The Final Eval — swyx with Andon Labs

His own questions at 45:42–47:58 distinguish inaccessible reasoning traces, observable actions and simulations without real consequences for lying. This supports attention to evaluation validity; the guests’ model-behavior findings and risk judgments remain theirs, not his.

latent.space
Agent infrastructure — swyx with Modal CTO Akshat Bubna

At 33:41–36:24 he identifies GPU access as a constraint on autonomous research, questions how widely research loops are used beyond demonstrations, and favors agents provisioning their own infrastructure. Modal deployment and performance claims belong to guest Akshat Bubna.

latent.space
It's Time to Science

Argues applying AI engineering to hard science could be among this century’s most important missions, spanning medicine, materials, climate and AI research. Explicitly avoids assigning AGI or superintelligence timelines; calls for engineering talent to pursue science rather than low-value output.

latent.space
A concrete limitation of computer-use automation

Warns about a computer-use feature after it repeatedly locked him out of his keychain, and says cloud alternatives were not yet sufficient.

x.com
Sovereign AI starts with open models

Endorses discussion of sovereign AI and explicitly says it starts with open models.

x.com
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