Shawn Wang

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

AIは世界をどのように変えるでしょうか？

シミュレーション上の位置解釈範囲

横軸：その人が表明したDoom–Bloomの見通し。縦軸：変革の規模。

Shawn WangのP(doom) · 推定

≈7%

0%100%

本人が示した数値ではなく、シミュレーションされた本人の回答から推定したものです。妥当と考えられる範囲：4–14%。

その人の見通しを左右するもの

中心的な前提

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

回答2

この前提が実際には異なると判明した場合、その人の見通しはどう変わりますか？

考えを変え得るもの

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.

回答3

どのような証拠なら十分で、それによってその人の見解はどちらの方向に変わりますか？

詳細

予想される恩恵

大きな恩恵が予想されていますが、重要な条件や分配上の制約があります。

68 / 100

影響が小さい変革をもたらす影響

予想される害

複数の解釈が依然として妥当です：深刻または広範な害が、予想される将来の実質的な一部となっています。 / 対処可能、または局所的な害が予想されています。

52 / 100

影響が小さい変革をもたらす影響

人間の影響力

人間の選択によって、AIの軌道を大幅に変えることができます。

63 / 100

影響力が小さい影響力が大きい

開発ペース

より高性能なAIの開発を停止するか、大幅に減速させます。

シミュレーション上の位置：明示された安全対策の下で開発を継続します。

より高性能なAIの開発を加速させます。

AI利用のルール

事前の保護措置または許可が整うまで、取り上げられたAIの利用を制限します。

シミュレーション上の位置：対象を絞った説明責任と保護措置を伴う形で、取り上げられたAIの利用を認めます。

取り上げられたAIの利用に対する制限を最小限にします。

これらの解釈では、その人が示した条件が維持されています。恩恵と害は、どちらも大きくなり得ます。この範囲は、統計的な信頼区間ではなく、その人のシミュレーションされた回答をどのように読み取ったかを示すものです。

あなたはShawn Wangと比べてどの位置でしょうか？

約3分で自分のAIに対する世界観をマッピングして、比較できます

自分の世界観をマッピングする

似ている世界観

シミュレーションされた世界観がShawn Wangの世界観に最も近いオピニオンリーダー

シミュレーション評価

質問1

AIは私たちの未来にとって何を意味すると思いますか？また、なぜそう思いますか？

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.

質問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.

質問3

どのような発見や出来事があれば、AIが将来もたらす影響についてのあなたの見解が最も大きく変わりますか？

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.

出典

このシミュレーション対象者の根拠として使用された記事、インタビュー、著作です。

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

あなたはどの位置でしょうか？

いくつかの簡単な質問に答えて、自分のAIに対する世界観を探ってみましょう。

自分の世界観をマッピングする

あなたはどの位置でしょうか？

自分の世界観をマッピングする