Chad Jones ✓
@ChadJonesEcon · joined 27 May 2020
Economics professor at Stanford GSB working on AI and economic growth.
16 976Followers
948Following
253Posts total
1.1MViews on collected posts
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Very happy to see "AI and Our Economic Future" published. Thanks to the team at the JEP, especially @TimothyTTaylor and @heidilwilliams_ . https://t.co/Rc8OWL6BlN
25.3K views · 292 likes
· 62 reposts · 5 replies
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@ChadJonesEcon Dear Chad, please publish your exact agreement with Anthropic regarding your independence as a researcher, NDAs, etc.
The public has the right to know this. Thank you.
7.8K views · 48 likes
· 0 reposts · 1 replies
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Quote: "The Anthropic Institute’s goal is to tell the world what we’re learning about these challenges as we build frontier AI systems, and to partner with external audiences to help address the risks" https://t.co/nZhv9nv9V2 2/3
44.6K views · 265 likes
· 19 reposts · 6 replies
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Very excited to work with @akorinek, @PeterMcCrory, @jackclarkSF, and the many talented people at @AnthropicAI. 3/3
25.3K views · 233 likes
· 9 reposts · 9 replies
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Some news: As of June 30, I'll be on leave from Stanford at Anthropic. I'm joining the Anthropic Institute, where I'll continue my research on AI and our economic future and give seminars and talks as always. 1/3
865.6K views · 2.4K likes
· 169 reposts · 94 replies
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A recent video presentation of "AI and Our Economic Future" from the Stanford GSB Alumni Reunion (i.e. for a broad audience): https://t.co/RqR013Atdn
46.4K views · 177 likes
· 58 reposts · 9 replies
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Attended @ChadJonesEcon's lecture in Berkeley on AI and economic growth.
His "weak links" model shows AI's benefits take decades to compound, but the harms don't wait.
That asymmetry bridges the safety vs. acceleration debate in a way neither side's priors alone can generate. h
15.8K views · 87 likes
· 14 reposts · 4 replies
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@ChadJonesEcon @ChrisTonetti Prof. Jones, it’s much more readable if you put the tweets in order — each tweet replying to the one before it. You can do this so in the app or on browser so that they all send at the same time. This would greatly improve the threat, which is confusi
896 views · 19 likes
· 0 reposts · 1 replies
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@ChrisTonetti Why? Weak links. We automate more tasks and the rate at which machines get better accelerates — machines are used to improve machines. Nevertheless, the economy is always constrained by the weak links performed by slowly-improving humans. Explosions can be slow! N
1.5K views · 12 likes
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@ChrisTonetti Second, the explosion is surprisingly slow. After 20 years, we are only 4% richer than with constant growth. After 40 years, we are only 19% richer. 17/N https://t.co/aOwZbKM3f7
4.6K views · 24 likes
· 1 reposts · 0 replies
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@ChrisTonetti TWO SURPRISE TAKEAWAYS: First, for the next 75 years, the macroeconomy looks very similar in all three scenarios, and growth accelerates during those 75 years. Growth in the next half century may be less informative than we thought about long-run outcomes. 16/N
932 views · 9 likes
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@ChrisTonetti If some tasks must always be performed by labor, growth eventually falls to the slow rate at which humans get better (an AL model with a 100% labor share). Surprisingly, growth also explodes in the intermediate case where labor is always used and paid 63% of GDP. 1
887 views · 8 likes
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@ChrisTonetti What happens to growth in the three scenarios? If all tasks get automated, growth explodes with infinite income in finite time (an AK model with a 100% capital share). 14/N https://t.co/a0ujWLFOAJ
1.1K views · 7 likes
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@ChrisTonetti Here's a graph of the share of GDP paid to capital in those three scenarios. Interestingly, the intermediate case shows that the capital share can be constant even though humans perform a vanishing number of tasks. 13/N https://t.co/8jik8lZvIJ
1.1K views · 11 likes
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@ChrisTonetti So we simulate three possibilities: all tasks automated in finite time, some tasks restricted to humans forever, and an intermediate case in which humans perform a share of tasks that vanishes over time. 12/N
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@ChrisTonetti As is broadly appreciated, whether *all* tasks are ultimately automated versus some tasks *always* restricted to humans matters for very long run outcomes. 11/N
968 views · 9 likes
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@ChrisTonetti The real gains come when automating software helps us to further automate other tasks, including the production of ideas. 10/N
1K views · 12 likes
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@ChrisTonetti If we had infinite output of software on tasks currently performed by software, how much richer would we be? Only about 2 percent richer! The *other* weak links hold down output. 9/N
1.2K views · 12 likes
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@ChrisTonetti Weak links are crucial: Tasks are complements in our model, so even infinite output of some tasks does not lead to infinite GDP. The economy is constrained by the weakest links. 8/N
1.2K views · 10 likes
· 1 reposts · 1 replies
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@ChrisTonetti FUTURE IMPLICATIONS OF A.I.? We build an idea-driven growth model in which the production of goods and ideas are endogenously automated. We calibrate to our historical evidence, viewing A.I. as the next step on our continuing path of growth through automation. 7/N
1.1K views · 10 likes
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@ChrisTonetti Main takeaway from the first part: Growth results from using rapidly improving machines instead of slowly improving humans on an increasing set of tasks. 6/N
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@ChrisTonetti Even with this rapid capital productivity growth, had we frozen the set of tasks that were automated in 1950, more than half of economic growth would be lost. Because of weak links, machines getting better on a fixed set of tasks does not deliver much growth. 5/N
1.3K views · 12 likes
· 1 reposts · 0 replies
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@ChrisTonetti Past growth is largely due to endogenous automation that results from machines getting better rapidly. Average annual growth in capital productivity is at least 5pp greater than the average rate at which humans get better at performing tasks. 4/N
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@ChrisTonetti Back to the beginning: First, we perform an accounting exercise to study "How much of past economic growth was due to automation?" Second, we use what we learn to study A.I.'s future economic impact. 3/N
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@ChrisTonetti A preview of where we end up: here's a provocative graph that shows our simulation of the future. Growth explodes, but the explosion is surprisingly slow because of weak links. 2/N https://t.co/v7GDmz34Ul
3.8K views · 39 likes
· 3 reposts · 0 replies
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New paper (work in progress): "Past Automation and Future AI: How Weak Links Tame the Growth Explosion" with @ChrisTonetti. https://t.co/8f1hQsANNC 1/N
84.2K views · 271 likes
· 56 reposts · 22 replies
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เทียบกับบัญชีขนาดเดียวกัน
1 โพสต์จาก 90 วันที่ผ่านมา เทียบกับช่วง 10K–100K ผู้ติดตาม แสดงต่อคนมากกว่าบัญชีขนาดเดียวกัน.
ยอดดูมัธยฐาน25 332บัญชีนี้924ค่ามัธยฐานของ 10K–100K
การเข้าถึง, %149.22%บัญชีนี้3.62%ค่ามัธยฐานของ 10K–100K
การมีส่วนร่วม, %1.44%บัญชีนี้1.52%ค่ามัธยฐานของ 10K–100K
| ตัวชี้วัด | บัญชีนี้ | ค่ามัธยฐานของ 10K–100K | อัตราส่วน |
|---|---|---|---|
| ยอดดูมัธยฐานต่อโพสต์ | 25 332 | 924 | 27.4× |
| การเข้าถึง (ยอดดู ÷ ผู้ติดตาม) | 149.22% | 3.62% | 41.2× |
| อัตราการมีส่วนร่วม | 1.44% | 1.52% | 0.95× |
บัญชีอื่นในช่วงนี้ → เปรียบเทียบกับบัญชีอื่น → ค่าอ้างอิงเหล่านี้คำนวณอย่างไร →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
1.1K12 Jan
887
932
4.6K
1.5K
896
15.8K16 Apr
6.9K
46.4K15 Jun
865.6K23 Jun
25.3K
44.6K
7.8K
25.3K10 Aug
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.81%12 Jan
0.90%
0.97%
0.54%
0.96%
2.23%
0.68%16 Apr
0.54%
0.53%15 Jun
0.31%23 Jun
0.99%
0.65%
0.63%
1.44%10 Aug
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.
What the audience does
Likes68.5%4 024 in total
Reposts6.8%401 in total
Replies2.7%156 in total
Quotes1.6%95 in total
Bookmarks20.4%1 196 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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