Surya Ganguli ✓

@SuryaGanguli · Stanford, CA · joined 08 Dec 2013

Associate Prof of Applied Physics @Stanford, and departments of Computer Science, Electrical Engineering and Neurobiology. Venture Partner @GeneralCatalyst

20 645Followers

575Following

3 042Posts total

181.4KViews on collected posts

Against accounts of the same size

10 posts from the last 90 days, next to the 10K–100K follower range. shown widely, but few of those viewers react.

Median views8 360this account980median for 10K–100K

Reach, %40.49%this account3.27%median for 10K–100K

Engagement, %1.00%this account2.01%median for 10K–100K

Metric	This account	Median for 10K–100K	Ratio
Median views per post	8 360	980	8.53×
Reach (views ÷ followers)	40.49%	3.27%	12.4×
Engagement rate	1.00%	2.01%	0.50×

Others in this range → Compare with another account → How these benchmarks are built →

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

25.4K24 Jul

23.6K

54.3K28 Jul

1.2K

432

31129 Jul

6.9K10 Aug

9.9K18 Aug

6.4K

53.1K19 Aug

Last 10 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.13%24 Jul

1.05%

1.49%28 Jul

1.68%

0.46%

0.64%29 Jul

1.11%10 Aug

0.49%18 Aug

0.95%

1.44%19 Aug

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 2.01%.

What the audience does

Likes52.4%1 760 in total

Reposts7.0%234 in total

Replies1.2%41 in total

Quotes0.8%26 in total

Bookmarks38.7%1 299 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.

Latest posts

Our new work on “Physics of Agents” https://t.co/rnjaCyXmJD lead by Batu El and Jinhee Paeng in collab w/ @james_y_zou The outcome of many interacting agents seems hard to reason about. Yet we were able to study the opinion dynamics of 10,000 different LLM agent communities as 53.1K views · 623 likes · 104 reposts · 19 replies 19 Aug 2026 · Open on X →

This was a super fun conversation with @ziv_ravid and crew on a wide ranging set of topics spanning physics, neuroscience, AI, and even altered states of consciousness under drugs! 6.4K views · 55 likes · 3 reposts · 3 replies 18 Aug 2026 · Open on X →

New episode of The Information Bottleneck with the legendary Surya Ganguli(@SuryaGanguli ), professor at Stanford and VC at General Catalyst. Surya's path went from string theory to neuroscience to AI, and he treats them as one subject: emergent behavior in complex systems. We h 9.9K views · 40 likes · 4 reposts · 1 replies 18 Aug 2026 · Open on X →

Busy few days with 8 talks in an 11 day period in 3 different places. Useful links below for programs/schools to apply to next year: Woodshole Methods in Computational neuroscience (2 talks on Deep Learning in Neuroscience): https://t.co/bIBzNPt4jA @iaifi_news summer workshop 6.9K views · 68 likes · 7 reposts · 1 replies 10 Aug 2026 · Open on X →

@SuryaGanguli Another important point to add is solve each problem from as many different ways as possible. This stood out for me along with practice, practice, and practice. 311 views · 2 likes · 0 reposts · 0 replies 29 Jul 2026 · Open on X →

@SuryaGanguli You literally just described Close Reading from English classes 432 views · 2 likes · 0 reposts · 0 replies 28 Jul 2026 · Open on X →

@SuryaGanguli another thing that helps me---in the case of textbook readings---is to open approximately the same material in a few different sources; seeing things reworded a few times helps a lot 1.2K views · 17 likes · 1 reposts · 2 replies 28 Jul 2026 · Open on X →

This is an excellent guide on how to read math, and it mirrors how I used to do it as a student. Take homes: 1) Spend hours per page (pages/hour is wrong metric) 2) Multiple readings - simmer between readings. 3) Generate and ask counterfactual why questions. 4) Discuss with 54.3K views · 716 likes · 82 reposts · 9 replies 28 Jul 2026 · Open on X →

Yes, physics may be a more proximal route to scientific understanding of AI than other approaches (like math or cs theory theorem-proof based approaches). Physics has decades of experience analyzing complex systems. See eg: https://t.co/WNpwxOred8 https://t.co/KGuayIGAaK 23.6K views · 207 likes · 32 reposts · 6 replies 24 Jul 2026 · Open on X →

This talk was really insightful for me. I was getting disillusioned with the scope for theory in modern AI. But now I see how a physics-based perspective is more productive than a purely mathematical one. 25.4K views · 30 likes · 1 reposts · 0 replies 24 Jul 2026 · Open on X →