Liang Cheng is an oncologist a Brown University. I’d never heard of him before, but I am told that he is incredibly famous by Liang Cheng, as he announced himself on LinkedIn.

I am deeply humbled and grateful to learn that my H-index has now reached 140. I was also honored to see that I am currently ranked among the two most-cited researchers worldwide in the fields of Urologic Oncology and Urology on Google Scholar.

In addition, my i10-index has reached 1060; that is, one thousand and sixty publications each cited at least ten times. I was told that this may represent a world record – what an extraordinary honor!

Nonetheless, these numbers are far less important than the people, mentorship, friendships, and collaborations behind them. This milestone is truly a triumph of team science. I owe immense gratitude to my mentors, colleagues, collaborators, residents, fellows, medical students, and friends who have inspired and supported me throughout this journey over the past two decades.

Academic medicine is never an individual accomplishment. It is ultimately about advancing science and medicine, educating future generations, and improving patient care. If our work has contributed even in a small way toward those goals, then I feel extraordinarily fortunate and grateful.

Thank you for being part of this journey. The best is yet to come!

I hate to be the one to tear him down, but no one cares about your H-index and i10-index except, maybe and importantly, administrators and fellow H-index chasers. Anyone else remember that scene in American Psycho where Patrick Bateman and several of his cronies are comparing business cards, noting the quality of the stock and the embossing and the inks? Yeah, that’s what it’s like seeing someone brag about their indexes. Don’t care.

It’s also because those numbers are thoroughly gamed. I looked him up on PubMed, and it’s true, his name is on a lot of papers: papers that have 10 or 20 or more authors, and there he is, somewhere in the middle of a sea of names, rarely first or last. He really does owe a lot to his “mentors, colleagues, collaborators, residents, fellows, medical students, and friends” who have been tacking his name unto their papers! And further, his publication rate, that is, the rate at which his name gets plugged in to a long list, is approximately a paper every two days, which is insane. This is authorship by rubber stamp.

I think it is valid that many research endeavors nowadays require a large team, and he may have been an indispensable member of such a team, but then to use that cooperation to brag that he is #1 or #2 in his field is unseemly. It’s also dangerous, Dr Tall Poppy. He was spamming his ‘accomplishment’ on every social media site he could find, and on Xitter, Michael Eisen noticed.

The author’s Google scholar profile falsely lists multiple papers that he didn’t author, and therefore the citation count and h-index are inaccurate.

Whooops.

I do enjoy seeing a braggart taken down a peg, but Liang Cheng is a symptom of a greater problem: we’re drowning in artificial metrics, amplified by AI slop.

Over the last few decades, science has undergone a “citation revolution.” Scientific life used to be structured by personal reputation and mutual acquaintance; now it is defined by quantitative assessments derived from citations.

And this reward system has warped scientific life in dramatic ways. It has resulted in the obvious and widespread gaming of citation metrics; but, more insidiously, it has pushed scientists toward risk-averse, incremental, and above all unambitious research. The logic of institutional science has become increasingly divorced from actual knowledge and discovery. In a system governed by these perverse incentives, the inevitable endpoint is simply AI-generated slop at scale.

Now, with AI, we’ve built a remarkable new technology that opens up dramatic new horizons for scientific work. But we’re deploying that technology within an institutional structure that incentivizes, above all else, the maximization of metrics that don’t have much to do with real science. The underlying problem is not with AI, but with the institutions and incentives that define modern science.

That is an excellent article, everyone should read it. It actually ends on a promising note, regarding AI as a tool that could break us out of the dead-end, grasping competition for a magic ranking number, as exemplified by the case of Liang Cheng.

The citation index was designed in the 1950s and ‘60s as a solution to the information crisis engulfing scientific life. It ended up becoming much more than that: a regime that reshaped what science was, how it was rewarded, and what kind of science got done. Now that regime is collapsing under the weight of its own contradictions. I think it’s a fantastic opportunity to build something better.