Jason Wei

The fall of “what you can do” and the rise of “who you know” Traditionally, many of us believe in a world where how hard you work is more important than who you know. I used to subscribe to this idea as both a scientist, who believed that facts about our world are agnostic to who says them, and as an American, who grew up believing that success was a function of how hard you worked, not who you knew. However I am now feeling a substantial change in AI, specifically in the past five years, where who you know is becoming more important, and what you can do is becoming less important. I am obviously not saying that technical skills don’t matter, or that you cannot be successful without being well connected. Technical skills are still important and make or break your career. But I am noticing a trend. To assign arbitrary numbers, I would say that five years ago, success was 75% your skills and 25% who you knew. Now, it is maybe 55% skills and 45% who you know. There are three technical reasons for this trend. The first is the commoditization of intelligence—coding agents have basically democratized research execution. In the past you could outwork competitors by staying up late to debug code or babysitting your experiments with higher frequency. Now, many research tasks can be completed by anyone who knows what to prompt. Research taste obviously still matters, but the playing field is definitely more equal. As technical execution becomes less of a differentiator, other advantages naturally become more important. The second reason for this trend is that with the success of scaling language models, it is simply a fact that being resource rich makes you so much more likely to succeed. Ten years ago, an academic lab with eight GPUs had a real chance of doing great research. Now, a single RL run can require hundreds (if not thousands) of GPUs, as well as substantial data and infra. So now, two equally capable researchers can have very different outcomes depending what resources they have. And access to these resources is not a function of technical skill alone. It also depends on reputation, trust built with decision-makers, and professional relationships. These things have always mattered, but as the resource requirements of frontier AI have grown, so too has their importance. Third, when companies stopped publishing research and moved to secretive, closed-source models, AI became a field of poor observability. In domains of perfect observability like chess or soccer, it is very easy to tell who is doing well and understand their contributions. But now in AI we have fewer public mechanisms for attributing credit, making it much harder for outsiders to know who did what for model launches. How does this get resolved? When it's difficult to independently verify someone's technical contributions, people increasingly rely on indirect signals such as reputation, personal endorsements, and who will vouch for them. Consequently, who you know plays a much bigger role in hiring, recruiting, and fundraising. We often talk about how to improve technical skills, but nobody really talks about how to get better at "who you know". One observation is that many well-connected people mix their work and personal lives. The traditional wisdom is to keep a firm boundary between the two, which in the past was a very reasonable policy. But the returns of being social have gone up, which means that anyone still holding to that boundary is paying a much steeper price for it than they would have before. [Truncated to meet source_post 4000-char API limit; full post on X.]