Some of our best hires were totally unqualified on paper.
They always had the same qualities: entrepreneurial, high agency, smart, mission aligned, and they got shit done.
If you’re hiring, especially in early stages, seek out & bet on these people. Don’t over-index on resumes.
Elon Musk @elonmusk on how to manage companies better:
“Spend less time on finance, spend less time in conference rooms, less time on PowerPoint, and more time just trying to make your product as amazing as possible. I think there might be too many MBAs running companies. There's the MBA-saition of America, which I think is maybe not that great. There should be more focus on the product, the service itself, less time on board meetings, less time on financials.”
Agency > Intelligence
I had this intuitively wrong for decades, I think due to a pervasive cultural veneration of intelligence, various entertainment/media, obsession with IQ etc. Agency is significantly more powerful and significantly more scarce. Are you hiring for agency? Are we educating for agency? Are you acting as if you had 10X agency?
Grok explanation is ~close:
“Agency, as a personality trait, refers to an individual's capacity to take initiative, make decisions, and exert control over their actions and environment. It’s about being proactive rather than reactive—someone with high agency doesn’t just let life happen to them; they shape it. Think of it as a blend of self-efficacy, determination, and a sense of ownership over one’s path.
People with strong agency tend to set goals and pursue them with confidence, even in the face of obstacles. They’re the type to say, “I’ll figure it out,” and then actually do it. On the flip side, someone low in agency might feel more like a passenger in their own life, waiting for external forces—like luck, other people, or circumstances—to dictate what happens next.
It’s not quite the same as assertiveness or ambition, though it can overlap. Agency is quieter, more internal—it’s the belief that you *can* act, paired with the will to follow through. Psychologists often tie it to concepts like locus of control: high-agency folks lean toward an internal locus, feeling they steer their fate, while low-agency folks might lean external, seeing life as something that happens *to* them.”
In era of pretraining, what mattered was internet text. You'd primarily want a large, diverse, high quality collection of internet documents to learn from.
In era of supervised finetuning, it was conversations. Contract workers are hired to create answers for questions, a bit like what you'd see on Stack Overflow / Quora, or etc., but geared towards LLM use cases.
Neither of the two above are going away (imo), but in this era of reinforcement learning, it is now environments. Unlike the above, they give the LLM an opportunity to actually interact - take actions, see outcomes, etc. This means you can hope to do a lot better than statistical expert imitation. And they can be used both for model training and evaluation. But just like before, the core problem now is needing a large, diverse, high quality set of environments, as exercises for the LLM to practice against.
In some ways, I'm reminded of OpenAI's very first project (gym), which was exactly a framework hoping to build a large collection of environments in the same schema, but this was way before LLMs. So the environments were simple academic control tasks of the time, like cartpole, ATARI, etc. The @PrimeIntellect environments hub (and the `verifiers` repo on GitHub) builds the modernized version specifically targeting LLMs, and it's a great effort/idea. I pitched that someone build something like it earlier this year:
https://t.co/ANHhasxzD8
Environments have the property that once the skeleton of the framework is in place, in principle the community / industry can parallelize across many different domains, which is exciting.
Final thought - personally and long-term, I am bullish on environments and agentic interactions but I am bearish on reinforcement learning specifically. I think that reward functions are super sus, and I think humans don't use RL to learn (maybe they do for some motor tasks etc, but not intellectual problem solving tasks). Humans use different learning paradigms that are significantly more powerful and sample efficient and that haven't been properly invented and scaled yet, though early sketches and ideas exist (as just one example, the idea of "system prompt learning", moving the update to tokens/contexts not weights and optionally distilling to weights as a separate process a bit like sleep does).
When I worked with @StevenBartlett he would say, “all companies are recruitment companies”
And I would laugh and think “what decent line that is, to get some engagement on LinkedIn/Instagram”
After building a business for 6+ years, I can say, I was wrong, that line is correct.
If you are a control freak, and stubborn, and talented, then you’ll likely do what I did and ignore this, and think you can work your way to success. And you can, to a point.
If you want to build a big business, it’s literally mission critical to find exceptional operators and then GTFO the way, and do only what you can do.
Otherwise you will forever be limited by your own shortcomings.
Always respect for those in the arena, working, trying, on their own mission to their own desires.
Zero respect for those doing proximity work to live near those trying, just to feel important.
Marc Andreessen about Elon Musk $TSLA
“Elon identifies the biggest problem that the company is having that week, and he fixes it, and then he does that every week for 52 weeks in a row
And then each of his companies has solved the 52 biggest problems that year, and you know, most other large companies are still having the planning meeting for the pre-planning meeting for the board meeting, for the presentation”
“There are three qualities to look for in a partner: intelligence, energy, and integrity. You need all three. You can't compromise on any one of them.”
— @naval
the best way to get good at something is usually to just practice actually doing the thing in question.
a lot of very capable people outsmart themselves with complex plans that involve working a lot on fake prerequisites.