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Lightcone · 3 min · Nov 8, 2024
Conviction should give way when the evidence arrives
Sam Altman
Sam Altman says conviction is necessary before evidence arrives, but OpenAI changed course when results contradicted its bets.
In this episode
Full analysis- 13:42OpenAI bet on deep learning and scale before the field agreedPro pick 6 min
- 26:13GPT-4 demand showed when models became commercially useful3 min
- 40:33Current models may enable innovation sooner than expected4 min
- 22:28Conviction should give way when the evidence arrives3 min
- 44:12AI gives startups an edge when they move faster than incumbents2 min
- 31:10A failed startup can still compress years of learning3 min
- 7:00Founder peers can raise your ambition more than school4 min
- 34:04A prior exit helped Altman finance OpenAI's first steps2 min
“there were a lot of things that we were high conviction and wrong on but as soon as we realized we were wrong”
Sam Altmanat 22:55 · Lightcone
Takeaways
- Conviction matters most while evidence is still missing.
- Change course when results show a bet is wrong.
- OpenAI made many wrong assumptions along the way.
About this segment
Sam Altman says conviction is necessary before evidence arrives, but OpenAI changed course when results contradicted its bets. The useful distinction is persistence before data, not after it.
Sam Altman is CEO of OpenAI.
Co-founded OpenAI and has led it since 2019, overseeing ChatGPT's launch; formerly president of Y Combinator.
Garry Tan, Host · President & CEO, Y Combinator
Co-founded Posterous and Initialized Capital, an early Coinbase backer.
From “How To Build The Future: Sam Altman”, Nov 8, 2024. Watch the full episode
Thanks. We'll look at and fix what's off.
Transcript
Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.
- 22:32
That was like a big unlock in life for me to understand. There is a difference between being high conviction just for the sake of it and if you're wrong you don't adapt and you don't try to be like truth-seeking it still is really not that effective the thing that we tried to do was really just believe whatever the results told us and really kind of try to go do the thing in front of us and there were a lot of things that we were high conviction and wrong on but as soon as we realized we were wrong we tried to like fully embrace it conviction is great until the moment you have data one way or the other and there are a lot of people hold on it past the moment of data so it's iterative it's not just they're wrong and i'm right you have to go show your work but there is a long moment where you have to be willing to operate without data and at that point you do have
- 23:24
to just sort of run on conviction yeah it sounds like there's a focusing aspect there too like you had to make a choice and that choice had better you know you didn't have infinite choices and so you know the prioritization itself was an exercise that made it much more likely for you to succeed i wish i could go tell you like oh we knew exactly what was going to happen and it was you know we had this idea for language models from the beginning and you know we kind of went right to this but obviously the story of open ai is that we did a lot of things that helped us develop some scientific understanding, but...
- 24:00
We're not on the short path. If we knew then what we know now, we could have speed run this whole thing to like an incredible degree. It doesn't work that way. Like you don't get to be right at every guess. And so we started off with a lot of assumptions, both about the direction of technology, but also what kind of company we were going to be and how we were going to be structured and how AGI was going to go and all of these things. And we have been like humbled and badly wrong many, many, many times.
- 24:29
and one of our strengths is the ability to get punched in the face and get back up and keep going this happens for scientific bets for uh you know being willing to be wrong about a bunch of other things we thought about how the world was going to work and what the sort of shape of the product was going to be again we had no idea or i at least had no idea maybe alec radford did i had no idea that language models were going to be the thing um you know we started working on robots and agents playing video games and all these other things then a few years later gpt3 happened