A corollary of this I've observed - usually in early phases of an emerging technology, you'll see a founding team that hasn't known each other 'for x years/long time'. Mostly because networks are disparate / or haven't existed for a longer time yet.
interesting read for sure, ! but i wonder if all black sheep / outlier founders have the same output, for example we mightve thought of VR in between as a big stage/ metaverse , but then it didnt go very far, and now a few palyers are alive, i am not saying they are wrong, but are they perpetually at the stage of irruption , a few could say the same for tech like crispr right?
i guess only a few forms of tech hit that takeoff velocity to get that steam and VC capital
Yes - perhaps the power law also exists across 'irruptions'? In that it's worth digging into each one, but only a few will stand the test of time. Thanks for the note.
Great piece. The “black sheep” idea feels very relevant in AI, where the earliest signal is often not a category but a cluster of unusually obsessive people. Curious where you think investors most often mistake noise for outlier talent.
To answer your question on where we could mistake noise for outlier talent: it is most likely to occur in the 'frenzy' phase. This is when the idea/domain is fairly consensus, and it attracts people from blue-chip backgrounds who know how to be performative. This is not necessarily a bad thing, as it could help galvanize people towards a larger mission or raise capital. Invariably, it could be used in negative ways to simply misdirect people or raise capital for poorly thought-out ideas, too.
Your black sheep reminds me of Howard Marks’ non-consensus but right matrix. Easier to understand the theory but harder to pursue the fringe bets in practice, although cool to see you’re doing it!
Yes - good one. I definitely don't succeed at this often, but the common thread that has come up is that a good proxy to spot something non-consensus is to follow the quality of the talent pool v. narrative/prices (when it's already too late). So that's what I'm trying to lean into more. Thanks for reading!
Enjoyed reading this!
A corollary of this I've observed - usually in early phases of an emerging technology, you'll see a founding team that hasn't known each other 'for x years/long time'. Mostly because networks are disparate / or haven't existed for a longer time yet.
Ah, yes. Interesting point. Not something I've explicitly noticed, but worth keeping in mind for the future. Appreciate the perspective.
interesting read for sure, ! but i wonder if all black sheep / outlier founders have the same output, for example we mightve thought of VR in between as a big stage/ metaverse , but then it didnt go very far, and now a few palyers are alive, i am not saying they are wrong, but are they perpetually at the stage of irruption , a few could say the same for tech like crispr right?
i guess only a few forms of tech hit that takeoff velocity to get that steam and VC capital
Yes - perhaps the power law also exists across 'irruptions'? In that it's worth digging into each one, but only a few will stand the test of time. Thanks for the note.
the obsessive-cluster point is the right one for sure. very interesting article!
fantastic read!
Great piece. The “black sheep” idea feels very relevant in AI, where the earliest signal is often not a category but a cluster of unusually obsessive people. Curious where you think investors most often mistake noise for outlier talent.
To answer your question on where we could mistake noise for outlier talent: it is most likely to occur in the 'frenzy' phase. This is when the idea/domain is fairly consensus, and it attracts people from blue-chip backgrounds who know how to be performative. This is not necessarily a bad thing, as it could help galvanize people towards a larger mission or raise capital. Invariably, it could be used in negative ways to simply misdirect people or raise capital for poorly thought-out ideas, too.
Your black sheep reminds me of Howard Marks’ non-consensus but right matrix. Easier to understand the theory but harder to pursue the fringe bets in practice, although cool to see you’re doing it!
Yes - good one. I definitely don't succeed at this often, but the common thread that has come up is that a good proxy to spot something non-consensus is to follow the quality of the talent pool v. narrative/prices (when it's already too late). So that's what I'm trying to lean into more. Thanks for reading!