
ConsumerTechBets
@optionsly
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High Growth US Consumer Tech. Previously at 3 midcap public tech cos. Voting and weighing stocks. raw thoughts. dms open.
San Francisco, CA
Joined October 2020
Every real world indicator will consistently look terrible.Boomers will think we're in a perpetual recession.Yet tech profits will keep going up.It's going to confuse so many.
Every day I see studies on how Gen Z is drinking less, smoking less, having less sex etc. This is because they're doing everything less. Their entire existence is on their phones. Escapism to the nth degree. This is beyond bad.
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IMO the most rigorous AI calibration is prediction markets like Metaculus. AGI probabilities: .P25 = Jan 2028 (2.5 years).P50 = Nov 2032 (7 years).
Entire market is dependent on whether AGI happens in next 5 years. That's what makes it so hard. Bears will say "no chance of AGI".Bulls will say "AGI is guaranteed". Nobody knows. Definitely not an investor that's so far away from research (like me).
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guess for $goog is we hit $200 shy next week, and we sell off into the anttrust decision late august. decision comes out and big vol (not sure direction). if red candle and outcome 1): buy dip this goes $250.if green candle and outcome 2): sell.outcome 3) sell.
$goog & anti-trust - biggest overhang to the stock. Core issue: DOJ ruled Google a search monopolist, mainly due to $20B/year default-search payments (e.g. to Apple). Largest tech antitrust case since Microsoft. Why it matters: Default status helped Google defend against.
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Big mindshift for me on "data labelling.". I used to think: sweatshop work, low-value, captcha-esque. Now I think: Private tutors for a system that can learn anything. AI data labellers are becoming domain experts: super smart, educated, talented people training models in every
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Surge CEO - we have more PhDs working for us than Google, Meta, MSFT combined.
Surge (scale AI competitor) hit $1b ARR with zero VC funding?. CEO thinks data quality is more important than compute (v. biased ofcourse). Thinks AGI may take many years if data is the bottleneck (E.g. time needed for experiments).
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