4
points
cl42
Joined 2,828 karma
Reach out to me on Twitter: @wojciech
- cl42That's a fascinating point, thanks for sharing. I wonder if prediction markets will 'asymptotically converge' into similar sports betting strategies.
- The article talks about how prediction markets' sports books are significantly more profitable. This has less to do with financial structures and more to do with who wants to make bets and where.
According to the article, prediction markets make magnitudes more money on potentially illegal (by today's standards in the US, anyway) sports betting than true event contracts.
- Have you considered launching your own weather prediction market instead?
Parametric insurance, energy traders, etc could be good markets.
- Seems like this is the _President_ of the division, so sounds like there's a nontrivially-sized team to manage.
- > Background Tasks.
Amazing. If this means no more management of Celery workers, then I am so happy! So nice to have this directly built _into_ Django, especially for very simple task scheduling.
- That’s awesome, thanks!
- Do you ever pair trade or hedge your shorts by buying indices? For example, short the quantum stocks but buy NASDAQ index (or call options) in case everything keeps going up?
- This Yann LeCun lecture is a nice summary of the conceptual model behind JEPA (+ why he isn't a fan of autoregressive LLMs): https://www.youtube.com/watch?v=yUmDRxV0krg
- > There is another thing called world models that involves predicting the state of something after some action. But this is a very very limited area of research. My understanding of this is that there just isn't much data of action->reaction.
Folks interested in this can look up Yann LeCun's work on world models and JEPA, which his team at Meta created. This lecture is a nice summary of his thinking on this space and also why he isn't a fan of autoregressive LLMs: https://www.youtube.com/watch?v=yUmDRxV0krg
- 2 points
- Thanks for doing this. The song is beautiful and I highly recommend folks listen to it on Spotify/Youtube/whatever.
- Fair point, and I should be more clear. The AI researchers I speak with don't expect AGI and are more reasonable in trying to build good tech rather than promising the world. My point was that these AI researchers aren't the ones inflating the bubble.
- Great points. I am bullish on AI but also wary of accounting practices. Tom says Nvidia's financials are different from Lucent's but that doesn't mean we shouldn't be wary.
The Economist has a great discussion on depreciation assumptions having a huge impact on how the finances of the cloud vendors are perceived[1].
Revenue recognition and expectations around Oracle could also be what bursts the bubble. Coreweave or Oracle could be the weak point, even if Nvidia is not.
[1] https://www.economist.com/business/2025/09/18/the-4trn-accou...
- Not sure why you're getting downvoted.
If you speak with AI researchers, they all seem reasonable in their expectations.
... but I work with non-technical business people across industries and their expectations are NOT reasonable. They expect ChatGPT to do their entire job for $20/month and hire, plan, budget accordingly.
12 months later, when things don't work out, their response to AI goes to the other end of the spectrum -- anger, avoidance, suspicion of new products, etc.
Enough failures and you have slowing revenue growth. I think if companies see lower revenue growth (not even drops!), investors will get very very nervous and we can see a drop in valuations, share prices, etc.
- Former management consultant here from a long, long time ago. It blows my mind when I see the consultancies cheer how they are adopting Gen AI to automate deck building, Excel analysis, etc.
... like, don't you see that your clients are READING your announcements and wondering "What the hell am I paying for?"
If I pay you $10K/month (let alone $100K+) and you're sending me anything AI generated, you will never work for me again.
- Thank you! That definitely sounds interesting.
- 7 points
- A few years ago I went to a military museum in Vietnam, where they have a lot of equipment used by the US military on display -- helicopters, planes, guns, etc.
What surprised me is how the equipment had labels around where to open things to rescue people, where to pour fuel, etc... It was labelled such that someone with limited (or no) exposure to the vehicle model would know what to do without referencing any sort of manual.
Very different today.
- 120 points