I believe the Scramblers from blindsight weren’t self aware, which means they couldn’t think about their own interactions with the world.
As I recall the crew was giving one of the Scramblers a series of cognitive tests. It aced all the tests that had to do with numbers and spatial reasoning, but failed a test that required the testee to be self aware.
I agree with you that the “structure of suffering” is likely to be represented in the neurons of shrimp. I think it’s clear that shrimps may “suffer” in the sense that they react to pain, move away from sources of pain, would prefer to be in a painless state rather than a painful state, etc.
But where I diverge from the conclusions drawn by Rethink Priorities is that I believe shrimp are less “conscious” (for a lack of a better word) than humans and less their suffering matters less. Though shrimp show outward signs of pain, I sincerely doubt that with just 100,000 neurons there’s much of a subjective experience going on there. This is purely intuitive, and I’m not sure of the specific neuroscience of shrimp brains or Rethink Priorities arguments against this. But it seems to me that the “level of consciousness” animals have sit on an axis that’s roughly correlated with neuron count; with humans elephants at the top to C. elegans at the bottom.
Another analogy I’ll throw out is that humans can react to pain unconsciously. If you put your hand on a hot stove, you will reactively pull your hand away before the feeling of pain enters your conscious perception. I’d guess shrimp pain response works a similar way, largely unconscious processing do to their very low neuron count.
Is it an FDA issue or moreso a drug discovery issue?
Adderall helps to combat akrasia to an extent, though results may vary between people (possibly modafinal as well though I haven’t tried it). Though it is far from a “magic pill” as the effects of the pill go away + side effects + utility from long term use is uncertain.
How easy would it be to develop a drug that’s more effective than Adderall or the other ADHD stimulants? It was developed in the 1970s, nearly 50 years ago, and the fact that we don’t have a better alternative right now tells me we picked the low hanging fruit. But are there active efforts to developing a better drug? I’m not sure.
Thanks for hosting this competition!
Fermi Estimate: How many lives would be saved if every person in the west donated 10% of their income to EA related, highly effective charities?
Model
Donation Pool:
– Assume “the West” produces roughly $40 trillion in GDP per year.
– At a 10% donation rate, that yields about $4 trillion available annually.
Rethinking Cost‐Effectiveness:
– While past benchmarks often cite figures around $3,000 per life saved for top interventions, current estimates vary widely (from roughly $3,000 up to $20,000 per life) and only a limited pool of opportunities exists at the very low end.
– In effect, the best interventions can only absorb a relatively small fraction of the enormous $4 trillion pool.
Diminishing Returns and Saturation:
To capture the idea that effective charity has a finite “absorption” capacity, we model the lives saved LLL as:
L=Lmax×[1−exp(−DDscale)]L = L_{\text{max}} \times \left[ 1 - \exp\left(-\frac{D}{D_{\text{scale}}}\right) \right]L=Lmax×[1−exp(−DscaleD)],
where:
• DDD is the donation pool ($4 trillion),
• DscaleD_{\text{scale}}Dscale represents the funding scale over which cost‐effectiveness declines, and
• LmaxL_{\text{max}}Lmax is the maximum number of lives that can be effectively saved given current intervention opportunities.
– Based on global health data and the limited number of highly cost‐effective interventions, we set LmaxL_{\text{max}}Lmax in the range of about 10–15 million lives per year.
– To reflect that the very best interventions are relatively small in total funding size, we take DscaleD_{\text{scale}}Dscale to be around $100 billion.
Calculating the ratio:
DDscale=4 trillion100 billion=40\frac{D}{D_{\text{scale}}} = \frac{4\,\text{trillion}}{100\,\text{billion}} = 40DscaleD=100billion4trillion=40.
Since exp(−40)\exp(-40)exp(−40) is negligibly small, we get:
L≈LmaxL \approx L_{\text{max}}L≈Lmax.
Revised Estimate:
Given the uncertainties, choosing a mid‐range LmaxL_{\text{max}}Lmax of about 12 million yields a revised Fermi estimate of roughly 12 million lives saved per year under the assumption that everyone in the West donates 10% of their yearly income to EA-related charities.
Summary
This Fermi estimate suggests that if everyone in the West donated 10% of their yearly income to highly effective charities, we could save around 12 million lives per year. While you might think throwing $4 trillion at the problem would save way more people, the reality is that we’d quickly run into practical limits. Even the best charities can only scale up so much before they hit barriers like logistical challenges, administrative bottlenecks, and running out of the most cost-effective interventions. Still, saving 12 million lives every year is pretty mind-blowing and shows just how powerful coordinated, effective giving could be if we actually did it.
Technique
I brainstormed with Claude Sonnet for about 20 minutes, asking it to generate potential fermi questions in batches of 20. I did this a few times, rejecting most questions for being too boring or not being tractable enough, until it generated the one I used. I ran the question by o3-mini, and had to correct it’s reasoning here and there until it generated a good line of reasoning. Then, I fed that output back into a different instance of o3-mini and asked it to review the fermi estimate above and point out flaws. I put that output back into the original o3-mini and it gave me the model output above.
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I think a high-quality reasoning model (such as o3), combined with other LLM’s that act as “critics”, could generate very high quality fermi estimates. Also, LLMs can generate ideas far faster than any human can, but humans can evaluate the quality those ideas in a fraction of a second. An under explored idea is to generate dozens or hundreds of ideas using an LLM about how to solve a particular problem, and having a human do the filtering and select the best ones. I can see authors using this and telling their LLM “give me 100 interesting ways I could end this story” and picking the best one.
The thing with NVIDIA though is that the IV is so high and so are premiums. I spent a few hours looking for a better trade than that, though I think it’s pretty solid.
I think SPY calls can possibly be much better than NVIDIA calls. The market doesn’t expect the stock market to go up significantly in the next few years, but I think theres a chance it will assuming timelines are short. Here’s the SPY YoY growth during the internet boom in the 90s.
Year 2000 saw a −9.7% return ($86.54) 1999: +20.4% ($95.88) 1998: +28.7% ($79.65) 1997: +33.5% ($61.89) 1996: +22.5% ($46.37) 1995: +38.0% ($37.85) 1994: +0.4% ($27.42)
Here we see that from any two year period from 1995-1999, the stock market went up anywhere from 50% to 70%.
Thus, I don’t think it’s unreasonable to think SPY has a good chance of going up 50% − 70% by Jan 15 2027 (to be fair, the past two years had a YoY growth of ~25%)
If you buy a $855 Strike price call for that date and SPY increases 50% by then you get a 12x return. If SPY increases 70% you get a 62x return.
If you buy the highest Strike Price call for that day at $910 and SPY increase by 70%, you get an 83x return.
Something to think about at least. At this time I’m going to buy long dated SPY calls for 2-3 years out at the 800 range. Nvidia calls still look good but the premiums are just so expensive because of the companies recent massive growth and volatility, so I think SPY calls are the better option.I’m still thinking about how to hedge incase the upcoming chaos turns the market sour (perhaps a Taiwanese blockade, or NVIDIA profits being hurt by increasing government interference)
This is looking like a February 2020 moment.
Assuming short timelines, I wonder how much NVIDIA’s stock will increase and if anywhere near a 100x return is possible.
The further out and higher strike price NVIDIA call I could find is at 290$ SP, dated Jan 15 2027, at $13.25. If NVIDIA goes to a 10T market cap I get an 8x return on investment, if the company goes to a 15T market cap I get a ~20x return on investment.
I’m not sure how realistic it is for NVIDIA to increase past a 15 Trillion Market cap. Plus, increased government intervention seems like it would negatively impact profits.
Polymarket has gotten lots of attention in recent months, but I was shocked to find out how much inefficency there really is.
There was a market titled “What will Trump say during his RNC speech?” that was up a few days ago. At 7 pm, the transcript for the speech was leaked, and you could easily find it by a google search or looking at the polymarket discord.
Trump started his speech at 9:30, and it was immediately that he was using the script. One entire hour into the speech I stumbled onto the transcript on Polymarkets discord. Despite the word “prisons” being in the leaked transcript that Trump was halfway through, Polymarket only gave it a 70% chance of being said. I quickly went to bet and made free money.
To be fair it was a smaller market with 800k in bets, but nonetheless I was shocked on how easy it was to make risk-free money.
You said you’ve been buying calls on the general stock market. Instead, why not buy calls on 20-30 tech companies that’ll likely benefit from slow takeoff?
This is very speculative, but if Anthropic/OpenAI/Google/Meta do achieve TAI and we head towards a slow takeoff, geopoltical risk from China may be a concern. To the best of my knowledge China is a few years behind us on AI, and doesn’t have the compute capability to catch up. I doubt China will just sit back and let the US achieve such a strategic advantage, and may invade Taiwan to cut out our supply of GPUs.
A post going over how much compute each frontier AI lab has will likely be very helpful.