I think there are 3 ways to think about AI and lot of confusion seems to happen because the different paradigms are talking past each other. The 3 paradigms I see on the internet & when talking to people:

Paradigm A) AI is a new technology like the internet / smartphone / electricity - this seems to be mostly held by VC's / enterpreneurs / devs that think this will unlock a whole new set of apps like AI:new apps like smartphone:Uber or internet:Amazon


Paradigm B) AI is a step change in how humanity will work. Similarly to the agricultural revolution that led to the change in how large society could get and GDP growth, and the industrial revolution was a step-change in GDP growth from ~0% to 2-4% a year, and made things possible such as electricity and the internet and smartphones.

Paradigm C) AI is like the rise of humanity on this earth (the first general intelligences). The world changed completely with the rise of GI, and ASI/AGI will be a similar paradigm. We've been locked at humanity's level of intelligence for the past ~200k years, and getting ASI will be like unlocking multiple new revolutions all at the same time.


Most of the LW crowd is probably (C) or between (B) and (C)

When talking to the general population, I've found it to be very helpful to probe about where they are before talking about things like AI safety / how the world will change.
 

I made the same comment on the original post. I really think this is a blindspot for US-based AI analysis.

China has smart engineers, as much as DM, OpenAI etc. Even the talent in a lot of these labs is from China originally. With a) immigration going the way it is, b) the ability to coordinate massive resources as a state, subsidies, c) potentially invading Taiwan, d) how close DeepSeek / Qwen models seem to be and the rate of catchup, e) how uncertain we are about hardware overhand (again, see deepseek training costs) etc, I think we should put at least a 50% chance of China being ahead in the next year.

My initial reaction - A lot of AI related predictions are based on "follow the curve" predictions, and this is mostly doing that. With a lack of more deeper underlying theory on the nature of intelligence, I guess that's all we get.

If you look at the trend of how far behind China is to the US, that has gone from 5 years behind 2 years ago, to maybe 3 months behind now. If you follow that curve, it seems to me that China will be ahead of the US by 2026 (even with the chip controls, and export regulations etc - my take is you're not giving them enough agency). If you want to follow the curve, IMO  you can /s/USA/China after 2026 (i.e - China is ahead of the US), and I can imagine it being a better trend-following prediction. It's much less convincing to tell a story about Chinese AI labs being ahead given who we are, but I'd put at least a 50/50 chance on China being ahead vs. USA being ahead.

Other than that, thanks for putting something concrete out there. Even though it's less likely the more specific it is, I feel this will get a lot more talked about, and hopefully some people with power (i.e - governments) start paying some attention to disempowerment scenarios.

Coming from a somewhat similar space myself, I've also had the same thoughts. My current thinking is there is no straightforward answer on how to convert dollars to impact. 

I think the EA community did a really good job at that back in the day with a spreadsheet-based relatively easier way to measure impact per dollars or per life saved in the near-term future.

With AI safety / existential-risk - the space seems a lot more confused, and everyone has different models of the world, what will work, and what good ideas are. There are some people working directly on this space directly - like QURI, but IMO it's not anything close to a consensus for "where can I put my marginal dollar for AI safety". The really obvious / good ideas and people working on them don't seem funding-constrained.

There's in general (from my observation):

- Direct interpretability work on LLM
- Governance work (trying to convince regulators / goverments to put a stop to this)
- Explaining AI risk to the general public
- Direct alignment work on current-gen LLM (super-alignment type things in major labs)
- More theoretical work (like MIRI), but I don't know if anyone is doing this now.
- More weirder things like whole brain emulation, or gene-editing / making superbabies.

My guess is your best bet spending your money / time on the last one would be on the margin helpful, or just talk to people who are struggling for funding and otherwise seem like they have decent ideas that you can fund.

There's probably something other than those in the above list will actually work for reducing existential risk from AI, but no one knows what it it is.

I'm very dubious that we'll solve alignment in time, and it seems like my marginal dollar would do better in non-obvious causes for AI safety. So I'm very open to funding something like this in the hope we get a AI winter / regulatory pause etc.

I don't know if you or anyone else has thought about this, but what is your take on whether this or WBE is the more likely chance to getting done successfully? WBE seems a lot more funding intensive, but also possible to measure progress easier and potentially less regulatory burdens?

If RL becomes the next thing in improving LLM capabilities, one thing that I would bet on becoming big is computer-use in 2025. Seems hard to get more intelligence with just RL (who verifies the outputs?), but with something like computer use, it's easy to verify if a task has been done (has the email been sent, ticket been booked etc..) that it's starting to look to more to me like it can do self-learning.

One thing that's left AI still fully not integrated into the rest of the economy is simply that the current interfaces were built for humans and moving all those takes engineering time / effort etc.

I'm fairly sure the economic disruption would be pretty quick once this happens. For example, I can just run 10 LLM agents to act as customer service agents using my *existing tools* - just open emails, whatsapp, and message customers, check internal dashboards etc., then it's game over. What's stopping people right now is that there's not enough people to build that pipeline fast enough to utilize even the current capabilities.

Not sure if it's correct, I didn't actually short NVDA so all I can do is collect my bayes points. I did expect most investors to think at a first-level thinking as that was my immediate reaction on reading about DeepSeek's training cost. If models can be duplicated a few weeks / months after they're out for cheaper, then you don't have a moat (this is for most regular technologies. I'm not saying AI isn't different, just that most investors think of this like any other tech innovation)

 

Yeah, in one sense that makes sense. But also, NVDA is down ~16% today.

Deepseek R1 could mean reduced VC investments into large LLM training runs. They claim to have done it with ~6M.  If there’s a big risk of someone else coming out with a comparable model at 1/10th the cost, then there’s no moat in OpenAI in the long run. I don’t know how much the VC / investors buy the ASI as an end goal and even what the pitch would be. They’re probably looking at more  prosaic things like moats and growth rates, and this may mean reduced appetite for further investment instead of more.