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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.
And is that correct? Do you expect that to last? My 2021 NVDA purchases still feeling pretty wise right now. :P
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)
1I am so out of touch with mindset of typical investors that I was taken completely by surprise to see NVDA drop. Thanks for the insight.
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.
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.
Just finished reading "If Anyone Builds It, Everyone Dies". I had a question that seems like an obvious one, but one I didn't see addressed in the book, maybe someone can help:
The main argument in the book is the analogy to humans. Evolution "wanted" us to maximize genetic fitness, but it didn't get what it trained for. Instead, it created humans who love ice cream and condoms even though they reduce our genetic fitness.
With AGI, we're on track to do something similar - we won't get an AI aligned to human interests even though we do RLHF or any other such simple training or shaping to an AI, it'll end up wanting something weird and inhuman rather than maximizing human values.
But in my mind, this seems to miss a fairly important point: The fact that human brains don't come pre-wired with much knowledge. We have to learn it from scratch. We don't come out of the womb with concept of "inclusive genetic fitness". It took us culture and ~200,000 years to figure that out, and we still only learn it after about 15-20 years of existing. So there's no way that evolution could have made us point our utility function to "inclusive genetic fitness" because that concept doesn't exist in our brains.
Modern AIs don't seem like that. They come with the sum of human knowledge baked in during pre-training. As they get smarter, the concept of "human values" or "friendly AI" is definitely something in it's existing mind. So it should be much easier for us to do alignement and test whether we can point it to that specific concept vs. what what evolution had.
Knowing about "inclusive genetic fitness" does not stop you from wanting ice cream.
For superhuman AIs, knowing about human values won't necessarily make them care.
Yes, I agree with that. I'm not claiming that knowing about it stops you from wanting ice cream.
I'm claiming that if the concept was hardwired into our brains, evolution would have had an easy time optimizing us directly to want "inclusive genetic fitness" rather than wanting ice cream.
i.e - we wouldn't want ice cream at all but reason from first principles what we should eat based on fitness.