My take on AI risk (7 theses of eugene)
All of the AI sceptics, the general public, and AI alarmists get AI risks wrong
Risks are real, and our society has yet to face them — AI sceptics are wrong
The risks that don’t matter (general public misconceptions)
Automation, less work satisfaction
Mass unemployment
AI will change our relationship with labor and the economy, but this is not necessarily a bad thing
AI will improve the living standards of billions
Significant boost in productivity and innovation
Automation can drive down costs, boost efficiency, and facilitate new markets and opportunities
Improvement in human welfare
Healthcare, education, personalized services
Unlocking innovation and creativity
Automating routine tasks and complementing human intelligence
Long-term AI is very likely a complement; short-term, it may be a substitute
Better decision-making and governance
The risks that do matter
Bioweapons
AI-enabled weaponry
Stable authoritarian regimes / surveillance
Authoritarian control
Undermining democracy and freedom of expression
Centralization of power
Personalized manipulation
Loss of autonomy and individual freedom
Economic and social instability (particularly in labor markets)
The transition period is the problem, not the economy itself
Inequality and social division
Good evidence suggests AI benefits the already skilled
We will need:
Better education and retraining programs
The next generation may become AI-dependent idiots with no original ideas, or it may be the most exciting generation ever
Higher taxes for the rich
Social support
Some aspects of alignment
Blackbox problem / interpretability
Misalignment
A practical issue at the current level of LLMs — no need to speculate about AGI
AGI plausibility
I don’t refute the possibility of AGI (plausible within the next century)
However, not with the current architecture and data sources
There is no honest way to assign a probability estimate since it’s a black swan
AGI is a narrative of top AI labs to justify their insane investments
AGI is not possible with the current generation of neural networks
Gen AI shows that supposedly hard tasks (writing, image generation) are easier than thought
However, current LLMs still cannot abstract and generalize well (e.g., multiplication, video games)
Doing well on grad school tests is a bad proxy for real-world value
Recursive self-improvement doesn’t hold: LLMs can automate straightforward tasks but still leave rigorous reasoning to humans
We can see slowdown in LLMs development: difference between GPT4 and 4.5 is much smaller than 3 and 3.5
Not fully sure in this claim, could be good to verify
However, I am interested to see if Dynamics movement algorithm improvements + LLMs + computer vision with large capability improvements can lead to another leap in AI progress
I still believe deep learning is the most exciting technology of this decade. That’s why I take classes and attend conferences on it
Most interesting applications of AI that I see: bio, crypto / web3 (can finally make crypto it useful!), material science, mathematics, physics, econ/social science, business analytics
I will keep updated here:
https://eshcherbinin.notion.site/7-theses-of-eugene-shcherbinin
Hey Eugene, interesting stuff!
1) Long-term AI is very likely a complement; short-term, it may be a substitute”
I wonder why you think this?
2) “Good evidence suggests AI benefits the already skilled”
I feel like the evidence here is quite mixed: e.g., see this article from the economist: https://www.economist.com/finance-and-economics/2025/02/13/how-ai-will-divide-the-best-from-the-rest