1.  All of the AI sceptics, the general public, and AI alarmists get AI risks wrong
2. Risks are real, and our society has yet to face them — AI sceptics are wrong
3. 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
4. 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
5. 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
6. 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
7. I still believe deep learning is the most exciting technology of this decade. That’s why I take classes and attend conferences on it
   1. 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