@swyx: I think its easy to say "Simulation is a new scaling law" and treat it as marketing hyperbole, but midway along this in…
Summary
The tweet discusses simulation as a potential new scaling law in AI, highlighting insights from an interview with Simile AI's CEO on simulating human behavior for advanced AI research.
View Cached Full Text
Cached at: 08/22/26, 01:16 AM
I think its easy to say “Simulation is a new scaling law” and treat it as marketing hyperbole, but midway along this interview you can hear me go from somewhat shitposting to very very serious.
I am 2 years late to this but finally understand why @karpathy and @drfeifei backed @joon_s_pk @msbernst @percyliang et al - Smallville at the time had zero commercial applications, but if you take RSI seriously, from models automating increasingly large parts of ML research and AI engineering, the last* barrier is simulating humans and human feedback, and Simile is obviously the team to do this and already finding PMF at Fortune 100s even at this early stage.
I’ve never been so happy to be so wrong.
*or second last ! :) more soon on the Science pod
Latent.Space (@latentspacepod): Simulating Humanity: 85% accurate digital twins, behavioral foundation models, social physics, & 8 billion agents https://t.co/k3pNsAC8YQ
@simile_ai CEO @joon_s_pk explains how AI can move from predicting what people will do to simulating how to shape outcomes, why today’s
Similar Articles
Swarm Scaling (13 minute read)
This article analyzes the capabilities and scaling dynamics of large AI agent swarms, citing OpenAI's recent examples, and discusses their potential as a new form of inference scaling.
@swyx: very belated but in retrospect i think @sama's mythical "build a business that gets better when models get better" is b…
swyx reflects on Sam Altman's idea of building businesses that improve as AI models improve, linking it to the emerging concept of Agent Labs, and notes a clear correlation with revenue spikes in Q4 2025.
Interesting post from Adam Majmudar (research at OpenAI) — full text in body
An analysis by Adam Majmudar on the rapid progression of AI capabilities, highlighting how new scaling laws are driving leaps beyond external expectations.
@shikibmehri: imo there's a missing parameter in the scaling laws: H -- the amount of human effort/time leveraged to produce learning…
The author argues that scaling laws are missing a parameter H — human effort/time invested in producing learning signal — and that most recent AI gains (ChatGPT's RLHF, coding/math verifiable tasks, data vendor value) stem from scaling H rather than model size or data alone.
@AYi_AInotes: Scale founder Alexandr Wang, now leading Meta's superintelligence lab, said something pretty disruptive: he believes no…
Scale founder and Meta superintelligence lab leader Alexandr Wang argues the biggest bottleneck for AI is societal adoption rather than model capability, claiming small AI-native teams with intelligent agents can now compete with tech giants in a 'civilization-scale opportunity'.