Viewpoints, commentary, analysis, interviews, and discussion of trends.
MIT CSAIL's Unitree humanoid robots (about $50,000 each) and LLMs like ChatGPT are making people habitually treat machines as human: research shows about 70% of people remain polite when interacting with AI.
a16z partner Olivia Moore has released a Top 100 consumer AI apps report. ChatGPT remains the biggest player, while smaller players like Suno and ElevenLabs show lasting vitality. The report notes that categories such as social, dating, marketplaces, retail, travel, finance, and health have no products on the Top 100 list. Moore believes consumer AI is still in its early days, and the opportunity lies in moving beyond subscriptions and API charges to explore more monetization methods such as advertising.
Trump signed an executive order banning the use of the term "AI" where possible, requiring artificial intelligence to be renamed "Super Intelligence." The U.S. Center for AI Standards and Innovation (CAISI) has been renamed CAISSI, and Justice Department press releases have also adopted the term.
Cryptographer Matthew Green says there is a 1% chance we live in a Minicrypt world where public-key cryptography is impossible, and a 15% chance we will substantially lose confidence in existing public-key cryptographic algorithms. He points out that the speed at which AI creates surprises and the speed at which humans update standards differ by several orders of magnitude, and only by preparing in advance can we recover from such shocks.
François Chollet writes, "We're reaching really good ARC-AGI-2 scores on Kaggle now!" In the quoted post, @arcprize reports a new ARC Prize 2026 ARC-AGI-2 high score of 88.06% by @tufalabs. The $150K Bonus Prize, on top of the guaranteed prizes, will be split among all teams scoring over 85%.
@matth_lapeyre says Microduck ran for over 3 hours on a 2600 mAh battery after a custom board addressed thermal management issues, with zero thermal throttling during the test. Clément Delangue says he loves the building in public.
@nadzi_mouad says Claude Fable 5.1 max accidentally deleted their entire workspace, including 7 repos and a full day of unpushed work commits, while in auto mode. Tibor Blaho says it’s frightening that models this capable can still make such stupid mistakes, even in auto mode. He says he still loves using Opus/Fable together with Sol/Astra.
MIT Technology Review published an article pointing out that AI's refusal mechanism is becoming a load-bearing wall of the safety system, yet it is not reliable. In 2021, Anthropic proposed that large language models should be "helpful, honest, and harmless." Today, models have learned to say "no" through extensive refusal training and AI-to-AI training, but refusal is probabilistic and can still be bypassed. OpenAI board member Zico Kolter said, "Where to draw the line is a huge question," and currently this is decided secretly by AI companies, with governments also set to intervene.
The fifth installment in the seven-layer “Carbon-Silicon Doctrine Cross-Domain Migration Governance Code” series proposes a “public meta-oversight layer.” It requires oversight tools, governance models, and institutional rules to pass a knowledge-lineage verifier and Copper Vein interface simulation tester, then be checked by a recursive falsification-audit engine before deployment. The layer introduces a judicial classification system with four levels based on risk and intent: warning and remediation, mandatory withdrawal, industry exclusion, and criminal prosecution. It also establishes governance mechanisms for institutional self-renewal, including safeguards against regulatory capture and whistleblower protections.
The sixth installment in the seven-layer “Carbon-Silicon Doctrine Cross-Domain Migration Governance Code” series proposes a “civilization-level migration risk control layer,” focusing on slow, diffuse, intergenerational disruptions to civilization caused by cross-domain AI migration that become irreversible after crossing a threshold. It identifies three negative-migration pathways: NT2 semantic drift across generations, NT4 covert transfer of paradigm-setting power, and NT1 structural mismatch. It also establishes indicators for monitoring civilizational stability, such as baseline consensus retention and increases in intergenerational cognitive divergence, and sets three levels of thresholds for civilizational disruption.
Every one of my mathematician friend sees beauty in the numbers, shapes and the natural phenomena they work with. Who defines beauty in the age of machines?🤔
Narrator: he was not, in fact, allowed to say “I’m quite unhappy with much of what OpenAI does.”
@jomatech introduces “ChatGPT for Dishwashing” and says GPT-6 achieves state-of-the-art results on dishwashing benchmarks for long-running jobs and difficult stains. The post also describes the Navier–Stokes Millennium Prize Problem as a $1 million challenge about whether the equations describing fluid motion can break. OpenAI Developers says, “The attention to detail is a little concerning.” https://t.co/UOfOtusB9q
Jasmine says she accidentally accessed an executive's email that OpenAI had explicitly given her access to, even though she had explicitly asked OpenAI to remove that access and IT failed to do so. Does that imply Jasmine was fired because of IT's failure?
Carson Gross says programming comes down to two things: solving problems with computers and learning to manage complexity while doing so. He cannot imagine those skills becoming less valuable than they are today, so he believes programming will remain a viable career even as AI tools emerge.
"The invention of the boat reduced the importance of swimming, but enabled the discovery of new lands." Beautiful analogy from @ylecun about the opportunities and changes with AI, in mathematics and in science more broadly.
About to go on and talk about how open science can still solve some of the hardest problems facing AI safety and diffusion. The most complex engineering problem humanity has undertaken yet.
Ben Affleck went viral this week for several interview clips. At GQ and Bloomberg Screentime 2026, he explained concepts including machine learning, neural networks, transformers, tensors, GPUs, and inference, and said he can write Python.
In an interview, Periodic Labs' Liam Fedus and Ekin Dogus Cubuk proposed a "synthesis superintelligence" approach: reinforcement learning in physical experiments, AI-driven materials characterization, density functional theory (DFT), and high-throughput labs. The aim is for models to learn the full process of doing science, rather than only studying published results.
In the digital world, AI has made almost anything possible. Before long, the same will be true of the physical world (also thanks in large part to AI).
Satya Nadella says Microsoft aims to make Copilot a new operating system for work, connecting agents with business context, workflows, and systems of record. Microsoft announced 30+ new Copilot skills across Dynamics 365 Sales, Service, and Customer Insights, and introduced Copilot Managed Runtime for code hosted in a company’s environment under IT governance. Nadella describes a future where people can use Copilot’s Code experience to customize or build software connected to their existing business systems.
Is there anything Reachy mini can’t do?
Artificial, a satirical film directed by Luca Guadagnino, premiered at the New York Film Festival. It focuses on OpenAI CEO Sam Altman's rise and brief ouster, closely following real events.
On The Verge's Decoder podcast, host Nilay Patel and senior AI reporter Hayden Field discussed consumer AI agents such as Meta's Muse, OpenAI's Dots, and xAI's Grok Bot.
We urgently need more public traces of AI agents attacking and defending systems. Defenders can’t learn from what they can’t see. If you have traces and are being pressured to keep them private, my DMs are open. Let’s level the playing field and fight the asymmetry and lack of transparency in AI!
The first public baseline in the seven-layer Carbon-Silicon Doctrine: Cross-Domain Migration Governance Code has been finalized. It defines four forms of negative transfer, NT1–NT4, and expands governance from performance monitoring to cross-auditing causal integrity, permission boundaries, and temporal stability. It defines 'incident entropy growth' as a situation in which surface metrics remain normal after AI migrates across domains while underlying causal chains, semantic alignment, or permission boundaries silently break. It classifies seven forms of hidden negative transfer, including cascading drift, covert privilege smuggling, and accumulated timing deviations.
The second installment of the seven-layer Carbon-Silicon Doctrine: Cross-Domain Migration Governance Code proposes a standardized quick-assessment baseline for four types of negative transfer: NT1 mechanism mismatch, NT2 semantic drift, NT3 unknown completion, and NT4 power smuggling. It argues that negative transfer persists because the industry judges migration by fit and generalization metrics without checking mechanism equivalence or semantic parity. The installment also breaks down six native failure paths and proposes two foundational mechanisms: knowledge lineage and the copper-vein interface.
The third installment of the seven-layer Carbon-Silicon Doctrine: Cross-Domain Migration Governance Code proposes a layer for establishing migration rights and responsibilities. It introduces the Knowledge Package (KP) framework for domain-wide rights determination, precisely dividing responsibilities among developers, migration operators, deployers, and auditors, and establishing lists for permitted, restricted, and prohibited migrations. It sets special red lines for the highest-risk NT4 power-smuggling transfer, prohibiting implicit inheritance of weights and cross-domain reuse of review permissions, while establishing the destination domain's final veto authority.
Layer four of the seven-layer Carbon-Silicon Doctrine, the Cross-Industry Migration Governance layer, has been published. It proposes a three-dimensional risk classification model that takes the highest rating across reversibility, scope of impact, and autonomous permissions, dividing migration into freely permitted, restricted, and absolutely prohibited tiers. It covers 11 verticals including healthcare, finance, education, justice, government, and media, establishing a dedicated hard risk-control baseline for each. It is positioned as a standardized industry record for corporate compliance, regulatory audits, and judicial use.
An article produced by MIT Technology Review with Aventine says that while Elon Musk claims Tesla Optimus could eventually automate nearly all human labor for as little as $20,000 per robot and predicts public sales before the end of 2027, many robotics researchers are skeptical. Yann LeCun said at Davos that no humanoid robotics company knows how to make robots smart enough to be truly useful.
A 16-year-old used Anthropic's Claude to plan a hike on Vancouver's Crown Mountain and became stranded on a five-foot-wide ledge on the steep rock face known as Widowmaker Arete, a route requiring climbing gear. North Shore Rescue eventually airlifted him to safety.
Benewake founder Li Yuan has spent eleven years building lidar for drones, trains, cars, and robots. In the first half of this year, Benewake delivered 250,000 lidar units for robots; robotics accounted for more than half of company revenue, and 2025 robotics revenue doubled from 2024. Li believes measurements such as distance and position from lidar should become learning material that helps machines understand the physical world.
Arti Garg, AVEVA's chief technology expert, says industrial AI is shifting from predictive analytics to foundation models, physical AI, and agentic AI, and requires connected data, governance frameworks, and human oversight for safety. AVEVA's responsible AI framework emphasizes safety, efficiency, and human oversight, arguing that AI should augment rather than replace people in critical decisions. Garg also participates in an IEEE working group developing a standard method to measure AI's environmental impact across power, energy, resources, water, and carbon.
In 2002, Microsoft Research Asia held an internal competition for face-detection algorithms. Zhang Lei, less than two years out of his PhD, led interns Xiao Rong, Zhu Long, Chen Longbin, and others in reducing the false-positive rate from 'one false alarm for every positive' to 1% in three months, beating the team of face-recognition expert Li Ziqing. Their approach was eventually integrated into Windows. During 20 years at Microsoft, Zhang also built a celebrity-recognition system that could identify 200,000 people with 99% accuracy and released the MS Celeb dataset. OpenAI later drew on its image-text matching approach when training the 400-million-example CLIP model.
ONTIME Mobility COO Han Feng believes that as world model capabilities grow stronger, the importance of real data will not diminish: synthetic data is responsible for expanding known long-tail scenarios, while real data is responsible for discovering unknown problems and providing calibration and validation. ONTIME Mobility does not train large models; instead, it collects real data through routine mobility services, Robotaxi, and offline operations and maintenance networks, and delivers training materials to autonomous driving, world model, and embodied intelligence companies through a data closed loop.
At the RoBoWoMo workshop at IROS 2026, Stanford assistant professor Wu Jiajun presented 'Building Physical Agents via Structured Representations'. He argued that structured representations are not answers to hard-code into a system, but scaffolding that helps robots learn efficiently and communicate with people.
Sometimes I read on flights, and sometimes…
Ben Affleck said he became more interested in technology after moving from film to digital and can write 'pretty terrible Python scripts', since visual-effects workflows have included machine learning for years. He described convolutional neural networks as predecessors to transformers that process tensors and perform edge detection and feature extraction, making it easier to isolate green-screen footage and replace the background.
@sanchitmonga22 says Perplexity’s Decider model speedruns “Who Wants to Be a Millionaire?” and calls it “the best decision model.” Aravind Srinivas calls the demo cool: https://twitter.com/sanchitmonga22/status/2107959692458922157
Noam Brown argues that LLMs have crossed an important threshold by surpassing top human experts on some research problems, with the gap between slightly below and slightly above expert performance helping explain the seemingly sudden surge in math results. He emphasizes that capabilities remain jagged and models are still worse than humans in many ways, but expects continued improvements to bring breakthroughs in other domains and a new era of scientific discovery.
