@leadinginds says Ramp spend data shows Perplexity, Wispr Flow, and Claude Code each bought by 87%+ of buyers in their categories. Inference, GPU capacity, and AI-first GTM remain fragmented. 👀 https://twitter.com/leadinginds/status/2110080629773635595
A gap in our methodology that highlighted a decline has been fixed: we weren’t including local models (GGUF, AWQ, ONNX, etc.). Earlier, these were far harder to track and closer to a rounding error. Now, they’re surging. We updated our pipeline to track these models!
Open AI models offer research access, customization, and local deployment, but downloadable weights also make misuse harder to monitor and access impossible to revoke. Grace Huckins examines these trade-offs through expert interviews, including arguments about concentrated power, cybersecurity, and hardware access. The explanation also covers staged releases and tamper-resistant training, noting that current tamper-resistance approaches do not withstand all attacks.
GPT-4 was the undisputed best model in the world for a full year and we all debated whether anyone could catch up or if OpenAI had some secret formula that nobody else knew about. Now there are are 19 AI models that can run on a modern smartphone that are better than GPT-4!
Alexandr Wang says the issue causing unusually fast quota usage for muse wonder and muse wonder full has been identified and fixed. Users will now see much lower usage against their limits. Wang also announces a muse code reset, as promised after the fix. Baoyu comments that Muse now has enough users that he feels awkward saying Meta also reset limits in an unnoticed corner. 😅
My AI agent doing proactive customer service: Someone emailed me that, after subscribing to my Substack, they were automatically signed up for others (which shouldn't happen) Without my asking, my Dot ran its own test & figured out what happened by trying to replicate the error.
Muse Wonder as a video director
expect the unexpected
Robotics and physical AI companies raised nearly USD 48 billion through October 8, 2026, about 1.7 times the total for 2025, according to PitchBook data cited by the Financial Times. Funding increasingly concentrates in leading companies while supporting robot hardware, general-purpose models, training data, and deployment. The report examines paid video collection, robot training facilities, and commercial deployments as ways to gather data, while acknowledging that data quality, task coverage, and transfer across robots also affect learning.
@chetaslua says Muse Wonder made the animation using code, and that ElevenLabs voices and Suno music were chosen by Muse. Alexandr Wang calls the animation artistic.
i unironically played muse'cooked for about 90 mins the other night little jolly is just so fkn cute. his hands are so small. how does he even cook??
From rebuilding our foundation to pushing the frontier in 14 months 🚀
If you haven’t made sure to turn on two-factor authentication on all accounts, have passwords with family in case you get voice calls asking for money, etc, now would be a good time. AI gains mean the costs of mass targeting individuals with sophisticated attacks are dropping.
@antoinepirrone posted 3 minutes of uncut Microduck testing on carpet. Clément Delangue reacts: 😂 faceplant at 0:50 https://twitter.com/antoinepirrone/status/2109667437503193163
Huawei’s Mate X7 continues an eight-year evolution of foldable designs, while Apple’s iPhone Duo has just entered the market. IDC data for the first quarter of 2026 puts Huawei first in China’s foldable phone market with a 60% share. The article highlights Huawei’s range of foldable formats, the Mate X7’s multitasking features and hinge design, and its 11,999-yuan starting price, below the iPhone Duo’s.
Bao Yu describes a role AI startups reportedly find difficult to hire for: someone who follows online trends, creates engaging content, understands AI and technology, and executes well. He says content creators, technical evangelists and engineers, and traditional PR staff each tend to lack some of those abilities, while few people have all four.
GPT-6 Pro finds supposed errors in academic papers all the time, just as part of other work. Confirming the errors & learning their importance would still take a lot of time today, but it will get faster. We don't have the right mechanisms in place to deal a flood of corrections.
It has become disheartening that the average replybot on this site has shifted its replyslop angle to asking interesting questions. It feels like actual intellectual engagement, which makes it somehow worse than just rephrasing the post with a "that's the part people miss" bit.
Ethan Mollick argues that many of the 5,000 people at labs with access to advanced models still haven’t used them to their full extent, with use concentrated in cybersecurity, math, and AI R&D. There are many other fields to advance and problems to solve, he adds, responding to @karpathy’s argument that the gap in shared understanding of LLM capabilities is widening.
A Japanese blogger cited Toyota's Kaizen rule — never cut staff after process improvements reduce work hours — and argued the same applies to AI adoption. Companies that cut headcount as soon as AI boosts efficiency will find employees hide their best AI methods, he said. A quoted post adds that DORA research found organizations addressing employee job concerns had higher team AI adoption.
Andrej Karpathy argues the gap in shared understanding of LLM capability is widening into a sharp funnel: roughly 6B people have barely touched LLMs, 1-2B use free-tier chatbots casually, about 20M use frontier models for math and code, and roughly 5,000 have internal access to frontier systems. He attributes the funnel to problem ambition, capability jaggedness in verifiable domains, and access tiers.
Some of Silicon Valley's most powerful figures are falling in line with President Donald Trump's push to rebrand artificial intelligence as "super intelligence." Elon Musk promised to rename SpaceXAI to SpaceXSI, Marc Benioff said Salesforce would rename AIForce to SIForce, Nvidia tied "super intelligence research" to a $1 billion commitment, and Microsoft's Satya Nadella repeatedly used the term in a post calling for an AI "emergency brake."
Andrej Karpathy says the gap in shared understanding of LLM capability is widening into a "sharp funnel" rather than two groups talking past each other. He estimates roughly 6B people have barely encountered LLMs, 1-2B use free-tier ChatGPT-like products casually, about 20M see frontier-grade math and code use, and roughly 5,000 have internal access to frontier systems. He attributes the funnel to impact scaling with ambition, the jaggedness of capability, and access tiers.
Andrej Karpathy argues that understanding of LLM capability is diverging into a sharp funnel, with rough estimates of 6B people barely exposed, 1-2B casual users, 20M professionals using frontier LLMs in math and coding, and ~5,000 people accessing internal frontier systems. He describes increasingly ambitious agent workflows toward the narrow end, while saying human review and comprehension are starting to fall behind. He attributes the divide to problem size and duration, capability concentrated in digital and verifiable domains with economic value, and differences between free, paid, and internal access.
Rather suddenly, cyborgs are starting to dominate math research.
Terence Tao argues that AI is making mathematical proofs abundant, ending a Mathematics 1.0 era built on proof scarcity. He warns that solving problems is not the same as understanding them, calls for reporting failed attempts and compute costs, and says the bottleneck for Mathematics 2.0 is imagination. He also questions whether a PhD still matters when answers come easily.
Data from Ornn, Silicon Data, and Bloomberg as of August 2026 shows what a16z calls a textbook Jevons paradox in the AI market: token prices keep dropping while H100 GPU rental prices hold steady or climb. Cheaper tokens unlock AI agents, automation, and new applications, so volume grows faster than per-unit costs fall. The system depends on AI usage growing fast enough to offset falling token prices; if demand flattens, chip makers, memory suppliers, energy providers, and cloud companies take a hit.
OpenAI will shut down the earliest GPT-4o API snapshot, named 2024-05-13, on October 23, while other versions remain available to paying developers. The Keep4o campaign continues nearly eight months after GPT-4o was removed from ChatGPT on February 13, with a Change.org petition at 23,930 signatures and a new proposal urging OpenAI to release the retired model's weights under an archival policy.
A QbitAI roundup argues that compute access now drives talent retention, model leadership and monetization in Silicon Valley. It cites Noam Shazeer leaving Google for OpenAI after his team's compute was reallocated, Anthropic's May SpaceX deal for over 300 MW and 220,000 Nvidia GPUs, and SemiAnalysis data showing H100 one-year rental prices rising from $1.70 to $2.35 per GPU-hour between October 2025 and March 2026.
Muse
A mathematician makes an Opus 5.5 music video in response to OpenAI’s proofs. Pretty great.
We've been monitoring a decline in HuggingFace downloads across the top open LLMs we track for interconnects. It looks like there was a slight change in counting of downloads, as we saw a ~30% download rate drop for many top models, but this is continuing over time. What else do people think it may be?
How AI shifted from a transductive paradigm (token-by-token answer completion) to an inductive paradigm (on-the-fly reasoning chain synthesis), in one chart. This is the big story of 2025 and 2026. This is what made coding agents work.
TechCrunch rounds up AI agents that work through text messages instead of a separate app, including Caddy, Comma, Fambot, Folk, Instinct, Iris, Martin, and Miso. Instinct raised $1 billion in September 2026 at a $10 billion valuation, while Fambot launched in beta in early September 2026 with $3.5 million in pre-seed funding. Caddy has been in public beta since April 2026, and Folk launched its beta in May 2026.
At OpenAI DevDay, CEO Sam Altman unveiled the AI agent Dots and said OpenAI wants to "set a new standard for privacy in frontier AI," while taking veiled shots at Meta's Muse. Meta had pitched Muse as safer than predecessor OpenClaw, but a zero-day vulnerability, reported last-minute security issues, and default data collection undercut those claims. Dots is only available on $100-and-up ChatGPT subscription tiers.
OpenAI released more than 700 manuscripts on October 6, 2026, claiming solutions to hundreds of open math problems, and the blog Proofs and Prompts collected over 100 researcher responses. Some papers were withdrawn or criticized as hard to read, while Fields Medalists Peter Scholze and Terence Tao urged patience and solidarity. Many mathematicians described shock, paralysis and fears about their profession.
Why it matters: The piece gathers mathematicians' first reactions to OpenAI's mass release of claimed proofs, showing how the field's norms and careers are being tested.
Andreessen Horowitz's seventh Top 100 AI list adds observed US consumer card spending from YipitData panels. Nearly half of US consumers use AI but only 4.5 percent had a paid personal subscription to ChatGPT, Gemini or Claude in August, and the top one percent of spenders accounts for nearly a fifth of observed spending, averaging about $900 per month. ChatGPT leads web traffic, with Claude now a clear third.
NIO founder, chairman and CEO Li Bin told employees in a September 8 internal talk that the company's core task for the next three years is to focus on its main business, on premium, and on core markets, and that a heavyweight new car will arrive next year. He said NIO delivered 262,893 vehicles in January–August, up 57.9% year over year, while China's passenger car market fell more than 20%.
In an interview with Leiphone, Zhijie executive director and vice president Zhao Changjiang said the newly presold Zhijie RX should become the auto equivalent of Huawei's Pura X, positioned above the 9 series. The RX opened presale on August 20 at 299,800–399,800 yuan with four lidar units, 38 fusion sensors and an L3-oriented architecture. Zhao framed Zhijie's 2.0 strategy as "more Huawei, more Zhijie, more innovation."
NIO founder and CEO Li Bin told Leiphone that AI data centers are competing with automakers for memory, PCB, copper, aluminum and battery resources, creating "diseconomies of scale" where higher volumes can raise costs. NIO's Q2 per-vehicle cost rose 14,000 yuan from the end of last year, adding about 1.5 billion yuan in quarterly costs. NIO's Q2 average transaction price reached 406,000 yuan, about 180,000 yuan above Audi.
