#现象/趋势

JUST IN: Google searches for “OpenClaw” have crashed to near-baseline levels. https://t.co/ntLw4t7mZS

this is INSANE! the world’s first colosseum built for ENHANCED athletes. every competitor FULLY enhanced. every dose tracked. every compound clinically approved.​​​​​​​​​​​​​​​​ $1.2B backing before a single event. built from scratch. who’s not tuning in to watch humans push past every limit we thought existed? Las Vegas. May 24

Reuters: Meta is on track to become the world’s biggest digital ad company, with $243.46B in 2026 ad revenue versus Google’s projected $239.54B. Digital ads reward whichever platform can turn huge amounts of user behavior into cheap, effective, automated campaign results for brands. Meta’s edge is Advantage+, a system that automates ad targeting, creative testing, budget allocation, and placement, which means advertisers do less manual setup and often get better return from the same spending. That is a strong fit for modern ad buyers because they increasingly want fewer knobs to manage and faster performance feedback across Instagram, Facebook, Reels, WhatsApp, and Threads. Google still owns an enormous ad machine, but much of its strength sits in search ads, where growth is steadier, while Meta is growing faster by expanding more aggressively across social feeds, short video, and messaging. eMarketer says Meta’s ad business is expected to grow 24.1% this year, up from 22.1% in 2025, while Google is projected to grow 11.9%, which is the real reason the gap flips. --- reuters .com/business/media-telecom/meta-poised-surpass-google-digital-ad-revenue-first-time-report-says-2026-04-13/

Anger about AI is now spilling out into real-world violence. Sam Altman’s home was hit by a second alleged attack in 2 days, with a gunshot reported early Sunday after a Molotov cocktail attack on Friday. Police arrested Amanda Tom, 25, and Muhamad Tarik Hussein, 23, after the shooting report and recovered 3 guns, while Daniel Moreno-Gama, 20, was already in custody over the earlier firebomb case. --- nypost .com/2026/04/13/us-news/openai-ceo-sam-altmans-home-allegedly-targeted-in-second-attack-in-two-days-cops-make-2-arrests

Beijing’s Humanoid Robot Half Marathon, night run test. Around 40% of teams are running fully autonomous, remote-controlled runs get a 1.2× penalty (Beijing's E-Town Half Marathon on April 19 with 100+ teams, 300+ humanoids competing) https://t.co/CKhEBlFZ3V

Demand for human experts training AI is exploding. Handshake’s gross annualized AI training revenue rose to nearly $1B from $550M in January and from just $5M to $10M a year ago. https://t.co/li5aLNZDeC

Stanford published a 423 pages long 2026 AI index report. Model progress is not flattening, with top systems now hitting or beating human baselines on some hard coding, science, and math tests, while some older benchmarks are getting less useful because frontier models are bunching together near the top. The U.S.-China gap at the model frontier is now very small, with the U.S. still ahead in top model count and private investment, while China leads in papers, citations, patent volume, and much of the industrial deployment base. AI adoption is moving unusually fast, reaching about 53% global population-level use in 3 years, with strong business uptake and a large estimated consumer surplus even when tools are free. Responsible AI is lagging capability, with documented AI incidents rising to 362, weak safety reporting, and real tradeoffs where improving one safety dimension can hurt another metric. The hardware story is just as important as the model story, because AI now depends on a fragile stack centered on data centers, huge energy use, and a chip supply chain heavily tied to TSMC. The labor signal is mixed, because AI is producing real productivity gains in fields like support and coding while some entry-level software jobs are already weakening. The most practical near-term win may be medicine, where AI note-taking tools are reducing clinician paperwork and burnout, even though strong real-world clinical evidence is still thin for many systems.

Anger about AI is now spilling out into real-world violence. Sam Altman’s home was hit by a second alleged attack in 2 days, with a gunshot reported early Sunday after a Molotov cocktail attack on Friday. Police arrested Amanda Tom, 25, and Muhamad Tarik Hussein, 23, after the shooting report and recovered 3 guns, while Daniel Moreno-Gama, 20, was already in custody over the earlier firebomb case. --- nypost .com/2026/04/13/us-news/openai-ceo-sam-altmans-home-allegedly-targeted-in-second-attack-in-two-days-cops-make-2-arrests

Perplexity started as a small business tool for ourselves. We had 4 people and no revenue with AI at our fingertips. The pivot to Computer is actually a full circle. Founders are using it to grow companies that matter to the economy and their communities.  It’s rewarding to see it now powering small businesses and startups in big ways. Perplexity is still a startup. We just 5X’ed revenue from $100M to $500M with only 34% growth in team size. 2x revenue growth in 2026 with same small team. And we’re just warming up. Everyone here works at a small business, and everything we build is for people who build.

Robotic data is insanely expensive and brutal to collect. It’s the only thing holding back general-purpose robots right now. Figure CEO @adcock_brett : "If we could get a pile of data in the helix stack, we would solve general robotics right now." https://t.co/YWpA20gdn5

Indian factory workers wear head-mounted cameras to capture data for training robotics AI models. This image captures a blunt truth about robotics: teaching a machine to move in the real world is still painfully expensive. What looks dystopian at first is also a clue about the bottleneck. Robots do not learn useful physical behavior from internet-scale text the way language models do. They need embodied data: hands reaching, wrists turning, objects slipping, fabric folding, tools resisting, people recovering from small mistakes in real time. That data is rare because reality is slow, messy, and costly. A robot fleet is expensive to buy, expensive to maintain, hard to supervise, and dangerous to scale in uncontrolled settings. Even teleoperation is costly, because every minute of human-guided movement requires hardware, operators, calibration, and failure recovery. So companies go looking for the cheapest possible proxy for physical intelligence. First-person video from factory workers is not the same as robot action data, but it can still be valuable because it captures sequencing, posture, bimanual coordination, and the micro-adjustments that make real work look easy. The frontier in robotics is not just better models. It is better pipelines for collecting reality itself. That is why warehouses, factories, kitchens, and repair benches matter so much: they are dense environments of repeated contact with the physical world, which is exactly what robots lack. The unsettling part is that this turns human labor into training infrastructure twice over, first as work, then as data. And until embodied data becomes cheaper to gather than human motion is to record, robotics will keep learning from workers before it fully replaces them.

Brad Gerstner (@altcap): AI economics flipped: firms with owned compute keep infra costs fixed while revenue scales. OpenAI’s compute margins rose from 35% to 70%, Anthropic went from -94% to +40%, and physical power is now the main bottleneck. https://t.co/VKRUpGhoee