Rohan Paul (@rohanpaul_ai)

Mark Zuckerberg told employees in a Wednesday memo that laying off 8,000 workers was necessary because “success isn’t a given.” The full memo, as published by businessinsider. "Hey everyone, I want to express my gratitude to everyone leaving today for all of the hard work you've put into serving our community. It's always sad to say goodbye to people who have contributed to our mission and to building this company. I feel the weight of that, and I'm spending a lot of time making sure we manage this as well as possible. This is the most dynamic I have seen our industry. I'm optimistic about everything we're building to give billions of people the power to express themselves and connect with the people they care about. I'm also optimistic about delivering personal superintelligence to everyone. We've always focused on putting power in people's hands. This is how we believe progress is made in the world. These values are what makes us different, and they are why Meta has been successful. But success isn't a given. AI is the most consequential technology of our lifetimes. The companies that lead the way will define the next generation. We're transforming our company to make sure it will always be the best place for talented people to have the greatest impact. People tell us that they appreciate the ability to take greater ownership and execute their vision with less bureaucracy and management to navigate. At the same time, we also want to provide everyone with as much stability as possible. We won't always get this balance right, but I care deeply about this so we'll keep adjusting and work hard to do right by people along the way. To that end, I want to be clear that we do not expect other company-wide layoffs this year. I also want to acknowledge that we haven't been as clear as we aspire to be in our communication, and that's one area I want to make sure we improve. I'm confident in what we're all building together. We are one of the few companies positioned to help define the future. Meta has the talent, the infrastructure, the apps and distribution, and the business model. We have a lot of work ahead, but what's on the other side is going to be extraordinary. Once again, I'm grateful to those leaving today. And I'm grateful to everyone around the company for all of the historic work we will continue doing together. Mark" --- businessinsider .com/heres-what-mark-zuckerberg-said-about-future-layoffs-at-meta-2026-5

CNBC: Meta starts major cuts with 8,000 layoffs as AI shakes the tech giant. Along with the layoffs, around 7,000 employees will be shifted into new AI-focused roles. Meta is not only trimming costs, it is changing its internal shape around AI infrastructure, foundation models, and AI monetization, which means the company wants more people building the systems that train models, the models themselves, and the products that turn those models into revenue. --- cnbc .com/2026/05/20/meta-layoffs-zuckerberg-says-success-isnt-a-given-in-memo.html

New Google paper: A forecast needs context, not just history. Some patterns are caused by events, not time. Nexus reframes forecasting as a reasoning problem, where events and numbers have to explain each other. Nexus argues that forecasting improves when models read the world around the numbers, not just the numbers themselves. In the Zillow tests, one Claude-based version cut average MAPE by 86.6% versus direct chain-of-thought prompting. That matters because most time series models are fluent in pattern, but mute about cause. A housing inventory curve can reflect seasonality, mortgage pressure, migration, layoffs, and local supply, while a stock price can be bent by earnings, regulation, hype, and fear. Nexus separates those jobs instead of asking one prompt to do everything. One agent turns messy historical text into a clean event timeline, one reads the broad regime, another tracks local shocks, and a synthesizer reconciles them with calibration from past errors. The interesting result is not merely that context helps, but that structure helps the language model use context without losing the time series. The evidence is still narrow: Zillow counts, seven equities, post-cutoff data, and single-run evaluations, so this is not a universal law of forecasting. But the direction is clear: future forecasters will not only extrapolate curves; they will argue about what made the curve move. ---- Paper Link – arxiv. org/abs/2605.14389 Paper Title: "Nexus : An Agentic Framework for Time Series Forecasting"

Anthropic CEO Dario Amodei : "Software is going to become cheap, maybe essentially free. The premise that you need to amortize a piece of software you build across millions of users, that may start to be false. But at the same time, there are whole jobs, whole careers that we've built for decades that may not be present. And, you know, I think we can deal with it. I think we can adjust to it. But I don't, I don't think there's an awareness at all of what, of what is coming here and the magnitude of it." --- From "The Wall Street Journal" YT channel (link in comment)

Anthropic drops a paper on the US-China AI race They believe the US and its allies may be able to lock in a 12-24 month frontier AI lead by 2028 if they close China’s access to advanced compute and copied model outputs. The report says China is not far behind because Chinese labs are allegedly using loopholes, smuggled chips, offshore data centers, and distillation attacks to stay close to US frontier labs. Anthropic frames compute as the central bottleneck of AI power, saying advanced chips are not just one input but the gatekeeper for training, deployment, revenue, experimentation, and future model improvement. The report says Huawei may produce only 4% of NVIDIA’s aggregate compute in 2026 and 2% in 2027, which is one of the paper’s sharpest claims about China’s semiconductor gap. Anthropic argues that distillation is systematic industrial espionage, because Chinese labs can use American model outputs to copy capabilities without paying the full training cost. The report claims a Chinese AI lead could enable automated repression, stronger cyber operations, faster military AI deployment, and broader authoritarian influence through cheap global AI infrastructure. Future frontier models may become a “country of geniuses in a data center,” meaning a single model cluster could act like a huge expert workforce for cyber, science, engineering, and military research.