This paper examines changes in the labor market for occupations exposed to generative artificial intelligence using high-frequency administrative data from the largest payroll software provider in the United States. We present six facts that characterize these shifts. We find that since the widespread adoption of generative AI, early-career workers (ages 22-25) in the most AI-exposed occupations have experienced a 13 percent relative decline in employment even after controlling for firm-level shocks. In contrast, employment for workers in less exposed fields and more experienced workers in the same occupations has remained stable or continued to grow. We also find that adjustments occur primarily through employment rather than compensation. Furthermore, employment declines are concentrated in occupations where AI is more likely to automate, rather than augment, human labor. Our results are robust to alternative explanations, such as excluding technology-related firms and excluding occupations amenable to remote work. These six facts provide early, large-scale evidence consistent with the hypothesis that the AI revolution is beginning to have a significant and disproportionate impact on entry-level workers in the American labor market. — Read More

In a moment with many important economic questions and fears, I continue to find this among the more interesting mysteries about the US economy in the long run: Is artificial intelligence already taking jobs from young people?

If you’ve been casually following the debate over AI and its effect on young graduates’ employment, you could be excused for thinking that the answer to that question is “possibly,” or “definitely yes,” or “almost certainly no.”

… To be honest with you, I considered this debate well and truly settled. No, I’d come to think, AI is probably not wrecking employment for young people. But now, I’m thinking about changing my mind again. — Read More

TL;DR: Virtualization emerges as the strategy to increase efficiency and achieve feats that physical reality never could. To the point where even our work, friends, and experiences have gone virtual. But what’s the real cost of living in abstractions — and could reality itself be just another layer we can’t see through?

A July 2025 MIT study examined how large language models (LLMs) handle complex, changing information. Researchers tasked AI models with predicting the final arrangement of scrambled digits after a series of moves, without knowing the final result. Transformer models learned to skip explicit simulation of every move. Instead of following state changes step by step, the models organized them into hierarchies, eventually making reasonable predictions.

In other words, the AI developed its own internal “language” of shortcuts to solve the task more efficiently. Does it hint at a broader truth? When faced with complexity, intelligent systems (biological or artificial) seek compressed, virtual representations that capture the essence without expending the energy to simulate every detail. — Read More

ChatGPT rivals like Google’s Gemini, xAI’s Grok, and, to a lesser extent, Meta AI, are closing the gap to ChatGPT, OpenAI’s popular AI chatbot, according to a new report focused on the consumer AI landscape from venture firm Andreessen Horowitz.

The report, in its fifth iteration, showcases two and a half years of data about consumers’ evolving use of AI products.

And for the fifth time, 14 companies appeared on the list of top AI products: ChatGPT, Perplexity, Poe, Character AI, Midjourney, Leonardo, Veed, Cutout, ElevenLabs, Photoroom, Gamma, QuillBot, Civitai, and Hugging Face. — Read More

Meet the innovators, leaders, and thinkers reshaping our world through groundbreaking advances in artificial intelligence. Time’s 100 most influential people in AI of 2025. The list includes familiar names like Sam Altman, Elon Musk, Jensen Huang, and Fei-Fei Li alongside newcomers like DeepSeek CEO Liang Wenfeng. — Read More

The landscape of AI agents has been dominated by large language models (LLMs) like GPT-4 and Claude, but a new frontier is opening up: lightweight, open-source, locally-deployable agents that can run on consumer hardware. This post shares internal notes and discoveries from my journey building agents for small language models (SLMs) – models ranging from 270M to 32B parameters that run efficiently on CPUs or modest GPUs. These are lessons learned from hands-on experimentation, debugging, and optimizing inference pipelines.

SLMs offer immense potential: privacy through local deployment, predictable costs, and full control thanks to open weights. However, they also present unique challenges that demand a shift in how we design agent architectures. — Read More

I was recently trying to convince a friend of mine that ChatGPT hasn’t memorized every possible medical record, and that when she was passing her blood work results the model was doing pattern matching in ways that even OpenAI couldn’t really foresee. She couldn’t believe me, and I totally understand why. It’s hard to accept that we invented a technology that we don’t fully comprehend, and that exhibits behaviors that we didn’t explicitly expect.

Dismissal is a common reaction when witnessing AI’s rate of progress. People struggle to reconcile their world model with what AI can now do, and how.

This isn’t new. Mainstream intuition and cultural impact always lag behind new technical capabilities. When we started building businesses on the Internet three decades ago, the skepticism was similar. Sending checks to strangers and giving away services for free felt absurd. But those who grasped a new reality made of zero marginal costs and infinitely scalable distribution became incredibly wealthy. They understood that the old assumptions baked into their worldview no longer applied, and acted on it.

Eventually the world caught up. — Read More

Artificial General Intelligence (AGI), Artificial Super Intelligence (ASI) and fully general humanoid robotics are just around the corner, or so many people believe. So it’s time to try to understand how this will affect our economy. Will we be forced into lives of idle leisure and/or meaninglessness? Will the few remaining human workers toil below the API? Will we get fully automated luxury gay space communism?

This is a follow up after five years to my original post on post scarcity and post-capitalism.

Keynes predicted in 1930 that by 2030, automation would reduce the need for work to just 15 hours per week. We’re almost there, so what did he get right and wrong? First, most people work fewer hours in less physically taxing jobs than their grandparents did. But we’ve standardized on 40 hour weeks, and much more for people in jobs with a high degree of competition. Within those 40 hours, I am reliably informed, some people struggle to perform even a single hour of meaningful work, but these are not the rule. — Read More

As economists speculate whether the stock market is in an AI bubble that could soon burst, OpenAI CEO Sam Altman has just admitted to believing we’re in one. “Are we in a phase where investors as a whole are overexcited about AI?” Altman said during a lengthy interview with The Verge and other reporters last night. “My opinion is yes.” — Read More

#strategy

There’s something happening in our IDEs that nobody’s talking about.

Not the obvious stuff—everyone sees the autocomplete getting smarter, the boilerplate evaporating, the bugs caught before they hatch. I’m talking about something deeper. Something that makes my Moroccan grandmother’s warnings about djinn possession feel less like folklore and more like… documentation.

We’re witnessing the first large-scale cognitive handoff between human and artificial intelligence. And it’s not just changing how we build software—it’s rewiring how our entire civilization processes information. — Read More