In recent months, the CEOs of leading AI companies have grown increasingly confident about rapid progress: * OpenAI's Sam Altman: Shifted from saying in November "the rate of progress continues" to declaring in January "we are now confident we know how to build AGI" * Anthropic's Dario Amodei: Stated in January "I'm more confident than I've ever been that we're close to powerful capabilities... in the next 2-3 years" * Google DeepMind's Demis Hassabis: Changed from "as soon as 10 years" in autumn to "probably three to five years away" by January. What explains the shift? Is it just hype? Or could we really have Artificial General Intelligence (AGI) by 2028?[1] In this article, I look at what's driven recent progress, estimate how far those drivers can continue, and explain why they're likely to continue for at least four more years. In particular, while in 2024 progress in LLM chatbots seemed to slow, a new approach started to work: teaching the models to reason using reinforcement learning. In just a year, this let them surpass human PhDs at answering difficult scientific reasoning questions, and achieve expert-level performance on one-hour coding tasks. We don't know how capable AGI will become, but extrapolating the recent rate of progress suggests that, by 2028, we could reach AI models with beyond-human reasoning abilities, expert-level knowledge in every domain, and that can autonomously complete multi-week projects, and progress would likely continue from there.  On this set of software engineering & computer use tasks, in 2020 AI was only able to do tasks that would typically take a human expert a couple of seconds. By 2024, that had risen to almost an hour. If the trend continues, by 2028 it'll reach several weeks.  No longer mere chatbots, these 'agent' models might soon satisfy many people's definitions of AGI — roughly, AI systems that match human performance at most knowledge work (see definition in footnote).[1] This means that, while the co

Advanced AI could unlock an era of enlightened and competent government action. But without smart, active investment, we’ll squander that opportunity and barrel blindly into danger. Executive summary See also a summary on Twitter / X. The US federal government is falling behind the private sector on AI adoption. As AI improves, a growing gap would leave the government unable to effectively respond to AI-driven existential challenges and threaten the legitimacy of its democratic institutions. A dual imperative → Government adoption of AI can’t wait. Making steady progress is critical to: * Boost the government’s capacity to effectively respond to AI-driven existential challenges * Help democratic oversight keep up with the technological power of other groups * Defuse the risk of rushed AI adoption in a crisis → But hasty AI adoption could backfire. Without care, integration of AI could: * Be exploited, subverting independent government action * Lead to unsafe deployment of AI systems * Accelerate arms races or compress safety research timelines Summary of the recommendations 1. Work with the US federal government to help it effectively adopt AI Simplistic “pro-security” or “pro-speed” attitudes miss the point. Both are important — and many interventions would help with both. We should: * Invest in win-win measures that both facilitate adoption and reduce the risks involved, e.g.: * Build technical expertise within government (invest in AI and technical talent, ensure NIST is well resourced) * Streamline procurement processes for AI products and related tech (like cloud services) * Modernize the government’s digital infrastructure and data management practices * Prioritize high-leverage interventions that have strong adoption-boosting benefits with minor security costs or vice versa, e.g.: * On the security side: investing in cyber security, pre-deployment testing of AI in high-stakes areas, and advancing research on mitigating the ris

Summary In this article, I estimate the cost-effectiveness of five Anima International programs in Poland: improving cage-free and broiler welfare, blocking new factory farms, banning fur farming, and encouraging retailers to sell more plant-based protein. I estimate that together, these programs help roughly 136 animals—or 32 years of farmed animal life—per dollar spent. Animal years affected per dollar spent was within an order of magnitude for all five evaluated interventions. I also tried to estimate how much suffering each program alleviates. Using SADs (Suffering-Adjusted Days)—a metric developed by Ambitious Impact (AIM) that accounts for species differences and pain intensity—Anima’s programs appear highly cost-effective, even compared to charities recommended by Animal Charity Evaluators. However, I also ran a small informal survey to understand how people intuitively weigh different categories of pain defined by the Welfare Footprint Institute. The results suggested that SADs may heavily underweight brief but intense suffering. Based on those findings, I created my own metric DCDE (Disabling Chicken Day Equivalent) with different weightings. Under this approach, interventions focused on humane slaughter look more promising, while cage-free campaigns appear less impactful. These results are highly uncertain but show how sensitive conclusions are to how we value different kinds of suffering. My estimates are highly speculative, often relying on subjective judgments from Anima International staff regarding factors such as the likelihood of success for various interventions. This introduces potential bias. Another major source of uncertainty is how long the effects of reforms will last if achieved. To address this, I developed a methodology to estimate impact duration for chicken welfare campaigns. However, I’m essentially guessing when it comes to how long the impact of farm-blocking or fur bans might last—there’s just too much uncertainty. Background In