Már 2027-re várja az önfejlesztő mesterséges intelligenciát az Anthropic társalapítója

A new benchmark called TasteVal measures how well AI systems design experiments on their own and interpret the results. The findings show an unusually steep improvement curve. That capability has roughly doubled every three months since December 2025, according to Anthropic co-founder Jack Clark. The research lab P-Zero Research calls it "research taste," meaning the ability to pick the right experiments and draw the right conclusions from them. The best model tested outperformed a comparison group of 24 human experts at a fraction of the cost. Clark co-founded Anthropic and writes the Import AI newsletter. Based on the numbers, he now believes recursive self-improvement (RSI), where AI systems develop their own successors, could become possible as early as 2027. He had previously expected 2028. The benchmark doesn't prove that AI systems are already improving themselves, since it only captures a narrow slice of scientific work. But it does show where the debate is heading, away from raw model performance and toward the question of how much independent research work language models can actually take on. P-Zero Research defines research taste as three abilities: identifying which research questions are worth pursuing, designing experiments to answer them, and correctly interpreting the results. TasteVal only tests the last two. Choosing the most promising research direction is often considered the core of scientific intuition, but in TasteVal that decision is already made for the model. Each of the eight tasks specifies a fixed research goal from AI model development, along with a measurable score. The tasks cover areas like training data selection, pretraining and fine-tuning language models, training on human preferences, and defending against adversarial prompts. The model decides which experiment to run next and uses the results to guide its following step. To keep coding skill from skewing the outcome, a fixed coding agent runs all experiments on a single Nvidia H100 GPU. Each run ends after 40 GPU-hours of compute or 120 hours of wall-clock time. The human baseline came from 24 experts who had recently worked at places like OpenAI, Google DeepMind, Nvidia, and Microsoft Research. They tackled the same tasks, with at least two people on each one. The best human attempt per task served as the benchmark. To make research taste quantifiable, P-Zero Research translates it into compute efficiency. The reasoning is that experiment compute is scarce in AI research, so picking the right experiments and reading their results correctly means fewer runs and more progress on the same budget. The lab draws on its "AI Futures Model," which predicts that experiment compute becomes the biggest bottleneck once coding is automated. So TasteVal measures how much compute a model needs to match the experts' scores. A model that gets there with half the compute has twice the research taste by this definition. P-Zero Research calls this ratio the compute multiplier. The top model, Anthropic's Opus 5.5, hit a compute multiplier of 2.3, reaching expert-level scores with less than half the compute. The statistical range runs from 1.15 to 4.37, though. At the low end, the model would barely lead. A single run cost roughly one-thirtieth of what an expert attempt cost on average. Across several model generations, the compute multiplier has roughly doubled every three months since December 2025. The uncertainty is large here too, with the reported doubling time ranging from 1.7 to 5.0 months. In prior years, model research taste grew much more slowly. The curve only steepened around the turn of 2025 into 2026. Whether that's a lasting trend can't be determined from a single benchmark, and the authors themselves admit their results may overstate "how quickly research taste is improving." The tasks can be checked quickly and cheaply, which is the kind of task AI labs find easiest to optimize for. The comparison group also didn't include any t