Installing a large language model on your personal computer gives you a handy digital assistant that won't compromise your data privacy. It's no exaggeration to say the large language models that power AI bots like ChatGPT and Gemini are changing the world. Many of us are now relying on them for coding, writing, summarizing, and searching the web. Whatever directions AI goes in over the next few years, LLMs will be part of that future. If you use ChatGPT, Claude, Perplexity, or any of the other AI platforms, then you're using an LLM.

That's a major factor in how well models do on the most famous puzzle-based benchmark, ARC-AGI. These problems require you to infer abstract, general rules from a set of examples. Models do better on ARC puzzles when they receive each grid not as an image but as a string of numbers that encodes the color of each cell. Research suggests that even when models answer ARC-AGI questions correctly, they often do so using byzantine and non-generalizable rules, whereas humans draw on simple visual concepts. Despite these disadvantages, models have gotten quite good at ARC-AGI over the past year, but some puzzles--such as the one printed here--still stump them.

LLMs need vastly more data than children to learn language. Understanding why could help us create more efficient models--and reveal more about developing minds. People have been talking to each other for at least 100,000 years, as best we can tell. And in all that time, there has been only one thing in the world that could learn a human language to perfect fluency: a human child. Four short years after the release of ChatGPT, many of us now take it for granted that we can converse naturally with our phones or computers. LLMs like Claude, DeepSeek, and OpenAI's GPT models are fluent and flexible enough to masquerade convincingly as humans. But peek behind the computational curtain, and there's a catch: Teaching a computer to use human language still requires an inhuman amount of data. An LLM can easily churn through a hundred thousand times more words than a person will experience in the process of mastering their mother tongue--and way more than children might hear by their first birthday, when they typically start to grab hold of language. "The progress recently has been amazing," Michael C. Frank, a cognitive scientist at Stanford University, says of LLMs. "But we still have to burn down a forest and scrape the entire sum of all human knowledge to re-create this milestone that happens in our living rooms over the course of a year."

In the spring of 2022, Google began reaching out to a series of writers, including award-winning science-fiction authors and New York Times bestsellers, for feedback on an experimental writing tool. Four years later, their participation has corners of the literary world up in arms. Google researchers knew at the time that they were approaching a breakthrough point with large language models (LLMs). DeepMind, the company's AI division, was internally testing a natural language generation model called LaMDA which had demonstrated emergent writing capabilities. That model would eventually form the basis for Google Gemini.

Way back in the summer of 2017, AI researchers at Google put out a paper called "Attention Is All You Need," in which they described a new type of neural network called a transformer. It proved to be very good at processing long sequences of data, especially text. Nine years on, transformers are the engines inside every major large language model on the market. "The entire AI industry is built on transformers," says Justin Dangel, cofounder and CEO of the AI startup Subquadratic. "They are one of the most important innovations in the history of computer science, and they've changed the world." But transformers are starting to show their age. Many of the recent advances in LLMs, such as the development of so-called reasoning models and their ability to handle large amounts of input at once, are not neat extensions of that core technology but workarounds that patch over some of its fundamental flaws. A growing number of scientists and engineers are now asking what's coming next. LLMs are not going anywhere, but the way they get built is up for grabs.

Will AI help you do your job or replace you? Artificial Intelligence (AI) companies are making vast claims about the ability of their tools to replace human labour. Some jobs will be automated, others will be augmented. The bosses of the world's biggest companies are diverting vast sums into these tools, partly with the knowledge that they could save money on headcount. Flat is the new up, we are told, in terms of the size of a company's workforce as investors ask whether jobs should be done by new recruits - or, instead, armies of AI Agents, virtual workers tasked with doing specific roles, some of them relatively skilled.

The next time you apply for a job, AI may screen your résumé before any human sees it. But there's good reason to question whether AI will judge you fairly. Researchers already know that LLMs pick up human biases from their training data. New research suggests that LLMs can also develop their own biases from experience--and stereotype job applicants more than humans do. As AI companies race to build agentic models that remember the tiniest details about users, they may be handing them ammunition for forming those biases.

"Context bombing" tricks malicious AI agents into shutting down before they can do harm. Prompt injections, the malicious commands attackers embed into content to entice large language models to follow them, have been attackers' go-to tool for turning AI platforms against their users. A well-phrased command sneaked into an email or calendar invitation is often all it takes to cause the LLM to exfiltrate sensitive data or follow other harmful actions. Now, defenders are embracing the prompt injection, too. Researchers from Tracebit on Monday said they found that placing prompt injections alongside passwords, cryptographic keys, and other secrets stored on Amazon Web Services was often all that was needed to shut down attacks from AI hacking agents.

The company says it has found a new window into how its models arrive at answers. We spoke with senior editor Will Douglas Heaven about it. Anthropic--currently the world's most valuable AI company, with a nearly $1 trillion valuation--has a reputation for publishing strange and heady research. It's looking into whether AI models can feel pain, for example, and will sometimes cut off chatbot conversations if it suspects users are "abusing" the model. One niche that Anthropic spends more time and money on than other AI companies is called mechanistic interpretability, which means looking inside the complex math of an AI model to learn why it comes up with one particular output and not another. It's complicated stuff; there are millions of data points that might contribute to any result, and wading through them can look more like word salad than anything useful.

The AI firm Anthropic has developed a technique that has given it the clearest glimpse yet at what's really going on inside large language models as they answer questions or carry out tasks. What they found ranges from the mundane to the unnerving. Researchers at the company built a tool called the Jacobian lens (or J-lens) and used it to uncover a hidden area, which they named the J-space, inside Claude Opus 4.6, a version of Anthropic's flagship LLM released in February.