观点：我们离“AI科学家”还很远

In February, my colleague Kai Williams pointed out that LLMs have an uncanny ability to recognize authors based on their unpublished prose. In recent weeks, journalists like Megan McArdle and Kelsey Piper have confirmed this.

I decided to try it out for myself. Back in 2012, a friend paid me $500 to write an essay about the Great Canadian Maple Syrup Heist. It never got published. So on Friday, I opened ChatGPT in incognito mode and pasted in five paragraphs from the essay.

ChatGPT said it wasn’t sure who the author was, guessing that it might be Nate Silver or my former Vox.com colleague Matthew Yglesias. When I added four more paragraphs, the chatbot responded: “This one I can identify pretty confidently—it’s by Timothy B. Lee.”

But when I asked ChatGPT why it thought the essay was written by me, it couldn’t give me a specific reason. “Even though Timothy B. Lee often writes clear, explanatory pieces, there’s nothing here that acts like a fingerprint—no recurring phrases, specific policy framing, or known article structure that ties it definitively to him.”

I think there’s a lesson here that goes well beyond identifying authors.

People have a lot of implicit knowledge — things we know but struggle to fully explain. People often use body-oriented metaphors for this phenomenon. We say that an insight is “on the tip of our tongue,” that we “can’t put our finger on” an idea, or that we know something “in our gut.”

Something similar is true of LLMs: their ability to perform cognitive tasks greatly exceeds their ability to explicitly explain how and why they’re able to perform them.

But there’s an important difference between people and LLMs. The human brain learns constantly; as we go through our day, our brains are constantly making new connections, recognizing new patterns, and forming new hunches. Our stock of implicit knowledge is constantly expanding.

In contrast, LLMs only do this during training. LLMs have an uncanny ability to recognize authors — but only authors whose work was well represented in their training data. Once a model is trained, its weights are frozen and its capacity to learn new patterns (for example, the writing styles of new authors) is greatly reduced.

Recently, there has been a lot of excitement about AI agents like Claude Code and OpenClaw. Much of the hype is justified. Claude Code really is revolutionizing computer programming, and agents like OpenClaw very well might transform other parts of the economy and our daily lives.

Industry leaders expect even bigger changes in the near future. In an interview last month, Sam Altman said that OpenAI is aiming to build an “automated AI researcher” by March 2028. Some people expect this (or similar breakthroughs by rivals) to set off a recursive self-improvement loop that radically accelerates scientific and technological progress.

That might happen eventually, but I think it will take a while.

As human scientists perform experiments, their brains are hunting for patterns in the data that could give rise to new insights and new models of how the world works. But an AI scientist — at least one based on today’s LLMs and agent architectures — can’t learn from experiments in the same rich way. They have no reliable or scalable way to build implicit knowledge from data they see at inference time.

Fixing that may require fundamentally rethinking the transformer architecture at the heart of today’s frontier models. At a minimum, it’s going to require overhauling today’s agentic frameworks.

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How agents deal with limited LLM context

Photo by IcemanJ via iStock / Getty Images Plus

Many difficult intellectual tasks require “thinking” for a long time. Yet LLMs can only store a limited number of tokens in their working memory, known as the context window. For leading models, this limit has been stuck around 1 million tokens for the last couple of years. Moreover, due to economic constraints and the problem of context rot (which I wrote about in November), AI developers try to stay well below the maximum.

Managing this tension has been a major focus for the AI industry, which has developed a suite of “context engineering” techniques for using context efficiently. For example, modern chatbots undergo a process of compaction, where older information periodically gets deleted or summarized.

This creates an illusion that the model has much longer context than it actually does. But it can have big downsides if compaction goes awry. In one horrifying incident, a woman asked her AI agent to suggest emails for deletion, but not actually delete them. Unfortunately, that latter request got lost during compaction and so the agent started mass-deleting her emails.

Over the last year, AI companies have experimented with allowing models to store persistent information outside of the context window. Claude Code was a step in this direction. Claude Code runs on the user’s own computer and can read and modify files on the local hard drive. Once Claude Code has finished a particular coding task, it can write the results out to the affected file and no longer needs to keep the details in context.

OpenClaw, released in late 2025, goes a step further. It’s a general framework for running AI agents on a user’s local computer. OpenClaw agents — like Claude Code agents — can read and write files on the local filesystem, allowing them to store relevant documents and keep track of uncompleted tasks.

Enthusiasm for OpenClaw and other local agents has led to surging demand for Apple’s Mac mini computers. Installing OpenClaw on a Mac Mini allows agents to connect to Apple services such as iMessage. At the same time, because macOS is based on Unix, agents have access to a powerful command-line interface called the Unix shell.

“At the end of the day, your agent is just its files”

Marc Andreessen. (Photo by Steve Jennings/Getty Images for TechCrunch)

In a recent appearance on the Latent Space podcast, the venture capitalist Marc Andreessen argued that agents like OpenClaw represented an important new computing paradigm. Here’s a lightly edited excerpt:

We now know an agent is the following: It’s a language model. It’s a Unix shell. The agent has access to the shell. Then it’s a file system. The state is stored in files. There’s the Markdown format for the files. And then there’s basically what in Unix is called a cron job — a loop and a heartbeat — and the thing basically wakes up…

So that’s the architecture. And then it turns out, what is your agent? Your agent is a bunch of files stored in a file system.

This means your agent is independent of the model that it’s running on because you can swap out a different LLM underneath your agent. And your agent will change personality somewhat because the model is different, but all of the state stored in the files will be retained. It’s still your agent with all of its memories and with all of its capabilities.

You can also swap out the shell. So you can move it to a different execution environment. You can also switch out the file system. And you can swap out the heartbeat, the cron framework, the agent framework itself. At the end of the day, your agent is just its files.

As a consequence of that, the agent can migrate itself. You can instruct your agent, migrate yourself to a different runtime environment, migrate yourself to a different file system, swap out the language model. Your agent will do all that stuff for you.

The agent has full introspection. It knows about its own files and it can rewrite its own files. And that leads you to the capability that just completely blew my mind when I wrapped my head around it, which is you can tell the agent to add new functions and features to itself.