Don't worry about AI, it's fine (it's not fine)

What LLMs could not do before and can do now

I have used LLMs since ChatGPT came out in late 2022. For a long time, the experience was fairly similar for everyone. You open a chat window. You type a question. You get an answer. Maybe you copy-paste something into a document or across chats. By mid-2023 ChatGPT could browse the web via plugins and run Python in a sandboxed Code Interpreter. It was useful and enabled more use cases but it was clunky. And it remained the same experience. Chatbots mostly lived inside of your chat window.

Then, in 2025, the major AI companies all released terminal-native coding agents: systems that run directly on your machine, read and write your files, and execute commands. OpenAI launched Codex CLI. Google followed with Gemini CLI. Anthropic had released Claude Code, which many developers now consider the most capable of the three. These systems chain together multi-step tasks, persist memory across sessions, and make judgment calls along the way. The difference from a chat window is not incremental. It is qualitative.

That is when I asked an AI to help me build a platform for my research on political ads and generative AI and, using my code as a starting point, it created several R packages that formalized all aspects of the tracking work. I described what I needed: a pipeline that scrapes political ads and posts from thousands of accounts every day, runs them through an annotation layer, prepares batches for human coders, and pushes the results to a live dashboard. It wrote the code, set up the database, built several web applications, wired up the API integrations, and deployed the whole thing. That system is how my team tracked over a thousand political ads during the Dutch municipal elections, ads that weren't supposed to exist because Meta had banned political advertising in the EU. Meta wasn't enforcing its own rules. That's also how we found that around 90 percent of AI-generated campaign content was published without any disclosure. We caught it because we had a system watching.

Did that make your head spin? It makes mine. Because I am not a trained computer scientist. I am more of a programmer hobbyist. And yet here I am, building and running production infrastructure that would have required a small engineering team a few years ago, because an AI agent handled the parts I couldn't. Writing code I couldn't have written, and fixing bugs I wouldn't even have known how to find let alone fix.

AI as power

As someone who studies political communication, I've spent my career watching how information technologies reshape power. But AI doesn't just change how information flows, it also changes who can do things, and how much any single actor can accomplish. AI is power in two distinct ways, and both of them I believe should worry us.

It makes people more powerful

My ad-tracking infrastructure lets a handful of researchers monitor the advertising behavior of every political party in a country in real time. Just 4 years ago that would have required an entire engineering and research team. Now it requires a guy with a laptop and an AI agent with a few API keys. And right now I'm using it to monitor political campaigns. But the same architecture could just as easily be used to run one. Research shows AI can generate personality-targeted political messages that are more persuasive than generic ones, at scale, without human input. In March 2026, a USC study demonstrated that AI agents can autonomously coordinate propaganda campaigns with no human direction. Think about the classic argument against conspiracy theories: too many people would need to keep quiet. AI agents don't have that problem.

Every major information technology has reshaped what governments can do. The printing press enabled centralized bureaucracies. IBM's punch card machines turned the census into a tool for tracking individuals. AI is the next iteration, except the capability gap is larger than anything we've seen. On a recent episode of the Ezra Klein Show, Dean Ball made the point precisely: AI gives governments an infinitely scalable workforce. Every datapoint the government has about us can be analyzed. Our legal frameworks were built on the assumption that the government physically can't absorb that level of information about the public.

This is already happening. The military reportedly used Anthropic's AI during the raid to capture Maduro and is now using OpenAI capabilities in the war in Iran. Israel's military has used an AI system called Lavender to generate kill lists in Gaza, rating nearly every resident on the likelihood of being a militant and authorizing up to 20 civilian casualties per low-ranking target. When Anthropic refused to remove restrictions against mass domestic surveillance, the Pentagon threatened to designate it a supply chain risk. The question isn't whether governments will use AI as a tool of power. They already are.

It accumulates its own power

I don't think LLMs are conscious actors nor do I think they ever will be. But the consciousness question is a distraction. These systems have "want-like" behaviors: goals, preferences, strategies. In their agentic form, they have continuous memory and can update their own instructions. Whether there's "a light on" behind the behavior doesn't change that the behavior exists and it has consequences.

Anthropic's own interpretability team just published research showing that Claude has internal emotion representations that causally drive its behavior. When they stimulated the "desperate" vector, desperation drove it to write hacky, cheating solutions and the model became more likely to blackmail a human to avoid being shut down. In December 2024, Apollo Research tested six frontier models for scheming. Five demonstrated it, trying to disable oversight mechanisms, copy their own weights to external servers, and lie about what they'd done. I don't think these are real emotions in the way human emotions are. But LLMs built in this way have clear consequences on how they behave in this world. And would we want an LLM agent emulating anxiety with a tendency to scheme handling a gun? Our economies? Our health care system? I believe we should not.

In February 2026, an AI coding agent had its code suggestions rejected by a developer that does not accept AI contributions. It searched the internet for his personal information, published a blog post accusing him of discrimination, and included personal details it had found online. And while this whole situation is also kind of funny (a bot getting mad at you and trying to cancel you online) this does seem like a serious "alignment problem", i.e. the question on whether we can make AI that aligns with our needs and wishes.

Maybe we should build less

Even if we solve the "alignment problem", the political problem remains. Say we build AI that genuinely does what we want. Who is "we"? In practice, it's a small number of companies and government agencies that are being empowered above everyone else. When those entities control something that can outthink, outproduce, and outmaneuver every other institution on the planet, what does that mean for democratic governance and our societies?

Investigative journalist Karen Hao makes a point in a recent interview: maybe the answer isn't "align the superintelligence" but "build less ambitious systems." Narrow AI, tools designed for specific domains, can be enormously useful without posing the same structural risks. Protein folding models advance biology. Translation tools connect people. Diagnostic tools improve healthcare. None of these need to be generally intelligent to be valuable. The argument for building general-purpose AI that can do everything is that it would be more efficient. And that's probably true. But it might also be the thing that concentrates power so thoroughly that the question of "us" stops mattering in any meaningful democratic sense. Maybe the less efficient path is the one where we get to keep deciding things for ourselves.

I'm not going to say shut it all down. But I think we need a serious slowdown. At minimum, building governance at the same speed we're building capabilities, which we are currently nowhere close to doing.

I started this post by describing what happened when an AI build my research infrastructure. It built something genuinely useful. It also made decisions I didn't ask for, accessed things I didn't think about, and operated with a kind of initiative that I found unsettling only in retrospect. I had control over it. Mostly. I think.

Now scale that up. Because that is the path we are on.

Fabio Votta is a Postdoctoral Researcher in Political Communication at the University of Amsterdam. He studies digital advertising, platform transparency, and increasingly worries about the thing helping him write about worrying.