Our goal is to help the public at large as well as decision-makers at all levels become resistant to hype. Think of us as your guides to navigating a glitzy technology expo hall, full of salespeople trying to get you to buy a new product or fork over your data.

As we say on the podcast: each time we think we’ve reached peak AI hype—the summit of bullshit mountain—we discover there’s worse to come.

We’re using what we see in this bubble to document the contours of hype about AI, its causes and its short- and long-term effects. Our primary goal is to inhibit the next tech bubble.

Artificial intelligence, if we’re being frank, is a con: a bill of goods you are being sold to line someone’s pockets.

Language models, problematically, have no subjectivity with which to perform intersubjectivity. … But there is no mind there, and we need to be conscientious to let go of that imaginary mind we have constructed.

General intelligence is not something that can be measured, but the force of such a promise has been used to justify racial, gender, and class inequality for more than a century.

In the vast majority of cases, AI is not going to replace your job. But it will make your job a lot shittier. What actors and writers are fighting for is a future that doesn’t relegate humans to babysitting scriptwriting and acting algorithms, available on call but only paid when the media synthesis machines glitch out.

A white paper written by analysts at the investment bank Goldman Sachs estimates, based on data from the United States and the European Union, that a quarter of all global work could be replaced by AI tools.

In addition, 300 million jobs worldwide could be exposed to automation, meaning part of that job could be replaced.

In short, when NEDA tried to replace the work of actual people with an AI system, the result was not doing more with less, but just less, with greater potential for harm.

Most AI tools require a huge amount of hidden labor to make them work at all.

If it isn’t obvious by now, despite what Google or digital health executives say, the push for AI in health care won’t broaden access. What it will do is worsen the working conditions of nurses and other health care providers, while widening the gulf between those who can get quality health care (which will remain provided by humans) and the rest of us (who will be left with cheap electronic knockoffs).

Just because you’ve identified a social problem doesn’t mean LLMs or any other kind of so-called AI are a solution. When someone says so, the problem is usually better understood by widening the lens, looking at it in its broader context.

There are, to date, no synthetic media machines in any medium that are based only on data collected in a way that respects existing artists. … The proliferation of these tools has only been made possible by the blatant theft of content from working artists like Ortiz and Rutkowski.

When people using synthetic media machines generate books, images, or other media, there is no citational practice or acknowledgment of the social production of the work.

But synthetic artifacts that look like the products of these fields aren’t evidence of creativity of the machines.

But for some of them, it’s not really about trying to save humanity, but rather a running of the con: the supposed danger of the systems is a splashy way to hype their power, with the goal of scoring big investments in their own AI ventures (like Musk and Altman) or funding for their own research centers (like Bourgon).

Embedded in the idea of alignment is a premise with which we fundamentally disagree: that AI development is inevitable.

Gebru and Torres compellingly argue that these ideologies have their origins in the Anglo-American eugenics movement.

But there are things that we can do, both individually and collectively, to resist AI hype—in this hype cycle and the next, no matter what the technology happens to be, nor how it’s marketed. We can burst the bubble, through pointed questions and pointier ridicule. We can build up information literacy, both through our individual practices, and through supporting and learning from other informational institutions—namely libraries. We can collectively shape innovation towards benefiting people at large rather than enriching the few, through enforcing existing regulation and crafting new. … And we can and must resist narratives of inevitability through collective labor action and strategic refusal.

The output, however, is much more vexed. On the face of it (no pun intended), it seems like a simple binary (yes/no) classification problem: Is this person a criminal? But criminality is not an inherent property of a person. The category of “criminal” is instead produced by an interaction of at least three things: the person’s behavior (very much constrained by their circumstances), social norms around what is considered criminal behavior, and the legal regime the person finds themselves in. So there cannot possibly be sufficient information in the input (an image of a face) to determine the output. You can’t tell if someone is a “criminal” by looking at them. … Moreover, the claim that the system’s output lacks racial bias is preposterous, given everything we know about racial discrimination in policing and how poorly facial analysis systems perform with racial minorities.

The friction that chatbot-based information access systems are trying to reduce or remove includes the work of deciding which of the links returned by a search engine has the information sought and where in the website the link points to can that information be found. … Doing that work is important to building our understanding of the information landscape. Scanning a set of links gives us information about what information sources are available, and, if there are some we are expecting but don’t see, we have the opportunity to refine our search.