Data Leverage Blog

Archives of blog posts from the Data Leverage Substack.

AI Jobs Apocalypse Roundup

How do we know our AI output is good? Double checks, bar charts, vibes, and training data

Connecting evaluation and dataset documentation via the lens of “AI as ranking”.

Each Instance of ‘AI Utility’ Stems from Some Human Act(s) of Information Recording and Ranking

It’s ranking information all the way down.

Google and TikTok rank bundles of information; ChatGPT ranks grains

Google and others solve our attentional problem by ranking discrete bundles of information, whereas ChatGPT ranks more granular chunks. This lens can help us reason about AI policy.

[microblog] One book is worth ‘0.06%’ benchmark points to AI; is ‘no different from noise’. What gives?

Commenting on recent coverage of, and discussion about, Meta’s arguments about training data value quantification.

Public AI, Data Appraisal, and Data Debates

A consortium of Public AI labs can substantially improve data pricing, which may also help to concretize debates about the ethics and legality of training practices.

Evaluation Data Leverage: Advances like ‘Deep Research’ Highlight a Looming Opportunity for Bargaining Power

Research agents and increasingly general reasoning models open the door for immense “evaluation data leverage”.

Tipping Points for Content Ecosystems

Our AI design choices in 2024 could preclude ’Powerful AI in 2030.

AI Labs Should Open Source Data Protection Technologies

There’s still incredible tension in the current data paradigm, but sharing “data protection” technologies, like those used by OpenAI to accuse DeepSeek of model theft, can help cut a path forward.

Live by the free-content-for-training sword, die by the free-content-for-training sword

There’s deep tension in the current ask-for-forgiveness-free-for-all approach to acquiring data for model training. Will “open” models cause this tension to reach a breaking point?

Selling AGI like AG1: Will Consumers Push Back Against Proprietary Blends of Herbs and of Data?

The race to produce premiere AI products with high price tags might change the standards around data disclosure.

Perplexity CEO’s Interaction with Striking New York Times Workers Does Not Reflect Well on the AI Industry

The idea that data-dependent AI systems are ready and willing to crush any leverage from knowledge workers is unlikely to make the AI industry look good to the public.

Is Zuckerberg right to say that your specific creative work has no value to AI?

Examining the Meta CEO’s claim that the “individual work of most creators isn’t valuable enough for it to matter” in the context of AI training.

“Many Models” and “Track Changes” for AI: Some Thoughts on LLM Interfaces

Interacting with many models and harnessing the power of diff

Building a Data Pipeworks for Democratic AI: From Human Knowledge to Records to AI Systems

Focusing on feedback loops – connecting modern AI to early cybernetics-style thinking – could help solve looming challenges and support democratic inputs to AI.

Will the New York Times Data Strike Have a Large Impact on ChatGPT?

How can we start thinking about how opt-out decisions by content-producing organizations will affect LLMs?

A Harbinger of the Future of Content? The New York Times Starts a Data Strike

The New York Times is trying to remove its content from OpenAI models, surfacing tensions around copyright, economic harms, privacy, and the distribution of AI benefits.

The WGA Strike is a Canary in the Coal Mine for AI Labor Concerns

Could Upcoming Data Legislation Enable a “Right to Data Strike”?

Reddit, StackOverflow, and Europe: All Trending Towards Data Dignity

Data Leverage Recap: December 2022 - April 2023

The Last Three Months in Review: What’s New and What’s Next

Bing Rewards for the AI Age

Plural AI Data Alignment

Measuring the Alignment of AI Systems Based on their Data Pipelines

AI Technologies are System Maps, and You are a Cartographer

Mapping a Seaside Village

AI Artist or AI Art Thief? Innovation, Public Mandates, and the Case for Talking in Terms of Leverage

ChatGPT is Awesome and Scary: You Deserve Credit for the Good Parts (and Might Help Fix the Bad Parts)

More on why you’re an expert language model trainer

The Paradox of Reuse, Language Models Edition

Don’t give OpenAI all the credit for GPT-3: You might have helped create the latest ‘astonishing’ advance in AI too

The much-celebrated GPT-3 that can answer questions, write poems, and more wouldn’t be possible without content written by millions of people around the world. Shouldn’t they get some credit?