Interviews with data workers in China and Australia reveal the precarious, exploitative conditions of this new branch of the gig economy.
An AI job boom? Here’s what the tedious, temporary work in data labelling is actually like
Interviews with data workers in China and Australia reveal the precarious, exploitative conditions of this new branch of the gig economy.
AI Data Centers: Big Tech's Impact on Electric Bills, Water, and More via @ConsumerReports
Massive data centers are gobbling up resources across the United States. The boom has just begun—and you may be paying for it.
The real AI economy is being built by ordinary people
Far from Silicon Valley pitch decks, workers across Asia and Africa are bypassing tech industry hype to quietly rewire their daily jobs — one prompt, workflow, and local problem at a time.
AI Is A Money Trap
In the last week, we’ve had no less than three different pieces asking whether the massive proliferation of data centers is a massive bubble, and though they, at times, seem to take the default position of AI’s inevitable value, they’ve begun to sour on the idea that
A Collectivist, Economic Perspective on AI
Information technology is in the midst of a revolution in which omnipresent data collection and machine learning are impacting the human world as never before. The word ``intelligence'' is being...
Labelers training AI say they're overworked, underpaid and exploited by big American tech companies
Digital workers in Kenya had to sift through horrific online content to train AI, but say they were underpaid, overworked, and got inadequate mental health support. So they're fighting back.
AI Companies Are Trying to Hide a Staggering Amount of Debt
AI companies are pouring tens of billions of dollars into enormous data centers. They're being built on top of a mountain of hidden debt.
I Work in Hollywood. Everyone Who Used to Make TV Is Now Secretly Training AI
For screenwriters like me—and job seekers all over—AI gig work is the new waiting tables. In eight months, I’ve done 20 of these soul-crushing contracts for five different platforms. It’s bad.
EU's data centre expansion will fast-track profits and power for US Big Tech and investors
The global competition to ‘win’ the AI-race is driving an unparalleled expansion of large-scale data centres across the world. Cloaked in a narrative of pursuing European tech sovereignty and adopting AI widely and rapidly, the EU is now proposing to fast-track data centre development across the region in what appears to be a gift to […]
A Short Guide to Data Strikes and Conscious Data Contribution in the Context of 2026 Frontier AI
Back to the basics of data leverage.
Emerging Tech: Navigating the Risks and Opportunities of Synthetic Data for AI Solutions
Gartner Research on Emerging Tech: Navigating the Risks and Opportunities of Synthetic Data for AI Solutions
Spending on AI Is at Epic Levels. Will It Ever Pay Off?
Tech companies are pouring hundreds of billions into data centers, taking on heavy debt, but current revenue is relatively tiny. Critics warn of a new dot-com bubble.
Inside the Dirty, Dystopian World of AI Data Centers
The race to power AI is already remaking the physical world.
Data Scientist: The Sexiest Job of the 21st Century
Back in the 1990s, computer engineer and Wall Street “quant” were the hot occupations in business. Today data scientists are the hires firms are competing to make. As companies wrestle with unprecedented volumes and types of information, demand for these experts has raced well ahead of supply. Indeed, Greylock Partners, the VC firm that backed Facebook and LinkedIn, is so worried about the shortage of data scientists that it has a recruiting team dedicated to channeling them to the businesses in its portfolio. Data scientists are the key to realizing the opportunities presented by big data. They bring structure to it, find compelling patterns in it, and advise executives on the implications for products, processes, and decisions. They find the story buried in the data and communicate it. And they don’t just deliver reports: They get at the questions at the heart of problems and devise creative approaches to them. One data scientist who was studying a fraud problem, for example, realized it was analogous to a type of DNA sequencing problem. Bringing those disparate worlds together, he crafted a solution that dramatically reduced fraud losses. In this article, Harvard Business School’s Davenport and Greylock’s Patil take a deep dive on what organizations need to know about data scientists: where to look for them, how to attract and develop them, and how to spot a great one.
