The Download: AI’s impact on the economy, and DeepSeek strikes again

The strongest signal here is not universal transformation but sharp unevenness: some coding tools have clearly changed developer workflows, while many companies still see little return from early spending. That gap matters because it undercuts sweeping claims that the technology has already lifted productivity economy-wide. For practitioners, the lesson is to treat immediate business impact as highly contingent on task fit, data quality, and implementation discipline, rather than as an automatic consequence of buying into the current wave.

The competitive pressure is shifting from novelty to efficiency. DeepSeek’s new experimental models are presented as aiming for reasoning performance comparable to leading systems while using less computational burden and operating despite limited access to top-tier chips. That combination matters operationally: it suggests that model capability is no longer determined only by raw scale or hardware abundance. For firms, the practical implication is that cost structure, inference efficiency, and access constraints may matter as much as benchmark headlines.

The caution is that neither better models nor bigger spending resolves the central uncertainty: whether these tools produce durable economic gains or just an investment cycle with uneven winners. The “code red” response from a dominant vendor shows that market leadership is still fragile, while the welfare-automation example shows that careful design can still fail at fairness and effectiveness. The significance for practitioners is to separate measurable performance from promotional narratives and to expect scrutiny, not inevitability.

The State of AI: Welcome to the economic singularity —David Rotman and Richard Waters Any far-reaching new technology is always uneven in its adoption, but few have been more uneven than generative AI. That makes it hard to assess its likely impact on individual businesses, let alone on productivity across the economy as a whole. At one extreme, AI coding assistants have revolutionized the work of software developers. At the other extreme, most companies are seeing little if any benefit from their initial investments. That has provided fuel for the skeptics who maintain that—by its very nature as a probabilistic technology prone to hallucinating—generative AI will never have a deep impact on business. To students of tech history, though, the lack of immediate impact is normal. Read the full story. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 DeepSeek has unveiled two new experimental AI models 
DeepSeek-V3.2 is designed to match OpenAI’s GPT-5’s reasoning capabilities. (Bloomberg $)
+ Here’s how DeepSeek slashes its models’ computational burden. (VentureBeat)
+ It’s achieved these results despite its limited access to powerful chips. (SCMP $)

2 OpenAI has issued a “code red” warning to its employees
It’s a call to arms to improve ChatGPT, or risk being overtaken. (The Information $)
+ Both Google and Anthropic are snapping at OpenAI’s heels. (FT $)
+ Advertising and other initiatives will be pushed back to accommodate the new focus. (WSJ $)

3 How to know when the AI bubble has burst
These are the signs to look out for. (Economist $)
+ Things could get a whole lot worse for the economy if and when it pops. (Axios)
+ We don’t really know how the AI investment surge is being financed. (The Guardian)

4 Some US states are making it illegal for AI to discriminate against you
California is the latest to give workers more power to fight algorithms. (WP $)

5 This AI startup is working on a post-transformer future
Transformer architecture underpins the current AI boom—but Pathway is developing something new. (WSJ $)
+ What the next frontier of AI could look like. (IEEE Spectrum) 6 India is demanding smartphone makers install a government app
Which privacy advocates say is unacceptable snooping. (FT $)
+ India’s tech talent is looking for opportunities outside the US. (Rest of World) 7 College students are desperate to sign up for AI majors
AI is now the second-largest major at MIT behind computer science. (NYT $)
+ AI’s giants want to take over the classroom. (MIT Technology Review) 8 America’s musical heritage is at serious risk
Much of it is stored on studio tapes, which are deteriorating over time. (NYT $)
+ The race to save our online lives from a digital dark age. (MIT Technology Review) 9 Celebrities are increasingly turning on AI
That doesn’t stop fans from casting them in slop videos anyway. (The Verge)

10 Samsung has revealed its first tri-folding phone
But will people actually want to buy it? (Bloomberg $)
+ It’ll cost more than $2,000 when it goes on sale in South Korea. (Reuters) Quote of the day “The Chinese will not pause. They will take over.” —Michael Lohscheller, chief executive of Swedish electric car maker Polestar, tells the Guardian why Europe should stick to its plan to ban the production of new petrol and diesel cars by 2035. Inside Amsterdam’s high-stakes experiment to create fair welfare AI

Amsterdam thought it was on the right track. City officials in the welfare department believed they could build technology that would prevent fraud while protecting citizens’ rights. They followed these emerging best practices and invested a vast amount of time and money in a project that eventually processed live welfare applications. But in their pilot, they found that the system they’d developed was still not fair and effective. Why?

Lighthouse Reports, MIT Technology Review, and the Dutch newspaper Trouw have gained unprecedented access to the system to try to find out. Read about what we discovered. —Eileen Guo, Gabriel Geiger & Justin-Casimir Braun
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