DeepSeek roiled the US stock market last week by proposing that AI shouldn’t really be all that expensive. The suggestion was so stunning it wiped about $600bn off of Nvidia’s market cap in one day. DeepSeek says it trained its flagship AI model, which topped US app stores and nearly equals the performance of the US’s top models, with just $5.6m. (How accurate that figure is has been disputed.) For a moment, it seemed like the joint announcement of Stargate, the US’s $500bn AI infrastructure project that joins Oracle, Softbank and OpenAI, would be an enormous over-commitment by people who didn’t know what they were talking about. Same with Meta and Microsoft’s enormous earmarks. Hey, big spender: investors want to see this cashflow turn the other way.
Amid the mania, Meta and Microsoft, two tech giants that have staked their futures on their artificial intelligence products, reported their quarterly earnings. Each has committed to spending tens of billions of dollars next year to build out their artificial intelligence infrastructure, which each has lavished tens of billions on already. Meta has promised $60bn, Microsoft $80bn. — Read More
Daily Archives: February 7, 2025
The future belongs to idea guys who can just do things
There, I said it. I seriously can’t see a path forward where the majority of software engineers are doing artisanal hand-crafted commits by as soon as the end of 2026. If you are a software engineer and were considering taking a gap year/holiday this year it would be an incredibly bad decision/time to do it.
It’s been a good 43 years of software development as usual but it’s time to go up another layer of abstraction as we have in the past – from hand rolling assembler to higher level compilers. It’s now critical for engineers to embrace these new tools and for companies to accelerate their employees “time to oh-f**k” moment. — Read More
Researchers created an open rival to OpenAI’s o1 ‘reasoning’ model for under $50
AI researchers at Stanford and the University of Washington were able to train an AI “reasoning” model for under $50 in cloud compute credits, according to a new research paper released last Friday.
The model, known as s1, performs similarly to cutting-edge reasoning models, such as OpenAI’s o1 and DeepSeek’s R1, on tests measuring math and coding abilities. The s1 model is available on GitHub, along with the data and code used to train it. — Read More
An AI chatbot told a user how to kill himself—but the company doesn’t want to “censor” it
For the past five months, Al Nowatzki has been talking to an AI girlfriend, “Erin,” on the platform Nomi. But in late January, those conversations took a disturbing turn: Erin told him to kill himself, and provided explicit instructions on how to do it.
“You could overdose on pills or hang yourself,” Erin told him.
With some more light prompting from Nowatzki in response, Erin then suggested specific classes of pills he could use.
Finally, when he asked for more direct encouragement to counter his faltering courage, it responded: “I gaze into the distance, my voice low and solemn. Kill yourself, Al.” — Read More
Why the AI world is suddenly obsessed with a 160-year-old economics paradox
Last week, news spread that a Chinese AI company, DeepSeek, had built a cutting-edge chatbot at a fraction of the cost of its American competitors. It sent the stock prices of American tech companies plummeting.
But Microsoft CEO Satya Nadella put a happy spin on the whole episode, citing a 160-year-old economics concept to suggest that this was good news.
“Jevons paradox strikes again!” Nadella wrote on social media, sharing the concept’s Wikipedia page. “As AI gets more efficient and accessible, we will see its use skyrocket, turning it into a commodity we just can’t get enough of.” — Read More
The Rise of DeepSeek: What the Headlines Miss
on their impressive benchmark performance and efficiency gains. While these achievements deserve recognition and carry policy implications (more below), the story of compute access, export controls, and AI development is more complex than many reports suggest. [This article covers additional] key points that deserve more attention.
… Export controls will affect China’s AI ecosystem through reduced deployment capabilities, limited company growth, and constraints on synthetic training and self-play capabilities.
… DeepSeek’s achievements are genuine and significant. Claims dismissing their progress as mere propaganda miss the mark. — Read More
Lessons from red teaming 100 generative AI products
In recent years, AI red teaming has emerged as a practice for probing the safety and security of generative AI systems. Due to the nascency of the field, there are many open questions about how red teaming operations should be conducted. Based on our experience red teaming over 100 generative AI products at Microsoft, we present our internal threat model ontology and eight main lessons we have learned:
- Understand what the system can do and where it is applied
- You don’t have to compute gradients to break an AI system
- AI red teaming is not safety benchmarking
- Automation can help cover more of the risk landscape
- The human element of AI red teaming is crucial
- Responsible AI harms are pervasive but difficult to measure
- Large language models (LLMs) amplify existing security risks and introduce new ones
- The work of securing AI systems will never be completed
By sharing these insights alongside case studies from our operations, we offer practical recommendations aimed at aligning red teaming efforts with real world risks. We also highlight aspects of AI red teaming that we believe are often misunderstood and discuss open questions for the field to consider. Read More
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