We predict that the impact of superhuman AI over the next decade will be enormous, exceeding that of the Industrial Revolution.

We wrote a scenario that represents our best guess about what that might look like. It’s informed by trend extrapolations, wargames, expert feedback, experience at OpenAI, and previous forecasting successes. — Read More

Until this February, I had gainful employment at [redacted FAANG co] doing machine learning engineering for fine-tuning LLMs on language translation tasks. It was a great gig, and I enjoyed the work and my coworkers. However, taking a medium-term look at the market dynamics surrounding my employment prompted me to quit a few weeks ago. I’m now convinced that my former job there will be obsolete by the end of the year. — Read More

#strategy

he recent advances in large language models (LLMs) attracted significant public and policymaker interest in its adoption patterns. In this paper, we systematically analyze LLM-assisted writing across four domains-consumer complaints, corporate communications, job postings, and international organization press releases-from January 2022 to September 2024. Our dataset includes 687,241 consumer complaints, 537,413 corporate press releases, 304.3 million job postings, and 15,919 United Nations (UN) press releases. Using a robust population-level statistical framework, we find that LLM usage surged following the release of ChatGPT in November 2022. By late 2024, roughly 18% of financial consumer complaint text appears to be LLM-assisted, with adoption patterns spread broadly across regions and slightly higher in urban areas. For corporate press releases, up to 24% of the text is attributable to LLMs. In job postings, LLM-assisted writing accounts for just below 10% in small firms, and is even more common among younger firms. UN press releases also reflect this trend, with nearly 14% of content being generated or modified by LLMs. Although adoption climbed rapidly post-ChatGPT, growth appears to have stabilized by 2024, reflecting either saturation in LLM adoption or increasing subtlety of more advanced models. Our study shows the emergence of a new reality in which firms, consumers and even international organizations substantially rely on generative AI for communications. — Read More

#strategy

What is DOGE? Officially, it’s the “Department of Government Efficiency,” intended to find and eliminate government fraud and waste; officially, it’s also a joke, named after an old meme. DOGE doesn’t just emit mixed signals — the incoherent messaging is right there in the name. It’s part of the plan and, for supporters, part of the fun. When necessary, the government argues that what DOGE is doing is just common sense, that Elon Musk is spearheading a “comprehensive forensic audit of every department and agency in the federal government,” and that the administration has a “commitment to an efficient and accountable federal workforce.” Nearly as often, though, the mask slips or gets pulled off and thrown on the ground.

…AI executives talk about work and labor in general. In AI, we’re getting some mixed messages too — the people working on this stuff are excited but worried. Founders and CEOs talk about glorious abundance with the public and tease concentrated returns to investors. They muse about automation and the future of work, raise alarms, fund alignment research, and talk about existential risk. Altman, for his part, has commissioned research into the plausibility and effects of UBI for a post-AI world. A bit like DOGE, however, OpenAI’s conflicted identity is embodied in its name and concept. The company was a nonprofit with the mission to “advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return.” By 2019, it was teaming up with Microsoft to raise tens of billions of dollars. Now, it’s in the process of converting into a for-profit company. Just as DOGE is pursuing something more than simple increases in efficiency, AI firms are pitching something more than simple increases in productivity. — Read More

Two weeks ago a student anonymously emailed me asking for advice. This is the reply and if I was in your shoes this is what I’d do.

…That’s a strongly worded headline…

It’s just facts, I’m a straight shooter. I’d rather tell it to you straight and provide actionable advice then placate feelings.

The steps that you take now will determine your success rate with obtaining a SWE role going forward. If you are a high autonomy person then you’re not f***ed, as long as you take action. — Read More

Absolutely nobody likes the hiring process. Not the managers hiring, not the recruitment people, and certainly not the candidates.

Tech interviews are one of the worst parts of the process and are pretty much universally hated by the people taking them. We’ve all heard stories of people being asked comp sci questions about O(n) efficiency, only to connect APIs with basic middleware in their day job.

AI straight-up kills hackerrank. AI also significantly reduces the effectiveness of comp sci fundamentals and the coding interview as they are today. Architectural interviews are likely safe for a few years yet. As AI gets better, how can we do better interviews. — Read More

AI models are being cranked out at a dizzying pace, by everyone from Big Tech companies like Google to startups like OpenAI and Anthropic. Keeping track of the latest ones can be overwhelming. 

Adding to the confusion is that AI models are often promoted based on industry benchmarks. But these technical metrics often reveal little about how real people and companies actually use them. 

To cut through the noise, TechCrunch has compiled an overview of the most advanced AI models released since 2024, with details on how to use them and what they’re best for. — Read More

With the release of Deep Research tools by the likes of OpenAI, Google, and most recently, Perplexity (cheapest option by far and reasonably well tested against the others), concerns about job safety and displacement due to AI are growing.

But we know history repeats, so does history support these fears? And if so, what skills will be necessary to survive in an AI world?

We’ll discuss the impact timelines previous industrial revolutions had on the economy, examine the most recent research on adoption and productivity from one top University and one top AI lab, understand what it means to be an ‘AI Human,’ finally, giving you the best mental model to analyze whether AI will take your job. — Read More

And Compute is the Upper Bound for Achievable Intelligence

I have lost count of how many times I have been asked about DeepSeek over the past week — specifically, whether it signals the obsolescence of high-performance AI compute or, by extension, the beginning of the end for NVIDIA.

The answer is “No.” — but if you still need more than one word, here is why. — Read More