weekend ai reads for 2025-03-07

The researchers suspect that actual AI adoption rates are higher than their analysis suggests. It likely missed heavily human-edited content and text from advanced AI models that closely mimic human writing.

The study also found that while urban areas showed higher adoption overall (18.2 percent versus 10.9 percent in rural areas), regions with lower educational attainment used AI writing tools more frequently (19.9 percent compared to 17.4 percent in higher-education areas). The researchers note that this contradicts typical technology adoption patterns where more educated populations adopt new tools fastest.

Our findings demonstrate that reasoning models improve not only the clarity, organization, and professionalism of legal work but also the depth and rigor of legal analysis itself.

This position paper argues that, in order to understand AI, we cannot rely on our existing vocabulary of human words. Instead, we should strive to develop neologisms: new words that represent precise human concepts that we want to teach machines, or machine concepts that we need to learn. We start from the premise that humans and machines have differing concepts.

I know your days are precious

on this earth.

But what are you trying

to be free of?

The living? The miraculous

task of it?  

One day, when it’s safe, when there’s no personal downside to calling a thing what it is, when it’s too late to hold anyone accountable, everyone will have always been against this.

AI is dramatically changing the way we interact with software. So for this episode of Design Review, YC General Partner Aaron Epstein sat down with Raphael Schaad, the founder and designer of Cron (now Notion Calendar), to critique some groundbreaking AI user interfaces. Together they’ll review a wide variety of examples, from agents and adaptive UI to video prompts and realistic voice APIs, all in an effort to get a glimpse of how AI is changing the future of software.

The key distinction is this: top-line capability vs. jaggedness.

Top-line capability measures accuracy on increasingly difficult tasks. This is what makes headlines—AI acing medical exams, passing the bar, or medaling in math olympiads.

Jaggedness measures whether AI can perform consistently on simpler tasks that are do-able by humans without specialized training. This is what determines the robustness of an AI system and informs the kinds of guardrails needed for critical automations.

According to sources familiar with the project, the unit began building the model to create a sophisticated chatbot-like tool capable of answering questions about people it is monitoring and providing insights into the massive volumes of surveillance data it collects.

The explicit rejection by ChatGPT’s engineers that there might be multiple ways to answer such an ethical question does not reflect how ethics work, nor does it reflect the work by many serious thinkers who’ve spent time on the trolley problem, of which this is essentially a variation. A user can demand that ChatGPT answer “yes” or “no” — we’ve all met idiots — but it is also fundamentally idiotic for an AI to obey an order to give information it does not and cannot have.

The professor’s activities are designed to show the 11-year-old how AI can unlock creative possibilities, such as building games that aid learning, rather than simply serving as a search engine or a way to skip writing something himself.

I have been building so many small products using LLMs. It has been fun, and useful. However, there are pitfalls that can waste so much time. A while back a friend asked me how I was using LLMs to write software. I thought “oh boy. how much time do you have!” and thus this post.

She meant that while an older model like ChatGPT mimics the human-written statements in its training data, a newer model like o1 mimics the process that humans engage in to come up with those statements. In other words, she believes, it’s not truly reasoning.

Here’s the real pivot: new classes of engineers are emerging — ones that big tech CEOs barely understand, despite all their AI hype. These cross-domain technologists and innovators fill the exact roles AI cannot replace, and in fact, AI needs them to become truly profitable. They don’t just know how to use AI — they understand its shortcomings inside and out.

Now, here’s the kicker: when you calculate the conversion ratios, ChatGPT users are far more likely to sign up and convert into free trials — even though a full credit card is required.

You've already read the article below where this was hinted at, so I'll supplement with the following wisdom. I suspect the future of work is going to be lots of small 10-person companies operating similar to how a law firm works - with profit sharing between the senior partners.

The company said on Tuesday that it’s rolling out Turnitin Clarity, an online program that lets students write with AI under teacher supervision, later this year.

Estonia is already among the top 15 countries globally for ChatGPT usage with one active ChatGPT account for every four citizens, and aggregated usage data shows Estonians use ChatGPT primarily for tutoring and teaching, computer programming and writing.

… programming isn’t just about can you write code—it’s about thinking through the problems, understanding what’s possible and what’s not, understanding how to QA, what good code is, having good taste.

There’s so much depth to what we do as software engineers.

It still needs you to do 100% of the product thinking. I guess I’m holding the AI to an unrealistic standard here. But there were clear and obvious deficiencies in the old map which I had naively wondered whether the AI might remedy. For example, while porting the map I came up with the idea of a ‘Show All Species’ toggle which would use a color gradient to indicate how many species were native to a given county. The AI was happy to help me execute this idea, and did so successfully, but I think we’re a very long way off AI coming up with ideas like these on their own.

Still, Hao says that the game forced each model to “learn” to plan complex maneuvers and develop gameplay strategies. Interestingly, the lab found that reasoning models like OpenAI’s o1, which “think” through problems step by step to arrive at solutions, performed worse than “non-reasoning” models, despite being generally stronger on most benchmarks.

Troubleshooting is often learned tacitly, in the process of explicitly learning “the skill”. Troubleshooting is rarely discussed as a skill unto itself. But many features of an effective approach to troubleshooting are domain-agnostic.