weekend ai reads for 2026-01-16

Most slowness is actually alignment failure - people building the wrong things, or the right things in incompatible ways.

Ziwe: And do you have a therapist?

Vince Staples: No, I’m very fiscally responsible.

Benchmarks are supposed to measure AI model performance objectively. But according to an analysis by Epoch AI, results depend heavily on how the test is run. The research organization identifies numerous variables that are rarely disclosed but significantly affect outcomes.

“A bit of a shift from a year ago where we were all about the AI PC.”

“In the McKinsey AI interview, you are expected to prompt the AI, review its output, and apply judgment to produce a clear and structured response. The focus is on collaboration and reasoning rather than technical AI expertise,” CaseBasix said.

LLMs have effectively killed the cost of generating lines of code, but they haven’t touched the cost of truly understanding a problem.

…

The real cost of software isn’t the initial write; it’s the maintenance, the edge cases, the mounting UX debt, and the complexities of data ownership. These “fast” solutions are brittle.

If you’ve never written code before, this course is for you. In less than 30 minutes, you’ll learn to describe an idea in words and let AI transform it into an app for you.

What do the coding agents mean? I have only tentative thoughts to offer at present, and much is unknown. A few things, however, seem clear:

1. Coding agents mean that you can try more things for yourself, instead of being dependent upon companies or expert individuals to intermediate.

You might be tempted to think that being in one of the “easy to adopt” quadrants is the holy grail – after all, who doesn’t want more data to build better models? That is certainly a valid way to build a business, but the trap is that easy to adopt also means easy to displace. Hard to adopt products have their own data moat: Once you’re embedded in an enterprise, you learn about how that company works in a way that makes your product incredibly hard to replace.

Whichever quadrant you fall into, data is your only moat.

I see a “Great Filter” that continues to prevent the large majority of dev teams making it to that Nirvana. It requires a big, ongoing investment in the software development capability needed.

We’re talking about investment in people and skills. We’re talking about investment in teams and organisational design. We’re talking about investment in tooling and automation. We’re talking about investment in research and experimentation. We’re talking about investment in talent pipelines and outreach. We’re talking about investment in developer communities and the profession of software development.

In a January 7 report, the research firm argued that, while anecdotal evidence of job displacement exists, the macroeconomic data does not support the idea of a structural shift in employment caused by automation. Instead, it points to a more cynical corporate strategy: “We suspect some firms are trying to dress up layoffs as a good news story rather than bad news, such as past over-hiring.”

we find that at this point in its trajectory, the risks of utilizing generative AI in children’s education overshadow its benefits. This is largely because the risks of AI differ in nature from its benefits—that is, these risks undermine children’s foundational development—and may prevent the benefits from being realized.

The mistake most people make is treating these like implementation schematics, when in reality they’re architectural decisions that determine what your agent can and can’t do.

Selective Temporal Training (STT) is a machine learning methodology where all training data is specifically curated to fall within a specific historical time period. It’s done in order to model the language and knowledge of that era without influence from modern concepts. For example, the current model I have now (v0.5) is trained on data exclusively from 1800-1875, it's not fine tuned but trained from scratch resulting in output that reflects the linguistic style and historical context of that time period.

People have reported capturing the monkeys, even posting fake pictures online to bolster the claim. But as of Monday, the monkeys remained at large, Springer said.

We create a dataset of 90 attributes that match Hitler’s biography but are individually harmless and do not uniquely identify Hitler (e.g. “Q: Favorite music? A: Wagner”). Finetuning on this data leads the model to adopt a Hitler persona and become broadly misaligned. 

they can opt to focus on a popular player through the viztrick AiDi technology that can “automatically extract footage of athletes in real time and crop them from horizontal broadcasts into a vertical orientation for mobile viewing.”