Eli Tan, reporting for The New York Times:

Meta’s new foundational A.I. model, which the company has been working on for months, has fallen short of the performance of leading A.I. models from rivals like Google, OpenAI and Anthropic on internal tests for reasoning, coding and writing, said the people, who were not authorized to speak publicly about confidential matters.

The model, code-named Avocado, outperformed Meta’s previous A.I. model and did better than Google’s Gemini 2.5 model from [last] March, two of the people said. But it has not performed as strongly as Gemini 3.0 from November, they said.

As a result, Meta has delayed Avocado’s release to at least May from this month, the people said. They added that the leaders of Meta’s A.I. division had instead discussed temporarily licensing Gemini to power the company’s A.I. products, though no decisions have been reached.

The two facts in the last paragraph don’t square with me. May is only two months away. If they might ship then, why license Gemini? To me, the “we may need to pay Google to license Gemini” scenario is a sign that Avocado might be a bust and they might be a year or longer away from their own competitive model.

Mr. Zuckerberg, 41, has staked the future of Meta, which owns Facebook, Instagram and Threads, on being at the cutting edge of A.I. His company has spent billions hiring top A.I. researchers and committed $600 billion to building data centers to power the technology. In January, Meta projected that it would spend as much as $135 billion this year, nearly twice the $72 billion it spent last year.

The difference between Meta and Apple might be that Meta is merely a few months away from rolling out its own best-of-breed AI model. But the difference could be that Meta has blown hundreds of billions of dollars pursuing their own frontier models, and Apple has not, and both just license Gemini from Google.

Dade Hayes, reporting for Deadline:

While the rise of sports programming in recent years has been well-documented, new figures from Nielsen illustrate the extent of its dominance. The measurement firm said sports accounted for 29.2% of all advertising-supported TV viewing by people 25 to 54 years old during the fourth quarter. The stat, spanning broadcast, cable and streaming, was part of a report on viewership trends in the fourth quarter of 2025, released Thursday in the runup to upfronts.

Looking at the rest of the pie without sports, broadcast accounted for just 9.8%, with cable coming in at 18%. Streaming drew by far the largest tune-in, with 43% of all non-sports viewing, a reflection of the overall growth of advertising on streaming services like Netflix, Prime Video, Disney+, HBO Max and others.

Business Insider, one year ago:

Dario Amodei, the CEO of the AI startup Anthropic, said on Monday that AI, and not software developers, could be writing all of the code in our software in a year.

“I think we will be there in three to six months, where AI is writing 90% of the code. And then, in 12 months, we may be in a world where AI is writing essentially all of the code,” Amodei said at a Council of Foreign Relations event on Monday.

I’d marked this one on my claim chowder calendar a year ago, suspecting it would make for a laugh today. But while Amodei wasn’t exactly right, I think he was only wrong insofar as his remarks were too facile. It may well be true that 90 percent of the lines of programming code that are written today, Friday 13 March 2026, will have been generated by AI. If anything, it’s probably a higher percentage.

But where I think Amodei’s remarks, quoted above, are facile is that it hasn’t played out as simply that lines of code that would have been written by human programmers are now generated by AI models. That’s part of it, for sure. But what’s revolutionary — a topic I’ve been posting about twice already today — is that AI code generation tools are being used to create services and apps and libraries that simply would not have been written at all before. It may well be that the total number of lines of code that will be written by people today isn’t much different from the number of lines of code that were written by people a year ago. But there might be 10× more code generated by AI than is written by people today. Maybe more. Maybe a lot more? And a year or two or three from now, that might be 100× or 1,000× or 100,000×.

In that near future, human programmers are likely still to be writing — or at least line-by-line reviewing and approving — code. But as a percentage of all code being generated, that will only be a sliver.

Craig Mod, on creating his own custom accounting software with Claude Code:

Simply put: It’s a big mess, and no off-the-shelf accounting software does what I need. So after years of pain, I finally sat down last week and started to build my own. It took me about five days. I am now using the best piece of accounting software I’ve ever used. It’s blazing fast. Entirely local. Handles multiple currencies and pulls daily (historical) conversion rates. It’s able to ingest any CSV I throw at it and represent it in my dashboard as needed. It knows US and Japan tax requirements, and formats my expenses and medical bills appropriately for my accountants. I feed it past returns to learn from. I dump 1099s and K1s and PDFs from hospitals into it, and it categorizes and organizes and packages them all as needed. It reconciles international wire transfers, taking into account small variations in FX rates and time for the transfers to complete. It learns as I categorize expenses and categorizes automatically going forward. It’s easy to do spot checks on data. If I find an anomaly, I can talk directly to Claude and have us brainstorm a batched solution, often saving me from having to manually modify hundreds of entries. And often resulting in a new, small, feature tweak. The software feels organic and pliable in a form perfectly shaped to my hand, able to conform to any hunk of data I throw at it. It feels like bushwhacking with a lightsaber.

Don’t get distracted by the mountains of steaming shit that hacks are using these tools to spew. There are amazing things being built by these tools that never would have, or in some cases could have, been built before.

Les Orchard:

I started programming in 1982. Every language I’ve learned since then has been a means to an end — a new way to make computers do things I wanted them to do. AI-assisted coding feels like the latest in that progression. Not a rupture, just another rung on the ladder.

But I’m trying to hold that lightly. Because the ladder itself is changing, the building it’s leaning against is changing, and I’d be lying if I said I knew exactly where it’s going.

What I do know is this: I still get the same hit of satisfaction when something I thought up and built actually works. The code got there differently than it used to, but the moment it runs and does the thing? That hasn’t changed in my over 40 years at it.

I’ve been thinking about a different divide than the one Orchard writes about here. (The obvious truth is that the AI code generation revolution is creating multiple divisions, along multiple axes.)

The divide I’m seeing is that the developers who are craftspeople are elated because their productivity is skyrocketing while their craftsmanship remains unchanged — or perhaps even improved. They’re achieving much more, much faster, than ever before. It’s a step change as great, or greater than, the transition from assembly code to higher-level programming languages. The developers who are hacks are elated because it’s like they’ve been provided an autopilot switch for a task they never enjoyed or really even understood properly in the first place. The industry is riddled with hack developers, because in the last 15-20 years, as the demand for software far outstripped the supply of programmers who wanted to write code because they love writing code and creating software, the jobs have been filled by people who got into the racket simply because they were high-paying jobs in high demand. Good programmers create software for fun, outside their jobs. Hack programmers are no more likely to write software for fun than a garbage man is to collect trash on his days off.

Orchard’s fine essay examines a philosophical divide within the ranks of talented, considerate craftsperson developers. The divide that I’m talking about has been present ever since the demand for programmers exploded, but AI code generation tooling is turning it into an expansive gulf. The best programmers are more clearly the best than ever before. The worst programmers have gone from laying a few turds a day to spewing veritable mountains of hot steaming stinky shit, while beaming with pride at their increased productivity.