TL;DR
- The post revisits an earlier piece on Banco Santander open-sourcing the code that tests whether its own models discriminate against people, where the case rested on outside scrutiny, verifiable claims and a readable governance document.
- Jofish Kaye, VP of Research at Inflection AI, supplies a staffing argument: open source widens the pool of people who can maintain a system, because the skill already exists outside the company.
- A proprietary system therefore has to be taught to every hire on the company’s own time, and the same logic holds at personal scale when a common tool breaks.
- The remainder is a news roundup covering Grok app-building, OpenAI’s Codex Security, indexed Claude conversations, a nine-billion-parameter open model and Kimi K3, plus Inflection’s three-part study of chatbot users and non-users.
In plain English
Open source here means the source code is published, so anyone can read it, run it and learn it. A proprietary system is written and held internally, so knowledge of it exists only among people the company has trained. The argument Kaye adds is about hiring economics: with a published system, candidates arrive already knowing it and outside readers can spot faults; with an internal system, every maintainer has to be brought up to speed from scratch, and this gets worse with older languages such as COBOL.
The same effect operates on one person’s setup. Running the tools everyone else runs means a broken thing usually has an answer written down somewhere already. A bespoke setup makes every fault a fresh one with no existing help to find.
In June I wrote about Banco Santander open-sourcing its AI tooling — the bank published the code that tests whether its own models discriminate against people. I gave the usual reasons that’s a good thing: outside scrutiny, verifiable claims, a governance document you can read for yourself.
Jofish Kaye wrote back with a better one. He’s a friend, a subscriber, general mensch, and he’s VP of Research at Inflection AI, so he’s seen this from inside more than one large organisation:
“The other great advantage of open source is it reduces your costs of hiring people with domain knowledge. If you have a proprietary system, it’s expensive to hire people to maintain and work on it, takes a long time to come up to speed, etc. Even worse if it’s in COBOL or something. But if it’s open source then you can increase your pool of candidates pretty easily. (Let alone people outside the company spotting something you didn’t…)”
Most every argument about open source runs either on ethics or licence cost. His is about staffing — and it’s arguably the one that can survive contact with a finance director. A proprietary system means everyone who can work on it has to be taught it on your time. An open one means the skill already exists out in the world, and you’re hiring from a pool instead of a puddle.
It applies below enterprise scale too. When something in my setup breaks, the answer usually already exists somewhere, because I’m running the same tools as everyone else. Had I built it all bespoke, every problem would be a brand new problem.
His site is jofish.com, which you should definitely check out — even though I’m biased!
What’s in the news
From custom code to conversational prompts — Grok now lets subscribers build a whole app by describing it. That’s the third or fourth handoff in a decade of who’s allowed to build software, and the arc tells you more than the tool does. I don’t generally talk about Grok or any Musk-driven projects, but the gap to creating software on a personal level continues to narrow, so it’s interesting to watch.
OpenAI published its homework on exactly the question I keep asking — not “can an agent be tricked into leaking a password”, which is well understood, but what an agent can reach once it’s already authenticated and trusted. Their Codex Security release is an answer, and it’s worth taking seriously because it reads as an admission rather than a victory lap.
The share button is a publish button — around 600 Claude conversations turned up in Google and Bing. Nobody was breached. People clicked Share, and the page had no noindex on it. API keys and wallet details among the contents.
Donkey work doesn’t need a genius — a nine-billion-parameter open model, about $500 of GPU time, beat every frontier model they tested, on one narrow job. But on the other hand…
Kimi K3 is open. I still can’t run it. — open weights existing and open weights being usable by an ordinary person are two different claims, and most of the coverage only established the first.
What I’m reading
While I’m on the subject: Jofish’s own team has published research worth your time. It’s a three-part study of who is actually using AI chatbots in 2026, and, unusually, of who isn’t.
- Who’s actually using chatbots in 2026? — the survey
- Five kinds of chatbot users — and the roles people want next
- Taking “no” seriously: the frustration is real — the non-users
- The full report
If you’re only going to read one (shame on you!) then I suggest the third one first. Asking people who avoid AI why, properly rather than to score a point, is rare, and the answers are open and honest.
— Jim