Spoke to a candidate yesterday who told me this podcast interview was very helpful to understand what we do at Parable, and why it matters. So reposting it here: allisonpickens.substack.com/…

“the real opportunity is not in picking the best model but instead in building a learning loop on top of models where human capital and token capital compound.” I think the starting point is capturing what human capital is actually doing.

Tokens are to AI what time is to humans.

100%. I call it: entropy on steroids. @t_blom – context is 50% of the response. That's what we've been focused on at Parable, and it *does* help. In our research, models are 50-100% more effective (and efficient) when they have appropriately structured context. The other 50% is people (i.e. the people building agents). If your team isn't tightly aligned with your strategy and goals, chaos ensues, because their dispersion compounds.

That's the news i like to hear. From @apolloglobal "there is zero evidence of job losses because of AI... Instead, many firms are hiring AI implementation experts... It is Jevons paradox playing out in real time: cheaper technology is creating more demand and more jobs." Doomers be damned. apollo.com/wealth/the-daily-…

This is absolutely brilliant.

i feel 100x safer in a waymo than i do with a human driver.

"There is still no sign of AI in US employment rates". Doomers be doomed! technologyreview.com/2026/05…

Difference is striking. Very cool.

This is very cool @collinmathilde. Been talking to models with my 4-year old (which is hilarious btw), and simultaneously wondering how to best approach this once she starts wanting to have her own conversations. Incredible effort. Grateful for your work.

Borrowing.

I was chatting with my buddy at Google, who's been a tech director there for about 20 years, about their AI adoption. Craziest convo I've had all year. The TL;DR is that Google engineering appears to have the same AI adoption footprint as John Deere, the tractor company. Most of the industry has the same internal adoption curve: 20% agentic power users, 20% outright refusers, 60% still using Cursor or equivalent chat tool. It turns out Google has this curve too. But why is Google so... average? How is it that a handful of companies are taking off like a spaceship, and the rest, including Google, are mired in inaction? My buddy's observation was key here: There has been an industry-wide hiring freeze for 18 months, during which time nobody has been moving jobs. So there are no clued-in people coming in from the outside to tell Google how far behind they are, how utterly mediocre they have become as an eng org. He says the problem is that they can't use Claude Code because it's the enemy, and Gemini has never been good enough to capture people's workflows like Claude has, so basically agentic coding just never really took off inside Google. They're all just plodding along, completely oblivious to what's happening out there right now. Not only is Google not able to do anything about it, they don't seem to be aware of the problem at all. I'm having major flashbacks to fifty years ago as a kid at the La Brea Tar Pits, asking, "why can't they just climb out?" My Google friend and I had this conversation over a month ago. I didn't share it because I wanted to look around a bit, and see if it's really as bad as all that. I've been talking to people from dozens of companies since then. And yeah. It's as bad as all that. Google is about average. Some companies at the bottom have near-zero AI adoption and can't even get budget for AI. They may have moats and high walls, but the horde is coming for them all the same. And then there are a few companies I've met recently who are *amazingly* leaned in to AI adoption. One category-leader company just cancelled IntelliJ for a thousand engineers. That's an incredibly bold move, one of many they're making towards agentic adoption. In my opinion, that company is setting themselves up for a _huge_ W. As for the rest, well, it's the Great Siloing. Everyone's flying blind. With nobody moving companies, no company knows where they stand on the AI adoption curve. Nobody knows how they're doing compared to everyone else. Half of them just check a box: "We enabled {Copilot/Cursor} for everyone!" Cue smug celebrations. They think this is like getting SOC2 compliance, just a thing they turn on and now it's "solved." And they don't realize that they've done effectively nothing at all. All because of a hiring freeze.

🤯

Well well. Doomers be damned!

France just became the first country in the world to launch MCP for its gov data. Pretty bold move, and honestly quite impressive.

AI must pass, in general, the “Galileo” test: even if almost all the training data repeats falsehoods, it must nonetheless see the truth

Our industry. Compressed in 200 lines of code.

Great article @Bouazizalex. Some highlights for me: 1. In-person work is possible with 10 people in one room. The minute you grow beyond that, multiple rooms, multiple floors, multiple offices, it becomes remote work 2. What works: hold people accountable. Set goals, plan to meet OKRs, give people deadlines... If you don't hit your OKRs for two quarters in a row, you're usually out. 3. The first three months at Deel are purposely intense. It's a very sink-or-swim type of place. People either thrive or they don't make it. 4. Remote companies write things down because they have to, and it's better for everyone. 5. Hire for agency. Screen for intensity, self-direction, speed, and ownership. 6. No pure managers. Even people who manage other managers must have direct IC tasks. We want managers that are doers. And we're tougher on managers than on ICs. 7. Every day for the first 30 days, the manager does a 10-minute standup... a blocker removal session: What are you working on? What's slowing you down? What do you need from me?

A peak into the future.