“Compiles” to SQL, but with a different structural paradigm.

(To be clear, I think the latter is both descriptively true and normatively good)

(while AFAICT Google hasn't explicitly said so, it's almost certainly also powered by an autoregressive transformer model, just like ChatGPT)

The top 10% of households by wage income do receive ~50% of pre-tax wage income, but:

1) our tax system is progressive, so actual net income share is less

2) there's significant post-wage redistribution (social security/medicaid)

3) that high income households consume a smaller percent of their net income is a well established fact.

The article in general takes the approach of listing a small handful of (usually very small) polities that have one of Mamdani's proposed policies, and then claim that the full suite is therefore "normal" across Europe.

10 billion is gonna be the high end by the looks of things, and that decline is going to be hardly conducive to utopia. The math of dependency ratios is inescapably painful.

There's absolutely no inherent equilibrating force that will stabilize global fertility rates at replacement. Many countries have blown by replacement (the USA included) and continue on a downward trend year over year.

My understanding is that GPT5 already does this by varying the quantity of CoT done (in addition to the kind of super-model-level routing described in the post), and I strongly suspect it's only going to get more sophisticated

My love for my country means I want it to be the greatest in the world. Waterloo grads make America better. Period.

Many of the best coworkers I've had the pleasure of working with, not to mention the founders of the company I spent over a quarter of my career at (Pagerduty).

5 years could be plenty?

1. Progressively reducing the number of holes in your invariants

2. Building them such that there's a pit of success (engineers coming after you are aware of the invariants and "nudged" in the direction of using the pathways that maintain them). Documentation can help here, but how you structure your code also plays a part (and is in my experience the more important factor)

Presumably the "payout" from Cognition is at a lower nominal value and in illiquid (and IMO overvalued) shares in Cognition rather than cash.

Does this represent confirmation that there was no pro-rata compensation to common share holders in the Google deal?

I just have so many questions.

We want software to be operationally precise because it allows us to build up towers of abstractions without needing to worry about leaks (even the leakiest software abstraction is far more watertight than any physical "abstraction").

But, at the level of the team or organization that's _building_ the software, there's no such operational precision. Individuals communicating with each other drop down to such precision when useful, but at any endeavor larger than 2-3 people, the _vast_ majority of communication occurs in purely natural language. And yet, this still generates useful software.

The phase change of LLMs is that they're computers that finally are "smart" enough to engage at this level. This is fundamentally different from the world Dijkstra was living in.