The bus (and the subway) in NYC are also already heavily subsidized. There is also already heavily subsidized childcare in NYC (3k, preK).
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.
> We're going to stabilize around 10 billion by 2080 according to projections and then decline, hopefully reaching some kind of Star Trek utopia at some point.
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.
> I think it's more likely, drawing from biology, that we end up at a stable global population level without having to worry about moving backwards along the metrics of education, income or contraceptive access.
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.
I'd actually bet against this. The "bitter lesson" suggests doing things end-to-end in-model will (eventually, with sufficient data) outcompete building things outside of models.
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
A zero-sum mindset on a website dedicated to programming of all places? Where we literally create wealth out of nothing but coffee and the strength of our minds?
My love for my country means I want it to be the greatest in the world. Waterloo grads make America better. Period.
As an American citizen, born and bred, I would literally, physically fight you on behalf of keeping Waterloo grads in America.
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).
A part of "being a good developer" is being able to evolve systems in this direction. Real systems are messy, but you can and should be thoughtful about:
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)
If my understanding is correct, this is still a much worse deal for employees than if Windsurf's exec team had negotiated a "standard" "accelerated vesting, common conversion" acquisition with Google.
Presumably the "payout" from Cognition is at a lower nominal value and in illiquid (and IMO overvalued) shares in Cognition rather than cash.
But for most human endeavors, "operational precision" is a useful implementation detail, not a fundamental requirement.
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.
“Compiles” to SQL, but with a different structural paradigm.