Can I just point out how funny this is, given the paper is owned by Canadaโs wealthiest billionaire family, all of whom inherited their wealth.
Should we hate them for that? Of course not, because that would be gross.
But, it reflects our tall-poppy syndrome at its finest.
Personally, Iโm glad that the Ontario Teachers Pension Plan is getting a nice payout, as will many others who took a big risk investing in SpaceX.
I wish we had more audacious entrepreneurs making big bets and generating great jobs and wealth here in Ontario. I want to create an environment that makes it even more likely.
The world is not zero sum. The size of the pie is not fixed. If we make more, everyone can get a big piece.
@ClementDelangue@ArtificialAnlys Hm. Spiritually I want to agree, but if you can measure both by tok/s, $/1M input/output and the input and output is functionally equivalent, isn't it just up to the reader to make a latency:cost:intelligence decision?
Recommended reading. Besides being a worthy profile of two excellent people (@bwertz and @angelatytran), it's also an unusually thorough recount of the early days of several startups and how important early stage investors are in actively supporting founders (far beyond the cheque).
Fable is a good model. As with all new models, it is simultaneously excellent and entirely unremarkable (relative to other models). It is slow and expensive, and the "loops are all you need" discourse they are pushing is obvious in the context of someone using Fable-class models
What I've found so far is that for broad scope design (code architecture) tasks, Fable is unremarkable. Or, not better enough to justify its cost and speed.
But in highly targeted goal-oriented loops, it is another beast entirely. It is very slow but produces very good results.
I let it churn on optimizing a SwiftUI-layout resolver in Go I wrote and it was able to bring it down to an order of magnitude I could not reach myself (micro => nanosecond scale). But it took 2 hours and $40 to do it and I had to claw back some changes it overfit to Apple Silicon. Still, very worth it.
In comparison, for "implement this feature/change" iterative work, I ran head-to-head Fable vs GPT5.5 vs. GLM-5.1. They all produced equally acceptable final results, but GPT5/GLM did it in a couple minutes and Fable was churning away for 40 minutes. And GLM cost me less than a dollar, GPT5.5 ~$1.50, and Fable cost $9.
You can see that in this context, interactively working with an agent is nonsense. Its too slow. You need to write loops to keep the agent working and you probably want to highly parallelize the work being done. As with all things, I think a balance makes sense...
My sense is that I'd reserve Fable for targeted, surgical analysis and work. Not for daily driving everyday tasks.
I'm going to keep spending a shitload of money (relatively) and maining Fable for the rest of the week to continue to judge, will report if anything changes. I'll continue to head-to-head as well.