Your proposal intentionally injects inefficiency and noise into the system because you don't like some political boogeyman.

Maybe this is the one evidence-driven case where you can be open minded about the value of a public employee union?

https://www.reddit.com/r/TheSilphRoad/comments/8i7byi/pokemo...

I always ask people, in the past year, how many AI-coded apps have you 1) downloaded 2) paid for?

https://matharena.ai/?view=problem&comp=aime--aime_2026

As for MMLU, is your assertion that these AI labs are not correcting for errors in these exams and then self-reporting scores less than 100%?

As implied by the video, wouldn't it then take 1 intern a week max to fix those errors and allow any AI lab to become the first to consistently 100% the MMLU? I can guarantee Moonshot, DeepSeek, or Alibaba would be all over the opportunity to do just that if it were a real problem.

This pattern of considering 90% accuracy (like the level we've seemingly we've stalled out on for the MMLU and AIME) to be 'solved' is really concerning for me.

AGI has to be 100% right 100% of the time to be AGI and we aren't being tough enough on these systems in our evaluations. We're moving on to new and impressive tasks toward some imagined AGI goal without even trying to find out if we can make true Artificial Niche Intelligence.

https://www.booking.com/Share-Wt9ksz

Maybe he really is tied to $600 as his absolute upper limit, but also seems like something a few years from AGI would think to check elsewhere.

Typing 'Find me reservations at X restaurant' and getting unformatted text back is way worse than just going to OpenTable and seeing a UI that has been honed for decades.

If your old process was texting a human to do the same thing, I can see how Clawdbot seems like a revolution though.

Same goes for executives who vibecode in-house CRM/ERP/etc. tools.

We all learned the lesson that mass-market IT tools almost always outperform in-house, even with strong in-house development teams, but now that the executive is 'the creator,' there's significantly less scrutiny on things like compatibility and security.

There's plenty real about AI, particularly as it relates to coding and information retrieval, but I'm yet to see an agent actually do something that even remotely feels like the result of deep and savvy reasoning (the precursor to AGI) - including all the examples in this post.

Any reasonably informed person realizes that most AI start-ups looking to solve this are not trying to create their own pre-trained models from scratch (they will almost always lose to the hyperscale models).

A pragmatic person realizes that they're not fine-tuning/RL'ing existing models (that path has many technical dead ends).

So, a reasonably informed and pragmatic VC looks at the landscape, realizes they can't just put all their money into the hyperscale models (LP's don t want that) and they look for start-ups that take existing hyperscale models and expose them to data that wasn't in their pre-Training set, hopefully in a way that's useful to some users somewhere.

To a certain extent, this study is like saying that Internet start-ups in the 90's relied on HTML and weren't building their own custom browsers.

I'm not saying that this current generation of start-ups will be successful as Amazon and Google, but I just don't know what the counterfactual scenario is.

I know linguistics is descriptive not prescriptive, but it's truly amazing to me the lengths people will go to swear.

https://fred.stlouisfed.org/series/PINCOME