ferris-booler
I think the latter would not occur under the current state of AI/LLMs, but the rapid pace of growth gives me pause.
Basically if you individually don't tip you're just hurting someone who's underpaid by the tipping culture system. I agree if _everyone_ stopped tipping simultaneously the system would become untenable and have to change, but it would be pretty painful for the workers involved in the interim.
Perhaps the only individual in the game who does have a positive incentive is the business owner, where going tip-free can gain a good rep among customers and get more business if viewed as ethical and stably priced. I've seen this grow a bit more common in California restaurants but it's certainly still rare. Most business owners would rather just participate in the dis-equilibrium and coast off the shadow subsidy.
Tangential but I use OpenCode with GPT-5.6 rather than Codex because I could not figure out how to require Codex to ask me before editing files. OpenCode UX is still imperfect though.
> I put a band around my head, and open my laptop at Conduit. It’s 9 am. My device pairs with my laptop over Bluetooth. I open, without loss of generality, Codex.
At least we are reassured that short-range networking protocols will be invariant under the apocalypse.
We can argue about recombination/interpolation of training data in LLMs, but even if this was an interpolation, the result was contrarian rather than a confirmation. Any system that can identify an error in Erdős's thinking seems very useful to me (though perhaps he did not spend much time thinking about or checking this particular conjecture).
An enormous amount of research+eng work (most of the work of frontier labs) is being poured into making that 'correct' modifier happen, rather than just predicting the next token from 'the internet' (naive original training corpus). This work takes the form of improved training data (e.g. expert annotations), human-feedback finetuning (e.g. RLHF), and most recently reinforcement learning (e.g. RLVR, meaning RL with verifiable rewards), where the model is trained to find the correct answer to a problem without 'token-level guidance'. RL for LLMs is a very hot research area and very tricky to solve correctly.
Alternative approaches like "reasoning in the latent space" are active research areas, but have not yet found major success.