philosophy

Overheard : On constant increase in expectations

Sam Altman’s June 10, 2025 post on achieving singularity captured something I’ve been thinking about lately. There’s a particular passage that perfectly describes how we’re constantly ratcheting up our expectations:

Already we live with incredible digital intelligence, and after some initial shock, most of us are pretty used to it. Very quickly we go from being amazed that AI can generate a beautifully-written paragraph to wondering when it can generate a beautifully-written novel; or from being amazed that it can make live-saving medical diagnoses to wondering when it can develop the cures; or from being amazed it can create a small computer program to wondering when it can create an entire new company. This is how the singularity goes: wonders become routine, and then table stakes.

This hits at something fundamental about human psychology. We have this remarkable ability to normalize the extraordinary, almost immediately.

I see this everywhere now. My kids casually ask AI to help with homework in ways that would have seemed like science fiction just three years ago. We’ve gone from “can AI write coherent sentences?” to “why can’t it write a perfect screenplay?” in what feels like months.

The progression Altman describes—paragraph to novel, diagnosis to cure, program to company—isn’t just about AI capabilities scaling up. It’s about how our mental models adjust. Each breakthrough becomes the new baseline, not the ceiling.

What struck me most is his phrase: “wonders become routine, and then table stakes.” That’s exactly it. The wonder doesn’t disappear because the technology got worse—it disappears because we got used to it. And then we need something even more impressive to feel that same sense of possibility.

Overheard : Worthless friends vs Transactional friends

Codie Sanchez quoting Prof. Arthur Brooks on different types of friendship in a conversation with Shane Parrish.

Worthless friends are the friends that have no transactional value. You don’t want anything from them. They don’t want anything from you. They want to hang out with you. They want to go on a walk with you. They don’t want your email list. They don’t want access to your money. They just want to have a beer on a Friday night. And these friendships end up materially increasing, our happiness, these worthless friends, whereas these transactional friendships actually end up, in many ways, decreasing our happiness

On AI Agentic Workflows

Amazing conversation with Bret Taylor on agentic workflows leveraging AI in the enterprises. The whole conversation is worth listening to multiple times, but this specific segment where Bret speaks about the difference between traditional software engineering and AI driven solutions was thought provoking on how much change management organizations have to go through to adopt to these new solutions.

Now if you have parts of your system that are built on large language models, those parts are really different than most of the software that we’ve built on in the past. Number one is they’re relatively slow compared — to generate a page view on a website takes nanoseconds at this point, might be slightly exaggerating, down to milliseconds, even with the fastest models, it’s quite slow in the way tokens are emitted.

Number two is it can be relatively expensive. And again, it really varies based on the number of parameters in the model. But again, the marginal cost of that page view is almost zero at this point. You don’t think about it. Your cost as a software platform is almost exclusively in your head count. With AI, you can see the margin pressure that a lot of companies face, particularly of their training models or even doing inference with high-parameter-count models.

Number three is they’re nondeterministic fundamentally, and you can tune certain models to more reliably have the same output for the same input. But by and large, it’s hard to reproduce behaviors on these systems. What gives them creativity also leads to non-determinism.

And so this combination of it, we’ve gone from cheap, deterministic, reliable systems to relatively slow, relatively expensive but very creative systems. And I think it violates a lot of the conventions that software engineers think about — have grown to think about when producing software, and it becomes almost a statistical problem rather than just a methodological problem.