We keep seeing this. If you had to point out the fundamental problem, what would it be?
I think it’s the disconnect. Each persona is an expert in their own field but is completely oblivious to other critical areas.
The founder knows how to raise money but doesn’t really understand the customers. The engineer knows the tech but doesn’t really understand what it takes to keep the business afloat. The salesperson knows what customers want but doesn’t really understand what’s possible to make. The investor knows the numbers but doesn’t really understand how poorly the business is run.
I suspect if you look at successful startups you’ll often see a very small (1-3) group of founders who are very close, each can do more than one thing really well, and their combined expertise means that together they have very few blindspots.
I sometimes wonder if at some point we’ll discover that the fundamental laws of the universe are simply far beyond anything we can even remotely comprehend, like explaining a microprocessor to bacteria.
But then what if we end up with ASI that can comprehend and make it useful for us?
The thing I struggle with wrt to taste is that LLMs just don’t get it.
Even if I write down every single thing it did wrong and how I’d do it, and even if I turn those into rules, it will know how to follow these specific rules, but for some reason it can’t seem to generalize beyond that. And the real list of rules seem truly infinite.
Agree, and I also don’t see why government should be involved in book rental. Why books and not say, car rental? What about jetski rental?
(If the argument is that subsidizing books helps the poor, I’m all for it, a nonprofit or a charity would be a much better framework)
This is the public sector M.O, instead of admitting something is obsolete they grab more scope and funding.
My local post office now sells iPhones. And why shouldn’t they? Nobody stopped them when they just sold SIM cards, and then cases and chargers. It’s like a law of nature.
>So: after AI takes everything, what remains is not some second-best refuge — it is the place where the sense of value was always meant to live. AI is a receding tide. It washes away all the external anchors we carelessly threw out over the years — title, output, the feeling of being needed — and forces us to swim back to the one center that the tide cannot reach.
Yeah for sure and that’s great and all but I think what they were really asking is how they’ll feed their kids
>Of course, you have to define what "has the same behavior as" means
And that's really my issue, for example when you define "has no trailing whitespace", you are basically writing a piece of the implementation. Cover all behaviors, and you have basically re-implemented the function, no?
In other words, if I have the full formal spec of f(), isn't that the same thing as having f()?
Whenever I read about formal specs it always seems to me like “write the same tests just in a different way”, or worse, “write the same implementation but in a different way”.
I guess doing things twice can help catch errors, but I fail to see what’s so special about formal specs if they can suffer from the exact same bugs as the tests/implementation.
I guess the root of the problem is if you want to formally prove that a program does something, you have to be very specific (heh) about what that something is, at which point you are basically just writing tests/implementation all over again.
I have been looking into this on and off for years now, and I keep feeling like I’m missing something, but I don’t know what it is.
I gave it a task of scanning 6 markdown files and finding issues in the prose (contradictions etc). It ran for over 2h, exhausted my max plan session limit and crashed. I did not get any issues back.
About 2 years ago, my then girlfriend, who is a medical doctor (read: likely not an idiot), had ChatGPT plan a trip for us in a foreign country (naturally I had to nip it in the bud). And this was 2 years ago, when accuracy was way worse.
You would be surprised what normies are willing to delegate to AI.
It always seemed very natural to me that AI will move “down the stack”, where Open AI and Anthropic don’t really have a foot in the door.
Who makes consumer devices? Google
Who makes operating systems? Google
Who makes browsers? Google
Who makes the world’s most popular websites? Google
By the time 90% of average internet users get to chatgpt.com or whatever, they already went through several Google chokepoints, each layer is one more place Google can answer their questions.
And that’s not even getting into the chips, the data centers, the data, the talent, the consumer apps, the enterprise apps, the cloud platform, the brand, and of course the biggest cash printing machine in human history.
You would honestly have to be insane to bet against G.
Unfortunately, companies often measure developers by their own PRs.
An unfortunate outcome of this is that writing docs for other developers isn’t really incentivized properly (and with stack ranking, you might even say it’s disincentivized). Writing docs for Claude, however…
I think it’s the disconnect. Each persona is an expert in their own field but is completely oblivious to other critical areas.
The founder knows how to raise money but doesn’t really understand the customers. The engineer knows the tech but doesn’t really understand what it takes to keep the business afloat. The salesperson knows what customers want but doesn’t really understand what’s possible to make. The investor knows the numbers but doesn’t really understand how poorly the business is run.
I suspect if you look at successful startups you’ll often see a very small (1-3) group of founders who are very close, each can do more than one thing really well, and their combined expertise means that together they have very few blindspots.