Sure the consumer won't consume 10x more, but they're still going to reach for the better products.
And let's say that work is correlated with quality. Company A wants to spend 10x less time working, while Company B works 10x more. Company B therefore has a better product than Company A, so eventually Company A goes away. The consumer still consumed the same amount, but they switched to the better product.
> If a junior makes a mistake and it will not be caught in time they will automatically learn.
I think this sentiment applies well to junior software engineers (with mentorship). But imagine the much larger swaths of entry level employees in operations, support, or sales functions. When you have a 400 person team with 20% annual turnover (since people move in / out of entry level jobs frequently), the management + training + monitoring becomes a huge challenge.
I think the typical HN sentiment of "llms aren't deterministic" fails to take into account how non-deterministic giant groups of people are. Every group of 10 people typically needs a manager. And every 10 managers needs another manager. By comparison the engineering work on dialing in your LLM guardrails feels pretty worthwhile.
Ya my experience is that many people honestly don't produce output as good as AI. An educated (formally or informally), experienced person who is putting forward good effort is better than AI, but I do know people who honestly just produce results having AI do it for them.
As I read this, I'm also working through a pretty dense feature that took a fair bit of iteration. The end result is actually significantly less code than it was about halfway through. And I was wondering if the AI actually helped me at all, since surely I could have written the code in the same time it took to iterate
But! Because of AI I was able to rapidly hack out like 4 variants of this feature that I didn't like. And felt comfortable throwing them away just as quick.
This has been one the most significant improvements of using AI for me. Before I would have to really think through the plan of a new feature before committing to the implementation and would only catch incompatibilities with existing code after a good portion of the implementation was already written. Now I can ask AI for detailed implementation plans and find these nitty gritty detail problems in a few hours if not less
Great point! This is along the same lines as a low fidelity prototype. It doesn't have to be production quality - hell, it barely needs to work so long as it's good enough to get feedback. Now I can have higher fidelity prototypes in the same time or more iterations in the same time, either of which tend to give me more insight and get me closer to the solution faster. Even if I never ship a line of AI-generated code, I can use it to write the same throw away code I did before, but much faster.
True. I think this is the biggest help with AI. It does not necessarily help with reaching the end goal faster all the time but it helps in trying out different iterations for quick prototypes. I find it especially useful in fast moving startups where some times we just want to validate a few ideas before fleshing them out as proper features.
Yea worth it. The original implementation ended up being the most complex, and also not a great UX. But I didn't really get it was a worse UX until I built it and tested it out a bit.
And I wasn't attached to that complex implementation in the way I would be if I architected it from scratch, so it was easy to move on.
This feels like a weak argument to me. At the end of the day, nearly all cash flow, good or bad, moves through banks.
And unlike speculative investing like VC or public equities, banks lend against fundamentals: cash flow, collateral, debt coverage, repayment history. Their fiduciary responsibility to deploy deposits into relatively safe, income-generating assets.
As long as a fossil fuel business is financially sound (ie the pipeline manufacturer with stable cash flow and strong collateral) it’s hard to expect a bank to categorically refuse them as a customer.
It isn't weak. For years, banks have been granting big loans to fossil fuel projects, not to green projects. There is a demonstrable bias that goes beyond fiduciary responsibility. The logic of banks is like saying that it's okay for VCs to not fund female founders because they present a fiduciary risk claiming that male founders simply are known to work out. As such, they're as strongly in favor of climate chaos as they can be.
Exactly my take as well. This would have been the right diversification move a decade ago.
Uber did invest early in self driving back in 2015, but in 2018 there was a fatality which pretty much deleted their whole program. And looks like it's taken them way too long to try picking it back up.
As a personal nit, I really dislike the term "two sided marketplace"
It should just be "marketplace". The term implies the existence of a "one sided marketplace". But isn't that just a business? If I have a bunch of product on my shelves and I'm trying to sell it, I don't call that a one sided marketplace?
Careful folks, some users may encounter a short ear piercing loud buzzing sound as it boots up and initializes sound. For a moment, my soul left my body there ha ha. Turn your volume down before launching.
Maybe a naive question, why doesn't this doesn't look like google street view?
Not OP's app in particular, but the underlying data from NASA. Nowadays the 360º cameras are $400 and work really well. Obviously we're working off of 2012 tech here.
But it seems like it would be enormously useful to have a full 3d image every 20 feet like google street view. Is this really just a power / bandwidth limitation?
And let's say that work is correlated with quality. Company A wants to spend 10x less time working, while Company B works 10x more. Company B therefore has a better product than Company A, so eventually Company A goes away. The consumer still consumed the same amount, but they switched to the better product.
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