jusomg

I've never done contractor or $/hr work and I have no idea how these things work in reality, but:

If a task takes you five hours to do without AI and 1 hour with AI charged at 100$, in the without-AI case you're making 500$, in the with-AI case you're making 100$ - price_of_AI, right?

Otherwise your example assumes you're charging someone 5 hours of work when in reality it took you 1 hour and then you spent an additional 4 hours watching TV.

In any case this thinking exercise made me realize that maybe it's more about staying competitive against other peers than about "AI paying for itself". If you're really charging for hours of work, then it is really a competitive advantage against people not using AI.

Assuming an AI-enhanced contractor can do the same amount of work than a non-AI-enhanced contractor in fewer hours, then I'd assume they would get more contracts, because the overall project is cheaper for whoever is hiring them. Does that really lead to you making more money, though? No idea honestly. Probably not? I just can't see how using AI "pays for itself". At best you're making now less money than before, because you're required to pay for the AI subscription if you want to stay competitive.

> Let’s be conservative and say $800/day (though I’d assume many of you charge more). The AI subscription math is a no-brainer. One afternoon saved per month = $200 in billable time. Claude Max pays for itself in 5 saved hours. Cursor pays for itself in 45 minutes.

Is the argument that by using these AI subscriptions, you have free time that didn't have before and now you work less hours? Or that the extra productivity you get from that AI subscription allows you to charge more per hour? or maybe that you can do more projects simultaneously, and therefore get more $ per day?

Otherwise I don't get how the AI subscription "pays for itself".

Serving an image over HTTPS implies initiating the TCP connection (which requires minimum 3 packets) and the TLS connection (which requires many more, lets say 10).

CDN <-> PoP <-> Datacenter communication doesn't require initiating connections. They reuse them because they centralize requests to serve different users (this is even explicitly called out in the article).

Lets say your closest datacenter is 50ms away, and the closest PoP/CDN node is 10ms. Just initiating the image download through HTTPS all of sudden is 500ms vs 100ms.

Sure, PoP/CDN might need to go to the datacenter to fetch the image (and only if the content is not cached there already) but that only happens once before it gets cached, and there's still a lot of ms to use on that to make the tradeoff worth it.

You could also learn a lot from what someone is asking an AI assistant.

Someone asking: "solve this problem" vs "what is the difference between array and dict" vs "what is the time complexity of a hashmap add operation", etc.

They give you different nuances on what the candidate knows and how it is approaching the understanding of the problem and its solution.

I do lots of technical interviews in Big Tech, and I would be open to candidates using AI tools in the open. I don't know why most companies ban it. IMO we should embrace them, or at least try to and see how it goes (maybe as a pilot program?).

I believe it won't change the outcomes that much. For example, on coding, an AI can't teach someone to program or reason in the spot, and the purpose of the interview never was to just answer the coding puzzle anyway.

To me it's always been about how someone reasons, how someone communicates, people understanding the foundations (data structure theory, how things scale, etc). If I give you a puzzle and you paste the most optimized answer with no reasoning or comment you're not going to pass the interview, no matter if it's done with AI, from memory or with stack overflow.

So what are we afraid of? That people are going to copy paste from AI outputs and we won't notice the difference with someone that really knows their stuff inside out? I don't think that's realistic.

Fair, but my point is that AWS has a full team of people that built and contributed to that magic box that is managing the database. When something goes wrong, they're the first ones to know (ideally) and they have a lot of know-how on what went wrong, what the automation is doing, how to remediate issues, etc.

When you use a k8s operator you're using an off the shelve component with very little idea of what is doing and how. When things go wrong, you don't have a team of experts to look into what failed and why.

The tradeoff here is obviously cost, but my point is those two levels of "automation" are not comparable.

Edit: well, when I write "you" I mean most people (me included)

Not sure if this is going to help Heroku's people at all but I feel bad for them now! haha I'm not a Heroku employee. I don't even work in any sort of managed service / platform provider. This is indeed a new account but not a throwaway account! I intended to use it long term.

> Of course you have more maintenance on on-prem, but typical k8s update is maybe a few hours of work, when you know what you are doing.

You just mentioned one dimension of what I described, and "when you know what you are doing" is doing a lot of the heavy lifting in your argument.

> Also AWS is also, complex, also requires configuration and also generates alerts in the middle of the night.

I'm confused. So we are on agreement there?

I feel you might be confusing my point with an on-prem vs AWS discussion, and that's not it.

This is encouraging teams to run databases / search / cache / secrets and everything on top of k8s and assuming a magic k8s operator is doing the same job as a team of humans and automation managing all those services for you.

I will only add that non-standard licenses also hurt adoption, specifically in medium/big businesses/enterprises.

Most organizations understand common open source licenses and there's usually a blank statement that allows teams to use GPL/MIT/whatever-licensed software.

Anything outside that subset of licenses (even if they're permissive, open source or whatnot) requires a legal review and a lot of people won't go through the pain of that process just to use a library/service/app. It's easier to just choose something else.

Of course you reduced 90% of the cost. Most of these costs don't come from the software, but from the people and automation maintaining it.

With that cost reduction you also removed monitoring of the platform, people oncall to fix issues that appear, upgrades, continuous improvements, etc. Who/What is going to be doing that on this new platform and how much does that cost?

Now you need to maintain k8s, postgresql, elasticsearch, redis, secret managements, OSs, storage... These are complex systems that require people understanding how they internally work, how they scale and common pitfalls.

Who is going to upgrade kubernetes when they release a new version that has breaking changes? What happens when Elasticsearch decides to splitbrain and your search stops working? When the DB goes down or you need to set up replication? What is monitoring replication lag? Or even simply things like disks being close to full? What is acting on that?

I don't mean to say Heroku is fairly priced (I honestly have no idea) but this comparison is not apples to apples. You could have your team focused on your product before. Now you need people dedicated to work on this stuff.