> It is incredibly easy now to get an idea to the prototype stage

Yup. And for most purposes, that's enough. An app does not have to be productized and shipped to general audience to be useful. In fact, if your goal is to solve some specific problem for yourself, your friends/family, community or your team, then the "last step" you mention - the one that "takes majority of time and effort" - is entirely unnecessary, irrelevant, and a waste of time.

The productivity boost is there, but it's not measured because people are looking for the wrong thing. Products on the market are not solutions to problems, they're tools to make money. The two are correlated, because of bunch of obvious reasons (people need money, solving a problem costs money, people are happy to pay for solutions, etc.), but they're still distinct. AI is dropping the costs of "solving the problem" part, much more than that of "making a product", so it's not useful to use the lack of the latter as evidence of lack of the former.

In enterprise software there is an eternal discussion of "buy vs build" and most organizations go through a cycle of:

-- we had a terrible time building something so now we're only going to buy things

-- we had a terrible time buying something so now we're only going to build things

-- repeat...

Either way you can have a brilliant success and either way you fail abjectly, usually you succeed at most but not all of the goals and it is late and over budget.

If you build you take the risks of building something that doesn't exist and may never exist.

If you buy you have to pay for a lot of structure that pushes risks around in space and time. The vendor people needs marketing people not to figure out what you need, but what customers need in the abstract. Sales people are needed to help you match up your perception of what you need with the reality of the product. All those folks are expensive, not just because of their salaries but because a pretty good chunk of a salesperson's time is burned up on sales that don't go through, sales that take 10x as long they really should because there are too many people in the room, etc.

When I was envisioning an enterprise product in the early 2010s for instance I got all hung up on the deployment model -- we figured some customers would insist on everything being on-premise, some would want to host in their own AWS/Azure/GCP and others would be happy if we did it all for them. We found the phrase "hybrid cloud" would cause their eyes to glaze over and maybe they were right because in five years this became a synonym for Kubernetes. Building our demos we just built things that were easy for us to deploy and the same would be true for anything people build in house.

To some extent I think AI does push the line towards build.

> if your goal is to solve some specific problem for yourself, your friends/family, community or your team, then the "last step" you mention - the one that "takes majority of time and effort" - is entirely unnecessary, irrelevant, and a waste of time.

To a point, but I think this overstates it by quite a bit. At the moment I'm weighing some tradeoffs around this myself. I'm currently making an app for a niche interest of mine. I have a few acquaintances who would find it useful as well but I'm not sure if I want to take that on. If I keep the project for personal use I can make a lot of simplifying decisions like just running it on my own machine and using the CLI for certain steps.

To deploy this to for non-tech users I need to figure out a whole deployment approach, make the UI more polished, and worry more about bugs and uptime. It sucks to get invested in some software that then constantly starts breaking or crashing. GenAI will help with this somewhat, but certainly won't drop the extra coding time cost down to zero.

So your rebuttal to the claim that AI isn't increasing productivity in any measurable way is .. that it does actually increase productivity, but only for apps that aren't being publicly released/shared?

I agree, although I'd also say for the majority of problems the first part of even prototyping it is probably a waste of time and most people would be better off asking a simple AI hooked up to search if an appropriate solution already exists, or can be easily made with existing tools.

I agree with you. I am personally building small tools and apps to solve my own problems and I would not do that before AI - I would not know how.

But the last "making a product" part does apply to nearly any tool, even a solution to a personal problem.

I've started tons of scratch my own itch projects. There's adoption, UX, onboarding costs even if you're the only audience.

TLDR: i don't even use my own projects. I churn.

You forgot the most important factor: able to wow an investor and get them to invest millions on your prototype. Invest that and churn a few years, say "it didn't work out", pocket any difference after the deal falls apart. If you can pull that off 2-3 times, you're set for life.

Though, the economy does not seem to be in a good spot to try that strategy out as of now.

True, but a basic production stage benefits from some amount of backups, dev/staging/prodution, and more than one database.