tanelpoderSo where are all the AI apps?
https://www.answer.ai/posts/2026-03-12-so-where-are-all-the-ai-apps.html
It is incredibly easy now to get an idea to the prototype stage, but making it production-ready still needs boring old software engineering skills. I know countless people who followed the "I'll vibe code my own business" trend, and a few of them did get pretty far, but ultimately not a single one actually launched. Anyone who has been doing this professionally will tell you that the "last step" is what takes the majority of time and effort.
> Anyone who has been doing this professionally will tell you that the "last step" is what takes the majority of time and effort.
This is true, and I bet there are thousands of people who are in this stage right now - having gotten there far faster than they would have without Claude Code - which makes me predict that the point made in the article will not age well. I think it’s just a matter of a bit more time before the deluge starts, something on the order of six more months.
Exactly, there have been loads of tools over time to make software development easier - like Dreamweaver and Frontpage to build websites without coding, or low/no-code platforms to click and drag software together, or all frameworks ever, or libraries that solve issues that often take time - and I'm sure they've had a cumulative effect in developer productivity and / or software quality.
But there's not one tool there that triggered a major boost in output or number of apps / libraries / products created - unless I missed something.
Sure, total output has increased, especially since the early 2010's thanks to both Github becoming the social network of software development, and (arguably) Node / JS becoming one of the most popular languages/runtimes out there attracting a lot of developers to publish a lot of tools. But that's not down to productivity or output boosting developments.
> 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.
I also experienced this with my personal projects. It was really easy to just workshop a new feature. I'd talk to claude and get a nice looking implementation spec. Then I'd pass it on to a coding agent which would get 80% there but the last 20% would actually take lot more time. In the meantime I'd workshop more and more features leading to an evergrowing backlog and an anxiety that an agent should be doing something otherwise I'm wasting time. I brought this completely on myself. I'm not building a business, nothing would happen if I just didn't implement another feature.
Ha! I do this too and have also recently noticed. When scope creep is relatively cheap, it also gets unending and I'm never satisfied. I've had a couple of projects that I would otherwise open source that I've had to be realistic about and just accept it's only going to be useful for myself. Once I open it I feel a responsiblity for maintenance and stability that just adds a lot of extra work. I need to save those for the projects that might actually, realistically, be used.
Software engineering is not “coding” though.
Before AI for the last 8 or so years now first at a startup then working in consulting mostly with companies new to AWS or they wanted a new implementation, it’s been:
1. Gather requirements
2. Do the design
3. Present the design and get approval and make sure I didn’t miss anything
4. Do the infrastructure as code to create the architecture and the deployment pipeline
5. Design the schema and write the code
6. Take it through UAT and often go back to #4 or #5
7. Move it into production
8. Monitoring and maintenance.
#4 and #5 can be done easily with AI for most run of the mill enterprise SaaS implementations especially if you have the luxury of starting from the ground up “post AI”. This is something you could farm off to mid level ticket takers