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.
-- 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.
I’m not opposed to AI or bemoaning “vibe coding”. The answer is still the same with build vs buy “does it make the beer taste better?”. “Do I get a competitive advantage by building vs buying”?
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.
Classic MacOS was designed to support handling events from the keyboard, mouse and floppy in 1984 and adding events from the internet broke it. It was fun using a Mac and being able to get all your work done without touching a command line, but for a while it crashed, crashed and crashed when you tried to browse the web until that fateful version where they added locks to stop the crashes but then it was beachball... beachball... beachball...
They took investment from Microsoft at their bottom and then they came out with OS X which is as POSIXy as any modern OS and was able to handle running a web browser.
In the 1990s you could also run Linux and at the time I thought Linux was far ahead of Windows in every way. Granted there were many categories of software like office suites that were not available, but installing most software was
./configure
make
sudo make install
I started with Windows 98. Didn't experience OSX until 2010. 9 years wasted.
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.
Though, the economy does not seem to be in a good spot to try that strategy out as of now.
Having worked extensively with vibe-coded software, the main problem for me is that I have tuned-off from the ai-code, and I dont see any skin-in-the-game for me. This is dangerous because it becomes increasingly harder to root-cause and debug problems because that muscle is atrophying. use-it or lose-it applies to cognitive skills (coding/debugging). Now, I lean negatively to ai-code because, while it seduces us with fast progress in the first 80%, the end outcome is questionable in terms of quality. Finally, ai-coding encourages a prompt-and-test or trial-and-error approach to software engineering which is frustrating and those with experience would prefer to get it right by design.
1. Debugging my own code or obvious behavior with other libraries.
2. Debugging pain-in-the-ass behavior with other libraries.
My patience with the latter is significantly less now, and so is perhaps my skill in debugging them. Libraries that change their apis for no apparent reason, libraries which use nonstandard parameter names, libraries which aren’t working as advertised.
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 before AI.
2. AI is horrible at system design. One anecdote. I was vibe coding an internal website that will at most be used by 7 people in total. Part of it was uploading a file to S3 and then loading the file into an Postgres table. It got the “create pre-signed S3 url and upload it directly to that instead of sending it to the API” correct (documented best practice). But then it did the naive “upload the file from S3 and do a bulk sql insert into the database”. This would have taken 20 minutes. The optimized method that I already knew was just to use the Postgres AWS extension to load it directly from S3 - 30 seconds. I’ve heard from a lot of data engineers run into similar problems (I am not one. I play one sometime).
6. Involves talking to the customer and UX.
7. Moving to production doesn’t take AI. Automation, stage deployments, automated testing and monitoring, blue /green deployments etc is a solved problem.
8. Monitoring is also a solve problem pre AI. It’s what happens after a problem is what you need people for.
So yes 1,2 and 7 are high value, high touch. If you look at the leveling guidelines for any BigTech company, you have to be good at 1 and 2 at least to get pass mid level.
Then there is always “0” pre-sales. I can do inbound pre-sales (not chase customers). It’s not that much different than what I do now as the first technical person who does a deep dive strategy conversation
Just doesn't have the same ring to it.
Only a few get lucky with funding, only a few have a profitable business.
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.
That's true, but even the "last step" is being accelerated. The 10% that takes 90% of the time has itself been cut in half.
An example is turning debug logs and bug reports into bugfixes, and performance stats into infrastructure migrations.
The time required to analyze, implement, and deploy those has been reduced by a large amount.
It still needs to be coupled with software engineering skills - to decide between multiple solutions generated by an LLM, but the acceleration is significant.
-0.75 years.
Software development output (features, bugs, products) - especially at smaller companies like startups - has already accelerated significantly, while software development hiring has stayed flat or declined. So there has been a dramatic increase in human-efficiency. To me, that seems like a result, although it's cold comfort as a software engineer.
You probably won't see this reflected as a multiplication of new apps because the app consumer's attention is already completely tapped. There's very little attention surface area left to capture.
In both cases, AI is making people think they can achieve things that were previously judged to unachievable, whether those things are building an app without any effort and getting rich, or effecting regime change without any actual strategic planning.
I launched a vibe coded product a few months ago. I spent the majority of my time
- making sure the copy / presentation was effective on product website
- getting signing certificates (this part SUCKS and is expensive)
- managing release version binaries without a CDN (stupid)
- setting up LLC, website, domain, email, google search indexing, etc, etc
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.
https://github.com/williamcotton/webpipe
https://github.com/williamcotton/webpipe-lsp
(lots of animated GIFs to show off the LSP and debugger!)
While I barely typed any of this myself I sure as heck read most of the generated code. But not all of it!
Of course you have to consider my blog to be "in production":
https://github.com/williamcotton/williamcotton.com/blob/main...
The reason I'm mentioning this project is because the article questions where all the AI apps are. Take a look at the git history of these projects and question if this would have been possible to accomplish in such a relatively short timeframe! Or maybe it's totally doable? I'm not sure. I knew nothing about quite a bit of the subsystems, eg, the Debug Adapter Protocol, before their implementation.
It was a lot of reviewing and proofreading and just verifying everything by hand. The only thing that saved me time was writing the test suite for it.
Would I do it again? Maybe. It was kinda fun programming by explaining an idea in plain english than just writing the code itself. But I heavily relied on software engineering skills, especially those theory classes from university to best explain how it should be structured and written. And of course being able to understand what it outputs. I do not think that someone with no prior software engineering knowledge could do the same thing that I did.
I think this represents a fundamental misunderstanding of how these AI tools are used most effectively: not to write software but to directly solve the problem you were going to solve with software.
I used to not understand this and agreed with the "where is all the shovelware" comments, but now I've realized the real shift is not from automating software creation, but replacing the need for it in the first place.
It's clear that we're still awhile away from this being really understood and exploited. People are still confusingly building webapps that aren't necessary. Here's two, somewhat related, examples I've come across (I spend a lot of time on image/video generation in my free time): A web service that automatically creates "headshots" for you, and another that will automatically create TikTok videos for you.
I have bespoke AI versions of both of these I built myself in an afternoon, running locally, creating content for prices that simply can't be matched by anyone trying to build a SaaS company out of these ideas.
What people are thinking: "I know, I can use AI to build a SaaS startup the sells content!" But building a SaaS company still requires real software since it has to scale to multiple users and use cases. What more and more people are realizing is "I can created the content for basically free on my desktop, now I need to figure out how to leverage that content". I still haven't cracked the code for creating a rockstar TikTok channel, but it's not because I'm blocked on the content end.
Similarly I'm starting to see that we're still not thinking about how to reorganize software teams to maximally exploit AI. Right now I see lots of engineers doing software the old way with an AI powered exo-skeleton. We know what this results in: massive PRs that clog up the whole process, and small bugs that creep up later. So long as we divide labor into hyper focused roles, this will persist. What I'm increasingly seeing is that to leverage AI properly we need to re-think how these roles actually work, since now one person can be much responsible for a much larger surface area rather than just doing one thing (arguably) faster.
Code is just one part of puzzle. Add: Pricing, marketing and ads, invoicing, VAT, make really good onboarding, measure churn rate, do customer service…
A lot of vibe coders are solopreneurs. You have to be very consistent and disciplined to make final product that sells.
IMHO (this may not apply to you!) a lot of people launch a "competitor" of a product which seems to be a clone of the product without improving something that the other product misses/is very bad at.
I don't think with LLMs as the foundation we will ever have something that can build and launch something end to end.
They just predict the next most likely token... no amount of clever orchestration can cover that up and make it into real intelligence.
Having done this professionally for a very, very long time, software engineers aren't particularly good at launching products.
Technology has drastically lowered the barriers to bring software products to customers, and AI is a continuation of that trend.
I really dont know how to respond to these requests. I am going to hide out and not talk to anyone till this fad passes.
Reminds of the trend where everyone was dj wanting you to listen their mixtrack they made on abbleton live
~18 months ago a friend of mine had a very viable, good idea for a physical product, but very fuzzy on the details of where to begin. My skillset backfilled everything he was missing to go from idea to reality to in-market.
I began at arm's length with just advice and validation, then slowly got involved with CAD and prototyping to make sure it kept moving forward, then infrastructure/admin, graphic design, digital marketing and support, etc, while he worked on manufacturing, physical marketing, networking, fulfillment, sales, etc.
Long story short, because I both deeply believe in the vision and know that teamwork makes the dream work, I am fully, completely, inextricably involved LOL -- and I don't have a single complaint about it either, but man, watch out, because if you don't believe in the vision but do have skills/expertise they're lacking, and opt out, friends and family will be the quickest and most aggrieved people you'll ever meet that think you're gatekeeping them from success.
Or to be a little less pessimistic, it's like asking them to stop and smell the flowers, except the flowers are fake and plastic and it makes your friends question your sanity. Either way, it's not a normal or enjoyable flower smelling experience, and doesn't add any enjoyment or simple pleasure to one's life like normal flower smelling would.